From 0036086b16e804366ec5274faa7dc013f03e3337 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Mon, 27 Apr 2026 13:26:02 -0400 Subject: [PATCH 001/104] fix addon links --- docs/addons/qiskit-addon-obp/_toc.json | 42 + .../how_tos/bound_error_using_p_norm.ipynb | 450 +++++++++ .../simulating_circuits_with_obp.ipynb | 539 ++++++++++ .../how_tos/truncate_operator_terms.ipynb | 921 ++++++++++++++++++ docs/addons/qiskit-addon-obp/index.mdx | 48 + docs/addons/qiskit-addon-obp/install.mdx | 76 ++ .../tutorials/01_getting_started.ipynb | 513 ++++++++++ package.json | 1 + scripts/config/addon-docs.ts | 25 + scripts/js/commands/updateAddonDocs.ts | 95 ++ scripts/js/lib/addons/addonContentPipeline.ts | 192 ++++ scripts/js/lib/addons/generateAddonToc.ts | 101 ++ scripts/js/lib/api/updateLinks.ts | 22 + 13 files changed, 3025 insertions(+) create mode 100644 docs/addons/qiskit-addon-obp/_toc.json create mode 100644 docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb create mode 100644 docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb create mode 100644 docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb create mode 100644 docs/addons/qiskit-addon-obp/index.mdx create mode 100644 docs/addons/qiskit-addon-obp/install.mdx create mode 100644 docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb create mode 100644 scripts/config/addon-docs.ts create mode 100644 scripts/js/commands/updateAddonDocs.ts create mode 100644 scripts/js/lib/addons/addonContentPipeline.ts create mode 100644 scripts/js/lib/addons/generateAddonToc.ts diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json new file mode 100644 index 00000000000..cd35fe4fc03 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -0,0 +1,42 @@ +{ + "title": "Operator backpropagation (OBP)", + "children": [ + { + "title": "Documentation Home", + "url": "/docs/addons/qiskit-addon-obp" + }, + { + "title": "Installation Instructions", + "url": "/docs/addons/qiskit-addon-obp/install" + }, + { + "title": "Tutorials", + "children": [ + { + "title": "Reducing depth of circuits with operator backpropagation", + "url": "/docs/addons/qiskit-addon-obp/tutorials/01_getting_started" + } + ], + "url": "/docs/addons/qiskit-addon-obp/tutorials/index" + }, + { + "title": "How-To Guides", + "children": [ + { + "title": "Using different Lp-norms for Pauli term truncation", + "url": "/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm" + }, + { + "title": "Classically simulating circuits with OBP", + "url": "/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp" + }, + { + "title": "Truncating Pauli terms during backpropagation", + "url": "/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms" + } + ], + "url": "/docs/addons/qiskit-addon-obp/how_tos/index" + } + ], + "collapsed": true +} diff --git a/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb b/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb new file mode 100644 index 00000000000..f35818eea62 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb @@ -0,0 +1,450 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8a7c9df5-d836-4ecb-8aec-8a1f2faa017a", + "metadata": {}, + "source": [ + "# Using different Lp-norms for Pauli term truncation" + ] + }, + { + "cell_type": "markdown", + "id": "5958e635-64c1-4599-82ca-225b83312906", + "metadata": {}, + "source": [ + "**Note:** If you have not yet read the how-to guide on [truncating Pauli terms](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms) you should do so before reading this guide." + ] + }, + { + "cell_type": "markdown", + "id": "2780c587-cd2a-4398-8faa-de5e0c661a10", + "metadata": {}, + "source": [ + "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit_addon_obp.backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating.TruncationErrorBudget).\n", + "In this guide, you will learn about its additional keyword argument `p_norm`, which can be used to change the Lp-norm used to estimate the error incurred by the truncated Pauli terms." + ] + }, + { + "cell_type": "markdown", + "id": "872aeae7-0312-41db-8ebb-f7b02838b54d", + "metadata": {}, + "source": [ + "## Constructing an example circuit\n", + "\n", + "For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d6f281d4-706e-41ad-b7b5-bc7ebe106996", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import rustworkx.generators\n", + "from qiskit.synthesis import LieTrotter\n", + "from qiskit_addon_utils.problem_generators import (\n", + " PauliOrderStrategy,\n", + " generate_time_evolution_circuit,\n", + " generate_xyz_hamiltonian,\n", + ")\n", + "from qiskit_addon_utils.slicing import combine_slices, slice_by_gate_types\n", + "\n", + "# we generate a linear chain of 10 qubits\n", + "num_qubits = 10\n", + "linear_chain = rustworkx.generators.path_graph(num_qubits)\n", + "\n", + "# we use an arbitrary XY model\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " linear_chain,\n", + " coupling_constants=(0.05, 0.02, 0.0),\n", + " ext_magnetic_field=(0.02, 0.08, 0.0),\n", + " pauli_order_strategy=PauliOrderStrategy.InteractionThenColor,\n", + ")\n", + "# we evolve for some time\n", + "circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0)\n", + "# slice the circuit by gate type\n", + "slices = slice_by_gate_types(circuit)\n", + "\n", + "# for visualization purposes only, we recombine the slices with barriers between them and draw the resulting circuit\n", + "combine_slices(slices, include_barriers=True).draw(\"mpl\", fold=50, scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "7a7c7299-0c82-4279-b3dc-4f39e8dddd7e", + "metadata": {}, + "source": [ + "The observable that we will look at is the total magnetization:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ea9e7adc-b003-4762-a889-10e8d84a63d5", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "obs = SparsePauliOp.from_sparse_list(\n", + " [(\"Z\", [i], 1.0) for i in range(num_qubits)], num_qubits=num_qubits\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "423150d8-80d2-4b80-9665-033808d30272", + "metadata": {}, + "source": [ + "For reference, we compute the exact expectation value:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4dc72426-2eb5-4f8d-8bba-79650da06b54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.318197859862146\n" + ] + } + ], + "source": [ + "from qiskit.primitives import StatevectorEstimator\n", + "\n", + "estimator = StatevectorEstimator()\n", + "job = estimator.run([(circuit, obs)])\n", + "res = job.result()\n", + "exact_exp = res[0].data.evs\n", + "print(exact_exp)" + ] + }, + { + "cell_type": "markdown", + "id": "d21490c0-e86c-4fe0-af90-c9625813e0c0", + "metadata": {}, + "source": [ + "## Using the L1 norm\n", + "\n", + "By default, and as you have already seen in the [other how-to guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms), `p_norm=1` which means that the error is estimated as:\n", + "$$\n", + "|\\langle\\psi|\\Delta|\\psi\\rangle| \\leq \\sum_{P\\in\\mathcal{T}} |c_P|\n", + "$$\n", + "where $\\psi$ is the quantum state, $\\Delta$ is the real difference between the exact and truncated observable (which is unknown), $\\mathcal{T}$ is the set of Pauli terms that were truncated and $c_P$ is the Pauli terms coefficient.\n", + "This inequality is rigorous but a very loose upper bound for most scenarios." + ] + }, + { + "cell_type": "markdown", + "id": "425293ce-49fb-4bd0-8d2f-44a3a43db967", + "metadata": {}, + "source": [ + "For this simple guide, we will backpropagate 6 slices of our example circuit.\n", + "We will use a constant error per slice of `0.001`. This value is to be understood as the budget within the given `p_norm`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b46531b3-055b-4112-903a-11f2dd52a9ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TruncationErrorBudget(per_slice_budget=[0.001], max_error_total=inf, p_norm=1)\n" + ] + } + ], + "source": [ + "from qiskit_addon_obp.utils.truncating import setup_budget\n", + "\n", + "l1_truncation_error_budget = setup_budget(max_error_per_slice=0.001, p_norm=1)\n", + "print(l1_truncation_error_budget)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bc252678-58a0-407e-91fe-b7c545d57ae8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backpropagated 6 circuit slices.\n", + "New observable contains 116 terms and 10 commuting groups.\n" + ] + } + ], + "source": [ + "from qiskit_addon_obp import backpropagate\n", + "\n", + "max_slices = 6\n", + "l1_bp_obs, l1_remaining_slices, l1_metadata = backpropagate(\n", + " obs, slices[-max_slices:], truncation_error_budget=l1_truncation_error_budget\n", + ")\n", + "l1_reduced_circuit = combine_slices(slices[:-max_slices] + l1_remaining_slices)\n", + "print(f\"Backpropagated {max_slices - len(l1_remaining_slices)} circuit slices.\")\n", + "print(\n", + " f\"New observable contains {len(l1_bp_obs)} terms and {len(l1_bp_obs.group_commuting(qubit_wise=True))} commuting groups.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9f2782f8-1e93-4319-af26-dc467d7ed459", + "metadata": {}, + "source": [ + "We can now compute the expectation value of the backpropagated observable, as well as the error with respect to the exact reference:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5be8c457-6569-452d-8502-0fc5e39bf918", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.317869899338842 0.00032796052330397174\n" + ] + } + ], + "source": [ + "estimator = StatevectorEstimator()\n", + "job = estimator.run([(l1_reduced_circuit, l1_bp_obs)])\n", + "res = job.result()\n", + "l1_exp = res[0].data.evs\n", + "l1_error = exact_exp - l1_exp\n", + "print(l1_exp, l1_error)" + ] + }, + { + "cell_type": "markdown", + "id": "c0eccc5b-dc71-45b3-ad79-3d3dcb491f2d", + "metadata": {}, + "source": [ + "Finally, we can plot the error incurred during the backpropagation of each slice, as well as the accumulated error.\n", + "The accumulated error is simply the accumulated sum of the slice errors. We can see clearly that the accumulated error is a very loose upper bound to the actual error." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ff72e77f-e2c0-4cce-8575-c5aa587f78fb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "from qiskit_addon_obp.utils.visualization import (\n", + " plot_accumulated_error,\n", + " plot_slice_errors,\n", + ")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "axes[1].plot([6], [l1_error], \"x\", color=\"red\", label=\"actual error\")\n", + "plot_slice_errors(l1_metadata, axes[0])\n", + "plot_accumulated_error(l1_metadata, axes[1])" + ] + }, + { + "cell_type": "markdown", + "id": "10dd1139-c597-43c2-9f58-0a0988de768f", + "metadata": {}, + "source": [ + "## Using the L2 norm\n", + "\n", + "One can argue that the L2 norm is a better approximation of the incurred error than the L1 norm.\n", + "That is because we can assume the quantum state $|\\psi\\rangle$ to be drawn from a Haar random ensemble in which case the incurred error follows a distribution with a vanishing mean and a variance that can be approximately bound by the L2 norm:\n", + "$$\n", + "|\\langle\\psi|\\Delta|\\psi\\rangle| \\lesssim \\left( \\sum_{P\\in\\mathcal{T}} |c_P|^2 \\right)^{1/2}\n", + "$$\n", + "While the bound is not rigorous, it will only be violated in pathological cases." + ] + }, + { + "cell_type": "markdown", + "id": "f302d808-b341-49cb-8bbd-b03f2bfc6dd6", + "metadata": {}, + "source": [ + "Once again, we will backpropagate 6 slices of our example circuit using a maximum error per slice of `0.001` (this time, interpreted on the L2 norm)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c7b7e21f-22e4-479d-954c-39400974f8c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TruncationErrorBudget(per_slice_budget=[0.001], max_error_total=inf, p_norm=2)\n" + ] + } + ], + "source": [ + "l2_truncation_error_budget = setup_budget(max_error_per_slice=0.001, p_norm=2)\n", + "print(l2_truncation_error_budget)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e92680c5-497e-47e0-ae0f-69465028dd66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backpropagated 6 circuit slices.\n", + "New observable contains 84 terms and 6 commuting groups.\n" + ] + } + ], + "source": [ + "max_slices = 6\n", + "l2_bp_obs, l2_remaining_slices, l2_metadata = backpropagate(\n", + " obs, slices[-max_slices:], truncation_error_budget=l2_truncation_error_budget\n", + ")\n", + "l2_reduced_circuit = combine_slices(slices[:-max_slices] + l2_remaining_slices)\n", + "print(f\"Backpropagated {max_slices - len(l2_remaining_slices)} circuit slices.\")\n", + "print(\n", + " f\"New observable contains {len(l2_bp_obs)} terms and {len(l2_bp_obs.group_commuting(qubit_wise=True))} commuting groups.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a05f992a-3a38-4912-9e1b-bcd40463c35e", + "metadata": {}, + "source": [ + "Once again, we can compute the expectation value of the backpropagated observable, as well as the error with respect to the exact reference:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "73fe737a-c3b0-4639-83dc-d614521dd77a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.317829770853422 0.00036808900872387085\n" + ] + } + ], + "source": [ + "estimator = StatevectorEstimator()\n", + "job = estimator.run([(l2_reduced_circuit, l2_bp_obs)])\n", + "res = job.result()\n", + "l2_exp = res[0].data.evs\n", + "l2_error = exact_exp - l2_exp\n", + "print(l2_exp, l2_error)" + ] + }, + { + "cell_type": "markdown", + "id": "81fd099c-f144-4c59-9b76-72fd8b06d8ad", + "metadata": {}, + "source": [ + "Plotting the incurred errors per slice and the accumulated error yields a similar picture to before." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fe7f454c-7d79-4c61-b1dd-6a98564fb5f1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "axes[1].plot([6], [l2_error], \"x\", color=\"red\", label=\"actual error\")\n", + "plot_slice_errors(l2_metadata, axes[0])\n", + "plot_accumulated_error(l2_metadata, axes[1])" + ] + }, + { + "cell_type": "markdown", + "id": "9ef23a93-5c3b-48be-a02a-e666179c8c65", + "metadata": {}, + "source": [ + "Note, that the accumulated error is once again simply the sum of the single slice errors. This is yet another loose bound due to the Minkowski inequality since we have to compute this bound recursively:\n", + "$$\n", + "|\\langle\\psi|\\Delta_{i}|\\psi\\rangle| \\leq |\\langle\\psi|\\tilde{\\Delta}_{i-1}|\\psi\\rangle| + \\left( \\sum_{P\\in\\mathcal{T_i}} |c_P|^2 \\right)^{1/2} = |\\langle\\psi|\\tilde{\\Delta}_{i}|\\psi\\rangle|\n", + "$$\n", + "where the new subscript $i$ indicates the current slice iteration making $\\Delta_i$ the actual error at backpropagation iteration $i$, $\\tilde{\\Delta}_{i-1}$ the approximated truncation error from iteration $i-1$ and $\\mathcal{T}_i$ the set of Pauli terms truncated at iteration $i$." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb b/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb new file mode 100644 index 00000000000..792091bda97 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb @@ -0,0 +1,539 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "26d926db-2e2f-41ff-947e-9757a9f426d8", + "metadata": {}, + "source": [ + "# Classically simulating circuits with OBP" + ] + }, + { + "cell_type": "markdown", + "id": "7642d6db-fe6d-4506-8e49-6c840f166e2b", + "metadata": {}, + "source": [ + "In this guide, you will learn how to classically simulate `QuantumCircuit` instances to estimate expectation values entirely through the means of OBP.\n", + "\n", + "Since OBP will take an observable and backpropagate it through a given circuit, the \"simulation\" of a circuit amounts to computing the expectation value of the target observable with respect to this circuit.\n", + "As you will see later, the `qiskit-addon-obp` package is even capable of handling simple noise models, allowing you to compute noisy expectation values, too!" + ] + }, + { + "cell_type": "markdown", + "id": "2e996d79-36fd-4a71-bd6b-737fc8e44987", + "metadata": {}, + "source": [ + "## Constructing an example circuit\n", + "\n", + "For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b7946767-904b-422e-b076-7f956f3fdb70", + "metadata": {}, + "outputs": [], + "source": [ + "import rustworkx.generators\n", + "from qiskit.synthesis import LieTrotter\n", + "from qiskit_addon_utils.problem_generators import (\n", + " PauliOrderStrategy,\n", + " generate_time_evolution_circuit,\n", + " generate_xyz_hamiltonian,\n", + ")\n", + "from qiskit_addon_utils.slicing import combine_slices, slice_by_gate_types\n", + "\n", + "# we generate a linear chain of 10 qubits\n", + "num_qubits = 10\n", + "linear_chain = rustworkx.generators.path_graph(num_qubits)\n", + "\n", + "# we use an arbitrary XY model\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " linear_chain,\n", + " coupling_constants=(0.05, 0.02, 0.0),\n", + " ext_magnetic_field=(0.02, 0.08, 0.0),\n", + " pauli_order_strategy=PauliOrderStrategy.InteractionThenColor,\n", + ")\n", + "# we evolve for some time\n", + "circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0)\n", + "# slice the circuit by gate type\n", + "slices = slice_by_gate_types(circuit)" + ] + }, + { + "cell_type": "markdown", + "id": "63bcf4d0-7abe-4467-bc2b-f95d8d66dee5", + "metadata": {}, + "source": [ + "However, the above is purely the circuit describing the time evolution under a chosen Hamiltonian.\n", + "We also need an initial state to start from, with respect to which we compute the expectation values of our observable.\n", + "\n", + "Of course, we could choose the all-zero (or vacuum) state as our initial state, but to show how one would insert their own initial state, we choose a different one below.\n", + "\n", + "One possibility, would be to prepend the initial state to our time-evolution circuit above: `circuit.compose(initial_state, front=True)`.\n", + "But since we have already sliced our `circuit`, it is easier to simply insert the initial state as the first slice, which we do below.\n", + "\n", + "In this way, we can simply replace the first slice with another initial state, if we want to exchange that in the future, without having to recompute our slices." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ffaa07e2-af75-4424-9e7b-61c81b3bb9ff", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit.circuit import QuantumCircuit\n", + "\n", + "initial_state = QuantumCircuit(num_qubits)\n", + "for i in range(0, num_qubits, 2):\n", + " initial_state.x(i)\n", + "\n", + "slices.insert(0, initial_state)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8758f393-8691-4116-94ca-1185c6188bb4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for visualization purposes only, we recombine the slices with barriers between them and draw the resulting circuit\n", + "combine_slices(slices, include_barriers=True).draw(\"mpl\", fold=50, scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "bc9d51da-0a4e-46e2-8a5a-3501456aeb81", + "metadata": {}, + "source": [ + "## Simulating a noiseless expectation value" + ] + }, + { + "cell_type": "markdown", + "id": "1125cb39-4eda-4928-86b2-24e1872ed87e", + "metadata": {}, + "source": [ + "As our target observable, we choose the `ZZ` observable on the central qubits:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ee438fc3-8196-43d9-bcdb-956fd9ec4cf4", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "obs = SparsePauliOp(\"IIIIZZIIII\")" + ] + }, + { + "cell_type": "markdown", + "id": "d06f10b0-683e-476e-9ac3-5c110364a0c8", + "metadata": {}, + "source": [ + "At this point, we are already set to classically simulate the expectation value using OBP.\n", + "To do so, we simply provide the _all_ the slices to the `backpropagate` method, like so:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6402f5f5-08e0-4d05-ab2b-0c8863e9f966", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_obp import backpropagate\n", + "\n", + "vacuum_state_obs, _, metadata = backpropagate(obs, slices)" + ] + }, + { + "cell_type": "markdown", + "id": "7b2d555e-7fab-49f1-82d4-74a770f1be75", + "metadata": {}, + "source": [ + "We have now backpropagated our target observable `obs` through the _entire_ circuit (**including** the `initial_state` which we placed on `slices[0]`) resulting in a new `SparsePauliOp` whose expectation value we obtain by projecting it on the _vacuum state_ (`|00...00>`).\n", + "\n", + "This can be achieved in a straight forward manner by summing up the coefficients of all Pauli terms defined in the computational basis:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "63ae0edc-e8f6-467b-896a-ddd451d40a9a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.complex128(-0.8285688012239535+4.9487770271457865e-20j)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vacuum_state_obs.coeffs[~vacuum_state_obs.paulis.x.any(axis=1)].sum()" + ] + }, + { + "cell_type": "markdown", + "id": "107a0d89-323c-469b-8442-5e3f48169cd4", + "metadata": {}, + "source": [ + "As a sanity check (and to prove that this works) we can compare our result against Qiskit's `Statevector`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f3cb999d-688a-4e82-bf12-d958ef0e2ec0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.complex128(-0.8285687255430366+0j)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.quantum_info import Statevector\n", + "\n", + "Statevector(combine_slices(slices)).expectation_value(obs)" + ] + }, + { + "cell_type": "markdown", + "id": "02207cde-e89e-4cf6-8b3e-727a750d245c", + "metadata": {}, + "source": [ + "### Some notes on performance\n", + "\n", + "The computational efficiency of the `backpropagate` call above will heavily depend on many things, including:\n", + "- the structure of the `circuit`\n", + "- the method of slicing the circuit\n", + "- the target observable\n", + "- the truncation parameters\n", + "\n", + "Since the `backpropagate` method simplifies the observable after every _slice_ has been applied, the number of gates in a slice can dramatically influence the computational burden.\n", + "The most aggressive strategy in terms of operator simplification can be achieved by slicing your circuit into slices of individual gates.\n", + "\n", + "Additionally, you can leverage all of the truncation mechanism built into the `backpropagate` method.\n", + "We did not do so above, effectively resulting in an exact expectation value, but you can learn how to in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms)." + ] + }, + { + "cell_type": "markdown", + "id": "1ba37abf-0f78-4053-8995-2ab8340237ab", + "metadata": {}, + "source": [ + "## Simulating a noisy expectation value" + ] + }, + { + "cell_type": "markdown", + "id": "3a5e753b-c479-42d9-84d0-5ce4860ec091", + "metadata": {}, + "source": [ + "The `qiskit-addon-obp` package also supports handling of noise models in the form of `PauliLindbladError`s.\n", + "This is especially useful when you have characterized the noise model of the 2-qubit layers in your circuit, for example using the [`NoiseLearner`](/api/qiskit-ibm-runtime/noise-learner-noise-learner).\n", + "\n", + "In this section, you will see how you can use the `LayerError` objects returned by the `NoiseLearner` to compute noisy expectation values using OBP." + ] + }, + { + "cell_type": "markdown", + "id": "2d50dbf1-3abd-4945-bed9-1ed49758e9cc", + "metadata": {}, + "source": [ + "### Obtaining a noise model\n", + "\n", + "Normally, you would execute the `NoiseLearner` to obtain a noise model of your specific circuit.\n", + "To avoid complexity (and randomness) in this tutorial, we will refrain from doing so, and instead hard-code some noise model for our circuit below.\n", + "\n", + "However, we make sure that the structure of our data matches that of the [`NoiseLearnerResult`](/api/qiskit-ibm-runtime/noise-learner-result)." + ] + }, + { + "cell_type": "markdown", + "id": "54a7aba1-a7ee-4d0f-b95b-095158511ce0", + "metadata": {}, + "source": [ + "In its current (non-transpiled) form, our circuit contains 4 unique layers of 2-qubit gates:\n", + "- `slices[1]`: which has `Rxx` gates acting on all odd pairs of qubits\n", + "- `slices[2]`: which has `Rxx` gates acting on all even pairs of qubits\n", + "- `slices[3]`: which has `Ryy` gates acting on all odd pairs of qubits\n", + "- `slices[4]`: which has `Ryy` gates acting on all even pairs of qubits\n", + "\n", + "In the cell below, we manually construct 4 `LayerError` instances for each one of these layers with some randomized error rates." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e7a3b1f9-b60c-4735-8d6b-5151a7c5b92b", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit.quantum_info import PauliList\n", + "from qiskit_ibm_runtime.utils.noise_learner_result import LayerError, PauliLindbladError\n", + "\n", + "# fmt: off\n", + "pauli_errors_even = ['IIIIIIIIIX', 'IIIIIIIIIY', 'IIIIIIIIIZ', 'IIIIIIIIXI', 'IIIIIIIIXX', 'IIIIIIIIXY', 'IIIIIIIIXZ', 'IIIIIIIIYI', 'IIIIIIIIYX', 'IIIIIIIIYY', 'IIIIIIIIYZ', 'IIIIIIIIZI', 'IIIIIIIIZX', 'IIIIIIIIZY', 'IIIIIIIIZZ', 'IIIIIIIXII', 'IIIIIIIXXI', 'IIIIIIIXYI', 'IIIIIIIXZI', 'IIIIIIIYII', 'IIIIIIIYXI', 'IIIIIIIYYI', 'IIIIIIIYZI', 'IIIIIIIZII', 'IIIIIIIZXI', 'IIIIIIIZYI', 'IIIIIIIZZI', 'IIIIIIXIII', 'IIIIIIXXII', 'IIIIIIXYII', 'IIIIIIXZII', 'IIIIIIYIII', 'IIIIIIYXII', 'IIIIIIYYII', 'IIIIIIYZII', 'IIIIIIZIII', 'IIIIIIZXII', 'IIIIIIZYII', 'IIIIIIZZII', 'IIIIIXIIII', 'IIIIIXXIII', 'IIIIIXYIII', 'IIIIIXZIII', 'IIIIIYIIII', 'IIIIIYXIII', 'IIIIIYYIII', 'IIIIIYZIII', 'IIIIIZIIII', 'IIIIIZXIII', 'IIIIIZYIII', 'IIIIIZZIII', 'IIIIXIIIII', 'IIIIXXIIII', 'IIIIXYIIII', 'IIIIXZIIII', 'IIIIYIIIII', 'IIIIYXIIII', 'IIIIYYIIII', 'IIIIYZIIII', 'IIIIZIIIII', 'IIIIZXIIII', 'IIIIZYIIII', 'IIIIZZIIII', 'IIIXIIIIII', 'IIIXXIIIII', 'IIIXYIIIII', 'IIIXZIIIII', 'IIIYIIIIII', 'IIIYXIIIII', 'IIIYYIIIII', 'IIIYZIIIII', 'IIIZIIIIII', 'IIIZXIIIII', 'IIIZYIIIII', 'IIIZZIIIII', 'IIXIIIIIII', 'IIXXIIIIII', 'IIXYIIIIII', 'IIXZIIIIII', 'IIYIIIIIII', 'IIYXIIIIII', 'IIYYIIIIII', 'IIYZIIIIII', 'IIZIIIIIII', 'IIZXIIIIII', 'IIZYIIIIII', 'IIZZIIIIII', 'IXIIIIIIII', 'IXXIIIIIII', 'IXYIIIIIII', 'IXZIIIIIII', 'IYIIIIIIII', 'IYXIIIIIII', 'IYYIIIIIII', 'IYZIIIIIII', 'IZIIIIIIII', 'IZXIIIIIII', 'IZYIIIIIII', 'IZZIIIIIII', 'XIIIIIIIII', 'XXIIIIIIII', 'XYIIIIIIII', 'XZIIIIIIII', 'YIIIIIIIII', 'YXIIIIIIII', 'YYIIIIIIII', 'YZIIIIIIII', 'ZIIIIIIIII', 'ZXIIIIIIII', 'ZYIIIIIIII', 'ZZIIIIIIII']\n", + "pauli_errors_odd = ['IIIIIIIIXI', 'IIIIIIIIYI', 'IIIIIIIIZI', 'IIIIIIIXII', 'IIIIIIIXXI', 'IIIIIIIXYI', 'IIIIIIIXZI', 'IIIIIIIYII', 'IIIIIIIYXI', 'IIIIIIIYYI', 'IIIIIIIYZI', 'IIIIIIIZII', 'IIIIIIIZXI', 'IIIIIIIZYI', 'IIIIIIIZZI', 'IIIIIIXIII', 'IIIIIIXXII', 'IIIIIIXYII', 'IIIIIIXZII', 'IIIIIIYIII', 'IIIIIIYXII', 'IIIIIIYYII', 'IIIIIIYZII', 'IIIIIIZIII', 'IIIIIIZXII', 'IIIIIIZYII', 'IIIIIIZZII', 'IIIIIXIIII', 'IIIIIXXIII', 'IIIIIXYIII', 'IIIIIXZIII', 'IIIIIYIIII', 'IIIIIYXIII', 'IIIIIYYIII', 'IIIIIYZIII', 'IIIIIZIIII', 'IIIIIZXIII', 'IIIIIZYIII', 'IIIIIZZIII', 'IIIIXIIIII', 'IIIIXXIIII', 'IIIIXYIIII', 'IIIIXZIIII', 'IIIIYIIIII', 'IIIIYXIIII', 'IIIIYYIIII', 'IIIIYZIIII', 'IIIIZIIIII', 'IIIIZXIIII', 'IIIIZYIIII', 'IIIIZZIIII', 'IIIXIIIIII', 'IIIXXIIIII', 'IIIXYIIIII', 'IIIXZIIIII', 'IIIYIIIIII', 'IIIYXIIIII', 'IIIYYIIIII', 'IIIYZIIIII', 'IIIZIIIIII', 'IIIZXIIIII', 'IIIZYIIIII', 'IIIZZIIIII', 'IIXIIIIIII', 'IIXXIIIIII', 'IIXYIIIIII', 'IIXZIIIIII', 'IIYIIIIIII', 'IIYXIIIIII', 'IIYYIIIIII', 'IIYZIIIIII', 'IIZIIIIIII', 'IIZXIIIIII', 'IIZYIIIIII', 'IIZZIIIIII', 'IXIIIIIIII', 'IXXIIIIIII', 'IXYIIIIIII', 'IXZIIIIIII', 'IYIIIIIIII', 'IYXIIIIIII', 'IYYIIIIIII', 'IYZIIIIIII', 'IZIIIIIIII', 'IZXIIIIIII', 'IZYIIIIIII', 'IZZIIIIIII']\n", + "# fmt: on\n", + "\n", + "np.random.seed(42)\n", + "\n", + "layer_error_odd_xx = LayerError(\n", + " circuit=slices[1],\n", + " qubits=list(range(num_qubits)),\n", + " error=PauliLindbladError(\n", + " PauliList(pauli_errors_odd),\n", + " 0.0001 + 0.0004 * np.random.rand(len(pauli_errors_odd)),\n", + " ),\n", + ")\n", + "\n", + "layer_error_even_xx = LayerError(\n", + " circuit=slices[2],\n", + " qubits=list(range(num_qubits)),\n", + " error=PauliLindbladError(\n", + " PauliList(pauli_errors_even),\n", + " 0.0001 + 0.0004 * np.random.rand(len(pauli_errors_even)),\n", + " ),\n", + ")\n", + "\n", + "layer_error_odd_yy = LayerError(\n", + " circuit=slices[3],\n", + " qubits=list(range(num_qubits)),\n", + " error=PauliLindbladError(\n", + " PauliList(pauli_errors_odd),\n", + " 0.0001 + 0.0004 * np.random.rand(len(pauli_errors_odd)),\n", + " ),\n", + ")\n", + "\n", + "layer_error_even_yy = LayerError(\n", + " circuit=slices[4],\n", + " qubits=list(range(num_qubits)),\n", + " error=PauliLindbladError(\n", + " PauliList(pauli_errors_even),\n", + " 0.0001 + 0.0004 * np.random.rand(len(pauli_errors_even)),\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a9dff7ad-2c7a-4fc2-b7b5-5aa1a0e9b9db", + "metadata": {}, + "source": [ + "If you would have used the `NoiseLearner` to identify the unique 2-qubit gate layers of your circuit and characterize their noise, you would obtain a `NoiseLearnerResult` object.\n", + "This result would contain a list of `LayerError` objects, just like the ones we have manually constructed above.\n", + "\n", + "For each unique 2-qubit layer, the `LayerError` contains:\n", + "- the `QuantumCircuit` representing that 2-qubit gate layer\n", + "- the qubit indices which this circuit is acting upon\n", + "- the `PauliLindbladError` which represents the characterized noise model of this layer\n", + "\n", + "The `PauliLindbladError` will contain two objects:\n", + "- the list of Pauli errors that have been characterized\n", + "- the error rates corresponding to each one of those Pauli errors\n", + "\n", + "Normally, the list of Pauli errors will be sparse. More specifically, it will contain the single-qubit Pauli errors on all qubits that have gates acting upon them as well as the two-qubit Pauli errors on all those qubits that are connected." + ] + }, + { + "cell_type": "markdown", + "id": "9c3a1a9d-6fcb-4345-b9ba-e86e2e362a7e", + "metadata": {}, + "source": [ + "### Inserting the noisy layers into our circuit\n", + "\n", + "In the previous section, we have specifically constructed one `LayerError` for each of our known unique 2-qubit gate layers.\n", + "This means, we know which `LayerError` matches a specific one of our slices exactly.\n", + "\n", + "Normally, when using the `LayerError`, you will need to figure out what the unique 2-qubit gate layer is, and where it occurs inside of your circuit.\n", + "You will then need to adjust your `circuit` and/or `slices` to insert the `LayerError` accordingly.\n", + "How to do this in the general case, is beyond the scope of this how-to guide.\n", + "**TODO: link to external documentation, once it exists!**" + ] + }, + { + "cell_type": "markdown", + "id": "dcb5937e-83d8-41ab-a526-d6677201c8f4", + "metadata": {}, + "source": [ + "Here, our life is simpler because we know which slice a `LayerError` corresponds to.\n", + "Therefore, it is now just a matter of inserting new slices to represent the noise.\n", + "\n", + "Note, that we must wrap each `PauliLindbladError` from `LayerError.error` in a `PauliLindbladErrorInstruction` for it to be a valid `QuantumCircuit` instruction." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1f5c4655-f6af-439f-8475-0ecf0c673de5", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_obp.utils.noise import PauliLindbladErrorInstruction\n", + "\n", + "noisy_slices = []\n", + "for slice_ in slices:\n", + " if slice_ == layer_error_even_xx.circuit:\n", + " noisy_slices.append(\n", + " QuantumCircuit.from_instructions(\n", + " [(PauliLindbladErrorInstruction(layer_error_even_xx.error), slice_.qubits)]\n", + " )\n", + " )\n", + " elif slice_ == layer_error_odd_xx.circuit:\n", + " noisy_slices.append(\n", + " QuantumCircuit.from_instructions(\n", + " [(PauliLindbladErrorInstruction(layer_error_odd_xx.error), slice_.qubits)]\n", + " )\n", + " )\n", + " elif slice_ == layer_error_even_yy.circuit:\n", + " noisy_slices.append(\n", + " QuantumCircuit.from_instructions(\n", + " [(PauliLindbladErrorInstruction(layer_error_even_yy.error), slice_.qubits)]\n", + " )\n", + " )\n", + " elif slice_ == layer_error_odd_yy.circuit:\n", + " noisy_slices.append(\n", + " QuantumCircuit.from_instructions(\n", + " [(PauliLindbladErrorInstruction(layer_error_odd_yy.error), slice_.qubits)]\n", + " )\n", + " )\n", + " noisy_slices.append(slice_)" + ] + }, + { + "cell_type": "markdown", + "id": "b3778d94-4d04-4094-990d-a354f2062932", + "metadata": {}, + "source": [ + "We can check our work and draw the circuit below:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "dadc314d-452f-465c-a382-d54685ce3f2a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+X6MQZ6MI5BPmbBAudM0usoEL9MwusoErdM0usoEL9MyuMGfDnY1dPmTDTxpGUmVkZGjIkCFBHwMRQNfsIhu4QM/sIhu4QtfsIhu4QM/sCms2sbIKFW8slCQVFWzW4uemqt89lysjJ0vlO4qDPl5ChDUbRSCfMGeDcKFrdpENXKBndpENXKFrdpENXKBndoU1G9/vaxTibBSBfMKcDcKFrtlFNnCBntlFNnCFrtlFNnCBntkV5my4s7HLh2z4ScNIqqKiIg0bNkxFRUVBHwWeo2t2kQ1coGd2kQ1coWt2kQ1coGd2+ZBNVvsD1PXsfopVVKqqMhb0cRLGh2zkaT6+ZAP76JpdZAMX6JldZANX6JpdZAMX6JldPmTj432NPMlGnubjSzawj67ZRTZwgZ7ZRTZwha7ZRTZwgZ7Z5Us23NnYFdZs+EnDSKpYLKYVK1YoFgvPHwqEE12zi2zgAj2zi2zgCl2zi2zgAj2zK6zZ5B5/hC5e9qxSUlOVntVEkrTg8UmqKC6VJGXnttaZk+/R5B/fopJN25SWlalzpj6o6Zc/oMJFqwI+ff2ENRvVI5+Txt6of7/3mZY8N1WS1PrIbvrhY9frH6fdrMrS8kDPXh9hzgbhQtfsIhu4QM/sIhu4QtfsIhu4QM/sCms23KfZxn0akBh0zS6ygQv0zC6ygSt0zS6ygQv0zK4wZ+P7nZrP2YThPi3UP2n4s88+0znnnKOWLVuqRYsWGjJkiNatW6fmzZvrggsuCPp4AAAAAAAACKGN+Us16dSbNXnQCM1/6GVtmLtY88a8WPN8UcFmffHkZP1g1C8lSXk3DtXKt2aH4jLWB3XlM+eOcep13U/VpHVzKSVF/e+7QrNve9rMhSwAAAAAAIBvuE+zjfs0AAAAAAAAe7hTs8uH+7TQ/qThadOm6eyzz9ZBBx2k22+/XVlZWRo/frwGDRqkHTt2KC8vL+gjwqjz5zymnM7t9nh8y+LVev2kGwI5E/zT8ZTeOubWi9SyRycVb9iiL55+U188OTnoY4EZAEfa9ztcR/zXYLU+sqtyOrVV/pgX9a+H/xb0sfAfzAG4wC5gFzMALrALhF9lSZm2f10gSZr/wAQ175qrvvdcro9ueqLmY758+i2dPWWMDh9+pg46s68mDbwpwBNHS135FBVs1sInJ+vYOy7VN/OWaetX67Tug88DPnW48foJF9ihbWMOINnYoW1jBsAFdgHbmANINnaB8OM+zTbu09zjtRMusEPbxhxAsrFD28YMgAvsArYxB5Bs7AJ+4E7NLh/u00L5puGNGzdq2LBh6tOnj6ZOnaqsrCxJ0qWXXqpu3bpJEm8aNqJp06Z65JFH1LRp06CPUuMfg0YoNXXXD9lOb9ZU50x/UCte/zDQc2H/WOpam6MP0cDxt2jBE5P03tUPq23vHuo/5kpVFpdp8TNvB3085yxlI2aAt6z1LD27qQqXrtZXr83UcXf9KujjBMpaNmIOeMtS19gF4lnKRswAb1nrGbvALtayaaz5f5ygn77/iBY/+442fbZcklQVi2nuyPE649VRmn7Z/aooLg36mA3iSzbaSz6Lxk3RWW+MVocBR+ofg0YEfcQGsZgNr59+stQ1duh4lrKpxhzwj7WesUPvYi0bMQO8Zalr7ALxLGVTjTngH2s9YxfYxVo2jcV9mm3cpyUfr51+stQ1duh4lrKpxhzwj7WesUPvYi0bMQO8Zalr7ALxLGVTjTngH2s9YxfYxVo2+8O3OzXfs7F+nxbKNw2PGTNGW7Zs0bhx42reMCxJLVu2VJ8+fTRt2jTeNGxEenq6+vfvH/Qx4pRu2hb36x6nn6rU9DQtfX5qYGfC/rPUtSOuPFvfzF+u/NEvSJK2Ll2rVod2Vq9rh0TyX8QsZSNmgLes9Wzt9HlaO32eJOnY2y8J+jiBspaNmAPestQ1doF4lrIRM8Bb1nrGLrCLtWwaa/uKAq1+5xP1GXGh3rnwDzWPdxzYW0UFm3XAYV206q05gZ6xoXzJRnvLp6pKi595RwceffAes986i9nw+uknS11jh45nKZtqzAH/WOsZO/Qu1rIRM8BblrrGLhDPUjbVmAP+sdYzdoFdrGXTWNyn2cZ9WvLx2uknS11jh45nKZtqzAH/WOsZO/Qu1rIRM8BblrrGLhDPUjbVmAP+sdYzdoFdrGWzP3y7U/M+G+P3aaF80/BLL72kE088UT179qz1+fbt2ys3N1eSlJOTE/dcaWmpDj/8cP3rX/+q19eqqKhQQUFBAk7tn507d9brY4YOHaqJEyeqWbNm+/zYNWvWJPB0Unl5Rb0+7tBLT9Pqdz5V8YbChH79qCovr0h4lla7treOtTvuMC19YVrcY2tnzNeRV5+j7A6tVbRuc0K+fmMkOh+r2VSrzxxgBiRWlGaAGvBaY0kiM7KcjdgFAhOlOcAuYDebauwC7kVpBohdwHQ2tdnfvBY8Nkln/eMe5fY/QgWzFqrVYV3U5YzjNHnQCJ35j3u0/G/va8eqDfU6R7K/16hlo1rykSTFYqqKVTXoHFHPhh06GFF6/WSHtptNNeaAe1GaAWKHNp2NmAGBidIcYBewm0015oB7UZoBYhcwnU1trNynycG9TdSyUYLu00Q2vHYGJEqvn+zQdrOpxhxwL0ozQOzQprMRMyAwUZoD7AJ2s6nGHHAvSjNA7AKms6lNMu5srP43amTj9j4tNzdX6ekNextw6N40XFBQoLVr12rYsGF7PBeLxfT555+rd+/eNY/t2LEj7mOOOuooXXDBBQ36ep07d97PU/tp+PDhdX5MWVmZioqKNHHiRGVmZu7zY5966qkEnk76Q5vT1DGjxT4/ps3Rh+jAow9R/n0vJPRrR9mSJUs0NMF/Zqx2bW8dy2rXSsUb4xf64g1b/vPcAYH+i1ii87GaTbW65gAzIPGiNANUz9caaxKZkeVsxC4QmCjNAXYBu9lUYxdwL0ozQOwCprOpTX3z+uC3f6n18Y2fLNb4DufX/Lr/mCs1d+R4FRVs1rz7X1Lfey7XtEvvrfP3T8afk+/yNRs1IJ/GIJv/396dh1dVnu3fP5PshCQERFCJMiMgDmigVkjRVkStiCJVC461dWqdH2utw2vFoQ7YarX+nAoW6oSgdaCoiAzOCGgABWUUmUoACWHInOy8f9hMEMjA3ve61lrfz3H0eMzeO+TeOc/c6/J+XIQZ2ithun4yQ9vNpgr7gHth2gPEDG06G7EHeCZM+wCzgN1sqrAPuBemPUDMAqazqY+V8zQ5OLcJajaK83mayIZrp0fCdP1khrabTRX2AffCtAeIGdp0NmIP8EyY9gFmAbvZVGEfcC9Me4CYBUxnU594nNlY/W/UyKb5mpPN2rVr1bFjxyZ9TmIT1+W5qjvRExISdnvuzTff1KZNm5SVlVXv586dO1dff/21fv3rX8d9nfCHwy4+RTtWb9R/31/o9VIAeIA9AAD7ABBu7AEAmqvnhSer+PttWjcjR5K08pUPlNwyVZ1P7+/10oC44/oJgH0ACDf2AADsAwCag/M0hBnXTgDsA0C4sQcAYB8A0FycqWFf+O43DXfq1ElJSUn64IMP6jy+evVqXXfddZK0x5uGn332WQ0ZMkSHHHJIo79eZmam1q5du4+rDqYVK1Y0+JqCggI999xzGjFiRIO/SnzUqFExXJ00e8SDKliVu8fnkzPS1G34QH356L9j+nXDrlevXlo76Z8x/TOtdm1PHSvalK+0A9vUeSz1fx9X/S1OXol1PlazqbK3fYA9ID7CtAeoEdcai2KZkeVsxCzgmTDtA8wCdrOpwizgXpj2ADELmM6mPrHMa/mL07X8xel1Hpt6duPWH4+fk12FOZvaVkx6Xysmvd/o15MNM7RXwnT9ZIa2m00V9gH3wrQHiBnadDZiD/BMmPYBZgG72VRhH3AvTHuAmAVMZ1MfK+dpcnBuE+ZsamvqeZrIhmunR8J0/WSGtptNFfYB98K0B4gZ2nQ2Yg/wTJj2AWYBu9lUYR9wL0x7gJgFTGdTn1jnZfm/UQt7NlVcnadlZmY2cWU+vGk4JSVFv/rVrzRu3DidddZZGjp0qNauXasxY8aoffv2Wr9+fb03DRcUFOjll1/Wc88916SvF4lEmvzrm8Niw4YNDb4mNTVVEyZMULt27ZSUlLTX18b6+5ycvPd6dz/np0pMjmj5y7Ni+nXDLjk59j8zVru2p45tmrtEh5yYpYV/e7X6sQ6DsrRz7SYVbsiLyddurljnYzWbKnvbB9gD4iNMe4Aaca2xKJYZWc5GzAKeCdM+wCxgN5sqzALuhWkPELOA6WzqYyWvePyc7IpsmodsmKG9EqbrJzO03WyqsA+4F6Y9QIau+00RphmaPcAbYdoHmAXsZlOFfcC9MO0BYhYwnU19LOUV73Mbsmm+sGfDtdMbYbp+MkPbzaYK+4B7YdoDZOy631hhmqHZA7wRpn2AWcBuNlXYB9wL0x4gZgHT2dTHUl5hP7PZVZiyqZIY968QB3//+9915ZVXas6cObrppps0Z84cvf766zrkkEOUnp6uXr167fY5r7zyitLT0zV06FBP1hxWiYmJat++vRIT7VXtsItP0Zqp81T8/Tavl4IYsNS1xf+YogP79lDfW8/Xfj0O0aG//JkOv3SIvvp/b3i9NE9YyqY29oBgsdazSHqq2h7ZVW2P7KrE5IjSDmyjtkd2VauuTf8bXvzOWja1sQ8Ei6WuMQvUZSmb2tgDgsVaz5gFaljLBjXIxi7L2XD9DBZLXWOGrstSNrtiHwgOaz1jhq5hLZva2AOCxVLXmAXqspTNrtgHgsNaz5gFaljLBjXIxi7L2XDtDBZLXWOGrstSNrtiHwgOaz1jhq5hLZva2AOCxVLXmAXqspTNrtgHgsNaz5gFaljLBjXIxlu+/K5nZGTomWeeUW5urnbs2KFp06YpOztbixYtUp8+feot09ixY3XJJZcoErFzZ3gYFBQU6KSTTlJBQYHXS6njwH491fbIrlr2/HteLwUxYqlrWxau1MzfPKROJ/9Iw6Y/rL5/PE85oydo6XPTvF6aJyxlU4U9IHis9eyAYw7VsOl/1bDpf1V6ZlsdfukQDZv+Vw18+Cqvl+actWyqsA8Ej6WuMQvUZSmbKuwBwWOtZ8wCNaxlgxpkY5fVbLh+Bo+lrjFD12Upm9rYB4LFWs+YoWtYy6YKe0DwWOoas0BdlrKpjX0gWKz1jFmghrVsUINs7LKaDdfO4LHUNWbouixlUxv7QLBY6xkzdA1r2VRhDwgeS11jFqjLUja1sQ8Ei7WeMQvUsJYNapCNtwJzB21+fr7WrVtX728SXrp0qT799FONGzfOk7XBns05yzX+4HO9XgYCbN2MHK2bkeP1MrAH7AGIt9zZi+mYcewDiDdmAdvYAxBvzAIAgojrJ+KNGdo+9gHEEzO0fewBiDdmAfvYBxBPzAIAgohrJ+KNGdo+9gHEEzO0fewBiDdmAfvYBxBPzAIAGhKYm4a/+uorSVJWVtZuzz377LM64YQT1LNnTw9WBgAAAAAAAGt6XjBYPc87SZWVUc2+ZYzyl6ypfi6j80Ea+MjVSkyOaM07c7X46clq06ujsv/yW1VGK1VZXqFPbnpKO9dskiT1uXa4Dj7haCVGkpQzeoK+n79Cp778p+o/78Bje2ni0ZerdBt/a2JT7S2nrJtGqMd5g7Rt+Tq9d8F9e/2cY248Vwcf30eS1KpbphY98aa+efZtT94TAAAAAACAH3Ge5g+cpwEAAAAAANjAeZo/BPU8LRQ3DT/00EMerAgAAAAAAAAWpbTJ0GGXnKq3ht6uVl3aK/vBK/TuL++ufv7YOy5WzgMvafMXy3Taa3dr9VufqXjLdk2/6AGV7ShUh0FZOubGc/XJjU+qw0l9lZTWQtNG3lPna0w9Z5Qkqe2RXfWjOy7iQLYZGspp6fPTtOKV95X94BUNfs7Cv72qhX97VZJ05rSHtPqtzzx5TwAAAAAAAH7EeZo/cJ4GAAAAAABgA+dp/hDk87RET796DF199dWqrKzUgAEDvF4KamnZsqVmzpypli1ber0UBBxds4ts4AI9s4ts4Apds4ts4AI9s8tqNgf27aHcTxersrxC21f+Vy3atpYSEqqf369nB23+Ypkkad30HLUfcLiKt2xX2Y5CSVK0rEKVFVFJUtczsxVJb6FTJ43S8Y9eo0jL1Dpfq/vZJ+jb1z5y+v4aw2o2tTWUU9GmfCla2aTPadOro0q3FagwN8/pe2kKP2SDYKBrdpENXKBndpENXKFrdpENXKBndlnNhvM0u9nUxnma3WwQDHTNLrKBC/TMLrKBK3TNLrKBC/TMLqvZcJ5mN5vagnyeFpibhmFTNBrVxo0bFY1GvV4KAo6u2UU2cIGe2UU2cIWu2UU2cIGe2WU1m5Q2GXX+ZsWynUVKaZ1e/XFCYs0hXsm2ArXYv1X1x0mpKcq6eYS+Hvu2JCk9s60qyyo0bcTdylv8nY763bCaL5SQoM6nHafVb8+J/5tqIqvZ1NZQTs35nO7n/FTfvv5xnFYcG37IBsFA1+wiG7hAz+wiG7hC1+wiG7hAz+yymg3naXazqY3zNLvZIBjoml1kAxfomV1kA1foml1kAxfomV1Ws+E8zW42tQX5PI2bhhFXRUVFOv/881VUVOT1UhBwdM0usoEL9MwusoErdM0usoEL9Mwuq9mUbitQSuuav8EwOSNNpdsLqz+urPWXA6a0TlfJ1h2SpISkRP30yRu0+KnJyl+yRpJUsnWn1s9aIElaP2u+9j+iS/XnZmYfoS2LVqm8oNjF22oSq9nU1lBOzfmcLqf31+ops+O04tjwQzYIBrpmF9nABXpmF9nAFbpmF9nABXpml9VsOE+zm01tnKfZzQbBQNfsIhu4QM/sIhu4QtfsIhu4QM/sspoN52l2s6ktyOdpEa8XAABhd/yj16jHyEGSpGhFhYo25mvDJ4uUc/+Lnv86egAA4AbzAAC4tTlnubL+MEIJSYnK6HSQSvK21zmJ3bZsnQ7I6qHvF6xQx8H99Mnvn5QkDXz4Kv33/YVaM3Ve9WtzZy9Wu2MOrf6/21dtqH6u+9kn6NvXPnL87oKjoZya+jkHHddb+cvXNXiwC8A+5mcAAMA8AABucZ7mD5ynAdgT5mcAAMA8AABucZ7mD0E+T+OmYQAwIPezr/XBlY8oISlRrbq214D7L9eJ/7hJbw/7/7xeGgAAcIR5AADcKc3fqeUvzdCQ1+9VZWVUn902Vh0GZSmlTYZWvf6xvrj/RQ18+ColRJK09t152rlmkzoMylLXYT9RRqeD1O2sgcpbvEpz7xyvFRNnaeDDV+nnr96lipIyfXT945KkxJSIDj6+jz67bazXb9e3Gsqp10Un69Bf/kz79eigUyfeqY+uf1xFG7fu9jlVfjgk/9jT9wQgdpifAQAA8wAAuMN5mj9wngZgb5ifAQAA8wAAuMN5mj8E+TyNm4YRdy1btmzEq4B95+euRUvLVbQ5X5JUmJunpS9M14D7LlNyRprKdhZ5vbx95uds4B/0zC6ygSt+71qQ5wG/ZwN/oGd2Wc1m2QvTteyF6dUfb/16dfU/7/guV1PPGVXn9etnLdAL3S/c7c+Jlpbro+ser/fxfw+4JubrjiWr2dS2t5x2fa6hxz+7dUwcVxpbfsgGweDnrgV5fpbPs4F/0DO7yAau+L1rQZ4H/J4N/IGe2WU1G87T7GZTG+dpQHz5uWtBnp/l82zgH/TMLrKBK37vWpDnAb9nA3+gZ3ZZzYbzNLvZ1BbU8zRuGkZcZWRkaNasWV4vAyEQpK6ltd9fXc8YoGh5hSorol4vZ58FKRvYRc/sIhu4ErSuBWkeCFo2sIme2UU2dpGNXWQDV4LUtSDNzwpYNrCLntlFNnAlaF0L0jwQtGxgEz2zi2zsIhu7yAauBKlrQZqfFbBsYBc9s4ts4ErQuhakeSBo2cAmemYX2dhFNt5K9HoBCLby8nLNnj1b5eXlXi8FAef3rmX+5EhduOJ5XfTtixq5YIwys4/U12PeUnlRiSQpPbOtzv38KaW2ay1JSkpL0dmfPK42vTvv9TkL/J4N/IGe2UU2cCUIXWtoHjhxzE3qddHJ1a9ve1Q3Df/w0T0+ntQi2ZP3sasgZAP76JldZGMX2dhFNnDF713jPA3YN/TMLrKBK0HoGudpQPPRM7vIxi6ysYts4Irfu8Z5GrBv6JldZANXgtA1ztOA5qNndpGNXWTjLW4aRlwVFxfrhhtuUHFxsddLQcD5vWubc5Zr8sk3a8qQW7XgkVe0ad5SzR89ofr5wtw8ff3MFP347l9LkrJuGqHV78xR/pI1e33OAr9nA3+gZ3aRDVwJQtcamgfm/mmc+lz3C7Vo20pKSFD2g1dozu3P7vHxipIyT99PlSBkA/vomV1kYxfZ2EU2cMXvXeM8Ddg39MwusoErQega52lA89Ezu8jGLrKxi2zgit+7xnkasG/omV1kA1eC0DXO04Dmo2d2kY1dZOOtiNcLAABIFcWl2vFdriRpwV8mqlXXTPW/7zJ9+oenq1/zzbPv6Iypo3X45aery+n9NXnwHxr1HID4adU10+slNJkf1wyERUPzQGFunhY/M0XH/ulifT9/hbZ9u0EbPv5Kkvb4OID48uN11Y9rjhUr793KOiyx8j2xsg4AjcN5GuBPfrze+nHNQFhwngb4jx+vq35cc6xYeu+W1mKBpe+HpbUA2DvO0wB/8uO11o9rBsKC8zTAf/x4XfXjmmPF0nu3tBYLLH0/XK2Fm4YBwKAFf52oX3z4mJY+/562LFwpSaqMRjVv1Hid9trdmnnpQyovKql+/d6eAxA/g/91q9dLABBg9c0DS8ZN1dC37tfBA4/Sf4bU7EF7ehxAfDEL+At52UU2AGKB8zTAH7juA4gnztMA+5gF/IW87CIbALHAeRrgD1z3AcQT52mAfcwC/kJedoUxm0SvF4BgS0xMVLdu3ZSYSNUQX0Hr2o5VuVr73ufqd+v5dR7vMLivCnPztH/vzrt9zt6e81LQsoFN9MwusoErQexavfNAZaWWPvee1s3IUcmW7Q0/bkAQs4E99MwusrGLbOwiG7gStK5xngY0DT2zi2zgShC7xnka0Hj0zC6ysYts7CIbuBK0rnGeBjQNPbOLbOBKELvGeRrQePTMLrKxi2y8xXcdcZWenq6JEycqPT3d66Ug4ILYtUVPTlaHE7OUmX2kJKlN787qfNpxmjLkVvW8YLAyOh9U/dq9Pee1IGYDe+iZXWQDV4LatV3nAUlSNKrKaOXuL97T4x4LajawhZ7ZRTZ2kY1dZANXgtg1ztOAxqNndpENXAlq1zhPAxqHntlFNnaRjV1kA1eC2DXO04DGo2d2kQ1cCWrXOE8DGoee2UU2dpGNt7hpGHFVVlamN954Q2VlZV4vBQHn5659/H9PaNrIe3Z7fPPnSzX+4HOVO3uxJCl79JWaN2q8CnPzNP+hl9X/vsuqX7u357zm52zgH/TMLrKBK37vWmPnAT/yezbwB3pmF9nYRTZ2kQ1c8XPXOE8D9h09s4ts4Irfu8Z5GrBv6JldZGMX2dhFNnDFz13jPA3Yd/TMLrKBK37vGudpwL6hZ3aRjV1k4y1uGkZclZSU6P7771dJSYnXS0HABb1rPS88WcXfb9O6GTmSpJWvfKDklqnqfHr/vT5nQdCzgQ30zC6ygSt0zS6ygQv0zC6ysYts7CIbuBL0rnGeBuwdPbOLbOAKXbOLbOACPbOLbOwiG7vIBq4EvWucpwF7R8/sIhu4QtfsIhu4QM/sIhu7yMZbEa8XAABo2PIXp2v5i9PrPDb17FF1nt/TcwAAIFhWTHpfKya93+jHAQAAgDDiPA0AAFThPA0AAABoGOdpAACgCudpAAD4H79pGAAAAAAAAAAAAAAAAAAAAAAAAAAAAPA5bhpGXCUlJal///5KSkryeikIOLpmF9nABXpmF9nAFbpmF9nABXpmF9nYRTZ2kQ1coWt2kQ1coGd2kQ1coWt2kQ1coGd2kY1dZGMX2cAVumYX2cAFemYX2cAVumYX2cAFemYX2dhFNt7ipmHEVVJSkvr06cMPOOKOrtlFNnCBntlFNnCFrtlFNnCBntlFNnaRjV1kA1foml1kAxfomV1kA1foml1kAxfomV1kYxfZ2EU2cIWu2UU2cIGe2UU2cIWu2UU2cIGe2UU2dpGNt7hpGHFVWlqqsWPHqrS01OulIODoml1kAxfomV1kA1foml1kAxfomV1kYxfZ2EU2cIWu2UU2cIGe2UU2cIWu2UU2cIGe2UU2dpGNXWQDV+iaXWQDF+iZXWQDV+iaXWQDF+iZXWRjF9l4i5uGAQAAAAAAAAAAAAAAAAAAAAAAAAAAAJ/jpmEAAAAAAAAAAAAAAAAAAAAAAAAAAADA57hpGHEViUQ0bNgwRSIRr5eCgKNrdpENXKBndpENXKFrdpENXKBndpGNXWRjF9nAFbpmF9nABXpmF9nAFbpmF9nABXpmF9nYRTZ2kQ1coWt2kQ1coGd2kQ1coWt2kQ1coGd2kY1dZOMtvuuIq9TUVN1xxx1eLwMh4Leu9bxgsHqed5IqK6OafcsY5S9ZU/1cRueDNPCRq5WYHNGad+Zq8dOTJUntju6ufrdfqMRIkjbNW6L5o1+WJPW5drgOPuFoJUaSlDN6gr6fv0Knvvyn6j/vwGN7aeLRl6t0W4EH79R/2cCf6JldZANX/Na1ps4CbXp1VPZffqvKaKUqyyv0yU1PaeeaTRKzACDRM9PIxi6ysYts4IrfusZ5GhBb9MwusoErfusa52lAbNEzu8jGLrKxi2zgit+6xnkaEFv0zC6ygSt+6xrnaUBs0TO7yMYusvEWv2kYcVVcXKw///nPKi4u9nopCDg/dS2lTYYOu+RUvXP2nfrk90+p/72/qfP8sXdcrJwHXtI7Z92hTqf+SBmdDlJickT9bj1fsy79i949967qA9kOJ/VVUloLTRt5j6aeM0qb5i5RtKxcU88ZpannjNLcO8cp99PFnv1LmHyWDfyLntlFNnDFT11rzixQvGW7pl/0gKb+4k4tevJNHXPjuRKzAFCNntlFNnaRjV1kA1f81DXO04DYo2d2kQ1c8VPXOE8DYo+e2UU2dpGNXWQDV/zUNc7TgNijZ3aRDVzxU9c4TwNij57ZRTZ2kY23uGkYcVVeXq7JkyervLzc66Ug4PzUtQP79lDup4tVWV6h7Sv/qxZtW0sJCdXP79ezgzZ/sUyStG56jtoPOFwHHttLZYUl+tnTN+rUSaN04I96SZK6npmtSHoLnTpplI5/9BpFWqbW+Vrdzz5B3772keN3WJefsoF/0TO7yAau+KlrzZkFirdsV9mOQklStKxClRVRiVkAqEbP7CIbu8jGLrKBK37qGudpQOzRM7vIBq74qWucpwGxR8/sIhu7yMYusoErfuoa52lA7NEzu8gGrvipa5ynAbFHz+wiG7vIxlsRrxcQNMOue08r1233ehk6tGNrTX78FK+XAaAeKW0y6vxtSmU7i5TSOr36sYTEmn8pK9lWoBb7t1K0rFxtD++iyaferJT9WuqUF27XmyfdpPTMtirevE3TRtytI64YqqN+N0wLHp70wycnJKjzacdpwSOvuH+TQEjMuORB7fgu1+tlNEmrrpka/K9bvV4GEGrNmQWqJKWmKOvmEZp9yxhJYhYAPMYs4C9W8gpzBntCNgAawnkaEBxWrvtNwYwAeI/zNCA4mAX8xVJeYc6hPmQDoCGcpwHBYem631jMB4D3OE8DgoNZwF8s5RXmHOoTxmy4aTjGVq7brq9X5nu9DACGlW4rUErrltUfJ2ekqXR7YfXHlZU1r01pna7i77epJH+nNs1bovKC4h/+V1ii5Iw0lWzdqfWzFkiS1s+ar363XVj9uZnZR2jLolUqLyh29daA0NnxXa7yl63zehkAfKY5s4AkJSQl6qdP3qDFT01W/pI1ksQsAHiMWcBfyMsusgHQEM7TgODgug+gOThPA4KDWcBfyMsusgHQEM7TgODgug+gOThPA4KDWcBfyMuuMGaT6PUCEGwpKSm6/PLLlZKS4vVSEHB+6trmnOVqP+BwJSQlqlXXTJXkba/zb1/blq3TAVk9JEkdB/fTxjnf6Puc5Wrd/RAlJCUquVW6klulqWxnkXJnL1a7Yw6VJLU75lBtX7Wh+s/pfvYJ+va1jzx4h3X5KRv4Fz2zi2zgip+61pxZQJIGPnyV/vv+Qq2ZOq/6tcwCwA/omV1kYxfZ2EU2cMVPXeM8DYg9emYX2cAVP3WN8zQg9uiZXWRjF9nYRTZwxU9d4zwNiD16ZhfZwBU/dY3zNCD26JldZGMX2XiL3zSMuEpJSdGVV17p9TIQAn7qWmn+Ti1/aYaGvH6vKiuj+uy2seowKEspbTK06vWP9cX9L2rgw1cpIZKkte/O0841myRJS5+fptNeu1uJkYg+v+d5SdKKibM08OGr9PNX71JFSZk+uv5xSVJiSkQHH99Hn9021tP3Kp9lA/+iZ3aRDVzxU9eaMwt0GJSlrsN+ooxOB6nbWQOVt3iV5t45nlkA+B96ZhfZ2EU2dpENXPFT1zhPA2KPntlFNnDFT13jPA2IPXpmF9nYRTZ2kQ1c8VPXOE8DYo+e2UU2cMVPXeM8DYg9emYX2dhFNt7ipmHEVVFRkf74xz/qoYceUlpamtfLQYD5rWvLXpiuZS9Mr/5469erq/95x3e5mnrOqN0+Z+UrH2jlKx/UeSxaWq6Prnt8t9dGS8v17wHXxHzdzeG3bOBP9MwusoErfutaU2eB9bMW6IXuF+725zALAD+gZ3aRjV1kYxfZwBW/dY3zNCC26JldZANX/NY1ztOA2KJndpGNXWRjF9nAFb91jfM0ILbomV1kA1f81jXO04DYomd2kY1dZOOtRK8XgGCrqKjQnDlzVFFR4fVSEHB0zS6ygQv0zC6ygSt0zS6ygQv0zC6ysYts7CIbuELX7CIbuEDP7CIbuELX7CIbuEDP7CIbu8jGLrKBK3TNLrKBC/TMLrKBK3TNLrKBC/TMLrKxi2y8xW8aBgAAAAAAAP7n+EevUY+RgyRJ0YoKFW3M14ZPFinn/hdVmJvn9fJCj3wAAAAAAABs4bzGNvIBAAAAAACwhzMbu4KSDb9pGAAAAAAAAKgl97OvNfHoy/XqsVfpw2seVbujuurEf9zk9bLwP+QDAAAAAABgC+c1tpEPAAAAAACAPZzZ2BWEbLhpGHHVokUL3X777WrRooXXS0HA0TW7yAYu0DO7yAau0DW7yAYu0DO7/JpNtLRcRZvzVZibp42ffaOlL0zXQT8+TMkZaV4vLWb8mo1CkI+fs4G/0DW7yAYu0DO7yAau0DW7yAYu0DO7/JpN0M9r5ONsFIJ8/JwN/IWu2UU2cIGe2UU2cIWu2UU2cIGe2eXnbDizsSsI2US8XgCCLTk5WcOHD/d6GQgBumYX2cAFemYX2cAVumYX2cAFemZXELJJa7+/up4xQNHyClVWRL1eTswEIRsFNJ+gZAP76JpdZAMX6JldZANX6JpdZAMX6JldQcgmiOc1Ckg2Cmg+QckG9tE1u8gGLtAzu8gGrtA1u8gGLtAzu4KSDWc2dvk1G37TMOKqsLBQI0eOVGFhoddLQcDRNbvIBi7QM7vIBq7QNbvIBi7QM7v8mk3mT47UhSue10XfvqiRC8YoM/tIfT3mLZUXlUiS0jPb6tzPn1Jqu9aSpKS0FJ39yeNq07uzxytvPL9mo0bkc+KYm9TropOrX9/2qG4a/uGjSmqR7OGqG8/P2cBf6JpdZAMX6JldZANX6JpdZAMX6Jldfs2G8zTbOE8DYoOu2UU2cIGe2UU2cIWu2UU2cIGe2eXnbIJ+phbkbPxwnubrm4YXLlyos846S/vtt59at26t4cOHa8OGDWrVqpXOO+88r5cHSdFoVKtWrVI06p876eFPdM0usoEL9MwusoErdM0usoEL9Mwuv2azOWe5Jp98s6YMuVULHnlFm+Yt1fzRE6qfL8zN09fPTNGP7/61JCnrphFa/c4c5S9Z4+Gqm8av2agR+cz90zj1ue4XatG2lZSQoOwHr9Cc259VRUmZp+tuLD9nA3+ha3aRDVygZ3aRDVyha3aRDVygZ3b5NRvO02zjPA2IDbpmF9nABXpmF9nAFbpmF9nABXpml5+zCfqZWpCz8cN5WsTrBTTXjBkzdMYZZ6hLly664447lJaWpvHjx2vIkCHauXOnsrKyvF5ik0wfM0TJkUSdeOlbqqysefyNx05Wh4PSlX3xf1ReXrm3PwKNlZCgY/7vHB36y58pPbOtivO2a80785Rz/4vVd/wD+6L9gMN15G+Hqe1RXZXR8UDljJ6gLx/9t9fLQhX2ADjQ4aS++tFtF2i/nh1VtGmrvn72bX39zBSvl4Uq7AOIM2YB49gD4ACzgP9VFJdqx3e5kqQFf5moVl0z1f++y/TpH56ufs03z76jM6aO1uGXn64up/fX5MF/8HDF4dJQPoW5eVr8zBQd+6eL9f38Fdr27QZt+Pgrj1ftc1w/EWfM0MaxB8ABZmjj2AcQZ8wCxrEHwAFmAf/jPM02ztM8wPUTccYM7QPsA4gzZmjj2AMQZ8wCPsA+gDhjFggGztTsCsJ5mi9vGt68ebNGjhypfv36afr06UpLS5MkXXzxxerWrZsk+e6m4Uvu+EBfvnq2brn0aD347JeSpCvPPUynDOigfiPf4IbhGDryd2fqyKuG6ZMbn9CWhd+qdY9DdPzfrlFSi4hm//EfXi8PARBJT1X+8rX69vWPdNw9v/F6OdgFewDird0xh2rw+Fu06OnJ+uDqR3Vg357KHn2lKopKtfS5aV4vD+wDcIBZwDb2AMQbs0AwLfjrRP3iw8e09Pn3tGXhSklSZTSqeaPG67TX7tbMSx/i/6njofryWTJuqoa+db8OHniU/jPkVq+X6HtcPxFvzNC2sQcg3pih7WMfQLwxC9jGHoB4YxYIJs7TbOM8Lf64fiLemKHtYx9APDFD28cegHhjFrCPfQDxxCwQXJyp2eXH8zRf3jQ8evRobd26VePGjau+YViS9ttvP/Xr108zZszw3U3D6zcW6qo/f6Ln7/+Zpn6yXoXF5Xrk5v66+ZG5WvrdNq+X12ypqal67LHHlJqa6vVSqrU/rrf+++GXWv3WHEnSznWb9e0bH+vggX28Xhr2gaWurZ85X+tnzpckHXvHRV4vx3OWshF7QGBZ6tmRV56h7xesVM79L0mSti1frzaHdVKfa4eH8l/ELGVThX0gmCx1jVmgLkvZiD0gsCz1jFmgLkvZ7Isdq3K19r3P1e/W8/Xe+X+ufrzD4L4qzM3T/r07a807cz1dY1MFJRvtKZ/KSi197j0dcEx3lWzZ7vUSm8RiNlw/g8lS15ih67KUjdgDAstSz5ih67KUTRX2gWCy1DVmgbosZSP2gMCy1DNmgbosZbMvOE+zjfO0+OP6GUyWusYMXZelbKqwDwSPpZ4xQ9dlKZsq7AHBZKlrzAJ1WcqmCvtA8FjqGbNAXZay2VdBO1MLfDbGz9MSvV5Ac7z88ss64YQT1KtXr3qfb9++vTIzMyVJGzZs0DnnnKMDDjhA7dq10/Dhw7Vu3TrHK26cSe+u0qR3V+nFB36mFx/4mT78IldPTvzG62Xtk0gkouzsbEUidu5P3zhnidr/+DDtf3gXSVJG54PUcXA/rZvxhddLwz6w2DX8wFo27AHBZKlnBx3XW+tnza/z2PpZC5TR6SClH9zWs3V5xVI2VdgHgsli1/ADa9mwBwSTpZ4xC9RlKZt9tejJyepwYpYys4+UJLXp3VmdTztOU4bcqp4XDFZG54O8XmKTBCkb1ZOPJCkaVWW00stlNYvFbLh+BpPFruEH1rJhDwgmSz1jhq7LUjZV2AeCyWLX8ANr2bAHBJOlnjEL1GUpm33FeZptnKfFF9fPYLLYNfzAYjbsA8FjqWfM0HVZyqYKe0AwWewafmAxG/aB4LHUM2aBuixlEwtBOlMLejaS7fM0333Xc3NztX79eo0cOXK356LRqL766iv17du3+rGrr75a5eXlWrVqlZKSknTFFVfo0ksv1bRpjfvbE8rLy5Wbm9vo9ZWXlTX6tfW59oHZWj/9PEWjlTrj2vea/eeUl5XF/ebogoKCRr1mxIgRmjRpklq2bLnX18Z6vWVl5fU+vvjpyUpKTdaZ0x6SKiuVmBzR0hfe0/zRL8f064dVWVl5zLO02rU9dcyyWOdjNZsq9WXEHhBfYdoDtIeOpR3URkWb8+s8VrRp6/+e21+FG/Ji9vWbI5YZWc5GzAKeCdM+wCxgN5sqzALuhWkPELOA6Wzq09h9++P/e6Lexzd/vlTjDz63+uPs0Vdq3qjxKszN0/yHXlb/+y7TjIsfaNQ6wn5ms6umXFMbm09z1xH2bJihvRGm6ycztN1sqjBDuxemPUDM0KazEbOAZ8K0DzAL2M2mCrOAe2HaA8QsYDqb+lg5T5ODc5ugZqM4n6eJbJihPRKm6ycztN1sqrAPuBemPUDM0KazEXuAZ8K0DzAL2M2mCvuAe2HaA8QsYDqb+sTjzMbqf6NGNvu2lqa+38zMzCbffO27m4arSpWQkLDbc2+++aY2bdqkrKys6sdWrlypP/zhD2rVqpUk6YILLtBll13W6K+Xm5urTp06NX6BPe+WUjs0/vW7uGjooUpQgtJTk/SjIw7Q2x+tbdafs2zZMnXqdH6z19EYl19+eYOvKS0tVWFhoSZNmqSUlJS9vnbs2LExXJ3053anqENy690e73JGtnpf8nN9fOMTylv0nfY79BAdd/ev1ffW8zX/wQkxXUMYLVu2TCOa8jPTCFa7tqeOWRbrfKxmU6W+jNgD4itMe4DYB0xnI2YBz4RpHwj7HiDD2VRhFnAvTHuA2AdMZ1OfWObV88KTVfz9Nq2bkSNJWvnKB+p5/knqfHp/rXl7zl4/Nx4/J7sKczb7gmyYob0SpuunlZ/3pmCGZg+ItzDtAWIfMJ2NmAU8E6Z9IOx7gAxnU4VZwL0w7QFiHzCdTX2snKfJwblNmLPZV2HPhhnaG2G6flr6eW8sZugfsA/ET5j2ALEPmM5G7AGeCdM+EPY9QIazqcI+4F6Y9gCxD5jOpj6xzsvyf6MW9mz2RXOyWbt2rTp27Nikz/HdTcOdOnVSUlKSPvjggzqPr169Wtddd50k1blp+Pe//71effVVDRs2TElJSXr++ed15plnOl93Y/Tutp8euvE43fDQZzqiexuNvet49TnnNW3JL/F6aYHy47t+pa/Hvq1vX/1QkpS/ZI2S0lJ0/CNX68u/vaqKkn37bdEAbGMPQLwVbcpX2oFt6jyW+r+Pq/4WJ3iLfQAIN/YAxBuzQDgsf3G6lr84vc5jU88e5dl6UL8Vk97Xiknve72MQOD6CYQbewDijRnaPvYBINzYAxBvzALhwHmaP3CeFjtcPwGwDyCemKHtYw8AwD6AeGIWCA/O1OyzfJ7mu5uGU1JS9Ktf/Urjxo3TWWedpaFDh2rt2rUaM2aM2rdvr/Xr19e5afj444/XP//5T7Vt21YJCQk6+uijNW3atEZ/vczMTK1d2/jf9jv4dx9r2ZqGf8X2riKRBL3wwImaPme9xv57qVqkJOmU7A565s6BOvf3M5v85/Xq1UszZjbvtxQ31ooVKxp8TUFBgZ577jmNGDGiwV8lPmpUbDeu2SMeVMGq3N0ej6SlqjIarfNYZUVUSkj44X/YJ7169dLaSf+M6Z9ptWt76phlsc7HajZV6suIPSC+wrQHaA8d2zR3iQ45MUsL//Zq9WMdBmVp59pNKtyQF7Ov3VyxzMhyNmIW8EyY9gFmAbvZVGEWcC9Me4CYBUxnUx8r+3Y8fk52RTbNQzbM0F4J0/XTys97UzBDswfEW5j2ADFDm85GzAKeCdM+wCxgN5sqzALuhWkPELOA6WzqY2nfjve5Ddk0X9izYYb2Rpiun5Z+3huLGfoH7APxE6Y9QMzQprMRe4BnwrQPMAvYzaYK+4B7YdoDxCxgOpv6WNq3w35msyu/Z5OZmdnkr+O7m4Yl6e9//7uSk5P15ptvaubMmcrOztbrr7+ue+65RytWrFCvXr0kSdFoVCeffLLOPvtsvf3220pKStJDDz2kE088UQsWLFBycnKDXysSiTTp1zdHGvFn1ueeq3+kju1basjV70qSSkordNFt72vuS8N08Zk99Px/Gv5h2nUdTf210021YcOGBl+TmpqqCRMmqF27dkpKStrra2O93uTk+uu9ZupcHXXVMO34Lld5X61S6x6HqN8t52v9zPmqKC6N6RrCKDm5aT8zjWG1a3vqWCQ9Va27/bAhJyZHlHZgG7U9sqvKCoq14ztvLzKxzsdqNlXqy4g9IL7CtAdoDx1b/I8pGvqf+9T31vP17asf6IC+PXX4pUM0765/xezr7otYZmQ5GzELeCZM+wCzgN1sqjALuBemPUDMAqazqc+e9m3X4vFzsiuyaR6yYYb2Spiun8zQdrOpwgztXpj2ADFDm85GzAKeCdM+wCxgN5sqzALuhWkPELOA6WzqY+XMRg7Obcim+cKeDTO0N8J0/WSGtptNFfYB98K0B4gZ2nQ2Yg/wTJj2AWYBu9lUYR9wL0x7gJgFTGdTH85s6iKb+rn4b9Tk15uGMzIy9Mwzz+iZZ56p8/iiRYvUp08fJSYmSpLy8vK0evVqXX/99crIyJAk/f73v9ddd92llStXqnfv3p6sf1cD+7bXzb/uo1/cOF2b84qrH1+4NE+jnszR328ZoPfnbdDa3Kb/BmOvJSYmqn379tWZWDD3jn+qNH+nfjzqEqW3319FW7Zp3XtfKGf0y14vDfvAUtcOOOZQnfba3dUfH37pEB1+6RDlfrpYU8+J799+YZGlbMQeEFiWerZl4UrN/M1D6nfbBTrqd8NUtDlfOaMnaOlz07xemicsZVOFfSCYLHWNWaAuS9mIPSCwLPWMWaAuS9mgLrKxy2I2XD+DyVLXmKHrspSN2AMCy1LPmKHrspRNFfaBYLLUNWaBuixlI/aAwLLUM2aBuixlg7rIxi6L2XD9DCZLXWOGrstSNlXYB4LHUs+YoeuylE0V9oBgstQ1ZoG6LGVThX0geCz1jFmgLkvZoC6y8ZYvbxquT35+vtatW6ehQ4dWP3bAAQeoR48eeuKJJ3TPPfcoKSlJjz32mPbff3917drV0/XW9sn8jUruN67e5x589ks9+OyXztcUKwUFBTrppJM0c+bM6hu3vVZeVKLP731en9/7vNdLQQxZ6lru7MUaf/C5nq7BEkvZiD0gsKz1bN2MHK2bkeP1Mkywlo3YBwLLUteYBeqylI3YAwLLWs+YBWpYywY1yMYui9lw/QwmS11jhq7LUjZiDwgsaz1jhq5hLRuxDwSWpa4xC9RlKRuxBwSWtZ4xC9Swlg1qkI1dFrPh+hlMlrrGDF2XpWyqsA8Ej7WeMUPXsJaN2AMCy1LXmAXqspRNFfaB4LHWM2aBGtayQQ2y8VZgbtX+6quvJElZWVl1Hn/zzTf11VdfqWPHjmrfvr3effddTZkyRampqR6tFAAAAAAAAAAAAAAAAAAAAAAAAAAAAIitwPym4T3dNHzEEUdo6tSpHq0KAAAAAAAAFvW8YLB6nneSKiujmn3LGOUvWVP9XEbngzTwkauVmBzRmnfmavHTk9WmV0dl/+W3qoxWqrK8Qp/c9JR2rtkkSepz7XAdfMLRSowkKWf0BH0/f4VOfflP1X/egcf20sSjL1fptgJP3quf7S2nrJtGqMd5g7Rt+Tq9d8F9e/2cY248Vwcf30eS1KpbphY98aa+efZtT94TAAAAAACAH3Ge5g+cpwEAAAAAANjAeZo/BPU8LTA3DV999dW6+uqrvV4GAAAAAAAAjEtpk6HDLjlVbw29Xa26tFf2g1fo3V/eXf38sXdcrJwHXtLmL5bptNfu1uq3PlPxlu2aftEDKttRqA6DsnTMjefqkxufVIeT+ioprYWmjbynzteYes4oSVLbI7vqR3dcxIF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" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "combine_slices(noisy_slices, include_barriers=True).draw(\"mpl\", fold=100, scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "10b88555-0263-4cdb-a4c1-6895d7e55a5b", + "metadata": {}, + "source": [ + "### Simulating a noisy expectation value\n", + "\n", + "At this point, classically simulating the expectation value works exactly the same as before, just" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0e3ecb85-08c4-4fda-85e2-1000983c8842", + "metadata": {}, + "outputs": [], + "source": [ + "vacuum_state_noisy_obs, _, metadata = backpropagate(obs, noisy_slices)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a74e9e88-2f1c-4d40-873d-bf4524e2b7fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.complex128(-0.7230801696448901+7.082755280463563e-19j)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vacuum_state_noisy_obs.coeffs[~vacuum_state_noisy_obs.paulis.x.any(axis=1)].sum()" + ] + }, + { + "cell_type": "markdown", + "id": "7a4d2ff5-7c49-463e-8515-6fc9daa951ad", + "metadata": {}, + "source": [ + "We point out again, that multiple performance concerns should be considered.\n", + "Please go back to the [corresponding section above](#some-notes-on-performance)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb b/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb new file mode 100644 index 00000000000..2121983b8b1 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb @@ -0,0 +1,921 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "11cf5076-2777-499c-be50-531e305bf011", + "metadata": {}, + "source": [ + "# Truncating Pauli terms during backpropagation" + ] + }, + { + "cell_type": "markdown", + "id": "c1453472-3506-4008-b432-f8ef9ccbf3ee", + "metadata": {}, + "source": [ + "This guide explains how to configure the Pauli term truncation mechanism provided by the [qiskit_addon_obp.utils.truncating](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating) module.\n", + "\n", + "Operator backpropagation (OBP) can be used to reduce the depth of quantum circuits at the cost of a more complex observable. In order to get meaningful results from OBP, one usually needs to truncate terms from their observable to prevent it from growing too large. One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms' coefficients.\n", + "\n", + "The [backpropagate](/docs/api/qiskit-addon-obp/qiskit_addon_obp.backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating.TruncationErrorBudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", + "The amount of terms that are truncated depends on various configuration parameters specified by the user.\n", + "As of now, only a single truncation strategy is available -- the [truncate_binary_search](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating.truncate_binary_search) method.\n", + "Given an observable and some _budget_ it will perform a binary search over the Pauli terms and coefficients within that observable to find the optimal threshold,\n", + "such that the sum of the truncated coefficients is maximal but lower than the budget.\n", + "\n", + "**Note**: By default, the L1 norm is used to evaluate and bound the truncation error; however, the `p_norm` setting enables the specification of the Lp-norm to be used.\n", + "For more information on how to use that setting, please refer to [its how-to guide](/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm)." + ] + }, + { + "cell_type": "markdown", + "id": "aaf5f046-6d5e-4b6f-add7-b60604edef30", + "metadata": {}, + "source": [ + "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating.TruncationErrorBudget) via the accompanying [setup_budget](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating.setup_budget) function." + ] + }, + { + "cell_type": "markdown", + "id": "c83c0741-4a9e-4f89-92a9-4756ef133130", + "metadata": {}, + "source": [ + "## Constructing an example circuit\n", + "\n", + "For the purposes of this guide, we will use the following circuit slices:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "297fc3b0-d3b4-432d-96cd-7e175b7e6a52", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import rustworkx.generators\n", + "from qiskit.synthesis import LieTrotter\n", + "from qiskit_addon_utils.problem_generators import (\n", + " PauliOrderStrategy,\n", + " generate_time_evolution_circuit,\n", + " generate_xyz_hamiltonian,\n", + ")\n", + "from qiskit_addon_utils.slicing import combine_slices, slice_by_gate_types\n", + "\n", + "# we generate a linear chain of 10 qubits\n", + "linear_chain = rustworkx.generators.path_graph(10)\n", + "\n", + "# we use an arbitrary XY model\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " linear_chain,\n", + " coupling_constants=(0.05, 0.02, 0.0),\n", + " ext_magnetic_field=(0.02, 0.08, 0.0),\n", + " pauli_order_strategy=PauliOrderStrategy.InteractionThenColor,\n", + ")\n", + "# we evolve for some time\n", + "circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0)\n", + "# slice the circuit by gate type\n", + "slices = slice_by_gate_types(circuit)\n", + "\n", + "# for visualization purposes only, we recombine the slices with barriers between them and draw the resulting circuit\n", + "combine_slices(slices, include_barriers=True).draw(\"mpl\", fold=50, scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "89ce4fa9-06ba-4009-8594-d1b85f5e719f", + "metadata": {}, + "source": [ + "We will look at a single simple observable:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b168d34c-f147-4524-9af9-264f56440a55", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "obs = SparsePauliOp(\"IIIIIZIIII\")" + ] + }, + { + "cell_type": "markdown", + "id": "723dcf4e-1dab-4c26-a8e4-cd30b1564189", + "metadata": {}, + "source": [ + "## The simplest case: a fixed truncation budget for each slice" + ] + }, + { + "cell_type": "markdown", + "id": "5e04e5c4-1e6b-4720-9563-becbb9edc4d7", + "metadata": {}, + "source": [ + "The available budget for truncation of Pauli terms can vary at each step of the backpropagation.\n", + "As a first step towards understanding how this works, we look at the simplest case of a fixed truncation budget as specified by the user.\n", + "\n", + "The most straight-forward way to specify the truncation budget is via the `max_error_per_slice` argument.\n", + "In fact, this is what is being done in [this tutorial](/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb). Setting `max_error_per_slice` to a `float` results in each slice being allotted a budget equaling that value.\n", + "In the example below, we set this value to `0.001` which guarantees us an implicit truncation error of at most `0.018`, if the entire 18 slices were to be backpropagated. \n", + "\n", + "
\n", + "Note that any error budget remaining after backpropagating a slice and truncating terms with small coefficients will always be added to the following slice's error budget.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5f20a9b9-c3c4-43c4-9f13-fdf451958081", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TruncationErrorBudget(per_slice_budget=[0.001], max_error_total=inf, p_norm=1)\n" + ] + } + ], + "source": [ + "from qiskit_addon_obp.utils.truncating import setup_budget\n", + "\n", + "truncation_error_budget = setup_budget(max_error_per_slice=0.001)\n", + "print(truncation_error_budget)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c3b8b9ef-7d5c-43dd-8cb9-90756d4753b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backpropagated 11 circuit slices.\n", + "New observable contains 29 terms and 10 commuting groups.\n" + ] + } + ], + "source": [ + "from qiskit_addon_obp import backpropagate\n", + "from qiskit_addon_obp.utils.simplify import OperatorBudget\n", + "\n", + "op_budget = OperatorBudget(max_qwc_groups=10)\n", + "bp_obs, remaining_slices, metadata = backpropagate(\n", + " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", + ")\n", + "reduced_circuit = combine_slices(remaining_slices)\n", + "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", + "print(\n", + " f\"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4a772af6-edc6-4e53-81c7-53f3b72bf58a", + "metadata": {}, + "source": [ + "Let's use the [OBPMetadata](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.metadata.OBPMetadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", + "\n", + "- **The top/left plot** shows that we have enough budget to begin truncating observable terms after backpropagating the third slice. Starting from the third slice, we know that we truncate at least one term from each slice we backpropagate because we are incurring some truncation error after each slice.\n", + "- **The top/right plot** shows that the error budget ramps up to `.003` for the third slice. We see a sharp drop in remaining budget, which means terms were truncated from the observable. This is consistent with what we inferred from the top/left plot.\n", + "- **The bottom/left plot** shows that as we truncate terms from our observable, our overall accumulated error is growing monotonically. This graph also confirms that no terms were truncated until after the third slice had been backpropagated.\n", + "- **The bottom/right plot** shows that the number of commuting Pauli groups in our observable has grown to the specified limit of `10`. This plot also shows how backpropagating one more layer would cause our observable to outgrow the specified bound as you can see from the cross-over of the black and red lines." + ] + }, + { + "cell_type": "markdown", + "id": "8ff436fa-3b52-4387-a9f4-d8d9df13c480", + "metadata": {}, + "source": [ + "
\n", + "Note that in all of these plots, the x-axis enumerates the backpropagated slices, but since OBP works on the end of the circuit, slice 1 is in fact the very last slice, slice 2 the one before that, and so on.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "68f118df-6718-49b2-ba80-c37ef461f71a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "from qiskit_addon_obp.utils.visualization import (\n", + " plot_accumulated_error,\n", + " plot_left_over_error_budget,\n", + " plot_num_qwc_groups,\n", + " plot_slice_errors,\n", + ")\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(20, 10))\n", + "plot_slice_errors(metadata, axes[(0, 0)])\n", + "plot_left_over_error_budget(metadata, axes[(0, 1)])\n", + "plot_accumulated_error(metadata, axes[(1, 0)])\n", + "plot_num_qwc_groups(metadata, axes[(1, 1)])" + ] + }, + { + "cell_type": "markdown", + "id": "523f5a81-afbc-4eee-9d66-05cc3b4a781a", + "metadata": {}, + "source": [ + "## Specifying slice budget explicitly" + ] + }, + { + "cell_type": "markdown", + "id": "80f9337d-ea37-454c-ae0b-b101259568c9", + "metadata": {}, + "source": [ + "If one has knowledge of how to assign a budget to each slice, such that the backpropagation performance is optimized, they may want to explicitly assign a budget for each slice. For demonstration purposes, let's assign the first 3 slices zero budget and use the same budget budget of `.001` per slice for the remaining slices." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "17ce7232-7e3a-44e4-8081-1b1b27430bad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TruncationErrorBudget(per_slice_budget=[0.0, 0.0, 0.0, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001], max_error_total=inf, p_norm=1)\n" + ] + } + ], + "source": [ + "# Zero out the first 3 slices' budgets\n", + "max_error_per_slice = [0.0] * 3 + [0.001] * (len(slices) - 3)\n", + "\n", + "truncation_error_budget = setup_budget(max_error_per_slice=max_error_per_slice)\n", + "print(truncation_error_budget)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7cd711a7-1aea-4b45-a46a-63d771230969", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backpropagated 11 circuit slices.\n", + "New observable contains 32 terms and 10 commuting groups.\n" + ] + } + ], + "source": [ + "bp_obs, remaining_slices, metadata = backpropagate(\n", + " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", + ")\n", + "reduced_circuit = combine_slices(remaining_slices)\n", + "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", + "print(\n", + " f\"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3e853723-454b-4c69-b9c5-180eb1b2e6c2", + "metadata": {}, + "source": [ + "Removing the budget from the first 3 layers resulted in no terms being truncated until after the fourth layer, as can be confirmed in 3 of these 4 plots. What may be somewhat surprising is that although the 4th slice had no leftover budget passed to it, a term was still truncated using the allocated `.001` budget. This is obvious from the **top/left** plot but can also be observed in the **top/right** plot, as the left-over budget curve flattens between slice ID's 3 and 4. Another notable detail is that at least one term was truncated from this point on, same as above.\n", + "\n", + "The key takeaway is that although we incurred less truncation error in this second example due to zeroing out some slices' budgets, we were able to backpropagate the same number of slices, and our observable contains the same number of commuting Pauli groups. We can confirm that the bound on our error is smaller in the second example by inspecting the **bottom/left** plot." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b7167f80-5d29-4c32-be97-cba733f18b1d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(20, 10))\n", + "plot_slice_errors(metadata, axes[(0, 0)])\n", + "plot_left_over_error_budget(metadata, axes[(0, 1)])\n", + "plot_accumulated_error(metadata, axes[(1, 0)])\n", + "plot_num_qwc_groups(metadata, axes[(1, 1)])" + ] + }, + { + "cell_type": "markdown", + "id": "f03a92ee-e9a0-4707-b23a-ebdc3c9f35a3", + "metadata": {}, + "source": [ + "## Specifying the budget cyclically" + ] + }, + { + "cell_type": "markdown", + "id": "62f0ea1f-eaec-408d-99df-25a8647c1eb9", + "metadata": {}, + "source": [ + "If one has a circuit with some repeatable pattern, such as a Trotter circuit, it may be desired to specify a budget for that repeated subset of slices and have that budget used for all of the following repetitions of those slices.\n", + "\n", + "More concretely, the example circuit we are using has 6 slices which are repeated 3 times for a total of 18 slices. Here we will arbitrarily assign zero budget to the single-qubit and `RYY` layers and `.003` budget to each of the `RXX` layers. We will observe how specifying the budget as a length-6 sequence causes the budget to be applied to all 18 slices cyclically.\n", + "\n", + "
\n", + "Once more pay attention to the fact that the slices are backpropagated in reverse order (i.e. starting at the end). Therefore, the first entry in our cyclic budget actually gets used for the last slice, the second entry for the slice before that, and so on.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4f37a576-5a61-4589-a896-8304056d0e19", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TruncationErrorBudget(per_slice_budget=[0.0, 0.0, 0.0, 0.0, 0.003, 0.003], max_error_total=inf, p_norm=1)\n" + ] + } + ], + "source": [ + "# Specify a length-6 per-slice budget.\n", + "# This will be cycled over 3 times to be applied to the 18 slices\n", + "max_error_per_slice = [0.0] * 4 + [0.003] * 2\n", + "\n", + "truncation_error_budget = setup_budget(max_error_per_slice=max_error_per_slice)\n", + "print(truncation_error_budget)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7f78cfe4-13e4-472c-b6cc-9d1e867cbd9c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backpropagated 13 circuit slices.\n", + "New observable contains 49 terms and 14 commuting groups.\n" + ] + } + ], + "source": [ + "op_budget = OperatorBudget(max_qwc_groups=20)\n", + "bp_obs, remaining_slices, metadata = backpropagate(\n", + " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", + ")\n", + "reduced_circuit = combine_slices(remaining_slices)\n", + "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", + "print(\n", + " f\"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3511206b-2972-41ba-b981-6c20ccb7712b", + "metadata": {}, + "source": [ + "As seen in the **top/left** and **bottom/left**, no truncation was performed over the first four slices since they were allocated no error budget. Slices 5 and 6 had some of their terms truncated as budget became available.\n", + "\n", + "As seen in the **top/left**, a relatively large amount of error was incurred after backpropagating slice 7, even though that slice was allocated no budget. This is because only about `.002` of the `.006` total budget allocated to slices 5 and 6 was used, so the remaining was forwarded to slice 7 and mostly expended, as seen in the **top/right**.\n", + "\n", + "**top/left** and **top/right** show that the small amount of leftover budget is used up between slices 8-10, and new budget becomes available in slice 11, as expected. **top/left**, **top/right**, and **bottom/left** all demonstrate the cyclical behavior of the `max_error_per_slice` argument when its length is less than the number of slices. This cyclical behavior would have continued through all the slices, but the `max_qwc_groups` stopping criteria was reached after backpropagating 13 slices, as seen in **bottom/right**.\n", + "\n", + "Another interesting thing is that the number of Pauli groups actually drops after the second round of budget becomes available at slice 11. This is because there were groups with small coefficients which could not be truncated until there was more budget after backpropagating slice 11, so they accumulated in the observable for several iterations. This particular case also highlights that `max_qwc_groups` must be _exceeded_ for the algorithm to terminate." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4c36768e-03e7-4e1b-81b3-59b6e9eab43e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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//hhPPPEEUlNT632q7v3338f7778PoPpze+jQoRCJRFCpVHjhhRcgFotx4MABDB48+EH+kxERETWJXQkZAICbxRV4aXMCvnx9IBSyph81TESk1Wpxp1ItyLktTCW665x74XVT27xuErx4A1T/MNQ3TdpfK3o1wsLCEBYWVu/xRCIRFi9ejMWLFzfo/G5ubnVW7nr16oWjR4826BhERG1Bc0+/1BAPMkUTEbUcly5dglarRffu3evc3r17d9y+fRt5eXlwcHAAAAwaNAhz584FAHh6euK3337Dhx9+iOHDhyMjIwMKhQJBQUEwNTVFp06ddBcBGRkZ2Lp1KzIyMuDk5AQAmDVrFmJjY7F161b85z//AQBUVlbiv//9L3r37q3LMWbMGOzatUt3ERIXF4eCggI899xzAABnZ2fMmjVL1/+NN97A999/j88//1zvIsTV1RUffvghRCIRvLy8cO7cOXz44Yd1XoT8+uuvOHHiBHJzc3VT837wwQfYv38/vvjii3ofKnrttdcwZswYbNu2DcePH0dMTAwOHjyIbdu24fPPPwdQ/xqOREREQlCqyvBTSi4AwEkmRWbBHYzfkoDPXw2EnaWZwOmIyNjdqVSjR9T3gpz7j8XBsDS7/y16Xje1zeumFlG8ISKi1qVm+qUWQSN0ACJqDPcaAv93NWsd/vXv0dHRAKof8omOjkaXLl0QEhKCkSNH4umnn4aJiQnOnTsHtVqtmzK3Rnl5ud7inmZmZujVq5den3HjxmHAgAHIysqCk5MTdu7ciaeeegp2dnYAqp8o+89//oPPP/8cmZmZqKioQHl5OSwtLfWOM2DAAL0n6wIDA7Fq1Sqo1WpIJBK9vmfOnEFxcXGthUfv3LmDK1eu1Pv9sbOzg52dHU6cOIHnnnsObm5uOH36NEaNGgU3N7d69yMiIhLKl6duQKMF+rm1w+oXfBEWE4/UnGJM3HoSO18JgJU5b18REQG8bmpr103814+IiIiIBNOtWzeIRCJcuHABzz77bK3tFy5cQLt27dCxY8cGHc/V1RUpKSn44YcfcPjwYfz73//GypUr8csvv6C4uBgSiQRJSUm1fuG3trbW/dnCwqLW1AX9+vVD165dsXv3brz++uv43//+h23btum2r1y5EmvWrEF0dDR8fHxgZWWFGTNmoKKiwoDvhr7i4mI4OjrWORK95uLn744ePYonn3wSQPVoyZ9//hkzZ87EnTt3YGpqivfffx/z58/H/PnzHzgXERFRY9JotPg88ToA4AV/V7i2t8Snk/oj7ON4JF8vwGv/l4TNE/xhbiK5z5GIiB6MhakEfywOFuzcDcHrpvoZ83UTizdEREREJJgOHTpg+PDh+O9//4uZM2fqzd+sVCqxc+dOjB8/Xu+i4Pjx43rHOH78uN70ARYWFnj66afx9NNPY8qUKfD29sa5c+fQp08fqNVq5Obm4tFHHzU467hx47Bz5064uLhALBbjqaee0m377bff8Mwzz+Cll14CAGg0GqSmpqJHjx56x0hISKiV3cPDo9ZFEQD07dsXSqUSJiYmDX7yy9/fH8nJyUhKSsKcOXMQFxeHjIwMjBo1CqdOnYJYLK53zmciIiIhJKTl49qtUlibm+CpXo4AAA+5DbZO7IdxmxNw9NJNRH5+BmvH9IFEfP91IYiIDCUSiRo0dZmQeN3UNq+bxEIHICIiIqK2bf369SgvL0dwcDCOHDmC69evIzY2FsOHD4ezs7Nuccsav/32G1asWIHU1FRs2LABe/fuxfTp0wEA27ZtwyeffILz58/j6tWr+L//+z9YWFigc+fO8PT0xLhx4zB+/Hjs27cPaWlpOHHiBJYtW4Zvv/32vjnHjRuHU6dO4b333sPzzz+vm08ZADw8PHD48GEcO3YMFy5cwKuvvoqcnJxax8jIyEBkZCRSUlLw2WefYd26dbrsfxcUFITAwECEhobi0KFDSE9Px7Fjx/D2228jMTGxzn0sLCzQrVs3pKWl4bHHHkO3bt1w48YNDBo0CJ6enujWrVuti5A//vgDycnJyM/Ph0qlQnJyMpKTk+/7/SAiImoMNaNunu7tqHfztE+ndvj4X34wlYjw7dlsLPzqvEHTBRERGRteN7W966aWXVIkIiIiokYhE5c1yxpRMnGZwft4eHggMTERixYtwgsvvID8/HwoFAqEhoZi0aJFtX5pfvPNN5GYmIh3330Xtra2WL16NYKDq6c5sLOzw/vvv4/IyEio1Wr4+Pjgm2++0c1/vHXrVixduhRvvvkmMjMzYW9vjwEDBuAf//jHfXN269YN/fv3x4kTJ3RzRddYsGABrl69iuDgYFhaWmLy5MkIDQ2FSqXS6zd+/HjcuXMH/fv3h0QiwfTp0+tdQFMkEuHgwYN4++23ER4ejry8PCgUCgwZMgRyufyeWX/++WeEhYUBAH755RcMGTKk3r4jR47EtWvXdH/v06cPAMPm0yYiInoQqjuVOHguG0D1lGl/96hHR0SP7oOpn53CroQMtLc0w6xgr+aOSUTUIvC6qe1dN4m0vCprMoWFhZDJZFCpVLC1tRU6DhHRQ8vOzsbGjRvxdVl33NJaCR0HANBBVIJR0guYPHkyHB0dhY5DJKiysjKkpaXB3d0dUqkUAKBSqbB23Xpo1FXNlkMsMcG0N6ZCJpM12zmp+dT1c1aDv/+SofgzQ9S2fXr8GhbuPw9PuTW+nzGk1toJNXYlZGD+/84BABY81R2vPNqlOWMSkRG51++yRI2pMa6bOPKGiIiIyIjJZDJMe2MqSktLm+2clpaWLNwQERHRfX1+snrKtBf8Xest3ADAiwGdcLu0Aiu/T8HSby/AztIMz/u5NFdMIiIiQbB4Q0RERGTkZDIZiylERETUovyepcK5TBVMJSL8s+/9CzH/fqwrCkorsOloGt768ixspSYY8YiiGZISEREJQyx0ACIiIiIiIiIialtqRt2M6KFAeyuz+/YXiUSYP7I7nvdzgVqjxdTPTiP+yq2mjklERCQYFm+IiIiIiIiIiKjZlFWqsT85CwDwQj/XBu8nEonw/j99MKKHHBVVGkTsSMT5TNX9dyQiImqFWLwhIiIiIiIiIqJm8/3vSqjuVMJJJsXgbvYG7WsiEWPt2D4Y0KU9isurMGHLCVzNK26ipERERMJh8YaIiIjIyGi1WqEjkBHjzxcRET2szxOrp0x73t8VErHI4P2lphJsGu8PH2cZbpVU4F+fnEC26k5jxyQiI8bfaampNcbPGIs3REREREbC1NQUAFBaWipwEjJmNT9fNT9vREREhrieX4rfLt+CSASE+bk88HFspKbYFt4PXeytkFlwB//65ATySyoaMSkRGSNeM1FzaYzrJpPGCkNEREREwpJIJLCzs0Nubi4AwNLSEiKR4U+zEtVFq9WitLQUubm5sLOzg0QiEToSERG1QnvvjroZ1NUeru0tH+pYHazN8ekrAXj+o2O4nFuM8K0nsDNiAKzNebuLiOrGayZqao153cR/zYiIiIiMiEKhAADdxQhRY7Ozs9P9nBERERlCrdFib9INAMAL/Vwb5ZjOdhb4dFJ/hMXE48wNFV79NBFbJvaDuQkfMiCiuvGaiZpDY1w3sXhDREREZEREIhEcHR3h4OCAyspKoeOQkTE1NeWIGyIiemBHL+UhW1UGmYUpRvSQN9pxuznYYFt4f7y46Th+u3wLM3YnY/2LfR9oPR0iMn68ZqKm1ljXTSzeEBERERkhiUTCm+xERETUouw5WT1l2rN9nCE1bdzfU3q72mHjeH+Ebz2J784r8fb/zmHZP304HRIR1YvXTNTSiYUOQERERERERERExu1WcTl+uJADABjdSFOm/d2gbvZYO9YXYhGw++R1LI9NaZLzEBERNQcWb4iIiIiIiIiIqEn973QmKtVa9HKRobujbZOdJ6SnI5b90wcAEPPLFXz8y5UmOxcREVFTYvGGiIiIiIiIiIiajFar1U2Z9oJ/04y6+avR/Tph7pPeAIBl313E53fPTURE1JqweENERERERERERE3m9PUCXMothtRUjFG+Ts1yzteGdsWrQ7sAAObuO4vY88pmOS8REVFjYfGGiIiIiIiIiIiaTM3Il5E9HWErNW22884N8cZof1dotMC0z07j2OWbzXZuIiKih8XiDRERERERERERNYmS8ip8cyYLAPBCv6afMu2vRCIR3nu2J0IeUaBCrUHEjkScvVHQrBmIiIgeFIs3RERERERERETUJL49l42SCjXcOlgiwL19s5/fRCLGmrG+GNStA0oq1Ji49SQu5xY3ew4iIiJDsXhDRERERERERERNombKtBf6uUIkEgmSwdxEgo//5Y/eLjLkl1TgX58kILPgjiBZiIiIGorFGyIiIiIiIiIianSXc4uQeO02JGIRnu/rImgWa3MTbA3vj64drZCtKsO/PknAreJyQTMRERHdC4s3RERERERERETU6D5PvAEAGObVEQ62UoHTAO2tzPDppAA4yaS4mleCiVtPoqisUuhYREREdWLxhoiIiIiIiIiIGlWlWoN9p6qLNy/4uwqc5k9Odhb49JUAtLcyw7lMFSbvSEJZpVroWERERLWweENERERERERERI0q7kIubhZXwN7aHMO8HYSOo6drR2tsD+8Pa3MTxF+9hWmfnUaVWiN0LCIiIj0s3hARERERERERUaP6PPE6AOA5P2eYSlre7ScfFxk2jfeHmYkYh/7Iwbx956DVaoWORUREpNPy/vUkIiIiIiIiIqJWS6kqw88puQBa1pRpfxfYtQPWje0DsQjYm3QDy767yAIOERG1GC2ieLNhwwa4ublBKpUiICAAJ06cuGf/vXv3wtvbG1KpFD4+Pjh48KDedq1Wi6ioKDg6OsLCwgJBQUG4dOmSXp9Ro0ahU6dOkEqlcHR0xL/+9S9kZWXptqenp0MkEtV6HT9+vPHeOBERERERERGRkfny1A1otEA/t3bo2tFa6Dj3FPyIAu8/1wsAsPHIVcT8clXgRERERNUEL97s2bMHkZGRWLRoEU6dOoXevXsjODgYubm5dfY/duwYxo4di0mTJuH06dMIDQ1FaGgozp8/r+uzYsUKrF27FjExMUhISICVlRWCg4NRVlam6zNs2DB8/vnnSElJwZdffokrV67g+eefr3W+H374AdnZ2bqXn59f438TiIiIiIiIiIiMgEaj1U2ZNrpfJ4HTNMwL/q54e2R3AMDy2Iv47ESGwImIiIhaQPFm9erViIiIQHh4OHr06IGYmBhYWlpiy5YtdfZfs2YNQkJCMHv2bHTv3h1LlixB3759sX79egDVo26io6OxYMECPPPMM+jVqxd27NiBrKws7N+/X3ecmTNnYsCAAejcuTMGDhyIuXPn4vjx46isrNQ7X4cOHaBQKHQvU1PTJvteEBERERERERG1ZsfTbuHarVJYm5tgpI9C6DgNFjGkC/79WFcAwNv/O4eD57IFTkRERG2doMWbiooKJCUlISgoSNcmFosRFBSE+Pj4OveJj4/X6w8AwcHBuv5paWlQKpV6fWQyGQICAuo9Zn5+Pnbu3ImBAwfWKs6MGjUKDg4OGDx4ML7++usHep9ERERERERERG3B5yerR9083dsJlmYmAqcxzOxgL4zt3wkaLTBjdzJ+vXRT6EhERNSGCVq8uXnzJtRqNeRyuV67XC6HUqmscx+lUnnP/jVfG3LMt956C1ZWVujQoQMyMjLw1Vdf6bZZW1tj1apV2Lt3L7799lsMHjwYoaGh9yzglJeXo7CwUO9FRERERERERNQWqO5U4rvz1fdeRvdzFTiN4UQiEZaG9sRTPo6oUGsw+dNEnM64LXQsIiJqowSfNk1Is2fPxunTp3Ho0CFIJBKMHz8eWq0WAGBvb4/IyEgEBASgX79+eP/99/HSSy9h5cqV9R5v2bJlkMlkupera+v7RYWIiIiIiIiI6EF8nZyJ8ioNvOQ26O0iEzrOA5GIRVg9ujce9bBHaYUa4dtO4lJOkdCxiIioDRK0eGNvbw+JRIKcnBy99pycHCgUdc+LqlAo7tm/5mtDjmlvbw9PT08MHz4cu3fvxsGDB3H8+PF68wYEBODy5cv1bp83bx5UKpXudf369Xr7EhEREREREREZkz2J1fdBXujnCpFIJHCaB2duIkHMS37wdbVDQWkl/vXJCdy4XSp0LCIiamMELd6YmZnBz88PcXFxujaNRoO4uDgEBgbWuU9gYKBefwA4fPiwrr+7uzsUCoVen8LCQiQkJNR7zJrzAtVTn9UnOTkZjo6O9W43NzeHra2t3ouIiIiIiIiIyNj9nqXC+cxCmEpEeLaPs9BxHpqVuQm2TuwHDwdrKAvL8K9PTuBmcf33jIiIiBqb4CvHRUZGYsKECfD390f//v0RHR2NkpIShIeHAwDGjx8PZ2dnLFu2DAAwffp0DB06FKtWrcJTTz2F3bt3IzExERs3bgRQPT/pjBkzsHTpUnh4eMDd3R0LFy6Ek5MTQkNDAQAJCQk4efIkBg8ejHbt2uHKlStYuHAhunbtqivwbN++HWZmZujTpw8AYN++fdiyZQs2b97czN8hIiIiIiIiIqKW7fOT1aNuRvRQoL2VmcBpGkc7KzN8OikAz310DGk3SzBhywl8NnkAbKWmQkcjIqI2QPDizejRo5GXl4eoqCgolUr4+voiNjYWcrkcAJCRkQGx+M8BQgMHDsSuXbuwYMECzJ8/Hx4eHti/fz969uyp6zNnzhyUlJRg8uTJKCgowODBgxEbGwupVAoAsLS0xL59+7Bo0SKUlJTA0dERISEhWLBgAczNzXXHWbJkCa5duwYTExN4e3tjz549eP7555vpO0NERERERERE1PKVVaqxPzkLADC6n3Gt/6uQSfF/rwTg+Y+O4fesQryyPRE7Xu4PqalE6GhERGTkRFqtVit0CGNVWFgImUwGlUrFKdSIyChkZ2dj48aN+LqsO25prYSOAwDoICrBKOkFTJ48+Z5TWxIRUdPj779kKP7MEBmHr5IzMX13MpztLHB0zjCIxa13vZv6nM9UYezG4ygqr0JQdzliXuoLE4mgqxEQEVEr1dDfgfmvDBERERERERERPbA9d6dMe97PxSgLNwDQ01mGzRP8YW4ixg8XcvDWl+eg0fB5aCIiajos3hARERERERER0QPJuFWKY1duQSQCwvxdhI7TpAK6dMCGF/tCIhbhy1M38N7BC+CENkRE1FRYvCEiIiIiIiIiogeyN6l61M3gbvZwaWcpcJqmF9RDjpXP9wIAfPJrGv778xWBExERkbFi8YaIiIiIiMgIbNiwAW5ubpBKpQgICMCJEyfu2X/v3r3w9vaGVCqFj48PDh48qLddq9UiKioKjo6OsLCwQFBQEC5dulTnscrLy+Hr6wuRSITk5OTGektE1MKpNVp8kXQDAPCCv6vAaZrPP/u6IOofPQAAK79Pwf8dvyZwIiIiMkYs3hAREREREbVye/bsQWRkJBYtWoRTp06hd+/eCA4ORm5ubp39jx07hrFjx2LSpEk4ffo0QkNDERoaivPnz+v6rFixAmvXrkVMTAwSEhJgZWWF4OBglJWV1TrenDlz4OTk1GTvj4hapiOX8pCtKoOdpSlGPCIXOk6zenmwO954vBsAYOFX53H0Up7AiYiIyNiweENERERERNTKrV69GhEREQgPD0ePHj0QExMDS0tLbNmypc7+a9asQUhICGbPno3u3btjyZIl6Nu3L9avXw+getRNdHQ0FixYgGeeeQa9evXCjh07kJWVhf379+sd67vvvsOhQ4fwwQcfNPXbJKIW5vOT1VOmhfo6w9xEInCa5hc53BPP9nGGVgvsP50ldBwiIjIyLN4QERERERG1YhUVFUhKSkJQUJCuTSwWIygoCPHx8XXuEx8fr9cfAIKDg3X909LSoFQq9frIZDIEBAToHTMnJwcRERH49NNPYWl5/7UuysvLUVhYqPciotbpVnE5friQAwAY3a/tTJn2VyKRCMF3Rxyl5hQJnIaIiIwNizdERERERESt2M2bN6FWqyGX609ZJJfLoVQq69xHqVTes3/N13v10Wq1mDhxIl577TX4+/s3KOuyZcsgk8l0L1fXtnnDl8gY/O90JirVWvR2kaG7o63QcQTjpah+76k5RVBrtAKnISIiY8LiDRERERERERls3bp1KCoqwrx58xq8z7x586BSqXSv69evN2FCImoqWq0Wu+9OmfZCGx11U6NTe0tITcUor9IgI79U6DhERGREWLwhIiIiIiJqxezt7SGRSJCTk6PXnpOTA4VCUec+CoXinv1rvt6rz48//oj4+HiYm5vDxMQE3bpVL9zt7++PCRMm1Hlec3Nz2Nra6r2IqPU5lVGAy7nFkJqK8XRvJ6HjCEoiFsHDwQYAkKLkVJBERNR4WLwhIiIiIiJqxczMzODn54e4uDhdm0ajQVxcHAIDA+vcJzAwUK8/ABw+fFjX393dHQqFQq9PYWEhEhISdH3Wrl2LM2fOIDk5GcnJyTh48CAAYM+ePXjvvfca9T0SUcvy+d1RNyN9HGErNRU4jfA85TXFm2KBkxARkTExEToAERERERERPZzIyEhMmDAB/v7+6N+/P6Kjo1FSUoLw8HAAwPjx4+Hs7Ixly5YBAKZPn46hQ4di1apVeOqpp7B7924kJiZi48aNAKoX4Z4xYwaWLl0KDw8PuLu7Y+HChXByckJoaCgAoFOnTnoZrK2tAQBdu3aFi4tLM71zImpuJeVVOHA2CwAw2r9tT5lWw1txt3iTw5E3RETUeFi8ISIiIiIiauVGjx6NvLw8REVFQalUwtfXF7GxsZDL5QCAjIwMiMV/TrwwcOBA7Nq1CwsWLMD8+fPh4eGB/fv3o2fPnro+c+bMQUlJCSZPnoyCggIMHjwYsbGxkEqlzf7+iKjl+PZsNkoq1HC3t0J/9/ZCx2kRvGqKN8oigZMQEZExYfGGiIiIiIjICEydOhVTp06tc9vPP/9cqy0sLAxhYWH1Hk8kEmHx4sVYvHhxg87v5uYGrVbboL5E1HrtSayeMi3M3wUikUjgNC1DTfEm/VYpyirVkJpKBE5ERETGgGveEBERERERERHRfV3OLULStduQiEV4vi+nR6zhYGMOO0tTqDVaXM7lujdERNQ4WLwhIiIiIiIiIqL7+jzxBgBgmJcDHGw5hWINkUgET3n16JvUHE6dRkREjYPFGyIiIiIiIiIiuqeKKg32naou3ozu5ypwmpbHm+veEBFRI2PxhoiIiIiIiIiI7unHizm4WVyBjjbmGObVUeg4LU7NyJsUjrwhIqJGwuINERERERERERHd056T1wEAz/V1gYmEt5P+jiNviIiosfFfWyIiIiIiIiIiqpdSVYZfUvMAAC/4uwicpmXyuDvyJltVBtWdSoHTEBGRMWDxhoiIiIiIiIiI6vVF0nVotEB/t/bo0tFa6DgtkszCFE4yKQAglVOnERFRI2DxhoiIiIiIiIiI6qTRaPF54g0AwAv9XAVO07J5ceo0IiJqRCzeEBERERERERFRnY6n3UJGfilszE0w0kchdJwWzZPFGyIiakQs3hARERERERERUZ0+P3kdAPC0rxMszUwETtOyebN4Q0REjYjFGyIiIiIiIiIiqkV1pxLfnVcCAEb7c8q0+/GU3y3e5BRBq9UKnIaIiFo7Fm+IiIiIiIiIiKiWr5MzUV6lgbfCBr1cZELHafG6drSGRCyC6k4lcgrLhY5DREStHIs3RERERERERERUy+67U6a94O8KkUgkcJqWT2oqgVsHSwDVo2+IiIgeBos3RERERERERESk53ymCr9nFcJMIsazfZyFjtNqeCtsAQApykKBkxARUWvH4g0REREREREREen5PLF61M3wR+RoZ2UmcJrWQ7fujbJY4CRERNTasXhDREREREREREQ6ZZVq7D+dCQAY7e8qcJrWxUtxt3iTw5E3RET0cFi8ISIiIiIiIiIine9/V6KwrArOdhYY3M1e6Ditivfd4s2lnGKoNVqB0xARUWvG4g0REREREREREensOVk9ZVqYvwvEYpHAaVoX1/aWkJqKUV6lwbVbJULHISKiVqxFFG82bNgANzc3SKVSBAQE4MSJE/fsv3fvXnh7e0MqlcLHxwcHDx7U267VahEVFQVHR0dYWFggKCgIly5d0uszatQodOrUCVKpFI6OjvjXv/6FrKwsvT5nz57Fo48+CqlUCldXV6xYsaJx3jARERERERERUQuUcasUx67cgkgEhHHKNINJxKK/rHtTJHAaIiJqzQQv3uzZsweRkZFYtGgRTp06hd69eyM4OBi5ubl19j927BjGjh2LSZMm4fTp0wgNDUVoaCjOnz+v67NixQqsXbsWMTExSEhIgJWVFYKDg1FWVqbrM2zYMHz++edISUnBl19+iStXruD555/XbS8sLMSIESPQuXNnJCUlYeXKlXjnnXewcePGpvtmEBEREREREREJaG9S9aibwd3s4WxnIXCa1klXvMlh8YaIiB6c4MWb1atXIyIiAuHh4ejRowdiYmJgaWmJLVu21Nl/zZo1CAkJwezZs9G9e3csWbIEffv2xfr16wFUj7qJjo7GggUL8Mwzz6BXr17YsWMHsrKysH//ft1xZs6ciQEDBqBz584YOHAg5s6di+PHj6OyshIAsHPnTlRUVGDLli145JFHMGbMGEybNg2rV69u8u8JEREREREREVFzU2u02Jt4AwAwuh9H3TyomnVvOPKGiIgehqDFm4qKCiQlJSEoKEjXJhaLERQUhPj4+Dr3iY+P1+sPAMHBwbr+aWlpUCqVen1kMhkCAgLqPWZ+fj527tyJgQMHwtTUVHeeIUOGwMzMTO88KSkpuH37dp3HKS8vR2Fhod6LiIiIiIiIiKg1OJKaB2VhGdpZmmJ4D7nQcVotjrwhIqLGIGjx5ubNm1Cr1ZDL9X8hkMvlUCqVde6jVCrv2b/ma0OO+dZbb8HKygodOnRARkYGvvrqq/ue56/n+Ltly5ZBJpPpXq6ufEqFiIiIiIiIiFqHPSerp0wL7eMMcxOJwGlar5qRN+k3S1BWqRY4DRERtVaCT5smpNmzZ+P06dM4dOgQJBIJxo8fD61W+8DHmzdvHlQqle51/fr1RkxLRERERERERNQ0bhaX44cLOQA4ZdrD6mhjDjtLU2i0wOXcYqHjEBFRK2Ui5Mnt7e0hkUiQk5Oj156TkwOFQlHnPgqF4p79a77m5OTA0dFRr4+vr2+t89vb28PT0xPdu3eHq6srjh8/jsDAwHrP89dz/J25uTnMzc3v866JiIiIiIiIiFqW/53KRJVGi94uMngrbIWO06qJRCJ4yW2QkJaPFGURejrLhI5EREStkKAjb8zMzODn54e4uDhdm0ajQVxcHAIDA+vcJzAwUK8/ABw+fFjX393dHQqFQq9PYWEhEhIS6j1mzXmB6nVras5z5MgRVFZW6p3Hy8sL7dq1M/CdEhERERERERG1TFqtFnsSq2cPGd2vk8BpjEPN1GmpXPeGiIgekODTpkVGRmLTpk3Yvn07Lly4gNdffx0lJSUIDw8HAIwfPx7z5s3T9Z8+fTpiY2OxatUqXLx4Ee+88w4SExMxdepUANVPN8yYMQNLly7F119/jXPnzmH8+PFwcnJCaGgoACAhIQHr169HcnIyrl27hh9//BFjx45F165ddQWeF198EWZmZpg0aRJ+//137NmzB2vWrEFkZGTzfoOIiIiIiMjoHDlyBFVVVbXaq6qqcOTIEQESEVFbdiqjAJdzi2FhKsHTvR3vvwPdl+fd4s1FJYs3RET0YASdNg0ARo8ejby8PERFRUGpVMLX1xexsbGQy+UAgIyMDIjFf9aYBg4ciF27dmHBggWYP38+PDw8sH//fvTs2VPXZ86cOSgpKcHkyZNRUFCAwYMHIzY2FlKpFABgaWmJffv2YdGiRSgpKYGjoyNCQkKwYMEC3bRnMpkMhw4dwpQpU+Dn5wd7e3tERUVh8uTJzfjdISIiIiGoVCqUlpYKHUOPpaUlZDJOuUFkLIYNG4bs7Gw4ODjotatUKgwbNgxqNRe4JqLm8/nJ6lE3I30cYSM1FTiNcagZeZPC4g0RET0gwYs3ADB16lTdyJm/+/nnn2u1hYWFISwsrN7jiUQiLF68GIsXL65zu4+PD3788cf75urVqxeOHj16335ERERkPFQqFdauWw+NuvYT8UISS0ww7Y2pLOAQGQmtVguRSFSr/datW7CyshIgERG1VcXlVfjmbBYAYHQ/V4HTGA8PeXXxRllYBlVpJWSWLIoREZFhWkTxhoiIiKilKC0thUZdhV8q3KHSSIWOAwCQicsw1CwNpaWlLN4QtXL//Oc/AVQ/cDZx4kTdyH8AUKvVOHv2LAYOHChUPCJqg749m4XSCjW62FuhnxvX+G0stlJTONtZILPgDlJyitDfvb3QkYiIqJVh8YaIiIioDiqNFLe0LeTpd43QAYiosdQUYLVaLWxsbGBhYaHbZmZmhgEDBiAiIkKoeETUBu25O2VamL9rnSMC6cF5yq1ZvCEiogfG4g0REREREVEz2bp1KwDAzc0Ns2bN4hRpRCSoy7lFOJVRAIlYhOf8nIWOY3S8FLb4KSUPKcpCoaMQEVErJBY6ABERERERUVuzaNEimJub44cffsDHH3+MoqLqBa2zsrJQXFwscDoiaitqRt0M83KAg03LmC7WmHgprAEAqUp+rhMRkeE48oaIiIiIiKiZXbt2DSEhIcjIyEB5eTmGDx8OGxsbLF++HOXl5YiJiRE6IhEZuYoqDfadygQAjOnnKnAa4+QltwUAXFQWQqvVclo6IiIyCEfeEBERERERNbPp06fD398ft2/f1lv35tlnn0VcXJyAyYiorfjxYg5ulVTAwcYcj3l1FDqOUerqYAWJWITCsirkFJYLHYeIiFoZjrwhIiIiIiJqZkePHsWxY8dgZmam1+7m5obMzEyBUhFRW1IzZdpzfi4wkfDZ3qZgbiKBu70VLucW46KyEAoZp6YjIqKGM+hf58rKSnTt2hUXLlxoqjxERERERERGT6PRQK1W12q/ceMGbGxsBEhERG1JtuoOfknNAwC84M8p05qSl6L6Mz1FWSRwEiIiam0MKt6YmpqirKysqbIQERERERG1CSNGjEB0dLTu7yKRCMXFxVi0aBFGjhwpXDAiahO+SLwBjRbo794e7vZWQscxal7yu8WbHBZviIjIMAaPi50yZQqWL1+OqqqqpshDRERERERk9FatWoXffvsNPXr0QFlZGV588UXdlGnLly8XOh4RGTGNRovPk6qnTBvNUTdNjiNviIjoQRm85s3JkycRFxeHQ4cOwcfHB1ZW+k9o7Nu3r9HCERERERERGSMXFxecOXMGu3fvxtmzZ1FcXIxJkyZh3LhxsLCwEDoeERmx41dv4Xr+HdiYm2Ckj6PQcYxezcibS7nFUGu0kIhFAiciIqLWwuDijZ2dHZ577rmmyEJERERERNRmmJiY4KWXXhI6BhG1MXsSq0fdPO3rBAszicBpjF+n9paQmopRVqlB+q0SdO1oLXQkIiJqJQwu3mzdurUpchAREREREbUZX3/9dZ3tIpEIUqkU3bp1g7u7ezOnIiJjpyqtxHfnlQCAMf04ZVpzEItF8JTb4OwNFVKVRSzeEBFRgxlcvKmRl5eHlJQUAICXlxc6duzYaKGIiIiIiIiMWWhoKEQiEbRarV57TZtIJMLgwYOxf/9+tGvXTqCURGRsvjqTiYoqDbwVNvBxlgkdp83wulu8uagswpOcqo6IiBpIbOgOJSUlePnll+Ho6IghQ4ZgyJAhcHJywqRJk1BaWtoUGYmIiIiIiIzK4cOH0a9fPxw+fBgqlQoqlQqHDx9GQEAADhw4gCNHjuDWrVuYNWuW0FGJyIjsOVk9Zdrofq4Qibj2SnPxUlSve5OaUyRwEiIiak0MLt5ERkbil19+wTfffIOCggIUFBTgq6++wi+//II333yzKTISEREREREZlenTp2P16tV44oknYGNjAxsbGzzxxBNYuXIlZs+ejUGDBiE6OhqHDx8WOioRGYnzmSr8nlUIM4kYob7OQsdpU2qKNylKFm+IiKjhDJ427csvv8QXX3yBxx57TNc2cuRIWFhY4IUXXsBHH33UmPmIiIiIiIiMzpUrV2Bra1ur3dbWFlevXgUAeHh44ObNm80djYiM1OeJ1aNuRjwiRzsrM4HTtC01xZv0WyUoq1RDaioROBEREbUGBo+8KS0thVwur9Xu4ODAadOIiIiIiIgawM/PD7Nnz0ZeXp6uLS8vD3PmzEG/fv0AAJcuXYKrKxcUJ6KHV1apxv9OZwKonjKNmldHa3O0szSFRgtczi0WOg4REbUSBhdvAgMDsWjRIpSVlena7ty5g3fffReBgYGNGo6IiIiIiMgYffLJJ0hLS4OLiwu6deuGbt26wcXFBenp6di8eTMAoLi4GAsWLBA4KREZg9jzShSVVcHZzgKDutoLHafNEYlEutE3Fzl1GhERNZDB06ZFR0cjJCQELi4u6N27NwDgzJkzkEql+P777xs9IBERERERkbHx8vLCH3/8gUOHDiE1NVXXNnz4cIjF1c/YhYaGCpiQiIzJnpPVU6aF+btALBYJnKZt8pLb4PjVfKTmsHhDREQNY3DxxsfHB5cuXcLOnTtx8eJFAMDYsWMxbtw4WFhYNHpAIiIiIiIiYyQWixESEoKQkBChoxCREbt2qwTxV29BJALC/DllmlC8FNXrnHHkDRERNZRBxZvKykp4e3vjwIEDiIiIaKpMRERERERERmft2rUN7jtt2rQmTEJEbcnexBsAgEc9OsLZjg/dCsVLYQ0ASGXxhoiIGsig4o2pqaneWjdERERERETUMB9++KHe3/Py8lBaWgo7OzsAQEFBASwtLeHg4MDiDRE1CrVGiy+Sqos3oznqRlCe8uo1b5SFZVCVVkJmaSpwIiIiaunEhu4wZcoULF++HFVVVU2Rh4iIiIiIyCilpaXpXu+99x58fX1x4cIF5OfnIz8/HxcuXEDfvn2xZMkSoaMSkZE4kpoHZWEZ2lmaIqiHg9Bx2jQbqalu5FMK170hIqIGMHjNm5MnTyIuLg6HDh2Cj48PrKys9Lbv27ev0cIREREREREZo4ULF+KLL76Al5eXrs3Lywsffvghnn/+eYwbN07AdERkLPacvA4AeLaPC8xNJAKnIS+FDTIL7iBFWYj+7u2FjkNklEorqgccWJoZfNubqMUx+KfYzs4Ozz33XFNkISIiIiIiahOys7PrnM1ArVYjJydHgEREZGxuFpfjhwvVnyej+3HKtJbAS2GDHy/m4iLXvSEymEajRX5pBZSqMuQUliH77lelqgzKwj//XFhWBXMTMf7370Ho4WQrdGyih2JQ8aaqqgrDhg3DiBEjoFAomioTERERERGRUXviiSfw6quvYvPmzejbty8AICkpCa+//jqCgoIe6JgbNmzAypUroVQq0bt3b6xbtw79+/evt//evXuxcOFCpKenw8PDA8uXL8fIkSN127VaLRYtWoRNmzahoKAAgwYNwkcffQQPDw9dn1GjRiE5ORm5ublo164dgoKCsHz5cjg5OT3QeyCixrPv1A1UabTo7WoHL4WN0HEIgNfddW9SOW0akZ6ySjVyC8uhLLxbiLlbkFH+5WtuURkq1doGHa+8SoP//nwZ61/s28TJiZqWQcUbExMTvPbaa7hw4UJT5SEiIiIiIjJ6W7ZswYQJE+Dv7w9T0+pFq6uqqhAcHIzNmzcbfLw9e/YgMjISMTExCAgIQHR0NIKDg5GSkgIHh9rrXBw7dgxjx47FsmXL8I9//AO7du1CaGgoTp06hZ49ewIAVqxYgbVr12L79u1wd3fHwoULERwcjD/++ANSqRQAMGzYMMyfPx+Ojo7IzMzErFmz8Pzzz+PYsWMP8d0hooel1Wp1U6aN9ueom5aipoh2UVkErVYLkUgkcCKipqXVaqG6UwllzUiZv42SURaWQ6m6g9ullQ06nkgEdLAyh0JmDoWtFHJbafVXmRSOsuo/q+5U4vmYeBw8l43r+aVwbW/ZxO+SqOkYPG1a//79cfr0aXTu3Lkp8hARERERERm9jh074uDBg0hNTcXFixcBAN7e3vD09Hyg461evRoREREIDw8HAMTExODbb7/Fli1bMHfu3Fr916xZg5CQEMyePRsAsGTJEhw+fBjr169HTEwMtFotoqOjsWDBAjzzzDMAgB07dkAul2P//v0YM2YMAGDmzJm6Y3bu3Blz585FaGgoKisrdUUpImp+pzJu40peCSxMJXi6t6PQceiuLh2tIBGLUFRWBWVhGRxlFkJHInpglWoNcovKddOYKf82WqamrbxK06DjmZmIobhbjFHIql9y3d/NIbeVwsFGCjMT8X2P9aiHPY5euoktv6Vh0dOPPOxbJRKMwcWbf//733jzzTdx48YN+Pn5wcrKSm97r169Gi0cERERERGRMfP09Hzggk2NiooKJCUlYd68ebo2sViMoKAgxMfH17lPfHw8IiMj9dqCg4Oxf/9+AEBaWhqUSqXeFG4ymQwBAQGIj4/XFW/+Kj8/Hzt37sTAgQNZuCESWM2om6d6OcJGyv8fWwpzEwm62FvhUm4xLiqLWLyhFk2j0eJkej4y8kurCzGFZVCqyqEsvAOlqhy3SsqhbdgsZmhnaVpdiLk7OkZuWz1SRn737wpbKewsTRttNNrkIV1w9NJN7Dl5HTOe8ITMkp+D1DoZXLyp+SV92rRpujaRSKQb7qlWqxsvHRERERERkRF6+eWX77l9y5YtDT7WzZs3oVarIZfL9drlcrluVM/fKZXKOvsrlUrd9pq2+vrUeOutt7B+/XqUlpZiwIABOHDgQL1Zy8vLUV5ervt7YWHhfd4dERmqvEqNA2ezAQCj+3HKtJbGU2GDS7nFSFUWYZhX7WktiVqKT35Nw3sH7710holYBLmtFHJbczjKLO4WaMz/MmKmulAjNZU0U+pqg7vZw1thg4vKIvxfwjVMGdatWc9P1FjuP87sb9LS0mq9rl69qvv6IDZs2AA3NzdIpVIEBATgxIkT9+y/d+9eeHt7QyqVwsfHBwcPHtTbrtVqERUVBUdHR1hYWCAoKAiXLl3SbU9PT8ekSZPg7u4OCwsLdO3aFYsWLUJFRYVeH5FIVOt1/PjxB3qPRERERERENW7fvq33ys3NxY8//oh9+/ahoKBA6HgGmT17Nk6fPo1Dhw5BIpFg/Pjx0NbzKO6yZcsgk8l0L1dX3lgmamznM1UorVDD3toM/p3bCR2H/sZbXr3uTYqySOAkRPf2S2oeAOARJ1s87+eCqcO6YUloT2we748DbwzGybeDkLr0Sfw293Hs+/cgbBjXF1FP98DkIV3xjK8zArp0QOcOVs1euAGqBxpMHtIFALDtWDrKqzjYgFong0feNPZaN0IsrHnx4kVoNBp8/PHH6NatG86fP4+IiAiUlJTggw8+0DvfDz/8gEce+XNuxA4dOjTq+yciIiIiorbnf//7X602jUaD119/HV27djXoWPb29pBIJMjJydFrz8nJgUKhqHMfhUJxz/41X3NycuDo6KjXx9fXt9b57e3t4enpie7du8PV1RXHjx9HYGBgrfPOmzdPb7q2wsJCFnCIGlnStdsAgL6d2jXaFETUeDwVd4s3OSzeUMtVpdbgdEb1Z8kHYb3R3dFW4ESG+0cvJ6yITYGysAxfnc7CCxyJSK2QwSNvAODTTz/FoEGD4OTkhGvXrgEAoqOj8dVXXxl8rL8urNmjRw/ExMTA0tKy3mkC/rqwZvfu3bFkyRL07dsX69evB4BaC2v26tULO3bsQFZWlm7+5pCQEGzduhUjRoxAly5dMGrUKMyaNQv79u2rdb4OHTpAoVDoXpy7mYiIiIiImoJYLEZkZCQ+/PBDg/YzMzODn58f4uLidG0ajQZxcXF1FlAAIDAwUK8/ABw+fFjX393dHQqFQq9PYWEhEhIS6j1mzXkB6E2N9lfm5uawtbXVexFR40pMr77h6u/GUTctkffd4s2l3GJUqRu2kDtRc0vJKUJJhRo25ibwvDtarLUxMxHj5cFuAICNR69Co2ngAj1ELYjBxZuPPvoIkZGRGDlyJAoKCnRr3NjZ2SE6OtqgY9UsrPnXRTAbsrDmX/sD1Qtr1vS/38Ka9VGpVGjfvn2t9lGjRsHBwQGDBw/G119/fc/3U15ejsLCQr0XERERERFRQ125cgVVVVUG7xcZGYlNmzZh+/btuHDhAl5//XWUlJQgPDwcADB+/HjMmzdP13/69OmIjY3FqlWrcPHiRbzzzjtITEzE1KlTAVRPNzJjxgwsXboUX3/9Nc6dO4fx48fDyckJoaGhAICEhASsX78eycnJuHbtGn788UeMHTsWXbt2vWeBh4iajlarxam7T8v7ccq0Fsm1nSUsTCWoqNIg/Vap0HGI6lQzgs+3kx0k4tY7gm9M/06wNjfB5dxi3TRwRK2JwdOmrVu3Dps2bUJoaCjef/99Xbu/vz9mzZpl0LGEXlizxuXLl7Fu3Tq9KdOsra2xatUqDBo0CGKxGF9++SVCQ0Oxf/9+jBo1qs7jLFu2DO++++493jERERERERH0pg4Dqm+4Zmdn49tvv8WECRMMPt7o0aORl5eHqKgoKJVK+Pr6IjY2VnddlJGRAbH4z2f3Bg4ciF27dmHBggWYP38+PDw8sH//ft1U1AAwZ84clJSUYPLkySgoKMDgwYMRGxsLqVQKALC0tMS+ffuwaNEilJSUwNHRESEhIViwYAHMzc0f5NtCRA/p2q1S3CyugJmJGD2dZULHoTqIxSJ4yq1x5oYKqTlF6OZgLXQkolpqRvC19iKwrdQUY/u7YtPRNGw8chXDvGsv0UHUkhlcvElLS0OfPn1qtZubm6OkpKRRQjWnzMxMhISEICwsDBEREbp2e3t7vQuqfv36ISsrCytXrqy3eMP5m4mIiIiIqCFOnz6t93exWIyOHTti1apVePnllx/omFOnTtWNnPm7n3/+uVZbWFgYwsLC6j2eSCTC4sWLsXjx4jq3+/j44Mcff3ygrETUNGqelu/lLIO5SfMvEk4N46WwwZkbKlxUFmGkj+P9dyBqZjWfJf6da89S1NqED3LH1t/SEX/1Fs7dUMHHhYVtaj0MLt64u7sjOTkZnTt31muPjY1F9+7dDTqW0AtrZmVlYdiwYRg4cCA2btx437wBAQE4fPhwvdvNzc35hBkREREREd3XTz/9JHQEIjJCideM42l5Y1ezhkiqskjgJES1KVVlyCy4A7Goetq01s7JzgJP93bC/05nYuPRq1g3tvagBKKWyuA1byIjIzFlyhTs2bMHWq0WJ06cwHvvvYd58+Zhzpw5Bh1LyIU1MzMz8dhjj8HPzw9bt27Vm0KgPsnJyXoFISIiIiIiooeRm5uLo0eP4ujRo8jNzRU6DhG1cknX8gGweNPSeStsAVQvCk/U0tSMuvFW2MLa3ODn/lukVx51BwAcPJeN6/lca4paD4P/D3zllVdgYWGBBQsWoLS0FC+++CKcnJywZs0ajBkzxuAAkZGRmDBhAvz9/dG/f39ER0fXWljT2dkZy5YtA1C9sObQoUOxatUqPPXUU9i9ezcSExN1I2f+urCmh4cH3N3dsXDhQr2FNWsKN507d8YHH3yAvLw/F6yqGbmzfft2mJmZ6aaI27dvH7Zs2YLNmzcb/B6JiIiIiIj+qrCwEFOmTMFnn30GjUYDAJBIJBg9ejQ2bNgAmYxTehCRYVR3KpGaUwwA6MviTYvmqahe5yb9VgnKKtWQmnKKO2o5Eu8Wgf3djOdz5BEnGQZ3s8evl29i62/piHq6h9CRiBrkgcqn48aNw7hx41BaWori4mI4ODz4Yk9CLKx5+PBhXL58GZcvX4aLi4teHq1Wq/vzkiVLcO3aNZiYmMDb2xt79uzB888//8DvlYiIiIiICAAiIiJw+vRpfPvtt7oZAuLj4zF9+nS8+uqr2L17t8AJiai1OZVR/bS8u70V7K05pXtL1tHaHO2tzJBfUoFLOcVcg4NalFNGOv3i5CFd8Ovlm9h9MgPTn/CAzNJU6EhE9/VQY98sLS1haWn50CGae2HNiRMnYuLEiffMNGHCBEyYMOGefYiIiIiIiB7EgQMH8P3332Pw4MG6tuDgYGzatAkhISECJiOi1qrmhmvfTsZ1w9UYiUQieMqtcfxqPlJyili8oRbjToUav2cVAjC+4s2jHvbwVtjgorIIO09cw78f6yZ0JKL7MnjNGyIiIiIiIno4HTp0qHNqNJlMhnbtjOtmCRE1j8T06uKNMU11ZMx0694oCwVOQvSn5OsFqNJoIbc1h7OdhdBxGpVIJELEo10AANt+S0d5lVrgRET3x+INERERERFRM1uwYAEiIyOhVCp1bUqlErNnz8bChQsFTEZErVGVWoPk6wUAjO9peWPlpbABAKTcXaeIqCWomX7Rv3N7iEQigdM0vqd7O0FhK0VuUTm+Ts4SOg7RfT3UtGlERERERETUMH369NG7EXLp0iV06tQJnTp1AlC93qe5uTny8vLw6quvChWTiFqhC9lFuFOphq3UBN06WgsdhxrAU363eMORN9SCJKbnAwD6GmkR2MxEjPBBblj23UVsOnoVz/u5GGWRiozHQxVvysrKIJVKGysLERERERGR0QoNDRU6AhEZqaRrf95wFYt5I7I18JRXF9lyCstRUFoBO0szgRNRW6fRaHEqowAA4G+kxRsAGBvQCet+vIzUnGL8nJqHYV4OQkciqpfBxRuNRoP33nsPMTExyMnJQWpqKrp06YKFCxfCzc0NkyZNaoqcRERERERErdqiRYuEjkBERirxWs1UR8Z7w9XY2EhN4WxngcyCO0hRFiGgSwehI1EbdyWvGKo7lZCaitHDyVboOE3GVmqKMf1csfnXNGw6cpXFG2rRDF7zZunSpdi2bRtWrFgBM7M/nwro2bMnNm/e3KjhiIiIiIiIiIjo3pLuFm/8OrcXOAkZwlu37k2RwEmI/vwc6e1iB1OJcS+THj7YHRKxCMeu3ML5TJXQcYjqZfD/iTt27MDGjRsxbtw4SCQSXXvv3r1x8eLFRg1HRERERERERET1yyq4g2xVGSRiEXq7yoSOQwbwrCneKFm8IeHpRvC5Gf8IPmc7CzzdyxEAsPHIVYHTENXP4OJNZmYmunXrVqtdo9GgsrKyUUIREREREREREdH91dxwfcTJFpZmD7W0MTUzbxZvqAU5pRvBZ/zFGwB45dEuAIBvz2Xjxu1SgdMQ1c3g4k2PHj1w9OjRWu1ffPEF+vTp0yihiIiIiIiIiIjo/mpuuPbt1DZuuBoTT/mf06ZptVqB01Bbdqu4HFdvlgBoO58lPZ1lGNStA9QaLbb+li50HKI6GfxIRlRUFCZMmIDMzExoNBrs27cPKSkp2LFjBw4cONAUGYmIiIiIiIxGZWUlvL29ceDAAXTv3l3oOETUyiVeywfQNqY6MjZdO1rDRCxCUVkVslVlcLKzEDoStVE16910c7CGnaXZfXobj8lDuuK3y7ew+0QGpj3hAZmFqdCRiPQYPPLmmWeewTfffIMffvgBVlZWiIqKwoULF/DNN99g+PDhTZGRiIiIiIjIaJiamqKsrEzoGERkBErKq3Ahu3rKrbYy1ZExMTMRo0tHKwDVo2+IhJKUcXe9mzb2OTLEwx5echuUVKixKyFD6DhEtRhcvAGARx99FIcPH0Zubi5KS0vx66+/YsSIEY2djYiIiIiIyChNmTIFy5cvR1VVldBRiKgVO3O9AGqNFs52FnCUcdRGa6SbOo3r3pCAktLvTr/Yxoo3IpEIEUOq177Z+lsaKqo0Aici0mfwtGknT56ERqNBQECAXntCQgIkEgn8/f0bLRwREREREZExOnnyJOLi4nDo0CH4+PjAyspKb/u+ffsESkZErUnNVEdt7YarMfFW2ODA2WwWb0gw5VVqnM1UAWh7I28AYFRvJ6z8/iJyCsvx9ZksPO/nInQkIh2DR95MmTIF169fr9WemZmJKVOmNEooIiIiIiIiY2ZnZ4fnnnsOwcHBcHJygkwm03sRETVE4rW2OdWRMeHIGxLa+cxCVFRp0N7KDO72VvffwciYmYgRPsgdALDpyFVotVqBExH9yeCRN3/88Qf69u1bq71Pnz74448/GiUUERERERGRMdu6davQEYioldNotDh1d50KrnfTenkrbAEAl/OKUaXWwETyQCscED2wUzUj+Dq1g0gkEjiNMMb274R1cZeQklOEX1Lz8JiXg9CRiAA8wMgbc3Nz5OTk1GrPzs6GiYnBtSAiIiIiIqI2Ky8vD7/++it+/fVX5OXlCR2HiFqRS7nFKCqrgqWZBN4KG6Hj0ANyaWcBSzMJKqo0SL9VKnQcaoMSr+UDAPzd2m4RWGZhijH9OwEANh29KnAaoj8ZXLwZMWIE5s2bB5VKpWsrKCjA/PnzMXz48EYNR0REREREZIxKSkrw8ssvw9HREUOGDMGQIUPg5OSESZMmobSUN++I6P5qbrj26WTH0RqtmFgsggenTiOBaLVa3dpZbX0EX/ggN0jEIvx2+RbOZ6ruvwNRMzD4X/cPPvgA169fR+fOnTFs2DAMGzYM7u7uUCqVWLVqVVNkJCIiIiIiMiqRkZH45Zdf8M0336CgoAAFBQX46quv8Msvv+DNN98UOh4RtQK6G66d2vYNV2PgJbcGAKTksHhDzSsjvxQ3iytgKhHBx7ltr7nn0s4S/+jlCICjb6jlMLh44+zsjLNnz2LFihXo0aMH/Pz8sGbNGpw7dw6urq5NkZGIiIiIiMiofPnll/jkk0/w5JNPwtbWFra2thg5ciQ2bdqEL774Quh4RNQK6Io3bu0FTkIPy+vuujcpykKBk1Bbk5he/TnS01kGqalE4DTCi3i0CwDgwNlsZBbcETgNEfBAi9RYWVlh8uTJjZ2FiIiIiIioTSgtLYVcLq/V7uDgwGnTiOi+8orKce1WKUSi6mnTqHWrWbMoNadY4CTU1iRlVBdv/Nv4lGk1ejrLMLBrBxy7cgtbf03Dgn/0EDoStXENKt58/fXXePLJJ2Fqaoqvv/76nn1HjRrVKMGIiIiIiIiMVWBgIBYtWoQdO3ZAKpUCAO7cuYN3330XgYGBAqcjopauZtSNl9wGtlJTgdPQw/K8u+ZN+q0S3KlQw8KMIyCoeSSl16x3wxF8NSYP6YJjV27hsxMZeOMJD8gs+BlLwmlQ8SY0NBRKpRIODg4IDQ2tt59IJIJarW6sbEREREREREYpOjoaISEhcHFxQe/evQEAZ86cgVQqxffffy9wOiJq6ZKu5QMA+vJpeaPQ0cYcHazMcKukApdyi9DLxU7oSNQGqO5UIjW3ep0lP36W6Az17AgvuQ1Scorw2YkMvDa0q9CRqA1r0Jo3Go0GDg4Ouj/X92LhhoiIiIiI6P58fHxw6dIlLFu2DL6+vvD19cX777+PS5cu4ZFHHhE6HhG1cDUjbzjVkfGoGX2ToiwSOAm1FaczbkOrBTp3sERHG3Oh47QYIpEIrzzqDgDY+lsaKqo0AieituyB1rwhIiIiIiKiB1NZWQlvb28cOHAAERERQscholamrFKN85nVC9vzaXnj4aWwQfzVWyzeULM5dbcI7NeJnyN/N8rXCSu/T0FOYTm+OZOF5/xchI5EbVSDijdr165t8AGnTZv2wGGIiIiIiIiMnampKcrKyoSOQUSt1PlMFSrUGthbm6NTe0uh41Aj8VLcHXmTw+INNY/EmuKNG4s3f2duIkH4IHcsj72ITUev4p99nSESiYSORW1Qg4o3H374YYMOJhKJWLwhIiIiIiK6jylTpmD58uXYvHkzTEw4IQIRNVziX6ZM481E46Er3nDkDTWDKrUGydcLAHAEX31eDOiE9T9ewkVlEY5cuomhnh2FjkRtUIOuEtLS0po6BxERERERUZtx8uRJxMXF4dChQ/Dx8YGVlZXe9n379gmUjIhaupr1bnjD1bjUrHmTW1SO2yUVaGdlJnAiMmYXlUUorVDDxtwEng42QsdpkWQWphjdrxO2/JaGTUeusnhDghA/7AHUajWSk5Nx+/btxshDRERERERk9Ozs7PDcc88hODgYTk5OkMlkei8iorpotdo/16ngVEdGxdrcBC7tLABw6jRqeonp+QCAPp3bQSzmCL76vDzYDRKxCL9evonfs1RCx6E2yODx+TNmzICPjw8mTZoEtVqNIUOGID4+HpaWljhw4AAee+yxJohJRERERERkHKqqqjBs2DCMGDECCoVC6DhE1Iqk3yrFrZIKmJmI8YiTrdBxqJF5K2xw4/YdpOYUYUCXDkLHISOWlFEAoHr6RaqfSztLPOXjiK/PZGHTkauIHtNH6EjUxhg88uaLL75A7969AQDffPMN0tPTcfHiRcycORNvv/12owckIiIiIiIyJiYmJnjttddQXl4udBQiamVqnpbv7SKDuYlE4DTU2GqmTrvIdW+oiSXd/Sxh8eb+Ih7tAgD45mw2sgruCJyG2hqDizc3b97UPR128OBBhIWFwdPTEy+//DLOnTvX6AGJiIiIiIiMTf/+/XH69GmhYxBRK1Oz3k1f3nA1Sl6K6uJNCos31ISyCu4gS1UGiViE3q52Qsdp8XxcZAjs0gFqjRZbf+O68NS8DC7eyOVy/PHHH1Cr1YiNjcXw4cMBAKWlpZBIHuypjw0bNsDNzQ1SqRQBAQE4ceLEPfvv3bsX3t7ekEql8PHxwcGDB/W2a7VaREVFwdHRERYWFggKCsKlS5d029PT0zFp0iS4u7vDwsICXbt2xaJFi1BRUaF3nLNnz+LRRx+FVCqFq6srVqxY8UDvj4iIiIiI6K/+/e9/480338T69esRHx+Ps2fP6r2IiOpSU7zx79xe4CTUFGqKN6nKImi1WoHTkLGq+Rzp7mgDK3ODV9RokyYPrR5989mJ6ygsqxQ4DbUlBhdvwsPD8cILL6Bnz54QiUQICgoCACQkJMDb29vgAHv27EFkZCQWLVqEU6dOoXfv3ggODkZubm6d/Y8dO4axY8di0qRJOH36NEJDQxEaGorz58/r+qxYsQJr165FTEwMEhISYGVlheDgYJSVlQEALl68CI1Gg48//hi///47PvzwQ8TExGD+/Pm6YxQWFmLEiBHo3LkzkpKSsHLlSrzzzjvYuHGjwe+RiIiIiIjor8aMGYO0tDRMmzYNgwYNgq+vL/r06aP7SkT0dwWlFbiUWwwA6NvJTtgw1CS62FvDRCxCUXkVslRlQschI1VTvPHrxBF8DfWYZ0d4OFijuLwKnyVkCB2H2hCDy6vvvPMOevbsievXryMsLAzm5uYAAIlEgrlz5xocYPXq1YiIiEB4eDgAICYmBt9++y22bNlS5/HWrFmDkJAQzJ49GwCwZMkSHD58GOvXr0dMTAy0Wi2io6OxYMECPPPMMwCAHTt2QC6XY//+/RgzZgxCQkIQEhKiO2aXLl2QkpKCjz76CB988AEAYOfOnaioqMCWLVtgZmaGRx55BMnJyVi9ejUmT55s8PskIiIiIiKqkZbGaTeIyDCn7y4w3sXeCh2szYUNQ03CzESMLh2tkJpTjFRlEZztLISOREZIV7xx4wi+hhKJRIgY0gVzvjiLrb+lI3yQO8xMDB4TQWSwB/ope/755zFz5ky4uLjo2iZMmKArljRURUUFkpKSdKN3AEAsFiMoKAjx8fF17hMfH6/XHwCCg4N1/dPS0qBUKvX6yGQyBAQE1HtMAFCpVGjf/s8Prfj4eAwZMgRmZmZ650lJScHt27cNep9ERERERER/1blz53u+iIj+LvFa9QLjflzvxqh5KWwBABe57g01gZLyKvyRXQiAnyWGesbXCQ425lAWluHA2Syh41AbIWiJ8ObNm1Cr1ZDL5XrtcrkcSqWyzn2USuU9+9d8NeSYly9fxrp16/Dqq6/e9zx/PcfflZeXo7CwUO9FRERERERUl08//RSDBg2Ck5MTrl27BgCIjo7GV199JXAyImqJdE/L84arUfOSWwMAUnNYvKHGd+ZGAdQaLRxlUo7sMpC5iQQTB7kBADYeucp1qahZtPnxXZmZmQgJCUFYWBgiIiIe6ljLli2DTCbTvVxdXRspJRERERERGZOPPvoIkZGRGDlyJAoKCqBWqwEAdnZ2iI6OFjYcEbU4lWoNkq8XAAD83Vi8MWYceUNNKSmdReCHMa5/Z1iaSXBRWYSjl24KHYfaAEGLN/b29pBIJMjJydFrz8nJgUKhqHMfhUJxz/41XxtyzKysLAwbNgwDBw7Exo0bG3Sev57j7+bNmweVSqV7Xb9+vc5+RERERETUtq1btw6bNm3C22+/DYlEomv39/fHuXPnBExGRC3RhexClFVqILMwRRd7a6HjUBPyVtgAAK7kFqNKrRE4DRmbpAwWbx6GzNIUo/tVP6y/6ehVgdNQWyBo8cbMzAx+fn6Ii4vTtWk0GsTFxSEwMLDOfQIDA/X6A8Dhw4d1/d3d3aFQKPT6FBYWIiEhQe+YmZmZeOyxx+Dn54etW7dCLNb/VgQGBuLIkSOorKzUO4+Xlxfatav7A87c3By2trZ6LyIiIiIior9LS0tDnz59arWbm5ujpKREgERE1JIl/uVpebFYJHAaakrOdhawNJOgQq1B+i3+e0CNR6PR4tTd6Rf9O7e/T2+qz8uD3CERi3D00k38kcUlM6hpNah48/d1XO71MlRkZCQ2bdqE7du348KFC3j99ddRUlKC8PBwAMD48eMxb948Xf/p06cjNjYWq1atwsWLF/HOO+8gMTERU6dOBQCIRCLMmDEDS5cuxddff41z585h/PjxcHJyQmhoKIA/CzedOnXCBx98gLy8PCiVSr21bF588UWYmZlh0qRJ+P3337Fnzx6sWbMGkZGRBr9HIiIiIiKiv3J3d0dycnKt9tjYWHTv3r35AxFRi8b1btoOsVgET3n16BtOnUaN6XJeMQrLqmBhKoG3o43QcVot1/aWGOnjCICjb6jpmTSkk52dHUSihj3ZUTNXc0ONHj0aeXl5iIqKglKphK+vL2JjYyGXywEAGRkZeqNiBg4ciF27dmHBggWYP38+PDw8sH//fvTs2VPXZ86cOSgpKcHkyZNRUFCAwYMHIzY2FlKpFED1CJrLly/j8uXLcHFx0ctTs9iUTCbDoUOHMGXKFPj5+cHe3h5RUVGYPHmyQe+PiIiIiIjo7yIjIzFlyhSUlZVBq9XixIkT+Oyzz7Bs2TJs3rxZ6HhE1IJotVokXssHwOJNW+Elt0Hy9QKkKouAXkKnIWNRM4LP19UOppI2vwz6Q4l41B3fnMnCN2eyMDvYC052FkJHIiPVoOLNTz/9pPtzeno65s6di4kTJ+qmIYuPj8f27duxbNmyBwoxdepU3ciZv/v5559rtYWFhSEsLKze44lEIixevBiLFy+uc/vEiRMxceLE++bq1asXjh49et9+REREREREhnjllVdgYWGBBQsWoLS0FC+++CKcnJywZs0ajBkzRuh4RNSCZBbcQU5hOUzEIvR2sRM6DjUDLwVH3lDjYxG48fRyscOALu1x/Go+th1Lx/yRHDVNTaNBxZuhQ4fq/rx48WKsXr0aY8eO1bWNGjUKPj4+2LhxIyZMmND4KYmIiIiIiIzMuHHjMG7cOJSWlqK4uBgODg5CRyKiFqhmyrRHnGxhYSYROA01h5riTWoOizfUeGrWu/FzY/GmMbw6pCuOX83HroQMTH28G2ylpkJHIiNk8Bi5+Ph4+Pv712r39/fHiRMnGiUUERERERFRW2FpacnCDRHVq6Z405dPy7cZNcWba/mlKK2oEjgNGYO8onKk3yoFAPR15WdJYxjq2REeDtYoLq/C7hMZQschI2Vw8cbV1RWbNm2q1b5582a4uro2SigiIiIiIiIiIvqzeOPfub3ASai52Fubo4OVGbRa4HJusdBxyAicyqj+HPGUW0NmyREijUEsFiHi0S4AgC2/pqOiSiNwIjJGDZo27a8+/PBDPPfcc/juu+8QEBAAADhx4gQuXbqEL7/8stEDEhERERERERG1RcXlVbiQXQgA8OdUR22Kl8IGx67cwkVlEXpxrSN6SDVFYD8WgRvVM32csPJQCpSFZfj2XBae7eMidCQyMgaPvBk5ciRSU1Px9NNPIz8/H/n5+Xj66aeRmpqKkSNHNkVGIiIiIiIiIqI258z1Ami0gLOdBeS2UqHjUDPSrXuj5Lo39PD+LN6wCNyYzE0kmDjQDQDw8S9XodVqhQ1ERsfgkTdA9dRp//nPfxo7CxERERERkdGrrKxESEgIYmJi4OHhIXQcImrBEtPvTpnGUTdtjpe8uniTksPiDT2csko1zt1QAQD8WbxpdOMCOmHDT5dxUVmEXy/fxKMeHYWOREbE4JE3AHD06FG89NJLGDhwIDIzMwEAn376KX799ddGDUdERERERGRsTE1NcfbsWaFjEFErkHgtHwCflm+LakbeXOTIG3pIv2epUKHWoIOVGTp3sBQ6jtGxszTDC/7V68BvPHJV4DRkbAwu3nz55ZcIDg6GhYUFTp06hfLycgCASqXiaBwiIiIiIqIGeOmll/DJJ58IHYOIWjC1RovkjAIALN60RR53R97kFZUjv6RC4DTUmtWM4PPr3A4ikUjgNMZp0mB3iEXA0Us3deuUETUGg6dNW7p0KWJiYjB+/Hjs3r1b1z5o0CAsXbq0UcMREREREREZo6qqKmzZsgU//PAD/Pz8YGVlpbd99erVAiUjopYiNacIReVVsDKT6KbQorbD2twEru0tcD3/DlKURQjs2kHoSNRKJXK9mybn2t4SI30cceBsNjYduYrVo32FjkRGwuCRNykpKRgyZEitdplMhoKCgsbIREREREREZNTOnz+Pvn37wsbGBqmpqTh9+rTulZyc/EDH3LBhA9zc3CCVShEQEIATJ07cs//evXvh7e0NqVQKHx8fHDx4UG+7VqtFVFQUHB0dYWFhgaCgIFy6dEm3PT09HZMmTYK7uzssLCzQtWtXLFq0CBUVfEKcqDHULDDep1M7mEgeaNZ7auVqinapXPeGHpBWq8Wpa1w7qzlMHtIFAPD1mSxkq+4InIaMhcEjbxQKBS5fvgw3Nze99l9//RVdunRprFxERERERERG66effmrU4+3ZsweRkZGIiYlBQEAAoqOjERwcjJSUFDg4ONTqf+zYMYwdOxbLli3DP/7xD+zatQuhoaE4deoUevbsCQBYsWIF1q5di+3bt8Pd3R0LFy5EcHAw/vjjD0ilUly8eBEajQYff/wxunXrhvPnzyMiIgIlJSX44IMPGvX9EbVFNcWbvnxavs3yUtjghwu5XPeGHlj6rVLcKqmAmUSMns4yoeMYtV4udghwb4+EtHxs+y0d80Z2FzoSGQGDH92IiIjA9OnTkZCQAJFIhKysLOzcuROzZs3C66+/3hQZiYiIiIiIjNLly5fx/fff486d6ic0tVrtAx1n9erViIiIQHh4OHr06IGYmBhYWlpiy5YtdfZfs2YNQkJCMHv2bHTv3h1LlixB3759sX79el2O6OhoLFiwAM888wx69eqFHTt2ICsrC/v37wcAhISEYOvWrRgxYgS6dOmCUaNGYdasWdi3b98DvQci0ldTvPFn8abN8uTIG3pINZ8jPi4ymJtIBE5j/F4dWj2wYVdCBorKKgVOQ8bA4OLN3Llz8eKLL+KJJ55AcXExhgwZgldeeQWvvvoq3njjjabISEREREREZFRu3bqFJ554Ap6enhg5ciSys7MBAJMmTcKbb75p0LEqKiqQlJSEoKAgXZtYLEZQUBDi4+Pr3Cc+Pl6vPwAEBwfr+qelpUGpVOr1kclkCAgIqPeYAKBSqdC+fft6t5eXl6OwsFDvRUS15RaVISO/FCIR0KeTndBxSCDeClsAQKqy6IGL+9S2JV3LB8AicHN5zNMB3RysUVRehd0nrgsdh4yAQcUbtVqNo0ePYsqUKcjPz8f58+dx/Phx5OXlYcmSJU2VkYiIiIiIyKjMnDkTpqamyMjIgKWlpa599OjRiI2NNehYN2/ehFqthlwu12uXy+VQKpV17qNUKu/Zv+arIce8fPky1q1bh1dffbXerMuWLYNMJtO9XF1d7/3miNqomjUqvOQ2sJGaCpyGhOJubwUTsQhF5VXIUpUJHYdaIU6/2LzEYhEiHnUHAGz5LQ2Vao3Aiai1M6h4I5FIMGLECNy+fRtmZmbo0aMH+vfvD2tr66bKR0REREREZHQOHTqE5cuXw8XFRa/dw8MD165dEyjVg8vMzERISAjCwsIQERFRb7958+ZBpVLpXtev86lUorokpnOBcQLMTMTo2rH6nluKkiMVyTCq0kqk5hQDAPxYvGk2z/g6w97aHNmqMnx7NlvoONTKGTxtWs+ePXH16tWmyEJERERERNQmlJSU6I24qZGfnw9zc3ODjmVvbw+JRIKcnBy99pycHCgUijr3USgU9+xf87Uhx8zKysKwYcMwcOBAbNy48Z5Zzc3NYWtrq/ciotoS7z4tzxuu5KWoXvfmopLr3pBhTmVUf464dbCEvbVhv1vQg5OaShA+yA0A8PGRq5zykB6KwcWbpUuXYtasWThw4ACys7M5XzEREREREZGBHn30UezYsUP3d5FIBI1GgxUrVmDYsGEGHcvMzAx+fn6Ii4vTtWk0GsTFxSEwMLDOfQIDA/X6A8Dhw4d1/d3d3aFQKPT6FBYWIiEhQe+YmZmZeOyxx+Dn54etW7dCLDb4EpOI/qasUo3fs1QAAP/O9a8hRW1DTfEmlcUbMlCSrgjMz5HmNi6gEyxMJbiQXYjfLt8SOg61YiaG7jBy5EgAwKhRoyASiXTtWq0WIpEIarW68dIREREREREZoRUrVuCJJ55AYmIiKioqMGfOHPz+++/Iz8/Hb7/9ZvDxIiMjMWHCBPj7+6N///6Ijo5GSUkJwsPDAQDjx4+Hs7Mzli1bBgCYPn06hg4dilWrVuGpp57C7t27kZiYqBs5IxKJMGPGDCxduhQeHh5wd3fHwoUL4eTkhNDQUAB/Fm46d+6MDz74AHl5ebo89Y34IaL7O3tDhUq1Fh1tzOHSzkLoOCQwLzlH3tCDSbyWD4DTLwrBztIMo/u5YtuxdGw8ehWDPeyFjkStlMHFm59++qkpchAREREREbUZPXv2RGpqKtavXw8bGxsUFxfjn//8J6ZMmQJHR0eDjzd69Gjk5eUhKioKSqUSvr6+iI2NhVwuBwBkZGTojYoZOHAgdu3ahQULFmD+/Pnw8PDA/v370bNnT12fOXPmoKSkBJMnT0ZBQQEGDx6M2NhYSKVSANUjdS5fvozLly/XWruHU4QQPbiap+X9O7fTe2iW2qaakTdX80pQqdbAVMIRjnR/lWoNzlyvHsHH6ReFMWmwO3bEp+NIah4uZBeiuyOniiXDGVS8qaysxOLFixETEwMPD4+mykRERERERGT0ZDIZ3n777UY73tSpUzF16tQ6t/3888+12sLCwhAWFlbv8UQiERYvXozFixfXuX3ixImYOHHig0QlontIuvu0PG+4EgA421nAykyCkgo10m+WwOPuSByie7mQXYg7lWrYSk3QraO10HHaJNf2lnjSxxHfns3GpqNXsfoFX6EjUStkULne1NQUZ8+ebaosREREREREbUK3bt3wzjvv4NKlS0JHIaIWRKvV/mWdChZvCBCLRbqCTUoOp06jhqn5HOnbuR3EYo7gE8rkR7sAAL5OzkK26o7Aaag1Mnis5UsvvYRPPvmkKbIQERERERG1CVOmTMG3334LLy8v9OvXD2vWrIFSqRQ6FhEJ7OrNEtwurYS5iRiPOMmEjkMthPfdqdNSuO4NNVDiX6ZfJOH0drVDf/f2qNJose1YutBxqBUyeM2bqqoqbNmyBT/88AP8/PxgZWWlt3316tWNFo6IiIiIiMgYzZw5EzNnzkRqaip27tyJDRs2YNasWRg2bBheeukljB8/XuiIRCSAmqfle7vYwcyEa5tQNU85izdkmFN/GXlDwnp1SBecSMvHruMZmDqsG2ykpkJHolbE4N8Ezp8/j759+8LGxgapqak4ffq07pWcnNwEEYmIiIiIiIyTp6cn3n33XaSmpuLo0aPIy8tDeHi40LGISCBJ6XenTHPjDVf6k27kDadNowbILLiDbFUZJGIRfF3thI7T5g3zckDXjlYoKq/CnpPXhY5DrYzBI29++umnpshBRERERETUJp04cQK7du3Cnj17UFhYiLCwMKEjEZFAEq/lAwD8OrF4Q3/yulu8ycgvRWlFFSzNDL6dR21IYnr150gPR1v+rLQAYrEIEY92wdx957Dl1zRMGOgGUwlHVlLD8CeFiIiIiIiomaWmpmLRokXw9PTEoEGDcOHCBSxfvhw5OTnYvXu30PGISAC3SypwJa8EAKc6In0drM1hb20GrRa4lFMsdBxq4WqmTPPj50iLEdrHGfbW5shSleHguWyh41ArYnD5ddiwYRCJRPVu//HHHx8qEBERERERkbHz9vZGv379MGXKFIwZMwZyuVzoSEQksFMZ1Tdcu3S0QnsrM4HTUEvjpbDBzcu3kKIsQm9OhUX3kHi3eOPP6RdbDKmpBBMHdsYHh1Kx8chVjOrtdM/760Q1DC7e+Pr66v29srISycnJOH/+PCZMmNBYuYiIiIiIiIxWSkoKPDw8hI5BRC1IUs0NVz4tT3XwlNvgt8u3uO4N3VNJeRUuZBcC4MiblmZcQGds+OkKfs8qxLErtzCom73QkagVMLh48+GHH9bZ/s4776C4mEM3iYiIiIiI7qemcJOUlIQLFy4AAHr06IG+ffsKGYuIBJTIqY7oHrzvrnuTomTxhuqXfL0AGi3gbGcBR5mF0HHoL9pZmeEFfxdsj7+GjUeusnhDDdJoa9689NJL2LJlS2MdjoiIiIiIyGjl5uZi2LBh6NevH6ZNm4Zp06bB398fTzzxBPLy8oSOR0TNrFKtwZnrBQAAv87thQ1DLZKn/G7xhiNv6B5qRvBx3ayWadLgLhCLgF9S81iIpQZptOJNfHw8pFJpYx2OiIiIiIjIaL3xxhsoLi7G77//jvz8fOTn5+P8+fMoLCzEtGnThI5HRM3s96xClFdpYGdpii72VkLHoRaopniTV1SO/JIKgdNQS5XI6RdbtE4dLPFkT0cAwMYjVwVOQ62BwcWbf/7zn3qvZ599FgMGDEB4eDheffVVgwNs2LABbm5ukEqlCAgIwIkTJ+7Zf+/evfD29oZUKoWPjw8OHjyot12r1SIqKgqOjo6wsLBAUFAQLl26pNfnvffew8CBA2FpaQk7O7s6zyMSiWq9du/ebfD7IyIiIiIi+rvY2Fj897//Rffu3XVtPXr0wIYNG/Ddd98JmIyIhFDztLxfp3YQi7mINdVmZW4C1/bV02DxiX2qi1qjxWlOv9jivfKoOwDg6zOZUKrKBE5DLZ3BxRuZTKb3at++PR577DEcPHgQixYtMuhYe/bsQWRkJBYtWoRTp06hd+/eCA4ORm5ubp39jx07hrFjx2LSpEk4ffo0QkNDERoaivPnz+v6rFixAmvXrkVMTAwSEhJgZWWF4OBglJX9+T9DRUUFwsLC8Prrr98z39atW5Gdna17hYaGGvT+iIiIiIiI6qLRaGBqalqr3dTUFBqNRoBERCSkpGv5AAA/N95wpfp5yW0BACnKQoGTUEt0KbcIReVVsDST6NZIopanT6d26O/WHpVqLbYdSxc6DrVwJobusHXr1kY7+erVqxEREYHw8HAAQExMDL799lts2bIFc+fOrdV/zZo1CAkJwezZswEAS5YsweHDh7F+/XrExMRAq9UiOjoaCxYswDPPPAMA2LFjB+RyOfbv348xY8YAAN59910AwLZt2+6Zz87ODgqForHeLhEREREREQDg8ccfx/Tp0/HZZ5/ByckJAJCZmYmZM2fiiSeeEDgdETUnrVaLxPQ/R94Q1cdbYYMfLuQgJadY6CjUAtV8jvTpZAcTSaOtlEFNYPKQLjiRno+dCdcw9fFusDY3+BY9tREG/2ScPHkSGo0GAQEBeu0JCQmQSCTw9/dv0HEqKiqQlJSEefPm6drEYjGCgoIQHx9f5z7x8fGIjIzUawsODsb+/fsBAGlpaVAqlQgKCtJtl8lkCAgIQHx8vK5401BTpkzBK6+8gi5duuC1115DeHg4RCIOXyYiIqKWSaVSobS0VOgYeiwtLSGTyYSOQdTirF+/HqNGjYKbmxtcXV0BANevX0fPnj3xf//3fwKnI6LmdOP2HeQWlcNELEJvVzuh41AL5nl3NAVH3lBdTl1jEbi1eNzbAV06WuFqXgl2n8jAK492EToStVAGF2+mTJmCOXPm1CreZGZmYvny5UhISGjQcW7evAm1Wg25XK7XLpfLcfHixTr3USqVdfZXKpW67TVt9fVpqMWLF+Pxxx+HpaUlDh06hH//+98oLi6+5+Kh5eXlKC8v1/29sJD/mBIREVHzUKlUWLtuPTTqKqGj6BFLTDDtjaks4BD9jaurK06dOoUffvhBd/3TvXt3vQfRiKhtqFnv5hFnGaSmEoHTUEtWMxVWak4xtFotHzAmPYk1xRu39gInofsRi0WIeLQL5u07h62/pWPCQDeYcrQU1cHg4s0ff/yBvn371mrv06cP/vjjj0YJ1RIsXLhQ9+c+ffqgpKQEK1euvGfxZtmyZbop2YiIiIiaU2lpKTTqKvxS4Q6VRip0HACATFyGoWZpKC0tZfGGqA4ikQjDhw/H8OHDhY5CRAKqKd74c4Fxug93eyuYSkQoLq9CZsEduLSzFDoStRC5RWXIyC+FSFQ9bRq1fM/2ccaqQynILLiDg+ey8Yyvs9CRqAUyuKRnbm6OnJycWu3Z2dkwMWl4Lcje3h4SiaTWsXJycupdZ0ahUNyzf81XQ47ZUAEBAbhx44beyJq/mzdvHlQqle51/fr1hzonERERkaFUGiluaa1axKulFJGIWjofHx9eOxC1Ybqn5Vm8ofswlYjRtaM1ACBFWSRwGmpJaqZM85LbwFZqKnAaagipqQQTAt0AAJuOXoVWqxU2ELVIBhdvRowYoStS1CgoKMD8+fMNemLMzMwMfn5+iIuL07VpNBrExcUhMDCwzn0CAwP1+gPA4cOHdf3d3d2hUCj0+hQWFiIhIaHeYzZUcnIy2rVrB3Nz83r7mJubw9bWVu9FRERERER0L+np6aisrBQ6BhEJoKisUrd+CYs31BCe8rvr3uSweEN/qhnB15efI63KSwM6Q2oqxvnMQsRfuSV0HGqBDJ427YMPPsCQIUPQuXNn9OnTB0B1YUMul+PTTz816FiRkZGYMGEC/P390b9/f0RHR6OkpATh4eEAgPHjx8PZ2RnLli0DAEyfPh1Dhw7FqlWr8NRTT2H37t1ITEzExo0bAVRPOzBjxgwsXboUHh4ecHd3x8KFC+Hk5ITQ0FDdeTMyMpCfn4+MjAyo1WokJycDALp16wZra2t88803yMnJwYABAyCVSnH48GH85z//waxZswz9dhERERERERER1Sn5egE0WsClnQXkthyxSvfnpbABznDkDelL5PSLrVI7KzO84O+KHfHXsPHoVQzsZi90JGphDC7eODs74+zZs9i5cyfOnDkDCwsLhIeHY+zYsTA1NWxY3ujRo5GXl4eoqCgolUr4+voiNjYWcrkcQHWRRSz+c3DQwIEDsWvXLixYsADz58+Hh4cH9u/fj549e+r6zJkzByUlJZg8eTIKCgowePBgxMbGQir985egqKgobN++Xff3miLUTz/9hMceewympqbYsGEDZs6cCa1Wi27dumH16tWIiIgw9NtFRERERER0T48++igsLCyEjkFEAuB6N2Qor5qRNyze0F1llWqcz6yeIYkj+FqfSYPd8X/Hr+HnlDykKIuqC7REdxlcvAEAKysrTJ48uVECTJ06FVOnTq1z288//1yrLSwsDGFhYfUeTyQSYfHixVi8eHG9fbZt24Zt27bVuz0kJAQhISH1biciIiIiInoYR44cwcCBA2FiYoKDBw/q2quqqnDs2DEMGTJEwHRE1FySuN4NGajmxu6VvGJUqjUwlRi8IgIZmXOZKlSqtbC3Nken9pZCxyEDde5ghZCeChw8p8Smo1fxQVhvoSNRC/JAxZtLly7hp59+Qm5uLjQajd62qKioRglGRERERERkrIYNG4bs7Gw4ODjotatUKgwbNgxqtVqgZETUXNQaLU5nFAAA/Dq3FzYMtRou7SxgZSZBSYUa6TdL4CHnU/ptXWL6nyP4RCKRwGnoQUQ82gUHzynxVXImIod7wsmOI7KpmsHFm02bNuH111+Hvb09FAqF3oeCSCRi8YaIiIiIDKZSqVBaWip0DD2WlpaQyWRCxyAjpdVq67zBcuvWLVhZWQmQiIiaW4qyCMXlVbA2N+E0OdRgIpEIngobnM4owEVlEYs3xBF8RqBPp3YIcG+PhLR8bDxyFe+MekToSNRCGFy8Wbp0Kd577z289dZbTZGHiIiIiNoYlUqFtevWQ6OuEjqKHrHEBNPemMoCDjWqf/7znwCqb75NnDgR5ubmum1qtRpnz57FwIEDhYpHRM0o6Vo+AKBPJztIxHxanhrO+27xJkVZhKc5w1KbptVqcSrjbvHGjcWb1mzaEx4YtzkBn53IwL+HdYWDjfT+O5HRM7h4c/v27XuuOUNEREREZIjS0lJo1FX4pcIdKk3LuEiRicsw1CwNpaWlLN5Qo6r5edJqtbCxsYGFxZ/TYpiZmWHAgAGIiIgQKh4RNSM+LU8PyvPuaJuUnCKBk5DQ0m6WIL+kAmYmYjziZCt0HHoIA7t2QN9OdjiVUYBNR67i7ad6CB2JWgCDizdhYWE4dOgQXnvttabIQ0RERERtlEojxS1tC5kuSnP/LkSGioyMxPr162FlZYX09HRs3rwZ1tbWQsciIoEksnhDD6hmmr0UJYs3bV3N50hvFxnMTSQCp6GHIRKJ8MYTHgjfehL/dzwDrw3tig7W5vffkYyawcWbbt26YeHChTh+/Dh8fHxgamqqt33atGmNFo6IiIiIiMhYrFu3Dm+99RasrKxw5MgRlJaWsnhD1EblFJbhxu07EIsAX1c7oeNQK+N1d+RNRn4pSiuqYGlm8O09MhJJ6dXFm74sAhuFxzw7opeLDGdvqPDJr2mYE+ItdCQSmMGf7hs3boS1tTV++eUX/PLLL3rbRCIRizdERERERER1cHNzw9q1azFixAhotVrEx8ejXbu6b7YMGTKkmdMRUXOqmTLNS2ELG6npfXoT6etgbQ57a3PcLC5Hak4xC4BtWNLd9W78O7cXOAk1BpFIhKnDumHyp0nYEX8Nk4d0gZ2lmdCxSEAGF2/S0tKaIgcREREREZFRW7lyJV577TUsW7YMIpEIzz77bJ39RCIR1Gp1M6cjouaUmF5zw5VPy9OD8VJY4+blcqQqi1i8aaMKSitwObcYAKdfNCbDe8jhrbDBRWURtv6WjpnDPYWORAISCx2AiIiIiIioLQgNDYVSqURhYSG0Wi1SUlJw+/btWq/8/HyhoxJRE6t5Wp43XOlBecmrF6e/yHVv2qxTdz9Huthbob0VR2cYC5FIhDce9wAAbP0tDUVllQInIiE1aORNZGQklixZAisrK0RGRt6z7+rVqxslGBERERERkTGytrbGTz/9BHd3d5iYcJ0CorbmToUav2eqALB4Qw/OW1G97k1qDos3bVXNCD5+jhifJ3sq0M3BGpdzi7Ej/hqmDOsmdCQSSIOuFE6fPo3Kykrdn+sjEokaJxUREREREZERGzp0KK5cuYKtW7fiypUrWLNmDRwcHPDdd9+hU6dOeOSRR4SOSERN5OyNAlRptJDbmsOlnYXQcaiV8rxbvOHIm7arZu0sFm+Mj1gswpRhXTFzzxlsPnoVEwe6wcqcD/y0RQ36r/7TTz/V+WciIiIiIiIy3C+//IInn3wSgwYNwpEjR/Dee+/BwcEBZ86cwSeffIIvvvhC6IhE1EQS/3LDlQ/B0oPylFsDAG4Wl+NWcTk6WJsLnIiaU6VagzM3CgAA/m4s3hijp3s5IfqHS7h2qxQ7E65h8pCuQkciAXDNGyIiIiIiomY2d+5cLF26FIcPH4aZ2Z/z1D/++OM4fvy4gMmIqKmd0hVv2guchFozSzMTdGpvCQBI4dRpbc4fWYUoq9RAZmGKLvbWQsehJmAiEWPKY9XTpW08koaySrXAiUgILN4QERERERE1s3PnzuHZZ5+t1e7g4ICbN28KkIiImoNGo0VSBqc6osbhdXfqtBROndbm/HUEn1jMEXzG6tm+znC2s8DN4nLsPpEhdBwSAIs3REREREREzczOzg7Z2dm12k+fPg1nZ2cBEhFRc7h6swQFpZWQmorxiJOt0HGolfOSVxdvUjnyps1JupYPgEVgY2cqEeP1x6qnS4v55SrKqzj6pq1h8YaIiIiIiKiZjRkzBm+99RaUSiVEIhE0Gg1+++03zJo1C+PHjxc6HhE1kZobrr1c7GAq4S0Zejg1I28ucuRNm6LVapF0jSP42oowfxcobKVQFpbhi6QbQsehZmYidAAiooehUqlQWloqdAw9lpaWkMlkQscgIiKiFuw///kPpkyZAldXV6jVavTo0QNqtRovvvgiFixYIHQ8ImoiienVN1z9ecOVGkFN8SZVWQStVguRiNNntQU3bt9BTmE5TMQi9HaxEzoONTFzEwleHdoF737zBz76+Qpe8Hdl8b8NYfGGiFotlUqFtevWQ6OuEjqKHrHEBNPemMoCDhEREf0/e3ceFlXZvwH8nhmWYUdQGEBQVFTcURJRS1MSzUzU3F7KJdI3EzfKevXnVmaa5r7mllaipimZGqbgluICuC+4i4osijAsss2c3x/I5MiqAmcY7s91zWVzznPO3HOYYJ75zvM8xTIyMsKaNWswdepUXLx4Eenp6fDw8ICbm5vY0YioAnG9GypPrjXNYCiTICNHhftPnsLZxlTsSFQJop/9HmnqaAkTI5nIaagyDG7rguUHb+L+k6fYeeYBBng6ix2JKgmLN0RUZWVmZkKtysPhHFekquVixwEAWEmz0MnoNjIzM1m8ISIiolK5uLjAxcVF7BhEVAmSM3JwKykDANDahcUben2GMinq1zLH1fg0XEtIY/GmmigYwdemjo3ISaiyyA1lGPmWK77bexUrDt5AXw8nGHD0TbXA4g0RVXmpajkeC2Zix8inFjsAERER6aqgoKAyt12wYEEFJiEiMUQ/W6Oifi0z1DAzEjkN6YtGCgtcjU/D1fg0dHW3FzsOVQKud1M9+XvVwcpDN3HncSZ2n38IPw8nsSNRJWDxhoiIiIiIqBKcOXOmTO24ZgGRfoq8W7DeDb8tT+WnYN2bmPg0kZNQZUjPzsPVeCUAwLMuizfViZmxAQI6uuKHv69h2cEbeL+lI6RSvmfUdyzeEBERERERVYKDBw+KHYGIRFQw8qYNP3ClctTIPr94cy2BxZvq4GxsCtQC4GRtAntL3Zg+nirPkPZ1sfrILdxITMdfF+PRs4WD2JGognFyPCIiIiIiIiKiCpSTp8a5+ykAONURla+CkTc3k9KRq+I83vou8m4yAI66qa4s5YYY1sEVALA0/DrUakHkRFTRWLwhIiIiIiIiIqpAl+JSkZ2nRg1TQ9SrqSPrdZJecLI2gbmxAXJVAm4/yhA7DlWwKM30iyzeVFcfd6gLMyMZrsan4cCVBLHjUAVj8YaIiIiIiIiIqAI9v8A417Wi8iSRSNDQ3hwAcJXr3ug1lVrAmdgUAEBrFm+qLWtTIwxpXxcAsOzgDQgCR9/oMxZviIiIiIiIiIgqUOSdguKNjchJSB8VTJ12jcUbvRYTn4b07DyYGcnQWGEpdhwS0ScdXWFiKMP5+6k4fC1J7DhUgVi8ISIiIiIiIiKqIIIgICr235E3ROWtkX1+8YYjb/Rbwe8RD5cakEk5gq86szU3hr+XCwBgaThH3+gzFm+IiIiIiIiIiCrIveSnSErLhqFMgha1rcSOQ3qo0bNRGNcSWLzRZ1F3kgGwCEz5Rr5VD0YGUkTdfYKIm4/FjkMVhMUbIiIiIiIiIqIKEhWb/4FrU0cryA1lIqchfVQwbVpsciYysvNETkMVhSP46Hl2lnIMfsMZALAk/LrIaaiisHhDRERERERERFRBCta78eQHrlRBbMyMUMvCGABH3+irRGUW7iU/hVQCeLhYix2HdMR/O9WHoUyCE7eScfrZyCzSLyzeEBERERERERFVkKi7z4o3dVm8oYpTsO4Nizf6KfLZ75FGCktYyA1FTkO6wtHaBB+0qQ0AWBLG0Tf6iMUbIiIiIiIiIqIKoMzKRcyzD9Nbc+QNVaCCqdOuxrN4o48KisBt6liLG4R0zqhODSCTSnD0+iOcvZcidhwqZ6IXb5YvX466detCLpfDy8sLp06dKrH9tm3b0LhxY8jlcjRv3hx79+7V2i8IAqZNmwYHBweYmJjAx8cH169rVx5nzZqF9u3bw9TUFNbW1kU+TmxsLHr27AlTU1PY2dlh4sSJyMvjvKFEREREREREVDZnY1MgCICLjSnsLORixyE9xpE3+q1g5I1nHRuRk5CucbE1hV8rJwDAUo6+0TuiFm+2bt2KoKAgTJ8+HdHR0WjZsiV8fX2RmJhYZPvjx49j8ODBCAgIwJkzZ+Dn5wc/Pz9cvHhR02bu3LlYsmQJVq1ahZMnT8LMzAy+vr7IysrStMnJyUH//v0xatSoIh9HpVKhZ8+eyMnJwfHjx7Fx40Zs2LAB06ZNK98LQERERERERER6K/IuFxinylEw8iaGI2/0TlauCpcepALg7xIq2ui360MqAcKuJuLis9cK6QdRizcLFizAiBEjMHz4cDRp0gSrVq2Cqakp1q9fX2T7xYsXo3v37pg4cSLc3d0xc+ZMtG7dGsuWLQOQP+pm0aJFmDJlCnr37o0WLVrg559/RlxcHEJCQjTn+frrrzFhwgQ0b968yMf5+++/cfnyZfz6669o1aoVevTogZkzZ2L58uXIyckp9+tARERERET0unR1VgOi6izqbv4C0vzAlSqam705JBLgUXoOHqVnix2HytG5eynIUwuwszBG7RomYschHVSvljnea+EIAFgWfkPkNFSeRCve5OTkICoqCj4+Pv+GkUrh4+ODiIiIIo+JiIjQag8Avr6+mva3b99GfHy8VhsrKyt4eXkVe87iHqd58+awt7fXehylUolLly4Ve1x2djaUSqXWjYiIiIiIqKLp6qwGRNVZnkqNs7EpAFi8oYpnamQAFxtTAMA1jr7RK1Gx/47gk0gkIqchXRXYpQEAIPRSPEfg6RHRijePHj2CSqXSKpAAgL29PeLj44s8Jj4+vsT2Bf++zDlf5nGef4yizJ49G1ZWVpqbs7NzmR+TiIiIiIjoVenqrAZE1dnV+DRk5KhgYWyAhs/WIyGqSAXr3sRw3Ru9EnWH0y9S6RraW6BHMwUAYPlBjr7RF6JOm6ZvJk2ahNTUVM3t3r17YkciIiIiIiI9p8uzGryIsxVQdRL97NvyrVysIZPy2/JU8bjujf4RBEFr5A1RSQpG3+w+H4dbSekip6HyIFrxpmbNmpDJZEhISNDanpCQAIVCUeQxCoWixPYF/77MOV/mcZ5/jKIYGxvD0tJS60ZERERERFSRdHlWgxdxtgKqTiKffVves46NyEmouigo3lxl8UZv3EzKQEpmLowNpGjqaCV2HNJxTR2t4ONuB7UALD94U+w4VA5EK94YGRmhTZs2CAsL02xTq9UICwuDt7d3kcd4e3trtQeA/fv3a9q7urpCoVBotVEqlTh58mSx5yzucS5cuKA1P/T+/fthaWmJJk2alPk8RERERERE9C/OVkDVSdRdflueKlfBtGnXE9KgVgsip6HyEHU3GQDQ0tkaRgacQIlKN6aLGwAg5OwDxD7OFDkNvS5R/68PCgrCmjVrsHHjRly5cgWjRo1CRkYGhg8fDgAYMmQIJk2apGk/btw4hIaGYv78+bh69SpmzJiByMhIBAYGAgAkEgnGjx+Pb7/9Frt27cKFCxcwZMgQODo6ws/PT3Oe2NhYnD17FrGxsVCpVDh79izOnj2L9PT84WTdunVDkyZN8NFHH+HcuXPYt28fpkyZgtGjR8PY2LjyLhAREREREVEpdHlWgxdxtgKqLuJTs/Ag5Smkkvxp04gqQ92aZjCSSZGRo8KDlKdix6FywCIwvayWztZ4q2EtqNQCVh7m2jdVnajFm4EDB+KHH37AtGnT0KpVK5w9exahoaGaofmxsbF4+PChpn379u0RHByM1atXo2XLlti+fTtCQkLQrFkzTZsvv/wSY8aMwciRI/HGG28gPT0doaGhkMvlmjbTpk2Dh4cHpk+fjvT0dHh4eMDDwwORkZEAAJlMht27d0Mmk8Hb2xsffvghhgwZgm+++aaSrgwREREREVHZ6PKsBkTVVcEHru4OljA3NhA5DVUXhjIp6tUyA8B1b/RF5N2C6RdZvKGyG/Ns7ZvtUfdZyK3iRH8HERgYqBk586JDhw4V2ta/f3/079+/2PNJJBJ88803JRZaNmzYgA0bNpSYq06dOti7d2+JbYiIiIiIiHRBUFAQhg4dCk9PT7Rt2xaLFi0qNKuBk5MTZs+eDSB/VoNOnTph/vz56NmzJ7Zs2YLIyEisXr0agPasBm5ubnB1dcXUqVOLnNUgOTlZa1YDAGjQoAHMzc0r9RoQ6ZLIZ1Md8dvyVNkaKyxwNT4NMQlp8GliX/oBpLOSM3JwKykDANDahb9LqOzeqGuDdvVscOJWMn48fBPf9G5W+kGkk0Qv3hAREREREdHrGThwIJKSkjBt2jTEx8ejVatWhWY1kEr/nXihYFaDKVOmYPLkyXBzcytyVoOMjAyMHDkSKSkp6NixY5GzGmzcuFFz38PDAwBw8OBBdO7cuYKfNZHu4lRHJJaGivx1bzjypuqLfvZ7pH4tM9QwMxI5DVU1Y7u44cStk9hy+h4C324AO0t56QeRzmHxhoiIiIiISA/o6qwGRNVNZk4eLsUpAbB4Q5WvMYs3eiMqlkVgenXe9W3Rpk4NRN19gh+P3MLU95qIHYlegahr3hARERERERER6ZNz91KhUgtQWMrhZG0idhyqZhopLAEAN5PSkZOnFjkNvY6oOwXr3diInISqIolEoln7ZtPJu3iUni1yInoVLN4QEREREREREZWT6Oe+LS+RSEROQ9WNo5UcFsYGyFMLuP0oQ+w49Ipy8tQ4dz8FANCmLkfe0Kvp1LAWWtS2QlauGmuP3hY7Dr0CFm+IiIiIiIiIiMpJ5J1kAJzqiMQhkUj+XfcmgVOnVVWX4lKRnadGDVND1KtpJnYcqqLyR9+4AQB+ibiDlMwckRPRy2LxhoiIiIiIiIioHKjVAqJjUwCweEPiaWhfsO6NUuQk9Kqi7nIEH5UPH3c7uDtYIiNHhfXH7ogdh14SizdEREREREREROXgZlI6Up/mwsRQhiaOlmLHoWqqccHIm3iOvKmqCoo3rVkEptf0/No3Px27DWVWrsiJ6GWweENEREREREREVA4KPnBt6WwFQxk/ciFxaEbecNq0KkkQBEQ++13iWcdG5DSkD7o3VaCBnTnSsvLw8/E7Ysehl8B3EkRERERERERE5SDyuamOiMTS6NnIm3vJT5GenSdyGnpZ9588RVJaNgxlErSobSV2HNIDUqkEgW/nj75Z989tZPD3QpXB4g0RERERERERUTmI4rflSQfYmBmhloUxAOA6R99UOZF3kwEATR2tIDeUiZyG9MV7LRxQ19YUTzJz8euJu2LHoTJi8YaIiIiIiIiI6DU9Ts/G7UcZAAAPF2txw1C1x3Vvqq4ojuCjCmAgk+KzZ6Nv1hy9hac5KpETUVmweENERERERERE9JoKPnB1szOHtamRyGmoumvEdW+qrMg7BSP4WLyh8tXHwwm1a5jgUXoONp+KFTsOlQGLN0RERERERERErykqlt+WJ93RkCNvqiRlVq6m4MbfJVTeDGVSjOpcHwDw45GbyMrl6Btdx+INEREREREREdFrirrD4g3pjoJp065x5E2VcjY2BYIAONuYwM5SLnYc0kMftKkNBys5EpTZ2BZ1X+w4VAoDsQMQEVVXqampyMzMFDuGFlNTU1hZWYkdg4iIiIioSsnOU+H8g1QALN6QbnCzs4BEAjxKz8Gj9GzUNDcWOxKVQeTdginTbEROQvrK2ECG/75VDzP+vIxVh25ioKczjAw4vkNXsXhDRCSC1NRULFm6DGpVnthRtEhlBhg7JpAFHCIiIiKil3DxgRI5eWrYmBnBtaaZ2HGIYGIkQx0bU9x5nImY+DTUbMDiTVUQ/ax405pFYKpAg9q6YNnBm3iQ8hQ7z9zHwDdcxI5ExWDxhohIBJmZmVCr8nA4xxWpat0YCm0lzUIno9vIzMxk8YaIiIiI6CVoPnB1qQGJRCJyGqJ8De0tNMWbDg1qih2HSpGnUuNMbMHIGxZvqOLIDfNH38zaewUrDt1Ev9a1YSDj6BtdxOINEZGIUtVyPBZ05Jt5arEDEBERERFVTZF3kwEAnnX5gSvpjsYKC/x9OQEx8Vz3piqISUhDRo4KFsYGaGhvIXYc0nP+7Vyw4tAN3H2ciT/Px6GPR22xI1ERWFIjIiIiIiIiInpFgiAg6i6/LU+6p6EivwAQk8DiTVVQ8HuklYs1ZFKO4KOKZWpkgE/erAcAWBZ+Ayq1IHIiKgqLN0REREREREREryg2OROP0nNgJJOimROnHybd0fhZ8eZaQhrU/GBW5/1bBLYROQlVF0O868BSboCbSRn46+JDseNQEVi8ISIiIiIiIiJ6RZF38j9wbeZkCbmhTOQ0RP+qY2sGI5kUmTkqPEh5KnYcKkXB75I2HMFHlcRCbojhHVwB5I++YZFX97B4Q0RERERERET0iqJi+YEr6SZDmRT17cwBAFe57o1Oi0/NwoOUp5BK8qdNI6osH3dwhbmxAa7Gp2H/lQSx49ALWLwhIiIiIiIiInpFUZpvy3OqI9I9z0+dRrqrYMq0xgpLmBsbiJyGqhMrU0MM8a4DAFgafh2CwNE3uoTFGyIiIiIiIiKiV5D6NBfXEvM/FOfIG9JFDe3zizcceaPbIu8mAwA86/L3CFW+gI6uMDGU4eIDJQ7FJIkdh57D4g0RERERERER0Ss4E/sEggDUsTVFLQtjseMQFVIw8iYmXilyEipJ9F1Ov0jisTU3xoftXAAASzj6RqeweENERERERERE9Ao0H7i68ANX0k0NnxVvbiVlICdPLXIaKsrTHBUuxeUX11i8IbGMeKsejA2kOBObgmM3Hosdh55h8YaIiIiIiIiI6BVEFhRvONUR6ShHKzksjA2QpxZw61G62HGoCOfupyBPLUBhKYeTtYnYcaiasrOQY3Dbf0ffkG5g8YaIiIiIiIiI6CXlqdQ4ey8FAOBZx0bcMETFkEgkmtE3MVz3RidFPTdlmkQiETkNVWf/7VQPhjIJTt1OxslbHH2jC1i8ISIiIiIiIiJ6SVfj05CZo4KF3ABuduZixyEqViMWb3Ra5J1kAJwyjcTnYGWCD9o4AwCWHbwhchoCWLwhIiIiIiIiInppBR+4tnapAamU35Yn3dXIPr94cy2BxRtdo1YLiI5NAcDiDemGzzrXh0wqwdHrj3Am9onYcao9Fm+IiIiIiIiIiF5SFD9wpSqiYOTNVY680Tk3k9KR+jQXckMpmjhaih2HCM42pujj4QQAWBrO0Tdi04nizfLly1G3bl3I5XJ4eXnh1KlTJbbftm0bGjduDLlcjubNm2Pv3r1a+wVBwLRp0+Dg4AATExP4+Pjg+nXthZaSk5Ph7+8PS0tLWFtbIyAgAOnp/y7cdufOHUgkkkK3EydOlN8TJyIiIiIiIqIqKerZyBtPFm9IxxWMvLn/5CnSs/NETkPPK1jvpmVtaxjKdOJjWiKMfrsBpBIg/GoiLj5IFTtOtSb6b4WtW7ciKCgI06dPR3R0NFq2bAlfX18kJiYW2f748eMYPHgwAgICcObMGfj5+cHPzw8XL17UtJk7dy6WLFmCVatW4eTJkzAzM4Ovry+ysrI0bfz9/XHp0iXs378fu3fvxpEjRzBy5MhCj3fgwAE8fPhQc2vTpk35XwQiIiIiIiIiqjLiUp4iLjULMqkELZ2txY5DVKIaZkawszAGwKnTdE3ks+KNZ10WgUl3uNY0Q6+WjgCApeHXS2lNFUn04s2CBQswYsQIDB8+HE2aNMGqVatgamqK9evXF9l+8eLF6N69OyZOnAh3d3fMnDkTrVu3xrJlywDkj7pZtGgRpkyZgt69e6NFixb4+eefERcXh5CQEADAlStXEBoairVr18LLywsdO3bE0qVLsWXLFsTFxWk9nq2tLRQKheZmaGhYodeDiIiIiIiIiHRbwbfl3R0sYGZsIHIaotIVTJ0Ww6nTdEr0s98lnH6RdE3g2w0gkQD7LiXgarxS7DjVlqjFm5ycHERFRcHHx0ezTSqVwsfHBxEREUUeExERodUeAHx9fTXtb9++jfj4eK02VlZW8PLy0rSJiIiAtbU1PD09NW18fHwglUpx8uRJrXO///77sLOzQ8eOHbFr164Sn092djaUSqXWjYiIiIiIiIj0S0Hxpo0LP3ClqqFg6jQWb3TH4/Rs3HqUAQBozd8lpGPc7C3Qo5kCALCMa9+IRtTizaNHj6BSqWBvb6+13d7eHvHx8UUeEx8fX2L7gn9La2NnZ6e138DAADY2Npo25ubmmD9/PrZt24Y9e/agY8eO8PPzK7GAM3v2bFhZWWluzs7OpV0CIiIiIiIiIqpiNMWbujYiJyEqG4680T3RsSkAADc7c1ibGokbhqgIgW+7AQD2XHiIG4nppbSmiiD6tGm6qmbNmggKCoKXlxfeeOMNzJkzBx9++CHmzZtX7DGTJk1Camqq5nbv3r1KTExEREREREREFS0jOw+XH+bPtMGpjqiqKCjecM0b3RF5NxkAf4+Q7mriaAkfd3sIArDiIEffiEHU4k3NmjUhk8mQkJCgtT0hIQEKhaLIYxQKRYntC/4trU1iYqLW/ry8PCQnJxf7uADg5eWFGzeKf6EaGxvD0tJS60ZERERERERE+uPc/RSo1AIcrORwsjYROw5RmbjZWUAiAR5n5CApLVvsOAQg6g7XuyHdN7ZrAwDAH+ficPdxhshpqh9RizdGRkZo06YNwsLCNNvUajXCwsLg7e1d5DHe3t5a7QFg//79mvaurq5QKBRabZRKJU6ePKlp4+3tjZSUFERFRWnahIeHQ61Ww8vLq9i8Z8+ehYODw8s/USIiIiIiIiLSC/zAlaoiEyMZ6tiYAuDoG12QnafC+QepAPi7hHRbi9rW6NSwFlRqASsO3hQ7TrVjIHaAoKAgDB06FJ6enmjbti0WLVqEjIwMDB8+HAAwZMgQODk5Yfbs2QCAcePGoVOnTpg/fz569uyJLVu2IDIyEqtXrwYASCQSjB8/Ht9++y3c3Nzg6uqKqVOnwtHREX5+fgAAd3d3dO/eHSNGjMCqVauQm5uLwMBADBo0CI6OjgCAjRs3wsjICB4eHgCAHTt2YP369Vi7dm0lXyEiIiIiIiIi0hVRsSzeUNXUSGGBO48zcTU+DR0a1BQ7TrV28YESOXlq2JgZwbWmmdhxiEo0pksDHL6WhN+j72OsjxtHnVYi0Ys3AwcORFJSEqZNm4b4+Hi0atUKoaGhsLe3BwDExsZCKv13gFD79u0RHByMKVOmYPLkyXBzc0NISAiaNWumafPll18iIyMDI0eOREpKCjp27IjQ0FDI5XJNm02bNiEwMBBdu3aFVCpFv379sGTJEq1sM2fOxN27d2FgYIDGjRtj69at+OCDDyr4ihARERERERGRLlKrBUTfzS/eeNaxETkN0ctppLDEvksJuBbPkTdiK/g90tqlBiQSichpiErmWdcG3vVsEXHrMVYduomZfs1KP4jKhejFGwAIDAxEYGBgkfsOHTpUaFv//v3Rv3//Ys8nkUjwzTff4Jtvvim2jY2NDYKDg4vdP3ToUAwdOrT40ERERERERERUrdxISocyKw8mhjI0drAQOw7RS2lkn/+avcpp00QXeTcZAOBZlyP4qGoY07UBIm49xtbIewjs0gD2lvLSD6LXJuqaN0REREREREREVUXks/VuWjlbw1DGj1SoammkyC/eXE9Ig1otiJym+hIEAVF3UwAAnpx+kaoI73q28KxTAzl5avx4+JbYcaoNvtMgIiIiIiIiIiqDgm/Lc70bqorq2prCyECKzBwV7j95Knacais2OROP0rNhJJOimZOV2HGIykQikWBMVzcAQPCpu3iUni1youqBxRsiIiIiIiIiojIoWKeiDac6oirIQCZFg1rmAICr8UqR01RfBSP4mjlZQm4oEzkNUdm95VYTLWtbIStXjTVHOfqmMrB4Q0RERERERERUiqS0bNx5nAkAaO3M4g1VTQVTp13jujeiiYp9VgTmCD6qYiQSCcZ0yR9980vEXTzJyBE5kf5j8YaIiIiIiIiIqBTRzz5wbWhvDitTQ5HTEL2aguLN1XgWb8QSdaegeGMjchKil9fV3Q5NHCyRmaPC+mO3xY6j91i8ISIiIiIiIiIqRdRdfuBKVV8je468EVPq01xcS8y/9hx5Q1VR/uibBgCADcfuIPVprsiJ9BuLN0REREREREREpfi3eMMPXKnqKhh5cyspAzl5apHTVD9nYp9AEIA6tqaoZWEsdhyiV+LbVAE3O3OkZedh4/E7YsfRayzeEBERERERERGVICtXhQv3UwEAnizeUBXmYCWHhdwAeWoBtx6lix2n2okuKAK78PcIVV1SqQSBz0bfrD92G+nZeSIn0l8s3hARERERERERleBSXCpyVGrYmhmhjq2p2HGIXplEItFMnRbDdW8qXWRB8aYuizdUtb3XwhGuNc2QkpmLqSEXcfR6EqdQqwAGYgcgIiIiIiIiItJlkXf+nTJNIpGInIbo9TRSWCDy7hNcjU9Db7HDVCN5KjXO3ksBAHhy7Syq4mRSCQLfboDPt53DzjMPsPPMAwBAvVpmaOVsDQ9na7RyroHGDhYwlHH8yKti8YaIiIiIiIiIqASRXO+G9EjBujfXOPKmUl2NT0NmjgoWcgO42ZmLHYfotfVt7QSpFAi/moSz957gXvJT3ErKwK2kDOyIzi/mGBtI0czJCq2crTW32jVM+EWIMmLxhoiIiIiIiIioGIIgaNap8ORUR6QHCqZNu8riTaWKvJMMAGjtUgNSKT+4pqpPIpGgj0dt9PGoDQB4lJ6Nc/dScO5eCs48+1eZlYeou08Q9ezvKADUNDdCy9rPijku1mhR2xpWJoZiPQ2dxuINEREREREREVEx7jzOxOOMHBjJpGjqaCV2HKLXVjDy5kHKU6Rl5cJCzg9NK0NUbAoAjuAj/VXT3Bhd3e3R1d0eAKBWC7j9OANnY1Nw9l4Kzt1PweU4JR6l5yDsaiLCriZqjq1fywytnGuglbMVp1t7Dos3RERERERERETFKPi2cPPaVpAbykROQ/T6rE2NYG9pjARlNrrOPwxHaxM4WMmhsJLD0coECiu55r6dhRxGBvwAtTxEPRt548niDVUTUqkE9WuZo34tc/Rrkz86JytXhUtxSpy9l/Lslj/d2s2kDNxMysDv0fcBcLq1AizeEBEREREREREVI+pu/geu/LY86ZMezRyw4fgdJKZlIzEtG2fvFd1OIsn/Nr2DlRwKy4Kizr/FHgcrOewt5SxsliIu5SniUrMgk0rQ0tla7DhEopEbytCmTg2tv6mP07Nx7n4KzsaWPt1aK2fr/CnXqsl0ayzeEBERERER6YHly5dj3rx5iI+PR8uWLbF06VK0bdu22Pbbtm3D1KlTcefOHbi5ueH777/Hu+++q9kvCAKmT5+ONWvWICUlBR06dMDKlSvh5uamaZOcnIwxY8bgzz//hFQqRb9+/bB48WKYm3MhZtIfBR8csXhD+mTG+03xWef6eJiahYepWYhPfYqHyizEa+7n33JUaiSlZSMpLRvnkVrs+WzMjJ4r7vxb5HF8dl9hJYepUfX9GLLg94i7gwXMjKvvdSAqiq25Mbo0tkeXxoWnWzt3P3+ETsF0aweuJOLAlSKmW3OxhoezNRop9Gu6Nf62ICIiIiIiquK2bt2KoKAgrFq1Cl5eXli0aBF8fX0RExMDOzu7Qu2PHz+OwYMHY/bs2XjvvfcQHBwMPz8/REdHo1mzZgCAuXPnYsmSJdi4cSNcXV0xdepU+Pr64vLly5DL5QAAf39/PHz4EPv370dubi6GDx+OkSNHIjg4uFKfP1FFSc3MxbWEdAAs3pD+sbOUw85SjpbORe8XBAHJGTmaYs5DZRYepjz9t8CjzMLD1KfIylUjOSMHyRk5uPxQWezjWZkYahd3LE1eKPbI9Xb9nYLijWcdG5GTEOm+sky3du5eCmKTM/V+ujUWb4iIiIiIiKq4BQsWYMSIERg+fDgAYNWqVdizZw/Wr1+P//3vf4XaL168GN27d8fEiRMBADNnzsT+/fuxbNkyrFq1CoIgYNGiRZgyZQp69+4NAPj5559hb2+PkJAQDBo0CFeuXEFoaChOnz4NT09PAMDSpUvx7rvv4ocffoCjo2MlPXuiihN9L/8D17q2pqhpbixyGqLKJZFIYGtuDFtzYzRzsiqyjSAISH2a+2+Bp2AUj6a4k1/wychRIfVpLlKf5uJqfFqxj2lubPBccafoadqsTAyr3AexBcWb1iwCE72S8phuLf9WAy2crWBZRQrFLN4QERERERFVYTk5OYiKisKkSZM026RSKXx8fBAREVHkMREREQgKCtLa5uvri5CQEADA7du3ER8fDx8fH81+KysreHl5ISIiAoMGDUJERASsra01hRsA8PHxgVQqxcmTJ9GnT59yfJblT6UWcCb2SekNqVrbdzEeANCG35YnKpJEIoG1qRGsTY3g7mBZbLu0rFztKdpStadoe5j6FMqsPKRn5+FGYjpuJKaX+LgyqQQGz24yqQQGMum/22QSGEj/vf/v9sLbZFIpDGXa9w2kEshkEhgW3NfaL4FhMefJf9yCbf+2kUigGZHkyeINUbkparq1O48zNKNzzt5LwZWHxU+3NritCz55s55Y8cuExRsiIiIiIqIq7NGjR1CpVLC3t9fabm9vj6tXrxZ5THx8fJHt4+PjNfsLtpXU5sUp2QwMDGBjY6Np86Ls7GxkZ2dr7iuVxU+vU9FyVWp8sKro4hbRizhlGtHrsZAbwkJuiIb2FsW2ycjOQ7yyiBE8qVmIe3b/SWYugPwCvEotILvYs+keBys5HK1NxI5BpLekUgnq1TJHvVrm6Nu68HRr554VdAqmW1M+zRU5celYvCEiIiIiIqJKMXv2bHz99ddix9Coa2sqdgSqAuws5ejRTCF2DCK9Z2ZsoFnnojhZuSqkZeVBpRaQp1Y/+ze/kJOnyt/2/P3n2+W+cF/TTi1Apco/TvtYtVabPJX2/X//VT937mfHqbTbqQUBQ73rVt7FJCIAJU+35mJjJmKysmHxhoiIiIiIqAqrWbMmZDIZEhIStLYnJCRAoSj6A2eFQlFi+4J/ExIS4ODgoNWmVatWmjaJiYla58jLy0NycnKxjztp0iSt6dqUSiWcnYtZKbuCyQ1lODTxbVEem4iIXo3cUAa5oUzsGERUhRVMt1YVsHhDRERERPSKUlNTkZmZKXYMLaamprCyKnpRYdJPRkZGaNOmDcLCwuDn5wcAUKvVCAsLQ2BgYJHHeHt7IywsDOPHj9ds279/P7y9vQEArq6uUCgUCAsL0xRrlEolTp48iVGjRmnOkZKSgqioKLRp0wYAEB4eDrVaDS8vryIf19jYGMbGXPSdiIiIiKg0LN4QEREREb2C1NRULFm6DGpVnthRtEhlBhg7JpAFnGomKCgIQ4cOhaenJ9q2bYtFixYhIyMDw4cPBwAMGTIETk5OmD17NgBg3Lhx6NSpE+bPn4+ePXtiy5YtiIyMxOrVqwHkL0A9fvx4fPvtt3Bzc4OrqyumTp0KR0dHTYHI3d0d3bt3x4gRI7Bq1Srk5uYiMDAQgwYNgqOjoyjXgYiIiIhIX7B4Q0RERET0CjIzM6FW5eFwjitS1XKx4wAArKRZ6GR0G5mZmSzeVDMDBw5EUlISpk2bhvj4eLRq1QqhoaGwt8+fEiI2NhZSqVTTvn379ggODsaUKVMwefJkuLm5ISQkBM2aNdO0+fLLL5GRkYGRI0ciJSUFHTt2RGhoKOTyf1/vmzZtQmBgILp27QqpVIp+/fphyZIllffEiYiIiIj0FIs3RERERESvIVUtx2NBRxa7VIsdgMQUGBhY7DRphw4dKrStf//+6N+/f7Hnk0gk+Oabb/DNN98U28bGxgbBwcEvnZWIiIiIiEomLb0JERERERERERERERERVRYWb4iIiIiIiIiIiIiIiHQIp00jIqJqITU1FZmZmWLHKMTU1JTrUhARERERERERkRYWb4iISO+lpqZiydJlUKvyxI5SiFRmgLFjAlnAISIiIiIiIiIiDRZviIhI72VmZkKtysPhHFekquVix9Gwkmahk9FtZGZmsnhDREREREREREQaOrHmzfLly1G3bl3I5XJ4eXnh1KlTJbbftm0bGjduDLlcjubNm2Pv3r1a+wVBwLRp0+Dg4AATExP4+Pjg+vXrWm2Sk5Ph7+8PS0tLWFtbIyAgAOnp6Vptzp8/jzfffBNyuRzOzs6YO3du+TxhIiISRapajseCmc7cdKmQREREREREREREukP04s3WrVsRFBSE6dOnIzo6Gi1btoSvry8SExOLbH/8+HEMHjwYAQEBOHPmDPz8/ODn54eLFy9q2sydOxdLlizBqlWrcPLkSZiZmcHX1xdZWVmaNv7+/rh06RL279+P3bt348iRIxg5cqRmv1KpRLdu3VCnTh1ERUVh3rx5mDFjBlavXl1xF4OIiIiIiIiIiIiIiKo90Ys3CxYswIgRIzB8+HA0adIEq1atgqmpKdavX19k+8WLF6N79+6YOHEi3N3dMXPmTLRu3RrLli0DkD/qZtGiRZgyZQp69+6NFi1a4Oeff0ZcXBxCQkIAAFeuXEFoaCjWrl0LLy8vdOzYEUuXLsWWLVsQFxcHANi0aRNycnKwfv16NG3aFIMGDcLYsWOxYMGCSrkuRERERERERERERERUPYm65k1OTg6ioqIwadIkzTapVAofHx9EREQUeUxERASCgoK0tvn6+moKM7dv30Z8fDx8fHw0+62srODl5YWIiAgMGjQIERERsLa2hqenp6aNj48PpFIpTp48iT59+iAiIgJvvfUWjIyMtB7n+++/x5MnT1CjRo3yuARERESlSk1NRWZmptgxtJiamnKdHiIiIiIiIiKiCiJq8ebRo0dQqVSwt7fX2m5vb4+rV68WeUx8fHyR7ePj4zX7C7aV1MbOzk5rv4GBAWxsbLTauLq6FjpHwb6iijfZ2dnIzs7W3E9NTQWQPwWbWNLS0pCRkSHa4xfFzMwMFhYWJbZh7vJVVbOXljstLQ1ZWVkwy30ClVo3Ptg2k2YjC1lIS0uDmZlZse2qanbmLl9lyZ6amopVP66GWpVXyelKJpUZ4NP/jiyxgFMVf68Auvl6qaqvc/5OrHxlveYVpeB9ryAIlf7YVDUVvFbE7DMREREREVWmsvabRC3e6JvZs2fj66+/LrTd2dlZhDREJJajAObMmSN2jFdSVbNX1dxA1c7+3axvxY5QrVTV10pVzQ1U3ey6kDstLY2j86hM0tLSALDPRERERETVT2n9JlGLNzVr1oRMJkNCQoLW9oSEBCgUiiKPUSgUJbYv+DchIQEODg5abVq1aqVpk5iYqHWOvLw8JCcna52nqMd5/jFeNGnSJK0p3dRqNZKTk2FrawuJRAKlUglnZ2fcu3cPlpaWRZ6DyheveeXi9a58vOaVj9e88vGaVz5e88qlT9dbEASkpaXB0dFR7ChURTg6OuLevXuwsLCARCKp9MfXp///qgJe78rDa125eL0rD6915eL1rjy81pVL7Otd1n6TqMUbIyMjtGnTBmFhYfDz8wOQX/AICwtDYGBgkcd4e3sjLCwM48eP12zbv38/vL29AQCurq5QKBQICwvTFGuUSiVOnjyJUaNGac6RkpKCqKgotGnTBgAQHh4OtVoNLy8vTZv/+7//Q25uLgwNDTWP06hRo2LXuzE2NoaxsbHWNmtr60LtLC0t+T9hJeM1r1y83pWP17zy8ZpXPl7zysdrXrn05XpzxA29DKlUitq1a4sdQ2/+/6sqeL0rD6915eL1rjy81pWL17vy8FpXLjGvd1n6TdJKyFGioKAgrFmzBhs3bsSVK1cwatQoZGRkYPjw4QCAIUOGYNKkSZr248aNQ2hoKObPn4+rV69ixowZiIyM1BR7JBIJxo8fj2+//Ra7du3ChQsXMGTIEDg6OmoKRO7u7ujevTtGjBiBU6dO4dixYwgMDMSgQYM01a7//Oc/MDIyQkBAAC5duoStW7di8eLFWiNriIiIiIiIiIiIiIiIypvoa94MHDgQSUlJmDZtGuLj49GqVSuEhobC3t4eABAbGwup9N8aU/v27REcHIwpU6Zg8uTJcHNzQ0hICJo1a6Zp8+WXXyIjIwMjR45ESkoKOnbsiNDQUMjlck2bTZs2ITAwEF27doVUKkW/fv2wZMkSzX4rKyv8/fffGD16NNq0aYOaNWti2rRpGDlyZCVcFSIiIiIiIiIiIiIiqq5EL94AQGBgYLHTpB06dKjQtv79+6N///7Fnk8ikeCbb77BN998U2wbGxsbBAcHl5irRYsWOHr0aIltXoaxsTGmT59eaGo1qji85pWL17vy8ZpXPl7zysdrXvl4zSsXrzeRePj/X+Xi9a48vNaVi9e78vBaVy5e78rDa125qsr1lgiCIIgdgoiIiIiIiIiIiIiIiPKJvuYNERERERERERERERER/YvFGyIiIiIiIiIiIiIiIh3C4g0REREREREREREREZEOYfGmkixfvhx169aFXC6Hl5cXTp06JXYkvTV79my88cYbsLCwgJ2dHfz8/BATEyN2rGplzpw5kEgkGD9+vNhR9NqDBw/w4YcfwtbWFiYmJmjevDkiIyPFjqW3VCoVpk6dCldXV5iYmKB+/fqYOXMmuHRc+Tly5Ah69eoFR0dHSCQShISEaO0XBAHTpk2Dg4MDTExM4OPjg+vXr4sTVg+UdL1zc3Px1VdfoXnz5jAzM4OjoyOGDBmCuLg48QLrgdJe48/79NNPIZFIsGjRokrLR1QdsZ9W8dg/Ew/7ZRWPfbLKw/5YxWE/rHKxH1Z59KH/xeJNJdi6dSuCgoIwffp0REdHo2XLlvD19UViYqLY0fTS4cOHMXr0aJw4cQL79+9Hbm4uunXrhoyMDLGjVQunT5/Gjz/+iBYtWogdRa89efIEHTp0gKGhIf766y9cvnwZ8+fPR40aNcSOpre+//57rFy5EsuWLcOVK1fw/fffY+7cuVi6dKnY0fRGRkYGWrZsieXLlxe5f+7cuViyZAlWrVqFkydPwszMDL6+vsjKyqrkpPqhpOudmZmJ6OhoTJ06FdHR0dixYwdiYmLw/vvvi5BUf5T2Gi+wc+dOnDhxAo6OjpWUjKh6Yj+tcrB/Jg72yyoe+2SVi/2xisN+WOViP6zy6EX/S6AK17ZtW2H06NGa+yqVSnB0dBRmz54tYqrqIzExUQAgHD58WOwoei8tLU1wc3MT9u/fL3Tq1EkYN26c2JH01ldffSV07NhR7BjVSs+ePYWPP/5Ya1vfvn0Ff39/kRLpNwDCzp07NffVarWgUCiEefPmabalpKQIxsbGwubNm0VIqF9evN5FOXXqlABAuHv3buWE0nPFXfP79+8LTk5OwsWLF4U6deoICxcurPRsRNUF+2niYP+s4rFfVjnYJ6tc7I9VDvbDKhf7YZWnqva/OPKmguXk5CAqKgo+Pj6abVKpFD4+PoiIiBAxWfWRmpoKALCxsRE5if4bPXo0evbsqfV6p4qxa9cueHp6on///rCzs4OHhwfWrFkjdiy91r59e4SFheHatWsAgHPnzuGff/5Bjx49RE5WPdy+fRvx8fFav1+srKzg5eXFv6eVJDU1FRKJBNbW1mJH0VtqtRofffQRJk6ciKZNm4odh0ivsZ8mHvbPKh77ZZWDfbLKxf6YONgPEx/7YRWnKvS/DMQOoO8ePXoElUoFe3t7re329va4evWqSKmqD7VajfHjx6NDhw5o1qyZ2HH02pYtWxAdHY3Tp0+LHaVauHXrFlauXImgoCBMnjwZp0+fxtixY2FkZIShQ4eKHU8v/e9//4NSqUTjxo0hk8mgUqkwa9Ys+Pv7ix2tWoiPjweAIv+eFuyjipOVlYWvvvoKgwcPhqWlpdhx9Nb3338PAwMDjB07VuwoRHqP/TRxsH9W8dgvqzzsk1Uu9sfEwX6YuNgPq1hVof/F4g3ptdGjR+PixYv4559/xI6i1+7du4dx48Zh//79kMvlYsepFtRqNTw9PfHdd98BADw8PHDx4kWsWrWKHYUK8ttvv2HTpk0IDg5G06ZNcfbsWYwfPx6Ojo685qTXcnNzMWDAAAiCgJUrV4odR29FRUVh8eLFiI6OhkQiETsOEVGFYP+sYrFfVrnYJ6tc7I9RdcN+WMWqKv0vTptWwWrWrAmZTIaEhASt7QkJCVAoFCKlqh4CAwOxe/duHDx4ELVr1xY7jl6LiopCYmIiWrduDQMDAxgYGODw4cNYsmQJDAwMoFKpxI6odxwcHNCkSROtbe7u7oiNjRUpkf6bOHEi/ve//2HQoEFo3rw5PvroI0yYMAGzZ88WO1q1UPA3k39PK1dBh+Hu3bvYv38/v+1VgY4ePYrExES4uLho/pbevXsXn3/+OerWrSt2PCK9w35a5WP/rOKxX1a52CerXOyPiYP9MHGwH1bxqkr/i8WbCmZkZIQ2bdogLCxMs02tViMsLAze3t4iJtNfgiAgMDAQO3fuRHh4OFxdXcWOpPe6du2KCxcu4OzZs5qbp6cn/P39cfbsWchkMrEj6p0OHTogJiZGa9u1a9dQp04dkRLpv8zMTEil2n82ZTIZ1Gq1SImqF1dXVygUCq2/p0qlEidPnuTf0wpS0GG4fv06Dhw4AFtbW7Ej6bWPPvoI58+f1/pb6ujoiIkTJ2Lfvn1ixyPSO+ynVR72zyoP+2WVi32yysX+mDjYD6t87IdVjqrS/+K0aZUgKCgIQ4cOhaenJ9q2bYtFixYhIyMDw4cPFzuaXho9ejSCg4Pxxx9/wMLCQjMHp5WVFUxMTEROp58sLCwKzVltZmYGW1tbzmVdQSZMmID27dvju+++w4ABA3Dq1CmsXr0aq1evFjua3urVqxdmzZoFFxcXNG3aFGfOnMGCBQvw8ccfix1Nb6Snp+PGjRua+7dv38bZs2dhY2MDFxcXjB8/Ht9++y3c3Nzg6uqKqVOnwtHREX5+fuKFrsJKut4ODg744IMPEB0djd27d0OlUmn+ntrY2MDIyEis2FVaaa/xFztmhoaGUCgUaNSoUWVHJaoW2E+rHOyfVR72yyoX+2SVi/2xisN+WOViP6zy6EX/S6BKsXTpUsHFxUUwMjIS2rZtK5w4cULsSHoLQJG3n376Sexo1UqnTp2EcePGiR1Dr/35559Cs2bNBGNjY6Fx48bC6tWrxY6k15RKpTBu3DjBxcVFkMvlQr169YT/+7//E7Kzs8WOpjcOHjxY5O/voUOHCoIgCGq1Wpg6dapgb28vGBsbC127dhViYmLEDV2FlXS9b9++Xezf04MHD4odvcoq7TX+ojp16ggLFy6s1IxE1Q37aRWP/TNxsV9Wsdgnqzzsj1Uc9sMqF/thlUcf+l8SQRCE8iwGERERERERERERERER0avjmjdEREREREREREREREQ6hMUbIiIiIiIiIiIiIiIiHcLiDRERERERERERERERkQ5h8YaIiIiIiIiIiIiIiEiHsHhDRERERERERERERESkQ1i8ISIiIiIiIiIiIiIi0iEs3hAREREREREREREREekQFm+IiIiIiIiIiIiIiIh0CIs3RESvoXPnzhg/fnyFPsawYcPg5+dXoY9R3bzONT106BAkEglSUlIAABs2bIC1tXW5ZdM1EokEISEhYscgIiIioiqKfaaqiX2msmOfiYgqCos3RERUJehqh2zgwIG4du2a2DGIiIiIiKiaY5+JiEi/GIgdgIiIKl9OTg6MjIzEjqEXTExMYGJiInaMKoWvPyIiIiLSdXzPWn7YZ3p5fP0REcCRN0REry0vLw+BgYGwsrJCzZo1MXXqVAiCoNn/yy+/wNPTExYWFlAoFPjPf/6DxMRErXNcunQJ7733HiwtLWFhYYE333wTN2/eLPLxTp8+jVq1auH7778HAMyYMQOtWrXCjz/+CGdnZ5iammLAgAFITU3VHFPwDaxZs2bB0dERjRo1AgBcuHABXbp0gYmJCWxtbTFy5Eikp6cXOu7rr79GrVq1YGlpiU8//RQ5OTmaNqGhoejYsSOsra1ha2uL9957r1D248ePo1WrVpDL5fD09ERISAgkEgnOnj0LAFCpVAgICICrqytMTEzQqFEjLF68WHP8jBkzsHHjRvzxxx+QSCSQSCQ4dOgQAODevXsYMGAArK2tYWNjg969e+POnTuaY1UqFYKCgjT5vvzyS62fT1Hu3r2LXr16oUaNGjAzM0PTpk2xd+/eItsWNQXAn3/+iTfeeANyuRw1a9ZEnz59NPuys7PxxRdfwMnJCWZmZvDy8tI8l+JIJBKsXbsWffr0gampKdzc3LBr164SMxRc4wIFr5P169fDxcUF5ubm+Oyzz6BSqTB37lwoFArY2dlh1qxZhR7/4cOH6NGjB0xMTFCvXj1s375da39pP4PiXn9EREREVD2wz8Q+E/tM7DMR0ctj8YaI6DVt3LgRBgYGOHXqFBYvXowFCxZg7dq1mv25ubmYOXMmzp07h5CQENy5cwfDhg3T7H/w4AHeeustGBsbIzw8HFFRUfj444+Rl5dX6LHCw8PxzjvvYNasWfjqq68022/cuIHffvsNf/75J0JDQ3HmzBl89tlnWseGhYUhJiYG+/fvx+7du5GRkQFfX1/UqFEDp0+fxrZt23DgwAEEBgYWOu7KlSs4dOgQNm/ejB07duDrr7/W7M/IyEBQUBAiIyMRFhYGqVSKPn36QK1WAwCUSiV69eqF5s2bIzo6GjNnztTKDgBqtRq1a9fGtm3bcPnyZUybNg2TJ0/Gb7/9BgD44osvMGDAAHTv3h0PHz7Ew4cP0b59e+Tm5sLX1xcWFhY4evQojh07BnNzc3Tv3l3TWZo/fz42bNiA9evX459//kFycjJ27txZ4s909OjRyM7OxpEjR3DhwgV8//33MDc3L/GYAnv27EGfPn3w7rvv4syZMwgLC0Pbtm01+wMDAxEREYEtW7bg/Pnz6N+/P7p3747r16+XeN6vv/4aAwYMwPnz5/Huu+/C398fycnJZcpU4ObNm/jrr78QGhqKzZs3Y926dejZsyfu37+Pw4cP4/vvv8eUKVNw8uRJreOmTp2Kfv364dy5c/D398egQYNw5coVACjTzwAo/PojIiIiouqDfSb2mZ7HPhP7TERURgIREb2yTp06Ce7u7oJardZs++qrrwR3d/dijzl9+rQAQEhLSxMEQRAmTZokuLq6Cjk5OUW2Hzp0qNC7d29hx44dgrm5ubBlyxat/dOnTxdkMplw//59zba//vpLkEqlwsOHDzXnsLe3F7KzszVtVq9eLdSoUUNIT0/XbNuzZ48glUqF+Ph4zXE2NjZCRkaGps3KlSsFc3NzQaVSFZk3KSlJACBcuHBB097W1lZ4+vSpps2aNWsEAMKZM2eKvU6jR48W+vXrV+g6PO+XX34RGjVqpHX9s7OzBRMTE2Hfvn2CIAiCg4ODMHfuXM3+3NxcoXbt2oXO9bzmzZsLM2bMKHLfwYMHBQDCkydPBEEQhJ9++kmwsrLS7Pf29hb8/f2LPPbu3buCTCYTHjx4oLW9a9euwqRJk4rNA0CYMmWK5n56eroAQPjrr7+KzCAIgrBz507h+T/z06dPF0xNTQWlUqnZ5uvrK9StW1frZ9moUSNh9uzZWo/96aefap3by8tLGDVqlCAIZfsZFPX6IyIiIqLqgX2mwthnYp+JfSYiKguOvCEiek3t2rXTGmrt7e2N69evQ6VSAQCioqLQq1cvuLi4wMLCAp06dQIAxMbGAgDOnj2LN998E4aGhsU+xsmTJ9G/f3/88ssvGDhwYKH9Li4ucHJy0sqgVqsRExOj2da8eXOtOXOvXLmCli1bwszMTLOtQ4cOhY5r2bIlTE1Ntc6dnp6Oe/fuAQCuX7+OwYMHo169erC0tETdunW1nl9MTAxatGgBuVyuOcfz36oqsHz5crRp0wa1atWCubk5Vq9erTlHcc6dO4cbN27AwsIC5ubmMDc3h42NDbKysnDz5k2kpqbi4cOH8PLy0hxjYGAAT0/PEs87duxYfPvtt+jQoQOmT5+O8+fPl9j+eWfPnkXXrl2L3HfhwgWoVCo0bNhQk9fc3ByHDx8udsqHAi1atND8t5mZGSwtLQtNJVGaunXrwsLCQnPf3t4eTZo0gVQq1dr24nm9vb0L3S/4FllpP4MCL77+iIiIiKj6YJ+Jfabnsc/EPhMRlY2B2AGIiPRZwTB7X19fbNq0CbVq1UJsbCx8fX01w6PLsnBj/fr1YWtri/Xr16Nnz54ldlqK83yHozz16tULderUwZo1a+Do6Ai1Wo1mzZppDf8uzZYtW/DFF19g/vz58Pb2hoWFBebNm1doKPqL0tPT0aZNG2zatKnQvlq1ar30cynwySefwNfXF3v27MHff/+N2bNnY/78+RgzZkypx5b080xPT4dMJkNUVBRkMpnWvtKmGHjxZy6RSDTTLEil0kJzUufm5pbpHCWdtyzK+jOoqNcfEREREVVt7DOVDftM7DMRUfXDkTdERK/pxTfLJ06cgJubG2QyGa5evYrHjx9jzpw5ePPNN9G4ceNC39Bp0aIFjh49WuQbxwI1a9ZEeHg4bty4gQEDBhRqGxsbi7i4OK0MUqm0xEUO3d3dce7cOWRkZGi2HTt2rNBx586dw9OnT7XObW5uDmdnZzx+/BgxMTGYMmUKunbtCnd3dzx58kTrcRo1aoQLFy4gOztbs+306dNabY4dO4b27dvjs88+g4eHBxo0aFDoW1VGRkaab+YVaN26Na5fvw47Ozs0aNBA62ZlZQUrKys4ODho/Yzy8vIQFRVV7HUp4OzsjE8//RQ7duzA559/jjVr1pR6DJD/8wwLCytyn4eHB1QqFRITEwvlVSgUZTp/UWrVqoW0tDStn2XBwqbl4cSJE4Xuu7u7Ayj9Z0BERERExD4T+0zPY5+JfSYiKhsWb4iIXlNsbCyCgoIQExODzZs3Y+nSpRg3bhyA/KH5RkZGWLp0KW7duoVdu3Zh5syZWscHBgZCqVRi0KBBiIyMxPXr1/HLL79oDcMHADs7O4SHh+Pq1asYPHiw1uKccrkcQ4cOxblz53D06FGMHTsWAwYMKPHNrb+/v+a4ixcv4uDBgxgzZgw++ugj2Nvba9rl5OQgICAAly9fxt69ezF9+nQEBgZCKpWiRo0asLW1xerVq3Hjxg2Eh4cjKChI63H+85//QK1WY+TIkbhy5Qr27duHH374AQA0Uye4ubkhMjIS+/btw7Vr1zB16tRCnZW6devi/PnziImJwaNHj5Cbmwt/f3/UrFkTvXv3xtGjR3H79m0cOnQIY8eOxf379wEA48aNw5w5cxASEoKrV6/is88+Q0pKSok/0/Hjx2Pfvn24ffs2oqOjcfDgQc0b79JMnz4dmzdvxvTp03HlyhXN4p0A0LBhQ/j7+2PIkCHYsWMHbt++jVOnTmH27NnYs2dPmc5fFC8vL5iammLy5Mm4efMmgoODsWHDhlc+34u2bduG9evX49q1a5g+fTpOnTqlWaS1LD8DIiIiIqre2Gdin+l57DOxz0REZcPiDRHRaxoyZAiePn2Ktm3bYvTo0Rg3bhxGjhwJIP/bPRs2bMC2bdvQpEkTzJkzR/MmvICtrS3Cw8ORnp6OTp06oU2bNlizZk2Rw/wVCgXCw8Nx4cIF+Pv7a75V1aBBA/Tt2xfvvvsuunXrhhYtWmDFihUl5jY1NcW+ffuQnJyMN954Ax988AG6du2KZcuWabXr2rUr3Nzc8NZbb2HgwIF4//33MWPGDAD5Q8+3bNmCqKgoNGvWDBMmTMC8efO0jre0tMSff/6Js2fPolWrVvi///s/TJs2DQA0czr/97//Rd++fTFw4EB4eXnh8ePH+Oyzz7TOM2LECDRq1Aienp6oVasWjh07BlNTUxw5cgQuLi7o27cv3N3dERAQgKysLFhaWgIAPv/8c3z00UcYOnSoZnqBPn36lHhtVCoVRo8eDXd3d3Tv3h0NGzYs9XoW6Ny5M7Zt24Zdu3ahVatW6NKlC06dOqXZ/9NPP2HIkCH4/PPP0ahRI/j5+eH06dNwcXEp0/mLYmNjg19//RV79+5F8+bNsXnzZs3PqDx8/fXX2LJlC1q0aIGff/4ZmzdvRpMmTQCgTD8DIiIiIqre2Gdin+l57DOxz0REZSMRXpzwkYiIqpQZM2YgJCSkXId8Fxg2bBhSUlIQEhJSrufdtGkThg8fjtTU1DLNX01ERERERPSq2GciIqKqyEDsAEREpP9+/vln1KtXD05OTjh37hy++uorDBgwgJ0QIiIiIiIisM9ERESFsXhDREQVLj4+HtOmTUN8fDwcHBzQv39/zJo1S+xYREREREREOoF9JiIiehGnTSMiIiIiIiIiIiIiItIhUrEDEBERERERERERERER0b9YvCEiIiIiIiIiIiIiItIhLN4QERERERERERERERHpEBZviIiIiIiIiIiIiIiIdAiLN0RERERERERERERERDqExRsiIiIiIiIiIiIiIiIdwuINERERERERERERERGRDmHxhoiIiIiIiIiIiIiISIeweENERERERERERERERKRDWLwhIiIiIiIiIiIiIiLSISzeEBERERERERERERER6RAWb4iIiIiIiIiIiIiIiHQIizdEREREREREREREREQ6hMUbIiIiIiIiIiIiIiIiHcLiDRERERERERERERERkQ5h8YaIiIiIiIiIiIiIiEiHsHhDRERERERERERERESkQ1i8ISIiIiIiIiIiIiIi0iEs3hAREREREREREREREekQFm+IiIiIiIiIiIiIiIh0CIs3REREREREREREREREOoTFGyIiIiIiIiIiIiIiIh3C4g0REREREREREREREZEOYfGGiIiIiIiIiIiIiIhIh7B4Q0REREREREREREREpEMMxA6gz9RqNeLi4mBhYQGJRCJ2HCIiIiKiCiUIAtLS0uDo6AiplN8To9Kxz0RERERE1U1Z+00s3lSguLg4ODs7ix2DiIiIiKhS3bt3D7Vr1xY7BlUB7DMRERERUXVVWr+JxZsKZGFhASD/h2BpaSlyGiIiIiKiiqVUKuHs7Kx5H0xUGvaZiIiIiKi6KWu/icWbClQw7N/S0pIdESIiIiKqNjj9FZUV+0xEREREVF2V1m/iRNRERERERER6bvbs2XjjjTdgYWEBOzs7+Pn5ISYmRrM/OTkZY8aMQaNGjWBiYgIXFxeMHTsWqampJZ532LBhkEgkWrfu3btX9NMhIiIiItJ7LN4QERERERHpucOHD2P06NE4ceIE9u/fj9zcXHTr1g0ZGRkA8teeiYuLww8//ICLFy9iw4YNCA0NRUBAQKnn7t69Ox4+fKi5bd68uaKfDhERERGR3uO0aURERERERHouNDRU6/6GDRtgZ2eHqKgovPXWW2jWrBl+//13zf769etj1qxZ+PDDD5GXlwcDg+K7jsbGxlAoFBWWnYiIiIioOmLxRkSCICAvLw8qlUrsKKSHZDIZDAwMOOc8ERERERVSMB2ajY1NiW0sLS1LLNwAwKFDh2BnZ4caNWqgS5cu+Pbbb2Fra1tuWdlvIl3FPhcRERFVJBZvRJKTk4OHDx8iMzNT7Cikx0xNTeHg4AAjIyOxoxARERGRjlCr1Rg/fjw6dOiAZs2aFdnm0aNHmDlzJkaOHFniubp3746+ffvC1dUVN2/exOTJk9GjRw9ERERAJpMVap+dnY3s7GzNfaVSWeL52W8iXcc+FxEREVUUiSAIgtgh9JVSqYSVlZXmG2sF1Go1rl+/DplMhlq1asHIyIjf1KFyJQgCcnJykJSUBJVKBTc3N0ilXOKKiIiIKlZx739Jt4waNQp//fUX/vnnH9SuXbvQfqVSiXfeeQc2NjbYtWsXDA0Ny3zuW7duoX79+jhw4AC6du1aaP+MGTPw9ddfF9pe1GuG/SbSZexzERER0asqa7+JI29EkJOTA7VaDWdnZ5iamoodh/SUiYkJDA0NcffuXeTk5EAul4sdiYiIiIhEFhgYiN27d+PIkSNFFm7S0tLQvXt3WFhYYOfOnS9VuAGAevXqoWbNmrhx40aRxZtJkyYhKChIc1+pVMLZ2bnIc7HfRLqOfS4iIiKqSCzeiIjfyqGKxtcYEREREQH5owTGjBmDnTt34tChQ3B1dS3URqlUwtfXF8bGxti1a9crfRB9//59PH78GA4ODkXuNzY2hrGx8Uudk+9pSZfx9UlEREQVpUq8y1i+fDnq1q0LuVwOLy8vnDp1qsT227ZtQ+PGjSGXy9G8eXPs3btXa/+OHTvQrVs32NraQiKR4OzZs1r7k5OTMWbMGDRq1AgmJiZwcXHB2LFjNYt6EhERERERVSWjR4/Gr7/+iuDgYFhYWCA+Ph7x8fF4+vQpgPzCTbdu3ZCRkYF169ZBqVRq2qhUKs15GjdujJ07dwIA0tPTMXHiRJw4cQJ37txBWFgYevfujQYNGsDX11eU50lEREREpC90vnizdetWBAUFYfr06YiOjkbLli3h6+uLxMTEItsfP34cgwcPRkBAAM6cOQM/Pz/4+fnh4sWLmjYZGRno2LEjvv/++yLPERcXh7i4OPzwww+4ePEiNmzYgNDQUAQEBFTIc9RXdevWxaJFi8SO8Uo6d+6M8ePHl9imKj8/IiIiIqpeVq5cidTUVHTu3BkODg6a29atWwEA0dHROHnyJC5cuIAGDRpotbl3757mPDExMZovtclkMpw/fx7vv/8+GjZsiICAALRp0wZHjx596dE1RERERESkTeenTVuwYAFGjBiB4cOHAwBWrVqFPXv2YP369fjf//5XqP3ixYvRvXt3TJw4EQAwc+ZM7N+/H8uWLcOqVasAAB999BEA4M6dO0U+ZrNmzfD7779r7tevXx+zZs3Chx9+iLy8PBgY6Pxlq1D37t3D9OnTERoaikePHsHBwQF+fn6YNm0abG1txY5XZc2bNw9nzpxBcHAwgoODsXbtWoSHh2v2Z2Vl4dNPP0VUVBSuXLmC9957DyEhIeIFJiIiIqIqQxCEEvd37ty51DYvnsfExAT79u177WxERERERFSYTo+8ycnJQVRUFHx8fDTbpFIpfHx8EBERUeQxERERWu0BwNfXt9j2ZZWamgpLS8sSCzfZ2dlQKpVaN31z69YteHp64vr169i8eTNu3LiBVatWISwsDN7e3khOThYtm0qlglqtFu3xX1dERAQ6dOgAADh69KjmvwuoVCqYmJhg7NixhV7jREREVP0IgoC0rFzEPs7EmdgnCL+agO1R97HmyC2o1KV/CE9EVFXk5OSIHYGIiIj0xKVLlzBz5kyt0eW6SqeLN48ePYJKpYK9vb3Wdnt7e8THxxd5THx8/Eu1L2uOmTNnYuTIkSW2mz17NqysrDQ3Z2fnV35MXTV69GgYGRnh77//RqdOneDi4oIePXrgwIEDePDgAf7v//5Pq31aWhoGDx4MMzMzODk5Yfny5Zp9giBgxowZcHFxgbGxMRwdHTF27FjN/uzsbHzxxRdwcnKCmZkZvLy8cOjQIc3+DRs2wNraGrt27UKTJk1gbGyMtWvXQi6XIyUlRSvHuHHj0KVLFwDA48ePMXjwYDg5OcHU1BTNmzfH5s2bCz3XvLw8BAYGwsrKCjVr1sTUqVNL/DZiSkoKPvnkE9SqVQuWlpbo0qULzp07V+Zr+3zx5p9//ilUvDEzM8PKlSsxYsQIKBSKMp+XiIiIqobsPBXiU7NwOU6JYzceYde5OGw8fgcL91/DtD8uYnRwNP6z5gS6LzqCtrMOoOGUv9B8xt94a95B9FlxHB9viMQX285h1t4rSMnkB51EYurcuTPGjBmD8ePHo0aNGrC3t8eaNWuQkZGB4cOHw8LCAg0aNMBff/0FIP+LWgEBAXB1dYWJiQkaNWqExYsXa86XlZWFpk2bavVJb968CQsLC6xfv75MmTZs2AAXFxeYmpqiT58+mD9/PqytrQHkf1lRJpMhMjISAKBWq2FjY4N27dppjv/111+1+rj379/H4MGDYWNjAzMzM3h6euLkyZNlyvLtt9/Czs4OFhYW+OSTT/C///0PrVq10uwfNmwY/Pz8MGvWLDg6OqJRo0YAgAsXLqBLly4wMTGBra0tRo4cifT0dK3r/uL0135+fhg2bJjmft26dTFz5sxX7qcSERFR1bZ69WpMmzYNEyZMEDtKqar3/F9loFQq0bNnTzRp0gQzZswose2kSZMQFBSkdWxZCziCIOBprqr0hhXAxFAGiURSarvk5GTs27cPs2bNgomJidY+hUIBf39/bN26FStWrNCcb968eZg8eTK+/vpr7Nu3D+PGjUPDhg3xzjvv4Pfff8fChQuxZcsWNG3aFPHx8VrFjsDAQFy+fBlbtmyBo6Mjdu7cie7du+PChQtwc3MDAGRmZuL777/H2rVrYWtri9q1a2PatGn4/fffNWsUqVQqbN26FbNmzQKQ3/Fp06YNvvrqK1haWmLPnj346KOPUL9+fbRt21bz+Bs3bkRAQABOnTqFyMhIjBw5Ei4uLhgxYkSR16d///4wMTHBX3/9BSsrK/z444/o2rUrrl27BhsbmyKPmTNnDubMmQMgv8PUqVMnSCQSpKamYsCAAZBKpdi9ezc6duxY6s+HiIiIdIdaLSD1aS6SM3OQnJF/e5KRg8fP/k3OfPav5r9zkZ6d90qPZWokQw1TI9iY/XvjuBvSS4IAZGeL89jGxkAZ+kzP27hxI7788kucOnUKW7duxahRo7Bz50706dMHkydPxsKFC/HRRx8hNjYWhoaGqF27NrZt2wZbW1scP34cI0eOhIODAwYMGAC5XI5NmzbBy8sLPXv2xHvvvYcPP/wQ77zzDj7++ONSs5w8eRIBAQGYPXs2/Pz8EBoaiunTp2v2W1lZoVWrVjh06BA8PT1x4cIFSCQSnDlzBunp6TA3N8fhw4fRqVMnAEB6ejo6deoEJycn7Nq1CwqFAtHR0WWaCWHTpk2YNWsWVqxYgQ4dOmDLli2YP38+XF1dtdqFhYXB0tIS+/fvB5C/dq2vry+8vb1x+vRpJCYm4pNPPkFgYCA2bNjwEj+Z1+unEhERUdWVlZWFX3/9FQCqxPr2Ol28qVmzJmQyGRISErS2JyQkFDvyQKFQvFT7kqSlpaF79+6wsLDAzp07YWhoWGJ7Y2PjV16Y82muCk2miTNf9OVvfGFqVPpL4fr16xAEAe7u7kXud3d3x5MnT5CUlAQ7OzsAQIcOHTRrEzVs2BDHjh3DwoUL8c477yA2NhYKhQI+Pj4wNDSEi4uLpngSGxuLn376CbGxsXB0dAQAfPHFFwgNDcVPP/2E7777DgCQm5uLFStWoGXLlpocgwYNQnBwsOZ/wLCwMKSkpKBfv34AACcnJ3zxxRea9mPGjMG+ffvw22+/aRVvnJ2dsXDhQkgkEjRq1AgXLlzAwoULiyze/PPPPzh16hQSExM1r4EffvgBISEh2L59e7Gjtj799FMMGjQIGzZswIkTJ7Bq1Srs3bsXGzZswG+//QYAHGVDREQkMkEQkJmjyi/APFeMefH+k4x/izUpmTl4lZnLZFIJapgawdbMCDXMDGFjZvTc/X+LM88Xa+SGsvJ/0kS6KDsb6NFDnMf+6y9ALn+pQ1q2bIkpU6YAyP+i35w5c1CzZk1Nf2LatGlYuXIlzp8/j3bt2uHrr7/WHOvq6oqIiAj89ttvGDBgAACgVatW+Pbbb/HJJ59g0KBBuHv3Lnbv3l2mLAVrw3755ZcA8vtmx48fR2hoqKZN586dcejQIXzxxRc4dOgQ3nnnHVy9ehX//PMPunfvjkOHDmmODw4ORlJSEk6fPq35olqDBg3KlGXp0qUICAjQrGs7bdo0/P3331ojaID8mQfWrl0LIyMjAMCaNWuQlZWFn3/+GWZmZgCAZcuWoVevXvj+++8LzcBRklftpxIREVHVFhISguTkZNSuXRvdunUTO06pdLp4Y2RkhDZt2iAsLAx+fn4A8odvh4WFITAwsMhjvL29ERYWpjVUev/+/fD29n6px1YqlfD19YWxsTF27doF+Uu+UddnZVnItMCL193b2xuLFi0CkD9SZdGiRahXrx66d++Od999F7169YKBgQEuXLgAlUqFhg0bah2fnZ0NW1tbzX0jIyO0aNFCq42/vz/atWuHuLg4ODo6YtOmTejZs6dmSgCVSoXvvvsOv/32Gx48eICcnBxkZ2fD1NRU6zzt2rXTGpHk7e2N+fPnQ6VSQSbT/pDk3LlzSE9P18oGAE+fPsXNmzeLvT7W1tawtrbGqVOn0K9fP9StWxdnzpzB+++/j7p16xZ7HBEREZWvnDw1tp6Oxc2kDE1R5nH6v8WZ7LxXW1fPUm6QX2wxe1aAeVZ00RRiTJ/bZ2YES7lBmUZEE5Hue76fIpPJYGtri+bNm2u2FRQbEhMTAQDLly/H+vXrERsbi6dPnyInJ0drKjEA+PzzzxESEoJly5bhr7/+KtT/KM6VK1fQp08frW3e3t5axZtOnTph3bp1UKlUOHz4MLp16waFQoFDhw6hRYsWuHHjBjp37gwAOHv2LDw8PIqdYaAkMTEx+Oyzz7S2tW3bFuHh4VrbmjdvrincFDyHli1bago3QH4RRq1WIyYm5qWKN6/aTyUiIqKqbd26dQDyp2h98fNdXaTz7z6CgoIwdOhQeHp6om3btli0aJFmnmAAGDJkCJycnDB79mwA+WubdOrUCfPnz0fPnj2xZcsWREZGYvXq1ZpzJicnIzY2FnFxcQDy3zwC+SMcFAoFlEolunXrhszMTPz6669QKpVQKpUAgFq1alXID9bEUIbL3/iW+3nL+thl0aBBA0gkkiLf+AP5b6Zr1KiBWrVqlel8zs7OiImJwYEDB7B//3589tlnmDdvHg4fPoz09HTIZDJERUUVut7m5ub/ZjcxKfQBxxtvvIH69etjy5YtmqkJnh9GP2/ePCxevBiLFi1C8+bNYWZmhvHjx7/WIpjp6elwcHDQWpOnQEHR6EVHjx5Fj2ffHMzMzMShQ4cwYcIEPH36FIaGhpgzZw4mT56MyZMnv3IuIiIiKt39J5kYHXwG5+6llNjOyEAK22JGv9R4Voj5974hapgawVCm00tMElUtxsb5I2DEeuyX9OLMDRKJRGtbQT9GrVZjy5Yt+OKLLzB//nx4e3vDwsIC8+bNK7SGTGJiIq5duwaZTIbr16+je/fur/BkivbWW28hLS0N0dHROHLkCL777jsoFArMmTMHLVu2hKOjo2b66hen0a4IzxdpykoqlRb6smFubu5LnaOkfmpps3EQERGR7rpz5w4OHDgAAGWadlYX6HzxZuDAgUhKSsK0adMQHx+PVq1aITQ0VPOtmtjYWEil/3aK27dvj+DgYEyZMgWTJ0+Gm5sbQkJC0KxZM02bXbt2aYo/QP40WwAwffp0zJgxA9HR0Zo3yS8O/b59+3aFjIiQSCRlmrpMTLa2tnjnnXewYsUKTJgwQesNe3x8PDZt2oQhQ4ZoFVNOnDihdY4TJ05oTbtmYmKCXr16oVevXhg9ejQaN26MCxcuwMPDAyqVComJiXjzzTdfOqu/vz82bdqE2rVrQyqVomfPnpp9x44dQ+/evfHhhx8CyO8sXbt2DU2aNNE6x4sdpRMnTsDNza3I4l3r1q0RHx8PAwODMr8+PD09cfbsWURFReHLL79EWFgYYmNj8f777yM6OhpSqfSVvslGREREZRd2JQFBv51D6tNcWJkYYnBbF9Q0N4KtuVGhdWTKuk4gEVUQieSlpy6rKo4dO4b27dtrjUgpagT/xx9/jObNmyMgIAAjRoyAj49PsdNaP8/d3b3I/s3zrK2t0aJFCyxbtgyGhoZo3Lgx7OzsMHDgQOzevVuz3g2QP6po7dq1SE5Ofuk+S6NGjXD69GkMGTJEs+306dNleg4bNmxARkaGprBz7NgxSKVSNGrUCED+ly0fPnyoOUalUuHixYt4++23S3zuZe2ntm7d+qWeKxEREemOn376CQDQtWvXQmvt6SrdrhY8ExgYWOw0aUWNdOjfvz/69+9f7PmGDRuGYcOGFbu/c+fOLzU1WHWybNkytG/fHr6+vvj222/h6uqKS5cuYeLEiXBycsKsWbO02h87dgxz586Fn58f9u/fj23btmHPnj0AgA0bNkClUsHLywumpqb49ddfYWJigjp16sDW1hb+/v4YMmQI5s+fDw8PDyQlJSEsLAwtWrTQKsYUxd/fHzNmzMCsWbPwwQcfaK1F5Obmhu3bt+P48eOoUaMGFixYgISEhELFm9jYWAQFBeG///0voqOjsXTpUsyfP7/Ix/Px8YG3tzf8/Pwwd+5cNGzYEHFxcdizZw/69OkDT0/PQseYmJigQYMG2L59Ozp37owGDRrg+PHj6NChQ6Hp4gpcvnwZOTk5SE5ORlpaGs6ePQsAhaZTICIiopLlqtT44e8Y/Hj4FgCgpbM1lv/HA7VrmJZyJBFR+XNzc8PPP/+Mffv2wdXVFb/88gtOnz6t9cHC8uXLERERgfPnz8PZ2Rl79uyBv78/Tpw4oTW9WFHGjh2LDh064IcffkDv3r2xb98+rSnTCnTu3BlLly7FBx98AACwsbGBu7s7tm7diuXLl2vaDR48GN999x38/Pwwe/ZsODg44MyZM3B0dCx1yvIxY8ZgxIgR8PT0RPv27bF161acP38e9erVK/E4f39/TJ8+HUOHDsWMGTOQlJSEMWPG4KOPPtJ8ubNLly4ICgrCnj17UL9+fSxYsAApKSmFzvWq/VQiIiKqmlQqlaZ4U7BOelXAeRzopbi5uSEyMhL16tXDgAEDUL9+fYwcORJvv/02IiIiCn3r6vPPP0dkZCQ8PDzw7bffYsGCBfD1zZ8eztraGmvWrEGHDh3QokULHDhwAH/++adm3uaffvoJQ4YMweeff45GjRrBz88Pp0+fhouLS6k5GzRogLZt2+L8+fPw9/fX2jdlyhS0bt0avr6+6Ny5MxQKhWZNpecNGTIET58+Rdu2bTF69GiMGzcOI0eOLPLxJBIJ9u7di7feegvDhw9Hw4YNNYuIljb38qFDh/DWW28BAA4fPqz576K8++678PDwwJ9//olDhw7Bw8MDHh4epVwNIiIiel58ahb+s+aEpnDzcQdXbPuvNws3RCSa//73v+jbty8GDhwILy8vPH78WGsUztWrVzFx4kSsWLECzs7OAIAVK1bg0aNHmDp1aqnnb9euHdasWYPFixejZcuW+PvvvzFlypRC7Tp16gSVSqVZ2wbIL+i8uM3IyAh///037Ozs8O6776J58+aYM2dOmaYY9/f3x6RJk/DFF1+gdevWuH37NoYNG1bqOrOmpqbYt28fkpOT8cYbb+CDDz5A165dsWzZMk2bjz/+GEOHDsWQIUPQqVMn1KtXr9CoG+D1+qlERERU9Rw4cAD37t1DjRo1ilwORFdJBA4xqTBKpRJWVlZITU2FpaWlZntWVhZu374NV1fXUt+gEr0OvtaIiIi0HbmWhPFbzyI5IwcWxgaY+0EL9GjuIHYsvVHc+1+i4pT0muF72Yq1YcMGjB8/vsiRKZXtnXfegUKhwC+//FLhj1W3bl2MHz8e48ePL5fz8XVKRESk+wYMGIBt27YhMDAQS5cuFTtOmftNVWLaNCIiIiKi16FSC1gcdh1Lw69DEICmjpZY4d8adWxffkFsIiJ6dZmZmVi1ahV8fX0hk8mwefNmHDhwAPv37xc7GhEREemhR48eISQkBEDVmjIN4LRpRERERKTnktKy8dG6k1gSll+48fdywe+j2rNwQ0R6o0ePHjA3Ny/y9t1331VqlqZNmxabZdOmTVpTTrdp0wZ//vknfv/9d/j4+FRqTiIiIqoefv31V+Tm5qJ169ZVbt1wjrwhIiIiIr0VcfMxxm45g6S0bJgayTC7b3P0buUkdiwionK1du1aPH36tMh9L65LWmDYsGEYNmxYuWfZu3cvcnNzi9xnb28PExMTHDhwoNwft6zu3Lkj2mMTERFR5RIEAevWrQNQ9UbdACzeEBEREZEeUqsFrDx8E/P/joFaABram2OFfxs0sDMXOxoRUblzctKdonSdOnXEjkBEREQEADh16hQuXrwIuVyO//znP2LHeWks3hARERGRXknOyMGErWdx+FoSAOCDNrUxs3czmBjJRE5GRERERERElaVg1E2/fv1gbW0tbphXwOKNiARBEDsC6Tm+xoiIqLqJupuMwOAzeJiaBbmhFN/0boYBns5ixyKi18D3tKTL+PokIiLSTRkZGdiyZQuAqjllGsDijSgMDQ0BAJmZmTAxMRE5DemzzMxMAP++5oiIiPSVIAhY989tzPnrKvLUAurVMsMK/9ZorLAUOxoRvSL2m6gqYJ+LiIhIN23btg1paWmoX78+OnXqJHacV8LijQhkMhmsra2RmJgIADA1NYVEIhE5FekTQRCQmZmJxMREWFtbQybjNDFERKS/UjNz8cX2c9h/OQEA0KulI2b3bQ5zY77VJarK2G8iXcY+FxERkW4rmDLt448/hlQqFTnNq2GPViQKhQIANB0RoopgbW2tea0RERHpo/P3UzA6OBr3kp/CSCbFtF5N4O/lwg94ifQE+02k69jnIiIi0j0xMTH4559/IJVKMXToULHjvDIWb0QikUjg4OAAOzs75Obmih2H9JChoSG//UVERHpLEAT8cuIuvt19BTkqNVxsTLHCvzWaOVmJHY2IyhH7TaTL2OciIiLSTevXrwcA9OjRA05OTiKneXUs3ohMJpPxzR4RERHRS0jLysX/dlzAnvMPAQC+Te0x94OWsDLhegNE+or9JiIiIiIqi9zcXGzcuBEAEBAQIHKa18PiDRERERFVGZfjlBgdHI3bjzJgIJVg0rvu+LhDXU6TRkRERERERNi7dy8SEhJgZ2eH9957T+w4r4XFGyIiIiLSeYIg4LfIe5j2xyVk56nhaCXHMv/WaO1SQ+xoREREREREpCPWrVsHABgyZAgMDav27Aws3hARERGRTsvMycOUkIvYEf0AAPB2o1pYMKAVapgZiZyMqGI9ffoUgiDA1NQUAHD37l3s3LkTTZo0Qbdu3UROR0RERESkW+Li4rBnzx4AVX/KNIDFGyIiIiLSYTcS0zDq12hcT0yHTCrBF90a4b9v1YNUymnSSP/17t0bffv2xaeffoqUlBR4eXnB0NAQjx49woIFCzBq1CixIxIRERER6YyNGzdCrVajffv2aNy4sdhxXptU7ABEREREREXZeeY+ei09huuJ6bCzMEbwJ14Y1bk+CzdUbURHR+PNN98EAGzfvh329va4e/cufv75ZyxZskTkdEREREREukMQBKxfvx6Afoy6ATjyhoiIiIh0TFauCl//eQmbT90DAHRsUBOLBrVCTXNjkZMRVa7MzExYWFgAAP7++2/07dsXUqkU7dq1w927d0VOR0RERESkO44cOYIbN27A3NwcAwYMEDtOueDIGyIiIiLSGbcfZaDPiuPYfOoeJBJgvI8bNn7cloUbqpYaNGiAkJAQ3Lt3D/v27dOsc5OYmAhLS0uR0xERERER6Y5169YBAAYNGgRzc3OR05QPFm+IiIiISCfsOf8QvZb+gysPlbA1M8IvH3thvE9DyDhNGlVT06ZNwxdffIG6deuibdu28Pb2BpA/CsfDw0PkdEREREREuiE1NRXbt28HoD9TpgEs3hARERGRyLLzVJix6xJGB0cjPTsPbV1tsHfcm+joVlPsaESi+uCDDxAbG4vIyEjs27dPs71r165YuHDhS51r9uzZeOONN2BhYQE7Ozv4+fkhJiZGq01WVhZGjx4NW1tbmJubo1+/fkhISCjxvIIgYNq0aXBwcICJiQl8fHxw/fr1l8pGRERERPQ6Nm/ejKdPn6JJkybw8vISO065YfGGiIiIiERzLzkTA1ZFYMPxOwCAzzrXR/AnXrC3lIsbjEhHKBQKeHh44MGDB7h3L38dqLZt26Jx48YvdZ7Dhw9j9OjROHHiBPbv34/c3Fx069YNGRkZmjYTJkzAn3/+iW3btuHw4cOIi4tD3759Szzv3LlzsWTJEqxatQonT56EmZkZfH19kZWV9fJPloiIiIjoFRRMmRYQEACJRH9mbpAIgiCIHUJfKZVKWFlZITU1lXNSExEREb1g/+UEfP7bWSiz8mBtaoiFA1rh7cZ2Ysei18D3v+UrLy8PX3/9NZYsWYL09HQAgLm5OcaMGYPp06fD0NDwlc+dlJQEOzs7HD58GG+99RZSU1NRq1YtBAcH44MPPgAAXL16Fe7u7oiIiEC7du0KnUMQBDg6OuLzzz/HF198ASB/ygp7e3ts2LABgwYNKjWH5jWTkMDXDBERERG9tPMXLqBl27YwNDTEg5s3UatWLbEjlUqpVMLK3r7UfpNBJWYiIiIiIkKuSo0f9sXgxyO3AAAeLtZY9p/WcLI2ETkZkW4ZM2YMduzYgblz52rWu4mIiMCMGTPw+PFjrFy58pXPnZqaCgCwsbEBAERFRSE3Nxc+Pj6aNo0bN4aLi0uxxZvbt28jPj5e6xgrKyt4eXkhIiKiyOJNdnY2srOzNfeVSmX+f/TrBxiwe0pERFTe7mVlYX18PCbUrg1L/q0lPbTu2ZS971tZodaQISKnKaO8vDI14/+xRERERFRpHqY+RWDwGUTdfQIACOjoiq+6N4aRAWfzJXpRcHAwtmzZgh49emi2tWjRAs7Ozhg8ePArF2/UajXGjx+PDh06oFmzZgCA+Ph4GBkZwdraWqutvb094uPjizxPwXZ7e/syHzN79mx8/fXXr5SbiIiIXt7wq1cRlpIClSDgG1dXseMQlasslQq/PFujMcDBQeQ05Y/FGyIiIiKqFIevJWHC1rNIzsiBhdwA8z5oie7NFGLHItJZxsbGqFu3bqHtrq6uMDIyeuXzjh49GhcvXsQ///zzGulezaRJkxAUFKS5r1Qq4ezsDPz+O8Bp04iIiMrVufPnEfZs8fYDCgW++esvkRMRla+Q337Dk6NHUdvJCd0iIgCZTOxIZaNUAi98AaooLN4QERERUYVSqQUsOnANyw7egCAAzZwssfw/rVHH1kzsaEQ6LTAwEDNnzsRPP/0EY2NjAPnTjs2aNQuBgYGvfM7du3fjyJEjqF27tma7QqFATk4OUlJStEbfJCQkQKEoushasD0hIQEOz33TMSEhAa1atSryGGNjY81z0SKX59+IiIio3Cx6bpTuqchIpOXmwsLCQsREROVr3S+/AACGDR8OmVkV6l/m5JSpGYs3RERERFRhEtOyMG7zWUTcegwA+LCdC6b0bAK5YRX5RhSRiM6cOYOwsDDUrl0bLVu2BACcO3cOOTk56Nq1K/r27atpu2PHjhLPJQgCxowZg507d+LQoUNwfWHalDZt2sDQ0BBhYWHo168fACAmJgaxsbGa9XZe5OrqCoVCgbCwME2xRqlU4uTJkxg1atSrPm0iIiIqB/Hx8QgODgYAmJubIz09HUePHsW7774rcjKi8nHnzh0cOHAAAPDxxx+LnKZisHhDRERERBXi+M1HGLv5LB6lZ8PMSIbZ/Vrg/ZaOYsciqjKsra01hZQCzs7Or3Su0aNHIzg4GH/88QcsLCw0a9JYWVnBxMQEVlZWCAgIQFBQEGxsbGBpaYkxY8bA29sb7dq105yncePGmD17Nvr06QOJRILx48fj22+/hZubG1xdXTF16lQ4OjrCz8/vlZ83ERERvb6VK1ciJycH3t7eaNq0KdauXYvw8HAWb0hv/PTTTwCArl27Fvpikr5g8YaIiIiIypVaLWDFoRtYsP8a1ALQWGGB5f6tUb+WudjRiKqUgg5peVj5bNqUzp07F3qMYcOGAQAWLlwIqVSKfv36ITs7G76+vlixYoVW+5iYGKSmpmruf/nll8jIyMDIkSORkpKCjh07IjQ0FHJOgUZERCSap0+fav6GT5gwAXl5eVi7di3CwsJETkZUPlQqlea9ckBAgMhpKo5EEARB7BD6SqlUwsrKCqmpqbDk4ptERERUDSRn5GD81rM4ci0JADDAsza+fr8ZTIw4TVp1wPe/9LL4miEiIip/a9euxYgRI1CnTh3cuHEDjx490qxP9+jRI9ja2oqckOj17Nu3D927d0eNGjUQFxdX5b44VNb3wBx5Q0RERETlIvJOMgKDzyBemQW5oRQzezdDf89Xm+KJiPLXlJFIJMXuv3XrViWmISIioqpAEAQsXLgQADBmzBgYGBhAoVCgSZMmuHz5Mg4dOlRoWlaiqmbdunUAAH9//ypXuHkZLN4QERER0WsRBAFrjt7C96ExUKkF1KtlhpX+bdBIYSF2NKIqbfz48Vr3c3NzcebMGYSGhmLixInihCIiIiKd9vfff+Py5cswNzfHJ598otnepUsXXL58GeHh4SzeUJX26NEjhISEANDvKdMAFm+IiIiI6DVkZOdh4vZz2Hshf/Hz3q0c8V2f5jAz5ttMotc1bty4IrcvX74ckZGRlZyGiIiIqoKCUTcBAQGwsrLSbO/SpQuWLVuG8PBwsaIRlYtff/0Vubm5aN26NVq1aiV2nAolFTsAEREREVVN95Iz0W/lcey9EA9DmQTf+jXDooGtWLghqmA9evTA77//LnYMIiIi0jGXL1/Gvn37IJFIMHbsWK19nTp1gkQiwdWrVxEXFydSQqLXIwgC1q5dC0D/R90ALN4QERER0Ss4fvMR3l/2D67Gp6GmuTG2jGyHD9vVKXF9DiIqH9u3b4eNjY3YMYiIiEjHLFq0CADg5+eHevXqae2zsbFB69atAQAHDx6s7GhE5eLUqVO4dOkS5HI5/vOf/4gdp8Lxa5FEREREVGaCIODniLv4ZvdlqNQCWtS2wo8ftYGDlYnY0Yj0joeHh1ZBVBAExMfHIykpCStWrBAxGREREemapKQk/PzzzwCAoKCgItt06dIFUVFRCA8Ph7+/f2XGIyoX69atAwD069cP1tbW4oapBCzeEBEREVGZZOepMP2PS9hy+h4AoI+HE2b3bQ65oUzkZET6yc/PT+u+VCpFrVq10LlzZzRu3FicUERERKSTVq1ahezsbHh6eqJDhw5FtunSpQvmzZvHdW+oSsrIyMCWLVsAAJ988onIaSoHizdEREREVKqktGx8+msUou4+gVQC/K9HY4x4sx6nSSOqQNOnTxc7AhEREVUB2dnZWL58OQBgwoQJxb5H79ixIwwMDHDnzh3cvn0brq6ulRmT6LVs27YNaWlpqF+/Pjp16iR2nErB4g0RERERlej8/RT895coPEzNgoXcAEsHe6BzIzuxYxFVCyqVCiEhIbhy5QoAoGnTpnj//fchk3HEGxEREeXbsmULEhIS4OTkhP79+xfbztzcHF5eXjh27BjCw8OrxYLvpD8Kpkz7+OOPq82XCKViByAiIiIi3RVy5gH6r4rAw9Qs1K9lhj9Gd2DhhqiS3LhxA+7u7hgyZAh27NiBHTt24MMPP0TTpk1x8+ZNseMRERGRDhAEAQsWLAAAjBkzBoaGhiW279KlCwBw6jSqUmJiYvDPP/9AKpVi6NChYsepNFWieLN8+XLUrVsXcrkcXl5eOHXqVIntt23bhsaNG0Mul6N58+bYu3ev1v4dO3agW7dusLW1hUQiwdmzZwudIysrC6NHj4atrS3Mzc3Rr18/JCQklOfTIiIiItJZKrWA2XuvYPzWs8jOU6NLYzvsHN0B9WqZix2NqNoYO3Ys6tevj3v37iE6OhrR0dGIjY2Fq6srxo4dK3Y8IiIi0gEHDx7E+fPnYWpqipEjR5ba/vnijSAIFR2PqFysX78eANCjRw84OTmJnKby6HzxZuvWrQgKCsL06dMRHR2Nli1bwtfXF4mJiUW2P378OAYPHoyAgACcOXMGfn5+8PPzw8WLFzVtMjIy0LFjR3z//ffFPu6ECRPw559/Ytu2bTh8+DDi4uLQt2/fcn9+RERERLom9WkuPt5wGj8euQUAGP12fawZ4glLecnf4iOi8nX48GHMnTsXNjY2mm22traYM2cODh8+LGIyIiIi0hULFy4EAAwbNgw1atQotX27du0gl8sRHx+Pq1evVnQ8oteWm5uLjRs3AkC1m+pP54s3CxYswIgRIzB8+HA0adIEq1atgqmpqaba9qLFixeje/fumDhxItzd3TFz5ky0bt0ay5Yt07T56KOPMG3aNPj4+BR5jtTUVKxbtw4LFixAly5d0KZNG/z00084fvw4Tpw4USHPk4iIiEgX3EhMh9/yYzh8LQlyQymWDvbARN/GkEmrx5zCRLrE2NgYaWlphbanp6fDyMhIhERERESkS65du4bdu3dDIpFg3LhxZTpGLpejQ4cOADh1GlUNe/fuRUJCAuzs7PDee++JHadS6XTxJicnB1FRUVpFFqlUCh8fH0RERBR5TERERKGijK+vb7HtixIVFYXc3Fyt8zRu3BguLi4lnic7OxtKpVLrRkRERFRVhF9NQJ/lx3D7UQacrE2w/dP26NXSUexYRNXWe++9h5EjR+LkyZMQBAGCIODEiRP49NNP8f7774sdj4iIiES2aNEiAPnvGRo2bFjm47juDVUla9euBQAMGTKk1DWd9I1OF28ePXoElUoFe3t7re329vaIj48v8pj4+PiXal/cOYyMjGBtbf1S55k9ezasrKw0N2dn5zI/JhEREZFYBEHAikM3ELAxEmnZeWhb1wZ/BHZAMycrsaMRVWtLlixB/fr14e3tDblcrvmmbIMGDbB48WKx4xEREZGIkpOTNVNJTZgw4aWOLSjeHDx4ECqVqtyzEZWXuLg4zXr21W3KNAAwEDuAPpk0aRKCgoI095VKJQs4REREpNOe5qgwcfs57D7/EADg7+WC6b2awshAp7/jQ6T3BEGAUqnEli1b8ODBA1y5cgUA4O7ujgYNGoicjoiIiMS2evVqZGZmomXLlujcufNLHevp6QkLCws8efIE586dQ+vWrSsmJNFr2rhxI9RqNdq3b4/GjRuLHafS6XTxpmbNmpDJZEhISNDanpCQAIVCUeQxCoXipdoXd46cnBykpKRojb4p7TzGxsYwNjYu8+MQERERielBylOM/DkSl+KUMJBKMOP9pviwXR2xYxER8os3DRo0wKVLl+Dm5saCDREREWnk5ORg6dKlAICgoCBIJC+3PqWBgQHeeust7NmzB+Hh4SzekE4SBEGz7v0nn3wichpx6PRXKo2MjNCmTRuEhYVptqnVaoSFhcHb27vIY7y9vbXaA8D+/fuLbV+UNm3awNDQUOs8MTExiI2NfanzEBEREemqU7eT8f7Sf3ApTglbMyMEj2jHwg2RDpFKpXBzc8Pjx4/FjkJEREQ6Ztu2bYiLi4NCocCgQYNe6Rxc94Z03ZEjR3Djxg2Ym5ujf//+YscRhU6PvAHyq8dDhw6Fp6cn2rZti0WLFiEjIwPDhw8HkL9QkZOTE2bPng0AGDduHDp16oT58+ejZ8+e2LJlCyIjI7F69WrNOZOTkxEbG4u4uDgA+YUZIH/EjUKhgJWVFQICAhAUFAQbGxtYWlpizJgx8Pb2Rrt27Sr5ChARERGVr00n72L6H5eQpxbQxMESq4e0Qe0apmLHIqIXzJkzBxMnTsTKlSvRrFkzseMQERGRDhAEAQsXLgQAjB49GkZGRq90noLizZEjR5Cbm1vtFoIn3bdu3ToAwKBBg2Bubi5yGnHofPFm4MCBSEpKwrRp0xAfH49WrVohNDQU9vb2AIDY2FhIpf8OIGrfvj2Cg4MxZcoUTJ48GW5ubggJCdHq7OzatUtT/AGgqVBPnz4dM2bMAAAsXLgQUqkU/fr1Q3Z2Nnx9fbFixYpKeMZEREREFSMnT41vdl/CrydiAQDvtXDAvA9awsRIJnIyIirKkCFDNHPZGxkZwcTERGt/cnKySMmIiIhILP/88w+ioqIgl8vx6aefvvJ5WrRoAVtbWzx+/BinT59G+/btyzEl0etJTU3F9u3bAQABAQEipxGPRBAEQewQ+kqpVMLKygqpqamwtLQUOw4RERFVY4/Ss/HZpmicup0MiQT4olsjfNa5/kvPj01UEr7/LV8bN24scf/QoUMrKUnF4WuGiIjo5fTp0wchISEYOXIkfvzxx9c6V//+/bF9+3bMnDkTU6ZMKaeERK9v1apVGDVqFJo0aYKLFy/qXb+1rO+BdX7kDRERERG9nktxqRj5cxQepDyFubEBFg9qha7u9mLHIqJS6ENxhoiIiMrPzZs38ccffwAAxo8f/9rn69KlC7Zv347w8HAWb0inFEyZFhAQoHeFm5fB4g0RERGRHtt9Pg5fbDuHrFw1XGuaYc2QNmhgZyF2LCIqA6VSWeR2iUQCY2PjV57jnoiIiKqmJUuWQBAEdO/eHe7u7q99voJ1b44fP46nT58WmqKVSAznzp1DZGQkDA0N8dFHH4kdR1TS0psQERERUVWjVguYt+8qAoPPICtXjbca1kLIZx1YuCGqQqytrVGjRo1CN2tra5iYmKBOnTqYPn061Gq12FGJiIiogqWkpGD9+vUAgAkTJpTLORs2bAhHR0dkZ2cjIiKiXM5J9LoKRt28//77qFWrlshpxMXiDREREZGeScvKxYifI7H84E0AwH/fqoefhr0BK1NDkZMR0cvYsGEDHB0dMXnyZISEhCAkJASTJ0+Gk5MTVq5ciZEjR2LJkiWYM2eO2FGJiIiogq1duxbp6elo2rQp3nnnnXI5p0Qi0Yy+CQ8PL5dzEr2OrKws/PrrrwDyp0yr7jhtGhEREZEeuf0oAyN+jsSNxHQYGUjxfb/m6ONRW+xYRPQKNm7ciPnz52PAgAGabb169ULz5s3x448/IiwsDC4uLpg1axYmT54sYlIiIiKqSHl5eVi6dCmA/FE35bkGSJcuXfDrr7+yeEM6ISQkBE+ePEHt2rXRrVs3seOIjiNviIiIiPTE4WtJ6L3sH9xITIfCUo7tn3qzcENUhR0/fhweHh6Ftnt4eGimNunYsSNiY2MrOxoRERFVoh07diA2Nha1atWCv79/uZ67YOTNqVOnkJaWVq7nJnpZBVOmDR8+HDKZTOQ04mPxhoiIiKiKEwQBa47cwvCfTkGZlYfWLtbYNaYDWtS2FjsaEb0GZ2dnTQf2eevWrYOzszMA4PHjx6hRo0ZlRyMiIqJKtHDhQgDAZ599BrlcXq7nrlOnDurVqweVSoWjR4+W67mJXsadO3dw4MABAPnFG+K0aURERERVWlauCpN2XMDOMw8AAAM9nfGNX1MYG/BbSkRV3Q8//ID+/fvjr7/+whtvvAEAiIyMxNWrV7F9+3YAwOnTpzFw4EAxYxIREVEFioiIwIkTJ2BkZIRRo0ZVyGN06dIFt27dQnh4ON59990KeQyi0vz0008AgK5du8LV1VXkNLqBI2+IiIiIqqj41CwM+DECO888gEwqwdfvN8Wcfs1ZuCHSE++//z5iYmLw7rvvIjk5GcnJyejRoweuXr2K9957DwAwatQoLFiwoNRzHTlyBL169YKjoyMkEglCQkK09kskkiJv8+bNK/acM2bMKNS+cePGr/WciYiISFvBqBt/f3/Y29tXyGMUTJ0WFhZWIecnKo1KpdIUbwICAkROozs48oaIiIioCoq6+wSf/hqFpLRs1DA1xPL/tEb7BjXFjkVE5axu3bqYPXv2a58nIyMDLVu2xMcff4y+ffsW2v/w4UOt+3/99RcCAgLQr1+/Es/btGlTzfQWAGBgwC4mERFRebl79y5+//13AMCECRMq7HHefvttAMDZs2fx+PFj2NraVthjERXlwIEDuHfvHmrUqIE+ffqIHUdn8J01ERERURXz2+l7mBJyETkqNRorLLBmiCecbUzFjkVEOqxHjx7o0aNHsfsVCoXW/T/++ANvv/026tWrV+J5DQwMCh1LRERE5WPp0qVQq9Xw8fFB8+bNK+xxFAoFmjRpgsuXL+PQoUOlfnmDqLwVrPPo7+9f7us6VWWcNo2IiIioishVqTFj1yV8+ft55KjU6N5Ugd9HtWfhhojKVUJCAvbs2VOmKSuuX78OR0dH1KtXD/7+/oiNjS2xfXZ2NpRKpdaNiIiICktLS8OaNWsAVOyomwIFU6eFh4dX+GMRgibc2gAAoe5JREFUPS8pKUkzpS+nTNPG4g0RERFRFfAkIwdD15/ChuN3AABB7zTECv/WMDPmQGoiKl8bN26EhYVFkdOrPc/LywsbNmxAaGgoVq5c+f/s3Xd0VNXexvHvTDqQhNASApGELjUUCVUgoAgWmgrIBaSLICWggtItoBgpUqV7UUEEURHxYkIn0oN0KaETSoCEhJAymfcPLvOaS5GSyUl5PmvNyswpez+DYzIzv7P3JioqigYNGnD9+vV7njNu3Dg8PT1tNz8/v4yOLyIikiPMmzePuLg4ypUrx3PPPWf3/lS8EaMsWrSIlJQUqlevTmBgoNFxshQVb0RERESyuEPRcbw0bRNbjsWQ19mBWZ1q0L9JGcxmk9HRRCQHmjdv3gNNWdG8eXNeeeUVqlSpQrNmzVi1ahXXrl3ju+++u+c5w4YNIzY21nY7ffp0RscXERHJ9iwWC5MnTwZg4MCBmM32/wq3UaNGmEwmDh06xLlz5+zenwiA1Wq1TZmmUTd3UvFGREREJAtbve88baZv4fSVRJ4okIflb9ajWUWtLyEi9rFx40YOHz5Mjx49Hvrc/PnzU7ZsWY4ePXrPY1xcXPDw8Eh3ExERkfR+/PFHoqKiKFCgAJ07d86UPr28vKhevToAa9euzZQ+RbZt28b+/ftxdXXltddeMzpOlqN5NkRERESyoLQ0K5PDjjA57AgA9UoXZGqH6njldTY4mYjYU/Xq1QkLC8PLy4tq1aphMt17hN2uXbsyvP+5c+dSo0YNqlat+tDnxsfHc+zYMTp16pThuURERHKTiRMnAvDGG2+QJ0/mrW8ZHBzMzp07CQ8Pp2PHjpnWr+Ret0fdvPzyy+TPn9/YMFmQijciIiIiWUx8UiqDv4vkt/0XAOhWL4D3WpTH0UGDpkVyupYtW+Li4gJAq1atMqzd+Pj4dCNioqKiiIyMpECBAjzxxBMAxMXFsXTpUkJDQ+/aRpMmTWjdujX9+vUDYMiQIbz44ouUKFGCc+fOMWrUKBwcHOjQoUOG5RYREcltduzYwaZNm3BycqJv376Z2ndwcDATJkzQujeSKRISEli8eDGgKdPuRcUbERERkSzkVMwNen61g8MXruPsYOaj1pV4paYW9BbJLUaNGnXX+49rx44dNG7c2PY4JCQEgC5durBgwQIAFi9ejNVqvWfx5dixY1y+fNn2+MyZM3To0IGYmBgKFy5M/fr1+eOPPyhcuHCG5RYREcltbo+6adeuHb6+vpnad/369XF0dOTEiRNERUUREBCQqf1L7rJ06VKuX79OqVKlaNiwodFxsiST1Wq1Gh0ip4qLi8PT05PY2FjN5SwiIiL/aPPRy/T9ZhfXbqRQ2N2FWZ1qUP0JL6NjiTwwvf+Vh6XXjIiIyP87c+YMAQEBpKamsnPnTtsaNJmpfv36bN68mTlz5mg0hNhVgwYN2LRpEx999BHvvfee0XEy1YO+B9bIGxERERGDWa1W5m8+wUerDmJJs1LVLz+z/lUDH09Xo6OJSCbz8vK67zo3f3flyhU7pxEREZHMNHXqVFJTU2nYsKEhhRu4NXXa5s2bCQ8PV/FG7Obw4cNs2rQJs9lMly5djI6TZal4IyIiImKgpFQLw3/Yx9KdZwBoU60YH7epjKuTg8HJRMQIkyZNMjqCiIiIGCAhIYEvv/wSgEGDBhmWIzg4mA8++IDw8HCsVusDX1Qi8jDmzZsHQPPmzSlWrJjBabIuFW9EREREDGK1Wun79S5+P3gRswnea/Ek3esH6AOSSC6mKw9FRERyp4ULF3L16lVKly7NCy+8YFiO2rVr4+rqSnR0NIcOHeLJJ580LIvkTCkpKbY1FzW66/5UvBERERExyE97zvH7wYs4O5iZ06UmT5fVIt8i8v9OnTp13/1PPPFEJiURERERe0pLS7ONvh0wYAAODsaNwnd1daVevXqEhYURHh6u4o1kuF9++YWLFy9SpEgRQwuV2YGKNyIiIiIGuJqQzNifDwDwVnBpFW5E5A7+/v73HYlnsVgyMY2IiIjYyy+//MKRI0fInz8/r7/+utFxCA4OthVv+vbta3QcyWHmzp0LQOfOnXFycjI4Tdam4o2IiIiIAT5adZCYhGTKeuejd8NSRscRkSxo9+7d6R6npKSwe/duPv/8cz766CODUomIiEhGmzhxIgA9e/YkX758Bqe5VbwBWLt2LRaLxdCRQJKznDt3jlWrVgGaMu1BqHgjIiIiksm2HL3M9zvPYDLBuDZVcHY0Gx1JRLKgqlWr3rGtZs2a+Pr6MmHCBNq0aWNAKhEREclIkZGRrF27FgcHB9566y2j4wC33m+4u7tz9epV9uzZQ/Xq1Y2OJDnEwoULSUtLo27dupQvX97oOFmevikQERERyUQ3Uyy898NeAP4VVIIaJbwMTiQi2U25cuXYvn270TFEREQkA9wedfPKK6/g5+dncJpbHB0defrppwEIDw83OI3kFFarlXnz5gHQo0cPg9NkDyreiIiIiGSiKWFHOBFzA28PF95+rpzRcUQkC4uLi0t3i42N5dChQwwfPpwyZcoYHU9EREQe0/nz5/n2228BGDRokMFp0mvSpAmg4o1knA0bNnD06FHy5cvHK6+8YnScbEHTpomIiIhkkoPn4/hyw3EAxrashIerFmcUkXvLnz8/JpMp3Tar1Yqfnx+LFy82KJWIiIhklOnTp5OSkkLdunWpVauW0XHSub3uzYYNG0hJSdHC8vLY5s6dC0D79u2zxNpO2YGKNyIiIiKZwJJmZdjyvaSmWWlW0ZtmFX2MjiQiWdzatWvTPTabzRQuXJjSpUvj6KiPciIiItlZYmIiM2bMACAkJMTgNHeqXLkyBQsWJCYmhu3bt1O3bl2jI0k2Fhsby/fffw9A9+7dDU6Tfegdv4iIiEgm+HfECSJPX8PdxZExL1UyOo6IZAMNGzY0OoKIiIjYyb///W9iYmLw9/enVatWRse5g9lspnHjxnz//feEh4ereCOP5dtvvyUxMZEKFSoQFBRkdJxsQ8UbERERETs7dy2RCb8dBuCd5uXx8XQ1OJGIZCcHDhzg1KlTJCcnp9v+0ksvGZRIREREHofVamXSpEkA9O/fHwcHB2MD3UNwcLCteDN8+HCj40g2NmfOHODWqJv/nRZY7k3FGxERERE7slqtjPxxHwnJFmqU8KJjrSeMjiQi2cTx48dp3bo1e/fuxWQyYbVaAWwfeC0Wi5HxRERE5BH99ttvHDx4EHd39yw9hdTtdW+2bNlCYmIibm5uBieS7GjPnj3s3LkTJycnOnXqZHScbMVsdAARERGRnOzXfdH8fvAiTg4mxrWpjNmsq4xE5MEMGDCAgIAALl68SJ48edi/fz8bNmygZs2arFu3zuh4IiIi8og+//xzAHr06IGHh4fBae6tbNmy+Pr6kpSUREREhNFxJJuaO3cucGvUeOHChQ1Ok72oeCMiIiJiJ7GJKYz6aT8AfRqWoqy3u8GJRCQ7iYiIYOzYsRQqVAiz2YzZbKZ+/fqMGzeO/v37Gx1PREREHsG+fftYs2YNZrM5y/89N5lMttE34eHhBqeR7OjmzZssWrQIIEuPMsuqVLwRERERsZPxvx7i0vUkShbOy5uNSxsdR0SyGYvFgrv7raJvoUKFOHfuHAAlSpTg8OHDRkYTERGRR3R7rZvWrVvj7+9vaJYHoeKNPI4VK1Zw9epVihcvzrPPPmt0nGwnw4s3qampfPXVV1y4cCGjmxYRERHJNrZFXeHbbacA+Lh1ZVydsuYipCKSdVWqVIk9e/YAEBQUxKeffsrmzZsZO3YsJUuWNDidiIiIPKyLFy/aRiEMGjTI4DQP5nbxZtu2bVy/ft3gNJLd3J4yrWvXrjg46DPxw8rw4o2joyNvvPEGN2/ezOimRURERLKFpFQLw5b/CUD7p/yoXbKgwYlEJDsaPnw4aWlpAIwdO5aoqCgaNGjAqlWrmDJlisHpRERE5GHNmDGDpKQkatWqRd26dY2O80BKlChByZIlsVgsbNy40eg4ko2cOHGC33//HbhVvJGHZ5dp02rVqkVkZKQ9mhYRERHJ8qavPcaxSwkUyufCsOZPGh1HRLKpRo0a0axZMwBKly7NoUOHuHz5MhcvXrRdBSsiIiLZw82bN5k+fTpwa9SNyWQyONGD09Rp8ijmz58PQJMmTQgICDA4TfZkl+LNm2++SUhICFOnTiUiIoI///wz3U1EREQkpzp68TrT1x0FYPRLFfDM42RwIhHJbi5dukTz5s3Jly8fHh4e1K5dm6NHb/1eKVCgQLb6skdERERu+fbbb7l48SLFixenbdu2Rsd5KLeLN2FhYQYnkezCYrHYijfdu3c3OE325WiPRtu3bw9A//79bdtMJhNWqxWTyYTFYrFHtyIiIiKGSkuzMnTZXlIsVpqUL8LzlYsaHUlEsqF3332XyMhIxo4di6urK7NmzaJnz56sXbvW6GgiIiLyCKxWKxMnTgRufV/q5JS9LvBq3LgxAJGRkcTExFCwoKaFlvv7/fffOX36NF5eXrRu3droONmWXYo3UVFR9mhWREREJEv7dvspdpy8Sh5nB8a2qqSr40XkkaxZs4YFCxbYpkx74YUXePLJJ0lKSsLFxcXgdCIiIvKwwsLC2Lt3L3nz5qVnz55Gx3loPj4+VKhQgQMHDrBu3bpsN3JIMt+cOXMA6NixI66urganyb7sMm1aiRIl7nt7WNOmTcPf3x9XV1eCgoLYtm3bfY9funQp5cuXx9XVlcqVK7Nq1ap0+61WKyNHjqRo0aK4ubnRtGlTjhw5ku6Yv/76i5YtW1KoUCE8PDyoX7++rnQTERGRe7oQd5Pxqw4BMOTZchTL72ZwIhHJrs6dO0fVqlVtj8uUKYOLiwvnz583MJWIiIg8qtujbrp27Ur+/PmNDfOImjRpAmjdG/lnly5d4scffwQ0ZdrjskvxBuDYsWO89dZbNG3alKZNm9K/f3+OHTv20O0sWbKEkJAQRo0axa5du6hatSrNmjXj4sWLdz1+y5YtdOjQge7du7N7925atWpFq1at2Ldvn+2YTz/9lClTpjBz5ky2bt1K3rx5adasGTdv3rQd88ILL5Camkp4eDg7d+6katWqvPDCC0RHRz/8P4aIiIjkeKN/2s/1pFSqFvekS11/o+OISDbn4OBwx2Or1WpQGhEREXlUhw4dYtWqVZhMJgYMGGB0nEd2e90bFW/knyxatIiUlBSqV69OYGCg0XGyNZPVDp8AfvvtN1566SUCAwOpV68eAJs3b2bPnj38/PPPPPPMMw/cVlBQEE899RRTp04FIC0tDT8/P9566y2GDh16x/Ht2rUjISGBlStX2rbVrl2bwMBAZs6cidVqxdfXl8GDBzNkyBAAYmNj8fb2ZsGCBbRv357Lly9TuHBhNmzYQIMGDQC4fv06Hh4erFmzhqZNmz5Q9ri4ODw9PYmNjcXDw+OBn7OIiIhkL//ZH02vf+/EwWzi5371qeCrv/uSO+n9b8Ywm814enqmm3rx2rVreHh4YDb///V3V65cMSJehtJrRkREcro+ffowc+ZMWrZsyYoVK4yO88iuXr1KwYIFsVqtnD17Fl9fX6MjSRZktVqpXLky+/fvZ9q0abz55ptGR8qSHvQ9sF3WvBk6dCiDBg1i/Pjxd2x/9913H7h4k5yczM6dOxk2bJhtm9lspmnTpkRERNz1nIiICEJCQtJta9asme2XY1RUFNHR0ekKMJ6engQFBREREUH79u0pWLAg5cqV46uvvqJ69eq4uLgwa9YsihQpQo0aNR4ou4iIiOQO12+mMPLH/QD0erqkCjci8tjmz59vdAQRERHJADExMSxcuBCAQYMGGZzm8Xh5eVG9enV27tzJ2rVr6dixo9GRJAvatm0b+/fvx9XVlddee83oONmeXYo3Bw8e5Lvvvrtje7du3Zg0adIDt3P58mUsFgve3t7ptnt7e3Po0KG7nhMdHX3X429Pd3b75/2OMZlM/P7777Rq1Qp3d3fMZjNFihRh9erVeHl53TNvUlISSUlJtsdxcXEP+ExFREQku/rst8NEx92kRME8DGhSxug4IpIDdOnSxegIIiIikgFmzZpFYmIi1apV4+mnnzY6zmMLDg5m586dhIeHq3gjdzV37lwAXn755Wy7vlNWYpc1bwoXLkxkZOQd2yMjIylSpIg9usxQVquVvn37UqRIETZu3Mi2bdto1aoVL7744n0XCR03bhyenp62m5+fXyamFhERkcy269RVvvrjJAAftaqMq5PDP5whIiIiIiK5QXJysm0ZiEGDBqWbDjW70ro3cj8JCQksXrwYgO7duxucJmewS/GmZ8+e9OrVi08++YSNGzeyceNGxo8fT+/evenZs+cDt1OoUCEcHBy4cOFCuu0XLlzAx8fnruf4+Pjc9/jbP+93THh4OCtXrmTx4sXUq1eP6tWrM336dNzc3GxDHe9m2LBhxMbG2m6nT59+4OcqIiIi2UtyahrDlu3FaoW21YtTv0whoyOJiNzThg0bePHFF/H19cVkMt0x5/7rr7+OyWRKd3vuuef+sd1p06bh7++Pq6srQUFBbNu2zU7PQEREJHtZsmQJ58+fp2jRorRr187oOBmifv36ODo6cuLECaKiooyOI1nM0qVLuX79OqVKlaJhw4ZGx8kR7FK8GTFiBCNHjuSLL76gYcOGNGzYkKlTpzJ69GiGDx/+wO04OztTo0YNwsLCbNvS0tIICwujTp06dz2nTp066Y4HWLNmje34gIAAfHx80h0TFxfH1q1bbcfcuHEDIN1ioLcfp6Wl3TOvi4sLHh4e6W4iIiKSM83eeJzDF65TIK8z7z//pNFxRETuKyEhgapVqzJt2rR7HvPcc89x/vx52+3bb7+9b5tLliwhJCSEUaNGsWvXLqpWrUqzZs24ePFiRscXERHJVqxWKxMnTgSgX79+ODs7G5woY+TLl4+goCBAo2/kTrenTOvWrVuOGGmWFWR48SY1NZV///vfvPbaa5w5c8Y2CuXMmTMMGDDgof/DhYSEMHv2bBYuXMjBgwfp06cPCQkJdO3aFYDOnTszbNgw2/EDBgxg9erVhIaGcujQIUaPHs2OHTvo168fcGs9m4EDB/Lhhx/y008/sXfvXjp37oyvry+tWrUCbhWAvLy86NKlC3v27OGvv/7i7bffJioqiueffz5j/qFEREQk2zp+KZ7JYUcAGPlCBQrkzRkfxkQk52revDkffvghrVu3vucxLi4u+Pj42G73W+8T4PPPP6dnz5507dqVChUqMHPmTPLkycO8efMyOr6IiEi2smHDBnbv3o2bmxu9e/c2Ok6G0tRpcjeHDx9m06ZNmM1mrd+YgTK8eOPo6Mgbb7zBzZs3AXB3d8fd3f2R22vXrh2fffYZI0eOJDAwkMjISFavXo23tzcAp06dSrcOTd26dfnmm2/48ssvqVq1Kt9//z0rVqygUqVKtmPeeecd3nrrLXr16sVTTz1FfHw8q1evxtXVFbg1Xdvq1auJj48nODiYmjVrsmnTJn788UeqVq36yM9FREREsj+r1cp7P+wlOTWNBmUK0TLQ1+hIIiIZYt26dRQpUoRy5crRp08fYmJi7nlscnIyO3fupGnTprZtZrOZpk2bEhERcc/zkpKSiIuLS3cTERHJaW6PuuncuTMFCxY0OE3G+nvxxmq1GpxGsorbo26aN29OsWLFDE6Tc5isdvi/rFGjRgwcONA2kiW3iouLw9PTk9jYWE2hJiIikkN8t+M073z/J65OZv4zsCFPFMxjdCSRLEPvfzNWSEjIXbebTCZcXV0pXbo0LVu2pECBAg/Vrslk4ocffkj3eW3x4sXkyZOHgIAAjh07xnvvvUe+fPmIiIjAwcHhjjbOnTtHsWLF2LJlS7oprd955x3Wr1/P1q1b79r36NGjGTNmzB3b9ZoREZGc4ujRo5QtWxar1crBgwcpX7680ZEy1M2bN/Hy8uLmzZscOHCAJ5/UFNK5XUpKCsWLF+fixYssX778viO95ZYH/dzkaI/O33zzTQYPHsyZM2eoUaMGefPmTbe/SpUq9uhWRERExK4uXU/io18OAhDyTFkVbkTErnbv3s2uXbuwWCyUK1cOgL/++gsHBwfKly/P9OnTGTx4MJs2baJChQqP1Vf79u1t9ytXrkyVKlUoVaoU69ato0mTJo/V9t8NGzYsXVEqLi4OPz+/DGtfRETEaJMnT8ZqtdKiRYscV7gBcHV1pV69eoSFhREeHq7ijfDLL79w8eJFihQpwgsvvGB0nBwlw6dNg1tv/KOioujfvz/16tUjMDCQatWq2X6KiIiIZEcfrDxAbGIKFYp60K1egNFxRCSHa9myJU2bNuXcuXPs3LmTnTt3cubMGZ555hk6dOjA2bNnefrppxk0aFCG912yZEkKFSrE0aNH77q/UKFCODg4cOHChXTbL1y4gI+Pzz3bdXFxwcPDI91NREQkp7h27Rrz588HsMvf56xC697I392eMq1z5844OTkZnCZnscvIm6ioKHs0KyIiImKYtYcv8tOec5hN8EnbKjg62OUaGBERmwkTJrBmzZp0BQ5PT09Gjx7Ns88+y4ABAxg5ciTPPvtshvd95swZYmJiKFq06F33Ozs7U6NGDcLCwmzTr6WlpREWFka/fv0yPI+IiEh2MHv2bBISEqhcuXKGjlzNam4Xb9auXYvFYrnrFKuSO5w7d45Vq1YB0L17d4PT5DwZ/q1DSkoKwcHB3LhxgxIlStz1JiIiIpKdJCSlMvyHfQB0qxdA5eKeBicSkdwgNjaWixcv3rH90qVLxMXFAZA/f36Sk5P/sa34+HgiIyOJjIwEbl1wFxkZyalTp4iPj+ftt9/mjz/+4MSJE4SFhdGyZUtKly5Ns2bNbG00adKEqVOn2h6HhIQwe/ZsFi5cyMGDB+nTpw8JCQl07dr1MZ+5iIhI9pOSksKUKVOAW6NuTCaTwYnsp2bNmri7u3P16lX27NljdBwx0MKFC0lLS6NevXo5cppAo2V48cbJyYmbN29mdLMiIiIihpm45i/OXkukWH43Bj1T1ug4IpJLtGzZkm7duvHDDz9w5swZzpw5ww8//ED37t1to122bdtG2bL//Htpx44dVKtWzTaNdUhICNWqVWPkyJE4ODjw559/8tJLL1G2bFm6d+9OjRo12LhxIy4uLrY2jh07xuXLl22P27Vrx2effcbIkSMJDAwkMjKS1atX4+3tnbH/ECIiItnAsmXLOHPmDEWKFKFDhw5Gx7ErR0dHGjZsCGjqtNzMarUyb948QKNu7MVktVqtGd3oxx9/zF9//cWcOXNwdLTLzGzZQlxcHJ6ensTGxmouZxERkWzqzzPXaDVtM2lWmN/1KRqXK2J0JJEsS+9/M1Z8fDyDBg3iq6++IjU1Fbj1ZUmXLl2YOHEiefPmtY2kCQwMNC7oY9BrRkREcgKr1Urt2rXZtm0bo0ePZtSoUUZHsruJEycSEhJC8+bNbdNmSe6yfv16GjVqRL58+Th//jz58uUzOlK28aDvge1SWdm+fTthYWH85z//oXLlyuTNmzfd/uXLl9ujWxEREZEMlWpJY+iyvaRZ4aWqvirciEimypcvH7Nnz2bixIkcP34cgJIlS6b7YJxdizYiIiI5SUREBNu2bcPFxYU+ffoYHSdT3F73ZsOGDaSkpGih+lxo7ty5ALRv316FGzuxS/Emf/78tG3b1h5Ni4iIiGSauZuiOHA+Dk83J0a8UMHoOCKSS+XLl48qVaoYHUNERETu4fPPPwfgX//6F0WK5I4LvipXrkzBggWJiYlh+/bt1K1b1+hIkomuXbvG999/D2jKNHuyS/Fm/vz59mhWREREJNOcirnBxN//AuD955+ksLvLP5whIpKxEhISGD9+PGFhYVy8eJG0tLR0+2+PxhERERHjREVF8cMPPwAwcOBAY8NkIrPZTOPGjfn+++8JDw9X8SaX+fbbb0lMTKRChQoEBQUZHSfHstuCNKmpqaxbt45jx47x2muv4e7uzrlz5/Dw8NAwKhEREcnSrFYr76/Yy82UNOqULMgrNYobHUlEcqEePXqwfv16OnXqRNGiRTGZTEZHEhERkf/xxRdfkJaWxjPPPEOlSpWMjpOpgoODbcWb4cOHGx1HMtHtKdO6d++u96h2ZJfizcmTJ3nuuec4deoUSUlJPPPMM7i7u/PJJ5+QlJTEzJkz7dGtiIiISIZYEXmWjUcu4+xo5uM2lfVmVEQM8euvv/LLL79Qr149o6OIiIjIXcTFxTFnzhwAQkJCDE6T+W6ve7NlyxYSExNxc3MzOJFkhj179rBz506cnJzo1KmT0XFyNLM9Gh0wYAA1a9bk6tWr6f6nbd26NWFhYfboUkRERCRDXElI5oOVBwEY0KQMAYXyGpxIRHIrLy8vChQoYHQMERERuYe5c+dy/fp1nnzySZo1a2Z0nExXtmxZfH19SUpKIiIiwug4kkluj7p56aWXKFy4sMFpcja7FG82btzI8OHDcXZ2Trfd39+fs2fP2qNLERERkQzx0S8HuZKQTDlvd3o2KGl0HBHJxT744ANGjhzJjRs3jI4iIiIi/8NisTBlyhTg1lo3uXG0vslkso2+CQ8PNziNZIabN2+yaNEi4NaUaWJfdpk2LS0tDYvFcsf2M2fO4O7ubo8uRURERB7bpiOXWbbrDCYTjGtbGWdHu1znIiLyQEJDQzl27Bje3t74+/vj5OSUbv+uXbsMSiYiIiIrVqzgxIkTFCxYMFdPHRUcHMyiRYtUvMklVqxYwdWrVylevDjPPvus0XFyPLsUb5599lkmTZrEl19+CdyqwsbHxzNq1ChatGhhjy5FREREHktisoX3ftgLQOfaJaj+hJfBiUQkt2vVqpXREUREROQePv/8cwD69OmTq9d6uT3yZtu2bVy/fl0X7udgKSkpTJw4EYCuXbvi4OBgcKKcz2S1Wq0Z3eiZM2do1qwZVquVI0eOULNmTY4cOUKhQoXYsGEDRYoUyegus6S4uDg8PT2JjY3Fw8PD6DgiIiJyH+N/PcTM9cco6unKmpCG5HOxyzUuIjma3v/Kw9JrRkREsqNt27YRFBSEk5MTJ0+epGjRokZHMlSpUqU4fvw4v/zyiy7cz8GGDBlCaGgo7u7u7N+/Hz8/P6MjZVsP+h7YLt9KFC9enD179rBkyRL27NlDfHw83bt3p2PHjrm6Ei0iIiJZ04FzcczeeByAsS0rqXAjIiIiIiL3dHv0QYcOHXJ94QZujb45fvw44eHhKt7kUN9//z2hoaEALFiwQIWbTGK3byYcHR3p2LEjHTt2tFcXIiIiIo/NkmZl2PI/saRZaV7Jh2cqeBsdSURysQIFCvDXX39RqFAhvLy87rv48ZUrVzIxmYiIiACcPn2apUuXAjBo0CCD02QNwcHBzJkzh7CwMKOjiB0cPnyYbt26AbdG37Rp08bgRLmHLisVERGRXG3hlhPsOROLu6sjY16qaHQcEcnlJk6caJsrfuLEifct3oiIiEjm++KLL7BYLDRu3JjAwECj42QJjRs3BiAyMpKYmBgKFixocCLJKPHx8bRp04br16/z9NNPM27cOKMj5Soq3oiIiEiudfZaIp/95zAAQ5uXp4iHq8GJRCS369Kli+3+66+/blwQERERuUN8fDxffvkloFE3f+fj40PFihXZv38/69ato23btkZHkgxgtVrp1asXBw4cwMfHhyVLluDoqHJCZjIbHUBERETECFarlREr9nEj2cJT/l50eOoJoyOJiKTj4ODAxYsX79geExODg4ODAYlERERytwULFhAbG0uZMmV4/vnnjY6TpQQHBwMQHh5ucBLJKNOnT+fbb7/FwcGB7777Dh8fH6Mj5Toq3oiIiEiu9Mve84QfuoiTg4lxbSpjNmtqIhHJWqxW6123JyUl4ezsnMlpREREcjeLxcLkyZMBGDhwIGazvlb9OxVvcpY//vjDNrrs008/pUGDBgYnyp00zklERERyndgbKYz+6QAAbzYqTeki7gYnEhH5f1OmTAHAZDIxZ84c8uXLZ9tnsVjYsGED5cuXNyqeiIhIrrRy5UqOHj2Kl5dXumlO5ZaGDRtiMpk4dOgQ586dw9fX1+hI8oguXbrEK6+8QkpKCi+//LKmCDRQhhVvvLy8HngxzStXrmRUtyIiIiIPbdyvB7kcn0Spwnl5s3Epo+OIiKQzceJE4NbIm5kzZ6abIs3Z2Rl/f39mzpxpVDwREZFc6fbf5169epE3b16D02Q9Xl5eVK9enZ07d7J27Vo6duxodCR5BBaLhQ4dOnDmzBnKlSvH3LlzH/g7f8l4GVa8mTRpku1+TEwMH374Ic2aNaNOnToARERE8NtvvzFixIiM6lJERETkof1xPIbF208DML5tFVwctW6EiGQtUVFRADRu3Jjly5fj5eVlcCIREZHcbffu3axfvx5HR0f69etndJwsKzg4mJ07dxIeHq7iTTY1atQowsLCyJMnD8uWLcPDw8PoSLlahhVv/j5csG3btowdOzbdL7P+/fszdepUfv/9dw21EhEREUPcTLHw3vK9ALwW9ARP+RcwOJGIyL2tXbvW6AgiIiLC/4+6efXVVylevLjBabKu4OBgJkyYoHVvsqmVK1fy0UcfATB79mwqVqxocCIxWe+1CuZjyJcvH5GRkZQuXTrd9qNHjxIYGEh8fHxGd5klxcXF4enpSWxsrKqUIiIiWcDn/znMlPCjFHZ34feQhni6ORkdSSRH0fvfjNWtW7f77p83b14mJbEfvWZERCSrO3fuHCVKlCA1NZXt27dTs2ZNoyNlWfHx8Xh5eZGamsrx48cJCAgwOpI8oOPHj1OjRg2uXbtGv379+OKLL4yOlKM96Htgsz06L1iwID/++OMd23/88UcKFixojy5FRERE7uuvC9eZsf4YAGNeqqjCjYhkeVevXk13u3jxIuHh4Sxfvpxr164ZHU9ERCRXmDZtGqmpqdSvX1+Fm3+QL18+goKCADT6JhtJTEykbdu2XLt2jdq1axMaGmp0JPmvDJs27e/GjBlDjx49WLdune1/2K1bt7J69Wpmz55tjy5FRERE7iktzcqw5XtJsVhp+qQ3zSv5GB1JROQf/fDDD3dsS0tLo0+fPpQqVcqARCIiIrnLjRs3mDlzJoCWgXhAwcHBbN68mfDwcLp37250HHkA/fr1IzIykkKFCrF06VKcnZ2NjiT/ZZeRN6+//jqbN2/Gw8OD5cuXs3z5cjw8PNi0aROvv/66PboUERERuaevt51i58mr5HV2YGzLiphMJqMjiYg8ErPZTEhIiG3ufREREbGfr776iitXrhAQEEDLli2NjpMtBAcHA7dG3thhtQ7JYHPnzmXevHmYzWYWL16sNZ2yGLuMvAEICgri66+/tlfzIiIiIg8kOvYmn/56CIB3niuPb343gxOJiDyeY8eOkZqaanQMERGRHO3mzZt89tlnAAwYMAAHBweDE2UPtWvXxtXVlejoaA4dOsSTTz5pdCS5h127dtG3b18APvjgA5o0aWJwIvlfdiveHDt2jPnz53P8+HEmTZpEkSJF+PXXX3niiSeoWLGivboVERERSWfUT/u4npRKoF9+/lW7hNFxREQeWEhISLrHVquV8+fP88svv9ClSxeDUomIiOQOn3zyCceOHcPHx4du3boZHSfbcHV1pV69eoSFhREeHq7iTRZ15coV2rZtS1JSEi+88AJDhw41OpLchV2mTVu/fj2VK1dm69atLFu2jPj4eAD27NnDqFGj7NGliIiIyB1W74vmt/0XcDSbGN+2Mg5mTZcmItnH7t27093+/PNPAEJDQ5k0aZKx4URERHKwv/76i48//hiASZMm4e7ubnCi7OXvU6dJ1pOWlkanTp04ceIEJUuW5KuvvsJstkuZQB6TXUbeDB06lA8//JCQkJB0v9yCg4OZOnWqPboUERERSSfuZgqjftoHQO+GJSnv42FwIhGRh7N27VqjI4iIiOQ6VquVN998k+TkZJo1a8arr75qdKRsp0mTJrz//vusXbsWi8WiKeeymI8//phVq1bh6urKsmXL8PLyMjqS3INdSmp79+6ldevWd2wvUqQIly9ftkeXIiIiIulMWH2YC3FJ+BfMw1vBZYyOIyJiqA0bNvDiiy/i6+uLyWRixYoVtn0pKSm8++67VK5cmbx58+Lr60vnzp05d+7cfdscPXo0JpMp3a18+fJ2fiYiIiL29c033xAWFoarqyvTpk3DZNLo/YdVo0YN3N3duXr1Knv27DE6jvzNmjVrGDlyJADTp08nMDDQ2EByX3Yp3uTPn5/z58/fsX337t0UK1bMHl2KiIiI2Ow8eYVFW08C8HHryrg66UovEcl+YmJi6Nu3LxUqVKBQoUIUKFAg3e1hJCQkULVqVaZNm3bHvhs3brBr1y5GjBjBrl27WL58OYcPH+all176x3YrVqzI+fPnbbdNmzY9VC4REZGs5OrVq7Y154YPH06pUqUMTpQ9OTo60rBhQ0BTp2Ulp06dokOHDlitVnr06EHXrl2NjiT/wC7TprVv3553332XpUuXYjKZSEtLY/PmzQwZMoTOnTvbo0sRERERAJJT0xi2fC9WK7xSozh1SxcyOpKIyCPp1KkTR48epXv37nh7ez/Wlb/NmzenefPmd93n6enJmjVr0m2bOnUqtWrV4tSpUzzxxBP3bNfR0REfH59HziUiIpKVDBs2jIsXL/Lkk0/y9ttvGx0nWwsODmblypWEh4czZMgQo+PkeklJSbzyyivExMRQvXp1vvjiC6MjyQOwS/Hm448/pm/fvvj5+WGxWKhQoQIWi4XXXnuN4cOH26NLEREREQBmrT/GXxfiKZjXmfdaPGl0HBGRR7Zx40Y2bdpE1apVM73v2NhYTCYT+fPnv+9xR44cwdfXF1dXV+rUqcO4cePuW+wRERHJqiIiIpg1axYAM2bMwNnZ2eBE2VtwcDBwa+rWlJQUnJycDE6Uu4WEhLBt2za8vLz4/vvvcXV1NTqSPAC7FG+cnZ2ZPXs2I0eOZO/evcTHx1OtWjXKlNF88yIiImI/xy7F80X4UQBGvlgBr7z6wCUi2Vf58uVJTEzM9H5v3rzJu+++S4cOHfDw8LjncUFBQSxYsIBy5cpx/vx5xowZQ4MGDdi3bx/u7u53PScpKYmkpCTb47i4uAzPLyIi8rBSUlLo3bs3AK+//rptyi95dJUrV6ZgwYLExMSwfft26tata3SkXGvRokVMnz7ddj8gIMDgRPKg7LLmzdixY7lx4wZ+fn60aNGCV199lTJlypCYmMjYsWPt0aWIiIjkcmlpVoYt30uyJY2GZQvzUlVfoyOJiDyW6dOn8/7777N+/XpiYmKIi4tLd7OHlJQUXn31VaxWKzNmzLjvsc2bN+eVV16hSpUqNGvWjFWrVnHt2jW+++67e54zbtw4PD09bTc/P7+MfgoiIiIPbfLkyezdu5cCBQowYcIEo+PkCGazmcaNGwNa98ZIe/fupVevXgCMGDGCFi1aGJxIHoZdijdjxowhPj7+ju03btxgzJgx9uhSREREcrmlO0+zLeoKbk4OfNiq0mOtDSEikhXkz5+fuLg4goODKVKkCF5eXnh5eZE/f368vLwyvL/bhZuTJ0+yZs2a+466uVfesmXLcvTo0XseM2zYMGJjY22306dPP25sERGRx3Lq1ClGjRoFwIQJEyhUSGtmZpTbU6epeGOMuLg42rZtS2JiIs8884ztdS7Zh12mTbNarXf9wmTPnj0UKFDAHl2KiIhILnbx+k0++uUgAIOfLYtfgTwGJxIReXwdO3bEycmJb775Bm9vb7sWpW8Xbo4cOcLatWspWLDgQ7cRHx/PsWPH6NSp0z2PcXFxwcXF5XGiioiIZKi33nqLGzdu0KBBA15//XWj4+Qot4s3W7ZsITExETc3N4MT5R5Wq5WuXbty5MgR/Pz8+Oabb3BwcDA6ljykDB154+XlRYECBTCZTJQtW5YCBQrYbp6enjzzzDO8+uqrD93utGnT8Pf3x9XVlaCgILZt23bf45cuXUr58uVxdXWlcuXKrFq1Kt1+q9XKyJEjKVq0KG5ubjRt2pQjR47c0c4vv/xCUFAQbm5ueHl50apVq4fOLiIiIvY39ucDxN1MpXIxT16v6290HBGRDLFv3z7mz59Pu3btaNSoEQ0bNkx3exjx8fFERkYSGRkJQFRUFJGRkZw6dYqUlBRefvllduzYwddff43FYiE6Opro6GiSk5NtbTRp0oSpU6faHg8ZMoT169dz4sQJtmzZQuvWrXFwcKBDhw4Z8vxFRETs7ccff+Snn37C0dGRGTNmYDbbZZKiXKts2bL4+vqSlJRERESE0XFyldDQUJYvX46TkxNLly7ViLJsKkNH3kyaNAmr1Uq3bt0YM2YMnp6etn3Ozs74+/tTp06dh2pzyZIlhISEMHPmTIKCgpg0aRLNmjXj8OHDFClS5I7jt2zZQocOHRg3bhwvvPAC33zzDa1atWLXrl1UqlQJgE8//ZQpU6awcOFCAgICGDFiBM2aNePAgQO4uroCsGzZMnr27MnHH39McHAwqamp7Nu37zH+dURERMQewg9dYOWf53EwmxjXpjKODvrAJSI5Q82aNTl9+jTlypV77LZ27Nhhm3ceICQkBIAuXbowevRofvrpJwACAwPTnbd27VoaNWoEwLFjx7h8+bJt35kzZ+jQoQMxMTEULlyY+vXr88cff1C4cOHHzisiImJv8fHxvPXWWwC8/fbbVKxY0eBEOY/JZCI4OJhFixYRHh5uG4kj9rV+/XqGDh0K3FrPKSgoyOBE8qhMVqvVmtGNrl+/nrp16+Lk5PTYbQUFBfHUU0/ZrvBKS0vDz8+Pt956y/Yi/Lt27dqRkJDAypUrbdtq165NYGAgM2fOxGq14uvry+DBgxkyZAgAsbGxeHt7s2DBAtq3b09qair+/v6MGTOG7t27P3L2uLg4PD09iY2Nfej5okVEROSfJSSl8uzEDZy9lkivp0vyXosnjY4kkqvp/W/GWrp0KaNHj+btt9+mcuXKd3y+qlKlikHJMo5eMyIiYpQhQ4YQGhpKQEAA+/btI08eTb1sD/Pnz6dbt27UqVOHLVu2GB0nxzt//jzVqlXjwoUL/Otf/+Krr77SerBZ0IO+B7bLmjd/H8J/8+bNdEPtgQd+U56cnMzOnTsZNmyYbZvZbKZp06b3HGoXERFhu4rstmbNmrFixQrg1vQA0dHRNG3a1Lbf09OToKAgIiIiaN++Pbt27eLs2bOYzWaqVatGdHQ0gYGBTJgwwTZ6R0REcp9USxqJKRYSUywkpaSRZrWSZr01HWeaFeDWzzSrFevfflqtYOXux6Y/zoqV/zmf/263kq7tux1Lusf/f571f/v6W5t2uIYjHfu2DtuirnD2WiLFvdwY2LSMnXsTEclc7dq1A6Bbt262bSaTybbGqMViMSqaiIhItrZnzx4mTZoEwNSpU1W4saPbo222bdvG9evXcXd3NzhRznV7DcMLFy5QqVIlZs6cqcJNNmeX4s2NGzd45513+O6774iJiblj/4N+yLh8+TIWiwVvb+902729vTl06NBdz4mOjr7r8dHR0bb9t7fd65jjx48DMHr0aD7//HP8/f0JDQ2lUaNG/PXXXxQoUOCufSclJZGUlGR7HBcX90DPU0REHl9ampWbqRYSk28VV27/vPHfnzeT/3b/b9sTk9MfezPlf+4nW7iRnMrNlDSSLWlGP025h49aVyaPs13e1oiIGCYqKsroCCIiIjmOxWKhd+/eWCwWXn75ZVq0aGF0pBytRIkSlCxZkuPHj7Nx40b9e9vRsGHD2LRpE+7u7ixbtoy8efMaHUkek12+5Xj77bdZu3YtM2bMoFOnTkybNo2zZ88ya9Ysxo8fb48uM1Ra2q0v595//33atm0L3BriV7x4cZYuXUrv3r3vet64ceMYM2ZMpuUUEcmOUi1pnLpyw1YcuaOgcpfii62I8t8izK3ttwoqN5JT/3t+5hVWTCZwcTTjYDJhMpkwmcD895/wt+1gwnTr53+33T7u/4+9te/2seke29q+daz5rsf+T/93ORb+t73/P9/+/1727eQpfy8altX6CiKS85QoUcLoCCIiIjnO7Nmz2bp1K+7u7kyePNnoOLlCcHAwx48fJzw8XMUbO/n+++8JDQ0FYMGCBZQtW9bgRJIR7FK8+fnnn/nqq69o1KgRXbt2pUGDBpQuXZoSJUrw9ddf07Fjxwdqp1ChQjg4OHDhwoV02y9cuICPj89dz/Hx8bnv8bd/XrhwgaJFi6Y75vbinLe3V6hQwbbfxcWFkiVLcurUqXvmHTZsWLop2+Li4vDz8/unpykikmtYrVY6ztnK1qgrdu3HxdGMm7MDbk4O///zb/fzON+673r7vtPt+464OZv/e6yj7VjX/56b578/XRzNGnosIiKZ4ty5c2zatImLFy/aLjK7rX///galEhERyZ6io6Nta2h/9NFH+Pr6Gpwod2jSpAlz5swhLCzM6Cg50uHDh+natStwa1BFmzZtDE4kGcUuxZsrV65QsmRJ4Nb6Nleu3PqSrn79+vTp0+eB23F2dqZGjRqEhYXRqlUr4NaomLCwMPr163fXc+rUqUNYWBgDBw60bVuzZg116tQBICAgAB8fH8LCwmzFmri4OLZu3WrLVqNGDVxcXDh8+DD169cHbs0ZeOLEifte/ebi4oKLi8sDPz8Rkdxm09HLbI26gtkEhfK5pC+MpCui/Pe+swN5nP5bUPlvMeWOgsr/nOfq5ICDWYUVERHJ/hYsWEDv3r1xdnamYMGC6S4cMJlMKt6IiIg8pMGDBxMbG0uNGjV48803jY6TazRu3BiAyMhIYmJiKFiwoMGJco74+HjatGlDfHw8Tz/9NB9//LHRkSQD2aV4U7JkSaKionjiiScoX7483333HbVq1eLnn38mf/78D9VWSEgIXbp0oWbNmtSqVYtJkyaRkJBgqyZ27tyZYsWKMW7cOAAGDBhAw4YNCQ0N5fnnn2fx4sXs2LGDL7/8Erj1IWfgwIF8+OGHlClThoCAAEaMGIGvr6+tQOTh4cEbb7zBqFGj8PPzo0SJEkyYMAGAV155JWP+kUREcqEZ644B0LmOP6NfqmhwGhERkaxtxIgRjBw5kmHDhmE2m42OIyIikq2tWbOGb775BrPZzKxZs3BwcDA6Uq7h7e1NxYoV2b9/P+vWrbMtUyGPx2q10qtXLw4cOICPjw9LlizB0VFrweYkdvmv2bVrV/bs2UPDhg0ZOnQoL774IlOnTiUlJYXPP//8odpq164dly5dYuTIkURHRxMYGMjq1avx9vYG4NSpU+k+yNStW5dvvvmG4cOH895771GmTBlWrFhBpUqVbMe88847JCQk0KtXL65du0b9+vVZvXo1rq6utmMmTJiAo6MjnTp1IjExkaCgIMLDw/Hy8nrMfx0Rkdwp8vQ1thyLwdFsoufTJY2OIyIikuXduHGD9u3bq3AjIiLymG7evGkbadO3b19q1KhhcKLcJzg4mP379xMeHq7iTQaZPn063377LQ4ODnz33Xf3XGZEsi+T1Wq12ruTkydPsnPnTkqXLk2VKlXs3V2WERcXh6enJ7GxsXh4eBgdR0TEUL3/vYPf9l+gbfXihL5a1eg4IiJiB3r/m7HeeecdChQoYJubPyfSa0ZERDLDqFGjGDt2LEWLFuXQoUP6m2OAFStW0Lp1a8qXL8/BgweNjpPt/fHHHzz99NOkpKQQGhqabh12yfoe9D1wphRvcit9EBERueXoxes0/XwDAGsGPU0Zb3eDE4mIiD3o/W/GslgsvPDCCyQmJlK5cmWcnJzS7X/YWQ2yIr1mRETE3g4fPkyVKlVITk7mu+++05IIBrl69SoFCxbEarVy9uxZfH19jY6UbV26dIlq1apx9uxZXn75Zb777rt0ayNK1veg74EzbNq0KVOmPPCxWlhTRCR3mbn+OADPVvBW4UZEROQBjRs3jt9++41y5coBpPtQrg/oIiIi/8xqtfLmm2+SnJxM8+bNefnll42OlGt5eXlRvXp1du7cydq1a+nYsaPRkbIli8VChw4dOHv2LOXKlWPu3Ll6X5iDZVjxZuLEiQ90nMlkUvFGRCQXOXstkRW7zwLQp1Epg9OIiIhkH6GhocybN4/XX3/d6CgiIiLZ0qJFiwgPD8fV1ZVp06bpS26DBQcHs3PnTsLDw1W8eUSjRo0iLCyMPHnysGzZMo1czuEyrHgTFRWVUU2JiEgOMmfjcVLTrNQpWZBqT3gZHUdERCTbcHFxoV69ekbHEBERyZauXLnC4MGDARg5ciQBAQEGJ5Lg4GAmTJhAeHi40VGypZ9//pmPPvoIgNmzZ1OxYkWDE4m9mY0OICIiOdeVhGQWbzsNaNSNiIjIwxowYABffPGF0TFERESypaFDh3Lp0iUqVKhgK+KIserXr4+joyMnTpzQQICHdPz4cTp16gRAv379eO211wxOJJkhw0be/F23bt3uu3/evHn26FZERLKYBZujSEyxUKmYBw3KFDI6joiISLaybds2wsPDWblyJRUrVsTJySnd/uXLlxuUTEREJGvbsmULs2fPBmDmzJk4OzsbnEgA8uXLR1BQEJs3byY8PJzu3bsbHSlbSExMpG3btsTGxlK7dm1CQ0ONjiSZxC7Fm6tXr6Z7nJKSwr59+7h27RrBwcH26FJERLKY+KRUFkacBKBPw9KaW1hEROQh5c+fnzZt2hgdQ0REJFtJSUmhd+/ewK0LzBs0aGBwIvm74OBgFW8eUr9+/YiMjKRQoUIsXbpUxchcxC7Fmx9++OGObWlpafTp04dSpTRtjohIbvDt1lPEJqZQslBenqvkY3QcERGRbGf+/PlGRxAREcl2Jk2axL59+yhYsCCffPKJ0XHkfwQHB/PBBx8QHh6O1WrVhZ7/YO7cucybNw+z2czixYspXry40ZEkE2Xamjdms5mQkBAmTpyYWV2KiIhBklItzNl0HIDeDUviYNabMRERkUd16dIlNm3axKZNm7h06ZLRcURERLKskydPMnr0aAA+++wzChXS9N1ZTe3atXF1dSU6OppDhw4ZHSdL27lzJ3379gXggw8+oEmTJgYnksyWacUbgGPHjpGampqZXYqIiAF+2HWWC3FJeHu40KpaMaPjiIiIZEsJCQl069aNokWL8vTTT/P000/j6+tL9+7duXHjhtHxREREshSr1Uq/fv24ceMGDRs2pEuXLkZHkrtwdXWlXr16AISHhxucJuu6cuUKL7/8MklJSbz44osMHTrU6EhiALtMmxYSEpLusdVq5fz58/zyyy/6xSkiksNZ0qzM2nBr1E3PBiVxcXQwOJGIiEj2FBISwvr16/n5559tX3Js2rSJ/v37M3jwYGbMmGFwQhERkaxjxYoVrFy5EicnJ2bMmKHpuLKwJk2aEBYWRnh4uG1kify/tLQ0OnXqxIkTJyhZsiQLFy7EbM7UMRiSRdileLN79+50j81mM4ULFyY0NJRu3brZo0sREckiVu+LJupyAp5uTrSv9YTRcURERLKtZcuW8f3339OoUSPbthYtWuDm5sarr76q4o2IiMh/Xb9+nf79+wPwzjvv8OSTTxqcSO4nODgYgLVr12KxWHBw0EWff/fxxx+zatUqXF1dWbZsGV5eXkZHEoPYpXizdu1aezQrIiJZnNVqZcb6owB0qetPPhe7/JkRERHJFW7cuIG3t/cd24sUKaJp00RERP5m1KhRnDlzhpIlS/L+++8bHUf+QY0aNXB3d+fq1avs2bOH6tWrGx0py1izZg0jR44EYPr06QQGBhobSAyl8VYiIpJhNh65zL6zcbg5OfB6XX+j44iIiGRrderUYdSoUdy8edO2LTExkTFjxlCnTh0Dk4mIiGQdu3fvZvLkyQBMmzYNNzc3gxPJP3F0dKRhw4aA1r35u1OnTtGhQwesVis9evSga9euRkcSg9mleBMTE0Pfvn2pUKEChQoVokCBAuluIiKSM01fd2vUTftafhTI62xwGhERkext8uTJbN68meLFi9OkSROaNGmCn58fW7ZssX1JJSIikptZLBZ69+5NWloar776Ks8995zRkeQB3Z46TcWbW5KSknjllVeIiYmhevXqfPHFF0ZHkizALvPZdOrUiaNHj9K9e3e8vb21QJiISC6w69RV/jh+BUeziZ4NShodR0REJNurVKkSR44c4euvv+bQoUMAdOjQgY4dO+qqYhEREWDWrFls374dDw8PJk6caHQceQi3izcbNmwgJSUFJycngxMZKyQkhG3btuHl5cX333+Pq6ur0ZEkC7BL8Wbjxo1s2rSJqlWr2qN5ERHJgmasOwZAq2rF8M2vL5REREQyQp48eejZs6fRMURERLKc6Ohohg0bBsBHH32Er6+vwYnkYVSuXJmCBQsSExPD9u3bqVu3rtGRDLNo0SKmT59uux8QEGBwIskq7DJtWvny5UlMTLRH0yIikgUduXCdNQcuYDLBGw016kZERCQjjBs3jnnz5t2xfd68eXzyyScGJBIREck6Bg0aRFxcHDVr1qRPnz5Gx5GHZDabady4MZC7p07bu3cvvXr1AmDEiBG0aNHC4ESSldileDN9+nTef/991q9fT0xMDHFxceluIiKSs8xYf2vUzbMVvCldxN3gNCIiIjnDrFmzKF++/B3bK1asyMyZMw1IJCIikjX85z//YfHixZjNZmbNmoWDg4PRkeQR5PZ1b2JjY2nbti2JiYk888wzjBo1yuhIksXYZdq0/PnzExcXZ/sf8Dar1YrJZMJisdijWxERMcCZqzf4KfIcAH0alTY4jYiISM4RHR1N0aJF79heuHBhzp8/b0AiERER4yUmJvLmm28C8NZbb1G9enWDE8mjuv3d8ZYtW0hMTMxVa/pZrVa6du3KkSNH8PPz45tvvlERUu5gl+JNx44dcXJy4ptvvsHb2xuTyWSPbkREJAuYszGK1DQrdUsVJNAvv9FxREREcgw/Pz82b958x7znmzdv1rz+IiKSa40bN45jx45RrFgxPvjgA6PjyGMoW7Ysvr6+nDt3joiIiDsGAuRkoaGh/PDDDzg5OfH9999TqFAhoyNJFmSXadP27dvH/PnzadeuHY0aNaJhw4bpbiIikjPExCexePspAN7UqBsREZEM1bNnTwYOHMj8+fM5efIkJ0+eZN68eQwaNIiePXs+VFsbNmzgxRdfxNfXF5PJxIoVK9Ltt1qtjBw5kqJFi+Lm5kbTpk05cuTIP7Y7bdo0/P39cXV1JSgoiG3btj1ULhERkYdx6NAhxo8fD8DkyZNxd9e03dmZyWTKlVOnrV+/nqFDhwK3Xse1atUyOJFkVXYp3tSsWZPTp0/bo2kREclCFmw5wc2UNCoX86Re6YJGxxEREclR3n77bbp3786bb75JyZIlKVmyJG+99Rb9+/dn2LBhD9VWQkICVatWZdq0aXfd/+mnnzJlyhRmzpzJ1q1byZs3L82aNePmzZv3bHPJkiWEhIQwatQodu3aRdWqVWnWrBkXL158qGwiIiIPwmq18sYbb5CSksLzzz9PmzZtjI4kGSC3FW/Onz9Pu3btsFgs/Otf/+KNN94wOpJkYSar1WrN6EaXLl3K6NGjefvtt6lcuTJOTk7p9lepUiWju8yS4uLi8PT0JDY2Fg8PD6PjiIhkqOs3U6g3Ppy4m6nM6Fid5pXvnJNfRERyF73/tY/4+HgOHjyIm5sbZcqUwcXF5bHaM5lM/PDDD7Rq1Qq49WWYr68vgwcPZsiQIcCtBXS9vb1ZsGAB7du3v2s7QUFBPPXUU0ydOhWAtLQ0/Pz8eOutt2xXk/4TvWZERORBffXVV3Tp0gU3NzcOHDiAv7+/0ZEkA5w8eRJ/f38cHBy4evVqjh5NlZKSQnBwMJs2baJSpUr88ccf5M2b1+hYYoAHfQ9slzVv2rVrB0C3bt1s20wmE1arFZPJhMVisUe3IiKSib7ddoq4m6mULJyXZhV9jI4jIiKSY+XLl4+nnnrKbu1HRUURHR1N06ZNbds8PT0JCgoiIiLirsWb5ORkdu7cmW4EkNlspmnTpkRERNgtq4iI5E4xMTEMHjwYgFGjRqlwk4OUKFGCkiVLcvz4cTZu3EiLFi2MjmQXaWlpDBkyhE2bNuHh4cGyZctUuJF/ZJfiTVRUlD2aFRGRLCIp1cKcjbd+17/xdCnMZpPBiURERORRRUdHA+Dt7Z1uu7e3t23f/7p8+TIWi+Wu5xw6dOiefSUlJZGUlGR7HBcX96ixRUQkFxk6dCiXL1+mYsWKhISEGB1HMliTJk04fvw44eHhObJ489dff9GzZ082bNgAwIIFCyhbtqzBqSQ7sEvxpkSJEvZoVkREsojlu85y8XoSPh6utKpWzOg4IiIikk2MGzeOMWPGGB1DRESykU2bNjFnzhwAZs2adcfyDJL9BQcHM3v2bMLCwoyOkqFSUlIIDQ1l9OjRJCUlkSdPHj7//HNat25tdDTJJuxSvPnqq6/uu79z58726FZERDKBJc3KrPXHAOjRIABnR7PBiURERORx+Pjcmv70woULFC36/2vYXbhwgcDAwLueU6hQIRwcHLhw4UK67RcuXLC1dzfDhg1Ld8V0XFwcfn5+j5FeRERyspSUFNuC7j169KBevXoGJxJ7aNy4MQCRkZHExMRQsGBBgxM9vt27d9O9e3d2794NwLPPPsusWbM05Z88FLsUbwYMGJDucUpKCjdu3MDZ2Zk8efKoeCMiko39uu88J2JukD+PEx1qPWF0HBEREXlMAQEB+Pj4EBYWZivWxMXFsXXrVvr06XPXc5ydnalRowZhYWG0atUKuDWXe1hYGP369btnXy4uLri4uGT0UxARkRzq888/Z//+/RQqVIjx48cbHUfsxNvbm4oVK7J//37WrVtH27ZtjY70yBITExkzZgyfffYZFosFLy8vJk6cSOfOnTGZNOW8PBy7XC599erVdLf4+HgOHz5M/fr1+fbbb+3RpYiIZAKr1cr0tbdG3XSp409eF7tcAyAiIiL/9e9//5t69erh6+vLyZMnAZg0aRI//vjjQ7UTHx9PZGQkkZGRwK11SiMjIzl16hQmk4mBAwfy4Ycf8tNPP7F37146d+6Mr6+vrTADt+ajnzp1qu1xSEgIs2fPZuHChRw8eJA+ffqQkJBA165dH/t5i4iInDhxwjbVZmhoaI4YjSH3FhwcDEB4eLjBSR7dhg0bqFq1Kp988gkWi4VXX32VgwcP0qVLFxVu5JFk2lw3ZcqUYfz48XeMyhERkexjw5HLHDgfRx5nB16v6290HBERkRxtxowZhISE0KJFC65du4bFYgEgf/78TJo06aHa2rFjB9WqVaNatWrArcJLtWrVGDlyJADvvPMOb731Fr169eKpp54iPj6e1atX4+rqamvj2LFjXL582fa4Xbt2fPbZZ4wcOZLAwEAiIyNZvXo13t7ej/nMRUQkt7NarfTr14/ExEQaNWpEp06djI4kdpadizdxcXH06dOHhg0bcuTIEXx9fVmxYgVLlizR+yJ5LCar1WrNrM4iIyN5+umniYuLy6wuDRUXF4enpyexsbF4eHgYHUdE5LG1mxXB1qgrdK8fwIgXKhgdR0REshi9/81YFSpU4OOPP6ZVq1a4u7uzZ88eSpYsyb59+2jUqFG6Qkp2pdeMiIjczbJly3j55ZdxcnLizz//pHz58kZHEju7evUqBQsWxGq1cvbsWXx9fY2O9EB+/vln+vTpw9mzZwHo1asXn3zyCfnz5zc2mGRpD/oe2C7z3fz000/pHlutVs6fP8/UqVO1sJiISDa18+RVtkZdwcnBRI8GAUbHERERyfGioqJsI2X+zsXFhYSEBAMSiYiI2N/169fp378/AEOHDlXhJpfw8vKievXq7Ny5k7Vr19KxY0ejI93XxYsX6d+/P0uWLAGgdOnSzJ49m0aNGhkbTHIUuxRv/j4vMoDJZKJw4cIEBwcTGhpqjy5FRMTOZqy7tdZN62rFKOrpZnAaERGRnC8gIIDIyEhKlCiRbvvq1at58sknDUolIiJiXyNGjODcuXOUKlWKYcOGGR1HMlFwcDA7d+4kPDw8yxZvrFYrixYtYuDAgVy5cgWz2cyQIUMYPXo0bm76rkQyll2KN2lpafZoVkREDHI4+jq/H7yAyQS9G5YyOo6IiEiuEBISQt++fbl58yZWq5Vt27bx7bffMm7cOObMmWN0PBERkQy3a9cuvvjiCwCmT5+uL8NzmeDgYCZMmJBl1705efIkvXv35rfffgOgatWqzJ07lxo1ahicTHIquxRvREQkZ5m1/taom+cq+lCqcD6D04iIiOQOPXr0wM3NjeHDh3Pjxg1ee+01fH19mTx5Mu3btzc6noiISIayWCz07t2btLQ02rdvz7PPPmt0JMlk9evXx9HRkRMnThAVFUVAQNaYsj0tLY1p06YxbNgwEhIScHFxYdSoUQwZMgQnJyej40kOZrZHo23btuWTTz65Y/unn37KK6+8Yo8uRUTETk5fucGPe84B0KeRRt2IiIhkpo4dO3LkyBHi4+OJjo7mzJkzdO/e3ehYIiIiGW7mzJns2LEDT09PJk6caHQcMUC+fPkICgoCyDKjbw4cOED9+vXp378/CQkJ1K9fnz179jBs2DAVbsTu7FK82bBhAy1atLhje/PmzdmwYYM9uhQRETuZs/E4ljQr9UsXokrx/EbHERERyTUSExO5ceMGAHny5CExMZFJkybxn//8x+BkIiIiGevcuXO89957AHz88cf4+PgYnEiMEhwcDBhfvElOTuaDDz6gWrVqREREkC9fPqZPn8769espV66codkk97BL8SY+Ph5nZ+c7tjs5OREXF2ePLkVExA4uxyexePtpQKNuREREMlvLli356quvALh27Rq1atUiNDSUli1bMmPGDIPTiYiIZJyQkBDi4uKoVasWvXv3NjqOGOjvxRur1WpIhm3btlGzZk1GjhxJcnIyzz//PAcOHKBPnz6YzXb5Ol3kruzyaqtcuTJLliy5Y/vixYupUKGCPboUERE7WLD5BEmpaVQt7kndUgWNjiMiIpKr7Nq1iwYNGgDw/fff4+Pjw8mTJ/nqq6+YMmWKwelEREQyxm+//caSJUswm83MmjULBwcHoyOJgerUqYOrqyvR0dEcOnQoU/tOSEhg8ODB1KlTh71791KoUCG+/vprfv75Z/z8/DI1iwiAoz0aHTFiBG3atOHYsWO2amlYWBjffvstS5cutUeXIiKSwa7fTGFhxAkA+jQqjclkMjaQiIhILnPjxg3c3d0B+M9//kObNm0wm83Url2bkydPGpxORETk8SUmJvLmm28CMGDAAAIDA40NJIZzcXGhfv36/P7774SHh/Pkk09mSr9hYWH07NmTqKgo4Na6g5MmTaJQoUKZ0r/I3dhl5M2LL77IihUrOHr0KG+++SaDBw/mzJkz/P7777Rq1coeXYqISAb7eusprt9MpVThvDxbwdvoOCIiIrlO6dKlWbFiBadPn+a3337j2WefBeDixYt4eHgYnE5EROTxffTRRxw/fpzixYszZswYo+NIFpGZ695cvXqV7t2707RpU6KiovDz8+OXX35h0aJFKtyI4ew2Sd/zzz/P5s2bSUhI4PLly4SHh9OwYcNHamvatGn4+/vj6upKUFAQ27Ztu+/xS5cupXz58ri6ulK5cmVWrVqVbr/VamXkyJEULVoUNzc3mjZtypEjR+7aVlJSEoGBgZhMJiIjIx8pv4hIdnMzxcLcTbeuNnmjYSnMZo26ERERyWwjR45kyJAh+Pv7ExQURJ06dYBbo3CqVatmcDoREZHHc/DgQT799FMApkyZYhttKnK7eLN27VosFovd+lm+fDkVKlRg3rx5APTt25f9+/fTokULu/Up8jDsUrzZvn07W7duvWP71q1b2bFjx0O1tWTJEkJCQhg1ahS7du2iatWqNGvWjIsXL971+C1bttChQwe6d+/O7t27adWqFa1atWLfvn22Yz799FOmTJnCzJkz2bp1K3nz5qVZs2bcvHnzjvbeeecdfH19HyqziEh2t2zXGS5dT6KopystA4sZHUdERCRXevnllzl16hQ7duxg9erVtu1NmjRh4sSJBiYTERF5PFarlTfeeIOUlBRefPFFzdQj6dSoUQN3d3euXr3Knj17Mrz98+fP07ZtW9q2bUt0dDTlypVj48aNTJ06VUVEyVLsUrzp27cvp0+fvmP72bNn6du370O19fnnn9OzZ0+6du1KhQoVmDlzJnny5LFVRP/X5MmTee6553j77bd58skn+eCDD6hevTpTp04Fbv1xmDRpEsOHD6dly5ZUqVKFr776inPnzrFixYp0bf3666/85z//4bPPPnuozCIi2VmqJY1Z648D0LNBSZwd7TZIU0RERP6Bj48P1apVw2z+/7/HtWrVonz58gamEhEReTwLFy5kw4YN5MmThy+++EJrrEo6jo6OthmcMnLqNKvVyrx586hQoQLLly/H0dGR999/n8jISOrXr59h/YhkFEd7NHrgwAGqV69+x/Zq1apx4MCBB24nOTmZnTt3MmzYMNs2s9lM06ZNiYiIuOs5ERERhISEpNvWrFkzW2EmKiqK6OhomjZtatvv6elJUFAQERERtG/fHoALFy7Qs2dPVqxYQZ48eR44s4hIdrdqXzSnrtzAK48T7Wv5GR1HREQkV2nTpg0LFizAw8ODNm3a3PfY5cuXZ1IqERGRjBMTE8OQIUMAGD16NCVKlDA4kWRFwcHBrFy5kvDwcNvr5XEcP36cXr16ERYWBtwa3TN37lyqVq362G2L2ItdijcuLi5cuHCBkiVLptt+/vx5HB0fvMvLly9jsVjw9k6/ULa3tzeHDh266znR0dF3PT46Otq2//a2ex1jtVp5/fXXeeONN6hZsyYnTpx4oLxJSUkkJSXZHsfFxT3QeSIiWYXVamXGumMAvF43gDzOdvkzISIiIvfg6elpu/rY09PT4DQiIiIZ75133iEmJobKlSszcOBAo+NIFnV73ZsNGzaQkpKCk5PTI7VjsViYNGkSI0aMIDExEVdXVz744AMGDhz4UN9TixjBLq/QZ599lmHDhvHjjz/aPnBcu3aN9957j2eeecYeXWaoL774guvXr6cb8fMgxo0bx5gxY+yUSkTE/tb9dYmD5+PI4+xAl7q6+klERCSzzZ8//673RUREcoKNGzfalkKYOXPmI38hLzlf5cqVKViwIDExMWzfvp26des+dBt79+6le/fubN++HYDGjRvz5ZdfUrp06YyOK2IXdlnI4LPPPuP06dOUKFGCxo0b07hxYwICAoiOjiY0NPSB2ylUqBAODg5cuHAh3fYLFy7g4+Nz13N8fHzue/ztn/c7Jjw8nIiICFxcXHB0dLT9D12zZk26dOlyz7zDhg0jNjbWdrvbuj8iIlnZjLW3Rt28VusJ8udxNjiNiIhI7jZv3jyioqKMjiEiIpIhkpOTeeONNwDo1avXI30ZL7mH2WymcePGwMOve5OUlMSIESOoXr0627dvx9PTk9mzZxMWFqbCjWQrdineFCtWjD///JNPP/2UChUqUKNGDSZPnszevXvx83vw9ROcnZ2pUaOGbS5CgLS0NMLCwqhTp85dz6lTp0664wHWrFljOz4gIAAfH590x8TFxbF161bbMVOmTGHPnj1ERkYSGRnJqlWrAFiyZAkfffTRPfO6uLjg4eGR7iYikl3sOHGFbSeu4ORgokeDkv98goiIiNjVuHHjKF26NE888QSdOnVizpw5HD161OhYIiIijyQ0NJQDBw5QuHBhxo0bZ3QcyQZuT532MMWbLVu2UK1aNT788ENSU1Np1aoVBw4coEePHrapaUWyC7tN7Jc3b1569er12O2EhITQpUsXatasSa1atZg0aRIJCQl07doVgM6dO1OsWDHbL/0BAwbQsGFDQkNDef7551m8eDE7duzgyy+/BMBkMjFw4EA+/PBDypQpQ0BAACNGjMDX15dWrVoB8MQTT6TLkC9fPgBKlSpF8eLFH/s5iYhkRbfXumlTrTg+nq4GpxEREZEjR45w9uxZ1q1bx4YNG/jss8/o3bs3RYsWpVGjRixatMjoiCIiIg/k+PHjjB07FoDPP/+cAgUKGJxIsoPbxZstW7aQmJiIm5vbPY+9fv067733HtOmTcNqteLt7c3UqVNp27atijaSbdl1VaYDBw5w6tQpkpOT021/6aWXHriNdu3acenSJUaOHEl0dDSBgYGsXr0ab29vAE6dOoXZ/P8DiOrWrcs333zD8OHDee+99yhTpgwrVqygUqVKtmPeeecdEhIS6NWrF9euXaN+/fqsXr0aV1d9WSkiudOh6DjCDl3EZILeDTXqRkREJKsoVqwYHTt2pHXr1mzcuJFvv/2Wr7/+msWLF6t4IyIi2YLVaqVfv37cvHmT4OBgOnbsaHQkySbKli2Lr68v586dIyIiwlbM+V+rV6+md+/enDp1CoDXX3+d0NBQFQkl2zNZrVZrRjd6/PhxWrduzd69ezGZTNzu4naV02KxZHSXWVJcXByenp7ExsZqCjURydIGLt7NishztKjsw/SONYyOIyIi2ZTe/2as//znP6xbt45169axe/dunnzySRo2bEijRo14+umn8fLyMjriY9NrRkQk5/v+++955ZVXcHZ25s8//6RcuXJGR5JspFOnTixatIj333+fDz/8MN2+mJgYBg0axL///W8A/P39+fLLL3nmmWeMiCrywB70PbBd1rwZMGAAAQEBXLx4kTx58rB//342bNhAzZo1WbdunT26FBGRR3T6yg1+/vM8AH0aauE+ERGRrOK5555j7ty5tGrVivPnz7Nr1y4mTpxIy5Ytc0ThRkREcr64uDgGDBgAwLBhw1S4kYd2t3VvrFYrixcv5sknn+Tf//63bZmMffv2qXAjOYpdijcRERGMHTuWQoUKYTabMZvN1K9fn3HjxtG/f397dCkiIo/oyw3HsaRZaVCmEJWLexodR0RERP7r888/p169enz66adUrFiR1157jS+//JK//vrL6GgiIiIPZMSIEZw7d44yZcowdOhQo+NINnS7eLNt2zauX7/OmTNneOmll+jQoQOXLl2iYsWKbNmyhYkTJ5I3b16D04pkLLsUbywWC+7u7gAUKlSIc+fOAVCiRAkOHz5sjy5FROQRXLqexHc7TgPQp1Epg9OIiIjI3w0cOJDly5dz+fJlVq9eTd26dVm9ejWVKlWiePHiRscTERG5r507dzJ16lQApk+frrWm5ZGUKFGCUqVKYbFYGDBgABUqVGDlypU4OTkxevRodu3aRe3atY2OKWIXjvZotFKlSuzZs4eAgACCgoL49NNPcXZ25ssvv6RkSS2ELSKSVczfHEVSahqBfvmpU7Kg0XFERETkf1itVnbv3s26detYu3YtmzZtIi0tjcKFCxsdTURE5J5iYmLo2bMnaWlpvPbaazRt2tToSJKNBQcHc+zYMebPnw9A7dq1mTNnDhUrVjQ4mYh92WXkzfDhw0lLSwNg7NixREVF0aBBA1atWsWUKVPs0aWIiDykuJsp/DviJHBr1I3JZDI4kYiIiPzdiy++SMGCBalVqxZff/01ZcuWZeHChVy+fJndu3cbHU9EROQOf1+LZPfu3eTPn5/PP//c6FiSzTVv3hyAPHnyMGnSJDZt2qTCjeQKdhl506xZM9v90qVLc+jQIa5cuYKXl5e+HBQRySK+/uMU15NSKV0kH8886W10HBEREfkf5cuXp3fv3jRo0ABPT61LJyIiWduZM2fo06cPK1euBKBixYosXLgQb2993pTH06pVK3788UeqVauGn5+f0XFEMo1dijd3U6BAgczqSkRE/sHNFAtzN0UB8EbDUpjNKqyLiIhkNRMmTLDdP3PmDL6+vpjNdpk8QURE5JGlpaXx5Zdf8s4773D9+nWcnJwYPnw4Q4cOxdnZ2eh4kgOYTCZeeuklo2OIZLpMK96IiEjW8f3OM1yOT8LX05WWgb5GxxEREZF/UKFCBSIjI7WGqIiIZCl//fUXPXv2ZMOGDYDWIhERyUi6bEtEJJdJtaQxa8MxAHo+XRInB/0pEBERyeqsVqvd+/D398dkMt1x69u3712PX7BgwR3Hurq62j2niIgYLyUlhfHjx1OlShU2bNhAnjx5mDx5stYiERHJQBp5IyKSy/yy9zynryRSIK8z7Z96wug4IiIikkVs374di8Vie7xv3z6eeeYZXnnllXue4+HhweHDh22PtcapiEjOt2vXLnr06MHu3bsBePbZZ5k1axb+/v7GBhMRyWFUvBERyUWsVisz1t0adfN6XX/cnB0MTiQiIiIP4r333rP7OqKFCxdO93j8+PGUKlWKhg0b3vMck8mEj4+PXXOJiEjWkJiYyJgxY/jss8+wWCx4eXkxadIkOnXqpOK9iIgdaK4cEZFcZN3hSxyKvk5eZwe61PE3Oo6IiIjcx9ixY7lx4wYAw4YNI3/+/MCtL8/Gjh1r176Tk5NZtGgR3bp1u+8XcvHx8ZQoUQI/Pz9atmzJ/v3779tuUlIScXFx6W4iIpL1rV+/nqpVq/LJJ59gsVh49dVXOXjwIJ07d1bhRkTETlS8ERHJRaavOwpAx9ol8MzjZHAaERERuZ8xY8YQHx9/x/YbN24wZswYu/a9YsUKrl27xuuvv37PY8qVK8e8efP48ccfWbRoEWlpadStW5czZ87c85xx48bh6elpu/n5+dkhvYiIZJTY2FjeeOMNGjVqxJEjR/D19WXFihUsWbIEb29vo+OJiORoKt6IiOQS209cYfuJqzg7mOleP8DoOCIiIvIPrFbrXa9m3rNnj92nUJs7dy7NmzfH19f3nsfUqVOHzp07ExgYSMOGDVm+fDmFCxdm1qxZ9zxn2LBhxMbG2m6nT5+2R3wREckAP//8MxUrVrT9Xu/VqxcHDhygZcuWBicTEckdtOaNiEgucXutm7Y1iuHt4WpwGhEREbkXLy8vTCYTJpOJsmXLpivgWCwW4uPjeeONN+zW/8mTJ/n9999Zvnz5Q53n5OREtWrVOHr06D2PcXFxwcXF5XEjioiIHV28eJH+/fuzZMkSAEqXLs3s2bNp1KiRscFERHIZFW9ERHKBg+fjCD90EbMJej1dyug4IiIich+TJk3CarXSrVs3xowZg6enp22fs7Mz/v7+1KlTx279z58/nyJFivD8888/1HkWi4W9e/fSokULOyUTERF7slqtLFq0iIEDB3LlyhUcHBwYPHgwo0ePxs3Nzeh4IiK5joo3IiK5wMz1t0bdNK9clIBCeQ1OIyIiIvfTpUsXAAICAqhbty5OTpm3Tl1aWhrz58+nS5cuODqm/7jYuXNnihUrxrhx4wAYO3YstWvXpnTp0ly7do0JEyZw8uRJevTokWl5RUQkY5w8eZLevXvz22+/AVC1alXmzp1LjRo1DE4mIpJ7qXgjIpLDnYq5wc97zgHQp6FG3YiIiGRlcXFxeHh4AFCtWjUSExNJTEy867G3j8tIv//+O6dOnaJbt2537Dt16hRm8/8vm3r16lV69uxJdHQ0Xl5e1KhRgy1btlChQoUMzyUiIvZhsViYPn06w4YNIyEhARcXF0aNGsWQIUMy9eIBERG5k8lqtVqNDpFTxcXF4enpSWxsrF0+WImIPIjhK/ay6I9TPF22MF91q2V0HBERycH0/vfxOTg4cP78eYoUKYLZbE633s1tVqsVk8mExWIxIGHG0mtGRMQ4Bw4coEePHkRERABQv3595syZQ7ly5QxOJiKSsz3oe2CNvBERycEuXr/JdzvOAPBmI426ERERyerCw8MpUKAAAGvXrjU4jYiI5ETJycl88sknfPjhhyQnJ+Pu7s4nn3xC7969042wFBERY6l4IyKSg83ffILk1DSqPZGfoIACRscRERGRf9CwYcO73hcREckI27Zto3v37uzbtw+A559/nhkzZuDn52dwMhER+V8q3oiI5FBxN1NYFHESgDcblb7rtCsiIiKStV29epW5c+dy8OBBACpUqEDXrl1to3NEREQeREJCAiNGjGDy5MmkpaVRqFAhpkyZQvv27fVZUUQki9JYSBGRHOrfESe5npRKmSL5aFK+iNFxRERE5CFt2LABf39/pkyZwtWrV7l69SpTpkwhICCADRs2GB1PRESyibCwMCpXrszEiRNJS0ujY8eOHDx4kA4dOqhwIyKShWnkjYhIDnQzxcL8zVEA9GlUCrNZb8hFRESym759+9KuXTtmzJiBg4MDABaLhTfffJO+ffuyd+9egxOKiEhWdvXqVYYMGcK8efMA8PPzY9asWTRv3tzgZCIi8iA08kZEJAdauuM0l+OTKZbfjRer+hodR0RERB7B0aNHGTx4sK1wA+Dg4EBISAhHjx41MJmIiGR1y5Yto0KFCsybNw+TyUTfvn3Zv3+/CjciItmIijciIjlMqiWNWRuOA9Dr6ZI4OehXvYiISHZUvXp121o3f3fw4EGqVq1qQCIREcnqzp8/T9u2bXn55ZeJjo6mfPnybNy4kalTp+Lu7m50PBEReQiaNk1EJIdZ+ed5zlxNpGBeZ16t6Wd0HBEREXkIf/75p+1+//79GTBgAEePHqV27doA/PHHH0ybNo3x48cbFVFERLIgq9XKvHnzGDJkCNeuXcPR0ZF3332X4cOH4+rqanQ8ERF5BCreiIjkIFarlRnrjgHQtZ4/bs4O/3CGiIiIZCWBgYGYTCasVqtt2zvvvHPHca+99hrt2rXLzGgiIpJFHTt2jF69ehEeHg5AzZo1mTNnjkZpiohkcyreiIjkIOGHLnL4wnXyuTjSqY6/0XFERETkIUVFRRkdQUREsgmLxcKkSZMYMWIEiYmJuLm58cEHHzBgwAAcHfWVn4hIdqff5CIiOcjtUTcdg57A083J4DQiIiLysEqUKGF0BBERyQb+/PNPevTowfbt2wFo3Lgxs2fPplSpUgYnExGRjKLijYhIDrEt6go7Tl7F2cFM9/oBRscRERGRx/TVV1/dd3/nzp0zKYmIiGQVSUlJfPjhh4wfP57U1FQ8PT0JDQ2lW7dumEwmo+OJiEgGUvFGRCSHmL7uKABtaxSniIcWpBQREcnuBgwYkO5xSkoKN27cwNnZmTx58qh4IyKSy2zZsoUePXpw8OBBAFq1asW0adPw9fU1OJmIiNiD2egAIiLy+A6ci2Pd4UuYTdD76ZJGxxEREZEMcPXq1XS3+Ph4Dh8+TP369fn222+NjiciIpnk+vXrvPXWW9SvX5+DBw/i7e3N0qVLWb58uQo3IiI5mIo3IiI5wIz1t9a6aVG5KP6F8hqcRkREROylTJkyjB8//o5ROSIikjOtXr2aSpUqMXXqVKxWK127duXAgQO8/PLLmiZNRCSH07RpIiLZ3MmYBH758xwAfRppcUoREZGcztHRkXPnzhkdQ0RE7Ojy5csMGjSIRYsWAeDv78+XX37JM888Y3AyERHJLCreiIhkc7M2HCfNCo3KFaair6fRcURERCSD/PTTT+keW61Wzp8/z9SpU6lXr55BqURExF4sFgtbtmxh+fLlLFq0iMuXL2M2mxkwYAAffPABefNqlgURkdxExRsRkWzsYtxNvt9xBoA+DTXqRkREJCdp1apVuscmk4nChQsTHBxMaGioMaFERCRDpaSksG7dOpYtW8aKFSu4cOGCbV/FihWZO3cuQUFBBiYUERGjqHgjIpKNzd0cRbIljRolvKgVUMDoOCIiIpKB0tLSjI4gIiJ2cPPmTdasWcOyZcv46aefuHr1qm2fp6cnL730Em3atKFFixY4OzsbmFRERIyk4o2ISDYVm5jC13+cAm6NutFilSIiIiIiIllTfHw8v/76K8uXL2flypXEx8fb9hUuXJhWrVrRtm1bGjdurIKNiIgAKt6IiGRbi/44SXxSKuW83QkuX8ToOCIiIpLBQkJCHvjYzz//3I5JRETkUVy7do2ff/6Z5cuXs3r1am7evGnbV6xYMdq0aUPbtm2pX78+Dg4OBiYVEZGsSMUbEZFsKDHZwrxNUQC80agkZrNG3YiIiOQ0u3fvZvfu3aSkpFCuXDkA/vrrLxwcHKhevbrtOI2+FRHJOi5dusSKFStYtmwZYWFhpKam2vaVLFmStm3b0rZtW5566inMZrOBSUVEJKtT8UZEJBtauvM0MQnJFPdy48UqvkbHERERETt48cUXcXd3Z+HChXh5eQFw9epVunbtSoMGDRg8eLDBCUVEBODMmTP88MMPLFu2jI0bN6Zbs6xChQq2gk2VKlVUcBcRkQeWLUr806ZNw9/fH1dXV4KCgti2bdt9j1+6dCnly5fH1dWVypUrs2rVqnT7rVYrI0eOpGjRori5udG0aVOOHDli23/ixAm6d+9OQEAAbm5ulCpVilGjRpGcnGyX5yci8jBSLGnMWn8cgF5Pl8TRIVv8KhcREZGHFBoayrhx42yFGwAvLy8+/PBDQkNDDUwmIiLHjx9nwoQJ1K5dGz8/P/r378/69etJS0ujRo0afPTRRxw8eJD9+/czduxYqlatqsKNiIg8lCw/8mbJkiWEhIQwc+ZMgoKCmDRpEs2aNePw4cMUKXLnGg9btmyhQ4cOjBs3jhdeeIFvvvmGVq1asWvXLipVqgTAp59+ypQpU1i4cCEBAQGMGDGCZs2aceDAAVxdXTl06BBpaWnMmjWL0qVLs2/fPnr27ElCQgKfffZZZv8TiIiks/LPc5y9lkihfM68WtPP6DgiIiJiJ3FxcVy6dOmO7ZcuXeL69esGJBIRyd0OHDjAsmXLWLZsGXv27LFtN5lM1K1blzZt2tCmTRv8/f2NCykiIjmGyWq1Wo0OcT9BQUE89dRTTJ06FYC0tDT8/Px46623GDp06B3Ht2vXjoSEBFauXGnbVrt2bQIDA5k5cyZWqxVfX18GDx7MkCFDAIiNjcXb25sFCxbQvn37u+aYMGECM2bM4Pjx4w+cPS4uDk9PT2JjY/Hw8HiYpy0icldpaVaem7yBvy7E83azcvRtXNroSCIiIjZ6/5uxOnfuzMaNGwkNDaVWrVoAbN26lbfffpsGDRqwcOFCgxM+Pr1mRCQrs1qt7N69m2XLlrF8+XIOHTpk2+fg4EDDhg1p27YtrVu3pmjRogYmFRGR7ORB3wNn6ZE3ycnJ7Ny5k2HDhtm2mc1mmjZtSkRExF3PiYiIICQkJN22Zs2asWLFCgCioqKIjo6madOmtv2enp4EBQURERFxz+JNbGwsBQoUeMxnJCLyeMIPXeSvC/Hkc3HkX7VLGB1HRERE7GjmzJkMGTKE1157jZSUFAAcHR3p3r07EyZMMDidiEjOlJaWxh9//GEr2Jw4ccK2z8nJiWeeeYa2bdvy0ksvUahQIeOCiohIjpelizeXL1/GYrHg7e2dbru3t3e6qx3+Ljo6+q7HR0dH2/bf3navY/7X0aNH+eKLL/5xyrSkpCSSkpJsj+Pi4u57vIjIw7h2I5kv1h4F4F+1S+Dp5mRwIhEREbGnPHnyMH36dCZMmMCxY8cAKFWqFHnz5jU4mYhIzpKamsqGDRtYtmwZP/zwA+fPn7ftc3Nzo3nz5rRt25bnn38eT09PA5OKiEhukqWLN1nB2bNnee6553jllVfo2bPnfY8dN24cY8aMyaRkIpIbWK1Wtp+4yrfbTvHL3vMkp6bh7GimW31/o6OJiIhIJsmbNy9VqlQxOoaISI6SlJREWFgYy5Yt48cffyQmJsa2z8PDgxdffJE2bdrw3HPPkSdPHgOTiohIbmU2OsD9FCpUCAcHBy5cuJBu+4ULF/Dx8bnrOT4+Pvc9/vbPB2nz3LlzNG7cmLp16/Lll1/+Y95hw4YRGxtru50+ffofzxERuZtrN5KZuymKZyZu4NVZEfyw+yzJqWk8WdSDWf+qQRF3V6MjioiISA4yevRoTCZTulv58uXve87SpUspX748rq6uVK5cmVWrVmVSWhGRR5OQkMDy5cvp2LEjRYoU4fnnn2fevHnExMRQsGBBunfvzi+//MLFixdZtGgRbdq0UeFGREQMk6VH3jg7O1OjRg3CwsJo1aoVcGvu0bCwMPr163fXc+rUqUNYWBgDBw60bVuzZg116tQBICAgAB8fH8LCwggMDARuTW+2detW+vTpYzvn7NmzNG7cmBo1ajB//nzM5n+uc7m4uODi4vJoT1ZEcr27jbIBcHNy4KWqvrwW9ARVintiMpkMTioiIiI5UcWKFfn9999tjx0d7/1xccuWLXTo0IFx48bxwgsv8M0339CqVSt27dpFpUqVMiOuiMgDiY2NZeXKlSxfvpxff/2VxMRE276iRYvSunVr2rZty9NPP33f33siIiKZLcv/VQoJCaFLly7UrFmTWrVqMWnSJBISEujatSsAnTt3plixYowbNw6AAQMG0LBhQ0JDQ3n++edZvHgxO3bssI2cMZlMDBw4kA8//JAyZcoQEBDAiBEj8PX1tRWIzp49S6NGjShRogSfffYZly5dsuW514gfEZFHde1GMst2neXbbac4ejHetv3Joh68FvQErQJ9cXfV+jYiIiJiX46Ojg/8eWfy5Mk899xzvP322wB88MEHrFmzhqlTpzJz5kx7xhQR+UeXL1/mp59+YtmyZfz+++8kJyfb9vn7+9OmTRvatm1L7dq1H+hiXRERESNk+eJNu3btuHTpEiNHjiQ6OprAwEBWr16Nt7c3AKdOnUr3h7Zu3bp88803DB8+nPfee48yZcqwYsWKdFd/vfPOOyQkJNCrVy+uXbtG/fr1Wb16Na6ut6YhWrNmDUePHuXo0aMUL148XR6r1ZoJz1pEcjqNshEREZG7qV69OmFhYXh5eTF27FiGDBmSaVP2HDlyBF9fX1xdXalTpw7jxo3jiSeeuOuxERERhISEpNvWrFkzVqxYcd8+kpKSSEpKsj2Oi4t77NwikjEOHDjAW2+9xR9//GF0lMeWmJiY7vubcuXK0bZtW9q2bUu1atX0OUtERLIFk1XVCLuJi4vD09OT2NhYPDw8jI4jIlmARtmIiEhOpve/j8/NzY0jR45QvHhxHBwcOH/+PEWKFLF7v7/++ivx8fGUK1eO8+fPM2bMGM6ePcu+fftwd3e/43hnZ2cWLlxIhw4dbNumT5/OmDFj7lhf9O9Gjx7NmDFj7tiu14yIcZKTk/nkk0/48MMP041Qye4CAwNp27Ytbdq0oUKFCkbHERERsXnQz01ZfuSNiEh2p1E2IiIi8qACAwPp2rUr9evXx2q18tlnn5EvX767Hjty5MgM67d58+a2+1WqVCEoKIgSJUrw3Xff0b179wzrZ9iwYelG7MTFxeHn55dh7YvIw9m2bRvdu3dn3759ADz//PN8/PHHdy3aZid58uSxzdgiIiKSXal4IyJiJxplIyIiIg9rwYIFjBo1ipUrV2Iymfj111/vuoC2yWTK0OLN/8qfPz9ly5bl6NGjd93v4+NzxwibCxcu/OOaOS4uLri4uGRYThF5NAkJCYwcOZJJkyaRlpZGoUKFmDJlCu3bt9dFZSIiIlmEijciIhlIo2xERETkcZQrV47FixcDYDabCQsLy5Rp0/5XfHw8x44do1OnTnfdX6dOHcLCwhg4cKBt25o1a6hTp04mJRSRRxUWFkbPnj2JiooCoGPHjkyaNIlChQoZnExERET+TsUbEZEMoFE2IiIiktHS0tIyra8hQ4bw4osvUqJECc6dO8eoUaNwcHCwrWnTuXNnihUrxrhx4wAYMGAADRs2JDQ0lOeff57FixezY8cOvvzyy0zLLCIP5+rVqwwZMoR58+YB4Ofnx6xZs9JNmygiIiJZh4o3IiKPSKNsRERExN6OHTvGpEmTOHjwIAAVKlRgwIABlCpVKkP7OXPmDB06dCAmJobChQtTv359/vjjDwoXLgzAqVOnMJvNtuPr1q3LN998w/Dhw3nvvfcoU6YMK1asoFKlShmaS0QyxrJly+jXrx/R0dGYTCbefPNNxo0bl+3XthEREcnJTFar1Wp0iJwqLi4OT09PYmNj8fDwMDqOiGQQjbIRERG5O73/zVi//fYbL730EoGBgdSrVw+AzZs3s2fPHn7++WeeeeYZgxM+Pr1mROzr/Pnz9OvXj+XLlwNQvnx55syZY/udIiIiIpnvQd8Da+SNiMgD0CgbERERyWxDhw5l0KBBjB8//o7t7777bo4o3oiIfVitVubPn8/gwYO5du0ajo6OvPvuuwwfPhxXV1ej44mIiMgDUPFGROQ+NMpGREREjHLw4EG+++67O7Z369aNSZMmZX4gEckWjh8/Tq9evQgLCwOgZs2azJ07lypVqhicTERERB6GijciIv9Do2xEREQkKyhcuDCRkZGUKVMm3fbIyEiKFCliUCoRyaosFguTJk1ixIgRJCYm4ubmxgcffMCAAQNwdNTXPyIiItmN/nqLiPyXRtmIiIhIVtKzZ0969erF8ePHqVu3LnBrzZtPPvmEkJAQg9OJSFby559/0qNHD7Zv3w5A48aNmT17NqVKlTI4mYiIiDwqFW9EJFfTKBsRERHJqkaMGIG7uzuhoaEMGzYMAF9fX0aPHk3//v0NTiciWUFSUhIffvgh48ePJzU1FU9PT0JDQ+nWrZs+w4iIiGRzKt6ISK6kUTYiIiKS1ZlMJgYNGsSgQYO4fv06AO7u7ganEpGsYsuWLfTo0YODBw8C0Lp1a6ZOnYqvr6/ByURERCQjqHgjIrmGRtmIiIhIdqWijYjcdv36dd577z2mTZuG1WrF29ubadOm0bZtW6OjiYiISAZS8UZEcoUNf13ig5UHOKJRNiIiIiIikk2tXr2a3r17c+rUKQC6du3KZ599RoECBQxOJiIiIhlNxRsRydHibqbw0cqDLNlxGtAoGxERERERyX4uX77MoEGDWLRoEQABAQF8+eWXNG3a1OBkIiIiYi8q3ohIjrX20EWGLd9LdNxNAF6v60/Is2Xx0CgbERERERHJBqxWK0uWLKF///5cunQJs9nMgAED+OCDD8ibN6/R8URERMSOVLwRkRwn9kYKY1ceYNmuMwD4F8zDpy9XpVaAphIQEREREZHs4cyZM/Tp04eVK1cCUKlSJebOnUutWrUMTiYiIiKZwWx0ABGRjLTmwAWembieZbvOYDJBj/oB/DrgaRVuREREJFvq168fV65cMTqGiGSitLQ0Zs6cSYUKFVi5ciVOTk6MGTOGnTt3qnAjIiKSi6h4IyI5wtWEZAYu3k3Pr3Zw8XoSJQvn5fs36jD8hQq4OTsYHU9ERETkgZ05c8Z2/5tvviE+Ph6AypUrc/r0aaNiiUgm+Ouvv2jcuDF9+vTh+vXr1K5dm927dzNy5EicnZ2NjiciIiKZSNOmiUi2t3rfeYav2M/l+CTMJuj5dEkGNS2Lq5OKNiIiIpL9lC9fnoIFC1KvXj1u3rzJ6dOneeKJJzhx4gQpKSlGxxMRO0hJSSE0NJTRo0eTlJRE3rx5+fjjj+nbty8ODvpcIyIikhupeCMi2VZMfBIjf9rPL3+eB6BMkXxMeKUqgX75jQ0mIiIi8hiuXbvGrl272LhxI8uXL6dFixZ4e3uTlJTEb7/9Rps2bfD29jY6pohkkF27dtG9e3ciIyMBaNasGTNnzsTf39/QXCIiImIsTZsmItmO1Wpl5Z/neGbiBn758zwOZhN9G5diZf/6KtyIiIhItpeSkkKtWrUYPHgwbm5u7N69m/nz5+Pg4MC8efMICAigXLlyRscUkceUmJjI0KFDqVWrFpGRkRQoUICvvvqKX3/9VYUbERER0cgbEcleLl1PYsSKfazeHw1AeR93JrxclcrFPQ1OJiIiIpIx8ufPT2BgIPXq1SM5OZnExETq1auHo6MjS5YsoVixYmzfvt3omCLyGNavX0/Pnj05cuQIAO3atWPy5MkaVSciIiI2Kt6ISLZgtVr5ac85Rv20n2s3UnA0m3izcWn6NS6Ns6MGEYqIiEjOcfbsWSIiItiyZQupqanUqFGDp556iuTkZHbt2kXx4sWpX7++0TFF5BHExsby7rvvMmvWLAB8fX2ZMWMGL730ksHJREREJKsxWa1Wq9Ehcqq4uDg8PT2JjY3Fw8PD6Dgi2dbFuJu898M+fj94AYAKRT2Y8EoVKvpqtI2IiEhWove/Gc/Ly4sNGzZw8OBBOnfujI+PDxcuXKBWrVqsX7/e6HiPTa8ZyU1++ukn3nzzTc6ePQtA7969+eSTT/D01OcaERGR3ORB3wNr5I2IZFlWq5Vlu84y9uf9xN1MxcnBRP/gMrzRqBRODhptIyIiIrmDp6cnr776Kt27dyc8PJw8efLkiMKNSG5x8eJF+vfvz5IlSwAoXbo0c+bMoWHDhgYnExERkaxMxRsRyZLOxyby3vK9rD18CYDKxTyZ8EoVyvvoikwRERHJPf7880+KFSsGQIkSJXBycsLHx4d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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(20, 10))\n", + "plot_slice_errors(metadata, axes[(0, 0)])\n", + "plot_left_over_error_budget(metadata, axes[(0, 1)])\n", + "plot_accumulated_error(metadata, axes[(1, 0)])\n", + "plot_num_qwc_groups(metadata, axes[(1, 1)])" + ] + }, + { + "cell_type": "markdown", + "id": "269536c5-8ba4-48da-8e99-6b8ce3a52bd0", + "metadata": {}, + "source": [ + "## Capping the total error\n", + "\n", + "In addition to specifying the per-slice error budget, one can specify the maximum amount of error to incur from truncation. Once that limit is hit, no more truncation will be performed; however, backpropagation will continue until the observable becomes too large and one of the stopping criteria is met.\n", + "\n", + "Let's re-run the above experiment but set a cap on `max_error_total` such that the error budget is expended after backpropagating slice 7." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8ef6a100-fb4b-4b66-be6e-a094db2c29d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TruncationErrorBudget(per_slice_budget=[0.0, 0.0, 0.0, 0.0, 0.003, 0.003], max_error_total=0.006, p_norm=1)\n" + ] + } + ], + "source": [ + "# Specify a length-6 per-slice budget.\n", + "# This will be cycled over 3 times to be applied to the 18 slices\n", + "max_error_per_slice = [0.0] * 4 + [0.003] * 2\n", + "\n", + "truncation_error_budget = setup_budget(\n", + " max_error_per_slice=max_error_per_slice, max_error_total=0.006\n", + ")\n", + "print(truncation_error_budget)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c58bccd5-aa40-482f-acaf-4ef50d420053", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backpropagated 10 circuit slices.\n", + "New observable contains 67 terms and 20 commuting groups.\n" + ] + } + ], + "source": [ + "bp_obs, remaining_slices, metadata = backpropagate(\n", + " obs,\n", + " slices,\n", + " operator_budget=op_budget,\n", + " truncation_error_budget=truncation_error_budget,\n", + ")\n", + "reduced_circuit = combine_slices(remaining_slices)\n", + "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", + "print(\n", + " f\"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "dadb77a5-00d7-427d-921b-4a005f5ab36f", + "metadata": {}, + "source": [ + "As expected, our truncation error caps out at `.006` (**bottom/left**). It is notable that in this run we were not able to backpropagate slice 11. This is because we did not have enough budget to truncate terms and the number of commuting Pauli groups grew past the limit of `20`, as seen in **bottom/right**." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "69c9069a-9038-4319-8dc6-37a1be973ebf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(20, 10))\n", + "plot_slice_errors(metadata, axes[(0, 0)])\n", + "plot_left_over_error_budget(metadata, axes[(0, 1)])\n", + "plot_accumulated_error(metadata, axes[(1, 0)])\n", + "plot_num_qwc_groups(metadata, axes[(1, 1)])" + ] + }, + { + "cell_type": "markdown", + "id": "2049d223-1b91-4042-ba98-a86f7a59483c", + "metadata": {}, + "source": [ + "It may be desirable to simply cap the overall budget with `max_error_total` without specifying `max_error_per_slice`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "fac59f4f-3528-49c1-9d4d-9be98d2c7d78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TruncationErrorBudget(per_slice_budget=[0.018], max_error_total=0.018, p_norm=1)\n" + ] + } + ], + "source": [ + "truncation_error_budget = setup_budget(max_error_total=0.018)\n", + "print(truncation_error_budget)" + ] + }, + { + "cell_type": "markdown", + "id": "20c55e75-f619-4ac9-aecd-29491867ab82", + "metadata": {}, + "source": [ + "The output of the cell above, might be slightly surprising because the `per_slice_budget` is set to the `max_error_total`. This indicates, that the entire available budget will be consumed **greedily**.\n", + "You can think of it this way: the entire budget is available to each slice (because we loop over the `per_slice_budget`). But any budget that has already been consumed, will be deducted from the budget that is available at that given point in the algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5d65a057-eb5e-4308-b84d-82bfbea06989", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backpropagated 9 circuit slices.\n", + "New observable contains 25 terms and 9 commuting groups.\n" + ] + } + ], + "source": [ + "op_budget = OperatorBudget(max_qwc_groups=10)\n", + "bp_obs, remaining_slices, metadata = backpropagate(\n", + " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", + ")\n", + "reduced_circuit = combine_slices(remaining_slices)\n", + "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", + "print(\n", + " f\"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a77ceb1e-bc6d-462d-aebe-ec478d312233", + "metadata": {}, + "source": [ + "**top/right** shows how the entire `.018` error budget is made available to the first slice. No truncation is performed until the third slices, so the left-over budget remains constant. The budget monotonically decreases, as the full budget is made available to each backpropagated slice until it is expended.\n", + "\n", + "It is notable that this experiment yielded two fewer backpropagated slices compared to the first experiment in this notebook, which is almost identical. This demonstrates that for some problems distributing the budget evenly might be optimal. For other problems, allowing slices to greedily expend the full budget might yield better performance." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "51d36d7e-0bd5-4388-bbb8-6831bf1f02e6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(20, 10))\n", + "plot_slice_errors(metadata, axes[(0, 0)])\n", + "plot_left_over_error_budget(metadata, axes[(0, 1)])\n", + "plot_accumulated_error(metadata, axes[(1, 0)])\n", + "plot_num_qwc_groups(metadata, axes[(1, 1)])" + ] + }, + { + "cell_type": "markdown", + "id": "8859d36c-258f-441f-a7cf-3997b9f6e8ff", + "metadata": {}, + "source": [ + "## Capping the number of backpropagated slices and the total error together\n", + "\n", + "If one doesn't want to distribute their error budget all the way through the circuit but doesn't want to greedily expend it either, one can specify a the number of slices they intend to backpropagate (`num_slices`), along with a total error budget (`max_error_total`). This will distribute the error budget uniformly (accordingly to `p_norm`) across the input slices.\n", + "\n", + "Here, we will cap the number of slices we may backpropagate at `12`, and we will keep the same total error budget." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "474cf708-5447-4984-873c-5f62f81a402d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TruncationErrorBudget(per_slice_budget=[0.0014999999999999998], max_error_total=0.018, p_norm=1)\n" + ] + } + ], + "source": [ + "num_slices = 12\n", + "\n", + "truncation_error_budget = setup_budget(max_error_total=0.018, num_slices=num_slices, p_norm=1)\n", + "print(truncation_error_budget)" + ] + }, + { + "cell_type": "markdown", + "id": "1ac2fb58-db97-4c9e-9a96-fce97b52e7e2", + "metadata": {}, + "source": [ + "Now we will attempt to backpropagate the `12` slices for which we allocated some budget in the previous step. We do this by just passing in the final `12` slices in our circuit. With `p_norm=1`, each of the `12` slices has an available budget of `0.018 / num_slices = 0.0015`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6efc92bc-3433-4ea6-a683-684205201f4f", + "metadata": {}, + "outputs": [], + "source": [ + "bp_obs, remaining_slices, metadata = backpropagate(\n", + " obs,\n", + " slices[-num_slices:],\n", + " operator_budget=op_budget,\n", + " truncation_error_budget=truncation_error_budget,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "b15c2a20-19c3-417c-b32d-173c10f13852", + "metadata": {}, + "source": [ + "Since we passed a subset of our slices (i.e. ``slices[-num_slices:]``) to ``backpropagate``, we must combine the slices remaining after backpropagation with the slices that were never sent for backpropagation (i.e. ``slices[:-num_slices]``).\n", + "\n", + "Once we have combined all of the remaining slices, we can use [combine_slices](/docs/api/qiskit-addon-utils/qiskit_addon_utils.slicing.combine_slices.html#qiskit_addon_utils.slicing.combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7c42256f-e7f2-40ed-999e-6a51fd9351c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backpropagated 12 circuit slices.\n", + "New observable contains 29 terms and 9 commuting groups.\n" + ] + } + ], + "source": [ + "# Recombine the slices remaining after backprop with the rest of the original circuit\n", + "reduced_circuit = combine_slices(slices[:-num_slices] + remaining_slices)\n", + "\n", + "print(f\"Backpropagated {num_slices - len(remaining_slices)} circuit slices.\")\n", + "print(\n", + " f\"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ae2d383c-c11c-457e-856f-85602f2df306", + "metadata": {}, + "source": [ + "The plots show us that we did successfully backpropagate all `12` slices while keeping our observable under `10` commuting Pauli groups. We can also see that using `num_slices` along with `max_error_total` results in a distribution of budgets across the slices and unused budget is again forwarded to the next slice. This is most apparent in the **top/right** plot, as the budget is both expended and replenished throughout backpropagation.\n", + "\n", + "It is notable that this method of distributing error produced the best results (more backpropagated slices), compared with the first experiment in this notebook and the example just above this one. All of these examples allotted `.018` error budget, but backpropagation performed differently depending on how that budget was distributed." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b0bf5050-2928-41a1-9c3d-c9d79e918f6f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bt+fUqVMcP34cb29vAG644Qb7Iu1t2rThp59+4vXXX6dv376kp6fj6+tLdHQ0Tk5OBAYG2m8C0tPTef/990lPT8ff3x+AJ554goSEBN5//33++c9/AnDu3Dn+9a9/lfmU1qBBg1i4cKH9JiQxMZHTp09z1113AdC8eXOeeOIJe/tHH32Ub775hk8++aTMTUhAQACvv/46JpOJtm3bsn37dl5//fVyb0J+/PFHNmzYwLFjx+xvoL/66qssX76cTz/99KJrMT788MMMGjSIDz74gPXr1xMfH8/KlSv54IMP+OSTTwC0uHwt4uzsTHh4OImJicTGxgJgtVpJTEy86NTTkZGRJCYmllnAdfXq1URGRgIQHByMr68viYmJ9mJNbm4uycnJjB49GoBZs2YxdepU+/EZGRnExMSwZMkSIiIiLpp369atZQpCf+Ti4nJBQUjkUhYkpwPQNbAxO47ksvHgKTYeyKZbkDHTwoiIiBhB9031875JxRsRERERMVx56w9ezPk3oP/3+5kzZwKln7iaOXMmISEh9OvXj9tuu4077rgDR0dHtm/fTklJCW3atClzfGFhYZnFPZ2dnenUqVOZNkOGDKFnz55kZGTg7+/PggUL+Mtf/mKfDqCkpIR//vOffPLJJxw5coSioiIKCwsvWOS9Z8+eZYb6R0ZGMmPGDEpKSnBwcCjT9pdffuHs2bMXLDz622+/kZqaetHnp3HjxjRu3JgNGzZw1113ERQUxJYtW+jfvz9BQUEXPU5qrri4OO6//366detGjx49mDlzJnl5eQwfPhyAYcOG0bx5c6ZNmwaUTk1x0003MWPGDP7yl7+wePFiNm7cyJw5c4DSNULHjRvH1KlTad26NcHBwTz77LP4+/vbC0SBgYFlMjRs2BCA0NBQWrRoAZR+ItLZ2ZkuXboA8NlnnzFv3jzmzp1b5c+J1A+/FZWwdOMhAB79U2u++TWTxT8f4p21+1W8ERGRSuPq5MDOF2IMO3dF6L6pft03qXgjIiIiIoZp1aoVJpOJXbt28f/+3/+7YP+uXbto0qQJzZo1u6L+AgIC2L17N99++y2rV6/mb3/7G6+88gpr1qzh7NmzODg4sGnTpgte8J9/YxrA1dX1grmUu3fvTmhoKIsXL2b06NH8+9//5oMPPrDvf+WVV3jjjTeYOXMmHTt2pEGDBowbN46ioqIKPBtlnT17Fj8/P77//vsL9v3vHNL/64cffuDPf/4zUDqNwffff8/f//53fvvtN5ycnHjppZd4+umnefrpp686l1S/e+65h+PHjzN58mQyMzMJCwsjISEBH5/SqaPS09PLLA4bFRXFwoULmTRpEk8//TStW7dm+fLldOjQwd5mwoQJ5OXlMWrUKE6fPk2vXr1ISEjAYqnYVLAvvvgiBw8exNHRkXbt2rFkyRIGDBhQORcu9d4X2zLILSimRRNXerdpRqCnG0s2HmL1ziz2HTtLK++Gl+9ERETkMkwm0xVNXWYk3TddXF2+b6rZv5UiIiIiUqd5enrSt29f/vWvf/H3v/+9zPzNmZmZLFiwgGHDhpW5KVi/fn2ZPtavX19m+gBXV1fuuOMO7rjjDh555BHatWvH9u3b6dKlCyUlJRw7dowbb7yxwlmHDBnCggULaNGiBWazmb/85S/2fT/99BN33nknQ4cOBUqntdqzZw/XXXddmT6Sk5MvyN66desLbooAunbtSmZmJo6Ojlf8ya9u3bqxdetWNm3axIQJE0hMTCQ9PZ3+/fuzefNmzGbzRed8lpptzJgxF50mrbwb1YEDBzJw4MCL9mcymXjhhRd44YUXruj8QUFBF3zS8/777+f++++/ouNFrsbH6w8CMCSiJQ5mE6HNGtK3vQ+rdmYxZ20q0wdc2SLEIiIitZ3um+rnfZP58k1EREREpLbzMBfgacqr8oeHuaDC2WbPnk1hYSExMTGsXbuWQ4cOkZCQQN++fWnevLl9ccvzfvrpJ6ZPn86ePXt46623WLp0KWPHjgXggw8+4L333mPHjh3s37+fjz/+GFdXV1q2bEmbNm0YMmQIw4YN47PPPiMtLY0NGzYwbdo0vvrqq8vmHDJkCJs3b+Yf//gHAwYMKLNuR+vWrVm9ejXr1q1j165dPPTQQxcsGA+lIyTi4uLYvXs3ixYt4s0337Rn/6Po6GgiIyOJjY1l1apVHDhwgHXr1vHMM8+wcePGco9xdXWlVatWpKWlcfPNN9OqVSsOHz7MDTfcQJs2bWjVqtUFNyE7d+5k69atZGdnk5OTw9atW9m6detlnw8Rkaq07fBpth3OwdnBzN3dWti3P3RTKAD/3nKErNyK/58jIiJSW+m+qf7dN2nkjYiIiEgd5ubmhtnBkZuc06rtnGYHxwvmLL6U1q1bs3HjRqZMmcLdd99NdnY2vr6+xMbGMmXKlAteND/++ONs3LiR559/Hnd3d1577TViYkrnqG7cuDEvvfQScXFxlJSU0LFjR7744gv7/Mfvv/8+U6dO5fHHH+fIkSN4eXnRs2dPbr/99svmbNWqFT169GDDhg32uaLPmzRpEvv37ycmJgY3NzdGjRpFbGwsOTk5ZdoNGzaM3377jR49euDg4MDYsWMvuoCmyWRi5cqVPPPMMwwfPpzjx4/j6+tL79697dNlXcz3339vH3WxZs0aevfufdG2t912GwcPHrR/f379korMpy0iUtnOj7q5raMvng3/+6ZPeMsm9AhqyoYD2cz7KY2Jfy5/4WYREZG6RvdN9e++yWTTXVmVyc3NxcPDg5ycHNzd3Y2OIyKV7OjRo8yZM4cVBe05aWtgdJxL8jTl0d+yi1GjRuHn52d0HBGpIgUFBaSlpREcHFxm3YqcnBzy8/OrLYebmxseHh7Vdj6pXhf7PQO9/pWK0++MlCcn/xwR076l4JyVTx+OpFtQ2TejEndlMWL+Rhq5OPLTxD/hbnEyKKmIiNQ2l3otK1KZKuO+SSNvREREROo4Dw8PFVNERKTW+HTzYQrOWWnn24jwlk0u2N+nrTetvRuy99hZFiWn26dSExEREalLtOaNiIiIiIiIiNQINpuNBb9PmTa0Z8syCy+fZzabGNU7BIB5P6VRWFxSrRlFREREqoOKNyIiIiIiIiJSI6xLPcn+E3k0cHYgtkvzi7a7M6w5Pu4uZOUW8vmWjGpMKCIiIlI9VLwRERERERERkRrho6TSUTd/7dqChi4Xn+nd2dHMiF7BALyzNhWrVcv5ioiISN2i4o2IiIhIHWOz6Q0sqTr6/RKRqpKZU8DqXVlA6ZRplzO4RyCNLI6kHs8jMeVYVccTEZE6RK9ppapVxu+YijciIiIidYSTkxMA+fn5BieRuuz879f53zcRkcqyaEM6JVYbPYKa0ta30WXbN7I42Ys876xJrep4IiJSB+ieSapLZdw3XXwMsoiIiIjUKg4ODjRu3Jhjx0o/fezm5lbuQs8iV8Nms5Gfn8+xY8do3LgxDg4ORkcSkTrkXImVxT+nAzCkZ+AVHzc8Koj3fkhj48FTbDyQTbegplUVUURE6gDdM0lVq8z7JhVvREREROoQX19fAPvNiEhla9y4sf33TESksny7M4us3EK8GjrTr8OV/43xdrfw167NWfzzId5Zu1/FGxERuSzdM0l1qIz7JhVvREREROoQk8mEn58f3t7enDt3zug4Usc4OTlpxI2IVImPkw8CcHe3AFwcK/Z3ZmTvEJZsPMTqnVnsO3aWVt4NqyKiiIjUEbpnkqpWWfdNKt6IiIiI1EEODg56k11ERGqF1ONn+WnfSUwmuDfiyqdMOy+0WUP6tvdh1c4s5qxNZfqAzlWQUkRE6hrdM0lNZzY6gIiIiIiIiIjUXwvWl65186e23rRo4nZVfTx0UygA/95yhKzcgkrLJiIiImIUFW9ERERERERExBC/FZXw6aZDAAyNbHnV/YS3bEKPoKacK7Ex76e0yoonIiIiYhgVb0RERERERETEEF/8kkFuQTEBTV25qXWza+rroZtCAFi4Pp3cAq1hICIiIrWbijciIiIiIiIiYoiPkw8CcG+PlpjNpmvqq09bb1p7N+RMYTGLktMrI56IiIiIYVS8EREREREREZFq98uh02w7nIOzg5m7u7W45v7MZhOjepeOvpn3UxqFxSXX3KeIiIiIUVS8EREREREREZFq9/H60lE3t3X0xbOhS6X0eWdYc3zcXcjKLeTzLRmV0qeIiIiIEVS8EREREREREZFqdTq/iBW/lBZX7otsWWn9OjuaGdErGIB31qZitdoqrW8RERGR6qTijYiIiIiIiIhUq083Haaw2Eo730Z0DWxSqX0P7hFII4sjqcfzSEw5Vql9i4iIiFQXFW9EREREREREpNpYrTYWJKcDpaNuTCZTpfbfyOLE0J6lo3neWZNaqX2LiIiIVBcVb0RERERERESk2qxLPUnaiTwaujgSG9a8Ss4xPCoIZwczGw+eYuOB7Co5h4iIiEhVUvFGRERERERERKrNx+sPAvDXrs1p4OJYJefwdrfw166lhaF31u6vknOIiIiIVCUVb0RERERERESkWmTmFLB6VxaAfWqzqjKydwgmE6zemcW+Y2eq9FwiIiIilU3FGxERERERERGpFos2pFNitdEjuCltfBpV6blCmzWkb3sfAOZo9I2IiIjUMjWiePPWW28RFBSExWIhIiKCDRs2XLL90qVLadeuHRaLhY4dO7Jy5coy+202G5MnT8bPzw9XV1eio6PZu3dvmTb9+/cnMDAQi8WCn58f9913HxkZGfb9Bw4cwGQyXfBYv3595V24iIiIiIiISD1xrsTKog3pQNWPujnvoZtCAfj3liNk5RZUyzlFREREKoPhxZslS5YQFxfHlClT2Lx5M507dyYmJoZjx46V237dunUMHjyYESNGsGXLFmJjY4mNjWXHjh32NtOnT2fWrFnEx8eTnJxMgwYNiImJoaDgvy/U+vTpwyeffMLu3btZtmwZqampDBgw4ILzffvttxw9etT+CA8Pr/wnQURERERERKSO+3ZnFsfOFOLV0Jl+1/tWyznDWzahR1BTzpXYmPdTWrWcU0RERKQyGF68ee211xg5ciTDhw/nuuuuIz4+Hjc3N+bNm1du+zfeeIN+/foxfvx42rdvz4svvkjXrl2ZPXs2UDrqZubMmUyaNIk777yTTp068eGHH5KRkcHy5cvt/fz973+nZ8+etGzZkqioKJ566inWr1/PuXPnypzP09MTX19f+8PJyanKngsRERERERGRuuqj9QcBuKd7AM6O1fd2xEM3hQCwcH06uQXnLtNaREREpGYwtHhTVFTEpk2biI6Otm8zm81ER0eTlJRU7jFJSUll2gPExMTY26elpZGZmVmmjYeHBxERERftMzs7mwULFhAVFXVBcaZ///54e3vTq1cvVqxYccnrKSwsJDc3t8xDREREREREpL7bd+ws61JPYjLB4B6B1XruPm29ae3dkDOFxSxKTq/Wc4uIiIhcLUOLNydOnKCkpAQfH58y2318fMjMzCz3mMzMzEu2P//1Svp88sknadCgAZ6enqSnp/P555/b9zVs2JAZM2awdOlSvvrqK3r16kVsbOwlCzjTpk3Dw8PD/ggICLjMMyAiIiIiIiJS9y1ILh11c0s7b1o0cavWc5vNJkb1Lh19896PaRQWl1Tr+UVERESuhuHTphlp/PjxbNmyhVWrVuHg4MCwYcOw2WwAeHl5ERcXR0REBN27d+ell15i6NChvPLKKxftb+LEieTk5Ngfhw4dqq5LEREREREREamR8ouK+XTTYQCG9GxpSIY7w5rj4+7CsTOFfL4lw5AMIiIiIhVhaPHGy8sLBwcHsrKyymzPysrC17f8xQt9fX0v2f781yvp08vLizZt2tC3b18WL17MypUrWb9+/UXzRkREsG/fvovud3Fxwd3dvcxDREREREREpD774pcMzhQUE9DUlZtaNzMkg7OjmRG9ggF4Z20qVqvNkBwiIiIiV8rQ4o2zszPh4eEkJibat1mtVhITE4mMjCz3mMjIyDLtAVavXm1vHxwcjK+vb5k2ubm5JCcnX7TP8+eF0nVrLmbr1q34+fld/sJEREREREQq0VtvvUVQUBAWi4WIiAg2bNhwyfZLly6lXbt2WCwWOnbsyMqVK8vst9lsTJ48GT8/P1xdXYmOjmbv3r3l9lVYWEhYWBgmk4mtW7eW2bdt2zZuvPFGLBYLAQEBTJ8+/ZquU+oem83GR+tLp0wbEtESs9lkWJbBPQJpZHEk9XgeiSnHDMshIiIiciUMnzYtLi6Od999l/nz57Nr1y5Gjx5NXl4ew4cPB2DYsGFMnDjR3n7s2LEkJCQwY8YMUlJSeO6559i4cSNjxowBwGQyMW7cOKZOncqKFSvYvn07w4YNw9/fn9jYWACSk5OZPXs2W7du5eDBg3z33XcMHjyY0NBQe4Fn/vz5LFq0iJSUFFJSUvjnP//JvHnzePTRR6v3CRIRERERkXptyZIlxMXFMWXKFDZv3kznzp2JiYnh2LHy33xet24dgwcPZsSIEWzZsoXY2FhiY2PZsWOHvc306dOZNWsW8fHxJCcn06BBA2JiYigoKLigvwkTJuDv73/B9tzcXG699VZatmzJpk2beOWVV3juueeYM2dO5V281Hq/HM5hx5FcnB3N3N3N2HVhG1mcGPr7tG3vrEk1NIuIiIjI5RhevLnnnnt49dVXmTx5MmFhYWzdupWEhAR8fHwASE9P5+jRo/b2UVFRLFy4kDlz5tC5c2c+/fRTli9fTocOHextJkyYwKOPPsqoUaPo3r07Z8+eJSEhAYvFAoCbmxufffYZt9xyC23btmXEiBF06tSJNWvW4OLiYu/nxRdfJDw8nIiICD7//HOWLFliLyqJiIiIiIhUh9dee42RI0cyfPhwrrvuOuLj43Fzc2PevHnltn/jjTfo168f48ePp3379rz44ot07dqV2bNnA6UjIWbOnMmkSZO488476dSpEx9++CEZGRksX768TF9ff/01q1at4tVXX73gPAsWLKCoqIh58+Zx/fXXM2jQIB577DFee+21Sn8OpPb6+PdRN3/p6EfTBs4Gp4HhUUE4O5jZePAUGw9kGx1HRERE5KIcjQ4AMGbMGPvImT/6/vvvL9g2cOBABg4ceNH+TCYTL7zwAi+88EK5+zt27Mh33313yUz3338/999//yXbiIiIiIiIVKWioiI2bdpUZjYCs9lMdHQ0SUlJ5R6TlJREXFxcmW0xMTH2wkxaWhqZmZlER0fb93t4eBAREUFSUhKDBg0CStcNHTlyJMuXL8fNza3c8/Tu3Rtn5/++IR8TE8PLL7/MqVOnaNKkyQXHFBYWlpmqOjc39wqeBamtTucX8cUvGQD2ES9G83a38NeuzVn88yHeWbufbkFNjY4kIiIiUi7DR96IiIiIiIhI+U6cOEFJSYl9ZoLzfHx8yMzMLPeYzMzMS7Y///VSbWw2Gw888AAPP/ww3bp1q9B5/vccfzRt2jQ8PDzsj4AAY6fRkqr16abDFBZbae/nTtfAxkbHsRvZOwSTCVbvzGLfsTNGxxEREREpl4o3IiIiIiIiUsabb77JmTNnyoz4qQwTJ04kJyfH/jh06FCl9i81h9VqY0FyOgD39WyJyWQyONF/hTZrSN/2pYXGOWv3G5xGREREpHwq3oiIiIiIiNRQXl5eODg4kJWVVWZ7VlYWvr6+5R7j6+t7yfbnv16qzXfffUdSUhIuLi44OjrSqlUrALp162afXvpi5/nfc/yRi4sL7u7uZR5SN/2UeoK0E3k0dHHkzjB/o+Nc4OGbQwH495YjZOUWGJym7rBabWw9dJpzJVajo4iIiNR6Kt6IiIiIiIjUUM7OzoSHh5OYmGjfZrVaSUxMJDIystxjIiMjy7QHWL16tb19cHAwvr6+Zdrk5uaSnJxsbzNr1ix++eUXtm7dytatW1m5ciUAS5Ys4R//+If9PGvXruXcuXNlztO2bdty17uR+uXj9QcBuKtrcxq41IjldsvoGtiEHkFNOVdiY95PaUbHqRNsNhtPfPoLsW/9xLs/aESTiIjItVLxRkREREREpAaLi4vj3XffZf78+ezatYvRo0eTl5fH8OHDARg2bFiZ6c3Gjh1LQkICM2bMICUlheeee46NGzcyZswYAEwmE+PGjWPq1KmsWLGC7du3M2zYMPz9/YmNjQUgMDCQDh062B9t2rQBIDQ0lBYtWgBw77334uzszIgRI/j1119ZsmQJb7zxBnFxcdX47EhNdDTnN1bvLB2FNaRnS4PTXNxDN4UAsHB9OrkF5y7TWi7n4+R0Ptt8BIBvd2ZdprWIiIhcTs37+IuIiIiIiIjY3XPPPRw/fpzJkyeTmZlJWFgYCQkJ+PiUrtmRnp6O2fzfz+VFRUWxcOFCJk2axNNPP03r1q1Zvnw5HTp0sLeZMGECeXl5jBo1itOnT9OrVy8SEhKwWCxXnMvDw4NVq1bxyCOPEB4ejpeXF5MnT2bUqFGVd/FSKy3acAirDXoEN6WNTyOj41xUn7betPZuyN5jZ1mUnM5DN4UaHanW2px+ihe++NX+/S+HczhbWEzDGjjqSkREpLYw2Ww2m9Eh6qrc3Fw8PDzIycnRXM4iddDRo0eZM2cOKwrac9LWwOg4l+RpyqO/ZRejRo3Cz8/P6DgiIlJH6fWvVJR+Z+qecyVWbnjpO46dKeTNwV24o3PNW+/mfy3deIjxn27Du5ELPzzZBxdHB6Mj1TonzhZyx5s/cjSngH7X+/Lr0RwOZf/G+8O706ett9HxREREapwrfQ2sadNEREREREREpFKs3pnFsTOFeDV0IeZ6X6PjXNadYc3xcXfh2JlCPt+SYXScWqe4xMpji7ZwNKeAkGYNeGVgJ6JCvABISj1pcDoREZHaTcUbEREREREREakUHyUdBGBQ9wCcHWv+Ww7OjmZG9AoG4J21qVitmpykImas3sO61JO4OTsQPzScRhYnIkM9ARVvRERErlXNfyUlIiIiIiIiIjXevmNnSdp/ErMJBkcEGh3nig3uEUgjiyOpx/NITDlmdJxa45tfM3n7+1QAXr6rk319o/PFm18zcsjJP2dYPhERkdpOxRsRERERERERuWYLkktH3fypnQ/NG7sanObKNbI4MbRnSwDeWZNqcJraIe1EHk988gsAD94QXGZtIx93CyHNGmC1QXKaRt+IiIhcLRVvREREREREROSa5BcV8+mmwwAM7Vl7Rt2cNzwqCGcHMxsPnmLjgWyj49Ro+UXFPPzRJs4UFtM9qAkTb2t3QZvIkN+nTtuv4o2IiMjVUvFGRERERERERK7JF79kcKagmMCmbvRu3czoOBXm7W7hr12bAxC/Zr/BaWoum83GxM+2szvrDF4NXXjr3q44OVz41lJUqBegdW9ERESuhYo3IiIiIiIiInLVbDYbH60vnTJtSEQgZrPJ4ERXZ2TvEEwm+HZXFvuOnTE6To30YdJBPt+agYPZxFv3dsHb3VJuu54hTQFIyTzDybOF1RlRRESkzlDxRkRERERERESu2i+Hc9hxJBdnRzMDuwUYHeeqhTZrSN/2PgDMWavRN3+06WA2L365E4CJf25HxO9To5XHs6ELbX0aAZCcpmnoREREroaKNyIiIiIiIiJy1T5KKh11c3tHP5o2cDY4zbV5+OZQAP695QhZuQUGp6k5jp8p5G8LNlNstfGXjn6M6BV82WMiQ0uLO+tST1R1PBERkTpJxRsRERERERERuSqn8or4clsGAEMjWxqc5tp1DWxCj6CmnCuxMe+nNKPj1AjFJVYeXbSZrNxCQps14OUBnTCZLj813vnijda9ERERuToq3oiIiIiIiIjIVfl002EKi61c5+dOl4DGRsepFA/dFALAwvXp5BacMziN8V5ZtZv1+7Np4OzAO/eF09DF8YqO6xnsickEqcfzOKZRTCIiIhWm4o2IiIiIiIiIVJjVamNBcumUaUN7tryi0Ri1QZ+23rT2bsiZwmIWJqcbHcdQCTuO8s6a0vV/pg/oTCvvRld8rIebE9f7uwOQtF+jb0RERCpKxRsRERERERERqbAf953gwMl8Grk4cmeYv9FxKo3ZbGJU79LRN/N+TKOwuMTgRMZIPX6WJ5ZuA+D/egXzl05+Fe4jMkRTp4mIiFwtFW9EREREREREpMI+Xl866uavXZvT4Aqn0qot7gxrjo+7C8fOFPL5lgyj41S7vMJiHv5oE2cLi+kR3JQn/9zuqvqJCvUCNPJGRETkaqh4IyIiIiIiIiIVcjTnN77dlQWUTplW1zg7mhnRKxiAd9amYrXaDE5UfWw2G099tp29x87i3ciF2fd2wcnh6t4+6h7cFAeziYMn8zly+rdKTioiIlK3qXgjIiIiIiIiIhWyKDkdqw0igpvS2ufK10GpTQb3CKSRxZHU43kkphwzOk61+WDdAb74JQNHs4m3hnTFu5Hlqvtq6OJIx+YegKZOExERqSgVb0RERERERETkip0rsbLo50MA3BdZ90bdnNfI4mQfVfTOmlSD01SPjQey+cdXuwB4+rb2dA9qes19RoWWrnuzLvXENfclIiJSn6h4IyIiIiIiIiJXbNWvWRw/U4hXQxduvc7X6DhVanhUEM4OZjYePMXGA9lGx6lSx84U8LcFmym22ri9kx/DbwiqlH4jfy/erE89ic1Wf6afExERuVYq3oiIiIiIiIjIFft4/UEABvcIwNmxbr+t4O1u4a9dmwMQv2a/wWmqzrkSK2MWbuHYmUJaezfk5bs6YTKZKqXvbi2b4uRgIiOngPTs/ErpU0REpD6o26+yRERERERERKTS7Dt2hqT9JzGbSteEqQ9G9g7BZIJvd2Wx79gZo+NUiekJKWxIy6ahiyPx94XTwMWx0vp2dXagS0ATANZp3RsREZErpuKNiIiIiIiIiFyRj9enA3BLex/8G7sanKZ6hDZrSN/2PgDMWVv3Rt+s3H6Ud39IA+CVAZ0Ibdaw0s/R8/ep05JUvBEREbliKt6IiIiIiIiIyGXlFxWzbNNhAIb2bGlwmur18M2hAPx7yxGycgsMTlN59h07w/ilvwDwUO8Q/tzRr0rOE3W+eLNf696IiIhcKRVvREREREREROSyVmzN4ExhMS093bixlZfRcapV18Am9AhqyrkSG/N+SjM6TqU4W1jMwx9vJq+ohJ4hTRkf07bKztUlsDEujmaOnykk9fjZKjuPiIhIXaLijYiIiIiIiIhcks1m46P1BwEYEhGI2Vw5i9nXJg/dFALAwvXp5BacMzjNtbHZbDz56Tb2HTuLj7sLbw7uiqND1b1F5OLoQHjL0nVvNHWaiIjIlakRxZu33nqLoKAgLBYLERERbNiw4ZLtly5dSrt27bBYLHTs2JGVK1eW2W+z2Zg8eTJ+fn64uroSHR3N3r17y7Tp378/gYGBWCwW/Pz8uO+++8jIyCjTZtu2bdx4441YLBYCAgKYPn165VywiIiIiIiISC2y9dBpfs3IxdnRzMDwAKPjGKJPW29aezfkTGExC5PTjY5zTd77MY2vth/F0WziX0O60qyRS5Wf8/zUaetUvBEREbkihhdvlixZQlxcHFOmTGHz5s107tyZmJgYjh07Vm77devWMXjwYEaMGMGWLVuIjY0lNjaWHTt22NtMnz6dWbNmER8fT3JyMg0aNCAmJoaCgv/OS9unTx8++eQTdu/ezbJly0hNTWXAgAH2/bm5udx66620bNmSTZs28corr/Dcc88xZ86cqnsyRERERERERGqgj9eXFitu7+RHkwbOBqcxhtlsYlTv0tE3835Mo7C4xOBEV2dDWjbTvk4BYNJf2hPesmm1nDfy9+LN+v0nsVq17o2IiMjlGF68ee211xg5ciTDhw/nuuuuIz4+Hjc3N+bNm1du+zfeeIN+/foxfvx42rdvz4svvkjXrl2ZPXs2UDrqZubMmUyaNIk777yTTp068eGHH5KRkcHy5cvt/fz973+nZ8+etGzZkqioKJ566inWr1/PuXOlQ58XLFhAUVER8+bN4/rrr2fQoEE89thjvPbaa1X+nIiIiIiIiIjUFKfyivhiW+lMFUN7tjQ4jbHuDGuOj7sLx84U8vmWjMsfUMMcyy3gkYWbKbHauDPMn/ujgqrt3J1aNMbN2YFT+efYnXWm2s4rIiJSWxlavCkqKmLTpk1ER0fbt5nNZqKjo0lKSir3mKSkpDLtAWJiYuzt09LSyMzMLNPGw8ODiIiIi/aZnZ3NggULiIqKwsnJyX6e3r174+z8308UxcTEsHv3bk6dOnV1FywiIiIiIiJSy3y66TBFxVau93enS0Bjo+MYytnRzIhewQC8sza1Vo0gOVdi5ZGFmzl+ppC2Po2Y9teOmEzVt3aRk4OZ7kGlo3w0dZqIiMjlGVq8OXHiBCUlJfj4+JTZ7uPjQ2ZmZrnHZGZmXrL9+a9X0ueTTz5JgwYN8PT0JD09nc8///yy5/nfc/xRYWEhubm5ZR4iIiIiIiIitZXVauPj5INA6aib6nyzv6Ya3COQRhZHUo/nkZhS/pTvNdG0lSn8fOAUjVwceXtoV9ycHas9w/mp05JUvBEREbksw6dNM9L48ePZsmULq1atwsHBgWHDhmGzXf2nZqZNm4aHh4f9ERBQPxdxFBERERERkbrhx30nOHgyn0YujtwZ5m90nBqhkcXJPn3cO2tSDU5zZb7clsG8n9IAePXuzoQ0a2hIjqjfizfJaScpqUWjlkRERIxgaPHGy8sLBwcHsrKyymzPysrC19e33GN8fX0v2f781yvp08vLizZt2tC3b18WL17MypUrWb9+/SXP87/n+KOJEyeSk5Njfxw6dOii1y4iIiIiIiJS0320vnTUzV3hLQwZqVFTDY8KwtnBzMaDp9h4INvoOJe0N+sMEz7dBsDDN4USc33572lUh+v9PWhkceRMQTG/ZuQYlkNERKQ2MLR44+zsTHh4OImJifZtVquVxMREIiMjyz0mMjKyTHuA1atX29sHBwfj6+tbpk1ubi7JyckX7fP8eaF06rPz51m7di3nzp0rc562bdvSpEmTcvtwcXHB3d29zENERERERESkNso4/RuJu0o/xDi0Z6DBaWoWb3cLf+3aHID4NfsNTnNxZwrO8dDHm8gvKiEq1JMnbm1jaB4Hs4mI4NJ1bzR1moiIyKUZPm1aXFwc7777LvPnz2fXrl2MHj2avLw8hg8fDsCwYcOYOHGivf3YsWNJSEhgxowZpKSk8Nxzz7Fx40bGjBkDgMlkYty4cUydOpUVK1awfft2hg0bhr+/P7GxsQAkJycze/Zstm7dysGDB/nuu+8YPHgwoaGh9gLPvffei7OzMyNGjODXX39lyZIlvPHGG8TFxVXvEyQiIiIiIvXeW2+9RVBQEBaLhYiICDZs2HDJ9kuXLqVdu3ZYLBY6duzIypUry+y32WxMnjwZPz8/XF1diY6OZu/evWXa9O/fn8DAQCwWC35+ftx3331kZGTY9x84cACTyXTB4/xsBlL7LdqQjtUGPUOa0sq7kdFxapyRvUMwmeDbXVnsO3bG6DgXsNlsTPh0G/uP5+HrbmHW4C44Ohj+NhCRoV4ArFPxRkRE5JIM/1/7nnvu4dVXX2Xy5MmEhYWxdetWEhIS8PHxASA9PZ2jR4/a20dFRbFw4ULmzJlD586d+fTTT1m+fDkdOnSwt5kwYQKPPvooo0aNonv37pw9e5aEhAQsFgsAbm5ufPbZZ9xyyy20bduWESNG0KlTJ9asWYOLiwsAHh4erFq1irS0NMLDw3n88ceZPHkyo0aNqsZnR0RERERE6rslS5YQFxfHlClT2Lx5M507dyYmJoZjx8pfKH3dunUMHjyYESNGsGXLFmJjY4mNjWXHjh32NtOnT2fWrFnEx8eTnJxMgwYNiImJoaCgwN6mT58+fPLJJ+zevZtly5aRmprKgAEDLjjft99+y9GjR+2P8PDwyn8SpNoVFVtZ/HPpVOD39QwyNkwNFdqsIX3bl753MWdtzRt9M/eHNL7ekYmTg4l/De2KV0MXoyMBEBlSuu7NzweyOVdiNTiNiIhIzWWy2WxaIa6K5Obm4uHhQU5OjqZQE6mDjh49ypw5c1hR0J6TtgZGx7kkT1Me/S27GDVqFH5+fkbHERGROkqvf0utXbuWqKgoHB3Lrg9SXFzMunXr6N27d4X6i4iIoHv37syePRsonfI5ICCARx99lKeeeuqC9vfccw95eXl8+eWX9m09e/YkLCyM+Ph4bDYb/v7+PP744zzxxBMA5OTk4OPjwwcffMCgQYPKzbFixQpiY2MpLCzEycmJAwcOEBwczJYtWwgLC6vQNZ2n35ma68ttGYxZuIVmjVxY99SfcKoBIzZqos3pp/jrv9bh5GDixyf/hI+7xehIAKzff5Ihc5Mpsdp48c7ruS8yyOhIdlarjfCpqzmVf45lo6MIb1n+1PQiIiJ11ZW+BtarLxERERERkUrUp08fsrMvXMA8JyeHPn36VKivoqIiNm3aRHR0tH2b2WwmOjqapKSkco9JSkoq0x4gJibG3j4tLY3MzMwybTw8PIiIiLhon9nZ2SxYsICoqCicnJzK7Ovfvz/e3t706tWLFStWXPJ6CgsLyc3NLfOQmunj9QcBGNQ9QIWbS+ga2IQeQU05V2Jj3k9pRscBIDOngDELN1NitfH/ujRnaM+WRkcqw2w20fP30TdJqScMTiMiIlJz6RWYiIiIiIhIJbLZbJhMpgu2nzx5kgYNKjZa98SJE5SUlNinlT7Px8eHzMzMco/JzMy8ZPvzX6+kzyeffJIGDRrg6elJeno6n3/+uX1fw4YNmTFjBkuXLuWrr76iV69exMbGXrKAM23aNDw8POyPgICAyzwDYoS9WWdYvz8bswkG9wg0Ok6N99BNIQAsXJ9ObsE5Q7MUFVt5ZOFmTpwtop1vI/75/zqW+/fIaJGhvxdv9mvdGxERkYtxvHwTERERERERuZy//vWvAJhMJh544AH7epoAJSUlbNu2jaioKKPiXZXx48czYsQIDh48yPPPP8+wYcP48ssvMZlMeHl5ERcXZ2/bvXt3MjIyeOWVV+jfv3+5/U2cOLHMMbm5uSrg1EALktMBuKW9D/6NXQ1OU/P1aetNa++G7D12loXJ6Tx8U6hhWf65chebDp6ikcWR+KHhuDo7GJblUqJ+L95sPHCKwuISXBxrZk4REREjaeSNiIiIiIhIJTg/msRms9GoUaMyI0x8fX0ZNWoUH3/8cYX69PLywsHBgaysrDLbs7Ky8PX1LfcYX1/fS7Y///VK+vTy8qJNmzb07duXxYsXs3LlStavX3/RvBEREezbt++i+11cXHB3dy/zkJolv6iYZZsOA3BfDZtuq6Yym02M6l06+mbej2kUFpcYkuPzrUf4YN0BAF67O4wgr5q7Lmdos4Y0a+RCYbGVLemnjY4jIiJSI2nkjYiIiIiISCV4//33AQgKCuKJJ56o8BRp5XF2diY8PJzExERiY2MBsFqtJCYmMmbMmHKPiYyMJDExkXHjxtm3rV69msjISACCg4Px9fUlMTGRsLAwoHQETHJyMqNHj75oFqvVCpSuW3MxW7duxc/PrwJXKDXN51szOFNYTEtPN3q18jI6Tq1xZ1hzZqzaQ2ZuAZ9vyeDu7tU7omx35hmeWrYdgEf6hNL3Op/LHGEsk6l03ZsvfskgKfWkfQ0cERER+S+NvBEREREREalEU6ZMwcXFhW+//ZZ33nmHM2fOAJCRkcHZs2cr3F9cXBzvvvsu8+fPZ9euXYwePZq8vDyGDx8OwLBhw5g4caK9/dixY0lISGDGjBmkpKTw3HPPsXHjRnuxx2QyMW7cOKZOncqKFSvYvn07w4YNw9/f314gSk5OZvbs2WzdupWDBw/y3XffMXjwYEJDQ+1FoPnz57No0SJSUlJISUnhn//8J/PmzePRRx+9lqdPDGSz2fgo6SAAQyNaYjbXvLVSaipnRzMjegUD8M7aVKxWW7WdO7fgHKM/3sRv50ro1cqLuL5tq+3c1+L81GlJqVr3RkREpDwaeSNSg+Tk5JCfn290jCvm5uaGh4eH0TFEREREapSDBw/Sr18/0tPTKSwspG/fvjRq1IiXX36ZwsJC4uPjK9TfPffcw/Hjx5k8eTKZmZmEhYWRkJCAj0/pJ+vT09Mxm//7ubyoqCgWLlzIpEmTePrpp2ndujXLly+nQ4cO9jYTJkwgLy+PUaNGcfr0aXr16kVCQgIWiwUofZ332WefMWXKFPLy8vDz86Nfv35MmjSpzFo+L774IgcPHsTR0ZF27dqxZMkSBgwYcC1Pnxhoy6HT7Dyai7OjmQHhLYyOU+sM6hHArO/2kno8j8SUY9Uy+sVmszF+6S/sP5GHv4eFNwaF4VBLim6Rv4+22XLoFL8VldTY9XlERESMYrLZbNX3cZB6Jjc3Fw8PD3JycjSXs1xWTk4Os96cjbWk2OgoV8zs4Mhjj46ptwWco0ePMmfOHFYUtOekrebOJw3gacqjv2UXo0aN0lQmIiJSZfT6t1RsbCyNGjXivffew9PTk19++YWQkBC+//57Ro4cyd69e42OWGPod6ZmiftkK59tPsJdXVsw4+7ORseplV5OSOHt71MJb9mEZaOjqvx88WtSeenrFJwdzHzycCRhAY2r/JyVxWazEfXSdxzNKeDjERH0aq1p+kREpH640tfAGnkjUkPk5+djLSlmTVEwOVaL0XEuy8NcwE3OaeTn59fb4o2IiIhIeX744QfWrVuHs7Nzme1BQUEcOXLEoFQil3Yqr4gvtx0FYGjPQIPT1F7Do4J474c0Nh08xcYD2XQLalpl51q37wTTE1IAmHzHdbWqcAOlUzhGhnry2eYjrEs9oeKNiIjIH1SoeHPu3DnatWvHl19+Sfv27asqk0i9lmO11PhRHABYjQ4gIiIiUjNZrVZKSkou2H748GEaNWpkQCKRy1u66RBFxVY6NHevdUWAmsTb3cJfuzZn8c+HiF+zn7lVVLw5mvMbjy7agtUGd3VtwZCI2llwiwwpLd4k7de6NyIiIn9kvnyT/3JycqKgoKCqsoiIiIiIiNR6t956KzNnzrR/bzKZOHv2LFOmTOG2224zLpjIRVitNhYkpwMwNKIlJlPtWDOlphrZOwSTCb7dlcW+Y2cqvf+iYit/W7CZk3lFtPdzZ2psh1r7M4sMLV33ZtvhHM4W1p4pxEVERKpDhYo3AI888ggvv/wyxcX6T1VEREREROSPZsyYwU8//cR1111HQUEB9957r33KtJdfftnoeCIX+GHfCQ6ezKeRxZH+Yf5Gx6n1Qps1pG97HwDmrN1f6f1P/WonW9JP425xJH5oV1ydHSr9HNWlRRM3Apu6UWK18XNattFxREREapQKr3nz888/k5iYyKpVq+jYsSMNGpSd3umzzz6rtHAiIiIiIiK1TYsWLfjll19YvHgx27Zt4+zZs4wYMYIhQ4bg6upqdDyRC3yUdBAonX7LzVlL41aGh28OZdXOLP695QiP39oWH/fKWdd0+ZYjfPj7z+v1e8Jo6VkLpty+jMgQT9Kz80naf5I+7byNjiMiIlJjVPhVWePGjbnrrruqIouIiIiIiEid4OjoyNChQ42OIXJZR07/xncpWQAM7Vk7102piboGNqFHUFM2HMhm3k9pTPzzta8bvOtoLk99tg2AR//Uilt+H91T20W18mTJxkOsSz1hdBQREZEapcLFm/fff78qcoiIiIiIiNQJK1asKHe7yWTCYrHQqlUrgoODqzmVSPkWb0jHaisd/dDKu5HRceqUh24KYcOBbBauT+eRPq1wtzhddV85v51j9MebKDhn5cbWXoyLblOJSY0VGVK67s2vGbnk5J/Dw+3qnycREZG65KrHQx8/fpzdu3cD0LZtW5o1a1ZpoURERERERGqr2NhYTCYTNputzPbz20wmE7169WL58uU0adLEoJQipQvfL9pwCIChPVsanKbu6dPWm9beDdl77CwLk9N5+KbQq+rHarXxxNJfOHAyn+aNXZk1qAsOZlMlpzWOt7uFkGYN2H88j+S0k9x6va/RkURERGoEc0UPyMvL48EHH8TPz4/evXvTu3dv/P39GTFiBPn5+VWRUUREREREpNZYvXo13bt3Z/Xq1eTk5JCTk8Pq1auJiIjgyy+/ZO3atZw8eZInnnjC6KhSz63amcmJs4U0a+TCrdfXjSm4ahKz2cSo3iEAzPsxjcLikqvq5+01qazemYWzg5m3h3alSQPnyoxZI0SFlo6+WZd60uAkIiIiNUeFizdxcXGsWbOGL774gtOnT3P69Gk+//xz1qxZw+OPP14VGUVERERERGqNsWPH8tprr3HLLbfQqFEjGjVqxC233MIrr7zC+PHjueGGG5g5cyarV682OqrUcx/9vvD94O4BODlU+O0BuQJ3hjXH193CsTOFfL4lo8LH/7TvBDNWlc568vyd19OpReNKTlgzRIZ4AbB+v4o3IiIi51X41dmyZct47733+POf/4y7uzvu7u7cdtttvPvuu3z66adVkVFERERERKTWSE1Nxd3d/YLt7u7u7N+/H4DWrVtz4oQW5xbj7M06Q3JaNg5mE4MjAo2OU2c5O5oZ0at0jat31qZitdouc8R/ZZz+jUcXbcFqg4HhLRjUPaCqYhquZ0hTAFIyz3DybKHBaURERGqGChdv8vPz8fG5cDi1t7e3pk0TEREREZF6Lzw8nPHjx3P8+HH7tuPHjzNhwgS6d+8OwN69ewkIqLtvxErN9/H60lE3t7Tzxs/D1eA0ddugHgE0sjiSejyPxJRjV3RMYXEJoxdsJjuviOv93XkxtgMmU91Z5+aPPBu60M63EQDr92cbnEZERKRmqHDxJjIykilTplBQUGDf9ttvv/H8888TGRlZqeFERERERERqm/fee4+0tDRatGhBq1ataNWqFS1atODAgQPMnTsXgLNnzzJp0iSDk0p9lVdYzGebjwBwX2RLg9PUfY0sTgztWfo8x69JvaJjXvxyJ78cOo2HqxPxQ8OxODlUZcQaoWdI6bo3Sfs1KlFERATAsaIHzJw5k379+tGiRQs6d+4MwC+//ILFYuGbb76p9IAiIiIiIiK1Sdu2bdm5cyerVq1iz5499m19+/bFbC79/FxsbKyBCaW++3xrBmcKiwnydOOGUC+j49QLw6OCeO+HNDYdPMXGA9l0C2p60bbLNh3m4/XpmEwwc1AYAU3dqjGpcaJCPflg3QHWpWrdGxEREbiK4k3Hjh3Zu3cvCxYsICUlBYDBgwczZMgQXF011FpERERERMRsNtOvXz/69etndBSRMmw2m33KtKE9W2I2192puGoSb3cLf+3anMU/HyJ+zX7mXqR4szMjl6f/vR2Ax/7Umj5tvaszpqEigj0xmWD/8TyycgvwcbcYHUlERMRQFSrenDt3jnbt2vHll18ycuTIqsokIiIiFZSTk1Nr1p5zc3PDw8PD6BgiIpVq1qxZV9z2scceq8IkIpe2Of00O4/m4uJoZkB4C6Pj1Csje4ewZOMhvt2Vxb5jZ2jl3ajM/pz8czz88SYKi63c1KYZY29pbVBSY3i4OXG9vzs7juSyfv9J7gxrbnQkERERQ1WoeOPk5FRmrRsRERExXk5ODrPenI21pNjoKFfE7ODIY4+OUQFHROqU119/vcz3x48fJz8/n8aNGwNw+vRp3Nzc8Pb2VvFGDLXg91E3d3T2p7Gbs8Fp6pfQZg3p296HVTuzmLN2P9MHdLbvs1ptxH2ylfTsfFo0ceWNQWH1clRUVKgXO47ksm6fijciIiIVnjbtkUce4eWXX2bu3Lk4Olb4cBEREalk+fn5WEuKWVMUTI61Zk8v4WEu4CbnNPLz81W8EZE6JS0tzf7vhQsX8q9//Yv33nuPtm3bArB7925GjhzJQw89ZFREEbLzivhy+1GgdMo0qX4P3xzKqp1Z/HvLER6/ta19arB/fb+PxJRjODuaiR8aXm8La5EhnsxZu5+k/Vr3RkREpMLVl59//pnExERWrVpFx44dadCgQZn9n332WaWFExERkSuXY7Vw0tbg8g2NZDU6gIhI1Xv22Wf59NNP7YUbgLZt2/L6668zYMAAhgwZYmA6qc+WbjxEUbGVDs3d6dxCH6IwQtfAJvQIasqGA9nM+zGNibe1Z+2e48xYvQeAqXd2oEPz+vuz6R7cFAezifTsfA6fyqdFEzejI4mIiBimwsWbxo0bc9ddd1VFFhERERERkVrv6NGjFBdfOJVlSUkJWVlZBiQSKZ2Wa0FyOgD39WyJyVT/puSqKR66KYQNB7JZkJzO/+vanLGLt2CzwaDuAdzdPcDoeIZq6OJIpxYebEk/TVLqSQZ2U/FGRETqrwoVb4qLi+nTpw+33norvr6+VZVJRERERESk1rrlllt46KGHmDt3Ll27dgVg06ZNjB49mujoaIPTSX21du9x0rPzaWRx5I7O/kbHqdf6tPWmtXdD9h47y1//tY78ohI6Nvfguf7XGx2tRogM8Swt3uw/ycBu9buYJSIi9Zu5Io0dHR15+OGHKSwsrKo8IiIiIiIitdq8efPw9fWlW7duuLi44OLiQo8ePfDx8WHu3LlGx5N66uP1paNuBoS3wM1Z69cayWw2Map3CAD5RSU0dnPiX0O6YnFyMDhZzRAV6gVAUupJbDabwWlERESMU+FXbD169GDLli20bKnFDUVERERERP6oWbNmrFy5kj179pCSkgJAu3btaNOmjcHJpL46cvo3vkspnbJvSITu5WuCO8OaM+u7vRw59RtvDOpCQFNND3ZeeMsmODmYOJpTwMGT+QR51fA1HUVERKpIhYs3f/vb33j88cc5fPgw4eHhNGhQ9j/RTp06VVo4ERERERGR2qpNmzYq2EiNsCg5HasNokI9aeXd0Og4Ajg7mlk2Oorc34r1M/kDV2cHugQ0YcOBbJL2n1TxRkRE6q0KTZsGMGjQINLS0njssce44YYbCAsLo0uXLvavV+Ott94iKCgIi8VCREQEGzZsuGT7pUuX0q5dOywWCx07dmTlypVl9ttsNiZPnoyfnx+urq5ER0ezd+9e+/4DBw4wYsQIgoODcXV1JTQ0lClTplBUVFSmjclkuuCxfv36q7pGERERERGpHx588MFLPkSqU1GxlcU/l06ZNrSnRt3UJN6NLCrcXERkqCcA61JPGpxERETEOBUeeZOWllapAZYsWUJcXBzx8fFEREQwc+ZMYmJi2L17N97e3he0X7duHYMHD2batGncfvvtLFy4kNjYWDZv3kyHDh0AmD59OrNmzWL+/PkEBwfz7LPPEhMTw86dO7FYLKSkpGC1WnnnnXdo1aoVO3bsYOTIkeTl5fHqq6+WOd+3337L9df/d9FAT0/PSr1+ERERERGpW06dOlXm+3PnzrFjxw5Onz7Nn/70J4NSSX31za+ZnDhbhHcjF/pe52N0HJErEhnqyRuJe+3r3phMJqMjiYiIVLsKF28qe62b1157jZEjRzJ8+HAA4uPj+eqrr5g3bx5PPfXUBe3feOMN+vXrx/jx4wF48cUXWb16NbNnzyY+Ph6bzcbMmTOZNGkSd955JwAffvghPj4+LF++nEGDBtGvXz/69etn7zMkJITdu3fz9ttvX1C88fT0xNfXt1KvWURERERE6q5///vfF2yzWq2MHj2a0NBQAxJJffbR+oMADOoRiJNDhSffEDFEl8DGuDiaOXG2kNTjZ2nl3cjoSCIiItXuql65ffTRR9xwww34+/tz8GDpC8GZM2fy+eefV6ifoqIiNm3aRHR09H8Dmc1ER0eTlJRU7jFJSUll2gPExMTY26elpZGZmVmmjYeHBxERERftEyAnJ4emTZtesL1///54e3vTq1cvVqxYccnrKSwsJDc3t8xDRERERETEbDYTFxfH66+/bnQUqUf2ZJ1hQ1o2DmYTg3sEGB1H5Iq5ODrQLagJoKnTRESk/qpw8ebtt98mLi6O2267jdOnT1NSUgJA48aNmTlzZoX6OnHiBCUlJfj4lB267ePjQ2ZmZrnHZGZmXrL9+a8V6XPfvn28+eabPPTQQ/ZtDRs2ZMaMGSxdupSvvvqKXr16ERsbe8kCzrRp0/Dw8LA/AgL04lhEREREREqlpqZSXFxsdAypRxb8Puomur03fh6uBqcRqZjIkNJp65NUvBERkXqqwtOmvfnmm7z77rvExsby0ksv2bd369aNJ554olLDVYcjR47Qr18/Bg4cyMiRI+3bvby8iIuLs3/fvXt3MjIyeOWVV+jfv3+5fU2cOLHMMbm5uSrgiIiIiIjUM/97TwBgs9k4evQoX331Fffff79BqaS+ySssZtnmIwDc1zPI2DAiVyEy1AvYQ9L+k1itNsxmrXsjIiL1S4WLN2lpaXTp0uWC7S4uLuTl5VWoLy8vLxwcHMjKyiqzPSsr66LrzPj6+l6y/fmvWVlZ+Pn5lWkTFhZW5riMjAz69OlDVFQUc+bMuWzeiIgIVq9efdH9Li4uuLi4XLYfERERERGpu7Zs2VLme7PZTLNmzZgxYwYPPvigQamkvvl8awZnC4sJ9mpAVKin0XFEKqxTCw/cnB04nX+OlMwzXOfvbnQkERGRalXh4k1wcDBbt26lZcuWZbYnJCTQvn37CvXl7OxMeHg4iYmJxMbGAqULeSYmJjJmzJhyj4mMjCQxMZFx48bZt61evZrIyEh7Pl9fXxITE+3FmtzcXJKTkxk9erT9mCNHjtCnTx/Cw8N5//33MZsvP4Pc1q1byxSERERERERE/ug///mP0RGknrPZbHz0+5RpQyICNWJBaiUnBzM9gpvy/e7jJO0/qeKNiIjUOxVe8yYuLo5HHnmEJUuWYLPZ2LBhA//4xz+YOHEiEyZMqHCAuLg43n33XebPn8+uXbsYPXo0eXl5DB8+HIBhw4YxceJEe/uxY8eSkJDAjBkzSElJ4bnnnmPjxo32Yo/JZGLcuHFMnTqVFStWsH37doYNG4a/v7+9QHTkyBFuvvlmAgMDefXVVzl+/DiZmZll1sSZP38+ixYtIiUlhZSUFP75z38yb948Hn300Qpfo4iIiIiI1D/Hjh3jhx9+4IcffuDYsWPX1Ndbb71FUFAQFouFiIgINmzYcMn2S5cupV27dlgsFjp27MjKlSvL7LfZbEyePBk/Pz9cXV2Jjo5m7969Zdr079+fwMBALBYLfn5+3HfffWRkZJRps23bNm688UYsFgsBAQFMnz79mq5TKsfm9NPsOpqLi6OZAeEtjI4jctX+u+7NCYOTiIiIVL8Kj7z5v//7P1xdXZk0aRL5+fnce++9+Pv788YbbzBo0KAKB7jnnns4fvw4kydPJjMzk7CwMBISEvDx8QEgPT29zKiYqKgoFi5cyKRJk3j66adp3bo1y5cvp0OHDvY2EyZMIC8vj1GjRnH69Gl69epFQkICFosFKB2ps2/fPvbt20eLFmVfyNpsNvu/X3zxRQ4ePIijoyPt2rVjyZIlDBgwoMLXKCIiIiIi9Udubi6PPPIIixYtwmq1AuDg4MA999zDW2+9hYeHR4X6W7JkCXFxccTHxxMREcHMmTOJiYlh9+7deHt7X9B+3bp1DB48mGnTpnH77bezcOFCYmNj2bx5s/2+afr06cyaNYv58+cTHBzMs88+S0xMDDt37rTfN/Xp04enn34aPz8/jhw5whNPPMGAAQNYt26d/TpvvfVWoqOjiY+PZ/v27Tz44IM0btyYUaNGXctTKNfo499H3dzR2Z/Gbs4GpxG5epG/T/mXnJZNidWGg0aRiYhIPWKy/W+1ooLy8/M5e/ZsuTcMUnoz4+HhQU5ODu7uGt4rl3b06FHmzJnDioL2nLQ1MDrOZXma8uhv2cWoUaPq7XSCtelnpp9X3abfRRGpKfT6t9Q999zDli1bePPNN+3TOyclJTF27FjCwsJYvHhxhfqLiIige/fuzJ49GyidajogIIBHH32Up556qtzz5+Xl8eWXX9q39ezZk7CwMOLj47HZbPj7+/P444/zxBNPAJCTk4OPjw8ffPDBRT+Ut2LFCmJjYyksLMTJyYm3336bZ555hszMTJydSwsETz31FMuXLyclJeWKrk2/M5UvO6+Inv9MpKjEyueP3EDngMZGRxK5aiVWG2EvrOJMQTErxtxApxaNjY4kIiJyza70NXCFp037X25ubirciIiIiIiI/I8vv/ySefPmERMTg7u7O+7u7sTExPDuu+/yxRdfVKivoqIiNm3aRHR0tH2b2WwmOjqapKSkco9JSkoq0x4gJibG3j4tLY3MzMwybTw8PIiIiLhon9nZ2SxYsICoqCicnJzs5+ndu7e9cHP+PLt37+bUqVPl9lNYWEhubm6Zh1SuTzYeoqjESsfmHircSK3nYDYREVw6+mZd6kmD04iIiFSvayreiIiIiIiISFmenp7lTo3m4eFBkyZNKtTXiRMnKCkpsU8rfZ6Pj0+ZNTv/V2Zm5iXbn/96JX0++eSTNGjQAE9PT9LT0/n8888ve57/PccfTZs2DQ8PD/sjICCg3HZydaxWGwuSS6dMu69nS4PTiFSO81OnJal4IyIi9YyKNyIiIiIiIpVo0qRJxMXFlSlgZGZmMn78eJ599lkDk1Xc+PHj2bJlC6tWrcLBwYFhw4ZxDTNvM3HiRHJycuyPQ4cOVWJaWbP3OIeyf8Pd4sgdnf2NjiNSKaJ+L978fCCbcyVWg9OIiIhUH0ejA4iIiIiIiNR2Xbp0wWT670Lae/fuJTAwkMDAQADS09NxcXHh+PHjPPTQQ1fcr5eXFw4ODmRlZZXZnpWVha+vb7nH+Pr6XrL9+a9ZWVll1h/LysoiLCzsgvN7eXnRpk0b2rdvT0BAAOvXrycyMvKi5/nfc/yRi4sLLi4ul7lquVoL1peOuhkQHoCrs4PBaUQqR1ufRjRxc+JU/jm2HT5NeMumRkcSERGpFtdUvCkoKMBisVRWFhERERERkVopNja2Svp1dnYmPDycxMRE+zmsViuJiYmMGTOm3GMiIyNJTExk3Lhx9m2rV68mMjISgODgYHx9fUlMTLQXa3Jzc0lOTmb06NEXzWK1ln7ivbCw0H6eZ555hnPnztnXwVm9ejVt27at8PRwcu0On8onMeUYAEN6BhqcRqTymM0mIkM9Wbk9k6TUkyreiIhIvVHh4o3VauUf//gH8fHxZGVlsWfPHkJCQnj22WcJCgpixIgRVZFTRERERESkxpoyZUqV9R0XF8f9999Pt27d6NGjBzNnziQvL4/hw4cDMGzYMJo3b860adMAGDt2LDfddBMzZszgL3/5C4sXL2bjxo3MmTMHAJPJxLhx45g6dSqtW7cmODiYZ599Fn9/f3uBKDk5mZ9//plevXrRpEkTUlNTefbZZwkNDbUXge69916ef/55RowYwZNPPsmOHTt44403eP3116vsuZCLW7QhHZsNbmjlSWizhkbHEalUkSGlxZt1qScZ86fWRscRERGpFhVe82bq1Kl88MEHTJ8+HWdnZ/v2Dh06MHfu3EoNJyIiIiIiUt/dc889vPrqq0yePJmwsDC2bt1KQkICPj4+QOmUbEePHrW3j4qKYuHChcyZM4fOnTvz6aefsnz5cjp06GBvM2HCBB599FFGjRpF9+7dOXv2LAkJCfaZFdzc3Pjss8+45ZZbaNu2LSNGjKBTp06sWbPGPu2Zh4cHq1atIi0tjfDwcB5//HEmT57MqFGjqvHZEYCiYitLfi5dP2hoREuD04hUvsjf173ZdPAUhcUlBqcRERGpHhUeefPhhx8yZ84cbrnlFh5++GH79s6dO5OSklKp4URERERERATGjBlz0WnSvv/++wu2DRw4kIEDB160P5PJxAsvvMALL7xQ7v6OHTvy3XffXTZXp06d+OGHHy7bTqpWwq+ZnDhbhI+7C9HX+RgdR6TShTZrSLNGLhw/U8iW9NP0DPE0OlKt95+UYyTsyCSmgw83tfHGwWy6/EEiIlKtKly8OXLkCK1atbpgu9Vq5dy5c5USSkRERERERESuzMfrDwIwqHsgTg4VnmBDpMYzmUxEhniy4pcM1qWeVPHmGv1WVMLfP9nK6fxzLNl4CH8PC3d3D+DubgH4N3Y1Op6IiPyuwq/qrrvuunI/WfXpp5/SpUuXSgklIiIiIiIiIpe3O/MMG9KycTCbGNwj0Og4IlXm/NRp61NPGpyk9vt082FO55+jaQNnGrs5kZFTwMxv99Lr5e948IOfWb0zi+ISq9ExRUTqvQqPvJk8eTL3338/R44cwWq18tlnn7F7924+/PBDvvzyy6rIKCIiIiIiUiucO3eOdu3a8eWXX9K+fXuj40g9sCC5dNRN3/Y++HpYDE4jUnWifi/ebDl0it+KSnB1djA4Ue1ktdqY92MaAI/9qRWDegTyza+ZLNqQzvr92XyXcozvUo7h4+7CPd0CuLt7AC2auBmcWkSkfqrwyJs777yTL774gm+//ZYGDRowefJkdu3axRdffEHfvn2rIqOIiIiIiEit4OTkREFBgdExpJ7IKyzms81HABjas6XBaUSqVmBTN/w9LJwrsbHxYLbRcWqtxJRjpJ3Iw93iyMBuAVicHLgzrDmLR0Xy3eM38VDvEJo2cCYrt5BZ3+3jxun/4f55G0jYkck5jcYREalWFR55A3DjjTeyevXqys4iIiIiIiJS6z3yyCO8/PLLzJ07F0fHq7rlErkiy7ce4WxhMSFeDeyjEkTqKpPJRGSoF8s2HyYp9SQ3tm5mdKRaae4P+wG4N6IlDVzK/h8V0qwhE29rT9ytbVi9M4tFG9L5ad9J1uw5zpo9x2nWyIW7u7VgUPdAAppqNI6ISFWr8J3Ezz//jNVqJSIiosz25ORkHBwc6NatW6WFExERERERqW1+/vlnEhMTWbVqFR07dqRBgwZl9n/22WcGJZO6xGaz8VFS6ZRp90YEYjabDE4kUvUiQz1Ztvkw67TuzVXZfjiH5LRsHM0m7o+6+Gg9F0cHbu/kz+2d/DlwIo/FPx/i002HOH6mkLf+k8pb/0nlxtZeDO4RSHR7H5wdKzyxj4iIXIEKF28eeeQRJkyYcEHx5siRI7z88sskJydXWjgREREREZHapnHjxtx1111Gx5A6bnP6KVIyz2BxMjMwPMDoOCLVIvL3EWbbj+RwtrCYhi4a3VgRc38sHXVzR2d//Dxcr+iYIK8GPPXndsT1bUPiriwWbkjnh70n7A+vhs4MCA9gUPcAgrwaXL5DERG5YhX+X27nzp107dr1gu1dunRh586dlRJKRERERESktnr//feNjiD1wPlRN3d08sfDzcngNCLVo3ljV1p6unHwZD4/p2XTp5230ZFqjYzTv/HVtqMAjOgVXOHjnR3N/LmjH3/u6Meh7HyW/HyIJRtLR+PEr0klfk0qUaGeDO4RyK3X++Di6FDZlyAiUu9UuHjj4uJCVlYWISEhZbYfPXpU8zmLiIiIiIj87vjx4+zevRuAtm3b0qyZ1meQynHybCErt2cCMLTnxac+EqmLIkM8OXgyn3WpJ1S8qYD56w5QbLURGeJJh+Ye19RXQFM3nohpy9jo1nyXcoxFG9JZs+c461JPsi71JE0bODMgvAWDugcQ0qxhJV2BiEj9U+FJKW+99VYmTpxITk6Ofdvp06d5+umn6du3b6WGExERERERqW3y8vJ48MEH8fPzo3fv3vTu3Rt/f39GjBhBfn6+0fGkDli66TBFJVY6tfCgc0Bjo+OIVKvzU6cl7de6N1fqbGExCzekA/B/N1Z81M3FODmYibnelw+G9+CHCX147JbW+LpbyM4rYs7a/fxpxhoGzUni861HKDhXUmnnFRGpLypcvHn11Vc5dOgQLVu2pE+fPvTp04fg4GAyMzOZMWNGVWQUERERERGpNeLi4lizZg1ffPEFp0+f5vTp03z++eesWbOGxx9/3Oh4UstZrTYWJJdOmaZRN1IfRYaUFm9+zcjldH6RwWlqh09+PsSZgmJCmjWgT9uqGa3UookbcX3b8OOTfZg7rBu3tPPGbIL1+7MZu3grPacl8uKXO9l37EyVnF9EpC6q8DxnzZs3Z9u2bSxYsIBffvkFV1dXhg8fzuDBg3Fy0jy7IiIiIiJSvy1btoxPP/2Um2++2b7ttttuw9XVlbvvvpu3337buHBS663Ze5xD2b/hbnHkjk7+RscRqXbe7hZCmzUg9XgeyWnZxFzva3SkGq3EamPeT2lA6Vo3ZrOpSs/n6GAm+jofoq/z4WjOb3zy82GW/JxORk4B7/2Yxns/ptE9qAmDewRyW0c/LE5aG0dE5GKuapGaBg0aMGrUqMrOIiIiIiIiUuvl5+fj4+NzwXZvb29NmybX7OOk0lE3A7sF4OqsNz2lfooK9SL1eB5JqSdVvLmMVb9mcvjUbzRxc+KvXVpU67n9PFwZG92aMX9qxdo9x1m4IZ3vUo7x84FT/HzgFM+t+JW/dm3B4B6BtPVtVK3ZRERqgysq3qxYsYI///nPODk5sWLFiku27d+/f6UEExERERERqY0iIyOZMmUKH374IRaLBYDffvuN559/nsjISIPTSW12KDuf73YfA2BIRKDBaUSMExnqyUfrD5KUqnVvLufdH/YDcF/PloYVfB3MJvq086ZPO2+ycgtYuvEQizYc4sjp3/hg3QE+WHeA8JZNGNQ9gNs7+aswLSLyuysq3sTGxpKZmYm3tzexsbEXbWcymSgp0QJkIiIiIiJSf82cOZN+/frRokULOnfuDMAvv/yCxWLhm2++MTid1GaLNqRjs8ENrTwJadbQ6Dgihun5+7o3u7POcPJsIZ4NXQxOVDNtOniKzemncXYwMzSyZqyR5eNuYcyfWvO3m1vxw74TLEpO59tdWWw6eIpNB0/xwpc7+X9dmjOoeyDX+bsbHVdExFBXVLyxWq3l/ltERERERETK6tixI3v37mXBggWkpKQAMHjwYIYMGYKrq6vB6aS2Kiwu4ZONh4DST9CL1GdNGzjTzrcRKZlnWL8/m7908jM6Uo303o+lo25iu/jj3chicJqyzGYTN7Vpxk1tmnHsTAGfbjrM4g2HSM/O58Okg3yYdJCwgMbc2yOQ2zv74eZ8VSs/iIjUavrLJyIiIiIiUknOnTtHu3bt+PLLLxk5cqTRcaQOSdiRyYmzRfi4uxDd/sI1lUTqm8hQT1Iyz7Au9YSKN+U4lJ1Pwo5MAEb0CjE4zaV5N7Lwt5tb8XDvUNalnmTRhnRW7cxk66HTbD10mhe+3MmdYf4M7hFIh+YeRscVEak2V1S8mTVr1hV3+Nhjj111GBERERERkdrMycmJgoICo2NIHbRgfToAg3sE4uhgNjiNiPEiQzx5/6cDJO3XujflmfdTGlYb3Njai7a+jYyOc0XMZhO9WnvRq7UXJ84WsmzTYRZtSOfAyXwWJKezIDmdTi08GNQ9kP5h/jR00WfSRaRuu6K/cq+//voVdWYymVS8ERERERGReu2RRx7h5ZdfZu7cuTg66o0luXYpmblsOJCNg9nEoO6BRscRqREiQjwxm2D/8Tyycgvwca9Z04IZKee3c3zyc+k0iyNvrNmjbi7Gq6ELD90UyqjeISTtP8miDYf4Zkcm2w7nsO3wdqZ+9d/ROB2be2AymYyOLCJS6a7oTiItLa2qc4iIiIiIiNQJP//8M4mJiaxatYqOHTvSoEGDMvs/++wzg5JJbXV+1M2t1/ng66E3qEUAPFyduN7fg+1HckhKPUlsl+ZGR6oxFm9IJ6+ohLY+jbixtZfRca6JyWQiKtSLqFAvsvOK+GzzYRZuSGf/8TwWbTjEog2HuN7fncE9ArkzzJ9GFiejI4uIVJpr/hhYSUkJ27dvp2XLljRp0qQyMomIiIiIiNRajRs35q677jI6htQRZwuL+WzzYQCG9mxpcBqRmiUq1FPFmz84V2Llg3UHABhxY3CdGpHStIEz/3djCCN6BbMhLZvFPx/iq+1H+TUjl0nLd/CPr3ZxR2c/Hr+1rUZiiUidUOHizbhx4+jYsSMjRoygpKSE3r17k5SUhJubG19++SU333xzFcQUERERERGp+YqLi+nTpw+33norvr6+RseROmD5liPkFZUQ0qwBUaGeRscRqVF6hnryztr9rNt/wugoNcbK7Uc5mlOAV0MX7gzzNzpOlTCZTESEeBIR4smUO67js81HWLQhnb3HzvLJxsPsyTrLv/8WVacKVyJSP1V4lcNPP/2Uzp07A/DFF19w4MABUlJS+Pvf/84zzzxT6QFFRERERERqC0dHRx5++GEKCwuNjiJ1gM1m4+P1BwEYEtFSb0SK/EH3oKY4mE0cyv6Nw6fyjY5jOJvNxtwfSpc+uD+yJS6ODgYnqnqN3Zx5sFcwq/7em08eisTN2YGth06TsCPT6GgiIteswsWbEydO2D9BtnLlSgYOHEibNm148MEH2b59+1WFeOuttwgKCsJisRAREcGGDRsu2X7p0qW0a9cOi8VCx44dWblyZZn9NpuNyZMn4+fnh6urK9HR0ezdu9e+/8CBA4wYMYLg4GBcXV0JDQ1lypQpFBUVleln27Zt3HjjjVgsFgICApg+ffpVXZ+IiIiIiNQfPXr0YMuWLUbHkDpg08FTpGSeweJkZkDXFkbHEalxGro40rmFBwBJqScNTmO8DWnZbD+Sg4ujmSH1bJpFk8lEj+Cm/N+NIQBM/2Y350qsBqcSEbk2FS7e+Pj4sHPnTkpKSkhISKBv374A5Ofn4+BQ8Yr+kiVLiIuLY8qUKWzevJnOnTsTExPDsWPHym2/bt06Bg8ezIgRI9iyZQuxsbHExsayY8cOe5vp06cza9Ys4uPjSU5OpkGDBsTExFBQUABASkoKVquVd955h19//ZXXX3+d+Ph4nn76aXsfubm53HrrrbRs2ZJNmzbxyiuv8NxzzzFnzpwKX6OIiIiIiNQff/vb33j88ceZPXs2SUlJbNu2rcxD5EqdH3XTv7M/Hm5ahFukPJG/Tyeo4g28+/uom7vCW9C0gbPBaYwxqncIng2cSTuRx5KfDxkdR0TkmlS4eDN8+HDuvvtuOnTogMlkIjo6GoDk5GTatWtX4QCvvfYaI0eOZPjw4Vx33XXEx8fj5ubGvHnzym3/xhtv0K9fP8aPH0/79u158cUX6dq1K7NnzwZKR93MnDmTSZMmceedd9KpUyc+/PBDMjIyWL58OQD9+vXj/fff59ZbbyUkJIT+/fvzxBNP8Nlnn9nPs2DBAoqKipg3bx7XX389gwYN4rHHHuO1116r8DWKiIiIiEj9MWjQINLS0njssce44YYbCAsLo0uXLvavIlfi5NlCVm4vnfbnvp5BxoYRqcEiQ7wASNp/EpvNZnAa4+w/fpbElCwARvQKNjiNcRq6OPLYLa0BmPntXvIKiw1OJCJy9SpcvHnuueeYO3cuo0aN4qeffsLFxQUABwcHnnrqqQr1VVRUxKZNm+wFIACz2Ux0dDRJSUnlHpOUlFSmPUBMTIy9fVpaGpmZmWXaeHh4EBERcdE+AXJycmjatGmZ8/Tu3Rtn5/9+UiEmJobdu3dz6tSpCl2niIiIiIjUH2lpaRc89u/fb/8qciU+2XiYohIrnVt40PH3aaFE5ELhLZvg7GDmaE4BB07W33Vv5v2Uhs0Gt7TzJrRZQ6PjGGpwj0Baerpx4myhfQ0gEZHayPFqDhowYMAF2+6///4K93PixAlKSkrw8fEps93Hx4eUlJRyj8nMzCy3fWZmpn3/+W0Xa/NH+/bt48033+TVV18tc57g4LKfVDjfZ2ZmJk2aNLmgn8LCwjILk+bm5pZ7PhERERERqbtatqxf6wxI5Sux2liQXDplWn1bt0KkolydHQgLbMyGtGySUk8S7NXA6EjV7lReEZ9uOgxgX/OlPnN2NPPErW15dNEW5qxNZUjPQLwauhgdS0Skwio88qauOXLkCP369WPgwIGMHDnymvqaNm0aHh4e9kdAQEAlpRQRERERkdrko48+4oYbbsDf35+DB0vfhJ85cyaff/65wcmkNli75ziHT/2Gh6sTd3TyNzqOSI0XdX7dm/31c92bhRvSKThn5Xp/d3qGNL38AfXAXzr60amFB3lFJbyZuNfoOCIiV8XQ4o2XlxcODg5kZWWV2Z6VlYWvr2+5x/j6+l6y/fmvV9JnRkYGffr0ISoqijlz5lzRef73HH80ceJEcnJy7I9Dh7QwmoiIiIhIffP2228TFxfHbbfdxunTpykpKQGgcePGzJw509hwUit8tL604DcgvAWuzg4GpxGp+SJDfi/epNa/dW8Ki0v4YN0BAEbeGILJZDI2UA1hNpt4ql/p2twLktM5eDLP4EQiIhVnaPHG2dmZ8PBwEhMT7dusViuJiYlERkaWe0xkZGSZ9gCrV6+2tw8ODsbX17dMm9zcXJKTk8v0eeTIEW6++WbCw8N5//33MZvLPhWRkZGsXbuWc+fOlTlP27Zty50yDcDFxQV3d/cyDxERERERqV/efPNN3n33XZ555hkcHP77xnu3bt3Yvn27gcmkNjiUnc9/dh8DYEhEoMFpRGqHsMDGuDiaOXG2kH3Hzhodp1p98ctRjp8pxNfdwl86+Rkdp0aJauXFTW2aUWy18co3u42OIyJSYYZPmxYXF8e7777L/Pnz2bVrF6NHjyYvL4/hw4cDMGzYMCZOnGhvP3bsWBISEpgxYwYpKSk899xzbNy4kTFjxgBgMpkYN24cU6dOZcWKFWzfvp1hw4bh7+9PbGws8N/CTWBgIK+++irHjx8nMzOzzJo49957L87OzowYMYJff/2VJUuW8MYbbxAXF1d9T46IiIiIiNQ6aWlpdOnS5YLtLi4u5OXpk79yaQs3pGOzQa9WXoTU80XHRa6Ui6MD3YNKpwurT1On2Ww25v6wH4AHbgjCycHwt/lqnCf7tcNkgi+3HeWXQ6eNjiMiUiFX9Fc9Nzf3ih8Vdc899/Dqq68yefJkwsLC2Lp1KwkJCfj4+ACQnp7O0aNH7e2joqJYuHAhc+bMoXPnznz66acsX76cDh062NtMmDCBRx99lFGjRtG9e3fOnj1LQkICFosFKB1Bs2/fPhITE2nRogV+fn72x3keHh6sWrWKtLQ0wsPDefzxx5k8eTKjRo2q8DWKiIiIiEj9ERwczNatWy/YnpCQQPv27a+qz7feeougoCAsFgsRERFs2LDhku2XLl1Ku3btsFgsdOzYkZUrV5bZb7PZmDx5Mn5+fri6uhIdHc3evf9dE+DAgQOMGDGC4OBgXF1dCQ0NZcqUKRQVFZVpYzKZLnisX7/+qq5RSqc/+uTn0um3h/ZsaXAakdol8vd1b9btqz/Fm5/2nSQl8wxuzg4M7q6ReuW5zt+d/xfWHICXvk6pd9PqiUjt5ngljRo3bnzFc2aen8+5IsaMGWMfOfNH33///QXbBg4cyMCBAy/an8lk4oUXXuCFF14od/8DDzzAAw88cNlcnTp14ocffrhsOxERERERkfPi4uJ45JFHKCgowGazsWHDBhYtWsS0adOYO3duhftbsmQJcXFxxMfHExERwcyZM4mJiWH37t14e3tf0H7dunUMHjyYadOmcfvtt7Nw4UJiY2PZvHmz/UNv06dPZ9asWcyfP5/g4GCeffZZYmJi2LlzJxaLhZSUFKxWK++88w6tWrVix44djBw5kry8PF599dUy5/v222+5/vrr7d97enpW+BqlVMKOTE7mFeHrbiG6/YU/WxG5uJ6/r3uzPu0kVqsNs7nur/3y7u+jbu7uFoCHm5PBaWquuFvb8OW2oyTtP8maPce5ua3+vopI7XBFxZv//Oc/9n8fOHCAp556igceeMC+hkxSUhLz589n2rRpVZNSRERERESklvi///s/XF1dmTRpEvn5+dx77734+/vzxhtvMGjQoAr399prrzFy5Ej71NLx8fF89dVXzJs3j6eeeuqC9m+88Qb9+vVj/PjxALz44ousXr2a2bNnEx8fj81mY+bMmUyaNIk777wTgA8//BAfHx+WL1/OoEGD6NevH/369bP3GRISwu7du3n77bcvKN54enri6+tb4euSC328/iAAg3sE4qjpj0QqpFMLDxo4O3A6/xy7MnO53t/D6EhVak/WGdbsOY7JBA/eEGx0nBqtRRM3hkW2ZO6Pabz0dQq9WzerF8U9Ean9rujV4E033WR/fPjhh7z22mtMmzaN/v37079/f6ZNm8arr77K+++/X9V5RUREREREarwhQ4awd+9ezp49S2ZmJocPH2bEiBEV7qeoqIhNmzYRHR1t32Y2m4mOjiYpKancY5KSksq0B4iJibG3T0tLIzMzs0wbDw8PIiIiLtonQE5ODk2bNr1ge//+/fH29qZXr16sWLHiktdTWFh4zVNv11Upmbn8fOAUDmYTg3oEGB1HpNZxcjDTPfj3dW9S6/7Uae/9kAZAzHW+BHq6GZym5nukTysaWRxJyTzD8q1HjI4jInJFKvxRnqSkJLp163bB9m7dul123mUREREREZH6xM3Nrdypza7UiRMnKCkpsa8Jep6Pjw+ZmZnlHpOZmXnJ9ue/VqTPffv28eabb/LQQw/ZtzVs2JAZM2awdOlSvvrqK3r16kVsbOwlCzjTpk3Dw8PD/ggIUJHivPOjbmKu98HH3WJwGpHaKer3dW/W76/bxZvjZwr59+8FiJG9NermSjRp4Mzfbm4FwIxVeyg4V/FlH0REqluFizcBAQG8++67F2yfO3euXniLiIiIiIjUMUeOHKFfv34MHDiQkSNH2rd7eXkRFxdHREQE3bt356WXXmLo0KG88sorF+1r4sSJ5OTk2B+HDh2qjkuo8c4WFvPvzaVvxA6NaGlwGpHaKzLEC4Dk/dkUl1gNTlN1Pl5/kKJiK2EBjeka2MToOLXG8BuC8HW3cOT0b/aCuYhITXZFa978r9dff5277rqLr7/+moiICAA2bNjA3r17WbZsWaUHFBERERERqa+8vLxwcHAgKyurzPasrKyLrjPj6+t7yfbnv2ZlZeHn51emTVhYWJnjMjIy6NOnD1FRUcyZM+eyeSMiIli9evVF97u4uODi4nLZfuqbf285Ql5RCSHNGhD5+8gBEam46/zdcbc4kltQzK8ZuXQOaGx0pEpXcK6Ej34vPIy8MQSTSWu3XCmLkwNxfdswYdk2Zv9nHwO7BeDh6mR0LBGRi6rwyJvbbruNPXv2cMcdd5CdnU12djZ33HEHe/bs4bbbbquKjCIiIiIiIvWSs7Mz4eHhJCYm2rdZrVYSExOJjIws95jIyMgy7QFWr15tbx8cHIyvr2+ZNrm5uSQnJ5fp88iRI9x8882Eh4fz/vvvYzZf/vZx69atZQpCcnk2m40Fv78ROzSipd6IFbkGDmYTESGlBdCkOjp12r+3HCE7r4jmjV2Jud7n8gdIGXeFt6CNT0NO55/j7e9TjY4jInJJFR55A6VTp/3zn/+s7CwiIiIiIiK12rlz5+jXrx/x8fG0bt26UvqMi4vj/vvvp1u3bvTo0YOZM2eSl5fH8OHDARg2bBjNmzdn2rRpAIwdO5abbrqJGTNm8Je//IXFixezceNG+8gZk8nEuHHjmDp1Kq1btyY4OJhnn30Wf39/YmNjgf8Wblq2bMmrr77K8ePH7XnOj9yZP38+zs7OdOnSBYDPPvuMefPmMXfu3Eq57vpi48FTpGSeweJk5q7wFkbHEan1IkM8Wb0zi3WpJ3n4plCj41Qqq9XG3B/2A6VTgDk6VPgz2fWeg9nEhJh2/N+HG3n/pzTuj2qJn4er0bFERMp1VcWbH374gXfeeYf9+/ezdOlSmjdvzkcffURwcDC9evWq7IwiIiIiIiK1gpOTE9u2bavUPu+55x6OHz/O5MmTyczMJCwsjISEBHx8Sj9xnZ6eXmZUTFRUFAsXLmTSpEk8/fTTtG7dmuXLl9OhQwd7mwkTJpCXl8eoUaM4ffo0vXr1IiEhAYvFApSO1Nm3bx/79u2jRYuyBQWbzWb/94svvsjBgwdxdHSkXbt2LFmyhAEDBlTq9dd159dduLNzc03fI1IJzk89uPFANudKrDjVoQLHmj3HST2eRyMXR+7prnWnr9Yt7b3pEdSUDQeyeX31HqYP6Gx0JBGRclX4f7Bly5YRExODq6srmzdvprCwEICcnByNxhERERERkXpv6NChvPfee5Xa55gxYzh48CCFhYUkJyfb1x8F+P777/nggw/KtB84cCC7d++msLCQHTt2XDDFtclk4oUXXiAzM5OCggK+/fZb2rRpY9//wAMPYLPZyn2cd//997Nz507y8vLIyckhOTlZhZsKOnG2kJXbjwIwtGdLg9OI1A1tfRrRtIEz+UUlbDt82ug4lerd30fdDOoRQCOLir1Xy2Qy8eSf2wHw6abD7Mk6Y3AiEZHyVXjkzdSpU4mPj2fYsGEsXrzYvv2GG25g6tSplRpORERERESktikuLmbevHl8++23hIeH06BBgzL7X3vtNYOSSU3zycZDnCux0TmgMR1beBgdR6ROMJtN9Axpysrtmazbd5Lwlk2NjlQpfs3IYV3qSRzMJh64IdjoOLVeeMsm9Lvel4RfM5mekMLc+7sbHUlE5AIVLt7s3r2b3r17X7Ddw8OD06dPV0YmERERERGRWmvHjh107doVgD179pTZp8Xo5bwSq40F69MBGBoRaHAakbolMtSLldszSdp/kkdvqZz1x4z23o9pANzW0Y/mjbVGS2UY368tq3dl8e2uY2xIy6ZHcN0o9IlI3VHh4o2vry/79u0jKCiozPYff/yRkJCQysolIiIiUmfl5OSQn59vdIwr4ubmhoeHPg0uUhH/+c9/jI4gtcCaPcc4cvo3PFyduKOzv9FxROqUyJDf1705eIqCcyVYnBwMTnRtsnIL+OKXDABG3qhRN5UltFlD7ukewMLkdKZ9vYvPRkfpQxYiUqNUuHgzcuRIxo4dy7x58zCZTGRkZJCUlMQTTzzBs88+WxUZRUREROqMnJwcZr05G2tJsdFRrojZwZHHHh2jAo7IVdi3bx+pqan07t0bV1dXbDab3hQSu49/H3UzMLxFrX9jWaSmCW3WgGaNXDh+ppAt6aeJDPU0OtI1mb/uAOdKbPQIakqnFo2NjlOnjLulNf/efIQt6af55tdM+nXwMzqSiIhdhYs3Tz31FFarlVtuuYX8/Hx69+6Ni4sLTzzxBI8++mhVZBQRERGpM/Lz87GWFLOmKJgcq8XoOJfkYS7gJuc08vPzVbwRqYCTJ09y991385///AeTycTevXsJCQlhxIgRNGnShBkzZhgdUQx2KDuf/+w+BsCQni0NTiNS95hMJqJCPfl8awZJ+0/W6uJNflExC5JLi70jNOqm0nm7Wxh5YzCzvtvH9ITd3NLeBycHs9GxRESAChZvSkpK+Omnn3jkkUcYP348+/bt4+zZs1x33XU0bNiwqjKKiIhUito0VRVouqq6Lsdq4aStweUbGslqdACR2unvf/87Tk5OpKen0759e/v2e+65h7i4OBVvhIUb0rHZ4MbWXgR71fD/C0RqqciQ34s3qSegbxuj41y1TzcdJue3cwR5uhHd3sfoOHXSyN4hfJyczv4TeXyy8RBDIlRUF5GaoULFGwcHB2699VZ27dpF48aNue6666oql4iISKWqbVNVgaarEhGprVatWsU333xDixYtymxv3bo1Bw8eNCiV1BSFxSUs+fkQAEM16kakypwfbbP10Gl+KyrB1bn2TU9YYrXx3o9pADzYKxgHs6berAqNLE489qdWPPfFTmZ+u5f/16U5bs4VnqxIRKTSVfgvUYcOHdi/fz/BwRqqKSIitUdtmqoKNF2ViEhtlpeXh5ub2wXbs7OzcXFxMSCR1CQJOzLJzivCz8PCLe28jY4jUmcFNnWjeWNXjpz+jY0Hs7mxdTOjI1XYt7uyOHgyHw9XJwaEt7j8AXLV7o1oybyfDpCenc97P6Tx6C2tjY4kIlLx4s3UqVN54oknePHFFwkPD6dBg7JDvN3d3SstnIiISGWrFVNVgaarEhGpxW688UY+/PBDXnzxRaB07QWr1cr06dPp06ePwenEaB8llY6+GtwjEEetqyBSZUwmEz1DPFm2+TDrUk/WyuLNez+UjroZEhGokSBVzNnRzBMxbXls0RbeWbufeyMC8WyoD1yIiLEq/Jf/tttuA6B///6YTP8drmmz2TCZTJSUlFReOhERERERkVpm+vTp3HLLLWzcuJGioiImTJjAr7/+SnZ2Nj/99JPR8cRAu47msvHgKRzNJgZ1DzA6jkidFxVaWrxJSj1pdJQK++XQaTYcyMbJwcT9UUFGx6kXbu/ox7tr97P9SA5vfreP5/pfb3QkkRrBarVh1rSNhqhw8eY///lPVeT4/+zdeVwV9f7H8fdhXxRcEBBFwX1X3BDTrKSwrBvVdSlLM9NbaWrcNvu5lNa1LL2uN9MyUzPNe4sWjTKzNEVUFHNfEUwFXEFAATnz+4M8RaJCAsPyej4e8zBmvjPzPmeA+J7PfL8DAAAAABVCq1atdODAAc2ePVtVq1ZVenq6HnzwQQ0fPly1a9c2Ox5MtGRT3qibu1r6yNuj7E/jCpR3V557s/N4qi5cylFVF0eTExXe+7896+a+tn7y4fdFqbCzs+jlu5tpwPsx+jgmQYNvCVD9muVg1gaghBxKuaDRy+OUdvGyPnumq7wYjVbqilS8ycnJ0cSJEzV37lw1bszcjwAAAABQEE9PT/3f//2f2TFQhly4lKPI7cclSY92qW9yGqBy8Kvmqvo13ZRwJlNbjp7VHc18zI5UKMfPX9SqnSclSU92a2BymsrllkZeurVJLa07cErvfHdAsx4OMjsSYIrPt/+qVz7bpYs5ebNsLducqBF3UA8obUWaYNfR0VG//PJLSWUBAAAAgHKvUaNGevXVV3Xw4EGzo6AMidx+XBnZuWpYy10hDWqaHQeoNLr+NvqmPE2dtnBDvHKthm5pVFMt/Hi2dGl7uVczWSzSVztOaOevqWbHAUrVpZxcvfy/X/Tc8h26mJMr/xqukqSPYxJ1OZeH85a2Ij8d8dFHH9UHH3xQElkAAAAAoNwbPny4Vq5cqaZNm6pTp06aMWOGkpKSzI4FExmGoSWbEiXljbr54/NjAZSsLr8VSzeWk+LNhUs5Wrb5mCRG3ZilhZ+HwtvVkSS9GbVXhmGYnAgoHUdOpSt8zgYt23JMFos0OrSxvhvdQzXcnXQy9ZK+35tidsRKp8jFm8uXL+vdd99Vx44d9Y9//EMRERH5FgAAAACozJ577jlt2bJF+/bt0z333KM5c+bI399fd911lxYtWmR2PJhgy9Fz2p98Qa6O9nqwfV2z4wCVypWRbntOpul8ZrbJaW5s+ZZjupB1WY28q6hHk1pmx6m0Iu5sIid7O204dEbrDp42Ow5Q4r7acUL3zfpZ+5IuyKuKkxY/EazRoU3k6mSvfp38JUmLNx01N2QlVOTiza5du9S+fXtVrVpVBw4c0Pbt221LXFxcCUQEAAAAgPKnSZMmeu2113TgwAGtX79ep06d0uDBg82OBRMs2ZQgSbq/nZ88XcvPA9OBisDbw0WNvKvIMKSY+LNmx7muy7lWfbjhqCRpSLdA2dkxSs8s/jXc9FhI3vPJ3vxmn6xWRt+gYsq6nKtxkbv07CfblZGdq86BNbRyZHd1a+xla/NI53qyWKQNh87oUEq6iWkrH4ei7rB27dqSyAEAAAAAFc7mzZu1dOlSLV++XGlpaerTp4/ZkVDKTqdn6ZtdeQ8ef7RLfZPTAJVTSIOaOpSSrujDZxTW0tfsONcUtTtJx89fVE13Jz0QVMfsOJXeiNsb6dOtx7T3ZJq+2HFcDwQxchIVS+KZTD2zNFa7jqdJkp65raEi7mwiB/v84z38a7ipZzNvfb83RUs2JejVv7U0I26lVOSRNwAAAACAaztw4IAmTJigJk2a6JZbbtHevXv11ltvKTk5WcuWLTM7HkrZ8i3HlJNrqJ1/NbWq42l2HKBS6towb+q06DL83BvDMDR/fbykvEKvi6O9yYlQ3d1JT9/WUJL0zrcHdCkn1+REQPGJ2pWk3rPWa9fxNFV3c9SHgzvpxV7NrircXPFYSIAk6X+xvyoj63IpJq3cijzy5vbbb7/uwxV/+OGHmwoEAAAAAOVZs2bN1KlTJw0fPlz9+/eXj4+P2ZFgklyroaUxiZIYdQOYKfi3597sT76g0+lZ8qribHKiq8UmnNOOY+fl5GBnm64L5hvcNVCLNibo+PmLWrIpQU92b2B2JOCmZF+26s1v9mnBhrxicYf61TXr4SD5VXO97n7dG3kpoKabjp7JVGTccQ0I5vdUaSjyyJt27dqpbdu2tqVFixbKzs7Wtm3b1Lp165LICAAAAADlxv79+xUTE6NRo0ZRuKnkftyfouPnL6qam6PubVPb7DhApVXD3UnNfKtKkjYdKZujb97/bdTNg0F1ymRxqbJydbLXc3c2liTNXntIqRdzTE4E/HW/nstU3/eibYWbYbc20LJhXW5YuJEkOzuL7UaUxdEJMgyeA1Uaijzy5t///neB61999VWlp/PAIgAAAACVW+PGeR/yxMbGau/evZKkFi1aqH379mbGggmWbEqQJPXpUJcpkACTdW3opX1JFxR9+IzubeNndpx8Es5k6Ns9SZKkId0CTU6DP3uofV29vz5eB1PS9d5Ph/Vir2ZmRwKKbM3eZEV8ukOpF3Pk4eKgqX3b6c4WRbvJqE8Hf73z3X7tS7qgLUfPqXNgjRJKiyuK7Zk3jz76qBYsWFBchwMAAACAciklJUW33367OnXqpJEjR2rkyJHq2LGjevbsqVOnTpkdD6Xk2NlM/Xgg73oztQhgvpAy/NybDzcclWFItzWtpcY+Vc2Ogz9xsLezFWwWbIhXUuolkxMBhZeTa9XkVXs15KOtSr2Yo7Z1PbVyZPciF24kydPNUeHt6kiSFkUfLeakKEixFW+io6Pl4uJSXIcDAAAAgHLp2WefVXp6unbv3q2zZ8/q7Nmz2rVrl9LS0jRy5Eiz46GUfByTKMOQujf2UoCXu9lxgEqvc2AN2VmkI6czlJxWdj58T83M0adbj0mSnuzG81TKqtDm3uoUUF2Xcqz69+oDZscBCuVk6kU9PG+T3lt3RJL0eNcArXiqq/xruP3lY155JlfUriSllKHfpRVVkYs3Dz74YL7lgQceUJcuXTR48GD94x//KHKAOXPmKCAgQC4uLgoODtbmzZuv237FihVq1qyZXFxc1Lp1a61atSrfdsMwNH78eNWuXVuurq4KDQ3VwYMH87V544031LVrV7m5ualatWoFnsdisVy1LFu2rMivDwAAAEDlEhUVpf/85z9q3ry5bV2LFi00Z84cffPNNyYmQ2nJupxr+zD2yvzwAMzl6eqoVnU8JZWt0TdLNycqMztXzXyr6pZGNc2Og2uwWCx6+e68/6+viD2mg8kXTE4EXN9PB06p98yftTXhnKo6O+jdAe316t9aysnh5sZytPTzVPt61XTZauiTzceKKS2upchXy9PTM99So0YN3XbbbVq1apUmTJhQpGMtX75cERERmjBhgrZt26a2bdsqLCxMKSkpBbbfuHGjHn74YQ0ZMkTbt29XeHi4wsPDtWvXLlubKVOmaObMmZo7d65iYmLk7u6usLAwXbr0eyUwOztbffr00dNPP33dfB9++KFOnjxpW8LDw4v0+gAAAABUPlarVY6Ojletd3R0lNVqNSERSts3O5N0NiNbtT1d1LOZt9lxAPwmpEFecWTj4dMmJ8mTfdmqhRvzHhz+ZPcGslgsJifC9XSoX11hLX1kNaS3ovabHQcoUK7V0NTv9uvxDzfrbEa2Wvp56OuR3XR369rFdo6BIQGSpKWbE5STy9+2JcmhqDt8+OGHxXbyadOmaejQoRo8eLAkae7cuVq5cqUWLFigl19++ar2M2bMUK9evfTCCy9IkiZNmqTVq1dr9uzZmjt3rgzD0PTp0zV27Fjdf//9kqRFixbJx8dHkZGR6t+/vyTptddekyQtXLjwuvmqVasmX1/f4nq5AAAAACqBO+64Q6NGjdInn3wiP7+8h2IfP35czz33nHr27GlyOpSGxZsSJEkPd64nB/tim60cwE0KaVhT7607ougjZWPkzcqdJ5SclqVaVZ11X9vi+2AVJefFXs30/d4Ufb83WVuOnlWnAB7YjrIjJe2SRi7brk1HzkqSBgTX07h7W8jF0b5Yz3N3a19N+tpJyWlZ+n5PcrEWhpBfkf+K3LJli2JiYq5aHxMTo61btxb6ONnZ2YqNjVVoaOjvYezsFBoaqujo6AL3iY6OztdeksLCwmzt4+PjlZSUlK+Np6engoODr3nM6xk+fLi8vLzUuXNnLViwQIZhXLd9VlaW0tLS8i0AAAAAKpfZs2crLS1NAQEBatiwoRo2bKjAwEClpaVp1qxZZsdDCdtzIk2xCefkYGdR/07+ZscB8AedAmrIwc6iY2cv6tjZTFOzGIah99fnjbp5vGuAnB2K98NVlIyGtaqob8e83+2TV+294WeFKNiHG+LVa/o6zVpzUOcyss2OUyFsPHRa98z8WZuOnJW7k71m9G+nNx5oXeyFG0lydrBX/855PweLohOK/fj4XZGLN8OHD9exY1fPZ3f8+HENHz680Mc5ffq0cnNz5ePjk2+9j4+PkpKSCtwnKSnpuu2v/FuUY17LxIkT9emnn2r16tV66KGH9Mwzz9ywozV58uR8U8r5+/OHOgAAAFDZ+Pv7a9u2bVq5cqVGjx6t0aNHa9WqVdq2bZvq1q1rdjyUsCUxeR9ihLX0lbeHi8lpAPyRu7OD2tT97bk3Jo++iT5yRrtPpMnF0U6PdK5nahYUzXOhjeXqaK9tief17e5ks+OUK4ZhaErUPr321R7tS7qgqasPKOTNNRoXuUtHT2eYHa9cyrUamrnmoB79IEan07PUzLeqvny2m+5vV6dEz/tIcH3ZWfJ+l/EMqJJT5GnT9uzZo/bt21+1PigoSHv27CmWUGXBuHHjbP8dFBSkjIwMvf322xo5cuQ19xkzZowiIiJsX6elpVHAAQAAqABSU1OVmWnuHbqF5ebmJk9PT7NjVHoWi0V33nmn7rzzTrOjoBRduJSjyO3HJUmPdqlvchoABena0EvbEs9r0+EzthEUZvjgt1E3fTr4q7q7k2k5UHTeHi56snugZv1wSFO+3afQ5t5MkVkIVquh8V/u0pJNiZKkR7vUU9yx89p1PE2LNyVoSUyCwlr4auitDdShfnWT05YPp9Oz9NzyOK0/mPccr34d/fXq31rK1ankR/LVqeaq0OY++m5PshZvStDE+1uV+DkroyIXb5ydnZWcnKwGDRrkW3/y5Ek5OBT+cF5eXrK3t1dycv4KdXJy8jWfM+Pr63vd9lf+TU5OVu3atfO1adeuXaGzFSQ4OFiTJk1SVlaWnJ2dC2zj7Ox8zW0AAAAon1JTUzVz1mxZcy+bHaVQ7OwdNPLZERRwyojWrVtr1apV3NRVSURuP67M7Fw18q6iLg14DgJQFoU0rKnZaw9p4+EzMgxDFoul1DMcSknXmn0pslikJ7oFlvr5cfOG3dpAH8ck6sipDH269Vc9EszoqevJybXqhRU7FBl3QhaL9Hp4Kw0Iri/DMBR95IzmrzuitftPKWp3kqJ2J6lD/eoa2j1Qd7bwlb1d6f+MlgcxR85o5LLtSk7LkoujnV4Pb62/dyjdEd4DQwL03Z5kfbbtuF7s1UxVnItcasANFPkdveuuuzRmzBh98cUXtg7h+fPn9corrxTprjInJyd16NBBa9asUXh4uCTJarVqzZo1GjFiRIH7hISEaM2aNRo9erRt3erVqxUSEiJJCgwMlK+vr9asWWMr1qSlpSkmJkZPP/10UV9qPnFxcapevTrFGQAAgEomMzNT1tzL+ik7UKnWsj0FkqfdJfVwildmZibFmzLi6NGjysnJMTsGSoFhGFq8KW/KtEeD65nygTCAG+tQv7qc7O2UlHZJR89kKtDLvdQzLNiQN+omtLmPKefHzavq4qhn72ik177ao39/f0DhQX5yc+KD64JcysnViKXb9P3eFDnYWTStXzv9ra2fpLyRyl0beqlrQy8dSL6g99cfUeT2E4pNOKfYhHOqX9NNT3YL1N87+JfKaJLywGo19N66I3rnu/3KtRpq5F1F/xnQXk18qpZ6llsa1VSDWu46cipDn2/7VY+FBJR6hoquyL9V3nnnHd16662qX7++goKCJOUVNnx8fLR48eIiHSsiIkKDBg1Sx44d1blzZ02fPl0ZGRkaPHiwJGngwIGqU6eOJk+eLEkaNWqUevTooalTp6p3795atmyZtm7dqnnz5knK+4EfPXq0Xn/9dTVu3FiBgYEaN26c/Pz8bAUiSUpMTNTZs2eVmJio3NxcxcXFSZIaNWqkKlWq6KuvvlJycrK6dOkiFxcXrV69Wv/617/0/PPPF/XtAgAAQAWRanXRGaOMf8BiNTsAUHltOXpOB5LT5eporwdL+c5XAIXn4mivoHrVFBN/VtGHz5R68eRMepb+F/urJOlJRt2UawOC62vBhngdO3tRC36O14g7GpsdqcxJz7qsoR9tVfSRM3J2sNO7j7bXHc18CmzbxKeqpvy9rZ4Pa6pFGxO0eFOCEs5katwXuzVt9QE91qW+HgsJUK2qlffG+nMZ2Yr4NE5r95+SJD0YVEeTwlvJ3aQRLxaLRY8G19fEr/doUXSCHu1Sn5tXilmRJ2SsU6eOfvnlF02ZMkUtWrRQhw4dNGPGDO3cubPIUwH069dP77zzjsaPH6927dopLi5OUVFR8vHJ+yFOTEzUyZMnbe27du2qpUuXat68eWrbtq3++9//KjIyUq1a/T6n3osvvqhnn31Ww4YNU6dOnZSenq6oqCi5uPx+l+T48eMVFBSkCRMmKD09XUFBQQoKCtLWrVslSY6OjpozZ45CQkLUrl07vffee5o2bZomTJhQ1LcLAAAAQCXWvXt3ubq63vRx5syZo4CAALm4uCg4OFibN2++bvsVK1aoWbNmcnFxsU3d9keGYWj8+PGqXbu2XF1dFRoaqoMHD9q2Hz16VEOGDFFgYKBcXV3VsGFDTZgwQdnZ2fmO88svv6h79+5ycXGRv7+/pkyZctOvtby6MuomPMhPHi6OJqcBcD0hDWtKkjYePl3q5/44JlFZl61qXcdTnQOZXrE8c3Kw0/N3NZUkzf3piM6kZ5mcqGw5l5GtAfM3KfrIGVVxdtBHT3S+ZuHmj7yruuj5sKaKHnOHXvtbS9Wr4aZzmTma+cMh3fLWD3r5f7/oUMqFUngFZUtswjn1nrlea/efkrODnd58sLWm9m1rWuHmioc61JWro70OpqRr05GzpmapiP7S1XV3d9ewYcOKJcCIESOuOU3ajz/+eNW6Pn36qE+fPtc8nsVi0cSJEzVx4sRrtlm4cKEWLlx4ze29evVSr169rrkdAAAAAK5l3bp16tq1qxwcHPIVTS5fvqyNGzfq1ltvLdLxli9froiICM2dO1fBwcGaPn26wsLCtH//fnl7e1/VfuPGjXr44Yc1efJk3XvvvVq6dKnCw8O1bds2241vU6ZM0cyZM/XRRx/ZZiwICwvTnj175OLion379slqteq9995To0aNtGvXLg0dOlQZGRl65513JOVNUX3XXXcpNDRUc+fO1c6dO/XEE0+oWrVqxdZfLC9OXchS1K68Gw8HBNc3OQ2AG+na0EvTvz+oTUdK97k3l3JytSj6qCTpye6B3KFeAdzXxk/z1x/RruNpmr32kCbc19LsSGVCctolPfp+jA6mpKu6m6MWPRGs1nWLNqWvm5ODBnUN0KNd6uu73Ul6b90RxR07r2VbjmnZlmPq2cxbQ29toODAGhX6Z8kwDH3wc7ze/GafLlsNBXq5a84j7dXCz8PsaJIkT1dHhQfV0SebE7VkU4KtOI7i8ZeKNwcPHtTatWuVkpIiqzX/3Azjx48vlmAAAAAAUB7dfvvtOnny5FWFldTUVN1+++3Kzc0t0vGmTZumoUOH2qaXnjt3rlauXKkFCxbo5Zdfvqr9jBkz1KtXL73wwguSpEmTJmn16tWaPXu25s6dK8MwNH36dI0dO1b333+/JGnRokXy8fFRZGSk+vfvf9UNbQ0aNND+/fv17rvv2oo3H3/8sbKzs7VgwQI5OTmpZcuWiouL07Rp0ypd8ebTrceUk2soqF41tarD86aAsq6tv6dcHO10Oj1bB1PSS+1ZEV/GndDp9GzV9nTRPa1rl8o5UbLs7Cx6uVdzPfpBjJZsStDgroGqV9PN7FimSjyTqQEfbNKxsxfl6+GixUM6q/FN/IzZ21l0d+va6tXKV7EJ5zRv3RGt3pusNftStGZfilrX8dTQWxvonla+crAv8iRTZVpqZo6e/+8Ord6TLEm6t01tTX6wtaqWsRG+A0Pq65PNifp2d5KS0y7Jx6NsPye0PCnyd/T8+fPVvHlzjR8/Xv/973/1+eef25bIyMgSiAgAAAAA5ce17uI+c+aM3N2L9myF7OxsxcbGKjQ01LbOzs5OoaGhio6OLnCf6OjofO0lKSwszNY+Pj5eSUlJ+dp4enoqODj4mseU8opPNWr8PsVPdHS0br31Vjk5OeU7z/79+3Xu3Lkivc7yLNdqaGlMoiTpUUbdAOWCs4O9OtbP+30WffhMqZzTMAy9//MRSdLgWwLkWME+ZK7MujX2UvfGXsrJNfTOd/vNjmOqA8kX9Pe5G3Xs7EXVr+mmFU+F3FTh5o8sFos6BtTQvIEd9cM/b9OjXerJ2cFOO4+nauQn29Xj7R/1wc/xSs+6XCznM9svv55X71nrtXpPspzs7TTp/paa9XBQmSvcSFLz2h7qFFBdl//wNxGKR5FH3rz++ut644039NJLL5VEHgAAAAAolx588EFJeR8uPP7443J2/v2Burm5ufrll1/UtWvXIh3z9OnTys3NtT0X9AofHx/t27evwH2SkpIKbJ+UlGTbfmXdtdr82aFDhzRr1izbqJsrxwkMzP+w7SvHTEpKUvXq1a86TlZWlrKyfn8mQFpaWoHnK0/W7kvR8fMXVc3NUb3bcCc9UF6ENKypnw+dVvThMxrUNaDEz7fu4GkdSE6Xu5O9+nWqV+LnQ+l6+e5mWn/wZ32544SG3dqgUo7CjDt2Xo9/uFnnM3PU1KeqFg/pLO8SGoER6OWu18Nb67nQJlqyKVGLoo/q+PmLmvT1Hk3//oAeCa6nwV0D5etZ/kaAGIahRdEJemPlXmXnWuVfw1X/eaRDkaedK22PhQRoy9Fz+mRzokbc0YgCdTEp8rt47ty56z5zBgAAAAAqI09PT3l6esowDFWtWtX2taenp3x9fTVs2DAtWbLE7JhFdvz4cfXq1Ut9+vTR0KFDb+pYkydPzve++Pv7F1NK8yyJSZAk9e3oLxdHe5PTACisK89l2BR/RlarUeLne3993qibfp3qydO17N05j5vT0s9T4e38JElvflPwzRUV2cbDpzVg/iadz8xRO/9qWv6PLiVWuPmjmlWcNSq0sTa8fIcmP9haDWq568Kly3rvpyPq9tYPivg0TntPlp8bRdIu5WjE0u2a8OVuZedaFdbSR18/273MF24kqVdLX3lVcVbKhSx9u7vgm4FQdEUeedOnTx999913euqpp0oiDwAAAACUOxEREZo9e7bc3d119OhRvf/++6pSpcpNH9fLy0v29vZKTk7Otz45OVm+vr4F7uPr63vd9lf+TU5OVu3atfO1adeuXb79Tpw4odtvv11du3bVvHnzCnWeP57jz8aMGaOIiAjb12lpaeW6gJN4JlM/HTglSXqkM3fSA+VJ6zqecney1/nMHO1NSlNLv5L7cHRfUprWHzwtO0velGmomP55V1Ot2pmknw+d1roDp3Rrk1pmRyoVq/cka/jSbcq+bNUtjWpq3mMd5e78lx6z/pe5ONrr4c711K+jv37Yl6J5649oc/xZfbbtuD7bdlzdG3tp2K0N1K2RV4FT25YFu0+kavjH23T0TKYc7Cwac09zPXFLQJnN+2dODnZ6pLO/Zv5wSIuiE3RvGz+zI1UIRR5506hRI40bN06PP/64pk6dqpkzZ+ZbAAAAAKCymTVrltLT0yVJ69atU2ZmZrEc18nJSR06dNCaNWts66xWq9asWaOQkJAC9wkJCcnXXpJWr15tax8YGChfX998bdLS0hQTE5PvmMePH9dtt92mDh066MMPP5SdXf7uY0hIiNatW6ecnJx852natGmBU6ZJkrOzszw8PPIt5dnHmxNkGNKtTWopwKtozzMCYC5Hezt1Diyd5958sD5ektSrla/8a1Tuh9lXZP413PRYSN6zz978Zl+pjOgyW+T243pqSayyL1t1VwsffTCoU6kXbv7Izs6i0BY++vQfIYocfot6t6ktO4u0/uBpPfbBZt09Y73+F/ursi9bTcv4Z4aR95yYB/6zUUfPZKpONVeteCpEQ7oFlpvCzRUPB9eTvZ1Fm+PPal9S+RnxVJYV+adp3rx5qlKlin766Sf99NNP+bZZLBaNHDmy2MIBAAAAQHkQEBCgmTNn6q677pJhGIqOjr5mAePWW28t0rEjIiI0aNAgdezYUZ07d9b06dOVkZGhwYMHS5IGDhyoOnXqaPLkyZKkUaNGqUePHpo6dap69+6tZcuWaevWrbaRMxaLRaNHj9brr7+uxo0bKzAwUOPGjZOfn5/Cw8Ml/V64qV+/vt555x2dOnXKlufKqJpHHnlEr732moYMGaKXXnpJu3bt0owZM/Tvf/+7SK+vvLqUk6sVW3+VJD0azKgboDwKaVhTa/efUvThM3qye4MSOUfKhUv6Iu6EJJXYOVB2DL+9kT7dckx7Tqbpyx0nFB5Ux+xIJWZx9FGN/3K3DEN6sH0dTXmojRzK0HNO2vlX05xH2uvY2Uwt2BCv5VuOaV/SBf1zxQ5N+XafBt8SqIc7mzuNYUbWZb3y+U7b74iezbw1tW9bVXNzMi3Tzajt6ao7m/soaneSFkcn6I0HWpsdqdwrcvEmPj6+JHIAAAAAQLn19ttv66mnntLkyZNlsVj0wAMPFNjOYrEoNze3SMfu16+fTp06pfHjxyspKUnt2rVTVFSUfHx8JEmJiYn5RsV07dpVS5cu1dixY/XKK6+ocePGioyMVKtWrWxtXnzxRWVkZGjYsGE6f/68unXrpqioKLm45M1Pv3r1ah06dEiHDh1S3bp18+UxjLw7iT09PfXdd99p+PDh6tChg7y8vDR+/HgNGzasSK+vvPpm10mdzciWn6eL7mjmbXYcAH9B14ZekqTN8Wd1OddaIh88L45OUHauVe3rVVP7egUX9VFx1HB30lO3NdTb3+7XO9/t192tfeXsULGeh2YYhv7z42G9/e1+SdLjXQM0/t4WsrMrm6NE/Gu4acJ9LTW6ZxN9vDlBCzccVXJalt78Zp9mrTmofp3q6YluAapbvXRHxe1PuqBnPo7V4VMZsrez6IWwphrWvUGZfR8La2BIfUXtTtLn24/r5bubqaoLz/i6GeaNYwMAAACACiI8PFzh4eFKT0+Xh4eH9u/fL2/v4vtAf8SIERoxYkSB23788cer1vXp00d9+vS55vEsFosmTpyoiRMnFrj98ccf1+OPP37DXG3atNH69etv2K4iWrIpUZL0cOd6ZepOYwCF17y2hzxcHJR26bJ2nUhTO/9qxXr8i9m5WrIpQZI0lFE3lcYTtwRqUfRR/XruopZsStSQboFmRyo2hmHozW/26b11RyRJI3s21nOhjcvF9F6ebo565rZGGtItUF/tOKn5645of/IFLdgQr4+ij+qe1rU1tHug2tStVuJZVmw9pnFf7NKlHKt8PVw065EgdQqoUeLnLQ0hDWuqkXcVHUpJ12fbjmtQ1wCzI5VrhSreREREaNKkSXJ3d8/3cMmCTJs2rViCAQAAAEB5U6VKFa1du1aBgYFycOBeuYpqz4k0xSack4OdRf06+5sdB8BfZG9nUXCDmlq9J1nRh88Ue/Hmf9t+1bnMHPnXcNVdLX2L9dgou1yd7PVcaBO9/NlOzf7hoPp0rCuPCjD6INdqaGzkTn2y+ZgkaWzv5uVyKkBnB3v9vUNdPdS+jtYdPK35647o50On9dWOE/pqxwl1aVBDQ7s30O1NvYt9FMzF7FyN+2KX/hubN+3qrU1q6d9926pmFediPY+ZLBaLHutSXxO+3K3FmxI0MKR+uSjulVWF6k1s377d9hDK7du3X7MdFwIAAABAZdejRw8dPnxYH374oQ4fPqwZM2bI29tb33zzjerVq6eWLVuaHRE3aUlM3p30Ya185V3VxeQ0AG5G14a/FW+OnNHTtzUstuNarYYW/Jz36IEnbgmUfTmfCglF8/cOdfX+z/E6lJKu9346rBfCmpkd6aZkX7Yq4tM4ff3LSdlZpDcfbKO+ncr3zQsWi0U9mtRSjya1tPtEqj5YH68vd5zQpiNntenIWTWs5a6h3RsoPKiOXBxvfuq7QynpGv7xNu1PviA7ixRxZxM9c1ujcj9NWkEebF9HU6L26VBKuqIPn1HXRl5mRyq3ClW8Wbt2bYH/DQAAAADI76efftLdd9+tW265RevWrdMbb7whb29v7dixQx988IH++9//mh0RN+HCpRxFbj8uSXqsS32T0wC4WSENa0qStsSfVfZlq5wcimcaxB/2pejI6QxVdXFQn47l+0NuFJ2DvZ1eDGuqYYtj9cHP8XqsS4B8Pctnsf9idq6e/jhWP+4/JUd7i2b0D9I9rWubHatYtfTz1LR+7fRCr6ZauOGolsYk6vCpDL382U69891+DQwJ0KNd6quGu9NfOn7k9uN65fOdyszOVa2qzprZP8j2u6ciquriqAfa19GSTYlaFJ1A8eYmMDEvAAAAABSjl19+Wa+//rpWr14tJ6ffO/l33HGHNm3aZGIyFIfPtx9XZnauGntXUXBgxZifHqjMmnhXVQ13J13MydUvv54vtuO+/3PeM0Ee6VxPVZyZRrMyurOFjzrWr65LOVbNWHPA7Dh/SdqlHA1asFk/7j8lF0c7vT+oU4Ur3PxRbU9XjbmnuTaOuUNjezeXn6eLTqdna9rqA+r65hqNi9ylo6czCn28Szm5GvPZTo1eHqfM7FyFNKiplSO7VejCzRWPdQmQJK3em6yTqRfNDVOOUbwBAAAAgGK0c+dOPfDAA1et9/b21unTp01IhOJiGIYWR+dNmfZoF+ZwByoCOzuLQhrkfZAaffhMsRxz1/FUbTpyVg52Fj1+S0CxHBPlj8Vi0Zh78qZLW77lmA6lXDA5UdGcSc/SI/M3afPRs6rq4qAlQ4LVo0kts2OViqoujnqyewP99OLtmtG/nVrV8dClHKsWb0rQ7VN/1FOLYxWbcPa6xzh6OkMP/mejPtmcKItFGtmzsZY8GVxppltt6ltVnQNrKNdqaGlMotlxyi2KNwAAAABQjKpVq6aTJ09etX779u2qU6eOCYlQXDbHn9XBlHS5OtrrgfZcS6Ci6PLbXfAbi6l48/76vFE3vdvUVm1P12I5JsqnDvVr6K4WPrIa0ltR+82OU2gnUy+q73vR2nU8TTXdnbRsWBd1DKh8o00d7e10f7s6+mpENy0dGqzbm9aSYUhRu5P00LvRevA/GxS166RyrUa+/Vb+clL3zvpZe07mvX+LnuisiDubVLpnXw0MyZte9pPNx5R92WpymvKJ4g0AAAAAFKP+/fvrpZdeUlJSkiwWi6xWqzZs2KDnn39eAwcONDsebsLiTXmjbsKD/OTh4mhyGgDFpetvxZvYxHO6lJN7U8c6mXpRX/+SV8B/sluDm86G8u/FXs1kZ5FW70nW1qPXH61RFsSfztDf343W4VMZ8vN00adPhailn6fZsUxlsVjUtaGXPhzcWaufu1X9OvrLyd5O2xLP66kl23TH1B+1OPqoUjNzNOGLXRq+dJvSsy6rc0ANrRrVXd0bV44RS38W1tJX3lWddTo9S1G7k8yOUy5RvAEAAACAYvSvf/1LzZo1k7+/v9LT09WiRQvdeuut6tq1q8aOHWt2PPxFKRcu6dvfPnh4tEt9k9MAKE4NvNzlXdVZ2Zet2pZ47qaOtXDjUV22GgoOrKHWdSv3B97I08i7ivp18pckTf5mnwzDuMEe5tl7Mk195kbr+PmLauDlrhVPd1XDWlXMjlWmNPapqrf+3kY/v3y7RtzeSJ6ujko4k6lxX+xW0KTv9NFv06s+fVtDLR0aLB+PyjFNWkEc7e30cOd6kqTF0UfNDVNOUbwBAAAAgGLk5OSk+fPn6/Dhw/r666+1ZMkS7du3T4sXL5a9vb3Z8fAXfbrlmHJyDQXVq1bp70AGKhqLxWJ7gPimm5g6LSPrsu3ZDkO7M+oGvxsd2kQujnaKTTin7/Ykmx2nQLEJ59TvvWidTs9Si9oe+vSpENWpxrR/1+Jd1UXPhzVV9Jg79NrfWqpeDTdZDamam6M+fLyTXurVTA72fPT+SHA9OdhZtOXoOe09mWZ2nHLHwewAAAAAAFAR1atXT/Xq1TM7BopBrtXQJ5uPSZIeY9QNUCF1bVhTX8SdUPSRv168+XTrMV24dFmBXu66o5l3MaZDeefj4aInuzXQ7LWHNCVqn3o28y5TH+yvP3hKwxbF6mJOrjrWr64PHu8kT1emBy0MNycHDeoaoEe71FdM/Bk19q6qWlWdzY5VZvh4uCispa9W7jypRdEJmvxga7MjlSsUbwAAAADgJkVERBS67bRp00owCUrC2n0pOn7+oqq7Oeqe1rXNjgOgBIQ08JIkxR07r8zsy3JzKtpHZrlWQws2xEuSnugWKLtK9mBy3NiwHg30cUyCDp/K0IrYX23TSZktaleSRn6yXdm5Vt3apJbmPtq+yN//kOzt8p6Lg6s9FlJfK3eeVOT243r57mYUBouAn0QAAAAAuEnbt28vVDuLhQ/zyqPFm/Lmr+/b0V8ujkx9B1RE/jVcVaeaq46fv6itR8/p1iZFe8D4d7uTdOzsRVVzc9Tf29ctoZQozzxcHPXsHY018es9+vfqAwpvV0euTub+P2XF1mN66X+/yGpI97T21fR+QXJyKDsjglAxBAfWUBOfKjqQnK7/xf6qJ7oFmh2p3KB4AwAAAAA3ae3atWZHQAlJOJOhdQdPScqbtx1AxXTluTf/jf1V0UfOFLl48/7PeaNuHg2ub/oH8ii7BnSppw83xuvY2YtasCFew29vZFqWBT/Ha+LXeyRJfTvW1eQH28ieEWMoARaLRY91qa9xX+zWkk0JerxrAKMTC4lSKgAAAAAA17A0JlGGIfVoUkv1a7qbHQdACQppUFOStPFw0Z57sy3xnGITzsnJ3k4Du/JcLFybs4O9nr+rqSRp7o+HdTYju9QzGIahGd8ftBVunuwWqLceonCDkvVA+7qq4uygI6cztOHwabPjlBsUbwAAAAAAKMClnFx9uvWYJOnRLnwgC1R0IQ3zijc7fz2vtEs5hd7vg/V5o27+1s5P3lVdSiQbKo772vippZ+HLmRd1uwfDpXqua1WQ5O+3qt/f39AkvTPO5vo/3o3Z1pXlLgqzg56sH0dSdLi6AST05QfFG8AAAAAACjAqp0ndS4zR3WqueqOZt5mxwFQwvyquSqgppushrQl/myh9jl2NlPf7DopSXqyO89xwI3Z2Vn08t3NJEmLNx3VsbOZpXLey7lWvfS/X7RgQ16x8dX7WujZno0p3KDUPPbbjTDf703W8fMXTU5TPlC8AQAAAACgAEs25d0Z+nBnf6aTASqJK6Nvogs5ddqHG47KakjdG3upma9HSUZDBdK9cS11b+ylnFxD73y3v8TPl3U5VyOWbteK2F9lb2fR1D5t9fgtFBtRuhr7VFVIg5qyGtLSGEbfFAbFGwAAAAAA/mT3iVRtSzwvR3uL+nbyNzsOgFIS0tBLkhR95MbFm7RLOVq+JVGSNKQbH4SjaF7qlTf65ou4E9p1PLXEzpOZfVlPfrRVUbuT5GRvp/8MaK+HOtQtsfMB1zMwJG/0zbLNx5R1OdfkNGUfxRsAAAAAAP5kyaa8D2TDWvryDAugEunSoIYkac/JNJ3PvP7D5JdtTlRGdq4ae1dRjya1SiMeKpBWdTx1fzs/SdJbUftK5BypF3P02Aebtf7gabk52WvB450U1tK3RM4FFMadLXzk6+GiMxnZ+mZnktlxyjyKNwAAAAAA/EHapRxFbj8uSXr0t/nZAVQO3lVd1Mi7igxD2nTk2s+9ycm1auGGo5LynnXDc0PwVzx/V1M52dtp/cHTWn/wVLEe+9SFLPWft0mxCefk4eKgJU8Gq1tjr2I9B1BUDvZ2eiS4niRpUfRRc8OUAxRvAAAAAAD4g8+3HdfFnLy76YMDa5gdB0Ap6/rbc282XWfqtFU7T+pE6iV5VXHS/e3qlFY0VDD+NdxsNwm8+c0+Wa1GsRz313OZ6vtetPaeTFOtqs769KkQta9XvViODdys/p385WBn0bbE8yU6ZWBFQPEGAAAAAIDfGIahxZvyHqL7aJf63E0PVEIhDfKKNxsPny5wu2EY+uDneEnSY10C5OJoX2rZUPGMuKORqjo7aPeJNH31y4mbPt6hlHT1mRut+NMZqlPNVSv+EaJmvh7FkBQoHt4eLurVKm/6vsXRCSanKdtML97MmTNHAQEBcnFxUXBwsDZv3nzd9itWrFCzZs3k4uKi1q1ba9WqVfm2G4ah8ePHq3bt2nJ1dVVoaKgOHjyYr80bb7yhrl27ys3NTdWqVSvwPImJierdu7fc3Nzk7e2tF154QZcvX76p1woAAAAAKNti4s/qUEq63Jzs9UB77qYHKqMuvxVvDiSn69SFrKu2bzl6Tr/8mipnBzs92qVeacdDBVPD3UlP3dZQkvT2t/tv6iHuu46nqt970TqZekmNvKvof093VYCXe3FFBYrNwJAASdIXO44rNTPH3DBlmIOZJ1++fLkiIiI0d+5cBQcHa/r06QoLC9P+/fvl7e19VfuNGzfq4Ycf1uTJk3Xvvfdq6dKlCg8P17Zt29SqVStJ0pQpUzRz5kx99NFHCgwM1Lhx4xQWFqY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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(20, 10))\n", + "plot_slice_errors(metadata, axes[(0, 0)])\n", + "plot_left_over_error_budget(metadata, axes[(0, 1)])\n", + "plot_accumulated_error(metadata, axes[(1, 0)])\n", + "plot_num_qwc_groups(metadata, axes[(1, 1)])" + ] + }, + { + "cell_type": "markdown", + "id": "5c554774-2986-43a0-bdcd-45eaeb81ef5a", + "metadata": {}, + "source": [ + "## Working with multiple observables\n", + "\n", + "The `qiskit_addon_obp.backpropagate` method allows one to pass in a sequence of observables. This simplifies the workflow when dealing with multiple target observables.\n", + "\n", + "We explicitly mention this here to teach you how the truncation strategy handles such a case. For the sake of this example, we add an extra observable to the one which we have been using so far:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0857ea4c-82c1-4641-a268-1a9ba44a312b", + "metadata": {}, + "outputs": [], + "source": [ + "obs = [SparsePauliOp(\"IIIIIZIIII\"), SparsePauliOp(\"IIIIIXIIII\")]" + ] + }, + { + "cell_type": "markdown", + "id": "ba3ac81c-7395-4287-907a-742a1751cbd0", + "metadata": {}, + "source": [ + "For simplicity, we repeat the very first experiment from this tutorial. The only difference is that we now have two observables to backpropagate the circuit into.\n", + "\n", + "For this particular example, this does not affect the number of slices which could be backpropagated. However, we can see now that the two observables resulted in different numbers of Pauli terms and commuting groups." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "10c50f14-5bbd-427d-ae77-24389498a1e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TruncationErrorBudget(per_slice_budget=[0.001], max_error_total=inf, p_norm=1)\n" + ] + } + ], + "source": [ + "truncation_error_budget = setup_budget(max_error_per_slice=0.001)\n", + "print(truncation_error_budget)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "862edaec-fc2c-488b-875d-23841ecc80b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backpropagated 11 circuit slices.\n", + "The new first observable contains 29 terms and 10 commuting groups.\n", + "The new second observable contains 23 terms and 8 commuting groups.\n" + ] + } + ], + "source": [ + "bp_obs, remaining_slices, metadata = backpropagate(\n", + " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", + ")\n", + "reduced_circuit = combine_slices(remaining_slices)\n", + "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", + "print(\n", + " f\"The new first observable contains {len(bp_obs[0])} terms and {len(bp_obs[0].group_commuting(qubit_wise=True))} commuting groups.\"\n", + ")\n", + "print(\n", + " f\"The new second observable contains {len(bp_obs[1])} terms and {len(bp_obs[1].group_commuting(qubit_wise=True))} commuting groups.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "467c614d-6bac-4826-a42b-39937b9d0c72", + "metadata": {}, + "source": [ + "The plots below shed some light on how the backpropagation algorithm handles multiple observables.\n", + "\n", + "First, the plots **top/left**, **top/right** and **bottom/left** teach us that the budget for truncating terms is set individually for each observable. In other words, both observables have the ability to truncate terms assuming an error of `0.001` per backpropagated slice.\n", + "Due to the different nature of the observables, this results in different consumption of the budget. In this example, they exhibit a lot of overlap which is not necessarily the case in general.\n", + "\n", + "**bottom/right** teaches us that `max_qwc_groups` is actually taking _all_ observables combined into account. That means that the terms of all observables are grouped together to arrive at one final number of qubit-wise commuting groups which is compared against `max_qwc_paulis`. The same is done for the `max_paulis` threshold (which we have not discussed in this notebook) which allows you to set a limit on the number of Pauli terms across all observables." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "82271b0d-ddaf-4ad2-b5c6-e9e4c2a4cff7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(20, 10))\n", + "plot_slice_errors(metadata, axes[(0, 0)])\n", + "plot_left_over_error_budget(metadata, axes[(0, 1)])\n", + "plot_accumulated_error(metadata, axes[(1, 0)])\n", + "plot_num_qwc_groups(metadata, axes[(1, 1)])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-obp/index.mdx b/docs/addons/qiskit-addon-obp/index.mdx new file mode 100644 index 00000000000..b585d40fee5 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/index.mdx @@ -0,0 +1,48 @@ +--- +title: "Qiskit addon: operator backpropagation (OBP)" +--- + +# Qiskit addon: operator backpropagation (OBP) + +[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. + +This package contains the Qiskit addon for operator backpropagation (OBP). Experimental errors limit the depth of quantum circuits that can be executed on near-term devices. OBP is a technique to reduce circuit depth by trimming operations from its end at the cost of more operator measurements. + +As one backpropagates an operator further through a circuit, the size of the observable will grow exponentially, which results in both a classical and quantum resource overhead. However, for some circuits, the resulting distribution of Pauli observables is more concentrated than the worst-case exponential scaling, meaning that some terms in the Hamiltonian with small coefficients can be truncated to reduce the quantum overhead. The error incurred by doing this can be controlled to find a suitable tradeoff between precision and efficiency. + +There are a number of ways in which operator backpropagation can be performed, this package uses a method based on Clifford perturbation theory, which has the benefit that the overhead incurred by backpropagating various gates is determined by the non-Cliffordness of that gate. This leads to an increased efficiency for some families of circuits relative to tensor-network based methods for OBP, which currently have high classical overheads even in cases where the quantum overhead remains tame. + +## Documentation + +All documentation is available [here](/docs/addons/qiskit-addon-obp). + +## Installation + +We encourage installing this package via `pip`, when possible: + +```bash +pip install 'qiskit-addon-obp' +``` + +For more installation information refer to the [installation instructions](install) in the documentation. + +## Deprecation Policy + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [release notes](/docs/addons/qiskit-addon-obp/release-notes). + +## Contributing + +The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-obp). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-obp/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). + +We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-obp/issues/new/choose) for tracking requests and bugs. + +## References + +1. 2. Fuller et al. [Improved Quantum Computation using Operator Backpropagation](https://arxiv.org/abs/2502.01897), arXiv:2502.01897 \[quant-ph]. + +## License + +[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-obp/blob/main/LICENSE.txt) + diff --git a/docs/addons/qiskit-addon-obp/install.mdx b/docs/addons/qiskit-addon-obp/install.mdx new file mode 100644 index 00000000000..b6cd49f0394 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/install.mdx @@ -0,0 +1,76 @@ +--- +title: "Installation Instructions" +--- + +# Installation Instructions + +Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: + +* [Option 1: Install from PyPI](#option-1) +* [Option 2: Install from Source](#option-2) + +## Pre-Installation + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +Note: If you are using Windows, use the following commands in PowerShell: + +```pwsh +python3 -m venv c:\path\to\virtual\environment +c:\path\to\virtual\environment\Scripts\Activate.ps1 +``` + + + +## Option 1: Install from PyPI + +The most straightforward way to install the `qiskit-addon-obp` package is via `PyPI`. + +```sh +pip install 'qiskit-addon-obp' +``` + + + +## Option 2: Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +If so, the first step is to clone the `qiskit-addon-obp` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-obp.git +``` + +Next, upgrade pip and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-obp +``` + +The next step is to install `qiskit-addon-obp` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab +``` + diff --git a/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb new file mode 100644 index 00000000000..6e21df19967 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb @@ -0,0 +1,513 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5f9e3f0d-7cc1-4ea7-826b-35dd2a87af1e", + "metadata": {}, + "source": [ + "# Reducing depth of circuits with operator backpropagation\n", + "\n", + "Operator backpropagation is a technique which involves absorbing operations from the end of a quantum circuit into a Pauli operator, generally reducing the depth of the circuit at the cost of additional terms in the operator. The goal is to backpropagate as much of the circuit as possible without allowing the operator to grow too large.\n", + "\n", + "One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms' coefficients." + ] + }, + { + "cell_type": "markdown", + "id": "2cbd99bb-edc8-4032-8253-2f7195080aaa", + "metadata": {}, + "source": [ + "In this tutorial we will implement a [Qiskit pattern](/docs/guides/serverless#qiskit-patterns-with-quantum-serverless) for simulating the quantum dynamics of a Heisenberg spin chain using operator backpropagation:\n", + "\n", + "- **Step 1: Map to quantum problem**\n", + " - Map the time-evolved Hamiltonian onto a quantum circuit\n", + "- **Step 2: Optimize the problem**\n", + " - Slice the circuit\n", + " - Backpropagate slices from the circuit onto a Pauli observable\n", + " - Combine the remaining slices into a single circuit\n", + " - Transpile the circuit for the backend\n", + "- **Step 3: Execute experiments**\n", + " - Calculate the expectation value using the reduced circuit and expanded observable with a [StatevectorEstimator](/docs/api/qiskit/qiskit.primitives.StatevectorEstimator) for sake of simplicity in this notebook\n", + "- **Step 4: Reconstruct results**\n", + " - N.A.\n", + "\n", + "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit_addon_obp.backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." + ] + }, + { + "cell_type": "markdown", + "id": "706ce970-5dd2-431a-890b-6476f232ae7f", + "metadata": {}, + "source": [ + "## Step 1: Map to quantum problem\n", + "### Map the time-evolution of a quantum Heisenberg model to a quantum experiment.\n", + "\n", + "The [qiskit_addon_utils](/docs/addons/qiskit-addon-utils) package provides some reusable functionalities for various purposes.\n", + "\n", + "Its [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/qiskit_addon_utils.problem_generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", + "This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/docs/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows.\n", + "\n", + "In the following, we first generate a heavy-hex `CouplingMap` out of which we carve a linear chain of 10 qubits. Note, that the indices of this new `reduced_coupling_map` are again zero-based." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "de8ce35e-a2c5-474f-adb9-88c9afb2bd76", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "\n", + "coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False)\n", + "\n", + "# Choose a 10-qubit linear chain on this coupling map\n", + "reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 5, 12, 8, 18])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c79b8484-7de0-47cb-be7b-147b502ad1e5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from rustworkx.visualization import graphviz_draw\n", + "\n", + "graphviz_draw(reduced_coupling_map.graph, method=\"circo\")" + ] + }, + { + "cell_type": "markdown", + "id": "f95ba561-f211-4935-bde6-989c0d364ec8", + "metadata": {}, + "source": [ + "Next, we generate a Pauli operator modeling a Heisenberg XYZ Hamiltonian.\n", + "\n", + "$$\\hat{H} = \\sum_{(j,k)\\in E} (J_{x} \\sigma_j^{x} \\sigma_{k}^{x} +\n", + " J_{y} \\sigma_j^{y} \\sigma_{k}^{y} + J_{z} \\sigma_j^{z} \\sigma_{k}^{z}) +\n", + " \\sum_{j\\in V} (h_{x} \\sigma_j^{x} + h_{y} \\sigma_j^{y} + h_{z} \\sigma_j^{z})$$\n", + "\n", + "Where $G(V,E)$ is the graph of the coupling map provided." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "056df48a-0f8d-4a42-9c47-784343a5d6d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIXXI', 'IIIIIIIYYI', 'IIIIIIIZZI', 'IIIIIXXIII', 'IIIIIYYIII', 'IIIIIZZIII', 'IIIXXIIIII', 'IIIYYIIIII', 'IIIZZIIIII', 'IXXIIIIIII', 'IYYIIIIIII', 'IZZIIIIIII', 'IIIIIIIIXX', 'IIIIIIIIYY', 'IIIIIIIIZZ', 'IIIIIIXXII', 'IIIIIIYYII', 'IIIIIIZZII', 'IIIIXXIIII', 'IIIIYYIIII', 'IIIIZZIIII', 'IIXXIIIIII', 'IIYYIIIIII', 'IIZZIIIIII', 'XXIIIIIIII', 'YYIIIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIIY', 'IIIIIIIIIZ', 'IIIIIIIIXI', 'IIIIIIIIYI', 'IIIIIIIIZI', 'IIIIIIIXII', 'IIIIIIIYII', 'IIIIIIIZII', 'IIIIIIXIII', 'IIIIIIYIII', 'IIIIIIZIII', 'IIIIIXIIII', 'IIIIIYIIII', 'IIIIIZIIII', 'IIIIXIIIII', 'IIIIYIIIII', 'IIIIZIIIII', 'IIIXIIIIII', 'IIIYIIIIII', 'IIIZIIIIII', 'IIXIIIIIII', 'IIYIIIIIII', 'IIZIIIIIII', 'IXIIIIIIII', 'IYIIIIIIII', 'IZIIIIIIII', 'XIIIIIIIII', 'YIIIIIIIII', 'ZIIIIIIIII'],\n", + " coeffs=[0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j,\n", + " 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j,\n", + " 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j,\n", + " 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j,\n", + " 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j,\n", + " 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j,\n", + " 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, 1.04719755+0.j,\n", + " 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j,\n", + " 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j,\n", + " 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j,\n", + " 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j,\n", + " 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j,\n", + " 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j,\n", + " 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j,\n", + " 0.34906585+0.j])\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "# Get a qubit operator describing the Heisenberg XYZ model\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " reduced_coupling_map,\n", + " coupling_constants=(np.pi / 8, np.pi / 4, np.pi / 2),\n", + " ext_magnetic_field=(np.pi / 3, np.pi / 6, np.pi / 9),\n", + ")\n", + "print(hamiltonian)" + ] + }, + { + "cell_type": "markdown", + "id": "3a858a3f-fc3d-4e09-9db9-8d064e036045", + "metadata": {}, + "source": [ + "From the qubit operator, we can generate a quantum circuit which models its time evolution.\n", + "Once again, the [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/qiskit_addon_utils.problem_generators) module comes to the resuce with a handy function do just that:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1d68f197-ffa4-49de-9fe8-243b1facbd00", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.synthesis import LieTrotter\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", + "\n", + "circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " time=0.2,\n", + " synthesis=LieTrotter(reps=2),\n", + ")\n", + "circuit.draw(\"mpl\", style=\"iqp\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "ddc43a7d-6558-4f82-90a6-7ec5370a7fd5", + "metadata": {}, + "source": [ + "## Step 2: Optimize the problem\n", + "### Create circuit slices to backpropagate\n", + "\n", + "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](/docs/api/qiskit-addon-utils/qiskit_addon_utils.slicing.slice_by_gate_types) function.\n", + "\n", + "For a more detailed discussion on circuit slicing, check out this [how-to guide](/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices) of the [qiskit-addon-utils](/docs/addons/qiskit-addon-utils/index) package." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5d7f0d4e-ce4a-4f11-976b-437a74244b08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Separated the circuit into 18 slices.\n" + ] + } + ], + "source": [ + "from qiskit_addon_utils.slicing import slice_by_gate_types\n", + "\n", + "slices = slice_by_gate_types(circuit)\n", + "print(f\"Separated the circuit into {len(slices)} slices.\")" + ] + }, + { + "cell_type": "markdown", + "id": "c7e3ef27-8c58-4790-9269-26dacfafa0a1", + "metadata": {}, + "source": [ + "### Constrain how large the operator may grow during backpropagation\n", + "\n", + "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.simplify.OperatorBudget) instance.\n", + "\n", + "Here we specify that backpropagation should stop when the number of qubit-wise commuting Pauli groups in the operator grows past 8." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1279b567-ba19-4542-9dc1-c57a91118a20", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_obp.utils.simplify import OperatorBudget\n", + "\n", + "op_budget = OperatorBudget(max_qwc_groups=8)" + ] + }, + { + "cell_type": "markdown", + "id": "6306e6a1-56fb-432d-80aa-5277233c9ed9", + "metadata": {}, + "source": [ + "### Backpropagate slices from the circuit\n", + "\n", + "First, we will specify the Pauli-Z observable on qubit 0, and we will backpropagate slices from the time-evolution circuit until the terms in the observable can no longer be combined into 8 or fewer qubit-wise commuting Pauli groups.\n", + "\n", + "Below you will see that we backpropagated 7 slices but only used 6 of the 8 allotted Pauli groups. This implies that backpropagating one more slice would cause the number of Pauli groups to exceed 8. We can verify that this is the case by inspecting the returned metadata." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "65ec9cb1-a4ed-497b-a616-180e9659956f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backpropagated 7 slices.\n", + "New observable has 18 terms, which can be combined into 8 groups.\n", + "Note that backpropagating one more slice would result in 27 terms across 12 groups.\n", + "The remaining circuit after backpropagation looks as follows:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "from qiskit_addon_obp import backpropagate\n", + "from qiskit_addon_utils.slicing import combine_slices\n", + "\n", + "# Specify a single-qubit observable\n", + "observable = SparsePauliOp(\"IIIIIIIIIZ\")\n", + "\n", + "# Backpropagate slices onto the observable\n", + "bp_obs, remaining_slices, metadata = backpropagate(observable, slices, operator_budget=op_budget)\n", + "# Recombine the slices remaining after backpropagation\n", + "bp_circuit = combine_slices(remaining_slices, include_barriers=True)\n", + "\n", + "print(f\"Backpropagated {metadata.num_backpropagated_slices} slices.\")\n", + "print(\n", + " f\"New observable has {len(bp_obs.paulis)} terms, which can be combined into {len(bp_obs.group_commuting(qubit_wise=True))} groups.\"\n", + ")\n", + "print(\n", + " f\"Note that backpropagating one more slice would result in {metadata.backpropagation_history[-1].num_paulis[0]} terms \"\n", + " f\"across {metadata.backpropagation_history[-1].num_qwc_groups} groups.\"\n", + ")\n", + "print(\"The remaining circuit after backpropagation looks as follows:\")\n", + "bp_circuit.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "d25b9701-f1b1-4230-9dcc-554b85e91cf7", + "metadata": {}, + "source": [ + "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating.setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", + "\n", + "In order to enable this truncation, we need to setup our error budget like so:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3ba04824-dbe7-4e11-80b9-ea2320a131d5", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_obp.utils.truncating import setup_budget\n", + "\n", + "truncation_error_budget = setup_budget(max_error_per_slice=0.005)" + ] + }, + { + "cell_type": "markdown", + "id": "c6dda21f-4a28-4c4e-9727-15288973150e", + "metadata": {}, + "source": [ + "Note that by allocating `5e-3` error per slice for truncation, we are able to remove 3 more slices from the circuit, while remaining within the original budget of 8 commuting Pauli groups in the observable. By default, `backpropagate` uses the L1 norm of the truncated coefficients to bound the total error incurred from truncation. For other options refer to the [how-to guide on specifying the p_norm](/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm).\n", + "\n", + "In this particular example where we have backpropagated 10 slices, the total truncation error should not exceed ``(5e-3 error/slice) * (10 slices) = 5e-2``.\n", + "For further discussion on distributing an error budget across your slices, check out [this how-to guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5e8bae1a-ef18-4eb0-9d2a-1ac7bbdced3b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backpropagated 10 slices.\n", + "New observable has 19 terms, which can be combined into 8 groups.\n", + "After truncation, the error in our observable is bounded by 4.933e-02\n", + "Note that backpropagating one more slice would result in 27 terms across 13 groups.\n", + "The remaining circuit after backpropagation looks as follows:\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run the same experiment but truncate observable terms with small coefficients\n", + "bp_obs_trunc, remaining_slices_trunc, metadata = backpropagate(\n", + " observable, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", + ")\n", + "\n", + "# Recombine the slices remaining after backpropagation\n", + "bp_circuit_trunc = combine_slices(remaining_slices_trunc, include_barriers=True)\n", + "\n", + "print(f\"Backpropagated {metadata.num_backpropagated_slices} slices.\")\n", + "print(\n", + " f\"New observable has {len(bp_obs_trunc.paulis)} terms, which can be combined into {len(bp_obs_trunc.group_commuting(qubit_wise=True))} groups.\\n\"\n", + " f\"After truncation, the error in our observable is bounded by {metadata.accumulated_error(0):.3e}\"\n", + ")\n", + "print(\n", + " f\"Note that backpropagating one more slice would result in {metadata.backpropagation_history[-1].num_paulis[0]} terms \"\n", + " f\"across {metadata.backpropagation_history[-1].num_qwc_groups} groups.\"\n", + ")\n", + "print(\"The remaining circuit after backpropagation looks as follows:\")\n", + "bp_circuit_trunc.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "1e258829-af9f-43ad-8c79-9455243d7ced", + "metadata": {}, + "source": [ + "### Now that we have our reduced ansatze and expanded observables, we can transpile our experiments to the backend.\n", + "\n", + "Here we will use the 14-qubit [FakeMelbourneV2](/docs/api/qiskit-ibm-runtime/fake-provider-fake-melbourne-v2) from [qiskit-ibm-runtime](/docs/api/qiskit-ibm-runtime) to demonstrate how to transpile to a QPU backend." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b219035b-c5e7-4a08-a8df-cd275bdfe7a0", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", + "from qiskit_ibm_runtime.fake_provider import FakeMelbourneV2\n", + "\n", + "# Specify a backend and a pass manager for transpilation\n", + "backend = FakeMelbourneV2()\n", + "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n", + "\n", + "# Transpile original experiment\n", + "circuit_isa = pm.run(circuit)\n", + "observable_isa = observable.apply_layout(circuit_isa.layout)\n", + "\n", + "# Transpile backpropagated experiment\n", + "bp_circuit_isa = pm.run(bp_circuit)\n", + "bp_obs_isa = bp_obs.apply_layout(bp_circuit_isa.layout)\n", + "\n", + "# Transpile the backpropagated experiment with truncated observable terms\n", + "bp_circuit_trunc_isa = pm.run(bp_circuit_trunc)\n", + "bp_obs_trunc_isa = bp_obs_trunc.apply_layout(bp_circuit_trunc_isa.layout)" + ] + }, + { + "cell_type": "markdown", + "id": "966bcc9b-48a4-4a6a-8b03-e5aded1ab9df", + "metadata": {}, + "source": [ + "## Step 3: Execute quantum experiments\n", + "### Calculate expectation value\n", + "\n", + "Finally, we can run the backpropagated experiments and compare them with the full experiment using the noiseless [StatevectorEstimator](/docs/api/qiskit/qiskit.primitives.StatevectorEstimator). We can see that the backpropagated expectation value without truncation is equivalent to the exact value within the limits of numerical precision.\n", + "\n", + "The expectation value on the operator with truncated terms has some error on the order of ``1e-4``, which is within the expected tolerance.\n", + "\n", + "**Note:** We use a statevector-based ``Estimator`` primitive to illustrate the effect of truncation on the output. To run on the backend to which the experiments were transpiled in Step 2, one would import the [EstimatorV2](/docs/api/qiskit-ibm-runtime/estimator-v2) from ``qiskit-ibm-runtime`` and pass the backend instance into the constructor." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ae47b4e1-f40e-4308-a32d-2ad7ecc56c03", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exact expectation value: 0.8854160687717507\n", + "Backpropagated expectation value: 0.8854160687717532\n", + "Backpropagated expectation value with truncation: 0.8850236647156059\n", + " - Expected Error for truncated observable: 4.933e-02\n", + " - Observed Error for truncated observable: 3.924e-04\n" + ] + } + ], + "source": [ + "from qiskit.primitives import StatevectorEstimator as Estimator\n", + "\n", + "estimator = Estimator()\n", + "\n", + "# Run the experiments using Estimator primitive\n", + "result_exact = estimator.run([(circuit_isa, observable_isa)]).result()[0].data.evs.item()\n", + "result_bp = estimator.run([(bp_circuit_isa, bp_obs_isa)]).result()[0].data.evs.item()\n", + "result_bp_trunc = (\n", + " estimator.run([(bp_circuit_trunc_isa, bp_obs_trunc_isa)]).result()[0].data.evs.item()\n", + ")\n", + "\n", + "print(f\"Exact expectation value: {result_exact}\")\n", + "print(f\"Backpropagated expectation value: {result_bp}\")\n", + "print(f\"Backpropagated expectation value with truncation: {result_bp_trunc}\")\n", + "print(f\" - Expected Error for truncated observable: {metadata.accumulated_error(0):.3e}\")\n", + "print(f\" - Observed Error for truncated observable: {abs(result_exact - result_bp_trunc):.3e}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/package.json b/package.json index fd0672cd084..0248925f7bb 100644 --- a/package.json +++ b/package.json @@ -32,6 +32,7 @@ "check:qiskit-versions": "tsx scripts/js/commands/checkQiskitApiVersions.ts", "regen-apis": "tsx scripts/js/commands/api/regenerateApiDocs.ts", "gen-api": "tsx scripts/js/commands/api/updateApiDocs.ts", + "gen-addon-docs": "tsx scripts/js/commands/updateAddonDocs.ts", "generate-historical-redirects": "tsx scripts/js/commands/api/generateHistoricalRedirects.ts", "save-internal-links": "tsx scripts/js/commands/saveInternalLinks.ts" }, diff --git a/scripts/config/addon-docs.ts b/scripts/config/addon-docs.ts new file mode 100644 index 00000000000..fa5e1baa4e7 --- /dev/null +++ b/scripts/config/addon-docs.ts @@ -0,0 +1,25 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +type AddonDocsConfig = Record; + +export const ADDON_DOCS_CONFIG: AddonDocsConfig = { + "qiskit-addon-obp": { + version: "0.3.0", + include: [ + "index.html", + "install.html", + "how_tos/*.ipynb", + "tutorials/*.ipynb", + ], + }, +}; diff --git a/scripts/js/commands/updateAddonDocs.ts b/scripts/js/commands/updateAddonDocs.ts new file mode 100644 index 00000000000..2b3ca577e21 --- /dev/null +++ b/scripts/js/commands/updateAddonDocs.ts @@ -0,0 +1,95 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import yargs from "yargs/yargs"; +import { hideBin } from "yargs/helpers"; + +import { Pkg } from "../lib/api/Pkg.js"; +import { zxMain } from "../lib/zx.js"; +import { parseMinorVersion } from "../lib/apiVersions.js"; +import { pathExists, rmFilesInFolder } from "../lib/fs.js"; +import { downloadSphinxArtifact } from "../lib/api/sphinxArtifacts.js"; +import { runAddonContentPipeline } from "../lib/addons/addonContentPipeline.js"; +import { ADDON_DOCS_CONFIG } from "../../config/addon-docs.js"; + +const readArgs = () => { + return yargs(hideBin(process.argv)) + .version(false) + .option("package", { + alias: "p", + type: "string", + choices: Object.keys(ADDON_DOCS_CONFIG), + description: "Which addon to update. Defaults to all configured addons.", + }) + .option("skip-download", { + type: "boolean", + default: false, + description: + "Reuse the already-downloaded artifact instead of downloading again.", + }) + .parseSync(); +}; + +async function runForAddon( + pkgName: string, + skipDownload: boolean, +): Promise { + const addonConfig = ADDON_DOCS_CONFIG[pkgName]; + const { version } = addonConfig; + const minorVersion = parseMinorVersion(version); + if (minorVersion === null) { + throw new Error( + `Invalid version "${version}" in addon-docs.ts for ${pkgName}`, + ); + } + + const pkg = await Pkg.fromArgs(pkgName, version, minorVersion, "latest"); + const sphinxArtifactFolder = pkg.sphinxArtifactFolder(); + + if ( + skipDownload && + (await pathExists(`${sphinxArtifactFolder}/artifact`)) + ) { + console.log(`Skip downloading sources for ${pkg.name}:${minorVersion}`); + } else { + await downloadSphinxArtifact(pkg, sphinxArtifactFolder); + } + + const addonDir = `docs/addons/${pkg.name}`; + if (await pathExists(addonDir)) { + await rmFilesInFolder(addonDir); + } + const addonImagesDir = `public/docs/images/addons/${pkg.name}`; + if (await pathExists(addonImagesDir)) { + await rmFilesInFolder(addonImagesDir); + } + + console.log(`Run addon prose pipeline for ${pkg.name}:${minorVersion}`); + await runAddonContentPipeline( + `${sphinxArtifactFolder}/artifact`, + "docs", + "public/docs", + pkg, + addonConfig.include, + ); +} + +zxMain(async () => { + const args = readArgs(); + const packages = args.package + ? [args.package] + : Object.keys(ADDON_DOCS_CONFIG); + + for (const pkgName of packages) { + await runForAddon(pkgName, args.skipDownload); + } +}); diff --git a/scripts/js/lib/addons/addonContentPipeline.ts b/scripts/js/lib/addons/addonContentPipeline.ts new file mode 100644 index 00000000000..540794c3b2d --- /dev/null +++ b/scripts/js/lib/addons/addonContentPipeline.ts @@ -0,0 +1,192 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { readFile, writeFile } from "fs/promises"; +import { dirname, parse, relative } from "path"; + +import { load } from "cheerio"; +import { globby } from "globby"; +import { mkdirp } from "mkdirp"; +import { isEmpty, uniqBy } from "lodash-es"; + +import { sphinxHtmlToMarkdown } from "../api/htmlToMd.js"; +import { generateAddonToc } from "./generateAddonToc.js"; +import { HtmlToMdResultWithUrl } from "../api/HtmlToMdResult.js"; +import { Pkg } from "../api/Pkg.js"; +import { updateLinks, relativizeLink } from "../api/updateLinks.js"; +import { DOCS_BASE_PATH } from "../api/conversionPipeline.js"; +import addFrontMatter from "../api/addFrontMatter.js"; +import { dedupeHtmlIdsFromResults } from "../api/dedupeHtmlIds.js"; +import removeMathBlocksIndentation from "../api/removeMathBlocksIndentation.js"; +import { saveImages } from "../api/saveImages.js"; + +export async function runAddonContentPipeline( + htmlPath: string, + docsBaseFolder: string, + publicBaseFolder: string, + pkg: Pkg, + include: string[], +): Promise { + const outputDir = `${docsBaseFolder}/addons/${pkg.name}`; + await mkdirp(outputDir); + + const allFiles = await globby(include, { cwd: htmlPath }); + const htmlFiles = allFiles.filter((f) => f.endsWith(".html")); + const notebookFiles = allFiles.filter((f) => f.endsWith(".ipynb")); + + if (isEmpty(htmlFiles) && isEmpty(notebookFiles)) { + console.log(`No addon content files found for ${pkg.name}`); + return; + } + + const results = await convertProseFiles( + pkg, + htmlPath, + docsBaseFolder, + publicBaseFolder, + htmlFiles, + ); + + await updateLinks(results, { + kebabCaseAndShorten: false, + pkgName: pkg.name, + pkgOutputDir: `${DOCS_BASE_PATH}/addons/${pkg.name}`, + }); + await dedupeHtmlIdsFromResults(results); + addFrontMatter(results, pkg); + removeMathBlocksIndentation(results); + + await writeMdxFiles(docsBaseFolder, results); + await saveImages( + uniqBy(results.flatMap((r) => r.images), (img) => img.fileName), + `${htmlPath}/_images`, + publicBaseFolder, + pkg, + ); + await copyNotebooks(htmlPath, outputDir, notebookFiles); + await writeTocFile(htmlPath, outputDir, pkg, docsBaseFolder); +} + +async function convertProseFiles( + pkg: Pkg, + htmlPath: string, + docsBaseFolder: string, + publicBaseFolder: string, + filePaths: string[], +): Promise { + const results: HtmlToMdResultWithUrl[] = []; + const outputDir = `${docsBaseFolder}/addons/${pkg.name}`; + const imageDestination = `${publicBaseFolder}/images/addons/${pkg.name}`; + + for (const file of filePaths) { + const html = await readFile(`${htmlPath}/${file}`, "utf-8"); + const result = await sphinxHtmlToMarkdown({ + html, + fileName: file, + determineGithubUrl: pkg.determineGithubUrlFn(), + imageDestination, + releaseNotesTitle: "", + hasSeparateReleaseNotes: false, + isCApi: false, + hasRootNamespaceFile: false, + }); + + if (result.markdown === "") continue; + + const title = extractTitle(html); + const description = extractDescription(html); + const quotedTitle = `"${title.replace(/\\/g, "\\\\").replace(/"/g, '\\"')}"`; + const frontmatterLines = [`title: ${quotedTitle}`]; + if (description) { + const quotedDesc = `"${description.replace(/\\/g, "\\\\").replace(/"/g, '\\"')}"`; + frontmatterLines.push(`description: ${quotedDesc}`); + } + // Content-only (no --- delimiters) — addFrontMatter wraps it. + result.meta.hardcodedFrontmatter = frontmatterLines.join("\n"); + + const { dir, name } = parse(`${outputDir}/${file}`); + const url = `/${relative(docsBaseFolder, dir)}/${name}`; + results.push({ ...result, url }); + } + return results; +} + +function extractTitle(html: string): string { + const $ = load(html); + const h1 = $("h1").first(); + // Remove Sphinx permalink anchors before extracting text. + h1.find("a.headerlink").remove(); + return h1.text().trim() || "Untitled"; +} + +function extractDescription(html: string): string | null { + const $ = load(html); + const desc = $('meta[name="description"]').attr("content"); + return desc?.trim() || null; +} + +async function writeMdxFiles( + docsBaseFolder: string, + results: HtmlToMdResultWithUrl[], +): Promise { + for (const result of results) { + const filePath = `${docsBaseFolder}${result.url}.mdx`; + await mkdirp(dirname(filePath)); + await writeFile(filePath, result.markdown); + } +} + +async function copyNotebooks( + htmlPath: string, + outputDir: string, + notebookFiles: string[], +): Promise { + if (notebookFiles.length === 0) return; + + for (const file of notebookFiles) { + const dest = `${outputDir}/${file}`; + await mkdirp(dirname(dest)); + const raw = await readFile(`${htmlPath}/${file}`, "utf-8"); + const notebook = JSON.parse(raw); + for (const cell of notebook.cells ?? []) { + const lines: string[] = Array.isArray(cell.source) + ? cell.source + : [cell.source]; + cell.source = lines.map((line) => + line.replace(/\[([^\]]*)\]\(([^)]+)\)/g, (match, text, url) => { + const rewritten = relativizeLink({ url, text }); + return rewritten ? `[${rewritten.text ?? text}](${rewritten.url})` : match; + }), + ); + } + await writeFile(dest, JSON.stringify(notebook, null, 1)); + } +} + +async function writeTocFile( + htmlPath: string, + outputDir: string, + pkg: Pkg, + docsBaseFolder: string, +): Promise { + console.log("Generating addon toc"); + const toc = await generateAddonToc( + htmlPath, + outputDir, + pkg.title, + docsBaseFolder, + ); + await writeFile( + `${outputDir}/_toc.json`, + JSON.stringify(toc, null, 2) + "\n", + ); +} diff --git a/scripts/js/lib/addons/generateAddonToc.ts b/scripts/js/lib/addons/generateAddonToc.ts new file mode 100644 index 00000000000..65a5fe93a77 --- /dev/null +++ b/scripts/js/lib/addons/generateAddonToc.ts @@ -0,0 +1,101 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { readFile } from "fs/promises"; + +import { load } from "cheerio"; + +import { TocEntry } from "../api/generateToc.js"; + +export type AddonToc = { + title: string; + children: TocEntry[]; + collapsed: boolean; +}; + +// Sphinx artifact folders that belong to the API pipeline, not prose. +const EXCLUDED_HREF_PREFIXES = ["apidocs/", "stubs/", "release-notes"]; + +/** Build a _toc.json for addon content by parsing the Sphinx sidebar nav. */ +export async function generateAddonToc( + htmlPath: string, + outputDir: string, + pkgTitle: string, + docsBaseFolder: string, +): Promise { + const indexHtml = await readFile(`${htmlPath}/index.html`, "utf-8"); + const $ = load(indexHtml); + + // Collect the set of URLs we actually wrote so we can filter out entries + // that weren't included (e.g. apidocs children under a section we skip). + const outputBase = `/${outputDir.startsWith(docsBaseFolder) + ? outputDir + : `${docsBaseFolder}/${outputDir}`}`; + + const children: TocEntry[] = []; + + $(".sidebar-tree .toctree-l1").each((_, l1) => { + const l1Link = $(l1).children("a").first(); + const href = l1Link.attr("href") ?? ""; + + // Skip external links, API reference, release notes. + if ( + l1Link.hasClass("reference external") || + EXCLUDED_HREF_PREFIXES.some((prefix) => href.startsWith(prefix)) + ) { + return; + } + + const title = l1Link.text().trim(); + const url = hrefToUrl(href, outputBase); + + const l2Items = $(l1).find(".toctree-l2"); + if (l2Items.length === 0) { + children.push({ title, url }); + } else { + const subChildren: TocEntry[] = []; + l2Items.each((_, l2) => { + const l2Link = $(l2).children("a").first(); + const l2Href = l2Link.attr("href") ?? ""; + if ( + l2Link.hasClass("reference external") || + EXCLUDED_HREF_PREFIXES.some((prefix) => l2Href.startsWith(prefix)) + ) { + return; + } + subChildren.push({ + title: l2Link.text().trim(), + url: hrefToUrl(l2Href, outputBase), + }); + }); + + if (subChildren.length > 0) { + // If the section index page exists as a converted file, link it; + // otherwise emit a section header with no url. + const sectionUrl = href === "#" ? undefined : url; + const entry: TocEntry = { title, children: subChildren }; + if (sectionUrl) entry.url = sectionUrl; + children.push(entry); + } + } + }); + + return { title: pkgTitle, children, collapsed: true }; +} + +function hrefToUrl(href: string, outputBase: string): string { + if (href === "#") return outputBase; + // Strip .html and anchor fragment, prepend output base path. + const withoutAnchor = href.split("#")[0]; + const withoutExt = withoutAnchor.replace(/\.html$/, ""); + return `${outputBase}/${withoutExt}`; +} diff --git a/scripts/js/lib/api/updateLinks.ts b/scripts/js/lib/api/updateLinks.ts index 70f32ff584d..662a7d1554c 100644 --- a/scripts/js/lib/api/updateLinks.ts +++ b/scripts/js/lib/api/updateLinks.ts @@ -164,6 +164,26 @@ export function normalizeUrl( } export function relativizeLink(link: Link): Link | undefined { + // Handle qiskit.github.io addon links before the generic github.io rules. + // stubs/ and apidocs/ map to /docs/api/; everything else maps to /docs/addons/. + const githubIoMatch = link.url.match( + /^https:\/\/qiskit\.github\.io\/(qiskit[^/]+)\/(stubs|apidocs|apidoc)\/(.+)$/, + ); + if (githubIoMatch) { + const [, pkg, , rest] = githubIoMatch; + const page = rest.replace(/\.html$/, ""); + return { url: `/docs/api/${pkg}/${page}` }; + } + const githubIoProseMatch = link.url.match( + /^https:\/\/qiskit\.github\.io\/(qiskit[^/]+)\/?(.*)$/, + ); + if (githubIoProseMatch) { + const [, pkg, rest] = githubIoProseMatch; + const page = rest.replace(/\.html$/, "").replace(/\/$/, ""); + const url = page ? `/docs/addons/${pkg}/${page}` : `/docs/addons/${pkg}`; + return { url }; + } + const priorPrefixToNewPrefix = new Map([ ["https://qiskit.org/documentation/apidoc/", "/api/qiskit"], ["https://qiskit.org/documentation/stubs/", "/api/qiskit"], @@ -171,6 +191,8 @@ export function relativizeLink(link: Link): Link | undefined { ["https://docs.quantum-computing.ibm.com/", ""], ["https://quantum.cloud.ibm.com/docs", "/docs"], ["https://quantum.cloud.ibm.com/learning", "/learning"], + ["https://quantum.test.cloud.ibm.com/docs", "/docs"], + ["https://quantum.test.cloud.ibm.com/learning", "/learning"], ]); const priorPrefix = Array.from(priorPrefixToNewPrefix.keys()).find((prefix) => link.url.startsWith(prefix), From 20bb262ed87e63568ced23aac36a12231ad48019 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Wed, 29 Apr 2026 16:22:46 -0400 Subject: [PATCH 002/104] fix links to addon api docs --- .../how_tos/bound_error_using_p_norm.ipynb | 2 +- .../how_tos/truncate_operator_terms.ipynb | 12 ++-- .../tutorials/01_getting_started.ipynb | 12 ++-- scripts/js/lib/addons/addonContentPipeline.ts | 69 +++++++++++++++++-- scripts/js/lib/api/updateLinks.ts | 20 ------ 5 files changed, 77 insertions(+), 38 deletions(-) diff --git a/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb b/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb index f35818eea62..96ca9c4be6d 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb @@ -21,7 +21,7 @@ "id": "2780c587-cd2a-4398-8faa-de5e0c661a10", "metadata": {}, "source": [ - "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit_addon_obp.backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating.TruncationErrorBudget).\n", + "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget).\n", "In this guide, you will learn about its additional keyword argument `p_norm`, which can be used to change the Lp-norm used to estimate the error incurred by the truncated Pauli terms." ] }, diff --git a/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb b/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb index 2121983b8b1..bd83e4fd410 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb @@ -13,13 +13,13 @@ "id": "c1453472-3506-4008-b432-f8ef9ccbf3ee", "metadata": {}, "source": [ - "This guide explains how to configure the Pauli term truncation mechanism provided by the [qiskit_addon_obp.utils.truncating](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating) module.\n", + "This guide explains how to configure the Pauli term truncation mechanism provided by the [qiskit_addon_obp.utils.truncating](/docs/addons/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating) module.\n", "\n", "Operator backpropagation (OBP) can be used to reduce the depth of quantum circuits at the cost of a more complex observable. In order to get meaningful results from OBP, one usually needs to truncate terms from their observable to prevent it from growing too large. One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms' coefficients.\n", "\n", - "The [backpropagate](/docs/api/qiskit-addon-obp/qiskit_addon_obp.backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating.TruncationErrorBudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", + "The [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", "The amount of terms that are truncated depends on various configuration parameters specified by the user.\n", - "As of now, only a single truncation strategy is available -- the [truncate_binary_search](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating.truncate_binary_search) method.\n", + "As of now, only a single truncation strategy is available -- the [truncate_binary_search](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search) method.\n", "Given an observable and some _budget_ it will perform a binary search over the Pauli terms and coefficients within that observable to find the optimal threshold,\n", "such that the sum of the truncated coefficients is maximal but lower than the budget.\n", "\n", @@ -32,7 +32,7 @@ "id": "aaf5f046-6d5e-4b6f-add7-b60604edef30", "metadata": {}, "source": [ - "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating.TruncationErrorBudget) via the accompanying [setup_budget](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating.setup_budget) function." + "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) via the accompanying [setup_budget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function." ] }, { @@ -193,7 +193,7 @@ "id": "4a772af6-edc6-4e53-81c7-53f3b72bf58a", "metadata": {}, "source": [ - "Let's use the [OBPMetadata](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.metadata.OBPMetadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", + "Let's use the [OBPMetadata](/docs/addons/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.metadata.OBPMetadata) instance and the tools provided by the [visualization](/docs/addons/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", "\n", "- **The top/left plot** shows that we have enough budget to begin truncating observable terms after backpropagating the third slice. Starting from the third slice, we know that we truncate at least one term from each slice we backpropagate because we are incurring some truncation error after each slice.\n", "- **The top/right plot** shows that the error budget ramps up to `.003` for the third slice. We see a sharp drop in remaining budget, which means terms were truncated from the observable. This is consistent with what we inferred from the top/left plot.\n", @@ -714,7 +714,7 @@ "source": [ "Since we passed a subset of our slices (i.e. ``slices[-num_slices:]``) to ``backpropagate``, we must combine the slices remaining after backpropagation with the slices that were never sent for backpropagation (i.e. ``slices[:-num_slices]``).\n", "\n", - "Once we have combined all of the remaining slices, we can use [combine_slices](/docs/api/qiskit-addon-utils/qiskit_addon_utils.slicing.combine_slices.html#qiskit_addon_utils.slicing.combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became." + "Once we have combined all of the remaining slices, we can use [combine_slices](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.slicing.combine_slices#qiskit_addon_utils.slicing.combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became." ] }, { diff --git a/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb index 6e21df19967..88d280b002b 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb @@ -31,7 +31,7 @@ "- **Step 4: Reconstruct results**\n", " - N.A.\n", "\n", - "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit_addon_obp.backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." + "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." ] }, { @@ -44,7 +44,7 @@ "\n", "The [qiskit_addon_utils](/docs/addons/qiskit-addon-utils) package provides some reusable functionalities for various purposes.\n", "\n", - "Its [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/qiskit_addon_utils.problem_generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", + "Its [qiskit_addon_utils.problem_generators](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", "This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/docs/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows.\n", "\n", "In the following, we first generate a heavy-hex `CouplingMap` out of which we carve a linear chain of 10 qubits. Note, that the indices of this new `reduced_coupling_map` are again zero-based." @@ -159,7 +159,7 @@ "metadata": {}, "source": [ "From the qubit operator, we can generate a quantum circuit which models its time evolution.\n", - "Once again, the [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/qiskit_addon_utils.problem_generators) module comes to the resuce with a handy function do just that:" + "Once again, the [qiskit_addon_utils.problem_generators](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators) module comes to the resuce with a handy function do just that:" ] }, { @@ -200,7 +200,7 @@ "## Step 2: Optimize the problem\n", "### Create circuit slices to backpropagate\n", "\n", - "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](/docs/api/qiskit-addon-utils/qiskit_addon_utils.slicing.slice_by_gate_types) function.\n", + "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.slicing.slice_by_gate_types) function.\n", "\n", "For a more detailed discussion on circuit slicing, check out this [how-to guide](/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices) of the [qiskit-addon-utils](/docs/addons/qiskit-addon-utils/index) package." ] @@ -233,7 +233,7 @@ "source": [ "### Constrain how large the operator may grow during backpropagation\n", "\n", - "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.simplify.OperatorBudget) instance.\n", + "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget) instance.\n", "\n", "Here we specify that backpropagation should stop when the number of qubit-wise commuting Pauli groups in the operator grows past 8." ] @@ -320,7 +320,7 @@ "id": "d25b9701-f1b1-4230-9dcc-554b85e91cf7", "metadata": {}, "source": [ - "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](/docs/api/qiskit-addon-obp/qiskit_addon_obp.utils.truncating.setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", + "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", "\n", "In order to enable this truncation, we need to setup our error budget like so:" ] diff --git a/scripts/js/lib/addons/addonContentPipeline.ts b/scripts/js/lib/addons/addonContentPipeline.ts index 540794c3b2d..d4a701bc563 100644 --- a/scripts/js/lib/addons/addonContentPipeline.ts +++ b/scripts/js/lib/addons/addonContentPipeline.ts @@ -22,8 +22,10 @@ import { sphinxHtmlToMarkdown } from "../api/htmlToMd.js"; import { generateAddonToc } from "./generateAddonToc.js"; import { HtmlToMdResultWithUrl } from "../api/HtmlToMdResult.js"; import { Pkg } from "../api/Pkg.js"; +import { pathExists } from "../fs.js"; import { updateLinks, relativizeLink } from "../api/updateLinks.js"; import { DOCS_BASE_PATH } from "../api/conversionPipeline.js"; +import { kebabCaseAndShortenPage } from "../api/normalizeResultUrls.js"; import addFrontMatter from "../api/addFrontMatter.js"; import { dedupeHtmlIdsFromResults } from "../api/dedupeHtmlIds.js"; import removeMathBlocksIndentation from "../api/removeMathBlocksIndentation.js"; @@ -39,6 +41,7 @@ export async function runAddonContentPipeline( const outputDir = `${docsBaseFolder}/addons/${pkg.name}`; await mkdirp(outputDir); + const stubsMap = await buildStubsMap(htmlPath, pkg.name); const allFiles = await globby(include, { cwd: htmlPath }); const htmlFiles = allFiles.filter((f) => f.endsWith(".html")); const notebookFiles = allFiles.filter((f) => f.endsWith(".ipynb")); @@ -56,6 +59,9 @@ export async function runAddonContentPipeline( htmlFiles, ); + for (const result of results) { + result.markdown = resolveGithubIoLinks(result.markdown, stubsMap, pkg.name); + } await updateLinks(results, { kebabCaseAndShorten: false, pkgName: pkg.name, @@ -72,7 +78,7 @@ export async function runAddonContentPipeline( publicBaseFolder, pkg, ); - await copyNotebooks(htmlPath, outputDir, notebookFiles); + await copyNotebooks(htmlPath, outputDir, notebookFiles, stubsMap, pkg.name); await writeTocFile(htmlPath, outputDir, pkg, docsBaseFolder); } @@ -149,6 +155,8 @@ async function copyNotebooks( htmlPath: string, outputDir: string, notebookFiles: string[], + stubsMap: Map, + pkgName: string, ): Promise { if (notebookFiles.length === 0) return; @@ -161,17 +169,68 @@ async function copyNotebooks( const lines: string[] = Array.isArray(cell.source) ? cell.source : [cell.source]; - cell.source = lines.map((line) => - line.replace(/\[([^\]]*)\]\(([^)]+)\)/g, (match, text, url) => { + cell.source = lines.map((line) => { + const resolved = resolveGithubIoLinks(line, stubsMap, pkgName); + return resolved.replace(/\[([^\]]*)\]\(([^)]+)\)/g, (match, text, url) => { const rewritten = relativizeLink({ url, text }); return rewritten ? `[${rewritten.text ?? text}](${rewritten.url})` : match; - }), - ); + }); + }); } await writeFile(dest, JSON.stringify(notebook, null, 1)); } } +async function buildStubsMap( + htmlPath: string, + pkgName: string, +): Promise> { + const stubsDir = `${htmlPath}/stubs`; + const map = new Map(); + if (!(await pathExists(stubsDir))) return map; + + const files = await globby("*.html", { cwd: stubsDir }); + for (const file of files) { + const html = await readFile(`${stubsDir}/${file}`, "utf-8"); + const match = html.match(/var target = "([^"]+)"/); + if (!match) continue; + const apidocsMatch = match[1].match(/\.\.\/apidocs\/([^#"]+\.html)(#.*)?/); + if (!apidocsMatch) continue; + const modulePage = apidocsMatch[1].replace(/\.html$/, ""); + const anchor = apidocsMatch[2] ?? ""; + const kebabPage = kebabCaseAndShortenPage(modulePage, pkgName); + const symbolName = file.replace(/\.html$/, ""); + map.set(symbolName, `/docs/api/${pkgName}/${kebabPage}${anchor}`); + } + return map; +} + +function resolveGithubIoLinks( + text: string, + stubsMap: Map, + pkgName: string, +): string { + return text.replace( + /https:\/\/qiskit\.github\.io\/([^/"#)\s]+)\/?([^"#)\s]*)/g, + (match, pkg, rest) => { + // Stubs links for this package — resolve via artifact redirect map + if (pkg === pkgName) { + const stubsPrefix = "stubs/"; + if (rest.startsWith(stubsPrefix)) { + const symbol = rest + .slice(stubsPrefix.length) + .replace(/\.html$/, ""); + const resolved = stubsMap.get(symbol); + if (resolved) return resolved; + } + } + // All other github.io links → /docs/addons// + const page = rest.replace(/\.html$/, "").replace(/\/$/, ""); + return page ? `/docs/addons/${pkg}/${page}` : `/docs/addons/${pkg}`; + }, + ); +} + async function writeTocFile( htmlPath: string, outputDir: string, diff --git a/scripts/js/lib/api/updateLinks.ts b/scripts/js/lib/api/updateLinks.ts index 662a7d1554c..7babd7a07c2 100644 --- a/scripts/js/lib/api/updateLinks.ts +++ b/scripts/js/lib/api/updateLinks.ts @@ -164,26 +164,6 @@ export function normalizeUrl( } export function relativizeLink(link: Link): Link | undefined { - // Handle qiskit.github.io addon links before the generic github.io rules. - // stubs/ and apidocs/ map to /docs/api/; everything else maps to /docs/addons/. - const githubIoMatch = link.url.match( - /^https:\/\/qiskit\.github\.io\/(qiskit[^/]+)\/(stubs|apidocs|apidoc)\/(.+)$/, - ); - if (githubIoMatch) { - const [, pkg, , rest] = githubIoMatch; - const page = rest.replace(/\.html$/, ""); - return { url: `/docs/api/${pkg}/${page}` }; - } - const githubIoProseMatch = link.url.match( - /^https:\/\/qiskit\.github\.io\/(qiskit[^/]+)\/?(.*)$/, - ); - if (githubIoProseMatch) { - const [, pkg, rest] = githubIoProseMatch; - const page = rest.replace(/\.html$/, "").replace(/\/$/, ""); - const url = page ? `/docs/addons/${pkg}/${page}` : `/docs/addons/${pkg}`; - return { url }; - } - const priorPrefixToNewPrefix = new Map([ ["https://qiskit.org/documentation/apidoc/", "/api/qiskit"], ["https://qiskit.org/documentation/stubs/", "/api/qiskit"], From eb9eea0df223306229d424de0b90e3f7920a9516 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Wed, 29 Apr 2026 16:39:15 -0400 Subject: [PATCH 003/104] rem updateLinks mapping --- scripts/js/lib/api/updateLinks.ts | 2 -- 1 file changed, 2 deletions(-) diff --git a/scripts/js/lib/api/updateLinks.ts b/scripts/js/lib/api/updateLinks.ts index 7babd7a07c2..70f32ff584d 100644 --- a/scripts/js/lib/api/updateLinks.ts +++ b/scripts/js/lib/api/updateLinks.ts @@ -171,8 +171,6 @@ export function relativizeLink(link: Link): Link | undefined { ["https://docs.quantum-computing.ibm.com/", ""], ["https://quantum.cloud.ibm.com/docs", "/docs"], ["https://quantum.cloud.ibm.com/learning", "/learning"], - ["https://quantum.test.cloud.ibm.com/docs", "/docs"], - ["https://quantum.test.cloud.ibm.com/learning", "/learning"], ]); const priorPrefix = Array.from(priorPrefixToNewPrefix.keys()).find((prefix) => link.url.startsWith(prefix), From 92cd49f673b67209fa6e9b90aadc63456ad14320 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Thu, 30 Apr 2026 13:44:33 -0400 Subject: [PATCH 004/104] run ./fix for new content --- .../how_tos/bound_error_using_p_norm.ipynb | 43 +++++++---- .../simulating_circuits_with_obp.ipynb | 65 ++++++++++++---- .../how_tos/truncate_operator_terms.ipynb | 73 +++++++++++------- .../tutorials/01_getting_started.ipynb | 59 +++++++++----- ...6f281d4-706e-41ad-b7b5-bc7ebe106996-0.avif | Bin 0 -> 11411 bytes ...e7f454c-7d79-4c61-b1dd-6a98564fb5f1-0.avif | Bin 0 -> 6961 bytes ...f72e77f-e2c0-4cce-8575-c5aa587f78fb-0.avif | Bin 0 -> 7140 bytes ...758f393-8691-4116-94ca-1185c6188bb4-0.avif | Bin 0 -> 11411 bytes ...adc314d-452f-465c-a382-d54685ce3f2a-0.avif | Bin 0 -> 32495 bytes ...97fc3b0-d3b4-432d-96cd-7e175b7e6a52-0.avif | Bin 0 -> 11411 bytes ...c36768e-03e7-4e1b-81b3-59b6e9eab43e-0.avif | Bin 0 -> 15073 bytes ...1d36d7e-0bd5-4388-bbb8-6831bf1f02e6-0.avif | Bin 0 -> 12043 bytes ...8f118df-6718-49b2-ba80-c37ef461f71a-0.avif | Bin 0 -> 13855 bytes ...9c9069a-9038-4319-8dc6-37a1be973ebf-0.avif | Bin 0 -> 12758 bytes ...2271b0d-ddaf-4ad2-b5c6-e9e4c2a4cff7-0.avif | Bin 0 -> 16712 bytes ...0bf5050-2928-41a1-9c3d-c9d79e918f6f-0.avif | Bin 0 -> 15126 bytes ...7167f80-5d29-4c32-be97-cba733f18b1d-0.avif | Bin 0 -> 13213 bytes ...d68f197-ffa4-49de-9fe8-243b1facbd00-0.avif | Bin 0 -> 8000 bytes ...e8bae1a-ef18-4eb0-9d2a-1ac7bbdced3b-1.avif | Bin 0 -> 5491 bytes ...5ec9cb1-a4ed-497b-a616-180e9659956f-1.avif | Bin 0 -> 7085 bytes ...79b8484-7de0-47cb-be7b-147b502ad1e5-0.avif | Bin 0 -> 5016 bytes 21 files changed, 159 insertions(+), 81 deletions(-) create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm/extracted-outputs/d6f281d4-706e-41ad-b7b5-bc7ebe106996-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm/extracted-outputs/fe7f454c-7d79-4c61-b1dd-6a98564fb5f1-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm/extracted-outputs/ff72e77f-e2c0-4cce-8575-c5aa587f78fb-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp/extracted-outputs/8758f393-8691-4116-94ca-1185c6188bb4-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp/extracted-outputs/dadc314d-452f-465c-a382-d54685ce3f2a-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/truncate_operator_terms/extracted-outputs/297fc3b0-d3b4-432d-96cd-7e175b7e6a52-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/truncate_operator_terms/extracted-outputs/4c36768e-03e7-4e1b-81b3-59b6e9eab43e-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/truncate_operator_terms/extracted-outputs/51d36d7e-0bd5-4388-bbb8-6831bf1f02e6-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/truncate_operator_terms/extracted-outputs/68f118df-6718-49b2-ba80-c37ef461f71a-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/truncate_operator_terms/extracted-outputs/69c9069a-9038-4319-8dc6-37a1be973ebf-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/truncate_operator_terms/extracted-outputs/82271b0d-ddaf-4ad2-b5c6-e9e4c2a4cff7-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/truncate_operator_terms/extracted-outputs/b0bf5050-2928-41a1-9c3d-c9d79e918f6f-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/how_tos/truncate_operator_terms/extracted-outputs/b7167f80-5d29-4c32-be97-cba733f18b1d-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/tutorials/01_getting_started/extracted-outputs/1d68f197-ffa4-49de-9fe8-243b1facbd00-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/tutorials/01_getting_started/extracted-outputs/5e8bae1a-ef18-4eb0-9d2a-1ac7bbdced3b-1.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/tutorials/01_getting_started/extracted-outputs/65ec9cb1-a4ed-497b-a616-180e9659956f-1.avif create mode 100644 public/docs/images/addons/qiskit-addon-obp/tutorials/01_getting_started/extracted-outputs/c79b8484-7de0-47cb-be7b-147b502ad1e5-0.avif diff --git a/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb b/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb index 96ca9c4be6d..1cd49633864 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb @@ -43,9 +43,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 1, @@ -75,7 +74,9 @@ " pauli_order_strategy=PauliOrderStrategy.InteractionThenColor,\n", ")\n", "# we evolve for some time\n", - "circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0)\n", + "circuit = generate_time_evolution_circuit(\n", + " hamiltonian, synthesis=LieTrotter(reps=3), time=2.0\n", + ")\n", "# slice the circuit by gate type\n", "slices = slice_by_gate_types(circuit)\n", "\n", @@ -202,10 +203,16 @@ "\n", "max_slices = 6\n", "l1_bp_obs, l1_remaining_slices, l1_metadata = backpropagate(\n", - " obs, slices[-max_slices:], truncation_error_budget=l1_truncation_error_budget\n", + " obs,\n", + " slices[-max_slices:],\n", + " truncation_error_budget=l1_truncation_error_budget,\n", + ")\n", + "l1_reduced_circuit = combine_slices(\n", + " slices[:-max_slices] + l1_remaining_slices\n", + ")\n", + "print(\n", + " f\"Backpropagated {max_slices - len(l1_remaining_slices)} circuit slices.\"\n", ")\n", - "l1_reduced_circuit = combine_slices(slices[:-max_slices] + l1_remaining_slices)\n", - "print(f\"Backpropagated {max_slices - len(l1_remaining_slices)} circuit slices.\")\n", "print(\n", " f\"New observable contains {len(l1_bp_obs)} terms and {len(l1_bp_obs.group_commuting(qubit_wise=True))} commuting groups.\"\n", ")" @@ -259,9 +266,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -341,10 +347,16 @@ "source": [ "max_slices = 6\n", "l2_bp_obs, l2_remaining_slices, l2_metadata = backpropagate(\n", - " obs, slices[-max_slices:], truncation_error_budget=l2_truncation_error_budget\n", + " obs,\n", + " slices[-max_slices:],\n", + " truncation_error_budget=l2_truncation_error_budget,\n", + ")\n", + "l2_reduced_circuit = combine_slices(\n", + " slices[:-max_slices] + l2_remaining_slices\n", + ")\n", + "print(\n", + " f\"Backpropagated {max_slices - len(l2_remaining_slices)} circuit slices.\"\n", ")\n", - "l2_reduced_circuit = combine_slices(slices[:-max_slices] + l2_remaining_slices)\n", - "print(f\"Backpropagated {max_slices - len(l2_remaining_slices)} circuit slices.\")\n", "print(\n", " f\"New observable contains {len(l2_bp_obs)} terms and {len(l2_bp_obs.group_commuting(qubit_wise=True))} commuting groups.\"\n", ")" @@ -397,9 +409,8 @@ "outputs": [ { "data": { - "image/png": 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Pn65jx45p4cKFWrNmjYYPHy7p+qwl//73v3Xw4EGdPHlS//d//yd3d3fVr19fjRs3Vt++fdWvXz+tXbtWSUlJ2rVrl6ZOnaovv/zyD3P27dtXe/fu1VtvvaWnn37aNu6SdH1sgk2bNmnHjh06fPiw/vnPfxY4QGdycrIiIiJ09OhRffTRR5o/f74t+++FhIQoODhYYWFh2rhxo06dOqUdO3bojTfe0O7duwvcxt3dXY0aNVJSUpK6dOmiRo0a6eeff9b999+vxo0bq1GjRvkuPg8dOqSEhARdunRJly9fVkJCQr6LTAAAcBNmc8EDid+Y7e53xZ3iUKVKFVWrVk2LFi3S8ePHtWXLFkVERNi16dOnj3x9fRUWFqbt27fr5MmT+uSTTxQXFyfp+qxxSUlJSkhI0MWLF5WdnS13d3fdd999euedd3T48GFt27ZN48aNs9tvQECAdu/erQ0bNujYsWMaP368vv/++yLlf+2117Rjxw4NHTpUCQkJSkxM1KeffqqhQ4cW+bMICAjQE088oUGDBum7777T/v379dxzz8nPz09PPPFEkfcHAMjPbLHaxnb6+/3+uquy6x9sUfLo8VSKeHh4yMHRSZ1dku7YMR0cnfKNbXQrNy5gJkyYoGeeeUaXLl2yXShNmDAhX6HklVde0e7duzVx4kR5eXlp9uzZCg0NlXT9EbR33nlHERERMpvNCgwM1Oeff24bJ2np0qWaMmWKXnnlFZ05c0bVq1fXfffdp8cee+wPczZq1EgdOnTQrl27bGNK3TBu3DidPHlSoaGh8vDw0ODBgxUWFqbLly/btevXr5+uXr2qDh06yNHRUcOHD7/pIOEmk0nr16/XG2+8oQEDBujChQvy9fXVgw8+qJo1a94y69atW22DaW7btk0PPvjgTdv26NFDP/30k+39jTucRRl3CwCACuvNN2++roQes3NwcNDKlSs1bNgwtWjRQk2aNNG8efPUpUsXWxsXFxdt3LhRr7zyinr06KG8vDw1b95cCxculHR9cPK1a9fqoYceUlpampYuXarnn39eS5Ys0cCBA9WuXTs1adJE06dPV7du3Wz7/ec//6l9+/apd+/eMplM6tOnj1566SV99dVXhc7fsmVLbdu2TW+88YYeeOABWa1WNWzYUL17976tz2Pp0qUaPny4HnvsMeXk5OjBBx/U+vXrbTPaAQD+nE8TzijxfIa83Z016MG7jY4jSTJZ+Yu1xKSnp8vb21uXL1+Wl5eXbfm1a9eUlJQkf39/20wgN9zp6bGZlrp8u9XPGoCy69y5c1q0aJE+u9asQo3xVM2UqV5uh23TwRenm/3Oxp11q/8O/E7DncLPGoCyKifPoodnb9XpS1f1WvemerFLwxI9XmGvn+jxVMp4e3tTCAIAAAAAAEWy6vtknb50VXdVdlX/jvWNjmPDGE8AAAAAAABl2NUcs+ZtOS5JerlrI3m4lJ5+RhSeAAAAAAAAyrAP4k7pwpVs1anirmfvrWd0HDsUngAAAAAAAMqoy1dz9e7WE5KkkSGN5eJUuko9pStNBcO47ihp/IwBAAAAQPm2+NuTunw1VwE1KimsjZ/RcfKh8GSAG9PF3snZ61Ax3fgZY4piAAAAACh/LmZk69/fJUmSXunWRI4OJoMT5Vd6RpuqQBwdHeXj46Pz589Lkjw8PGQylb4fDpRdVqtVWVlZOn/+vHx8fOTo6Gh0JAAAAABAMVv49XFl5ZjVso63Qu+paXScAlF4Moivr68k2YpPQEnw8fGx/awBAAAAAMqPM2lXtXxnsiRpdGiTUtuhpVQUnhYuXKgZM2YoJSVFrVq10vz589WhQ4ebtl+zZo3Gjx+vU6dOKSAgQNOmTVOPHj1s661WqyZMmKD3339faWlpuv/++/Xuu+8qICBAknTq1ClNnjxZW7ZsUUpKimrXrq3nnntOb7zxhlxcXGxt/P398x07Li5O9913358+Z5PJpFq1aqlGjRrKzc390/sDfs/Z2ZmeTgAAlDENGjTQiBEjNGLECKOjFFmXLl3UunVrRUVF3bRNWT4/ACht5m1OVI7ZouC7q6lTo+pGx7kpwwtPq1atUkREhKKjoxUUFKSoqCiFhobq6NGjqlGjRr72O3bsUJ8+fTR16lQ99thjWrFihcLCwrR37161aNFCkjR9+nTNmzdPH3zwgfz9/TV+/HiFhobq0KFDcnNz05EjR2SxWPTee++pUaNGOnjwoAYNGqTMzEzNnDnT7nibN2/WPffcY3tfrVq1Yj1/R0dHigMAAADl3OnTpzVhwgTFxMTo4sWLqlWrlsLCwhQZGVns15cVyYwZM7Rv3z6tWLFCK1as0OLFi7Vlyxbb+mvXrumFF17Qnj17dPjwYT322GNat26dcYEBoJicuJChNXtOS5JGdy+9vZ2kUjC4+OzZszVo0CANGDBAzZs3V3R0tDw8PLRkyZIC28+dO1fdu3fX6NGj1axZM02ePFlt27bVggULJF3v7RQVFaVx48bpiSeeUMuWLfXhhx/q7Nmztl8y3bt319KlS9WtWzfdfffd6tWrl0aNGqW1a9fmO161atXk6+trezFIMwAAAIri5MmTat++vRITE/XRRx/p+PHjio6OVmxsrIKDg3Xp0iXDspnNZlksFsOO/2fFxcXp/vvvlyR9++23tu9vMJvNcnd317BhwxQSEmJERAAoEbM3HZPFKoU0q6m29aoYHeeWDC085eTkaM+ePXa/BBwcHBQSEqK4uLgCt4mLi8v3SyM0NNTWPikpSSkpKXZtvL29FRQUdNN9StLly5dVtWrVfMt79eqlGjVqqFOnTvrss89ueT7Z2dlKT0+3ewEAAKBiGzJkiFxcXLRx40Z17txZ9erV06OPPqrNmzfrzJkzeuONN+zaX7lyRX369JGnp6f8/Py0cOFC2zqr1ao333xT9erVk6urq2rXrq1hw4bZ1mdnZ2vUqFHy8/OTp6engoKCtHXrVtv6ZcuWycfHR5999pmaN28uV1dXLV68WG5ubkpLS7PLMXz4cHXt2lWS9Msvv6hPnz7y8/OTh4eHAgMD9dFHH+U717y8PA0dOlTe3t6qXr26xo8fL6vVetPPJi0tTf/4xz901113ycvLS127dtX+/fsL/dn+tvD03Xff5Ss8eXp66t1339WgQYMY9xJAuXHwzGV9+cM5mUzSK90aGx3nDxlaeLp48aLMZrNq1rQfeb1mzZpKSUkpcJuUlJRbtr/xtSj7PH78uObPn69//vOftmWVKlXSrFmztGbNGn355Zfq1KmTwsLCbll8mjp1qry9vW2vunXr3rQtAAAA/hyr1aqsnDxDXrcqpvzWpUuXtGHDBr300ktyd3e3W+fr66u+fftq1apVdvubMWOGWrVqpX379un111/X8OHDtWnTJknSJ598ojlz5ui9995TYmKi1q1bp8DAQNu2Q4cOVVxcnFauXKkffvhB4eHh6t69uxITE21tsrKyNG3aNC1evFg//vij+vbtKx8fH33yySe2NmazWatWrVLfvn0lXX9krV27dvryyy918OBBDR48WH/729+0a9cuu3P64IMP5OTkpF27dmnu3LmaPXu2Fi9efNPPJzw8XOfPn9dXX32lPXv2qG3btnr44Ydv2QvsnXfekY+Pj3x8fJSSkqLOnTvLx8dHBw8e1DPPPCMfHx999913t/rPAgBl2syNRyVJvVrVVrNaXgan+WOGj/FktDNnzqh79+4KDw/XoEGDbMurV6+uiIgI2/t7771XZ8+e1YwZM9SrV68C9zVmzBi7bdLT0yk+AQAAlJCruWY1j9xgyLEPTQqVh8sfX0onJibKarWqWbNmBa5v1qyZfv31V124cME2vun999+v119/XZLUuHFjbd++XXPmzNEjjzyi5ORk+fr6KiQkRM7OzqpXr55tUp7k5GQtXbpUycnJql27tiRp1KhRiomJ0dKlS/X2229LknJzc/Wvf/1LrVq1suV49tlntWLFCg0cOFCSFBsbq7S0ND311FOSJD8/P40aNcrW/uWXX9aGDRu0evVqu0mB6tatqzlz5shkMqlJkyY6cOCA5syZY3edfcN3332nXbt26fz583J1dZUkzZw5U+vWrdPHH3+swYMHF/iZvfDCC3r22We1bNky7dy5U9HR0Vq/fr2WLVum1atXSxK9mwCUW7uSLmnr0QtycjBpZEjp7+0kGdzjqXr16nJ0dFRqaqrd8tTU1Jv+svD19b1l+xtfC7PPs2fP6qGHHlLHjh21aNGiP8wbFBSk48eP33S9q6urvLy87F4AAABGWLhwoRo0aCA3NzcFBQXl65nye2vWrFHTpk3l5uamwMBArV+/3m691WpVZGSkatWqJXd3d4WEhNj1opGuz1hmMpnsXu+8806xn1tZVNgeUpIUHByc7/3hw4clXe8hdPXqVd19990aNGiQ/vvf/yovL0+SdODAAZnNZjVu3FiVKlWyvbZt26YTJ07Y9ufi4qKWLVvaHaNv377aunWrzp49K0lavny5evbsKR8fH0nXe0BNnjxZgYGBqlq1qipVqqQNGzYoOTnZbj/33Xef3QC3wcHBSkxMlNlsznee+/fvV0ZGhqpVq2aXNykpyS7v7/n4+KhBgwbatWuXnnrqKTVo0ED79u1Tr1691KBBA9vPPQCUN1arVTM2HJEkPXNvXTWo7mlwosIxtMeTi4uL2rVrp9jYWIWFhUmSLBaLYmNjNXTo0AK3CQ4OVmxsrN0UrJs2bbL9gvb395evr69iY2PVunVrSdd7HsXHx+vFF1+0bXPmzBk99NBDateunZYuXSoHhz+uwSUkJKhWrVq3d7IAAAB3iBGzBt8wadIku94tlStXLrHzdHd21KFJoSW2/z86dmE0atRIJpNJhw8f1pNPPplv/eHDh1WlShXdddddhdpf3bp1dfToUW3evFmbNm3SSy+9pBkzZmjbtm3KyMiQo6Oj9uzZk2/W5EqVKv0vu7t7vtmP7r33XjVs2FArV67Uiy++qP/+979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", "text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -428,7 +439,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -442,9 +453,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb b/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb index 792091bda97..99d95be2bc7 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb @@ -57,7 +57,9 @@ " pauli_order_strategy=PauliOrderStrategy.InteractionThenColor,\n", ")\n", "# we evolve for some time\n", - "circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0)\n", + "circuit = generate_time_evolution_circuit(\n", + " hamiltonian, synthesis=LieTrotter(reps=3), time=2.0\n", + ")\n", "# slice the circuit by gate type\n", "slices = slice_by_gate_types(circuit)" ] @@ -102,9 +104,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 3, @@ -303,7 +304,10 @@ "source": [ "import numpy as np\n", "from qiskit.quantum_info import PauliList\n", - "from qiskit_ibm_runtime.utils.noise_learner_result import LayerError, PauliLindbladError\n", + "from qiskit_ibm_runtime.utils.noise_learner_result import (\n", + " LayerError,\n", + " PauliLindbladError,\n", + ")\n", "\n", "# fmt: off\n", "pauli_errors_even = ['IIIIIIIIIX', 'IIIIIIIIIY', 'IIIIIIIIIZ', 'IIIIIIIIXI', 'IIIIIIIIXX', 'IIIIIIIIXY', 'IIIIIIIIXZ', 'IIIIIIIIYI', 'IIIIIIIIYX', 'IIIIIIIIYY', 'IIIIIIIIYZ', 'IIIIIIIIZI', 'IIIIIIIIZX', 'IIIIIIIIZY', 'IIIIIIIIZZ', 'IIIIIIIXII', 'IIIIIIIXXI', 'IIIIIIIXYI', 'IIIIIIIXZI', 'IIIIIIIYII', 'IIIIIIIYXI', 'IIIIIIIYYI', 'IIIIIIIYZI', 'IIIIIIIZII', 'IIIIIIIZXI', 'IIIIIIIZYI', 'IIIIIIIZZI', 'IIIIIIXIII', 'IIIIIIXXII', 'IIIIIIXYII', 'IIIIIIXZII', 'IIIIIIYIII', 'IIIIIIYXII', 'IIIIIIYYII', 'IIIIIIYZII', 'IIIIIIZIII', 'IIIIIIZXII', 'IIIIIIZYII', 'IIIIIIZZII', 'IIIIIXIIII', 'IIIIIXXIII', 'IIIIIXYIII', 'IIIIIXZIII', 'IIIIIYIIII', 'IIIIIYXIII', 'IIIIIYYIII', 'IIIIIYZIII', 'IIIIIZIIII', 'IIIIIZXIII', 'IIIIIZYIII', 'IIIIIZZIII', 'IIIIXIIIII', 'IIIIXXIIII', 'IIIIXYIIII', 'IIIIXZIIII', 'IIIIYIIIII', 'IIIIYXIIII', 'IIIIYYIIII', 'IIIIYZIIII', 'IIIIZIIIII', 'IIIIZXIIII', 'IIIIZYIIII', 'IIIIZZIIII', 'IIIXIIIIII', 'IIIXXIIIII', 'IIIXYIIIII', 'IIIXZIIIII', 'IIIYIIIIII', 'IIIYXIIIII', 'IIIYYIIIII', 'IIIYZIIIII', 'IIIZIIIIII', 'IIIZXIIIII', 'IIIZYIIIII', 'IIIZZIIIII', 'IIXIIIIIII', 'IIXXIIIIII', 'IIXYIIIIII', 'IIXZIIIIII', 'IIYIIIIIII', 'IIYXIIIIII', 'IIYYIIIIII', 'IIYZIIIIII', 'IIZIIIIIII', 'IIZXIIIIII', 'IIZYIIIIII', 'IIZZIIIIII', 'IXIIIIIIII', 'IXXIIIIIII', 'IXYIIIIIII', 'IXZIIIIIII', 'IYIIIIIIII', 'IYXIIIIIII', 'IYYIIIIIII', 'IYZIIIIIII', 'IZIIIIIIII', 'IZXIIIIIII', 'IZYIIIIIII', 'IZZIIIIIII', 'XIIIIIIIII', 'XXIIIIIIII', 'XYIIIIIIII', 'XZIIIIIIII', 'YIIIIIIIII', 'YXIIIIIIII', 'YYIIIIIIII', 'YZIIIIIIII', 'ZIIIIIIIII', 'ZXIIIIIIII', 'ZYIIIIIIII', 'ZZIIIIIIII']\n", @@ -410,25 +414,53 @@ " if slice_ == layer_error_even_xx.circuit:\n", " noisy_slices.append(\n", " QuantumCircuit.from_instructions(\n", - " [(PauliLindbladErrorInstruction(layer_error_even_xx.error), slice_.qubits)]\n", + " [\n", + " (\n", + " PauliLindbladErrorInstruction(\n", + " layer_error_even_xx.error\n", + " ),\n", + " slice_.qubits,\n", + " )\n", + " ]\n", " )\n", " )\n", " elif slice_ == layer_error_odd_xx.circuit:\n", " noisy_slices.append(\n", " QuantumCircuit.from_instructions(\n", - " [(PauliLindbladErrorInstruction(layer_error_odd_xx.error), slice_.qubits)]\n", + " [\n", + " (\n", + " PauliLindbladErrorInstruction(\n", + " layer_error_odd_xx.error\n", + " ),\n", + " slice_.qubits,\n", + " )\n", + " ]\n", " )\n", " )\n", " elif slice_ == layer_error_even_yy.circuit:\n", " noisy_slices.append(\n", " QuantumCircuit.from_instructions(\n", - " [(PauliLindbladErrorInstruction(layer_error_even_yy.error), slice_.qubits)]\n", + " [\n", + " (\n", + " PauliLindbladErrorInstruction(\n", + " layer_error_even_yy.error\n", + " ),\n", + " slice_.qubits,\n", + " )\n", + " ]\n", " )\n", " )\n", " elif slice_ == layer_error_odd_yy.circuit:\n", " noisy_slices.append(\n", " QuantumCircuit.from_instructions(\n", - " [(PauliLindbladErrorInstruction(layer_error_odd_yy.error), slice_.qubits)]\n", + " [\n", + " (\n", + " PauliLindbladErrorInstruction(\n", + " layer_error_odd_yy.error\n", + " ),\n", + " slice_.qubits,\n", + " )\n", + " ]\n", " )\n", " )\n", " noisy_slices.append(slice_)" @@ -450,9 +482,8 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "execution_count": 10, @@ -461,7 +492,9 @@ } ], "source": [ - "combine_slices(noisy_slices, include_barriers=True).draw(\"mpl\", fold=100, scale=0.8)" + "combine_slices(noisy_slices, include_barriers=True).draw(\n", + " \"mpl\", fold=100, scale=0.8\n", + ")" ] }, { @@ -502,7 +535,9 @@ } ], "source": [ - "vacuum_state_noisy_obs.coeffs[~vacuum_state_noisy_obs.paulis.x.any(axis=1)].sum()" + "vacuum_state_noisy_obs.coeffs[\n", + " ~vacuum_state_noisy_obs.paulis.x.any(axis=1)\n", + "].sum()" ] }, { @@ -517,7 +552,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -531,9 +566,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.12" + "version": "3" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb b/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb index bd83e4fd410..12f0db7a886 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb @@ -53,9 +53,8 @@ "outputs": [ { "data": { - "image/png": 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", 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" + "\"Output" ] }, "execution_count": 1, @@ -84,7 +83,9 @@ " pauli_order_strategy=PauliOrderStrategy.InteractionThenColor,\n", ")\n", "# we evolve for some time\n", - "circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0)\n", + "circuit = generate_time_evolution_circuit(\n", + " hamiltonian, synthesis=LieTrotter(reps=3), time=2.0\n", + ")\n", "# slice the circuit by gate type\n", "slices = slice_by_gate_types(circuit)\n", "\n", @@ -130,7 +131,7 @@ "\n", "The most straight-forward way to specify the truncation budget is via the `max_error_per_slice` argument.\n", "In fact, this is what is being done in [this tutorial](/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb). Setting `max_error_per_slice` to a `float` results in each slice being allotted a budget equaling that value.\n", - "In the example below, we set this value to `0.001` which guarantees us an implicit truncation error of at most `0.018`, if the entire 18 slices were to be backpropagated. \n", + "In the example below, we set this value to `0.001` which guarantees us an implicit truncation error of at most `0.018`, if the entire 18 slices were to be backpropagated.\n", "\n", "
\n", "Note that any error budget remaining after backpropagating a slice and truncating terms with small coefficients will always be added to the following slice's error budget.\n", @@ -179,7 +180,10 @@ "\n", "op_budget = OperatorBudget(max_qwc_groups=10)\n", "bp_obs, remaining_slices, metadata = backpropagate(\n", - " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", + " obs,\n", + " slices,\n", + " operator_budget=op_budget,\n", + " truncation_error_budget=truncation_error_budget,\n", ")\n", "reduced_circuit = combine_slices(remaining_slices)\n", "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", @@ -219,9 +223,8 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -278,7 +281,9 @@ "# Zero out the first 3 slices' budgets\n", "max_error_per_slice = [0.0] * 3 + [0.001] * (len(slices) - 3)\n", "\n", - "truncation_error_budget = setup_budget(max_error_per_slice=max_error_per_slice)\n", + "truncation_error_budget = setup_budget(\n", + " max_error_per_slice=max_error_per_slice\n", + ")\n", "print(truncation_error_budget)" ] }, @@ -299,7 +304,10 @@ ], "source": [ "bp_obs, remaining_slices, metadata = backpropagate(\n", - " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", + " obs,\n", + " slices,\n", + " operator_budget=op_budget,\n", + " truncation_error_budget=truncation_error_budget,\n", ")\n", "reduced_circuit = combine_slices(remaining_slices)\n", "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", @@ -326,9 +334,8 @@ "outputs": [ { "data": { - "image/png": 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v3jxSUlIIDAzkt99+4/z584SEhPD4449TpkyZjMUtb/vjjz+YNm0aJ06cYP78+Xz77bcMHToUuNUW9/PPP+fw4cOcOXOGL7/8EhcXFypUqEDVqlXp2bMnvXr1YtWqVYSHh7Nr1y6mTJnCunXr/jJnz5492bdvH5MnT+bZZ5/N6KcM4Ovry8aNG9m+fTtHjx7ltddeIyoq6o5jREREMHz4cI4fP87XX3/N3LlzM7L/r7Zt2xIQEECXLl3YsGEDZ8+eZfv27YwePZo9e/bcdR8XFxeqVKlCeHg4rVq1okqVKly4cIFmzZpRtWpVqlSpcsdFSFhYGKGhocTExBAXF0doaCihoaF/+X6IiIhI7hKfnMagr/czatUhUtKttKhagp+GPnrfws1t9csXYfLTt9aTnr35JCGHL+d0XBHJIl03FbzrJsNm3hQvXhw7O7s7/udERUXdcyEgb2/v+46//WdUVBSlSpXKNKZu3boPnO32vv9d8StRogTFixcnIiLinvs5OTll+mYUERERyQ1cXV0x29nT0jH8oZzPbGd/R8/i+/H19WXPnj2MHz+e5557jpiYGLy9venSpQvjx4+/45fmN998kz179jBhwgTc3d2ZOXMmgYG3elR7enry4YcfMnz4cCwWC7Vq1WLNmjUZ/Y8XL17MpEmTePPNN7l48SLFixenSZMmdOrU6S9zVqlShcaNG7Nr166MXtG3jRkzhjNnzhAYGIirqyv9+/enS5cuxMXFZRrXq1cvbt68SePGjbGzs2Po0KH3XEDTZDKxfv16Ro8eTZ8+fYiOjsbb25sWLVrcMdP8f/36669069YNgK1bt9KiRYt7ju3QoQPnzp3L+LpevXpA1vppi4iIiLFCz8cy+Ot9nI+5ib3ZxIjAavR7tFKW2p91a1iOo5dvsOiPcIZ/c4CKxd2o7u2eg6lFJCt03VTwrptMNgOvyvz9/WncuDFz584Fbs14KV++PIMGDeKdd965Y/zzzz9PUlISa9asydjWtGlTateuTXBwMDabjdKlS/PWW2/x5ptvArfWnSlZsiRLliy5Y82bJUuWMGzYMGJjYzNtP3HiBNWqVWPTpk20adMGgJiYGEqUKMFPP/1Eu3btHuj1xcfH4+HhQVxcHO7u+sdORERyxuXLl1mwYAE/JvtxzeZmdJz7KmZK5Cnno/Tv3z/TjRaSPZKTkwkPD8fHxwdnZ+dMz8XFxZGUlPRQcri6uuLh4fFQziUP3/2+z/T7b86ZP38+06dPJzIykjp16jB37lwaN258z/HffvstY8eO5ezZs/j6+jJ16lQ6dOiQ8bzNZmP8+PEsXLiQ2NhYmjVrxieffIKvry8AZ8+e5f333+eXX34hMjKS0qVL8+KLLzJ69OhMbaQPHjzIwIED2b17NyVKlGDw4MGMHDnygV+XvmdERP4Zq9XGZ7+fYVrIcdKtNsoWcWFuj3rUK1/kbx0v3WKl9+Jd/HHqGmWLuPDjoOYUdbv78gEiedH9fpcVyU7Zcd1k6EpMw4cPp3fv3jRs2JDGjRsTFBREYmIiffr0AW5V2MqUKcOUKVMAGDp0KC1btmTGjBl07NiRFStWsGfPHhYsWADcqrINGzaMSZMm4evri4+PD2PHjqV06dJ06dIl47wRERHExMQQERGBxWLJmN5UpUoVChUqRNWqVencuTNDhw5lwYIFuLu7M2rUKKpXr07r1q0f6nskIiIikh08PDxUUBHJo1auXMnw4cMJDg7G39+foKAgAgMDOX78OCVLlrxj/Pbt2+nRowdTpkyhU6dOLF++nC5durBv3z5q1rzVEmfatGnMmTOHpUuXZlw3BQYGEhYWhrOzM8eOHcNqtfLpp59SpUoVDh8+TL9+/UhMTOSjjz4Cbl10tmvXjrZt2xIcHMyhQ4d45ZVX8PT0vOedkSIikn2uJqTw5jcH2HoiGoAOtbyZ8kxtPFwc/vYx7e3MzOtRn87z/yAiJomBX+1jWd/GONhp5QURkYfN0J+8zz//PB999BHjxo2jbt26hIaGEhISkjGdKSIigsuX/7/HZtOmTVm+fDkLFiygTp06fPfdd6xevTrjAgRg5MiRDB48mP79+9OoUSMSEhIICQnJVN0aN24c9erVY/z48SQkJFCvXj3q1auXqQfesmXL8Pf3p2PHjrRs2RIHBwdCQkJwcPj7/wCKiIiIiIhk1cyZM+nXrx99+vShRo0aBAcH4+rqyqJFi+46fvbs2bRv354RI0bg5+fH+++/T/369Zk3bx5wa9ZNUFAQY8aMoXPnztSuXZtly5Zx6dIlVq9eDUD79u1ZvHgx7dq1o1KlSjz11FO89dZbrFq1KuM8X331FampqSxatIhHHnmE7t27M2TIEGbOnJnj74mISEG3/dRVOszextYT0TjZm5n8dE3mv1D/HxVubivi5shnvRvi5mjHjjPXmLzuaDYkFhGRrDK8bD5o0CDOnTtHSkoKO3fuxN/fP+O5X3/9lSVLlmQa361bN44fP05KSgqHDx/ONPUfbs2+mThxIpGRkSQnJ7Np0yaqVq2aacySJUuw2Wx3PFq1apUxxt3dnc8//5zr169z7do1Vq1aRbly5bL99YuIiIiIiNxLamoqe/fupW3bthnbzGYzbdu2ZceOHXfdZ8eOHZnGAwQGBmaMDw8PJzIyMtMYDw8P/P3973lMuNV+8b97qe/YsYMWLVpkaqN2e0bQ9evXs/ZCRUTkgaRbrMzYcJyen+/kyo0UqpQsxA+DmtHTvwIm04Ovb/NXqnoVZubzdQFYsv0sK3ffew1oERHJGYYXb0REREREROTurl69isViuWOxVS8vLyIjI++6T2Rk5H3H3/4zK8c8deoUc+fO5bXXXvvL8/z3Of5XSkoK8fHxmR4iIvJgLsXepMfCP5n7yylsNni+YTl+HNSM6t45s2ZY4CPevNH21g3RY1YfZu+5mBw5j4iI3J2KNyIiIiL5jM1mMzqC5GP6/ip4Ll68SPv27enWrRv9+vX7R8eaMmVKxhpcHh4e6m4gIvKANhyJ5InZ29h99jqFnOyZ3b0uU5+tjatjzi5nPfixKjxR05s0i43XvtjH5bibOXo+kYdFv9NKTsuO7zEVb0RERETyidtr8yUlJRmcRPKz299fWgvy4ShevDh2dnZERUVl2h4VFYW3t/dd9/H29r7v+Nt/PsgxL126ROvWrWnatCkLFix4oPP89zn+16hRo4iLi8t4nD9//q7jRETklpR0C+/9eIT+X+wl7mYatct6sG5IczrXLfNQzm82m/ioWx2qexfmakIKr32xl+Q0y0M5t0hO0DWTPCzZcd2Us+V5EREREXlo7Ozs8PT05MqVKwC4urpma+9zKdhsNhtJSUlcuXIFT09P7OzsjI5UIDg6OtKgQQM2b95Mly5dALBarWzevJlBgwbddZ+AgAA2b97MsGHDMrZt3LiRgIAAAHx8fPD29mbz5s3UrVsXgPj4eHbu3MmAAQMy9rl48SKtW7emQYMGLF68GLM5871/AQEBjB49mrS0tIyL0o0bN1KtWjWKFCly12xOTk44OTn9nbdCRKTAOROdwOCv93Pk0q0Wk68292Fk++o42j/ce7HdnOxZ2KshT837nYMX4njn+4PMer6ufs+UPEnXTJLTsvO6ScUbERERkXzk9t3uty9GRLKbp6fnPWdVSM4YPnw4vXv3pmHDhjRu3JigoCASExPp06cPAL169aJMmTJMmTIFgKFDh9KyZUtmzJhBx44dWbFiBXv27MmYOWMymRg2bBiTJk3C19cXHx8fxo4dS+nSpTMKRBcvXqRVq1ZUqFCBjz76iOjo6Iw8t///v/DCC0yYMIG+ffvy9ttvc/jwYWbPns2sWbMe4rsjIpI/rdp3gTGrD5OUaqGIqwMznqvDY9W9/nrHHFKuqCvze9bnpc93sTr0EjVKu9O/RWXD8oj8E7pmkochO66bVLwRERERyUdMJhOlSpWiZMmSpKWlGR1H8hkHBwfNuDHA888/T3R0NOPGjSMyMpK6desSEhKCl9etD/EiIiIyzYpp2rQpy5cvZ8yYMbz77rv4+vqyevVqatasmTFm5MiRJCYm0r9/f2JjY2nevDkhISE4OzsDt2bQnDp1ilOnTlG2bNlMeW737/bw8GDDhg0MHDiQBg0aULx4ccaNG0f//v1z+i0REcm3ElPSGffDEb7fdwEAf5+izO5eD28PZ4OTQdPKxRnXqQbjfzzChz8do6pXYVpVK2l0LJEs0zWT5LTsum4y2bQ6U46Jj4/Hw8ODuLg43N3djY4jIiL51OXLl1mwYAE/JvtxzeZmdJz7KmZK5Cnno/Tv359SpUoZHUeyWVxcXJ7qHe3q6oqHh4fRMfIV/f4rWaXvGRGR/3fkUhyDl+/nzNVEzCYY2qYqgx6rgp0597R0stlsjFp1iBW7z1PY2Z4fBjajUolCRscSEclTHvR3YM28EREREZF/LC4ujjlz52G1pBsd5YGZ7ewZMniQCjgiIiJiKJvNxhd/nmPSuqOkplvxdncmqHtdmlQqZnS0O5hMJiZ0foSTVxLYe+46/Zbt4d8Dm+Hu/PcX5BYRkbtT8UZERERE/rGkpCSslnS2pvoQZzW+rcdf8TAn09IxnKSkJBVvRERExDCxSamM/O4gG8KiAGhTvSTTu9WhqJujwcnuzcnejk9erE/neX9wOjqRYStCWdirYa6aISQikh+oeCMiIiIi2SbO6pzr2/cBYDU6gIiIiBR0e8/FMOTrUC7G3sTBzsQ7T/jxSrOKmEy5vwhSsrAzn77UgG7BO/jl2BVmbDjOyPbVjY4lIpKvmP96iIiIiIiIiIiIiGQHq9XG/C2neO7TP7kYe5MKxVz5fkBT+jb3yROFm9tql/Vk2rO1Afj419OsOXDJ4EQiIvmLZt6IiIiIiIiIiIg8BFduJDN85QF+P3UVgKfqlGby0zUpnEfXjOlctwxhl+P5dOsZRnx3AJ/ibtQso5a0IiLZQTNvREREREREREREcthvJ6LpMHsbv5+6irODmWldazO7e908W7i5bWRgdVpWLUFympX+y/ZwNSHF6EgiIvmCijciIiIiIiIiIiI5JM1i5cOfjtFr0S6uJqRS3bswawc357lG5fJUm7R7sTObmNOjHpWKu3EpLpnXv9xHaroWGBQR+adUvBEREREREREREckB52OSeO7THQRvPQ3Ai03Ks3pgM6qULGxwsuzl4eLAgl4NKexkz66zMby35ojRkURE8jwVb0RERERERERERLLZT4cu02HONvZHxFLY2Z6Pe9ZnUpdaODvYGR0tR1QpWYjZPepiMsHynRF8+ec5oyOJiORpKt6IiIiIiIiIiIhkk+Q0C6P/fYgBX+3jRnI69cp7sn7Io3SoVcroaDnusepejAisBsB7Px5h55lrBicSEcm7VLwRERERERERERHJBqeu3KDL/D/4amcEAP9qWZlvXgugXFFXg5M9PANaVubJOqVJt9oY8NU+LlxPMjqSiEiepOKNiIiIiIiIiIjIP2Cz2fhm93menPsHxyJvULyQI8teacw7T1THwa5gffxmMpmY1rU2Ncu4E5OYSv9le0lKTTc6lohInlOw/vUQERERERERERHJRjeS0xi2MpSR3x/kZpqF5lWKs37oo7SoWsLoaIZxcbTj05caUryQI2GX4xnx7UFsNpvRsURE8hQVb0RERERERERERP6Ggxdi6TT3d34IvYSd2cSIwGose6UxJQs7Gx3NcGU8XfjkxQY42JlYd+gyH/962uhIIiJ5ioo3IiIiIiIiIiIiWWCz2fj893C6frKdc9eSKOPpwjevNWFg6yqYzSaj4+UajSoWZcJTNQH4aMNxNoVFGZxIRCTvUPFGRERERERERETkAcUkpvLq0j28vzaMNIuNdjW8WDekOQ0qFDU6Wq70gn95XmpSAZsNhq0M5WTUDaMjiYjkCSreiIiIiIiIiIiIPIA/z1zjidm/sfnYFRztzEzs/AifvtQAT1dHo6PlauOerEFjn6IkpKTTb9ke4pLSjI4kIpLrqXgjIiIiIiIiIiJyHxarjaBNJ3hh4Z9ExadQqbgb/x7YlF4BFTGZ1CbtrzjYmfmkZ33KeLpw9loSg77eR7rFanQsEZFcTcUbERERERERERGRe4iMS+aFhX8StOkkVht0rV+WNYOb80hpD6Oj5SnFCjmxoFcDXBzs2HbyKlNDjhkdSUQkV1PxRkRERERERERE5C7ik9N4+uM/2Bkeg6ujHTOfq8OM5+rg5mRvdLQ86ZHSHnzUrQ4AC7eFs2rfBYMTiYjkXvqXRkRECoy4uDiSkpKMjvHAXF1d8fDQ3XwiIiIiIkaZ98spLsclU66oC0v7NKZSiUJGR8rzOtYuxbHIKsz95RTvrDpEpRKFqFvO0+hYIiK5joo3IiJSIMTFxTFn7jyslnSjozwws509QwYPUgFHRERERMQA4VcTWfxHOAATnnpEhZts9Ebbqhy9HM+mo1d47Ys9rBnUnJLuzkbHEhHJVVS8ERGRAiEpKQmrJZ2tqT7EWXP/RYGHOZmWjuEkJSWpeCMiIiIiYoAP1h8lzWKjRdUStK5W0ug4+YrZbGLW83V55uPtnLySwGtf7mVF/yY42dsZHU1EJNfIFWvezJ8/n4oVK+Ls7Iy/vz+7du267/hvv/2W6tWr4+zsTK1atVi/fn2m5202G+PGjaNUqVK4uLjQtm1bTp48mWnM5MmTadq0Ka6urnh6et73fNeuXaNs2bKYTCZiY2P/zksUEZFcIs7qzDWbW65/5IUCk4iIiIhIfvXHqatsDIvCzmxibEc/TCaT0ZHyncLODizs1RB3Z3v2R8Qy5t+HsdlsRscSEck1DC/erFy5kuHDhzN+/Hj27dtHnTp1CAwM5MqVK3cdv337dnr06EHfvn3Zv38/Xbp0oUuXLhw+fDhjzLRp05gzZw7BwcHs3LkTNzc3AgMDSU5OzhiTmppKt27dGDBgwF9m7Nu3L7Vr1/7nL1ZERERERERERHK1dIuViWvCAHipSQV8vQobnCj/qljcjXkv1Mdsgm/3XmDJ9rNGRxIRyTUML97MnDmTfv360adPH2rUqEFwcDCurq4sWrToruNnz55N+/btGTFiBH5+frz//vvUr1+fefPmAbdm3QQFBTFmzBg6d+5M7dq1WbZsGZcuXWL16tUZx5kwYQJvvPEGtWrVum++Tz75hNjYWN56661se80iIiIiIiIiIpI7fb37PMejbuDh4sDQNr5Gx8n3WlQtwbsd/ACYtO4of5y6anAiEZHcwdDiTWpqKnv37qVt27YZ28xmM23btmXHjh133WfHjh2ZxgMEBgZmjA8PDycyMjLTGA8PD/z9/e95zHsJCwtj4sSJLFu2DLP5r9+qlJQU4uPjMz1ERERERERERCRviLuZxswNxwF4o60vRdwcDU5UMPRt7sMz9ctgsdoYuHwfEdeSjI4kImI4Q4s3V69exWKx4OXllWm7l5cXkZGRd90nMjLyvuNv/5mVY95NSkoKPXr0YPr06ZQvX/6B9pkyZQoeHh4Zj3Llyj3w+URERERERERExFhzNp/kelIaVUoWomeTCkbHKTBMJhMfPF2LOuU8iU1K49Vlu0lISTc6loiIoQxvm5ZbjRo1Cj8/P1588cUs7RMXF5fxOH/+fA4mFBERERERERGR7HI6OoGl/1lzZWynGjjY6WOzh8nZwY4FLzWgZGEnTkQlMHxlKFarzehYIiKGMfRfoeLFi2NnZ0dUVFSm7VFRUXh7e991H29v7/uOv/1nVo55N7/88gvffvst9vb22Nvb06ZNm4zM48ePv+s+Tk5OuLu7Z3qIiIiIiIiIiEjuN3ndUdKtNlpXK0HLqiWMjlMgebk7E/xSAxztzGwIi2L25pNGRxIRMYyhxRtHR0caNGjA5s2bM7ZZrVY2b95MQEDAXfcJCAjINB5g48aNGeN9fHzw9vbONCY+Pp6dO3fe85h38/3333PgwAFCQ0MJDQ3ls88+A2Dbtm0MHDjwgY8jIiIiIiIiIiK529YT0fxy7Ar2ZhNjOtUwOk6BVr98ESY/XROA2ZtPEnL4ssGJRESMYW90gOHDh9O7d28aNmxI48aNCQoKIjExkT59+gDQq1cvypQpw5QpUwAYOnQoLVu2ZMaMGXTs2JEVK1awZ88eFixYANzqkTls2DAmTZqEr68vPj4+jB07ltKlS9OlS5eM80ZERBATE0NERAQWi4XQ0FAAqlSpQqFChahcuXKmnFevXgXAz88PT0/PnH1TRERERERERETkoUi3WJm0NgyAXgEVqVyikMGJpFvDchy9fINFf4Qz/JsDVCzuRnVvdbiR3Mtms7Hm4GXKFXGhXvkiRseRfMLw4s3zzz9PdHQ048aNIzIykrp16xISEoKXlxdwq8hiNv//BKGmTZuyfPlyxowZw7vvvouvry+rV6+mZs2aGWNGjhxJYmIi/fv3JzY2lubNmxMSEoKzs3PGmHHjxrF06dKMr+vVqwfAli1baNWqVQ6/ahERERERERERyQ2+2hnBySsJFHF1YGgbX6PjyH+826E6x6Pi+ePUNV5duocfBzWnqJuj0bFE7mpqyHGCt57G0d7MT0MfVRFYskWuWHlt0KBBnDt3jpSUFHbu3Im/v3/Gc7/++itLlizJNL5bt24cP36clJQUDh8+TIcOHTI9bzKZmDhxIpGRkSQnJ7Np0yaqVq2aacySJUuw2Wx3PO5VuGnVqhU2m02zbkRERERERERE8onYpFRmbToBwPDHq+Lh6mBwIrnN3s7MvB71KV/UlQvXbzLwq32kWaxGxxK5w6dbTxO89TQAqelWRn53EIvVZnAqyQ9yRfFGRERERERERETkYQvadJLYpDSqeRWmR+PyRseR/1HEzZHPejfEzdGOHWeuMXndUaMjiWTyze7zTPnpGAD9HvWhkJM9e89dZ8n2s8YGk3xBxRsRERERERERESlwTl25wRd/ngNgbKca2NvpY7LcqKpXYWY9XxeAJdvPsnJ3hLGBRP7j5yORvLPqIACvtajE6I41eLeDHwDTfz7G2auJRsaTfED/KomIiIiIiIiISIHz/tqjWKw22vp50dy3uNFx5D7aPeLN8MdvLYkwZvVh9p6LMTiRFHTbT19l8Nf7sdrguYZleeeJ6gD0aFyOZlWKkZxmZeT3B7GqfZr8AyreiIiIiIiIiIhIgbLl2BW2nojGwc7E6I5+RseRBzCodRWeqOlNmsXGa1/s43LcTaMjSQF16EIc/ZftJTXdSrsaXnzwdC1MJhNway32D5+pjaujHbvCYzJm94n8HSreiIiIiIiIiIhIgZFmsfL+ujAAXm5aEZ/ibgYnkgdhNpv4qFsdqnsX5mpCCq99sZfkNIvRsaSAOR2dQO/Fu0hISadJpaLM6VHvjpaL5Yq6ZszEmRpyjPMxSUZElXxAxRsRERERERERESkwvthxjjPRiRRzc2RwG1+j40gWuDnZs7BXQ4q4OnDwQhzvfH8Qm01tqeThuBx3k16f7yImMZWaZdxZ2Kshzg52dx37on8F/H2KkpRq4W19n8rfpOKNiIiIiIiIiIgUCDGJqQRtOgHAm+2q4e7sYHAiyapyRV2Z37M+dmYTq0MvsXDbGaMjSQFwPTGVlz7fxcXYm1Qq7saSPo0pfJ+fH2azialda+PsYGb76Wss3xXxENNKfqHijYiIiIiIiIiIFAizNp4gPjmd6t6Feb5ROaPjyN/UtHJxxnWqAcCHPx3j1+NXDE4k+VliSjovL9nNqSsJeLs7s6xvY4oXcvrL/SoWd2NE4K32aVPWH+NirNZpkqxR8UZERERERERERPK945E3+GrnrcXDxz1ZAzuzyeBE8k/0CqhA90blsNpg8Nf7OROdYHQkyYdS0i3868u9HDgfi6erA1/0bUzZIq4PvP/LTSvSoEIRElLS1eZPskzFGxERERERERERyddsNhuT1oVhtUHgI140rVzc6EjyD5lMJiZ0foQGFYpwIzmdfsv2EJ+cZnQsyUcsVhvDVx5g28mruDrasfjlRvh6Fc7SMezMJqY9WxsnezPbTl7l2z0Xciit5Ecq3oiIiIiIiIiISL62+egVtp28iqOdmXc7+BkdR7KJk70dwS82oJSHM6ejExm2IhSLVTMb5J+z2WyM/eEw6w5dxsHOxKcvNaBe+SJ/61iVSxRi+ONVAXh/XRiRccnZGVXyMRVvREREREREREQk30pNtzJ5/VEA+jSvSIVibgYnkuxUorATn77UACd7M78cu8LsTSeMjiT5wIwNJ1i+MwKTCYKer8ejviX+0fFefbQSdct5ciM5nXf/fUjt0+SBqHgjIiIiIiIiIiL51rIdZwm/mkjxQk4Mal3F6DiSA2qX9WRq19oAfLL1NGevJhqcSPKyz7adYd6WUwBM6lKTjrVL/eNj2plNTH+2No52t4qM/95/8R8fU/I/FW9ERERERERERCRfupaQwuzNJwEYEViVws4OBieSnNKlXhlaVi1BmsXGhz8dMzqO5FHf773ApHW3ZuqNCKxGT/8K2XZsX6/CDG3rC8CENWFciVf7NLk/FW9ERERERERERCRfmrHxBDeS03mktDvPNihndBzJYaM7+mE2QciRSHaeuWZ0HMljNoVFMfL7gwD0be7D660qZ/s5XmtRiVplPIi7mcbo1YfVPk3uS8UbERERERERERHJd45ejmfFrggAxnWqgZ3ZZHAiyWlVvQrTo3F5ACatO4rVqg/G5cHsPHONgcv3YbHaeKZ+GUZ38MNkyv6fGfZ2ZqZ3q42DnYmNYVH8eOBStp9D8g8Vb0REREREREREJF+x2WxMXBOG1QYdannjX6mY0ZHkIXnj8aoUcrLn0MU4VodqXRH5a0cuxfHq0j2kpFtp61eSqV1rY87BYm91b3cGtb7VPu29H48QfSMlx84leZuKNyIiIiIiIiIikq9sCItix5lrONqbGfWEn9Fx5CEqXsiJga2rADAt5Dg3Uy0GJ5LcLPxqIr0X7eJGSjqNKxZl3gv1cbDL+Y/MX29dGb9S7lxPSmP8j4dz/HySN6l4IyIiIiIiIiIi+UZKuoUP1t9acLzfoz6UK+pqcCJ52Po0q0gZTxci45NZuO2M0XEkl4qKT+alz3dyNSEVv1LufPZyQ5wd7B7KuR3szEx/tjb2ZhPrD0Wy/tDlh3JeyVtUvBERERERERERkXxj8R9nOXctiRKFnXi9VRWj44gBnB3seOeJ6gAEbz3NlfhkgxNJbhOblEqvz3dx4fpNKhZzZdkrjXF3dnioGWqW8WBAq8oAjF19mJjE1Id6fsn9VLwREREREREREZF8IfpGCvN+OQXAyMBquDnZG5xIjNKpdinqlfckKdXCjA0njI4juUhSajqvLNnN8agblCzsxBd9/SlR2MmQLIMeq0JVr0JcS0zlvR+PGJJBci8Vb0REREREREREJF+YseE4CSnp1C7rQdf6ZY2OIwYymUyM6VgDgG/2nifsUrzBiSQ3SE23MuDLfeyLiMXd2Z4v+vob2lrRyd6O6c/WwWyCHw9cYsORSMOySO6j4o2IiIiIiIiIiOR5hy/GsXLPeQDGdaqB2WwyOJEYrUGFInSqXQqbDSatC8NmsxkdSQxktdp489sDbD0RjYuDHYv7NKaad2GjY1GnnCf9W9xqnzZ69WFik9Q+TW5R8UZERERERERERPI0m83GxLVh2GzwZJ3SNKxY1OhIkku83b46jvZmtp++xi/HrhgdRwxis9l4b80R1hy4hL3ZxCcv1qdBhSJGx8owrK0vlUu4EX0jhYlrw4yOI7mEijciIiIiIiIiIpKnhRyOZFd4DE725oyF6kUAyhV15ZVmPgBMXn+UNIvV4ERihKBNJ1m24xwmE8x4rg6tqpU0OlImzg52TO92q33aqn0X+eVYlNGRJBdQ8UZERERERERERPKs5DQLk9cfBeC1FpUo4+licCLJbV5vXZlibo6ciU5k+c4Io+PIQ7bkj3Bmbz4JwMSnHqFz3TIGJ7q7+uWL0Lf5rULjqFWHiLuZZnAiMZqKNyIiIiIiIiIikmd9/ns4F67fxNvdmX+1qmx0HMmF3J0deOPxqgAEbTpBXJI+FC8oVu+/yHtrbrUhe6NtVV4KqGhsoL/wZrtq+BR3Iyo+hcnr1D6toFPxRkRERERERERE8qQr8cnM33IKgLefqIaro73BiSS36t6oHL4lC3E9KY15W04aHUcegi3HrvDWtwcAeLlpRYa0qWJwor/m7GDHtGdrYzLBN3susPVEtNGRxEAq3oiIiIiIiIiISJ40/efjJKVaqFPOk851cmcrJMkd7O3MjO7oB8CS7Wc5dy3R4ESSk/acjWHAV3tJt9roXLc04zrVwGQyGR3rgTSqWJTe/5khNOr7g9xI1kyxgkrFGxERERERERERyXMOXYjju30XABj/ZA3M5rzxwawYp1W1kjzqW5w0i42pIceMjiM55OjleF5ZspvkNCutqpXgo2518tzPh5Htq1G+qCuX4pKZ8pO+VwuqXFG8mT9/PhUrVsTZ2Rl/f3927dp13/Hffvst1atXx9nZmVq1arF+/fpMz9tsNsaNG0epUqVwcXGhbdu2nDyZeTrk5MmTadq0Ka6urnh6et5xjgMHDtCjRw/KlSuHi4sLfn5+zJ49+x+/VhERERERERER+WdsNhsT1hzBZoMudUtTv3wRoyNJHjG6ox9mE6w/FMnuszFGx5FsFnEtiV6LdhGfnE7DCkX4pGcDHOxyxUfgWeLqaM/UrrUBWL4zgu2nrhqcSIxg+HfuypUrGT58OOPHj2ffvn3UqVOHwMBArly5ctfx27dvp0ePHvTt25f9+/fTpUsXunTpwuHDhzPGTJs2jTlz5hAcHMzOnTtxc3MjMDCQ5OTkjDGpqal069aNAQMG3PU8e/fupWTJknz55ZccOXKE0aNHM2rUKObNm5e9b4CIiIiIiOQ7v/32G+np6XdsT09P57fffjMgkYhI/rL24GX2nLuOs4OZt5+obnQcyUOqe7vzfKPyAExaG4bVajM4kWSXKzeSefHznUTfSKG6d2E+790IF0c7o2P9bQGVi/Fik1vfqyO/P0hiyp2/W0r+ZnjxZubMmfTr148+ffpQo0YNgoODcXV1ZdGiRXcdP3v2bNq3b8+IESPw8/Pj/fffp379+hlFFZvNRlBQEGPGjKFz587Url2bZcuWcenSJVavXp1xnAkTJvDGG29Qq1atu57nlVdeYfbs2bRs2ZJKlSrx4osv0qdPH1atWpXt74GIiIiIiOQvrVu3Jibmzrt54+LiaN26dZaPlxu7FQCYTKY7HitWrMjy6xMRyYrkNAsf/qeN0L9aVqaUh4vBiSSvGf54Vdwc7ThwIY4fD1wyOo5kg7ibafT6fBcRMUmUK+rCslca4+HqYHSsf+ydJ/wo4+nChes3maZWfwWOocWb1NRU9u7dS9u2bTO2mc1m2rZty44dO+66z44dOzKNBwgMDMwYHx4eTmRkZKYxHh4e+Pv73/OYDyouLo6iRYve8/mUlBTi4+MzPUREREREpOCx2Wx3XRT32rVruLm5ZelYubVbwW2LFy/m8uXLGY8uXbpk6fWJiGTVwt/OcDH2JqU9nHmtRWWj40geVKKwE6+3rgLA1JBj3Ey1GJxI/ombqRZeXbqbY5E3KF7IiS/7+lPS3dnoWNmikJM9H3a9Nflg6Y5z/HnmmsGJ5GEytHhz9epVLBYLXl5embZ7eXkRGRl5130iIyPvO/72n1k55oPYvn07K1eupH///vccM2XKFDw8PDIe5cqV+9vnExERERGRvOeZZ57hmWeewWQy8fLLL2d8/cwzz9C5c2cCAwNp2rRplo6ZW7sV3Obp6Ym3t3fGw9k5f3xYIiK5U2RcMh//ehqAt5+onqdbIomx+jb3oYynC5fjkvn89zNGx5G/Kc1iZeDyfew+e53CzvYse6UxFYpl7UaZ3O5R3xJ0b3Trc+a3vz+oYmMBYnjbtLzg8OHDdO7cmfHjx9OuXbt7jhs1ahRxcXEZj/Pnzz/ElCIiIiIiYrTbN3LZbDYKFy6c6eYub29v+vfvz5dffvnAx8sL3QoGDhxI8eLFady4MYsWLcJmu/faAepWICL/1LSQY9xMs1C/vCdP1SltdBzJw5wd7BjZvhoAH/96mis3kv9iD8ltrFYbI787yC/HruBkb2bRy42oUdrd6Fg54t2OfpTycObctSSm/3zc6DjykNgbefLixYtjZ2dHVFRUpu1RUVF4e3vfdR9vb+/7jr/9Z1RUFKVKlco0pm7dulnOGBYWRps2bejfvz9jxoy571gnJyecnJyyfA4REREREckfFi9eDEDFihV56623stwi7X/dr1vBsWN373v+MLsVTJw4kcceewxXV1c2bNjA66+/TkJCAkOGDLnr+ClTpjBhwoQsnUNE5LbQ87Gs2n8RgPFPPnLX9pQiWfFUndIs/uMsoedjmbnhBB92rW10JHlANpuNiWvD+Pf+i9iZTXzyYn0aVbz3chd5nbuzA1OeqcXLi3ezeHs4HWp50zAfv165xdCZN46OjjRo0IDNmzdnbLNarWzevJmAgIC77hMQEJBpPMDGjRszxvv4+ODt7Z1pTHx8PDt37rznMe/lyJEjtG7dmt69ezN58uQs7SsiIiIiIgXX+PHjcXJyYtOmTXz66afcuHEDgEuXLpGQkGBwuuwzduxYmjVrRr169Xj77bcZOXIk06dPv+d4dSsQkb/LZrMxcc0RAJ6pX4Y65TyNDST5gslkYmwnPwBW7jlP2CXNCM0r5v1yiiXbzwLwUbfaPFbd6/475AOtqpXk2QZlsdlg5HcHSU5T+7T8zvC2acOHD2fhwoUsXbqUo0ePMmDAABITE+nTpw8AvXr1YtSoURnjhw4dSkhICDNmzODYsWO899577Nmzh0GDBgG3fugOGzaMSZMm8eOPP3Lo0CF69epF6dKlMy2cGRERQWhoKBEREVgsFkJDQwkNDc24kDp8+DCtW7emXbt2DB8+nMjISCIjI4mOjn54b46IiIiIiORJ586do1atWnTu3JmBAwdmXEdMnTqVt95664GPk9PdCh70mA/K39+fCxcukJKSctfnnZyccHd3z/QQEXkQPx64xL6IWFwd7Xi7fXWj40g+0qBCUTrWLoXNBh+sP3rf9p+SO3zx5zlmbDwBwPgna/B0vbIGJ3p4xnasQcnCTpy5msis/7wHkn8ZXrx5/vnn+eijjxg3bhx169YlNDSUkJCQjCn8ERERXL58OWN806ZNWb58OQsWLKBOnTp89913rF69mpo1a2aMGTlyJIMHD6Z///40atSIhIQEQkJCMi2cOW7cOOrVq8f48eNJSEigXr161KtXjz179gDw3XffER0dzZdffkmpUqUyHo0aNXpI74yIiIiIiORVQ4cOpWHDhly/fh0XF5eM7U8//fQdnQTuJ7d3K/hfoaGhFClSRO2kRSRbJaWm8+FPt1pFvt6qMl7uzn+xh0jWvNO+Oo52Zn4/dZVfj+vG7dxszYFLjPvhMABDHqtCn2Y+Bid6uDxcHfjg6VoALNx2hv0R1w1OJDnJ0DVvbhs0aFDGzJn/9euvv96xrVu3bnTr1u2exzOZTEycOJGJEyfec8ySJUtYsmTJPZ9/7733eO+99+75vIiIiIiIyL1s27aN7du34+jomGl7xYoVuXjxYpaONXz4cHr37k3Dhg1p3LgxQUFBd3QrKFOmDFOmTAFuFY5atmzJjBkz6NixIytWrGDPnj0sWLAAyNytwNfXFx8fH8aOHXvXbgUxMTGZuhUAVKlShUKFCrFmzRqioqJo0qQJzs7ObNy4kQ8++CBLM4tERB7Egt/OcDkumTKeLrz6aCWj40g+VK6oK32aVeTT384waV0YzX2L42Bn+D3v8j+2nohm+Deh2GzwYpPyvPF4VaMjGaJtDS+61C3N6tBLjPzuIGuHNMfJ3s7oWJIDslS8SUtLo3r16qxduxY/P7+cyiQiIiIiIpKnWa1WLJY7+5BfuHCBwoULZ+lYzz//PNHR0YwbN47IyEjq1q17R7cCs/n/P2C63a1gzJgxvPvuu/j6+t61W0FiYiL9+/cnNjaW5s2b37VbwdKlSzO+rlevHgBbtmyhVatWODg4MH/+fN544w1sNhtVqlRh5syZ9OvXL0uvT0Tkfi7F3iR462kARnWojrODPqCUnPF66yp8u/cCp6MTWbErgpcCKhodSf7Lvojr/OuLvaRZbHSqXYoJT9XEZDIZHcsw4598hN9PXePklQTmbD7JiEC1k8yPslRCdnBwIDk5OaeyiIiIiIiI5Avt2rUjKCgo42uTyURCQgLjx4+nQ4cOWT7eoEGDOHfuHCkpKezcuRN/f/+M53799dc7ugp069aN48ePk5KSwuHDh+845+1uBZGRkSQnJ7Np0yaqVs189+qSJUuw2Wx3PFq1agVA+/bt2b9/Pzdu3CAhIYHQ0FBee+21TIUkEZF/amrIMZLTrDSuWJSOtUoZHUfyMQ8XB95o6wvArE0nibuZZnAiue1E1A36LN7NzTQLj/oWZ+ZzdbEzF9zCDUARN0cmdXkEgOCtZzh0Ic7gRJITsvxb9cCBA5k6dSrp6ek5kUdERERERCTPmzFjBn/88Qc1atQgOTmZF154IaNl2tSpU42OJyKSJ+w9d50fQi9hMsG4J2sU6Lvs5eHo0bg8VUoWIiYxlY+3nDI6jgDnY5J46fOdxN1Mo155Tz59qQGO9rpRBKB9zVJ0rF0Ki9XGiO8OkJpuNTqSZLMsr3mze/duNm/ezIYNG6hVqxZubm6Znl+1alW2hRMREREREcmLypYty4EDB1ixYgUHDx4kISGBvn370rNnT1xcXIyOJyKS61mtNiauDQOgW4Oy1CzjYXAiKQjs7cyM7uBHnyW7WfzHWXr6V6B8MVejYxVY0TdSeOnznUTFp1DVqxCLX26Eq2OuWMI915j41CPsOH2NY5E3mL/lVIFdByi/yvJ3u6enJ127ds2JLCIiIiIiIvmGvb09L774otExRETypNWhFzlwPhY3RzveCqxmdBwpQFpVK8GjvsXZdvIqU0OOMb9nfaMjFUjxyWm8vHgXZ68lUcbThWWv+OPp6mh0rFynWCEnJjz1CIO/3s/8Lado94gXj5RWsTu/yHLxZvHixTmRQ0REREREJN/48ccf77rdZDLh7OxMlSpV8PHxecipRETyhsSUdKaGHANg4GNVKFnY2eBEUpCYTCZGd/Sjw+xtrDt0mT5nY2hYsajRsQqU5DQLry7dw5FL8RRzc+TLV/3x9tDPgXvpVLsU6w5eJuRIJCO+PcgPg5rhYKfWcvnB355nFh0dzfHjxwGoVq0aJUqUyLZQIiIiIiIieVmXLl0wmUzYbLZM229vM5lMNG/enNWrV1OkSBGDUoqI5E7BW08TFZ9CuaIuvNJMhW55+Kp7u/N8o3J8ves87687yr8HNMVs1ppLD0O6xcqg5fvZFR5DYSd7lr7SGJ/ibn+9YwFmMpl4v0tN/gy/RtjleIJ/Pc3gNr5Gx5JskOUSXGJiIq+88gqlSpWiRYsWtGjRgtKlS9O3b1+SkpJyIqOIiIiIiEiesnHjRho1asTGjRuJi4sjLi6OjRs34u/vz9q1a/ntt9+4du0ab731ltFRRURylQvXk1jw2xkARnfww9nBzuBEUlC98XhV3BztOHA+ljUHLxkdp0CwWm28/f0hNh2NwtHezMLeDbXe1QMqUdiJ9558BIA5v5zkeOQNgxNJdshy8Wb48OFs3bqVNWvWEBsbS2xsLD/88ANbt27lzTffzImMIiIiIiIiecrQoUOZOXMmbdq0oXDhwhQuXJg2bdowffp0RowYQbNmzQgKCmLjxo1GRxURyVU+/OkYKelW/H2KEviIt9FxpAArWdiZAa0qAzAt5DjJaRaDE+VvNpuND9Yf5ft9F7Azm5j/Qn2aVCpmdKw8pXPd0rT1K0maxcaI7w6QbrEaHUn+oSwXb77//ns+//xznnjiCdzd3XF3d6dDhw4sXLiQ7777LicyioiIiIiI5CmnT5/G3d39ju3u7u6cOXPrjnJfX1+uXr36sKOJiORau8/GsPbgZUwmGPdkDUwmtakSY736aCVKezhzMfYmn/8ebnScfO2Traf57D/v8dSutXm8hpfBifIek8nE5Kdr4e5sz8ELcSzcpu/ZvC7LxZukpCS8vO78y1OyZEm1TRMREREREQEaNGjAiBEjiI6OztgWHR3NyJEjadSoEQAnT56kXLlyRkUUEclVrFYbE9eEAdC9UTkeKa1WSWI8Zwc7RravDsDHW04RfSPF4ET509e7IpgWcmtt9TEd/Xi2QVmDE+VdXu7OjO1UA4BZm05w6kqCwYnkn8hy8SYgIIDx48eTnJycse3mzZtMmDCBgICAbA0nIiIiIiKSF33++eeEh4dTtmxZqlSpQpUqVShbtixnz57ls88+AyAhIYExY8YYnFREJHf4ft8FDl2Mo5CTPW+2q2Z0HJEMT9UpTZ2yHiSmWpi58YTRcfKd9YcuM/rfhwB4vVVlXn20ksGJ8r5nG5SlVbUSpKZbGfHdASxWm9GR5G+yz+oOQUFBtG/fnrJly1KnTh0ADhw4gLOzMz///HO2BxQREREREclrqlWrRlhYGBs2bODEiRMZ2x5//HHM5lv30HXp0sXAhCIiuUdCSjrTfr511/3gx6pQvJCTwYlE/p/ZbGJMpxp0C97Byt0R9G5agered7ZGlaz7/eRVhq0IxWqDHo3LMSJQhdvsYDKZ+ODpWrSb9Rv7I2JZ/Ee4imJ5VJaLN7Vq1eLkyZN89dVXHDt2DIAePXrQs2dPXFxcsj2giIiIiIhIXmQ2m2nfvj3t27c3OoqISK52ux1VhWKuvNysotFxRO7QqGJROtTyZv2hSCavO8qyVxprTaZ/KPR8LP2/2EOqxcoTNb2Z1KWW3tNsVNrThdEd/Ri16hDTfz5OGz8vfIq7GR1LsihLxZu0tDSqV6/O2rVr6devX05lEhERERERyXPmzJnzwGOHDBmSg0lERPKO8zFJGYuUj+7gh5O9ncGJRO7u7fbV2RR2hW0nr/LriWhaVytpdKQ860TUDfos3kVSqoVmVYoR1L0udmYVbrJb90blWHfwMr+fusrI7w6wsn8AZr3PeUqWijcODg6Z1roRERERERGRW2bNmpXp6+joaJKSkvD09AQgNjYWV1dXSpYsqeKNiMh/TPnpKKnpVppVKcbjNbyMjiNyTxWKufFys4os+O0Mk9cd5dEqxbG3y/Jy4gXe4Ytx9Fq0i+tJadQp68GnLzVU0TaHmEwmpjxTi8Cg39h99jpLd5ylTzMfo2NJFmT5J8zAgQOZOnUq6enpOZFHREREREQkTwoPD894TJ48mbp163L06FFiYmKIiYnh6NGj1K9fn/fff9/oqCIiucKfZ66x/lAkZhOM7VRDLZMk1xvYugpFXB04dSWBFbvPGx0nz9l7LoYeC/8kJjGV2mU9WNKnMYWcsryqh2RBuaKujHqiOgDTQo5z7lqiwYkkK7JcvNm9ezerVq2ifPnyBAYG8swzz2R6iIiIiIiIFHRjx45l7ty5VKv2/wvvVqtWjVmzZjFmzBgDk4mI5A4Wq42Ja8IA6NG4vBaAlzzBw8WBYW2rAjBr4wnik9MMTpR3bD91lZc+38WN5HQaVSzCV6/6U8TN0ehYBUJP/wo0qVSUm2kW3v7+IFarzehI8oCyXLzx9PSka9euBAYGUrp0aTw8PDI9RERERERECrrLly/ftVuBxWIhKirKgEQiIrnLt3vOE3Y5nsLO9gx/vKrRcUQe2Av+5alUwo1rial8vOW00XHyhM1Ho3h5yW6SUi086lucpa80prCzg9GxCgyz2cS0rnVwcbDjzzMxfLUrwuhI8oCyNC8tPT2d1q1b065dO7y9vXMqk4iIiIiISJ7Wpk0bXnvtNT777DPq168PwN69exkwYABt27Y1OJ2IiLFuJKfx0YbjAAxt40uxQk4GJxJ5cA52ZkZ38KPv0j0s+j2cnv7lKVfU1ehYudaaA5d4Y2Uo6VYbj9fwYt4L9bTGjQHKF3NlZPtqTFgTxofrj9K6WgnKFtH3bW6XpZk39vb2/Otf/yIlJSWn8oiIiIiIiOR5ixYtwtvbm4YNG+Lk5ISTkxONGzfGy8uLzz77zOh4IiKGmrflFFcTUqlU3I1eARWNjiOSZY9VL0mzKsVItViZGnLM6Di51jd7zjN0xX7SrTY61y3Nxz3rq3BjoN4BFWlUsQiJqRZGrTqEzab2abldltumNW7cmP379+dEFhERERERkXyhRIkSrF+/nmPHjvHtt9/y7bffcvToUdavX0/JkiWNjiciYphz1xJZ/PtZAEZ39MPRPssfTYkYzmQyMbpDDUwmWHvwMnvPXTc6Uq6z5I9wRn53EKsNejQux8zn6uJgp7/vRjKbTUx7tg5O9ma2nbzKyt3njY4kfyFLbdMAXn/9dd58800uXLhAgwYNcHNzy/R87dq1sy2ciIiIiIhIXla1alWqVtVaDiIit01ed5RUi5VHfYvzWHUVsyXvqlHanecalGPlnvO8vzaMf7/eFJPJZHSsXGH+llNM//lWa8S+zX0Y09FP700u4VPcjbfaVWPy+qNMXneUltVKUMrDxehYcg9ZLt50794dgCFDhmRsM5lM2Gw2TCYTFosl+9KJiIiIiIjkQa+88sp9n1+0aNFDSiIikntsP3WVDWFR2JlNjO1UQx/mSp73ZruqrDl4idDzsaw5eJmn6pQ2OpKhbDYbH204zvwtpwEY0saXN9r66u96LvNKcx/WH77M/ohYRq06xOKXGxWo/0c2m43T0QmYTSYqlShkdJz7ynLxJjw8PCdyiIiIiIiI5BvXr2dun5KWlsbhw4eJjY3lscceMyiViIhxLFYbE9eGAdDTvzxVvQobnEjknyvp7syAlpWZsfEEU386RrsaXjg7FMw1Xaz/+Tu+ZPtZAEY9UZ3XWlY2NpTclZ3ZxPRna9Nhzu/8ejya7/dd5NkGZY2OlWNsNhtnriay4/Q1/jxzjT/PxHA1IYUejcsx5Znc3UUsy8WbChUq5EQOERERERGRfOPf//73HdusVisDBgygcmV9kCEiBc+K3REci7yBh4sDb7RVO0nJP159tBLLd0VwMfYmi/84y4BWBe/feYvVxjvfH+TbvRcAeL9LTV5qos+Qc7MqJQszrK0v00KOM3HNER71LY6Xu7PRsbKFzWbj7LWk/yrWXOPKjZRMY5zszaRbbAYlfHBZLt4AfPHFFwQHBxMeHs6OHTuoUKECQUFB+Pj40Llz5+zOKCIiIiIikueZzWaGDx9Oq1atGDlypNFxREQemribaczYcAKAYW19KeLmaHAikezj4mjHiMBqDP/mAPO3nKJbw7IUL+RkdKyHJs1i5Y2Voaw9eBmzCaY/W4eu+XgWR37S/9FKhByO5OCFOEb/+xALezXMk+3TbDYb564l8eeZa+z4T7EmKj5zscbR3kz98p4EVCpOk0pFqVveEyf73D9LLsvFm08++YRx48YxbNgwJk+enLHGjaenJ0FBQSreiIiIiIiI3MPp06dJT083OoaIyEM175eTxCSmUqVkIV7U3fiSD3WpW4bFf5zl0MU4Zm08weSnaxkd6aFITrMwaPk+Nh29goOdiTnd6/FErVJGx5IHZG9nZvqzdeg0dxubjl7hh9BLdKlXxuhYf8lms3E+5mamYs3luORMYxztzNQr70mTSsUIqFyMuuU882RLwywXb+bOncvChQvp0qULH374Ycb2hg0b8tZbb2VrOBERERERkbxo+PDhmb622WxcvnyZdevW0bt3b4NSiYg8fOFXEzPWwBjT0Q8HO7OxgURygNlsYkxHP55f8Cdf74qgd9OK+X5dp8SUdPp/sYc/Tl3Dyd5M8EsNaF2tpNGxJIuqeRdm8GO+zNx4gvfWHKFplWKULJz72qedj0nKKNTsPBPDxdibmZ53sDNRr1wRmlQqSpPKxahfvkieLNb8rywXb8LDw6lXr94d252cnEhMTMyWUCIiIiIiInnZ/v37M31tNpspUaIEM2bM4JVXXjEolYjIwzd5XRhpFhutqpWglT7YlXzMv1Ix2j/iTciRSCavO8rSVxobHSnHxN1M45Ulu9l77jpujnZ81rsRAZWLGR1L/qYBrSoTcjiSsMvxjFt9hE9erG94+7QL15P480zMrdk1p6/dUayxN5uoW+7/Z9bUL18EF8e8X6z5X1ku3vj4+BAaGkqFCpmnuYaEhODn5/e3QsyfP5/p06cTGRlJnTp1mDt3Lo0b3/sH3LfffsvYsWM5e/Ysvr6+TJ06lQ4dOmQ8b7PZGD9+PAsXLiQ2NpZmzZrxySef4OvrmzFm8uTJrFu3jtDQUBwdHYmNjb3jPBEREQwYMIAtW7ZQqFAhevfuzZQpU7C3/1tLBYmIiIiISAGxZcsWoyOIiBhu28loNh29gr3ZxJiONYyOI5Lj3nmiOpuPRbH1RDS/Hr+SLwuW1xJS6LVoF0cuxePubM/SVxpTr3wRo2PJP+BgZ+ajbnV4at7vhByJZN2hy3SqXfqhZrgUezOjUPNn+DXOx9xZrKld1oOAysVoUqkYDSoUwdUx/39Gn+VXOHz4cAYOHEhycjI2m41du3bx9ddfM2XKFD777LMsB1i5ciXDhw8nODgYf39/goKCCAwM5Pjx45QseecPuO3bt9OjRw+mTJlCp06dWL58OV26dGHfvn3UrFkTgGnTpjFnzhyWLl2Kj48PY8eOJTAwkLCwMJydb037Sk1NpVu3bgQEBPD555/fcR6LxULHjh3x9vZm+/btXL58mV69euHg4MAHH3yQ5dcpIiIiIiIFz5UrVzh+/DgA1apVu+s1johIfpRusfL+2jAAXgqoQJWShQxOJJLzKhZ3o3dART77PZwP1h+leZXi2OejVoFR8cn0/Gwnp64kUMzNkS/6+lOjtLvRsSQb1CjtzuutqzBn80nG/XCEgErFKFbIKcfOFxmXzI4zV/nzdAx/hl/j3LWkTM/bmU3UKvP/xZqGFYrg5pT/izX/K8uv+NVXX8XFxYUxY8aQlJTECy+8QOnSpZk9ezbdu3fPcoCZM2fSr18/+vTpA0BwcDDr1q1j0aJFvPPOO3eMnz17Nu3bt2fEiBEAvP/++2zcuJF58+YRHByMzWYjKCiIMWPG0LlzZwCWLVuGl5cXq1evzsg4YcIEAJYsWXLXXBs2bCAsLIxNmzbh5eVF3bp1ef/993n77bd57733cHR0zPJrFRERERGRgiE+Pp6BAwfy9ddfY7VaAbCzs+P5559n/vz5eHh4GJxQRCRnfb0rghNRCXi6OjCsTVWj44g8NIMf8+W7fRc4EZXAyj3n6elf4a93ygPOxyTR87OdRMQk4e3uzFf9/KlcQkXZ/GRQ6ypsOBLJscgbjP/xCPNeqJ9tx46KT+bP/6xZs+P0Nc7+T7HGbIJaZT1pUqkoAZWK0bBiUQoVwGLN//pbpd+ePXty8uRJEhISiIyM5MKFC/Tt2zfLx0lNTWXv3r20bdv2/wOZzbRt25YdO3bcdZ8dO3ZkGg8QGBiYMT48PJzIyMhMYzw8PPD397/nMe91nlq1auHl5ZXpPPHx8Rw5cuSu+6SkpBAfH5/pISIiIiIiBU+/fv3YuXMn69atIzY2ltjYWNauXcuePXt47bXXjI4nIpKj4pLSmLnxBADDH6+Kh6uDwYlEHh4PVweGtbm1dMPMDSe4kZxmcKJ/7nR0As99uoOImCTKF3Xl238FqHCTDznam5n+bB3szCbWHrxMyOHIv32sKzeS+fHAJUatOsRjH/2K/webGboilK93nefstSTMJqhd1oP+LSqx+OVGHBjfjh8GNmPUE360qlZShZv/+EfvgqurK66urn97/6tXr2KxWDIVSAC8vLw4duzYXfeJjIy86/jIyMiM529vu9eYB3Gv8/z3Of7XlClTMmb0iIiIiIhIwbV27Vp+/vlnmjdvnrEtMDCQhQsX0r59ewOTiYjkvKDNJ7ielEZVr0K80Li80XFEHrqeTSqwbMc5zlxN5JNfTzOyfXWjI/1tRy/H89LnO7makEqVkoX46lV/vNydjY4lOaRWWQ9ea1GJj389zZjVh/H3KUoRt7/uQBV9I4Wd4f9Zs+bMNU5HJ2Z63mSCR0q7E1DpP23QKhbFw0WF/b+iElY2GjVqFMOHD8/4Oj4+nnLlyhmYSEREREREjFCsWLG7tkbz8PCgSBEt6isi+depKwl8seMcAGM71chX632IPCgHOzOjOvjRb9kePvs9nBf8y1O2yN+/Ad4ooedj6b1oF3E303iktDvLXmmco+ugSO4wpI0vG8KiOHUlgQlrjhDUvd4dY64lpPDnmZiMVmgnryRket5kAj9v94w1axr7qFjzdxhavClevDh2dnZERUVl2h4VFYW3t/dd9/H29r7v+Nt/RkVFUapUqUxj6tat+8DZvL292bVr1x3n+e9z/C8nJyecnPQDTERERESkoBszZgzDhw/niy++yLh+iIyMZMSIEYwdO9bgdCIiOWfyujDSrTba+pXkUd8SRscRMUxbv5IEVCrGjjPXmBZynDk97vwAPDf788w1+i7ZTWKqhfrlPVncp7E+fC8gnB3smP5sbbp+sp3VoZfoVLs09SsUYeftNWvOXONEVMId+1X3LpxRrPH3KYqnq9aM/6cMLd44OjrSoEEDNm/eTJcuXQCwWq1s3ryZQYMG3XWfgIAANm/ezLBhwzK2bdy4kYCAAAB8fHzw9vZm8+bNGcWa+Ph4du7cyYABAx44W0BAAJMnT+bKlSuULFky4zzu7u7UqFEj6y9WRERERETytXr16mEymTK+PnnyJOXLl6d8+VstgyIiInByciI6Olrr3ohIvpOcZiFo00m2HI/Gwc7Eux38jI4kYiiTycTojn48Oe93fjxwiZebVaR++bwx+/bX41d47Yu9pKRbaVq5GAt7NcRNa5AUKPXKF+HVRyux4LczvL58H6np1jvGVPcuTJNKxWhSqSj+PsUeqL2aZM0/+luXnJyMs/M/63E4fPhwevfuTcOGDWncuDFBQUEkJibSp08fAHr16kWZMmWYMmUKAEOHDqVly5bMmDGDjh07smLFCvbs2cOCBQuAWz8Yhw0bxqRJk/D19cXHx4exY8dSunTpjAIR3LpwiomJISIiAovFQmhoKABVqlShUKFCtGvXjho1avDSSy8xbdo0IiMjGTNmDAMHDtTsGhERERERucN/X2+IiBQkW09EM3b1YSJikgAY0KoKlbSYuQg1y3jwbP2yfLv3ApPWhvH9gKaZbvTIjUIOX2bw1/tJs9hoU70k83vWx9nBzuhYYoDhj1dl09Eozvxn/ZqqXoVoUqkYAf9pg6YWejkvy8Ubq9XK5MmTCQ4OJioqihMnTlCpUiXGjh1LxYoV6du3b5aO9/zzzxMdHc24ceOIjIykbt26hISE4OXlBdwqspjN/98ftWnTpixfvpwxY8bw7rvv4uvry+rVq6lZs2bGmJEjR5KYmEj//v2JjY2lefPmhISEZCo0jRs3jqVLl2Z8Xa/eramLW7ZsoVWrVtjZ2bF27VoGDBhAQEAAbm5u9O7dm4kTJ2b1LRMRERERkQJg/PjxRkcQEXmortxI5v21R1lz4BIA3u7OvPfUIwQ+4mVwMpHc463Aaqw9eJl9EbGsO3SZTrVLGx3pnlbtu8CI7w5isdroWLsUQc/XxUHrVhVYzg52rOjfhMMX46hd1pPiKtY8dFku3kyaNImlS5cybdo0+vXrl7G9Zs2aBAUFZbl4AzBo0KB7tkn79ddf79jWrVs3unXrds/jmUwmJk6ceN9Cy5IlS1iyZMl9c1WoUIH169ffd4yIiIiIiIiISEFitdpYviuCqSHHuJGcjtkEvZtW5M121Sik1koimXi5O/OvlpWZtekEH/50jLZ+XrlyJsuXf55jzOrDAHRrUJYPu9bGzpy7ZwlJzitZ2JnHqv+zzlvy92W5dLps2TIWLFhAz549sbP7/x80derU4dixY9kaTkREREREREREco+jl+PpGrydMasPcyM5nVplPPhxUHPGP/mICjci99CvhQ/e7s5cuH6TJdvPGh3nDgt+O51RuHm5aUWmqnAjkitkuXhz8eJFqlSpcsd2q9VKWlpatoQSEREREREREZHcIyk1nSnrj9Jp7u/sj4ilkJM97z1Zg9UDm1GzjIfR8URyNVdHe0YEVgNg/i+nuJqQYnCiW2w2G7M2nuCD9bduyH+9VWXGP1kDswo3IrlClos3NWrUYNu2bXds/+677zLWjRERERERERERkfxh89EoHp/5G5/+dgaL1cYTNb3ZNLwlLzfz0d35Ig/o6XplqFnGnRsp6QRtOmF0HGw2G5PXHWX25pMAjAisxsj21TGZ9HdaJLfI8nzWcePG0bt3by5evIjVamXVqlUcP36cZcuWsXbt2pzIKCIiIiIikmekpaVRvXp11q5di5+fn9FxRET+tsi4ZN778QghRyIBKOPpwsTOj9DGz8vgZCJ5j9lsYkzHGnRf8Cdf7zpP74CK+HoVNiSL1WpjzA+HWb4zAoD3nqzBy818DMkiIveW5Zk3nTt3Zs2aNWzatAk3NzfGjRvH0aNHWbNmDY8//nhOZBQREREREckzHBwcSE5ONjqGiMjfZrHaWPxHOG1nbiXkSCR2ZhOvtajExuEtVLgR+QeaVCpGuxpeWKw2Plh/1JAM6RYrw78JZfnOCMwmmNa1tgo3IrnU31pJ7tFHH2Xjxo3ZnUVERERERCRfGDhwIFOnTuWzzz7D3l4LeItI3nHoQhzv/vsQhy7GAVCvvCcfPF0Lv1LuBicTyR9GdfDjl2NX2HI8mt9ORNOiaomHdu6UdAuDl+9nQ1gU9mYTs56vy5N1Sj+084tI1mT5KmL37t1YrVb8/f0zbd+5cyd2dnY0bNgw28KJiIiIiIjkRbt372bz5s1s2LCBWrVq4ebmlun5VatWGZRMROTuElLSmbHhOEu3n8Vqg8LO9rzdvjovNC6vxctFspFPcTd6BVRk0R/hTF53lGZVij+UtaNuplro/8Uetp28iqO9mY9fqE/bGppJJ5KbZblt2sCBAzl//vwd2y9evMjAgQOzJZSIiIiIiEhe5unpSdeuXQkMDKR06dJ4eHhkeoiI5BY2m42Qw5dpO2Mri/+4Vbh5qk5pNr/ZkhebVFDhRiQHDGlTBQ8XB45H3eCbPXd+zprdbiSn0XvRLradvIqrox2LX26kwo1IHpDlmTdhYWHUr1//ju316tUjLCwsW0KJiIiIiIjkZYsXLzY6gojIX7pwPYn3fjzCpqNXAChf1JVJXWo+1DZOIgWRp6sjQ9v4MnFtGDM2HOfJOqUp5JQzbVZjk1LpvWgXBy7EUdjZniV9GtGgQtEcOZeIZK8sz7xxcnIiKirqju2XL19WL2cREREREZH/Eh0dze+//87vv/9OdHS00XFERABIs1hZ8NtpHp/5G5uOXsHBzsSg1lXY8EYLFW5EHpIXm1TAp7gbVxNS+eTXUzlyjis3knn+0z85cCGOIq4OfN2viQo3InlIlqst7dq1Y9SoUfzwww8Z0/1jY2N59913efzxx7M9oIiIiIiI0eLi4khKSjI6xgNxdXVVW65cIDExkcGDB7Ns2TKsVisAdnZ29OrVi7lz5+Lq6mpwQhEpqPZFXOfdVYc4FnkDgMYVizL56Zr4ehU2OJlIweJob2bUE9Xp/8VeFm4Lp0fj8pQtkn2/H1yMvcmLn+0k/GoiJQs78dWr/vp7LpLHZLl489FHH9GiRQsqVKhAvXr1AAgNDcXLy4svvvgi2wOKiIiIiBgpLi6OOXPnYbWkGx3lgZjt7BkyeJAKOAYbPnw4W7duZc2aNTRr1gyA33//nSFDhvDmm2/yySefGJxQRAqauJtpTP/5GF/tjMBmA09XB959wo9nG5TVujYiBnm8hhdNKhXlzzMxTP/5OLO718uW4569mkjPz3ZyMfYmZTxdWN7PnwrF3LLl2CLy8GS5eFOmTBkOHjzIV199xYEDB3BxcaFPnz706NEDBweHnMgoIiIiImKYpKQkrJZ0tqb6EGd1NjrOfXmYk2npGE5SUpKKNwb7/vvv+e6772jVqlXGtg4dOuDi4sJzzz2n4o2IPDQ2m421By8zcW0Y0TdSAHimfhlGd/CjWCEng9OJFGwmk4kxHWvw5Lzf+SH0En2a+VC3nOc/OuaJqBv0/Gwn0TdSqFTcjS9f9ae0p0v2BBaRh+pvLVLj5uZG//79szuLiIiIiEiuFWd15potl9+xaDU6gNyWlJSEl5fXHdtLliyZZ1rwiUjeF3EtiTE/HOa3E7fW3KpU3I1JT9ekaeXiBicTkdtqlvHgmXpl+X7fBSatDePbfwVgMv292XCHLsTRa9FOrielUd27MF/09adEYRVpRfKqByre/PjjjzzxxBM4ODjw448/3nfsU089lS3BRERERERE8qqAgADGjx/PsmXLcHa+NWPr5s2bTJgwgYCAAIPTiUh+l5puZeG2M8zZfJKUdCuOdmZeb12ZAa0q42RvZ3Q8EfkfIwKrse7QJfacu85PhyPpUKtUlo+x52wMfRbv5kZKOnXKebK0TyM8XR1zIK2IPCwPVLzp0qULkZGRlCxZki5dutxznMlkwmKxZFc2ERERERGRPCkoKIj27dtTtmxZ6tSpA8CBAwdwdnbm559/NjidiORnu8/G8O6qQ5y8kgBA08rFmNSlJpVKFDI4mYjci7eHM6+1qMzszSeZ8tNR2viVzFKh9feTV+m3bA830yw09inKopcbUcjpbzVcEpFc5IH+Flut1rv+t4iIiIiIiNypVq1anDx5kq+++opjx44B0KNHD3r27ImLi/rOi0j2i01K5cOfjrFi93kAiro5MqajH0/XK/O3WzCJyMPzWstKfL0rgvMxN1m6/Sz9W1R+oP02hkUx8Kt9pFqstKxaguAXG+DiqBl2IvmBSrAiIiIiIiLZKC0tjerVq7N27Vr69etndBwRyedsNhv/3n+RyeuOci0xFYDujcrxzhPV1TJJJA9xdbRnRGA1Rnx3kLmbT9G1flmKFbr/ejU/HrjEGytDsVhttH/Em9k96qo1okg+8kDFmzlz5jzwAYcMGfK3w4iIiIiIiOR1Dg4OJCcnGx1DRAqAM9EJjFl9mO2nrwHgW7IQHzxTi0YVixqcTET+jq71y7Jk+1mOXIpn9uaTTOxc855jV+6O4J1Vh7DZ4Jl6ZZj2bG3s7cwPMa2I5LQHKt7MmjXrgQ5mMplUvBERERERkQJv4MCBTJ06lc8++wx7ezU8EJHslZJu4ZNfT/PxltOkWqw42ZsZ0saXfo9WwtFeH96K5FVms4nRHf14YeFOvtoZQa+AClQpWfiOcYt+D2fi2jAAevqX5/3ONTGb1R5RJL95oKuI8PDwnM4hIiIiIiKSb+zevZvNmzezYcMGatWqhZubW6bnV61aZVAyEcnrtp++yph/H+bM1UQAWlQtwaTONSlfzNXgZCKSHZpWLs7jNbzYGBbFB+uPsejlRhnP2Ww25m85xUcbTgDQv0UlRj1RXetaieRT//gWMIvFwqFDh6hQoQJFihTJjkwiIiIiIiJ5mqenJ127djU6hojkI9cSUpi8/iir9l0EoERhJ8Z1qkGn2qX0wa1IPjPqiepsOXaFX45d4feTV2nuWxybzcbUkOMEbz0NwPDHqzL4sSr6+y+Sj2W5eDNs2DBq1apF3759sVgstGjRgh07duDq6sratWtp1apVDsQUERERERHJG9LT02ndujXt2rXD29vb6DgiksdZrTa+3XueKT8dIzYpDZPpVpukEYHV8XBxMDqeiOSASiUK8WKTCizZfpZJ68JYO7g5E9eGsWzHOQDGdPTj1UcrGZxSRHJalhuhfvfdd9SpUweANWvWcPbsWY4dO8Ybb7zB6NGjsz2giIiIiIhIXmJvb8+//vUvUlJSjI4iInncyagbdF/wJ29/f4jYpDSqexdm1YCmTOpSS4UbkXxuaBtf3J3tORZ5g6fm/cGyHecwmeCDp2upcCNSQGS5eHP16tWMu8fWr19Pt27dqFq1Kq+88gqHDh3K9oAiIiIiIiJ5TePGjdm/f7/RMUQkj0pOszD952N0mLONXWdjcHGw490O1VkzuDn1yqtlvUhBUMTNkSFtfAEIuxyPndnErOfq8oJ/eYOTicjDkuW2aV5eXoSFhVGqVClCQkL45JNPAEhKSsLOzi7bA4qIiIiIiOQ1r7/+Om+++SYXLlygQYMGuLm5ZXq+du3aBiUTkdzutxPRjFl9mIiYJADa+pXkvaceoWwRV4OTicjD1iugIqv2XeRUdAJzutejfU21YxUpSLI886ZPnz4899xz1KxZE5PJRNu2bQHYuXMn1atXz/aAIiIiIiIieU337t0JDw9nyJAhNGvWjLp161KvXr2MP7Nq/vz5VKxYEWdnZ/z9/dm1a9d9x3/77bdUr14dZ2dnatWqxfr16zM9b7PZGDduHKVKlcLFxYW2bdty8uTJTGMmT55M06ZNcXV1xdPT867niYiIoGPHjri6ulKyZElGjBhBenp6ll+fiMCVG8kM+Xo/vRbtIiImCW93Z4JfbMDCXg1VuBEpoBztzax6vSl7x7RV4UakAMryzJv33nuPmjVrcv78ebp164aTkxMAdnZ2vPPOO9keUEREREREJK8JDw/PtmOtXLmS4cOHExwcjL+/P0FBQQQGBnL8+HFKlix5x/jt27fTo0cPpkyZQqdOnVi+fDldunRh37591KxZE4Bp06YxZ84cli5dio+PD2PHjiUwMJCwsDCcnZ0BSE1NpVu3bgQEBPD555/fcR6LxULHjh3x9vZm+/btXL58mV69euHg4MAHH3yQba9fJL+zWm0s3xXB1JBj3EhOx2yC3k0r8ma7ahRyyvLHNiKSzzg72OHsoG5HIgXR3/ot4Nlnn71jW+/evf9xGBERERERkfygQoUK2XasmTNn0q9fP/r06QNAcHAw69atY9GiRXe9gW727Nm0b9+eESNGAPD++++zceNG5s2bR3BwMDabjaCgIMaMGUPnzp0BWLZsGV5eXqxevZru3bsDMGHCBACWLFly11wbNmwgLCyMTZs24eXlRd26dXn//fd5++23ee+993B0dMy290Akvzp6OZ53/32I/RGxANQq48EHT9eiVlkPY4OJiIiI4bLcNk1ERERERET+2hdffEGzZs0oXbo0586dAyAoKIgffvjhgY+RmprK3r17M9pVA5jNZtq2bcuOHTvuus+OHTsyjQcIDAzMGB8eHk5kZGSmMR4eHvj7+9/zmPc6T61atfDy8sp0nvj4eI4cOXLXfVJSUoiPj8/0ECmovt4VQae5v7M/IhY3RzvGP1mD1QObqXAjIiIiQC4p3hjRvzkmJoaePXvi7u6Op6cnffv2JSEhIdOYn3/+mSZNmlC4cGFKlChB165dOXv2bLa8ZhERERERyb8++eQThg8fTocOHYiNjcVisQDg6elJUFDQAx/n6tWrWCyWTAUSAC8vLyIjI++6T2Rk5H3H3/4zK8fMynn++xz/a8qUKXh4eGQ8ypUr98DnE8lPUtOtTA05hsVqI/ARLza92ZI+zXywM5uMjiYiIiK5hOHFm9v9m8ePH8++ffuoU6cOgYGBXLly5a7jb/dv7tu3L/v376dLly506dKFw4cPZ4y53b85ODiYnTt34ubmRmBgIMnJyRljevbsyZEjR9i4cSNr167lt99+o3///hnPh4eH07lzZx577DFCQ0P5+eefuXr1Ks8880zOvRkiIiIiIpIvzJ07l4ULFzJ69Gjs7P6/T33Dhg05dOiQgcmMNWrUKOLi4jIe58+fNzqSiCG2nYwmNimNEoWd+LhnA0p5uBgdSURERHIZw4s3/92/uUaNGgQHB+Pq6sqiRYvuOv6/+zf7+fnx/vvvU79+febNmwdwR//m2rVrs2zZMi5dusTq1asBOHr0KCEhIXz22Wf4+/vTvHlz5s6dy4oVK7h06RIAe/fuxWKxMGnSJCpXrkz9+vV56623CA0NJS0t7aG8NyIiIiIikjeFh4dTr169O7Y7OTmRmJj4wMcpXrw4dnZ2REVFZdoeFRWFt7f3Xffx9va+7/jbf2blmFk5z3+f4385OTnh7u6e6SFSEP144NZnD51ql9JsGxEREbmrByre/G9P4vs9ssKo/s07duzA09OThg0bZoxp27YtZrOZnTt3AtCgQQPMZjOLFy/GYrEQFxfHF198Qdu2bXFwcLhrNvVvFhERERERAB8fH0JDQ+/YHhISgp+f3wMfx9HRkQYNGrB58+aMbVarlc2bNxMQEHDXfQICAjKNB9i4cWPGeB8fH7y9vTONiY+PZ+fOnfc85r3Oc+jQoUxdEzZu3Ii7uzs1atR44OOIFDRJqelsOHKr0Nm5bhmD04iIiEhuZf8ggzw9PTGZHuxOkNu9nB/E/fo3Hzt27K77ZEf/5sjISEqWLJnpeXt7e4oWLZoxxsfHhw0bNvDcc8/x2muvYbFYCAgIuGN9nf82ZcoUJkyY8FcvW0RERERE8rnhw4czcOBAkpOTsdls7Nq1i6+//popU6bw2WefZflYvXv3pmHDhjRu3JigoCASExPp06cPAL169aJMmTJMmTIFgKFDh9KyZUtmzJhBx44dWbFiBXv27GHBggUAmEwmhg0bxqRJk/D19cXHx4exY8dSunRpunTpknHeiIgIYmJiiIiIwGKxZBSjqlSpQqFChWjXrh01atTgpZdeYtq0aURGRjJmzBgGDhyIk5PTP38TRfKpjWFR3EyzUKGYK3XKehgdR0RERHKpByrebNmyJeO/z549yzvvvMPLL7+ccVfWjh07WLp0acbFQn4QGRlJv3796N27Nz169ODGjRuMGzeOZ599lo0bN961mDVq1CiGDx+e8XV8fLwW4BQRERERKYBeffVVXFxcGDNmDElJSbzwwguULl2a2bNn07179ywd6/nnnyc6Oppx48YRGRlJ3bp1CQkJybhhLSIiArP5/5sqNG3alOXLlzNmzBjeffddfH19Wb16NTVr1swYM3LkSBITE+nfvz+xsbE0b96ckJAQnJ2dM8aMGzeOpUuXZnx9uw3cli1baNWqFXZ2dqxdu5YBAwYQEBCAm5sbvXv3ZuLEiX/rPRMpKNb8p2XaU3VKP/CNsiIiIlLwPFDxpmXLlhn/PXHiRGbOnEmPHj0ytj311FPUqlWLBQsW0Lt37wc+eU73by5VqlSmMXXr1s0Y899T+wHS09OJiYnJ2H/+/Pl4eHgwbdq0jDFffvkl5cqVY+fOnTRp0uSObE5OTrrDTEREREREAOjZsyc9e/YkKSmJhISEO2b/Z8WgQYMYNGjQXZ/79ddf79jWrVs3unXrds/jmUwmJk6ceN9Cy5IlS1iyZMl9c1WoUOG+3QlEJLPYpFS2nogGbhVvRERERO7lgYo3/23Hjh0EBwffsb1hw4a8+uqrWTrWf/dvvj09/3b/5ntdmNzu3zxs2LCMbffq33y7WHO7f/OAAQMyjhEbG8vevXtp0KABAL/88gtWqxV/f38AkpKSMt29BmBnZ5eRUUSyJi4ujqSkJKNjPBBXV1c8PNS+QERERLKHq6srrq6uRscQkVzgp8ORpFls+JVyx9ersNFxREREJBfLcvGmXLlyLFy4MNOMFIDPPvvsb7UIM6J/s5+fH+3bt6dfv34EBweTlpbGoEGD6N69O6VL37rzpWPHjsyaNYuJEydmtE179913qVChQka7ABF5MHFxccyZOw+rJd3oKA/EbGfPkMGDVMAREREREZFs9UPoRQA619WsGxEREbm/LBdvZs2aRdeuXfnpp58yZqns2rWLkydP8v3332c5gFH9m7/66isGDRpEmzZtMJvNdO3alTlz5mQ8/9hjj7F8+XKmTZvGtGnTcHV1JSAggJCQEFxcXLL8OkUKsqSkJKyWdLam+hBndf7rHQzkYU6mpWM4SUlJKt6IiIiIiEi2iYxLZmd4DABPqmWaiIiI/IUsF286dOjAiRMn+OSTTzh27BgATz75JP/617/+1swbMKZ/c9GiRVm+fPl9c3Xv3j3Li4mKyL3FWZ25ZnMzOsb9qSuiiIiIiIjkgLUHL2GzQaOKRSjjqZtCRURE5P6yXLyBW63TPvjgg+zOIiIiIiIikuelpaXRvn17goOD8fX1NTqOiOQSPx64BMBTmnUjIiIiD8D810PutG3bNl588UWaNm3KxYu3+rV+8cUX/P7779kaTkREREREJK9xcHDg4MGDRscQkVzkTHQCBy/EYWc20aFWKaPjiIiISB6Q5eLN999/T2BgIC4uLuzbt4+UlBTg1oLkmo0jIiIiIiICL774Ip9//rnRMUQkl7g966Z5leIUK+RkcBoRERHJC7LcNm3SpEkEBwfTq1cvVqxYkbG9WbNmTJo0KVvDiYiIiIiI5EXp6eksWrSITZs20aBBA9zcMq/7N3PmTIOSicjDZrPZMoo3neuqZZqIiIg8mCwXb44fP06LFi3u2O7h4UFsbGx2ZBIREYPFxcWRlJRkdIwH4urqioeHh9ExREREMjl8+DD169cH4MSJE5meM5lMRkQSEYMcuRTPmehEnOzNtHvE2+g4IiIikkdkuXjj7e3NqVOnqFixYqbtv//+O5UqVcquXCIiYpC4uDjmzJ2H1ZJudJQHYrazZ8jgQSrgiIhIrrJlyxajI4hILnF71k1bPy8KOWX5YxgREREpoLL8W0O/fv0YOnQoixYtwmQycenSJXbs2MFbb73F2LFjcyKjiIg8RElJSVgt6WxN9SHO6mx0nPvyMCfT0jGcpKQkFW9ERCRXOnXqFKdPn6ZFixa4uLhgs9k080akALFabaz5T/HmKbVMExERkSzIcvHmnXfewWq10qZNG5KSkmjRogVOTk689dZbDB48OCcyioiIAeKszlyzuf31QCNZjQ4gIiJyd9euXeO5555jy5YtmEwmTp48SaVKlejbty9FihRhxowZRkcUkYdg99kYLsclU9jZnlbVShgdR0RERPIQc1YGWywWtm3bxsCBA4mJieHw4cP8+eefREdH8/777+dURhERERERkTzljTfewMHBgYiICFxdXTO2P//884SEhBiYTEQeph/+M+vmiZreONnbGZxGRERE8pIszbyxs7OjXbt2HD16FE9PT2rUqJFTuURERERERPKsDRs28PPPP1O2bNlM2319fTl37pxBqUTkYUpNt7L+0GUAnqpTxuA0IiIiktdkaeYNQM2aNTlz5kxOZBEREREREckXEhMTM824uS0mJgYnJycDEonIw/b7qWhik9IoXsiJgMrFjI4jIiIieUyWizeTJk3irbfeYu3atVy+fJn4+PhMDxERERERkYLu0UcfZdmyZRlfm0wmrFYr06ZNo3Xr1gYmE5GH5cfQWy3TOtUuhZ3ZZHAaERERyWuy1DYNoEOHDgA89dRTmEz//8uHzWbDZDJhsViyL52IiIiIiEgeNG3aNNq0acOePXtITU1l5MiRHDlyhJiYGP744w+j44lIDktKTWdDWBQAneuWNjiNiIiI5EVZLt5s2bIlJ3KIiIiIiIjkGzVr1uTEiRPMmzePwoULk5CQwDPPPMPAgQMpVaqU0fFEJIdtOnqFpFQL5Yu6Urecp9FxREREJA/KUvEmLS2NiRMnEhwcjK+vb05lEhERERERyfM8PDwYPXq00TFExAC3W6Y9Vad0pq4lIiIiIg8qS2veODg4cPDgwZzKIiIiIiIiki9UqVKF9957j5MnTxodRUQestikVLaeuALAU2qZJiIiIn9Tloo3AC+++CKff/55TmQRERERERHJFwYOHMi6deuoVq0ajRo1Yvbs2URGRhodS0QegpDDkaRZbFT3LkxVr8JGxxEREZE8Kstr3qSnp7No0SI2bdpEgwYNcHNzy/T8zJkzsy2ciIiIiIhIXvTGG2/wxhtvcOLECb766ivmz5/PW2+9RevWrXnxxRfp1auX0RFFJIf88J+WaZ3rljE4iYiIiORlWZ55c/jwYerXr0/hwoU5ceIE+/fvz3iEhobmQEQREREREZG8qWrVqkyYMIETJ06wbds2oqOj6dOnj9GxRCSHRMUn82f4NQCerFPK4DQiIiKSl2V55s2WLVtyIoeIiIiIiEi+tGvXLpYvX87KlSuJj4+nW7duRkcSkRyy5sAlbDZoWKEIZYu4Gh1HRERE8rAsF29EREREHpa4uDiSkpKMjvFAXF1d8fDwMDqGiOQSt9ulff3114SHh/PYY48xdepUnnnmGQoVKmR0PBHJIWsO3GqZ9lTd0gYnERERkbwuy8Wb1q1bYzKZ7vn8L7/88o8CiYiIiMCtws2cufOwWtKNjvJAzHb2DBk8SAUcEQGgevXqNGrUiIEDB9K9e3e8vLyMjiQiOSz8aiIHLsRhZzbRoZZapomIiMg/k+XiTd26dTN9nZaWRmhoKIcPH6Z3797ZlUtEREQKuKSkJKyWdLam+hBndTY6zn15mJNp6RhOUlKSijciAsDx48fx9fU1OoaIPEQ/ht6addOsSnGKF3IyOI2IiIjkdVku3syaNeuu29977z0SEhL+cSARERGR/xZndeaazc3oGPdnNTqAiOQ2tws3e/fu5ejRowDUqFGD+vXrGxlLRHKIzWbjhwMXAehcRy3TRERE5J/LtjVvXnzxRRo3bsxHH32UXYcUERERERHJk65cucLzzz/P1q1b8fT0BCA2NpbWrVuzYsUKSpQoYWxAEclWRy7FcyY6ESd7M+0eUZtEERER+efM2XWgHTt24Oycu1uaiIiIiIiIPAyDBw8mISGBI0eOEBMTQ0xMDIcPHyY+Pp4hQ4YYHU9EstmaA7daprXxK0lhZweD04iIiEh+kOWZN88880ymr202G5cvX2bPnj2MHTs224KJiIiIiIjkVSEhIWzatAk/P7+MbTVq1GD+/Pm0a9fOwGQikt2sVhs//qd485RapomIiEg2yXLx5n8X4TWbzVSrVo2JEyfqIkRERERERASwWq04ONx5972DgwNWqxbKEslP9py7zuW4ZAo72dOqWkmj44iIiEg+keXizeLFi3Mih4iIiIiISL7x2GOPMXToUL7++mtKl751J/7Fixd54403aNOmjcHpRCQ7/RB6EYD2Nb1xdrAzOI2IiIjkF1ku3uzevRur1Yq/v3+m7Tt37sTOzo6GDRtmWzgRERGR/CguLo6kpCSjYzwwV1fXO2Zfi8j9zZs3j6eeeoqKFStSrlw5AM6fP0/NmjX58ssvDU4nItklzWJl/aHLADxVVy3TREREJPtkuXgzcOBARo4ceUfx5uLFi0ydOpWdO3dmOcT8+fOZPn06kZGR1KlTh7lz59K4ceN7jv/2228ZO3YsZ8+exdfXl6lTp9KhQ4eM5202G+PHj2fhwoXExsbSrFkzPvnkE3x9fTPGxMTEMHjwYNasWYPZbKZr167Mnj2bQoUKZTrOjBkzWLBgAefOnaN48eK8/vrrjB49OsuvUURERARuFW7mzJ2H1ZJudJQHZrazZ8jgQSrgiGRBuXLl2LdvH5s2beLYsWMA+Pn50bZtW4OTiUh2+v3kVa4npVG8kBMBlYoZHUdERETykSwXb8LCwqhfv/4d2+vVq0dYWFiWA6xcuZLhw4cTHByMv78/QUFBBAYGcvz4cUqWvLNX7Pbt2+nRowdTpkyhU6dO/B979x6fc/3/cfx5XZsdzA7msAOWOY8wh6yhVFZTwlRO7ZtD4vvFioZEDhUiRc6EhL5EOkhopTkVa5hzOZZTmDluNuxw7fr94ev6dWWYwufaPO63225cn8/rc32en+tzpeu91/V5fxYsWKCoqCht2bJF999/vyRpzJgxmjhxoubOnavg4GANGTJEkZGR+vXXX+Xm5iZJio6O1okTJ7Ry5UplZ2erS5cu6t69uxYsWGDbV+/evfX999/r/fffV82aNXX27FmdPXv2lo8RAADgqosXLyrXkqO1WcFKzXUzOs5NeZsvq4nLQV28eJHmDXCLTCaTHn/8cT3++ONGRwFwhyzdflyS9HStADk7mQ1OAwAACpNbbt64urrq5MmTqlChgt3yEydOyNn5lp9O48aNU7du3dSlSxdJ0vTp07V8+XLNnj1br7/++jX1EyZMULNmzdS/f39J0vDhw7Vy5UpNnjxZ06dPl9Vq1fjx4zV48GC1atVKkjRv3jz5+flpyZIlat++vXbv3q24uDht2rTJNs3bpEmT9NRTT+n9999XYGCgdu/erWnTpmnXrl2qWrWqJCk4OPiWjw8AACAvqbluOmP1MDrGzXFfdeAfq1mzplasWGGbPg1A4XApy6LvfkmWxJRpAADg9rvlr4U88cQTGjhwoFJTU23Lzp8/r0GDBt3yN8qysrKUlJRkN3WA2WxWRESEEhIS8twmISHhmqkGIiMjbfUHDx5UcnKyXY23t7fCwsJsNQkJCfLx8bG7P09ERITMZrNt2rdvvvlGFSpU0LJlyxQcHKzy5cvrpZdeuuGVN5mZmUpLS7P7AQAAAHBvO3TokLKzs42OAeA2+2H3SV3Msqicr7vqlPMxOg4AAChkbrl58/777+vo0aO677779Oijj+rRRx9VcHCwkpOTNXbs2Ft6rtOnT8tiscjPz89uuZ+fn5KTk/PcJjk5+Yb1V/+8Wc1fp2RzdnaWr6+vreb333/X4cOHtXjxYs2bN09z5sxRUlKSnnvuuesez6hRo+Tt7W374Zt1AAAAAAAUTlenTGtZO1Amk8ngNAAAoLC55XnOypQpox07dmj+/Pnavn273N3d1aVLF3Xo0EFFihS5ExkNkZubq8zMTM2bN09VqlSRJH300UeqV6+e9u7da5tK7c8GDhyo2NhY2+O0tDQaOAAAAMA97qGHHpK7u7vRMQDcRqkXs7Vmb4okqWXtMganAQAAhdGt36RGkoeHh7p37/6Pd16yZEk5OTnp5MmTdstPnjwpf3//PLfx9/e/Yf3VP0+ePKmAgAC7mtDQUFtNSkqK3XPk5OTo7Nmztu0DAgLk7Oxsa9xIUkhIiCTpyJEjeTZvXF1d5erqetPjBgAAAFC4rVu3Tg0bNpSzs7NWrFhhW56Tk6MNGzbo4YcfNjAdgH8q7pcTyrZYVc3fU1X9PY2OAwAACqG/1bzZv3+/Vq9erZSUFOXm2t/FdujQofl+HhcXF9WrV0/x8fGKioqSdOWKl/j4eMXExOS5TXh4uOLj49WnTx/bspUrVyo8PFySFBwcLH9/f8XHx9uaNWlpaUpMTFSPHj1sz3H+/HklJSWpXr16kqRVq1YpNzdXYWFhkqRGjRopJydHv/32mypWrChJ2rdvnyTpvvvuy/cxAgAAALj3PProozpx4sQ10zWnpqbq0UcflcViMSgZgNvh623/mzItNNDgJAAAoLC65ebNzJkz1aNHD5UsWVL+/v5287qaTKZbat5IUmxsrDp16qT69eurQYMGGj9+vDIyMtSlSxdJUseOHVWmTBmNGjVKktS7d281adJEY8eOVfPmzbVw4UJt3rxZM2bMsGXo06ePRowYocqVKys4OFhDhgxRYGCgrUEUEhKiZs2aqVu3bpo+fbqys7MVExOj9u3bKzDwygeviIgI1a1bVy+++KLGjx+v3Nxc9erVS48//rjd1TgAAAAA8FdWqzXPe2CcOXNGHh4eBiQCcLukpF1Wwu9nJEktatG8AQAAd8YtN29GjBihkSNHasCAAbclQLt27XTq1CkNHTpUycnJCg0NVVxcnPz8/CRdmaLMbDbb6hs2bKgFCxZo8ODBGjRokCpXrqwlS5bo/vvvt9W89tprysjIUPfu3XX+/Hk1btxYcXFxcnNzs9XMnz9fMTExatq0qcxms5599llNnDjRtt5sNuubb77Ryy+/rIcfflgeHh568sknNXbs2Nty3AAAAAAKn2eeeUbSlS+Vde7c2W5aZYvFoh07dqhhw4ZGxQNwG3yz44SsVqnefcVVzreo0XEAAEAhdcvNm3PnzqlNmza3NURMTMx1p0lbs2bNNcvatGlzwwwmk0lvv/223n777evW+Pr6asGCBTfMFRgYqC+++OKGNQAAAABwlbe3t6QrV954enrK3d3dts7FxUUPPvigunXrZlQ8ALfB0u3/mzKtNlfdAACAO+eWmzdt2rTR999/r//85z93Ig8AAAAAFEixsbGaPHmyPDw8dOjQIc2aNUvFihUzOhaA2+jQ6QxtP3peTmaTnqoZYHQcAABQiN1y86ZSpUoaMmSIfv75Z9WsWVNFihSxW//KK6/ctnAAAAAAUFBMmjRJAwYMkIeHh9atW6eLFy/SvAEKmatX3TSsWEKlPF1vUg0AAPD33XLzZsaMGSpWrJjWrl2rtWvX2q0zmUw0bwAAAADck8qXL6+JEyfqiSeekNVqVUJCgooXL55n7cMPP3yX0wH4p6xWq77edkyS1Cq0jMFpAABAYXfLzZuDBw/eiRwAAAAAUKC99957+s9//qNRo0bJZDKpdevWedaZTCZZLJa7nA7AP/XriTT9dipDLs5mRdbwMzoOAAAo5G65eQMAAAAAuFZUVJSioqKUnp4uLy8v7d27V6VLlzY6FoDb5OqUaU2rlZanW5GbVAMAAPwz+WrexMbGavjw4fLw8FBsbOwNa8eNG3dbggEAAABAQVSsWDGtXr1awcHBcnbm+3JAYZCba9U32640b1rWDjQ4DQAAuBfkaySxdetWZWdn2/5+PSaT6fakAgAAAIACrEmTJvrtt9/08ccf67ffftOECRNUunRpffvttwoKClKNGjWMjgjgFiQdOafjqZfl6eqsR6txRR0AALjz8tW8Wb16dZ5/BwAAAABca+3atXryySfVqFEjrVu3TiNHjlTp0qW1fft2ffTRR/r888+NjgjgFny97ZgkKfJ+f7kVcTI4DQAAuBeYjQ4AAAAAAIXN66+/rhEjRmjlypVycXGxLX/sscf0888/G5gMwK3KtuRqxc5kSUyZBgAA7h6aNwAAAABwm+3cuVOtW7e+Znnp0qV1+vRpAxIB+Lt+OnBaZzOyVLKYixpWLGF0HAAAcI+geQMAAAAAt5mPj49OnDhxzfKtW7eqTJkyBiQC8Hct3XZcktS8ZoCcnfg1CgAAuDv41AEAAAAAt1n79u01YMAAJScny2QyKTc3V+vXr1e/fv3UsWNHo+MByKdLWRZ9/8v/pkwLpfEKAADuHpo3AAAAAHCbvfPOO6pWrZrKlSun9PR0Va9eXQ8//LAaNmyowYMHGx0PQD7F7zmpjCyLyhZ3V90gH6PjAACAe4iz0QEAAAAAoLBxcXHRzJkzNWTIEO3atUvp6emqU6eOKleubHQ0ALfg6pRpLWsHymQyGZwGAADcS2jeAAAAAMAdEhQUpKCgIKNjAPgbUi9la83eU5KklqGBBqcBAAD3Gpo3AAAAAHAbxMbG5rt23LhxdzAJgNvhu13JyrLkqqqfp6r5exkdBwAA3GNo3gAAAADAbbB169Z81TH1ElAwfL39mCSuugEAAMageQMAAAAAt8Hq1auNjgDgNklJu6yE385IunK/GwAAgLvNbHQAAAAAAAAAR7JsxwnlWqW6QT4q51vU6DgAAOAeRPMGAAAAAADgT5ZuPy6Jq24AAIBxaN4AAAAAgIObMmWKypcvLzc3N4WFhWnjxo03rF+8eLGqVasmNzc31axZUytWrLBbb7VaNXToUAUEBMjd3V0RERHav3+/Xc3Zs2cVHR0tLy8v+fj4qGvXrkpPT7etP3TokEwm0zU/P//88+07cMAAh89kaNvR8zKbpOa1aN4AAABj0LwBAAAAAAe2aNEixcbGatiwYdqyZYtq166tyMhIpaSk5Fm/YcMGdejQQV27dtXWrVsVFRWlqKgo7dq1y1YzZswYTZw4UdOnT1diYqI8PDwUGRmpy5cv22qio6P1yy+/aOXKlVq2bJnWrVun7t27X7O/H374QSdOnLD91KtX7/a/CMBdtHTblatuGlUqqVKerganAQAA9yqaNwAAAADgwMaNG6du3bqpS5cuql69uqZPn66iRYtq9uzZedZPmDBBzZo1U//+/RUSEqLhw4erbt26mjx5sqQrV92MHz9egwcPVqtWrVSrVi3NmzdPx48f15IlSyRJu3fvVlxcnGbNmqWwsDA1btxYkyZN0sKFC3X8+HG7/ZUoUUL+/v62nyJFitzR1wO4k6xWq75myjQAAOAAaN4AAAAAgIPKyspSUlKSIiIibMvMZrMiIiKUkJCQ5zYJCQl29ZIUGRlpqz948KCSk5Ptary9vRUWFmarSUhIkI+Pj+rXr2+riYiIkNlsVmJiot1zt2zZUqVLl1bjxo21dOnSf3bAgMF2n7igAynpcnE2K/J+f6PjAACAe5iz0QEAAAAAAHk7ffq0LBaL/Pz87Jb7+flpz549eW6TnJycZ31ycrJt/dVlN6opXbq03XpnZ2f5+vraaooVK6axY8eqUaNGMpvN+uKLLxQVFaUlS5aoZcuWeWbLzMxUZmam7XFaWtoNjx+425b+76qbx6qWlpcbV5EBAADj0LwBAAAAANyykiVLKjY21vb4gQce0PHjx/Xee+9dt3kzatQovfXWW3crInBLcnOt+ubqlGmhTJkGAACMxbRpAAAAAOCgSpYsKScnJ508edJu+cmTJ+Xvn/eUTv7+/jesv/rnzWpSUlLs1ufk5Ojs2bPX3a8khYWF6cCBA9ddP3DgQKWmptp+jh49et1a4G7bcuScjp2/pGKuznqsWumbbwAAAHAH0bwBAAAAAAfl4uKievXqKT4+3rYsNzdX8fHxCg8Pz3Ob8PBwu3pJWrlypa0+ODhY/v7+djVpaWlKTEy01YSHh+v8+fNKSkqy1axatUq5ubkKCwu7bt5t27YpICDguutdXV3l5eVl9wM4iq+3Xbnq5okafnIr4mRwGgAAcK9j2jQAAAAAcGCxsbHq1KmT6tevrwYNGmj8+PHKyMhQly5dJEkdO3ZUmTJlNGrUKElS79691aRJE40dO1bNmzfXwoULtXnzZs2YMUOSZDKZ1KdPH40YMUKVK1dWcHCwhgwZosDAQEVFRUmSQkJC1KxZM3Xr1k3Tp09Xdna2YmJi1L59ewUGXplOau7cuXJxcVGdOnUkSV9++aVmz56tWbNm3eVXCPjnsi25WrHzhCSpVWgZg9MAAADQvAEAAAAAh9auXTudOnVKQ4cOVXJyskJDQxUXFyc/Pz9J0pEjR2Q2//+kCg0bNtSCBQs0ePBgDRo0SJUrV9aSJUt0//3322pee+01ZWRkqHv37jp//rwaN26suLg4ubm52Wrmz5+vmJgYNW3aVGazWc8++6wmTpxol2348OE6fPiwnJ2dVa1aNS1atEjPPffcHX5FgNtv/YHTOpORpRIeLmpUsYTRcQAAABxj2rQpU6aofPnycnNzU1hYmDZu3HjD+sWLF6tatWpyc3NTzZo1tWLFCrv1VqtVQ4cOVUBAgNzd3RUREaH9+/fb1Zw9e1bR0dHy8vKSj4+PunbtqvT09Dz3d+DAAXl6esrHx+cfHScAAAAA/B0xMTE6fPiwMjMzlZiYaDd12Zo1azRnzhy7+jZt2mjv3r3KzMzUrl279NRTT9mtN5lMevvtt5WcnKzLly/rhx9+UJUqVexqfH19tWDBAl24cEGpqamaPXu2ihUrZlvfqVMn/frrr8rIyFBqaqoSExNp3KDAWvq/KdOa1wqQs5ND/KoEAADc4wz/RLJo0SLFxsZq2LBh2rJli2rXrq3IyMhrbo551YYNG9ShQwd17dpVW7duVVRUlKKiorRr1y5bzZgxYzRx4kRNnz5diYmJ8vDwUGRkpC5fvmyriY6O1i+//KKVK1dq2bJlWrdunbp3737N/rKzs9WhQwc99NBDt//gAQAAAACAoS5nW/TdL8mSpFahgQanAQAAuMLw5s24cePUrVs3denSRdWrV9f06dNVtGhRzZ49O8/6CRMmqFmzZurfv79CQkI0fPhw1a1bV5MnT5Z05aqb8ePHa/DgwWrVqpVq1aqlefPm6fjx41qyZIkkaffu3YqLi9OsWbMUFhamxo0ba9KkSVq4cKGOHz9ut7/BgwerWrVqatu27R19HQAAAAAAwN0XvztFGVkWlfFxV92g4kbHAQAAkGRw8yYrK0tJSUmKiIiwLTObzYqIiFBCQkKe2yQkJNjVS1JkZKSt/uDBg0pOTrar8fb2VlhYmK0mISFBPj4+ql+/vq0mIiJCZrNZiYmJtmWrVq3S4sWLNWXKlH9+sAAAAAAAwOEs3X5MktQyNFAmk8ngNAAAAFc4G7nz06dPy2Kx2G60eZWfn5/27NmT5zbJycl51icnJ9vWX112o5rSpUvbrXd2dpavr6+t5syZM+rcubP++9//ysvLK1/Hk5mZqczMTNvjtLS0fG0HAAAAAADuvtRL2Vq955QkqWVtpkwDAACOw/Bp0xxVt27d9Pzzz+vhhx/O9zajRo2St7e37adcuXJ3MCEAAAAAAPgnvvslWVmWXFXxK6Zq/p5GxwEAALAx9MqbkiVLysnJSSdPnrRbfvLkSfn7++e5jb+//w3rr/558uRJBQQE2NWEhobaalJSUuyeIycnR2fPnrVtv2rVKi1dulTvv/++pCv30snNzZWzs7NmzJihF1988ZpsAwcOVGxsrO1xWloaDRygkEtNTdXFixeNjpEvRYsWlbe3t9ExAAAAAIexdNuV+962rM2UaQAAwLEY2rxxcXFRvXr1FB8fr6ioKElSbm6u4uPjFRMTk+c24eHhio+PV58+fWzLVq5cqfDwcElScHCw/P39FR8fb2vWpKWlKTExUT169LA9x/nz55WUlKR69epJutKsyc3NVVhYmKQr98WxWCy2fXz99dd69913tWHDBpUpUybPbK6urnJ1df3brweAgiU1NVUTJ01WriXH6Cj5YnZy1isvx9DAAQAAACSlXLisDb+dliS1rJ33OB8AAMAohjZvJCk2NladOnVS/fr11aBBA40fP14ZGRnq0qWLJKljx44qU6aMRo0aJUnq3bu3mjRporFjx6p58+ZauHChNm/erBkzZkiSTCaT+vTpoxEjRqhy5coKDg7WkCFDFBgYaGsQhYSEqFmzZurWrZumT5+u7OxsxcTEqH379goMDLTV/NnmzZtlNpt1//3336VXBoCju3jxonItOVqbFazUXDej49yQt/mymrgc1MWLF2neAAAAAJKW7zihXKtUJ8hHQSWKGh0HAADAjuHNm3bt2unUqVMaOnSokpOTFRoaqri4OPn5+UmSjhw5IrP5/2/N07BhQy1YsECDBw/WoEGDVLlyZS1ZssSuqfLaa68pIyND3bt31/nz59W4cWPFxcXJze3/f7k6f/58xcTEqGnTpjKbzXr22Wc1ceLEu3fgAAqN1Fw3nbF6GB3jxnKNDgAAAAA4lq//NGUaAACAozG8eSNJMTEx150mbc2aNdcsa9Omjdq0aXPd5zOZTHr77bf19ttvX7fG19dXCxYsyHfGzp07q3PnzvmuBwAAAAAAjunImYvadvS8zCapea2Am28AAABwl5lvXgIAAAAAAFB4LN1+TJLUsGJJlfZ07CmQAQDAvYnmDQAAAAAAuGdYrdb/nzItlCnTAACAY6J5AwAAAAAA7hl7ki9of0q6XJzMiqzhb3QcAACAPNG8AQAAAAAA94yl269cdfNotVLydi9icBoAAIC80bwBAAAAAAD3BKvVqqVXp0yrXcbgNAAAANdH8wYAAAAAANwTthw5p2PnL8nDxUlNQ0obHQcAAOC6aN4AAAAAAIB7wtf/u+omsoa/3Io4GZwGAADg+mjeAAAAAACAQi/HkqvlO05IklqGBhqcBgAA4MZo3gAAAAAAgEJv/W9ndCYjSyU8XNSoUkmj4wAAANwQzRsAAAAAAFDofb3tmCTpqZoBKuLEr0MAAIBj49MKAAAAAAAo1C5nW/T9LyclSa2YMg0AABQANG8AAAAAAEChtmpPitIzc1TGx111g4obHQcAAOCmaN4AAAAAAIBCbem245KkFrUDZTabDE4DAABwczRvAAAAAABAoZV2OVur9qZIklrWZso0AABQMNC8AQAAAAAAhdZ3u5KVlZOryqWLKSTA0+g4AAAA+ULzBgAAAAAAFFpLt1+ZMq1l7UCZTEyZBgAACgaaNwAAAAAAoFA6dSFT6w+cliS1DGXKNAAAUHDQvAEAAAAAAIXS8h3HlWuVapfz0X0lPIyOAwAAkG80bwAAAAAAQKH09f+mTGtVm6tuAABAwULzBgAAAAAAFDpHzlzU1iPnZTZJT9cKMDoOAADALaF5AwAAAAAACp1vdly56ia8YgmV9nIzOA0AAMCtoXkDAAAAAAAKnaXbrk6ZVsbgJAAAALeO5g0AAAAAAChU9iSnae/JC3JxMivyfn+j4wAAANwymjcAAAAAAKBQuXrVzSNVS8nbvYjBaQAAAG4dzRsAAAAAAFBoWK1WLd1+pXnTMjTQ4DQAAAB/D80bAAAAAABQaGw5cl5/nLskDxcnNa3mZ3QcAACAv4XmDQAAAAAAKDSWbjsmSXqihr/cXZwMTgMAAPD30LwBAAAAAACFQo4lV8t3npDElGkAAKBgo3kDAAAAAAAKhQ2/ndHp9Cz5eriocaWSRscBAAD422jeAAAAAACAQuHrbcclSU/V9FcRJ37lAQAACi6H+CQzZcoUlS9fXm5ubgoLC9PGjRtvWL948WJVq1ZNbm5uqlmzplasWGG33mq1aujQoQoICJC7u7siIiK0f/9+u5qzZ88qOjpaXl5e8vHxUdeuXZWenm5bv2bNGrVq1UoBAQHy8PBQaGio5s+ff/sOGgAAAAAA3DaXsy367pdkSVKr0DIGpwEAAPhnDG/eLFq0SLGxsRo2bJi2bNmi2rVrKzIyUikpKXnWb9iwQR06dFDXrl21detWRUVFKSoqSrt27bLVjBkzRhMnTtT06dOVmJgoDw8PRUZG6vLly7aa6Oho/fLLL1q5cqWWLVumdevWqXv37nb7qVWrlr744gvt2LFDXbp0UceOHbVs2bI792IAAAAAAIC/ZfWeFKVn5ijQ2031goobHQcAAOAfMbx5M27cOHXr1k1dunRR9erVNX36dBUtWlSzZ8/Os37ChAlq1qyZ+vfvr5CQEA0fPlx169bV5MmTJV256mb8+PEaPHiwWrVqpVq1amnevHk6fvy4lixZIknavXu34uLiNGvWLIWFhalx48aaNGmSFi5cqOPHr1xiPWjQIA0fPlwNGzZUxYoV1bt3bzVr1kxffvnlXXldAAAAAABA/i3dfmU83yI0UGazyeA0AAAA/4yhzZusrCwlJSUpIiLCtsxsNisiIkIJCQl5bpOQkGBXL0mRkZG2+oMHDyo5OdmuxtvbW2FhYbaahIQE+fj4qH79+raaiIgImc1mJSYmXjdvamqqfH19b/1AAQAAAADAHZN2OVvxe67M4NGydqDBaQAAAP45ZyN3fvr0aVksFvn5+dkt9/Pz0549e/LcJjk5Oc/65ORk2/qry25UU7p0abv1zs7O8vX1tdX81WeffaZNmzbpww8/vO7xZGZmKjMz0/Y4LS3turUAAAAAAOD2+P6Xk8rKyVWl0sVUPcDL6DgAAAD/mOHTphUEq1evVpcuXTRz5kzVqFHjunWjRo2St7e37adcuXJ3MSUAAAAAAPemr7cdk3TlqhuTiSnTAABAwWdo86ZkyZJycnLSyZMn7ZafPHlS/v7+eW7j7+9/w/qrf96sJiUlxW59Tk6Ozp49e81+165dqxYtWuiDDz5Qx44db3g8AwcOVGpqqu3n6NGjN6wHAAAAAAD/zKkLmdrw2xlJTJkGAAAKD0ObNy4uLqpXr57i4+Nty3JzcxUfH6/w8PA8twkPD7erl6SVK1fa6oODg+Xv729Xk5aWpsTERFtNeHi4zp8/r6SkJFvNqlWrlJubq7CwMNuyNWvWqHnz5nr33XfVvXv3mx6Pq6urvLy87H4AAAAAAMCds2LnCVlyrapd1lvlS3oYHQcAAOC2MPSeN5IUGxurTp06qX79+mrQoIHGjx+vjIwMdenSRZLUsWNHlSlTRqNGjZIk9e7dW02aNNHYsWPVvHlzLVy4UJs3b9aMGTMkSSaTSX369NGIESNUuXJlBQcHa8iQIQoMDFRUVJQkKSQkRM2aNVO3bt00ffp0ZWdnKyYmRu3bt1dg4JVv6axevVpPP/20evfurWeffdZ2LxwXFxf5+vre5VcJAAAAAADkxTZlWmgZg5MAAADcPoY3b9q1a6dTp05p6NChSk5OVmhoqOLi4uTn5ydJOnLkiMzm/79AqGHDhlqwYIEGDx6sQYMGqXLlylqyZInuv/9+W81rr72mjIwMde/eXefPn1fjxo0VFxcnNzc3W838+fMVExOjpk2bymw269lnn9XEiRNt6+fOnauLFy9q1KhRtsaRJDVp0kRr1qy5g68IAAAAAADIj6NnL2rLkfMymaQWtQKMjgMAAHDbGN68kaSYmBjFxMTkuS6vRkmbNm3Upk2b6z6fyWTS22+/rbfffvu6Nb6+vlqwYMF118+ZM0dz5sy57noAAAAAAGCspduPS5LCK5RQaS+3m1QDAAAUHIbe8wYAAAAAAODv+uZ/zZtWoYEGJwEAALi9aN4AAAAAAIACZ2/yBe1JvqAiTiY1q8GUaQAAoHCheQMAAAAAAAqcpduPSZIeqVpa3kWLGJwGAADg9qJ5AwAAAAAAChSr1Wq7303L2kyZBgAACh+aNwAAAAAAoEDZevS8jp69pKIuTooI8TM6DgAAwG1H8wYAAAAAABQoS7dduermiep+cndxMjgNAADA7UfzBgAAAAAAFBg5llwt23GledMqtIzBaQAAAO4MmjcAAAAAAKDASPj9jE6nZ6l40SJqXLmk0XEAAADuCJo3AAAAAACgwPj6f1OmPVUzQEWc+LUGAAAonPiUAwAAAAAACoTL2RZ9tytZElOmAQCAwo3mDQAAAAAAKBDW7E3RhcwcBXi7qf59xY2OAwAAcMfQvAEAAAAAAAXC0u1XpkxrWTtQZrPJ4DQAAAB3Ds0bAAAAAADg8C5cztYPu1MkSS1qBxqcBgAA4M6ieQMAAAAAABze97+cVFZOriqW8lCNQC+j4wAAANxRNG8AAAAAAIDD+9o2ZVoZmUxMmQYAAAo3mjcAAAAA4OCmTJmi8uXLy83NTWFhYdq4ceMN6xcvXqxq1arJzc1NNWvW1IoVK+zWW61WDR06VAEBAXJ3d1dERIT2799vV3P27FlFR0fLy8tLPj4+6tq1q9LT0+1qduzYoYceekhubm4qV66cxowZc3sOGPiL0+mZWn/gtCSpZShTpgEAgMKP5g0AAAAAOLBFixYpNjZWw4YN05YtW1S7dm1FRkYqJSUlz/oNGzaoQ4cO6tq1q7Zu3aqoqChFRUVp165dtpoxY8Zo4sSJmj59uhITE+Xh4aHIyEhdvnzZVhMdHa1ffvlFK1eu1LJly7Ru3Tp1797dtj4tLU1PPPGE7rvvPiUlJem9997Tm2++qRkzZty5FwP3rBU7T8iSa1Wtst4KLulhdBwAAIA7ztnoAAAAAACA6xs3bpy6deumLl26SJKmT5+u5cuXa/bs2Xr99devqZ8wYYKaNWum/v37S5KGDx+ulStXavLkyZo+fbqsVqvGjx+vwYMHq1WrVpKkefPmyc/PT0uWLFH79u21e/duxcXFadOmTapfv74kadKkSXrqqaf0/vvvKzAwUPPnz1dWVpZmz54tFxcX1ahRQ9u2bdO4cePsmjyObNvR88qx5BodA/nwedIfkqSWtbnqBgAA3Bto3gAAAACAg8rKylJSUpIGDhxoW2Y2mxUREaGEhIQ8t0lISFBsbKzdssjISC1ZskSSdPDgQSUnJysiIsK23tvbW2FhYUpISFD79u2VkJAgHx8fW+NGkiIiImQ2m5WYmKjWrVsrISFBDz/8sFxcXOz28+677+rcuXMqXrz4NdkyMzOVmZlpe5yWlnZrL8ht9tLcTTqdnmVoBuSfySS1oHkDAADuETRvAAAAAMBBnT59WhaLRX5+fnbL/fz8tGfPnjy3SU5OzrM+OTnZtv7qshvVlC5d2m6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"text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -384,7 +391,9 @@ "# This will be cycled over 3 times to be applied to the 18 slices\n", "max_error_per_slice = [0.0] * 4 + [0.003] * 2\n", "\n", - "truncation_error_budget = setup_budget(max_error_per_slice=max_error_per_slice)\n", + "truncation_error_budget = setup_budget(\n", + " max_error_per_slice=max_error_per_slice\n", + ")\n", "print(truncation_error_budget)" ] }, @@ -406,7 +415,10 @@ "source": [ "op_budget = OperatorBudget(max_qwc_groups=20)\n", "bp_obs, remaining_slices, metadata = backpropagate(\n", - " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", + " obs,\n", + " slices,\n", + " operator_budget=op_budget,\n", + " truncation_error_budget=truncation_error_budget,\n", ")\n", "reduced_circuit = combine_slices(remaining_slices)\n", "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", @@ -437,9 +449,8 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -536,9 +547,8 @@ "outputs": [ { "data": { - "image/png": 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TDRgwQAMHDlRMTIyioqKuev6oqKhLCkHff/+9w/3AwEA1bdpUM2bMUKVKldSwYUOFhISoZ8+e+vzzz+373UjnZxW9++67OnnyZLFn3zRo0EAbN25U37597W0bN24s0nOYP3++srKy7IWddevWyWw2q0GDBpKkatWq6ciRI/bHWCwW/fjjj7rjjjuu+Ny///57h5+jt7e3unXrpm7dumnw4MFq2LChduzYoZYtWxbruQIAAOeQkZGh6dOnSzo/68ZUzL0MjeL0xRtJio+Pv+wyaatWrbqk7YEHHrjiDBmTyaSXX35ZL7/88mX7VKpUSa+//rpef/31YucFAMAoX+88v+9bj5Y1NbB9pL0w4+vhVm7enAAAUNHUr19fCxYs0PLlyxUZGan3339fGzduVGRkpL3PzJkzlZKSoh9++EERERH64osv1KdPH33//fcOy4sV5qmnntJtt92m119/Xd27d9fy5csdlkwr0LFjR02fPl1///vfJUlBQUGKiorSRx99pJkzZ9r79e7dW6+++qri4uKUmJio6tWra+vWrQoPD79kSbK/evLJJzVw4EC1bt1at956qz766CP98MMPqlu37hUf16dPH40dO1b9+vXTiy++qGPHjunJJ5/Uww8/bP9w55133qmEhAR98cUXuuGGG/TGG2/o1KlTl5xr3bp1mjhxouLi4pSUlKTFixfriy++kCTNnz9fFotF0dHR8vHx0b///W95e3tfcQl1AADg3GbNmqWMjAxFRUVdspSsMzNfvQsAACgP8i1WfbP7/Lr5D7SuqYZh/goP9FZlT3cKNwAAOLF//vOfuv/++9WzZ09FR0frxIkTDrNwdu/erWeffVazZs1SRESEpPN/hDh+/LhGjx591fPfcsstmjNnjqZOnapmzZrp66+/1qhRoy7p16FDB1ksFvveNtL5gs5f2zw8PPT1118rJCREd999t5o0aaIJEybIzc3tqln69OmjESNGaNiwYWrZsqUOHDig/v37O6yEURgfHx8tX75cJ0+e1M0336y///3v6tSpk2bMmGHv8+ijj6pfv37q27evOnTooLp1614y60Y6v3fQpk2b1KJFC40bN05vvPGGYmNjJZ2fgTRnzhzddtttatq0qVasWKHPPvusyPsLAQAA55Kdna0333xTkjRixAiHVbycncn21wVhUWIyMzMVEBCgjIwM+fv7Gx0HAODi1u8/oZ7vfK9An0ra9EKM3N3KzxsSAK6B978oriv9zpw7d04HDhxQZGTkVf+wj+KbP3++hg4dWujMlLLWuXNnhYWF6f333y/1a9WpU0dDhw7V0KFDS+R8/J4CAODcpk6dqqFDhyoyMlI///yz3N2NX4ysqOMm45MCAIASsWLX+SXT7mwQQuEGAAA4pezsbM2ePVuxsbFyc3PThx9+qBUrVigpKcnoaAAAwMXk5uZq0qRJkqThw4c7ReGmOPjLDgAALsBmsynpwn43MY1CDU4DAADK0l133aXKlSsXenv11VfLNMtNN9102SwffPCBTCaTvvzyS7Vv316tWrXSZ599pv/973+KiYkp05wAAMD1LViwQIcOHVJ4eLj69+9vdJxiK1+lJgAAUKh9x87o1xPZ8nAzq/2N1YyOAwAAytC7776rs2fPFnosKCio0Pb+/fuXyh8xvvzyS+Xl5RV6LDQ0VN7e3lqxYkWJX7eofv31V8OuDQAAyk5+fr4mTJggSRo2bJg8PT0NTlR8FG8AAHABSTvTJUltb6iqyp787x0AgIqkRo0aRkewq127ttERAAAA9J///Ef79u1T1apVNWjQIKPjXBOWTQMAwAUk7UyTxJJpAAAAAACgYrNarfalY4cOHSpfX1+DE10bijcAAJRzx07naOvBU5KkmKgQY8MAAFDCbDab0RGAy+L3EwAA5/Ppp5/qp59+kr+/v+Lj442Oc80o3gAAUM6t3H1UNpvUpEaAqgd4Gx0HAIASUalSJUlSdna2wUmAyyv4/Sz4fQUAAMay2WwaP368JGnw4MEKDAw0NtB1YFF8AADKuYL9bjqzZBoAwIW4ubkpMDBQ6enn/z/n4+Mjk8lkcCrgPJvNpuzsbKWnpyswMFBubm5GRwIAAJKSkpK0adMmeXt76+mnnzY6znWheAMAQDl2Nteitb8ckyTFRFG8AQC4lrCwMEmyF3AAZxMYGGj/PQUAAMYrmHUzaNAgVatWzeA014fiDQAA5djaX47rXJ5VNQK9FVXdz+g4AACUKJPJpOrVqyskJER5eXlGxwEcVKpUiRk3AAA4kbVr12rNmjWqVKmShg0bZnSc60bxBgCAcmzFzqOSzi+ZxlIyAABX5ebmxh/JAQAAcEWvvvqqJKl///6qWbOmwWmun9noAAAA4NpYrDYl7z5fvGHJNAAAAAAAUFFt2bJFX331lcxms4YPH250nBJB8QYAgHJq28FTOn4mV35e7oquG2R0HAAAAAAAAEMUzLrp3bu3brjhBoPTlAyKNwAAlFNJF5ZM69ggRJXc+F86AEA6e/assrOz7fd/++03TZkyRV9//bWBqQAAAIDSs2vXLi1ZskSS9PzzzxucpuTwlx4AAMqpFbv+3O8GAABJ6t69uxYsWCBJOnXqlKKjozV58mR1795db731lsHpAAAAgJKXmJgom82muLg4NW7c2Og4JYbiDQAA5dCB41n6Jf2M3M0mdbixmtFxAABOYsuWLbr99tslSf/9738VGhqq3377TQsWLNC0adMMTgcAAACUrP3792vhwoWSpBdeeMHgNCWL4g0AAOXQigtLpkXXDVKAdyWD0wAAnEV2drb8/PwkSV9//bXuv/9+mc1m3XLLLfrtt98MTgcAAACUrIkTJ8pisahLly5q3bq10XFKFMUbAADKoaSCJdOiWDINAPCnevXqaenSpTp48KCWL1+uLl26SJLS09Pl7+9vcDoAAACg5Bw6dEjz5s2T5HqzbiSKNwAAlDsns3K16deTkqQY9rsBAFxkzJgxGjZsmOrUqaM2bdqobdu2ks7PwmnRokWxzvXWW2+padOm8vf3l7+/v9q2bauvvvrKfvzcuXMaPHiwqlatqsqVK6tHjx46evRoiT4fAAAA4HLeeOMN5ebmql27dmrfvr3RcUocxRsAAMqZb3any2qToqr7q2YVH6PjAACcyN///nelpqZq06ZNWr58ub29U6dOevPNN4t1rpo1a2rChAnavHmzNm3apDvvvFPdu3fXTz/9JEl6+umn9dlnn2nx4sVavXq1Dh8+rPvvv79Enw8AAABQmOPHj2v27NmSXHPWjSS5Gx0AAAAUT9LOgiXTQgxOAgBwRmFhYQoLC9PBgwclSREREWrTpk2xz9OtWzeH++PHj9dbb72l77//XjVr1tS//vUvLVy4UHfeeackad68eYqKitL333+vW2655fqfCAAAAHAZU6dOVXZ2tlq1aqXY2Fij45QKZt4AAFCOnMuzaM3eY5Kkzo3CDE4DAHA2+fn5Gj16tAICAlSnTh3VqVNHAQEBGjVqlPLy8q75vBaLRYsWLVJWVpbatm2rzZs3Ky8vTzExMfY+DRs2VK1atZSSknLZ8+Tk5CgzM9PhBgAAABRHRkaGpk+fLkkaOXKkTCaTwYlKBzNvAAAoR1L2n1B2rkWh/p5qXIONpwEAjp588kktWbJEEydOtO93k5KSohdffFEnTpzQW2+9Vazz7dixQ23bttW5c+dUuXJlffzxx2rUqJG2bdsmDw8PBQYGOvQPDQ1VWlraZc+XmJiol156qdjPCwAAACgwa9YsZWRkKCoqSnFxcUbHKTUUbwAAKEcKlkyLiQp12U+WAACu3cKFC7Vo0SLddddd9ramTZsqIiJCvXv3LnbxpkGDBtq2bZsyMjL03//+V/369dPq1auvOd+IESOUkJBgv5+ZmamIiIhrPh8AAAAqluzsbPtejiNGjJDZ7LqLi1G8AQCgnLBabUredWG/m0ahBqcBADgjT09P1alT55L2yMhIeXh4FPt8Hh4eqlevniSpVatW2rhxo6ZOnaqePXsqNzdXp06dcph9c/ToUYWFXX5ZT09PT3l6ehY7BwAAACBJc+bM0bFjxxQZGanevXsbHadUuW5ZCgAAF7PjUIaOZubI18NNbW+oanQcAIATio+P1yuvvKKcnBx7W05OjsaPH6/4+PjrPr/ValVOTo5atWqlSpUqKTk52X5sz549Sk1NtS/XBgAAAJSknJwcTZo0SZI0fPhwubu79twU1352AAC4kBUXZt10aFBNnu5uBqcBADijrVu3Kjk5WTVr1lSzZs0kSdu3b1dubq46deqk+++/3953yZIlVzzXiBEjdNddd6lWrVo6ffq0Fi5cqFWrVmn58uUKCAjQgAEDlJCQoKCgIPn7++vJJ59U27Ztdcstt5TqcwQAAEDFtGDBAh06dEjh4eHq37+/0XFKHcUbAADKiYv3uwEAoDCBgYHq0aOHQ9u17imTnp6uvn376siRIwoICFDTpk21fPlyde7cWZL05ptvymw2q0ePHsrJyVFsbKxmzZp13c8BAAAA+Kv8/Hy99tprkqRhw4ZViKV4TTabzWZ0CFeVmZmpgIAAZWRkyN/f3+g4AIBy7ODJbN0+8Ru5mU3aPCpGgT7F37cAAEob739RXPzOAAAAoCgWLlyoPn36KDg4WL/++qt8fX2NjnTNivoemD1vAAAoBwqWTGtduwqFGwAAAAAAUGFYrVa9+uqrkqShQ4eW68JNcbBsGgAA5UDBkmmdG7FkGgDg8iIjI2UymS57fP/+/WWYBgAAALh+n376qX766Sf5+/tr8ODBRscpMxRvAABwchnZeVp/4KQkijcAgCsbOnSow/28vDxt3bpVy5Yt07PPPmtMKAAAAOAa2Ww2jR8/XpIUHx+vwMBAYwOVIYo3AAA4uVU/p8tital+SGXVrloxpgYDAK7NkCFDCm2fOXOmNm3aVMZpAAAAgOuTlJSkTZs2ydvb+5IPKrk69rwBAMDJsWQaAOB63XXXXfrf//5ndAwAAACgWApm3QwaNEjVqlUzOE3ZongDAIATy823avWeY5KkGIo3AIBr9N///ldBQUFGxwAAAACKbO3atVqzZo0qVaqkYcOGGR2nzLFsGgAATmz9gRM6nZOv4Mqeal4z0Og4AAAn16JFC5lMJvt9m82mtLQ0HTt2TLNmzTIwGQAAAFA8r776qiSpf//+qlmzpsFpyh7FGwAAnNiKC0umxUSFyGw2XaU3AKCii4uLc7hvNptVrVo1dezYUQ0bNjQmFAAAAFBMW7Zs0VdffSWz2azhw4cbHccQFG8AAHBSNpvNvt9NTBRLpgEArm7s2LFGRwAAAACuW8Gsm969e+uGG24wOI0xKN4AAOCkdh7J1OGMc/KqZFa7+sFGxwEAlBMWi0VLly7Vrl27JEk33XST7r33Xrm5uRmcDAAAALi6Xbt2acmSJZKkESNGGJzGOBRvAABwUgWzbm6vX01elfiDGwDg6n755RfdfffdOnTokBo0aCBJSkxMVEREhL744osK+6lFAAAAlB+JiYmy2Wy67777dNNNNxkdxzBmowMUxcyZM1WnTh15eXkpOjpaGzZsuGL/xYsXq2HDhvLy8lKTJk305ZdfOhy32WwaM2aMqlevLm9vb8XExGjv3r0OferUqSOTyeRwmzBhQok/NwAALmfFrvPFm86NWDINAFA0Tz31lG644QYdPHhQW7Zs0ZYtW5SamqrIyEg99dRTRscDAAAArmj//v1auHChJGnkyJEGpzGW0xdvPvroIyUkJGjs2LHasmWLmjVrptjYWKWnpxfa/7vvvlPv3r01YMAAbd26VXFxcYqLi9OPP/5o7zNx4kRNmzZNs2fP1vr16+Xr66vY2FidO3fO4Vwvv/yyjhw5Yr89+eSTpfpcAQAocPjUWf14KFMmk3RnwxCj4wAAyonVq1dr4sSJCgoKsrdVrVpVEyZM0OrVqw1MBgAAAFzdxIkTZbFY1KVLF7Vu3droOIZy+uLNG2+8oYEDB+qRRx5Ro0aNNHv2bPn4+Gju3LmF9p86daq6du2qZ599VlFRUXrllVfUsmVLzZgxQ9L5WTdTpkzRqFGj1L17dzVt2lQLFizQ4cOHtXTpUodz+fn5KSwszH7z9fUt7acLAIAkKfnCrJuWtaoouLKnwWkAAOWFp6enTp8+fUn7mTNn5OHhYUAiAAAAoGgOHTqkefPmSZJeeOEFg9MYz6mLN7m5udq8ebNiYmLsbWazWTExMUpJSSn0MSkpKQ79JSk2Ntbe/8CBA0pLS3PoExAQoOjo6EvOOWHCBFWtWlUtWrTQpEmTlJ+fX1JPDQCAK0radX6GKUumAQCK429/+5sGDRqk9evXy2azyWaz6fvvv9djjz2me++91+h4AAAAwGVNnjxZubm5ateundq3b290HMO5Gx3gSo4fPy6LxaLQUMc/XIWGhmr37t2FPiYtLa3Q/mlpafbjBW2X6yOdXyu6ZcuWCgoK0nfffacRI0boyJEjeuONNy6bNycnRzk5Ofb7mZmZRXiWAAA4On0uTyn7jkuSYqIo3gAAim7atGnq16+f2rZtq0qVKkmS8vPzde+992rq1KkGpwMAAAAKd/z4cb399tuSmHVTwKmLN0ZKSEiwf9+0aVN5eHjon//8pxITE+XpWfjyNYmJiXrppZfKKiIAwEWt+fm48iw21Q32Vb2QykbHAQCUEzabTZmZmVq0aJEOHTqkXbt2SZKioqJUr149g9MBAAAAlzd16lRlZ2erVatWio2NNTqOU3DqZdOCg4Pl5uamo0ePOrQfPXpUYWFhhT4mLCzsiv0LvhbnnJIUHR2t/Px8/frrr5ftM2LECGVkZNhvBw8evGxfAAAuJ2nn+ZmgMSyZBgAoBpvNpnr16un3339XvXr11K1bN3Xr1o3CDQAAAJxaRkaGpk+fLkkaOXKkTCaTwYmcg1MXbzw8PNSqVSslJyfb26xWq5KTk9W2bdtCH9O2bVuH/pKUlJRk7x8ZGamwsDCHPpmZmVq/fv1lzylJ27Ztk9lsVkhIyGX7eHp6yt/f3+EGAEBx5FmsWrmb/W4AAMVnNptVv359nThxwugoAAAAQJHNmjVLGRkZatSokeLi4oyO4zScftm0hIQE9evXT61bt1abNm00ZcoUZWVl6ZFHHpEk9e3bVzVq1FBiYqIkaciQIerQoYMmT56se+65R4sWLdKmTZv0zjvvSJJMJpOGDh2qcePGqX79+oqMjNTo0aMVHh5u/8VISUnR+vXrdccdd8jPz08pKSl6+umn9X//93+qUqWKIT8HAEDFsOnXP5R5Ll9VfCqpZS3+nwMAKJ4JEybo2Wef1VtvvaXGjRsbHQcAAAC4ouzsbL355puSzq9sZTY79XyTMuX0xZuePXvq2LFjGjNmjNLS0tS8eXMtW7ZMoaHnP42cmprq8ILeeuutWrhwoUaNGqWRI0eqfv36Wrp0qcPA5bnnnlNWVpYGDRqkU6dOqV27dlq2bJm8vLwknZ9Bs2jRIr344ovKyclRZGSknn76aYd9cAAAKA1JO88v63lnw1C5mZkmDAAonr59+yo7O1vNmjWTh4eHvL29HY6fPHnSoGQAAADApebMmaNjx46pbt266tWrl9FxnIrJZrPZjA7hqjIzMxUQEKCMjAyWUAMAXJXNZlP7Sd/o4Mmzmv1/rdS18eX3YgMAZ8T7X+O99957Vzzer1+/MkpSNPzOAAAAVFw5OTm64YYbdOjQIb399tsaNGiQ0ZHKRFHfAzv9zBsAACqKn4+e0cGTZ+Xhbtbt9YONjgMAKIecrTgDAAAAXM6CBQt06NAhhYeH8z62EBRvAABwEit2nV8yrV29YPl68r9oAEDxZWZmFtpuMpnk6ekpDw+PMk4EAAAAXCo/P18TJkyQJA0bNkyenp4GJ3I+/GUIAAAn8fWF/W5iokINTgIAKK8CAwNlMl1+z7SaNWuqf//+Gjt2LJvBAgAAwDD/+c9/tH//fgUHB1eY5dKKi+INAABOID3znLYfPCVJ6hQVYmwYAEC5NX/+fL3wwgvq37+/2rRpI0nasGGD3nvvPY0aNUrHjh3T66+/Lk9PT40cOdLgtAAAAKiIrFarXn31VUnS0KFD5evra3Ai50TxBgAAJ5C8O12S1CwiUKH+XganAQCUV++9954mT56sBx980N7WrVs3NWnSRG+//baSk5NVq1YtjR8/nuINAAAADPHpp5/qp59+kr+/vwYPHmx0HKfFPHkAAJxA0oUl0zoz6wYAcB2+++47tWjR4pL2Fi1aKCUlRZLUrl07paamlnU0AAAAQDabTePHj5ckxcfHKzAw0NhAToziDQAABsvOzdfaX45Lkjo3CjM4DQCgPIuIiNC//vWvS9r/9a9/KSIiQpJ04sQJValSpayjAQAAAEpKStKmTZvk4+OjoUOHGh3HqbFsGgAABlvz83Hl5lsVEeStG0MrGx0HAFCOvf7663rggQf01Vdf6eabb5Ykbdq0Sbt379Z///tfSdLGjRvVs2dPI2MCAACggiqYdTNo0CBVq1bN4DTOjeINAAAGW7GrYMm0MJlMJoPTAADKs3vvvVd79uzR22+/rT179kiS7rrrLi1dulR16tSRJD3++OMGJgQAAEBFtXbtWq1Zs0aVKlXSsGHDjI7j9CjeAABgIIvVppW70yVJMY3Y7wYAcP3q1KmjxMREo2MAAAAADgpm3fTv3181atQwOI3zY88bAAAMtCX1D53MypW/l7turhNkdBwAAAAAAIASt3nzZi1btkxms1nDhw83Ok65QPEGAAADrdh5fsm0OxuGqJIb/1sGAAAAAACup2BmeO/evXXDDTcYnKZ84K9EAAAYKOlC8SamUajBSQAAAAAAAErerl27tGTJEknSiBEjDE5TflC8AQDAIPuOndH+41mq5GZShxurGR0HAAAAAACgxCUmJspms+m+++7TTTfdZHSccoPiDQAABimYdXNL3ary86pkcBoAAAAAAICStX//fi1cuFCS9MILLxicpnxxNzoAAAAVVcF+N11YMg0AcB1atmyp5ORkValSRS1atJDJZLps3y1btpRhMgAAAFR0EydOlMViUWxsrFq1amV0nHKF4g0AAAY4fiZHm1P/kCR1iqJ4AwC4dt27d5enp6ckKS4uztgwAAAAwAWHDh3SvHnzJDHr5lpQvAEAwAArd6fLZpNuCvdXeKC30XEAAOXY2LFjC/0eAAAAMNLkyZOVm5ur22+/XbfffrvRccod9rwBAMAABUumdWbJNACAk0pMTNTNN98sPz8/hYSEKC4uTnv27HHo07FjR5lMJofbY489ZlBiAAAAOIvjx4/r7bfflsSsm2vFzBsAAMrYuTyLvt17XJIUw5JpAIDrVKVKlSvuc3OxkydPFvm8q1ev1uDBg3XzzTcrPz9fI0eOVJcuXbRz5075+vra+w0cOFAvv/yy/b6Pj0/RwwMAAMAlTZkyRdnZ2WrVqpW6dOlidJxyieINAABlbN0vx3U2z6LwAC/dFO5vdBwAQDk3ZcqUUjnvsmXLHO7Pnz9fISEh2rx5s9q3b29v9/HxUVhYWKlkAAAAQPmTkZGhGTNmSJJGjhxZ5A8awRHFGwAAyljShSXTYhqF8gYGAHDd+vXrVybXycjIkCQFBQU5tH/wwQf697//rbCwMHXr1k2jR49m9g0AAEAFNmvWLGVkZKhRo0aKi4szOk65RfEGAIAyZLXatGJXuiSWTAMAlLzU1NQrHq9Vq9Y1nddqtWro0KG67bbb1LhxY3v7Qw89pNq1ays8PFw//PCDhg8frj179mjJkiWFnicnJ0c5OTn2+5mZmdeUBwAAAM4pOztbb775piRpxIgRMpvNBicqvyjeAABQhrb9fkrHz+Sosqe7bqlb1eg4AAAXU6dOnSvO6rRYLNd03sGDB+vHH3/U2rVrHdoHDRpk/75JkyaqXr26OnXqpH379umGG2645DyJiYl66aWXrikDAAAAnN+cOXN07Ngx1a1bV7169TI6TrlG8QYAgDK04sKSaR0aVJOHO58+AQCUrK1btzrcz8vL09atW/XGG29o/Pjx13TO+Ph4ff7551qzZo1q1qx5xb7R0dGSpF9++aXQ4s2IESOUkJBgv5+ZmamIiIhrygUAAADnkpOTo0mTJkmShg8fLnd3yg/Xg58eAABlaMWu88WbLo1YMg0AUPKaNWt2SVvr1q0VHh6uSZMm6f777y/yuWw2m5588kl9/PHHWrVqlSIjI6/6mG3btkmSqlevXuhxT09PeXp6FjkDAAAAyo8FCxbo0KFDqlGjRpnty+jKKN4AAFBGfjuRpZ+PnpGb2aSON4YYHQcAUIE0aNBAGzduLNZjBg8erIULF+qTTz6Rn5+f0tLSJEkBAQHy9vbWvn37tHDhQt19992qWrWqfvjhBz399NNq3769mjZtWhpPAwAAAE4qPz9fEyZMkCQNGzaMD+yUAIo3AACUkaQLS6ZFRwYpwKeSwWkAAK4oMzPT4b7NZtORI0f04osvqn79+sU611tvvSVJ6tixo0P7vHnz1L9/f3l4eGjFihWaMmWKsrKyFBERoR49emjUqFHX9RwAAABQ/nz00Ufav3+/goODNXDgQKPjuASKNwAAlJGC4k1MFEumAQBKR2BgoEwmk0ObzWZTRESEFi1aVKxz2Wy2Kx6PiIjQ6tWri50RAAAArsVqtSoxMVGSNHToUPn6+hqcyDVQvAEAoAz8kZWrTb/9IUnqzH43AIBS8s033zjcN5vNqlatmurVq8eGsQAAACgVn376qX766Sf5+/tr8ODBRsdxGbx7BwCgDHyzJ10Wq00Nw/wUEeRjdBwAgIvq0KGD0REAAABQgdhsNo0fP16SFB8fr8DAQGMDuRCKNwAAlIEVu1gyDQBQdnbu3KnU1FTl5uY6tN97770GJQIAAIArSkpK0qZNm+Tj46OhQ4caHcelULwBAKCU5eRbtHrPMUksmQYAKF379+/Xfffdpx07dshkMtn3rSnYB8disRgZDwAAAC6mYNbNoEGDVK1aNYPTuBaz0QEAAHB1KftOKCvXohA/TzWpEWB0HACACxsyZIgiIyOVnp4uHx8f/fTTT1qzZo1at26tVatWGR0PAAAALmTt2rVas2aNPDw8NGzYMKPjuBxm3gAAUMrsS6Y1CpXZbDI4DQDAlaWkpGjlypUKDg6W2WyW2WxWu3btlJiYqKeeekpbt241OiIAAABcRMGsm/79+6tGjRoGp3E9zLwBAKAU2Ww2rdiZLknqzH43AIBSZrFY5OfnJ0kKDg7W4cOHJUm1a9fWnj17jIwGAAAAF7J582YtW7ZMbm5uGj58uNFxXFKJF2/y8/O1YMECHT16tKRPDQBAufPjoUylZZ6Tj4eb2t5Q1eg4AAAX17hxY23fvl2SFB0drYkTJ2rdunV6+eWXVbduXYPTAQAAwFW8+uqrkqTevXvzPrOUlHjxxt3dXY899pjOnTtX0qcGAKDcSbqwZFr7+tXkVcnN4DQAAFc3atQoWa1WSdLLL7+sAwcO6Pbbb9eXX36padOmGZwOAAAArmDnzp1asmSJJGnEiBEGp3FdpbLnTZs2bbRt2zbVrl27NE4PAEC5kbTzz/1uAAAobR07dlR+fr4kqV69etq9e7dOnjypKlWqyGRi3zUAAABcvwkTJkiS7rvvPjVq1MjgNK6rVIo3TzzxhBISEnTw4EG1atVKvr6+DsebNm1aGpcFAMCp/P5HtnYdyZTZJN3ZMMToOAAAF3bs2DH17dtXK1askNVq1c0336x///vfqlevnoKCgoyOBwAAABexf/9+LVy4UJL0wgsvGJzGtZVK8aZXr16SpKeeesreZjKZZLPZZDKZZLFYSuOyAAA4lRUXZt20rh2kIF8Pg9MAAFzZ8OHDtW3bNr388svy8vLS22+/rYEDB+qbb74xOhoAAABcyMSJE2WxWBQbG6tWrVoZHcellUrx5sCBA6VxWgAAypUVu9IlSZ1ZMg0AUMqSkpI0f/58xcbGSpL+9re/KSoqSjk5OfL09DQ4HQAAAFzBoUOHNG/ePEnMuikL5tI4ae3ata94K66ZM2eqTp068vLyUnR0tDZs2HDF/osXL1bDhg3l5eWlJk2a6Msvv3Q4brPZNGbMGFWvXl3e3t6KiYnR3r17Cz1XTk6OmjdvLpPJpG3bthU7OwCgYso4m6fv95+QxH43AIDSd/jwYTVr1sx+v379+vL09NSRI0cMTAUAAABXMnnyZOXm5ur222/X7bffbnQcl1cqxRtJ2rdvn5588knFxMQoJiZGTz31lPbt21fs83z00UdKSEjQ2LFjtWXLFjVr1kyxsbFKT08vtP93332n3r17a8CAAdq6davi4uIUFxenH3/80d5n4sSJmjZtmmbPnq3169fL19dXsbGxOnfu3CXne+655xQeHl7s3ACAim31z8eUb7Xphmq+igz2vfoDAAC4Tm5ubpfct9lsBqUBAACAKzl+/LjefvttScy6KSulUrxZvny5GjVqpA0bNqhp06Zq2rSp1q9fr5tuuklJSUnFOtcbb7yhgQMH6pFHHlGjRo00e/Zs+fj4aO7cuYX2nzp1qrp27apnn31WUVFReuWVV9SyZUvNmDFD0vlZN1OmTNGoUaPUvXt3NW3aVAsWLNDhw4e1dOlSh3N99dVX+vrrr/X6669f088BAFBxFex307lRmMFJAAAVgc1m04033qigoCD77cyZM2rRooVDGwAAAHAtpkyZouzsbLVq1UpdunQxOk6FUCp73jz//PN6+umnNWHChEvahw8frs6dOxfpPLm5udq8ebNGjBhhbzObzYqJiVFKSkqhj0lJSVFCQoJDW2xsrL0wc+DAAaWlpSkmJsZ+PCAgQNHR0UpJSVGvXr0kSUePHtXAgQO1dOlS+fj4FCkvAACSlGex6ps9BfvdhBicBgBQERSsPQ4AAACUtIyMDPvkiBdeeEEmk8ngRBVDqRRvdu3apf/85z+XtD/66KOaMmVKkc9z/PhxWSwWhYY67hUQGhqq3bt3F/qYtLS0QvunpaXZjxe0Xa6PzWZT//799dhjj6l169b69ddfi5Q3JydHOTk59vuZmZlFehwAwLVsOHBSp8/lK7iyh5pHVDE6DgCgAujXr5/REQAAAOCiZs2apYyMDDVq1Ejdu3c3Ok6FUSrLplWrVk3btm27pH3btm0KCXH+TyBPnz5dp0+fdpjxUxSJiYkKCAiw3yIiIkopIQDAmSVdWDLtzoYhcjPzaRQAAAAAAFA+ZWdn680335QkjRgxQmZzqZQUUIhS+UkPHDhQgwYN0muvvaZvv/1W3377rSZMmKB//vOfGjhwYJHPExwcLDc3Nx09etSh/ejRowoLK3wPgbCwsCv2L/h6pT4rV65USkqKPD095e7urnr16kmSWrdufcVPtI0YMUIZGRn228GDB4v8XAEArsFms9mLNzFRoVfpDQAAAAAA4LzmzJmjY8eOqW7duvYtR1A2SqV4M3r0aI0ZM0bTp09Xhw4d1KFDB82YMUMvvviiRo0aVeTzeHh4qFWrVkpOTra3Wa1WJScnq23btoU+pm3btg79JSkpKcnePzIyUmFhYQ59MjMztX79enufadOmafv27dq2bZu2bdumL7/8UpL00Ucfafz48ZfN6+npKX9/f4cbAKBi2XXktA6dOitPd7Nur1/N6DgAAAAAAADXJCcnR5MmTZIkDR8+XO7upbILCy6jxH/a+fn5WrhwoR566CE9/fTTOn36tCTJz8/vms6XkJCgfv36qXXr1mrTpo2mTJmirKwsPfLII5Kkvn37qkaNGkpMTJQkDRkyRB06dNDkyZN1zz33aNGiRdq0aZPeeecdSZLJZNLQoUM1btw41a9fX5GRkRo9erTCw8MVFxcnSapVq5ZDhsqVK0uSbrjhBtWsWfOangcAoGJYsev8rJvb6wfL28PN4DQAAAAAAADXZsGCBTp06JBq1KjBHosGKPHijbu7ux577DHt2rVL0rUXbQr07NlTx44d05gxY5SWlqbmzZtr2bJlCg09vxRNamqqwzp7t956qxYuXKhRo0Zp5MiRql+/vpYuXarGjRvb+zz33HPKysrSoEGDdOrUKbVr107Lli2Tl5fXdWUFAKCgeNO5EUumAQAAAACA8ik/P18TJkyQJA0bNkyenp4GJ6p4TDabzVbSJ+3YsaOGDh1qn8lSUWVmZiogIEAZGRksoQYAFUBaxjndkpgsk0naMDJG1fx4YwOgYuH9r/ESEhIKbTeZTPLy8lK9evXUvXt3BQUFlXGywvE7AwAA4Jw++OAD/d///Z+Cg4P166+/ytfX1+hILqOo74FLZZG6J554Qs8884x+//13tWrV6pIXtmnTpqVxWQAADFUw66ZFRCCFGwCAIbZu3aotW7bIYrGoQYMGkqSff/5Zbm5uatiwoWbNmqVnnnlGa9euVaNGjQxOCwAAAGdktVr16quvSpKefvppCjcGKZXiTa9evSRJTz31lL3NZDLJZrPJZDLJYrGUxmUBADBU0s7zxZsYlkwDABikYFbNvHnz7J/iy8jI0D/+8Q+1a9dOAwcOtO9Punz5coPTAgAAwBl98skn2rlzpwICAjR48GCj41RYpVK8OXDgQGmcFgAAp3UmJ18p+05IkjpHUbwBABhj0qRJSkpKclh+ISAgQC+++KK6dOmiIUOGaMyYMerSpYuBKQEAAOCsbDabfdZNfHy8AgICDE5UcZV48SYvL0933nmnPv/8c0VFRZX06QEAcEprfj6mXItVdar6qF5IZaPjAAAqqIyMDKWnp1+yJNqxY8eUmZkpSQoMDFRubq4R8QAAAODkkpKStGnTJvn4+Gjo0KFGx6nQzCV9wkqVKuncuXMlfVoAAJzaioIl06JCZTKZDE4DAKiounfvrkcffVQff/yxfv/9d/3+++/6+OOPNWDAAMXFxUmSNmzYoBtvvNHYoAAAAHBK48ePlyQNGjRIwcHBBqep2Eq8eCNJgwcP1muvvab8/PzSOD0AAE4l32LVyj3pkqTO7HcDADDQ22+/rU6dOqlXr16qXbu2ateurV69eqlTp06aPXu2JKlhw4Z69913DU4KAAAAZ7N27VqtWbNGHh4eGjZsmNFxKrxS2fNm48aNSk5O1tdff60mTZrI19fX4fiSJUtK47IAABhi029/6FR2ngJ9KqlV7SpGxwEAVGCVK1fWnDlz9Oabb2r//v2SpLp166py5T+X9GzevLlB6QAAAODMCmbd9O/fXzVq1DA4DUqleBMYGKgePXqUxqkBAHA6BUum3dkwRO5upTKpFQCAYqlcubKaNm1qdAwAAACUE5s3b9ayZcvk5uam4cOHGx0HKqXizbx580rjtAAAOB2bzaakXeeLN52jWDINAGCsrKwsTZgwQcnJyUpPT5fVanU4XjAbBwAAALjYq6++Kknq3bu36tata3AaSKVUvJGk/Px8rVq1Svv27dNDDz0kPz8/HT58WP7+/g5T9gEAKM9+ST+j305ky8PNrNtvrGZ0HABABfePf/xDq1ev1sMPP6zq1avLZDIZHQkAAABObufOnfatTkaMGGFwGhQoleLNb7/9pq5duyo1NVU5OTnq3Lmz/Pz89NprryknJ8e+USYAAOXd1xeWTLu1XlVV9iy1z0QAAFAkX331lb744gvddtttRkcBAABAOTFhwgRJ0v33369GjRoZnAYFSmVh/iFDhqh169b6448/5O3tbW+/7777lJycXBqXBADAECsuLJkWw5JpAAAnUKVKFQUFBRkdAwAAAOXE/v37tXDhQknSyJEjDU6Di5VK8ebbb7/VqFGj5OHh4dBep04dHTp0qDQuCQBAmUs/fU7bDp6SRPEGAOAcXnnlFY0ZM0bZ2dlGRwEAAEA5MHHiRFksFnXt2lWtWrUyOg4uUirru1itVlkslkvaf//9d/n5+ZXGJQEAKHMrd6XLZpOa1gxQWICX0XEAANDkyZO1b98+hYaGqk6dOqpUqZLD8S1bthiUDAAAAM7m0KFDmjdvniRm3TijUinedOnSRVOmTNE777wjSTKZTDpz5ozGjh2ru+++uzQuCQBAmWPJNACAs4mLizM6AgAAAMqJyZMnKzc3V7fffrtuv/12o+PgL0qleDN58mTFxsaqUaNGOnfunB566CHt3btXwcHB+vDDD0vjkgAAlKns3Hx9u/e4JKlzI4o3AADnMHbsWKMjAAAAoBw4fvy43n77bUnSCy+8YHAaFKZUijc1a9bU9u3b9dFHH2n79u06c+aMBgwYoD59+sjb27s0LgkAQJlau/e4cvKtqhHorYZhLAkKAAAAAADKjylTpig7O1utWrVSly5djI6DQpRK8UaS3N3d1adPH/Xp06e0LgEAgGEKlkzr3ChUJpPJ4DQAgIosKChIP//8s4KDg1WlSpUr/n/p5MmTRT5vYmKilixZot27d8vb21u33nqrXnvtNTVo0MDe59y5c3rmmWe0aNEi5eTkKDY2VrNmzVJoKLNSAQAAnFVGRoZmzJgh6fysG/6u4ZxKrXgDAICrslhtSt6VLokl0wAAxnvzzTfl5+dn/76kBt+rV6/W4MGDdfPNNys/P18jR45Uly5dtHPnTvn6+kqSnn76aX3xxRdavHixAgICFB8fr/vvv1/r1q0rkQwAAAAoeTNnzlRGRoZuuukmde/e3eg4uAyTzWazGR3CVWVmZiogIEAZGRny9/c3Og4AoIRs/u2keryVIj8vd20Z3VmV3MxGRwIAp8D7X9d27NgxhYSEaPXq1Wrfvr0yMjJUrVo1LVy4UH//+98lSbt371ZUVJRSUlJ0yy23XPWc9t+Zo0f5nQEAACgD2dnZqt2ggY4fP65/z52rPr17Gx2pwsnMzFRAaOhVx03MvAEAoJi+3nl+ybQ7GoRQuAEAOBU3NzcdOXJEISEhDu0nTpxQSEiILBbLNZ87IyND0vll2iRp8+bNysvLU0xMjL1Pw4YNVatWrcsWb3JycpSTk2O/n5mZef6bHj0kd4anAAAApW3yr7/q+PHjquvlpZ7z50sLFhgdqeLJzy9SN/7iBABAMa24ULyJYck0AICTudzCCjk5OfLw8Ljm81qtVg0dOlS33XabGjduLElKS0uTh4eHAgMDHfqGhoYqLS2t0PMkJiYqICDAfouIiLjmTAAAACieRUePasyvv0qSxtapI3cz5QFnxkebAAAohv3HzmjfsSy5m03q2KCa0XEAAJAkTZs2TZJkMpn07rvvqnLlyvZjFotFa9asUcOGDa/5/IMHD9aPP/6otWvXXlfOESNGKCEhwX4/MzPzfAHnf/+TWDYNAACg1HyzerX63XuvJGnI4MF6eNIkqYT2SkQxZWZKoVf/QHCJFW+qVKlS5I0xT548WVKXBQCgTK3YdX7WzS11q8rfq5LBaQAAOO/NN9+UdH7mzezZs+Xm5mY/5uHhoTp16mj27NnXdO74+Hh9/vnnWrNmjWrWrGlvDwsLU25urk6dOuUw++bo0aMKCwsr9Fyenp7y9PS89ICX1/kbAAAAStwPP/yguAcfVG5urh544AG9MW2aTMy6MU5ubpG6lVjxZsqUKfbvT5w4oXHjxik2NlZt27aVJKWkpGj58uUaPXp0SV0SAIAyt2JnuiSpM0umAQCcyIEDByRJd9xxh5YsWaIqVapc9zltNpuefPJJffzxx1q1apUiIyMdjrdq1UqVKlVScnKyevToIUnas2ePUlNT7eNAAAAAGCs1NVV33XWXMjMz1b59ey1YsEBmCjflgsl2uUWRr0OPHj10xx13KD4+3qF9xowZWrFihZYuXVrSl3RKmZmZCggIUEZGhvxZAgAAyr2TWblqPS5JVpu0dvgdqlnFx+hIAOBUeP/rWp544gktXLhQn3zyiRo0aGBvDwgIkLe3tyTp8ccf15dffqn58+fL399fTz75pCTpu+++K9I1+J0BAAAoPSdPnlS7du20a9cu3XTTTfr2229L5EM+uD5FfQ9cKnveLF++XK+99tol7V27dtXzzz9fGpcEAKDUrdydLqtNalTdn8INAMApPfroo1c8Pnfu3CKf66233pIkdezY0aF93rx56t+/v6Tzy7WZzWb16NFDOTk5io2N1axZs4qVGQAAACXv3Llz6t69u3bt2qWaNWtq2bJlFG7KmVIp3lStWlWffPKJnnnmGYf2Tz75RFWrVi2NSwIAUOqSdqZJkmJYMg0A4KT++OMPh/t5eXn68ccfderUKd15553FOldRFmnw8vLSzJkzNXPmzGKdGwAAAKXHYrGoT58+Wrt2rQICAvTVV1857F2I8qFUijcvvfSS/vGPf2jVqlWKjo6WJK1fv17Lli3TnDlzSuOSAACUqnN5Fq35+bgkqXMUxRsAgHP6+OOPL2mzWq16/PHHdcMNNxiQCAAAAGXJZrNpyJAhWrJkiTw8PPTJJ5+ocePGRsfCNSiVnYn69++vdevWyd/fX0uWLNGSJUvk7++vtWvX2qfXAwBQnny377jO5lkU5u+lxjVYkx8AUH6YzWYlJCTozTffNDoKAAAAStnEiRM1c+ZMmUwmvf/+++rQoYPRkXCNSmXmjSRFR0frgw8+KK3TAwBQppJ2pkuSYhqFyGQyGZwGAIDi2bdvn/Lz842OAQAAgFL0/vvv2/ecf/PNN/Xggw8anAjXo9SKN/v27dO8efO0f/9+TZkyRSEhIfrqq69Uq1Yt3XTTTaV1WQAASpzValPyrqOSpM6NwgxOAwDA5SUkJDjct9lsOnLkiL744gv169fPoFQAAAAobUlJSXr00UclSc8++6yGDBlicCJcr1JZNm316tVq0qSJ1q9fr//97386c+aMJGn79u0aO3ZsaVwSAIBS88OhDKWfzpGvh5tuqRtkdBwAAC5r69atDrcffvhBkjR58mRNmTLF2HAAAAAoFVu2bNH999+v/Px8PfTQQ5owYYLRkVACSmXmzfPPP69x48YpISFBfn5+9vY777xTM2bMKI1LAgBQalbsPD/rpkODavJ0dzM4DQAAl/fNN98YHQEAAABl6MCBA7r77rt15swZderUSfPmzZPZXCpzNlDGSuVV3LFjh+67775L2kNCQnT8+PHSuCQAAKUmaWfBkmmhBicBAAAAAAA47/jx4+ratauOHj2qZs2aacmSJfLw8DA6FkpIqRRvAgMDdeTIkUvat27dqho1apTGJQEAKBWpJ7K15+hpuZlNuqNBiNFxAAC4ohMnTmjw4MFq1KiRgoODFRQU5HADAACAa8jOzla3bt30888/q3bt2vryyy/l7+9vdCyUoFJZNq1Xr14aPny4Fi9eLJPJJKvVqnXr1mnYsGHq27dvaVwSAIBSkbTr/Kybm+tUUaAPn14BADi3hx9+WL/88osGDBig0NBQmUwmoyMBAACghOXn56tXr176/vvvFRQUpGXLlik8PNzoWChhpVK8efXVVzV48GBFRETIYrGoUaNGslgseuihhzRq1KjSuCQAAKWiYL+bmCiWTAMAOL9vv/1Wa9euVbNmzYyOAgAAgFJgs9n0xBNP6LPPPpOXl5c+/fRTNWzY0OhYKAWlUrzx8PDQnDlzNGbMGO3YsUNnzpxRixYtVL9+/dK4HAAApSIjO08bfj0pif1uAADlQ8OGDXX27FmjYwAAAKCUjBs3TnPmzJHZbNaHH36o2267zehIKCWlsufNyy+/rOzsbEVEROjuu+/Wgw8+qPr16+vs2bN6+eWXS+OSAACUuG/2pMtitenG0MqqXdXX6DgAAFzVrFmz9MILL2j16tU6ceKEMjMzHW4AAAAov+bOnasxY8ZIkv6/vfsOi+J63wZ+79JRiiBdUFRAVBQrYo8l2Ii4xhYSazQx1mCLRiyJfrERS2xRo2hiN4gdC3ZFrBgNoKhELCBYYKXD7rx/+HPfbECDyjIL3J/r4go7Z8q9JTJnn5lzli9fDj8/P3EDkUZppHgze/ZsZGRkFFqelZWF2bNna+KQREREJe71fDccMo2IiMoKc3NzyOVydOjQAdbW1qhSpQqqVKkCc3NzVKlSRex4RERERPSeDh48iBEjRgAAvv/+e4wcOVLkRKRpGhk2TRCEIifGvH79OiwsLDRxSCIiohKVW6DAqVupADhkGhERlR3+/v7Q09PDli1bYGNjU2S/jIiIiIjKlosXL6JPnz5QKBQYNGgQfvzxR7EjUSko0TtvqlSpAgsLC0gkEri6usLCwkL1Y2Zmhs6dO6Nv377vvN8VK1agRo0aMDQ0hJeXFy5evPjW9Xfu3Ik6derA0NAQHh4eOHjwoFq7IAiYMWMG7OzsYGRkhE6dOiE+Pl5tnU8++QROTk4wNDSEnZ0dvvjiCzx+/PidsxMRUdkUde85MnILYGVigIbVzMWOQ0REVCw3b97Ehg0b0K9fP7Rv3x7t2rVT+yEiIiKisuXOnTvo3r07srKy4OPjg7Vr1/ICnQqiRO+8WbJkCQRBwNChQzF79myYmZmp2vT19VGjRg14e3u/0z63b9+OgIAArF69Gl5eXliyZAl8fHxw69YtWFtbF1r//PnzGDBgAIKCgtCjRw9s2bIFfn5+uHr1KurXrw8AWLBgAZYtW4aNGzfC2dkZgYGB8PHxQUxMDAwNDQEAH330EaZNmwY7Ozs8evQIEydOxKefforz589/wCtERERlxdGY10OmWUMq5UkRERGVDU2bNsWDBw/g5uYmdhQiIiIi+kBPnjyBj48Pnj59iiZNmmDXrl3Q09MTOxaVEokgCEJJ7/TUqVNo2bJliXyQvLy80KxZMyxfvhwAoFQq4ejoiDFjxuC7774rtH6/fv2QmZmJ/fv3q5a1aNECnp6eWL16NQRBgL29PSZMmICJEycCANLT02FjY4OQkBD079+/yBx79+6Fn58fcnNzi/285HI5zMzMkJ6eDlNT03d96kREJBJBENBy3nEkpefg10FN0ZFz3hARFQvPf8W3c+dOzJo1C5MmTYKHh0ehvkuDBg1ESlY0fmaIiIiIipaRkYGPPvoIly9fRs2aNXH+/HnY2PD7ifKguOfAGpnz5p+34+fk5CAvL0+tvbgn5Xl5ebhy5QqmTp2qWiaVStGpUydERkYWuU1kZCQCAgLUlvn4+CAsLAwAkJCQgOTkZHTq1EnVbmZmBi8vL0RGRhZZvHn+/Dk2b95cYgUpIiLSbn89liMpPQdGejpoVbuq2HGIiIiKrV+/fgCAoUOHqpZJJBLVvKQKhUKsaERERERUTPn5+ejTpw8uX76MqlWrIjw8nIWbCkgjxZusrCxMnjwZO3bswLNnzwq1F7fD8PTpUygUikIfTBsbG8TFxRW5TXJycpHrJycnq9pfL3vTOq9NmTIFy5cvR1ZWFlq0aKF2N09RcnNzkZubq3osl8vfuj4REWmn10OmtXGpCkM9HZHTEBERFV9CQoLYEYiIiIjoAwiCgBEjRiA8PBzGxsY4cOAAXFxcxI5FIpBqYqeTJk3C8ePHsWrVKhgYGGDdunWYPXs27O3tsWnTJk0cUiMmTZqEa9eu4ciRI9DR0cHAgQPxtlHmgoKCYGZmpvpxdHQsxbRERFRSjsX+33w3dXlVCxERlS3Vq1d/6w8RERERabcZM2YgJCQEOjo62LFjB5o3by52JBKJRu682bdvHzZt2oT27dtjyJAhaNOmDWrXro3q1atj8+bN8Pf3L9Z+qlatCh0dHTx58kRt+ZMnT2Bra1vkNra2tm9d//V/nzx5Ajs7O7V1PD09Cx2/atWqcHV1hbu7OxwdHXHhwgV4e3sXeeypU6eqDdkml8tZwCEiKmMepWXjr8dySCRAxzrWYschIiJ6Z48fP8bZs2eRkpICpVKp1jZ27FiRUhERERHRf1m9ejXmzJmj+r179+4iJyIxaaR48/z5c9SsWRPAq/ltnj9/DgBo3bo1Ro4cWez96Ovro0mTJoiIiICfnx8AQKlUIiIiAqNHjy5yG29vb0RERGD8+PGqZUePHlUVXJydnWFra4uIiAhVsUYulyMqKuqt2V53ev45LNq/GRgYwMDAoNjPj4iItE/E/91108SpCiwr8990IiIqW0JCQvDVV19BX18flpaWkEgkqjaJRMLiDREREZGW2rNnD0aNGgUAmD17Nr788kuRE5HYNFK8qVmzJhISEuDk5IQ6deqobu/at28fzM3N32lfAQEBGDRoEJo2bYrmzZtjyZIlyMzMxJAhQwAAAwcOhIODA4KCggAA48aNQ7t27RAcHIzu3btj27ZtuHz5MtasWQPgVYdl/PjxmDNnDlxcXODs7IzAwEDY29urCkRRUVG4dOkSWrdujSpVquDu3bsIDAxErVq13njXDRERlQ+v57vpzCHTiIioDAoMDMSMGTMwdepUSKUaGSWbiIiIiErY+fPn0b9/fyiVSgwfPhyBgYFiRyItoJHizZAhQ3D9+nW0a9cO3333HXx9fbF8+XLk5+fjp59+eqd99evXD6mpqZgxYwaSk5Ph6emJ8PBw2Ni8+lItMTFRrVPSsmVLbNmyBdOnT8e0adPg4uKCsLAw1K9fX7XO5MmTkZmZiREjRiAtLQ2tW7dGeHg4DA0NAQDGxsYIDQ3FzJkzkZmZCTs7O3Tp0gXTp0/nnTVEROWYPCcfF+49A8D5boiIqGzKffnqgAAAbiVJREFUyspC//79WbghIiIiKiPi4uLg6+uLnJwc9OjRAytXrlS7e5oqLokgCIKmD3L//n1cuXIFtWvXRoMGDTR9OK0hl8thZmaG9PR0mJqaih2HiIj+w/4/H2P0lmuoaVUJxye0FzsOEVGZw/Nf8U2ePBkWFhb47rvvxI5SLPzMEBERUUWWlJQEb29v3L9/H15eXoiIiEClSpXEjkUaVtxzYI3cefNv1atXR/Xq1UvjUERERO9NNWSaO++6ISKisikoKAg9evRAeHg4PDw8oKenp9b+riMhEBEREZFmyOVydOvWDffv34eLiwv27dvHwg2pKbHizbJly4q9LifJJCIibZOvUOJEXAoADplGRERlV1BQEA4fPgw3NzcAUBtyg8NvEBEREWmHvLw8yGQyREdHw8bGBuHh4bCyshI7FmmZEiveLF68uFjrSSQSFm+IiEjrXEp4DnlOASwq6aOxUxWx4xAREb2X4OBgrF+/HoMHDxY7ChEREREVQalUYujQoYiIiEDlypVx8OBB1KxZU+xYpIVKrHiTkJBQUrsiIiIqdUdjXw2Z1qGONXSkvDKZiIjKJgMDA7Rq1UrsGERERET0BlOnTsXmzZuhq6uLP/74A40bNxY7EmkpqdgBiIiIxCYIAo79X/GmM4dMIyKiMmzcuHH4+eefxY5BREREREVYtmwZFixYAAD49ddf8fHHH4uciLRZid15809Dhw59a/v69es1cVgiIqL3cuvJSzx4ng19XSnauFQVOw4REdF7u3jxIo4fP479+/ejXr160NPTU2sPDQ0VKRkRERFRxbZr1y6MHz8ewKt5CgcOHChuINJ6GinevHjxQu1xfn4+bt68ibS0NHTo0EEThyQiInpvx2Je3XXTunZVGOtr5E8jERFRqTA3N4dMJhM7BhERERH9w+nTp/H5559DEASMGjUKU6ZMETsSlQEa+YZq9+7dhZYplUqMHDkStWrV0sQhiYiI3tvRGA6ZRkRE5cOGDRvEjkBERERE/3Dz5k188sknyM3NRa9evbB06VJIJJxrl/5bqc15I5VKERAQgMWLF5fWIYmIiP7TE3kOrj9MBwB0rGMtchoiIqKSkZqairNnz+Ls2bNITU0VOw4RERFRhfTw4UN07doV6enpaNWqFTZv3gwdHR2xY1EZUWrFGwC4e/cuCgoKSvOQREREb3Us9tVdN56O5rA2NRQ5DRER0YfJzMzE0KFDYWdnh7Zt26Jt27awt7fHsGHDkJWVJXY8IiIiogojLS0NXbt2xcOHD+Hu7o69e/fCyMhI7FhUhmhk2LSAgAC1x4IgICkpCQcOHMCgQYM0cUgiIqL3coxDphERUTkSEBCAU6dOYd++fWjVqhUA4OzZsxg7diwmTJiAVatWiZyQiIiIqPzLycmBn58fbt68CXt7e4SHh8PCwkLsWFTGaKR4c+3aNbXHUqkUVlZWCA4OxtChQzVxSCIioneWmVuAc3efAWDxhoiIyoc//vgDu3btQvv27VXLunXrBiMjI/Tt25fFGyIiIiINUyqVGDhwIE6dOgVTU1McOnQITk5OYseiMkgjxZsTJ05oYrdEREQl6kx8KvIKlHCyMIaLdWWx4xAREX2wrKws2NgUviDB2tqaw6YRERERaZggCAgICMDOnTuhp6eH3bt3o0GDBmLHojKqVOe8ISIi0iZHY1IAAJ3cbSCRSEROQ0RE9OG8vb0xc+ZM5OTkqJZlZ2dj9uzZ8Pb2FjEZERERUfn3008/YenSpQCATZs2oUOHDiInorJMI3fePHv2DDNmzMCJEyeQkpICpVKp1v78+XNNHJaIiKjYChRKHI/jfDdERFS+LF26FD4+PqhWrRoaNmwIALh+/ToMDQ1x+PBhkdMRERERlV9bt27FxIkTAQDBwcHo37+/yImorNNI8eaLL77AnTt3MGzYMNjY8GpmIiLSPlcT0/AiKx9mRnpoVqOK2HGIiIhKRP369REfH4/NmzcjLi4OADBgwAD4+/vDyMjonfZ1+vRpLFy4EFeuXEFSUhJ2794NPz8/VfvgwYOxceNGtW18fHwQHh7+wc+DiIiIqCyJiIjAoEGDAADffvstAgICRE5E5YFGijdnzpzB2bNnVVd6ERERaZujMckAgA51rKGrw1FEiYio/DA2Nsbw4cM/eD+ZmZlo2LAhhg4dCplMVuQ6Xbp0wYYNG1SPDQwMPvi4RERERGXJ9evX0atXL+Tn56Nv375YtGiR2JGonNBI8aZOnTrIzs7WxK6JiIg+mCAIOBrzasi0Tu4cMo2IiMqPoKAg2NjYYOjQoWrL169fj9TUVEyZMqXY++ratSu6du361nUMDAxga2v7XlmJiIiIyrr79++ja9euePnyJdq3b49NmzZBKuUFolQyNPJJWrlyJb7//nucOnUKz549g1wuV/shIiIS093UTPz9LAv6OlK0c7MSOw4REVGJ+eWXX1CnTp1Cy+vVq4fVq1eX+PFOnjwJa2truLm5YeTIkXj27Nlb18/NzWX/kIiIiMqF58+fo0uXLkhKSoKHhwd2797Nu5CpRGnkzhtzc3PI5XJ06NBBbbkgCJBIJFAoFJo4LBERUbG8vuumRS1LVDbQyJ9CIiIiUSQnJ8POzq7QcisrKyQlJZXosbp06QKZTAZnZ2fcvXsX06ZNQ9euXREZGQkdHZ0itwkKCsLs2bNLNAcRERFRacvOzoavry/i4uLg6OiIQ4cOwdzcXOxYVM5o5Bsrf39/6OnpYcuWLbCxsYFEItHEYYiIiN7LsdhXxZvO7tYiJyEiIipZjo6OOHfuHJydndWWnzt3Dvb29iV6rP79+6t+9/DwQIMGDVCrVi2cPHkSHTt2LHKbqVOnqk3gK5fL4ejoWKK5iIiIiDRJoVDgs88+w/nz52Fubo5Dhw7BwcFB7FhUDmmkeHPz5k1cu3YNbm5umtg9ERHRe0t9mYuriS8AAJ3qcr4bIiIqX4YPH47x48cjPz9fNRJCREQEJk+ejAkTJmj02DVr1kTVqlVx586dNxZvDAwMOJwIERERlVmCIGDs2LEICwuDgYEB9u7di3r16okdi8opjRRvmjZtigcPHrB4Q0REWudEXAoEAajvYAo7MyOx4xAREZWoSZMm4dmzZ/jmm2+Ql5cHADA0NMSUKVMwdepUjR774cOHePbsWZHDthERERGVB/PmzcPKlSshkUjw+++/o02bNmJHonJMI8WbMWPGYNy4cZg0aRI8PDygp6en1t6gQQNNHJaIiOg/HYl5PWSarchJiIiISp5EIsH8+fMRGBiI2NhYGBkZwcXF5b3udsnIyMCdO3dUjxMSEhAdHQ0LCwtYWFhg9uzZ6N27N2xtbXH37l1MnjwZtWvXho+PT0k+JSIiIiKtsHHjRkybNg0AsHTpUnz66aciJ6LyTiIIglDSO5VKpYUPJJFAEARIJBIoFIqSPqRWksvlMDMzQ3p6OkxNTcWOQ0RU4WXnKdDoxyPIyVfiwNjWqGdvJnYkIqJyhee/5cvJkyfx0UcfFVo+aNAgrFq1Cn5+frh27RrS0tJgb2+Pjz/+GD/++CNsbIo/LCk/M0RERFQWhIeHw9fXFwUFBZgyZQrmzZsndiQqw4p7DqyRO28SEhI0sVsiIqIPcu7OU+TkK+FgboS6dvyCiIiI6G3at2+Pt13rd/jw4VJMQ0RERCSOK1eu4NNPP0VBQQE+//xz/O9//xM7ElUQGineVK9eXRO7JSIi+iBH/2/ItE7u1pBIJCKnISIiIiIiIiJtdu/ePXTr1g2ZmZno1KkTfv311yJHnSLSBI0UbzZt2vTW9oEDB2risERERG+kVAqIiPu/4k3d4g/nQkREREREREQVT2pqKrp06YKUlBR4enrijz/+gL6+vtixqALRSPFm3Lhxao/z8/ORlZUFfX19GBsbs3hDRESl7tqDNDzNyIOJgS68nC3FjkNEREREREREWiozMxM9evRAfHw8atSogUOHDnF+Pip1GrnH68WLF2o/GRkZuHXrFlq3bo2tW7dq4pBERERvdSz21V037dysoK/LW5yJiKj8+u2339CqVSvY29vj/v37AIAlS5Zgz549IicjIiIi0n4FBQXo168fLl68CAsLC4SHh8PW1lbsWFQBldq3Vy4uLpg3b16hu3KIiIhKw7H/m++mM4dMIyKicmzVqlUICAhAt27dkJaWBoVCAQAwNzfHkiVLxA1HREREpOUEQcDIkSNx4MABGBoaYv/+/XBzcxM7FlVQpXrpsa6uLh4/flyahyQiIsLfTzMRn5IBXakE7V2txY5DRESkMT///DPWrl2L77//Hjo6OqrlTZs2xY0bN0RMRkRERKT9fvjhB6xbtw5SqRTbtm2Dt7e32JGoAtPInDd79+5VeywIApKSkrB8+XK0atVKE4ckIiJ6o9dDpjV3toCZsZ7IaYiIiDQnISEBjRo1KrTcwMAAmZmZIiQiIiIiKhvWrVuHWbNmAQBWrlyJnj17ihuIKjyNFG/8/PzUHkskElhZWaFDhw4IDg7WxCGJiIje6AiHTCMiogrC2dkZ0dHRqF69utry8PBwuLu7i5SKiIiISLvt378fX3/9NQAgMDAQX331lciJiDRUvFEqlZrYLRER0Tt7kZmHy38/BwB0cmfxhoiIyreAgACMGjUKOTk5EAQBFy9exNatWxEUFIR169aJHY+IiIhI60RFRaFv375QKBQYMmQIZs+eLXYkIgAaKt4QERFpi+NxKVAKQB1bEzhaGIsdh4iISKO+/PJLGBkZYfr06cjKysJnn30Ge3t7LF26FP379xc7HhEREZFWuX37Nnr06IHs7Gx07doVv/zyCyQSidixiAAAUk3stHfv3pg/f36h5QsWLECfPn00cUgiIqIivZ7vhkOmERFRReHv74/4+HhkZGQgOTkZDx8+xLBhw8SORURERKRVnjx5gi5duuDp06do2rQpduzYAT09zpNL2kMjxZvTp0+jW7duhZZ37doVp0+f1sQhiYiICsnJV+DU7VQALN4QEVHFkJ2djaysLACAsbExsrOzsWTJEhw5ckTkZERERETa4+XLl+jWrRsSEhJQq1YtHDhwAJUrVxY7FpEajRRvMjIyoK+vX2i5np4e5HK5Jg5JRERUSOS9Z8jKU8DG1AD17c3EjkNERKRxPXv2xKZNmwAAaWlpaN68OYKDg9GzZ0+sWrVK5HRERERE4svPz8enn36Kq1evwsrKCuHh4bC2thY7FlEhGineeHh4YPv27YWWb9u2DXXr1tXEIYmIiAo5FvNqyLSO7jaQSjlmLRERlX9Xr15FmzZtAAC7du2Cra0t7t+/j02bNmHZsmUipyMiIiISV3p6OmQyGY4cOQJjY2McOHAAtWvXFjsWUZF0NbHTwMBAyGQy3L17Fx06dAAAREREYOvWrdi5c6cmDklERKRGqRQ43w0REVU4WVlZMDExAQAcOXIEMpkMUqkULVq0wP3790VOR0RERCSeuLg4+Pn54datWzAwMMCuXbvQrFkzsWMRvZFG7rzx9fVFWFgY7ty5g2+++QYTJkzAw4cPcezYMfj5+WnikERERGpuPk7HE3kujPV14F3TUuw4REREpaJ27doICwvDgwcPcPjwYXz88ccAgJSUFJiamoqcjoiIiEgce/fuRfPmzXHr1i04Ojri3Llz6Nq1q9ixiN5KI8UbAOjevTvOnTuHzMxMPH36FMePH0e7du3ea18rVqxAjRo1YGhoCC8vL1y8ePGt6+/cuRN16tSBoaEhPDw8cPDgQbV2QRAwY8YM2NnZwcjICJ06dUJ8fLyq/e+//8awYcPg7OwMIyMj1KpVCzNnzkReXt575SciotJ39P+GTGvnagVDPR2R0xAREZWOGTNmYOLEiahRowa8vLzg7e0N4NVdOI0aNRI5HREREVHpUiqVmDVrFnr27ImXL1+iXbt2uHz5Mpo0aSJ2NKL/pJHizaVLlxAVFVVoeVRUFC5fvvxO+9q+fTsCAgIwc+ZMXL16FQ0bNoSPjw9SUlKKXP/8+fMYMGAAhg0bhmvXrsHPzw9+fn64efOmap0FCxZg2bJlWL16NaKiolCpUiX4+PggJycHwKtb6JRKJX755Rf89ddfWLx4MVavXo1p06a9U3YiIhLP6+JNJ3cOmUZERBXHp59+isTERFy+fBnh4eGq5R07dsTixYtFTEZERERUuuRyOXr16oXZs2cDAMaMGYOjR4/C2tpa5GRExSMRBEEo6Z02b94ckydPxqeffqq2PDQ0FPPnzy+ysPMmXl5eaNasGZYvXw7gVbXU0dERY8aMwXfffVdo/X79+iEzMxP79+9XLWvRogU8PT2xevVqCIIAe3t7TJgwARMnTgTwaqIqGxsbhISEoH///kXmWLhwIVatWoV79+4VO7tcLoeZmRnS09M5RAERUSl68DwLbRacgFQCXJ7eGRaV9MWORERUIfD8l94VPzNERESkCf+e3+aXX37BoEGDxI5FBKD458C6mjh4TEwMGjduXGh5o0aNEBMTU+z95OXl4cqVK5g6dapqmVQqRadOnRAZGVnkNpGRkQgICFBb5uPjg7CwMABAQkICkpOT0alTJ1W7mZkZvLy8EBkZ+cbiTXp6OiwsLIqdnYiIxHMs9tVdN01rWLBwQ0RE5Z5MJkNISAhMTU0hk8neum5oaGgppSIiIiISx759++Dv74+XL1+iWrVqCA0NRbNmzcSORfTONFK8MTAwwJMnT1CzZk215UlJSdDVLf4hnz59CoVCARsb9SFvbGxsEBcXV+Q2ycnJRa6fnJysan+97E3r/NudO3fw888/Y9GiRW/Nm5ubi9zcXNVjuVz+1vWJiEgzXhdvOnPINCIiqgDMzMwgkUhUvxMRERFVREqlEnPmzMHMmTMBAG3atMHOnTsLfQ9MVFZopHjz8ccfY+rUqdizZ4+q85CWloZp06ahc+fOmjikxjx69AhdunRBnz59MHz48LeuGxQUpBpDkYiIxHEvNQNR954DADrV5QkaERGVfxs2bCjydyIiIqKKQi6XY+DAgdizZw8AYPTo0fjpp5+gp6cncjKi9yfVxE4XLVqEBw8eoHr16vjoo4/w0UcfwdnZGcnJyQgODi72fqpWrQodHR08efJEbfmTJ09ga2tb5Da2trZvXf/1f4uzz8ePH+Ojjz5Cy5YtsWbNmv/MO3XqVKSnp6t+Hjx48J/bEBFRycgtUGDpsXh0WXoGBUoB9exN4Vy1ktixiIiIStX69euRkJAgdgwiIiKiUnPr1i14eXlhz5490NfXx/r16/Hzzz+zcENlnkaKNw4ODvjzzz+xYMEC1K1bF02aNMHSpUtx48YNODo6Fns/+vr6aNKkCSIiIlTLlEolIiIi4O3tXeQ23t7eausDwNGjR1XrOzs7w9bWVm0duVyOqKgotX0+evQI7du3R5MmTbBhwwZIpf/9UhkYGMDU1FTth4iINO/cnafouuQMFh+7jbwCJVrXrorVnzcROxYREVGpCwoKQu3ateHk5IQvvvgC69atw507d8SORURERKQR+/fvR/PmzREXFwcHBwecOXMGQ4YMETsWUYnQyLBpAFCpUiWMGDHig/cTEBCAQYMGoWnTpmjevDmWLFmCzMxM1f+EAwcOhIODA4KCggAA48aNQ7t27RAcHIzu3btj27ZtuHz5surOGYlEgvHjx2POnDlwcXGBs7MzAgMDYW9vDz8/PwD/v3BTvXp1LFq0CKmpqao8b7rjh4iISl/KyxzM2R+LvdcfAwCsTAwQ2KMufBvYqcb+JyIiqkji4+Px6NEjnDx5EqdPn8aiRYvw1Vdfwc7ODu3bt8fvv/8udkQiIiKiD6ZUKjF37lzMnDkTgiCgdevW2LVrF+e3oXJFY8UbAIiJiUFiYiLy8vLUln/yySfF3ke/fv2QmpqKGTNmIDk5GZ6enggPD1f9j5iYmKh2V0zLli2xZcsWTJ8+HdOmTYOLiwvCwsJQv3591TqTJ09GZmYmRowYgbS0NLRu3Rrh4eEwNDQE8OpOnTt37uDOnTuoVq2aWh5BEN75dSAiopKlUArYHHUfCw/fwsucAkgkwMAW1THBxw2mhrwtmoiIKjYHBwf4+/ujV69eOHPmDLZu3YrNmzdj27ZtLN4QERFRmffy5UsMHDgQYWFhAIBvvvkGixcvhr6+vrjBiEqYRNBANeLevXvo1asXbty4AYlEoip4vL4KWqFQlPQhtZJcLoeZmRnS09M5hBoRUQm58TAd34fdwJ8P0wEAHg5mmNurPhpUMxc3GBER8fxXCxw5cgQnT57EyZMnce3aNbi7u6Ndu3Zo37492rZtiypVqogdUQ0/M0RERPQubt++DT8/P8TGxkJfXx+rVq3C0KFDxY5F9E6Kew6skTtvxo0bB2dnZ0RERMDZ2RkXL17Es2fPMGHCBCxatEgThyQionJOnpOP4MO38NuF+1AKgImBLiZ1cYO/V3XoSDlEGhEREQB06dIFVlZWmDBhAg4ePAhzc3OxIxERERGViAMHDsDf3x/p6emwt7dHaGgovLy8xI5FpDEaKd5ERkbi+PHjqFq1KqRSKaRSKVq3bo2goCCMHTsW165d08RhiYioHBIEAfv/TMIP+2OQ+jIXAPBJQ3tM7+4Oa1NDkdMRERFpl59++gmnT5/GggULsHTpUtVdN+3bt4erq6vY8YiIiIjemSAI+N///ofAwEAIgoBWrVph165dnJucyj2NFG8UCgVMTEwAAFWrVsXjx4/h5uaG6tWr49atW5o4JBERlUMJTzMxY89NnIl/CgBwrloJP/asj9YuVUVORkREpJ3Gjx+P8ePHAwBu3LiBU6dOITw8HKNHj4a1tTUePnwobkAiIiKid/Dy5UsMHjwYoaGhAICRI0diyZIlnN+GKgSNFG/q16+P69evw9nZGV5eXliwYAH09fWxZs0a1KxZUxOHJCKiciQnX4HVp+5i5cm7yCtQQl9Xim/a18LX7WrBUE9H7HhERERaTRAEXLt2DSdPnsSJEydw9uxZKJVKWFlZiR2NiIiIqNji4+Ph5+eHmJgY6OvrY8WKFfjyyy/FjkVUajRSvJk+fToyMzMBAD/88AN69OiBNm3awNLSEtu3b9fEIYmIqJw4E5+KwLCb+PtZFgCgjUtV/NCzPpyrVhI5GRERkfbz9fXFuXPnIJfL0bBhQ7Rv3x7Dhw9H27ZtOf8NERERlRmHDh3CgAEDkJ6eDjs7O4SGhqJFixZixyIqVRop3vj4+Kh+r127NuLi4vD8+XNUqVIFEgknlSYiosJS5Dn48UAs9l1/DACwNjFAYI+66NHAjn87iIiIiqlOnTr46quv0KZNG5iZmYkdh4iIiOidCIKAoKAgTJ8+HYIgoGXLlti1axfs7OzEjkZU6jRSvCmKhYVFaR2KiIjKEIVSwO8X7mPR4Vt4mVsAqQQY6F0DAR+7wtRQT+x4REREZcrChQtVvz98+BD29vaQSqUiJiIiIiIqnoyMDAwePBh//PEHAOCrr77CsmXLOL8NVVilVrwhIiL6tz8fpuH73Tdx41E6AKBhNTPM8fOARzVeKUxERPSh6tati+joaM47SkRERFrvzp076NWrF27evAk9PT2sWLECw4cPFzsWkahYvCEiolInz8nHosO38NuF+xAEwMRQF5N93PCZV3XoSDlEGhERUUkQBEHsCERERET/KTw8HAMGDEBaWhrs7Ozwxx9/wNvbW+xYRKJj8YaIiEqNIAjYe/0x5hyIRerLXABAT097fN/dHdYmhiKnIyIiIiIiIqLSIggC5s+fj2nTpkEQBHh7e2PXrl2wt7cXOxqRVmDxhoiISsW91AzM2PMXzt55CgCoWbUSfvSrj1a1q4qcjIiIqHyaNm0a5x4lIiIirZSRkYGhQ4di586dAIDhw4fj559/hoGBgcjJiLQHZ64kIiKNyslX4Kejt9FlyRmcvfMU+rpSBHR2xaHxbVi4ISIiKmE//PADsrKyAABTp06Fubk5ACA7Oxs//PCDiMmIiIiIXrl79y5atmyJnTt3Qk9PD6tXr8aaNWtYuCH6F4nAgZA1Ri6Xw8zMDOnp6TA1NRU7DhFRqTt9OxUz9tzE389efYnU1tUKP3xSDzWqVhI5GRERaQLPf8Wno6ODpKQkWFtbqy1/9uwZrK2toVAoREpWNH5miIiIKpbDhw9jwIABePHiBWxtbbFr1y60atVK7FhEpaq458C884aIiErcE3kORm25ioHrL+LvZ1mwMTXAis8aY+OQZizcEBERaZAgCJBIJIWWX79+/Z2HUDt9+jR8fX1hb28PiUSCsLCwQseaMWMG7OzsYGRkhE6dOiE+Pv5D4hMREVE5JQgCFixYgG7duuHFixfw8vLClStXWLghegvOeUNERCVGoRSwKfJvBB+5jYzcAkglwKCWNRDQ2RUmhnpixyMiIiq3qlSpAolEAolEAldXV7UCjkKhQEZGBr7++ut32mdmZiYaNmyIoUOHQiaTFWpfsGABli1bho0bN8LZ2RmBgYHw8fFBTEwMDA0NP/g5ERERUfmQmZmJoUOHYseOHQCAL7/8EsuXL+cwaUT/gcUbIiIqEdcfpOH7sBu4+UgOAGjoaI65fvVR38FM5GRERETl35IlSyAIAoYOHYrZs2fDzOz///3V19dHjRo14O3t/U777Nq1K7p27VpkmyAIWLJkCaZPn46ePXsCADZt2gQbGxuEhYWhf//+7/9kiIiIqNy4d+8e/Pz8cOPGDejq6uLnn3/GV199VeSdwkSkjsUbIiL6IOnZ+Vh0+BZ+j7oPQQBMDXUxuUsdDGjuBB0pT8aIiIhKw6BBgwAAzs7OaNmyJfT0NHvHa0JCApKTk9GpUyfVMjMzM3h5eSEyMpLFGyIiIsLRo0fRr18/vHjxAjY2Nti1axdat24tdiyiMoPFGyIiei+CIGBP9GPMORCLpxm5AIBejRwwrZs7rEx46zMREVFpkcvlqolOGzVqhOzsbGRnZxe57tsmRH0XycnJAAAbGxu15TY2Nqq2ouTm5iI3N1f1WC6Xl0geIiIi0h6CIGDRokX47rvvoFQq0bx5c4SGhsLBwUHsaERlCos3RET0zu6mZiAw7CbO330GAKhpVQlzetZHy9pVRU5GRERU8VSpUgVJSUmwtraGubl5kcOQCIIAiUQChUIhQsL/LygoCLNnzxY1AxEREWlOZmYmvvzyS2zbtg0AMHToUKxYsYLz4RG9BxZviIio2HLyFVh54g5Wn7qHPIUSBrpSjOlQG8Pb1oSBro7Y8YiIiCqk48ePw8LCAgBw4sSJUjmmra0tAODJkyews7NTLX/y5Ak8PT3fuN3UqVMREBCgeiyXy+Ho6KixnERERFR6EhIS4Ofnhz///BO6urpYunQpRo4cyfltiN4TizdERFQsJ2+lYObev3D/WRYAoL2bFX74pD6cLI1FTkZERFSxtWvXrsjfNcnZ2Rm2traIiIhQFWvkcjmioqIwcuTIN25nYGAAAwMOr0pERFTeHDt2DP369cPz589hbW2NXbt2oU2bNmLHIirTWLwhIqK3Sk7PwY/7Y3DgRhIAwNbUEDN966JLfVtePUNERKSFXrx4gV9//RWxsbEAgLp162LIkCGqu3OKKyMjA3fu3FE9TkhIQHR0NCwsLODk5ITx48djzpw5cHFxgbOzMwIDA2Fvbw8/P7+SfDpERESkxQRBQHBwMKZMmQKlUolmzZohNDQU1apVEzsaUZnH4g0RERWpQKHEpsj7+OnobWTkFkBHKsHgljXwbWdXVDbgnw8iIiJtdPr0afj6+sLMzAxNmzYFACxbtgw//PAD9u3bh7Zt2xZ7X5cvX8ZHH32kevx6uLNBgwYhJCQEkydPRmZmJkaMGIG0tDS0bt0a4eHhHNOeiIiogsjKysKXX36JrVu3AgAGDx6MVatW8VyAqIRIBEEQxA5RXsnlcpiZmSE9PR2mpqZixyEiKrboB2n4fvcN/PVYDgDwdDTH3F71Uc/eTORkRESkzXj+Kz4PDw94e3tj1apV0NF5NR+dQqHAN998g/Pnz+PGjRsiJ1THzwwREVHZ9Pfff6NXr16Ijo6Grq4ulixZgm+++YYjdBAVQ3HPgXnpNBERqaRn5WPB4ThsuZgIQQBMDXXxXVd39G/mCKmUJ2BERETa7s6dO9i1a5eqcAMAOjo6CAgIwKZNm0RMRkREROVFREQE+vXrh2fPnsHKygq7du16p7t7iah4WLwhIiIIgoCw6EeYeyAWTzPyAACyxg6Y1s0dVStzUmEiIqKyonHjxoiNjYWbm5va8tjYWDRs2FCkVERERFQeCIKAxYsXY9KkSVAqlWjatClCQ0Ph6OgodjSiconFGyKiCu5OSgYCw24i8t4zAEAtq0qY4+cB71qWIicjIiKi4vjzzz9Vv48dOxbjxo3DnTt30KJFCwDAhQsXsGLFCsybN0+siERERFTGZWVlYcSIEdi8eTOAV3PgrVq1CkZGRiInIyq/OOeNBnH8ZiLSZjn5Ciw/fge/nL6LfIUAA10pxnZ0wfA2NaGvKxU7HhERlUE8/xWHVCqFRCLBf3XtJBIJFApFKaUqHn5miIiItN/9+/fh5+eH6Oho6OjoYPHixRg9ejTntyF6T5zzhoiI3ujErRTM3PMXEp9nAQA+crPCDz3rw9HCWORkRERE9K4SEhLEjkBERETl1PHjx9G3b1/V/DY7d+5Eu3btxI5FVCGweENEVIEkp+fgh/1/4eCNZACAnZkhZvrWhU89W14xQ0REVEZVr15d7AhERERUzgiCgKVLl2LixIlQKBRo0qQJQkND4eTkJHY0ogqDxRsiogqgQKHExsj7+OnILWTmKaAjlWBIyxoY39kVlQ34p4CIiKi82LRp01vbBw4cWEpJiIiIqKzKzs7GiBEj8PvvvwMAvvjiC/zyyy+c34aolPEbOyKicu5q4gtM330TMUlyAEBjJ3PM7eUBdzuOK09ERFTejBs3Tu1xfn4+srKyoK+vD2NjYxZviIiI6K0SExPRq1cvXL16FTo6OggODsbYsWM5WgeRCFi8ISIqp9Kz8jH/cBy2XkyEIABmRnr4rmsd9GvqCKmUJ11ERETl0YsXLwoti4+Px8iRIzFp0iQREhEREVFZcfLkSfTp0wdPnz5F1apVsWPHDnz00UdixyKqsFi8ISIqZwRBQFj0I8zZH4tnmXkAgN6Nq2FatzqwrGwgcjoiIiIqbS4uLpg3bx4+//xzxMXFiR2HiIiItIwgCFi2bBkmTJgAhUKBRo0aYffu3ZxXj0hkLN4QEZUzi4/exrLjdwAALtaVMcevPrxqWoqcioiIiMSkq6uLx48fix2DiIiItEx2dja+/vpr1bx5n3/+OdasWcP5bYi0AIs3RETlyNJj8arCzdiOLhj9UW3o60pFTkVERESlZe/evWqPBUFAUlISli9fjlatWomUioiIiLRRYmIiZDIZrly5Ah0dHSxatAjjxo3j/DZEWoLFGyKicmL58XgsPnYbAPB9N3cMb1tT5ERERERU2vz8/NQeSyQSWFlZoUOHDggODhYnFBEREWmdU6dOoU+fPkhNTYWlpSV27NiBDh06iB2LiP6BxRsionJg1cm7WHTkVeFmSpc6LNwQERFVUEqlUuwIREREpKXy8/Nx4sQJ/PHHH/j111+hUCjg6emJ3bt3o0aNGmLHI6J/YfGGiKiMW3v6HuaHv5p8eOLHrhjZvpbIiYiIiIiIiIhIG2RlZeHIkSMIDQ3Fvn37kJaWpmr77LPPsHbtWhgbG4sXkIjeiMUbIqIybP3ZBMw9GAsAGN/JBaM7uIiciIiIiMQUEBBQ7HV/+uknDSYhIiIisaSnp+PAgQMIDQ3FoUOHkJWVpWqztraGn58f+vTpg44dO3J+GyItxuINEVEZtSnyb/ywPwYAMKZDbYzryMINERFRRXft2jVcu3YN+fn5cHNzAwDcvn0bOjo6aNy4sWo9flFDRERUvqSmpmLv3r0IDQ3FsWPHkJeXp2pzcnKCTCaDTCZDy5YtoaOjI2JSIiouFm+IiMqgzVH3MWPPXwCAke1rIaCzK7+EISIiIvj6+sLExAQbN25ElSpVAAAvXrzAkCFD0KZNG0yYMEHkhERERFRSHjx4gLCwMISGhuL06dNqc9+5ubmhd+/ekMlkaNy4Mb8zICqDpGIHKI4VK1agRo0aMDQ0hJeXFy5evPjW9Xfu3Ik6derA0NAQHh4eOHjwoFq7IAiYMWMG7OzsYGRkhE6dOiE+Pl5tnblz56Jly5YwNjaGubl5ST8lIqL3tv1SIr7ffRMAMLyNMyb7uPEkjIiIiAAAwcHBCAoKUhVuAKBKlSqYM2cOgoODRUxGREREJSE+Ph7z58+Hl5cXnJycMHbsWJw8eRJKpRKNGzfGnDlzEBMTg7i4OMydOxdNmjThdwZEZZTWF2+2b9+OgIAAzJw5E1evXkXDhg3h4+ODlJSUItc/f/48BgwYgGHDhuHatWvw8/ODn58fbt68qVpnwYIFWLZsGVavXo2oqChUqlQJPj4+yMnJUa2Tl5eHPn36YOTIkRp/jkRExbXz8gN8F3oDADCkVQ1M6+bOkzAiIiJSkcvlSE1NLbQ8NTUVL1++FCERERERfQhBEHD9+nXMnDkTHh4ecHV1xXfffYeLFy9CIpGgdevW+Omnn5CQkIArV67g+++/h7u7u9ixiagESARBEMQO8TZeXl5o1qwZli9fDgBQKpVwdHTEmDFj8N133xVav1+/fsjMzMT+/ftVy1q0aAFPT0+sXr0agiDA3t4eEyZMwMSJEwG8msTLxsYGISEh6N+/v9r+QkJCMH78eKSlpb1zdrlcDjMzM6Snp8PU1PSdtyci+qfd1x4iYMd1CAIw0Ls6Zn9Sj4UbIiLSKjz/Fd/AgQNx5swZBAcHo3nz5gCAqKgoTJo0CW3atMHGjRtFTqiOnxkiIqLClEoloqKiEBoaitDQUNy7d0/Vpquriw4dOkAmk6Fnz56wtbUVMSkRvY/ingNr9Zw3eXl5uHLlCqZOnapaJpVK0alTJ0RGRha5TWRkJAICAtSW+fj4ICwsDACQkJCA5ORkdOrUSdVuZmYGLy8vREZGFireEBFpgz3RjzDh/wo3/l5OLNwQERFRkVavXo2JEyfis88+Q35+PoBXX/IMGzYMCxcuFDkdERERvUlBQQFOnTqF0NBQ7N69G0lJSao2Q0NDdOnSBTKZDD169FAbHpWIyi+tLt48ffoUCoUCNjY2asttbGwQFxdX5DbJyclFrp+cnKxqf73sTeu8r9zcXOTm5qoey+XyD9ofEREAHPgzCd9uj4ZSAPo3c8SPPeuzcENERERFMjY2xsqVK7Fw4ULcvXsXAFCrVi1UqlRJ5GRERET0bzk5OTh27BhCQ0OxZ88ePH/+XNVmYmICX19fyGQydOnShX/LiSogrS7elDVBQUGYPXu22DGIqBwJv5mEsduuQSkAnzaphv/18oBUysINERERvV2lSpXQoEEDsWMQERHRv7x8+RKHDh1CaGgoDhw4gIyMDFVb1apV0bNnT8hkMnTs2BEGBgYiJiUisWl18aZq1arQ0dHBkydP1JY/efLkjeM52travnX91/998uQJ7Ozs1Nbx9PT8oLxTp05VG7JNLpfD0dHxg/ZJRBXXkb+SMXrLNSiUAmSNHDC/dwMWboiIiIiIiIjKmGfPnmHfvn0IDQ3FkSNH1EbucXBwgEwmg0wmQ+vWraGrq9Vf1xJRKdLqfw309fXRpEkTREREwM/PD8CrCbsiIiIwevToIrfx9vZGREQExo8fr1p29OhReHt7AwCcnZ1ha2uLiIgIVbFGLpcjKioKI0eO/KC8BgYGrIgTUYmIiH2CUVuuokAp4JOG9ljYpyF0WLghIiIiIiIiKhMeP36MsLAwhIaG4uTJk1AoFKq22rVro3fv3pDJZGjatCmkUqmISYlIW2l18QYAAgICMGjQIDRt2hTNmzfHkiVLkJmZiSFDhgAABg4cCAcHBwQFBQEAxo0bh3bt2iE4OBjdu3fHtm3bcPnyZaxZswYAIJFIMH78eMyZMwcuLi5wdnZGYGAg7O3tVQUiAEhMTMTz58+RmJgIhUKB6OhoAK/+ca1cuXKpvgZEVLGcvJWCkb9fRb5CQHcPO/zUl4UbIiIiIiIiIm137949hIaGIjQ0FJGRkWptDRs2VN1hU69ePc5lS0T/SeuLN/369UNqaipmzJiB5ORkeHp6Ijw8HDY2NgBeFVn+WZ1u2bIltmzZgunTp2PatGlwcXFBWFgY6tevr1pn8uTJyMzMxIgRI5CWlobWrVsjPDwchoaGqnVmzJiBjRs3qh43atQIAHDixAm0b99ew8+aiCqqM/GpGPHbFeQplOhSzxZL+ntCV4dX4BAREdGbNW7cGBEREahSpQp++OEHTJw4EcbGxmLHIiIiKvcEQUBMTIyqYPP64u/XvL29IZPJ0KtXL9SqVUuckERUZkkEQRDEDlFeyeVymJmZIT09HaampmLHISItd/7OUwwJuYTcAiU617XBis8aQ1+XhRsiIio7eP4rDiMjI8THx6NatWrQ0dFBUlISrK2txY5VLPzMEBFRWSMIAi5fvqwq2Ny+fVvVpqOjg/bt20Mmk6Fnz55wcHAQMSkRaavingNr/Z03REQVwYV7zzB046vCTcc61izcEBERUbF5enpiyJAhaN26NQRBwKJFi9441POMGTNKOR0REVHZp1AocPbsWVXB5uHDh6o2fX19fPzxx+jduzd8fX1haWkpYlIiKk9YvCEiEtmlv59jaMgl5OQr0d7NCis/Z+GGiIiIii8kJAQzZ87E/v37IZFIcOjQIejqFu7qSSQSFm+IiIiKKTc3F8ePH0doaCj27NmD1NRUVVulSpXQvXt3yGQydOvWDSYmJiImJaLyisUbIiIRXbn/AoPXX0RWngJtXKpi9edNYKCrI3YsIiIiKkPc3Nywbds2AIBUKkVERESZGTaNiIhIm2RmZiI8PByhoaHYv38/5HK5qs3CwgKffPIJZDIZOnfurDZ3NhGRJrB4Q0QkkmuJLzBo/UVk5inQspYl1nzRFIZ6LNwQERHR+1MqlWJHICIiKlNevHiB/fv3IzQ0FOHh4cjJyVG12dnZoVevXpDJZGjbti309PRETEpEFQ2LN0REIvjzYRoGrr+IjNwCeDlbYN2gpjDSZ+GGiIiIPtzdu3exZMkSxMbGAgDq1q2LcePGoVatWiInIyIiEl9ubi7i4+Nx7tw5hIaG4vjx4ygoKFC116xZEzKZDDKZDF5eXpBKOaw5EYmDxRsiolJ281E6Pl8XhZc5BWhWowrWD24GY33+c0xEREQf7vDhw/jkk0/g6emJVq1aAQDOnTuHevXqYd++fejcubPICYmIiEqHXC5HXFwcYmNj1X7u3bsHhUKhtm79+vVVBZsGDRpAIpGIlJqI6P/jt4VERKUo5rEcn/8aBXlOARo7mWPDkOaoZMB/iomIiKhkfPfdd/j2228xb968QsunTJnC4g0REZUrgiAgJSWlUIEmNjYWjx49euN2pqam8PDwgK+vL3r16gVXV9dSTE1EVDz8xpCIqJTcSn6Jz3+NQlpWPjwdzbFxaHNUZuGGiIiISlBsbCx27NhRaPnQoUOxZMmS0g9ERERUApRKJe7fv19kkebFixdv3M7W1hbu7u6Ffuzs7Hh3DRFpPX5rSERUCuKfvMRnay/geWYeGlQzw8ahzWFiyIkOiYiIqGRZWVkhOjoaLi4uasujo6NhbW0tUioiIqLiycvLw507dwoVaOLi4pCdnV3kNhKJBM7OzkUWaczNzUv3CRARlSAWb4iINOxOSgYGrI3Cs8w81LM3xW9DvWBmxMINEX04QRBQUFBQaMxuIk3R0dGBrq4ur1TVYsOHD8eIESNw7949tGzZEsCrOW/mz5+PgIAAkdMRERG9kpGRUeR8NHfu3Hnjua2+vj5cXV0LFWhcXV1hZGRUys+AiEjzWLwhItKghKeZ+GztBTzNyEUdWxP8PswLZsYs3BDRh8vLy0NSUhKysrLEjkIVjLGxMezs7KCvry92FCpCYGAgTExMEBwcjKlTpwIA7O3tMWvWLIwdO1bkdEREVNGkpqYWOdTZgwcP3riNiYlJkXfRODs7Q1eXX2USUcUhEQRBEDtEeSWXy2FmZob09HSYmpqKHYeIStn9Z5no98sFJMtz4GZjgi3DvWBZ2UDsWERUDiiVSsTHx0NHRwdWVlbQ19fnnRCkcYIgIC8vD6mpqVAoFHBxcYFUKlVbh+e/2uXly5cAXn0Jpq34mSEiKvuUSiUePHhQZJHm2bNnb9zO2tpaVZipW7eu6nd7e3ue2xJRuVbcc2CWq4mINODB8ywMWPOqcONiXRmbWbghohKUl5cHpVIJR0dHGBsbix2HKhAjIyPo6enh/v37yMvLg6GhodiR6C1Ko2gza9YszJ49W22Zm5sb4uLiNH5sIiIqXfn5+W+cj+ZNd4NLJBJUr169yDtpLCwsSvkZEBGVLSzeEBGVsIcvstB/zQU8Ts9BTatK2DzcC1VZuCEiDfj3XQ9EpYGfO/q3evXq4dixY6rHHNKGiKhsy8zMfON8NAUFBUVuo6enBxcXl0IFGjc3N15sRET0nnhWTURUgh6nZeOztVF4lJYN56qVsHV4C1ib8KpkIiIiKr90dXVha2srdgwioneWn58vdgRRpaenIy4uDjExMWpFmsTExDduU7lyZdSpU6dQkaZmzZrQ0+P8rkREJYnFGyKiEpKcnoPP1l5A4vMsOFkYY8twL9iYsnBDRERly6xZsxAWFobo6Gixo1AZER8fD3t7exgaGsLb2xtBQUFwcnIqct3c3Fzk5uaqHsvl8tKKSUQEQRBw+fJlhIaGIjQ0FLdv3xY7ktaysrIqcqizatWqcT4aIqJSwuINEVEJSJG/Ktz8/SwL1aoYYeuIFrAzMxI7FhERlQODBw9GWloawsLCSmU7onfh5eWFkJAQuLm5ISkpCbNnz0abNm1w8+bNIufcCQoKKjRHDhGRJikUCpw9exahoaHYvXs3Hjx4IHYkreLk5AR3d3fUrVtXrUhjaWkpdjQiogqPxRsiog+U+jIXA9ZewL2nmXAwN8LW4S3gYM7CDRFReZWXlwd9fX21ZQqFAhKJ5J3ng3nf7YjeZvTo0fjhhx9KZSLorl27qn5v0KABvLy8UL16dezYsQPDhg0rtP7UqVMREBCgeiyXy+Ho6KjxnERUseTm5uL48eMIDQ3Fnj17kJqaqmqrVKkSunfvDplMho8++qjQ3/SKxMDAAEZG7LsSEWkr9hKJiD7A04xcfLb2Au6mZsLOzBBbh7eAowUnYyQiepP27dtjzJgxGD9+PKpUqQIbGxusXbsWmZmZGDJkCExMTFC7dm0cOnQIwKvixrBhw+Ds7AwjIyO4ublh6dKlqv3l5OSgXr16GDFihGrZ3bt3YWJigvXr1xcr09mzZ9GmTRsYGRnB0dERY8eORWZmpqq9Ro0a+PHHHzFw4ECYmppixIgRCAkJgbm5Ofbu3Yu6devCwMAAiYmJePHiBQYOHIgqVarA2NgYXbt2RXx8vGpfb9ruTWbNmoWNGzdiz549kEgkkEgkOHnyJADgxo0b6NChA4yMjGBpaYkRI0YgIyPjP7ebMmUKXF1dYWxsjJo1ayIwMLDCj/lfHjx8+FD1+5YtW1SfBQ8Pj1K9ytzc3Byurq64c+dOke0GBgYwNTVV+yEiKgmZmZn4448/4O/vD2tra3Tr1g3r1q1DamoqLCwsMHjwYOzduxdPnz7F9u3b0a9fP1hbW8Pc3LzC/rBwQ0Sk3XjnDRHRe3qemYfP10UhPiUDNqYG2Dq8BZwsWbghIpEIAvCPeSRKjYEB8I7jnm/cuBGTJ0/GxYsXsX37dowcORK7d+9Gr169MG3aNCxevBhffPEFEhMToaenh2rVqmHnzp2wtLTE+fPnMWLECNjZ2aFv374wNDTE5s2b4eXlhe7du6NHjx74/PPP0blzZwwdOvQ/s9y9exddunTBnDlzsH79eqSmpmL06NEYPXo0NmzYoFpv0aJFmDFjBmbOnAkAOHPmDLKysjB//nysW7cOlpaWsLa2xoABAxAfH4+9e/fC1NQUU6ZMQbdu3RATE6OaxLeo7d5k4sSJiI2NhVwuV+WxsLBAZmYmfHx84O3tjUuXLiElJQVffvklRo8ejZCQkDduBwAmJiYICQmBvb09bty4geHDh8PExASTJ09+p/eRtEudOnVgaWmJVq1aIScnBw8ePICTkxP+/vvvUi3OZWRk4O7du/jiiy9K7ZhEVHGlpaVh//79CA0NRXh4OLKzs1VtdnZ26NWrF2QyGdq2bav6O0xERFRWSARBEMQOUV7J5XKYmZkhPT2dV5QRlTNpWXn4bG0UYpLksDIxwPYRLVDTqrLYsYiogsjJyUFCQgKcnZ1haGj4eiHwj+GLSs2hQ8DrDMXQvn17KBQKnDlzBsCrO2vMzMwgk8mwadMmAEBycjLs7OwQGRmJFi1aFNrH6NGjkZycjF27dqmWLVy4EAsWLED//v3xxx9/4MaNG8Uaq/3LL7+Ejo4OfvnlF9Wys2fPol27dsjMzIShoSFq1KiBRo0aYffu3ap1QkJCMGTIEERHR6Nhw4YAXk3a7urqinPnzqFly5YAgGfPnsHR0REbN25Enz59itzuvxQ1d83atWsxZcoUPHjwAJUqVQIAHDx4EL6+vnj8+DFsbGyKPefNokWLsG3bNly+fBnAq7t2wsLCEB0dXeT6RX7+/g/Pf8VTUFCAq1ev4syZM/j+++9hYGAAGxsb/P3331i6dClkMhlsbGxK/LgTJ06Er68vqlevjsePH2PmzJmIjo5GTEwMrKys/nN7fmaI6F09efIEe/bsQWhoKCIiIlBQUKBqc3Z2Ru/evSGTyeDl5cVhSYmISCsV9xyYd94QEb2j9Kx8fP7rq8JN1cqv7rhh4YaIqPgaNGig+l1HRweWlpbw8PBQLXv9BXNKSgoAYMWKFVi/fj0SExORnZ2NvLw8eHp6qu1zwoQJCAsLw/Lly3Ho0KFiT7J7/fp1/Pnnn9i8ebNqmSAIUCqVSEhIgLu7OwCgadOmhbbV19dXey6xsbHQ1dWFl5eXapmlpSXc3NwQGxv7xu3eR2xsLBo2bKgq3ABAq1atoFQqcevWrbd+Sb99+3YsW7YMd+/eRUZGBgoKCvileTmQn5+P5s2bo3nz5pgzZw6uXLmCpKQkdOrUCevXr8eECRPg6OiIW7dulehxHz58iAEDBuDZs2ewsrJC69atceHChWIVboiIiuv+/fvYvXs3QkNDcfbsWfzzOuT69etDJpNBJpOhQYMGkLzjHcFERETaisUbIqJ3IM/Jx8D1Ubj5SA7LSvrYMtwLta1ZuCEiLWBg8OouGDGO+47+PWyJRCJRW/b6SxelUolt27Zh4sSJCA4Ohre3N0xMTLBw4UJERUWp7SMlJQW3b9+Gjo4O4uPj0aVLl2JlycjIwFdffYWxY8cWanNyclL9/s8iyWtGRkbv9QXR+25XEiIjI+Hv74/Zs2fDx8cHZmZm2LZtG4KDg0XJQyXH3Nwcnp6eaNWqFfLy8pCdnY1WrVpBV1cX27dvh4ODAy5dulTix922bVuJ75OICADi4uIQGhqK0NBQXLlyRa2tefPmkMlk6NWrF1xdXUVKSEREpFks3hARFdPLnHwMWn8R1x+mo4qxHn7/0guuNiZixyIiekUieafhy8qK10OQffPNN6pld+/eLbTe0KFD4eHhgWHDhmH48OHo1KmT6q6Zt2ncuDFiYmJQu3btD87q7u6OgoICREVFqQ2bduvWLdStW/e996uvrw+FQlHoWCEhIcjMzFQVls6dOwepVAo3N7c3bnf+/HlUr14d33//vWrZ/fv33zsbaY9Hjx4hMjIS58+fR0FBAZo0aYJmzZohLy8PV69eRbVq1dC6dWuxYxIRvZEgCLh27ZqqYPPPu1alUinatGmjKtg4OjqKmJSIiKh0cPBPIqJiyMgtwOANl3AtMQ1mRq8KN+52HGKGiEjTXFxccPnyZRw+fBi3b99GYGBgobsHVqxYgcjISGzcuBH+/v7w8/ODv78/8vLy/nP/U6ZMwfnz5zF69GhER0cjPj4ee/bswejRo98ra8+ePTF8+HCcPXsW169fx+effw4HBwf07Nnznff3Wo0aNfDnn3/i1q1bePr0KfLz8+Hv7w9DQ0MMGjQIN2/exIkTJzBmzBh88cUXqiHTitrOxcUFiYmJ2LZtG+7evYtly5apzeVDZVfVqlXh6+uLoKAgGBsb49KlSxgzZgwkEgkmTpwIMzMztGvXTuyYRERqFAoFzp49i4CAADg7O6NJkyaYO3cuYmNjoaenh65du2Lt2rVISkrCyZMnMXbsWBZuiIiowmDxhojoP2TlFWDohku4cv8FTA11sflLL9SzNxM7FhFRhfDVV19BJpOhX79+8PLywrNnz9TuwomLi8OkSZOwcuVK1Zc5K1euxNOnTxEYGPif+2/QoAFOnTqF27dvo02bNmjUqBFmzJgBe3v798q7YcMGNGnSBD169IC3tzcEQcDBgwcLDRX3LoYPHw43Nzc0bdoUVlZWOHfuHIyNjXH48GE8f/4czZo1w6effoqOHTti+fLlb93uk08+wbfffovRo0fD09MT58+fL9brRGWPmZkZ+vbtCz09PRw/fhwJCQlq/+8QEYklPz8fR44cwddffw0HBwe0adMGixcvxv3792FsbIzevXtj8+bNSE1NxcGDB/Hll1/C2tpa7NhERESlTiL8c5Y3KlFyuRxmZmZIT0/nJLBEZVR2ngJDQi7iwr3nMDHQxe9feqGho7nYsYiogsvJyUFCQgKcnZ1hWA6HSiPt9rbPH89/tcODBw/g4OAAqVSK+vXr49ChQ1p7pTo/M0QVQ3Z2No4cOYI//vgD+/btQ1pamqrNzMwMn3zyCWQyGT7++GMYGxuLF5SIiKgUFPccmHPeEBG9QU6+AsM3XcaFe89R2UAXG4c1Z+GGiIiItN4/CzU3b94UMQkRVWRyuRwHDhxAaGgoDh48iKysLFWbtbU1evXqBZlMhvbt20NfX1/EpERERNqJxRsioiLk5Csw4rcrOHvnKSrp62Dj0GZo7FRF7FhERPSOunbtijNnzhTZNm3aNEybNq2UExVWuXLlN7YdOnQIbdq0KcU0RERE7y81NRV79+5FaGgojh07pjb/XPXq1SGTySCTyeDt7Q0dHR0RkxIREWk/Fm+IiP4lt0CBkb9fwenbqTDS08GGIc3RpLqF2LGIiOg9rFu3DtnZ2UW2WVhox7/t0dHRb2xzcHAovSBERETv4cGDBwgLC0NoaChOnz4NpVKpanNzc0Pv3r0hk8nQuHFjSCQSEZMSERGVLSzeEBH9Q16BEqM2X8WJW6kw1JNi/eBmaO6sHV/uERHRuysLxY/atWuLHYGIiOidxMfHIzQ0FKGhobh48aJaW+PGjVV32Li7u4uUkIiIqOxj8YaI6P/kK5QYveUqjsWmwEBXil8HNYN3LUuxYxEREREREYlKEAT8+eefqoLNP+fTkkgkaNWqFWQyGXr16oUaNWqIF5SIiKgcYfGGiAivCjdjt17DkZgn0NeVYu3ApmhVu6rYsYiIiIiIiEShVCoRFRWlKtjcu3dP1aarq4sOHTpAJpOhZ8+esLW1FTEpERFR+cTiDRFVeAUKJb7dHo1DN5OhryPFL583QVtXK7FjERERERERlaqCggKcOnUKoaGh2L17N5KSklRthoaG6NKlC2QyGXr06IEqVaqImJSIiKj8Y/GGiCo0hVLAxJ3Xsf/PJOjpSLDSvzE+qmMtdiwiIiIiIqJSkZOTg2PHjiE0NBR79uzB8+fPVW0mJibw9fWFTCZDly5dUKlSJRGTEhERVSws3hBRhaVQCpi06zrCoh9DVyrB8s8ao1NdG7FjERERERFpjCAISEpKQmxsLGJjY9W+qKeKRRAExMbG4sCBA8jIyFAtr1q1Knr27AmZTIaOHTvCwMBAxJREREQVF4s3RFQhKZUCpob+idCrj6AjlWDZgEbwqcdxmomItEWNGjUwfvx4jB8/Xuwo76x9+/bw9PTEkiVL3rhOWX5+RFQ2KBQKJCQkqIo0r3/i4uKQnp4udjzSMg4ODpDJZJDJZGjdujV0dfl1ERERkdj415iIKhylUsD3YTew4/JDSCXAkn6e6OZhJ3YsIqIK4cGDB5g5cybCw8Px9OlT2NnZwc/PDzNmzIClpaXY8cqshQsX4tq1a9iyZQu2bNmCdevW4fjx46r2nJwcfP3117hy5QpiY2PRo0cPhIWFiReYiEpMTk4Obt++XahIc/v2beTm5ha5jVQqRa1ateDu7g47OztIJJJSTk3awtLSEp988gmaNm0KqVQqdhwiIiL6BxZviKhCEQQBM/bexNaLDyCVAIv7ecK3ob3YsYiIKoR79+7B29sbrq6u2Lp1K5ydnfHXX39h0qRJOHToEC5cuAALCwtRsikUCkgkkjL7xVVkZCQ6duwIADhz5gxatWql1q5QKGBkZISxY8fijz/+ECMiEX2g9PR0xMXFISYmRq1Ik5CQAKVSWeQ2hoaGcHNzg7u7u9qPi4sLh8IiIiIi0nJls3dKRPQeBEHA7H0x+P1CIiQSYOGnDdHT00HsWEREFcaoUaOgr6+PI0eOoF27dnByckLXrl1x7NgxPHr0CN9//73a+i9fvsSAAQNQqVIlODg4YMWKFao2QRAwa9YsODk5wcDAAPb29hg7dqyqPTc3FxMnToSDgwMqVaoELy8vnDx5UtUeEhICc3Nz7N27F3Xr1oWBgQHWrVsHQ0NDpKWlqeUYN24cOnToAAB49uwZBgwYAAcHBxgbG8PDwwNbt24t9FwLCgowevRomJmZoWrVqggMDIQgCG98bdLS0vDll1/CysoKpqam6NChA65fv17s1zYyMlJVsDl79myh4k2lSpWwatUqDB8+HLa2HCaUSFsJgoDk5GScOHECK1euxJgxY9CpUyc4ODjA3NwcLVq0wNChQ7Fw4ULs378fd+/ehVKphJmZGVq0aIEhQ4ZgwYIFqraMjAxER0dj69atmDFjBvr06YP69euzcENERERUBvDOGyKqEARBwJwDsQg5/zcAYH7vBujdpJq4oYiISpAgCMjOV5T6cY30dIo13M7z589x+PBhzJ07F0ZGRmpttra28Pf3x/bt27Fy5UrV/hYuXIhp06Zh9uzZOHz4MMaNGwdXV1d07twZf/zxBxYvXoxt27ahXr16SE5OVit2jB49GjExMdi2bRvs7e2xe/dudOnSBTdu3ICLiwsAICsrC/Pnz8e6detgaWmJatWqYcaMGfjjjz8wbNgwAK/uWNm+fTvmzp0L4NXwRE2aNMGUKVNgamqKAwcO4IsvvkCtWrXQvHlz1fE3btyIYcOG4eLFi7h8+TJGjBgBJycnDB8+vMjXp0+fPjAyMsKhQ4dgZmaGX375BR07dsTt27ffeDfSvHnzMG/ePACvrshv164dJBIJ0tPT0bdvX0ilUuzfvx+tW7f+z/eHiEqXQqHA/fv3Cw11FhsbW6iA/E92dnaF7qJxd3eHra0thz4jIiIiKmdYvCGick8QBMw7FIdfzyYAAIJkHujb1FHkVEREJSs7X4G6Mw6X+nFjfvCBsf5/n1LGx8dDEAS4u7sX2e7u7o4XL14gNTUV1tbWAIBWrVrhu+++AwC4urri3LlzWLx4MTp37ozExETY2tqiU6dO0NPTg5OTk6p4kpiYiA0bNiAxMRH29q+Gxpw4cSLCw8OxYcMG/O9//wMA5OfnY+XKlWjYsKEqR//+/bFlyxZV8SYiIgJpaWno3bs3gFcTOk+cOFG1/pgxY3D48GHs2LFDrXjj6OiIxYsXQyKRwM3NDTdu3MDixYuLLN6cPXsWFy9eREpKiupq+EWLFiEsLAy7du3CiBEjinzNvv76a/Tv3x8hISG4cOECVq9ejYMHDyIkJAQ7duwAAN5lQySy3NxcxMfHFyrQ3Lp1Czk5OUVuI5VK4ezsXKhAU6dOHZibm5fuEyAiIiIi0ZSJ4s2KFSuwcOFCJCcno2HDhvj555/VOsf/tnPnTgQGBuLvv/+Gi4sL5s+fj27duqnaBUHAzJkzsXbtWqSlpaFVq1ZYtWqV6ipM4NXVoWPGjMG+ffsglUrRu3dvLF26FJUrV9bocyWikiUIAhYevoVfTt8DAPzoVx8DmjuJnIqIqOJ629Bh/+bt7V3o8ZIlSwC8ulNlyZIlqFmzJrp06YJu3brB19cXurq6uHHjBhQKBVxdXdW2z83NhaWlpeqxvr4+GjRooLaOv78/WrRogcePH8Pe3h6bN29G9+7dVV+YKhQK/O9//8OOHTvw6NEj5OXlITc3F8bGxmr7adGihdpV8N7e3ggODoZCoYCOjo7autevX0dGRoZaNgDIzs7G3bt33/j6mJubw9zcHBcvXkTv3r1Ro0YNXLt2DZ988glq1Kjxxu2IqOTJ5XLExcUVKtLcu3cPCkXRd0Xq6+sXOR+Nq6srDA0NS/kZEBEREZG20frizfbt2xEQEIDVq1fDy8sLS5YsgY+PD27duqW6KvOfzp8/jwEDBiAoKAg9evTAli1b4Ofnh6tXr6J+/foAgAULFmDZsmXYuHEjnJ2dERgYCB8fH8TExKhOkv39/ZGUlISjR48iPz8fQ4YMwYgRI7Bly5ZSff5E9GEWH4vHypOvvvia5VsXX7SoLnIiIiLNMNLTQcwPPqIctzhq164NiUSC2NhY9OrVq1B7bGwsqlSpAisrq2Ltz9HREbdu3cKxY8dw9OhRfPPNN1i4cCFOnTqFjIwM6Ojo4MqVK4UKJf+8EMfIyKjQMEPNmjVDrVq1sG3bNowcORK7d+9GSEiIqn3hwoVYunQplixZAg8PD1SqVAnjx49HXl5esXIXJSMjA3Z2dmpz8rz2pqvsz5w5g65duwJ4NfzbyZMn8e233yI7Oxt6enqYN28epk2bhmnTpr13LiJSJwgCUlJSihzq7NGjR2/czsTEBO7u7qhbt65akcbZ2bnQv1FERERERK9pffHmp59+wvDhwzFkyBAAwOrVq3HgwAGsX79eNYzGPy1duhRdunTBpEmTAAA//vgjjh49iuXLl2P16tUQBAFLlizB9OnT0bNnTwDApk2bYGNjg7CwMPTv3x+xsbEIDw/HpUuX0LRpUwDAzz//jG7dumHRokWq4TeISLstPRaPZRHxAIDp3d0xuJWzyImIiDRHIpEUa/gysVhaWqJz585YuXIlvv32W7V5b5KTk7F582YMHDhQrZhy4cIFtX1cuHBBbdg1IyMj+Pr6wtfXF6NGjUKdOnVw48YNNGrUCAqFAikpKWjTps07Z/X398fmzZtRrVo1SKVSdO/eXdV27tw59OzZE59//jkAQKlU4vbt26hbt67aPqKiogpld3FxKfKL2saNGyM5ORm6urrFvmOmadOmiI6OxpUrVzB58mREREQgMTERn3zyCa5evQqpVPrGuXKI6O2USiUSExNVhZmYmBjV7y9evHjjdjY2NkXOR2Nvb8/5aIiIiIjonWlvDx9AXl4erly5gqlTp6qWSaVSdOrUCZGRkUVuExkZiYCAALVlPj4+CAsLAwAkJCQgOTkZnTp1UrWbmZnBy8sLkZGR6N+/PyIjI2Fubq4q3ABAp06dIJVKERUVVeTVotrmcVo2Hqdlix2DSDSnb6di2fE7AIBp3ergyzY1RU5ERETLly9Hy5Yt4ePjgzlz5sDZ2Rl//fUXJk2aBAcHB8ydO1dt/XPnzmHBggXw8/PD0aNHsXPnThw4cAAAEBISAoVCAS8vLxgbG+P333+HkZERqlevDktLS/j7+2PgwIEIDg5Go0aNkJqaioiICDRo0ECtGFMUf39/zJo1C3PnzsWnn36qmocGAFxcXLBr1y6cP38eVapUwU8//YQnT54UKt4kJiYiICAAX331Fa5evYqff/4ZwcHBRR6vU6dO8Pb2hp+fHxYsWABXV1c8fvwYBw4cQK9evdTOSV8zMjJC7dq1sWvXLrRv3x61a9fG+fPn0apVq0LDxb0WExODvLw8PH/+HC9fvkR0dDQAwNPT862vB1F5VVBQgNu3bxc5H01WVlaR20gkEtSoUaPIIk2VKlVK+RkQERERUXmm1cWbp0+fQqFQwMbGRm25jY0N4uLiitwmOTm5yPWTk5NV7a+XvW2dfw/JpqurCwsLC9U6RcnNzUVubq7qsVwuf9vT06hdVx7ip6O3RTs+kbaY3MUNI9rWEjsGERHhVeHj8uXLmDlzJvr27Yvnz5/D1tYWfn5+mDlzZqE7RSZMmIDLly9j9uzZMDU1xU8//QQfn1dDw5mbm2PevHkICAiAQqGAh4cH9u3bp5o3ZsOGDZgzZw4mTJiAR48eoWrVqmjRogV69Ojxnzlr166N5s2b4+LFi6o5dl6bPn067t27Bx8fHxgbG2PEiBHw8/NDenq62noDBw5EdnY2mjdvDh0dHYwbNw4jRowo8ngSiQQHDx7E999/jyFDhiA1NRW2trZo27ZtoXPWfzt58iT69OkDADh16hTatm37xnW7deuG+/fvqx43atQIwLvNQ0RUnqSkpKBevXpFtunp6cHV1bXI+Wj+PccVEREREZEmaHXxpqwJCgrC7NmzxY4BADA31kMNS3YqqOLSkUrweYvqGMKh0oiItEr16tXV5pB5k7///vut7X5+fvDz83tju56eHmbPnv3Gc7PBgwdj8ODBb9z+38OevWZhYaG6o/tN/jl3zapVq4pc59/Pz8TEBMuWLcOyZcveuu9/Cw8PV/3+66+/vnXd/3pNiSoaOzs7VKtWDXZ2doWKNDVr1oSuLrvLRERERCQerT4brVq1KnR0dPDkyRO15U+ePIGtrW2R29ja2r51/df/ffLkCezs7NTWeT1khK2tLVJSUtT2UVBQoLo69E2mTp2qNmSbXC6Ho6PjfzxLzRjoXQMDvWuIcmwiIiIiIiJtJ5FIkJiYyPloiIiIiEgrScUO8Db6+vpo0qQJIiIiVMuUSiUiIiLg7e1d5Dbe3t5q6wPA0aNHVes7OzvD1tZWbR25XI6oqCjVOt7e3khLS8OVK1dU6xw/fhxKpRJeXl5vzGtgYABTU1O1HyIiIiIiItJOLNwQERERkbbS6jtvACAgIACDBg1C06ZN0bx5cyxZsgSZmZkYMmQIgFfjiTs4OCAoKAgAMG7cOLRr1w7BwcHo3r07tm3bhsuXL2PNmjUAXp2cjx8/HnPmzIGLiwucnZ0RGBgIe3t71dAb7u7u6NKlC4YPH47Vq1cjPz8fo0ePRv/+/WFvby/K60BERERERERERERERBWD1hdv+vXrh9TUVMyYMQPJycnw9PREeHi4avLWxMRESKX//waili1bYsuWLZg+fTqmTZsGFxcXhIWFoX79+qp1Jk+ejMzMTIwYMQJpaWlo3bo1wsPDYWhoqFpn8+bNGD16NDp27AipVIrevXu/8xjkRERERERERERERERE70oiCIIgdojySi6Xw8zMDOnp6RxCjYiIiEpMTk4OEhIS4OzsrHbxCVFpeNvnj+e/9K74mSEiIiKiiqa458BaPecNEREREb0Zr8EhMfBzR0REREREpHks3hARERGVMXp6egCArKwskZNQRfT6c/f6c0hEREREREQlT+vnvCEiIiIidTo6OjA3N0dKSgoAwNjYGBKJRORUVN4JgoCsrCykpKTA3NwcOjo6YkciIiIiIiIqt1i8ISIiIiqDbG1tAUBVwCEqLebm5qrPHxEREREREWkGizdEREREZZBEIoGdnR2sra2Rn58vdhyqIPT09HjHDRERERERUSlg8YaIiIioDNPR0eGX6URERERERETljFTsAERERERERERERERERPT/sXhDRERERERERERERESkRVi8ISIiIiIiIiIiIiIi0iKc80aDBEEAAMjlcpGTEBERERFp3uvz3tfnwUT/hX0mIiIiIqpoittvYvFGg16+fAkAcHR0FDkJEREREVHpefnyJczMzMSOQWUA+0xEREREVFH9V79JIvCyOI1RKpV4/PgxTExMIJFIxI5T4cjlcjg6OuLBgwcwNTUVOw6VMr7/FRvf/4qN73/FxvdfXIIg4OXLl7C3t4dUyhGa6b+xzyQu/ptZsfH9r9j4/ldsfP8rNr7/4ituv4l33miQVCpFtWrVxI5R4ZmamvIfogqM73/Fxve/YuP7X7Hx/RcP77ihd8E+k3bgv5kVG9//io3vf8XG979i4/svruL0m3g5HBERERERERERERERkRZh8YaIiIiIiIiIiIiIiEiLsHhD5ZaBgQFmzpwJAwMDsaOQCPj+V2x8/ys2vv8VG99/IqLi47+ZFRvf/4qN73/Fxve/YuP7X3ZIBEEQxA5BREREREREREREREREr/DOGyIiIiIiIiIiIiIiIi3C4g0REREREREREREREZEWYfGGiIiIiIiIiIiIiIhIi7B4Q+VKUFAQmjVrBhMTE1hbW8PPzw+3bt0SOxaJZN68eZBIJBg/frzYUaiUPHr0CJ9//jksLS1hZGQEDw8PXL58WexYVAoUCgUCAwPh7OwMIyMj1KpVCz/++CM4tV/5dfr0afj6+sLe3h4SiQRhYWFq7YIgYMaMGbCzs4ORkRE6deqE+Ph4ccISEWkZ9pvoNfaZKib2myou9psqFvaZyj4Wb6hcOXXqFEaNGoULFy7g6NGjyM/Px8cff4zMzEyxo1Epu3TpEn755Rc0aNBA7ChUSl68eIFWrVpBT08Phw4dQkxMDIKDg1GlShWxo1EpmD9/PlatWoXly5cjNjYW8+fPx4IFC/Dzzz+LHY00JDMzEw0bNsSKFSuKbF+wYAGWLVuG1atXIyoqCpUqVYKPjw9ycnJKOSkRkfZhv4kA9pkqKvabKjb2myoW9pnKPonA0iqVY6mpqbC2tsapU6fQtm1bseNQKcnIyEDjxo2xcuVKzJkzB56enliyZInYsUjDvvvuO5w7dw5nzpwROwqJoEePHrCxscGvv/6qWta7d28YGRnh999/FzEZlQaJRILdu3fDz88PwKsryOzt7TFhwgRMnDgRAJCeng4bGxuEhISgf//+IqYlItI+7DdVPOwzVVzsN1Vs7DdVXOwzlU2884bKtfT0dACAhYWFyEmoNI0aNQrdu3dHp06dxI5CpWjv3r1o2rQp+vTpA2trazRq1Ahr164VOxaVkpYtWyIiIgK3b98GAFy/fh1nz55F165dRU5GYkhISEBycrLa3wEzMzN4eXkhMjJSxGRERNqJ/aaKh32miov9poqN/SZ6jX2mskFX7ABEmqJUKjF+/Hi0atUK9evXFzsOlZJt27bh6tWruHTpkthRqJTdu3cPq1atQkBAAKZNm4ZLly5h7Nix0NfXx6BBg8SORxr23XffQS6Xo06dOtDR0YFCocDcuXPh7+8vdjQSQXJyMgDAxsZGbbmNjY2qjYiIXmG/qeJhn6liY7+pYmO/iV5jn6lsYPGGyq1Ro0bh5s2bOHv2rNhRqJQ8ePAA48aNw9GjR2FoaCh2HCplSqUSTZs2xf/+9z8AQKNGjXDz5k2sXr2anZAKYMeOHdi8eTO2bNmCevXqITo6GuPHj4e9vT3ffyIiordgv6liYZ+J2G+q2NhvIipbOGwalUujR4/G/v37ceLECVSrVk3sOFRKrly5gpSUFDRu3Bi6urrQ1dXFqVOnsGzZMujq6kKhUIgdkTTIzs4OdevWVVvm7u6OxMREkRJRaZo0aRK+++479O/fHx4eHvjiiy/w7bffIigoSOxoJAJbW1sAwJMnT9SWP3nyRNVGRETsN1VE7DMR+00VG/tN9Br7TGUDizdUrgiCgNGjR2P37t04fvw4nJ2dxY5Epahjx464ceMGoqOjVT9NmzaFv78/oqOjoaOjI3ZE0qBWrVrh1q1bastu376N6tWri5SISlNWVhakUvXTGh0dHSiVSpESkZicnZ1ha2uLiIgI1TK5XI6oqCh4e3uLmIyISDuw31Rxsc9E7DdVbOw30WvsM5UNHDaNypVRo0Zhy5Yt2LNnD0xMTFRjNJqZmcHIyEjkdKRpJiYmhcbprlSpEiwtLTl+dwXw7bffomXLlvjf//6Hvn374uLFi1izZg3WrFkjdjQqBb6+vpg7dy6cnJxQr149XLt2DT/99BOGDh0qdjTSkIyMDNy5c0f1OCEhAdHR0bCwsICTkxPGjx+POXPmwMXFBc7OzggMDIS9vT38/PzEC01EpCXYb6q42Gci9psqNvabKhb2mco+iSAIgtghiEqKRCIpcvmGDRswePDg0g1DWqF9+/bw9PTEkiVLxI5CpWD//v2YOnUq4uPj4ezsjICAAAwfPlzsWFQKXr58icDAQOzevRspKSmwt7fHgAEDMGPGDOjr64sdjzTg5MmT+OijjwotHzRoEEJCQiAIAmbOnIk1a9YgLS0NrVu3xsqVK+Hq6ipCWiIi7cJ+E/0T+0wVD/tNFRf7TRUL+0xlH4s3REREREREREREREREWoRz3hAREREREREREREREWkRFm+IiIiIiIiIiIiIiIi0CIs3REREREREREREREREWoTFGyIiIiIiIiIiIiIiIi3C4g0REREREREREREREZEWYfGGiIiIiIiIiIiIiIhIi7B4Q0REREREREREREREpEVYvCEiIiIiIiIiIiIiItIiLN4QEX2A9u3bY/z48Ro9xuDBg+Hn56fRY1Q0H/Kanjx5EhKJBGlpaQCAkJAQmJubl1g2bSORSBAWFiZ2DCIiIiIqo9hnKpvYZyo+9pmISFNYvCEiojJBWztk/fr1w+3bt8WOQUREREREFRz7TERE5Yuu2AGIiKj05eXlQV9fX+wY5YKRkRGMjIzEjlGm8PNHRERERNqO56wlh32md8fPHxEBvPOGiOiDFRQUYPTo0TAzM0PVqlURGBgIQRBU7b/99huaNm0KExMT2Nra4rPPPkNKSoraPv766y/06NEDpqamMDExQZs2bXD37t0ij3fp0iVYWVlh/vz5AIBZs2bB09MTv/zyCxwdHWFsbIy+ffsiPT1dtc3rK7Dmzp0Le3t7uLm5AQBu3LiBDh06wMjICJaWlhgxYgQyMjIKbTd79mxYWVnB1NQUX3/9NfLy8lTrhIeHo3Xr1jA3N4elpSV69OhRKPv58+fh6ekJQ0NDNG3aFGFhYZBIJIiOjgYAKBQKDBs2DM7OzjAyMoKbmxuWLl2q2n7WrFnYuHEj9uzZA4lEAolEgpMnTwIAHjx4gL59+8Lc3BwWFhbo2bMn/v77b9W2CoUCAQEBqnyTJ09We3+Kcv/+ffj6+qJKlSqoVKkS6tWrh4MHDxa5blFDAOzbtw/NmjWDoaEhqlatil69eqnacnNzMXHiRDg4OKBSpUrw8vJSPZc3kUgkWLduHXr16gVjY2O4uLhg7969b83w+jV+7fXnZP369XByckLlypXxzTffQKFQYMGCBbC1tYW1tTXmzp1b6PhJSUno2rUrjIyMULNmTezatUut/b/egzd9/oiIiIioYmCfiX0m9pnYZyKid8fiDRHRB9q4cSN0dXVx8eJFLF26FD/99BPWrVunas/Pz8ePP/6I69evIywsDH///TcGDx6san/06BHatm0LAwMDHD9+HFeuXMHQoUNRUFBQ6FjHjx9H586dMXfuXEyZMkW1/M6dO9ixYwf27duH8PBwXLt2Dd98843athEREbh16xaOHj2K/fv3IzMzEz4+PqhSpQouXbqEnTt34tixYxg9enSh7WJjY3Hy5Els3boVoaGhmD17tqo9MzMTAQEBuHz5MiIiIiCVStGrVy8olUoAgFwuh6+vLzw8PHD16lX8+OOPatkBQKlUolq1ati5cydiYmIwY8YMTJs2DTt27AAATJw4EX379kWXLl2QlJSEpKQktGzZEvn5+fDx8YGJiQnOnDmDc+fOoXLlyujSpYuqsxQcHIyQkBCsX78eZ8+exfPnz7F79+63vqejRo1Cbm4uTp8+jRs3bmD+/PmoXLnyW7d57cCBA+jVqxe6deuGa9euISIiAs2bN1e1jx49GpGRkdi2bRv+/PNP9OnTB126dEF8fPxb9zt79mz07dsXf/75J7p16wZ/f388f/68WJleu3v3Lg4dOoTw8HBs3boVv/76K7p3746HDx/i1KlTmD9/PqZPn46oqCi17QIDA9G7d29cv34d/v7+6N+/P2JjYwGgWO8BUPjzR0REREQVB/tM7DP9E/tM7DMRUTEJRET03tq1aye4u7sLSqVStWzKlCmCu7v7G7e5dOmSAEB4+fKlIAiCMHXqVMHZ2VnIy8srcv1BgwYJPXv2FEJDQ4XKlSsL27ZtU2ufOXOmoKOjIzx8+FC17NChQ4JUKhWSkpJU+7CxsRFyc3NV66xZs0aoUqWKkJGRoVp24MABQSqVCsnJyartLCwshMzMTNU6q1atEipXriwoFIoi86ampgoAhBs3bqjWt7S0FLKzs1XrrF27VgAgXLt27Y2v06hRo4TevXsXeh3+6bfffhPc3NzUXv/c3FzByMhIOHz4sCAIgmBnZycsWLBA1Z6fny9Uq1at0L7+ycPDQ5g1a1aRbSdOnBAACC9evBAEQRA2bNggmJmZqdq9vb0Ff3//Ire9f/++oKOjIzx69EhteceOHYWpU6e+MQ8AYfr06arHGRkZAgDh0KFDRWYQBEHYvXu38M8/8zNnzhSMjY0FuVyuWubj4yPUqFFD7b10c3MTgoKC1I799ddfq+3by8tLGDlypCAIxXsPivr8EREREVHFwD5TYewzsc/EPhMRFQfvvCEi+kAtWrRQu9Xa29sb8fHxUCgUAIArV67A19cXTk5OMDExQbt27QAAiYmJAIDo6Gi0adMGenp6bzxGVFQU+vTpg99++w39+vUr1O7k5AQHBwe1DEqlErdu3VIt8/DwUBszNzY2Fg0bNkSlSpVUy1q1alVou4YNG8LY2Fht3xkZGXjw4AEAID4+HgMGDEDNmjVhamqKGjVqqD2/W7duoUGDBjA0NFTt459XVb22YsUKNGnSBFZWVqhcuTLWrFmj2sebXL9+HXfu3IGJiQkqV66MypUrw8LCAjk5Obh79y7S09ORlJQELy8v1Ta6urpo2rTpW/c7duxYzJkzB61atcLMmTPx559/vnX9f4qOjkbHjh2LbLtx4wYUCgVcXV1VeStXroxTp069cciH1xo0aKD6vVKlSjA1NS00lMR/qVGjBkxMTFSPbWxsULduXUilUrVl/96vt7d3ocevryL7r/fgtX9//oiIiIio4mCfiX2mf2KfiX0mIioeXbEDEBGVZ69vs/fx8cHmzZthZWWFxMRE+Pj4qG6PLs7EjbVq1YKlpSXWr1+P7t27v7XT8ib/7HCUJF9fX1SvXh1r166Fvb09lEol6tevr3b793/Ztm0bJk6ciODgYHh7e8PExAQLFy4sdCv6v2VkZKBJkybYvHlzoTYrK6t3fi6vffnll/Dx8cGBAwdw5MgRBAUFITg4GGPGjPnPbd/2fmZkZEBHRwdXrlyBjo6OWtt/DTHw7/dcIpGohlmQSqWFxqTOz88v1j7ett/iKO57oKnPHxERERGVbewzFQ/7TOwzEVHFwztviIg+0L9Pli9cuAAXFxfo6OggLi4Oz549w7x589CmTRvUqVOn0BU6DRo0wJkzZ4o8cXytatWqOH78OO7cuYO+ffsWWjcxMRGPHz9WyyCVSt86yaG7uzuuX7+OzMxM1bJz584V2u769evIzs5W23flypXh6OiIZ8+e4datW5g+fTo6duwId3d3vHjxQu04bm5uuHHjBnJzc1XLLl26pLbOuXPn0LJlS3zzzTdo1KgRateuXeiqKn19fdWVea81btwY8fHxsLa2Ru3atdV+zMzMYGZmBjs7O7X3qKCgAFeuXHnj6/Kao6Mjvv76a4SGhmLChAlYu3btf24DvHo/IyIiimxr1KgRFAoFUlJSCuW1tbUt1v6LYmVlhZcvX6q9l68nNi0JFy5cKPTY3d0dwH+/B0RERERE7DOxz/RP7DOxz0RExcPiDRHRB0pMTERAQABu3bqFrVu34ueff8a4ceMAvLo1X19fHz///DPu3buHvXv34scff1TbfvTo0ZDL5ejfvz8uX76M+Ph4/Pbbb2q34QOAtbU1jh8/jri4OAwYMEBtck5DQ0MMGjQI169fx5kzZzB27Fj07dv3rSe3/v7+qu1u3ryJEydOYMyYMfjiiy9gY2OjWi8vLw/Dhg1DTEwMDh48iJkzZ2L06NGQSqWoUqUKLC0tsWbNGty5cwfHjx9HQECA2nE+++wzKJVKjBgxArGxsTh8+DAWLVoEAKqhE1xcXHD58mUcPnwYt2/fRmBgYKHOSo0aNfDnn3/i1q1bePr0KfLz8+Hv74+qVauiZ8+eOHPmDBISEnDy5EmMHTsWDx8+BACMGzcO8+bNQ1hYGOLi4vDNN98gLS3tre/p+PHjcfjwYSQkJODq1as4ceKE6sT7v8ycORNbt27FzJkzERsbq5q8EwBcXV3h7++PgQMHIjQ0FAkJCbh48SKCgoJw4MCBYu2/KF5eXjA2Nsa0adNw9+5dbNmyBSEhIe+9v3/buXMn1q9fj9u3b2PmzJm4ePGiapLW4rwHRERERFSxsc/EPtM/sc/EPhMRFQ+LN0REH2jgwIHIzs5G8+bNMWrUKIwbNw4jRowA8OrqnpCQEOzcuRN169bFvHnzVCfhr1laWuL48ePIyMhAu3bt0KRJE6xdu7bI2/xtbW1x/Phx3LhxA/7+/qqrqmrXrg2ZTIZu3brh448/RoMGDbBy5cq35jY2Nsbhw4fx/PlzNGvWDJ9++ik6duyI5cuXq63XsWNHuLi4oG3btujXrx8++eQTzJo1C8CrW8+3bduGK1euoH79+vj222+xcOFCte1NTU2xb98+REdHw9PTE99//z1mzJgBAKoxnb/66ivIZDL069cPXl5eePbsGb755hu1/QwfPhxubm5o2rQprKyscO7cORgbG+P06dNwcnKCTCaDu7s7hg0bhpycHJiamgIAJkyYgC+++AKDBg1SDS/Qq1evt742CoUCo0aNgru7O7p06QJXV9f/fD1fa9++PXbu3Im9e/fC09MTHTp0wMWLF1XtGzZswMCBAzFhwgS4ubnBz88Ply5dgpOTU7H2XxQLCwv8/vvvOHjwIDw8PLB161bVe1QSZs+ejW3btqFBgwbYtGkTtm7dirp16wJAsd4DIiIiIqrY2Gdin+mf2Gdin4mIikci/HvARyIiKlNmzZqFsLCwEr3l+7XBgwcjLS0NYWFhJbrfzZs3Y8iQIUhPTy/W+NVERERERETvi30mIiIqi3TFDkBEROXfpk2bULNmTTg4OOD69euYMmUK+vbty04IERERERER2GciIqLCWLwhIiKNS05OxowZM5CcnAw7Ozv06dMHc+fOFTsWERERERGRVmCfiYiI/o3DphEREREREREREREREWkRqdgBiIiIiIiIiIiIiIiI6P9j8YaIiIiIiIiIiIiIiEiLsHhDRERERERERERERESkRVi8ISIiIiIiIiIiIiIi0iIs3hAREREREREREREREWkRFm+IiIiIiIiIiIiIiIi0CIs3REREREREREREREREWoTFGyIiIiIiIiIiIiIiIi3C4g0REREREREREREREZEW+X+XL7ccWeZFxQAAAABJRU5ErkJggg==", "text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -607,7 +617,10 @@ "source": [ "op_budget = OperatorBudget(max_qwc_groups=10)\n", "bp_obs, remaining_slices, metadata = backpropagate(\n", - " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", + " obs,\n", + " slices,\n", + " operator_budget=op_budget,\n", + " truncation_error_budget=truncation_error_budget,\n", ")\n", "reduced_circuit = combine_slices(remaining_slices)\n", "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", @@ -634,9 +647,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -680,7 +692,9 @@ "source": [ "num_slices = 12\n", "\n", - "truncation_error_budget = setup_budget(max_error_total=0.018, num_slices=num_slices, p_norm=1)\n", + "truncation_error_budget = setup_budget(\n", + " max_error_total=0.018, num_slices=num_slices, p_norm=1\n", + ")\n", "print(truncation_error_budget)" ] }, @@ -760,9 +774,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -846,7 +859,10 @@ ], "source": [ "bp_obs, remaining_slices, metadata = backpropagate(\n", - " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", + " obs,\n", + " slices,\n", + " operator_budget=op_budget,\n", + " truncation_error_budget=truncation_error_budget,\n", ")\n", "reduced_circuit = combine_slices(remaining_slices)\n", "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", @@ -879,9 +895,8 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -899,7 +914,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -913,9 +928,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb index 88d280b002b..966aa483260 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb @@ -62,7 +62,9 @@ "coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False)\n", "\n", "# Choose a 10-qubit linear chain on this coupling map\n", - "reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 5, 12, 8, 18])" + "reduced_coupling_map = coupling_map.reduce(\n", + " [0, 13, 1, 14, 10, 16, 5, 12, 8, 18]\n", + ")" ] }, { @@ -81,9 +83,8 @@ "outputs": [ { "data": { - "image/png": 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W+PpqAp9smPzgIdkbgmRvCF87dW2sj08enmrFihU0YcIEOvHEE2n06NEkExbJHyRq6CoeCxBLMAVM4ZPj6l64i3b79u2JuznrXOrjU6ddcas77vmzNp+sv3zajvj9o3Opj0/kz/AjY0r+dDzlz8t169ZpH9LVsac+Pp1j8NqwAnHPnw17qOeI+vhE/gw/JibkT/49IW/oEA8HJB9oC9/pLGqoj88sLpP4Q+OePxcuXJgWw5kzZ5LsiURNmjRJ2xfVhvr4RP4MPwom5E/+jc45lH8PuRe+Z8Pbdf6Gr49Pt61Yr1+BOOfPt99+WzlXUlLicZKHW+P79v/6178826Ms1Mdn2pwdsruUGjdbdueL0kZPXe+//74qy14cnu2nnXaaKjv7PTsjKvA4+PIJgIhqS041zjwoPBYcj93ep08fkq10dPvtt6vGN0cJE/h0bDHx1Y/Pd955RzUgdejQgQYOHKgaaQ4cOKDe547uJvoSR5tM5nP37t3UpUsXrbL68anVIEsqd97Hcc2fPIcTf67y3DI6Fz8+kT+ji4hJ+VN211Zzcekci7a28n581j4G5ewViHv+zN7jcM7w4xP5Mxyt/a5qQv6UPfTp4MGDahxvPxt1bvPjU6c9ttQd9/zJ88s8++yz2h9q8OMT+TO6d4kJ+ZMfSmzbtq26cez2XPbkVNvlCA3uzZGu+/EZqQGWVhan/Ok0BvM8Ge6F5+Ti5cMPP1SvOv7Vx2daE7bzZKUcWkiHrapOOU6dem3durXHhuOPP16VdbZssi6sUXl5ucc2FDJXwGkZrOsMbr3mZcOGDfTb3/6W7rjjDnUT7JprrlE9EnifTj65flMX1oV7ynCDoBwHn2bPnq0+NDkxObrKbpD1mj9//nwqLCys9xjs9Ffgo48+UjtM45O/KHGvOH5ySeeC/BlcfZvyJze6VlZW0t13361y+4wZM4ILFOAKyJ8BxMvDqSblT+5hOmXKFDrllFPy4Fl+LoH8GVxHW/InPznPv5H4+54cepQuvvhikvN4qB6cwVXK7QrIn7nplq+zTMiffMOQF77pceWVV9K///1v4ocw5PAw9Pvf/56GDBmi9uv4h/wZXHVb8qdbiT/96U9qtIba95zcx0SxjvwZhcp112FC/uSbyb/73e/o5ptvJjmnrRplZ+nSpfTCCy/Qvffeq7XnEfJn3exkuifu+dPpCMGf6+7fRk5PJG441rU4+dO3/tpja8kbnWo8LnljtPauyMq/+tWvlA3vvvuup04uSyfEtGnTPNujLDz55JM1c02wLfjLTQPZjStr7XjybDm0mTpPJ59u3njsTGbAlDk7mE/5YSl69uyZtb5gOTeW3brxpGJcNoVPh9VbbrlFdO3atWZCKWd71K/In8EZY75syJ/ypog455xzhOxxpuaSmTt3rtb5uPi9gPyZHz7dOTGbdVPy54IFC8RVV11Vkx7lwy0qr+ucoNzh05nrLBtdcayX67jnTzmkSQ2bvPKf//xHfeeTT7aJqqoqz74oC8ifXs6ift+ZkD95smf2Wz6oJvbs2aPwYyZHjhypxpz/5JNPokTSUxe+f+aHz7jnTzcUPEclz2dowhxIyJ/54TPXvGtC/mQ2mcmhQ4eKVq1aCf5Mb9GihZAPM4gvvvjCjW7k68if+eEzzvlz7969Qg6pJi644AIhOx4oBnmicvmgovrc/8Mf/hA5l06FzCfPd+OzpM/Zwd2neJFfUtSrjn/yja2qrf0EuvNkus6nzvkpKm49kmLiL0cNeDxXnkOgocUZ75W7JvETQfx0+qxZs9RpOvlsyG6d+5lPHtuRn6qqqKig2267jTp16qRMqt3trC47+SlB8J3b+9vp8WUSnytXrqTHHnuMnnvuOZJfnuoKeyTbkT9z48r9frQlf/KwlPIBBvXU57x589SQhXLiXar9uR8JmP+vBPkzOJ9uVrNdNyF/7ty5k2699VZ68MEHo0Qvo7qQP4PzaUP+lD+WPbzwEAK//OUv6eOPPybZUOfZF2UB+TM4n9nmTPfxJuTP5s2bK+SKi4vJGY2Bt8mHGFXvcp7rUNeC/BmcTxvyp5s/HjZ72LBhNb+T3fuiXkf+DM6nOx9mu25C/uT7nNz7jXMmfxflJ+h5tBvefuGFF9aM0BE1m1wf8mdwPuOeP9u0aUOvvPKK6mHE3zt5uoHvf//7ilVm5LjjjuMXLQvz6fQwqW1A2jBWfCOfF+6KomtsOJ5bgBe+6epenBuIzn73vqjWWRdHo6jqTFI9znBL3OX56quvVn/8ZnIWhpkXnXw6tpj46uaTJxDiP75x88Ybb6ih12TLJ/HcDdyQ5IwTaKIfcbXJyQ2m8Pnee+8RD//GE0KXlpZql9XNp3ZjLDQgrvmTb4xww+yYMWPU2MnyqXot0XHzifwZfQhMyJ881F///v3ptddeqxFg1apVav31118nnsvjiiuuqNkX5YqbzyjrTUpdcc2fHJ9u3bqpMPHNEV2Lm0/kz+ijYEL+7Nixo3LcGRLbUUH2LFarsmeHsynyVzefkVeegArjlj95rjgeGoh/H5mwuPlE/ow+IibkT37wix8CW7x4cc1w4nyDmR9muOSSS9Tvox/+8IfRiyNrdPOpxQDLK41L/uQ5lZcsWeKJxpw5c1T5jDPO8GyPslAfn2mNHXxjmXtWrFmzRt0ojdJQpy7nptyWLVucTerVGQu0d+/enu1RFjgJ6aw/Sl+jqst5g/PETHyTa/To0SS7SPlWbwKfvoYZstGPT34KkPXkvz/+8Y8qST3xxBPqSX85DBfxfjksgiEexNsMk/jkG3TXXXcd/e1vfzMmZ/nxGe+I67felvzJPT144bmadC1+fCJ/RhcNE/Inj6nL3zUfeuihNMd5/jB+cokbPHQsfnzqsMOmOm3Jn05MnJ7xTjnKVz8+kT+ji4AJ+ZNvhHDPfe5l5F6c3xh8407X4senLltsqTfO+ZN/B/fq1YvOPvtsI8LhxyfyZ3ShMSF/rl+/Xjnsng+BN3Tu3Fltd/arQsT//PiM2ATrqotz/nQHgx+s5Yeo+/Xr594c6Xp9fHr7QkuzuOuUHEdbPQkeqZWuyuS8DMRPh6xevdq1lWjFihVq+3nnnefZHlWBu8TxDcQBAwZEVaW19ThffHnYtAkTJhAPtcOT2PFEYXU1dLAYJvBpalAy4VPOe0JyLEjiVlhuBX3qqafou9/9rtZJr0zVMxe7TOGTJza7/vrraeHChcY0dGTCZy6aJ/GcOOfPZ599Vg2rVjtuGzduVJu4a6yOJRM+kT/DjYwJ+XPTpk1pwzg6wxvIOTu0NXRkwme40bHn6nHOn9wQx72Pai9r165Vm3T9PsmET+TP2lHLb9mE/Mk36QYOHEivvvqqxzndDytmwqfHYBTqVCDO+dNxin2Q48vTlClTnE1aXzPhE/kz3BCZkD+dh774/ox7kfMjqGKXLl3cmyNbz4TPyIyJeUU25E93CHiUGO6RNGrUqDqHkXIfH8Z6g3zKA9KW6dOnCznOv5BjZ6fti2qDHNdMyPE+hfyCpKqUX+SFTERCjvcZlQlp9SxdulTIIAm2BUvuCpSVlYmxY8eKl19+Wcg3SdYXMoFPx2iTJigPwqcc2ko88sgjQo4JqX0Sa0fbuL7q5lMOtaImNpPDlwmeLLL2nxwHVIu0QfjUYrChlcY9f7700ktixIgRQo5BW6OwvMEsevToIWSvSW0T7AbhE/mzJpSBV3TnTz8HZGOH+u6nc4LyIHz6+ZTUbXHPn/ImiJq0lHOOs8g52oQcJkhceumlzqbIX4PwifyZv3CZkD/feust9XtdPjmvHKuurhbjxo1T9xUOHjyYP2ezuFIQPrOoxvpD454/nQA988wzQg4V5RS1vwbhE/kzf+HTnT/l8H9CDtUvfvSjH9Xcg62qqhKXXXaZkA3JQg7lnj9ns7hSED6zqMb6Q+OeP2XPIiGHRq+JE/+OnzhxopA9j4Qcjalme9QrDfBZVsAG1W5l4TE1ec4EfiqYn/rWtfDEUQ8//LCa5EzOAE833HADjR8/Xpc5auz7jz76iOQXOW02oGIiE/h8/vnn6YUXXqCtW7eq9wm3xvMTddwj6fbbb6eioqLIQ8VzM4DPyGVPq1A3n3/+85/pxhtvTLPL2cA95nT0jgOfTgT0vurmk8eUnzRpksqd3LOPF34yRH6Zp5tuuklL7mQbwCeroH/RzaefAvzUEg+vyT07uGekjgV86lA9vU7dfPI48zxnDA8TxGOMc1neTFZ8Tp06VfV+Trc6/C3gM3yNM6lBN5+Ojdxbf9q0aYrNL774gvg30syZM0k+1OAcEukr+IxU7jorM4VPHsHkZz/7GV177bV12hrlDvAZpdp112UCn3wfh+cwrKysrJmovHv37nTnnXdqG6kBfNbNTJR7dPPJ9zyHDx+u5pPhnmb82d63b1/FpjNfV5R6OHU1wGe5b2MHnzxkyBD1JuNZ17EQ8fwhnGx4MisedgmLXgXAp1d/8OnVQ3cJfHojAD69eugugU9vBMCnVw/dJfDpjQD49OqhuwQ+vREAn149dJfApzcC4NOrh+4S+PRGAHx69dBdAp/eCIBPrx66S+DTG4EM+Ky7sWP58uU0ePBg4saOYcOGea+cwBK3/stuMurJeR7XD4teBcCnV3/w6dVDdwl8eiMAPr166C6BT28EwKdXD90l8OmNAPj06qG7BD69EQCfXj10l8CnNwLg06uH7hL49EYAfHr10F0Cn94IgE+vHrpL4NMbgQz4rLuxgy91+eWXqyGD3n77bWrRooX36gkq8aTkPGn27NmzSY6jlyDPzXYVfH4dH/BpJqfgE3yaSebXVoFP8Ak+TVYAfJocHeRP8Ak+TVYAfJocHeRP8Ak+TVYAfJocHeTPrPisv7Fj8+bNVFpaSmPGjKH77rvP5LiHZtuXX35Jffr0ITn5H8mJVamgoCC0unDh7BQAn0TgMztmojwafILPKHnLti7wCT6zZSbK48En+IySt2zrAp/gM1tmojwefILPKHnLti7wCT6zZSbK48En+IySt2zrAp9Z8VlODc2Y/vTTTwt5g18888wzDR1q3f5jx46JkSNHig4dOojt27db558NDoFP8Gkyx+ATfIJPMxXA57uZcXFbhfyJ/OnmwbR18Ak+TWPSbQ/4BJ9uHkxbB5/g0zQm3faAT/Dp5sG0dfCZMZ9lDTZ2cHAnT54s5DwVYvHixabFOlR7Jk2aJOTwXUKOjxZqPbh4MAXAJ/gMRlC4Z4NP8BkuYcGuDj7BZzCCwj0bfILPcAkLdnXwCT6DERTu2eATfIZLWLCrg0/wGYygcM8Gn+AzXMKCXR18ZsRnZo0d/ATk1VdfLVq3bi1ee+21YJGJwdnV1dXi5ptvFo0bNxbz58+PgcXJNhF8Jjv+pnsPPk2PULLtA5/Jjr/p3oNP0yOUbPvAZ7Ljb7r34NP0CCXbPvCZ7Pib7j34ND1CybYPfGYU/8waO/hSR44cET/4wQ9UDw/uOmPrcvjwYSEnIRfNmjUTc+fOtdVN6/wCn9aF1CqHwKdV4bTOGfBpXUitcgh8WhVO65wBn9aF1CqHwKdV4bTOGfBpXUitcgh8WhVO65wBnw2GNPPGDr4UtyDddNNNag6PqVOnqgaQBquI0QGbNm0S559/vigqKhKLFi2KkeUwlRUAn+DAZAXAp8nRgW3gEwyYrAD4NDk6sA18ggGTFQCfJkcHtoFPMGCyAuDT5OjANvBZLwPZNXY4l3r00UfFcccdJ/r37y82bNjgbI71a3l5uWjbtq0oKSkRFRUVsfYl6caDz6QTYLb/4NPs+CTdOvCZdALM9h98mh2fpFsHPpNOgNn+g0+z45N068Bn0gkw23/waXZ8km4d+PQlILfGDr7UBx98IEpLS0VhYaG44447RFVVlW8Npm/85JNPxPDhwwURiQkTJoiDBw+abjLsy0AB8JmBSDhEmwLgU5v0qDgDBcBnBiLhEG0KgE9t0qPiDBQAnxmIhEO0KQA+tUmPijNQAHxmIBIO0aYA+NQmPSrOQAHwmSZS7o0dfCkeJ2zGjBmiVatW4swzzxRz5swRR48eTavFxA07d+4UPBQXN9YUFxeLJUuWmGgmbAqgAPgMIB5ODV0B8Bm6xKgggALgM4B4ODV0BcBn6BKjggAKgM8A4uHU0BUAn6FLjAoCKAA+A4iHU0NXAHyGLjEqCKAA+PSIF6yxw7nU5s2bxdixY0WTJk1Ejx49xOOPPy4OHTrk7DbqtbKyUtxyyy2qgebkk08W99xzj3VzjxgluAHGgE8DggAT6lQAfNYpDXYYoAD4NCAIMKFOBcBnndJghwEKgE8DggAT6lQAfNYpDXYYoAD4NCAIMKFOBcBnndJghwEKgE8VhPw0djjxXL9+vRg3bpxo1qyZaNeunZg8ebIR819wC9ff//53cdlll4nGjRuLDh06iJkzZ4oDBw44puM1AQqAzwQEOcYugs8YBy8BpoPPBAQ5xi6CzxgHLwGmg88EBDnGLoLPGAcvAaaDzwQEOcYugs8YBy8Bpiecz/w2dji8bN++Xdx1113ijDPOUHNh9OzZU0ybNk2sXr06smGu9u3bJxYsWKB6nHDDS0FBgbjoootEWVmZOHz4sGMqXhOoAPhMYNBj5DL4jFGwEmgq+Exg0GPkMviMUbASaCr4TGDQY+Qy+IxRsBJoKvhMYNBj5DL4jFGwEmhqQvksK+BYy8m5Q1mqq6tp5cqV9Nxzz9H8+fPp008/pTZt2tAFF1yg/vr06UNnnXUWdezYMVD9cp4Qkq1WVFFRQatWraLXX3+d1qxZQ1z/gAEDaOTIkerv9NNPD1QPTrZLAfBpVzxt8wZ82hZRu/wBn3bF0zZvwKdtEbXLH/BpVzxt8wZ82hZRu/wBn3bF0zZvwKdtEbXLn4TxWR5qY0dtNLgxYtmyZbR8+XJasWIFbdu2TR0ie15Q9+7dSQ4vRZ07dyY5l4ZqFGnevDm1bNmS+HX//v3EjRr8KnttkByHjHbs2EFyDg76+OOPSQ5VRXLOEOrVqxcNHjyYBg0apP7at29f2wyUoYCvAuDTVxZsNEQB8GlIIGCGrwLg01cWbDREAfBpSCBghq8C4NNXFmw0RAHwaUggYIavAuDTVxZsNEQB8GlIIGCGrwKW8xltY0dthXft2kVr166ldevW0YYNG0h2r6GtW7eqRgxu0JDDTZGcV0M1ZLRq1YqaNm1KrVu3pqKiIjr11FNV40inTp1IDpNFJSUlVFxcrBpGateDMhTIRQHwmYtqOCcqBcBnVEqjnlwUAJ+5qIZzolIAfEalNOrJRQHwmYtqOCcqBcBnVEqjnlwUAJ+5qIZzolIAfEalNOrJRQHL+NTb2JFLAHAOFIACUAAKQAEoAAWgABSAAlAACkABKAAFoAAUgAJQAApAASgABVwKlDdyFbAKBaAAFIACUAAKQAEoAAWgABSAAlAACkABKAAFoAAUgAJQAApAgdgpgMaO2IUMBkMBKAAFoAAUgAJQAApAASgABaAAFIACUAAKQAEoAAWgABSAAm4F0NjhVgPrUAAKQAEoAAWgABSAAlAACkABKAAFoAAUgAJQAApAASgABaBA7BRoIi0uj53VMBgKQAEoAAWgABSAAlAACkABKAAFoAAUgAJQAApAASgABaAAFIACXyuw6n/Epl6dejGgRAAAAABJRU5ErkJggg==", "text/plain": [ - "" + "\"Output" ] }, "execution_count": 2, @@ -170,9 +171,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 4, @@ -182,7 +182,9 @@ ], "source": [ "from qiskit.synthesis import LieTrotter\n", - "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", + "from qiskit_addon_utils.problem_generators import (\n", + " generate_time_evolution_circuit,\n", + ")\n", "\n", "circuit = generate_time_evolution_circuit(\n", " hamiltonian,\n", @@ -280,9 +282,8 @@ }, { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 7, @@ -299,7 +300,9 @@ "observable = SparsePauliOp(\"IIIIIIIIIZ\")\n", "\n", "# Backpropagate slices onto the observable\n", - "bp_obs, remaining_slices, metadata = backpropagate(observable, slices, operator_budget=op_budget)\n", + "bp_obs, remaining_slices, metadata = backpropagate(\n", + " observable, slices, operator_budget=op_budget\n", + ")\n", "# Recombine the slices remaining after backpropagation\n", "bp_circuit = combine_slices(remaining_slices, include_barriers=True)\n", "\n", @@ -367,9 +370,8 @@ }, { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "execution_count": 9, @@ -380,11 +382,16 @@ "source": [ "# Run the same experiment but truncate observable terms with small coefficients\n", "bp_obs_trunc, remaining_slices_trunc, metadata = backpropagate(\n", - " observable, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", + " observable,\n", + " slices,\n", + " operator_budget=op_budget,\n", + " truncation_error_budget=truncation_error_budget,\n", ")\n", "\n", "# Recombine the slices remaining after backpropagation\n", - "bp_circuit_trunc = combine_slices(remaining_slices_trunc, include_barriers=True)\n", + "bp_circuit_trunc = combine_slices(\n", + " remaining_slices_trunc, include_barriers=True\n", + ")\n", "\n", "print(f\"Backpropagated {metadata.num_backpropagated_slices} slices.\")\n", "print(\n", @@ -475,23 +482,33 @@ "estimator = Estimator()\n", "\n", "# Run the experiments using Estimator primitive\n", - "result_exact = estimator.run([(circuit_isa, observable_isa)]).result()[0].data.evs.item()\n", - "result_bp = estimator.run([(bp_circuit_isa, bp_obs_isa)]).result()[0].data.evs.item()\n", + "result_exact = (\n", + " estimator.run([(circuit_isa, observable_isa)]).result()[0].data.evs.item()\n", + ")\n", + "result_bp = (\n", + " estimator.run([(bp_circuit_isa, bp_obs_isa)]).result()[0].data.evs.item()\n", + ")\n", "result_bp_trunc = (\n", - " estimator.run([(bp_circuit_trunc_isa, bp_obs_trunc_isa)]).result()[0].data.evs.item()\n", + " estimator.run([(bp_circuit_trunc_isa, bp_obs_trunc_isa)])\n", + " .result()[0]\n", + " .data.evs.item()\n", ")\n", "\n", "print(f\"Exact expectation value: {result_exact}\")\n", "print(f\"Backpropagated expectation value: {result_bp}\")\n", "print(f\"Backpropagated expectation value with truncation: {result_bp_trunc}\")\n", - "print(f\" - Expected Error for truncated observable: {metadata.accumulated_error(0):.3e}\")\n", - "print(f\" - Observed Error for truncated observable: {abs(result_exact - result_bp_trunc):.3e}\")" + "print(\n", + " f\" - Expected Error for truncated observable: {metadata.accumulated_error(0):.3e}\"\n", + ")\n", + "print(\n", + " f\" - Observed Error for truncated observable: {abs(result_exact - result_bp_trunc):.3e}\"\n", + ")" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -505,9 +522,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/public/docs/images/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm/extracted-outputs/d6f281d4-706e-41ad-b7b5-bc7ebe106996-0.avif b/public/docs/images/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm/extracted-outputs/d6f281d4-706e-41ad-b7b5-bc7ebe106996-0.avif new file mode 100644 index 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zjbqBYFfmkKVq8c|!E@u&niy+Aj4_ASuCcGFMM4*QFKaFXySl*~d7{`J@2BM}bb2jt%Tt_f&vqUWG?`n>@ zP%^3^8bnX-Avfx!pVuE5(!m5YsFa-0Y69Gh>yTngTc1qc6x4#$Ov18!Y|dXfpTg{v zAF8->;Qc2^6ckeI%45E_&=n99monpP5$5h)-mNKO$2SNA_!Ol5DxVmJa@VmS>aO-{ zl|)y4JPxMa$9vL_%Klg}7$yD$qHKao*_V_JlBzC@dfo`ya4|LIK_#filg1;j{wZUL zxtFC*VeAfJz=4ktAGX=^#}V4Uk0VSUPWorUAl^tiDd=(jwF6_GtQz(3D4x9eeoALg R+C; export const ADDON_DOCS_CONFIG: AddonDocsConfig = { + "qiskit-addon-aqc-tensor": { + version: "0.2.0", + include: [ + "index.html", + "install.html", + "release-notes.html", + "how-tos/*.ipynb", + "tutorials/*.ipynb", + "explanation/index.html", + ], + }, "qiskit-addon-mpf": { version: "0.3.0", include: [ @@ -24,6 +35,16 @@ export const ADDON_DOCS_CONFIG: AddonDocsConfig = { "explanations/*.ipynb", ], }, + "qiskit-addon-sqd": { + version: "0.12.0", + include: [ + "index.html", + "install.html", + "release-notes.html", + "how_tos/*.ipynb", + "tutorials/*.ipynb", + ], + }, "qiskit-addon-obp": { version: "0.3.0", include: [ @@ -34,4 +55,24 @@ export const ADDON_DOCS_CONFIG: AddonDocsConfig = { "tutorials/*.ipynb", ], }, + "qiskit-addon-cutting": { + version: "0.10.0", + include: [ + "index.html", + "install.html", + "release-notes.html", + "how-tos/*.ipynb", + "tutorials/*.ipynb", + "explanation/index.html", + ], + }, + "qiskit-addon-utils": { + version: "0.3.1", + include: [ + "index.html", + "install.html", + "release-notes.html", + "how-tos/*.ipynb", + ], + }, }; diff --git a/scripts/js/lib/addons/addonContentPipeline.ts b/scripts/js/lib/addons/addonContentPipeline.ts index d4a701bc563..95b52f87e95 100644 --- a/scripts/js/lib/addons/addonContentPipeline.ts +++ b/scripts/js/lib/addons/addonContentPipeline.ts @@ -75,7 +75,7 @@ export async function runAddonContentPipeline( await saveImages( uniqBy(results.flatMap((r) => r.images), (img) => img.fileName), `${htmlPath}/_images`, - publicBaseFolder, + "public", pkg, ); await copyNotebooks(htmlPath, outputDir, notebookFiles, stubsMap, pkg.name); @@ -91,7 +91,7 @@ async function convertProseFiles( ): Promise { const results: HtmlToMdResultWithUrl[] = []; const outputDir = `${docsBaseFolder}/addons/${pkg.name}`; - const imageDestination = `${publicBaseFolder}/images/addons/${pkg.name}`; + const imageDestination = `docs/images/addons/${pkg.name}`; for (const file of filePaths) { const html = await readFile(`${htmlPath}/${file}`, "utf-8"); diff --git a/scripts/js/lib/api/processHtml.ts b/scripts/js/lib/api/processHtml.ts index eafa4942eac..df5e27e19d5 100644 --- a/scripts/js/lib/api/processHtml.ts +++ b/scripts/js/lib/api/processHtml.ts @@ -114,7 +114,11 @@ export function loadImages( return $main .find("img") .toArray() - .filter((img) => $(img).attr("src")) + .filter((img) => { + // filter out external URLs before processing: + const src = $(img).attr("src"); + return !!src && !src.startsWith("http://") && !src.startsWith("https://"); + }) .map((img) => { const $img = $(img); @@ -526,6 +530,9 @@ export function preserveMathBlockWhitespace( .toArray() .map((el) => { const $el = $(el); + // Remove equation number labels — the anchor IDs are on the parent divs, + // not the eqno span, so links to equations still resolve correctly. + $el.find("span.eqno").remove(); $el.replaceWith(`

${$el.html()}
`); }); } diff --git a/scripts/js/lib/api/saveImages.ts b/scripts/js/lib/api/saveImages.ts index 2cdff78ed94..51e8cc3f83e 100644 --- a/scripts/js/lib/api/saveImages.ts +++ b/scripts/js/lib/api/saveImages.ts @@ -11,7 +11,7 @@ // that they have been altered from the originals. import { copyFile } from "fs/promises"; -import { extname } from "node:path"; +import { dirname, extname } from "node:path"; import pMap from "p-map"; import { $ } from "zx"; @@ -64,6 +64,7 @@ export async function saveImages( const source = `${originalImagesFolderPath}/${img.fileName}`; const dest = `${publicBaseFolder}/${img.dest}`; + await mkdirp(dirname(dest)); if (extname(source) === extname(dest)) { await copyFile(source, dest); } else { From a51e7c4d9838d56813555e968c929109b772d54b Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Thu, 30 Apr 2026 18:03:19 -0400 Subject: [PATCH 008/104] fix addon utils includes --- .../tutorials/04_automatic_cut_finding.ipynb | 40 +- .../02_fermionic_lattice_hamiltonian.ipynb | 54 +-- .../color_device_edges_to_improve_depth.ipynb | 288 +++++++++++++++ .../how_tos/create_circuit_slices.ipynb | 347 ++++++++++++++++++ ...22fe96-e627-4648-99cd-5113399009bb-0.avif} | Bin ...255310-93cd-48e9-b06c-fdbb1403f1eb-0.avif} | Bin ...a9ae2a-e7d2-4c50-8559-502ead4a0923-0.avif} | Bin ...9f1001-04c9-4ae6-8ab0-4e2020050b67-1.avif} | Bin ...e2c8fa-bdb0-4713-9990-7791bcd950ef-0.avif} | Bin ...05546d-d33b-449f-918e-a17b1e6ec6ba-1.avif} | Bin ...3f1e21-dd43-4534-92ed-818ae0c29e87-0.avif} | Bin ...c00cd0-07b0-4bbc-9845-ddfc280e9887-0.avif} | Bin ...470344-b123-4fe0-a962-12d503cadba0-0.avif} | Bin ...3a5f389-94c0-4496-9fe6-262dca62a0e3-1.avif | Bin 0 -> 29995 bytes ...806ec31-05e8-4a4c-832b-888150506191-1.avif | Bin 0 -> 168928 bytes ...b986eac-93c6-453a-bfe1-2546681ab38b-0.avif | Bin 0 -> 75832 bytes ...3f482a1-6868-4cda-b8ed-8a8489794707-0.avif | Bin 0 -> 77450 bytes ...10b5844-972e-46d1-bfc5-20a30b69f814-0.avif | Bin 0 -> 4011 bytes ...a5d5aff-f988-4f14-ad25-9f0105202b77-0.avif | Bin 0 -> 4062 bytes ...91bd07b-739a-45ec-a3e9-2d068f8bd960-0.avif | Bin 0 -> 4711 bytes ...48aa715-22e8-41e1-943d-de7eb0aa379c-0.avif | Bin 0 -> 3584 bytes ...da8d173-8ce9-47a9-a24f-ab32e45f8337-0.avif | Bin 0 -> 3240 bytes ...f766d69-e10c-4aa8-a3bc-2057dab7a69a-0.avif | Bin 0 -> 2867 bytes ...00c8743-f3b9-460c-ba2c-ffc634e99e0e-0.avif | Bin 0 -> 2452 bytes ...c5ebecb-6c8b-4f51-8199-b236a0ba3328-0.avif | Bin 0 -> 4078 bytes ...883fa2e-f943-4c49-af45-fb26cadb72b6-0.avif | Bin 0 -> 4493 bytes scripts/config/addon-docs.ts | 2 +- 27 files changed, 683 insertions(+), 48 deletions(-) create mode 100644 docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth.ipynb create mode 100644 docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices.ipynb rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding/extracted-outputs/{3272590c-2155-4058-8a69-7805f0ae2b9d-0.avif => 0322fe96-e627-4648-99cd-5113399009bb-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding/extracted-outputs/{aab2325a-6043-45c3-9240-963412e15784-0.avif => 5b255310-93cd-48e9-b06c-fdbb1403f1eb-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding/extracted-outputs/{41f67453-4a5e-4f93-965c-1fd63570b868-0.avif => 60a9ae2a-e7d2-4c50-8559-502ead4a0923-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding/extracted-outputs/{580dbe6d-9a91-4349-9bee-d5c22d742d45-1.avif => b49f1001-04c9-4ae6-8ab0-4e2020050b67-1.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding/extracted-outputs/{bef68fc6-0626-45ff-b881-19314a943601-0.avif => e2e2c8fa-bdb0-4713-9990-7791bcd950ef-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian/extracted-outputs/{45b2ca6b-c570-4724-b811-e42e25ef7918-1.avif => 5b05546d-d33b-449f-918e-a17b1e6ec6ba-1.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian/extracted-outputs/{6060559e-3520-4f3d-bd12-afbae24f171b-0.avif => 663f1e21-dd43-4534-92ed-818ae0c29e87-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian/extracted-outputs/{a9b37df5-8a09-46d1-ac9c-56336ef95669-0.avif => 89c00cd0-07b0-4bbc-9845-ddfc280e9887-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian/extracted-outputs/{22b7a11c-bdc6-4767-885a-569fd981c69f-0.avif => 96470344-b123-4fe0-a962-12d503cadba0-0.avif} (100%) create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth/extracted-outputs/03a5f389-94c0-4496-9fe6-262dca62a0e3-1.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth/extracted-outputs/2806ec31-05e8-4a4c-832b-888150506191-1.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth/extracted-outputs/9b986eac-93c6-453a-bfe1-2546681ab38b-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth/extracted-outputs/c3f482a1-6868-4cda-b8ed-8a8489794707-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/create_circuit_slices/extracted-outputs/110b5844-972e-46d1-bfc5-20a30b69f814-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/create_circuit_slices/extracted-outputs/1a5d5aff-f988-4f14-ad25-9f0105202b77-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/create_circuit_slices/extracted-outputs/791bd07b-739a-45ec-a3e9-2d068f8bd960-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/create_circuit_slices/extracted-outputs/948aa715-22e8-41e1-943d-de7eb0aa379c-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/create_circuit_slices/extracted-outputs/9da8d173-8ce9-47a9-a24f-ab32e45f8337-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/create_circuit_slices/extracted-outputs/9f766d69-e10c-4aa8-a3bc-2057dab7a69a-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/create_circuit_slices/extracted-outputs/a00c8743-f3b9-460c-ba2c-ffc634e99e0e-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/create_circuit_slices/extracted-outputs/bc5ebecb-6c8b-4f51-8199-b236a0ba3328-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how_tos/create_circuit_slices/extracted-outputs/f883fa2e-f943-4c49-af45-fb26cadb72b6-0.avif diff --git a/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb index 985d1785977..9d82f0c3a5d 100644 --- a/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb +++ b/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "49524788-7b8e-4499-9108-83dfc4d3db79", + "id": "5fea9061-062a-4671-aaa5-ba1d57be19da", "metadata": {}, "source": [ "# Automatically find cuts" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "c45659c6-0948-44af-926b-afcd2773c46a", + "id": "4333fa28-4aea-4e25-a060-a669e85a68ee", "metadata": {}, "source": [ "## Step 1: Map\n", @@ -21,7 +21,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "3272590c-2155-4058-8a69-7805f0ae2b9d", + "id": "0322fe96-e627-4648-99cd-5113399009bb", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:55.792854Z", @@ -34,7 +34,7 @@ { "data": { "text/plain": [ - "\"Output" + "\"Output" ] }, "execution_count": 1, @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "a37a82a8-0fe7-4a74-b271-c5dd750106cc", + "id": "a3234a89-ba6d-4b53-8aec-79142956defd", "metadata": {}, "source": [ "## Step 2: Optimize\n", @@ -67,7 +67,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "580dbe6d-9a91-4349-9bee-d5c22d742d45", + "id": "b49f1001-04c9-4ae6-8ab0-4e2020050b67", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:56.802390Z", @@ -96,7 +96,7 @@ { "data": { "text/plain": [ - "\"Output" + "\"Output" ] }, "execution_count": 2, @@ -130,7 +130,7 @@ }, { "cell_type": "markdown", - "id": "11e995ae-6f8b-448a-a895-b9ce22e41a9e", + "id": "84ea51fd-1935-434c-acdd-4b7db6daf434", "metadata": {}, "source": [ "#### Add ancillas for wire cuts and expand the observables to account for ancilla qubits" @@ -139,7 +139,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "41f67453-4a5e-4f93-965c-1fd63570b868", + "id": "60a9ae2a-e7d2-4c50-8559-502ead4a0923", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.491081Z", @@ -152,7 +152,7 @@ { "data": { "text/plain": [ - "\"Output" + "\"Output" ] }, "execution_count": 3, @@ -170,7 +170,7 @@ }, { "cell_type": "markdown", - "id": "7bb0a17b-2213-4ac3-a09f-db61ee1bc6d7", + "id": "21e66074-f582-49b0-8a8c-f6a6c12047ba", "metadata": {}, "source": [ "#### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires." @@ -179,7 +179,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "80736dfb-a5f4-43b9-81d5-1731f32ac501", + "id": "1409f406-ab1f-418d-b5ab-b76796fa8031", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.755174Z", @@ -213,7 +213,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "65155899-b8d7-4509-a86a-4f11a8bb86ff", + "id": "6ddec420-a8a4-4b71-9672-4704b0562234", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.770081Z", @@ -242,7 +242,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "aab2325a-6043-45c3-9240-963412e15784", + "id": "5b255310-93cd-48e9-b06c-fdbb1403f1eb", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.776009Z", @@ -255,7 +255,7 @@ { "data": { "text/plain": [ - "\"Output" + "\"Output" ] }, "execution_count": 6, @@ -270,7 +270,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "bef68fc6-0626-45ff-b881-19314a943601", + "id": "e2e2c8fa-bdb0-4713-9990-7791bcd950ef", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.933278Z", @@ -283,7 +283,7 @@ { "data": { "text/plain": [ - "\"Output" + "\"Output" ] }, "execution_count": 7, @@ -297,7 +297,7 @@ }, { "cell_type": "markdown", - "id": "e45c0046-c8c6-4e27-b6f6-30d71c6c2645", + "id": "73004424-5863-464e-a35e-41d9d6bba1c5", "metadata": {}, "source": [ "#### Generate the experiments to run on the backend." @@ -306,7 +306,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "ec23c94c-0969-4964-a30d-275c21dacbe0", + "id": "627b2304-d72c-49eb-adc0-c886909f925b", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:53:00.112773Z", @@ -337,7 +337,7 @@ }, { "cell_type": "markdown", - "id": "8c548ee8-7c46-4a22-b18a-84e69bdd23aa", + "id": "88e64707-57d9-440b-b623-3ee453f5560d", "metadata": {}, "source": [ "Steps 3 and 4 of a [Qiskit pattern](/guides/intro-to-patterns) can then be performed as in the preceding tutorials." diff --git a/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb index e80a745695a..03b87c3c002 100644 --- a/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb +++ b/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e95ccc49-13da-4dc5-8bc8-e8c313d5c868", + "id": "014ac1a2-2060-4274-92c9-63e5431e5226", "metadata": {}, "source": [ "# Improving energy estimation of a Fermionic lattice model with SQD\n", @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "c324e5a3-9f5f-4fa0-ab16-10c1b21227de", + "id": "435bd534-2431-438e-b53a-ec935a25fac0", "metadata": {}, "source": [ "### Step 1: Map problem to a quantum circuit" @@ -35,7 +35,7 @@ }, { "cell_type": "markdown", - "id": "0c4a7125-fac3-4876-84fe-4eaf52313b95", + "id": "de134b7d-bfa1-45c6-9dd1-736e94038b3a", "metadata": {}, "source": [ "First, we will create the one- and two-body Hamiltonians describing the one-dimensional single-impurity Anderson model (SIAM) with ``7`` bath sites (``8`` electrons in ``8`` orbitals). This model is used to describe magnetic impurities embedded in metals.\n", @@ -46,7 +46,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "81c1127b-82fc-453b-b77b-7824a096dfe3", + "id": "eba34f72-77da-41d8-89ba-d05fcb3ff129", "metadata": {}, "outputs": [], "source": [ @@ -75,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "546ddc26-b91a-46f4-8dc0-f3987626dd60", + "id": "bd1a9d09-54cf-4423-92ce-6e38d88a00f6", "metadata": {}, "source": [ "Next, we will generate the quantum Krylov subspace with a set of Trotterized quantum circuits. Here we create helpers for generating the initial (reference) state as well as the time evolution for the one- and two-body parts of the Hamiltonian. For a more detailed description of this model and how the circuits are designed, please refer to [the paper](https://arxiv.org/abs/2501.09702)." @@ -84,7 +84,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "0ca03aea-723c-41b6-aab7-6c021f558c9b", + "id": "df328264-0c9f-4093-b87c-18ccb27fbeba", "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "markdown", - "id": "8ee0bd78-cc2e-4a81-9e21-3eff832399a9", + "id": "be7484ab-921f-4c05-94ac-b47e17a20235", "metadata": {}, "source": [ "Generate ``d`` time-evolved states that specify the quantum Krylov subspace. Here, we have chosen ``d=8``. The error from sampling Krylov basis states converges with increasing ``d``. Note that the particulars of this problem instance allow for an efficient compilation of the one-body evolution using `OrbitalRotationJW`; however, in general, one could use Qiskit's [PauliEvolutionGate](/docs/api/qiskit/qiskit.circuit.library.PauliEvolutionGate) to implement the Trotterized time evolution of the full Hamiltonian." @@ -141,7 +141,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "95cc557d-daf4-4489-ae55-ce37ba7e6398", + "id": "00c75502-36b6-4214-a484-0e3381b29e41", "metadata": {}, "outputs": [], "source": [ @@ -166,13 +166,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "a9b37df5-8a09-46d1-ac9c-56336ef95669", + "id": "89c00cd0-07b0-4bbc-9845-ddfc280e9887", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "\"Output" + "\"Output" ] }, "execution_count": 4, @@ -187,13 +187,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "22b7a11c-bdc6-4767-885a-569fd981c69f", + "id": "96470344-b123-4fe0-a962-12d503cadba0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "\"Output" + "\"Output" ] }, "execution_count": 5, @@ -207,7 +207,7 @@ }, { "cell_type": "markdown", - "id": "efc1ec7a-621d-4833-8087-252b82cfcbf1", + "id": "8fff210c-4786-4ade-9d12-c87101fbf767", "metadata": {}, "source": [ "### Step 2: Optimize the problem" @@ -215,7 +215,7 @@ }, { "cell_type": "markdown", - "id": "b2a21d4b-ec8b-4803-bfe8-8686a9d12e06", + "id": "6943d6b0-89de-4a87-b83b-e6b6ed981b88", "metadata": {}, "source": [ "After we have created the Trotterized circuits, we will optimize them for a target hardware. We need to choose the hardware device to use before optimization. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." @@ -224,7 +224,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "44010b25-740d-454e-b7e9-ea95ffce211e", + "id": "fcd03338-9cde-4608-a0b6-cfb221e1aeac", "metadata": {}, "outputs": [], "source": [ @@ -235,7 +235,7 @@ }, { "cell_type": "markdown", - "id": "0d4f202a-a487-41c8-8567-a313e92bc03d", + "id": "ec99f5bd-1bf8-4b0f-b48d-28fe45ce5f2c", "metadata": {}, "source": [ "Next, we will transpile the circuits to the target backend using Qiskit." @@ -244,7 +244,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "f12c69fb-d308-4092-ba87-689ff4f1192a", + "id": "31840bfc-06e9-49d3-9845-40729406c107", "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ }, { "cell_type": "markdown", - "id": "7bf409f6-5581-48a1-9bf9-ae10561f844c", + "id": "473d09df-97ca-4db8-b1d2-66e9429d3c6e", "metadata": {}, "source": [ "### Step 3: Execute experiments" @@ -265,7 +265,7 @@ }, { "cell_type": "markdown", - "id": "a5bb4ecb-a094-44da-a483-79b1b6c67572", + "id": "6328511a-0465-45c6-b75b-4ebc243c8857", "metadata": {}, "source": [ "After optimizing the circuits for hardware execution, we are ready to run them on the target hardware and collect samples for ground state energy estimation. Here we use ``SamplerV2`` from ``qiskit-ibm-runtime`` to simulate noisy samples taken from the ``ibm_sherbrooke`` backend. We then combine the counts from each of the Krylov basis states into a single counts dictionary and plot the top 20 most commonly sampled bitstrings.\n", @@ -276,13 +276,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "6060559e-3520-4f3d-bd12-afbae24f171b", + "id": "663f1e21-dd43-4534-92ed-818ae0c29e87", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "\"Output" + "\"Output" ] }, "execution_count": 8, @@ -307,7 +307,7 @@ }, { "cell_type": "markdown", - "id": "b89a4190-eea3-4b09-8202-755655126e57", + "id": "57159228-cde5-4e7b-b182-644a0f6b9be0", "metadata": {}, "source": [ "### Step 4: Post-process the results" @@ -315,7 +315,7 @@ }, { "cell_type": "markdown", - "id": "ac0f8e6a-ce60-40ce-be3b-efce3c74bcba", + "id": "4ffde66b-34f6-4907-8fba-d5e791175342", "metadata": {}, "source": [ "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function." @@ -324,7 +324,7 @@ { "cell_type": "code", "execution_count": null, - "id": "eee1179e-2947-42f0-97d5-49c91ce551ef", + "id": "8ba7ec2b-7ffd-42dc-92fa-d7737bff82a3", "metadata": {}, "outputs": [ { @@ -409,7 +409,7 @@ }, { "cell_type": "markdown", - "id": "ba6c24e6-af10-4f2a-9ad2-ccf9c2785de8", + "id": "85556909-385f-41f5-af52-261fe461a5e2", "metadata": {}, "source": [ "Now, we plot the results. The first plot shows that after a few iterations, we obtain the exact ground state energy.\n", @@ -422,7 +422,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "45b2ca6b-c570-4724-b811-e42e25ef7918", + "id": "5b05546d-d33b-449f-918e-a17b1e6ec6ba", "metadata": {}, "outputs": [ { @@ -437,7 +437,7 @@ { "data": { "text/plain": [ - "\"Output" + "\"Output" ] }, "metadata": {}, diff --git a/docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth.ipynb b/docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth.ipynb new file mode 100644 index 00000000000..cd02ef4e0d4 --- /dev/null +++ b/docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth.ipynb @@ -0,0 +1,288 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4de02100-ba7b-4ac3-8e92-f94844073337", + "metadata": {}, + "source": [ + "# Use edge coloring to reduce the depth of quantum circuits\n", + "\n", + "This guide will show how to use the [qiskit_addon_utils.coloring](/docs/addons/qiskit-addon-utils/apidocs/qiskit_addon_utils.coloring) module to color edges of a coupling map and use the coloring to more efficiently place gates in a quantum circuit, leading to shallower circuits.\n", + "\n", + "The point of edge-coloring a graph is to find a set of edge colors such that no two edges of the same color share a common node. For a quantum processor, this means that gates along like-colored edges (qubit connections) may be run simultaneously and that the circuit will execute faster. We can use this to define our circuit in a way that is more amenable to the hardware it should run on." + ] + }, + { + "cell_type": "markdown", + "id": "4b16cf04-941e-4b02-932e-f5e6301b9a5b", + "metadata": {}, + "source": [ + "First, we will specify a backend and visualize its qubit topology." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9da8d173-8ce9-47a9-a24f-ab32e45f8337", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeSherbrooke\n", + "\n", + "backend = FakeSherbrooke()\n", + "coupling_map = backend.coupling_map" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9b986eac-93c6-453a-bfe1-2546681ab38b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from rustworkx.visualization import graphviz_draw\n", + "\n", + "graphviz_draw(coupling_map.graph, method=\"neato\")" + ] + }, + { + "cell_type": "markdown", + "id": "eb4e5115-f2a9-4c62-8c5d-96f6bb6c8762", + "metadata": {}, + "source": [ + "Next we use the [qiskit_addon_utils.coloring.auto_color_edges](/docs/api/qiskit-addon-utils/coloring#qiskit_addon_utils.coloring.auto_color_edges#qiskit_addon_utils.coloring.auto_color_edges) function to generate an edge coloring. This is a light wrapper for [rustworkx.graph_greedy_edge_color](https://www.rustworkx.org/dev/apiref/rustworkx.graph_greedy_edge_color.html) that does some minor post-processing of the output. Namely, this function removes an arbitrary edge direction if an edge is bi-directional and combines the edge and color information into a dictionary format." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6700931f-cf94-4ad6-8012-d73c0bfd7efa", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 144 edges and 3 unique colors.\n", + "The edges are specified by length-2 tuples: (e.g. (1, 0)). \n", + "The \"colors\" are actually specified by a set of integers: {0, 1, 2}\n" + ] + } + ], + "source": [ + "from qiskit_addon_utils.coloring import auto_color_edges\n", + "from rustworkx import PyDiGraph\n", + "\n", + "coloring = auto_color_edges(coupling_map)\n", + "\n", + "# Create a colored graph for visualization\n", + "eagle_r3 = PyDiGraph()\n", + "eagle_r3.extend_from_weighted_edge_list(\n", + " [(source, target, color) for ((source, target), color) in coloring.items()]\n", + ")\n", + "print(f\"There are {len(coloring)} edges and {len(set(coloring.values()))} unique colors.\")\n", + "print(\n", + " f'The edges are specified by length-2 tuples: (e.g. {next(iter(coloring))}).\\\n", + " \\nThe \"colors\" are actually specified by a set of integers: {set(coloring.values())}'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c3f482a1-6868-4cda-b8ed-8a8489794707", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def color_edge_3color(edge):\n", + " \"\"\"Return the color of an edge.\"\"\"\n", + " # Map integers of a 3-coloring to color names\n", + " color_dict = {0: \"red\", 1: \"green\", 2: \"blue\"}\n", + " return {\"color\": color_dict[edge]}\n", + "\n", + "\n", + "graphviz_draw(eagle_r3, edge_attr_fn=color_edge_3color, method=\"neato\")" + ] + }, + { + "cell_type": "markdown", + "id": "90d56de5-d43e-4509-8c68-f42ec0f198bb", + "metadata": {}, + "source": [ + "Next, let's make a circuit with a ``CZGate`` on each edge and observe its depth. For this first circuit, we will just naively place a gate on each edge. We will place barriers in ``depth-1`` increments (using [qiskit_addon_utils.slicing.slice_by_depth](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.slice_by_depth)) to illustrate how crucial edge-coloring is when designing a hardware-efficient quantum circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2806ec31-05e8-4a4c-832b-888150506191", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The circuit has depth: 37\n" + ] + }, + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit\n", + "from qiskit_addon_utils.slicing import combine_slices, slice_by_depth\n", + "\n", + "circuit = QuantumCircuit(backend.num_qubits)\n", + "\n", + "for edge in coupling_map.graph.edge_list():\n", + " circuit.cz(edge[0], edge[1])\n", + "\n", + "print(f\"The circuit has depth: {circuit.depth()}\")\n", + "\n", + "# Add barriers in depth-1 increments\n", + "slices = slice_by_depth(circuit, max_slice_depth=1)\n", + "circuit = combine_slices(slices, include_barriers=True)\n", + "\n", + "circuit.draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "15cf56a0-954e-482a-8ff8-4645b7d18444", + "metadata": {}, + "source": [ + "Now let's place all of the gates of a given color at the same time. We can see the depth is reduced from ``37`` to ``3``. This is because we can take advantage of the fact that all gates on a given color can run at the same time." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "03a5f389-94c0-4496-9fe6-262dca62a0e3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The circuit has depth: 3\n" + ] + }, + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import defaultdict\n", + "\n", + "circuit = QuantumCircuit(backend.num_qubits)\n", + "\n", + "# Make a reverse coloring dict\n", + "color_to_edge = defaultdict(list)\n", + "for edge, color in coloring.items():\n", + " color_to_edge[color].append(edge)\n", + "\n", + "# Place edges in order of color\n", + "for edges in color_to_edge.values():\n", + " for edge in edges:\n", + " circuit.cz(edge[0], edge[1])\n", + "\n", + "print(f\"The circuit has depth: {circuit.depth(lambda x: x.name != 'barrier')}\")\n", + "\n", + "# Add barriers in depth-1 increments\n", + "slices = slice_by_depth(circuit, max_slice_depth=1)\n", + "circuit = combine_slices(slices, include_barriers=True)\n", + "\n", + "circuit.draw(\"mpl\", fold=-1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + }, + "toc": { + "base_numbering": 0 + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices.ipynb b/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices.ipynb new file mode 100644 index 00000000000..64e11ef578d --- /dev/null +++ b/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices.ipynb @@ -0,0 +1,347 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4de02100-ba7b-4ac3-8e92-f94844073337", + "metadata": {}, + "source": [ + "# Slicing circuits using ``qiskit_addon_utils.slicing``\n", + "\n", + "Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. Some Qiskit addons make use of the term **slices** to describe layers of arbitrary depth. More concretely, slices can be defined as time-like partitions of a [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) which span all qubits. Similar to layers, composing all slices of a `QuantumCircuit` produces a circuit which is semantically equivalent to the original." + ] + }, + { + "cell_type": "markdown", + "id": "9f419feb-5d35-489e-909c-c8e7ea2caaec", + "metadata": {}, + "source": [ + "The [qiskit_addon_utils.slicing](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.slicing) module provides a few utilities for partitioning ``QuantumCircuit``s into slices. This is for example useful for [operator backpropagation](https://github.com/qiskit/qiskit-addon-obp/) We will give an overview of those tools in this guide.\n", + "\n", + "**Note:** Throughout this guide, we will slice the circuit and use [qiskit_addon_utils.slicing.combine_slices](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.combine_slices) to recombine the slices with barriers to make it easier to visualize the slices." + ] + }, + { + "cell_type": "markdown", + "id": "c6c74021-216c-43fd-b37a-91817e3d3978", + "metadata": {}, + "source": [ + "First, we'll create a circuit from which we'll create slices." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9da8d173-8ce9-47a9-a24f-ab32e45f8337", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from qiskit import QuantumCircuit\n", + "\n", + "num_qubits = 9\n", + "qc = QuantumCircuit(num_qubits)\n", + "qc.ry(np.pi / 4, range(num_qubits))\n", + "qubits_1 = [i for i in range(num_qubits) if i % 2 == 0]\n", + "qubits_2 = [i for i in range(num_qubits) if i % 2 == 1]\n", + "qc.cx(qubits_1[:-1], qubits_2)\n", + "qc.cx(qubits_2, qubits_1[1:])\n", + "qc.cx(qubits_1[-1], qubits_1[0])\n", + "qc.rx(np.pi / 4, range(num_qubits))\n", + "qc.rz(np.pi / 4, range(num_qubits))\n", + "qc.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "968d1e85-ab12-4383-865f-f0199308a9f2", + "metadata": {}, + "source": [ + "In the case where there is no clear way to exploit the structure of the circuit for back-propagation, a user may wish to simply partition their circuit into slices of a given depth. Here, we'll separate this circuit into depth-1 slices." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f883fa2e-f943-4c49-af45-fb26cadb72b6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_utils.slicing import combine_slices, slice_by_depth\n", + "\n", + "slices = slice_by_depth(qc, 1)\n", + "combined_slices = combine_slices(slices, include_barriers=True)\n", + "combined_slices.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "4debef73-ba54-4eb1-887c-df6d022c9082", + "metadata": {}, + "source": [ + "Now let's try depth-2." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bc5ebecb-6c8b-4f51-8199-b236a0ba3328", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slices = slice_by_depth(qc, 2)\n", + "combined_slices = combine_slices(slices, include_barriers=True)\n", + "combined_slices.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "482bc11c-47a3-45ba-9633-98bcca5b44a9", + "metadata": {}, + "source": [ + "In many cases, such as Trotter circuits, it may be advantageous to slice by gate type. Slices holding a given gate type will be further split out into depth-1 slices, as there is little downside in doing so." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1a5d5aff-f988-4f14-ad25-9f0105202b77", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_utils.slicing import slice_by_gate_types\n", + "\n", + "slices = slice_by_gate_types(qc)\n", + "combined_slices = combine_slices(slices, include_barriers=True)\n", + "combined_slices.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "5e389e7e-1d29-4e9c-b44e-a5182da5cb5b", + "metadata": {}, + "source": [ + "If your circuit was designed to exploit the physical qubit connectivity, you may want to create slices based on an edge coloring. Here, we will assign a 3-coloring to the circuit edges and slice the circuit with respect to the edge coloring. This only affects non-local gates. Single qubit gates will be added to their own slices by gate type." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "110b5844-972e-46d1-bfc5-20a30b69f814", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_utils.slicing import slice_by_coloring\n", + "\n", + "# Assign a color to each set of connected qubits\n", + "coloring = {}\n", + "for i in range(num_qubits - 1):\n", + " coloring[(i, i + 1)] = i % 3\n", + "coloring[(num_qubits - 1, 0)] = 2\n", + "\n", + "# Create a circuit with operations added in order of color\n", + "qc = QuantumCircuit(num_qubits)\n", + "qc.ry(np.pi / 4, range(num_qubits))\n", + "edges = [edge for color in range(3) for edge in coloring if coloring[edge] == color]\n", + "for edge in edges:\n", + " qc.cx(edge[0], edge[1])\n", + "qc.rx(np.pi / 4, range(num_qubits))\n", + "qc.rz(np.pi / 4, range(num_qubits))\n", + "\n", + "# Create slices by edge color\n", + "slices = slice_by_coloring(qc, coloring=coloring)\n", + "combined_slices = combine_slices(slices, include_barriers=True)\n", + "combined_slices.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "d05b0cad-bbed-48cd-8520-3e2904cde058", + "metadata": {}, + "source": [ + "For more custom slicing strategies a user may wish to place barriers in the locations they want to slice and use the [slice_by_barriers](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.slice_by_barriers) function. Here, we will create 3 slices, one for each rotation layer and one for the entangling layer." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "948aa715-22e8-41e1-943d-de7eb0aa379c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = QuantumCircuit(num_qubits)\n", + "qc.ry(np.pi / 4, range(num_qubits))\n", + "qc.barrier()\n", + "qubits_1 = [i for i in range(num_qubits) if i % 2 == 0]\n", + "qubits_2 = [i for i in range(num_qubits) if i % 2 == 1]\n", + "qc.cx(qubits_1[:-1], qubits_2)\n", + "qc.cx(qubits_2, qubits_1[1:])\n", + "qc.cx(qubits_1[-1], qubits_1[0])\n", + "qc.barrier()\n", + "qc.rx(np.pi / 4, range(num_qubits))\n", + "qc.rz(np.pi / 4, range(num_qubits))\n", + "qc.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "6511c580-bcc6-462c-9d17-2435adae311a", + "metadata": {}, + "source": [ + "We will not draw the re-combined slices as a single circuit since it would look identical to the input circuit. Instead, below we draw each slice on its own." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9f766d69-e10c-4aa8-a3bc-2057dab7a69a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_utils.slicing import slice_by_barriers\n", + "\n", + "slices = slice_by_barriers(qc)\n", + "slices[0].draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a00c8743-f3b9-460c-ba2c-ffc634e99e0e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slices[1].draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "791bd07b-739a-45ec-a3e9-2d068f8bd960", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slices[2].draw(\"mpl\", scale=0.6)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/public/docs/images/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding/extracted-outputs/3272590c-2155-4058-8a69-7805f0ae2b9d-0.avif b/public/docs/images/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding/extracted-outputs/0322fe96-e627-4648-99cd-5113399009bb-0.avif similarity index 100% rename from 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    CYW%WTX{S@?L(z>wEQ27FaudE7g4?e$|ra8ULDOS-x2$d_bi9 zX(Qz>YXFV7QAf`Dy+;Q9(MjiNbhNYa-Vu=Z#I{%O*9>KTeWtvLU(PvApi_GZmaH%G z-$#JN$TcZNfze!6levveXR4m}TyDkj6h+UIc#ampwSh&=xuRR-+m(Qvxs7iKb;C}bUGz6N_v_C+h|RrKYv{!%%@1gXKI^TZDarrLV!MeHB+D+UxHl1k=)>iUVi_~AaflH^|V}#@Ni=hb2S03;0SDr9Jm<>aC<=g0HBtuS%`v?~Ay}~^_K2?-D OD$+{ZE-fTUmH!{bj2Gnq literal 0 HcmV?d00001 diff --git a/scripts/config/addon-docs.ts b/scripts/config/addon-docs.ts index 5662d57d00d..bd9a79250a3 100644 --- a/scripts/config/addon-docs.ts +++ b/scripts/config/addon-docs.ts @@ -72,7 +72,7 @@ export const ADDON_DOCS_CONFIG: AddonDocsConfig = { "index.html", "install.html", "release-notes.html", - "how-tos/*.ipynb", + "how_tos/*.ipynb", ], }, }; From 48c4b9195129283afb5091d1b3c11b8a3ade8338 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Tue, 5 May 2026 16:27:05 -0400 Subject: [PATCH 009/104] move to single pipeline --- .../commands/docs/qiskit-addon-aqc-tensor.ts | 41 +++ .../js/commands/docs/qiskit-addon-cutting.ts | 41 +++ scripts/js/commands/docs/qiskit-addon-mpf.ts | 41 +++ scripts/js/commands/docs/qiskit-addon-obp.ts | 41 +++ scripts/js/commands/docs/qiskit-addon-sqd.ts | 41 +++ .../js/commands/docs/qiskit-addon-utils.ts | 41 +++ scripts/js/commands/docs/qiskit-c.ts | 32 ++ .../js/commands/docs/qiskit-ibm-runtime.ts | 29 ++ .../js/commands/docs/qiskit-ibm-transpiler.ts | 29 ++ scripts/js/commands/docs/qiskit.ts | 29 ++ scripts/js/commands/docs/utils.ts | 75 +++++ scripts/js/commands/updateDocs.ts | 69 ++++ scripts/js/lib/sphinx/conversionPipeline.ts | 314 ++++++++++++++++++ scripts/js/lib/sphinx/generateToc.ts | 126 +++++++ 14 files changed, 949 insertions(+) create mode 100644 scripts/js/commands/docs/qiskit-addon-aqc-tensor.ts create mode 100644 scripts/js/commands/docs/qiskit-addon-cutting.ts create mode 100644 scripts/js/commands/docs/qiskit-addon-mpf.ts create mode 100644 scripts/js/commands/docs/qiskit-addon-obp.ts create mode 100644 scripts/js/commands/docs/qiskit-addon-sqd.ts create mode 100644 scripts/js/commands/docs/qiskit-addon-utils.ts create mode 100644 scripts/js/commands/docs/qiskit-c.ts create mode 100644 scripts/js/commands/docs/qiskit-ibm-runtime.ts create mode 100644 scripts/js/commands/docs/qiskit-ibm-transpiler.ts create mode 100644 scripts/js/commands/docs/qiskit.ts create mode 100644 scripts/js/commands/docs/utils.ts create mode 100644 scripts/js/commands/updateDocs.ts create mode 100644 scripts/js/lib/sphinx/conversionPipeline.ts create mode 100644 scripts/js/lib/sphinx/generateToc.ts diff --git a/scripts/js/commands/docs/qiskit-addon-aqc-tensor.ts b/scripts/js/commands/docs/qiskit-addon-aqc-tensor.ts new file mode 100644 index 00000000000..0a49a0927a7 --- /dev/null +++ b/scripts/js/commands/docs/qiskit-addon-aqc-tensor.ts @@ -0,0 +1,41 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { Pkg } from "../../lib/api/Pkg.js"; +import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { + downloadArtifact, + runPipeline, + API_INCLUDE, + PROSE_EXCLUDE, +} from "./utils.js"; + +const NAME = "qiskit-addon-aqc-tensor"; +const VERSION = "0.2.0"; + +export async function run(skipDownload: boolean): Promise { + const minorVersion = parseMinorVersion(VERSION)!; + const pkg = await Pkg.fromArgs(NAME, VERSION, minorVersion, "latest"); + const artifactPath = await downloadArtifact(pkg, skipDownload); + + await runPipeline(artifactPath, { + include: API_INCLUDE, + output: `docs/api/${NAME}`, + pkg, + }); + + await runPipeline(artifactPath, { + exclude: PROSE_EXCLUDE, + output: `docs/addons/${NAME}`, + pkg, + }); +} diff --git a/scripts/js/commands/docs/qiskit-addon-cutting.ts b/scripts/js/commands/docs/qiskit-addon-cutting.ts new file mode 100644 index 00000000000..5d9321e31dc --- /dev/null +++ b/scripts/js/commands/docs/qiskit-addon-cutting.ts @@ -0,0 +1,41 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { Pkg } from "../../lib/api/Pkg.js"; +import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { + downloadArtifact, + runPipeline, + API_INCLUDE, + PROSE_EXCLUDE, +} from "./utils.js"; + +const NAME = "qiskit-addon-cutting"; +const VERSION = "0.10.0"; + +export async function run(skipDownload: boolean): Promise { + const minorVersion = parseMinorVersion(VERSION)!; + const pkg = await Pkg.fromArgs(NAME, VERSION, minorVersion, "latest"); + const artifactPath = await downloadArtifact(pkg, skipDownload); + + await runPipeline(artifactPath, { + include: API_INCLUDE, + output: `docs/api/${NAME}`, + pkg, + }); + + await runPipeline(artifactPath, { + exclude: PROSE_EXCLUDE, + output: `docs/addons/${NAME}`, + pkg, + }); +} diff --git a/scripts/js/commands/docs/qiskit-addon-mpf.ts b/scripts/js/commands/docs/qiskit-addon-mpf.ts new file mode 100644 index 00000000000..ad9ff182674 --- /dev/null +++ b/scripts/js/commands/docs/qiskit-addon-mpf.ts @@ -0,0 +1,41 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { Pkg } from "../../lib/api/Pkg.js"; +import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { + downloadArtifact, + runPipeline, + API_INCLUDE, + PROSE_EXCLUDE, +} from "./utils.js"; + +const NAME = "qiskit-addon-mpf"; +const VERSION = "0.3.0"; + +export async function run(skipDownload: boolean): Promise { + const minorVersion = parseMinorVersion(VERSION)!; + const pkg = await Pkg.fromArgs(NAME, VERSION, minorVersion, "latest"); + const artifactPath = await downloadArtifact(pkg, skipDownload); + + await runPipeline(artifactPath, { + include: API_INCLUDE, + output: `docs/api/${NAME}`, + pkg, + }); + + await runPipeline(artifactPath, { + exclude: PROSE_EXCLUDE, + output: `docs/addons/${NAME}`, + pkg, + }); +} diff --git a/scripts/js/commands/docs/qiskit-addon-obp.ts b/scripts/js/commands/docs/qiskit-addon-obp.ts new file mode 100644 index 00000000000..b212ace5fec --- /dev/null +++ b/scripts/js/commands/docs/qiskit-addon-obp.ts @@ -0,0 +1,41 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { Pkg } from "../../lib/api/Pkg.js"; +import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { + downloadArtifact, + runPipeline, + API_INCLUDE, + PROSE_EXCLUDE, +} from "./utils.js"; + +const NAME = "qiskit-addon-obp"; +const VERSION = "0.3.0"; + +export async function run(skipDownload: boolean): Promise { + const minorVersion = parseMinorVersion(VERSION)!; + const pkg = await Pkg.fromArgs(NAME, VERSION, minorVersion, "latest"); + const artifactPath = await downloadArtifact(pkg, skipDownload); + + await runPipeline(artifactPath, { + include: API_INCLUDE, + output: `docs/api/${NAME}`, + pkg, + }); + + await runPipeline(artifactPath, { + exclude: PROSE_EXCLUDE, + output: `docs/addons/${NAME}`, + pkg, + }); +} diff --git a/scripts/js/commands/docs/qiskit-addon-sqd.ts b/scripts/js/commands/docs/qiskit-addon-sqd.ts new file mode 100644 index 00000000000..3746ba7ecc5 --- /dev/null +++ b/scripts/js/commands/docs/qiskit-addon-sqd.ts @@ -0,0 +1,41 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { Pkg } from "../../lib/api/Pkg.js"; +import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { + downloadArtifact, + runPipeline, + API_INCLUDE, + PROSE_EXCLUDE, +} from "./utils.js"; + +const NAME = "qiskit-addon-sqd"; +const VERSION = "0.12.0"; + +export async function run(skipDownload: boolean): Promise { + const minorVersion = parseMinorVersion(VERSION)!; + const pkg = await Pkg.fromArgs(NAME, VERSION, minorVersion, "latest"); + const artifactPath = await downloadArtifact(pkg, skipDownload); + + await runPipeline(artifactPath, { + include: API_INCLUDE, + output: `docs/api/${NAME}`, + pkg, + }); + + await runPipeline(artifactPath, { + exclude: PROSE_EXCLUDE, + output: `docs/addons/${NAME}`, + pkg, + }); +} diff --git a/scripts/js/commands/docs/qiskit-addon-utils.ts b/scripts/js/commands/docs/qiskit-addon-utils.ts new file mode 100644 index 00000000000..acde56c182b --- /dev/null +++ b/scripts/js/commands/docs/qiskit-addon-utils.ts @@ -0,0 +1,41 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { Pkg } from "../../lib/api/Pkg.js"; +import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { + downloadArtifact, + runPipeline, + API_INCLUDE, + PROSE_EXCLUDE, +} from "./utils.js"; + +const NAME = "qiskit-addon-utils"; +const VERSION = "0.3.1"; + +export async function run(skipDownload: boolean): Promise { + const minorVersion = parseMinorVersion(VERSION)!; + const pkg = await Pkg.fromArgs(NAME, VERSION, minorVersion, "latest"); + const artifactPath = await downloadArtifact(pkg, skipDownload); + + await runPipeline(artifactPath, { + include: API_INCLUDE, + output: `docs/api/${NAME}`, + pkg, + }); + + await runPipeline(artifactPath, { + exclude: PROSE_EXCLUDE, + output: `docs/addons/${NAME}`, + pkg, + }); +} diff --git a/scripts/js/commands/docs/qiskit-c.ts b/scripts/js/commands/docs/qiskit-c.ts new file mode 100644 index 00000000000..e67aed0c619 --- /dev/null +++ b/scripts/js/commands/docs/qiskit-c.ts @@ -0,0 +1,32 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { Pkg } from "../../lib/api/Pkg.js"; +import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { downloadArtifact, runPipeline } from "./utils.js"; + +const VERSION = "2.4.1"; + +// The C API uses a different content directory (cdoc/) instead of apidocs/. +const C_API_INCLUDE = ["cdoc/**", "release_notes.html", "release-notes.html"]; + +export async function run(skipDownload: boolean): Promise { + const minorVersion = parseMinorVersion(VERSION)!; + const pkg = await Pkg.fromArgs("qiskit-c", VERSION, minorVersion, "latest"); + const artifactPath = await downloadArtifact(pkg, skipDownload); + + await runPipeline(artifactPath, { + include: C_API_INCLUDE, + output: `docs/api/qiskit-c`, + pkg, + }); +} diff --git a/scripts/js/commands/docs/qiskit-ibm-runtime.ts b/scripts/js/commands/docs/qiskit-ibm-runtime.ts new file mode 100644 index 00000000000..c912c7ee066 --- /dev/null +++ b/scripts/js/commands/docs/qiskit-ibm-runtime.ts @@ -0,0 +1,29 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { Pkg } from "../../lib/api/Pkg.js"; +import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { downloadArtifact, runPipeline, API_INCLUDE } from "./utils.js"; + +const VERSION = "0.46.1"; + +export async function run(skipDownload: boolean): Promise { + const minorVersion = parseMinorVersion(VERSION)!; + const pkg = await Pkg.fromArgs("qiskit-ibm-runtime", VERSION, minorVersion, "latest"); + const artifactPath = await downloadArtifact(pkg, skipDownload); + + await runPipeline(artifactPath, { + include: API_INCLUDE, + output: `docs/api/qiskit-ibm-runtime`, + pkg, + }); +} diff --git a/scripts/js/commands/docs/qiskit-ibm-transpiler.ts b/scripts/js/commands/docs/qiskit-ibm-transpiler.ts new file mode 100644 index 00000000000..686312b8cf5 --- /dev/null +++ b/scripts/js/commands/docs/qiskit-ibm-transpiler.ts @@ -0,0 +1,29 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { Pkg } from "../../lib/api/Pkg.js"; +import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { downloadArtifact, runPipeline, API_INCLUDE } from "./utils.js"; + +const VERSION = "0.18.0"; + +export async function run(skipDownload: boolean): Promise { + const minorVersion = parseMinorVersion(VERSION)!; + const pkg = await Pkg.fromArgs("qiskit-ibm-transpiler", VERSION, minorVersion, "latest"); + const artifactPath = await downloadArtifact(pkg, skipDownload); + + await runPipeline(artifactPath, { + include: API_INCLUDE, + output: `docs/api/qiskit-ibm-transpiler`, + pkg, + }); +} diff --git a/scripts/js/commands/docs/qiskit.ts b/scripts/js/commands/docs/qiskit.ts new file mode 100644 index 00000000000..68d4def625a --- /dev/null +++ b/scripts/js/commands/docs/qiskit.ts @@ -0,0 +1,29 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { Pkg } from "../../lib/api/Pkg.js"; +import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { downloadArtifact, runPipeline, API_INCLUDE } from "./utils.js"; + +const VERSION = "2.4.1"; + +export async function run(skipDownload: boolean): Promise { + const minorVersion = parseMinorVersion(VERSION)!; + const pkg = await Pkg.fromArgs("qiskit", VERSION, minorVersion, "latest"); + const artifactPath = await downloadArtifact(pkg, skipDownload); + + await runPipeline(artifactPath, { + include: API_INCLUDE, + output: `docs/api/qiskit`, + pkg, + }); +} diff --git a/scripts/js/commands/docs/utils.ts b/scripts/js/commands/docs/utils.ts new file mode 100644 index 00000000000..7fe070b1ea8 --- /dev/null +++ b/scripts/js/commands/docs/utils.ts @@ -0,0 +1,75 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { $ } from "zx"; +import { mkdirp } from "mkdirp"; + +import { Pkg } from "../../lib/api/Pkg.js"; +import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { pathExists, rmFilesInFolder } from "../../lib/fs.js"; +import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; +import { + runSphinxPipeline, + SphinxPipelineConfig, +} from "../../lib/sphinx/conversionPipeline.js"; + +export const DOCS_FOLDER = "docs"; +export const PUBLIC_FOLDER = "public"; + +// Glob patterns that identify API reference content in Sphinx artifacts. +export const API_INCLUDE = [ + "apidocs/**", + "apidoc/**", + "stubs/**", + "release_notes.html", + "release-notes.html", +]; + +// Glob patterns to exclude from prose runs (API content + release notes which +// contain links to API pages, + Sphinx internals already excluded by the pipeline). +export const PROSE_EXCLUDE = [ + "apidocs/**", + "apidoc/**", + "stubs/**", + "release_notes.html", + "release-notes.html", +]; + +export async function downloadArtifact( + pkg: Pkg, + skipDownload: boolean, +): Promise { + const artifactFolder = pkg.sphinxArtifactFolder(); + const artifactPath = `${artifactFolder}/artifact`; + + if (skipDownload && (await pathExists(artifactPath))) { + console.log(`Reusing cached artifact for ${pkg.name}`); + return artifactPath; + } + + await downloadSphinxArtifact(pkg, artifactFolder); + return artifactPath; +} + +export async function clearOutputDir(dir: string): Promise { + if (await pathExists(dir)) { + await rmFilesInFolder(dir); + } +} + +export async function runPipeline( + artifactPath: string, + config: SphinxPipelineConfig, +): Promise { + await clearOutputDir(config.output); + await runSphinxPipeline(artifactPath, DOCS_FOLDER, PUBLIC_FOLDER, config); +} diff --git a/scripts/js/commands/updateDocs.ts b/scripts/js/commands/updateDocs.ts new file mode 100644 index 00000000000..78df5ca8fab --- /dev/null +++ b/scripts/js/commands/updateDocs.ts @@ -0,0 +1,69 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { basename } from "path"; +import { fileURLToPath, pathToFileURL } from "url"; + +import yargs from "yargs/yargs"; +import { hideBin } from "yargs/helpers"; +import { globby } from "globby"; + +import { zxMain } from "../lib/zx.js"; + +const COMMANDS_DIR = new URL("./docs/", import.meta.url); + +const VALID_PACKAGES = await discoverPackages(); + +const readArgs = () => { + return yargs(hideBin(process.argv)) + .version(false) + .option("package", { + alias: "p", + type: "string", + choices: VALID_PACKAGES, + description: "Which package to update. Defaults to all packages.", + }) + .option("skip-download", { + type: "boolean", + default: false, + description: "Reuse already-downloaded artifacts instead of downloading again.", + }) + .parseSync(); +}; + +async function discoverPackages(): Promise { + const dir = fileURLToPath(COMMANDS_DIR); + const files = await globby("*.ts", { cwd: dir }); + return files + .map((f) => basename(f, ".ts")) + .filter((name) => name !== "utils") + .sort(); +} + +async function loadAndRun(pkgName: string, skipDownload: boolean): Promise { + const moduleUrl = new URL(`${pkgName}.ts`, COMMANDS_DIR); + const mod = await import(moduleUrl.toString().replace(/\.ts$/, ".js")); + if (typeof mod.run !== "function") { + throw new Error(`Package command ${pkgName}.ts does not export a run() function`); + } + console.log(`\n=== ${pkgName} ===`); + await mod.run(skipDownload); +} + +zxMain(async () => { + const args = readArgs(); + const packages = args.package ? [args.package] : VALID_PACKAGES; + + for (const pkgName of packages) { + await loadAndRun(pkgName, args.skipDownload); + } +}); diff --git a/scripts/js/lib/sphinx/conversionPipeline.ts b/scripts/js/lib/sphinx/conversionPipeline.ts new file mode 100644 index 00000000000..def8cc0e5a6 --- /dev/null +++ b/scripts/js/lib/sphinx/conversionPipeline.ts @@ -0,0 +1,314 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { dirname, parse, relative } from "path"; +import { readFile, writeFile } from "fs/promises"; + +import { globby } from "globby"; +import { mkdirp } from "mkdirp"; +import { uniqBy } from "lodash-es"; + +import { sphinxHtmlToMarkdown } from "../api/htmlToMd.js"; +import { ObjectsInv } from "../api/objectsInv.js"; +import { HtmlToMdResultWithUrl } from "../api/HtmlToMdResult.js"; +import { Pkg } from "../api/Pkg.js"; +import { saveImages } from "../api/saveImages.js"; +import { normalizeResultUrls } from "../api/normalizeResultUrls.js"; +import { updateLinks, relativizeLink } from "../api/updateLinks.js"; +import { mergeClassMembers } from "../api/mergeClassMembers.js"; +import { specialCaseResults } from "../api/specialCaseResults.js"; +import addFrontMatter from "../api/addFrontMatter.js"; +import { dedupeHtmlIdsFromResults } from "../api/dedupeHtmlIds.js"; +import removeMathBlocksIndentation from "../api/removeMathBlocksIndentation.js"; +import { generateSphinxToc } from "./generateToc.js"; +import { DOCS_BASE_PATH } from "../api/conversionPipeline.js"; +import { kebabCaseAndShortenPage } from "../api/normalizeResultUrls.js"; + +// Sphinx build artifacts that are never content. +const SPHINX_INTERNALS = [ + "_static/**", + "_sources/**", + "_downloads/**", + "genindex.html", + "py-modindex.html", + "search.html", + "objects.inv", +]; + +export type SphinxPipelineConfig = { + /** Glob patterns to include relative to artifact root. Defaults to everything. */ + include?: string[]; + /** Glob patterns to exclude relative to artifact root. */ + exclude?: string[]; + /** Output directory, e.g. "docs/api/qiskit-addon-aqc-tensor" */ + output: string; + pkg: Pkg; +}; + +export async function runSphinxPipeline( + artifactPath: string, + docsBaseFolder: string, + publicBaseFolder: string, + config: SphinxPipelineConfig, +): Promise { + const { output, pkg } = config; + await mkdirp(output); + + const objectsInv = await loadObjectsInv(artifactPath, pkg); + const allFiles = await selectFiles(artifactPath, config); + const htmlFiles = allFiles.filter((f) => f.endsWith(".html")); + const notebookFiles = allFiles.filter((f) => f.endsWith(".ipynb")); + + if (htmlFiles.length === 0 && notebookFiles.length === 0) { + console.log(`No content files found for ${pkg.name} at ${output}`); + return; + } + + console.log( + `Processing ${htmlFiles.length} HTML + ${notebookFiles.length} notebooks for ${pkg.name} → ${output}`, + ); + + // Convert HTML files. + let results = await convertHtmlFiles( + pkg, + artifactPath, + docsBaseFolder, + output, + htmlFiles, + ); + + results = await mergeClassMembers(results); + + normalizeResultUrls(results, { + kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, + pkgName: pkg.name, + }); + + specialCaseResults(results); + + // Rewrite github.io links to internal paths before updateLinks runs. + for (const result of results) { + result.markdown = resolveGithubIoLinks( + result.markdown, + objectsInv, + pkg.name, + output, + ); + } + + await updateLinks( + results, + { + kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, + pkgName: pkg.name, + pkgOutputDir: `${DOCS_BASE_PATH}/${output.replace(/^docs\//, "")}`, + }, + objectsInv, + ); + + await dedupeHtmlIdsFromResults(results); + addFrontMatter(results, pkg); + removeMathBlocksIndentation(results); + + await writeMdxFiles(results); + await saveImages( + uniqBy(results.flatMap((r) => r.images), (img) => img.fileName), + `${artifactPath}/_images`, + publicBaseFolder, + pkg, + ); + + await copyNotebooks(artifactPath, output, notebookFiles, objectsInv, pkg.name); + await writeTocFile(artifactPath, output, pkg, docsBaseFolder, config); + await writePackageFile(output, pkg); +} + +async function loadObjectsInv( + artifactPath: string, + pkg: Pkg, +): Promise { + try { + return await ObjectsInv.fromFile(artifactPath, pkg.language); + } catch { + return undefined; + } +} + +async function selectFiles( + artifactPath: string, + config: SphinxPipelineConfig, +): Promise { + const include = config.include ?? ["**"]; + const exclude = [...SPHINX_INTERNALS, ...(config.exclude ?? [])]; + return globby(include, { + cwd: artifactPath, + ignore: exclude, + }); +} + +async function convertHtmlFiles( + pkg: Pkg, + artifactPath: string, + docsBaseFolder: string, + outputDir: string, + filePaths: string[], +): Promise { + const results: HtmlToMdResultWithUrl[] = []; + + for (const file of filePaths) { + const html = await readFile(`${artifactPath}/${file}`, "utf-8"); + const result = await sphinxHtmlToMarkdown({ + html, + fileName: file, + determineGithubUrl: pkg.determineGithubUrlFn(), + imageDestination: pkg.outputDir(`${DOCS_BASE_PATH}/images`), + releaseNotesTitle: pkg.releaseNotesTitle(), + hasSeparateReleaseNotes: pkg.hasSeparateReleaseNotes(), + isCApi: pkg.isCApi(), + hasRootNamespaceFile: pkg.hasRootNamespaceFile, + }); + + if (result.markdown === "") continue; + + const { dir, name } = parse(`${outputDir}/${file}`); + const url = `/${relative(docsBaseFolder, dir)}/${name}`; + results.push({ ...result, url }); + } + + return results; +} + +async function writeMdxFiles( + results: HtmlToMdResultWithUrl[], +): Promise { + for (const result of results) { + const filePath = `docs${result.url}.mdx`; + await mkdirp(dirname(filePath)); + await writeFile(filePath, result.markdown); + } +} + +async function copyNotebooks( + artifactPath: string, + outputDir: string, + notebookFiles: string[], + objectsInv: ObjectsInv | undefined, + pkgName: string, +): Promise { + for (const file of notebookFiles) { + const dest = `${outputDir}/${file}`; + await mkdirp(dirname(dest)); + const raw = await readFile(`${artifactPath}/${file}`, "utf-8"); + const notebook = JSON.parse(raw); + + for (const cell of notebook.cells ?? []) { + const lines: string[] = Array.isArray(cell.source) + ? cell.source + : [cell.source]; + cell.source = lines.map((line) => { + const resolved = resolveGithubIoLinks(line, objectsInv, pkgName, outputDir); + return resolved.replace(/\[([^\]]*)\]\(([^)]+)\)/g, (match, text, url) => { + const rewritten = relativizeLink({ url, text }); + return rewritten ? `[${rewritten.text ?? text}](${rewritten.url})` : match; + }); + }); + } + + await writeFile(dest, JSON.stringify(notebook, null, 1)); + } +} + +async function writeTocFile( + artifactPath: string, + outputDir: string, + pkg: Pkg, + docsBaseFolder: string, + config: SphinxPipelineConfig, +): Promise { + console.log(`Generating TOC for ${outputDir}`); + const toc = await generateSphinxToc( + artifactPath, + outputDir, + pkg.title, + docsBaseFolder, + config.include, + config.exclude, + ); + await writeFile( + `${outputDir}/_toc.json`, + JSON.stringify(toc, null, 2) + "\n", + ); +} + +async function writePackageFile(outputDir: string, pkg: Pkg): Promise { + const pkgJson = { name: pkg.name, version: pkg.version }; + await writeFile( + `${outputDir}/_package.json`, + JSON.stringify(pkgJson, null, 2) + "\n", + ); +} + +/** + * Resolve qiskit.github.io links to internal paths. + * + * For stubs links (symbol references), use objects.inv for resolution — + * it has complete symbol→URL mappings. For other github.io links (prose + * page links), rewrite to the configured output path. + */ +function resolveGithubIoLinks( + text: string, + objectsInv: ObjectsInv | undefined, + pkgName: string, + outputDir: string, +): string { + return text.replace( + /https:\/\/qiskit\.github\.io\/([^/"#)\s]+)\/?([^"#)\s]*)/g, + (match, pkg, rest) => { + const stubsPrefix = "stubs/"; + if (rest.startsWith(stubsPrefix)) { + const symbol = rest.slice(stubsPrefix.length).replace(/\.html$/, ""); + // Look up in objects.inv entries for this package. + if (objectsInv) { + const resolved = resolveSymbolFromObjectsInv(objectsInv, symbol, pkg); + if (resolved) return resolved; + } + // Fallback: derive URL from symbol name using kebab-case convention. + const kebabPage = kebabCaseAndShortenPage(symbol, pkg); + return `/docs/api/${pkg}/${kebabPage}`; + } + // Non-stubs github.io link — only rewrite for the package currently + // being processed. Links to other packages' github.io pages are left + // as external URLs since we may not have their prose content. + if (pkg !== pkgName) return match; + const page = rest.replace(/\.html$/, "").replace(/\/$/, ""); + return page ? `/docs/addons/${pkg}/${page}` : `/docs/addons/${pkg}`; + }, + ); +} + +function resolveSymbolFromObjectsInv( + objectsInv: ObjectsInv, + symbol: string, + pkgName: string, +): string | undefined { + const entry = objectsInv.entries.find((e) => e.name === symbol); + if (!entry) return undefined; + // URI format: "apidocs/qiskit_addon_obp.utils.truncating.html#symbol" + // Strip .html (which appears before the # fragment, not at the end). + const uri = entry.uri.replace(/\.html(?=#|$)/, ""); + const [rawPage, anchor] = uri.split("#"); + // Strip Sphinx artifact root folders (apidocs/, stubs/, etc.) before kebab-casing, + // matching the same normalization applied to converted API pages. + const strippedPage = rawPage.replace(/^(apidocs|apidoc|stubs|cdoc)\//, ""); + const kebabPage = kebabCaseAndShortenPage(strippedPage, pkgName); + return `/docs/api/${pkgName}/${kebabPage}${anchor ? `#${anchor}` : ""}`; +} diff --git a/scripts/js/lib/sphinx/generateToc.ts b/scripts/js/lib/sphinx/generateToc.ts new file mode 100644 index 00000000000..1bc498d79a9 --- /dev/null +++ b/scripts/js/lib/sphinx/generateToc.ts @@ -0,0 +1,126 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { readFile } from "fs/promises"; + +import { load } from "cheerio"; +import { globby } from "globby"; + +import { TocEntry } from "../api/generateToc.js"; + +export type SphinxToc = { + title: string; + children: TocEntry[]; + collapsed: boolean; +}; + +// Sphinx build artifact folders that are never prose content. +const SPHINX_INTERNAL_PREFIXES = ["_static/", "_sources/", "_downloads/"]; + +/** + * Build a _toc.json by parsing the Sphinx sidebar nav, filtered to match + * the same include/exclude config used for file selection. + */ +export async function generateSphinxToc( + artifactPath: string, + outputDir: string, + pkgTitle: string, + docsBaseFolder: string, + include?: string[], + exclude?: string[], +): Promise { + const indexHtml = await readFile(`${artifactPath}/index.html`, "utf-8"); + const $ = load(indexHtml); + + // Build set of files that were actually selected for this run so we can + // filter TOC entries to only those included. + const selectedFiles = new Set( + await globby(include ?? ["**"], { + cwd: artifactPath, + ignore: [...SPHINX_INTERNAL_PREFIXES.map((p) => `${p}**`), ...(exclude ?? [])], + }), + ); + + const outputBase = `/${outputDir.startsWith(docsBaseFolder) + ? outputDir + : `${docsBaseFolder}/${outputDir}`}`; + + const children: TocEntry[] = []; + + $(".sidebar-tree .toctree-l1").each((_, l1) => { + const l1Link = $(l1).children("a").first(); + const href = l1Link.attr("href") ?? ""; + + if (l1Link.hasClass("reference external")) return; + if (SPHINX_INTERNAL_PREFIXES.some((p) => href.startsWith(p))) return; + + // Filter by whether this href was selected for this run. + const hrefFile = href.split("#")[0]; + if (hrefFile && hrefFile !== "#" && !isHrefSelected(hrefFile, selectedFiles)) { + return; + } + + const title = l1Link.text().trim(); + const url = hrefToUrl(href, outputBase); + + const l2Items = $(l1).find(".toctree-l2"); + if (l2Items.length === 0) { + children.push({ title, url }); + } else { + const subChildren: TocEntry[] = []; + l2Items.each((_, l2) => { + const l2Link = $(l2).children("a").first(); + const l2Href = l2Link.attr("href") ?? ""; + if (l2Link.hasClass("reference external")) return; + if (SPHINX_INTERNAL_PREFIXES.some((p) => l2Href.startsWith(p))) return; + const l2HrefFile = l2Href.split("#")[0]; + if (l2HrefFile && l2HrefFile !== "#" && !isHrefSelected(l2HrefFile, selectedFiles)) { + return; + } + subChildren.push({ + title: l2Link.text().trim(), + url: hrefToUrl(l2Href, outputBase), + }); + }); + + if (subChildren.length > 0) { + const sectionUrl = href === "#" ? undefined : url; + const entry: TocEntry = { title, children: subChildren }; + if (sectionUrl) entry.url = sectionUrl; + children.push(entry); + } + } + }); + + return { title: pkgTitle, children, collapsed: true }; +} + +/** + * Check whether a sidebar href corresponds to a file that was selected + * for this pipeline run. + */ +function isHrefSelected(href: string, selectedFiles: Set): boolean { + const normalized = href.replace(/\.html$/, ""); + // Check both with and without .html extension, and also .ipynb + return ( + selectedFiles.has(`${normalized}.html`) || + selectedFiles.has(`${normalized}.ipynb`) || + selectedFiles.has(href) + ); +} + +function hrefToUrl(href: string, outputBase: string): string { + if (href === "#") return outputBase; + const withoutAnchor = href.split("#")[0]; + const withoutExt = withoutAnchor.replace(/\.html$/, ""); + return `${outputBase}/${withoutExt}`; +} From e002534856a24234891035ff07c856664813366b Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Tue, 5 May 2026 17:10:08 -0400 Subject: [PATCH 010/104] keep api and addon seperation --- .../qiskit-addon-aqc-tensor/_package.json | 4 + .../explanation/index.mdx | 9 +- .../how-tos/01-quimb-tnoptimizer.mdx | 220 ++ .../how-tos/01_quimb_tnoptimizer.ipynb | 9 +- .../qiskit-addon-aqc-tensor/how-tos/index.mdx | 13 + docs/addons/qiskit-addon-aqc-tensor/index.mdx | 5 +- .../qiskit-addon-aqc-tensor/install.mdx | 5 - .../qiskit-addon-aqc-tensor/release-notes.mdx | 72 - .../tutorials/01-initial-state-aqc.mdx | 435 +++ .../tutorials/01_initial_state_aqc.ipynb | 12 +- .../tutorials/index.mdx | 15 + .../addons/qiskit-addon-cutting/_package.json | 4 + .../explanation/index.mdx | 21 +- ...enerate-exact-quasi-dists-from-sampler.mdx | 76 + ...o-generate-exact-sampling-coefficients.mdx | 248 ++ .../how-to-provide-multiple-observables.mdx | 207 ++ .../how-tos/how-to-specify-cut-wires.mdx | 277 ++ ...erate_exact_quasi_dists_from_sampler.ipynb | 6 +- ...generate_exact_sampling_coefficients.ipynb | 15 +- .../how_to_provide_multiple_observables.ipynb | 9 +- .../how-tos/how_to_specify_cut_wires.ipynb | 21 +- .../qiskit-addon-cutting/how-tos/index.mdx | 28 + docs/addons/qiskit-addon-cutting/index.mdx | 41 +- docs/addons/qiskit-addon-cutting/install.mdx | 5 - .../qiskit-addon-cutting/release-notes.mdx | 462 --- ...1-gate-cutting-to-reduce-circuit-width.mdx | 310 ++ ...gate_cutting_to_reduce_circuit_width.ipynb | 15 +- ...2-gate-cutting-to-reduce-circuit-depth.mdx | 343 ++ ...gate_cutting_to_reduce_circuit_depth.ipynb | 18 +- .../03-wire-cutting-via-move-instruction.mdx | 385 +++ ...03_wire_cutting_via_move_instruction.ipynb | 18 +- .../tutorials/04-automatic-cut-finding.mdx | 205 ++ .../tutorials/04_automatic_cut_finding.ipynb | 36 +- .../qiskit-addon-cutting/tutorials/index.mdx | 30 + docs/addons/qiskit-addon-mpf/_package.json | 4 + .../qiskit-addon-mpf/explanations/index.mdx | 11 + .../explanations/mpf-stability.mdx | 157 + .../explanations/mpf_stability.ipynb | 9 +- .../how_tos/choose-trotter-steps.mdx | 136 + .../how_tos/choose_trotter_steps.ipynb | 8 +- .../addons/qiskit-addon-mpf/how_tos/index.mdx | 12 + .../how_tos/using-approximate-model.mdx | 607 ++++ .../how_tos/using_approximate_model.ipynb | 12 +- docs/addons/qiskit-addon-mpf/index.mdx | 3 +- docs/addons/qiskit-addon-mpf/install.mdx | 5 - .../addons/qiskit-addon-mpf/release-notes.mdx | 71 - .../tutorials/01-getting-started.mdx | 445 +++ .../tutorials/01_getting_started.ipynb | 28 +- .../qiskit-addon-mpf/tutorials/index.mdx | 11 + docs/addons/qiskit-addon-obp/_package.json | 4 + .../how_tos/bound-error-using-p-norm.mdx | 269 ++ .../how_tos/bound_error_using_p_norm.ipynb | 15 +- .../addons/qiskit-addon-obp/how_tos/index.mdx | 13 + .../how_tos/simulating-circuits-with-obp.mdx | 353 ++ .../simulating_circuits_with_obp.ipynb | 12 +- .../how_tos/truncate-operator-terms.mdx | 564 ++++ .../how_tos/truncate_operator_terms.ipynb | 38 +- docs/addons/qiskit-addon-obp/index.mdx | 3 +- docs/addons/qiskit-addon-obp/install.mdx | 5 - .../addons/qiskit-addon-obp/release-notes.mdx | 171 - .../tutorials/01-getting-started.mdx | 372 +++ .../tutorials/01_getting_started.ipynb | 28 +- .../qiskit-addon-obp/tutorials/index.mdx | 11 + docs/addons/qiskit-addon-sqd/_package.json | 4 + ...onic-excitations-to-configuration-pool.mdx | 129 + ...ic_excitations_to_configuration_pool.ipynb | 10 +- .../how_tos/benchmark-pauli-projection.mdx | 361 +++ .../how_tos/benchmark_pauli_projection.ipynb | 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mode 100644 index 00000000000..a08b147e6d6 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/_package.json @@ -0,0 +1,4 @@ +{ + "name": "qiskit-addon-aqc-tensor", + "version": "0.2.0" +} diff --git a/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx index 41fbe752e76..c945320e369 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx +++ b/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx @@ -1,5 +1,6 @@ --- -title: "Explanatory Material on AQC-Tensor" +title: Approximate quantum compilation (AQC-Tensor) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-aqc-tensor. --- @@ -24,9 +25,9 @@ The technique is to then use an iterative optimization method to make the *good* ## Ansatz generation (motivation) -As mentioned in the previous section, three closely related things are needed as input to AQC-Tensor. Rather than code all three individually, this package provides a [`generate_ansatz_from_circuit()`](ansatz_generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") function that can generate (2) and (3) from a circuit. In the case of Trotter evolution (and likely other circuit classes of interest), one can use the same circuit-generation function to generate (1) as well as the input to [`generate_ansatz_from_circuit()`](ansatz_generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"), thus enabling the generation of all three inputs from a single function. The tutorial is a good demonstration of this re-use of a single circuit-generation function. +As mentioned in the previous section, three closely related things are needed as input to AQC-Tensor. Rather than code all three individually, this package provides a [`generate_ansatz_from_circuit()`](ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") function that can generate (2) and (3) from a circuit. In the case of Trotter evolution (and likely other circuit classes of interest), one can use the same circuit-generation function to generate (1) as well as the input to [`generate_ansatz_from_circuit()`](ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"), thus enabling the generation of all three inputs from a single function. The tutorial is a good demonstration of this re-use of a single circuit-generation function. -Specifically, [`generate_ansatz_from_circuit()`](ansatz_generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") will take an input circuit and generate the ansatz circuit (2) and initial parameters (3) that follow the two-qubit connectivity of the input circuit. However, the returned circuit will be parametrized, based on generalized one- and two-qubit rotations. The initial parameters returned by the function, when plugged into the ansatz, will result in a circuit that is exactly equivalent to the circuit passed as input, up to a global phase. +Specifically, [`generate_ansatz_from_circuit()`](ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") will take an input circuit and generate the ansatz circuit (2) and initial parameters (3) that follow the two-qubit connectivity of the input circuit. However, the returned circuit will be parametrized, based on generalized one- and two-qubit rotations. The initial parameters returned by the function, when plugged into the ansatz, will result in a circuit that is exactly equivalent to the circuit passed as input, up to a global phase. Given the ansatz generation function, the user must provide only two things in order to use AQC-Tensor: @@ -65,7 +66,7 @@ This package provides a few different methods for calculating the gradient. The Aer backend always uses the explicit gradient code in the `explicit_gradient` module. -The Quimb backend will typically be used with an automatic differentiation backend; the user is to select a backend from among those supported by Quimb. Alternatively, one can instead pass `"explicit"` as the `autodiff_backend` when instantiating the [`QuimbSimulator`](qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator#qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator "qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator") object; in this case, the `explicit_gradient` module will be used. It is only recommended to use explicit gradients with Quimb’s eager [`CircuitMPS`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.CircuitMPS) simulator, not the lazy [`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.Circuit) simulator. +The Quimb backend will typically be used with an automatic differentiation backend; the user is to select a backend from among those supported by Quimb. Alternatively, one can instead pass `"explicit"` as the `autodiff_backend` when instantiating the [`QuimbSimulator`](simulation-quimb-quimb-simulator#qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator "qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator") object; in this case, the `explicit_gradient` module will be used. It is only recommended to use explicit gradients with Quimb’s eager [`CircuitMPS`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.CircuitMPS) simulator, not the lazy [`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.Circuit) simulator. Regardless of which backend is chosen, the gradient code can understand linear parameter expressions (ParameterExpression objects). This support is essential, as linear expressions are returned by the ansatz generation code. diff --git a/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer.mdx b/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer.mdx new file mode 100644 index 00000000000..32ed5042af4 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer.mdx @@ -0,0 +1,220 @@ + + +# How to use `quimb.tensor.TNOptimizer` directly + +Quimb provides [a mechanism for optimizing tensor networks](https://quimb.readthedocs.io/en/latest/tensor-optimization.html) through its `TNOptimizer` interface. The Quimb backend provided by this addon uses this under the hood. This how-to guide demonstrates how to work with this object directly, in case some users want more direct access to it. + + + +## Set up a model Hamiltonian + +``` +[1]: +``` + +```python +from qiskit.transpiler import CouplingMap +from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian + +# Generate some coupling map to use for this example +coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False) + +# Choose a 10-qubit circle on this coupling map +reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 4, 15, 3, 9]) + +# Get a qubit operator describing the Ising field model +hamiltonian = generate_xyz_hamiltonian( + reduced_coupling_map, + coupling_constants=(0.0, 0.0, 1.0), + ext_magnetic_field=(0.4, 0.0, 0.0), +) +``` + + + +## Set up quimb simulator with default options + +``` +[2]: +``` + +```python +import quimb.tensor as qtn + +from qiskit_addon_aqc_tensor.simulation.quimb import ( + QuimbSimulator, + qiskit_ansatz_to_quimb, + recover_parameters_from_quimb, +) + +simulator = QuimbSimulator(qtn.Circuit) +``` + +```python +/home/runner/work/qiskit-addon-aqc-tensor/qiskit-addon-aqc-tensor/.tox/docs/lib/python3.9/site-packages/cotengra/hyperoptimizers/hyper.py:57: UserWarning: Couldn't find `optuna`, `cmaes`, or `nevergrad` so will use completely random sampling in place of hyper-optimization. + warnings.warn( +/home/runner/work/qiskit-addon-aqc-tensor/qiskit-addon-aqc-tensor/.tox/docs/lib/python3.9/site-packages/cotengra/hyperoptimizers/hyper.py:76: UserWarning: Couldn't find `optuna`, `cmaes`, or `nevergrad` so will use completely random sampling in place of hyper-optimization. + warnings.warn( +``` + + + +## Generate target circuit + +``` +[3]: +``` + +```python +from qiskit.synthesis import SuzukiTrotter +from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit + +from qiskit_addon_aqc_tensor.simulation import ( + compute_overlap, + tensornetwork_from_circuit, +) + +evolution_time = 0.4 + +target_circuit = generate_time_evolution_circuit( + hamiltonian, + synthesis=SuzukiTrotter(reps=8), + time=evolution_time, +) + +target_tns = tensornetwork_from_circuit(target_circuit, simulator) +``` + + + +## Generate ansatz from a shallower circuit + +``` +[4]: +``` + +```python +from qiskit_addon_aqc_tensor.ansatz_generation import ( + AnsatzBlock, + generate_ansatz_from_circuit, +) + +initial_shallow_circuit = generate_time_evolution_circuit( + hamiltonian, + synthesis=SuzukiTrotter(reps=2), + time=evolution_time, +) +ansatz, initial_parameters = generate_ansatz_from_circuit(initial_shallow_circuit) +ansatz = ansatz.decompose(AnsatzBlock) +ansatz.draw("mpl", fold=-1) +``` + +``` +[4]: +``` + +![../\_images/how-tos\_01\_quimb\_tnoptimizer\_7\_0.png](/docs/images/api/qiskit-addon-aqc-tensor/how-tos_01_quimb_tnoptimizer_7_0.avif) + + + +## Initialize objective function + +``` +[5]: +``` + +```python +from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity + +objective = MaximizeStateFidelity(target_tns, None, None) +``` + + + +## Convert Qiskit ansatz and initial parameters to a Quimb parametrized circuit + +``` +[6]: +``` + +```python +circ, conversion_context = qiskit_ansatz_to_quimb(ansatz, initial_parameters) +``` + + + +## Perform optimization of Quimb circuit using automatic differentiation + +``` +[7]: +``` + +```python +from qiskit_addon_aqc_tensor.simulation.quimb import tnoptimizer_objective_kwargs + +tnopt = qtn.TNOptimizer( + circ, + **tnoptimizer_objective_kwargs(objective), + autodiff_backend="jax", # OPTIONS: jax, autograd, torch, etc. +) +circ_opt = tnopt.optimize(20) +``` + +```python ++0.000019788644 [best: +0.000019788644] : : 36it [00:36, 1.00s/it] +``` + + + +## Recover final parameters from the quimb circuit + +``` +[8]: +``` + +```python +final_parameters = recover_parameters_from_quimb(circ_opt, conversion_context) +``` + + + +## Check fidelity of final, compressed state w/ respect to target state + +``` +[9]: +``` + +```python +compressed_circuit = ansatz.assign_parameters(final_parameters) +compressed_state = tensornetwork_from_circuit(compressed_circuit, simulator) +abs(compute_overlap(target_tns, compressed_state)) ** 2 +``` + +``` +[9]: +``` + +```python +0.9999984444299815 +``` + + + +## Compare with fidelity of initial shallow state w/ respect to target state + +``` +[10]: +``` + +```python +initial_shallow_state = tensornetwork_from_circuit(initial_shallow_circuit, simulator) +abs(compute_overlap(target_tns, initial_shallow_state)) ** 2 +``` + +``` +[10]: +``` + +```python +0.9998389457702321 +``` diff --git a/docs/addons/qiskit-addon-aqc-tensor/how-tos/01_quimb_tnoptimizer.ipynb b/docs/addons/qiskit-addon-aqc-tensor/how-tos/01_quimb_tnoptimizer.ipynb index 97afa60750e..0ec131d7b15 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/how-tos/01_quimb_tnoptimizer.ipynb +++ b/docs/addons/qiskit-addon-aqc-tensor/how-tos/01_quimb_tnoptimizer.ipynb @@ -151,8 +151,9 @@ "outputs": [ { "data": { + 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A4D5MLGMSpxtRgZFxXi6Jd0Q1ONWRyElN93JJvMfs5wDgbcQh19uwPVnPvLfKoW2WfzNI+/+4Qcu/GVThbZauP6Y3Pl/vaPFQCuoBzIy2GIiBIA4AgLnRFgBc79e/9zk84bAzuaGvZ+7Uj3P3OFg6nIk4CMDsyAuAayGIAwBgbrQFXO/trzdpwaojDm3jTG7o+Y9Wa82W444WD2egDsDs6A+AOAjiAAAAAMyMPhHgWjabTfc8v0jHU3Mc2s7RHLnNJt0+ZoHSM/OcKSb+hTgIwOzIj5ob10EQAwAAtAdcKzevQLc+9bfy8gsrvI0zYyczs/N12zML3DZ5jZlQB2B29AnMjRgIYgAAAO4R6O0CeEJSUpLGjRunH374QQcOHFBCQoKuvvpqvfTSSxo5cqQ+/fRTvfPOO7rvvvu8XVSXKMzJVP6JJNlsNuWnHNaxWR8oa9dqhTftqtDazbxdPLcLDAtWSFyUZLEoLCFWzW/pq/i2jXRs1Xad2HXI28XzCLOfA47a884dSlk4RbaCfNkK8rT6+khJUoev02QJCPBy6eCLiEPuV1BQqGHPLFBuXsVvcEhSjarhqlM9wuHjPfPeKg3qXV8tG8U6vK1ZUQ9gZrTFQAwEcQCOoE8K+B/aAoD7pZ3M1V3PLXJ4O2dzQ/c8v0gXdKmpuJgQh7c1I+IgALMjLwCuhSAOwBHkBwH/Q1vA/XbsO6En3l7h8HbO5Iby820a+vTfWv71FQoKsjp8TDOiDsDs6A+AOAjiABxBXgAAAPgb+kSA+035bZd++nOfw9s5kyPfk5iux95crveePM/h45kVcRCA2ZEfNTeugyAGwBHcIwH8E+0B93tp4lqt25bs0DbOPle7cPURvfP1Rj1wUxuHtzUr6gDMjj6BuREDQQwAAMBz/H5imTVr1uiyyy7T4cOHFRERoVatWikxMVFvv/22du7cqeTkU8mRDh06eLegLnTomzE69M2YEq/Fnnu16t39npdK5FkdR9+gjqNvKPHanl//0dLHP/ZSiTzP7OdAzuFdOvz9Kzq58W/lHtsna1CIgqrUUHjTrqp64VBFtetTYv0G93+sBvd/rKS5k3V83mQ1f/Ev7xTcTdJO5uqLX3Zo0vRtOnQsU5J0NDlLb36xQUOvaKoq0fwYoqsRh9zvt4UHtGJjkseOl5tXqHGT1mnS8708dkxfRz0wjoKCQv369369/+0WHUrKkiQdTsrSQ+P+0b2DW6pZgxgvl9D/mL0tBmIgiANmR58URpOZla8ps3Zp4vdbivICR45n6aWJa3T71c1VPT7MyyX0P7QFAPf7+IetSjya6bHjHU3O1offbdHjd7T32DF9GXEQMJZjyVn6dPo2ffnLTiUW3SfK1kfTtmhI/8aKCA/ycgn9D3kBcC0EccDcyA/i32w2m5ZvSNKEqZs1b1liUXssOS1HC1cd1vkdq8tisXi5lHA12gLu99rkdcrOKfDY8dZuTdaMv/bqmksaeuyYvow6YCxpJ3P12YztmvzT9hLjR9/6coNuHdRUsYwfdTn6AyAOgjhgbuQFAAAwlu170/TBd1s0fd7eovxsUmq2ps/bo4G96ikwkImEXY0+EeBeNptNYz9Y7dFjfvT9Fj11VwfVTAj36HF9FXEQMA6bzaZl64/9//36QyXu1y9afUTndajG/Xo3ID9qblwHQQwwN+6R4ExZ2fmaOnuXJn6/tcSzzc9/uFp3XN2cfqafoj3gXiczcvXG5xs8esyXP1mne69vqeAgJv2qCOqAsazblqwJUzdr1qIDRXmB46k5+mPJQV3UrZasVvICrkafwNyIgSAGAADgOX49sUxSUpIuv/xyHT58WKNGjdKYMWMUFRUlSRo3bpwee+wxBQYGymKxqF27dl4uretUvfQuVTnvOtkK8pS1d70O//CqcpMOyBIUWrTOyY0LtOO5y87a1pafK1thgTr/6LmHbl1t6xe/a8/PS2QNClSVFvXUZsSViqgZr4Kc3KJ1rMGBuvz317T7xwVa99YPRa/3GD9CoQmxmjPkRW8U3WXMfA5kbF+hbU9eIEtgkOJ636Kweq1VmJulnMTtOrHmdwWERZ11o8mfTZq+Tfe/vEQZWfklXs/Lt+nh15bqyXdW6PWHu2r4Da28VEL/VJE4dMH7D0lWi+bf/b+i14JjI3XlX29qxXOfa9cPC7xRdJ8xYepmjx9zyqxden1UV8XHhpa/MqgHBrFmy3Fd8/Bc7TpwssTrBYU2jf9yo8Z/uVE3DWysiWN6KDTEr7sGHmXmthhOoU8C4oB50SeF0Uyft0fDnl6g1JO5JV7PL7DpyXdW6tn3V+upuzro6bs78ECMC9EfAtyrsNCm97/1fG7og+82a/SwtgoI4AclykMcBIzBZrPp1U/XacyEVcrNKyyxLC+/UHc/t0ij31yuiWN66Lq+/DiyK5EXAPlBEAfMi/wg/u1YcpYGP/qn/lp+6KxlWTkF6jn0V3Vrm6Dv/3eRaleP8EIJ4S70i90r7WSuvvxlp8ePO2HqZiaWqSDqgHFMnLZFD45bqszss8ePPjhuqZ54e6X+92g33X1dCy+V0D/RHwB5ARAHzIu8AAAAxpGTW6B7nl+kyT9tL2VZoa56cK4a1IrU9/+7SJ1aVfVCCf0XuSHAvf5cdkhb96R59Jj5+TZ9/MNWPX13R48e11cRBwFjOHo8S4Mfnaf5Kw6ftSwrp0A9bv1F3dudul9fqxr3612J/Ki5cY8ExADz4h4JzvTL/H269am/lZyWU+L1/AKbnnlvlZ77YLUev6O9xg7vxLPNfoZ+sXt9+ctOpWfmefSYR45n6ce5e3V9v0YePa6vog4Yw4n0XN30+Hz9PH/fWcuycwvU9+5ZatOkin4cf7Ga1Iv2Qgn9F30CcyMvAGIAAACe49e/Hj1y5EgdOHBA9913n15//fUSy0aPHq2vv/5aa9euVcOGDRUd7T+dupCaTRXd4WJJUkznyxTZsoe2Pt5D+96/R40enSJJimrdUx2nppfYLvd4oraM6qKEAfd5vMyudGLXYR1asF6SdHDeah1ZtkX9f3pe5756t+bf+6YkqTA3XwtHvqN+Pz6n/X+sVMqmvarX7xzVuaSLfrrwYW8W3yXMfA4cmjpWhTmZavnqGoU3bH/W8ryUswd/+Kv3pmzSfS8tsbtOVnaBRry0ROmZ+Rp9m/9MsOVtFYlDSx6fqCvmvaGGV56v3dMXSZK6v3SHji7bQmK3HHsOntSsRQc8ftzsnAJ9NmO7Hr6lrceP7YuoB963enOSLrhtpk5m2L8h+OUvO3XkeJZ+ebevgoMCPFQ6/2bmthhOoU8C4oB50SeFkUz5baf+89+/ZLOVvU5efqHGTFiltPRcvfFIN88Vzs/RHwLca96yRO3cf7L8FV1s36EM/bbwgAZeUM/jx/Y1xEHAGJ58e6Ve/mSt3XXSTuZq8CPz9PmLvXTz5U09VDL/R14A5AdBHDAv8oM47XhqtnoN+1Vbdtv/Ya+l64/p/Ft/0eLPB/JjNX6EfrF7ffnrjrMmyfCEecsOadueNDVrEOPxY/sa6oAxjP9igx56banddTKz83XP84uUnpmnUbcyJs5V6A+AvACIA+ZFXgAAAGPIyyvUVQ/O0W8L7T97ticxXb2G/aq/Pu2vLq0TPFQ6/0duCHCvD77b4pXjfvjdFj15ZwdZrfzYb3mIg4D3JaVkq+fQX7Vtr/379f+sO6Yet/6qxV8MVI2q4R4qnf8jP2pu3CMBMcC8uEeCf/thzh5d98g8FRaW/XBzfoFNz3+4RikncvX2f7szuYwfoV/sXh9O805u6P1vNzOxTAVRB7wvIzNPF9/1m5ZvSLK73oYdKTr/lp+16PPLmVzGhegTmBt5ARADAADwHKu3C+Aumzdv1tSpU1W1alW9/PLLpa7TuXNnSVL79iWT0XPmzFH37t0VGhqqatWq6Z577lFamv2bxkYW2fI8xfW+WSkLpyp98+JS1ynMy9GuV65WZKseqnndEx4uoXsdW7FVO6f9rYZXnq+ELs2LXj++bpc2vj9DPd++X+E143Tua/do6RMfK+tIihdL6x5mOgeyE7crICq+1JtMkhRUpYaHS+QdKzYe0/0v259U5t8eG79cC1ZyE85dSotDuanpWjzqfXV78Q6FVa+i+gO6q8Z5rbXksQ+9XFrjm7/isN0fRnanv5ZTT5xFPfCs3LwCDRo5p9xJZU77Y0minvtgtZtLZV5maouhdPRJQBwwD/qkMIrdB07q1qf+rnDf6X+fb9APc/a4tUxmRn8IcK0/lx3y2rH/Wu69Y/sy4iDgeb/M31fupDL/dtszC7R1d6r7CmRy5AVAfhDEAfMgP4jT7hy7sNxJZU7bm5iumx6f7+YSwZvoF7uWN3ND81eQG3IGdcDzlq47qodftz+pzL898sYyLVl7xI0lMjf6AyAvAOKAeZAXAADAGF76eE25k8qclpGVr0Ej5yg7x/MTGZsFuSHAdWw2m/700hjGg0cztWPfCa8c29cRBwHPu33MgnInlTlt98GTuvkJ7te7E/lRc+MeCYgB5sE9Epy2/3C6hjz+l91JZf7t3W82acpvu9xcKngT/WLXSU7L0dqtyV459uI1R5WTW+CVY/s66oDnPTjun3InlTntaHK2rnl4rmze+kFDE6BPYG7kBUAMAADAffx2YplvvvlGhYWFGjJkiCIjI0tdJywsTFLJiWXmz5+vfv36qXbt2vrxxx/14osvatq0abryyit9utNX8/qnJWuAEr9+ptTl+ybco8K8bDV4YLJnC+Yha9+cpsL8AnV89PqSr4//XoUFBRr0x2s6vGiDdv+0yEsldD+znAMhNRqr4ORxpSz5wdtF8ap3v9ns8MQbb3+90T2FgaTS49DBP9doz8+L1evdker+yp1aPOp95aSk29kLJGnl5oolbf3t2P6AeuA5P8zZowNHMhza5oPvtvAwjBuZpS2GstEnAXHAHOiTwig++G6zcvMKHdrmra/IC7gT/SHAdVZu8mZu6LjXju3riIOAZznatssvsOn9b7e4qTSQyAuA/CCIA2ZBfhDSqUmfp8/b69A2fy4/pHXbvPOwJzyDfrHrkBvyTdQBz3rnm00Ojx995+tN7ikMJNEfAHkBEAfMgrwAAADel5NboAlTNzu0zaFjmZr2xx73FAiSyA0BrrL/cIaSUrK9dnxv5ud9HXEQ8Jyd+09oxl/7HNpmzj+J2riDH610J/Kj5sY9EhADzIF7JDjto2lblZ3j2OQTPNvs/+gXu8YqL/7uWV5+oTbQb3IadcBzjqdm64tfdjq0zbptyfrLSxN6mwV9AnMjLwBiAAAA7uG3E8vMmzdPktSnT58y1zlw4ICkkhPLPPfcc2ratKm+++47XXbZZbrzzjv10Ucf6a+//tKvv/7q3kK7UWjNJorreYNOrpurkxsXlFh29Oe3lbbiFzV+fLqsIeFeKqF7ndxzWLt/WqRavdqpWreWRa/b8gt0bPlWhcbHaMfUP71YQvczyzlQc/BTsgQGadcr12jDvc205+3bdOy395W137EBwb7seGq2psza5fB2P87bq8Sjjk1CgIorKw6tGPu5ohrW0MF5q3Vg7iovltB3rPbijzQkHs3U4aRMrx3f11EPPMfRB2Ek6Xhqjr77fbcbSgPJPG0xlI0+CYgD5kCfFEaQk1ugT37c5vB2f688rA3b+fFKd6E/BLjOKi/mhlZtTpLN0V9khCTiIOBJ2/akac4/iQ5vN3nGdmVk5rmhRJDIC4D8IIgDZkF+EJL04bQtDv+YvyS978Q9XvgO+sWukXIiR3sSvffgLD+a5zzqgOccS85yavzPtD/26MjxLDeUCBL9AZAXAHHALMgLAADgfT/M2aOjyY5PuuDMMzioOHJDgGt488dDJXLklUEcBDzng2+3OLXd+9/SHnQn8qPmxj0SEAPMgXskkKS8vEJN/H6rw9stXX9Mq+hz+jX6xa6xapP3nqs9dXzqqbOoA54zafp25eQ6NsGZxH0id6NPYG7kBUAMAADAPQK9XQB32bt3rySpfv36pS7Pz8/XokWnZiX898QyS5cu1bBhw2S1Fs+507dvX0nS9OnTNXDgQIfL0qVLFx0+fLjEa5bgMFUfv93hfVVGjeueVPKCb5T49TNq/uKpxvPJdX/qwOePqekzvymkegO3HbtZs6ay5ZZ84DDIZtUYdXXbMc+07q3v1fDK89Xx0es1+9pnJUnVurVUk+v7aPMnM9X1uWGaccmjKsjOdfmxmzVtpjxLYYnXOAfsc/bziWxxrlq+sVJHfnpDaSt/0/G5k3R87qRTy1r1VIMHJiukRqPSjxkQJGtwmMPHPM3R9+guOYH1lRN9m8PbFRTY1K7bAIXm7XBDqcrn6ZjgDaXFofysHKXvPaqUzfu8WzgXKy3uucqRmPukgIRSly3/ZpBqVC07MVCjaljR//f/cYPd4xxOytQ5N8446/X2nXsoqOCoAyWuGDPUAck89cCddaAiEqs8KVmCHd7u3lHj9Pi9v7mhRMbgjfbXv3mzLXYmo7RbPM3bsdabfZIzeTtOeYO3Y4BknDhg1hjgCPqk8GV51ngdjx3p1LY9L71ZEblrXVyiivH2ddoTzNIfkszZ1oBn2CQdixtb5nJX5YbKygudSM9Tnbr1ZZHrz2/iIHEQcJXM4DZS5HUOb5d2MleNWnZXUMERN5TKGLydGyAv4F1GuNYaJT9o1uuUt2OARBzwJeQHURlJUbdIQY0d3u6Tb+bq5w+vd0OJfIMRrtXuRr+48vKtcVLsA6UuKy8vJFU+N7R63XbVqVPHgRJXHHWAOuAqOYENlRs91OHt8vIL1brzpQrN3+X6QhmEt/sERukPSOZsOxohzpIX8C5vxwDJOHHAjDHAUeQFAADwXWlhl0ph5zm83ZI1iapdp44sbihTRRihz+Ju5IaAyssI6SxFDCp1mSdy5O9P/EJTxl/jQIkrxgwxUCIOAp5yLGqoFNTQ4e0++vJ3TZ8w2PUFMgjyo8XMmkfz9vXWKPdIJPNep7wdB4wSAyTzxoGK4h4JKiPfGqsjsQ85te1Fl9+uiJyVLi6Rb/D2ddpT6BdXXlpYXyns/FKXeeI31x7571iNfXBBKVtUDnWAOuBKyRHXSiFtHd7ux1mrVWfqrW4okTF4uz8gGadPYNa2o7djLXkB7/N2HDBKDJDMGwcAAMZUo0YNrVixwqlt/XZimYyMDElSVlbpF+ypU6cqKSlJUVFRatiw+MZwQECAgoNL/gh3UFCQLBaLNm7c6FRZDh8+rIMHD5Z4zRoSrupO7a1sUW17q/NPtjKXh9Vtqc4/Fs+imnNkj3a9Nlh1hr6mqLa9XVyakhITE1WYk1nitWBLgFz5IRxeslGTa15b5vK07Qf1eZ3ih+8Dw0PVY/wIrXzxK235bLYu+/E5dXr8P1o+ZrLrCvX/Eg8lKtdWcgZbzgH7KvP5hDVoqwYPTJYk5Rzdq/QN85X0x8dK37RAO166Qi3fWClr0Nk/th/fe4jiew9x8qiOv0e3iYyVop3b9HhyunTiYPkruoGrY4I3OBqH/Flpcc9lImxSQOmLalQNV53qEeXuIjDAWqH1SnP0aJKU7fp64g91QKIenObWOlARcY5PKiNJGZl5yjjoneuAJ7ij/fVvRm6Lnckw7RYPc3esNXKf5Exej1Ne4O4YIPlOHDBrDHAEfVL4tNAAKda5TVPTMpWaTF7AWfSHipmxrQFPsUpxZS/1RG4oMfGIZHP9wCTioH8hDsKrqjSUIp3b9GhSqpRJftBZ5AWMzRPXWl/JD5r1OkV+sJhZ44AjyA+iUhrZpCDHN8vLt5w1xtBM6Bf7F7e1N0IKy8x/VzQvJDmfGyoolNvqKXXAv3i1zR1VtRLjR09KJ/33WkReoJgZ247kBYqRF3AfX4kDZowBjiIvAACAD6uVKznzO6SWQCUePCzJO21lckP+xaz9LnhAXFOpjNS2J3LkmVk5ynRDjtwfYqBEHPw34iC8qjH360tDfrSYWfNoPFtdzKzXKe6VFjNrHKgo7pGgUkJslXi2OUupx/23PWYP/WL/47b2Rs3MMvPfnniu9sTJDJ04Sm6oLNSBYl5tc9cvkEIc36zAFkBeoJJ8pU9g1rYjeYFi5AXcw1digGTeOAAA8D9+O7FMjRo1lJKSolWrVuncc88tsezQoUN69NFHJUnt2rWTxWIpWtasWTMtXbq0xPrLly+XzWZTcnKy02U5k6USs6S7QmFOpna+fKViug5StQH3uf14tWrVOmtWviCbVfLiZI3nPHuL0vcd1ZbJsyRJCx94V4PmvK59vy3VkX82u/RYtWrWOmtmSs4B+1z1+YRUq6+QC29RXJ+btfXxnsrYvEiZ25cpslUPl+z/3xx9j+6SExipJCe3ja8SrtCo2i4tT0V5OybAtUqLe65yJFDKL2PZ4ST7HfUaVcMUGGBVfkGhDifZr69l7ataQryCCl3/3qgD/sWddaAiEguzZbOGOrxdZJhVMbW9cx3wBG+3v/7N022xMxml3eJpRou1nuyTnMnbccobjBQDJO/GAbPGAEfQJ4Uvy7dG64gk2WzSv/KeFVElJlThYeQFUHlmbGvAM2ySEm2FksVa6nJX5Ybs7adWreqyuCFgEQf9C3EQ3pQVFCbn7mpL1eKjFFSF/KAnkBfwPCNea72VHzTrdcpIMUAiDhgd+UFURlKQTTlObBcckK8EP75XWx4jXqvhPHe1N/KtVU7lv0tRXl5IqnxuKMBqUw031VPqgH/xZps7JzDc6fGjVePCFRLtv9ciI/UJGDfkeUaMs+QFPMtIMUAiL2B05AUAAPBdaWEBSndiO4stV7Vqn/0cuKcYsc8C55m13wX3ywiOVGoZyzyRIw8PC1YVN+TIiYH+hzgIb0oKKnTyfn2BX9+vJz9azKx5NKNdb3m22vOMFAe4V2ps3CNBZRRYonVYcurZ5tjoYEWE+m97zB6jXadRee5qb6SFhZWZ//bEb65FR4UrKojcEMrnzTZ3cohFzrQKAiy5bhsfbARG6g9I5AW8wWixlryA5xkpDpAXAACgWGnzllSU304sc/HFF2vz5s169dVXdckll6hZs2aSTk0Sc/PNNysp6dRjcx06dCix3ciRI3XLLbfohRde0D333KMDBw5o+PDhCggIkNVa+g+VlWfFihVnvZaVL/Wc6dTuXCJl8ffK2r1W2Qe3KWXh1LOWt353k4IT6rnseNu2bVfYGWdbXma2vmp8k8uO4YjaF3ZUw0Hn66eLRhW9dnLvEa188Sud/+YIzbhwlPKznBkyULpt27cpKLzkj7tzDtjn6s/HYrEoolk3ZWxepFw3zU7v6Ht0l5zcAtXrO0VHk7Md2i4mKlh7/5mriPAgN5XMPm/GBLheaXHPVfrdM0uzF5dej8+5cYbdbff/cYPqVI/Q4aQs1b1kisPHtlik7ZuWKjoy2OFty0Md8C/urAMVcePoPzVl1i6Ht/vpyxd0YbdP3VAiY/B2++vfPN0WO5NR2i2eZqRY6+k+yZm8Hae8wUgxQPJuHDBrDHAEfVL4MpvNppZXfK+te9Ic2i4o0KrNy39Q9Xjv3BA20nUalWfGtgY8p9Fl32r3wZOlLnN3bqhWtXAdXLfP4e0qgjjoX4iD8KbjqdmqffEU5eQWOLRdw9pR2rFmtaxWxx7g8SVGyg2QF/A8o11rvZkfNOt1ykgxQCIOGB35QVTGO19v1MhX/nF4u6cfulJP3TXWDSXyDUa7VqNy3NXeyMrOV9S5n6ugwHbWsvLyQlLlc0O9urfWvE8OOLxdBc3u1AAAPdBJREFURVAH/Is329zZOfmqc8kUHU91rD8RFxOifcvmKyzUfxsURuoTMG7I84wWZ8kLeJ6RYoBEXsDoyAsAAOC7/l5xSBfc5viF/Jq+zfTdG+7J+1SE0fosqByz9rvgfnP+OahL7ppV6jJP5MhHP3i7xtz7nsPblYcY6H+Ig/Cm8V9s0EOvLXV4u2dHXaPH73jODSUyBvKjxcyaRzPS9ZZnq73DSHGAe6XGxj0SVIbNZlP7a3/U+u0pDm0XEGDR+sVTVadGhJtKZmxGuk7DNdzV3pgwZZNGvLSk1GXufq5Wkj54+0Xd2L+xU9vaQx3wP95sc38zc6f+89+/HN7uvlsu0PjHHnN9gQzCSP0BibyANxgp1pIX8A4jxQHyAgAAuIbfXs5Gjx6tr7/+Wvv371fr1q3VokULZWdna8eOHbrsssvUoEEDzZ49W+3bty+x3U033aSNGzfq+eef19NPP62AgACNGDFCwcHBio6O9tK7cb34Pjcrvs/N3i6G1xyct1pft7j1rNe3TJ5VNHOlv/PXc+DEmj8U1baPLAElw1thTpZOrPldkhRWt5U3iuYxIcEBuuPq5nrp47UObTd0UFOvTSpjdrOuGePtIviUzq2qljmxjLs1qx/jlkllQD1wteHXt3R4YpnmDWLUp2tNN5UIZ/LXthgqjj4JiAP+iT4pjMBisWj49S31wKuO/XjltZc08NqkMmZHfwhwTOdW8WVOLOP2Y7es6pXj+jviIOBa8bGhuqFfI302Y7tD2907uIVfTypjNOQFQH4QxAH/RH4QknTL5U313/ErlJmdX+FtAgMsuuPq5m4sFYyMfnHFhYUGqlWjWId/fMBVOrciN+QO1AHXCg0J1O1XNdO4Sesd2u62K5v59aQyRkN/AOQFQBzwT+QFAADwvp6da6h141ht3Jnq0HbDr2/pngKhXOSGgIrr5OXxi+TI3YM4CLjWrVc01RPvrFBWdkGFtwkKtOr2q5q5sVQ4E/lRc+MeCYgB/ol7JJCKn22+94XFDm13Re/6pp1UBvSLHeHt3Iy3j++vqAOudfXFDVQtLlRHk7Md2u7ewS3cVCKUhj6BuZEXADEAAADX8NsnwOrUqaMFCxbo0Ucf1fz587Vnzx61atVKH374oe688041bnxq1tszJ5axWCx65ZVX9OSTT2r37t2qXbu2YmJiFB8fr/vvv98bbwWAA/Z/8pDyTx5XbNdBCqvfVtaQcOUm7Vfy/K+Vk7hNcX1uUViDtt4uptvdO7ilPvhui5LTKjbjanRkkEYO4QYcfIM3bzJwgwO+oken6up9Tk39tfxQhbd56q4Oslj44UgAACqDPimM4tZBTfX6Z+u1/3BGhdYPDrLqkVs5NwH4hs6tqmraH3u8dOx4rxwXABw16pY2mjp7l7JzKvZwdM2EcN3Gg9EAAFQa+UFIUkxUsB4Y0lovf7K2wtvceU1z1aga7sZSAf6jc6uqXpxYhtwQfMOIG1rpo2lblXoyt0Lrx0QGa8QN/IAuAACVRV4AAADvs1gsevrujrph9J8V3qZHx1PP3wCA0cXFhKhh7SjtPnjSK8fn2VoAvqBKdIjuv7GVxk1aX+Ft7hncQtXiw9xYKgAA/B/3SHDakAGN9eqn67QnMb1C6wcFWjV6GOcGUBHtmsUpIMCiggKbx48dFRGkJvWiPX5cwFEhwQEaPaydHnljWYW3GXxpQzVvGOu+QgEAAACAG/jtxDKS1LJlS/3yyy9nvZ6enq49e/bIarWqTZs2pW4bFRWldu3aSZImTpyorKwsDRs2zK3lBVB5dW/7n1KX/qT0zQuVsvh7FWSkKiAiRmH126nGNY8p/sKh3i6iR9SpEaGf37lE/e6drZMZeXbXDQ8N1I9vXqxGdUjcwjdc2LWmwkIDlJVdsR/Ec6WBvep6/JiAMywWi77/30Xqc/tMrduWXO76Y+7pqJsGNvFAyQAA8G/0SWEUMVHBmvleX114x286lpJtd93AAIu+erm3OvGwHwAfMbBXXT3+1grvHPuCel45LgA4qm2zOE0d10fXPTJPuXmFdteNjw3RzPf6Kj421EOlAwDAf5EfxGnP39dJuw+e1JRZu8pdt3/POhr/WHcPlArwD5dfUE+Tf9ru8eOGBAfo4u61PX5cwBn1akZqxtuXqP+I2UrPzLe7bkRYoH56+2I1qB3lodIBAOC/yAsAAGAM1/drpB37Tuipd1eWu27rxrH6cfzFslgsHigZAFTe5RfU1dtfb/L4cTu1jFfNhHCPHxcAnPHSyC7ak5iub2fvLnfdyy+opzdGdfNAqQAA8G/cI8FpURHB+u39S9Xn9pk6nJRld92AAIs+f7GXurWr5qHSAb4tLDRQl3SvrVmLDnj82AN61pXVSh4dvuHhW9po14GTmjB1c7nr9uhYXZOe6+WBUgEAAACAa/n1xDJl2bhxo2w2m5o1a6bw8JKDWFasWKE//vhDnTp1Un5+vubMmaO3335br7/+uho3buylEgOoqOiOfRXdsa+3i2EI53WoroWfDdTIV5Zo/orDpa5zfsfqGj+6m7q0TvBw6QDnxUaHaEj/xvr4h20ePW61uFBdc0kDjx4TqIy4mBD9PWmAHnrtH309c5dycs+ejKl+rUiNuaejhl3ZzAslBADA/9AnhZG0aRqnf766XCNeXKLZiw/IZjt7nY4t4vX6qK66sFstzxcQAJzUpmmcenaqrgWrjnj0uN3aJqgzk3AB8CGD+tTXHx/208OvL9PKTUmlrnPJubX07uPnqVmDGA+XDgAA/0R+EKcFBFj11Su91apxrN76aqOOp+actU5MVLDuHdxCzw3vrKAgqxdKCfimQb3rqVa1cCUezfTocQf3baiqVZiQE76jZ+ca+nvSAD04bqn+Xln6+NEeHavrrce6qxN5TwAAXIK8AAAAxvHkXR1Ut0aExkxYpT2J6WctDw6y6oZ+jfTWY90VGx3ihRICgHPuvb6lVyaWGXFDK48fEwCcFRBg1dev9FarRrF6++tNSk4r/X79iOtbauzwTgoM5H49AACVxT0S/FuLhrFa8sXluu/lJZq5YH+pzza3axan10d11SXn1vZ8AQEfdu/gFl6ZWGbEDS09fkzAWRaLRe8+ca6a1IvWuEnrSp3oLCIsULdd1UzjHjpHoSGm/DlmAAAAAD7OlD2Z9evXS5Lat29/1rKQkBD9/PPPevnll5Wfn6+2bdtq6tSpuvbaaz1dTACotHbN4vTXpwO0cUeKPpuxXfsOp8tmk+pUj9AtlzdR++bx3i4i4JTh17f0+MQyd1zdXMFBAR49JlBZMVHB+vS5Xhr3UFdN/mm71m47rqzsAlWJDtYVferrsh51FBDAoEcAAAB/1ahOtH57/1Lt3H9Ck6Zv0879J5VfUKgaVcN104DG6to2QRaLxdvFBACHDb++pccnlrl3MINfAfieXl1qasWUK7R8wzF9+csOHUrKUoDVooa1ozTsyqZqWp8JZQAAANzFarXo6bs76tGhbfXd77s1d+khnczIU2R4oHp0rK7/9G+siPAgbxcT8DmBgVbdfW0LjZmwyqPHvff6Fh49HuAKHVtW1fxJA7Rhe7I+m7FD+4+cGj9at3qEbh3UVG2bxXm7iAAAAAAAuM0tg5pqyIDGmr34oKbP26vktByFhgSoXdM4DbuyqRLiwrxdRABwWIuGsbqwa03NW3bIY8eMjQrWDf0aeex4AOAKAQFWjbm3k0YPa6dvZ+/Wn8tP3a+PighSz07VdUO/RtyvBwAAcKMGtaP0y7t9tfvASU36aZt27DuhvPxCVY8P03/6N9a57avxbDPghAG96qpezQjtO5ThsWO2bVpF53es7rHjAa5gsVj00M1tdN8NrTT9z736beF+pZ3MU0RYoLq2TdDNA5soJirY28UEAAAAAKcxscwZ2rZtq8WLF3u6SADgVq2bVNG4h7t6uxhOCQgJ0gUfPKSYpnVUkJ2r7KQ0LfnvRJ3cc7honQ6jBmvH1D+VfuBY0d/r3/1RBTl5ResMPTRNXze/RbknMit87NL2Y8/QQ9OUsnmvVrzwpQ7OW62uz9+mepd2UWTdappx8SNK3rinQu8pqmEN9XzrfoXERSnvZKYWPvCuUredmin+0mnPKq5Vfa19c5o2Tfy1wu+lMuoP6K46F3dSSGyk3e/Bkzq2rKqbBjbWl7/s9MjxalUL1yND23rkWI6qP6C7avZsq+VjJpdbV5zlTD08/dq/65Az9bC0/dhT0XpYkfdlb1tv1MXKqFol1LDnMAAAANyvcd1ovXB/F28Xw2lmzA3U7tNBHR+7UdagQBVk5Wjx6A+VsmmvJM/3RzzR7wQcdV3fhnrrq436Z92x8ld2gc6tqmrIgMYeOZajyA2V5EhuSJIumfK0whJipcJC5WVka+lTnyp5w25JxsvTApVxTpsEndMmwdvFAAAAMKXQkEDdfHlT3Xx5U28XxSf5am7w8KINTo8NMtpYBSOOG3rwptb6+Iet2n/YMw9HX9e3oc5tb8wHo8kNleRIPZTKrovlvWdfyw21aRqn10b55vhRAAAAAAAqIyDAqv4966p/z7reLopT/DU/ai8HarTxOkbMjwLjHu6qbkNmqKDA5pHjvXh/Z4WHGfMnSciRl+RojtxePDba/SrAWWGhgbr1iqa69Qru1wMAAHhDwzpRem5EZ28Xw2f5a37wtCbX91GP8SM0b9ir2jdruSTJGhyoc8bcqtq9O6ggJ1fJm/ZqwX1vSyI/KJ3Keb/2cFdd/+ifHjvmG490M+xEUOSGSnKmHkpn18WQKpG69NsxxZ9BWIii6lfXlLa3Kzc13adyQ0FBVl3Xt6Gu69vQ20UBAAAAAJeyersA3mBvYhlvSvzmWRXmZkuS9rw1VEdmjHd4H6n/TFf61n8qtG7S3MlafWOMNj9c/KOSe94apg33NtOmB9pry2PnK2P78qJlO1+6Spse7FD038orrUpdOkOSdOSnN7Xh7iba9GAHh8v8bx1GDVZASJAkqcf4EWp15wCH91Gv3zlK6FSxG/tNBvfWf7Z+poGzXi167fw3h+uqhW9r0JzXddlPLyi+ffGPxUU1rKFLpz2rQX+8pqsWvKUuY26R/j/h1equgbp68Tsa9MdrDpf5NKOfA9mJ27XtmUu06YH22nhfayUvmFq0zFXnAFCarV/8oR97jNSMix/RvtnLdf4b90o6ldjt/ORNCo4JV9WOTdTz3ZEKqRKpDo8Ux5LKcGY/v135tA7OWy1J2vvrEs284iml7z9a4fckSeeNu1vbvjy1fP2709XjrfuKls2+9lnt/32Fk+/IOfX6d9O+Wcvtltkb3nrsXNWoGuaRY330zPmqEh3ikWM56tT3s0yS/fOqshyth5Jzdag0ju6novVQsv+Z2dvWG3UR7mGE9teh717Shnubn2pf/zO9xPr22maSdHTmBG0c0VIbR7bVpgfaF72XA5Me1brb62nHS1c6/H7Mxgh9kLYjr9ZVC97SrQe/Vb1+55RY317/pM+nj2rQH68V/XfrwW9Vt++pc8sV/ROz8OU4kJ24XVuf7KNND3bQhuEttP/TUbIVFkqijwbA/5gpNxAcE6Ge7z6ghSPf0YyLRmn581+o13sPFG3n6f6Ip/qdgCMCAqya9HwvhQQHuP1YQYFWTX6+pwIDjXlLjdzQ2RzJDc2/6w3NuGiUZlzyqDZ++LN6jB9RtMxoeVoA7uELeYFNI9tp04MdtHnUOTqxdm7Rsq1P9tb6OxsW3b8/8tObRct2vzFEa2+tof0fP+jw+zEbX8gPDpr7hgb98ZoG/vaKavYoOYl8h1GDddXCt3XFvDd06bRni17v+d4Dun7tRHV9bqjD78dMfDkGbH6kW1H933h/G628wqLMPeskcY8AgP/x1dygs2ODjDZWwYjjhqIjg/Xxsz08cqyEKqF674lzPXIsZ5AbOltF66Fkvy7a25bckP8wQp/A3piAwpxM7Xr9Rm24u4k23NtMKYumFS3zxLh+MzBCXsDe2KCAsGD1mvCgrl78jq5a+LbqD+hetMzdzzWYgS/HAJ7rAAAAMA9/zI/ay4EabbyOEfOjQOdWVfXf29p55Fh9zqmpewa39MixnEGO/GyO5MjtxWOj3a8C4B5Gz5EmzflUG0e21cqrAkstG89XV47R75E0ueFCXTHvDd2yf+pZZePZatfw5RjAs9UAzMQf84OSFFknQc2GXKyjK7aWeL3zkzdJNpt+OP9+/XThKK0Y+3nRMvKDpwy+tJGuvaSBR45117XNdcm5tT1yLGeQGzqbI/VQKr0u5qSka8Yljxb9t+3LOTo4b7VyU9MlkRvyJ0bvE+x5a6jWDatdNEbywKRHi5Yd/OIJbRjeQpseaK/ND3dR2qrZRcvoE1SM0fMCPcaP0HWrPizq+3d5+uaiZR3/e6OumPdG0bKGV5xftKzL0zfr2hXv68JJox1+P2bjyzHgtKz9m7XquvASz1ETAwAAZhHo7QJ4w7x587xdhFIdmjJW1S5/UNbgUKf3kbp0usIadlBk8+7lrywpqm0fNXlietHfsd2vUv37JsoSEKjU5b9o17jr1HbiHklS4yd+LFovY/sKbR/bT9Gd+kmSql/xkMIbddT+Tx50uuzSqQTGpom/VHiW6tLU69dVyRv36Niq7RVa//DijZo3bFzR3/t+W6bFj3wgW0Gh6lzcWX0mjtK0rsMlSec8c4v2/bZMmz+ZqYCQIA387RUd6rNeB+et1qaPflHy+t2V+mEWo58De94aqqoXDVPVvncoL+2YtozqoshWPRQcX9tl5wBwpoKcvKJEpSQdW7Vdbe4dJEna++s/Sj9wTJd+N0ZVWjXQnCEvqutzwyRJl01/XraCQv1+w/PKPn5CktR8aD/V63eOQuOjtfZ/07Rj6qmZ1+PbN1aXp25SUFS4LFar1r39g2r1bFvqfnq+94BiGteSNShQGYlJWvzw+8o6llpq2Y/8s9nh9xQaH6349o31+w3PF73H7i/drqgGNdwyS31wdLiu+PNNBYQGKzMxSdaQIEXVq66d0+Zr8SMfyBIYoOrnNNfCB96VLb+g1DJ7S1xMiD4Z21OX3/+HCgttFdrmcFJmif9XxJ3XNNeAXvWcKqMr2PuOlvx3Yonvp6zzqrIcrYcFOXk699W7JJWsQ5Jj9XDvL0tK3c85zw2rdD0s732Vty38hxHaX9HtL1Zczxu0553bzlrXXtssdelPSp7/lVqM+0cBETHKSzsmS8CpZH2dYa8ptF5rpS6dftY+UZIR+iCH/l6n3dMXqsebI85a117/5M/bigc2xrdvrEu+flIH/1wjSS7pn5iFL8eBA5MfVZXuV6na5SNVmJutzY+coxOrLlJMl/700QD4FbPlBqIa1FBOykmlbjsgSTq6dLMialdVXNuGSl6/29mPsUxG6HcCzmjRMFavj+qq+19eUuFtnMkNvTSyi9o0jXO4fK5ihDrqr7khSco9UXwuBEeFS7ZTeUZP52kBeI/R8wJ1bn9TgZGxkqTMXau17emL1P6LJFmspyY8q3v7m4rtfuVZ2zUc9ZUSv3lWBRmpjr4d0zF6fnD5mMlF16u4Ng116bfP6JvWt0k2m1re0V9VWtXXT30eVmFevsISYou2WzDiLXUYNVjBMeFOvy8z8OUY0PL1pUXrpSyapsSpYxXe4NSPB3GPAIA/8dXcYGXGBnlyrIIvjxvqe14d3XdjK737zaYKb+NobshikSY+20MJcWFOldEVyA05nxsqb1xQeXWRfLg5GKFPYG9MwOEfX5c1KERtPtyhnCO7teXRbopq20eB0fEeGddvBkbIC9gbG9TmnkEqzM3TD+fdr8i61TRg5ss6vHiDclLS3f5cgxn4cgzguQ4AAABz8Mf8qFT2eB6eqwQq7um7O2rWooNauSmpwts4miM//fyu1WpxqoyuQI7cfTlyyf74Sp6tBczB6DnS8Mad1ejRb3V42sulHJfnqyvL6PdIjq/bqb/u/p/a3n/1Wfvh2WrX8OUYwLPVAMzCX/ODslh03hv3aulTn+icMbcWvRwYFqKmN16o7zrdXfRaWc8MuoIv5wcnPHme/ll3TAeOZFRofWeeq21aP1qvPdzVqfK5Crkh9+aGyqqLZ2r6nwu16qWvHP/wYHhG7xNIUvWrHlX1QQ+etZ/IVj1Vc/DTsoaEKXP3Wm19opfaTUpUQGgEfYIKMnpeQJI2vj9Dmyb+etZ+Nk74Satf+UaSFF4jTlf+PV6JC9YpJ/mkVjz/hVK37le9y7x7DfMFvhwDJMmWn6d9E+5Sle5XlXidGAAAMAtTTixjRHsn3CNJ2vpET1msAQqKqyXLgc3a9vRFyk3ar7B6bdTwkSmyBgXLlp+ng189rZPr58mWn6vQWs1Ub/iHytiyRKnLZujEmj90fN5kVet/n2LOGahdr9+owqwTKszNVlTbPqp759tFPzRzpthuxZ3+yObdlXv8oGwF+bIElDxVkuZ8ovjeN8kaFOyyz+DMBEbmkRTFNK2tvt+OUUSteKVu3a/597ypwrx8WQID1Gn0DarRo40CggKVtuuQloz+UAmdm6lu3y6q2audmgzurc2TZunAHyvV6/0HFRQVpoCQYB1etEFLn/q06MfBzvTvWXCPrdqm8BpxsgRYZSsolM1mU1D0qR9eCQgNljUoUFlHU13y/n3hHMjas1bRnftLkoJiEhTWoL1SFk5V9SsedslnAFREqzv6a9/sU7OJ1uvfTQkdm2rH1D91dNkWnfvaPVo+ZpKa39JXv135dIkfBZSkwpw8/dr/ccU0qaWBv72qndPmKygiVOe9do/m3PSiso6mKiQuSpf/Pk4zBz5Z6n6WPTNJOf9/w6TtfVeqwyODteSxj1z2niJqV1XWkRTZCgqLlqcfTFJE7apuGQCdeyJTu35coLyMbK17c5pq9W6vdiOv1uJHPpAk1Ty/jY4u31ri5saZZfam/j3r6uNne+j2MQvKCuslnHPjDIf2f9VF9TXhyfOcLJ1r2PuOal3QvtTvR3Lvd1SRerjksY9KrUOO1MNjK7aWuh931MMz3xfMwSjtr4hmZSeh7bXNDv/4mmrdMEYBETGSTrXP4Bij9EGS1uwos4z2+if/1vTGC7Xr+79VmJfvgk/GPHw9DlhkUUFmmiSpMDdLtvw8BcXVdOEnBADG5O+5gRO7DimkSpQSujTXsRVbVbdvFwVHhSuybjW3TCxjxH4nUFH33dhKh45l6qWP11ZofUdzQ6NuaaNRt7ZxpmguY8Q66m+5oR5v36+a57WWJP1x00uSPJ+nBeAdvpAXOD2hhCQVZKS59P3DN/KDZ02C9i9t7r1Cs697tign6M6HpvyRP8WApDmfqOrFtzv3QQCAj/HV3KA3xwbZ4+vjhsaP7qajyVn6dnbF8qaO5obef+p8XdGnvjNFcxlyQ67LDZ35mThSF41yzsO1jNInsDcmIGXhVNW//xNJUkj1hopq01up//yoqn3vKLEPd4zrNwOj5AXsjQ1qcMX5WvzwBElS+v6jOrx4o+pd1k3bv57r1ucazMDXYwDPdQAAAJiTP+RH7eG5SqDiQoID9NuEvrrgtpnavCu1Qts4kiOPjgzSrPcvVcM6UU6W0DXIkbsvRw4AvpAjDW/YXpJK3ZbnqyvHF+6RpGzae2pBYWGp257Gs9XO8fUYwLPVAMzKX/KDre++XEeXb9HxdbtKrBfVoIZyU9PVbuTVqtmrnQqyc7Xm9W91aOF6pz8ze3w5P5gQF6Y/PuqnC4b9qqPJ2eWu7+jYyXo1I/THh/0UHendsUjkhtybGyqrLv5bQpfmComJ0P4/Vjr8WcHYfKFPYE9M58uK/h1Wv61ksyk/7ZgCQiNc8On4P1/IC9jz79gaGBEqi8Uii8Xigk/GPHw9BkhS4tTnVOW865SfnqyCjFSXfC4AAPgSJpYxiPrDP1DS7A/V/KUFCoyM1Z63hipz1xo1f/FPWQJDtPWJXkpd8r3iet2owz++poDQCLV8fZkk6dDU55X45VOqd897iu06SGENOxTNqleYm60mT/2sgLBI2QoKtPOlK5Sy8FvF9bqh3DId+fktxXTuf1ajqjAnSykLvlHzlxa49DM4M4HRY/wIxbVuqFnXjlFhTr76/fic6g/opt3TF6nN8CuUl5WjX/s/Lklq99C16vjYjVr6xMfa//sKJW/cUzS7ZEBIkObe8oryM7NlsVp14eTH1HDQedr906Jyy9TqjgE6MHd1Uedi2TOTdNFnj6vFLX0VHBOhteO/V/IG1/yYoy+cA+GNOyt5/peqcfVo5RzepfQtixVcrYFL3j9QEW1HXq2oBjW0ePBYSdK+mUu1b+ZSdRg1WEmrd2jPz0vsbr/rh1NxK21HogrzCxRWLVZVWtZXVP1quuSrJ0usG924Vqn7aHRVDzW+9gIFhAQpICRIOcknXfqeKqv/zy8qulHpN7xnXPKoMhOPn/V6XJuG2vzxTElSfLvGJeJavX7naO9vy9xa5soadmUzhQQFaOjTfysv334yyBE3D2yiT8b2VGBg6ckITyrrOyrt+5HK/46cOU/K2re762Hm4eSz9uHqelja+4I5GLH9Zc+ZbbPs/ZuUsWOFEqeMlS0vR/F9blG1y0dW6hhmY8Q+iD1n9k9OCwgNVqMre2jmlU9Xav9m5OtxoM4d47Xzhct17Lf3lZ+RopqDn1Z4o46VOgYAGJ0ZcgN5JzP1152vq/MT/1FgRKiOrdimlK37Sx3cVhpX5gac7XcCnvTC/Z0VHhqop9517cDEZ+7uqGeHdzTEQCJyQyW5Og4vHPmOJKnxdReoy1M3ac7/Ty4DwP/5Sl7gwGf/Vcri71SQnqLGj31fYhDlgc//q4NfP62wOq1U+5aXFVKjUaU/FzPxlfxg5yeGqP7l5yokJkJ/3vG6ZLMpKDJMYQkxqnvpOWow8FxJ0sYPf9aeGYtd8+GYgD/EAEnKPbZfJzfMV8OHvqjU5wEAvsBXc4OezKWZbdxQQIBVX73cW5HhQfr0x20u229goEUTx/TQ0CuauWyflUFuqCRP10MjnfNwLSP2Cc4cE5B7bJ9CEoonuAqu1kC5x/aV2MZd4/rNwIh5gTPHBkXWrqr0A8eKlqfvP6qI2lUlufe5BjPw9RjAcx0AAADmQ360fGbLjwIJcWGa/2l/9R/xu1ZsTHLZfqvFheq3CZeqU6uqLttnZZAjL8nosRiA7/CFHKk9PF9dOb5wj6QieLbaeb4eA3i2GoAZ+Ut+MLZ5XdUf0E2/XfXMWetaAq2KrFtNqdsPaOVLXymuTUP1nfq0pl/wkLKT0so9ltnygy0axurvSQN06b2ztTcx3YX7jdHsD/qpXs1Il+2zMsgNleSq3JC9uvhvTW+8UDu+m+9QOx2+wVf6BEd/fkvH53yq4IR6qjXkBYU36nDWdsfnTlJIjUYKrlb/rGUona/kBVre0V9NbrhQGQeTtPrVb5S8cU/xstv7q8XQSxVeK16LR72v7P+fdAsV4+sxIGPrUmVsWaKmz/2hQ1O83y4DAMAbmFjGwKp0v0rWkHBJUkTTrso5vFOSlLp0ugoy0pSy5HtJki0vV8HVG5S+E1uhDn72mNI3L5RsNuWlHVVovTblNqyO//WlUhZ+q+Yv/33WspTF0xRSq5nCGrR1/s1V0L7flqogK1eSlLRmh6Ia1JB0KqETHBWuBv27SZKswYFK33+s9J1YLer81E2q3rWFZLEorGq0UrfsK7eD0eianmpw+bklkh4tbu2n3dMXav07Pyo0PlqXfj9WSWt26NDf61zwbs9mtHOgwQOf6cCkUdr0YAcFJ9RXdLuLKnRDDnCF1vcMUv3+3fT74LFFceG0NW98W6F9FOQUb2crLJQ1IEAWi0WpWw9o5qAn7Wx5SrWuLdTy9v6aOfAJZR8/obp9u6jjo9c79kb+pbT3lHEwSWHVq5SYMTeydlVlHKzYwM6Zl5f/Ps4U17pBUcI8vl0j7f/XzOK1enfQiue/tFtmI/jPgMZq16yKhj69QCs3VW4QbGxUsN7+b3fdNLCJIX44VCr7Ozrz+5Eq9h05c56Ut29frYeScc9reIc321/2lNY2sxXkK/fIbjV/6W8VZKRo6xMXKLhGI8WeM9Dp48C7fRB7SuufnFZ/YHel7UpU6pZ9pWwJR/lSHDj22wRV6XWjal77uPJSj2rbU30U0fQcRXe4xOnjAICR+WOfpKz3dHjxRs26eoykU+2O69d+rNRtByq0T1fmBpztdwKeZLFY9ORdHXReh2q6fcxC7T5YuUlG6tWM0Cdje+ri7rVdVMLKIzdUzB25odN2fjdf5756l0KqRFY6TwvAdxkxL1Dn1ldU59ZXdGLNHB34bLSav7JI1qBgNXzwCwUn1JXNZtOxme9px/MD1fq9TU4dA8WMmB9c+dJXWvnSV6rZs626PH2zZg56SpbAAFmDAhUYGqxfBzyuyDoJ6v/zi0rbcVApm/Y6dRz4Vgw47fi8yYo9Z6ACo43xoz4A4C6+2id1x9gge8w4bigw0KqPn+2hS7rX0n0vL9Hx1JxK7a9DizhNfr6X2jePd1EJK4/cUDFX1UOpYnXRiOc83Mto47YrwpPj+s3AaM8u2OPp5xrMwJdiAM91AAAAmIu/5WXKwnOVgOMS4sK06POBev7DNXr5k7UqKLBVan/X9W2o9544VwlxYS4qYeWRIy/miVgMwNx8KUfK89Wu50v3SE7j2WrX8qUYwLPVAMzGn/qk1bu1VGTdarpm8TuSpLCEWJ372j0Kq1ZFe35ZosKCAu36/tTkG8kbdit931FVaVlPhxasL7eMZswPNm8Yq7XfXaVRry/VJz9uq9S+LBZp5H9a66WRXRQeZpzxB+SGirkyN2SvLm79/HdJUmB4qBoOOk+/XPZYhd4ffJ/R+gS1bnpRQVVqymK1KmXJj9r+3GVq8/52BYQVT3x1Yu1cJU4Zq2Zj/zDM70X6KqPlBVa98o0yj6RINpvqXdZVF3/1pH44737lZ2ZLkjZ/MlObP5mpKq3qq9e7I5U4f61yUlw30ZoZ+UoMsFit2vfhcDV6bBr1HgBgasbpueMsluDQ4n8HBMhWkH/qD5tN9e56R9Ed+5a7jyM//U95aUfV4rWlsgaHav8nD8uWl213m+QFU3Voylg1e36ugmKrn7U8ac4nqnrx7Y69GScV5OQV/dtWcCoJIp36obilT32qxPlry91H67svV1jVGP064HEV5OTpnGdvVUBokN1tGgw6Tx0eHqzZg8eWmKm6xbBL9cP5IyVJ2cdP6ODcVapxXmu3PYBltHMgpHoDNf7v90V/b3+2X4XKAFRWq7sHquFV5+v3wc8p90RmuevnnsxUUHR4hdY9umKrIutVU82ebYtuIMS1bqDUbQfO2k9wTITy07OUk5Iua1Cgmt3s/E3lst5T9vETSl6/W42v6aUd3/6l+gO6K+NQsk7uOez0sewJrxEn2WxFs5PHtayvdW+dqudVOzZV2vaDRUkUR78HT2vTNE7/fHm53vxig978cqMOHXOsjMFBVt3Qr5FeefAc1UwId1MpHVfWd3Tm9yO59ztyZt8VrYv26mFhXn6J/biyHjr7vuDfvNX+sqestllwQj1V6XWjLAEBCoyuqpjO/ZWx9R8GPlaSt/og9pTVPzmt2Y0Xafs385zeP0rypThwbOZ7aj3h1ACPoNhqiuncXyfX/8XgRwB+yUy5AUkKqxarrKOpkqT2D12rQ4s2eDw34Ol+J1BZfbrW0rrvr9JzH6zWR99vVdpJxwbnRkUE6Y6rm2ns8E6KiggufwMPMUod9cfcUHB0uALCQpR1JEXSqcFtOSnpRQPHPJmnBWAcRswLnBbd4WLt++g+Ze1dr4gmnRWcUPdUOS0WVRtwnw5MekT5J44rMNo4P4Dti4yYHzzt0IL1CooMU5WW9XR83S7lpWdp5/enBsumHzimo8u3qmqHJkwsUwm+FANOFcumpLmTVO+e9yu9fwAwMl/NDRplbJA9/jJuyGKx6IbLGqtP11p6/K3l+nrmLuXkFji0j+rxYXpgSGs9cmtbBQVZ3VRSx5EbqlxuyF65y6uLRj7n4T5GG7cdnFBPOcf2KiiupiQp9+geRXcoWQZPjus3A6M9u5B+MEmRdRKK7htG1q1WVAZPP9dgBr4UA3iuAwAAwDz8LT9qD89VAs4JDgrQ8/d11lUX1tdj45drzj+JDu+jTZMqGnNPR13bt6EbSug8cuTuy5EDQGmMliO1h+erXc9o90gqgmerXcuXYgDPVgMwE3/LD279/PeiSSskqd/3Y7Vp4i/aN+vUZCGHFm5Qrd7tdXDeakXWrabIetWUtv1gue/FGf6SH4yJCtbHY3vqur4N9fR7K7V8Q8Um6v63Hh2r66WRXdSzcw03lNB55Ibclxsqry5KUsMrzlPypj1K2+F4vhG+yWh9guD42kX/rnLuVTr4+X+VfXBr0TNVJzfM1563h6nJUz8rtE5zR94qSmG0vMDp2C9J+35bps5PDFFMk1o6vm5Xie1TNu1V5uFk1Tivtfb+urRC7xWl85UYYA0MVu6xfdr2VB9JUkFGqmyFhcpPT1HDBz9z5C0DAODTmFjGQKxhUSrITFNgZKzd9WK7XakjM95UZKsesoaEqzAnUzlHdiusXmtZw6JVkFncIM5PT1FQlRqyBocqL+WwUhZ9pyrnXVPmvpMXfqvEr55S0+fmKDih3lnLsw/tUOaOFWry5Ayn36c9FU2E7Ju1TK3uGqgjyzarICtXAWHBiqpb7f8TklkKiiqeECA4JkJZR1NUkJOnsIRYNRh4rvbO/KfMfTe4/Fx1euxGzb5+rDIOlkyQndx7VLX7dNSOKfMUGBaiGue30cYPXPdZGP0cyEs9osDoBFmsVqWtmq2s/ZvUuNd/KvWegfKE14xT12eH6sSew+o37VlJUkFuvn4d8HiZ22z84Gf1nfKMCrJy9PsNzyv7+Iky181Ny9Ccm1/SOc/conPG3CprUIAyDiZp3rBxZ+3n4J9r1PiaXrpq4VvKSUlX4oJ1iqgRV+a+zx13l+pc1Flh1WJ1yTdPKS89Sz+cd3+572nx6A/VY/wItR15tfLSs7Twwfcc/+AqKK5Nw6IZ2SUp90SGWtx6qRY/8oHqX9ZV+2Ytk+Tc9+ANgYFWPTqsnR68qY1++nOvPv5hq5asO6oT6XllrG9RmyZVdEO/RrrtymZKiAvzcInLV9Z3lJN8suj7kdz7HTm77zPrUFns1cOz9nPjC0rbmVjpeliR92VvW/gPI7S/7LHXNovr9R+dWDVL0e0uVGFOlk5u+Es1rh7t1HHMzAh9EHvs9U8kKapBDcW3b6S5t77i1P7h23EgpHojnVg9S1Uvvk0F2Rk6uf5PVb9ylFPHAQAjM2NuoOPoG1S9W0tZAqw6tnKbFj88wfEProKM0O8EXCUyPEjjHu6qMfd01De/7dJnM7Zr5eYkZWWX/kOioSEB6tQyXrdc3lRDBjRWZHjlf/Dd1YxQR/01NxQUHa7eH41SYGiwbIU2ZR8/obm3vFy0rSfztAC8x8h5AVt+nnKO7VVozSaSpIxty5SfdlQhNRrJVpCv/JPHiwZKpiz+XkGx1ZlUxglGzg9aAgMUWSeh6IeSqnZootD4aJ3ce0SStGv6QtXu01FbP5ut4NhIVe3YRBsm/OTwcczMV2PAaSfXzZOtIJ+HoQH4NV/NDVZmbJAnxyr427ih6vFh+vS5Xnrt4a6aNH27vvltp9ZtT1Z+vq3U9aMigtS9XYJuv6q5rrqovoKDAjxc4vKRG3I+N1SRcpdVF33lnEflGaFPYG9MQJXzr1PSrA8U2by7co7s1skNf6nePcX3jNw9rt8MjJAXsDc2aO/PS9T8lr46tmq7IutWU43zWuufxydKcv9zDWbgyzGA5zoAAADMwV/zo/ZyoDxXCTivU6uq+uOjy7RtT5re/3azfl2wX9v3lh0DaiaEq3eXGrp3cEv16FRdFovFg6WtGHLk7s2R24vHPFsLmIPRc6T28Hx15Rn9Hkl5eLa68nw5BvBsNQCz8Nf8oD1LRn+o8/83XF2eukm2QpuWjP6wxA/Lu5K/5QcvPb+OLj2/jpZvOKb3v92sOf8kav/hjDLXb1ArUv3Or6N7r2+pds3Kfj7Tm8gNuTc3VJ6mN16kbV/NqfD68D1G7xPkJh1QcNU6kqT0rf8o/+TxomesTm78W7vfvFlNnvxJ4Q3bO/kJmJvR8wLhNeOUeehUGyChU1OFxEXpxO5Tz1jGNKujtG0HJElR9asrrk1Dpf7/36g4X40BARExav/FsaL1Er95VgUZqap7x3jHPwQAAHyYxWazlf7EJNwqK1/qObPka4lTxip5/leyhoQrKK6Wojv0VfVBD0qSDkx6RNbQSNW68VnZCvJ1aMpzSln6oyw6NUin+tWPKb73EGVsX649bw2VJTBY1fqPUHTHvtr56rUqzM5QUFwtBcfXUUBEjOreMV5Jcycrdel0NXlielEZVl4dpKDYGiV+cKbZc3OL/j74xRPKPX6w1Jn4Tq7/S/s/eVCtxq85a9mC/lLYGdMY5WVm66vGN5V4rf3D16nR1T1VkJWjzCMpSpy/Vpsm/ipJ6vLMLcrPyNaaN76VJcCq9g9fp/qXddXpM3jDe9O164cFim/fWD3euk+FufnaMnmWEuevVe+JoxQYHqqsw8nKOHRceSczteyZyWoyuLfqXda1KIEiSbfsm6Kso6nKSTlZ9NrswWOVk5KuuDYN1e3F2xUUESprcKD2z16hlS9+WbRejXNbq+tzQzXjkkdLvK8hO79UUHhoidd88RxI+v1jHf7+FckaoKC4Wqp31zsKq9+maD1HzwF7Svt8jM7R94iSSosJvmbooWn6uvktbp9Vvsf4EUreuKcoPrrLFX+9qdnXjLF7g6gspcU9bykstGnn/hNauzVZJzLyVFBQqLDQQDWrH6N2zaooNMQYFdfROlCZ78efeaoeSvbropHqAIoZtf116NsXdGzWB8pPOyZrWJSswaFq+eZqBcUk2G2bFeZma++Eu5W5fblksajKudeo1pDnitYr7VinmbXdYtQ+SLsHr1Hzm/sqND5aeRlZKsjO04y+jyrn+Am7/RNJ6vT4fxReI04LH3j3rPdbVv9EMmecKquP4ctxIHPXau378D4VZqfLlp+rmK6DVPuWV4oe6Cmrj2bWGOAI+qSA5/lDXkDyr9yAv+QFgPz8Qm3elaqNO1OUkZWvwkKbIsOD1LpJFbVqFKvAQKu3iyiJ3JCrkBsCYI+v5QcDQiO07ZlLVJCZJktAoKwhEao15HlFt7tQBdkZ2vbEBSrMz5HFYlVgdFXVue1/JQZClzUg0qz9x7KutUbOD+ZnZqvvlGcUHB0uW36B8jJztHrcFB1etEGSFFIlUue/OUJR9U9NMLRl8mxt/Wx20X47jBqs4JhwLXtmcon3bNbrlD/FgNN2vfEfhdZsqlr/GXvW++UeQeWQHwQ8zx/yg/6UG5T8Jz+Yk1ug9duTtWV3mrKy8xUQYFVURJDaN4tTk3rRslqN8UN55IZcg9wQ7DFqn8DemICC7Aztffs2ZexcIYs1QLWGvKC4HoOL1nPluH5/Z+S8gL2xQYFhITr/zeGKb99YtoJCrX71G+35eYkkufS5BjPwtxjgyec6zIi8AAAA8DTyoxVHfhRwTOqJHK3eclz7DmUoJ69AwUFWVY0NVaeW8apVLcLbxZPkXAwkR146cuQAymLk5yrt/r7N3MlK/OopFaSnyBIYJGtopJo89bPCG3V0+vlqs+bRjPpstb17JE0G91bHx25UcGyEbHkFysvI1txbXyn6YXGerXaML94nsRcDnH22WjJvHKgo7pEAnucPuUGJ/OC/Gam9cfR4llZuStKR5Czl5hUqJChAtaqFq1PLeMXHGqOM5IZch9wQyuKreYFtT1+svLQjslgDZA0OU+2bXlJUuz6SpA33NFVB5gkFxdUs2q7hg18orEFbSfze0pl8MS/Qd+ozCkuIka2gUPnZuVr18tc6vHijJOmizx9XZL1qsuXlq7CgUOvfna69vywp2kdpxzrNrDHKF/MC9mJAifdRynPU5AUAAGbAxDJeYoQkur0fkXCGKyaW8TR7jX5nVHZiGU/z9jlgjxE+H0fRSagcI8SEyrp+7URlHUvType+0sF5q91yjEunPavIugla9/aP2m7g2bzNmjipDH+oA0bgiXoolV8XqQPGZIT2havbX84ey6ztFiPEWlf3Qexh8GNJRogBkmfjADc6nWeU88URfK/wdUa4TrsCuYFTzNjWACrLX+Kgt5EbAmCPEfp6nswLMLFMSUa51noyP8jEMiWZLQZwj6ByjHC+OIrvFb7OKNfqyiA3WMys7Y3K8Ic6YATkhmCPEdp4RhgvIJmz7WiUOGuEcUNmjVHEgGJmjAGOMsL54ii+VwAAfJtR+iyVQX60mFn7XYCz/CEGGgU5cgBlMUq+ywhjp8yaRzPC9dYI90gk816njBAHuE/iG4xwrjiK7xS+zgjXaVcgP1jMrO0NZ/lLHTACckMoi1HaeEboE5i17WiEWOvJvAATy5zNCHHACDFAMm8cAAD4Hy5nJmYNDlPW7jXa/HAXtfzfikrt68hPbyrp94kKiqvlotJ5Rn52ruJaN9DAWa/ql36PVWpfre4aqGZDLlLm4RQXlc79OAcA15ra/k63H2P2tc+6/RiAL/NEPZSoi3CeK9tf9hyY9KhSl81QZIvz3HYMOMeVfRB7fLF/YhaeigP00QDAO8gNAIB3kRsCYHSeygvsfmOIMrYtVVyv/7jtGHCOp/KDPd97QAkdm2jXjwvddgw4jnsEAODfyA0C3kduCEbHeAEwbsjciAEAAADwZ+RHAcD7yJEDMDrGTpkb90jAfRIA8G/kBwHvIzcEo6NPYG6eygt0efpm1e3bRUdXbHXbMeAcYgAAAK5lsdlsNm8XwoyMMGOfJ5U2K58RZq70lNJmpuQcsM8XPx9mn6wcM8UEMzDrjLyVQR3wL9QBY/LF9oW7mLXdQqwtZsY4RQwoZtYY4AhfPF/4XuHruE77FzO2NYDKIg76F+IgYEy+2NdzB7P2H7nWFjPrdYoYUMysccARvni+8L3C13Gt9i9mbW9UBnXAv1AHjMkX23juYsa2I3G2mFljFDGgmBljgKN88XzhewUAwLfRZ/EvZu13Ac4iBvof4iBgPL6Y73IXs+bRuN4WM+t1ijhQzKxxoKJ88VzhO4Wv4zrtf8za3nAWdcD/UAeMxxfbeO5i1rYjsbaYWWMUcaCYWeMAAMD/WL1dAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABASUwsAwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGY7HZbDZvF8KMbDYpu8DbpfCc0ADJYin5ms1mU35WjncK5GGBYSGynPEBcA7Y54ufj6PvESWZKSaYQWlxD/ZRB/wLdcCYfLF94S5mbbcQa4uZMU4RA4qZNQY4whfPF75X+Dqu0/7FjG0NoLKIg/6FOAgYky/29dzBrP1HrrXFzHqdIgYUM2sccIQvni98r/B1XKv9i1nbG5VBHfAv1AFj8sU2nruYse1InC1m1hhFDChmxhjgKF88X/heAQDwbfRZ/ItZ+12As4iB/oc4CBiPL+a73MWseTSut8XMep0iDhQzaxyoKF88V/hO4eu4Tvsfs7Y3nEUd8D/UAePxxTaeu5i17UisLWbWGEUcKGbWOAAA8D9MLAMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABmP1dgEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUxsQwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAwTywAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAwTCxDAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAYDBPLAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIDBMLEMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABgME8sAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgMEwsQwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAwTywAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAwTCxDAAAAAAAAAAAAAAAAAAAAAAAAAA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"text/plain": [ - "\"Output" + "

    " ] }, "execution_count": 4, @@ -707,7 +708,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -721,9 +722,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.9.21" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-aqc-tensor/how-tos/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/how-tos/index.mdx new file mode 100644 index 00000000000..c8eda5c3eee --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/how-tos/index.mdx @@ -0,0 +1,13 @@ +--- +title: Approximate quantum compilation (AQC-Tensor) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-aqc-tensor. +--- + +# AQC-Tensor How-To Guides + +* [How to use Quimb’s TNOptimizer directly](01-quimb-tnoptimizer) + +[![](/docs/images/api/qiskit-addon-aqc-tensor/how-tos_01_quimb_tnoptimizer_7_0.avif)](01-quimb-tnoptimizer) + +[How to use quimb.tensor.TNOptimizer directly](01-quimb-tnoptimizer) + diff --git a/docs/addons/qiskit-addon-aqc-tensor/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/index.mdx index b5fa83c76de..799965fd481 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/index.mdx +++ b/docs/addons/qiskit-addon-aqc-tensor/index.mdx @@ -1,5 +1,6 @@ --- -title: "AQC-Tensor Qiskit addon" +title: Approximate quantum compilation (AQC-Tensor) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-aqc-tensor. --- # AQC-Tensor Qiskit addon @@ -13,7 +14,7 @@ It has been tested primarily on Trotter circuits to date. It may, however, be ap 1. A *great* intermediate state, known as the “target state,” that can be achieved by tensor-network simulation; and, 2. A *good* circuit that prepares an approximation to the target state, but with fewer layers when compiled to the target hardware device. -![\_images/aqc-compression.png](docs/images/addons/qiskit-addon-aqc-tensor/aqc-compression.avif) +![\_images/aqc-compression.png](/docs/images/api/qiskit-addon-aqc-tensor/aqc-compression.avif) (Figure is taken from [arXiv:2301.08609](https://arxiv.org/abs/2301.08609).) diff --git a/docs/addons/qiskit-addon-aqc-tensor/install.mdx b/docs/addons/qiskit-addon-aqc-tensor/install.mdx index b552a9f8825..54a703817ba 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/install.mdx +++ b/docs/addons/qiskit-addon-aqc-tensor/install.mdx @@ -1,7 +1,3 @@ ---- -title: "Installation Instructions" ---- - # Installation Instructions @@ -84,4 +80,3 @@ jupyter lab ## Platform Support We expect this package to work on [any Tier 1 platform supported by Qiskit](/docs/start/install#operating-system-support). - diff --git a/docs/addons/qiskit-addon-aqc-tensor/release-notes.mdx b/docs/addons/qiskit-addon-aqc-tensor/release-notes.mdx deleted file mode 100644 index 9594395fbe6..00000000000 --- a/docs/addons/qiskit-addon-aqc-tensor/release-notes.mdx +++ /dev/null @@ -1,72 +0,0 @@ ---- -title: "Release Notes" ---- - - - - - -# - - - - - -## 0.2.0 - - - -### Prelude - -This release renames the primary objective to [`MaximizeStateFidelity`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity") and adds support for Qiskit 2.0. - - - -### New Features - -* Adds the [`parametrize_circuit()`](ansatz_generation#qiskit_addon_aqc_tensor.ansatz_generation.parametrize_circuit "qiskit_addon_aqc_tensor.ansatz_generation.parametrize_circuit") function for generating a parametrized version of a given circuit. In contrast to [`generate_ansatz_from_circuit()`](ansatz_generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"), `parametrize_circuit` does not change the types of gates in the circuit. It simply replaces numerical parameters with free parameters. - -* Support for Python 3.13. - - - -### Upgrade Notes - -* The `OneMinusFidelity` objective function has been renamed and is now known as [`MaximizeStateFidelity`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity"). As a related change, one should now call the [`loss_function()`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") method to obtain the value and gradient of the loss function, instead of calling the instance directly (through its `__call__` method). - -* Qiskit SDK version 1.3 or higher is now required. Qiskit SDK version 2.0 is now supported. - - - -### Bug Fixes - -* This release fixes the import of `quimb_gate()` to work on versions of qiskit-quimb greater than 0.0.5. The aforementioned function is now imported from the toplevel `qiskit_quimb` instead of `qiskit_quimb.circuit`. - - - - - -## 0.1.1 - - - - - -### New Features - -* This release adds quimb-autograd as an optional dependency, so that one can now run `pip install 'qiskit-addon-aqc-tensor[quimb-autograd]'` to install the addon along with quimb and autograd. - - - - - -## 0.1.0 - - - - - -### Prelude - -Initial release. - diff --git a/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.mdx b/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.mdx new file mode 100644 index 00000000000..38f08a310ad --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.mdx @@ -0,0 +1,435 @@ + + +# Reducing circuit depth with the AQC-Tensor Qiskit addon + +In this notebook, we will work through the steps of a [Qiskit pattern](/docs/guides/intro-to-patterns) while using **approximate quantum compilation with tensor networks (AQC-Tensor)** to achieve a lower circuit depth than would ordinarily be needed to perform Trotter evolution. + +These are the steps that we will take: + +* **Step 1: Map to quantum problem** + + * Initialize our problem’s Hamiltonian and observable(s) + * Generate a target tensor-network state for the initial portion of the circuit + * Generate a low-depth circuit which approximates the portion being compressed + * Generate a general ansatz from that circuit + * Optimize the parameters to bring the ansatz as close as possible to the target + * Add subsequent Trotter steps to the optimized ansatz + +* **Step 2: Optimize for target hardware** + + * Transpile the circuit for hardware + +* **Step 3: Execute experiments** + + * Use a fake backend for simplicity + +* **Step 4: Reconstruct results** + + * N/A; instead, we just output the measured observable + + + +## Step 1: Map to quantum circuit and operator + + + +### Set up a model Hamiltonian and observable + +In this notebook, we use the Ising model on a circle of 10 sites: + +$$ +\hat{\mathcal{H}}_{\text{Ising}} = \sum_{i=1}^{10} J_{i,(i+1)} Z_i Z_{(i+1)} + h_i X_i \, , +$$ + +where the periodic boundary conditions imply that for $i=10$ we obtain $i+1=11\rightarrow1$, $J$ is the coupling strength between two sites and $h$ is the external magnetic field. + +``` +[1]: +``` + +```python +from qiskit.transpiler import CouplingMap +from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian + +# Generate some coupling map to use for this example +coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False) + +# Choose a 10-qubit circle on this coupling map +reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 4, 15, 3, 9]) + +# Get a qubit operator describing the Ising field model +hamiltonian = generate_xyz_hamiltonian( + reduced_coupling_map, + coupling_constants=(0.0, 0.0, 1.0), + ext_magnetic_field=(0.4, 0.0, 0.0), +) +``` + +The observable we will measure is the total magnetization. + +``` +[2]: +``` + +```python +from qiskit.quantum_info import SparsePauliOp + +L = reduced_coupling_map.size() +observable = SparsePauliOp.from_sparse_list([("Z", [i], 1 / L / 2) for i in range(L)], num_qubits=L) +``` + + + +### Determine how much of the time evolution to simulate classically + +Our overall goal is to simulate time evolution of the above model Hamiltonian. We do so by Trotter evolution, which we split into two portions: + +1. An initial portion that is simulable with matrix product states (MPS). We will “compile” this portion using AQC as presented in [https://arxiv.org/abs/2301.08609](https://arxiv.org/abs/2301.08609). +2. A subsequent portion of the circuit that will be executed on hardware. + +Let’s plan to use AQC-Tensor to compress our time evolution circuit up to time $t=4$, then evolve using ordinary Trotter steps up to $t=5$. + + + +### Generate circuits before and after split + +Now that we have chosen to split at $t=4$, we will generate two circuits: + +1. A “target” circuit for the AQC portion of the evolution, from $t_i=0$ to $t_f=4$. Because this is being simulated by a tensor-network simulator, the number of layers affects execution time only by a constant factor, so we might as well use a generous number of layers to minimize Trotter error. + +``` +[3]: +``` + +```python +from qiskit.synthesis import SuzukiTrotter +from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit + +aqc_evolution_time = 4.0 +aqc_target_num_trotter_steps = 45 + +aqc_target_circuit = generate_time_evolution_circuit( + hamiltonian, + synthesis=SuzukiTrotter(reps=aqc_target_num_trotter_steps), + time=aqc_evolution_time, +) +``` + +2. A subsequent evolution circuit, which evolves from $t_i=4$ to $t_f=5$. Because this is being run on quantum hardware, it is desirable to use as few Trotter layers as possible. + +``` +[4]: +``` + +```python +subsequent_evolution_time = 1.0 +subsequent_num_trotter_steps = 5 + +subsequent_circuit = generate_time_evolution_circuit( + hamiltonian, + synthesis=SuzukiTrotter(reps=subsequent_num_trotter_steps), + time=subsequent_evolution_time, +) +``` + +For the sake of later comparison, let’s also generate a third circuit: one that evolves for `aqc_evolution_time` but which has the same evolution time per Trotter step as the subsequent circuit. This is the circuit we would have working with had we not used a generous number of Trotter steps for the target circuit. We will refer to this as the *comparison circuit*. + +``` +[5]: +``` + +```python +aqc_comparison_num_trotter_steps = int( + subsequent_num_trotter_steps / subsequent_evolution_time * aqc_evolution_time +) +aqc_comparison_num_trotter_steps +``` + +``` +[5]: +``` + +```python +20 +``` + +``` +[6]: +``` + +```python +comparison_circuit = generate_time_evolution_circuit( + hamiltonian, + synthesis=SuzukiTrotter(reps=aqc_comparison_num_trotter_steps), + time=aqc_evolution_time, +) +``` + + + +### Generate an ansatz and initial parameters from a Trotter circuit with fewer steps + +First, we construct a “good” circuit that has the same evolution time as the target circuit, but with fewer Trotter steps (and thus fewer layers). + +Then we pass this “good” circuit to AQC-Tensor’s `generate_ansatz_from_circuit` function. This function analyzes the two-qubit connectivity of the circuit and returns two things: + +1. a general, parametrized ansatz circuit with the same two-qubit connectivity as the input circuit; and, +2. parameters that, when plugged into the ansatz, yield the input (good) circuit. + +Soon we will take these parameters and iteratively adjust them to bring the ansatz circuit as close as possible to the target MPS. + +``` +[7]: +``` + +```python +from qiskit_addon_aqc_tensor import generate_ansatz_from_circuit + +aqc_ansatz_num_trotter_steps = 5 + +aqc_good_circuit = generate_time_evolution_circuit( + hamiltonian, + synthesis=SuzukiTrotter(reps=aqc_ansatz_num_trotter_steps), + time=aqc_evolution_time, +) + +aqc_ansatz, aqc_initial_parameters = generate_ansatz_from_circuit( + aqc_good_circuit, qubits_initially_zero=True +) +aqc_ansatz.draw("mpl", fold=-1) +``` + +``` +[7]: +``` + +![../\_images/tutorials\_01\_initial\_state\_aqc\_15\_0.png](/docs/images/api/qiskit-addon-aqc-tensor/tutorials_01_initial_state_aqc_15_0.avif) + +``` +[8]: +``` + +```python +print(f"Comparison circuit: depth {comparison_circuit.depth()}") +print(f"Target circuit: depth {aqc_target_circuit.depth()}") +print(f"Ansatz circuit: depth {aqc_ansatz.depth()}, with {len(aqc_initial_parameters)} parameters") +``` + +```python +Comparison circuit: depth 120 +Target circuit: depth 270 +Ansatz circuit: depth 23, with 515 parameters +``` + + + +### Choose settings for tensor network simulation + +Here, we use the Qiskit Aer matrix-product state (MPS) simulator, which is currently the only supported tensor network backend. + +``` +[9]: +``` + +```python +from qiskit_aer import AerSimulator + +simulator_settings = AerSimulator( + method="matrix_product_state", + matrix_product_state_max_bond_dimension=100, +) +``` + + + +### Construct matrix-product state representation of the AQC target state + +Next, we build a matrix-product representation of the state to be approximated by AQC. + +``` +[10]: +``` + +```python +from qiskit_addon_aqc_tensor.simulation import tensornetwork_from_circuit + +aqc_target_mps = tensornetwork_from_circuit(aqc_target_circuit, simulator_settings) +``` + +Note that because we chose a generous number of Trotter steps for the target state, it actually has less Trotter error than the comparison circuit. We can calculate the fidelity ($| \langle \psi_1 | \psi_2 \rangle |^2$) of the state prepared by the comparison circuit vs. the target state: + +``` +[11]: +``` + +```python +from qiskit_addon_aqc_tensor.simulation import compute_overlap + +comparison_mps = tensornetwork_from_circuit(comparison_circuit, simulator_settings) +comparison_fidelity = abs(compute_overlap(comparison_mps, aqc_target_mps)) ** 2 +comparison_fidelity +``` + +``` +[11]: +``` + +```python +0.9997111919739693 +``` + + + +### Optimize the parameters of the ansatz using MPS calculations + +Here, we minimize the simplest possible cost function, `MaximizeStateFidelity`, by using the L-BFGS optimizer from scipy. + +We choose a stopping point for the fidelity such that it will be above what the comparison circuit would have been, without using AQC. Once this is reached, the compressed circuit has less Trotter error *and* less depth than the original circuit. Given more processing time, further optimization steps can be performed to bring the fidelity even higher. + +``` +[12]: +``` + +```python +from scipy.optimize import OptimizeResult, minimize + +from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity + +objective = MaximizeStateFidelity(aqc_target_mps, aqc_ansatz, simulator_settings) + +stopping_point = 1 - comparison_fidelity + + +def callback(intermediate_result: OptimizeResult): + print(f"Intermediate result: Fidelity {1 - intermediate_result.fun:.8}") + if intermediate_result.fun < stopping_point: + # Good enough for now + raise StopIteration + + +result = minimize( + objective.loss_function, + aqc_initial_parameters, + method="L-BFGS-B", + jac=True, + options={"maxiter": 100}, + callback=callback, +) +if result.status not in ( + 0, + 1, + 99, +): # 0 => success; 1 => max iterations reached; 99 => early termination via StopIteration + raise RuntimeError(f"Optimization failed: {result.message} (status={result.status})") + +print(f"Done after {result.nit} iterations.") +aqc_final_parameters = result.x +``` + +```python +Intermediate result: Fidelity 0.95084365 +Intermediate result: Fidelity 0.98409893 +Intermediate result: Fidelity 0.99142033 +Intermediate result: Fidelity 0.99521405 +Intermediate result: Fidelity 0.99566673 +Intermediate result: Fidelity 0.99650054 +Intermediate result: Fidelity 0.99683487 +Intermediate result: Fidelity 0.99720426 +Intermediate result: Fidelity 0.99761726 +Intermediate result: Fidelity 0.99809073 +Intermediate result: Fidelity 0.99838244 +Intermediate result: Fidelity 0.99861841 +Intermediate result: Fidelity 0.99874617 +Intermediate result: Fidelity 0.99892696 +Intermediate result: Fidelity 0.99908129 +Intermediate result: Fidelity 0.99917737 +Intermediate result: Fidelity 0.99925456 +Intermediate result: Fidelity 0.99933134 +Intermediate result: Fidelity 0.99947173 +Intermediate result: Fidelity 0.99956469 +Intermediate result: Fidelity 0.99964488 +Intermediate result: Fidelity 0.99967419 +Intermediate result: Fidelity 0.99968821 +Intermediate result: Fidelity 0.9997448 +Done after 24 iterations. +``` + + + +### Construct the final circuit to pass to the transpiler + +``` +[13]: +``` + +```python +final_circuit = aqc_ansatz.assign_parameters(aqc_final_parameters) +final_circuit.compose(subsequent_circuit, inplace=True) +final_circuit.draw("mpl", fold=-1) +``` + +``` +[13]: +``` + +![../\_images/tutorials\_01\_initial\_state\_aqc\_26\_0.png](/docs/images/api/qiskit-addon-aqc-tensor/tutorials_01_initial_state_aqc_26_0.avif) + + + +## Step 2: Transpile for execution on target hardware + +In Step 2 of a [Qiskit pattern](/docs/guides/intro-to-patterns), we transpile this circuit and any desired observable(s) for execution on a target device. Here we are using a fake backend provided by `qiskit-ibm-runtime`. + +``` +[14]: +``` + +```python +from qiskit import transpile +from qiskit_ibm_runtime.fake_provider import FakeMelbourneV2 + +backend = FakeMelbourneV2() + +isa_circuit = transpile(final_circuit, backend) +isa_observable = observable.apply_layout(isa_circuit.layout) +``` + +The resulting ISA circuit can then be sent for execution on the backend (step 3 of a [Qiskit pattern](/docs/guides/intro-to-patterns)). + + + +## Step 3: Execute on quantum hardware + +``` +[15]: +``` + +```python +from qiskit_ibm_runtime import EstimatorV2 as Estimator + +estimator = Estimator(backend) +job = estimator.run([(isa_circuit, isa_observable)]) +pub_result = job.result()[0] +``` + + + +## Step 4: Reconstruct + +Reconstruction is not necessary in our case. We can just look at the result. + +``` +[16]: +``` + +```python +pub_result.data.evs[()] +``` + +``` +[16]: +``` + +```python +np.float64(0.054003906250000004) +``` diff --git a/docs/addons/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.ipynb b/docs/addons/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.ipynb index 94830a07502..8dba50e0e8c 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.ipynb +++ b/docs/addons/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.ipynb @@ -284,8 +284,9 @@ "outputs": [ { "data": { + "image/png": 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7dZSW3v26EqNi5Rnor5sXT9GZTfu0ftzH6r/0zUwjoB+atUq7Z86TJIU0q6n208fotw6P5nv/C4Or/yauVm9kX0Us21osB1kEAAAAAADID2ftB7q6P8evfLDiI87Yp8cfj5Jv+eBrTsutpHMX9O+Un1SpdystHz4l0zRrcqrm93lGATXKqd/CyTo0a6Vkkzp9+LhWj5mu8wdPyuRtUd95k+wPUV19TE/+9a+O/LYmY3srhKjv/In6peX/Mr0UKTtmb0/VuqubNk/87r/vZt7XtItJSolPlHeZUvaXFuXXge+Xqfqgjtr9yTwdW/SPJKnGkM4KblJdf9w0VvHHo9R8wl1qOOYWrR/3sUq3rK1qt7TXwoHPy5qSptKt66rjjMf0e+fHsz2mG1/4wv7QUsMxt6jJU0PsD545UrZDQ1kCfO0vi/crH6yojXvs0+OPR6nqLe2yfC+gVgWFNK+l9eNn5uu4AAAAAAAAFEeu0j94bvthVR/UUTtn/C6/SqVVumVtxR/P6CfL2j94RiHNa+X9WIz7WK1eGZap5k+SLCV8teDmZ+UZ6K9B69/XwR9XKCEyWu3fHaPt787W6fW7ZTAZddM3z6hyvzBtmzZLNe/oqpWjpil619GMZQT4auGA52WzWmUp6af+S6bqxF//ZnoJenYMRqPq3tdbx/7M6Ke7cPiUPEv5K6RFbZ3ZtE8Ve7SQxd9HfhVLK3rHEYfLyi1nraP0DimpKv3aaOnQNyRJ57YfUp1hPeUdUlKJZ2JV7dYOsvj7yFLSz/6QVnbHEAAAAAAAAJc5W/9g8wl3qfLNYfIM8NWKkW/aX6y09vEP1PXL8Wo6/nZ5BZbQ+vEf258nLo71g0YPs/otmCTfCiGK2ROuZfdOlpTx4vhdH8/T4I0zlBwTr/SUVC0a+EKe9vVanK1+sHLfNmoy9jaVqBKqLZO+V/TOjH7QbdNmqcunT+m27Z/K7GXR4d9W6/jirC+Hon4QAAAAAAAge87UN1i5bxvZrDYdX7wpy0CLJWtWUFDj6mr8xP/J5OmhQ7+s1J7PFhTYcXC2vsG2b47Sple/UdrFpMzHoVYFXTx1TmGT71dQ4+pKjonX5te+tfeXXZKferkTy7dm+VuGhtVXQI3yWvvEh/r7mU9Ve2gPNXv6Di254zX5VSqtJk8O0ZI7XlNqfKL8q4Sq95xXNavV/7Ktx9zxwRxteuVrSVLVAe3U+tXhWnLn69lui6WEj1o8f4+W3PGaStaqmOVYpKekqtvXz8i3bKCi9xzTPy9/Ze939KsQoq5fjpd/lTKKWLpFe7/8M8/HAgAAAAAAoLhzpv5BSWr+7N1qOu52xe6P0OaJ3yn+WJTDsUc8/LzlHRKgij1bqkq/MEnSrplzdfSPdXk6Ds72PK1/lVDdvHiKbOlWHfhxhfZ9ldG3laVOMuJMtnWSNe/sqkOzVsqWlp6n4yAp22ejK/VqpZBmNTW35zglx8Sr04ePq/Y93bXjvd9UdWB7BdQopwX9npXNalW1wR3VZtL9WnbPpCzHVJJWjX5HyTEZz/62eeN+1R3RWzven5Pj9tQd2Uc1bu+qiyfOauvkH+zb5KhmdOMLX6jbV8+oztAesgT4ats7v2bpQwUAV8VAiwCQC8cWblB6Yook6ey/B+VfJVSSVKlXS1n8fVTlv4IEo8Vsf2lRFkaDmj93t8q0qiMZDPIOLqHYvccKdVC9gpaWmKzw+RskSVGb9tuPQ0iL2vKvXFrdv3s20/wlqpdTfPjpLMsJalBVjR69VZ6l/GVLS1dAjfIyeVmUnpRS+DtRQFzxN1FtUAdVuTlMCwv4gTEAAAAAAIDirCj7gZytP+fw7NWSpPMHT8qali7v0iXl4e+jkrUqqNOHj9vn8/DzUslaFXRu26Esy/CrVEYdP3hUPmWDZEtLl6Wkn/wrldb5gydzXK/Rw6xOM5/Qib+26djCjQW/Y3l0ZvN+xR/PGMwxavN+1b2vtySpYs+WKlWvsvrNn2Sf17Okn0xelmyXU21ge1Uf3EkmTw+ZPD2UHB13zXWXrFNJ7ac9pJWjpiktMTnX2+xTNlDdvhiv9eM/vuZL6wEAAAAAAHCZs/UPrnnkfbV46V71XzJV8RFndWr1DtnS8/6gz/U48ltG/2BydJziwqPkV6m0Us5fVNn2DeUdHGCfz+zrpYAa5bJdhmcpf7V7e7RKVCsrW1q6PEv5qVSdStfss2rzxkgln4+3P2CWGpegv+5/U80n3Cmzr5fObNqvmH3Hr+uhp7wqyjpKDz9vdfv6ae2Y8bu9/zVy3S7t/PAPdfvmGdnSrTq2MGPbrj4WVx9DAAAAAAAAXFtR9Q9unvidNk/8TmU7NFSL5+/Rgv7PyZqapoZjBmrzxO905Lc18qtUWr1nv6Kz2w7p/P6Igt3xbBRF/aA1NU1/dB8ro4dZrV+/T7Xv6a6dM36XX8XSqtyntX4NG6PE0zGqM7yXOs18XAsHPF84O3+FoqofDJ//t8Ln/y2/CiHq8vk4HV+6WRcOnVTVAe0Uuz9Ci4e8IpO3Rd2+elo17+ymA98vs3+X+kEAAAAAAIC8u9F9g94hJdX4sUFaNOjFbBdlMJvkX6m0Fg18QZaSvuo1+xXFhZ9WxNLNBbC3jt3ovsGad3bTxRNnFbl2Z5ZpBrNJwU1raMuk77V+3Mcq37Wpun39tGa1Gp2pZq4w6uXijkbq7NYDkqSoTftUf9TNkqTyXZqqRNVQ9f7tFfu8Nqs12xe8S1K5jo1U977e8vDzlsFokGdJvxzX2XriSG2fPltJ/w2eeCWDyaRyHRppfr8JSoiMVrMJdyrsjfv11/1vScp4yfwfNz0ls4+XOn7wiCr3ae1S77wEAAAAAAAoSkVRO7jq4XeVcPKcJKnO8F666ZtnNKfT49nOe4nBbJLRwyyzl0Xz+z4jvwoh6jP3dZ0/eEIxu8OvZ9czKYrnac/tOKyfmz2o1LgE+ZQN1E3fPqvk6As6Ond9rrbZ7O2pqgPaaX6/CXndXYdOrPjXPkBi1OZ9KlWnkqSMQRiDm1TXzX9OliQZTMacF2IwqN4D/VShW3MZzUZ5lPDRmX/25zj7ljd+UMLpGMlmU6XerXTTd89qdtuHlZaQ5HBb69zbS0fmrNGO936TV1AJ9fz1ZZ3996BOrdqex70GAOfDQIsAkAvpyan2f9vSrTKaTJIkg8GgDc99rpMrt11zGfUfvFnewQGa3/cZpSenquVL98rk5VGg25kcEy9rerq8Q0raR5b3qxiiiydySLLyyJqSZv+3Ld0qo/nycYjdF6EF/Z/N8h2/CiGZ/t/oYVaXz57SokEv6dy2Q/Lw89ZdB76RyWJ2qYEWXeU3cUmV/m3V5Ikh+nPIy0o6e75Q1gEAAAAAAFAcFVU/UE79OfEnzsqvQogSo2IlSX4VS9u3wdG0gpCefLn/zmbNOBYGScmx8fqj+9hcLaPTh49p8+vfKXz+35KkO3Z/IZNn9i8SkjKKODrNfFyJUTHa+Pzn9s/jI85m6ns0+3rJ4u+jxNMxedyrvMvym7iin/TQLyu1ZdL311xG6VZ1VHdEHy3oN0FJ5y6oYo8Wajr2NoffCahVQTd984zWPDFDURv32j+/9He/xK9iaV08cdb+/95lSqnnzy9q2zuzFD4vd4UiAAAAAAAAyOB0/YMRZ/TXyDft/9/9+2ez9A+e2ZzxUE1B1g5KVx2L//oHZTBIkub3m5Bpek7CJj+giOVbtGLEVEnSzYunyOTp+Fi0fu0++ZYL1vLhUySbzf555LpdWnRrxkuljBazbtv2qWJvwAvli6qO0uzrpe7fP6djf/6j3TPnZZq276s/te+rPyVJIc1q6uKJs0qNT7RPz+kYAgAAAAAAwLGifo701Ood8vDzVqm6lRQfcUaVerfSyv9NkyTFH4vSmS0HVLplHZ3fH1Es6wcvsaam6eCPK9T2zVHaOeN3Ve7bRjF7j9nrBQ/+uEJtJo6U0cMsa2raNZaWP0VVP3hJfMQZnd16QBVvaq5dh06qzrCeWjd2pmxWq9IuJil83t8KbdfAPtAi9YMAAAAAAADX50b3DQY1qibvMqXUf2lGfaBnoL8q9mghz6AS2vrGD7p44qwO/7ZGNqtVydFxOrFsi0Ka17whAy3e6L7B0HYNFNq6rip2b27/rP/yt7R82BRdPHFWCZHRily3S5J0YvlWmSxm+VUIUdzRSEmFVy93ZW3f1X2DJ1du16qHpmf5jm9oUOb/Lx+sNq+P0LzeTysu/LRK1a2caYDGq5VpVUdlWtVRyxeHyuTpIUtJPw1cPV2/dXhUF0+c1al1u5QQGS1JOjxrlbr/8HyWZaQlJOnInLWqdmsHBloEAAAAAADIpaKoHbw0yKIk7f1ikVq+MFSepfwcjj2SEhuv1PhEHfp1laSM+rKof/YpuEmNAhlosSiep73y2diEU9E6MmeNyrSuq6Nz1yv+xFmV+G+wx0vruvJ9e5JU5eYwxe47rvMF/Lxxpn7Sq47Fjvd+0/5vl15zGdVuba+y7Rpq0a0vKDU+UXVH9FHZ9g1ynP9S358kHVu4Uc0n3KWAGuV0bvthhzWjdYb31Ox2j0iSks5d0IllWxTatj4DLQIoFhwMZwsA7iklLkEeJXxyNe+xRRtV74F+Mnln3Kw3eVtUslaF/5aTKA//y8uxBPgqMSpG6cmp8g4pqSr9wgp+4yWFz12v2kN7SJKCGleXT2igItfvznbe4CY11OPnF7OdlhKfkGn7HYnatE9+lUqrbIeG9s8C61eR0cOslPhEmbwsMnpkjO1r8vSQ0cNsTzzqjuid630rTO3ffViVerfKdpqr/yaq3BymZuPv0J+3vZwl4QMAAAAAAMBlztIP5Kg/58r+P7+KpRXatr6OLdp4zWlX8wkN1MDVWR/akaSU+MRc9w2eP3RSqfGJqnFbF/tn/lVCZSnpl7Gsq46pJcBP8cejJEnVBnWQZyn/HJdtMBnV6aPHlRwTr3VPfZRp2rnth2UwmxTatr4kqfY9PXR8yaZcvcz9anW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"text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 7, @@ -695,8 +696,9 @@ "outputs": [ { "data": { + "image/png": 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/GArL7Q8YK3XzfN/yHOl/m/0bE4DA+my79PNu99attEkPrHT9YFKEl6dWSemH3Fs3t8T+AHobD6CPGKUV0r3LpeIK99b/I1d6c6N/YwJMRk7EZxAMz66Wtue7t+7BMnsOyVgAiBw7C6QnV7m//rdp0iwuFY0oszLt36u7nvzDXm8QOV5eJ21w8zqgQ+X260fdnVsG4DnTcyLTyx8MP2fZ5wnc9exqaetB/8UDILBsNunBX+3zP+5IPyT924McEqEvr8Q+/1vp5hj/pyzpcw/6DYS+pdnSex5cB/TyemktQzDAb0zPiUwvfzAUlEkTVkplbl4/ujhb+mCLf2MCEFifbrP/TswdFTbpvpX236HBc1HBDsBfPvvsM1VWVurqq69WUlJSjeskJCRIklJSUqpe27p1q77++mslJyerX79+AYk1nCQP6qW+D/9NB7ZmasHtr7pc/4R7L1fD7m21/PEPVbhrnyQpe/lGbXhvptqNHKAOFw4+av2Tn7lJTU/ootWvfqP0Gcv8UgYACGcch+FKkz6dVVlRoZzfj86SK0rKlLs2VU36dApSZAAA+AbjIQAwG/0AHCEfBgCYgLEQAIC+AMciHwYAmISxEACYjX4ANSEvBgCYhPEQAJiNfgDuIlcGAEQixkIAEFwch+EuTx8QUWGTvk71SygIgoIyadpOz7bZXST9sts/8QCwW/XSZFWWV+iE8Zcf/frLX6uyokKj5jyn3YvWaseURUGK0L9ML3+gfb7Ds/Vtkr7ycBsAoWvdfmm1h8/nWbhHyjjkn3gQeFN3SoXlnm3zVSoPH48k3jw48kvGAoBfmZ4TmV7+QJuVIR0o82ybr1Ol0gq/hAMgwCpsno/tiiqk79P9Ew8CL/OQNN/DOZ/1edK6PH9Eg2D4MUvKKfFsm+/SpCIPzyUAcB85EZ9BIOUUS7N3ebbN9nxpRY5/4gEQeJNTPZ/z+YI5gojymRfXj36T5p9YEHgHS6XpHl4/mllov2YAgP+YnhOZXv5A8/T60UrZx5AAIsOa/fZ5H0/M3y3tKvRLOAiCKen2+V9PfLlDquT60YjhzXkerh8F/Mv0nMj08gfazAwp38PrRyenSuWVfgkHQICVV0pfpnq2TWG55/elgJ012AH4y7x58yRJQ4cOdbhORkaGJCklJaXqtVNPPVVZWVmSpMcff1yLFtG5H5bQrIFOf/tuRVmjlTp9qdqc3dfhuvvXp8uaEKte/xilnXN/1dbP5x21/Ldn/k+tzzxRA54ao6yFa1Wcc0BdrjpTXa86U0V783RgS6Y6XnyKw/2nz1iu8iIPrzAFgDDHcRjuSGzeUCW5+aosrf6risLduWrev7uiYqyqLONXFwCA8MN4CADMRj8AZ8iHAQCRjrEQAIC+ADUhHwYAmIKxEACYjX4AjpAXAwBMwXgIAMxGPwBPkCsDACINYyEACC6Ow3DXlgPS6v2eb/d9unRbDyk+2vcxIbCmZ3j+UBHJfjPxM1r6PBwAf8pP3a0dUxap08WnqtmAHspetkGSZCuv0N4Vm9Tk+E7a+sVPQY7Sf0wvfyDtLJCW7fV8u5kZ0p29pKQY38cEILC+9uIh4jZJX6dKd/TydTQINJvN/l16aneRtGiPdGqyz0NCgO0pkn7Z7fl2P+6S7jlOahTn+5gAkBOZXv5A8+ZB8rml0rwsaURrn4cDIMCWZHv3IPnJqdI1nSSLxechIcC+TbPn+Z76aod0XEOfh4Mg8GYsUFAu/ZApXdjO5+EAEDmRxGcQSFPSpQovBgNfpUr9m/o8HAABVlIhfe/Fg+TX7pc25kndG/g6IgTahjxpfZ7n201Nl27tLsVx/WjYm7ZTKqn0fLvJO6TTmCsG/Mb0nMj08gdSar60Msfz7WbslG7vKdWx+j4mAIHlzRyBTfb5pdt6+DoaBJrNJn3jxTXEmYX2eebBzX0fEwIrq1BauMfz7ebsku7uJTXg+lHAL0zPiUwvf6B5Mx7MKZF+3i2dxe/LgbC3KNv+myJPTU6VruzI9aOeithTaWlp9syyXbuaryYsLy/XokWLJEkpKSlVr0dFRfk8lr59+2r3bi9+JeelGFuUHlN/n++3fqdWim9cX5KUcsfFTtdd89p3ajuin8oKirT43reqLa8oLtWiu9/QiG+e0MD/3qyfxjyn5v27S5ISmjbQKa/c7nT/k/v9QwUZXtyFwIWuXbqqzOLFLGUI8Nf3DnOFc3s4VqS0DxOOw4EWrvXcWZ2OTohTRWlZjcsqSuyvWxNiVcpN5OGFUGkzkXJcR/gJlTbgL+HQthgPuSfc62o41EXAkXBvf0cKxbZIP+BYJNU9R1zVSfJhBFIotLlQPE4jsoVCvfe1cGtHjIW8F+71N9zqKswRbm0rEtoSfYFvhFvdlZgfRmgKVluKhOM5wlM49h+1EYptjbGQY5FeP0OxPgJS+LW9cG9L9AO+E2l1l7wYwUJeDNOEW//hSji2JcZD3gn3uhuOdRXmCPf25YlQaIv0A38xqe45Qq6MQArFNhcKx2WYJRTbgafCvd0wFvKdcK3P4V6HEVl80Y7CrU5zHPZeuB53vRU/4BI1uO5lj7crKJe6DThdFbu3+j4oBFS9a55X4qArPN5uaWquWl92vB8iQqho+tQKRTdsoaysLLVu3S/Y4YQ0f40TVk/8Wh0uHKwTxl+uHy55XJLUbEAPdb58qDa8N0P9//V3TR02XhXFpR7v25f9nenlD3dxJ5yrhjdN8ni7kkrpuNPPVXnaKj9EhVBBX2CGxvfPVEzb3h5v9+60+Xru7Kv9EBECyRKXqOYvbfZq25seeU6HZk70cUQItNieQ9Vo7Mceb1duk/qOuFSlW5b4ISqECsYC7iEn8s9n4M/yS+SFVSwWNX81TRYvnvN053/eUMF3T/shKIQK+gEz1Blxu+qOus/j7TILpbZdustWXOCHqBBIDW/7RHG9Tvd4u++Wrtd7o4b7PiAEXLOXNisqLtHj7R6Y+IHGfvmwHyJCqGAs4D7TcyLy4vDW4MZJij/xXI+3m7M6Va1HD/FDRAgV9ANmiG7WUU0f/8Wrbc+/6R4VLfnCxxEh0BIGXan61zzn8XYHyqSufU9Rxd4dfogKgVT/2peUcPKlHm+3cGu2Wl96oh8iQqhgLOA+03Mi08sf7uJPGqUGY97weLvCCqnXkOEqz1jvh6gQKugLzND4obmKadXd4+3e+naenhl2rR8iQiBZEuqp+QveHctvePAZHfrhdR9HhECL632WGv7jA4+3K6uUThg+WmXbVvg+KIQMxgLuISdirjSsRVmV/FqqV5uOfXKiCr73/Nwywgf9gBnqjLxLdc+7x+Pt0g9JbTp2lsqK/RBVaEtOTtbKlSu92tbq41hCxqFDhyRJRUVFNS7/4osvlJOTo7p166pDhw5+jWX37t3KzMz063scKdYSLTX3/X53L1mnD1pc4vb6vz71idPl2cs36qPWl1f9vfDO17XwzuAm9buydqnUVhHUGLzlr+8d5grn9nCsSGkfJhyHAy1c67mzOl1RVKKYOvVrXBYdFyNJKi/y7sQFECptJlKO6wg/odIG/CUc2hbjIfeEe10Nh7oIOBLu7e9IodgW6Qcci6S654irOkk+jEAKhTYXisdpRLZQqPe+Fm7tiLGQ98K9/oZbXYU5wq1tRUJboi/wjXCruxLzwwhNwWpLkXA8R3gKx/6jNkKxrTEWcizS62co1kdACr+2F+5tiX7AdyKt7pIXI1jIi2GacOs/XAnHtsR4yDvhXnfDsa7CHOHevjwRCm2RfuAvJtU9R8iVEUih2OZC4bgMs4RiO/BUuLcbxkK+E671OdzrMCKLL9pRuNVpjsPeC9fjrreaFJaogZfb5uTlqzCA9+mEf8SWVcrzR89LNmtcQO/TisBrVFGhaEkVFRV81y54O05w1V8f2JJ5VP9rTYzXkJdv069PfaqNH/6gc779l0584CqteOwDj9/bl/2d6eUPd426FKqhl9vuy8tXAceHiEZfYIYGUTGK8WK7Mls09SICWOs18fp0R0FJuXZRB8JegzaH1MjLbfcdLNBB6kBEYyzgHnIi7z6DYJZfIi88zBIbr+SoKK+2LSwt59gQ4egHzNCyuEx1vdx29748left8Wk8CLw6NovivNiuwmLl2BAhmsfEe7VdURn9Q6RjLOA+03Mi8uLwllBpkTc9QWV0LMeGCEc/YIaE2CZq6uW2BwpLtJe6EfaaFhar5l8zurZ3/wEVUQfCXmy5TQnebMj1oxGPsYD7TM+JTC9/uGvcvdj735IcKNAhjg8Rjb7ADA2irF5dP1pqs1AvIoC1YYXX14/mF5cpizoQ9hq2r8VvSQ4UKJ86ENEYC7iHnIi50nAWlVBXyV5ue6iUY0Okox8wQ6uS2lw/ul8V+bk+jSfSWYMdgL8kJydr//79+u233zRw4MCjlmVlZWn8+PGSpOOPP14Wi8XvsQRSjC1KqgzoW0aMli1aqswSnh8e3zt8LZzbw7FoH3AkXOu5szpduGe/6ndtrahYqypLy49alpjcSMX7DqiyrLzmjQEXQqXNcFxHsIRKG/AX2lbkCPe6Sl1EOAv39nck2mJ4iaS654irOkk+jEAKhTbHcRqBFgr13tdoR+YI9/pLXUWoCre2RVvCYeFWdyXmhxGagtWWOJ4jWMKx/6gN2lp4ifT6SX1EqAq3tkdbwmGRVnfJixEs5MUwTbj1H67QlswR7nWXuopQFu7tyxO0xdBiUt1zhFwZgRSKbY7jMgItFNuBp2g3OCxc6zN1GKHEF+2IOm2OcD3ueis+1vN7edpsNlksFjWpE6eKVq38EBUCKU6l3m1YnK9WfP8RLTo6uuq/fNfOBWqc0O/xa1WQnq2NH8ySJC284zWNmvu80mcu056lGzzaly/7O9PLH+7i4qK83rZxUpzqc3yIaPQFZogqPeTVdtaKIupFJIj25jGTdknRldSBCBCb4P2jTholxqgudSCiMRZwDzlRYD4DX5ZfIi88kq2sWJaYeI+3S4xiLBDp6AfMkBjt/bGweYMkqU7EPjrPGDEVRV5tF1VWyLEhQtgKD8iS5Pkj6BMs5dSBCMdYwH2m50TkxeEttrLEq+2iSgo4NkQ4+gEzRCXFSfrrekBP1Iu1KJa6EfZqdf1o3XhVUgfCXryN60dRM8YC7jM9JzK9/OHOm+tHD48FGteJVQOODxGNvsAM0aUFXm0XU1FCvYgEXlwrclhStI06EAFi46O93rZRnVjVow5ENMYC7iEnYq40rFksslWUyeLFb0oSoyo4NkQ4+gEz1Iny7lhoq6xUcoO6Ur0EH0cU+pKTk73eNmKvtj3rrLO0YcMG/fe//9WwYcPUtWtXSdKKFSv0t7/9TTk5OZKkPn36+D2WlStX+v09jlRWWKxPO10T0PeMFJu3bFZMovcnJoKJ7x2+Fs7t4Vi0DzgSrvXcWZ3O+WOrWp3eR01O6KLsZX+dnIiOi1Gj49p7dcICOCxU2gzHdQRLqLQBf6FtRY5wr6vURYSzcG9/R6IthpdIqnuOuKqT5MMIpFBocxynEWihUO99jXZkjnCvv9RVhKpwa1u0JRwWbnVXYn4YoSlYbYnjOYIlHPuP2qCthZdIr5/UR4SqcGt7tCUcFml1l7wYwUJeDNOEW//hCm3JHOFed6mrCGXh3r48QVsMLSbVPUfIlRFIodjmOC4j0EKxHXiKdoPDwrU+U4cRSnzRjqjT5gjX46638kqlkbOlUg/uI2uxWNSxrrTij8Xy8PmUCEGLs6VxSz3f7rKUZD2QkeH7gBAyRs6WsoulFi1a6He+a6cCMU5odcYJ6jBqsKaceU/Va/lpe/TrU59q8Eu3aeoZ96i8yP2HiPuyvzO9/OHuULl0zg9SYYVn27VKlJYtnaNoxgIRjb7ADO9vkV73YhrqyWuGafRD1ItIMG6pPS/w1KxXHlKHug/5PiAEVEmF/Xh/oMyz7RrHSUvnT5XV82cWI4wwFnAPOZH/PwNfl18iLzzSfSukH7M83+6b/9ypnm/d6fN4EDroB8yQWiBdMs/z7U5uKr22Y6vvA0LAfZcm/XuV59uNG9FHN47j2BAJHv9dmrbT8+0+efQWnfTKLb4PCCGDsYD7TM+JyIvD2+xM6cFfPd9uzClddTvHhohGP2AGm0268mdpa75nE/8xUdKKz19Vw7hX/RMYAia3xN7ey23ub2OxWNS1nrRi9XKuH40AC3ZLdy33fLsr+7bWBPqHiMZYwH2m50Smlz/cFZRJI2ZLxR5cP2qxWNS2jrR8xc+KYiwQ0egLzPDOJmnSJs+3e/r6ERr1KPUiEvxzsbQ8x/Pt5rzxmNolPeb7gBBQxeXSOXOkfA+vH20WLy1dMJ3rRyMcYwH3kBMxVxru7l0u/bzb8+2mPj9e3d4Z7/uAEDLoB8yw7aB0+c+ebzckOUoT01N9HU7Ei9jh84QJE9S4cWPt3LlTvXr1Uu/evdWlSxf1799fHTt21BlnnCFJSklJCXKkAAAAvrNjymLZKivV86Zzj3q9y9VnKSYxXtu/+SVIkQEAAAAA4D/kwwAAAAAAE5EPAwAAAABMRl4MAAAAAMDRyJUBAAAAAMHQIFYa3srz7S5tLx4yGCFObiq1SvR8u0va+zwUAE5kzvtd/9f9Oh3KPPopQBs/mKVvBo71+IEK4cb08vtTHat0bhvPt7uonRTNWACICBe0lawetuc6VmmEF3kEQpM3Y/u+TaQOdX0eCoIgLloa1dbz7S5qJx4yCASQ6TmR6eX3t0s7eL5Nzwb2fwDCX/skqX8Tz7djjiByjGglJVk92ybaIl3oRR6B0ORNe+5YVzqxsc9DAeAAORGfgT8NbSE1jvNsG4vs5wcBhD+LRbrEi3NDZ7WUGnp47EBoahRn/z49dUl7rh+NFIOaSy0SPN+Oc0NAYJmeE5lefn9KipHOae35dhe3l6IYCwAR4UIvrgevFyMN9yKPQGjy5pqRAU2ldkm+jwWBF2+VzvfytyRcPwoEjuk5kenl9zdvzvMd31DqVt/noQAIgk71vLsGzJs8ApKHl2mGj9atW2vBggUaP3685s+fr9TUVPXs2VOTJk3STTfdpE6dOkmSUlJSghwpEBgdLzlVSa2bSpLiG9dTVIxVx995sSSpIGOvtk/mpsIAEAnyNqZr4/uz1GPMSA19b7wyfvxN9bu0Us8xI7V78Tpt/2ZhsEMEAACoFfJbAEBNyIcBAIDJyJUBwFzkwwAAAOTFAGAy8mIAAGAy8mEAQE3IlQEAgGnIjwEgdFzTSZq7SyqucG/9VonePZAIoSnKIt3UTXr8d/e3GZosdeFG4gAQMa7qKM3IkA6Vu7d+s3jpAh4+D0SMRnH2B0R8tt39bf7WWUqI2KcjmGdwc6lnA2l9nnvrR0m6oYsfA0LAXd5RmpIuHSxzb/1GsfaHDgMAIsNJje0PGfttn3vrWyTd1NWvIQEIsBu6Siv3SZU299bvXl8a0ty/MSFw4q3SdV2k1ze4v83F7aUm8X4LCQHWq4E0qJm0ONv9bW7qKlksfgsJABBAMVHS37tIz691f5vz2kit6vgvJgCBNbK19MlWKaPQvfXjoqRrO/k3JgTWtZ2ln7Kkkkr31m9TRxrB9aMRI9oi3dhNevIP97cZ1lLqWNdvIQEAAuzqTtIPGVKhm78lSU6Qzm/j35gABE7TeOmidtJXqe5vc21n+/wSIsMpze3zvxsPuLd+lIXrRyPNFR2laTvdv360cZw0mt+SAEDE6N9USmkkrcp1b32L7OcTAUSOG7tKY5dIbk4TqVcDaWAzf0YUuSI6le7Ro4emTZtW7fWCggKlpqYqKipKxx13XBAiAwKv65VnKnlQr6NeO/G+KyXJflNhbqoHABFj+aMfqGDnXnW95iy1PvNEFece1Ib/zdTvz34h2dz8hRYAAECIIr8FADhCPgwAAExFrgwAZiMfBgAApiMvBgCzkRcDAABTkQ8DABwhVwYAACYhPwaA0NG5nvSfvtKEFVKpi7vINo2XJp4sJcUEJjYExnltpKxCadIm1+v2aST960T/xwQACJw2SdLz/aS7l0tFLh422DBWeuVkqUFsYGIDEBh39JT2FEnzslyve0FbaQwPmYso0Rbppf7SrYulHQXO142S9GCK/YFEiBzJCdJLA6RxS6VD5c7XrRcjvXyy1CQ+MLEBAPzPYpGe6yf9Y7G0+aDr9e/tLZ2S7P+4AARO3ybSoynSv/5w/bDBdknSywMka1QgIkOgXN/ZPk/0TZrrdU9Plu7u5Xo9hA+LRXr6JGnsUmntftfr395DGtbK/3EBAALn8g7SrkLp/7a7XndgU+mB4/0fE4DASbTarwG4dbGUXex83dgo6Zm+Upf6gYkNgdG1vv17vX+l6+tHm8dLrwyw1xtEjgva2s8LvLvZ9bonNpYe6+P3kAAAAdQ+SXq2n3TvCqnYxfWjjeOkiQOkelw/CkSUe46z54Pzd7te9+J20nWd/R8TAscaZZ//vWWxlObG9aOP9pFOahKIyBAoLROlF/tL45ZJhS6uH60fYx8LNOb6UQCIGFEW6YV+9jmCrfnO17VIuq+3NKhZQEIDECD9m9p/J/b0KtfXj3ZIso8doy0BCS3iGDm9tm7dOtlsNnXt2lWJiYnVlk+ePFmStH79+qP+bt++vfr27Ru4QAEfmnXxY8EOAQAQILbKSq2b9L3WTfo+2KEAAAD4HPktAMAR8mEAAGAqcmUAMBv5MAAAMB15MQCYjbwYAACYinwYAOAIuTIAADAJ+TEAhJYhzaVJg6RXN0i/7au+PNoinZYs3X2clJwQ+Pjgfzd1k1ol2h82mH6o+vI6VvsDCW/rIcVFBz4+AIB/9WsqvTNYmrheWpFTfXmUpFOSpbt6Sa3rBDw8AH5m/fOh8u9vkb7YLu0vrb5Os3jpmk7SlR0lCw+UiDiN46X3hkgvr5d+yJBKani6SNd69nxgcPPAxwf/S2lkrwMT10lL9lZfbpH9oVJ39ZLa1w14eAAAP6sfK709WHplvTQjo+aH0HeqK/2ju3R6i8DHB8D/zmtrzwte3yBtPFB9eWyUdHYr6c5e9mMGIovFIj1wvNSxrvTxVmlPcfV1GsRKl3WQxnTlQZORKClGemugfa74+3SpsIaxQPsk+3zi2a0CHx8AwL8sFvt5v3ZJ0gdbpKyi6uvUj5Euai/d0s0+pwAgsrRNkj44RXpxnfRTllRhq75On0bS7T3t8wmIPKcmS28Okl5dL/2RW315tEUa2kK6u5fUjOtHI9Kt3aXWidJ7W6SdNVw/mmSVRreznyOO5fpRAIg4Jzf7a65wpYPrR0/987ckLRMDHh4AP7NGSc/2k97bLH25Q8qr4frR5vHS3zpLl3fg+tFI1CRe+t8Q6eV10g+ZUmkN1492r2+/fnRgs8DHB//r01h6b7D9GuJlDq4fHdLcfr1Au6SAhwcA8LMGcdK7Q+y/KZyxs+bfknSpZz+HeFpy4OMD4H8XtrPnBW9skDYfrL48Lko6u7V0Z0+pHtePes0a7ACCYc2aNZKklJSUGpdfeumlNf593XXX6YMPPvBrbAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEC46N3I/oChrQelWRnSvhIpyiK1SpTOa8MDBk0wso00orW0fK+0cI/0XZpUXCnVtUrTh0uJRt4BFwDM0b2B/cHDO/KlGRnSZ9ul4gqpjlX64nQpmQcMAhEt2iLd2FW6tpM0L8v+APLCcvsxoH9T6ZTm9gcSInLVi5Ue7SON6ylN3ymlFkilFVKDWOmsltJxDXnIZKTrXE96daC0s0CaniF9ss0+FkiMlv7vdKl1nWBHCADwp6QY6cEU6fY/xwKvbbD3AwnR0isnS30aMRYAIt3AZtLJTaV1edLcXdL+Eik22v6g6fPa2HMDRC6LRbqio3RJe/sc0bK90qFy+9zQ8Q3teWFsdLCjhD/FW6XxvaV/9pBm7rQ/iP7wWODF/lLfJowFACCSWSzSxe3tD6Beki0tzpamHnPNSDxjASCiNUuQ/tNX2lssTdspZRySKmxS4zhpRCupS/1gRwh/S2kkvTtE2nJAmpVpv37UarHPD53XRmoSH+wI4W/ntbVfQ7p8r7RgjzTliLHAjOFSAtePAkBE69lAemuQtD1fmnns9aNDpWR+SwJEtGiLdHM36frO9nnC1fv/un50QFNpCNePRrz6sdJjJ0h39LKfF0j78/rRhnH2ecJeDZgninRd6kuvD7R/9zMypE+3/TUW+Ox0qSW/JQGAiJYUIz2UIt3ew94PHHn96Ksn288fMxYAItuQ5tLgZtLa/fa8MK/Ufr1ghyTp3Db2nAG1Y+Q0y5o1ayRJKSkpNS632WyBDCcg6nZI1ikTb1dco7oqyy/UwjteU97mDJfb9X/yBrU9u6+S2jTT1LPuVe661BrXSx7YS2d9+qAObttV9dr08x9SRXGpZLGo7yN/U6uhfRRljdae5Ru19P53VFlW7nb8Q16+TZ0vH6r/63atSg8W1rhO84E91e/RaxWdECeLRVp095va++tmWRPiNODpMWqS0klRMValz1yuX5/+1O33BgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgHOd60ljewY7CgRLlEU6uZn937wsqbjY/oDBRCPvfgv4h7v3GO5y5RnqPXa0FGXR7kVrteT+d2Qrr5AsFvV77Fq1GtpHleWVKtmfr8X3vqX81N1BKI13+AxCW4e60m09pOk7/3q4UDIPFgKMERstjWht/wczNYiVru4U7CgQTG2SpFu7S1PT7WOBpBipdZ1gRwVEFtNzItPLH+rqxkhXdJQ+2mrvB+rGSCc0DnZUAALFYpGOa2j/BzNZo6TTW9j/wUx1rNIlHaT/bflrLNCvabCjAiKL6TmR6eUPddEW+wOohzSXfj7impH46GBHBiBQmsZLf+8S7CgQTF3q2//BTEdeP/rTEWOBBK4fBXyGnIjPINR1rOn60YRgRwUgUGKjpZFt7P9gpgax0jVcP2q0dknSP7pL36f/NRZoyW9JAJ8yPScyvfyhrl5s9etH+3D9KGAMi0Xq3cj+D75n5FTLmjVrJEkpKSlBjiRwBj17izZ/Mkdbv/xZ7c49WUMmjtW0c+53uV3a9CVa+8Z3Gjnl3y7XPbhtl6YOG1/t9S5XnanGvTvo++ETVFlWrkHP36oeN47UujenuhV725EDVFle4XSdhOYNdcrEsZpz9VM6sCVTUbFWWeNjJUm977hIluhoTTnjHlms0Trzw/vV7ryBSpu2xK33B4DouBid9tZdqt+ltSqKS1Wcc0BL7n+nxoTPVXJ5yfI3VFFSporiUknS6le/VerUxT6J093E1tm6rso67PNHlNC0gVRZqbJDxVr28P+Uu3aHy2W11f/JG9T27L5KatNMU8+6V7nrUh2u68/PGAAAOObuWMTVmKHV0D464b4rFRVjVUVRiRZPmKT969Pc2tYRd8cSzsYRrc44QSfef6UsFoss1mitfWOKtn01363Pxt24ncXpz7EWAPiDL459nuzH0/0e5q9jv7u5qbP3iIq1qt9j16nV6X1UUVKq3PVpWjD2FbfeHwAA+J8n5+QlqfPlQzXk5ds07+//VfqsFVWvh+t4x93yO1vPWRkcbRfXMElnf/lY1XrRCXGq2665Pu89RqV5BW7FDgC14Yvjn6vlzs6ROjp2ezKnfSxfzcV606+4E7c7x37OnwIAEFp8lTN7Mk45ki9y5tqco/fVHInk+LMBgGAIZk7s7nlBT4+bwcyJJff7DEfvzflSAACCw91z8rXpqyNhPtjXc8W1LRMABIuvzhd6e84y2OdL3b6m2sk8OQAACD6f/X7YQV4XzBy6Nu8diLmTw2qaA+G3Z4C5fJHrSf7PNb3dvy/uoeXufmo6vjrLUbmvFQAglLhzj+GkNs10woQr9P3wCSram6czPrhP3a4Zpo0fzFLbs/uqWb/umnLmvbKVV+j4Oy/WiQ9cpfm3vBikEnmOzwAAAACAyUzPiUwvPwAAAACzmZ4TmV5+AAAAAGYjJ+IzAAAAAGA203Mi08sPADCXNdgBBMO8efOCHUJAxTeup8YpnTT7iiclSWnTl+rkp8eobvtklw+k3bN0Q63fv1HPdtq1YI0qy8olSRnzflefey7Tujenuo69SX0dP+4izbr4MXW9+iyH63W//mxt/3ahDmzJlCRVlpartLT8z/dvr+3fLpAk2cortOuXVep0yalKm7aktkUDYJBNH89R5rzfJUnd/z5Cg1/4h2Zd/Fi19dxJLuff+pLbD830hDvv7c66zso6/+YXVHqwUJLU9pz+GvLybZp61r0ul9VW2vQlWvvGdxo55d9ure+vzxgAADjm7ljE2Zghtn4dnfLaHZo1+hHlbc5QswE9dOrrd2jK0LtdbuuMJ2MJR+OIU18bp1kXP679G9KU1LqpRi+YqLQZy1R+qNj1Pt2M21mc/hxrAYA/+OLY58l+PN3vUe/hh2O/s/26+x4nPXSNZLPpm8G3S5L9QS0AACBkeHJOPql1U3W9+ixlr9xUbVm4jnfcLb+z9ZyVwdF2JfsLNHXY+Kr1et06SskDe7p8EBwA+Iovjn/Olrs6R+ro2G0rr3B7TvtYvpqL9bZfcRW3O8d+zp8CABBafJUzezpO8WY7R2Ob2pyj99UcibPPBgCCIZg5sTu5oTfHzWDnxJ7kszW9N+dLAQAIHnfOydemr46E+WBfzxXXtkwAECy+Ol/o7TnLYJ8vdbbfw1zNkwMAgODzxe+HJcd5XTBz6Nq8t7/nTg5zNAfCb88Ac/ki15P8n2t6u39f3UPLm+OrOzkq97UCAIQCd+8x3O68k7Vz9koV7c2TJG36aLaOv+Mibfxglmw2KTrWqui4GJWXVygmKUGFWfuCURyv8BkAAAAAMJnpOZHp5QcAAABgNtNzItPLDwAAAMBs5ER8BgAAAADMZnpOZHr5AQBmiwp2APC/Oq2aqGjPftkqKqteK8jMUZ1WTXz6PnXbJ+v82c/qvJn/Ubfrzq56fd/q7Wo7vK9ikhJksUarw/mDlNSmqVv7HPT8rVr55McubxTfoGsbRcfHavgXj2rUnOc04N83yJoQ9+f7b1P78wcqKtYqa2K82o7or6Q2zbwvKADjVJSUVT08RJL2/ralxuPY4eRy29e/SLInl3VaNlbd9sl+j9GT93a2rquyHr4prCTF1k2UbDa3ltXWnqUbVJiV67P9AQAA3/JkLOJszFC3fbJK9ucrb3OGJCl72QbVadVEjXp3cLmtM74YS9hsUmz9RElSTN1EFe/PV2VpuVvbuhu3szj9OdYCAH/wxbHPk/14ul931ObYX9v3sCbEqcuVZ+i3/3xWte7hSWoAABB8Hs0HWCwa9MI/tOzh92ocS4TjeMfd8rtaz1EZPPl8u1x1hrZ89qMHpQYA7/nq+OdsuatzpI6O3e7OadfEV3Ox3vQr3sRd07Gf86cAAIQOX+bM3o5Taju+qe05ep/Mkbj4bAAg0EIhJz5StdzQy+NmMHNiyff5LOdLAQAIDG/PyXvSV4f7fLC/5ooDcU0fAPiaL84X1uacZbDPl7rDk3MCAAAg8Hz1+2HJ/bwu0Dm0N+8diLkTSQ7nQPjtGWA2Xxz7/J1rert/X91Dy9vjKzkqACBcuHuP4aRWTVSQsfevdTL2Vq2zc/ZK7V6yTpevfleXr3pHLU7prd+f/SIwBfABPgMAAAAAJjM9JzK9/AAAAADMZnpOZHr5AQAAAJiNnIjPAAAAAIDZTM+JTC8/AMBs1mAHgMiwb812fXniLSrLL1Rii0Y665OHVJJ7UKnfL9HWL35SUuumGvHNv1RRXKpdC1ar5WnHu9xnl6vO1KHMHO1etNblupboKDU/uYdmX/YvlR0q1pCXb1Of8Zdr5b8+0prXvtNJD12t86Y/o9L8QuX8vlUthhzni2IDMFTPG0cq/YcV1V53llzmp+6uem3IK7fLYpH2/r5Vvz79qUr2Hax1TO6+t6fr1lTWIa/crhaDekmS5lzztNvLAskfnzEAAHDMk/GF5HjMcHB7luIa1lXTvt20d+UmtRneV7F1E5XUpply1+xwuq2vOBpHzL/1RQ19b7zKC0sUW7+OfhrznCrL3H+wmS/iDpWxFgC4y1fHrXA+9rvKTR29R/3OrVSaV6Djx12kFqcer4riUv3x/JfKWrjGZ+UGAADe8yQP7nXL+cpesVH7Vm93uL9wG++4W35Pzxd4ul3Tvt0UV7+Ods751b0PAgBqyVfHP2fL963e7vQcqbvHbkdz2r7grzxach23s2M/508BAAgNvs6Z/a2msU3d9sm1Pkdf27FJKHw2AHCkUMiJD6spNwzUcdMfObG7fYar+WfOlwIAEDzunJP3Z18divPBZfmFfpkr9sVcBAAEQ23PF/rinKW7cfrrfKmznNad3xIBAIDg8dXvhyX3ctVg5NDevHcg5k7yU3c7nAMJ1BgRQPhzdOzz93HE2/376h5ars5ROjq+unu/C+5rBQCIBE1SOqlBt7b66oSbVZpfpJMeuloDn71ZC8a+EuzQAobPAAAAAIDJTM+JTC8/AAAAALOZnhOZXn4AAAAAZiMn4jMAAAAAYDbTcyLTyw8ACF/WYAcA/zuUmaOE5g1liY6qumlSUqsmOpSZ47P3KCsoqvr/wqxc7fhuoZoP6KHU75dIkv544Uv98cKXkqQOFwxW3uYMl/tMHnyckgf0UJthJ1W9NmreC5p3/bPKXXv0TYUPZeYod12qSg8ckiTt+Haheo8bLUmqKC7V8kfer1q399gLlbdpp5clBRCJRn7/lOp1bFHjsqnDxqtw176qv3uPu0h12ydr8WVPePVeM0c/qkOZObJYo3XifVfqlIljNdeNG8u7itEfHJV14bhXJUmdLj1NfR++5qj4nS1zxJPP3x3efsYAAMAxX49FHI0ZyvIL9fNNz+ukB6+StU689q7crP2bdspWXuFyW19wNI6wREcp5c5L9NOY57Rn6QY1TumkMz+8X1POuFslufm1KrMn/Fl2APCEu3mcr45b4Xjsdyc3dfYeFmuUkto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"text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 13, @@ -821,7 +823,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -835,9 +837,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.9.21" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx new file mode 100644 index 00000000000..3c67cf257c4 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx @@ -0,0 +1,15 @@ +--- +title: Approximate quantum compilation (AQC-Tensor) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-aqc-tensor. +--- + + + +# AQC-Tensor Tutorials + +* [Tutorial 1](01-basic-workflow-ipynb): Show the basic end-to-end workflow of the AQC-Tensor addon. + +[![](/docs/images/api/qiskit-addon-aqc-tensor/tutorials_01_initial_state_aqc_26_0.avif)](01-initial-state-aqc) + +[Reducing circuit depth with the AQC-Tensor Qiskit addon](01-initial-state-aqc) + diff --git a/docs/addons/qiskit-addon-cutting/_package.json b/docs/addons/qiskit-addon-cutting/_package.json new file mode 100644 index 00000000000..2c3e49e17d2 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/_package.json @@ -0,0 +1,4 @@ +{ + "name": "qiskit-addon-cutting", + "version": "0.10.0" +} diff --git a/docs/addons/qiskit-addon-cutting/explanation/index.mdx b/docs/addons/qiskit-addon-cutting/explanation/index.mdx index 5a7511683ed..41b8a4b993a 100644 --- a/docs/addons/qiskit-addon-cutting/explanation/index.mdx +++ b/docs/addons/qiskit-addon-cutting/explanation/index.mdx @@ -1,5 +1,6 @@ --- -title: "Explanatory material" +title: Circuit cutting API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-cutting. --- @@ -12,9 +13,9 @@ Circuit cutting is a technique to increase the size of circuits we can run on qu ## Key terms -* subcircuits: The set of circuits resulting from cutting gates in a [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) and then separating the disconnected qubit subsets into smaller circuits. These circuits contain [`SingleQubitQPDGate`](qiskit_addon_cutting.qpd.SingleQubitQPDGate#qiskit_addon_cutting.qpd.SingleQubitQPDGate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s and will be used to instantiate each unique subexperiment. -* subexperiments: A term used to describe the unique circuit samples associated with a subcircuit. These circuits have had their [`BaseQPDGate`](qiskit_addon_cutting.qpd.BaseQPDGate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate")s decomposed into local Qiskit gates and measurements. Subexperiments are the circuits sent to the backend for execution. -* decompose: We try to honor the Qiskit notion of “decompose” in the documentation and API, which loosely means transforming a gate into a less-abstracted representation. *Occasionally*, we may use the term “decompose” to refer to the act of inserting [`BaseQPDGate`](qiskit_addon_cutting.qpd.BaseQPDGate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate") instances into quantum circuits as “decomposing” a gate or wire; however, we try to use terms like “partition” and “cut” when referring to this to avoid ambiguity with Qiskit language. +* subcircuits: The set of circuits resulting from cutting gates in a [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) and then separating the disconnected qubit subsets into smaller circuits. These circuits contain [`SingleQubitQPDGate`](qpd-single-qubit-qpd-gate#qiskit_addon_cutting.qpd.SingleQubitQPDGate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s and will be used to instantiate each unique subexperiment. +* subexperiments: A term used to describe the unique circuit samples associated with a subcircuit. These circuits have had their [`BaseQPDGate`](qpd-base-qpd-gate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate")s decomposed into local Qiskit gates and measurements. Subexperiments are the circuits sent to the backend for execution. +* decompose: We try to honor the Qiskit notion of “decompose” in the documentation and API, which loosely means transforming a gate into a less-abstracted representation. *Occasionally*, we may use the term “decompose” to refer to the act of inserting [`BaseQPDGate`](qpd-base-qpd-gate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate") instances into quantum circuits as “decomposing” a gate or wire; however, we try to use terms like “partition” and “cut” when referring to this to avoid ambiguity with Qiskit language. ## Circuit cutting as a quasiprobability decomposition (QPD) @@ -63,7 +64,7 @@ As shown in \[[2](https://arxiv.org/abs/1909.07534)], a quantum circuit which im Equation [(1)](#equation-eq-qpd) for [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) can thus be written as a sum of the six terms listed above. The following plot shows the magnitude of each coeffient (negative coefficients are in orange) as a function of $\theta$. The square root of the optimal sampling overhead, denoted by $\gamma$, is given by the sum of the absolute coefficients. -![../\_images/index-1.svg](docs/images/addons/qiskit-addon-cutting/index-1.svg) +![../\_images/index-1.svg](/docs/images/api/qiskit-addon-cutting/index-1.svg) Let’s consider some special points in this plot: @@ -89,15 +90,15 @@ The code that generates subexperiments from the KAK decomposition currently foll -## Wire cutting phrased as a two-qubit [`Move`](qiskit_addon_cutting.instructions.Move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") operation +## Wire cutting phrased as a two-qubit [`Move`](instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") operation -A wire cut is represented fundamentally by this package as a two-qubit [`Move`](qiskit_addon_cutting.instructions.Move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction, which is defined as a reset of the second qubit followed by a swap of both qubits. Equivalently, the operation is defined as transferring the state of the first qubit wire to the second qubit wire, while simultaneously discarding the state of the second qubit wire (the first qubit ends up in state $\lvert 0 \rangle$). +A wire cut is represented fundamentally by this package as a two-qubit [`Move`](instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction, which is defined as a reset of the second qubit followed by a swap of both qubits. Equivalently, the operation is defined as transferring the state of the first qubit wire to the second qubit wire, while simultaneously discarding the state of the second qubit wire (the first qubit ends up in state $\lvert 0 \rangle$). -We have chosen to represent wire cuts in this way primarily because it is consistent with the way one must treat wire cuts when acting on physical qubits: for instance, a wire cut might take the state of physical qubit $n$ and continue it as physical qubit $m$ after the cut. Our choice also has the benefit of allowing us to think of “instruction cutting” as a unified framework for considering both wire cuts and gate cuts in the same formalism, being that a wire cut is just a cut [`Move`](qiskit_addon_cutting.instructions.Move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction. +We have chosen to represent wire cuts in this way primarily because it is consistent with the way one must treat wire cuts when acting on physical qubits: for instance, a wire cut might take the state of physical qubit $n$ and continue it as physical qubit $m$ after the cut. Our choice also has the benefit of allowing us to think of “instruction cutting” as a unified framework for considering both wire cuts and gate cuts in the same formalism, being that a wire cut is just a cut [`Move`](instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction. More information on this formalism is given in Sec. 3 of Ref. \[[4](https://arxiv.org/abs/2302.03366)] -If you prefer to place cut wires abstractly on a single qubit wire, please see the [how-to guide on placing wire cuts using a single-qubit instruction](how-tos/how_to_specify_cut_wires), which explains how to use the [`cut_wires()`](qiskit_addon_cutting#qiskit_addon_cutting.cut_wires "qiskit_addon_cutting.cut_wires") function to convert a circuit with [`CutWire`](qiskit_addon_cutting.instructions.CutWire#qiskit_addon_cutting.instructions.CutWire "qiskit_addon_cutting.instructions.CutWire") instructions to a circuit with [`Move`](qiskit_addon_cutting.instructions.Move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move")s on additional qubits. +If you prefer to place cut wires abstractly on a single qubit wire, please see the [how-to guide on placing wire cuts using a single-qubit instruction](how-tos/how-to-specify-cut-wires), which explains how to use the [`cut_wires()`](qiskit-addon-cutting#qiskit_addon_cutting.cut_wires "qiskit_addon_cutting.cut_wires") function to convert a circuit with [`CutWire`](instructions-cut-wire#qiskit_addon_cutting.instructions.CutWire "qiskit_addon_cutting.instructions.CutWire") instructions to a circuit with [`Move`](instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move")s on additional qubits. ## Sample weights in the Qiskit addon for circuit cutting @@ -118,7 +119,7 @@ The below table provides the sampling overhead factor for a variety of two-qubit | [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), [`RZXGate`](/docs/api/qiskit/qiskit.circuit.library.RZXGate) | \$( | \theta/2 | , 0, 0)\$ | \$\left\[1 + 2 \left | \sin(\theta)\right | \right]^2\$ | | | | [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate), [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) | \$( | \theta/4 | , 0, 0)\$ | \$\left\[1 + 2 \left | \sin(\theta/2)\right | \right]^2\$ | | | | [`XXPlusYYGate`](/docs/api/qiskit/qiskit.circuit.library.XXPlusYYGate), [`XXMinusYYGate`](/docs/api/qiskit/qiskit.circuit.library.XXMinusYYGate) | \$( | \theta/4 | , | \theta/4 | ,0)\$ | \$\left\[1+4\left | \sin(\theta/2)\right | +2\sin^2(\theta/2)\right]^2$(independent of$\beta\$ parameter) | -| [`Move`](qiskit_addon_cutting.instructions.Move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") (cut wire) without classical communication (i.e., in the LO setting) | not applicable | $4^2=16$ | | | | | | | +| [`Move`](instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") (cut wire) without classical communication (i.e., in the LO setting) | not applicable | $4^2=16$ | | | | | | | ## Current limitations diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-quasi-dists-from-sampler.mdx b/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-quasi-dists-from-sampler.mdx new file mode 100644 index 00000000000..5e75facf582 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-quasi-dists-from-sampler.mdx @@ -0,0 +1,76 @@ + + +# How to generate exact quasiprobability distributions from Sampler + +This how-to guide is intended to show users how they can generate statevector-based quasiprobability distributions through the `qiskit.primitives.BaseSampler` interface. + +``` +[1]: +``` + +```python +from qiskit import QuantumCircuit +from qiskit.quantum_info import SparsePauliOp + +from qiskit_addon_cutting import ( + partition_problem, + generate_cutting_experiments, +) +``` + +Prepare inputs to `generate_cutting_experiments` + +``` +[2]: +``` + +```python +circuit = QuantumCircuit(2) +circuit.h(0) +circuit.cx(0, 1) +observable = SparsePauliOp(["ZZ"]) +partitioned_problem = partition_problem( + circuit=circuit, partition_labels="AB", observables=observable.paulis +) +subcircuits = partitioned_problem.subcircuits +subobservables = partitioned_problem.subobservables +``` + +Call `generate_cutting_experiments` + +``` +[3]: +``` + +```python +subexperiments, coefficients = generate_cutting_experiments( + circuits=subcircuits, + observables=subobservables, + num_samples=1000, +) +``` + +In order to calculate exact quasiprobability distributions for circuits with mid-circuit measurements, users will need to use the `ExactSampler` class from `qiskit_addon_cutting.utils.simulation`. The Qiskit Samplers do not support mid-circuit measurements in statevector mode. + +``` +[4]: +``` + +```python +from qiskit_addon_cutting.utils.simulation import ExactSampler + +exact_sampler = ExactSampler() +``` + +If `ExactSampler` is used, the quasiprobability distributions returned from `generate_cutting_experiments` will be exact and generated from the statevectors of the subexperiments. + +``` +[5]: +``` + +```python +results = { + label: exact_sampler.run(subexperiment).result() + for label, subexperiment in subexperiments.items() +} +``` diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.mdx b/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.mdx new file mode 100644 index 00000000000..61bab34585d --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.mdx @@ -0,0 +1,248 @@ + + +# How to generate exact sampling coefficients + +This how-to guide is intended to show users how they can generate exact sampling coefficients to be used in reconstructing the simulated expectation value of the original circuit. + +First, we set up a simple cutting problem following the [first tutorial](tutorials/01-gate-cutting-to-reduce-circuit-width). + +``` +[1]: +``` + +```python +import numpy as np +from qiskit.circuit.library import efficient_su2 +from qiskit.quantum_info import SparsePauliOp + +from qiskit_addon_cutting import ( + partition_problem, + generate_cutting_experiments, +) +``` + +``` +[2]: +``` + +```python +circuit = efficient_su2(4, entanglement="linear", reps=2) +circuit.assign_parameters([0.8] * len(circuit.parameters), inplace=True) +observable = SparsePauliOp(["ZZZZ"]) +circuit.draw("mpl", scale=0.8) +``` + +``` +[2]: +``` + +![../\_images/how-tos\_how\_to\_generate\_exact\_sampling\_coefficients\_2\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_generate_exact_sampling_coefficients_2_0.avif) + +Partition the circuit between qubits 1 and 2 by cutting 2 CNOT gates. + +``` +[3]: +``` + +```python +partitioned_problem = partition_problem( + circuit=circuit, partition_labels="AABB", observables=observable.paulis +) +subcircuits = partitioned_problem.subcircuits +bases = partitioned_problem.bases +subobservables = partitioned_problem.subobservables +subcircuits["A"].draw("mpl", scale=0.6) +``` + +``` +[3]: +``` + +![../\_images/how-tos\_how\_to\_generate\_exact\_sampling\_coefficients\_4\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_generate_exact_sampling_coefficients_4_0.avif) + +``` +[4]: +``` + +```python +subcircuits["B"].draw("mpl", scale=0.6) +``` + +``` +[4]: +``` + +![../\_images/how-tos\_how\_to\_generate\_exact\_sampling\_coefficients\_5\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_generate_exact_sampling_coefficients_5_0.avif) + + + +## Demonstrate how to obtain all weights exactly + +If you wish to calculate all weights exactly, no matter how small, you can achieve this by passing infinity (`np.inf`) to `num_samples`: + +``` +[5]: +``` + +```python +subexperiments, coefficients = generate_cutting_experiments( + circuits=subcircuits, + observables=subobservables, + num_samples=np.inf, +) +coefficients +``` + +``` +[5]: +``` + +```python +[(np.float64(0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(0.24999999999999992), ), + (np.float64(-0.24999999999999992), ), + (np.float64(0.24999999999999992), )] +``` + + + +## Demonstrate how to find the minimum `num_samples` needed to retrieve all exact weights for 2 CNOT cuts + +When `num_samples` is set to a finite number, each weight whose absolute value is above a threshold of 1 / `num_samples` will be evaluated exactly. The remaining weights – those in the tail of the distribution – will then be sampled from, resulting in at most `num_samples` unique weights. + +In the case of a CNOT gate – or any gate equivalent to it up to single-qubit unitaries – each of the six weights of the quasi-probability decomposition have the same magnitude, so each gets sampled with a probability of $1/6$: + +``` +[6]: +``` + +```python +print(f"Mapping probabilities for a CNOT decomposition: {bases[0].probabilities}") +``` + +```python +Mapping probabilities for a CNOT decomposition: [0.16666667 0.16666667 0.16666667 0.16666667 0.16666667 0.16666667] +``` + +In this example, we have cut two CNOT gates. Given that the probability of any given mapping in a CNOT decomposition is $1/6$, the probability of any given mapping in the *joint* distribution combining the two cut CNOT gates is $(1/6)^2$. Therefore, we need to take at least $6^2$ weights in order to retrieve all exact weights from `generate_cutting_experiments`. + +``` +[7]: +``` + +```python +from qiskit_addon_cutting.qpd import QPDBasis +from qiskit.circuit.library.standard_gates import CXGate + +qpd_basis_cx = QPDBasis.from_instruction(CXGate()) + + +def _min_nonzero(seq, /): + """Return the minimum value in a sequence, ignoring values near zero.""" + return min(x for x in seq if not np.isclose(x, 0)) + + +num_cx_cuts = 2 + +print( + f"Number of samples needed to retrieve exact weights: {1 / _min_nonzero(qpd_basis_cx.probabilities)**num_cx_cuts}" +) +``` + +```python +Number of samples needed to retrieve exact weights: 36.0 +``` + + + +### Observe the coefficient weights returned from `generate_cutting_experiments` are `WeightType.EXACT` + +Above, we determined 36 samples would trigger the coefficients to be returned as exact. Here we set `num_samples` to exactly 36 to test this. + +``` +[8]: +``` + +```python +subexperiments, coefficients = generate_cutting_experiments( + circuits=subcircuits, + observables=subobservables, + num_samples=36, +) +coefficients +``` + +``` +[8]: +``` + +```python +[(np.float64(0.25), ), + (np.float64(0.25), ), + (np.float64(0.25), ), + (np.float64(-0.25), ), + (np.float64(0.25), ), + (np.float64(-0.25), ), + (np.float64(0.25), ), + (np.float64(0.25), ), + (np.float64(0.25), ), + (np.float64(-0.25), ), + (np.float64(0.25), ), + (np.float64(-0.25), ), + (np.float64(0.25), ), + (np.float64(0.25), ), + (np.float64(0.25), ), + (np.float64(-0.25), ), + (np.float64(0.25), ), + (np.float64(-0.25), ), + (np.float64(-0.25), ), + (np.float64(-0.25), ), + (np.float64(-0.25), ), + (np.float64(0.25), ), + (np.float64(-0.25), ), + (np.float64(0.25), ), + (np.float64(0.25), ), + (np.float64(0.25), ), + (np.float64(0.25), ), + (np.float64(-0.25), ), + (np.float64(0.25), ), + (np.float64(-0.25), ), + (np.float64(-0.25), ), + (np.float64(-0.25), ), + (np.float64(-0.25), ), + (np.float64(0.25), ), + (np.float64(-0.25), ), + (np.float64(0.25), )] +``` diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables.mdx b/docs/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables.mdx new file mode 100644 index 00000000000..327fe3093e3 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables.mdx @@ -0,0 +1,207 @@ + + +# How to perform a cutting workflow with multiple observables + + + +## Create a circuit to cut + +``` +[1]: +``` + +```python +from qiskit.circuit.library import EfficientSU2 + +qc = EfficientSU2(4, entanglement="linear", reps=2).decompose() +qc.assign_parameters([0.4] * len(qc.parameters), inplace=True) + +qc.draw("mpl", scale=0.8) +``` + +``` +[1]: +``` + +![../\_images/how-tos\_how\_to\_provide\_multiple\_observables\_2\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_provide_multiple_observables_2_0.avif) + + + +## Specify the observables of interest + +``` +[2]: +``` + +```python +from qiskit.quantum_info import Pauli, SparsePauliOp + +observables = [ + Pauli("-XZII"), + SparsePauliOp(["ZZII", "IZZI", "-IIZZ", "XIXI", "ZIZZ", "IXIX"]), +] +``` + + + +## Gather unique observable terms + +``` +[3]: +``` + +```python +from qiskit_addon_cutting.utils.observable_terms import gather_unique_observable_terms + +unique_observable_terms = gather_unique_observable_terms(observables) +``` + + + +## Perform cutting workflow + +``` +[4]: +``` + +```python +from qiskit_addon_cutting import partition_problem + +partitioned_problem = partition_problem( + circuit=qc, partition_labels="AABB", observables=unique_observable_terms +) +subcircuits = partitioned_problem.subcircuits +subobservables = partitioned_problem.subobservables +``` + +``` +[5]: +``` + +```python +import numpy as np +from qiskit_addon_cutting import generate_cutting_experiments + +subexperiments, coefficients = generate_cutting_experiments( + circuits=subcircuits, observables=subobservables, num_samples=np.inf +) +``` + +``` +[6]: +``` + +```python +from qiskit_ibm_runtime.fake_provider import FakeManilaV2 + +backend = FakeManilaV2() +``` + +``` +[7]: +``` + +```python +from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager + +# Transpile the subexperiments to ISA circuits +pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend) +isa_subexperiments = { + label: pass_manager.run(partition_subexpts) + for label, partition_subexpts in subexperiments.items() +} +``` + +``` +[8]: +``` + +```python +from qiskit_ibm_runtime import SamplerV2, Batch + +# Submit each partition's subexperiments as a single batch +with Batch(backend=backend) as batch: + sampler = SamplerV2(mode=batch) + jobs = { + label: sampler.run(subsystem_subexpts, shots=2**12) + for label, subsystem_subexpts in isa_subexperiments.items() + } +``` + +``` +[9]: +``` + +```python +# Retrieve results +results = {label: job.result() for label, job in jobs.items()} +``` + + + +## Reconstruct the expectation value of each unique term + +``` +[10]: +``` + +```python +from qiskit_addon_cutting import reconstruct_expectation_values + +# Get expectation values for each observable term +reconstructed_term_expvals = reconstruct_expectation_values( + results, + coefficients, + subobservables, # or unique_observable_terms if the circuit did not separate +) +``` + + + +## Reconstruct the expectation value of the original operators + +``` +[11]: +``` + +```python +from qiskit_addon_cutting.utils.observable_terms import ( + reconstruct_observable_expvals_from_terms, +) + +reconstructed_expvals = reconstruct_observable_expvals_from_terms( + observables, dict(zip(unique_observable_terms, reconstructed_term_expvals)) +) +``` + + + +## Compare the reconstructed expectation values with the exact expectation value from the original circuit and observables + +``` +[12]: +``` + +```python +from qiskit_aer.primitives import EstimatorV2 + +estimator = EstimatorV2() +exact_expvals = estimator.run([(qc, observables)]).result()[0].data.evs +print( + f"Reconstructed expectation values: {np.real(np.round(reconstructed_expvals, 8))}" +) +print(f"Exact expectation values: {np.round(exact_expvals, 8)}") +print( + f"Error in estimation: {np.real(np.round(reconstructed_expvals-exact_expvals, 8))}" +) +print( + f"Relative error in estimation: {np.real(np.round((reconstructed_expvals-exact_expvals) / exact_expvals, 8))}" +) +``` + +```python +Reconstructed expectation values: [-0.20063001 0.48694563] +Exact expectation values: [-0.17994235 0.56254612] +Error in estimation: [-0.02068766 -0.07560049] +Relative error in estimation: [ 0.11496824 -0.13438986] +``` diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires.mdx b/docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires.mdx new file mode 100644 index 00000000000..4218e8ec18c --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires.mdx @@ -0,0 +1,277 @@ + + +# How to place wire cuts using a single-qubit `CutWire` instruction + +This how-to guide is intended to demonstrate how to place wire cuts using single-qubit `CutWire` instructions, which may at times be more convenient than specifying them as two-qubit `Move` instructions. + +``` +[1]: +``` + +```python +import numpy as np +from qiskit import QuantumCircuit +from qiskit.quantum_info import SparsePauliOp +from qiskit_aer.primitives import EstimatorV2, SamplerV2 + +from qiskit_addon_cutting import ( + partition_problem, + generate_cutting_experiments, + reconstruct_expectation_values, +) + +from qiskit_addon_cutting.instructions import CutWire +from qiskit_addon_cutting import cut_wires, expand_observables +``` + + + +## Prepare a circuit for cutting + +As in the [tutorial for wire cutting](tutorials/03-wire-cutting-via-move-instruction), we have used a circuit inspired by Fig. 1(a) of [arXiv:2302.03366v1](https://arxiv.org/abs/2302.03366v1). The cut locations are marked manually here with `CutWire` instructions. + +``` +[2]: +``` + +```python +qc_0 = QuantumCircuit(7) +for i in range(7): + qc_0.rx(np.pi / 4, i) +qc_0.cx(0, 3) +qc_0.cx(1, 3) +qc_0.cx(2, 3) +qc_0.append(CutWire(), [3]) +qc_0.cx(3, 4) +qc_0.cx(3, 5) +qc_0.cx(3, 6) +qc_0.append(CutWire(), [3]) +qc_0.cx(0, 3) +qc_0.cx(1, 3) +qc_0.cx(2, 3) +qc_0.draw("mpl") +``` + +``` +[2]: +``` + +![../\_images/how-tos\_how\_to\_specify\_cut\_wires\_3\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_3_0.avif) + + + +## Recover the uncut circuit + +`CutWire` instructions decompose to nothing (they are equivalent to the identity), so the uncut circuit can be recovered as follows: + +``` +[3]: +``` + +```python +qc_0.decompose("cut_wire").draw("mpl") +``` + +``` +[3]: +``` + +![../\_images/how-tos\_how\_to\_specify\_cut\_wires\_5\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_5_0.avif) + + + +## Specify an observable + +This observable has 7 qubits, just like the original circuit. + +``` +[4]: +``` + +```python +observable = SparsePauliOp(["ZIIIIII", "IIIZIII", "IIIIIIZ"]) +``` + + + +## Transform cuts to moves + +The next step is to call `cut_wires`, which transforms each `CutWire` into a `TwoQubitQPDGate` which wraps a `Move` instruction. An additional qubit is added to the circuit for each `CutWire` in the input circuit. + +Notice that, unlike in the [wire cutting tutorial](tutorials/03-wire-cutting-via-move-instruction), where `Move` operations were placed manually, this function does not result in the *re*-use of a qubit. Because any method for qubit re-use is based on heuristics, this function naively allocates an additional qubit for each cut. Users wishing to re-use qubits might wish to experiment with [qiskit-qubit-reuse](https://github.com/qiskit-community/qiskit-qubit-reuse). + +``` +[5]: +``` + +```python +qc_1 = cut_wires(qc_0) +qc_1.draw("mpl") +``` + +``` +[5]: +``` + +![../\_images/how-tos\_how\_to\_specify\_cut\_wires\_9\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_9_0.avif) + + + +## Update the observable terms to account for the extra qubit + +The transformed circuit contains additional qubits (one for each `CutWire` instruction), so the observables must be updated for the new circuit. This can be done using the `expand_observables` function. Since the observable coefficients are ignored until reconstruction of the final expectation value, the input and output types of the observables for `expand_observables` are `PauliList`s. + +The resulting observables have 9 qubits, just like the transformed circuit. + +``` +[6]: +``` + +```python +observable_expanded_paulis = expand_observables(observable.paulis, qc_0, qc_1) +observable_expanded_paulis +``` + +``` +[6]: +``` + +```python +PauliList(['ZIIIIIIII', 'IIIZIIIII', 'IIIIIIIIZ']) +``` + + + +## Separate the circuit and observables + +In this case, `partition_labels` need not be passed to `partition_problem`, as the labels can be determined automatically from the connectivity of the circuit. + +``` +[7]: +``` + +```python +partitioned_problem = partition_problem( + circuit=qc_1, observables=observable_expanded_paulis +) +subcircuits = partitioned_problem.subcircuits +subobservables = partitioned_problem.subobservables +``` + +From here forward, the cutting workflow is the same as usual, but the remaining steps are spelled out here explicitly so one can follow along with the results. + + + +## Visualize the decomposed problem + +Notice that once the circuits have been cut, some of the instructions are able to commute past each other. For instance, in subcircuit “A”, half of the second `Move` operation is actually the *first* operation on the final qubit. + +``` +[8]: +``` + +```python +subobservables +``` + +``` +[8]: +``` + +```python +{0: PauliList(['IIIII', 'ZIIII', 'IIIIZ']), + 1: PauliList(['ZIII', 'IIII', 'IIII'])} +``` + +``` +[9]: +``` + +```python +subcircuits[0].draw("mpl") +``` + +``` +[9]: +``` + +![../\_images/how-tos\_how\_to\_specify\_cut\_wires\_17\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_17_0.avif) + +``` +[10]: +``` + +```python +subcircuits[1].draw("mpl") +``` + +``` +[10]: +``` + +![../\_images/how-tos\_how\_to\_specify\_cut\_wires\_18\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_18_0.avif) + + + +## Generate and run the cutting experiments; reconstruct and compare against uncut expectation values + +``` +[11]: +``` + +```python +subexperiments, coefficients = generate_cutting_experiments( + circuits=subcircuits, + observables=subobservables, + num_samples=np.inf, +) +``` + +``` +[12]: +``` + +```python +sampler = SamplerV2() +results = { + label: sampler.run(subexperiment, shots=2**12).result() + for label, subexperiment in subexperiments.items() +} +``` + +``` +[13]: +``` + +```python +reconstructed_expval_terms = reconstruct_expectation_values( + results, + coefficients, + subobservables, +) +reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs) +``` + +``` +[14]: +``` + +```python +estimator = EstimatorV2() +exact_expval = ( + estimator.run([(qc_0.decompose("cut_wire"), observable)]).result()[0].data.evs +) +print(f"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}") +print(f"Exact expectation value: {np.round(exact_expval, 8)}") +print(f"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}") +print( + f"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}" +) +``` + +```python +Reconstructed expectation value: 1.62048799 +Exact expectation value: 1.59099026 +Error in estimation: 0.02949773 +Relative error in estimation: 0.01854048 +``` diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb index 6a41797a153..3b4ade2637b 100644 --- a/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb +++ b/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb @@ -153,7 +153,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -167,9 +167,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.9.21" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb index cb09664a410..df5b5676e19 100644 --- a/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb +++ b/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb @@ -51,8 +51,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 2, @@ -90,8 +91,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 3, @@ -124,8 +126,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 4, @@ -381,7 +384,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -395,9 +398,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.9.21" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how_to_provide_multiple_observables.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how_to_provide_multiple_observables.ipynb index 87f2c1e2c90..f35df28146d 100644 --- a/docs/addons/qiskit-addon-cutting/how-tos/how_to_provide_multiple_observables.ipynb +++ b/docs/addons/qiskit-addon-cutting/how-tos/how_to_provide_multiple_observables.ipynb @@ -31,8 +31,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 1, @@ -360,7 +361,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -374,9 +375,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.9.21" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how_to_specify_cut_wires.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how_to_specify_cut_wires.ipynb index f87093ebc2f..227213bf544 100644 --- a/docs/addons/qiskit-addon-cutting/how-tos/how_to_specify_cut_wires.ipynb +++ b/docs/addons/qiskit-addon-cutting/how-tos/how_to_specify_cut_wires.ipynb @@ -64,8 +64,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 2, @@ -116,8 +117,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 3, @@ -183,8 +185,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 5, @@ -331,8 +334,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 9, @@ -359,8 +363,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 10, @@ -484,7 +489,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -498,9 +503,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.9.21" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/how-tos/index.mdx b/docs/addons/qiskit-addon-cutting/how-tos/index.mdx new file mode 100644 index 00000000000..986d7f75b26 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/index.mdx @@ -0,0 +1,28 @@ +--- +title: Circuit cutting API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-cutting. +--- + +# How-To Guides + +* [Generate exact quasi-distributions](how-to-generate-exact-quasi-dists-from-sampler): Use the [`ExactSampler`](utils-simulation#qiskit_addon_cutting.utils.simulation.ExactSampler "qiskit_addon_cutting.utils.simulation.ExactSampler") interface to generate exact quasi-distributions for circuits containing mid-circuit measurements. +* [Generate exact sampling coefficients](how-to-generate-exact-sampling-coefficients): Generate exact sampling coefficients and run all unique samples from the distribution. +* [Specify cut wires as a single-qubit instruction](how-to-specify-cut-wires): Perform wire cutting with a single-qubit CutWire instruction, rather than a two-qubit Move operation. +* [Perform a cutting workflow with multiple observables](how-to-provide-multiple-observables): Use the [`observable_terms`](utils-observable-terms#module-qiskit_addon_cutting.utils.observable_terms "qiskit_addon_cutting.utils.observable_terms") utility functions to gather unique observable terms, perform circuit cutting experiments, and reconstruct the desired expectation values. + +[![](/docs/images/api/qiskit-addon-cutting/nbsphinx-no-thumbnail.svg)](how-to-generate-exact-quasi-dists-from-sampler) + +[How to generate exact quasiprobability distributions from Sampler](how-to-generate-exact-quasi-dists-from-sampler) + +[![](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_generate_exact_sampling_coefficients_5_0.avif)](how-to-generate-exact-sampling-coefficients) + +[How to generate exact sampling coefficients](how-to-generate-exact-sampling-coefficients) + +[![](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_provide_multiple_observables_2_0.avif)](how-to-provide-multiple-observables) + +[How to perform a cutting workflow with multiple observables](how-to-provide-multiple-observables) + +[![](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_18_0.avif)](how-to-specify-cut-wires) + +[How to place wire cuts using a single-qubit CutWire instruction](how-to-specify-cut-wires) + diff --git a/docs/addons/qiskit-addon-cutting/index.mdx b/docs/addons/qiskit-addon-cutting/index.mdx index 2ca74551654..ff700b74a0f 100644 --- a/docs/addons/qiskit-addon-cutting/index.mdx +++ b/docs/addons/qiskit-addon-cutting/index.mdx @@ -1,5 +1,6 @@ --- -title: "Qiskit addon: circuit cutting" +title: Circuit cutting API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-cutting. --- # Qiskit addon: circuit cutting @@ -10,7 +11,7 @@ title: "Qiskit addon: circuit cutting" This package implements circuit cutting. In this technique, a handful of gates and/or wires are cut, resulting in smaller circuits that are better suited for execution on hardware. The result of the original circuit can then be reconstructed; however, the trade-off is that the overall number of shots must be increased by a factor exponential in the number of cuts. -For a more detailed discussion on circuit cutting, check out our [technical guide](./circuit_cutting/explanation/index.rst#overview-of-circuit-cutting). +For a more detailed discussion on circuit cutting, check out our [technical guide](./circuit_cutting/explanation/index-rst#overview-of-circuit-cutting). We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [Release Notes](release-notes#release-notes). @@ -71,10 +72,10 @@ The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qi * [Tutorials](tutorials/index) - * [Gate Cutting to Reduce Circuit Width](tutorials/01_gate_cutting_to_reduce_circuit_width) - * [Gate Cutting to Reduce Circuit Depth](tutorials/02_gate_cutting_to_reduce_circuit_depth) - * [Wire Cutting Phrased as a Two-Qubit `Move` Instruction](tutorials/03_wire_cutting_via_move_instruction) - * [Automatically find cuts](tutorials/04_automatic_cut_finding) + * [Gate Cutting to Reduce Circuit Width](tutorials/01-gate-cutting-to-reduce-circuit-width) + * [Gate Cutting to Reduce Circuit Depth](tutorials/02-gate-cutting-to-reduce-circuit-depth) + * [Wire Cutting Phrased as a Two-Qubit `Move` Instruction](tutorials/03-wire-cutting-via-move-instruction) + * [Automatically find cuts](tutorials/04-automatic-cut-finding) * [Explanatory Material](explanation/index) @@ -91,23 +92,23 @@ The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qi * [How-To Guides](how-tos/index) - * [How to generate exact quasiprobability distributions from Sampler](how-tos/how_to_generate_exact_quasi_dists_from_sampler) - * [How to generate exact sampling coefficients](how-tos/how_to_generate_exact_sampling_coefficients) - * [How to perform a cutting workflow with multiple observables](how-tos/how_to_provide_multiple_observables) - * [How to place wire cuts using a single-qubit `CutWire` instruction](how-tos/how_to_specify_cut_wires) + * [How to generate exact quasiprobability distributions from Sampler](how-tos/how-to-generate-exact-quasi-dists-from-sampler) + * [How to generate exact sampling coefficients](how-tos/how-to-generate-exact-sampling-coefficients) + * [How to perform a cutting workflow with multiple observables](how-tos/how-to-provide-multiple-observables) + * [How to place wire cuts using a single-qubit `CutWire` instruction](how-tos/how-to-specify-cut-wires) * [API References](index) - * [Circuit cutting (`qiskit_addon_cutting`)](qiskit_addon_cutting) - * [Instructions (`qiskit_addon_cutting.instructions`)](qiskit_addon_cutting.instructions) - * [Quasi-Probability Decomposition (QPD) (`qiskit_addon_cutting.qpd`)](qiskit_addon_cutting.qpd) - * [Bitwise utilities (`qiskit_addon_cutting.utils.bitwise`)](qiskit_addon_cutting.utils.bitwise) - * [Iteration utilities (`qiskit_addon_cutting.utils.iteration`)](qiskit_addon_cutting.utils.iteration) - * [Observable grouping (`qiskit_addon_cutting.utils.observable_grouping`)](qiskit_addon_cutting.utils.observable_grouping) - * [Observable terms (`qiskit_addon_cutting.utils.observable_terms`)](qiskit_addon_cutting.utils.observable_terms) - * [Simulation utilities (`qiskit_addon_cutting.utils.simulation`)](qiskit_addon_cutting.utils.simulation) - * [Transforms (`qiskit_addon_cutting.utils.transforms`)](qiskit_addon_cutting.utils.transforms) - * [Transpiler passes (`qiskit_addon_cutting.utils.transpiler_passes`)](qiskit_addon_cutting.utils.transpiler_passes) + * [Circuit cutting (`qiskit_addon_cutting`)](qiskit-addon-cutting) + * [Instructions (`qiskit_addon_cutting.instructions`)](instructions) + * [Quasi-Probability Decomposition (QPD) (`qiskit_addon_cutting.qpd`)](qpd) + * [Bitwise utilities (`qiskit_addon_cutting.utils.bitwise`)](utils-bitwise) + * [Iteration utilities (`qiskit_addon_cutting.utils.iteration`)](utils-iteration) + * [Observable grouping (`qiskit_addon_cutting.utils.observable_grouping`)](utils-observable-grouping) + * [Observable terms (`qiskit_addon_cutting.utils.observable_terms`)](utils-observable-terms) + * [Simulation utilities (`qiskit_addon_cutting.utils.simulation`)](utils-simulation) + * [Transforms (`qiskit_addon_cutting.utils.transforms`)](utils-transforms) + * [Transpiler passes (`qiskit_addon_cutting.utils.transpiler_passes`)](utils-transpiler-passes) * [GitHub](https://github.com/Qiskit/qiskit-addon-cutting) diff --git a/docs/addons/qiskit-addon-cutting/install.mdx b/docs/addons/qiskit-addon-cutting/install.mdx index 4053a3f7b52..ea82b6758ca 100644 --- a/docs/addons/qiskit-addon-cutting/install.mdx +++ b/docs/addons/qiskit-addon-cutting/install.mdx @@ -1,7 +1,3 @@ ---- -title: "Installation Instructions" ---- - # Installation Instructions Let’s see how to install the Qiskit addon for circuit cutting. The first thing to do is choose how you’re going to run and install the packages. There are three primary ways to do this: @@ -124,4 +120,3 @@ The home directory includes a subdirectory named `persistent-volume`. All work y ## Platform Support We expect this package to work on [any platform supported by Qiskit](/docs/start/install#operating-system-support). If you are experiencing issues running the software on your device, you may consider [using this package within Docker](#option-3). - diff --git a/docs/addons/qiskit-addon-cutting/release-notes.mdx b/docs/addons/qiskit-addon-cutting/release-notes.mdx deleted file mode 100644 index 01c3210737b..00000000000 --- a/docs/addons/qiskit-addon-cutting/release-notes.mdx +++ /dev/null @@ -1,462 +0,0 @@ ---- -title: "Release Notes" ---- - - - - - -# - - - - - -## 0.10.0 - - - -### Prelude - -This release adds compatibility with Qiskit SDK 2.0. - - - -### New Features - -* This release adds utility functions and a how-to guide for performing circuit cutting with `SparsePauliOp` observables, rather than a `PauliList` of observables. - - - -### Upgrade Notes - -* Qiskit SDK 1.3.1 or higher is now required. Qiskit SDK 2.0 is now supported. - - - -### Other Notes - -* This addon is now marked as being incompatible with Qiskit 1.3.0, as the second cutting tutorial fails to execute due to a bug in Qiskit 1.3.0. See discussion in [issue #714](https://github.com/Qiskit/qiskit-addon-cutting/issues/714) for more information. - - - - - -## 0.9.0 - - - - - -### Prelude - -The most notable change in this release is that the package has been renamed to `qiskit-cutting-addon`. It also marks the removal of this package’s original implementation of wire cutting, CutQC. - - - - - -### New Features - -* A new `minimum_reached` field has been added to the metadata outputted by `circuit_knitting.cutting.find_cuts()` to check if the cut-finder found a cut scheme that minimized the sampling overhead. Note that the search algorithm employed by the cut-finder is *guaranteed* to find the optimal solution, that is, the solution with the minimum sampling overhead, provided it is allowed to run long enough. The user is free to time-restrict the search by passing in suitable values for `max_backjumps` and/or `max_gamma` to [`OptimizationParameters`](qiskit_addon_cutting#qiskit_addon_cutting.OptimizationParameters "qiskit_addon_cutting.OptimizationParameters"). If the search is terminated prematurely in this way, the metadata may indicate that the minimum was not reached, even though the returned solution was actually the optimal solution. This would mean that the search that was performed was not exhaustive enough to prove that the returned solution was optimal. - -* When specifying instances of [`OptimizationParameters`](qiskit_addon_cutting#qiskit_addon_cutting.OptimizationParameters "qiskit_addon_cutting.OptimizationParameters") that are inputted to `circuit_knitting.cutting.find_cuts()`, the user can now control whether the cut-finder looks only for gate cuts, only for wire cuts, or both, by setting the bools `gate_lo` and `wire_lo` appropriately. The default value of both of these is set to `True` and so the default search considers the possibility of both gate and wire cuts. - - - - - -### Upgrade Notes - -* This package now requires updated versions of some dependencies: `qiskit` 1.1 or later, and `qiskit-ibm-runtime` 0.24.0 or later. - -* The search engine inside the automated cut-finder has been primed to avoid extraneous searches and is therefore expected to run faster. - -* The `CutQC` subpackage has been removed, along with its two associated utility modules, `circuit_knitting.utils.metrics` and `circuit_knitting.utils.conversion`. Users are now encouraged to use the automatic cut-finding and gate/wire cutting from the `circuit_knitting.cutting` package. - -* The behavior of [`separate_circuit()`](qiskit_addon_cutting.utils.transforms#qiskit_addon_cutting.utils.transforms.separate_circuit "qiskit_addon_cutting.utils.transforms.separate_circuit") and [`partition_problem()`](qiskit_addon_cutting#qiskit_addon_cutting.partition_problem "qiskit_addon_cutting.partition_problem") have changed so that idle qubits are discarded by default. Previously, each idle qubit was placed in its own subcircuit if `partition_labels` was not provided. - - - - - -## 0.7.1 - - - - - -### Prelude - -The 0.7.1 release provides a workaround to ensure that the experiments generated by the circuit cutting workflow will execute on IBM Quantum’s hardware backends. - - - -### Bug Fixes - -* Added a workaround so that the classical registers in the generated circuits will always contain at least one bit. This is currently necessary for the experiments to be able to reach IBM Quantum’s hardware backends due to an [openqasm parser issue](https://github.com/openqasm/qe-qasm/issues/37). - - - - - -### Other Notes - -* The [`generate_cutting_experiments()`](qiskit_addon_cutting#qiskit_addon_cutting.generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") function has been optimized for faster execution. - - - - - -## 0.7.0 - - - - - -### Prelude - -The 0.7 release introduces an automated cut finding code for the new circuit cutting workflow. With this milestone, the older cutting workflow (CutQC) is now deprecated. Additionally, this is the first CKT release to support version 2 of the Qiskit Runtime primitives. User are encouraged to [migrate to v2 primitives](/docs/guides/v2-primitives) as soon as possible. - - - - - -### New Features - -* Added a cut-finder function, `circuit_knitting.cutting.find_cuts()`, for automatically identifying locations to place LO gate and wire cuts such that the circuit is separable and runnable, given the maximum number of qubits per subcircuit as a parameter. The cut-finder will search for cut schemes which minimize the sampling overhead. Note, however, that for larger circuits, the number of cuts needed to separate the circuit will naturally grow larger, leading to an exponentially increasing sampling overhead. For instances of wire cuts, the cut-finder assumes no qubit reuse. Therefore, for each wire cut, a new wire is added to the circuit. In addition, the cut-finder requires that every gate in an input circuit be at most a two qubit gate. The search algorithm used by the cut-finder to identify cut locations is Dijkstra’s best first search algorithm which is guaranteed to find solutions with the lowest sampling overhead, provided any user-specified value for the maximum number of allowed backjumps or for the maximum sampling overhead does not prematurely stop the search. If the user wishes to time-restrict the search when running the cut-finder on large circuits, they can specify a maximum sampling overhead and/or a maximum number of allowed backjumps, in which case the cut-finder will return a valid albeit suboptimal cut scheme. - -* Circuit cutting reconstruction can now interpret the [`PrimitiveResult`](/docs/api/qiskit/qiskit.primitives.PrimitiveResult) object, which is returned by version 2 of the sampler primitive ([`BaseSamplerV2`](/docs/api/qiskit/qiskit.primitives.BaseSamplerV2)). See the [migration guide](/docs/guides/qiskit-1.0-features#qiskitprimitives) for details on upgrading to version 2 of the Qiskit primitives. - - - - - -### Upgrade Notes - -* CKT now requires updated versions of some dependencies: `qiskit` 1.0 or later, `qiskit-aer` 0.14.0 or later, and `qiskit-ibm-runtime` 0.23.0 or later. - -* The code in the `circuit_knitting.cutting.qpd.qpd` submodule has been split into three separate files. If you were importing directly from this submodule, you will now need to import from `circuit_knitting.cutting.qpd` instead. - - - -### Deprecation Notes - -* The `circuit_knitting.cutting.cutqc` package is deprecated and will be removed no sooner than Circuit Knitting Toolbox 0.8.0. The wire cutting functionality in the `circuit_knitting.cutting` package is what will be maintained going forward. Additionally, there is a new automated gate and wire cut-finding functionality in the `circuit_knitting.cutting.automated_cut_finding` module. A [tutorial](https://github.com/Qiskit/qiskit-addon-cutting/blob/stable/0.7/docs/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb) has been added to demonstrate automated cut-finding. - - - - - -### Other Notes - -* The cutting tutorials have been rephrased with the goal of reconstructing the expectation value of a single [`SparsePauliOp`](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) with many terms, rather than multiple independent [`Pauli`](/docs/api/qiskit/qiskit.quantum_info.Pauli) observables. - -* The [circuit cutting explanation](/docs/addons/qiskit-addon-cutting/explanation/index) document has been expanded significantly. - - - - - -## 0.6.0 - - - - - -### Upgrade Notes - -* The minimum supported version of `qiskit` is now 0.45.0, and the minimum supported version of `qiskit-ibm-runtime` is now 0.12.2. CKT also now explicitly requires a version of `qiskit` less than 1.0, as there is no guarantee that the current version of CKT will work with Qiskit 1.0. - -* Removed the `circuit_knitting.cutting.qpd.QPDBasis.from_gate` method, which has been deprecated since the 0.3 release. [`QPDBasis.from_instruction()`](qiskit_addon_cutting.qpd.QPDBasis#qiskit_addon_cutting.qpd.QPDBasis.from_instruction "qiskit_addon_cutting.qpd.QPDBasis.from_instruction") should be used instead. - -* Removed the `circuit_knitting_toolbox` import path. Users should now import from `circuit_knitting` instead. - -* Removed the `circuit_knitting.cutting.decompose_gates` function, which has been deprecated since the 0.3 release. [`cut_gates()`](qiskit_addon_cutting#qiskit_addon_cutting.cut_gates "qiskit_addon_cutting.cut_gates") should be used instead. - -* Removed the `circuit_knitting.cutting.cutting_evaluation` module, which has been deprecated since the 0.4 release. Users should first call `circuit_knitting.cutting.generate_cutting_experiments()` to generate the subexperiment circuits and then submit these circuits directly to the desired Sampler(s). - -* Removed the `circuit_knitting.cutting.qpd.generate_qpd_samples` function, which has been deprecated since the 0.3 release. [`generate_qpd_weights()`](qiskit_addon_cutting.qpd#qiskit_addon_cutting.qpd.generate_qpd_weights "qiskit_addon_cutting.qpd.generate_qpd_weights") should be used instead. - -* Removed the `circuit_knitting.cutting.qpd.qpdbasis_from_gate` function, which has been deprecated since the 0.3 release. [`qpdbasis_from_instruction()`](qiskit_addon_cutting.qpd#qiskit_addon_cutting.qpd.qpdbasis_from_instruction "qiskit_addon_cutting.qpd.qpdbasis_from_instruction") should be used instead. - -* The [`generate_cutting_experiments()`](qiskit_addon_cutting#qiskit_addon_cutting.generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") function now performs some optimizations on the generated circuits before returning them to the user. In particular, it performs the [`RemoveResetInZeroState`](/docs/api/qiskit/qiskit.transpiler.passes.RemoveResetInZeroState), [`RemoveFinalReset`](qiskit_addon_cutting.utils.transpiler_passes.RemoveFinalReset#qiskit_addon_cutting.utils.transpiler_passes.RemoveFinalReset "qiskit_addon_cutting.utils.transpiler_passes.RemoveFinalReset"), and [`ConsolidateResets`](qiskit_addon_cutting.utils.transpiler_passes.ConsolidateResets#qiskit_addon_cutting.utils.transpiler_passes.ConsolidateResets "qiskit_addon_cutting.utils.transpiler_passes.ConsolidateResets") passes, so that circuits with cut wires and no re-used qubits are transformed into subexperiments that contain no `Reset`s. This allows such circuits to work on a greater variety of hardware backends. - - - - - -### Bug Fixes - -* It is now possible to serialize [`SingleQubitQPDGate`](qiskit_addon_cutting.qpd.SingleQubitQPDGate#qiskit_addon_cutting.qpd.SingleQubitQPDGate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s using [`qpy`](/docs/api/qiskit/qpy#module-qiskit.qpy), but some other issues with serialization and deserialization still remain. See issue [#455](https://github.com/Qiskit/qiskit-addon-cutting/issues/445) for details. - - - - - -### Other Notes - -* Removed the entanglement forging tool, as the Qiskit application modules are no longer supported, and packages in `Qiskit-Extensions` may not have dependency on those modules. With this change, CKT no longer depends on `qiskit-algorithms` or Qiskit Nature. - - - - - -## 0.5.0 - - - - - -### Prelude - -The primary purpose of this release is to swap the order of the classical registers in the circuits generated the `circuit_knitting.cutting` module. With this change, `"observable_measurements"` now comes before `"qpd_measurements"`, and there is no longer a need to insert `num_qpd_bits` into the result metadata by hand. - - - - - -### Upgrade Notes - -* CKT now depends on the [qiskit-algorithms](https://github.com/qiskit-community/qiskit-algorithms) package. This new package replaces the `qiskit.algorithms` module, which was deprecated in Qiskit 0.44. - -* The order of the classical registers in the generated experiments has been swapped. The `"observable_measurements"` register now comes first, and the `"qpd_measurements"` register now comes second. As a result of this change, it is no longer necessary to manually insert `num_qpd_bits` into the `metadata` for each experiment’s result. - -* Removed the `circuit_knitting.cutting.wire_cutting` module. Users are now expected to import from the `circuit_knitting.cutting.cutqc` module instead. - - - - - -### Other Notes - -* `qiskit-nature` is now pinned to version 0.6.X, as the entanglement forging code has not yet been updated to work with Qiskit Nature 0.7.0. Compatibility with Qiskit Nature 0.7.0 is tracked by [issue #406](https://github.com/Qiskit/qiskit-addon-cutting/issues/406). - - - - - -## 0.4.0 - - - - - -### Prelude - -The primary goal of this release is to modify the circuit cutting workflow to enable direct use of the Sampler primitive. Previously, the Sampler was called in `execute_experiments()`, a function which is now deprecated in favor of [`generate_cutting_experiments()`](qiskit_addon_cutting#qiskit_addon_cutting.generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments"). - - - - - -### New Features - -* Added a module, `circuit_knitting.cutting.cutting_experiments`, which is intended to hold functions used for generating the quantum experiments needed for circuit cutting. This module will initially hold one function, [`generate_cutting_experiments()`](qiskit_addon_cutting#qiskit_addon_cutting.generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments"), which can be used to generate quantum experiments, given an input circuit containing [`BaseQPDGate`](qiskit_addon_cutting.qpd.BaseQPDGate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate") instances, some observables, and a number of times the joint quasi-probability distribution for the cuts should be sampled. - - - - - -### Upgrade Notes - -* The `circuit-knitting-toolbox` Python package now depends on `qiskit` rather than `qiskit-terra`. This should have no user-visible effects, but it is something to keep in mind if one sees dependency errors when upgrading CKT. - -* The `execute_experiments()` function now returns a [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instance for each circuit partition, rather than the 3D list of quasi-distributions returned previously. The quasi-distribution for each subexperiment can be accessed via the `quasi_dists` field of [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult). The number of QPD bits contained in each subexperiment will be included in the `num_qpd_bits` field of the `metadata` dictionary for each experiment result. The output of this function is still valid as input to [`reconstruct_expectation_values()`](qiskit_addon_cutting#qiskit_addon_cutting.reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values"). - -* [`reconstruct_expectation_values()`](qiskit_addon_cutting#qiskit_addon_cutting.reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") now takes, as its first argument, a [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instance or a dictionary mapping partition labels to [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instances. This new `results` argument replaces the old `quasi_dists` argument. The [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instances are expected to contain the number of QPD bits used in each circuit input to the Sampler. This should be specified in the `num_qpd_bits` field of the experiment result metadata. - - - - - -### Deprecation Notes - -* The `execute_experiments()` function has been deprecated. Going forward, users should first call [`generate_cutting_experiments()`](qiskit_addon_cutting#qiskit_addon_cutting.generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") to generate the subexperiment circuits and then submit these circuits directly to the desired Sampler(s). The [tutorials](/docs/addons/qiskit-addon-cutting/tutorials/index) have been updated with this new workflow. - - - - - -## 0.3.0 - - - - - -### Prelude - -The 0.3.0 release introduces significant new features while maintaining backwards compatibility with the 0.2.0 release. The most striking change in this release is the shortened module names: `circuit_knitting_toolbox` has been renamed to `circuit_knitting`, `entanglement_forging` has been renamed to `forging`, and `circuit_cutting` has been renamed to `cutting`. The new circuit cutting module contains significant enhancements, including support for wire cutting and for the cutting of arbitrary two-qubit gates. - - - - - -### New Features - -* The circuit cutting module now supports the cutting of arbitrary two-qubit gates. This is supported via a KAK decomposition, using Qiskit’s `TwoQubitWeylDecomposition`, following the method in [arXiv:2006.11174](https://arxiv.org/abs/2006.11174). - - Additionally, this release adds *explicit* support (i.e., without relying on a KAK decomposition) for the following gates: - - * [`CHGate`](/docs/api/qiskit/qiskit.circuit.library.CHGate) - * [`CYGate`](/docs/api/qiskit/qiskit.circuit.library.CYGate) - * [`CSGate`](/docs/api/qiskit/qiskit.circuit.library.CSGate) - * [`CSXGate`](/docs/api/qiskit/qiskit.circuit.library.CSXGate) - * [`CSdgGate`](/docs/api/qiskit/qiskit.circuit.library.CSdgGate) - * [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) - * [`ECRGate`](/docs/api/qiskit/qiskit.circuit.library.ECRGate) - * [`SwapGate`](/docs/api/qiskit/qiskit.circuit.library.SwapGate) - * [`iSwapGate`](/docs/api/qiskit/qiskit.circuit.library.iSwapGate) - * [`DCXGate`](/docs/api/qiskit/qiskit.circuit.library.DCXGate) - -* [`partition_problem()`](qiskit_addon_cutting#qiskit_addon_cutting.partition_problem "qiskit_addon_cutting.partition_problem") now works even if `partition_labels` is not explicitly provided. In this case, the labels are determined automatically from the connectivity of the input circuit. For the sake of determining connectivity, [`TwoQubitQPDGate`](qiskit_addon_cutting.qpd.TwoQubitQPDGate#qiskit_addon_cutting.qpd.TwoQubitQPDGate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s are ignored, as these instructions are already marked for cutting. To support this workflow, this release also introduces a new method, [`TwoQubitQPDGate.from_instruction()`](qiskit_addon_cutting.qpd.TwoQubitQPDGate#qiskit_addon_cutting.qpd.TwoQubitQPDGate.from_instruction "qiskit_addon_cutting.qpd.TwoQubitQPDGate.from_instruction"), which allows one to create a [`TwoQubitQPDGate`](qiskit_addon_cutting.qpd.TwoQubitQPDGate#qiskit_addon_cutting.qpd.TwoQubitQPDGate "qiskit_addon_cutting.qpd.TwoQubitQPDGate") that wraps a given instruction. - -* Dynamic Definition code has been added to the `cutqc` module. See pull request [#285](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/pull/285) for details. - -* Users may now bypass experiments associated with the Hartree-Fock bitstring and replace their results with a specified Hartree-Fock value using the `hf_energy` class field in `circuit_knitting.forging.EntanglementForgingGroundStateSolver`. Refer to the `explanatory material ` for more information. - -* `generate_qpd_weights()` now returns a mixture of exact and sampled weights when appropriate. Specifically, it exactly evaluates all weights greater than or equal to `1 / num_samples` and samples from the remaining weights (ones which are below this threshold). Previously, this function would only return exact weights if *all* weights were greater than or equal to `1 / num_samples`; otherwise, all weights were sampled. The new behavior is expected to improve performance on non-uniform quasi-probability decompositions, e.g. for cut instantiations of [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), and [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate) away from $\theta=\pi/2$. - -* The `circuit_knitting.cutting` module now supports wire cutting. There is a [new tutorial](/docs/addons/qiskit-addon-cutting/tutorials/index) that explains how to use it. - - - - - -### Upgrade Notes - -* `execute_experiments()` no longer creates separate jobs for each subcircuit by default. Now, separate jobs are only created if separate [BaseSampler](/docs/api/qiskit/1.4/qiskit.primitives.BaseSampler) instances are provided for each circuit partition. - -* Numpy 1.23.0 or later is now required. The [`kron()`](https://numpy.org/doc/stable/reference/generated/numpy.kron.html#numpy.kron) method in earlier versions [has known performance issues](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/pull/70#issuecomment-1500514513), and this method is used heavily by the CutQC wire cutting module. - -* The dependency on the `qiskit.opflow` module has been removed from entanglement forging. With this change, Qiskit Nature 0.6.0 is now required. However, Qiskit Nature 0.6.0 is incompatible with Quantum Serverless, so users that wish to use entanglement forging with Quantum Serverless must remain on version 0.2 of the Circuit Knitting Toolbox until [issue #108](https://github.com/Qiskit/qiskit-addon-cutting/issues/108) is resolved. - -* [`BaseQPDGate`](qiskit_addon_cutting.qpd.BaseQPDGate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate") instances in subcircuits returned from [`partition_circuit_qubits()`](qiskit_addon_cutting#qiskit_addon_cutting.partition_circuit_qubits "qiskit_addon_cutting.partition_circuit_qubits") and [`partition_problem()`](qiskit_addon_cutting#qiskit_addon_cutting.partition_problem "qiskit_addon_cutting.partition_problem") will now have labels prefixed with “cut”, rather than “qpd”. - - - - - -### Deprecation Notes - -* `decompose_gates()` is deprecated and will be removed no sooner than v0.4.0. Users should migrate to the identical [`cut_gates()`](qiskit_addon_cutting#qiskit_addon_cutting.cut_gates "qiskit_addon_cutting.cut_gates") function. - -* `generate_qpd_samples()` has been renamed to `generate_qpd_weights()`. The original name will be removed no sooner than version 0.4 of the Circuit Knitting Toolbox. - -* `QPDBasis.from_gate()` has been renamed to [`QPDBasis.from_instruction()`](qiskit_addon_cutting.qpd.QPDBasis#qiskit_addon_cutting.qpd.QPDBasis.from_instruction "qiskit_addon_cutting.qpd.QPDBasis.from_instruction"). The original name is deprecated and will be removed no sooner than CKT v0.4.0. - -* The top-level name for imports has been renamed from `circuit_knitting_toolbox` to `circuit_knitting`. Furthermore, the following renames have occurred one level deeper: - - * `circuit_knitting_toolbox.entanglement_forging` has been moved to `circuit_knitting.forging`. - * `circuit_knitting_toolbox.circuit_cutting` has been moved to `circuit_knitting.cutting`. - - The old import locations are now deprecated and will be removed in a future release of the Circuit Knitting Toolbox. - - - - - -### Bug Fixes - -* Fixed a bug in `decompose_qpd_instructions()` which would cause index errors if `TwoQubitQPDGate` indices were not specified in ascending order. - -* Fixed a bug in `circuit_knitting.forging.EntanglementForgingGroundStateSolver` which was causing `AttributeError`s when instantiating the :class:`circuit_knitting.forging.EntanglementForgingResult` in certain conditions, such as when reducing the orbitals over which to solve. - - - - - -## 0.2.0 - - - - - -### Prelude - -0.2.0 is centered around the addition of functions which allow for the easy implementation of a circuit cutting technique called gate cutting. For more details on circuit cutting, check out our [explanation guide](/docs/addons/qiskit-addon-cutting/explanation/index). - -The foundation of the `circuit_cutting` package is the `circuit_knitting_toolbox.circuit_cutting.qpd` sub-package. The `qpd` package allows for easy transformation of [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) gates and wires into elements which may be decomposed to a probabilistic set of basis gates. See `QPDBasis` and `BaseQPDGate` classes for more information. - -Additionally, 0.2.0 includes a set of functions which allow for easy implementation of gate cutting workflows. These functions are built on top of the `circuit_knitting_toolbox.circuit_cutting.qpd` package. Like all circuit knitting techniques, gate cutting can be described as three consecutive stages: *decomposition* of a problem, *execution* of many subexperiments, and *reconstruction* of a simulated output of the original problem. These steps may be implemented with the `circuit_cutting` package using only a few primary functions, namely, the [`partition_problem()`](qiskit_addon_cutting#qiskit_addon_cutting.partition_problem "qiskit_addon_cutting.partition_problem"), `decompose_gates()`, `execute_experiments()`, and [`reconstruct_expectation_values()`](qiskit_addon_cutting#qiskit_addon_cutting.reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") functions. Check out the [tutorials](/docs/addons/qiskit-addon-cutting/tutorials/index) for a look at a couple of example circuit cutting workflows. - - - - - -### New Features - -* Addition of a `qpd` package which allows for easy transformation of [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) gates and wires into elements which may be decomposed to a probabilistic set of basis gates. See `QPDBasis` and `BaseQPDGate` classes for more information. - -* Addition of `cutting_decomposition`, `cutting_execution`, and `cutting_reconstruction` modules. These modules provide several functions which allow for easy implementation of gate cutting workflows, namely, the [`partition_problem()`](qiskit_addon_cutting#qiskit_addon_cutting.partition_problem "qiskit_addon_cutting.partition_problem"), `decompose_gates()`, `execute_experiments()`, and [`reconstruct_expectation_values()`](qiskit_addon_cutting#qiskit_addon_cutting.reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") functions. - - - -### Known Issues - -* The `circuit_cutting` package only supports [`PauliList`](/docs/api/qiskit/qiskit.quantum_info.PauliList) observables for calculating expectation values. Support for calculating expectation values for more observable types, including [`SparsePauliOp`](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp), is expected no sooner than 0.3.0. - -* The `Sampler`s from Qiskit and Qiskit Aer do not support mid-circuit measurements in statevector mode. For more on generating exact quasi-distributions using the [BaseSampler](/docs/api/qiskit/1.4/qiskit.primitives.BaseSampler) interface, check out our [how-to guide](https://github.com/Qiskit/qiskit-addon-cutting/blob/stable/0.7/docs/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb). - -* The `circuit_cutting` package generally does not yet support input circuits with user-added classical bits, so by extension, it does not yet support dynamic circuits. - - - - - -### Upgrade Notes - -* Support for running with Python 3.7 has been removed. To run the Circuit Knitting Toolbox, you now need Python version 3.8 or higher. - - - - - -### Deprecation Notes - -* The `circuit_knitting_toolbox.circuit_cutting.wire_cutting` namespace is now deprecated. It has been renamed to `circuit_knitting_toolbox.circuit_cutting.cutqc`. Both gate and wire cutting for simulating expectation values will be implemented directly into the `circuit_cutting` package, and the `circuit_cutting.cutqc` package will remain available for users of the automatic cut finding and/or the full probability distribution reconstruction. - - - - - -## 0.1.0 - - - - - -### New Features - -* Support for Python 3.11. - -* Support for Qiskit Nature 0.5. - - - - - -### Upgrade Notes - -* The minimum supported version of each Qiskit dependency has been updated. This release depends on `qiskit-terra>=0.23.3`, `qiskit-aer>=0.12.0`, `qiskit-nature>=0.5.2`, and `qiskit-ibm-runtime>=0.9.2`. `qiskit-ibmq-provider` is now deprecated and is no longer required by the Circuit Knitting Toolbox. - -* Support for Qiskit Nature \< 0.5 has been removed upon this release. - - Users are now be required to use the `qiskit_nature.second_q.problems.ElectronicStructureProblem`, as input to the `EntanglementForgingGroundStateSolver`, rather than the deprecated `qiskit_nature.problems.second_quantization.ElectronicStructureProblem`. - - For more information on migrating to Qiskit Nature 0.5, check out the [Qiskit Nature migration guide](https://qiskit-community.github.io/qiskit-nature/migration/0.5_a_intro.html). - - For more information on adapting your entanglement forging workflows, check out the `tutorials ` and `how-to guides `. - -* The `~circuit_knitting_toolbox.utils.IntegralDriver` class has been removed. The new Qiskit Nature API allows for a more flexible build-up of the `qiskit_nature.second_q.problems.ElectronicStructureProblem`, and this driver is no longer needed. - -* DOcplex and cplex are now optional dependencies. They must be installed for automatic wire cut finding to work. - - - - - -### Deprecation Notes - -* Support for running with Python 3.7 has been deprecated. Future versions of the Circuit Knitting Toolbox will require Python 3.8 or higher. - diff --git a/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width.mdx b/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width.mdx new file mode 100644 index 00000000000..fb634c4fd6d --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width.mdx @@ -0,0 +1,310 @@ + + +# Gate Cutting to Reduce Circuit Width + +In this notebook, we will work through the steps of a [Qiskit pattern](/docs/guides/intro-to-patterns) while using **circuit cutting** to reduce the number of qubits in a circuit. We will cut gates to enable us to reconstruct the expectation value of a four-qubit circuit using only two-qubit experiments. + +These are the steps that we will take: + +* **Step 1: Map problem to quantum circuits and operators**: + + * Map the hamiltonian onto a quantum circuit. + +* **Step 2: Optimize for target hardware** \[*Uses the cutting addon*]: + + * Cut the circuit and observable. + * Transpile the subexperiments for hardware. + +* **Step 3: Execute on target hardware**: + + * Run the subexperiments obtained in Step 2 using a `Sampler` primitive. + +* **Step 4: Post-process results** \[*Uses the cutting addon*]: + + * Combine the results of Step 3 to reconstruct the expectation value of the observable in question. + + + +## Step 1: Map + + + +### Create a circuit to cut + +``` +[1]: +``` + +```python +from qiskit.circuit.library import efficient_su2 + +qc = efficient_su2(4, entanglement="linear", reps=2) +qc.assign_parameters([0.4] * len(qc.parameters), inplace=True) + +qc.draw("mpl", scale=0.8) +``` + +``` +[1]: +``` + +![../\_images/tutorials\_01\_gate\_cutting\_to\_reduce\_circuit\_width\_2\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_01_gate_cutting_to_reduce_circuit_width_2_0.avif) + + + +### Specify an observable + +``` +[2]: +``` + +```python +from qiskit.quantum_info import SparsePauliOp + +observable = SparsePauliOp(["ZZII", "IZZI", "-IIZZ", "XIXI", "ZIZZ", "IXIX"]) +``` + + + +## Step 2: Optimize + + + +### Separate the circuit and observable according to a specified qubit partitioning + +Each label in `partition_labels` corresponds to the `circuit` qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut. + +**Note:** The `observables` kwarg to `partition_problem` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value. + +``` +[3]: +``` + +```python +from qiskit_addon_cutting import partition_problem + +partitioned_problem = partition_problem( + circuit=qc, partition_labels="AABB", observables=observable.paulis +) +subcircuits = partitioned_problem.subcircuits +subobservables = partitioned_problem.subobservables +bases = partitioned_problem.bases +``` + + + +### Visualize the decomposed problem + +``` +[4]: +``` + +```python +subobservables +``` + +``` +[4]: +``` + +```python +{'A': PauliList(['II', 'ZI', 'ZZ', 'XI', 'ZZ', 'IX']), + 'B': PauliList(['ZZ', 'IZ', 'II', 'XI', 'ZI', 'IX'])} +``` + +``` +[5]: +``` + +```python +subcircuits["A"].draw("mpl", scale=0.8) +``` + +``` +[5]: +``` + +![../\_images/tutorials\_01\_gate\_cutting\_to\_reduce\_circuit\_width\_9\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_01_gate_cutting_to_reduce_circuit_width_9_0.avif) + +``` +[6]: +``` + +```python +subcircuits["B"].draw("mpl", scale=0.8) +``` + +``` +[6]: +``` + +![../\_images/tutorials\_01\_gate\_cutting\_to\_reduce\_circuit\_width\_10\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.avif) + + + +### Calculate the sampling overhead for the chosen cuts + +Here we cut two CNOT gates, resulting in a sampling overhead of $9^2$. + +For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](explanation/index). + +``` +[7]: +``` + +```python +import numpy as np + +print(f"Sampling overhead: {np.prod([basis.overhead for basis in bases])}") +``` + +```python +Sampling overhead: 81.0 +``` + + + +### Generate the subexperiments to run on the backend + +`generate_cutting_experiments` accepts `circuits`/`observables` args as dictionaries mapping qubit partition labels to the respective `subcircuit`/`subobservables`. + +To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates’ joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](explanation/index). + +``` +[8]: +``` + +```python +from qiskit_addon_cutting import generate_cutting_experiments + +subexperiments, coefficients = generate_cutting_experiments( + circuits=subcircuits, observables=subobservables, num_samples=np.inf +) +``` + + + +### Choose a backend + +Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator). + +``` +[9]: +``` + +```python +from qiskit_ibm_runtime.fake_provider import FakeManilaV2 + +backend = FakeManilaV2() +``` + + + +### Prepare the subexperiments for the backend + +We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime. + +``` +[10]: +``` + +```python +from qiskit.transpiler import generate_preset_pass_manager + +# Transpile the subexperiments to ISA circuits +pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend) +isa_subexperiments = { + label: pass_manager.run(partition_subexpts) + for label, partition_subexpts in subexperiments.items() +} +``` + + + +## Step 3: Execute + + + +### Run the subexperiments using the Qiskit Runtime Sampler primitive + +``` +[11]: +``` + +```python +from qiskit_ibm_runtime import SamplerV2, Batch + +# Submit each partition's subexperiments to the Qiskit Runtime Sampler +# primitive, in a single batch so that the jobs will run back-to-back. +with Batch(backend=backend) as batch: + sampler = SamplerV2(mode=batch) + jobs = { + label: sampler.run(subsystem_subexpts, shots=2**12) + for label, subsystem_subexpts in isa_subexperiments.items() + } +``` + +``` +[12]: +``` + +```python +# Retrieve results +results = {label: job.result() for label, job in jobs.items()} +``` + + + +## Step 4: Post-process + + + +### Reconstruct the expectation value + +Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable. + +``` +[13]: +``` + +```python +from qiskit_addon_cutting import reconstruct_expectation_values + +# Get expectation values for each observable term +reconstructed_expval_terms = reconstruct_expectation_values( + results, + coefficients, + subobservables, +) + +# Reconstruct final expectation value +reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs) +``` + + + +### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable + +``` +[14]: +``` + +```python +from qiskit_aer.primitives import EstimatorV2 + +estimator = EstimatorV2() +exact_expval = estimator.run([(qc, observable)]).result()[0].data.evs +print(f"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}") +print(f"Exact expectation value: {np.round(exact_expval, 8)}") +print(f"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}") +print( + f"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}" +) +``` + +```python +Reconstructed expectation value: 0.58167797 +Exact expectation value: 0.56254612 +Error in estimation: 0.01913185 +Relative error in estimation: 0.03400939 +``` diff --git a/docs/addons/qiskit-addon-cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb index efcf4e0ddbb..ea3a688c41f 100644 --- a/docs/addons/qiskit-addon-cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb +++ b/docs/addons/qiskit-addon-cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb @@ -47,8 +47,9 @@ "outputs": [ { "data": { + "image/png": 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DYyiJiIiISBQ2lEREREQkCo+htDL3AL3cJQCwTh2+CrrVrpJqUQKlbGeAY21rSqmDpMHcSEMpdZC8eAwlkZmUdCwYkb1gbojuDdzlTURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYnChpKIiIiIRGFDSURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYniJHcBjiZi4ofIumqQuwy4B+jRf90cUWPMiAQScq1UkEi+rsDSbnJXQVJhbqTB3Dg25kYazE3tsKG0sqyrBmTExstdhlUk5AKXs+Sugu4FzA2R5ZgbUhLu8iYiIiIiUdhQEhEREZEobCiJiIiISBQeQ0lUjeS0PPx6LBHHz6Qg6lxq6fI3Pz+OPl190KuLHi39PWSskEh5UtLz/5ebZJw4W5abN5aX5SawKXND5EjYUBJV4u+zKVjyXTR+3HsFRcWmCs9/9/NFfPfzRQDAgO5NMHVcOzzUpylUKpWtSyVSjBNnU7B0QzS2/HIFhUUVc7N+50Ws31mSm37/54OpT7TDyL7NmBsiB8CGUiYPLJuMwLF9AQAmoxF5SRlI/CMaJxZ9j1xDmszVWe7qZ08j9cC6kgdqNbT1feAe3A++Ez6As5evvMVZIC+/GG9/8TeWrI+GIJj3mv1Hb2D/0RsY0acpvn67J3wauUpb5D2MuVGm/IJivPPVCXyyLhomk3nBOfBXIg78lYhhvfzxzbyeaNLYTeIq713MDdkCj6GUkeHoWWzu+Bx+uv9l/DZ5Gbw6BKDPN7PkLqvWdO3C0HFtIoJXX0fzWT8g90oULi8eLXdZZjOk5KL7Uz/j0+/MbybvtOPgdQQ/+h8ci062fnFUirlRlqTUPPQYvxMfhZ82u5m8039/i0Pwo1tx9NRNCaqj25gbkhobShmZCouRl5yBXEMako6eQ8yG/WjctTW0Ohe5S6sVlZMztPX1cPbyhXv7Xmg06AXkxByBMfeW3KXVKCU9H32f3YV/Yqv+tK7RqODr7Qpfb1doNJXvokvNKMCAF3bjxNkUqUq95zE3ypGWWYD+z+1C1PnUKtcxJzdpmQUY+OJufhiTEHNDUmNDqRAu3vURMLw7TMVGCMaKxx7Zm8LUG0j/8ydArSn5o2CCIGDiW4dw/kpmtevpG7ogft84xO8bB33DqifhW9lFeGRmBLJyCq1dKt2FuZGPIAj417zfcOZSRrXrmZub7NxiPDozAplZzI3UmBuSAo+hlJG+R3s8eXE9VGo1nFzqAACiV+xAcV4BAKDPqlm4cegUYjfsBwA06NAcvb6ahp8HzoaxoEi2uquSFX0QUWN1EEwmCIV5AADvUbOgqVtybFT6ka1I3Pxuudfkx52F/3OfodGQl21e723rdlzArt+te7eJazey8dqSY1jxdk+rjkvMDaCM3Pyw6xK2/3rdqmPGGXLw6qeRWDU/zKrjEnMDKCM3jsyuG8pTp05h3rx5OHjwIARBQL9+/bBixQoEBQVh2LBh2LRpk9wlViv5xAUcnvYFNHW0CBjRA03COiJq8cbS5/96OxxDti/AtV2RKEjPRuiHzyPyjTWKDDcAuAV1Q8D0dRAK85F+eAtundqPJk8uLH2+fujDqB/6cOnjjKPbkLD+DXj1myhHuQCAwiIj5iw7LsnYX/94HjPGd0BQQD1Jxq+t64nZ+Oan8zj6TzKMJgGtA+rhhUdbI6RdQ7lLMwtzI39uiotNeH3pMUnGXv2fWMwY3wHtWtaXZPzaijfk4Jt/n8eRUzdRbBTQqqkHXnisNe5v30ju0szC3MifG0dnt7u8IyIi0L17d8TExOCtt97CokWLEB8fjyFDhiA7OxudOnWSu8QaGfMLS+7FGhOHkx9vRlbcTXR7/9nS53MNaTizcifuf3s8Wo8fiMzLiUg8fFrGiqundnZBXZ9AuDTrgCZPvoc63s0R983UStctTInH9ZWT0Xz2JqjryHdW9NaIa0hKzZNs/K9/PCfZ2JYSBAFvfX4czQdvxvurTuHAXzdw6HgiVv54Hl0e345R0/YhO1eZvzzuxNzIn5sdB68j4WauZON//eN5yca2lCAImP/VCQQM3owFK08i4mhJblb9OwZdx+3AQ1P22sXhLcyN/LlxdHbZUCYnJ2Ps2LEICQlBVFQUZs+ejSlTpiAiIgLXr5fsgrGHhvJuJz/ZjMCxfeF1X8vSZefD98CztT+Cp4zCsXfXyVid5XzGzUdKRDhyLpT/BlAwmXBl6VPQPzoHrgEdZaquxHc/X5B4/IsQanPKuATe+eoE3l91CrdPxBUElDubffuv1/HozAgY7eyYKubG9qTOzfqfL9bqjHEpLFh5Eu9+HQXj/+oRUD43O3+Lw6hp+1FUyXU3lYy5IWuzy4Zy8eLFSE9PR3h4OFxcyg7yrlevHkJCQgDYZ0OZdcWAuH3HETJnXNlCQUDMd/sQH3ECBan2dfZa3Sat4Nn1IdzY8Ga55YlbFkLj4oHGwyv/NGkrgiAg8rS0Z5WmZhTgcnyWpO9hjoSkHCxadarG9fb+mYCdv8XZoCLrYW5sT+rcZGQV4sK16k+SswVDSi4WfBNV43oH/krE9l+v2aAi62FuyNrs8hjKTZs2ISwsDEFBQZU+7+3tDb1eDwDYsmULli9fjpMnT6Jhw4a4evWqRe9VXFwMg8Fg9vpFRcUWjX+36K92YNjP70Mf2h6GI2dKFppMECz8tF5UVIz4eHEnmhQVeQPQihrD++HZiJnTE1mnD8I9uA+yz/2B1P1r0HbJCQtrKUJ8fJKoWu6WcDMPqRkF5ZZpNKoqz0T1uWO5TxXrGFLyYDSW/7/adzgWdcL0IqsVZ8mGi6XfsNS47roodGll2zMlmZvylJybpLQCGFLKHyYiRW72Ho6Fm7aJyGrFWb7pEoqLzduGln53Et3bift/txRzU56Sc2NP9Ho9nJwsbw9VglL2x5nJYDDAx8cHM2fOxKefflruOZPJBB8fH3Tu3Bl79uwBAOzbtw+pqalISkrC0qVLLW4o4+Pj4e/vb/b6C70Gwldr3XvUBo7pA6/7WiLyzTVmvyah6BbeSt0n6n3bfR4Nl6btRY1xp+LsDJybGYKAKWvg3rGvRa/Nu34GZ6d2sFotAACXZkDg2+UW+Xq7In7fuCpeUDO/gRuRkHTXsWUJ3wNpv9Z6TKto9grgHgyYc4s7Yy5w9hXpa7oDc1M1xeWmrj/Q6p1yiyTJzY1NQOr+Wo9pFU0nAx6dzMxNPnB2iuQl3Ym5qZricmNH4uLi4OfnZ/Hr7O4bypycHACo9N6v27dvx82bN8vt7h44cCAAYNu2bbYoj6qRvGcFitITEfftjHLLvfpOhPfIGVW8Sko2un+wEu5TrLLk6BYF1EullJcbG7G33FiUMZLaPZsbGdndN5SFhYVwdXVF586dcexY2WUrrl27hp49eyIhIQEbN27E448/Xu5127Ztw/Tp0yXf5X1kzIfIuWL++lJxa65H6JY5osaYetYbcfm23YVTFf+6Rfi8nXV3QVxJyEGv5w+XW1bTrrtjG0cBALqO24bElIpnh1e26+6zV4PxSD95d93N/+Y81myr+RgvFYCOQR7YuSxU+qLuwNxIQ4rcXDfkoue/fi+3TIrcfDK9A8YOkve+zAvXxGDlv6/WuJ5KBbRr7o49X/SQvqg7MDfSkCI39qS2u7zt7htKZ2dnTJgwAeHh4Rg5ciSGDRuGuLg4rFq1Ct7e3khISLDqCTlOTk4WffWr1Srjn1SrtazuSse4ACDfOvWIpdVqRf88d2vSRIDONbLcpXKMRqHirrdKJKbkmbUeAPQLDYSfX4Na12kNs57WmdVQCgCmPXWf1f+ta8LcSEOK3Pj6CqjnHlnujjZS5KZ/j0D4+XnVuk5rmDnR3ayGUhCAV57qyNyIGcPBc3MvsMvv6JcvX44XXngBkZGRmDVrFiIjI7F161Y0adIErq6uVZ6sQ3QntVqFkLbS/sJyreuENs09JX0Pc7Rt4Yknh7U0a72xD7awQUVkr1QqFe6X+CL4deto0F4BFzYPCqiHiSNa1bxeMw88ObTmfBE5MrtsKHU6HVauXAmDwYCsrCzs3bsXoaGhiI6ORnBwMNRqu/yxSAaPD5a2eRo9qDmcnJSxPa6e/wBG9WtWYfntI9XatfDE3q8Hw9VFGd96kHJJnZtHBwRAq1VGblbO64lHBwRUWH47N60D6mHvysFwc1XG7loiuSgjsVaQkZGB+Pj4Cru7jUYj8vPzUVRUBEEQkJ+fj4KCgsoHoXvOU8NbQifhL4JJY9tKNral6tZxwr+X9McvXz+IgaFlx6aFtPPCuoW9cHzTSPjp3WSskOzFuCEtUM/dWbLxlZSbOs4abPmkH/Z9MxgP9ijLTee2XghfEIaoLaPQrIm7jBUSKYPDNJSnT5fcIuruhnL9+vVwcXHBmDFjcP36dbi4uKB169YyVFi5Vk/0x9Ad72PI9gXwbNO00nUG//tdhC5+wcaV1U7K3tU4/1oPnJ/zAPKuVn7brpg3++DaVy/ZuLLKubs5Y/bTwZKMPTTMD107KOv+2Gq1CoN6+OHbd8NKl21bNhATRrSCS137+WaSuZGXm6sWrz8jzV1HBvXwReh9jSUZu7bUahUGdPfF6vlludn+2UA8PTKIuZGRveXG0Tl8Q/n0009DEIRyfyw901sqzp46tJ44CLsfmYc/Zq5AtwXPVFjHb0AXFGVLd69payrOSkPynhVovegQAqasQdzqaRXWyTi2ExoXZX2an/vsfejUxronzdRzd8Y38x6o9PJWJA5zowyznw7G/e2t+4HJ3U2LVe8wN1JgbkhqDtNQTpo0CYIgoHv37nKXYrZGnQNh+PMMhGIjbl26gToNPMpfe02lQptnBuP82j3yFWmBnAt/QdehD1ROWtT1a43iWykQTGX3txVMJiTv+hKNhk6WscqKtFo1Ni7uCy/POtWuZ0jJg9/AjfAbuLHCnULupNGosG5hL/h6c/exFJgbZXByUuOHD/ugUf261a5nbm7UahXC3wtDUx+dtUslMDckPYdpKO2Rs6cOhZk5pY+LsvPg7OFa+jhwTB9c2xUJY35RZS9XHGNWGpx0ZWdmql3cYcwtux9v6oF18Ax9BGpt9b+A5NCmuSf2fzOk2l+Oty+NkpCUW+Gaebc5aVT44cM+GNm34skvZB3MjXK0alYP+1cNgbdX5degBMzLjUajwnfv98KjA5tLVeo9j7khqbGhlFFhZg6cPcq+xdLqXFB4q+QabZo6WrR4JAwXNx2QqzyLaXT1YczJKH1sysuCxrVeyd8L85F26Hs07F9xN4tSdGrjhb83jyx34L0l2rbwxOF1wzGGl92RFHOjLB2DGuD4xpEY8kDtrtsX1KwefgsfhieHBVq5MroTc0NSY0Mpo+QTF+DdvS1UGjXcA/QoSLtVcoVcALqmjeFczw0D1s9Fl7efgm//zmg5urfMFVfPLagbss78BsFYjPzEi3DyaAjV/y7hVJB0BcacDFxcMBzx615D5t+7kHrgO5krrshfr8PuFQ/iu/d7IbiVedfBa9LYFQundMGJzSPRraOyTiZwRMyN8nLjp3fDf78chA0f9MZ9rc07HtmnkSvemxyCkz+OQo9O3hJXSMyN8nLjaOzn9DQHVJiRjQs/RGDI1gUQBBOOzl0N376d4Oypw5Wth7Fz8OsAAH1oezQf1ROXfjwkc8XVc3JvgIYDn0PM3F6AWo2mL36JzBN7YMxKQ4PeT6DtkuMAgKzTB5H2+yZ49Zsgb8FVUKlUGP9QKzw1PBB/nryJPX/E4++zKTh3OQO5+cVw1mrQws8dXdp5oVcXPYaFNVXMNfPuBcyNcnPz5LBAPDG0JY7+cxO7D5fk5uylktxondT/y01D9Oqix/BezI0tMTfKzI0jsbt7eSvdtt7TkREbL3cZ8Azyw6hDy0SNMeZX4HKWdeoRq4U7sKWv3FU4jnhDDvwHbQIAxO19XPbrTzI30mBurIu5qRxzQwB3eRMRERGRSGwoiYiIiEgUNpREREREJApPyrEy9wC93CUAsE4dvq41r2MrSqqFrI+5kYaSaiHrY26koaRa7AlPyiG6Bynt5AIie8DcEFWNu7yJiIiISBQ2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUdhQEhEREZEobCiJiIiISBQ2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUZzkLsDRREz8EFlXDXKXAfcAPfqvmyNqjBmRQEKulQoSydcVWNpN7iqIaqaUOQBwrHmAc4BjY26kYcvcsKG0sqyrBmTExstdhlUk5AKXs+Sugsi+ONIcAHAeINtgbuwfd3kTERERkShsKImIiIhIFDaURPcYQRAQZ8gufXzheiaKi00yVkSkfHfnJvYac0N0Jx5DSXQPKCwy4j/7r2LdjouIPH0T6bcKS5/r99xuuNTVoFNrL4wZ1BwTR7ZCfY86MlZLpAyFRUZsO3ANa7dfQOTpZKRlFpQ+1//53ahbR4NOrRtg9KDmeHpkEBrUY27o3sWGUiYPLJuMwLF9AQAmoxF5SRlI/CMaJxZ9j1xDmszVWe7qZ08j9cC6kgdqNbT1feAe3A++Ez6As5evvMXdwwRBwLodFzBn2XEkpeZVuV5evhFHTt3EkVM38cbnxzH9yQ545+XOqOOssWG19x5HmgccaQ4QBAEbdl7Ea0uPwZBSdW7yC4w4+k8yjv6TjDc//xuvPNEO704KQd06/NUqJeZGmbjLW0aGo2exueNz+On+l/Hb5GXw6hCAPt/MkrusWtO1C0PHtYkIXn0dzWf9gNwrUbi8eLTcZd2zUtLzMXzKXjzz9u/VNpN3y8s34oM1pxAydhtOx9rX5GyPHGkecIQ5IC2zAKOm7ceEN3+rtpm8W36BER+Fn0bnMdtw8nyqhBUSwNwoERtKGZkKi5GXnIFcQxqSjp5DzIb9aNy1NbQ6F7lLqxWVkzO09fVw9vKFe/teaDToBeTEHIEx95bcpd1zklLz0OuZ/2LX77W/DMfZSxkIe+a/iPznphUro7s50jxg73NAcloeej/zX+w4eL3WY5y/kolez/wXf55MsmJldDfmRnnYUCqEi3d9BAzvDlOxEYLR/g/0Lky9gfQ/fwLUmpI/ZDP5BcUY/PIenLucUeU6Go0Kvt6u8PV2hUajqnK9zKxCDJn0Cy7F2dfEZq8caR6wtzmgoNCIoZP3IvpiepXrmJubrJwiDJ28F7FXM6Uole7C3CgDD/SQkb5Hezx5cT1UajWcXEoO5o5esQPFeSUHfvdZNQs3Dp1C7Ib9AIAGHZqj11fT8PPA2TAWFMlWd1Wyog8iaqwOgskEobBkV5H3qFnQ1HUDAKQf2YrEze+We01+3Fn4P/cZGg152eb1Oqr5K6Jw8nz1u6r1DV0Qv28cAMBv4EYkJFV9S4f0W4X417zf8euaoVCrq/4lSrXjSPOAPc8BC1ZG4fiZlGrXsSQ3mVmF+Nc7v+PQt0Oh0fC7G2tjbpSRmzvZdUN56tQpzJs3DwcPHoQgCOjXrx9WrFiBoKAgDBs2DJs2bZK7xGoln7iAw9O+gKaOFgEjeqBJWEdELd5Y+vxfb4djyPYFuLYrEgXp2Qj98HlEvrFGcWG4zS2oGwKmr4NQmI/0w1tw69R+NHlyYenz9UMfRv3Qh0sfZxzdhoT1b8Cr30Q5ynVIp2JS8fHa01Yf97e/DVj9nxi88Fgbq48tliAIOH8lE0mpeXB30+K+oAZwcrKfX+CONA/Y6xwQfSENH377j9XH/SMqCSt/PI9Jj7ez+tjWEHMlA4kpedC5OqFTay/mRib2mpu72W1DGRERgeHDh6NZs2Z466234OLigrVr12LIkCHIzs5Gp06d5C6xRsb8wtJ7l578eDPcA/To9v6z+PPVrwEAuYY0nFm5E/e/PR4pUReReTkRiYet3yxYi9rZBXV9AgEALs06oMBwCXHfTEWzKasqrFuYEo/rKycj8J3dUNdxtXWpDmvZhjMwmQRJxv50XTSef7Q1VCplfEspCAJ+2HUJS76LxolzZSdB+DZ2xctj2uLVp4Pt4ix1R5oH7HUO+Oz7MzAapcnNkvXReGlMW0V9u7/xf7k5frbsG9kmjVzx0pg2mP10sF2cpc7cyJ+bu9nPx5E7JCcnY+zYsQgJCUFUVBRmz56NKVOmICIiAtevlxxMbQ8N5d1OfrIZgWP7wuu+lqXLzofvgWdrfwRPGYVj766TsTrL+Yybj5SIcORcOF5uuWAy4crSp6B/dA5cAzrKVJ3jScsswKY9lyUbP/ZaJg5EJko2viUEQcDrS4/hqbmHEHXXGbU3knPx1hd/Y8jLvyAvv1imCmvPkeYBe5gDMm4V4PtdlyQb/1JcFvb+mSDZ+JZ647PjeGLOQfx9rvzu/cSUXMz78gQefOkX5OYxN3Kyh9xUxi4bysWLFyM9PR3h4eFwcSk7o6tevXoICQkBYJ8NZdYVA+L2HUfInHFlCwUBMd/tQ3zECRSk2teJEXWbtIJn14dwY8Ob5ZYnblkIjYsHGg+fKlNljunQ8UTkFxglfY9f/qz9WePWtHnP5dJd+8JdXyzdfvzrsUS8+ulfNq5MPEeaB+xhDjgclYS8/HsjN//edwUfrDkFoOrc/Pa3ATM+PmrjysRjbuRnlw3lpk2bEBYWhqCgoEqf9/b2hl6vR0FBAZ5//nm0aNEC7u7uCAoKwueff27jai0T/dUO+PbpBH1o+7KFJhMEiXZjSs374dm4dXIvsk4fBABkn/sDqfvXIOCVcFnrckR/n63+hAJ7eQ9zLF0fDXP2vH+7LRYZtwpqXlFhHGkeUPocwNxUtG7HBaRm5EtfkJUxN/JS/oESdzEYDEhISMDYsWMrPGcymXD69Gl07twZAFBcXAy9Xo+9e/eiRYsW+Oeff/Dggw/C29sbY8aMMev9iouLYTAYzK6vqMi8XQWHp39Z6fLk4zFY6/OY2e9XXR3x8eI+FRcVeQPQmrVuwLS1lS7Xte2BLttLwlycnYErS8cj4JW1cPLwsrCWIsTH87pu1Yk6W3471WhU0Des/JpsPncs96liHQAwpOSVO7Ys+mKa6O1KrCs3cvBXtHm/oPMLjAj/z0mMHmC7O06YOwcAjjUP2Osc8PeZ8odxSJGbMwrITXxSHv44ad41ZQsKTfj2p5MYN9hP4qrKMDflyZkbvV4PJyfL20O7ayhzcnIAoNITA7Zv346bN2+W7u52c3PDggULSp/v1KkTRowYgcOHD5vdUBoMBvj7+5td30KvgfDVepi9vlRiY2MxxoK6K9Pu82i4NG1f84pmSt6zAkXpiYj7dka55V59J8J75IwqXlUiNjYW/g92sFotDqnZK4BH2XE1d17ipDrHNo6q8rm7L41yMznNojxIwrUF0PINs1efOXs+ZqbskbCg8pQyBwDKmwcUOQc0mwx4dC59KEVu0tKz5M+NSwAQ+JbZq7/2xgK89vwu6eq5C3NTNVvnJi4uDn5+ln+YsLuG0t/fHxqNBocOHSq3/Nq1a5g6teS4gqqOnywqKsLvv/+OV199VeoyreriloO4uOWg3GWI5vPYXPg8NlfuMhyXIO1xYAAAkw3eoyZGC3fFmcy/fZ6SOcI8oMg5wBa5scV71MRoYQ5M9rfLuzLMje2oBOHuQ3OV71//+hfCw8MxYsQIDBs2DHFxcVi1ahW8vb3xzz//4Ny5c2jTpuL18l588UWcOHECf/zxB5ydnc16L0t3eR8Z8yFyrpi/vlTcmusRumWOqDGmnvVGXL55u7yl5l+3CJ+34y7v6ry36jxWbb1W+rimXXe3v2HpOm4bEqu4Z/Hdu+66tPXEtk+7Wa/oWjCZBIQ99zvikvIqnFhwN7UaOLq2N3wa1rVNcVDOHAA41jwg1Ryw6NsYrPjpauljKXLTsZUH/vtZqNVqrg1BENDnhcO4ciO3xtyoVMAf3/aCv7ftbmPI3EijNrm5Z3Z5A8Dy5cuh1Wqxfft2HDhwAKGhodi6dSvee+89XLx4sdKTdWbOnIkjR47gwIEDZjeTAODk5GTRV79arTL+SbVay+qudIwLABTyIVWr1Yr+eRxdn/8rKNdQGo1CtXfyuC0xJc+s9QAgtFMTRfw/TB/fETM/jqxxvZF9m6Frp0AbVFRGKXMA4FjzgFRzQJ9uheUaSkfOzYwJ92HqB0dqXG94r6YI7dLKBhWVYW6kYcvfnXZ5lrdOp8PKlSthMBiQlZWFvXv3IjQ0FNHR0QgODoZaXf7Hmj59Ovbt24eIiAg0bNhQpqqJpBXWxdusMzjF6N1FL+0bmGnK4+0wNKz6SbK5rw5fvdnDRhWRvXqgs7fkFx1XSm5eGt0GI/o0rXadpno3fP02c0OWs8uGsjIZGRmIj4+vcPzkK6+8gv379+PAgQNo1KiRPMUR2YC/XodhYdId+O/t5YIRfav/ZWQrWq0aW5cNwOyng+HuVn63kkatwuhBzfHn+oegb6isO0mQ8jRp7FZjkyVGw/p18ciAAMnGt4STkxo/fdofr/+rIzzuyo1arcKjAwJw9PsRaNLYTaYKyZ45TEN5+nTJRY7vbCivXbuGzz//HBcvXkTz5s2h0+mg0+kwZMgQmaokktYrT1rvrPy7vTS6DZy1yrmVobNWg49m/h9uRIzDF3PLjk87+v0IbPmkH5tJMtsrT0h3r+0XH2utqFuAarVqfDi9K25EjMOXb5R9E3l0w0P4aUl/+DRibqh2HLqhbNasGQRBQH5+PrKzs0v/7N69W6YqK2r1RH8M3fE+hmxfAM82lX9KHvzvdxG6+AUbV1Y7KXtX4/xrPXB+zgPIu1r5fVNj3uyDa1+9ZOPK7g0DQ30xdnBzq48b1KweXv+X8m71BQA6Vy1G9m1W+ljvZbsTCazFkeYBe5wD+v5fEzw5rGXNK1qopb875j57n9XHtQY3V225b2Z97PADGHOjLA7TUE6aNAmCIKB79+5yl2I2Z08dWk8chN2PzMMfM1eg24JnKqzjN6ALirLt47InxVlpSN6zAq0XHULAlDWIWz2twjoZx3ZC4+IuQ3X3ji/m9qjyLNXbDCl58Bu4EX4DN8JQxZmqtzlpVAhfEAaXuso5aN6RONI8YM9zwPI5ofBtXH1TZUluNBoVvn03DG6u8p/p64iYG+VxmIbSHjXqHAjDn2cgFBtx69IN1GnggXJnVahUaPPMYJxfa7uLMouRc+Ev6Dr0gcpJi7p+rVF8KwWCyVT6vGAyIXnXl2g0dLKMVTq+hvXr4pevB6NBvTpVrnP7TNaEpNxylze5m1qtwvpFvdGjk7cUpRIcax6w5zmgQb062LPiQXh5is+NSgWEvxeGXvf7SFEqgblRIjaUMnL21KEwM6f0cVF2Hpw9yj4hB47pg2u7ImHML5KjPIsZs9LgpKtf+ljt4g5jbmbp49QD6+AZ+gjUWttdE/Be1TGoAQ59OxSBTWt/54l67s7495J+eHyI9XcFUhlHmgfsfQ7o0KoBfl87HEHN6tV6DA+dFls+7ofxD9n2sjv3GuZGedhQyqgwMwfOHmVn02l1Lii8VXJdM00dLVo8EoaLmw7IVZ7FNLr6MOZklD425WVB41oyMZsK85F26Hs07F9xtwRJo0OrBjj148OYMb69xZcTGtbLH2f+8whG9QuQpDYq40jzgCPMAW1beOLkj6Pw6sRgiy8nNLinH6L/8wgeG2T945ipPOZGeXhQlIyST1xAp1fHQKVRQ+ffGAVpt3D7Fga6po3hXM8NA9bPhbOnDi6NPdFydG9c+vFQDaPKxy2oG25sfAeCsRgFN6/CyaMhVP+7JmhB0hUYczJwccFwFGenoSjdgNQD38Gr3wSZq3Zsri5OWDK7O155oj1W/nQea7dfqPLYL52rFo8NDMCksW3RtQMvsWUrjjQPOMoc4FLXCR/P+j9MGdcW3/wUg/DtF5CYXPlFzN1cnPDogABMfrwdunZoCJXUF4MlAMyNEnPDhlJGhRnZuPBDBIZsXQBBMOHo3NXw7dsJzp46XNl6GDsHvw4A0Ie2R/NRPRUbhtuc3Bug4cDnEDO3F6BWo+mLXyLzxB4Ys9LQoPcTaLvkOAAg6/RBpP2+SZGBcFQBvu74YFpXLHrlfsQn5eDvsylISs2HySTA090Zndp4IaiZBzQa7rSwNUeaBxxtDmjWxB3vv3I/Fk7tgoSkXPx9LgVJqSW3VfR0d8Z9rRugdUA95kYGzI3y2OW9vJVsW+/pyIiNl7sMeAb5YdShZaLGGPMrcDnLOvWI1cId2NJX7ipIqeINOfAftAkAELf3cfjp5bsws1LmAMCx5gHOAdbH3FSOuakdfqwiIiIiIlHYUBIRERGRKDyG0srcA/RylwDAOnX4KujGCUqqhag6SpkDAMeaB5RSB0mDuZGGLevgMZREZPeUdCwYkb1gbsiauMubiIiIiERhQ0lEREREorChJCIiIiJR2FASERERkShsKImIiIhIFDaURERERCQKG0oiIiIiEoUNJRERERGJwoaSiIiIiERhQ0lEREREorChJCIiIiJR2FASERERkShsKImIiIhIFDaURERERCQKG0oiIiIiEsVJ7gIcTcTED5F11SB3GXAP0KP/ujmixpgRCSTkWqkgkXxdgaXd5K6CqGZKmQMAx5oHOAc4NuZGGrbMDRtKK8u6akBGbLzcZVhFQi5wOUvuKojsiyPNAQDnAbIN5sb+cZc3EREREYnChpKIiIiIRGFDSURERESi8BhKIrJLqRn5+PWvRPx9LgUnzqaWLn/7y7/R+349enXRo4Wfh4wVEilPWmYBfv3rBv4+m4q/z6aULn/ri5LchIXoEdiUuSHLsaEkIrsSdS4FS9efwZa9V1BQaKzw/NrtF7B2+wUAwMDQJpg6rj2G9/aHSqWydalEinEqJhVL15/Bpj2XK83Nuh0XsG5HSW76d2uCqU+0w4g+TZkbMhsbSpk8sGwyAsf2BQCYjEbkJWUg8Y9onFj0PXINaTJXZ7mrnz2N1APrSh6o1dDW94F7cD/4TvgAzl6+8hZHDiG/oBjvfHUCn6yLhskkmPWafUduYN+RGxjRpylWzusJfUNXiau0jCPNA5wDlKmg0Ij3vo7C4vB/YDSal5uIyBuIiLyB4b38sXJeTzRp7CZxlZZhbpSJx1DKyHD0LDZ3fA4/3f8yfpu8DF4dAtDnm1lyl1VrunZh6Lg2EcGrr6P5rB+QeyUKlxePlrsscgA3U/PQY/xOfBR+2uxm8k47Dl5H8KNbcSw6WYLqxHGkeYBzgLKkpOfjgYk7sWj1KbObyTvt/C0OwY9uxdFTNyWoThzmRnnYUMrIVFiMvOQM5BrSkHT0HGI27Efjrq2h1bnIXVqtqJycoa2vh7OXL9zb90KjQS8gJ+YIjLm35C6N7FhqRj76PbcLUedTq1xHo1HB19sVvt6u0Ggq30WXkp6PAS/sxok7jhtTAkeaBzgHKEf6rQL0f343jp+pens3JzdpmQUY+OJuxX0YY26Uhw2lQrh410fA8O4wFRshGE1ylyNaYeoNpP/5E6DWlPwhqgVBEPDM27/jzKWMatfTN3RB/L5xiN83DvqGVf9CuZVdhEdmRiArp9DKlVqHI80DnAPkIwgCnnvnMP6JrX73r7m5yc4txiMzIpCZxdxIzZ5zw2MoZaTv0R5PXlwPlVoNJ5c6AIDoFTtQnFcAAOizahZuHDqF2A37AQANOjRHr6+m4eeBs2EsKJKt7qpkRR9E1FgdBJMJQmEeAMB71Cxo6pYcf5N+ZCsSN79b7jX5cWfh/9xnaDTkZZvXS8r3/X8v4edD16065rUb2XhtyTGseLunVcetLUeaBzgHKMOWX67gPxFXrTpmfFIOZn0SidXvhll13NpibpSXG7tuKE+dOoV58+bh4MGDEAQB/fr1w4oVKxAUFIRhw4Zh06ZNcpdYreQTF3B42hfQ1NEiYEQPNAnriKjFG0uf/+vtcAzZvgDXdkWiID0boR8+j8g31iguDLe5BXVDwPR1EArzkX54C26d2o8mTy4sfb5+6MOoH/pw6eOMo9uQsP4NePWbKEe5pHBFRSa8vuyYJGN//eN5zBjfAUEB9SQZ3xKONA9wDpCf0WjC7CV/STL2mq2xmDmhA9q1rC/J+JZgbpSXG7vd5R0REYHu3bsjJiYGb731FhYtWoT4+HgMGTIE2dnZ6NSpk9wl1siYX1hy/9KYOJz8eDOy4m6i2/vPlj6fa0jDmZU7cf/b49F6/EBkXk5E4uHTMlZcPbWzC+r6BMKlWQc0efI91PFujrhvpla6bmFKPK6vnIzmszdBXUdZZ96SMuw4eA03buZKNv7XP56TbGxLONI8wDlAfv/9LQ5xhhzJxl+x5bxkY1uCuVFebuyyoUxOTsbYsWMREhKCqKgozJ49G1OmTEFERASuXy/ZPWYPDeXdTn6yGYFj+8Lrvpaly86H74Fna38ETxmFY++uk7E6y/mMm4+UiHDkXDhebrlgMuHK0qegf3QOXAM6ylQdKd26HRclH782Z4xLzZHmAc4Btid1btbvvAijAo9TZG7kZ5cN5eLFi5Geno7w8HC4uJQdSFyvXj2EhIQAsM+GMuuKAXH7jiNkzriyhYKAmO/2IT7iBApS7euMr7pNWsGz60O4seHNcssTtyyExsUDjYdX/gmMSBAERJ6W9lIlaZkFuByfJel71IYjzQOcA2xP6txkZhUi9prytkHmRn52eQzlpk2bEBYWhqCgoEqf9/b2hl6vBwBMmjQJP//8MzIzM+Hu7o7Ro0fjo48+grOzs1nvVVxcDIPBYHZtRUXFZq9bmeivdmDYz+9DH9oehiNnShaaTBAs/CalqKgY8fHxomopKvIGoBU1hvfDsxEzpyeyTh+Ee3AfZJ/7A6n716DtkhMW1lKE+PgkUbWQ/UhMycfNtPxyyzQaVZVnovrcsdyninUMKXkVrsX3y+8xqNvbR2S15YmdAwDHmgc4B9hOSkYBEu46TESK3Ow7HAt35yYiqy2PuSlPztzo9Xo4OVneHqoEQVDePp9qGAwG+Pj4YObMmfj000/LPWcymeDj44POnTtjz549AICzZ8+iWbNmcHNzQ0pKCkaPHo3evXtj/vz5Zr1ffHw8/P39za5voddA+Gqtex/UwDF94HVfS0S+ucbs1yQU3cJbqftEvW+7z6Ph0rS9qDHuVJydgXMzQxAwZQ3cO/a16LV518/g7NQOVquFFK5uU6DVvHKLfL1dEb9vXBUvqJnfwI1ISLrrmMwbPwCpB2o9ZmWkmAMAx5gHOAdIrI4vEFT+bGBJcpO4GUgRt13djbmpmq1zExcXBz8/P4teA9jhN5Q5OSUHG1d2f9Ht27fj5s2b5XZ3t2vXrvTvgiBArVbjwoULktdJFSXvWYGi9ETEfTuj3HKvvhPhPXJGFa+ie5Ot7h/M+xTbEucAidnsvtvMjS3ZS27s7hvKwsJCuLq6onPnzjh2rOySIteuXUPPnj2RkJCAjRs34vHHHy997sMPP8TChQuRk5MDLy8v7N69G127djXr/Szd5X1kzIfIuWL++lJxa65H6JY5osaYetYbcfnidnlbi3/dInzejru77hVXE3MR9uzv5ZbVtOvu2MZRAICu47YhMSWvwjqV7bpbMrMDRg+w7v1ylTIHAI41D3AOqFnCzTx0f/q3csukyM3H09rj8Qct/warOsyNNGqTm9ru8ra7byidnZ0xYcIEhIeHY+TIkRg2bBji4uKwatUqeHt7IyEhocIJOXPmzMGcOXNw7tw5fP/99/DxMf+YKScnJ4u++tVqlfFPqtVaVnelY1wAkF/jajah1WpF/zxkP5o0EeDudhRZOWXXjDMahYq73iqRmJJn1noA0C80EH5+XrWuszJKmQMAx5oHOAfUzNdXQH2Po0i/VXZHGyly0zc0EH5+DWtdZ2WYG2nYMjd2eZb38uXL8cILLyAyMhKzZs1CZGQktm7diiZNmsDV1bXKk3Xatm2L++67D+PHj7dxxURkCbVahS7trPsL625162jQroX8F2gmshaVSoX720ubG2etGh0CmRuqyC4bSp1Oh5UrV8JgMCArKwt79+5FaGgooqOjERwcDLW66h+rqKgIsbGxNqyWiGpj7IPNJR3/sYEB0GrtcgokqtLYB1tIOv7D/ZuhjrN93WOabMNhZtOMjAzEx8eX292dmZmJtWvXIiMjA4Ig4J9//sHChQvx4IMPylcoEZnlyWEt4e4m3TFIk8a2lWxsIrmMG9IS9dzNuyxebUwaw9xQ5RymoTx9uuSWSnc2lCqVChs2bECLFi3g7u6OUaNGYejQofj8889lqrKiVk/0x9Ad72PI9gXwbNO00nUG//tdhC5+wcaV1U7K3tU4/1oPnJ/zAPKuVn6bq5g3++DaVy/ZuDKyN+5uznjtmWBJxn6why+6d2wsydi14UjzAOcAebm6OGHOv6S5i0r/bk0Q1kUvydi1wdwoi0M3lB4eHti/fz/S0tKQnZ2Ny5cv45NPPoGbm5tMVZbn7KlD64mDsPuRefhj5gp0W/BMhXX8BnRBUXbFM++UqDgrDcl7VqD1okMImLIGcaunVVgn49hOaFzcZaiO7NHrz9yHzm2se9KMh06LVfMfqPTSY3JwpHmAc4AyvDoxGF07WPdYSp2rFquZG0k4Sm4cpqGcNGkSBEFA9+7d5S7FbI06B8Lw5xkIxUbcunQDdRp4lL+OmEqFNs8Mxvm1e+Qr0gI5F/6CrkMfqJy0qOvXGsW3UiCYyu75KphMSN71JRoNnSxjlWRPtFo1fljcB16edapdz5CSB7+BG+E3cCMMlVz65Da1WoXw93rBX6+zdqm15kjzAOcAZXByUuP7D/qgUf261a5nbm5UKmDNuw8gwFc5DQ1zozwO01DaI2dPHQozc0ofF2XnwdnDtfRx4Jg+uLYrEsb8osperjjGrDQ46crO/lO7uMOYm1n6OPXAOniGPgK1tvpJjuhObZp7Yv83Q6r95Xj70igJSbkVrpl3m0ajwvr3e+ORAQESVVo7jjQPcA5QjlbN6iFi1RB4e1V+DUrA/NysXdALYyQ+2cdSzI3ysKGUUWFmDpw9yna/a3UuKLxVch0wTR0tWjwShoubrHtbOClpdPVhzMkofWzKy4LGtV7J3wvzkXboezTsX3G3BFFNOrXxwvFNIzEwtHb3Dw5qVg+/hQ/DE8NaWrky8RxpHuAcoCzBQQ1wfONIDO5Zu+sQBjb1wK+rh2LCiFZWrkw85kZ52FDKKPnEBXh3bwuVRg33AD0K0m4B/7txka5pYzjXc8OA9XPR5e2n4Nu/M1qO7i1zxdVzC+qGrDO/QTAWIz/xIpw8GkL1v0s4FSRdgTEnAxcXDEf8uteQ+fcupB74TuaKyZ409dHhl68HY+2CXmZfB0/f0AXzX+6Mkz+OQo9O3hJXWDuONA9wDlAeP70bdn01COsX9UbHoAZmvcbbywXzXuyMUz8+rKiTcO7E3CiPci5Nfw8qzMjGhR8iMGTrAgiCCUfnroZv305w9tThytbD2Dn4dQCAPrQ9mo/qiUs/HpK54uo5uTdAw4HPIWZuL0CtRtMXv0TmiT0wZqWhQe8n0HbJcQBA1umDSPt9E7z6TZC3YLI7KpUKE0e2woQRgTh8Igl7/ojH32dTcO5KBnLzjXDWqtHc1x1d2nqh9/0+eKh3U8Vfa9KR5gHOAcqkUqnw1PBAPDmsJf48eRO7D8fh77OpOHs5Hbn5Rmid1Gjuq0OXdg3RK0SPEX2bwlmr7GtNMjfKY3f38la6bb2nIyM2Xu4y4Bnkh1GHlokaY8yvwOUs69QjVgt3YEtfuasgqplS5gDAseYBzgGOjbmRhi1zo+yP7kRERESkeGwoiYiIiEgUNpREREREJApPyrEy9wBlnBFnjTp8XWtex1aUVAtRdZQyBwCONQ8opQ6SBnMjDVvWwZNyiIiIiEgU7vImIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYnChpKIiIiIRGFDSURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYnChpKIiIiIRGFDSURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYny/+nhttSBnktAAAAAAElFTkSuQmCC", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 1, @@ -182,8 +183,9 @@ "outputs": [ { "data": { + "image/png": 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hpt/fy88ffOW+Iu2Qc+hHgtv3xsfPTEB0a4ovncVmtZY+b7NayVi7gEb3jXVjleIsRtueXUX7jRiJcsB+ygDPZIhmesyYMdhsNrp2Ne5MCf6hwRRezCl9XJSdh3+9K3dvbDm0NyfWJmHJL3JHeXazZJ3HL7hB6WNTYAiW3Ctf/jz3zRJCu8ViMlft7pPiXYy2PbuK9hsxEuWA/ZQBnskQzXRtUHgxB/96dUsfm4MDKbyUC4BvHTPXx/bg8Ipv3FWe3XyDG2DJySx9bM3Lwjeofsn/F+ZzfvM/CL+7/Ck/MQajbc+uov1GjEQ5YD9lgGfyutk8aquMHYfo+Keh+PiaCG7WmILzl7h8Z4Dg5o3xr1+Xfsum4h8aTGDjUG54qBdHPt5cyajuU7dVF04tfwGbpZiCM8fxqxeOz//NxFJw+hiWnEwOz7yf4uzzFF1I59w3SwnrO8LNVUtNMdr27Crab8RIlAP2UwZ4JjXTXqIwM5tD/0xk4KqZ2GxWfpi6iKg+HfEPDebYqi2sufdZACK7taPF4Ds9PnD8QhoS3n8UB6b2BJOJ5v+zgIs7vsKSdZ6GvYbR5o2Suwlm7dnE+X+vUBgYjNG2Z1fRfiNGohywnzLAM/nYbFe78ak422e9JpJ5MMXdZRDaKprBm+dWa4yhG+Goh9wo8voQWNnHvTWkpOfQbMAKAJLXPUx0ZN1KXuH9PGV7hprZpl1B+41x1cYMAM/JAW/JAPCcHFAGOE7XTIuIiIiIOEjNtIiIiIiIg3TNtBuFxES6uwSgZuqICqp8HVfxpFpqE0/ZnsGzarkWT9pWPakW8V6esu95Sh1V4Sn7nqfU4Y10zbSIE9TW6yVFpIQyQKT20GUeIiIiIiIOUjMtIiIiIuIgNdMiIiIiIg5SMy0iIiIi4iA10yIiIiIiDlIzLSIiIiLiIDXTIiIiIiIOUjMtIiIiIuIgNdMiIiIiIg5SMy0iIiIi4iA10yIiIiIiDlIzLSIiIiLiIDXTIiIiIiIOUjMtIk7Rr18/Ro4c6e4yysjKyuLJJ58kLCyMunXrMnDgQI4cOeLuskQMyxNzYOLEiXTp0oWgoCD8/PzcXY4YgJppEak1HnvsMRITE/nkk0/YsmULNpuN/v37k5eX5+7SRMRFLBYLw4YNY8yYMe4uRQxCH8ncKPHxV8g6nu7uMgiJieTuJc9Va4xJSZCaW0MFVVNUELzZxd1VGMOCBQtYsGABR44coX79+vTo0YNPP/2UmJgYRo0axfTp00vXHTVqFIcPH2bTpk2MHDmSxMREAJYsWQLAxo0b6d279zXfr7i4mFmzZrF06VJSUlIIDw8nNjaW+fPns3XrVnr16sXKlSsZMmRI6ZgDBgxgzZo13HPPPdcc++DBg6xevZqvv/6aPn36ALB8+XIiIyP56KOP3HL0zFMyAIyVA8qAmmWkHACYP38+AB988IED/xo1z1NywEgZAK7NATXTbpR1PJ3MgynuLqNGpObC0Sx3VyE16YUXXuD111/nlVdeYcCAAWRnZ/Pll19W6bXz5s3j6NGjNGnShHnz5gHQsGHDSl/3xBNP8OWXX/L666/TvXt3MjIy+P777wHo3r07L774Ik888QS33XYbgYGBPProo0yaNKlKf0C/++47zGYzd999d+myBg0acMcdd7Blyxa3NNNGygBQDhiR0XLAExkpB2prBqiZFpFycnJyePXVV5k5cybjxo0rXd6pU6cqvb5+/fr4+/sTGBhIZGRklV5z+PBhli5dyscff8yDDz4IwA033EDXrl1L15k6dSobN25k+PDhhISEEBUVxaxZs6o0flpaGuHh4fj6+pZZHhkZSVpaWpXGEKlNjJgDIs6gZlpEytm3bx/5+fkMGDDAZe+5Y8cOgGu+p8lkYtmyZbRp04bi4mL++9//YjabXVWiSK2iHBCpGn0BUUTsZjKZsNlsZZYVFRW55L137dpFTk4O+fn5JCcnV/l1TZo04ezZs1gsljLLT58+TZMmTWq6TBHD88YcEHEGNdMiNSTjfB4frzvGc3O38cQL/y5d/sJbO1j6+SGOp3rPhWRt27YlICCAdevWVfh848aNOXXqVJllO3fuLPPY39+/XON6LZdPHV/tPQHS09N5/PHHef755xk3bhyPPvoo58+fr9L4d955J0VFRXzzzTelyzIzM0lKSuKuu+6qcp0iV2Oz2fhxTwbz/7mPya8llS4fM2srf1m0m/Xfp1JYVPV9wt2MmAMizqDLPESqafu+DN5cto+P1x2jqNha7vm/f3aQv392EIB774xmwrC2DOzRzNVl2iU4OJj4+HhefPFFAgMDS6ePW7t2LVOnTqVfv3689dZbDBkyhOuuu4533nmHEydOlPlyUYsWLdi4cWPpDAD169e/5qnYli1bMnz4cMaMGUN+fj7dunXj/PnzbN26laeffhqbzcaIESO46aabmDFjBhaLhW+//ZY//OEPfPbZZ5X+TK1atWLQoEE89dRTvP/++9SvX59p06YRFRVFXFxcTfyzSS2VX1DMe58e4K2P9vPzsYvlnv9i80m+2HwSgMYNAxgV25oJw9sRERbo6lLtYsQcgJLrsrOzszl5suR3smvXrtL3Dg4OdvjfS2ovNdNe4K65Y2kZVzKVl9ViIe90Jmnf7WXH7H+Qm+59n8aPzxvJuW9KpknCZMLcoAkhHfoSNeIv+IdFubc4O+TmFTNjwX94c9lefnWm86q++i6Fr75L4cH+MSyY1p3GHvzHdObMmTRq1IiEhAQmTZpEgwYN6NmzJwDPPvssJ06cIC4uDrPZzJgxY3jooYc4fPhw6evj4+PZs2cPt9xyCzk5OVWaEmvx4sW89NJLTJ8+nVOnTtG4cePSLyG9+uqrbN++nd27d+Pr64uvry8rVqzgtttuY8GCBYwdO7bSn2nZsmVMnjyZIUOGkJ+fT8+ePVm3bh2BgZ77e7jMSDlglAwASPrvGUbO+LbCJroiZ87nM3vRbt75+GfmP9eNR+67Hh8fHydX6Tgj5sCoUaPYvHlz6eNbb70VqNq0fe5kpAwAY+WAj+3XFzyJy3zWa2KVpsO5a+5Ygq+LYPPoN/DxNRESE0HX2aMoys5n7QPPV7uO0FbRDN48t1pjDN1Y9elwjs8bSUH6Ua5/ZiU2q4WC9COcXDgW34AQbnp1a7XqALg+BFb2qfYw15SWkcs9f/yKPYcuODxG44YBfP3OvXS8KawGKxNvUtUMAGPlgBEyAOCdlfsZO/t7rFbH/4yOim3FOzPuxNdXV13WVuoFvDsHQNdMew1rYTF5GZnkpp/n9A/7OfDhBhp3bo052POPqFXEx88fc4NI/MOiCGnXk0YDRpNz4HssuZfcXVqlzpzLo88Ta6/ZSPv6+hAVEURURBC+vhUfdTpzPp++o9ay56D3HVEQ9zBSDnhzBkBJI/3Uy1uv2khXJQMAFv3rIE+8sKVaDbnUHkbKAPD+HLisVlzmYbVamTdvHgsXLuT48eM0atSIoUOH8tJLL1G3bl13l2e3wIgGxNzfFWuxBZul/DW63qbw3CkubP0ETL4l/3kwm83GiOc3c+D4tU/pRoYHkrL+EQCi+y8n9XTFt4S6cKmQIZM2sPvjIdQNMvbUTmPGjLnmHcc2b95MYWEhH3zwASNHjsTf3/+a43Xu3LnM43bt2nHixIkK13300Ud555137K7ZkxkpB7wpA6Dk0o6xs7+/5jpVzQCAJZ8fonP7cMY+3LZG6/Q027ZtY/HixdfMgfXr1zucAVC7csBIGQDelwO/VCua6UmTJpGQkMCQIUOIj49n//79JCQksHPnTjZs2IDJ5PkH6CO7t2P44WX4mEz4BdYBYO/bn1OcVwBA7/fiObV5Nwc/3ABAw/Yt6PnW03zRfwqWAtdMVWSPrL2b2BkXjM1qxVaYB0DE4Hh8A0o+3Fz4fhVpH/25zGvyk3+i2ah5NBr4lMvrvezvqw7y9dbUGh3zSHIWz8//D3Of7Vr5yl4sNjaWfv36XXOdwsJCFi1axLBhwyr9Q/pra9euveq0XPXq1bNrLE9lpBzw1gzILyjm9//77xo/kvzMG9sYeFc010cbY1u9mspyoDoZAMbPASNlAHhvDvya4Zvpffv2MX/+fGJjY/n0009Ll7do0YIJEyawYsUKhg0b5sYKqyZjxyG2PP03fOuYiXmgO0173MzOOctLn/9xxmIGrp7JibVJFFzIptsrT5I07X2P3HkA6rbqQszEJdgK87mwZSWXdm+g6fCXS59v0G0IDboNKX2c+cNnpC6bRljfx91RLgAFhRamztvulLET/rmPp4e3o0V0iFPG9wSXv8nvLNddd53TxvYURsoBb8wAKPlAvf9oZo2Pm5tfzAtv7WDZ7N41PrYnqSwHsrOzqzW+0XPASBkA3psDv+b5h2SvYffu3QwaNIj69etTr149Bg8eTFpaGiEhITz88MMALF++HJvNxsSJE8u89sknnyQoKIgPP/zQDZXbz5JfSNbxdDIPJLPrrx+RlXyGLrOeKH0+N/08+xau4fYZj9H6sf5cPJpG2pY9bqz42kz+gQQ0aUngde1pOvwl6kS0IPnd8RWuW3g2hZMLx9JiygpMdYJcXOkVn64/TsaFfKeMbbPBu5/+7JSxxTiMlAPemAE2m423PtrvtPFXfn2MjPN5ThtfvJ+RMgC8Mwcq4rXNdGJiIl27duXAgQNMnz6d2bNnk5KSwsCBA8nOzqZjx45AyTVaJpOJO+64o8zrAwIC6NixI9u2bXND9dW367WPaBnXh7Bbbihd9vPirwht3YwO4waz7c9L3Fid/Zo88iJnExeTc6jskV+b1cqxNx8l8nfPERRzs5uqK7Hki0NOHf+D1YfK3U2stvHz8+OBBx7Az8/wJ81qhJFywBsyYMf+c+w7kum08QuLrKz46qjTxvcGygD7GCkDwDtyoCJe2UxnZGQQFxdHp06d2LlzJ1OmTGHcuHEkJiaWTsJ+uZk+deoU4eHh1KlTp9w4UVFRnD17lsLCQleWXyOyjqWTvH47nZ575MpCm40DS9eTkriDgnPe9U3YgKY3Etr5t5z6sOz0PmkrX8Y3sB6N76/4k6qrXL6zmTOln80j5XSOU9/D0wUEBDB9+nQCAgLcXYpXMFIOeHoGQMkXD53tx73OzRlPpwywj5EyALwjByrilR/95syZw4ULF1i8eHGZmy3Ur1+fTp06kZiYWNpM5+bmVthIA6U7a25urkNfdPi14uJi0tPTq7x+UVFxtd5v71uf85svZhHZrR3p3+8rWWi1YrPzizFFRcWkpFRtrturjxEBVG82ioghUzjw3J1k7dlESIfeZO//jnMb3qfNGzvsrKWIlJTT1arl106k5ZKZVfZDl6+vD5HhFU9H1OQXy5tcZZ30s3lYLGV/V19/e4B7u0dUs1rPlJNT+QeFgoICEhISmDBhwlX328uqu816gupmABgrBzw5AwD+vf1kmcfVzQAonwNJu9MNsW1XRBlQMfUCZbkzByIjIx06K+KVN22Jjo6mZcuWbNq0qdxz/fr1Y+/evaVNbYcOHThz5gynT5f/Bx06dCgff/wxBQUFpc30ypUrSUhIYNeuXYSHh3P8+PEq15WSkkKzZlW/TfTLYf2JMtfst4tbDu1N2C03kPT8+1V+TWrRJaafW1+t9207fy+BzdtVa4xfKs7OZP/kTsSMe5+Qm+2bdT3v5D5+Gt++xmoBIOh6uGFamUVREUGlU185osLpslKWwoVvHR7Tk40aNarSdQoLC1m6dCkjRoyo9APuokWLaqo0t3FGBoAxcsDjMgDguvFQ75bSh9XNAKggB4qzYP+kao3pqZQBFVMvcHWuzoHk5GSio6Pteg144ZHp9PR0UlNTiYuLK/ec1Wplz549pbcGBWjatCk//fQTBQUF5T7lpqamEh4eXmaHbdCgAePGjeP06dO8+eabzvtB5JoyvnqbogtpJP+97B+VsD6PEzHIHX9oXHS7Xw++rbCIK3leBriKV159KeIU3pIDXndk+siRI7Rs2ZL4+Hhee+21Ms+tWrWK2NhYnn32WV555RUApk+fzqxZs/j222/p0aNH6br5+fmEhYXRs2dPvvzyy3Lv89lnnzFx4kS7jkzbe5nH90NfIedY1dd3lrotIum28rlqjTH+pwiS8z3jpiPNAoqY37ZmT/EePJnN3X/8rsyyyk7xbls+GIDOj3xG2tny39Cv6DKPBc/ezAO9mtRM0R7m8OHDla6Tk5PD/fffz5o1ayq9oVLLli1rqjS38ZQMAGPlgDMyAGDCX//Lqo1ppY+rmwFQPgeaRwby3d971lzRHkQZUDFPyQEjZQA4lgOOXubhdUemmzVrhq+vL5s3by6z/MSJE4wfX3Jh+uXrpQHi4uKYPXs2c+fOLdNMv/fee+Tm5jJ8+PAaq83Pz8+u0wNms2f885vN9tVd4RiHAOfMGmc3s9lc7Z/n1yIjrQQG/EBevqV0mcViu+ZdzS5LO5tXpfUA7u5+I9HRoY6W6dHS0tIqXcdsNjNq1ChCQ0MrPcVb079jd/CUDABj5YAzMgCg+60XyjTTzsiA29tFGGLbrogyoGKekgNGygBwXg5UxDN+g3bw9/dnxIgRLF68mEGDBvGb3/yG5ORk3nvvPSIiIkhNTS3TTHfo0IGxY8fyt7/9jdjYWO67777SOyD26tXLK27YIu7n52eiY+swvt/tvG/zh9Q1c+N1zrupiTfw9/dn9OjR7i5DpEK3twt3+nvc1jbM6e/hyZQB4o288uKshIQERo8eTVJSEvHx8SQlJbFq1SqaNm1KUFAQrVq1KrP+3Llzee2119i3bx9jx45lxYoVjB8/njVr1njFrcTFM8Td08Kp4w8d0AKTqXZfM52Xl8f48ePJy9ONK8Tz3NkxgqaNnXuziKH3XO/U8T2dMkC8kVd2ksHBwSxcuJD09HSysrJYt24d3bp1Y+/evXTo0KFcg+zr60t8fDwHDhygoKCA1NRU3njjDYKDg930E4g3evyBGwkKcN7JnDFxbZw2trewWCwkJSVhsVgqX1nExcxmE6N/19pp49/TPYqWzWt+dhdvogwQb+SVzXRFMjMzSUlJKXOJhyMsFgv5+fkUFRVhs9nIz8+noKCgZoqsphuH3c19n89i4OqZhN7UvMJ17v30z3Sb4x2nyM6uW8TPz3Tn5+fuIu94xbc7PfB8b0689UcXV1ax0Hp1mDzCCdNtAQ/0bk6nts4/hSzez0g54G0ZADD24bY0alDzNxTx8YEXnrq18hWl1jNSBoB35sCvGaaZ3rOn5BdQ3WZ62bJlBAYGMnToUE6ePElgYCCtWzvvSERV+YcG0/rxAXwZ+798N/ltusz8fbl1ovvdRlG2d5waK846T8ZXb9N69mZixr1P8qKny62TuW0NvoEhbqju6qaP7kj7lg1qdMwG9fx5Z8adNTqmGJORcsBbMyC8QQBvPd+9xsed/Fh7ut1izBs2Sc0xUgaA9+bAr6mZ/pWRI0dis9nK/GfP9HjO0ujWlqRv3Yet2MKlI6eo07Be2TmJfXy46ff38vMHX7mvSDvkHPqR4Pa98fEzExDdmuJLZ7FZraXP26xWMtYuoNF9Y91YZXl1/H1ZPqc3oSHX/pZ5+tk8ovsvJ7r/ctKvMiUWlEyttXRWL5o0cu51mN6iTp06TJs2rdI7n9VWRsoBb80AgAcHtGDsw9e+LKuqGQDQvWNjZo67rSZL9FrKgGszUgaAd+fALxmmmR4zZgw2m42uXbu6uxSn8A8NpvDilVuxFmXn4V/vSgPWcmhvTqxNwpJf5I7y7GbJOo9f8JUjvKbAECy5F0sfn/tmCaHdYjGZa/50anW1v7Eh69+9l4b1rx72l6fMSj2dW24u6cvMfiZWzOnD/b0qPk1XG5nNZgYPHozZ7BnzlHoaI+WAN2cAQMJz3Rj94NXPWlYlAwDuvDWC//e3AQQ68fsY3kQZcG1GygDw/hy4zDDNtNEVXszBv96VCezNwYEUXiqZt9S3jpnrY3tweMU37irPbr7BDbDkZJY+tuZl4RtUMi2ctTCf85v/Qfjd5U9feYrb2zVi+/JB9Ons2A1W2lwfypYl9/PgAOfOEOJtcnNziYuLIze3anPy1jZGygFvzwCTyYd3ZtzJu/97JyF17W/8fHzgT493YP3Cewmtp6OwlykDrs1IGQDenwOXqZn2Ehk7DhHRtQ0+viZCYiIpOH8J/u/mlcHNG+Nfvy79lk3lthmPEnX3rdzwUC83V3xtdVt1IWvft9gsxeSnHcavXjg+/zcLS8HpY1hyMjk8835SljzDxf+s5dw3S91ccXktokPY8N5AFr14F22uD63Sa5o2DuLlcbex46NB3NGhkXML9EJWq5Vjx45h/cVpPrnCSDlghAzw8fHhyQdvYu+/Yhk56EYC6vhW4TUw8K5ovlt6P3+Nv0NHpH9FGXBtRsoAMEYOgBfetKW2KszM5tA/Exm4aiY2m5Ufpi4iqk9H/EODObZqC2vufRaAyG7taDH4To58vLmSEd3LL6Qh4f1HcWBqTzCZaP4/C7i44yssWedp2GsYbd7YDkDWnk2c//cKwvqOcG/BV2Ey+fBEbGv+MKQVm7ens25rKv/Zf5b9RzPJK7DgbzZxQ3QIt7UNp9ftkfymR3PMZn2GFccYKQeMkgEAzZsEs3hmT17/UxdWfn2UH/dm8J+fznH6XB5Wq43Qev50bB3GbW3DeLB/C25oVrunvxPHGSkDwDg54GOz2a5+MZc41We9JpJ5MMXdZRDaKprBm+dWa4yhG+FoVs3UU13Xh8DKPu6uQn5t27Ztla6TnZ1N3759+eabbyqdB75z5841VZrbeEoGgLFyQBngmZQBFfOUHDBSBoBrc0CHyETEYwQEBDBv3jwCAjz7yyYi4hzKAPFGusxDRDyGn58f3bp1c3cZIuImygDxRjoyLSIeIzs7mz59+pCdne3uUkTEDZQB4o10ZNqNQmIi3V0CUDN1RHnQPUc8qRaxX05OTuUrGYSnZAAYKwc8pQ5xTG3KAPCcHDBSBoBra1Ez7UZ3L3nO3SXUmDe7uLsCEe9jpAwA5YCII4yUA7U1A3SZh4iIiIiIg9RMi4jHCAwMZPny5QQGBrq7FBFxA2WAeCM10yLiMUwmExEREZhMiiaR2kgZIN5IW6uIeIycnBz69u1b676AJCIllAHijdRMi4iIiIg4SM20iIiIiIiD1EyLiIiIiDjIx2az2dxdhIgIgM1mIysri5CQEHx8fNxdjoi4mDJAvJGaaRERERERB+kyDxERERERB6mZFhERERFxkJppEREREREHqZkWEREREXGQmmkREREREQepmRYRERERcZCaaRERERERB6mZFhERERFxkJppEREREREHqZkWEREREXGQmmkREREREQepmRYRERERcZCaaRERERERB/1/zF06fXTJ81gAAAAASUVORK5CYII=", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 5, @@ -210,8 +212,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 6, @@ -506,7 +509,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -520,9 +523,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.9.21" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth.mdx b/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth.mdx new file mode 100644 index 00000000000..7b27af8c265 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth.mdx @@ -0,0 +1,343 @@ + + +# Gate Cutting to Reduce Circuit Depth + +In this tutorial, we will reduce a circuit’s depth by cutting distant gates, avoiding the swap gates that would otherwise be introduced by routing. + +These are the steps that we will take in this [Qiskit pattern](/docs/guides/intro-to-patterns): + +* **Step 1: Map problem to quantum circuits and operators**: + + * Map the hamiltonian onto a quantum circuit. + +* **Step 2: Optimize for target hardware** \[*Uses the cutting addon*]: + + * Cut the circuit and observable. + * Transpile the subexperiments for hardware. + +* **Step 3: Execute on target hardware**: + + * Run the subexperiments obtained in Step 2 using a `Sampler` primitive. + +* **Step 4: Post-process results** \[*Uses the cutting addon*]: + + * Combine the results of Step 3 to reconstruct the expectation value of the observable in question. + + + +## Step 1: Map + + + +### Create a circuit to run on the backend + +``` +[1]: +``` + +```python +from qiskit.circuit.library import efficient_su2 + +circuit = efficient_su2(num_qubits=4, entanglement="circular") +circuit.assign_parameters([0.4] * len(circuit.parameters), inplace=True) +circuit.draw("mpl", scale=0.8) +``` + +``` +[1]: +``` + +![../\_images/tutorials\_02\_gate\_cutting\_to\_reduce\_circuit\_depth\_2\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_02_gate_cutting_to_reduce_circuit_depth_2_0.avif) + + + +### Specify an observable + +``` +[2]: +``` + +```python +from qiskit.quantum_info import SparsePauliOp + +observable = SparsePauliOp(["ZZII", "IZZI", "-IIZZ", "XIXI", "ZIZZ", "IXIX"]) +``` + + + +## Step 2: Optimize + + + +### Specify a backend + +You can provide either a fake backend or a hardware backend from Qiskit Runtime. + +``` +[3]: +``` + +```python +from qiskit_ibm_runtime.fake_provider import FakeManilaV2 + +backend = FakeManilaV2() +``` + + + +### Transpile the circuit, visualize the swaps, and note the depth + +We choose a layout that requires two swaps to execute the gates between qubits 3 and 0 and another two swaps to return the qubits to their initial positions. + +``` +[4]: +``` + +```python +from qiskit.transpiler import generate_preset_pass_manager + +pass_manager = generate_preset_pass_manager( + optimization_level=1, backend=backend, initial_layout=[0, 1, 2, 3] +) + +transpiled_qc = pass_manager.run(circuit) +print(f"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}") +``` + +```python +Transpiled circuit depth: 30 +``` + +```python +/tmp/ipykernel_3574/3423257129.py:8: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes. + print(f"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}") +``` + +``` +[5]: +``` + +```python +transpiled_qc.draw("mpl", scale=0.4, idle_wires=False, fold=-1) +``` + +``` +[5]: +``` + +![../\_images/tutorials\_02\_gate\_cutting\_to\_reduce\_circuit\_depth\_9\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_02_gate_cutting_to_reduce_circuit_depth_9_0.avif) + + + +### Replace distant gates with `TwoQubitQPDGate`s by specifying their indices + +`cut_gates` will replace the gates in the specified indices with `TwoQubitQPDGate`s and also return a list of `QPDBasis` instances – one for each gate decomposition. + +``` +[6]: +``` + +```python +from qiskit_addon_cutting import cut_gates + +# Find the indices of the distant gates +cut_indices = [ + i + for i, instruction in enumerate(circuit.data) + if {circuit.find_bit(q)[0] for q in instruction.qubits} == {0, 3} +] + +# Decompose distant CNOTs into TwoQubitQPDGate instances +qpd_circuit, bases = cut_gates(circuit, cut_indices) + +qpd_circuit.draw("mpl", scale=0.8) +``` + +``` +[6]: +``` + +![../\_images/tutorials\_02\_gate\_cutting\_to\_reduce\_circuit\_depth\_11\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.avif) + + + +### Generate the subexperiments to run on the backend + +`generate_cutting_experiments` accepts a circuit containing `TwoQubitQPDGate` instances and observables as a `PauliList`. + +To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates’ joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](explanation/index). + +**Note:** The `observables` kwarg to `generate_cutting_experiments` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value. + +``` +[7]: +``` + +```python +import numpy as np +from qiskit_addon_cutting import generate_cutting_experiments + +# Generate the subexperiments and sampling coefficients +subexperiments, coefficients = generate_cutting_experiments( + circuits=qpd_circuit, observables=observable.paulis, num_samples=np.inf +) +``` + + + +### Calculate the sampling overhead for the chosen cuts + +Here we cut three CNOT gates, resulting in a sampling overhead of $9^3$. + +For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](explanation/index). + +``` +[8]: +``` + +```python +print(f"Sampling overhead: {np.prod([basis.overhead for basis in bases])}") +``` + +```python +Sampling overhead: 729.0 +``` + + + +### Demonstrate that the QPD subexperiments will be shallower after cutting distant gates + +Here is an example of an arbitrarily chosen subexperiment generated from the QPD circuit. Its depth has been reduced by more than half. Many of these probabilistic subexperiments must be generated and evaluated in order to reconstruct an expectation value of the deeper circuit. + +``` +[9]: +``` + +```python +# Transpile the decomposed circuit to the same layout +transpiled_qpd_circuit = pass_manager.run(subexperiments[100]) + +print( + f"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}" +) +print( + f"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}" +) +transpiled_qpd_circuit.draw("mpl", scale=0.8, idle_wires=False, fold=-1) +``` + +```python +Original circuit depth after transpile: 30 +QPD subexperiment depth after transpile: 7 +``` + +```python +/tmp/ipykernel_3574/4252024425.py:5: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes. + f"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}" +/tmp/ipykernel_3574/4252024425.py:8: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes. + f"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}" +``` + +``` +[9]: +``` + +![../\_images/tutorials\_02\_gate\_cutting\_to\_reduce\_circuit\_depth\_17\_2.png](/docs/images/api/qiskit-addon-cutting/tutorials_02_gate_cutting_to_reduce_circuit_depth_17_2.avif) + + + +### Prepare subexperiments for the backend + +``` +[10]: +``` + +```python +# Transpile the subeperiments to the backend's instruction set architecture (ISA) +isa_subexperiments = pass_manager.run(subexperiments) +``` + + + +## Step 3: Execute + + + +### Run the subexperiments using the Qiskit Runtime Sampler primitive + +``` +[11]: +``` + +```python +from qiskit_ibm_runtime import SamplerV2 + +# Set up the Qiskit Runtime Sampler primitive. For a fake backend, this will use a local simulator. +sampler = SamplerV2(backend) + +# Submit the subexperiments +job = sampler.run(isa_subexperiments) +``` + +``` +[12]: +``` + +```python +# Retrieve the results +results = job.result() +``` + + + +## Step 4: Post-process + + + +### Reconstruct the expectation value + +Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable. + +``` +[13]: +``` + +```python +from qiskit_addon_cutting import reconstruct_expectation_values + +reconstructed_expval_terms = reconstruct_expectation_values( + results, + coefficients, + observable.paulis, +) +# Reconstruct final expectation value +reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs) +``` + + + +### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable + +``` +[14]: +``` + +```python +from qiskit_aer.primitives import EstimatorV2 + +estimator = EstimatorV2() +exact_expval = estimator.run([(circuit, observable)]).result()[0].data.evs +print(f"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}") +print(f"Exact expectation value: {np.round(exact_expval, 8)}") +print(f"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}") +print( + f"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}" +) +``` + +```python +Reconstructed expectation value: 0.51977539 +Exact expectation value: 0.50497603 +Error in estimation: 0.01479936 +Relative error in estimation: 0.02930705 +``` diff --git a/docs/addons/qiskit-addon-cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb index ce41f29bfc3..bfee58d2b3c 100644 --- a/docs/addons/qiskit-addon-cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb +++ b/docs/addons/qiskit-addon-cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb @@ -47,8 +47,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 1, @@ -187,8 +188,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 5, @@ -225,8 +227,9 @@ "outputs": [ { "data": { + "image/png": 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GTqfDp59+ipdeesmkC3sQFUd/4IxFrhZF5efoqMX/zX4Uk388hrlrziElPbfgPgetBkMeC8L3H4bBx8tZwSqJSAnMaOV1aOmDPUv6Yfw3h7HnWOGjeGr5umHKm63wyqAQhaojIqUwn5Wn1Wqw7IsuaBBUBd//egaJd++vtarVAE90r405E9sj0NdNwSqJyNK0ShdgKREREQBQ6EIeAPDRRx+hS5cuaNKkCerVq4dGjRph5syZClRIRJak02nx1bi2uBU+Aj99fP/IvsMrn8Dq//Rg04+ISEFtmnhj9+J+2Davd8G25TO64vLmoWz6EREpyMFBiylvtsLNHU9j/pSOBdsPLB+A9d89yqYfkYCEb/w5Ojpizpw5SEpKwt27d7Fo0SI4O6unIVD/mZ7ou/EL9NkwDZ4NaxU7pvfvnyFs5kgrV1Y+8dsW4vwHHXB+YidkXosodsyFj7vh+k9vWrkyEpWriw4Dut7/7NSopp7Pt6lEygFmABE9qFGwZ8G/u7Xxg4ODbe16ipTPADOaiO6rXMkRfToFFtyuWcNVwWrKR6SMZj6TnGxr76sUo0ePhiRJaN++vdKlmMzJ0w0NXnwMWwZPwf735qLdtJeLjAl4tDVy0zIVqM58eamJiNs6Fw1m7EbQmEWIXji2yJjkI5vg4GzahVWI7IFIOcAMICKRiJTPADOaiMQiUkYzn0luwjT+bJF3q3rQ/3sGUp4BKZdvoVI1j/xVr+/RaNDw5d44v2SrckWaIT3qMNyadoPGUYfKAQ2QlxIPyWgsuF8yGhG3+Ud4931LwSqJ1EWkHGAGEJFIRMpngBlNRGIRKaOZzyQ3Nv4U5OTphpy76QW3c9My4eThUnC73rBuuL75EAxZucU9XHUMqYlwdKtacFvr7A5Dxt2C2wk7l8IzbDC0uspKlEekSiLlADOAiEQiUj4DzGgiEotIGc18Jrmx8aegnLvpcPK4v5aCzs0ZOSkZAACHSjrUGdwZl1bvVKo8szm4VYUhPbngtjEzFQ4uVfL/nZOFxN2/onrPoodgE9kzkXKAGUBEIhEpnwFmNBGJRaSMZj6T3ByVLsCexR2PQsv3h0HjoIVbYA1kJ6YAkgQAcKtVA05VXPHo8klw8nSDcw1P1B3aFZd/261w1SVzDWmHW6umQjLkIfvONTh6VIdGm99bzr59FYb0ZFya1h95aYnITdIjYecyePV4QeGqiZQlUg4wA4hIJCLlM8CMJiKxiJTRzGeSGxt/CspJTkPUynD0WT8NkmTEwUkL4d+9JZw83XB1/T5s6v0hAMA3rAmCB3ZUbVDd4+heDdV7vYYLk7oAWi1qvfEj7h7fCkNqIqp1fQaNZh0FAKRG7ELi3tUMKyKIlQPMACISiUj5DDCjiUgsImU085nkxsafwi6u2IGLK3YU3E46e73IGP2BM9AfOGPNssrN+/GR8H78gculB7coMsa9WTe4N+tmvaKIVE6kHGAGEJFIRMpngBlNRGIRKaOZzyQnrvFHREREREREREQkIDb+iIiIiIiIiIiIBMRTfS3MPchX6RIAWKYOf5eyx1iLmmohKo1aMgAQKwfUUgcR2Ta1ZLRI+QyoqxYisk1qyWdArIxWSx2kLDb+LKzn0olKl2Axs9spXQGR7REpAwDmABGJRaSMZj4TkUhEymeAGU3qwlN9iYiIiIiIiIiIBMTGHxERERERERERkYDY+CMiIiIiIiIiIhIQG39EREREREREREQCYuOPiIiIiIiIiIhIQGz8ERERERERERERCYiNPyIiIiIiIiIiIgGx8UdERERERERERCQgNv6IiIiIiIiIiIgExMYfERERERERERGRgNj4IyIiIiIiIiIiEhAbf0RERERERERERAJi44+IiIiIiIiIiEhAbPwREREREREREREJiI0/IiIiIiIiIiIiATkqXYBowl/8CqnX9EqXAfcgX/RcOrFCc7x7CLiZYaGCKsjfBZjdTukqiOwPM00ezDQiqii15DMgVkYzn4nIEtSS0SLlM8CMLi82/iws9ZoeyRdjlC7DIm5mAFdSla6CiJTETCMiUieR8hlgRhORWETKaOaz7eOpvkRERERERERERAJi44+IiIiIiIiIiEhAbPwREREREREREREJiGv8EZFNikvMxD9HYnHsbDyOn00o2P7xD0fRtY0furT2Rb1aHgpWSERknyRJwpHIeBw8fQd7j91f2PyNz/ehfYsaaNO4Oro/4ofKlbgbSkRkbUkp2dh56BaOnU3A0bPxBdsnfX8EXVr7onNrXzQM9lSuQCKyOO5xEZFNOX42HrOWR+K3bVeRk2sscv+yPy9h2Z+XAAA9HvHDO882wRPdakGj0Vi7VCIiu5KdY8DC/7uAn9acw9nLyUXu37wvBpv35S90Xq1KJbwyMATjnmsCfx9XK1dKRGR/IqMSMXvFGazachmZWYYi96/46zJW/HUZANCplQ/GjGiMYY8Hcx+aSABs/Cmk03dvod7w7gAAo8GAzNvJiN0fieMzfkWGPlHh6sx37fuXkLBzaf4NrRa6qn5wb9YD/i98CScvf2WLIyFkZuVhyo/HMWt5JIxGyaTH7Dwci52HY9G/SyDmTemImjX4x6VcmGlE9u3omTi89MkenCmm4VecxLvZ+GZpBOb/fh6zJ7THywPr849LGYmU0cxnIvPk5BowY8EpfLHgJPIMpu1D7ztxG/tO3MaC3y9g0WedULumu8xV2jdmNMmNa/wpSH/wLNY0fw3r2ozCnre+g1fTIHSbP17pssrNrXFnNF8Si2YLbyB4/EpkXD2BKzOHKl0WCeB2QibCnv8T3yyNMLnp96BNe6LRfMh6HDp9R4bq6B5mGpF9WvR/F9D+uT9Nbvo9KCUtF69O3YvnJu1GbjFHcZPliJTRzGci0ySlZKPbK5vx2c8nTG76PSj80C00e2o9/jl8S4bq6EHMaJITG38KMubkITMuGRn6RNw+eA4XVuxAjbYNoHNzVrq0ctE4OkFX1RdOXv5wb9IF3o+NRPqFAzBkpChdGtmw+KQsdH91M05dKPnbLgcHDfx9XODv4wIHh+KPGElIzkavN7bi2ANrmZBlMdOI7M8v6y/itU/3wVDCH5Sm5DMArNx8Gc9O2gWDgc0/uYiU0cxnorKlpOXgsTe24sCpkr/4NiWjU9Nz0fetbdhzNFauUgnMaJIXG38q4exTFUH928OYZ4AkwE5vTsItJP27DtA65P9HVA6SJOHlyXtw7kpyqeN8qzsjZvsIxGwfAd/qJf9yTE3PxeB3dyAlLcfCldLDmGlE4jt5PgFvfL6v1DGm5jMA/LbtKr5dGmnJEqkEImU085moeG/N+BdHz5T+hbepGZ2VbcCQ8TsRl5hp6TKpGMxosjSu8acg3w5N8Oyl5dBotXB0rgQAiJy7EXmZ2QCAbgvG49buU7i4YgcAoFrTYHT5aSz+7DUBhuxcxeouSWrkLpwY7gbJaISUk/9LwWfgeDhUzl9XLenAesSu+azQY7KizyLwte/h3WeU1esl9Vux6RI27Ym26Jw3YtMxYdZhzJvSyaLzEjMNYKaR/cjJNeClyXvKdepYaab8dBwDutVCozqeFp2XxMpo5jNR6Tb+cx0rNl226JxxSVkY8+UBrPm6h0XnpXzMaGa0nGy68Xfq1ClMmTIFu3btgiRJ6NGjB+bOnYuQkBD069cPq1evVrrEUsUdj8K+sf+FQyUdgp7ogJqdm+PEzFUF9x+evBh9NkzD9c2HkJ2UhrCvXsehjxap7oN9j2tIOwSNWwopJwtJ+9Yi5dQO1Hx2esH9VcMGoWrYoILbyQf/wM3lH8Grx4tKlEsql5trxIffHZVl7vnrLuDd55uiYbCnLPOXV7Q+DfPXXcDB03eQZ5AQUtsDI4c0ROvG1ZUuzSTMNGYa2Y8Vmy6XugRDeWXnGPDRnKNY/92jFp+7IgwGI7buj8HyTZcQG5cJV2dH9OsSiOf714OHm5PS5ZlEpIxmPhOVzGiUMGHWEVnmXvv3Vbz3/B20a15DlvnLSx+fgYX/dwF7jumRk2tEnQB3vP5UA7RvXsNmLhzFjGZGy8lmG3/h4eHo378/ateujU8++QTOzs5YsmQJ+vTpg7S0NLRs2VLpEstkyMpB6jU9AODk12vgHuSLdl+8in/f/xkAkKFPxJl5m9Bm8vOIP3EJd6/EInZfhJIll0rr5IzKfvUAAM61myJbfxnR899G7TELiozNiY/BjXlvod7ULdBWcrF2qWQDNvxzHbFxGbLN//Pa8/juw/ayzW8OSZIw9afj+GL+SRgl4N7+ya4jsZi/7gIGdK2FX7/qCndXdf9xyUxjppF9kCQJP64+K9v8G3fdQIw+HQG+6rgS++XoFAx4e3vBshMaDSBJwJZ9Mfhw9hEsnd4FT/UKVrZIE4iU0cxnopLtPHwLF6/flW3+uWvPq6bxJ0kSvl0aiUnfH0GeQYIGADTA7qN6LP4jCj0e8cNv3/ZEtSqVlC61TMxoZrScbHKNv7i4OAwfPhyhoaE4ceIEJkyYgDFjxiA8PBw3btwAAJto/D3s5DdrUG94d3i1qFuw7fzirfBsEIhmYwbiyGdLFazOfH4jPkV8+GKkRxU+aksyGnF19nPwfWoiXIKaK1Qdqd3SjVGyzr9806VyXSFYDp/NPYFp8/KbfkD+H5TSA6X9ufsGBr8bjrw821rjg5lGJKazl5Nx/FyCbPMbjRJ+3XxJtvnNoY/PQPdXNxdaa/bBfM7IysOw93firz03rF9cBYmU0cxnovvk3ode8/cVZGXnyfocpvp+xRlMmHW4YNkJCYUzeufhWPQd/bdq6jUHM5osySYbfzNnzkRSUhIWL14MZ+f7i5BWqVIFoaGhAGyz8Zd6VY/o7UcROnHE/Y2ShAvLtiMm/DiyE2zrKjiVa9aHZ9sBuLXi40LbY9dOh4OzB2r0f1uhykjtJEnCoYg4WZ8j8W42Lkcr/5m6dScdXyw4Wea4HQdvYeMu2/rDkplGJCa58xkADkeo4wrs3yyJQLQ+vcT7JSn/D813/3MIkqSOL5NMJVJGM5+J7pM7o7OyDYiISpL1OUyRnJKNj+YcRVkn8h6KiMOqLVesUpMlMaPJkmzyVN/Vq1ejc+fOCAkJKfZ+Hx8f+Pr6AgDWrl2LOXPm4OTJk6hevTquXbtm1nPl5eVBr9ebPD43t2LfJkT+tBH9/vwCvmFNoD9wJn+j0QjJzCOTcnPzEBMTU6FacnN9AOgqNIfPoAm4MLEjUiN2wb1ZN6Sd24+EHYvQaNZxM2vJRUzM7QrVYq6Kvpa2yhLvnYqKjc9CXFJWoW0ODpoSrzbm98B2vxLG6OMzYXhoEfpt+y7C2cGvgtVWzHcrL5m8OP7sZSfwSEPrxjYzrTBbzjTR2GNGqyGfAWDPkWuFblc0n4GiGX0k8rbiP2tmtgELfj8PDfKbeyWRJCDqRgrW/HUanVp6Was8i3wGRMpo5rN62GM+A+rI6LSMPERdL9wQkmMfOvzfS/DzzK5gtRXzy4bryMw2lDlOA+C75afQq411TyNVS0arIZ8BZrQl+fr6wtHRvL8JNZKNfT2p1+vh5+eH9957D99++22h+4xGI/z8/NCqVSts3boVALB9+3YkJCTg9u3bmD17ttmNv5iYGAQGBpo8frpXL/jrPMx6jrLUG9YNXi3q4tDHi0x+zM3cFHySsL1Cz9v4h0g412pSoTkelJeWjHPvhSJozCK4N+9u1mMzb5zB2bebWqwWU8jxWtoCS7x3KqxyLaD+lEKb/H1cELN9RAkPKFtAr1W4efuhNQNvrQQSdpZ7Touo/Tbg3vz+wn6lMWQCZ637jRgzrWS2lmmisceMVkU+A0DgG4Bn24KbFc1noJiMNmYDZ96q0JwVVqkmEPK56eP1vwNxW+Sr5yFyfQZEyGjms7LsMZ8BlWS0rjrQ8KtCm2TZh779B3BnU7nntIiAV4GqYaaNlYxA5Bso/Wscy1JLRqstnwFmdEVFR0cjICDArMfY3BF/6en5p1sUd3WeDRs24M6dO4VO8+3VqxcA4I8//rBGeVSKuK1zkZsUi+hf3i203av7i/B58t0SHkX2yVpX31LBVb40Zqy4YM5Ykh0zjeyWVa6QaGP5DMBGV9AREvOZ7JbVrmBrYxmt0SI/o8s+QpDkx4y2Pps74i8nJwcuLi5o1aoVjhy5f5ny69evo2PHjrh58yZWrVqFp59+utDj/vjjD4wbN072U30PDPsK6VdNHy8X12BfhK2dWKE53j7rg+isih3SaymBlXPxQ2PrHtKrltfS2izx3qmoa7EZ6Pzq3kLbyjpN4ciqgQCAtiP+QGx8ZpExxZ2mMOu9phj6qL9lii6nzxecx4L118scp9EATet6YPMcE7/ZtBC1fA6YafQwtbw3rUkN+QwAH3wfiVV/3yy4XdF8BopmdI1qlXBsRTeL1VweKem5aPXMLuTkmnZhpXkftUDfTr4yV3Wfmj4DImU087ni1PTetCY1ZHRSSg6aP/1PoW1y7EN/9kZDvPJkbcsUXU7frbyMb1eUfSEojQao5euMfYu6WKGq+9TyORApnwFmNFC+U31t7og/JycnvPDCC1i8eDGefPJJ9OvXD9HR0ViwYAF8fHxw8+ZNi17Yw9HR0azDKHU6dfwv1enMq7vYOaIAZJU5zCp0Ol2Ffx7zn1Mdr6W1WeK9U1E1a0pwdz2I1PTcgm0Gg1T0NINixMZnmjQOAHp2qI+AgGrlrtMSxr/kZlLjT5KAd55tbrefA2YaPUwt701rUkM+A0DH1imFGn9y5HPrxt6q+Fmf7ReNxX+UfoVMjQaoUc0ZLz3VCk46BytVpq7PgEgZzXyuODW9N61JDRkdACDA5zBibt+/KJEcGd2tXR0EBCi7Tva7L1bFdysvw1DGeneSBIwZ0Yz70BWZQyX5DDCjy8smz0mYM2cORo4ciUOHDmH8+PE4dOgQ1q9fj5o1a8LFxaXEi34QkW3QajVo3bi6rM/hXNkBjet4yvocpmgQ7Inn+9crc1zD4CoY0aeOFSoiIipdG5nzGQDaNJH/OUzxwcvN4ebiWOrZc5IETH3Tuk0/IqKSyJ2fWq0GrRpZ70JGJfH3ccXopxuVOa6WryteHcz+ANk3m2z8ubm5Yd68edDr9UhNTcW2bdsQFhaGyMhINGvWDFqtTf5YRPSAp3vL2+Qa8mgwHB3VkRXzp3bEoJ5FT5e493dmw+Aq+Pvn3nB1Ucch9kRk39o29Uawv7uszzH8cXV80dEw2BN//fcxeLjm529xDcBpY0IxanjZf3wSEVmD3PvQ/ToHwt3VSdbnMNWs99vhhQFFv0C/l9VBNd2wfX4fVPWoZOXKiNRFHX/1WkBycjJiYmKKnOZrMBiQlZWF3NxcSJKErKwsZGcre+lxIirbs/3qwsNNvkbXaBX9kVa5kiPWfdsT2+b1xmNh99ccDG3khSXTuuD4moGo5eemYIVERPdptRqMGtZQtvm7tvFFk3pVZZvfXF3a+CFq01B8Na4N6gXev0Ljc/3r4dS6QfhkZCsFqyMiKmxQz9rw8Sp+TT9LUNM+tKOjFkumd8HuX/qif9fAgu3N61fD/CkdEfF/gxESVEXBConUQZjGX0REBAAUafwtX74czs7OGDZsGG7cuAFnZ2c0aNBAgQqLV/+Znui78Qv02TANng1rFTum9++fIWzmSCtXVj7x2xbi/AcdcH5iJ2Reiyh2zIWPu+H6T29auTKyNW4uOnzwUnNZ5u7dMQDtmnvLMnd5abUa9Arzx6LPOhds++P7XnjxyfpwrqyONUJMwUwjsg8jhzSEfw0XWeae+qb6Gmne1Zzx4SstsHNh34JtX77TBs1DlF0n1lwiZTTzmah4TjoHTH6jpSxzd2hZA491UPbCeA/TaDTo0sYPcz/uWLBt038fw+tDGsLNhs6WESmfAWa02gjf+HvppZcgSVKh/8y9sq9cnDzd0ODFx7Bl8BTsf28u2k17uciYgEdbIzet+CvgqU1eaiLits5Fgxm7ETRmEaIXji0yJvnIJjg4y3t6EInjg5ebI9TCa4h4uOmwYGonaEpbsInKhZlGZD+quDth/tROFp939PBG6P5ITYvPS2JlNPOZqHSjhjVC1zaWvcp45UoOWPx5F2i13Ie2NJHyGWBGq5Ewjb/Ro0dDkiS0b99e6VJM5t2qHvT/noGUZ0DK5VuoVM2j8OIxGg0avtwb55dsVa5IM6RHHYZb027QOOpQOaAB8lLiIRmNBfdLRiPiNv8I775vKVgl2RKdTouVX3VD9aqVSx2nj89EQK9VCOi1Cvr4kn8harUaLJnWBQG+rpYulcBMI7I3fTsHYsJLzUodY2o+A0DbptUx8922liyRHiBSRjOfiUqn1Wqw7IuuZR6ZbU5Gz5vckafNykSkfAaY0WokTOPPFjl5uiHn7v1LreemZcLJ43441xvWDdc3H4IhK1eJ8sxmSE2Eo9v9NXm0zu4wZNwtuJ2wcyk8wwZDqyu9iUP0oAbBntgxvzdqVCv5fWMwSLh5OwM3b2fAYJCKHePooMGKGV0xqGeQTJUSM43I/sx8ty3GPdekxPtNyWcAeKSpN7b89LhNnZZla0TKaOYzUdlq+blh58K+CCzlC29TMlqjAeZP6YgXnqgvV6l2T6R8BpjRasTGn4Jy7qbDyeN+EOvcnJGTkgEAcKikQ53BnXFp9U6lyjObg1tVGNKTC24bM1Ph4JL/rZAxJwuJu39F9Z5FD1smKkuLBl44uvpJ9O4YUK7HNwiqgr1L+mNE37oWrowexEwjsj8ajQazJrTDsi+6wNPd/Ks8ajTAuOea4J9FfeHlyT8A5CRSRjOfiUwTElQFR1Y9icHl/OI72N8d4Qv64PUh8l3QicTKZ4AZrUZs/Cko7ngUfNo3gsZBC/cgX2QnpgBS/jctbrVqwKmKKx5dPgmtJz8H/56tUHdoV4UrLp1rSDukntkDyZCHrNhLcPSoDo02/y2WffsqDOnJuDStP2KWfoC7xzYjYecyhSsmWxLo64bNPz2G5TO6mryYes0aLpg2JhQn1g5E+xY1ZK6QmGnMNLJPGo0Gzw+oj7N/PIU3hjaEq7NpFyTq3TEAe5f0x+wP2sPFxMdQ+YmU0cxnItP5eDlj3aweWPdtD7RtWt2kx3hXrYxPRrbE6d8Hcd1VKxApnwFmtBpxL0tBOclpiFoZjj7rp0GSjDg4aSH8u7eEk6cbrq7fh029PwQA+IY1QfDAjrj8226FKy6do3s1VO/1Gi5M6gJotaj1xo+4e3wrDKmJqNb1GTSadRQAkBqxC4l7V8OrxwvKFkw2R6PR4Ln+9fBsv7r49+QdbN0fg2Nn43HuSjIysvLgpHNAsL8bWjeujq5tfNGvcy3odPx+w1qYacw0sm9+3i74eXJHzBzXFuu2X8XB03dw7GwCbidmwmCQUMXNCS0aVEPrxl546tFg1KvloXTJdkWkjGY+E5lHo9HgqV7BeKpXMI5ExmHz3mgcPROPM5eTkZ6ZC52jFrVr5u9Dd2rlgye710YlJwely7YbIuUzwIxWIzb+FHZxxQ5cXLGj4HbS2etFxugPnIH+wBlrllVu3o+PhPfjD1xiPLhFkTHuzbrBvVk36xVFwtFoNOjYygcdW/koXQo9hJlGRFXcnfDq4AZ4dXADpUuhh4iU0cxnovJp29QbbZt6K10GPUSkfAaY0WrDQ2GIiIiIiIiIiIgExMYfERERERERERGRgNj4IyIiIiIiIiIiEhDX+LMw9yBfpUsAYJk6/F0sUIiFqKkWInvCTJOHmmohItuklnwGxMpotdRBRLZNLRktUj4D6qrFlrDxZ2E9l05UugSLmd1O6QqISGnMNCIidRIpnwFmNBGJRaSMZj7bPp7qS0REREREREREJCA2/ohU6rE1U9Dpu7eULoOIiB7CfCYiUi9mNBFRYWz8ERERERERERERCYiNPyIZNXypNwbuno3nr63C8IhF6LbwfQDAkMM/ofm4pwqN7fDNm+j9+2cAgE7fvYWaXZqj3vDueCl2HV6KXQffsCZlPp/GQYsW7w3F4AP/xfPXVmHo8XloN/0VAIB3mwZ44cZq1OrzSMF43w5N8MKN1ajZrYWlfmQiIpvAfCYiUi9mNBGR5fDiHkQyafn+MDR5cwCOffErbu0+BUfXygjo0cqkxx6avBhutX2QeTsJhycvBgBkJ6eV+biOs0bDv0crHPlsKeKOXEBlLw94t2kAAIg7egEnv12Ljt+OQsLpK8jLykHn/76DM/M34dauU+X/QYmIbAzzmYhIvZjRRESWxcYfkQwcnSuh6egnceI/a3B+8daC7YkRV016fG5qBow5eTBk5SAzLtmkx7gH+aLesG7457VvcP2vgwCA1Ou3EXc8qmDM6Tnr4duhKbr8OBa5aZnI0Cfi+FerTP/BiIhsHPOZiEi9mNFERJbHU32JZODZIBCOzpVwa7f1vgX0ahYMAKU/pyRh79tzULVhLfi0b4Tdb86GlGewUoVERMpjPhMRqRczmojI8tj4I1KAZJQAjabQNq3OOgfgVmsaDEeXSnCo5ARX/+pWeU4iIlvBfCYiUi9mNBGR+dj4I5JB8sUY5GVmo2bX4hf8zYq/CxefqoW2VWsaXOi2MTcPGgfTP6IJ/zsFoqTnBABnb090+n4MTn//fzi/eAu6/PAOnDzdTH4OIiJbx3wmIlIvZjQRkeWx8Uckg7yMLJyZ9ydavj8UDV/qDY86fqjauDaavT0IAHBr72kEP9EBNbu2gEfdmmj72UtwCyj8zWHqjTvwal4H7rV9UKmaOzSODqU+Z+o1PS7/vgftv3oddZ7qDPfaPvBqUReNXutbMKbTD2/j7qWbODV7HY5OW4GsxBR0mj3a8v8DiIhUivlMRKRezGgiIsvjxT2IZHJi5mpkJaSg0at90PazF5FzNx23D54DAET89w+4BXij68/vwphnwIUlf+PanwfgEexX8PgzP29E1Ua18ET4N9C5OmPr4KnQHzhT6nPuG/cjWr43FKEfjoCzT1Vkxafg+l8HAABN3xqI6s3rYMOj70MyGiEZjdj95mwM+Ps/aPhSb5xfsrXUuYmIRMF8JiJSL2Y0EZFlaSRJkpQugkiN/ug6DskXY5Quw+o8QwIwcPd3Spdht2L06Qh8bDUAIHrb0wjwdVW4IiJ1sseMZj4rjxlNVDZ7zGeAGa005jNRyXiqLxERERERERERkYB4qi+RjWj2zmA0f2dQiff/Wu95K1ZDRET3MJ+JiNSLGU1E9o6NPyIbcWHZNlzb+K/SZRAR0UOYz0RE6sWMJiJ7x8afhYW/+BVSr+mVLgPuQb7ouXRiheZ49xBwM8NCBVWQvwswu53SVSgrJzkNOclpSpdBZLPUks+AWBnNfGY+E1mCWjJapHwGmNEAM5qootSSz4BYGW3NfGbjz8JSr+mFWcz2ZgZwJVXpKoiILEOkfAaY0UQkFpEymvlMRCIRKZ8B+8xoXtyDiIiIiIiIiIhIQGz8ERERERERERERCYiNPyIilZAkCdH6+2vQXLx+F3l5RgUrIiKiezIy83DmclLB7YTkLAWrISKieyRJwq076QW3z19NRm4u96GJ7uEaf0RECsrJNWB9+HUs2RCFw5FxSLybXXBfz9e3wLmyA1o28MLQx4Lx0pP1UdWjkoLVEhHZl6sxqfj5t3PYvDcG564mw2CQCu5rOewPBPq6onOoL0YOaYAurX2h0WgUrJaIyH7k5Rmxac8N/LI+CgdO30F80v0vY3q9sRWVnBzQPKQqnno0CK8MDIF3NWcFqyVSFht/Cun03VuoN7w7AMBoMCDzdjJi90fi+IxfkaFPVLg68137/iUk7Fyaf0Orha6qH9yb9YD/C1/Cyctf2eKIVEiSJCzbeAkffncEtxMySxyXmWXAgVN3cODUHXz8w1GMfbYJpr7ZCpUrMb7lJFJGM5+JzKePz8DYmQfx27arkKSSx0Xr07Fy82Ws3HwZzepXxc+TO6JDSx/rFWqHRMpngBlNVB7rtl3Fe98cQrQ+vcQx2TkGHImMx5HIeEz58TjeHNoQM95pA1cXnRUrtT8iZbRI+cxTfRWkP3gWa5q/hnVtRmHPW9/Bq2kQus0fr3RZ5ebWuDOaL4lFs4U3EDx+JTKunsCVmUOVLotIdRKSszDg7e14afKeUpt+D8vMMuCrRafR+ukNOH3Rtn5x2iKRMpr5TGS6/9txDU0G/R/W/l160+9hEVFJ6PTiJkz49jCXaZCZSPkMMKOJTJWSloPhE3Zi6Ps7S236PSwn14g5K8+i+ZD1OHT6jowVEiBWRouSz2z8KciYk4fMuGRk6BNx++A5XFixAzXaNoDOzTYPQ9Y4OkFX1RdOXv5wb9IF3o+NRPqFAzBkpChdGpFq3EnIRJeX/8Jfe6LLPcfZy8no/NImHDzFHRc5iZTRzGci0yxYdx5DxocXWnbBHJIEfLM0Ak9/8A/Xl5KRSPkMMKOJTJGcko2er2/B2r+vlnuOKzGp6P7aZoQfvGXByuhhImW0KPnMxp9KOPtURVD/9jDmGSAZbH9HMSfhFpL+XQdoHfL/IyJk5xjQZ/TfOHs5ucQxDg4a+Pu4wN/HBQ4OJa8VlZKWiz6j/8alG7b1S8dWiZTRzGei4m345zremLa/1KP8TM3o33dcw1sz/pWhSnqYSPkMMKOJipOXZ8STY3fg6Jn4EseYms+ZWQY8OXY7z56xEpEy2pbzmYtEKci3QxM8e2k5NFotHJ3zF+yPnLsReZn53zJ3WzAet3afwsUVOwAA1ZoGo8tPY/FnrwkwZOcqVndJUiN34cRwN0hGI6Sc/NMXfQaOh0NlVwBA0oH1iF3zWaHHZEWfReBr38O7zyir10tkbZ/NPYHj5xJKHeNb3Rkx20cAAAJ6rcLN2xkljk1OzcErU/Zg1y/9oNVyQXlLEymjmc9EpYtLzMTrn+4r89ReczJ6we8XMKBrLQzoVsuSpRLEymeAGU1UllnLIrHnmL7UMebkc3pmHl78ZDcO//okdDoeC2VpImW0KPls042/U6dOYcqUKdi1axckSUKPHj0wd+5chISEoF+/fli9erXSJZYq7ngU9o39Lxwq6RD0RAfU7NwcJ2auKrj/8OTF6LNhGq5vPoTspDSEffU6Dn20SHUfhntcQ9ohaNxSSDlZSNq3FimndqDms9ML7q8aNghVwwYV3E4++AduLv8IXj1eVKJcIqs6fTERMxeftvi8e4/fxvx15/HmsEYWn7uiJEnChWt3oY/PhJuLI1o28IKjo+3sXImU0cxnotKN/+Yw4h64IqSlvDFtP3o84qfKxeTTMnIREZWI3Fwj6gR4IMDXVemSTCZSPgPMaKLSXIlJwZSfjlt83pPnE/HtsghMfLWFxee2hKjrd3HzTgZcKjuiZcNqcNLZzhFmImW0KPlss42/8PBw9O/fH7Vr18Ynn3wCZ2dnLFmyBH369EFaWhpatmypdIllMmTlIPVa/jcXJ79eA/cgX7T74lX8+/7PAIAMfSLOzNuENpOfR/yJS7h7JRax+yKULLlUWidnVParBwBwrt0U2frLiJ7/NmqPWVBkbE58DG7Mewv1pm6BtpKLtUslsrrvfz0Do9GMVeLNMGt5JEYOaaiqo/5W/nUZs5ZF4NgDRzj613DBm8Ma4v0Xm9nEVYlFymjmM1HJbt1Jx8otl2WZOzYuA6u3XsGrgxvIMn95xMZl4IsFJ7F0YxTSMvIAABoN0KdTACa92gKdQn0VrrBsIuUzwIwmKs2Pq88hO8cgy9zf/3oG419opqqj/taHX8PXSyJw4IG1vGtUq4yRQxriw1eaw02FXyQ9TKSMFiWf1fMON0NcXByGDx+O0NBQnDhxAhMmTMCYMWMQHh6OGzduAIBNNP4edvKbNag3vDu8WtQt2HZ+8VZ4NghEszEDceSzpQpWZz6/EZ8iPnwx0qOOFtouGY24Ovs5+D41ES5BzRWqjsh6klKysXKzPH9UAkDU9RSEH1LPIsUTvzuCZyftwvHzhU9rvhWXgcn/PY7eo/5GZlaeQtWVn0gZzXwmum/B7xdgMMjzxQwA/LTmnGxzm+vazVS0e3Yjflx9rqDpB+RflGTz3hh0f3Uz1v59RcEKy0ekfAaY0UT3ZGblYfEfF2WbXx+fiT/+uS7b/Ob6cuEpDH43HAcfuvJwXFIWps8/iW6vbMbd1ByFqis/kTLaVvPZJht/M2fORFJSEhYvXgxn5/tXhqlSpQpCQ0MB2GbjL/WqHtHbjyJ04oj7GyUJF5ZtR0z4cWQn2NYi/pVr1odn2wG4teLjQttj106Hg7MHavR/W6HKiKxrzzE9srLl+abynr/3x8g6v6nW/n0FM3/JP6X54bWy7t3efVSP974+ZOXKKk6kjGY+E923Veb8PH4uAfEynEZsLkmSMGjcDkTr00scYzBKeG7Sbly8dteKlVWcSPkMMKOJ7jkcGYekFHkbXWrZh/57fww+mpPfTCppH/rY2Xi8OW2/lSurOJEy2lbz2SYbf6tXr0bnzp0REhJS7P0+Pj7w9fVFdnY2Xn/9ddSpUwfu7u4ICQnBDz/8YOVqzRP500b4d2sJ37Am9zcajZBkOkVQbj6DJiDl5DakRuwCAKSd24+EHYsQ9M5iResisqZjZ0u+ApnFnqOMi4ZYy+zlkTDlhOPFGy4iKSVb9nosTaSMZj4T5V8p8pQVruxojd8DZdlzTI+TF0r/WSUJyM0zYu5a9RylaCqR8hlgRhMB1tqHVj6fAeC7FWdMGvfbtquIKeULHLUSKaNtMZ/Vv8jSQ/R6PW7evInhw4cXuc9oNCIiIgKtWrUCAOTl5cHX1xfbtm1DnTp1cPr0aTz++OPw8fHBsGHDTHq+vLw86PWlX0HoQbm5pp2+tm/cj8Vujzt6AUv8hpj8fKXVERNTsW8vcnN9AJi2hkDQ2CXFbndr1AGtN+R/mPPSknF19vMIemcJHD28zKwlFzExt816TEWZ+lqKxhLvHSrsxJnYQrcdHDTwre5c7Fi/B7b7lTAGyD814cFT0yKjEhR/3a7HZuDg6TiTxmbnGLFo3Qk8/ViAzFXdZ85nWqSMFjGf85/X/jKa+Wx512MzkJlV+Ijsimb0w/kMAP8ev4YmtStYbAXNX2vaH5UAsGzjRYx/xnr5DHAf+mG2nNH2mM8AM1oOxyILL2Ujxz702UtJir9uiXdzTD763GCUMH/tCYwcHCRvUQ/gPnRhSuezr68vHB3Na+XZXOMvPT2/u63RFD2mZMOGDbhz507Bab6urq6YNm1awf0tW7bEE088gX379pnc+NPr9QgMDDS5vuleveCv8zB5vFwuXryIYWbUXZzGP0TCuVaTsgeaKG7rXOQmxSL6l3cLbffq/iJ8nny3hEflu3jxIgIfb2qxWkyhltfS2izx3qGH1H4b8Lh/xTDf6s6I2T6ilAfkO7JqYIn3BfRahZu3MwpuxyfcNSurZOEcDNT7uOxx/zNh4ueY8OoWGQsqTE2fabVltK3lM6Cu19NamM8yqFQTCPm80KaKZvTD+QwAn0/7Ep+/s7ncZVpErdGAR6v8K3mUITEl1+q/U9TymVZbPgO2l9FqeS2tjRktg4BXgaphBTfl2IfOyZMQGFgLgIJHnjn5AA2+MHn4tBnfYdrY32UsqDA1fabVltFK5HN0dDQCAsz7cs7mGn+BgYFwcHDA7t27C22/fv063n47/3zqktb3y83Nxd69e/H+++/LXaZFXVq7C5fW7lK6jArzGzIJfkMmKV0GkfVJ8q7vl/8cKvh23WjmGlYG5de8sgQRMpr5THbLGvkMqCejTWj6QZIAo+0txVAcEfIZYEaTHbPKPrQBijb9APP3oc0dr1IiZLSt5LNGkh5eOlL9XnnlFSxevBhPPPEE+vXrh+joaCxYsAA+Pj44ffo0zp07h4YNGxZ53BtvvIHjx49j//79cHJyMum5zD3V98Cwr5B+1fTxcnEN9kXY2okVmuPtsz6IzlLH5cIDK+fih8bWPU1BLa+ltVnivUOFTV90AfN+v1Zwu6zTFO59S9l2xB+Ijc8sdtzDpym0DKmCP79rb7Gay8NolNB15D5cj80osijxw7QaYP8vXRDgU/KpGJamps+0SBmtRD4D6no9rYX5bHmZ2QY0HhKOvAfytKIZXdypvvM/aYk+HXwsV3g5bN6nxxszTpk0dnB3P3w/wbpXJVTLZ1qkfAa4D21NzGjLm7P6Mr5edqngthz70HX8XbB7QWfLFV1O/cYeQERUikktyO0/dUDDIHfZa7pHTZ9pkTK6vPlsF6f6AsCcOXOg0+mwYcMG7Ny5E2FhYVi/fj0+//xzXLp0qdiLfrz33ns4cOAAdu7caXLTDwAcHR3NOoxSp1PH/1Kdzry6i50jCoBKvkzQ6XQV/nnMf051vJbWZon3DhXW7ZHsQo0/g0EqchpYcWLjM00aBwBhLf1U8bqNe64Zxv2n7Cv2DuhWC+1b17dCRfep6TMtUkYrkc/5z6ue19NamM/yaFKvKk49cNELOTL68c4NEODnVu4aLeHlp2ri84VRuBVX9pczE15pg4CAGtYp7H/U8pkWKZ8B7kNbEzPa8nq0R6HGnxz53K65rypet/deaImXJu8pc1zXNr54tFMjK1R0n5o+0yJltDXz2Sav6uvm5oZ58+ZBr9cjNTUV27ZtQ1hYGCIjI9GsWTNotYV/rHHjxmH79u0IDw9H9erVFaqaiOxVp1a+0GpNudZt+XVt4yfr/KZ66+nG6N+l9HU3avu5Ye4nHa1UERFR6bq29pV1/mB/dwT6usr6HKbQ6bRY+00PVHZyKPaM33vbPh3VCu1bWLfpR0RUnHbNvVHJyUHW51DLPvTzA+rhmb51AQAl/dXg41UZiz/vYr2iSBg22fgrTnJyMmJiYoqs7/fOO+9gx44d2LlzJ7y9vZUpjojsWoCva5nNsIqoUa0yBvZQ+HKR/+PoqMXvs3vig5ebwd218CH0Wq0GQ3oF4cCKAfDzdlGoQiKiwt4YWnR5GMvO36DYi9IpoUNLH+xZ0g8dWxY97di/hgvmTemIqaNCFaiMiKioqh6VMPzxYNnmd3PR4Zm+dWSb3xxarQbLvuiCT0e1gqdH4TMUNRpgQNdaOLjiCQQHWO8UXxKHMI2/iIgIAIUv7HH9+nX88MMPuHTpEoKDg+Hm5gY3Nzf06dNHoSqJyF6984zlru73sDeGNpT921BzOOkcMPPdRxAbPgL/nXT/SmwHVwzAb9/2ZNOPiFSlcd2qeLR9TVnmdqnsiFcGFl2CRkltmnhj79L+2PZz74Jty6Z3wbWtwzFyiLxNUCIic739TGPZ5n55YH24u5q+DJjcHBy0mDoqFDd3jMDPD5wds39pf2z8oReC/Nn0o/IRuvFXu3ZtSJKErKwspKWlFfy3ZcsWhaosqv4zPdF34xfos2EaPBvWKnZM798/Q9jMkVaurHzity3E+Q864PzETsi8FlHsmAsfd8P1n960cmVEyurZviZG9LH8N4r1a3tg4istLD6vJbi66PBk9/tHIvpVt72Gn0gZzXwmKtl/J4XJ8gXKzHfbwrua9S5iZI5GdTwL/t39kZpwcLCtPwtEymeAGU1UkjZNvDFqmOW/lPCv4YLPR6vzCGfnyo7o98DZQoG+yq4RWx4iZbQI+Wxbv+FLMXr0aEiShPbtlb2qpTmcPN3Q4MXHsGXwFOx/by7aTXu5yJiAR1sjN634KxKpTV5qIuK2zkWDGbsRNGYRoheOLTIm+cgmODjzmwqyTz9MCivzaDd9fCYCeq1CQK9V0JdwNbJ7HBw0WPx5F7g4q2fBXZGIlNHMZ6LSNQj2xJdj25Q5zpyM7vGIH0YPt+4C7PZCpHwGmNFEZZn5blvUKeMUV3PyWaMBFkztBE+PSpYsk/5HpIwWJZ+FafzZIu9W9aD/9wykPANSLt9CpWoeKLTaskaDhi/3xvklW5Ur0gzpUYfh1rQbNI46VA5ogLyUeEhGY8H9ktGIuM0/wrvvWwpWSaQcL8/K+Hvu4/DyLHkn497Vym7ezoDBUPJlF7VaDZZO74KOrYqu00SWIVJGM5+JyjbuuSZ4p4xTykzN6JYNq2HdrJ6yX9jJXomUzwAzmqgs7q5O+Pvn3qV+gW5qPgPAjx91QJ/O8q2/be9EymhR8pmNPwU5eboh5256we3ctEw4edwPs3rDuuH65kMwZOUqUZ7ZDKmJcHSrWnBb6+wOQ8bdgtsJO5fCM2wwtLrKSpRHpArNQqph9y/9UL+2R7nn8HDTYd23PfBsv3oWrIweJlJGM5+JyqbRaPDdh+3x6ahWFWrY9QqriX8W9kVVHkkiG5HyGWBGE5miXi0P7FvaD83qVy17cAlcKjti2RddMIpHY8tKpIwWJZ/Z+FNQzt10OHm4FtzWuTkjJyUDAOBQSYc6gzvj0uqdSpVnNge3qjCkJxfcNmamwsGlSv6/c7KQuPtXVO9Z9DBfInvTpF5VnFw7CO+90NTsPy77dArAmf97CoN6BslTHBUQKaOZz0Sm0Wg0mDoqFAeWD0Djup5mPdbdVYefJ3fE3z/35uljMhMpnwFmNJGp6gR44OjqJzH5jZZwdDRvH7prG1+c/n0Qnh9QX6bq6B6RMlqUfObCUAqKOx6Flu8Pg8ZBC7fAGshOTAGk/MOS3WrVgFMVVzy6fBKcPN3gXMMTdYd2xeXfditcdclcQ9rh1qqpkAx5yL5zDY4e1aHR5veWs29fhSE9GZem9UdeWiJyk/RI2LkMXj1eULhqImW4ODvi2/fb4Z1nGmPebxewZGMUYuMyih3r6uyIIb2C8dbTjdC2qbeVK7VfImU085nIPI8088bpdYOweW8Mflx9FuGHbyEvr/hTx5rU9cTIIQ3x4hP1UcVdPVeHFJlI+Qwwo4nM4aRzwOdvtcabQxtiwe8XsGj9RUTr04sd61zZAQO718bo4Y3QsZUPNBouv2ANImW0KPnMxp+CcpLTELUyHH3WT4MkGXFw0kL4d28JJ083XF2/D5t6fwgA8A1rguCBHVX7YbjH0b0aqvd6DRcmdQG0WtR640fcPb4VhtREVOv6DBrNOgoASI3YhcS9q1X5gSCytto13TFjbBt88U5rxNxOx7Gz8biTmAWDQYKnuxNaNvRCSG0Pm7vaoghEymjmM5H5HBy0GNCtFgZ0q4Ws7DxERCXhzOUkZGTmQeeoRXCAO0IbVUe1Kjy6z9pEymeAGU1UHjVruGLqqFBMHRWKW3fScexsAmLj89f383DToUVINTQM9oSjI/ehrU2kjBYln9n4U9jFFTtwccWOgttJZ68XGaM/cAb6A2esWVa5eT8+Et6PP3BJ7uAWRca4N+sG92bdrFcUkQ3QaDQI9HVDoK+b0qXQA0TKaOYzUflVruSItk29edS1ioiUzwAzmqgiatZwRc0armUPJKsRKaNFyGe2v4mIiIiIiIiIiATExh8REREREREREZGAeKqvhbkH+SpdAgDL1OHvUvYYa1FTLURkm9SSz4BYGa2WOojItqklo0XKZ0BdtRCRbVJLPgNiZbQ162Djz8J6Lp2odAkWM7ud0hUQEVmOSPkMMKOJSCwiZTTzmYhEIlI+A/aZ0TzVl4iIiIiIiIiISEBs/BEREREREREREQmIjT8iIiIiIiIiIiIBsfFHREREREREREQkIDb+iIiIiIiIiIiIBMTGHxERERERERERkYDY+CMiIiIiIiIiIhIQG39EREREREREREQCYuOPiIiIiIiIiIhIQGz8ERERERERERERCYiNPyIiIiIiIiIiIgGx8UdERERERERERCQgNv6IiIiIiIiIiIgExMYfERERERERERGRgNj4IyIiIiIiIiIiEpCj0gWIJvzFr5B6Ta90GXAP8kXPpRMrNMe7h4CbGRYqqIL8XYDZ7ZSugohsmVryGRAro5nPRGQJaslokfIZYEYTUcWpJZ8BsTLamvnMxp+FpV7TI/lijNJlWMTNDOBKqtJVEBFZhkj5DDCjiUgsImU085mIRCJSPgP2mdE81ZeIiIiIiIiIiEhAbPwREREREREREREJiI0/IiIiIiIiIiIiAXGNPyIisqiE5CzsOhKLY2cTcOxsfMH2yT8eQ9c2vugc6ou6gR4KVkhEZJ8kScLxcwk4ePoO9hy7v1D7qOn70b55DbRpUh1d2/iiciX+iUBEZG3JKdnYdTR/H/romfv70B/NOYqubXzRqZUPGgR7Klcg2Sz+ViciIos4eT4Bs5dHYs3fV5GdYyhy/5INUViyIQoA8Gj7mnh7RGMM6FYLGo3G2qUSEdmVnFwDfll/ET+tOYeIqKQi92/aE41Ne6IBAF6elfDKwBCMfbYJ/H1crV0qEZHdOXclGbOXR+LXvy4jIyuvyP3LN13C8k2XAACdQ30wZkRjDH0smPvQZDI2/hTS6bu3UG94dwCA0WBA5u1kxO6PxPEZvyJDn6hwdea79v1LSNi5NP+GVgtdVT+4N+sB/xe+hJOXv7LFEZGssrLz8NnPJ/CfxREwGiWTHrPj4C3sOHgLA7rWwrwpHeHn7SJzleYRKaOZz0T27fjZeLw0eU+xDb/iJCRn4+slEZi37jxmT2iPlwfWV9UflyLlM8CMJrJnublGfPXLKUybdxK5eUaTHrP3+G3sPX4b89ddwKLPOqF2TXeZqzSPSBktUj5zjT8F6Q+exZrmr2Fdm1HY89Z38GoahG7zxytdVrm5Ne6M5kti0WzhDQSPX4mMqydwZeZQpcsiIhndSchExxc24atFp01u+j3oz9030Oyp/8PhiDgZqqsYkTKa+Uxkn5ZuiEK7Zzea3PR7UEpaLl6duhfPTdqN3FzT/iC1FpHyGWBGE9mj5JRs9HhtM6b8eNzkpt+Dwg/dQrOn1uOfw7dkqK5iRMpoUfKZjT8FGXPykBmXjAx9Im4fPIcLK3agRtsG0Lk5K11auWgcnaCr6gsnL3+4N+kC78dGIv3CARgyUpQujYhkkHg3G4+O3ILj5xJKHOPgoIG/jwv8fVzg4FD8ESMJyfnzPLgeoBqIlNHMZyL7s/zPKLw0eQ/yDMV/KWNKPgPAys2X8eykXTAY1NP8EymfAWY0kb1Jy8hF71F/Y9+J2yWOMSWjU9Nz0fetbdhzNFauUstFpIwWJZ/Z+FMJZ5+qCOrfHsY8AyQV7ViVV07CLST9uw7QOuT/R0RCkSQJr07dW+ZRJL7VnRGzfQRito+Ab/WSf9mnpudi0LgdSEnLsXSpFiFSRjOficR3+mIiXp26r9QxpuYzAPy27Sq+WRphyRItRqR8BpjRRPbg7S8P4FAZZ7uYmtFZ2QYMGb8TcYmZli7TIkTKaFvOZ67xpyDfDk3w7KXl0Gi1cHSuBACInLsReZnZAIBuC8bj1u5TuLhiBwCgWtNgdPlpLP7sNQGG7FzF6i5JauQunBjuBslohJSTHzw+A8fDoXL+wtBJB9Yjds1nhR6TFX0Wga99D+8+o6xeLxGV3+otV/DHzusWnTNan44Jsw5j3pROFp23vETKaOYzkf3IzTXi5cl7ynXqWGmm/HgcA7rWQuO6VS06b3mIlM8AM5rInvy150bBxe4sJS4pC2O+PIA1X/ew6LzlJVJGi5LPNt34O3XqFKZMmYJdu3ZBkiT06NEDc+fORUhICPr164fVq1crXWKp4o5HYd/Y/8Khkg5BT3RAzc7NcWLmqoL7D09ejD4bpuH65kPITkpD2Fev49BHi1T3YbjHNaQdgsYthZSThaR9a5FyagdqPju94P6qYYNQNWxQwe3kg3/g5vKP4NXjRSXKJaJyyssz4oPZR2SZe/66C3j3+aZoGOwpy/zmECmjmc9E9mPVlsulLsFQXjm5Rnz8wzGs/+5Ri89tLpHyGWBGE9kLSZIwYZY8+9Br/76K956/g3bNa8gyvzlEymhR8tlmT/UNDw9H+/btceHCBXzyySeYMWMGYmJi0KdPH6SlpaFly5ZKl1gmQ1YOUq/pkXwhGie/XoPU6Dto98WrBfdn6BNxZt4mtJn8PBo83wt3r8Qidp86T7MAAK2TMyr71YNz7aao+eznqOQTjOj5bxc7Nic+BjfmvYXgCauhraSuq3mawr9HKzyx/Ws8f20Vhhz+CY3f6K90SURWs2nPDcTcTpdt/p/XnpdtbnOIlNHMZyL78dOac7LNvXHXDcTo5ct/U4mUzwAzmshe7D6qx7krybLNP5f70BYnSj7bZOMvLi4Ow4cPR2hoKE6cOIEJEyZgzJgxCA8Px40bNwDAJhp/Dzv5zRrUG94dXi3qFmw7v3grPBsEotmYgTjy2VIFqzOf34hPER++GOlRRwttl4xGXJ39HHyfmgiXoOYKVVd+Xi3qoueSDxHzzwls7PU+Tn6zFq0nPoMGLzymdGlEVrF04yVZ51/2Z1S5rhAsN5EymvlMJKYLV5PLXDeqIoxGCSv+kvd3QHmIlM8AM5pIVEs3WvYU34et3noFWdl5sj5HeYiU0baazzbZ+Js5cyaSkpKwePFiODvfX+iySpUqCA0NBWCbjb/Uq3pEbz+K0Ikj7m+UJFxYth0x4ceRnWBbV46pXLM+PNsOwK0VHxfaHrt2OhycPVCjf/GdcrVrMrI/4k9exvEZK3E36iYurd2Fc79sQbMxA5Uujcgq5PyjEgCSUnJw6Yb68k6kjGY+E4lJ7nwGgMOR8j+HuUTKZ4AZTSQquTM6O8eA0xdLv/CeEkTKaFvNZ5tc42/16tXo3LkzQkJCir3fx8cHvr6+AIDRo0fjzz//xN27d+Hu7o6hQ4fiP//5D5ycnEx6rry8POj1epNry82tWIc98qeN6PfnF/ANawL9gTP5G41GSGYe/ZKbm4eYmJgK1ZKb6wNAV6E5fAZNwIWJHZEasQvuzboh7dx+JOxYhEazjptZSy5iYkq+3LkcSnotazzSEFErwwttu/nPSTQd/SRc/KohIzbRGuXJxhLvHRLX7cRsxMZlFNrm4KAp8Wpjfg9s9ythjD4+EwZD4Yz7e+8FuDjWrGC1hVU0nwGxMtqW8zn/eYu+nsxnsne7D18rdLui+QwUzehDp2/L8j7kPnRhtpzR3IcmKio9Mw/nryYX2ibHPvT2/VGoWTW7YsU+hPvQhSmdz76+vnB0NK+VZ3ONP71ej5s3b2L48OFF7jMajYiIiECrVq0Kto0ZMwZff/01XF1dER8fj6FDh2LGjBn49NNPTX6+wMBAk+ub7tUL/jqPMsftG/djsdvjjl7AEr8hJj9fSS5evIhhZtRdnMY/RMK5VhOTxgaNXVLsdrdGHdB6Q/6HOS8tGVdnP4+gd5bA0cPLrFouXryIwMebmvWYiirptXSu4YnMuORC2zLvJP3vvqo2v9NiifcOCaxyIFB/aqFNvtWdEbN9RAkPuO/IqoHFbg/otQo3bxduJr7z7sd4J2FHucssjqn5DIiV0SLmM1D868l8JrsXOBLwfKTgZkXzGSia0bdup5q1b2wq7kMXZssZzX1oomLovICGMwttkmMf+pOpM/HJqI3lLrM43IcuTOl8jo6ORkBAgFmPsbnGX3p6/oLCGo2myH0bNmzAnTt3Cp3m27hx44J/S5IErVaLqCh5z62n4sVtnYvcpFhE//Juoe1e3V+Ez5PvlvAoIrJLxWQ8yYf5TCQKa2Qn89namNFEIrBWdjKjrclW8lkjSZL6VlAvRU5ODlxcXNCqVSscOXL/UtjXr19Hx44dcfPmTaxatQpPP/10wX1fffUVpk+fjvT0dHh5eWHLli1o27atSc9n7qm+B4Z9hfSrpo+Xi2uwL8LWTqzQHG+f9UF0VsVOU7CUwMq5+KGxdU9TKOm1HHJkLqJWhuPU7HUF23w7NkXvdZ9ibehIm/+20hLvHRLXDX0GOr6yt9C2sk5TuPctZdsRfyA2PrPImOJOU/hmXFMMf8zfMkX/j1ryGRAro5XIZ6D415P5TPZu4g9n8OuW+6dAVTSfgaIZXd3TCSdWdrdc0f+jlowWKZ8B7kNbEzOaSpOcmotmw3cW2ibHPvSU1xvg9UFBFqn5HrXkMyBWRpc3n+3iVF8nJye88MILWLx4MZ588kn069cP0dHRWLBgAXx8fHDz5s0iF/aYOHEiJk6ciHPnzuHXX3+Fn5+fyc/n6Oho1mGUOp06/pfqdObVXewcUQCyLFNPRel0ugr/POY/Z/Gv5Z3D51GzW8tCOy3+3VsiLfqOze+wAJZ575C4/P0lVHE/hLupOQXbDAapyGkGxYmNzzRpHAD0CKuLgIDq5a6zOGrJZ0CsjFYin/Oft+jryXwme9cxNLVQ40+OfA5t7C3L+1AtGS1SPgPch7YmZjSVJgBALT9X3IhNL9gmR0Z3a1cHAQGWXSdbLfkMiJXR1sxnm7yq75w5czBy5EgcOnQI48ePx6FDh7B+/XrUrFkTLi4uJV70o1GjRmjRogWef/55K1dMIjkzfxO8W9VDq4kjUKVeTdQd2hWNXumDiP/+oXRpRLLTaDRo09iyDbmHVXJyQJN6VWV9DhIT85nsXdum8uYzANl/B5C4mNFk79o28ZZ1fo0GCG3EjKai1NO6NYObmxvmzZuHefPmFdoeGRmJZs2aQastuZ+Zm5uLixcvyl0iCSzh1GXsfPk/CJ30DJq++QQy45JxfOYqXFi2TenSiKxi+OPBCD90S7b5n3o0CE46B9nmJ3Exn8nehTaqjrqB7rgcnSrbcwzvXUe2uUlszGiyd8N718HvO67JNn+fTgGo4u4k2/xku2yy8Vec5ORkxMTEoF+/fgXb7t69i/Xr12PgwIGoUqUKIiIiMH36dDz++OMKVkoiiAk/jphw8y7XTSSKZ/rWxfuzDiMlLVeW+UcPbyTLvGQfmM9kz7RaDUYNa4T3vz0sy/ydWvmgeUg1WeYm+8CMJns2sHtt+FZ3hr6ENVUrivvQVBKbPNW3OBEREQBQaH0/jUaDFStWoE6dOnB3d8fAgQPRt29f/PDDDwpVWVT9Z3qi78Yv0GfDNHg2rFXsmN6/f4awmSOtXFn5xG9biPMfdMD5iZ2QeS2i2DEXPu6G6z+9aeXKiMhSXF10+PDl5rLM3SusJjq0rCHL3OUhUkYzn4nsw2uDG8C/hossc386KlSWectDpHwGmNFE9kCn02LyyJayzN2umTd6d1TPGpMiZbQI+Sx048/DwwM7duxAYmIi0tLScOXKFXzzzTdwdXVVqMrCnDzd0ODFx7Bl8BTsf28u2k17uciYgEdbIzdNnm8ELC0vNRFxW+eiwYzdCBqzCNELxxYZk3xkExyc3RWojogs6YOXm6O1hdd5cnfVYeGnnaHRaCw6b3mJlNHMZyL7UcXdCQumdrL4vG8MbYie7S27YHx5iZTPADOayJ68OawRurU1/WKjpqjk5IAl07vAwUEd7R2RMlqUfFbHO8MCRo8eDUmS0L59e6VLMZl3q3rQ/3sGUp4BKZdvoVI1j/wVOe/RaNDw5d44v2SrckWaIT3qMNyadoPGUYfKAQ2QlxIPyWgsuF8yGhG3+Ud4931LwSqJyBIcHbVY+VU3VK9audRx+vhMBPRahYBeq0o9rUGjAX75rDNq+blZutRyEymjmc9E9qVP50B88HKzUseYms8AENrIC1+/19aSJVaISPkMMKOJ7IlWq8HS6V3KPDLbnIz+6eMOaBjsacEqK0akjBYln4Vp/NkiJ0835Ny9fznv3LRMOHncD4B6w7rh+uZDMGTJs46WpRlSE+Hodv9KnFpndxgy7hbcTti5FJ5hg6HVld4oICLbEBJUBTvm90aNaiV/pg0GCTdvZ+Dm7QwYDFKxYxwcNFj2RVcMeSxYrlLLRaSMZj4T2Z+vxrXFuOealHi/KfkMAK0bV8ffP/eGu6t6FowXKZ8BZjSRvanl54adC/si0LfkMxFNzegfPwrDK4NC5Ciz3ETKaFHymY0/BeXcTYeTx/0Pu87NGTkpGQAAh0o61BncGZdW71SqPLM5uFWFIT254LYxMxUOLlXy/52ThcTdv6J6z6KH+RKR7WrRwAtHVz+Jxzv4l+vx9Wt7YNeivniufz0LV1ZxImU085nI/mg0Gsya0A5Lp3eBZzmv8jhmRGPs+qVvmUd3W5tI+Qwwo4nsUUhQFRxZ9SQG9axdrsfX8nPFtnm9MfrpxhaurOJEymhR8pmNPwXFHY+CT/tG0Dho4R7ki+zEFEDK7+a71aoBpyqueHT5JLSe/Bz8e7ZC3aFdFa64dK4h7ZB6Zg8kQx6yYi/B0aM6NNr8t1j27aswpCfj0rT+iFn6Ae4e24yEncsUrpiILCHQ1w1b5j6OpdO7oFn9qmU/AICPlzOmvtkKJ9cOQqdQX5krLB+RMpr5TGSfNBoNXniiPs6sH4zXn2oAl8qOJj3u0fY1seuXvvhhUhjcXHQyV2k+kfIZYEYT2SsfL2f8Pqsn1n7Tw+S1s708K+HDV5oj4vfB6BVWvi/e5SZSRouSz6b99idZ5CSnIWplOPqsnwZJMuLgpIXw794STp5uuLp+Hzb1/hAA4BvWBMEDO+Lyb7sVrrh0ju7VUL3Xa7gwqQug1aLWGz/i7vGtMKQmolrXZ9Bo1lEAQGrELiTuXQ2vHi8oWzARWcy9Py6fH1AP+0/cxtb9MTh2NgFnryQhI8sAnaMWdQLc0bqRF7q09sWAbrXgpHNQuuxSiZTRzGci+1azhivmT+2Er997BL9tu4qDp+/g2Ll46OMzYTBIqOLuhBYh1dC6cXU89WgQQoKqKF1yqUTKZ4AZTWTPNBoNhj4WjKGPBeNwRBz+2hONY+fiEXkpCemZedA5alHbzxWtG1dHp1Y+GNijNipXUncbR6SMFiWfNZIklXzCOJntj67jkHwxRuky4BkSgIG7v6vQHMP+Aa6kWqaeiqrjDqztbt3nVMtraW2WeO8QqZGaPtMiZbQS+Qyo6/W0FuYziUwtn2mR8hngPrQ1MaNJVGr6TIuU0dbMZ57qS0REREREREREJCA2/oiIiIiIiIiIiATExh8REREREREREZGA1L0qpA1yD1LH1SktUYe/iwUKsRAlalHLa2lt9vpzk/jU9N4WKaOVqkNNr6e12OPPTPZDLe9vkfIZ4D60Ndnrz03iU9N7W6SMtmYdvLgHERERERERERGRgHiqLxERERERERERkYDY+CMiIiIiIiIiIhIQG39EREREREREREQCYuOPiIiIiIiIiIhIQGz8ERERERERERERCYiNPyIiIiIiIiIiIgGx8UdERERERERERCQgNv6IiIiIiIiIiIgExMYfERERERERERGRgNj4IyIiIiIiIiIiEhAbf0RERERERERERAJi44+IiIiIiIiIiEhAbPwREREREREREREJiI0/IiIiIiIiIiIiAbHxR0REREREREREJCA2/oiIiIiIiIiIiATExh8REREREREREZGA2PgjIiIiIiIiIiISEBt/REREREREREREAmLjj4iIiIiIiIiISEBs/BEREREREREREQmIjT8iIiIiIiIiIiIB/T/J0WUXMQ9swwAAAABJRU5ErkJggg==", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 6, @@ -367,8 +370,9 @@ }, { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 9, @@ -551,7 +555,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -565,9 +569,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.9.21" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction.mdx b/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction.mdx new file mode 100644 index 00000000000..1890da9f0b9 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction.mdx @@ -0,0 +1,385 @@ + + +# Wire Cutting Phrased as a Two-Qubit `Move` Instruction + +In this tutorial, we will reconstruct expectation values of a seven-qubit circuit by splitting it into two four-qubit circuits using wire cutting. + +These are the steps that we will take in this [Qiskit pattern](/docs/guides/intro-to-patterns): + +* **Step 1: Map problem to quantum circuits and operators**: + + * Map the hamiltonian onto a quantum circuit. + +* **Step 2: Optimize for target hardware** \[*Uses the cutting addon*]: + + * Cut the circuit and observable. + * Transpile the subexperiments for hardware. + +* **Step 3: Execute on target hardware**: + + * Run the subexperiments obtained in Step 2 using a `Sampler` primitive. + +* **Step 4: Post-process results** \[*Uses the cutting addon*]: + + * Combine the results of Step 3 to reconstruct the expectation value of the observable in question. + + + +## Step 1: Map + + + +### Create a circuit to cut + +First, we begin with a circuit inspired by Fig. 1(a) of [arXiv:2302.03366v1](https://arxiv.org/abs/2302.03366v1). + +``` +[1]: +``` + +```python +import numpy as np +from qiskit import QuantumCircuit + +qc_0 = QuantumCircuit(7) +for i in range(7): + qc_0.rx(np.pi / 4, i) +qc_0.cx(0, 3) +qc_0.cx(1, 3) +qc_0.cx(2, 3) +qc_0.cx(3, 4) +qc_0.cx(3, 5) +qc_0.cx(3, 6) +qc_0.cx(0, 3) +qc_0.cx(1, 3) +qc_0.cx(2, 3) +``` + +``` +[1]: +``` + +```python + +``` + +``` +[2]: +``` + +```python +qc_0.draw("mpl") +``` + +``` +[2]: +``` + +![../\_images/tutorials\_03\_wire\_cutting\_via\_move\_instruction\_3\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_03_wire_cutting_via_move_instruction_3_0.avif) + + + +### Specify an observable + +``` +[3]: +``` + +```python +from qiskit.quantum_info import SparsePauliOp + +observable = SparsePauliOp(["ZIIIIII", "IIIZIII", "IIIIIIZ"]) +``` + + + +## Step 2: Optimize + + + +### Create a new circuit where `Move` instructions have been placed at the desired cut locations + +Given the above circuit, we would like to place two wire cuts on the middle qubit line, so that the circuit can separate into two circuits of four qubits each. One way to do this is to manually place two-qubit `Move` instructions that move the state from one qubit wire to another. A `Move` instruction is conceptually equivalent to a reset operation on the second qubit, followed by a SWAP gate. The effect of this instruction is to transfer the state of the first (source) qubit to the second (detination) qubit, while discarding the incoming state of the second qubit. For this to work as intended, it is important that the second (destination) qubit share no entanglement with the remainder of the system; otherwise, the reset operation will cause the state of the remainder of the system to be partially collapsed. + +Here, we build a new circuit with one additional qubit and the `Move` operations in place. In this example, we are able to reuse a qubit: the source qubit of the first `Move` becomes the destination qubit of the second `Move` operation. + +Note: As an alternative to working directly with `Move` instructions, one may choose to mark wire cuts using a single-qubit `CutWire` instruction. The `cut_wires` function exists to transform `CutWire`s to `Move` instructions on newly allocated qubits. However, in contrast to the manual method, this automatic method does not allow for the re-use of qubit wires. See the `CutWire` [how-to guide](how-tos/how-to-specify-cut-wires) for details. + +``` +[4]: +``` + +```python +from qiskit_addon_cutting.instructions import Move + +qc_1 = QuantumCircuit(8) +for i in [*range(4), *range(5, 8)]: + qc_1.rx(np.pi / 4, i) +qc_1.cx(0, 3) +qc_1.cx(1, 3) +qc_1.cx(2, 3) +qc_1.append(Move(), [3, 4]) +qc_1.cx(4, 5) +qc_1.cx(4, 6) +qc_1.cx(4, 7) +qc_1.append(Move(), [4, 3]) +qc_1.cx(0, 3) +qc_1.cx(1, 3) +qc_1.cx(2, 3) + +qc_1.draw("mpl") +``` + +``` +[4]: +``` + +![../\_images/tutorials\_03\_wire\_cutting\_via\_move\_instruction\_7\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_03_wire_cutting_via_move_instruction_7_0.avif) + + + +### Create observable to go with the new circuit + +This observable corresponds with `observable`, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an “I” at index 4). Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character. + +``` +[5]: +``` + +```python +observable_expanded = SparsePauliOp(["ZIIIIIII", "IIIIZIII", "IIIIIIIZ"]) +``` + + + +### Separate the circuit and observables + +As in the previous tutorials, qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut. + +``` +[6]: +``` + +```python +from qiskit_addon_cutting import partition_problem + +partitioned_problem = partition_problem( + circuit=qc_1, partition_labels="AAAABBBB", observables=observable_expanded.paulis +) +subcircuits = partitioned_problem.subcircuits +subobservables = partitioned_problem.subobservables +bases = partitioned_problem.bases +``` + + + +### Visualize the decomposed problem + +``` +[7]: +``` + +```python +subobservables +``` + +``` +[7]: +``` + +```python +{'A': PauliList(['IIII', 'ZIII', 'IIIZ']), + 'B': PauliList(['ZIII', 'IIII', 'IIII'])} +``` + +``` +[8]: +``` + +```python +subcircuits["A"].draw("mpl") +``` + +``` +[8]: +``` + +![../\_images/tutorials\_03\_wire\_cutting\_via\_move\_instruction\_14\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_03_wire_cutting_via_move_instruction_14_0.avif) + +``` +[9]: +``` + +```python +subcircuits["B"].draw("mpl") +``` + +``` +[9]: +``` + +![../\_images/tutorials\_03\_wire\_cutting\_via\_move\_instruction\_15\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_03_wire_cutting_via_move_instruction_15_0.avif) + + + +### Calculate the sampling overhead for the chosen cuts + +Here we cut two wires, resulting in a sampling overhead of $4^4$. + +For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](explanation/index). + +``` +[10]: +``` + +```python +print(f"Sampling overhead: {np.prod([basis.overhead for basis in bases])}") +``` + +```python +Sampling overhead: 256.0 +``` + + + +### Generate the subexperiments to run on the backend + +`generate_cutting_experiments` accepts `circuits`/`observables` args as dictionaries mapping qubit partition labels to the respective `subcircuit`/`subobservables`. + +To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates’ joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](explanation/index). + +``` +[11]: +``` + +```python +from qiskit_addon_cutting import generate_cutting_experiments + +subexperiments, coefficients = generate_cutting_experiments( + circuits=subcircuits, observables=subobservables, num_samples=np.inf +) +``` + + + +### Choose a backend + +Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator). + +``` +[12]: +``` + +```python +from qiskit_ibm_runtime.fake_provider import FakeManilaV2 + +backend = FakeManilaV2() +``` + + + +### Prepare the subexperiments for the backend + +We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime. + +``` +[13]: +``` + +```python +from qiskit.transpiler import generate_preset_pass_manager + +# Transpile the subexperiments to ISA circuits +pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend) +isa_subexperiments = { + label: pass_manager.run(partition_subexpts) + for label, partition_subexpts in subexperiments.items() +} +``` + + + +## Step 3: Execute + + + +### Run the subexperiments using the Qiskit Runtime Sampler primitive + +``` +[14]: +``` + +```python +from qiskit_ibm_runtime import SamplerV2, Batch + +# Submit each partition's subexperiments to the Qiskit Runtime Sampler +# primitive, in a single batch so that the jobs will run back-to-back. +with Batch(backend=backend) as batch: + sampler = SamplerV2(mode=batch) + jobs = { + label: sampler.run(subsystem_subexpts, shots=2**12) + for label, subsystem_subexpts in isa_subexperiments.items() + } +``` + +``` +[15]: +``` + +```python +# Retrieve results +results = {label: job.result() for label, job in jobs.items()} +``` + + + +## Step 4: Post-process + + + +### Reconstruct the expectation value + +Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable. + +``` +[16]: +``` + +```python +from qiskit_addon_cutting import reconstruct_expectation_values + +reconstructed_expval_terms = reconstruct_expectation_values( + results, + coefficients, + subobservables, +) +reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs) +``` + + + +### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable + +``` +[17]: +``` + +```python +from qiskit_aer.primitives import EstimatorV2 + +estimator = EstimatorV2() +exact_expval = estimator.run([(qc_0, observable)]).result()[0].data.evs +print(f"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}") +print(f"Exact expectation value: {np.round(exact_expval, 8)}") +print(f"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}") +print( + f"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}" +) +``` + +```python +Reconstructed expectation value: 1.40369761 +Exact expectation value: 1.59099026 +Error in estimation: -0.18729265 +Relative error in estimation: -0.1177208 +``` diff --git a/docs/addons/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb index 9d4d19b1c87..8a7aee7a681 100644 --- a/docs/addons/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb +++ b/docs/addons/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb @@ -91,8 +91,9 @@ "outputs": [ { "data": { + "image/png": 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IJIyBnoiIiIhIwuRiF0CN02q1UFVWi11Gs8ntbSEIgthlEBEREbU4DPRmSlVZjQ2dnha7jGaLzlgPawc7scsgIiIianE45IaIiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMI4D70F8eoXghFb5jVYVlteidLMfGRsOoC0tb9Cq9aIVB0RERERGQMDvQXK3JKAnPhkQBBg7+mKzhMGove8KWgV2B5H5qwWuzwiIiIiMiAGegtUlJKFzM0J9T+nr9uNxxKWIuipoUievxHVRaUiVkdEREREhsQx9C2AqrIahckXIchkcOnQVuxyiIiIiMiAGOhbCOeOdUG++toNkSshIiIiIkNqEYFeqVQiJiYGnTt3hp2dHXx9fTFr1iyUl5fjueeegyAIWLFihdhlGozc3ga2bs6wdXeBa1c/9Pn4ebiHBqAw+SJKM/PFLo+IiIiIDMjix9CfOnUKI0eOhEKhgKOjI7p164a8vDwsW7YMGRkZKC4uBgD06NFD3EINqGfMJPSMmdRgWXbcURx780uRKiIxabVaHD1TgFWx53H2zxJU1ajh3soW44Z1xJQxgWjtYit2iUREZGSX8srw303p2Hc0D2UVtXC0l+OBnm3x8hPBCOrYSuzy6B5ZdKBXKpUYPXo0FAoFZs+ejblz58LZ2RkA8Omnn+L//u//IJfLIQgCwsLCRK7WcNK/24PsX45AZi1H665+6D59LBy93aGurqnfRmYjx+g9C5G1NQFnlm6pX/7Akumw83TFvuiPxCidDOxC9nU8+X/7kZxWdNu6gyev4u3lSZgzOQxzX+4JmUwQoUIiIjKm8opavPjBIWzcmQmNRttgXVKqEkvWp2L0QD9889EAdvBImEUPuZk5cyZycnIwY8YMLFq0qD7MA0BMTAzCw8OhUqnQsWNHuLi4iFipYZVmKpCfkILc+JM4u3Ibfps8Hx49OqHfghfrt9HUqHBw5nKEzhyH1t06AAD8RkTCZ3gEDr22UqzSyYDOZZSg3zO/NBrmb6qsUuP91Scxbd5BaLXaJrcjIiLpqahU4aGXdmFDXMZtYf5Wv/xxGQ9O3oGS0moTVkeGZLGBPi0tDbGxsfDw8MAnn3zS6Da9evUCAISHh9cvu/kFoHfv3rC1tYUgSL/XsjApHRmbDsB/bH94RnSpX150JhOpq7bjwWX/hIO3G/otfAnH3voSlVdLRKyWDKGmVo1R0/eg+HrzTs5rt17Aqtg0I1dFRESmNHP+ERw+VdCsbVMzruHZt/4wckVkLBYb6Ddu3AiNRoPo6Gg4OTk1uo29vT2AhoH+zz//xObNm+Hl5YXIyEiT1GoKpxdvgkalRs85ExsuX7IZGrUaUXsXQnHoLLK2HRKpQjKkLfuykZ2n24xGn3939o49OEREJB1Xiyrx7S9/6tRmx4ErOJ91zTgFkVFZbKCPj48HAAwePLjJbXJycgA0DPQDBgxAfn4+tm/fjmHDhhm3SBMqy1Yga9shtBsQhjZ9guuXa1VqFCamw869Ff6M3S9ihWRI//lB9972jCtl2HM41wjVEBGRqa3dko5alUbndrxaK00We1PspUuXAAAdOnRodL1KpcKhQ3W90bcGepnM8N9xIiIioFAodGpjrZVhLnobtI4zSzfDf2x/9JwzEbsffw8A0KZPMDpPHIy0tb+i9/tTsX34HKirau68o0YEBQahVtD9xGFq+a6vAbJWyFfkw8fHR+xyjEILAXmt3wUE3X+XJ0x9G60qfzNCVURE5qElfA4AgNLpacAmUOd2q77dh80rJhihIrobLy8vJCUl6dXWYgN9eXk5AKCysrLR9bGxsVAqlXB2doa/v79Ra1EoFMjN1a3n00awAnR8qKviSCrWeT/e5PrrF3Pxrc//htzIHezwwJLpOPHRBpz/ZjdGbn0f9735FBLnrtPtwADy8vNQo1Xr3M7knNWADNCo1Tr/nUiGzBZw0++L6Y3yGtzIt9D3hYgIaBmfAwAQoAVsdG9Wq5ZZ9vtioSw20Ht5eaGkpATJycno169fg3X5+fmYM2cOACAsLMzoN756eXnp3MZaKwOM3OEd+d6zuHG5AOfX7QIAHJy1AlH7FuHyzmO4elS3S27tvNtJo4feygoaADIrK3i3by92OUahhYA8rUavHnpnR2u4WOj7QkQEtIzPAQBQWgP6zFljbaVGGwt+X8yZPnnxJosN9MOGDUNaWhoWLFiA4cOHIygoCACQmJiIZ555BkqlEoBpHiilz+WT2ooqbOj0tBGqqdN+SE/4R/XHtqGz65eVXbqKEx9tQP/F07F9yGyoKpt/Krhw8QKsHeyMUapB+QzbiNyCCnh7eSPnbI7Y5RjNsBd24rdjeTq327ZhPgb3bmeEioiIzENL+Rz47JsUvP7ZcZ3bvfrCCCx49V0jVETGZLE3xcbExMDd3R1XrlxBSEgIQkNDERgYiN69eyMgIABDhgwB0HD8fEuSG38S33edjPJcZYPl59ftwpZ+M3QK82R+XpkYfPeN/qarfysMivQ2QjVERGRqU8YEws7WSqc2ggC8OKGrkSoiY7LYQO/j44OEhASMGjUKdnZ2yM7OhpubG1avXo24uDhcuHABQMsN9GTZogb5oau/bo/y/r9/GH/4GRERmYa7qx1eGN/l7hveYsJD/gjwsZwHbbYkFjvkBgCCg4OxY8eO25bfuHED2dnZkMlk6N69uwiVERmXXC5D3H8ewoNT4pBXUHHX7Wc/2x1TxgSZoDIiIjKVRbN7Iz37erOmJO7d3RNr5z1ogqrIGCy2h/5OUlNTodVqERgYCAcHh9vWb9q0CZs2bcK5c+ca/KzvVEJEYgjwccHR9aMxtE/TY+LdWtni8zl9sHC2YadIJSIi8dlYW+GX5cMx48lusLVpfPiN3ErAs6M7I/7LkXBysDZxhWQoFt1D35SUlBQATQ+3mTBhQqM/T548GevWrTNqbUSG5OvlhH1rRuJcRglW/3Qeq386j+paDexsrLDqnfsx8eEA2Nu1yNMAEVGLYGNtheVv9sN7L/fE1z9fxL6judh/PB81Kg2cHa1xftt4tGvjKHaZdI9aZA/93QK9Vqtt9D+GeZKqbp1aY+kb/eDRum4mIndXW0wZE8QwT0TUQri72uH1KaHY9cUIeLrVfRa4OFozzFsIBvoWpsOovug7/4UGyzpPHIwp+ZvgNyJSpKqIiIiISF8tsnsuPj5e7BJE4/dIH2T89Hv9z04+ngiKHoaCpHTxiiIiIiIivbXIQG/JbFwcMGb/YljZ2aAiTwmZrTWc/doiY9MfOPLGGrSN7IKDs1bUbSwIuP+zl3Hs32sROXeyuIUTERERkV4Y6C1MTWkFMrcmoLa8CmcWb0K7QeEImzkOh1//Au0GhqMgMR1alRoAEPLiaBQknkfRmUyRqyYiIiIifbXIMfSWzq27P4pTsgAA7mGdUHy27s9+IyJxaWfdY6Bdu/iiw6g+OL1ks2h1EhEREdG9Yw+9BXIL6Vgf4t3DAnBldyIAoN2gHkj6YD0AoG2fYDj5tsH4w8sBAPaerui38CXYt2mN9G/3iFM4EREREemMgd7COHi5AVotKhTFAAC34A44s3QzPHoG4vrFXKgqqgAA6d/uaRDcR2yeh3NrduDyrkRR6iYiIiIi/TDQWxi37v71vfMAUFNajq6TH0Z1cRku7zouYmVEREREZAwM9BYmZ98J5Ow7Uf/zjpFvAADG/L4Yu8fPbbLdrjusIyIiIiLzxUDfQmwb9KrYJRARERGREXCWGyIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJ4xh6MyW3t0V0xnqxy2g2ub2t2CUQERERtUgM9GZKEARYO9iJXQYRERERmTkOuSEiIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgmTi10ANU6r1UJVWS12Gc0mt7eFIAhil0FERETU4jDQmylVZTU2dHpa7DKaLTpjPawd7MQug4iIiKjF4ZAbIiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJ4zz0FsSrXwhGbJnXYFlteSVKM/ORsekA0tb+Cq1aI1J1RERERGQMDPQWKHNLAnLikwFBgL2nKzpPGIje86agVWB7HJmzWuzyiIiIiMiAGOgtUFFKFjI3J9T/nL5uNx5LWIqgp4Yief5GVBeVilgdERERERkSx9C3AKrKahQmX4Qgk8GlQ1uxyyEiIiIiA2KgbyGcO9YF+eprN0SuhIiIiIgMiUNuLJDc3ga2bs71Y+i7PPsQ3EMDUJh8EaWZ+WKXR0REREQG1CICvVKpxKeffootW7YgJycHnp6eGDduHD7++GPMnDkTX331FZYvX44ZM2aIXapB9IyZhJ4xkxosy447imNvfilSRUTiu5x/A7sP5eBaWQ0c7OWI6OaB3qGeEARB7NKIiMgEyitq8csfl5FXWAGZICDAxxkjH/CFtbX0B6xYfKA/deoURo4cCYVCAUdHR3Tr1g15eXlYtmwZMjIyUFxcDADo0aOHuIUaUPp3e5D9yxHIrOVo3dUP3aePhaO3O9TVNfXbyGzkGL1nIbK2JuDM0i31yx9YMh12nq7YF/2RGKUTGdzR0wWY/9Vp/PLHFWg02gbrenZ1x6zoEDwb1ZnBnojIQuUVlGP+V2fwzfaLKL1R22Cdl4c9XhjfBXOmhMLZ0UakCu+d9L+S3IFSqcTo0aOhUCgwe/Zs5OfnIzk5GQqFAgsWLEBcXBwSExMhCALCwsLELtdgSjMVyE9IQW78SZxduQ2/TZ4Pjx6d0G/Bi/XbaGpUODhzOUJnjkPrbh0AAH4jIuEzPAKHXlspVulEBvV9XAYenLID2/Zfvi3MA8DJ80WY8s4BTJt3sNH1REQkbWmZ19An+hcs//7cbWEeABTKSnyw+hQGTI1DQVGlCBUahkUH+pkzZyInJwczZszAokWL4OzsXL8uJiYG4eHhUKlU6NixI1xcXESs1LgKk9KRsekA/Mf2h2dEl/rlRWcykbpqOx5c9k84eLuh38KXcOytL1F5tUTEaokMY++RXDz77z+gUt89qH+55QLeXJpogqqIiMhUrhZVYsTLu5Bztfyu2546X4xH/7kHVdUqE1RmeBYb6NPS0hAbGwsPDw988sknjW7Tq1cvAEB4eHj9sk2bNmH8+PHo0KEDHBwc0LVrV7z99tu4cUPas8OcXrwJGpUaPedMbLh8yWZo1GpE7V0IxaGzyNp2SKQKiQxHq9UiZvFxqJsR5m/67NuzyFHc/aRPRETSsHR9Ki7nN/+8nnhWidhdWUasyHgsNtBv3LgRGo0G0dHRcHJyanQbe3t7AA0D/aJFi2BlZYWPP/4YO3fuxMsvv4xVq1ZhxIgR0Gg0JqndGMqyFcjadgjtBoShTZ/g+uValRqFiemwc2+FP2P3i1ghkeEcPVOAU+eLdWqjVmuxZnO6kSoiIiJTqq5R48stup/TV8amGaEa47PYQB8fHw8AGDx4cJPb5OTkAGgY6H/55Rf8+OOPiI6OxsCBAzFr1iysWLEChw4dwsGDB41btJGdWVrXG39rL32bPsHoPHEw0tb+it7vT4WVnXRvCCG6aePOTP3a7cowcCVERCSG+GN5KCyp0rnd8bOFyMwpNUJFxmWxs9xcunQJANChQ4dG16tUKhw6VDe85NZA7+npedu2ERERAIDc3Fy9aomIiIBCodCpjbVWhrnorVMbxZFUrPN+vMn11y/m4luf/4V5uYMdHlgyHSc+2oDz3+zGyK3v4743n0Li3HU6HRcAggKDUCuY/xWMfNfXAFkr5Cvy4ePjI3Y5JtdSXn+x4+OAbajO7f7MKrDo94WIWs558E5awntQbhMOOI3Tq22fBx6CrSrHwBXdnZeXF5KSkvRqa7GBvry8bsxUZWXjdyzHxsZCqVTC2dkZ/v7+d9zX/v11Q1GCg4PvuF1TFAqFzl8GbAQroK1eh2u2yPeexY3LBTi/bhcA4OCsFYjatwiXdx7D1aO6XXLKy89DjVZtjDINy1kNyACNWq33FzRJaymv36ccsNW9mVZTa9nvCxG1nPPgnbSE98DVB2h8xPVdKQsUQKW03heLDfReXl4oKSlBcnIy+vXr12Bdfn4+5syZAwAICwu74/zTubm5eOeddzBixAi956r38vLSuY21VgYYscO7/ZCe8I/qj21DZ9cvK7t0FSc+2oD+i6dj+5DZUFVWN3t/7bzbSaOH3soKGgAyKyt4t28vdjkm11Jef6ltJcr0aGejLYGnBb8vRNRyzoN30hLegxorLQoBQKsFdHnOiFYNL3cbWGlN/77okxdvErRarUVOvjxz5kwsX74cvr6+2LdvH4KCggAAiYmJeOaZZ5CZmYna2lpMnz4dK1asaHQfN27cwKBBg6BQKJCYmAhvb2+T1V9bUYUNnZ422fHuVXTGelg72Ildxl35DNuI3IIKtG/jgJx9T4pdjsm1lNd/RXEDHUf8qPPc8l+9/yCmjg0yUlVEZA5aynnwTlrCe6DVatHziZ9xOl23CRKeeNgfsQuHGKkq47HYm2JjYmLg7u6OK1euICQkBKGhoQgMDETv3r0REBCAIUPq/rJuHT9/q8rKSowePRpZWVnYs2ePScM8Ed0bXy8nRA3y06lNaxcbTHw4wEgVERGRKQmCgFcm6j5UWp825sBiA72Pjw8SEhIwatQo2NnZITs7G25ubli9ejXi4uJw4cIFAI0H+traWjz++ONISkrCzp070a1bN1OXT0T3aPkb/dC+jUOztpXJBHz70UA42FvsKEQiohbnH2ODMGqAb7O3nxUdgoER0uzAtehPr+DgYOzYseO25Tdu3EB2djZkMhm6d+/eYN3Nuet/++03/Prrr+jdW7eZZojIPPh4OeL3r0Zh5Cu78eflpqcgs7O1wvfzB+HRgbr16BMRkXmTy2X4adEQRL/5O7b+dumO2772bHcsfE26mc+iA31TUlNTodVqERQUBAeHhj1406dPx08//YQ33ngDDg4OOHr0aP26Tp06NTqtJRGZp85+Ljj902P4YVcm/vPDOSSnFdWvk8kEvDOtB14Y3wXt2zqKWCURERmLvZ0cmz4bivjjeVgZm4Zt+y83uL9q6thAvPxEMCK7SzvftchAn5KSAqDx4TY7d+4EAMyfPx/z589vsO7rr7/GlClTjF4fERmOg70c/3gsCFPHBqKguAph47egoLgKXu52eO+V+8Quj4iIjEwmEzCsb3sM69se18tq0GX0T7haXAVvT3t89f4AscszCIsdQ38ndwr02dnZ0Gq1jf5nCWG+w6i+6Dv/hQbLOk8cjCn5m+A3IlKkqoiMTxAEtHW3h7VcVv8zERG1LK2cbSD/63NAZkGfAwz0LYzfI31wedfx+p+dfDwRFD0MBUnpIlZFRERERPpqkUNu4uPjxS7BaGxcHDBm/2JY2dmgIk8Jma01nP3aImPTHzjyxhq0jeyCg7P+mndfEHD/Zy/j2L/XInLuZHELJyIiIiK9tMhAb8lqSiuQuTUBteVVOLN4E9oNCkfYzHE4/PoXaDcwHAWJ6dCq1ACAkBdHoyDxPIrOZIpcNRERERHpq0UOubF0bt39UZySBQBwD+uE4rN1f/YbEYlLO+uG27h28UWHUX1weslm0eokIiIionvHHnoL5BbSsT7Eu4cF4MruRABAu0E9kPTBegBA2z7BcPJtg/GHlwMA7D1d0W/hS7Bv0xrp3+4Rp3AiIiIi0hkDvYVx8HIDtFpUKIoBAG7BHXBm6WZ49AzE9Yu5UFVUAQDSv93TILiP2DwP59bswOVdiaLUTURERET6YaC3MG7d/et75wGgprQcXSc/jOrisgaz2xARERGRZWCgtzA5+04gZ9+J+p93jHwDADDm98XYPX5uk+123WEdEREREZkvBvoWYtugV8UugYiIiIiMgLPcEBERERFJGAM9EREREZGEMdATEREREUkYAz0RERERkYTxplgzJbe3RXTGerHLaDa5va3YJRARERG1SAz0ZkoQBFg72IldBhERERGZOQ65ISIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjC52AVQ47RaLVSV1WKX0Wxye1sIgiB2GUREREQtDgO9mVJVVmNDp6fFLqPZojPWw9rBTuwyiIiIiFocDrkhIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMM5yQxZLq9UiOa0ISalKnDinxPmsaygorgIAKEuq8NrCo+jVzQN9w9qgk6+LyNUaR15BOQ6fKsCJNCVOnS+uf/2FJVV49q0/EBHigV7dPNAn1BNyueV9v6+qVuHomUKcOKdEUqoSlxU3UFBcCQBQXqvCu/85gV7dPHB/eBt4utmLXC0RGUN2bhmOnC7AiXNKnL7Q8Dz43NwERHTzqD8XymSWN/3yjYra+teflKpEXmFF/Xmw6Fo1Plh9sv486OpiK3K1hqfVanE6vRiJZwtxIq0IaZnXGvwOzJp/BBEhdVkgsEMrkavVn6DVarViF0G3q62o4rSVerpWWo1vtl/EytjzuHDperPaDIzwwisTg/HYkI6wtpZ2sNVotNh7JBcrY9Ow48AVaDR3/yfevo0Dpj3eFS+M7wJvTwcTVGlcWTll+OKnNKzdegFF1+7+PAdruQzjh3XE9EnB6N+zLZ+pQCRxKpUGv/xxGStj07DvaF6z2nTydcZLE4IxdWwg3F3N4/PsXpzLKMGqH8/jm+0XUVZee9ft7e2s8NTITnhlYjDu6+ZhggqNq/RGDb7b8SdWxqbhXMa1ZrXp37MtXnkiGOOHd4StjZVxCzQwBnozpU+g9+oXghFb5jXcT3klSjPzkbHpANLW/gqtWmPIMuuZQ6DXarX476Z0zPn8eLNOXo0J8HHGV+8/iIER3gauzjRSLhRj6rsJOHFOqVd7a7kMb78Qjree7yHJLzYVlSq8vTwJSzekQt8z2+BIb6yd9yD8fZwNWxwRmcSR01cx9Z0EpGc3r0Pn7xzs5PhkVgRmPNlNkj32JaXVePXTY/hm+0W99/HY0A5Y+fb98PKQXgePVqvFt9v/xL8+PYprZTV67cPP2xFr5j6Ah+73MXB1xsNAb6buJdBnbklATnwyIAiw93RF5wkD0Tq4A9LX78WROauNUq/Ygf6K4gb+8W5Cs3ti7mbGk93w6auRsLeTxqg0jUaL+WtP471VJ1GruvcvbT26uuG7jwaie6CbAaozjcOnrmLyvw/gz8ul97wvR3s5Fr7WGy890ZW99UQSUV2jxr+Xn8Dn351t1pXJu3nwvrb45sOBkvpyvzPhCp6fdxB5BRX3vC+3VrZY+fb9mDgiwACVmYZCWYHn3zuIuANXDLK/F8Z3wedz+sDJwdog+zMmBnozdS+BPnHet0j9Ynv9crm9LR5LWAoHbzf8EPY8qovuPfD8nZiB/nzWNQyftgs5V8sNut+BEV7Yvmw4XJxsDLpfQ6ut1WDKOwfw/a8ZBt2vs6M1diwfjgESuFrxc3w2Js7Zj5paw16B+tfTIfh8Th+GeiIzV15Ri3Gv/YY9h3MNut+27vbY/cXDCO/ibtD9GsOXm9Mx7f2Del+dbMonsyLwxnPhht2pEWTmlGLYC7uQlVtm0P32CfXErysfhlsr876/QHrX1ElnqspqFCZfhCCTwaVDW7HLMaiMK6UY8vxOg4d5APgjSYFHZ+xBRaXK4Ps2FLVag8n//sPgYR4Ayspr8cj0PThy+qrB921IcQcuY8Lr8QYP8wCwZH0qZi86BvZ7EJmv6ho1Hnt1n8HDPABcLarEsGm7kJZ5zeD7NqR12y7ghXmGD/MA8ObSJCz8+ozhd2xAVxQ3MPi5Xw0e5gHgWEohRry8C2Xl+g3fMRUG+hbCuWNdkK++dkPkSgynukaNsbP2Ib/w3i8tNiUh+Sr+Of+I0fZ/rz79OgUbd2Yabf/llSqMmbkPhX/NiGBuMq6U4onX90OlMl7gXvxdKr7d/qfR9k9E92bO58ex94hhhls2RllShaiZe822cycptRDPv3fQqMeIWZyI3YdyjHoMfalUGox79Tdczjd8x95NiWeVeGGecd/je8VAb4Hk9jawdXOGrbsLXLv6oc/Hz8M9NACFyRdRmpkvdnkG8/4XJ3H2zxKd2iRujMKVvZOQuDGq2W2+2noBuw6a34ks9c8SvLcqWac2+rz+wpIqs/xSo9Fo8Y93E1BRpduHrD7vwaxPjyLXCFeBiOje/JGUj+Xfn9OpjT7ngD8vl+Lt5Um6lmd01TVqTPn3AajVunVq6PMePP/eQVzX8yZTY1q4LgVJqbpNBKHP64/dlYXNe7N0Lc9kWkSgVyqViImJQefOnWFnZwdfX1/MmjUL5eXleO655yAIAlasWCF2mQbTM2YSnkz9Gk+e/Qpj93+O4KkjkB13FPFTFohdmsGcOl+EBXpcAvTycIBPW0ed79x/Yd5B3KjQb+YcY9Bq68KsrsNM9H39sbuy8HN8tk5tjG31T+dx4IRC53b6vAfXy2rw8oeHdT6WudFotLheVoNrpdUGuWlQiiqrVCi6VoVaIwzRkgK1WoPi69W4UVEr+aFk1TVq/OPdBJ3b6XseXLoh1eyGIH605hRSmzkl4630eQ9yrpYjZvFxnY9lTOlZ13Tu2AL0/x14+cPDKCm9+1TIYpDGFB734NSpUxg5ciQUCgUcHR3RrVs35OXlYdmyZcjIyEBxcTEAoEePHuIWakDp3+1B9i9HILOWo3VXP3SfPhaO3u5QV//vm7XMRo7RexYia2sCzizdUr/8gSXTYefpin3RH4lRerMt+iZF5x6Je5FztRzf/5qBaY93Ndkx72T/8XwcP1to0mMu+OoMxg7paNJjNkWt1uBTE4/p/OWPyziXUYJunVqb9LiGkJ51Dat+PI912y/W97A5O1rjmUc745WJwQjpLL3XpIsbFbVY/9d81CkX/3dVb0hvb7wyMRhRgzpIcprW5tJqtThwQoGVsWnY8lt2/RA1Xy9HvPh4Vzw/vgvaukvvwWqb9mYhM8fwY6abotXW9QZvWWwe96KVV9RimY5XJ+7V1z9fxPvTe5nN78vi9alGuX+qKYUlVVi37SJefaa7yY7ZXJZ7BkNdz/zo0aOhUCgwe/Zs5OfnIzk5GQqFAgsWLEBcXBwSExMhCALCwsLELtdgSjMVyE9IQW78SZxduQ2/TZ4Pjx6d0G/Bi/XbaGpUODhzOUJnjkPrbh0AAH4jIuEzPAKHXlspVunNUlhciZ/2mP6y18rYNLPp0VoZm2byYx49U4hkPee3N7Rdh3KQnWf6+0FW/Xje5Me8F1qtFm8vS0LXMZuxdENqg8vlZeW1WBmbhu7jtuC1hUcttsf+yOmr8B/5I17+8HCDMA8A8cfz8fjsePR8Yisu51vO/UW3KiuvwajpezDoH7/ix91ZDe43uaIox79XnIDfQz/g+zjD31hvbGKcB7ftv4wchXkMv9u4M9PkQ2BqVRqs3ZJu0mM25XpZDdbvMP39Tat+TDPL86VFB/qZM2ciJycHM2bMwKJFi+Ds/L+5ZGNiYhAeHg6VSoWOHTvCxcVFxEqNqzApHRmbDsB/bH94RnSpX150JhOpq7bjwWX/hIO3G/otfAnH3voSlVd1G5duat/t+NOk38hvOp1erPM4PWMoKKrEz/sviXLsNZvN40QuVh3f/nIR1TVqUY6tjzmfHcfHX56+63aLv0vF9I8Om80XVkM5nlKIoc/vhLKk6o7bpWZcw4CpcUa9wV4MVdUqjJq+Bzvvcg9QTa0G0W/+Lko40te5jBIcPlVg8uNqNFp89fMFkx+3MV+KFKzN5XPgh12ZKBfhRuWLl0rxR5L53Y9osYE+LS0NsbGx8PDwwCeffNLoNr169QIAhIf/b37VhIQEDBs2DN7e3rC1tYWPjw8mTpyItDTT9wQY0unFm6BRqdFzzsSGy5dshkatRtTehVAcOousbYdEqrD5Dp4UbwzjIRGPfdOxlEKTDje61aFT4r9+rVYr2u9A6Y1anW/EFsu+o7n47Nuzzd7+i5/OY/vvl41YkWmp1Ro8MSceldXN+wJ2Ke8GXvrA/M9/uvjkyzNISG7+v5Xn5iZI5kuNqJ8DZnAerKxSIUmkK6bZeTeQVyD+VYqDJ3W/h8pQzOF34O8sNtBv3LgRGo0G0dHRcHJyanQbe/u6MWC3BvqSkhKEhoZi2bJl2LNnDxYsWIDU1FT069cPOTnmN9NJc5VlK5C17RDaDQhDmz7B9cu1KjUKE9Nh594Kf8buF7HC5jsh4rCPE2ni99CL+frPZV4Tfeq2y/k3UHRNvJuSxHz/dfGfH3TvhPjPD6Ydj2tMcQeu4JKOw7J2HLiCS3mmG5NtTDW1avx3s25DxGpqNaL1+upK1M+Bc0rRr2aduVAsWscOAJw4VyTasc2hBnN4/X9nsYE+Pj4eADB48OAmt7kZ0G8N9FFRUVi8eDEmTJiAgQMHIjo6Glu2bMH169exefNm4xZtZGeW1vXG39pL36ZPMDpPHIy0tb+i9/tTYWVn3k9FLb5ebdS5Zu/mZJr4/4hPnhevBrVaK3oP9an0YlGPbw6/A3eTV1CuV2/73iN5yLhi+CdJi2H1Jt3vd9BotPhyi3kMp7hXv/x+GQql7s+PWP3TebMcH/x3p86Ldx4oulZtlIcZ6kLMzwFzOH5llQrns66JdvxTIr/+xghasb9mGomvry9ycnJw8uTJRmewUalU8Pb2hlKpREZGBgICAprcV1FRETw8PLBixQpMnz5d51oiIiKgUOh2achaK8NcTW+dj6ULuYMdon5bhHOrd+D8N7sxcuv7UJ7OQOLcdTrva57sOGoF449rV8la46rrv5pcn7gx6o7TUHl52ENuJYNKrbnjh51CWYHIJ7fftlymKYX3tc90qtnQCp2nosa6Y6PrDPX6gabfA/fSb2GnEu8GunKbcFxzGtfouru9fuDefwfsq1PgVr5Jt6JNrFreEUqXqXq1dSvbAPta6YdaRat/QW2l++w9djXn4H4j1ggVmVap3UCUOQzRq613yceQac1zar6brrb6J1RWHo2uM8V5sM31/8Babfox/DeV2T2AUofhja4zxXnQseoIXCt26Va0AakFRyhaxzS53thZQNBUot21+boV3QxeXl5IStLveQcWO21leXndt+fKysb/omJjY6FUKuHs7Ax/f//b1qvVamg0Gly6dAlvvvkmvLy88MQTT+hVi0KhQG6ubo+kthGsACPPjBX53rO4cbkA59fV/aM8OGsFovYtwuWdx3D1qG6X6/Py81CjNcHNgjYqwLXp1Tfnlr0buZWsWdv9nUYNnf8uDS6gFrBufJWxXz8AFBWXAGUivget/YHGR9E1+/UD+r8HlVXV4v8O3I2TK6Dnff7FxdeBUjN/fc3hLABWujerqqo1/7/f5mhbCeg2xXa9/PxCQG3mQ48cNU3+/ZriPFhQoASqRPw98bzR5N+vKc6D5TcqUJ4v4uuXtwLu8H3d2L8DWq1gducJiw30Xl5eKCkpQXJyMvr169dgXX5+PubMmQMACAsLgyAIt7UfOHAgDh2qu0Gqc+fOiI+Ph6enp9616MpaKwOM2OHdfkhP+Ef1x7ahs+uXlV26ihMfbUD/xdOxfchsqCqb30PTzrudiXroXXCnW1EUyjvf0KXLt/LGWMnU8GrfvjmlGo3SRoam/mYM9frvtC8PNxfYuoj3HlTYOKGpQT93e/3Avf8O2NtZw03k34G7qbFygL5PKfBobQdbZ/N+fc1xVaiGPnd7ONhq0NrM/36b44atFa7r01CrQTuv1hD0/UZoIlettE3+/ZriPNjG0w3WGvEeTlZm54CmBseZ4jzo5GiLViL+O9EI9rjTPDPGzgIyQQ1vI7x+ffLiTRY75GbmzJlYvnw5fH19sW/fPgQFBQEAEhMT8cwzzyAzMxO1tbWYPn16o0+JTU9Px7Vr15CVlYWFCxeioKAAhw4dgp+fn0nqr62owoZOT5vkWIYQnbEe1g52Rj+OSqWBy/3forJKv6sBV/ZOgk9bR+RcLYfv8B90bj+sbzvs/e9IvY5tKC99cAirf9JvPvR7ff0AcHnPRPh6NdFFbgLHzhSg79O/6N3+Xt+Dea/ch3df6qn38U1BrdYg8NFNyMrVrZfVy8Mel3dPsoiHLM1blYz3Vp3Uud0vy4fj0YGmOc8bU3ZuGQIe+RG6fsI/NrQDtiweZpyiDGjMzL16z8p0r+cAKysBZUeehb2deH2i2/ZfwthZ+/Ruf6/vwX/f7Y8XRHzQolarhfuD61FSqt88/Pf6+vuGeeLI+ii9jm0s0j9rNyEmJgbu7u64cuUKQkJCEBoaisDAQPTu3RsBAQEYMqRubOGtN8TeqkuXLujTpw8mTZqE3377DWVlZfj0009N+RKoEXK5DD26uIt2/F7dGh+zadIagsV7/Z6t7fS+RG0oYUFusLK6/aqaqfTqJt7731xWVjK8NEH3D9tp47taRJgHgBfGd4Fcx9+TDu2cMPIBHyNVZFod2ztj1IO+Ord7ZWLw3TcyA2Kei0M6tRY1zANAr2BxP4vE/iwUBEHUGsR+/Y2xjDN3I3x8fJCQkIBRo0bBzs4O2dnZcHNzw+rVqxEXF4cLF+pu+moq0N/K1dUVnTt3xp9/SuehG5YsIqRl/yMW+/U3NkTNlOzt5OjeWfebHQ3FHH4HmuOFx7sgwMf57hv+xaetI6ZPkkaYa452bRwxKzpEpzYf/bMXrKws52Nx7ss9YWfb/BsJhvVth6F92hmxIsMR9zwo/pf69m0d0NbdXpRj21jL0D1QvHPwTWJ/FpobyzlzNSI4OBg7duxAWVkZysrKcOzYMUybNg3l5eXIzs6GTCZD9+7d77qfgoICpKeno1OnTiaomu7m8WEdRTmuk4M1Hr5f/LG14V3c0cm3+UHNkCY8dPsN5GJ4fHhHUY77QM+2d509wly0drHFzpUPw9fr7ldUvDzssXPlQ2gjUkAwlgWvRuLpR5t33l74Wm9Ej+ps5IpMKyLEEz8uHNKsUN83zBObPhsq+hf25hoc6Y3WLuJMszxhuPjnQUEQRDsPjhncATbWetxxbmCPi/T3YGtjhdFmOCzPogN9U1JTU6HVahEYGAgHh4Yfzk8//TTee+89/Pzzz/j999+xZs0aDBo0CHK5HK+++qpIFdOtHuzlhZBOriY/7jOPdoKLk/jz9MtkAl5+wvQ9qa7ONpg0ounpXU3p+XFdIJebPnhIZTjCTUEdW+Ho+tF4flwQ7O1u/wC2tbHClDGBOLYhCt0D3USo0LisrGT45sOBWP5mvya/BPcJ9cTPS4fh9SmhJq7ONEYP8sOBr0dh1ABfNJbVPVvb4a3nwxH/5SNo5Sz++a257O3k+MfYIJMf17+9Mx7ubx7DssT4HADM5zzYq5sHenfXb7KSe/HEQ/7waG38ewZ11SIDfUpKCoDGh9v07dsXv/76K6ZOnYqRI0di4cKFePDBB3Hq1Cl07mxZvTdSJQgCpk/qZvLjinXybMzUsY0HNGP6x2NBcLA3j4mxvDwc8Pgw0/bOtHW3xziRrg7di3ZtHLHmvQeRt+9JrH63P5wd6v4OWzlZI3ffJHz9wQD4eYt3k7OxyWQCZjzZDRd+mYBdqx6Gs2PdnK8ujtZI+mEMjm6IwpjBHUSu0rgiu3tix4qHkBH3BBbN7g2nv34HWrvY4MreSfhoZoToY8L18dITwZDJTPvF/pWJpj9mU0I6t8agSG+THrNbJ1cMjNB/JhZDE2OYoLkOTWSg/5sZM2bg+PHjKCkpQWVlJS5cuIDVq1ejQwfLPuFLzfPjuqBnV9ONY3xlYjBCg8ynB9OtlS0++meEyY7n7emAf0/rYbLjNceCVyPrw5kpLInpA1sb8S8z68vVxRbTHu9af5XJycEa7q7m18tkLDKZgIf7+8Dlr98ZZ0drsxwHa0z+Ps6YPTkUrf76HXCwk0v6d7qznwtmP3v3YbOGEhzgihlPmleYWxLTx6RXK//z1v1mNSwrelQn3N+jjcmONzkqEH3CTHc8XTDQtzAdRvVF3/kvNFjWeeJgTMnfBL8RkSJVpTtraxnWfTgA1nLj/wp3bOeEBa+a33sz86lu6N/TyE8f+8t/3+2P1i62JjlWc/l5O2HRbOM+TfmmcUM7YqKZDDciov+Z98p96NKxldGPI5MJWPfBANjZmteVjPAu7nhnmmmm0Z0+KdjkVwTuxspKhq/fH6DTzd/6atfGAYtj+hj9OPpqkYE+Pj4eWq0Wo0aNErsUk/N7pA8u7zpe/7OTjyeCooehICldxKr0Exbkhk91DNoKZQVyrpY368EbQN0Y4+8+HggnB9P1BDeXlZUM6z4YAHfX5gdtXV8/UHd1wlzn5X5hfBeMG9pRpza6vgcd2jlh1b/Nq1eKiOrY28mx/pOBcNBhyJA+58F5r/RE71DTj9dujjefC8eD9+nWuaPrexAa2Brz/2V+HVtA3b1CS/+vr05tdH39crmAbz4cYHYdW7cyr6+adM9sXBwwZv9iWNnZoCJPCZmtNZz92iJj0x848sYatI3sgoOz/nqQliDg/s9exrF/r0Xk3MniFq6nfz3THUXXq/Hhf081a/vIJ7c3e9821jL8tGgIHrjPfMYL/l1nPxfsWvUwhk/bhWtld3/Ahi6vHwCeeqQTlr2h24nSlARBwIb5AzFmVi32HG7eY7h1eQ/atXHAvv+OtLjZX4gsSURI3Y3NUTP3oqr67g8d1PU8+K+nQ/D2Cz30rM74rK1l+GX5Qxg2bSeSUpXNaqPLe9ClYyvsWT3CLDu2bpr2eFcUXavGW8uSmrW9Lq/fykrAhk8GYVhf8We5u5MW2UNvyWpKK5C5NQHnvozD9uFzcPzdr1GYfAGHX/8C3v27oyAxHVpV3Qkv5MXRKEg8j6IzmSJXfW8+mNELn74aadAblVydbfDL8uEYPcg8e6ZvFRHiiT++HtWs6Ql1MX1SML79aIDZz8ttZyvH9mXDMXGEYW+S7erfCgfXPYrOfi4G3S8RGd7wfu2xe9XDBp19RBCA917uic/n9DH7K3StnG3w25qRGN7PsM8R6N3dEwe+HiWJ6XrffD4cy9/sZ9B7CpwdrbHl86F44mHzH3Jp3p/UpBe37v4oTskCALiHdULx2bo/+42IxKWddcNtXLv4osOoPji9ZLNodRrSnKlhOLp+NLoZYDrLRwf4InXrODx0v3lMTdYcYUFuSNk8Ds+Pu/dp3Nq1cUDcfx7CirfuN/swf5OtjRU2LhiMDZ8Mglure7skKpMJmDMlFMmxY+Gvw4OZiEhcAyK8kbplHMYbYDaqwA4uOPD1KMx9+T6zD/M3uTjZYNeqEVjxVj843uOMZDbWMnw8MwKHvn1UUlcoZzzZDYnfj0F4l3ufxGJ4v3Y4u2UcoiQyC5Y0Pq1JJ24hHetDvHtYAIr+CvftBvVAbvxJAEDbPsFw8m2D8YeX4/HjK+F5XyD6LXwJXZ59SLS671Vkd0+c+GEMPpkVoVdvda9uHti4YBC2Lx+Odm0M29ttCq2cbbDmvQex978j9Hrao7urLWKmhiJ1yzg8oscj48UmCAKeGtUJqVvHYfqkYJ1nwBEEYPRAPxz+9lF8+lpvSU7jR9TStXG3x6bPh2LL4qHoF677bCTt2jjg/en34dSPj5n1cMumyGR10zqf2fwYpo4N1PlmUblcwKQRAUiOHYs3nw+H3AQTTxhaj67uOP59FD57vTf82+veKRPexQ3ffjQAu78YIakpfQWtVqsVuwi6XW1FFTZ0elrndg5ebhi951PEhj0PABh3aDl+f+lzyORyhL/6OH579pNG243YPA/n1uzA5V2JetUbnbEe1g7mMwWeSqVBXMIVbNyZgaRUJTKulN22jVwuIKRTa/QNa4PnHgtCpAgPqDCm81nX8OXmdBw8eRWn0otRXXP72FKfto7o1c0d44d1xISH/M1uBod7UVZeg/U7MrDjwGWcOFeEq0WVt23jaC9Hj67uGBThhefHdUFHPU7+UuMzbCNyCyrQvo0DcvY9KXY5JtfSXz/Qst6Dk2lKrN16AUdOFyDlYglqVZrbtvFv74xe3dwxaUQAogZ1gLW19EJsU4qvV2PdtgvYdSgHJ84Vofh69W3buDhZ475gDwzr0w7PjQuSxPCa5lKrNdh9OBcb4jKQmFqIi5dKb9vGykpAtwBX9An1xNSxQegX3kYyV2VuZTmf3gTgr+E2f/XOA0BNaTm6Tn4Y1cVlDWa3sXRyuQxjBneof2DMtdJqpGdfR0WVClYyAc6O1ggOcLWoAPt3Xf1dsej1uim2ams1SM++hpLSGtSqNLC3s0InHxdJXUrVlbOjDV6eGIyXJwZDq9Uir6ACl/JvoKpaDRtrGTxa2yHQz0Uyw4qISHc9gz2wIrjueQPVNWqcz7qG62U1UGu0cLCTI7BDq3sepmfO3FrZ4rVnQ/Has6HQarW4lHcDuQUVqK6pOw96eTggwMfZbB6WZWhWVjI88qBv/VXn62U19VlAEABnh7osYAlXZKX/CqiBnH0nkLPvRP3PO0a+AQAY8/ti7B4/t8l2u+6wzhK4utia7cMgTMHaWobugebzYCxTEwQB7ds6on1b6Q2lIiLDsLWxQngX0z2Q0NwIgoCO7Z1bxJXIprRytjHb6UfvFQN9C7Ft0Ktil0BERERERsBrzUREREREEsZAT0REREQkYQz0REREREQSxjH0Zkpub4vojPVil9FscnvLnSWAiIiIyJwx0JspQRDMal53IiIiIjJPHHJDRERERCRhDPRERERERBLGQE9EREREJGEM9EREREREEsZAT0REREQkYQz0REREREQSxkBPRERERCRhDPRERERERBLGQE9EREREJGEM9EREREREEsZAT0REREQkYQz0REREREQSxkBPRERERCRhDPRERERERBLGQE9EREREJGEM9EREREREEsZAT0REREQkYQz0REREREQSJhe7AGqcVquFqrJa7DKaTW5vC0EQxC6DiIiIqMVhoDdTqspqbOj0tNhlNFt0xnpYO9iJXQYRERFRi8MhN0REREREEsZAT0REREQkYQz0REREREQSxkBPRERERCRhvCmWiMjC1dSqcfZiCVIzSnCjohYAUFGpQlJqIUID3WBrYyVyhUREdC8Y6ImILFBZeQ3W78jAN9sv4uT5ItTUahqsLymrQeST22EtlyG8ixueebQznh3dGa4utiJVTERE+mKgJyKyIKU3ajB3ZTK+3HKhvjf+TmpVGiSlKpGUqsSbS5MwdWwgPpzRi8GeiEhCGOgtiFe/EIzYMq/BstrySpRm5iNj0wGkrf0VWrWmidZEJHX7jubiubkJuJxfrlf7iioV/vNDGrbGX8KauQ/gkQd9DVwhEREZAwO9BcrckoCc+GRAEGDv6YrOEwai97wpaBXYHkfmrBa7PCIyMK1Wi/e/OIn3Vp00yP7yCiowavoexEwNxfx/RfIp0EREZo6B3gIVpWQhc3NC/c/p63bjsYSlCHpqKJLnb0R1UamI1RGRob25NAkLvjpj8P1++nUKKqrUWPZGX4Z6IiIzxmkrWwBVZTUKky9CkMng0qGt2OUQkQEt/z7VKGH+phUbz2H+WuPtn4iI7h0DfQvh3LEuyFdfuyFyJURkKOezrmHO54k6tUncGIUreychcWNUs9u8u/IETp0v0rU8IiIyEQZ6CyS3t4GtmzNs3V3g2tUPfT5+Hu6hAShMvojSzHyxyyMiA1CrNZj6zgFU16h1aufl4QCfto7w8nBodhuVSosp7xxATa1uxyIiItOw+ECvVCoRExODzp07w87ODr6+vpg1axbKy8vx3HPPQRAErFixQuwyDapnzCQ8mfo1njz7Fcbu/xzBU0cgO+4o4qcsELs0IjKQTXuzcfRMocmOdzq9GOt3ZJjseERE1HwWfVPsqVOnMHLkSCgUCjg6OqJbt27Iy8vDsmXLkJGRgeLiYgBAjx49xC3UwNK/24PsX45AZi1H665+6D59LBy93aGurqnfRmYjx+g9C5G1NQFnlm6pX/7Akumw83TFvuiPxCidiJppZWyayY/5nx/OYerYQN4gS0RkZiy2h16pVGL06NFQKBSYPXs28vPzkZycDIVCgQULFiAuLg6JiYkQBAFhYWFil2tQpZkK5CekIDf+JM6u3IbfJs+HR49O6LfgxfptNDUqHJy5HKEzx6F1tw4AAL8RkfAZHoFDr60Uq3QiaoazF4tx4ITC5MdNTivC8RTTXRUgIqLmsdhAP3PmTOTk5GDGjBlYtGgRnJ2d69fFxMQgPDwcKpUKHTt2hIuLi4iVGl9hUjoyNh2A/9j+8IzoUr+86EwmUldtx4PL/gkHbzf0W/gSjr31JSqvlohYLRHdTVzClRZ5bCIiapxFBvq0tDTExsbCw8MDn3zySaPb9OrVCwAQHh7e5H5GjhwJQRDw3nvvGaNMkzq9eBM0KjV6zpnYcPmSzdCo1YjauxCKQ2eRte2QSBUSUXOdOCfejDMnzilFOzYRETXOIgP9xo0bodFoEB0dDScnp0a3sbe3B9B0oP/xxx9x6tQpY5VocmXZCmRtO4R2A8LQpk9w/XKtSo3CxHTYubfCn7H7RayQiJpLzFB94lwRtFqtaMcnIqLbWWSgj4+PBwAMHjy4yW1ycnIANB7oS0tL8a9//QuLFi0yToEiObO0rjf+1l76Nn2C0XniYKSt/RW9358KKzsbESskoua4rBDveRJXiyqhUjHQExGZE0FrgV0tvr6+yMnJwcmTJxudwUalUsHb2xtKpRIZGRkICAhosP6f//wnUlJS8Pvvv0MQBMydO/eeht1ERERAodDtBjZrrQxzNb31PmZzyB3sEPXbIpxbvQPnv9mNkVvfh/J0BhLnrtN5X/Nkx1EraAxfJBE1oIWAPLf3mlyfuDHqjnPMe3nYQ24lg0qtgUJZ2eR2CmUFIp/c3ug67+KPIENNo+vMXb7ra9DIWkGmuQ7va5+LXY4o+B4QmScvLy8kJSXp1dYip60sLy8HAFRWNv5hFRsbC6VSCWdnZ/j7+zdYl5SUhDVr1uDEiRMGq0ehUCA3N1enNjaCFdDWYCU0KvK9Z3HjcgHOr9sFADg4awWi9i3C5Z3HcPWoblPi5eXnoUbLh84QmURrNSBYNbrq5oOj7kZuJWvWdo3Jz7sCaFV6tRWdsxqQARq1WufzssXge0BkcSwy0Ht5eaGkpATJycno169fg3X5+fmYM2cOACAsLKzBfMpqtRovvvgiZsyYgZCQEIPWoytrrQwwYod3+yE94R/VH9uGzq5fVnbpKk58tAH9F0/H9iGzoaqsbvb+2nm3Yw89kYnkayugEZwbXadQVtyxrS499I0RtFXwbtcWUp2JPt/KChoAMisreLdvL3Y5ouB7QGSe9MmLN1lkoB82bBjS0tKwYMECDB8+HEFBQQCAxMREPPPMM1Aq624o+/twnBUrVuDq1asGn9VGn8sntRVV2NDpaYPWcavc+JP4vuvk25afX7ervsdeFxcuXoC1g50hSiOiuxg1fTd+TchpdF1Tw2RuurJ3EnzaOkKhrITv8B90PvaAyI74/avGjy0FPsM2IregAt5e3sg5K93XcS/4HhBZHou8KTYmJgbu7u64cuUKQkJCEBoaisDAQPTu3RsBAQEYMmQIgIY3xCqVSrzzzjt49913oVKpcO3aNVy7dg0AUFVVhWvXrkGjYQ80EYmvVzePFnlsIiJqnEUGeh8fHyQkJGDUqFGws7NDdnY23NzcsHr1asTFxeHChQsAGgb6nJwclJWV4cUXX0Tr1q3r/wOABQsWoHXr1rh8+bIor4eI6FaDI71b5LGJiKhxFjnkBgCCg4OxY8eO25bfuHED2dnZkMlk6N69e/3yzp07Y//+2+dhHzx4MCZPnowpU6bc09gmIiJDGRTpjS4dWyE9+7pJj+vn7YiRD/iY9JhERHR3Fhvom5KamgqtVougoCA4OPxvajcnJycMGjSo0TYdO3Zsch0RkakJgoBXJgZj1oKjJj3uSxOCYWVlkRd2iYgkrcWdmVNSUgA0/YRYIiIpmDo2EL5e+k07qY+27vZ4cUJXkx2PiIiaj4H+LrRarcFnvRFTh1F90Xf+Cw2WdZ44GFPyN8FvRKRIVRGRrpwdbfDlew+Y7HhfvHM/3FrZmux4RETUfAz0LYzfI31wedfx+p+dfDwRFD0MBUnpIlZFRPp46H4fnXvNFcoK5Fwtv+t89beKHtUJY4d01LE6IiIylRY3hj4+Pl7sEozKxsUBY/YvhpWdDSrylJDZWsPZry0yNv2BI2+sQdvILjg4a0XdxoKA+z97Gcf+vRaRc2+fk56IzN+yN/riUt4N7DrUvPnE7zZP/d8NjPDCmrmmuxJARES6a3E99JauprQCmVsTcO7LOGwfPgfH3/0ahckXcPj1L+DdvzsKEtOhVakBACEvjkZB4nkUnckUuWoi0peNtRW2LB6KRwf4Gnzfw/u1w44VD8HersX1/RARSQoDvQVy6+6P4pQsAIB7WCcUn637s9+ISFzaWTfcxrWLLzqM6oPTSzaLVicRGYa9nRxblwzDhzN6wVp+76d1KysB77zYAztWPAQnB2sDVEhERMbEQG+B3EI61od497AAFP0V7tsN6oHc+JMAgLZ9guHk2wbjDy/H48dXwvO+QPRb+BK6PPuQaHUTkf7kchnentYDJ34Yg75hnnrv575gdxzfEIX3p/eCjbWVASskIiJj4XVUC+Pg5QZotahQFAMA3II74MzSzfDoGYjrF3OhqqgCAKR/uwfp3+6pbzdi8zycW7MDl3clilI3ERlGaJAbDn83GsdTCrEyNg2xu7NQXaO+YxtruQwTHvLHKxODcX+PNhAEwUTVEhGRITDQWxi37v71vfMAUFNajq6TH0Z1cVmD2W2IyHIJgoA+YW3QJ6wNVr/bH2culODEOSXO/lmCGxW10GoBR3s5uge2Rq9gD4R3ceM4eSIiCeMZ3MLk7DuBnH0n6n/eMfINAMCY3xdj9/i5TbbbdYd1RCRddrZy9A71RO9Q/YfhEBGReWOgbyG2DXpV7BKIiIiIyAh4UywRERERkYQx0BMRERERSRgDPRERERGRhDHQExERERFJGG+KNVNye1tEZ6wXu4xmk9vbil0CERERUYvEQG+mBEGAtYOd2GUQERERkZnjkBsiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJk4tdADVOq9VCVVktdhnNJre3hSAIYpdBRERE1OIw0JspVWU1NnR6Wuwymi06Yz2sHezELoOIiIioxeGQGyIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiImoxtFptg/8TEVkCznJDREQWSaPRYu+RXMQfz0NSqhLJaUW4VlYDAMgrrES7oRvRK9gdESEeGDXAFxEhniJXTESkHwZ6IiKyKNdKq7FmczpW/XgeWbllTW6XX1iBHYUV2HHgCt5bdRIRIR54ZWIwokd1go21lQkrJiK6Nwz0FsSrXwhGbJnXYFlteSVKM/ORsekA0tb+Cq1aI1J1RETGF3fgMqa9fwh5BRU6t01KVeIf7yZg6YZUfPPhAIR3cTdChUREhsdAb4EytyQgJz4ZEATYe7qi84SB6D1vCloFtseROavFLo+IyOCqqlV45aPD+Prni/e8r9PpxYh4chs+mN4L//ePMD4Fm4jMHgO9BSpKyULm5oT6n9PX7cZjCUsR9NRQJM/fiOqiUhGrIyIyrIpKFaJm7sVvx/IMtk+VSos3lyYhr7ACS/+vL0M9EZk1znLTAqgqq1GYfBGCTAaXDm3FLoeIyGBqazUY/9pvBg3zt1r+/Tm8sSTRKPsmIjIUBvoWwrljXZCvvnZD5EqIiAxn/lensetQjlGP8enXKYg7cNmoxyAiuhcccmOB5PY2sHVzrh9D3+XZh+AeGoDC5IsozcwXuzwiIoM4c6EYH6w+pVObxI1R8PJwgEJZgcgntze73QvzDiF1a1u0drHVsUoiIuNrET30SqUSMTEx6Ny5M+zs7ODr64tZs2ahvLwczz33HARBwIoVK8Qu02B6xkzCk6lf48mzX2Hs/s8RPHUEsuOOIn7KArFLIyIyCK1Wi2nzDqJWpdvMXV4eDvBp6wgvDwed2uUXVuDtZUk6tSEiMhWL76E/deoURo4cCYVCAUdHR3Tr1g15eXlYtmwZMjIyUFxcDADo0aOHuIUaUPp3e5D9yxHIrOVo3dUP3aePhaO3O9TVNfXbyGzkGL1nIbK2JuDM0i31yx9YMh12nq7YF/2RGKUTETXL8ZRCHEspNOkx122/iI9nRsCVvfREZGYsuodeqVRi9OjRUCgUmD17NvLz85GcnAyFQoEFCxYgLi4OiYmJEAQBYWFhYpdrMKWZCuQnpCA3/iTOrtyG3ybPh0ePTui34MX6bTQ1KhycuRyhM8ehdbcOAAC/EZHwGR6BQ6+tFKt0IqJm+c8PaSY/ZmWVGt9sv/dpMYmIDM2iA/3MmTORk5ODGTNmYNGiRXB2dq5fFxMTg/DwcKhUKnTs2BEuLi4iVmpchUnpyNh0AP5j+8Mzokv98qIzmUhdtR0PLvsnHLzd0G/hSzj21peovFoiYrVERHdWVa3Cj3uyRDn2t7/8KcpxiYjuxGIDfVpaGmJjY+Hh4YFPPvmk0W169eoFAAgPD69f9vvvv0MQhNv+k/qQnNOLN0GjUqPnnIkNly/ZDI1ajai9C6E4dBZZ2w6JVCERUfOkXCxBdY1alGOfuViMqmqVKMcmImqKxY6h37hxIzQaDaKjo+Hk5NToNvb29gAaBvqb/vOf/+C+++6r/9nR0dE4hZpIWbYCWdsOodP4AWjTJxgFx+ouV2tVahQmpsMjrBP+jN0vcpVERHd34pxStGOrVFqcuVCC3qGeotVARPR3FttDHx8fDwAYPHhwk9vk5NTNXdxYoO/WrRv69u1b/19oaKhxCjWhM0vreuNv7aVv0ycYnScORtraX9H7/amwsrMRsUIiortLzbgm8vE5LJGIzIug1Wq1YhdhDL6+vsjJycHJkycbHS6jUqng7e0NpVKJjIwMBAQEAKgbcjN48GDs378fgwYNMkgtERERUCgUOrWx1sowV9PbIMdvitzBDlG/LcK51Ttw/pvdGLn1fShPZyBx7jqd9zVPdhy1gm7TxxER6aPEcSwqbHs2uu7mPPNN8fKwh9xKBpVaA4Wy8o7HaWqu+lblv8Kp+phuRZuRfNfXoJG1gkxzHd7XPhe7HCL6i5eXF5KS9Jse12KH3JSXlwMAKisbP2HHxsZCqVTC2dkZ/v7+t62fOHEilEol3N3dERUVhfnz58PDw0OvWhQKBXJzc3VqYyNYAW31OlyzRb73LG5cLsD5dbsAAAdnrUDUvkW4vPMYrh7VbQaJvPw81GjFGdNKRC1M+3KgiZkjb84zfzdyK1mztmvM9esluF6k2zndrDirARmgUat1/mwiIvNksYHey8sLJSUlSE5ORr9+/Rqsy8/Px5w5cwAAYWFhEAShfl2rVq0wZ84cDBgwAE5OTjhy5Ag++eQTHD16FElJSbCzs9OrFl1Za2WAETu82w/pCf+o/tg2dHb9srJLV3Hiow3ov3g6tg+ZDVVldbP31867HXvoicgkrjnIUd7EOoWy4o5tde2hb4yriwMc7do3p1SzlG9lBQ0AmZUVvNtL93UQWRp98uJNFjvkZubMmVi+fDl8fX2xb98+BAUFAQASExPxzDPPIDMzE7W1tZg+ffpdnxL7yy+/ICoqCl999RWmTp1qivJRW1GFDZ2eNsmxDCE6Yz2sHXT/skNEpKvVP53HSx/oNyPXlb2T4NPWETlXy+E7/Ae99nH8+yhEdpfuTbE+wzYit6AC7ds4IGffk2KXQ0QGYLE3xcbExMDd3R1XrlxBSEgIQkNDERgYiN69eyMgIABDhgwB0PgNsX/36KOPwtHRUe9xTUREZDi9urmLdmy5XEBoYGvRjk9E1BiLDfQ+Pj5ISEjAqFGjYGdnh+zsbLi5uWH16tWIi4vDhQsXADQv0N9069AcIiISR2igG+xsrUQ5dniQO+xsLXa0KhFJlEWflYKDg7Fjx47blt+4cQPZ2dmQyWTo3r37Xfezfft2lJeXo3dv4846Q0REd2drY4WJDwfgm+0XTX7syVGdTX5MIqK7sehA35TU1FRotVoEBQXBwaHh9GZPP/00AgICcN9999XfFPvpp5+iR48emDRpkkgVExHRrV6ZGGzyQO9gJ8ezowNNekwiouZokYE+JSUFQOPDbUJCQvD9999jyZIlqKyshI+PD1544QXMnTsXNjZ86BIRkTnoHeqJ+3u0weFTBSY75j8eC0IrZ34OEJH5sdgx9Hdyp0D/5ptvIiUlBaWlpaitrUVWVhY+//xztGrVytRlGkWHUX3Rd/4LDZZ1njgYU/I3wW9EpEhVERHpbvU7/WFjbZqPMZ+2jvhwRi+THIuISFcM9C2M3yN9cHnX8fqfnXw8ERQ9DAVJ6SJWRUSku+6Bbpj7UuNPjG2KQlmBnKvld52v/u/WzH2AvfNEZLZa5JCb+Ph4sUswGhsXB4zZvxhWdjaoyFNCZmsNZ7+2yNj0B468sQZtI7vg4Ky/5t0XBNz/2cs49u+1iJw7WdzCiYj0EDM1DEdOF2DHgSvN2j7yye06H+PtF8Ix4gEfndsREZlKi+yht2Q1pRXI3JqAc1/GYfvwOTj+7tcoTL6Aw69/Ae/+3VGQmA6tSg0ACHlxNAoSz6PoTKbIVRMR6Ucul+HHRUPw8P3GeeLpq8+E4AMOtSEiM8dAb4HcuvujOCULAOAe1gnFZ+v+7DciEpd21g23ce3iiw6j+uD0ks2i1UlEZAj2dnJsXz4c0x7vYrB9WstlWPhab3z2eh8+g4SIzB4DvQVyC+lYH+LdwwJQ9Fe4bzeoB3LjTwIA2vYJhpNvG4w/vByPH18Jz/sC0W/hS+jy7EOi1U1EpC8bayusfvcB7Fr1MHzaOt7Tvnp180By7Bi8PiWUYZ6IJKFFjqG3ZA5eboBWiwpFMQDALbgDzizdDI+egbh+MReqiioAQPq3e5D+7Z76diM2z8O5NTtweVeiKHUTERnCw/19kLp1HL7aegErf0zDxUulzW57f482ePmJYEwaEQC5nP1dRCQdDPQWxq27f33vPADUlJaj6+SHUV1c1mB2GyIiS+XiZIN/PdMdM6NDsP94PvYn5uHEuSIkpylRUFzXqSEIgJ+3E3oFe6BXN3c88qAvenR1F7lyIiL9CFqtVit2EXS72ooqbOj0tMH2N+b3xdg9fi6qiprfW6WL6Iz1sHawM8q+iYgMRavVQqXSQi4XWuxwGp9hG5FbUIH2bRyQs+9JscshIgNgD30LsW3Qq2KXQEQkOkEQYG3dMoM8EVkuDhIkIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCeNNsWZKbm+L6Iz1YpfRbHJ7W7FLICIiImqRGOjNlCAInAaSiIiIiO6KQ26IiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkTC52AdQ4rVYLVWW12GU0m9zeFoIgiF0GERERUYvDQG+mVJXV2NDpabHLaLbojPWwdrATuwwiIiKiFodDboiIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMs9wQERFZsMLiSpw4V4QT55T480opiq/XTYl8rawGazadR69uHuge2Bo21lYiV0pE+mKgJyIisjA1tWr8HH8JK2PT8EeSotFtyitVmPb+IQCAi5M1JkcF4uUnghEc4GrCSonIEAStVqsVuwi6XW1FFeehJyIinW3em4WZC44ir6BCr/ZjBvth5dv3o10bRwNXRkTGwh56C+LVLwQjtsxrsKy2vBKlmfnI2HQAaWt/hVatEak6IiIyJmVJFaZ/fBg/7s66p/1s238ZfyQpsPT/+uKZ0Z35FHAiCWCgt0CZWxKQE58MCALsPV3RecJA9J43Ba0C2+PInNVil0dERAaWlVOGYdN2IjOnzCD7u1ZWg8n/PoBT6UX47PU+DPVEZo6B3gIVpWQhc3NC/c/p63bjsYSlCHpqKJLnb0R1UamI1RERkSFdzr+Bgf+IwxVFucH3vfi7VGg0wOIYhnoic8ZpK1sAVWU1CpMvQpDJ4NKhrdjlEBGRgVTXqPHojD1GCfM3Ld2QilWxaUbbPxHdOwb6FsK5Y12Qr752Q+RKiIjIUN7/4iRSLpbo1CZxYxSu7J2ExI1RzW4z5/NEZObw6i6RubL4QK9UKhETE4POnTvDzs4Ovr6+mDVrFsrLy/Hcc89BEASsWLFC7DINSm5vA1s3Z9i6u8C1qx/6fPw83EMDUJh8EaWZ+WKXR0REBnDinBILvj6jczsvDwf4tHWEl4dDs9tUVKnw3NyD4MR4RObJosfQnzp1CiNHjoRCoYCjoyO6deuGvLw8LFu2DBkZGSguLgYA9OjRQ9xCDaxnzCT0jJnUYFl23FEce/NLkSoiIiJD+2jNKajVpgvYvyfmI+GEAgMivE12TCJqHovtoVcqlRg9ejQUCgVmz56N/Px8JCcnQ6FQYMGCBYiLi0NiYiIEQUBYWJjY5RpU+nd7sPuJedgb/RGSPvgOVcVlcPR2h7q6pn4bmY0cY35fjLBZ4xq0fWDJdAzb8LapSyYiIh3kKMqxbf9lkx935Y8cS09kjiw20M+cORM5OTmYMWMGFi1aBGdn5/p1MTExCA8Ph0qlQseOHeHi4iJipYZXmqlAfkIKcuNP4uzKbfht8nx49OiEfgterN9GU6PCwZnLETpzHFp36wAA8BsRCZ/hETj02kqxSiciomZYuzUdGo3ph79s3peNgqJKkx+XiO7MIgN9WloaYmNj4eHhgU8++aTRbXr16gUACA8Pv23d1q1bcf/998PR0RGtWrVC//79kZqaatSajakwKR0Zmw7Af2x/eEZ0qV9edCYTqau248Fl/4SDtxv6LXwJx976EpVXdbvBioiITGt/ojj3Q6lUWhw6dVWUYxNR0ywy0G/cuBEajQbR0dFwcnJqdBt7e3sAtwf6ZcuW4YknnsADDzyA7du3Y+PGjRg2bBgqK6XdI3F68SZoVGr0nDOx4fIlm6FRqxG1dyEUh84ia9shkSokIqLm0Gi0SE4rEu34J84pRTs2ETXOIm+KjY+PBwAMHjy4yW1ycnIANAz0GRkZmDNnDhYvXowZM2bUL3/kkUeMVKnplGUrkLXtEDqNH4A2fYJRcKxuHKRWpUZhYjo8wjrhz9j9IldJRER3k3GlFGXltaIdX8wvE0TUOIsM9JcuXQIAdOjQodH1KpUKhw7V9UTfGui/+uorWFtb44UXXjBoPREREVAoFDq1sdbKMBe9DVrHmaWb4T+2P3rOmYjdj78HAGjTJxidJw5G2tpf0fv9qdg+fA7UVTV33lEjggKDUCtoDFovERHdrlruC7g83+i6xI1Rd52O0svDvv7/V/ZOanI7hbICkU9uv235vt+PwsfnOR0qJqLm8PLyQlJSkl5tLTLQl5fXPTGvqWEysbGxUCqVcHZ2hr+/f/3yw4cPo0uXLli/fj0+/PBDXLlyBYGBgXj33Xfx5JNP6l2PQqFAbm6uTm1sBCtAx4e6Ko6kYp33402uv34xF9/6/G/IjdzBDg8smY4TH23A+W92Y+TW93Hfm08hce463Q4MIC8/DzVatc7tiIhIR46OQBNzOdycY7455FayZm97q9pajc6faURkXBYZ6L28vFBSUoLk5GT069evwbr8/HzMmTMHABAWFgZBEBqsy83NxZtvvokFCxbA19cXa9euxVNPPQVPT08MGzZM73p0Za2VAUbu8I5871ncuFyA8+t2AQAOzlqBqH2LcHnnMVw9qtvUZO2827GHnojIBGqsWqOwiXUKZcVd23t52ENuJYNKrYFC2fT9YU3ty9paQJv27ZtTKhHpQJ+8eJOgtcDHvs2cORPLly+Hr68v9u3bh6CgIABAYmIinnnmGWRmZqK2thbTp09v8JTYoKAgXLx4EVu3bsXYsWMBAFqtFj169ICrqyv++OMPk72G2ooqbOj0tNH2335ITwxc+S9sGzob5bn/u8Gp65QR6Pbio9g+ZDZUldXN3l90xnpYO9gZo1QiIrpFXkE52g/7Qe/2V/ZOgk9bR+RcLYfvcN33M+Ehf/y4aIjexyciw7PIWW5iYmLg7u6OK1euICQkBKGhoQgMDETv3r0REBCAIUPqTkR/n+HGzc0NABr0xAuCgGHDhuHs2bOmewEmkBt/Et93ndwgzAPA+XW7sKXfDJ3CPBERmU67No7w9rzzOHlj6tXNXbRjE1HjLDLQ+/j4ICEhAaNGjYKdnR2ys7Ph5uaG1atXIy4uDhcuXABwe6APCQlpcp9VVVVGrZmIiKi5egWLF6p7dfMQ7dhE1DiLDPQAEBwcjB07dqCsrAxlZWU4duwYpk2bhvLycmRnZ0Mmk6F79+4N2owZMwYAsGfPnvplGo0Ge/fuRWRkpEnrJyIiasroQX6iHLe1iw3uD9dxxgYiMjqLvCn2TlJTU6HVahEUFAQHh4aXLEePHo0HH3wQ06ZNQ1FREfz8/PDll18iNTUVe/fuFaliIiKihp56pBNe/+y4yeejnzo2CA72LS46EJk9i+2hb0pKSgqA24fbAHXj5bdv347x48fjrbfeQlRUFC5duoRff/21ftw9ERGR2JwcrDE5KtDkx31pQleTH5OI7o6B/m9cXV2xevVqFBYWorq6GsePH8fDDz9syhKJiIju6u0XwuHWytZkx3tlYjACO7Qy2fGIqPkY6FuYDqP6ou/8hk/C7TxxMKbkb4LfCN4nQEQkFV4eDlj+Rr+7b2gAHds5YcGr/IwgMlctbiBcfHy82CWIyu+RPsj46ff6n518PBEUPQwFSeniFUVERHp58pEA/PLHZfywK7PZbW4+MKo5D6ECAGu5DOs+HAAnB2u9aiQi42txgd7S2bg4YMz+xbCys0FFnhIyW2s4+7VFxqY/cOSNNWgb2QUHZ/31MC1BwP2fvYxj/16LyLmTxS2ciIh0JggC1n04AMWl1dhzOLdZbSKf3N7s/VtZCdgwfxAGRnjrWyIRmUCLG3Jj6WpKK5C5NQHnvozD9uFzcPzdr1GYfAGHX/8C3v27oyAxHVqVGgAQ8uJoFCSeR9GZ5vfsEBGRebG1scK2pcPw2NAOBt2vna0VNn8+FBMe8jfofonI8BjoLZBbd38Up2QBANzDOqH4bN2f/UZE4tLO4wAA1y6+6DCqD04v2SxanUREZBh2tnJs/nwoVr/b3yBDY/r3bIszmx7DmMGG/ZJARMbBQG+B3EI61od497AAFP0V7tsN6oHc+JMAgLZ9guHk2wbjDy/H48dXwvO+QPRb+BK6PPuQaHUTEZH+BEHAtMe74uyWxzBpRADkckHnffh6OWLZG33xx1ePcEYbIgnhGHoL4+DlBmi1qFAUAwDcgjvgzNLN8OgZiOsXc6GqqAIApH+7B+nf/u+JuCM2z8O5NTtweVeiKHUTEZFhdGjnjI2fDsbnhX3w5ZZ0/LQnC+cyr0Gt1ja6vauzDfr3bIsXxnfBqAd9IZezr49IahjoLYxbd//63nkAqCktR9fJD6O6uAyXdx0XsTIiIjIlb08HvPNiT7zzYk9UVKpw+kIR/rxcispqNeRWMrg626BHVzf4t3eGIOjem09E5kPQarWNf2UnUdVWVGFDp6cNtr8xvy/G7vFzUVVUarB93io6Yz2sHeyMsm8iIiIiahp76FuIbYNeFbsEIiIiIjICDpQjIiIiIpIwBnoiIiIiIgljoCciIiIikjDeFGumtFotVJXVYpfRbHJ7W86SQERERCQCBnoiIiIiIgnjkBsiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgn7f2Ec/HOg66AzAAAAAElFTkSuQmCC", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 2, @@ -162,8 +163,9 @@ "outputs": [ { "data": { + "image/png": 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AADdFqAcAAAAAwE0R6gEAAAAAcFOEegAAAAAA3BShHgAAAAAAN0WoBwAAAADATRHqAQAAAABwU4R6AAAAAADcFKEeAAAAAAA35WN0ASifzWaTJb/Q6DIqzSfQXyaTyegyAAAAAKBGIdRXU5b8Qs1vMdboMiotLvkz+QYFGF0GAAAAANQoDL8HAAAAAMBNEeoBAAAAAHBThHoAAAAAANwUoR4AAAAAADdFqAcAAAAAwE0R6gEAAAAAcFOEegAAAAAA3BShHgAAAAAAN+VjdAFwnKg+7TV4yfQy24pz85V1NF3Ji9Yr6cNvZSuxGlQdAAAAAMDRCPUe6OiSBKXG75BMJgVG1lHLUTer5/Txqt2qkTZPmWt0eQAAAAAAByHUe6Bze47p6OKE0j8fnLdKdyW8qdj7b9GOVxeo8FyWgdUBAAAAAByFOfU1gCW/UGd3HJbJy0uhTesbXQ4AAAAAwEEI9TVESLNLYb7wQo7BlQAAAAAAHKVGhPqMjAxNnTpVLVu2VEBAgBo3bqwnn3xSubm5evjhh2UymTR79myjy3QYn0A/+YeFyD88VHXaNFGvl3+r8I7NdXbHYWUdTTe6PBgkK6dIPx88py0/n9GRE1my2WxGlwQAAAAXOnMuX9v3Zyhx71mlnc41uhw4iMfPqd+1a5eGDBkis9ms4OBgtWvXTqdOndJbb72l5ORkZWZmSpK6dOlibKEO1HXqGHWdOqbMtpQVW7T12Q8MqghG2pmUoTkLkzT/22TlF5SUbu/Yqq6eGN1WY+9oqVpBvgZWCAAAAGex2WxaszlNcxYm6ZsfT8pq/e+Dnf49GuiJ0W01YkBT+fjUiOe9HsmjQ31GRoaGDRsms9msyZMna9q0aQoJCZEkvfbaa/rTn/4kHx8fmUwmderUyeBqHefgp6uV8s1mefn6qG6bJuowYYSCG4SrpLCo9BgvPx8NW/26jn2VoN1vLindfsMbExQQWUdr414yonQ42Mx5ezTlnz+Vu2/P4fN6/O+b9MZn+/TdnNsUEx3i4uoAAADgTMXFVj0yfYP+vexwuft/SEzXD4npGtSnoRb94xaF1vJzcYVwBI/+OmbSpElKTU3VxIkTNXPmzNJAL0lTp05V586dZbFY1KxZM4WGhhpYqWNlHTUrPWGP0uJ3au+cpfp+3KuK6NJCfWY8WnqMtciiDZPeVsdJd6tuu6aSpCaDeyh6UHdtfHqOUaXDgd78bG+Fgf7XDqZc1C2PfKfT5/JdUBUAAABcwWaz6bfPJ1QY6H9tzeZTuuuptSoqLrnqsah+PDbUJyUlaeHChYqIiNArr7xS7jHdunWTJHXu3Ll02y9fAvTs2VP+/v4ymUwuqdeZzm47qORF6xUzoq8iu7cu3X5u91Hte2eZbnzr9wpqEKY+rz+mrc99oPzT5w2sFo6Qas7V5H9cPdD/4lhatv781jYnVgQAAABXWrkhVZ98c6TSx8f/lK73Fx90YkVwFo8N9QsWLJDValVcXJxq1apV7jGBgYGSyob6I0eOaPHixYqKilKPHj1cUqsr/DxrkayWEnWdMrrs9jcWy1pSouFrXpd5414dW7rRoArhSO8vPqiSEvsWwvv8u2Sdzyp0UkUAAABwpTkLk+xu887CJBZTdkMeG+rj4+MlSf3796/wmNTUVEllQ/1NN92k9PR0LVu2TAMHDnRukS6UnWLWsaUb1fCmTqrXq23pdpulRGcTDyogvLaOLPzBwArhSB9+Zf+3rPkFJVrwbbITqgEAAIArpZ/N04qEk3a325d8QVt3n3VCRXAmj10o7/jx45Kkpk2blrvfYrFo48ZLT6V/Heq9vBz/PUf37t1lNpvtauNr89I09XRoHbvfXKyYEX3VdcporbrneUlSvV5t1XJ0fyV9+K16vvCQlg2aopKCoiufqByxrWJVbLI6tF5nSK/ztORVW+nmdEVHRxtdjlPY5K1TYX+rUts//XWmXv7jagdXBAAAUH3UhPvBIu9o2Wo/UqW2t9/1oIKK9jq4IlRGVFSUtm2zf0qsx4b63NxL713Mzy9/8a+FCxcqIyNDISEhiomJcWotZrNZaWlpdrXxM3lL9e3sZ/M+zWtwT4X7Lx5O0yfR/x1+7xMUoBvemKDtL83XgX+v0pCvXtB1z96vxGnz7OtY0qn0UyqyucHCGiElkpdkLSmx+7+J2zD5SmFVa5qTm6ucdA/9uQAAAEg1434wKFCqXbWm589f1PkLHvpz8VAeG+qjoqJ0/vx57dixQ3369CmzLz09XVOmTJEkderUyemL4UVFRdndxtfmJTn5wXeP5x9UzokzOjBvpSRpw5OzNXztTJ34bqtOb7FvDk7DBg3d40m9t7eskry8vdWgUSOjy3GaU9Z82bwC7W4XGiSFePDPBQAAoCbcD1q8AnRakmw2yc6sE17bRwHBnvlzqe6qkhslDw71AwcOVFJSkmbMmKFBgwYpNjZWkpSYmKgHHnhAGRkZkqQuXbo4vZaqDKEozivQ/BZjnVDNJY0GdFXM8L5aesvk0m3Zx09r+0vz1XfWBC0bMFmW/Movmnbo8CH5BgU4o1SHih64QGln8tQgqoFS96YaXY7TTHx5k/71H/u+mPHyMmnvhg/VOKr8hSUBAAA8QU25H7z+gW+0+eczdrVpVC9IKTt+lI+Pxy695pE89r/W1KlTFR4erpMnT6p9+/bq2LGjWrVqpZ49e6p58+YaMGCApLLz6WuStPid+rzNOOWmZZTZfmDeSi3pM9GuQI/q5/F72179oP8xvF8TAj0AAICHeGK0/feDj45qQ6B3Qx77Xyw6OloJCQkaOnSoAgIClJKSorCwMM2dO1crVqzQoUOHJNXcUA/P1r5lXbsu5LVr+eml33dzYkUAAABwpXtvi9FN3So/nLt1s9qaeF87J1YEZ/HY4feS1LZtWy1fvvyy7Tk5OUpJSZGXl5c6dOhgQGWA8735p97Kzbfo38sOX/G4uqF++ubtW9WuRV0XVQYAAABn8/P11tdvDtQdE1dr064rD8Nv3ay2Vr17m+qG+ruoOjiSR4f6iuzbt082m02xsbEKCgq6bP+iRYskSfv37y/z52bNmql79+6uKxS4Bj4+Xvr4xRs1qE9Dvf35fm3dU/ado7WCfPTgsFaaPK6DmkeHGlQlAAAAnKVuqL/WvjdEcxYm6d0vD+jIiawy+xtEBul3I1trUlx7hdUm0LurGhnq9+zZI6niofejRo0q98/jxo3TvHnznFob4Egmk0lxQ1sqbmhL7TpwTgN++63OZxUprLafUlaOVkiwn9ElAgAAwIkCA3w0eVxH/eGBDkrYYdaIJ9fqQnaRwmv76/jK0fL19dgZ2TUGob4cNpvNleUALtGlTbiCAnx0PqtIgf4+BHoAAIAaxMvLpJu7N1BwoI8uZBcpwN+bQO8hauR/xauFek/WdGhv9X71kTLbWo7ur/Hpi9RkcA+DqgIAAAAAVEWNfFIfHx9vdAmGaXJ7LyV/ua70z7WiIxUbN1Bnth00rigAAAAAQJXUyFDvyfxCg3TnD7PkHeCnvFMZ8vL3VUiT+kpe9KM2P/O+6vdorQ1Pzr50sMmk6//xuLb+5UP1mDbO2MIBAAAAAHYj1HuYoqw8Hf0qQcW5Bdo9a5Ea9uusTpPu1qY/vquGN3fWmcSDsllKJEntHx2mM4kHdG73UYOrBgAAAABURY2cU+/pwjrEKHPPMUlSeKcWytx76d+bDO6h49/9JEmq07qxmg7tpZ/fWGxYnQAAAACAa8OTeg8U1r5ZaZAP79RcJ1clSpIa9uuibS9+Jkmq36utajWup5Gb3pYkBUbWUZ/XH1Ngvbo6+MlqYwoHAAAAANiFUO9hgqLCJJtNeeZMSVJY26ba/eZiRXRtpYuH02TJK5AkHfxkdZnwPnjxdO1/f7lOrEw0pG4AAAAAgP0I9R4mrENM6VN6SSrKylWbcbepMDNbJ1b+ZGBlAAAAAABHI9R7mNS125W6dnvpn5cPeUaSdOe6WVo1clqF7VZeYR8AAAAAoHoi1NcQS/v9wegSAAAAAAAOxur3AAAAAAC4KUI9AAAAAABuilAPAAAAAICbYk59NeUT6K+45M+MLqPSfAL9jS4BAAAAAGocQn01ZTKZ5BsUYHQZAAAAAIBqjOH3AAAAAAC4KUI9AAAAAABuilAPAAAAAICbItQDAAAAAOCmCPUAAAAAALgpQj0AAAAAAG6KUA8AAAAAgJsi1AMAAAAA4KYI9QAAAAAAuClCPQAAAAAAbopQDwAAAACAmyLUAwAAAADgpgj1AAAAAAC4KUI9AAAAAABuilAPAAAAAICbItQDAAAAAOCmCPUAAAAAALgpQj0AAAAAAG7Kx+gCUD6bzSZLfqHRZVSaT6C/TCaT0WUAAAAAQI1CqK+mLPmFmt9irNFlVFpc8mfyDQowugwAAAAAqFEYfg8AAAAAgJsi1AMAAAAA4KYI9QAAAAAAuClCPQAAAAAAbopQDwAAAACAmyLUAwAAAADgpgj1AAAAAAC4Kd5T70Gi+rTX4CXTy2wrzs1X1tF0JS9ar6QPv5WtxGpQdQAAAAAARyPUe6CjSxKUGr9DMpkUGFlHLUfdrJ7Tx6t2q0baPGWu0eUBAAAAAByEUO+Bzu05pqOLE0r/fHDeKt2V8KZi779FO15doMJzWQZWBwAAAABwFObU1wCW/EKd3XFYJi8vhTatb3Q5AAAAAAAHIdTXECHNLoX5wgs5BlcCAAAAAHAUht97IJ9AP/mHhZTOqW/94K0K79hcZ3ccVtbRdKPLAwAAAAA4SI0I9RkZGXrttde0ZMkSpaamKjIyUnfffbdefvllTZo0SR999JHefvttTZw40ehSHaLr1DHqOnVMmW0pK7Zo67MfGFQRYKzT5/L1weKDWpFwUheyixQU4KNu7cL1xOi26tw63OjyAAAA4GQHj13QO18c0MZdp5Wbb1FIkK8G9m6oR0e1UZMGtYwu75p4fKjftWuXhgwZIrPZrODgYLVr106nTp3SW2+9peTkZGVmZkqSunTpYmyhDnTw09VK+WazvHx9VLdNE3WYMELBDcJVUlhUeoyXn4+GrX5dx75K0O43l5Ruv+GNCQqIrKO1cS8ZUTrgUMXFVj09c6vmfnlAxZayr3Pcvj9D7y06qJu7R2n+K/3UqH6wQVUCAADAWTIvFmrcn3/U8vUnL9v3096zevWj3Yq7vYXe/WtfBQW6Zzz26Dn1GRkZGjZsmMxmsyZPnqz09HTt2LFDZrNZM2bM0IoVK5SYmCiTyaROnToZXa7DZB01Kz1hj9Lid2rvnKX6ftyriujSQn1mPFp6jLXIog2T3lbHSXerbrumkqQmg3soelB3bXx6jlGlAw5jsVg18unvNXvB/ssC/a/9uM2sPg98o5Nm1psAAADwJOcuFOjG8cvLDfS/sFpt+nT5Ed322ErlF1hcWJ3jeHSonzRpklJTUzVx4kTNnDlTISEhpfumTp2qzp07y2KxqFmzZgoNDTWwUuc6u+2gkhetV8yIvors3rp0+7ndR7XvnWW68a3fK6hBmPq8/pi2PveB8k+fN7BawDGmv7tT3/x4olLHnjTn6q6nvpfNZnNyVQAAAHCVB//8o/YnX6jUsRt2ntaTM7Y4tyAn8dhQn5SUpIULFyoiIkKvvPJKucd069ZNktS5c+fSbYsWLdLIkSPVtGlTBQUFqU2bNvrzn/+snBz3for386xFslpK1HXK6LLb31gsa0mJhq95XeaNe3Vs6UaDKgQcJy/fon/9Z79dbbbvz9D67WYnVQQAAABX2p98Xt8mpNrV5t/LDutsZr6TKnIejw31CxYskNVqVVxcnGrVKn/hg8DAQEllQ/3MmTPl7e2tl19+Wd99950ef/xxvfPOOxo8eLCs1oqH8FZ32SlmHVu6UQ1v6qR6vdqWbrdZSnQ28aACwmvryMIfDKwQcJz/rDyq81lFVz/wf/zrP0lOqAYAAACu9s4XB+xuU1Rs1YdfHXJCNc7lnisBVEJ8fLwkqX///hUek5p66ZubX4f6b775RpGRkaV/vvnmmxUZGam4uDht2LBBN910k921dO/eXWazfU8AfW1emqaedvd1JbvfXKyYEX3VdcporbrneUlSvV5t1XJ0fyV9+K16vvCQlg2aopIC+8NQbKtYFZuq/5ce6XWelrxqK92crujoaKPLcbma8vnPB4+Q/Lva3W7xyp8V/Z8HHV8QAACoNmrK/VBFasrnPxP6mOTTwO52z7/+uWZPG+qEiq4uKipK27Zts7udx4b648ePS5KaNm1a7n6LxaKNGy8NNf91qP91oP9F9+7dJUlpaWlVqsVsNtvd1s/kLdW3s5/N+zSvwT0V7r94OE2fRP93+L1PUIBueGOCtr80Xwf+vUpDvnpB1z17vxKnzbOvY0mn0k+pyFZidzuXCymRvCRrSUmV/3u6tZry+ZuUSP72N7Pa/Dz75wIAAGrO/VBFasrnD/auUtotLLK53c/FY0N9bm6uJCk/v/w5EQsXLlRGRoZCQkIUExNzxXP98MOlYelt27a94nEViYqKsruNr81LcvKD7x7PP6icE2d0YN5KSdKGJ2dr+NqZOvHdVp3eYt8w5IYNGrrHk3pvb1kleXl7q0GjRkaX43I15fOfD/BSXhXaeZmKPPrnAgAAas79UEVqyuc/421RcRXa+ftJEQb9XKqSGyUPDvVRUVE6f/68duzYoT59+pTZl56erilTpkiSOnXqJJPJVOF50tLS9Ne//lWDBw+u8rvsqzKEojivQPNbjK1Sf5XRaEBXxQzvq6W3TC7dln38tLa/NF99Z03QsgGTZckvrPT5Dh0+JN+gAGeU6lDRAxco7UyeGkQ1UOpe+xbO8AQ15fPPX3FEY5/90e52ccOv0ycve+7PBQAA1Jz7oYrUlM//x5lb9Y9P9trd7pU/P6g/PPCaEypyHo9dKG/gwIGSpBkzZujQof8udpCYmKj+/fsrIyNDkq4Y1HNycnTnnXfKz89PH330kVPrdbW0+J36vM045aZllNl+YN5KLekz0a5AD1Q39wyKUURd+79kemJ01UbjAAAAoHp57F777+sCA7w1/s5WTqjGuTw21E+dOlXh4eE6efKk2rdvr44dO6pVq1bq2bOnmjdvrgEDBkgqO5/+1/Lz8zVs2DAdO3ZMq1evVoMG9i+yAMAY/n7e+sPY9na1ufG6+urV6fI1NQAAAOB+WjYJ1T2DmtnV5ncj26huaBUWZjKYx4b66OhoJSQkaOjQoQoICFBKSorCwsI0d+5crVixovTpfXmhvri4WPfcc4+2bdum7777Tu3atXN1+QCu0TMPd1bc0BaVOja2aW0t+sctV5yKAwAAAPfy0Qs3qnv7iEodO7hvtF57uoeTK3IOj51TL11a2G758uWXbc/JyVFKSoq8vLzUoUOHMvt+ebf9999/r2+//VY9ezr2tXIAXMPLy6RPXrpZMY1C9MZne5WTZ7nsGJNJGt6viT6cfqPC61T/NSEAAABQeSHBfvrhw9v1+N83asF3R1VSYrvsGH8/b/3untaaObmn/Hy9Dajy2nl0qK/Ivn37ZLPZFBsbq6CgoDL7JkyYoC+//FLPPPOMgoKCtGXLltJ9LVq0KPeVdwCqJy8vk16c2E1TxnfUp8uPaMX6k/p+yykVWayqFeSj3YvuVkx0iNFlAgAAwElqBfnq05f7acZTPfTeooN69cOfVVhslb+fl/4+sbseGtHK7R/ueOzw+yvZs2ePpPKH3n/33XeSpFdffVV9+vQp88+KFStcWicAxwit5acJY9rp2zm3KTLs0kW7di0/Aj0AAEAN0bBesJ5/4rrSxZQj6gToj+M7un2glwj1l+1LSUmRzWYr95/x48e7uFLHazq0t3q/+kiZbS1H99f49EVqMtg955AAAAAAQE1FqK9hmtzeSydW/lT651rRkYqNG6gz2w4aWBUAAAAAoCpq5Jz6+Ph4o0twGr/QIN35wyx5B/gp71SGvPx9FdKkvpIX/ajNz7yv+j1aa8OTsy8dbDLp+n88rq1/+VA9po0ztnAAAAAAgN1qZKj3ZEVZeTr6VYKKcwu0e9YiNezXWZ0m3a1Nf3xXDW/urDOJB2WzlEiS2j86TGcSD+jc7qMGVw0AAAAAqIoaOfze04V1iFHmnmOSpPBOLZS599K/NxncQ8e/uzT0vk7rxmo6tJd+fmOxYXUCAAAAAK4NT+o9UFj7ZqVBPrxTc51clShJativi7a9+JkkqX6vtqrVuJ5GbnpbkhQYWUd9Xn9MgfXq6uAnq40pHAAAAABgF0K9hwmKCpNsNuWZMyVJYW2bavebixXRtZUuHk6TJa9AknTwk9VlwvvgxdO1//3lOrEy0ZC6AQAAAAD2I9R7mLAOMaVP6SWpKCtXbcbdpsLM7DKr3gMAAAAA3B+h3sOkrt2u1LXbS/+8fMgzkqQ7183SqpHTKmy38gr7AAAAAADVE6G+hlja7w9GlwAAAAAAcDBWvwcAAAAAwE0R6gEAAAAAcFOEegAAAAAA3BRz6qspn0B/xSV/ZnQZleYT6G90CQAAAABQ4xDqqymTySTfoACjywAAAAAAVGMMvwcAAAAAwE0R6gEAAAAAcFOEegAAAAAA3BShHgAAAAAAN0WoBwAAAADATRHqAQAAAABwU4R6AAAAAADcFKEeAAAAAAA3RagHAAAAAMBNEeoBAAAAAHBThHoAAAAAANwUoR4AAAAAADdFqAcAAAAAwE0R6gEAAAAAcFOEegAAAAAA3BShHgAAAAAAN0WoBwAAAADATRHqAQAAAABwUz5GF4Dy2Ww2WfILjS6j0nwC/WUymYwuA8AVuNt1xdm4bgE1C9fAy3EdBDwDob6asuQXan6LsUaXUWlxyZ/JNyjA6DIAXIG7XVecjesWULNwDbwc10HAMzD8HgAAAAAAN0WoBwAAAADATRHqAQAAAABwU4R6AAAAAADcFAvloUYoLrbqhDlHxRarJMlSYpXNZqsxK77abDaZM/JLP3+xxaqCQosC/GvOJSA3r1ipp3NLfwYlVpvBFQEAAFfKy7fo5K/uB2vavYDVaivz+S0lVlksVvn41JznvJkXC8vcD2flFCm0lp/BVV27mnNHjxrFZrNp064zWvBdsrbty9DPhzJVUFhSuv/0uQKF3/iZrmsboT6d62n8na3UonGogRU7njkjT/OWHlbCDrO27z+n0+fyS/edySxQSJ9P1KFlXXVrG6G7BzbTbdc3kre351zUi4utWvrDcX3z4wlt35+hpGMXZf3VL29zRr4aD/qPurULV7/uDfTg8FYKq+1vYMUAAMCRLBarViSc1Nfxx7V9f4b2H72gkpKy9wKNBi5Qt7YRurl7lB4c1lKRYYEGVux4B45d0L+XHdaW3We1IylDWTnFpftOnytQ6PWfqGubcPVoH6m4oS3Uo0OkgdU6XnZukeavSNb3W09p+/5zOpaWXbrvTGaBal//qWKb1la3duEackO0Rt0a45YPvUw2m61mfUXlJorzCtzqtSvV5ZUoJSVWffz1Yb29YL92H8q0q+1t1zfSH8d31MDejZxUnWts35+h1z7erSXfp8hiqfz/vWMahejxe9to4n3tFBjgfhezX1zMLtKsT/fqvcUHlX42r9LtAvy9dd+Q5pr6UCe1ianjvAIN5G7XFWerLtctAK7BNfBynnodzMkr1huf7dXcLw8q9XRupdv5+Xrp3ttiNHV8J3WMDXNihc63Yv0J/fOTvYr/Kd2udt3bR2jS/e0UN7SlvLzcd0Tr8VPZeu3jPfrkmyPKySu+eoP/F17HX78ZEas/juuoeuHu8wUPob6aqsovnqg+7TV4yfSy58nNV9bRdCUvWq+kD7+VrcTqyDJLVYdfCgePXdBDf0vQ5p/PXNN5xt/ZSrOm9FKdUPd6altQaNHz7+zU6/P2lHkiba/WzWrr4xdvVJ/O9R1YnWus2piq3z6/wa5f4P/L389b05/oqskPdvS44Wjc0JZVHa5bAFyHa+DlPPE6+MNPp/TwtA1lnsjay8fHpL/+rquefbizfH3d617gbGa+Jr6yWV+sOnZN5+nfo4E+nH6jYqJDHFSZa9hsNs398oCm/DPRrjD/vyLqBmjOn6/XqFtjHFid87jX31JUytElCVo/8U2t//1b2vWPL+Xl462e08er96uPGF2a03z01SF1uffraw70kjRv6WG1v3uJtu6+9nO5SvLJLHUbs1QzPtp9TYFekg6mXNQN41bo+Tk75C7f+ZWUWDXx5U0a/Piqawr0klRYVKJn3timG8YtLzNlAQAAVF9Wq01T/vGTBvz2u2sK9JJksdg0bc4O9YpbplTztd1XuNK6xHS1v2vJNQd6SfohMV0dRy7Rf75LdkBlrnExu0iDH1+lx/++6ZoCvSRlnC/QvX+M1/1/+kGFRSVXb2AwQr0HOrfnmI4uTtDRReu1751lWjH0OeWmZSj2/lvkH+5Z88Yl6Y1P9+rhaQll5sxfq1Nn8nTLI99p/Tb7hiwZ4cCxC7px/ArtT77gsHNarTZNf3enJr26pdoHe4vFqvufWad//SfJoefduuesbhy/XGnX+CUBAABwLqvVpoenJWjmv/c49Lw7D5zTDeOX61jqtX1J4ArfJZzU4MdX6ez5AoedMzffovufWacPFh902Dmd5XxWoQb89lut3pTm0PMu+O6ohv1+tfILLA49r6MR6msAS36hzu44LJOXl0Kbut+Q6iv5+OtD+sPrW51y7tx8i+74/RrtTMpwyvkdIdWcq0G/W2nX3HF7zF6wX3+dvd0p53YEm82mR1/c6JBvpMtz+HiWbn1spS5kFTrl/AAA4No99doWzVt62CnnPn4qR4Me/U5nM6vv6L1Nu07r7qe/d8oTZZtN+t0LG/TlaufcazlCQaFFtz+xSjuSzjnl/Gs2n9LoKT9c82hYZyLU1xAhzS6F+cILOQZX4jiHj1/UhJc32dUmccFwnVwzRokLhlfq+OzcYsU9+6MKCqvft3M2m00PP59g13Bzez+/JL30/s9al1g9RywsXHlUH311yK429v4M9idf0FOvOeeLIwAAcG2+WXdCb3++36429t4LJJ/M1hMv2XfP6SrZuUW6/5l1do1Ytffz22zSb59PqLZTEf72rx3asvtspY+vyv3wNz+e0OwF9v09cyVCvQfyCfSTf1iI/MNDVadNE/V6+bcK79hcZ3ccVtbR6hnO7FVSYtVDf0tQfoF930hGRQQpun6woiKCKt0m6egFPf/OTntLdLoPlxyye4hRVT6/JP3mbwnXPDfJ0U6fu7QQjL2q8jP497LDWrH+hN19AQAA5zmfVahHX9xod7uq3AssWpOiL1YdtbsvZ/vTrEQdP2XfQ7uqfP6snGI9Mn1DtZuWueXnM/rHJ3vtalPV++Fn3kzUkRNZdrVxlRoR6jMyMjR16lS1bNlSAQEBaty4sZ588knl5ubq4Ycflslk0uzZs40u02G6Th2j+/Z9rPv2fqQRP/xTbR8arJQVWxQ/fobRpTnMp8uPaOPO0y7r7/V5e3Qo5aLL+ruarJwiTf6H654eH0vL1oyPdrusv8p47q1tOnfBdcPiH//7Jlksznl7BAAAsN/zc3Y4bQpieX7/yuZqNXpzZ1KG3vnigMv6W7kxVV/HH3dZf1djs9n0+EsbXTYsPr+gRH94bYtL+rKX+76MupJ27dqlIUOGyGw2Kzg4WO3atdOpU6f01ltvKTk5WZmZl95l3qVLF2MLdaCDn65Wyjeb5eXro7ptmqjDhBEKbhCuksKi0mO8/Hw0bPXrOvZVgna/uaR0+w1vTFBAZB2tjXvJiNIrxWaz2T3M6lpZrTa9+2WS/jmlt0v7rciny48oK8e1T87nfnlAf/ldF/n7ebu03/Kcu1Cgz7917WqsJ825+ubHE7rrlmYu7delTCa1e2SoWj8wSLWiI1VwLkvHvtmkXa8tlCWfdQUAeL6Ov79L4R2bK7xTc4U0ra+ck2e0qOcTRpeFcmTnFuljJ82jr8iZzAJ9ufqYHhjWyqX9VmS2gxcJroy3P99fbe6FNu06o10HMl3a54qEkzqamqXm0dVr8XGPflKfkZGhYcOGyWw2a/LkyUpPT9eOHTtkNps1Y8YMrVixQomJiTKZTOrUqZPR5TpM1lGz0hP2KC1+p/bOWarvx72qiC4t1GfGo6XHWIss2jDpbXWcdLfqtmsqSWoyuIeiB3XXxqfnGFV6pSTuzXDaQhhX8vHXh5WXb/y3szabTXMWuv4ifvZ8gRavSXF5v+X5+OvDDn3bQWUZ8XN3pZ4vjFfP6eN14VCqtvzlI6Us36x2D9+uWz55RjKZjC4PAJyu23NxiurbQdkpZhWer/4rntdk81ckKzvX9VMDq8u9wPmsQpc/4JAuveou6egFl/dbHiP+W9hs0rsuHB1RWR4d6idNmqTU1FRNnDhRM2fOVEhISOm+qVOnqnPnzrJYLGrWrJlCQ6vXty2OdHbbQSUvWq+YEX0V2b116fZzu49q3zvLdONbv1dQgzD1ef0xbX3uA+WfPm9gtVf3n5XGvC/zQnaRVm9ONaTvX9t7+LxDX19nj/+srB5zyRYaNKdt7ZZTynDgq2Kqkzqx0Wr7myFKWbFFPzz8ug7PX6vE5/+tn57/txrc0FExI/oaXSIAON2iXk/oP+0f0uoxLyqvmt8P1XRG3ZNs2X1Wx08Z/4XPN+tOGPKAQ7q0ULHRiopLtHhtiiF9V5f74V/z2FCflJSkhQsXKiIiQq+88kq5x3Tr1k2S1Llz59JtCQkJGjhwoBo0aCB/f39FR0dr9OjRSkqqHt/KVdXPsxbJailR1ymjy25/Y7GsJSUavuZ1mTfu1bGl9i824mrb9hn3ijkj+y6tYb+Bn9/Avn9RVFyi3YdcO9Tq17ZXg5+BM8TcdYNMXl7a//6KMtsPz1+r4rwCtRh5k0GVAYDr5Jw4Y3QJqASr1WbIqM1fbN9vXN+/qOn3g/uOnHfKK/wq46Q5t9q94tBjQ/2CBQtktVoVFxenWrVqlXtMYGCgpLKh/vz58+rYsaPeeustrV69WjNmzNC+ffvUp08fpaYa/5S2qrJTzDq2dKMa3tRJ9Xq1Ld1us5TobOJBBYTX1pGFPxhYYeVYrTbtdPHcmV+rDhdxI0Nl+tk8ly5IU569h8+rqNi4Bes8NdRHdGkpa0mJMnaWnZ9YUliszL0piujSwqDKAAAo6/Dxi4YMvf9FdbgXMLKG6vH5jb0nN7r//+WxoT4+Pl6S1L9//wqP+SWk/zrUDx8+XLNmzdKoUaN08803Ky4uTkuWLNHFixe1ePFi5xbtZLvfvPRU/tdP6+v1aquWo/sr6cNv1fOFh+Qd4GdghVd36kyeoa9WO1gNVsA3ugb6N/7vgDME1a+rwsxsWYsuXzciz5ypgPDa8vL1+LVVAQBuwOjfxUb3b3QN5ox8ZeUUXf1AJzL6v8HBlAuG9v+/TLbq9rJBB2ncuLFSU1O1c+fOcle2t1gsatCggTIyMpScnKzmzZtXeK5z584pIiJCs2fP1oQJE+yupXv37jKbzXa18bV5aZq1p9192cMnKEDDv5+p/XOX68C/V2nIVy8o4+dkJU6bZ/e5pnv9pGKT85+eWrzCdLrOkxXuT1ww/IrvnIyKCJSPt5csJVaZMyoeNmPOyFOP+5Zdtt3Lmq0GF2baV7SDnQ35jYp8m5a7z1GfX6r4ZxCe/akCio/YV7QD5fp11YVaI8rdd7XPL13734GAon0Kz/nCrpqriytdV+7ePFtevt5a1P3xy/bd8Nbv1XLUzfq89YMqyjJ2pIYjueq6BaB6sPfe6s4f/inf4ACPXv3eXa+DeX7tdb7WveXuc8W9gH/RIUXkzLevaAc7VffPspnKfxjn7PthSYo6/5q8bbn2Fe1AF4JuV25Ar3L3ueJ+ODRvrUIKEuwruhKioqK0bds2u9t57GOX3NxLf8ny88v/D7Vw4UJlZGQoJCREMTExl+0vKSmR1WrV8ePH9eyzzyoqKkr33lv+xeNqzGaz0tLS7GrjZ/KW6lepu0rr8fyDyjlxRgfmrZQkbXhytoavnakT323V6S32rSFwKv2UimwumNfiVyTVqXh3VESQousHX/U0Pt5elTruf1lLLHb/t3S4mALJt/xdzv78knQu46yUY+DPoE5TqfwZNZX+/FLVfwYFebnG/x2ooitdV0ryC+UbXLvcfd7+l/7CWfKN/Vbe0Vx23QJQLbji3srduO11MDTK0HuBwoJ84+8FalulCt4y7Ir7QXP6Kakkp0ptHaJBjhRQ/i5XfP6sixeUlVF97gc9NtRHRUXp/Pnz2rFjh/r06VNmX3p6uqZMmSJJ6tSpk0zlvKrp5ptv1saNlxaNa9mypeLj4xUZGVnlWuzla/OSnPjFaaMBXRUzvK+W3jK5dFv28dPa/tJ89Z01QcsGTLbrvdQNGzR0yTe9JaYgXWnMgznjyk8R7flmsjw+XhbVb9SoMqU6zTk/qaL11x31+a90rojwEPnXNu5nkO8bqIpWVbja55eu/e9AUICX6hr8d6CqrnRdyTt9XrVjo+Xl53PZEPygqDAVnLsoa7Hxr3R0JFddtwBUD86+t3JH7nodLPAJVkUzml1xLxDob1KYwfcC6aZCWStItc6+H5bNqoZR4TKp/IcBrpAV6KOK3kHgivvhOqH+CvZ3/N+BquRGyYOH30+aNElvv/22GjdurLVr1yo2NlaSlJiYqAceeEBHjx5VcXGxJkyYoNmzZ1/W/uDBg7pw4YKOHTum119/XWfOnNHGjRvVpEkTl9RfnFeg+S3GuqQvR4hL/ky+QRV8XeZgDW9ZUOXF2k6uGaPo+sFKPZ2rxoP+Y3f70YNj9J/XBlSpb0f581vb9PIHP1ep7bV+fpNJytr8oGoFVTBUwAWST2ap5dAvq9z+Wn8Gs5/rowlj2lW5fyNd6brS9U9j1Pmpe/TtiL/qzNb/jtTx9vfVmP0f6/SWJK2Ne8lVpbqEK69bAIxn771VTRh+767XQXNGnhoMWFDl9td6LzDjqR6a+ptOVe7fEW5/YpW+21C1Rbyv9fO3blZbB5bdU6W+HeXL1cd07x/jq9T2Wj+/JG369A716Vx9hv547EJ5U6dOVXh4uE6ePKn27durY8eOatWqlXr27KnmzZtrwIBLwezXi+T9WuvWrdWrVy+NGTNG33//vbKzs/Xaa6+58iOgAt3bRRjYd9VGazi0hvbGff42MXUMDfSS1Dw6RHVCjFvQ0ci/f850bOkm2axWtXtkaJntreIGyjcoQEeXrDeoMgAAyoqKCFKjeleeN+9M3arBvYCRNRh5L1pag4Gf38vLpM6x4Yb1Xx6PDfXR0dFKSEjQ0KFDFRAQoJSUFIWFhWnu3LlasWKFDh06JKniUP9rderUUcuWLXXkiHGLg+G/+nSuVyP7/kWvjpEqZ8aIS1SHz28ymdS7kzF1BAZ4q1NsmCF9O9uFAyd04OOVaja0t/p/OEWt7r9F3ac9qJ7Pj5N50z4dXbLB6BIBwOma33OTOj01Up2eGqmA8FD5hgSV/rn5PTcZXR5+xah7El8fL3VrZ3ygq+n3w80a1VJURKAhfXdpHaagwOo1i716VeNgbdu21fLlyy/bnpOTo5SUFHl5ealDhw5XPc+ZM2d08OBB9epV/gqLcK0Hh7XUX/+1XSUlrp050rpZbV3fxfiLWMN6wRpyQ7S+TajakKtr8fBdsS7vszwP3xWrlRtd//njbm+hwADPvWz+9Ld5yjl5VrFjByr6lutUkJmlpI++087XFkqeOVMLAMqIve8WRV3fvsy26/50nyRd+oJzEaOWqouH72qtRWtSXN7vyIHNVCfU3+X9/q9b+zRSo3pBSjvj2rfSBPh7674hLVzaZ3lMJpN+MyK2ylNSr0V1uR/+Nc+9O72Cffv2yWazKTY2VkFBZYfujB07Vi1btlSXLl1Up04dHT58WLNmzZKPj4/+8Ic/GFQxfq1R/WCN6N9Ui9emuLTfJ0a3LXdRRSM8Mbqty0N959Zh1eKbWUm6s39TNawXpFMu/kX2+L1tXdqfq9msVu2b+432zf3G6FIAwBArR04zugRU0q3XN1KLxiFKPlnRcmnO8cTo6nEv4OPjpUdHtdHf/rXDpf3eN6S5wmob/6WGJP3untZ69aPdslpd9+ChVpCvxt7R0mX9VZbHDr+/kj179kgqf+h979699e233+qhhx7SkCFD9Prrr+vGG2/Url271LJl9fsPWFNNHnf1ERaOFFE3QA8Oqz7//Qf3jVb7FnVc2ueU8R2rzZcavr5eeiqu/dUPdKABPRvoumowhw4AAFya1zz5wY4u7bNXx0jdcF31WRztd/e0UWgt16115O1t0lNjXXsPfiVNG4Zo9G2Xv5rcmR4b1UahtYxb26kihPr/MXHiRP300086f/688vPzdejQIc2dO1dNmzZ1dZm4gj6d6+v397tuBfI5f76+Wgy1+oW3t5c+fvEmeXu7JmTffmO07r/d+KFWv/aHBzq4bJGYoAAfvf/8DS7pCwAAVM6jo9roRheFbD9fL330wo3V5gGHJNUPD9Q//+i66cHP/KZTtVtb6I2pvRVR1zVvcGjROETPP97VJX3Zi1BfwzQd2lu9X32kzLaWo/trfPoiNRncw6CqquaVSd3VPDrErjbmjDylns6t1DtMfzHq1hiNutW13wJWRo8OkZo63r7XqVTl89cO8dN7f7uhWv0Sky4NO5v34o3y87XvMlaVn8GMP/RQ8+hQe0sEAABO5OVl0kcv3KTAAG+72lXlXmD6E9epXYu69pbodL+5K1aD+0bb1aYqn79Dy7r666PVL9DWCw/Uv57rY1ebqnx+k0n6+IWbFGzwW6Aq4rHvqXd3znpP/Y3/elLJX67TqXWXFpWoFR2pm+Y8JZmkvf/6WidWJlbpvEa953TPoUzd/JsVOp9V5JTzd2kTph8+uL1aPaX/teJiq+76w1qtWH/SKef38/XSN28P0q3X2/fLwpU+X5Gssc+tc9o6buPvbKUPp98oL6/q9aVGVTjruuKu3PX9zACqhmvg5TzlOvh1fIrumRzvtEWU770tRp+/2k/e3tXzeejZzHzd9NAKHTh20Snnrx8eqIR5Q9WqaW2nnP9a2Ww2PfNGol77eI/T+nhjai89WY2mHvyv6vk3E1XmFxqkUdvnasy+jzV8zesasf4NPZCyQNfPfEwmH2/V79Fa6Rv2XjrYZNL1/3hcW//yoaxFFmMLr6KOsWFaPXewwus4PnR3bROu1e8OrraBXro0t/zLmQM05AbHh25/P28t+sct1TrQS9L9Q1vooxecE7ofuKOl3p92g0cEegAAPNWIAc00/5V+8vFx/O/rewY106cv31xtA70kRYYFau17Q9S2eR2HnzsqIlBr3xtcbQO9dGkl/Fef6qE/POCc9ZZef7pntQ70EqHe4xRl5enoVwna/8EKLRs0RT/97WOd3XFIm/74rhr07aAziQdls5RIkto/OkxnEg/o3O6jBld9bbq3j9TGf9/h0PnVYwY31w8f3q7IMGPef2mPwAAfff3mQP3hgfYOe3998+gQxX8wRMP6NXHMCZ1s/J2xWv72IIe9r9THx6QXJlyneX+/ST4+XCYBAKjuRg9urlXvDFZ0/WCHnM/b26TnfttZ/3mtv/x87Rveb4RG9YO1/uOhurO/4+7dru9ST5s+GaYOrarXPPrymEwm/eOPvfT2s33sno5RkTohfvr81X7643jXLshYFdyteqCwDjHK3HNMkhTeqYUy91769yaDe+j4dz9Jkuq0bqymQ3vp5zcWG1anI7WOqaPNnw7T3yd2k+81hLB6YQFa/M9btOC1/qodUv1WtqyIn6+3/jmlt9Z/PFQtm1zb3O/f399Ouxfdpeu7VJ/VXStjyI2Nte+rkRp7x7Ut6NelTZi2LbhTf320K0/oAQBwIwN6NdTeJXdf83vE27eooy2fDdNLk7pX6yf0/yuiboC+emOgPnvlZtUNrfp9bIC/t/45pZfWfzxUMXauX2Ukk8mkife1089f3qUbul7bfeywm5to/9cjdV81Wyi6Isypr6auZd7X6D0f6ptBU5RnzlS/D/6ok6sSlfzlj7p782wtu+WPsuQVqPWDt6rz06NkLSqWJAVG1lFRTr52vb5QBz9ZbXef1WlO1klzjt5bdFDvLz6o0+fyK9WmfYs6emJ0W429o2W1fE2FPQqLSrRozTHNWZikTbvOVKpNaC1fjRveSo+NalMtF4Gx1/b9GXpnYZI+/y5Z+QUllWozqE9DPTG6re64qYnHPp1nPmlZ1em6BcD5uAZezpOvg7sPZeqdhUn6dPkR5eZXbpppvx4N9MTothrRv6l87VyIt7q5kFWofy87rDkLD+jQ8crNtW9UL0iPjmqj397dWg0ig5xcoXNZrTat2ZymOQuTtHz9yUq9y97Xx0v3DGqmJ0a3Vd+u9avdItFXQqivpqr6iycoKkzDVr+mhZ1+K0m6e+PbWvfYP+Xl46POf7hH3z/4SrntBi+erv3vL3e7hfKupKi4ROsS07VtX4a27z+npGMXlFdgkbeXSSHBvuocG6Zu7SLUu1M99egQ4Vb/x62sfUfOa8PO09q+P0M7D5zT+axCFVusCvT3UYvGIereLlLd2oVrQK+GqlVNV/O8FuezChW/9ZS27z+nbfvP6vipXBUUWeTn662IOv66rm2EurWL0E3doq55hIM74Ia2rOp43QLgPFwDL1cTroNZOUX6fuspbd9/6X7waGq2Coos8vXxUnjtAF3XNlzd2kXoxuvqq3VMHaPLdTibzabNP5/R1j1ntX1/hvYcPq/s3GLZbDYFB/qobfM66t4+Qj3aR+qmblEe+WDjpDlH6xLT//9+MEOnz+WrqLhE/n7eiq4XrG7tIi7dD/dsqHrh1X/qbXl8jC4AjhXWIaZ0uL0kFWXlqs2421SYma0TK38ysDLX8/P11q3XR1f7hd6cqX3Lumrf0v2fvFdV3VB/jRwUo5GDqt8rCQEAgPOF1vLTXbc00123NDO6FEOYTCZd36W+202rdKTGUbX0wLBWemBYK6NLcRpCvYdJXbtdqWu3l/55+ZBnJEl3rpulVSOnVdhu5RX2AQAAAACqJ0J9DbG03x+MLgEAAAAA4GCeN2kCAAAAAIAaglAPAAAAAICbItQDAAAAAOCmmFNfTfkE+isu+TOjy6g0n0B/o0sAAAAAgBqHUF9NmUwmj39vKAAAAADg2jD8HgAAAAAAN0WoBwDYLapPe41PX6Tx6YvU66WHyz0mIDxUDxxfoPHpizR48XQXVwgAzsV1EEB1QagHAFSZJb9Qze+6QV5+l8/manHPzTKZTLIWWwyoDABcg+sgAKMR6gEAVXbiu5/kXzdETW7rcdm+lmP6K/X7nSopKjagMgBwDa6DAIxGqAcAVNm5PceUuS9FLccMKLM9oktL1W3TREcWxpfbrsngHhqy9O+KS/5McUc+1ZClf1fj/7khHrriFY3e/YFM3pf/qmrYr7PGpy9Su0eGltne+sFbdceqGRp7dL7ijnyq2xY9r6jr21/jpwSAinEdBGA0Qj0A4Joc/k+8Gt7cSUFRYaXbWt03QPlnL+jkmu2XHd963G0a8PGf5F+3ln7+5yL9PGux/OvW0i3z/qTYsQNLjzvyxToFRtZRo/5dLztHi1H9ZC226OiShNJtN86epF4vP6zsY2Zte/FT7Xr9C/mFBunWhX9T41u7O/hTA8B/cR0EYCRCPQDgmhxdvF42i1Ut7u0nSfIO8FPMnX2VvGi9bCXWMsf61Q5W97+OVdaxdK24/Vnt/dfX2vuvr7Xi9meVlWJWj2nj5BcaJEk6tnSjSgqL1WLUzWXO4RMcoCaDeyg1fqcKzmVJkpoM6akWI2/Slmfe14+PzdKBj1dq39xvtHzIM8rcl6KeLz7k/B8EgBqL6yAAIxHqAQDXpPB8jk6u3qaW/38z2/T2XvKrHazD/7l8yGnDmzrJNzhQSR9+p+Kc/NLtxTn5SvrwW/nWClSDGztJkoou5Ojkmm1qPKhb6Q2uJDW7o498gwKU/OW60m3NR96kouy8S3Nbw0JK//GrHayTq7cppEl9hTZv4JwfAIAaj+sgACNdvkwnAAB2OvyfeA2a/2fV69lGLccM0Nkdh3XxUOplx9VqUl+SdOHgycv2/bItpGn90m1HvlinZnf0UbPh1+vQZ2slSS1G3azC89k6ufq/Q1rrtGokv5Agjdn7UYU1BkTWUdbR9Kp9QAC4Cq6DAIxCqAcAXLNT635W7qlz6vL0KDXo216bn3nfIedNi9+p/IyLajHqZh36bK2CG0Uoqk87HfxkddlXRJlMys+4qPVPvFHhuS4cOOGQmgCgPFwHARiFUA8AuGY2q1XJi35Up0l3y5JfqGNfbSj3uOzjpyVJdVo3VvqGPWX21YltXOYYSbKVWHXsqwS1e+QO1WpST83vukEmLy8d+eLHMm2zjqYremADnd1+WJa8Akd+NACoFK6DAIzCnHoAgEMc/GS1ds38Qpv/9F6ZeaK/lr7+ZxXn5qvtb4bIJzigdLtPcIDa/maIinPydWr97jJtfrlxbTmqn1rcc7MuHklTxs7DZY5J/vJHeXl7q9tz95fbb0BE7Wv5aABQKVwHARiBJ/UAAIfITcvQrn98ccVjirLytO3Fz9Tn1Ud0x7ev6MjCdZKklqP7KbR5A22a8q6Ks/PKtMnce0yZ+4+r3e+Gyi80WNtfnn/ZeY+v2KLDC+LV9uHbFdaxuVLXbldBZraCG4QpsntrhTaL0uLeExz2WQGgPFwHARiBUA8AcKmD/16l/DPn1eHxO9V58ihJ0vl9xxX/0AydWJlYbpvkL9apx/PjZC0pUfLi9eUes/HpOUrftFetxw5Sx9/fJW9fH+WfvaBze46VewMMAEbhOgjAkUw2m81mdBEAAOcrzivQ/BZjjS6j2ohL/ky+QQFXPxCAR+AaeDmug4BnYE49AAAAAABuilAPAAAAAICbItQDAAAAAOCmCPUAAAAAALgpQj0AAAAAAG6KUA8AAAAAgJsi1AMAAAAA4KYI9QAAAAAAuClCPQAAAAAAbopQDwAAAACAmyLUAwAAAADgpgj1AAAAAAC4KUI9AAAAAABuilAPAAAAAICbItQDAAAAAOCmCPUAAAAAALgpQj0AAAAAAG6KUA8AQA1RUmJV5sVC5eQVy2azGV2Oy9lsNmXnFul8VqFKSqxGl2OIouISnbtQoIJCi9GlAAAcxMfoApwtIyNDr732mpYsWaLU1FRFRkbq7rvv1ssvv6xJkybpo48+0ttvv62JEycaXSoAGKrj7+9SeMfmCu/UXCFN6yvn5Bkt6vmE0WXhGtlsNq3fbtachUla8n2KLJZLYb5xVLAevaeNfjuyteqHBxpcpXOlnc7Ve4sO6v0lB5V+Nk+S5OfrpVG3xmjCmLbq3ameTCaTwVU6T1FxiZasTdGchUlK2HG6dHu3dhF6YnRbjRncXEGBHn9LWDkmk9o9MlStHxikWtGRKjiXpWPfbNKu1xbKkl9odHUAUC6TzYO/qt+1a5eGDBkis9ms4OBgxcbG6tSpUzp9+rSGDh2qzMxMbd68WQkJCbrhhhuMLhcAnKo4r0DzW4ytcP/49EUqyMxW5p6jCu/UXMU5+R4d6uOSP5NvUIDRZThVdm6RRk/5Qd9tSK3wGD9fL338wk26f2gLF1bmOu8tOqAJL22SpaTi252RA5vp05dvVmCA5wXbw8cv6vYJq3XkRFaFxzSsF6Tlbw9S17YRLqzM9a52DZSkni8+pHa/Harj325VavxO1WnVSG1/M0SntyZp1b0vSB5221wTroNATeB5v73+X0ZGhoYNGyaz2azJkydr2rRpCgkJkSS99tpr+tOf/iQfHx+ZTCZ16tTJ4GoBwHiLej2hnBNnJEl3/vBP+QZzo+fOCgotGjphdZkns+UpKrYq7tl1stpsGntHSxdV5xrvfpGkx/++6arHLV6botx8i5a9NUi+vp4zMzElLVs3PbRC5oz8Kx536kye+j38rTbMu0MdY8NcVF31Uyc2Wm1/M0QpK7Zo3W9nlm7PPnFGvV96WDEj+urYVxsMrBAAyuc5v7n+x6RJk5SamqqJEydq5syZpYFekqZOnarOnTvLYrGoWbNmCg0NNbBSAKgefgn08AyvfLD7qoH+1x6ellA6NN0TJJ/M0oSXN1f6+JUbU/XW5/ucWJHr/eZvCVcN9L/IyinWfX/6oUautfCLmLtukMnLS/vfX1Fm++H5a1WcV6AWI28yqDIAuDKPDPVJSUlauHChIiIi9Morr5R7TLdu3SRJnTt3rvA8Q4YMkclk0vPPP++MMgEAcIqi4hK9t/iAnW2s+mDJQSdV5HrvfnFAVqt9AXXOwiS721RX+5PP64fEdLva7Eu+oPXbzU6qqPqL6NJS1pISZew8XGZ7SWGxMvemKKKLZ05RAeD+PDLUL1iwQFarVXFxcapVq1a5xwQGXloUqKJQ/8UXX2jXrl3OKhEAAKf5Zt2JSj+h/bW5X9ofhKujouISffT1IbvbHU3N1totaU6oyPXeW1S1L2je/cK+L4M8SVD9uirMzJa16PI3A+SZMxUQXltevh47cxWAG/PIK1N8fLwkqX///hUek5p6adGg8kJ9VlaWnnrqKc2cOVNjx155QZXK6N69u8zmmvvNN4DqwdfmpWnqaXQZ1UZsq1gVmzzztWZZATdLQQPsbpd2Jk/RTVvKy+beq3xbvEKVWWdyldre+8BTqlW4xcEVuV5GyIOSr/1Plhct36SEzx9wQkXGu9o10DvQXyVFxeXuKym8tN0n0E9FxZ7zOkBPvg4C7igqKkrbtm2zu51Hhvrjx49Lkpo2bVrufovFoo0bN0oqP9T/+c9/VmxsrOLi4hwS6s1ms9LSPOObfwDuy8/kLdU3uorq41T6KRXZSowuwznq50tBVWuann5WKsl2bD2u5lcs1ala04tZubqY4QG/s5uXSL72N7OUyGPvWa52DSzJL5RvcO1y93n7X/phWvKLnFGaYTz6OgjUIB4Z6nNzcyVJ+fnlDz1cuHChMjIyFBISopiYmDL7tm3bpvfff1/bt293WD1RUVEOOxcAVJWvzUvigUyphg0aeuwTqhx/b12sSkObVQ2j6sok915A1moKkH2zyf+rToivgv0bObQeI5zztaigCu38vAoV2cj9P395rnYNzDt9XrVjo+Xl53PZEPygqDAVnLsoqwc9pZc8+zoIuKOq5kaPDPVRUVE6f/68duzYoT59+pTZl56erilTpkiSOnXqJJPJVLqvpKREjz76qCZOnKj27ds7rJ6qDKEAAEerzDuaa5JDhw957PuZU9Ky1fz2L+x+pfZdA2O0ZNZx5xTlYgMf+U7fbz1lVxtfHy8dTFyoeuGBTqrKdT5fkay4Z9fZ3W7Gc/fqqQdecHxB1cDVroEZu46oUb8uiujaSme2JpVu9/b3VViHZjq9JanCtu7Kk6+DQE3ikQvlDRw4UJI0Y8YMHTr034VyEhMT1b9/f2VkZEiSunTpUqbd7Nmzdfr0aVa7BwC4tWaNQjT0xsZ2t3tidFsnVGOMCWPs/yyjbo3xiEAvSSMHNVNkXfvCWmCAt8bd2cpJFVV/x5Zuks1qVbtHhpbZ3ipuoHyDAnR0yXqDKgOAK/PIJ/VTp07V559/rpMnT6p9+/Zq06aNCgoKdOTIEQ0ZMkTNmjXTqlWrysynz8jI0F//+lfNnDlTFotFFy5cKN1XUFCgCxcuKDQ0VF5eHvk9CACo+T03qVZ0pCQpIDxUXr4+6vTUSElSTupZHV3EDa07mfZ4V63dekoFhZWbLzuwd0Pd0quhk6tynWE3N1GfzvW0+eczlTq+VpCPnvttxa+5dTf+ft56YcJ1evzvmyrd5k8PdVLdUH8nVlW9XThwQgc+Xqm2D9+u/h9OUer3O1S7VSO1e/h2mTft09ElG4wuEQDKZbLZ7B2c5x6SkpI0ZcoU/fjjj5Kkdu3a6eGHH9YjjzyiFi1a6NixY9q6dat69ry0CuquXbvUtWvXK57z2LFjatasmbNLBwCnuNrQ08GLpyvq+vKnHpk37dPKkdOcVZoh4pI/8/hhp9+sO6F7p8RfNdj37hSple8MVu0QPxdV5hoZ5ws06NHvtOtA5hWPCw700dI3B+mW3p7zpcYv/vav7Xpx7q6rHvfYqDaa85fry0xL9DSVmYJk8vJSu0eGKnbsQNVqXE8FmVlKWbZJO19bKEteVVYpqN5qwnUQqAk8NtRXJCcnR6GhoTKZTMrOzlZQUFDp9vLmvvfv31/jxo3T+PHj1bt3bwUEcOED4J6YU19WTbmZTdx7VtPf3alvE05eNsc+sm6AHhnZWn/5XRcFBnjk4D1l5xZp+js79dHXh3Q+q+zK5V5eJt3Zv4mmPdZVnVuHG1Sh8y1ceVQzPtqtnQfOXbavTUxtTX6wox6+O9ajA73ENbA8NeU6CHg6z/wNfgX79u2TzWZTbGxsaaCXpFq1aqlfv37ltmnWrFmF+wAAqM56dIjU8tm36lhqtpZ8n6Ln39mhnDyL6ob66eSaMfL38za6RKcKCfbTzD/20osTu2nx2hQ98feNys6zqHYtX+1ZcrcaR9UyukSnGz24ue69LUY/7TmrtVtO6dWPflZOnkURdfy1/+uRHh/mAcDT1bgJ4nv27JFU/vvpAQDwVDHRIZo8rqNq17o0xD4owMfjA/2vBQb4aOwdLRX6/5+/VpBvjQj0vzCZTOrVqZ7+/LsupX8H/P28CfQA4AFq3JN6e0N9DZudAAAAAABwIzypBwAAAADATdW4J/Xx8fFGlwAAAAAAgEPUuCf1AAAAAAB4CkI9AAAAAABuilAPAAAAAICbItQDAAAAAOCmCPUAAAAAALgpQj0AAAAAAG6KUA8AAAAAgJsi1AMAAAAA4KYI9QAAAAAAuClCPQAAAAAAbsrH6AIAAK7hE+ivuOTPjC6j2vAJ9De6BAAuxDXwclwHAc9AqAeAGsJkMsk3KMDoMgDAEFwDAXgqht8DAAAAAOCmCPUAAAAAALgpQj0AAAAAAG6KUA8AAAAAgJsi1AMAAAAA4KYI9QAAAAAAuClCPQAAAAAAbopQDwAAAACAmyLUAwAAAADgpgj1AAAAAAC4KUI9AAAAAABuilAPAAAAAICbItQDAAAAAOCmCPUAAAAAALgpQj0AAAAAAG6KUA8AAAAAgJsi1AMAAAAA4KYI9QAAAAAAuCkfowtA+Ww2myz5hUaXUWk+gf4ymUxGlwEAAAAANQqhvpqy5BdqfouxRpdRaXHJn8k3KMDoMgAAAACgRmH4PQAAAAAAbopQDwAAAACAmyLUAwAAAADgpgj1AAAAAAC4KRbKAwDAw2XnFmln0jkdOZmtnLxiSVJegUUHjl1QbNPa8vLi7SUAALgrQj0AAB7opDlH7y06qEVrjulgykXZbGX3n88qUts7F6tWkK/6dI7UIyPbaET/pvL1ZRAfAADuhFAPAIAHOZqapSn/SNTXPxyX1Wq76vE5ecVas/mU1mw+pQaRQZr8YAc9GddePj6EewAA3AGh3oNE9WmvwUuml9lWnJuvrKPpSl60XkkffitbidWg6gAAzmS12jRnYZL+NCtReQWWKp0j/Wye/viPn/TFqmOa9/eb1LZ5HccWCQAAHI5Q74GOLklQavwOyWRSYGQdtRx1s3pOH6/arRpp85S5RpcHAHCw/AKLRk/5Qd/8eMIh5/tp71l1vfdrffbyzbrn1hiHnBMAADgHY+s80Lk9x3R0cYKOLlqvfe8s04qhzyk3LUOx998i//BQo8sDADhQQaFFd0xc7bBA/4vCohKNnvqDFnyb7NDzAgAAxyLU1wCW/EKd3XFYJi8vhTatb3Q5AAAHsdlseuC5HxX/U7pTzm+12vTgX37UDz+dcsr5AQDAtSPU1xAhzS6F+cILOQZXAgBwlM+WH9GiNSl2tUlcMFwn14xR4oLhlTreYrHpob8lKDu3qAoVAgAAZyPUeyCfQD/5h4XIPzxUddo0Ua+Xf6vwjs11dsdhZR11ztMcAIBrpZ/N06RXt9jdLioiSNH1gxUVEVTpNsdP5WjKPxPt7gsAADhfjQj1GRkZmjp1qlq2bKmAgAA1btxYTz75pHJzc/Xwww/LZDJp9uzZRpfpMF2njtF9+z7WfXs/0ogf/qm2Dw1Wyootih8/w+jSAAAO8tL7u3Qh23VPz+d+eUAHj11wWX8AAKByPH71+127dmnIkCEym80KDg5Wu3btdOrUKb311ltKTk5WZmamJKlLly7GFupABz9drZRvNsvL10d12zRRhwkjFNwgXCWF/7358/Lz0bDVr+vYVwna/eaS0u03vDFBAZF1tDbuJSNKBwBUQnZukT755ojL+333ywOaNbW3y/sFAAAV8+gn9RkZGRo2bJjMZrMmT56s9PR07dixQ2azWTNmzNCKFSuUmJgok8mkTp06GV2uw2QdNSs9YY/S4ndq75yl+n7cq4ro0kJ9Zjxaeoy1yKINk95Wx0l3q267ppKkJoN7KHpQd218eo5RpQMAKmH+imRl5xa7vN+Plx5Wbp7r+wUAABXz6FA/adIkpaamauLEiZo5c6ZCQkJK902dOlWdO3eWxWJRs2bNFBrqua96O7vtoJIXrVfMiL6K7N66dPu53Ue1751luvGt3yuoQZj6vP6Ytj73gfJPnzewWgDA1Sxff9KQfi9mF2nTz2cM6RsAAJTPY0N9UlKSFi5cqIiICL3yyivlHtOtWzdJUufOnUu3rVu3TiaT6bJ/3H14/s+zFslqKVHXKaPLbn9jsawlJRq+5nWZN+7VsaUbDaoQAFBZ2/dn1Mi+AQDA5Tx2Tv2CBQtktVoVFxenWrVqlXtMYGCgpLKh/hf/+te/dN1115X+OTg42DmFukh2ilnHlm5Ui5E3qV6vtjqzNUmSZLOU6GziQUV0aqEjC38wuEoAwNWcOpMrc0a+Yf0T6gEAqF489kl9fHy8JKl///4VHpOamiqp/FDfrl079e7du/Sfjh07OqdQF9r95qWn8r9+Wl+vV1u1HN1fSR9+q54vPCTvAD8DKwQAXM2J9FxD+z9pNrZ/AABQlsc+qT9+/LgkqWnTpuXut1gs2rjx0lDz8kK9I3Xv3l1ms9muNr42L01TT7vamDfv07wG91S4/+LhNH0S/d9A7xMUoBvemKDtL83XgX+v0pCvXtB1z96vxGnz7OpXkmJbxarYZLW7HQDAPoU+TaXQ35S7L3HB8Ku+fz4qIrD0f0+uGVPhceaMPPW4b9ll23fs3K3o6CfsqLh6Sa/ztORVW+nmdEVHRxtdjiH4GQBA9RQVFaVt27bZ3c5jQ31u7qUnCfn55Q9RXLhwoTIyMhQSEqKYmJjL9o8ePVoZGRkKDw/X8OHD9eqrryoiIqJKtZjNZqWlpdnVxs/kLdWvUneV1uP5B5Vz4owOzFspSdrw5GwNXztTJ77bqtNbkuw616n0UyqylTijTADArwUFSBWs7RoVEaTo+pWbLubj7VXpY3+tuKjA7t9p1UpIieQlWUtK3PtzXAt+BgDgUTw21EdFRen8+fPasWOH+vTpU2Zfenq6pkyZIknq1KmTTCZT6b7atWtrypQpuummm1SrVi1t3rxZr7zyirZs2aJt27YpICCgSrXYy9fmJTnxwXejAV0VM7yvlt4yuXRb9vHT2v7SfPWdNUHLBkyWJb+w0udr2KAhT+oBwAUsXkE6XcE+c0beVdtHRQTKx9tLlhLrFefmV3Quf59iRTRqVJlSq6V0b29ZJXl5e6uBG3+Oa8HPAACqp6rkRkky2Ww2m4NrqRYmTZqkt99+W40bN9batWsVGxsrSUpMTNQDDzygo0ePqri4WBMmTNDs2bOveK5vvvlGw4cP10cffaSHHnrIFeWrOK9A81uMdUlfjhCX/Jl8g+z/wgMAYB+r1aa6N3yqrJyqvS/+5Joxiq4frNTTuWo86D92t//L77roxYndqtR3dRA9cIHSzuSpUb0gpa69z+hyDMHPAAA8i8culDd16lSFh4fr5MmTat++vTp27KhWrVqpZ8+eat68uQYMGCCpcvPp77jjDgUHB1dpfgMAAI7k5WXSdW2rNh3MEbq1CzesbwAAcDmPDfXR0dFKSEjQ0KFDFRAQoJSUFIWFhWnu3LlasWKFDh06JMm+RfJ+PUwfAACj9O/RwJB+fX281LeLkxd8AQAAdvHYOfWS1LZtWy1fvvyy7Tk5OUpJSZGXl5c6dOhw1fMsW7ZMubm56tnTvtXoAQBwhofvitULc3eqpMS1M+hGDmymyLBAl/YJAACuzKNDfUX27dsnm82m2NhYBQWVffXP2LFj1bx5c1133XWlC+W99tpr6tKli8aMqfjVPwAAuEqj+sEa0b+pFq9NcWm/E8a0dWl/AADg6jx2+P2V7NmzR1L5Q+/bt2+vr776Sg8++KCGDBmijz76SI888ojWrVsnPz8/V5cKAEC5/vxIZ3l7u25a2ICeDdS3K0PvAQCobgj1/+PZZ5/Vnj17lJWVpeLiYh07dkz//Oc/Vbt2bVeX6RRNh/ZW71cfKbOt5ej+Gp++SE0G9zCoKgCAvbq2jdCzD1d+XZhrUSvIVx9Ov5G1ZQAAqIYI9TVMk9t76cTKn0r/XCs6UrFxA3Vm20EDqwIAVMVfH+2izq3D7GpjzshT6uncSr3T/hczJ/dUs0Yh9pYHAABcoEbOqY+Pjze6BKfxCw3SnT/MkneAn/JOZcjL31chTeoredGP2vzM+6rfo7U2PDn70sEmk67/x+Pa+pcP1WPaOGMLBwDYzc/XWytm36obxi1XyqmcSrXpcd8yu/qY/GAH/e6e1lUpDwAAuECNfFLvyYqy8nT0qwTt/2CFlg2aop/+9rHO7jikTX98Vw36dtCZxIOyWUokSe0fHaYziQd0bvdRg6sGAFRVo/rBWvfR7WrVNNTh5/7Tbzrp9ck9GXYPAEA1Rqj3QGEdYpS555gkKbxTC2XuvfTvTQb30PHvLg29r9O6sZoO7aWf31hsWJ0AAMdo2jBEWz4brrF3tHDI+cJq++vzV/vp1ad6EOgBAKjmCPUeKKx9s9IgH96puc79f8Bv2K+L0uJ3SpLq92qrWo3raeSmt3XPT3MUeV0r9Xn9MbV+8FbD6gYAVF1YbX99+nI/ff3mQDWOCq7yee4Z1Ez7vrpb993umC8IAACAc9XIOfWeLCgqTLLZlGfOlCSFtW2q3W8uVkTXVrp4OE2WvAJJ0sFPVuvgJ6tL2w1ePF3731+uEysTDakbAOAYd/ZvqqE3Ntby9Sc0Z2GS1mw+ddU2dUP99JsRsXrs3rZq2cTxw/gBAIDzEOo9TFiHmNKn9JJUlJWrNuNuU2FmdplV7wEAnsvHx0sjBjTTiAHNdCGrUDuSzmnbvgwdPnFR+YUl8vH2Ut1QP3VpHa5u7cLVJqaOfHwYvAcAgDsi1HuY1LXblbp2e+mflw95RpJ057pZWjVyWoXtVl5hHwDAfdUJ9deAXg01oFdDo0sBAABOQKivIZb2+4PRJQAAAAAAHIyxdgAAAAAAuClCPQAAAAAAbopQDwAAAACAmyLUAwAAAADgplgor5ryCfRXXPJnRpdRaT6B/kaXAAAAAAA1DqG+mjKZTPINCjC6DAAAAABANcbwewAAAAAA3BShHgAAAAAAN0WoBwAAAADATRHqAQAAAABwU4R6AAAAAADcFKEeAAAAAAA3RagHAAAAAMBNEeoBAAAAAHBThHoAAAAAANwUoR4AAAAAADdFqAcAAAAAwE0R6gEAAAAAcFOEegAAAAAA3BShHgAAAAAAN0WoBwAAAADATRHqAQAAAABwU4R6AAAAAADclI/RBaB8NptNlvxCo8uoNJ9Af5lMJqPLAAAAAIAahVBfTVnyCzW/xVijy6i0uOTP5BsUYHQZAAAAAFCjMPweAAAAAAA3RagHAAAAAMBNEeoBAAAAAHBThHoAAAAAANwUoR4AANQINptNNput9N8BAPAErH4PAAA8Ul6+RUu+T9GmXae1ff857T6cqYLCEknSqbP5ih32pbq3i1D39hG6Z1CMmjSoZXDFAADYj1APAAA8ytHULL39+X7NW3pYF7KLKjzu8PEsHT6epQXfHdWUfyZq6I2NNfG+thrUp5FMJpMLKwYAoOoI9QAAwCOUlFg169N9+svs7SosKrGrrdVq0zc/ntA3P57QyIHNNOfP16teeKCTKgUAwHEI9R4kqk97DV4yvcy24tx8ZR1NV/Ki9Ur68FvZSqwGVQcAgPOkmnN175R4bf75zDWfa/HaFP243ax///0m3X5jYwdUBwCA8xDqPdDRJQlKjd8hmUwKjKyjlqNuVs/p41W7VSNtnjLX6PIAAHCo5JNZuuWR73T8VI7DzplxvkDDJ63RJ3+/WfcPbeGw8wIA4Gisfu+Bzu05pqOLE3R00Xrte2eZVgx9TrlpGYq9/xb5h4caXR4AAA5z6kyuBjo40P+ipMSmB/78o76OT3H4uQEAcBRCfQ1gyS/U2R2HZfLyUmjT+kaXAwCAQ9hsNo3/63qlOCHQ/8JqtWncX9brpNl5fQAAcC0I9TVESLNLYb7wAjclAADP8MHig1qz+ZRdbRIXDNfJNWOUuGB4pdtk5RTrkec38G57AEC15PGhPiMjQ1OnTlXLli0VEBCgxo0b68knn1Rubq4efvhhmUwmzZ492+gyHcon0E/+YSHyDw9VnTZN1Ovl3yq8Y3Od3XFYWUfTjS4PAIBrdu5CgSb/4ye720VFBCm6frCiIoLsardqU5oWrjxqd38AADibRy+Ut2vXLg0ZMkRms1nBwcFq166dTp06pbfeekvJycnKzMyUJHXp0sXYQh2s69Qx6jp1TJltKSu2aOuzHxhUEQAAjvXx14eVnVvs0j7fnL9PY4awaB4AoHrx2Cf1GRkZGjZsmMxmsyZPnqz09HTt2LFDZrNZM2bM0IoVK5SYmCiTyaROnToZXa5DHfx0tVbdO11r4l7Sthc/VUFmtoIbhKuksKj0GC8/H925bpY6PXl3mbY3vDFBA+f/2dUlAwBQaVarTe98keTyfrfsPqsd+zNc3i8AAFfisaF+0qRJSk1N1cSJEzVz5kyFhISU7ps6dao6d+4si8WiZs2aKTTUs1aEzzpqVnrCHqXF79TeOUv1/bhXFdGlhfrMeLT0GGuRRRsmva2Ok+5W3XZNJUlNBvdQ9KDu2vj0HKNKBwDgqjbtOq2jqdmG9P3JN0cM6RcAgIp4ZKhPSkrSwoULFRERoVdeeaXcY7p16yZJ6ty582X7vvrqK11//fUKDg5W7dq11bdvX+3bt8+pNTvT2W0HlbxovWJG9FVk99al28/tPqp97yzTjW/9XkENwtTn9ce09bkPlH/6vIHVAgBwZT/tPWtY34n7jOsbAIDyeGSoX7BggaxWq+Li4lSrVq1yjwkMDJR0eah/6623dO+99+qGG27QsmXLtGDBAg0cOFD5+flOr9uZfp61SFZLibpOGV12+xuLZS0p0fA1r8u8ca+OLd1oUIUAAFTO9v3nDOt754FzslishvUPAMD/8siF8uLj4yVJ/fv3r/CY1NRUSWVDfXJysqZMmaJZs2Zp4sSJpdtvv/12J1XqOtkpZh1bulEtRt6ker3a6szWS3MRbZYSnU08qIhOLXRk4Q8GVwkAwNXtSzZuRFl+QYlSTuWoZRPPmroHAHBfHhnqjx8/Lklq2rRpufstFos2brz0RPrXof6jjz6Sr6+vHnnkEYfW0717d5nNZrva+Nq8NE09HVrH7jcXK2ZEX3WdMlqr7nleklSvV1u1HN1fSR9+q54vPKRlg6aopKDoyicqR2yrWBWbeHIBAHA+c+1Jknd4ufsSFwy/4uvqoiICS//35JoxFR4nSeaMPPW4b9ll26+/cYD8Suz7vV6dpNd5WvKqrXRzuqKjo40uBwDw/6KiorRt2za723lkqM/NzZWkCofML1y4UBkZGQoJCVFMTEzp9k2bNql169b67LPP9Pe//10nT55Uq1at9Le//U333Xdflesxm81KS0uzq42fyVuqb2c/m/dpXoN7Ktx/8XCaPon+7/B7n6AA3fDGBG1/ab4O/HuVhnz1gq579n4lTptnX8eSTqWfUpGtxO52AADYLdgieZe/65f30F+Nj7dXpY4rz9kzp6UC+36vVyshJZKXZC0psfv+BABQ/XhkqI+KitL58+e1Y8cO9enTp8y+9PR0TZkyRZLUqVMnmUymMvvS0tL07LPPasaMGWrcuLE+/PBD3X///YqMjNTAgQOrXI+9fG1ekpMffPd4/kHlnDijA/NWSpI2PDlbw9fO1Invtur0FvteFdSwQUOe1AMAXOKMt1UVvaHenJF3xbZREYHy8faSpcQqc8aV18up6Fz1I+vKx2qrTKnVUrq3t6ySvLy91aBRI6PLAQD8v6rkRslDQ/3AgQOVlJSkGTNmaNCgQYqNjZUkJSYm6oEHHlBGxqV3zHbp0qVMO6vVqpycHH366acaMWKEJOmWW27R/v379eKLL1Y51FdlCEVxXoHmtxhbpf4qo9GArooZ3ldLb5lcui37+Gltf2m++s6aoGUDJsuSX1jp8x06fEi+QQHOKBUAgDLGPrtO81ckl7uvvOHyv3ZyzRhF1w+WOSNfjQf9x+6+gwJ8lHZsl7y93Xet4eiBC5R2Jk8NohoodW+q0eUAAK6R+/5GuoKpU6cqPDxcJ0+eVPv27dWxY0e1atVKPXv2VPPmzTVgwABJl698HxYWJkllwrvJZNLAgQO1d+9e130AF0iL36nP24xTblpGme0H5q3Ukj4T7Qr0AAC4Urd2EYb13aVNmFsHegCA5/HI30rR0dFKSEjQ0KFDFRAQoJSUFIWFhWnu3LlasWKFDh06JOnyUN++ffsKz1lQUODUmgEAQOX07BBZI/sGAKA8HhnqJalt27Zavny5srOzlZ2dra1bt+p3v/udcnNzlZKSIi8vL3Xo0KFMmzvvvFOStHr16tJtVqtVa9asUY8ePVxaPwAAKF+fzvXUonGIIX2PG97KkH4BAKiIR86pv5J9+/bJZrMpNjZWQUFlX3kzbNgw3Xjjjfrd736nc+fOqUmTJvrggw+0b98+rVmzxqCKAQDAr3l5mfT4vW31x3/85NJ++3Supy5tyn+VHgAARvHYJ/UV2bNnj6TLh95Ll+bPL1u2TCNHjtRzzz2n4cOH6/jx4/r2229L5+EDAADjPTQiVqG1fF3a51NjK56mBwCAUQj1/6NOnTqaO3euzp49q8LCQv3000+67bbbXFkiAAC4irDa/po1pbfL+ht6U2ONujXGZf0BAFBZhPoapunQ3ur96iNltrUc3V/j0xepyWDWDQAAuI+HRrTSkBui7WpjzshT6uncq77P/tdqh/hp7l/7ymQy2VsiAABOV+Pm1MfHxxtdgqGa3N5LyV+uK/1zrehIxcYN1JltB40rCgCAKjCZTProhRt1w7jlSj6ZXak2V3uP/f/y9jbps5dvVqP6wVUpEQAAp6txT+o9nV9okEZtn6sx+z7W8DWva8T6N/RAygJdP/MxmXy8Vb9Ha6Vv2HvpYJNJ1//jcW39y4eyFlmMLRwAgCqIigjS2veGKKaR41fD9/Ex6fNX++mOm5s4/NwAADgKod7DFGXl6ehXCdr/wQotGzRFP/3tY53dcUib/viuGvTtoDOJB2WzlEiS2j86TGcSD+jc7qMGVw0AQNU1axSiDf8eqhuvq++wc9YLC9Dyt2/Vvbc1d9g5AQBwBkK9BwrrEKPMPcckSeGdWihz76V/bzK4h45/d+n1P3VaN1bTob308xuLDasTAABHaVgvWOs+Gqo3pvZSYID3NZ3rviHNtf/rkbqtr33z9QEAMEKNm1NfE4S1b1Ya5MM7NdfJVYmSpIb9umjbi59Jkur3aqtajetp5Ka3JUmBkXXU5/XHFFivrg5+stqYwgEAuAZeXiY9ObaDRgxoqjkLk/ThV4d07kJhpdp6e5s0on9TTbyvnfr1aODkSgEAcBxCvYcJigqTbDblmTMlSWFtm2r3m4sV0bWVLh5OkyWvQJJ08JPVZcL74MXTtf/95TqxMtGQugEAcJSmDUM04w89Nf2J67T0hxPa/PMZbd+foZ8PZSo7t1iS5OvjpdimoerWLkLd2kVo5MBmLIYHAHBLhHoPE9YhpvQpvSQVZeWqzbjbVJiZrRMrfzKwMgAAXCvA30ejBzfX6MH/nRdvtdpktdrk48MMRACAZyDUe5jUtduVunZ76Z+XD3lGknTnullaNXJahe1WXmEfAACewsvLJC8v3jcPAPAchPoaYmm/PxhdAgAAAADAwRh7BgAAAACAmyLUAwAAAADgpgj1AAAAAAC4KebUV1M+gf6KS/7M6DIqzSfQ3+gSAAAAAKDGIdRXUyaTSb5BAUaXAQAAAACoxhh+DwAAAACAmyLUAwAAAADgpgj1AAAAAAC4KUI9AAAAAABuilAPAAAAAICbItQDAAAAAOCmCPUAAAAAALgpQj0AAAAAAG6KUA8AAAAAgJsi1AMAAAAA4KYI9QAAAAAAuClCPQAAAAAAbopQDwAAAACAmyLUAwAAAADgpgj1AAAAAAC4KUI9AAAAAABuilAPAAAAAICb8jG6AJTPZrPJkl9odBmV5hPoL5PJZHQZAAAAAFCjEOqrKUt+oea3GGt0GZUWl/yZfIMCjC4DAAAAAGoUht8DAAAAAOCmCPUAAAAAALgpQj0AAAAAAG6KUA8AAAAAgJsi1AMAAAAA4KZY/R4AAMBD2Ww2paTlaPv+DG1PylDa6TxlXrz0ytyLOUVa8G2yurWLUMsmofLy4tW0AOCOCPUAAAAe5kJWof697LDe+eKADqZcLPeYnDyL7n9mnSSpacNaevSeNnr4rljVCw90YaUAgGtlstlsNqOLwOWK8wp4Tz0AALBLcbFVMz7+WS9/8LPyC0rsbu/n66Un49pr+hPXKTCAZz8A4A64WnuQqD7tNXjJ9DLbinPzlXU0XcmL1ivpw29lK7EaVB0AAHCmPYcyNf6v67Uj6VyVz1FUbNXr8/Zo2boT+vjFG9Wnc30HVggAcAZCvQc6uiRBqfE7JJNJgZF11HLUzeo5fbxqt2qkzVPmGl0eAABwsNWbUnXXU98rr8DikPMdTLmomx5aoc9e7qfRg5s75JwAAOcg1Hugc3uO6ejihNI/H5y3SnclvKnY+2/RjlcXqPBcloHVAQAAR1q7JU3Dfr9GRcWOHY1nsdh0359+kJeXSaNujXHouQEAjsMr7WoAS36hzu44LJOXl0KbMowOAABPkXwyS3c99b3DA/0vbDYp7pl12rE/wynnBwBcO0J9DRHS7FKYL7yQY3AlAADAEaxWm37ztwTl5BXb1S5xwXCdXDNGiQuGV+r4YotV4/+6XkXF9i+8BwBwPkK9B/IJ9JN/WIj8w0NVp00T9Xr5twrv2FxndxxW1tF0o8sDAAAOMGdhktZvN9vdLioiSNH1gxUVEVTpNnsOn9ff39tld18AAOerEaE+IyNDU6dOVcuWLRUQEKDGjRvrySefVG5urh5++GGZTCbNnj3b6DIdpuvUMbpv38e6b+9HGvHDP9X2ocFKWbFF8eNnGF0aAABwgOJiq156f5dL+/znJ3uVlVPk0j4BAFfn8Qvl7dq1S0OGDJHZbFZwcLDatWunU6dO6a233lJycrIyMzMlSV26dDG2UAc6+OlqpXyzWV6+Pqrbpok6TBih4AbhKin87y9iLz8fDVv9uo59laDdby4p3X7DGxMUEFlHa+NeMqJ0AABQCV//cFzmjHyX9pmbb9Gny49owph2Lu0XAHBlHv2kPiMjQ8OGDZPZbNbkyZOVnp6uHTt2yGw2a8aMGVqxYoUSExNlMpnUqVMno8t1mKyjZqUn7FFa/E7tnbNU3497VRFdWqjPjEdLj7EWWbRh0tvqOOlu1W3XVJLUZHAPRQ/qro1PzzGqdAAAUAlzvzxgSL/vfmFMvwCAinl0qJ80aZJSU1M1ceJEzZw5UyEhIaX7pk6dqs6dO8tisahZs2YKDQ01sFLnOrvtoJIXrVfMiL6K7N66dPu53Ue1751luvGt3yuoQZj6vP6Ytj73gfJPnzewWgAAcCXFxVZt2HnakL73HjmvcxcKDOkbAFA+jw31SUlJWrhwoSIiIvTKK6+Ue0y3bt0kSZ07dy7d1q9fP5lMpnL/eeyxx1xSuzP8PGuRrJYSdZ0yuuz2NxbLWlKi4Wtel3njXh1butGgCgEAQGXsSz6vwiLjVqLfzuvtAKBa8dg59QsWLJDValVcXJxq1apV7jGBgYGSyob6OXPmKCsrq8xxK1as0N///nfdcccdzivYybJTzDq2dKNajLxJ9Xq11ZmtSZIkm6VEZxMPKqJTCx1Z+IPBVQIAgKvZkXTO0P637z+nW6+PNrQGAMB/eWyoj4+PlyT179+/wmNSU1MllQ317dpdvvjLSy+9pMjISA0ePLhKtXTv3l1ms32vnPG1eWmaelapv4rsfnOxYkb0Vdcpo7XqnuclSfV6tVXL0f2V9OG36vnCQ1o2aIpKCuxf2Ta2VayKTVaH1gsAAC6XHXCDFDSo3H2JC4Zf9VV1URGBpf97cs2YCo8zZ+Spx33LLtv+8oy39K/nV9lRMQCgMqKiorRt2za723lsqD9+/LgkqWnTpuXut1gs2rjx0lDzX4f6/3X27FmtXLlSTzzxhHx8qvbjMpvNSktLs6uNn8lbqm9nP5v3aV6Deyrcf/Fwmj6J/u/we5+gAN3wxgRtf2m+Dvx7lYZ89YKue/Z+JU6bZ1/Hkk6ln1KRzbihgAAA1BiROVIFuf2Xd9BXho+3V6WP/bWc3HzlpNt3XwMAcB6PDfW5ubmSpPz88l/3snDhQmVkZCgkJEQxMTEVnmfBggWyWCx64IEHqlxLVFSU3W18bV6Skx9893j+QeWcOKMD81ZKkjY8OVvD187Uie+26vSWJLvO1bBBQ57UAwDgAtkBwcqqYJ85I++q7aMiAuXj7SVLifWKr8Wr6Fy1ggNUu1GjypQKALBDVXKjJJlsNpvNwbVUC+3atVNSUpJmz56tCRMmlNmXnp6ubt26KT09XX379tWGDRsqPE+PHj2Um5ur/fv3O7vkMorzCjS/xVinnb/RgK66ec5TWnrLZOWm/XfBmzbjB6vdo3do2YDJsuQXVvp8ccmfyTcowBmlAgCAX/li1VGNnlL1dXBOrhmj6PrBSj2dq8aD/mN3+7ef7aOJ9/GuegCoLjx29fuBAwdKkmbMmKFDhw6Vbk9MTFT//v2VkXEpyHbp0qXCcxw4cEDbtm27pqf01VVa/E593mZcmUAvSQfmrdSSPhPtCvQAAMB1urWLMLj/cEP7BwCU5bGhfurUqQoPD9fJkyfVvn17dezYUa1atVLPnj3VvHlzDRgwQNKV59N/+umnMplMiouLc1XZAAAAV9Q8OkR1QvwM6dvLy6TOsYR6AKhOPDbUR0dHKyEhQUOHDlVAQIBSUlIUFhamuXPnasWKFaVP7ysK9TabTfPnz1e/fv3UpEkTV5YOAABQIZPJpDtubmxI37f0aqCgQI9dkgkA3JJHX5Xbtm2r5cuXX7Y9JydHKSkp8vLyUocOHcptu379eh0/flzTpk1zdpkAAAB2efzetvpsebLL+31idFuX9wkAuDKPfVJ/Jfv27ZPNZlOrVq0UFFT+O2E+/fRTBQYG6p57Kn5FHAAAgBH6dK6nzq3DXNpndP1g3XEToxcBoLqpkaF+z549kioeel9QUKBFixZpxIgRCgkJcWVpAAAAV2UymfSPyb1c2ufrT/eQj0+NvHUEgGqtRl6ZrxbqAwICdOHCBX3++eeuLMslmg7trd6vPlJmW8vR/TU+fZGaDO5hUFUAAMBet/RuqMdGtXFJX3ff0kyjBzd3SV8AAPsQ6muYJrf30omVP5X+uVZ0pGLjBurMtoMGVgUAAKritad7qHWz2na1MWfkKfV0rswZeZU6vmG9IM35y/UymUxVKREA4GQevVBeReLj440uwWn8QoN05w+z5B3gp7xTGfLy91VIk/pKXvSjNj/zvur3aK0NT86+dLDJpOv/8bi2/uVD9Zg2ztjCAQCA3UKC/bTmvcG6cfwKHT+VU6k2Pe5bVunzR9YN0Nr3hqh+eGBVSwQAOFmNfFLvyYqy8nT0qwTt/2CFlg2aop/+9rHO7jikTX98Vw36dtCZxIOyWUokSe0fHaYziQd0bvdRg6sGAABV1TiqlhLmDVXb5nUcet4mDYK13gnnBQA4FqHeA4V1iFHmnmOSpPBOLZS599K/NxncQ8e/uzT0vk7rxmo6tJd+fmOxYXUCAADHaBxVS9sW3KmnxraXI0bJ/+auWP385V1qE1Pn2k8GAHAqQr0HCmvfrDTIh3dqrnP/H/Ab9uuitPidkqT6vdqqVuN6Grnpbd3z0xxFXtdKfV5/TK0fvNWwugEAQNUFBfpo1tTeWv/xUN14Xf0qnaNbuwh9+69b9eH0G1Un1N/BFQIAnKFGzqn3ZEFRYZLNpjxzpiQprG1T7X5zsSK6ttLFw2my5BVIkg5+sloHP1ld2m7w4una//5ynViZaEjdAADAMW64Lkrr592hPYcy9c4XSVq5MU3H0rIrPD66frAG9m6oJ0a3VY8OkS6sFADgCIR6DxPWIab0Kb0kFWXlqs2421SY+X/t3DEuhGEUhtH7i8EoJ8ICJAqJSqmxA4WSTegllqCzAhag1FmBVqVVSCYRkjGTIb+WUChGxmvOWcDNbZ98X+7zp6v3AMD/trXRq7Pjnaqq6j8O6+a2X/cPgxqN32qhM1drvW5tb67UqiN4ANGatm3baS/BV+PBsC7WDyc2b+/6tK72T2rYf5rYzI8O7s6rs7z0K7MBAAD4npf6GXG5ezTtFQAAAJgwh/IAAAAglKgHAACAUKIeAAAAQjmU90e1bVuvL6Npr/Fj893Fappm2msAAADMFFEPAAAAoXy/BwAAgFCiHgAAAEKJegAAAAgl6gEAACCUqAcAAIBQoh4AAABCiXoAAAAIJeoBAAAglKgHAACAUKIeAAAAQol6AAAACCXqAQAAIJSoBwAAgFCiHgAAAEKJegAAAAgl6gEAACCUqAcAAIBQoh4AAABCiXoAAAAIJeoBAAAglKgHAACAUKIeAAAAQol6AAAACCXqAQAAIJSoBwAAgFDvoQfW5IUaACsAAAAASUVORK5CYII=", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 4, @@ -305,8 +307,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 8, @@ -333,8 +336,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 9, @@ -617,7 +621,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -631,9 +635,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.9.21" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.mdx b/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.mdx new file mode 100644 index 00000000000..e0fb987fb76 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.mdx @@ -0,0 +1,205 @@ + + +# Automatically find cuts + + + +## Step 1: Map + + + +### Create a circuit and observables + +``` +[1]: +``` + +```python +import numpy as np +from qiskit.circuit.random import random_circuit +from qiskit.quantum_info import SparsePauliOp + +circuit = random_circuit(7, 6, max_operands=2, seed=1242) +observable = SparsePauliOp(["ZIIIIII", "IIIZIII", "IIIIIIZ"]) + + +circuit.draw("mpl", scale=0.8) +``` + +``` +[1]: +``` + +![../\_images/tutorials\_04\_automatic\_cut\_finding\_2\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_2_0.avif) + + + +## Step 2: Optimize + + + +### Find cut locations, given a maximum of 4 qubits per subcircuit. This circuit can be separated in two by making a single wire cut and cutting one `CRZGate` + +``` +[2]: +``` + +```python +from qiskit_addon_cutting.automated_cut_finding import ( + find_cuts, + OptimizationParameters, + DeviceConstraints, +) + +# Specify settings for the cut-finding optimizer +optimization_settings = OptimizationParameters(seed=111) + +# Specify the size of the QPUs available +device_constraints = DeviceConstraints(qubits_per_subcircuit=4) + +cut_circuit, metadata = find_cuts(circuit, optimization_settings, device_constraints) +print( + f'Found solution using {len(metadata["cuts"])} cuts with a sampling ' + f'overhead of {metadata["sampling_overhead"]}.\n' + f'Lowest cost solution found: {metadata["minimum_reached"]}.' +) +for cut in metadata["cuts"]: + print(f"{cut[0]} at circuit instruction index {cut[1]}") +cut_circuit.draw("mpl", scale=0.8, fold=-1) +``` + +```python +Found solution using 8 cuts with a sampling overhead of 59776343.19975524. +Lowest cost solution found: False. +Gate Cut at circuit instruction index 6 +Gate Cut at circuit instruction index 8 +Gate Cut at circuit instruction index 9 +Wire Cut at circuit instruction index 10 +Gate Cut at circuit instruction index 15 +Gate Cut at circuit instruction index 18 +Gate Cut at circuit instruction index 19 +Gate Cut at circuit instruction index 21 +``` + +``` +[2]: +``` + +![../\_images/tutorials\_04\_automatic\_cut\_finding\_4\_1.png](/docs/images/api/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_4_1.avif) + + + +### Add ancillas for wire cuts and expand the observables to account for ancilla qubits + +``` +[3]: +``` + +```python +from qiskit_addon_cutting import cut_wires, expand_observables + +qc_w_ancilla = cut_wires(cut_circuit) +observables_expanded = expand_observables(observable.paulis, circuit, qc_w_ancilla) +qc_w_ancilla.draw("mpl", scale=0.8, fold=-1) +``` + +``` +[3]: +``` + +![../\_images/tutorials\_04\_automatic\_cut\_finding\_6\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_6_0.avif) + + + +### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires. + +``` +[4]: +``` + +```python +from qiskit_addon_cutting import partition_problem + +partitioned_problem = partition_problem( + circuit=qc_w_ancilla, observables=observables_expanded +) +subcircuits = partitioned_problem.subcircuits +subobservables = partitioned_problem.subobservables +print( + f"Sampling overhead: {np.prod([basis.overhead for basis in partitioned_problem.bases])}" +) +``` + +```python +Sampling overhead: 59776343.19975523 +``` + +``` +[5]: +``` + +```python +subobservables +``` + +``` +[5]: +``` + +```python +{0: PauliList(['IIII', 'IIZI', 'IIIZ']), + 1: PauliList(['ZIII', 'IIII', 'IIII'])} +``` + +``` +[6]: +``` + +```python +subcircuits[0].draw("mpl", style="iqp", scale=0.8) +``` + +``` +[6]: +``` + +![../\_images/tutorials\_04\_automatic\_cut\_finding\_10\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_10_0.avif) + +``` +[7]: +``` + +```python +subcircuits[1].draw("mpl", style="iqp", scale=0.8) +``` + +``` +[7]: +``` + +![../\_images/tutorials\_04\_automatic\_cut\_finding\_11\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_11_0.avif) + + + +### Generate the experiments to run on the backend. + +``` +[8]: +``` + +```python +from qiskit_addon_cutting import generate_cutting_experiments + +subexperiments, coefficients = generate_cutting_experiments( + circuits=subcircuits, observables=subobservables, num_samples=1_000 +) +print( + f"{len(subexperiments[0]) + len(subexperiments[1])} total subexperiments to run on backend." +) +``` + +```python +2000 total subexperiments to run on backend. +``` + +Steps 3 and 4 of a [Qiskit pattern](/docs/guides/intro-to-patterns) can then be performed as in the preceding tutorials. diff --git a/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb index 9d82f0c3a5d..bb6428c4f34 100644 --- a/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb +++ b/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb @@ -2,7 +2,6 @@ "cells": [ { "cell_type": "markdown", - "id": "5fea9061-062a-4671-aaa5-ba1d57be19da", "metadata": {}, "source": [ "# Automatically find cuts" @@ -10,7 +9,6 @@ }, { "cell_type": "markdown", - "id": "4333fa28-4aea-4e25-a060-a669e85a68ee", "metadata": {}, "source": [ "## Step 1: Map\n", @@ -21,7 +19,6 @@ { "cell_type": "code", "execution_count": 1, - "id": "0322fe96-e627-4648-99cd-5113399009bb", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:55.792854Z", @@ -33,8 +30,9 @@ "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 1, @@ -56,7 +54,6 @@ }, { "cell_type": "markdown", - "id": "a3234a89-ba6d-4b53-8aec-79142956defd", "metadata": {}, "source": [ "## Step 2: Optimize\n", @@ -67,7 +64,6 @@ { "cell_type": "code", "execution_count": 2, - "id": "b49f1001-04c9-4ae6-8ab0-4e2020050b67", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:56.802390Z", @@ -95,8 +91,9 @@ }, { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 2, @@ -130,7 +127,6 @@ }, { "cell_type": "markdown", - "id": "84ea51fd-1935-434c-acdd-4b7db6daf434", "metadata": {}, "source": [ "#### Add ancillas for wire cuts and expand the observables to account for ancilla qubits" @@ -139,7 +135,6 @@ { "cell_type": "code", "execution_count": 3, - "id": "60a9ae2a-e7d2-4c50-8559-502ead4a0923", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.491081Z", @@ -151,8 +146,9 @@ "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 3, @@ -170,7 +166,6 @@ }, { "cell_type": "markdown", - "id": "21e66074-f582-49b0-8a8c-f6a6c12047ba", "metadata": {}, "source": [ "#### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires." @@ -179,7 +174,6 @@ { "cell_type": "code", "execution_count": 4, - "id": "1409f406-ab1f-418d-b5ab-b76796fa8031", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.755174Z", @@ -213,7 +207,6 @@ { "cell_type": "code", "execution_count": 5, - "id": "6ddec420-a8a4-4b71-9672-4704b0562234", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.770081Z", @@ -242,7 +235,6 @@ { "cell_type": "code", "execution_count": 6, - "id": "5b255310-93cd-48e9-b06c-fdbb1403f1eb", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.776009Z", @@ -254,8 +246,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 6, @@ -270,7 +263,6 @@ { "cell_type": "code", "execution_count": 7, - "id": "e2e2c8fa-bdb0-4713-9990-7791bcd950ef", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.933278Z", @@ -282,8 +274,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 7, @@ -297,7 +290,6 @@ }, { "cell_type": "markdown", - "id": "73004424-5863-464e-a35e-41d9d6bba1c5", "metadata": {}, "source": [ "#### Generate the experiments to run on the backend." @@ -306,7 +298,6 @@ { "cell_type": "code", "execution_count": 8, - "id": "627b2304-d72c-49eb-adc0-c886909f925b", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:53:00.112773Z", @@ -337,7 +328,6 @@ }, { "cell_type": "markdown", - "id": "88e64707-57d9-440b-b623-3ee453f5560d", "metadata": {}, "source": [ "Steps 3 and 4 of a [Qiskit pattern](/guides/intro-to-patterns) can then be performed as in the preceding tutorials." @@ -346,7 +336,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -360,9 +350,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.9.21" } }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/tutorials/index.mdx b/docs/addons/qiskit-addon-cutting/tutorials/index.mdx new file mode 100644 index 00000000000..a554fa522d5 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/index.mdx @@ -0,0 +1,30 @@ +--- +title: Circuit cutting API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-cutting. +--- + + + +# Tutorials + +* [Tutorial 1](01-gate-cutting-to-reduce-circuit-width): Separate sets of qubits by cutting nonlocal gates. +* [Tutorial 2](02-gate-cutting-to-reduce-circuit-depth): Cut gates requiring many SWAPs to decrease circuit depth. +* [Tutorial 3](03-wire-cutting-via-move-instruction): Specify wire cuts as a two-qubit [`Move`](instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") operation. +* [Tutorial 4](04-automatic-cut-finding): Automatically find optimal gate and wire cut locations for reducing circuit width. + +[![](/docs/images/api/qiskit-addon-cutting/tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.avif)](01-gate-cutting-to-reduce-circuit-width) + +[Gate Cutting to Reduce Circuit Width](01-gate-cutting-to-reduce-circuit-width) + +[![](/docs/images/api/qiskit-addon-cutting/tutorials_02_gate_cutting_to_reduce_circuit_depth_17_2.avif)](02-gate-cutting-to-reduce-circuit-depth) + +[Gate Cutting to Reduce Circuit Depth](02-gate-cutting-to-reduce-circuit-depth) + +[![](/docs/images/api/qiskit-addon-cutting/tutorials_03_wire_cutting_via_move_instruction_15_0.avif)](03-wire-cutting-via-move-instruction) + +[Wire Cutting Phrased as a Two-Qubit Move Instruction](03-wire-cutting-via-move-instruction) + +[![](/docs/images/api/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_11_0.avif)](04-automatic-cut-finding) + +[Automatically find cuts](04-automatic-cut-finding) + diff --git a/docs/addons/qiskit-addon-mpf/_package.json b/docs/addons/qiskit-addon-mpf/_package.json new file mode 100644 index 00000000000..802fd0f5ff9 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/_package.json @@ -0,0 +1,4 @@ +{ + "name": "qiskit-addon-mpf", + "version": "0.3.0" +} diff --git a/docs/addons/qiskit-addon-mpf/explanations/index.mdx b/docs/addons/qiskit-addon-mpf/explanations/index.mdx new file mode 100644 index 00000000000..3d1088d0cbd --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/explanations/index.mdx @@ -0,0 +1,11 @@ +--- +title: Multi-product formulas (MPF) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-mpf. +--- + +# Explanations + +This page summarizes additional explanatory content. + +* [Understanding the stability of MPFs](mpf-stability) + diff --git a/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.mdx b/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.mdx new file mode 100644 index 00000000000..548932261f7 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.mdx @@ -0,0 +1,157 @@ + + +# Understanding the stability of MPFs + +Over in the guide [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps) we introduced a heuristic that limits the **smallest** $k_j$ value ($k_{\text{min}}$) based on the total evolution time, $t$, that we would like to reach. In particular, we state that $t/k_{\text{min}} \lt 1$ must be satisfied (noting that $t/k_{\text{min}} \leq 1$ empirically seems to work fine, too). + +On this page, we will analyze when the behavior of MPFs is not guaranteed to work well, such as when this constraint gets violated. + + + +## Setting up a simple model problem + +For this simple example, we will reuse the model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial; the Ising model on a line of 10 sites: + +$$ +\hat{\mathcal{H}}_{\text{Ising}} = \sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \sum_{i=1}^{10} h_i X_i \, , +$$ + +where $J$ is the coupling strength between two sites and $h$ is the external magnetic field. + +``` +[1]: +``` + +```python +from qiskit.transpiler import CouplingMap +from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian + +coupling_map = CouplingMap.from_line(10, bidirectional=False) + +hamiltonian = generate_xyz_hamiltonian( + coupling_map, + coupling_constants=(0.0, 0.0, 1.0), + ext_magnetic_field=(0.4, 0.0, 0.0), +) +print(hamiltonian) +``` + +```python +SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'], + coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, + 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, + 0.4+0.j, 0.4+0.j, 0.4+0.j]) +``` + +The observable that we will be measuring is the total magnetization which we can simply construct as shown below: + +``` +[2]: +``` + +```python +from qiskit.quantum_info import SparsePauliOp + +L = coupling_map.size() +observable = SparsePauliOp.from_sparse_list([("Z", [i], 1 / L / 2) for i in range(L)], num_qubits=L) +print(observable) +``` + +```python +SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'], + coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, + 0.05+0.j, 0.05+0.j, 0.05+0.j]) +``` + + + +## Simulating various Trotter circuits + +Our analysis simply consists of comparing the behavior of product formulas of various order (here, second and fourth order) to the exact solution (which we can compute for a problem as simple as this one). + +In the code cell below, we do exactly that, for evolution times ranging from $2$ up to $10$ and Trotter steps ranging from $1$ to $25$. + +``` +[3]: +``` + +```python +import numpy as np +from qiskit.primitives import StatevectorEstimator +from qiskit.synthesis import SuzukiTrotter +from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit +from scipy.linalg import expm + +estimator = StatevectorEstimator() +times = np.array(range(2, 11)) +ks = np.array(range(1, 25)) + +exact = [] +order2 = [] +order4 = [] + +for time in times: + exp_H = expm(-1j * time * hamiltonian.to_matrix()) + initial_state = np.zeros(exp_H.shape[0]) + initial_state[0] = 1.0 + time_evolved_state = exp_H @ initial_state + exact_evs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state + exact.append(float(exact_evs)) + + for k in ks: + order2_circ = generate_time_evolution_circuit( + hamiltonian, synthesis=SuzukiTrotter(reps=k, order=2), time=time + ) + order4_circ = generate_time_evolution_circuit( + hamiltonian, synthesis=SuzukiTrotter(reps=k, order=4), time=time + ) + + job = estimator.run([(order2_circ, observable), (order4_circ, observable)]) + result = job.result() + order2.append(result[0].data.evs) + order4.append(result[1].data.evs) +``` + + + +## Analyzing the results + +We can now analyze the computed results by plotting them. + +``` +[4]: +``` + +```python +from matplotlib import pyplot as plt + +order2_reshaped = np.asarray(order2).reshape((len(times), len(ks))) +order4_reshaped = np.asarray(order4).reshape((len(times), len(ks))) + +fig, axes = plt.subplots(3, 3, figsize=(20, 10)) + +for i, t in enumerate(times): + ax = axes[i // 3, i % 3] + + ax.axvline(1.0, color="grey", label="$t/k=1$") + ax.axhline(exact[i], color="tab:green", label="exact") + + ax.plot(t / ks, order2_reshaped[i], label=r"$\chi=2$") + ax.plot(t / ks, order4_reshaped[i], label=r"$\chi=4$") + + ax.semilogx() + ax.set_xticks([10**p for p in range(-1, 1)]) + ax.set_xlabel("$t/k$") + + ax.set_ylim([-0.33, 0.55]) + ax.set_ylabel(r"$\langle O \rangle$") + + ax.set_title(f"time={t}") + +fig.legend(*ax.get_legend_handles_labels(), ncols=4, loc="center", bbox_to_anchor=(0.5, 0.0)) +fig.tight_layout() +``` + +![../\_images/explanations\_mpf\_stability\_8\_0.png](/docs/images/api/qiskit-addon-mpf/explanations_mpf_stability_8_0.avif) + +We can now clearly see that in the regime $t/k \gt 1$ the dynamics are no longer reproduced faithfully by the Trotterized circuits. Thus, if an MPF were to use a product formula with a $k_j$ from that regime, its extrapolated expectation value cannot faithfully produce good results. diff --git a/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb b/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb index e12ac085532..1e1ae9be310 100644 --- a/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb +++ b/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb @@ -229,8 +229,9 @@ "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -285,7 +286,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -299,9 +300,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-mpf/how_tos/choose-trotter-steps.mdx b/docs/addons/qiskit-addon-mpf/how_tos/choose-trotter-steps.mdx new file mode 100644 index 00000000000..126e71ec38b --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/how_tos/choose-trotter-steps.mdx @@ -0,0 +1,136 @@ + + +# How to choose the Trotter steps for an MPF + +This guide teaches you how to find a good set of Trotter steps for an MPF. We assume that you have already read and understood the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial. + + + +## Understanding some heuristics + +In principle, any $k_j$ values can be chosen but some choices of Trotter steps will lead to a larger noise amplification on real devices than others. Thus, it is important that one tries to find *“good”* values of $k_j$. In the following we will discuss some heuristics to help guide you in doing so. + +1. In practice, our **largest** $k_j$ ($k_{\text{max}}$) value is simply bound by the depth of quantum circuit we can reliably execute. +2. Since the Trotter error is only well behaved for $\text{d}t = t/k \lt 1$ (see [Understanding the stability of MPFs](/docs/addons/qiskit-addon-mpf/explanations/mpf_stability)), our **smallest** $k_j$ ($k_{\text{min}}$) should satisfy: $t/k_{\text{min}} \lt 1$ (Note: empirically $\text{d}t \leq 1$ seems to work fine, too). +3. None of our $x_j$ values should be close to $0$, as this would imply that we might as well exclude the corresponding $k_j$ from the expansion. +4. Similarly, we do not want the $x_j$ for $k_{\text{max}}$ to be the only dominant one, since that implies that we are essentially just recovering a single product formula with $k=k_{\text{max}}$. +5. Finally, we want the L1-norm of our coefficients, $x_j$, to be small, as this indicates a well-conditioned MPF (see [Carrera Vazquez et al., 2023](https://quantum-journal.org/papers/q-2023-07-25-1067/)). Furthermore, small coefficients minimize the variance amplification of the MPF. + + + +## Applying these heuristics + +Reusing the example from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial, we want to simulate up to time $t=8.0$. Thus, $k_{\text{min}}=8$. + +For the sake of this example, let us assume that our deepest circuit is bound by $k_{\text{max}}=20$. + +In addition to the constraints above, we will follow the heuristic presented by [Zhuk et al., 2023](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309), where they minimize a re-weighted L1-norm of the coefficients: + +$$ +\text{min}_{k_j} \sum_j \frac{|c_j|}{k_j^{2\chi}} \, , +$$ + +where $\chi$ is the order of our individual product formulas. + + + +### Preparing our LSE + +In a first step, we prepare the LSE, $Ax=b$, leveraging a feature of the [setup\_static\_lse](/docs/api/qiskit-addon-mpf/static#qiskit_addon_mpf.static.setup_static_lse) function that allows us to use a [cvxpy.Parameter](https://www.cvxpy.org/api_reference/cvxpy.expressions.html#parameter) object for our $k_j$ values. + +``` +[30]: +``` + +```python +order = 2 +symmetric = True + +min_k = 8 +max_k = 20 + +max_l1_norm = 5.0 +min_coeff = 0.01 +``` + +``` +[31]: +``` + +```python +import cvxpy as cp +from qiskit_addon_mpf.static import setup_static_lse + +ks = cp.Parameter(3, integer=True) +lse = setup_static_lse(ks, order=order, symmetric=symmetric) +``` + +Next, we loop over all possible 3-tuples of $k_j$ values within our specified range. For each one, we compute the analytical coefficients, $x_j$, and then apply our filter criteria. We keep track of those $k_j$ tuples which satisfy our criteria. + +``` +[32]: +``` + +```python +import numpy as np + +possible_ks = {} + +for k1 in range(min_k, max_k): + for k2 in range(k1 + 1, max_k): + for k3 in range(k2 + 1, max_k): + ks.value = [k1, k2, k3] + coeffs = lse.solve() + + if np.any(np.isclose(coeffs, 0.0, atol=min_coeff)): + continue + + norm1 = np.linalg.norm(coeffs, ord=1) + if norm1 > max_l1_norm: + continue + + weighted = np.sum([np.abs(c) / k ** (2 * order) for c, k in zip(coeffs, ks.value)]) + possible_ks[tuple(int(k) for k in ks.value)] = { + "coeffs": tuple(coeffs), + "weighted": float(weighted), + "norm1": float(norm1), + } +``` + +Finally, we sort our $k_j$ by the re-weighted L1-norm presented by [Zhuk et al., 2023](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309): + +``` +[33]: +``` + +```python +for ks_val in sorted(possible_ks, key=lambda ks_val: possible_ks[ks_val].get("weighted")): + print(ks_val, possible_ks[ks_val]) +``` + +```python +(8, 14, 19) {'coeffs': (0.10447913478216586, -1.7638200183654775, 2.6593408835833117), 'weighted': 9.182736455463762e-05, 'norm1': 4.527640036730955} +(8, 13, 19) {'coeffs': (0.13134519801186473, -1.4167162698412699, 2.285371071829405), 'weighted': 9.920634920634922e-05, 'norm1': 3.8334325396825397} +(8, 12, 19) {'coeffs': (0.17239057239057254, -1.1944700460829496, 2.022079473692377), 'weighted': 0.00011520737327188946, 'norm1': 3.388940092165899} +(9, 13, 19) {'coeffs': (0.2662743506493499, -1.6904000946969673, 2.4241257440476174), 'weighted': 0.00011837121212121191, 'norm1': 4.380800189393934} +(8, 13, 18) {'coeffs': (0.15003663003663004, -1.7549001536098316, 2.6048635235732016), 'weighted': 0.00012288786482334872, 'norm1': 4.509800307219663} +(8, 12, 18) {'coeffs': (0.19692307692307653, -1.4399999999999984, 2.243076923076922), 'weighted': 0.0001388888888888887, 'norm1': 3.879999999999997} +(8, 11, 19) {'coeffs': (0.24195168054817162, -1.070248538011695, 1.8282968574635234), 'weighted': 0.00014619883040935662, 'norm1': 3.14049707602339} +(9, 12, 19) {'coeffs': (0.37193877551020393, -1.5167873601053319, 2.144848584595128), 'weighted': 0.00014629507717065315, 'norm1': 4.033574720210664} +(8, 12, 17) {'coeffs': (0.22755555555555573, -1.7875862068965531, 2.5600306513409974), 'weighted': 0.00017241379310344843, 'norm1': 4.575172413793107} +(8, 11, 18) {'coeffs': (0.27638326585694917, -1.265318468585254, 1.988935202728305), 'weighted': 0.00017284590787313073, 'norm1': 3.530636937170508} +(9, 12, 18) {'coeffs': (0.42857142857142816, -1.8285714285714276, 2.3999999999999995), 'weighted': 0.00017636684303350958, 'norm1': 4.657142857142855} +(9, 11, 19) {'coeffs': (0.5858035714285708, -1.5251041666666654, 1.9393005952380946), 'weighted': 0.00020833333333333313, 'norm1': 4.05020833333333} +(8, 11, 17) {'coeffs': (0.3193762183235871, -1.5289264828738525, 2.2095502645502654), 'weighted': 0.00020885547201336693, 'norm1': 4.0578529657477045} +(8, 10, 19) {'coeffs': (0.383090160867938, -1.0642826734780744, 1.6811925126101364), 'weighted': 0.00021285653469561486, 'norm1': 3.1285653469561487} +(9, 11, 18) {'coeffs': (0.6750000000000009, -1.8030788177339916, 2.1280788177339907), 'weighted': 0.0002463054187192121, 'norm1': 4.606157635467984} +(8, 10, 18) {'coeffs': (0.43760683760683694, -1.2400793650793638, 1.8024725274725268), 'weighted': 0.0002480158730158727, 'norm1': 3.4801587301587276} +(8, 11, 16) {'coeffs': (0.3742690058479534, -1.9026640675763484, 2.528395061728395), 'weighted': 0.0002599090318388565, 'norm1': 4.805328135152697} +(8, 10, 17) {'coeffs': (0.5056790123456789, -1.4697236919459142, 1.9640446796002353), 'weighted': 0.00029394473838918284, 'norm1': 3.9394473838918285} +(8, 10, 16) {'coeffs': (0.592592592592593, -1.7806267806267817, 2.1880341880341887), 'weighted': 0.0003561253561253563, 'norm1': 4.561253561253563} +(8, 9, 19) {'coeffs': (0.8112497524262232, -1.3783613445378153, 1.5671115921115921), 'weighted': 0.00042016806722689084, 'norm1': 3.756722689075631} +(8, 9, 18) {'coeffs': (0.9266968325791858, -1.5882352941176474, 1.6615384615384616), 'weighted': 0.00048414427499394834, 'norm1': 4.176470588235295} +(8, 9, 17) {'coeffs': (1.0708496732026151, -1.855486425339368, 1.7846367521367528), 'weighted': 0.0005656108597285072, 'norm1': 4.710972850678736} +``` + +Recalling the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial, from these possible values we picked `ks=(8, 12, 19)` since it strikes a good balance of minimizing the re-weighted and plain L1-norms. diff --git a/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb b/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb index 80266c720fb..ba4f43cc53b 100644 --- a/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb +++ b/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb @@ -93,7 +93,7 @@ "source": [ "### Preparing our LSE\n", "\n", - "In a first step, we prepare the LSE, $Ax=b$, leveraging a feature of the [setup_static_lse](/docs/addons/qiskit-addon-mpf/stubs/qiskit_addon_mpf.static.setup_static_lse) function that allows us to use a [cvxpy.Parameter](https://www.cvxpy.org/api_reference/cvxpy.expressions.html#parameter) object for our $k_j$ values." + "In a first step, we prepare the LSE, $Ax=b$, leveraging a feature of the [setup_static_lse](/docs/api/qiskit-addon-mpf/static#qiskit_addon_mpf.static.setup_static_lse) function that allows us to use a [cvxpy.Parameter](https://www.cvxpy.org/api_reference/cvxpy.expressions.html#parameter) object for our $k_j$ values." ] }, { @@ -275,7 +275,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -289,9 +289,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-mpf/how_tos/index.mdx b/docs/addons/qiskit-addon-mpf/how_tos/index.mdx new file mode 100644 index 00000000000..847d80880ae --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/how_tos/index.mdx @@ -0,0 +1,12 @@ +--- +title: Multi-product formulas (MPF) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-mpf. +--- + +# How-To Guides + +This page summarizes the available how-to guides. + +* [How to choose the Trotter steps for an MPF](choose-trotter-steps) +* [How to use the approximate model](using-approximate-model) + diff --git a/docs/addons/qiskit-addon-mpf/how_tos/using-approximate-model.mdx b/docs/addons/qiskit-addon-mpf/how_tos/using-approximate-model.mdx new file mode 100644 index 00000000000..90766605c31 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/how_tos/using-approximate-model.mdx @@ -0,0 +1,607 @@ + + +# How to use the approximate model + +You have already seen a first example of the [setup\_sum\_of\_squares\_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem) function in the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial. In this guide, we are going to revisit that function in more detail. + + + +## Setting up a simple model problem + +For this guide, we will reuse the simple model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial; the Ising model on a line of 10 sites: + +$$ +\hat{\mathcal{H}}_{\text{Ising}} = \sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \sum_{i=1}^{10} h_i X_i \, , +$$ + +where $J$ is the coupling strength between two sites and $h$ is the external magnetic field. + +``` +[1]: +``` + +```python +from qiskit.transpiler import CouplingMap +from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian + +coupling_map = CouplingMap.from_line(10, bidirectional=False) + +hamiltonian = generate_xyz_hamiltonian( + coupling_map, + coupling_constants=(0.0, 0.0, 1.0), + ext_magnetic_field=(0.4, 0.0, 0.0), +) +print(hamiltonian) +``` + +```python +SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'], + coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, + 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, + 0.4+0.j, 0.4+0.j, 0.4+0.j]) +``` + + + +## Choosing $k_j$ + +In this guide, we are going to focus on the tweaks that we can apply to the [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) and its solver that are returned by the [setup\_sum\_of\_squares\_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem). Therefore, we are not concerned with choosing any specific $k_j$ values and can simply reuse the ones from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial. + +``` +[2]: +``` + +```python +trotter_steps = (8, 12, 19) +``` + + + +## Computing $x$ analytically + +First, we have to construct our LSE, $Ax=b$. Once again, we reuse the settings from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial. + +``` +[3]: +``` + +```python +from qiskit_addon_mpf.static import setup_static_lse + +lse = setup_static_lse(trotter_steps, order=2, symmetric=True) +``` + +Next, we compute the analytical solution of $x$ and its L1-norm as our reference values. + +``` +[4]: +``` + +```python +coeff_analytical = lse.solve() +print(coeff_analytical) +``` + +```python +[ 0.17239057 -1.19447005 2.02207947] +``` + +``` +[5]: +``` + +```python +import numpy as np + +print(np.linalg.norm(coeff_analytical, ord=1)) +``` + +```python +3.388940092165899 +``` + + + +## Setting up the approximate model + +Now, we will construct the approximate model. The idea of using approximate solutions $\tilde{x}$ in order to ensure a smaller L1-norm was proposed by [Zhuk et al., 2023](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309). The model is already explained in detail in the API documentation of [setup\_sum\_of\_squares\_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem) so we refrain from repeating it here. Suffice to say, that the model consists of two constraints which ensure that the coefficients, $\tilde{x}$, sum to $1$ and it enforces their L1-norm to be smaller than the user-provided `max_l1_norm` value. The optimization objective is to minimize the deviation of $A\tilde{x}=b$. + +We can see all of this in the output below. Here, we are setting `max_l1_norm=3` because we already know that the L1-norm of the exact solution is approximately $3.4$ and we want to find a $\tilde{x}$ that has a smaller L1-norm. + +``` +[6]: +``` + +```python +from qiskit_addon_mpf.costs import setup_sum_of_squares_problem + +model, coeff_var = setup_sum_of_squares_problem(lse, max_l1_norm=3) +print(model) +``` + +```python +minimize quad_over_lin(Vstack([1. 1. 1.] @ x + -1.0, [0.015625 0.00694444 0.00277008] @ x + -0.0, [2.44140625e-04 4.82253086e-05 7.67336039e-06] @ x + -0.0), 1.0) +subject to Sum(x, None, False) == 1.0 + norm1(x) <= 3.0 +``` + + + +## Solving the model with default settings + +First, let us see what happens when we solve the `model` with only default settings. + +``` +[7]: +``` + +```python +model.solve(verbose=True) +``` + +```python +=============================================================================== + CVXPY + v1.5.3 +=============================================================================== +(CVXPY) Sep 05 09:28:06 AM: Your problem has 3 variables, 2 constraints, and 0 parameters. +(CVXPY) Sep 05 09:28:06 AM: It is compliant with the following grammars: DCP, DQCP +(CVXPY) Sep 05 09:28:06 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.) +(CVXPY) Sep 05 09:28:06 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution. +(CVXPY) Sep 05 09:28:06 AM: Your problem is compiled with the CPP canonicalization backend. +------------------------------------------------------------------------------- + Compilation +------------------------------------------------------------------------------- +(CVXPY) Sep 05 09:28:06 AM: Compiling problem (target solver=OSQP). +(CVXPY) Sep 05 09:28:06 AM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> OSQP +(CVXPY) Sep 05 09:28:06 AM: Applying reduction CvxAttr2Constr +(CVXPY) Sep 05 09:28:06 AM: Applying reduction Qp2SymbolicQp +(CVXPY) Sep 05 09:28:06 AM: Applying reduction QpMatrixStuffing +(CVXPY) Sep 05 09:28:06 AM: Applying reduction OSQP +(CVXPY) Sep 05 09:28:06 AM: Finished problem compilation (took 1.053e-02 seconds). +------------------------------------------------------------------------------- + Numerical solver +------------------------------------------------------------------------------- +(CVXPY) Sep 05 09:28:06 AM: Invoking solver OSQP to obtain a solution. +----------------------------------------------------------------- + OSQP v0.6.3 - Operator Splitting QP Solver + (c) Bartolomeo Stellato, Goran Banjac + University of Oxford - Stanford University 2021 +----------------------------------------------------------------- +problem: variables n = 9, constraints m = 11 + nnz(P) + nnz(A) = 33 +settings: linear system solver = qdldl, + eps_abs = 1.0e-05, eps_rel = 1.0e-05, + eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04, + rho = 1.00e-01 (adaptive), + sigma = 1.00e-06, alpha = 1.60, max_iter = 10000 + check_termination: on (interval 25), + scaling: on, scaled_termination: off + warm start: on, polish: on, time_limit: off + +iter objective pri res dua res rho time + 1 0.0000e+00 1.00e+00 2.00e+02 1.00e-01 6.26e-05s + 200 4.0661e-09 7.52e-06 1.44e-08 1.22e-05 1.28e-04s + +status: solved +solution polish: unsuccessful +number of iterations: 200 +optimal objective: 0.0000 +run time: 1.53e-04s +optimal rho estimate: 2.09e-06 + +------------------------------------------------------------------------------- + Summary +------------------------------------------------------------------------------- +(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal +(CVXPY) Sep 05 09:28:07 AM: Optimal value: 4.092e-09 +(CVXPY) Sep 05 09:28:07 AM: Compilation took 1.053e-02 seconds +(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 1.028e-03 seconds +``` + +``` +[7]: +``` + +```python +4.092215822096244e-09 +``` + +We can see from the final value of the cost function that the sum of squares of $A\tilde{x}-b$ is almost zero. + +In the output above, we also get a lot more information from [cvxpy.Problem.solve](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem.solve), some of which we will get back to later. + +For now, let us inspect the final values of $\tilde{x}$ and its L1-norm: + +``` +[8]: +``` + +```python +coeff_approx_plain = coeff_var.value +print(coeff_approx_plain) +print(np.linalg.norm(coeff_approx_plain, ord=1)) +``` + +```python +[-0.39646076 0.55556835 0.84089365] +1.7929227539822414 +``` + +We can clearly see that the L1-norm has decreased compared to the analytical solution that we have computed before. + +Nonetheless, let us see if we can tweak our solution a bit. In the end, we will compare the results obtained for this set of coefficients with all the other ones. + + + +## Tweaking the approximate model + +Rather than tweaking the input parameters like the chosen `trotter_steps` and `max_l1_norm` values, we will focus on the solver settings in this guide. + + + +### Using a different solver + +Notice, that we used the default solver method: `OSQP`. We can change that solver to see whether this has any effect. + +``` +[9]: +``` + +```python +model.solve(verbose=True, solver="CLARABEL") +``` + +```python +=============================================================================== + CVXPY + v1.5.3 +=============================================================================== +(CVXPY) Sep 05 09:28:07 AM: Your problem has 3 variables, 2 constraints, and 0 parameters. +(CVXPY) Sep 05 09:28:07 AM: It is compliant with the following grammars: DCP, DQCP +(CVXPY) Sep 05 09:28:07 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.) +(CVXPY) Sep 05 09:28:07 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution. +(CVXPY) Sep 05 09:28:07 AM: Your problem is compiled with the CPP canonicalization backend. +------------------------------------------------------------------------------- + Compilation +------------------------------------------------------------------------------- +(CVXPY) Sep 05 09:28:07 AM: Compiling problem (target solver=CLARABEL). +(CVXPY) Sep 05 09:28:07 AM: Reduction chain: Dcp2Cone -> CvxAttr2Constr -> ConeMatrixStuffing -> CLARABEL +(CVXPY) Sep 05 09:28:07 AM: Applying reduction Dcp2Cone +(CVXPY) Sep 05 09:28:07 AM: Applying reduction CvxAttr2Constr +(CVXPY) Sep 05 09:28:07 AM: Applying reduction ConeMatrixStuffing +(CVXPY) Sep 05 09:28:07 AM: Applying reduction CLARABEL +(CVXPY) Sep 05 09:28:07 AM: Finished problem compilation (took 8.657e-03 seconds). +------------------------------------------------------------------------------- + Numerical solver +------------------------------------------------------------------------------- +(CVXPY) Sep 05 09:28:07 AM: Invoking solver CLARABEL to obtain a solution. +------------------------------------------------------------- + Clarabel.rs v0.8.1 - Clever Acronym + + (c) Paul Goulart + University of Oxford, 2022 +------------------------------------------------------------- + +problem: + variables = 9 + constraints = 11 + nnz(P) = 3 + nnz(A) = 30 + cones (total) = 2 + : Zero = 1, numel = 4 + : Nonnegative = 1, numel = 7 + +settings: + linear algebra: direct / qdldl, precision: 64 bit + max iter = 200, time limit = Inf, max step = 0.990 + tol_feas = 1.0e-8, tol_gap_abs = 1.0e-8, tol_gap_rel = 1.0e-8, + static reg : on, ϵ1 = 1.0e-8, ϵ2 = 4.9e-32 + dynamic reg: on, ϵ = 1.0e-13, δ = 2.0e-7 + iter refine: on, reltol = 1.0e-13, abstol = 1.0e-12, + max iter = 10, stop ratio = 5.0 + equilibrate: on, min_scale = 1.0e-4, max_scale = 1.0e4 + max iter = 10 + +iter pcost dcost gap pres dres k/t μ step +--------------------------------------------------------------------------------------------- + 0 +7.1341e-05 -2.3333e+00 2.33e+00 2.40e-01 7.10e-01 1.00e+00 1.88e+00 ------ + 1 +7.1330e-05 -1.5432e-01 1.54e-01 1.64e-02 1.12e-01 3.28e-02 1.80e-01 9.61e-01 + 2 +6.9440e-05 -1.7266e-03 1.80e-03 2.10e-04 1.89e-03 5.65e-04 2.43e-03 9.87e-01 + 3 +2.2053e-05 -5.0002e-05 7.21e-05 5.56e-06 4.83e-05 1.98e-05 7.80e-05 9.87e-01 + 4 +1.1119e-06 -2.5543e-05 2.67e-05 1.80e-07 1.41e-06 3.21e-06 1.04e-05 9.90e-01 + 5 +7.9250e-09 -2.0484e-06 2.06e-06 2.06e-09 1.59e-08 2.11e-07 6.43e-07 9.90e-01 + 6 +2.2543e-09 -2.8390e-08 3.06e-08 2.07e-11 1.59e-10 3.12e-09 9.42e-09 9.90e-01 + 7 +1.8480e-09 -8.5733e-10 2.71e-09 1.79e-12 2.36e-10 3.19e-10 9.40e-10 9.15e-01 +--------------------------------------------------------------------------------------------- +Terminated with status = Solved +solve time = 113.289µs +------------------------------------------------------------------------------- + Summary +------------------------------------------------------------------------------- +(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal +(CVXPY) Sep 05 09:28:07 AM: Optimal value: 1.848e-09 +(CVXPY) Sep 05 09:28:07 AM: Compilation took 8.657e-03 seconds +(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 8.504e-04 seconds +``` + +``` +[9]: +``` + +```python +1.8479854472132682e-09 +``` + +``` +[10]: +``` + +```python +coeff_approx_clarabel = coeff_var.value +print(coeff_approx_clarabel) +print(np.linalg.norm(coeff_approx_clarabel, ord=1)) +``` + +```python +[-0.21284352 -0.00792683 1.22077035] +1.4415406931636157 +``` + +Indeed, the outcome has changed. But now we have one major contribution from the largest $k_j$ value which is also not ideal. + +An exhaustive search of the various available solvers is beyond the scope of this guide. Instead we encourage you to try this yourself. + + + +### Changing the convergence thresholds + +Rather than changing the solver altogether, we can also tweak the convergence parameters. For the `OSQP` solver the relevant ones are called `eps_abs` and `eps_rel` and they are both set to `1e-5` by default. + +``` +[11]: +``` + +```python +model.solve(verbose=True, eps_abs=1e-8, eps_rel=1e-8) +``` + +```python +=============================================================================== + CVXPY + v1.5.3 +=============================================================================== +(CVXPY) Sep 05 09:28:07 AM: Your problem has 3 variables, 2 constraints, and 0 parameters. +(CVXPY) Sep 05 09:28:07 AM: It is compliant with the following grammars: DCP, DQCP +(CVXPY) Sep 05 09:28:07 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.) +(CVXPY) Sep 05 09:28:07 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution. +(CVXPY) Sep 05 09:28:07 AM: Your problem is compiled with the CPP canonicalization backend. +------------------------------------------------------------------------------- + Compilation +------------------------------------------------------------------------------- +(CVXPY) Sep 05 09:28:07 AM: Compiling problem (target solver=OSQP). +(CVXPY) Sep 05 09:28:07 AM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> OSQP +(CVXPY) Sep 05 09:28:07 AM: Applying reduction CvxAttr2Constr +(CVXPY) Sep 05 09:28:07 AM: Applying reduction Qp2SymbolicQp +(CVXPY) Sep 05 09:28:07 AM: Applying reduction QpMatrixStuffing +(CVXPY) Sep 05 09:28:07 AM: Applying reduction OSQP +(CVXPY) Sep 05 09:28:07 AM: Finished problem compilation (took 8.835e-03 seconds). +------------------------------------------------------------------------------- + Numerical solver +------------------------------------------------------------------------------- +(CVXPY) Sep 05 09:28:07 AM: Invoking solver OSQP to obtain a solution. +----------------------------------------------------------------- + OSQP v0.6.3 - Operator Splitting QP Solver + (c) Bartolomeo Stellato, Goran Banjac + University of Oxford - Stanford University 2021 +----------------------------------------------------------------- +problem: variables n = 9, constraints m = 11 + nnz(P) + nnz(A) = 33 +settings: linear system solver = qdldl, + eps_abs = 1.0e-08, eps_rel = 1.0e-08, + eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04, + rho = 1.00e-01 (adaptive), + sigma = 1.00e-06, alpha = 1.60, max_iter = 10000 + check_termination: on (interval 25), + scaling: on, scaled_termination: off + warm start: on, polish: on, time_limit: off + +iter objective pri res dua res rho time + 1 0.0000e+00 1.00e+00 2.00e+02 1.00e-01 5.02e-05s + 200 4.0661e-09 7.52e-06 1.44e-08 1.22e-05 2.10e-04s + 400 3.8209e-09 4.95e-06 2.88e-09 2.09e-06 3.72e-04s + 600 3.3607e-09 6.09e-06 1.96e-09 2.09e-06 4.33e-04s + 800 2.8975e-09 6.24e-06 1.84e-09 2.09e-06 4.87e-04s +1000 2.4704e-09 6.08e-06 1.70e-09 2.09e-06 5.40e-04s +1200 2.0921e-09 5.78e-06 1.56e-09 2.09e-06 5.92e-04s +1400 1.7645e-09 5.40e-06 1.44e-09 2.09e-06 6.44e-04s +1600 1.4845e-09 5.01e-06 1.32e-09 2.09e-06 7.01e-04s +1800 1.2471e-09 4.63e-06 1.21e-09 2.09e-06 7.53e-04s +2000 1.0467e-09 4.26e-06 1.11e-09 2.09e-06 8.05e-04s +2200 8.7801e-10 3.91e-06 1.01e-09 2.09e-06 8.57e-04s +2400 7.3890e-10 3.25e-06 7.74e-10 2.09e-06 9.10e-04s +2600 6.4003e-10 2.47e-06 7.11e-10 2.09e-06 9.62e-04s +2800 5.6692e-10 2.03e-06 6.68e-10 2.09e-06 1.01e-03s +3000 5.0872e-10 1.76e-06 6.33e-10 2.09e-06 1.07e-03s +3200 4.5991e-10 1.58e-06 6.02e-10 2.09e-06 1.12e-03s +3400 4.1754e-10 1.46e-06 5.74e-10 2.09e-06 1.17e-03s +3600 3.7999e-10 1.37e-06 5.47e-10 2.09e-06 1.22e-03s +3800 3.4628e-10 1.29e-06 5.22e-10 2.09e-06 1.32e-03s +4000 3.1580e-10 1.22e-06 4.99e-10 2.09e-06 1.37e-03s +4200 2.8813e-10 1.16e-06 4.76e-10 2.09e-06 1.43e-03s +4400 2.6295e-10 1.11e-06 4.55e-10 2.09e-06 1.48e-03s +4600 2.4000e-10 1.06e-06 4.35e-10 2.09e-06 1.53e-03s +4800 2.1907e-10 1.01e-06 4.15e-10 2.09e-06 1.58e-03s +5000 1.9997e-10 9.67e-07 3.97e-10 2.09e-06 1.63e-03s +5200 1.8255e-10 9.23e-07 3.79e-10 2.09e-06 1.69e-03s +5400 1.6664e-10 8.82e-07 3.62e-10 2.09e-06 1.74e-03s +5600 1.5219e-10 1.01e-06 2.60e-10 2.09e-06 1.79e-03s +5800 1.4049e-10 6.59e-07 2.47e-10 2.09e-06 1.84e-03s +6000 1.3096e-10 5.75e-07 2.39e-10 2.09e-06 1.90e-03s +6200 1.2274e-10 5.24e-07 2.31e-10 2.09e-06 1.95e-03s +6400 1.1538e-10 4.90e-07 2.24e-10 2.09e-06 2.00e-03s +6600 1.0863e-10 4.67e-07 2.17e-10 2.09e-06 2.05e-03s +6800 1.0237e-10 4.48e-07 2.11e-10 2.09e-06 2.10e-03s +7000 9.6519e-11 4.32e-07 2.05e-10 2.09e-06 2.16e-03s +7200 9.1024e-11 4.19e-07 1.99e-10 2.09e-06 2.21e-03s +7400 8.5854e-11 4.06e-07 1.93e-10 2.09e-06 2.29e-03s +7600 8.0984e-11 3.94e-07 1.88e-10 2.09e-06 2.36e-03s +7800 7.6393e-11 3.82e-07 1.82e-10 2.09e-06 2.41e-03s +8000 7.2065e-11 3.71e-07 1.77e-10 2.09e-06 2.46e-03s +8200 6.7982e-11 3.60e-07 1.72e-10 2.09e-06 2.51e-03s +8400 6.4131e-11 3.50e-07 1.67e-10 2.09e-06 2.56e-03s +8600 6.0499e-11 3.40e-07 1.62e-10 2.09e-06 2.62e-03s +8800 5.7072e-11 3.30e-07 1.57e-10 2.09e-06 2.67e-03s +9000 5.3859e-11 8.77e-06 8.55e-12 2.09e-06 2.72e-03s +9200 5.1785e-11 5.78e-07 2.18e-13 2.09e-06 2.79e-03s +9400 5.0736e-11 7.72e-08 8.32e-14 2.09e-06 2.87e-03s +9550 5.0303e-11 4.69e-08 8.73e-14 2.09e-06 2.96e-03s +plsh 4.9640e-11 1.94e-14 1.50e-12 -------- 3.00e-03s + +status: solved +solution polish: successful +number of iterations: 9550 +optimal objective: 0.0000 +run time: 3.00e-03s +optimal rho estimate: 2.96e-06 + +------------------------------------------------------------------------------- + Summary +------------------------------------------------------------------------------- +(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal +(CVXPY) Sep 05 09:28:07 AM: Optimal value: 4.964e-11 +(CVXPY) Sep 05 09:28:07 AM: Compilation took 8.835e-03 seconds +(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 3.988e-03 seconds +``` + +``` +[11]: +``` + +```python +4.9640211694779584e-11 +``` + +``` +[12]: +``` + +```python +coeff_approx_tight = coeff_var.value +print(coeff_approx_tight) +print(np.linalg.norm(coeff_approx_tight, ord=1)) +``` + +```python +[ 0.10925065 -1. 1.89074935] +3.0 +``` + +As you can see, we have now obtained a comparatively higher L1-norm (which is still constrained by our choice of `max_l1_norm=3`). While the coefficient of the smallest $k_j$ value is still fairly small in magnitude, at least the other two provide a reasonable amount of mixing. + + + +## Comparing our results + +We will now briefly repeat the computations from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial in order to assess the quality of the various coefficients. + +``` +[13]: +``` + +```python +from qiskit.synthesis import SuzukiTrotter +from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit + +time = 8.0 +circuits = [] +for k in trotter_steps: + circ = generate_time_evolution_circuit( + hamiltonian, + synthesis=SuzukiTrotter(order=2, reps=k), + time=time, + ) + circuits.append(circ) +``` + +``` +[14]: +``` + +```python +from qiskit.quantum_info import SparsePauliOp + +L = 10 +observable = SparsePauliOp.from_sparse_list([("Z", [i], 1 / L / 2) for i in range(L)], num_qubits=L) +print(observable) +``` + +```python +SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'], + coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, + 0.05+0.j, 0.05+0.j, 0.05+0.j]) +``` + +``` +[15]: +``` + +```python +from qiskit.primitives import StatevectorEstimator + +estimator = StatevectorEstimator() +job = estimator.run([(circ, observable) for circ in circuits]) +result = job.result() +``` + +``` +[16]: +``` + +```python +evs = [res.data.evs for res in result] +print(evs) +``` + +```python +[array(0.23799162), array(0.35754312), array(0.38649906)] +``` + +``` +[17]: +``` + +```python +from scipy.linalg import expm + +exp_H = expm(-1j * time * hamiltonian.to_matrix()) + +initial_state = np.zeros(exp_H.shape[0]) +initial_state[0] = 1.0 + +time_evolved_state = exp_H @ initial_state + +exact_obs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state +``` + +``` +[18]: +``` + +```python +print("Analytical coeff solution:", evs @ coeff_analytical) +print("Plain approx. solution:", evs @ coeff_approx_plain) +print("CLARABEL approx. solution:", evs @ coeff_approx_clarabel) +print("Tight approx. solution:", evs @ coeff_approx_tight) +print("Exact solution:", exact_obs.real) +``` + +```python +Analytical coeff solution: 0.39548478559794153 +Plain approx. solution: 0.42928990901998165 +CLARABEL approx. solution: 0.41833743748333746 +Tight approx. solution: 0.3992304700751579 +Exact solution: 0.40060242487900255 +``` + +We can clearly see now, that the various approximate solutions can yield quite different results. Luckily, testing out different expansion coefficients for a fixed choice of $k_j$ values does not require us to rerun our quantum experiments. Therefore, we encourage you to play around and tweak your expansion coefficients when using the approximate solutions. diff --git a/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb b/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb index 556a1c10498..19c5974460b 100644 --- a/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb +++ b/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb @@ -14,7 +14,7 @@ "source": [ "# How to use the approximate model\n", "\n", - "You have already seen a first example of the [setup_sum_of_squares_problem](/docs/addons/qiskit-addon-mpf/stubs/qiskit_addon_mpf.costs.setup_sum_of_squares_problem) function in the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial.\n", + "You have already seen a first example of the [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem) function in the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial.\n", "In this guide, we are going to revisit that function in more detail." ] }, @@ -92,7 +92,7 @@ "source": [ "## Choosing $k_j$\n", "\n", - "In this guide, we are going to focus on the tweaks that we can apply to the [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) and its solver that are returned by the [setup_sum_of_squares_problem](/docs/addons/qiskit-addon-mpf/stubs/qiskit_addon_mpf.costs.setup_sum_of_squares_problem). Therefore, we are not concerned with choosing any specific $k_j$ values and can simply reuse the ones from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial." + "In this guide, we are going to focus on the tweaks that we can apply to the [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) and its solver that are returned by the [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem). Therefore, we are not concerned with choosing any specific $k_j$ values and can simply reuse the ones from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial." ] }, { @@ -222,7 +222,7 @@ "\n", "Now, we will construct the approximate model.\n", "The idea of using approximate solutions $\\tilde{x}$ in order to ensure a smaller L1-norm was proposed by [Zhuk et al., 2023].\n", - "The model is already explained in detail in the API documentation of [setup_sum_of_squares_problem](/docs/addons/qiskit-addon-mpf/stubs/qiskit_addon_mpf.costs.setup_sum_of_squares_problem) so we refrain from repeating it here.\n", + "The model is already explained in detail in the API documentation of [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem) so we refrain from repeating it here.\n", "Suffice to say, that the model consists of two constraints which ensure that the coefficients, $\\tilde{x}$, sum to $1$ and it enforces their L1-norm to be smaller than the user-provided `max_l1_norm` value.\n", "The optimization objective is to minimize the deviation of $A\\tilde{x}=b$.\n", "\n", @@ -1007,7 +1007,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1021,9 +1021,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-mpf/index.mdx b/docs/addons/qiskit-addon-mpf/index.mdx index 116e92a4d7f..92f0ee9b65d 100644 --- a/docs/addons/qiskit-addon-mpf/index.mdx +++ b/docs/addons/qiskit-addon-mpf/index.mdx @@ -1,5 +1,6 @@ --- -title: "Qiskit addon: multi-product formulas (MPF)" +title: Multi-product formulas (MPF) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-mpf. --- # Qiskit addon: multi-product formulas (MPF) diff --git a/docs/addons/qiskit-addon-mpf/install.mdx b/docs/addons/qiskit-addon-mpf/install.mdx index 89bac4edda7..bd571abad14 100644 --- a/docs/addons/qiskit-addon-mpf/install.mdx +++ b/docs/addons/qiskit-addon-mpf/install.mdx @@ -1,7 +1,3 @@ ---- -title: "Installation Instructions" ---- - # Installation Instructions Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: @@ -73,4 +69,3 @@ If you installed the notebook dependencies, you can get started by running the n cd docs/ jupyter lab ``` - diff --git a/docs/addons/qiskit-addon-mpf/release-notes.mdx b/docs/addons/qiskit-addon-mpf/release-notes.mdx deleted file mode 100644 index 3fcac204f98..00000000000 --- a/docs/addons/qiskit-addon-mpf/release-notes.mdx +++ /dev/null @@ -1,71 +0,0 @@ ---- -title: "Release Notes" ---- - - - - - -# - - - - - -## 0.3.0 - - - -### New Features - -* TeNPy can disable `Sz` conservation. Previously, the [`initialize_from_lattice()`](qiskit_addon_mpf.backends.tenpy_tebd.MPOState#qiskit_addon_mpf.backends.tenpy_tebd.MPOState.initialize_from_lattice "qiskit_addon_mpf.backends.tenpy_tebd.MPOState.initialize_from_lattice") method did not support this, but a new keyword argument `conserve` has been added which allows the disabling of `Sz` conservation. The default remains to be `True`. - -* Guards against unexpected behavior when `N_steps != 1` in [`qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver.evolve()`](qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver#qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver.evolve "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver.evolve"). - - - -### Upgrade Notes - -* This release adds support for Python 3.13. No code changes were necessary, so older releases are expected to work on Python 3.13 too. - -* This package is now compatible with Qiskit SDK 2.0. - -* The `keep_only_odd` attribute of [`quimb_layers.LayerModel`](qiskit_addon_mpf.backends.quimb_layers.LayerModel#qiskit_addon_mpf.backends.quimb_layers.LayerModel "qiskit_addon_mpf.backends.quimb_layers.LayerModel") has been removed. The internal workings have been refactored to ensure that data reported by its `terms` attribute (which is inherited from the base class) is already taking the `keep_only_odd` argument of the [`quimb_layers.LayerModel.from_quantum_circuit()`](qiskit_addon_mpf.backends.quimb_layers.LayerModel#qiskit_addon_mpf.backends.quimb_layers.LayerModel.from_quantum_circuit "qiskit_addon_mpf.backends.quimb_layers.LayerModel.from_quantum_circuit") constructor method. - -* The `scaling_factor` keyword argument of the `from_quantum_circuit` constructor methods has been removed. It is not actually needed and was merely adding an additional (confusing) re-scaling. - - - -### Bug Fixes - -* Fixes the [`DynamicMPF.evolve()`](qiskit_addon_mpf.dynamic#qiskit_addon_mpf.dynamic.DynamicMPF.evolve "qiskit_addon_mpf.dynamic.DynamicMPF.evolve") method when providing `time` values with numerical inaccuracies (e.g. `time=1.00000001`). Previously this could cause the LHS and RHS time evolvers to advance too far. The new default accuracy is 8 decimal places, but it may be configured via the [`DynamicMPF.TIME_DECIMALS`](qiskit_addon_mpf.dynamic#qiskit_addon_mpf.dynamic.DynamicMPF.TIME_DECIMALS "qiskit_addon_mpf.dynamic.DynamicMPF.TIME_DECIMALS") value. - - - - - -## 0.2.0 - - - - - -### New Features - -* Adds the ability to compute dynamic (i.e. time-dependent) MPF coefficients. For more details, refer to [`qiskit_addon_mpf.dynamic`](qiskit_addon_mpf.dynamic#module-qiskit_addon_mpf.dynamic "qiskit_addon_mpf.dynamic"). - - - - - -### Upgrade Notes - -* The code for the static MPF coefficients has been moved. It functions the same as before, but you have to update your imports and function names as summarized in the table below: - - | Old | New | - | ------------------------------------------------------------- | ----------------------------------------------------------------- | - | `from qiskit_addon_mpf.static import setup_lse` | `from qiskit_addon_mpf.static import setup_static_lse` | - | `from qiskit_addon_mpf.static import LSE` | `from qiskit_addon_mpf.costs import LSE` | - | `from qiskit_addon_mpf.static import setup_exact_model` | `from qiskit_addon_mpf.costs import setup_exact_problem` | - | `from qiskit_addon_mpf.static import setup_approximate_model` | `from qiskit_addon_mpf.costs import setup_sum_of_squares_problem` | - diff --git a/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started.mdx b/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started.mdx new file mode 100644 index 00000000000..661bdb0df6e --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started.mdx @@ -0,0 +1,445 @@ + + +# Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas + +In this notebook, you will learn how to use a **Multi-Product Formula (MPF)** to achieve a lower Trotter error on our observable compared to the one incurred by the deepest Trotter circuit that we will actually execute. You will do so by working through the steps of a **Qiskit pattern**: + +* **Step 1: Map to quantum problem** + + * Initialize our problem’s Hamiltonian + * Use an MPF to generate the Trotterized time-evolution circuits + +* **Step 2: Optimize the problem** + + * Here we transpile our circuits for a [GenericBackendV2](/docs/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2) + +* **Step 3: Execute experiments** + + * Use a [StatevectorEstimator](/docs/api/qiskit/qiskit.primitives.StatevectorEstimator) for sake of simplicity in this notebook + +* **Step 4: Reconstruct results** + + * Compute the MPF expectation value + + + +## Step 1: Map to quantum problem + + + +### 1a: Setting up our Hamiltonian + +We use the Ising model on a line of 10 sites: + +$$ +\hat{\mathcal{H}}_{\text{Ising}} = \sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \sum_{i=1}^{10} h_i X_i \, , +$$ + +where $J$ is the coupling strength between two sites and $h$ is the external magnetic field. + +The [qiskit\_addon\_utils](https://qiskit.github.io/qiskit-addon-utils/) package provides some reusable functionalities for various purposes. + +Its [qiskit\_addon\_utils.problem\_generators](/docs/api/qiskit-addon-utils/problem-generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph. This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/docs/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows. + +In the following, we create a simple line of 10 qubits using the `CouplingMap.from_line` method. + +``` +[1]: +``` + +```python +from qiskit.transpiler import CouplingMap + +# Generate some coupling map to use for this example +coupling_map = CouplingMap.from_line(10, bidirectional=False) +``` + +``` +[2]: +``` + +```python +from rustworkx.visualization import graphviz_draw + +graphviz_draw(coupling_map.graph, method="circo") +``` + +``` +[2]: +``` + +![../\_images/tutorials\_01\_getting\_started\_4\_0.png](/docs/images/api/qiskit-addon-mpf/tutorials_01_getting_started_4_0.avif) + +Next, we generate the [SparsePauliOp](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) on the provided connectivity with the desired constants. + +``` +[3]: +``` + +```python +from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian + +# Get a qubit operator describing the Ising field model +hamiltonian = generate_xyz_hamiltonian( + coupling_map, + coupling_constants=(0.0, 0.0, 1.0), + ext_magnetic_field=(0.4, 0.0, 0.0), +) +print(hamiltonian) +``` + +```python +SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'], + coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, + 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, + 0.4+0.j, 0.4+0.j, 0.4+0.j]) +``` + +The observable that we will be measuring is the total magnetization which we can simply construct as shown below: + +``` +[4]: +``` + +```python +from qiskit.quantum_info import SparsePauliOp + +L = coupling_map.size() +observable = SparsePauliOp.from_sparse_list([("Z", [i], 1 / L / 2) for i in range(L)], num_qubits=L) +print(observable) +``` + +```python +SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'], + coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, + 0.05+0.j, 0.05+0.j, 0.05+0.j]) +``` + + + +### 1b: Multi-Product Formulas + +MPFs reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions. + +To make this more concrete, we define an MPF as: + +$$ +\mu(t) = \sum_{j} x_j \rho^{k_j}_{j}\left(\frac{t}{k_j}\right) + \text{some remaining Trotter error} \, , +$$ + +where $x_j$ are our weighting coefficients, $\rho^{k_j}_j$ is the density matrix corresponding to the pure state obtained by evolving the initial state with the product formula, $S^{k_j}$, involving $k_j$ Trotter steps, and $j$ indexes the number of product formulas that make up the MPF. + +The key here is that the remaining Trotter error is smaller than the Trotter error that one would obtain by simply using the largest $k_j$ value! + +You can view the usefulness of MPF from two perspectives: + +1. For a fixed budget of Trotter steps that you are able to execute, you can obtain results with a Trotter error that is smaller in total. +2. For a number of Trotter steps which results in deep circuits, you can use MPF to find several shorter-depth circuits to run which result in a similar Trotter error. + + + +#### An introduction to static MPFs + +*Static* MPFs are those where the $x_j$ values do **NOT** depend on the evolution time, $t$. + +Determining the static MPF coefficients for a given set of $k_j$ values amounts to solving a linear system of equations: $Ax=b$, where $x$ are our coefficients of interest, $A$ is a matrix depending on $k_j$ and the type of PF we use ($S$), and $b$ is a vector of constraints. For brevity, we are not going to go into more detail here and instead refer you to the documentation of [LSE](/docs/addons/qiskit-addon-mpf/apidocs/qiskit_addon_mpf.costs#qiskit_addon_mpf.costs.LSE). + +We can find a solution for $x$ analytically as $x = A^{-1}b$, see e.g. [Carrera Vazquez et al., 2023](https://quantum-journal.org/papers/q-2023-07-25-1067/) or [Zhuk et al., 2023](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309). However, this exact solution can be *“ill-conditioned”* resulting in very large L1-norms of our coefficients, $x$, which can lead to bad performance of the MPF. Instead, one can also obtain an approximate solution which minimizes the L1-norm of $x$ in order to attempt and optimize the MPF behavior. + +In the following, you will learn how to do all of this. + + + +#### Choosing $k_j$ + +The choice of $k_j$ is up to the end-user. In principle, any values can be chosen but some $k_j$’s will lead to a larger noise amplification on real devices than other choices. Thus, it is important that one tries to find *“good”* values of $k_j$. + +Here, we will simply pick some fixed values for $k_j$. The smallest value is motivated by the target evolution time of $t=8.0$ which normally tells us to satisfy $t/k_{\text{min}} \lt 1$ but empirically we know that setting it equal to $1$ usually works, too. If you want to learn more about this and how to choose your other $k_j$ values, refer to the respective guide: [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps). + +``` +[5]: +``` + +```python +time = 8.0 +trotter_steps = (8, 12, 19) +``` + + + +#### Setting up the LSE + +Now that we have chosen our $k_j$s, we must first construct the LSE, $Ax=b$ as explained above. The matrix $A$ depends not only on $k_j$ but also our choice of product formula (PF) – in particular its *order*. Additionally, one may take into account whether the PF is symmetric or not (see [Carrera Vazquez et al., 2023](https://quantum-journal.org/papers/q-2023-07-25-1067/)), by setting `symmetric=True`. However, this is not required as shown by [Zhuk et al., 2023](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309). + +Here, we are going to use a second-order Suzuki-Trotter formula yielding `order=2` and we will set `symmetric=True`. + +``` +[6]: +``` + +```python +from qiskit_addon_mpf.static import setup_static_lse + +lse = setup_static_lse(trotter_steps, order=2, symmetric=True) +print(lse) +``` + +```python +LSE(A=array([[1.00000000e+00, 1.00000000e+00, 1.00000000e+00], + [1.56250000e-02, 6.94444444e-03, 2.77008310e-03], + [2.44140625e-04, 4.82253086e-05, 7.67336039e-06]]), b=array([1., 0., 0.])) +``` + + + +#### Solving for $x$ analytically + +As mentioned before, we can find $x$ analytically: + +``` +[7]: +``` + +```python +import numpy as np + +coeffs_analytical = lse.solve() +print(coeffs_analytical) +``` + +```python +[ 0.17239057 -1.19447005 2.02207947] +``` + + + +#### Optimizing for $x$ using an exact model + +Alternatively to computing $x=A^{-1}b$, you can also use [setup\_exact\_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_exact_problem) to construct a [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which uses the LSE as constraints and whose optimal solution will yield $x$. + +In the next section, it will be clear why this interface exists. + +``` +[8]: +``` + +```python +from qiskit_addon_mpf.costs import setup_exact_problem + +model_exact, coeffs_exact = setup_exact_problem(lse) +model_exact.solve() +print(coeffs_exact.value) +``` + +```python +[ 0.17239057 -1.19447005 2.02207947] +``` + +As an indicator whether an MPF constructed with these coefficients will yield good results, we can use the L1-norm (see also [Carrera Vazquez et al., 2023](https://quantum-journal.org/papers/q-2023-07-25-1067/)). + +``` +[9]: +``` + +```python +print(np.linalg.norm(coeffs_exact.value, ord=1)) +``` + +```python +3.3889400921655914 +``` + + + +#### Optimizing for $x$ using an approximate model + +It may happen that the L1 norm for the chosen set of $k_j$ values is deemed too high. If that is the case and you cannot choose a different set of $k_j$ values, you can use an approximate solution to the LSE instead of an exact one. + +To do so, simply use [setup\_sum\_of\_squares\_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem) to construct a different [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which constrains the L1-norm to a chosen threshold while minimizing the difference of $Ax$ and $b$. + +``` +[10]: +``` + +```python +from qiskit_addon_mpf.costs import setup_sum_of_squares_problem + +model_approx, coeffs_approx = setup_sum_of_squares_problem(lse, max_l1_norm=3.0) +model_approx.solve() +print(coeffs_approx.value) +print(np.linalg.norm(coeffs_approx.value, ord=1)) +``` + +```python +[-0.40454257 0.57553173 0.8290123 ] +1.8090865903790838 +``` + +Note, that you have complete freedom over how to solve this optimization problem, meaning that you can change the optimization solver, its convergence thresholds, and so on. Check out the respective guide on [How to use the approximate model](/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model). + + + +### 1c: Setting up the Trotter circuits + +At this point, we have found our expansion coefficients, $x$, and all that is left to do is to generate the Trotterized quantum circuits. Once again, the [qiskit\_addon\_utils.problem\_generators](/docs/api/qiskit-addon-utils/problem-generators) module comes to the rescue to do just that: + +``` +[11]: +``` + +```python +from qiskit.synthesis import SuzukiTrotter +from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit + +circuits = [] +for k in trotter_steps: + circ = generate_time_evolution_circuit( + hamiltonian, + synthesis=SuzukiTrotter(order=2, reps=k), + time=time, + ) + circuits.append(circ) +``` + +``` +[12]: +``` + +```python +circuits[0].draw("mpl", fold=-1) +``` + +``` +[12]: +``` + +![../\_images/tutorials\_01\_getting\_started\_26\_0.png](/docs/images/api/qiskit-addon-mpf/tutorials_01_getting_started_26_0.avif) + +``` +[13]: +``` + +```python +circuits[1].draw("mpl", fold=-1) +``` + +``` +[13]: +``` + +![../\_images/tutorials\_01\_getting\_started\_27\_0.png](/docs/images/api/qiskit-addon-mpf/tutorials_01_getting_started_27_0.avif) + +``` +[14]: +``` + +```python +circuits[2].draw("mpl", fold=-1) +``` + +``` +[14]: +``` + +![../\_images/tutorials\_01\_getting\_started\_28\_0.png](/docs/images/api/qiskit-addon-mpf/tutorials_01_getting_started_28_0.avif) + + + +## Step 2: Optimize the problem + +Normally, this is the step in the pattern during which you optimize your circuits for execution on hardware. Here, since we only use a noiseless simulator, we simply transpile our circuit for a [GenericBackendV2](/docs/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2). + +``` +[15]: +``` + +```python +from qiskit.providers.fake_provider import GenericBackendV2 +from qiskit.transpiler import generate_preset_pass_manager + +backend = GenericBackendV2(num_qubits=10) +transpiler = generate_preset_pass_manager(optimization_level=2, backend=backend) + +transpiled_circuits = [transpiler.run(circ) for circ in circuits] +``` + + + +## Step 3: Execute quantum experiments + +As explained in the very beginning, we will skip the optimization step 2 because we are simply going to compute our target observable’s expectation values using a noise-free simulator, namely the [StatevectorEstimator](/docs/api/qiskit/qiskit.primitives.StatevectorEstimator). + +``` +[16]: +``` + +```python +from qiskit.primitives import StatevectorEstimator + +estimator = StatevectorEstimator() +job = estimator.run([(circ, observable) for circ in transpiled_circuits]) +result = job.result() +``` + + + +## Step 4: Reconstruct results + +First, we extract the individual expectation values obtained for each of the Trotter circuits: + +``` +[17]: +``` + +```python +evs = [res.data.evs for res in result] +print(evs) +``` + +```python +[array(0.23799162), array(0.35754312), array(0.38649906)] +``` + +Next, we simply recombine them with our MPF coefficients to yield the total expectation values of the MPF. Below, we do so for each of the different ways by which we have computed $x$. + +``` +[18]: +``` + +```python +print("Analytical solution:", evs @ coeffs_analytical) +print("Exact model solution:", evs @ coeffs_exact.value) +print("Approx. model solution:", evs @ coeffs_approx.value) +``` + +```python +Analytical solution: 0.3954847855980006 +Exact model solution: 0.39548478559800204 +Approx. model solution: 0.42991214253489807 +``` + +Finally, for this small problem we can compute the exact reference value using [scipy.linalg.expm](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.expm.html) as follows: + +``` +[19]: +``` + +```python +from scipy.linalg import expm + +exp_H = expm(-1j * time * hamiltonian.to_matrix()) + +initial_state = np.zeros(exp_H.shape[0]) +initial_state[0] = 1.0 + +time_evolved_state = exp_H @ initial_state + +exact_obs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state +print(exact_obs.real) +``` + +```python +0.40060242487899755 +``` + +We can clearly see that the MPF has reduced the Trotter error compared to the one obtained with the deepest individual PF with $k_j=19$. However, we also see that the approximate model is not flawless since it actually resulted in an expectation value that is worse than the exact solution. This shows the importance of using tight convergence criteria on the approximate model as you will learn in the guide [How to use the approximate model](/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model). diff --git a/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb b/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb index f99c34cd431..010863b04c3 100644 --- a/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb +++ b/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb @@ -65,9 +65,9 @@ "tags": [] }, "source": [ - "The [qiskit_addon_utils](/docs/addons/qiskit-addon-utils) package provides some reusable functionalities for various purposes.\n", + "The [qiskit_addon_utils](https://qiskit.github.io/qiskit-addon-utils/) package provides some reusable functionalities for various purposes.\n", "\n", - "Its [qiskit_addon_utils.problem_generators](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", + "Its [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", "This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows.\n", "\n", "In the following, we create a simple line of 10 qubits using the `CouplingMap.from_line` method." @@ -108,8 +108,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "" ] }, "execution_count": 2, @@ -432,7 +433,7 @@ "source": [ "#### Optimizing for $x$ using an exact model\n", "\n", - "Alternatively to computing $x=A^{-1}b$, you can also use [setup_exact_problem](/docs/addons/qiskit-addon-mpf/stubs/qiskit_addon_mpf.costs.setup_exact_problem) to construct a [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which uses the LSE as constraints and whose optimal solution will yield $x$.\n", + "Alternatively to computing $x=A^{-1}b$, you can also use [setup_exact_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_exact_problem) to construct a [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which uses the LSE as constraints and whose optimal solution will yield $x$.\n", "\n", "In the next section, it will be clear why this interface exists." ] @@ -523,7 +524,7 @@ "It may happen that the L1 norm for the chosen set of $k_j$ values is deemed too high.\n", "If that is the case and you cannot choose a different set of $k_j$ values, you can use an approximate solution to the LSE instead of an exact one.\n", "\n", - "To do so, simply use [setup_sum_of_squares_problem](/docs/addons/qiskit-addon-mpf/stubs/qiskit_addon_mpf.costs.setup_sum_of_squares_problem) to construct a different [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which constrains the L1-norm to a chosen threshold while minimizing the difference of $Ax$ and $b$." + "To do so, simply use [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem) to construct a different [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which constrains the L1-norm to a chosen threshold while minimizing the difference of $Ax$ and $b$." ] }, { @@ -588,7 +589,7 @@ "### 1c: Setting up the Trotter circuits\n", "\n", "At this point, we have found our expansion coefficients, $x$, and all that is left to do is to generate the Trotterized quantum circuits.\n", - "Once again, the [qiskit_addon_utils.problem_generators](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators) module comes to the rescue to do just that:" + "Once again, the [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators) module comes to the rescue to do just that:" ] }, { @@ -631,8 +632,9 @@ "outputs": [ { "data": { + "image/png": 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LpYdXSBcPlq47gBu9blXRJN23TPpfsZn8s0Njq3Tj51LvDOn6A6Wz+seuj25EFuIYxJP3CqXHV0qF9R2/Xt8qnTvHFI+7dSwLAtyqNSA9vVr6zwbzcK+99wqlx1ZI5w+UphzEjV63qm6WHlhm7gX6Ah2/9+ZmqWe69JMDzPsA7rS2Wrp/mSkW1F5Dq/TjT6QDOks3jZYOyY1N/9yKLMQxiBf+gPTPtdIL69om/OxQ65POeFc6d4D0y1FmsSjcp84nPbjMFIxr8Xf83lsFUm6a9OPh0iWDmQDmVhtrpfu+lj4v3f17r2w0mwz9cpR0RM/o9w3R8ekWcx94fU3Hr9e3ShfMlY7oId02VuqXFZv+uZXtWcj28ceTRp/00HJzD6CpXRZo8ku/WyL9OVW6cph0+VCygFsV1Uv3fi19tnX3783aKA3KNlngmF7R7xuiY9E2cx74tnr3772wTprQXbptjDSIIpKuFAhIM/LN/NHSpo7fq/NJZ71vCgbdPJrFgeFmex6yffzxZGuDdM/Xpmhc+8dEja3SlAVS/yxpykizKATutLRM+n/fSCurdv/eS+vNhoO3jpGGM3/UtV7fJD25Wtqy6/zRIump1dIpfcxaEhYHupPPb+YMzdzYcf5ok1/641dmLcklQ6RrD2AD6nCzPQ/ZPv54sqLSzBtaVtHx6w2t0uUfSWO6ms+Eo7rGpHuIgncKpL+u2n3+qCQ9u2b7/NExUi7zR12pNSD9fbU0fb2ZI9Leu4VmzekFA6UbRrKWxK2qms3cwTlFu6wl8Us3LzIFZK89wGwugPAhC3EM4skHReZz4aa6jl+vb5W+N8c8I7x9rNSTgtKu5A+YzPfSOlNEvL33CqXHVprNRaYexKaTblXbIj34jcl+u84fnV1gNqC+eoR04SDmjLhVfo107zJp8S7zRxtapWs+lYZ3km4cJR3WIzb9cyuyEMcgXgQCZm7Qv9ZKFXtYS3Lme6a2wI2jpHTXV7+zU4NP+vNys8lg8y5Z4O0C6c/fSFcOlyYPIQt8F66+rVZaWqpzzjlHJSUluummm1RcXKwlS5aopKRE9913n2bPnq1FixbJ4/Fo7Nixse6u6zy+0kz62bVw5A6+gPllvvoTqbJpz69B4mpqlX650EzuaV84sr3qFvMQ4DdfdLwBjO+uen2JiuctU+HcL/XNE6/pgx/eq+7jh+rI+67b+Rp/s0+fTH1UY6ZeoK4HmSoNA844TP1OnaBPb3wiVl0PC9vHHy/mb5Wu+2z3wpHtfVUuXfuJ9GXZ3l+DxPXMt9Lvl+5eOHKH1oApInPVJ2YnObiLzy/dskh6bu3uhSN3qPNJ/1gjTVtsXg932dZofr93fdjfXnGDdMeX0nNrots3tyMLcQzixb/XSb/+Ys8T/yTJL+l/JdKP50kle3kNEldrwHzef+rb3QtH7lDfKr243tw/aN7LvQMkroom80D37cLdC0fusLVR+tNX0hMro9s3RMc3FdI1n+xeOLK91VXSDfOlj0ui1y8bkIU4BvHAH5D+sNQ8K9q1cOQOTX6z4cSU+aawENylpkW69lPpjc27T/zboazJTA7883IzQQTu8m2VdNW8PReO3GFtjfSLhWZCMNznrc3SrxbuXjiyvQXbzH2BdXsoKIXQ2Z6FbB9/vKj3ST+db4oFN+0lC1Q0mwKzd39NFnCj/Bpzjt9T4cidr6mVblxodhaH+3xQZAqD7alw5A6LS83zxFWVUesWouiRFWajwV0LR+7Q4jcFhn/y6d7vHSA0tuch28cfLwrqTBaYt0vhyPY210m3LpZe3hDVriFKPtki/Wz+ngtH7rC0XLr6U+nr8uj1C9Hz99XSXUt3Lxy5Q2vALBy/6hOpjPmjrtPiN5tLP79u7/NHa31mnvFtzB8NO9vzkO3jjxeLS82zwl0LR7a3rMKsN1m0LXr9QvQ8v1b67ZK9zx9tDUhzi00W2FteQOLy+c01/plvdy8cuUOdz2SFGz/f+5wCJK6yRrNu/N3Cva8l2dJg5hb9fXVUu+Z6ZCGOQbx4eYO597dr4cgdAjL3Dn88z9xLhLv4A9IdS6S/rdq9cOQOja3SfzZIP19g/hvuUtVsngG+uY/5o6VN5lniIyui2zdEx6pK83lv18KR7a2pNnMKPiiKWresQBbiGMSDQMDMCXx4xe6FI3do8pvNp34638w1hLvUtph7v7M27l44cofyZrPR2P3LmD/6Xbi6eOTUqVNVUFCgKVOm6MEHH1ROTtsW1dOmTdO4cePk8/k0aNAgderUKYY9dZ/XN5lCQMFYX2MKBvGL7C73Ldv3YrD23i+SnlwV2f7Ybtvi1Vo342MNPu9o9ZhwwM6vl329Xsv/+rqOfeTnyuzdTUc+cL0W/vppNWzZxxPaBGT7+GNhU61066K9B7n26lulmz7nga/bzCkyu0QGY1OdeeDrJwu4yl+Wm4c4wfi4xNwAgHu0Bsxiv81BPsB7bCU3eSOJLMQxiIVPtpgCMMEobpCmLmQiuNv8bZXJhMFYuM3c5IV7BAKmkPi+isS09+waMzEA7lHWaAoF7W3yb3stfun2xRQMiiSyEMcgFv6xxhQNDMaX5dIfvopsfxB9v15sigQH46X10iv5Ee0Ooqy62RSF3Nvk3/ZaA2ZzkZWVEe8WomhZuXTnUrNxxP5UNJv7ArVBvF8QGtuzkO3jj5Xff7nvheHtzdpodhmHezT4zLm9LIjNhP0ynweWsuGkq6yukn63JLjNhKtbTHaspHigq7y60Sz+D8aaalNMgPmjkWN7HrJ9/LHQ3GrO7VuCLAZ3/zJpwT4KTiPx5Neac3tQ80d95pkSm0+7yzsFwReA2Vgr3byILOA2/++bfW8m0N7/is1mZIgc2/OQ7eOPhaJ6sz4kmAIwja3mOlBIwSBX+bA4+LUBhfVm82nmj7rL4yvNNT4Yn22VHmT+qKsEAubcnl8b3Ov/vtp8hkBkkIU4BrGwYGvwawO2NJp7ic0UD3SVp7+V3g5yM+EvyqS7mT/qKoGAWRuwJsi1Ac+vM/NG4B6V2+ePVgc5f/R3S4KfbwznyEIcg1h40cG5fVmFmUcOd/m/L6UVlcG99uV8s54EoXFt8ciVK1dq+vTp6t69u+655549vubQQw+VJI0bN27n10444QR5PJ49/rn++uuj0vdE5w8EXzhyhyVlZvdQuENJg/TmJmdt/rOeBUGR9tVDM+T3tergWy7p+PW/vCJ/a6vOff8BlXz6jTa89mmMehhZto8/2l5ab4pCBqu6RZqRH7HuIMoCAbNDoBMrKqUF7BrqGuVN0isOb9i+ki9VBrGADIlh/lZppcMbts+uYRJwJJGFOAbR9qzDLLC+RvqwJDJ9QfTVtpjP+U68sVnaSkF51/gihHt9z35LFnCTWRv3vjvcnjT5pX/zoCeiyEIcg2hq9JkH/k68WyhtDnLSOOLf8gppvsN7ff9Yy4IgN3ljs7TNwYL/Fn/whWWQGP65NrhiUTtsaZDeZkFQRNmehWwff7Str5HmBrkodId/rWVBkJu8XWCKBATLHzDXDrjHC+uCKxa1Q1mT9BoLglyjNYT5o4tKpW9YfxFRtuch28cfbR8Um2JwwQpIeo4s4Cr/XhdcsagdqlrYXMZNAgEzD8yJZRXS56WR6Q+ir7TR+YL//24wmxIhcmzPQ7aPP9r+u0GqC2Kz0R3qfGaBMNzDaRZYU202LIc7VDeb84ATr26ioLybfF4a/AZjO7CWJLLIQhyDaHturbnnF6yNteaeItyhIYT5o06fLyO+hXKv77k1zuaaIb69tjG4zUZ3aPaz6WykkYU4BtHU3Op8Htj/is2cQ7jDmirpY4drxf+51qwngHOuLR750ksvye/3a/LkycrOzt7jazIyMiR1LB75xBNPaP78+R3+/Pa3v5UknX322ZHvuAt8vk3aHMKOXzzocY9ZGyWn5+T6VhYERVpNfok2vPap+hw3Vj0PH7nz6wFfq7YtWq303M5aO/1/MexhZNk+/miq80lvhfD7/NpGFgS5xVflwe8K097LDh8QI369vsn5B7Rmv/T65sj0B9EXyu/z6ioWBEUSWYhjEE2rq6SvQ/h9ppi4e8wukBocZvvWALsFukkov8+b6swCYSQ+n1+aGcLv8zsFLAiKJLIQxyCa3isKbrfYXTndiALxK5QssKWBBUFu4Q+Etth/bhELgtyipMH5pB/JzBdgQVDk2J6FbB9/tIWSBSqanRecRHwKBEKbA/bJFhYEuUVFkzSnyHm7mRtZEOQW87dKhSH8PjN/NLJsz0O2jz/aQpkzsrhU2sCCIFeobQltPvjMjWwu4xZLykJb4DeD+aOu8eom59m+yW82JULk2J6HbB9/NDW2mnnkTr2+yVnxacSv5RXSikrn7Zg/6h5vbDbXdidaAyZDwB1Cyfbra8xnCUQGWYhjEE0basy9PqdYV+oe7xQ6KyYvmWKjM/Mj0RvEQijP/ArrzTNGJL7WQGhrSeYUmbkGiAyyEMcgmuYWm7mATnFvyD1C+VmWNUkfMn80JK4tHjl37lxJ0oknnrjX1xQUmJkJ7YtHHnTQQTriiCM6/Fm6dKl69OihM844I7KddonPQgzmn7EgzDVC/VmG+t5B8L5+2FQ+b18RvefhIzXskhO18pm3NPGuHyspPTWGPYws28cfLcvKnd/ck6TyZlNoCIlv/rYQ221lYahbhJwHyQKu4A9IC0I8D/AeiCyyEMcgWkJ9YLe4lGLibsF9AYR6HuA94A7ra6StIRR+avIzATTSyEIcg2jhOgDuDdmtsM4UBnfKFwht8jjiz+fbnG80KJkcuYUCohFlexayffzRFGoe/JQs4AplTaFtNBiQtID3gCt8URbaTvCF9dKm2vD3B9HHZ8L4ZXsesn380VLnC22jQYnzgFssLXe+0aBkcuS3IeRIxB+eEYD3QPyyPQ/ZPv5oWV4R2kaD1S2mLRJfqGtJFmyjmLhbhJwFWFvsGp+xliQukYU4BtES6u/y1xVmUxIkPtaSgHtDdttcG9pGgy1+5o9GGlmIYxAt1BxDqPcHyQKh8QQC7izR079/fxUUFOjLL7/U+PHjd/u+z+dT7969VVpaqnXr1mnIkCF7/Hu2bdumPn366Gc/+5kefvjhkPoyYcIElZSUhNQ2EXW6/EFlHnWp43YBv19bpgyIQI8Qbd1/P0/JPQc7bte8ZoHKH7owAj1KPCkBr+7wT4z4v5Ocma5zP3hQK558U6v++a4mzbpLpV+t06I7nnP8d93p/VwtnvA8qbN9/Iku7eCz1PUnT4bUtvyxy9W84sPwdghRl3PRXco68aqQ2pb8YpjUwurQRJd7+ztK6T/acbuWTV+r7N4zI9AjRJMnLVO9Hvo2pLZ1c59WzYzfh7dDCSoaeSicWUgiD5IH22R/7zZlnz4lpLZbp42Tv5bKYYmu240zlTrM+e+wb8s6ld55fAR6hKjyeJT3+OaQmtZ/8m9V/3tamDuEaEsZdrhyb3wlpLaV//yFGheG1tZtyIPkwUTW9YYXlDbqBMftWiuKte03h4W/Q4i6Xn9ZI09qhuN2DYteVdU/QvssgfiR3H+Mut/+dkhtq/7zazV8/K8w9wjRlnni1ep00Z0htS394ynyFa0Kc48SU6LlwXBnoUQbv0QebK/HfUuVlNPdcbvGr99T5d9Ce8aI+JHUc4h6/P7jkNpWz/yj6uf8Lcw9QrRlHPUDdb78gZDalt1/rlryl4S5R4i2zj/8izIOdz4HMNDaoi0/dz7n0I24N0YeTGTeLr3V8+5FIbWtnf1n1c7+c5h7hGhLn/A9dbnq8ZDalj98iZpXfxrmHiHaOl36J2Ue98OQ2pb8fLDUSqWIRJf7mzlK6Xug43bNG75U+QPnRKBHiYc8mHh5kCzYJm3Mqer603+E1Lbib1ep6ev3wtwjRFvO+b9V1qnXh9R2y82jFaivDG+HEHXdbnldqYMPcdyupWiVyv54SgR6hKjyJivvsfyQmtZ//C9V/+fX4e1Pgkq0LCQxd5A82Cb7rBuVfdaNIbXd+pvD5K8oDnOPEG1dp/5HaQce47hda9lmbfvdkRHoEaKt1yPr5Ul2XnytYeErqvrnLyLQI0RTyqCDlTvtjZDaVr04TQ2f/jvMPUpM5EHyYCLrct0zSh93uuN2/poybb11XAR6hGjr+cA38mZ1cdyucenbqvz7T8LfoQSQl5enxYsXh9Q2Ocx9iRt1dXWSpIaGhj1+f/r06SotLVVOTo4GD977hLOXXnpJPp9PV1xxRch9KSkpUWFhYcjtE423fJsyQ2jnb6q36ji5WafaKiX3dN6uobqC98B2qZ4kqVfk/53Dfn+lajdt1arn3pEkffKLx3TunAe16e2F2rJgpaO/q6i4SM2BELYL3gPbx5/oOuUVqGuIbbcW5KuO80DC61u+VVkhtAv4/SrcuF5yZ21zq2TXViklhHaNNZVkATfwekO+jFeXbVER7wFJ0clD4cxCEnmQPNgmb1uJskNsW5i/Vv6mELYYQ1zJqKlQKPtsNZEFXKNnU4O8ac4LRtWUb+U94AKZGRuVG2LbsuICVfIekEQeJA8mtrTqcqWF0K6lvprrgEvkNtQqJYTikXWVpbwHXCBN2XJeLswoLylQOe+BhNd9a7E6hdi2eOM6NW/lPSAlXh4MdxZKtPFL5MH2utbXhlQ8sr6yjCzgAilNHvUIsW3l1iJt4z2Q8LpuKVTnENuWFGxQI++BhJdUvk3OPxFKrQ21XAe2494YeTCRJVXVK4Tpw5Kkym3F2sJ5IOF17lugLiG23VqwkfmjblAW2lqSgK9FhZvyw90bxEBOXajzR1lLsgN5MPHyIFmwTU7uppDXkmwr3KgazgMJr3fZlpDWkkhS4YY1Cviaw9ofRF9mTWVI80eba6vIAi7Ry9ciT7LzRFhdxvzRHRItC0nMHSQPtum1rTjktSRF+evUWlsR1v4g+tJDnD9KFnCP7o31Ss52nghrWUviCunJXUNfS1JSoAreA5LIg+TBxJZaVa70ENq11NdwHXCJbg21Sg2heGRdBWtJQuHa4pF5eXmqqKjQkiVLdOSRHavMFxcX65ZbbpEkjR07Vh6PZ69/z/PPP6+RI0dqwoQJ36kvNkktXRtSO9+mr9S3b98w9wYxUbRcGjLecbOkklW8B7ZLCXilCBcW73vSwRp87tF67eSbdn6tZuMWffGnF3X0Qzfo9ZNukq+hKei/r0/vPmGtBm/z+BOdt6FEgVafPEnOYkaguUFdWyrUhfNAwksrWxdSu5aNX6lvnz5h7g1iwVO0Qhp5tON23uIVZAGXaNn4lVIGOt/hI71sHe+B7SKdh8KdhSTyIHmwTWrFeklSIBDY5z2XXbUUrVbv7l2lkKePIl4kFa+UDna+Q5SnaDnXAZfwbfpKqcOPcNwurXQt7wEX8Pir5W+okTcjx1G7gN+vTrWFyuI9IIk8SB5MbMlbVofULlDwDdcBl2jd/LVSOp/suF3K1jW8B9wguVn+mjJ5c4KfArjj82N29SZl8B5IeMlV+ZKc3xdordqiHmkBifeApMTLg+HOQok2fok82J6/YJnUa5DjdslbV5MF3MDrVWtFsZK69g66yY5rRmZlPu8BF0iq3izJeRbw11Uq19NAFnCB1G1rQmrXyvzRnbg3Rh5MdL6tG5Tcc3DQr99xzcio2MB5wAW89UUK+Fvl8SY5audvqlPX1irmj7pAelloa0laNi7lHOAWRSukEYc7buYtXsl7YDvyYOLlQbJgG0/jVgVamuRJcVYuJtDSpM5N29SJ80DCSysPcS3J5uXq0yvUbWkQT7zFK6WxJzlvWMRaErdo2bhUqUMPc9wuvYz5ozskWhaSmDtIHmyTUrFBkvPnRL6tG5TXJUvqHMqWFIgnSSWrJJ3ruF2gkPmjbtG6+SsljzzecbvUbcwfdQVPg/x1lfI6KBq245qRU7NZmbwHJJEHyYOJLXlraGtJ/AXLuA64hL9gmdS9n+N2Kdu+tfY98F1qE3oCgUAgjH2JG1OnTtWjjz6q/v37a86cORoxYoQkadGiRbriiiu0fv16tbS06IYbbtBjjz22x79j1apVGjlypO6++27dfvvt0ex+Qmtqlc58X6pyuNHTvROkU6gX5QqrqqTLP3LWJskjvX6K1CuUbcddqKW+US8OvTzW3XBk8roXlJIZSg3w3dk+fje4dZH0QbGzNucOkP5vfES6gyjz+aVz5kjbGp21u2O8dM6AiHQJUZZfK10413m7WSdJ/UPdXgxx5bVN0h+WOmvTK0N67WQp2RuRLiUc2/OQ7eNPdP6AdMEHUkG9s3bTxkgXB7+GCHGspEE6933nz2leOl4a3jkiXUKUvVco/foLZ226pkqzT5VSna0jQ5y672vp5XxnbY7tJT3kfA2Ra9meh2wff6Irb5LOfE/yOXwC+ewx0thukekTomteifSrz521yUqW3j5NynTt1od2eWSF9C+Ha8QPzZWedL4fDeLUVfOkryuctblmhHT9gZHpTyJKtDwU7iyUaOOXyIPtLS6Vrv/MWZs0r8kCnVIj0ydE19Orpb85nAd8UBfpX8dFpDuIgZ99Jn1e6qzN5CHSr0ZHpj+IrkafNOl9qabFWbsHD5NOCL7urKuRhTgGie6FddJfljtrMyRHmn6C5GA9OeLYzZ9LH5Y4a3PBQOnXzvepRRxq8Utnvy+VOVtXqj8cIk1yvo4McWhdtXTJh87aeCS9erLUNysSPUo8ZKHEOwZkwY5+94X0dqGzNpP6Sn84NDL9QXS1BqTzP5CKHM4fvX2s9P1BEekSoqywTjrvA8npovXpJ0hDO0WiR4i2twuk3y1x1iY3TXrzVCmFtSSSEi8LScwdJA+2CQTMZ8L1Nc7a/XKUdPnQiHQJUVbaKJ31vsmFTvzzWGlU18j0CdH1YbF08yJnbXJSpLdPldKZP+oKD30jvbjeWZuJ3aUnjopMfxKR7XnI9vEnuupmadJ7UpPDhaV/O0qa0D0yfUJ0LdgqTVngrE16kvTOaVJ2SmT65GauvZUybdo05ebmavPmzRo1apTGjBmj4cOHa+LEiRoyZIhOOsnsXjNu3N5nGTz//PPyeDyaPHlytLrtCmlJ0vcHOmvTO0M6IfQiqIgzB3aWDsl11ubkPhSOBNzkB0Ocvd4rCgW5SbJXusThzzM3TTrVzkLwrjQoWzq6p7M2x/aicKSbnN7H/F47cclgCkcCbuH1SJc6zIOdU6QzWQTgGnkZ0kkONwiZ0J3CkW5yYm/zPnDiwkEUjnSTiwebzWKccHrtABC/uqU5X+A5qos0hol/rnFUL2mAwwWe5w+kcKSbfH+glOrwPo/T5wqIb05/nmlecx4A4A6H5kojHC7wPLs/hSPd5LyBZkKnE5eRBVzlMoeL+1K85v4g3CE92RQAc6JvpnRMr8j0B0D0ndNfynZ4n+fSwRSOdBOnz3ySPMwfdZMUr3SRw59nz3TpZIpIu8bQTtLhPZy1OSGPwpGAm1wyxBSFDZZnexu4Q5LH+VqSLqkUkXaTvlnO1wkf3oPCkW5ycm+T8Z24aDCFIwG38HjMvT4nspLNPUW4Q/d06TSH64THdqVwpJsc00vql+mszQUDKRzpJhcOcp7tnM4xABC/OqWauYBOjOhk5hzCHSb2kIbmOGtz7gAKR4bKtbdT+vXrp3nz5umss85Senq68vPz1a1bNz355JOaPXu2vv32W0l7Lx4ZCAT04osv6oQTTtCAAQOi2XVXuPYA6cggH/hmJ0sPHU6hGLf506GmKGgwhuVIvx4b2f4AiK7xudIvDgr+9beONYVn4R5XDJNODPKBb0aS9OeJzhcQIb79/uDgiwQMzJbuODiy/UF0pSdL/8/B7/VJvaXJ3OAFXOXiwdIZQT7wTfVKD07k5p7b/GZc8Dd5+2RKfzwksv1BdKV4TcbPCvIB/lE9patHRLZPiK7BOdLv9r5v026uP9D5AiIA8e2WMdJBXYJ7bY906b4JLA53kySPyQKdgsz4h+ZKPzswsn1CdPXNku46JPjJCD8cJp3A4nBXObVv8AUkvR7zbJmNBgH38Hik+w8zRcWDMaar9KtRke0Toqt7unTvhOA3lrh4sHQ6Gw26yjG9pKuGB/daj6Q7xrPRoNtcf6A0sXtwr81JMZ8hmT8KuEfnVOmBw4JfGHhOfzYUcJsJ3aUpI4N//e1jpWEUinGVHw0zm0kHI3P7/FE2GnSXuw4OvkjA4Gzpt+Mj2h0AUTa6q3TT6OBff9No0wbu8YMh0qlBbj6d5jVzztlo0F1+O95c44PRL9NkB7hH6vaMH+zv9bG9zGcIAO5x/sDgi0GmeM29xM5sNOgqt44JfsPJXunSPRMi2x9EV/L2jB/s/NGJ3c2zRbhH/2wzByDYaeFXDWejQcBtfjXKFIcORrc0M9eQtSTu4fVIDx4mdQ0y44/rJk11UJsIHbl6utXIkSP15ptvqqamRjU1NVq4cKGuvfZa1dXVKT8/X16vV6NH7/lpxMcff6yNGzfqiiuuiHKv3WFHqD+n/75D3YAs6eljmPDhRj3SpWeOMZP89+WontLfj6ZICOBGVwyTbhtrCgPuTVaydOfB0vcHRa1biJIkj7lp+/2B+w6cfTLNdYCdgdyna5rJeYfsZ6eHCd2lp482O4bCXUZ3Nb/fffYxCdQr6aJB0t2HBr+AEEBi8HqkOw8xkwD39fvdK13661HSwewM5Do5KeY6cMR+isGN7WruH3R3uMsw4t+IzuZnu6+C4h6Z+4cPHsbCYDc6e4B0z6HmfLA3aV6zCOAaiocCrpOZLD1x5P4Xh47sLD17jJTncJdhxL9BOeZnO2Q/BcVP7ys9fAQLg93olD5mo4B9Te5O9ZpCAk6KCSBx3DhKuv6AfRcL6ZIqPTSR4qGAG/XLMllgfwtCTuwtPX6k2ZQK7nJML+nhw6Vu+8gCyR7pJyOkW0YzAdiNfnqgmdibto8s0CnFTAA/o1/0+oXoSPGaDcXP3M/PdlC2uY88lPmjgOsc1sPkvB77eAaY5JGuGCr9bjxZwI1+NFyaNmbfG89mJZtNBs+jeKjrJHtNzjt/4L7XkvTLlJ46RjqwS7R6hmjJTTfzR8d32/frJnaXnjqaIiGAG106xOS8zH1kgcwk6bfjzGvhLl6P9IdDpEsG73stSV6G9OTRZoE43KVzqrnG729zkfHdTGbIZf6o6xzYxbwH9lVQ3CPzmeF+5o8CruPxmCx4xdB9ryXpkW7uIU5k83nXyU4xOW9/xeAO6iI9cywbzrrR0E4m5w3aT0HxSf3MM8VgN6NC4jijn8l5+yoimuY1cwp+SvFQwHXSk6XHjjRzA/dlRCczx7DfPtYeIjH1zzY/22H7WUtySh/psSP2/UwZ+2bl1Nvly5crEAhoxIgRyszc892n559/XhkZGbrwwguj3Dv3SE2S7jhYunqE9Eq+9PEWaVOtFJBZCPTgRFNAwMuEH9fqmWFO5ssqpJfzpa/LpTqfmexzeA/pwkGmkAAA97pwkHRGX2l2gTR7s7SlwZz38zJMgZAz+rFDoJsle6Xbx5mJoDM3Sv8rlja2ywL3TZCO6kXBODfrlmaKRi2vkGbkm3OBP2DOA2f3N+eIg7rEuJOIqIO6SLNOlj7ZYj4TrK+RmlrNovATe0sXDKRACOBmSR5TEOyKodKsjdIHxVJFk7lfMDjbFBA/thcTftysc6q50b+6ymSBz7dJRfUmD6Z5pb8dZYoNsxjMvYZ1kmacJM3fat4Dn26R/DLnh8uHmizQlwc8rnZqX3Ouf7dIem2jVNxgPhP0SJfO6i+d1U/qxEIgwLWyU8ykrrXV5jPh/K1Src/cDxzdVbp4kFkEQhZwr0E50n9OkBaVSjM2SB+VtGWBSwebzwQD9jM5EIntuDzprVOlOUXSqxulr8rNeyDZI90wUjpnAJvKuJnHI11zgPldf22T9G6hVN5kzgH9ssxCoJN7UzwWcLN+WdKLx0tLysx9geWVUr3PbDJwVE/znGjwfiYHIrEd0VN681RpbrH06iZpSWlbHrz+QOl7A8zzRLiTxyNdOcz8nN/YLL1dIK2pMu+BFI/0m3HSKX2Z/OtmaUnSXYeYIrEzN5rPhDUt5mc+orPZZHAi80cBVzskV3rjFOnDEmlmvpk31uKXuqRJp/UxBQPZYM7dLh5sCgm/udnMH11d1XZv6LaxZmOZDOaPulaK12S+Hw83c0Z2nT96/2HSkT2ZP+pm3dNNwaDllea+wJdlbWtJDutu7gtQOBRwt+8NMIt/395s7g1saTBf77V9Lcmk/uacAHdK9kq3jJGuGGaeE35QJOW3ywJ3H2qKCTF/1L26pElPHCWtqjRZYFFp2/zRdK/0t6OlUV2YM+JmB3SWXjnZzBd6JV9aU23WknRKlU7Mky4YJPVhLQngWl6P9ItR0uShJgu8X2TWkqR4pYHZ5hxwQh5ZwM1yUqS/bJ8/OiNfWrBVKmy3luTxI5k/6nZDcqT/nmjWEb2cL81rN3/0siFmLUl/5o+62om9zT3gOYVmzkhRvdQaMPcNJ/Uz9wbYVAZwr8xk6YHDTF2BV/Klz7aaOSOZyab2wEWDzPNksoB79c+WXjpB+qJMenmD9L8Ss6YwyWOeI39/oFlvgu/Gylvsy5YtkySNGzduj99vbGzUjBkzdN555yknh3fZd9Uvy3zA/8Uo6cz3pK2NZhHQUT1j3TNEg8cjje1m/gCwU3aK2THwksGx7glipXemWQh8w8iOWeDYvFj3DNEyqqv5s3Cb+fl3T5P+b3yse4VoSfJIx+eZPwDs1DNDuu5A8wd2OqCzWRQiteXBzqnSGO4VWMHrkY7uZf7s+Pnnpkk/PyjWPUO0pCebBQHfGxDrngCIlWGdpFvHxroXiBWvx2wodniPjlngV6Nj3TNES1rS9qLR/dveA93SzEIx2KFrmtlk6kfDY90TALHg8UiHdjd/YKfUJLOp5Bn9OubBH3NdsEbnVLORzOVD294DXdOks7lXZI3+2W3zRwHYJ9lrCgad0ifWPUGsZKdIlw4xf9rfGzpvYKx7hmjps5f5o8f0inXPEA0ej9lQbHTXWPcEQKxkJUsXDjZ/YKe8DLORzPUHdswCJ/SOdc8QLQd2kX473vz3jvdAp1TygS2SPCb7k/8Be3VPNxuPXnNArHuCWBnWyWwkI3VcSzI+N7b9QnR4PWbTySN6dpwvwHNDe6QnmbkBzA8A7DUkx2wwAjt5PNKE7uZP+yxwE2tJwobikXuQnp6uysrKKPYIAMIrZ3Cejn3450rrlqOWmnp98ovHVPltwV5ff/rLdyh3zBD9+8AfRrGXkWP7+AEAAGzPQ7aPHwAAwPY8ZPv4AQCA3chCHAMAAGA3shDHAAAA2M32LGT7+AEAAGzPQ7aPHwAAwPY8ZPv4AdjFG+sOxML+ikcCQKI76v7r9O0L72vWMVO17LFXdczDU/b62oOuO1s1G7dEsXeRZ/v4AQAAbM9Dto8fAADA9jxk+/gBAIDdyEIcAwAAYDeyEMcAAADYzfYsZPv4AQAAbM9Dto8fAADA9jxk+/gB2MXK4pFz585VIBDQWWedFeuuAEDYped2Uu64oVr3yseSpI2zFyirT65yBuXt9touI/ppwBkTtezRWdHuZsTYPn4AAADb85Dt4wcAALA9D9k+fgAAYDeyEMcAAADYjSzEMQAAAHazPQvZPn4AAADb85Dt4wcAALA9D9k+fgD2sbJ4JAC4WVbf7mrYUqFAq3/n12oLS5XVt3uH13mSk3TUgz/V/GlPdnhtorN9/AAAALbnIdvHDwAAYHsesn38AADAbmQhjgEAALAbWYhjAAAA7GZ7FrJ9/AAAALbnIdvHDwAAYHsesn38AOxD8UgAsNT4my7SxrcWqmpNYay7EhO2jx8AAMD2PGT7+AEAAGzPQ7aPHwAA2I0sxDEAAAB2IwtxDAAAgN1sz0K2jx8AAMD2PGT7+AEAAGzPQ7aPH4B7JMe6AwCA8KorLFVGr67yJHl3VjnP7ttddYWlHV6Xd+QoZfXtrpFXnSFPUpJScjJ04edP6I1Jt6mprDoWXQ8L28cPAABgex6yffwAAAC25yHbxw8AAOxGFuIYAAAAu5GFOAYAAMButmch28cPAABgex6yffwAAAC25yHbxw/APhSPBACXaSyrVvmyDRr6/eO09r8fauBZR6iuuFw1+SUdXvf2eb/b+d/Z/Xro3DkPasbEn0W7u2Fn+/gBAABsz0O2jx8AAMD2PGT7+AEAgN3IQhwDAABgN7IQxwAAANjN9ixk+/gBAABsz0O2jx8AAMD2PGT7+AHYxxvrDgAAwu+zaU9qxBWn6vxPHtGYn5+vT375uCTpqAevV//TJsS4d5Fn+/gBAABsz0O2jx8AAMD2PGT7+AEAgN3IQhwDAABgN7IQxwAAANjN9ixk+/gBAABsz0O2jx8AAMD2PGT7+AHYxRMIBAKx7gTsceZ70tZGqWe69NZpse4NEL9a6hv14tDLY90NRyave0Epmelh+btsHz/gZmQBu/HzB4Jnex6yffyAm5EH7MbPHwie7XnI9vEDbkUWAO8BIHiJlofCnYUSbfwSeRAIBlkAvAeA4JCFOAaAW5EFwHsACA5ZKPGOAVkQCA5ZALwHgOAkWhaSmDtIHgSCQxawGz9/IHi25yHbxw+4FVkgMryx7gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD2juKRAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAcYzikQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHGM4pEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABxjOKRAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAcSw51h0AAOwuOSNNk9e9EOtuOJKckRbWv8vm8QMAANieh2wfPwAAgO15yPbxAwAAJFoeCncWSrTxS+RBAAAQPmQhjgEAALAbWSjxjgFZEAAAhFOiZSGJuYPkQQAAEE625yHbxw8ATlA8EgDikMfjUUpmeqy7ETO2jx8AAMD2PGT7+AE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m6MEHH9xZOFKSpk2bpnHjxsnn82nQoEHq1InZZ0A4bWmQrv5k98KRu/q8VLr2U6m6ed+vgzPV60tUPG+ZCud+qW+eeE0f/PBedR8/VEfed93O1/ibffpk6qMaM/UCdT1ooCRpwBmHqd+pE/TpjU/EqutwkefWSA8s271wZHsN2yeK/3dD1LoFAIiSr8ql6z7dvXBkewGZHeRuXbz7AnKEjiyIeOAPSL9bYnLePuKgSpukG+abhUMAAHd5bZOZ3LVr4cj2mv3Swyukv6+OXr9sQB5EPKjzSdd/tufCke19Wy1d9cnuE8UBAIktEJAe+EZ6+tt93/erapFu+lz6oCh6fbMBeRDxYHOtyXm7Fo7c1ccl0s/mSw37+OwIAEg8za3SLxfuuXBke5vrpGs+kdZURadftiAPIh4s2CpNmb974cj2/JJeWi/d+aX5HAkAcI/SRpPzdi0cuaslZdI1n0qV+7hewDnyIOLBi+uku7/evXBke42t5jUvrItevwAA0bG8wqwZ3t98oDc2m43IfPuabA5HyIKIB4GAuef37z0UjmyvrMncQ1ywnzmGAIDE89Zm6fbFuxeObK/FLz2+0vxB+JAHEQ8afGbN8J4KR7a3rsbMMdxcG51+AW7k2uKRK1eu1PTp09W9e3fdc889e3zNoYceKkkaN25ch6/PmTNHRxxxhNLT09WzZ09df/31qqpidhrgxG++MAUkg7G+xjzwQ+RsW7xa62Z8rMHnHa0eEw7Y+fWyr9dr+V9f17GP/FyZvbvpyAeu18JfP62GLfuZqQHsx5Iys+tDsB5cJq2qjFh3AABR1uCTbv583wWE2/uoRHp+bWT7ZDOyIGJh+gbp/SALPzT7pVsWmd2EAADusK5auvur4F//1LdM/ook8iBi4f6vpdVBPlora5JuXcQCcQBwk/eKgt84zC+zAUVJkM+W4Rx5ENEWCEjTFksVQW4iuqJS+vPyiHYJABBlf10V/MZhtT7pJhaIRxR5ENFW2WzyoC/I+31vFUivbIxsnwAA0fV/S6TCIDcO21gr3eXg2TKcIw8i2paVSw85uN/3l+WmDQDAHZpazQaCDfsoINzeZ1ulZ9dEtk82IwsiFl7ZaO75BcO3/dlyZZDPlgEA8W9jrXTXUinYaeHPrd1/gTmEjjyIWPjzcml5ZXCvrWg2a4tZSwKExrXFI1966SX5/X5NnjxZ2dnZe3xNRkaGpI7FIz/66COdccYZ6tu3r2bNmqU//elPmjFjhs477zwFONMAQVlZKS11+OBubhELgiLtq4dmyO9r1cG3XNLx6395Rf7WVp37/gMq+fQbbXjt0xj1EG7y0npnr/fLFBgCALjDu4XBLwrd4b8bWBAUSWRBRJM/IP3HYR6saZHe3ByZ/gAAou/lfKnV4e10p/cS4Ax5ENFU1mg+Fzqxskr6igVBAOAaTrNds1+aRaGQiCIPIpoWl0prqp21eWszC4IAwC0afM6zXVG9NG9LZPoDgzyIaHpjk1Tvc9bmP+tZEAQAbrG2Wvo8yELiO8wrkQrqItMfGORBRFMo60JYSwIA7jGnSCptctZmxgaphbUkEUMWRDQFQlhLUu8z9xQBAO4wIz/4DcZ2cHrtgDPkQURTZbOZC+jE2prgNygF0JFri0fOnTtXknTiiSfu9TUFBWbbgvbFI++66y4NHz5cL7/8siZNmqSf/OQn+vvf/64PP/xQs2fPjmynAZeYke+8jV8sCIq0mvwSbXjtU/U5bqx6Hj5y59cDvlZtW7Ra6bmdtXb6/2LYQ7jF1obQdnh4r1CqYkEQALjCy/nO22xtZEFQJJEFEU0LtkmF9c7bzchnQRAAuEGdz/mDPsnsIM6CoMghDyKaXtvkfNKPFNqzBQBA/FlVKX0TwubTr25kQVAkkQcRTaE8I2jyS2+yIAgAXOHdQqnWYdE4SXqZQiERRR5EtPgDod3ny6+VvigLe3cAADHwSr7zNoEQ2yF45EFES3mTKRrm1Jwi0xYAkPhCuS9Q3izNLQ57V7AdWRDR9EWZudfn1Ix8c28RAJDYGkMsCPx5aWjXDwSHPIhoenOTmQvoVChzDgFIybHuQKRs3Giq0A0cOHCP3/f5fPr0U1P1uH3xyIULF+rHP/6xvN62upqnnXaaJOnVV1/V2Wef7bgvEyZMUElJCFW0gASVe/u7Suk/ynG7J2b9T396/IoI9CjxpAS8ukMTw/73fv3wKxp83tE6+JZL9O6Fv5ck9Tx8pIZdcqJWPvOWJt71Y71+6i1qbXRewW/E8BFq8bCiC1La6JPU9Wf/ctyuyS+NP/V8taxbFIFeAQCixuNV3uOhrfD86R8eUe3r94e5Q4kpEnkwkllQIg+iTdYZU5Vz7jTH7fJrpQFDhyvQ3BCBXgEAoiV5wFh1v+0tx+0Cko6/5Fo1LXXe1o3Ig0hkXa59SunjJzlu9+aSdXrue8dHoEcAgGjKOHqyOk++z3G7siZp2MFHqrUshErkLpRoeZAsiPZ6/GG+knL7O253zz9f1W3/mBKBHgEAoqnTpXcr87grHbdbsKlS/S4eHYEeJR7mDiKReTK7qNeD34TU9rKb/6j6OX8Lc48AANHW7ZY3lDr4YMftnn7rUz1w+iUR6FHiIQ8ikaUecLS6/WK643a+gDThzEvUvPrTCPQKABBNvR7dIE9SiuN2v7zvb6qZ+ccI9CjxJNqzYok8iDaZp1yvThf81nG7wnpp4AGjFaivDH+nAABRk9znQHX/7ZyQ2p5y+RQ1Ln41vB1KUORBJLLOP35MGYed57jd+99sVr/zjwx/h4AEkJeXp8WLF4fU1rXFI+vq6iRJDQ17Ljgwffp0lZaWKicnR4MHD9759aSkJKWmpnZ4bUpKijwej5YvXx5SX0pKSlRYWBhSWyARdUlKlfPbu1Kzkvhd2S7VkyT1ct6uZP5yPdf7wr1+v2pNof7Vr21SRXJmuo75yw364k8vatU/39WkWXfpkNsv06I7nnP8bxcVF6k50Oq803CdLgMb1DXEtuU19armPAAACc2bnqW8ENvWNfvJg9uFkgdjmQUl8iDa9GluVU6IbUvKq+Sr2hbW/gAAoiu78xB1D7FtZX2jysmDksiDSGwZfo/SQ2jnT0rjMyEAuEDPhiZ1DrHt1soaNXItkJR4eZAsiPZyk9KUFEK7poCXPAgALpDSKmWG1DCT68B2zB1EIkvJDentK0mqbfKpmPMAACS8zp7Qlqn5PMnkwe3Ig0hknfs2qFuIbctrG1TFeQAAEltSsvJCKBwpSfUtAfLgdon2rFgiD6JN7yafOoXYdktFlVpKOQ8AQCLLzOob8lqSqvomlZIHJZEHkdjS/R5lhNDOn5zOZ0IgBK4tHpmXl6eKigotWbJERx7ZsbJscXGxbrnlFknS2LFj5fF4dn5vxIgRWrhwYYfXL1q0SIFAQOXl5SH3BbBJUktdSO1SWhvVt2/fMPcmMaUEvFIUCqsf9vsrVbtpq1Y9944k6ZNfPKZz5zyoTW8v1JYFKx39XX1696EaPCRJqZmhPeSRpG6ZqcrhPAAAic3jUaDVJ0+S84+bWUl+8uB20ciD4cyCEnkQbbKSAiG37dUlW8pO3f8LAQBxKzk7LeS2XdKSlEEelEQeRGJLDYS246inuZbPhADgAhmpXsdtAoGAPB6PeuRkyM+1QFLi5UGyINrzNIc2ZyQt0EweBAAXSJcvpHaBxmquA9sxdxCJzJOWFXLb7OQA5wEAcIEkX0NI7ZJ9DVwHtiMPIpGlZIS+lqRrRoqyOQ8AQMILNDfIk+q8VEiGt5U8uF2iPSuWyINok5n8HdaSdM5SII3zAAAksqTs9JDbdkrzKo08KIk8iMSWppaQ2nmaWEsCe32X2oSeQCAQ+qewODZ16lQ9+uij6t+/v+bMmaMRI0ZIMoUgr7jiCq1fv14tLS264YYb9Nhjj+1s9/zzz+vKK6/UH/7wB11//fUqKCjQlVdeqRUrVmj48OFaudL5RQ6wzZOrpKe+dd7utrHShYPC3p2E1FLfqBeHXh7Rf6PvSQfr+Cd+qddOvkl1haU7v37gj87QQdedrddPukm+hqag/77J615QSmboH+jgHvU+adJ7Up3DueC5adKbp0opztcUAgDizK8WSvO2OG/33LHS6K7h708iinQeDHcWlMiDaLOmSvrBR87bTewuPXFU+PsDAIgun1/63gfSFodrgtKTpLdPk3JCX0fgKuRBJLI3N0m/X+q83RVDpV+MCnt3AABRtqVBOneO1OpwJsqwHOmlE6R2e39aLdHyIFkQ7T24TPrPBuft/nSodDrzPwEg4X22VZq6wHm7s/tLvz84/P1JRMwdRKK75hNpabnzdjNOkgZlh78/AIDoevZb6YlVztvdNFr6wZDw9ycRkQeRyJpapTPfk6ocrhHvnCK9dZqUlhSZfgEAomfaImlusfN2Tx0tHZwb/v4kokR7ViyRB9Emv1a6cK7zduO7SU8fE/7+AACiyx+Qzv9AKqx31i7VK711qtQlLTL9SjTkQSSydwul33zhvN2lg6Wbx4S/P4DbubY807Rp05Sbm6vNmzdr1KhRGjNmjIYPH66JEydqyJAhOumkkyRJ48aN69Du8ssv16233qo//OEP6tGjhyZMmKATTzxR48ePV+/evWMxFCDhnDdQSnK4qCcrWZrULzL9wZ4Vzv1S/z7whx0CvSSteu4dzTxyiuNAD+yQmWwmdDt13kAKRwKAW4RSEHxkZ2lUl3D3BHtDFkQkDe9sHt47xWYCAOAOyV7pgoHO203qR+HIaCIPIpJO6WsW9zjhkfT9QZHoDYD/397dBtlZ1mcAv87Z3exLdsnbhrzsuklIFgghhJeQhBAUeSnaSBQFrZSq2BGpVgSlTp1+cJxxCqhYcahTrC39YD/QTqctkBmgLeMIUpnSmWLKTAu1jUQaYiNQNiEgIdsPj9VJzeLZ7e45Z5/n95vZL3ueM/PfD+fkyvPc93VDsy3pTd44hQNQL1+lOLKZ5EFm0lTu8y2ck1xgWRZAKWxZnAz1Tf59nhM1lzzITJrK53nToOJIgLJ4+0jSOcn7fN31qa09Z+rkQWZKd0eyY2Ty79sxojgSoCyuWDn596wemNrac6ZGFmQmrewv7vVNlmcEAOVQr03tO/3i5Yojm0keZCZdsKxYCzhZ8iBMTWkrmoaHh/PQQw9l+/bt6enpye7du7Nw4cLccccd2blzZ5588skkP18eWavVcvPNN2f//v15/PHHs2/fvtx666156qmnsnXr1lb8KTDrLOlNLp3kw/srTygKJIFyeO8JRYlko+Z1Je+aQrEEAO3pnOOTU+ZP7j2/fqLN4VAmV48WBUCNGj1uasUSALSny1YkCyfx8L6no7g/CJRDT0dy1ZrJvectw8nw3JmZB4Dme/+ayW0QX97noEEok5UDyUXLJ/ee9486aBCgLOq14tnvZGxe7KBBKJMLlyerJlEEWU/ygdEZGweAJlvUUzwvnoxfOcFBg1Am7141uc/0QFfxHgDKYeNgsmGSRZD2kkC5fGB0cgUmq/odNAhQJjtGksU9jV8/p55ctXrm5gGaq6terAWcjIuWF2sOgckr9bLbtWvX5t57783Y2FjGxsby6KOP5pprrsnBgweze/fu1Ov1nHrqqcd878DAQE477bQsWrQod955Zw4dOpSrr766yX8BzF6fWp+cs7ixa986nHzopJmdB2iu4bnJF88uNor/InM7ky9tTo7vnfm5AGiOei350qZkRYObAa4/JTnfgz4olXOXJDeub+zaob7ky5uTzlLfpQKoloXdyW2bk+Ma2Awwp57cvDFZ5UEflMoH1iTvGGns2o2Dye9s+MXXATB7nLog+eyZSUcDG3wW/SQ7OmgQyuUzpyenN7gx8IqVDhQAKJsdI8VBY404aV5xf9DmcCiPrnrylS3JsgbWA9aS/PZpyaYG1xsDMDt88tTkvCWNXXvx8uQja2d2HqC5lvUlt25K+hrYS9LbUew7WdY383MB0By1WvHdfkKD6wE/cnLyS0MzOxPQXJsWJ5/eUNz7+0WW9ia3bUnmNJAdAZgd5s0p1gPOa2AvSVc9+d2zktF5Mz8X0DxXnlCsCWzE6QuLtYbA1FRyW/4TTzyR8fHxjI6Opq/v6KcLjz32WG666abcf//92blzZ2644YZce+21ueWWW7J6tbpqaNScjuT3Nie/tnrijT7z5xQ3dz97RlEwBJTLpsXJHVuT0xZMfM1Zi5Kvnzv5E8UAaH+DPckfbUveMpR0TpD1hvuSz52ZXLWmubMBzfGeVcktG5ORucd+vaNWLAC/87xkiSJxgNJZO7/Ig5sGJ75m3fzkq+ck2xrcOATMHrVaUQh53SnJwjnHvqavI3nvCclXNjd2CA0As8slQ8Ui0NHjjv16LUUOvPM8ReJQRr2dye3nFPcIeyfIeou6k0+sKw4nVRgGUD4fXVvcG5joGVB3PXn7SPK1c5OBBjYOAbPLsr7i/3tvXjbxwQIr+5MvnJ28c2VTRwOgCTrrxXf81aNJ/wR7SeZ1JR8+KfncWY0dQgPMLmcuSr62LTnjdfaJnLEw+cNtyVmvs64EgNlpQXexdnD7cFEIdCxDfcW+4g+e2NzZgOa4bEXx/8JV/cd+vaNW3Dv8k/OS5YrEAUrnxHnFc6Itr3N42Np5ye1bkvOXNW8uoDlqtWJN4CfWFWsEj6WnI3n3qmKNYa/D52HKKvnx2bVrV5Jkw4YNP/dad3d37rnnntx00005fPhw1q9fn7vuuiuXX355s8eEWa+znnx8XfKhk5L7fpA8/lzy0mvFAoBNi5MLlzkNBMpu3YLkj89LnvzvZOee5IcvF79f2ptcOtL4KWIAzE7z5xSLO69fl9yzJ/n3F5NXjhSnB12wLNm8WIk4lN2Fy4vP+z/sT/72P5MXfpzMqScrB4oNgYt7Wj0hADNp1UDy1a3J7gPJPU8new8lR8aL7/9fHi4KJoHyqtWS960pCiIf3Jt854fJgcNFedD6Bclbh5N+5RAApbbl+OIe4HefL54XP/dK8Qx5eG6y4w3J0AQHTgDl0NOR/Nb65DdOTnb+IHni+eTQT9aMnLskOX9p8Z0AQHldtiK59A3Jt3+YfOvZZOzVpLsjOem45G0jxfNkoLwGe4oN4vsOJXc/nXz/QPLqkeKzf/FQcfC0EnGA8uqsF4XiHxxN7n8m+afnkoOHk7mdycbB5KLlDheDsjt5XlEO+W8vJvfuKXJhUhwy8LY3JGsmOHwKgHIY6Eo+e2axl+Tup4t/D/53L8mblibnHK9EHMru/GXF5/0ff1TsJXn+laJQdkV/smNk4sOnACiHkf6iFO7pA8Xe4mdeKvaSDHYXa8jXLWj1hMBMqtWSK1cXBZHffDb59r6f7SU5ZX5xf9BeEvj/Ux75f6xfvz6PPPJIs0eCUuvrLE4Gdjpw8wysWprzbvtYuhcO5NWxl/Lwx2/PC0/+4OiLarWc/Zn3ZejNp+fI4SN55fmxPHLjH2Rs97OtGZpSO3Fe8QNANQ32FKeI0zzyIO2kVisOENj0OqeFAVBuK/uTj53S6imqRR6knXTVk0uGih8AqqdWSzYsLH5oDlmQdtPflbxnVZJVrZ4EgFborBebQ9+0tNWTVIc8SLtZ0lscQg9ANfV2Ju9YUfzQHPIg7WbNcUVxGADVtKA7eb+9JE0lD9JOarXiAIGNg62eBIBWGekvDpmheeRB2klnvThM6qLlrZ4EyqmS57e/XnkkQBls/fyH8+Q3/iZ/ue267Lr9r7Lttt/8uWtGLtmY488+OX994Y25+8JPZu/Du3Lmp69swbQAAEw3eRAAoNrkQQCA6pIFAQCqTR4EAKg2eRAAoNrkQQCAapMHAaqjkuWRDz74YMbHx7N9+/ZWjwIw7XoWHZdFG1bne3/xrSTJ93d+J3OXL8rAyqOPbx8fTzrmdKajuytJ0tXfm5f2/qjp8wIAML3kQQCAapMHAQCqSxYEAKg2eRAAoNrkQQCAapMHAQCqTR4EqJbOVg8AwPSaOzSYQ/uez/hrR376uwPP7M/cocGM7X72p7/b88BjWXbuurznu1/P4QOHcvDZ53LfZZ9pxcgAAEwjeRAAoNrkQQCA6pIFAQCqTR4EAKg2eRAAoNrkQQCAapMHAaql3uoBAGiNwQ2rM/+kkfz5GdfkrtOvyd6HduWcz1/T6rEAAGgSeRAAoNrkQQCA6pIFAQCqTR4EAKg2eRAAoNrkQQCAapMHAcpBeSRAyRx8Zn96lyxIreNnX/H9Q4M5+Mz+o65bfcWbsvfb/5wfv/hSMj6e7/3ZN7N067pmjwsAwDSTBwEAqk0eBACoLlkQAKDa5EEAgGqTBwEAqk0eBACoNnkQoFqURwKUzMs/ejHP7fqPrH7XG5MkK7ZvycG9z2Vs97NHXTf29L4sO/fU1Ls6kyTDF2/MC/+6p+nzAgAwveRBAIBqkwcBAKpLFgQAqDZ5EACg2uRBAIBqkwcBAKpNHgSols5WDwDA9HvkU3dk25c/mvXXvTOvHjiUh6///STJ1i9emz0PPJY9DzyWf7nzvswfHc6Ov/tijrz6Wg791wv5+0/d0eLJAQCYDvIgAEC1yYMAANUlCwIAVJs8CABQbfIgAEC1yYMAANUmDwJUR218fHy81UMAcLRXX3o5f7r6qlaPMSm/+r1vpKuvp9VjAACUgjwIAFBt8iAAQLXNtjwoCwIATJ/ZlgUTeRAAYDrJgwAA1SYPAgBUmzwIQKPqrR4AAAAAAAAAAAAAAAAAAAAAAAAAAICJKY8EAAAAAAAAAAAAAAAAAAAAAAAAAGhjyiMBAAAAAAAAAAAAAAAAAAAAAAAAANqY8kgAAAAAAAAAAAAAAAAAAAAAAAAAgDamPBIAAAAAAAAAAAAAAAAAAAAAAAAAoI3VxsfHx1s9BABHGx8fz+FDr7R6jEnp7O1OrVZr9RgAAKUgDwIAVJs8CABQbbMtD8qCAADTZ7ZlwUQeBACYTvIgAEC1yYMAANUmDwLQKOWRAAAAAAAAAAAAAAAAAAAAAAAAAABtrN7qAQAAAAAAAAAAAAAAAAAAAAAAAAAAmJjySAAAAAAAAAAAAAAAAAAAAAAAAACANqY8EgAAAAAAAAAAAAAAAAAAAAAAAACgjSmPBAAAAAAAAAAAAAAAAAAAAAAAAABoY8ojAQAAAAAAAAAAAAAAAAAAAAAAAADamPJIAAAAAAAAAAAAAAAAAAAAAAAAAIA2pjwSAAAAAAAAAAAAAAAAAAAAAAAAAKCNKY8EAAAAAAAAAAAAAAAAAAAAAAAAAGhjyiMBAAAAAAAAAAAAAAAAAAAAAAAAANqY8kgAAAAAAAAAAAAAAAAAAAAAAAAAgDamPBIAAAAAAAAAAAAAAAAAAAAAAAAAoI0pjwQAAAAAAAAAAAAAAAAAAAAAAAAAaGPKIwEAAAAAAAAAAAAAAAAAAAAAAAAA2pjySAAAAAAAAAAAAAAAAAAAAAAAAACANqY8EgAAAAAAAAAAAAAAAAAAAAAAAACgjSmPBAAAAAAAAAAAAAAAAAAAAAAAAABoY8ojAQAAAAAAAAAAAAAAAAAAAAAAAADamPJIAAAAAAAAAAAAAAAAAAAAAAAAAIA2pjwSAAAAAAAAAAAAAAAAAAAAAAAAAKCNKY8EAAAAAAAAAAAAAAAAAAAAAAAAAGhjyiMBAAAAAAAAAAAAAAAAAAAAAAAAANqY8kgAAAAAAAAAAAAAAAAAAAAAAAAAgDamPBIAAAAAAAAAAAAAAAAAAAAAAAAAoI0pjwQAAAAAAAAAAAAAAAAAAAAAAAAAaGPKIwEAAAAAAAAAAAAAAAAAAAAAAAAA2tj/AD9ENB9Ltm0MAAAAAElFTkSuQmCC", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 12, @@ -658,8 +660,9 @@ "outputs": [ { "data": { + "image/png": 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ckLzjD9XvEAwOWcg1GA76atUh4o/2/md1b/UM8dHOoqD9LN9Y/b6/fdHp7d2zunp+MG/Nn/4+2s/Ny6t3gNsXnd7eJQ9U+8vXWz/acc67J3n/NX96L0l/qjUFn7rRmpHBJAu5BsPB6k3J3132yEWntzZ/bZUZHu1cKtrPnSurZz6Ptmf4ikXV+tE1skDH+em8ao/A9kWnt1ZL8u27qz0HskBnqdWqPUL/8whFp7e2vi/512uS8+/9E99E3UrPQqWPf7i4Z3WVBbYvOr29a5ZWe5BXPsq5VLSfXy2ozhLbvuj09s6eW90L+mWBjvPZm6szRP7U+tEN/dVZJGfNaV2/aI0NfdVZYo9UdHprSzZUvxNe/yjvEqhP6Xmo9PEPF5c9WM0D2xed3t6P76uKjzh/tPP89+1VHvhT60c39VfviL5u/WjH6e2vfh/Yvuj09pZvrM4qvspZ5B1n0eb1o49UdHprt69MXv+76uxyBk/peaj08Q8X39h8ltj2Rae31lerapf8122t6xet0VdL3nvVjkWnt7dy81nklz7KWRS0n2UbqrMnty86vb27V1W1zOZbP9pxblxWPfd7tJozv3EW+aCThVyD4aC/lnzgmkcuOr21Nb1VgfpHO4uC9rNqU5UFti86vb371lR7SeY6i7xhHVt4+pZbbslZZ52V6dOn56Mf/egjfs/RRx+dJDniiCMe/tpDhaqPO+64jB49Ol1dXS3pb6f5z1uTawf4And9X/Uw2ObgzlGrVQfBLR1gMen71lQLP2ieRVfdlrvO/m32fuGTsvMxj3n460uuvzs3/ecP8+TPvi3jdp2aJ3zyb3PFP3456x7orGrwpY9/KFyxKPny7QP//s/eXD0IoHN89fbq52AgNvZXiz7W2hDUUd53zaMXFnrIwnXJ+65ubn9orTW91X/Xf+ol39YuX1RtEqc5ZCHXYCh8++7ktwM8AKivlrznyuoQQTrHv1376IWFHrJkQ3W4FJ1jQ1/18uZPHRKztWuWJv91a3P7RGvdviL51A0D//5v3JVc4uC4ppGFXIOh8LEbBn4A0IpNyXuuclBIJ+ntT07/w58uMri1m5ZXz4jpHPesrg4AGqiz5yYXWvjTUS6Y9+gLwLf2gWssAm6m0rNQ6eMfCv216h3BygEWk75jZbVBmM6xcF31rnCgEf+CeQ6O6zQXL6jvAKCPXe8Q2U7zH7cm1w/wlrpu8/rRgb5bpn6l56HSxz8Ulm5+9zfQwwAvXpB8z8FxHeWyB+tbB/Tpm5JbljetOwyBL9/+6MUEHrKxP3nXlYrLdJp/+WPy4ACLSc9fm3zw2qZ2p3il56HSxz8UrllS7SscqP+69dE3EdNevnHXwA8A6q0l776qKkhE5/jgtdU9fiAeXJ/8s/WjHWX95sM/Ng7wed9Vi6sDZ2kOWcg1GArfn1MVlh+I/s1Fqgd6HgHt4aPXVevHBmLZxioPWj/aOTZtXj/6p4oMbu2GZdVBsnSOOauqNQADdVYd9w3qJwu5BkPh32+sigUMxMpN1VpDheY6R1+tei7waEUGH3LbiuT/WT/aUeavqfYGDNQP700uuK95/aH1fnF/tUdooD5ynUNkm6n0LFT6+IdCrVbtFV4+wLNj7l5V7UWmcyxev3n96AC//8L5yTlzm9kjWu2ShdWagYH61A2PXpCO9vLF2wa+DmhD/+aziZxF3jSl56HSxz8UVm6s8mDfAJ/3XfJA8q067hsMf1ctTr5URwHJz9+SXDfAGha0h6/fUZ0tPBCbNp9FvsZeko7y/muSBQM8i/yBdcm/1vE8mfqVnodKH/9QuHFZcmYd58h9pY4aFrSH79yd/HqAZ8r2by5SPdDnybSHD1+X3DvAc+SWbkje+0frRzvJxs1nx6wf4PO+a5fWtweR+shCrsFQOO+e5KfzB/a9tST/enXy4AB/h6Q9fKKOc+QeWj8qCzSmYwtPf+c730l/f39OOeWUTJgw4RG/Z+zYsUm2LTx955135pxzzsnMmTNz7LHHtqSvnWZdb/KDe+prs3iDzSCd5Kbl1Z96/Hahw8Sb7bpPn53+3r4cdfrLtv36Z85Jf19fnv+LT2bh727MnPN/N0Q9bK7Sx99q3727/jb1HDrM8Laxr/7FnCs2JT+b15TuMATuXDnwgyMf8ofFDhPvJD+9b+AFJR5y9lyHiTeTLOQatFJfrf6Dwdf2JT+yMbRjzFsz8IMjH3LDsuQm71Y6xi/urwqK1+P8ex0m3km+N2fgxaUe8l3PBZpKFnINWmnR+uSXdRaQvWNl8keHiXeM3y4c+CaAh/z4PoeJd5Kz5w58Q+BDzmrgvQLDU61W/3ui3lpybp1rDKhP6Vmo9PG32hWL6j8A7OfzHSbeSc6dW/87n7PmWADaSepd/9FXq4oQ0BnW9FYHgtbjwfXJr60fbarS81Dp42+18+8d+IbAh3xvjsPEO0m9zwVqUXy8k2zoS86p8znP8o3Jz+t8r8DwdeuK5Jo6DwC77MGBF6SiMaXnodLH32qN7AuRBTpHb3/9z3nW9Fbvi+kM96yu7u31uHZplSHoDD+/vyoiWo9z73GYeDPJQq5BK/XX6s+D6/uqZ4p0hoXrkl/V+c7nthUOE+8kv1qQPLC+vjY/us9h4p3ke3PqXz/quUBzyUKuQSst3ZD8bIAHRz5k7mqHiXeSyx9M7qvz/LCfzXOYeCc5955qb0A9vmv9aEepd81IX03B0WYrPQuVPv5W++OSaq9wPX55f7Unmc7wg3uSjfaSFK3eM0NqsZekk6xv4CzyJRuqM4pontLzUOnjb7Uf3pesrfOdz/fn1v9egeGrkbPIvSfqHJv6q/+m67FyU3V2MZ3h7lX1v/O5anF1hjnNU3oeKn38rdbIXpJG8gPDUyNnka/rS35k/WjHmL8m+c0AC48/5Obl9dc1Y/j65YKq9mA9fnBPVeOQ5pCFXINWqjWwl2Rjf1Wsms6weH1yYZ3vfO5eVX9dMyodW3j64osvTpKccMIJ/+f3zJtXVTfcuvD0X/zFX2TBggX54Q9/mGc84xnN7WSH+vn8ZHUDwcwL/85x9tz629TiMPFmWzV3Yeac/7vs9heHZ5fHHfTw12u9fVl05W0ZM22n3HnWr4awh81V+vhbaeHa+ovMJclF9yfLHSbeEX61MFnawMaeRu4fDE+NburwM9A5Gvl3uWSDw8SbSRZyDVrpsgeqw2Lqdc5cm0E6xbn31F9wNpEFOkkjeXDlpvpfDDA8rd6U/HRe/e2uWJTc6zDxppGFXINW+sE9jW3skQU6RyP/Ltf1OUy8U6xv8N/l9cuS2x0m3hFuWt7YwfDn35tsdJh405SehUoff6s1kgU29ddfpJThqbe//kNCkuqAKYeJd4a7ViZXL6m/3U/m1X+wBMPTz+Y1djB8vYcKUJ/S81Dp42+l/gYPg71vTfIHh4l3hPlrqsPE63Xh/GSFw8Q7wi/vb+zf5dn2knQM60eHp9LzUOnjb6XF6+svMpdUbRY7TLwjXPJA8mAD/y7dBzpHo1lAYZHO0ci/yxUbq32FNIcs5Bq00h8W1V9kLqnmjn57STrCD+5J6qwrk0Qe7CSN/Ltc09vY/gOGn3W91bv/el29pFprQHPIQq5BK/3w3motYL2sGekcjWSBDf0OE+8Um/qrPQH1unWFw8Q7xR0rqr1B9frRvdVeJJqj9CxU+vhbrZF3BH01h4l3ir5aY2fJzl1dFRqj/d23uv4ic0n1bHD1psHvD633i/urs4Pq5R1Bc5Weh0offyvVGtxLsnBd8rsGzq9m+HlgXfLbOovMJdX+g6XOIu8Iv1lYnSlcL+8IOoe9JMNT6Xmo9PG30vINja0FvvSBqqYJ7e/yB5P7G/h3efZcZ5F3ivMaPItcnbrO0UiuW91b1TikOWQh16CVrllaFRGu13n3VGfW0f7Ov7exs8g9G2pMz1B3oFnuuadadbDXXns94ue9vb353e9+l2TbwtPd3YNfi/uYY47JwoUNPPVuUxP/6kMZ/9TX1N3u2oVrs/vuBwx+h2i5af94YUbufnDd7b50/q/z8We9ogk9aj8ja915X44b9L/3+jPPyd4vfFKOOv1l+flL358k2eVxB2W/l52QW77ykxz3wdfmh888PX3r6z/x64D9D8imrsFJY6WPv92NPvTpmfKW/6m73ab+5KhnvTib7vpDE3pFK0144T9mwrPeUne725b3Z/c99vKUtwNMfdcPMmqfY+pu981fXJHPPuclTegRLdU9IjM/39iK/rd9+HNZ/cOPD3KH2lMz8lAzs1AiD8qDW4x/ztsz8aTT625335pkz/0OSG2Dt/7tbsrbvpXRBz2l7nbn/u6G/PdJz2lCj2i1GWfema6RY+pu9+5PfyVv+f77mtAjWqlnryMy/d0XNNT2hL9+czZc01jbTiMPyoPtbPKbvpwxRz677nY/u+bufPOFf9GEHtFqu3zi+nRPmFp3uw9+6bt51zff1YQe0UojZu6Xnf/11w21fe7r3pH1V5w9uB2i5cYef0p2+pv6n/Et35js/9gnpm+Jk8OS9suDg52F2m38iTy4tZ3/7fcZMW33utt98hvn55+/+tYm9IhW6p6yW3b5cGPv/P/qbf+Utb+tf60Bw8uYY1+Uya/9XN3t1vQmBz/pmemdf0sTekUrTfrrj2bck19Zd7ur7luR3Xc/pAk9aj+ejcmD7axr/OTM+OSNDbV99bs/nDW/+M9B7hGtNvqIv8yUN3+l7nYb+pMjn/78bJpzdRN6RStNfPG/ZPwz3lx3u5uX9mb33WcPfodoualn/DijZh9Zd7uv/+yyfPrZfzX4HWpD8mD75UFZcItRjzk+U9/+3brb9dWS45738my87dIm9IpWmnDiuzLhue+ou93c1cnus/dNep0g2e6mvON7GX3AE+tud9Zvrs1/Pu/EJvSIVpvxubnpGlH/0QXv+uQX8+Zz/60JPWo/7ZaFEmsH5cEtxj/z7zLxRf9Ud7uF65K9Djw0tTXLB79TtNTkv/taxhz2zLrb/ejK2/L1Fzy9CT2i1Xb595vTPXZS3e3+9T++kXd8571N6BGt1DProEz/p1801PYvX/22rL/yvEHuUXuSB+XBdrbT6/4jY495Qd3tfnXTvOz+4sc3oUe02s4fuSojJs+su93Hvn5u3vv105rQI1ppxLQ9s/O/XdZQ2xf93buz7tJvDXKPaLUxj3tpJr/6M3W3W92bPOZxT03fwjsHv1NtqN3yYOnvihN5cGvT3/fb9MzYp+52nz3rZ/ngl97QhB7RSt0Tp2eXj1/bUNtT3vmBrL34vwe3Q7Tc6KOelylv/GLd7Tb0J4c+9Xnpvee6JvSKVpp48gcy/oTX193uhgfXZ/fd92tCj9qPZ2PyYDvrGj0uMz59e0Nt3/gvn8yan545yD2i1UYdfEKmnvqNutv11pJjnn1yNt5xeRN6RStNeP67M+HZb6u73V2rkt333Cvp72tCr2ilqf9wTkbt97i623374qvy+ee+cPA71IbkwfbLg7LgFiP3PS7T3nlu3e1qSZ704ldnw42/HPxO0VLjn/22THz+u+tuN39tsuf+B6a2fnUTekUrTXnrNzP6kKfW3e4Hv785X3n+swa/Q7TcLp++Pd2jx9Xd7r1nfj2nfu+fm9Cj9tNuWSixdlAe3GLcCa/PpJM/UHe7xRuSfQ47Ov0rHmhCr2ilyW/4YsY89nl1t/vF9XOz+4uOb0KPhr+ZM2fmqquuaqhtxxaeXrNmTZJk3bp1j/j5WWedlcWLF2fixInZe++9m9qXhQsXZv78+U39Zwwne23qy/gG2nWNHFPUdepkk7tHZmQD7TbWuvwMbDaqa0Qyo/52Cy+/KV/f9aX/5+cr7pif/939ZQ//755xY3L8Z96aP374W7n1f36e55z3wTz2vX+TK9/39br/2fcvuD8ba4Pzgqb08be7yXutzZQG2y5dtSYrzQNtb4+NvZnQQLuu7u7c/+Di1DauH/Q+0VqTMiKjGmi3KSNkgQ7QPWZ86t8OWFmzsdfPwGaN5KGhzEKJPCgPbrHbht5MbLDtwsXL0rty8aD2h9abUBuR0Q206+se6T7QCbq6MrOBotNJsnZjn5+BDjBhp30yvcG2y1evzVI/A0nkQXmwvY3tTxq5E/SPkAU6xS4NZoH1m/r9DHSAcWNmZOcG265YuyGL/Qy0vV3WbshODbZ9cPnKrPczkKT98uBgZ6F2G38iD25t2ohRGdFAuw19kQU6wOiMzy4Ntl25bkMe8DPQ9qYdvC6TG2y7aPnKrPUz0PZG9tZS/1agJCPHug9s5tmYPNjORk5r6Mc3SbJq3cYsMA+0vSn7NL5+dMmK1VnlZ6Dt7bGxwb0kI3py/wMPpta7adD7RGvt1NBTgWRTrVse3EwebL88KAtusdOstZnaYNulq9dmhXmg7c3asKmhvSRJsnDx0vQpNtn2JtS6G1s/ai9JZxjRk5kNFJ1OkjXWjz6s3bJQYu2gPLjFrus3NbyX5IEly7NpiXmg3Y3r725w/ego94EOsUtPY+tH1/XW/Ax0gHHjZzW+l2TNuizxM5BEHpQH29uYvmRsA+1kgc4xfUQjp4wk62WBjjBmxOSG95KsXLshD/oZaHvT125oeP3o4uWrrB/drN3yYOnvihN5cGtTRzRy+mhVdFYWaH+jdulpeC/JqvUbs9DPQNubuv+fsX50+aqs9jPQ9vbc1N/4WeT335/UaoPep3bj2Zg82M56Jk1veC/J6g29ud99oO1N3mNNw+tHl6xc7SzyDrB7g2eRJ8mCRUvSv37NoPaH1pvY4FnkvdaPPkwebL88KAtuMWmXNZnWYNulq9ZmuXmg7e32Z6wfXbhkeXqXKzbZ7sbXuhrbS9LVIwt0iBkNnj+6bpO9JA9ptyyUWDsoD24xY92GTGqw7QNLl2fjQvNAuxvb32UvSQt1bOHpmTNnZtmyZbn66qvzhCc8YZvPFixYkNNPPz1Jcvjhh6erq6vpfSnJ2K7GDvuprVuZWbNmDXJvGArdGxt7SD+yb72fgc1G1rqT/ub/c459/6uy+t4Hc+vXf5YkufTtn8/zL/pU7v3pFXng97fU9Xfttutu2dQ1OJ0uffztbtSYxg4MS5KpY0dmonmg7Y3rauyX29qmDdlt50ZfDzCcjNi0tqF2Pb3rZIEOUevblK4GNgOM6+73M7BZK/LQYGahRB6UB7cYP6Lx6zBjysRkYiOvCRlOevrWNdSue+Ma94EO0b92RbrH1V9qbmx3n5+BDtAzvrEDIpJk8pgRGetnIIk8KA+2t1F96xtq17VBFugUtXUrk9H1lxkb07XJz0AHGDG++p2uVqvV/R5+p1FdGe1noO2NaeCMkId+XnaeMCb9fgaStF8eHOws1G7jT+TBrXVtWJU0cFzM6NpGWaADdE+oln42kgUmjuxKj5+Btjd6VHfdbR76eZk+fnT6/Ay0vTFpdP3oCveBzTwbkwfbWVcDz4QeMmFkzAMd4M9aPzp+VCb5GWh747p7G2rXv2FtdpvR6NGjDCcjNjW4l6TXXpKHyIPtlwdlwS1Gjml8q+qUMT2ZYB5oe+O7G9xL0t+XmVMmJpMbOYKY4WRkb2PrR0f0rpUFOkT/+jXpHlP/f8vjrB99WLtlocTaQXlwi3E9jRcEmLHT+NTGmAfaXaPrR7s3rHYf6BC1dSuSkfWXGxwT60c7wYjNe0kaWj86ekTG+BlIIg/Kg+1tdP+Ghtp1yQKdY/3KZGL95WVGywId4c9ZPzppVFdG+hloe2NG1X+Wp/WjO2q3PFj6u+JEHtxa14bVDbUb1b9BFugAXWMbXz86sScZ4Weg7Y0eXf9ekodMmzA6O/kZaHtjuxpcP7puZWbtttsg96Y9eTYmD7a1Bs4dfciEEc4f7QSjxja+fnTqOGeRd4KGzyLv25Rdp01OMnkwu8MQ6GnwLPIRziJ/mDzYfnlQFtxi5NjG8+CUsT0Zbx5oe+P+nLPIJ09Ixnds6bxijGz0LPJN9pJ0itraFemaMKXudmO7ev0MbNZuWSixdlAe3GLsn7GXZJdJY1MbYR5od6MaXD9a8l6SP6eucVetVmv8v7ph7LTTTsvnPve57LHHHrnoootywAEHJEmuvPLKvPKVr8zdd9+dTZs25a1vfWs+//nPP+Lf8f73vz8f+MAH0qGXqGn+uDh582X1tztxj+T9Rw1+f2i9L9+e/Net9bf75yOSF+41+P1pR5vWrs+39n1FU/8Zs552VJ7yhXfk/Ke/M2vmL3746we+5tk5+M0n5odPe2d61w38pnzKXd/MyHFjBqVvpY+/3a3vTZ77i2RlnefI7jwm+dEzkp7G140xTNy8PHnVb+tv9/Rdk48fO+jdYQh8667k0zfV3+5dhyYv32fw+0Prnf6H5FcL62/3zb9IDpw86N1pS83OQ4OdhRJ5UB7cYs6q5ORf1d/uibskn3384PeH1jvvnuTD19Xf7q0HJa/df/D7Q+v9y9XJT+fV3+7LT0qOnDb4/aG1+mrJi3+ZzK9zDei4EclP/9Kan4fIg/JgO/vZvOSfr66/3Wv3r/IA7e+j1yXn3FN/u88+vvq9gPZWqyUv+3Vy96r62o3qTn7yrGTyqKZ0ixZavD553i+qXFiPA3dKvvEXSZ1nTHWsdsuDg52F2m38iTy4tc/clHzzrvrbffyY5OnOiOgIr/5tctPy+tqM6KrWC+wytildooVWbkyec2Gyoc418XtNSM4+QRboBH9YlLzl8vrbvXDP5J+PHPTutCXPxuTBdve3lyVXLX7079veD56e7K7GXNtb15s8+8JkTZ1nx80cm5z/jCoX0t5uWJq89tL62z1rVvKRowe/P7Te/96ZfPbm+tu9+7Dk5L0Hvz/tSB5svzwoC26xsS858RfJ0o31tZs6KvnxM5NRI5rTL1rnjhXJX/+m/nZPnpF8+nGD3x9a7/tzko/fUH+70w5OXrXf4PeH1vvHPyYXzq+/3deOTw6rvy5VR2q3LJRYOygPbjFvTfLCX9bf7pjpyX89cfD7Q+v9+L7k/dfU3+6NByRvPnDw+0Pr/du1yfn31t/uC09Ijqu/XjXDTK2WvPRXyT111hkb3Z389FnJJOtHk8iD8mB7++X9ybuvqr/dK/ZN3nHI4PeH1vv3G5Pv3F1/u08dmzx118HvD61VqyWv/G1y64r62o3oSi54ZjLdVNr2lm9MnnthsrHO9aP7TEzOeqr1ow9ptzxY+rviRB7c2n/cknztjvrbfeixybN3H/z+0HpvvDS5Zml9bbpSrR3crfG61QwTa3qT5/w8WVtnvcFZ45Jzn279aCe4dknyht/V3+45uyf/9tjB70878mxMHmx3p/0+uezB+tt9/4Rk74mD3x9aa0Nf9VxgRZ1nkU8bXT0bchZ5+7t1efKKBs4iP2Fm8snjBr07DIHv3p186sb62/39Ickp+w5+f9qRPNh+eVAW3KK3PznpomTR+vraTRqZ/OSZyRjnj7a9uauTl15cf7vH75x8/gmD3x9a7wf3JB9q4Czyvz0wecMBg98fWu/911TriOv1xScmR08f/P60o3bLQom1g/LgFgvXJs+/qP7a4YdPSb765KZ0iRa7cH61r7Ber94vedvBg9+fTtexj1PPOOOMTJs2Lffdd18OOeSQHHbYYdl///1z3HHHZZ999snTnva0JMkRRxwxxD3tPI+dVi3kq9dLZw96VxgiL9yz/oUbE3qSZ89qTn94ZPMvvibfPvDV24TZJLn16z/LuU84te5A325KH38zjelJTtqj/nYv3suL3k5x8OTqT70cGNc5Ttqj2uhbjzEjkuc1MHcwPL20gf+eD5ui6HQryUKuQTPtPbE6+Kdengt0jmfPqr9wbE9X8oI9m9MfWu/k2fW32W9icoRDAzvCiK7qd/x6PW8PRadbSRZyDZrpabsmU+o8+Ks7jc0dDE+NZPtZ46oFoLS/rq7G8uCzZik63Smmj6nuBfV66WwHBbVS6Vmo9PE320sayHU7j0meMnPw+8LQaCQLPGWmotOdYtKoxg7+kgU6x7HTq0Li9fKeqLVKz0Olj7/ZGvnv+Ym7KDrdKcb+GetHHRrYGQ6dkjxmp/rbNfJ7BMPT8/dIRtW5fnTciOrwSFqn9DxU+vibadSI5AUNPB98wV6KTneK/XdKjmxgDZi9JJ3jObtX9/Z6jOquMgSdoZFs/5idqt8laA1ZyDVopt3HV8/66uUdQed45m7JTiPrazOiK3mR9aMdo5EssNeE6h0j7a+rq7E5/dm7KzrdSrKQa9BMT5lZrQWsVyNrDhmeXjK7/jYzxibHzxj0rjAEGs0CT9tV0elOMXlUtTeoXifPtn60lUrPQqWPv9levFf9hxtPGdXYPjSGp0bOHDt+hqLTnWJ8T2NnCFo/2jmOmFqdHVQv60dbq/Q8VPr4m62R5wLHTFd0ulOMHpE8v4EzBJ1F3jkOnFydKVyvRn6PYHh63h7V2eL1GN3d2D40Gld6Hip9/M3U093YGYIn7aHodKeYPSE5zlnkRXv2rKrmWD1GdFW1zegMjfz3vM/EqsYhrSELuQbNNHNc8uQGzhC0r7RznLBrMm10fW264izyRnXsI9Xdd989l1xySZ73vOdlzJgxmTt3bqZOnZovfvGLueCCC3L77bcnUXi6Gbq6kjceUF+bJ+6SHDK5Kd1hCEwfU3+of/X+HuxAJ3n5PsmkOjYHTxstzHWaNz2mCukD9dhpydEe7HSMSaOSv96nvjZ/s08ysc5DBRi+jp1e36FhXUneUOfvEMDw9vr963vodPDk5Ek2BneMsT3Jq/arr83JeydT63wozPB12JT6C0e+4TE2BneSF+yVzKhjs//4nup3AqAzjBqRvHb/+to8f89kVxuDO8b+O1Uv/evxxsck3bJAx3juHlUx8YEaMyJ5xb7N6w+t9+r96ysus+f45C8bOGAIGJ72mJA8t85iUa/f38bgTvKMWcnedRSdHdWdvKbO3yEY3l6xbzK2js3Bu45NTrQxuGM0sn70yTOqgwWAzvDUmckBkwb+/SO66n+eyPD28n3q2xy88xiFZTpJI1mg3rVmDG+TRycvq3OT5yn7JhOsH4WOcfLsqrDAQE0e5QDZTvOGA+pbP9rIWjOGrwkjq3t7Pf5q7ypD0BmOnFr/oWFvPMD6Uegkr9u/vgIRB0yqninSGUaPqP/d7wv3THYZ25z+0HoHTq7e/dVDFugsJ+1RrQEYqLHWj0JH6emu1gLW47m7V2sO6QyzJ9S/JvwNB1g/2kmePavaGzBQo7qrvQd0jlfsW19xmVnjkufUue4cGL52HVd/obnX7l/tSaYzPG3X+orO9lg/2nH+Zp/q7JCB2mVMdTYJnaGrqzovoB6P37mxApXA8PSkGdVZggPVner9Mp3jZXWeRT51VPKS2U3rDkPgDQfUdxb5kVOr/SR0hokj6z9H8K/3qc4wBzrDi/eqr9DcpJHVXlQ6x+sOqO8cwQN3So53FnnHGNNT/7vfl8yuapvRGQ6ZXNUerIf1o9BZXrt/9f5voPaZmDy9znOLGb5Gdtf/7vfEPZJZdaw1Y4uOXnJ50EEH5cc//nFWrVqVVatW5Yorrsib3vSmrFmzJnPnzk13d3cOPfTQoe5mR3rmrORtBw3sew+ZnHzkaGGu0/z9IQPf6PmivZLX1FmQChjedh2X/L/jknEDWPw1aWTymccl0zzY6SjHz0jeddjAvveAScknj5UFOs3fHVRtChqI5+ye/O2Bze0PrdXdlXzquGT/ARwk3JXk3YcpOAud5tidk386cmAPnmZPSD59XH2HCzH8vW7/6gCggThh1+TtBze3P7RWV1fysWMGvhD87Qcnz9itqV2ixSaPSs58fLW4+9GMHZF86liHhECn+et9qj8D8cRdkncf3tz+0HofPGrghULe9BhF5jrN+J4qC+w8gOf+o7qr7LhfHQWpGP4O3KlaAzByAA8GZoxNPvv4ZGwdBwoAw98/HZE8boCFQl61n43BnWbMiCoLDOQg4Z6u5N8eW9+BAgx/e09MPnFsMnoAWWDa6CoLTFRkrqM8e/fkLQNcA3D4lORDRze3P0Br9XRXWWAgBwl3dyX/emRy1LSmd4sW2n188u/HJeMGcCDo5FHV+tEpisx1lKfumrxzgNt0Dtwp+fgx1o92mlMPTp45wDUAJ+1R/2GTwPC2y9jq/j6Q3/Un9FTfq8hcZ3n8Lsl7jxjY4YH7TKyyYz2HCzH8vfEx1T1+IJ65W/I260c7ykPrRw/caWDf/w+HVL9DAJ3jyGnJ+44c2P19z/FVHlRkrrO8Yt/k5NkD+94nz0hOH+A+ZNrHh46u3gEOxN8dWL1bpHNMGFmtARjIQcKju6u1BXvXUZAKGP5eMrtaEzgQx02v1hrSWf7lyOSYARYKee3+1bljdI4xPVUWmDGA5/4ju6s9BwN9jkR72G9S9Xxw1AB+1995TLXGaIL1o9BR3n148qQBFhV4+d4D34dMexi5ef3o7uMe/XtHdCXvPyo5fID7kGkPe0yozg4ZO4D1o1NGVdlxsiJzHeXpuw38DKmDJ1fZ0fpR6BwjuqqzBPcewBlS3Un+8YjkuP/P3n3HV1ne/x9/n+wEwt4bBQRkqThArDjrrlrtQlu/2lZbqXZp7fi2tb+qtfqtdXTYOjq0rYoD90BEURFBVBDZJEBCEsgig+zcvz+uhCRAyDmnJ2fcn9fz8eDRcs6543Xdubnv97nvz3VdQY5DRmIYkinddbybb6QrvVKl35/AInN+c+JgN7dwMJf38b3cnMXUj/rLNROlc4KsAThruJu7HIB/9M9w9YC9grjvn5Xi1jAZGsR9JCSOmQOkn08Pbi7y0T2pH/WjK8a5ReiDMXeIG0sA/wgEXA3AlCDrR78zya1tCMA/pvR1c8kFs9bI8CzpnuOltCCeKSFxfHGs9JUgawBmDZR+zFzkYTMZo9euXSvP8zR+/HhlZR34bXLBggVasGCBPv300w5/X7lyZbSbmtC+Nt6FujGdPOzJSnb/2O+fTeGfH6UkSbcfK119hCvqOJjBGdIPp0g/mcbDfsCPZvSXHpojndDJg/yA3KDgh06SJvWJZssQLV8c6woAD+9kwGdGsrsB+JcTpd4U/vlOckD61dHS/EmdDxAekC5dN1m6+Sge9vtRnzTprye6wX6dTSo/LtsVe1wyNrptAxAdnxvlCgCP6GTAZ3qSm1juoTmuQAD+Egi4Qf/fP1Ia1Mnvt2+adM0RbhAAD/v9p2equ+/3hbGdTyo/tqd02zHS5UFOJoHEMq6X+85/8pDOH0QcO8BlxmMZBAD4TiDgcsBNUztfbK53qnTVBFf8GczCpEgsmSnSH2a5h/6dDQoa1cPdF/omC0r40pie0sMnSacN7bz45+j+LjPOGRzdtiE65g6V/jRLmt7J5A/JATeR/N9OcgtSAfCX9GQ3wOOKcZ0PChqeJf1suntWRM2I/wzLclngs8Pd4tIHM62v9MfZblIR+M+sQdL9J7rMdzDJAemUoS4LMIm0P105Qfr10Z0vPNsjxU0W98fZwU0mASCxDMxwz4HPHdH5RLKTeruBQOcGuRgZEssxA6QH5rjJ4g9mX/3onM5rCpDYvnyY9NuZbjHJg8lMli4Z4+pHe1E/6jvJAemWY6RvT+y8fnRghvTdydLPZ1A/CvjRlL7Sg3Ok2YMOPnlcQO69h04KfjIJJJaLRrv60Qm9Dv5+epKrMX1wjtQviMXIkFiSAu4a/90j3TX/YPqluaxwyzHBTSaBxNIrzWX9S8d0Pqn8YdnS7TOlrxwe1aYBiJJzRrp7f5M6ue+TluTuHT40RxoUxGJkSCyBgHTjVPens8XmeqdJ35gg3XEsY0n8qEeKewb45S7qR399tKsjhv+MzXa1AKd0VT96oqstAOAvgYCrCfzZdFcjeDC9Ul1t4d0nuFpD+EtGsvs+cPnhUs9OssCILHf/6FoWlPClET1cFjhjWOdZYHo/N9Zg7tDotg3RMWewGyt0qPrRU4e6OuPO5qcEkLhSk6T/O8595+/dyViSoZlu7PEPpjCWxI8GZ7pz/NkjOh87fmQf6b4TpLOCXIwMieXYgS1ziHRSP5okNwfJwye5OUngP5ePc3NJdbbwbFaye5bMXOSAP/XPcDVhF4zqfP7RCb1cbdmFQS5GhsQyvZ87BmYdYi7yEwe5eoHJfaLZMkTLJWPdHMPjOhlLkp7kakz/eqKbsxj+khSQfnmUW0Sws7Ek/dPdXOW/CnIxMgCJZVIf953/pMEHH0siuTVLHjzRrWEC/zlvlKsFmHiI+tHWucgHMBe57wQCbgHJH05xa5EdTJ80N+/k7dSP+lLPVOnPs9waRZ3NRT6mpxtL9LXx0W0bgOg4Y7ibh7izccOpSdLZw11mHNJJfSESVyAgfe9IlwcONRf5/4x394hZeDx8JqdrW7NmjSRp+vTpB33/0ksvPejfv/a1r+lvf/tbt7bNb84c7gpAPyiR3iyUns6VaptdUfALZzJhoN8lB6RvHCF9bZz0eoH0cam0t9H93o8f6ApE+TIH+Nu4XtJ9s6TtVdKLeVJRjQt6QzLdwPDhLCTge3OHugK/j0qlxQUds8BzZ0jZFHz5WlJAumK8NO9waUmB9KuPpJomN3HML46S5g4hC/hdz1S36Oh3JrnrwJZKqb7JTQ5x6lBXGMQAAMDfThzsJoj8pExatFMqr3c38sb0dBOIU/Dlb4GAmxDuC2OlpUXS+7ul57a7PNgrVXrhDG7s+l1mipsw6tsTpZfypLs/lWpb8uBdx0vH9CcL+N2IHm5waOFe6fkdUkGN1Oy5Ip9zRrCoEOB3gYAbDHDRGOndIundXVJ1o7sOTO0rnT7cTSgD/0pPlr4/RbpmovRynvS7tW1Z4I5jpeMGspiE3w3JdMWdu2pcFnhokzsGspLdQgIMCve/Gf3dwMCNe6RX8qWSOrf46Ige0nkjKf4G/C41SZo/Wfr6BOnVndLaMvecqGequ2c4exBZwO8GZLhC/+8f6bLAXze2ZYG/zOl8oBD8Y0pft7jIlgrp5Xzp31vdMdAjRXr8lM4nmod/nDX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xyIyvrd8jPZEjvZwn1bXc2Cmtl768xJ0DzhnhFiNH5JCF2AfxpLZRejnfZYENe9peL66Tbv7Q5cEj+8aqdYiG4tq2LNBa/ClJfdPcPYGLR7PIjN9trnBZ4MU8qaap7fWxPaXPj5HOG0khqJ95nrsf8ESuuz/QupZESZ30rXfdMTB3CJOKRxpZiH0QT8rqpGe2S0/lukXmWhXXSXevdeeBET1i1TpEQ26luw48v8NNJNxqVA/p4jHSBSOlXmmxah26m+dJH5S4PLiksGPx91H93L2h04aSBfysoVlatNMdA6vL2l4vrpN+vNLdFzi6P4WgkWY9C1nvf7zZUeVqhp7bIVU0uNdK66WLX3f3hT43SurDhCG+9mGJy4OLd7pJA1tN7euuA2cMY1CInzU2S28UuGNgVUnb68kB6TND3DFw3ACygJ9Vtqsf3daufrS4Trp9tXTpWOmw7Jg1z7es5yHr/Y83a0rddWDRTqm+5SFBSb301bfcvcHPDmdQiJ81edJbhe6+wPvFba8nSTpxcFv9KFnAv6obpRdassDWdvWjmcnSWSOkS8dIE6gf9bXCGveM6Jlt7n6A5P73/EXShaPcc4KBjCWJOOt5yHr/40mzJ71d5K4Dy3ZJrbeGSuqk695zWWDOYMaS+FlNo/RSnjsGNla0vZ6eJJ05XPrCWGlSn1i1DtGwu1396O529aP9WutHx0hDGEvia5v2SI/nurEkrfWjpfXSF5e468C5I6UejCWJKLIQ+yCe1DZJr+a7LPBpedvrxXXSz1e5+wJT+8WocYiKklpXP/r0NnePoFXvNFcr8PnR0nDqR31ta2XbWJL29aOje7rf//mjmEjUzzxPWlHsnhO91a5+tKRO+uY7Lg+eSv1oxJGF2AfxpLzOzTX11DY3kXSr4jrpd5+488ConjFrHqJgW0v96PM7XA1Rq+FZ7vd/wSiXDeFPnid9WOry4OKCjmNJpvV1dWOnDaV+1M8am6XXC6QFOe5YaFVcJ924wt0XmEn9aMRZz0LW+x9v8qrd/KPPbncLjEnuGcGFi9wzos+NYvFZv1vdkgUWFbRNJi9Jk/u468CZw6V0soBvNTZLbxa67wQr9qsfPWmIOwaOH0gW8LPWucifzJVy9htLctvH7jsBc5FHnvU8ZL3/8WZtmbsOvJrfNv9oSb102Vvu3tDZw6UMakZ8q6m1fjRHWtZuLvKA3BiSS8cyF7nf7W10z4kX5Eib240lSU9qGUvCXOS+t6vdXOTF7eYiL6mT/rzezUU+mPrRiLOeh6z3P540e9K7u1wefKeo41iSa5e5+wInDXFzTsCfWuciX5Dr1iZoldYyluSSMdIU5iL3teLW+tFcqWi/ucg/N8rNMcBc5P62ucKdA17cIe1tN5bk0jfcOYC5yCOPLMQ+iCd1TdJrO9154JP95h/92QfuPDCjf6xah2goa6kffTK341zkvVJd3eAlY5iL/L/l6xL84uJinX/++SosLNQPfvADFRQUaNWqVSosLNTtt9+uF154QStWrFAgENC0adNi3Vzfya2SvvKmdOcnHRedllywW5ArfWWJu/kLf3qrULpksfS3zW2LTrcqqpX+vEH6whvSRyUH3x7hq9haqIKla5S/+EN98seFev1rv9GAGYdr1u1X7/tMc32j3r7uXk297mL1nTxakjTqrGM14oyZeuf7f4xV0yPCev/jRU2j9MP3pR+u6LjodKu3i9zi0z/9wAV/+E9+tXT5W9Jv1nRcdFpyEwc8s1267E3pn5tj0z50v/d2uZt4D2zsuOi05L7Y/3Wje/9g5wj4w983uX/nC7e3FX212lwp/Wa1m0x4596Db4/wkIXYB/GisEb62lLp1x93XHS61XM73Pt/3eAGkMJ/VpW47/33b+i46LTk7hM8vMllgaWFsWkfut9jOdKXl0hPbuu46LTkBobc+Yk0782OC47APxqapV9+KM1/zw0K2y8OakWxdNNKd2+g/URS+O+RhdgH8eKTMpcF/rCu44NeyRWC/nOLe3/Rzpg0D1GwcLubLPixnAPP9durpd+vlb78pisOg/80Nrt7P9e86yaLadrve9+Hpe75wLeXSRX1B/8ZSGzl9dLV70j/u6rjotOtXtspXf2u+16w//GB/471LGS9//Hk5TzpC0ukR7e2LTrdKn+vdO86lxXWl8egceh2zZ6bIPQb77jJARr3O9evKZN+8aH0zXddkSj8p7LB3Rf68QcdF52W3LX/jQI3MPCWj112hP9srXT1of/3yYH3gD25Cca/vMQtRIjIsp6HrPc/Xnie9Kf10v+87SYKqd/vXP9pufT/PpKuXNpx8TH4x95G6fvLpRtWdFx0WnLPjJYWSdcvd/cNGsgCvpRX7erGfrum46LTknt2/PQ296z431tj0z50v3d3SZculh7a1LbodKvdtS31o4ullcUH3x7hs56HrPc/XtQ2ST9aKX3/fXc+2P8xwLu73Hs/WuE+C/8p3OvqQ29d3XHRacnVlD+3w401eWgj9aN+tbLYXev/suHA732l9S4jXLrYnQ/gT//e6saXP32Q+tGtle67YCUpHwABAABJREFUwmVvSjuoH40oshD7IF7srnX3/n71UcdFp1u9mOfuHf5xHVnAr1aXuvrQP63vuOi0JO2pl/6x2Y0leaMgNu1D93sq1z0LfCL3wPrRbVXS79a69/e/dwh/aGx2tQDfXub+ne9fH7iqRPrJB66moKrh4D8D4SELsQ/ixfpyVxt477qOi05L7l7hv7a691/Oi37bEB0v7JC++Ia7P1B5kPrRuz917x9s7DkSX5Pnasa++Y4bM7B/Flhd5moFrnnXLVIP/6mod1nwpx90XHS61eIC6VvLpNtWUz8aadazkPX+x5PXd7p7Q//c0rbodKuCGjfm+ItvdJxkHP7hedJ9n0pXvi29lH9gfeCn5dLNH0lff9stPgL/qW6Uvrfc1Y2sOEj96JuF7r7QLz+kftSvtlW5+tA7P+m46HSrJ7e5+8OPMRd5xFnPQ9b7Hy88T3pgg6sde27HgfOPbtjjniF8bemBzxHhD7WN0o0rpB+833HRacndH35nl5tv7CfMRe5bO/e6OYZ/s7rjotOSOycsbJmL/O+bYtM+dL/3d7uakL9u7LjotOS+EzzQMhf5e9SPRpz1PGS9//Givsk9H/jucrcGyf7lgct3uzVLfvi+W8ME/rOrxtWH/vrjjotOS26s+fM7pCuWSvevp37Urz5qmYv8z+s7LjotubnI/7bZrWH2JnOR+9aCnJa1CHPbFp1uldsyF/lX3jxwHUP8d8hC7IN4UVrn5pz75YcHfx78cr709Xeku9e6OergP2vLXD3AfQeZi7yiQXqkZS7y1/Jj0z6/8PXC09ddd53y8vI0f/583XnnncrOzt733o033qjp06ersbFRY8aMUa9evWLYUv8prJG+9W7XC8g1yy1GuXB7VJqFKFq+200Wtv8Dvv1VNEjfeY+JhLvb7pUbtGXBWxp74YkaOPOIfa+XrN6qtX96Vifd8x1lDe2nWXdco+U/eUA1Rf6qxrPe/1hobHaLRy0t6vqzr+10E0oT6v2lpNYV+QezgNzdn0r/YfJA3/moxE0ItreLBzitRaKrDzJYBIntX1vcgNCu5Fa57w6lDArrNmQh9kEslNdL335X2hLEBCD3b3AP/eAvn5ZL17134KJC+6ttcoUfK3Yf+nNIPE/lSnesObDYZ3/5e10WKKIQ3Fc8zy0Y8UIQE4C8t1u64X0GhXUnshD7IBY2V7gFxMq6WEy2vln6yUpXIAp/eSnPXQu6Wky2qMZ9d8ivjkqzECWe57Lgk9u6/uyqElcozqIC/lLT6L4THmzB6f09luMWokf3sZ6FrPc/Vt4oCG4BuZI6N7FYLhMJ+869n7oJQrvySZmrG+rqmSISS12TW2gymAXkntnuBpAzKMxfClru++5f/L2/Js8tQPU89aPdynoest7/WPnLBunBjV1/bmPLfcT9J5pGYmtsdhMFvRPEBCAv50s3Uz/qO8W1bpLwHV3c9/XkJhxfwOSBvvNBsZssbP8FBvdX1Shd/x4TCXc363nIev9joclzEwUFs4DcG4Xus109U0RiKa9zY0mCWUDuj+vdhPPwl0/K3DW+qov7vjVNLjN8EMR9RCSWBTku63d1et9R7c4XLCrQfchC7INYqGxw9/w2VnT92Yc2uXuJ8JeNe6T5yw5cVGh/9c1u4ZF3mUjYd57f7p4BdvVdr3VOmoIu5qRBYvE8VwPwTBDPgFf+f/buOzqO6m7j+LPSqhdLsmzJvRt3G2xsUwyY3juBAGmkQF4cQkIgISQhJIHQEmoKIZCEQBI6piU007spBoy7LXfZlmVZxeo77x9XsuQm7wy7q5253885Ojl49zr3jsazz8787r0VZh56E/WjcUMW4hh0h7IaUxO4eS/rBjRHTI1hNPcR4S8vtq0h1LKXLFDRaOaSrGIh4cC5db70nyieAX+yRbrkXTYVCJqGVjNH6MPNe3/vYyvb5qHznChubM9Cto+/u7yxQbpyrrn305UtTeY+4tIo7iPCX/60MLo1hBZsNXNJaqkfDZTmiFk7ZOdNJnfnmbZ56GSBYNnQdt937V7u+zoyWfCxskT0yl625yHbx99d/r5U+nMUz4CX1Zh7Q1V7WZMG/tISka78ILoN5F5cZ+4RM5ckWCobTRaIZgO5OxaYNYsRLJ9UmvuDdXu577utxTwr/jiK+4jwzvY8ZPv4u0PEkX7xkdlzZG9e32D2MGlh/dFAqW4y9QJLorjve/di6Z4l8e8TEmthlbnvu7e1yBvb5qG/y1rkgTN7ldmDcG+X93Vta9KUsxZ53JCFOAbdoa7FzCP4vGrv7/3nMumPUexjBH9Z1raGUGUUa5Ff9YH0WhT3EbF7gd14esGCBXrwwQdVXFys3/72t7t9z+TJkyVJEydO3P5nr7/+uo488kj16dNHGRkZ6t+/v84++2wtWMCVxo1b50ubXEz2veETHvQESUtE+tVH0S/8Ud8q/XoeRR/xNu+WRxRpadW+l5+945/f+qgira06+YWbVP7mZ1ox+81u6mF82T7+RHt2TXSLBrZ7cR0TgoLmzgXmpk20bpkvbeTmTmBEHOlXH++9ALxdU0S65mOyQJBsqDebykdr7TZu7sQbWYhjkGh/WSStcrFx3B8WSGvYaC4wHEf6zcfRbxzX6pgswAKiwbGlUbrps+jfv7HBXXZA8ntzo7k3EK33KqTZUWxMCe/IQhyDRLtu3t4LwNtFZDYWYdGw4KhtNudAtCqbpJtdZAckvw83R7fpdLtPtkgPsblMoDywPLqir3b/Xi59Whm37kBkIdvHn2gNrebeULS3eqqbpRs+jWePkGgLqtxtFrNwq/TPKBYWgn88UiZ95OKz/YlV0vtsLhMov/9s7wsId/bbT8xEQsSP7XnI9vEn2ooaM+E3WstrotukGv7x1GrpHReTff+31iwUgOC4/XN3k31v/oyN5oKkvX60Ocr60caIeT/1o/Flex6yffyJ9sLa6BYNbPdquWmD4PjTQrOZbLTu+Nzd3BMkN6ctCzRGmQWa27IAC4gGx+YGdzUg5fXmOwTihyzEMUi0exabe37RunuxuaeI4Lh2nrQtynrQiGPWpGAB0eCobjLP/qK1udE8W0RwvF8R3abT7T7cLD1cFrfuQGQhiWOQaDd8uvcFhNs5MpuMRTsPFcmvvsXUj0Zra7N0I/WjgfJppZkbEK3Pq8zcAwTHQyvMHKFoPboyuk2q4Z3tWcj28SdaU6u51xPtrZ66Fnf3EZD8llZL97rYLGZJtfR3NpcJlCdWmrVDouV2vVIkv9s+N2sIReumz8waRYgf2/OQ7eNPtDV17tYTXVVn1ipEcLidGzJnvfRSFBtTwj/+uMCsLRytW+ez0VyQOB7WIqd+NP5sz0O2jz/RXl5v9hqJltv1SpH87l4sldVG//4/L5RWuXg/kpvjSL+ZZ/Yei0arY9YfpX40OKqapBtdPPfZ1CDdNj9+/QFZSOIYJNrfl0iLq128f6m0aGv8+oPEu/4TqdbFWuS/+lhqpH7Uk8BuPP3vf/9bkUhE5513nnJzc3f7nqysLEk7bjy9ZcsWjR8/Xrfffruef/553XDDDZo/f74OOOAArVnDN89oVDS43zy0KSI95WLyCJLbGxukDS4Xf1q0VZpfFZfuoE1NWblWzH5TfQ+ZoN7TRm//c6elVZveX6TMnj209MGXu7GH8WX7+BPtkTL3bR710AbJaWuT9LzLxZ9aHXcTSZHc3tvkbrNRSVpZK81lMfHAeHyl+81D/7tGqolyIincIwtxDBJpW4v0zGr37ciDwfHpFnc3+CVT+Pcmi4kHxuxV0S8i3W7OOrPgIILBy32Bh8tYTDyeyEIcg0RavNXdAhGStKXJTAhBMDy7JvrCv3ZvbGAx8SDx+oyAySDB0BKRHi9z387LeYPo2Z6FbB9/or2w1iwG6Mb7FSwmHiReFgR+fCWTQYIi4ni7389C0sGxoV56zeX9/saI9AzluXFlex6yffyJ5uX73VOrWEw8KBxHeniF+3aPeGiD5FTVKL3gcvGnFsc8Y0YwvL3R3WJRktmQ7KPK+PQHhu15yPbxJ5qXPMgzguCobXa/+JMjc38QwfBRpbvNRiWTHd5mMfHAeGKVyfhuvLDOfJdAfJCFOAaJ1NDqbb0I8mBwLKhyv15ERaP0Snk8eoPu8PRq8+zPjdc2mGeMCAYvz/6pH40vshDHIJFW1JhaQDeqm03NIYLhubXRLxzZ7p1NLCYeJF6+3z1eRv1oUHitH+W+QHzZnoVsH3+izVkvVTa5azOv0sxJRjB4uaY/scpsWg7/cxxqRmy3ucGsHeRGc4T60XizPQ/ZPv5Ee7TM1IK58cxqs2YhgoEsYLeaZrOmsBsRSU9QPxoYcyvcbTYqmbXL39sUn/7AsD0P2T7+ROMZgd3qW7zVj7IWeXDMr5IWurzfv7HBrD+JYHhqlfv60TnrzR6HiA+yEMcgkZoj0mwP3/HJg8GxtNr9ehFVTdJLLp8twQjsxtNz5syRJM2cOXOP72nfSLrzxtMnn3yybrnlFp111lk69NBDdd555+mxxx7T1q1b9eijj8a30wHxzGr3m8xJbDYZJF4f3HOTP/4+ue1RRVpbte/lZ2//s97TRmv42TO14J5nNfVX31BqZno39jC+bB9/oizZKn1e5b7dexXSWpcb1SI5PbfW/Y0dydsXQSQnz1mAPBgYXs6Bxoj0HIuJxxVZiGOQKHPWSXUeCjmfXMUiEUFBFsCTHn6XLY77RUeRnDY1eNtIflmN+4XG4A5ZiGOQKF4+ByQmBQaJl9+lI29Fo0g+1R43kl+7TfrA5UJjSE7vbpI2eCjkfMHj/QREz/YsZPv4E8lrrvOaI5FcGlq8Pe+paJTeZGORQJhXaSb6uvVaubSFjUUCwXP9KDUjcWd7HrJ9/InSEjGbSri1tVl6xcP9BCSfRVulxdXu2729SSp3uVEtktN/15qJgW7xjCA4mEuSvGzPQ7aPP1HKaqWPPWwk/3Gl+4XGkJxeXCfVe1gU/ImVZhFq+J/XeUHUjwaHl+c9zRHqR+ONLMQxSJRXy829PreeXs0mY0HhuX6U+wKB4eXeUKvj7dkCks+WRvPs361Vdd7uJyB6ZCGOQaJ4zQLcFwgO6kftVtdi5gS4taHBbEAO//tws5kb5Nac9dJWlxvVwh3bs5Dt408k5hbbralVetbDPZ6qJuk1NhYJhPlVZs0Qt97cYNYogf/9d41ZO8gtPgfiz/Y8ZPv4EyXieMuDdS1sLBIUy6qlz7a4b/fBZmk19aOB4HUtcuYRBIfXXEcejD/b85Dt40+UddvMHiNufV4lLXa5US2S05z1Uq2Xtcg9rk2B5MNa5KB+NDmRhTgGifLGBqnSQ+3Hf9dIDR7moyL5UD+aWCHHCeaU7AEDBmjNmjX66KOPNGnSpF1eb2lpUZ8+fVRRUaFly5Zp6NChe/y7Nm/erOLiYt155526+OKLXfdlypQpKi/3MDvCp/K//Ftlz/iK63ZOc6M2fH9YHHqEROv5s5eU1ncf1+0aF76uLbd/OQ498p80J0VXR6bG/f8nnJ2pk1+6WZ/f9bQW/uM5Hff4r1Qxb5nev/rvrv+ua1LeU3MoNjN7bR+/32VMPEaFF97jqW3lrV9S0+K3YtwjJFre6T9XzpEXempbPmuwFGFnCb8ruuJppQ+e5Lpd0/IPVHnzKbHvEBIrnK7S25d7alr7wp9U+/i1Me6QPyUiD8UyC0nkQfJgh9wTLlPuCT/w1HbDZWPk1HtYhRpJpfD7Dypjn4Nct2teu0Cbrz0qDj1CopXcUaZQath1u7pX/66aB38Whx4hkdKG7Keelz/pqW3VPf+nhg+8tQ0a8iB50M8Kvvs3ZY53/5neUrFSFb9wnyGQfHrfPF8p2T1ct6t/52Ftvc/bdwkkj3DfUSr+2Yue2m69/0eqf+s/Me4REi37kK8p/xxv9/g2/XKGWjeuiHGP/MlveTDWWchv45fIg531um6uUgtKXbdr+PAZVf3V2zNGJI/UngPV69fenvlXP3y1tr3srdYAySNz2pkq+NqtntpW/PZYtaz+LLYdQsLln3eTsg9yXwMYaajTxh+6rzkMIu6NkQf9LCWvWL1v+NhT25rZ16vuuTtj2yEkXMa+J6jw23d5arv5d6eredl7Me4REi3vrGuUM/Obrts5kYg2fG8QO04GQM+f/FdpA8e7bte09F1V/v6MOPTIf8iD/suDZMEO6WMOU9Gs+z21rbzzfDV9/kpsO4SEyz35x8o99nue2m64dIScpvoY9wiJVvTDR5U+fJrrds2rPtXm64+LQ4+QUKGQSu5YqVBKiuumdS/fo5qHr45Dp/zHb1lIonaQPNgh55hZyjvlJ57abvzxJEVqPKw8iaRSePE/lTF2put2LRuWqeKaQ+PQIyRa71sWKyUj23W7bW/+W9UPXB6HHiGRwgPGqfjK/3lqW/WPS9Xw7iMx7pE/kQfJg35W8K27lLnfCa7btVaVa9NPp8ShR0i0Xtd/pNT8Xq7b1c+dra33ul/XD8kltfcQ9frl657aVv/nKm177R8x7hESLevAc9Tj/Js9ta34zZFqWbcwxj3yJ7/lQdufFUvkwc6Kf/WmwsWDXLdr+PQFVf3pG3HoERIppUepev92rqe2NY9fq7oX/hTjHiHRMiefrIJv/tFT2803nazmFR/GuEdItLyzf6OcQ7/uup3T2qIN3xsc8/74EffGyIN+FsrKV8nvPvfUtvaZW1T7zO9i3CMkWsb4I1X43b97alt52zlqWvRGbDuEhMs97SrlHvVdT23LLxkqtXjYnQpJpehHs5U+dLLrdk1lH6vyxhPj0CP/IQ/6Lw+SBTukjzxQRZc+5Kntlru+qcZ5z8W4R0i0nON/oLwTL/PUdsOPxsnZVhXbDiHhCi/5tzJGzXDdrnndIm3+zRFx6BESreS2ZQqlZbhut+31f6r631fGoUf+47csJFE7SB7skH3Ed5R/xi88td141VRFtqyLcY+QaAUX/lWZE4913a518xpt+vn0OPQo+ZWWlmruXG/P2N3v/uETdXV1kqT6+t1PNn/wwQdVUVGhvLw8DRkyZJfXW1tbFYlEtHLlSl155ZUqLS3Vl770JU99KS8v19q1az219aPU+ga5nwokKSXVquMUZAWOlOahXVNzC+dAm/RQqlQS//+f/X/5VdWu2qiFfzcTuN74/p06+cWbteq/72rDOwtc/V3r1q9Tk9Mak37ZPn6/KxhYpUKPbSsqt6iG64Dv9d9WrxyPbdetXy+Hh72+l9fSqnQP7ZpbWskCARBKz5L77SSMurptnANtEpGHYpmFJPIgebBD323blOux7foNG9RaUxnT/iDxclpa5f4xn9QScfgcCIJQSCUKeWq6rb6BcyAAcvIHq6fHtlu2blUl54Ak8iB50N+yGpuV6aFda0R8DgREr1Cqp3bbGho5BwIgK72Xij22raquUQXngO/1qqlRvse2Gys2q4FzQJL/8mCss5Dfxi+RBzvr6Uhe0kB9UxNZIAAyIllyv2yksbW6Rhs5B3yv59ZqFXhsu6lis7ZxDvheuKGR+tEviHtj5EE/SyuKqLfHttW1dSrnOuB7hUO+SP1opWo5B3xvgMf60VBKitauWy9FuJb6XX5rxNtcEupHtyMP+i8PkgU79OhTpSKPbSu3VGkr1wHf61fnvX503YaNimyrjml/kHi5XueStEbIAkGQkqpSD5tOS1LdtnrOgTZ+y0IStYPkwQ596rYpz2Pb8o0b1Vy5Pqb9QeJlN7d4mktC/Whw9A55ywLUjwZDdlYf7/WjW6u1mXNAEnmQPOhvGU1NnuaSRByyQFAUe5xX2tBI/WgQZKbke64fraqp0SbOAd8rrq5RD49tN1ZsVj3ngCT/5UHbnxVL5MHOCiPeFjdubGwmCwRAWmOK9/rRmlrqRwOgaPhW73NJNleqjnPA9wbWN3pbfzSUorXr1kmOE+su+Q73xsiDfpaaV+/5V1ezbZvW8Tngez36e59LsnnLFlVzDvhe/y9SP7q+XE7T7veSgX+wFvkXRx70Xx4kC3bIK97yheaSVHEd8L0+dXVfqH60ZeummPYHiZfjuX6UtciDosRj/Wgda5Fv57csJFE7SB7sUFJT63n90Q0bN6lpI9cBv8tsavG2Fjn1o54EduPp0tJSbdmyRR9++KEOOOCAHV5bv369Lr/8cknShAkTFArtWrB66KGH6s0335QkDR8+XHPmzFGvXt7KGktLvW675k9ZLXWe2kWqN6pfv34x7g26Q6hus6d24YatnANt0pwUKRLf/49+h++rIScfpNlHXLb9z2pWbtAH1z6gg265WE8efpla6huj/vv69umr5lBsOm37+P0uLdwiSXIcZ7efsbvT/t6itFblcx3wvezWbZ7aReqq1LfE6zQSJJPU+i2e25EFgiFSX62ULPe3d7Ii2zgH2sQ7D8U6C0nkQfJgB69ZwGlqUGlBrpSfFeMeIdHCHrNASt1mPgcCIlKzSakF7u+JZrbUcQ4EQGq6mczj5r5Aux4pzcriHJBEHiQP+ltak7eFoENkgcBwqjdKWe6ng2Q213IOBEBKW7WHlyyQH2pUBueA72XIXYZo50QiKs5KkcM5IMl/eTDWWchv45fIgzuorZAK3d8XyGiqJgsEQCgzLCfSqlCK++3H80KNSuMc8L30lGbXbdqzY89MqZBzwPcym2s8tYtUb+BzoA33xsiDvhbOkNPSpFDY/TIRuU4D14EASEttcd2mo340oh6cA76X1eptLklrTYX69bFr3k1QpW7zVjMSrq/ic6ANedB/eZAs2CH8BeaSFIRblMt1wPeyIx7nkjTUqU9RvlTodakhJItwfZWndqnbmEsSFK01FUrNc7/dYFYr9aPt/JaFJGoHyYMdsiLeFgF2WprUOy9LyuI64HdpDVWe2oXqKvgcCIhI9UalFA903S6zuYZzIABS2lYO9VQ/mtKsTM4BSeRB8qC/ZXicS6JaskBQODUVUr77+wLpTWSBIAhlpsiJRBRKcb+YdJ4alc454HsZIfdzSdqzY3FmSBHOAUn+y4O2PyuWyIOdmfVHB7lul8ZckmBIS5fT2qxQaprrprmifjQIvtBcknRHBZwDvpfZUuupXaR6o/r17Rvj3vgT98bIg74WSpHT1KBQuvutRXJaWX80CNLCZpMlL/WjhWkR5XEO+F6W1/rR+mr17eV1q1IkE9Yi/+LIg/7Lg2TBDuE071mgINyiHK4Dvue5frS5QSX52VIu54DfhRu2emoX2lZJFgiISM0mpRa6v8+XxVrk2/ktC0nUDpIHO2R6XX+0tUW9c9PlpHEd8Lv0Ro9ZwOK5JF9kX+OQ4zhODPuSNC655BLdcccdGjBggF588UWNHDlSkvT+++/rK1/5ipYvX67m5mZdfPHFuvPOO3dpv2jRIlVVVWnFihW66aabtHHjRr355psaOND9JBfblNVKZ85x3+7rw6VZY2LfHyTeEyul38xz3+7WadLBJbHvjx81b2vQA8PO7+5uuHLesvuVlu3+Ae/u2D5+v4s40mkvSWtdPu8bmS89cKjkch4hktD6bdIpL7r/Xn72EOny8XHpEhLsubXSVR+4b3f9FOlIav8C4YZPpIfL3LVJDUlPHimVsN+sJPKQ7eP3u8pG6fjnpRaXd5xOGiBdvW98+oTEer1c+sF77ttdPUk6iVtvgXDbfOmfy9y3e+xwaaD7PSqRhL7xuvSpyxrQkkxp9pFS2P26AoFkex6yffx+N7dCuugt9+0uH2/uD8H/7l4k3bXIfbsHDpX26RH7/iDx/u8t6b0Kd20K0qVnj5LS3e9RiSTT0CId+7xU63KfqYNLzPNiGH7LQ7HOQn4bv0Qe7Oz+pdKtn7tvd8/B0kTmhQbCZe9Jr5a7a5MTlv57tJQdjk+fkDjNEenEF6TNLmvB9+sp/eWg+PQJibWsWjr7FfftvjVSumhUzLvjS2QhjoHf/ewD6X9r3bXJSJGePVrq4X6/aiSZVkc69UVpvcs54mMKpPsOiUuXkGBr6kwNsdtJSucOlX44Li5dQoI9u1r6xUfu2920vzSzT+z740dkIf8dA7JgB8eRvvSytMLlOrJDcqWHZjKXJAg2NZh7Q60uw8Bpg6SrJsanT0isl9dLl7/vvt2v9pWOHxD7/iDxfv+Z9K/l7tqEJD1+hNQ/Jy5d8h2/ZSGJ2kHyYIetTWYuSaPLiaXH9pN+Mzk+fUJivbNRmvWO+3ZXTpDOGBzz7qAb/GmhdM9i9+0ePEwalh/z7qAbfOdN6cPN7tr0zJCePkpKYy6JJPKQ7eP3u3mV0jffcN/u0jHS+cNj3x8k3t+XSHcu8NBuhjSuMPb9QeJd+q70xgZ3bXLD0v+OljKpH/W9plbp+BekqiZ37aYWS388MD598iO/5SHbnxVL5MHOHlwh3fSp+3Z/PlCaUhz7/iDxrpwrvbDOXZvMVDOXJM/9ftVIMi0Rs/bkhgZ37cYXSn+bEZ8+IbFW1Uqne1iL/CvDpO+PjX1//IgsxDHwu2s+kp5a7a5NOGTmkhRlxKdPSJyIY/akWFXnrt3wPOnfh1E/GgQb6qWTX3RfP3rWYOnHE+LSJSTYi+ukn8x13+7aydIxdu4vtQuykP+OAVmwg+NI570qLa52165ftqkhTiEL+N6WRvOcqNll/eiJA6RfshZ5ILyxwTwvdutnE6VTB8W+P0i8Oz+X/r7UfbtHDpcGsxa5JP9lIYnaQfJgh9pm6bjnpfpWd+0O7yPduH98+oTE+nCzqSN367Jx0peHxr4/QRfYsvsrrrhCPXv21OrVqzV27FiNHz9eI0aM0NSpUzV06FAdfvjhkqSJE3c/I32fffbRtGnTdM455+ill15STU2NbrzxxkQOwbcG55pCPjdCkk4fHI/eoDsc288U9LrRL1s6oHd8+gMgsVJC0pmD3bc7awgPeoOiT7Z0cKn7dl7OGySnw/uYib5uFGdIh3k4b5CcvPx7PqSETaeBoCjKkI7s677dWWwyGBgHlkh9XF7T89Okoyj6CowzBpv7fW5M78Wm00HiJQ+eNphNp4GgmNzTffFOVqp0Qv/49AeJd+ogKdVlGJhQyKbTQeLl+92pA9l0Oigyw9JJA9234xkBEBwnDTQbB7oxMt/kAQTDWYPdtzlhAJtOB0VairdJPV7OGySnYflmI3E3UkNmgykAweDl+93R/dh0OihSQ942iOG+QHD0z5EO9DAngHMgOI7oKxW4vKaXZEozSuLTHwCJFQpJZ3p4TnQmc0kCo1emNLOP+3ZkgeCYUWI+290oSDcZAsHg5d/zgb3ZdBoIih7p5l6fW2SB4JjaSxro8pqeE5aOo340ME4b5H4Ro/16sul0kHh59n/qIDadBoJiQqGpBXQjI8Vb3TGS0ykD3V/TR/eQxhbEpTvoBl6+3500kE2ngyI91cwNcos1JoDgOKG/mSvsxpBcMycZweAlCxzXn02ngyKcYtYMcYtnBMExMNesHeRGSN7qjgEkJy/f747sy6bTQZHidS4J9aOBUZJl1hR2izwYHIeVmrXF3eiZYdYwB+B/oZC3PHjmYDadDopCj2uRkwWC44DeZs8xN3LDZm8zBMPpg92vRT61mE2ngaDITZOO9zAngDXHgmPfImlonrs2manSiQPi05+gC2zpff/+/fX666/rhBNOUGZmpsrKylRUVKS77rpLzzzzjBYvXixpzxtPd1ZQUKDhw4dr6dKl8e52YHxvjPmHGa2vDpf6uvwSgOSVGZa+Pzb694ck/XCc+00IACSv0wdLw10EurEFTAwOmotHmQnf0TprsDTE5ZcAJK+0FOkHLrKAJP1gHJvMBcmwfHcPbXLD0ndHx607ALrBd/YxGwlH66QB0piCuHUHCZYaki4b5+5h3yUu7yUhufXPkc4fFv37s1KlWWPi1x8k3tH9zMOeaA3Olc5mcjgQGKG2LOCmmPPi0aZQAMHQK1O6YET0709PkS51eS8JyW1GibvJwf2ypXNd5Eckv68NNxPDonVwiSkcBhAMPdKli0ZF//5wyNSMMDE4OKb2kg4tjf79vTOlrw+PX3+QeF8eKvV3UQu4f7G3DYmQvC4ZYxYGjpbb/AgguU0sko5xMcmzMF361sj49QeJd+ZgdxOCJhQyMThoZo2Wsl3Uj355qFlwEMGQker+nj/1o0CwnDxAGtUj+veP6mHaIDguGuWufvTUgdI+Ls4ZJLdwirnn78alY02GQDAMzDUZP1rZYfMdAkBwfGukuecXrWP6mXuKCIaUtuf/br7mf8/lvSQkt9Is6esu6kczUsyzRQTHzD6mBiBaA3KkLzOXBAiMUFsWCLuoBbxolKk5RDAUZpj55dFKSzHPiagfDY4De5u5AdEqyTK1YwiO84a5W1B+Wi8zBwlAMOSmmbnC0UphLkng7NdTOsrF5jI9M6RvuriXhOT3pcHuNgnZt8isTYLgmDXGrCEUrfOHmTWKAATDmAKzlmC08tPc3UtC8jttkDQyP/r3jymQTmQt8kD5v9FmbeFonTnYrF2MYPBSP/qDseZZAYBgOK6/NK4w+vcPzzN7mSA4LtxHKnDx/P/4/mZ/GgSDl7XIvz/W7G2GYOibbfYejFZmqtnbEEBwfGOkVJwR/ftn9pGmuKg5RnILhaQfjnW3/+j/jZLyWIvck0DfThk9erSefvpp1dTUqKamRu+++66+853vqK6uTmVlZUpJSdG4cXu/C7Vx40YtWrRIw4ax2nW0RhdIv5sa3SSvMwe7KxKCP5w2KLpJXqkh6epJ7hacBZD8csLS7dOlYVEsHjimQLp1GpvMBc2wfOm2adGF9JMGSD8aH/8+IbGO7S/9ePzeb/KmSPrJBHcLzsIffjTOPLzZm/w08zngZsFZAMlvYK50x/ToHvge1Vf66cT49wmJdVgf6eeTottw8tIx0qmD4t4lJNj3xkinR/F7zQ6b+4huFpxF8ktLkW6eajYK2JtBbZ8ZPOQBguWA3tKv94tuwaCLRknnuFhwFv7wnX2kc6P4vWamSjfuL01g8dBACadI10+JrpCnX7bJAkUuioSQ/IozpT9MNwuJ7s20XtJ1k90VCQFIfucPi27zwPQU6drJFH8GTUpIunY/s4Dg3pRkmizQmw1nA6UgXbrzALNA9N7s11O6aX82mQuacYXm/mA0CwadPUT67qj49wlA4oTa6oJnRlEX3DNDuuMAqR8LhgVKbpqpH42mFmhcofT7aVI69aOBMqKHdMvU6BYMOmWg+02KkfxOHGDqB6OpH71qonSkiwVnASS/rLCpC45m8cCR+ea9WSwSEiiDc81ckh5R1AId08/MJUCwHNFX+tnEvU9eD8lkhhPZfD5wLh1rNpXfm9yw+e4wgvpRIFD65Zh7fj2jqAWaWWruJbKxTLAcXCJds190tUAXj5bOZMPZwPnuKOmcKH6vWanSTVPdLTiL5BdOMTUA+/Xc+3sH5LTNP6R+FAiUKcWmLjg9ilqgb440tYYIlq8PNz97k54i/XZydJ8Z8I+UkLkGTO+19/eWZpk5B8WZ8e8XEqcwQ7pzenSbT08plm6YQv0oEDTnDDVzhvcmHDJzkA+IYs4B/CMUkn65r3RIFPWjxRnmvkBpFJ8Z8I/8dJMFBkWx+fSEtjkHbDIXLKN6SL+Pci3y0wexsQwQRD+daNYU3JuCdDPnYGAUnxnwj+ywdNt0aUQU9aOjerStRU79aKAMyTPnQH4U9aPH9zf1gwiWo9vqgqOpH71ivFm7HEBwZKaauuAxBXt/77A8kwdzyAKB0r+tFqgoirXIj2hbs5r60WA5pFS6et/o6kcvGWP2NEOwXDxaOmvw3t+XnWrWIh9dEO8eAUik0iyz5livKGqBZpSY58VkgWCZ3lv6zX7RPf/7zj7Sl1mL3DMrv0rNnz9fjuNo5MiRys7esdLg/PPP1/DhwzVp0iQVFBRoyZIluuWWWxQOh/WDH/ygm3rsT9N6SfcdIj2wTPrvGqmhdcfX9y2Szh5qvtRxEQ+mrw6XRveQ/rVcemOD5HR6LTUkHd5HOm8Yk8GAoOqdJd1zsPSfFdLjZdKGhh1f75stnTFI+tIQFgoKqkk9pX+2ZYFnVkvbdsoC4wvN7//YfmSBoDpriDQ835wDr5VLkU6vpYbMTeDzhppzBcETTpGu2ddMCnuoTPpsy46v54RNscd5w8xDIQDBM7bQZIF/LZeeWiXVtuz4+qgeJgucOCC6zYnhPycPlIbkSg8sl15eL7U6O74+o8R8DrCxUDClhKQrJ0j7F0sPrpA+rtzx9cxU6bj+ZnGIaCYNwX96pEt/PFB6ZIX0SJm0ZtuOrxdnmEKPc4aa9wIInmP6SQNzzH2BF9dJLTtlgQN7m4e8TAwPplBI+uE4swjQf1ZIcyt2fD0jxUwWOH+YNCyKSUPwn9w0UwT8aJn0cJm0snbH14vSpVMHmetAIYsGBtLgPOkfM6R/L5eeWCVVNe34+tA86czBZnI4CwUBwRMKmcWCxhWa68C7m3Z8PS3FTB4/f5g0ks0EAikzbBYKeWKleU60vGbH13ukmw3mzh3KooFB1T9H+tsM6T/LpcdXSpsbd3x9YI7JAmcOZqPJoDqgt6kfvX+Z9L81UmNkx9f362k2nT6c+lEgkNJTpev3N8+JH1ohLa7e8fW8NOmkAeZZYUlW9/QR8VXaXj+6XHpspbRpp/rRftnSGYOlLw1moaCgmlxsakbuXyY9u0aq36l+dEJb/egx1I8G1jlDzaJh/1ouvb6b+tFDS83nwMSibusigDgqzpTuPthkwUfLpPL6HV8vzWrLAkNYKCioxhdJ/zzU1As8vVqq26l+dEyByYLHUz8aWKcOMs8D718mvVq+a/1o+1ySydSPBlJqSLpqorR/L+nB5dInO80lyUo1c0nOHyYNoH4UCKRRPTrWmHhqtVTTvOPrI/NNFjxpYHSLy8F/jutv5gjcv0yas5v60YPa6kenUz8aSKGQdFmn+tEPN+/4ekaKWTz6/GFm4XEET26a2WDokTLzs6pux9d7ZrTVjw5h02kgqA7vK/2tbS7JC+uk5p1qRqb1Mlng4JLu6R/iKxSSZo0xz4D+vVx6b6e5JOmd6kdHUD8aSNlhs2nQYytNFti5frQgXTp1oLkO9KR+NJAG5Ep/b59LslKq3GkuyaDcjvpRNpoEgulbI6WxBeY68NbGHV8Lh6Qj+5qaETYTCKaMVOmm/aXZK8280iU71Y/mp5m1aM4datapRPCUZkt/a1t/9LEyqWKnuST9s9uywBCz7gyCZ/9eHfWju1uLfFKRmUtyZF/qR4EgSkuRrp1s1pN5aIW0YOuOr+eEO+aS9Mne/d8Bf+uVKd19kFlz7rGV0oad6kf7dKofzaZ+NJAmFnXUjDy7Ztf60XGFpn70uP5kgaA6c7A0PM+sP/raTvWjKWqrHx0m7cta5EAgFWZIdx3YNpdkpbRup/VHSzKl0wZL5wwx9SUIntEFbVlgD2uR79O2FvlJzCUJrBMHSINzTR6cs4e1yL88VJraq3v6h/hKCUlXjDdzhR5cLn20h7XIzxtq1igEEDzD800W+Ncy6clV0tad5pIMzzN7WJ0ykPVHg+qofqZ26IGl0ovrd60fPaCX9OVh5h4yvAs5juPs/W3B8te//lXf/va39aUvfUkPPvjgDq/deeeduu+++7RkyRI1NDRowIABmjlzpn76059q0KBB3dRj/6tplt7bJP3mY6mmxSwk/vyx3d0rJNLaOunTLWbRqJywmSjGwrF71rytQQ8MO7+7u+HKecvuV1p2bH6pto8/iFoiZmORjQ1SSGahoP2KmRRuk9q2LPDrj00WKEyXXiALWKW8Xjr3Fam62RT//vswFo61zcKt0vJqqSliNhOY2ovF4rpiex6yffxBVN9iNpepajKFoUPypNE9KPiyyaYG6aPN0m/nmTzYM116jjxolaXV0nfe7MiDTx5JsY9NIo5ZMKp8m9QqUyA+tZgHfF2xPQ/ZPv4g2twgfbDZTAbISjWTxlk41i4raqRvvtGRBR4/wtwfgB0cR/q4Uvrhe+a5cY806X/HsECMTZpazaJhFQ0mA/bPNhPGuC+wZ37LQ7HOQn4bv0Qe3JuVtdKCKlMzkhuWphSbCUOwg+OYmqHvv9uRBZ492iwoBDs0R0y9wM8+MPeHC9Kl549hIphNqptMHqxtNv/2R+ZLw/K7u1fJiyzEMQgax5HmV5lM2BwxnwPTe7HZsE3a60evnNuWBdLMPAKygD2oH8X6bWazwW0tZoGwfYtYOLYrZCH/HQOyYNdaHZMF2hcPLMky9weZS2KPbW31o7/6iCxgq431ZrGY69vrRzOk547p7l4hkRZvNTWkTRFTMzK1F/WjXfFbFpKoHSQPdq2hRXq3QtrSaGqFBuWa+kFqRuxR0WDqyJlLYq9l1dK3O80leeIIKZ/6UWtE2upH19WZuSTFGSYPUj+6Z7bnIdvHH0RbGs39wdq2uSSjC0wmhD3KaqULXu/IAo8dLhVQP2oNxzHPCS+lftRa7XNJNjdKqZL65ZiNBrkvsGd+y0O2PyuWyIN7s7rW1A+2rz86uafUk8Nljfb60e+905EFnjmazYZt0hIxWeAq6ketVdtsakZq2uaSjMg3m05g98hCHIOgcRyz8fSKmo65JNN6SVnMJbHG7uaSPHcs9aM2qWsxc0moH7XXhnrzvLh9LsnEIrMvAXaPLOS/Y0AW7FqrI31YYfYlcCT1zjRzSVh/1B7ta5Ff0ykLPH8Mz4lsQv0ollZLS6qlxlYpr20uSR5zSfbIb1lIonaQPNi1hlZzX6Cy0WTAQTnSuEKygE0qG6UPKqTr5rFnbaxZeZv9008/lSRNnDhxl9dmzZqlWbNmJbpLgZeXJh3RV/rdZ+YfMV/o7dMvx/wgdvKGlGrGbd9TRlGemmu26Y3v36mqxWt2eE/pQeM0+arzlJaTKceR1rz4gT649gHz9FVSTr9iTb/uW8of2kdOJKKF/3heC+/9r/oeNlFTruoI1JnFPVS/qUpPHX1FQsfYFdvH7zfhFGl67+7uBbpTbpp0eF/p5rYswIRQ+5RmmYLf6mbzv2w6bZ9RPcwPYsf2PGT7+P0mKywd1qe7e4Hu1CtTOrqfdOt8kwdTyYPWGZ6/Yx5k0UC7pIRMoRdihyzEMfCbnm1ZAPYakrdjFmDTabuEQtK+Pc1iYe2Tg7lHbJf0VOngku7uRbCQhTgGfjMol8UibRYKSROKdswCLBpol7QU6aAS86ygpkVKT2GhINvkp0tH9u3uXgSL7VnI9vH7TShkJv+MK+zunqC7tNePbs8CqWQB21A/ij7Z5gexY3sesn38fpMaMotFwl7ZYWlmH+mmT8kCtuqdJR3TT7qtvX6U7wPWGdnD/CB2bM9Dto/fbzLD0qGl3d0LdKdi5pJYb9hOc0nYdNouKSFpv57mB7FBFuIY+E1hhnQUc0msNjh3xyzAptN2CYXMRiLUj9qLuSSxRxbiGPjNgFzzAzu11492zgJsOm2XcIp0IPWjVsttW4s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"text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 13, @@ -685,8 +688,9 @@ "outputs": [ { "data": { + "image/png": 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XSG9WSA1de0nm50mnTGQviRXVXetH13WtHx2SJh06RjpiDFnAis317nug93OB96tYPxorZCHuwUDT1im9WCq99om9JNva3P5C+F9tm9tT+GGtywI5adLBo6Sjx7GXxIrCBrdmpIj1oyaFw9L7XetHez8XyG+QprCXJCasZyHr/R+I6ttd3cHea0YeL5KOHefWEMH/Shrde6LCBqk9LA1Ll44dL+03gvWjFoTD0qoaV4e4e/3o2GzpVNaPmtERkpb2Wj8qufWElS2sH40V63nIev8HorKd7CV5bat04CiygBVrat3vAFube/aSnDJR2oP1oyZ0hqXXt7r3BN17SaYPdvuJRrF+1ISmDunpYundqp482NzRs5YU/c96HrLe/4Hoo23b15qpb5dW10hz8hLbLsRHKCy9UeHqT9a2SWkBV1/k9EnUIreipUN6dotbR9xdi3zBMOlkapGbUdUiPVq0/V6SF7dIh7F+NCbIQtyDgSYcdvsJP7mXpLhRmjAosW1DfLR2un3Fyyrcv31mijSvay/JENaPmlDb6rLAmtqeWuSHjJaOpBa5GQUNO74nWl7l9pizfrT/kYW4BwNNe8i9I3p16/ZrRmpbpVzqj5pQ11WLfFVNz16SA0e6vSSsH7WhuNFlgYKGnr0kx45z60fJAv4XDksru9aP9j6/flO9NI1a5DFhPQtZ7/9A1NAuPfGJ+qOPFkrHjXfPieB/pV3rRzfXu70keenSMeOkA6hFbsbqGjcOlPdaP3rqRGkm60dN6Ay7ZwIvlW7/XKCiRRrJXpKYsJ6HrPd/INravOM7gqVl0kGjpRSygAnrtrm9JGVNUliu/vQpE6U9cxPdMsRDqLsWeWnP+tFpg6XTJktj2EtiQkuHO7P4ncrta441tLN+NFas5yHr/R+INtTtWH90VY00J5f3RBaEutePlkjVXVlgco7bVzqe9aMmtHa69cNvlm9fi/zkiW4dIfyvpvUTzwXa3P4yapHHBlmIezDQhMOutsAzJdvvJSlskCZRf9SE3rXIu/eSzMmVTpkk5bKXxIRtXetHV9dsX4v8qLFSOmtGTCjayV6SdyqpRR4rZCHuwUDTEZJeKnPvh3uvGalplfLYS2JCfbv0RJG0osbVGstJdesGj2H9qBlbmqSHC9xeku71o8eOk/Zn/agJ4bC0utaNA733kmyooxZ5rFjPQtb7PxA1dkhPfeJckocLpBPGS5nUHzWhrFl6pMDtI2wLuWeCR49ztchZP/rp+P5H6P3339eJJ56osrIyDRo0SHvttZe2bNmiW2+9VRs3blR1dbUkaeHChYltqE89kC/97SOpvGX7P39ui7RkrTtg6Op5vPT3q7ZO6bY1btJu7uz589aQ9LP3pd+tls6bJl08i1/s+tu6O59V/mPLFExLVd7sSZr7zdM1aOxwdba2ffw1wfRUfebZ32jzQ0u14pYHP/7zQ373TWWOzNXz5/8yEU3vF9b7P5CsrJZ+s8odNtxbc6f0+ZddIeGr5kmz2RzqW48XSn9e5wJ9b89L+utH7mXv9+dRNMqvOkLSn9dK9+W7X+x7e6ZEuuVD6bNTpUv3YAGgX9W0Sr9eKf2v1G0Q79bSKV35ljQ2S/rabLcQGP2HLMQ9GEieLZH+sEYqadr+z5s6pVOflw4fI31/PoeO+lVnWLp9nfSfze7FXm/Plki//1A6Y7J02V4Uk/arujbpNyvds8CO8Pafe7xIGpUpfXUP930Af9pQJ9240hUN7625U/ryq+6w0e/OlfYZnpj2+RVZiHswUITC0j83SHdt7HnR362hQzrhGenUSdK351BM2q8aO6SbVroCIe2h7T/3ZLE0PEP68kzp3KlsBvCrggbp1yuktyp3/NwDBdKMwW4MWDwq/m1DfLy21T0H3lS//Z83dUpnvigtHildM5+Dxvqb9Sxkvf8DSUuH9NvV7hlAa68s0BqSfrxcujld+sIM6YLpZAG/2tIk3bBCer18x889VCBNyXFZ4JDR8W8b4uPtCjcOfFS34+fu2igtGiFdM0+awuEivhQOS/fnu/Wjla3bf66xQzr5Oen48dL35lJAsr9Zz0PW+z+QlDdLv1rhDhPo/ZqopVO67A1p4iDpsj3dphD40/tV0v+tktZs2/Fz92ySFg5zawc5XMS/Hi2UlqxzBwr09vwW6a/r3AFDV8+ThlI0ypc6Qm7N0IMF268fbQ1Jv/jA7SU5d5p0yR5sDOxv1vOQ9f4PJB/WunVDK2u2//PmTumCl6V5ee53wjl5CWke4uDpYulPa3dcPypJf1/ftX50njSc9aO+1BmW/rJOuneTWyPS2zMlbs/pmZOlb+7JXhK/2tbm1g4+v+UTe0lC0vfedodMXbKHO1gA/YcsxD0YSF7Y4n4vLGzc/s+bOqXTnnfvCK+dL43ioDFfCoVd5rtno7RtZ3tJ1kinT5Iu34ti0n7V0C7dtMplv0+uH32iWBqRIV00Szp7CmtG/Cq/XrphpSsa3ltzp3Txa9LMIdKVc6T9RiamfX5FFuIeDBThsFsb9K8NUs1O9pKc9KyrLXDlHApI+lVzh3Tzalc8uO0TWeCpYunmVdIXZkrnTyML+FVxo1s/+kbFjp97qMAdPn3FXtLBrB/1rTcrpN+ukjbsZC/J2S9K+49wNSYmc9BYv7Kehaz3fyBp7XT7yR4p2HEvyU/ec+vLL5gufXEG9Uf9qqzZ7Sv95PpRSXq4UJo0yD0bPGJsQpqHOHivyj0fXLeT9aP/3uTqi1w9j8NF/OyhArdO9JO1yBs7pFOecwcMXTVPGsL60X5lPQ9Z7/9AUtnissDLZVLvR0MtndIVb0rjs6Vv7On2lcGfVtW49aOra3f83D2bpPl5LgvMzo13yxAvTxS5WtSlO6lFfvtH0pFdtcg5dNSfOkJuL9F/N+9Yi7y+QzrxWbdW4OuzWT/a36znIev9H0jWbXNriD+o3v7PmzulLy6V9sp1e0nmD0tI8xAHz29x9aaLP7mXpFT6+0fSoWNczbGR7CXxpVDYZb7/bJLqdlGL/PTJ0rf2ZP2oX9W3u98Jny3ZvhZ5S0i6+h1Xi/yiWdJZUxLWRF8iC3EPBpKXy6RbP3S1aHtr6pTOeEE6aJTbSzImOzHtQ2yFe9Uir/3E+tHnuvYZnTpJumKOlEkW8KWmDldr6KniHdePdtci/9JM6TxqkftWUYOrOfbJWuQtndIlr7n1o1dSi7zfkYW4BwNFOOzOJ/vHeqlqJ/VHT3pOOnGCO58om70kvtTS6eqKPVa4/fpRqWsvyWrpwumuHjlZwJ9Km1wWWFa+4/rRhwrc/oEr9pIOG5OQ5iEO3ql0+8Y+WYu8uVP67P+kfYe7vSTTqEXer6xnIev9H0jaOl1dsYcL3M99t9aQ9LP3XU743DT3noC9JP5U0eL2lS79xPpRSXqkUJqQ7c4sPoZa5L61otrtJfmwdsfP3bNJWjDMrR/dg1rkvvVYVy3ysk+sH+3eS3LUWJcHc9lL0q+s5yHr/R9IqlvdXpKXyj5Rf7RT+s5b0tgst374pImJayNia02tWze0ombHz927WZrbVYt8LrXIfeuZEumPa3asRf5CqTu77PAxLguMYP2oL3WGpdvXuXdF9Z9YP9pdc+zMKa4WeRp7SfqV9Txkvf8DyYY6t5dkedX2f97cKX1pqTR7qHtXvPfwxLQPsfe/Uum2D3esRS5Jd6x3tcivme/OJoD/hMJuvcjdm9z5FL111yI/dZJ7X5zB+lFfamh360ef/kQt8paQdM07rhb5l2dJn53CmpH+RBbiHgwkr26Vblktbd7JXpIzX5QOHOmeC0wYlJj2IbbCYenurlrk1TvZS/Knte7Z8JVzpCzWj/pSc4erMfpE0c7Xj+alu7WjF0wnC/hVSaP065XS6+Xb/3lLp/S116WpOW4/2SHUIu9XZCHuwUARDkv35bt6IpW72Ety/Hj3rjAnLSFNRIy1drq95Y8UurG/t6dKXE74/HTpyzNZP+pXW5vd+tGd1SJ/pKsW+WV7SkexftS33q9yzwfXfKIWeXOndN5L0t7D3HMBapH3L+tZyHr/B5KOkKsl8kC+ex7YrTUk/eIDd2bJZ6e688tTyAK+VNXingu8VLrjXpLHilwt8q/Plk6YkJDm+YKvl1tVVlbqM5/5jMrKyvTd735XpaWlWr58ucrKyvTrX/9aTzzxhN5++20FAgHNnz8/0c31nT+scRvDP3mgSLeOsHvIe9GrUm3rzr8Gyau1U/r2m27TV3Pnzr+mrl36yzrph+9uv0kAn17dpjKVLl2pkhff06o/PqIXvniDRiycrgN/fenHXxNq69Crl9+meZefqby9JkuSJp2wnyYcu0ivXfnHRDW9X1jv/0CxrFy69PWdbwjt9kG1dMmr7iAy+M/fPpJ+8v6OG0K7dYbdS/+vvOoOIYK/dISkq96W/rFhxwNFujV2uEXAV7/jvh7+UtHifr6f37LrrFfaLF33nlskjP5DFuIeDBT/3ij94N0dN4R2C0n6X5n05aVS2S6+BsmrM+x+3//rRztuCO3W1Ok2iXz7TVdYCv5S0ypd/Jpb2NGxiyxQ3iL98gO3eRz+s6pGuvjVHTeE9rZum/TNZdIrZfFrlwVkIe7BQBAKSz9/370r+uSG0G6tIbc49LJlUssunh0gedW3u0MjHivafkNob1WtrqjgzavdYmH4y0fbpK8s3fFAkd421LtD6J8tiV+7ED9PFknfeVPaVL/rr3mjwj0X2Fi366+Bd9azkPX+DxRNHdLXl0kPFOy4IbRbTZtbAHr9CrKAH+XXuzH+kxtCt/uaBunKN12hYfjPC1uky97Y8UCR3t6pdO8T19bGrVmIo1s/dAvAP7khtFt7SHq8SPrqa7t+doDoWM9D1vs/UBQ3uiywdCcbQrsVNUrff0e6b3Ncm4Y4eXWr9I1lO24I7e39aumi19zhI/Cfv6xzh0ht7WP96DMlLg9WsX7Ud9pD0pVvSXdu3PX60YYOt874GtaP9jvrech6/weKdyrdu8KVOzlQpNvKGrff5O2K+LUL8XPnBulHy3e9frQzLL1Y6rLArvICkldHV6Hov33k5vydaexwWeHKt3a9pgDJq6rF7Rt/pmTXe0m2Nru1RX9ZF9em+R5ZiHswUNy32T3729mBIpJ7Zrh0q3uGWLyLr0HyCoWl65ZLf14rbdvFXpKWTnf42Lfe2LGwJJLftjb3DvDxPtaPVra6d4m3fhjftiE+1ta63/fe6WP96Po6t6bghS1xa5YJZCHuwUAQDrs1gbd86NYI7kxrSHqwwK0xbGIvie80tLtnvw8VSG27yALVbdLvVrvDKFk/6j8b69zve2/08fx/U73ba/BkUfzahfh5rkS6/A23Z2hX3qp03yfr+lhXAu+sZyHr/R8oWjrcnuH/bt71XpJtbW7v8c/ed8+S4C9FDbtfP1rY6GrTPZgfz5YhXl4pc+tH+5rnl1e594mr+lhXguT1p7WullCftchLXE2iGmqR9yvrech6/weKsiaXBf5XtuOBIt1Kmlxturs3xrVpiJM3K6RLX5NW1+76a1bUuPeJ7/bxLgnJ6x/rXY3h0j72kjzfVYu8gr0kvtMRcmuG7ljfdy3yf25wvxeyl6R/Wc9D1vs/ULxXJX31VXf2yK58WOsOoV/WRy0SJK//bHJ7CYr7qEX+clct8i3UIvedzrDbS/SXde4ssp1p7nRnmH37TXemGfylttXVoX6yuO9a5L9a4d4Vof+QhbgHA8XDBdL33pIKGnb++bCk18qlLy3d9dcgeYXD0s8/kH6/RqrdTS3yby6Tmlk/6jsNXbXIHync9frRqlbp/1a5euSsH/WfDXXSl1/tuxb5pnrp8jelp4vj1y4LyELcg4EgHHbj+/+tcuP9zrSF3Dxx6Wtu3oC/NHdI33hduj9/1+tHa9uk29ZIv/iALOBH+Q3u973Xy3e9frSgQfruW+73R/jP/0rdOvK+apG/27V+tK8z7uGd9Sxkvf8DRWunO3vqnk3ufdDO1LVLS9ZJP16+65pUSF5bmlwWeLmP9aPFTe598r3UIvelZeVuPUhf8/wH1W5dyft9nGuL5HX7R9JP35fK+lg/+twWd55hJetH+5X1PGS9/wNFebP7+X6hdNdZr7RZ+n/vufXm8J93u2qRr+hjz+iqGvd8+E1qkfvS3RvdfrFd1SIPye03+8qru84LSF6dYffv/9eP3DnmO9PUKd210dUbamP9aL+ynoes93+gWFntnv8v7+N3/rXbXB2KpWXxaxfi54F8t19wd7XIv7TU1aWBv4TC0k/fk/64dtfn0bZ0unpU33rD1aeCv9S1ud8JH9tNLfLfrHQ1SFkz0n/IQtyDgeKJIunKN6XNfczzyyrcM8RNfdQoRXIKh6UbVki/+9DVm96Z1pCrU/2113ddhwLJq7HD1Zl/sGDX60dr2ly9+l+tIAv40eZ6Vzvg9T7qR2xucHPF44Xxa5cFZCHuwUAQDrtzp25c6X7325n2kDu/6pLX3O+Q8JeWTvfM597Nuz6Dblu7q1P7k/eoRe5HxY3u973d1SK/+h3pftaP+tKrW93vBGv6qEX+Xte7pBV91CWDd9azkPX+DxTtIXcu/Z0b3dqQnalvd+fbX/MO9Uf9aGuzWxP2Ymnftch/tFy6a0Ncm+YrwUQ3IJYuv/xyFRcX67LLLtNNN92kwYMHf/y5q6++WgsWLFBHR4emTJmiIUOGJLCl/vNooSsgHYlN9S7U84DXX369su8iQb09t0Vasja27bGu4p112nj/K5p6+sEauWiPj/+8asUmrf7Tozr01m8pe+wwHfibr+nNH9yu5q3+Ou3Jev8TobBB+v7buy4W11tTpysUspWNAL7y/Bb34DYShY3ulz8e8PrL71a7B7uReKXMvfSHf3SG3Uv8ol0s/v6k36+RXtgS2zZZRhbiHiTCq1ulm1dH9rWlza6ILA94/eXPa10mjMSbFW5hCPwjHHYbwSJd2P339W7xD/yjqsVt+G6IYGF3e0i69h1pYx8FJvHpkIW4B4lwx3q3GTAS71W7A0jgLz94R1rXx4Kv3u7Z5DaSwz/q2lzx2G0RHBLQGXaHU6+pjXmzEEcrq13RyEh+1a9pc88FOFQidqxnIev9T5SfvCetjPBWPlTgCofBP5o73Ni+q0OFegvJ/T5AIWl/Wbct8kMC6tpddtzVgZRITg8XuA0AkVhf5zYCsH40dqznIev9T4S2rkNltkZ4SMCNK6U3+igkgeST3+DG9ojWj3a4d0ocKuEvTxdLf1kX2dcWNEjfe5ss4Df/t6rvIkG9/a9U+sOa2LbHOut5yHr/E2FLk9sfsqsCMb21dLp5oCTC9cZIDi+VRr43oKRJ+jbrR33nD2vcHB+J18ulm1g/6ivhsBvb8yM8JOAv69zvEIgNshD3IBHeKI98b8DWFvcskQOm/OX2j6SnSiL72nerpOtZP+or4bDbG7A+wr0Bd25060bgH7Vd60frIlw/+uPlka83hndkIe5BItztYWxfWePWkcNf/t970oe1kX3tffluPwn8o6FrPWBNBOsBQ3J7Digk7S9rat04EOn60W+/yaESsWQ9C1nvf6L84gO3ZzgSjxdFXq8WyaG10+0lqYhgPWBY7kDCdyKsV4vksLHOPR/c1WHDvTV2rR+tYv2orzxe5A6LiMSmelebiPWjsWM9D1nvfyJ0hNxzgdIIa0v/drW0tCy2bUJ8FTe6sX1Xhw331ty1frSsKfbtQvy8uMXVFo5EUaPLg5E8R0LyuPVD6eUIx/alW91cgNixnoes9z8Rtja7vSS7OmCwt7aQO8OkMML1xkgOr5dLN62K7GvLmqUr3ojsORKSx1/WSc9GuH70rUr3jBj+EQ67swc3RliL/I710mOFsW2TZWQh7kEivFvp9gZE8qt+Zat0+RtSSwS165E8/rnBnVsbiQ+qpZ+9H9PmIAF++K60NsK9Afdulv6bH9PmIM7q293YHkk9yVDY1atdzfQbM2Qh7kEi/Dffje+RWLNN+tHymDYHCfDT96UVEf4oPVLo8iP8o6XDPfONpBZ5WNL1K6Tl1CL3lfXb3O8EHRE8GKhvl779hlQTwfcLomM9C1nvf6LcsEJ6O8K9Ac+URF6vFsmhPeSywNYI14/etFJaRi1yXylskK5+O8Ja5J3SlW9J5RF+vyA5PFvizi+PRGGj+x4IsX40ZqznIev9T4TOsPSdt6TiCPcG/H6NW3cO/yjrqkXeHMH60daQ23dSTC1yX1laFvnegC1N7tkQtcj95U9rpOcjHNvfqJB+E+F6Y0THeh6y3v9EqGxxebAxgvWA7SF3ntmmCNcbIzm8VSH9OsK9ARVdtchbqUXuK39fLz0R4TkTy6tcfSr4yw/elT6KsBb53ZukB6hFHjNkIe5BIqzo2hsQya/61W1uvXEk2RHJ498exvbVtdL/Y/2o7/xkubQqwuH0wQJXvx7+0dThxvbqCGuR/+wD6X3Wj8YMWYh7kAgPF7pzpyLxUZ17Pkj9UX+5/oPI9wY8WRx5vVokh9ZOlwW2Rlhb+tcrpTcrYtsmxNfmeje2e6lFXkkt8pixnoWs9z9Rblrpas9F4n+l7rx7+EdHyJ05VRLh+tHfeahXi+0FE92AWFmzZo3uvfdejRgxQr/61a92+jX77ruvJGnBggUf/9kRRxyhQCCw04+vfe1rcWl7sguFvR8WtbxKep+DBn2jrFl63GMhyP9scgdUInY++O39CnV0au+rzt3+z3/3gEKdnTr1ud+o7LVV2vzIawlqYWxZ73+83bMpsiLi3erapfvzY9YcxFk47P1h7Ye1bjMA/KG61ftCvgfypVoKxfjGsnJXCM6Lv6/nRV8skYW4B/H2d49ZYFO99BIP93yjod39nu/FY0UUivGTd6N41vf3j8gCfvJQgVQTwcLPbq0ht2gcsUMW4h7EU0uH90X9z5RIRRw06Bura6RlHp/13bGBQjF+8liR2/QfqfZQ5AvGkRz+ucHbQeJbm6WnIiwmgOhYz0LW+x9vm+qlF0u9XfOvDVIbhWJ846liVwwwUqEwB8v4zV0bIysi3q2qVXqEQjG+0RnF+tG3KyMvJoDoWM9D1vsfby+USgUenvWFJf2DLOAr/94otXjI99va3dox+EM47NaBebGyRnorwsOoMPBVtrj3xV78d7NU5+H9Mryznoes9z/e/rvZWyHIxg7pvvyYNQcJ4DULrK+TXt0am7Yg/ura3DjgxcOFFIrxk7cqXcb3gr0ksUUW4h7E2z82uGd+kSpocM8U4Q/NUawf9fp+GQNbNM/6/rHe21ozDGyPFLg1AJFqC7k1BogdshD3IJ7aOr2vA/tfKQcN+sn6bdIrHveK/3NDZAWHkRyeKna15yLVGXZriOEfd2709jNd0eL2ICF2rGch6/2Pt6IG6ekSb9fcvdHtSYY/PFciFTVG/vUheV93joHt35tc7ZBI1bR5X2uGgSsc9l5z7P1qV6MIsWM9D1nvf7y9VCZt9PisjyzgL//Z5A6djVR9O+tH/SQclv7m8Wd67TZXuxj+UNvq/XyBBwtcDXPEjvU8ZL3/8XZ/vjtrJFJNne4sE/iH1+cCmxukl1g/6hsN7dI9HteBPVHk7f0yBrb3qyM/eL7b39e7ulOIDbIQ9yDe/rnBvf+LVEmT9MyWmDUHcdbSKd3pcR3Yc1u81SfCwLa2VnrN47O+f66nFrmfPF4klXvYK94RphZ5rJGFuAfx1BFyewS9eHWrtM7j+ZYYuPLrpec95vs7N0qt1CL3jadL3O95kQqFvY8bGNju3uStFnl1m/RIYezaA7KQ9f7HW1mTe+/jxX82eatViIHtxVL3/jdSYbF+1G/u2SQ1e8j3de3e15ph4IpmL8mHtdKbHs+3hDfW85D1/sfb61E866P+qL/cu1lq8JDvmzrcNfAPr7XIN9R7r0+Egau+XfqPx5/pRwu9nXUN76znIev9j7eHCqRaD2cNtYbcuWbwj3+s97Z+tLDR+/tlDFwtUdQif6ZEKvZQnwgD26oa6Q2Pz/qoRR5bZCHuQbz9c4O3n+myZukp6o/6RnvIe03hl8ukDXWxaQ/ib2Od9D+Pz/r+tcHVsYc/PFkslXrYKx4KS//iuUBMkYW4B/HUGcWakbcqpdW1MWkOEqCk0Z1L4cXdG935dvCHF7a4Z76RCou9JH7z702uzkCktrVLD+THrDkQWch6/+OtssWdR+/Ffze7tSbwh6VbpfUen/WxlyQ6wUQ3IFbuuecehUIhnX/++crJydnp12RlZUmSFixY8PGf/fGPf9SyZcu2+/jRj34kSTrllFNi33AfeKvC28FC3Sgm7x8PFXhb8CO5IsJeHwTAm/r8Mm1+5DWNO2y+Rh2w58d/Hu7oVMXb65Q5fKg23Pu/BLYwtqz3P54aO9xLHq8eKeBFn198UO09zEvSfWwG8o1HC70fIt4Wkh5l0Y9vRPPzvG6bWzSK2CALcQ/iad02aUUUP88UCfGPJ4q9FYmR3CIRDh32j2h+ngsbpbcr+70pSICOkDs00Kuni6U6DxtJ4Q1ZiHsQT89u8XbIYLcHyAK+EU0W2NrsCsoj+YXC0S3ie3GLWyyE5FfWHF3hp/vyKRoXS9azkPX+x1s0WaCmzRWfRvILh6NbA/bqVmmLh4NIMHDVtEZX+OnBAgrF+MWycm8HC3Vj/WhsWc9D1vsfb9GsGXmnUtpc3/9tQfw1tEe3HvzBAg4d9ovlVdKmKH6e72f9qG88XOg927eGpMdYPxpT1vOQ9f7HU0unW0fu1aOF3opKYOBaXeMODPOK9aP+8ViRm9u96Ax7LyqBgSuabL+p3v0ugdggC3EP4mlzvXvW5xX7Sv3j6RLvh4iHJT2YH4vWIBGieedX0uTeMSL5dYaj20vy/Ba31gCxQRbiHsTTi6VuLaBXPBvyj2j+LatapZdYP+oL0a4ffaXM7UFA8qtscXuDvHog3+1FQmxYz0LW+x9v0ewRrmt3e5LhD9FkgTcrpIKGfm8KEqCuzdUM8Yr1o/7xdqW3g4W68VwgtqznIev9j7dofp5X1LiahUh+zR3RrQd/pFBqZf2oL6yK8ueZNSP+8WiRqy3uRXsourXniJz1PGS9//HU1unOGPHqyWLv680wMK3fJr1f7f06akz4x1PF7swxL0KiFrmfRPPzXNTozjhEbJCFuAfxVNwovR7FevD7NlN/1C+e3yJti6IWOe+J/COaf8vyFndgOZJfOBzds97/lUoV1CKPGbIQ9yCeXi6L7ueZLOAf0awf3dYWXe1iDDzR7iVZVu5+n0Tyq22Tni3xft0D+dQijyXrWch6/+PtgQL33seLxo7oahdjYIqm5tjyKmlDXf+3BfHX2CE9EcX60YcL3PoxJL8PqqUNUdQiZ81IbFnPQ9b7H2/R/Dyv3Satru3vliARoq1F/lih24eC5Le2VlpZ4/063hH4xxNF3s8W6AyzfjTWrOch6/2Pp45QdPVHnyqW6qNYb4aBJ79BeiuKWuRkAf94usT7z3NY7l0h/CGan+eyZuk11o/GDFmIexBPZU3S0jLv192Xz14Sv/hfqasr7RV50D+i+besbnN17JH8wuHo1oy8WiaVNvV/e+CQhbgH8fT6Vqk0irMFqDnmHw8UuGc9XjR0SM9EsfYcA1M0efDtSnfOJZJfQ3t068EfohZ5TFnPQtb7H28PFXjfG9Yakh6PYu05BqZosv2qGmlNbb83xfeCiW5ArLz44ouSpCOPPHKXX1Nc7BLHggULPv6zvfbaS4sXL97u4/3339fIkSN1wgknxLbRPhFNsTDJPQyAP0T7bxnt9w4it+KWBxTq7NTeV5378Z+NOmBPzTj3SK3525Pa/2dfVkpmegJbGFvW+x8vK6ujKwhe3cbBMn6xLMpCwMvKWfDhF1HnQbKAL4TC0htRjgN8D8QWWYh7EC/LovxZfqfSHUqD5MdzAUQ7DvA94A+b6l1heK9aQ65oGGKHLMQ9iBfmAfBsyLaSRqkwioLgHWH3eyGS31sV3gsISy5HbuVgmZiynoWs9z+eos2Dr5EFfKGqVVofRUHwsKQ3+B7whXeroisIXtIkFTb0f3sQf/xOOHBZz0PW+x8vjR3SiigKCEuMA37xfrXUHMV7/6pW6SMOlvEF3hGA74GBy3oest7/eFldI9VFURC8rt1di+QX7V6SNyooEuIXUWcB9hb7xuvsJRmQyELcg3iJ9md5RY0rNobkx14S8GzItqIG9+7fq/YQ60djjSzEPYgXao4h2ueDZAF/2Nri9gR4FZL0ZpTfOxhY3ql0e4O8Kmx0e5EQO9azkPX+xxNriG2ra5NW10Z3bbTPlDCwvFftaoZ4Vd4ibeRgGV/gucDAZT0PWe9/vLR1Rv+snyzgDytroqtFXtvGwTJ+EW0WWFbuahgj+bFeYOCynoes9z9e1m1zZ4x41dghraju//Yg/qJ9V7y8SmqJIkdi4OHZEKg5NjCRhbgH8RJt7bi129zzISQ/1guA7wHbtjRFV4u8M+xqWCN2yELcg3ihxgTIArbVRnkGObXI/ePdSqktivWjpc1SPutHY8p6FrLe/3hizYhtzR1uL0E0WD/qDyuqpaYoapFXR5kjMfB8mvUCYdaPxpT1PGS9//ESCkf/+z150B/W1krboqgh2dAhraIWuS9Eu370rcrozrfDwMM7goHLeh6y3v942VAvVURxBnlryO0lQPKL9veBlTVSPbXIfYF3BOB7YGAiC3EP4uXNSldP1quN9a7uGJIf8wB4NmRbZat7NuAVtchjjyzEPYgX5gFQY8K2hnZ37mQ0WD/qD+9VSS1RrB+tbJXW1/V/e9DDehay3v94Ym+xbR0htwYsGvxO4F0gHPbn0vuJEyequLhY7733nhYuXLjD5zs6OjR27FhVVlZq48aNmjZt2k7/noqKCo0bN07f+MY3dMstt0TVlkWLFqmsrCyqa5PRkAtuUvZB53m+LhwKaetlk2LQIsTbiJ8sVeqoqZ6va1v/hqp/e3YMWpR80sJBXRfaP+b/P6nZmTr1hZv04ZLHtfafz+jEh36myg826u3r/uH57/pp8C21B/pnBbf1/ie7jL1PVt5Xl0R1bfXvL1Dbhy/1b4MQd4PP+ZkGHfmVqK4tu2KG1M6qj2Q3/NqnlTZxrufr2gtXqOqGk2LQIsRTICNbo3/7UVTXNr54u+rv/0n/NihJxSMP9WcWksiD5MEeOaddo5zjL4vq2vKrFyjUwI6gZDfsygeVPsP7z3DH1o2q/OnhMWgR4ioQ0Jg/FEV1adOr/1bdv6/u5wYh3tJmHKDhVz4Q1bW1/7xCLW9Gd63fkAfJg8ks75t3KWPOEZ6v66wpVcUP9+v/BiHuRv9uvQLpWZ6va377YW27I7rfJTBwpE6cpxHXPhXVtdv+8wM1v/Kvfm4R4i37yIs05JyfRnVt5S+OUceWtf3couSUbHmwv7NQsvVfIg/2NvLX7ytl8AjP17WseFa1f47uHSMGjpRR0zTyJ69EdW3dg79Q0/N/7ucWId6yDvqchl7wm6iurbrxVLXnL+/nFiHehn7xd8o6wPsawHBnu7Z+y/uaQz/i2Rh5MJkFc8dq1PVvR3VtwxM3q+GJm/u5RYi3zEWnKfcrf4jq2upbzlXbutf6uUWItyHn/VLZh30xqmvLvjVV6qSCaLIb/sPnlTZ+tufr2ja/p+rffCYGLUo+5MHky4NkwR4Z845V3tfviOramj9/Ra0rnu3nFiHeBp/xIw069mtRXbv1e3MVbqrt3wYh7oZd9ajSp+7j+br2LWtV9YtjYtAixFUwVWN+nx/VpU2v/Et1//lB/7YnSSVbFpJYO0ge7JFz8pXKOfnKqK4t/+F+CtWU9nOLEG95l/9HGbMP8XxdZ1WRKn58YAxahHgbfesmBVK9F19rfvMBbfvnFTFoEeIpbcreGn71Y1Fdu+3uq9X82r/7uUXJiTxIHkxmuZf+TZkLjvd8Xai+SuXfXxCDFiHeRv1mlYKDcj1f1/L+U6r9y1f7v0GIq9Sxe2jEj1+I6tq6+3+iphdv7+cWId6yDr1QQz/3q6iurbzhJHUUrujnFiWnZMuD1t8VS+TB3kb+8i2l5I3zfF3rhy+p5vcXxKBFiKeUYRM08hdvRHVt/aM3qvHpW/u5RYi3zP3PUu6XoqvZXfXbs9W+PrrvHwwcQz5/o7IP+XxU15Z9c5IUZj7l2Rh5MJkFcoZp9I3R/V7X8Mwf1PBIdL9PYuDIWHii8i75a1TXVv/hC2pb/WI/twjxNvis6zTo6Oie8279zh4Ktzb2c4sQb8O//4TSJnt/39NetFpVv/L+fsmPyIPJlwfJgj3S9zxcw751d1TX1t7+NbUsf7yfW4R4y/nMVco5Mbr1X+XX7KNQHacNJrth37lf6TMXe76uo3yzKn9yaAxahHgb/ftCBYJBz9c1vf4f1d31vRi0KPkkWxaSWDtIHuwx6LhvavDp10Z1bcV1h6izIr9/G4S4y/vGv5Qx9yjP13Vu26qKa/eNQYsQb6NuXqtgZo7n65rffUzb/vb1GLQI8ZQ6YS+N+EF0dSLq/vtjNb0UXX0KvyEPkgeT2dCL/qSsfb3XjQo116v8u3vGoEWIt5G/elcpQ0d7vq5l1Quq/WN09eowcKSMnKKRP301qmvrH/6VGp+Nrm4lBo6sA8/V0Av/L6prq246Xe2b3unnFiWnZMuD1t8VS+TB3kb85FWljpri+bq2j5ap+nfn9H+DEFfBIaM06obozpVoePJ3anj8pn5uEeItc59TlHtxdOfLVN/2ebWtie5cGwwcgz/7cw064stRXVt2xQypvaWfW5R8eDZGHkxmgcwcjb45uvMmG1/4q+ofiO6cSwwcGXOPUt43ojt3tmbJxWr94Ol+bhHiLee0a5Vz/DejunbrVfMUbqzp5xYh3oZ99yGlT/d+Dn1H2XpV/uzIGLQo+ZAHky8PkgV7pM88UMO+c19U19b+4wq1vPVAP7cI8TboxCs0+DNXRXVtxY8Wq7O6uJ9bhHjL+9bdytjzcM/XddZsUcUPYz//IfZG/26DAumZnq9rfushbfvHt2LQouSTbFlIYu0gebBH9lEXa8jZP4nq2sqfH6WO0o/6t0GIu9xL/qrMhSd6vi7UWKvyq+bGoEWIt1E3rlAwZ5jn61o+eEa1Sy6KQYsQTyljZmjk/3spqmvrHvy5mp5f0r8NSlLkQfJgMhv6xVuUdcBZnq8Lt7dq6xXTY9AixNvIny9TyvCJnq9rXbtUNbd+LgYtQjwF88Zq1C/fjura+sf/T41P/rafW4R4y9zvDOV++baorq3+3WfV9tHr/dyi5JRsedD6u2KJPNjb8B+9oLRxe3i+rm3zclX/5tQYtAjxFMjO1eibVkV1bcNzf1LDQ7/s5xYNfGPGjNE770S3lzK1n9syYDQ2umLUzc3NO/38vffeq8rKSg0ePFhTp07d5d9zzz33qKOjQxdeeGHUbSkrK1NJSUnU1yebYHWFsqO4LtTaZOo++dmQhm1KHeX9uua6Gr4HuqQHUiTvNTY82+8nX1BDYbnW/sMtvH/1it/r1OdvUuFTb2rrG2s8/V1bSreoLdzZL+2y3v9kN2RMsfKivLa8OF+NjANJb3x1uQZFcV04FFJJwSYpHO73NiG+chq2KS2K61rqa8kCfhAMRj2N11Vt1Ra+ByTFJw/1ZxaSyIPkwR5jKsrkvXywU5K/QaHWpn5tD+Ivq75G6VFc10oW8I1Rrc0KZmR5vq6+upzvAR/IzirQ8CivrSotVi3fA5LIg+TB5JZRV62MKK5rb6pjHvCJ4c0NSkv3ngUaayv5HvCBDOVoRJTXVpcVq5rvgaQ3orxUQ6K8trRgo9rK+R6Qki8P9ncWSrb+S+TB3vKaGpQy2Pts0FRbRRbwgbTWgEZGeW1t+RZV8D2Q9PK2lmholNeWFW9WC98DSS+lukLefyOUOpsbmAe68GyMPJjMUrY1KYrlw5Kk2opSbWUcSHpDxxcrN8pry4sLWD/qB1XR7SUJd7SrpDC/v1uDBBjcGO36UfaSdCMPJl8eJAv2GDy8MOq9JBUlBapnHEh6Y6u2RrWXRJJKNq9XuKOtX9uD+Muur41q/WhbwzaygE+M7mhXINV7IqyrYv1ot2TLQhJrB8mDPUZXlEa9l2RL/kZ1NnDAWLLLjHL9KFnAP0a0NCk1x3sibGAviS9kpuZFv5ekrFg1fA9IIg+SB5Nb+rZqeT9aSmpvqmce8IlhzQ1KH5Tr+brGGvaS+EF6e1rUe0lqtm5RJd8DSW/Yp1k/WrhJrXwPSEq+PGj9XbFEHuwtt6leKVG8MG7aVk0W8IHUhrbo95JUlKqc74Gkl1v2adaP5quJ74GkF6guj7IWebNKiov6vT3JiGdj5MFkFsyoifqfbltFqcqYB5LekFFFn2r9aAPfA0lvXJS1yCWpOH+DFOKQrmQ3KMpa5K0N1B/tRh5MvjxIFuwxaEiBhkV5bcWWItUxDiS9MZVbP0Ut8vUKNTf0a3sQf1l10dUiZ/2of4xsbVJKlveRoKG6gu+BLsmWhSTWDpIHe4ysKNXgKK/dUrBRHTVl/doexF/Gtqooa5GzftQvhjc3KJjpPQs01pAF/CAjnP2papFX8T0giTxIHkxuqbVV1B81Lq+pQSlDvf8QU4vcH9KaQ1GvH62hFrkv5JUVR72XZGvRZjXzPSAp+fKg9XfFEnmwtyFNdUqN4rrmOvaS+EGwpj76WuSVZdQi94GhY6lFbt34qujWj4Y7O1VSsEkKh/u9TcmGZ2PkwaQWTIn6n66uskxbmAeSXs6wT7GXZEshtch9YGzVp1k/ukHhtpZ+bQ/iL9pa5K2sH/0YeTD58iBZsEd2Vn7Ue0mqSotUyziQ9EaVR79+tCR/gzrrq/q1PYi/zG011CI3bkRLo1LTvVehbWD96MeSLQtJrB0kD/YYUV6qIVFeW1q4SW3ljAPJLq22Kqpa5B3N7CXxi7ymemXkeP/NkPWj/pDelhL9+tGtJdQi70IeJA8ms5Sayuj2krQ0Mg/4xNCmemVFcUhVM7XIfSGlrjnq9aPbKkpZP+oDQycURb1+dGtJAbXIuyRbHrT+rlgiD/Y2uLEuqvqjLewl8YVAWmX060crylTK94An0ezbSgpjxoxRTU2Nli9frgMPPHC7z5WWluqqq66SJM2fP1+BQGCXf8+dd96pPffcU4sWLfpUbbEkvXJDVNd1FH6g8ePH93NrkBBbVkvTFnq+LKVsLd8DXdLCQSnGNfXHH7W3pp56sB45+rsf/1l9wVa9+8u7dfBvv6lHj/quOppbI/77xo0dp/ZA/zTaev+TXbC5TOHODgVSvMWMcFuz8tprlMs4kPQyqjZGdV17wQcaP25cP7cGiRDY8qG058GerwuWfkgW8In2gg+UNnmB5+syqzbyPdAl1nmov7OQRB4kD/ZIr9kkSQqHw30+c/mk9i3rNHZEnhT1tmIMFCmla6S9j/d8XWDLauYBn+go/EDpMxd7vi6jcgPfAz4QCNUp1FyvYJa3bYHhUEhDGko0iO8BSeRB8mByS926LqrrwsWrmAd8orNohdKGHu35urTy9XwP+EFqm0L1VQoOjnwFcPfvjzl1hcrieyDppW7Ll+T9uUDntq0amRGW+B6QlHx5sL+zULL1XyIP9hYqXimNnuL5utTydWQBPwgG1VlTqpS8sRFf0j1nZNfm8z3gAyl1RZK8Z4FQY62GB5rJAj6QXrE+qus6WT/6MZ6NkQeTXUf5ZqWOmhrx13fPGVk1mxkHfCDYtEXhUKcCwRRP14VaG5XXuY31oz6QWRXdXpL2gvcZA/xiy4fSrAM8XxYsXcP3QBfyYPLlQbJgj0BLucLtrQqkeSsjHG5v1dDWCg1hHEh6GdVR7iUpWq1xo6MtN4eBJFi6Rpp/lPcLt7CXxC/aC95X+vT9PF+XWcX60W7JloUk1g6SB3uk1WyW5P09UUf5Zo3JHSQNzY5V0xAnKWVrJZ3q+bpwCetH/aKz6AOl7nm45+vSK1g/6guBZoUaaxUclBvxJd1zxuD6ImXzPSCJPEgeTG6p5dHtJQkVr2Qe8IlQ8UppxATP16VVfMT3gB8Ew+qsLVNKbuT1FruzwKBt+crgeyDppdQVSopi/Wh9lUaktrF+tEuy5UHr74ol8mBv4eJV0vg9PF+XupW9JL4QCKijskCpIyZHfMnHe0lYP+oLKQ0lCodCCgSDnq4LNddrWKheeXwPJL0MapF/ajwbIw8mu/Yt65Q2LvI82LOXZBPjgA8EW7Yq3NmuQIq344XCbS3KbavSUL4Hkt6nqkU+NvI96Ri4gqUfSnMO83xdgPWjHyMPJl8eJAv2CLRXK9zWrEC6t6Onw50dGtpcpsGMA0kvvTq6WuQdZes1dthQSUNj1DLEi1s/eqL3C6lF7hsdhR8oZQ/vZ9OkU4v8Y8mWhSTWDpIHe6TV5EuKov5oVbFGD0qTshkHkl0KtcjN6yxaobQ872c1s37UJ1La1VlfqZTBIyK+5ONa5NsKlcn3gCTyIHkwuaVVfBTVdZ1FK5gHfCJUvFIaO93zddQi94lAUJ1VxUoZHvl+oo/3ktTmK53vgaSXUl8sKYq9JE3bNEyN7CXpkmx50Pq7Yok8uJ2S1dKU+Z4vC5atJQv4REfZBqWOmRHx13+8l6Sa9aN+EGwqVbizQ4GUVE/XhdualddRQy1yH4h6/WjhBxo/blw/tyY58WyMPJjs2gtXKG2S9zyYWb2RLOADgdYKhdtbFEjL9HRduKNNuS3l1CL3ge71o161l6zVuJGRn3WMgStYtkbSsZ6vC7B+9GPkweTLg2TBHoFQnUItDQpm5ni6LhwKaXBjiQYxDiS9tNooa5FX5GvM0CxpCN8DyS5l69roLqQWuW90Fq1Q6pwjPV9HLfIeyZaFJNYOkgd7pNbmS4piL0ltmUZmiDUjPpBWHt360VARtcj9Ily8UhoVee3Jbqwf9Ymgoq9FXkst8m7kQfJgMkuvXB/VdZ2F7CXxjeJV0sS9PF+WspX1o74QCKijPF+po6ZEfElP/VFqkftBsLFE4VCnAsEUT9eFWhs1rHMbtci7JFsetP6uWCIPbmfLammm97PLg6VrmAd8or1kjdLG7+n5ukyje0nGjPFek6VbIBwOh/uxLQPG5Zdfrttuu00TJ07U888/r1mzZkmS3n77bV144YXatGmT2tvb9c1vflO///3vd/p3rF27Vnvuuaeuv/56XXvttfFsflJr7ZROek7a1ubtuhsWScewD8QX1m6TLnjZ2zUpAenRY6TR3uoO+1Z7U4vunn5Bopvhyfkb71JatreF/7tivf9+8P23pRdKvV1z6iTp/y2MSXMQZx0h6TPPSxUt3q67bqH0mUkxaRLiLL9BOvtF79c9dJQ00du6cQxQjxRKP3/f2zWjs6RHjpZSvZ1P6VvW85D1/ie7UFg68wWpuMnbdVfPkz47NTZtQnyVNUunPuf9Hc09h0szOVPGF54tkX7wrrdr8tKlJ46V0r29H8YA9esV0n353q45dLT02wNi0pykZD0PWe9/sqtulU56Vurw+Aby74dI84fFpk2Ir6Vl0nfe8nbNoFTpqeOkbG+1BjFA3fqh9K8N3q7Zd7i0xPu5dBigvrJUWlHj7ZqLZ0lfmx2b9iSjZMtD/Z2Fkq3/Enmwt3cqpa+97u2ajKDLAkPSY9MmxNft66Q/ezxrcK9c6V+HxaQ5SIBvvC69VentmvOnSd+ZG5v2IL5aOqQTn5Pq271dd9N+0hFjY9OmZEMW4h4ku7s2Sr9b7e2aaYOle4+QPNQWwgD2vbekl8q8XXPmZOkHC2LTHsRXe0g65TmpytueSv18H+nEyM8mxAC2sU469yVv1wQkPXy0NH5QLFqUfMhCyXcPyILb+/G70lMl3q45cbz0831j0x7EV2dYOuMFaYvH9aPXzpfOmhKTJiHOShql01+QvG5av/cIafqQWLQI8fZUsfTj5d6uGZ4hPX6slMZeEknJl4Uk1g6SB3uEw+53wk313q779hzpgukxaRLirLJFOvk5lwu9+Oeh0py82LQJ8fVSqfS9t71dMzhNeupYKZP1o77w21XS3R7Pm9x/hPTHg2LTnmRkPQ9Z73+yq2uTTnxWavW4sfTPB0mLRsSmTYivN8qly97wdk1mivT0cVJOWmzahPj681rpdo/nC83Pk/5+aGzag/i79DXp3Spv13xhhnS59zMIfCvZ8pD1d8USebC3FdXSV171dk1a0NWYGJYRmzYhvv65XrptjbdrZg6R/n0460f94ttvSq9u9XbNOVOk78+PSXMQZ22d7h1Bjcda5NfvKx1n7yyBnSILcQ+S3X83Szeu9HbNhGzpwaOlIFnAF659R3pui7drTpko/WTv2LQH8dURkk57Qdra7O26Hy+UTqMWuS8UNUhnRFGL/P6jpCnUIpdEFpKS7x6QBbf3s/elRwu9XXP0WOnX3s8mxAAUCrszKQobvV33vbnSedNi0ybE19Zm6dTnva8fvetwaTa1yH3h+S3SNe94u2ZouvTksVIGtcglJV8Wklg7SB7sEQ5L578sfVTn7bpv7il9eWZs2oT4qm11Z9a2eVw/evvB0sLhsWkT4uu1rdIVb3q7JjtFeup4V5Mcye8Pa6Q71nu7Zu9h0l8PiU17kpH1PGS9/8muod2dS9LU6e262xZLB46KTZsQX8urpEte83ZNelB68jgpl1rkvvC3j6Q/rfV2zeyh0p2HsX7UL761TFpW4e2az02Tvkst8o8lWx6y/q5YIg/2trZWuuAVb9ekBKTHjpFGZcWkSYizezZJ/7fK2zWTc6T7jyQL+MXVb0svlnq75vRJ0o8WxqQ5iLOOrlrklR5rkf9kb7eOGGQhiXuQ7B4ukH7xgbdrxmRJjxzjciGS33XLpSeKvV1z3Hi3rxDJL9RVi7zEYy3ya+ZJZ0+NTZsQX6VN0mnPSx6XjOg/R0gzqEUuiSwkJd89IAtu74YV0v353q45dLT02wNi0hzEWTgsnfeStNFjLfLL93J1x5D8qrpqkXd43EtyxyHSvGGxaRPi65Uy6cq3vF2Tk+rOr89i/aik5MtCEmsHyYPb+/JSaWWNt2u+Oku6dHZs2oP4qm93tchbPK4f/eOB0v4jY9MmxNfbFdLXl3m7JiPossAQ1o/6wl/WuQ8v5uRK/zwsJs1JStbzkPX+J7uWDumEZ6WGDm/X/d/+0uFjYtMmxNeqGulLS71dkxpw55WO4MfIF+7cIN3yobdrpg125xazftQfrnzLPSP04qzJ0rULYtOeZJRsecj6u2KJPNjbhjr3rtCLgNzawXHZsWgR4u3+fLdmwIvx2dJD1CL3LJjoBsTK1VdfreHDh6uoqEhz5szRvHnzNHPmTO2///6aNm2ajjrqKEnSggW7Tg933nmnAoGAzj///Hg12xcyUlww82JslnQEv9D7xuyh0j4eC38dPU4azYZQwDc+57EYdFDSZ9kA4BupQelcj/+ewzOkYzlczDem5EgHeyz8dehoaSIHivjG8ePcz7UX50514weA5BcMeD8cZGiadNKE2LQH8TcmSzpqnLdrFo2QZnKgiG8cOdZ9H3hx9hQpnQNFfOOzU70X++BgKcA/hmVIJ3rMdnNypXl5MWkOEuCg0dKkQd6uOWOylM2GUN84a7IrDu+F1/cKGNi8/ntmBN04AMAf9h0uzfJY+OuUiWwI9ZPTJ0uZHp/zfJ4s4Cufn+7t69OC7vkg/CEzVTrTY7Ybny0dMjo27QEQf5+Z6AqAeXHeVDaE+onXdz4pAdaP+klaUDrH47/nqEzp6LGxaQ/ib/oQ6QCPhb+OGCON9/heAcDAde40t9k7UoGua+APKQHve0ly072vMcDANX6Q933CB4x0GQL+cPRYl/G9OGeq+10CQPILBNyzPi8GpbpnivCHEZnu0EAv5udJc1g/6huHjJYmeCz8deZk944R/nD2FO/ZzusaAwAD15B074fIzxri1hzCH/YfKU0f7O2aUydJOWmxaQ/i74zJbm+AF+wl8Rev/57pQe/1KgEMXPPy3F5hL04Y7/Ykwx9Om+R9n/DnprF+1E+8ZoGUgPf1hhi40lO87w0aneVqFAHwh5MmuFqCXpw3jQNF/OQ8j+tHg/K+3hADVzS1yIelu9rF8IeJOa62uBcHj3I1zAH4w7lTvR9+Rv1R/wgGvD8bGpwmncz6Ud8YneXOHPNin+HubDP4wxFjvB8ce/YUd8YhgOQXCHivIZed4t4vwh9yo6hFvudQacGw2LQH8XdgFM95Tpvs9pXBH86kFjlgWk6aG9e9mJrjvUYVBq69h3l/znPyRFdvBv5w+iQpy+NzHtaP+svnPO4TTg1IZ02JSVMAJMDsXJcHvDhmnDTK4xmnGLhOmeje/3pBFvAXr895gtQi95XUoPd/zxEZ0rGsHwV84/jxbl24F+dGcd45Bq5zp3lbPxqQ93qVGLiiWT86NE06kfWjvjE22/s+4f1HSDOoRQ74xjlTvGc71oz4RyCKLJCdKp1KFvCN4ZnSCR7Xj87Ncx/wh4NHS5M8njl3xmQpi/WjgG94zQIZQTcOwB8Gp3k/Z2bGYGm/EbFpD+Jv0QhppsfnPJ+Z5OrYwx/OmCxlRrF+FIA/ZKZKZ07xds3EQe5cK/jDnFx37pwXx49359vBHz4zyfs+4c+zftRXPudx/Qe1yAF/mTHErQHx4oix3mtUYeA6MYpa5OdOpRZ5NLzW+EwaEyZM0NKlS3XyyScrMzNT+fn5GjZsmJYsWaInnnhCH330kSRpwYIFO70+HA7r7rvv1hFHHKFJk6hk5dUle0gHRljwISdV+u0BbgMJ/OOX+0pjI9zkOWOw9IP5sW0PgPhaOFy6Yq/Iv/778ykg7DcXzpCOHBPZ12alSDfv7/3FIAa2n+wd+eK/yTnSdXvHtj2Ir8xU6f88/FwfNVY632NxIQAD22enuoOkI5EelG7a3xWchH/8cIE0fXBkXzsuW/rFPrFtD+IrLegyfqQv/A8aJV00K7ZtQnxNHSz9eOeP3nfqa7MpIAz4zVXzpL1yI/vakZnSrxex6MtPUgIuCwyJMOPvO1z6xuzYtgnxNX6Q9LN9Il+M8MUZbtEP/OPY8ZFv7gkG3Lvl0RQQBnwjEJBu3E8alhHZ18/Lk74zJ7ZtQnyNyJRuWBR5wajPTnWbgeAfh4yWvjIzsq8NSLpuoTTR48GEGNi+NjvyjQCD09zvkKwfBfxjaLr0m/3c+6JIfGYihYL8ZtEI6bI9I//6a+dTQNhvvjRDOjTCgg/ZXetH01k/6is/21uaEOEmz6k50o8WxrQ5AOJsbp703bmRf/1351JA2G8+Ny3yA8Mygm7NeTYFhH3lRwvdHB+JCdkuO8A/0rsyfqQ/14eOdr9DAPCPMyZHXkg6LeieJQ6lgLCvfH+eNCvC532jM6VfLYptexBfqV0ZP9L1o/uPcO8W4R8Tc9wagEiXhX9lJgWEAb/5zpzIC0kPy3BrDdlL4h/BgHTTflJehBl/wTDpcg+1iTDwjc5yewMiLQb6uWlu7wH844ixbo9QJIKSfrq324MEwB8CAbdXeFSEh4TsOdTtRYZ/5GZINy6SUiPMAl6eJyM5HDDS2/O+Hy+QpkVYmwjJ4aJZroZQJAalunfLka45BzDw5aS5WoLpEf5cnzDe7S2FfywYJn3bw37xq+dJe+bGrDlIgPOnu9rCkchMkW4+wNUuhn9ct7erMR6JSYNc7XIA/rHHUHfWSKQu30vae3js2oP4O3uKdNKEyL42PejeLQ+mFrmv/GC+NDPC9aNjut4twz9Su2qR50SY8Q8cKX2VWuSAr5w8UTorwtoxKQHpBg91CpEcvjc38lrkIzKkX7N+1FeCAfeOINLDx/cZ7q02EQa+sdnSzz3UIr9wunRUhPUIACSHy/Z043skctPdvBHpmnMMfN21yEdEmPHn5EpXUovcV4Z37RePtBa5l+fJSA4HjYr8eV9A0v9bKE2hFjngK9cvcu9/IjFriKtDDP8YnObqB0W6fvQUD8+TkRz2Hu5tv/i186VZQ2PXHsTfF2dKR4yJ7GuzutaPZlCLHPCNrFRXcywzwp/ro8e6defwj71ype952C/+7TnS/GExaw4S4LNT3T6xSKR31SkcxF4SX/mhh/3i47PdOdcA/GP6EOlHCyL/+q/PlvYfGbv2IP5OmySdPimyr00NuLo0uawf9ZWr5kmzI3zeN6rrnGvWj/pHSsBbLfL9Rri5AIB/HDvO1ZWNRDDg3i2PivDdMpLDFV5qkXetHyUL+Ecg4NYLDIuwFvn8POnb1CL3lZGZ0vX7Rr5+9Nyp0vHUIgd85euz3blTkRiS5n6HjHTMwMAXCLh9BKMjrEW+x1DpataP+kpuusuDkdYVPnWSe6YM/9hvpPQND8/7frBAmhFhbSIAyeHn+7i1IJGYNtidSQD/GNS1fjQjwixw3HjpvAifJ2N7vj7GYc8999Tjjz+u+vp61dfX680339Qll1yixsZG5efnKxgMau7cuTu99pVXXlFBQYEuvPDCOLfaH7oPnv7MxL4PHp40SLr9EIKcH43MlP52iDRvNy96Dhol/eVgdwgFAH+5cIZ0zXy34W9XBqW6g0bPmhK3ZiFOUroe8J41ue/AOS7bzQNzIlwYgOSRl+Fy3u6Khi0aId1+sHsgDH+Zm+d+vsf18XAnKOmcKd4WBgBIDsGA9NN93ALQvn6+R2dKfzqIQ2X8aHCamwcW72aT1/w89/xgRIQLA5A8Zg11/7aTBu36awJyzw9v2s89T4S/nDJJ+tW+fR8alRGUvjtXuphDZQDfyU6V/nigdOjovr9uz6HS3w+RxkS4MADJY8pg92+7u0Ihx4+XblkspVMwzneOGdd1uEwfz/3Sg+7gAQ6V8acr50hf26PvRcC56dJv95eOiPCQcgDJY8IglwVm7WY90JFjpT8cKGVSMM53Dhkt3XJA35uDUwPu4Imr5rIx3I++PtsVE+9rEfCQNHcI0QkcKuM7aUHptwfs/sCgKTnuOfJ01o8CvrPfSJfzRvbxDjAl4A4a/fFCsoAffWmmdPW8vgvKD0qVfrGPdDqHyvhOatDlvDMm972XZEK29NdDpNm58WoZ4mV4pls/unA3hwTsP0L668F9P0cGkJzOm+ZyXnYfWSA7xRUcZ1Ow/wQDrkDAuVP73ksyJktacrC0gENlfGdoupvjd1c0bOEwlxmGs37Ud2bnuu+BCX2sBwrI/c5wI+tHAd8JBFwWvHB633tJRma6Z4gcKuM/OWku5x2ym/Wje+VKfztUGk0hed+ZPsTlvCk5fX/diRPcO8VIi0wieZwwweW8vg6XyQi6NQUcKgP4T2aq9PsD3drAvswa4tYYTuhj7yGS08Qc9287Yzd7SY4ZJ/1+ceSHlCN5HDHW7RHI6+MdYFpQunQPt+cA/tO9Tyi9j6w/NE36zf7SsRwqA/jOmGyXBfbK7fvrDh3tas1ks5fEdxaPcr8TDO/jMOmUgPTlmdK181k/6kcXz3I1RPraSzI4zdUiOYVDZXwnNehqCO2uFvnEQa725B4RHlIOIHnsPVz680F9HzSXEnC1CX+6j1trBn85f7r0wwV9rx8dlCr9ZKF09tS4NQtxkhJwtYXPmRJZLfK51CL3ndx0l/MW7Wb96D7D3bqCvD5+dwSQnM6a4s4cGdTHc7+sFHd2yRdmxK1ZiJNAQLpub+n83dQiH5XpalTuG+Eh5UgeOWnSkoPcGWR9mZfn3iX0VYcAyWlG1/rR3dUiP2WiO9OQvSSAvwQC0vfnS1+Z6WrL7crwDOm2xbufL5B8srpqkR8+pu+v22Oo9PdD+z7HCslpSo77t91dLfLjxku3HiBlsH7Ud44eJ918wO5rkXfXqATgLxkpbnw/dlzfXze96+yKybvZe4jkMy7bZYHdrQc6Yoz0x4NcfoS/HDJaunVx3+tHUwPSRbNcjUrWj/rPJXtI356z+1rkNyySTpoYv3YBiI+RmS7nzdvNeqCDRrk1hjl91CFAclo0wj0fHEUtcrO+MEO6Zp5bF7Irg1Kln+3tas/BX1ICLueduZta5OOzXX3C3e09BJB85g1z64bG9lFPLii3zvyX+7KXxI8+O1W6bmHf60ezU9x+k/Onx61ZiJNgwO0T+9xu1o+OznS/Ey4cHr+2IT6GdNUiX7yb2rLz89w5dSNYPwr4zmcmuX2FOX1kgYyg9L257l0R/CUQkH6wQPrSjL6zwIgMV49mMetHfWdQqst5h0ZQi/zvh7gzauAvU7vWA03dzXqgE8a7WuTprB8FfCUQcHVlL9mj77MG8tKlWw7Y/X4DJJ/MFJfzjt5NLfKZQ9waQ2qR+8+EQe7fdtaQvr/uqLHueyWT9aO+c9gYN8YP200t8q/Ocs8GWDMC+Eta0O0nO3FC3183Jce9I9jd3kMkn9FZ7vy53a0HOmS0W1fQ1ztlJKf9R7ozZ0bsphb5F2a488vJAv7zla6c19e5Qzmp0i/2kU6jFjngO8MzXc5bOKzvr9t/hKtBO6SP3x2RnBYOd+cW9/UOMCjpvKluLwHrR6NjMkavXr1a4XBYs2bNUnb2zqsU3XnnncrKytLZZ58d59b5R3qKKyB60SzpgXzpla1SYYMUlisQc9P+bmEgP7z+NSrLvfBfWSPdly+tqJYaO9wv8AeMlM6eIs3iQBnA186e4hb1PFEsPVEkbW124/6YLHfw1AkTOGDQz1KD0rULpC/NlB4skP5XKhX0ygK/XiQdNLrvxaFIbsMy3C/sq2uk+/PdWBAKu3HglIlujGBDsL/tlSs9dLT06lb3O8Gmeqm10x06c+RYt3F8DIVjAd9KCbgDZy+cLj1UIL1QKtW0uucFU3PcwTOHjuYgAT8bmu4W9a3b5rLAWxXSliaXBzOCbqPI3Dxe8vrZjCHS/UdJy8rd98BrW6WQ3PhwwXSXBcaz8NfXjh3vxvpntkiPFEilze53gpGZ0skTpZMn8IIP8LOcNLfZb0Od+51wWbnU0OGeB87Nkz47RVowjCzgZ1MGS/85Qnq7Urp/s/RyWU8WOG+q+51gEkXkfe2wMdKTx0rPb5EeLpA+qHbfA6kB6Zt7uiISuWQB3woEpIv3cD/rjxRKz5RI1a1uDJgwyBUMPXosm8IBP5swSLr7cGl5lXsusLpWauqQBqe5AuJnT3GFJOBfi0dJjx8rvVgqPVwoLa/syYNfm+0W/w/rY6MIkluga6PPaZOkx4qkp4ql9dvc90BawBUNPWZ83xtFkNwyUqSf7eM2fz9Y4H4nrG93/+azhrriwfuzfhTwtX2GS48dI71UJj2Y79aNtYek3AzpuHHS6ZMpHOt3n50qnTRBerzIrR9dt63n2dA186Xjx3PAoJ+lBV3m+/JMt2bkk+tHb9xPOnAU60f9bESmKya+utY9F3ivqmcvyX4j3HOB2bmJbSOA2DptknTMOOmpIvdsYGuz+/PRXXtJTpxIgRg/Sw1KV82TLpzh3hO+sEXK75UFrt/XFQpi/ah/5Wa4Q8XX1ros8HZlz/rRzKD054OlObmsGfGzPYZKDxzt1gs9kC+tr3N7SYakS0eOkc6c4g6pB+BPwYB0xRx3gNzDBdJzW9xekrSgNDnHjQFHjCEL+NngNOl3XetH78+X3iiXSnrtJfnDgawf9btpg6X/Hun2Ed2XLy3ttX7089PcXpKJrB/1tSPHumfAz5e4NSNbmqTOsHtueOIE92xgKOtHAd/KTpV+s5+rK/BAvvR6uVszkp3qag+cM8W9TyYL+NfEHOmeI6R3q6T7Nkv/K3N7ClMC7j3yWZPdfhP418GjpSe61o8+WCC9X9WzXqB7/Wge60d9KxBwtaZOnyw9VujWj1Z17SUZn+3+/Jhxbo0hAH8aky3981C3l/C/+dKqGreXJCfV/a541hRXhwL+tWiE20vyUqlbO/ZOr70kl+zhsgDrR/3tc9NcLZHu+qMf9dpLcs0C6fhxHDDoZ921yC/u2kvyUplU1+by38whbu3ggaPYSwL42fxh0iPHSEu76o9ubpDaOt2zgKPHuhoDo/o4dATJ74zJ0rHj3DOBx4rcGrLuZ0NXzXPviqhF7l+pQen786UvzpAeKpRe/MT60RsWueeH7CXxr7wMV2P0w1q3ZuTdyp69JPsMd3lwTl6CGwkgpk6e6NaNPN2VBcq66o+OzuqpP5qTluhWIlZSAtJ35natHy10tec217sskBaUfrmPq0vH+lH/GpIu3brYPRO8P1968xO1yP90kDSPWuS+1l2L/I0K9z2wfpvU0rWX5LDR7j3RBGqRA74VDEjf2FM6d6qrP/psV/3RtKCrPXzGZJcV08gCvpWTJv3f/tLGOumBAre3sLixJwvcdqC0N3tJfG1yjnTvEW5P8X350sulPesFzu2qRT6ZvSS+dshoV4v8hS3u98LiRreXZHiGO6v01IluDzoAf8pMlX61SLq0wb0rfm1rT/3RPXPdXpJFI8gCfjYuW7rrMOm9areX5IXSnr0kZ01x74mmsZfE1w4Y2asWeYF7V9idBy/tWj86nPWjvhXoOo/wMxNd/dGniqWKFve8YFy2+/c/jlrkgK+NypL+foi0ssZlgRU1PWtGDhjpssCsoYluJWJp4XDp0WPcWQQPFkhvV/RkgYtnuX2FI8kCvnb2VPcMqHsvSe/1o9+fL51ALXJfSw1KP+iqRf5gVy3ybV17SaYPdvPAQawfBXxtTp700NHSa131RzfWu70kuenSUePc++Ix7CXxtc9Mcv/W3etHP6zpyQLfnev2krB+1L9Suv6dL+iqP/pCac/60fSg9Mt9pUOpRe5rQ9Ol3x8ord3m5oG3KnqeCywY5mpOUYsc8Lfjxrux/pkS6dFCqbRrL8nITLeX5JSJrlYx/CkYkC7bSzpvmlsz9FyJq0PZvZfk5/tQi9zvctKk33bVIn8g360fpRa5LVMGS/d21SK/P9+dVdncKQ1Jc/PDWVPcenIA/hToqi13zhS3l+SZrr0kKQG3j+zMydJRY11dKvhTdqr06/3c86AH8t1z4t57SW5ZLO1LLXJfmzBIuvtwV4v8/ny3hrB7/eg5U1wWmMr6UV9bPKpn/ehDBW4M6AhLwzLcueWnTnL/HYA/Zaa45z+XzHL7Sl8p69lLssdQt2Zk/5HUH/WzMVk9tcjvy3dnFXZngTMmu+8BapH7277dtcjLpAfzt69F/tWu9aPUIve386a590FPFLmP8q69JGOzXBY8nvWjgK+NyJT+erC0utbtJXm/umfNyH4jXBaYnZvgRiKm5uZJDx8tvdpVi/zNXntJvtJ1dtVo1o9+Kian0ZUrV0qSFixYsNPPt7S06P7779fpp5+uwYN5+vhpTRjkDqG/Yo500rMu0OWmSweNSnTLEA+BgDtYYv6wRLcEQKLkpLkCcedOTXRLkChjs6Vv7uk+emeBQ8ckumWIlzl57uPNCvfvPyJD+n8LE90qxEtKQDp8jPsAYNOoLOnS2e4DNu0xVPph12O47jw4NF2ax7MCE4IBd5jgwaN7/v2HZ0jf2ivRLUO8ZKa6IpGnTUp0SwAkyowhrkAcbAoGXKHQA0ZunwW+MzfRLUO8ZKR0HSw6sed7YFiGdOGMRLcM8ZKXIX1ppvsAYE8g4DYD7Dsi0S1BoqSnuELSJ0zYPg9+mXnBjKHprnjkBdN7vgfyMqRTeFZkxsScnvWjAOxJDUrHjHMfsCknzW0OPW/a9s+GTp+c6JYhXsbtYv3oIaMT3TLEQyDgNgbOzUt0SwAkyqBUd9DY2ewlMWtMlvS12e6jdxY4YmyiW4Z4mZ0r/Wih++/d3wND0skHVqQEXPYn/wN2jciULt7DfcCmGUOka7rWj/beS7JweGLbhfgIBlwx6cWjtl8vwHtDOzJT3NoA1gcAdk0bLF01L9GtQKIEAtKiEe6jdxb4LntJzNjZ+tFhGewrsCQ33e0dYv8QYFMg4J4B8RzIrrSgdOx499E7D140K9EtQ7wMSZc+N8199N5LQt0RO8YPcrWFqC8E2JQalI4c6z5gU06adM5U99H72dBZUxLdMsTLmGzp67PdR+/1o4dRl9iMvXKpPQ5Ylp0qnTnFfcCmUVnSJXu4j4+fDaVLR7Hf3IxZQ6Uf7KQWOeeW2RA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mC9kHAEDeZCH7AADIW+5ZKPf5AwDknodynz8AQO55KPf5AwB5k4XsAwAgb7KQfQAA5C33LJT7/AEAcs9Duc8fACD3PJT7/AGAvMlC9gEAkDdZyD4AAPKWexbKff5AXloGegAA9L01982PJy6++al/n/e9X8QZN58Xh7zlxXHX538U29ZsGMDR9b/c5w8A5E0Wsg8AgLzJQvYBAJC33LNQ7vMHAMg9D+U+fwCA3PNQ7vMHAPImC9kHAEDeZCH7AADIW+5ZKPf5AwDknodynz8AQO55KPf5AwB5k4XsAwAgb7KQfQAA5C33LJT7/AEAcs9Duc8fACD3PJT7/AGAvMlC9gEAkDdZyD4AAPKWexbKff5AXpoHegAA9L+urdti1V2PRlNzc4zdf5+BHk7d5T5/ACBvspB9AADkTRayDwCAvOWehXKfPwBA7nko9/kDAOSeh3KfPwCQN1nIPgAA8iYL2QcAQN5yz0K5zx8AIPc8lPv8AQByz0O5zx8AyJssZB8AAHmThewDACBvuWeh3OcPAJB7Hsp9/gAAueeh3OcPAORNFrIPAIC8yUL2AQCQt9yzUO7zB8qteaAHAEB9jJmdguy2tk0DPJKBkfv8AYC8yUL2AQCQN1nIPgAA8pZ7Fsp9/gAAueeh3OcPAJB7Hsp9/gBA3mQh+wAAyJssZB8AAHnLPQvlPn8AgNzzUO7zBwDIPQ/lPn8AIG+ykH0AAORNFrIPAIC85Z6Fcp8/AEDueSj3+QMA5J6Hcp8/AJA3Wcg+AADyJgvZBwBA3nLPQrnPHyivloEeAAB9r2XEsBg+cUxEU1OMmDI+Dn37aTHpqANi1V2PxoYnlg308Ppd7vMHAPImC9kHAEDeZCH7AADIW+5ZKPf5AwDknodynz8AQO55KPf5AwB5k4XsAwAgb7KQfQAA5C33LJT7/AEAcs9Duc8fACD3PJT7/AGAvMlC9gEAkDdZyD4AAPKWexbKff4AALnnodznDwCQex7Kff4AQN5kIfsAAMibLGQfAAB5yz0L5T5/IC8tAz2A/rZ69er44he/GJdcckksXrw4pkyZEq973evis5/9bJx99tnxX//1X3H++efHBz7wgYEeaql1dEfcty5iW3f69+7KwI6H+mvriHi4LaK9O2JkS8QREyJGlf4MNHCe+fE3xzM//uZd/mzBVb+N2z717QEaUX3lPv/BqFKJmLchYuXWiKaImD4y4sD0OxeZ6OyJuF8WyNrGzpQJI9L/buyMGDN0YMdEfS3bEjF/U/r8xw2LOGpCREvzQI+qnGQh+2Aw6urNAm0dEUObI2aPjthv1ECPinra1BnxYNuOPNgjD2ZnxdYdn39HT8oEw4YM7Jior8c3RCzbmn7+p7RGHDbOfYH+IgvZB4PR1q6UBzd1RYwYEnHouIgJwwd6VNTT6vadskB3+v/DZYGsLNi44xjo7EmZoFkWyEZPJf1OuGZbxJCmiJmjIvYfPdCjKq/cs1Du8x+s1m6LeGR9qhkZPTTiyPERrWpGsrJwkyyQs0pl1/vDXT0DOhwGwLbudF9gY2f6XfCgsekeIf0j9zyU+/wHq8WbI57clHLAhOERR05Ivx+Sh0olYt56WSBn6kfZ0BHx0Pr0vGhES8QR49P9AfpH7nko9/kPRpVKxKMbUu1QRMQ+IyIOHqtmJCddu2cBeTA7mzojHmhTP5qz5VsjntiY6kXG9q4lGWotSb+QheyDwai7krLAum3pZ3//0REzrCXJypauiAfWyQI5W9W+61oS9aP5mb8xYsmWdE2YPDziGePdF+gvspB9MBi1d0Xc35buD7QOiThkXMREa0mysma3tSTtXepHc/Ok+tGs7b6WZMbIiNljBnpU5ZV7Fsp9/oPVum2pdqy9O/UdPWJC6kNKPhbtlgW6K+pHc1KpRDysfjRrHd3pvsCGjnRf+IAxqXaI/pF7Hsp9/oPVks0RC3rXkowfltaS6D+aj0ol4pENu2aBSsVzopx09XgvSe429vYf3b6W5Bnj9SLvT7nnodznPxhVKhGPb0w96SsRMXVExKHWkmTFWhI2qx/N3sqt6VqwrTvlwKMm6EXeX2Qh+2Aw6q5EPLguYm1Hej64/6iImfqPZmVrl3Wludu9F3l7d6olJh8LNkYs7l1LMql3LYn60f4hC9kHg1F7b//R7WtJDhobMVn/0ays3bZrFtj+vIh87N6LXP1oXiqV1HdwVXv63PcdmeoH6R+5Z6Hc5z9YtXVEPNyWcuHI3rUko2SBrCzevGsW6OpRP5qTSiVinvrRrG3vRb6+Iz0jPmB0xLSRAz2q8so9D+U+/8Fq2ZaI+ZvSfSFrSfJTqUQ8tmHXPCgL5EX9KJt615Js6UrPiQ4fHzFu2ECPqrxyz0O5z3+wemzDjrUkU1ojDhsnC+Rkey9yfUbypRc5q9vTO6q29b6//qgJepH3F1nIPhiMdu8/OnNUejcJ+djei1wWyJde5Dy5KWLR5vT74cRhqW7IWpL+IQvZB4PRtt61JBs70++CB41N9wjJR9u2lAEi0v9u6dKLPDe7149aS5KXSiW9j2Ble0RTREwfma4F9I/cs1Du8x+sNnT09h/dvpZkfHpWQD6Wbtn1nbXWkvSNUkfqe+65J17+8pfH8uXLY9SoUfGMZzwjli5dGl/96lfj8ccfj7Vr10ZExDHHHDOwAy2xlVsjfjw/4vKFEes6dvz5mm0RH7094k1zIp49ZeDGR/97uC3i/+ZHXLMknby3Gzkk4hUzI948xwsH+8O8718TC356azQPbYkJh82KI9//2hg1fVJ0b9vxg9g8rCVedc2/xfxLb457z7vkqT9/wVfeH61Txsd1Z/3rQAy9T+Q+/8GkvTviyoURFy1IzWN3dti4iDfMjjh9ZsRQoa60VrdH/GRBxKVPpuv/dmu2RXz4togz50Q8b+qADY86eGxDxP89EXH14ohtvVmgrTPi5ddEvHxGyoNu8pXbb1ak3wluWZkWAGw3eXjEGfuna8EkD/z6lCxkHwwm67ZFXLwg4pIn0wOenZ0wJeKNcyJO3MeioDKbv3FHFtjSvePPV2+L+MzdEW86IP1uQHndviriwvkRNy+P2H5rqK0j4pXXRrxm/4g3zk4vm6Kcunoifr443Rd4oG3Xr80ZHfH62RGvnaUQuK/JQvbBYLJwU7oOXLkovWRsu6HNES+ZnrLAkRMGbnz0v7vWRFz4RMQNy3e8aLitM+IV10a8ema6N6R5XHl1VyKuXZKywO/X7vjzdR0Rb/hlbxbYXyPZMtvUmZ4PXLwgvVRmZ8+cGPGGORGn7mtRUF/LPQvlPv/B5r61qWbk+qURXTs9JBgzNOKVvVlgxqiBGx/9q6eSPvuLFqRcuN26jogzro943f4Rr5udjgfKaUtXqhv8yYK0MHi7tR0Rf3VzyoMvm2FRUJkt3xpxUW/9aNtO9aNDmiJOmpauA8dNHrjxlVXueSj3+Q8mlUrEjctTzcjtq3f92rQRqWbk9bNTY3HKqb074qe99aNP7FQ/urYj4u03pc//9BkWBZXZ6vYda0l2rx/9m9tSFjhe/WipPbI+3Rf4xU71oxERI4ak3wXePCfiQPWjfS73PJT7/AeTju70nPgnCyIe2bDr1w4Zm+qHXzkzvXCKclq7bcdaklU71Y+u6Yg4+7dpLcnzp6ofLbMndqof3bpb/eg/3ZOywCHqR0vttysjfrwg4tc71Y9GREwaHvGaWek8MNlakj4lC9kHg0lbR8QlC9JakuVbd/3acyanc8ALp8kCZfbkppQFfrZ41/rR1dsi/uGuiDcfEPGM8QM2POrgztWphvimnetHOyJOvzbi1b1ZYJq1JKXVXUn3BX+yIOLedbt+bf/REa/fP9WPaiTbt2Qh+2AwWbx5x1qSjZ07/rylKeKUfdN9gaMnDtz46H+/X5vy4C+XPX0tyatmpiywn/rR0uqpRFy3NGWB3etHX3d9qhc4Y3+NZMtsc1fEZb1rSRZu3vVrR09IPSZO20/9aF/LPQvlPv/B5oF1qWbkuqXpRRLbjWrZsZZklhcOllalEvGrZal28I6d6kfXdUS89rq0juR1+0eMUz9aWu1dEZcvivjJ/Ij5u60l+fObUp+Zl6kfLbWVW9M54PIn0+e+XXNEnNi7luQ5epH3udzzUO7zH0wqlYibV6Q1Zbeu2vVrU1t39B+dMHxAhkcdbNupfvTRnepH13ZEvLU3C+hFXm5rdupFvnq3tSQf2l4/us+ADY86eGxDek509eK0vmy74c2pF/mbD9CLvD/knodyn/9g0tkTcVVvFnh4/a5fO3BMygKvnBXRai1JabVti/jJkxGXLohYsdtakg/cmp4TnaQXeakt2JiywFWLnt6L/B/vTjUjh40fsOFRB3es2lE/uvNakgnD0lqSN86J2Ef9aJ+SheyDwWRDR1pHcsmT6aXDOztucro3+OLpskCZLdqpF/mm3daS/P3v0n0BvcjL7Z41qWbkV7vVj55+TcSrZqVnhdP1Ii+tnkp6X/FPFkTcs3bXr80cteO9JOpH+5YsZB8MJsu2pCxwxcKIDTutJRnSFHHK9HQdOGbSwI2P/nf/urSW5LqdepFvf2ft9iygF3l5VSoR1y9LNSO/+wO9yF8/Wy/yMtu6Uy/yBZt2/dqRE9J9gZftp360r+WehXKf/2DzcFu6L3DNkoiO3daSvGJGek/dbGtJSmt7L/KLFkTctlP96LqOiNdcv6N+VC/y8mrvjriytxf543voRf6GOelcoH60vFa3p8//sid37UXeFBEv2CfVj56gF3mfyz0P5T7/weY3K9I7CW5ZGbHTa+pi8vAdWWCS/qOl1dGdeg5etCBi3k71o+s6It5y445e5MPVj5bWup16ka/crX70g731oyeqHy21+Rt39B/duttaktP2SzUjh+pF3udyz0O5z38w6epJa8kuWhDxYNuuXztgTHpG9JqZEa36j5bW+u31owsilu3Ui3xtR8T7bkl58GT1o6W2cKf60d17kf+/u9JakiPUj5baXWtSHrxxp17kEanH1Kt7e45NUz/ap2Qh+2Aw2dSZssDFCyKW7LaW5NhJKQu8ZN+IZlmgtJb09iL/6W69yFdvi/jUnem+wFy9yEvt3rWpZuSXu9ePXruj/6j60fLqqURcvzTdF9i5F3lExH4j032B1+lF3udkIftgMFm+dcf769fv1H90SFPESb39R4+bPHDjo/892LZjLcn2+tHta0lO780C+6sfLa1KJeKG5Wktye279SJ/zXU71pLoRV5e7V0RV/T2HHti465fe8b4dF/gFXqR97ncs1Du8x9s5q1PWeCaJRHbdlpLMmLIjv6jB4wZuPHRvyqViN+s3FE/ul1bR8Srr0vvKn3D7IiJepHXrLSX0NWrV8erXvWqWL58eZxzzjmxbNmyuOuuu2L58uXxhS98Ia666qq44447oqmpKY4++uiBHm4p3b8uFfl+77EU4Hd3w/KI994a8Z8Ppx92yueyJyPefnMq9th5QWhEaiT6kwURZ90YccOyARleqW14Ynksu/m+WPLLu+P+b1we1//552PyMQfGCV9491Pf09PRFb8++/w46uzXxYRn7B8REbNe9uyYcepx8ZuPfGOght4ncp//YNHWW9z3+ft2XRC63cPrI/7l9xFn/zYVBVA+89ZHvPXGiG8/suuC0O1+vSJ9/l95QBYoq58vTsfAZQt3/YU+Ii0av/TJiLfdlH7hp3wqlYh/vz/iQ7elX+x3/zFfvS3iW4+kl0w9un6PfwU1koXsg8Fi/sZ0nv/PebsuCN3u1lURH7k94vP37locTnn8aln6vf/iJ3d9och2P12UXjx8xcL6j43+V6lEfOOhiPfdmhaB7BYHY21HxHcfTcfIA+sGZIj0s61d6Tz/j/dEPND29K/P3xTxpfsj3nVLxNo9/M5I7WQh+2CwuGVlOs9fOH/XBaER6QWEVy+J+KubU1EY5fRfj0S86zepkfTumX99R8T3H0/PEu9Zs+ftaWzbuiM+eUfE398V8fu1T//6ws0RX34g4h03R6zc+vSv0/iWb4n4y5sjznswYvGWp3/97rURf/e7iL/9XWosRt/JPQvlPv/B5H8fj/irX0f8YsmOBaHbbeyM+NETKQvcvmrP29PYOnsi/uGuiE/97ukLQiNSs4DzH4r4i5uf/hJKymF1e8Q7f52eFz656elfv3ddxKfvjvjo7WnBCOVz79qIt9wQ8d+PpRqinXVX0jOE99wS8c15akb6Wu55KPf5DxZdPak28KN37LogdLvlWyP+4+H0rHBP1wkaX9u2iHf/JuIL9z19QWhEWjT+z/ekuqLd7x9TDg+3pWcE//XonutHb14R8YHfRnz1QVmgrH62KNUNXbGH+tGtO9WPXrd0YMZXZrnnodznP1hs7Ezn+c/eG/HIhqd//ZEN6WvvvzViwx7WHNL4HtsQ8bYb0+/9q/ZQP3rLyogP3xbxb/en5nKUz3VL01qSS57c9YUi212xMGWBqxbVf2z0v0ol4vwH07Xgpj3Uj67Zln5XOOvG9LsDfUcWsg8Giyc3pXt/33g43Qvc3e2r073Df/59updI+fx6RTrPX7Rgz/f/frY44i9uSi+doXwqlYhvzUvPAn+1h/rRto6I/3ksPUvcU20hja+9O9UC/MPdqTZgd09uijj3gVRTsHoPvzNSO1nIPhgsbl+VagN/9MSuLxSJSLWE1yyJeMevU40h5fT9x9JnfO3Sp2eBDZ0RP+ytH71zD/UENL6O7h3rBPZUP7p4S1pj8Jc3pzUHlM/KrWmt0JcfSGuHdnfvuvSyyU/ckbIjfSf3LJT7/AeTi+an8/zVi9O6gp1t7kprjs+6cdcG05RHV0/EP90T8fE7I+7YQ95btjXi6w+le8iL1I+W0tptqYfIv92Xeors7oG21IPkI7dHbFE/WkoPrEvn+e8+mnoL7awnUg+i992azgXqR/tW7nko9/kPFt2VtIbgI7enHoO7W9keccG81H90T+sMaHzrO9J5/nP3Rjy6h/rReb29yD+oF3lpPbo+/Yx/65HUc3h3v1mZ1hL9u/rR0rpmSaoPvfTJp9//29aTepS/9cbUs5y+lXseyn3+g8WmzvTOiX/5fXoHye4e35jeWfK+W57ef4ByWLC9F/nDESv2UBf021UR59ye1hTpRV5ONy7fUT+6p17kVy5K7zC77Mm6D406qFTSz/97b03vJNy9THxdR3qH4Vk3pnca0ndkIftgsFi8OfWU+9pDe+4rd+fqiE/emfqOWUtSTr/t7UX+f/MjNu3hWeDPl6R6gv97ov5joz6+92jEO3+T1hg/rRd5Z8QPenuR76m2kMa3rTud5//+roh79rBeaNHm9J7Kv/r1ntccUjtZyD4YLH63OuLPbkjn+w27PQvsrqT1Be/8TcR/Pzogw6MO/u+JlPd+vode5Ju6enuR35ByI+XT1duL/JN3RvxuD3lv6Zb0++Kf35R+f6R8Vren8/yX7o9YsIf60fvXRfzj3anPhF7kfSv3LJT7/AeTy55Mz4GuXBTRsYe1JBctiDjrhtSPivLprqTnwB+9I+K2PdSPrtianiO9/aY9XydofG0dEe+9JdWFPL6HGuGH1vf2Ilc/Wlrz1qdnBN955Om9yCuRepF/8LcR5z1gLUlfyz0P5T7/waKnkurDP3Rbqhff/cd89bZUX/7Wm1K9OeWzqTOd5//l9+masLtHN6R1Ju+7Na07oXye2Jh+xi+Yl9aP7e7WVWm92Rfvs5akrH61LOXBi/fQi3xbT8RPF6XfCa9cODDjK7Pc81Du8x8stnRF/M1tEZ+5J72PbHdPbEz9J951S+pHQfks2pTO819/KPWV2d3tq1Mfmn+6R/1oWf1mRaoNvHD+nnuRX7041RP8ZH79x0Z9fOeRiHf9JuKXe+hFvr4j4vu99aP3qB/tU7KQfTBYLNuSzvNffTBiyR7Wkty1JvWn/bvfPb0vIeVwR28v8v/dQy/yiFQ/+o5fR/zgsfqPjfr44eNpncA1e6gf3diZ6kfPujH1rad8OnvSOpJP/YFe5Eu2pGvEn9+85zWH1E4Wsg8Gi9+vTesE/uexpz8L7K6kZwjvuSXi2/PUjJTVxQvSe+h+tnjP9aM/7u1F/usVAzI8+llXT3oP5cfuSPeCd7d8a3qP5dtviliofrSU1vX2Iv/ifXvuMftgW3o+8OHb9CLva7lnodznP5hc2VsT8tNFqUZkZ1u703vt33pjxPVLB2Z89K+eSroGfPi2Pb97ZmV7xDfnpd6Ej++hTzXVaR7oAfSXs88+OxYvXhwf+MAH4ktf+lKMGTPmqa99/OMfj7lz50ZXV1fMnj07xo4dO4AjLaf5G1PxbzWNYb/9SMR/u8lfOtcuScXfeyvs3daTGgh44Wj/WnXnvHj8JzfFnNc+P6Ycd+hTf77m3ifigf+4Ik786gdj5PSJccK/vSdu+9tvx9YV5ermmfv8B0J7d8RHbtvzS+d3d8fqiI/f4YF/2SzeHPGBW/f8MpHd/eDxiP94uP/HRH3dvDw1Ctr9If/utj8QdpO/fL7xcCro2JtV7RHvvzViiYZR/UYWsg8Gwoqt6We7msawFz+ZGslSLrevSr/v7/6Qf3fdldQs5jo3+Uvnu49G/FcVjWHXdaT7iAu8cLRUunpS0eeeHu7s7sG21DBqqwf+/UYWsg8Gwj1rIj56+9MbQ+yuJ9LLqa9cVJdhUUc/eiLdG9ibDZ0RZ9+mYVTZ9FTSvcFfVdEY9rGNER/47Z4XjNG41ndEvP+3EfOrKOy9bmkqArUQoP/knoVyn/9AuezJiHMfeHrDyN1tbyjkhaPlUqlE/Ovv0wtl9ubJTem5cpuGUaWyvXHoI1UU9t68Ij0v9vLxcnl8Q8TZv336i8X25JvzUt0I/Sf3PJT7/AfKv98fcXkVjWGXbEnPlVfvockwjau9KzWRf6Bt799726qIT96heWTZLNqUfrZ3f5nInvzPY+nFApTLDcsiPn333nN+R09qHHerF472q9zzUO7zHwgd3RHn3L7nRmG7u3ttehFdx16eK9NYlvfm/BVV5Pwfz484/8H+HxP19duVqTFsNfWj/3h3ah5GuVS7ZnzNtnS+0DCq/8hC9sFAWL19nVgVjWGvWJheUE+53LUmNQxsr6J+9LP3Rvx8cV2GRR398PH0gsG92dCZnik+pmFUqfRUIv7f71ItwN48siHVFnj5eP+RheyDgfDAulQTuLfGsJVINYaXPVmXYVFHP1kQcV4V9/w299aPPtTW3yOiniqVtDbg2irWjM/flNYcePl4uWzqTGuEHqtizfgNyyM+fZeXj/en3LNQ7vMfKFctSmuG91YOtrU7rUH2wtFyqVTS5//TKtaML96Srhlr1I+Wytau1DtkTy+d390tKyM+daf60bJZsCnd81tXRc6vti8Rtcs9D+U+/4Fy3gPp/tDeFOlRSOPY1h3xkdvTyyb35s7VqX5UL/JyWbol3fNbVUXO/9ETepGX0W9614zv7We7q7cv0c1V9CWidrnnodznPxC6etK7Ru6o4p1D965L7zDZW40ZjWVlb85fVkXOv/TJiHPVj5bOnasjPnHH018yurueSnqX2TVV9CWisfz3Y2k9yd609fYin68Xeb+RheyDgbB2+zqxKt459LPFaS2B/qPlcu/adH9wy15yfiXSWqKfVtGXiMZy4fyIrz209+/b2Bnx4d9GzNOLvFR6eteM/7KKNeNPbIz44K0RG9SP9htZyD4YCPPWp/UBm6p459D5D6VeI5TL5b1rxvfai7z3ufJ9VTxXpnFs70V+dRX3/Bb2vuN6nV7kpbK5K60Zrybn/3pFxN/qRd6vcs9Cuc9/oFyzJD3/2ds6sW29z5XvrOK5Mo3l3PvTc+C9Wdrbo7CaGjMaR3t3+p3wvipOqbevjviEtSSls3hz9b3Iv19lXyJql3seyn3+A+UbD6X68L1Z1Z7qzZdW0aOQxtHZk3qR/66KNeO/X5u+d5v60VJZ3ls/uqKK+tGLFqT1Z5TLbasiPnlndb3IP3NPxC+r6EtE7XLPQ7nPfyB09aRzwK2r9v69D7ZFfPi21JeC8ljTm/Or6UX+096+RJTLPQV6kX/+vtSfinL538erWzO+oTPi7NsiHlU/2m9kIftgIKzvSL3k5lfxzqFrl6Z+tdaSlMuDbRF/c3uqH9qbrzwYcfGC/h4R9XbJgogvV3HPb3NXqh99wKm3VCqViH++p7o1409uSvWjbdaS9BtZyD4YCI9tSDXEG6p459B/zov4YRXPlWksP18c8bl7996LvL073UO6Wy/y0vm3+9J7KPdmSW/96Gr1o6WytSviQ7dV14v8t73PldWP9p/cs1Du8x8ov1oW8Zm7975OrKMn4m9/l953T7mc/2CqCdubp3qRqx+tSfNAD6A/PPTQQ3HhhRfG5MmT43Of+9wev+dZz3pWRETMnTt3lz+/+eab48UvfnFMnjw5xo8fH8cff3xccskl/T7msjnvwdQEqFr/8bAFYWXS0V2siKerEvF5TeP63e+//JPo6eqOZ37sTbv++Vcujp7u7nj1tf8Wy39zf8y//DcDNML+lfv86+2KhalBeLVuX62JcNl8/aHqXi623XcfTQtJKYeunlTQWe29up5KxBfu1SimTBZtSj/X1Vrb4eVC/U0Wsg/q7YJ5ESsL/J7/oydSgQDl0NP7e3611/ZKRHzxXi8aLJMVW4s1ftnQGfHVKpqO0zhuWJ4aQlbrofWKwPubLGQf1FOlEvHF+/beHGJnX7rP4vAyadsW8dUHq//+LV3VLRqhcdy6MuL6Kl4ost0TG6trLkbj+MHjaZFPtX6+RBPh/pZ7Fsp9/vW2qbPYS4S39UT8u5cOl8o9ayOuLND4ZeHm9IJayuPC+RGPFrjnf8PyiN8UuJfE4PeVB6p7udh2X38oNRmj/+Seh3Kff7091FbdIpDtlm+t7kX1NI5LF0Y80Fb999+6qti9JAa/8x+KWF9gLcm350UssyCsNLp6eteGVPn922tM9vYyMv40ueeh3Odfbz9bHHFXgcYvd61J21Ae//FwdS8X2+77j0fM39h/46G+eiqpUVSR+tEv3KtRTJks2xLxrQL1o+s7I76mfrRfyUL2Qb19+5F0z69aP1mQ7ilSDpXedaJF1ob82317fwEJjWNNe7Fr++YuLxosm1+viPjV8uq//9ENqcaA/iML2Qf19qX7U01gtc69P9UcUg4bOyO+XKAedGu3+tGyuXN1WhtQrSc3pbUHlMf/PpHWCFXr+mVpDRL9J/cslPv8621rV7rXU62OnrQWWf/R8nigLeLSJ6v//iVbivUoY/D7yYLUO6Rav1mZ1pNQHl99sLqXi233zXmpRxH9J/c8lPv86+2xDen3wmqtak/nAcrjp4sifr+2+u+/c3V6MSHl8Y2HItYWqB/97qOpdzHl0F1wbUhPpN7l6kf7V+55KPf519svlqR3jVTr3nXVvZyWxvGtRyJWFOgZceH8iEcK3EticKv0ZoGuAvf8v3hfercZ5bCqvdh7RjZ2FutXSnGykH1Qb999ND3/q9YVCyPu877v0qipF/n9qR815dDWEfHVAmtDtnQX61fK4Hf7qohrl1b//fM3FXuuRHGykH1Qb/9+fzq/V+u8ByI2FHjHLYPb5q5i1/aOnoh/kwVK5fdrU81AtRZvifie+tFSuWh+xCMFepHftDziZvWj/Sr3LJT7/OttW3e6N1Strt6+JNaSlMe89cV6RqzYWqxHGYPf5QXv+d9W8F4Sg9/XHkr3iav1nUcilmzuv/EgD+U+/3p7clPE9wq8c2zttoj/0H+0VK5eHPG7Ar3Ii77XjsHvgodT/Vi1fliwLwmDW0/v7/mFepHfV6xfKcXlnodyn3+9/WpZxC0F+kc92BZxcYG+JAx+33k0YmmB+tFLn4x4QP1oaVQq6dpetH60Xf1oaazblt5VWK0tXRFf1ou8X8lC9kG9ff+xdI+wWkXvJTH4fange0a+/IBe5GWyqbPYtb1dL/LSKfr+0YWbI/6nwHMlipOF7IN6+8oDaT1Btb5esEcZg1t7wfrRTr3IS+eBdcXu+S/bmmrHKI9LnkzPfqp1y8qIXy7rt+EQslDu86+3rp7Uc6zaS3vRfqUMfvM3Rny/wDvHVm+L+E9rSWrSPNAD6A8/+tGPoqenJ84666wYPXr0Hr9nxIgRERExd+7cp/7s97//fZx66qkxZMiQ+N73vhcXXnhhzJw5M97whjfElVdeWZexl8GSzRG/WVFsm+5KxGWKfkrj+mXFFgJFRCzY5EFff9u4YHnMv/w3se9JR8fU5x7+1J9Xurpj1R3zonXSuHjswl8N4Aj7V+7zr6dKJb1grKhatmFwWt1e/CZNJSIuXtAfo2Eg/HpF8RfGLdsacUvBDMngVUtB/3VLPejrT7KQfVBPGzoiflHDC+NkgfK4Y3Uq5CpibYcHfWVy2ZPVLwre7tfLI5YXWEDG4FbrfQEP+vqPLGQf1NN964o1j42I2NSVXk5IOVy+sHjDj9tXRyzQKKY0askClz7ppcNl0dFd27P/ixb0+VDYSe5ZKPf519vVi4u9VCYiZciH2/plOAyAWs7pVywstpicwau7khaCFKVmpDwWbYq4dVWxbboq6XdJ+k/ueSj3+ddbLef0ny0qtpicwatSSS8YK+onNWzD4LRya8SNBV8Y1xO1ZUgGpxuWp0V+RSzZEvHbghmSYnLPQ7nPv54qldruDV20QKOYsmjbVtsL49SPlsdvV6VrexGrt6UMQTlc8mTK+EXcuDz9LkH/kIXsg3ra3JXu9RXlOVF53LM24vGCdYDrOyOuUz9aGpcvTM/+irh1VXrGSDnUck6/pIY1SFRPFrIP6unhtlQLWMSW7lRzSDlcuShiW8EbA/esjXh0ff+Mh/qr5RnBZU+mNQg0vq6etDaoKPcF+lfuWSj3+dfbL5aktcJFPLIh4n4vHS6NWs7pP10UsVX9aCn0VGp79i8LlMfyLal3UBF6kfe/3PNQ7vOvt1quA79YnHoW0vgqldrWhegxUR5rt6WewkXV0ruYwemWFam3eBErtqYe5vSf3PNQ7vOvt1r7j1pLUg4bO2t79u/eUHn8bk1651gRbR3p3WaUQ029yFdELNWLvN/IQvZBPbV3pdqxotwbKo8H2iIeLlgHuLkr4ufqR0vjyoXF60d/tybiCb3IS6OWc7pe5P1LFrIP6unxDRF3FXwP+baeVDtGOVy9uHgfyQfbIh5QP1oatdaPtqsfLYXuGutH3RfoX7lnodznX2/XL03PfYqYvyndG6AcaskCVy+O2NTZ50NhANRcP6oXeWmsbo/4VcFn/5XQi7y/5Z6Hcp9/vdXyO+F1yyLWFexhz+ClfjRv6zsirqmhj6T60fK4fVXEws3FtlmzLeIG9aP9Kvc8lPv8662W+/0XL0j9KWh8W7oirtKLPGv3rot4dEOxbTZ2RvyihvXIDE6XL4zoLFgHePvqiCf1Iu83spB9UE8d3RGX1fAeclmgPB5Zn/JAEe3dta1BYnC6anHE1oI9pe9dV3wNEoNXLef0yxdGbNOLvN/IQvZBPS3cVPw95J09EVfUkCEZnK5dErGhYB3goxsifr+2f8ZD/dWSBX5WwxokBie9yAen3LNQ7vOvtxuWpxqQIhZvibitYIZk8KrlnH7NkuJrkIhoHugB9Idf/vKXERFx8skn/8HvWbw4dSaaO3fuU3924YUXRlNTU1x22WXxyle+Ml760pfG//3f/8XMmTPjhz/8Yf8OukSuXpwWdhTlBn951PpZ/tSNnX5373kXR093dzzzY2966s+mPvfwOOhNJ8dD3/lZPOef/jKGtA4bwBH2r9znXy8PttXW/O2+dcUbDzM4/XxJ8QbCEbUVjDI41ZwFHAOlUctn2VVJL6ql/8hC9kG9XLe0eAPhiJQFaskQDD61ZgF5sDxqyQI9kR740/iWbYm4c3Xx7RZvUfTT32Qh+6Beas0CnhOVx1U1XtNr3Y7BZd222l4evKo9LQ6m8d2yMmJdDcU7Ny5PTQLoP7lnodznX0+1Pu+RB8thc1fEL2to+LK+s7YMweBz5+qIFVuLb3fLytSAmsZX631e14H+l3seyn3+9dLZk2rHitrSHfErTeNK4b51xRsIR0TcvTZicQ3bMfhcvbi22g9ZoDxqrf2wlqT/5Z6Hcp9/vTy+MWJeDc3f5q1P29L4rlka0VFD/eiVi7xYpixqvaarHy2PWrJ9dyX9LkH/kYXsg3r51dJ0r6+ony9JTchpfDXXj7oOlEatx4C1JOWwuj09+y9qxdba1qBQPVnIPqgXPSawnihvGzvTmoCi1nXUliEYfG5fndYGFfXrFWktEv0n9yyU+/zrSR7MW3t3agpe1Oau1ISexvf7talnSFF3rk49Smh8P1ucegcV5TrQ/3LPQ7nPv166K7XV/2zrST0LaXzz1kc8VkMd6INtEfPVj5bCL5aknsJFyQLlocfE4JV7Hsp9/vXy5Ka0trSoJzamPEDju35pukdY1NWLI7pquaHAoFPrWhJZoDxq+SwrEfEzx0C/koXsg3q5cUVtfUSvWxqxtavvx0P91bo+VBYoD++myVtbR8TNNdR+rNkW8dtVfT8edpCF7IN6sY4AeTBvW7sirl9WfLsNnRE36UVeCneviVhWQy/y21ZFrKxhO6qXexbKff715L5A3rp6ausdtrW7tgzB4PNAW8T8TcW3u3ddqjeh8f1cL/JBK/c8lPv866VS41qSzp5Uf07je3xDbXWgj26IeGRDnw+HAXDt0rQ+rKirFkX06EVeCnpMNYSOTQAAmflJREFUDF6556Hc518vS7dE3LWm+HaLNkfcu7bvx0P93bAs9Y0p6polEdv0Ii8FzwhwDAxOspB9UC+/WZnqiIv61bKITTWsQWHwUT9Krdd0WaAcNnVG/LKG2o/1HRG/UT/ar2Qh+6BerqrxPVOeEZSHnmN56/gTepH/Sv1oKdy3LmLh5uLb3bUmYnEN21G93LNQ7vOvJz3H8lap1PZZdvREXKd+tLCmSqVSunK7mTNnxuLFi+Puu++OY4455mlf7+rqiunTp8fq1avj8ccfjwMOOCAiIj760Y/Gt771rWhra4umpqanvv/II4+MI444Ii688MKaxnPcccfF8uX5vDVn7Fu+ECNfcFbh7SrdnbHig3P6YUTU2+R/+FW0TDu48Hbb5v0m1p33pr1/YwaGVprj0z3P6ff/TsvI1nj19V+KBy+4Mh7+71/Eyy/9p1j9+8fjjk9/r/Df9Znm26OzqW86AOc+/0Y3/JiXx4R3faumbdee9+bomPfrPh4R9Tbm9Z+OUS/+65q2Xf7BORHdqj4a3aRPXBVD959beLuO+XfH2n97VT+MiLoa2hrTznuspk03X/efsfGSf+njATWmeuShvsxCEfKgPLjD6Fd+NEa/4sM1bbvio0dGZUtbn46H+pv44Yti2CEnFN6uc+m8WPMvL+6HEVFv+3ztyWhqHlJ4uy03/U9s+L+/7YcRUU9DDzguJn30spq2bfuvD0T7nbVtWzbyoDzYyMa/77+j9cji1/TuNYti1f8rniEYfKb++0PRPGJM4e223nZxrP/vD/XDiKinlv0Oj8l/d21N267/4Sdi629+2Mcjot5GvugvY+yZ/1zTtqv+6UXRvby2e4tl02h5sK+zUKPNP0Ie3NmUz/0uhozbp/B27fdcHW3frO0ZI4PHkCmzY8pnanvmv+En/xhbfvntPh4R9Tbi+DNj3NvPrWnb1Z9/RXQtvLePR0S9jX3rl2Lk895ceLtKR3us+PBB/TCixuPemDzYyJrHTo2pn7+rpm03XvHF2Pzzr/bxiKi31mNfGePf+Z81bbvmy2+Izkd/28cjot7GnPnPMepFf1nTtss/sH9Ejw6ijW7Sp34RQ2ceUXi7jsfviLX/fkY/jKjxyIONlwdlwR2GHXFKTHz//9S07bpvvD223f/LPh4R9Tb6NZ+K0S99f03brvjIYVFp96a5RjfxnEtj2IHPLrxd56IHYs3nXtoPI6Kumppj2tdr6xKy+YbvxsYf/78+HlBjarQsFKF2UB7cYdTLzo4xr/54Tduu/OSx0bNhZR+PiHqb8IEfxPBnvKjwdl0rF8Tqf3xB3w+IutvnK49F07DWwtttufXC2PD9c/phRNRTy6yjY/Inf1bTtuv/5yOx9bc/7uMRNSZ5UB5sZOPf9a1oPeblhbfrXr8iVn3qWf0wIupt6hd+H81jJhXebuvvfhrrv/PefhgR9TRknwNjyqdvrGnbDT/+f7Hlhu/28YiotxHPf0uMO+uLNW27+rOnRdfiB/t4RI2p0fJg7s+KI+TBnU3551tjyKSZhbdrv//6aPvGn/fDiKin5gnTY+q/3lHTthsv+1xsvubrfTwi6q31uNfG+L/6Wk3brvnSa6PziTv7eETU29g3fzZGnvT2wttVerpjxQdnpzcSZM69MXmwkTWNHB/7fOn+mrbddPV5semn/9bHI6Lehh99Wkx4z3/VtO3a898SHQ/d1Mcjot7GvO7vY9RL3lPTtss/dFBEZ3sfj4h6m/ixn8awOc8svF3nk7+PNV84vR9G1HjkwcbLg7LgDsMOfX5M/FBt7/Ja982/jm33XN3HI6LeRp9+Tow+/W9q2nbFx4+Oyqa1fTwi6m3Chy6M4Yc+v/B2XcsfjdX/dHI/jIh62+erT0RTS/GX92759Q9jw/9+oh9G1HgaLQtFqB2UB3cY+ZL3xNjX/X1N2676++Oje+3iPh4R9Tb+Pf8VrUefVni77nVLY9Xf9f/PPv1v6pfuj+aR4wtvt/WOy2L9dz/Q9wOirlr2PSwm//11NW27/kefiq03f7+PR9SY5EF5sJGN+8uvxYhnv7bwdj2b22Llx47s+wFRd1P+9Y4YMmF64e3af/+LaLvgHf0wIuppyKSZMeWfb61p2w2X/HNsue6CPh4R9db63NfH+D8/r6Zt13zxVdG54O4+HlFjarQ8mPuz4gh5cGeTP31jtOxzYOHttj18c6z76p/1w4iop+bRk2LqF39f07Ybr/z32PyzL/fxiKi34ce8Iia865s1bbv2K2dGxyO39PGIqLcxb/jHGHXKO2vadvkH50R0d/bxiBqPe2PyYEMb2hrTzqvtfZObr/3P2Hjpv/TxgKi3Yc94UUz8wA9q2nbdf/xFbLuvtmdMDB6jX/3xGP2ys2vadsU5z4jK1g19PCLqbeLf/CSGHXx84e06lzwca/71Jf0wosYjDzZeHpQFdxh64LNj0jmX1rRt23+9P9rvvLyPR0S9jXrp+2PMaz5V07Yr//bZ0dO2rI9HRL1NeP/3Y/gRxdeEdK1eGKv/4Xn9MCLqbeq586K5dVTh7bb+9qJY/z+1rUUrm0bLQhFqB+XBHUa+6K9i7Jn/VNO2q/7xpOhe+UQfj4h6G//O/4zWY19ZeLvujatj1SeO6fsBUXdTPndXDBk3tfB27Xf/LNq+9a5+GBH1NGTqnJjyjzfXtO2Giz4dW371nT4eUWOSB+XBRjbubefGiBPOLLxdz7YtsfJvDumHEVFvkz/z62iZMrvwdtseuCHWff2tfT8g6qp53D4x9XO/q2nbjVd8ITb//Pw+HhH11vqsV8X4d/xHTduuOff10fnYbX08osbUaHkw92fFEfLgzib93bUxdL/DC2/X8djtsfbc1/XDiKinptYxsc+5D9W07aafnx+brvhCH49o8Js2bVrceWdt7+Jo6eOxDAqbN2+OiIitW7fu8esXXnhhrF69OsaMGRNz5sx56s/f9ra3xde//vU455xz4hOf+ES0tLTEBRdcEI8++mh84xvfqHk8y5cvjyVLltS8faMZsmlTjKxhu0pPJav9VGbjOrtqOrlsa293DPQa1jQkYp/+/+88+x/fHpsWroyHv/fziIj49Ye+Fq++7kux8OrbYsVvi12Mly5bGh2V7j4ZV+7zb3TjZ66OCTVuu3r1qtjoPNDwZmzaGMVLvpIlSxZHdHf16XiovzEdHTG0hu06OrbJAiXQNHR4TKtx240bNzoGetUjD/VlFoqQB+XBHfbduDFG17jtsqVLontzW18OhwEwatu2KP46iYiuzk7XgZLYp8aXBm/atMkxUAKjxsyKSTVuu3btmljnGIgIeVAebGytW7dGaw3bdXV1uQ6UxJQas8CWLZsdAyUwYuikmFzjtm3r1sZqx0DDm7JuXYytcdsVy5fHNsdARDReHuzrLNRo84+QB3c2sbs7htSw3dYtW2SBEhjWPTym1Ljt+ra2WOkYaHiT1q2NcTVuu2rlitjiGGh4LVu21FY/WulxHejl3pg82MhaNndF8fYgyYYN62O580DDG7//mhhf47arV66MTY6BhjfzT60f7bGwstGN7ayxfnSb+tHt5MHGy4Oy4A5jp62OiTVuu3r16tjgPNDw9t24oeb60aVLl0TP1k19Oh7qb3SN9aOdnR2yQBk0N9e8lmTzJmtJtmu0LBShdlAe3GHahg0xpsZtly1bGl1tK/p0PNTfiG3bYngN23V1WUtSFlMrPdFUw3ZbNqsfLYORrfvUXD+6bt3aWOMYiAh5UB5sbMO3bKlpLUl3d7frQElM6u6O5hq227pV/WgZDI/RNdePtq1bF6scAw1v8rp1NdePrlyxIrY6BiKi8fJg7s+KI+TBnU3o6qppLUn71q2yQAkMbY+a60fXr2+LFY6BhjfhgNrrR1etWhmbHQMNb8jmzTWtJYlKJZYsXtzXw2lI7o3Jg41syKjNNX90GzdsiKWuAw1v3H5/Qi/yVXqRl8F+G2tfS7J0yeKodG7r0/FQf6M7aqsf7ehQP7qdPNh4eVAW3GHMpNrXkqxdszranAca3vQN6/+EXuRLo3vjmj4dD/U3sr29pvrRTr3IS2NqJWqqH92sF/lTGi0LRagdlAd32Gd9W839R5cvXxYdq5wHGl1re3tt9aN6kZfG5J5KTfWjepGXQ+uQ8bX3Im9bpxd5L3lQHmxkw7ZsiRE1bNdjLUlpTOxWP5qzYZ0tepFnbuK6dTXXj67Ui/wpjZYHc39WHCEP7mx8Z2e01LDdtvZ2WaAEWsZuq3ktycYN62OZY6DhjZ9Ze/3oqtWr9CIvgRl/ai/y7q4+HU8jcm9MHmxkTcNG1Nx/dOPGDfJgCYyduqrm+tE1q1fHesdAw9t3w5/Yi3zLhj4dD/U3Si/yP5k82Hh5UBbcYdTY2TGpxm3Xrl0T65wHGt609bX3Il++bGl0rl3ap+Oh/kZs3VrTWhL1o+UxpVLbuwY360X+lEbLQhFqB+XBHaa0rat5LcmKFctj21LngUY3rMZe5OpHy2NST09N9aN6kZfD8J4R6kf7gDwoDzayli2ba1pLUunpcR0oifFdXTXVj7a3W0tSBi1bumuuH92wfn0sdww0vAlz1ta8lmT1qpXqR3s1Wh7M/VlxhDy4s7EdHTG0hu22bdsmC5RA84gxtfci36gXeVG15O5Bb9q0abFu3bq466674oQTTtjla8uWLYuPfexjERFx9NFHR1PTjvZ2c+fOjeuvvz5e97rXxZe//OWIiBg1alRcdNFFcdJJJ/1J48lJa8f6mrbraVsa++23Xx+PhoHQvHFFRBxeeLuhW9Y4BnoNrTRH1FYzUbX9TnlmzHn18+PyF5/z1J9tfHJF/O5ffxjP//L744pTzomurdU39t93+r7R2dQ3g859/o2upSntt0qlsst19o/Z/r0ThnTEWOeBhjeis7aFHN3rV8Z+0+rwZId+N2Tz6pq2a9m8WhYoiZ6Na6J5TPGlACM7NzoGevV3HurrLBQhD8qDO4zo2ljTdj1bN8a0CWMixtfaXoLBomVLbVmgedMq14GS6GlbFkMmzSy83YjO9Y6BEmhuSY1+arkvMK6pPUY6BiJCHpQHG9uwrWtr2q5pwwrXgZLoaVsaQ0YWXxI2fFubY6AEmob1RKWnO5qaiy8HGhNbY7hjoOENr2ypabtKd2dMGdEcFcdARDReHuzrLNRo84+QB3exfkXExH0LbzasfZ0sUAJNw4dEpasjmlqKt40b3bMlhjoGGt6w2FrTdpWenpg0rCcmOAYaXuu2tpq262lb5jrQy70xebChDRkaPdu2RPPwkYU3HdW9yXmgBIb+CfWjE1u6YpxjoOG1dtRYP7puWew3fXofj4aB0LxpVU3bDVE/+hR5sPHyoCy4w5+0lqS5I8Y4DzS8mutHt7TF9InjImJc3w6Iuqt1LYn60fLoXrcshkwonu1bOzY4Bno1WhaKUDsoD+7Q2l1jFti2JfYZOyJilPNAoxu6eU1N2zVtXOk6UBI9bcuieZ8DC2/Xus1akjJoHtYTlZ6eaGpuLrzt2NgarY6BiJAH5cHGNqx9XW0brreWpDQ2LI8YX7yV9HD1o6XQ1NoUle7OaBpSvIXsmMqWGOYYaHjDal1L0tMdk4ZXrCXp1Wh5MPdnxRHy4M6aNqyI2GdO4e2GbV0rC5RBy/CodLRH07Dirxoc3b05WhwDDW9oU3tE1F4/Ot4x0PBaO9pq2s5akh3cG5MHG1pTU/Rs3RjNI8YU3nRkl7UkZaAXOSM7a6wZ2bgm9p06uY9Hw0BoqbF+1FqSHeTBxsuDsuAOQ5o7IqLW/qPbYpTzQMMb0b2ppu162jfFtPGjIsYWv6/I4DJ0S231o83qR0ujp21pNE+ZXXi71g71o9s1WhaKUDsoD+7Q2rO5pu0qne0xZdTQiGHOA41u6JbaepGHXuSlUWlbFjF6QuHthrfrRV4GTa1Rcy/ysZUtepH3kgflwUY2rMb+o5X1y10HymLDiojJxd9RNtRaklJoGjY0Kp3bomno8MLbjunZrBd5CQyrFO9Fvv050aSh3XqR92q0PJj7s+IIeXBnTRtXRsRhhbdr2bJGFiiD5iHR0745mluLv396lPrRUmhp/hN6kQ/p1Iu8BEbU2ot8/YrYb9o+fTyaxuTemDzY6Ho2rY3m0RMLbzeya6MsUAJ/Sv3o+OZtMdox0PBq7kW+dUNMnzg2YkLxtWgMLi211o/qRf4UebDx8qAsuENzS2dE1LiWJNpjpPNAw2utcS1JpaM9po5pjRjhGGh0Q7fWVj+qF3l5VNqWRdTQY6J1m/rR7RotC0WoHZQHdxhea//R7s6YMmKI/qMlMLzmXuTqR8uisn55xIRphbcbtlX9aBk0tQ6JSldHNLUMK7zt6MoW9aO95EF5sJG1bltf03aV9fqPlkXTxpUR0w8qvN1QvcjLYcjQqHRsjaZhIwpvOqp7s2OgBIZGWktSay9y9aNJo+XB3J8VR8iDO2vetKqm7awlKY+eLeujeeS4wtvlWj86bVrx+2jbNVUqlUofjmVQOPvss+P888+PmTNnxnXXXReHHHJIRETccccd8ba3vS2eeOKJ6OzsjPe///3xta997antHn300TjllFPimGOOife9730xZMiQ+OEPfxgXXXRRXHnllXHKKacM1JQayrItEa++LqLogfWewyLeeUi/DIk6+/niiL+/q/h2Fzwv4ll6yUdEROeW9vjhgW8d6GEUctbjP4ihI/um+W/u8290lUrEm2+IeLzgWoCjJ0T814n9MiTqbE17xOnXRnQVDAN/flDEB5/RP2Oivm5aHvGR24tv95XnRrzAuuBSOO+BiO8/Xmyboc0RPzs1YkLxHlOllHseyn3+jW5jZ8TLfhGxreC97jPnRHz8qP4ZE/V1x6qI995afLvPPSvi1Pzu7ZbSN+elf4pojogrTo2YVrxGgEHofbdE3F7wXYOzRkX85JSI5urqA0ov9zyU+/wb3f3rIv7i5uLb/cMxEa+e1efDYQB8/7GI8x4svt3Fp0TsP7rvx0P9feT2dI+wiKmtEVe8JKKluX/GRP109kScfk3E2o5i271k34jPH9c/Y2pEjZaH+joLNdr8I+TBnf1kQcTn7y2+3Q9fGHFo8XpBBqG//13Ez5cU22b8sIirTo0YXvy9ZAwy3ZWI11wXsbzgu0WePzXivOP7Z0zU1+LNEWdcX7x+9AOHR/zFwf0ypIYjC9kHje5ffx9x6ZPFthnVEvGz09L/0tgqlYg3/ipiQcH+kcdOivjm8/tnTNTXqvaIV16bcmER7zgk4r3F30nFIPTLpREfv7P4dl87PuL4qX0/nkYkCzXePpAFd6hUIt5+U8RDBfsFHT4u4n9OiqiypwSDWFtHek5UtH70zw6IOOfI/hkT9fXblREf+G3x7b54XMQp+/b9eKi//3g44juPFNtmSFPEladGTHE5jYjGy0IRagflwR02d0W84pr0v0WcsX/E383tnzFRX/esiXjnb4pv95lnRpw+s+/HQ/1979GIrz1UbJumiLj0xREzir+vnEHoQ7+N+M3KYttMGxFx+UtSLkQeyn3+jW7e+oizbiy+3SePjnjD7D4fDgPg/56I+NL9NWz3ooiDxvb5cBgAn7wz4rqlxbaZOCziqtNSvxEaW1dP6j25sr3YdidNizj3Of0zpkbUaHko92fFEfLgzq5YGPFP9xTf7nsnRhw5oc+HwwD4zN0RP11UbJvRLRE/Py2iVf1ow+upRLz+lxGLNhfb7jmTI77xvP4ZE/W1fGvEq68t/k6Qdx2a/kEWirAPGt0X74v48fxi2wxvjvj5SyPGDO2fMVE/lUq6P/zIhmLbHTkh/U5A41u3LeIV16Z+I0W87cCIDx3RP2Oivn69IuLDtxXf7tznpHuEyEIRjbcPZMFd/dXNEfeuK7bNgWPSs0JrSRrfps6Il18TsbW72HZvmJ1qBmh8v1sd8e5bim/3L8dGvGxG34+H+vv2IxH/+XCxbZoj1Q5OH9kvQ2o4jZaFItQOyoM7tHel+wIbOottd/qMiM8c2z9jor4ebEvry4v6+7kRr92/z4fDAPjh4xFffqD4dj85OWL2mL4fD/X3sdsjflWwF/nk4WltsV7kSe55KPf5N7r5G1PfuaLOOTL1GqHxXbIg4rM19CL/wUkRh43v69EwEP7fXRFXLy62zbihaS1Jq17kDa+7EvG66yOWbCm23QlTIs4/oX/G1IgaLQ/l/qw4Qh7c2c8XR/z9XcW3++bzUy9iGt/nfh9xccFe5COHpF7ko9WPNrxKJeJNN0Q8sbHYdsdMjPj2C/plSNTZ6vaI02voRf6XB0e8//D+GVOjkYXsg0b3lQcifvB4sW2GNUf87NSI8cP7Z0zU15/fFPFAW7FtDhmb3lWofrTxbehI9aNFe5G/eU7ER4/qnzFRX7evinjfrcW3+/xx6d3FyEIRjbcPZMFdveeWiDtXF9tm1qiIn5wS0SwLNLytXREvq6EX+atnRfzDMf0yJOrsvrURf/nr4tt9+piIV83q8+EwAP7nsYivPlh8u0tOiZg1uu/H04gaLQtFqB2UB3fo6E7vK13bUWy7U/eN+Nxx/TMm6uvR9RF/VkMv8o8fFXHmnL4fD/V30fyIL9xXfLv/fWHEIeP6fjzU39/+LuKaJcW2GT8sPScapn40IuSh3Off6BZvjnjt9cW3++DhEX9+cN+Ph/q7clHEP95dfLv/ekHE0RP7fjzU3z/fE3H5wmLbjGqJuPq0iJF6kTe8SiXiDb+KeHJTse2eNSniguf3z5gaUaPlodyfFUfIgzu7fmnEJ+4svt3Xjo84fmrfj4f6+/f7I370RLFthjentSTjhvXPmMqqlC05Pv7xj8ekSZNi0aJFccQRR8RRRx0VBx98cDznOc+JAw44IE455ZSIiJg7d9c3nf/t3/5tjBw5Mi699NJ4+ctfHqeddlr893//dzz3uc+Nc845ZyCm0pCmj4x4wT7FthnSFPFaD/pL45Tp6cWRRRwwxoJQKIumpog3zi6+3Rs95C2NSa0RLy5YzN8UEa/TNLI0nr9PxLQRxbbZb2TECX6hL43Xz04/10W8ZN+ICRaEQimMGRrx8hpeDPGG2X0+FAbIcZMj9i9YzD9peMSLpvfPeKi/M/ZP9/uKOGla8QzJ4FXLOf31sy0IhbI4YnzEYQWL+ccOjThNc4jSeNWs1PypiOdOKZ4hGbxqeUbwuv29UKQshjbX9pIo9wWgPF4+IxX1F3H0hIhDLQgtjVrO6a+ZFTHcgtBSGNKU7vMUpWakPGaMKv7sf2hzahgGlEMtWeAVNWRIBqemptqOgVruJzE4TWmNOLngs//mSM+YKYeTpqXjoIiZoyKeM6V/xgPUV1NTxBtq+B3/DXO8UKQsxg+LOG2/4tvVcj+Jwek5U9K1vYgprSlDUA5n7F+8ccHJ04tnSGBwGtWS7vUVpWakPOZOjDh4bLFtxg3zcrEyefWs9OyviBOmpmeMlEMtz/5fP7v4GiRgcDp0XKoFLGJUS23rkRmcXjkzorVgHeCxkyIOKpghGbxq+f3utfsXz5AMTi3NtfWOUjMC5XHavmmtcBGHj0trkimHWu4LvGpWRKv60VJornEtiWcE5TFtRPFn/0Oa1I9CmdRyTn/5jNSzkMZX61oSWaA8Jgwv/uy/KdSPlskJU1Nv8SKmjUg9zIFyqOXe0BtnW0tSFqP1Is/esZPSO8eKmDgsvduMcnjtrOJ1gC+Ylt5xCDS+1paIV80svp2eY+XxjPHpnyJGt0S8tIb1yAxOr5qZXiRexLMnR8wumCEZvGrpMXGGXuRQGnPGpPeUFTG8Oa0/oBxeVkMfySMnRBw2vl+GwwCoZU3Aq2cVX4PE4DSkqba1JLVkSGBwOmV6xIRhxbY5YEzEMyf2z3iov5p6kc9Mz5lpfE1NteVB9QLlMbm1+LP/pqgtQwKD0+tr+Hl+yb4R44f3/VgYGLVkgTfqRV4aY4ele8RFyYPl8ezJxd9DPnl4xIv0IofSqHVdabMsUAojaq0fnd3nQ2GAHFnDe8jHDo04Vf1oabx6ZsSwgnWAx0+JmFUwQwKD07AhEa+ppf+ompHSOHhcxDEFn/2PGFLb+2wYnF4+I2JkwTrAuRMjDimYIRm8avn97rWz0jUEaHwzRkU8b2qxbYY1R7xmVv+Mh/o7dd+IcQXrAA8ZG3FUwffZMHjVkgVeOTNipF7kpVBr/1H3BaA8Xjit+HvIZ41K772nHGrpJ3zafundxRRTyrYcM2bMiJtvvjlOP/30aG1tjQULFsTEiRPjggsuiKuuuioeeeSRiIiYO3fuLtvdd999MXfu3Ghp2TVVHnfccfHQQw/Vbfxl8OEjir1g6gOHpwUklMOwIRGfPDot8KnG0Obe71f0BaXxqlnFHvaeMCXdEKQ83n94xKQCi7v++tCI/Ub133ioryFNEX87t/qC7iFNEZ86unjjaQavGaMi3nlI9d8/eXjE+w/rv/EA9ffuwyL2GVH997/twOIvoWDwauq9trdUeW1vivT9Q0t5pzJPU1oj3lfg2j5uaMTZz+i/8VB/L5yeHvZV64jxtTUWAQanpqZ0z7/ISyU+flR6IQ3lMH5YelZYrdEtEX9T4PsZ/J47JRVwVOvgsRFvPqD/xkP9ve2gYr/nnz4j4lmT+m88QH2Naon42JHVf/+IIREfO6r/xkP9zZ1YbHHX/qMj3n5Q/42H+nvTnGLNgl48vfgiQga3vzkiYkyB+tGznxExUQNhKI1Dx0W8ucACv31HFqsxYfB77f4RRxdY6P2CfSJOLvgSCga3Dxxe7AVT7z4sYlqBGhMGt5bmiL89uvrFatvrRzUQhvJ4xYz0UoFqHTdZ08Cyec9hEVMLrBP9i4MiZmsgXBrNBdeGNEfKDi3qR0tj2oh0HqjWhGHpdwigPN55SLrnV62iz5UY3LbXjxZ5qcQnj4oYroFwaUwcXmxtyJih6kfL5nlT0wtkq3XouIgzNY6EUvn4UakmsFofPTLVHFIOo4emz7RaI4dEnFPg+xn8njUpNQav1gFjIt56YP+Nh/p78wFpjVC1TtsvrUECyqG1JeXBag1vjviE/qOl8ozxxV4sMmNkxF8d3F+jYSC8Yf/UO6RaJ01LvUkojw89o9gLpt57WPGXUACD1wFjUi/Bau0zItWRUx6vnBlxbIGeEc+dEvGyAn1JGPzef1jqLVytdxySehdTDkV7ixetNwUGv1P3Te8aqdYxE9O7TCiPdx1abJ3oWw6IOKjAcyUGt+29yKvtLd4U6RnBMPWjpTG5NeKDBdaGjB2a7icC5fGXB6cXSVfrjILPlRj8PnFUsV7kHzsqYoT60dIYOyzibwrUg45qUT9aNs+ZHPHyAvf8DxwTcZb6USiVc46IGFng2v6RI4v1K2VwG1mwfrR1SLHe5Qx+R01Iv+dVa9aoiD9XP1oqZ86JOLxAz4iTp6cepEA5DBuS+oxUWwIyrDk9V7KWpDwOHpee/1Zr+gi9yMvm1bNSHUi1ivYlYfD7wOHF3jPyrkMjphfoUQYMbjNHp7rwak0eXux99wx+L5uRnhVV69hJ6Z2llMe7D43Yp8A60bcfFDG7wDtuGdy214+2FOhF/qm5epFDmZw8PeLEAvf8j5wQ8boCz5UY/N5xSMR+BX7Pf+PsiMPH99doqLemptRbvEgv8o8fleoGKIfxwyM+VKC3+OgWvcihbN52YMScAu8ce9XMiGcWeK7E4PfRo1J/6Wqdc2TqX005jB6ajoFqjVA/WjrHTEzn9mrNHh3xtoP6bzxA/f3NEcXWhpz9jPS7JOUwfEjqG1KtYc299abqR0vjsPHF3jm238hiNSYMfmfsn9YUVevEfSJeNK3/xgPUV0vv2pBqHxO19NaYNMsCpTF7dMRfFPg9f2prsffds0NpS60OP/zwuPLKK2Pjxo2xcePGuO222+Jd73pXbN68ORYsWBDNzc1x5JG73lGcNm1a3HPPPdHV1bXLn99xxx2x3346nRex/+iIr58QMXHY3r/3PYd6yWAZnbJvxKefuffm4K1DIv7t2cVePgAMfsOHRJz7nOoe4B8/JeLzz1YAXjb7jkxZYGoVi0H+4qCIv3Zjr3SeNzXis8/aewHosOb0fcdPrc+4qJ93HZoWee3NPiPS+WKaheFQKlNa0892NYtB3jQn4oNeJlA6x01Ov+/vbXFHS1PEZ54Z8SIvHC6dtx+U8sDeTBqezhezChSMM/gNaYr412OrWxh65ISI854b0eplAlAqR06IOPe5e28m3twU8XdzU2MZyuXMOamod2/GDY04/3gvGSyb5qaIfzwmvXR0bw4dl44Bi8HKZcz2n+0qGkC9bL+Ivz9GATiUzStnpcYfe3sEPLol4svP1SSmbLY3jaumGeQBYyK+fnzEuCpqzGgcI1sivvrc6l4scvL0iH86VgF42cwZk/LghCp+tt9/eMSfFXj5ANAY/ubIiNdX0Qxy5qiIb5wQMalAw2EGv9YhKecfXcXC0OdPjfjcs9SPls2MUekZ8JQqfrbfcUjEX3nJYOmcOC3in58VMbSK+tEvHBfxnCn1GRdQH0ObU93Ys6t4schxkyO+9Oy9ny9oLNtrw6eP2Pv3vuWAiPcd3v9jor6eMyVd4/e2lmRoc8oMJ2oSUzp/eXB1LxDdXm8+Y1T/jwmon0mt6Z7fzCp+tl+3f8RHNBAunbkTI/79OXtvJj6kKeL/HRNxqjYupfNnB6RngHszYVh6pjjHSwZLpbl3ndjJVawTO3xcqi0YZS0JlMph4yO+8txUG/jHNEeqMXzVrHqMinp67f7pZTF7KwcbOzTivOPTegLKo6l3ndjLq8j5B/XWmI1VP1oqo3vXklTzs/2SfdPaI/WjUC4vm5GuBXv72R7Zku4hHVng5QM0ho8eFfHaKnL+9n7FE7xYqFRaW1LvkGpeLHLiPhGfPXbv/YppLDNHR3z9eamX0N6869CIP/eSQSidDz4j4s1VvGRsv5HV15vTOIYNSTm/mvcMPHdKxBf1Ii+dab0/2/tUUT/6tgMj3l1Fj0Iay/EFe5E/v4oehUDjaGlO7xo5oYp1Ys+cmN5hMnwvNWY0lsm99aPV9CJ/4+yIDx/R70Oizp45qbpe5EOaIj59TMSLq+hRSGM568DqXiA6cVj63WF/vcihVMb3vmdgdhU/26+eFfGJo/QfLZsjJqReM3urDW+O3h6FM+syLOroDbOry/ljh0Z8VS/y0mnqXSd2WhX1o4eMjfjaCXqRQ9kc3LtObOxefrabIuJvjoh4/ex6jIp6On1mxCeP3nsv8lEtEV9+TsqPlEdTU/o971VV5Pw5vfWj460lKZURLWmd2DPG7/17XzQt4p/Vj0LpvHjf9Pxnbz/bI4ak50nPrKLGjMby4SPSc+C9mTEyPVeerH60VIYPSXUgx0zc+/c+b2rE549TP1o2+/W+Z2BqFT/b1fYoBBrLew5N9eF7s71f8bQqasxoHC3NaX3Ic6roRf6sSWndyTD1o6UydURaV7pvFT/bf3ZAxAf0Ii+d4yan88DwveT8lqb0nsIX6kUOpTKkKa0TO7GKdWJHTYj4ynNSXwrKY0Jv/eisKnqRn7F/6ktDuRzVu05sb73Im5si/n5u6k9FubxpTsQHq8j544el2sED1Y9CqYzd/rNdxXsGXt7bo9BaknI5bFzEV46PGFNF/ehHj0x9qymXV8+K+NhRe+9FPmZo6lt/2Ph6jIp62d6L/BVV5PwDxqRrxjj1o1Aqc3rfMzChip/tDxwe8eYD+n9M1Ndp+6V1hXvtRd7bo/DoKmrMaCznHJneQ7k3s0ale8kT9SIvldYhqb/A0VWsE3vBPumZkvpRKJeTpqVakJa9ZIHhzamG/NlV9Ciksbzv8Ii3VJHzp49I9ebV9Cvm6bIrs3jggQeiUqnEIYccEiNH7lqZ+P73vz/OPPPMOOOMM+Ld7353DBkyJP73f/83brzxxjjvvPMGaMSN6/DxEf/7ooiLF0Rc8mTEmm07vjakKeKU6RFnzrEYsMxeOTPi0LERP14QcfXiiPbuHV8b3ZJeMnvm7PQSGqB8xvY2hv3Z4oiLFkTMW7/r14+akBrLvXQ/v9CX1UFjI374woiLn4y4dEHEivYdX2uOVPh/5hy/zJXZS/ZNRT8Xzo/42aKILTtlgZFDUkOpN82JmF1FYRCNp6kp4uxnpJdHXTQ/4qblET07fX2f1ojXzY54/f6p4TBQPrNHR/zPSRGXPpnuDSzbuuvXT9wn4o1zUpMQyunEaRH/+8KIH8+P+OmiiM1dO77WOiQV/r5pjgbSZdXUlF4mfNzkiAufiLhheUR3ZcfXJw9PC8FeP1ujqLJqbYn40nMirl2S7gv8fu2uXz94bLovcPrMvb+ACGhMz50S8aMXpnPAFQsjNnTu+vVXzYx40wFp0Qjl9PaDUuPAC+dHXL80omunLDBhWFoA9MbZqakM5TNsSMS/Pis1kr1ofsTv1uz69QPGpCzwqpmp6TTls8+IiO+cGPHThela8OSmXb/+nMnpGDh5ugWhUFZnzok4ckK6L3Dt0oiOnR4SjBsW8ZpZKQtM1zSylFqaI/7xmek8f9GCiNtW7fr1WaPSdeDVs7xcrKwmtUZ86/kRVy5KtWNPbNz168dOSsfAi/f1QpGyOnJCqh/9yYKIy3arH42IOHXf9IzgGPWjUEpDmtJL5k6clp4V3royYqdbQ7HfyPSM6LWzUp0h5TNuWMR/PK+3fnR+xCMbdv360b31o6epHy2tQ8b11o8uSGtJVu1UPzqkaUf96HFVNJynMb10v4iDxqTrwM8WR2zdqX50VEt6TnzmnFRfBJTP6KERXz0+4ue9a0kebNv1688Yn+4NvmxGxFBZoJTmjIn4/gt31I8u36l+tDkiXjAtrSk8Xv1oab1oesSPXpTqBa7arX50xE71oxpIl1NTU8R7Dkt5/8fzI27crX50SmtqJvX62RpFQVnNGLXrWpIlW3b9+vOmpt8Jnz9VzUhZnTA1PSfavpZk4071o8Ob0+8CZ86JOFT9aGn95cERz+ytH/3lsl2zwKThqX70DbNTLqB8hg9JzeCuX5qeF9+1h/rRN85O9aNeLgbl9KzJ6b7ARQsiLl8Ysb5j16+fPiOtJXnG+AEYHHXxZweknkIXzo+4bmlE5071o+N3qh/10vFyGtqcGsiePD1lgdtX7/r1/Uf3ZoFZ6bkh5TO5NeLbz0+/D140P2L+bmtJnrVT/ejeXjoANKYz9k99aC98IuKaJRHbdsoCY4emNQRvnB2xXxUvJqbxDOl9ydhJ01IWuGXlrl+fMTJdB167v7UkZTV+eKofvWpROgYe3a1+dO7EdA54yb7qR8vqsJ3qRy99MmL1bmtJXjw93Rc41loSKKXmpoiPHhXx/H3Ss8Jfr9h1Lcn0Eale5Iz9vWi0rMYMjfja8RFXL0n3BR7erRf5kb1rSV5mLUlpHTg24ocnpV7klyx4ei/yk6al/qPP1Yu8tF68Uy/yq3brRd4U6Trwpjmp1hgon1Et6YWjv1iS7gvct27Xrx86Lt0XeMWM1J+M8pk1OuL7J6U1pZc8GbF0p/rRpki/K7xxdqojVT9aTi/YJ/Uf/fGC1Htu0x56kZ85O+Jg9aOl1NQU8c5D0jPhH/+B+tHta0n0Iodymj4y4nsnRly2MD0nWLR5168fPyXdFzhpH1mgrJ4zpbd+dH7qRb5+p7Ukw5pTD4o3zYk4bPxAjZD+9tYDI+ZOSFngumW71o9u70X+htleOl5Ww4ZE/Mux6f3UP1kQcedu9aOzR+/oPzpS/SiU0jGTUhbY3n903W5rSV6+X6oZOXLCQIyOenjD7IgjxqfnRNcs2a0X+fb60TkR+1pLUkotzRH/cEzqL/iTBRG/3a0X+czeXuSv0Yu8tCYOj/jm8yKu7O0/+vhuvcifOTHdGzx1P73IoaxeOSs9E75wfsTVi3ddSzK6Ja0nPHN2xEz9R0upuSni40el54UXLYj4zW71o/uOTO+uP2N/vcjLauywiG+ckPoP/3gPvciP6q0ffan60dI6aGzED16YakcveTJiZfuuXz95enpGoBc5lFNTU8SHjkg9hn88P+Lm5RE7xcHYZ0Rvzcj+af0h5bN7L/IH2nb9+uHj0r3Bl+2nfrSsZvfWj27vP7psp17kTZF+VzhzTupRSTmdNG1HL/Ird+tFHpF+H3zTnJQbgfIZ0RLxb89O/eYuWhDx+7W7fv3gsb1rSWammnLKZ8aoiP8+KeLyJ9OzwsV76EX+htkRJ6ofLa3je3uRXzQ/4oo99CJ/aW8v8sOsJSmtPz841Q79eH7qR92100OCiTvVj05VPwqltM+IiP86Ma0huGh+xMLd1pI8Z3I6B5w8XRYoq2MnpXWlFy1Ia4p27kU+tHnH++uPUD9aWm+aE3Hk+JQFrl26W/1oby/yM2frRV5WLc0Rn3lmOs9fNP/pvchnjUrPCF6tFzmU1pET0n2B7WtJ1uzWf/S0/dJ14Bj9R0vrNbPSfZ8L50f8Yrf60TFD07upzpyT7iNSPkOaIj51dLr/e9GCiFtX7lo/ut9OvcjHWEtSSuOHpV7kP+tdS7J7/ejRE1IePFUvciitl81Iz4S3ryXZulP/0VEtEa/szQL7W0tSSs1NER85Mj0TvnAPvcin7dSLfLy1JDVrqlQqlb1/W3l8+9vfjr/+67+OM888My688MKnff3KK6+ML3zhC/Hggw9Gd3d3HHLIIfHhD3843vKWtwzAaMujqycVAG/sTA/654zRLDA3Gzsjzrguoq0znbR/+pJUGMaedW5pjx8e+NaBHkYhZz3+gxg6sm9+sHOffxlVKqlBxLt/k5oHThwWcc3LBnpU1FNXT8SDbREbOlPTyNmjFXvlZlNnxLz1EVu6UpPAw8Z7wJublVsj3nJDyoMThkVcfZqbun9M7nko9/mXUXcl4qG2iA/9NuXBScMjfvHSgR4V9bSlK71ocHNnuh9w6DgPeHOzuj1i/sZU9DFmaGoqKgvkZcGmiHfcnK4DE4ZFXPNSxf9/TO55KPf5l1F7d7o39LHb03lg8vCIn8uDWVm7LeLxDelYGDM04hnjNQjKzaJNEUu2pN8PJ7VGHDpWFshJpZJ+J/zArZ4TVavR8lBfZ6FGm3+EPLg3bdsi3vDLHTUjV50aMVwWyMrizemfrkq6P3zouFQgSh4qlbQQZHV7WiQ0faTC79x09kQ8sC7iHPcFqiIL2QdltGxLxNtu3FEz8ouXygI52V4/urI9NZCePiJi9piBHhX1tHv96JwxEVOcMrOyqTPitddHtHWk+wJXvMTLpv8YWajx9oEsuHePb4h4l7Uk2equRDy4Ln3+w5rTfaF9rCXJypauVEO8fS3JoeO8bDo3q9oj/uxX1pJUq9GyUITaQXnwj+vpXUtytrUk2Wrvinhwp7Ukh4z1suncrGmPeKJ3LcnoltRAfqgskJWFmyL+0lqSquWeh3Kffxlt611L8lE1I9laty09L97aldaSHD5e/Whudq8fPWycLJCTSiVi3oaI99/iOVG1Gi0P5f6sOEIe3Jv1HRGvv37HWpIrXxLRqmYkK0u3RCzanOrJJwxLeVD9aD4qlYhHN6Tnhc29a0lmW0uSle29yD9ym+dE1ZCF7IMyWr414q037KgZ+flL0/pC8rD7WpJ9RkQcYC1JVrp6Us3Ier3Is7W5K+LhtoiP3+E5UTVkocbbB7Lg3s3fGPHOX+94RvALNSNZ2d6LvK0j1YvtPypi2siBHhX1tLUr4iG9yLO2uj3izdaSVK3RslCE2kF58I/r6e0/+sHe/qOThkX8Qs1IVtq7Ux7c1JsFDhrrZdO5WbstrSXZXj+qF3l+Fm2K+AtrSaqWex7Kff5l1NG7lkT/0Xy1dUQ8tiFlgdG9a0laZYGsLNmc6ke7KhETe9eSqB/Nx/b60fdaS1K1RstDuT8rjpAH92ZDR8TrdlpL8tOXpHtE5GP5logne9eSjO9dS6J+NB+7149OG5F6UZOP7b3I/8ZakqrIQvZBGa3YGnHWDWpGcvbExnQcVCJiamt6Xkw+nlY/OjplQvKxpSvVDXl/fXVkocbbB7Lg3i3YFPGOm60lydX2XuTrerPAzFER+1pLkpXde5EfPDZinPrRrKxpj3jTr3Y8J7r6NL3I/5hGy0IRagflwT+u0ruW5APWkmRrW+9ako29WeDAMREThg/0qKintm0Rj23cUT/6jPF6kedm8eaIv7jJ++urlXseyn3+ZdTZE/HAOmtJcra+I9WRb+2KGDU04hnj9CLPzbItEQv1Is9WpZLWk73HWpKqNVoeyv1ZcYQ8uDebOiNee92OZwRXvCS9x558LN8a8eQma0n6WnY/Rvfdd19ERMydO3ePX3/lK18Zr3zlK+s5pCy0NEfMnTjQo2AgjRna2xyqt5m8BaF/ujFzpsWJ530whk8cE50bt8SvP/S1aHtk8S7fM3rGlHjBeR+IiUfOjk0LV8YVp35sl6+PP2xWHP+v74jWKeMiIuKuz/8oFv7stoimpnj2p98e+518TPR09cS2dRvjlo/+Z2xcsLxu89ub3OffaJqa0sKP4b3nAYuA8tPSHHG0LJC10UMjnjV5oEfBQJo6YkceHNrsWtAXcs9Duc+/0Qxpijhywo486KZefka2RBw7aaBHwUCa3Jr+IV+zR++4DgxttiC0L+Seh3Kff6NpHZKywPbzgIKv/EwcHjFxykCPgoE0c3T6hzw1NaUiH8+J+o4sZB80mvHDd60ZsSA0PzNGpX/IU1NTetH0oeMGeiQMlKHNEce4L9CnZCH7oNFMH7lrzYjzQF62149qHp4v9aOMHpruB0Sk/7Ug9E+XexbKff6N6EBrSbI2pCniKFkgayNbrCXJ3ZRWa0n6Wu55KPf5N5rmpogjrCXJWqu1JNmb1Jr+IV+zrCXpc7nnodzn32iGD4l4ppqRrE0YHnGcF8lkTf1o3pqaIg4b5zlRX5KF7INGM27YrmtJvFAkP/uO9KLpnDU1RRwyLv1Dnrb3IvecqO/IQvZBo5m2W/9R54G8WEtCS7P60dyN6q0f9Zyo7+SehXKffyOaM2bXZwRqRvKyvRc5+RqhfjR7k60l6XO556Hc599ompsinjF+p2cEzgHZae2tHyVfE4enf8jXTGtJ+lzueSj3+TeaYUP0H83d+GERx+kzkrX9RqV/yNP2+lFrSfqOLGQfNJqxu60lGWEtSXamjUz/kCf1o2zvRW4tSd+RheyDRrPPCDUjuTtgTPqHPKkfZWSL99f3tdyzUO7zb0SzR1tLkrPtvcjJl17kTGrd9TnRUPcF/mS556Hc599ompoiDh9vLUnOhvfWj5Kv8XqRZ2/GKO+v72u556Hc599ohjZbS5K7cdaSZG/6yPQPeWpqijjYWpI+JQvZB41m9NBdnxGMtJYkO9NGpH/oW9n9KN13330RETF37twBHgnAn+Z5X3x3PPKDa+OxH98Q+59+fLzgvA/ElS//5C7f07Fpa9z1hR/FsDEj49hP/tkuXxsyYli8+HufiJvPPj9W3v5wNDU3x7AJoyMiYtZLj4upzz4sLn/xR6PS1R1Hf/j1ceyn3hI3vvvcus1vb3KfPwBA7nko9/kDAOSeh3KfPwCQN1nIPgAA8iYL2QcAQN5yz0K5zx8AIPc8lPv8AQByz0O5zx8AyJssZB8AAHmThewDACBvuWeh3OcPAJB7Hsp9/gAAueeh3OcPAORNFrIPAIC8yUL2AQCQt9yzUO7zBwDIPQ/lPn8AgNzzUO7zBwDyJgvZBwAREc0DPYB6++UvfxmVSiVOP/30gR4KQM1aJ42NSXMPjMcvvikiIp686rcxat9JMWb2tF2+r6NtU6y8/eHo2rLtaX/HAWecGKt+90isvP3hiIio9PTEtjUb0v+vRAwZ1hJDhg+NiIiho0fElmVr+nNKheQ+fwCA3PNQ7vMHAMg9D+U+fwAgb7KQfQAA5E0Wsg8AgLzlnoVynz8AQO55KPf5AwDknodynz8AkDdZyD4AAPImC9kHAEDecs9Cuc8fACD3PJT7/AEAcs9Duc8fAMibLGQfAAB5k4XsAwAgb7lnodznDwCQex7Kff4AALnnodznDwDkTRayDwC2axnoAQBQ3Kj9JsfWFeui0t3z1J9tWrI6Ru03OTYuWF7V3zH+kBnR3dEZL/6fT8Wo6RNj7UML447P/HdsW7MhFl1zZ0x//hHxpnu/HV2btsbm5Wvj52d8ur+mU1ju8wcAyD0P5T5/AIDc81Du8wcA8iYL2QcAQN5kIfsAAMhb7lko9/kDAOSeh3KfPwBA7nko9/kDAHmThewDACBvspB9AAD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"text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 14, @@ -928,7 +932,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -942,9 +946,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-mpf/tutorials/index.mdx b/docs/addons/qiskit-addon-mpf/tutorials/index.mdx new file mode 100644 index 00000000000..70eaa755c8b --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/tutorials/index.mdx @@ -0,0 +1,11 @@ +--- +title: Multi-product formulas (MPF) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-mpf. +--- + +# Tutorials + +This page summarizes the available tutorials. + +* [Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas](01-getting-started) + diff --git a/docs/addons/qiskit-addon-obp/_package.json b/docs/addons/qiskit-addon-obp/_package.json new file mode 100644 index 00000000000..716d8c3c091 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/_package.json @@ -0,0 +1,4 @@ +{ + "name": "qiskit-addon-obp", + "version": "0.3.0" +} diff --git a/docs/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm.mdx b/docs/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm.mdx new file mode 100644 index 00000000000..55d19833c5c --- /dev/null +++ b/docs/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm.mdx @@ -0,0 +1,269 @@ + + +# Using different Lp-norms for Pauli term truncation + +**Note:** If you have not yet read the how-to guide on [truncating Pauli terms](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms) you should do so before reading this guide. + +You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget). In this guide, you will learn about its additional keyword argument `p_norm`, which can be used to change the Lp-norm used to estimate the error incurred by the truncated Pauli terms. + + + +## Constructing an example circuit + +For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms): + +``` +[1]: +``` + +```python +import rustworkx.generators +from qiskit.synthesis import LieTrotter +from qiskit_addon_utils.problem_generators import ( + PauliOrderStrategy, + generate_time_evolution_circuit, + generate_xyz_hamiltonian, +) +from qiskit_addon_utils.slicing import combine_slices, slice_by_gate_types + +# we generate a linear chain of 10 qubits +num_qubits = 10 +linear_chain = rustworkx.generators.path_graph(num_qubits) + +# we use an arbitrary XY model +hamiltonian = generate_xyz_hamiltonian( + linear_chain, + coupling_constants=(0.05, 0.02, 0.0), + ext_magnetic_field=(0.02, 0.08, 0.0), + pauli_order_strategy=PauliOrderStrategy.InteractionThenColor, +) +# we evolve for some time +circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0) +# slice the circuit by gate type +slices = slice_by_gate_types(circuit) + +# for visualization purposes only, we recombine the slices with barriers between them and draw the resulting circuit +combine_slices(slices, include_barriers=True).draw("mpl", fold=50, scale=0.6) +``` + +``` +[1]: +``` + +![../\_images/how\_tos\_bound\_error\_using\_p\_norm\_4\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_bound_error_using_p_norm_4_0.avif) + +The observable that we will look at is the total magnetization: + +``` +[2]: +``` + +```python +from qiskit.quantum_info import SparsePauliOp + +obs = SparsePauliOp.from_sparse_list( + [("Z", [i], 1.0) for i in range(num_qubits)], num_qubits=num_qubits +) +``` + +For reference, we compute the exact expectation value: + +``` +[3]: +``` + +```python +from qiskit.primitives import StatevectorEstimator + +estimator = StatevectorEstimator() +job = estimator.run([(circuit, obs)]) +res = job.result() +exact_exp = res[0].data.evs +print(exact_exp) +``` + +```python +9.318197859862146 +``` + + + +## Using the L1 norm + +By default, and as you have already seen in the [other how-to guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms), `p_norm=1` which means that the error is estimated as: + +$$ +|\langle\psi|\Delta|\psi\rangle| \leq \sum_{P\in\mathcal{T}} |c_P| +$$ + +where $\psi$ is the quantum state, $\Delta$ is the real difference between the exact and truncated observable (which is unknown), $\mathcal{T}$ is the set of Pauli terms that were truncated and $c_P$ is the Pauli terms coefficient. This inequality is rigorous but a very loose upper bound for most scenarios. + +For this simple guide, we will backpropagate 6 slices of our example circuit. We will use a constant error per slice of `0.001`. This value is to be understood as the budget within the given `p_norm`. + +``` +[4]: +``` + +```python +from qiskit_addon_obp.utils.truncating import setup_budget + +l1_truncation_error_budget = setup_budget(max_error_per_slice=0.001, p_norm=1) +print(l1_truncation_error_budget) +``` + +```python +TruncationErrorBudget(per_slice_budget=[0.001], max_error_total=inf, p_norm=1) +``` + +``` +[5]: +``` + +```python +from qiskit_addon_obp import backpropagate + +max_slices = 6 +l1_bp_obs, l1_remaining_slices, l1_metadata = backpropagate( + obs, slices[-max_slices:], truncation_error_budget=l1_truncation_error_budget +) +l1_reduced_circuit = combine_slices(slices[:-max_slices] + l1_remaining_slices) +print(f"Backpropagated {max_slices - len(l1_remaining_slices)} circuit slices.") +print( + f"New observable contains {len(l1_bp_obs)} terms and {len(l1_bp_obs.group_commuting(qubit_wise=True))} commuting groups." +) +``` + +```python +Backpropagated 6 circuit slices. +New observable contains 116 terms and 10 commuting groups. +``` + +We can now compute the expectation value of the backpropagated observable, as well as the error with respect to the exact reference: + +``` +[6]: +``` + +```python +estimator = StatevectorEstimator() +job = estimator.run([(l1_reduced_circuit, l1_bp_obs)]) +res = job.result() +l1_exp = res[0].data.evs +l1_error = exact_exp - l1_exp +print(l1_exp, l1_error) +``` + +```python +9.317869899338842 0.00032796052330397174 +``` + +Finally, we can plot the error incurred during the backpropagation of each slice, as well as the accumulated error. The accumulated error is simply the accumulated sum of the slice errors. We can see clearly that the accumulated error is a very loose upper bound to the actual error. + +``` +[7]: +``` + +```python +from matplotlib import pyplot as plt +from qiskit_addon_obp.utils.visualization import ( + plot_accumulated_error, + plot_slice_errors, +) + +fig, axes = plt.subplots(1, 2, figsize=(14, 5)) +axes[1].plot([6], [l1_error], "x", color="red", label="actual error") +plot_slice_errors(l1_metadata, axes[0]) +plot_accumulated_error(l1_metadata, axes[1]) +``` + +![../\_images/how\_tos\_bound\_error\_using\_p\_norm\_16\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_bound_error_using_p_norm_16_0.avif) + + + +## Using the L2 norm + +One can argue that the L2 norm is a better approximation of the incurred error than the L1 norm. That is because we can assume the quantum state $|\psi\rangle$ to be drawn from a Haar random ensemble in which case the incurred error follows a distribution with a vanishing mean and a variance that can be approximately bound by the L2 norm: + +$$ +|\langle\psi|\Delta|\psi\rangle| \lesssim \left( \sum_{P\in\mathcal{T}} |c_P|^2 \right)^{1/2} +$$ + +While the bound is not rigorous, it will only be violated in pathological cases. + +Once again, we will backpropagate 6 slices of our example circuit using a maximum error per slice of `0.001` (this time, interpreted on the L2 norm). + +``` +[8]: +``` + +```python +l2_truncation_error_budget = setup_budget(max_error_per_slice=0.001, p_norm=2) +print(l2_truncation_error_budget) +``` + +```python +TruncationErrorBudget(per_slice_budget=[0.001], max_error_total=inf, p_norm=2) +``` + +``` +[9]: +``` + +```python +max_slices = 6 +l2_bp_obs, l2_remaining_slices, l2_metadata = backpropagate( + obs, slices[-max_slices:], truncation_error_budget=l2_truncation_error_budget +) +l2_reduced_circuit = combine_slices(slices[:-max_slices] + l2_remaining_slices) +print(f"Backpropagated {max_slices - len(l2_remaining_slices)} circuit slices.") +print( + f"New observable contains {len(l2_bp_obs)} terms and {len(l2_bp_obs.group_commuting(qubit_wise=True))} commuting groups." +) +``` + +```python +Backpropagated 6 circuit slices. +New observable contains 84 terms and 6 commuting groups. +``` + +Once again, we can compute the expectation value of the backpropagated observable, as well as the error with respect to the exact reference: + +``` +[10]: +``` + +```python +estimator = StatevectorEstimator() +job = estimator.run([(l2_reduced_circuit, l2_bp_obs)]) +res = job.result() +l2_exp = res[0].data.evs +l2_error = exact_exp - l2_exp +print(l2_exp, l2_error) +``` + +```python +9.317829770853422 0.00036808900872387085 +``` + +Plotting the incurred errors per slice and the accumulated error yields a similar picture to before. + +``` +[11]: +``` + +```python +fig, axes = plt.subplots(1, 2, figsize=(14, 5)) +axes[1].plot([6], [l2_error], "x", color="red", label="actual error") +plot_slice_errors(l2_metadata, axes[0]) +plot_accumulated_error(l2_metadata, axes[1]) +``` + +![../\_images/how\_tos\_bound\_error\_using\_p\_norm\_24\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_bound_error_using_p_norm_24_0.avif) + +Note, that the accumulated error is once again simply the sum of the single slice errors. This is yet another loose bound due to the Minkowski inequality since we have to compute this bound recursively: + +$$ +|\langle\psi|\Delta_{i}|\psi\rangle| \leq |\langle\psi|\tilde{\Delta}_{i-1}|\psi\rangle| + \left( \sum_{P\in\mathcal{T_i}} |c_P|^2 \right)^{1/2} = |\langle\psi|\tilde{\Delta}_{i}|\psi\rangle| +$$ + +where the new subscript $i$ indicates the current slice iteration making $\Delta_i$ the actual error at backpropagation iteration $i$, $\tilde{\Delta}_{i-1}$ the approximated truncation error from iteration $i-1$ and $\mathcal{T}_i$ the set of Pauli terms truncated at iteration $i$. diff --git a/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb b/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb index aeb5dc7702e..96ca9c4be6d 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb @@ -43,8 +43,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 1, @@ -258,8 +259,9 @@ "outputs": [ { "data": { + "image/png": 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eeuqpK2a5dALw+0vl/iglJcWmmPVHzs7O1tku1VGzZs0wmUwcOHCABx544LL1Bw4coE6dOtSvX79U+wsICODQoUN8/vnnbN68maeffpoZM2awZcsWLly4gL29PTt37rysyFOrVi3r966urjbFISienXbjjTeyatUqnnrqKf71r3+xZMkS6/oZM2YwZ84cYmNjCQ4Oxt3dnWeffZaCgoIyvBu2Lly4gJ+fn00PqksuFbz+6Ouvv+aee+4Biv/h/dVXX/Hcc8/x22+/4ejoyGuvvca4ceMYN27cNecSqUou9berMYqMDiBSQ/2xp9Ol51Bhxaf777+fJk2a8O677+Lv709RURFt2rSxnnv8sYVBaVxqtv37DwQvXrTt37Fq1SpGjRrFzJkzCQsLo3bt2syYMYOkpKRSH+fChQs88cQTNn04L2ncuLH1e3f3y///XdJ5Wmlc63YiIjXdqfN5DHgvmYPp5/F0dWTJoC50aFzH6FjGX2oXHR3NwIED6dy5MyEhIcTGxpKTk2O9y92AAQNo2LAh06ZNA4qbRHfv3p2ZM2fSs2dPVq1axY4dO1iwYAEAJpOJZ599lqlTpxIUFERgYCATJkzA39/fWtxKSkpi+/btdOvWjTp16vDjjz8yYcIEbrzxRmsBa+nSpTg5OdGhQwcA1q1bx6JFi1i4cOF1focqj3r16nHXXXfx1ltv8dxzz9mcJKWnp7N8+XIGDBhgc6Kwbds2m31s27bN5lI9V1dX7r//fu6//36GDRtGy5Yt2bt3Lx06dMBsNnPq1CluvfXWMmft378/y5cvp1GjRtjZ2dGzZ0/rum+//ZbevXvzyCOPAMVFxx9++IHWrVvb7OOPJ2Xbtm0jKCjoskIYQMeOHUlPT8fBwaHUM5U6d+5MSkoKO3fu5IUXXiAhIYG0tDR69erFrl27sLOzM+wOgSIiItVSSY3Ef9/z6ffPy8mvv/7KoUOHePfdd63nNN98843NmLZt27Jw4ULOnj1b4t9+JycnzGazzbJLH/SdPHnSer76+0bjgPWStqefftq67GozsUvSsWNH9u/fb3Pp37Vq1aoVhYWFJCUlWS+1u/T+/PE8TEREyubYuVweWZjEz7/mUr+2M/8cHEoL39pGxwIqQeGpb9++nD59mpiYGNLT02nfvj0bN260NgdPS0uzuX1q165dWbFiBePHj2fcuHEEBQURHx9PmzZtrGNeeOEFcnJyGDp0KJmZmXTr1o2NGzdar0F3c3Nj3bp1TJw4kZycHPz8/OjRowfjx4+3mbE0ZcoUfvnlFxwcHGjZsiWrV6/moYceqvD3xNMu77p8Eu5pl1fmbebNm0fXrl2JiIhg6tSpBAYG8v333zN69GgaNmxobeB9ybfffsv06dOJjIxk8+bNrF27lk8++QQovmuJ2WwmNDQUNzc3/vnPf+Lq6kqTJk2oV68e/fv3Z8CAAcycOZMOHTpw+vRpEhISaNu2rU0hqST9+/fn5Zdf5pVXXuGhhx6y+bkGBQXxwQcfsHXrVurUqcOsWbPIyMi47IQnLS2N6OhonnjiCXbt2sWbb77JzJkzSzxeeHg4YWFhREZGMn36dJo3b86JEyf45JNPeOCBB0qcfu7q6kqzZs344IMPuP3222nWrBlbt27llltuuewSw0v2799PQUEBZ8+e5fz589YTzLJMmRcREamxzOaSG4lfev6H4k55qFOnDvXq1WPBggX4+fmRlpZm7X95SVRUFK+++iqRkZFMmzYNPz8/du/ejb+/P2FhYTRt2pTU1FRSUlJo1KgRtWvXxtXVlZtvvpnXXnuNwMBATp06xfjx4232GxQUxLJly/jss88IDAzk/fffZ/v27QQGBpY6/4svvsjNN9/M8OHDefzxx3F3d2f//v1s3ryZefPmlem9CAoKonfv3gwZMoR33nmH2rVrM2bMGBo2bEjv3r3LtC8REfmfI6cu8Oh7SZzMyqOhlyvLHw+lqXfluZLA8MITYL0zRklKunSpT58+1uaDJTGZTEyePJnJkyeXuD44OJgvvvjiqpkGDhzIwIEDrzqmvLm5uWFn70B3p9Trdkw7e4fLehtdTVBQEDt27GDixIk8/PDDnD17Fl9fXyIjI5k4ceJln9I9//zz7Nixg0mTJuHh4cGsWbOIiIgAii9Be+2114iOjsZsNhMcHMxHH31k7ZO0ePFipk6dyvPPP8/x48fx9vbm5ptv5r777vvTnM2aNSMkJITk5GRrT6lLxo8fz08//URERARubm4MHTqUyMhIsrKybMYNGDCA3377jZCQEOzt7Rk5cuQVm4SbTCY2bNjASy+9xKBBgzh9+jS+vr7cdtttl91h8Y+++uor6+/zli1buO2226449t577+WXX36xPr/0CWdZ+m6JiIjUWC+/fOV1FXSZnZ2dHatWrWLEiBG0adOGFi1aMHfuXG6//XbrGCcnJzZt2sTzzz/PvffeS2FhIa1bt2b+/PlAcXPydevWcccdd5CZmcnixYt57LHHWLRoEYMHD6ZTp060aNGC6dOnc/fdd1v3+8QTT7B792769u2LyWQiKiqKp59+mk8//bTU+du2bcuWLVt46aWXuPXWW7FYLNx444307dv3mt6PxYsXM3LkSO677z4KCgq47bbb2LBhg/WOdiIiUjb7jmcxcFEyv+YUcGN9d/75eCh+nmW/hLsimSz6F2uFyc7OxtPTk6ysLDw8PKzL8/LySE1NJTAw0DoL65LrfUtx3cq7erva75pIZXHy5EkWLFjA+rxWNarHUz1TDr1cDlhvbS7GutLfbLm+rvZz0N80uV70uyYiVcWOn88yaMl2zucV0qahB0sHhVCv1vXrO13a86dKMeNJ/sfT01OFIBERERERERG5oi0/nOaJ93eQd7GIkKZ1WfhYZzxcKufsURWeRERERERERESqiE/3nmTEqt1cNFu4vUV93u7fCVeny2+CVVmo8CQiIiIiIiIiUgWs2XGUMR9+R5EFerb1Y/bD7XFysPvzDQ2kwpOIiIiIiIiISCW36JtUJn+8H4B+XQJ45YFg7O1MBqf6cyo8GUh93aWi6XdMRERERESkarNYLMxJOEzs54cBeLxbIC/1bIXJVPmLTqDCkyEu3S42NzcXV9fKdZtDqV4u3SFRtygWERERERGpeiwWC1M+PsCib1MBeP6u5gy/s1mVKTqBCk+GsLe3x8vLi1OnTgHg5uZWpX5ppPKzWCzk5uZy6tQpvLy8sLevvI3mRERERERE5HLmIgtj133Hmh3HAJh4f2sG3RJocKqyU+HJIL6+vgDW4pNIRfDy8rL+romIiIiIiEjVkF9o5rnVKWzYm46dCaY/1I6HOjUyOtY1UeHJICaTCT8/Pxo0aMDFixeNjiPVkKOjo2Y6iYiIVDFNmzbl2Wef5dlnnzU6SpndfvvttG/fntjY2CuOqcqvT0TkevmtwMwT/9zJf344jZO9HXOj2tOjjZ/Rsa6ZCk8Gs7e3V3FAREREpJo7evQoEydOZOPGjZw5cwY/Pz8iIyOJiYmhXr16RsersmbMmMHu3btZsWIFK1asYOHChXzxxRfW9Xl5eTz55JPs3LmTAwcOcN999xEfH29cYBGRP5Gdd5HBS7az/edzuDras2BAJ24Nqm90rL/EzugAIiIiIiLV2U8//UTnzp05fPgwK1eu5MiRI8TFxZGQkEBYWBhnz541LJvZbKaoqMiw4/9ViYmJ3HLLLQB8/fXX1u8vMZvNuLq6MmLECMLDw42IKCJSamcu5BO1YBvbfz5HbRcH/vl4SJUvOoEKTyIiIiIiFWrYsGE4OTmxadMmunfvTuPGjbnnnnv4/PPPOX78OC+99JLN+PPnzxMVFYW7uzsNGzZk/vz51nUWi4WXX36Zxo0b4+zsjL+/PyNGjLCuz8/PZ9SoUTRs2BB3d3dCQ0P56quvrOuXLFmCl5cX69evp3Xr1jg7O7Nw4UJcXFzIzMy0yTFy5EjuvPNOAH799VeioqJo2LAhbm5uBAcHs3Llystea2FhIcOHD8fT0xNvb28mTJiAxWK54nuTmZnJ448/Tv369fHw8ODOO+9kz549pX5vf194+uabby4rPLm7u/P2228zZMgQ9b0UkUrtZNZvPPxOIt+fyMa7lhOrht5MpyZ1jY5VLlR4EhEREZEqyWKxkFtQaMjjasWU3zt79iyfffYZTz/9NK6urjbrfH196d+/P6tXr7bZ34wZM2jXrh27d+9mzJgxjBw5ks2bNwPw4YcfMnv2bN555x0OHz5MfHw8wcHB1m2HDx9OYmIiq1at4rvvvqNPnz706NGDw4cPW8fk5uby+uuvs3DhQr7//nv69++Pl5cXH374oXWM2Wxm9erV9O/fHyi+ZK1Tp0588skn7Nu3j6FDh/Loo4+SnJxs85qWLl2Kg4MDycnJzJkzh1mzZrFw4cIrvj99+vTh1KlTfPrpp+zcuZOOHTvyt7/97aqzwF577TW8vLzw8vIiPT2d7t274+Xlxb59+3j44Yfx8vLim2++udqPRUSkUkk9k8NDbyfy0+kc/D1dWPNEGDf5exodq9yox5OIiIiIVEm/XTTTOuYzQ469f3IEbk5/fip9+PBhLBYLrVq1KnF9q1atOHfuHKdPn6ZBgwYA3HLLLYwZMwaA5s2b8+233zJ79mzuuusu0tLS8PX1JTw8HEdHRxo3bkxISAgAaWlpLF68mLS0NPz9/QEYNWoUGzduZPHixbz66qsAXLx4kbfeeot27dpZc/Tr148VK1YwePBgABISEsjMzOTBBx8EoGHDhowaNco6/plnnuGzzz5jzZo11uMDBAQEMHv2bEwmEy1atGDv3r3Mnj2bIUOGXPbav/nmG5KTkzl16hTOzs4AvPHGG8THx/PBBx8wdOjQEt+zJ598kn79+rFkyRK2bdtGXFwcGzZsYMmSJaxZswZAs5tEpMo4cDKbR99L5syFfAK93fnn46E09HL98w2rEM14EhEREamG5s+fT9OmTXFxcSE0NPSymSl/tHbtWlq2bImLiwvBwcFs2LDBZr3FYiEmJgY/Pz9cXV0JDw+3mUUDxXcsM5lMNo/XXnut3F9bVVTaGVIAYWFhlz0/cOAAUDxD6LfffuOGG25gyJAh/Otf/6KwsBCAvXv3Yjabad68ObVq1bI+tmzZwo8//mjdn5OTE23btrU5Rv/+/fnqq684ceIEAMuXL6dnz554eXkBxTOgpkyZQnBwMHXr1qVWrVp89tlnpKWl2ezn5ptvxmQy2WQ/fPgwZrP5ste5Z88eLly4QL169Wzypqam2uT9Iy8vL5o2bUpycjIPPvggTZs2Zffu3fTq1YumTZtaf+9FRCq7XWnn6PtOImcu5NPKz4M1T4RVu6ITaMaTiIiISLWzevVqoqOjiYuLIzQ0lNjYWCIiIjh06JB1Vs3vbd26laioKKZNm8Z9993HihUriIyMZNeuXbRp0waA6dOnM3fuXJYuXUpgYCATJkwgIiKC/fv32/wjf/LkyTazW2rXrl1hr9PV0Z79kyMqbP9/duzSaNasGSaTiQMHDvDAAw9ctv7AgQPUqVOH+vVL1zw2ICCAQ4cO8fnnn7N582aefvppZsyYwZYtW7hw4QL29vbs3Lnzsrsm16pV63/ZXV1tikMAXbp04cYbb2TVqlU89dRT/Otf/2LJkiXW9TNmzGDOnDnExsYSHByMu7s7zz77LAUFBaXKXZILFy7g5+dn04PqkksFrz/6+uuvueeee4DiSwa/+uornnvuOX777TccHR157bXXGDduHOPGjbvmXCIi18O3R84wZNkOcgvMdGzsxeLHQvB0czQ6VoVQ4UlERESkmpk1axZDhgxh0KBBAMTFxfHJJ5+waNEi6yVcvzdnzhx69OjB6NGjAZgyZQqbN29m3rx5xMXFYbFYiI2NZfz48fTu3RuAZcuW4ePjQ3x8PP369bPuq3bt2tftMieTyVSqy92MVK9ePe666y7eeustnnvuOZs+T+np6SxfvpwBAwbYFIK2bdtms49t27bZXKrn6urK/fffz/3338+wYcNo2bIle/fupUOHDpjNZk6dOsWtt95a5qz9+/dn+fLlNGrUCDs7O3r27Gld9+2339K7d28eeeQRAIqKivjhhx9o3bq1zT6SkpIuyx4UFHRZIQygY8eOpKen4+DgQNOmTUuVsXPnzqSkpLBz505eeOEFEhISSEtLo1evXuzatQs7Ozvq1q0ezXhFpPra9H06w1fspsBcxK1B3rzzaKdK//fsr9CldiIiIiLVSEFBATt37rS5dbydnR3h4eEkJiaWuE1iYuJlt5qPiIiwjk9NTSU9Pd1mjKenJ6GhoZft87XXXqNevXp06NCBGTNmWC8DK0l+fj7Z2dk2j+po3rx55OfnExERwX/+8x+OHj3Kxo0bueuuu2jYsCGvvPKKzfhvv/2W6dOn88MPPzB//nzWrl3LyJEjgeK70r333nvs27ePn376iX/+85+4urrSpEkTmjdvTv/+/RkwYADr1q0jNTWV5ORkpk2bxieffPKnOfv378+uXbt45ZVXeOihh6x9lwCCgoLYvHkzW7du5cCBAzzxxBNkZGRcto+0tDSio6M5dOgQK1eu5M0337Rm/6Pw8HDCwsKIjIxk06ZN/Pzzz2zdupWXXnqJHTt2lLiNq6srzZo1IzU1ldtvv51mzZpx7NgxbrnlFpo3b06zZs0uKzzt37+flJQUzp49S1ZWFikpKaSkpPzp+yEiUhHW7TrGU8t3UWAuIuImHxYO7Fyti06gGU8iIiIi1cqZM2cwm834+PjYLPfx8eHgwYMlbpOenl7i+PT0dOv6S8uuNAZgxIgRdOzYkbp167J161bGjh3LyZMnmTVrVonHnTZtGpMmTSrbC6yCgoKC2LFjBxMnTuThhx/m7Nmz+Pr6EhkZycSJEy8rlDz//PPs2LGDSZMm4eHhwaxZs4iIKL6k0MvLi9dee43o6GjMZjPBwcF89NFH1KtXD4DFixczdepUnn/+eY4fP463tzc333wz991335/mbNasGSEhISQnJxMbG2uzbvz48fz0009ERETg5ubG0KFDiYyMJCsry2bcgAED+O233wgJCcHe3p6RI0desUm4yWRiw4YNvPTSSwwaNIjTp0/j6+vLbbfddtnv2h999dVX9OnTB4AtW7Zw2223XXHsvffeyy+//GJ93qFDB6BsfbdERMrD+4k/M+Hf3wPwYMdGvP5gMA721X8+kMmi/+NWmOzsbDw9PcnKysLDw8PoOCIildLJkydZsGAB6/Na8avF3eg41009Uw69XA4wdOhQ/Pz8jI5T41Wnv9knTpygYcOGbN261aZJ9QsvvMCWLVsuuxQKiptNL126lKioKOuyt956i0mTJpGRkcHWrVu55ZZbOHHihM3v68MPP4zJZGL16tUlZlm0aBFPPPEEFy5csJk9c0l+fj75+fnW59nZ2QQEBJT4c8jLyyM1NZXAwEA1jpYKpd81ESlvFouFt776kRmfHQLgsa5NibmvNXZ2pj/ZsnIr7flT9S+tiYiIiNQg3t7e2NvbX3YZVEZGxhV7L/n6+l51/KWvZdknQGhoKIWFhfz8888lrnd2dsbDw8PmISIiUp1YLBZe23jQWnQacWczJt5f9YtOZaHCk4iIiEg14uTkRKdOnUhISLAuKyoqIiEhwWYG1O+FhYXZjAfYvHmzdXxgYCC+vr42Y7Kzs0lKSrriPgFSUlKws7Mr8U56IiIi1Z25yMJL8ft4Z8tPALx0byui725x2Z1Fqzv1eBIRERGpZqKjoxk4cCCdO3cmJCSE2NhYcnJyrHe5GzBgAA0bNmTatGkAjBw5ku7duzNz5kx69uzJqlWr2LFjBwsWLACKe/E8++yzTJ06laCgIAIDA5kwYQL+/v5ERkYCxQ3Kk5KSuOOOO6hduzaJiYk899xzPPLII9SpU8eQ90FERMQoF81FPL9mD+v3nMBkgmkPBNMvpLHRsQyhwpOIiIhINdO3b19Onz5NTEwM6enptG/fno0bN1obNqelpWFn97+J7127dmXFihWMHz+ecePGERQURHx8PG3atLGOeeGFF8jJyWHo0KFkZmbSrVs3Nm7caO2B4+zszKpVq3j55ZfJz88nMDCQ5557jujo6Ov74kVERAyWd9HM08t38cXBUzjYmYjt15772vobHcswai5egapTo1IRkYqi5uJqLl4Z6G925XC1n4MaPsv1ot81Efkrzudd5PGlO0hKPYuzgx1xj3TijpbV85Lz0p4/acaTiIiIiFQZ+sxUKpp+x0TkWp3LKWDg4mS+O5ZFLWcH3hvYmdAb6hkdy3AqPImIiIhIpefo6AhAbm4urq6uBqeR6iw3Nxf43++ciEhpZGTn8cjCJA6fukAdN0eW/SOU4EaeRseqFFR4EhEREZFKz97eHi8vL06dOgWAm5tbjbsrkFQsi8VCbm4up06dwsvLC3t7e6MjiUgVkfZrLv3f28bRs7/h4+HMPweHEuRT2+hYlYYKTyIiIiJSJfj6+gJYi08iFcHLy8v6uyYi8md+yDjPIwuTOHU+nyb13Pjn4FAC6roZHatSUeFJRERERKoEk8mEn58fDRo04OLFi0bHkWrI0dFRM51EpNS+O5bJwEXJnMu9SAuf2rw/OIQGHropwR+p8CQiIiIiVYq9vb2KAyIiYqhtP/3K40t3cCG/kHYBXiwd1AUvNyejY1VKKjyJiIiIiIiIiJTSFwczeOqfu8gvLCLshnq8O7AztZxVXrkSvTMiIiIiIiIiIqWwfs8JolenUFhkIbxVA+b9vSMujpqFezUqPImIiIiIiIiI/IkVSWm8FL8XiwV6t/fnjT7tcLS3MzpWpafCk4iIiIiIiIjIVbyz5UemfXoQgP6hjZnSuw12diaDU1UNKjyJiIiIiIiIiJTAYrHwxqZDzP/yRwCeuv1GXohogcmkolNpqfAkIiIiIiIiIvIHRUUWXv7oe5Yl/gLACz1a8PTtzQxOVfWo8CQiIiIiIiIi8juF5iJGf/Ad/9p9HJMJJvduw6M3NzE6VpWkwpOIiIiIiIiIyH/lXTTzzMrdbN6fgb2diZl92hHZoaHRsaosFZ5ERERERERERICc/EKGvr+Db4/8ipODHW/9vSPhrX2MjlWlqfAkIiIiIiIiIjVeZm4Bg5ZsZ3daJu5O9rw7sDNdb/Q2OlaVZ2d0AID58+fTtGlTXFxcCA0NJTk5+arj165dS8uWLXFxcSE4OJgNGzbYrLdYLMTExODn54erqyvh4eEcPnzYZkyvXr1o3LgxLi4u+Pn58eijj3LixAmbMd999x233norLi4uBAQEMH369PJ5wSIiIiIiIiJSaZw6n0e/BdvYnZaJp6sj/3w8VEWncmJ44Wn16tVER0czceJEdu3aRbt27YiIiODUqVMljt+6dStRUVEMHjyY3bt3ExkZSWRkJPv27bOOmT59OnPnziUuLo6kpCTc3d2JiIggLy/POuaOO+5gzZo1HDp0iA8//JAff/yRhx56yLo+Ozubu+++myZNmrBz505mzJjByy+/zIIFCyruzRARERERERGR6+rYuVwejkvkYPp56td2Zs0TYXRoXMfoWNWGyWKxWIwMEBoaSpcuXZg3bx4ARUVFBAQE8MwzzzBmzJjLxvft25ecnBw+/vhj67Kbb76Z9u3bExcXh8Viwd/fn+eff55Ro0YBkJWVhY+PD0uWLKFfv34l5li/fj2RkZHk5+fj6OjI22+/zUsvvUR6ejpOTk4AjBkzhvj4eA4ePFiq15adnY2npydZWVl4eHiU6X0REakpTp48yYIFC1if14pfLe5Gx7lu6ply6OVygKFDh+Ln52d0nBpPf7MrB/0cRETkejty6gKPvpfEyaw8GtVxZfnjoTSpV3POSf+K0v7dNnTGU0FBATt37iQ8PNy6zM7OjvDwcBITE0vcJjEx0WY8QEREhHV8amoq6enpNmM8PT0JDQ294j7Pnj3L8uXL6dq1K46Ojtbj3Hbbbdai06XjHDp0iHPnzpW4n/z8fLKzs20eIiIiIiIiIlL57DueRd93EjmZlceN9d1Z+2SYik4VwNDC05kzZzCbzfj42HaI9/HxIT09vcRt0tPTrzr+0tfS7PPFF1/E3d2devXqkZaWxr///e8/Pc7vj/FH06ZNw9PT0/oICAgocZyIiIiIiIiIGGf7z2eJWrCNX3MKaNPQgzVPhOHn6Wp0rGrJ8B5PRho9ejS7d+9m06ZN2NvbM2DAAP7KlYdjx44lKyvL+jh69Gg5phURERERERGRv2rLD6d59L0kzucXEtK0LiuG3Ey9Ws5Gx6q2HIw8uLe3N/b29mRkZNgsz8jIwNfXt8RtfH19rzr+0teMjAybnhkZGRm0b9/+suN7e3vTvHlzWrVqRUBAANu2bSMsLOyKx/n9Mf7I2dkZZ2f9soqIiIiIiIhURhv2nmTkqt1cNFu4vUV93u7fCVcne6NjVWuGznhycnKiU6dOJCQkWJcVFRWRkJBAWFhYiduEhYXZjAfYvHmzdXxgYCC+vr42Y7Kzs0lKSrriPi8dF4r7NF06zn/+8x8uXrxoc5wWLVpQp46624uIiIiIiIhUJWt2HGX4il1cNFvo2daPBY92VtHpOjD8Urvo6Gjeffddli5dyoEDB3jqqafIyclh0KBBAAwYMICxY8dax48cOZKNGzcyc+ZMDh48yMsvv8yOHTsYPnw4ACaTiWeffZapU6eyfv169u7dy4ABA/D39ycyMhKApKQk5s2bR0pKCr/88gtffPEFUVFR3Hjjjdbi1N///necnJwYPHgw33//PatXr2bOnDlER0df3zdIRERERERERP6SRd+k8sIH31FkgX5dApjbrwNODoaXRGoEQy+1A+jbty+nT58mJiaG9PR02rdvz8aNG62NvNPS0rCz+98vQ9euXVmxYgXjx49n3LhxBAUFER8fT5s2baxjXnjhBXJychg6dCiZmZl069aNjRs34uLiAoCbmxvr1q1j4sSJ5OTk4OfnR48ePRg/frz1UjlPT082bdrEsGHD6NSpE97e3sTExDB06NDr+O6IiIiIiIiIyLWyWCzMSThM7OeHARhyayDj7m2FyWQyOFnNYbL8lW7aclXZ2dl4enqSlZWFh4eH0XFERCqlkydPsmDBAtbnteJXS825fW09Uw69XA4wdOhQm56EYgz9za4c9HMQEZHyVFRkYeonB1j0bSoAz9/VnOF3NlPRqZyU9u+24TOeRERERERERETKk7nIwpgPv2PtzmMAvHx/ax67JdDgVDWTCk8iIiIiIiIiUm3kF5p5bnUKG/amY2eC6Q+146FOjYyOVWOp8CQiIiIiIiIi1UJuQSFP/nMX//nhNE72dsyNak+PNmprYCQVnkRERERERESkysvOu8g/Fm9nxy/ncHW0Z8GATtwaVN/oWDWeCk8iIiIiIiIiUqWduZDPwEXJfH8im9ouDiwZ1IVOTeoaHUtQ4UlEREREREREqrATmb/xyHtJ/HQ6B+9aTiz9Rwg3+XsaHUv+S4UnEREREREREamSUs/k8MjCJI5n/oa/pwv/fDyUG+rXMjqW/I4KTyIiIiIiIiJS5Rw4mc2j7yVz5kI+N3i78/7joTT0cjU6lvyBCk8iIiIiIiIiUqXsSjvHY4uSyc4rpJWfB8v+EUL92s5Gx5ISqPAkIiIiIiIiIlXGN4fPMPT9HeQWmOnY2IvFj4Xg6eZodCy5AhWeRERERERERKRK2PR9OsNX7KbAXMStQd6882gn3JxU2qjM9NMRERERERERkUpv3a5jjP7gO8xFFiJu8mFuVAecHeyNjiV/QoUnEREREREREanUliX+TMy/vwfgwY6NeP3BYBzs7QxOJaWhwpOIiIiIiIiIVEoWi4W3vvqRGZ8dAuCxrk2Jua81dnYmg5NJaanwJCIiIiIiIiKVjsVi4bWNB3lny08AjLizGc/d1RyTSUWnqkSFJxERERERERGpVMxFFsbH72NlchoAL93biiG33WBwKrkWKjyJiIiIiIiISKVx0VxE9Jo9fLTnBCYTTHsgmH4hjY2OJddIhScRERERERERqRTyLpp5evkuvjh4Ckd7E7P7tue+tv5Gx5K/QIUnERERERERETHc+byLPL50B0mpZ3F2sCPu0U7c0aKB0bHkL1LhSUREREREREQMdTangMcWJ/PdsSxqOTvw3sDOhN5Qz+hYUg5UeBIRERERERERw2Rk5/HIwiQOn7pAXXcnlg4KIbiRp9GxpJyo8CQiIiIiIiIihkj7NZf+723j6Nnf8PFwZvnjoTRrUNvoWFKOVHgSERERERERkevuh4zzPLIwiVPn82lSz41/Dg4loK6b0bGknKnwJCIiIiIiIiLX1YGT2US9u43M3Iu08KnN+4NDaODhYnQsqQAqPImIiIiIiIjIdTXt04Nk5l6kXYAXSwd1wcvNyehIUkHsjA4gIiIiIiIiIjXH0bO5fH34NABv9uugolM1p8KTiIiIiIiIiFw3a3YcxWKBbs28aVxPPZ2qOxWeRERERKqh+fPn07RpU1xcXAgNDSU5Ofmq49euXUvLli1xcXEhODiYDRs22Ky3WCzExMTg5+eHq6sr4eHhHD58uMR95efn0759e0wmEykpKeX1kkREpBooNBexZsdRAPqFBBicRq4HFZ5EREREqpnVq1cTHR3NxIkT2bVrF+3atSMiIoJTp06VOH7r1q1ERUUxePBgdu/eTWRkJJGRkezbt886Zvr06cydO5e4uDiSkpJwd3cnIiKCvLy8y/b3wgsv4O/vX2GvT0REqq4vD50mIzufeu5O3N3a1+g4ch2o8CQiIiJSzcyaNYshQ4YwaNAgWrduTVxcHG5ubixatKjE8XPmzKFHjx6MHj2aVq1aMWXKFDp27Mi8efOA4tlOsbGxjB8/nt69e9O2bVuWLVvGiRMniI+Pt9nXp59+yqZNm3jjjTcq+mWKiEgVtCo5DYAHOzXCyUEliZpAP2URERGRaqSgoICdO3cSHh5uXWZnZ0d4eDiJiYklbpOYmGgzHiAiIsI6PjU1lfT0dJsxnp6ehIaG2uwzIyODIUOG8P777+Pm9uc9O/Lz88nOzrZ5iIhI9XUy6ze+PFQ8+7ZvF11mV1Oo8CQiIiJSjZw5cwaz2YyPj4/Nch8fH9LT00vcJj09/arjL3292hiLxcJjjz3Gk08+SefOnUuVddq0aXh6elofAQH6R4iISHW2ZvsxiiwQGliXG+vXMjqOXCcqPImIiIjIX/bmm29y/vx5xo4dW+ptxo4dS1ZWlvVx9OjRCkwoIiJGMhdZrE3Fo0IaG5xGricVnkRERESqEW9vb+zt7cnIyLBZnpGRga9vyU1cfX19rzr+0terjfniiy9ITEzE2dkZBwcHmjVrBkDnzp0ZOHBgicd1dnbGw8PD5iEiItXT14dPczzzNzxdHenRRk3FaxIVnkRERESqEScnJzp16kRCQoJ1WVFREQkJCYSFhZW4TVhYmM14gM2bN1vHBwYG4uvrazMmOzubpKQk65i5c+eyZ88eUlJSSElJYcOGDUDxHfZeeeWVcn2NIiJS9az8b1Px/+vYEBdHe4PTyPXkYHQAERERESlf0dHRDBw4kM6dOxMSEkJsbCw5OTkMGjQIgAEDBtCwYUOmTZsGwMiRI+nevTszZ86kZ8+erFq1ih07drBgwQIATCYTzz77LFOnTiUoKIjAwEAmTJiAv78/kZGRADRubHvZRK1axb07brzxRho1anSdXrmIiFRGp87nkXCguKm4LrOreVR4EhEREalm+vbty+nTp4mJiSE9PZ327duzceNGa3PwtLQ07Oz+N/G9a9eurFixgvHjxzNu3DiCgoKIj4+nTZs21jEvvPACOTk5DB06lMzMTLp168bGjRtxcXG57q9PRESqlg92HqOwyELHxl4096ltdBy5zkwWi8VidIjqKjs7G09PT7KystSzQETkCk6ePMmCBQtYn9eKXy3uRse5buqZcujlcoChQ4fi5+dndJwaT3+zKwf9HEREqp+iIgu3v/EVaWdzmfFQW/p01h1Mq4vS/t1WjycRERERERERqRCJP/1K2tlcajs70LOtPmyriVR4EhEREREREZEKcampeO8O/rg5qdtPTaTCk4iIiIiIiIiUu18v5PPZ9+mAmorXZJWi8DR//nyaNm2Ki4sLoaGhJCcnX3X82rVradmyJS4uLgQHB1tv13uJxWIhJiYGPz8/XF1dCQ8P5/Dhw9b1P//8M4MHDyYwMBBXV1duvPFGJk6cSEFBgc0Yk8l02WPbtm3l++JFREREREREqqF1u45z0WyhbSNPbvL3NDqOGMTwwtPq1auJjo5m4sSJ7Nq1i3bt2hEREcGpU6dKHL9161aioqIYPHgwu3fvJjIyksjISPbt22cdM336dObOnUtcXBxJSUm4u7sTERFBXl4eAAcPHqSoqIh33nmH77//ntmzZxMXF8e4ceMuO97nn3/OyZMnrY9OnTpVzBshIiIiIiIiUk1YLBZWbi++zK5fF812qskMLzzNmjWLIUOGMGjQIFq3bk1cXBxubm4sWrSoxPFz5syhR48ejB49mlatWjFlyhQ6duzIvHnzgOJf7tjYWMaPH0/v3r1p27Yty5Yt48SJE8THxwPQo0cPFi9ezN13380NN9xAr169GDVqFOvWrbvsePXq1cPX19f6cHR0rLD3QkRERERERKQ6SE49y0+nc3BzsqdXe3+j44iBDC08FRQUsHPnTsLDw63L7OzsCA8PJzExscRtEhMTbcYDREREWMenpqaSnp5uM8bT05PQ0NAr7hMgKyuLunXrXra8V69eNGjQgG7durF+/fqrvp78/Hyys7NtHiIiIiIiIiI1zartRwHo1c6fWs5qKl6TGVp4OnPmDGazGR8fH5vlPj4+pKenl7hNenr6Vcdf+lqWfR45coQ333yTJ554wrqsVq1azJw5k7Vr1/LJJ5/QrVs3IiMjr1p8mjZtGp6entZHQEDAFceKiIiIiIiIVEeZuQV8svckAP3UVLzGq/Flx+PHj9OjRw/69OnDkCFDrMu9vb2Jjo62Pu/SpQsnTpxgxowZ9OrVq8R9jR071mab7OxsFZ9ERERERESkRvnX7uMUFBbRys+Ddo3UVLymM3TGk7e3N/b29mRkZNgsz8jIwNfXt8RtfH19rzr+0tfS7PPEiRPccccddO3alQULFvxp3tDQUI4cOXLF9c7Oznh4eNg8RERERERERGoKi8XCquTiy+yiQgIwmUwGJxKjGVp4cnJyolOnTiQkJFiXFRUVkZCQQFhYWInbhIWF2YwH2Lx5s3V8YGAgvr6+NmOys7NJSkqy2efx48e5/fbb6dSpE4sXL8bO7s/fipSUFPz8/Mr0GkVERERERERqit1HMzmUcR4XRzt6t29odBypBAy/1C46OpqBAwfSuXNnQkJCiI2NJScnh0GDBgEwYMAAGjZsyLRp0wAYOXIk3bt3Z+bMmfTs2ZNVq1axY8cO64wlk8nEs88+y9SpUwkKCiIwMJAJEybg7+9PZGQk8L+iU5MmTXjjjTc4ffq0Nc+lWVFLly7FycmJDh06ALBu3ToWLVrEwoULr9dbIyIiIiIiIlKlrEpOA+DeYD88XXVXeKkEhae+ffty+vRpYmJiSE9Pp3379mzcuNHaHDwtLc1mNlLXrl1ZsWIF48ePZ9y4cQQFBREfH0+bNm2sY1544QVycnIYOnQomZmZdOvWjY0bN+Li4gIUz5A6cuQIR44coVGjRjZ5LBaL9fspU6bwyy+/4ODgQMuWLVm9ejUPPfRQRb4dIiIiIiIiIlXS+byLfLSnuKn439VUXP7LZPl9peVPXLx4kZYtW/Lxxx/TqlWrisxVLWRnZ+Pp6UlWVpb6PYmIXMHJkydZsGAB6/Na8avF3eg41009Uw69XA4wdOhQXcZdCehvduWgn4OISNX2z22/MD5+H80a1GLzc7epv1M1V9q/22Xq8eTo6EheXt5fDiciIiIi/1NYWMiyZcsuuzmKiIhIVbJqe/Fldv26qKm4/E+Zm4sPGzaM119/ncLCworIIyIiIlLjODg48OSTT+oDPhERqbL2Hsti3/FsnOzteLBjoz/fQGqMMvd42r59OwkJCWzatIng4GDc3W0vi1i3bl25hRMRERGpKUJCQkhJSaFJkyZGRxERESmzlf+d7dSjjS913J0MTiOVSZkLT15eXjz44IMVkUVERESkxnr66aeJjo7m6NGjdOrU6bIP99q2bWtQMhERkavLyS9kfcoJAPqFBBicRiqbMheeFi9eXBE5RERERGq0fv36ATBixAjrMpPJhMViwWQyYTabjYomIiJyVR9/d4IL+YU0redG2A31jI4jlUyZC0+XnD59mkOHDgHQokUL6tevX26hRERERGqa1NRUoyOIiIhck5XJRwHoF9JYTcXlMmUuPOXk5PDMM8+wbNkyioqKALC3t2fAgAG8+eabuLm5lXtIERERkepOvZ1ERKQqOnAym5SjmTjYmdRUXEpU5rvaRUdHs2XLFj766CMyMzPJzMzk3//+N1u2bOH555+viIwiIiIiNcKPP/7IM888Q3h4OOHh4YwYMYIff/zR6FgiIiJXtCq5uKn43Tf5UL+2s8FppDIqc+Hpww8/5L333uOee+7Bw8MDDw8P7r33Xt59910++OCDisgoIiIiUu199tlntG7dmuTkZNq2bUvbtm1JSkripptuYvPmzUbHExERuUzeRTP/2n0cgH5dGhucRiqrMl9ql5ubi4+Pz2XLGzRoQG5ubrmEEhEREalpxowZw3PPPcdrr7122fIXX3yRu+66y6BkIiIiJduw9yTZeYU0quNKt2beRseRSqrMM57CwsKYOHEieXl51mW//fYbkyZNIiwsrFzDiYiIiNQUBw4cYPDgwZct/8c//sH+/fsNSCQiInJ1K/97mV2/LgHY2ampuJSszDOeYmNj6dGjB40aNaJdu3YA7NmzBxcXFz777LNyDygiIiJSE9SvX5+UlBSCgoJslqekpNCgQQODUomIiJTsyKnzbP/5HPZ2Jvp0DjA6jlRiZS48BQcHc/jwYZYvX87BgwcBiIqKon///ri6upZ7QBEREZGaYMiQIQwdOpSffvqJrl27AvDtt9/y+uuvEx0dbXA6ERERW6uSjwJwR4sG+Hi4GJxGKrMyFZ4uXrxIy5Yt+fjjjxkyZEhFZRIRERGpcSZMmEDt2rWZOXMmY8eOBcDf35+XX36ZESNGGJxORETkf/ILzXy46xgAfw/VbCe5ujIVnhwdHW16O4mIiIjIX1dYWMiKFSv4+9//znPPPcf58+cBqF27tsHJRERELvfZ9xmcy72In6cL3ZvrcnC5ujI3Fx82bBivv/46hYWFFZFHREREpMZxcHDgySeftH7AV7t2bRWdRESk0lr136bifToHYK+m4vInytzjafv27SQkJLBp0yaCg4Nxd3e3Wb9u3bpyCyciIiJSU4SEhLB7926aNGlidBQREZEr+vlMDlt//BWTCfp20WV28ufKXHjy8vLiwQcfrIgsIiIiIjXW008/zfPPP8+xY8fo1KnTZR/utW3b1qBkIiIi/7Nqe3FT8e7N69PQSzcYkz9XpsJTYWEhd9xxB3fffTe+vr4VlUlERESkxunXrx+ATSNxk8mExWLBZDJhNpuNiiYiIgLARXMRH+wsbirer0tjg9NIVVGmwtOl/gMHDhyoqDwiIiIiNVJqaqrREURERK4q4UAGZy7k413Lmb+1UlNxKZ0yX2qn/gMiIiIi5evixYvceeedfPzxx7Rq1croOCIiIiVakVx8md3DnRvhaF/me5VJDVXmwpP6D4iIiIiUL0dHR+sd7URERCqjo2dz+frwaUBNxaVsylx4Uv8BERERkfI3bNgwXn/9dRYuXIiDQ5lP0URERCrU2h1HsVjglmb1aFLP/c83EPmvMp/VqP+AiIiISPnbvn07CQkJbNq0ieDg4Mtmla9bt86gZCIiUtMVmotYvaP4MruoEDUVl7Ipc+FJvZ1EREREyp+XlxcPPvig0TFEREQu89Wh02Rk51PX3Ym7WvsYHUeqmGuax/3+++8TFxdHamoqiYmJNGnShNjYWAIDA+ndu3d5ZxQRERGp9hYvXmx0BBERkRKt2p4GwIMdG+LsYG9wGqlqytyG/u233yY6Opp7772XzMxMa08nLy8vYmNjyzufiIiISI1RWFjI559/zjvvvMP58+cBOHHiBBcuXDA4mYiI1FQns37ji4OnAOiny+zkGpS58PTmm2/y7rvv8tJLL2Fv/79KZ+fOndm7d2+5hhMRERGpKX755ReCg4Pp3bs3w4YN4/Tp4jsHvf7664waNcrgdCIiUlOt3XGMIguEBNblxvq1jI4jVVCZC0+pqal06NDhsuXOzs7k5OSUSygRERGRmmbkyJF07tyZc+fO4erqal3+wAMPkJCQYGAyERGpqcxFFlZvv9RUPMDgNFJVlbnHU2BgICkpKZc1Gd+4cSOtWrUqt2AiIiIiNcnXX3/N1q1bcXJyslnetGlTjh8/blAqERGpyb4+fJrjmb/h6erIPW38jI4jVVSZC0/R0dEMGzaMvLw8LBYLycnJrFy5kmnTprFw4cKKyCgiIiJS7RUVFVl7Z/7esWPHqF27tgGJRESkpluVXDzb6YEODXFxVFNxuTZlLjw9/vjjuLq6Mn78eHJzc/n73/+Ov78/c+bMoV+/fhWRUURERKTau/vuu4mNjWXBggUAmEwmLly4wMSJE7n33nsNTiciIjXNqfN5fH4gA4AoNRWXv6DMhSeA/v37079/f3Jzc7lw4QINGjQo71wiIiIiNcrMmTOJiIigdevW5OXl8fe//53Dhw/j7e3NypUrjY4nIiI1zAc7j1FYZKFjYy9a+GrmrVy7ayo8XeLm5oabm1t5ZRERERGpsRo1asSePXtYvXo1e/bs4cKFCwwePJj+/fvbNBsXERGpaEW/ayreT7Od5C/6S4UnERERESk/Dg4O1pnlIiIiRtn206/88msutZ0duK+tmorLX2NndAARERERERERqTxWJKcB0LuDP25Omq8if40KTyIiIiIiIiICwNmcAjZ9X9xUvF8XXWYnf91fKjzl5eWVVw4RERERERERMdi6XccoMBcR3NCTNg09jY4j1UCZC09FRUVMmTKFhg0bUqtWLX766ScAJkyYwHvvvVfuAUVERERERESk4lksFutldlFqKi7lpMyFp6lTp7JkyRKmT5+Ok5OTdXmbNm1YuHBhuYYTERERERERketj+8/n+Ol0Dm5O9vRq7290HKkmylx4WrZsGQsWLKB///7Y29tbl7dr146DBw+WazgRERGR6qxOnTrUrVu3VI+ymj9/Pk2bNsXFxYXQ0FCSk5OvOn7t2rW0bNkSFxcXgoOD2bBhg816i8VCTEwMfn5+uLq6Eh4ezuHDh23G9OrVi8aNG+Pi4oKfnx+PPvooJ06cKHN2ERExxqr/zna6v60/tZzVVFzKR5kLT8ePH6dZs2aXLS8qKuLixYvlEkpERESkJoiNjWX27NnMnj2b8ePHAxAREcHLL7/Myy+/TEREBFDc0qAsVq9eTXR0NBMnTmTXrl20a9eOiIgITp06VeL4rVu3EhUVxeDBg9m9ezeRkZFERkayb98+65jp06czd+5c4uLiSEpKwt3dnYiICJuen3fccQdr1qzh0KFDfPjhh/z444889NBDZX1bRETEAFm5F/lk70kA+oUEGJxGqpMyF55at27N119/fdnyDz74gA4dOlxTiOv9idzPP//M4MGDCQwMxNXVlRtvvJGJEydSUFBgs5/vvvuOW2+9FRcXFwICApg+ffo1vT4RERGRkgwcOND6+Pbbb5k8eTIrV65kxIgRjBgxgpUrVzJ58mS2bNlSpv3OmjWLIUOGMGjQIFq3bk1cXBxubm4sWrSoxPFz5syhR48ejB49mlatWjFlyhQ6duzIvHnzgOJzq9jYWMaPH0/v3r1p27Yty5Yt48SJE8THx1v389xzz3HzzTfTpEkTunbtypgxY9i2bZs+nBQRqQL+tfsY+YVFtPStTfsAL6PjSDVS5sJTTEwMw4cP5/XXX6eoqIh169YxZMgQXnnlFWJiYsocwIhP5A4ePEhRURHvvPMO33//PbNnzyYuLo5x48ZZ95Gdnc3dd99NkyZN2LlzJzNmzODll19mwYIFZX6NIiIiIn/ms88+o0ePHpct79GjB59//nmp91NQUMDOnTsJDw+3LrOzsyM8PJzExMQSt0lMTLQZD8Uzry6NT01NJT093WaMp6cnoaGhV9zn2bNnWb58OV27dsXR0bHEMfn5+WRnZ9s8RETk+rNYLKzafhQobipuMpkMTiTVSZkLT7179+ajjz7i888/x93dnZiYGA4cOMBHH33EXXfdVeYARnwi16NHDxYvXszdd9/NDTfcQK9evRg1ahTr1q2zHmf58uUUFBSwaNEibrrpJvr168eIESOYNWtWmV+jiIiIyJ+pV68e//73vy9b/u9//5t69eqVej9nzpzBbDbj4+Njs9zHx4f09PQSt0lPT7/q+EtfS7PPF198EXd3d+rVq0daWlqJr+mSadOm4enpaX0EBOjSDhERI6QczeRg+nmcHeyIbN/Q6DhSzZS58ARw6623snnzZk6dOkVubi7ffPMNd999d5n3U1k+kQPIysqyadyZmJjIbbfdZnPnvoiICA4dOsS5c+dK3Ic+tRMREZFrNWnSJF588UXuv/9+pk6dytSpU7n//vsZM2YMkyZNMjpeqY0ePZrdu3ezadMm7O3tGTBgABaLpcSxY8eOJSsry/o4evTodU4rIiIAK//bVLxnWz883UqepSpyrcpceNq+fTtJSUmXLU9KSmLHjh1l2pfRn8hdcuTIEd58802eeOKJPz3O74/xR/rUTkRERK7VY489xrfffouHhwfr1q1j3bp1eHh48M033/DYY4+Vej/e3t7Y29uTkZFhszwjIwNfX98St/H19b3q+EtfS7NPb29vmjdvzl133cWqVavYsGED27ZtK/G4zs7OeHh42DxEROT6Op93kY/2FDcVjwppbHAaqY7KXHgaNmxYiZ9GHT9+nGHDhpVLqOvp+PHj9OjRgz59+jBkyJC/tC99aiciIiJ/RWhoKMuXL2fXrl3s2rWL5cuXExoaWqZ9ODk50alTJxISEqzLioqKSEhIICwsrMRtwsLCbMYDbN682To+MDAQX19fmzHZ2dkkJSVdcZ+XjgvFs8JFRKRyWr/nBL9dNNOsQS06N6ljdByphhzKusH+/fvp2LHjZcs7dOjA/v37y7Sviv5Ezs/Pz2ZM+/btbbY7ceIEd9xxB127dr2safiVjvP7Y/yRs7Mzzs7OJa4TERER+TM//vgjixcv5qeffiI2NpYGDRrw6aef0rhxY2666aZS7yc6OpqBAwfSuXNnQkJCiI2NJScnh0GDBgEwYMAAGjZsyLRp0wAYOXIk3bt3Z+bMmfTs2ZNVq1axY8cO6/mRyWTi2WefZerUqQQFBREYGMiECRPw9/cnMjISKJ79vn37drp160adOnX48ccfmTBhAjfeeONVi1MiImKsS5fZ9esSoKbiUiHKPOPJ2dn5soIMwMmTJ3FwKFsdy8hP5I4fP87tt99Op06dWLx4MXZ2tm9FWFgY//nPf2xu/7t582ZatGhBnTqqAouIiEj52rJlC8HBwSQlJfHhhx9y4cIFAPbs2cPEiRPLtK++ffvyxhtvEBMTQ/v27UlJSWHjxo3WtgFpaWmcPHnSOr5r166sWLGCBQsW0K5dOz744APi4+Np06aNdcwLL7zAM888w9ChQ+nSpQsXLlxg48aNuLi4AODm5sa6dev429/+RosWLRg8eDBt27Zly5Yt+mBORKSS2nc8i33Hs3Gyt+P/OjYyOo5UUybLlbo9XkFUVBQnT57k3//+N56engBkZmYSGRlJgwYNWLNmTZkCrF69moEDB/LOO+9YP5Fbs2YNBw8exMfH57JP5LZu3Ur37t157bXXrJ/Ivfrqq+zatct6cvT666/z2muvsXTpUusnct999x379+/HxcXFWnRq0qQJS5cuxd7e3prn0mymrKwsWrRowd13382LL77Ivn37+Mc//sHs2bMZOnRoqV5bdnY2np6eZGVlqWeBiMgVnDx5kgULFrA+rxW/WtyNjnPd1DPl0MvlAEOHDrWZoSvGqAx/s8PCwujTpw/R0dHUrl2bPXv2cMMNN5CcnMz//d//cezYMUNyXU+V4ecgIlKTvPSvvSxPSuP+dv68GdXB6DhSxZT273aZL7V74403uO2222jSpAkdOhT/YqakpODj48P7779f5qB9+/bl9OnTxMTEkJ6eTvv27S/7RO73s5EufSI3fvx4xo0bR1BQUImfyOXk5DB06FAyMzPp1q2bzSdymzdv5siRIxw5coRGjWyrupfqcJ6enmzatIlhw4bRqVMnvL29iYmJKXXRSURERKQs9u7dy4oVKy5b3qBBA86cOWNAIhERqc5y8gv5d8oJAKJCdGMsqThlnvEEkJOTw/Lly9mzZw+urq60bduWqKgoHB1128Xf06d2IiJ/TjOeNOOpMqgMf7MbNWrEmjVr6Nq1q82Mp3/961+MGjWKH3/80ZBc11Nl+DmIiNQUa7Yf5YUPv6NpPTe+HHW7+jtJmVXYjCcAd3d3zfwRERERKUf9+vXjxRdfZO3atZhMJoqKivj2228ZNWoUAwYMMDqeiIhUMyu3FzcV79ulsYpOUqFKVXhav34999xzD46Ojqxfv/6qY3v16lUuwURERERqkldffZVhw4YREBCA2WymdevWmM1m/v73vzN+/Hij44mISDVyMD2b3WmZONiZeKiTmopLxSpV4SkyMpL09HQaNGhgvWVuSUwmE2azubyyiYiIiNQYTk5OvPvuu8TExLB3714uXLhAhw4dCAoKMjqaiIhUM6uSjwJwV2sf6tfWnUelYtn9+RAoKiqiQYMG1u+v9FDRSUREROTaTJ48mdzcXAICArj33nt5+OGHCQoK4rfffmPy5MlGxxMRkWoi76KZdbuK75TaL6SxwWmkJihV4UlEREREKtakSZO4cOHCZctzc3OZNGmSAYlERKQ62rD3JNl5hTT0cuXWZt5Gx5EaoFSX2s2dO7fUOxwxYsQ1hxERERGpqSwWS4nNXffs2UPdunUNSCQiItXRpcvs+nUJwM5OTcWl4pWq8DR79uxS7cxkMqnwJCIiIlIGderUwWQyYTKZaN68uU3xyWw2c+HCBZ588kkDE4qISHVx5NQFkn8+i50J+nQOMDqO1BClKjylpqZWdA4RERGRGik2NhaLxcI//vEPJk2ahKenp3Wdk5MTTZs2JSwszMCEIiJSXaxKTgPgzpY++Hq6GJxGaopSFZ6uxmw2s3fvXpo0aUKdOnXKI5OIiIhIjTFw4EAAAgMD6dq1K46OjgYnEhGR6ii/0MyH/20qHhWi2U5y/ZS5ufizzz7Le++9BxQXnW677TY6duxIQEAAX331VXnnExEREakRunfvbi065eXlkZ2dbfMQERH5KzZ9n8G53Iv4erjQvXl9o+NIDVLmwtMHH3xAu3btAPjoo4/4+eefOXjwIM899xwvvfRSuQcUERERqQlyc3MZPnw4DRo0wN3dnTp16tg8RERE/oqV/73M7uHOjXCw1w3u5fop82/bmTNn8PX1BWDDhg306dOH5s2b849//IO9e/eWe0ARERGRmmD06NF88cUXvP322zg7O7Nw4UImTZqEv78/y5YtMzqeiIhUYb/8msPWH3/FZIKHu+gyO7m+ylx48vHxYf/+/ZjNZjZu3Mhdd90FFH9KZ29vX+4BRURERGqCjz76iLfeeosHH3wQBwcHbr31VsaPH8+rr77K8uXLjY4nIiJV2KrtRwG4Lag+jeq4GZxGapoyF54GDRrEww8/TJs2bTCZTISHhwOQlJREy5Ytyz2giIiISE1w9uxZbrjhBgA8PDw4e/YsAN26deM///mPkdFERKQKu2guYu0ONRUX45T5rnYvv/wybdq04ejRo/Tp0wdnZ2cA7O3tGTNmTLkHFBEREakJbrjhBlJTU2ncuDEtW7ZkzZo1hISE8NFHH+Hl5WV0PBERqaISDmRw5kI+3rWc+VsrH6PjSA1U5sITwEMPPXTZsku3AhYRERGRshs0aBB79uyhe/fujBkzhvvvv5958+Zx8eJFZs2aZXQ8ERGpolYmF19m16dzIxzVVFwMcE2FJxEREREpX88995z1+/DwcA4ePMjOnTtp1qwZbdu2NTCZiIhUVcfO5fKfw6cB6Kem4mIQFZ5EREREKqEmTZrQpEkTo2OIiEgVtmb7USwWuKVZPZrUczc6jtRQKjyJiIiIGGTu3LmlHjtixIgKTCIiItVNobmINf9tKt6vS2OD00hNpsKTiIiIiEFmz55dqnEmk0mFJxERKZMtP5wmPTuPOm6O3H2TmoqLcUpVeMrOzi71Dj08PK45jIiIiEhNkpqaanQEERGpplYmpwHwUKdGODvYG5xGarJSFZ68vLwwmUyl2qHZbP5LgURERERERETk2qVn5fHFwVMA9NVldmKwUhWevvzyS+v3P//8M2PGjOGxxx4jLCwMgMTERJYuXcq0adMqJqWIiIhINfePf/zjqusXLVp0nZKIiEhVt3bHUYosENK0Ls0a1DI6jtRwpSo8de/e3fr95MmTmTVrFlFRUdZlvXr1Ijg4mAULFjBw4MDyTykiIiJSzZ07d87m+cWLF9m3bx+ZmZnceeedBqUSEZGqpqjIwqrtRwGICg0wOI3INTQXT0xMJC4u7rLlnTt35vHHHy+XUCIiIiI1zb/+9a/LlhUVFfHUU09x4403GpBIRESqoq+PnOF45m94uDhwTxs/o+OIYFfWDQICAnj33XcvW75w4UICAlRNFRERESkvdnZ2REdHl/rudyIiIqv+21T8/zo2wsVRTcXFeGWe8TR79mwefPBBPv30U0JDQwFITk7m8OHDfPjhh+UeUERERKQm+/HHHyksLDQ6hoiIVAGnz+ezeX8GAP1CNDFEKocyF57uvfdefvjhB95++20OHjwIwP3338+TTz6pGU8iIiIi1yg6OtrmucVi4eTJk3zyySfqoSkiIqXywc5jFBZZ6NDYi5a+HkbHEQGuofAExZfbvfrqq+WdRURERKTG2r17t81zOzs76tevz8yZM//0jnciIiJFRRZWby++zC6qS2OD04j8zzUVnr7++mveeecdfvrpJ9auXUvDhg15//33CQwMpFu3buWdUURERKTa+/LLL42OICIiVdi2n37l519zqeXswH3t1FRcKo8yNxf/8MMPiYiIwNXVlV27dpGfnw9AVlaWZkGJiIiIiIiIGGDl9qMA9G7vj5vTNc0xEakQZS48TZ06lbi4ON59910cHR2ty2+55RZ27dpVruFEREREaopff/2VYcOG0bp1a7y9valbt67NQ0RE5ErO5hTw2b50AKJCdJmdVC5lLoMeOnSI22677bLlnp6eZGZmlkcmERERkRrn0Ucf5ciRIwwePBgfHx9MJpPRkUREpIpYt+sYBeYi2jT0oE1DT6PjiNgoc+HJ19eXI0eO0LRpU5vl33zzDTfccEN55RIRERGpUb7++mu++eYb2rVrZ3QUERGpQiwWCyuT/9tUXLOdpBIq86V2Q4YMYeTIkSQlJWEymThx4gTLly9n1KhRPPXUUxWRUURERKTaa9myJb/99pvRMUREpIrZ8cs5fjydg6ujPb3a+RsdR+QyZZ7xNGbMGIqKivjb3/5Gbm4ut912G87OzowaNYpnnnmmIjKKiIiIVHtvvfUWY8aMISYmhjZt2tj00gTw8PAwKJmIiFRml2Y73d/Oj9oujn8yWuT6K1PhyWw28+233zJs2DBGjx7NkSNHuHDhAq1bt6ZWrVoVlVFERESk2vPy8iI7O5s777zTZrnFYsFkMmE2mw1KJiIilVVW7kU++e4koMvspPIqU+HJ3t6eu+++mwMHDuDl5UXr1q0rKpeIiIhIjdK/f38cHR1ZsWKFmouLiEipxKccJ7+wiJa+tWkf4GV0HJESlflSuzZt2vDTTz8RGBhYEXlEREREaqR9+/axe/duWrRoYXQUERGpAn7fVLxflwB9YCGVVpmbi0+dOpVRo0bx8ccfc/LkSbKzs20eIiIiIlJ2nTt35ujRo0bHEBGRKiLlaCYH08/j7GDHAx0aGR1H5IrKPOPp3nvvBaBXr142FVX1HxARERG5ds888wwjR45k9OjRBAcHX9ZcvG3btgYlExGRymhVcvGHFT2D/fB0U1NxqbzKPOPpyy+/tD6++OIL6+PS87KaP38+TZs2xcXFhdDQUJKTk686fu3atbRs2RIXFxeCg4PZsGGDzXqLxUJMTAx+fn64uroSHh7O4cOHbca88sordO3aFTc3N7y8vEo8jslkuuyxatWqMr8+ERERkdLo27cvBw4c4B//+AddunShffv2dOjQwfpVRETkkvN5F/nouxMA9FNTcankyjTj6eLFi0yePJm4uDiCgoL+8sFXr15NdHQ0cXFxhIaGEhsbS0REBIcOHaJBgwaXjd+6dStRUVFMmzaN++67jxUrVhAZGcmuXbto06YNANOnT2fu3LksXbqUwMBAJkyYQEREBPv378fFxQWAgoIC+vTpQ1hYGO+9994V8y1evJgePXpYn1+pSCUiIiLyV6WmphodQUREqoj1e06QW2DmxvrudGlax+g4IldVpsKTo6Mj3333XbkdfNasWQwZMoRBgwYBEBcXxyeffMKiRYsYM2bMZePnzJlDjx49GD16NABTpkxh8+bNzJs3j7i4OCwWC7GxsYwfP57evXsDsGzZMnx8fIiPj6dfv34ATJo0CYAlS5ZcNZ+Xlxe+vr7l9XJFRERErqhJkyZGRxARkSri0mV2USGN1VRcKr0y93h65JFHeO+993jttdf+0oELCgrYuXMnY8eOtS6zs7MjPDycxMTEErdJTEwkOjraZllERATx8fFA8SeF6enphIeHW9d7enoSGhpKYmKitfBUWsOGDePxxx/nhhtu4Mknn2TQoEFX/Y86Pz+f/Px863M1WxcREZHSWrZs2VXXDxgw4DolERGRymzf8Sz2Hs/Cyd6O/+uopuJS+ZW58FRYWMiiRYv4/PPP6dSpE+7u7jbrZ82aVar9nDlzBrPZjI+Pj81yHx8fDh48WOI26enpJY5PT0+3rr+07EpjSmvy5MnceeeduLm5sWnTJp5++mkuXLjAiBEjrrjNtGnTrLOpRERERMpi5MiRNs8vXrxIbm4uTk5OuLm5qfAkIiIArExOAyCijS913Z0MTiPy58pceNq3bx8dO3YE4IcffrBZV52m+E2YMMH6fYcOHcjJyWHGjBlXLTyNHTvWZkZWdnY2AQEBFZpTREREqodz585dtuzw4cM89dRT1jYDIiJSs+UWFPLvlOKm4lFd9G9NqRrKXHj68ssvy+XA3t7e2Nvbk5GRYbM8IyPjin2VfH19rzr+0teMjAz8/PxsxrRv3/4v5Q0NDWXKlCnk5+fj7Oxc4hhnZ+crrhMREREpq6CgIF577TUeeeSRK84IFxGRmuPj705yIb+QJvXcuPmGekbHESkVO6MO7OTkRKdOnUhISLAuKyoqIiEhgbCwsBK3CQsLsxkPsHnzZuv4wMBAfH19bcZkZ2eTlJR0xX2WVkpKCnXq1FFhSURERK4rBwcHTpw4YXQMERGpBC5dZtevS2Ps7KrPFUdSvZV5xtMdd9xx1Uvqvvjii1LvKzo6moEDB9K5c2dCQkKIjY0lJyfHepe7AQMG0LBhQ6ZNmwYU9z7o3r07M2fOpGfPnqxatYodO3awYMECoPhSv2effZapU6cSFBREYGAgEyZMwN/fn8jISOtx09LSOHv2LGlpaZjNZlJSUgBo1qwZtWrV4qOPPiIjI4Obb74ZFxcXNm/ezKuvvsqoUaPK+G6JiIiIlM769ettnlssFk6ePMm8efO45ZZbDEolIiKVxaH08+xOy8TBzsRDndRUXKqOMhee/njJ2sWLF0lJSWHfvn0MHDiwTPvq27cvp0+fJiYmhvT0dNq3b8/GjRutzcHT0tKws/vfpKyuXbuyYsUKxo8fz7hx4wgKCiI+Pp42bdpYx7zwwgvk5OQwdOhQMjMz6datGxs3bsTFxcU6JiYmhqVLl1qfd+jQASi+jPD222/H0dGR+fPn89xzz2GxWGjWrBmzZs1iyJAhZXp9IiIiIqX1+w/JoPgDtfr163PnnXcyc+ZMY0KJiEilcWm2U3grH+rX1pU4UnWYLBaLpTx29PLLL3PhwgXeeOON8thdtZCdnY2npydZWVl4eHgYHUdEpFI6efIkCxYsYH1eK361uP/5BtVEPVMOvVwOMHToUJu+hGKM6vg3e/78+cyYMYP09HTatWvHm2++SUhIyBXHr127lgkTJvDzzz8TFBTE66+/zr333mtdb7FYmDhxIu+++y6ZmZnccsstvP322wQFBQHw888/M2XKFL744gvS09Px9/fnkUce4aWXXsLJqXR3XaqOPwcRkfKQd9FMyCufk51XyNJ/hNC9eX2jI4mU+u92ufV4euSRR1i0aFF57U5ERERErtHq1auJjo5m4sSJ7Nq1i3bt2hEREcGpU6dKHL9161aioqIYPHgwu3fvJjIyksjISPbt22cdM336dObOnUtcXBxJSUm4u7sTERFBXl4eAAcPHqSoqIh33nmH77//ntmzZxMXF8e4ceOuy2sWEanOPt13kuy8Qhp6uXJrM2+j44iUSbkVnhITE20uZxMRERGR0nvwwQd5/fXXL1s+ffp0+vTpU6Z9XWoRMGjQIFq3bk1cXBxubm5X/JBwzpw59OjRg9GjR9OqVSumTJlCx44dmTdvHlA82yk2Npbx48fTu3dv2rZty7Jlyzhx4gTx8fEA9OjRg8WLF3P33Xdzww030KtXL0aNGsW6devK9kaIiMhlViYfBaBvlwA1FZcqp8w9nv7v//7P5vmlxpc7duxgwoQJ5RZMREREpCb5z3/+w8svv3zZ8nvuuadMPZ4KCgrYuXMnY8eOtS6zs7MjPDycxMTEErdJTEwkOjraZllERIS1qJSamkp6ejrh4eHW9Z6enoSGhpKYmEi/fv1K3G9WVhZ169YtdXYREbnckVMXSE49i50J+nRWU3GpespcePL09LR5bmdnR4sWLZg8eTJ33313uQUTERERqUkuXLhQYi8kR0dHsrOzS72fM2fOYDabrTdrucTHx4eDBw+WuE16enqJ49PT063rLy270pg/OnLkCG+++eZV+3/m5+eTn59vfV6W1ykiUlOs3l7cVPzOlg3w83Q1OI1I2ZW58LR48eKKyCEiIiJSowUHB7N69WpiYmJslq9atYrWrVsblOraHD9+nB49etCnT5+r3hV42rRpTJo06TomExGpWvILzXy46zgA/bo0NjiNyLUpc+Fp+/btFBUVERoaarM8KSkJe3t7OnfuXG7hRERERGqKCRMm8H//93/8+OOP3HnnnQAkJCSwcuVK1q5dW+r9eHt7Y29vT0ZGhs3yjIwMfH19S9zG19f3quMvfc3IyLC5C2NGRgbt27e32e7EiRPccccddO3alQULFlw169ixY20u8cvOziYgIODqL1BEpAbZvD+DszkF+Hg4c3sL3clOqqYyNxcfNmwYR48evWz58ePHGTZsWLmEEhEREalp7r//fuLj4zly5AhPP/00zz//PMeOHePzzz8nMjKy1PtxcnKiU6dOJCQkWJcVFRWRkJBAWFhYiduEhYXZjAfYvHmzdXxgYCC+vr42Y7Kzs0lKSrLZ5/Hjx7n99tvp1KkTixcvxs7u6qeazs7OeHh42DxEROR/ViYXX2bXt3MADvbldm8wkeuqzDOe9u/fT8eOHS9b3qFDB/bv318uoURERERqop49e9KzZ8+/vJ/o6GgGDhxI586dCQkJITY2lpycHAYNGgTAgAEDaNiwIdOmTQNg5MiRdO/enZkzZ9KzZ09WrVrFjh07rDOWTCYTzz77LFOnTiUoKIjAwEAmTJiAv7+/tSh2qejUpEkT3njjDU6fPm3Nc6WZViIicmW//JrDt0d+xWSCh7toNqhUXWUuPDk7O5ORkcENN9xgs/zkyZM4OJR5dyIiIiJC+bYz6Nu3L6dPnyYmJob09HTat2/Pxo0brc3B09LSbGYjde3alRUrVjB+/HjGjRtHUFAQ8fHxtGnTxjrmhRdeICcnh6FDh5KZmUm3bt3YuHEjLi4uQPEMqSNHjnDkyBEaNbK965LFYinz+yEiUtOt3l58pdGtQfVpVMfN4DQi185kKeOZQFRUFCdPnuTf//639Q53mZmZREZG0qBBA9asWVMhQaui7OxsPD09ycrK0tRxEZErOHnyJAsWLGB9Xit+tbgbHee6qWfKoZfLAYYOHWrTM0eMURn+ZoeEhPDCCy/w0EMP2Sxft24dr7/+OklJSYbkup4qw89BRKQyuGguImzaF5y5kE/cIx3p0UbnClL5lPbvdpmnKL3xxhvcdtttNGnShA4dOgCQkpKCj48P77///rUnFhEREanB1M5AREQuSThwijMX8vGu5czfWvkYHUfkLylz4alhw4Z89913LF++nD179uDq6sqgQYOIiorC0dGxIjKKiIiIVHtqZyAiIpes2l7cVPyhTo1wVFNxqeKu6SzG3d2doUOHlncWERERkRrr7rvvZuzYsZe1Mxg3bhx33XWXwelEROR6OXYuly0/FN+goZ+aiks1cE2Fp8OHD/Pll19y6tQpioqKbNbFxMSUSzARERG5sqysLHJzc42Ocd25ublZizLVjdoZiIgIwJodx7BYoOuN9WjqXXP6X0r1VebC07vvvstTTz2Ft7c3vr6+mEwm6zqTyaTCk4iISAXLyspi7pvzKDIXGh3lurOzd2DEM8OrZfFJ7QxERKTQXMTaHcV3s+sX0tjgNCLlo8yFp6lTp/LKK6/w4osvVkQeERER+RO5ubkUmQvZUhBIVpGL0XGuG0+7PLo7pZKbm1stC0+gdgYiIjXdlh9OczIrjzpujkTcpKbiUj2UufB07tw5+vTpUxFZREREpAyyilz41VKDpuAX/fmQ6mD//v2kpaVRUFBgs7xXr14GJRIRketlZXLxbKcHOzbC2cHe4DQi5aPMhac+ffqwadMmnnzyyYrIIyIiIlIj/fTTTzzwwAPs3bsXk8mExWIBsLY1MJvNRsYTEZEKlp6Vx5eHTgHQL0RNxaX6KHPhqVmzZkyYMIFt27YRHBx8Wc+BESNGlFs4ERERkZpi5MiRBAYGkpCQQGBgIMnJyfz66688//zzvPHGG0bHExGRCrZ2x1HMRRZCmtalWYPaRscRKTdlLjwtWLCAWrVqsWXLFrZs2WKzzmQyqfAkIiIicg0SExP54osv8Pb2xs7ODjs7O7p168a0adMYMWIEu3fvNjqiiIhUkKIiC6utTcU120mqlzIXnlJTUysih4iIiEiNZjabqV27+BNub29vTpw4QYsWLWjSpAmHDh0yOJ2IiFSkb46c4di53/BwceDeYD+j44iUqzIXnkRERESk/LVp04Y9e/YQGBhIaGgo06dPx8nJiQULFnDDDTcYHU9ERCrQyuQ0AP6vYyNcHNVUXKqXUhWeoqOjmTJlCu7u7kRHR1917KxZs8olmIiIiEhNMn78eHJycgCYPHky9913H7feeiv16tVj9erVBqcTEZGKcvp8Ppv3ZwC6zE6qp1IVnnbv3s3Fixet31/JpbuuiIiIiEjZREREWL9v1qwZBw8e5OzZs9SpU0fnWCIi1diHu45RWGShfYAXLX09jI4jUu5KVXj68ssvS/xeRERERCpO3bp1jY4gIiIVyGKxsOq/l9n9PaSxwWlEKoad0QFEREREREREaqLEn37l519zqeXswH3t1FRcqicVnkREREREREQMsCr5KAC92vvj5qR7f0n1pMKTiIiIiIiIyHV2LqeAjfvSAYjqosvspPpS4UlERERERETkOvtw1zEKzEW0aehBcCNPo+OIVBgVnkRERERERESuI4vFwqrtxZfZ9dNsJ6nmVHgSERERERERuY52/nKOI6cu4OpoT+/2/kbHEalQKjyJiIiIiIiIXEcrktMAuL+dH7VdHA1OI1KxVHgSERERERERuU6yfrvIhr0nAegXosvspPpT4UlERERERETkOvl3ynHyLhbRwqc2HQK8jI4jUuFUeBIRERERERG5DiwWCyuSii+ziwoJwGQyGZxIpOKp8CQiIiIiIiJyHew5lsXB9PM4O9jxQIdGRscRuS5UeBIRERERERG5Dlb9t6n4vcF+eLqpqbjUDCo8iYiIiIiIiFSwC/mFrN9zAoAoNRWXGkSFJxEREREREZEKtj7lBLkFZm6s706XpnWMjiNy3ajwJCIiIiIiIlLBVm0vvsyuX5fGaiouNYoKTyIiIiIiIiIVaN/xLL47loWTvR0PdlJTcalZVHgSERERERERqUCXZjvdfZMPdd2dDE4jcn0ZXniaP38+TZs2xcXFhdDQUJKTk686fu3atbRs2RIXFxeCg4PZsGGDzXqLxUJMTAx+fn64uroSHh7O4cOHbca88sordO3aFTc3N7y8vEo8TlpaGj179sTNzY0GDRowevRoCgsL/9JrFRERERERkZolt6CQf+9WU3GpuQwtPK1evZro6GgmTpzIrl27aNeuHREREZw6darE8Vu3biUqKorBgweze/duIiMjiYyMZN++fdYx06dPZ+7cucTFxZGUlIS7uzsRERHk5eVZxxQUFNCnTx+eeuqpEo9jNpvp2bMnBQUFbN26laVLl7JkyRJiYmLK9w0QERERERGRau3j705yPr+QJvXcCLuhntFxRK47QwtPs2bNYsiQIQwaNIjWrVsTFxeHm5sbixYtKnH8nDlz6NGjB6NHj6ZVq1ZMmTKFjh07Mm/ePKB4tlNsbCzjx4+nd+/etG3blmXLlnHixAni4+Ot+5k0aRLPPfccwcHBJR5n06ZN7N+/n3/+85+0b9+ee+65hylTpjB//nwKCgrK/X0QERERERGR6mlVcvFldn27BGBnp6biUvMYVngqKChg586dhIeH/y+MnR3h4eEkJiaWuE1iYqLNeICIiAjr+NTUVNLT023GeHp6EhoaesV9Xuk4wcHB+Pj42BwnOzub77///orb5efnk52dbfMQERERERGRmulQ+nl2pWXiYGfiITUVlxrKsMLTmTNnMJvNNsUdAB8fH9LT00vcJj09/arjL30tyz7LcpzfH6Mk06ZNw9PT0/oICAgo9TFFRERERESkeln539lO4a18aFDbxeA0IsYwvLl4dTJ27FiysrKsj6NHjxodSURERERERAyQd9HMv3YfB6BfiCYlSM1lWOHJ29sbe3t7MjIybJZnZGTg6+tb4ja+vr5XHX/pa1n2WZbj/P4YJXF2dsbDw8PmISIiIiIiIjXPxn3pZP12kYZertwaVN/oOCKGMazw5OTkRKdOnUhISLAuKyoqIiEhgbCwsBK3CQsLsxkPsHnzZuv4wMBAfH19bcZkZ2eTlJR0xX1e6Th79+61ubve5s2b8fDwoHXr1qXej4iIiIiIiNRMly6ze7hzAPZqKi41mIORB4+OjmbgwIF07tyZkJAQYmNjycnJYdCgQQAMGDCAhg0bMm3aNABGjhxJ9+7dmTlzJj179mTVqlXs2LGDBQsWAGAymXj22WeZOnUqQUFBBAYGMmHCBPz9/YmMjLQeNy0tjbNnz5KWlobZbCYlJQWAZs2aUatWLe6++25at27No48+yvTp00lPT2f8+PEMGzYMZ2fn6/oeiYiIiIiISNXy4+kLJKWexc4ED3dRU3Gp2QwtPPXt25fTp08TExNDeno67du3Z+PGjdZG3mlpadjZ/W9SVteuXVmxYgXjx49n3LhxBAUFER8fT5s2baxjXnjhBXJychg6dCiZmZl069aNjRs34uLyv0ZuMTExLF261Pq8Q4cOAHz55Zfcfvvt2Nvb8/HHH/PUU08RFhaGu7s7AwcOZPLkyRX9loiIiIiIiEgVt3p7cb/fO1o0wM/T1eA0IsYytPAEMHz4cIYPH17iuq+++uqyZX369KFPnz5X3J/JZGLy5MlXLRItWbKEJUuWXDVXkyZN2LBhw1XHiIiIiIiIiPxefqGZD3YeA6BfSGOD04gYT3e1ExERERERESknm/dncDanAB8PZ+5ooabiIio8iYiIiIiIiJSTVcnFl9k93DkAB3v9k1tE/xWIiIiIiIiIlIO0X3P55sgZTKbiwpOIqPAkIiIiIiIiUi5WbU8D4Nag+gTUdTM4jUjloMKTiIiIiIiIyF900VzE2v82FY/qotlOIpeo8CQiIiIiIiLyF31x8BSnz+fjXcuJv7XyMTqOSKWhwpOIiIiIiIjIX7Qyufgyu4c6BeDkoH9qi1yi/xpERERERERE/oLjmb+x5YfTAPTTZXYiNlR4EhEREREREfkL1mw/isUCYTfUo6m3u9FxRCoVFZ5ERERERERErpG5yMKaHUcBiAptbHAakcpHhScRERERERGRa7Tlh1OczMqjjpsjETepqbjIH6nwJCIiIlINzZ8/n6ZNm+Li4kJoaCjJyclXHb927VpatmyJi4sLwcHBbNiwwWa9xWIhJiYGPz8/XF1dCQ8P5/DhwzZjXnnlFbp27YqbmxteXl7l/ZJERCqllcnFs53+r2MjnB3sDU4jUvmo8CQiIiJSzaxevZro6GgmTpzIrl27aNeuHREREZw6darE8Vu3biUqKorBgweze/duIiMjiYyMZN++fdYx06dPZ+7cucTFxZGUlIS7uzsRERHk5eVZxxQUFNCnTx+eeuqpCn+NIiKVQUZ2Hl8cLP5/a1SImoqLlESFJxEREZFqZtasWQwZMoRBgwbRunVr4uLicHNzY9GiRSWOnzNnDj169GD06NG0atWKKVOm0LFjR+bNmwcUz3aKjY1l/Pjx9O7dm7Zt27Js2TJOnDhBfHy8dT+TJk3iueeeIzg4+Hq8TBERw63dcRRzkYUuTevQrEFto+OIVEoqPImIiIhUIwUFBezcuZPw8HDrMjs7O8LDw0lMTCxxm8TERJvxABEREdbxqamppKen24zx9PQkNDT0ivssjfz8fLKzs20eIiJVRVGRhVXbiy+z69dFTcVFrkSFJxEREZFq5MyZM5jNZnx8bBvc+vj4kJ6eXuI26enpVx1/6WtZ9lka06ZNw9PT0/oICNBlKiJSdXxz5AzHzv2Gh4sDPdv6GR1HpNJS4UlEREREDDF27FiysrKsj6NHjxodSUSk1FZtTwPggQ4NcXFUU3GRK1HhSURERKQa8fb2xt7enoyMDJvlGRkZ+Pr6lriNr6/vVcdf+lqWfZaGs7MzHh4eNg8RkargzIV8Nu8v/n9ivxBdZidyNSo8iYiIiFQjTk5OdOrUiYSEBOuyoqIiEhISCAsLK3GbsLAwm/EAmzdvto4PDAzE19fXZkx2djZJSUlX3KeISHX24c5jXDRbaBfgRSs/Fc1FrsbB6AAiIiIiUr6io6MZOHAgnTt3JiQkhNjYWHJychg0aBAAAwYMoGHDhkybNg2AkSNH0r17d2bOnEnPnj1ZtWoVO3bsYMGCBQCYTCaeffZZpk6dSlBQEIGBgUyYMAF/f38iIyOtx01LS+Ps2bOkpaVhNptJSUkBoFmzZtSqVeu6vgciIhXFYvlfU/G/h6g3ncifUeFJREREpJrp27cvp0+fJiYmhvT0dNq3b8/GjRutzcHT0tKws/vfxPeuXbuyYsUKxo8fz7hx4wgKCiI+Pp42bdpYx7zwwgvk5OQwdOhQMjMz6datGxs3bsTFxcU6JiYmhqVLl1qfd+jQAYAvv/yS22+/vYJftYjI9bHtp7OknsnB3cme+9r6Gx1HpNJT4UlERESkGho+fDjDhw8vcd1XX3112bI+ffrQp0+fK+7PZDIxefJkJk+efMUxS5YsYcmSJWWNKiJSpVxqKt6rfUPcnfVPapE/ox5PIiIiIiIiIqVwLqeAT/emA/B3NRUXKRUVnkRERERERERKYd3u4xSYi7jJ34PgRp5GxxGpElR4EhEREREREfkTFouFVcnFl9n102wnkVJT4UlERERERETkT+z85RyHT13A1dGe3u3VVFyktFR4EhEREREREfkTK5OPAnBfWz88XBwNTiNSdajwJCIiIiIiInIVWb9d5JO9JwBdZidSVio8iYiIiIiIiFzFv1OOk3exiBY+tenY2MvoOCJVigpPIiIiIiIiIldgsVisl9n1CwnAZDIZnEikalHhSUREREREROQKvjuWxYGT2Tg52PFAh4ZGxxGpclR4EhEREREREbmClclpAPQM9sPLzcngNCJVjwpPIiIiIiIiIiW4kF/I+j3/bSreJcDgNCJVk4PRAUREqpusrCxyc3ONjnHdubm54enpaXQMERERkXLz0Z4T5BaYuaG+OyGBdY2OI1IlqfAkIlKOsrKymPvmPIrMhUZHue7s7B0Y8cxwFZ9ERESk2rh0mV1Ul8ZqKi5yjVR4EhEpR7m5uRSZC9lSEEhWkYvRca4bT7s8ujulkpubq8KTiIiIVAvfn8jiu2NZONqb+L+Oaioucq1UeBIRqQBZRS78anE3Osb1U2R0ABEREZHytSr5KAB33+RLvVrOBqcRqbrUXFxERERERETkd3ILConffRyAv4c0NjiNSNWmwpOIiIiIiIjI73zy3UnO5xfSuK4bYTfUMzqOSJWmwpOIiIiIiIjI76zaXnyZXd8uAdjZqam4yF+hHk8iIiIiIiIiwIGT2Sz6JpWdv5zD3s5En06NjI4kUuVVihlP8+fPp2nTpri4uBAaGkpycvJVx69du5aWLVvi4uJCcHAwGzZssFlvsViIiYnBz88PV1dXwsPDOXz4sM2Ys2fP0r9/fzw8PPDy8mLw4MFcuHDBuv7nn3/GZDJd9ti2bVv5vXARERERERExVFGRhc/3Z/D3d7dxz5yvWbvzGAADwprQwKPm3KVYpKIYXnhavXo10dHRTJw4kV27dtGuXTsiIiI4depUieO3bt1KVFQUgwcPZvfu3URGRhIZGcm+ffusY6ZPn87cuXOJi4sjKSkJd3d3IiIiyMvLs47p378/33//PZs3b+bjjz/mP//5D0OHDr3seJ9//jknT560Pjp16lT+b4KIiIiIiIhcVxfyC1nybSp3zvyKx5ftYOuPv2JvZ6JnWz8+fKorMfe1NjqiSLVg+KV2s2bNYsiQIQwaNAiAuLg4PvnkExYtWsSYMWMuGz9nzhx69OjB6NGjAZgyZQqbN29m3rx5xMXFYbFYiI2NZfz48fTu3RuAZcuW4ePjQ3x8PP369ePAgQNs3LiR7du307lzZwDefPNN7r33Xt544w38/f2tx6tXrx6+vr4V/TaIiIiIiIjIdXD0bC5Lt/7M6u1HOZ9fCICHiwNRoY0ZENaUhl6uBicUqV4MnfFUUFDAzp07CQ8Pty6zs7MjPDycxMTEErdJTEy0GQ8QERFhHZ+amkp6errNGE9PT0JDQ61jEhMT8fLyshadAMLDw7GzsyMpKclm37169aJBgwZ069aN9evXX/X15Ofnk52dbfMQERERERERY1ksFrb/fJan/rmT7jO+ZOE3qZzPL+QGb3em9L6JbeP+xth7WqnoJFIBDJ3xdObMGcxmMz4+PjbLfXx8OHjwYInbpKenlzg+PT3duv7SsquNadCggc16BwcH6tatax1Tq1YtZs6cyS233IKdnR0ffvghkZGRxMfH06tXrxKzTZs2jUmTJpXmpYuIiIiIiEgFKygs4pO9J1j0zc/sPZ5lXX5rkDf/uCWQ7s3r6651IhXM8EvtKitvb2+io6Otz7t06cKJEyeYMWPGFQtPY8eOtdkmOzubgICACs8qIiIiIiIi//PrhXxWJKWxbNsvnD6fD4Czgx3/17Ehj3UNpIVvbYMTitQchhaevL29sbe3JyMjw2Z5RkbGFfsq+fr6XnX8pa8ZGRn4+fnZjGnfvr11zB+blxcWFnL27Nmr9nMKDQ1l8+bNV1zv7OyMs7PzFdeLiIiIiIhIxTmYns3ib37mXynHKSgsAqBBbWcGdm1KVEhj6ro7GZxQpOYxtMeTk5MTnTp1IiEhwbqsqKiIhIQEwsLCStwmLCzMZjzA5s2breMDAwPx9fW1GZOdnU1SUpJ1TFhYGJmZmezcudM65osvvqCoqIjQ0NAr5k1JSbEpZomIiPx/e3ceF9V1/g/8MwNh32QTUDYrIi6IgUgQjGmgAbVWjEEwNBCl8k3CRC1JY7UKuBVjar5qksaiRWMqYsxXqYmKsSAuiGxuaBTRoLjhEkRgKuvc3x/+uHVkEVOHO+Dn/Xr5inPOufc+c2YiD8+ce4aIiIikpVIJyDp7E5HrjyJk1SFsLbqCxmYVPPubY3WEFw7PfQVxvxzIohORRCS/1S4+Ph7R0dHw8fHBqFGjsGrVKiiVSvFb7qKiotCvXz8kJycDAGbPno2xY8di5cqVmDBhAtLT01FUVISUlBQAgEwmw5w5c7B06VK4ubnB1dUVCxcuhIODA0JDQwEAHh4eCAkJwcyZM7F27Vo0NTVBoVAgIiJC/Ea7L7/8Enp6ehg5ciQAYPv27UhNTcX69eu7eYaIiIiIiIjoUcqGZnxTfBUbj1xC+R0lAEAuA8YNs8eMABc879QHMhn3byKSmuSFp/DwcNy+fRsJCQmorKyEl5cXMjMzxc3BKyoqIJf/Z2HW6NGjkZaWhgULFmD+/Plwc3NDRkYGhg0bJo758MMPoVQqERsbi+rqagQEBCAzMxMGBgbimM2bN0OhUCAwMBByuRxTpkzBmjVr1GJbsmQJLl++DF1dXQwePBhbt27F66+/ruEZISIiIiIioo5cqfo3NuVdQnrhFdTWNwMATA10MW2UE6L8nNG/j5HEERLRwyQvPAGAQqGAQqFoty8nJ6dNW1hYGMLCwjo8n0wmw+LFi7F48eIOx1haWiItLa3D/ujoaERHR3ccNBEREREREXULQRBQdPkuUg+XY++ZSqiEB+2u1saY7u+CKc/3h7G+Vvx6S0SP4P+ZREREREREpJUam1XYVXIdqYcvoeTaPbE9YKA1ZgS44OVBtpDLeTsdkTZj4YmIiIiIiIi0yk91DdhSUIFNeZdxq7YBAKCnK8drI/thur8r3O1MJY6QiLqKhSciIiIiIiLSCqWVtdiQW44dx6+hoVkFALA11UeUnzOmjXKClYm+xBES0ZNi4YmIiIiIiIgko1IJyDl/C6mHL+HwhTti+/B+5ogJcMX44fbQ05V3cgYiUpOUBOjoAAsXtu1bsgRoaXkwppuw8ERERERERETdTtnQjP87dhUbci+h/I4SACCXASHD7DDD3xXezn0gk3H/JqInpqMDJCQ8+PvDxaclSx60d/JFbJrAwhMRERERERF1m6t3/41NeZexpaACtfXNAABTA11MG+WEKD9n9O9jJHGERD1ca7Hp4eLTw0Wn9lZCaRALT0RERERERKRRgiCg+PJdpOaWI/N0JVTCg3YXKyNM93fFFO/+MNHnr6dET83DxaelS4HGRkmKTgALT0RERERERKQhjc0q7C65gdTccpy6ek9s9x9ohRn+rviluy3kct5OR6QRCxf+p+ikpydJ0Qlg4YmIiIiIiIiesiplI7YUVGBT3iXcrGkAAOjpyjHZqx+mB7hgsJ2ZxBESPQOWLPlP0amx8cFjrngiIiIiIiKinur8zVpsyC3H9mPX0NCsAgDYmOoj6kVnvOHrBCsTfYkjJHpGPLqnU+tjgHs8ERERERERUc+hUgk4cP42UnPLcajsjtg+rJ8ZYgJcMWG4A/R05RJGSPSMaW8j8fY2HO8mLDwRERERERHRE1M2NGP7savYkHsJP95RAgDkMiB4qB1mBLjCx7kPZDLu30TU7Vpa2t9IvPVxS0u3hsPCExEREREREXXZter72HTkErYUVKCmvhkAYKqvi4hRjojyc4GjpZHEERI945KSOu7jHk9ERERERESkbQRBwLGKu0g9fAmZZyrRohIAAC5WRpju74op3v1hos9fL4moLf7LQERERERERO1qalFhd8kNpB4ux8mr98T20b+wwgx/V7wy2BZyOW+nI6KOsfBEREREREREau4qG5FWUIFNeZdws6YBAKCnK0eolwOm+7vCw95M4giJqKdg4YmIiIiIiIgAAGU3a5Gaewnbj11FQ7MKAGBjqo83X3TGG75OsDbRlzhCIuppWHgiIiIiIiJ6hqlUAg6U3Ubq4XIcKrsjtg91MENMgCsmeNpDX1dHwgiJqCdj4YmIiIiIiOgZ9O/GZvzfsWvYkFuOH28rAQByGfDqEDvMCHDFCy59IJNx/yYi+u+w8ERERERERPQMuVZ9H5vyLmFLfgVq6psBAKb6ugh/wRHRo13gaGkkcYRE1Juw8ERERERERPQMKL58F6m55cg8XYkWlQAAcLYywvTRLnjdxxEm+vz1kIiePv7LQkRERERE1Es1taiw53QlUg+X48SVarF99C+sMMPfFb8cbAsdOW+nIyLNYeGJiIiIiIiol7mrbMSWwgpsOnIZlTX1AAA9XTlCvRww3d8VHvZmEkdIRM8KFp6IiIiIiIh6ibKbtdhw5BK2H7uK+iYVAMDaRB9vvuiMyBedYG2iL3GERPSskUsdABERERE9fZ9//jlcXFxgYGAAX19fFBQUdDp+27ZtGDx4MAwMDDB8+HDs3r1brV8QBCQkJMDe3h6GhoYICgpCWVmZ2piqqipERkbCzMwMFhYWiImJQV1d3VN/bkSkTqUSkFN6C1GpBfjV/x5EWn4F6ptUGGJvhpVhI5D7x19idpAbi05EJAkWnoiIiIh6ma1btyI+Ph6JiYk4duwYRowYgeDgYNy6davd8UeOHMG0adMQExOD48ePIzQ0FKGhoTh9+rQ4ZsWKFVizZg3Wrl2L/Px8GBsbIzg4GPX19eKYyMhInDlzBvv27cN3332HgwcPIjY2VuPPl+hZ9e/GZvzj6GX86n8P4K0NhTh4/jZkMiB4aF9sjX0Ru2YFYIp3f+jr6kgdKhE9w3irHREREVEv88knn2DmzJmYPn06AGDt2rXYtWsXUlNT8cc//rHN+NWrVyMkJAR/+MMfAABLlizBvn378Nlnn2Ht2rUQBAGrVq3CggULMGnSJADApk2b0LdvX2RkZCAiIgJnz55FZmYmCgsL4ePjAwD49NNPMX78ePzlL3+Bg4NDNz17ot7vevV9bMq7jC0FFbh3vwkAYKKvi/AXHBHt5wInKyOJIyQi+g8WnoiIiIh6kcbGRhQXF2PevHlim1wuR1BQEPLy8to9Ji8vD/Hx8WptwcHByMjIAACUl5ejsrISQUFBYr+5uTl8fX2Rl5eHiIgI5OXlwcLCQiw6AUBQUBDkcjny8/MxefLkNtdtaGhAQ0OD+LimpuZnPefHKbpUhQUZpx8/kKgHEATgwu06tKgEAICTpRGm+7vgde/+MDV4TuLoiIjaYuGJiIiIqBe5c+cOWlpa0LdvX7X2vn374ty5c+0eU1lZ2e74yspKsb+1rbMxtra2av26urqwtLQUxzwqOTkZixYt6uIz+/mUjS04V1mr8esQdSe/AVaYEeCKVwbbQkcukzocIqIOsfBERERERJKYN2+e2kqrmpoaODo6PvXrePYzxz9ifJ/6eYmkYmdugIG2JlKHQUTUJSw8EREREfUi1tbW0NHRwc2bN9Xab968CTs7u3aPsbOz63R8639v3rwJe3t7tTFeXl7imEc3L29ubkZVVVWH19XX14e+vua/ZauPsR4C3Kw1fh0iIiJqi99qR0RERNSL6OnpwdvbG1lZWWKbSqVCVlYW/Pz82j3Gz89PbTwA7Nu3Txzv6uoKOzs7tTE1NTXIz88Xx/j5+aG6uhrFxcXimOzsbKhUKvj6crURERHRs4ornoiIiIh6mfj4eERHR8PHxwejRo3CqlWroFQqxW+5i4qKQr9+/ZCcnAwAmD17NsaOHYuVK1diwoQJSE9PR1FREVJSUgAAMpkMc+bMwdKlS+Hm5gZXV1csXLgQDg4OCA0NBQB4eHggJCQEM2fOxNq1a9HU1ASFQoGIiAh+ox0REdEzjIUnIiIiol4mPDwct2/fRkJCAiorK+Hl5YXMzExxc/CKigrI5f9Z+D569GikpaVhwYIFmD9/Ptzc3JCRkYFhw4aJYz788EMolUrExsaiuroaAQEByMzMhIGBgThm8+bNUCgUCAwMhFwux5QpU7BmzZrue+JERESkdVh4IiIiIuqFFAoFFApFu305OTlt2sLCwhAWFtbh+WQyGRYvXozFixd3OMbS0hJpaWlPHCsRERH1XtzjiYiIiIiIiIiINIKFJyIiIiIiIiIi0ggWnoiIiIiIiIiISCNYeCIiIiIiIiIiIo1g4YmIiIiIiIiIiDSChSciIiIiIiIiItIIrSg8ff7553BxcYGBgQF8fX1RUFDQ6fht27Zh8ODBMDAwwPDhw7F79261fkEQkJCQAHt7exgaGiIoKAhlZWVqY6qqqhAZGQkzMzNYWFggJiYGdXV1amNOnTqFMWPGwMDAAI6OjlixYsXTecJERERERERERM8AyQtPW7duRXx8PBITE3Hs2DGMGDECwcHBuHXrVrvjjxw5gmnTpiEmJgbHjx9HaGgoQkNDcfr0aXHMihUrsGbNGqxduxb5+fkwNjZGcHAw6uvrxTGRkZE4c+YM9u3bh++++w4HDx5EbGys2F9TU4NXX30Vzs7OKC4uxscff4ykpCSkpKRobjKIiIiIiIiIiHoRyQtPn3zyCWbOnInp06djyJAhWLt2LYyMjJCamtru+NWrVyMkJAR/+MMf4OHhgSVLluD555/HZ599BuDBaqdVq1ZhwYIFmDRpEjw9PbFp0yZcv34dGRkZAICzZ88iMzMT69evh6+vLwICAvDpp58iPT0d169fBwBs3rwZjY2NSE1NxdChQxEREYFZs2bhk08+6ZZ5ISIiIiIiIiLq6SQtPDU2NqK4uBhBQUFim1wuR1BQEPLy8to9Ji8vT208AAQHB4vjy8vLUVlZqTbG3Nwcvr6+4pi8vDxYWFjAx8dHHBMUFAS5XI78/HxxzEsvvQQ9PT2165SWluLu3bv/5TMnIiIiIiIiIur9dKW8+J07d9DS0oK+ffuqtfft2xfnzp1r95jKysp2x1dWVor9rW2djbG1tVXr19XVhaWlpdoYV1fXNudo7evTp0+b2BoaGtDQ0CA+vnfvHoAHt+09bbW1tVAqlU/9vNrO2NgYpqamP+tYztnP8yzO23/7Pquvr4dx0120qP79lCPTXsbyBtSjHrW1tTA2Nn6iYzlnnLOu+m/m7HFaf1YLgvBUz0tPpnX+NZE7ERER0dPV1fxJ0sJTb5OcnIxFixa1aXd0dJQgGiKi7nUIwPLly6UOo0fhnD05Tc9ZbW0tzM3NNXZ+6lxtbS0A5k5EREQ9yePyJ0kLT9bW1tDR0cHNmzfV2m/evAk7O7t2j7Gzs+t0fOt/b968CXt7e7UxXl5e4phHNy9vbm5GVVWV2nnau87D13jUvHnzEB8fLz5WqVSoqqqClZUVZDJZu8f0NDU1NXB0dMSVK1dgZmYmdTg9AufsyXHOfh7O25PjnD253jpngiCgtrYWDg4OUofyTHNwcMCVK1dgamr61HOn3vre1Sac4+7Bee4enGfN4xx3D03Oc1fzJ0kLT3p6evD29kZWVhZCQ0MBPCjWZGVlQaFQtHuMn58fsrKyMGfOHLFt37598PPzAwC4urrCzs4OWVlZYqGppqYG+fn5eOedd8RzVFdXo7i4GN7e3gCA7OxsqFQq+Pr6imP+9Kc/oampCc8995x4HXd393ZvswMAfX196Ovrq7VZWFg88bz0BGZmZvzH4Qlxzp4c5+zn4bw9Oc7Zk+uNc8aVTtKTy+Xo37+/Rq/RG9+72oZz3D04z92D86x5nOPuoal57kr+JPm32sXHx2PdunX48ssvcfbsWbzzzjtQKpWYPn06ACAqKgrz5s0Tx8+ePRuZmZlYuXIlzp07h6SkJBQVFYmFKplMhjlz5mDp0qXYuXMnSkpKEBUVBQcHB7G45eHhgZCQEMycORMFBQXIzc2FQqFARESEWKl74403oKenh5iYGJw5cwZbt27F6tWr1VY0ERERERERERFRxyTf4yk8PBy3b99GQkICKisr4eXlhczMTHEj74qKCsjl/6mPjR49GmlpaViwYAHmz58PNzc3ZGRkYNiwYeKYDz/8EEqlErGxsaiurkZAQAAyMzNhYGAgjtm8eTMUCgUCAwMhl8sxZcoUrFmzRuw3NzfH999/j7i4OHh7e8Pa2hoJCQmIjY3thlkhIiIiIiIiIur5JC88AYBCoejw1rqcnJw2bWFhYQgLC+vwfDKZDIsXL8bixYs7HGNpaYm0tLRO4/L09MShQ4c6HfOs0dfXR2JiYptbCqljnLMnxzn7eThvT45z9uQ4Z9RT8b2reZzj7sF57h6cZ83jHHcPbZhnmcDvDSYiIiIiIiIiIg2QfI8nIiIiIiIiIiLqnVh4IiIiIiIiIiIijWDhiYiIiIiIiIiINIKFJyIiIiIiIiIi0ggWnqhLDh48iIkTJ8LBwQEymQwZGRlSh6T1kpOT8cILL8DU1BS2trYIDQ1FaWmp1GFptS+++AKenp4wMzODmZkZ/Pz8sGfPHqnD6lGWL18OmUyGOXPmSB2KVktKSoJMJlP7M3jwYKnD0nrXrl3Db3/7W1hZWcHQ0BDDhw9HUVGR1GERdYo5jOYx5+kezJO6H/MqzWAe1n20JXdj4Ym6RKlUYsSIEfj888+lDqXHOHDgAOLi4nD06FHs27cPTU1NePXVV6FUKqUOTWv1798fy5cvR3FxMYqKivDKK69g0qRJOHPmjNSh9QiFhYX429/+Bk9PT6lD6RGGDh2KGzduiH8OHz4sdUha7e7du/D398dzzz2HPXv24IcffsDKlSvRp08fqUMj6hRzGM1jztM9mCd1L+ZVmsU8TPO0KXfT7fYrUo80btw4jBs3TuowepTMzEy1xxs3boStrS2Ki4vx0ksvSRSVdps4caLa42XLluGLL77A0aNHMXToUImi6hnq6uoQGRmJdevWYenSpVKH0yPo6urCzs5O6jB6jI8++giOjo7YsGGD2Obq6iphRERdwxxG85jzdA/mSd2HeZXmMQ/TPG3K3bjiiaib3Lt3DwBgaWkpcSQ9Q0tLC9LT06FUKuHn5yd1OFovLi4OEyZMQFBQkNSh9BhlZWVwcHDAgAEDEBkZiYqKCqlD0mo7d+6Ej48PwsLCYGtri5EjR2LdunVSh0VEWog5j+YxT9Is5lWaxzxM87Qpd+OKJ6JuoFKpMGfOHPj7+2PYsGFSh6PVSkpK4Ofnh/r6epiYmGDHjh0YMmSI1GFptfT0dBw7dgyFhYVSh9Jj+Pr6YuPGjXB3d8eNGzewaNEijBkzBqdPn4apqanU4WmlH3/8EV988QXi4+Mxf/58FBYWYtasWdDT00N0dLTU4RGRlmDOo1nMkzSPeZXmMQ/rHtqUu7HwRNQN4uLicPr0ad673AXu7u44ceIE7t27h2+++QbR0dE4cOAAk6oOXLlyBbNnz8a+fftgYGAgdTg9xsO33Xh6esLX1xfOzs74+uuvERMTI2Fk2kulUsHHxwd//vOfAQAjR47E6dOnsXbtWhaeiEjEnEezmCdpFvOq7sE8rHtoU+7GW+2INEyhUOC7777D/v370b9/f6nD0Xp6enoYOHAgvL29kZycjBEjRmD16tVSh6W1iouLcevWLTz//PPQ1dWFrq4uDhw4gDVr1kBXVxctLS1Sh9gjWFhYYNCgQbhw4YLUoWgte3v7Nr/YeHh4cGk8EYmY82ge8yTNYl4lDeZhmqFNuRtXPBFpiCAIeO+997Bjxw7k5ORwE96fSaVSoaGhQeowtFZgYCBKSkrU2qZPn47Bgwdj7ty50NHRkSiynqWurg4XL17Em2++KXUoWsvf37/N16OfP38ezs7OEkVERNqCOY90mCc9XcyrpME8TDO0KXdj4Ym6pK6uTq0CXV5ejhMnTsDS0hJOTk4SRqa94uLikJaWhn/+858wNTVFZWUlAMDc3ByGhoYSR6ed5s2bh3HjxsHJyQm1tbVIS0tDTk4O9u7dK3VoWsvU1LTNHhrGxsawsrLi3hqd+OCDDzBx4kQ4Ozvj+vXrSExMhI6ODqZNmyZ1aFrr97//PUaPHo0///nPmDp1KgoKCpCSkoKUlBSpQyPqFHMYzWPO0z2YJ2ke86ruwTyse2hV7iYQdcH+/fsFAG3+REdHSx2a1mpvvgAIGzZskDo0rTVjxgzB2dlZ0NPTE2xsbITAwEDh+++/lzqsHmfs2LHC7NmzpQ5Dq4WHhwv29vaCnp6e0K9fPyE8PFy4cOGC1GFpvW+//VYYNmyYoK+vLwwePFhISUmROiSix2IOo3nMeboH8yRpMK96+piHdR9tyd1kgiAI3VnoIiIiIiIiIiKiZwM3FyciIiIiIiIiIo1g4YmIiIiIiIiIiDSChSciIiIiIiIiItIIFp6IiIiIiIiIiEgjWHgiIiIiIiIiIiKNYOGJiIiIiIiIiIg0goUnIiIiIiIiIiLSCBaeiLTUyy+/jDlz5mj0Gm+99RZCQ0M1eo1nzX8zpzk5OZDJZKiurgYAbNy4ERYWFk8tNm0jk8mQkZEhdRhERNSLMH/qmZg/dR3zJ+qJWHgiomeetiaQ4eHhOH/+vNRhEBEREbXB/ImIukpX6gCIqHdpbGyEnp6e1GH0CoaGhjA0NJQ6jB6F7z8iIuqJ+PPr6WH+9OT4/iNN44onIi3W3NwMhUIBc3NzWFtbY+HChRAEQez/6quv4OPjA1NTU9jZ2eGNN97ArVu31M5x5swZ/PrXv4aZmRlMTU0xZswYXLx4sd3rFRYWwsbGBh999BEAICkpCV5eXvjb3/4GR0dHGBkZYerUqbh37554TOunXcuWLYODgwPc3d0BACUlJXjllVdgaGgIKysrxMbGoq6urs1xixYtgo2NDczMzPD222+jsbFRHJOZmYmAgABYWFjAysoKv/71r9vEfuTIEXh5ecHAwAA+Pj7IyMiATCbDiRMnAAAtLS2IiYmBq6srDA0N4e7ujtWrV4vHJyUl4csvv8Q///lPyGQyyGQy5OTkAACuXLmCqVOnwsLCApaWlpg0aRIuXbokHtvS0oL4+Hgxvg8//FDt9WnP5cuXMXHiRPTp0wfGxsYYOnQodu/e3e7Y9paKf/vtt3jhhRdgYGAAa2trTJ48WexraGjABx98gH79+sHY2Bi+vr7ic+mITCbD+vXrMXnyZBgZGcHNzQ07d+7sNIbWOW7V+j5JTU2Fk5MTTExM8O6776KlpQUrVqyAnZ0dbG1tsWzZsjbXv3HjBsaNGwdDQ0MMGDAA33zzjVr/416Djt5/RET07GL+xPyJ+RPzJ9IuLDwRabEvv/wSurq6KCgowOrVq/HJJ59g/fr1Yn9TUxOWLFmCkydPIiMjA5cuXcJbb70l9l+7dg0vvfQS9PX1kZ2djeLiYsyYMQPNzc1trpWdnY1f/epXWLZsGebOnSu2X7hwAV9//TW+/fZbZGZm4vjx43j33XfVjs3KykJpaSn27duH7777DkqlEsHBwejTpw8KCwuxbds2/Otf/4JCoWhz3NmzZ5GTk4MtW7Zg+/btWLRokdivVCoRHx+PoqIiZGVlQS6XY/LkyVCpVACAmpoaTJw4EcOHD8exY8ewZMkStdgBQKVSoX///ti2bRt++OEHJCQkYP78+fj6668BAB988AGmTp2KkJAQ3LhxAzdu3MDo0aPR1NSE4OBgmJqa4tChQ8jNzYWJiQlCQkLE5G7lypXYuHEjUlNTcfjwYVRVVWHHjh2dvqZxcXFoaGjAwYMHUVJSgo8++ggmJiadHtNq165dmDx5MsaPH4/jx48jKysLo0aNEvsVCgXy8vKQnp6OU6dOISwsDCEhISgrK+v0vIsWLcLUqVNx6tQpjB8/HpGRkaiqqupSTK0uXryIPXv2IDMzE1u2bMHf//53TJgwAVevXsWBAwfw0UcfYcGCBcjPz1c7buHChZgyZQpOnjyJyMhIRERE4OzZswDQpdcAaPv+IyKiZxvzJ+ZPD2P+xPyJtIBARFpp7NixgoeHh6BSqcS2uXPnCh4eHh0eU1hYKAAQamtrBUEQhHnz5gmurq5CY2Nju+Ojo6OFSZMmCdu3bxdMTEyE9PR0tf7ExERBR0dHuHr1qti2Z88eQS6XCzdu3BDP0bdvX6GhoUEck5KSIvTp00eoq6sT23bt2iXI5XKhsrJSPM7S0lJQKpXimC+++EIwMTERWlpa2o339u3bAgChpKREHG9lZSXcv39fHLNu3ToBgHD8+PEO5ykuLk6YMmVKm3l42FdffSW4u7urzX9DQ4NgaGgo7N27VxAEQbC3txdWrFgh9jc1NQn9+/dvc66HDR8+XEhKSmq3b//+/QIA4e7du4IgCMKGDRsEc3Nzsd/Pz0+IjIxs99jLly8LOjo6wrVr19TaAwMDhXnz5nUYDwBhwYIF4uO6ujoBgLBnz552YxAEQdixY4fw8I+PxMREwcjISKipqRHbgoODBRcXF7XX0t3dXUhOTla79ttvv612bl9fX+Gdd94RBKFrr0F77z8iInp2MX9qi/kT8yfmTyQ1rngi0mIvvvii2pJcPz8/lJWVoaWlBQBQXFyMiRMnwsnJCaamphg7diwAoKKiAgBw4sQJjBkzBs8991yH18jPz0dYWBi++uorhIeHt+l3cnJCv3791GJQqVQoLS0V24YPH652X/jZs2cxYsQIGBsbi23+/v5tjhsxYgSMjIzUzl1XV4crV64AAMrKyjBt2jQMGDAAZmZmcHFxUXt+paWl8PT0hIGBgXiOhz/BavX555/D29sbNjY2MDExQUpKiniOjpw8eRIXLlyAqakpTExMYGJiAktLS9TX1+PixYu4d+8ebty4AV9fX/EYXV1d+Pj4dHreWbNmYenSpfD390diYiJOnTrV6fiHnThxAoGBge32lZSUoKWlBYMGDRLjNTExwYEDBzq8NaCVp6en+HdjY2OYmZm1ueXgcVxcXGBqaio+7tu3L4YMGQK5XK7W9uh5/fz82jxu/cTuca9Bq0fff0RE9Gxj/sT86WHMn5g/kfS4uThRD9W6HDs4OBibN2+GjY0NKioqEBwcLC6j7crGir/4xS9gZWWF1NRUTJgwodMkqyMPJ0hP08SJE+Hs7Ix169bBwcEBKpUKw4YNU1sm/Djp6en44IMPsHLlSvj5+cHU1BQff/xxmyXLj6qrq4O3tzc2b97cps/GxuaJn0ur3/3udwgODsauXbvw/fffIzk5GStXrsR777332GM7ez3r6uqgo6OD4uJi6OjoqPU9bin6o6+5TCYTl+PL5fI2+y40NTV16RydnbcruvoaaOr9R0REvQ/zp65h/sT8iehp4oonIi326A/3o0ePws3NDTo6Ojh37hx++uknLF++HGPGjMHgwYPbfBri6emJQ4cOtfuDrpW1tTWys7Nx4cIFTJ06tc3YiooKXL9+XS0GuVze6SaEHh4eOHnyJJRKpdiWm5vb5riTJ0/i/v37auc2MTGBo6MjfvrpJ5SWlmLBggUIDAyEh4cH7t69q3Ydd3d3lJSUoKGhQWwrLCxUG5Obm4vRo0fj3XffxciRIzFw4MA2n2Dp6emJn4K2ev7551FWVgZbW1sMHDhQ7Y+5uTnMzc1hb2+v9ho1NzejuLi4w3lp5ejoiLfffhvbt2/H+++/j3Xr1j32GODB65mVldVu38iRI9HS0oJbt261idfOzq5L52+PjY0Namtr1V7L1o1Hn4ajR4+2eezh4QHg8a8BERFRe5g/MX96GPMn5k8kPRaeiLRYRUUF4uPjUVpaii1btuDTTz/F7NmzATxYwq2np4dPP/0UP/74I3bu3IklS5aoHa9QKFBTU4OIiAgUFRWhrKwMX331ldpybQCwtbVFdnY2zp07h2nTpqltnmlgYIDo6GicPHkShw4dwqxZszB16tROfxhHRkaKx50+fRr79+/He++9hzfffBN9+/YVxzU2NiImJgY//PADdu/ejcTERCgUCsjlcvTp0wdWVlZISUnBhQsXkJ2djfj4eLXrvPHGG1CpVIiNjcXZs2exd+9e/OUvfwEAcYm9m5sbioqKsHfvXpw/fx4LFy5sk1y5uLjg1KlTKC0txZ07d9DU1ITIyEhYW1tj0qRJOHToEMrLy5GTk4NZs2bh6tWrAIDZs2dj+fLlyMjIwLlz5/Duu++iurq609d0zpw52Lt3L8rLy3Hs2DHs379fTBQeJzExEVu2bEFiYiLOnj0rbq4JAIMGDUJkZCSioqKwfft2lJeXo6CgAMnJydi1a1eXzt8eX19fGBkZYf78+bh48SLS0tKwcePGn32+R23btg2pqak4f/48EhMTUVBQIG6i2pXXgIiI6FHMn5g/PYz5E/Mnkh4LT0RaLCoqCvfv38eoUaMQFxeH2bNnIzY2FsCDT1I2btyIbdu2YciQIVi+fLmYNLSysrJCdnY26urqMHbsWHh7e2PdunXtLge3s7NDdnY2SkpKEBkZKX6CNXDgQLz22msYP348Xn31VXh6euKvf/1rp3EbGRlh7969qKqqwgsvvIDXX38dgYGB+Oyzz9TGBQYGws3NDS+99BLCw8Pxm9/8BklJSQAeLFFOT09HcXExhg0bht///vf4+OOP1Y43MzPDt99+ixMnTsDLywt/+tOfkJCQAADivgX/8z//g9deew3h4eHw9fXFTz/91OZbZWbOnAl3d3f4+PjAxsYGubm5MDIywsGDB+Hk5ITXXnsNHh4eiImJQX19PczMzAAA77//Pt58801ER0eLy9Af/nre9rS0tCAuLg4eHh4ICQnBoEGDHjufrV5++WVs27YNO3fuhJeXF1555RUUFBSI/Rs2bEBUVBTef/99uLu7IzQ0FIWFhXBycurS+dtjaWmJf/zjH9i9ezeGDx+OLVu2iK/R07Bo0SKkp6fD09MTmzZtwpYtWzBkyBAA6NJrQERE9CjmT8yfHsb8ifkTSU8mPHrzKRHR/5eUlISMjIynujS41VtvvYXq6mpkZGQ81fNu3rwZ06dPx71797q0RwMRERHR08T8iYhIHTcXJ6IebdOmTRgwYAD69euHkydPYu7cuZg6dSqTJiIiIqIOMH8iou7EwhMR9WiVlZVISEhAZWUl7O3tERYWhmXLlkkdFhEREZHWYv5ERN2Jt9oREREREREREZFGcHNxIiIiIiIiIiLSCBaeiIiIiIiIiIhII1h4IiIiIiIiIiIijWDhiYiIiIiIiIiINIKFJyIiIiIiIiIi0ggWnoiIiIiIiIiISCNYeCIiIiIiIiIiIo1g4YmIiIiIiIiIiDSChSciIiIiIiIiItKI/wcm8RzLAKLCJwAAAABJRU5ErkJggg==", "text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -395,8 +397,9 @@ "outputs": [ { "data": { + "image/png": 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    " ] }, "metadata": {}, @@ -425,7 +428,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -439,9 +442,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-obp/how_tos/index.mdx b/docs/addons/qiskit-addon-obp/how_tos/index.mdx new file mode 100644 index 00000000000..2f7b6d1f313 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/how_tos/index.mdx @@ -0,0 +1,13 @@ +--- +title: Operator backpropagation (OBP) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-obp. +--- + +# How-To Guides + +This page summarizes the available how-to guides. + +* [Using different Lp-norms for Pauli term truncation](bound-error-using-p-norm) +* [Classically simulating circuits with OBP](simulating-circuits-with-obp) +* [Truncating Pauli terms during backpropagation](truncate-operator-terms) + diff --git a/docs/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp.mdx b/docs/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp.mdx new file mode 100644 index 00000000000..eb6f9c87bf0 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp.mdx @@ -0,0 +1,353 @@ + + +# Classically simulating circuits with OBP + +In this guide, you will learn how to classically simulate `QuantumCircuit` instances to estimate expectation values entirely through the means of OBP. + +Since OBP will take an observable and backpropagate it through a given circuit, the “simulation” of a circuit amounts to computing the expectation value of the target observable with respect to this circuit. As you will see later, the `qiskit-addon-obp` package is even capable of handling simple noise models, allowing you to compute noisy expectation values, too! + + + +## Constructing an example circuit + +For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms): + +``` +[1]: +``` + +```python +import rustworkx.generators +from qiskit.synthesis import LieTrotter +from qiskit_addon_utils.problem_generators import ( + PauliOrderStrategy, + generate_time_evolution_circuit, + generate_xyz_hamiltonian, +) +from qiskit_addon_utils.slicing import combine_slices, slice_by_gate_types + +# we generate a linear chain of 10 qubits +num_qubits = 10 +linear_chain = rustworkx.generators.path_graph(num_qubits) + +# we use an arbitrary XY model +hamiltonian = generate_xyz_hamiltonian( + linear_chain, + coupling_constants=(0.05, 0.02, 0.0), + ext_magnetic_field=(0.02, 0.08, 0.0), + pauli_order_strategy=PauliOrderStrategy.InteractionThenColor, +) +# we evolve for some time +circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0) +# slice the circuit by gate type +slices = slice_by_gate_types(circuit) +``` + +However, the above is purely the circuit describing the time evolution under a chosen Hamiltonian. We also need an initial state to start from, with respect to which we compute the expectation values of our observable. + +Of course, we could choose the all-zero (or vacuum) state as our initial state, but to show how one would insert their own initial state, we choose a different one below. + +One possibility, would be to prepend the initial state to our time-evolution circuit above: `circuit.compose(initial_state, front=True)`. But since we have already sliced our `circuit`, it is easier to simply insert the initial state as the first slice, which we do below. + +In this way, we can simply replace the first slice with another initial state, if we want to exchange that in the future, without having to recompute our slices. + +``` +[2]: +``` + +```python +from qiskit.circuit import QuantumCircuit + +initial_state = QuantumCircuit(num_qubits) +for i in range(0, num_qubits, 2): + initial_state.x(i) + +slices.insert(0, initial_state) +``` + +``` +[3]: +``` + +```python +# for visualization purposes only, we recombine the slices with barriers between them and draw the resulting circuit +combine_slices(slices, include_barriers=True).draw("mpl", fold=50, scale=0.6) +``` + +``` +[3]: +``` + +![../\_images/how\_tos\_simulating\_circuits\_with\_obp\_6\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_simulating_circuits_with_obp_6_0.avif) + + + +## Simulating a noiseless expectation value + +As our target observable, we choose the `ZZ` observable on the central qubits: + +``` +[4]: +``` + +```python +from qiskit.quantum_info import SparsePauliOp + +obs = SparsePauliOp("IIIIZZIIII") +``` + +At this point, we are already set to classically simulate the expectation value using OBP. To do so, we simply provide the *all* the slices to the `backpropagate` method, like so: + +``` +[5]: +``` + +```python +from qiskit_addon_obp import backpropagate + +vacuum_state_obs, _, metadata = backpropagate(obs, slices) +``` + +We have now backpropagated our target observable `obs` through the *entire* circuit (**including** the `initial_state` which we placed on `slices[0]`) resulting in a new `SparsePauliOp` whose expectation value we obtain by projecting it on the *vacuum state* (`|00...00>`). + +This can be achieved in a straight forward manner by summing up the coefficients of all Pauli terms defined in the computational basis: + +``` +[6]: +``` + +```python +vacuum_state_obs.coeffs[~vacuum_state_obs.paulis.x.any(axis=1)].sum() +``` + +``` +[6]: +``` + +```python +np.complex128(-0.8285688012239535+4.9487770271457865e-20j) +``` + +As a sanity check (and to prove that this works) we can compare our result against Qiskit’s `Statevector`: + +``` +[7]: +``` + +```python +from qiskit.quantum_info import Statevector + +Statevector(combine_slices(slices)).expectation_value(obs) +``` + +``` +[7]: +``` + +```python +np.complex128(-0.8285687255430366+0j) +``` + + + +### Some notes on performance + +The computational efficiency of the `backpropagate` call above will heavily depend on many things, including: + +* the structure of the `circuit` +* the method of slicing the circuit +* the target observable +* the truncation parameters + +Since the `backpropagate` method simplifies the observable after every *slice* has been applied, the number of gates in a slice can dramatically influence the computational burden. The most aggressive strategy in terms of operator simplification can be achieved by slicing your circuit into slices of individual gates. + +Additionally, you can leverage all of the truncation mechanism built into the `backpropagate` method. We did not do so above, effectively resulting in an exact expectation value, but you can learn how to in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms). + + + +## Simulating a noisy expectation value + +The `qiskit-addon-obp` package also supports handling of noise models in the form of `PauliLindbladError`s. This is especially useful when you have characterized the noise model of the 2-qubit layers in your circuit, for example using the `` `NoiseLearner `` \<[api/qiskit-ibm-runtime/noise-learner-noise-learner](/docs/api/qiskit-ibm-runtime/noise-learner-noise-learner)>\`\_\_. + +In this section, you will see how you can use the `LayerError` objects returned by the `NoiseLearner` to compute noisy expectation values using OBP. + + + +### Obtaining a noise model + +Normally, you would execute the `NoiseLearner` to obtain a noise model of your specific circuit. To avoid complexity (and randomness) in this tutorial, we will refrain from doing so, and instead hard-code some noise model for our circuit below. + +However, we make sure that the structure of our data matches that of the `` `NoiseLearnerResult `` \<[api/qiskit-ibm-runtime/noise-learner-result](/docs/api/qiskit-ibm-runtime/noise-learner-result)>\`\_\_. + +In its current (non-transpiled) form, our circuit contains 4 unique layers of 2-qubit gates: + +* `slices[1]`: which has `Rxx` gates acting on all odd pairs of qubits +* `slices[2]`: which has `Rxx` gates acting on all even pairs of qubits +* `slices[3]`: which has `Ryy` gates acting on all odd pairs of qubits +* `slices[4]`: which has `Ryy` gates acting on all even pairs of qubits + +In the cell below, we manually construct 4 `LayerError` instances for each one of these layers with some randomized error rates. + +``` +[8]: +``` + +```python +import numpy as np +from qiskit.quantum_info import PauliList +from qiskit_ibm_runtime.utils.noise_learner_result import LayerError, PauliLindbladError + +# fmt: off +pauli_errors_even = ['IIIIIIIIIX', 'IIIIIIIIIY', 'IIIIIIIIIZ', 'IIIIIIIIXI', 'IIIIIIIIXX', 'IIIIIIIIXY', 'IIIIIIIIXZ', 'IIIIIIIIYI', 'IIIIIIIIYX', 'IIIIIIIIYY', 'IIIIIIIIYZ', 'IIIIIIIIZI', 'IIIIIIIIZX', 'IIIIIIIIZY', 'IIIIIIIIZZ', 'IIIIIIIXII', 'IIIIIIIXXI', 'IIIIIIIXYI', 'IIIIIIIXZI', 'IIIIIIIYII', 'IIIIIIIYXI', 'IIIIIIIYYI', 'IIIIIIIYZI', 'IIIIIIIZII', 'IIIIIIIZXI', 'IIIIIIIZYI', 'IIIIIIIZZI', 'IIIIIIXIII', 'IIIIIIXXII', 'IIIIIIXYII', 'IIIIIIXZII', 'IIIIIIYIII', 'IIIIIIYXII', 'IIIIIIYYII', 'IIIIIIYZII', 'IIIIIIZIII', 'IIIIIIZXII', 'IIIIIIZYII', 'IIIIIIZZII', 'IIIIIXIIII', 'IIIIIXXIII', 'IIIIIXYIII', 'IIIIIXZIII', 'IIIIIYIIII', 'IIIIIYXIII', 'IIIIIYYIII', 'IIIIIYZIII', 'IIIIIZIIII', 'IIIIIZXIII', 'IIIIIZYIII', 'IIIIIZZIII', 'IIIIXIIIII', 'IIIIXXIIII', 'IIIIXYIIII', 'IIIIXZIIII', 'IIIIYIIIII', 'IIIIYXIIII', 'IIIIYYIIII', 'IIIIYZIIII', 'IIIIZIIIII', 'IIIIZXIIII', 'IIIIZYIIII', 'IIIIZZIIII', 'IIIXIIIIII', 'IIIXXIIIII', 'IIIXYIIIII', 'IIIXZIIIII', 'IIIYIIIIII', 'IIIYXIIIII', 'IIIYYIIIII', 'IIIYZIIIII', 'IIIZIIIIII', 'IIIZXIIIII', 'IIIZYIIIII', 'IIIZZIIIII', 'IIXIIIIIII', 'IIXXIIIIII', 'IIXYIIIIII', 'IIXZIIIIII', 'IIYIIIIIII', 'IIYXIIIIII', 'IIYYIIIIII', 'IIYZIIIIII', 'IIZIIIIIII', 'IIZXIIIIII', 'IIZYIIIIII', 'IIZZIIIIII', 'IXIIIIIIII', 'IXXIIIIIII', 'IXYIIIIIII', 'IXZIIIIIII', 'IYIIIIIIII', 'IYXIIIIIII', 'IYYIIIIIII', 'IYZIIIIIII', 'IZIIIIIIII', 'IZXIIIIIII', 'IZYIIIIIII', 'IZZIIIIIII', 'XIIIIIIIII', 'XXIIIIIIII', 'XYIIIIIIII', 'XZIIIIIIII', 'YIIIIIIIII', 'YXIIIIIIII', 'YYIIIIIIII', 'YZIIIIIIII', 'ZIIIIIIIII', 'ZXIIIIIIII', 'ZYIIIIIIII', 'ZZIIIIIIII'] +pauli_errors_odd = ['IIIIIIIIXI', 'IIIIIIIIYI', 'IIIIIIIIZI', 'IIIIIIIXII', 'IIIIIIIXXI', 'IIIIIIIXYI', 'IIIIIIIXZI', 'IIIIIIIYII', 'IIIIIIIYXI', 'IIIIIIIYYI', 'IIIIIIIYZI', 'IIIIIIIZII', 'IIIIIIIZXI', 'IIIIIIIZYI', 'IIIIIIIZZI', 'IIIIIIXIII', 'IIIIIIXXII', 'IIIIIIXYII', 'IIIIIIXZII', 'IIIIIIYIII', 'IIIIIIYXII', 'IIIIIIYYII', 'IIIIIIYZII', 'IIIIIIZIII', 'IIIIIIZXII', 'IIIIIIZYII', 'IIIIIIZZII', 'IIIIIXIIII', 'IIIIIXXIII', 'IIIIIXYIII', 'IIIIIXZIII', 'IIIIIYIIII', 'IIIIIYXIII', 'IIIIIYYIII', 'IIIIIYZIII', 'IIIIIZIIII', 'IIIIIZXIII', 'IIIIIZYIII', 'IIIIIZZIII', 'IIIIXIIIII', 'IIIIXXIIII', 'IIIIXYIIII', 'IIIIXZIIII', 'IIIIYIIIII', 'IIIIYXIIII', 'IIIIYYIIII', 'IIIIYZIIII', 'IIIIZIIIII', 'IIIIZXIIII', 'IIIIZYIIII', 'IIIIZZIIII', 'IIIXIIIIII', 'IIIXXIIIII', 'IIIXYIIIII', 'IIIXZIIIII', 'IIIYIIIIII', 'IIIYXIIIII', 'IIIYYIIIII', 'IIIYZIIIII', 'IIIZIIIIII', 'IIIZXIIIII', 'IIIZYIIIII', 'IIIZZIIIII', 'IIXIIIIIII', 'IIXXIIIIII', 'IIXYIIIIII', 'IIXZIIIIII', 'IIYIIIIIII', 'IIYXIIIIII', 'IIYYIIIIII', 'IIYZIIIIII', 'IIZIIIIIII', 'IIZXIIIIII', 'IIZYIIIIII', 'IIZZIIIIII', 'IXIIIIIIII', 'IXXIIIIIII', 'IXYIIIIIII', 'IXZIIIIIII', 'IYIIIIIIII', 'IYXIIIIIII', 'IYYIIIIIII', 'IYZIIIIIII', 'IZIIIIIIII', 'IZXIIIIIII', 'IZYIIIIIII', 'IZZIIIIIII'] +# fmt: on + +np.random.seed(42) + +layer_error_odd_xx = LayerError( + circuit=slices[1], + qubits=list(range(num_qubits)), + error=PauliLindbladError( + PauliList(pauli_errors_odd), + 0.0001 + 0.0004 * np.random.rand(len(pauli_errors_odd)), + ), +) + +layer_error_even_xx = LayerError( + circuit=slices[2], + qubits=list(range(num_qubits)), + error=PauliLindbladError( + PauliList(pauli_errors_even), + 0.0001 + 0.0004 * np.random.rand(len(pauli_errors_even)), + ), +) + +layer_error_odd_yy = LayerError( + circuit=slices[3], + qubits=list(range(num_qubits)), + error=PauliLindbladError( + PauliList(pauli_errors_odd), + 0.0001 + 0.0004 * np.random.rand(len(pauli_errors_odd)), + ), +) + +layer_error_even_yy = LayerError( + circuit=slices[4], + qubits=list(range(num_qubits)), + error=PauliLindbladError( + PauliList(pauli_errors_even), + 0.0001 + 0.0004 * np.random.rand(len(pauli_errors_even)), + ), +) +``` + +If you would have used the `NoiseLearner` to identify the unique 2-qubit gate layers of your circuit and characterize their noise, you would obtain a `NoiseLearnerResult` object. This result would contain a list of `LayerError` objects, just like the ones we have manually constructed above. + +For each unique 2-qubit layer, the `LayerError` contains: + +* the `QuantumCircuit` representing that 2-qubit gate layer +* the qubit indices which this circuit is acting upon +* the `PauliLindbladError` which represents the characterized noise model of this layer + +The `PauliLindbladError` will contain two objects: + +* the list of Pauli errors that have been characterized +* the error rates corresponding to each one of those Pauli errors + +Normally, the list of Pauli errors will be sparse. More specifically, it will contain the single-qubit Pauli errors on all qubits that have gates acting upon them as well as the two-qubit Pauli errors on all those qubits that are connected. + + + +### Inserting the noisy layers into our circuit + +In the previous section, we have specifically constructed one `LayerError` for each of our known unique 2-qubit gate layers. This means, we know which `LayerError` matches a specific one of our slices exactly. + +Normally, when using the `LayerError`, you will need to figure out what the unique 2-qubit gate layer is, and where it occurs inside of your circuit. You will then need to adjust your `circuit` and/or `slices` to insert the `LayerError` accordingly. How to do this in the general case, is beyond the scope of this how-to guide. **TODO: link to external documentation, once it exists!** + +Here, our life is simpler because we know which slice a `LayerError` corresponds to. Therefore, it is now just a matter of inserting new slices to represent the noise. + +Note, that we must wrap each `PauliLindbladError` from `LayerError.error` in a `PauliLindbladErrorInstruction` for it to be a valid `QuantumCircuit` instruction. + +``` +[9]: +``` + +```python +from qiskit_addon_obp.utils.noise import PauliLindbladErrorInstruction + +noisy_slices = [] +for slice_ in slices: + if slice_ == layer_error_even_xx.circuit: + noisy_slices.append( + QuantumCircuit.from_instructions( + [(PauliLindbladErrorInstruction(layer_error_even_xx.error), slice_.qubits)] + ) + ) + elif slice_ == layer_error_odd_xx.circuit: + noisy_slices.append( + QuantumCircuit.from_instructions( + [(PauliLindbladErrorInstruction(layer_error_odd_xx.error), slice_.qubits)] + ) + ) + elif slice_ == layer_error_even_yy.circuit: + noisy_slices.append( + QuantumCircuit.from_instructions( + [(PauliLindbladErrorInstruction(layer_error_even_yy.error), slice_.qubits)] + ) + ) + elif slice_ == layer_error_odd_yy.circuit: + noisy_slices.append( + QuantumCircuit.from_instructions( + [(PauliLindbladErrorInstruction(layer_error_odd_yy.error), slice_.qubits)] + ) + ) + noisy_slices.append(slice_) +``` + +We can check our work and draw the circuit below: + +``` +[10]: +``` + +```python +combine_slices(noisy_slices, include_barriers=True).draw("mpl", fold=100, scale=0.8) +``` + +``` +[10]: +``` + +![../\_images/how\_tos\_simulating\_circuits\_with\_obp\_27\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_simulating_circuits_with_obp_27_0.avif) + + + +### Simulating a noisy expectation value + +At this point, classically simulating the expectation value works exactly the same as before, just + +``` +[11]: +``` + +```python +vacuum_state_noisy_obs, _, metadata = backpropagate(obs, noisy_slices) +``` + +``` +[12]: +``` + +```python +vacuum_state_noisy_obs.coeffs[~vacuum_state_noisy_obs.paulis.x.any(axis=1)].sum() +``` + +``` +[12]: +``` + +```python +np.complex128(-0.7230801696448901+7.082755280463563e-19j) +``` + +We point out again, that multiple performance concerns should be considered. Please go back to the [corresponding section above](#some-notes-on-performance). diff --git a/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb b/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb index aecdb5154e7..792091bda97 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb @@ -102,8 +102,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 3, @@ -449,8 +450,9 @@ "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 10, @@ -515,7 +517,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -529,9 +531,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.11.12" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-obp/how_tos/truncate-operator-terms.mdx b/docs/addons/qiskit-addon-obp/how_tos/truncate-operator-terms.mdx new file mode 100644 index 00000000000..6b32c7409af --- /dev/null +++ b/docs/addons/qiskit-addon-obp/how_tos/truncate-operator-terms.mdx @@ -0,0 +1,564 @@ + + +# Truncating Pauli terms during backpropagation + +This guide explains how to configure the Pauli term truncation mechanism provided by the [qiskit\_addon\_obp.utils.truncating](/docs/api/qiskit-addon-obp/utils-truncating#module-qiskit_addon_obp.utils.truncating) module. + +Operator backpropagation (OBP) can be used to reduce the depth of quantum circuits at the cost of a more complex observable. In order to get meaningful results from OBP, one usually needs to truncate terms from their observable to prevent it from growing too large. One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms’ coefficients. + +The [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice. The amount of terms that are truncated depends on various configuration parameters specified by the user. As of now, only a single truncation strategy is available – the [truncate\_binary\_search](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search) method. Given an observable and some *budget* it will perform a binary search over the Pauli terms and coefficients within that observable to find the optimal threshold, such that the sum of the truncated coefficients is maximal but lower than the budget. + +**Note**: By default, the L1 norm is used to evaluate and bound the truncation error; however, the `p_norm` setting enables the specification of the Lp-norm to be used. For more information on how to use that setting, please refer to [its how-to guide](/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm). + +In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) via the accompanying [setup\_budget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function. + + + +## Constructing an example circuit + +For the purposes of this guide, we will use the following circuit slices: + +``` +[1]: +``` + +```python +import rustworkx.generators +from qiskit.synthesis import LieTrotter +from qiskit_addon_utils.problem_generators import ( + PauliOrderStrategy, + generate_time_evolution_circuit, + generate_xyz_hamiltonian, +) +from qiskit_addon_utils.slicing import combine_slices, slice_by_gate_types + +# we generate a linear chain of 10 qubits +linear_chain = rustworkx.generators.path_graph(10) + +# we use an arbitrary XY model +hamiltonian = generate_xyz_hamiltonian( + linear_chain, + coupling_constants=(0.05, 0.02, 0.0), + ext_magnetic_field=(0.02, 0.08, 0.0), + pauli_order_strategy=PauliOrderStrategy.InteractionThenColor, +) +# we evolve for some time +circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0) +# slice the circuit by gate type +slices = slice_by_gate_types(circuit) + +# for visualization purposes only, we recombine the slices with barriers between them and draw the resulting circuit +combine_slices(slices, include_barriers=True).draw("mpl", fold=50, scale=0.6) +``` + +``` +[1]: +``` + +![../\_images/how\_tos\_truncate\_operator\_terms\_4\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_4_0.avif) + +We will look at a single simple observable: + +``` +[2]: +``` + +```python +from qiskit.quantum_info import SparsePauliOp + +obs = SparsePauliOp("IIIIIZIIII") +``` + + + +## The simplest case: a fixed truncation budget for each slice + +The available budget for truncation of Pauli terms can vary at each step of the backpropagation. As a first step towards understanding how this works, we look at the simplest case of a fixed truncation budget as specified by the user. + +The most straight-forward way to specify the truncation budget is via the `max_error_per_slice` argument. In fact, this is what is being done in [this tutorial](/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb). Setting `max_error_per_slice` to a `float` results in each slice being allotted a budget equaling that value. In the example below, we set this value to `0.001` which guarantees us an implicit truncation error of at most `0.018`, if the entire 18 slices were to be backpropagated. + + + Note that any error budget remaining after backpropagating a slice and truncating terms with small coefficients will always be added to the following slice’s error budget. + + +``` +[3]: +``` + +```python +from qiskit_addon_obp.utils.truncating import setup_budget + +truncation_error_budget = setup_budget(max_error_per_slice=0.001) +print(truncation_error_budget) +``` + +```python +TruncationErrorBudget(per_slice_budget=[0.001], max_error_total=inf, p_norm=1) +``` + +``` +[4]: +``` + +```python +from qiskit_addon_obp import backpropagate +from qiskit_addon_obp.utils.simplify import OperatorBudget + +op_budget = OperatorBudget(max_qwc_groups=10) +bp_obs, remaining_slices, metadata = backpropagate( + obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget +) +reduced_circuit = combine_slices(remaining_slices) +print(f"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.") +print( + f"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups." +) +``` + +```python +Backpropagated 11 circuit slices. +New observable contains 29 terms and 10 commuting groups. +``` + +Let’s use the [OBPMetadata](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process. + +* **The top/left plot** shows that we have enough budget to begin truncating observable terms after backpropagating the third slice. Starting from the third slice, we know that we truncate at least one term from each slice we backpropagate because we are incurring some truncation error after each slice. +* **The top/right plot** shows that the error budget ramps up to `.003` for the third slice. We see a sharp drop in remaining budget, which means terms were truncated from the observable. This is consistent with what we inferred from the top/left plot. +* **The bottom/left plot** shows that as we truncate terms from our observable, our overall accumulated error is growing monotonically. This graph also confirms that no terms were truncated until after the third slice had been backpropagated. +* **The bottom/right plot** shows that the number of commuting Pauli groups in our observable has grown to the specified limit of `10`. This plot also shows how backpropagating one more layer would cause our observable to outgrow the specified bound as you can see from the cross-over of the black and red lines. + + + Note that in all of these plots, the x-axis enumerates the backpropagated slices, but since OBP works on the end of the circuit, slice 1 is in fact the very last slice, slice 2 the one before that, and so on. + + +``` +[5]: +``` + +```python +from matplotlib import pyplot as plt +from qiskit_addon_obp.utils.visualization import ( + plot_accumulated_error, + plot_left_over_error_budget, + plot_num_qwc_groups, + plot_slice_errors, +) + +fig, axes = plt.subplots(2, 2, figsize=(20, 10)) +plot_slice_errors(metadata, axes[(0, 0)]) +plot_left_over_error_budget(metadata, axes[(0, 1)]) +plot_accumulated_error(metadata, axes[(1, 0)]) +plot_num_qwc_groups(metadata, axes[(1, 1)]) +``` + +![../\_images/how\_tos\_truncate\_operator\_terms\_13\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_13_0.avif) + + + +## Specifying slice budget explicitly + +If one has knowledge of how to assign a budget to each slice, such that the backpropagation performance is optimized, they may want to explicitly assign a budget for each slice. For demonstration purposes, let’s assign the first 3 slices zero budget and use the same budget budget of `.001` per slice for the remaining slices. + +``` +[6]: +``` + +```python +# Zero out the first 3 slices' budgets +max_error_per_slice = [0.0] * 3 + [0.001] * (len(slices) - 3) + +truncation_error_budget = setup_budget(max_error_per_slice=max_error_per_slice) +print(truncation_error_budget) +``` + +```python +TruncationErrorBudget(per_slice_budget=[0.0, 0.0, 0.0, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001], max_error_total=inf, p_norm=1) +``` + +``` +[7]: +``` + +```python +bp_obs, remaining_slices, metadata = backpropagate( + obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget +) +reduced_circuit = combine_slices(remaining_slices) +print(f"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.") +print( + f"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups." +) +``` + +```python +Backpropagated 11 circuit slices. +New observable contains 32 terms and 10 commuting groups. +``` + +Removing the budget from the first 3 layers resulted in no terms being truncated until after the fourth layer, as can be confirmed in 3 of these 4 plots. What may be somewhat surprising is that although the 4th slice had no leftover budget passed to it, a term was still truncated using the allocated `.001` budget. This is obvious from the **top/left** plot but can also be observed in the **top/right** plot, as the left-over budget curve flattens between slice ID’s 3 and 4. Another notable detail is that at least one term was truncated from this point on, same as above. + +The key takeaway is that although we incurred less truncation error in this second example due to zeroing out some slices’ budgets, we were able to backpropagate the same number of slices, and our observable contains the same number of commuting Pauli groups. We can confirm that the bound on our error is smaller in the second example by inspecting the **bottom/left** plot. + +``` +[8]: +``` + +```python +fig, axes = plt.subplots(2, 2, figsize=(20, 10)) +plot_slice_errors(metadata, axes[(0, 0)]) +plot_left_over_error_budget(metadata, axes[(0, 1)]) +plot_accumulated_error(metadata, axes[(1, 0)]) +plot_num_qwc_groups(metadata, axes[(1, 1)]) +``` + +![../\_images/how\_tos\_truncate\_operator\_terms\_19\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_19_0.avif) + + + +## Specifying the budget cyclically + +If one has a circuit with some repeatable pattern, such as a Trotter circuit, it may be desired to specify a budget for that repeated subset of slices and have that budget used for all of the following repetitions of those slices. + +More concretely, the example circuit we are using has 6 slices which are repeated 3 times for a total of 18 slices. Here we will arbitrarily assign zero budget to the single-qubit and `RYY` layers and `.003` budget to each of the `RXX` layers. We will observe how specifying the budget as a length-6 sequence causes the budget to be applied to all 18 slices cyclically. + + + Once more pay attention to the fact that the slices are backpropagated in reverse order (i.e. starting at the end). Therefore, the first entry in our cyclic budget actually gets used for the last slice, the second entry for the slice before that, and so on. + + +``` +[9]: +``` + +```python +# Specify a length-6 per-slice budget. +# This will be cycled over 3 times to be applied to the 18 slices +max_error_per_slice = [0.0] * 4 + [0.003] * 2 + +truncation_error_budget = setup_budget(max_error_per_slice=max_error_per_slice) +print(truncation_error_budget) +``` + +```python +TruncationErrorBudget(per_slice_budget=[0.0, 0.0, 0.0, 0.0, 0.003, 0.003], max_error_total=inf, p_norm=1) +``` + +``` +[10]: +``` + +```python +op_budget = OperatorBudget(max_qwc_groups=20) +bp_obs, remaining_slices, metadata = backpropagate( + obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget +) +reduced_circuit = combine_slices(remaining_slices) +print(f"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.") +print( + f"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups." +) +``` + +```python +Backpropagated 13 circuit slices. +New observable contains 49 terms and 14 commuting groups. +``` + +As seen in the **top/left** and **bottom/left**, no truncation was performed over the first four slices since they were allocated no error budget. Slices 5 and 6 had some of their terms truncated as budget became available. + +As seen in the **top/left**, a relatively large amount of error was incurred after backpropagating slice 7, even though that slice was allocated no budget. This is because only about `.002` of the `.006` total budget allocated to slices 5 and 6 was used, so the remaining was forwarded to slice 7 and mostly expended, as seen in the **top/right**. + +**top/left** and **top/right** show that the small amount of leftover budget is used up between slices 8-10, and new budget becomes available in slice 11, as expected. **top/left**, **top/right**, and **bottom/left** all demonstrate the cyclical behavior of the `max_error_per_slice` argument when its length is less than the number of slices. This cyclical behavior would have continued through all the slices, but the `max_qwc_groups` stopping criteria was reached after backpropagating 13 slices, as seen in **bottom/right**. + +Another interesting thing is that the number of Pauli groups actually drops after the second round of budget becomes available at slice 11. This is because there were groups with small coefficients which could not be truncated until there was more budget after backpropagating slice 11, so they accumulated in the observable for several iterations. This particular case also highlights that `max_qwc_groups` must be *exceeded* for the algorithm to terminate. + +``` +[11]: +``` + +```python +fig, axes = plt.subplots(2, 2, figsize=(20, 10)) +plot_slice_errors(metadata, axes[(0, 0)]) +plot_left_over_error_budget(metadata, axes[(0, 1)]) +plot_accumulated_error(metadata, axes[(1, 0)]) +plot_num_qwc_groups(metadata, axes[(1, 1)]) +``` + +![../\_images/how\_tos\_truncate\_operator\_terms\_25\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_25_0.avif) + + + +## Capping the total error + +In addition to specifying the per-slice error budget, one can specify the maximum amount of error to incur from truncation. Once that limit is hit, no more truncation will be performed; however, backpropagation will continue until the observable becomes too large and one of the stopping criteria is met. + +Let’s re-run the above experiment but set a cap on `max_error_total` such that the error budget is expended after backpropagating slice 7. + +``` +[12]: +``` + +```python +# Specify a length-6 per-slice budget. +# This will be cycled over 3 times to be applied to the 18 slices +max_error_per_slice = [0.0] * 4 + [0.003] * 2 + +truncation_error_budget = setup_budget( + max_error_per_slice=max_error_per_slice, max_error_total=0.006 +) +print(truncation_error_budget) +``` + +```python +TruncationErrorBudget(per_slice_budget=[0.0, 0.0, 0.0, 0.0, 0.003, 0.003], max_error_total=0.006, p_norm=1) +``` + +``` +[13]: +``` + +```python +bp_obs, remaining_slices, metadata = backpropagate( + obs, + slices, + operator_budget=op_budget, + truncation_error_budget=truncation_error_budget, +) +reduced_circuit = combine_slices(remaining_slices) +print(f"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.") +print( + f"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups." +) +``` + +```python +Backpropagated 10 circuit slices. +New observable contains 67 terms and 20 commuting groups. +``` + +As expected, our truncation error caps out at `.006` (**bottom/left**). It is notable that in this run we were not able to backpropagate slice 11. This is because we did not have enough budget to truncate terms and the number of commuting Pauli groups grew past the limit of `20`, as seen in **bottom/right**. + +``` +[14]: +``` + +```python +fig, axes = plt.subplots(2, 2, figsize=(20, 10)) +plot_slice_errors(metadata, axes[(0, 0)]) +plot_left_over_error_budget(metadata, axes[(0, 1)]) +plot_accumulated_error(metadata, axes[(1, 0)]) +plot_num_qwc_groups(metadata, axes[(1, 1)]) +``` + +![../\_images/how\_tos\_truncate\_operator\_terms\_30\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_30_0.avif) + +It may be desirable to simply cap the overall budget with `max_error_total` without specifying `max_error_per_slice`. + +``` +[15]: +``` + +```python +truncation_error_budget = setup_budget(max_error_total=0.018) +print(truncation_error_budget) +``` + +```python +TruncationErrorBudget(per_slice_budget=[0.018], max_error_total=0.018, p_norm=1) +``` + +The output of the cell above, might be slightly surprising because the `per_slice_budget` is set to the `max_error_total`. This indicates, that the entire available budget will be consumed **greedily**. You can think of it this way: the entire budget is available to each slice (because we loop over the `per_slice_budget`). But any budget that has already been consumed, will be deducted from the budget that is available at that given point in the algorithm. + +``` +[16]: +``` + +```python +op_budget = OperatorBudget(max_qwc_groups=10) +bp_obs, remaining_slices, metadata = backpropagate( + obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget +) +reduced_circuit = combine_slices(remaining_slices) +print(f"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.") +print( + f"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups." +) +``` + +```python +Backpropagated 9 circuit slices. +New observable contains 25 terms and 9 commuting groups. +``` + +**top/right** shows how the entire `.018` error budget is made available to the first slice. No truncation is performed until the third slices, so the left-over budget remains constant. The budget monotonically decreases, as the full budget is made available to each backpropagated slice until it is expended. + +It is notable that this experiment yielded two fewer backpropagated slices compared to the first experiment in this notebook, which is almost identical. This demonstrates that for some problems distributing the budget evenly might be optimal. For other problems, allowing slices to greedily expend the full budget might yield better performance. + +``` +[17]: +``` + +```python +fig, axes = plt.subplots(2, 2, figsize=(20, 10)) +plot_slice_errors(metadata, axes[(0, 0)]) +plot_left_over_error_budget(metadata, axes[(0, 1)]) +plot_accumulated_error(metadata, axes[(1, 0)]) +plot_num_qwc_groups(metadata, axes[(1, 1)]) +``` + +![../\_images/how\_tos\_truncate\_operator\_terms\_36\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_36_0.avif) + + + +## Capping the number of backpropagated slices and the total error together + +If one doesn’t want to distribute their error budget all the way through the circuit but doesn’t want to greedily expend it either, one can specify a the number of slices they intend to backpropagate (`num_slices`), along with a total error budget (`max_error_total`). This will distribute the error budget uniformly (accordingly to `p_norm`) across the input slices. + +Here, we will cap the number of slices we may backpropagate at `12`, and we will keep the same total error budget. + +``` +[18]: +``` + +```python +num_slices = 12 + +truncation_error_budget = setup_budget(max_error_total=0.018, num_slices=num_slices, p_norm=1) +print(truncation_error_budget) +``` + +```python +TruncationErrorBudget(per_slice_budget=[0.0014999999999999998], max_error_total=0.018, p_norm=1) +``` + +Now we will attempt to backpropagate the `12` slices for which we allocated some budget in the previous step. We do this by just passing in the final `12` slices in our circuit. With `p_norm=1`, each of the `12` slices has an available budget of `0.018 / num_slices = 0.0015`. + +``` +[19]: +``` + +```python +bp_obs, remaining_slices, metadata = backpropagate( + obs, + slices[-num_slices:], + operator_budget=op_budget, + truncation_error_budget=truncation_error_budget, +) +``` + +Since we passed a subset of our slices (i.e. `slices[-num_slices:]`) to `backpropagate`, we must combine the slices remaining after backpropagation with the slices that were never sent for backpropagation (i.e. `slices[:-num_slices]`). + +Once we have combined all of the remaining slices, we can use [combine\_slices](/docs/api/qiskit-addon-utils/slicing-combine-slices#qiskit_addon_utils.slicing.combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became. + +``` +[20]: +``` + +```python +# Recombine the slices remaining after backprop with the rest of the original circuit +reduced_circuit = combine_slices(slices[:-num_slices] + remaining_slices) + +print(f"Backpropagated {num_slices - len(remaining_slices)} circuit slices.") +print( + f"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups." +) +``` + +```python +Backpropagated 12 circuit slices. +New observable contains 29 terms and 9 commuting groups. +``` + +The plots show us that we did successfully backpropagate all `12` slices while keeping our observable under `10` commuting Pauli groups. We can also see that using `num_slices` along with `max_error_total` results in a distribution of budgets across the slices and unused budget is again forwarded to the next slice. This is most apparent in the **top/right** plot, as the budget is both expended and replenished throughout backpropagation. + +It is notable that this method of distributing error produced the best results (more backpropagated slices), compared with the first experiment in this notebook and the example just above this one. All of these examples allotted `.018` error budget, but backpropagation performed differently depending on how that budget was distributed. + +``` +[21]: +``` + +```python +fig, axes = plt.subplots(2, 2, figsize=(20, 10)) +plot_slice_errors(metadata, axes[(0, 0)]) +plot_left_over_error_budget(metadata, axes[(0, 1)]) +plot_accumulated_error(metadata, axes[(1, 0)]) +plot_num_qwc_groups(metadata, axes[(1, 1)]) +``` + +![../\_images/how\_tos\_truncate\_operator\_terms\_44\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_44_0.avif) + + + +## Working with multiple observables + +The `qiskit_addon_obp.backpropagate` method allows one to pass in a sequence of observables. This simplifies the workflow when dealing with multiple target observables. + +We explicitly mention this here to teach you how the truncation strategy handles such a case. For the sake of this example, we add an extra observable to the one which we have been using so far: + +``` +[22]: +``` + +```python +obs = [SparsePauliOp("IIIIIZIIII"), SparsePauliOp("IIIIIXIIII")] +``` + +For simplicity, we repeat the very first experiment from this tutorial. The only difference is that we now have two observables to backpropagate the circuit into. + +For this particular example, this does not affect the number of slices which could be backpropagated. However, we can see now that the two observables resulted in different numbers of Pauli terms and commuting groups. + +``` +[23]: +``` + +```python +truncation_error_budget = setup_budget(max_error_per_slice=0.001) +print(truncation_error_budget) +``` + +```python +TruncationErrorBudget(per_slice_budget=[0.001], max_error_total=inf, p_norm=1) +``` + +``` +[24]: +``` + +```python +bp_obs, remaining_slices, metadata = backpropagate( + obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget +) +reduced_circuit = combine_slices(remaining_slices) +print(f"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.") +print( + f"The new first observable contains {len(bp_obs[0])} terms and {len(bp_obs[0].group_commuting(qubit_wise=True))} commuting groups." +) +print( + f"The new second observable contains {len(bp_obs[1])} terms and {len(bp_obs[1].group_commuting(qubit_wise=True))} commuting groups." +) +``` + +```python +Backpropagated 11 circuit slices. +The new first observable contains 29 terms and 10 commuting groups. +The new second observable contains 23 terms and 8 commuting groups. +``` + +The plots below shed some light on how the backpropagation algorithm handles multiple observables. + +First, the plots **top/left**, **top/right** and **bottom/left** teach us that the budget for truncating terms is set individually for each observable. In other words, both observables have the ability to truncate terms assuming an error of `0.001` per backpropagated slice. Due to the different nature of the observables, this results in different consumption of the budget. In this example, they exhibit a lot of overlap which is not necessarily the case in general. + +**bottom/right** teaches us that `max_qwc_groups` is actually taking *all* observables combined into account. That means that the terms of all observables are grouped together to arrive at one final number of qubit-wise commuting groups which is compared against `max_qwc_paulis`. The same is done for the `max_paulis` threshold (which we have not discussed in this notebook) which allows you to set a limit on the number of Pauli terms across all observables. + +``` +[25]: +``` + +```python +fig, axes = plt.subplots(2, 2, figsize=(20, 10)) +plot_slice_errors(metadata, axes[(0, 0)]) +plot_left_over_error_budget(metadata, axes[(0, 1)]) +plot_accumulated_error(metadata, axes[(1, 0)]) +plot_num_qwc_groups(metadata, axes[(1, 1)]) +``` + +![../\_images/how\_tos\_truncate\_operator\_terms\_51\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_51_0.avif) diff --git a/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb b/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb index 862fab2f568..e60bad309c1 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb @@ -13,7 +13,7 @@ "id": "c1453472-3506-4008-b432-f8ef9ccbf3ee", "metadata": {}, "source": [ - "This guide explains how to configure the Pauli term truncation mechanism provided by the [qiskit_addon_obp.utils.truncating](/docs/addons/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating) module.\n", + "This guide explains how to configure the Pauli term truncation mechanism provided by the [qiskit_addon_obp.utils.truncating](/docs/api/qiskit-addon-obp/utils-truncating#module-qiskit_addon_obp.utils.truncating) module.\n", "\n", "Operator backpropagation (OBP) can be used to reduce the depth of quantum circuits at the cost of a more complex observable. In order to get meaningful results from OBP, one usually needs to truncate terms from their observable to prevent it from growing too large. One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms' coefficients.\n", "\n", @@ -53,8 +53,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 1, @@ -129,7 +130,7 @@ "\n", "The most straight-forward way to specify the truncation budget is via the `max_error_per_slice` argument.\n", "In fact, this is what is being done in [this tutorial](/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb). Setting `max_error_per_slice` to a `float` results in each slice being allotted a budget equaling that value.\n", - "In the example below, we set this value to `0.001` which guarantees us an implicit truncation error of at most `0.018`, if the entire 18 slices were to be backpropagated.\n", + "In the example below, we set this value to `0.001` which guarantees us an implicit truncation error of at most `0.018`, if the entire 18 slices were to be backpropagated. \n", "\n", "
    \n", "Note that any error budget remaining after backpropagating a slice and truncating terms with small coefficients will always be added to the following slice's error budget.\n", @@ -192,7 +193,7 @@ "id": "4a772af6-edc6-4e53-81c7-53f3b72bf58a", "metadata": {}, "source": [ - "Let's use the [OBPMetadata](/docs/addons/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.metadata.OBPMetadata) instance and the tools provided by the [visualization](/docs/addons/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", + "Let's use the [OBPMetadata](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", "\n", "- **The top/left plot** shows that we have enough budget to begin truncating observable terms after backpropagating the third slice. Starting from the third slice, we know that we truncate at least one term from each slice we backpropagate because we are incurring some truncation error after each slice.\n", "- **The top/right plot** shows that the error budget ramps up to `.003` for the third slice. We see a sharp drop in remaining budget, which means terms were truncated from the observable. This is consistent with what we inferred from the top/left plot.\n", @@ -218,8 +219,9 @@ "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -324,8 +326,9 @@ "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -434,8 +437,9 @@ "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -532,8 +536,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -629,8 +634,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -708,7 +714,7 @@ "source": [ "Since we passed a subset of our slices (i.e. ``slices[-num_slices:]``) to ``backpropagate``, we must combine the slices remaining after backpropagation with the slices that were never sent for backpropagation (i.e. ``slices[:-num_slices]``).\n", "\n", - "Once we have combined all of the remaining slices, we can use [combine_slices](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.slicing.combine_slices#qiskit_addon_utils.slicing.combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became." + "Once we have combined all of the remaining slices, we can use [combine_slices](/docs/api/qiskit-addon-utils/slicing-combine-slices#qiskit_addon_utils.slicing.combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became." ] }, { @@ -754,8 +760,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -872,8 +879,9 @@ "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -891,7 +899,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -905,9 +913,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-obp/index.mdx b/docs/addons/qiskit-addon-obp/index.mdx index b585d40fee5..6d18bdbabdd 100644 --- a/docs/addons/qiskit-addon-obp/index.mdx +++ b/docs/addons/qiskit-addon-obp/index.mdx @@ -1,5 +1,6 @@ --- -title: "Qiskit addon: operator backpropagation (OBP)" +title: Operator backpropagation (OBP) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-obp. --- # Qiskit addon: operator backpropagation (OBP) diff --git a/docs/addons/qiskit-addon-obp/install.mdx b/docs/addons/qiskit-addon-obp/install.mdx index b6cd49f0394..8c5e1e7c54c 100644 --- a/docs/addons/qiskit-addon-obp/install.mdx +++ b/docs/addons/qiskit-addon-obp/install.mdx @@ -1,7 +1,3 @@ ---- -title: "Installation Instructions" ---- - # Installation Instructions Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: @@ -73,4 +69,3 @@ If you installed the notebook dependencies, you can get started by running the n cd docs/ jupyter lab ``` - diff --git a/docs/addons/qiskit-addon-obp/release-notes.mdx b/docs/addons/qiskit-addon-obp/release-notes.mdx deleted file mode 100644 index ebf4396e714..00000000000 --- a/docs/addons/qiskit-addon-obp/release-notes.mdx +++ /dev/null @@ -1,171 +0,0 @@ ---- -title: "Release Notes" ---- - - - - - -# - - - - - -## 0.3.0 - - - -### New Features - -* [`PauliLindbladError`](/docs/api/qiskit-ibm-runtime/utils-noise-learner-result-pauli-lindblad-error "(in Qiskit Runtime IBM Client)") objects can now be embedded in slices of `QuantumCircuit` using the [`PauliLindbladErrorInstruction`](qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction#qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction "qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction") and will be handled properly by [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate"). - - - -### Bug Fixes - -* Fixed a bug in [`qiskit_addon_obp.backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") which often caused resets to be applied to the wrong qubit. - - - - - -## 0.2.0 - - - - - -### New Features - -* A new parameter `show_legend` has been added to each function in the [`qiskit_addon_obp.utils.visualization`](qiskit_addon_obp.utils.visualization#module-qiskit_addon_obp.utils.visualization "qiskit_addon_obp.utils.visualization") module that can show or hide the legend on a plot. The legend is shown by default. This can be useful when the legend becomes long and obstructs the plot. - -* [`qiskit_addon_obp.utils.simplify.OperatorBudget`](qiskit_addon_obp.utils.simplify#qiskit_addon_obp.utils.simplify.OperatorBudget "qiskit_addon_obp.utils.simplify.OperatorBudget") now holds `atol` and `rtol` fields which are absolute and relative tolerances used to determine whether coefficients are zero while simplifying an operator. - -* [`truncate_binary_search()`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search "qiskit_addon_obp.utils.truncating.truncate_binary_search") now accepts a `tol` kwarg. Once an optimal truncation threshold, up to this value, has been found, the search for an optimal threshold will stop. The default tolerance is `1e-8`; whereas, the tolerance used prior to this release was `1e-10`. - - [`TruncationErrorBudget`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") now holds a `tol` field. This field is used by [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") as the `tol` argument to [`truncate_binary_search()`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search "qiskit_addon_obp.utils.truncating.truncate_binary_search"). - - - -### Upgrade Notes - -* This release adds support for Python 3.13. No code changes were necessary, so older releases are expected to work on Python 3.13 too. - -* This package is now compatible with Qiskit SDK 2.0. - - - - - -### Bug Fixes - -* The [`num_duplicate_paulis`](qiskit_addon_obp.utils.simplify#qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_duplicate_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_duplicate_paulis") was previously unable to differentiate from Pauli terms that were trimmed due to their coefficient being close to zero. This is now tracked correctly with these trimmed terms only counting towards [`num_trimmed_paulis`](qiskit_addon_obp.utils.simplify#qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_trimmed_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_trimmed_paulis"). - -* The reported number of unique Paulis in [`num_unique_paulis`](qiskit_addon_obp.utils.simplify#qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_unique_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_unique_paulis") is now correct in cases where all gates in a slice commute with an observable. - -* Fixed a bug in [`qiskit_addon_obp.backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") which caused slices with depth > 1 to be incorrectly forward-propagated. Gates from a given slice will now be correctly propagated into the observable in reverse order (i.e., from the back of the slice). - - - - - -## 0.1.0 - - - - - -### New Features - -* Added a [`OperatorBudget`](qiskit_addon_obp.utils.simplify#qiskit_addon_obp.utils.simplify.OperatorBudget "qiskit_addon_obp.utils.simplify.OperatorBudget") class for specifying how large an operator may grow during back-propagation. - -* Adds the `max_seconds` keyword-argument to the [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") function, allowing the end-user to specify a maximum wall clock time for the algorithm. This can (for example) be useful for exploring different truncation error budget strategies while limiting the CPU time. - -* Introduced a new `dataclass`, [`TruncationErrorBudget`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget"), for holding information about the observable truncation strategy. - -* Introduced a new function, [`setup_budget()`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.setup_budget "qiskit_addon_obp.utils.truncating.setup_budget"), which generates a [`TruncationErrorBudget`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") class, given an observable truncation strategy (e.g. `max_error_total`, `max_error_per_slice`, `p_norm`). - - - - - -### Upgrade Notes - -* The [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") function no longer accepts `max_paulis` and `max_qwc_groups` kwargs for constraining the size of the operator during back-propagation. Users should instead use the new `operator_budget` kwarg, which takes an [`OperatorBudget`](qiskit_addon_obp.utils.simplify#qiskit_addon_obp.utils.simplify.OperatorBudget "qiskit_addon_obp.utils.simplify.OperatorBudget") instance. - - To migrate, change this code - - ```python - from qiskit_addon_obp import backpropagate - - bp_obs, remaining_slices, metadata = backpropagate( - obs, - slices, - max_paulis=100, - max_qwc_groups=10, - simplify=True - ) - ``` - - to this - - ```python - from qiskit_addon_obp import backpropagate - from qiskit_addon_obp.utils.simplify import OperatorBudget - - op_budget = OperatorBudget(max_paulis=100, max_qwc_groups=10, simplify=True) - bp_obs, remaining_slices, metadata = backpropagate(obs, slices, operator_budget=op_budget) - ``` - -* The `max_slices` kwarg has been removed from [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate"). Users should now only pass in slices which they intend to back-propagate. If a user wants to attempt to only back-propagate the last `20` slices of an `N`-slice circuit, they would simply pass in the last `20` slices to [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") and, recombine any slices remaining after back-propagation with the original `N-20` slices. - - For example - - ```python - from qiskit_addon_obp import backpropagate - from qiskit_addon_obp.utils.truncating import setup_budget - from qiskit_addon_utils.slicing import combine_slices - - num_slices = 20 - truncation_error_budget = setup_budget(max_error_total=0.02, num_slices=num_slices, p_norm=1) - bp_obs, remaining_slices, metadata = backpropagate( - obs, slices[-num_slices:], truncation_error_budget=truncation_error_budget - ) - reduced_circuit = combine_slices(slices[:-num_slices] + remaining_slices) - ``` - -* The `max_slices` kwarg in [`setup_budget()`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.setup_budget "qiskit_addon_obp.utils.truncating.setup_budget") has been renamed to `num_slices`. - -* The `max_slices` attribute in [`OBPMetadata`](qiskit_addon_obp.utils.metadata.OBPMetadata#qiskit_addon_obp.utils.metadata.OBPMetadata "qiskit_addon_obp.utils.metadata.OBPMetadata") has been renamed to `num_slices`. - -* The project’s root Python namespace has been changed from `obp` to `qiskit_addon_obp`. All package imports must be updated. - - For example: - - ```python - from obp import backpropagate - ``` - - should be changed to: - - ```python - from qiskit_addon_obp import backpropagate - ``` - -* Removed the `max_error_total`, `max_error_per_slice`, and `p_norm` kwargs from the [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") signature. Instead, users must specify their observable truncation strategy with the new `truncation_error_budget` kwarg which accepts a [`TruncationErrorBudget`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") instance. - -* Removed the `per_slice_budget`, `max_error_total`, and `p_norm` fields from the [`OBPMetadata`](qiskit_addon_obp.utils.metadata.OBPMetadata#qiskit_addon_obp.utils.metadata.OBPMetadata "qiskit_addon_obp.utils.metadata.OBPMetadata") class. These fields will now be accessed through the new `truncation_error_budget` field, which holds a [`TruncationErrorBudget`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") instance. - - - - - -### Bug Fixes - -* The [`setup_budget()`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.setup_budget "qiskit_addon_obp.utils.truncating.setup_budget") erroneously distributed the `max_error_total` when `num_slices` was also set. This has been fixed now, such that the budget always gets distributed evenly, regardless of the value of `p_norm`. - -* When the `max_seconds` argument to the [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") method is used, but the timeout is not reached during the actual OBP execution, the signal will now be reset properly, thereby avoiding cancellations at a (seemingly) random later point in time (of course, it is not random but actually after the specified amount of time has passed, but the rest of the code being executed after OBP could be doing anything at this point). - -* The computation of the [`accumulated_error()`](qiskit_addon_obp.utils.metadata.OBPMetadata#qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error "qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error") and [`left_over_error_budget()`](qiskit_addon_obp.utils.metadata.OBPMetadata#qiskit_addon_obp.utils.metadata.OBPMetadata.left_over_error_budget "qiskit_addon_obp.utils.metadata.OBPMetadata.left_over_error_budget") were fixed to respect the [Minkowski inequality](https://en.wikipedia.org/wiki/Minkowski_inequality). This is necessary, because a general Lp-norm (other than `p=2`) does not satisfy the [parallelogram law](https://en.wikipedia.org/wiki/Parallelogram_law) which resulted in a non-rigorous upper bound of the actual accumulated errors (and left-over error budgets by extension). - diff --git a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.mdx b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.mdx new file mode 100644 index 00000000000..3ce0d447f90 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.mdx @@ -0,0 +1,372 @@ + + +# Reducing depth of circuits with operator backpropagation + +Operator backpropagation is a technique which involves absorbing operations from the end of a quantum circuit into a Pauli operator, generally reducing the depth of the circuit at the cost of additional terms in the operator. The goal is to backpropagate as much of the circuit as possible without allowing the operator to grow too large. + +One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms’ coefficients. + +In this tutorial we will implement a [Qiskit pattern](/docs/guides/serverless#qiskit-patterns-with-quantum-serverless) for simulating the quantum dynamics of a Heisenberg spin chain using operator backpropagation: + +* **Step 1: Map to quantum problem** + + * Map the time-evolved Hamiltonian onto a quantum circuit + +* **Step 2: Optimize the problem** + + * Slice the circuit + * Backpropagate slices from the circuit onto a Pauli observable + * Combine the remaining slices into a single circuit + * Transpile the circuit for the backend + +* **Step 3: Execute experiments** + + * Calculate the expectation value using the reduced circuit and expanded observable with a [StatevectorEstimator](/docs/api/qiskit/qiskit.primitives.StatevectorEstimator) for sake of simplicity in this notebook + +* **Step 4: Reconstruct results** + + * N.A. + +**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit\_addon\_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below. + + + +## Step 1: Map to quantum problem + + + +### Map the time-evolution of a quantum Heisenberg model to a quantum experiment. + +The [qiskit\_addon\_utils](https://qiskit.github.io/qiskit-addon-utils/) package provides some reusable functionalities for various purposes. + +Its [qiskit\_addon\_utils.problem\_generators](/docs/api/qiskit-addon-utils/problem-generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph. This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/docs/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows. + +In the following, we first generate a heavy-hex `CouplingMap` out of which we carve a linear chain of 10 qubits. Note, that the indices of this new `reduced_coupling_map` are again zero-based. + +``` +[1]: +``` + +```python +from qiskit.transpiler import CouplingMap + +coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False) + +# Choose a 10-qubit linear chain on this coupling map +reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 5, 12, 8, 18]) +``` + +``` +[2]: +``` + +```python +from rustworkx.visualization import graphviz_draw + +graphviz_draw(reduced_coupling_map.graph, method="circo") +``` + +``` +[2]: +``` + +![../\_images/tutorials\_01\_getting\_started\_4\_0.png](/docs/images/api/qiskit-addon-obp/tutorials_01_getting_started_4_0.avif) + +Next, we generate a Pauli operator modeling a Heisenberg XYZ Hamiltonian. + +$$ +\hat{H} = \sum_{(j,k)\in E} (J_{x} \sigma_j^{x} \sigma_{k}^{x} + + J_{y} \sigma_j^{y} \sigma_{k}^{y} + J_{z} \sigma_j^{z} \sigma_{k}^{z}) + + \sum_{j\in V} (h_{x} \sigma_j^{x} + h_{y} \sigma_j^{y} + h_{z} \sigma_j^{z}) +$$ + +Where $G(V,E)$ is the graph of the coupling map provided. + +``` +[3]: +``` + +```python +import numpy as np +from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian + +# Get a qubit operator describing the Heisenberg XYZ model +hamiltonian = generate_xyz_hamiltonian( + reduced_coupling_map, + coupling_constants=(np.pi / 8, np.pi / 4, np.pi / 2), + ext_magnetic_field=(np.pi / 3, np.pi / 6, np.pi / 9), +) +print(hamiltonian) +``` + +```python +SparsePauliOp(['IIIIIIIXXI', 'IIIIIIIYYI', 'IIIIIIIZZI', 'IIIIIXXIII', 'IIIIIYYIII', 'IIIIIZZIII', 'IIIXXIIIII', 'IIIYYIIIII', 'IIIZZIIIII', 'IXXIIIIIII', 'IYYIIIIIII', 'IZZIIIIIII', 'IIIIIIIIXX', 'IIIIIIIIYY', 'IIIIIIIIZZ', 'IIIIIIXXII', 'IIIIIIYYII', 'IIIIIIZZII', 'IIIIXXIIII', 'IIIIYYIIII', 'IIIIZZIIII', 'IIXXIIIIII', 'IIYYIIIIII', 'IIZZIIIIII', 'XXIIIIIIII', 'YYIIIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIIY', 'IIIIIIIIIZ', 'IIIIIIIIXI', 'IIIIIIIIYI', 'IIIIIIIIZI', 'IIIIIIIXII', 'IIIIIIIYII', 'IIIIIIIZII', 'IIIIIIXIII', 'IIIIIIYIII', 'IIIIIIZIII', 'IIIIIXIIII', 'IIIIIYIIII', 'IIIIIZIIII', 'IIIIXIIIII', 'IIIIYIIIII', 'IIIIZIIIII', 'IIIXIIIIII', 'IIIYIIIIII', 'IIIZIIIIII', 'IIXIIIIIII', 'IIYIIIIIII', 'IIZIIIIIII', 'IXIIIIIIII', 'IYIIIIIIII', 'IZIIIIIIII', 'XIIIIIIIII', 'YIIIIIIIII', 'ZIIIIIIIII'], + coeffs=[0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j, + 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j, + 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, + 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j, + 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j, + 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, + 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, 1.04719755+0.j, + 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, + 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j, + 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, + 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, + 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j, + 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, + 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, + 0.34906585+0.j]) +``` + +From the qubit operator, we can generate a quantum circuit which models its time evolution. Once again, the [qiskit\_addon\_utils.problem\_generators](/docs/api/qiskit-addon-utils/problem-generators) module comes to the resuce with a handy function do just that: + +``` +[4]: +``` + +```python +from qiskit.synthesis import LieTrotter +from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit + +circuit = generate_time_evolution_circuit( + hamiltonian, + time=0.2, + synthesis=LieTrotter(reps=2), +) +circuit.draw("mpl", style="iqp", scale=0.6) +``` + +``` +[4]: +``` + +![../\_images/tutorials\_01\_getting\_started\_8\_0.png](/docs/images/api/qiskit-addon-obp/tutorials_01_getting_started_8_0.avif) + + + +## Step 2: Optimize the problem + + + +### Create circuit slices to backpropagate + +Remember, the `backpropagate` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice\_by\_gate\_types](/docs/api/qiskit-addon-utils/slicing-slice-by-gate-types) function. + +For a more detailed discussion on circuit slicing, check out this [how-to guide](https://qiskit.github.io/qiskit-addon-utils/how_tos/create_circuit_slices.html) of the [qiskit-addon-utils](https://qiskit.github.io/qiskit-addon-utils/index.html) package. + +``` +[5]: +``` + +```python +from qiskit_addon_utils.slicing import slice_by_gate_types + +slices = slice_by_gate_types(circuit) +print(f"Separated the circuit into {len(slices)} slices.") +``` + +```python +Separated the circuit into 18 slices. +``` + + + +### Constrain how large the operator may grow during backpropagation + +During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the `operator_budget` kwarg of the `backpropagate` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget) instance. + +Here we specify that backpropagation should stop when the number of qubit-wise commuting Pauli groups in the operator grows past 8. + +``` +[6]: +``` + +```python +from qiskit_addon_obp.utils.simplify import OperatorBudget + +op_budget = OperatorBudget(max_qwc_groups=8) +``` + + + +### Backpropagate slices from the circuit + +First, we will specify the Pauli-Z observable on qubit 0, and we will backpropagate slices from the time-evolution circuit until the terms in the observable can no longer be combined into 8 or fewer qubit-wise commuting Pauli groups. + +Below you will see that we backpropagated 7 slices but only used 6 of the 8 allotted Pauli groups. This implies that backpropagating one more slice would cause the number of Pauli groups to exceed 8. We can verify that this is the case by inspecting the returned metadata. + +``` +[7]: +``` + +```python +from qiskit.quantum_info import SparsePauliOp +from qiskit_addon_obp import backpropagate +from qiskit_addon_utils.slicing import combine_slices + +# Specify a single-qubit observable +observable = SparsePauliOp("IIIIIIIIIZ") + +# Backpropagate slices onto the observable +bp_obs, remaining_slices, metadata = backpropagate(observable, slices, operator_budget=op_budget) +# Recombine the slices remaining after backpropagation +bp_circuit = combine_slices(remaining_slices, include_barriers=True) + +print(f"Backpropagated {metadata.num_backpropagated_slices} slices.") +print( + f"New observable has {len(bp_obs.paulis)} terms, which can be combined into {len(bp_obs.group_commuting(qubit_wise=True))} groups." +) +print( + f"Note that backpropagating one more slice would result in {metadata.backpropagation_history[-1].num_paulis[0]} terms " + f"across {metadata.backpropagation_history[-1].num_qwc_groups} groups." +) +print("The remaining circuit after backpropagation looks as follows:") +bp_circuit.draw("mpl", scale=0.6) +``` + +```python +Backpropagated 7 slices. +New observable has 18 terms, which can be combined into 8 groups. +Note that backpropagating one more slice would result in 27 terms across 12 groups. +The remaining circuit after backpropagation looks as follows: +``` + +``` +[7]: +``` + +![../\_images/tutorials\_01\_getting\_started\_14\_1.png](/docs/images/api/qiskit-addon-obp/tutorials_01_getting_started_14_1.avif) + +Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup\_budet](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice’s budget. + +In order to enable this truncation, we need to setup our error budget like so: + +``` +[8]: +``` + +```python +from qiskit_addon_obp.utils.truncating import setup_budget + +truncation_error_budget = setup_budget(max_error_per_slice=0.005) +``` + +Note that by allocating `5e-3` error per slice for truncation, we are able to remove 3 more slices from the circuit, while remaining within the original budget of 8 commuting Pauli groups in the observable. By default, `backpropagate` uses the L1 norm of the truncated coefficients to bound the total error incurred from truncation. For other options refer to the [how-to guide on specifying the p\_norm](/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm). + +In this particular example where we have backpropagated 10 slices, the total truncation error should not exceed `(5e-3 error/slice) * (10 slices) = 5e-2`. For further discussion on distributing an error budget across your slices, check out [this how-to guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms). + +``` +[9]: +``` + +```python +# Run the same experiment but truncate observable terms with small coefficients +bp_obs_trunc, remaining_slices_trunc, metadata = backpropagate( + observable, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget +) + +# Recombine the slices remaining after backpropagation +bp_circuit_trunc = combine_slices(remaining_slices_trunc, include_barriers=True) + +print(f"Backpropagated {metadata.num_backpropagated_slices} slices.") +print( + f"New observable has {len(bp_obs_trunc.paulis)} terms, which can be combined into {len(bp_obs_trunc.group_commuting(qubit_wise=True))} groups.\n" + f"After truncation, the error in our observable is bounded by {metadata.accumulated_error(0):.3e}" +) +print( + f"Note that backpropagating one more slice would result in {metadata.backpropagation_history[-1].num_paulis[0]} terms " + f"across {metadata.backpropagation_history[-1].num_qwc_groups} groups." +) +print("The remaining circuit after backpropagation looks as follows:") +bp_circuit_trunc.draw("mpl", scale=0.6) +``` + +```python +Backpropagated 10 slices. +New observable has 19 terms, which can be combined into 8 groups. +After truncation, the error in our observable is bounded by 4.933e-02 +Note that backpropagating one more slice would result in 27 terms across 13 groups. +The remaining circuit after backpropagation looks as follows: +``` + +``` +[9]: +``` + +![../\_images/tutorials\_01\_getting\_started\_18\_1.png](/docs/images/api/qiskit-addon-obp/tutorials_01_getting_started_18_1.avif) + + + +### Now that we have our reduced ansatze and expanded observables, we can transpile our experiments to the backend. + +Here we will use the 14-qubit [FakeMelbourneV2](/docs/api/qiskit-ibm-runtime/fake-provider-fake-melbourne-v2) from [qiskit-ibm-runtime](/docs/api/qiskit-ibm-runtime) to demonstrate how to transpile to a QPU backend. + +``` +[10]: +``` + +```python +from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager +from qiskit_ibm_runtime.fake_provider import FakeMelbourneV2 + +# Specify a backend and a pass manager for transpilation +backend = FakeMelbourneV2() +pm = generate_preset_pass_manager(backend=backend, optimization_level=1) + +# Transpile original experiment +circuit_isa = pm.run(circuit) +observable_isa = observable.apply_layout(circuit_isa.layout) + +# Transpile backpropagated experiment +bp_circuit_isa = pm.run(bp_circuit) +bp_obs_isa = bp_obs.apply_layout(bp_circuit_isa.layout) + +# Transpile the backpropagated experiment with truncated observable terms +bp_circuit_trunc_isa = pm.run(bp_circuit_trunc) +bp_obs_trunc_isa = bp_obs_trunc.apply_layout(bp_circuit_trunc_isa.layout) +``` + + + +## Step 3: Execute quantum experiments + + + +### Calculate expectation value + +Finally, we can run the backpropagated experiments and compare them with the full experiment using the noiseless [StatevectorEstimator](/docs/api/qiskit/qiskit.primitives.StatevectorEstimator). We can see that the backpropagated expectation value without truncation is equivalent to the exact value within the limits of numerical precision. + +The expectation value on the operator with truncated terms has some error on the order of `1e-4`, which is within the expected tolerance. + +**Note:** We use a statevector-based `Estimator` primitive to illustrate the effect of truncation on the output. To run on the backend to which the experiments were transpiled in Step 2, one would import the [EstimatorV2](/docs/api/qiskit-ibm-runtime/estimator-v2) from `qiskit-ibm-runtime` and pass the backend instance into the constructor. + +``` +[11]: +``` + +```python +from qiskit.primitives import StatevectorEstimator as Estimator + +estimator = Estimator() + +# Run the experiments using Estimator primitive +result_exact = estimator.run([(circuit_isa, observable_isa)]).result()[0].data.evs.item() +result_bp = estimator.run([(bp_circuit_isa, bp_obs_isa)]).result()[0].data.evs.item() +result_bp_trunc = ( + estimator.run([(bp_circuit_trunc_isa, bp_obs_trunc_isa)]).result()[0].data.evs.item() +) + +print(f"Exact expectation value: {result_exact}") +print(f"Backpropagated expectation value: {result_bp}") +print(f"Backpropagated expectation value with truncation: {result_bp_trunc}") +print(f" - Expected Error for truncated observable: {metadata.accumulated_error(0):.3e}") +print(f" - Observed Error for truncated observable: {abs(result_exact - result_bp_trunc):.3e}") +``` + +```python +Exact expectation value: 0.8854160687717507 +Backpropagated expectation value: 0.8854160687717532 +Backpropagated expectation value with truncation: 0.8850236647156059 + - Expected Error for truncated observable: 4.933e-02 + - Observed Error for truncated observable: 3.924e-04 +``` diff --git a/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb index 4a74f1eaae3..e97e7dfd5d6 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb @@ -42,9 +42,9 @@ "## Step 1: Map to quantum problem\n", "### Map the time-evolution of a quantum Heisenberg model to a quantum experiment.\n", "\n", - "The [qiskit_addon_utils](/docs/addons/qiskit-addon-utils) package provides some reusable functionalities for various purposes.\n", + "The [qiskit_addon_utils](https://qiskit.github.io/qiskit-addon-utils/) package provides some reusable functionalities for various purposes.\n", "\n", - "Its [qiskit_addon_utils.problem_generators](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", + "Its [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", "This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/docs/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows.\n", "\n", "In the following, we first generate a heavy-hex `CouplingMap` out of which we carve a linear chain of 10 qubits. Note, that the indices of this new `reduced_coupling_map` are again zero-based." @@ -81,8 +81,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "" ] }, "execution_count": 2, @@ -158,7 +159,7 @@ "metadata": {}, "source": [ "From the qubit operator, we can generate a quantum circuit which models its time evolution.\n", - "Once again, the [qiskit_addon_utils.problem_generators](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators) module comes to the resuce with a handy function do just that:" + "Once again, the [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators) module comes to the resuce with a handy function do just that:" ] }, { @@ -169,8 +170,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 4, @@ -198,9 +200,9 @@ "## Step 2: Optimize the problem\n", "### Create circuit slices to backpropagate\n", "\n", - "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.slicing.slice_by_gate_types) function.\n", + "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](/docs/api/qiskit-addon-utils/slicing-slice-by-gate-types) function.\n", "\n", - "For a more detailed discussion on circuit slicing, check out this [how-to guide](/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices) of the [qiskit-addon-utils](/docs/addons/qiskit-addon-utils/index) package." + "For a more detailed discussion on circuit slicing, check out this [how-to guide](https://qiskit.github.io/qiskit-addon-utils/how_tos/create_circuit_slices.html) of the [qiskit-addon-utils](https://qiskit.github.io/qiskit-addon-utils/index.html) package." ] }, { @@ -278,8 +280,9 @@ }, { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 7, @@ -364,8 +367,9 @@ }, { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 9, @@ -487,7 +491,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -501,9 +505,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-obp/tutorials/index.mdx b/docs/addons/qiskit-addon-obp/tutorials/index.mdx new file mode 100644 index 00000000000..06c949cb7c4 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/tutorials/index.mdx @@ -0,0 +1,11 @@ +--- +title: Operator backpropagation (OBP) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-obp. +--- + +# Tutorials + +This page summarizes the available tutorials. + +* [Reducing depth of circuits with operator backpropagation](01-getting-started) + diff --git a/docs/addons/qiskit-addon-sqd/_package.json b/docs/addons/qiskit-addon-sqd/_package.json new file mode 100644 index 00000000000..9b4e87cc521 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/_package.json @@ -0,0 +1,4 @@ +{ + "name": "qiskit-addon-sqd", + "version": "0.12.0" +} diff --git a/docs/addons/qiskit-addon-sqd/how_tos/add-fermionic-excitations-to-configuration-pool.mdx b/docs/addons/qiskit-addon-sqd/how_tos/add-fermionic-excitations-to-configuration-pool.mdx new file mode 100644 index 00000000000..c060b77b77f --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/add-fermionic-excitations-to-configuration-pool.mdx @@ -0,0 +1,129 @@ + + +# Add fermionic transitions to the pool of configurations + +Here we demonstrate the functionalities to augment the pool of electronic configurations obtained by the action of transition operators on each electronic configuration. + +We demonstrate how to add single-electron hops of the type: + +$$ +c^\dagger_{p\sigma} c_{q\sigma} |\textbf{x} \rangle +$$ + +for $p, q = 1, ..., N_\textrm{orb}$ and $\sigma \in \{ \uparrow, \downarrow\}$, and for all $|\textbf{x} \rangle$ in the batch of electronic configurations. + +Let’s begin by generating a batch of random electronic configurations + +``` +[1]: +``` + +```python +import numpy as np + +n_qubits = 8 +n_orb = n_qubits // 2 + +rand_seed = 22 +np.random.seed(rand_seed) + + +# Generate some random bitstrings for testing +def random_bitstrings(n_samples, n_qubits): + return np.round(np.random.rand(n_samples, n_qubits)).astype("int").astype("bool") + + +bitstring_matrix = random_bitstrings(100, n_qubits) +``` + +The excitation operators are specified inside a numpy array whose length is equal to the number of fermionic nodes (or qubits). Each element of the array must be a string that can take values: + +* `'I'`: Identity +* `'+'`: Creation operator +* `'-'`: Annihilation operator + +Let’s generate all possible single-electron transitions (amongst same spin species). + +``` +[2]: +``` + +```python +transitions_single = np.array( + [["I" for i in range(2 * n_orb)] for j in range(4 * (n_orb**2 - n_orb) // 2 + 1)] +) +count = 1 +for i in range(n_orb): + for j in range(i + 1, n_orb): + # spin up + transitions_single[count, i] = "+" + transitions_single[count, j] = "-" + count += 1 + transitions_single[count, i] = "-" + transitions_single[count, j] = "+" + count += 1 + + # spin down + transitions_single[count, i + n_orb] = "+" + transitions_single[count, j + n_orb] = "-" + count += 1 + transitions_single[count, i + n_orb] = "-" + transitions_single[count, j + n_orb] = "+" + count += 1 + +print(transitions_single) +``` + +```python +[['I' 'I' 'I' 'I' 'I' 'I' 'I' 'I'] + ['+' '-' 'I' 'I' 'I' 'I' 'I' 'I'] + ['-' '+' 'I' 'I' 'I' 'I' 'I' 'I'] + ['I' 'I' 'I' 'I' '+' '-' 'I' 'I'] + ['I' 'I' 'I' 'I' '-' '+' 'I' 'I'] + ['+' 'I' '-' 'I' 'I' 'I' 'I' 'I'] + ['-' 'I' '+' 'I' 'I' 'I' 'I' 'I'] + ['I' 'I' 'I' 'I' '+' 'I' '-' 'I'] + ['I' 'I' 'I' 'I' '-' 'I' '+' 'I'] + ['+' 'I' 'I' '-' 'I' 'I' 'I' 'I'] + ['-' 'I' 'I' '+' 'I' 'I' 'I' 'I'] + ['I' 'I' 'I' 'I' '+' 'I' 'I' '-'] + ['I' 'I' 'I' 'I' '-' 'I' 'I' '+'] + ['I' '+' '-' 'I' 'I' 'I' 'I' 'I'] + ['I' '-' '+' 'I' 'I' 'I' 'I' 'I'] + ['I' 'I' 'I' 'I' 'I' '+' '-' 'I'] + ['I' 'I' 'I' 'I' 'I' '-' '+' 'I'] + ['I' '+' 'I' '-' 'I' 'I' 'I' 'I'] + ['I' '-' 'I' '+' 'I' 'I' 'I' 'I'] + ['I' 'I' 'I' 'I' 'I' '+' 'I' '-'] + ['I' 'I' 'I' 'I' 'I' '-' 'I' '+'] + ['I' 'I' '+' '-' 'I' 'I' 'I' 'I'] + ['I' 'I' '-' '+' 'I' 'I' 'I' 'I'] + ['I' 'I' 'I' 'I' 'I' 'I' '+' '-'] + ['I' 'I' 'I' 'I' 'I' 'I' '-' '+']] +``` + +Let’s now apply the transition operators to the configurations in `bitstring_matrix` + +``` +[3]: +``` + +```python +from qiskit_addon_sqd.fermion import enlarge_batch_from_transitions + +bitstring_matrix_aug = enlarge_batch_from_transitions(bitstring_matrix, transitions_single) + +print(bitstring_matrix_aug.shape) +print(bitstring_matrix_aug) +``` + +```python +(723, 8) +[[False False False ... False False True] + [False True False ... True False False] + [ True True True ... True False True] + ... + [ True False True ... False False True] + [ True True True ... True False True] + [False False False ... False False True]] +``` diff --git a/docs/addons/qiskit-addon-sqd/how_tos/add_fermionic_excitations_to_configuration_pool.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/add_fermionic_excitations_to_configuration_pool.ipynb index 4c2623e64cd..40253069f05 100644 --- a/docs/addons/qiskit-addon-sqd/how_tos/add_fermionic_excitations_to_configuration_pool.ipynb +++ b/docs/addons/qiskit-addon-sqd/how_tos/add_fermionic_excitations_to_configuration_pool.ipynb @@ -8,7 +8,7 @@ "# Add fermionic transitions to the pool of configurations\n", "\n", "Here we demonstrate the functionalities to augment the pool of electronic\n", - "configurations obtained by the action of transition operators on each electronic\n", + "configurations obtained by the action of transition operators on each electronic \n", "configuration.\n", "\n", "We demonstrate how to add single-electron hops of the type:\n", @@ -64,7 +64,7 @@ "- ``'+'``: Creation operator\n", "- ``'-'``: Annihilation operator\n", "\n", - "Let's generate all possible single-electron transitions (amongst same spin\n", + "Let's generate all possible single-electron transitions (amongst same spin \n", "species)." ] }, @@ -173,7 +173,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -187,9 +187,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/how_tos/benchmark-pauli-projection.mdx b/docs/addons/qiskit-addon-sqd/how_tos/benchmark-pauli-projection.mdx new file mode 100644 index 00000000000..a1f4ff56bb4 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/benchmark-pauli-projection.mdx @@ -0,0 +1,361 @@ + + +# Benchmark Pauli operator projection + + + +## Pauli string action on a computational basis state + +The action of a Pauli string on a computational basis state is rather trivial, and just a single computational basis state itself. This is a direct consequence of the structure of Pauli matrices, which only have a single nonzero element on each of their rows. Consequently, their action on a qubit is: + +*** + +$$ +\sigma_x |0 \rangle = |1 \rangle +$$ + +$$ +\sigma_x |1 \rangle = |0 \rangle +$$ + +*** + +$$ +\sigma_y |0 \rangle = i|1 \rangle +$$ + +$$ +\sigma_y |1 \rangle = -i|0 \rangle +$$ + +*** + +$$ +\sigma_z |0 \rangle = |0 \rangle +$$ + +$$ +\sigma_z |1 \rangle = -|1 \rangle +$$ + +*** + +$$ +I |0 \rangle = |0 \rangle +$$ + +$$ +I |1 \rangle = |1 \rangle +$$ + +*** + +Each bit on the bitstring labeling the computational basis will be labeled by $x \in \{0, 1 \}$. In order to keep the implementation at light as possible, we will represent the bitstrings with `bool` variables: $0\rightarrow \textrm{False}$ and $1\rightarrow \textrm{True}$. + +To represent the action of each Pauli operator in a computational basis state we will assign three variables to it: `diag`, `sign`, `imag`. + +* `diag` labels whether the operator is diagonal: + + * $\textrm{diag}(I) = \textrm{True}$ + * $\textrm{diag}(\sigma_x) = \textrm{False}$ + * $\textrm{diag}(\sigma_y) = \textrm{False}$ + * $\textrm{diag}(\sigma_z) = \textrm{True}$ + +* `sign` Identifies if there is a sign change in the matrix element connected to either 0 or 1: + + * $\textrm{sign}(I) = \textrm{False}$ + * $\textrm{sign}(\sigma_x) = \textrm{False}$ + * $\textrm{sign}(\sigma_y) = \textrm{True}$ + * $\textrm{sign}(\sigma_z) = \textrm{True}$ + +* `imag` Identifies if there is a complex component to the matrix element: + + * $\textrm{imag}(I) = \textrm{False}$ + * $\textrm{imag}(\sigma_x) = \textrm{False}$ + * $\textrm{imag}(\sigma_y) = \textrm{True}$ + * $\textrm{imag}(\sigma_z) = \textrm{False}$ + +Let’s label an arbitrary Pauli operator as $\sigma \in \{ I, \sigma_x, \sigma_y \sigma_z\}$. The action of the Pauli operator on a computational basis state can then be represented by the logic operation: + +$$ +\sigma |x \rangle = |x == \textrm{diag}(\sigma) \rangle (-1)^{x\textrm{ and sign}(\sigma)} +(i)^{\textrm{imag}(\sigma)}. +$$ + +The same is straightforwardly generalized to arbitrary number of qubits. + +Let’s check that this works: + +``` +[1]: +``` + +```python +def connected_element_and_amplitude_bool( + x: bool, diag: bool, sign: bool, imag: bool +) -> tuple[bool, complex]: + """ + Finds the connected element to computational basis state |x> under + the action of the Pauli operator represented by (diag, sign, imag). + + Args: + x: Value of the bit, either True or False. + diag: Whether the Pauli operator is diagonal (I, Z) + sigma: Whether the Pauli operator's rows differ in sign (Y, Z) + imag: Whether the Pauli operator is purely imaginary (Y) + + Returns: + A length-2 tuple: + - The connected element to x, either False or True + - The matrix element + """ + return x == diag, (-1) ** (x and sign) * (1j) ** (imag) + + +sigma_indices = [0, 1, 2, 3] +sigma_string = ["I", "SX", "SZ", "SY"] +sigma_diag = [True, False, True, False] +sigma_sign = [False, False, True, True] +sigma_imag = [False, False, False, True] +qubit_values = [False, True] + +for xi in sigma_indices: + print("-------------------") + print(sigma_string[xi]) + for x in qubit_values: + x_p, matrix_element = connected_element_and_amplitude_bool( + x, sigma_diag[xi], sigma_sign[xi], sigma_imag[xi] + ) + print("|" + str(x) + "> --> |" + str(x_p) + "> ME:" + str(matrix_element)) +``` + +```python +------------------- +I +|False> --> |False> ME:(1+0j) +|True> --> |True> ME:(1+0j) +------------------- +SX +|False> --> |True> ME:(1+0j) +|True> --> |False> ME:(1+0j) +------------------- +SZ +|False> --> |False> ME:(1+0j) +|True> --> |True> ME:(-1+0j) +------------------- +SY +|False> --> |True> ME:1j +|True> --> |False> ME:(-0-1j) +``` + +Let’s generate some large number of bitstrings (50 M) for a 40-qubit system + +``` +[2]: +``` + +```python +import numpy as np +from qiskit_addon_sqd.qubit import sort_and_remove_duplicates + +rand_seed = 22 +np.random.seed(rand_seed) + +# Generate some random bitstrings for testing + + +def random_bitstrings(n_samples, n_qubits): + return np.round(np.random.rand(n_samples, n_qubits)).astype("int").astype("bool") + + +n_qubits = 40 +bts_matrix = random_bitstrings(50_000_000, n_qubits) + +# We need to sort the bitstrings and just keep the unique ones +# NOTE: It is essential for the projection code to have the bitstrings sorted! +bts_matrix = sort_and_remove_duplicates(bts_matrix).astype("bool") + +# Final subspace dimension after getting rid of duplicated bitstrings +d = bts_matrix.shape[0] + +print("Total number of unique bitstrings: " + str(d)) +``` + +```python +Total number of unique bitstrings: 49998839 +``` + + + +## Benchmark SQD Pauli projection functions + +The Pauli string under consideration is $\sigma_z \otimes ... \otimes \sigma_z$. + +Different subspace dimensions are considered by just slicing the matrix of bitstrings. We time the subspace projection for the different subspace sizes. + +``` +[3]: +``` + +```python +import time + +from qiskit.quantum_info import Pauli +from qiskit_addon_sqd.qubit import matrix_elements_from_pauli + +pauli = Pauli("Z" * n_qubits) + +# Different subspace sizes to test +d_list = np.linspace(d / 1000, d, 20).astype("int") + +# To store the walltime +time_array = np.zeros(20) + +for i in range(20): + int_bts_matrix = bts_matrix[: d_list[i], :] + time_1 = time.time() + _ = matrix_elements_from_pauli(int_bts_matrix, pauli) + time_array[i] = time.time() - time_1 + print(f"Iteration {i} took {round(time_array[i], 6)}s") +``` + +```python +Iteration 0 took 0.201246s +Iteration 1 took 0.348222s +Iteration 2 took 0.576333s +Iteration 3 took 0.78356s +Iteration 4 took 1.016162s +Iteration 5 took 1.305325s +Iteration 6 took 1.392751s +Iteration 7 took 1.632433s +Iteration 8 took 1.826521s +Iteration 9 took 2.02903s +Iteration 10 took 2.297458s +Iteration 11 took 2.588042s +Iteration 12 took 2.738746s +Iteration 13 took 2.906144s +Iteration 14 took 3.148833s +Iteration 15 took 3.323253s +Iteration 16 took 3.664171s +Iteration 17 took 3.680663s +Iteration 18 took 4.008313s +Iteration 19 took 4.173532s +``` + +``` +[4]: +``` + +```python +import matplotlib.pyplot as plt + +# Data for energies plot +x1 = d_list +y1 = time_array + +# Plot energies +plt.title("Runtime vs subspace dimension 40 qubits") +plt.xlabel("Subspace dimension (millions)") +plt.ylabel("Wall time [s]") +plt.xticks([1e7, 2e7, 3e7, 4e7, 5e7], [str(i) for i in [10, 20, 30, 40, 50]]) +plt.plot(x1, y1, marker=".", markersize=20) +plt.tight_layout() +plt.show() +``` + +![../\_images/how\_tos\_benchmark\_pauli\_projection\_8\_0.png](/docs/images/api/qiskit-addon-sqd/how_tos_benchmark_pauli_projection_8_0.avif) + +Let’s do the same for 60 qubits + +``` +[5]: +``` + +```python +n_qubits = 60 +bts_matrix = random_bitstrings(50_000_000, n_qubits) + +# We need to sort the bitstrings and just keep the unique ones +bts_matrix = sort_and_remove_duplicates(bts_matrix).astype("bool") + +# Final subspace dimension after getting rid of duplicated bitstrings +d = bts_matrix.shape[0] + +print("Total number of unique bitstrings: " + str(d)) +``` + +```python +Total number of unique bitstrings: 50000000 +``` + +``` +[6]: +``` + +```python +pauli = Pauli("Z" * n_qubits) + +# Different subspace sizes to test +d_list = np.linspace(d / 1000, d, 20).astype("int") + +# It is better to do this once +row_array = np.arange(d) + +# To store the walltime +time_array = np.zeros(20) + +for i in range(20): + int_bts_matrix = bts_matrix[: d_list[i], :] + int_row_array = row_array[: d_list[i]] + time_1 = time.time() + _ = matrix_elements_from_pauli(int_bts_matrix, pauli) + time_array[i] = time.time() - time_1 + print(f"Iteration {i} took {round(time_array[i], 6)}s") +``` + +```python +Iteration 0 took 0.236567s +Iteration 1 took 0.424116s +Iteration 2 took 0.673399s +Iteration 3 took 0.905164s +Iteration 4 took 1.168936s +Iteration 5 took 1.454204s +Iteration 6 took 1.74778s +Iteration 7 took 1.920795s +Iteration 8 took 2.259994s +Iteration 9 took 2.550674s +Iteration 10 took 2.681287s +Iteration 11 took 3.04411s +Iteration 12 took 3.293262s +Iteration 13 took 3.471247s +Iteration 14 took 3.726639s +Iteration 15 took 4.072854s +Iteration 16 took 4.221037s +Iteration 17 took 4.498535s +Iteration 18 took 4.741108s +Iteration 19 took 5.159038s +``` + +``` +[7]: +``` + +```python +# Data for energies plot +x1 = d_list +y1 = time_array + +fig, axs = plt.subplots(1, 1, figsize=(6, 6)) + +# Plot energies +axs.plot(x1, y1, marker=".", markersize=20) +axs.set_title("Runtime vs subspace dimension 60 qubits") +axs.set_xlabel("Subspace dimension (millions)") +plt.xticks([1e7, 2e7, 3e7, 4e7, 5e7], [str(i) for i in [10, 20, 30, 40, 50]]) +axs.set_ylabel("Wall time [s]") + +plt.tight_layout() +plt.show() +``` + +![../\_images/how\_tos\_benchmark\_pauli\_projection\_12\_0.png](/docs/images/api/qiskit-addon-sqd/how_tos_benchmark_pauli_projection_12_0.avif) diff --git a/docs/addons/qiskit-addon-sqd/how_tos/benchmark_pauli_projection.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/benchmark_pauli_projection.ipynb index 64fba504741..f78442d628f 100644 --- a/docs/addons/qiskit-addon-sqd/how_tos/benchmark_pauli_projection.ipynb +++ b/docs/addons/qiskit-addon-sqd/how_tos/benchmark_pauli_projection.ipynb @@ -73,7 +73,7 @@ " - $\\textrm{diag}(\\sigma_y) = \\textrm{False}$\n", " - $\\textrm{diag}(\\sigma_z) = \\textrm{True}$\n", "\n", - "- `sign` Identifies if there is a sign change in the matrix element connected\n", + "- `sign` Identifies if there is a sign change in the matrix element connected \n", "to either 0 or 1:\n", "\n", " - $\\textrm{sign}(I) = \\textrm{False}$\n", @@ -89,7 +89,7 @@ " - $\\textrm{imag}(\\sigma_z) = \\textrm{False}$\n", "\n", "Let's label an arbitrary Pauli operator as $\\sigma \\in \\{ I, \\sigma_x, \\sigma_y\n", - "\\sigma_z\\}$. The action of the Pauli operator on a computational basis state\n", + "\\sigma_z\\}$. The action of the Pauli operator on a computational basis state \n", "can then be represented by the logic operation:\n", "$$\n", "\\sigma |x \\rangle = |x == \\textrm{diag}(\\sigma) \\rangle (-1)^{x\\textrm{ and sign}(\\sigma)}\n", @@ -298,8 +298,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -420,8 +421,9 @@ "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -449,7 +451,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -463,9 +465,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/how_tos/choose-subspace-dimension.mdx b/docs/addons/qiskit-addon-sqd/how_tos/choose-subspace-dimension.mdx new file mode 100644 index 00000000000..f3753894c67 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/choose-subspace-dimension.mdx @@ -0,0 +1,145 @@ + + +# Bounding the subspace dimension + +In this tutorial, we will show the effect of the subspace dimension in the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068). + +**A priori**, we do not know what is the correct subspace dimension to obtain a target level of accuracy. However, we do know that increasing the subspace dimension increases the accuracy of the method. Therefore, we can study the accuracy of the predictions as a function of the subspace dimension. + +Specify the molecule and its properties. + +``` +[1]: +``` + +```python +import warnings + +import pyscf +import pyscf.cc +import pyscf.mcscf + +warnings.filterwarnings("ignore") + +# Specify molecule properties +open_shell = False +spin_sq = 0 + +# Build N2 molecule +mol = pyscf.gto.Mole() +mol.build( + atom=[["N", (0, 0, 0)], ["N", (1.0, 0, 0)]], + basis="6-31g", + symmetry="Dooh", +) + +# Define active space +n_frozen = 2 +active_space = range(n_frozen, mol.nao_nr()) + +# Get molecular integrals +scf = pyscf.scf.RHF(mol).run() +num_orbitals = len(active_space) +n_electrons = int(sum(scf.mo_occ[active_space])) +num_elec_a = (n_electrons + mol.spin) // 2 +num_elec_b = (n_electrons - mol.spin) // 2 +cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b)) +mo = cas.sort_mo(active_space, base=0) +hcore, nuclear_repulsion_energy = cas.get_h1cas(mo) +eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals) + +# Compute exact energy +exact_energy = cas.run().e_tot +``` + +```python +converged SCF energy = -108.835236570775 +CASCI E = -109.046671778080 E(CI) = -32.8155692383188 S^2 = 0.0000000 +``` + +Generate some random bitstrings to proxy QPU samples. + +``` +[2]: +``` + +```python +import numpy as np +from qiskit_addon_sqd.counts import generate_bit_array_uniform + +# Create a seed to control randomness throughout this workflow +rng = np.random.default_rng(24) + +# Generate random samples +bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng) +``` + +Call SQD with increasing batch sizes. + +``` +[3]: +``` + +```python +from qiskit_addon_sqd.fermion import diagonalize_fermionic_hamiltonian + +list_samples_per_batch = [50, 200, 400, 600] + +# SQD options +max_iterations = 5 + +# Eigenstate solver options +num_batches = 10 +max_davidson_cycles = 200 + +energies = [] +subspace_dimensions = [] + +for samples_per_batch in list_samples_per_batch: + result = diagonalize_fermionic_hamiltonian( + hcore, + eri, + bit_array, + samples_per_batch=samples_per_batch, + norb=num_orbitals, + nelec=(num_elec_a, num_elec_b), + num_batches=num_batches, + max_iterations=max_iterations, + symmetrize_spin=True, + seed=rng, + ) + energies.append(result.energy) + subspace_dimensions.append(np.prod(result.sci_state.amplitudes.shape)) +``` + +This plot shows that increasing the subspace dimension leads to more accurate results. + +``` +[4]: +``` + +```python +import matplotlib.pyplot as plt + +# Data for energies plot +x1 = subspace_dimensions +y1 = np.array(energies) + nuclear_repulsion_energy + +fig, axs = plt.subplots(1, 1, figsize=(12, 6)) + +# Plot energies +axs.plot(x1, y1, marker=".", markersize=20, label="Estimated") +axs.set_xticks(x1) +axs.set_xticklabels(x1) +axs.axhline(y=exact_energy, color="red", linestyle="--", label="Exact") +axs.set_title("Approximated Ground State Energy vs subspace dimension") +axs.set_xlabel("Subspace dimension") +axs.set_ylabel("Energy (Ha)") +axs.legend() + + +plt.tight_layout() +plt.show() +``` + +![../\_images/how\_tos\_choose\_subspace\_dimension\_8\_0.png](/docs/images/api/qiskit-addon-sqd/how_tos_choose_subspace_dimension_8_0.avif) diff --git a/docs/addons/qiskit-addon-sqd/how_tos/choose_subspace_dimension.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/choose_subspace_dimension.ipynb index d832f333321..99151b59b2f 100644 --- a/docs/addons/qiskit-addon-sqd/how_tos/choose_subspace_dimension.ipynb +++ b/docs/addons/qiskit-addon-sqd/how_tos/choose_subspace_dimension.ipynb @@ -162,8 +162,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -197,7 +198,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -211,9 +212,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.13.3" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/how_tos/index.mdx b/docs/addons/qiskit-addon-sqd/how_tos/index.mdx new file mode 100644 index 00000000000..2259da1e85e --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/index.mdx @@ -0,0 +1,37 @@ +--- +title: Sample-based quantum diagonalization (SQD) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-sqd. +--- + +# How-To Guides + +This page summarizes the available how-to guides. + +[![](/docs/images/api/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](add-fermionic-excitations-to-configuration-pool) + +[Add fermionic transitions to the pool of configurations](add-fermionic-excitations-to-configuration-pool) + +[![](/docs/images/api/qiskit-addon-sqd/how_tos_benchmark_pauli_projection_12_0.avif)](benchmark-pauli-projection) + +[Benchmark Pauli operator projection](benchmark-pauli-projection) + +[![](/docs/images/api/qiskit-addon-sqd/how_tos_choose_subspace_dimension_8_0.avif)](choose-subspace-dimension) + +[Bounding the subspace dimension](choose-subspace-dimension) + +[![](/docs/images/api/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](integrate-dice-solver) + +[Scale SQD chemistry workflows with Dice solver](integrate-dice-solver) + +[![](/docs/images/api/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](project-pauli-operators-onto-hilbert-subspaces) + +[Project Pauli operators onto Hilbert subspaces](project-pauli-operators-onto-hilbert-subspaces) + +[![](/docs/images/api/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](select-open-closed-shell) + +[Understand open-shell vs closed-shell options and its effect in the subspace construction](select-open-closed-shell) + +[![](/docs/images/api/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](use-oo-to-optimize-hamiltonian-basis) + +[Optimize Hamiltonian basis with orbital optimization](use-oo-to-optimize-hamiltonian-basis) + diff --git a/docs/addons/qiskit-addon-sqd/how_tos/integrate-dice-solver.mdx b/docs/addons/qiskit-addon-sqd/how_tos/integrate-dice-solver.mdx new file mode 100644 index 00000000000..347464bbc7a --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/integrate-dice-solver.mdx @@ -0,0 +1,93 @@ + + +# Scale SQD chemistry workflows with Dice solver + +For information on how to install and use `qiskit-addon-dice-solver`, [visit the docs](https://qiskit.github.io/qiskit-addon-dice-solver/). + +For more details on the SQD code used in this example, check out [tutorial 1](/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian). + +``` +[1]: +``` + +```python +import numpy as np +import pyscf +import pyscf.cc +import pyscf.mcscf +from qiskit_addon_dice_solver import solve_sci_batch +from qiskit_addon_sqd.counts import generate_bit_array_uniform +from qiskit_addon_sqd.fermion import diagonalize_fermionic_hamiltonian + +# Specify molecule properties +num_orbitals = 16 +num_elec_a = num_elec_b = 5 +spin_sq = 0 + +# Build N2 molecule +mol = pyscf.gto.Mole() +mol.build( + atom=[["N", (0, 0, 0)], ["N", (1.0, 0, 0)]], + basis="6-31g", + symmetry="Dooh", +) + +# Define active space +n_frozen = 2 +active_space = range(n_frozen, mol.nao_nr()) + +# Get molecular integrals +scf = pyscf.scf.RHF(mol).run() +num_orbitals = len(active_space) +n_electrons = int(sum(scf.mo_occ[active_space])) +num_elec_a = (n_electrons + mol.spin) // 2 +num_elec_b = (n_electrons - mol.spin) // 2 +cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b)) +mo = cas.sort_mo(active_space, base=0) +hcore, nuclear_repulsion_energy = cas.get_h1cas(mo) +eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals) + +# Compute exact energy +exact_energy = cas.run().e_tot + +# Create a seed to control randomness throughout this workflow +rng = np.random.default_rng(24) + + +# Generate random samples +bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng) + +# Run SQD +result = diagonalize_fermionic_hamiltonian( + hcore, + eri, + bit_array, + samples_per_batch=300, + norb=num_orbitals, + nelec=(num_elec_a, num_elec_b), + num_batches=5, + max_iterations=5, + sci_solver=solve_sci_batch, + symmetrize_spin=True, + seed=rng, +) +``` + +```python +converged SCF energy = -108.835236570774 +CASCI E = -109.046671778080 E(CI) = -32.8155692383187 S^2 = 0.0000000 +``` + +``` +[2]: +``` + +```python +print(f"Exact energy: {exact_energy}") +print(f"Estimated energy: {result.energy + nuclear_repulsion_energy}") +``` + +```python +Exact energy: -109.04667177808028 +Estimated energy: -109.03402667558743 +``` diff --git a/docs/addons/qiskit-addon-sqd/how_tos/integrate_dice_solver.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/integrate_dice_solver.ipynb index 5e71994df7a..5fb539ebd4a 100644 --- a/docs/addons/qiskit-addon-sqd/how_tos/integrate_dice_solver.ipynb +++ b/docs/addons/qiskit-addon-sqd/how_tos/integrate_dice_solver.ipynb @@ -7,7 +7,7 @@ "source": [ "# Scale SQD chemistry workflows with Dice solver\n", "\n", - "For information on how to install and use ``qiskit-addon-dice-solver``, [visit the docs](/docs/addons/qiskit-addon-dice-solver).\n", + "For information on how to install and use ``qiskit-addon-dice-solver``, [visit the docs](https://qiskit.github.io/qiskit-addon-dice-solver/).\n", "\n", "For more details on the SQD code used in this example, check out [tutorial 1](/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian)." ] @@ -113,7 +113,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -127,9 +127,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/how_tos/project-pauli-operators-onto-hilbert-subspaces.mdx b/docs/addons/qiskit-addon-sqd/how_tos/project-pauli-operators-onto-hilbert-subspaces.mdx new file mode 100644 index 00000000000..e2632cc41f2 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/project-pauli-operators-onto-hilbert-subspaces.mdx @@ -0,0 +1,203 @@ + + +# Project Pauli operators onto Hilbert subspaces + +We show different ways of projecting a weighted linear combination of pauli strings into a subspace defined by a subset of size $d$ computational basis states. The projected operator is stored as a $d \times d$ `scipy.sparse.coo_matrix`. + +As an example we consider the Hamiltonian of the 1D Heisenberg model with periodic boundary conditions and $L = 22$ spins: + +$$ +H = \sum_{\langle i, j \rangle}\left( \sigma^x_i\sigma^x_j + \sigma^y_i\sigma^y_j + \sigma^z_i\sigma^z_j \right). +$$ + +This package provides two tools to perform this projection: + +* `qiskit_addon_sqd.qubit.matrix_elements_from_pauli()`: is a lower-level function that returns the non-zero matrix elements of a Pauli string and the corresponding indices of the non-zero elements. +* `qiskit_addon_sqd.qubit.project_operator_to_subspace()`: is a higher-level function that directly returns a `scipy.sparse` matrix. + +This notebook shows how to use these two tools to produce the same sparse operator. + + + +## Subspace definition + +For this example we just generate length-22 random bitstrings. For the projection functions to work as expected, it is essential that the bitstrings that define the subspace are unique and sorted in ascending order according to their unsigned integer representation. + +This can be achieved with the `qiskit_addon_sqd.qubit.sort_and_remove_duplicates()` function. + +``` +[1]: +``` + +```python +import numpy as np +from qiskit_addon_sqd.qubit import sort_and_remove_duplicates + +num_spins = 22 +rand_seed = 22 +np.random.seed(rand_seed) + + +def random_bitstrings(n_samples, n_qubits): + return np.round(np.random.rand(n_samples, n_qubits)).astype("int").astype("bool") + + +bitstring_matrix = random_bitstrings(50_000, num_spins) + +# NOTE: It is essential for the projection code to have the bitstrings sorted! +bitstring_matrix = sort_and_remove_duplicates(bitstring_matrix) + +print("Subspace dimension: " + str(bitstring_matrix.shape[0])) +print("Full Hilbert space dimension: " + str(2**num_spins)) +``` + +```python +Subspace dimension: 49718 +Full Hilbert space dimension: 4194304 +``` + + + +## First method: nonzero matrix elements and indices. + +``` +[2]: +``` + +```python +from qiskit.transpiler import CouplingMap +from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian + +coupling_map = CouplingMap.from_ring(num_spins) +hamiltonian = generate_xyz_hamiltonian(coupling_map) +print(hamiltonian) +``` + +```python +SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIXX', 'IIIIIIIIIIIIIIIIIIIIYY', 'IIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIXXII', 'IIIIIIIIIIIIIIIIIIYYII', 'IIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIXXIIII', 'IIIIIIIIIIIIIIIIYYIIII', 'IIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIXXIIIIII', 'IIIIIIIIIIIIIIYYIIIIII', 'IIIIIIIIIIIIIIZZIIIIII', 'IIIIIIIIIIIIXXIIIIIIII', 'IIIIIIIIIIIIYYIIIIIIII', 'IIIIIIIIIIIIZZIIIIIIII', 'IIIIIIIIIIXXIIIIIIIIII', 'IIIIIIIIIIYYIIIIIIIIII', 'IIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIXXIIIIIIIIIIII', 'IIIIIIIIYYIIIIIIIIIIII', 'IIIIIIIIZZIIIIIIIIIIII', 'IIIIIIXXIIIIIIIIIIIIII', 'IIIIIIYYIIIIIIIIIIIIII', 'IIIIIIZZIIIIIIIIIIIIII', 'IIIIXXIIIIIIIIIIIIIIII', 'IIIIYYIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIII', 'IIXXIIIIIIIIIIIIIIIIII', 'IIYYIIIIIIIIIIIIIIIIII', 'IIZZIIIIIIIIIIIIIIIIII', 'XXIIIIIIIIIIIIIIIIIIII', 'YYIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIII', 'XIIIIIIIIIIIIIIIIIIIIX', 'YIIIIIIIIIIIIIIIIIIIIY', 'ZIIIIIIIIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIIIIIIIXXI', 'IIIIIIIIIIIIIIIIIIIYYI', 'IIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIXXIII', 'IIIIIIIIIIIIIIIIIYYIII', 'IIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIXXIIIII', 'IIIIIIIIIIIIIIIYYIIIII', 'IIIIIIIIIIIIIIIZZIIIII', 'IIIIIIIIIIIIIXXIIIIIII', 'IIIIIIIIIIIIIYYIIIIIII', 'IIIIIIIIIIIIIZZIIIIIII', 'IIIIIIIIIIIXXIIIIIIIII', 'IIIIIIIIIIIYYIIIIIIIII', 'IIIIIIIIIIIZZIIIIIIIII', 'IIIIIIIIIXXIIIIIIIIIII', 'IIIIIIIIIYYIIIIIIIIIII', 'IIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIXXIIIIIIIIIIIII', 'IIIIIIIYYIIIIIIIIIIIII', 'IIIIIIIZZIIIIIIIIIIIII', 'IIIIIXXIIIIIIIIIIIIIII', 'IIIIIYYIIIIIIIIIIIIIII', 'IIIIIZZIIIIIIIIIIIIIII', 'IIIXXIIIIIIIIIIIIIIIII', 'IIIYYIIIIIIIIIIIIIIIII', 'IIIZZIIIIIIIIIIIIIIIII', 'IXXIIIIIIIIIIIIIIIIIII', 'IYYIIIIIIIIIIIIIIIIIII', 'IZZIIIIIIIIIIIIIIIIIII'], + coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, + 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, + 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, + 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, + 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, + 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, + 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, + 1.+0.j, 1.+0.j, 1.+0.j]) +``` + +``` +[3]: +``` + +```python +from qiskit_addon_sqd.qubit import matrix_elements_from_pauli +from scipy.sparse import coo_matrix + +d = bitstring_matrix.shape[0] + +# Initialize the coo_matrix object +operator_from_matrix_elements = coo_matrix((d, d), dtype="complex128") + +for pauli in hamiltonian.paulis: + matrix_elements, row_indices, col_indices = matrix_elements_from_pauli(bitstring_matrix, pauli) + operator_from_matrix_elements += coo_matrix( + (matrix_elements, (row_indices, col_indices)), (d, d) + ) +``` + + + +## Higher-level implementation + +``` +[4]: +``` + +```python +from qiskit_addon_sqd.qubit import project_operator_to_subspace + +operator = project_operator_to_subspace(bitstring_matrix, hamiltonian, verbose=True) +``` + +```python +Projecting term 1 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIXX ... +Projecting term 2 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIYY ... +Projecting term 3 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIZZ ... +Projecting term 4 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIXXII ... +Projecting term 5 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIYYII ... +Projecting term 6 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIZZII ... +Projecting term 7 out of 66: (1+0j) * IIIIIIIIIIIIIIIIXXIIII ... +Projecting term 8 out of 66: (1+0j) * IIIIIIIIIIIIIIIIYYIIII ... +Projecting term 9 out of 66: (1+0j) * IIIIIIIIIIIIIIIIZZIIII ... +Projecting term 10 out of 66: (1+0j) * IIIIIIIIIIIIIIXXIIIIII ... +Projecting term 11 out of 66: (1+0j) * IIIIIIIIIIIIIIYYIIIIII ... +Projecting term 12 out of 66: (1+0j) * IIIIIIIIIIIIIIZZIIIIII ... +Projecting term 13 out of 66: (1+0j) * IIIIIIIIIIIIXXIIIIIIII ... +Projecting term 14 out of 66: (1+0j) * IIIIIIIIIIIIYYIIIIIIII ... +Projecting term 15 out of 66: (1+0j) * IIIIIIIIIIIIZZIIIIIIII ... +Projecting term 16 out of 66: (1+0j) * IIIIIIIIIIXXIIIIIIIIII ... +Projecting term 17 out of 66: (1+0j) * IIIIIIIIIIYYIIIIIIIIII ... +Projecting term 18 out of 66: (1+0j) * IIIIIIIIIIZZIIIIIIIIII ... +Projecting term 19 out of 66: (1+0j) * IIIIIIIIXXIIIIIIIIIIII ... +Projecting term 20 out of 66: (1+0j) * IIIIIIIIYYIIIIIIIIIIII ... +Projecting term 21 out of 66: (1+0j) * IIIIIIIIZZIIIIIIIIIIII ... +Projecting term 22 out of 66: (1+0j) * IIIIIIXXIIIIIIIIIIIIII ... +Projecting term 23 out of 66: (1+0j) * IIIIIIYYIIIIIIIIIIIIII ... +Projecting term 24 out of 66: (1+0j) * IIIIIIZZIIIIIIIIIIIIII ... +Projecting term 25 out of 66: (1+0j) * IIIIXXIIIIIIIIIIIIIIII ... +Projecting term 26 out of 66: (1+0j) * IIIIYYIIIIIIIIIIIIIIII ... +Projecting term 27 out of 66: (1+0j) * IIIIZZIIIIIIIIIIIIIIII ... +Projecting term 28 out of 66: (1+0j) * IIXXIIIIIIIIIIIIIIIIII ... +Projecting term 29 out of 66: (1+0j) * IIYYIIIIIIIIIIIIIIIIII ... +Projecting term 30 out of 66: (1+0j) * IIZZIIIIIIIIIIIIIIIIII ... +Projecting term 31 out of 66: (1+0j) * XXIIIIIIIIIIIIIIIIIIII ... +Projecting term 32 out of 66: (1+0j) * YYIIIIIIIIIIIIIIIIIIII ... +Projecting term 33 out of 66: (1+0j) * ZZIIIIIIIIIIIIIIIIIIII ... +Projecting term 34 out of 66: (1+0j) * XIIIIIIIIIIIIIIIIIIIIX ... +Projecting term 35 out of 66: (1+0j) * YIIIIIIIIIIIIIIIIIIIIY ... +Projecting term 36 out of 66: (1+0j) * ZIIIIIIIIIIIIIIIIIIIIZ ... +Projecting term 37 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIXXI ... +Projecting term 38 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIYYI ... +Projecting term 39 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIZZI ... +Projecting term 40 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIXXIII ... +Projecting term 41 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIYYIII ... +Projecting term 42 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIZZIII ... +Projecting term 43 out of 66: (1+0j) * IIIIIIIIIIIIIIIXXIIIII ... +Projecting term 44 out of 66: (1+0j) * IIIIIIIIIIIIIIIYYIIIII ... +Projecting term 45 out of 66: (1+0j) * IIIIIIIIIIIIIIIZZIIIII ... +Projecting term 46 out of 66: (1+0j) * IIIIIIIIIIIIIXXIIIIIII ... +Projecting term 47 out of 66: (1+0j) * IIIIIIIIIIIIIYYIIIIIII ... +Projecting term 48 out of 66: (1+0j) * IIIIIIIIIIIIIZZIIIIIII ... +Projecting term 49 out of 66: (1+0j) * IIIIIIIIIIIXXIIIIIIIII ... +Projecting term 50 out of 66: (1+0j) * IIIIIIIIIIIYYIIIIIIIII ... +Projecting term 51 out of 66: (1+0j) * IIIIIIIIIIIZZIIIIIIIII ... +Projecting term 52 out of 66: (1+0j) * IIIIIIIIIXXIIIIIIIIIII ... +Projecting term 53 out of 66: (1+0j) * IIIIIIIIIYYIIIIIIIIIII ... +Projecting term 54 out of 66: (1+0j) * IIIIIIIIIZZIIIIIIIIIII ... +Projecting term 55 out of 66: (1+0j) * IIIIIIIXXIIIIIIIIIIIII ... +Projecting term 56 out of 66: (1+0j) * IIIIIIIYYIIIIIIIIIIIII ... +Projecting term 57 out of 66: (1+0j) * IIIIIIIZZIIIIIIIIIIIII ... +Projecting term 58 out of 66: (1+0j) * IIIIIXXIIIIIIIIIIIIIII ... +Projecting term 59 out of 66: (1+0j) * IIIIIYYIIIIIIIIIIIIIII ... +Projecting term 60 out of 66: (1+0j) * IIIIIZZIIIIIIIIIIIIIII ... +Projecting term 61 out of 66: (1+0j) * IIIXXIIIIIIIIIIIIIIIII ... +Projecting term 62 out of 66: (1+0j) * IIIYYIIIIIIIIIIIIIIIII ... +Projecting term 63 out of 66: (1+0j) * IIIZZIIIIIIIIIIIIIIIII ... +Projecting term 64 out of 66: (1+0j) * IXXIIIIIIIIIIIIIIIIIII ... +Projecting term 65 out of 66: (1+0j) * IYYIIIIIIIIIIIIIIIIIII ... +Projecting term 66 out of 66: (1+0j) * IZZIIIIIIIIIIIIIIIIIII ... +``` + +Check that both implementations yield the same coo\_matrix + +``` +[5]: +``` + +```python +print((operator.power(2) - operator_from_matrix_elements.power(2)).sum()) +``` + +```python +0j +``` diff --git a/docs/addons/qiskit-addon-sqd/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb index a41624b2ad1..d1d6baf81d7 100644 --- a/docs/addons/qiskit-addon-sqd/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb +++ b/docs/addons/qiskit-addon-sqd/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb @@ -14,7 +14,7 @@ "metadata": {}, "source": [ "We show different ways of projecting a weighted linear combination of pauli strings\n", - "into a subspace defined by a subset of size $d$ computational basis states. The\n", + "into a subspace defined by a subset of size $d$ computational basis states. The \n", "projected operator is stored as a $d \\times d$ ``scipy.sparse.coo_matrix``.\n", "\n", "As an example we consider the Hamiltonian of the 1D Heisenberg model with periodic\n", @@ -29,7 +29,7 @@ "that returns the non-zero matrix elements of a Pauli string and the corresponding indices\n", "of the non-zero elements.\n", "\n", - "- ``qiskit_addon_sqd.qubit.project_operator_to_subspace()``: is a higher-level function that\n", + "- ``qiskit_addon_sqd.qubit.project_operator_to_subspace()``: is a higher-level function that \n", "directly returns a ``scipy.sparse`` matrix.\n", "\n", "This notebook shows how to use these two tools to produce the same sparse operator." @@ -271,7 +271,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -285,9 +285,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/how_tos/select-open-closed-shell.mdx b/docs/addons/qiskit-addon-sqd/how_tos/select-open-closed-shell.mdx new file mode 100644 index 00000000000..84b41a3efa4 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/select-open-closed-shell.mdx @@ -0,0 +1,334 @@ + + +# Understand open-shell vs closed-shell options and its effect in the subspace construction + +In this “how-to”, we will show how to choose subpace dimensions in the `qiskit_addon_sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068). + +More importantly, this “how-to” also highlights some differences in the behaviour in the susbapce construction when run in `open_shell = False` or `open_shell = True` modes: + +* `open_shell = False` only works when the number of spin-up and spin-down electrons is the same. +* `open_shell = True` must be used when the number of spin-up and spin-down electrons is different. It can also be used when the number of spin-up and spin-down electrons is the same. However, in this last case, there is a difference in the sizes of the subspaces generated between `open_shell = False` and `open_shell = True`, as discussed in this notebook. + +**NOTE:** Some of the electronic-configuration (bitstring) manipulations in this package have as a goal to preserve the total spin symmetry $S^2$. Standard Selected Counfiguration Interaction (SCI) solvers cannot impose $S^2$ conservation exactly. Consequently, they do so approximately via a Lagrange multiplier. + +The choice of electronic configurations entering the eigenstate solver can also have a strong effect in the conservation of spin. For example, in a (2-electron,2-orbital) system, one may sample the configuration $|1001\rangle$ (having a single spin-up excitation over the RHF state $|0101\rangle$) which is a linear combination of the open-shell singlet and triplet states, respectively $(|1001\rangle ± |0110\rangle) /\sqrt{2}$. If the configuration |0110⟩ is not sampled, one can construct neither eigenfunction of total spin, leading to spin contamination or redundancy (i.e. the configuration |1001⟩ is involved in a CI calculation, but has coefficient 0 in the CI vector). Consider that a single sample $|1001\rangle$ is generated in the quantum computer, this is how the `sqd` package handles this situation: + +* `open_shell = False`: + + 1. The $1001$ bitstring is split in half, representing spin-up and spin-down configurations: $10$ (up) and $01$ (down). + 2. The list of unique spin-polarized configurations is constructed: $\mathcal{U} = [01, 10]$. + 3. We then consider all possible combinations of $\mathcal{U}$ elements to form the basis: $\left \{ |0101\rangle, |0110\rangle , |1001\rangle , |1010\rangle \right \}$, which contains the singlet and triplet states. + +* `open_shell = True`: + + 1. Contrary to the `open_shell = False` case, we do not combine the halves of the bitstring to form the basis. + + + +## Closed-Shell + +This example shows how the bitstrings are manipulated in a (2-electron, 4-orbital) system. + +``` +[1]: +``` + +```python +# Specify molecule properties +num_orbitals = 4 +num_elec_a = num_elec_b = 1 +open_shell = False +``` + +Specify by hand a dictionary of measurement outcomes + +``` +[2]: +``` + +```python +counts_dict = {"00010010": 1 / 2.0 - 0.01, "01001000": 1 / 2.0 - 0.01, "00010001": 0.02} +``` + +Transform the counts dict into a bitstring matrix and probability array for post-processing + +``` +[3]: +``` + +```python +from qiskit_addon_sqd.counts import counts_to_arrays + +# Convert counts into bitstring and probability arrays +bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict) +print(bitstring_matrix_full) +print(probs_arr_full) +``` + +```python +[[False False False True False False True False] + [False True False False True False False False] + [False False False True False False False True]] +[0.49 0.49 0.02] +``` + +Subsample a single batch of size two: + +* `n_batches = 1`: Number of batches of configurations used by the different calls to the eigenstate solver +* `samples_per_batch = 2`: Number of unique configurations to include in each batch + +``` +[4]: +``` + +```python +from qiskit_addon_sqd.subsampling import postselect_and_subsample + +n_batches = 1 +samples_per_batch = 2 + +# seed for random number generator +rand_seed = 48 + +# Generate the batches +batches = postselect_and_subsample( + bitstring_matrix_full, + probs_arr_full, + hamming_right=num_elec_a, + hamming_left=num_elec_b, + samples_per_batch=samples_per_batch, + num_batches=n_batches, + rand_seed=rand_seed, +) + +print(batches[0]) +``` + +```python +[[False False False True False False True False] + [False True False False True False False False]] +``` + +Obtain decimal representation of the spin-up and spin-down bitstrings used by the eigenstate solver + +The fist element in the tuple corresponds to the decimal representation of the spin-up configurations, while the second element in the tuple corresponds to the decimal representation of the spin-down configurations + +``` +[5]: +``` + +```python +from qiskit_addon_sqd.fermion import bitstring_matrix_to_ci_strs + +ci_strs = bitstring_matrix_to_ci_strs(batches[0], open_shell=open_shell) +print(ci_strs) +``` + +```python +(array([1, 2, 4, 8], dtype=int64), array([1, 2, 4, 8], dtype=int64)) +``` + +Note that while the number of samples per batch is 2, and the sampled bitstrings are: $00010010$ and $01001000$, four electronic configurations are generated per spin-species. In this case, the set of unique spin-polarized configurations is given by: + +$$ +\mathcal{U} = \{ 0001, 0010, 0100, 1000 \} +$$ + +whose base-10 decimal representation is + +$$ +\mathcal{U}_{10} = \{ 1, 2, 4, 8 \} +$$ + +Basis of the subspace: + +The eigenstate solver takes all possible pairs of spin-up and spin-down bitstrings to construnct the basis $\mathcal{B}$ of the subspace: + +* Element 1: $|00010001\rangle$ +* Element 2: $|00010010\rangle$ +* Element 3: $|00010100\rangle$ +* Element 4: $|00011000\rangle$ +* Element 5: $|00100001\rangle$ +* Element 6: $|00100010\rangle$ +* Element 7: $|00100100\rangle$ +* Element 8: $|00101000\rangle$ +* Element 9: $|01000001\rangle$ +* Element 10: $|01000010\rangle$ +* Element 11: $|01000100\rangle$ +* Element 12: $|01001000\rangle$ +* Element 13: $|10000001\rangle$ +* Element 14: $|10000010\rangle$ +* Element 15: $|10000100\rangle$ +* Element 16: $|10001000\rangle$ + +``` +[6]: +``` + +```python +ci_strs_up, ci_strs_dn = ci_strs + +print("Basis elements of the subspace:") + +for ci_str_up in ci_strs_up: + for ci_str_dn in ci_strs_dn: + format_name = "{0:0" + str(num_orbitals) + "b}" + print("|" + format_name.format(ci_str_up) + format_name.format(ci_str_dn) + ">") +``` + +```python +Basis elements of the subspace: +|00010001> +|00010010> +|00010100> +|00011000> +|00100001> +|00100010> +|00100100> +|00101000> +|01000001> +|01000010> +|01000100> +|01001000> +|10000001> +|10000010> +|10000100> +|10001000> +``` + +**The subspace dimension is upper-bounded by**: $2 \cdot$ (`samples_per_batch`)$^2$ + + + +## Open-Shell + +This example shows how the bitstrings are manipulated in a (2-electron, 4-orbital) system. + +``` +[7]: +``` + +```python +# Specify molecule properties +num_orbitals = 4 +num_elec_a = num_elec_b = 1 +open_shell = True +``` + +Specify by hand a dictionary of measurement outcomes + +``` +[8]: +``` + +```python +counts_dict = {"00010010": 1 / 2.0 - 0.01, "01001000": 1 / 2.0 - 0.01, "00010001": 0.02} +``` + +Transform the counts dict into a bitstring matrix and probability array for post-processing + +``` +[9]: +``` + +```python +# Convert counts into bitstring and probability arrays +bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict) +print(bitstring_matrix_full) +print(probs_arr_full) +``` + +```python +[[False False False True False False True False] + [False True False False True False False False] + [False False False True False False False True]] +[0.49 0.49 0.02] +``` + +Subsample a single batch of size two: + +* `n_batches = 1`: Number of batches of configurations used by the different calls to the eigenstate solver +* `samples_per_batch = 2`: Number of unique configurations to include in each batch + +``` +[10]: +``` + +```python +n_batches = 1 +samples_per_batch = 2 + +# seed for random number generator +rand_seed = 48 + +# Generate the batches +batches = postselect_and_subsample( + bitstring_matrix_full, + probs_arr_full, + hamming_right=num_elec_a, + hamming_left=num_elec_b, + samples_per_batch=samples_per_batch, + num_batches=n_batches, + rand_seed=rand_seed, +) + +print(batches[0]) +``` + +```python +[[False False False True False False True False] + [False True False False True False False False]] +``` + +Obtain decimal representation of the spin-up and spin-down bitstrings used by the eigenstate solver + +The fist element in the tuple corresponds to the decimal representation of the spin-up configurations, while the second element in the tuple corresponds to the decimal representation of the spin-down configurations + +``` +[11]: +``` + +```python +ci_strs = bitstring_matrix_to_ci_strs(batches[0], open_shell=open_shell) +print(ci_strs) +``` + +```python +(array([2, 8], dtype=int64), array([1, 4], dtype=int64)) +``` + +If we specify that `open_shell = True`, now we do not include all unique half-bitstrings as spin-up and spin-down configurations, thus yielding a smaller basis as when specifying `open_shell = False` + +Basis of the subspace: + +The eigenstate solver takes all possible pairs of spin-up and spin-down bitstrings to construnct the basis $\mathcal{B}$ of the subspace: + +* Element 1: $|00010010\rangle$ +* Element 2: $|00011000\rangle$ +* Element 3: $|01000010\rangle$ +* Element 4: $|01001000\rangle$ + +``` +[12]: +``` + +```python +ci_strs_up, ci_strs_dn = ci_strs + +print("Basis elements of the subspace:") + +for ci_str_up in ci_strs_up: + for ci_str_dn in ci_strs_dn: + format_name = "{0:0" + str(num_orbitals) + "b}" + print("|" + format_name.format(ci_str_up) + format_name.format(ci_str_dn) + ">") +``` + +```python +Basis elements of the subspace: +|00100001> +|00100100> +|10000001> +|10000100> +``` + +**The subspace dimension is upper-bounded by**: (`samples_per_batch`)$^2$ diff --git a/docs/addons/qiskit-addon-sqd/how_tos/select_open_closed_shell.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/select_open_closed_shell.ipynb index 7ca123516fe..dd2fa781b99 100644 --- a/docs/addons/qiskit-addon-sqd/how_tos/select_open_closed_shell.ipynb +++ b/docs/addons/qiskit-addon-sqd/how_tos/select_open_closed_shell.ipynb @@ -7,28 +7,28 @@ "source": [ "# Understand open-shell vs closed-shell options and its effect in the subspace construction\n", "\n", - "In this \"how-to\", we will show how to choose subpace dimensions in the `qiskit_addon_sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068).\n", + "In this \"how-to\", we will show how to choose subpace dimensions in the `qiskit_addon_sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068). \n", "\n", "More importantly, this \"how-to\" also highlights some differences in the behaviour in the susbapce construction when run in `open_shell = False` or `open_shell = True` modes:\n", "\n", - "- `open_shell = False` only works when the number of spin-up and spin-down electrons is the same.\n", + "- `open_shell = False` only works when the number of spin-up and spin-down electrons is the same. \n", "\n", "- `open_shell = True` must be used when the number of spin-up and spin-down electrons is different. It can also be used when the number of spin-up and spin-down electrons is the same. However, in this last case, there is a difference in the sizes of the subspaces generated between `open_shell = False` and `open_shell = True`, as discussed in this notebook.\n", "\n", - "**NOTE:** Some of the electronic-configuration (bitstring) manipulations in this package have as a goal to preserve the total spin symmetry $S^2$. Standard Selected Counfiguration Interaction (SCI) solvers cannot impose $S^2$ conservation exactly. Consequently, they do so approximately via a Lagrange multiplier.\n", + "**NOTE:** Some of the electronic-configuration (bitstring) manipulations in this package have as a goal to preserve the total spin symmetry $S^2$. Standard Selected Counfiguration Interaction (SCI) solvers cannot impose $S^2$ conservation exactly. Consequently, they do so approximately via a Lagrange multiplier. \n", "\n", "The choice of electronic configurations entering the eigenstate solver can also have a strong effect in the conservation of spin. For example, in a (2-electron,2-orbital) system, one may sample the configuration $|1001\\rangle$ (having a single spin-up excitation over the RHF state $|0101\\rangle$) which is a linear combination of the open-shell singlet and triplet states, respectively $(|1001\\rangle ± |0110\\rangle) /\\sqrt{2}$. If the configuration |0110⟩ is not sampled, one can construct neither eigenfunction of total spin, leading to spin contamination or redundancy (i.e. the configuration |1001⟩ is involved in a CI calculation, but has coefficient 0 in the CI vector). Consider that a single sample $|1001\\rangle$ is generated in the quantum computer, this is how the `sqd` package handles this situation:\n", "\n", - "- `open_shell = False`:\n", + "- `open_shell = False`: \n", "\n", " 1. The $1001$ bitstring is split in half, representing spin-up and spin-down configurations: $10$ (up) and $01$ (down).\n", - "\n", + " \n", " 2. The list of unique spin-polarized configurations is constructed: $\\mathcal{U} = [01, 10]$.\n", "\n", " 3. We then consider all possible combinations of $\\mathcal{U}$ elements to form the basis: $\\left \\{ |0101\\rangle, |0110\\rangle , |1001\\rangle , |1010\\rangle \\right \\}$, which contains the singlet and triplet states.\n", "\n", "\n", - "- `open_shell = True`:\n", + "- `open_shell = True`: \n", "\n", " 1. Contrary to the `open_shell = False` case, we do not combine the halves of the bitstring to form the basis." ] @@ -197,7 +197,7 @@ "$$\n", "\\mathcal{U} = \\{ 0001, 0010, 0100, 1000 \\}\n", "$$\n", - "whose base-10 decimal representation is\n", + "whose base-10 decimal representation is \n", "$$\n", "\\mathcal{U}_{10} = \\{ 1, 2, 4, 8 \\}\n", "$$" @@ -211,38 +211,39 @@ "Basis of the subspace:\n", "\n", "The eigenstate solver takes all possible pairs of spin-up and spin-down bitstrings to construnct the basis $\\mathcal{B}$ of the subspace:\n", + " \n", + "- Element 1: $|00010001\\rangle$ \n", "\n", - "- Element 1: $|00010001\\rangle$\n", - "\n", - "- Element 2: $|00010010\\rangle$\n", - "\n", - "- Element 3: $|00010100\\rangle$\n", + "- Element 2: $|00010010\\rangle$ \n", "\n", - "- Element 4: $|00011000\\rangle$\n", + "- Element 3: $|00010100\\rangle$ \n", "\n", - "- Element 5: $|00100001\\rangle$\n", + "- Element 4: $|00011000\\rangle$ \n", "\n", - "- Element 6: $|00100010\\rangle$\n", + "- Element 5: $|00100001\\rangle$ \n", "\n", - "- Element 7: $|00100100\\rangle$\n", + "- Element 6: $|00100010\\rangle$ \n", "\n", - "- Element 8: $|00101000\\rangle$\n", + "- Element 7: $|00100100\\rangle$ \n", "\n", - "- Element 9: $|01000001\\rangle$\n", + "- Element 8: $|00101000\\rangle$ \n", + " \n", + "- Element 9: $|01000001\\rangle$ \n", "\n", - "- Element 10: $|01000010\\rangle$\n", + "- Element 10: $|01000010\\rangle$ \n", "\n", - "- Element 11: $|01000100\\rangle$\n", + "- Element 11: $|01000100\\rangle$ \n", "\n", - "- Element 12: $|01001000\\rangle$\n", + "- Element 12: $|01001000\\rangle$ \n", "\n", - "- Element 13: $|10000001\\rangle$\n", + "- Element 13: $|10000001\\rangle$ \n", "\n", - "- Element 14: $|10000010\\rangle$\n", + "- Element 14: $|10000010\\rangle$ \n", "\n", - "- Element 15: $|10000100\\rangle$\n", + "- Element 15: $|10000100\\rangle$ \n", "\n", - "- Element 16: $|10001000\\rangle$" + "- Element 16: $|10001000\\rangle$ \n", + " " ] }, { @@ -459,14 +460,16 @@ "Basis of the subspace:\n", "\n", "The eigenstate solver takes all possible pairs of spin-up and spin-down bitstrings to construnct the basis $\\mathcal{B}$ of the subspace:\n", + " \n", + "- Element 1: $|00010010\\rangle$ \n", "\n", - "- Element 1: $|00010010\\rangle$\n", + "- Element 2: $|00011000\\rangle$ \n", "\n", - "- Element 2: $|00011000\\rangle$\n", + "- Element 3: $|01000010\\rangle$ \n", "\n", - "- Element 3: $|01000010\\rangle$\n", + "- Element 4: $|01001000\\rangle$ \n", "\n", - "- Element 4: $|01001000\\rangle$" + " " ] }, { @@ -509,7 +512,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -523,9 +526,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/how_tos/use-oo-to-optimize-hamiltonian-basis.mdx b/docs/addons/qiskit-addon-sqd/how_tos/use-oo-to-optimize-hamiltonian-basis.mdx new file mode 100644 index 00000000000..e70c0f0957a --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/use-oo-to-optimize-hamiltonian-basis.mdx @@ -0,0 +1,288 @@ + + +# Optimize Hamiltonian basis with orbital optimization + +In this tutorial, we will show how to use the `sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068) and then further optimize the ground state approximation using orbital optimization + +Refer to [Sec. II A 4](https://arxiv.org/pdf/2405.05068) for a more detailed discussion on this technique. + + + +## Specify the molecule and generate samples + +In this example, we will approximate the ground state energy of an $N_2$ molecule and then improve the answer using orbital optimization. This guide studies $N_2$ at equilibrium, which is mean-field dominated. This means the MO basis is already a good choice for our integrals; therefore, we will rotate our integrals **out** of the MO basis in order to illustrate the effects of orbital optimization. + +``` +[1]: +``` + +```python +%%capture +import numpy as np +import pyscf +import pyscf.cc +import pyscf.mcscf +from qiskit_addon_sqd.fermion import rotate_integrals + +# Specify molecule properties +open_shell = False +spin_sq = 0 + +# Build N2 molecule +mol = pyscf.gto.Mole() +mol.build( + atom=[["N", (0, 0, 0)], ["N", (1.0, 0, 0)]], + basis="6-31g", + symmetry="Dooh", +) + +# Define active space +n_frozen = 2 +active_space = range(n_frozen, mol.nao_nr()) + +# Get molecular integrals +scf = pyscf.scf.RHF(mol).run() +num_orbitals = len(active_space) +n_electrons = int(sum(scf.mo_occ[active_space])) +num_elec_a = (n_electrons + mol.spin) // 2 +num_elec_b = (n_electrons - mol.spin) // 2 +cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b)) +mo = cas.sort_mo(active_space, base=0) +hcore, nuclear_repulsion_energy = cas.get_h1cas(mo) +eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals) + +# Compute exact energy +exact_energy = cas.run().e_tot +``` + +The MO basis is already a good basis for this problem, so we will rotate out of that basis in this guide in order to highlight the effect of orbital optimization. + +``` +[2]: +``` + +```python +# Rotate our integrals out of MO basis +rng = np.random.default_rng(24) +num_params = (num_orbitals**2 - num_orbitals) // 2 # antisymmetric, specified by upper triangle +k_rot = (rng.random(num_params) - 0.5) * 0.5 +hcore_rot, eri_rot = rotate_integrals(hcore, eri, k_rot) +``` + +Generate samples + +``` +[3]: +``` + +```python +from qiskit_addon_sqd.counts import counts_to_arrays, generate_counts_uniform + +# Generate random samples +counts_dict = generate_counts_uniform(10_000, num_orbitals * 2, rand_seed=rng) + +# Convert counts into bitstring and probability arrays +bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict) +``` + + + +## Iteratively refine the samples using SQD and approximate the ground state + +``` +[4]: +``` + +```python +from qiskit_addon_sqd.configuration_recovery import recover_configurations +from qiskit_addon_sqd.fermion import solve_fermion +from qiskit_addon_sqd.subsampling import postselect_and_subsample + +# SQSD options +iterations = 5 + +# Eigenstate solver options +n_batches = 3 +samples_per_batch = 100 +max_davidson_cycles = 200 + +# Self-consistent configuration recovery loop +e_hist = np.zeros((iterations, n_batches)) # energy history +s_hist = np.zeros((iterations, n_batches)) # spin history +occupancy_hist = [] +avg_occupancy = None +for i in range(iterations): + print(f"Starting configuration recovery iteration {i}") + # On the first iteration, we have no orbital occupancy information from the + # solver, so we just post-select from the full bitstring set based on hamming weight. + if avg_occupancy is None: + bs_mat_tmp = bitstring_matrix_full + probs_arr_tmp = probs_arr_full + + # In following iterations, we use both the occupancy info and the target hamming + # weight to refine bitstrings. + else: + bs_mat_tmp, probs_arr_tmp = recover_configurations( + bitstring_matrix_full, + probs_arr_full, + avg_occupancy, + num_elec_a, + num_elec_b, + rand_seed=rng, + ) + + # Throw out samples with incorrect hamming weight and create batches of subsamples. + batches = postselect_and_subsample( + bs_mat_tmp, + probs_arr_tmp, + hamming_right=num_elec_a, + hamming_left=num_elec_b, + samples_per_batch=samples_per_batch, + num_batches=n_batches, + rand_seed=rng, + ) + + # Run eigenstate solvers in a loop. This loop should be parallelized for larger problems. + int_e = np.zeros(n_batches) + int_s = np.zeros(n_batches) + int_occs = [] + cs = [] + for j in range(n_batches): + energy_sci, coeffs_sci, avg_occs, spin = solve_fermion( + batches[j], + hcore_rot, + eri_rot, + open_shell=open_shell, + spin_sq=spin_sq, + max_cycle=max_davidson_cycles, + ) + energy_sci += nuclear_repulsion_energy + int_e[j] = energy_sci + int_s[j] = spin + int_occs.append(avg_occs) + cs.append(coeffs_sci) + + # Combine batch results + avg_occupancy = tuple(np.mean(int_occs, axis=0)) + + # Track optimization history + e_hist[i, :] = int_e + s_hist[i, :] = int_s + occupancy_hist.append(avg_occupancy) +``` + +```python +Starting configuration recovery iteration 0 +Starting configuration recovery iteration 1 +Starting configuration recovery iteration 2 +Starting configuration recovery iteration 3 +Starting configuration recovery iteration 4 +``` + + + +## Refine the subspace + +To refine the subspace, we will take the CI strings of the batch with the lowest energy from the last configuration recovery step. Other strategies may be used, like taking the union of the CI strings of the batches in the last configuration recovery iteration. + +``` +[5]: +``` + +```python +from qiskit_addon_sqd.fermion import bitstring_matrix_to_ci_strs + +best_batch = batches[np.argmin(e_hist[-1])] +ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(best_batch, open_shell=open_shell) +print(f"Subspace dimension: {len(ci_strs_up) * len(ci_strs_dn)}") +print(f"Energy of that batch from SQD: {e_hist[-1, np.argmin(e_hist[-1])]}") + +# Union strategy + +# batches_union = np.concatenate((batches[0], batches[1]), axis = 0) +# for i in range(n_batches-2): +# batches_union = np.concatenate((batches_union, batches[ i+ 2])) +# ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs( +# batches_union, open_shell=open_shell +# ) +# print (f"Subspace dimension: {len(ci_strs_up) * len(ci_strs_dn)}") +``` + +```python +Subspace dimension: 32761 +Energy of that batch from SQD: -108.7531706601421 +``` + + + +## Perform orbital optimization to improve the energy approximation + +We now describe how to optimize the orbitals to further improve the quality of the sqd calculation. + +The orbital rotations that are implemented in this package are those described by: + +$$ +U(\kappa) = e^{\sum_{pq, \sigma} \kappa_{pq} c^\dagger_{p\sigma} c_{q\sigma}}, +$$ + +where $\kappa_{p, q} \in \mathbb{R}$ and $\kappa_{p, q} = -\kappa_{q, p}$. The orbitals are optimized to minimize the variational energy: + +$$ +E(\kappa) = \langle \psi | U^\dagger(\kappa) H U(\kappa) |\psi \rangle, +$$ + +with respect to $\kappa$ using gradient descent with momentum. Recall that $|\psi\rangle$ is spanned in a subspace defined by determinants. + +Since the change of basis alters the Hamiltonian, we allow $|\psi\rangle$ to respond to the change in the Hamiltonian. This is done by performing a number of alternating self-consistent optimizations of $\kappa$ and $|\psi\rangle$. We recall that the optimal $|\psi\rangle$ is given by the lowest eigenvector of the Hamiltonian projected into the subspace. + +The `sqd.fermion.fermion` module provides the tools to perform this alternating optimization. In particular, the function `sqd.fermion.optimize_orbitals()`. + +Some of the arguments that define the optimization are: + +* `num_iters`: number of self-consistent iterations. +* `num_steps_grad`: number of gradient step updates performed when optimizing $\kappa$ on each self-consistent iteration. +* `learning_rate`: step-size in the gradient descent optimization of $\kappa$. + +``` +[6]: +``` + +```python +from qiskit_addon_sqd.fermion import optimize_orbitals + +k_flat = (rng.random(num_params) - 0.5) * 0.1 +num_iters = 20 +num_steps_grad = 10_000 # relatively cheap to execute +learning_rate = 0.05 + +e_improved, k_flat, orbital_occupancies = optimize_orbitals( + best_batch, + hcore_rot, + eri_rot, + k_flat, + open_shell=open_shell, + spin_sq=spin_sq, + num_iters=num_iters, + num_steps_grad=num_steps_grad, + learning_rate=learning_rate, + max_cycle=max_davidson_cycles, +) +``` + +Here we see that by optimizing rotation parameters for our Hamiltonian, we can improve the result from SQD. + +``` +[7]: +``` + +```python +print(f"Exact energy: {exact_energy}") +print(f"SQD energy: {np.min(e_hist[-1])}") +print(f"Energy after OO: {e_improved + nuclear_repulsion_energy}") +``` + +```python +Exact energy: -109.04667177808032 +SQD energy: -108.7531706601421 +Energy after OO: -108.80400806164377 +``` diff --git a/docs/addons/qiskit-addon-sqd/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb index 49b8965d9cf..f352bc8400a 100644 --- a/docs/addons/qiskit-addon-sqd/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb +++ b/docs/addons/qiskit-addon-sqd/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb @@ -225,7 +225,7 @@ "### Refine the subspace\n", "\n", "To refine the subspace, we will take the CI strings of the batch with the lowest energy\n", - "from the last configuration recovery step. Other strategies may be used, like taking the union\n", + "from the last configuration recovery step. Other strategies may be used, like taking the union \n", "of the CI strings of the batches in the last configuration recovery iteration." ] }, @@ -276,15 +276,15 @@ "$$\n", "U(\\kappa) = e^{\\sum_{pq, \\sigma} \\kappa_{pq} c^\\dagger_{p\\sigma} c_{q\\sigma}},\n", "$$\n", - "where $\\kappa_{p, q} \\in \\mathbb{R}$ and $\\kappa_{p, q} = -\\kappa_{q, p}$. The orbitals are optimized to\n", + "where $\\kappa_{p, q} \\in \\mathbb{R}$ and $\\kappa_{p, q} = -\\kappa_{q, p}$. The orbitals are optimized to \n", "minimize the variational energy:\n", "$$\n", "E(\\kappa) = \\langle \\psi | U^\\dagger(\\kappa) H U(\\kappa) |\\psi \\rangle,\n", "$$\n", - "with respect to $\\kappa$ using gradient descent with momentum. Recall that\n", + "with respect to $\\kappa$ using gradient descent with momentum. Recall that \n", "$|\\psi\\rangle$ is spanned in a subspace defined by determinants.\n", "\n", - "Since the change of basis alters the Hamiltonian, we allow $|\\psi\\rangle$ to\n", + "Since the change of basis alters the Hamiltonian, we allow $|\\psi\\rangle$ to \n", "respond to the change in the Hamiltonian. This is done by performing a number of alternating\n", "self-consistent optimizations of $\\kappa$ and $|\\psi\\rangle$. We recall that the optimal\n", "$|\\psi\\rangle$ is given by the lowest eigenvector of the Hamiltonian projected into the\n", @@ -296,7 +296,7 @@ "Some of the arguments that define the optimization are:\n", "\n", "- ``num_iters``: number of self-consistent iterations.\n", - "- ``num_steps_grad``: number of gradient step updates performed when optimizing\n", + "- ``num_steps_grad``: number of gradient step updates performed when optimizing \n", "$\\kappa$ on each self-consistent iteration.\n", "- ``learning_rate``: step-size in the gradient descent optimization of $\\kappa$." ] @@ -362,7 +362,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -376,9 +376,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/index.mdx b/docs/addons/qiskit-addon-sqd/index.mdx index 4d52b88810e..0d09f841487 100644 --- a/docs/addons/qiskit-addon-sqd/index.mdx +++ b/docs/addons/qiskit-addon-sqd/index.mdx @@ -1,5 +1,6 @@ --- -title: "Qiskit addon: sample-based quantum diagonalization (SQD)" +title: Sample-based quantum diagonalization (SQD) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-sqd. --- # Qiskit addon: sample-based quantum diagonalization (SQD) @@ -33,17 +34,17 @@ For more installation information refer to the [installation instructions](insta ## System sizes and computational requirements -The computational cost of SQD is dominated by the eigenstate solver calls. At each step of the self-consistent configuration recovery iteration, n\_batches of eigenstate solver calls are performed. The different calls are embarrassingly parallel. In this [tutorial](tutorials/01_chemistry_hamiltonian), those calls are inside a for loop. **It is highly recommended to perform these calls in parallel**. +The computational cost of SQD is dominated by the eigenstate solver calls. At each step of the self-consistent configuration recovery iteration, n\_batches of eigenstate solver calls are performed. The different calls are embarrassingly parallel. In this [tutorial](tutorials/01-chemistry-hamiltonian), those calls are inside a for loop. **It is highly recommended to perform these calls in parallel**. -The [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") function is multithreaded and capable of handling systems with \~25 spacial orbitals and \~10 electrons with subspace dimensions of \~\$10^7\$, using \~10-30 cores. +The [`qiskit_addon_sqd.fermion.solve_fermion()`](fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") function is multithreaded and capable of handling systems with \~25 spacial orbitals and \~10 electrons with subspace dimensions of \~\$10^7\$, using \~10-30 cores. ## Choosing subspace dimensions -The choice of the subspace dimension affects the accuracy and runtime of the eigenstate solver. The larger the subspace the more accurate the calculation, at the cost of increasing the runtime and memory requirements. The optimal subspace size of a given system is not known, thus a convergence study with the subspace dimension may be performed, as described in this [guide](how_tos/choose_subspace_dimension). +The choice of the subspace dimension affects the accuracy and runtime of the eigenstate solver. The larger the subspace the more accurate the calculation, at the cost of increasing the runtime and memory requirements. The optimal subspace size of a given system is not known, thus a convergence study with the subspace dimension may be performed, as described in this [guide](how_tos/choose-subspace-dimension). ## The subspace dimension is set indirectly -In this package, the user controls the number of bitstrings contained in each subspace with the samples\_per\_batch argument in [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"). The value of this argument sets an upper bound to the subspace dimension in the case of quantum chemistry applications. See this [example](how_tos/select_open_closed_shell) for more details. +In this package, the user controls the number of bitstrings contained in each subspace with the samples\_per\_batch argument in [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"). The value of this argument sets an upper bound to the subspace dimension in the case of quantum chemistry applications. See this [example](how_tos/select-open-closed-shell) for more details. ## Citing this project diff --git a/docs/addons/qiskit-addon-sqd/install.mdx b/docs/addons/qiskit-addon-sqd/install.mdx index 515607e6add..f560b8d9c9f 100644 --- a/docs/addons/qiskit-addon-sqd/install.mdx +++ b/docs/addons/qiskit-addon-sqd/install.mdx @@ -1,7 +1,3 @@ ---- -title: "Installation Instructions" ---- - # Installation Instructions Let’s see how to install the package. The first thing to do is ensure your Python environment is set up correctly. To create a new environment: @@ -59,4 +55,3 @@ If you installed the notebook dependencies, you can get started by running the n cd docs/ jupyter lab ``` - diff --git a/docs/addons/qiskit-addon-sqd/release-notes.mdx b/docs/addons/qiskit-addon-sqd/release-notes.mdx deleted file mode 100644 index cfbf3168eed..00000000000 --- a/docs/addons/qiskit-addon-sqd/release-notes.mdx +++ /dev/null @@ -1,420 +0,0 @@ ---- -title: "Release Notes" ---- - - - - - -# - - - - - -## 0.12.1 - - - -### Upgrade Notes - -* Python 3.10 is now the minimum required version. Support for Python 3.9 has been dropped. - - - - - -## 0.12.0 - - - -### New Features - -* Added the `max_dim` argument to the [`qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian "qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian") function. This argument is used to limit the dimension of the SCI subspace. - -* Introduced the [`qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left "qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left") function. - - - - - -### Upgrade Notes - -* [`qiskit_addon_sqd.fermion.SCIState`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.SCIState "qiskit_addon_sqd.fermion.SCIState") now takes two additional required arguments, `norb` and `nelec`. This is a breaking change. - - - -### Deprecation Notes - -* [`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight()`](qiskit_addon_sqd.configuration_recovery#qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight "qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight") is now deprecated. Instead, use [`qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left "qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left"). - -* [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample") is now deprecated. Instead, use [`qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left "qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left") and [`qiskit_addon_sqd.subsampling.subsample()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.subsample "qiskit_addon_sqd.subsampling.subsample"). - - - -### Bug Fixes - -* Fixed a bug in [`qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs "qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs") in which the conversion of bitstrings to CI strings could be incorrect when the number of orbitals is greater than 53. - -* Fixed a bug in which saving and loading [`qiskit_addon_sqd.fermion.SCIState`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.SCIState "qiskit_addon_sqd.fermion.SCIState") objects resulted in an error. - -* Added a release note to version 0.11 for a breaking change involving [`qiskit_addon_sqd.fermion.SCIState`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.SCIState "qiskit_addon_sqd.fermion.SCIState"). - - - - - -## 0.11.0 - - - - - -### New Features - -* Added a new function [`qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian "qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian") to serve as the main entrypoint to the SQD algorithm. See the updated [tutorials](/docs/addons/qiskit-addon-sqd/tutorials/index) for a demonstration of how to use this function. - -* [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") now accepts a `shift` argument used to shift states which have the wrong spin, $(H + shift * S^2)|ψ> = E|ψ>$. - - - - - -### Upgrade Notes - -* [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") now accept trailing `kwargs`, which will be passed directly to [pyscf.fci.selected\_ci.kernel\_fixed\_space](https://pyscf.org/pyscf_api_docs/pyscf.fci.html#pyscf.fci.selected_ci.kernel_fixed_space) under the hood to calculate the target state. The `max_davidson` argument should now be passed as `max_cycle` in both functions. - - - - - -### Bug Fixes - -* Fixed a float to integer type conversion affecting the `qubit` module - - - - - -## 0.10.0 - - - - - -### Upgrade Notes - -* Removed `qiskit_addon_sqd.fermion.flip_orbital_occupancies()`. Users no longer need to flip the orbital occupancies output from [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals"); they will be output in the order expected by [`qiskit_addon_sqd.configuration_recovery.recover_configurations()`](qiskit_addon_sqd.configuration_recovery#qiskit_addon_sqd.configuration_recovery.recover_configurations "qiskit_addon_sqd.configuration_recovery.recover_configurations"): `tuple(array([occ_a_0, ..., occ_a_N]), array([occ_b_0, ..., occ_b_N]))`. - - - - - -### Deprecation Notes - -* The `avg_occupancies` argument to [`qiskit_addon_sqd.configuration_recovery.recover_configurations()`](qiskit_addon_sqd.configuration_recovery#qiskit_addon_sqd.configuration_recovery.recover_configurations "qiskit_addon_sqd.configuration_recovery.recover_configurations") should now be a length-2 tuple containing the spin-up and spin-down occupancies, respectively. - - Old format: `array([occ_b_N, ..., occ_b_0, occ_a_N, ..., occ_a_0])` - - New format: `tuple(array([occ_a_0, ..., occ_a_N]), array([occ_b_0, ..., occ_b_N]))` - - - - - -### Bug Fixes - -* Fixed a bug which would cause the energy output from [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") to be incorrect when the input `spin_sq` deviates from the spin^2 of the output wavefunction. - - - - - -## 0.9.0 - - - - - -### Upgrade Notes - -* [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") and [`qiskit_addon_sqd.fermion.rotate_integrals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.rotate_integrals "qiskit_addon_sqd.fermion.rotate_integrals") now require `k_flat` to specify the upper triangle (not including diagonal) of the rotation matrix, rather than the entire matrix. - - - - - -### Bug Fixes - -* Fixed an indexing error in [`qiskit_addon_sqd.configuration_recovery()`](qiskit_addon_sqd.configuration_recovery#module-qiskit_addon_sqd.configuration_recovery "qiskit_addon_sqd.configuration_recovery") which caused bits in the right half of the bitstring to be flipped with respect to the occupancies of the oritals associated with the left half of the bitstring. - -* pyscf is no longer considered a dependency on Windows, where it fails to install. However, Windows remains an unsupported platform. - - - - - -## 0.8.0 - - - - - -### New Features - -* Added support for Python 3.9. - -* All functions which take a `rand_seed` argument now also accept a `numpy.random.Generator` instance as the `rand_seed`. - - - - - -### Upgrade Notes - -* The ground state returned by [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") will now be an instance of [`qiskit_addon_sqd.fermion.SCIState`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.SCIState "qiskit_addon_sqd.fermion.SCIState"), rather than a PySCF `SCIVector` instance. - - - - - -## 0.7.0 - - - - - -### Bug Fixes - -* Fixed a bug in [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") which causes the determinants for spin-up and spin-down to be incorrectly flipped before solving. - -* Fixed a bug in open-shell workflows which would cause [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") to crash with a `malloc` error. - - - - - -## 0.6.0 - - - - - -### Upgrade Notes - -* Specifying `addresses` as a keyword argument to [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") is no longer supported. Users may still pass `addresses` as the first positional argument; however, this usage is deprecated. Users are encouraged to pass the bitstring matrix defining the subspace as the first positional arguments to these functions, as shown below. - - To upgrade, change this code - - ```python - # DEPRECATED CODE - from qiskit_addon_sqd.fermion import ( - bitstring_matrix_to_sorted_addresses, - solve_fermion, - optimize_orbitals, - ) - - bitstring_matrix = ... - addresses = bitstring_matrix_to_sorted_addresses(bitstring_matrix, open_shell=open_shell) - energy, coeffs, occs, spin = solve_fermion( - addresses=addresses, - hcore=hcore, - eri=eri, - ) - ... - e_oo, rotation, occs_oo = optimize_orbitals( - addresses=addresses, - hcore=hcore, - eri=eri, - ) - - ### SHOULD BECOME ### - - # NEW CODE - from qiskit_addon_sqd.fermion import solve_fermion, optimize_orbitals - - bitstring_matrix = ... - energy, coeffs, occs, spin = solve_fermion( - bitstring_matrix, - hcore=hcore, - eri=eri, - ) - ... - e_oo, rotation, occs_oo = optimize_orbitals( - bitstring_matrix, - hcore=hcore, - eri=eri, - ) - ``` - - - - - -### Deprecation Notes - -* The `qiskit_addon_sqd.fermion.bitstring_matrix_to_sorted_addresses()` function has been deprecated in favor of [`qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs "qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs"). These two functions behave the same with one key exception – `qiskit_addon_sqd.fermion.bitstring_matrix_to_sorted_addresses()` returns the configurations as `tuple(spin_dn, spin_up)`; whereas, [`qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs "qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs") returns the configurations as `tuple(spin_up, spin_dn)`. - - To migrate - - ```python - from qiskit_addon_sqd.fermion import ( - bitstring_matrix_to_sorted_addresses, - bitstring_matrix_to_ci_strs, - ) - - # DEPRECATED CODE - bs_matrix = ... - addr_dn, addr_up = bitstring_matrix_to_sorted_addresses(bs_matrix, open_shell=True) - - ### SHOULD BECOME ### - - # NEW CODE - bs_matrix = ... - ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(bs_matrix, open_shell=True) - ``` - -* The `addresses` argument to [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") has been deprecated in favor of `bitstring_matrix`. Users are no longer required to convert their configurations to integers; instead, they should now pass in the bitstring matrix specifying the subspace onto which to project and diagonalize the Hamiltonian. The conversion to the integer representation of determinants will be done internally. - - - - - -### Bug Fixes - -* Fixed a bug in [`qiskit_addon_sqd.configuration_recovery.recover_configurations()`](qiskit_addon_sqd.configuration_recovery#qiskit_addon_sqd.configuration_recovery.recover_configurations "qiskit_addon_sqd.configuration_recovery.recover_configurations") which would sometimes cause a divide-by-zero error when calculating individual bit-flip probability. - -* Fixes a bug that caused configuration recovery to fail on bitstrings of length greater than 72. - - - - - -## 0.5.0 - - - - - -### Upgrade Notes - -* The [`qiskit_addon_sqd.counts.generate_counts_bipartite_hamming()`](qiskit_addon_sqd.counts#qiskit_addon_sqd.counts.generate_counts_bipartite_hamming "qiskit_addon_sqd.counts.generate_counts_bipartite_hamming"), [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"), and [`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight()`](qiskit_addon_sqd.configuration_recovery#qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight "qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight") now require the `hamming_right` and `hamming_left` arguments to be specified as keyword arguments. Additionally, the `samples_per_batch` and `n_batches` arguments to [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample") should now be passed as keyword arguments. - - To upgrade - - ```python - from qiskit_addon_sqd.configuration_recovery import post_select_by_hamming_weight - from qiskit_addon_sqd.subsampling import postselect_and_subsample - from qiskit_addon_sqd.counts import generate_counts_bipartite_hamming - - counts = generate_counts_bipartite_hamming(num_samples, num_bits, num_elec_a, num_elec_b) - - ... - - bs_mat = post_select_by_hamming_weight(bs_mat_full, num_elec_a, num_elec_b) - - ... - - batches = postselect_and_subsample( - bs_mat, - probs_arr, - num_elec_a, - num_elec_b, - samples_per_batch, - num_batches, - ) - ``` - - should be changed to - - ```python - from qiskit_addon_sqd.configuration_recovery import post_select_by_hamming_weight - from qiskit_addon_sqd.subsampling import postselect_and_subsample - from qiskit_addon_sqd.counts import generate_counts_bipartite_hamming - - counts = generate_counts_bipartite_hamming(num_samples, num_bits, hamming_right=num_elec_a, hamming_left=num_elec_b) - - ... - - bs_mat = post_select_by_hamming_weight(bs_mat_full, hamming_right=num_elec_a, hamming_left=num_elec_b) - - ... - - batches = postselect_and_subsample( - bs_mat, - probs_arr, - hamming_right=num_elec_a, - hamming_left=num_elec_b, - samples_per_batch=samples_per_batch, - num_batches=num_batches, - ) - ``` - - - - - -## 0.4.0 - - - -### Prelude - -This is a minor release which introduces a couple of small, but important, breaking changes to to the API. These changes allow for a more consistent pattern in specifying the number of alpha and beta electrons throughout both the chemistry and non-chemistry functions in the API. - - - - - -### Upgrade Notes - -* The [`qiskit_addon_sqd.counts.generate_counts_bipartite_hamming()`](qiskit_addon_sqd.counts#qiskit_addon_sqd.counts.generate_counts_bipartite_hamming "qiskit_addon_sqd.counts.generate_counts_bipartite_hamming"), [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"), and [`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight()`](qiskit_addon_sqd.configuration_recovery#qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight "qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight") now take the `hamming_right` positional argument before the `hamming_left` argument to better match the rest of the workflow. - - To upgrade - - ```python - from qiskit_addon_sqd.configuration_recovery import post_select_by_hamming_weight - from qiskit_addon_sqd.subsampling import postselect_and_subsample - from qiskit_addon_sqd.counts import generate_counts_bipartite_hamming - - counts = generate_counts_bipartite_hamming(num_samples, num_bits, num_elec_b, num_elec_a) - - ... - - bs_mat = post_select_by_hamming_weight(bs_mat_full, num_elec_b, num_elec_a) - - ... - - batches = postselect_and_subsample( - bs_mat, - probs_arr, - num_elec_b, - num_elec_a, - samples_per_batch, - n_batches, - ) - ``` - - should be changed to - - ```python - from qiskit_addon_sqd.configuration_recovery import post_select_by_hamming_weight - from qiskit_addon_sqd.subsampling import postselect_and_subsample - from qiskit_addon_sqd.counts import generate_counts_bipartite_hamming - - counts = generate_counts_bipartite_hamming(num_samples, num_bits, num_elec_a, num_elec_b) - - bs_mat = post_select_by_hamming_weight(bs_mat_full, num_elec_a, num_elec_b) - - ... - - batches = postselect_and_subsample( - bs_mat, - probs_arr, - num_elec_a, - num_elec_b, - samples_per_batch, - n_batches, - ) - ``` - diff --git a/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.mdx b/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.mdx new file mode 100644 index 00000000000..03d406df998 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.mdx @@ -0,0 +1,408 @@ + + +# Improving energy estimation of a chemistry Hamiltonian with SQD + +In this tutorial we implement a [Qiskit pattern](/docs/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of a chemistry Hamiltonian: the $N_2$ molecule at equilibrium in the 6-31G basis set. We will follow a [sample-based quantum diagonalization approach](https://arxiv.org/abs/2405.05068) to process samples taken from a `36`-qubit quantum circuit ansatz (in this case, an LUCJ circuit). In order to account for the effect of quantum noise, the configuration recovery technique is used. + +The pattern can be described in four steps: + +1. **Step 1: Map to quantum problem** + + * Generate an ansatz for estimating the ground state + +2. **Step 2: Optimize the problem** + + * Transpile the ansatz for the backend + +3. **Step 3: Execute experiments** + + * Draw samples from the ansatz using the `Sampler` primitive + +4. **Step 4: Post-process results** + + * Self-consistent configuration recovery loop + + * Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration. + * Probabilistically create batches of subsamples from recovered bitstrings. + * Project and diagonalize the molecular Hamiltonian over each sampled subspace. + * Save the minimum ground state energy found across all batches and update the avg orbital occupancy. + +For this example, the interacting-electron Hamiltonian takes the generic form: + +$$ +\begin{split}\hat{H} = \sum_{ \substack{pr\\\sigma} } h_{pr} \, \hat{a}^\dagger_{p\sigma} \hat{a}_{r\sigma} ++ +\sum_{ \substack{prqs\\\sigma\tau} } +\frac{(pr|qs)}{2} \, +\hat{a}^\dagger_{p\sigma} +\hat{a}^\dagger_{q\tau} +\hat{a}_{s\tau} +\hat{a}_{r\sigma}\end{split} +$$ + +$\hat{a}^\dagger_{p\sigma}$/$\hat{a}_{p\sigma}$ are the fermionic creation/annihalation operators associated to the $p$-th basis set element and the spin $\sigma$. $h_{pr}$ and $(pr|qs)$ are the one- and two-body electronic integrals. + +The SQD workflow with self-consistent configuration recovery is depicted in the following diagram. + +![SQD diagram](/docs/images/api/qiskit-addon-sqd/sqd_diagram.avif) + +SQD is known to work well when the target eigenstate is sparse: the wave function is supported in a set of basis states $\mathcal{S} = \{|x\rangle \}$ whose size does not increase exponentially with the size of the problem. In this scenario, the diagonalization of the Hamiltonian projected into the subspace defined by $\mathcal{S}$: + +$$ +H_\mathcal{S} = P_\mathcal{S} H P_\mathcal{S} \textrm{ with } P_\mathcal{S} = \sum_{x \in \mathcal{S}} |x \rangle \langle x |; +$$ + +yields a good approximation to the target eigenstate. The role of the quantum device is to produce samples of the members of $\mathcal{S}$ only. First, a quantum circuit prepares the state $|\Psi\rangle$ in the quantum device. The Jordan-Wigner encoding is used. Consequently, members of the computational basis represent Fock states (electronic configurations/determinants). The circuit is sampled in the computational basis, yielding the set of noisy configurations $\tilde{\mathcal{X}}$. The configurations are represented by bitstrings. The set $\tilde{\mathcal{X}}$ is then passed into the classical post-processing block, where the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068) is used. In the SQD framework, the role of the quantum device is to produce a probability distribution. + + + +## Step 1: Map problem to a quantum circuit + +In this tutorial, we will approximate the ground state energy of an $N_2$ molecule. First, we will specify the molecule and its properties. Next, we will create a [local unitary cluster Jastrow (LUCJ)](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) ansatz (quantum circuit) to generate samples from a quantum computer for ground state energy estimation. + +First, we will specify the molecule and its properties. + +``` +[1]: +``` + +```python +import warnings + +warnings.filterwarnings("ignore") + +import pyscf +import pyscf.cc +import pyscf.mcscf + +# Specify molecule properties +open_shell = False +spin_sq = 0 + +# Build N2 molecule +mol = pyscf.gto.Mole() +mol.build( + atom=[["N", (0, 0, 0)], ["N", (1.0, 0, 0)]], + basis="6-31g", + symmetry="Dooh", +) + +# Define active space +n_frozen = 2 +active_space = range(n_frozen, mol.nao_nr()) + +# Get molecular integrals +scf = pyscf.scf.RHF(mol).run() +num_orbitals = len(active_space) +n_electrons = int(sum(scf.mo_occ[active_space])) +num_elec_a = (n_electrons + mol.spin) // 2 +num_elec_b = (n_electrons - mol.spin) // 2 +cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b)) +mo = cas.sort_mo(active_space, base=0) +hcore, nuclear_repulsion_energy = cas.get_h1cas(mo) +eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals) + +# Compute exact energy +exact_energy = cas.run().e_tot +``` + +```python +converged SCF energy = -108.835236570775 +CASCI E = -109.046671778080 E(CI) = -32.8155692383188 S^2 = 0.0000000 +``` + +Next, we will create the ansatz. The `LUCJ` ansatz is a parameterized quantum circuit, and we will initialize it with `t2` and `t1` amplitudes obtained from a CCSD calculation. + +``` +[2]: +``` + +```python +# Get CCSD t2 amplitudes for initializing the ansatz +ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run() +t1 = ccsd.t1 +t2 = ccsd.t2 +``` + +```python +E(CCSD) = -109.0398256929734 E_corr = -0.2045891221988311 +``` + +We will use the [ffsim](https://github.com/qiskit-community/ffsim/tree/main) package to create and initialize the ansatz with `t2` and `t1` amplitudes computed above. Since our molecule has a closed-shell Hartree-Fock state, we will use the spin-balanced variant of the UCJ ansatz, [UCJOpSpinBalanced](https://qiskit-community.github.io/ffsim/api/ffsim.html#ffsim.UCJOpSpinBalanced). + +As our target IBM hardware has a heavy-hex topology, we will adopt the [zig-zag pattern for qubit interactions](https://pubs.rsc.org/en/content/articlehtml/2023/sc/d3sc02516k). In this pattern, orbitals (represented by qubits) with the same spin are connected with a line topology (red and blue circles) where each line take a zig-zag shape due the heavy-hex connectivity of the target hardware. Again, due to the heavy-hex topology, orbitals for different spins have connections between every 4th orbital (0, 4, 8, etc.) (purple circles). + +![lucj\_ansatz](/docs/images/api/qiskit-addon-sqd/lucj_ansatz_zig_zag_pattern.avif) + +``` +[3]: +``` + +```python +import ffsim +from qiskit import QuantumCircuit, QuantumRegister + +n_reps = 1 +alpha_alpha_indices = [(p, p + 1) for p in range(num_orbitals - 1)] +alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)] + +ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes( + t2=t2, + t1=t1, + n_reps=n_reps, + interaction_pairs=(alpha_alpha_indices, alpha_beta_indices), +) + +nelec = (num_elec_a, num_elec_b) + +# create an empty quantum circuit +qubits = QuantumRegister(2 * num_orbitals, name="q") +circuit = QuantumCircuit(qubits) + +# prepare Hartree-Fock state as the reference state and append it to the quantum circuit +circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits) + +# apply the UCJ operator to the reference state +circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits) +circuit.measure_all() +``` + + + +## Step 2: Optimize the problem + +Next, we will optimize our circuit for a target hardware. We need to choose the hardware device to use before optimizing our circuit. We will use a fake 127-qubit backend from `qiskit_ibm_runtime` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info. + +``` +[4]: +``` + +```python +from qiskit_ibm_runtime.fake_provider import FakeSherbrooke + +backend = FakeSherbrooke() +``` + +Next, we recommend the following steps to optimize the ansatz and make it hardware-compatible. + +* Select physical qubits (`initial_layout`) from the target hardware that adheres to the zig-zag pattern described above. Laying out qubits in this pattern leads to an efficient hardware-compatible circuit with fewer gates. +* Generate a staged pass manager using the [generate\_preset\_pass\_manager](/docs/api/qiskit/transpiler_preset#generate_preset_pass_manager) function from Qiskit with your choice of `backend` and `initial_layout`. +* Set the `pre_init` stage of your staged pass manager to `ffsim.qiskit.PRE_INIT`. `ffsim.qiskit.PRE_INIT` includes Qiskit transpiler passes that decompose gates into orbital rotations and then merges the orbital rotations, resulting in fewer gates in the final circuit. +* Run the pass manager on your circuit. + +``` +[5]: +``` + +```python +from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager + +spin_a_layout = [0, 14, 18, 19, 20, 33, 39, 40, 41, 53, 60, 61, 62, 72, 81, 82] +spin_b_layout = [2, 3, 4, 15, 22, 23, 24, 34, 43, 44, 45, 54, 64, 65, 66, 73] +initial_layout = spin_a_layout + spin_b_layout + +pass_manager = generate_preset_pass_manager( + optimization_level=3, backend=backend, initial_layout=initial_layout +) + +# without PRE_INIT passes +isa_circuit = pass_manager.run(circuit) +print(f"Gate counts (w/o pre-init passes): {isa_circuit.count_ops()}") + +# with PRE_INIT passes +# We will use the circuit generated by this pass manager for hardware execution +pass_manager.pre_init = ffsim.qiskit.PRE_INIT +isa_circuit = pass_manager.run(circuit) +print(f"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}") +``` + +```python +Gate counts (w/o pre-init passes): OrderedDict({'rz': 4420, 'sx': 3432, 'ecr': 1366, 'x': 239, 'measure': 32, 'barrier': 1}) +Gate counts (w/ pre-init passes): OrderedDict({'rz': 2460, 'sx': 2156, 'ecr': 730, 'x': 71, 'measure': 32, 'barrier': 1}) +``` + + + +## Step 3: Execute experiments + +After optimizing the circuit for hardware execution, we are ready to run it on the target hardware and collect samples for ground state energy estimation. As we only have one circuit, we will use Qiskit Runtime’s [Job execution mode](/docs/guides/execution-modes) and execute our circuit. + +**Note: We have commented out the code for running the circuit on a QPU and left it for the user’s reference. Instead of running on real hardware in this guide, we will just generate random samples drawn from the uniform distribution.** + +``` +[6]: +``` + +```python +import numpy as np +from qiskit_addon_sqd.counts import generate_bit_array_uniform + +# from qiskit_ibm_runtime import SamplerV2 as Sampler + +# sampler = Sampler(mode=backend) +# job = sampler.run([isa_circuit], shots=10_000) +# primitive_result = job.result() +# pub_result = primitive_result[0] +# bit_array = pub_result.data.meas + +rng = np.random.default_rng(24) +bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng) +``` + + + +## Step 4: Post-process results + +Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](fermion#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function. + +The solver included in the SQD addon uses PySCF’s implementation of selected CI, specifically [pyscf.fci.selected\_ci.kernel\_fixed\_space](https://pyscf.org/pyscf_api_docs/pyscf.fci.html#pyscf.fci.selected_ci.kernel_fixed_space). The example below also shows how to pass keyword arguments to that function via the included solver. Here we pass the `max_cycle` argument. + +``` +[7]: +``` + +```python +from functools import partial + +from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian, solve_sci_batch + +# SQD options +energy_tol = 1e-3 +occupancies_tol = 1e-3 +max_iterations = 5 + +# Eigenstate solver options +num_batches = 1 +samples_per_batch = 300 +symmetrize_spin = True +carryover_threshold = 1e-4 +max_cycle = 200 + +# Pass options to the built-in eigensolver. If you just want to use the defaults, +# you can omit this step, in which case you would not specify the sci_solver argument +# in the call to diagonalize_fermionic_hamiltonian below. +sci_solver = partial(solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle) + +# List to capture intermediate results +result_history = [] + + +def callback(results: list[SCIResult]): + result_history.append(results) + iteration = len(result_history) + print(f"Iteration {iteration}") + for i, result in enumerate(results): + print(f"\tSubsample {i}") + print(f"\t\tEnergy: {result.energy + nuclear_repulsion_energy}") + print(f"\t\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}") + + +result = diagonalize_fermionic_hamiltonian( + hcore, + eri, + bit_array, + samples_per_batch=samples_per_batch, + norb=num_orbitals, + nelec=nelec, + num_batches=num_batches, + energy_tol=energy_tol, + occupancies_tol=occupancies_tol, + max_iterations=max_iterations, + sci_solver=sci_solver, + symmetrize_spin=symmetrize_spin, + carryover_threshold=carryover_threshold, + callback=callback, + seed=rng, +) +``` + +```python +Iteration 1 + Subsample 0 + Energy: -105.45358671756313 + Subspace dimension: 5476 +Iteration 2 + Subsample 0 + Energy: -107.95172900082163 + Subspace dimension: 249001 +Iteration 3 + Subsample 0 + Energy: -108.97460330369815 + Subspace dimension: 339889 +Iteration 4 + Subsample 0 + Energy: -109.02739376648793 + Subspace dimension: 440896 +Iteration 5 + Subsample 0 + Energy: -109.030972328451 + Subspace dimension: 597529 +``` + +Now, we plot the results. + +The first plot shows that after a few iterations we estimate the ground state energy within `~16 mH` (chemical accuracy is typically accepted to be `1 kcal/mol` $\approx$ `1.6 mH`). Remember, the quantum samples in this demo were pure noise. The signal here comes from *a priori* knowledge of the electronic structure and molecular Hamiltonian. + +The second plot shows the average occupancy of each spatial orbital after the final iteration. We can see that both the spin-up and spin-down electrons occupy the first five orbitals with high probability in our solutions. + +``` +[8]: +``` + +```python +import matplotlib.pyplot as plt + +# Data for energies plot +x1 = range(len(result_history)) +min_e = [ + min(result, key=lambda res: res.energy).energy + nuclear_repulsion_energy + for result in result_history +] +e_diff = [abs(e - exact_energy) for e in min_e] +yt1 = [1.0, 1e-1, 1e-2, 1e-3, 1e-4] + +# Chemical accuracy (+/- 1 milli-Hartree) +chem_accuracy = 0.001 + +# Data for avg spatial orbital occupancy +y2 = np.sum(result.orbital_occupancies, axis=0) +x2 = range(len(y2)) + +fig, axs = plt.subplots(1, 2, figsize=(12, 6)) + +# Plot energies +axs[0].plot(x1, e_diff, label="energy error", marker="o") +axs[0].set_xticks(x1) +axs[0].set_xticklabels(x1) +axs[0].set_yticks(yt1) +axs[0].set_yticklabels(yt1) +axs[0].set_yscale("log") +axs[0].set_ylim(1e-4) +axs[0].axhline(y=chem_accuracy, color="#BF5700", linestyle="--", label="chemical accuracy") +axs[0].set_title("Approximated Ground State Energy Error vs SQD Iterations") +axs[0].set_xlabel("Iteration Index", fontdict={"fontsize": 12}) +axs[0].set_ylabel("Energy Error (Ha)", fontdict={"fontsize": 12}) +axs[0].legend() + +# Plot orbital occupancy +axs[1].bar(x2, y2, width=0.8) +axs[1].set_xticks(x2) +axs[1].set_xticklabels(x2) +axs[1].set_title("Avg Occupancy per Spatial Orbital") +axs[1].set_xlabel("Orbital Index", fontdict={"fontsize": 12}) +axs[1].set_ylabel("Avg Occupancy", fontdict={"fontsize": 12}) + +print(f"Exact energy: {exact_energy:.5f} Ha") +print(f"SQD energy: {min_e[-1]:.5f} Ha") +print(f"Absolute error: {e_diff[-1]:.5f} Ha") +plt.tight_layout() +plt.show() +``` + +```python +Exact energy: -109.04667 Ha +SQD energy: -109.03097 Ha +Absolute error: 0.01570 Ha +``` + +![../\_images/tutorials\_01\_chemistry\_hamiltonian\_19\_1.png](/docs/images/api/qiskit-addon-sqd/tutorials_01_chemistry_hamiltonian_19_1.avif) diff --git a/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian.ipynb index cefefd907a6..6b803ff7477 100644 --- a/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian.ipynb +++ b/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian.ipynb @@ -29,9 +29,9 @@ "\n", "$$\n", "\\hat{H} = \\sum_{ \\substack{pr\\\\\\sigma} } h_{pr} \\, \\hat{a}^\\dagger_{p\\sigma} \\hat{a}_{r\\sigma}\n", - "+\n", + "+ \n", "\\sum_{ \\substack{prqs\\\\\\sigma\\tau} }\n", - "\\frac{(pr|qs)}{2} \\,\n", + "\\frac{(pr|qs)}{2} \\, \n", "\\hat{a}^\\dagger_{p\\sigma}\n", "\\hat{a}^\\dagger_{q\\tau}\n", "\\hat{a}_{s\\tau}\n", @@ -233,7 +233,7 @@ "- Select physical qubits (`initial_layout`) from the target hardware that adheres to the zig-zag pattern described above. Laying out qubits in this pattern leads to an efficient hardware-compatible circuit with fewer gates.\n", "- Generate a staged pass manager using the [generate_preset_pass_manager](/docs/api/qiskit/transpiler_preset#generate_preset_pass_manager) function from Qiskit with your choice of `backend` and `initial_layout`.\n", "- Set the `pre_init` stage of your staged pass manager to `ffsim.qiskit.PRE_INIT`. `ffsim.qiskit.PRE_INIT` includes Qiskit transpiler passes that decompose gates into orbital rotations and then merges the orbital rotations, resulting in fewer gates in the final circuit.\n", - "- Run the pass manager on your circuit." + "- Run the pass manager on your circuit. " ] }, { @@ -448,8 +448,9 @@ }, { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "metadata": {}, @@ -509,7 +510,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -523,9 +524,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.13.3" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.mdx b/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.mdx new file mode 100644 index 00000000000..bcf1615d5a3 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.mdx @@ -0,0 +1,378 @@ + + +# Improving energy estimation of a Fermionic lattice model with SQD + +In this tutorial we implement a [Qiskit pattern](/docs/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of a Fermionic lattice Hamiltonian known as the single-impurity Anderson model. We will follow a sample-based quantum diagonalization approach to process samples taken from a set of `16`-qubit Krylov basis states over increasing time intervals. These states are realized on the quantum device using Trotterization of the time evolution. In order to account for the effect of quantum noise, the configuration recovery technique is used. Assuming a good initial state and sparsity of the ground state, [this approach is proven to converge efficiently](https://arxiv.org/abs/2501.09702). + +The pattern can be described in four steps: + +1. **Step 1: Map to quantum problem** + + * Generate a set of Krylov basis states (i.e., Trotterized time-evolution circuits) over increasing time intervals for estimating the ground state + +2. **Step 2: Optimize the problem** + + * Transpile the circuits for the backend + +3. **Step 3: Execute experiments** + + * Draw samples from the circuits using the `Sampler` primitive + +4. **Step 4: Post-process results** + + * Self-consistent configuration recovery loop + + * Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration + * Probabilistically create batches of subsamples from recovered bitstrings + * Project and diagonalize the Fermionic lattice Hamiltonian over each sampled subspace + * Save the minimum ground state energy found across all batches and update the avg orbital occupancy + + + +## Step 1: Map problem to a quantum circuit + +First, we will create the one- and two-body Hamiltonians describing the one-dimensional single-impurity Anderson model (SIAM) with `7` bath sites (`8` electrons in `8` orbitals). This model is used to describe magnetic impurities embedded in metals. + +Then we will create the `16`-qubit Trotter circuits used to generate the quantum Krylov subspace. + +``` +[1]: +``` + +```python +import numpy as np + +n_bath = 7 # number of bath sites + +V = 1 # hybridization amplitude +t = 1 # bath hopping amplitude +U = 10 # Impurity onsite repulsion +eps = -U / 2 # Chemical potential for the impurity + +# Place the impurity on the first qubit +impurity_index = 0 + +# One body matrix elements in the "position" basis +h1e = -t * np.diag(np.ones(n_bath), k=1) - t * np.diag(np.ones(n_bath), k=-1) +h1e[impurity_index, impurity_index + 1] = -V +h1e[impurity_index + 1, impurity_index] = -V +h1e[impurity_index, impurity_index] = eps + +# Two body matrix elements in the "position" basis +h2e = np.zeros((n_bath + 1, n_bath + 1, n_bath + 1, n_bath + 1)) +h2e[impurity_index, impurity_index, impurity_index, impurity_index] = U +``` + +Next, we will generate the quantum Krylov subspace with a set of Trotterized quantum circuits. Here we create helpers for generating the initial (reference) state as well as the time evolution for the one- and two-body parts of the Hamiltonian. For a more detailed description of this model and how the circuits are designed, please refer to [the paper](https://arxiv.org/abs/2501.09702). + +``` +[2]: +``` + +```python +import ffsim +import scipy +from qiskit import QuantumCircuit, QuantumRegister +from qiskit.circuit.library import CPhaseGate, XGate, XXPlusYYGate + +n_modes = n_bath + 1 +nelec = (n_modes // 2, n_modes // 2) + +dt = 0.2 +Utar = scipy.linalg.expm(-1j * dt * h1e) + + +# The reference state +def initial_state(q_circuit, norb, nocc): + """Prepare an initial state.""" + for i in range(nocc): + q_circuit.append(XGate(), [i]) + q_circuit.append(XGate(), [norb + i]) + rot = XXPlusYYGate(np.pi / 2, -np.pi / 2) + + for i in range(3): + for j in range(nocc - i - 1, nocc + i, 2): + q_circuit.append(rot, [j, j + 1]) + q_circuit.append(rot, [norb + j, norb + j + 1]) + q_circuit.append(rot, [j + 1, j + 2]) + q_circuit.append(rot, [norb + j + 1, norb + j + 2]) + + +# The one-body time evolution +free_fermion_evolution = ffsim.qiskit.OrbitalRotationJW(n_modes, Utar) + + +# The two-body time evolution +def append_diagonal_evolution(dt, U, impurity_qubit, num_orb, q_circuit): + """Append two-body time evolution to a quantum circuit.""" + if U != 0: + q_circuit.append( + CPhaseGate(-dt / 2 * U), + [impurity_qubit, impurity_qubit + num_orb], + ) +``` + +Generate `d` time-evolved states that specify the quantum Krylov subspace. Here, we have chosen `d=8`. The error from sampling Krylov basis states converges with increasing `d`. Note that the particulars of this problem instance allow for an efficient compilation of the one-body evolution using `OrbitalRotationJW`; however, in general, one could use Qiskit’s [PauliEvolutionGate](/docs/api/qiskit/qiskit.circuit.library.PauliEvolutionGate) to implement the Trotterized time evolution of the full Hamiltonian. + +``` +[3]: +``` + +```python +# Generate the initial state +qubits = QuantumRegister(2 * n_modes, name="q") +init_state = QuantumCircuit(qubits) +initial_state(init_state, n_modes, n_modes // 2) +init_state.draw("mpl", scale=0.4, fold=-1) + +d = 8 # Number of Krylov basis states +circuits = [] +for i in range(d): + circ = init_state.copy() + circuits.append(circ) + for _ in range(i): + append_diagonal_evolution(dt, U, impurity_index, n_modes, circ) + circ.append(free_fermion_evolution, qubits) + append_diagonal_evolution(dt, U, impurity_index, n_modes, circ) + circ.measure_all() +``` + +``` +[4]: +``` + +```python +circuits[0].draw("mpl", scale=0.4, fold=-1) +``` + +``` +[4]: +``` + +![../\_images/tutorials\_02\_fermionic\_lattice\_hamiltonian\_8\_0.png](/docs/images/api/qiskit-addon-sqd/tutorials_02_fermionic_lattice_hamiltonian_8_0.avif) + +``` +[5]: +``` + +```python +circuits[-1].draw("mpl", scale=0.4, fold=-1) +``` + +``` +[5]: +``` + +![../\_images/tutorials\_02\_fermionic\_lattice\_hamiltonian\_9\_0.png](/docs/images/api/qiskit-addon-sqd/tutorials_02_fermionic_lattice_hamiltonian_9_0.avif) + + + +## Step 2: Optimize the problem + +After we have created the Trotterized circuits, we will optimize them for a target hardware. We need to choose the hardware device to use before optimization. We will use a fake 127-qubit backend from `qiskit_ibm_runtime` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info. + +``` +[6]: +``` + +```python +from qiskit_ibm_runtime.fake_provider.backends import FakeSherbrooke + +backend = FakeSherbrooke() +``` + +Next, we will transpile the circuits to the target backend using Qiskit. + +``` +[7]: +``` + +```python +from qiskit.transpiler import generate_preset_pass_manager + +# The circuit needs to be transpiled to the AerSimulator target +pass_manager = generate_preset_pass_manager(optimization_level=3, backend=backend) +isa_circuits = pass_manager.run(circuits) +``` + + + +## Step 3: Execute experiments + +After optimizing the circuits for hardware execution, we are ready to run them on the target hardware and collect samples for ground state energy estimation. Here we use `SamplerV2` from `qiskit-ibm-runtime` to simulate noisy samples taken from the `ibm_sherbrooke` backend. We then combine the counts from each of the Krylov basis states into a single counts dictionary and plot the top 20 most commonly sampled bitstrings. + +**Note:** [Qiskit Aer](https://qiskit.github.io/qiskit-aer/index.html) **is required to simulate samples from transpiled circuits.** + +``` +[8]: +``` + +```python +from qiskit.primitives import BitArray +from qiskit.visualization import plot_histogram +from qiskit_ibm_runtime import SamplerV2 as Sampler + +# Sample from the circuits +noisy_sampler = Sampler(backend, options={"simulator": {"seed_simulator": 24}}) +job = noisy_sampler.run(isa_circuits, shots=500) + +# Combine the counts from the individual Trotter circuits +bit_array = BitArray.concatenate_shots([result.data.meas for result in job.result()]) + +plot_histogram(bit_array.get_counts(), number_to_keep=20) +``` + +``` +[8]: +``` + +![../\_images/tutorials\_02\_fermionic\_lattice\_hamiltonian\_17\_0.png](/docs/images/api/qiskit-addon-sqd/tutorials_02_fermionic_lattice_hamiltonian_17_0.avif) + + + +## Step 4: Post-process the results + +Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](fermion#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function. + +``` +[ ]: +``` + +```python +from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian + +# List to capture intermediate results +result_history = [] + + +def callback(results: list[SCIResult]): + result_history.append(results) + iteration = len(result_history) + print(f"Iteration {iteration}") + for i, result in enumerate(results): + print(f"\tSubsample {i}") + print(f"\t\tEnergy: {result.energy}") + print(f"\t\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}") + + +rng = np.random.default_rng(24) +result = diagonalize_fermionic_hamiltonian( + h1e, + h2e, + bit_array, + samples_per_batch=300, + norb=n_modes, + nelec=nelec, + num_batches=3, + max_iterations=10, + symmetrize_spin=True, + callback=callback, + seed=rng, +) +``` + +```python +Iteration 1 + Subsample 0 + Energy: -13.257128325607997 + Subspace dimension: 3969 + Subsample 1 + Energy: -13.257128325607997 + Subspace dimension: 3969 + Subsample 2 + Energy: -13.257128325607997 + Subspace dimension: 3969 +Iteration 2 + Subsample 0 + Energy: -13.319666127542039 + Subspace dimension: 4096 + Subsample 1 + Energy: -13.420534292304595 + Subspace dimension: 4624 + Subsample 2 + Energy: -9.136171594591085 + Subspace dimension: 4624 +Iteration 3 + Subsample 0 + Energy: -13.422491814612833 + Subspace dimension: 4900 + Subsample 1 + Energy: -13.422491814612833 + Subspace dimension: 4900 + Subsample 2 + Energy: -13.422491814612833 + Subspace dimension: 4900 +Iteration 4 + Subsample 0 + Energy: -13.422491814612833 + Subspace dimension: 4900 + Subsample 1 + Energy: -13.422491814612833 + Subspace dimension: 4900 + Subsample 2 + Energy: -13.422491814612833 + Subspace dimension: 4900 +``` + +Now, we plot the results. The first plot shows that after a few iterations, we obtain the exact ground state energy. + +This example is small enough that we are able to explore the full Hilbert space, as seen in the print statements above. Remember, the full Hilbert space is dimension `(num_orbitals choose nelec_a) * (num_orbitals choose nelec_b)`. So for this problem: `(8 choose 4)**2 = 4900`. The subspace dimensions increase due enhanced configuration recovery, and also the fact that the SQD algorithm carries important configurations from one iteration to the next. By the last iteration, we are diagonalizing in the entire Hilbert space. + +The second plot shows the average occupancy of each spatial orbital across all batches’ solutions. We see that with high probability each orbital contains one electron. + +``` +[10]: +``` + +```python +import matplotlib.pyplot as plt + +exact_energy = -13.422491814605827 +min_es = [min(result, key=lambda res: res.energy).energy for result in result_history] +min_id, min_e = min(enumerate(min_es), key=lambda x: x[1]) + +# Data for energies plot +x1 = range(len(result_history)) +yt1 = list(np.arange(-13.5, -13.1, 0.1)) +ytl = [f"{i:.1f}" for i in yt1] + +# Data for avg spatial orbital occupancy +y2 = np.sum(result.orbital_occupancies, axis=0) +x2 = range(len(y2)) + +fig, axs = plt.subplots(1, 2, figsize=(12, 6)) + +# Plot energies +axs[0].plot(x1, min_es, label="SQD energy", marker="o") +axs[0].set_xticks(x1) +axs[0].set_xticklabels(x1) +axs[0].set_yticks(yt1) +axs[0].set_yticklabels(ytl) +axs[0].axhline(y=exact_energy, color="#BF5700", linestyle="--", label="Exact energy") +axs[0].set_title("Approximated Ground State Energy vs SQD Iterations") +axs[0].set_xlabel("Iteration Index", fontdict={"fontsize": 12}) +axs[0].set_ylabel("Energy", fontdict={"fontsize": 12}) +axs[0].legend() + +# Plot orbital occupancy +axs[1].bar(x2, y2, width=0.8) +axs[1].set_xticks(x2) +axs[1].set_xticklabels(x2) +axs[1].set_title("Avg Occupancy per Spatial Orbital") +axs[1].set_xlabel("Orbital Index", fontdict={"fontsize": 12}) +axs[1].set_ylabel("Avg Occupancy", fontdict={"fontsize": 12}) + +print(f"Exact energy: {exact_energy:.5f}") +print(f"SQD energy: {min_e:.5f}") +print(f"Absolute error: {abs(min_e - exact_energy):.5f}") +plt.tight_layout() +plt.show() +``` + +```python +Exact energy: -13.42249 +SQD energy: -13.42249 +Absolute error: 0.00000 +``` + +![../\_images/tutorials\_02\_fermionic\_lattice\_hamiltonian\_22\_1.png](/docs/images/api/qiskit-addon-sqd/tutorials_02_fermionic_lattice_hamiltonian_22_1.avif) diff --git a/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb index 03b87c3c002..e216440f591 100644 --- a/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb +++ b/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb @@ -2,7 +2,6 @@ "cells": [ { "cell_type": "markdown", - "id": "014ac1a2-2060-4274-92c9-63e5431e5226", "metadata": {}, "source": [ "# Improving energy estimation of a Fermionic lattice model with SQD\n", @@ -27,7 +26,6 @@ }, { "cell_type": "markdown", - "id": "435bd534-2431-438e-b53a-ec935a25fac0", "metadata": {}, "source": [ "### Step 1: Map problem to a quantum circuit" @@ -35,7 +33,6 @@ }, { "cell_type": "markdown", - "id": "de134b7d-bfa1-45c6-9dd1-736e94038b3a", "metadata": {}, "source": [ "First, we will create the one- and two-body Hamiltonians describing the one-dimensional single-impurity Anderson model (SIAM) with ``7`` bath sites (``8`` electrons in ``8`` orbitals). This model is used to describe magnetic impurities embedded in metals.\n", @@ -46,7 +43,6 @@ { "cell_type": "code", "execution_count": 1, - "id": "eba34f72-77da-41d8-89ba-d05fcb3ff129", "metadata": {}, "outputs": [], "source": [ @@ -75,7 +71,6 @@ }, { "cell_type": "markdown", - "id": "bd1a9d09-54cf-4423-92ce-6e38d88a00f6", "metadata": {}, "source": [ "Next, we will generate the quantum Krylov subspace with a set of Trotterized quantum circuits. Here we create helpers for generating the initial (reference) state as well as the time evolution for the one- and two-body parts of the Hamiltonian. For a more detailed description of this model and how the circuits are designed, please refer to [the paper](https://arxiv.org/abs/2501.09702)." @@ -84,7 +79,6 @@ { "cell_type": "code", "execution_count": 2, - "id": "df328264-0c9f-4093-b87c-18ccb27fbeba", "metadata": {}, "outputs": [], "source": [ @@ -132,7 +126,6 @@ }, { "cell_type": "markdown", - "id": "be7484ab-921f-4c05-94ac-b47e17a20235", "metadata": {}, "source": [ "Generate ``d`` time-evolved states that specify the quantum Krylov subspace. Here, we have chosen ``d=8``. The error from sampling Krylov basis states converges with increasing ``d``. Note that the particulars of this problem instance allow for an efficient compilation of the one-body evolution using `OrbitalRotationJW`; however, in general, one could use Qiskit's [PauliEvolutionGate](/docs/api/qiskit/qiskit.circuit.library.PauliEvolutionGate) to implement the Trotterized time evolution of the full Hamiltonian." @@ -141,7 +134,6 @@ { "cell_type": "code", "execution_count": 3, - "id": "00c75502-36b6-4214-a484-0e3381b29e41", "metadata": {}, "outputs": [], "source": [ @@ -166,13 +158,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "89c00cd0-07b0-4bbc-9845-ddfc280e9887", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 4, @@ -187,13 +179,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "96470344-b123-4fe0-a962-12d503cadba0", "metadata": {}, "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 5, @@ -207,7 +199,6 @@ }, { "cell_type": "markdown", - "id": "8fff210c-4786-4ade-9d12-c87101fbf767", "metadata": {}, "source": [ "### Step 2: Optimize the problem" @@ -215,7 +206,6 @@ }, { "cell_type": "markdown", - "id": "6943d6b0-89de-4a87-b83b-e6b6ed981b88", "metadata": {}, "source": [ "After we have created the Trotterized circuits, we will optimize them for a target hardware. We need to choose the hardware device to use before optimization. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." @@ -224,7 +214,6 @@ { "cell_type": "code", "execution_count": 6, - "id": "fcd03338-9cde-4608-a0b6-cfb221e1aeac", "metadata": {}, "outputs": [], "source": [ @@ -235,7 +224,6 @@ }, { "cell_type": "markdown", - "id": "ec99f5bd-1bf8-4b0f-b48d-28fe45ce5f2c", "metadata": {}, "source": [ "Next, we will transpile the circuits to the target backend using Qiskit." @@ -244,7 +232,6 @@ { "cell_type": "code", "execution_count": 7, - "id": "31840bfc-06e9-49d3-9845-40729406c107", "metadata": {}, "outputs": [], "source": [ @@ -257,7 +244,6 @@ }, { "cell_type": "markdown", - "id": "473d09df-97ca-4db8-b1d2-66e9429d3c6e", "metadata": {}, "source": [ "### Step 3: Execute experiments" @@ -265,24 +251,23 @@ }, { "cell_type": "markdown", - "id": "6328511a-0465-45c6-b75b-4ebc243c8857", "metadata": {}, "source": [ "After optimizing the circuits for hardware execution, we are ready to run them on the target hardware and collect samples for ground state energy estimation. Here we use ``SamplerV2`` from ``qiskit-ibm-runtime`` to simulate noisy samples taken from the ``ibm_sherbrooke`` backend. We then combine the counts from each of the Krylov basis states into a single counts dictionary and plot the top 20 most commonly sampled bitstrings.\n", "\n", - "***Note: [Qiskit Aer](/docs/addons/qiskit-aer/index) is required to simulate samples from transpiled circuits.***" + "***Note: [Qiskit Aer](https://qiskit.github.io/qiskit-aer/index.html) is required to simulate samples from transpiled circuits.***" ] }, { "cell_type": "code", "execution_count": 8, - "id": "663f1e21-dd43-4534-92ed-818ae0c29e87", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 8, @@ -307,7 +292,6 @@ }, { "cell_type": "markdown", - "id": "57159228-cde5-4e7b-b182-644a0f6b9be0", "metadata": {}, "source": [ "### Step 4: Post-process the results" @@ -315,7 +299,6 @@ }, { "cell_type": "markdown", - "id": "4ffde66b-34f6-4907-8fba-d5e791175342", "metadata": {}, "source": [ "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function." @@ -324,7 +307,6 @@ { "cell_type": "code", "execution_count": null, - "id": "8ba7ec2b-7ffd-42dc-92fa-d7737bff82a3", "metadata": {}, "outputs": [ { @@ -409,7 +391,6 @@ }, { "cell_type": "markdown", - "id": "85556909-385f-41f5-af52-261fe461a5e2", "metadata": {}, "source": [ "Now, we plot the results. The first plot shows that after a few iterations, we obtain the exact ground state energy.\n", @@ -422,7 +403,6 @@ { "cell_type": "code", "execution_count": 10, - "id": "5b05546d-d33b-449f-918e-a17b1e6ec6ba", "metadata": {}, "outputs": [ { @@ -436,8 +416,9 @@ }, { "data": { + "image/png": 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g0X744Yc4n6sWi0Xp06dXrly5VLBgQQdFBgAAkHKS1NylUaNG1tn3UiJBERUVpYoVK6pJkybq2LFjvGVKliypli1bKnv27IqMjNSgQYP00ksv6ejRo3J2jn+2uLx582rMmDHKmTOnbt68qVGjRumll17SoUOH5O/vn+znkRpk9HLTmBYl1HTCBi3aeVplQjKodfkQR4cFAECq0KZNG0eHAAAAYHdJSko1a9ZMb7/9tqpVq6aOHTsqJCREHh4eccqVKFHiifb/5ptvSpIiIiISLNOpUyfr3yEhIfr0009VrFgxRUREKFeuXPFu06JFC5vnI0eO1OTJk7Vz5069+OKLTxTr86BkcHr1rVNAQxbu0aeL9qhoVh+FZk/v6LAAAHjmRUZG6t9//1XRokXjXb9r1y5lzZpV6dPzuQsAAFKPJCWlqlatav17zZo1cdYbY2SxWBQdHZ2UwyRaVFSUpkyZohw5cihbtmyJ2ubOnTuaOHGifHx8VKxYsRSO8NnXrkKItkREasnuM+r203YtfKei0nu6OjosAACeaT169ND+/fv1999/x7u+c+fOKlCggCZPnmznyAAAAFJOkpJSU6ZMSa44kmTs2LHq3bu3oqKilC9fPi1btkyurg9PlCxcuFDNmjXTjRs3FBgYqGXLlsnPzy/B8rdv39bt27etz69evZps8T9LLBaLhjcuqr2nryri4g31mBWuH1qXlpMT40sBAPCk/vrrL3Xp0iXB9fXr19f48ePtGBEAAEDKS1JSqnXr1skVh8LCwtS5c2fr8yVLliR66uOWLVuqZs2aOn36tL788ks1adJE69atk7u7e4LbVKtWTeHh4bpw4YImTZqkJk2aaOPGjcqUKVO85YcNG6bBgwc/3kmlUt7uaTS2ZUk1HLtOK/ef17hVh9W1Wm5HhwUAwDPr/PnzD/1xLGPGjDp37pwdIwIAAEh5Tsm1o9OnT2vHjh2Kiop6ou1feeUVhYeHWx+lSpVK9LY+Pj7KkyePKleurNmzZ2vfvn2aO3fuQ7fx9PRU7ty59cILL2jy5MlycXF5aJP4vn376sqVK9bHiRMnEh1falQwyFtDXi0sSfrqj/1af/iCgyMCAODZFRgYqO3btye4fuvWrUzGAgAAUp0kJ6Xmz5+v/PnzK2vWrCpRooQ2btwoSbpw4YJCQ0MfmRyKlS5dOuXOndv6iG/A9MQwxsgYY9PVLjFiYmIeuo2bm5u8vb1tHs+7JqWz6fWSWRVjpHd/3q6zV285OiQAAJ5JDRo00OTJk7VgwYI46+bPn68pU6aoYcOGDogMAAAg5SQpKfXbb7/ptddek5+fnwYOHChjjHWdn5+fsmTJoqlTpz7x/iMjIxUeHq49e/ZIkvbv36/w8HCdOXNGknTkyBENGzZMW7du1fHjx7V+/Xq9/vrr8vDwUJ06daz7yZ8/vzU5FhUVpX79+unvv//WsWPHtHXrVrVr104nT57U66+//sSxPq8+ebWw8gek04Xrd/TOT9t1LzrG0SEBAPDMGTRokPLly6eGDRuqRIkSatWqlVq1aqUSJUrotddeU968eRlGAAAApDpJSkp98sknqly5stauXauuXbvGWV+uXLmHNkV/lAULFig0NFR169aVJDVr1kyhoaHWgT7d3d21Zs0a1alTR7lz51bTpk2VLl06rV+/3mZsqP379+vKlSuSJGdnZ+3bt0+NGjVS3rx5Vb9+fV28eFFr1qxRoUKFnjjW55WHq7PGtiwhLzcXbYqI1Ig/9js6JAAAnjk+Pj76+++/9fHHH+vu3buaPXu2Zs+erbt376p///7auHGjfH19HR0mAABAskrSQOe7d+/WyJEjE1yfOXPmJA3K2aZNG7Vp0ybB9UFBQVq8ePEj93N/Cy53d3fNmTPniWNCXDn9vfRF46J6O2ybJqw6olLBGVSzYGZHhwUAwDPF09NTgwcPpkUUAAB4biSppVTatGkfOrD5kSNHlDFjxqQcAs+IOkUC1bZCiCTp/VnhOhF5w7EBAQDwDLl3756uXr2a4PqrV6/q3r17dowIAAAg5SUpKVWtWjVNmzYt3krSmTNnNGnSJL300ktJOQSeIX1rF1Bodl9dvXVPXcK26tbdaEeHBADAM+Hdd99V+fLlE1xfoUIFvf/++3aMCAAAIOUlKSn12Wef6d9//1Xp0qU1YcIEWSwW/f777/r4449VpEgRGWM0cODA5IoVTzlXFyd916KE0qdNo90nr2rIwj2ODgkAgGfC0qVL1bhx4wTXN27cOFFDFgAAADxLkpSUypcvn9auXauMGTOqf//+MsZoxIgRGjp0qIoUKaI1a9YoJCQkmULFsyDI10OjmhaXxSKFbTyuedtPOjokAACeeqdOnVKWLFkSXB8UFKSTJ/lMBQAAqUuSBjqXpEKFCunPP//UpUuXdOjQIcXExChnzpzy9/dPjvjwDKqaL5PeqZZb3/x1SH3n7FKhIG/lyZzO0WEBAPDUypgxo/bvT3gG271798rb29uOEQEAAKS8JLWUul/69OlVunRplS1bloQU9F6NvKqQO6Nu3o1Wl7BtirrN4KwAACTk5Zdf1oQJE7R9+/Y467Zt26aJEyeqdu3aDogMAAAg5SS5pVR0dLR+//13HTlyRJcuXZIxxma9xWJR//79k3oYPGOcnSz6ulmo6n6zRofOXVffObv0dbPislgsjg4NAICnzpAhQ7R06VKVKVNGr7zyigoVKiRJ2r17t3777TdlypRJQ4YMcXCUAAAAyStJSaktW7aoUaNG+vfff+Mko2KRlHp++Xm5aUyLEmo28W8t2HFKpXNk0JsvBDs6LAAAnjpBQUHasmWLPvzwQ82fP19z586VJHl7e6tly5YaOnSogoKCHBwlAABA8kpSUurtt9/WzZs3NW/ePFWqVEm+vr7JFBZSi9IhGfThy/n12eK9GvLbHhXL6qOiWX0dHRYAAE+dwMBATZs2TcYYnT9/XpLk7+9PK2MAAJBqJWlMqZ07d6pPnz6qX78+CSkkqEOlHHqpYGbdiY7R22HbdOXGXUeHBADAUykqKkpnzpxR2rRplSlTJhJSAAAgVUtSUipr1qwJdtsDYlksFo14vZiyZ0irfy/dVM9Z4YqJ4X0DAIAkRURE6O2331ZwcLC8vb2VNWtW+fj4KHv27OratauOHj3q6BABAABSRJKSUn369NGkSZN09erV5IoHqZSPRxqNbVlCri5OWr7vnCasPuLokAAAcLj58+eraNGiGj9+vJydnVW/fn21aNFC9evXl4uLi8aNG6eiRYtq/vz5jg4VAAAg2SVpTKlr167Jy8tLuXPnVrNmzZQtWzY5OzvblLFYLOrRo0eSgkTqUDiLjwa/Ukh95+zSiN/3KTS7r17ImdHRYQEA4BB79uxR06ZNlTNnTk2YMEGVKlWKU2bNmjV666231KxZM23dulUFCxZ0QKQAAAApI0lJqV69eln/HjNmTLxlSErhfs1KZ9PmiEjN2XZS7/y8XYverahM6dwdHRYAAHY3dOhQ+fn5ae3atcqQIUO8ZSpVqqQ1a9aoaNGiGjZsmH788Uc7RwkAAJBykpSUYowDPC6LxaJPGxTW7pNXdODsdb3783bNaF9WLs5J6kkKAMAzZ8WKFerQoUOCCalYGTJkULt27TR58mQ7RQYAAGAfj50J2LRpkyIjIyVJwcHBD30YY7Rq1apkDxrPtrSuLhr3Rkl5ujrr7yORGvXnAUeHBACA3V28eFEhISGJKpsjRw5dvHgxZQMCAACws8dOSpUrV05Lly61Po+MjFTatGnjTT6tW7dObdu2TVqESJVy+Xvp80ZFJUnfrTisv/addXBEAADYl5+fX6JbnR89elR+fn4pHBEAAIB9PXZSyhgT5/mtW7cUHR2dbEHh+VC/WJBalwuWJPWYuUMnIm84OCIAAOynatWqmjx5srUFekIiIyM1efJkVa1a1T6BAQAA2AkD+cCh+tUtoGJZfXTl5l11+2mbbt8juQkAeD7069dPFy9eVOXKlbV+/fp4y6xfv15VqlTRxYsX1bdvXztHCAAAkLKSNNA5kFRuLs76rmUJ1f1mrXb8e0WfLdqrT14t7OiwAABIcQULFtRPP/2kVq1aqVKlSgoJCVGxYsWULl06Xbt2TTt37tTRo0fl7u6uGTNmqFChQo4OGQAAIFnRUgoOlzV9Wo1uWlySNH3DMS3YccqxAQEAYCevvfaadu7cqY4dO+r27duaN2+efvzxR82bN083b95Uhw4dtGPHDjVu3NjRoQIAACS7J2opFRERoW3btkmSrly5Ikk6ePCgfH19bcoldvBOoFr+TOpaLZe+W3FYH/5vpwoGeit3Ji9HhwUAQIrLmTOnxo8fL0m6evWqrl27pnTp0snb29vBkQEAAKSsJ0pK9e/fX/3797dZ9vbbb8cpZ4yRxWJ5ssjw3OlRI6+2HbusDUcu6u2wrZrXtYLSutLDFADw/PD29iYZBQAAnhuP/Y1/ypQpKREHIBdnJ33dvLjqfrNWB85e10dzd2tkk2IkNgEAAAAASIUeOynVunXrlIgDkCRlSueuMc1D1eL7jZq7/aRKh2RQi7LZHR0WAAAAAABIZgx0jqdO2ZwZ1eulfJKkQb/9o90nrzg4IgAAAAAAkNxISuGp1LlyTtUokEl37sWoS9hWXbl519EhAQAAAACAZERSCk8lJyeLvnq9uLKm99CJyJvq9esOGWMcHRYAAAAAAEgmTG2Gp5ZP2jQa27KEGo/boGV7zmrSmiPqVDmXo8MCACDZrV69+qHrLRaL3N3dlTVrVgUGBtopKgAAgJRFUgpPtaJZfTWgfkF9PG+3hi/dr9Ds6VU6JIOjwwIAIFlVrVo10bPN5smTR4MHD1bTpk1TOCoAAICURfc9PPVals2uV4sHKTrGqGvYNp2/dtvRIQEAkKyWLl2qokWLKl++fPryyy81b948zZs3TyNGjFC+fPlUvHhxzZ49W1999ZUsFotatGih2bNnOzpsAACAJCEphaeexWLR0IZFlDuTl85du633ftmu6BjGlwIApB5Lly6Vu7u7wsPD1aNHD9WvX1/169dXz549tW3bNqVJk0Z///23unfvru3bt6tAgQIaPny4o8MGAABIEpJSeCZ4urloXMsS8kjjrPWHL+rrPw84OiQAAJJNWFiYWrRoIVdX1zjr3N3d1bJlS02bNs36/I033tCePXvsHSYAAECyIimFZ0aezOn0eaMikqRv/jqklfvPOTgiAACSR1RUlM6ePZvg+tOnT+v69evW576+vnJ2drZHaAAAACmGpBSeKa8Wz6KWZbNLknrMDNfJyzcdHBEAAElXvXp1jR49WgsXLoyz7rffftPXX3+t6tWrW5eFh4crJCTEjhECAAAkP5JSeOb0r1dQRbL46NKNu+oatk137sU4OiQAAJJkzJgxypw5s1599VVlz55d1apVU7Vq1ZQ9e3Y1aNBAmTNn1rfffitJunXrlo4fP64OHTo4OGoAAICkISmFZ457GmeNbVlC3u4uCj9xWcOW7HV0SAAAJEn27Nm1a9cujRgxQgUKFNDp06d1+vRpFShQQCNGjNCuXbsUHBws6b8xpRYvXqx3333XwVEDAAAkjYujAwCeRLYMaTWySXF1mL5FU9ZFqFRwBtUtGujosAAAeGJp06ZVz5491bNnT0eHAgAAYBe0lMIzq0bBzHqrSi5JUp//7dSR89cfsQUAAE+n3r17a/v27Y4OAwAAwK5ISuGZ1uulvCqTI4Ou376nt8O26eadaEeHBADAY/v2229VqlQp5cmTR/3799euXbscHRIAAECKIymFZ5qLs5PGNA+Vn5eb9p25pv7zdzs6JAAAHtu5c+c0ZcoU5c2bV1988YWKFy+uQoUKaciQIdq/f7+jwwMAAEgRJKXwzMvk7a5vmheXk0WavfVfzdp8wtEhAQDwWNKlS6dWrVpp0aJFOnv2rCZOnKisWbNqyJAhKliwoIoXL67PP//c0WECAAAkK5JSSBXK5/LT+y/lkyT1n79b/5y64uCIAAB4Mr6+vmrfvr1+//13nT59Wl999ZWOHj2qjz76yNGhAQAAJCuSUkg1ulTJpWr5/HX7XozeDtumq7fuOjokAACeyN27d7VgwQK9++67GjBggK5du6asWbM6OiwAAIBkRVIKqYaTk0UjmxRXFl8PHbt4Q71/3SljjKPDAgAgUe7du6fFixerdevW8vf3V4MGDbRy5Uq1bdtWa9eu1bFjxxwdIgAAQLJycXQAQHJK7+mq71qW0Ovj12vpP2c0ee1RdaiU09FhAQDwUO3bt9e8efN06dIl+fn5qXnz5mrWrJkqV64si8Xi6PAAAABSBC2lkOoUz+arj+sWlCR9vmSfth6LdHBEAAA83Lx589SwYUPrOFLjxo1TlSpVbBJSly5dcmCEAAAAyY+kFFKlVuWCVa9ooO7FGHUN266L1287OiQAABJ09uxZff/996pZs6acnZ2ty2/fvq1ff/1VDRo0UGBgoAMjBAAASH4kpZAqWSwWfd6oqHL6e+rM1VvqPjNc0TGMLwUAeDq5uPzfiArGGP35559q27atMmfOrKZNm2rDhg1q0aKFAyMEAABIfiSlkGp5ubloXMuSck/jpDUHL+jbvw46OiQAABK0detW9ezZU1myZNFLL72k6dOnq27dulq3bp3OnDmjH3744Yn3/d133ykkJETu7u4qW7asNm3alKjtfvnlF1ksFjVo0OCJjw0AAJAQklJI1fIFpNPQhkUkSV8vP6jVB847OCIAAP7PkSNHNGTIEOXPn19lypTR7Nmz1bJlS82cOVPGGDVq1EjlypVL0mDnM2fOVM+ePTVw4EBt27ZNxYoVU61atXTu3LmHbhcREaFevXqpUqVKT3xsAACAhyEphVTvtRJZ1bxMNhkjdZ8ZrtNXbjo6JAAAVK5cOeXJk0djxozRiy++qFWrVun48eMaMWKESpQokWzHGTlypDp27Ki2bduqYMGCGj9+vNKmTfvQllfR0dFq2bKlBg8erJw5mcUWAACkDJJSeC4MrF9IhYK8FRl1R91+2q670TGODgkA8JzbuHGjQkJCNHHiRH399deqWLFish/jzp072rp1q2rUqGFd5uTkpBo1amjDhg0JbvfJJ58oU6ZMat++faKOc/v2bV29etXmAQAA8CgkpfBccE/jrLEtSyidu4u2Hruk4Uv2OTokAMBzbsyYMQoMDFTDhg0VEBCgzp07a8WKFTIm+SbmuHDhgqKjo5U5c2ab5ZkzZ9aZM2fi3Wbt2rWaPHmyJk2alOjjDBs2TD4+PtZHtmzZkhQ3AAB4PpCUwnMjOKOnvny9mCTp+7VHtXT3aQdHBAB4nr399ttau3atDh8+rO7du2vNmjV68cUXlSVLFg0YMEAWiyVJY0k9iWvXrunNN9/UpEmT5Ofnl+jt+vbtqytXrlgfJ06cSMEoAQBAakFSCs+VWoUC1LFSDknSB7/uVMSFKAdHBAB43uXIkUMff/yx9uzZo82bN6tZs2ZauXKljDF6++231alTJy1cuFC3bt167H37+fnJ2dlZZ8+etVl+9uxZBQQExCl/+PBhRUREqH79+nJxcZGLi4umT5+uBQsWyMXFRYcPH473OG5ubvL29rZ5AAAAPApJKTx3er+cX6WC0+va7XvqErZNt+5GOzokAAAkSSVLltTIkSN14sQJ/fHHH6pVq5ZmzpypV1555bFaLsVydXVVyZIltXz5cuuymJgYLV++XOXKlYtTPn/+/Nq1a5fCw8Otj1deeUXVqlVTeHg43fIAAECyIimF504aZyeNaVFCGT1dtff0VQ2c/4+jQwIAwEbsYORTp07V2bNn9fPPP+vFF198on317NlTkyZN0rRp07R371516dJFUVFRatu2rSSpVatW6tu3ryTJ3d1dhQsXtnn4+voqXbp0Kly4sFxdXZPtHAEAAFwcHQDgCAE+7vq6Waje/GGjZm45oVIh6fV6KX79BQA8fdzd3dW0aVM1bdr0ibZv2rSpzp8/rwEDBujMmTMqXry4li5dah38/Pjx43Jy4ndKAABgfySl8NyqmMdPPWrk1chlB9R//m4Vyeqj/AGMgQEASH26deumbt26xbtu5cqVD9126tSpyR8QAACA6L6H51y3arlVOa+/bt2NUZcZ23Tt1l1HhwQAAAAAwHOBpBSea05OFo1uWlyBPu46eiFKH/5vl4wxjg4LAAAAAIBUj6QUnnsZPF01pkUJuThZtGjXaU1dH+HokAAAAAAASPVISgGSSganV786BSRJQxfv1bbjlxwcEQAAAAAAqRtJKeD/a1shRHWKBOhutFG3sG26FHXH0SEBAAAAAJBqkZQC/j+LxaLhjYoqh5+nTl25pe4zwxUTw/hSAAAAAACkBJJSwH3SuafR2JYl5ObipFUHzuu7FYccHRIAAAAAAKkSSSngAQUCvTWkQWFJ0qg/D2jdoQsOjggAAAAAgNSHpBQQjyalsqlJqayKMdJ7v2zXmSu3HB0SAAAAAACpCkkpIAGfvFpY+QPS6cL1O3rn5226Gx3j6JAAAAAAAEg1SEoBCXBP46xxb5SUl5uLNkdc0pe/73d0SAAAAAAApBokpYCHyOHnqRGNi0qSJqw+oj/+OePgiAAAAAAASB1ISgGPULtIoNpVyCFJev/XHTp+8YaDIwIAAAAA4NlHUgpIhA9r51eJ7L66duueuoRt1a270Y4OCQAAAACAZxpJKSARXF2cNKZFCaVPm0b/nLqqTxbu+X/t3Xl0E/X+//FXkjZt6cbaQtkKlUV2KIsoi1eQoojwRREQkSJekU2RK/5AvRZEKbjClU241+L3IooLcEVZRL6AIHhlq+wVgQKyL1KgQEuT+f1RicY2pcUmU9rn45w5MJN3Ju8pk8mHV2cmZrcEAAAAAMBNjVAKyKeo0kGa3LupLBZp3n8PacGWn81uCQAAAACAmxahFFAA7WtX0PC7akmSXli4Qz+euGByRwAAAAAA3JwIpYACerpDLbW5pbwuX3XoybmbdTEjy+yWAAAAAAC46RBKAQVks1o0pXcTVQwL1P5T6RqzYLsMwzC7LQAAAAAAbiqEUsANKBcSoKkPN5XNatHiH45q7ncHzW4JAAAAAICbCqEUcIOaR5fVmHvqSpJe/mKXfjh8ztyGAAAAAAC4iRBKAX/CwDY1FFc/UlcdhoZ8sEXnLmWa3RIAAAAAADcFQingT7BYLHrtwcaqXq6Ujpy7rJEf/yCnk/tLAQAAAABwPYRSwJ8UHuSv6X2bye5n1f/tOakZa/aZ3RIAAAAAAEUeoRRQCOpHhevl++tLkt78KkUb9p0xuSMAAAAAAIo2QimgkPRqUVUPNKsipyEN/3CrTp6/YnZLAAAAAAAUWYRSQCGxWCx6pXsD1YkM1emLGRr+4VZlOZxmtwUAAAAAQJFEKAUUoiC7TdMfaaZgu03/PXBWb6740eyWAAAAAAAokgilgEIWUyFEkx5sJEmasXqfVu4+YXJHAAAAAAAUPYRSgBfc1yhK8bdHS5KemZ+sw2cvmdsQAAAAAABFDKEU4CXP33urGlctrfNXsjR03hZlZDnMbgkAAAAAgCKDUArwErufVdMebqrSpfy17ec0vfLFbrNbAgAAAACgyCCUAryoSplSertXE0nSv787qP8kHzG3IQAAAAAAighCKcDL/lInQsP+coskacyC7frp5AWTOwIAAAAAwHyEUoAPPHN3bd0eU06XMh16cu4WpWdkmd0SAAAAAACmIpQCfMBmtWhK76aKCA3QTycv6oWF22UYhtltAQAAAABgGkIpwEcqhAbonT5NZbNatCj5qOZ9f8jslgAAAAAAMA2hFOBDrWqW06i4OpKkcZ/v0vaf00zuCAAAAAAAcxBKAT72RNua6nhrpDIdTg2Zt1lpl66a3RIAAAAAAD5HKAX4mNVq0Zs9G6tq2SAdPntZf/vkB+4vBQAAAAAocQilABOEl/LX9IdjZbdZ9fXuE5r1zX6zWwIAAAAAwKcIpQCTNKwSroT760mSXlueov/uP2NyRwAAAAAA+A6hFGCih1tWU/cmUXI4DQ3/cKtOXcgwuyUAAAAAAHyCUAowkcVi0av/01C1IkJ08kKGnvpwqxxO7i8FAAAAACj+CKUAkwUH+GnGI81Uym7Thv1n9PaKH81uCQAAAAAAryOUAoqAWyJCldijoSRp6qqftCrlpMkdAQAAAADgXYRSQBHRrUll9butuiTpmfnJOnLusskdAQAAAADgPYRSQBHy4n23qlGVcJ27dFVDPtiizCyn2S0BAAAAAOAVhFJAERLgZ9O0h5spPMhfPxw+pwlLdpvdEgAAAAAAXkEoBRQxVcuW0lsPNZYkzVmfqsU/HDW5IwAAAAAACh+hFFAEdbg1UoPvjJEkjf5sm/adumhyRwAAAAAAFC5CKaCI+tvdtdWqRlmlZzo0ZO4WXc50mN0SAAAAAACFhlAKKKL8bFa906epyocEKOXEBb2waLsMwzC7LQAAAAAACgWhFFCERYQF6p0+TWW1SAu2HNH8jYfNbgkAAAAAgEJRpEOpBQsWqFOnTipXrpwsFouSk5Nz1AwaNEgxMTEKCgpShQoV1K1bN+3Zsyffr/Hkk0/KYrFo8uTJhdc4UIhax5TT3zrVkSS99PlO7TiSZnJHAAAAAAD8eUU6lEpPT1ebNm00adIkjzWxsbFKSkrS7t27tXz5chmGoU6dOsnhuP79dxYuXKjvvvtOUVFRhdk2UOgGt4/RXXUjlJnl1NB5W5R2+arZLQEAAAAA8Kf4md1AXvr16ydJSk1N9VjzxBNPuP4eHR2tV155RY0bN1ZqaqpiYmI8Pu/IkSMaPny4li9fri5duhRaz4A3WK0WvfVQY3X5xzodPHNJoz75Qe/2i5XFYjG7NQAAAAAAbkiRPlOqoNLT05WUlKQaNWqoatWqHuucTqf69eunUaNGqX79+j7sELhxpUvZNb1vM/nbLPpq1wn9a90Bs1sCAAAAAOCGFYtQavr06QoJCVFISIiWLl2qFStWyG63e6yfNGmS/Pz89NRTT+X7NTIyMnT+/Hm3CfC1xlVL6+/31ZMkJS7do02pZ03uCAAAAACAG1NkQqkPPvjAFSyFhIRo7dq1+X5u3759tXXrVq1Zs0a1a9fWQw89pCtXruRau3nzZk2ZMkVz5swp0KVPiYmJCg8Pd015nYkFeFO/26qra+MoOZyGhs3bqtMXM8xuCQAAAACAAisyodT999+v5ORk19S8efN8Pzc8PFy1atVSu3bt9Omnn2rPnj1auHBhrrVr167VyZMnVa1aNfn5+cnPz08HDx7U3/72N0VHR3t8jTFjxigtLc01HT58uKCbCBQKi8WixB4NFVMhWMfPX9GIj5LlcBpmtwUAAAAAQIEUmRudh4aGKjQ09E+vxzAMGYahjIzczx7p16+fOnbs6LYsLi5O/fr104ABAzyuNyAgQAEBAX+6P6AwhAT4acYjseo29Vut++m0pqzcq5F31za7LQAAAAAA8q3InCmVm7Nnzyo5OVm7du2SJKWkpCg5OVnHjx+XJO3fv1+JiYnavHmzDh06pPXr16tnz54KCgrSvffe61pP3bp1XWdOlStXTg0aNHCb/P39VbFiRdWpU8f3GwncoNqRoXr1fxpIkt75v71a8+MpkzsCAAAAACD/inQo9fnnn6tp06bq0qWLJKl3795q2rSpZs6cKUkKDAzU2rVrde+99+qWW25Rr169FBoaqvXr1ysiIsK1npSUFKWlpZmyDYA39WhWRX1aVpNhSCM+2qqj5y6b3RIAAAAAAPlSZC7fy018fLzi4+M9Ph4VFaUlS5Zcdz2Gkff9dlJTUwvYGVB0JHStp20/n9POo+c1bN4WffREa9n9inTeDAAAAABA0T5TCsD1BfrbNKNvrEID/bTl0DlNXLrH7JYAAAAAALguQimgGKhWrpTe7NlYkvTetwe0dPsxkzsCAAAAACBvhFJAMdGpfkUNaldTkjTq0206cDrd5I4AAAAAAPCMUAooRp6Nq6MW0WV0MSNLg+du1pWrDrNbAgAAAAAgV4RSQDHib7Nq6sPNVD7Erj3HL+il/+wwuyUAAAAAAHJFKAUUM5FhgZrSu6ksFunjTT/r402HzW4JAAAAAIAcCKWAYuiOW8prZMfakqS/L9qhXUfPm9wRAAAAAADuCKWAYmroX25R+9oVlJHl1NB5W3ThylWzWwIAAAAAwIVQCiimrFaL3u7VRFHhgTpwOl3/77NtMgzD7LYAAAAAAJBEKAUUa2WD7Zrat5n8bRYt2X5cSd+mmt0SAAAAAACSCKWAYq9ZtTJ6/t5bJUkTluzW5oO/mNwRAAAAAACEUkCJEH97tLo0rKQsp6Fh87bobHqm2S0BAAAAAEo4QimgBLBYLJr4QEPVKB+sY2lXNGJ+spxO7i8FAAAAADAPoRRQQoQG+mvGI80U6G/VNz+e0tRVP5ndEgAAAACgBCOUAkqQuhXDNL5bA0nS21//qHV7T8vhNLRh3xn9J/mINuw7IwdnUAEAAAAAfMDP7AYA+FbP5lW1KfUXzd90WE/O3axSdptOXshwPV4pPFAJXeupc4NKJnYJAAAAACjuOFMKKIHGdauvyqWDdDEjyy2QkqTjaVc0eO4WLdtxzKTuAAAAAAAlAWdKASWQv82qzCxnro8ZkiySxi3epbvrVZTNavFpbyjeHE5D3x84q5MXrigiNFAta5RlH4PXsL8BAAAUbYRSQAn0/YGzOnUxw+PjhqRjaVf06L/+q4rhQfKzWmSzWbL/tF770+o+b/Ow3GqRn83Dcqv1d4/nXO5v+0Ndjj6sslqyv10QRd+yHcc0bvEuHUu74lrG5aLwFvY3AACAoo9QCiiBTl64cv0iSd/uO+PlTgpHzhDM+oeQK38h2PVDtF+X53j+H+pteYR3BQzp/Gx51Fktst4kZ30s23FMg+du0R9vo3/tctEZjzQjKEChYX/Ladq0aXr99dd1/PhxNW7cWO+8845atmyZa+3s2bP1v//7v9qxY4ckKTY2VhMmTPBYDwAAcKMIpf4kx5V0Oey2nA9YbbLZA93qPLJaZbMH3VhtxiXJ8PBtaRaLbAGlbqw287LkzP3yLkmyBQbfYO0VyekolFprQCnXGTLOqxkyHFmFU2sPksWafbs1Z1amjKyrhVQbKIvVdgO1V2VkZXqstfgHyGrzK1BtRGigrIZDfobnHhwWP/VpXVNVypSSI+uqnJkZcjidynIacjgNZRm//ul0Kkt+umr4Zc87sqSrGcpyOuX4tdZhGMpyZP89w7DqqvyU5TDkdGRJWVd+Xc9v63U6s+szDasyfl2vw+GQv+Fh25xSlsWmDIt/9nYaTs+1kpwWm7J+rZVhyG54PmusILWGrLpqtbvm7U7P4V/Bai26ag3ItdZikfuZY1arDL9AV2gVaMmQn8U91LK56m0y/INcIViAkSGbRbmEdJLVapXVHvxbWObMkL/FkM1qlS2XkM4aEOKat2ZdVuLS3fJ35r6/ZVoD9fzCHbLbrLI6MiTD8/te9t+OEbp6Je9a/1LZPyBJysqQnJ7f9wWq9QuSfn3fKytT8rBdBa8NlH593xeo1nFVcnje32ULkH49RhSsNktyeN7fZbNLNv+C1zodUlYewbjVX/Kz30CtU8q6LKfTUMLCHTn2N4fFJofFXxZJL3++Q3fVDPV4KZ/F5ierf/Z7zjAMOTMu5VqX5+d1ETJ//nyNHDlSM2fOVKtWrTR58mTFxcUpJSVFEREROepXr16tPn366Pbbb1dgYKAmTZqkTp06aefOnapcubIJWwAAAIorQqk/af3gKAX751xetum9avT/vnTNfzsowuOgNvzW9mqasNo1/93waF29cDrX2tCazRU7YaNr/vu/1VPG6YO51paqUk8t39jpmt/8Qgtd+nlXrrUB5aur9dRU13zy2Ha6sH9TrrX+oeV1x+xTrvltifcobfeaXGutAaXU7v3fBu07335AZ7cuybVWku786LfQbM+0fjr130891radc9EVYqXMHqQT37zvsfb2WSdlD6sgSfrp3yN19KvpHmtb/eOAgiKiJUkHPnpBh794w2Nti9d3KLhqfUnSwYUTdPCzcR5rm736vcJiWkiSfl46Rfs/eM5jbeO/r1KZ+ndKko6tnKW9ScM81jZ87guVa9ZFknRi3QdKmTnAY229ER8r4raealmjrDpok/oeGu+x9pOqozTu/vtls1p0ZsuX2v7afR5raw2YqspxQyVJv+xcrR/G/8Vjbc2+r6la11GSpPP7NmrLC3d5rK3+QIJq9BwrSbp4aIc2PdfQY23pjk+rzP+8qiynoYxTqTr5sudao0W8rnZOzA67Lp5WyJRGHmvP1e6hn++cqCynISMjXU3/3cxj7ZGojvquxWvZ4Z3D0MNLWnis3Vf6Nn1cZ5Ku/hrevbD1QY/B1J7AhppYMdE1/8bPjynMeT7X2v32Wno56u3fag8/pvKOk7n3619NL1T+7b3w6pEhqnz1UK61p20Rerbqe675l44+o5qZe3OtPW8N01PV5rnmRx8brdcyduRam2EJ0KDqn+lseqYee3+TnjkxVo0v537skaT46C9cfx96MlEtLn3rsfaJap8q05r9y4HHT72tNukrPdYOr/qBLtjCJUn9zsxQhwtfeqx9tvK/dNo/UpLU6+x7uuf8Ao+1z0dN01F7dUlS918+UPe0Dz3Wjqv0lg4E1JYk3ZP2mXr9kuSxdmLkBO0Jyt5vO5z/Qv3OzvRY+3ZEgn4olb0vtrnwtR4/M9lj7bQKo7UxuI0kqUX6Og09NdFj7T/LjdC60I6SpMaXNuqZk56Pf/8u+6RWhmUfQ+pe3qbRJ573WDu/zAAtDX9AklQj40clHBvpsXZReB8tKtNXkhSVeVATjg6VVdKkXGqXhvXQ/LKPyZCUeeaw1j/WyeN6ozoNUe3HpkmSrl44rfVP5AxuJCk9j9ywKHnrrbf017/+VQMGZH8+zJw5U19++aXee+89jR49Okf9Bx984Db/z3/+U5999plWrlypRx991Cc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    " ] }, "metadata": {}, @@ -492,7 +473,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -506,9 +487,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.13.3" } }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/tutorials/index.mdx b/docs/addons/qiskit-addon-sqd/tutorials/index.mdx new file mode 100644 index 00000000000..80199d8444d --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/tutorials/index.mdx @@ -0,0 +1,17 @@ +--- +title: Sample-based quantum diagonalization (SQD) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-sqd. +--- + +# Tutorials + +This page summarizes the available tutorials. + +[![](/docs/images/api/qiskit-addon-sqd/tutorials_01_chemistry_hamiltonian_19_1.avif)](01-chemistry-hamiltonian) + +[Improving energy estimation of a chemistry Hamiltonian with SQD](01-chemistry-hamiltonian) + +[![](/docs/images/api/qiskit-addon-sqd/tutorials_02_fermionic_lattice_hamiltonian_22_1.avif)](02-fermionic-lattice-hamiltonian) + +[Improving energy estimation of a Fermionic lattice model with SQD](02-fermionic-lattice-hamiltonian) + diff --git a/docs/addons/qiskit-addon-utils/_package.json b/docs/addons/qiskit-addon-utils/_package.json new file mode 100644 index 00000000000..648d449cc7c --- /dev/null +++ b/docs/addons/qiskit-addon-utils/_package.json @@ -0,0 +1,4 @@ +{ + "name": "qiskit-addon-utils", + "version": "0.3.1" +} diff --git a/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.mdx b/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.mdx new file mode 100644 index 00000000000..dbe5cb5da95 --- /dev/null +++ b/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.mdx @@ -0,0 +1,161 @@ + + +# Use edge coloring to reduce the depth of quantum circuits + +This guide will show how to use the [qiskit\_addon\_utils.coloring](/docs/addons/qiskit-addon-utils/apidocs/qiskit_addon_utils.coloring) module to color edges of a coupling map and use the coloring to more efficiently place gates in a quantum circuit, leading to shallower circuits. + +The point of edge-coloring a graph is to find a set of edge colors such that no two edges of the same color share a common node. For a quantum processor, this means that gates along like-colored edges (qubit connections) may be run simultaneously and that the circuit will execute faster. We can use this to define our circuit in a way that is more amenable to the hardware it should run on. + +First, we will specify a backend and visualize its qubit topology. + +``` +[1]: +``` + +```python +from qiskit_ibm_runtime.fake_provider import FakeSherbrooke + +backend = FakeSherbrooke() +coupling_map = backend.coupling_map +``` + +``` +[2]: +``` + +```python +from rustworkx.visualization import graphviz_draw + +graphviz_draw(coupling_map.graph, method="neato") +``` + +``` +[2]: +``` + +![../\_images/how\_tos\_color\_device\_edges\_to\_improve\_depth\_3\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_color_device_edges_to_improve_depth_3_0.avif) + +Next we use the [qiskit\_addon\_utils.coloring.auto\_color\_edges](/docs/api/qiskit-addon-utils/coloring#qiskit_addon_utils.coloring.auto_color_edges) function to generate an edge coloring. This is a light wrapper for [rustworkx.graph\_greedy\_edge\_color](https://www.rustworkx.org/dev/apiref/rustworkx.graph_greedy_edge_color.html) that does some minor post-processing of the output. Namely, this function removes an arbitrary edge direction if an edge is bi-directional and combines the edge and color information into a dictionary format. + +``` +[3]: +``` + +```python +from qiskit_addon_utils.coloring import auto_color_edges +from rustworkx import PyDiGraph + +coloring = auto_color_edges(coupling_map) + +# Create a colored graph for visualization +eagle_r3 = PyDiGraph() +eagle_r3.extend_from_weighted_edge_list( + [(source, target, color) for ((source, target), color) in coloring.items()] +) +print(f"There are {len(coloring)} edges and {len(set(coloring.values()))} unique colors.") +print( + f'The edges are specified by length-2 tuples: (e.g. {next(iter(coloring))}).\ + \nThe "colors" are actually specified by a set of integers: {set(coloring.values())}' +) +``` + +```python +There are 144 edges and 3 unique colors. +The edges are specified by length-2 tuples: (e.g. (1, 0)). +The "colors" are actually specified by a set of integers: {0, 1, 2} +``` + +``` +[4]: +``` + +```python +def color_edge_3color(edge): + """Return the color of an edge.""" + # Map integers of a 3-coloring to color names + color_dict = {0: "red", 1: "green", 2: "blue"} + return {"color": color_dict[edge]} + + +graphviz_draw(eagle_r3, edge_attr_fn=color_edge_3color, method="neato") +``` + +``` +[4]: +``` + +![../\_images/how\_tos\_color\_device\_edges\_to\_improve\_depth\_6\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_color_device_edges_to_improve_depth_6_0.avif) + +Next, let’s make a circuit with a `CZGate` on each edge and observe its depth. For this first circuit, we will just naively place a gate on each edge. We will place barriers in `depth-1` increments (using [qiskit\_addon\_utils.slicing.slice\_by\_depth](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.slice_by_depth)) to illustrate how crucial edge-coloring is when designing a hardware-efficient quantum circuit. + +``` +[5]: +``` + +```python +from qiskit import QuantumCircuit +from qiskit_addon_utils.slicing import combine_slices, slice_by_depth + +circuit = QuantumCircuit(backend.num_qubits) + +for edge in coupling_map.graph.edge_list(): + circuit.cz(edge[0], edge[1]) + +print(f"The circuit has depth: {circuit.depth()}") + +# Add barriers in depth-1 increments +slices = slice_by_depth(circuit, max_slice_depth=1) +circuit = combine_slices(slices, include_barriers=True) + +circuit.draw("mpl", fold=-1) +``` + +```python +The circuit has depth: 37 +``` + +``` +[5]: +``` + +![../\_images/how\_tos\_color\_device\_edges\_to\_improve\_depth\_8\_1.png](/docs/images/api/qiskit-addon-utils/how_tos_color_device_edges_to_improve_depth_8_1.avif) + +Now let’s place all of the gates of a given color at the same time. We can see the depth is reduced from `37` to `3`. This is because we can take advantage of the fact that all gates on a given color can run at the same time. + +``` +[6]: +``` + +```python +from collections import defaultdict + +circuit = QuantumCircuit(backend.num_qubits) + +# Make a reverse coloring dict +color_to_edge = defaultdict(list) +for edge, color in coloring.items(): + color_to_edge[color].append(edge) + +# Place edges in order of color +for edges in color_to_edge.values(): + for edge in edges: + circuit.cz(edge[0], edge[1]) + +print(f"The circuit has depth: {circuit.depth(lambda x: x.name != 'barrier')}") + +# Add barriers in depth-1 increments +slices = slice_by_depth(circuit, max_slice_depth=1) +circuit = combine_slices(slices, include_barriers=True) + +circuit.draw("mpl", fold=-1) +``` + +```python +The circuit has depth: 3 +``` + +``` +[6]: +``` + +![../\_images/how\_tos\_color\_device\_edges\_to\_improve\_depth\_10\_1.png](/docs/images/api/qiskit-addon-utils/how_tos_color_device_edges_to_improve_depth_10_1.avif) diff --git a/docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth.ipynb b/docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth.ipynb index cd02ef4e0d4..3c1ae0a2995 100644 --- a/docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth.ipynb +++ b/docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth.ipynb @@ -55,8 +55,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "" ] }, "execution_count": 2, @@ -134,8 +135,9 @@ "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "" ] }, "execution_count": 4, @@ -177,8 +179,9 @@ }, { "data": { + "image/png": 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"text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 5, @@ -227,8 +230,9 @@ }, { "data": { + "image/png": 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7olR77zadBuESzBwIZQZIkTcHdp6Qvm39zI0L/u4macHlN+sC6Ie42GKYlJSUKDU1VbW1tZowYYJuuukmjR49WlOmTNHIkSM1c+ZMST2vj/BxPp9Pc+fOVVVVlV555RVKBABwiNzcXO3cuVO5ubmmo9gmxiUtyAl+3eeHO7tEAKTomAGSNDNDSg3y3IakWKlwmD15ANiLIiFMsrKyVF5errvuuksJCQmqrq5WSkqKNmzYoO3bt+vtt9+W1HuRcO7cOS1YsED79u3Tjh07NH78+L6ODwCwSVxcnIYMGaK4OGefTXjvcCk7iLM+MpOk+Tm2xQH6jWiZAQNipG8GeXmvh8ZKyc7+tgCORZEQRuPGjdO2bdvU2tqq1tZW7dmzR4sXL1Z7e7uqq6sVExOjG2+8sccav9+voqIi/frXv9Z///d/a8qUKYbSAwDsUFdXp2XLll24BbBTDRwgrbtNykq68rE3JHUfO/ga+3MBpkXLDJCke7KlJRbLhPtzpa+OsjcPAPtQJPSBw4cPKxAIaPTo0UpK6vkK6+GHH9bmzZv1rW99S0lJSXr99dcvfLz//vuGEgMAwqWtrU3l5eVqawvtQmSRJDNZ+smnpC+PlAb28lvG5DjpCyOk//iklD2w7/MBJkTTDJCkvxwt/dMUqeASt3IZf630j5OkpeM/uhYpgMjDZqI+cPDgQUm9n9awY8cOSdIPfvAD/eAHP+jx2E9+8hP95V/+pe35AAAIlyHx0rIbpW/mSb/xSI0+KSDpugRpRoaUxCsPwPFuT+/+qGyW9n0g/dOh7jngkvTc7abTAQgHns77wOWKhOrq6j5OAwCA/RLjpMIs0ykAmDR6cPfH0x8rEgA4A6c29IHLFQkAAAAAAEQSdiT0gbKyMtMRAACGpKWlqbi4WGlpaaajADCAGQDAiSgSAACwUWpqqoqKikzHAGAIMwCAE3FqAwAANmppadGuXbvU0tJiOgoAA5gBAJyIIgEAABvV19drxYoVqq+vNx0FgAHMAABORJEAAAAAAAAso0gAAAAAAACWUSQAAAAAAADLKBIAALBRfHy8xo4dq/j4eNNRABjADADgRNz+EQAAG40YMUIbN240HQOAIcwAAE7EjgQAAAAAAGAZRQIAADY6duyYpk+frmPHjpmOAsAAZgAAJ6JIAADARoFAQOfOnVMgEDAdBYABzAAATkSRAAAAAAAALKNIAAAAAAAAllEkAAAAAAAAy7j9IwAANsrJyVFpaakyMzNNRwFgADMAgBNRJAAAYKOEhASNGjXKdAwAhjADADgRpzYAAGCjhoYGrVy5Ug0NDaajADCAGQDAiSgSAACwUXNzs7Zu3arm5mbTUQAYwAwA4EQUCQAAAAAAwDKKBAAAAAAAYBlFAgAAAAAAsIwiAQAAG8XExOjmm29WTAxPuUA0YgYAcCImGgAANvL7/XrzzTfl9/tNRwFgADMAgBNRJAAAAAAAAMsoEgAAAAAAgGUUCQAAAAAAwDKKBAAAbOR2uzV79my53W7TUQAYwAwA4ERxpgMAAOBkmZmZeuKJJ0zHAGAIMwCAE7EjAQAAG3V0dKi2tlYdHR2mowAwgBkAwIkoEgAAsFFVVZXmz5+vqqoq01EAGMAMAOBEFAkAAAAAAMAyigQAAAAAAGAZRQIAAAAAALCMIgEAAAAAAFjG7R8BALBRXl6e9u7dazoGAEOYAQCciB0JAAAAAADAMooEAABsVFNTowcffFA1NTWmowAwgBkAwIkoEgAAsJHP59OhQ4fk8/lMRwFgADMAgBNRJAAAAAAAAMsoEgAAAAAAgGUUCQAAAAAAwDKKBAAAbJSRkaHHH39cGRkZpqMAMIAZAMCJ4kwHAADAyQYPHqzCwkLTMQAYwgwA4ETsSAAAwEZNTU3avHmzmpqaTEcBYAAzAIATUSQAAGCjxsZGPfXUU2psbDQdBYABzAAATkSRYAOv16uSkhLl5uYqISFBw4YNU3Fxsdrb27Vo0SK5XC6tX7/edEwAAAAAAILGNRLC7MCBAyosLJTH41FycrLGjx+v+vp6rV27VsePH9epU6ckSQUFBWaD2uhsl1TWIL3aIDWfk66JkXIHSZ8fLmUlm04HAAAAu7Wek7bVShVeyf+nzwUkne6Qro03mQxAOFAkhJHX69XcuXPl8Xi0bNkyPfbYY3K73ZKkJ598Uo888oji4uLkcrk0ceJEw2nDLxCQNldLPzomNZ3t+djvTkob35GmXy99J18ammAkIgAAAGzU6ZfWvSW9UCOd6er5WEDSX/xKumuYtOxGKSHWSEQAYcCpDWG0dOlS1dXVacmSJVqzZs2FEkGSSkpKlJ+fr87OTuXk5GjQoEEGk9rjh0ekJw9eXCKcF5D0WqP0tXLJ4+vTaABgTFJSkqZOnaqkpCTTUQAYEE0zoNMv/d8K6WfvXlwinHfWL/2iRvrr30tnOvs2H4DwoUgIkyNHjmjTpk0aOnSoVq1a1esxkyZNkiTl5+df+Fx5eblmzZqljIwMxcfHKysrS1/84hd15MiRPskdLttqpZ++Y+3YBp/0rT1SV8DeTADQH2RnZ2vdunXKzs42HQWAAdE0A/75re5fGlnx5inpH/9obx4A9qFICJPS0lL5/X4VFRVp4MCBvR6TmJgoqWeR0NTUpJtuuklr167VK6+8otWrV+vw4cOaNm2a6urq+iT71QoEpP+vMrg1lS3S77h4MYAo0NXVpba2NnV1XeLXcwAcLVpmwOmz0ovVwa35ZZ3k+dCWOABsRpEQJmVlZZKkGTNmXPKY88XAx4uEu+++W08//bTuu+8+ffrTn1ZRUZFefPFFNTc364UXXrA3dJi88YFU1Rb8us3VYY8CAP1OZWWlZs6cqcrKIBtXAI4QLTNg23tSh//Kx32cX9KLNbbEAWAzioQwqanpnoLDhw/v9fHOzk7t3r1bUs8ioTepqamSpLi4yLgW5p73Q18X4PQGAACAiHc1rwcBRJ7IeKcaAdrb2yVJPl/vVxHctGmTvF6v3G63RowYcdHjXV1d8vv9qqmp0aOPPqr09HR94QtfCCnL5MmT5fF4QlobCvcX/l7Jd3wt6HVdAWnYyFzp3BkbUgGAPRYsWBDU8SdPnpQk7dixQ2+88YalNfPmzQs6F/qn69fXyBUTqy5/l7Kyev9lAyJPMHMglBkgRd4cSFn2C10z6tag1/3h6DvK+sod4Q8E4IrS09O1b9++kNZSJIRJenq6mpqatH//fk2bNq3HYw0NDVq+fLkkaeLEiXK5XBet//SnP31hx0Jubq7KysqUlpYWUhaPx6MTJ06EtDYUmR80KjmEdYGuTp2oPh72PABgp/PFsVXnC2afz2d5bV/OcNjr+vNb7wIBfq4OEswcCGUGSJE3B5JamnRNCOs6Wpsi7msFQJEQNrNmzdKRI0e0evVq3XnnnRozZowkqaKiQgsXLpTX65UkFRQU9Lr+3//933X69GlVVVXpqaee0mc/+1nt3r07pCv8pqenh/x1hCL+5LGQ1p2r2q/MzMwwpwEAeyUnB1ednn/jkJiYaHkts9FBzv/ywOXi5+ogwcyBUGaAFHlzIKb+kHTzZ4NfWHcw4r5WwCmu5n2jKxDgLPVwqKurU0FBgT744APFxcUpLy9PZ86c0TvvvKPCwkL5/X7t3LlTzz77rB566KHL/l2nT59WTk6OvvrVr2r9+vV99BWErisg3bNL8vR+VsclrbxFmp1lTyYAsEtFRUVQx3d2dqq1tVVut9vytW9uvTX47cHon6Zs7b6gXIykvXebToNwCWYOhDIDpMibAyd90txdwd/ee/MMaYTbnkwA7MPFFsMkKytL5eXluuuuu5SQkKDq6mqlpKRow4YN2r59u95++21JV77QoiRde+21ys3N1TvvvGN37LCIdUlfGhncmusTpZkZ9uQBgP4kLi5OQ4YMiZgL6AIIr2iZAdclSp+9Ibg1n7iOEgGIVBQJYTRu3Dht27ZNra2tam1t1Z49e7R48WK1t7erurpaMTExuvHGG6/495w8eVLHjh3TqFGj+iB1eHxlpFRocVeae4D0z1Ola2LtzQQA/UFdXZ2WLVt24RbAAKJLNM2Av8uXxg22dmzOQOmJW+zNA8A+FAl94PDhwwoEAho9erSSkpJ6PPbVr35V3//+9/XSSy/pN7/5jX70ox/pjjvuUFxcnL71rW8ZShy8GJf0/VukhaOkuIuvJXnBSLf075+Ucgf1XTYAMKmtrU3l5eVqa2szHQWAAdE0A5LjpH/9hDTjCrtOb0uTfjxdujaUqzMC6Becvceqnzh48KCk3k9ruO222/Tcc8/pn//5n3XmzBkNGzZMM2bM0IoVKzR8eGTdJirWJRVPkL46StpaK73aIL11+qPH/+0T0qTUj647BQAAAGcZOEB66lappk16oVra55XaO6XEWKkgVVqQwy+UACegSOgDlysSlixZoiVLlvR1JFulJkhfG9398fGLTE0eajoZAAAA+sLwgdLfXvmMXgARilMb+sDligQAAAAAACIJOxL6QFlZmekIAABD0tLSVFxcrLS0NNNRABjADADgRBQJAADYKDU1VUVFRaZjADCEGQDAiTi1AQAAG7W0tGjXrl1qaWkxHQWAAcwAAE5EkQAAgI3q6+u1YsUK1dfXm44CwABmAAAnokgAAAAAAACWUSQAAAAAAADLKBIAAAAAAIBlFAkAANgoPj5eY8eOVXx8vOkoAAxgBgBwIm7/CACAjUaMGKGNGzeajgHAEGYAACdiRwIAAAAAALCMIgEAABsdO3ZM06dP17Fjx0xHAWAAMwCAE1EkAABgo0AgoHPnzikQCJiOAsAAZgAAJ6JIAAAAAAAAllEkAAAAAAAAyygSAAAAAACAZdz+EQAAG+Xk5Ki0tFSZmZmmowAwgBkAwIkoEgAAsFFCQoJGjRplOgYAQ5gBAJyIUxsAALBRQ0ODVq5cqYaGBtNRABjADADgRBQJAADYqLm5WVu3blVzc7PpKAAMYAYAcCKKBAAAAAAAYBlFAgAAAAAAsIwiAQAAAAAAWEaRAACAjVJSUvTAAw8oJSXFdBQABjADADgRRQIAADaKiYnRgAEDFBPDUy4QjZgBAJyIiQYAgI28Xq9+/OMfy+v1mo4CwABmAAAnokgAAAAAAACWUSQAAAAAAADLKBIAAAAAAIBlFAkAANjI7XZr9uzZcrvdpqMAMIAZAMCJ4kwHAADAyTIzM/XEE0+YjgHAEGYAACdiRwIAADbq6OhQbW2tOjo6TEcBYAAzAIATUSQAAGCjqqoqzZ8/X1VVVaajADCAGQDAiSgSAAAAAACAZRQJAAAAAADAMooEAAAAAABgGUUCAAAAAACwjNs/AgBgo7y8PO3du9d0DACGMAMAOBE7EgAAAAAAgGUUCQAA2KimpkYPPvigampqTEcBYAAzAIATUSQAAGAjn8+nQ4cOyefzmY4CwABmAAAnokgAAAAAAACWUSQAAAAAAADLKBIAAAAAAIBlFAkAANgoIyNDjz/+uDIyMkxHAWAAMwCAE8WZDgAAgJMNHjxYhYWFpmMAMIQZAMCJ2JEAAICNmpqatHnzZjU1NZmOAsAAZgAAJ6JIAADARo2NjXrqqafU2NhoOgoAA5gBAJyIIsEGXq9XJSUlys3NVUJCgoYNG6bi4mK1t7dr0aJFcrlcWr9+vemYtjrTKe1ulAJ/+nPgskcDAACn6eiSfn9S+p9aaVe9dLzFdCL0taYO6TcN0vZa6dUGyXvGdCIA4cI1EsLswIEDKiwslMfjUXJyssaPH6/6+nqtXbtWx48f16lTpyRJBQUFZoPaxPOh9Py70su1Uuu5jz4fkPT13dIXRkgzMySXy1hEAABgo5M+qfRdaWut1Hy252P5KdJ9OdLnMnkt4GRHT0s/e7e7QDrn/+jzsS7pjnTpK6O6/10AELkoEsLI6/Vq7ty58ng8WrZsmR577DG53W5J0pNPPqlHHnlEcXFxcrlcmjhxouG04XfwlPQ3e6Tmc70//sYH3R/zh0uPTJRieAEBAICjHG2Wil+XPujo/fE/nOr++P1J6bsFUhx7Yx1nR530+JtSZy/bUbsC0q8bpLIGaflN3b9gAhCZGN9htHTpUtXV1WnJkiVas2bNhRJBkkpKSpSfn6/Ozk7l5ORo0KBBBpOGX3WrtPQyJcLHvVAjrX3L/kwA0B8kJSVp6tSpSkpKMh0FsFVdu/TXv790ifBx2+ukJw/an6k/iKYZUO6RHtvfe4nwcQF1//z/p7ZPYgGwAUVCmBw5ckSbNm3S0KFDtWrVql6PmTRpkiQpPz//kn9PYWGhXC6Xvv/979sR0zY/PNLzVIYr+c/jUk2bfXkAoL/Izs7WunXrlJ2dbToKYKsNx6Sms1c+7rwXa6Rjzfbl6S+iZQZ0BaQ1hyT/lQ+94J8Od19LA0DkoUgIk9LSUvn9fhUVFWngwIG9HpOYmCjp0kXCz3/+cx04cMCuiLZp9Em/9QS/7oXqsEcBgH6nq6tLbW1t6uri1TKcq6mj+3z4YG2pDnuUfidaZsDvT0onPgxuzemz0q9D+PcGgHkUCWFSVlYmSZoxY8Ylj6mrq5PUe5HQ0tKiv/mbv9GaNWvsCWijX9YF1z6f9z91YY8CAP1OZWWlZs6cqcrKStNRANv8+UX1rPplneR3+K2domUGbA/xNAVeDwKRiYsthklNTY0kafjw4b0+3tnZqd27d0vqvUj49re/rTFjxqioqEhf/epXryrL5MmT5fGEsEUgRO4F31fyzP8T9LrTZ6Ws4SOkriDOiQAAwxYsWBDU8SdPnpQk7dixQ2+88YalNfPmzQs6F/qn69fXyBUTqy5/l7Kyen+N4AQD7y7RwNlLg17n65KGj5mggC+yznEIZg6EMgOkyJsDKd/aomtG3xb0uvI/HFXWfbNsSATgStLT07Vv376Q1lIkhEl7e7skyefz9fr4pk2b5PV65Xa7NWJEz0vU7tu3Tz/60Y+CenK5HI/HoxMnToTl77Iiq61VySGuPXGiTurqDGseALDT+Xlv1fnnBZ/PZ3ltX85w2Ov6wJ9+3R4IOPrnekNzs3o/sfPK6uvr5P+wJax57BbMHAhlBkiRNweSOzp0TQjrzp09G3FfKwCKhLBJT09XU1OT9u/fr2nTpvV4rKGhQcuXL5ckTZw4Ua6P3Ti5q6tLX//617VkyRJNmDAhbFn6UmLH6ZDWdTU1KDP9+vCGAQCbJScHV52ef+OQmJhoeW1mZmbQudBPnX/Od7kc/XNNPBfajgJ/+2llpAyShrivfHA/EswcCGUGSJE3B+LaToa0LqbFE3FfK+AUV/O+kSIhTGbNmqUjR45o9erVuvPOOzVmzBhJUkVFhRYuXCiv1ytJKigo6LFu/fr1amxsDOtdGkLdnhKq0x3SX/xKOhvkuZFfn5qhb9RxYhyAyFJRURHU8UePHlVpaakKCwuVl5dnac0zzzwTQjL0R1O2dl9HKDYm9sK1kpyo7ZxU+Er3qQrBWHjTtfpWbeTdAzCYORDKDJAibw7s/0BavDv4desXfVZ3fMe5/20ATsXFFsOkpKREqampqq2t1YQJE3TTTTdp9OjRmjJlikaOHKmZM2dK6nl9BK/Xq+9+97v63ve+p87OTp0+fVqnT5+WJJ05c0anT5+W3x/KZQz71rXx0qwbglsT65LmOfdUUQC4IDc3Vzt37lRubq7pKIBtBg6Q/iIr+HXzc8Iepd+Jlhlwc4o0MsiNJdcnSJ9kcyoQkSgSwiQrK0vl5eW66667lJCQoOrqaqWkpGjDhg3avn273n77bUk9i4S6ujq1trbq61//uoYMGXLhQ5JWr16tIUOG6L333jPy9QRrybjuJ4Ogjk+0Lw8A9BdxcXEaMmSI4uLYBAhn+3qelJlk/fjFY6XsUC+sEEGiZQa4XNJ38qVrLL67iHFJ3ymQ4ng3AkQk/tMNo3Hjxmnbtm1qbW1Va2ur9uzZo8WLF6u9vV3V1dWKiYnRjTfeeOH43Nxcvfrqqxd9SNIDDzygV199tc+vdxCq6xKlf/mEtRcQ38yTvjrK/kwA0B/U1dVp2bJljt7WDkhSSrz0L9Ok4RbKgQdHSw+NsT9TfxBNM2BiivT0VCnpCp3JNTHS6snStOv6JheA8HN2NdpPHD58WIFAQGPGjFFS0kfvtAcOHKg77rij1zU5OTmXfKy/Gj5Q+s9PS1vfk7ZUS7UfuzDxgBjpzhukL4yQbhxiLCIA9Lm2tjaVl5froYceMh0FsF1msvTc7dK2WmlLlVTV1vPxz2VK9+VIBalG4hkRbTNgapr08xnSC9XSSzVS09mejxeNlBbkSMOiYDcK4GQUCX3g4MGDknqe1uBU7gFS0SjpyyOld1ul5rNSfKyUlSxdG8o9gQAAQERJjpO+OEL6Qk73a4Ev/UYKSHJJ+odJZrOhb6QnSg+P69518m6rtPB/P/p34Fs3Xmk1gEhAkdAHgi0SAufvOR3BYlxS7iDTKQAAgCkulzRqUPebx/NvIhFdromV8q7l3wHAibhGQh+Iph0JAAAAAABnY0dCHygrKzMdAQBgSFpamoqLi5WWlmY6CgADmAEAnIgiAQAAG6WmpqqoqMh0DACGMAMAOBGnNgAAYKOWlhbt2rVLLS0tpqMAMIAZAMCJKBIAALBRfX29VqxYofr6etNRABjADADgRBQJAAAAAADAMooEAAAAAABgGUUCAAAAAACwjCIBAAAbxcfHa+zYsYqPjzcdBYABzAAATsTtHwEAsNGIESO0ceNG0zEAGMIMAOBE7EgAAAAAAACWUSQAAGCjY8eOafr06Tp27JjpKAAMYAYAcCKKBAAAbBQIBHTu3DkFAgHTUQAYwAwA4EQUCQAAAAAAwDKKBAAAAAAAYBlFAgAAAAAAsIzbPwIAYKOcnByVlpYqMzPTdBQABjADADgRRQIAADZKSEjQqFGjTMcAYAgzAIATcWoDAAA2amho0MqVK9XQ0GA6CgADmAEAnIgiAQAAGzU3N2vr1q1qbm42HQWAAcwAAE5EkQAAAAAAACyjSAAAAAAAAJZRJAAAAAAAAMsoEgAAsFFMTIxuvvlmxcTwlAtEI2YAACdiogEAYCO/368333xTfr/fdBQABjADADgRRQIAAAAAALCMIgEAAAAAAFhGkQAAAAAAACyjSAAAwEZut1uzZ8+W2+02HQWAAcwAAE4UZzoAAABOlpmZqSeeeMJ0DACGMAMAOBE7EgAAsFFHR4dqa2vV0dFhOgoAA5gBAJyIIgEAABtVVVVp/vz5qqqqMh0FgAHMAABORJEAAAAAAAAso0gAAAAAAACWUSQAAAAAAADLKBIAAAAAAIBl3P4RAAAb5eXlae/evaZjADCEGQDAidiRAAAAAAAALKNIAADARjU1NXrwwQdVU1NjOgoAA5gBAJyIIgEAABv5fD4dOnRIPp/PdBQABjADADgRRQIAAAAAALCMIgEAAAAAAFhGkQAAAAAAACyjSAAAwEYZGRl6/PHHlZGRYToKAAOYAQCcKM50AAAAnGzw4MEqLCw0HQOAIcwAAE7EjgQAAGzU1NSkzZs3q6mpyXQUAAYwAwA4EUUCAAA2amxs1FNPPaXGxkbTUQAYwAwA4EQUCTbwer0qKSlRbm6uEhISNGzYMBUXF6u9vV2LFi2Sy+XS+vXrTccEAAAAACBoXCMhzA4cOKDCwkJ5PB4lJydr/Pjxqq+v19q1a3X8+HGdOnVKklRQUGA2qI3eaZE2V0m/8Uinz0rxMVLuIOnzOdKdN0jxsaYTAgAAwC6BgLTPK22u7v5f/58+75e0u1Gadp0U4zIYEMBVo0gII6/Xq7lz58rj8WjZsmV67LHH5Ha7JUlPPvmkHnnkEcXFxcnlcmnixImG04bfmS7p8TelX9X3/PyHXdIfm7o/1h6WVt8q3ZxqJiMAAADs8/4Z6f/ulQ6f7v3x4j3SKLf0/6ZIWcl9Gg1AGHFqQxgtXbpUdXV1WrJkidasWXOhRJCkkpIS5efnq7OzUzk5ORo0aJDBpOF3zi/97Z6LS4Q/d+qs9PDvpTe8fZMLAExLSkrS1KlTlZSUZDoKAAOiaQac6pD+z2uXLhHOO94qLXpNaviwT2IBsAFFQpgcOXJEmzZt0tChQ7Vq1apej5k0aZIkKT8//8LnfvOb38jlcl30EWmnPvz729Jei+XAWb9UUiF92GlvJgDoD7Kzs7Vu3TplZ2ebjgLAgGiaAU8ckE5YLAc+6JC+/YatcQDYiFMbwqS0tFR+v19FRUUaOHBgr8ckJiZK6lkknPfDH/5Qt9xyy4U/JydHzl6vs13SC9XBrWk+J+08Ic0bbkskAOg3urq65PP5lJiYqNhYLhIDRJtomQG1bdJrQd6Y4o9N0lunpfHX2pEIgJ3YkRAmZWVlkqQZM2Zc8pi6ujpJvRcJ48eP12233Xbh46abbrInqA3KGqSms8Gv21wV/iwA0N9UVlZq5syZqqysNB0FgAHRMgNerAlt3ZbqsMYA0EfYkRAmNTXd03P48N5/xd7Z2andu3dL6r1ICKfJkyfL4/HY+v/xcQPnfVsD7/yroNcdO+1X1rDh3Zf2BYAIsWDBgqCOP3nypCRpx44deuMNa/t4582bF3Qu9E/Xr6+RKyZWXf4uZWVF5zY8J34PgpkDocwAKfLmwJClpYrP+1TQ61547Y96ds5f2JAIwJWkp6dr3759Ia2lSAiT9vZ2SZLP5+v18U2bNsnr9crtdmvEiBEXPf7FL35RXq9Xqampuvvuu/WDH/xAQ4cODSmLx+PRiRMnQlobimEdner9ZI7Lc8XEqL7xfQXOdYQ9EwDY5fy8t+r884LP57O8ti9nOOx1/fmyPBCI2p+rE78HwcyBUGaAFHlzYGAgRvEhrOt0DYi4rxUARULYpKenq6mpSfv379e0adN6PNbQ0KDly5dLkiZOnCiX66Mb5w4ePFjLly/X7bffroEDB+r3v/+9Vq1apddff1379u1TQkJCSFn6UlJMV0jrAufO6IbrQitLAMCUYK9hc/6NQ2JiouW1mZmZQedCP3X+Od/lit6fqwO/B8HMgVBmgBR5cyCus/dfpl1J7Ln2iPtaAae4mveNrkCAfeXhsHTpUq1bt07Dhg3Trl27NGbMGElSRUWFFi5cqHfffVfnzp3Tww8/rPXr11/273r55Zd199136z/+4z/0ta99rS/iX5XDTdID5cGvm3WD9IPJ4c8DAHaqqKgI6vijR4/q/vvv13PPPae8vDxLa2699dZQoqEfmrJV8qv7olR77zadxgwnfg+CmQOhzAAp8ubAC9XSqj8Gv27JOOkvR4c9DgCbcbHFMCkpKVFqaqpqa2s1YcIE3XTTTRo9erSmTJmikSNHaubMmZKsXR9hzpw5Sk5ODvl8lb42YUhoV9tdkBPuJADQ/+Tm5mrnzp3Kzc01HQWAAdEyAwqzpOQg9zoPiJHudv5dMQFHokgIk6ysLJWXl+uuu+5SQkKCqqurlZKSog0bNmj79u16++23JQV3ocWPnwLR3z00Jrjjb0mVJqXakwUA+pO4uDgNGTJEcXGcTQhEo2iZAUlx0sJRwa1ZkCOlhHJhBQDGUSSE0bhx47Rt2za1traqtbVVe/bs0eLFi9Xe3q7q6mrFxMToxhtvvOLfs3XrVrW3t2vKlCl9kDo8PpUuLbd4x8oxg6Snbv3olEkAcLK6ujotW7bswi2AAUSXaJoBi8ZI91rcYfCZDKl4vL15ANjH2dVoP3H48GEFAgGNGTNGSUlJPR776le/qpEjR+qWW265cLHFJ598UgUFBfrSl75kKHFovjhCuj5B+rej0jutFz+eGCv9RZa0dELwW98AIFK1tbWpvLxcDz30kOkoAAyIphngcknfzpdGDZL+8x2p8czFx6RcI31xZPd1EWL5pRIQsXg71wcOHjwoqffTGiZMmKDnn39ezzzzjHw+n7KysvTQQw/pscce0zXXXNPXUa/aHRnSp9OlP5ySyhqk59/t/rxL0o7PSgMHGI0HAAAAG7lc0pdHSvflSOWNUoVXaj/XfepDfor0mRu6r40AILJRJPSByxUJjz76qB599NG+jmQrl0sqSO3++K93u6/U7BIlAgAAQLSIi5FmZHR/AHAe+sA+cLkiAQAAAACASMKOhD5QVlZmOgIAwJC0tDQVFxcrLS3NdBQABjADADgRRQIAADZKTU1VUVGR6RgADGEGAHAiTm0AAMBGLS0t2rVrl1paWkxHAWAAMwCAE1EkAABgo/r6eq1YsUL19fWmowAwgBkAwIkoEgAAAAAAgGUUCQAAAAAAwDKKBAAAAAAAYBlFAgAANoqPj9fYsWMVHx9vOgoAA5gBAJyI2z8CAGCjESNGaOPGjaZjADCEGQDAidiRAAAAAAAALKNIAADARseOHdP06dN17Ngx01EAGMAMAOBEFAkAANgoEAjo3LlzCgQCpqMAMIAZAMCJKBIAAAAAAIBlFAkAAAAAAMAyigQAAAAAAGAZt38EAMBGOTk5Ki0tVWZmpukoAAxgBgBwIooEAABslJCQoFGjRpmOAcAQZgAAJ+LUBgAAbNTQ0KCVK1eqoaHBdBQABjADADgRRQIAADZqbm7W1q1b1dzcbDoKAAOYAQCciCIBAAAAAABYRpEAAAAAAAAso0gAAAAAAACWUSQAAGCjlJQUPfDAA0pJSTEdBYABzAAATkSRAACAjWJiYjRgwADFxPCUC0QjZgAAJ2KiAQBgI6/Xqx//+Mfyer2mowAwgBkAwIkoEgAAAAAAgGUUCQAAAAAAwDKKBAAAAAAAYBlFAgAANnK73Zo9e7bcbrfpKAAMYAYAcKI40wEAAHCyzMxMPfHEE6ZjADCEGQDAidiRAACAjTo6OlRbW6uOjg7TUQAYwAwA4EQUCQAA2Kiqqkrz589XVVWV6SgADGAGAHAiigQAAAAAAGAZRQIAAAAAALCMIgEAAAAAAFhGkQAAAAAAACzj9o8AANgoLy9Pe/fuNR0DgCHMAABOxI4EAAAAAABgGUUCAAA2qqmp0YMPPqiamhrTUQAYwAwA4EQUCQAA2Mjn8+nQoUPy+XymowAwgBkAwIkoEgAAAAAAgGUUCQAAAAAAwDKKBAAAAAAAYBlFAgAANsrIyNDjjz+ujIwM01EAGMAMAOBEcaYDAADgZIMHD1ZhYaHpGAAMYQYAcCJ2JAAAYKOmpiZt3rxZTU1NpqMAMIAZAMCJKBJs4PV6VVJSotzcXCUkJGjYsGEqLi5We3u7Fi1aJJfLpfXr15uOCQDoA42NjXrqqafU2NhoOgoAA5gBAJyIUxvC7MCBAyosLJTH41FycrLGjx+v+vp6rV27VsePH9epU6ckSQUFBWaD2uxwk/Qbj+T/058Dkj7slJL4Nw4AokKjT9pRJ3l83X++PlEqzJTSk8zmAgAAV4+3dWHk9Xo1d+5ceTweLVu2TI899pjcbrck6cknn9QjjzyiuLg4uVwuTZw40XBae7zWKD17THrrdM/PByQVviLdnS19M09K5N88AHCk2jZp7RHpfz1SV6DnY/96RPpUuvTX46WcgWbyAQCAq8epDWG0dOlS1dXVacmSJVqzZs2FEkGSSkpKlJ+fr87OTuXk5GjQoEEGk9rjxWrpW3suLhHOa++USt+Vvv47qfVcXyYDAPSFY83S116TXm24uESQunep/dYjPVjevXMNAABEJoqEMDly5Ig2bdqkoUOHatWqVb0eM2nSJElSfn7+RY/94he/0Cc+8QklJydr8ODBmj59ug4fPmxr5nD63Ulp1R+7dx5cyVunpUcqpICVgwEgwiUlJWnq1KlKSnL2nv4PzkhLX5dOn73ysS3nuovn98/YnwswLVpmAIDoQpEQJqWlpfL7/SoqKtLAgb3v10xMTJR0cZGwdu1afeELX9AnP/lJbd26VaWlpZo1a5Z8Pp/tucPlR8eslQjn7fVKB07ZFgcA+o3s7GytW7dO2dnZpqPY6oUa6YMO68efOiv9vMq+PEB/ES0zAEB04Uz1MCkrK5MkzZgx45LH1NXVSepZJBw/flzLly/X008/rSVLllz4/F/8xV/YlDT8jp6WDoawRXVztXRzarjTAED/0tXVJZ/Pp8TERMXGxpqOY4tOv/SLmuDX/XeN9NAY6RpnflsASdExAwBEH3YkhElNTfcrqOHDh/f6eGdnp3bv3i2pZ5HwH//xHxowYIAeeugh+0Pa5LeeENc1cHoDAOerrKzUzJkzVVlZaTqKbY6cDu00hVNnpT9yrQQ4XDTMAADRhx0JYdLe3i5JlzwdYdOmTfJ6vXK73RoxYsSFz//ud7/T2LFj9Z//+Z9auXKlamtrNXr0aH3ve9/Tl7/85ZCyTJ48WR5PiO/uQ+D+wt8r+Y6vBb2uwy8NG5krneMkWQCRY8GCBUEdf/LkSUnSjh079MYbb1haM2/evKBzmRR/40wN+eZzIa394l8+pI4DO8KcqP+4fn2NXDGx6vJ3KSur9182OJ0TvwfBzIFQZoAUeXMAQORJT0/Xvn37QlpLkRAm6enpampq0v79+zVt2rQejzU0NGj58uWSpIkTJ8rlcvV47MSJE3r00Ue1evVqDRs2TP/+7/+ur3zlK0pLS9OsWbOCzuLxeHTixImr+4KCkNnkVXII6wJ+v07UvMu2BAAR5XxxbNX5gtnn81le25czPBzcKXUaEuJab0OdWiLs6w3G9eef4wKBiPu5hosTvwfBzIFQZoAUeXMAQHShSAiTWbNm6ciRI1q9erXuvPNOjRkzRpJUUVGhhQsXyuv1SpIKCgp6rPP7/Wpra9PGjRt17733SpI+85nP6K233tLf//3fh1QkpKenX9XXEqyE5tqQ1nXWH1XmDTeEOQ0A2Cs5Objq9Pwbh8TERMtrMzMzg85lUkxnkwJd5+SKHRDUuoC/S4PPeuWOsK83KOd/eeByRdzPNWwc+D0IZg6EMgOkyJsDACLP1bxvdAUC/Do4HOrq6lRQUKAPPvhAcXFxysvL05kzZ/TOO++osLBQfr9fO3fu1LPPPtvjegi33Xab9uzZo9bW1h53e1i2bJl++tOf6oMPPjDx5QSlo0v6i19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VSkqKXnrpJd17771KSEhQdna2JKlXr15asGCBvvrVr6pHjx7685//rCVLlujdd9/V3r17FRUV+P3REhODfUkPtLPz/024XEpKSjKbBbby1X+msMjAr0ge46/vcn83Ar2Gzfk3DtHR0ZbXdrXfSUT3pheJfr+/xXOhFX26+9Wji+0vEMgcCGYGSF1vDuASQv31UKjvfyd2Oe8bXX4/N1+6lLlz52rlypVKSUnRtm3bNHjwYEnSnj17NGvWLH388cc6d+6cHn30Ua1atap53eDBg3X06FFt2LBB06ZNk9T0AisrK0u9e/fW22+/fdE/84033tDtt9+ul19+Wd/+9rdt3T/ATmPzJZ+abg+z+3bTaWCnp/ZL+Z8EtsYl6fWbpaQudm3ZPXv2BLT94cOHAz4/+tprrw0mmjE+vzRju/RJYAdr6MoYacPNUnhg3QNgXCBzIJgZIHW9OYCLC/XXQ6G+/07F7R/bkJubq/j4eJWUlGjEiBG6+uqrNWjQII0dO1ZXXXWVJk2aJOnCOzbExTUd5Hn+yANJcrlcys7O1sGDBy/5Z06ZMkWxsbFBn68CAB1tRlrgayb063olQjAyMjK0ZcsWZWRkmI5imzCXND0t8HX/NIASAc4XCjMAQOihSGhDcnKyduzYodtuu01RUVEqKipSXFycVq9erU2bNunDDz+UdGGRMGLEiIv+zLNnz1r6swM9PBQATBneW/paAEcrRoVLDw+xLU6nEhERoT59+igiwtlnE96RKqUHcPeF1NimIgFwulCZAQBCC0WCBcOGDdPGjRtVU1Ojmpoa7dq1S7Nnz1ZdXZ2KiooUFhamkSNHtlhzxx13SJLeeuut5u/5fD5t3bq1zUPV8vPzVVdXp7Fjx7b/zgCATZ7Ikm7s1/Z20eHS8mulob3tTtQ5lJaWav78+c23CnaqHt2kldc1FQRtSY6RVlwn9exufy7AtFCZAQBCC9XoZSgoKJDf79fgwYMVE9PyImNTp07VjTfeqNmzZ+vUqVNKTU3Viy++qIKCAm3durV5u29+85u66qqr9JWvfKX5YovLli1TVlaWvvGNb3T0LgFA0LqHS89eK60rktYVXni+fIRLuvlK6duDpIyeRiIaUVtbqx07dhi5HXBHS4yRXr5ReuUj6TefSFWft3y8Zzfp9lTp/gypT6SZjEBHC6UZACB0UCRchgMHDki68LQGqem0hPz8fC1cuFCLFi1SdXW1MjMz9eabbzZfV0FqOgXiF7/4hZ5//nl5vV4lJyfroYce0hNPPKHu3fmoBkDXEhEm3XOV9PV0aZ9HeuTPkl9NF1bcdIsUH/iNaNDF9O4uzR0ufWeItOOEVOGV5Jf6RUs3Jjad1gIAALo2ioTLcKkiQZJ69+6t1atXa/Xq1Rf9GY899pgee+wxW/IBgClhLunahKYC4XyRQIkQWiLDpewrTacAAAB24BoJl6GtIgEAAAAAAKfhiITLsH37dtMRAACdXEJCgubNm6eEhATTUQAYwAwA4EQUCQAA2Cg+Pl4zZ840HQOAIcwAAE7EqQ0AANiourpa27ZtU3V1tekoAAxgBgBwIooEAABsVFZWpkWLFqmsrMx0FAAGMAMAOBFFAgAAAAAAsIwiAQAAAAAAWEaRAAAAAAAALKNIAADARpGRkRoyZIgiIyNNRwFgADMAgBNx+0cAAGyUnp6uNWvWmI4BwBBmAAAn4ogEAAAAAABgGUUCAAA2OnLkiCZMmKAjR46YjgLAAGYAACeiSAAAwEZ+v1/nzp2T3+83HQWAAcwAAE5EkQAAAAAAACyjSAAAAAAAAJZRJAAAAAAAAMu4/SMAADZKS0tTXl6ekpKSTEcBYAAzAIATUSQAAGCjqKgoDRw40HQMAIYwAwA4Eac2AABgo/Lyci1evFjl5eWmowAwgBkAwIkoEgAAsFFVVZXy8/NVVVVlOgoAA5gBAJyIIgEAAAAAAFhGkQAAAAAAACyjSAAAAAAAAJZRJAAAYKOwsDBdc801CgvjKRcIRcwAAE7ERAMAwEY+n0/vv/++fD6f6SgADGAGAHAiigQAAAAAAGAZRQIAAAAAALCMIgEAAAAAAFhGkQAAgI3cbrcmT54st9ttOgoAA5gBAJwownQAAACcLCkpSU899ZTpGAAMYQYAcCKOSAAAwEb19fUqKSlRfX296SgADGAGAHAiigQAAGxUWFio6dOnq7Cw0HQUAAYwAwA4EUUCAAAAAACwjCIBAAAAAABYRpEAAAAAAAAso0gAAAAAAACWcftHAABsNHToUO3evdt0DACGMAMAOBFHJAAAAAAAAMsoEgAAsFFxcbEeeOABFRcXm44CwABmAAAnokgAAMBGXq9XBw8elNfrNR0FgAHMAABORJEAAAAAAAAso0gAAAAAAACWUSQAAAAAAADLKBIAALBR//799eSTT6p///6mowAwgBkAwIkiTAcAAMDJevXqpZycHNMxABjCDADgRByRAACAjSorK7Vu3TpVVlaajgLAAGYAACeiSAAAwEYnTpzQs88+qxMnTpiOAsAAZgAAJ6JICIDH41Fubq4yMjIUFRWllJQUzZs3T3V1dXrwwQflcrm0atUq0zEBAAAAALAN10iwaP/+/crJyVFFRYViY2M1fPhwlZWVacWKFTp27JhOnz4tScrKyjIbFAA6gY9rpF8XSb6//bNP0sYS6ZYrpchwg8HQIfx+aa9HeqNEqvA2fa9flHRbijQ2QQpzmc0HAIDdPmuQflsq7TghVZ+TIsOkIb2kfxogpfQwne7yUSRY4PF4NHXqVFVUVGj+/Pl64okn5Ha7JUnLli3TwoULFRERIZfLpVGjRhlOCwDmVHwmPblf2uO58LF/e1967qD0z0Okb6RLLt5MOtJej7Tkr1Jx7YWPbT4upcZKC0dJ4xI6PhsAAHbz+6WXjkprPpLqGlo+ttsjrTkm3dBP+mGmFB9lJmN74NQGC+bOnavS0lLNmTNHy5cvby4RJCk3N1eZmZlqaGhQWlqaevbsaTApAJhTWid9+0+tlwjnVZ2T/v2gtOpQx+UyLSYmRuPGjVNMTIzpKLbbUSHN+XPrJcJ5n9RJc9+Vfl/ecbkAk0JpBgChzu+XfvQX6b8OX1gifNmfTkgP/EnynO24bO2NIqENhw4d0tq1a9W3b18tWbKk1W1Gjx4tScrMzLzgsQ0bNuj6669XbGysevXqpQkTJqigoCDo7QCgM2rwSfN2SZ9afEL8n4+kTSX2ZuosUlNTtXLlSqWmppqOYqtPaqXH9kkN/ra3bfRLP9jXdAoM4HShMgMASHkfS69/Ym3b459J39/dVD50RRQJbcjLy5PP59PMmTPVo0frJ7NER0dLurBIWLFihe6++27dcMMNys/PV15enrKzs+X1eoPaDgA6qz9UXPpT6Nb8z0dd98kzEI2NjaqtrVVjY6PpKLb6ZaF0NoBd/Nwnrf3YvjxAZxEqMwAIdQ0+6dVjga354Iy075QtcWzHNRLasH37dknSxIkTL7pNaWmppJZFwrFjx7RgwQI999xzmjNnTvP3b7311hZrrW4HAJ3Z+qLA13xcI71/WvpKfLvH6VSOHj2q++67T6+88oqGDh1qOo4tvA1NF1YM1Jul0r8Ml3p0a/9MQGcRCjMAQNNFFU8GcarCukJpTN/2z2M3jkhoQ3FxsSRpwIABrT7e0NCgnTt3SmpZJLz88svq1q2bHnrooUv+fKvbAUBn5fNL+y5xXYRL2fNp+2aBGYerLn0u6MV4G6WCM+0eBwCADnepa0TZsc40jkhoQ11dnSRd9DSDtWvXyuPxyO12Kz09vfn777zzjoYMGaJXX31VixcvVklJiQYNGqTHH39c99xzT8DbBWLMmDGqqKgIai3QnvqtKpYrLFyNvkYlJ7dexqHrc0XGqt9zR4Ja+/z/+5me/tWT7ZzIXjNmzAho+5MnT0qSNm/erH379llac+eddwacy6TIkZPU55FXglp777cfUv3+ze2cCLBXIHMgmBkgdb05gIsL9ddDobL/ve57TtHX3RXwuqqzDUpOTmv/QBYkJiZq7969Qa2lSGhDYmKiKisr9d5772n8+PEtHisvL9eCBQskSaNGjZLrS/cyKy8v1/Hjx/XYY49p6dKlSklJ0UsvvaR7771XCQkJys7ODmi7QFRUVOj48eOXsddA++h3/gR4v5+/k04WFqZ+QS6t9lSorIv93ThfMFt1voj2er2W13a1/1569C5WnyDXfnq8WDVdbH+BQOZAMDNA6npzABcX6q+HQmX/w06fVHQQ6xq9tV3y90KR0Ibs7GwdOnRIS5cu1S233KLBgwdLkvbs2aNZs2bJ42k6FiUrK6vFOp/Pp9raWq1Zs0bTpk2TJN1888364IMP9PTTTzcXBFa3C0RiYmJwOwu0t/PlmsulpKQks1lgq88/3qvuV42xvL3f75fL5VLkycNd7u9GbGxsQNuff+MQHR1teW1X+524zp6Ur/4zhUUGdns7/+dn1euzMvXsYvsLBDIHgpkBUtebA7iEUH89FCL7373ig6DWNRTuNfZ7uZz3jS6/PxSumR280tJSZWVl6dSpU4qIiNDQoUN19uxZffTRR8rJyZHP59OWLVv005/+tMV1Dq677jrt2rVLNTU1Le72MH/+fP385z/XqVOnAtoO6IrG5ks+NV2MZfftptPATm+WSI+/H9ialFjpV5OkMFfb23Yme/bsCWj7hoYG1dTUyO12KyLCWn9/7bXXBhPNqB/9RdpQHNiaqSnSE9fYkwewUyBzIJgZIHXNOYDWhfrroVDZ/88bpSlbpdOfB7buP8dJE4I9tNMgLrbYhuTkZO3YsUO33XaboqKiVFRUpLi4OK1evVqbNm3Shx9+KOnCWz+OGDHioj/z7NmzAW8HAJ3ZzVdKiQEezzfzqq5XIgQjIiJCffr0CegNRFf09XQpIoB/n+Eu6etX2ZcH6CxCZQYAoa57uHR3gM9rV7ml8VfYk8duFAkWDBs2TBs3blRNTY1qamq0a9cuzZ49W3V1dSoqKlJYWJhGjhzZYs0dd9whSXrrrbeav+fz+bR169YWDbPV7QCgM4sMl54fJ/W0eBu/aanS9DRbI3UapaWlmj9/fvOtgp0qo2fT0QVWugSXpP+bKQ3tZXcqwLxQmQEApG8Pkm7ub23b+EjpP8Z23Q9VqEYvQ0FBgfx+vwYPHqyYmJbnhU6dOlU33nijZs+erVOnTik1NVUvvviiCgoKtHXr1oC3A4DOLqOn9NIN0g/2SR9Wt75NZJh0X4b00JAvTpl0utraWu3YsSMkbvObkyxFh0vP/FXy1Le+TXyktOBqKfvKjs0GmBJKMwAIdeEu6UejpSs+kNYXSed8rW93dZ+m7a4M7NJCnQpFwmU4cOCApAtPa5Akl8ul/Px8LVy4UIsWLVJ1dbUyMzP15ptvatKkSQFvBwBdQbpbeu0fpL+cln5VJB2rkT73Sb26S5P6N50T37O76ZSw0039pRv6SX+okPI/kd45+cVjPxrd9PegG8dDAgAcKiJMmj+y6eiE/E+ktyukA5VfPP4/N0ojgr3VUSdCkXAZLlUkSFLv3r21evVqrV69+pI/x+p2ANAVuFxSVnzTF0JTRFjTEQfZV7a8yNbXnHuxbgAAWoiLlL41qOnry8+FTigRJK6RcFnaKhIAAAAAAHAajki4DNu3bzcdAQDQySUkJGjevHlKSEgwHQWAAcwAAE5EkQAAgI3i4+M1c+ZM0zEAGMIMAOBEnNoAAICNqqurtW3bNlVXX+RWFgAcjRkAwIkoEgAAsFFZWZkWLVqksrIy01EAGMAMAOBEFAkAAAAAAMAyigQAAAAAAGAZRQIAAAAAALCMIgEAABtFRkZqyJAhioyMNB0FgAHMAABOxO0fAQCwUXp6utasWWM6BgBDmAEAnIgjEgAAAAAAgGUUCQAA2OjIkSOaMGGCjhw5YjoKAAOYAQCciCIBAAAb+f1+nTt3Tn6/33QUAAYwAwA4EUUCAAAAAACwjCIBAAAAAABYRpEAAAAAAAAs4/aPAADYKC0tTXl5eUpKSjIdBYABzAAATkSRAACAjaKiojRw4EDTMQAYwgwA4ESc2gAAgI3Ky8u1ePFilZeXm44CwABmAAAnokgAAMBGVVVVys/PV1VVlekoAAxgBgBwIooEAAAAAABgGUUCAAAAAACwjCIBAAAAAABYRpEAAICN4uLidP/99ysuLs50FAAGMAMAOBFFAgAANgoLC1O3bt0UFsZTLhCKmAEAnIiJBgCAjTwej1588UV5PB7TUQAYwAwA4EQUCQAAAAAAwDKKBAAAAAAAYBlFAgAAAAAAsIwiAQAAG7ndbk2ePFlut9t0FAAGMAMAOFGE6QAAADhZUlKSnnrqKdMxABjCDADgRByRAACAjerr61VSUqL6+nrTUQAYwAwA4EQUCQAA2KiwsFDTp09XYWGh6SgADGAGAHAiigQAAAAAAGAZRQIAAAAAALCMIgEAAAAAAFhGkQAAAAAAACzj9o8AANho6NCh2r17t+kYAAxhBgBwIo5IAAAAAAAAllEkAABgo+LiYj3wwAMqLi42HQWAAcwAAE5EkQAAgI28Xq8OHjwor9drOgoAA5gBAJyIIgEAAAAAAFhGkQAAAAAAACyjSAAAAAAAAJZRJAAAYKP+/fvrySefVP/+/U1HAWAAMwCAE0WYDgAAgJP16tVLOTk5pmMAMIQZAMCJOCIBAAAbVVZWat26daqsrDQdBYABzAAATkSRAACAjU6cOKFnn31WJ06cMB0FgAHMAABORJEQAI/Ho9zcXGVkZCgqKkopKSmaN2+e6urq9OCDD8rlcmnVqlWmYwIAYFyjX9rrkfx/+2f/374HIHR4zkpbj0uvF0tbjksVn5lOBKC9cI0Ei/bv36+cnBxVVFQoNjZWw4cPV1lZmVasWKFjx47p9OnTkqSsrCyzQQEAMOizBumXhdKvi6WyL71p8Eu6Y5s0PU26O12K5RUI4FgHK6XXjknby1sWiGGSbkiUZl4lje5rLB6AdsARCRZ4PB5NnTpVFRUVmj9/vsrLy/Xee++poqJCS5cu1aZNm7Rnzx65XC6NGjXKdFwAAIw4XS99Z6e06lDLEuG8Cq/0k0PSQ39q+qQSgPO88Yn0wJ+krWUXHoXkk/THCunhd6RfHDMSD0A7oUiwYO7cuSotLdWcOXO0fPlyud3u5sdyc3OVmZmphoYGpaWlqWfPngaTAgA6m5iYGI0bN04xMTGmo9jqbIP03V3Soaq2t/2wWpr7btPRC4DThcoMkKQ/lEtP7Zd8bZzG5Jf0HwVNpQOArokioQ2HDh3S2rVr1bdvXy1ZsqTVbUaPHi1JyszMvOCxDRs26Prrr1dsbKx69eqlCRMmqKCgoPnxm266SS6Xq9Wvhx9+2J6dAgB0mNTUVK1cuVKpqammo9hqY6n0wRnr239YLf2GNxEIAaEyAxr90r8f/OK6KFY8XyCdbbQtEgAbcYZiG/Ly8uTz+TRz5kz16NGj1W2io6MlXVgkrFixQvPnz9f3vvc9Pf3006qvr9euXbvk9Xqbt3nhhRdUXV3dYt2mTZu0ePFiTZkypZ33BgDQ0RobG+X1ehUdHa3w8HDTcWzh90vrCwNft65Q+ka65HK1fyagswiFGSBJO09I5d62t/uyqnPStjJpSoo9mQDYhyKhDdu3b5ckTZw48aLblJaWSmpZJBw7dkwLFizQc889pzlz5jR//9Zbb22xdvjw4Rf8vB/96EdKSEjQ5MmTLys7AMC8o0eP6r777tMrr7yioUOHmo5ji4+qpY9qAl/3SZ1UcEYa2afdIwGdRijMAEnaXBrcujdLKBKArogioQ3FxcWSpAEDBrT6eENDg3bu3CmpZZHw8ssvq1u3bnrooYcC+vM+/fRT/fa3v9UjjzyiiIjg/vWMGTNGFRUVQa0F2lO/VcVyhYWr0deo5OTW/xsCupoZM2YEtP3JkyclSZs3b9a+ffssrbnzzjsDzmVS9+E3KW7Oq0GtvWPmA6r/61vtnAiwVyBzIJgZIHW9ORD3/V+pe8a4gNft/OsRJd99sw2JOo9Qfz0U6vsvdd7fQWJiovbu3RvUWoqENtTV1UlSi9MRvmzt2rXyeDxyu91KT09v/v4777yjIUOG6NVXX9XixYtVUlKiQYMG6fHHH9c999xz0T8vLy9PDQ0NmjVrVtCZKyoqdPz48aDXA+2ln/9vZ0r6/fydhGOcf16w6vzzh9frtby2q/334u57QnFBrj316UlVdbH9BQKZA8HMAKnrzYFYr1fdg1h3rv5sl9vXQIX666FQ33/Jmb8DioQ2JCYmqrKyUu+9957Gjx/f4rHy8nItWLBAkjRq1Ci5vnSSZ3l5uY4fP67HHntMS5cuVUpKil566SXde++9SkhIUHZ2dqt/3po1azRs2DCNGTPmsjIDncL5/yZcLiUlJZnNArST2NjYgLY//8YhOjra8tqu9t9LuKvpjZLf72/xXHgp57ftrc/Uo4vtLxDIHAhmBkhdcA7UnghqXVh1eZfb14CF+uuhUN9/qdP+Di7nfSNFQhuys7N16NAhLV26VLfccosGDx4sSdqzZ49mzZolj8cjScrKymqxzufzqba2VmvWrNG0adMkSTfffLM++OADPf30060WCYcPH9bevXv14x//+LIyB3t4CtDexuY33TM6PCy8+VoiQFe3Z8+egLY/fPiw8vLylJOTY/n86Oeffz6IZGbN3im9d8r6VRNdLpdG9pF+vme7jakAewQyB4KZAVLXmwP7T0n/vDPwdS88NFlf/aGzXyOE+uuhUN9/yZm/A27/2Ibc3FzFx8erpKREI0aM0NVXX61BgwZp7NixuuqqqzRp0iRJF96xIS6u6SDPLxcGLpdL2dnZOnjwYKt/1po1a+RyuTRz5kyb9gYA0NEyMjK0ZcsWZWRkmI5iqxlpHbMG6GpCZQZkxkkZ7sDWJEZLE/rZkweAvSgS2pCcnKwdO3botttuU1RUlIqKihQXF6fVq1dr06ZN+vDDDyVdWCSMGDHioj/z7NmzF3zP7/frtdde00033eT4+wwDQCiJiIhQnz59gr6Abldx85XSxACOkLyxnzS58xzdCdgmVGaAyyX9MEuKtPjuIsIlPZElhXP7V6BLokiwYNiwYdq4caNqampUU1OjXbt2afbs2aqrq1NRUZHCwsI0cuTIFmvuuOMOSdJbb31xJWqfz6etW7fq2muvveDP+OMf/6ji4uLLusgiAKDzKS0t1fz58x1zKOPFhLukp0dLk/q3ve1XE6Ulo6UIXoUgBITKDJCkEX2kFddJ7m6X3i4qXHr2WunahI7JBaD9ObsatVlBQYH8fr8GDx6smJiYFo9NnTpVN954o2bPnq1Tp04pNTVVL774ogoKCrR169YLftaaNWsUHR0d8G3FAACdW21trXbs2BHw7YC7oqhw6Zkx0s4T0roi6c8nJf+XHr8uoel0hhsT+RQSoSOUZoAkje4r/XKitKG46evTvzsQ91sZ0vQ0qX9Mq8sBdBEUCZfhwIEDki48rUFquh5Cfn6+Fi5cqEWLFqm6ulqZmZl68803m6+rcN7Zs2e1fv16TZs2TW53gCeXAQDQiYS5moqCGxMlz1np5FnJ75euiJYSokynA9AREqKk2UOkBwZJxbXSN/7QVCq6JM0ZbjgcgHZBkXAZLlUkSFLv3r21evVqrV69+pI/JyoqSmfOnGnveAAAGNU3qukLQGiKCJMG9mwqEM4XCQCcgbMTL0NbRQIAAAAAAE7DEQmXYft27n0NALi0hIQEzZs3TwkJXFUMCEXMAABORJEAAICN4uPjNXPmTNMxABjCDADgRJzaAACAjaqrq7Vt2zZVV1ebjgLAAGYAACeiSAAAwEZlZWVatGiRysrKTEcBYAAzAIATUSQAAAAAAADLKBIAAAAAAIBlFAkAAAAAAMAyigQAAGwUGRmpIUOGKDIy0nQUAAYwAwA4Ebd/BADARunp6VqzZo3pGAAMYQYAcCKOSAAAAAAAAJZRJAAAYKMjR45owoQJOnLkiOk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PmY4BwBBmAAAn4ogEAABsVF9fr5KSEtXX15uOAsAAZgAAJ6JIAADARoWFhZo+fboKCwtNRwFgADMAgBNRJAAAAAAAAMsoEgAAAAAAgGUUCQAAAAAAwDKKBAAAAAAAYBm3fwQAwEZDhw7V7t27TccAYAgzAIATcUQCAAAAAACwjCIBAAAbFRcX64EHHlBxcbHpKAAMYAYAcCKKBAAAbOT1enXw4EF5vV7TUQAYwAwA4EQUCQAAAAAAwDKKBAAAAAAAYBlFAgAAAAAAsIwiAQAAG/Xv319PPvmk+vfvbzoKAAOYAQCcKMJ0AAAAnKxXr17KyckxHQOAIcwAAE7EEQkAANiosrJS69atU2VlpekoAAxgBgBwIooEAABsdOLECT377LM6ceKE6SgADGAGAHAiioQAeDwe5ebmKiMjQ1FRUUpJSdG8efNUV1enBx98UC6XS6tWrTIdEwAAAAAA23CNBIv279+vnJwcVVRUKDY2VsOHD1dZWZlWrFihY8eO6fTp05KkrKwss0EBoBOoOSdtKpE+rpE+90m9ukmTrpRG9ZFcLtPp0BGO10lvlkq+v/2zT1JpnZQcazIVAABoDxQJFng8Hk2dOlUVFRWaP3++nnjiCbndbknSsmXLtHDhQkVERMjlcmnUqFGG0wKAObXnpBUfNL2BPNvY8rHXPpYG95T+v6HSjYlm8sF+RbXS8wXSzhOS/+8eu/N30vgrpO+OkK5yG4kHAADaAac2WDB37lyVlpZqzpw5Wr58eXOJIEm5ubnKzMxUQ0OD0tLS1LNnT4NJAcCcM59LD+2Ufl18YYlw3ofV0vd3S78q6tBoRsXExGjcuHGKiYkxHcV2H5yRHtgh/amVEkFq+t47J5u2OXC6g8MBhoTSDAAQOigS2nDo0CGtXbtWffv21ZIlS1rdZvTo0ZKkzMzMCx7bsGGDrr/+esXGxqpXr16aMGGCCgoKWmyzbds2XXfddYqKitIVV1yhhx9+WFVVVe2/MwBgE59f+j+7paPVbW/rl/TMX5veUIaC1NRUrVy5Uqmpqaaj2MpzVvruu1L1uba3rW2QvrtLqvDanwswLVRmAIDQQpHQhry8PPl8Ps2cOVM9evRodZvo6GhJFxYJK1as0N13360bbrhB+fn5ysvLU3Z2trzeL145vf3225o8ebKSkpK0YcMG/ehHP9L69es1bdo0+f2tfZ4DAJ3P7k+l/QF8wuyX9OIR2+J0Ko2NjaqtrVVj40UO03CI9UXS6c+tb191TlpXaFscoNMIlRkAILRwjYQ2bN++XZI0ceLEi25TWloqqWWRcOzYMS1YsEDPPfec5syZ0/z9W2+9tcXap556SoMGDdK6desUFtbU68THx2v69OnatGmTpkyZ0m77AgB2WV8U+Jq/VkpHqqQhvdo9Tqdy9OhR3XfffXrllVc0dOhQ03Fs0eCTNhQHvu43n0izh0iR4e2fCegsQmEGAAg9HJHQhuLipldGAwYMaPXxhoYG7dy5U1LLIuHll19Wt27d9NBDD13y5+/atUvZ2dnNJYIk/eM//qMk6fXXX7+c6ADQIXx+aUeQt0f/Y0X7ZoEZH5yRTtUHvu7M59KBynaPAwAAbMYRCW2oq6uTpBanI3zZ2rVr5fF45Ha7lZ6e3vz9d955R0OGDNGrr76qxYsXq6SkRIMGDdLjjz+ue+65p3m78PBwde/evcXP7Natm1wu1wXXUrBqzJgxqqjg1TmAjuGKjFW/54I7T+E/XvipnvzVU+2cyF4zZswIaPuTJ5suBrF582bt27fP0po777wz4FwmRY6cpD6PvBLU2q/f/8+q/8tv2zkRYK9A5kAwM0DqenMAF9dvVbFcYeFq9DUqObn1DyedLNT3X+q8v4PExETt3bs3qLUUCW1ITExUZWWl3nvvPY0fP77FY+Xl5VqwYIEkadSoUXJ96ebo5eXlOn78uB577DEtXbpUKSkpeumll3TvvfcqISFB2dnZkqTBgwdr165dLX7unj175Pf7dfp0cJe0rqio0PHjx4NaCwABC49QvyCXVp/+VGVdbF6dL5itOl9Ee71ey2u72gzvEVeiPkGu9ZSXqrqL7S8QyBwIZgZIXW8O4OL6nb/umd8fkv9eQ33/JWf+DigS2pCdna1Dhw5p6dKluuWWWzR48GBJTW/2Z82aJY/HI0nKyspqsc7n86m2tlZr1qzRtGnTJEk333yzPvjgAz399NPNRcLcuXN13333afHixXr44YdVWlqqRx55ROHh4S1OdwhEYiI3aAfQsc6VHVa3KwM/9zfqzCdKSkqyIZF9YmNjA9r+/BuH6Ohoy2u72u8k7Fyl/I3n5ArvFtA6v69RvT4/JXcX218gkDkQzAyQut4cwCWc/7DR5QrNf6+hvv9Sp/0dXM77RpefWwNcUmlpqbKysnTq1ClFRERo6NChOnv2rD766CPl5OTI5/Npy5Yt+ulPf9riegjXXXeddu3apZqamhZ3e5g/f75+/vOf69SpU5Ikv9+vxx57TM8995w+//xzhYeH69FHH9XOnTvVs2fP5os9AkBn9stCadmBwNbER0qbbpEiutjVevbs2RPQ9g0NDaqpqZHb7VZEhLX+/tprrw0mmlGP7ZW2lgW2ZmKi9OxYe/IAdgpkDgQzA6SuOQfQurH5kk9NF6fbfbvpNB0v1PdfcubvoIu9fOt4ycnJ2rFjh2677TZFRUWpqKhIcXFxWr16tTZt2qQPP/xQ0oW3fhwxYsRFf+bZs2eb/7/L5dIzzzwjj8ejv/zlLzpx4oT+/d//XUePHtX1119vz04BQDu7NVnqEeAxbjPSul6JEIyIiAj16dMnoDcQXdHX09ve5oI1V7V/DqCzCZUZACC0hMBLuMs3bNgwbdy4UTU1NaqpqdGuXbs0e/Zs1dXVqaioSGFhYRo5cmSLNXfccYck6a233mr+ns/n09atW1ttmN1ut0aNGqX4+Hj993//t7xer7797W/bu2MA0E56dJOWXStFuNreVpKuS5C+PcjeTJ1FaWmp5s+f33yrYKfKipceCeDslocGS2P62pcH6CxCZQYACC1Uo5ehoKBAfr9fgwcPVkxMTIvHpk6dqhtvvFGzZ8/WqVOnlJqaqhdffFEFBQXaunVr83Z79+7V1q1b9ZWvfEUNDQ3atm2bVqxYoeXLl2vgwIEdvUsAELSxCdKq8dLCvVLV5xffbnKS9MOs0DgaQZJqa2u1Y8eONm8H7AQPDJaiwqUVH0gNFzlxMtwlPTpMmsVTHEJEKM0AAKGDIuEyHDjQdELw35/WIDWdspCfn6+FCxdq0aJFqq6uVmZmpt58801NmjSpebvIyEi98cYbWrJkiRoaGnT11Vdr7dq1Ad9eDAA6gzF9m657sPW49Kti6WDlF4/dlSZNT5MyeppKh45w70BpcrL0m0+kNz6RTnglv6QroqQpqdK0VKlvlOmUAADgclAkXIZLFQmS1Lt3b61evVqrV6++6M+4+uqr9c4779iSDwBMiAqXpqY2fX354kILR5lOho4SF9l06kqonL4CAECoCZEDS+3RVpEAAAAAAIDTcETCZeDWjACAtiQkJGjevHlKSEgwHQWAAcwAAE5EkQAAgI3i4+M1c+ZM0zEAGMIMAOBEnNoAAICNqqurtW3bNlVXV5uOAsAAZgAAJ6JIAADARmVlZVq0aJHKyspMRwFgADMAgBNRJAAAAAAAAMsoEgAAAAAAgGUUCQAAAAAAwDKKBAAAbBQZGakhQ4YoMjLSdBQABjADADgRt38EAMBG6enpWrNmjekYAAxhBgBwIo5IAAAAAAAAllEkAABgoyNHjmjChAk6cuSI6SgADGAGAHAiigQAAGzk9/t17tw5+f1+01EAGMAMAOBEFAkAAAAAAMAyigQAAAAAAGAZRQIAAAAAALCM2z8CAGCjtLQ05eXlKSkpyXQUAAYwAwA4EUUCAAA2ioqK0sCBA03HAGAIMwCAE3FqAwAANiovL9fixYtVXl5uOgoAA5gBAJyIIgEAABtVVVUpPz9fVVVVpqMAMIAZAMCJKBIAAAAAAIBlFAkAAAAAAMAyigQAAAAAAGAZRQIAADaKi4vT/fffr7i4ONNRABjADADgRBQJAADYKCwsTN26dVNYGE+5QChiBgBwIiYaAAA28ng8evHFF+XxeExHAWAAMwCAE1EkAAAAAAAAyygSAAAAAACAZRQJAAAAAADAMooEAABs5Ha7NXnyZLndbtNRABjADADgRBGmAwAA4GRJSUl66qmnTMcAYAgzAIATcUQCAAA2qq+vV0lJierr601HAWAAMwCAE1EkAABgo8LCQk2fPl2FhYWmowAwgBkAwIkoEgAAAAAAgGUUCQAAAAAAwDKKBAAAAAAAYBlFAgAAAAAAsIzbPwIAYKOhQ4dq9+7dpmMAMIQZAMCJOCIBAAAAAABYRpEAAICNiouL9cADD6i4uNh0FAAGMAMAOBFFAgAANvJ6vTp48KC8Xq/pKAAMYAYAcCKKBAAAAAAAYBlFAgAAAAAAsIwiAQAAAAAAWEaRAACAjfr3768nn3xS/fv3Nx0FgAHMAABOFGE6AAAATtarVy/l5OSYjgHAEGYAACfiiAQAAGxUWVmpdevWqbKy0nQUAAYwAwA4EUUCAAA2OnHihJ599lmdOHHCdBQABjADADgRRUIAPB6PcnNzlZGRoaioKKWkpGjevHmqq6vTgw8+KJfLpVWrVpmOCQCdQtXnkv9v/98vye+/1NZwoorPpP2nmr7KPzOdBgAAtBeukWDR/v37lZOTo4qKCsXGxmr48OEqKyvTihUrdOzYMZ0+fVqSlJWVZTYoABh24LT0yyJpW1nLImHG76UZadLUFKlHN3P5YK9Gv/R2ubS+SNrtafnY6HjprnTppkQpgo8yAADosigSLPB4PJo6daoqKio0f/58PfHEE3K73ZKkZcuWaeHChYqIiJDL5dKoUaMMpwUAM/x+6cUPpdVHWn+8uFb694PSLwulFeOklB4dmw/2O9sgLXpP+mNF64/vO9X0NeEKackYKYZXIQAAdEl8HmDB3LlzVVpaqjlz5mj58uXNJYIk5ebmKjMzUw0NDUpLS1PPnj0NJgUAc/7no4uXCF9WUic98mfJc9b+TJ1BTEyMxo0bp5iYGNNRbNXolx7bd/ES4ct2npQW7pEafPbnAkwLlRkAILRQJLTh0KFDWrt2rfr27aslS5a0us3o0aMlSZmZmc3fu+mmm+RyuVr9evjhh1usLyws1O233y63260+ffrovvvu06lTp+zbKQBoZ2WfST85ZH37cq/0wmH78nQmqampWrlypVJTU01HsdXW49KOAK4l9+dPpc2l9uUBOotQmQEAQgsHFbYhLy9PPp9PM2fOVI8erR+HGx0dLallkfDCCy+ourq6xXabNm3S4sWLNWXKlObv1dTUaOLEiYqLi1NeXp68Xq9yc3M1ZcoU7dy5U2FhdD0AOr8NxV9cD8GqLaXSd4dLPbvbEqnTaGxslNfrVXR0tMLDw03Hsc36osDXrCuSpvLeCg4XKjMAQGjhXWobtm/fLkmaOHHiRbcpLW36SOXLRcLw4cN13XXXtfjav3+/EhISNHny5ObtfvrTn+r48eN6/fXXNWXKFN111136xS9+oXfffVf5+fk27RUAtK/8TwJfU++Tthxv/yydzdGjRzVp0iQdPXrUdBTbFNdK+08Hvu6DM9JH1W1uBnRpoTADAIQejkhoQ3FxsSRpwIABrT7e0NCgnTt3SmpZJPy9Tz/9VL/97W/1yCOPKCLii1/7xo0bdcMNN7Q43G38+PG66qqr9MYbb2jatGkBZx4zZowqKiycpAoA7SEiUokrjgW19If//oK+9/qP2zmQvWbMmBHQ9idPnpQkbd68Wfv27bO05s477ww4l0ndh31Vcf/yi6DWfu3u+1V/8HftnAiwVyBzIJgZIHW9OYCL67eqWK6wcDX6GpWc3Pp7CicL9f2XOu/vIDExUXv37g1qLUVCG+rq6iRJXq+31cfXrl0rj8cjt9ut9PT0i/6cvLw8NTQ0aNasWS2+/8EHH+iuu+66YPsRI0bogw8+CCpzRUWFjh8PgY/5AHQKru7RSgxybW3dZ11uXp1/XrDq/POH1+u1vLar/U56XnFacUGuPVV5RlVdbH+BQOZAMDNA6npzABfXz/+3k//8/pD89xrq+y8583dAkdCGxMREVVZW6r333tP48eNbPFZeXq4FCxZIkkaNGiWXy3XRn7NmzRoNGzZMY8aMafH9yspK9e7d+4Lt4+LidOSIhcufXyQzAHQk32dVCovpFfC6mMY6JSUl2ZDIPrGxsQFtf/6NQ3R0tOW1Xe13EhHRIEny+/2XfC78svPb9o5oUI8utr9AIHMgmBkgdb05gEs4PxddrtD89xrq+y912t/B5bxvpEhoQ3Z2tg4dOqSlS5fqlltu0eDBgyVJe/bs0axZs+TxeCRJWVlZF/0Zhw8f1t69e/XjH3fM4bvBHp4CAMFadkD6ZWFga8Jd0js/fUJ9o56wJ5RN9uzZE9D2hw8fVl5ennJycjR06FBLa55//vkgkpnj90sz35Y+rLZWIkiSy+VSeg9pz582yWL3AHQagcyBYGaA1PXmAC5ubL7kkxQeFt58bbVQEur7Lznzd8DFFtuQm5ur+Ph4lZSUaMSIEbr66qs1aNAgjR07VldddZUmTZok6dLXR1izZo1cLpdmzpx5wWN9+vTRmTNnLvj+6dOnFRcX7IGiANCxZqQFvmZSf6lvVLtH6XQyMjK0ZcsWZWRkmI5iG5cruL8DM9JFiQDHC4UZACD0UCS0ITk5WTt27NBtt92mqKgoFRUVKS4uTqtXr9amTZv04YcfSrp4keD3+/Xaa6/ppptuavX+wcOGDWv1WggffPCBhg0b1r47AwA2ucotff3il4m5QM9u0sPWP5jr0iIiItSnT58WF9p1oltTpKv7WN9+WC/p9hT78gCdRajMAAChhSLBgmHDhmnjxo2qqalRTU2Ndu3apdmzZ6uurk5FRUUKCwvTyJEjW137xz/+UcXFxRdcZPG8KVOm6E9/+lOLQ1x27dqlY8eOaerUqbbsDwDY4fsjpTsu7Esv0Ke7tPI6aUAP+zN1BqWlpZo/f75jDmW8mKhw6bmx0kgLZcKwXtLz46Ro3lchBITKDAAQWigSLkNBQYH8fr8GDRqkmJiYVrdZs2aNoqOjL3qboNmzZ6t///664447tHHjRq1fv1733HOPxo4dqzvuuMPO+ADQrsJd0v/NlJaMlr4Sf+HjPbtJM6+S1nxVGhHAJ9ddXW1trXbs2KHa2lrTUWzXO1L6r/HS90dIqa1cUy45RvrucGn1BCk+BE5rAaTQmgEAQgefBVyGAwcOSLr4aQ1nz57V+vXrNW3aNLnd7la36dmzp7Zv36558+bpG9/4hiIiIjRlyhQ999xzCguj5wHQtbhc0i1JTV8f1zR91TdKvbtLo/s2fWoNZ4uKkO4dKH3jKumvp6WHdkp+SS5Jv75ZCuOaCAAAdHkUCZehrSIhKiqq1Qsp/r2BAwdq48aN7RkNAIy7yt30hdAU5pKy4psKhPNFAiUCAADOwEfel6GtIgEAAAAAAKfhiITLsH37dtMRAACdXEJCgubNm6eEhATTUQAYwAwA4EQUCQAA2Cg+Pl4zZ840HQOAIcwAAE7EqQ0AANiourpa27ZtU3V1tekoAAxgBgBwIooEAABsVFZWpkWLFqmsrMx0FAAGMAMAOBFFAgAAAAAAsIwiAQAAAAAAWEaRAAAAAAAALKNIAADARpGRkRoyZIgiIyNNRwFgADMAgBNx+0cAAGyUnp6uNWvWmI4BwBBmAAAn4ogEAAAAAABgGUUCAAA2OnLkiCZMmKAjR46YjgLAAGYAACeiSAAAwEZ+v1/nzp2T3+83HQWAAcwAAE5EkQAAAAAAACyjSAAAAAAAAJZRJAAAAAAAAMu4/SMAADZKS0tTXl6ekpKSTEcBYAAzAIATUSQAAGCjqKgoDRw40HQMAIYwAwA4Eac2AABgo/Lyci1evFjl5eWmowAwgBkAwIkoEgAAsFFVVZXy8/NVVVVlOgoAA5gBAJyIIgEAAAAAAFhGkQAAAAAAACyjSAAAAAAAAJZRJAAAYKOwsDBdc801CgvjKRcIRcwAAE7ERAMAwEY+n0/vv/++fD6f6SgADGAGAHAiigQAAAAAAGAZRQIAAAAAALCMIgEAAAAAAFhGkQAAgI3cbrcmT54st9ttOgoAA5gBAJwownQAAACcLCkpSU899ZTpGAAMYQYAcCKOSAAAwEb19fUqKSlRfX296SgADGAGAHAiigQAAGxUWFio6dOnq7Cw0HQUAAYwAwA4EUUCAAAAAACwjCIBAAAAAABYRpEAAAAAAAAso0gAAAAAAACWcftHAABsNHToUO3evdt0DACGMAMAOBFHJAAAAAAAAMsoEgAAsFFxcbEeeOABFRcXm44CwABmAAAnokgAAMBGXq9XBw8elNfrNR0FgAHMAABORJEAAAAAAAAso0gAAAAAAACWUSQAAAAAAADLKBIAALBR//799eSTT6p///6mowAwgBkAwIkiTAcAAMDJevXqpZycHNMxABjCDADgRByRAACAjSorK7Vu3TpVVlaajgLAAGYAACeiSAAAwEYnTpzQs88+qxMnTpiOAsAAZgAAJ6JICIDH41Fubq4yMjIUFRWllJQUzZs3T3V1dXrwwQflcrm0atUq0zEBAAAAALAN10iwaP/+/crJyVFFRYViY2M1fPhwlZWVacWKFTp27JhOnz4tScrKyjIbtBM4XS/95hPp9+VS1edSZLg00C1NT5NGx0sul+mEAAAAgH28DdJvj0ubSyXf377nk/S7MukfEqUIPs5FF0eRYIHH49HUqVNVUVGh+fPn64knnpDb7ZYkLVu2TAsXLlRERIRcLpdGjRplOK05jX5p1QfS/xZK53wtH/u4Rtpa1lQoPDNGSnebyQgAAADY6ddF0n9+INU1XPjYwr1SQpT0RJZ03RUdnQxoP3RhFsydO1elpaWaM2eOli9f3lwiSFJubq4yMzPV0NCgtLQ09ezZ02BSc3x+6d/ek9Ycu7BE+LJjNdKDf5I+qu64bABgUkxMjMaNG6eYmBjTUQAYwAwILa98JP34r62XCOd9elaat0v6Q3nH5QLaG0VCGw4dOqS1a9eqb9++WrJkSavbjB49WpKUmZnZ/L2bbrpJLper1a+HH364ebvzBcXYsWMVGRkpVxc97j/vY2nzcWvbVp+TvrtL+rzR3kwA0BmkpqZq5cqVSk1NNR0FgAHMgNCx+1NpxQfWtm30Sz/YJ5V9Zm8mwC6c2tCGvLw8+Xw+zZw5Uz169Gh1m+joaEkti4QXXnhB1dUtP3bftGmTFi9erClTpjR/76OPPtKvfvUrXXvtterevbt27txpw17Yq9HfVCQEosIrbS+XJifbkwkAOovGxkZ5vV5FR0crPDzcdBwAHYwZEDp+EeDr4Xqf9Ksi6V+G2xIHsBVHJLRh+/btkqSJEydedJvS0lJJLYuE4cOH67rrrmvxtX//fiUkJGjy5MnN2331q19VeXm58vPzlZ2dbdNe2OtPJ5qKgUCtL2r3KADQ6Rw9elSTJk3S0aNHTUcBYAAzIDQcr5N2BnGHz9c/keo5ShddEEcktKG4uFiSNGDAgFYfb2hoaD6K4MtFwt/79NNP9dvf/laPPPKIIiK++LWHhbV/lzNmzBhVVFS0+8+9mB53PKYeX3s04HXvf9qg5OS09g8EADaaMWNGQNufPHlSkrR582bt27fP0po777wz4FydVb9VxXKFhavR16jk5NafS4GuJpA5EMwMkJw1B0JB1Ojb1fvBFwJeV/W5NGR8thrKDtuQqnPgeaDz/g4SExO1d+/eoNZSJLShrq5OkuT1tv6R+9q1a+XxeOR2u5Wenn7Rn5OXl6eGhgbNmjXLlpxfVlFRoePHLV6woB2knPOp9ZM+Ls0VHqGyT0/J//nZds8EAHY5/7xg1fnnD6/Xa3ltR85wu/Xz+5v+j9/vqP1CaAtkDgQzAyRnzYFQED+sXr2DXOuprlOdg/998zzgzN8BRUIbEhMTVVlZqffee0/jx49v8Vh5ebkWLFggSRo1atQlL5S4Zs0aDRs2TGPGjLE1r9SUuSPFBPm3yN94TlcmxLdvGACwWWxsbEDbn3/jEB0dbXltUlJSwLk6rfPPjS6Xs/YLIS2QORDMDJAcNgdCQFR0t6DX9nVHq7eT/33zPNBpfweX876RIqEN2dnZOnTokJYuXapbbrlFgwcPliTt2bNHs2bNksfjkSRlZWVd9GccPnxYe/fu1Y9//OOOiBz04SlB/3ke6eF3Al83oX83rfjb9SUAoKvYs2dPQNsfPnxYeXl5ysnJ0dChQy2tef7554NI1jmNzZd8ksLDwpuvKQR0dYHMgWBmgOSsORAKPGelKVulBn9g6/pFSe/u2q4IB1+5jucBZ/4OHPxXtn3k5uYqPj5eJSUlGjFihK6++moNGjRIY8eO1VVXXaVJkyZJuvT1EdasWSOXy6WZM2d2VOwONTpeSgvi3Ia70to9CgB0OhkZGdqyZYsyMjJMRwFgADMgNPSNkiZdGfi6O9Pk6BIBzsVf2zYkJydrx44duu222xQVFaWioiLFxcVp9erV2rRpkz788ENJFy8S/H6/XnvtNd10002OvX+wyyV9a1BgazLc0vX97MkDAJ1JRESE+vTp0+JCuwBCBzMgdHxzoBR+8TOdL9Czm3SnM98eIARQJFgwbNgwbdy4UTU1NaqpqdGuXbs0e/Zs1dXVqaioSGFhYRo5cmSra//4xz+quLi4Qy6yaNKUFOl+i0V7YrT0/HWBDVoA6KpKS0s1f/58xxzKCCAwzIDQMby39ESWtTdYMeHSf4yV4qNsDgXYhCLhMhQUFMjv92vQoEGKiYlpdZs1a9YoOjr6krcJWr9+vdavX68PPvigxT939LUOLtecYVLu1VKf7hffZsIV0s9vbCoTACAU1NbWaseOHaqtrTUdBYABzIDQcmuK9B/jpJRLXFdzeG/pZzdIWVxzHF0Yx1hdhgMHDki6+GkNZ8+e1fr16zVt2jS53e6L/py77rqr1X++//779fOf/7x9wnYAl0u6O12aliptL//i67zXb5aSA7vYOQAAANCl3NBPuv4Kafen0qZS6dRZKcwlXRkj3ZEqjehjOiFw+SgSLkNbRUJUVJTOnDnT5s/x+wO8vGsn1z1cmpzc9HX+CqVhokQAAABAaAhzSddd0fQFOBGnNlyGtooEAAAAAACchiMSLsP27dtNRwAAdHIJCQmaN2+eEhISTEcBYAAzAIATUSQAAGCj+Ph4zZw503QMAIYwAwA4Eac2AABgo+rqam3btk3V1dWmowAwgBkAwIkoEgAAsFFZWZkWLVqksrIy01EAGMAMAOBEFAkAAAAAAMAyigQAAAAAAGAZRQIAAAAAALCMIgEAABtFRkZqyJAhioyMNB0FgAHMAABOxO0fAQCwUXp6utasWWM6BgBDmAEAnIgjEgAAAAAAgGUUCQAA2OjIkSOaMGGCjhw5YjoKAAOYAQCciCIBAAAb+f1+nTt3Tn6/33QUAAYwAwA4EUUCAAAAAACwjCIBAAAAAABYRpEAAAAAAAAs4/aPAADYKC0tTXl5eUpKSjIdBYABzAAATkSRAACAjaKiojRw4EDTMQAYwgwA4ESc2gAAgI3Ky8u1ePFilZeXm44CwABmAAAnokgAAMBGVVVVys/PV1VVlekoAAxgBgBwIooEAAAAAABgGUUCAAAAAACwjCIBAAAAAABYRpEAAICN4uLidP/99ysuLs50FAAGMAMAOBFFAgAANgoLC1O3bt0UFsZTLhCKmAEAnIiJBgCAjTwej1588UV5PB7TUQAYwAwA4EQUCQAAAAAAwDKKBAAAAAAAYBlFAgAAAAAAsIwiAQAAG7ndbk2ePFlut9t0FAAGMAMAOFGE6QAAADhZUlKSnnrqKdMxABjCDADgRByRAACAjerr61VSUqL6+nrTUQAYwAwA4EQUCQAA2KiwsFDTp09XYWGh6SgADGAGAHAiigQAAAAAAGAZRQIAAAAAALCMIgEAAAAAAFhGkQAAAAAAACzj9o8AANho6NCh2r17t+kYAAxhBgBwIo5IAAAAAAAAllEkAABgo+LiYj3wwAMqLi42HQWAAcwAAE5EkQAAgI28Xq8OHjwor9drOgoAA5gBAJyIIgEAAAAAAFhGkQAAAAAAACyjSAAAAAAAAJZRJAAAYKP+/fvrySefVP/+/U1HAWAAMwCAE0WYDgAAgJP16tVLOTk5pmMAMIQZAMCJOCIBAAAbVVZWat26daqsrDQdBYABzAAATkSREACPx6Pc3FxlZGQoKipKKSkpmjdvnurq6vTggw/K5XJp1apVpmMCADqREydO6Nlnn9WJEydMRwFgADMAgBNxaoNF+/fvV05OjioqKhQbG6vhw4errKxMK1as0LFjx3T69GlJUlZWltmg6BQ+b5T+eEIqrZN8fik+SpqYKPXsbjoZAAAAYD+fX9r9qeT/2z/7JVV8JiXGmEyF9kKRYIHH49HUqVNVUVGh+fPn64knnpDb7ZYkLVu2TAsXLlRERIRcLpdGjRplOC1MqmuQfn5U+k2xdPrzlo8tC5O+liw9OEhKijWTDwAAALBTo1/6ZWHTV0ndF9/3S7p9m3RDovTAIGlkH2MR0Q44tcGCuXPnqrS0VHPmzNHy5cubSwRJys3NVWZmphoaGpSWlqaePXsaTAqTKuul2Tul/z56YYkgSfU+Kf8T6Vs7pMNnOjweAAAAYKtzPulf90j/frBliXCeT9IfK6SHdkq/K+vweGhHFAltOHTokNauXau+fftqyZIlrW4zevRoSVJmZmbz92666Sa5XK5Wvx5++OHm7davX6/p06drwIABiomJ0dChQ/WDH/xAtbW19u4Y2tU5n/S93dKRqra3rfxcmvtu06FdAJwvJiZG48aNU0wMx3ICoYgZgFDy479Iv69oe7tzPukH+6T3T9mfCfbg1IY25OXlyefzaebMmerRo0er20RHR0tqWSS88MILqq6ubrHdpk2btHjxYk2ZMqX5e8uXL1dqaqp+/OMfKzk5Wfv379eTTz6pt99+W3/84x8VFkbX0xX8rkw6GMDFmE9/Lq05Ji242r5MADqH1NRUrVy50nQMAIYwAxAqjlVLb5RY377BL71wSPrZDfZlgn0oEtqwfft2SdLEiRMvuk1paamklkXC8OHDL9juRz/6kRISEjR58uTm773xxhtKSEho/ud/+Id/UEJCgmbOnKk//elP+upXv3rZ+wD7rSsKfM3GEunRYVIM/xUCjtbY2Civ16vo6GiFh4ebjgOggzEDECrWFwW+5v3T0kfVUgZnh3c5fNzdhuLiYknSgAEDWn28oaFBO3fulNSySPh7n376qX7729/qnnvuUUTEF+8cv1winDdmzBhJ0vHjx4POjY5z0iv95XTg6+oapHdOtn8eAJ3L0aNHNWnSJB09etR0FAAGMAMQKrYGec2DYNfBLD4LbUNdXdNVQrxeb6uPr127Vh6PR263W+np6Rf9OXl5eWpoaNCsWbPa/DN///vfS5KGDRsWROKmIqKiwsLJSR2g36piucLC1ehrVHJy62VMVxeRPFx9F70V1Nr/b/5j8u5Y086JANhpxowZAW1/8mRTY7h582bt27fP0po777wz4FydVSg8DyD0BDIHgpkBkrPmAEKAy6XEnwRwXsOXrHxpjX6U91g7B+pcOutzYWJiovbu3RvUWoqENiQmJqqyslLvvfeexo8f3+Kx8vJyLViwQJI0atQouVyui/6cNWvWaNiwYc1HG1zM8ePH9cMf/lCTJ09WVlZWUJkrKio6zdEM/fx/u3Os399pMrW3qLBe6hvk2krPSZ1y6O8FcKrzBbNV54tor9drea2T5mUoPA8g9AQyB4KZAZKz5gBCQ7+Gc3JFdAt4XW1VpeP/vjvxuZAioQ3Z2dk6dOiQli5dqltuuUWDBw+WJO3Zs0ezZs2Sx+ORpEu+6T98+LD27t2rH//4x5f8s2pra3XHHXeoe/fuevnll4POnJiYGPTadne+XHG5lJSUZDaLTVzdG+X//Kxc3aMsr/H7/XK5XHJ/Xqkoh/5eAKeKjY0NaPvzbxyio6Mtr3XUvAyB5wGEnkDmQDAzQHLYHEBIaDj5sbpdOSTgdVF1J53/972TPhdezvtGl99/vh5Ba0pLS5WVlaVTp04pIiJCQ4cO1dmzZ/XRRx8pJydHPp9PW7Zs0U9/+lM99NBDrf6MH/zgB1qyZImKioqUmpra6jZer1e33nqr/vrXv2rHjh2tXqyxKxqb33S/2DBJu283ncY+T74f2FVqJSm9h/TLiV/MFQBdw549ewLa/vDhw7rvvvv0yiuvaOjQoZbWXHvttcFE65RC5XkAoSWQORDMDJCcNQcQGv73Y2n5wcDWRIZJm/9R6tndnkydhROfC7nYYhuSk5O1Y8cO3XbbbYqKilJRUZHi4uK0evVqbdq0SR9++KGki19o0e/367XXXtNNN9100RLh3LlzmjFjhvbu3avNmzc7pkQIJXdd/PIYFzUjnRIBCAUZGRnasmWLMjIyTEcBYAAzAKHithQpOsAbk0xOdn6J4FSc2mDBsGHDtHHjxgu+X1tbq6KiIoWFhWnkyJGtrv3jH/+o4uJiPfHEE60+7vP5NHPmTP3ud7/Tm2++qbFjx7ZrdnSM4b2lhwZLP/vQ2vbXXyFN7zzXWQFgo4iICPXp08d0DACGMAMQKtzdpH+7RvrXvZKVQ94H9JDm8vlpl8URCZehoKBAfr9fgwYNUkxMTKvbrFmzRtHR0Re9uu+jjz6qdevW6Xvf+55iYmL07rvvNn99+umndsZHO5s9RPqOhdPCJiZKy8ZIEfzXB4SE0tJSzZ8/X6WlpaajADCAGYBQcvOV0o9GS93beJ07pJf0X9dLvTgaocvircxlOHDggKSLn9Zw9uxZrV+/XtOmTZPb7W51m82bN0uSnnnmGY0fP77F16ZNm+wJDlu4XNJDQ6S1N0l3p0uxf3e8T/aVTQNz2bVSFMcCASGjtrZWO3bsUG1trekoAAxgBiDU/GOS9PrNTR+yJfzdtcjH9JWeGSP9z40XPoauhbczl6GtIiEqKkpnzpy55M8oKipq51QwbWBPKfdq6XsjpOs3Nh3a5VLT0AQAAACc7oropiLhwcHSmXqp3if17Cb1CPzukOikKBIuQ1tFAkJbt7D/n717j4+qvvM//p4kkBvDLUSSJkCAAOEiiRVBtHUBoSUVVApWbYpudaWuZaHdLLGl3fUHpVKU32qVuou9/szSNIWqxbBIi7SaUgtBxAKGEGkSCbnISMjNIZDM/P5IwaYk5EzIyTc583o+HnnsOnO+4X2myWeSd86ltUC4WCQAAAAAwSTUJcVw5IEjUSRchT179piOAAAAAABAj+IaCQAA2Cg2NlYrV65UbGys6SgADGAGAHAijkgAAMBGMTExysjIMB0DgCHMAABOxBEJAADYqK6uTrt371ZdXZ3pKAAMYAYAcCKKBAAAbFRRUaHVq1eroqLCdBQABjADADgRRQIAAAAAALCMIgEAAAAAAFhGkQAAAAAAACyjSAAAwEbh4eGaMGGCwsPDTUcBYAAzAIATcftHAABsNHr0aGVnZ5uOAcAQZgAAJ+KIBAAAAAAAYBlFAgAANioqKtLNN9+soqIi01EAGMAMAOBEFAkAANjI7/frwoUL8vv9pqMAMIAZAMCJKBIAAAAAAIBlFAkAAAAAAMAyigQAAAAAAGAZt38EAMBGSUlJysnJUUJCgukoAAxgBgBwIooEAABsFBERobFjx5qOAcAQZgAAJ+LUBgAAbFRZWal169apsrLSdBQABjADADgRRQIAADaqra3V9u3bVVtbazoKAAOYAQCciCIBAAAAAABYRpEAAAAAAAAso0gAAAAAAACWUSQAAGCjkJAQXXfddQoJ4S0XCEbMAABOxEQDAMBGPp9Pb7/9tnw+n+koAAxgBgBwIooEAAAAAABgGUUCAAAAAACwjCIBAAAAAABYRpEAAICN3G635s+fL7fbbToKAAOYAQCcKMx0AAAAnCwhIUFr1641HQOAIcwAAE7EEQkAANioqalJJ0+eVFNTk+koAAxgBgBwIooEAABsVFJSosWLF6ukpMR0FAAGMAMAOBFFAgAAAAAAsIwiAQAAAAAAWEaRAAAAAAAALKNIAAAAAAAAlnH7RwAAbJSSkqL9+/ebjgHAEGYAACfiiAQAAAAAAGAZRQIAADYqKyvTAw88oLKyMtNRABjADADgRBQJAADYyOv16siRI/J6vaajADCAGQDAiSgSAAAAAACAZRQJAAAAAADAMooEAAAAAABgGUUCAAA2io+P15o1axQfH286CgADmAEAnCjMdAAAAJxs0KBBSk9PNx0DgCHMAABOxBEJAADYqKamRlu3blVNTY3pKAAMYAYAcCKKBAAAbFRdXa0nn3xS1dXVpqMAMIAZAMCJKBIC4PF4lJWVpeTkZEVERGjEiBFauXKlGhsb9eCDD8rlcmnTpk2mYwK9gt8vNVyQTp9r/b9+v+lEAAAAQM861yxd/DHYST8Oc40Eiw4dOqT09HRVVVUpOjpakyZNUkVFhZ555hmdOHFCZ86ckSSlpaWZDQoY9lGz9Gq5tK1UOl738eMTBkl3JUnzE6QIJg8AAAAcqtknvVHV+vPwfs/Hj/sl/axYumOkNCTcVLruwREJFng8Hi1cuFBVVVXKzMxUZWWlDh48qKqqKm3YsEE7duxQQUGBXC6Xpk6dajouYMyxWunzr0mP/7ltiSBJRbXSunekz++RimvN5AMAAADsdPqcdH++lHWgbYlw0aZCaeHu1qKhL6NIsGDFihUqLy/X8uXLtXHjRrnd7kvPZWVlKTU1Vc3NzUpKStLAgQMNJgXMOVEnPbxX8jRdebsPzklf+aNUWt8zuQDToqKiNGPGDEVFRZmOAsAAZgAQPM6el76yt/UPaFdyrkX6twLpD3340ikUCZ0oLCxUbm6uhg0bpvXr17e7zfXXXy9JSk1NvfTYrFmz5HK52v14+OGHL22Xn5+vuXPnKj4+XuHh4UpMTNTdd9+twsJCe3cM6EZ+v/TvB6WGZmvb112Q/uNtezMBvcXIkSP17LPPauTIkaajADCAGQAEj+8fld5vtLatzy99+y3Ja/Hn596GM5U7kZOTI5/Pp4yMDA0YMKDdbSIjIyW1LRKee+451dW1PbZ7x44dWrdunRYsWHDpsZqaGl177bX6yle+omuuuUbl5eVav369Zs6cqSNHjigxMdGGvQK6159rLj+VoTPvnpWO1kiTh9gSCeg1Wlpa5PV6FRkZqdDQUNNxAPQwZgAQHM6el3adCmxNQ7P06ilp0Sh7MtmJIqETe/bskSTNnj27w23Ky8sltS0SJk2adNl23/3udxUbG6v58+dfeuz222/X7bff3ma7G264QRMmTNCvfvUrrVy58qryAz3hxdIuriujSIDzFRcX67777tMLL7yglJQU03EA9DBmABAcdpyUzvsCX/diKUWCI5WVlUmSRo1q/3/d5uZm7d27V1LbIuHvnT59Wq+++qoeeeQRhYVd+WWPiYmRpE6368i0adNUVdU7rt4xfFOZXCGhavG1KDGxD36HXKVg2f+hq15R/9HXBbxu65792rzg8zYkAuyzZMmSgLb/4IMPJEk7d+7UW2+9ZWnNokWLAs7VWwXLHERwCWQOdGUGSM6aA0AwGHjv9xT16S8FvO5odaMSEyfYkKhzcXFxOnDgQJfWUiR0orGx9SQXr9fb7vO5ubnyeDxyu90aPXp0h58nJydHzc3NWrp0abvPt7S0yOfzqaysTN/85jcVFxenL3zhC13KXFVVpVOnAjyuxibD/X+9W6rf32sy9aRg2f+Bfql/F9Zd8Lsc/brAmS6+L1h18f3D6/VaXuuk74tgmYMILoHMga7MAMlZcwAIBmHnm9WVS6q6wvr3ye93ioROxMXFqaamRgcPHtTMmTPbPFdZWalVq1ZJkqZOnSqXy9Xh58nOztbEiRM1bdq0dp//h3/4h0tHNiQnJ2vPnj2KjY3tcuZe4+Jr4nIpISHBbBYTgmT/Q5sCvEDCX4U11Tv6dYEzRUdHB7T9xV8cIiMjLa911PdFkMxBBJdA5kBXZoDksDkABIFI37kurfM1fGjs+/1qfm+kSOjE3LlzVVhYqA0bNmjevHkaP368JKmgoEBLly6Vx9N6c9C0tLQOP8exY8d04MABPf744x1u8+Mf/1hnz55VSUmJnnzySX3mM5/R3r17u3SF364enmKH6dsln6TQkNBL15IIJsGy/y+VSd99J/B1j39pjhaudu7rAmcqKCgIaPtjx44pJydH6enpls+Pfvrpp7uQrHcKljmI4BLIHOjKDJCcNQeAYHC0Rro/P/B1X/pknDL74Psjt3/sRFZWlmJiYnTy5ElNnjxZ1157rcaNG6fp06drzJgxmjNnjqQrXx8hOztbLpdLGRkZHW4zYcIEzZgxQ/fcc49ee+011dfX64knnuj2/QHsMD9BGhBgLTmonzSPP7YgCCQnJ2vXrl1KTk42HQWAAcwAIDhMHiJNGhz4usVJ3Z2kZ1AkdCIxMVH5+fm67bbbFBERodLSUg0dOlSbN2/Wjh07dPz4cUkdFwl+v19btmzRrFmzLB9dMHjwYCUnJ+u9997rtv0A7BQZJi0L8BoxX0mRIrgLFoJAWFiYhgwZ0uUL6ALo25gBQPB4JEUK6fhs98vcMVJKGmBfHjtRJFgwceJE5eXlqb6+XvX19dq3b5+WLVumxsZGlZaWKiQkRFOmTGl37RtvvKGysrIOL7LYng8++EBFRUUaO3Zsd+0CYLt7x0j/aPGPLQ+Ol77Q8bVJAUcpLy9XZmYmh/UDQYoZAASPG6+RHkuTQi2UCbPjpG9OtT2SbahGr8LRo0fl9/s1fvx4RUW1f43O7OxsRUZGdniboC996UtKTk5WWlqaBg8erOLiYj311FMKCwvT17/+dTvjA93K5ZKWT5LGDZKy35OO1V6+zaTB0n3J0txP9Hg8wJiGhgbl5+froYceMh0FgAHMACC43DZCio+SfnpcevP05c8nRLX+Qe2eMdYKh96KIuEqHD58WFLHpzWcO3dO27Zt05133im3293uNjfeeKNeeOEFff/739e5c+c0YsQIzZ49W6tXr9aoUdxvG33PZxOkz3xCOnpW+nK+5JfkkvSzT7eeOwYAAAA42SdjpE/OlN5vkPKrpYYLUnioNH6QdGNsYKc/9FYUCVehsyIhIiJCZ8+eveLnWL58uZYvX97d0QCjXC5pypDWAuFikUCJAAAAgGAycoCU0UevgdAZrpFwFTorEgAAAAAAcBqOSLgKe/bsMR0BANDLxcbGauXKlYqNjTUdBYABzAAATkSRAACAjWJiYpSRkWE6BgBDmAEAnIhTGwAAsFFdXZ12796turo601EAGMAMAOBEFAkAANiooqJCq1evVkVFhekoAAxgBgBwIooEAAAAAABgGUUCAAAAAACwjCIBAAAAAABYRpEAAICNwsPDNWHCBIWHh5uOAsAAZgAAJ+L2jwAA2Gj06NHKzs42HQOAIcwAAE7EEQkAAAAAAMAyigQAAGxUVFSkm2++WUVFRaajADCAGQDAiSgSAACwkd/v14ULF+T3+01HAWAAMwCAE1EkAAAAAAAAyygSAAAAAACAZRQJAAAAAADAMm7/CACAjZKSkpSTk6OEhATTUQAYwAwA4EQUCQAA2CgiIkJjx441HQOAIcwAAE7EqQ0AANiosrJS69atU2VlpekoAAxgBgBwIooEAABsVFtbq+3bt6u2ttZ0FAAGMAMAOBFFAgAAAAAAsIwiAQAAAAAAWEaRAAAAAAAALKNIAADARiEhIbruuusUEsJbLhCMmAEAnIiJBgCAjXw+n95++235fD7TUQAYwAwA4EQUCQAAAAAAwDKKBAAAAAAAYBlFAgAAAAAAsIwiAQAAG7ndbs2fP19ut9t0FAAGMAMAOFGY6QAAADhZQkKC1q5dazoGAEOYAQCciCMSAACwUVNTk06ePKmmpibTUQAYwAwA4EQUCQAA2KikpESLFy9WSUmJ6SgADGAGAHAiigQAAAAAAGAZRQIAAAAAALCMIgEAAAAAAFhGkQAAAAAAACzj9o8AANgoJSVF+/fvNx0DgCHMAABOxBEJAAAAAADAMooEAABsVFZWpgceeEBlZWWmowAwgBkAwIkoEgAAsJHX69WRI0fk9XpNRwFgADMAgBNRJAAAAAAAAMsoEgAAAAAAgGUUCQAAAAAAwDKKBAAAbBQfH681a9YoPj7edBQABjADADhRmOkAAAA42aBBg5Senm46BgBDmAEAnIgjEgAAsFFNTY22bt2qmpoa01EAGMAMAOBEFAkAANiourpaTz75pKqrq01HAWAAMwCAE1EkBMDj8SgrK0vJycmKiIjQiBEjtHLlSjU2NurBBx+Uy+XSpk2bTMcEAAAAAMA2XCPBokOHDik9PV1VVVWKjo7WpEmTVFFRoWeeeUYnTpzQmTNnJElpaWlmgwIwzueX9p+WXj0lnWmSQl1SYrR050hp7EDT6QD7+f3S0bPSK+9Lvr8+5pP0zhlp6hDJ5TIYDgAAXDWKBAs8Ho8WLlyoqqoqZWZm6rHHHpPb7ZYkPfHEE3r00UcVFhYml8ulqVOnGk4LwKTXq6Snj0onGy9/Lucv0idjpNWpUtKAns8G9IQjNdL3/iwdq738uQf/II0fKGVdK6XF9Hw2AADQPTi1wYIVK1aovLxcy5cv18aNGy+VCJKUlZWl1NRUNTc3KykpSQMH8udGIFhtf1/6t/3tlwgXHfxQeiBfKmrnlyw4U1RUlGbMmKGoqCjTUWy377S0bG/7JcJFx+ukR96U9nK6OIJEMM0AAMGDIqEThYWFys3N1bBhw7R+/fp2t7n++uslSampqZcemzVrllwuV7sfDz/8cIf/Xnp6ulwul/7P//k/3bofAOx16ENp3TuS38K2dRekr+2T6i/YHgu9wMiRI/Xss89q5MiRpqPY6lSjtKpAOu/rfNvzPukbB6T3G+zPBZgWLDMAQHDh1IZO5OTkyOfzKSMjQwMGtH8scmRkpKS2RcJzzz2nurq6Ntvt2LFD69at04IFC9r9PL/85S916NCh7gkOoEdln2i9NoJVp89JO05K94yxLxN6h5aWFnm9XkVGRio0NNR0HNtsLZU+ara+vbdFyi2RVl1rWySgVwiWGQAguHBEQif27NkjSZo9e3aH25SXl0tqWyRMmjRJN954Y5uPQ4cOKTY2VvPnz7/sc9TV1elrX/uaNm7c2M17AMBuVR9J+VWBr9ta2npROjhbcXGx5syZo+LiYtNRbHOupfXUnkDlnQysfAD6omCYAQCCD0VCJ8rKyiRJo0aNavf55uZm7d27V1LbIuHvnT59Wq+++qruvfdehYVdfiDIt771LY0fP14ZGRndkBpAT9rv+fjK9IEoa5CqvN0eB+hxx862nrITqMZm6XBNt8cBAAA249SGTjQ2tl41zett/6f93NxceTweud1ujR49usPPk5OTo+bmZi1duvSy5w4cOKAf/vCHeuutt7ol87Rp01RV1YU/j9pg+KYyuUJC1eJrUWJi+2WMk7H/wbH/UbMf1MC71nRp7czZ89R8qrCbE8FOS5YsCWj7Dz74QJK0c+dOy3N+0aJFAecyKXzKHA155IUurf3ilx9S06Gd3ZwIsFcgc6ArM0Dqe3MAQN8TFxenAwcOdGktRUIn4uLiVFNTo4MHD2rmzJltnqusrNSqVaskSVOnTpXrCjfGzs7O1sSJEzVt2rQ2j7e0tOgrX/mKli9frsmTJ3dL5qqqKp06dapbPtfVGn7xuG2/v9dk6knsf3Dsf0z1KXX1fi2VZSd0vtq5r40TXSyYrbpYRHu9Xstr+9r3y4Ah72tIF9eePlWm+j62v0Agc6ArM0Dqe3MAQHChSOjE3LlzVVhYqA0bNmjevHkaP368JKmgoEBLly6Vx+ORJKWlpXX4OY4dO6YDBw7o8ccfv+y5TZs2qbq6ulvv0hAXF9dtn+uqXSxXXC4lJCSYzWIC+3/p/zp5/8POlkqS/H7/FQvFv9dytkqx/X2Sg18bJ4qOjg5o+4u/OERGRlpe29e+X1znTst/3itX/8iA1vkvNGmwt1oD+9j+AoHMga7MAKnvzQEAfc/V/N7o8vu51NeVlJeXKy0tTR9++KHCwsKUkpKic+fO6b333lN6erp8Pp927dql559/Xg899FC7n+Nb3/qW1q9fr9LS0ja3/vF4PBozZow2btyoL3zhC5ceHzJkiB599FF94xvf0MCBAxUS0ncvZTF9e+u54yGS9t9uOk3PY/+DZ/8f+oP09pkA14yXvpJiTx7Yp6CgIKDtm5ubVV9fL7fb3e41ctpzww03dCWaUWvell45Gdia9ATpO9fbkwewUyBzoCszQOqbcwBA8Oi7v6H2kMTEROXn5+u2225TRESESktLNXToUG3evFk7duzQ8ePHJXV8oUW/368tW7Zo1qxZl90/uLy8XPX19frKV76iIUOGXPqQpA0bNmjIkCF6//0uXAYbQI/74tjAto8IlRY597IR+BthYWEaMmRIQL9A9EV3j5FCrB+Qo5C/rgGcLlhmAIDgQpFgwcSJE5WXl6f6+nrV19dr3759WrZsmRobG1VaWqqQkBBNmTKl3bVvvPGGysrK2r3IYnJysn73u99d9iFJ999/v373u9/1rtMUAHRodrz0j8nWtg11Sd+bJl0T2FHg6KPKy8uVmZl56VbBTpUySFo91fr2q66VpnT1wgpAHxIsMwBAcKEavQpHjx6V3+/X+PHjFRUV1e422dnZioyMbPfqvgMGDNCsWbPaXZeUlNThcwB6p69OlAb2lzYfk5o6uB/ksHBpzSelGbE9mw3mNDQ0KD8/v8PT35zkzlGtR9s8cbjj20EOCJP+7VppwYiezQaYEkwzAEDwoEi4CocPH5bU8WkN586d07Zt23TnnXfK7Xb3ZDQABrhc0n3J0p0jpbyT0q5T0tGzHz//vWnSrDgpjGPB4GDzE6VZ8dJvT7VeM6HaK/klXRPRWh58NkGK5KcPAAD6NN7Kr0JnRUJERITOnj0b8Ofl+pdA3zawf+s1E744tu0FJ+d+wnQyoGdEhEoLR7Z+AAAA5+HvYlehsyIBAAAAAACn4YiEq7Bnzx7TEQAAvVxsbKxWrlyp2FgujAEEI2YAACeiSAAAwEYxMTHKyMgwHQOAIcwAAE7EqQ0AANiorq5Ou3fvVl1dnekoAAxgBgBwIooEAABsVFFRodWrV6uiosJ0FAAGMAMAOBFFAgAAAAAAsIwiAQAAAAAAWEaRAAAAAAAALKNIAADARuHh4ZowYYLCw8NNRwFgADMAgBNx+0cAAGw0evRoZWdnm44BwBBmAAAn4ogEAAAAAABgGUUCAAA2Kioq0s0336yioiLTUQAYwAwA4EQUCQAA2Mjv9+vChQvy+/2mowAwgBkAwIkoEgAAAAAAgGUUCQAAAAAAwDKKBAAAAAAAYBm3fwQAwEZJSUnKyclRQkKC6SgADGAGAHAiigQAAGwUERGhsWPHmo4BwBBmAAAn4tQGAABsVFlZqXXr1qmystJ0FAAGMAMAOBFFAgAANqqtrdX27dtVW1trOgoAA5gBAJyIIgEAAAAAAFhGkQAAAAAAACyjSAAAAAAAAJZRJAAAYKOhQ4fq/vvv19ChQ01HAWAAMwCAE1EkAABgo5CQEPXr108hIbzlAsGIGQDAiZhoAADYyOPx6Ec/+pE8Ho/pKAAMYAYAcCKKBAAAAAAAYBlFAgAAAAAAsIwiAQAAAAAAWEaRAACAjdxut+bPny+32206CgADmAEAnCjMdAAAAJwsISFBa9euNR0DgCHMAABOxBEJAADYqKmpSSdPnlRTU5PpKAAMYAYAcCKKBAAAbFRSUqLFixerpKTEdBQABjADADgRRQIAAAAAALCMIgEAAAAAAFhGkQAAAAAAACyjSAAAAAAAAJZx+0cAAGyUkpKi/fv3m44BwBBmAAAn4ogEAAAAAABgGUUCAAA2Kisr0wMPPKCysjLTUQAYwAwA4EQUCQAA2Mjr9erIkSPyer2mowAwgBkAwIkoEgAAAAAAgGUUCQAAAAAAwDKKBAAAAAAAYBlFAgAANoqPj9eaNWsUHx9vOgoAA5gBAJwozHQAAACcbNCgQUpPTzcdA4AhzAAATsQRCQAA2KimpkZbt25VTU2N6SgADGAGAHAiigQAAGxUXV2tJ598UtXV1aajADCAGQDAiSgSAuDxeJSVlaXk5GRFRERoxIgRWrlypRobG/Xggw/K5XJp06ZNpmMCQK/wfoP0+0rpN6ekfael8y2mEwHoSRd8UsHp1hnw+0qprMF0IgBAd+EaCRYdOnRI6enpqqqqUnR0tCZNmqSKigo988wzOnHihM6cOSNJSktLMxsUAAzy+6XXq6Rflkj7PW2fG9xfumOkdM8YKTbCTD4A9vOck3JLpF+XSWfOt33u+hjprtHSrfGSy2UmHwDg6lEkWODxeLRw4UJVVVUpMzNTjz32mNxutyTpiSee0KOPPqqwsDC5XC5NnTrVcFoAMMPnl544LG0rbf/5s+el//eelHdS+v4MKWVwT6YD0BOKa6UV+6TT59p//q0PWz/uGCmtTpVCKRMAoE/i1AYLVqxYofLyci1fvlwbN268VCJIUlZWllJTU9Xc3KykpCQNHDjQYFIAMOf7RzsuEf7Wh03S8j9J5Y22R+oVoqKiNGPGDEVFRZmOAtiq4qPW7+2OSoS/9ev3pY2H7c/UGzADADgRRUInCgsLlZubq2HDhmn9+vXtbnP99ddLklJTUy89NmvWLLlcrnY/Hn744Uvb/f73v293G06RANCXlNRLW/5iffuz56XnjtmXpzcZOXKknn32WY0cOdJ0FMBWm4+1FoVWbS1tPYLB6ZgBAJyIUxs6kZOTI5/Pp4yMDA0YMKDdbSIjIyW1LRKee+451dXVtdlux44dWrdunRYsWHDZ5/jBD36gT37yk5f+Ozo6ujviA0CP+FVp4Gv2VEieydIwh18voaWlRV6vV5GRkQoNDTUdB7DF2fPSbysCX7etVPpmaqeb9WnMAABOxBEJndizZ48kafbs2R1uU15eLqltkTBp0iTdeOONbT4OHTqk2NhYzZ8//7LP8ffbX3vttd28JwBgD79f+t/ywNc1+1uv5u50xcXFmjNnjoqLi01HAWyz+5R03hf4up3lrddXcTJmAAAn4oiETpSVlUmSRo0a1e7zzc3N2rt3r6S2RcLfO336tF599VU98sgjCguz92WfNm2aqqqqbP03rBq+qUyukFC1+FqUmNj+a+hk7H9w778UJK9BvwjFff+9Li39ztP/rayX1nVzIHstWbIkoO0/+OADSdLOnTv11ltvWVqzaNGigHMBJg24PUsD5q8IeN1HLdKo8ZPl9/atcxwCmQNdmQEScwCA/eLi4nTgwIEuraVI6ERjY+vVwLxeb7vP5+bmyuPxyO12a/To0R1+npycHDU3N2vp0qXtPn/33XfL4/EoJiZGt99+u773ve9p2LBhXcpcVVWlU6d6x5/5hvv/+mcGv7/XZOpJ7H9w778UHK+Bq3+E4rq4tr6+rs+9LhffF6y6+P7h9Xotr+1rrwnwidpatX8CaOcqKsrl+6iu8w17kUDmQFdmgMQcANC7USR0Ii4uTjU1NTp48KBmzpzZ5rnKykqtWrVKkjR16lS5rnBD5OzsbE2cOFHTpk1r8/igQYO0atUq3XLLLRowYIDefPNNrV+/Xn/605904MABRUQEfvJwXFxXf6S3wcXXxOVSQkKC2SwmsP+X/m9Q7r8UNK9BS71Hoe7Ay8+oC3V97nUJ9Bo2F39xiIyMtLy2r70mQOSFrhUBvo9qFT90oDTE3fnGvUggc6ArM0BiDgCw39X83kiR0Im5c+eqsLBQGzZs0Lx58zR+/HhJUkFBgZYuXSqPxyNJV7zLwrFjx3TgwAE9/vjjlz133XXX6brrrrv037NmzdKUKVN0++23KycnR1/+8pcDztzVw1PsMH275JMUGhJ66VoSwYT9D+79l4LnNXjmXemFAM9uCA+R9v90nQb371unNhQUFAS0/bFjx5STk6P09HSlpKRYWvP00093IRlgTsMFaf5vpHMtga3LmDJImSdP2hPKRoHMga7MAIk5AKB342KLncjKylJMTIxOnjypyZMn69prr9W4ceM0ffp0jRkzRnPmzJF05esjZGdny+VyKSMjw9K/uWDBAkVHR/eqQgAAruTzo6SOj8lq32cSpMH9bYnTqyQnJ2vXrl1KTk42HQWwzYB+Unpi4OsWJ3V7lF6HGQDAiSgSOpGYmKj8/HzddtttioiIUGlpqYYOHarNmzdrx44dOn78uKSOiwS/368tW7Zo1qxZAd8/+EqnSgBAb5IYLX1lgvXth0dI/2z9D3N9WlhYmIYMGWL7hXYB074yQYqPtL79A+OkpK5eWKEPYQYAcCKKBAsmTpyovLw81dfXq76+Xvv27dOyZcvU2Nio0tJShYSEaMqUKe2ufeONN1RWVtbhRRbbs337djU2Nmr69OndtQsAYLsHx7d+dCYhSvrBTdI1AfzC0ZeVl5crMzPT0ae2AJI0LEL6r5ukERYuA7B0bPCUicwAAE5ENXoVjh49Kr/fr/HjxysqKqrdbbKzsxUZGdnhbYK+9KUvacyYMfrkJz956WKLTzzxhNLS0nTPPffYGR8AupXL1fqLwY2x0i9LpD2VUsvf3B8+IUpakiTdMVIaGASnNFzU0NCg/Px8PfTQQ6ajALZLjJayb5F+/b60rVQ6+Xc3Kbg1XrprtDStazem6pOYAQCciCLhKhw+fFhSx6c1nDt3Ttu2bdOdd94pt7v9qxFPnjxZP//5z/X000/L6/UqMTFRDz30kB577DH17x9EP2kDcIzrYlo/PjzXevE1v1qvn/DSrVIIZ2wBjjegn5QxVrp3jFRcJ33p9Y/nwIYbTKcDAHQHioSr0FmREBERobNnz17xc3zzm9/UN7/5ze6OBgDGxUS0/uJw8RcISgQguIS4pAmD2s4BAIAzcI2Eq9BZkQAAAAAAgNNwRMJV2LNnj+kIAIBeLjY2VitXrlRsbKzpKAAMYAYAcCKKBAAAbBQTE6OMjAzTMQAYwgwA4ESc2gAAgI3q6uq0e/du1dXVmY4CwABmAAAnokgAAMBGFRUVWr16tSoqKkxHAWAAMwCAE1EkAAAAAAAAyygSAAAAAACAZRQJAAAAAADAMooEAABsFB4ergkTJig8PNx0FAAGMAMAOBG3fwQAwEajR49Wdna26RgADGEGAHAijkgAAAAAAACWUSQAAGCjoqIi3XzzzSoqKjIdBYABzAAATkSRAACAjfx+vy5cuCC/3286CgADmAEAnIgiAQAAAAAAWEaRAAAAAAAALKNIAAAAAAAAlnH7RwAAbJSUlKScnBwlJCSYjgLAAGYAACeiSAAAwEYREREaO3as6RgADGEGAHAiTm0AAMBGlZWVWrdunSorK01HAWAAMwCAE1EkAABgo9raWm3fvl21tbWmowAwgBkAwIkoEgAAAAAAgGUUCQAAAAAAwDKKBAAAAAAAYBlFAgAANgoJCdF1112nkBDecoFgxAwA4ERMNAAAbOTz+fT222/L5/OZjgLAAGYAACeiSAAAAAAAAJZRJAAAAAAAAMsoEgAAAAAAgGUUCQAA2Mjtdmv+/Plyu92mowAwgBkAwInCTAcAAMDJEhIStHbtWtMxABjCDADgRByRAACAjZqamnTy5Ek1NTWZjgLAAGYAACeiSAAAwEYlJSVavHixSkpKTEcBYAAzAIATUSQAAAAAAADLKBIAAAAAAIBlFAkAAAAAAMAyigQAAAAAAGAZt38EAMBGKSkp2r9/v+kYAAxhBgBwIo5IAAAAAAAAllEkAABgo7KyMj3wwAMqKyszHQWAAcwAAE5EkQAAgI28Xq+OHDkir9drOgoAA5gBAJyIIgEAAAAAAFhGkQAAAAAAACyjSAAAAAAAAJZRJAAAYKP4+HitWbNG8fHxpqMAMIAZAMCJwkwHAADAyQYNGqT09HTTMQAYwgwA4EQckQAAgI1qamq0detW1dTUmI4CwABmAAAnokgAAMBG1dXVevLJJ1VdXW06CgADmAEAnIgiIQAej0dZWVlKTk5WRESERowYoZUrV6qxsVEPPvigXC6XNm3aZDomAAAAAAC24RoJFh06dEjp6emqqqpSdHS0Jk2apIqKCj3zzDM6ceKEzpw5I0lKS0szGxQADDvfIu2plH5VKvn++phP0v89Ii1OkpIGmMuGnnH2vPTK+9IrJ6Vqr+T3S8MjpdtGSHeOlAaHm04IAACuBkWCBR6PRwsXLlRVVZUyMzP12GOPye12S5KeeOIJPfroowoLC5PL5dLUqVMNpwUAc97+UPrGAenDpsufy/lL68eCEdLqqVL/0J7PB/ttLZGePio1+do+XtIgbSqUni+Slk+UvjjWTD4AAHD1OLXBghUrVqi8vFzLly/Xxo0bL5UIkpSVlaXU1FQ1NzcrKSlJAwcONJgUAMx5yyN99c32S4S/lXdSyjogNfuuvJ1TREVFacaMGYqKijIdxXbZ70kbDl9eIvyt8z7pP49KPz7ec7kAk4JpBgAIHhQJnSgsLFRubq6GDRum9evXt7vN9ddfL0lKTU299NisWbPkcrna/Xj44Ycv+xwvvfSSbrrpJkVHR2vQoEG6+eabdfToUXt2CgC6WWOzlFXQ+kuiFX+oll54z95MvcXIkSP17LPPauTIkaaj2OrwGen771rf/r+OSQc/tC8P0FsEywwAEFw4taETOTk58vl8ysjI0IAB7Z/YGxkZKaltkfDcc8+prq6uzXY7duzQunXrtGDBgjaPP/PMM8rMzNTXv/51fec731FTU5P27dsnr9fbzXsDAPbYWS7VXghszdZS6b5kKczhlXZLS4u8Xq8iIyMVGurc8zlySwJf84u/SJ+M6f4sQG8SLDMAQHChSOjEnj17JEmzZ8/ucJvy8nJJbYuESZMmXbbdd7/7XcXGxmr+/PmXHjtx4oRWrVqlp556SsuXL7/0+Oc+97mrzg4APeVXpYGvOX1Oer1KuvUT3R6nVykuLtZ9992nF154QSkpKabj2KKmSdpdEfi616tavw5iI7o/E9BbBMMMABB8KBI6UVZWJkkaNWpUu883Nzdr7969ktoWCX/v9OnTevXVV/XII48oLOzjl/0nP/mJ+vXrp4ceeqjbMk+bNk1VVVXd9vmuxvBNZXKFhKrF16LExPZfQydj/4N7/6UgeQ1CwhS3qbRLS7+67hk1bH+ie/PYbMmSJQFt/8EHH0iSdu7cqbfeesvSmkWLFgWcy6T+42/S0K/9MuB1LX5pxu1f0vl3f9/9odBrOHEOBjIHujIDpL43BwD0PXFxcTpw4ECX1lIkdKKxsVGSOjzNIDc3Vx6PR263W6NHj+7w8+Tk5Ki5uVlLly5t8/gf//hHTZgwQf/zP/+jdevW6eTJkxo3bpz+4z/+Q/fee2+XMldVVenUqVNdWtvdhvv9rf+P399rMvUk9j+4918KjtcgJHKA4rq4tvF8S597XS6+L1h18f3D6/VaXtvXXpOBcY0a2sW1NfUf6Wwf218ExolzMJA50JUZIPW9OQAguFAkdCIuLk41NTU6ePCgZs6c2ea5yspKrVq1SpI0depUuVyuDj9Pdna2Jk6cqGnTpl32OU6dOqVvfvOb2rBhg0aMGKEf//jH+uIXv6jY2FjNnTu3S5l7jYuviculhIQEs1lMYP8v/d+g3H8pOF4Dl0v+lma5QgN/S4lytfS51yU6Ojqg7S/+4hAZGWl5bV97TfpFdf3HiSGRYYruY/uLADlwDgYyB7oyA6S+NwcA9D1X83sjRUIn5s6dq8LCQm3YsEHz5s3T+PHjJUkFBQVaunSpPB6PJCktLa3Dz3Hs2DEdOHBAjz/++GXP+Xw+NTQ0KDs7W3feeack6dZbb9W7776r73znO10qErp6eIodpm+XfJJCQ0IvXUsimLD/wb3/UvC8Bv+6X3qjC2dU/frJf1PKD/+t+wPZqKCgIKDtjx07ppycHKWnp1s+P/rpp5/uQjJzLvik234jnTkf2LpB/aQ//PaXiuD6c47mxDkYyBzoygyQ+t4cABBcHH6t7KuXlZWlmJgYnTx5UpMnT9a1116rcePGafr06RozZozmzJkj6crXR8jOzpbL5VJGRsZlzw0d2now6N8WBi6XS3PnztWRI0e6eW8AwB53JQW+ZsoQKWVwdyfpfZKTk7Vr1y4lJyebjmKbfiHSnV049f32kaJEgOMFwwwAEHwoEjqRmJio/Px83XbbbYqIiFBpaamGDh2qzZs3a8eOHTp+/LikjosEv9+vLVu2aNasWe3eP3jy5Mkd/tvnzp3rnp0AAJvNiJWuHWJ9e5ekfxpvW5xeJSwsTEOGDGlzoV0nWpIkDe5vffuB/aS7O760EOAYwTIDAAQXigQLJk6cqLy8PNXX16u+vl779u3TsmXL1NjYqNLSUoWEhGjKlCntrn3jjTdUVlZ22UUWL7rjjjskSb/5zW8uPebz+fTb3/5WN9xwQ/fvDADYIMQl/ed0aYzb2vb/dq30qeH2ZuotysvLlZmZ6ZhDujtyTaT01HRpgIXflaLCWr9e4qLszwWYFiwzAEBwoUi4CkePHpXf79e4ceMUFdX+T0PZ2dmKjIzs8DZBCxcu1Kc//WktW7ZMP/zhD7Vr1y7dfffdOnr0qP7jP/7DzvgA0K2GhEs//pS0cITUv4N3lzFu6ckbgusv0Q0NDcrPz1dDQ4PpKLa7dmjr18CNsR1vM32Y9OObpbSYnssFmBRMMwBA8OAYq6tw+PBhSR2f1nDu3Dlt27ZNd955p9zu9v9M53K5tH37dj366KNavXq16urqlJqaqv/93/+9dP0FAOgr3P2kx66TVk6W8k5Kf6mXzrdIg/pLc+KlT8Z8fAF3ONPYgdKmmdL7Da1fAz8p/vi5bbOlJItHrQAAgN6LIuEqdFYkRERE6OzZs51+nsGDB2vz5s3avHlzd8YDAGMG95e+NNZ0Cpg0coD0yETpZ8WtV+wPESUCAABOwakNV6GzIgEAAAAAAKfhiISrsGfPHtMRAAC9XGxsrFauXKnY2CtcOACAYzEDADgRRQIAADaKiYlRRkaG6RgADGEGAHAiTm0AAMBGdXV12r17t+rq6kxHAWAAMwCAE1EkAABgo4qKCq1evVoVFRWmowAwgBkAwIkoEgAAAAAAgGUUCQAAAAAAwDKKBAAAAAAAYBlFAgAANgoPD9eECRMUHh5uOgoAA5gBAJyI2z8CAGCj0aNHKzs723QMAIYwAwA4EUckAAAAAAAAyygSAACwUVFRkW6++WYVFRWZjgLAAGYAACeiSAAAwEZ+v18XLlyQ3+83HQWAAcwAAE5EkQAAAAAAACyjSAAAAAAAAJZRJAAAAAAAAMu4/SMAADZKSkpSTk6OEhISTEcBYAAzAIATUSQAAGCjiIgIjR071nQMAIYwAwA4Eac2AABgo8rKSq1bt06VlZWmowAwgBkAwIkoEgAAsFFtba22b9+u2tpa01EAGMAMAOBEFAkAAAAAAMAyigQAAAAAAGAZRQIAAAAAALCMIgEAABsNHTpU999/v4YOHWo6CgADmAEAnIgiAQAAG4WEhKhfv34KCeEtFwhGzAAATsREAwDARh6PRz/60Y/k8XhMRwFgADMAgBNRJAAAAAAAAMsoEgAAAAAAgGUUCQAAAAAAwDKKBAAAbOR2uzV//ny53W7TUQAYwAwA4ERhpgMAAOBkCQkJWrt2rekYAAxhBgBwIo5IAADARk1NTTp58qSamppMRwFgADMAgBNRJAAAYKOSkhItXrxYJSUlpqMAMIAZAMCJKBIAAAAAAIBlFAkAAAAAAMAyigQAAAAAAGAZRQIAAAAAALCM2z8CAGCjlJQU7d+/33QMAIYwAwA4EUckAAAAAAAAyygSAACwUVlZmR544AGVlZWZjgLAAGYAACeiSAAAwEZer1dHjhyR1+s1HQWAAcwAAE5EkQAAAAAAACyjSAAAAAAAAJZRJAAAAAAAAMsoEgAAsFF8fLzWrFmj+Ph401EAGMAMAOBEYaYDAADgZIMGDVJ6errpGAAMYQYAcCKOSAAAwEY1NTXaunWrampqTEcBYAAzAIATUSQEwOPxKCsrS8nJyYqIiNCIESO0cuVKNTY26sEHH5TL5dKmTZtMxwQA9CLV1dV68sknVV1dbToKAAOYAQCciFMbLDp06JDS09NVVVWl6OhoTZo0SRUVFXrmmWd04sQJnTlzRpKUlpZmNmgvcaxWer1S8v31v/2SzjVLEXzFAUBQ8JyTXi1v+z5w+pwUG2EyFYCe4vNL+09LBzxSY7MUGSZdN1S6abgU6jKdDsDV4tc6CzwejxYuXKiqqiplZmbqsccek9vtliQ98cQTevTRRxUWFiaXy6WpU6caTmvWnz6QNhdJh//u6D2/pM/9VrpjpPSVFCki1Eg8AIDNTjVKmwqlPZVSi//jx/2SFvxWmhUnLZ8ojRhgLCIAG/n90q/fl154T3q/se1zL0iKj5TuHdP64aJQAPosTm2wYMWKFSovL9fy5cu1cePGSyWCJGVlZSk1NVXNzc1KSkrSwIEDDSY169fvSyv+dHmJcFHdBSn7hPTIH6WGCz2bDQBgv+Ja6cv50m8r2pYIF7X4pdcqpS//QTp2tsfjAbCZ3y/951Fp3TuXlwgXVXpbt/k/b7cetQCgb6JI6ERhYaFyc3M1bNgwrV+/vt1trr/+eklSamrqpcdmzZoll8vV7sfDDz8c8Ha93Z8+kL576ONDWK/kzzXS6rda32wAwOmioqI0Y8YMRUVFmY5iqzNN0sp90pnznW979nzrtp5z9ucCTAuWGSBJW05IOX+xtu2Ocum/jtmbB4B9OLWhEzk5OfL5fMrIyNCAAe0fhxkZGSmpbZHw3HPPqa6urs12O3bs0Lp167RgwYKAt+vtni+yViJc9McPpHfOSGkxtkUCgF5h5MiRevbZZ03HsN2LpdIHARQDHzZJW0ulf06xKxHQOwTLDDjXLP2kOLA1Pz8hZYyVBve3JxMA+1AkdGLPnj2SpNmzZ3e4TXl5uaS2RcKkSZMu2+673/2uYmNjNX/+/IC3682KaluPMgjUtlKKBADO19LSIq/Xq8jISIWGOvMCMc0+6cWywNe9XCb903ipH8dHwsGCYQZI0m8qWk9jDUSTT8p7X/pSsj2ZANiHt+5OlJW1/mQ0atSodp9vbm7W3r17JbUtEv7e6dOn9eqrr+ree+9VWFjH/Y3V7XqT31d2bd3vqro3BwD0RsXFxZozZ46KiwP8U10fUng2sKMRLvqwSfrzmW6PA/QqwTADJOl3/DwIBJW+8ZuqQY2NrVeK8Xq97T6fm5srj8cjt9ut0aNHd/h5cnJy1NzcrKVLl17x37O63ZVMmzZNVVU9N5XdX/iOomd9OeB1TS1SYtJYqbnJhlS9w/BNZXKFhKrF16LExPbLKCcL9v2XeA2caMmSJQFt/8EHH0iSdu7cqbfeesvSmkWLFgWcy6TwKXM05JEXurT2C/f/k5reebWbE6E3ceIcDGQOdGUGSH1vDgzNfEn9x94Q8Lq33i1W4j0dH/kLwD5xcXE6cOBAl9ZSJHQiLi5ONTU1OnjwoGbOnNnmucrKSq1atUqSNHXqVLmucA+b7OxsTZw4UdOmTbviv2d1uyupqqrSqVOnurw+UAk1HkV3YZ3f59Op90scfdXF4Rf3ze/v0f9Neotg33+J18CJLhbMVl0sor1er+W1fe1rxT20XEO6uNZTdUp1fWx/ERgnzsFA5kBXZoDU9+ZAVEOdunKpg/ON9X1uXwFQJHRq7ty5Kiws1IYNGzRv3jyNHz9eklRQUKClS5fK4/FIktLS0jr8HMeOHdOBAwf0+OOPX/HfsrpdZ+Li4q5qfaAiasu7tK658rgSPvGJbk7Ty1wsl1wuJSQkmM1iQrDvv8Rr4EDR0YFVpxd/cYiMjLS8tq99rYQ0n5W/pVmu0MB+rPD7fBp04YzcfWx/ESAHzsFA5kBXZoDU9+ZAqOcvkm4NeJ3r9Ik+t6+AU1zN740uv9/Bfw7uBuXl5UpLS9OHH36osLAwpaSk6Ny5c3rvvfeUnp4un8+nXbt26fnnn9dDDz3U7uf41re+pfXr16u0tFQjR47s8N+yul1vc65Fuu03Um2AF9jJulb6QsdngzjC9O2td7MIkbT/dtNpel6w77/Ea+BEBQUFAW1/7Ngx3XfffXrhhReUkmLtFgU33BD44cGmrSoI/BzpTw+XnpphTx70Hk6cg4HMga7MAKnvzYHSemnJ7wJf95NPSVOHdn8eAPbiYoudSExMVH5+vm677TZFRESotLRUQ4cO1ebNm7Vjxw4dP35cUscXWvT7/dqyZYtmzZp1xXLA6na9UUSodGeApzxGh0mfS7QnDwD0JsnJydq1a5eSk519WfK7u1AMO71MBqTgmQFJbmlGbGBrUgZJ13b1vCgARnFqgwUTJ05UXl7eZY83NDSotLRUISEhmjJlSrtr33jjDZWVlemxxx674r9hdbveatkE6dAZ6R0LV98OdUnfmyYN6Gd/LgAwLSwsTEOGOP8n5WnDpAfGWb+P/NKx0sxr7M0E9AbBMgMk6bE06cv5UrWFu7gM6ic9fv3HZ74A6Fs4IuEqHD16VH6/X+PGjVNUVFS722RnZysyMrLTq/ta3a63Cg+VnrlRurmTHwqjw6SnZ/DDI4DgUV5erszMTJWXd+16Mn3JP6dID6dc+YcLl6SHxksrJvVUKsCsYJoB10RKP/yUNMZ95e0SoqQffUoaOaBncgHofhQJV+Hw4cOSOj6t4dy5c9q2bZvuvPNOud0dT1Sr2/V2F0uCn3yq9bSFyNDWx0MkJbulb1wr/e9nKBEABJeGhgbl5+eroaHBdBTbuVzSP42Xts9tPTohNuL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"text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 6, @@ -263,7 +267,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -277,7 +281,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.11.9" }, "toc": { "base_numbering": 0 @@ -285,4 +289,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-utils/how_tos/create-circuit-slices.mdx b/docs/addons/qiskit-addon-utils/how_tos/create-circuit-slices.mdx new file mode 100644 index 00000000000..9510f09437d --- /dev/null +++ b/docs/addons/qiskit-addon-utils/how_tos/create-circuit-slices.mdx @@ -0,0 +1,206 @@ + + +# Slicing circuits using `qiskit_addon_utils.slicing` + +Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. Some Qiskit addons make use of the term **slices** to describe layers of arbitrary depth. More concretely, slices can be defined as time-like partitions of a [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) which span all qubits. Similar to layers, composing all slices of a `QuantumCircuit` produces a circuit which is semantically equivalent to the original. + +The [qiskit\_addon\_utils.slicing](/docs/api/qiskit-addon-utils/slicing#module-qiskit_addon_utils.slicing) module provides a few utilities for partitioning `QuantumCircuit`s into slices. This is for example useful for [operator backpropagation](https://github.com/qiskit/qiskit-addon-obp/) We will give an overview of those tools in this guide. + +**Note:** Throughout this guide, we will slice the circuit and use [qiskit\_addon\_utils.slicing.combine\_slices](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.combine_slices) to recombine the slices with barriers to make it easier to visualize the slices. + +First, we’ll create a circuit from which we’ll create slices. + +``` +[1]: +``` + +```python +import numpy as np +from qiskit import QuantumCircuit + +num_qubits = 9 +qc = QuantumCircuit(num_qubits) +qc.ry(np.pi / 4, range(num_qubits)) +qubits_1 = [i for i in range(num_qubits) if i % 2 == 0] +qubits_2 = [i for i in range(num_qubits) if i % 2 == 1] +qc.cx(qubits_1[:-1], qubits_2) +qc.cx(qubits_2, qubits_1[1:]) +qc.cx(qubits_1[-1], qubits_1[0]) +qc.rx(np.pi / 4, range(num_qubits)) +qc.rz(np.pi / 4, range(num_qubits)) +qc.draw("mpl", scale=0.6) +``` + +``` +[1]: +``` + +![../\_images/how\_tos\_create\_circuit\_slices\_3\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_3_0.avif) + +In the case where there is no clear way to exploit the structure of the circuit for back-propagation, a user may wish to simply partition their circuit into slices of a given depth. Here, we’ll separate this circuit into depth-1 slices. + +``` +[2]: +``` + +```python +from qiskit_addon_utils.slicing import combine_slices, slice_by_depth + +slices = slice_by_depth(qc, 1) +combined_slices = combine_slices(slices, include_barriers=True) +combined_slices.draw("mpl", scale=0.6) +``` + +``` +[2]: +``` + +![../\_images/how\_tos\_create\_circuit\_slices\_5\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_5_0.avif) + +Now let’s try depth-2. + +``` +[3]: +``` + +```python +slices = slice_by_depth(qc, 2) +combined_slices = combine_slices(slices, include_barriers=True) +combined_slices.draw("mpl", scale=0.6) +``` + +``` +[3]: +``` + +![../\_images/how\_tos\_create\_circuit\_slices\_7\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_7_0.avif) + +In many cases, such as Trotter circuits, it may be advantageous to slice by gate type. Slices holding a given gate type will be further split out into depth-1 slices, as there is little downside in doing so. + +``` +[4]: +``` + +```python +from qiskit_addon_utils.slicing import slice_by_gate_types + +slices = slice_by_gate_types(qc) +combined_slices = combine_slices(slices, include_barriers=True) +combined_slices.draw("mpl", scale=0.6) +``` + +``` +[4]: +``` + +![../\_images/how\_tos\_create\_circuit\_slices\_9\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_9_0.avif) + +If your circuit was designed to exploit the physical qubit connectivity, you may want to create slices based on an edge coloring. Here, we will assign a 3-coloring to the circuit edges and slice the circuit with respect to the edge coloring. This only affects non-local gates. Single qubit gates will be added to their own slices by gate type. + +``` +[5]: +``` + +```python +from qiskit_addon_utils.slicing import slice_by_coloring + +# Assign a color to each set of connected qubits +coloring = {} +for i in range(num_qubits - 1): + coloring[(i, i + 1)] = i % 3 +coloring[(num_qubits - 1, 0)] = 2 + +# Create a circuit with operations added in order of color +qc = QuantumCircuit(num_qubits) +qc.ry(np.pi / 4, range(num_qubits)) +edges = [edge for color in range(3) for edge in coloring if coloring[edge] == color] +for edge in edges: + qc.cx(edge[0], edge[1]) +qc.rx(np.pi / 4, range(num_qubits)) +qc.rz(np.pi / 4, range(num_qubits)) + +# Create slices by edge color +slices = slice_by_coloring(qc, coloring=coloring) +combined_slices = combine_slices(slices, include_barriers=True) +combined_slices.draw("mpl", scale=0.6) +``` + +``` +[5]: +``` + +![../\_images/how\_tos\_create\_circuit\_slices\_11\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_11_0.avif) + +For more custom slicing strategies a user may wish to place barriers in the locations they want to slice and use the [slice\_by\_barriers](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.slice_by_barriers) function. Here, we will create 3 slices, one for each rotation layer and one for the entangling layer. + +``` +[6]: +``` + +```python +qc = QuantumCircuit(num_qubits) +qc.ry(np.pi / 4, range(num_qubits)) +qc.barrier() +qubits_1 = [i for i in range(num_qubits) if i % 2 == 0] +qubits_2 = [i for i in range(num_qubits) if i % 2 == 1] +qc.cx(qubits_1[:-1], qubits_2) +qc.cx(qubits_2, qubits_1[1:]) +qc.cx(qubits_1[-1], qubits_1[0]) +qc.barrier() +qc.rx(np.pi / 4, range(num_qubits)) +qc.rz(np.pi / 4, range(num_qubits)) +qc.draw("mpl", scale=0.6) +``` + +``` +[6]: +``` + +![../\_images/how\_tos\_create\_circuit\_slices\_13\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_13_0.avif) + +We will not draw the re-combined slices as a single circuit since it would look identical to the input circuit. Instead, below we draw each slice on its own. + +``` +[7]: +``` + +```python +from qiskit_addon_utils.slicing import slice_by_barriers + +slices = slice_by_barriers(qc) +slices[0].draw("mpl", scale=0.6) +``` + +``` +[7]: +``` + +![../\_images/how\_tos\_create\_circuit\_slices\_15\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_15_0.avif) + +``` +[8]: +``` + +```python +slices[1].draw("mpl", scale=0.6) +``` + +``` +[8]: +``` + +![../\_images/how\_tos\_create\_circuit\_slices\_16\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_16_0.avif) + +``` +[9]: +``` + +```python +slices[2].draw("mpl", scale=0.6) +``` + +``` +[9]: +``` + +![../\_images/how\_tos\_create\_circuit\_slices\_17\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_17_0.avif) diff --git a/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices.ipynb b/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices.ipynb index 64e11ef578d..2908c94ff5f 100644 --- a/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices.ipynb +++ b/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices.ipynb @@ -15,7 +15,7 @@ "id": "9f419feb-5d35-489e-909c-c8e7ea2caaec", "metadata": {}, "source": [ - "The [qiskit_addon_utils.slicing](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.slicing) module provides a few utilities for partitioning ``QuantumCircuit``s into slices. This is for example useful for [operator backpropagation](https://github.com/qiskit/qiskit-addon-obp/) We will give an overview of those tools in this guide.\n", + "The [qiskit_addon_utils.slicing](/docs/api/qiskit-addon-utils/slicing#module-qiskit_addon_utils.slicing) module provides a few utilities for partitioning ``QuantumCircuit``s into slices. This is for example useful for [operator backpropagation](https://github.com/qiskit/qiskit-addon-obp/) We will give an overview of those tools in this guide.\n", "\n", "**Note:** Throughout this guide, we will slice the circuit and use [qiskit_addon_utils.slicing.combine_slices](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.combine_slices) to recombine the slices with barriers to make it easier to visualize the slices." ] @@ -36,8 +36,9 @@ "outputs": [ { "data": { + "image/png": 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nDqJy8cQhqCs+g56j4+L9dFy8H8d2PWk1boEkmWR3mpmQkEC/fv1YtGgRGzZsYMeOHcyaNYuQkBCUSml7b/9P5qFQKHAJ8uXAzBUYKvUUpZzHaDDw4OKpnP0i9qaHtja3LXszuXi5aZf2P9/2K++8HIaLk2W+8D3uZD7vfHyK2CM5KBQwvF8gf53aha4dvSyy/7tNXXUGkPDeRgZ8Mo/cA6ckO+1MWzIKo8FARd45guZ8hdLWzrSsLPkQ1doi3MKGSpJNdkdm0dHRzJgxg9mzZxMUFMSsWbPw8/OjS5cuUkdj37PL2PLQS/w0YwV93p2JvXfNFcKEdzfSomcw6VsPmT3Dqg3JKJq4Da1Ozxfb05olz61s2ZvBnyZ/z46fs7hSrker0/NNbDrhT2xj/y+5Fslwt6mvzopSs3Dy9yZzZ7xk2TSvbaHT6lTum7uezA+nUVWUD4BRX0X2Z/NoPfV9ybLJqpmlpaVx8OBBoqOjr3tdrVabmtnIkSNp1aoVEyZMkCIiAJnb48jZf5IuL44CQJtdwJWcArPvN+NCKQeO5VH/kH/jKBQ1R2fmVl6hZ8qCAxgMRvTVV1Pr9Uaq9AYmz/8JQwMXMO51N9ZZ8ORBpK7dTchzwyROBh59x+IaOoi8b5YAkLd5GV4RT2Pr2UqyTLI6zUxISMDd3Z3AwEDTazqdjt9++83UzD788EPS0tJYs2ZNo7c7YsQI0tLqPxJxrbYlina3lfXY218y/IelnP7wW3QXixr9vkcHPkqJTdVt7avWFfzA5moT37oy8qbxMU3rmp8TN4++7vW07BJGvhgLgNEIx06lExISckc5GquE9pQoHwPFzZ+ZRiOczy3jvs6DcOKCWXM0RppyEoDZ/05ut9Zq6yxxzXcEDAxj9/hF9PlHFJ6dg7h8Or3B995OrSnc/bB/9fbGe/0nLSH5lR649x5D6ekfab8otlHve3TgQIxFOfUu12g0bNu27baygMyOzBQKBdXV1dcN9MfExHDlyhVTM2vdurUk2b4Jf57ic1d/AaUZeazrOPm2GllTGbCV5bbqo1c4oaChizYG9Aons+e4m9RXZx0mPkLyxzvAaOTk+9/Q7ZVxFs/W+d8Z2AcEm362b9WO0K+KuXxgHZUF2Zx9vT+p8yNIWzK6ga2Yj8JorOuamDTOnz+PRqPh9ddfZ9KkScTGxjJ//nxUKhU5OVd/wfv372fNmjWsX7++WfZbmnWRTeHPN8u2bmVM/Cpc/pjKcbsOJ+TTd9L2BtepPSLrNHpzg+sFtHQka8/EO8rRWDt/zmJY9J4GTyWPrR9J9we8zZqjMUJGbQIgacsYs+5HrrWWcwVGNO7Aqsm2RYKfY/NvV1ZHZoGBgXz44YesWbOG0NBQjh49yoQJE2Qx+C8HQf4uKJs6+g8ogPaB5r+96dE+/vh6O9SZ2cZGQZcOHoTeL65oCs1DVs0MICoqitzcXIqKioiJiSE9PV00sz+08nFkWL/AW694C0Zg+piOTQ90CzY2Sja/9wgO9irUtldLTW2rxN1ZzfplA1AomqE7CwIybGY3SkxMvK6Zvfjii8yZM4f9+/cTGRnZ4MC+NXp+/P1Ner9CAT4e9oyObNs8gW7hwS4tSN46hrmTO2OnVmKnVrIgqhtnvh3D/fe5WySDcG+QdTPTarVkZmZe18xWrlzJsWPHyMvLIzY2Fo1GI0m2kKjhOLRwB8DO04XRhz/AOcDH7Psd2Nuf9oGu9c41S8suIS27pN73G40wY1wwdmob8wSsQ2tfZxa/GIamtSua1q68/lwoLbzEDe+NIVWdNUb+t+9SdblmrqC+pIDEqHZU5GdIlkdWUzNu5OTkJJtbmAKHhBM6bwKlGfk4+nmSdyjJdCXzgemPcd5CExmVSgWb3nuEvpO2U6aruumWptrpF/WJCPNl/vRu5gsoNIlc6qwuhXFbyFm3ADtfDVWXsnHp3N80ryx/2wrce42SLBvIvJnJifZCASmf7qIkPRcHH3dcg2p+if4DQrn4Syo+oe0tlqVzB092rHqU4dF7KCqtbPT7Hu7hy5YVkRY9KhNuj5zq7EZqn0BaDH0Bu1btqCrKpyK3ZuJ18bGdOAf3QfurdI0WZH6aKScewYEUJmfi+UBb7DxcyDucBECLsI74R3TFv3832j/xiMXy/Km7L3FfDGdw34B616kdW3d2VPHKpE7sjhmMu6tdvesL0pNbnV1Ll3kah7ad0WWcpLr0Ei6dIgAoSzlMScJuSo7voiD2Y0mygTgyazT34EAyd8bjPyCUFj2DSfn8BwBOLKuZ69ZtzjjObdhn0UzBQe7sXD2Ic+dLiPk6hS++P8fFyzoMhporhvff587Mcffz5GManB3NP0lWaDo51lmt8vOJePQaRfGxnZQlH8JnyEwA/J98E4CcrxbiNWCKJNlANLNGO7poLQAnlq4n7I1JN307RsK7G6WIBUC7QFf+MSecf8wJx2Coue9RnEreneRcZwHPLAfA/6m3yP50LgrV9R+QfhMXSpDqKnGaeQdqC06OlEqFaGRWQs51VtvY5EQ0M0EQrIJoZtQ8/VnlbG/2/aic7VE7i/lV9zK51pqTquYBvebmqKrZlzmIMTPAzsOFsfGrzf6AXrWzg3ia+T1OrrXmpq550ri5HtBby0llnqeZg2hmJnYeLqLRCBYh11pzU5uv0ViCOM0UBMEqiGYmCIJVEM1MEASrIJqZIAhWQTQzQRCsgmhmgiBYBdHMBEGwCqKZCYJgFUQzEwTBKog7AP5QUVgqu1tMBOsk11orrhS3M931KgpL+Tp8JvqycrPuR+Vsz9j41aKh3cPkWmvFlTA8Fq6YuZk5qmruATVHQxOnmUBlmc7sxQWgLys3+yeyIG9yrTWt3vyNDGr2Ya6jP9HMBEGwCuI0UzAro9FI3MmL7P8ll/wCHSjgnY9PMrhvAN2CvaSOJ1gR0cwa6fH4VejLKzFU6lGqVSSt+Y5f1+2VOpZsaa9U8eWONP61PplTZy9ft+y1fx7ltX8epXeXFjw/4X4eH9gWeztRiiDvOjs9vS1KtQMKlRqjvpKWI+fg/eg0qWOZiAq6DfueXUbxuRzcO7Zm+O5lZO89ji6/UOpYsnPufAmDZ+wiLbvU9Li7uhw5dZG4UxdZ/vlpdvzrUfxaOFkupIzJuc40r23BPiAYXWYiya90x7XHUNReflLHAsSY2R0pSs2isliLUysvIv4zF+fWLQBoP3EAwVMGS5xOWmcziun91DbSsksBbnri+rVqF51MvUzvp74j56LW/AHvItfWmW/vEHounAyAV1cNvd6ZLmk2hzadsHHyoOpSNr//8BGp8yNInR9B8is9SJnXW5JMopndgRbhwVQUlXH5TAYJyzfSbe44lGoV941+iNQv9kgdTzKFJRUMnrmLgqKKOpdvXRnJ1pWRdS47n6dl6KzdlFdY4JLaXeLaOsuLS8KljS+Ofl50nf04J9//RtJsZWcOonLxxCGoKz6DnqPj4v10XLwfx3Y9aTVugSSZZHmaefz4cWbMmMGpU6fo3Lkz0dHRzJgxg7KyMpRK6fpv/0/moVAocAny5cDMFRgq9RSlnMdoMPDg4qmc/SL2puccWpJeb+Dn43lcLq7gAY0H99/nbtH9f7z5LOkXyupdrglwbfD9J1Mv8/XudJ4e3r65o91V6qozgIT3NjLgk3nkHjgl2Wln2pJRGA0GKvLOETTnK5S2dqZlZcmHqNYW4RY2VJJssmtmCQkJ9OvXj0WLFrFhwwZ27NjBrFmzCAkJkbSRwdWxjDbDetP3vZnkxZ2hvKCYhHc3EvnFfOL+EiNZtq37MoladIiLl3WobBRU6Y38KbQlXy3tT4Cv+ceiDAYjqzacQaFo+NSyIQoFrNqQfM83s/rqrCg1Cyd/bzJ3xkuWrXbMrPDQ12R8MBWXTv2wdW+JUV9F9mfz0Lwq3RGj7E4za4/CZs+eTVBQELNmzcLPz48uXbpIHc0kc3scOftP0uXFUQBoswu4klMgWZ79v+Qy+uVY8i/pMBqhSl/TTf53+ncemrKdKzrzn7rtPnyB9Atld9zIoKYJHjn1O8fPSPd3KSc31lnw5EGkrt1NyHPDJE4GHn3H4ho6iLxvlgCQt3kZXhFPY+vZSrJMsmpmaWlpHDx4kOjo6OteV6vVdOnShbNnz/Lwww/z0EMP0atXL7Zv3y5RUjj29pe0G98fhxbukmWo9bdVx6nrsmGV3kB2vpb1u34ze4Zdh7Jlua27XW2dOfp5ETAwjITlG6nS6vDsHCR1NPwnLaFg76eUJv1M6ekf8R4cJWkehdHYlM/S5rVp0yamTZtGYeHV8QCdToe3tzfbt2+nc+fOKBQKvLy8uHjxIj169CArK+uW2x0xYgRpaWn1LnettiWquF2Tsg9cN589Tyy+5Xoxbucosalq0r6uZcCGFJuX6l/BaMSZ3wg0bG22fdblgmIwxcoHTD9vXRl50xiZpnXNz2lZJde9npZdwsgXY00/exmO0tJ4wIxpb5amnASAxrDWrPu501rrNmcclxLTyfrhF5z8vXnwran8+MzSBt9zO7WmcPfD/tU7u3iVuXompad+NB2VqZw90by2ud71y5cOxFiUU+9yjUbDtm3bbjuHrMbMFAoF1dXVGAwG0/hYTEwMV65coUuXLnh5XZ0x7uDggKKhSUwW1phGZh63+DtQKMBoib+n5tuHbD5dZSTh3Y2mP2svFNyykVlSm5mrpY4AyOzI7Pz582g0Gl5//XUmTZpEbGws8+fPR6VSkZNztZMbjUaefvpp+vTpw/PPP9/k/ZZmXWRTeNO30xhj4lfh8se8tOYSNuFbjidfqnO8Sq1SsnxuONFPhDTrPm8UvSSOD7860+A6iZtHA9BpdP2f2gBvvtCd158LbbZsjREyahMASVvGmHU/cq21nCswIvbW6zWHbZHg59j825XVmFlgYCAffvgha9asITQ0lKNHjzJhwoSbBv9feOEF2rZt2yyNzBq8EVX3P3wbGwVuLmomWeDqYESYbzNuS7pBZOHuJatmBhAVFUVubi5FRUXExMSQnp5+XTN76aWXsLe356233pIwpbyM6N+GNQv6Ym9nA9ScWSoU0KaVM/s/GYqbi5m+De/aDBFtaOXt0ODtS7eiUECndh70DW3ZfMGEe4bsmtmNEhMTTc0sNjaWf/3rXxw7doyIiAgiIiIoK6t/kua95LnHg7m4/wn8WjjS0suBPTFD+HX7WB7QeFhk/7a2SqLGBjd5asasCffLaixUuHvIuplptVoyMzNNzSwyMhK9Xs/+/ftN/zk7O0uSLSRquGlahp2nC6MPf4BzgI8kWWq5OKlxd1Hj6WbHI738UCot2xSixgbj42Ff79FZWnYJadkldS8E2vo58+RjGjOluzvJsc5q5X/7LlWXcwHQlxSQGNWOivwMyfLI6mrmjZycnDAYDFLHACBwSDih8yZQmpGPo58neYeS0F0sAuCB6Y9xXsJZ2XLh6+3I9/96lH7PfE95ZfVNR2nXTr+4kYerml2rB+HiZP5TYjmTc50Vxm0hZ90C7Hw1VF3KxqVzf9N0jPxtK3DvNUqybCDzZiYn2gsFpHy6i5L0XBx83HENqvkl+g8I5eIvqfiE3tu34NTq2cmHnz59jMdm7eb3wvIGb2+qXdamlTO71gyiY5C7RbPKkZzrTO0TSIuhL2DXqh1VRflU5P4KQPGxnTgH90H7q7Qf6KKZNZJHcCCFyZn4dO+AQV9N3uEkAFqEdcTW2R6f7h1Q2Cg5sWy9xEml17OTD6c2jSLm6xTWfJ1CXkHd30Uf6OvM8+PvZ9qYjni62dW5zr1GznWmyzyNQ9vOaFOPoLCxxaVTBABlKYcx6ErRph7BaKjG/8k3LZ4NRDNrNPfgQDJ3xuM/IJQWPYNJ+fwHAFNRdZszjnMb9kkZUVZ8vR3528zu/N+0bmzdl8n+o7kUllSiUICnmx2D+wYwqI8/NjayHra1ODnXWfn5RDx6jaL42E7Kkg/hM2QmgKl55Xy1EK8BUyTJBqKZNdrRRTW3uZxYup6wNybd9FU/187QFq6ytVXy+KNBPP6o9PcS3g3kXGcBzywHwP+pt8j+dC4Kle11y/0mLpQg1VXiY/EO1BacIJiTnOustrHJiWhmgiBYBdHMqHmUvcrZ3uz7UTnbo3Z2MPt+BPmSa605qWqeNm5ujqqafZmDGDMD7DxcGBu/2uxPG1c7O2Dn4WLWfQjyJtdac1PDd5Hme9p4LSdVzb7MQTSzP9h5uIhGI1iEXGvNTW2+RmMJ4jRTEASrIJqZIAhWQTQzQRCsgmhmgiBYBdHMBEGwCqKZCYJgFUQzEwTBKohmJgiCVRDNTBAEqyDuAPhDRWGp7G4xEayTXGutuFLcznTXqygs5evwmejLys26H5WzPWPjV4uGdg+Ta60VV8LwWLhi5mbmqKq5B9QcDU2cZgKVZTqzFxeAvqzc7J/IgrzJtda0evM3MqjZh7mO/kQzEwTBKojTTCtQWFLBZ1t/Zd2ONM5mFGMwQtCQDYRoPIh6PJihDwWI79oXrJ5oZo30ePwq9OWVGCr1KNUqktZ8x6/r9kqaKeeilgX/Os66HWmUV1Rf91i3jAtlZFwo4/sDWQT6OvHSUyG8/FQniz8YWLg9cqyzWqent0WpdkChUmPUV9Jy5By8H50mdSwT0cxuw75nl1F8Lgf3jq0ZvnsZ2XuPo8svlCRL0rlCBs/cRXb+FdNr9T2fMitfy5zl8RxOuMgXS/phbyd+7XImpzq7kea1LdgHBKPLTCT5le649hiK2stP6liAGDO7I0WpWVQWa3Fq5UXEf+bi3LoFAO0nDiB4ymCz7z/jQimPTNtxXSNrSG2T2xSbwVOv/YTBUE/XE2Tl2jrz7R1Cz4WTAfDqqqHXO9MlzebQphM2Th5UXcrm9x8+InV+BKnzI0h+pQcp83pLkkmWzez48eOEh4djb29Pz549Wbt2LY6OjhgMBqmjAdAiPJiKojIun8kgYflGus0dh1Kt4r7RD5H6xR6z7ttoNDLmlb3kX677itjWlZFsXRlZ7/s3xWbw/n8TzRVPaEbX1lleXBIubXxx9POi6+zHOfn+N5JmKztzEJWLJw5BXfEZ9BwdF++n4+L9OLbrSatxCyTJJLvzjYSEBPr168eiRYvYsGEDO3bsYNasWYSEhKBUStt7+38yD4VCgUuQLwdmrsBQqaco5TxGg4EHF0/l7BexNz3nsLkdOpHP8eRL9S7XBLg2+H6FAv75ZRIvPxVisYsCJWWV/Hf7OXYdykapUDCyfxsmDL4PRwfZlZ8s1FVnAAnvbWTAJ/PIPXBKstPOtCWjMBoMVOSdI2jOVyhtrz6Jviz5ENXaItzChkqSTXbVFB0dzYwZM5g9ezYAs2bNYuXKlXTp0kXiZFfHMtoM603f92aSF3eG8oJiEt7dSOQX84n7S4zZM6zakNyk9xuNkJWn5fsDWYzo36aZUtUvJb2Ifs98T2FJJVX6miPrHQezeDPmBD9/NowAXyezZ7jb1FdnRalZOPl7k7kzXrJstWNmhYe+JuODqbh06oete0uM+iqyP5uH5lXpjhhldZqZlpbGwYMHiY6Ovu51tVptamZ9+/YlIiKCsLAw/vGPf0gRk8ztceTsP0mXF0cBoM0u4EpOgdn3W1BYztd70ptlW2u+TmmW7TTEaDQy8sU9XCquMDUyAL3eyIWLWia8us/sGe5mN9ZZ8ORBpK7dTchzwyROBh59x+IaOoi8b5YAkLd5GV4RT2Pr2UqyTLJqZgkJCbi7uxMYGGh6TafT8dtvv5ma2b59+9i/fz9xcXGsWbOGixcvSpL12Ntf0m58fxxauFtsn+fOl6DXN8/gfVKa+U9Tfjqax6/nS6iuvjlzld7IoRP5nLFAjrtZbZ05+nkRMDCMhOUbqdLq8OwcJHU0/CctoWDvp5Qm/Uzp6R/xHhwlaR6F0VjfBX3L27x5M1OmTKGoqMg0PrZixQpmz55NQUEBXl5epnWLi4sZNmwYsbGx2NnZ1bdJAEaMGEFaWlq9y12rbYkqbtek7APXzWfPE4tvuV6M2zlKbKruaB9ltOG8zRjTz1tXRt40RqZpXfNzWlbJda+nZZcw8sVY089KYwXBhn/dUY7GuqzoRr7iYYyKekYzjNUEGHbgyq9mzdEYacpJAGgMa826nzuttW5zxnEpMZ2sH37Byd+bB9+ayo/PLG3wPbdTawp3P+xfvbOLV5mrZ1J66kfTUZnK2RPNa5vrXb986UCMRTn1LtdoNGzbtu22c8hqzCwsLAydTsebb77JpEmTiI2N5e2336ZVq1amRlZdXc0jjzxCUlISzz77LGq1PB7015hG1lQKmu+mtubcVn1sKMdIQ5N0lSgx/32K1iDh3Y2mP2svFNyykVlSm5mrpY4AyOzIDCAmJoaFCxei0+kYP348dnZ2nD17ll27dl23Xnl5OUOHDuXVV19l0KBBTdpnadZFNoU/36RtNNaY+FW4/DEv7XadzSim44iGB1gTN48GoNPo+j8ZFQro1tGL4xv/fEc5GqtUW0nLiHXoKuq+wuvjYU/O3omoVNKPdoSM2gRA0pYxt1izaeRaazlXYETsrddrDtsiwc+x+bcrfRXdICoqitzcXIqKioiJiSE9Pd00XlZZWWmaa2ZnZ4ejoyMODg5SxrWo9m1c6XG/F4om3pFkNMITQzXNE6oBLk5qVrzaC4WC6zIrFTX/rVnQVxaNTLAOsq+kxMREUzNLT08nIiKCiIgI+vTpQ8+ePXn44YclTmg5CoWCmePvr/e2pcZS2yp55s/tmyfULTz3eDBbVkQSGnx1vLN3t5bsjhnC6Mi2Fskg3Btk3cy0Wi2ZmZmmZtaxY0cOHDhgupr5t7/9TbJsIVHDTVcy7TxdGH34A5wDfMy+34lDNLg62Tbp6GziEA1e7vbNF+oWRvZvw7ENfyY4yI3gIDcOfj6MR3rJ434+uZOqzhoj/9t3qbqcC4C+pIDEqHZU5GdIlkdWFwBu5OTkJJtbmAKHhBM6bwKlGfk4+nmSdygJ3cUiAB6Y/hjnLTSR0dFBxZoFfXnir/vrXJ6WXVLn6wAKoJWPI2+90MM84W5BfGPHrcmlzupSGLeFnHULsPPVUHUpG5fO/U1XMPO3rcC91yjJsoHMm5mcaC8UkPLpLkrSc3Hwccc1qOaX6D8glIu/pOITapnTNoCJQzVcvKzj5WX/u+5rf4Drpl/cyNvDnl2rB4lZ9zImpzq7kdonkBZDX8CuVTuqivKpyK2ZUlN8bCfOwX3Q/ipdowXRzBrNIziQwuRMfLp3wKCvJu9wEgAtwjpi62yPT/cOKGyUnFi23iJ5XnqqEy08HZjx5iFKtFU3NbUbde3oyeb3H+G+W9y7KUhLbnV2LV3maRzadkabegSFjS0unSIAKEs5jEFXijb1CEZDNf5PvmnxbCDzMTM5cQ8OpDAlC7W7E4FDwsn/peZ2oBPL1hP/xmdc2Jdg8S/RmzhUQ87eifxn4Z/o1tHzpuUqGwXjBgXx0ydDObHxz6KR3QXkWGe1ys8n4hDYCX1ZIYVHtuB8f18A/J98k9bTVuDafTDekVMlyQbiyKzRji6qmRl+Yul6wt6YdNO3Y1w7qdGSnBxtmTq6I8+O6kBqRjF5BTp05XrcXNS0a+1KC697Z+qKNZBrnQEEPLMcAP+n3iL707koVLbXLfebuFCCVFeJZnYHagtOThQKBcFB7gQHuUsdRWgmcqyzWrWNTU7EaaYgCFZBNDNqnv6scjb/vCuVsz1qZ3Hady+Ta605qWoe0GtujqqafZmDOM0E7DxcGBu/2uwP6FU7O4inmd/j5FprbuqaJ42b6wG9tZxU5nmaOYhmZmLn4SIajWARcq01N7X5Go0liNNMQRCsgmhmgiBYBdHMBEGwCqKZCYJgFUQzEwTBKohmJgiCVRDNTBAEqyCamSAIVkE0M0EQrIJoZoIgWAVxO9MfKgpLZXe/nGCd5FprxZXi3sy7XkVhKV+Hz0RfZt6na6uc7Rkbv1o0tHuYXGutuBKGx8IVMzczR1XNDe3maGjiNBOoLNOZvbgA9GXlZv9EFuRNrrWm1Zu/kUHNPsx19CeamWB2V3R6jpy8SKm2irIrVcSf/p3Kqupbv1EQboM4zRTM5mxGMWu+TuaTLWcpLqsyvf7gk9vw8bDnucc78tzjwQS2cpYwpWAtRDNrpMfjV6Evr8RQqUepVpG05jvJnpIjd7pyPdP/fpAvv0+rd52ConIW//skS/5zkpnj72fFvF6oVOJEQc51dnp6W5RqBxQqNUZ9JS1HzsH70WlSxzIRzew27Ht2GcXncnDv2Jrhu5eRvfc4uvxCqWPJStmVKgbP2MWhhIsNrlf7jE+DEf61PpmMnDI2v/8IalsbC6SUNznXmea1LdgHBKPLTCT5le649hiK2stP6liAGDO7I0WpWVQWa3Fq5UXEf+bi3LoFAO0nDiB4ymCJ00lHrzcw/i8/1tvItq6MZOvKyDqXfX8gi+kLD2Js6EnG95hr68y3dwg9F04GwKurhl7vTJc0m0ObTtg4eVB1KZvff/iI1PkRpM6PIPmVHqTM6y1JJlk2s+PHjxMeHo69vT09e/Zk7dq1ODo6YjAYpI4GQIvwYCqKyrh8JoOE5RvpNnccSrWK+0Y/ROoXeyTNVlFZTWFJBQaD5ZvC17vT2fFzdr3LNQGuaBp4EPHa786xLz7XHNHuStfWWV5cEi5tfHH086Lr7Mc5+f43kmYrO3MQlYsnDkFd8Rn0HB0X76fj4v04tutJq3ELJMkku9PMhIQE+vXrx6JFi9iwYQM7duxg1qxZhISEoFRK23v7fzIPhUKBS5AvB2auwFCppyjlPEaDgQcXT+XsF7E3PbTVUtKzS5n/wVG+3p2OvtqIt4cdLz0ZwqvPdMXW1jJ/b6s2JKMAmtJGV21IZsCD8jhtkUpddQaQ8N5GBnwyj9wDpyQ77UxbMgqjwUBF3jmC5nyF0tbOtKws+RDV2iLcwoZKkk12R2bR0dHMmDGD2bNnExQUxKxZs/Dz86NLly5SR2Pfs8vY8tBL/DRjBX3enYm9txtQ85TpFj2DSd96SJJcGRdKCZu4la/31DQygILCChatSWDMK7EWOUo7dfYyB0/kN6mRAXz7YyYX8rXNkuluVV+dFaVm4eTvTebOeMmyaV7bQqfVqdw3dz2ZH06jqigfAKO+iuzP5tF66vuSZZNVM0tLS+PgwYNER0df97parb6umel0Otq2bctf//pXS0cEIHN7HDn7T9LlxVEAaLMLuJJTIEkWgAX/OkapthK9/vpWUqU38P2BLPbEXTB7hvU7f2uW7VQbjHy9O71ZtnW3u7HOgicPInXtbkKeGyZxMvDoOxbX0EHkfbMEgLzNy/CKeBpbz1aSZZLVaWZCQgLu7u4EBgaaXtPpdPz222/XNbPly5cTGhra6O2OGDGCtLT6pwm4VtsSRbvbynrs7S8Z/sNSTn/4LbqLRY1+36MDH6XEpurWKzaSEQXJyhdBUfdVQIPBwLioFQQYdzbbPuuSo3gUlJ1MP29dGXnT+Jimdc3PiZtHX/d6WnYJI1+MNf381tIP+Pc7B82Y9mZpykkAhISEmHU/t1trtXWWuOY7AgaGsXv8Ivr8IwrPzkFcPt1w07+dWlO4+2H/6u2N9/pPWkLyKz1w7z2G0tM/0n5R7K3fBDw6cCDGopx6l2s0GrZt23ZbWUBmR2YKhYLq6urrBvpjYmK4cuWKqZlduHCB+Ph4Ro4cadFs34Q/T/G5q7+A0ow81nWcfFuNzByMqOptZAAolFQrzP8UdUMzfi4a5fUZa1H11VmHiY+Q/PEOMBo5+f43dHtlnMWzdf53BvYBwaaf7Vu1I/SrYi4fWEdlQTZnX+9P6vwI0paMbmAr5qMwyuha+Pnz59FoNLz++utMmjSJ2NhY5s+fj0qlIien5hc8efJkXn75ZU6ePElKSgrvvPNOk/dbmnWRTeHPN3k7jTEmfhUuf0zlaA5GoxH/R74it6Du+/DUtkpefiqEpbPDm22fdZnx5iFivk5pcJ3aI7JOozc3uN4bUaH8fVb3ZsvWGCGjNgGQtGWMWfcj11rLuQIjGndg1WTbIsHPsfm3K6sjs8DAQD788EPWrFlDaGgoR48eZcKECaajsv/973/Y2Njc1immtVMoFLwyqTO2KkWdy6sNRqLGBte5rDmFaNybb1vtmm9bwr1DVs0MICoqitzcXIqKioiJiSE9Pf26Znbu3DkGDx7Mu+++y8aNG1m7dq3EiaU3++kQ/jygLYpr+pnaVomNjYIvl0RwXwNzu5rL08PaYW/XtNn7CgX4eNjz5wFtmimVcC+RXTO7UWJioqmZvfjiixw4cIBdu3YxZ84cxo0bx6RJkyROKD0bGyUb/tGfvf8egpuzLc6OKuZO7sy57WMZP/g+i2Rwd7Xjqcc0TdqG0QjPPd5R3NIk3BFZNzOtVktmZmadc8ymTJnSLONldyokajgOLdwBsPN0YfThD3AO8JEsj0KhoH+4H/4tnQhs5cziF8No62/ZL4F8YeID2CgV1x0hXistu4S07JI6lykUYG9nw3OPm/+U+G4itzq7Vv6371J1ueaODX1JAYlR7ajIz5Asj6wvGzk5OcnmFqbAIeGEzptAaUY+jn6e5B1KMl3JfGD6Y5yXcCKjXHTt6MWaBX2Z/veDKBRXbyavde30i2vVNr/1y/rf818HJOc6K4zbQs66Bdj5aqi6lI1L5/6meWX521bg3muUZNlA5s1MTrQXCkj5dBcl6bk4+LjjGlTzS/QfEMrFX1LxCW0vcUJ5mDamIxVV1by4JA5o3K1NKhsln7/1MCP7i7EyOdeZ2ieQFkNfwK5VO6qK8qnI/RWA4mM7cQ7ug/ZXaT/QRTNrJI/gQAqTM/Hp3gGDvpq8w0kAtAjriK2zPT7dO6CwUXJi2XqJk0pv1oQH6NDGjTdjTvDz8fwG1x3Ux583ZoTSp1tLC6WTNznXmS7zNA5tO6NNPYLCxhaXThEAlKUcxqArRZt6BKOhGv8n37R4NhDNrNHcgwPJ3BmP/4BQWvQMJuXzHwBMRdVtzjjObdgnZURZGdjbn4G9/Tl99jKrNyaz/2gel4vLUaDAy92OwX0DiBobTPs2blJHlRU511n5+UQ8eo2i+NhOypIP4TNkJoCpeeV8tRCvAVMkyQaimTXa0UU1U0BOLF1P2BuTbvp2jIR3N0oRS/Y6d/Bk1et9pY5x15BznQU8sxwA/6feIvvTuShUttct95u4UIJUV8n6aqZc1RacIJiTnOustrHJiWhmgiBYBdHMqHn6s8rZ3uz7UTnbo3Y2/03fgnzJtdacVDUP6DU3R1XNvsxBjJkBdh4ujI1fbfYH9KqdHcTTzO9xcq01N3XNk8bN9YDeWk4q8zzNHEQzM7HzcBGNRrAIudaam9p8jcYSxGmmIAhWQTQzQRCsgmhmgiBYBdHMBEGwCqKZCYJgFUQzEwTBKohmJgiCVRDNTBAEqyCamSAIVkHcAfCHisJS2d1iIlgnudZacaW4nemuV1FYytfhM9GXlZt1Pypne8bGrxYN7R4m11orroThsXDFzM3MUVVzD6g5Gpo4zQQqy3RmLy4AfVm52T+RBXmTa61p9eZvZFCzD3Md/YlmJgiCVRCnmVYiIeUSX+1M40K+FoMRpv3tZx7QuDNpeHu8Pcz//VmCIDXRzBrp8fhV6MsrMVTqUapVJK35jl/X7ZU0k15vYP2u31i1IZm4kxevW/bxlrMAvPbPo0wYfB/RTzxAWIg8Hh4r1E+OdVbr9PS2KNUOKFRqjPpKWo6cg/ej06SOZSKa2W3Y9+wyis/l4N6xNcN3LyN773F0+YWSZCnVVjJu7j52Hcqu9wniAJVVBtZ+d47/bj/HP1/tRfQTIZYLKdwROdXZjTSvbcE+IBhdZiLJr3THtcdQ1F5+UscCxJjZHSlKzaKyWItTKy8i/jMX59YtAGg/cQDBUwabff+6cj1DZv7ArkPZwM1PDq/Pi+8cYflnp82YTGhO19aZb+8Qei6cDIBXVw293pkuaTaHNp2wcfKg6lI2v//wEanzI0idH0HyKz1Imddbkkyimd2BFuHBVBSVcflMBgnLN9Jt7jiUahX3jX6I1C/2mH3/zy06yKGEi3Uu27oykq0rI2963WgEBfCX9+L5bv95MycUmsO1dZYXl4RLG18c/bzoOvtxTr7/jaTZys4cROXiiUNQV3wGPUfHxfvpuHg/ju160mrcAkkyyfI08/jx48yYMYNTp07RuXNnoqOjmTFjBmVlZSiV0vXf/p/MQ6FQ4BLky4GZKzBU6ilKOY/RYODBxVM5+0XsTc85bG5pWSV8sT2t3uWaANd6lxkBhQLe+ugEwyMCzZCufmfSCtn7vxyUSgVD/hTAfQ3kvNfVVWcACe9tZMAn88g9cEqy0860JaMwGgxU5J0jaM5XKG3tTMvKkg9RrS3CLWyoJNlk18wSEhLo168fixYtYsOGDezYsYNZs2YREhIiaSODq2MZbYb1pu97M8mLO0N5QTEJ724k8ov5xP0lxuwZ1mxMadL7jUaITyzgaNLvFrkgUKqtZMK8fez4ORs7dc3vL/rtOCYO1fDJooewU9uYPcPdpr46K0rNwsnfm8yd8ZJlqx0zKzz0NRkfTMWlUz9s3Vti1FeR/dk8NK9Kd8Qou9PM2qOw2bNnExQUxKxZs/Dz86NLly5SRzPJ3B5Hzv6TdHlxFADa7AKu5BSYfb+6cj0fb0mlgfH+RlvdxKbYWE+8up89cTkAVFQaqKg0YAS+3p1O9JI4i2S4W91YZ8GTB5G6djchzw2TOBl49B2La+gg8r5ZAkDe5mV4RTyNrWcryTLJqpmlpaVx8OBBoqOjr3tdrVabmplarSYiIoKIiAgWLVokRUwAjr39Je3G98ehhbvF9nnmtyIKSypp5Hh/gw4czW2GrTQsJb2I7QeyqNIbblpWpTfwybdnuXhJ3BHRkNo6c/TzImBgGAnLN1Kl1eHZOUjqaPhPWkLB3k8pTfqZ0tM/4j04StI8CqOxsdfCzG/Tpk1MmzaNwsKr4wE6nQ5vb2+2b99O//798fX1JS8v77a2O2LECNLS6h9ncq22Jaq43R3nBhi4bj57nlh8y/Vi3M5RYlN1R/soI5DzNo+bft66MvKmMTJN65qf07JKrns9LbuEkS/Gmn5WGnUEG1bfUY7GuqzoQr4iAqOintEMo4EAw3ZcOWfWHI2RppwEgMaw1qz7udNa6zZnHJcS08n64Rec/L158K2p/PjM0gbfczu1pnD3w/7VO7t4lbl6JqWnfjQdlamcPdG8trne9cuXDsRYlFPvco1Gw7Zt2247h6zGzBQKBdXV1RgMBtP4WExMDFeuXDEdmRUXFxMREYGDgwNvv/02oaGhUkY2aUwjaypFsxyTNf+2mrIPS+SwBgnvbjT9WXuh4JaNzJLazDTvh2JjyerI7Pz582g0Gl5//XUmTZpEbGws8+fPR6VSkZNT08l///13fHx8OH78OOPHjyc1NbXJFwZKsy6yKfz55vi/cEtj4lfh8se8tNuVkHKJ0HHfNrhO4ubRAHQaXf8nI0CHNq6kfjf2jnI0VlZeGW0Gbah3Hpyd2ob8fU/g5iL9k2dDRm0CIGnLGLPuR661lnMFRsTeer3msC0S/Bybf7uyGjMLDAzkww8/ZM2aNYSGhnL06FEmTJhw3eC/j0/NFbju3bvj7u5uanL3gk7tPPDzcWxwxn9jDX2oddM3cgutfZ2ZOqoDtqqby0xlo+AvUzrLopEJ1kFWzQwgKiqK3NxcioqKiImJIT093dTMSktLqa6umceVlZXFxYsXadmypZRxLUqlUhI1NrjRM/4bMmNscNM30gir5vdl5rjg6xqavZ0N/zetK39/vrtFMgj3Btk1sxslJiaamlliYiJhYWE8/PDDjBs3jo8//hhbW1uJE1rWtNEdUNk07dAsspcfHYPcmyfQLdjaKvnnX3uTt+8JWvs60drXiYv7n+Dvs3qgVDbHJBNBqCHrZqbVasnMzDQ1s969e3PixAkOHDhAXFwckZE337ZjKSFRw03TMuw8XRh9+AOcA8w/CdWvhRMvPVn/zeJp2SWkZZfUuUyhAFuVkoUzLX9E5Olmh4uT7R//iVPLxpKqzhoj/9t3qbpcM8VHX1JAYlQ7KvIzJMsjq6uZN3JycsJguHmOkhQCh4QTOm8CpRn5OPp5kncoCd3FIgAemP4Y5y04K3vp7J5k5JSxKTbjpmXXTr+4lkJRc2/m5289TN/Qe+fU/G4jpzq7UWHcFnLWLcDOV0PVpWxcOvc3TcfI37YC916jJMsGMm9mcqK9UEDKp7soSc/Fwccd16CaX6L/gFAu/pKKT2h7i2WxsVGyfll/nl98mH9vSm1wXYWi5hYmO7UN65f1Z2T/NhZKKdwJOdXZjdQ+gbQY+gJ2rdpRVZRPRe6vABQf24lzcB+0v0rXaEE0s0bzCA6kMDkTn+4dMOiryTucBECLsI7YOtvj070DChslJ5att0gelUpJzBt9mTyiPas2JPP17t+o0t98ZaCllwPPjQlm+piOBPg6WSSbcOfkVmfX0mWexqFtZ7SpR1DY2OLSKQKAspTDGHSlaFOPYDRU4//kmxbPBqKZNZp7cCCZO+PxHxBKi57BpHz+A4CpqLrNGce5DfssmkmhUNA3tCV9Q1vy3l8eZNOedPIu6dCVV+PmYsv9Qe6MiGiDra2sh0aFa8ixzmqVn0/Eo9coio/tpCz5ED5DZgKYmlfOVwvxGjBFkmwgmlmjHV1Uc5vLiaXrCXtj0k1f9XPtDG0ptPRy4PkJD0iaQWg6OddZwDPLAfB/6i2yP52LQnX9TAK/iQslSHWV+Mi+A7UFJwjmJOc6q21sciKaGTVPf1Y5m/8JRipne9TODmbfjyBfcq01J1XNA3rNzVFVsy9zEKeZgJ2HC2PjV5v9Ab1qZwfxNPN7nFxrzU1d86Rxcz2gt5aTyjxPMwfRzEzsPFxEoxEsQq615qY2X6OxBHGaKQiCVRDNTBAEqyCamSAIVkE0M0EQrIJoZoIgWAXRzARBsAqimQmCYBVEMxMEwSqIZiYIglUQzUwQBKsgbmf6Q0VhqezulxOsk1xrrbhS3Jt516soLOXr8Jnoy8rNuh+Vsz1j41eLhnYPk2utFVfC8Fi4YuZm5qiquaHdHA1NnGYClWU6sxcXgL6s3OyfyIK8ybXWtHrzNzKo2Ye5jv7EkZlgMQaDseYRUYJgBqKZCWZTXW1gx8/ZrNqQzL5fcqmorPkKaMfwzxjStzXPj7+fAQ+2QqEQHU5oOtHMGunx+FXoyysxVOpRqlUkrfmOX9ftlTqWbH28OZVFMSc4n6u9aZmuvJrNezPYvDeDDm1cWRwdxuOPBkmQUn7kXGenp7dFqXZAoVJj1FfScuQcvB+dJnUsE9HMbsO+Z5dRfC4H946tGb57Gdl7j6PLL5Q6lqwYjUbmvfcLyz8/TWMOuH49X8LYuT+y9OWezHu2i/kD3gXkXGea17ZgHxCMLjOR5Fe649pjKGovP6ljAeICwB0pSs2isliLUysvIv4zF+fWLQBoP3EAwVMGS5xOWovWnGD556eBmocP30rtOq+u+IXVG5LNmOzuc22d+fYOoefCyQB4ddXQ653pkmZzaNMJGycPqi5l8/sPH5E6P4LU+REkv9KDlHm9Jckky2Z2/PhxwsPDsbe3p2fPnqxduxZHR0cMBoPU0QBoER5MRVEZl89kkLB8I93mjkOpVnHf6IdI/WKP1PEkc/B4HgtXn6h3+daVkWxdGVnnMoUCXnj7MCnpRWZKd/e5ts7y4pJwaeOLo58XXWc/zsn3v5E0W9mZg6hcPHEI6orPoOfouHg/HRfvx7FdT1qNWyBJJtmdZiYkJNCvXz8WLVrEhg0b2LFjB7NmzSIkJASlUtre2/+TeSgUClyCfDkwcwWGSj1FKecxGgw8uHgqZ7+Ivek5h5ZSUVnNuh1pfLLlLL8XltOtoyfRT4TQN7SlxTJ8uP5Mg8s1Aa71LjMawQis3pDMP/8qzSe7XNRVZwAJ721kwCfzyD1wSrLTzrQlozAaDFTknSNozlcobe1My8qSD1GtLcItbKgk2WR3ZBYdHc2MGTOYPXs2QUFBzJo1Cz8/P7p0kX48Zd+zy9jy0Ev8NGMFfd6dib23G1DzYNYWPYNJ33pIklxXdHoGTN3Bc4sOcvBEPqkZxWzem8FDU7bz3trTFsmQV3CFTXsymrydT7f+ivZKVdMD3cXqq7Oi1Cyc/L3J3BkvWTbNa1votDqV++auJ/PDaVQV5QNg1FeR/dk8Wk99X7JssmpmaWlpHDx4kOjo6OteV6vVpmZ25swZHnvsMQYMGMDgwdKMT2VujyNn/0m6vDgKAG12AVdyCiTJAvD2fxI4mlSAXn91kKpKb8RohDnL4zl99rLZM6zddg59dSMGyW6hVFvF13vSmyHR3e/GOguePIjUtbsJeW6YxMnAo+9YXEMHkffNEgDyNi/DK+JpbD1bSZZJVs0sISEBd3d3AgMDTa/pdDp+++03unTpQlVVFTNnzuTzzz/nxx9/ZNeuXZJlPfb2l7Qb3x+HFu6SZYCaiairNiRTqa97PFFtq+SjTalmz3Euq6TZtpWWVdps27rb1daZo58XAQPDSFi+kSqtDs/O0k9l8Z+0hIK9n1Ka9DOlp3/Ee3CUpHkURmNjrjlZxubNm5kyZQpFRUWm8bEVK1Ywe/ZsCgoKOHPmDO+88w52dnZcunSJyZMn8+yzz95yuyNGjCAtLa3e5a7VtkQVt2tS9oHr5rPnicW3XC/G7RwlNs13GmXAlhSb6AbXcTJm0sawqdn2WZdsxVBKlMGmn7eujLxpjEzTuubntBsaX1p2CSNfjDX97Gk4jq9xv/nC1iFNOQkAjWGtWfdzp7XWbc44LiWmk/XDLzj5e/PgW1P58ZmlDb7ndmpN4e6H/at3dvEqc/VMSk/9aDoqUzl7onltc73rly8diLEop97lGo2Gbdu23XYOWV0ACAsLQ6fT8eabbzJp0iRiY2N5++23adWqFV5eXly4cIGjR49y+vRpnJyceOihh+jbty8dO3aUOnqjGpk5KNCjMFZhVNjWvYKxGltj8x011UdJ8zXo5tyWtUh4d6Ppz9oLBbdsZJbUZuZqqSMAMjsyA4iJiWHhwoXodDrGjx+PnZ0dZ8+eZdeuXezevZuVK1eyfft2AF5++WX69u3L2LFjm7TP0qyLbAp/vjni39KY+FW4/DEvrbm8sPgwH21KpaqeU81Da4fRp5t5r2r+ffXxBqdlACRuHg1Ap9H1f2oDrJrfh5nj72+2bI0RMqrmyDVpyxiz7keutZZzBUbE3nq95rAtEvwcm3+7shozA4iKiiI3N5eioiJiYmJIT083Df736tWLjIwMdDodBoOBo0eP0r59e4kTS2/h891p7euE2vbqr1OhABulglnj76d31+ZtnnV5ali7Rs34vxU7tZJxg6QfDxLuPrJrZjdKTEw0NTNXV1f+/ve/ExkZSZ8+fRgyZAjdunWTNqAMeHvY88tXI3n12S6obBQoFND9fi8+X/wwH/xfb4vcyK1p7crgvgFN3s7EIRq83O2bIZFwr5F1M9NqtWRmZl43x2zMmDEcOnSII0eOMH/+fMmyhUQNN13JtPN0YfThD3AO8JEsj6ebHYtm9aBDWzfuv8+do+v/zJOPtbPoN1I83wynhs2xDWsitzq7Vv6371J1ORcAfUkBiVHtqMjPkCyPrC4A3MjJyUk2tzAFDgkndN4ESjPycfTzJO9QErqLRQA8MP0xzks4kVEuHnu4Nc+O6sAnW87WuTwtu+ELEa9N7UrPTvL4hyoVOddZYdwWctYtwM5XQ9WlbFw69zddwczftgL3XqMkywYyb2Zyor1QQMqnuyhJz8XBxx3XoJpfov+AUC7+kopPqBi7UygUrHm9LyVllXxTx90A106/uFHU2GDeiu5hxnR3BznXmdonkBZDX8CuVTuqivKpyP0VgOJjO3EO7oP2V2k/0EUzaySP4EAKkzPx6d4Bg76avMNJALQI64itsz0+3TugsFFyYtl6iZNKy9ZWyYZ/DOCNfx3jvbWJ6CqqUShu/gaN2tdcnWx5/bluzJ3SWXxJI/KuM13maRzadkabegSFjS0unSIAKEs5jEFXijb1CEZDNf5PvmnxbCDzMTM5cQ8OpDAlC7W7E4FDwsn/JQWAE8vWE//GZ1zYlyCbL9GTmlKp4K3oMHL2TuSfr/aifeDNN5h37eDJv//2J3L2TuQvz3QRjewPcq6z8vOJOAR2Ql9WSOGRLTjf3xcA/yffpPW0Fbh2H4x35FRJsoE4Mmu0o4tqZoafWLqesDcm3fTtGNdOahRquLva8eKTIUQ/8QD5l3QUllSiUICnqx0+nvaigdVBznUW8MxyAPyfeovsT+eiUF0/Udtv4kIJUl0lmtkdqC04oXEUCgW+3o74epthpqQVk3Od1TY2ORGnmYIgWAXRzKh5+rPK2fwTNVXO9qidHcy+H0G+5FprTqqaB/Sam6OqZl/mIE4zATsPF8bGrzb7A3rVzg7iaeb3OLnWmpu65knj5npAby0nlXmeZg6imZnYebiIRiNYhFxrzU1tvkZjCeI0UxAEqyCamSAIVkE0M0EQrIJoZoIgWAXRzARBsAqimQmCYBVEMxMEwSqIZiYIglUQzUwQBKsgmpkgCFZB3M70h4rCUtndLydYJ7nWWnGluDfzrldRWMrX4TPRl5WbdT8qZ3vGxq8WDe0eJtdaK66E4bFwxczNzFFVc0O7ORqaOM0EKst0Zi8uAH1Zudk/kQV5k2utafXmb2RQsw9zHf2JZmYlKquq2XUwm6KSCi4XV/Dl9+c4kVwgdSxBsBhxmtlIj8evQl9eiaFSj1KtImnNd7J4gMn53DI++iaFj75J5ffCq5/4T732EwDhnbx5fvwDjBsUhIO9+HXLnVzrDOD09LYo1Q4oVGqM+kpajpyD96PTpI5lIqr7Nux7dhnF53Jw79ia4buXkb33OLr8QkmyGI1Gln5yivkfHMVgqHl0W11+SSpgyoIDvLoinu8+ePSef8ju3UBOdXYjzWtbsA8IRpeZSPIr3XHtMRS1l5/UsQBxmnlHilKzqCzW4tTKi4j/zMW5dQsA2k8cQPCUwWbfv9FoZM7y//HaP49iNNS+Vt+6Nf978XI5/Z75nn3xOWbPJzSPa+vMt3cIPRdOBsCrq4Ze70yXNJtDm07YOHlQdSmb33/4iNT5EaTOjyD5lR6kzOstSSbRzO5Ai/BgKorKuHwmg4TlG+k2dxxKtYr7Rj9E6hd7zL7/9/+byPv/rXk47I09bOvKSLaujLzpPUYjlFdW8+eXYjmTJo9PeaFh19ZZXlwSLm18cfTzouvsxzn5/jeSZis7cxCViycOQV3xGfQcHRfvp+Pi/Ti260mrcQskySTL08zjx48zY8YMTp06RefOnYmOjmbGjBmUlZWhVErXf/t/Mg+FQoFLkC8HZq7AUKmnKOU8RoOBBxdP5ewXsTc957C5FZZUMP+DY/Uu1wTc/MDdWkYjlGirWPDhcTa9/4g54tVLrzeQkl6EUqkgOMgdpVI8M7M+ddUZQMJ7GxnwyTxyD5yS7LQzbckojAYDFXnnCJrzFUpbO9OysuRDVGuLcAsbKkk22TWzhIQE+vXrx6JFi9iwYQM7duxg1qxZhISESNrI4OpYRpthven73kzy4s5QXlBMwrsbifxiPnF/iTF7hs+3/kp5RdMa5tZ9mWTnaQnwdWqmVPUzGo18sO4Mb32UYLpAEdDSkbdeCGPyyPZm3//dqL46K0rNwsnfm8yd8ZJlqx0zKzz0NRkfTMWlUz9s3Vti1FeR/dk8NK9Kd8Qou9PM2qOw2bNnExQUxKxZs/Dz86NLly5SRzPJ3B5Hzv6TdHlxFADa7AKu5Jh/GoTRaGTVhuR6B/sbq9pg5N+bUpsn1C38ffUJXvnH/6670pqdf4Vn3zjA6g3JFslwt7qxzoInDyJ17W5CnhsmcTLw6DsW19BB5H2zBIC8zcvwingaW89WkmWSVTNLS0vj4MGDREdHX/e6Wq2mS5cupKamEhERYfrP1taWxMRESbIee/tL2o3vj0MLd4vtM+lcIb+eL6l3sL+xFMCm2PRmydSQgsJy3v5PAtWGmwMbjPDqil/QlVtgpuZdrLbOHP28CBgYRsLyjVRpdXh2DpI6Gv6TllCw91NKk36m9PSPeA+OkjSPwmhs6j+N5rNp0yamTZtGYeHV8QCdToe3tzfbt2+nf//+pteTk5MZP348p06duuV2R4wYQVpaWr3LXattiSpu16TsA9fNZ88Ti2+5XozbOUpsqu5oH1oCyLQZZ/p568rIm8bINK1rfk7LKrnu9bTsEka+GGv62cZ4hY6GNXeUo7GKFA+QoxgICpu6VzAaaG3Yigvmb6y3kqacBIDGsNas+7nTWus2ZxyXEtPJ+uEXnPy9efCtqfz4zNIG33M7taZw98P+1Tu7eJW5eialp340HZWpnD3RvLa53vXLlw7EWFT/VXWNRsO2bdtuO4esxswUCgXV1dUYDAbT+FhMTAxXrly56TTzs88+45lnnpEiZp0a08iaykg9TeGOtmX+g3IDtigw3nTF9doURnmVoGwlvLvR9GfthYJbNjJLajNztdQRAJkdmZ0/fx6NRsPrr7/OpEmTiI2NZf78+ahUKnJyrnby6upq7rvvPo4ePYqPT9MngZZmXWRT+PNN3k5jjIlfhcsf89JuV/zp33nwyYY/sRI3jwag0+j6PxkB2vo7k75z/B3laKyjSb/Tc2L9eRXAbzvH0dZf+hvvQ0ZtAiBpyxiz7keutZZzBUbE3nq95rAtEvwcm3+7shozCwwM5MMPP2TNmjWEhoZy9OhRJkyYcNNR2a5du+jRo0ezNLK7SXCQG47NdEtSzxDz/92FhfjwYGcfbFU3l5mtSsnwiEBZNDLBOsiqmQFERUWRm5tLUVERMTExpKeny/4U01JcndU8NUzTLNt6fvz9zbKdW9myIpL2bVy5dlqZQgE9HvDi87cetkgG4d4gu2Z2o8TExOua2eXLl4mPj2fIkCESppLOzHFNa0IKRc0RXr8w32ZK1LBWPo6c+mYU3334KJ5udni62bH330M4/N/huLva3XoDgtBIsm5mWq2WzMzM65qZp6cnmZmZqFTSDhyHRA03Tcuw83Rh9OEPcA4w/6lbt2AvHu3tX+/ytOwS0rJL6l1uNMKrz3ZB0dTJarfBxkbJ0Ida4+vtgK+3A/3D/Sy6/7uZVHXWGPnfvkvV5VwA9CUFJEa1oyI/Q7I8sr6U5OTkhMFgkDoGAIFDwgmdN4HSjHwc/TzJO5SE7mIRAA9Mf4zzFpyV/dWy/vR+ahtnM29uWtdOv6hL9BMPMHmEmHkvV3KqsxsVxm0hZ90C7Hw1VF3KxqVzf9N0jPxtK3DvNUqybCDzZiYn2gsFpHy6i5L0XBx83HENqvkl+g8I5eIvqfiEWq5BeLrZse/joQydtZuTqZdRKOr/1gyouWpoBF6Z1Il/vBIujopkTE51diO1TyAthr6AXat2VBXlU5H7KwDFx3biHNwH7a/SNVoQzazRPIIDKUzOxKd7Bwz6avIO13xrRYuwjtg62+PTvQMKGyUnlq23SB6/Fk78/NljrPgiiTUbU8j5/Qrwx/eaGYFrGtyfurfkpSdDGDNQ+lnjQsPkVmfX0mWexqFtZ7SpR1DY2OLSKQKAspTDGHSlaFOPYDRU4//kmxbPBqKZNZp7cCCZO+PxHxBKi57BpHz+A4CpqLrNGce5DfssmsnFSc2CqFBem9qV7346z7odaeQV6NBVVOPmbMv997kT9XgwnTt4WjSXcOfkWGe1ys8n4tFrFMXHdlKWfAifITMBTM0r56uFeA2YIkk2EM2s0Y4uqrnN5cTS9YS9Memmr/q5doa2palUSkY90pZRj7SVLIPQPORcZwHPLAfA/6m3yP50LgqV7XXL/SYulCDVVbK+milXtQUnCOYk5zqrbWxyIpqZIAhWQTQzap7+rHK2N/t+VM72qJ0dzL4fQb7kWmtOqpoH9Jqbo6pmX+YgxswAOw8XxsavNvsDetXODuJp5vc4udaam7rmSePmekBvLSeVeZ5mDqKZmdh5uIhGI1iEXGvNTW2+RmMJ4jRTEASrIJqZIAhWQTQzQRCsgmhmgiBYBdHMBEGwCqKZCYJgFUQzEwTBKohmJgiCVRDNTBAEqyDuAPhDRWGp7G4xEayTXGutuFLcznTXqygs5evwmejLys26H5WzPWPjV4uGdg+Ta60VV8LwWLhi5mbmqKq5B9QcDU2cZgKVZTqzFxeAvqzc7J/IgrzJtda0evM3MqjZh7mO/kQzEwTBKojTTMGsCgrL+fTbs+w/mkt6dikoYPgLuxn8pwCeHtYOV+e7+GsaBFkRzayRHo9fhb68EkOlHqVaRdKa7/h13V6pY8lW4q+X+cdnp1m/6zcqq65/9un2A1lsP5DFvPd+YdLwdsyd0hlNa1eJksqLnOvs9PS2KNUOKFRqjPpKWo6cg/ej06SOZSKa2W3Y9+wyis/l4N6xNcN3LyN773F0+YVSx5KdbfsyGT9vH+UV1Q2ud6Vcz5qvU1i/6ze2/jOSh8NaWSihvMm5zjSvbcE+IBhdZiLJr3THtcdQ1F5+UscCxJjZHSlKzaKyWItTKy8i/jMX59YtAGg/cQDBUwZLnE5aO37OYvTsvVRUNtzIrlVcVsmjM3YRdzLfjMnuPtfWmW/vEHounAyAV1cNvd6ZLmk2hzadsHHyoOpSNr//8BGp8yNInR9B8is9SJnXW5JMsmxmx48fJzw8HHt7e3r27MnatWtxdHTEYDDc+s0W0CI8mIqiMi6fySBh+Ua6zR2HUq3ivtEPkfrFHqnjSea37BLGzvkRg9FY5xPWt66MZOvKyJteNxqhssrA8Bf2UFBo/it9d4tr6ywvLgmXNr44+nnRdfbjnHz/G0mzlZ05iMrFE4egrvgMeo6Oi/fTcfF+HNv1pNW4BZJkkt1pZkJCAv369WPRokVs2LCBHTt2MGvWLEJCQlAqpe29/T+Zh0KhwCXIlwMzV2Co1FOUch6jwcCDi6dy9ovYm55zeC/51/pkrpTXf91dE1D/uJjRCJeKK/hky1nmPdvFHPHqVFRSwZd/PDxZZaPgQr4W/5ZOFtt/XeqqM4CE9zYy4JN55B44JdlpZ9qSURgNBiryzhE05yuUtnamZWXJh6jWFuEWNlSSbLI7MouOjmbGjBnMnj2boKAgZs2ahZ+fH126WK7A67Pv2WVseeglfpqxgj7vzsTe2w2oeTBri57BpG89JHFC6VzR6flky9kmbUOhgNUbk6mutswR+LZ9mbQa8BWzl/2Py8UV/H65nMBH1/P+fxMtsv/61FdnRalZOPl7k7kzXrJsmte20Gl1KvfNXU/mh9OoKqoZGjDqq8j+bB6tp74vWTZZNbO0tDQOHjxIdHT0da+r1WpTM/vb3/5Gz549CQ8P55133pEiJpnb48jZf5IuL44CQJtdwJWcAkmyyMWGH36jqLSySdswGiEjp4wfDl9oplT1S00vYswreymvrKZKX9M8jYDBCHOW/4/vD5w3e4ZbubHOgicPInXtbkKeGyZxMvDoOxbX0EHkfbMEgLzNy/CKeBpbT+ku4siqmSUkJODu7k5gYKDpNZ1Ox2+//UaXLl3Iy8vjyy+/5MiRI8TFxfHxxx/z+++/S5L12Ntf0m58fxxauEuyf7k5mtR8zbw5t1WfD786g1KpqHOZAlj6ySmzZ2iM2jpz9PMiYGAYCcs3UqXV4dk5SOpo+E9aQsHeTylN+pnS0z/iPThK0jwKo7GuoVppbN68mSlTplBUVGQaH1uxYgWzZ8+moKAAZ2dnIiMj2blzJwDh4eHEx8fj7Ozc4HZHjBhBWlpavctdq22JKm7XpOwD181nzxOLb7lejNs5SmyqmrQvOcpWDKFEeb/p560rI28aI6udS5aWVXLd62nZJYx8Mdb0s6fhGL7Gn8yYFtKUT1Oh8Kl3ucKo537Dymbf753WWrc547iUmE7WD7/g5O/Ng29N5cdnljb4ntupNYW7H/av3tnFq8zVMyk99aPpqEzl7Inmtc31rl++dCDGopx6l2s0GrZt23bbOWR1ASAsLAydTsebb77JpEmTiI2N5e2336ZVq1Z4eXkBMHDgQIKDgzEajbz00ku3bGSW0phGZs0UNN84V3Nuqz5KGv5HrkBeF3IS3t1o+rP2QsEtG5kltZm5WuoIgMyOzABiYmJYuHAhOp2O8ePHY2dnx9mzZ9m1axd79+7l7bffNh2ZDR06lMWLF/Pggw82aZ+lWRfZFP58c8S/pTHxq3D5Y16aNZm7/H+8u7bhgfPEzaMB6DS6/k9tgKUv9zT7Fc0P1iUxZ3m8abzsWrYqJc/8uT0xb/yp2fcr11rLuQIjYm+9XnPYFgl+js2/XVmNmQFERUWRm5tLUVERMTExpKenmwb/q6ur8fDwQK1Wo1arcXNz49KlSxInFgBGPdK22bb15wFtmm1b9Zkysj1t/JyxVV3/T8BGqcDB3oZXLTg9RGgesmtmN0pMTDQ1s8jISLy9venduze9e/fGy8uLQYMGSZxQAOjTrQWd23ugqHtMvdEG9vajQ1u35gnVABcnNYc+H8aIiEBsrrkQ8KfQlsT9dzj3NTAnTpAnWTczrVZLZmamqZkplUrWrFlDXFwccXFxfPTRR9jY2EiSLSRquOlKpp2nC6MPf4BzQP0DytZOoVAwa8IDdc78vx3Pj7//1is1kxZeDnzz3iPk/jiRo+tHcn73ePZ/+hgPaDwsluFW5Fxn+d++S9XlXAD0JQUkRrWjIj9DsjyyugBwIycnJ9ncwhQ4JJzQeRMozcjH0c+TvENJ6C4WAfDA9Mc4L+FERrl4elg7/rMplaNn6p5akZZdUufrtSJ7+THs4cAG1zEHH08HfDwdLL7fusi5zgrjtpCzbgF2vhqqLmXj0rm/6Qpm/rYVuPcaJVk2kHkzkxPthQJSPt1FSXouDj7uuAbV/BL9B4Ry8ZdUfELbS5xQeo4OKr77cCB/mrydtKzSm5ZfO/3iRqHBXmx67xFUKlmfLJidnOtM7RNIi6EvYNeqHVVF+VTk/gpA8bGdOAf3QfurtB/o93bl3AaP4EAKkzPxfKAtdh4u5B1OAqBFWEf8I7ri378b7Z94ROKU0vP1diTuv8Pp263xV2yH/CmAnz4dKr6oEXnXmS7zNA5tO6PLOEl16SVcOkUAUJZymJKE3ZQc30VB7MeSZAPRzBrNPTiQwpQs1O5OBA4JJ/+XFABOLFtP/BufcWFfgmy+RE9qPp4OHPhsGLtjBvPnAW2oa6K9SqXgiaEaDn4+jO//9SguTqKRgbzrrPx8Ig6BndCXFVJ4ZAvO9/cFwP/JN2k9bQWu3QfjHTlVkmwgw3lmUrjduT9hb0zi6KK1d7Qva51n1pCsvDIOHs+nsKQChUKBp5sdET1b0dJLHuNUlnQ7tdaUOgPzzjPL/nQuAc8sv6Nc5ppnJsbM7kBTCuxe1NrXmYlD5XGnxt1EznV2p43MnMRppiAIVkE0M2qe/qxytjf7flTO9qid771TK+Equdaak6rmAb3m5qiq2Zc5iDGzP1QUlpr9Ab1qZwfxNHNBtrVWXGm+B/TWclKZ52nmIJqZIAhWQpxmCoJgFUQzEwTBKohmJgiCVRDNTBAEqyCamSAIVkE0M0EQrIJoZoIgWAXRzARBsAqimQmCYBVEMxMEwSqIZiYIglUQzUwQBKsgmpkgCFZBNDNBEKyCaGaCIFgF0cwEQbAKopkJgmAVRDMTBMEq/D/I84aHGQRmHAAAAABJRU5ErkJggg==", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 1, @@ -78,8 +79,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 2, @@ -111,8 +113,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 3, @@ -142,8 +145,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 4, @@ -175,8 +179,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 5, @@ -224,8 +229,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 6, @@ -264,8 +270,9 @@ "outputs": [ { "data": { + "image/png": "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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 7, @@ -288,8 +295,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 8, @@ -309,8 +317,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
    " ] }, "execution_count": 9, @@ -325,7 +334,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -339,9 +348,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-utils/how_tos/index.mdx b/docs/addons/qiskit-addon-utils/how_tos/index.mdx new file mode 100644 index 00000000000..21ed11db483 --- /dev/null +++ b/docs/addons/qiskit-addon-utils/how_tos/index.mdx @@ -0,0 +1,12 @@ +--- +title: Qiskit addon utilities API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-utils. +--- + +# How-To Guides + +This page summarizes the available how-to guides. + +* [Use edge coloring to reduce the depth of quantum circuits](color-device-edges-to-improve-depth) +* [Slicing circuits using `qiskit_addon_utils.slicing`](create-circuit-slices) + diff --git a/docs/addons/qiskit-addon-utils/index.mdx b/docs/addons/qiskit-addon-utils/index.mdx index 474e54f027d..c294db8b409 100644 --- a/docs/addons/qiskit-addon-utils/index.mdx +++ b/docs/addons/qiskit-addon-utils/index.mdx @@ -1,5 +1,6 @@ --- -title: "Qiskit addon utilities" +title: Qiskit addon utilities API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-utils. --- # Qiskit addon utilities diff --git a/docs/addons/qiskit-addon-utils/install.mdx b/docs/addons/qiskit-addon-utils/install.mdx index e7ff94c2ebe..054046adca3 100644 --- a/docs/addons/qiskit-addon-utils/install.mdx +++ b/docs/addons/qiskit-addon-utils/install.mdx @@ -1,7 +1,3 @@ ---- -title: "Installation Instructions" ---- - # Installation Instructions Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: @@ -73,4 +69,3 @@ If you installed the notebook dependencies, you can get started by running the n cd docs/ jupyter lab ``` - diff --git a/docs/addons/qiskit-addon-utils/release-notes.mdx b/docs/addons/qiskit-addon-utils/release-notes.mdx deleted file mode 100644 index 31d21da94a9..00000000000 --- a/docs/addons/qiskit-addon-utils/release-notes.mdx +++ /dev/null @@ -1,93 +0,0 @@ ---- -title: "Release Notes" ---- - - - - - -# - - - - - -## 0.3.1 - - - -### Bug Fixes - -* Fixed bugs in [`qiskit_addon_utils.exp_vals.executor_expectation_values()`](qiskit_addon_utils.exp_vals#qiskit_addon_utils.exp_vals.executor_expectation_values "qiskit_addon_utils.exp_vals.executor_expectation_values") causing indexing errors when bit\_array dimensions associated with twirling randomizations were not specified in avg\_axis. - - - - - -## 0.3.0 - - - -### New Features - -* Added a new sub-package, [`qiskit_addon_utils.exp_vals`](qiskit_addon_utils.exp_vals#module-qiskit_addon_utils.exp_vals "qiskit_addon_utils.exp_vals"), containing functions for calculating expectation values from the outputs of Qiskit Runtime’s new `Executor` primitive prototype. This includes: - - > * [`qiskit_addon_utils.exp_vals.executor_expectation_values()`](qiskit_addon_utils.exp_vals#qiskit_addon_utils.exp_vals.executor_expectation_values "qiskit_addon_utils.exp_vals.executor_expectation_values") for expectation value calculation - > * [`qiskit_addon_utils.exp_vals.get_measurement_bases()`](qiskit_addon_utils.exp_vals#qiskit_addon_utils.exp_vals.get_measurement_bases "qiskit_addon_utils.exp_vals.get_measurement_bases") for identifying a minimal set of measurement bases to cover all terms in the observable. - > * [`qiskit_addon_utils.exp_vals.map_observable_isa_to_canonical()`](qiskit_addon_utils.exp_vals#qiskit_addon_utils.exp_vals.map_observable_isa_to_canonical "qiskit_addon_utils.exp_vals.map_observable_isa_to_canonical"), [`qiskit_addon_utils.exp_vals.map_observable_virtual_to_canonical()`](qiskit_addon_utils.exp_vals#qiskit_addon_utils.exp_vals.map_observable_virtual_to_canonical "qiskit_addon_utils.exp_vals.map_observable_virtual_to_canonical"), and [`qiskit_addon_utils.exp_vals.map_observable_isa_to_virtual()`](qiskit_addon_utils.exp_vals#qiskit_addon_utils.exp_vals.map_observable_isa_to_virtual "qiskit_addon_utils.exp_vals.map_observable_isa_to_virtual") for transforming the qubit order of Pauli observables. - -* Added two new functions to the [`qiskit_addon_utils.noise_management`](qiskit_addon_utils.noise_management#module-qiskit_addon_utils.noise_management "qiskit_addon_utils.noise_management") sub-package: - - > * [`qiskit_addon_utils.noise_management.trex_factors()`](qiskit_addon_utils.noise_management#qiskit_addon_utils.noise_management.trex_factors "qiskit_addon_utils.noise_management.trex_factors") for calculating TREX readout error mitigation scaling factors - > * [`qiskit_addon_utils.noise_management.gamma_from_noisy_boxes()`](qiskit_addon_utils.noise_management#qiskit_addon_utils.noise_management.gamma_from_noisy_boxes "qiskit_addon_utils.noise_management.gamma_from_noisy_boxes") for calculating the gamma factor for a given circuit and noise model. - - - - - -## 0.2.0 - - - - - -### New Features - -* Add the transpiler passes required to pre-process the circuits, as well as the `PostSelector` and `PostSelectionSummary` objects required to post-process the results. - - - -### Upgrade Notes - -* This addition requires bumping the version of Qiskit SDK to at least 2.0. - -* This package is now compatible with Qiskit SDK 2.0. - - - - - -### Bug Fixes - -* Support circuits that include classical bits when slicing a circuit by gate types. See #115 \<[https://github.com/Qiskit/qiskit-addon-utils/issues/115](https://github.com/Qiskit/qiskit-addon-utils/issues/115)> for details. - - - -### Other Notes - -* This release adds support for Python 3.13. No code changes were necessary, so older releases are expected to work on Python 3.13 too. - - - - - -## 0.1.0 - - - - - -### Upgrade Notes - -* The keyword argument `include_slice_barriers` of [`combine_slices()`](qiskit_addon_utils.slicing#qiskit_addon_utils.slicing.combine_slices "qiskit_addon_utils.slicing.combine_slices") has been renamed to `include_barriers`. - diff --git a/package.json b/package.json index 0248925f7bb..860f4cb498b 100644 --- a/package.json +++ b/package.json @@ -32,7 +32,7 @@ "check:qiskit-versions": "tsx scripts/js/commands/checkQiskitApiVersions.ts", "regen-apis": "tsx scripts/js/commands/api/regenerateApiDocs.ts", "gen-api": "tsx scripts/js/commands/api/updateApiDocs.ts", - "gen-addon-docs": "tsx scripts/js/commands/updateAddonDocs.ts", + "gen-addon-docs": "tsx scripts/js/commands/updateDocs.ts", "generate-historical-redirects": "tsx scripts/js/commands/api/generateHistoricalRedirects.ts", "save-internal-links": "tsx scripts/js/commands/saveInternalLinks.ts" 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@@ -1,41 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { Pkg } from "../../lib/api/Pkg.js"; -import { parseMinorVersion } from "../../lib/apiVersions.js"; -import { - downloadArtifact, - runPipeline, - API_INCLUDE, - PROSE_EXCLUDE, -} from "./utils.js"; - -const NAME = "qiskit-addon-aqc-tensor"; -const VERSION = "0.2.0"; - -export async function run(skipDownload: boolean): Promise { - const minorVersion = parseMinorVersion(VERSION)!; - const pkg = await Pkg.fromArgs(NAME, VERSION, minorVersion, "latest"); - const artifactPath = await downloadArtifact(pkg, skipDownload); - - await runPipeline(artifactPath, { - include: API_INCLUDE, - output: `docs/api/${NAME}`, - pkg, - }); - - await runPipeline(artifactPath, { - exclude: PROSE_EXCLUDE, - output: `docs/addons/${NAME}`, - pkg, - }); -} diff --git a/scripts/js/commands/docs/qiskit-addon-cutting.ts b/scripts/js/commands/docs/qiskit-addon-cutting.ts deleted file mode 100644 index 5d9321e31dc..00000000000 --- a/scripts/js/commands/docs/qiskit-addon-cutting.ts +++ /dev/null @@ -1,41 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { Pkg } from "../../lib/api/Pkg.js"; -import { parseMinorVersion } from "../../lib/apiVersions.js"; -import { - downloadArtifact, - runPipeline, - API_INCLUDE, - PROSE_EXCLUDE, -} from "./utils.js"; - -const NAME = "qiskit-addon-cutting"; -const VERSION = "0.10.0"; - -export async function run(skipDownload: boolean): Promise { - const minorVersion = parseMinorVersion(VERSION)!; - const pkg = await Pkg.fromArgs(NAME, VERSION, minorVersion, "latest"); - const artifactPath = await downloadArtifact(pkg, skipDownload); - - await runPipeline(artifactPath, { - include: API_INCLUDE, - output: `docs/api/${NAME}`, - pkg, - }); - - await runPipeline(artifactPath, { - exclude: PROSE_EXCLUDE, - output: `docs/addons/${NAME}`, - pkg, - }); -} diff --git a/scripts/js/commands/docs/qiskit-addon-mpf.ts b/scripts/js/commands/docs/qiskit-addon-mpf.ts deleted file mode 100644 index ad9ff182674..00000000000 --- a/scripts/js/commands/docs/qiskit-addon-mpf.ts +++ /dev/null @@ -1,41 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { Pkg } from "../../lib/api/Pkg.js"; -import { parseMinorVersion } from "../../lib/apiVersions.js"; -import { - downloadArtifact, - runPipeline, - API_INCLUDE, - PROSE_EXCLUDE, -} from "./utils.js"; - -const NAME = "qiskit-addon-mpf"; -const VERSION = "0.3.0"; - -export async function run(skipDownload: boolean): Promise { - const minorVersion = parseMinorVersion(VERSION)!; - const pkg = await Pkg.fromArgs(NAME, VERSION, minorVersion, "latest"); - const artifactPath = await downloadArtifact(pkg, skipDownload); - - await runPipeline(artifactPath, { - include: API_INCLUDE, - output: `docs/api/${NAME}`, - pkg, - }); - - await runPipeline(artifactPath, { - exclude: PROSE_EXCLUDE, - output: `docs/addons/${NAME}`, - pkg, - }); -} diff --git a/scripts/js/commands/docs/qiskit-addon-obp.ts b/scripts/js/commands/docs/qiskit-addon-obp.ts deleted file mode 100644 index b212ace5fec..00000000000 --- a/scripts/js/commands/docs/qiskit-addon-obp.ts +++ /dev/null @@ -1,41 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { Pkg } from "../../lib/api/Pkg.js"; -import { parseMinorVersion } from "../../lib/apiVersions.js"; -import { - downloadArtifact, - runPipeline, - API_INCLUDE, - PROSE_EXCLUDE, -} from "./utils.js"; - -const NAME = "qiskit-addon-obp"; -const VERSION = "0.3.0"; - -export async function run(skipDownload: boolean): Promise { - const minorVersion = parseMinorVersion(VERSION)!; - const pkg = await Pkg.fromArgs(NAME, VERSION, minorVersion, "latest"); - const artifactPath = await downloadArtifact(pkg, skipDownload); - - await runPipeline(artifactPath, { - include: API_INCLUDE, - output: `docs/api/${NAME}`, - pkg, - }); - - await runPipeline(artifactPath, { - exclude: PROSE_EXCLUDE, - output: `docs/addons/${NAME}`, - pkg, - }); -} diff --git a/scripts/js/commands/docs/qiskit-addon-sqd.ts b/scripts/js/commands/docs/qiskit-addon-sqd.ts deleted file mode 100644 index 3746ba7ecc5..00000000000 --- a/scripts/js/commands/docs/qiskit-addon-sqd.ts +++ /dev/null @@ -1,41 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { Pkg } from "../../lib/api/Pkg.js"; -import { parseMinorVersion } from "../../lib/apiVersions.js"; -import { - downloadArtifact, - runPipeline, - API_INCLUDE, - PROSE_EXCLUDE, -} from "./utils.js"; - -const NAME = "qiskit-addon-sqd"; -const VERSION = "0.12.0"; - -export async function run(skipDownload: boolean): Promise { - const minorVersion = parseMinorVersion(VERSION)!; - const pkg = await Pkg.fromArgs(NAME, VERSION, minorVersion, "latest"); - const artifactPath = await downloadArtifact(pkg, skipDownload); - - await runPipeline(artifactPath, { - include: API_INCLUDE, - output: `docs/api/${NAME}`, - pkg, - }); - - await runPipeline(artifactPath, { - exclude: PROSE_EXCLUDE, - output: `docs/addons/${NAME}`, - pkg, - }); -} diff --git a/scripts/js/commands/docs/qiskit-addon-utils.ts b/scripts/js/commands/docs/qiskit-addon-utils.ts deleted file mode 100644 index acde56c182b..00000000000 --- a/scripts/js/commands/docs/qiskit-addon-utils.ts +++ /dev/null @@ -1,41 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { Pkg } from "../../lib/api/Pkg.js"; -import { parseMinorVersion } from "../../lib/apiVersions.js"; -import { - downloadArtifact, - runPipeline, - API_INCLUDE, - PROSE_EXCLUDE, -} from "./utils.js"; - -const NAME = "qiskit-addon-utils"; -const VERSION = "0.3.1"; - -export async function run(skipDownload: boolean): Promise { - const minorVersion = parseMinorVersion(VERSION)!; - const pkg = await Pkg.fromArgs(NAME, VERSION, minorVersion, "latest"); - const artifactPath = await downloadArtifact(pkg, skipDownload); - - await runPipeline(artifactPath, { - include: API_INCLUDE, - output: `docs/api/${NAME}`, - pkg, - }); - - await runPipeline(artifactPath, { - exclude: PROSE_EXCLUDE, - output: `docs/addons/${NAME}`, - pkg, - }); -} diff --git a/scripts/js/commands/docs/qiskit-c.ts b/scripts/js/commands/docs/qiskit-c.ts deleted file mode 100644 index e67aed0c619..00000000000 --- a/scripts/js/commands/docs/qiskit-c.ts +++ /dev/null @@ -1,32 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { Pkg } from "../../lib/api/Pkg.js"; -import { parseMinorVersion } from "../../lib/apiVersions.js"; -import { downloadArtifact, runPipeline } from "./utils.js"; - -const VERSION = "2.4.1"; - -// The C API uses a different content directory (cdoc/) instead of apidocs/. -const C_API_INCLUDE = ["cdoc/**", "release_notes.html", "release-notes.html"]; - -export async function run(skipDownload: boolean): Promise { - const minorVersion = parseMinorVersion(VERSION)!; - const pkg = await Pkg.fromArgs("qiskit-c", VERSION, minorVersion, "latest"); - const artifactPath = await downloadArtifact(pkg, skipDownload); - - await runPipeline(artifactPath, { - include: C_API_INCLUDE, - output: `docs/api/qiskit-c`, - pkg, - }); -} diff --git a/scripts/js/commands/docs/qiskit-ibm-runtime.ts b/scripts/js/commands/docs/qiskit-ibm-runtime.ts deleted file mode 100644 index c912c7ee066..00000000000 --- a/scripts/js/commands/docs/qiskit-ibm-runtime.ts +++ /dev/null @@ -1,29 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { Pkg } from "../../lib/api/Pkg.js"; -import { parseMinorVersion } from "../../lib/apiVersions.js"; -import { downloadArtifact, runPipeline, API_INCLUDE } from "./utils.js"; - -const VERSION = "0.46.1"; - -export async function run(skipDownload: boolean): Promise { - const minorVersion = parseMinorVersion(VERSION)!; - const pkg = await Pkg.fromArgs("qiskit-ibm-runtime", VERSION, minorVersion, "latest"); - const artifactPath = await downloadArtifact(pkg, skipDownload); - - await runPipeline(artifactPath, { - include: API_INCLUDE, - output: `docs/api/qiskit-ibm-runtime`, - pkg, - }); -} diff --git a/scripts/js/commands/docs/qiskit-ibm-transpiler.ts b/scripts/js/commands/docs/qiskit-ibm-transpiler.ts deleted file mode 100644 index 686312b8cf5..00000000000 --- a/scripts/js/commands/docs/qiskit-ibm-transpiler.ts +++ /dev/null @@ -1,29 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { Pkg } from "../../lib/api/Pkg.js"; -import { parseMinorVersion } from "../../lib/apiVersions.js"; -import { downloadArtifact, runPipeline, API_INCLUDE } from "./utils.js"; - -const VERSION = "0.18.0"; - -export async function run(skipDownload: boolean): Promise { - const minorVersion = parseMinorVersion(VERSION)!; - const pkg = await Pkg.fromArgs("qiskit-ibm-transpiler", VERSION, minorVersion, "latest"); - const artifactPath = await downloadArtifact(pkg, skipDownload); - - await runPipeline(artifactPath, { - include: API_INCLUDE, - output: `docs/api/qiskit-ibm-transpiler`, - pkg, - }); -} diff --git a/scripts/js/commands/docs/qiskit.ts b/scripts/js/commands/docs/qiskit.ts deleted file mode 100644 index 68d4def625a..00000000000 --- a/scripts/js/commands/docs/qiskit.ts +++ /dev/null @@ -1,29 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { Pkg } from "../../lib/api/Pkg.js"; -import { parseMinorVersion } from "../../lib/apiVersions.js"; -import { downloadArtifact, runPipeline, API_INCLUDE } from "./utils.js"; - -const VERSION = "2.4.1"; - -export async function run(skipDownload: boolean): Promise { - const minorVersion = parseMinorVersion(VERSION)!; - const pkg = await Pkg.fromArgs("qiskit", VERSION, minorVersion, "latest"); - const artifactPath = await downloadArtifact(pkg, skipDownload); - - await runPipeline(artifactPath, { - include: API_INCLUDE, - output: `docs/api/qiskit`, - pkg, - }); -} diff --git a/scripts/js/commands/docs/utils.ts b/scripts/js/commands/docs/utils.ts deleted file mode 100644 index 7fe070b1ea8..00000000000 --- a/scripts/js/commands/docs/utils.ts +++ /dev/null @@ -1,75 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { $ } from "zx"; -import { mkdirp } from "mkdirp"; - -import { Pkg } from "../../lib/api/Pkg.js"; -import { parseMinorVersion } from "../../lib/apiVersions.js"; -import { pathExists, rmFilesInFolder } from "../../lib/fs.js"; -import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; -import { - runSphinxPipeline, - SphinxPipelineConfig, -} from "../../lib/sphinx/conversionPipeline.js"; - -export const DOCS_FOLDER = "docs"; -export const PUBLIC_FOLDER = "public"; - -// Glob patterns that identify API reference content in Sphinx artifacts. -export const API_INCLUDE = [ - "apidocs/**", - "apidoc/**", - "stubs/**", - "release_notes.html", - "release-notes.html", -]; - -// Glob patterns to exclude from prose runs (API content + release notes which -// contain links to API pages, + Sphinx internals already excluded by the pipeline). -export const PROSE_EXCLUDE = [ - "apidocs/**", - "apidoc/**", - "stubs/**", - "release_notes.html", - "release-notes.html", -]; - -export async function downloadArtifact( - pkg: Pkg, - skipDownload: boolean, -): Promise { - const artifactFolder = pkg.sphinxArtifactFolder(); - const artifactPath = `${artifactFolder}/artifact`; - - if (skipDownload && (await pathExists(artifactPath))) { - console.log(`Reusing cached artifact for ${pkg.name}`); - return artifactPath; - } - - await downloadSphinxArtifact(pkg, artifactFolder); - return artifactPath; -} - -export async function clearOutputDir(dir: string): Promise { - if (await pathExists(dir)) { - await rmFilesInFolder(dir); - } -} - -export async function runPipeline( - artifactPath: string, - config: SphinxPipelineConfig, -): Promise { - await clearOutputDir(config.output); - await runSphinxPipeline(artifactPath, DOCS_FOLDER, PUBLIC_FOLDER, config); -} diff --git a/scripts/js/commands/updateDocs.ts b/scripts/js/commands/updateDocs.ts index 78df5ca8fab..3a006402efd 100644 --- a/scripts/js/commands/updateDocs.ts +++ b/scripts/js/commands/updateDocs.ts @@ -10,18 +10,45 @@ // copyright notice, and modified files need to carry a notice indicating // that they have been altered from the originals. -import { basename } from "path"; -import { fileURLToPath, pathToFileURL } from "url"; - import yargs from "yargs/yargs"; import { hideBin } from "yargs/helpers"; -import { globby } from "globby"; +import { Pkg } from "../lib/api/Pkg.js"; +import { parseMinorVersion } from "../lib/apiVersions.js"; +import { pathExists } from "../lib/fs.js"; +import { downloadSphinxArtifact } from "../lib/api/sphinxArtifacts.js"; +import { runSphinxPipeline } from "../lib/sphinx/conversionPipeline.js"; import { zxMain } from "../lib/zx.js"; +import { $ } from "zx"; + +const DOCS_FOLDER = "docs"; +const PUBLIC_FOLDER = "public"; + +// Glob patterns excluded from prose runs. Release notes link heavily to API +// pages and don't belong in the prose output. +const PROSE_EXCLUDE = [ + "apidocs/**", + "apidoc/**", + "stubs/**", + "release_notes.html", + "release-notes.html", +]; + +type AddonConfig = { + name: string; + version: string; +}; -const COMMANDS_DIR = new URL("./docs/", import.meta.url); +const ADDONS: AddonConfig[] = [ + { name: "qiskit-addon-aqc-tensor", version: "0.2.0" }, + { name: "qiskit-addon-cutting", version: "0.10.0" }, + { name: "qiskit-addon-mpf", version: "0.3.0" }, + { name: "qiskit-addon-obp", version: "0.3.0" }, + { name: "qiskit-addon-sqd", version: "0.12.0" }, + { name: "qiskit-addon-utils", version: "0.3.1" }, +]; -const VALID_PACKAGES = await discoverPackages(); +const VALID_NAMES = ADDONS.map((a) => a.name); const readArgs = () => { return yargs(hideBin(process.argv)) @@ -29,41 +56,56 @@ const readArgs = () => { .option("package", { alias: "p", type: "string", - choices: VALID_PACKAGES, - description: "Which package to update. Defaults to all packages.", + choices: VALID_NAMES, + description: + "Which addon to update. Defaults to all configured addons.", }) .option("skip-download", { type: "boolean", default: false, - description: "Reuse already-downloaded artifacts instead of downloading again.", + description: + "Reuse already-downloaded artifacts instead of downloading again.", }) .parseSync(); }; -async function discoverPackages(): Promise { - const dir = fileURLToPath(COMMANDS_DIR); - const files = await globby("*.ts", { cwd: dir }); - return files - .map((f) => basename(f, ".ts")) - .filter((name) => name !== "utils") - .sort(); -} +async function runForAddon( + config: AddonConfig, + skipDownload: boolean, +): Promise { + const minorVersion = parseMinorVersion(config.version)!; + const pkg = await Pkg.fromArgs( + config.name, + config.version, + minorVersion, + "latest", + ); -async function loadAndRun(pkgName: string, skipDownload: boolean): Promise { - const moduleUrl = new URL(`${pkgName}.ts`, COMMANDS_DIR); - const mod = await import(moduleUrl.toString().replace(/\.ts$/, ".js")); - if (typeof mod.run !== "function") { - throw new Error(`Package command ${pkgName}.ts does not export a run() function`); + const artifactFolder = pkg.sphinxArtifactFolder(); + const artifactPath = `${artifactFolder}/artifact`; + + if (skipDownload && (await pathExists(artifactPath))) { + console.log(`Reusing cached artifact for ${config.name}`); + } else { + await downloadSphinxArtifact(pkg, artifactFolder); } - console.log(`\n=== ${pkgName} ===`); - await mod.run(skipDownload); + + const proseOutput = `docs/addons/${config.name}`; + if (await pathExists(proseOutput)) await $`rm -rf ${proseOutput}`; + await runSphinxPipeline(artifactPath, DOCS_FOLDER, PUBLIC_FOLDER, { + exclude: PROSE_EXCLUDE, + output: proseOutput, + pkg, + }); } zxMain(async () => { const args = readArgs(); - const packages = args.package ? [args.package] : VALID_PACKAGES; + const addons = args.package + ? ADDONS.filter((a) => a.name === args.package) + : ADDONS; - for (const pkgName of packages) { - await loadAndRun(pkgName, args.skipDownload); + for (const addon of addons) { + await runForAddon(addon, args.skipDownload); } }); diff --git a/scripts/js/lib/api/htmlToMd.ts b/scripts/js/lib/api/htmlToMd.ts index 7abf0b466bf..ad8ecced71b 100644 --- a/scripts/js/lib/api/htmlToMd.ts +++ b/scripts/js/lib/api/htmlToMd.ts @@ -243,7 +243,7 @@ function buildAdmonition( handlers: Record, ): MdxJsxFlowElement { const titleNode = findNodeWithProperty(node.children, "admonition-title"); - const children: Array = without(node.children, titleNode).map( + const children: Array = without(node.children, titleNode ?? undefined).map( (node: any) => toMdast(node, { handlers }), ); @@ -261,7 +261,7 @@ function buildAdmonition( { type: "mdxJsxAttribute", name: "title", - value: toText(titleNode), + value: titleNode ? toText(titleNode) : "", }, { type: "mdxJsxAttribute", diff --git a/scripts/js/lib/api/processHtml.ts b/scripts/js/lib/api/processHtml.ts index df5e27e19d5..efd4329ec16 100644 --- a/scripts/js/lib/api/processHtml.ts +++ b/scripts/js/lib/api/processHtml.ts @@ -245,10 +245,8 @@ function detectLanguage( ): string | null { const defaultLanguage = options.isCApi ? "c" : "python"; // Two levels up from `pre` should have class `highlight-` - const detectedLanguage = $pre - .parent() - .parent()[0] - .attribs.class.match(/(?<=highlight-)\w+/); + const grandparentClass = $pre.parent().parent()[0]?.attribs?.class ?? ""; + const detectedLanguage = grandparentClass.match(/(?<=highlight-)\w+/); if (!detectedLanguage) return defaultLanguage; const langName = detectedLanguage[0]; if (langName === "none") return null; diff --git a/scripts/js/lib/api/saveImages.ts b/scripts/js/lib/api/saveImages.ts index 51e8cc3f83e..97768687f4e 100644 --- a/scripts/js/lib/api/saveImages.ts +++ b/scripts/js/lib/api/saveImages.ts @@ -62,8 +62,12 @@ export async function saveImages( return; } const source = `${originalImagesFolderPath}/${img.fileName}`; - const dest = `${publicBaseFolder}/${img.dest}`; + const dest = `${publicBaseFolder}/${img.dest.replace(/^\//, "")}`; + if (!(await pathExists(source))) { + console.warn(`Skipping missing image: ${source}`); + return; + } await mkdirp(dirname(dest)); if (extname(source) === extname(dest)) { await copyFile(source, dest); diff --git a/scripts/js/lib/sphinx/conversionPipeline.ts b/scripts/js/lib/sphinx/conversionPipeline.ts index def8cc0e5a6..e6799624aa1 100644 --- a/scripts/js/lib/sphinx/conversionPipeline.ts +++ b/scripts/js/lib/sphinx/conversionPipeline.ts @@ -38,6 +38,7 @@ const SPHINX_INTERNALS = [ "_static/**", "_sources/**", "_downloads/**", + "_modules/**", "genindex.html", "py-modindex.html", "search.html", From 02e198d485efd1df8e7a9c1a61d689a9343d2867 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Wed, 6 May 2026 14:36:48 -0400 Subject: [PATCH 011/104] cleanup old addon pipeline --- .../qiskit-addon-aqc-tensor/_package.json | 4 - .../addons/qiskit-addon-cutting/_package.json | 4 - docs/addons/qiskit-addon-mpf/_package.json | 4 - docs/addons/qiskit-addon-obp/_package.json | 4 - docs/addons/qiskit-addon-sqd/_package.json | 4 - docs/addons/qiskit-addon-utils/_package.json | 4 - scripts/config/addon-docs.ts | 78 ------ scripts/js/commands/updateAddonDocs.ts | 95 ------- scripts/js/lib/addons/addonContentPipeline.ts | 251 ------------------ scripts/js/lib/addons/generateAddonToc.ts | 101 ------- scripts/js/lib/sphinx/conversionPipeline.ts | 8 - 11 files changed, 557 deletions(-) delete mode 100644 docs/addons/qiskit-addon-aqc-tensor/_package.json delete mode 100644 docs/addons/qiskit-addon-cutting/_package.json delete mode 100644 docs/addons/qiskit-addon-mpf/_package.json delete mode 100644 docs/addons/qiskit-addon-obp/_package.json delete mode 100644 docs/addons/qiskit-addon-sqd/_package.json delete mode 100644 docs/addons/qiskit-addon-utils/_package.json delete mode 100644 scripts/config/addon-docs.ts delete mode 100644 scripts/js/commands/updateAddonDocs.ts delete mode 100644 scripts/js/lib/addons/addonContentPipeline.ts delete mode 100644 scripts/js/lib/addons/generateAddonToc.ts diff --git a/docs/addons/qiskit-addon-aqc-tensor/_package.json b/docs/addons/qiskit-addon-aqc-tensor/_package.json deleted file mode 100644 index a08b147e6d6..00000000000 --- a/docs/addons/qiskit-addon-aqc-tensor/_package.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "name": "qiskit-addon-aqc-tensor", - "version": "0.2.0" -} diff --git a/docs/addons/qiskit-addon-cutting/_package.json b/docs/addons/qiskit-addon-cutting/_package.json deleted file mode 100644 index 2c3e49e17d2..00000000000 --- a/docs/addons/qiskit-addon-cutting/_package.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "name": "qiskit-addon-cutting", - "version": "0.10.0" -} diff --git a/docs/addons/qiskit-addon-mpf/_package.json b/docs/addons/qiskit-addon-mpf/_package.json deleted file mode 100644 index 802fd0f5ff9..00000000000 --- a/docs/addons/qiskit-addon-mpf/_package.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "name": "qiskit-addon-mpf", - "version": "0.3.0" -} diff --git a/docs/addons/qiskit-addon-obp/_package.json b/docs/addons/qiskit-addon-obp/_package.json deleted file mode 100644 index 716d8c3c091..00000000000 --- a/docs/addons/qiskit-addon-obp/_package.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "name": "qiskit-addon-obp", - "version": "0.3.0" -} diff --git a/docs/addons/qiskit-addon-sqd/_package.json b/docs/addons/qiskit-addon-sqd/_package.json deleted file mode 100644 index 9b4e87cc521..00000000000 --- a/docs/addons/qiskit-addon-sqd/_package.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "name": "qiskit-addon-sqd", - "version": "0.12.0" -} diff --git a/docs/addons/qiskit-addon-utils/_package.json b/docs/addons/qiskit-addon-utils/_package.json deleted file mode 100644 index 648d449cc7c..00000000000 --- a/docs/addons/qiskit-addon-utils/_package.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "name": "qiskit-addon-utils", - "version": "0.3.1" -} diff --git a/scripts/config/addon-docs.ts b/scripts/config/addon-docs.ts deleted file mode 100644 index bd9a79250a3..00000000000 --- a/scripts/config/addon-docs.ts +++ /dev/null @@ -1,78 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -type AddonDocsConfig = Record; - -export const ADDON_DOCS_CONFIG: AddonDocsConfig = { - "qiskit-addon-aqc-tensor": { - version: "0.2.0", - include: [ - "index.html", - "install.html", - "release-notes.html", - "how-tos/*.ipynb", - "tutorials/*.ipynb", - "explanation/index.html", - ], - }, - "qiskit-addon-mpf": { - version: "0.3.0", - include: [ - "index.html", - "install.html", - "release-notes.html", - "how_tos/*.ipynb", - "tutorials/*.ipynb", - "explanations/*.ipynb", - ], - }, - "qiskit-addon-sqd": { - version: "0.12.0", - include: [ - "index.html", - "install.html", - "release-notes.html", - "how_tos/*.ipynb", - "tutorials/*.ipynb", - ], - }, - "qiskit-addon-obp": { - version: "0.3.0", - include: [ - "index.html", - "install.html", - "release-notes.html", - "how_tos/*.ipynb", - "tutorials/*.ipynb", - ], - }, - "qiskit-addon-cutting": { - version: "0.10.0", - include: [ - "index.html", - "install.html", - "release-notes.html", - "how-tos/*.ipynb", - "tutorials/*.ipynb", - "explanation/index.html", - ], - }, - "qiskit-addon-utils": { - version: "0.3.1", - include: [ - "index.html", - "install.html", - "release-notes.html", - "how_tos/*.ipynb", - ], - }, -}; diff --git a/scripts/js/commands/updateAddonDocs.ts b/scripts/js/commands/updateAddonDocs.ts deleted file mode 100644 index 2b3ca577e21..00000000000 --- a/scripts/js/commands/updateAddonDocs.ts +++ /dev/null @@ -1,95 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import yargs from "yargs/yargs"; -import { hideBin } from "yargs/helpers"; - -import { Pkg } from "../lib/api/Pkg.js"; -import { zxMain } from "../lib/zx.js"; -import { parseMinorVersion } from "../lib/apiVersions.js"; -import { pathExists, rmFilesInFolder } from "../lib/fs.js"; -import { downloadSphinxArtifact } from "../lib/api/sphinxArtifacts.js"; -import { runAddonContentPipeline } from "../lib/addons/addonContentPipeline.js"; -import { ADDON_DOCS_CONFIG } from "../../config/addon-docs.js"; - -const readArgs = () => { - return yargs(hideBin(process.argv)) - .version(false) - .option("package", { - alias: "p", - type: "string", - choices: Object.keys(ADDON_DOCS_CONFIG), - description: "Which addon to update. Defaults to all configured addons.", - }) - .option("skip-download", { - type: "boolean", - default: false, - description: - "Reuse the already-downloaded artifact instead of downloading again.", - }) - .parseSync(); -}; - -async function runForAddon( - pkgName: string, - skipDownload: boolean, -): Promise { - const addonConfig = ADDON_DOCS_CONFIG[pkgName]; - const { version } = addonConfig; - const minorVersion = parseMinorVersion(version); - if (minorVersion === null) { - throw new Error( - `Invalid version "${version}" in addon-docs.ts for ${pkgName}`, - ); - } - - const pkg = await Pkg.fromArgs(pkgName, version, minorVersion, "latest"); - const sphinxArtifactFolder = pkg.sphinxArtifactFolder(); - - if ( - skipDownload && - (await pathExists(`${sphinxArtifactFolder}/artifact`)) - ) { - console.log(`Skip downloading sources for ${pkg.name}:${minorVersion}`); - } else { - await downloadSphinxArtifact(pkg, sphinxArtifactFolder); - } - - const addonDir = `docs/addons/${pkg.name}`; - if (await pathExists(addonDir)) { - await rmFilesInFolder(addonDir); - } - const addonImagesDir = `public/docs/images/addons/${pkg.name}`; - if (await pathExists(addonImagesDir)) { - await rmFilesInFolder(addonImagesDir); - } - - console.log(`Run addon prose pipeline for ${pkg.name}:${minorVersion}`); - await runAddonContentPipeline( - `${sphinxArtifactFolder}/artifact`, - "docs", - "public/docs", - pkg, - addonConfig.include, - ); -} - -zxMain(async () => { - const args = readArgs(); - const packages = args.package - ? [args.package] - : Object.keys(ADDON_DOCS_CONFIG); - - for (const pkgName of packages) { - await runForAddon(pkgName, args.skipDownload); - } -}); diff --git a/scripts/js/lib/addons/addonContentPipeline.ts b/scripts/js/lib/addons/addonContentPipeline.ts deleted file mode 100644 index 95b52f87e95..00000000000 --- a/scripts/js/lib/addons/addonContentPipeline.ts +++ /dev/null @@ -1,251 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { readFile, writeFile } from "fs/promises"; -import { dirname, parse, relative } from "path"; - -import { load } from "cheerio"; -import { globby } from "globby"; -import { mkdirp } from "mkdirp"; -import { isEmpty, uniqBy } from "lodash-es"; - -import { sphinxHtmlToMarkdown } from "../api/htmlToMd.js"; -import { generateAddonToc } from "./generateAddonToc.js"; -import { HtmlToMdResultWithUrl } from "../api/HtmlToMdResult.js"; -import { Pkg } from "../api/Pkg.js"; -import { pathExists } from "../fs.js"; -import { updateLinks, relativizeLink } from "../api/updateLinks.js"; -import { DOCS_BASE_PATH } from "../api/conversionPipeline.js"; -import { kebabCaseAndShortenPage } from "../api/normalizeResultUrls.js"; -import addFrontMatter from "../api/addFrontMatter.js"; -import { dedupeHtmlIdsFromResults } from "../api/dedupeHtmlIds.js"; -import removeMathBlocksIndentation from "../api/removeMathBlocksIndentation.js"; -import { saveImages } from "../api/saveImages.js"; - -export async function runAddonContentPipeline( - htmlPath: string, - docsBaseFolder: string, - publicBaseFolder: string, - pkg: Pkg, - include: string[], -): Promise { - const outputDir = `${docsBaseFolder}/addons/${pkg.name}`; - await mkdirp(outputDir); - - const stubsMap = await buildStubsMap(htmlPath, pkg.name); - const allFiles = await globby(include, { cwd: htmlPath }); - const htmlFiles = allFiles.filter((f) => f.endsWith(".html")); - const notebookFiles = allFiles.filter((f) => f.endsWith(".ipynb")); - - if (isEmpty(htmlFiles) && isEmpty(notebookFiles)) { - console.log(`No addon content files found for ${pkg.name}`); - return; - } - - const results = await convertProseFiles( - pkg, - htmlPath, - docsBaseFolder, - publicBaseFolder, - htmlFiles, - ); - - for (const result of results) { - result.markdown = resolveGithubIoLinks(result.markdown, stubsMap, pkg.name); - } - await updateLinks(results, { - kebabCaseAndShorten: false, - pkgName: pkg.name, - pkgOutputDir: `${DOCS_BASE_PATH}/addons/${pkg.name}`, - }); - await dedupeHtmlIdsFromResults(results); - addFrontMatter(results, pkg); - removeMathBlocksIndentation(results); - - await writeMdxFiles(docsBaseFolder, results); - await saveImages( - uniqBy(results.flatMap((r) => r.images), (img) => img.fileName), - `${htmlPath}/_images`, - "public", - pkg, - ); - await copyNotebooks(htmlPath, outputDir, notebookFiles, stubsMap, pkg.name); - await writeTocFile(htmlPath, outputDir, pkg, docsBaseFolder); -} - -async function convertProseFiles( - pkg: Pkg, - htmlPath: string, - docsBaseFolder: string, - publicBaseFolder: string, - filePaths: string[], -): Promise { - const results: HtmlToMdResultWithUrl[] = []; - const outputDir = `${docsBaseFolder}/addons/${pkg.name}`; - const imageDestination = `docs/images/addons/${pkg.name}`; - - for (const file of filePaths) { - const html = await readFile(`${htmlPath}/${file}`, "utf-8"); - const result = await sphinxHtmlToMarkdown({ - html, - fileName: file, - determineGithubUrl: pkg.determineGithubUrlFn(), - imageDestination, - releaseNotesTitle: "", - hasSeparateReleaseNotes: false, - isCApi: false, - hasRootNamespaceFile: false, - }); - - if (result.markdown === "") continue; - - const title = extractTitle(html); - const description = extractDescription(html); - const quotedTitle = `"${title.replace(/\\/g, "\\\\").replace(/"/g, '\\"')}"`; - const frontmatterLines = [`title: ${quotedTitle}`]; - if (description) { - const quotedDesc = `"${description.replace(/\\/g, "\\\\").replace(/"/g, '\\"')}"`; - frontmatterLines.push(`description: ${quotedDesc}`); - } - // Content-only (no --- delimiters) — addFrontMatter wraps it. - result.meta.hardcodedFrontmatter = frontmatterLines.join("\n"); - - const { dir, name } = parse(`${outputDir}/${file}`); - const url = `/${relative(docsBaseFolder, dir)}/${name}`; - results.push({ ...result, url }); - } - return results; -} - -function extractTitle(html: string): string { - const $ = load(html); - const h1 = $("h1").first(); - // Remove Sphinx permalink anchors before extracting text. - h1.find("a.headerlink").remove(); - return h1.text().trim() || "Untitled"; -} - -function extractDescription(html: string): string | null { - const $ = load(html); - const desc = $('meta[name="description"]').attr("content"); - return desc?.trim() || null; -} - -async function writeMdxFiles( - docsBaseFolder: string, - results: HtmlToMdResultWithUrl[], -): Promise { - for (const result of results) { - const filePath = `${docsBaseFolder}${result.url}.mdx`; - await mkdirp(dirname(filePath)); - await writeFile(filePath, result.markdown); - } -} - -async function copyNotebooks( - htmlPath: string, - outputDir: string, - notebookFiles: string[], - stubsMap: Map, - pkgName: string, -): Promise { - if (notebookFiles.length === 0) return; - - for (const file of notebookFiles) { - const dest = `${outputDir}/${file}`; - await mkdirp(dirname(dest)); - const raw = await readFile(`${htmlPath}/${file}`, "utf-8"); - const notebook = JSON.parse(raw); - for (const cell of notebook.cells ?? []) { - const lines: string[] = Array.isArray(cell.source) - ? cell.source - : [cell.source]; - cell.source = lines.map((line) => { - const resolved = resolveGithubIoLinks(line, stubsMap, pkgName); - return resolved.replace(/\[([^\]]*)\]\(([^)]+)\)/g, (match, text, url) => { - const rewritten = relativizeLink({ url, text }); - return rewritten ? `[${rewritten.text ?? text}](${rewritten.url})` : match; - }); - }); - } - await writeFile(dest, JSON.stringify(notebook, null, 1)); - } -} - -async function buildStubsMap( - htmlPath: string, - pkgName: string, -): Promise> { - const stubsDir = `${htmlPath}/stubs`; - const map = new Map(); - if (!(await pathExists(stubsDir))) return map; - - const files = await globby("*.html", { cwd: stubsDir }); - for (const file of files) { - const html = await readFile(`${stubsDir}/${file}`, "utf-8"); - const match = html.match(/var target = "([^"]+)"/); - if (!match) continue; - const apidocsMatch = match[1].match(/\.\.\/apidocs\/([^#"]+\.html)(#.*)?/); - if (!apidocsMatch) continue; - const modulePage = apidocsMatch[1].replace(/\.html$/, ""); - const anchor = apidocsMatch[2] ?? ""; - const kebabPage = kebabCaseAndShortenPage(modulePage, pkgName); - const symbolName = file.replace(/\.html$/, ""); - map.set(symbolName, `/docs/api/${pkgName}/${kebabPage}${anchor}`); - } - return map; -} - -function resolveGithubIoLinks( - text: string, - stubsMap: Map, - pkgName: string, -): string { - return text.replace( - /https:\/\/qiskit\.github\.io\/([^/"#)\s]+)\/?([^"#)\s]*)/g, - (match, pkg, rest) => { - // Stubs links for this package — resolve via artifact redirect map - if (pkg === pkgName) { - const stubsPrefix = "stubs/"; - if (rest.startsWith(stubsPrefix)) { - const symbol = rest - .slice(stubsPrefix.length) - .replace(/\.html$/, ""); - const resolved = stubsMap.get(symbol); - if (resolved) return resolved; - } - } - // All other github.io links → /docs/addons// - const page = rest.replace(/\.html$/, "").replace(/\/$/, ""); - return page ? `/docs/addons/${pkg}/${page}` : `/docs/addons/${pkg}`; - }, - ); -} - -async function writeTocFile( - htmlPath: string, - outputDir: string, - pkg: Pkg, - docsBaseFolder: string, -): Promise { - console.log("Generating addon toc"); - const toc = await generateAddonToc( - htmlPath, - outputDir, - pkg.title, - docsBaseFolder, - ); - await writeFile( - `${outputDir}/_toc.json`, - JSON.stringify(toc, null, 2) + "\n", - ); -} diff --git a/scripts/js/lib/addons/generateAddonToc.ts b/scripts/js/lib/addons/generateAddonToc.ts deleted file mode 100644 index 65a5fe93a77..00000000000 --- a/scripts/js/lib/addons/generateAddonToc.ts +++ /dev/null @@ -1,101 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { readFile } from "fs/promises"; - -import { load } from "cheerio"; - -import { TocEntry } from "../api/generateToc.js"; - -export type AddonToc = { - title: string; - children: TocEntry[]; - collapsed: boolean; -}; - -// Sphinx artifact folders that belong to the API pipeline, not prose. -const EXCLUDED_HREF_PREFIXES = ["apidocs/", "stubs/", "release-notes"]; - -/** Build a _toc.json for addon content by parsing the Sphinx sidebar nav. */ -export async function generateAddonToc( - htmlPath: string, - outputDir: string, - pkgTitle: string, - docsBaseFolder: string, -): Promise { - const indexHtml = await readFile(`${htmlPath}/index.html`, "utf-8"); - const $ = load(indexHtml); - - // Collect the set of URLs we actually wrote so we can filter out entries - // that weren't included (e.g. apidocs children under a section we skip). - const outputBase = `/${outputDir.startsWith(docsBaseFolder) - ? outputDir - : `${docsBaseFolder}/${outputDir}`}`; - - const children: TocEntry[] = []; - - $(".sidebar-tree .toctree-l1").each((_, l1) => { - const l1Link = $(l1).children("a").first(); - const href = l1Link.attr("href") ?? ""; - - // Skip external links, API reference, release notes. - if ( - l1Link.hasClass("reference external") || - EXCLUDED_HREF_PREFIXES.some((prefix) => href.startsWith(prefix)) - ) { - return; - } - - const title = l1Link.text().trim(); - const url = hrefToUrl(href, outputBase); - - const l2Items = $(l1).find(".toctree-l2"); - if (l2Items.length === 0) { - children.push({ title, url }); - } else { - const subChildren: TocEntry[] = []; - l2Items.each((_, l2) => { - const l2Link = $(l2).children("a").first(); - const l2Href = l2Link.attr("href") ?? ""; - if ( - l2Link.hasClass("reference external") || - EXCLUDED_HREF_PREFIXES.some((prefix) => l2Href.startsWith(prefix)) - ) { - return; - } - subChildren.push({ - title: l2Link.text().trim(), - url: hrefToUrl(l2Href, outputBase), - }); - }); - - if (subChildren.length > 0) { - // If the section index page exists as a converted file, link it; - // otherwise emit a section header with no url. - const sectionUrl = href === "#" ? undefined : url; - const entry: TocEntry = { title, children: subChildren }; - if (sectionUrl) entry.url = sectionUrl; - children.push(entry); - } - } - }); - - return { title: pkgTitle, children, collapsed: true }; -} - -function hrefToUrl(href: string, outputBase: string): string { - if (href === "#") return outputBase; - // Strip .html and anchor fragment, prepend output base path. - const withoutAnchor = href.split("#")[0]; - const withoutExt = withoutAnchor.replace(/\.html$/, ""); - return `${outputBase}/${withoutExt}`; -} diff --git a/scripts/js/lib/sphinx/conversionPipeline.ts b/scripts/js/lib/sphinx/conversionPipeline.ts index e6799624aa1..7a4a5603a74 100644 --- a/scripts/js/lib/sphinx/conversionPipeline.ts +++ b/scripts/js/lib/sphinx/conversionPipeline.ts @@ -130,7 +130,6 @@ export async function runSphinxPipeline( await copyNotebooks(artifactPath, output, notebookFiles, objectsInv, pkg.name); await writeTocFile(artifactPath, output, pkg, docsBaseFolder, config); - await writePackageFile(output, pkg); } async function loadObjectsInv( @@ -250,13 +249,6 @@ async function writeTocFile( ); } -async function writePackageFile(outputDir: string, pkg: Pkg): Promise { - const pkgJson = { name: pkg.name, version: pkg.version }; - await writeFile( - `${outputDir}/_package.json`, - JSON.stringify(pkgJson, null, 2) + "\n", - ); -} /** * Resolve qiskit.github.io links to internal paths. From 6d8755c2496d5b543fb9eceef5b7b861af54d77b Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Wed, 6 May 2026 18:46:23 -0400 Subject: [PATCH 012/104] sync conversion pipelines --- docs/addons/qiskit-addon-aqc-tensor/_toc.json | 38 - .../explanation/index.mdx | 86 - .../how-tos/01-quimb-tnoptimizer.mdx | 220 -- .../how-tos/01_quimb_tnoptimizer.ipynb | 730 ----- .../qiskit-addon-aqc-tensor/how-tos/index.mdx | 13 - docs/addons/qiskit-addon-aqc-tensor/index.mdx | 76 - .../qiskit-addon-aqc-tensor/install.mdx | 82 - .../tutorials/01-initial-state-aqc.mdx | 435 --- .../tutorials/01_initial_state_aqc.ipynb | 845 ----- .../tutorials/index.mdx | 15 - docs/addons/qiskit-addon-cutting/_toc.json | 62 - .../explanation/index.mdx | 146 - ...enerate-exact-quasi-dists-from-sampler.mdx | 76 - ...o-generate-exact-sampling-coefficients.mdx | 248 -- .../how-to-provide-multiple-observables.mdx | 207 -- .../how-tos/how-to-specify-cut-wires.mdx | 277 -- ...erate_exact_quasi_dists_from_sampler.ipynb | 175 - 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"/docs/addons/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc" - } - ], - "url": "/docs/addons/qiskit-addon-aqc-tensor/tutorials/index" - }, - { - "title": "Explanatory Material", - "url": "/docs/addons/qiskit-addon-aqc-tensor/explanation/index" - }, - { - "title": "How-To Guides", - "children": [ - { - "title": "How to use quimb.tensor.TNOptimizer directly", - "url": "/docs/addons/qiskit-addon-aqc-tensor/how-tos/01_quimb_tnoptimizer" - } - ], - "url": "/docs/addons/qiskit-addon-aqc-tensor/how-tos/index" - } - ], - "collapsed": true -} diff --git a/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx deleted file mode 100644 index c945320e369..00000000000 --- a/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx +++ /dev/null @@ -1,86 +0,0 @@ ---- -title: Approximate quantum compilation (AQC-Tensor) API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-aqc-tensor. ---- - - - -# Explanatory Material on AQC-Tensor - -## Background - -This Qiskit addon allows one to compress the *initial portion* of a circuit using Approximate Quantum Compilation (AQC) with tensor networks, following the method introduced by Ref. [\[1\]](#id4). - -In the following, we will assume that the input circuit has already been truncated such that its entire depth can be simulated by a tensor network with a reasonable bond dimension. In order for AQC to be useful on quantum hardware, one must use the resulting circuit as a state preparation procedure and then perform subsequent quantum computation on that state in a way that is beyond the reach of a tensor network simulator alone. - -In essence, AQC-Tensor requires three things as input: - -1. A description of the **target state** in the form of a tensor network. This may be generated by simulating a circuit on a tensor network simulator (this is what Ref. [\[1\]](#id4) did), or it could be generated in some other way, e.g., by performing time-dependent variational principle (TDVP) time evolution directly on a matrix-product state. -2. A parametrized **ansatz circuit**, ideally with hardware-efficient connectivity, such that it will have a reasonable depth on the target hardware. -3. **Initial parameters** to plug into the ansatz circuit, such that the resulting state is already a *good* approximation to the target state. - -The technique is to then use an iterative optimization method to make the *good* state as close to the target state as possible. - -\[The third item is not required *in principle*, but it really helps to give the optimizer a sensible starting point; otherwise, it is much more likely to get stuck in a local (but not global) minimum.] - -## Ansatz generation (motivation) - -As mentioned in the previous section, three closely related things are needed as input to AQC-Tensor. Rather than code all three individually, this package provides a [`generate_ansatz_from_circuit()`](ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") function that can generate (2) and (3) from a circuit. In the case of Trotter evolution (and likely other circuit classes of interest), one can use the same circuit-generation function to generate (1) as well as the input to [`generate_ansatz_from_circuit()`](ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"), thus enabling the generation of all three inputs from a single function. The tutorial is a good demonstration of this re-use of a single circuit-generation function. - -Specifically, [`generate_ansatz_from_circuit()`](ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") will take an input circuit and generate the ansatz circuit (2) and initial parameters (3) that follow the two-qubit connectivity of the input circuit. However, the returned circuit will be parametrized, based on generalized one- and two-qubit rotations. The initial parameters returned by the function, when plugged into the ansatz, will result in a circuit that is exactly equivalent to the circuit passed as input, up to a global phase. - -Given the ansatz generation function, the user must provide only two things in order to use AQC-Tensor: - -1. The **target state**, i.e., a *great* description of the desired state; and, -2. A **circuit** with the desired connectivity of the ansatz that prepares a *good* approximation to the target state. - -The optimization procedure then takes the *good* ansatz parameters and brings them as close as possible to the *great* description (the target state). - -In the case of a Trotter circuit, the same function can actually provide both things. For instance, the `spinhalf_trotter_circuit` function could be called with `num_trotter_steps=50` to produce the target state, then again with `num_trotter_steps=5` to produce the ansatz structure to be passed to `generate_ansatz_from_circuit`. (Note that the `evolution_time` is fixed, such that the time step `dt = evolution_time / num_trotter_steps`. the time per single Trotter step as `dt = evolution_time / num_trotter_steps`, so these two function calls would leave the total `evolution_time` fixed, while changing the time per Trotter step, `dt`.) So, in order for a user to apply AQC-Tensor to a new physical model of Trotter circuit, they would only need to generalize a single function with the details of that model. - -The ansatz used by `generate_ansatz_from_circuit` uses 9 parameters per two-qubit block, which is the theoretical minimum (see, e.g., Fig. 2 of [https://arxiv.org/abs/quant-ph/0308033](https://arxiv.org/abs/quant-ph/0308033)). The automatic ansatz is based on the [KAK decomposition](https://qiskit-extensions.github.io/circuit-knitting-toolbox/circuit_cutting/explanation/index.html#more-general-cut-two-qubit-gates-via-the-kak-decomposition), which parametrizes any two-qubit gate in terms of three parameters, up to single-qubit rotations. The single-qubit rotation are then decomposed as ZXZ, each of which has three parameters. So, each two-qubit block of the original circuit will result in 3 parameters for the two-qubit rotation, plus 3 parameters for an outgoing single-qubit rotation on each of the two qubits, for a total of 9 parameters per block. The ansatz is completed by adding a layer of single-qubit rotations to each active qubit at the start of the circuit. - -## Tensor-network simulation - -The simplest tensor network is a matrix-product state (MPS). - -Currently, AQC-Tensor supports the following tensor-network simulators: - -* Qiskit Aer’s MPS simulator -* Quimb’s [eager](https://quimb.readthedocs.io/en/latest/tensor/tensor-circuit-mps.html) [`CircuitMPS`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.CircuitMPS) simulator -* Quimb’s [lazy](https://quimb.readthedocs.io/en/latest/tensor/tensor-circuit.html) [`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.Circuit) simulator (may work only on small circuits so far; we’re working to fix this soon with more clever contractions) - -The most important parameter of a tensor network is its maximum bond dimension, which limits how much entanglement it can represent (and thus to what depth a given circuit can be faithfully simulated). The bond dimension is often represented by the Greek letter $\chi$. - -Given a general circuit on $L$ qubits, a matrix-product state needs at most a bond dimension of $\chi_\mathrm{exact} = 2^{\lfloor L/2 \rfloor}$ in order to be able to simulate it to *any* depth. Of course, general circuits on 100+ qubits cannot be classically simulated, so it will be intractable to set the bond dimension this high for those circuits. - -For this reason, if you are attempting to experiment with AQC-Tensor on a toy problem with few qubits, it is important to ensure that $\chi < 2^{\lfloor L/2 \rfloor}$. Otherwise, any circuit can be simulated to any depth, and there is no point in performing AQC. - -### Objective function - -Currently, this addon provides one very simple objective, [`MaximizeStateFidelity`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity"), whose [`loss_function()`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") is equivalent to Eq. (7) in Ref. [\[1\]](#id4). When called with an array of parameters, the [`loss_function()`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") method returns the value and gradient of the objective function as a 2-tuple. - -### Gradient - -This package provides a few different methods for calculating the gradient. - -The Aer backend always uses the explicit gradient code in the `explicit_gradient` module. - -The Quimb backend will typically be used with an automatic differentiation backend; the user is to select a backend from among those supported by Quimb. Alternatively, one can instead pass `"explicit"` as the `autodiff_backend` when instantiating the [`QuimbSimulator`](simulation-quimb-quimb-simulator#qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator "qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator") object; in this case, the `explicit_gradient` module will be used. It is only recommended to use explicit gradients with Quimb’s eager [`CircuitMPS`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.CircuitMPS) simulator, not the lazy [`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.Circuit) simulator. - -Regardless of which backend is chosen, the gradient code can understand linear parameter expressions (ParameterExpression objects). This support is essential, as linear expressions are returned by the ansatz generation code. - -### Optimization method - -Users are encouraged to use [`scipy.optimize`](https://docs.scipy.org/doc/scipy/reference/optimize.html#module-scipy.optimize "(in SciPy v1.15.2)") to perform the optimization. - -L-BFGS is the optimizer demonstrated in the tutorial notebook. It works well in practice because it uses the function value and its gradient to approximate the Hessian. It works well when given an initial point and seems to work particularly well in the case of Trotter circuits. However, it might early terminate if it starts in a barren plateau. In that case, performing a handful of steps using the ADAM optimizer first may help. - -## References - - - -\[1] ([1](#id1),[2](#id2),[3](#id3)) - -[arXiv:2301.08609v6](https://arxiv.org/abs/2301.08609v6). - diff --git a/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer.mdx b/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer.mdx deleted file mode 100644 index 32ed5042af4..00000000000 --- a/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer.mdx +++ /dev/null @@ -1,220 +0,0 @@ - - -# How to use `quimb.tensor.TNOptimizer` directly - -Quimb provides [a mechanism for optimizing tensor networks](https://quimb.readthedocs.io/en/latest/tensor-optimization.html) through its `TNOptimizer` interface. The Quimb backend provided by this addon uses this under the hood. This how-to guide demonstrates how to work with this object directly, in case some users want more direct access to it. - - - -## Set up a model Hamiltonian - -``` -[1]: -``` - -```python -from qiskit.transpiler import CouplingMap -from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian - -# Generate some coupling map to use for this example -coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False) - -# Choose a 10-qubit circle on this coupling map -reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 4, 15, 3, 9]) - -# Get a qubit operator describing the Ising field model -hamiltonian = generate_xyz_hamiltonian( - reduced_coupling_map, - coupling_constants=(0.0, 0.0, 1.0), - ext_magnetic_field=(0.4, 0.0, 0.0), -) -``` - - - -## Set up quimb simulator with default options - -``` -[2]: -``` - -```python -import quimb.tensor as qtn - -from qiskit_addon_aqc_tensor.simulation.quimb import ( - QuimbSimulator, - qiskit_ansatz_to_quimb, - recover_parameters_from_quimb, -) - -simulator = QuimbSimulator(qtn.Circuit) -``` - -```python -/home/runner/work/qiskit-addon-aqc-tensor/qiskit-addon-aqc-tensor/.tox/docs/lib/python3.9/site-packages/cotengra/hyperoptimizers/hyper.py:57: UserWarning: Couldn't find `optuna`, `cmaes`, or `nevergrad` so will use completely random sampling in place of hyper-optimization. - warnings.warn( -/home/runner/work/qiskit-addon-aqc-tensor/qiskit-addon-aqc-tensor/.tox/docs/lib/python3.9/site-packages/cotengra/hyperoptimizers/hyper.py:76: UserWarning: Couldn't find `optuna`, `cmaes`, or `nevergrad` so will use completely random sampling in place of hyper-optimization. - warnings.warn( -``` - - - -## Generate target circuit - -``` -[3]: -``` - -```python -from qiskit.synthesis import SuzukiTrotter -from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit - -from qiskit_addon_aqc_tensor.simulation import ( - compute_overlap, - tensornetwork_from_circuit, -) - -evolution_time = 0.4 - -target_circuit = generate_time_evolution_circuit( - hamiltonian, - synthesis=SuzukiTrotter(reps=8), - time=evolution_time, -) - -target_tns = tensornetwork_from_circuit(target_circuit, simulator) -``` - - - -## Generate ansatz from a shallower circuit - -``` -[4]: -``` - -```python -from qiskit_addon_aqc_tensor.ansatz_generation import ( - AnsatzBlock, - generate_ansatz_from_circuit, -) - -initial_shallow_circuit = generate_time_evolution_circuit( - hamiltonian, - synthesis=SuzukiTrotter(reps=2), - time=evolution_time, -) -ansatz, initial_parameters = generate_ansatz_from_circuit(initial_shallow_circuit) -ansatz = ansatz.decompose(AnsatzBlock) -ansatz.draw("mpl", fold=-1) -``` - -``` -[4]: -``` - -![../\_images/how-tos\_01\_quimb\_tnoptimizer\_7\_0.png](/docs/images/api/qiskit-addon-aqc-tensor/how-tos_01_quimb_tnoptimizer_7_0.avif) - - - -## Initialize objective function - -``` -[5]: -``` - -```python -from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity - -objective = MaximizeStateFidelity(target_tns, None, None) -``` - - - -## Convert Qiskit ansatz and initial parameters to a Quimb parametrized circuit - -``` -[6]: -``` - -```python -circ, conversion_context = qiskit_ansatz_to_quimb(ansatz, initial_parameters) -``` - - - -## Perform optimization of Quimb circuit using automatic differentiation - -``` -[7]: -``` - -```python -from qiskit_addon_aqc_tensor.simulation.quimb import tnoptimizer_objective_kwargs - -tnopt = qtn.TNOptimizer( - circ, - **tnoptimizer_objective_kwargs(objective), - autodiff_backend="jax", # OPTIONS: jax, autograd, torch, etc. -) -circ_opt = tnopt.optimize(20) -``` - -```python -+0.000019788644 [best: +0.000019788644] : : 36it [00:36, 1.00s/it] -``` - - - -## Recover final parameters from the quimb circuit - -``` -[8]: -``` - -```python -final_parameters = recover_parameters_from_quimb(circ_opt, conversion_context) -``` - - - -## Check fidelity of final, compressed state w/ respect to target state - -``` -[9]: -``` - -```python -compressed_circuit = ansatz.assign_parameters(final_parameters) -compressed_state = tensornetwork_from_circuit(compressed_circuit, simulator) -abs(compute_overlap(target_tns, compressed_state)) ** 2 -``` - -``` -[9]: -``` - -```python -0.9999984444299815 -``` - - - -## Compare with fidelity of initial shallow state w/ respect to target state - -``` -[10]: -``` - -```python -initial_shallow_state = tensornetwork_from_circuit(initial_shallow_circuit, simulator) -abs(compute_overlap(target_tns, initial_shallow_state)) ** 2 -``` - -``` -[10]: -``` - -```python -0.9998389457702321 -``` diff --git a/docs/addons/qiskit-addon-aqc-tensor/how-tos/01_quimb_tnoptimizer.ipynb b/docs/addons/qiskit-addon-aqc-tensor/how-tos/01_quimb_tnoptimizer.ipynb deleted file mode 100644 index 0ec131d7b15..00000000000 --- a/docs/addons/qiskit-addon-aqc-tensor/how-tos/01_quimb_tnoptimizer.ipynb +++ /dev/null @@ -1,730 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d0791829-7938-47b6-8f01-068e981f60c5", - "metadata": {}, - "source": [ - "# How to use `quimb.tensor.TNOptimizer` directly\n", - "\n", - "Quimb provides [a mechanism for optimizing tensor networks](https://quimb.readthedocs.io/en/latest/tensor-optimization.html) through its `TNOptimizer` interface. The Quimb backend provided by this addon uses this under the hood. This how-to guide demonstrates how to work with this object directly, in case some users want more direct access to it.\n", - "\n", - "### Set up a model Hamiltonian" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8fdf1603-b541-46e7-871d-bf6b0ea8afce", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:31.116183Z", - "iopub.status.busy": "2025-03-28T17:48:31.115492Z", - "iopub.status.idle": "2025-03-28T17:48:31.665894Z", - "shell.execute_reply": "2025-03-28T17:48:31.665054Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.transpiler import CouplingMap\n", - "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", - "\n", - "# Generate some coupling map to use for this example\n", - "coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False)\n", - "\n", - "# Choose a 10-qubit circle on this coupling map\n", - "reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 4, 15, 3, 9])\n", - "\n", - "# Get a qubit operator describing the Ising field model\n", - "hamiltonian = generate_xyz_hamiltonian(\n", - " reduced_coupling_map,\n", - " coupling_constants=(0.0, 0.0, 1.0),\n", - " ext_magnetic_field=(0.4, 0.0, 0.0),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "3f46356f-e975-4705-8788-87566e2c4905", - "metadata": {}, - "source": [ - "### Set up quimb simulator with default options" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "fc38c412-f55d-4453-8c38-b84286f43ee8", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:31.669986Z", - "iopub.status.busy": "2025-03-28T17:48:31.669579Z", - "iopub.status.idle": "2025-03-28T17:48:50.572758Z", - "shell.execute_reply": "2025-03-28T17:48:50.571987Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/runner/work/qiskit-addon-aqc-tensor/qiskit-addon-aqc-tensor/.tox/docs/lib/python3.9/site-packages/cotengra/hyperoptimizers/hyper.py:57: UserWarning: Couldn't find `optuna`, `cmaes`, or `nevergrad` so will use completely random sampling in place of hyper-optimization.\n", - " warnings.warn(\n", - "/home/runner/work/qiskit-addon-aqc-tensor/qiskit-addon-aqc-tensor/.tox/docs/lib/python3.9/site-packages/cotengra/hyperoptimizers/hyper.py:76: UserWarning: Couldn't find `optuna`, `cmaes`, or `nevergrad` so will use completely random sampling in place of hyper-optimization.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "import quimb.tensor as qtn\n", - "\n", - "from qiskit_addon_aqc_tensor.simulation.quimb import (\n", - " QuimbSimulator,\n", - " qiskit_ansatz_to_quimb,\n", - " recover_parameters_from_quimb,\n", - ")\n", - "\n", - "simulator = QuimbSimulator(qtn.Circuit)" - ] - }, - { - "cell_type": "markdown", - "id": "b145bc16-7271-4647-8661-9f88023a790c", - "metadata": {}, - "source": [ - "### Generate target circuit" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "072bfaff-5a7f-4639-899a-a5a78e6af3ba", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:50.575445Z", - "iopub.status.busy": "2025-03-28T17:48:50.575150Z", - "iopub.status.idle": "2025-03-28T17:48:55.771949Z", - "shell.execute_reply": "2025-03-28T17:48:55.771099Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.synthesis import SuzukiTrotter\n", - "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", - "\n", - "from qiskit_addon_aqc_tensor.simulation import (\n", - " compute_overlap,\n", - " tensornetwork_from_circuit,\n", - ")\n", - "\n", - "evolution_time = 0.4\n", - "\n", - "target_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=8),\n", - " time=evolution_time,\n", - ")\n", - "\n", - "target_tns = tensornetwork_from_circuit(target_circuit, simulator)" - ] - }, - { - "cell_type": "markdown", - "id": "4b2735b7-0350-41e6-af80-6c9cda2083b7", - "metadata": {}, - "source": [ - "### Generate ansatz from a shallower circuit" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5d07640a-d7ee-4966-80e8-dd084ffb0da3", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:55.774797Z", - "iopub.status.busy": "2025-03-28T17:48:55.774309Z", - "iopub.status.idle": "2025-03-28T17:48:59.492081Z", - "shell.execute_reply": "2025-03-28T17:48:59.491177Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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dybZr++zcIr3z3Vb98ucBfTKpjwb3aeDgGvo+zgEA9AmMjesAiAHGlpicozufW6bf/jpo9z7+WZ2oXjf8rodv7qBJd3ZRcBDHi7chDgAAYFybdh7TmKcWa+22FLu2Ly626Id5+zRnaZwmj++h265ozbPNALwOfSJjIz8KYgAAgPaA+6Rm5Ou+l//V17N2272P/zYn6cLb5mjs6DZ69cEeCg8NcGANjYFzAEZHn8DYiIEgBgAA4HpcWX1Q9X7XqFrXIZLFosLURKX8/ZXiPn9QeXHb1Hjcx+6untPt/XmJ4hatlUwmhdSKUotR56vHM2NUrWV9rZjwkbur5xJGPwYAdyMOud6Xv+3Src8sUVGRpcr7ysgq1P8mLdH8FXH66oXzFRjg54AaGg/nAYyMthiIgSAOAICxpGbka+jd87V8/VGH7G/BisPqcMXP+vXNgerfo55D9gnXoi0AAICx7NiXpovHztOBw1kO2d/HM3Zo4b+HNf+jwWreMNIh+wTgWvQJjI17BCAGADA6roWuZbFYNPG9tXr+4/UO2V/ckWxdctc8PX5rJz1/T1deXmgHzgEA9AmMjesAiAHG89+mJA0ZN08paflV3ldJiUUvf7ZR85bFa84HF6tOjRAH1BCuRhwAAMBYvvtjj8Y8tViFRVV/KV5mdqHueHaZ5i+P17cvX6CgQJ5tBuB96BMZG/lREAMAALQHXGvrnlRdfOc8xR3Jdsj+Pvhhuxb8e1gLPhqsJvUjHLJPo+EcgNHRJzA2YiCIAQAAuI7Z3RWA44U2O0c1LrheNfrfoJjLJ6jNq/8qoEYDJS/4VIXpSe6untOlbNqnvT8t0d4Zi7Xlg5n649LHlR2frFbXXqigGsZ48Y7RjwHA3YhDrvXJjO0a89Rih0wqc6rpc/fpigf+VGEhsxzbg/MARkZbDMRAEAcAwDjSMvI14NbZDptU5oSMrEJdctd8LVgR79D9wjVoCwAAYBzb96Wp381/OGxSmRP2xmWqz02ztPtghkP3C8A16BMYG/cIQAwAYHRcC13HYrFowuv/OWxSmVO9+OkGPfjaSlksjh2TZwScAwDoExgb1wEQA4xlxYYjuvC2OQ6ZVOZU67anqN+YP3QkJdeh+4VrEAcAADCOqb/t1PWP/+2QSWVO9dPC/Rpx30IVFBY7dL8A4Ar0iYyN/CiIAQAA2gOus2V3qvrd/IfDJpU5YffBDPW5aZb2xWU6dL9GwTkAo6NPYGzEQBADAABwHSaWMQC/4DCFte4pWSzKT9zj7uq4XFFuvpLW7pLJbFZk4zruro5bGP0YsFbyn1O144kL3F0N+CDikPPM+ueg7nhumfP2v/iQ7nzeefs3Es4DGBltMRADQRwAAN9UVFSi4fct1Prtx5yy//yCYo24b6E27nTO/uE6tAUAAPBNyal5GnT7XB09lueU/Scm52rQ7XOUmuHYl5EBcD36BMbGPQIQA2ANxq3Bl3EtdJ4pX2/W619tdtr+3/xmiyZP3eS0/RsF5wAA+gTGxnUAxADftedQhobcNV9ZOYVO2f/OA+m6ZOw85RfwMnFvRxwAAMA3zVsWp/9NWipnzc09d1mc/jdpiXN2DgAuRJ/I2MiPghgAazBuCvBttAec40hKri66c65S0pzzvFH80RwNumOO0jMLnLJ/I+EcgNHRJzA2YiCIAQAAOA8TyxjEiUaUf3i0m2viHhFNjnck8tOy3FwT9zH6MQC4G3HI8VLS8nTr07YNvFw1bZgOLbhaq6YNs3qbz3/ZqZl/HbCjhjgd5wGMjLYYiIEgDgCA73n9q01avCbRpm1s7Zfm5BVpzJOLVVhYYk8V4UFoCwAA4Hvufmm54o5kW72+Pfco9h/O0v2v/GtP9QB4GPoExsY9AhADABgd10LH27zrmB57a7VN29jTL33inTXatPOYrdXDaTgHANAnMDauAyAG+J6SEotumbhEaTa8TMue9vi67Sl65oN19lQRHoY4AACAb0nNyNctk5aopMT6h5vtaQ9+M2uPZszfZ08VAcCj0CcyNvKjIAYAAGgPOJbFYtHY55fp8NEcq7exJy+x51Cmxk9eaU8VcRrOARgdfQJjIwaCGAAAgHP4u7sCcLyS/BwVZSTLYrGoKDVRSXM/VO7edQpt2UPB9Vu5u3pO5x8SqKDoCMlkUkitKLW+8SLV6NBMSWt3KWNvgrur5xJGPwZstf+dW5W69HtZiotkKS7UuqvCJUmdv0uXyc/PzbWDNyIOucY9L63QkZRcm7aJqRmqBnXCbC7rjueWqc85MYquFmTztkbFeQAjoy0GYiCIAwDg+7buSdXE99bavJ09/dJ121P00mcbNPHOLjaXB/egLQAAgO/7acE+TZ9r24sb7L1H8dXvu3XloKa67IJGNm8LwD3oExgb9whADIAtGLcGX8S10PmKikp088QlKrBxUnp7+qWFRSUa89Ri/fvNMAUEmG3a1qg4BwDQJzA2rgMgBhjDu9O2avGaRJu2sfc+0StfbNTICxure/taNm8L9yAOAADg+x54daVNL2+V7G8P3vXCcp3fLUa1okNs3hYA3IE+kbGRHwUxALZg3BTgm2gPON/0uXv1y58HbNrG3rzEZ7/s1JWDmmpwnwY2b2tUnAMwOvoExkYMBDEAAADXMcTEMsnJyXr11Vf1888/Ky4uTrVq1dLll1+uF198Uffee68+//xzvfPOO7r77rvdXVWHSJg2SQnTJpX5LKrX5Wp0x3tuqpFrdXn4anV5+Ooyn+3/41+tfOxTN9XI9Yx+DOQn7lXiTy8rc8tiFSQdlDkgSAHVYxTasodqDhijiI79y6zf5J5P1eSeT5X851SlLJqq1i/87Z6KO9GGHSn64tddSknPlySlZRZo9ZYkdWvHgwXOQBxyvjVbkzVtzl6XlZeYnKvJUzfpxfu6uaxMb8d54FmOpOTqs5936Nj/XweOZeTr54X7NeyCRvL356UPjmb0thiIgSAOAIARPPnuGptf2FYVL366QXddFaua1YNdVibsR1sAKCsvv0g/zt+necvjlZ5ZoLBQf/XqWFs3DWupqEgmcgbgfYqLSzThjVUuLfOh1//T0PMbymQyubRcOIat96/h/egTGBv3CEAMMDbGrXkei8Wif1YnatqcPUpJOz5mIj2rQLsOpKtl42purp1v4lrofNPn7dXqLckuK2/tthRNm7NHNw5r6bIyvRnnAE6XdCxXn/+6s8zYvR/n79OI/o2ZsMlH0ScwNq4DIAb4vuycQk18f63LyispseiRKau06LMhLisTVUMcAMpKSMrRZ7/s0MadqcrLL1Z0tSCNGNBIQ/vxPBMA77Rp5zF9OXOXy8pLSs3TK59v1OSHznVZmXAcxgzBiOgTGRv5URADjI1xUyjPrgPp+uyXnaXvPEvNKNDfqxJ0frcYno3wUbQHnKuoqEQPT3H180wrdXHv+pyzVuIc8Cz5BcWasWCf5i6LKx1DnJlTqGPp+YquxrPNzkCfwNiIgSAGAADgOj4/scz69et1ySWXKDExUWFhYWrbtq0OHz6st99+W3v27NGxY8ckSZ07d3ZvRR2o5sW3q/p5o2QpLlTugU1K/PkVFSTHyRRw8qWDmVuWaPezl5yxraWoQJaSYnX9pdiVVXaoHV/P1/7fV8gc4K/qbRqp/bgRCqtbQ8X5BaXrmAP9ddn817TvlyXa+NbPpZ/3eXOcgmtFaeF1L7ij6g5j5GMge9dq7XzifJn8AxR9wY0KadROJQW5yj+8Sxnr58svJMJQg2zWbUvW3S+t0PL1R8t8np1bpO7XzFSP9rX09qM9dW7H2m6qoW+yJg6d/8EDktmkf+54o/SzwKhwjfh7ilY/+5X2/rzEHVX3Gu9P3+byMj/9eYcmje2ioEA/l5ftjTgPPENqRr7ufXmFps/dp8Kiky+9zs0r1hUP/qn6tUM18c4uuv3KNm6spe8xclsMx9EnAXEAAHxbXGK2fvvroEvLzC8o1he/7tSEmzu6tFzYh7YAcFxJiUWvfL5Rr3+1qXTQ5QnT5+7TY2+v1i0jWmny+B4KDvL5W5YAfMi85fHaF5/p0jJ3HkjXopUJurBnPZeWi6rj/rUx0ScwNu4RgBhgXFz3Pc+cJYf00Bv/aeuetDKfZ+UUqdVlM3TxefX1/pPnqVmDSPdU0EcxZsj53DF27oMftjGxjJU4B3BCRlaB7nvlX303e48KCsuO3Rv90CLVrRWqx2/tpHFXx/LyDR9Dn8DYyAuAGOD7vpu9R+mZBWdf0YH+WpWgbXvTFNssyqXlwj7EAeC4Y+n5uvvF5fpx/j4VFVvKLPty5i41jAnT02PP0S0jW7mphgBgH3fkZz//daeeu7urQoIZZ+lNuHcIo6JPZGzkR0EMMC7aPjjd3rgMjX1+ueYvjy/zeU5ekfr/b7baNo/S5Ad76JK+Dd1UQzgL42ac648lh3QoMdulZW7Zk6YlaxLVr1tdl5brrTgHPIPFYtHkqZv02tRNSkrNK7MsI6tQ9QdO05hhLfX6Q+cqNIScmyPRJzA28gIgBgAA4Do+3ZNJTk7WZZddpsTERI0fP16TJk1SRESEJOnVV1/VI488In9/f5lMJnXs6Dsv5Auq21KRnQdKkqp1vUThsX2047E+OvjBnWo24XtJUkS7vuoyPavMdgUph7V9fDfVuvRul9fZkTL2JiphySZJUvyidTry33YN+e059XrlDv0zdookqaSgSEvvfUeDf3lWhxasUerWA2o0uLsaDOqm3wY86M7qO4SRj4GE6c+oJD9Hsa+sV2jTTmcsL0xNdEOt3GPx6gQNGTdf2blFFa7z3+YkXfC/2frtrYG66LwGLqydb7MmDq147BMNX/S6mo7orX2/LpMk9XzxVh39bzuJ3bNIzcjXd7P3uLzcpNQ8zViwT9dd2sLlZXsjzgP3S07N0wW3/KEtp70g5VTxR3N0x7PLdOBwll64t5vrKufjjNwWw3H0SUAcAADf9vFP21VSYjn7ig72wQ/bNf6mDjKbebGUp6MtAByfVOZ/k5Zo6m+7KlwnN69Y732/TRt3HtPcDwYzABOA13DHCyIk6b3pW5lYxgtx/9qY6BMYG/cIQAwwLq77nuXr33dpzFNLKs3lzlser57X/a6/Phuidi2qu7B2vo0xQ861YUeKlq8/6vJy/92YpLVbk3VO25ouL9vbcA5AOj7Otf//ZmvDjmMVrpOQlKN7XlqhffGZmjy+B5PL+BD6BMZGXgDEAN9msVj0/g/uuU/0/vRteuexXm4pG7YhDgDSkZRcXXDLH9q+L73CdQ4lZut/k5boUGKWJo09x4W1AwD7ZWQV6OtZu11ebmpGgb6fu1c3j2AyLm/CvUMYFX0iYyM/CmKAcdH2wam27E5V///NPuNF/qfauidNQ+9ZoC+f76frh/IeJ1/CuBnnct/zTNuYWMZKnAPuZ7FYdMezy/TJTzsqXCcvv1gf/rhdG3Ye0/yPBis8NMCFNfRt9AmMjbwAiAEAALiO2d0VcKZ7771XcXFxuvvuuzV58uTSSWUk6eGHH1anTp1UVFSkJk2aKDIy0o01da7w2PMUfcENSl06XVnblpe7Tklhvva+fLnC2/ZR3VGPu7iGzpW0eof2zFispiN6q1a31qWfp2zcqy0fzFTft+9RaN1o9XrtTq18/FPlHkl1Y22dw0jHQN7hXfKLqFHuTSZJCqge4+IaucehxCwNv29hpZPKnJCXX6wrHvxTuw9muKBmxlReHCpIy9Ly8R/o3BduVUid6mp8aU/FnNdOKx75yM219XyLVh5WXr57ZpP9Y/Eht5TrCzgPXMtisejyBxZWOqnMqV78dIO+rORFr6gaI7XFUD76JCAOAIBvmfWPe/qG++IztW1vmlvKRtXQFoARvfTphkonlTnVkrVHdOvTDHgF4B3yC4o1f3m8W8qeuyxORUUlbikb9uP+NST6BEbHPQIQA4yD677nWLHhiG6ZWPmkMickpeZpyLh5yswucEHNjIkxQ47lrnsUkvTHEsbO2YNzwHgsFouumrCo0kllTvXGV5v18YyKX2QA70efwNjIC4AY4FsOH83R+u3WXeMdjWdZvBdxAEZjsVg04r4FlU4qc6qnP1inabP3OLlWAOAY/6xOtOq5fWegPeh9uHcIHEefyNjIj4IYYBy0fXBCRlaBhoybV+mkMieUlFh081OLtWLDERfUDO7CuBnHyc0r0p8rD7ul7DlL46waj4kzcQ643uSpmyqdVOZUKzYc1ZgnFzu5RsZGn8DYyAuAGAAAgPP47MQy27Zt0/Tp01WzZk299NJL5a7TtWtXSVKnTicT0hdccIFMJlO5/7vzzjtdUndnqHvVU5LZT4e/m1ju8oPv36mSwjw1uW+qayvmIhumzFBJUbG6TLiq7Odv/qSS4mINW/CaEpdt1r7flrmphs5nlGMgKKa5ijNTlLriZ3dXxa3en75NaZnWP+yelVOkt7/b4sQaobw4FP/Xeu3/fbn6vXuver58m5aP/0D5qVmV7AWStGZriiHL9gWcB66zeE2ilqy17eb5C5+u5yaeExmlLYaK0ScBcQAAfEN+QbE273bfgIQ1W5PdVjaqhrYAjCQnt0ivf7XJpm2mzdnL5OcAvMLm3akqdNPkLrl5xdq+L80tZcN+3L/GCfQJjI17BCAGGAPXfc/xyucbVVRs/fiHgwnZ+mYWL690JsYMOc6abe67T8A9CvtxDhjLqs3JWrDCthd5vPTZBhUXM6GwL6NPYGzkBUAM8B3ubBPvi8/UsfR8t5WPqiEOwEj+XHlY/25Msmmb5z9ZL4uF55kAeD53tgfJz3of7h0CJ9EnMjbyoyAGGANtH5zwzazdOpiQbfX6RcUWvfqFbc+gwfswbsYxNuw4pmIbxkU6UmZ2oXYdsG4ycZyJc8B18guK9epU264rPy3cr21705xTIUiiT2B05AVADAAAwDl8dmKZadOmqaSkRNddd53Cw8PLXSckJERS2Yll3n//fa1YsaLM/5588klJ0tChQ51fcScJrttC0X2vVubGP5W5ZUmZZUd/f1vpq2ep+WO/yhwU6qYaOlfm/kTt+22Z6vXrqNrnxpZ+bikqVtKqHQquUU27p//lxho6n1GOgbqjn5TJP0B7X75Cm8e20v63b1HSnA+Ue2ibu6vmMvkFxfr05502b/flzF3Kyil0Qo0gVRyHVj/zlSKaxih+0TrF/bnWjTX0Hmvd+HD8zgPpysiyftImlMV54DrvT7f9urfrQIYW/WfbA+2wnlHaYqgYfRIQBwDAN2zadcxtLxKXeCjQm9EWgJFMn7dXqRm259A+/ME4eXwA3svd7bE1W1PcWj5sx/1rnECfwNi4RwBigDFw3fcMBxOy9Ps/h2ze7v3p23h5pRMxZshx3PviQvqk9uIcMBZ7xu4dOJylOUvjnFAbeAr6BMZGXgDEAN/h7jaxO5+lQdUQB2Ak9vSJtu5J0+I1iU6oDQA41tpt7msP7j+cpZS0PLeVD9tx7xA4iT6RsZEfBTHAGGj7QJIsFotduaGZfx/UoUQmU/BljJtxjDVuvk/k7vtk3oxzwHVmLNin5FTbc2gf8GyzU9EnMDbyAiAGAADgHD47scyiRYskSf37969wnbi44w/fnDqxTNu2bdWzZ88y/1u/fr1q1aqlwYMHO7fSThYz6gnJbC4zU1/mxr8U99Ujavbwjwqq08R9lXOBjW8dn5Xy1Nkqa58bqxZX9de2z2arx7M3yy840I01dD4jHAPhbXop9vU1qjHgJhVnpyvlzy908MO7tPXuttrxWD/lJ+6tcFuTX4DMgSEurK1zrN2WbFdiKyOrUCs2HHVCjXBCeXGoKDdfWQeOKnXbQTfWzLscTMx2a/lxR9xbvrfjPHCNucvse8ich9OdywhtMVSOPgmIAwDg/Q4muLdPeIg+qVejLQCjsDsvYed2AOBKBxPc+8DSoSM8MOVtqnL/Gr6HPoGxcY8AxADfx7g1z7Dw38MqKbF9gpjNu1MVfyTHCTXCCYwZqjqLxeLW+xRxR7LtOr9wHOeAccxZavsEZ8e3I0fu6+gTGBt5ARADfMNBN7/Y7pCbn6VB1RAHYBQ8zwTAl9EehC0YMwSURZ/I2MiPghjg+xg3Ben4uJIte9Js3q6kxKIFKw47vkLwKIybqTqeZ/JunAOuwT0Kz0WfwNjIC4AYAACA45ksFotPPuHVsGFDxcXFad26dercufMZy4uKilS3bl0lJydrz549atasWbn7SUpKUr169XTXXXfprbfesqsu3bp1U2JiYpnPTIEhqvPmLrv25yj5R/Zr+0PdVffqSap96d1OLevI/S1lKcgt81mAxaxJJT2cWm5l/EODNezPydr60Sxt/3KeLvnlWSVv2KNVk6Y6vKxnzP+p0FRS5jMjHQPl/f5n46jvJ//oAWVt/kfJCz5V1tYlCm7cXrGvr5E5wPGdR3v+TmfIC2ihlIgb7No2OnO6Qgq3OrhG9nF3jHClwT89o0ML1mjLhzPdXZUqKS/WOUNitftU7Bdd7rJV04YppmbFM87G1AyRv59ZRcUlSkyu/HxNTM5R92vO/E1qpX+gwOLEcrZwLCOdA5JvnAeuOgfOxiKTDkc/bde2oflrVT37N4fWx5O5uz3m7va40XlCnHV3n8To3B0DJOIAANdLiHpQJeZqMpekq27aG+6ujlfKCWyv1PBR5S47W59Usr5fWlGfNKhgp2pmfWtbpb2EJ1ybXc2VbQFX89S2B3HQNZLDr1d+YEubt+N3AZyPOFh16SEXKSukd7nLXHGPIiL3H0XmLrKt0rCZM9umzrp/7antL0/iiX0Od/UJjHq8eMJ9gVO58h7B6Yx6z8DT4oA78wJGjQPWYtyad8sK6qX0sMF2bVs7/V0FFCc5uEbW8bTrlCv5wpih0znrWmuRnw5HT6xwuaP6pRX1SSWp3rHnZFKR9ZW2EucA54Avia/+pGQKsHm7kPyNis7+yQk1guR5/QGJvICredq1hryA63laHCAv4J2OhV2h3KCO5S5zxX2iatmzFJ6/yrZKewFPOz9dhXFDrsf9cuezyKzD0ZPs2jYsb5WicmY5uEYATkUcrLoj1e5WkV+tcpe55tnmjxVYHG9bpWEzZ7VPnXnv0FPbX57GyHHQE/td5Eddi/zoSUbMjxIDyjJqHLAW46ZQFYV+tXW02ji7tq2WPVfh+SscXCPv4GnXaVdj3Iz10kIHKzu4V7nLXPI8U85fisz726Y6W4NzgHPAkVLCr1FeYBubtzOXZKlu2mtOqJFnoE9wklHbjp4Wa8kLuJ6nxQHyAgAAHBcTE6PVq1fbta2/g+viMbKzsyVJubnlX7CnT5+u5ORkRUREqGnTphXuZ9q0aSoqKtINN9g3SYMkJSYmKj6+7CANc1Co6ti9x6oryc/RnpdGqFqPYS5pSB0+fFgl+TllPgs0+cmdX0L3p29U1sGj2j51riRp6X3vatjCyTo4Z6WO/LvNoWUdTjisAktxmc+MdAyU9/ufjaO+n6DajRU04EZF979BOx7rq+xty5Sz6z+Ft+3jgL2XZc/f6RShoVKEfZseS06QsjxjUJm7YwRsV16sc4rQfMmv/EUxNUPVoE7YWXfh72e2ar3yJB1JkPIP27WtLTgHvI/LzgFrROVL5iCbN8vJPKacBM+4DriCO9tjntAeNzpPiLPu7pMYnZH6ZBJxAMD/iyiWzFJJcfEZ+UpYKTJGCi9/kbV9Usn+fml+brbP/nbuvja7mqvbAq7msW0P4qBrNMqU7HjWo6Qwl98FcDbiYNXVSZNCyl/kinsUmRmpyjzKb+dszmybOuv+tce2vzyIp/U53NknMOrx4gn3BU7lynsEpzPqPQNPigPuzgsYNQ5Yi3FrXi76qGRfc1tHEw5JhSmOrY+VPO06hapx3rXWJEVXvNQV/dLD8QclWezatjKcA77FqO3NUpH5kr/tE8vkZqUq/jB5H2fxpP6ARF7AHTztWkNewPU8KQ6QF/BiDTKlCobou6I9np6arPRU32sveNL56SrujgPO5rFxhvvlrlG9SDLZ/uqC7Mxjyk7kdwGcijhYdSH5bh03lHQ0Qcrjt3M2Z7VPnXnv0GPbX57GwHHQ0/pd5Eddj/zoSUbMjxIDyjJqHLAW46ZQJQH5UjX7Nk1PPeqT+W9reNp1GlXntPZGTLoUXMEilzzPdEyZSY4/TzkHfI9b29wNM+x7trnIt59tpk9wklHbjp4Wa8kLuJ4nxQHyAgAAOIbPTiwTExOj1NRUrV27Vr16lZ1hNyEhQRMmTJAkdezYUSaTqcL9fP3114qNjVW3bt2qVJfTmQIrGDXiIqnLf1Luvg3Ki9+p1KXTz1je7t2tCqzVyGHl1atX74xZ+QIsZslNkzXWH9BFTYf11m8Xji/9LPPAEa154Vv1njJOMweMV1FuvsPKq1e33hkzUxrpGCjv9z8bR38/JpNJYa3OVfa2ZSpIcU7yxp6/0xlKTCVKsBRKJhsfSrQUKyaqUH7V6junYjZyZ4yAfcqLdc6Q5FegggqWJSZX3lGPqRkifz+ziopLlJhc+fla0b5iaoXLz+L884RzwPu46hywRlLxYRWYK548sSLVg9MUWt8zrgOu4M72mCe0x43O3XHWE/okRmekPplEHABwXIKfn0okmf38VNdA7T5HyvcPUnIFy87WJ5Ws75dWtK/QoBJV99Hfzt3XZldzdVvA1Ty17UEcdI3MgBRl2LFdiI4omt8FcCriYNVlBfkpvYJlrrhHUS3cX+EB/HbO5oq2qaPvX3tq+8uTeFqfw519AqMeL+6+L3AqV98jOJ1R7xl4Uhxwd17AqHHAWoxb824FftlKkiSLRapkbPDpzCUZiqkdKlNFT147mSddp1B1zrzWJpRkq8Rc/sP9juqXVrQfU0mO6tWvZ31lbcA54FuM2t48IbkkQflqafN2UUFpCiNn5zSe1B+QyAu4gydda8gLuIcnxQHyAt4rPVjKqmCZK+4TRVcLVEio77UXPOn8dBV3xwFn89Q4w/1y10gqOqyCANuP3+oh6YZ6nglwB+Jg1SX7F6ii3por2oN1aoXLv4Tfztmc3T51xr1DT21/eRojx0FP63eRH3U98qMnGTE/Sgwoy6hxwFqMm0JVWGRWYkmGSsyRNmx0fIxXrYhsBfpg/tsannSdhmM4q72RGWyu8JlNV+QloiL8FRbo+POUc8D3uLPNnRl4zL5nmy2+/WwzfYKTjNp29KRYS17APTwpDpAXAADgpPLmLbGWyWKxWBxYF49x77336p133lHDhg21cOFCtWrVSpK0atUq3XDDDdq7d68KCws1btw4vfvuu+XuY/v27YqNjdWLL76oxx57zKH1yy2S+s526C492pIhUshp0xgV5uTp2+bXu6dCLnbdnm8UEFr2gWMjHQPl/f5nY+/3k7F+gSI69JfJr2yBJfm52jahh/IObFbbtzYqpEkH23d+Fvb8nc5y81OLNfW3XTZtM/rippr+2gAn1ch2RooRvqK8WOcM9728Qm9/t9WubQ8tuFoN6oQp7ki2Gg763ubt69cOVdzCa+wq21acA97HVeeANX6Yt1dXTfjLpm1qVg9W3IKrFRTo56RaeR7aY8ZmtDjrSTHKUxgpBkjEAQDHNRg4TfFHc1zat/E1GVkFqnbe13ZvX9V+6ftPnKexV8XaXb4nM9q12dd5atuDOOgaR1Ny1WDQ9yossm1g1fKvh6pXpzpOqhUAiTjoCItXJ+j8W+xrtFS1LShJ/35zmc7tWNuubWE9R7ZNXXX/2lPbX56EPsdJRj1ejHZfoDJGvWdAHDjJqHHAWoxb8369rp+pfzcm2bTNxDu66Jlx5zipRmfHdcq3OPNae/GdczV/uX0vW6lqv3Rgz3pa8PEldpV9NpwDvsWo7c0TZv51QMPvW2jTNtUiAhW/4GqFhQY4qVagP3CSUduOXGtOMmqcJg6cZNQ44AjTZu/RtY/+bde2jrhPdGj+1WoQU/5Ek96M89P3eGqc4X65a3z7x25d/9g/Nm0TUzNEB+ZdpcAA4zzPBLgDcbDqJrz+nyZ/ucmubavaHqxVPVhH/r5WJpPJrvJhPUe1T11579BT21+exshxkH7XSUY9X8iPnmTE/CgxoCyjxgFrMW4KVTXxvTV67qP1Nm3Tq1NtLf/6MudUyAtwnfY9zmpv/PnvYQ28fY5d2zriPtWa74frnLY17dq2MpwDvsedbe5j6fmqP3Ca8vKLbdru78+H6PxudZ1UK/ejT3CSUduOxNqTjJgXkIgDpzJqHAAA+B6fvZw9/PDD+u6773To0CG1a9dObdq0UV5ennbv3q1LLrlETZo00bx589SpU6cK9/H111/LZDLpuuuuc2HNAVTFoc8eUFFmiqJ6DFNI4w4yB4WqIPmQjv3znfIP71R0/xudcpPJ09xzTVt99ftulZRYP3fYvde2c2KNAMfp6oQbDN5QNmCLEQMaq1HdMB1MyLZ6m7Gj2xhqUhkAAADAHpHhgWrVuJp2Hkh3S/n0SwF4g9o1QnTdpc1tmvy8R/ta6slECQC8QJfYGjKZJIv1t+Acxs/PpI6tol1fMKqE+9cAABgH133Pcf/17XX1w39ZvX5IkJ9uu6K1E2sEOE7X2Jp2TyxT5bK5RwFY5dJ+DdWiUaR2H8ywepvbr2jNpDIAAHgBd7aJa0cHq36dULeVDwDWunJQUz0yZZXij+ZYvc24q2OZVAaAV+jatoYby67JpDJehnuHAADASGj74ITbr2ijyVM3KdeGF/rffz3vPAOscY4b8xKBAWa1b1ndbeUD1oquFqQbL2uhj2fssHqbzm2i1a9rjBNrBQAAAACOZ3Z3BZylQYMGWrJkiS699FIFBwdr//79io6O1kcffaQ//vhDO3fulKQKJ5axWCz69ttvdcEFF6hRo0aurDqAKmh4yxuq3vNyZe/4V4enPa0D792uo7PeVkB0PTW+5zM1ufcLd1fRJc5pW1PvPd7L6vUnj++h3l3qOLFGgOOc3y1G7hr/2L+7784qDt8SGOCnmW8PUmS4dQ+bD+7dQE/d3sXJtQIAAAB8Q/8e7ukbVo8MVKfWvEgcgHd457Fe6tbOupfq1Ksdqh9fH8BDzwC8QkRYoNXxzdF6daytkGB/t5QN+3H/GgAA4+C67zlGX9xU911n3QsH/PxMmvZqfzWICXNyrQDHcNc9Comxc4C1/PzM+u2tgaoeGWjV+gN61NXz93R1cq0AAIAjtGgUqQZ13NN/7N+jLvfUAXiFoMDjzzNFhFn3PNNl5zfSo7eU/5w/AHiavufEyGx2T5uM/Kz34d4hAAAwEto+OKFBTJimvdpffn7W9Z3uv76dRl/czMm1AnxD9cggdW7jnmfMe3euw+Tg8BpTJvRUz461rFo3pmaIfn5jIPdhAQAAAHgdn37rSGxsrGbNmnXG51lZWdq/f7/MZrPat29f7raLFy/WgQMHNGnSJGdXE4ADRXa5SJFdLnJ3NTzCnaNjFRrsr7tfWqHM7MJy1wkL8dfrD52rO0a1cXHtAPs1rhehS/o00OwlcS4tNzjITzcOa+nSMoGq6NS6hpZOHaorxv+pXQcyyl3HZJJuGtZSHz7VWwEBPjvnJAAAAOBQd45qo49+3O7ycm8e0UpBgQy8A+AdwkMD9Ocnl+j6x/7R7/8crHC9rm1r6qc3BqhR3XAX1g4AqubOUbFatXmJ68sdzf08b8T9awAAjIPrvucwmUya8vC5iq4WpBc+Wa+CwpJy16tVPVhfvXC+Bvdp4OIaAva78Nx6at4wQnsOZbq03Kb1IzSoV32Xlgl4s7bNq2vZV5fpigf/1La9aRWud92lzfXJpD68fAMAAC9hNpt0+5WtNfG9tS4v+85RsS4vEwDsdU7bmlr8xaW6cvyfFeYwTCbp1stb693He8nfn+eZAHiH+nXCNOyCRvp10QGXlhsYYNbNI3i22dtw7xAAABgJbR+canj/xvr97UG66cnFSkrNK3edwACznrits566o7NrKwd4uTtHxerO55a5vlyeZ4IXCQ3x14KPL9GNT/yjX/6sOI/XuU20fn5joJo2iHBh7QAAAADAMXx6YpmKbNmyRRaLRa1atVJoaGi563z99dcKCQnRlVde6eLaAYDj3DispS4f2ETfzd6jz3/ZqYOJ2bJYLGoYE6abhrXUDUNbKDI80N3VBGw27uq2Lp9Y5tohzRVdLcilZQJV1aFVtLb/dqXmL4/XBz9s0/odKcrNK1Z0tSAN799Id4xqo2YNIt1dTQAAAMCrdG5TQ7061daKDUddWu7Y0bwgAoB3iQwP1Mx3BmnzrmP64Iftmrc8TvviMlVikUKC/DT/o8Hq3aWOTCaTu6sKADa5enAzPfT6SqVmFLiszFrVg3XloKYuKw8AAADwdiaTSRPv7KKxo9vo81936rvZe3UkJVf+fia1aBSp269ooysGNWEyb3gds9mksaNj9dDr/7m03LGj28hsJo8H2CK2WZQ2/3y5/lx5WO9P36Z121OUnVuk6MggXXZ+Q90xqo1aNq7m7moCAAAb3XZFaz370ToVFVlcVmbb5lE6v1uMy8oDAEfo3KaGdsy8UvOWx+uD6ds0Z2mcikss8vcz6aExHXTHlW3UpD4vawPgfcZdHevyiWVGX9xUtaJDXFomAAAAAFTFJX0b6tCCq/XTgv36+Kft2n0wQ0XFFtWpEaJrhzTTLSNa0c8B7HDdpc014Y3/lJld6LIyY2qGaOSAJi4rD3CE8NAA/TxloLbuSdWHP27XnKVxSsssUHiIv3p0qKW7ropVv64xPNsMAAAAwGsZcmKZTZs2SZI6depU7vK8vDzNmDFDI0aMUEQEA9MAeLfw0ADdfmUb3X4ls37Dd1x8Xn2d26GWVm5Kckl5QYF+enhMB5eUBTia2WzS4D4NNLhPA3dXBQAAAPAZk+7sosFj57msvOuHNleLRkwKCcA7tW8ZrfeeOE+S1GDgNMUfzVF0tSD1OYcX3wDwTqEh/nrklo569M3VLivzsVs78cJrAAAAwA61okP0yC2d9Mgt5Y8XBrzRrZe31htfb9bhozkuKa9urVDddkVrl5QF+Bqz2aRBveprUK/67q4KAABwkJiaoRo7OlbvfLfVZWVOurMLLzUC4JX8/Mwa0rehhvRtWDpmqE6NEL10X3d3Vw0A7DagRz316VJHS9cdcUl5gQFmPco9DgAAAABeKCjQT9de2lzXXtrc3VUBfEZ4aIAmjOmgie+tdVmZT9zWWQEBZpeVBzhS2+bV9fajvdxdDQAAAABwOEP21M82sUxwcLDS0tL03XffubJaAAAr+AUFaMAXD2vk0rc1bOFkXfT9U4poUvYFhJ3Hj1Z4g1pl/u0XFFBmnTEJMxQYGWpT2eXtpzJjEmZo+KLXVX9AF0lS/QFddNn8VzVswWsa/tcbaj7q/DO2aXFVf41JmKFGg08OkL54xtO6ZusXanvbpTbVtyoaX9pTvafcddbv2l38/Mz64rl+LnuJ2vN3d1XrplEuKauqGl/aUz1fvs2qc8Ve9pyHJz479Ryy5zwsbz+VsfU8vPK/9zVyyVsatuA1DVvwmpoMO690mTvORQAAAACe6eLeDXTLyFYuKSumZojeeoRBSxVJXTZDBz4Yq5KCPO1+cYQ2j22lrfd10s6Jg5SX8H/s3WdgFEUfBvDnSnpvkABJCBAgoQXpVZo0EQQFRAWxAdJEEVGKFBULKFVAUUERKSJNpINSpPeSEGp6773c5d4PeTkIJJfc5cre7fP7lNzuzsyV/e/s7H937uilDk1lq1QqAEDcxrkoTIxQ///gtZKiAvX/FwZJoMjJ0Lr+x8vR5MIgCW5MbobM83sAAFE/TMa1t+viwiAJ8u5drnC7lENrcWGQBBmnd6hfK4i7jZsfdsT1dxoibGob5EfdUC8Ln9kdl19xR+KuJVq/HyIiIkswdVQztGnqaZS6OrWsickvBxulLiIiIiIiEhZzzpVr++kbePHsSoyO3wr3JnXL3ebxXDkbN0d1zs7Agwsx+MQyjIreDGtXRwDMo3vAxckaa+Z0Nlp9P3zSCa7ONkarTxvMlStLl/0QKD9vVVOeHfPoiIiISOwWTG6NgNpORqnrhV51MbR3gFHqMkfMGyrr0byhyj6TypZryjli3hAREYmZVCrBz/O7wM7WOPc2zxnXEk0auBmlLiIiIiIiIhIWc80d09Tuyt6T0PJVhJg79tEbLRDS2N0odT3d2hvjhwcZpS5dMHesrKruh5W9r9rdQzBg31cYePgbPLt7AdyC/dXbMW+MiIiIiIhIGDixjIA8muQZsXS0TomVGad3ICf8dJXWTTm8DpdGuCDs/dbq1yKWvq5OBr05vRNyb597uP6hn3FjcjNcGCx/om0xa6fh6pt+uLPgea3b/KhHBzA6L5mg08CBX9828HoqsErrNhjWDS+H/4IB+75Sv9Zp8Xj1QEe/nZ/Bo8XDGc+f+vjl0ofNH1qEAfu+Qq1uD39DwWMGYMjJ5Rh4cKHWbX5A6L+B2PUzcWNyM4ROCUHolBCkHdukXqav3wBRVYSvP4jtnSdjV68PELX/HDp98w6A0oHeVjNfhbWLPTxbNkCXFZNh4+aIkA+0u1BREV3K2fv8bMQeuQQA6LpiMk68+x12PTMNh0d+gY5fj4XcwVa9rmMdLzR8pReSzoeXKWP/i3MRfeB8tduvDb/+7RC171yFn7UQBNVzxWcTW2m1TUJKHmISc5GQklflbTqG1MB7I5to2zyTKf3uzgKoeF/RB233Q0C3fag82pajzX4IAEfHLcauZ6Zh1zPTELHrpPp1U+yLZHxC6I/l3j6Hm9M7IfTdFgidEoKsq0fUy0oK83Bv0QhcH9sA199piPT/tqqXsT+mH0I4J/FoUR/9dn6GgYcWYeDBhfDu1PTh+i/1wKAj32BU9OYn2tZ69ki8eH4Veqz9UOs2Uymhx4DY9TNwfXxjhL7bAmHvt0bmxf3qZYk7F+P62AYInRKidZuJiEg3337QDn4+Dlpto8t56fezO8HdRZgPbBOCjNPb4drueQCAV+8xaLIyHMFLr8C13SBErnhLb/VUVHbGyT8R88t0KHMzkHv7LCIWj4QiKxUAEL9pXpUf7KCJtuU0WnAcLq37AwDcOr2IRl+cgHUN/wrXL0yMQMqBNXBo1L7M61Erx8Kzzxg0XXUL3kOmI2Lp6Id1fP4PXNsO1O6NEBERWRC5XIq187vC1qbqD4nQpS9obyvHz/O6QCYTZWoHkcEJYTww/o8FuP5OI1x4XlpmosdHZV09gguDZWXaZ4wcHjEQwjWBZpOHYPDxpXgtdkuZB3k/yrtTU4yKKXtdwNA5TGJhznGAeVxE4mGuuXKRf5/CnkGzkBOdVO665eXKFabnqHN2dj0zDbd+O4TYI5dQlJEDgHl0j+rfxRdvDG6o1Ta6nJe+NjAQA57207Z5RsNcuSdpsx8CFeetasqzYx4dWRKhnxOEz+yGa28HqPv9iTsXq5dFLH0doZObI3RKCMKmtkHWlcPqZfe/eQVXXvNG9I9TtH4/YiL0cYG+f87DC2e+U0+6FzxmwBNlljdmwDzCqjPnGKBpGWOA4TnaW+Hn+V0glUqqvI0u/XEvN1usnNkREknV6xEb5g096dG8oco+E03LNeUcMW+IiIjELtDfBV++W/613Yro0h9s09QTH77eXNvmERFViRDGRTQ/26bi+yiT/lqGG5OaluZMTG6O1H9/Uy9LO74ZNyYE4/LLrlq/H7ERwviors93ajy6LwYd+Ub9wPmgN/urlzFvqurMOQ48UJyRhCujapbJkWIcICJLY665Y5ryeDQtE1q+ihBzx6yspFj3aVdYW1X9PiNdxiUc7a3w0zztrocZG3PHnlTV/bCi5dYuDuiy4l2cmLwcu3pOxblP16Prd++qt2HemGUR/jlBxfdLPJAfHYaLQ+3L5Efw+UtVI/RxgZYfjVCf+w88uBABgzqpl3VeMgFDL36vXtZ69kj1MuZNVZ25x4CkPStxY0JQ6TrvtlC/F95PRUREYiE3dQNM4ciRI5WvZALxm+ahxnNTILW2rXzlCmSc2QG7gBA4PvYAuIo4NeuOBjN2qP93bT8Y/hPXQCKTI+Pcbtz7eiiarYkAANjXb4V607YgYesXT5RT5/WFsPVrgowzO55Ypo2QD4YhdM1uKAuLdS7Dr29bpN2IQPLF21VaP+HkDRx5/Wv1/1F7z+LkB6uhUpagTq9W6L5mKra2HQ8ASDwThiuLt0JZUAS3YH/02z4fW0LGQJFfiNAfdiPt2n20nT9a57YL/TdQc/A01B75OQCgKDUWNyYEwTmkF+TOnnr7DRBVRllYrB64BIDki7fR9J3SZPTIv08jJyYZff6YA7fgujj0yudoO/91AEC/HZ9CpSzBgZc+RUFqFgCg0ei+8OvbBrYezrjy7Vbc2fwPgNKHubee9SqsnOwhkUpxddk21OrSrNxyunz3Llzq14LUSo7cuBScfH8V8pMzym27SgVYu5TOGm7lZI+C9GyUFClKF0ok6PjNOzgz6ye0mfOa3j+3R1k722PQP4shs7VGXlwKpDZWcPKribtbj+LkB6shkctQs00jnHh3BVQKpXq7Rz9roZj6WlPcjsrED1vDK18ZQJsRu7Qqv1FdF+xY0ktQD2zT9P2d+mhNme+uon2lurTdD5WFxejw1RgAZfchQLv9MHL3qXLLaTP/df3sh0QwfX9MpVLh7heDUXfyOjiH9EJB7C3c+qQXmq4Mh9TGDgnbF0FqZYOm399BYeJ93JzWDk7NukPu7MH+mJ4I4Zykx88f4sSUFYg/fg3O9XzQe8sn2N75XSgLipB69S7+Hfstmk0a8kQ55z9dj4zwaPj1a6tz28VO6DHAMbgLfIbNhtTGDnn3ryB8Rlc0XxsHma0Dag56D/b1WiL6pyk6t52IiLTj4mSNfav6osvo3UjNKKzSNtqel37zQVsM7F7xhCBioMjJQOjkpigpyoe1py9UxYUoTLgH924j4f/OKuTc/A91310HidxK/VAEAHBo2B6JOxbppQ1Sa9sKy3br9CKsa/jj1uyeyLt/BYFz9kJqbYvIleMAAOEzukAilSFw7gEAQPLelcg4swOKzGT4DP8Enr1Kx65yb59D7C/ToczLgqpECZ+hM+DWaWi55cT89B4KYsOhUhTBytMXdSf9BCs373Lb7tSkq8b3piopQeSKt+A7Zjli1k5Vv16ckYTcO+cROK+03a4dX0DUDxNREH8Htj4NdPkYiYiILE6TBm7Y+k0PPD/lEBQKVaXra9sXtJJLsX1JTzSs66JrE4moEqYeDwQA5xa94N7lJUQsf6Pc9ZW5mYj99SO4tOpf5nVj5PCIgRCuCcQfu4r7O06g8+IJ5a5v5WSPVjNfQezhS2VeN3QOk1iYcxxgHheROJhzrlzi6bCK31gVc+UCX+6Biws2VPnz0pa559GtnNkR0Qk5OHgqrkrra3te2qOtD1bP7qhL0/SGuXLVy5XTuB8CGvdF5tmRWJjDOYHvm4vh2v75J16v8+ZiyB1dAQB59y7h1uyeaLE+BRKpFAFTNyBu41woczO0fDfiYg7jAufmrEPUvnPlLqtozIB5hFVnzjFA0zLGAOPo1sYHa+Z0xptzjldpfW37404OVti7qg9qeNjp0jyLwbwh3fOGNLW7KssryzkiIiISu0kvB+N2VBZWbAyt0vra9gcb+Dlj17JnIJcL595mIrIsQhgX0fRsG033Udr6NUHjL/+DzMEFRcnRCH2vJRwbdYCNT324dxkOh4btEPZeiM7vSyyEMD6q6/Od7v55DDfX7QMAWDnaYdC/3yLxTBjSrt9n3pQWzDkOPBC1cixc2gyAIjtV/RrjABFZEnPNHdPUbk3LAOPmq5hz7liLRh7YsqgHXnj/MJRK/d/PZGMtw86lvVDf11nXJuoFc8d0zx2rbF+raLlTXW8Upmcj41YMACDpTBgcanvCvVkA0q7d18dHSgIi9HMCTfdLAIBKUYyolWPg1n5wmTr4/KWqEfq4wI2VO3Hpy40AAHtvdzx/bAnijl9FYVp26fJVuxC65u8n6mDeVNWZcwzIOLMTaUc3oPHXpyFzcEFxZjIkstKJkng/FRERiYUoJ5YRoseTPK3ca0ESE4Zbs3uiKCUadn5NEfDBJkitrKFSFCN2w2xkXzsClaIItrUawm/898i9eQoZZ3ch6/JBpB5Zhxr9J8KlzQDcWzQCJflZKCkqgFOz7vB9exkk0vKTSFzbPTzpd2zUHkWpsVApFZDI5LAPaAEAFW5bXY8PYOQlpsMlsDZ6b5kDh1oeyAiPxtFxi1FSrIBELsNTH74E785NIbOSI/NePE59+D28WjWEb+/W8OnaHA2GdUPY2n2IOXgBXVdNgZWTHWQ21kj47zrOzPq5dASxHI/OhJt88Rbsvd0hkUmhUpaUGQRJD4sCJBLYejgjJya52u/fHH4DD248AoCS/BxApYKqpKTa752oOoLf6o+o/aU3S/n1bwevloG4s/kfJJ29iQ4Lx+HcnLVoNKo39j4/G0VZZWdMLyksxt/9P4ZLg1oYsPcr3N16FFYOtui4cBwOvfo58pMyYOPuhOcOfI09A2aWW87ZT9ai8P8XUJpNfB4hHwzDqek/lNvWo+O+RfefpkGRVwhrFwf88+ZClBSXXrhoMvY5JJ27idSr9wzxMZVRlJWHe9uPozi3AFcXb0Wtbi3QfPIQnPxgNQDAp1NTJJ0LL3NBAyj7WQuFRCLBqlmdIJdJsXJzJTdBa6lZoBv2r+4LL3dh3Yij6fur9XSLcr87wLDfX1X2w1PTfyh3H9JmP0w+H15uOfraDx/ovGwSJBIg+dIdXFiwQV02WT4h9MeU2alQZCbDOaQXAMC2dkPIHVyReXEv3DoMQfqJzfCf9BMAwKZmAJyadkPG6e3w7P2W8T4oCyaEcxIbdyfYejgj/vg1AEDWvXgUZeahdo+WiNpzBumhkaUrsh+ud+YQA1xa9VOva+ffDFCpoMhMLpMISURExhVUzxX//tQfvcftR3xyXuUbaGHxtHaYMrKpXss0R3JHV7h3fRkyOyf4DJ+NzIv7kbB1AepO+hFZlw7AoXFHSORWT2yXtHspXNsOMkibHi07/dQ25N46A8+er8MhqBMivxsD3zcXw3/8aqTs/x6NFhwvM64qtbJB0KKzKIi5ibCpbeDRfSSU+TmI/G4MAj/ZAyt3HyiyUhD63lNwaNyx3HLqvLUEVi5eAICErV8ibuNc+I9frdN7Sdz5LRyDOsGhQasyrxelRMPKzQcSWellJIlEAmsvPxQlR3FiGSIiokc829UP277thWHTjqCg8MmxeV3Z2cqw7dte6N2xjt7KJKKyhDAeCAAODTUnqkf9MBE+Q2ch4/S2Mq8bOodHDIRwTQAAUi7f0djO9gvexNUlf8K/f7syrxsyh0kszD0OMI+LSJzMKVdOk6rkynm1bgQbFwdEH7ygdflVZe55dDbWMuxY8gxenHoYe0/E6LXsvp3q4M9ve8LWxrS3GjBXrnq5cpXRtC9WJc+OyNyZyzlBRR49J1DmZupUhpiZy7iAJhWNGVDVmHsMIGF4Y3BDAMDb806gpKTyh3ZVlbuLDfau7INWwZ56K9NcMW9If3lDlX0mhvzMiIiILJFEIsHS6e0hk0qwdMMNvZYdXN8VB1b3hbenvV7LJSJ6QCjjIpqebaPpPkrnFj3Vy6y9fGHl5o2ilGjY+NQ30CdmeYQyPqrr852Ksx9ef5Pb20Aq5+PTtGXucQAAUg7+BOuaAbCr25wPiyUi0TDX3DFNeTyPLzNmvoq5544N6u6PPxb1wEsf/oOiYv3lDzvYybFjaS/0aFdLb2Xqirlj+ssdq+wzebA86148bNyc4NW6EZLPh8O3d2tYO9nD0bcGJ5axMOZwTlDZ/RJxm+fDreNQKHLSoMzNMMjnZKnMYVzg0fgpd7CFRCKBRCIx7AcjIuYeAxK2L0Stl+ZA5uACAOocCiIiIjHhlRGBeDzJM2LpaOTdu4xGn/8DidwG4TO6IuPUn3DvOgIJ2xdCZuuAoEVnAQDxmz9F3G+z4DfuO7i2HQi7gBDUHDgFAFBSVIAGs/6CzM4RKqUSdxcMQvqJLXDv+lKlbUr8aylcWvVXPzjO0B4fwOi8ZALcmwRg34tzUFKoQN/t8+H/bDvc3/Efmo4fhOL8Qvzd/2MAQPP3XkTL6SNwZsaPiD5wHmk3ItQzSMpsrHB41JdQ5BVAIpWix7rpCBjYEfd3/ldpm4LfehYxhy9BpXxy0Czwpe7IiUzU2wMZzOU3kPTXMiTt+Q7FqTHwn/gjrFxr6OX9E+mi2eQhcKrrjZPD5gEAovacQdSeMwiZOgwpl+4g4q9TGre/t+04ACDzThxKFErY1XCFW5A/nPxr4JkNM8us61y//IHueoM7o/6LT0NmYwWZjZV6JtvHSWRStJjyIv55cyEST4fBo0V99PzlI+zs8T7svFzh/2w77B38ibYfAQCg/1+fw7meT7nLdj0zDXlxqU+87t40AGE/7gEAeDSvj7TrDwdt/fq2QeTes2XWf/yzFhKpVIIVMzqgVbAn3lt4Glk5us9+/MDbLzTCoqlt4exorYcW6l9F31953x1Q+feny2+oorINvR/mJaQ9UYY+9sMH2+wd/AlyY1NKBzOnj0CXpRNx6NUFGt8DWQ4h9Mfkzp6wcvdB2oktcO88DLm3z6EgNhxFSREAgKLkKNh4+avXt65RF0XJUQb/bMRCCOckhWnZyE9KR93nOiDir1PwaFEfLvVrwdGXg9eGZg4x4FGph9fCxrserGv4P7GMiIiMq2mgO879PhBj5p/AnuPVf3Bb7Rr2WDOnM/p18dVD6yxD3v3LqDFgcunfdy/Avl5LAEDGmR1waz/4ifXj/1iAwvg78P/0cLnl3fywAwribpe7LHjxJVh7VfzZP162a/vBcOswBHEb58IhsC3cOg3VmJTi/vQrAADbOo0hkclRnJ6A/IirKEy8h9vz+5VZtyA2HNYetZ8oI+3Y70j7Zz1KigugKiqA3Fm3B4nkR15Hxqk/0WjBMZ22JyIiolLPdfPDqfXP4bVZx3D11pNj2Np6KsgD6z7timYN3fXQOiKqiBDGAyuT/t9WQCKFa7uBT0woQdUnhGsClfF/tj1UJSpEHziv8SGx+s5hEgtLiAPM4yISF3PKldPEtZFvlXLlAkf0wJ0/jpabv1sRMebR2dvJsXPpM/jy5yuY//0lKBTVe5i1XC7BrLdDMOOtEFhZCWMSQ+bKlaWP/RDQvC9WJc+OyBKYwzkBAMT8+hFif58NuzrBqD3qC9h413u47JePkH7yDyhz0lF/+p+cgFYL5jAuAACtZr6Klh++hIxbMbiwYANyopIAVH3MgCpmETFAwzIynjcGN0R9Xye88clx3Iupfl+pexsf/DSvCwLqOOmhdZaBeUNl6ZI3VNlnUtlyIiIiKp9UKsHiD9vhqSAPvPvVaWRkF1W7zNefD8S3H7SDq7ONHlpIRFQ+IY6LaHq+kab7KLMuH4IyJx32gW2q96GIjBDHR7V9vpP/s+0RMm04nOt64+IXv5e5zkuVM/c4UJh4H8n7VqPRgmNIO7FZfx8MEZGAmWvumKY8nseXVTdfRYy5Y4N71sXJ9c/htZlHceNuRrXLa93EE+s+7YomDdyq3zg9Ye5YWfreDx9frswvwr9vL0KrGS9D7mCL5PO3kB4eXe4EPmTezOWcoKL7JXLDzyD35ikEzj+I+E3CiMnmxFzGBYLe7I/Go/vAvpYHTk5dhYL/T6wFAEFv9UeDl3ogNzYFl77aiLQbEfr9kCycuceAguhQ5N45j7hN86AqLoRH91Go8dxk/X9QREREAsaJZQTMrf1gSG3sAQAOgW1RmHAXQGnSqzI3E+mn/gQAqIqLYF2zbvmFqEoQ+8t05ISdAFQqFGcmwdavaaUdq9R/f0P6iS1o9IVpHygXtfcMlPmlSTQpl+/Aqa43gNIBHWsne9T9/w0QUms5cqIreDiCVIJWs15FzbaNAYkEdp7OyLgZVekJRr0XuqDucx3KvWHOp3MztJg6FAeGf1qNd1c5If4Gajw3GTWem4y8+1dwf/GrcA7pDbmzR/XeKJEOmowbCP/+7XDg/4ORj7r8zZYqlaEsfLidqqQEUpkMEokEGeEx2DNwpoYtS9Vo2xhBb/bHngEzUJCaBd/erdFy2vBy13VvGgC7mm5IPB0GAEi9chd58alwbxoA57recPStgRdOLgcA2Hm5osPCcbCr4YbwXw9U2o49z1Xe1ifa06SuepDco3k9RD8ym3itbiE4/+lv6v81fdZCIZFI8MbghnimQy2M/+wkdh+L1qmcenWcsGpWR/TuWEfPLdSvir6/x787oGrfny6/ocrKNrf9MP7YVQBAbmxKaVsUSoSu2Y0h/y2v0vsgy2WK/lj9GTsR+8t0JGz9AnZ+TeAY3BkSKU/dTMUU5ySHR3+F1rNeRbNJQ5BxKxqJZ29Cpaj6w3JIf4QaA7KuHEbcpnloOO+gxhtQiYjIeGrXdMDuFb3xy67bmPbtOaSkF2hdhkwmweiBgVg0tS1vBnxM/v3L6odC5N29ANe2A6FSqZB5aT9qv/Z1mXUTti9CxqltCJx/SH0cf1zjrzUn51WkvLIfHItrjZhbpTIkVrYP/5HKoFIqAKhg59sEjb8+Wen2OaEnkLR7GRp/dQpWrjWQcWYX4jbqNllxTuhxFCZF4Po7gQCA4vQEREaPQXF6PFw7vIDi9HiolApIZHKoVCoUJUfB2stPp7qIiIgsXUhjD5zbOBCf/3AFC3+5ivwC7RPm7W3l+Pit5pj+egvBPLyXSGxMmaPxuOL0BMT/8Rkafvavrm+HdGDKPKXH2Xm5osWUF7DvhTka1zNWDpNYmFscYB4XkXiYW46OJjXbBVWaKye3t0XAwI7Y3W+6VmWLNY/OykqK2WNbYmA3P4yZ9x/OXtdtsrnWTTyxZk5nhDQW1rGEuXIP6Ws/BDTviylX7laaZ0dkqYR0TgAAAVPWw9rLFyqVCsl7vsOdTwegyXeh6uV1XvsSdV77ElmXDyHmlw/R6Mv/ILWy1roeKiWkcQEAODZpmfqhNY1f74te6z/Gjqffq/KYAWnPnGJAZfGBjOvp1j64unUwPl52Hqu33ESxDjm/rk7W+HxSK4wbFgSplHmhj2Le0EO65A1V9plU5TMjIiKiikkkEowaGIie7Wph4hensONIpE7l1K3liO9mdkT/LhVPckdEZEhCfLYNoPk+yvyIa4hY9jrqTdsMma2Dlu+YHmduz3eK/Ps0Iv8+Dcc6Xuj+84eIPnQBWXfjtH3b9AhziQMqlQoRy96A35gVkNrY6f6GiYjMiLnmrGhqd3nLqvJcKE3EmjvWKtgTFzY/j/mrL+Hb9ddRUKj9/UwOdnLMfDsE00Y3g1wurPuZmDv2kL73w4qWJ5y8gX1DSvMRpNZyDL/yIzJuxVTpPZJ5E+I5QXn3S0ht7BD1/XjUm76Vz1zSIyGOC4T9tAdhP+2BW7A/uq6YjLijV1CYnoOLX25EXmI6oFLBr19b9NowE9s6ToIiT/tnvNBD5hID5M4eUCkVKEq8j0YLjkGZm47wGU/D2rseXNsMqN6HQEREZEb4dGIBk1g/TBSVyB4kiqK0AztmOZxb9q60jMSd36I4MwmNF56B1NoW0T+9D1Wx5g5v2vHNiN80Dw0/PQwr15rVeg/VpSwsVv+tUpYOggClSTZnZv2MuKNXKi2jydjnYOfpgr+f/RjKwmK0mfsaZLZWGrepO7AjQt4fhv3D5qEgJbPMspodgtFpyXgcHvWlwS8qCvk3YB/QAtbutZF9/V+4dXyh6m+KSA+Cxw5AwOBOODBsPoqy8ipdvyg7D1bO9lVaN+l8OBz9asCnSzPEH78GoHRwOeNWzBPlWLs4QJGTj8L0HEit5Gg48pkKy82NTYF9TTe4BNZG5u1YONX1hpN/TWTdjUP8satlJpDp++c8hK7Zjah95yosrzrsvd0BlUo9I7l7kD+uLi09WfdsGYjM27HqwRFtP2tT8/V2xF8reiPsXgZWbQnDL7tuIyunWOM2EgnQv4svxg8PQp+OtSGTCevixuMq+v4e/+4Aw35/upRd1X1R035YUqwoU46+9kMAkNvZQGolU7cvYHBnpP7/whKJlyn6Y/YBLRA4d5/6/xsTgmDr1wQAYO3lh8LkSFi5+wAAipIi4BxSeRtId6Y4J0kPjcTBlz9X///8sSXICNdt0jSqHqHFAADIvn4UEcteR4NZf8G2TiMd3hURERmKRCLB6EENMaJffWw9eB8rN4fh5OWkSrfz9rTDmBca4+0XGqGON29meVxRaiwACaw9agMA8iOuwmfoTOTdOgu7OkGQ2Tmq103c+S3Sj29E4PxDkDu66rUdupQttXOCMi+z0vUdGndEYdJ9ZF0+BOeQXgCAvHuXYesbDKmVdZlyFDnpkNk5Qe7kgZLiIiTv/17n9+TV7x149XtH/X/4zG6o+dwUuLZ/HgBgX/8ppP77Gzx7jkbGyT9h7VEHtj4NdK6PiIjI0llbyTBvwlOYMrIJftl5G6u23MStyMxKtwuq54rxw4MwckADuDjxoY9EpmSqHI3y5N29gOK0eIS9FwIAUGSlIOPsLigyk1F75OeaNyadmSpPqTwezevBrqYbBh5aBACwcXeCb+/WsPFwxqUvNwIwbg6TWJhrHGAeF5FlM8dcOU3Cfz1Qaa5cwKCOSAuNQOYdwx7fLC2PrkUjD5ze8BxOX03Cqs03sXn/PRQVa36gtbWVFEN7B2D88CB0aFFDcDfZMldO91y5ymjaF209XTTm2RFZMiGdEwCAtVfpw2QlEglqPDsRMWs/gCIr9YkJJZ1DeiHqh4nIj7wGhwatdKqLhDUuAEA9qQwA3Fy7D20+GQUbN8cqjRmQbswpBlQ1PpDxONhbYdlHHTDjrRb4adstfL/1JqITcivdrlWwJya8FIThferB3o63+j6OeUPVyxuqrN2G/MyIiIjEpnZNB2xf0gvh9zOw+o+bWLvzNjKzNT9kViIB+nSsg/HDg9C/Sx3B39tMRJZNiM+20XQfZX5UKO58NgB1J/8Mx+DOVXyXpIm5Pt8pJyYZKZduw7dXK9zgtaxqMZc4UJKXhfzIq7i3qPQh6sr8HJQU5uHW7J5o+Olhbd4yEZFZMNfcMU3trmhZZc+F0jdLyh2zsZbh88mtMfW1Zli38xZWbbmJO1FZlW7XpH7p/UyvDmgAZ0fh3c/E3LHq5Y5V1u6KltvVcEV+UgYAoMV7LyL+v+vIjkio0vsm8ybEc4IHHr1fwrZWQxQlR+HWrO4AAGVuBlQlJVDkpCNgyi9VfLf0OCGOCzyQHhqJvIQ0eHdsgsi/z6iPCwAQtfcsWs14BS4NaiH16r2qvFWqgLnEALeOL8Dayw9uXUdAIpNB7uwJl1b9kRt+mhPLEBGRqDDbVECqmizq2u55JO5aDMfgzpDa2KOkMA+Fifdh59cEUjtnKPMedogVOemwcvOG1NoWxekJSP/vD403j6ed2IK4DbMQOP8QrL389PXWqqyqAyFR+84ieMwAJJ4NgzK/CDI7azj51vj/gGQ+rJzs1etauzggPykdysJi2Hm5ou6ADojcc7rCsus+1wFPTR+B/cPnITc2pcyymu2D0GX5JBwZ/TXSQyOr92bLIfTfQH5UKOz8ggEAhfF3kXfvEmx9g3V/w0Q6sPdxR9u5o5EVkYC+W+cCAJRFCvz97McVbnNj9V/ovekTKPMLceClT1GQWvGgd1FmLg6NXIA2n4xCmzmvQWolQ25sCo68/vUT5cT+cxn1X+iKwSeWojA9B3HHr8LB273ccgtSMnFy2mp0+/59qEpUkEglOD3zpyfijDG4Nw1Qz8IOAEVZuWj8Wh+c/GA1/Pu1RdS+swB0+6yFIqieK5Z91AHfTG2H0HvpuBCaiivhqcjMKYaypAT2tnI09HdB6yaeaNnYQ5AXNipS0fdXmJat/u4Aw35/upb9+D5UEU374RPljPgMmXfj9LIf2nq5oPuP0yCRSSGRANmRSTgxabkOnxCZMyH0x4rT4tUTxyQfWAOpjQOcmvcAALh1GoqUfavh2Kg9ChPvI/v6v/Abt7L6b5zUhHBO8uhF1sBXekGRV4j4E9f08v5IM6HHgOwbx3B/8Ug0mLkT9gEtqv+GiYjIIGysZXjl2QZ45dkGSE7Lx4XQVFwITcH92Gz8vucu8guVcLST4/tPOqNVsAcC/V0glQrrQW1CknfvEuzrtVT/L3NwRdKelZA7e8K13fPq14tSYhDz81RYe9dTJwJJ5DYIWnSm2m3Qteyaz0/F7TnPQGpjj8C5BypcT+7ohsDZfyNm7QeIWTsVKkUxrL38UH/GjnLK2Q/bWo1wY3wjyJw84NyiFzLTYissO3LlWGSe/xvF6Qm4PbcPZHZOaPr9nSq9b/93vkfEstFI2LoAMjtn1J28tkrbERERiZ2bsw2mjGyKd19tguiEXFwITcH5GylY8tt15BUoYW8rw/ujmqFVsAdaBXuiTk0HwT24l0gshDAeWBGX1s+ixa+J6v8jlo6GXUAIag6conVZVDEhXBOoSMzhi9jc/C31/52XTEDajQiErvkbgOFzmMTCnOMA87iIxMFcc+UAoMPXY1CnZyvY1XDFMxtnoTgnH9s6TqrS+w4c0RO3Nhyq0rrVYYl5dBKJBB1a1ESHFjXx/SedcPVWGi6EpuD6nXTk5iugUgGO9nI0aeCG1sGeaN7QHXa2wr2dgLlyuufKAbrvh0LKdyUyNCGfE6iUCiiyU9U3S6ef/BNWrjUhd/aASlGMwuRI2Po0AADk3joLRWYSbLzraV2PmAl5XEAik8LGzUn9wAT/Z9shPyUThek5lY4ZUNWZbQzQsIxMz9vTHjPHhGDG2y1wNzobF0JTcDEsBSs2hqqvE01/o4X6OpG3p33lhYoY84Z0zxuqrN2VLa9OzhEREZGYNQpwxeIP22Ph+20Rei8DF0JTcPlmKn7cFq7uD84b3wqtgj3wVJAnXJzM595mIrIsQhgX0fRsG033UeZHh+HOp/3hN/4HOIdofpAyVUwI46O6Pt/JpWEdZN6KAQDYeDjDu1NTRPyt/Tis2JlrHJA5uCDkt4cTs6ccXoeMMzvQ4P9jKURElsRcc8c0tVvTMmPnq1hi7pi7iw3eH9UM741siqj4HJy/kYILoalITMtHYZESNtYy1K5hj1bBnmgV5InaNe0FfT8Tc8d0zx2rrN2alrf88CXUbBcEiUyK5Au3cPJ9PtfKUgn9nKCi+yXsfIPQYn2yer24jXOhzM2A71tLtP8QREzo4wKPnvs7+deEe9MAZPz/f3sfd+TFl04u4/VUIGzcnZB1nxNgactcYwAAuHd9GVkX98G5eQ+UFOYj+/q/8B7yoY6fBBERkXkS7p1gIvRokqeVey3YVbCe9wvTEb+pEGHT2kGC0gGZmkOmw86vCTy6j0TE0tHIOL0DNfpPQM3n3sXdr17EjYlNYOVeC84temlsw/1vX4GVqzfuLhikfq3h/MOQO3sg5fA6xG2YBWVOOjLO7EDijkVoMOuvMsm51fXoAEZeYnqF611bsQMt3rfCgL+/gEpV+tr173Yg41YM7m49is5LJ8Kvb1vcXLcPYT/uQbc1UzHo38XIT0hD3PGrGtvQ9bt3kZ+UgZ5rp6tf2z9sHgrTc9Dpm/GQWVuh8+Lx6mXHJi1Hxs2o6r3x/xP6byD2lw9RmHgfErkVJFI5/MaugJ1vkF7eO1FV5cWnYZ3Pi1ptc+XbP3Dl2z/KvPZ4GZuavKH+O+3afewfOq9K5Rwdt7jM/5e+3FhhO+7v+A/3d/xXaXv3vTCn0nWqI+bQBcQcuqD+f3e/j9R/1+ndGvv/X78un7XQWFlJ0aKRB1o0spybpCr6/gb9u1j93QGG/f50LfvxfUiX/bC8cvS1H+ZEJeGv3tMqfgMkCkLojyUf+AFpRzcAKhVs6wSh/sfb1Rdiaw6ehshlb+Da2PqQSGXwHbMCcmdPfX4EoieEc5KGrz6DekO6QCKRION2DI688bV6WYNh3dBy+ghYuzrAr29bNBk3EIdf+7LMxXDSndBjQOTyN6EqLkTEstfV6wdMWQ+7us308v6JiEj/vNzt0LdzHfTtXAcAsO+/GMQm5cHFyRovP1vfxK0zD65tBsC1zQD1/0HfnAMA3JjYBDU/+0f9urVnHbTaqTJIG3Qtu9ZLc1DrpYfn6o+XEfLbwwQX+/pPoeFnR6pUTr0PN5dZXnvk5xW2wX/891Vub6PP/y3zv22dRmj89akqb09ERERlSSQS+Pk4ws/HEYN71sUvu24jryAPbs42+HRiK1M3j4ggjPHA+C2fIXnfaigykxEReR3SHyYiaPElWLl4adzOGDk8YiCEawLNp7yARiN7w9bDGR0bv4N2n7+FXb2noVDDjZwADJ7DJBbmHAeYx0UkDuacK3fqwx+q1N7ycuX2DJxZpW2ry9Lz6Oxs5WjXvAbaNa9h6qbojLly1cuVq+p+CDy5L1Y135XI3An5nEBqY487859FiaIQEokUcmdP1J+5CwCgUhYjYslrUOZlQiKTQ2rjgHrTt0Lu6KbPj8fiCXlcQJlXiF6/zYDMWg5ViQoFadk4/NqX+nz7BPONASXFhRUuI+GQSCRo4OeMBn7OGN63Hjb8fVd9neiTcRzHrSrmDemeN1RZuytbrk3OERERET1JLpeieUN3NG9Y+nDPPw9FqPuDH4zmPUBEZHpCGBfR9GwbTfdRRq+ZDGVuJmJ/nY7YX0ufC1R71FdweapP9T4UkRHC+Kiuz3cKfqs/arYLQkmRApBIELrmb8Qf01wXPcmc4wARkViYa+6YpnZX9p6Mma9iybljEokE/rWc4F/LCS88E2Dq5uiMuWO6545V1m5Ny09+sLrC7ciyCP2cgPdLGJbQxwVazxoJR78aUBUrUKIswekZPyHzdiwAoPOSibDzcoFKWQJFQRH+ffsbFGdrniCHnmTOMaDmoPcRuXIsbkwIBiQSuHV4AW6dhurlcyEiIjIXEpVKZZiMSdIoXwF02WPaNqQcXoeMMzvQYMYOg5d3vD9g99g0RsV5BdhQ/1W91K2rBsO6wa9fW/XMvNXl3aEJ2s4fjV3PlH0o/St3f4OVvW2Z18T0Gyjv+6+MED4fbenyPqliQogR1TX8yhrkJ2fiwoINiD1ySedy+mydC0dfL1xdth23NxzSYwv1q7xYR7qzhH1ACPS1HwKV74vcB8yPEPobQu6PWTqhxFl9n5NUVB5j1JMsMQZkX/sX0T9NQfCSy08sYxwgIgCo02sjYpPyULuGPWIOjTB1c0SJ30HFhHBstgRXXvOGlWtN1B75BVxa9zdYPeEzu6MoKQLeQ2fAq/fbTywXat+D+6Dp8TsgMi3ug6bFz998mGPfVKj9LyERwveq7/FAXesS6+9FCNcF9H1NQJOKcpgA8V4zYBx4SKxxoKqE8FvRFr9T8yeE41R16TNHpyLMo7NclrAPCIEx9kOAeXRknoTSxzPmOUHcxrlQ5mbA960lZV4Xa99RCMcaY44LaKpLrHFaCHFACDEAEG8cMDVeJ6qYEPZPS8G8Ic24H5oWP38i0+N+aFr8/M2HOfZPhdr/Ehox74dC+F0bc1ykMDECYe+FIOT3jCeWiXV/Edv4KPOmyhJCDAAYB8yFUH4v2uB3SuZOCMdpfWDu2ENi7G9Uh6XsA0IglP2Q+4DwCKWPZ8xzgoqevyTWvqMQYi3zpkxLCHGA91MRERHpFw9nIia1tkP+/csIe781gr49X62yYtZOQ8bZXXBs3FFPrTMORUER3JvUxYB9X2F33+mVb6BB8JgBaPhKT+QlVDzjptDwN0BkWJtbPJkcr4v9L87VSzlEYqSv/RDgvkiGwf4Y6fOcpPXskfDt3RpJ58P11DoyNH3GgMSdi5FyYA2s3GvpqXVERERkjlr8kmCUehp9/o9R6iEiIiIisiT6HA/UhNcLhEuf1wQ0McccJrFgHCAiS6bPHJ2KMHeHSDNj7IcA90Wi6jDWOcH9b15B7q0zcO/6ssHqIO0Za1yAeYTCxRhARIbGvCEiIiIiIhIqY42LpB3fjPg/PoPcpabB6iDdMG+KGAeIiCwbc8eITI/7IQmdsc4J+PwlYWLeFPF+KiIiIv3ixDIi5t5lONy7DNdLWXVeX4g6ry/US1nGFLHrJCJ2ndRLWaE/7EboD7v1Upax8DdAREREZFrsj5E+z0nOf7oe5z9dr5eyyDj0GQNqDnoPNQe9p5eyiIiIiIiIiIiISP/0OR6oCa8XCJc+rwloYo45TGLBOEBEREREJG7GOicImLrB4HWQ9ow1LsA8QuFiDCAiIiIiIiIisTLWuIix6iHtMW+KGAeIiIiIiMTNWH11Pn9JmJg3RbyfioiISL+kpm4AERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERGVxYlliIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIgERm7qBoiVrQw43t/UrTAeW9mTr8ntbPDK3d+M3xgTkNvZPPGamH4D5X3/VdnG3D4fXd4nVUxMMcJSlBfrSHfcB8wP9wHzY479DV2xn/IkscVZxqgniSkGAIwDREQkfGI7Nls69j2IiIjInJlj35T9r8qZ4/dqKGL9vYjtuoAmYr1mwDjwkFjjQFWZ42+F36n543HKsoj1WFsd3AcsC/cBEiJz7OMZilj7jjzWPCTWOM048JBY4wAJF/dPy8M4Q0RERObMHPun7H9RZczxd20oYt1fOD76kBjHRxkDyhJrHKgqc/y98Dslc8fjtOURY3+jOrgPWB7uA8Jjjn08QxFr35Gx9iGxxijGgYfEGgeIiMjycGIZE5FIADuRf/oSiQRW9rambobJ8DegGT8fEnuMIOI+QGR47G+IG+MsMQYQEREJC4/NRERERCQU7JtaJn6vxOsCxDhAVcXfCpkCj1MkdtwHiMjQ2McjHmuIcYBIuLh/EhEREZGQsH9Kloi/a+L4qLgxBpA2+HshMj4ep0nsuA8QGR77eMRYS4wDRERElkdq6gYQEREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREVFZnFiGiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiEhgOLEMEREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREZHAcGIZIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKB4cQyREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREQCw4lliIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIgEhhPLEBEREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREJDCeWISIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiISGE4sQ0REREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREQkMJxYhoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhIYDixDBERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERGRwHBiGSIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIigeHEMkREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREAsOJZYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIBIYTyxARERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERCQwnliEiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiEhhOLENEREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREREJDByUzdArFQqoEBp6lYYj60MkEjKvqZSqaDILzRNg4xMbmcDyWMfgJh+A+V9/0SPE9M+IQbc74nI3JnjcYmxt3Lm+L0aihh/L2I6B61MeeeoYsAY8JA5xABz/L7M4XMlEjIeqy2LWPsbRNXBOGhZGAeJhMcczzMNRaznrzzWPiTW4xTjwENijQMkbNxHLQvjDBGZO3M8LjH2amaO36mhiPW3wnGBhzguQGKNA9owx98Lv1cyZzxOWx6x9jeIdMU4aHkYB4mExxzP8wyF54/ixP7GQ2I9TjMOPsQ4SEREQsPjtOVhf4OIzJk5HpcYdytnjt+roYj198LxwYfEOD7IGPCQWGMAEVkmTixjIgVKoMseU7fCeI73B+we+7Up8guxof6rpmmQkb1y9zdY2duWeU1Mv4Hyvn+ix4lpnxAD7vdEZO7M8bjE2Fs5c/xeDUWMvxcxnYNWprxzVDFgDHjIHGKAOX5f5vC5EgkZj9WWRaz9DaLqYBy0LIyDRMJjjueZhiLW81ceax8S63GKceAhscYBEjbuo5aFcYaIzJ05HpcYezUzx+/UUMT6W+G4wEMcFyCxxgFtmOPvhd8rmTMepy2PWPsbRLpiHLQ8jINEwmOO53mGwvNHcWJ/4yGxHqcZBx9iHCQiIqHhcdrysL9BRObMHI9LjLuVM8fv1VDE+nvh+OBDYhwfZAx4SKwxgIgsk9TUDSAiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiorI4sQwRERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERkcBwYhkiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIoHhxDJERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERAIjN3UDiIjIOFQqFQqLlFCpAFsbGSQSiambRERERqRSqVCsKIFCoYKNtRQyGeeYJCIiIiIiIiLjUSpLUKJSmboZREREREREJqFQlKCouATWVlLI5bxWS0RExsX8USIiIiIiIiIyFZVKBdX/c4ZUzB0iIiIiIiIiIhIVlUqFouISlJSoYGMtg1TKnBUiIjIupbIEhUUlsJJLYWXFPH4iIiIiIjJ/nFiGiMhCpWcV4o8D93HmWjIuhKbgxt10KBSlibcymQTB9VzRKtgTbZt6YWjvAHi62Zq4xUREpE/FxSX4+3gUjl1IwPkbKbh0Mw05ecXq5X4+DmgV7InWwZ4Y1N0fTRq4mbC1RERERERERGRJVCoVzl1PwZ7j0bgQmoLzoSlISMlXL49LzkOvt/eiVbAHerarhV7ta/PGACIiIiIishgqlQonLydh338xuBCagguhKUhKK1Av93C1QatgT7QK8sQzHWqhWxsfPuCfiIj0KjWjAFsPRuDMtSRcCE3FjbvpUCrL5o+2bvIwf9TDlfmjRERERERERKQfRcVK7D4ajeMXH9zPlIrcfAUAIC45H3X7bkarIE+0buKJ53v4I6ieq2kbTEREREREREREepOTV4xthyJw6koSLoSl4OqtdBQWKQEAEgnQ0N+l9Fk3TTwx9JkA1PF2MHGLiYjI0lwITcHuo1G4EJqKC2EpiEvKUy9zdbLGU0EeaBXsiR5ta+GZDrUgk3GyGSIiIiIiMi+cWIaIyMJcvpmKZb/fwMa991BQqCx3HaVShWu303HtdjrW7byN9xaewbDeAZj0cjDaNPUycouJiEifElPzsWpzGH74MxzxyXkVrhcVn4uo+FxsPxyJmcsvoGsrb4wfHoShvQP4IFciIiIiIiIi0klRsRK/7b6LlZvDcCE0pcL1VCrg8Jk4HD4Th6/XXkN9XyeMGxqEt19oBBcnayO2mIiIiIiISH/yCxT4ZddtrNwchmu30ytcLzWjEAdOxuLAyVh88dMVNKrrgneGNcabQxrB0d7KiC0mIiJLczE0Bct+D8WmfffUD+V43KP5o2t33MaUr89geJ8ATH6lCVoFexq5xURERERERERkKRJS8rBycxjW/BmOhJT8CteLjMtBZFwOth2OwIxl59G9jQ/GDw/CkF51eT8TEREREREREZGZuh2ZieUbQ/HLrtvIyikudx2VCgiPyER4RCZ+33MX0749i4Hd/DDxpWD0aFfLyC0mIiJLUlxcgg177mDl5jCcu17xvc0Z2UU4cjYeR87GY+G6awio7YSxQxthzIuN4eZsY8QWExERERER6Y4Ty1iQ7Gv/4tas7mVek9o6wKZWQ3h0G4kaAyZBIrPcr9y7QxP03TavzGvFufnIuhePu1uPIeynPVApS0zUOuMQ+29A7PILFJi14gIWr78OlUq7bQuLlFi/+w7W776DiSOC8eW7reHAB1VojfsgEZmSSqXChr/vYvKXp5CeVaT19scuJODYhQR8tykUP8/vigZ+zgZoJZFlY1+AeF5KjANEROLGvgCJ3cXQFIyefUzjw5Mrcjc6G9O+PYslv13Hmjmd0a+LrwFaSIbGOEhEYsdxAeKxUNwYA+j0lSSMnn0M4RGZWm8bHpGJKV+fwZLfbuDn+V3QvS1vkDZHjANEZEp5+QrMXH4eSzfc0Cl/9Ne/SvNHJ40IxoLJzB8l0gX7AsRxAXFjDCAiIvYFSMxUKhV+2VU6eW1mtvb3M/1zLh7/nItHtzY++GleZ9Srw/uZzBHjIBGJHccGSOzYFxA3xkAiInFTKEqwcN01zF11EUXF2h3vlUoVth+OxPbDkXipbz2smNEBHq62BmopGQr7AkRkaldvpWH0rGO4dDNV623vx2bjoyXnsXRDKL6f3QnPdfMzQAuJLB/7A+LGsUFiDCAiMj5GVQvk1nUEXFr1B1QqFKcnIPXfXxHz8/soiAmD/4QfTN08g7u37ThijlwEJBLYebmiwdCn0XbeaLgE1sapad+bunlGIfbfgBhdvZWGoVOP4Fak9g+neNyKjaHYczwamxd2R+smXnponfhwHyQiY8vKKcKomUex85+oapd1/GIimr+4DUs/bI+3X2ysh9YRiQ/7AsTzUmIcICISN/YFSGxUKhUWrLmCuasvQqHQ8qmVj4lNykP/CQfw5uCG+G5mR9hYy/TUSjImxkEiEjuOCxCPheLGGCA+JSUqzF5xAV/+fBUlJdU7J4qIy0GPt/Zi0svB+PaDdpDLpXpqJRkT4wARGdvlm6kYNu0IbkdmVasclQpY9nso/j4ejS0Le+CpYE89tZBIXNgXII4LiBtjABERsS9AYpORVYiRM45i97Hoapf177l4NH9hO1bM6IDRgxrqoXVkCoyDRCR2HBsgsWNfQNwYA4mIxCcyLhtDPziCc9dTql3Wpn33cORsHNYveBq9O9bRQ+vI2NgXICJjU6lUWLj2GmatuIBiRfUmLIhPzsPAyQfx2sBArJ7dEbY2fEwzkS7YHxA3jg0SYwARkfHwjMUC2dd7Ch7dXlX/79V/PG6Mb4yUgz+i1qufw8rFsieKSL12H/f+PK7+P3zdfgw+vhQNX+6Ji19uRGFq9W6cNAdi/w2IzZmrSej7zn5kZBfprcx7Mdno/uZe7F7xDJ5u7aO3csWC+yARGVNaZiF6j92HC6HVv9D+QH6BEmPm/4fk9ALMeDtEb+USiQX7AsTzUmIcICISN/YFSExUKhUmLjiFlZvD9FruT9tvIToxFzuW9IKdLS9nmhvGQSISO44LEI+F4sYYIC5KZQlen30c63ff0Wu5y38PRUxiLjZ93R3WVpxw09wwDhCRMZ28nIh+4/cjK6dYb2Xejc5Gtzf3YM93vdH5KW+9lUskFuwLEMcFxI0xgIiI2BcgMUlJL8AzY/fi8s00vZWZm6/A67OPIzmtANNeb663csl4GAeJSOw4NkBix76AuDEGEhGJy62ITPR8ey9iEnP1VmZSWgEGTDyI37/shhd7B+itXDIO9gWIyJhUKhXe+/oMlm64oddyf9l1G9EJOdi17Bk42FvptWwiMWB/QNw4NkiMAURExiM1dQPI8GS2DnBo1B5QqVCYcNfUzTE6RX4hki/ehkQqhbN/TVM3xyTE/huwZFdvpel9UpkHcvKKMWDiQZy7nqz3ssWG+yARGUpOXjH6vbNfr5PKPGrm8gtYsv66QcomEhP2BYjnpcQ4QEQkbuwLkCWb9s1ZvU8q88CBk7EY+sERKBQlBimfjIdxkIjEjuMCxGOhuDEGWC6VSoV3Pjup90llHth+OBKjZh5FSYnKIOWT8TAOEJGhXL6ZqvdJZR7Izi1G/wkHcNFAOUlEYsK+AHFcQNwYA4iIiH0BslTZuUXo+85+vU4q86gPF5/Dd5tCDVI2GRfjIBGJHccGSOzYFxA3xkAiIssVnZCDXmP0O6nMA8WKEoz46B/sOR6t97LJuNgXICJDmrnsgt4nlXngyNl4DHn/MIqKlQYpn0hM2B8QN44NEmMAEZHhcGIZkXhwAJU7upu4JabhVLe0E1mYkWPilpiO2H8DlqigUIHh044YZFKZB3LyijF82j/IydP/jcdiw32QiAzhw2/P4qyBJwCb+s1ZnL3GScaIqot9AeJ5KTEOCF/K4XUIn9nN1M0gIgvFvgBZop3/ROKbXw07Ie3fx6KxcN01g9ZBxsE4SERix3EB4rFQ3BgDLNOGv+9izZ/hBq1j8777BpvMk4yLcYCI9C0vX4Fh044YZFKZB7JzizFs2hHk5SsMVgeRWLAvQBwXEDfGAKoK5i0RWTb2BcgSvb/wDC4YeELad786zUlvLQTjIBGJHccGSOzYFxA3xkAiIstTUqLCqx8fRXSC/ieVeUChUOHVj/9FfHKeweog42BfgIgMYe/xaHzx0xWD1nHgZCy++NGwdRCJBfsD4saxQWIMICIyDLmpG0D6V1KYB0VWClQqFRTpCUjetxr59y7BPrAtbGs3NHXzDE5uZw0bdydAIoGdlysajeoNj2b1kHzxNrLuxZu6eUYh9t+AWMxZeRE372dqtc25jQPh7WmPhJQ8tBmxq0rb3I/NxkdLzmHFjI66NFOUuA8SkTEcOROHVVtuarWNLseBkhIVRs8+houbB8HWhqcPRFXBvgDxvJQYB4iIxI19ARKDtMxCjPv0P62302VsYu6qi3juaV80DWSyiLlgHCQiseO4APFYKG6MAeIQn5yHyV+e0no7Xc6Jpi8+h36d66C+r7PW9ZFpMA4QkTHMWnEetyOztNpGl+PQ3ehszFh2Hkumt9elmUSixL4AcVxA3BgDiIiIfQESg/3/xeDHbbe02kaXcQmlUoXXPzmOcxsHwtpKpktTyQQYB4lI7Dg2QGLHvoC4MQYSEYnDd5tCcexCglbb6DI2lJ5VhHGf/ocdS3tBIpHo0lQyMvYFiMgYMrIK8fa8E1pvp8ux6LM1lzGouz9CGntoXR+RWLE/IG4cGyTGACIi47H4J0OnpKTg66+/xrZt2xATEwMvLy8MGTIECxYswOTJk/Hzzz9j+fLlmDhxoqmbqjfxG+cgfuOcMq+5dhgCv7HfmahFxtXyw5fQ8sOXyrwW8fdpnPn4RxO1yPjE/hsQg+u307Dol+tab+ftaY86NR203u67TWEY9Vwg2jbz0npbMeI+KEy3IzNxOyoLCkUJarjboU1TT8hkUlM3i0gnCkWJThc4dD0OhN3LwMJ11zB7bEuttyUSI/YFiOelxDhgXiKWv4X0E5ugUiqgUhbj0nBHAEDI75mQyHgjKulHYZESp68mIb9QAQBQKktM3CIyJPYFSAw+XnoOCSn5Wm+ny9hEUXEJxn12Eid+GaB1fWQajINET0rPKkRBkRIAUFCkRGZ2EVycrE3cKjIUjgsQj4XixhggDu8vPIP0rCKtt9PlnCivQIGJC05h76o+WtdHpsE4IDwlJSqcu56MxNR8yOVSNPB1RsO6LqZuFpHOLt9MxZLfbmi9na55Q8t+v4FRzzXAU8GeWm9LJEbsCxDHBcSNMYC0xbwlcUhMzUdBYel1osIiJfLyFbC3s/jbekWLfQGydMXFJRgz33j3M129lYbF669j+hsttN6WTINxkOhJd6OzHvYHi5VQKkt4b7MF49gAiR37AuLGGEhEZPkSUvLw0ZLzWm+n69jQrn+jsONIJAb3rKv1tmR87AsIU1pmIS6EpiAnrxjODtZo09QTzo68n4nM1+zvLiI2KU/r7XQ5FikUKoyZfwJnNgzkJGdEVcT+gLhxbJAYA4iIjMeiM1AvX76Mfv36ISEhAQ4ODggODkZcXByWLVuGu3fvIi0tDQAQEhJi2obqmWefMXDrOBQqZTHyI68hYdtXKEqJgcTKVr1O9o3juDO/3xPbqhRFUJUo0Wq70phN1qvw9QcQ8dcpSK3kcGvsh6YTnoeDjweUhQ9v5pday/HcgYW4v/04ri7dpn6985IJsPVyxaFXPjdF0/VG7L8BMVixKQwlJSqj1rl84w2sb9bNqHWaK+6DwqFSqfDnwQis3ByGf86Vnam2bi1HjBvWGGNebAw3ZxsTtZBIN7uPReFeTLZR61y5OQzT32gOayvepKitwoR7SPjzS2TfOIai5ChIrWxg5eYN+8C28OwxGk7Nu5u6iaRn7AsQz0uJccC0tD321p30I+pO+hEph9ch9cg6NPr8X9M0nCxSTEIuVmwKxU/bbyElvUD9ekJqAYa8dwiTRgSje9taJmwhGUJV+gJPr3oPkEpwdOy36tesXR3x/L+LcX7+r7i37bgpmk5UJWmZhfj1rztGrfO/S4k4fyMZrZtw8nNzwDhI9NDVW2lYtuEGft97F/kFped5qRmFqNVzI14dUB+TX26CJg3cTNxK0jeOCxDHB8WNMcDyxSbm4o+D941a577/YnDzfgYaB7gatV7SDeOAcGRmF2HNn+FYtSXsiRyLrq28MX54EIb2DoBUyps9ybws3xgKlRHTR1Wq0jrXftrVeJVaEOYNiQ/7AsRxAXFjDCDmLdGjTl1JxLINofjzUASKFSUAgJSMQtTutRGvPx+ISSOaIKCOk4lbSfrG6+Vk6Xb8E4mo+Fyj1rliYximjmoGuZyTMJgDxkGiUiqVCjuOROK7TWE4fCZO/XpKeiHq9d+CsS82xrhhQXB34b3NloZjAyR2HB8VN8ZAIiLL9+O2cOQVKIxa59INNzixjJlgX0BYLt9MxbLfb2Dj3nvqCW8BwMFOjpEDGmDyK00QVM/VdA0k0kFmdhHW7rhl1DrPXU/B6atJ6NCiplHrtRTMnxQf9gfEjWODxBhARGQ8FjuxTEpKCp577jkkJCRg6tSpmDNnDpycShNtv/76a0yfPh1yuRwSiQTNmzc3cWv1y8YnEM4hvQAALq36wTGoM8I/7oyoVeNQb9omAIBTky5ouTmnzHZFqXG4ObU1vJ6daPQ261PWvQTEH78GAIg9cgmJZ2+i/85P0eGrsTj6zmIAQEmRAicmL0ff7fMRffAC0kMj4de3Deo80xo7e7xvyubrhdh/A5YuM7sIv+027kP7AGDL/vv4Zmo71PCwM3rd5ob7oDAUF5fgjTnH8Nvuu+Uuj4jLwUdLzuOn7bewb1Uf1KvjbOQWEunuu01hRq8zISUfO45EYlifekav25zl3j6PWzOfhkRuBfduo2Dn1wQlRfkojLuNrMsHILNz4gUOC8S+APG8lBgHTIfHXhKS01eSMGDSAaRmFJa7fPvhSGw/HIk541pizjstIZHw4YWWoip9gVMfr8GgI98g4PlOuL/jPwBA+wVvIensTd4UTYK3dsetMsnExrJycxh+ns+JZcwB4yBRqQ1/38Hrs4+rHxT2qLwCBX7YGo51O29j/YKnOe5qYTguQBwfFDfGAMv3w9ZwKJVGfJr//63echNLprc3er2kPcYBYYiMy0a/8QcQdi+j3OXHLiTg2IUEbDscgV8/fxo21jLjNpBIR+lZhfh9T/n5cIa0ad89LJraFh6utpWvTGq8dilO7AsQxwXEjTFA3Hjsp0ctXn8d7y88U+6yjOwiLF5/Az9tv4Xti3uhR7taRm4dGRKvl5Ol+25TqNHrjEnMxV9Ho/gAUTPBOEgEKBQlGDP/BNbuuF3u8qj4XMxcfkF9b3Ogv4uRW0iGxLEBEjuOj4obYyARkWVTKErw/R/hRq/36PkEXL+dhqaB7kavm7TDvoBw/LLzNt6aexyKcvKdc/MVWP3HTazdeQu/f9kdQ3rVNX4DiXT061+3kZtv3AnOgNJnvXFiGe0xh0Kc2B8QN44NEmMAEZHxSE3dAEOZPHkyYmJiMHHiRCxatEg9qQwAfPjhh2jRogUUCgXq1q0LZ2fLfpC8Y1BHuHcbifQTm5ETdrLcdUqKC3HvyyFwDO4Mn6EzjNxCw0o+H467W48h4PlO8GrdSP166tV7uLFqF7osmwR7H3d0WDgOZ2b8iPzEdBO21jDE/huwNNuPRJhkYKuouARbDtw3er2WgPug8alUKoz77L8KJ5V51O3ILPQeuw/JaflGaBlR9cUn5+HQ6TiT1P3rX8af2MzcxW+eh5LCPDT8/Cj8xiyDV9+xqDlwCvzGfYemq2/DZ9gsUzeRjIB9AeJ5KTEOGA+PvSQUoXfT0fed/RVOKvOoeasvYeHaa0ZoFZlKeX2BoowcnJy6Cu0+fwt2Nd3g/2x7eHdsglPTvzdxa4kqt94EE58DpQ+vLCo2/oQ2VH2MgyRGf/0bhVEzj5U7qcyjiopL8PJH/2LfiRgjtYxMgeMCxPFBcWMMsDymOidav/sOVCrjT2hD1cc4YHxpmYXoM25/hZPKPGrL/vt4c85x7l9kNv48GGGSSZ8LCpXYejDC6PWaO167JIB9AeK4gNgxBogLj/30wA9bb1Y4qcyjsnKK8dykgzh/I9kIrSJT4fVysiRR8Tk4ej7BJHXzfibzxThIYjTpi1MVTirzqHsx2Xhm7D4kpOQZoVVkKhwbILHj+Ki4MQYSEVmWo+cTEJOYa5K6TZW3SdXDvoBp/HnwPl7/5Fi5k8o8qrCoBMOnHcGh07FGahlR9ZnqePDHgfvILzD+cz/NHXMoCGB/QOw4NkiMAUREhmORE8uEhYVh8+bN8PT0xBdffFHuOq1atQIAtGjRoszrx48fR8+ePeHp6QlXV1e0b98e27ZtM3ibDc1n+GxAKkPc75+Uuzxq5TiUFBeg7rvrjNswI7myeCtKFEq0nDa87OtL/kSJUomBBxci4b/ruL/zPxO10PDE/huwJGevme6GgXPXebOCrrgPGtepK0n4efutKq9/NzobX/581YAtItIfU8bic9eT+TAVLRXE3YbMyQP2AS3KXW7l5m3kFpGpsC9APC8lxgHj4LGXhOKDb84iM6eoyuvPXH6eNwVauPL6ArH/XEbEXyfRdcVktP/ybZycugqF6TkmbCVR5fLyFbh22zRJOfkFSty4w4Qgc8U4SGKiUJTgnc/+Q0lJ1cZSlUoVxn9+Ekql5kloyLxxXIA4PihujAGWIyW9APdjs01Sd1pmIe7FmKZuqj7GAeNauO4qwiMyq7z+hr/v4t9z8QZsEZH+nDVx3hBph9cu6QH2BYjjAuLGGCAePPYTAGRmF1VpUpkH8goUePer0wZsEQkBr5eTpTD1/UxkvhgHSUzOXU/G6j9uVnn9yLgcfL7migFbRELAsQESO46PihtjIBGR5TBtzkqKyeqm6mFfwLiKipWYsOAUqvpoKMX/72eq6v1PRKZUWKTE5ZtpJqm7qLgEV2+Zpm5zxhwKeoD9AXHj2CAxBhARGYZFTiyzceNGlJSU4JVXXoGjo2O569jZ2QEoO7HMlStX8Mwzz0Amk2HdunXYvHkzfH198eKLL2L37t1Gabuh2Po0gHuXl5B99TCybxwvsyzpr2XIPL8b9T/eAamNvYlaaFjZEQm4v/M/1OraHDXaBalfVymUSD4XDlsPF9zZ/I8JW2h4Yv8NWJLzoaa70HHBhHWbO+6DxrVyc5jW26zdcQt5+ZwVnITvQmiqyepOSitATGKuyeo3Rzbe9aHMTkX6KfOfrJKqh30B4nkpMQ4YB4+9JAT3YrKw778YrbZRKFX4cVu4gVpEQlBRX+D8vF/hFOCN2COXEHP4oglbSFQ1l8NTTZoobMpxEaoexkESk7+ORiE2SbtJA+/HZmP/yVgDtYiEgOMCxPFBcWMMsBymzps5f4N5O+aKccB4CouU+HHbLa230yXXiMgUTHksMGXuqrnitUt6gH0B4riAuDEGiAeP/QQA63ffQa6W96WcvJyEyzd5LdyS8Xo5WQpTjg3EJuUhIUW76/AkHIyDJCartmh/veGXXbeRk1dsgNaQUHBsgMSO46PixhhIRGQ5TJmzcvFmKlRVnSmDBIV9AePafjgSian5Wm1zOzILh8/EGahFRPpz7XYaihUlJqvf1PcRmCPmUNAD7A+IG8cGiTGAiMgwLHJimSNHjgAAunfvXuE6MTGlD/Z7dGKZzZs3QyKRYMeOHRgwYAD69OmDTZs2wdfXFxs2bDBso43Ae+hMQCotM0tb9tV/EPPrdNT78A/Y1KxrusYZwdWlpTMSPjpTYY12QWgwvDvCftqDtvNfh8zW2oQtNDyx/wYsxY07GSarO+x+JpRK0w2smTvug8aRm1eMPw7c13q79Kwi/HU0ygAtItKvG3fTTVu/CY9D5shn2CxI5Fa49+ULuP5OQ0QsewPJe1chP5oPpREj9gWI56XEOGB4PPaSEKz/6w50yVFdt/O2/htDglJeX0CRX4icyCSkh3FMgszDjTsmHpcw8bgIVQ/jIImFrv069gctH8cFiOOD4sYYYBlMfU5i6vqpehgHjGPP8WikpBdovd2OI5HIzC4yQIuI9MuUx4Ibd9P5kA4t8dolPYp9AeK4gLgxBogDj/0E6H6955ddvE5k6Xi9nCyBqe8nMnX9VD2MgyQGhUVKbNx7T+vtsnOLsf1whP4bRILCsQESO46PihtjIBGRZTBlzkpmdhFiEznpsLliX8B4dL3exPuZyByY+hrBjbumrd8cMYeCHsX+gLhxbJAYA4iI9E+issA7vHx9fRETE4NLly4hJCTkieUKhQI+Pj5ISUnB3bt3Ua9ePQDABx98gDVr1iAjIwMSiUS9ftOmTdGkSRNs3rxZp/a0bt0aCQkJZV6TWNuh5hLTDqQUJkbg5gdt4PPSHNR4dqJB60qcEghVUdlZjK1UUswpaWvQejWR29ti4OFFCP1+N27+sh/9ts9HypW7ODdnnd7rmic9i2JJ2ck4xPQbKO/7J92oAMS5z6tw+bmNA+HtWfFMi96edpDLpFAoS5CQUvF3kpCShzYjdpW7zCftM0hRXOU2V5UQ9gljM2YcNjZT7vcKqSsSXd/TaVvnvP1wKjip5xYR6VeK46sotA4sd5m+jgNAxccC9+zNsCsO1a7RZkifx6X8iGtI3PkNMi/shSIzSf26Y3AX1H13HWy86+mlHva5Kie0/oYp+wJi/L2Y+hy0PMY8L31UeeeoYiC0GACYLg6YQwyozvel67E39d8NSDv6GwLn7NWpXnP4XONd30eJ1AXSkkz4ZHxr6uZYrHT7gcizbaX9hioFaqV/Cknla1okIR6rjaXvn/MQffACbqwufzzOHAm1v8E4WH05Nu2R6dCv3GWVjUsA1R+jti+4ALc8y9lXHsU4yDhoDIyDxpHkPBbF8lpab2eliEaNrB8N0CICOC7wKHM4fzUEIR5rOT5oXEKLA7xGYJ6ybJ9Gtn2PcpcZ45zIoeAkXPP2a9doMyG0fdQYmDdkGDk27ZDp0F+nbWtkLINVSaqeW0SkPypIEec+p8LlxsgfrZU2HxIoq95oM8W8IcsjxL4GxwWMi+MCD3FcQBg4LiBszFui6oh3nYYSqaPW29kW3YBHzhYDtEj4hHicNiZeLzceXi+vvhSn11BoVf6xzDj3M/0Ou+Jw7RptBhgHGQeNhXHQ8JQSJyS4faDTts55h+BUcFzPLaIHhDYuAHB80BTEHAeF2N/g+KhxCS0OcnyUiMg8PehPlccYY0M1MpbDqiRFu0abAaEdp43BknMnAdP2NxKdx0Mhr6n1dtbFEfDKXmuAFhHpT45NG2Q6DCh3mTHy+O0KL8M9d7t2jTZD+j4uGSN/kud5lRNif4Pjo8YltPFBU40NAuIcH2QMeEisMYCIhMvb2xvnz5/XaVu5ntsiCLm5uQCA/Pzyg/XmzZuRkpICJycnBAQEqF8fOXIkvvvuO0ydOhXTp0+HXC7H999/j9u3b2PlypU6tychIQGxsbFlXpPa2EP7oRf9KSnMw90vnodL24FGOYjGxcWhpLDsjN/WEhlM+SG0mTsKOVFJuLluHwDgxLsrMPDQIkTtPYPE0/qdyTQuPg5FqrI3UorpN1De90/V4F7xIm9Pe9Sp6VBpEXKZtErrlSc+Lg5QFem0rSam3ieMzdhx2NhMut9bFQCuum2alZmJrJTYylckMqW6BUAFEysb4ziQlpYKZFn+fqLP45Jd3Wao++46AEBhUiRyrh9FysEfkRN6HHcWDELQNxcgtar+bNnsc1VOSP0NU/cFxPh7MfU5aHmMeV76qPLOUcVASDEAMG0cMIcYUJ3vS9djr0e3V+DR7RWd22wOnyuclIAUKFEqnxivJD2qnQvY6rCdSoU4EX8vQjxWk+4E299gHKw+jwyggiGFqo5LALqPTeTl5SDPQr87xkHLwjgocnbFOmViFBcW8nsxII4LPGQW568GIMRjLccHjUtIcYDXCMxYjSyggnvOjHFOlJudg9wEy+wvCGkfNQZTxwFDM2mc0TB2UZmkxESgKFGvzSHSL6nJ80fj4mIBEfQlmTdkeYTW1+C4gPFxXOAhjguYnqnPB8QaB7TBvCWqlv9fj9NWQX6+aK8TCfE4TdUj2P4Gr5dXX0ABYFX+IqPcz5SaCmRb3nfHOGh5GAdFTO4CuOm2aVZWJrKS+b0YipDGBQCOD5qMiOOgEPsbHB81LiHFQY6PEhGZMUelSZ91k5SYYJG5XUI6ThuDqfsCxmDS/oa9bvczFfF+JjIH7oEmvbc5Py9XFPuJvo9Lxsif5Hle5YTW3+D4qPEJbXzQVGODgDjHBxkDHhJrDCAiy2SRE8t4e3sjPT0dFy9eRIcOHcosi4+Px7Rp0wAAzZs3h0QiUS9r0aIFDh8+jCFDhmDx4sUAAAcHB/zxxx/o2rVrtdrzOIm1nc7l6UP6yT+Rf/8KCmJvIf3E5ieWN1kRCmsvP73VV6tWrSdmZbNSSQETTdRXu0dLBAzshJ09p6pfy45MxIXPN6DT4gnY1WMqFPmFequvlk+tJ2YlFNNvoLzvn3QXpyqESmJT7rKEFM2dVG1mTC6XqgS1atWABKoqt7eqTL1PGJux47CxmXK/L4EV4lUKQKJ9N8fNCbC3qW2AVhHpT6qNFAUVLNPXcUBTWR7uTrB1svz9xFDHJZsa/rDpMQru3Uci/OMuyA37D3m3z8IxuHO1y2afq3JC6m+Yui8gxt+LKc9By2Ps89JHlXeOKgZCigGAaeOAOcQAfX1fhjz2Ps4cPtd4mQwlAKQyGXxqW36fzlSybBXI1mE7mSoL3iL+XoR2rKbqEWp/g3Gw+nJt7JFRwbLKxiWA6o9RO9hbw9VCvzvGQcvCOChuqbK8CsdwNbGTF8Cd34vBcFzgIXM4fzUEoR1rOT5ofEKKA7xGYL5ybGyRWcEyY5wTOTlaw9lC+wtC2keNwdRxwNBMGWfyrSRI02VDlRI+NRwgVVnmPkaWI1ZVDEjKf4Kr4fNHlahVyxuS8pdaFOYNWR6h9TU4LmB8HBd4iOMCpmfq8wGxxgFtMG+JqiNJkoNiuGi9nYNNkcVeC6+M0I7TVH1C7W/wenn1pVoL4H4mZ8v77hgHLQ/joHipIEO8qggqifYPQnRzBOyt+b0YipDGBQCOD5qKmOOg0PobHB81PiHFQY6PEhGZr0SZEooKlhljbMi7hjtkKst7ZKaQjtPGYOq+gDGYsr+RIs2DLj1pOyvez0TCl2dtj/QKlhnl3mY7K1FczzXkcclQORQ8z6uc0PobHB81PiGND5pybBAQ5/ggY8BDYo0BRCRc5c1bUlUSlUql/9kBTGzy5MlYvnw5fH19cejQITRs2BAAcO7cOYwcORL37t1DcXExJkyYgBUrVqi3u337Nnr06IGQkBCMHz8eMpkMGzZswB9//IHdu3ejR48eemtjvgLoskdvxQne8f6A3WNjssV5BdhQ/1XTNMjIXrn7G6zsbcu8JqbfQHnfP+mu3cu7cPZ6sk7bRh98CXVqOiAmMRe+z2zSevumDdxwbdsQnequjJj2CTEw9X4/asZRrN99R6tt3F1sEHPwJdjZMmCRsM1deRHzVl/SadvqHgcAIPrAS6jj7aDTtubEGMel6J+nImnntwj4YBPcuwyvdnmmjr3mgP2Nh8T4exHTOWhlyjtHFQPGgIfMIQYY4vvS97H3cebwudbptRGxSXmoXcMeMYdGmLo5Fut+TDbqP7sF2o68fz6pFWa8HWKQNpkDHqsti1D7G4yD1XfqSiI6jtyt8/bVHZv4cW5nvDmkkc71CxnjoGVhHBS3nf9E4vl3D2m93d6VfdC3cx0DtIgAjgs8yhzOXw2Bx9qHhHqcMjTGgYfEGgf04cDJGPQZt1/n7at7TrR5YXcM61NP5/qFjPuoZTFlnCksUsKv9yYkpWk33eOwPgHYvFB/OcJEhtJq+A5cDEvVadvqHodCGrvj0pbBOtVtbpg3ZHnY13hIrL8Vjgs8xHEBEmsc0Abzlqg6Vm4KxYQFp7Te7srWwWje0N0ALRI+Hqctj1D7G7xeXn0zlp7HFz9d0WlbfdzPlPDPy6jpIayH3ugD46DlYRwUt7fmHMdP229ptY2zoxXiDo2Ag335k4pT9XFc4CExnz+KOQ6yv/GQUI/ThsY4+JCY4yARUXW9+P5h/HkoQqdtqzs25OpkjbQTr0IikehUv5DxOG15TNnf+OPAfQz74IjW2x36oR96tq9lgBYR6c+F0BS0fmmnzttX91i0cmZHvDM8SOf6zYWxjkv6zKHgeV7l2N94SKy/F44PPiTG8UHGgIfEGgOIyDJJTd0AQ/jwww/h4eGB6OhoNGnSBM2aNUNgYCDatm2LevXqqSeIadGiRZntZsyYAXt7e2zfvh39+vVD79698csvv6Bdu3aYOnVqeVURERldq2APE9btabK6ibQxXocB2Deeb8hJZcgsmDIW13C3Re2a9iar3xxlXT4IlVLxxOslhfnIunwAAGDnG2zsZhEREVksHntJCALqOKF/F1+ttrGSSy12ogQisiwtGnpAKjVdEj7HqInIHDzbxRe+Wk7OXa+OE3p3rG2gFhEREZG+mPqcxNT1E5kDG2sZ3tJhrFWXXCMiUzDlsaBVEI9D2uK1SyIiInHhsZ8A4NUBDeBor919KZ1b1hTtpDJEZF5MeV9r7Rr2FjmpDBFZHl2uN4weGMhJZYiIiIiISPBMmbPyVJCHRU4qQ6Rvz3f3h7enduOojeq6oEc7HwO1iEh/mjZwg5XcdI9OZh6/9phDQURERERkOBb59PQ6derg+PHjmDZtGo4ePYqIiAgEBwfj+++/x9tvv4369esDeHJimWvXrqFFixaQy8t+LK1bt8by5cuN1n4iIk3aNauBVVtumqTutk05sEXmoV1zL4x5sRF+2BpepfUD/Z3x0ZvNDdwqIv1oY8JY3LapFy+2ayn6p/egyE6Fa9uBsPNvBqmNPYpSopF29HcUxt2Ce/dRsKvbzNTNJCIishg89pJQfDO1LU5eTkR6VlGV1v9ySmve9ExEZsHeTo7mDd1w+Waa0eu2s5WhSX03o9dLRKQtuVyK1bM7YeDkg1AqVZWvL5Ng9exOJp24i4iIiKrGw9UW9eo44V5MtgnqtkG9Ok5Gr5fIHH0wuhm2H4lE2L2MKq3/2sBAdG3lbdhGEelJu2ZeWPNn1XLi9K1tMy+T1GvOeO2SiIhIXHjsJwBwdrTGso864I1PjldpfQc7OZZ91N7ArSIi0g9Tjg1wXIKIzMVTwZ6YOCIYKzaGVmn9enWcMPPtEMM2ioiIiIiISA/acWyISPCsrErvZxo85RBUld/OBCu5FKtmdeSzpMgs2FjL0LKxB85eTzZ63dZWUjRvyHubtcUcCiIiIiIiw7HIiWUAICgoCLt3737i9ZycHEREREAqlaJp06Zllnl7e+Py5ctQKBRlJpc5d+4cateubfA2ExFVxeCe/piwQI7c/Cdn4TUkG2sZhvWpZ9Q6iXQlkUjw3YyOKFaUYO2O2xrXbRzggn2r+sDD1dZIrSOqHm9Pe/TuWBsHTsYave7XBgYavU5z5/vGt8g4sxM5YSeQfvJPKHMzIHNwgZ1/c3i/MB0ePUabuolEREQWhcdeEopGAa7Yv7ovBkw8gKS0Ao3rfjaxFd4b2VTjOkREQjLquUBcvnnG6PW+3K8+rKykRq+XiEgX/bv44vcvu2HkjKMoKi6pcD1bGxl+/7IbnunAfAQiIiJzMeq5Bpi76pJJ6uWNm0RV4+ZsgwOr+6Lf+P24fidd47qvPFsfP8zpxP2LzMYLvepi0penkF+gNGq9drYyDO0dYNQ6LQGvXRIREYkLj/30wOvPN0RuvgKTvzyl8aFdrk7W2Lm0F1oGeRqvcURE1eDr7YjubXzwz7l4o9fN+5mIyJws+bAdioqV+GGr5knCA/2dsW9VH9TwsDNSy4iIiIiIiHT3dGtv+Pk4ICo+1+h1jxzQwOh1EpmrQd39sX7B03h99nEUKyq+n8nORoZNX3dH97a1jNg6ouoZNbCBSSaWGd6nHmxtLPaxzQbDHAoiIiIiIsMR3RnKjRs3oFKp0LBhQ9jb25dZNmHCBAwbNgyDBw/G2LFjIZPJ8Pvvv+Po0aNYunSpiVpMRFSWs6M1Rg5ogNV/3DRqvcN6B8DTjRNvkPmQy6X4aV4XDOruj5Wbw56YhKOBnzPeGdYYbw5uBBcnaxO1kkg344cHGX1iGR8vewzq7m/UOi2Bc8vecG7Z29TNICIiEg0ee0lI2jT1wuU/BmPVljD8sDUcian56mVymQQvPFMXE18KRuenvE3YSiIi7Y0eFIiZy88b/eGV44cHGbU+IqLqGtanHpo2cMPy30Oxfvcd5OYr1MucHKww6rkGmDgiGI0DXE3XSCIiItLa2y80wmdrLkOh0PBUUAMYN5TnRETaqOPtgJPrB+Dn7bewcvNN3IrMLLO8Z7taGD88CIN7+nNSGTIrrs42eKV/ffy47ZZR6x3Rrz7cnG2MWqcl4LVLIiIiceGxnx41cUQw2jXzwvLfQ7F5/z0UFT98cJeHqw3eHNwQE14Khp+PowlbSUSkvfHDg4w+sYyvtwOe7eJr1DqJiKpDJpNi9exOeO5pP3y3KQz7/osps7xeHSe8MywIbw1pCFeOuxIRERERkZmQyaQY+2JjzFx+waj1dmvjg+D6bkatk8jcvfJsA7Ro6I7lG0Px2+67yCt4eD+Ts6MVXhsYiIkvBaNhXRcTtpJIeyMHNMBHS84jJ6/YqPXy3mbdMIeCiIiIiMhwRDexzLVr1wAALVq0eGLZ0KFD8ddff+Grr77Ca6+9BqVSiYYNG2LDhg14+eWXjd1UIqIKTXo5GGu2hUOpNN5DKia/Emy0uoj0RSKRYFB3fwzq7o/7Mdlo8/JOpGYUwsvNBuG7XoRUygdDkHka0NUXDfyccScqy2h1TnwpCFZWUqPVR0RERERkCXy87DF/QivMGhOC8zdSkJ5VCDsbOYLru8Lb077yAoiIBMjN2QajBwZi1RbjTX7e5amaeCrY02j1ERHpS3B9N6ya3QlfvdcGl26mIju3GE4OVngqyANODpz0noiIyBzVquGA4X3qYcPfd41W57NdfXnzJpEOnBys8e6rTTH5lSa4GJaKPmP3ITWzEDU9bHFoTT9TN49IZ5NeboKfd9xGSYlx8kclEmDSCOaPEhERERFpq01TL/y64Gl8O60drt1OQ26+Ai6O1mjT1BO2NqK7rZeILMSg7v6oW8sREXE5Rqtz0ohgyOW8n4mIzItEIsGAp/0w4Gk/RMZl43ZUFoqKS+DlZotWwZ68t5mIiIiIiMzSm0MaYcGPV5Cbr6h8ZT2Z8moTo9VFZEmaBrrj+0864+v32qLBs1uQklEIT1cb3N83HI72VqZuHpFOnB2t8ebghli64YbR6mzXzAvtmnsZrT4iIiIiIqKqEF0GqqaJZQBgwIABGDBggDGbRESkteD6bvhwdHN88dMVo9Q3+eVgtG4izIGt9P+2IuvqYfi+uRj3Fr2EguhQSK3tIHepAb93VsHWp0G16ygpKqiwbJVKBYlEgriNc+HRYzSsa/hDIilN6IvbOBfeL3wEqbUtAODCIAlabEiH3NFVq/ofL0eTC4MksPVvijqjvoJL6/6I+mEyMs/tQlFSJIIWX4J9vZAqvS9NywAgfGZ35Edcgc/wT1Bz4BSt3o+pBNRxgq21DABgbSVj4iWZNZlMijVzOqP7m3uMUl/TBm6Y+lozo9RFRERERGSJrK1k6BhS09TNMEsyGys8vfo9uATWgbKgCAUpmTj10RpkRySo1wmZOgx3Nv+DnJhk9f/XVmyHsrBYvc7o+K34vdEoFGXlVbnu8srRZHT8VqSHReL8Z78h9sgltP30Dfj1aQ1H3xrY1esDpN2IUK/7zKbZsPNyBUpKUJxbgDOzfkba9fsAgNo9WuKpj0ZAIpFAIpfh+sqduPvHUQBAn61z4R7sjyuLtyJ0zd9Vfi/V4f9se9Tp9RRsXB01fg8kLp9Pbo2d/0YhLqnq+5SubKxl+P6Tzgavx1z5P9sePl2a4dycdZXGS13pEosfvPZoHNUlFpdXjibaxGKgknjcPQQtp4+A1EoOZX4hTn74PdJDIwGYJh6TeXN2tMbTrX1M3QwiIiLSk0VT22LviRikZRYavC5Heyus+LiDwesxV8wbKuvRvCGn5j0q/UwK4m4jYslrUGSnQGbvgrrvroOdX+mN+Lfm9IYiPQGQSiGzc4Lv28tgX68lAPPLG5JIJGgV7Albm9K8IbmMD6Ek89a8oTumjmqKheuuGaW+90Y2RUhjD6PURURERERkiTzdbNG9bS1TN8MsmWveUMJ/1zW2W9N1bKFdp2beED3OykqKNXM745kx+4xSX0hjd0x5talR6jJHzBsqS5tYXNn7cgrwRpelk2Dj7oTi7DyceHcFMm7FAGDeEGnPv5YT/Gs5mboZRERERERE1VbTww4L32+L8Z+fNEp9g3v6Y2A3P6PUZY6YP1mWtvmTD6QcWovI5W+g/sfb4dr+eQBA5vk9iN0wC1CVQKVUwHvwNHj0eA2A+eVPujhZw+b/z1yzsZZxUhkye/MnPIVthyMQnZBr8Lqs5KXPeHsQ24iIiIiIiIRCdHdHVjaxjCnFbZyLkqICAEDE0tFI3LVE6zIyTu9ATvjpKq2bcngdLo1wQdj7rdWvRSx9HdffaYjQd1vg5vROyL197ont8qPDcHGoPaJ/nKJ+LXHnYlwf2wChU0K0bvOjQqYOg8ymdNCp85IJCH77Wa3L8OvbBl5PBVZp3QbDuuHl8F8wYN9X6tc6LR6PwSeWYeChRei38zN4tKivXtZ5yQQMvfg9Bh5ciIEHF6L17JHqZa1nj8SL51ehx9oPtW7zA+bwG0jasxI3JgThxuRmCH23hbq9MWun4eqbfriz4Hmt20y6mfNOSzSp76rVNgkpeYhJzEVCStWTL+v7OmHB5NaVr2giGae3w7Xd8wAAr95j0GRlOIKXXoFru0GIXPGW3uqpqOyMk38i5pfpUOZmIPf2WUQsHglFVioAIH7TPPU+Uh3altNowXG4tO4PAHDr9CIafXEC1jX8y11X02emaVmjz/+Ba9uBOr4jItKHbm18MHFEsFbb6HIckMkkWPdZV/VFQiJLI4Q+ePwfC3D9nUa48LwUGad3lFlfU/88dv3M0n75lBCETglB2rFN6mXsn1edEM5Dm00egsHHl+K12C3w69umzPqazlEfcAmsjVfvbUDb+aPVrwWPGYAhJ5dj4MGFWr8fsTHnOBCxdDSuvl5bHQdi1k5TL2McICJLEr7+ILZ3noxdvT5A1P5z6PTNOwBKb4puNfNVWLvYw7NlA3RZMRk2bo4I+eDh8b06dCln7/OzEXvkEgAg8u9T2DNoFnKik55Y7+iYb7Cr51TsemYabnz/FzovmaBe1nXFZJx49zvsemYaDo/8Ah2/Hgu5Q2ni6/4X5yL6wPlqvCvt+fVvh6h95yr8Hkic3Jxt8MMnnbTeTpexifnjn0JQPVet6xKL0n30LICK46U+aBuLAd3iaHm0LaeqsRioOB5buzigy4p3cWLycuzqORXnPl2Prt+9q97OFPGYiAxD6OMCD2RdPYILg2Xlts+QORyWTuhjgw94d2qKUTGby7TP0PkrYmHuMYA5PKbj7Wmv02QvupwTLXy/DerW5oOmKsK8oSc9mjdU2WcStXIsPPuMQdNVt+A9ZDoilo5WL6s3bQuCl11F8JLLqDHw/TLLmDdEZHrzJzyFxgEuWm2jy3Goob8LPpvYStvmEZkNIZwTaMoJKCnMw71FI3B9bANcf6ch0v/b+kSZHBeoHiGMDWjKDZLZWaPryikYcnI5Bp9YBv9n26uXcWyg+sw9BnBcgIjEwlzzhjRdv6/oOrYQr1Mzb4jK06t9bYwd2lirbXQZl5DLJVj3aVdYWYnuUQhVxryhJ1U1Fle2vOPXY3Hrt9Ll11bsQOelE9XLmDdEZBmEPi4Qu34Gro9vjNB3WyDs/dbIvLi/TNuvjPRS30t0/5tX1Ms4NkhVJfSx0ac+fhmDjy/FwEOLMGDfV6jVrewzovyfbYdBR77BoH++xaB/voVjHS8AvKdSG+YcB6NWT1DHwNApIbj4oi2S/loGAEg7vhk3JgTj8suuWr8fIiKqmrFDG6NHWx+tttFlbMjD1QarZnXiw/w1YP7kk7TJnwSAwsQIpBxYA4dGD6/Fq1Qq3F/8Kuq+uw7BSy6jwazdiFw5Fsq87NI6mD9JZFLOjtb4cW5nrbfT5Vj0ybgQNGvornVdROZA6OMCD5SXH1mckYi7XwxB6OTmuDEhqEzbOS5QdUIfH9SUH6kpr5K5k1Un9Dig6blqKYd+xo3JzXBhsPyJdjN/kojEQm7qBhjbkSNHTN2ECsVvmocaz02p0szIFck4swN2ASFwfGSQShOnZt3RYMYO9f+u7QfDf+IaSGRyZJzbjXtfD0WzNRHq5SpFMaJWjoFb+8Flyqk56D3Y12uJ6J+m6Nx2oDTBK3TNbigLi3Uuw69vW6TdiEDyxdtVWj/h5A0cef1r9f9Re8/i5AeroVKWoE6vVui+Ziq2th2vXn5j1S6Ervn7iXLOf7oeGeHR8OvXVue2C/03kHFmJ9KObkDjr09D5uCC4sxkSGSlJwN1Xl8IW78myDiz48lKyCBsrGXYvLAHur7+N9IyC6u0TZsRu7Sqw8nBClsW9oCDCWcZV+RkIHRyU5QU5cPa0xeq4kIUJtyDe7eR8H9nFXJu/oe6766DRG6lHtQHAIeG7ZG4Y5Fe2iC1tq2wbLdOL8K6hj9uze6JvPtXEDhnL6TWtohcOQ4AED6jCyRSGQLnHgAAJO9diYwzO6DITIbP8E/g2et1AEDu7XOI/WU6lHlZUJUo4TN0Btw6DS23nJif3kNBbDhUiiJYefqi7qSfYOXmXW7bnZp01el9aVpGRMLx1ZQ2uBCaglNXyn8w5uO0PQ4AwJIP26NVsKfW2xGZCyH0wZ1b9IJ7l5cQsfyNJ9bV1D+vOXgaao/8HABQlBqLGxOC4BzSC3JnT/bPtSCE89D4Y1dxf8cJdF484Yl1KztHlchl6LhwHCL3ni2zXegPu5F27X6ZyWaofOYcB4DSWFBz4JQntmMcICJLoSwsVt/kCwDJF2+j6TulSZeRf59GTkwy+vwxB27BdXHolc/Rdn7pWEu/HZ9CpSzBgZc+RUFqFgCg0ei+8OvbBrYezrjy7Vbc2fwPAMCjRX20nvUqrJzsIZFKcXXZNtTq0qzccrp89y5c6teC1EqO3LgUnHx/FfKTM8pte+LpsArfV1HWw8Qzayd7QKVS/69SAdYu9gAAKyd7FKRno6RIoe1HVyXWzvYY9M9iyGytkReXAqmNFZz8auLu1qM4+cFqSOQy1GzTCCfeXQGVQqne7tHvgcTr2a5+mP5Gc3z189Uqb6Pt2MSg7n54f1RTbZtmUTTtp6c+WlNmH60oXlaXtrFYWViMDl+NAVA2jgLaxeLI3afKLafN/Nf1EouBiuOxU11vFKZnI+NWDAAg6UwYHGp7wr1ZANKu3df2IyQiARP6uAAAKHMzEfvrR3Bp1f+JZYbO4bB0Qh8bBErPCVrNfAWxhy89scyQ+StiYc4xgDk8pvdSv3o4fjEBq7bcrPI22p4TvfJsfYx5UbuH81ka5g3pnjdUWe5PcUYScu+cR+C80ra5dnwBUT9MREH8Hdj6NIDc0VW9rjIvE+AN+kSCYmsjx5b/549mZBdVaRttj0MuTtbYsqg77GxFd7sBiYgQzgk05QQkbF8EqZUNmn5/B4WJ93FzWjs4NesOubMHAI4L6IMQxgY05QY1HTcQJUXF2NZxEhx9a+DZPV8g4eR1FKbnAODYQHWZcwzguAARiYW55g1pajdQ8XVsY1+nZt4QVceiqW1xKSwVZ68nV2l9Xe5nWvFxR7Ro5KH1dpaEeUO65w1VFos1Lbf1cIZHi/rqdkf+fRrtF7wJp7reyI5IqPZnSkTCIPRxAcfgLvAZNhtSGzvk3b+C8Bld0XxtHGS2DgAA96dfge9bS56og2ODVFVCHxtNPBOGK4u3QllQBLdgf/TbPh9bQsZAkV8I96YBeOqjl7HvxbnIT0yH3MEWqpISALynUhvmHAf9xn2nLqM4PQHXxgTArfMwAIB7l+FwaNgOYe+F6Py+iIhIM6lUgl8/fxqdXtuNyLicKm2j7diQlVyK37/shpoedro00WIwf9Jw+ZMAoCopQeSKt+A7Zjli1k4tW4BEAmVuBgBAmZ8FuZMHJFY21fgkiUifenesg9ljQ/Dp95ervI22x6L+Xepg+ustKl+RyEwJfVwAqDg/Muan92HrG4z6H2+DsiAX4R91gmNQJzgEtuG4gBaEPj4IVJwfqSmvkrmTVWcOcaCi56rZ12+FetO2IGHrF08sY/4kEYkF7/QSiMcHsKzca0ESE4Zbs3uiKCUadn5NEfDBJkitrKFSFCN2w2xkXzsClaIItrUawm/898i9eQoZZ3ch6/JBpB5Zhxr9J8KlzQDcWzQCJflZKCkqgFOz7vB9exkkUmm57XBt9zApyrFRexSlxkKlVEAiK/2pxG2eD7eOQ6HISVMPeunL4wleeYnpcAmsjd5b5sChlgcywqNxdNxilBQrIJHL8NSHL8G7c1PIrOTIvBePUx9+D69WDeHbuzV8ujZHg2HdELZ2H2IOXkDXVVNg5WQHmY01Ev67jjOzfi7zoL5HRR84r/47+eIt2Hu7QyKTQqUs0ev7fZw5/AYSti9ErZfmQObgAgCwcvEy6GdClWvSwA37VvVBn3H7kJ5VtZuDq8rZ0Qp/r+iNp0w8mYDc0RXuXV+GzM4JPsNnI/PifiRsXYC6k35E1qUDcGjcERL5kxPfJO1eCte2gwzSpkfLTj+1Dbm3zsCz5+twCOqEyO/GwPfNxfAfvxop+79HowXHyzxoQWplg6BFZ1EQcxNhU9vAo/tIKPNzEPndGAR+sgdW7j5QZKUg9L2n4NC4Y7nl1HlriXr/S9j6JeI2zoX/+NV6fV/aLCMi07G3k+Pv73qj77j9Vb4ZQxtfTWmDiSOC9V4ukVAIpQ/u0LDiQWhN/fNH+xgl+TmASqVOgKWqEcp5aMrlOxW2sbJz1JD3hyLir1OwcXVUP4Ceqs7c4wARkRgFv9UfUfvPAQD8+reDV8tA3Nn8D5LO3kSHheNwbs5aNBrVG3ufn11msgAAKCksxt/9P4ZLg1oYsPcr3N16FFYOtui4cBwOvfo58pMyYOPuhOcOfI09A2aWW87ZT9ai8P8PnGg28XmEfDAMp6b/oNN76bxsEnw6NgEAHHx1gfr1o+O+RfefpkGRVwhrFwf88+ZClBQbZmKZoqw83Nt+HMW5Bbi6eCtqdWuB5pOH4OQHpWNNPp2aIulceJmHQwBlvwcSty/ebY38AgWW/R6q97L7da6DTV93h1xefh9KLDTtp7WeblHuPgoYdj+tSiw+Nf2HcuOoNrE4+Xx4ueXoMxYD5cfjrHvxsHFzglfrRkg+Hw7f3q1h7WQPR98anFiGyIKYw7gAAET9MBE+Q2ch4/S2J5YZMofD0pnD2CAAtF/wJq4u+RP+/dvp/TMQO3OPAczhMT2JRIIVMzqioEiJtTuqdhOENl58pi7Wzu8KqVTck3kwb0h/eUOPfyZFKdGwcvNRX2+QSCSw9vJDUXIUbH0aAADuLx6F7GulD/YL/GRPtT87ItKvZg3dsW9VH/R9Z3+VJ5epKhcna+xd2Vv0D28lyyaUcwJNOQHpJzbDf9JPAACbmgFwatoNGae3w7P3WwA4LlBdQhkb0JQbVHdQJ5x8fyUAICc6CQknb8CvXzvc/v2w4T8gC2fuMYDjAkQkVuaaN1TV6/fGvk7NvCGqDkd7K+xZ2Rt9xu3HhdAUvZf/7bR2GDtU3BOvA8wb0mfeUGWfyaPLHWp7Ij8xvcxzBXJiU+BQ25MTyxBZCHMYF3Bp1U+9zM6/GaBSQZGZrJ5Yhqg6zGFs9NEJ4NLDogCJBLYezsiJSUaTcc/hxvd/IT8xHQCgyC0w4KdlmSwpDqYe+QXOLfuU+0B5IiIynNo1HXDoh37o+fYeRMXn6rVsayspNi/sjt4d6+i1XHPE/EnD5U8CQOLOb0sfQt+gVZnXJRIJ6n2wGXe/GAKZrQMUOemo/9E2SK2sq/HJEZG+zRv/FPLyFfjm1+t6L/uZDrXwx6KesLIS973NZLnMYVwAqDg/Mi/iCrz6TwAAyGwd4BjcFan/rIdDYBsDfWKWxxzGBzVhXmX1mUscqIh9QOnkbxWVS0QkBnwCpUA8PoAVsXQ08u5dRqPP/4FEboPwGV2RcepPuHcdgYTtCyGzdUDQorMAgPjNnyLut1nwG/cdXNsOhF1AiHpGtZKiAjSY9Rdkdo5QKZW4u2AQ0k9sgXvXlyptU+JfS+HSqr/6gJobfga5N08hcP5BxG+ap/fP4PEEr85LJsC9SQD2vTgHJYUK9N0+H/7PtsP9Hf+h6fhBKM4vxN/9PwYANH/vRbScPgJnZvyI6APnkXYjQj2zoMzGCodHfQlFXgEkUil6rJuOgIEdcX/nf5W2KfitZxFz+FKZjmXQW/3R4KUeyI1NwaWvNiLtRoRe3r85/AYKokORe+c84jbNg6q4EB7dR6HGc5P18v5Jd22aeuHY2mcxbNo/CLuXoZcyA/2dsfnr7mgZZNpJZR7Iu38ZNQaU/tby7l6Afb2WAEpnqHx8FlkAiP9jAQrj78D/0/JP7m5+2AEFceU/1CN48SVYe/lW2JbHy3ZtPxhuHYYgbuNcOAS2hVunoZBIKn6oh/vTrwAAbOs0hkQmR3F6AvIjrqIw8R5uz+9XZt2C2HBYe9R+ooy0Y78j7Z/1KCkugKqoAHLn6n9Pmj6zyj5PIjItN2cbHFrTF6/PPo4/D0XopUwHOzmWf9wBrz/fUC/lEQmVEPvgmjzePweApL+WIWnPdyhOjYH/xB9h5VqjWnWIjRDPQzV5/BzVs2UgvFo1xIHh8xEydVj1PgyRsow4sBSph36GtZcfar3yGezrhVSrDiIiIWs2eQic6nrj5LDS8emoPWcQtecMQqYOQ8qlO4j465TG7e9tOw4AyLwThxKFEnY1XOEW5A8n/xp4ZsPMMus6169Vbhn1BndG/RefhszGCjIbKxSmZev8fk5MXg4AqD/0abSe9SoOvboAEpkULaa8iH/eXIjE02HwaFEfPX/5CDt7vF+luvr/9Tmc6/mUu2zXM9OQF5f6xOvuTQMQ9mPpg0E9mtdH2vWHD6Lw69sGkXvPlln/8e+BxE0ikWDJ9PaoVcMes1dcRLFCP5NdjhvaGEs/ag9rK5leyjN3Fe2n5e2jQOX7qS6xoqKyDR2L8xLSnihDn7EYKD8eF2fn4d+3F6HVjJchd7BF8vlbSA+PLvdhHERkvsxhXCD9v62ARArXdgOfmFTC0Dkcls4cxgb9n20PVYkK0QfOlzuxjKHyV8TC3GMAc3iEQSqV4Me5XeBb0xGf/3gZSmX5NztoQyIB3hvZFF+/1wYyGRPcAeYNPU6XvCFdc38C3vsVQOmDWGJ+nc7JZYgEqF3zGji69lkMn3YEN+9n6qXMhv4u2LKoOyeVIYsnxHOCx3MCipKjYOPlr15uXaMuipKjAHBcQB+EODbweG6QY21P5MQkq5fnRCfBofbD/h/HBnRn7jGA4wJEJEbmmjekTZ5Nda9TM2+IjM3D1RZHfuyH12Ydw44jkXop08nBCitndsSrAxropTxLwLyhsgwRixnbiMTHHMYFHpV6eC1svOvBusbDcYL0//5A9rV/IHfygM+w2XBq3l0/Hw6JgjmMjT4q8KXuyIlMVI+Vujasg5zoJPTdNg9WjvaIOXQBlxdtgapEP3nsYmAJcfCBlEM/o87ri6r3gRARkU4a+DnjxC8DMOyDIzh9NbnyDarAx8sevy14Gj3alT9GK0bMnyxLX/mT+ZHXkXHqTzRacOyJ9VVKBeL/+Az1P94GpyZdkXv7HO58PhBNll3Ty3PeiEg/JBIJFk5ti1o17PHx0vMoKtbPOeFbQxpixYyOsLHmvc1kucxhXEBTfqR9/VZIO/Y7HBq1hyI7FVmX9sO2diP9fkgWzlzGByvKj6wsr5IqZw5xAOBz1YiINOHEMgLm1n4wpDb2AACHwLYoTLgLoHRAT5mbifRTfwIAVMVFsK5Zt/xCVCWI/WU6csJOACoVijOTYOvXtNKDauq/vyH9xBY0+qJ00KukMA9R349HvelbNQ7c6VvU3jNQ5hcBAFIu34FTXW8ApQlv1k72qPv/B2dIreXIia5gcFkqQatZr6Jm28aARAI7T2dk3IyqtHNZ74UuqPtcB+wd/In6tYtfbkReYjqgUsGvX1v02jAT2zpOgiKvQA/v9klC+g0ApQOeRYn30WjBMShz0xE+42lYe9eDa5sB1X+zVC1NA91xcfMgzFt9CV+vvYaSEt0eVCGRAFNebYLPJraGvZ1wDhH59y+rL2rk3b0A17YDoVKpkHlpP2q/9nWZdRO2L0LGqW0InH9Ivf88rvHXmhNUK1Je2Q9iYq0Rc6tUhsTK9uE/UhlUSgUAFex8m6Dx1ycr3T4n9ASSdi9D469Owcq1BjLO7ELcxk8q3U4TTZ9ZVT5PIjI9Jwdr/PFND2zZfx8TFpxEakahzmV1b+ODn+Z1QUAdJz22kMh8mLIPrkl5/XMAqPHcZNR4bjLy7l/B/cWvwjmkN+TOfLhLdZjyPFSTx89RZXbWaP/lW/j3rW90LpPKZ05xoNarn8PKzQcSqRTpp7bj9vx+aLrqNmR2jjrXQ0QkVE3GDYR//3Y4MGye+lj9wOVvtlSpDGXhw+1UJSWQymSQSCTICI/BnoEzNWxZqkbbxgh6sz/2DJiBgtQs+PZujZbThmv3Rspx94+j6PDVGNi4OcLRrybsaroh8XQYACD1yl3kxafCvWkA4o9drbSsPc9V/j4e596krvpmc4/m9RC9/5x6Wa1uITj/6W/q/zV9DyReEokE099ogX6dfTF61jFculnxwwUq4+fjgJ/mdUGv9k8mfotZRfvp4/soULX9VJdYUVnZ5h6LgbLxuDA9Bwknb2DfkDkASs8Bh1/5ERm3YvRSFxEJl5DGBYrTExD/x2do+Nm/TywzVQ6HpRPS2KCdlytaTHkB+16YU+5yY+eviIW5xACAOTxCIpVKMG/CU3iuW+k50Y27GTqXVd/XCWvnd0WXVt76a6AFYN7QQ7rkDVX0mVh7+qI4PR4qpQISmRwqlQpFyVGw9vJ7ogyPHq8hctU4KLJSeS2SSICaN3THxc3PY87Ki/jm1+vVyh+dOqoZ5k94Cna2wskfJTImoeXuV4TjAoYjtPtXNOHYgP6ZSwwAOC5AROJjrteqdcmzqc51auYNkSk4O1pj2+Ke2LjnHiZ9eQppmbrfz/RMh1r4cW4X+PkwD/pRzBt6yBCxuLzlubEpsKvpBolMWmbiz9zYlCq9TyIyT0IdF8i6chhxm+ah4byD6rFAr77j4DN0JiRyK+SE/Ye7XwxG40XnYFPOhAtEVSXUsVGfzs3QYupQHBj+qfo1iUwG96YBOPjy55BIJOjxy0do9Fpv3Fy7T5e3Tv9nTnHwgewbx1GSnw2XVv21fLdERKQvvt6OOPHLACxefwOzVlxAYVHVJskuz6jnGmDJ9PZwc7bRYwvNH/MnH9Jn/mRO6HEUJkXg+juBAErztiOjx6A4PR72DdqgOC0OTk26AgAcAtvA2qMO8u5dgnPIM1V6r0RkHBKJBO+PaoY+Hetg9OxjOH9D9zHcOjUdsGZOZ/TtXEePLSQyH0IaF6gsP9L3jW8Qs/YDhL3XEnKXGnBq1g2KTP1MdChmQhsfZH6k8QkpDgB8rhoRUWV415eASawfDoJJZA8GwVDasRmzHM4te1daRuLOb1GcmYTGC89Aam2L6J/eh6pYc0co7fhmxG+ah4afHoaVa00AQGH8XRQlR+HWrO4AAGVuBlQlJVDkpCNgyi86vsPKKQuL1X+rlKVJYkDpifyZWT8j7uiVSstoMvY52Hm64O9nP4aysBht5r4Gma2Vxm3qDuyIkPeHYf+weShIyVS/npeQpv47au9ZtJrxClwa1ELq1XvavrUqEdJvAACsvfzg1nUEJDIZ5M6ecGnVH7nhp3nziUDY2sjxxbtt8Er/+lixKQy/7b6D3HxFFbeV4eX+9TFpRDBCGgvr5v+i1FgAEvUM8vkRV+EzdCbybp2FXZ2gMh37xJ3fIv34RgTOPwS5o6te26FL2VI7JyjzMitd36FxRxQm3UfW5UNwDukFAMi7dxm2vsGQWlmXKUeRkw6ZnRPkTh4oKS5C8v7vDfa+DPl5EpH+SSQSDO9bDz3b1cIPW29i9R83EZ2QW+Xte7WvhfHDgzCouz+kUt5sTuJlqj64JhX1zx9lH9AC1u61kX39X7h1fEHnush056GalHeO6uTvDcfanuj751wAgLWzAyCVwNrFESfeXaFzXWReceDBeRIAuHUYjNhfP0JBbDgcGrTSuS4iIiEKHjsAAYM74cCw+SjKyqt0/aLsvP+1d9/hUVRtH8d/u+k9JITee+8CKiBYEAGxoz5YwC4oFhQfK2IX9REbFlSwg6IiKoICijTpvfcWWkiB9LbvH7wkBpJNdrNlduf7uS4vyU47uzvnnnPuObNHQdHhFVr36IqtiqxXTTV7ttWhBeslnXoQO3XbgbP2ExwTofz0LOWkpMsaFKhmNzs3CDQ4OlwBYSHKOpIi6dRgipyUdOWkpMsSEKDw6lUU07S20rYfVFSDGoqqX10ndiY6dazyhNeIk2y2otx3XMv6WvfWqRv6VTs2Vdr2g0WDKxz9HmA+7ZrFaelXgzRl1k5NmLpZ/6yr+CCs5g1iNPz6lhp2ZVNFRQS7sZS+p6x6emYdldxbT53Zd0Xjsb1YXJiXX2I/rorFkv14LElh1WKVdTRVktT+oWt1aNEGndxz2OnjAfANRsoLZO5cqbzkQ9r8UAdJUv6JJKUum6H8tGOK63mDV8Zw+Dsj5Qbj2zVSWPUqGjTndUlSSFyU6vbtopD4aK1+5RuPj18xC1+JAbVvfpExPAbUpXWCVk69Ul/9ulPvTdmkVZsrPulm68axGnFDK906qKnCwxjS+W+MG6rcuCF75Q6Krabwxp10/K8vVfWioUpd/L2C4+sotGYT5aenqjAnU8HxtSRJqf9MV2BUvAKi4ir03gF4XlhooMY93FU3DWyid7/ZpK9+3anM7IqNHw0LDdCQ/o11342t1L65scaPAp5mxLH7Ocf2KiiupiQp9+geRXfo67VnO8zAaM+vpB9MUmSdhKJcdWTdakVlIDfger4SA04vIy8AwCx8ddyQs/fvPXmfmnFDcBWLxaL/DGisi7vX0ofTtujD77bo4NGKnyd9z6ut4de31KDe9Zg88wyMG6rcuKHyyl3W8uzjJ5S8frcaX9NLO779S/UHdFfGoWTGDQF+zmh5AUk6uWG+9rw9TE2e+lmhdZoXvR5UpUbRvyNbnq/whh2VuWMFE8ugUoyWG5Wk6ue20vnjh2vuLa+UeJ4i42CS9s5cqoLsUz90uW/mUiV0acbEMpXkS3HwtONzPlH8hbfK8v/nKwDAOwICrHpkaFtdc3EDvTtlkyZN36aUExWbmDow0KKrL2qg+25opZ6da5S/gckwftJ94ycTLrtXCZfdW/T31id7q/rlDyq2+5XKSz2ivORDytq/WWF1Wyr70A7lHN6p0Npnt0cAGEPrJlW05IvLNXX2Lr3/7RYtWn2kwts2qRet4de31O1XNVN0JM82w7yMlBcob3xkYHRVNXhgctE+9k64R6H1Wjv4jnEmo+UH7Y2PtDeuEs4zUhyQ+F01ACgPTyEbSEUTYbHdrtSRGW8qslUPWUPCVZiTqZwjuxVWr7WsYdEqyCxuDOWnpyioSg1Zg0OVl3JYKYu+s/sjw8kLv1XiV0+p6XNzFJxQr+j1sAZt1f6L4h8AS/zmWRVkpKruHeOdfr+lqehAsX2zlqnVXQN1ZNlmFWTlKiAsWFF1q/3/oN0sBUUVzwwdHBOhrKMpKsjJU1hCrBoMPFd7Z/5T5r4bXH6uOj12o2ZfP1YZB0vOPBteM06Zh041MBM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    " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_aqc_tensor.ansatz_generation import (\n", - " AnsatzBlock,\n", - " generate_ansatz_from_circuit,\n", - ")\n", - "\n", - "initial_shallow_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=2),\n", - " time=evolution_time,\n", - ")\n", - "ansatz, initial_parameters = generate_ansatz_from_circuit(initial_shallow_circuit)\n", - "ansatz = ansatz.decompose(AnsatzBlock)\n", - "ansatz.draw(\"mpl\", fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "172012af-5499-4bdf-82b1-c5ba2d0350fe", - "metadata": {}, - "source": [ - "### Initialize objective function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8b039335-3e00-409d-ac75-983cfbe88c3a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:59.495880Z", - "iopub.status.busy": "2025-03-28T17:48:59.495415Z", - "iopub.status.idle": "2025-03-28T17:48:59.499389Z", - "shell.execute_reply": "2025-03-28T17:48:59.498609Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity\n", - "\n", - "objective = MaximizeStateFidelity(target_tns, None, None)" - ] - }, - { - "cell_type": "markdown", - "id": "4ba05493-be0b-4d91-8ca7-1065f8b0bde8", - "metadata": {}, - "source": [ - "### Convert Qiskit ansatz and initial parameters to a Quimb parametrized circuit" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2d60342e-393d-4604-86af-f20e0674c982", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:59.501808Z", - "iopub.status.busy": "2025-03-28T17:48:59.501297Z", - "iopub.status.idle": "2025-03-28T17:48:59.675635Z", - "shell.execute_reply": "2025-03-28T17:48:59.674867Z" - } - }, - "outputs": [], - "source": [ - "circ, conversion_context = qiskit_ansatz_to_quimb(ansatz, initial_parameters)" - ] - }, - { - "cell_type": "markdown", - "id": "a05591a0-5877-49e8-82aa-5fe06c11b718", - "metadata": {}, - "source": [ - "### Perform optimization of Quimb circuit using automatic differentiation" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7f3eaa20-3332-4fd1-811c-48a7a3078242", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:59.678703Z", - "iopub.status.busy": "2025-03-28T17:48:59.678458Z", - "iopub.status.idle": "2025-03-28T17:49:36.521776Z", - "shell.execute_reply": "2025-03-28T17:49:36.521028Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 0%| | 0/20 [00:00 - -# Installation Instructions - -Let’s see how to install AQC-Tensor. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: - -* [Option 1: Pip Installation](#option-1) -* [Option 2: Install from Source](#option-2) - -## Pre-Installation - -Users who wish to install locally (using either [Option 1: Pip Installation](#option-1) or [Option 2: Install from Source](#option-2)) are encouraged to follow a brief set of common instructions to prepare a Python environment for installation of AQC-Tensor: - -First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). - -```sh -python3 -m venv /path/to/virtual/environment -``` - -Activate your new environment. - -```sh -source /path/to/virtual/environment/bin/activate -``` - -Note: If you are using Windows, use the following commands in PowerShell: - -```pwsh -python3 -m venv c:\path\to\virtual\environment -c:\path\to\virtual\environment\Scripts\Activate.ps1 -``` - - - -## Option 1: Pip Installation - -Upgrade pip and install the AQC-Tensor package. To meaningfully use the package, you must also install at least one tensor network backend. The below snippet installs the addon, along with quimb (for tensor network support) and jax (for automatic differentiation). - -```sh -pip install --upgrade pip -pip install qiskit-addon-aqc-tensor[quimb-jax] -``` - - - -## Option 2: Install from Source - -Users who wish to develop in the repository or run the tutorials locally may want to install from source. - -In either case, the first step is to clone the AQC-Tensor repository. - -```sh -git clone git@github.com:Qiskit/qiskit-addon-aqc-tensor.git -``` - -Next, upgrade pip and enter the repository. - -```sh -pip install --upgrade pip -cd qiskit-addon-aqc-tensor -``` - -The next step is to install AQC-Tensor to the virtual environment. If you plan on running the tutorials, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. - -Adjust the options below to suit your needs. - -```sh -pip install tox jupyterlab -e '.[notebook-dependencies,dev]' -``` - -If you installed the notebook dependencies, you can get started with AQC-Tensor by running the notebooks in the docs. - -```python -cd docs/ -jupyter lab -``` - - - -## Platform Support - -We expect this package to work on [any Tier 1 platform supported by Qiskit](/docs/start/install#operating-system-support). diff --git a/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.mdx b/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.mdx deleted file mode 100644 index 38f08a310ad..00000000000 --- a/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.mdx +++ /dev/null @@ -1,435 +0,0 @@ - - -# Reducing circuit depth with the AQC-Tensor Qiskit addon - -In this notebook, we will work through the steps of a [Qiskit pattern](/docs/guides/intro-to-patterns) while using **approximate quantum compilation with tensor networks (AQC-Tensor)** to achieve a lower circuit depth than would ordinarily be needed to perform Trotter evolution. - -These are the steps that we will take: - -* **Step 1: Map to quantum problem** - - * Initialize our problem’s Hamiltonian and observable(s) - * Generate a target tensor-network state for the initial portion of the circuit - * Generate a low-depth circuit which approximates the portion being compressed - * Generate a general ansatz from that circuit - * Optimize the parameters to bring the ansatz as close as possible to the target - * Add subsequent Trotter steps to the optimized ansatz - -* **Step 2: Optimize for target hardware** - - * Transpile the circuit for hardware - -* **Step 3: Execute experiments** - - * Use a fake backend for simplicity - -* **Step 4: Reconstruct results** - - * N/A; instead, we just output the measured observable - - - -## Step 1: Map to quantum circuit and operator - - - -### Set up a model Hamiltonian and observable - -In this notebook, we use the Ising model on a circle of 10 sites: - -$$ -\hat{\mathcal{H}}_{\text{Ising}} = \sum_{i=1}^{10} J_{i,(i+1)} Z_i Z_{(i+1)} + h_i X_i \, , -$$ - -where the periodic boundary conditions imply that for $i=10$ we obtain $i+1=11\rightarrow1$, $J$ is the coupling strength between two sites and $h$ is the external magnetic field. - -``` -[1]: -``` - -```python -from qiskit.transpiler import CouplingMap -from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian - -# Generate some coupling map to use for this example -coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False) - -# Choose a 10-qubit circle on this coupling map -reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 4, 15, 3, 9]) - -# Get a qubit operator describing the Ising field model -hamiltonian = generate_xyz_hamiltonian( - reduced_coupling_map, - coupling_constants=(0.0, 0.0, 1.0), - ext_magnetic_field=(0.4, 0.0, 0.0), -) -``` - -The observable we will measure is the total magnetization. - -``` -[2]: -``` - -```python -from qiskit.quantum_info import SparsePauliOp - -L = reduced_coupling_map.size() -observable = SparsePauliOp.from_sparse_list([("Z", [i], 1 / L / 2) for i in range(L)], num_qubits=L) -``` - - - -### Determine how much of the time evolution to simulate classically - -Our overall goal is to simulate time evolution of the above model Hamiltonian. We do so by Trotter evolution, which we split into two portions: - -1. An initial portion that is simulable with matrix product states (MPS). We will “compile” this portion using AQC as presented in [https://arxiv.org/abs/2301.08609](https://arxiv.org/abs/2301.08609). -2. A subsequent portion of the circuit that will be executed on hardware. - -Let’s plan to use AQC-Tensor to compress our time evolution circuit up to time $t=4$, then evolve using ordinary Trotter steps up to $t=5$. - - - -### Generate circuits before and after split - -Now that we have chosen to split at $t=4$, we will generate two circuits: - -1. A “target” circuit for the AQC portion of the evolution, from $t_i=0$ to $t_f=4$. Because this is being simulated by a tensor-network simulator, the number of layers affects execution time only by a constant factor, so we might as well use a generous number of layers to minimize Trotter error. - -``` -[3]: -``` - -```python -from qiskit.synthesis import SuzukiTrotter -from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit - -aqc_evolution_time = 4.0 -aqc_target_num_trotter_steps = 45 - -aqc_target_circuit = generate_time_evolution_circuit( - hamiltonian, - synthesis=SuzukiTrotter(reps=aqc_target_num_trotter_steps), - time=aqc_evolution_time, -) -``` - -2. A subsequent evolution circuit, which evolves from $t_i=4$ to $t_f=5$. Because this is being run on quantum hardware, it is desirable to use as few Trotter layers as possible. - -``` -[4]: -``` - -```python -subsequent_evolution_time = 1.0 -subsequent_num_trotter_steps = 5 - -subsequent_circuit = generate_time_evolution_circuit( - hamiltonian, - synthesis=SuzukiTrotter(reps=subsequent_num_trotter_steps), - time=subsequent_evolution_time, -) -``` - -For the sake of later comparison, let’s also generate a third circuit: one that evolves for `aqc_evolution_time` but which has the same evolution time per Trotter step as the subsequent circuit. This is the circuit we would have working with had we not used a generous number of Trotter steps for the target circuit. We will refer to this as the *comparison circuit*. - -``` -[5]: -``` - -```python -aqc_comparison_num_trotter_steps = int( - subsequent_num_trotter_steps / subsequent_evolution_time * aqc_evolution_time -) -aqc_comparison_num_trotter_steps -``` - -``` -[5]: -``` - -```python -20 -``` - -``` -[6]: -``` - -```python -comparison_circuit = generate_time_evolution_circuit( - hamiltonian, - synthesis=SuzukiTrotter(reps=aqc_comparison_num_trotter_steps), - time=aqc_evolution_time, -) -``` - - - -### Generate an ansatz and initial parameters from a Trotter circuit with fewer steps - -First, we construct a “good” circuit that has the same evolution time as the target circuit, but with fewer Trotter steps (and thus fewer layers). - -Then we pass this “good” circuit to AQC-Tensor’s `generate_ansatz_from_circuit` function. This function analyzes the two-qubit connectivity of the circuit and returns two things: - -1. a general, parametrized ansatz circuit with the same two-qubit connectivity as the input circuit; and, -2. parameters that, when plugged into the ansatz, yield the input (good) circuit. - -Soon we will take these parameters and iteratively adjust them to bring the ansatz circuit as close as possible to the target MPS. - -``` -[7]: -``` - -```python -from qiskit_addon_aqc_tensor import generate_ansatz_from_circuit - -aqc_ansatz_num_trotter_steps = 5 - -aqc_good_circuit = generate_time_evolution_circuit( - hamiltonian, - synthesis=SuzukiTrotter(reps=aqc_ansatz_num_trotter_steps), - time=aqc_evolution_time, -) - -aqc_ansatz, aqc_initial_parameters = generate_ansatz_from_circuit( - aqc_good_circuit, qubits_initially_zero=True -) -aqc_ansatz.draw("mpl", fold=-1) -``` - -``` -[7]: -``` - -![../\_images/tutorials\_01\_initial\_state\_aqc\_15\_0.png](/docs/images/api/qiskit-addon-aqc-tensor/tutorials_01_initial_state_aqc_15_0.avif) - -``` -[8]: -``` - -```python -print(f"Comparison circuit: depth {comparison_circuit.depth()}") -print(f"Target circuit: depth {aqc_target_circuit.depth()}") -print(f"Ansatz circuit: depth {aqc_ansatz.depth()}, with {len(aqc_initial_parameters)} parameters") -``` - -```python -Comparison circuit: depth 120 -Target circuit: depth 270 -Ansatz circuit: depth 23, with 515 parameters -``` - - - -### Choose settings for tensor network simulation - -Here, we use the Qiskit Aer matrix-product state (MPS) simulator, which is currently the only supported tensor network backend. - -``` -[9]: -``` - -```python -from qiskit_aer import AerSimulator - -simulator_settings = AerSimulator( - method="matrix_product_state", - matrix_product_state_max_bond_dimension=100, -) -``` - - - -### Construct matrix-product state representation of the AQC target state - -Next, we build a matrix-product representation of the state to be approximated by AQC. - -``` -[10]: -``` - -```python -from qiskit_addon_aqc_tensor.simulation import tensornetwork_from_circuit - -aqc_target_mps = tensornetwork_from_circuit(aqc_target_circuit, simulator_settings) -``` - -Note that because we chose a generous number of Trotter steps for the target state, it actually has less Trotter error than the comparison circuit. We can calculate the fidelity ($| \langle \psi_1 | \psi_2 \rangle |^2$) of the state prepared by the comparison circuit vs. the target state: - -``` -[11]: -``` - -```python -from qiskit_addon_aqc_tensor.simulation import compute_overlap - -comparison_mps = tensornetwork_from_circuit(comparison_circuit, simulator_settings) -comparison_fidelity = abs(compute_overlap(comparison_mps, aqc_target_mps)) ** 2 -comparison_fidelity -``` - -``` -[11]: -``` - -```python -0.9997111919739693 -``` - - - -### Optimize the parameters of the ansatz using MPS calculations - -Here, we minimize the simplest possible cost function, `MaximizeStateFidelity`, by using the L-BFGS optimizer from scipy. - -We choose a stopping point for the fidelity such that it will be above what the comparison circuit would have been, without using AQC. Once this is reached, the compressed circuit has less Trotter error *and* less depth than the original circuit. Given more processing time, further optimization steps can be performed to bring the fidelity even higher. - -``` -[12]: -``` - -```python -from scipy.optimize import OptimizeResult, minimize - -from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity - -objective = MaximizeStateFidelity(aqc_target_mps, aqc_ansatz, simulator_settings) - -stopping_point = 1 - comparison_fidelity - - -def callback(intermediate_result: OptimizeResult): - print(f"Intermediate result: Fidelity {1 - intermediate_result.fun:.8}") - if intermediate_result.fun < stopping_point: - # Good enough for now - raise StopIteration - - -result = minimize( - objective.loss_function, - aqc_initial_parameters, - method="L-BFGS-B", - jac=True, - options={"maxiter": 100}, - callback=callback, -) -if result.status not in ( - 0, - 1, - 99, -): # 0 => success; 1 => max iterations reached; 99 => early termination via StopIteration - raise RuntimeError(f"Optimization failed: {result.message} (status={result.status})") - -print(f"Done after {result.nit} iterations.") -aqc_final_parameters = result.x -``` - -```python -Intermediate result: Fidelity 0.95084365 -Intermediate result: Fidelity 0.98409893 -Intermediate result: Fidelity 0.99142033 -Intermediate result: Fidelity 0.99521405 -Intermediate result: Fidelity 0.99566673 -Intermediate result: Fidelity 0.99650054 -Intermediate result: Fidelity 0.99683487 -Intermediate result: Fidelity 0.99720426 -Intermediate result: Fidelity 0.99761726 -Intermediate result: Fidelity 0.99809073 -Intermediate result: Fidelity 0.99838244 -Intermediate result: Fidelity 0.99861841 -Intermediate result: Fidelity 0.99874617 -Intermediate result: Fidelity 0.99892696 -Intermediate result: Fidelity 0.99908129 -Intermediate result: Fidelity 0.99917737 -Intermediate result: Fidelity 0.99925456 -Intermediate result: Fidelity 0.99933134 -Intermediate result: Fidelity 0.99947173 -Intermediate result: Fidelity 0.99956469 -Intermediate result: Fidelity 0.99964488 -Intermediate result: Fidelity 0.99967419 -Intermediate result: Fidelity 0.99968821 -Intermediate result: Fidelity 0.9997448 -Done after 24 iterations. -``` - - - -### Construct the final circuit to pass to the transpiler - -``` -[13]: -``` - -```python -final_circuit = aqc_ansatz.assign_parameters(aqc_final_parameters) -final_circuit.compose(subsequent_circuit, inplace=True) -final_circuit.draw("mpl", fold=-1) -``` - -``` -[13]: -``` - -![../\_images/tutorials\_01\_initial\_state\_aqc\_26\_0.png](/docs/images/api/qiskit-addon-aqc-tensor/tutorials_01_initial_state_aqc_26_0.avif) - - - -## Step 2: Transpile for execution on target hardware - -In Step 2 of a [Qiskit pattern](/docs/guides/intro-to-patterns), we transpile this circuit and any desired observable(s) for execution on a target device. Here we are using a fake backend provided by `qiskit-ibm-runtime`. - -``` -[14]: -``` - -```python -from qiskit import transpile -from qiskit_ibm_runtime.fake_provider import FakeMelbourneV2 - -backend = FakeMelbourneV2() - -isa_circuit = transpile(final_circuit, backend) -isa_observable = observable.apply_layout(isa_circuit.layout) -``` - -The resulting ISA circuit can then be sent for execution on the backend (step 3 of a [Qiskit pattern](/docs/guides/intro-to-patterns)). - - - -## Step 3: Execute on quantum hardware - -``` -[15]: -``` - -```python -from qiskit_ibm_runtime import EstimatorV2 as Estimator - -estimator = Estimator(backend) -job = estimator.run([(isa_circuit, isa_observable)]) -pub_result = job.result()[0] -``` - - - -## Step 4: Reconstruct - -Reconstruction is not necessary in our case. We can just look at the result. - -``` -[16]: -``` - -```python -pub_result.data.evs[()] -``` - -``` -[16]: -``` - -```python -np.float64(0.054003906250000004) -``` diff --git a/docs/addons/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.ipynb b/docs/addons/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.ipynb deleted file mode 100644 index 8dba50e0e8c..00000000000 --- a/docs/addons/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.ipynb +++ /dev/null @@ -1,845 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0f0b5824", - "metadata": {}, - "source": [ - "# Reducing circuit depth with the AQC-Tensor Qiskit addon\n", - "\n", - "In this notebook, we will work through the steps of a [Qiskit pattern](/guides/intro-to-patterns) while using **approximate quantum compilation with tensor networks (AQC-Tensor)** to achieve a lower circuit depth than would ordinarily be needed to perform Trotter evolution.\n", - "\n", - "These are the steps that we will take:\n", - "\n", - "- **Step 1: Map to quantum problem**\n", - " - Initialize our problem's Hamiltonian and observable(s)\n", - " - Generate a target tensor-network state for the initial portion of the circuit\n", - " - Generate a low-depth circuit which approximates the portion being compressed\n", - " - Generate a general ansatz from that circuit\n", - " - Optimize the parameters to bring the ansatz as close as possible to the target\n", - " - Add subsequent Trotter steps to the optimized ansatz\n", - "- **Step 2: Optimize for target hardware**\n", - " - Transpile the circuit for hardware\n", - "- **Step 3: Execute experiments**\n", - " - Use a fake backend for simplicity\n", - "- **Step 4: Reconstruct results**\n", - " - N/A; instead, we just output the measured observable" - ] - }, - { - "cell_type": "markdown", - "id": "79042f19-241d-48fb-aa87-134a93082c94", - "metadata": {}, - "source": [ - "## Step 1: Map to quantum circuit and operator\n", - "\n", - "### Set up a model Hamiltonian and observable\n", - "\n", - "In this notebook, we use the Ising model on a circle of 10 sites:\n", - "$$\n", - "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{10} J_{i,(i+1)} Z_i Z_{(i+1)} + h_i X_i \\, ,\n", - "$$\n", - "where the periodic boundary conditions imply that for $i=10$ we obtain $i+1=11\\rightarrow1$, $J$ is the coupling strength between two sites and $h$ is the external magnetic field." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "1f700ca7-ad7e-45ac-a5eb-50962ce67b48", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:51.132868Z", - "iopub.status.busy": "2025-03-28T17:48:51.132589Z", - "iopub.status.idle": "2025-03-28T17:48:51.531783Z", - "shell.execute_reply": "2025-03-28T17:48:51.531048Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.transpiler import CouplingMap\n", - "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", - "\n", - "# Generate some coupling map to use for this example\n", - "coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False)\n", - "\n", - "# Choose a 10-qubit circle on this coupling map\n", - "reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 4, 15, 3, 9])\n", - "\n", - "# Get a qubit operator describing the Ising field model\n", - "hamiltonian = generate_xyz_hamiltonian(\n", - " reduced_coupling_map,\n", - " coupling_constants=(0.0, 0.0, 1.0),\n", - " ext_magnetic_field=(0.4, 0.0, 0.0),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d88fd2c0-a4cd-46be-a1aa-7f6dec219791", - "metadata": {}, - "source": [ - "The observable we will measure is the total magnetization." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9c864767-c218-4656-b20d-3e4e8c6f47af", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:51.534447Z", - "iopub.status.busy": "2025-03-28T17:48:51.534173Z", - "iopub.status.idle": "2025-03-28T17:48:51.538492Z", - "shell.execute_reply": "2025-03-28T17:48:51.537835Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "L = reduced_coupling_map.size()\n", - "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)" - ] - }, - { - "cell_type": "markdown", - "id": "70aa63a7-7560-4b29-9391-e1f42abdf1fd", - "metadata": {}, - "source": [ - "### Determine how much of the time evolution to simulate classically\n", - "\n", - "Our overall goal is to simulate time evolution of the above model Hamiltonian. We do so by Trotter evolution, which we split into two portions:\n", - "\n", - "1. An initial portion that is simulable with matrix product states (MPS). We will \"compile\" this portion using AQC as presented in https://arxiv.org/abs/2301.08609.\n", - "2. A subsequent portion of the circuit that will be executed on hardware." - ] - }, - { - "cell_type": "markdown", - "id": "442de278-82f9-4fcf-8b30-88804e7c170d", - "metadata": {}, - "source": [ - "Let's plan to use AQC-Tensor to compress our time evolution circuit up to time $t=4$, then evolve using ordinary Trotter steps up to $t=5$." - ] - }, - { - "cell_type": "markdown", - "id": "bf2243b5-56c7-48e8-acc6-7c8c720a8997", - "metadata": {}, - "source": [ - "### Generate circuits before and after split\n", - "\n", - "Now that we have chosen to split at $t=4$, we will generate two circuits:\n", - "\n", - "1. A \"target\" circuit for the AQC portion of the evolution, from $t_i=0$ to $t_f=4$. Because this is being simulated by a tensor-network simulator, the number of layers affects execution time only by a constant factor, so we might as well use a generous number of layers to minimize Trotter error." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c7593867-ed56-4f01-b2a9-1641ea4a5d10", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:51.541167Z", - "iopub.status.busy": "2025-03-28T17:48:51.540901Z", - "iopub.status.idle": "2025-03-28T17:48:51.554770Z", - "shell.execute_reply": "2025-03-28T17:48:51.554075Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.synthesis import SuzukiTrotter\n", - "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", - "\n", - "aqc_evolution_time = 4.0\n", - "aqc_target_num_trotter_steps = 45\n", - "\n", - "aqc_target_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=aqc_target_num_trotter_steps),\n", - " time=aqc_evolution_time,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "bc3a6acc-8eb3-486e-9dd8-a58a82a7ccf9", - "metadata": {}, - "source": [ - "2. A subsequent evolution circuit, which evolves from $t_i=4$ to $t_f=5$. Because this is being run on quantum hardware, it is desirable to use as few Trotter layers as possible." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c97ac3e8-b7bd-4727-8495-a513bf143b1d", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:51.557394Z", - "iopub.status.busy": "2025-03-28T17:48:51.557174Z", - "iopub.status.idle": "2025-03-28T17:48:51.563062Z", - "shell.execute_reply": "2025-03-28T17:48:51.562342Z" - } - }, - "outputs": [], - "source": [ - "subsequent_evolution_time = 1.0\n", - "subsequent_num_trotter_steps = 5\n", - "\n", - "subsequent_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=subsequent_num_trotter_steps),\n", - " time=subsequent_evolution_time,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "3731468d-073b-460a-a87c-4920a35d44d9", - "metadata": {}, - "source": [ - "For the sake of later comparison, let's also generate a third circuit: one that evolves for `aqc_evolution_time` but which has the same evolution time per Trotter step as the subsequent circuit. This is the circuit we would have working with had we not used a generous number of Trotter steps for the target circuit. We will refer to this as the _comparison circuit_." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "557cc3a4-5909-4087-903d-2b603927071d", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:51.565090Z", - "iopub.status.busy": "2025-03-28T17:48:51.564894Z", - "iopub.status.idle": "2025-03-28T17:48:51.571882Z", - "shell.execute_reply": "2025-03-28T17:48:51.571204Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "20" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "aqc_comparison_num_trotter_steps = int(\n", - " subsequent_num_trotter_steps / subsequent_evolution_time * aqc_evolution_time\n", - ")\n", - "aqc_comparison_num_trotter_steps" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4c9bb0cd-0bd4-4236-9046-55a5222a034a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:51.573836Z", - "iopub.status.busy": "2025-03-28T17:48:51.573642Z", - "iopub.status.idle": "2025-03-28T17:48:51.580207Z", - "shell.execute_reply": "2025-03-28T17:48:51.579553Z" - } - }, - "outputs": [], - "source": [ - "comparison_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=aqc_comparison_num_trotter_steps),\n", - " time=aqc_evolution_time,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "0c7d8499-afd9-4fdd-88a6-8dc33e144ee2", - "metadata": {}, - "source": [ - "### Generate an ansatz and initial parameters from a Trotter circuit with fewer steps\n", - "\n", - "First, we construct a \"good\" circuit that has the same evolution time as the target circuit, but with fewer Trotter steps (and thus fewer layers).\n", - "\n", - "Then we pass this \"good\" circuit to AQC-Tensor's `generate_ansatz_from_circuit` function. This function analyzes the two-qubit connectivity of the circuit and returns two things:\n", - "1. a general, parametrized ansatz circuit with the same two-qubit connectivity as the input circuit; and,\n", - "2. parameters that, when plugged into the ansatz, yield the input (good) circuit.\n", - "\n", - "Soon we will take these parameters and iteratively adjust them to bring the ansatz circuit as close as possible to the target MPS." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "17c2f81f-dcfa-436a-b592-af3efb46d75a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:51.582608Z", - "iopub.status.busy": "2025-03-28T17:48:51.582412Z", - "iopub.status.idle": "2025-03-28T17:48:53.734541Z", - "shell.execute_reply": "2025-03-28T17:48:53.733760Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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MNS2mtAzL3wYPD3n6+SgjKSXP5wIAuD7Ocm+rPyRKW979TekJWY2cS7OH54cj3fcSDl2ub5hNJp3594BK1qmUYzujj5eq3d5Wf/V7Nd/nW6FjkxzfScKhGBk8jWo5bpDKNK8jg6dRK5/6QGe3HJAklW/fSI2e6Sejr7fMmSZtfPN7xazeocgJj6hYhWBFLZyoxONntGTAeDUbfb9CI+vJw9OotMRkrX7uE104kPtATLHrLwds0xOSdH7/CQVUCpHWZP0cjN6eMvp4KSMjU14Bfko6eTbf5wsAAID8c6T2gCS1HDtQWyf/rso9WmRbfm5n1sN9mfIe97qaI7UHJMk3OEgNn7xd8/u+olr33GJZnnLmvFLOnLf8f3rTPtV9qHu+z9eR2gOSVK5tA/mXK6XdX8zLNtFiZnKa5W/jpcFSbfxGAAB540j3+DP/7s9Tmmve3Un7flpi+b9K79Za/WzWg/zEo7GKWb1DYd1baN+Pi/N8HRzp/m9Ky1DMqu0KqBhiM81+ZUuqXJv6WvXMh5ZlJ1dsy/M5W1Pz7k4q3aiamr82QI2H9dfGcT9Kkjz9fdXuo6dVsk4lZaZlaNkj7yjxSFaHw2r92qnug93l4WVU+sUUrX3xCyWfOpfrNW374VMKql5eHl6eunjijFY/+7HVjhUXDp6UJFXu3iLX9ZdU799eJ5ZvsezHVowRAAAAl7lCe8DV+gcUpD1wiav0HUg6Gaekk3EqUauizTSHdYtQ0okzOrv1oCT6FQBAfjhSXeB6+wO6WmzQVl/BwCqhCm5YXUsHvm3Z5sq4mqvFBk+uvHw+SSfOKjk2XsXKBysh+pTqDOiqg3+s1Pl9xyVl1aHS/nu/gNggAABA3rlCm8DV4oMFaRNc4irxQVt1+2p92ur0xr2WQVLNJpPluyc+CAB550h1gUsDHkmSd2DeB7SrEhWp6ZFPSJLObjmgpFNxCo2sl69YWanwKqp9f2cZjB4KjQxX9Ny1OvDbcklZg0lV7NxU3oH+WvvSlzq+JGtQx9KNqqvZS/fKK9BfBg8PbX1vuqJnr1HkhEfkGeCrqIUTZco0aXa3kar3aC9Vu62NPDyNMmVkau1LX+r0xr25puV6+8unxScqdt1uhUaG5/m8c+MKv4nCGFcFAADAXbhC/a/+Y7311/9elSk963nx9Qz66QptgisFN6kp3+AgHflrw7U3vkqjZ/rJr2xJ3fzJM8pMSbNMSuRXpqQ6ffO8AquUVXJsvJY+/I7S4rMGKw0fHKUqUZHy8DQq5cx5rR4xVT5BATmu6db3puuW70fJp2SgjL7eOrfjsFY/94kyklNzTcuVvwnPYr4yGAwyGAzXPAez2Syv4lm/IaOvtzy8PJUcG5/vawEArs6R6gGS9bFDJKnxs//T4Vlr5FMiINvA6FWiWmnfD4tkNpmUFp+oQ3+uUtU+bbR5/M/5uxa5PNOSsib0aPnWw/IrU0L7flqirZN/lyT5hZRQxJsPKaBSiDx9vXXkr/XaPP7nXO+jPiUD1WTknTL6eMvD21M7p87K9i7E1Yx+3moxdqCWDnpbPWa8kX2lwaBW7zymtS99oeavPJBtVV7fxbDFkepEklSiVkWFdYvQqqc/VJVekdnWFfX4KwAAAO7AFdoE4YNv1Y6ps5R86pwkKePi9c1P4Qptgitd/Q52XjlSP0rJer3/WtfJbJbld+IV6K+UcwnZ5jMBAGRxtLqANbb6B17v88UrOVpMzJrKvVrq6IINlmehe75doIZP3W6ZaNFWLC2vHOk5oS2F8U65K2KiReRQrEKwkk+dy9Z5NvH4GRWrEGxzosU1Iz9V7fu7aN5tL2craEuFV9X8fq/IlJqhbn+8rso9W+jQjFWqP6S30pNTNadH1uzvDZ/ppyYj79LaUZ/r6IINittx2DL7qdHHS4vvf0sZSSkyeHio49cjVTWqVZ5meq83qKeOLd6c787AKWcv6N8Jvyise0SOWX5Nqema0+MFBdUor17zxuvAtOWSWbr542e0YugUnd9/QkY/b/WcPc7SQLr62pxY9q8O/bFSkhRQMUQ954zVb80fs9oI8fDyVK+541SsYojO7YrW4gfG5+t8AAD55yz3thI1K6p0o+pq9Oz/ZPTx0oHflmvXF3Pzda6Odt+7xNPPR7Xu6aSNY3/IsS6sRwslHDmVbQb6vDq+ZHOO7yQ0MlxBNSpo1bMf658XPlft+7vopufv0sK73lRAWBk1HtZfC+96U+mJyQqsEqruM97QtIjHtGbEp4p4fUC2GeC3fThDG17/VpJUtXdrtXjjQS28e8w10xVUq6JCmtbSmpFTJWW95FSudbju2Pq5MhKTdTEmTvP7vJLv8wUAAED+OVJ7oHLPljKbzDq6YEOuHSMKytHaA63eHqwNb3xnsyOHwcNDdR/qriN/rc/3+TpSe8C7uL+avXyfFt71pkrUyjnBfEDFEHX8eqQCq5TVsUWbtPvrv/J9vgCA7BzpHp8X/uVLKzSynlY88Z5lWUCFYCUeO235P/ForIpVCM7Xfh3t/p8XNe7ooGNLNivlOgaKtGXfj4tVvW877fxsto7Mz6pb1OjfXsGNq2vmLcOVeDRWTUfdowZDb9OaEZ+qTPPaqnZbG83r87JMaRkq06Ku2n30tP5s/0yu13Td6K8sAxw2GHqbGj/X39K58nrVvLOD1v9X37ja1TFGAAAAXOYK7QFX7h9gi632gCv2HbCl5t2dtPeKF7/oVwAAeeeodYH89Ad05djg1X0FS9SqqIsnzypy/MMq3ai6Us8lauOb3ytu+6F8na8tjhobLNe2gbyDilkGhCpRq5ISj51Wl19Gy7dUoE6t3aWNY37I8WIVsUEAAADbXKFN4MrxwettE7hifPDqun2JWhWVmZauTt++oGLlSilu1xGtf+0bpZ69QHwQAPLB0eoCTUfdo8q3RsonqJiWDno728BKTV+8V01G3Kn4vce0cewPSjwSK5+SAfLw9Mw2kUri0dMqViEkX9chbsdh7fl2obyD/LVu9NeSsu7Z3kHFdG5XtP59+1dV6NBYEa8/qD+WbJZ3cX+1mjhYi+4do+TYePmUCtStCybo9IY9WjPiU0Utejvb/e/AtL+1c+psSVLITTXVZspQ/dH2KavpsdVfPrBKqG5dMEHmTJP2/bxUe74p3L70zv6buNr1jqsCAADgLpy9/ucV4Ce/kCBV6trcMmDojqmzdHjm6nxdB1drE9S8u6MOTFsuc0Zmvq6DJG2ZNE017+qo5YMnWeKLYd0iFHJTTc3qOkKp5xJ188fPqPZ9nbXt/T9UtU8bBdUor7m9XpTZZFK1fu3UctzDWnzfuBzXVJL+HjJZqeeyBl5t+dbDqjuwu7Z9MMNqeuoO7KE6A7rKv3xprR72cbb+knUH9VCNOzvq4vEz2jz+J0t6143+Sp2+eUF17u8i76Bi2jL590LtVwEArsKR6gG2xg4JblJTIU1racEdr6vxsP7Z1uXsO3haIU1r5f9a5PJMS5K8ixfT3FtflE+pQPVd84H2/7xUSTFxavPeUG19b7pOrdkpg9FDt3z3gir3isz1PuodVEzzer8ss8kk7xIBilo4UceX/aukk3G5pqXZS/dpzzcLlHTibI514Y/eqtj1u3V268F8n2NeOFKdyOBpVKu3H9OqYR/lGtsr6vFXAAAA3IErtAlK1KqoxKOx6jb9NXkF+OvYoo369+1fZTbl7/mwK7UJcnsHO68crR+lrXq/reu0fPC76vDFcGUkpco7qJiWDpwoUzoTLQLA1RypLiBdf/9AW88X88KRYmKS9WeBOWKhx05b3qO+ViwtrxzuOaGVZ4HXeqfc2udcHRMtosgdmbdWmclpkqQz/+5XYJVQSVJYt+byDvRXlf8acx7enko8ejr3nXgY1PSle1U2oo5kMMgvuLjidx+55otm1fq2VZVbIzWvz+jCOyFJB6evkCSd339CpoxM+ZUpIa9Af5WoVVE3f/yMZTuvAF+VqFVRZ7ccyLGPgLCyavfhU/IvV1rmjEx5lwhQYFgZnd9/ItdjmtIzNLPzcHl4earFmIdU+77O2v7Rn4V6XgCAvHG0e5vB06jAsDKa32e0vEsUU7fprysh+pSOLdpYCGdrn/uelDXJ8M1Tn9XxZVt0ZN66HOtr3tVJ+35ckssnr1/C4Rid2bxPkhS7YY/CB98qSarQoYmKVw1V9z9et2xrNpmsDlJVvl1D1X2ou7wC/GTwMMinRMA1j+1frpQ6fTVSa0Z+agkCBzeqrhK1w/Rbk0eUlpCspi/eo8gJj2jF0PwHcQEAAFA4bnR7wC+khBo93Vfz+9pn4Jsb3R6oeXcnXTx+RjGrtttMV8u3Bin1fKLlAVNhsEd7oMXYQdo6ZbrVCaMSj53WzFuek6e/r9p9+KQq92hRoMH5AQDW2TPmZ0uNOzro6MKNSo1LuO595Je94oHXUvPODlr70pfX/fn8Or1xrxKPZg1GFLtxr+o+1F2SVKlrc5WsV1m95oyzbOtTIkBGX+9c91OtTxtV73ezjD5eMvp4Ffi7LNOirrwC/HR88eYc63KLMQIAAODanKk94Kr9A67FVnvA1foO2FKsYrDKRtTR8sGTLMvoVwAABedu/QFtcaS+ggZPo4Kb1NCmcT9qzYhPVaFjE3X69nlNixhyXQMk5oc9Y4Ml6oSpzaTHtXzwJMtEigajh8q2rKsF/V9X+sUUtZn8uBoPv8MyYYtEbBAAAKAgnKlN4KrxwYK0CVwtPphb3d5gNKp824aa02uUkmLidNOouxX51sNa9vA7xAcBoBDYqy6wcewP2jj2B5Vr20DNXr5Pc6Nekik9Q38/8Z5lcLw6D3bTLd+9oBk3P2N1P4UlIzlV0XPWSpJiN+y1XIeQZrUVWLmMOv/wYrbti1cvr8ToUzn2U7p+VTV86nb5lAyUOSNTQTUqyOjrrcyUtFyPa62//NltB/XrTY8qPSFJ/uVK6ZbvX1Rq3AUdnrWmkM88J2f8TRTVuCoAAADuwFnqfwZPozy8POXp6605PV9QQMUQ9Zg1Ruf3H9e5ndEFvg7O2Cbw9PNR1d6tNafXqAKf/5WOL/3XMvBp7MY9KlknTFLW4KrBjavr1r/GS8p6lm+VwaB6j/RSxU5N5eHpIa/i/jq9fq/N4+76Yq52fTFXJetVVrsPntSJ5VuUei5Rm976SUmnzklms8K6R+iWH17U9FZPKCMpRXUe6KZDM1Zq2/t/yLd0cXX9/TWd+Xe/Tv69tXAuBgC4OEcaO8To562Wbw3SskHvFNLZ5c+hP7KeE6bGJSghOlYBYWWUdv6iyrVpIL/gIMt2nsV8FVSjfK778CkZqNbvDlHxauVkzsiUT8kAlawTlmt/tnLtGiqgYojWvvhFjnUlaldS5Z4t7BLrskedqPGw/yl67lqd33dcARVDsq2z5/grAAAA7sCZ2gQGo1Gl6lfVwrvHyGAwqOM3z6v2A120+6v513PqOThjm6CoxmRypHEXbV0ng9FDjZ7up6UDJ+rUP7tUulF1dfrmef3Z8dkbOk4VADgzezwnLEj/QGvPFwvKHjGx6+0faCuWVhjs8ZzQ1rNAW673c66AiRaRw8XjZ+RXtqQMRg/LLKwBFYJ18fiZ69pfZmq65W9zpkkeRqMkyWAwaO1LX+rE8i3X3Ef4o7fKLzhIc3q+oMzUdDV/9QEZfb1sfqZKVCs1fra//ur/mlLOnL+utFuTmXq5MDabss7JICk1PjHbrLm23Pzx09o45gdFz/lHknTXzq9k9Ml9kIUrmdIztP/npWr19mAmWgQAO3G0e9vF42d08I+VMptMSo1L0PHFmxTStGahvShtj/uewdOom6c+o+TYc1r3cs7BEgMqlVFI05paNmhi/k7mGq5scJkzTfLwvPzdnli+VX8/PiXHZ4qFls7+f4VgtRwzULO7P6+E6FMqWbdytpeoc+NXtqS6/vqKtkyepujZlxty1f93s06u2q60C0mSpAO/LlPnn1++7vMDAABAwd3o9kDphtXkV7akoha9LUnyKRWoSl2ayad0cW1+66dCOCPbbnR7ILR1fYW2qKtKnZtalkUteUdLBkxQ3PZDkqQWbz6kYuWDteTBCZLZfJ1nlpM92gNlI+qobEQdNX/lfhl9vORdIkB9VkzRH22fyrZdRlKKDs1YpWq3t2WiRQAoIvaK+V1LzTs6aM3zn2Zblnj8jAIqhig5Nl5SVqwsL+nLK3s+B7MmNDJcRh9vnVhWeOd5LTl+E1fUDQ78tlybxv14zX2UiaijugN7aG6vUUo5e0GVujRTk+F3FChdte7qqP2/LZPZZMq23FqMEQAAANfmTO0BV+wfcC222gOu1nfgWmre0VFH5q9XWnyiZRn9CgCg4JypP6Arxgat9RW8ePyMkmLiFLN6hyTp+JLNMnp7KqBiiBIOx1zH2eWdvWKDQbUq6pbvXtDKZz9S7LrdluUXj59R3I7DSjt/UZJ06I+VavBkH8t6YoMAAAAF40xtAleMDxakTeBq8UFrdfuLx8/o5OodSorJGnDq4LS/1fmnrBgg8UEAKDh7Pys8uWKbvAL8VLJumM5uPWgZREmSdn81X81H3y+fkgFKPZcoU2am/EJKKPl0vCQpoFKILh63MoBTPpnSLg+4dPX9L37PMc2NejHHZ64eqMjDy1MdvnhO8/u+qrNbDsgrwE/37PtORm9Pq5OqXHJ1f/n0xGTLuqSTcTo0Y6XKtqh7QyZadKbfhFS046oAAAC4A2eq/6UnJuvA739LypqgMHb9HgU3rlEoEy06Y5ugyq2Rit9zVOf3Hruuc7YmW5z0qmux7f0/tPf7RdfcR7Xb26hc6waaf/topScmq+7AHirXpn6ejn9uZ7SSYuIU2ipc0XPWWuKCknRk3jo1HXWPgmqU19mtB1Xnwa6a3vpJSVLK2Qs6vniTQluFM9EiAOSRI40dcmjGKgVUCFa331+VJHkXLyZ5GOQdFKCVT31g6Tt4emPWgNyFGRuUrroW/z0nlMEgSZrTa1S29dZEjn9Ex5Zs0tKBWc/ubl0wQUaf3K9FuTb1VapBVfVb95Ekyb9cad3y/SitHjFVxcqVVkClMuq7+n1JWZPRRE4cLL8yJbXn2wUFOs9rsUedKDQyXMUqBKvuQ91kMBrlFeinfus+0qzuz9t1/BUAAAB34ExtgovHzyh67lpLnfLI3LUKaVar0CZadMY2QW7vYBcGRxp30dZ1So6Nl1/Zkjr1zy5J0tktB5R08qxK1a9KfBAA8sgezwkLo3/g1c8XC8oeMTFbzwITj59R8f8me7x0rEtzpdmKpaWevVCwCyH7PCe09SzQ1jvltj7n6mxMcwl3lXL2guK2HVL1vu0kSZV7ttTFk3GWl4/avPeEwrpH5PrZtIQkeRX3z9Nxjsxfp3qP9JLRL6tyb/TzVolaFf/bT7K8Ai/vxzuomJJjzykzNV1+ISVUpVekzX1XuTVSN428S3/d8do1J4j0Dy2lPityvtgkSWmJ2dNhy/kDJ5SemKwad3SwLAusEirvEgH/nVP2a+MdFKDEo7GSpGp928qnZKDVfRerGGy5TjIYVPnWSMXtOpKndAEACsYZ7m0H/1ipCh0aZx3T11uhrcIVtyP3TpDOcN8zGD108yfPKPVcolY/90mu29S8q6OOzFtneRHYmrDuEWrz3hO5rrv6O7Hl+LJ/Va5dQ5WsW9myLLhxjaz9JGY/V69Af5kyMpUUe06SVOehbjb37VemhLr+9oq2fTBDB35bnm1dwpFTKte6vjy8suZHr9i5meL3HM1TmgF7CI0M14CT0xQ+OCrHurKR9XT3nm/U/9/PsuUlSbr5k2c04OQ0dfn1Fav7bjysvwacnKbSjarnWFfv0V564Piv6v7nm/LOY5kNAEBeOEJ74NjiTfql4SBNixiiaRFDFD37H22ZNO26Jll0hvbAisen6Ldmgy3nK0kzOw6zdPKNeOMhBVYtpyUPTZApPcPqfiTnaA9cOs9pEUO0fPBknd97zDLJYmCVUBn+e7Di4eWpsO4RittV8BfeAACOcY/Pi3JtGsjg6aETy7N3XouetUa17+8iKeuhd2ircB2Zvy7XfTjD/T8vat7dUft/XZpjcsH8qPNgN9006u5c16UlJOX5WhxZsEHV+rZVsQrBWQsMBku84upr6h1UTBmJyUo9lygPL0/Vuq/zdadfkrwC/FS5V0vt+2lJtuW2YozAjUR8EADgDJy9PeBq/QPywlZ7wJX6DlyTwaAad3bQvp8WZ1tMvwIAyB9HqQtcb39AV4sN2uoreHbLAaUnJFvuu8GNa0gGgy6esP1uQG6cITYYVLOCOn8/SquHf5LjheaDf6zMut97Z93vK3RsYvlNEBuEIyE+CABwBs7eJnC1+GBB2wSuFB+0Vbc/PGu1ghtXl1eAnySpQqebFLfzsCTigwCQX45QFzB4GhV4xSBAwY1ryLd0cSVEn5LB6CHf4CDLuso9Wyj5zHnLhHpXxgdLN6ou/9BSilmzM9fjBDeuYbWtnZaY95hY7IY9Cggro3JtG1iWlQqvIg8vT6UlJsvo6225Dxl9vOTh5Wmp39Qd2N3mvm31l/crU8IyaKNnMV9VvKWpzm4/nKc0X83Rx0cpyG8iP+OqAEWJ2CAAwFk4e/3v4IyVqtChSdYxSwQouIn1SRbdoU1Q8+5OOd6vyc1No+5WnQdzj8+lJSTn6zdR+/4ultiowdOoUvWrZu3nqmvqHRSglLgLSk9MlmcxX9W4o73NfQf999uSpMDKZVWqflXF/zeBpH+5UpZ1ITfVlE+pQF04lDUeZEJ0rOU34enno9DW9RW/m7EJASA3jlAPsDV2SPzuI/q5/kDLup2fzdH+n5do5VMfSMqKDda85xYZPDzkXSJAVXu31qE/V1s9Vp8VU+QfWirH8qufadmSkZSimFXb1WBoH8syv7IlLfemq++j3iWK6eKxrIHfy7asq5L1sscBrrRp7I/67aZHLeebdPKsFt07VscWbtSebxfo18YPW9ad3rRPa4Z/cl2TLDpDnWjebS9rWvPHNC1iiOb1fknpCcmaFjFEqWcvFOr4K0BRIj4IAHAGzt4mOPjHClW4uZFkMMhg9FD5mxspbsdhq8dy9TaBtXewr+YM/Sht1fttXaeLx8/Iv2xJBdWsYElPYOWyunDgRJ7OCQDcjSPUBa63f6Ct54u5cYaYmK1ngdFz/lGlLs3kF1JCklT7/i46NGOVJNuxtNw4w3NCW88Cbb1Tbutzrs7T3gmAY1o9YqraTH5cDZ68XemJyVr59IeWdcGNqmnXF3Nz/dyOT2apy8+jlZmcqgV3vmHzGNs+mKFGz3qp15xxMpuzlm3/cIbi9x7TgWnL1WbKUIV1i9Dur+dr1+dz1f6zYeq9bJKSY+J0YoXtxku7D59Scmy8On010rLsr/6vWW4SV/IPLSVTRmau+zm5YpvqD45S1OJ3dHrDHq0ZaX12enOmSYvvG6eI1x9UvUd6yWD0UGrcBf09ZIrScrk260Z/qfafDVPa+SSdXLVNicdyzgZ8Scm6lXXT81mDOxg8DDq77ZDWvfSFzWsAACgcznBv2zl1liInPKrblk+S2ZzVCIievSbX/TjDfa9q79aq0rOl4nYcVtTCiZKkU+v3aO2oz7M2MBhUo397rXjyfZvXTJKKVy2n9ITcX5i++jtJsNEASDgco7+HTFbkhEfk6ecjD29PxW07pL8fn6JzO6MVv+eYei99VwnRp7RkwHgd+nOVbls2SannEq0OZHVJkxF3qliFYNUb1EP1BvWQJO38fK72/7JUu7+arxI1Kypq8dsypWcq+XS81oyYes3zBhxNxVuaqv2nzyr5dLwW9H89WxDGp2SAwrpF6MKhkyrXOlwBFUNslhFXazLiTjV6pp+OL92sJQMnKjM5rShOAQDgphylPWBLjf7t1WTkXfIuUUxh3SIUPjhKix94y9Ix9krO0B6wpUzz2qo3qIfi9x1TrznjJEkJR2O19KGJuW7vDO0BW8q1qa+6A3vInGmSwdOokyu2aeukade9PwDAZY5yj2/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- "text/plain": [ - "
    " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_aqc_tensor import generate_ansatz_from_circuit\n", - "\n", - "aqc_ansatz_num_trotter_steps = 5\n", - "\n", - "aqc_good_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=aqc_ansatz_num_trotter_steps),\n", - " time=aqc_evolution_time,\n", - ")\n", - "\n", - "aqc_ansatz, aqc_initial_parameters = generate_ansatz_from_circuit(\n", - " aqc_good_circuit, qubits_initially_zero=True\n", - ")\n", - "aqc_ansatz.draw(\"mpl\", fold=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6864f636-91f6-4ba4-86d4-da7521474977", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:53.737555Z", - "iopub.status.busy": "2025-03-28T17:48:53.736896Z", - "iopub.status.idle": "2025-03-28T17:48:53.757790Z", - "shell.execute_reply": "2025-03-28T17:48:53.757151Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Comparison circuit: depth 120\n", - "Target circuit: depth 270\n", - "Ansatz circuit: depth 23, with 515 parameters\n" - ] - } - ], - "source": [ - "print(f\"Comparison circuit: depth {comparison_circuit.depth()}\")\n", - "print(f\"Target circuit: depth {aqc_target_circuit.depth()}\")\n", - "print(f\"Ansatz circuit: depth {aqc_ansatz.depth()}, with {len(aqc_initial_parameters)} parameters\")" - ] - }, - { - "cell_type": "markdown", - "id": "79845e2d-d563-4776-9e59-3a5c9eeb5687", - "metadata": {}, - "source": [ - "### Choose settings for tensor network simulation\n", - "\n", - "Here, we use the Qiskit Aer matrix-product state (MPS) simulator, which is currently the only supported tensor network backend." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "da640da1-cd8a-45b7-b73d-9c02c09c658b", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:53.759849Z", - "iopub.status.busy": "2025-03-28T17:48:53.759640Z", - "iopub.status.idle": "2025-03-28T17:48:53.794235Z", - "shell.execute_reply": "2025-03-28T17:48:53.793413Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_aer import AerSimulator\n", - "\n", - "simulator_settings = AerSimulator(\n", - " method=\"matrix_product_state\",\n", - " matrix_product_state_max_bond_dimension=100,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "cf375cc7-f8c5-4534-b0ec-b2e9d6582840", - "metadata": {}, - "source": [ - "### Construct matrix-product state representation of the AQC target state\n", - "\n", - "Next, we build a matrix-product representation of the state to be approximated by AQC." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a8a6bf5e-31ec-48e2-b96e-ce3d7cc54ff7", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:53.797398Z", - "iopub.status.busy": "2025-03-28T17:48:53.796654Z", - "iopub.status.idle": "2025-03-28T17:48:54.069958Z", - "shell.execute_reply": "2025-03-28T17:48:54.069033Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_aqc_tensor.simulation import tensornetwork_from_circuit\n", - "\n", - "aqc_target_mps = tensornetwork_from_circuit(aqc_target_circuit, simulator_settings)" - ] - }, - { - "cell_type": "markdown", - "id": "88071374-04e4-47ee-8463-16ad0f5fa33b", - "metadata": {}, - "source": [ - "Note that because we chose a generous number of Trotter steps for the target state, it actually has less Trotter error than the comparison circuit. We can calculate the fidelity ($| \\langle \\psi_1 | \\psi_2 \\rangle |^2$) of the state prepared by the comparison circuit vs. the target state:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "65c3febb-8688-4806-aca6-99e9b860b377", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:54.073025Z", - "iopub.status.busy": "2025-03-28T17:48:54.072767Z", - "iopub.status.idle": "2025-03-28T17:48:54.163511Z", - "shell.execute_reply": "2025-03-28T17:48:54.162747Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9997111919739693" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_aqc_tensor.simulation import compute_overlap\n", - "\n", - "comparison_mps = tensornetwork_from_circuit(comparison_circuit, simulator_settings)\n", - "comparison_fidelity = abs(compute_overlap(comparison_mps, aqc_target_mps)) ** 2\n", - "comparison_fidelity" - ] - }, - { - "cell_type": "markdown", - "id": "5859794d-efbd-4e5b-a1d2-2b7add48b8e4", - "metadata": {}, - "source": [ - "### Optimize the parameters of the ansatz using MPS calculations\n", - "\n", - "Here, we minimize the simplest possible cost function, `MaximizeStateFidelity`, by using the L-BFGS optimizer from scipy.\n", - "\n", - "We choose a stopping point for the fidelity such that it will be above what the comparison circuit would have been, without using AQC. Once this is reached, the compressed circuit has less Trotter error _and_ less depth than the original circuit. Given more processing time, further optimization steps can be performed to bring the fidelity even higher." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "66dddd19-51d6-4bd4-a3df-2c9b457d4149", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:48:54.165816Z", - "iopub.status.busy": "2025-03-28T17:48:54.165599Z", - "iopub.status.idle": "2025-03-28T17:49:47.483360Z", - "shell.execute_reply": "2025-03-28T17:49:47.482608Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.95084365\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.98409893\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99142033\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99521405\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99566673\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99650054\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99683487\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99720426\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99761726\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99809073\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99838244\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99861841\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99874617\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99892696\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99908129\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99917737\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99925456\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99933134\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99947173\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99956469\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99964488\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99967419\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99968821\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.9997448\n", - "Done after 24 iterations.\n" - ] - } - ], - "source": [ - "from scipy.optimize import OptimizeResult, minimize\n", - "\n", - "from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity\n", - "\n", - "objective = MaximizeStateFidelity(aqc_target_mps, aqc_ansatz, simulator_settings)\n", - "\n", - "stopping_point = 1 - comparison_fidelity\n", - "\n", - "\n", - "def callback(intermediate_result: OptimizeResult):\n", - " print(f\"Intermediate result: Fidelity {1 - intermediate_result.fun:.8}\")\n", - " if intermediate_result.fun < stopping_point:\n", - " # Good enough for now\n", - " raise StopIteration\n", - "\n", - "\n", - "result = minimize(\n", - " objective.loss_function,\n", - " aqc_initial_parameters,\n", - " method=\"L-BFGS-B\",\n", - " jac=True,\n", - " options={\"maxiter\": 100},\n", - " callback=callback,\n", - ")\n", - "if result.status not in (\n", - " 0,\n", - " 1,\n", - " 99,\n", - "): # 0 => success; 1 => max iterations reached; 99 => early termination via StopIteration\n", - " raise RuntimeError(f\"Optimization failed: {result.message} (status={result.status})\")\n", - "\n", - "print(f\"Done after {result.nit} iterations.\")\n", - "aqc_final_parameters = result.x" - ] - }, - { - "cell_type": "markdown", - "id": "4f668909-529d-4f2c-bfe7-e7766cd33ce6", - "metadata": {}, - "source": [ - "### Construct the final circuit to pass to the transpiler" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "dbf60899-6f9d-4222-a499-e68bd5eabaf5", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:49:47.486007Z", - "iopub.status.busy": "2025-03-28T17:49:47.485504Z", - 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- "text/plain": [ - "
    " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final_circuit = aqc_ansatz.assign_parameters(aqc_final_parameters)\n", - "final_circuit.compose(subsequent_circuit, inplace=True)\n", - "final_circuit.draw(\"mpl\", fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "817839e7-17e7-4dcb-b620-a7711aeb2edf", - "metadata": {}, - "source": [ - "## Step 2: Transpile for execution on target hardware\n", - "\n", - "In Step 2 of a [Qiskit pattern](/guides/intro-to-patterns), we transpile this circuit and any desired observable(s) for execution on a target device. Here we are using a fake backend provided by `qiskit-ibm-runtime`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "783aea48-3d0a-4d7b-a5ed-e3f4888c47a8", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:49:50.150161Z", - "iopub.status.busy": "2025-03-28T17:49:50.149739Z", - "iopub.status.idle": "2025-03-28T17:49:50.853074Z", - "shell.execute_reply": "2025-03-28T17:49:50.852356Z" - } - }, - "outputs": [], - "source": [ - "from qiskit import transpile\n", - "from qiskit_ibm_runtime.fake_provider import FakeMelbourneV2\n", - "\n", - "backend = FakeMelbourneV2()\n", - "\n", - "isa_circuit = transpile(final_circuit, backend)\n", - "isa_observable = observable.apply_layout(isa_circuit.layout)" - ] - }, - { - "cell_type": "markdown", - "id": "5ff0be42-b45a-4af3-8317-3c1f87758771", - "metadata": {}, - "source": [ - "The resulting ISA circuit can then be sent for execution on the backend (step 3 of a [Qiskit pattern](/guides/intro-to-patterns))." - ] - }, - { - "cell_type": "markdown", - "id": "6600071d-e0b6-45cd-ae4e-0ffe882b0955", - "metadata": {}, - "source": [ - "## Step 3: Execute on quantum hardware" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "bd8039e4-08c4-4a7f-98ba-f8c11c8da795", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:49:50.855921Z", - "iopub.status.busy": "2025-03-28T17:49:50.855424Z", - "iopub.status.idle": "2025-03-28T17:49:52.989199Z", - "shell.execute_reply": "2025-03-28T17:49:52.988574Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import EstimatorV2 as Estimator\n", - "\n", - "estimator = Estimator(backend)\n", - "job = estimator.run([(isa_circuit, isa_observable)])\n", - "pub_result = job.result()[0]" - ] - }, - { - "cell_type": "markdown", - "id": "f56257c7-55c0-426a-835f-6e08b306536a", - "metadata": {}, - "source": [ - "## Step 4: Reconstruct\n", - "\n", - "Reconstruction is not necessary in our case. We can just look at the result." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "84a212df-8e89-4576-a407-903da48a0c5a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T17:49:52.991715Z", - "iopub.status.busy": "2025-03-28T17:49:52.991328Z", - "iopub.status.idle": "2025-03-28T17:49:52.995743Z", - "shell.execute_reply": "2025-03-28T17:49:52.995238Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "np.float64(0.054003906250000004)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pub_result.data.evs[()]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.21" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx deleted file mode 100644 index 3c67cf257c4..00000000000 --- a/docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx +++ /dev/null @@ -1,15 +0,0 @@ ---- -title: Approximate quantum compilation (AQC-Tensor) API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-aqc-tensor. ---- - - - -# AQC-Tensor Tutorials - -* [Tutorial 1](01-basic-workflow-ipynb): Show the basic end-to-end workflow of the AQC-Tensor addon. - -[![](/docs/images/api/qiskit-addon-aqc-tensor/tutorials_01_initial_state_aqc_26_0.avif)](01-initial-state-aqc) - -[Reducing circuit depth with the AQC-Tensor Qiskit addon](01-initial-state-aqc) - diff --git a/docs/addons/qiskit-addon-cutting/_toc.json b/docs/addons/qiskit-addon-cutting/_toc.json deleted file mode 100644 index 7721219d701..00000000000 --- a/docs/addons/qiskit-addon-cutting/_toc.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "title": "Circuit cutting", - "children": [ - { - "title": "Documentation Home", - "url": "/docs/addons/qiskit-addon-cutting" - }, - { - "title": "Installation Instructions", - "url": "/docs/addons/qiskit-addon-cutting/install" - }, - { - "title": "Tutorials", - "children": [ - { - "title": "Gate Cutting to Reduce Circuit Width", - "url": "/docs/addons/qiskit-addon-cutting/tutorials/01_gate_cutting_to_reduce_circuit_width" - }, - { - "title": "Gate Cutting to Reduce Circuit Depth", - "url": "/docs/addons/qiskit-addon-cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth" - }, - { - "title": "Wire Cutting Phrased as a Two-Qubit Move Instruction", - "url": "/docs/addons/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction" - }, - { - "title": "Automatically find cuts", - "url": "/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding" - } - ], - "url": "/docs/addons/qiskit-addon-cutting/tutorials/index" - }, - { - "title": "Explanatory Material", - "url": "/docs/addons/qiskit-addon-cutting/explanation/index" - }, - { - "title": "How-To Guides", - "children": [ - { - "title": "How to generate exact quasiprobability distributions from Sampler", - "url": "/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler" - }, - { - "title": "How to generate exact sampling coefficients", - "url": "/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_sampling_coefficients" - }, - { - "title": "How to perform a cutting workflow with multiple observables", - "url": "/docs/addons/qiskit-addon-cutting/how-tos/how_to_provide_multiple_observables" - }, - { - "title": "How to place wire cuts using a single-qubit CutWire instruction", - "url": "/docs/addons/qiskit-addon-cutting/how-tos/how_to_specify_cut_wires" - } - ], - "url": "/docs/addons/qiskit-addon-cutting/how-tos/index" - } - ], - "collapsed": true -} diff --git a/docs/addons/qiskit-addon-cutting/explanation/index.mdx b/docs/addons/qiskit-addon-cutting/explanation/index.mdx deleted file mode 100644 index 41b8a4b993a..00000000000 --- a/docs/addons/qiskit-addon-cutting/explanation/index.mdx +++ /dev/null @@ -1,146 +0,0 @@ ---- -title: Circuit cutting API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-cutting. ---- - - - -# Explanatory material - -## Overview of circuit cutting - -Circuit cutting is a technique to increase the size of circuits we can run on quantum hardware at the cost of an additional sampling overhead. A larger quantum circuit can be decomposed by cutting its gates and/or wires, resulting in smaller circuits which can be executed within the constraints of available quantum hardware. The results of these smaller circuits are combined to reconstruct the outcome of the original problem. Circuit cutting can also be used to engineer gates between distant qubits which would otherwise require a large swap overhead. - -## Key terms - -* subcircuits: The set of circuits resulting from cutting gates in a [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) and then separating the disconnected qubit subsets into smaller circuits. These circuits contain [`SingleQubitQPDGate`](qpd-single-qubit-qpd-gate#qiskit_addon_cutting.qpd.SingleQubitQPDGate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s and will be used to instantiate each unique subexperiment. -* subexperiments: A term used to describe the unique circuit samples associated with a subcircuit. These circuits have had their [`BaseQPDGate`](qpd-base-qpd-gate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate")s decomposed into local Qiskit gates and measurements. Subexperiments are the circuits sent to the backend for execution. -* decompose: We try to honor the Qiskit notion of “decompose” in the documentation and API, which loosely means transforming a gate into a less-abstracted representation. *Occasionally*, we may use the term “decompose” to refer to the act of inserting [`BaseQPDGate`](qpd-base-qpd-gate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate") instances into quantum circuits as “decomposing” a gate or wire; however, we try to use terms like “partition” and “cut” when referring to this to avoid ambiguity with Qiskit language. - -## Circuit cutting as a quasiprobability decomposition (QPD) - -Quasiprobability decomposition is a technique which can be used to simulate quantum circuit executions that go beyond the actual capabilities of current quantum hardware while using that same hardware. It forms the basis of many error mitigation techniques, which allow simulating a noise-free quantum computer using a noisy one. Circuit cutting techniques, which allow simulating a quantum circuit using fewer qubits than would otherwise be necessary, can also be phrased in terms of a quasiprobability decomposition. No matter the goal, the cost of the quasiprobability decomposition is an exponential overhead in the number of circuit executions which must be performed. In certain cases, this tradeoff is worth it, because it can allow the estimation of quantities that would otherwise be impossible on today’s hardware. - -There are two types of cuts: gate cuts and wire cuts. Gate cuts, also known as “space-like” cuts, exist when the cut goes through a gate operating on two (or more) qubits. Wire cuts, also known as “time-like” cuts, are direct cuts through a qubit wire, essentially a single-qubit identity gate that has been cut into two pieces. In this package, a wire cut is represented by introducing a new qubit into the circuit and moving remaining operations after the cut identity gate to the new qubit; see [Wire cutting phrased as a two-qubit Move operation](#wire-cutting-as-move), below. - -There are [three settings](https://research.ibm.com/blog/circuit-knitting-with-classical-communication) to consider for circuit cutting. The first is where only local operations (LO) \[i.e., local *quantum* operations] are available. The other settings introduce classical communication between the circuit executions, which is known in the quantum information literature as LOCC, for [local operations and classical communication](https://en.wikipedia.org/wiki/LOCC). The LOCC can be either near-time, one-directional communication between the circuit executions (the second setting), or real-time, bi-directional communication (the third setting). - -As mentioned above, the cost of any simulation based on quasiprobability distribution is an exponential sampling overhead. The sampling overhead is the factor by which the overall number of shots must increase for the quasiprobability decomposition to result in the same amount of error, $\epsilon$, as one would get by executing the original circuit. The overhead of a cut gate depends on which gate is cut; see the final appendix of \[[1](https://arxiv.org/abs/2205.00016)] for details. For instance, a single cut CNOT gate incurs a sampling overhead of 9 \[[2](https://arxiv.org/abs/1909.07534),[6](https://arxiv.org/abs/2312.11638)]. A circuit with $n$ wire cuts incurs a sampling overhead of O($16^n$) when classical communication is not available (LO setting); this is reduced to O($4^n$) when classical communication is available (LOCC setting) \[[4](https://arxiv.org/abs/2302.03366)]. However, wire cutting with classical communication (LOCC) is not yet supported by this package (see issue [#264](https://github.com/Qiskit/qiskit-addon-cutting/issues/264)). - -The QPD can be given explicitly as follows: - -$$ -\mathcal{U} = \sum_i a_i \mathcal{F}_i , -$$ - -where $\mathcal{U}$ is the channel implementing the desired operation, and each $a_i$ is a real coefficient corresponding to a quantum channel, $\mathcal{F}_i$, that is realizable on hardware. - -Note that because this is a sum of channels, *not* a sum of unitaries, expectation values can be evaluated efficiently \[[2](https://arxiv.org/abs/1909.07534)]. - -Results equivalent to original unitary channel can be obtained by a post-processing method, given the coefficients $a_i$ and the outcome of each experiment corresponding to the different local ($\mathcal{F}_i$) channels. This post-processing boosts the magnitude of each measurement outcome by $\sum_i \left| a_i \right|$, resulting in a sampling overhead of that quantity squared \[[6](https://arxiv.org/abs/2312.11638)]: - -$$ -\mathrm{Sampling\ overhead} = \left( \sum_i \lvert a_i \rvert \right)^2 . -$$ - -For more detailed information on the quasiprobability decomposition technique, refer to the paper, Error mitigation for short-depth quantum circuits \[[5](https://arxiv.org/abs/1612.02058)]. - -The essential idea of gate cutting is to replace a two-qubit gate with a linear combination of quantum channels \[Eq. [(1)](#equation-eq-qpd)] that, when recombined, will allow reconstruction of the result for physically measurable quantities like expectation values. Note that “global phase” is not something that can be physically measured, so we can disregard it when specifying quasiprobability decompositions. - - - -## An example: cutting a [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) - -As a basic and explicit example, let us consider the decomposition of a cut [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate). - -As shown in \[[2](https://arxiv.org/abs/1909.07534)], a quantum circuit which implements an [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) can be simulated by performing six subexperiments where the [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) in the original circuit has been replaced with only local (single-qubit) operations \[the $\mathcal{F}_i$‘s in Eq. [(1)](#equation-eq-qpd)]. The result is then reconstructed by combining the subexperiment results with certain coefficients \[the $a_i$‘s in Eq. [(1)](#equation-eq-qpd)], which can be either positive or negative. Given the $\theta$ parameter of the [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), the six subexperiments are as follows: - -1. With coefficient $a_1 = \cos^2 (\theta/2)$, do nothing ($I \otimes I$, where $I$ is the identity operation on a single qubit). -2. With coefficient $a_2 = \sin^2 (\theta/2)$, perform a [`ZGate`](/docs/api/qiskit/qiskit.circuit.library.ZGate) on each qubit ($Z \otimes Z$). -3. With coefficient $a_3 = -\sin(\theta)/2$, perform a projective measurement in the Z basis on the first qubit and an [`SGate`](/docs/api/qiskit/qiskit.circuit.library.SGate) gate on the second qubit (denote this as $M_z \otimes S$). If the result of the measurement is 1, flip the sign of that outcome’s contribution during reconstruction. -4. With coefficient $a_4 = \sin(\theta)/2$, perform a projective measurement in the Z basis on the first qubit and an [`SdgGate`](/docs/api/qiskit/qiskit.circuit.library.SdgGate) gate on the second qubit (denote this as $M_z \otimes S^\dagger$). If the result of the measurement is 1, flip the sign of that outcome’s contribution during reconstruction. -5. Same as term 3 ($a_5 = a_3$), but swap the qubits ($S \otimes M_z$). -6. Same as term 4 ($a_6 = a_4$), but swap the qubits ($S^\dagger \otimes M_z$). - -Equation [(1)](#equation-eq-qpd) for [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) can thus be written as a sum of the six terms listed above. The following plot shows the magnitude of each coeffient (negative coefficients are in orange) as a function of $\theta$. The square root of the optimal sampling overhead, denoted by $\gamma$, is given by the sum of the absolute coefficients. - -![../\_images/index-1.svg](/docs/images/api/qiskit-addon-cutting/index-1.svg) - -Let’s consider some special points in this plot: - -* When $\theta = 0$, the gate has no effect, and the sampling overhead is 1. (Because the overhead is multiplicative, this is equivalent to there being no overhead.) -* When $\theta = \pi$, the gate is equivalent to $Z \otimes Z$ up to a global phase, and the sampling overhead is again 1. -* The maximum sampling overhead of $3^2 = 9$ is reached at a ZZ Rotation of $\theta=\pi/2$. We call this point the [`CXGate`](/docs/api/qiskit/qiskit.circuit.library.CXGate) family because this rotation is equivalent to a CXGate up to (single-qubit) local unitary operations. This point is also equivalent, up to local unitary operations, to [`CZGate`](/docs/api/qiskit/qiskit.circuit.library.CZGate), [`CYGate`](/docs/api/qiskit/qiskit.circuit.library.CYGate), [`CHGate`](/docs/api/qiskit/qiskit.circuit.library.CHGate), and [`ECRGate`](/docs/api/qiskit/qiskit.circuit.library.ECRGate). -* The ZZ rotation at $\theta=\pi/4$ has sampling overhead of $3+2\sqrt{2} \approx 5.828$. We call this the [`CSGate`](/docs/api/qiskit/qiskit.circuit.library.CSGate) family because this rotation is equivalent to a CSGate up to (single-qubit) local operations. This family also includes [`CSdgGate`](/docs/api/qiskit/qiskit.circuit.library.CSdgGate) and [`CSXGate`](/docs/api/qiskit/qiskit.circuit.library.CSXGate). -* Likewise, [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), and [`RZXGate`](/docs/api/qiskit/qiskit.circuit.library.RZXGate) are all locally equivalent to [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate). The controlled rotations [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate), and [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) at an angle of $2\theta$ are locally equivalent to [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) at an angle of $\theta$. - -## More general cut two-qubit gates via the KAK decomposition - -We can formalize this notion of local unitary equivalence and expand it to all two-qubit gates using the KAK decomposition, given by - -$$ -U = (V_1 \otimes V_2) \exp \left[ i \left( \theta_x \, X \otimes X + \theta_y \, Y \otimes Y + \theta_z \, Z \otimes Z \right) \right] (V_3 \otimes V_4) , -$$ - -where $V_1$, $V_2$, $V_3$, and $V_4$ are local, single-qubit operations, and the two-qubit portion of the interaction is parametrized entirely by $\vec{\theta} = (\theta_x, \theta_y, \theta_z)$. By convention, we have chosen $\vec{\theta}$ to be in the “Weyl chamber” restricted by $\pi/4 \geq \theta_x \geq \theta_y \geq | \theta_z | \geq 0$ \[[6](https://arxiv.org/abs/2312.11638)]. For more information on the KAK decomposition, see Ref. \[[7](https://arxiv.org/abs/quant-ph/0209120)]. - -The code that generates subexperiments from the KAK decomposition currently follows Ref. \[[3](https://arxiv.org/abs/2006.11174)], which is now known to be non-optimal. A provably optimal method has been presented in Ref. \[[6](https://arxiv.org/abs/2312.11638)], but this newer method has not yet been implemented in this package (see issue [#531](https://github.com/Qiskit/qiskit-addon-cutting/issues/531)). - - - - - -## Wire cutting phrased as a two-qubit [`Move`](instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") operation - -A wire cut is represented fundamentally by this package as a two-qubit [`Move`](instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction, which is defined as a reset of the second qubit followed by a swap of both qubits. Equivalently, the operation is defined as transferring the state of the first qubit wire to the second qubit wire, while simultaneously discarding the state of the second qubit wire (the first qubit ends up in state $\lvert 0 \rangle$). - -We have chosen to represent wire cuts in this way primarily because it is consistent with the way one must treat wire cuts when acting on physical qubits: for instance, a wire cut might take the state of physical qubit $n$ and continue it as physical qubit $m$ after the cut. Our choice also has the benefit of allowing us to think of “instruction cutting” as a unified framework for considering both wire cuts and gate cuts in the same formalism, being that a wire cut is just a cut [`Move`](instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction. - -More information on this formalism is given in Sec. 3 of Ref. \[[4](https://arxiv.org/abs/2302.03366)] - -If you prefer to place cut wires abstractly on a single qubit wire, please see the [how-to guide on placing wire cuts using a single-qubit instruction](how-tos/how-to-specify-cut-wires), which explains how to use the [`cut_wires()`](qiskit-addon-cutting#qiskit_addon_cutting.cut_wires "qiskit_addon_cutting.cut_wires") function to convert a circuit with [`CutWire`](instructions-cut-wire#qiskit_addon_cutting.instructions.CutWire "qiskit_addon_cutting.instructions.CutWire") instructions to a circuit with [`Move`](instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move")s on additional qubits. - -## Sample weights in the Qiskit addon for circuit cutting - -In this package, the number of samples taken from the distribution is generally controlled by a `num_samples` argument, and each sample has an associated weight which is used during expectation value reconstruction. Each weight with absolute value above a threshold of 1 / `num_samples` will be evaluated exactly. The remaining low-probability elements – those in the tail of the distribution – will then be sampled, resulting in at most `num_samples` unique weights. Setting `num_samples` to infinity indicates that all weights should be generated rigorously, rather than by sampling from the distribution. - -Much of the circuit cutting literature describes a process where we sample from the distribution, take a single shot, then sample from the distribution again and repeat; however, this is not feasible in practice, so we instead perform all sampling upfront. For now, because of limitations in version 1 of the Qiskit primitives, we take a fixed number of shots for each considered subexperiment and send the subexperiments to the backend(s) in batches. During reconstruction, each subexperiment contributes to the final result with proportion equal to its weight. One must ensure the number of shots taken is sufficient for the heaviest weighted subexperiment. In the future, we plan to support passing an individual `shots` count with each subexperiment to Qiskit Runtime, so that each subexperiment will be run with a number of shots proportional to that subexperiment’s weight in the final result (see issue [#532](https://github.com/Qiskit/qiskit-addon-cutting/issues/532)). This per-experiment shots count is a new feature enabled by version 2 of the Qiskit primitives. - -## Sampling overhead reference table - -The below table provides the sampling overhead factor for a variety of two-qubit instructions, provided that only a single instruction is cut. - -| Instruction(s) | KAK decomposition angles | Sampling overhead factor | | | | | | | -| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------ | --------------------------- | --------- | -------------------- | -------------------- | ----------------- | -------------------- | -------------------------------------------------------------- | -| [`CSGate`](/docs/api/qiskit/qiskit.circuit.library.CSGate), [`CSdgGate`](/docs/api/qiskit/qiskit.circuit.library.CSdgGate), [`CSXGate`](/docs/api/qiskit/qiskit.circuit.library.CSXGate) | $(\pi/8, 0, 0)$ | $3+2\sqrt{2} \approx 5.828$ | | | | | | | -| [`CXGate`](/docs/api/qiskit/qiskit.circuit.library.CXGate), [`CYGate`](/docs/api/qiskit/qiskit.circuit.library.CYGate), [`CZGate`](/docs/api/qiskit/qiskit.circuit.library.CZGate), [`CHGate`](/docs/api/qiskit/qiskit.circuit.library.CHGate), [`ECRGate`](/docs/api/qiskit/qiskit.circuit.library.ECRGate) | $(\pi/4, 0, 0)$ | $3^2=9$ | | | | | | | -| [`iSwapGate`](/docs/api/qiskit/qiskit.circuit.library.iSwapGate), [`DCXGate`](/docs/api/qiskit/qiskit.circuit.library.DCXGate) | $(\pi/4, \pi/4, 0)$ | $7^2=49$ | | | | | | | -| [`SwapGate`](/docs/api/qiskit/qiskit.circuit.library.SwapGate) | $(\pi/4,\pi/4,\pi/4)$ | | | | | | | | -| [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), [`RZXGate`](/docs/api/qiskit/qiskit.circuit.library.RZXGate) | \$( | \theta/2 | , 0, 0)\$ | \$\left\[1 + 2 \left | \sin(\theta)\right | \right]^2\$ | | | -| [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate), [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) | \$( | \theta/4 | , 0, 0)\$ | \$\left\[1 + 2 \left | \sin(\theta/2)\right | \right]^2\$ | | | -| [`XXPlusYYGate`](/docs/api/qiskit/qiskit.circuit.library.XXPlusYYGate), [`XXMinusYYGate`](/docs/api/qiskit/qiskit.circuit.library.XXMinusYYGate) | \$( | \theta/4 | , | \theta/4 | ,0)\$ | \$\left\[1+4\left | \sin(\theta/2)\right | +2\sin^2(\theta/2)\right]^2$(independent of$\beta\$ parameter) | -| [`Move`](instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") (cut wire) without classical communication (i.e., in the LO setting) | not applicable | $4^2=16$ | | | | | | | - -## Current limitations - -* The workflow only allows taking the *expectation value* of observables with respect to a circuit. Limited support for reconstructing an output probability distribution may be added to a future version of this package (see issue [#259](https://github.com/Qiskit/qiskit-addon-cutting/issues/259)). -* Due to current code limitations, some of the generated subexperiments are redundant. This can result in more subexperiments than expected, particularly when using wire cutting. This is tracked by issue [#262](https://github.com/Qiskit/qiskit-addon-cutting/issues/262). - -## References - -This module is based on the theory described in the following papers: - -\[1] Christophe Piveteau, David Sutter, *Circuit knitting with classical communication*, [https://arxiv.org/abs/2205.00016](https://arxiv.org/abs/2205.00016) - -\[2] Kosuke Mitarai, Keisuke Fujii, *Constructing a virtual two-qubit gate by sampling single-qubit operations*, [https://arxiv.org/abs/1909.07534](https://arxiv.org/abs/1909.07534) - -\[3] Kosuke Mitarai, Keisuke Fujii, *Overhead for simulating a non-local channel with local channels by quasiprobability sampling*, [https://arxiv.org/abs/2006.11174](https://arxiv.org/abs/2006.11174) - -\[4] Lukas Brenner, Christophe Piveteau, David Sutter, *Optimal wire cutting with classical communication*, [https://arxiv.org/abs/2302.03366](https://arxiv.org/abs/2302.03366) - -\[5] K. Temme, S. Bravyi, and J. M. Gambetta, *Error mitigation for short-depth quantum circuits*, [https://arxiv.org/abs/1612.02058](https://arxiv.org/abs/1612.02058) - -\[6] Lukas Schmitt, Christophe Piveteau, David Sutter, *Cutting circuits with multiple two-qubit unitaries*, [https://arxiv.org/abs/2312.11638](https://arxiv.org/abs/2312.11638) - -\[7] Jun Zhang, Jiri Vala, K. Birgitta Whaley, Shankar Sastry, *A geometric theory of non-local two-qubit operations*, [https://arxiv.org/abs/quant-ph/0209120](https://arxiv.org/abs/quant-ph/0209120) - diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-quasi-dists-from-sampler.mdx b/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-quasi-dists-from-sampler.mdx deleted file mode 100644 index 5e75facf582..00000000000 --- a/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-quasi-dists-from-sampler.mdx +++ /dev/null @@ -1,76 +0,0 @@ - - -# How to generate exact quasiprobability distributions from Sampler - -This how-to guide is intended to show users how they can generate statevector-based quasiprobability distributions through the `qiskit.primitives.BaseSampler` interface. - -``` -[1]: -``` - -```python -from qiskit import QuantumCircuit -from qiskit.quantum_info import SparsePauliOp - -from qiskit_addon_cutting import ( - partition_problem, - generate_cutting_experiments, -) -``` - -Prepare inputs to `generate_cutting_experiments` - -``` -[2]: -``` - -```python -circuit = QuantumCircuit(2) -circuit.h(0) -circuit.cx(0, 1) -observable = SparsePauliOp(["ZZ"]) -partitioned_problem = partition_problem( - circuit=circuit, partition_labels="AB", observables=observable.paulis -) -subcircuits = partitioned_problem.subcircuits -subobservables = partitioned_problem.subobservables -``` - -Call `generate_cutting_experiments` - -``` -[3]: -``` - -```python -subexperiments, coefficients = generate_cutting_experiments( - circuits=subcircuits, - observables=subobservables, - num_samples=1000, -) -``` - -In order to calculate exact quasiprobability distributions for circuits with mid-circuit measurements, users will need to use the `ExactSampler` class from `qiskit_addon_cutting.utils.simulation`. The Qiskit Samplers do not support mid-circuit measurements in statevector mode. - -``` -[4]: -``` - -```python -from qiskit_addon_cutting.utils.simulation import ExactSampler - -exact_sampler = ExactSampler() -``` - -If `ExactSampler` is used, the quasiprobability distributions returned from `generate_cutting_experiments` will be exact and generated from the statevectors of the subexperiments. - -``` -[5]: -``` - -```python -results = { - label: exact_sampler.run(subexperiment).result() - for label, subexperiment in subexperiments.items() -} -``` diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.mdx b/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.mdx deleted file mode 100644 index 61bab34585d..00000000000 --- a/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.mdx +++ /dev/null @@ -1,248 +0,0 @@ - - -# How to generate exact sampling coefficients - -This how-to guide is intended to show users how they can generate exact sampling coefficients to be used in reconstructing the simulated expectation value of the original circuit. - -First, we set up a simple cutting problem following the [first tutorial](tutorials/01-gate-cutting-to-reduce-circuit-width). - -``` -[1]: -``` - -```python -import numpy as np -from qiskit.circuit.library import efficient_su2 -from qiskit.quantum_info import SparsePauliOp - -from qiskit_addon_cutting import ( - partition_problem, - generate_cutting_experiments, -) -``` - -``` -[2]: -``` - -```python -circuit = efficient_su2(4, entanglement="linear", reps=2) -circuit.assign_parameters([0.8] * len(circuit.parameters), inplace=True) -observable = SparsePauliOp(["ZZZZ"]) -circuit.draw("mpl", scale=0.8) -``` - -``` -[2]: -``` - -![../\_images/how-tos\_how\_to\_generate\_exact\_sampling\_coefficients\_2\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_generate_exact_sampling_coefficients_2_0.avif) - -Partition the circuit between qubits 1 and 2 by cutting 2 CNOT gates. - -``` -[3]: -``` - -```python -partitioned_problem = partition_problem( - circuit=circuit, partition_labels="AABB", observables=observable.paulis -) -subcircuits = partitioned_problem.subcircuits -bases = partitioned_problem.bases -subobservables = partitioned_problem.subobservables -subcircuits["A"].draw("mpl", scale=0.6) -``` - -``` -[3]: -``` - -![../\_images/how-tos\_how\_to\_generate\_exact\_sampling\_coefficients\_4\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_generate_exact_sampling_coefficients_4_0.avif) - -``` -[4]: -``` - -```python -subcircuits["B"].draw("mpl", scale=0.6) -``` - -``` -[4]: -``` - -![../\_images/how-tos\_how\_to\_generate\_exact\_sampling\_coefficients\_5\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_generate_exact_sampling_coefficients_5_0.avif) - - - -## Demonstrate how to obtain all weights exactly - -If you wish to calculate all weights exactly, no matter how small, you can achieve this by passing infinity (`np.inf`) to `num_samples`: - -``` -[5]: -``` - -```python -subexperiments, coefficients = generate_cutting_experiments( - circuits=subcircuits, - observables=subobservables, - num_samples=np.inf, -) -coefficients -``` - -``` -[5]: -``` - -```python -[(np.float64(0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(0.24999999999999992), ), - (np.float64(-0.24999999999999992), ), - (np.float64(0.24999999999999992), )] -``` - - - -## Demonstrate how to find the minimum `num_samples` needed to retrieve all exact weights for 2 CNOT cuts - -When `num_samples` is set to a finite number, each weight whose absolute value is above a threshold of 1 / `num_samples` will be evaluated exactly. The remaining weights – those in the tail of the distribution – will then be sampled from, resulting in at most `num_samples` unique weights. - -In the case of a CNOT gate – or any gate equivalent to it up to single-qubit unitaries – each of the six weights of the quasi-probability decomposition have the same magnitude, so each gets sampled with a probability of $1/6$: - -``` -[6]: -``` - -```python -print(f"Mapping probabilities for a CNOT decomposition: {bases[0].probabilities}") -``` - -```python -Mapping probabilities for a CNOT decomposition: [0.16666667 0.16666667 0.16666667 0.16666667 0.16666667 0.16666667] -``` - -In this example, we have cut two CNOT gates. Given that the probability of any given mapping in a CNOT decomposition is $1/6$, the probability of any given mapping in the *joint* distribution combining the two cut CNOT gates is $(1/6)^2$. Therefore, we need to take at least $6^2$ weights in order to retrieve all exact weights from `generate_cutting_experiments`. - -``` -[7]: -``` - -```python -from qiskit_addon_cutting.qpd import QPDBasis -from qiskit.circuit.library.standard_gates import CXGate - -qpd_basis_cx = QPDBasis.from_instruction(CXGate()) - - -def _min_nonzero(seq, /): - """Return the minimum value in a sequence, ignoring values near zero.""" - return min(x for x in seq if not np.isclose(x, 0)) - - -num_cx_cuts = 2 - -print( - f"Number of samples needed to retrieve exact weights: {1 / _min_nonzero(qpd_basis_cx.probabilities)**num_cx_cuts}" -) -``` - -```python -Number of samples needed to retrieve exact weights: 36.0 -``` - - - -### Observe the coefficient weights returned from `generate_cutting_experiments` are `WeightType.EXACT` - -Above, we determined 36 samples would trigger the coefficients to be returned as exact. Here we set `num_samples` to exactly 36 to test this. - -``` -[8]: -``` - -```python -subexperiments, coefficients = generate_cutting_experiments( - circuits=subcircuits, - observables=subobservables, - num_samples=36, -) -coefficients -``` - -``` -[8]: -``` - -```python -[(np.float64(0.25), ), - (np.float64(0.25), ), - (np.float64(0.25), ), - (np.float64(-0.25), ), - (np.float64(0.25), ), - (np.float64(-0.25), ), - (np.float64(0.25), ), - (np.float64(0.25), ), - (np.float64(0.25), ), - (np.float64(-0.25), ), - (np.float64(0.25), ), - (np.float64(-0.25), ), - (np.float64(0.25), ), - (np.float64(0.25), ), - (np.float64(0.25), ), - (np.float64(-0.25), ), - (np.float64(0.25), ), - (np.float64(-0.25), ), - (np.float64(-0.25), ), - (np.float64(-0.25), ), - (np.float64(-0.25), ), - (np.float64(0.25), ), - (np.float64(-0.25), ), - (np.float64(0.25), ), - (np.float64(0.25), ), - (np.float64(0.25), ), - (np.float64(0.25), ), - (np.float64(-0.25), ), - (np.float64(0.25), ), - (np.float64(-0.25), ), - (np.float64(-0.25), ), - (np.float64(-0.25), ), - (np.float64(-0.25), ), - (np.float64(0.25), ), - (np.float64(-0.25), ), - (np.float64(0.25), )] -``` diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables.mdx b/docs/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables.mdx deleted file mode 100644 index 327fe3093e3..00000000000 --- a/docs/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables.mdx +++ /dev/null @@ -1,207 +0,0 @@ - - -# How to perform a cutting workflow with multiple observables - - - -## Create a circuit to cut - -``` -[1]: -``` - -```python -from qiskit.circuit.library import EfficientSU2 - -qc = EfficientSU2(4, entanglement="linear", reps=2).decompose() -qc.assign_parameters([0.4] * len(qc.parameters), inplace=True) - -qc.draw("mpl", scale=0.8) -``` - -``` -[1]: -``` - -![../\_images/how-tos\_how\_to\_provide\_multiple\_observables\_2\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_provide_multiple_observables_2_0.avif) - - - -## Specify the observables of interest - -``` -[2]: -``` - -```python -from qiskit.quantum_info import Pauli, SparsePauliOp - -observables = [ - Pauli("-XZII"), - SparsePauliOp(["ZZII", "IZZI", "-IIZZ", "XIXI", "ZIZZ", "IXIX"]), -] -``` - - - -## Gather unique observable terms - -``` -[3]: -``` - -```python -from qiskit_addon_cutting.utils.observable_terms import gather_unique_observable_terms - -unique_observable_terms = gather_unique_observable_terms(observables) -``` - - - -## Perform cutting workflow - -``` -[4]: -``` - -```python -from qiskit_addon_cutting import partition_problem - -partitioned_problem = partition_problem( - circuit=qc, partition_labels="AABB", observables=unique_observable_terms -) -subcircuits = partitioned_problem.subcircuits -subobservables = partitioned_problem.subobservables -``` - -``` -[5]: -``` - -```python -import numpy as np -from qiskit_addon_cutting import generate_cutting_experiments - -subexperiments, coefficients = generate_cutting_experiments( - circuits=subcircuits, observables=subobservables, num_samples=np.inf -) -``` - -``` -[6]: -``` - -```python -from qiskit_ibm_runtime.fake_provider import FakeManilaV2 - -backend = FakeManilaV2() -``` - -``` -[7]: -``` - -```python -from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager - -# Transpile the subexperiments to ISA circuits -pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend) -isa_subexperiments = { - label: pass_manager.run(partition_subexpts) - for label, partition_subexpts in subexperiments.items() -} -``` - -``` -[8]: -``` - -```python -from qiskit_ibm_runtime import SamplerV2, Batch - -# Submit each partition's subexperiments as a single batch -with Batch(backend=backend) as batch: - sampler = SamplerV2(mode=batch) - jobs = { - label: sampler.run(subsystem_subexpts, shots=2**12) - for label, subsystem_subexpts in isa_subexperiments.items() - } -``` - -``` -[9]: -``` - -```python -# Retrieve results -results = {label: job.result() for label, job in jobs.items()} -``` - - - -## Reconstruct the expectation value of each unique term - -``` -[10]: -``` - -```python -from qiskit_addon_cutting import reconstruct_expectation_values - -# Get expectation values for each observable term -reconstructed_term_expvals = reconstruct_expectation_values( - results, - coefficients, - subobservables, # or unique_observable_terms if the circuit did not separate -) -``` - - - -## Reconstruct the expectation value of the original operators - -``` -[11]: -``` - -```python -from qiskit_addon_cutting.utils.observable_terms import ( - reconstruct_observable_expvals_from_terms, -) - -reconstructed_expvals = reconstruct_observable_expvals_from_terms( - observables, dict(zip(unique_observable_terms, reconstructed_term_expvals)) -) -``` - - - -## Compare the reconstructed expectation values with the exact expectation value from the original circuit and observables - -``` -[12]: -``` - -```python -from qiskit_aer.primitives import EstimatorV2 - -estimator = EstimatorV2() -exact_expvals = estimator.run([(qc, observables)]).result()[0].data.evs -print( - f"Reconstructed expectation values: {np.real(np.round(reconstructed_expvals, 8))}" -) -print(f"Exact expectation values: {np.round(exact_expvals, 8)}") -print( - f"Error in estimation: {np.real(np.round(reconstructed_expvals-exact_expvals, 8))}" -) -print( - f"Relative error in estimation: {np.real(np.round((reconstructed_expvals-exact_expvals) / exact_expvals, 8))}" -) -``` - -```python -Reconstructed expectation values: [-0.20063001 0.48694563] -Exact expectation values: [-0.17994235 0.56254612] -Error in estimation: [-0.02068766 -0.07560049] -Relative error in estimation: [ 0.11496824 -0.13438986] -``` diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires.mdx b/docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires.mdx deleted file mode 100644 index 4218e8ec18c..00000000000 --- a/docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires.mdx +++ /dev/null @@ -1,277 +0,0 @@ - - -# How to place wire cuts using a single-qubit `CutWire` instruction - -This how-to guide is intended to demonstrate how to place wire cuts using single-qubit `CutWire` instructions, which may at times be more convenient than specifying them as two-qubit `Move` instructions. - -``` -[1]: -``` - -```python -import numpy as np -from qiskit import QuantumCircuit -from qiskit.quantum_info import SparsePauliOp -from qiskit_aer.primitives import EstimatorV2, SamplerV2 - -from qiskit_addon_cutting import ( - partition_problem, - generate_cutting_experiments, - reconstruct_expectation_values, -) - -from qiskit_addon_cutting.instructions import CutWire -from qiskit_addon_cutting import cut_wires, expand_observables -``` - - - -## Prepare a circuit for cutting - -As in the [tutorial for wire cutting](tutorials/03-wire-cutting-via-move-instruction), we have used a circuit inspired by Fig. 1(a) of [arXiv:2302.03366v1](https://arxiv.org/abs/2302.03366v1). The cut locations are marked manually here with `CutWire` instructions. - -``` -[2]: -``` - -```python -qc_0 = QuantumCircuit(7) -for i in range(7): - qc_0.rx(np.pi / 4, i) -qc_0.cx(0, 3) -qc_0.cx(1, 3) -qc_0.cx(2, 3) -qc_0.append(CutWire(), [3]) -qc_0.cx(3, 4) -qc_0.cx(3, 5) -qc_0.cx(3, 6) -qc_0.append(CutWire(), [3]) -qc_0.cx(0, 3) -qc_0.cx(1, 3) -qc_0.cx(2, 3) -qc_0.draw("mpl") -``` - -``` -[2]: -``` - -![../\_images/how-tos\_how\_to\_specify\_cut\_wires\_3\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_3_0.avif) - - - -## Recover the uncut circuit - -`CutWire` instructions decompose to nothing (they are equivalent to the identity), so the uncut circuit can be recovered as follows: - -``` -[3]: -``` - -```python -qc_0.decompose("cut_wire").draw("mpl") -``` - -``` -[3]: -``` - -![../\_images/how-tos\_how\_to\_specify\_cut\_wires\_5\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_5_0.avif) - - - -## Specify an observable - -This observable has 7 qubits, just like the original circuit. - -``` -[4]: -``` - -```python -observable = SparsePauliOp(["ZIIIIII", "IIIZIII", "IIIIIIZ"]) -``` - - - -## Transform cuts to moves - -The next step is to call `cut_wires`, which transforms each `CutWire` into a `TwoQubitQPDGate` which wraps a `Move` instruction. An additional qubit is added to the circuit for each `CutWire` in the input circuit. - -Notice that, unlike in the [wire cutting tutorial](tutorials/03-wire-cutting-via-move-instruction), where `Move` operations were placed manually, this function does not result in the *re*-use of a qubit. Because any method for qubit re-use is based on heuristics, this function naively allocates an additional qubit for each cut. Users wishing to re-use qubits might wish to experiment with [qiskit-qubit-reuse](https://github.com/qiskit-community/qiskit-qubit-reuse). - -``` -[5]: -``` - -```python -qc_1 = cut_wires(qc_0) -qc_1.draw("mpl") -``` - -``` -[5]: -``` - -![../\_images/how-tos\_how\_to\_specify\_cut\_wires\_9\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_9_0.avif) - - - -## Update the observable terms to account for the extra qubit - -The transformed circuit contains additional qubits (one for each `CutWire` instruction), so the observables must be updated for the new circuit. This can be done using the `expand_observables` function. Since the observable coefficients are ignored until reconstruction of the final expectation value, the input and output types of the observables for `expand_observables` are `PauliList`s. - -The resulting observables have 9 qubits, just like the transformed circuit. - -``` -[6]: -``` - -```python -observable_expanded_paulis = expand_observables(observable.paulis, qc_0, qc_1) -observable_expanded_paulis -``` - -``` -[6]: -``` - -```python -PauliList(['ZIIIIIIII', 'IIIZIIIII', 'IIIIIIIIZ']) -``` - - - -## Separate the circuit and observables - -In this case, `partition_labels` need not be passed to `partition_problem`, as the labels can be determined automatically from the connectivity of the circuit. - -``` -[7]: -``` - -```python -partitioned_problem = partition_problem( - circuit=qc_1, observables=observable_expanded_paulis -) -subcircuits = partitioned_problem.subcircuits -subobservables = partitioned_problem.subobservables -``` - -From here forward, the cutting workflow is the same as usual, but the remaining steps are spelled out here explicitly so one can follow along with the results. - - - -## Visualize the decomposed problem - -Notice that once the circuits have been cut, some of the instructions are able to commute past each other. For instance, in subcircuit “A”, half of the second `Move` operation is actually the *first* operation on the final qubit. - -``` -[8]: -``` - -```python -subobservables -``` - -``` -[8]: -``` - -```python -{0: PauliList(['IIIII', 'ZIIII', 'IIIIZ']), - 1: PauliList(['ZIII', 'IIII', 'IIII'])} -``` - -``` -[9]: -``` - -```python -subcircuits[0].draw("mpl") -``` - -``` -[9]: -``` - -![../\_images/how-tos\_how\_to\_specify\_cut\_wires\_17\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_17_0.avif) - -``` -[10]: -``` - -```python -subcircuits[1].draw("mpl") -``` - -``` -[10]: -``` - -![../\_images/how-tos\_how\_to\_specify\_cut\_wires\_18\_0.png](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_18_0.avif) - - - -## Generate and run the cutting experiments; reconstruct and compare against uncut expectation values - -``` -[11]: -``` - -```python -subexperiments, coefficients = generate_cutting_experiments( - circuits=subcircuits, - observables=subobservables, - num_samples=np.inf, -) -``` - -``` -[12]: -``` - -```python -sampler = SamplerV2() -results = { - label: sampler.run(subexperiment, shots=2**12).result() - for label, subexperiment in subexperiments.items() -} -``` - -``` -[13]: -``` - -```python -reconstructed_expval_terms = reconstruct_expectation_values( - results, - coefficients, - subobservables, -) -reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs) -``` - -``` -[14]: -``` - -```python -estimator = EstimatorV2() -exact_expval = ( - estimator.run([(qc_0.decompose("cut_wire"), observable)]).result()[0].data.evs -) -print(f"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}") -print(f"Exact expectation value: {np.round(exact_expval, 8)}") -print(f"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}") -print( - f"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}" -) -``` - -```python -Reconstructed expectation value: 1.62048799 -Exact expectation value: 1.59099026 -Error in estimation: 0.02949773 -Relative error in estimation: 0.01854048 -``` diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb deleted file mode 100644 index 3b4ade2637b..00000000000 --- a/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb +++ /dev/null @@ -1,175 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f9e40036", - "metadata": {}, - "source": [ - "## How to generate exact quasiprobability distributions from Sampler\n", - "\n", - "This how-to guide is intended to show users how they can generate statevector-based quasiprobability distributions through the `qiskit.primitives.BaseSampler` interface." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "072055cb", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:50.653585Z", - "iopub.status.busy": "2025-03-28T16:50:50.653274Z", - "iopub.status.idle": "2025-03-28T16:50:51.088193Z", - "shell.execute_reply": "2025-03-28T16:50:51.087485Z" - } - }, - "outputs": [], - "source": [ - "from qiskit import QuantumCircuit\n", - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "from qiskit_addon_cutting import (\n", - " partition_problem,\n", - " generate_cutting_experiments,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "940334fd", - "metadata": {}, - "source": [ - "Prepare inputs to `generate_cutting_experiments`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "dc4af922", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.091193Z", - "iopub.status.busy": "2025-03-28T16:50:51.090943Z", - "iopub.status.idle": "2025-03-28T16:50:51.116996Z", - "shell.execute_reply": "2025-03-28T16:50:51.116317Z" - } - }, - "outputs": [], - "source": [ - "circuit = QuantumCircuit(2)\n", - "circuit.h(0)\n", - "circuit.cx(0, 1)\n", - "observable = SparsePauliOp([\"ZZ\"])\n", - "partitioned_problem = partition_problem(\n", - " circuit=circuit, partition_labels=\"AB\", observables=observable.paulis\n", - ")\n", - "subcircuits = partitioned_problem.subcircuits\n", - "subobservables = partitioned_problem.subobservables" - ] - }, - { - "cell_type": "markdown", - "id": "d6361a9d", - "metadata": {}, - "source": [ - "Call `generate_cutting_experiments`" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d095701f", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.119755Z", - "iopub.status.busy": "2025-03-28T16:50:51.119556Z", - "iopub.status.idle": "2025-03-28T16:50:51.128723Z", - "shell.execute_reply": "2025-03-28T16:50:51.127940Z" - } - }, - "outputs": [], - "source": [ - "subexperiments, coefficients = generate_cutting_experiments(\n", - " circuits=subcircuits,\n", - " observables=subobservables,\n", - " num_samples=1000,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "bc59b1be", - "metadata": {}, - "source": [ - "In order to calculate exact quasiprobability distributions for circuits with mid-circuit measurements, users will need to use the `ExactSampler` class from `qiskit_addon_cutting.utils.simulation`. The Qiskit Samplers do not support mid-circuit measurements in statevector mode." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7a74f709", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.131476Z", - "iopub.status.busy": "2025-03-28T16:50:51.130959Z", - "iopub.status.idle": "2025-03-28T16:50:51.135282Z", - "shell.execute_reply": "2025-03-28T16:50:51.134551Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting.utils.simulation import ExactSampler\n", - "\n", - "exact_sampler = ExactSampler()" - ] - }, - { - "cell_type": "markdown", - "id": "8d050fbe", - "metadata": {}, - "source": [ - "If `ExactSampler` is used, the quasiprobability distributions returned from `generate_cutting_experiments` will be exact and generated from the statevectors of the subexperiments." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7019d781", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.137659Z", - "iopub.status.busy": "2025-03-28T16:50:51.137468Z", - "iopub.status.idle": "2025-03-28T16:50:51.148731Z", - "shell.execute_reply": "2025-03-28T16:50:51.147960Z" - } - }, - "outputs": [], - "source": [ - "results = {\n", - " label: exact_sampler.run(subexperiment).result()\n", - " for label, subexperiment in subexperiments.items()\n", - "}" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.21" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb deleted file mode 100644 index df5b5676e19..00000000000 --- a/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb +++ /dev/null @@ -1,406 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ccbf48d3", - "metadata": {}, - "source": [ - "## How to generate exact sampling coefficients\n", - "\n", - "This how-to guide is intended to show users how they can generate exact sampling coefficients to be used in reconstructing the simulated expectation value of the original circuit.\n", - "\n", - "First, we set up a simple cutting problem following the [first tutorial](../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "dc54656b", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:52.873918Z", - "iopub.status.busy": "2025-03-28T16:50:52.873283Z", - "iopub.status.idle": "2025-03-28T16:50:53.390761Z", - "shell.execute_reply": "2025-03-28T16:50:53.390002Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from qiskit.circuit.library import efficient_su2\n", - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "from qiskit_addon_cutting import (\n", - " partition_problem,\n", - " generate_cutting_experiments,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "dd147239", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:53.393546Z", - "iopub.status.busy": "2025-03-28T16:50:53.393230Z", - "iopub.status.idle": "2025-03-28T16:50:54.346876Z", - "shell.execute_reply": "2025-03-28T16:50:54.346177Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "circuit = efficient_su2(4, entanglement=\"linear\", reps=2)\n", - "circuit.assign_parameters([0.8] * len(circuit.parameters), inplace=True)\n", - "observable = SparsePauliOp([\"ZZZZ\"])\n", - "circuit.draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "id": "0bce456c", - "metadata": {}, - "source": [ - "Partition the circuit between qubits 1 and 2 by cutting 2 CNOT gates." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d4ccf5b8", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:54.349628Z", - "iopub.status.busy": "2025-03-28T16:50:54.349194Z", - "iopub.status.idle": "2025-03-28T16:50:54.584073Z", - "shell.execute_reply": "2025-03-28T16:50:54.583316Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "partitioned_problem = partition_problem(\n", - " circuit=circuit, partition_labels=\"AABB\", observables=observable.paulis\n", - ")\n", - "subcircuits = partitioned_problem.subcircuits\n", - "bases = partitioned_problem.bases\n", - "subobservables = partitioned_problem.subobservables\n", - "subcircuits[\"A\"].draw(\"mpl\", scale=0.6)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "44956cbb", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:54.586659Z", - "iopub.status.busy": "2025-03-28T16:50:54.586391Z", - "iopub.status.idle": "2025-03-28T16:50:54.811013Z", - "shell.execute_reply": "2025-03-28T16:50:54.810276Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[\"B\"].draw(\"mpl\", scale=0.6)" - ] - }, - { - "cell_type": "markdown", - "id": "fce55187", - "metadata": {}, - "source": [ - "### Demonstrate how to obtain all weights exactly\n", - "\n", - "If you wish to calculate all weights exactly, no matter how small, you can achieve this by passing infinity (`np.inf`) to `num_samples`:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8c56282f", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:54.813264Z", - "iopub.status.busy": "2025-03-28T16:50:54.813049Z", - "iopub.status.idle": "2025-03-28T16:50:54.867519Z", - "shell.execute_reply": "2025-03-28T16:50:54.866771Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[(np.float64(0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), ),\n", - " (np.float64(-0.24999999999999992), ),\n", - " (np.float64(0.24999999999999992), )]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subexperiments, coefficients = generate_cutting_experiments(\n", - " circuits=subcircuits,\n", - " observables=subobservables,\n", - " num_samples=np.inf,\n", - ")\n", - "coefficients" - ] - }, - { - "cell_type": "markdown", - "id": "b5fc6579", - "metadata": {}, - "source": [ - "### Demonstrate how to find the minimum `num_samples` needed to retrieve all exact weights for 2 CNOT cuts\n", - "\n", - "When `num_samples` is set to a finite number, each weight whose absolute value is above a threshold of 1 / `num_samples` will be evaluated exactly. The remaining weights -- those in the tail of the distribution -- will then be sampled from, resulting in at most `num_samples` unique weights.\n", - "\n", - "In the case of a CNOT gate -- or any gate equivalent to it up to single-qubit unitaries -- each of the six weights of the quasi-probability decomposition have the same magnitude, so each gets sampled with a probability of $1/6$:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "78539fcc", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:54.870158Z", - "iopub.status.busy": "2025-03-28T16:50:54.869670Z", - "iopub.status.idle": "2025-03-28T16:50:54.874200Z", - "shell.execute_reply": "2025-03-28T16:50:54.873577Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mapping probabilities for a CNOT decomposition: [0.16666667 0.16666667 0.16666667 0.16666667 0.16666667 0.16666667]\n" - ] - } - ], - "source": [ - "print(f\"Mapping probabilities for a CNOT decomposition: {bases[0].probabilities}\")" - ] - }, - { - "cell_type": "markdown", - "id": "5770cf75", - "metadata": {}, - "source": [ - "In this example, we have cut two CNOT gates. Given that the probability of any given mapping in a CNOT decomposition is $1/6$, the probability of any given mapping in the _joint_ distribution combining the two cut CNOT gates is $(1/6)^2$. Therefore, we need to take at least $6^2$ weights in order to retrieve all exact weights from `generate_cutting_experiments`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f07a6cc3", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:54.876590Z", - "iopub.status.busy": "2025-03-28T16:50:54.876398Z", - "iopub.status.idle": "2025-03-28T16:50:54.881758Z", - "shell.execute_reply": "2025-03-28T16:50:54.881069Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of samples needed to retrieve exact weights: 36.0\n" - ] - } - ], - "source": [ - "from qiskit_addon_cutting.qpd import QPDBasis\n", - "from qiskit.circuit.library.standard_gates import CXGate\n", - "\n", - "qpd_basis_cx = QPDBasis.from_instruction(CXGate())\n", - "\n", - "\n", - "def _min_nonzero(seq, /):\n", - " \"\"\"Return the minimum value in a sequence, ignoring values near zero.\"\"\"\n", - " return min(x for x in seq if not np.isclose(x, 0))\n", - "\n", - "\n", - "num_cx_cuts = 2\n", - "\n", - "print(\n", - " f\"Number of samples needed to retrieve exact weights: {1 / _min_nonzero(qpd_basis_cx.probabilities)**num_cx_cuts}\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "deb6ad1e", - "metadata": {}, - "source": [ - "#### Observe the coefficient weights returned from `generate_cutting_experiments` are `WeightType.EXACT`\n", - "\n", - "Above, we determined 36 samples would trigger the coefficients to be returned as exact. Here we set `num_samples` to exactly 36 to test this." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "43d32869", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:54.884011Z", - "iopub.status.busy": "2025-03-28T16:50:54.883576Z", - "iopub.status.idle": "2025-03-28T16:50:54.942752Z", - "shell.execute_reply": "2025-03-28T16:50:54.942054Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[(np.float64(0.25), ),\n", - " (np.float64(0.25), ),\n", - " (np.float64(0.25), ),\n", - " (np.float64(-0.25), ),\n", - " (np.float64(0.25), ),\n", - " (np.float64(-0.25), ),\n", - " (np.float64(0.25), ),\n", - " (np.float64(0.25), ),\n", - " (np.float64(0.25), ),\n", - " (np.float64(-0.25), ),\n", - " (np.float64(0.25), ),\n", - " (np.float64(-0.25), ),\n", - " (np.float64(0.25), ),\n", - " (np.float64(0.25), ),\n", - " (np.float64(0.25), ),\n", - " (np.float64(-0.25), ),\n", - " (np.float64(0.25), ),\n", - " (np.float64(-0.25), ),\n", - " 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"codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.21" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how_to_provide_multiple_observables.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how_to_provide_multiple_observables.ipynb deleted file mode 100644 index f35df28146d..00000000000 --- a/docs/addons/qiskit-addon-cutting/how-tos/how_to_provide_multiple_observables.ipynb +++ /dev/null @@ -1,383 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a42230b5-41d9-465c-904a-7d431cc72595", - "metadata": {}, - "source": [ - "# How to perform a cutting workflow with multiple observables" - ] - }, - { - "cell_type": "markdown", - "id": "6902602f-1392-4321-9cbe-5eb0921909b0", - "metadata": {}, - "source": [ - "### Create a circuit to cut" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5defa9fb-e928-4fcb-9ae3-bed8e8f2527b", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:57.096887Z", - "iopub.status.busy": "2025-03-28T16:50:57.096668Z", - "iopub.status.idle": "2025-03-28T16:50:58.481013Z", - "shell.execute_reply": "2025-03-28T16:50:58.480360Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit.circuit.library import EfficientSU2\n", - "\n", - "qc = EfficientSU2(4, entanglement=\"linear\", reps=2).decompose()\n", - "qc.assign_parameters([0.4] * len(qc.parameters), inplace=True)\n", - "\n", - "qc.draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "id": "99344052-a7cc-4857-a77e-0db02003cb7d", - "metadata": {}, - "source": [ - "### Specify the observables of interest" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "42c2d640-7cce-41c3-9683-920449215c9d", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:58.484791Z", - "iopub.status.busy": "2025-03-28T16:50:58.483701Z", - "iopub.status.idle": "2025-03-28T16:50:58.489908Z", - "shell.execute_reply": "2025-03-28T16:50:58.489261Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.quantum_info import Pauli, SparsePauliOp\n", - "\n", - "observables = [\n", - " Pauli(\"-XZII\"),\n", - " SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"]),\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "466343c4-35ce-496e-a887-5a126e0a6b40", - "metadata": {}, - "source": [ - "### Gather unique observable terms" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "defb33f4-820a-44aa-89ec-4f7eb13a49f5", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:58.492460Z", - "iopub.status.busy": "2025-03-28T16:50:58.491798Z", - "iopub.status.idle": "2025-03-28T16:50:58.561027Z", - "shell.execute_reply": "2025-03-28T16:50:58.560336Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting.utils.observable_terms import gather_unique_observable_terms\n", - "\n", - "unique_observable_terms = gather_unique_observable_terms(observables)" - ] - }, - { - "cell_type": "markdown", - "id": "81c1c9e1-e8a4-4a5c-80f3-35d00298459f", - "metadata": {}, - "source": [ - "### Perform cutting workflow" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9830f036-904e-4900-98b1-7991da2915d0", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:58.568971Z", - "iopub.status.busy": "2025-03-28T16:50:58.568447Z", - "iopub.status.idle": "2025-03-28T16:50:58.579358Z", - "shell.execute_reply": "2025-03-28T16:50:58.578737Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import partition_problem\n", - "\n", - "partitioned_problem = partition_problem(\n", - " circuit=qc, partition_labels=\"AABB\", observables=unique_observable_terms\n", - ")\n", - "subcircuits = partitioned_problem.subcircuits\n", - "subobservables = partitioned_problem.subobservables" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "77577d6f-4acc-468b-962c-81a09cc31fcd", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:58.581924Z", - "iopub.status.busy": "2025-03-28T16:50:58.581453Z", - "iopub.status.idle": "2025-03-28T16:50:58.854801Z", - "shell.execute_reply": "2025-03-28T16:50:58.850920Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from qiskit_addon_cutting import generate_cutting_experiments\n", - "\n", - "subexperiments, coefficients = generate_cutting_experiments(\n", - " circuits=subcircuits, observables=subobservables, num_samples=np.inf\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "37253e37-51f9-4aa4-9f5e-d8a032d639ce", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:58.859697Z", - "iopub.status.busy": "2025-03-28T16:50:58.859332Z", - "iopub.status.idle": "2025-03-28T16:50:59.766313Z", - "shell.execute_reply": "2025-03-28T16:50:59.765302Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", - "\n", - "backend = FakeManilaV2()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f15e198e-7f0b-4507-a73f-e4aab2f286ed", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:59.773825Z", - "iopub.status.busy": "2025-03-28T16:50:59.773001Z", - "iopub.status.idle": "2025-03-28T16:51:07.997499Z", - "shell.execute_reply": "2025-03-28T16:51:07.996531Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", - "\n", - "# Transpile the subexperiments to ISA circuits\n", - "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", - "isa_subexperiments = {\n", - " label: pass_manager.run(partition_subexpts)\n", - " for label, partition_subexpts in subexperiments.items()\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5383d427-3285-43b1-8564-f451e1b4287e", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:08.002734Z", - "iopub.status.busy": "2025-03-28T16:51:08.001594Z", - "iopub.status.idle": "2025-03-28T16:51:08.452499Z", - "shell.execute_reply": "2025-03-28T16:51:08.444876Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import SamplerV2, Batch\n", - "\n", - "# Submit each partition's subexperiments as a single batch\n", - "with Batch(backend=backend) as batch:\n", - " sampler = SamplerV2(mode=batch)\n", - " jobs = {\n", - " label: sampler.run(subsystem_subexpts, shots=2**12)\n", - " for label, subsystem_subexpts in isa_subexperiments.items()\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "154f4cf0-c45d-41b2-8091-b23a47b30c22", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:08.472013Z", - "iopub.status.busy": "2025-03-28T16:51:08.471699Z", - "iopub.status.idle": "2025-03-28T16:51:34.412339Z", - "shell.execute_reply": "2025-03-28T16:51:34.411608Z" - } - }, - "outputs": [], - "source": [ - "# Retrieve results\n", - "results = {label: job.result() for label, job in jobs.items()}" - ] - }, - { - "cell_type": "markdown", - "id": "35c57f33-a128-4b0f-9a23-705cfecae993", - "metadata": {}, - "source": [ - "### Reconstruct the expectation value of each unique term" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "9f5e64a7-a334-4278-9c61-e78bbbc36368", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:34.415031Z", - "iopub.status.busy": "2025-03-28T16:51:34.414827Z", - "iopub.status.idle": "2025-03-28T16:51:39.000491Z", - "shell.execute_reply": "2025-03-28T16:51:38.999738Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import reconstruct_expectation_values\n", - "\n", - "# Get expectation values for each observable term\n", - "reconstructed_term_expvals = reconstruct_expectation_values(\n", - " results,\n", - " coefficients,\n", - " subobservables, # or unique_observable_terms if the circuit did not separate\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "792853df-d4f9-43d1-9f7d-50e38768ceb8", - "metadata": {}, - "source": [ - "### Reconstruct the expectation value of the original operators" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b134b767-ce53-4438-866f-a25952cb985b", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:39.003144Z", - "iopub.status.busy": "2025-03-28T16:51:39.002785Z", - "iopub.status.idle": "2025-03-28T16:51:39.006758Z", - "shell.execute_reply": "2025-03-28T16:51:39.006291Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting.utils.observable_terms import (\n", - " reconstruct_observable_expvals_from_terms,\n", - ")\n", - "\n", - "reconstructed_expvals = reconstruct_observable_expvals_from_terms(\n", - " observables, dict(zip(unique_observable_terms, reconstructed_term_expvals))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "fd71a9ef-37cd-4687-9fe1-9b4ea3264f91", - "metadata": {}, - "source": [ - "### Compare the reconstructed expectation values with the exact expectation value from the original circuit and observables" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "986e5ce8-64a5-464a-9c43-45ccf1a65b2d", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:39.008795Z", - "iopub.status.busy": "2025-03-28T16:51:39.008594Z", - "iopub.status.idle": "2025-03-28T16:51:39.023519Z", - "shell.execute_reply": "2025-03-28T16:51:39.022762Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstructed expectation values: [-0.20063001 0.48694563]\n", - "Exact expectation values: [-0.17994235 0.56254612]\n", - "Error in estimation: [-0.02068766 -0.07560049]\n", - "Relative error in estimation: [ 0.11496824 -0.13438986]\n" - ] - } - ], - "source": [ - "from qiskit_aer.primitives import EstimatorV2\n", - "\n", - "estimator = EstimatorV2()\n", - "exact_expvals = estimator.run([(qc, observables)]).result()[0].data.evs\n", - "print(\n", - " f\"Reconstructed expectation values: {np.real(np.round(reconstructed_expvals, 8))}\"\n", - ")\n", - "print(f\"Exact expectation values: {np.round(exact_expvals, 8)}\")\n", - "print(\n", - " f\"Error in estimation: {np.real(np.round(reconstructed_expvals-exact_expvals, 8))}\"\n", - ")\n", - "print(\n", - " f\"Relative error in estimation: {np.real(np.round((reconstructed_expvals-exact_expvals) / exact_expvals, 8))}\"\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.21" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how_to_specify_cut_wires.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how_to_specify_cut_wires.ipynb deleted file mode 100644 index 227213bf544..00000000000 --- a/docs/addons/qiskit-addon-cutting/how-tos/how_to_specify_cut_wires.ipynb +++ /dev/null @@ -1,511 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b04b8bc7", - "metadata": {}, - "source": [ - "# How to place wire cuts using a single-qubit `CutWire` instruction\n", - "\n", - "This how-to guide is intended to demonstrate how to place wire cuts using single-qubit `CutWire` instructions, which may at times be more convenient than specifying them as two-qubit `Move` instructions." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "1aa871cb", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:41.715866Z", - "iopub.status.busy": "2025-03-28T16:51:41.715618Z", - "iopub.status.idle": "2025-03-28T16:51:42.298891Z", - "shell.execute_reply": "2025-03-28T16:51:42.298034Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from qiskit import QuantumCircuit\n", - "from qiskit.quantum_info import SparsePauliOp\n", - "from qiskit_aer.primitives import EstimatorV2, SamplerV2\n", - "\n", - "from qiskit_addon_cutting import (\n", - " partition_problem,\n", - " generate_cutting_experiments,\n", - " reconstruct_expectation_values,\n", - ")\n", - "\n", - "from qiskit_addon_cutting.instructions import CutWire\n", - "from qiskit_addon_cutting import cut_wires, expand_observables" - ] - }, - { - "cell_type": "markdown", - "id": "06ae4c39", - "metadata": {}, - "source": [ - "### Prepare a circuit for cutting\n", - "\n", - "As in the [tutorial for wire cutting](../tutorials/03_wire_cutting_via_move_instruction.ipynb), we have used a circuit inspired by Fig. 1(a) of [arXiv:2302.03366v1](https://arxiv.org/abs/2302.03366v1). The cut locations are marked manually here with `CutWire` instructions." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "0ae22516", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:42.302264Z", - "iopub.status.busy": "2025-03-28T16:51:42.301906Z", - "iopub.status.idle": "2025-03-28T16:51:43.264296Z", - "shell.execute_reply": "2025-03-28T16:51:43.263022Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qc_0 = QuantumCircuit(7)\n", - "for i in range(7):\n", - " qc_0.rx(np.pi / 4, i)\n", - "qc_0.cx(0, 3)\n", - "qc_0.cx(1, 3)\n", - "qc_0.cx(2, 3)\n", - "qc_0.append(CutWire(), [3])\n", - "qc_0.cx(3, 4)\n", - "qc_0.cx(3, 5)\n", - "qc_0.cx(3, 6)\n", - "qc_0.append(CutWire(), [3])\n", - "qc_0.cx(0, 3)\n", - "qc_0.cx(1, 3)\n", - "qc_0.cx(2, 3)\n", - "qc_0.draw(\"mpl\")" - ] - }, - { - "cell_type": "markdown", - "id": "7ee07a81", - "metadata": {}, - "source": [ - "### Recover the uncut circuit\n", - "\n", - "`CutWire` instructions decompose to nothing (they are equivalent to the identity), so the uncut circuit can be recovered as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "631286a6", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:43.267141Z", - "iopub.status.busy": "2025-03-28T16:51:43.266751Z", - "iopub.status.idle": "2025-03-28T16:51:43.652437Z", - "shell.execute_reply": "2025-03-28T16:51:43.651574Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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IJIyBnoiIiIhIwuRiF0CN02q1UFVWi11Gs8ntbSEIgthlEBEREbU4DPRmSlVZjQ2dnha7jGaLzlgPawc7scsgIiIianE45IaIiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMI4D70F8eoXghFb5jVYVlteidLMfGRsOoC0tb9Cq9aIVB0RERERGQMDvQXK3JKAnPhkQBBg7+mKzhMGove8KWgV2B5H5qwWuzwiIiIiMiAGegtUlJKFzM0J9T+nr9uNxxKWIuipoUievxHVRaUiVkdEREREhsQx9C2AqrIahckXIchkcOnQVuxyiIiIiMiAGOhbCOeOdUG++toNkSshIiIiIkNqEYFeqVQiJiYGnTt3hp2dHXx9fTFr1iyUl5fjueeegyAIWLFihdhlGozc3ga2bs6wdXeBa1c/9Pn4ebiHBqAw+SJKM/PFLo+IiIiIDMjix9CfOnUKI0eOhEKhgKOjI7p164a8vDwsW7YMGRkZKC4uBgD06NFD3EINqGfMJPSMmdRgWXbcURx780uRKiIxabVaHD1TgFWx53H2zxJU1ajh3soW44Z1xJQxgWjtYit2iUREZGSX8srw303p2Hc0D2UVtXC0l+OBnm3x8hPBCOrYSuzy6B5ZdKBXKpUYPXo0FAoFZs+ejblz58LZ2RkA8Omnn+L//u//IJfLIQgCwsLCRK7WcNK/24PsX45AZi1H665+6D59LBy93aGurqnfRmYjx+g9C5G1NQFnlm6pX/7Akumw83TFvuiPxCidDOxC9nU8+X/7kZxWdNu6gyev4u3lSZgzOQxzX+4JmUwQoUIiIjKm8opavPjBIWzcmQmNRttgXVKqEkvWp2L0QD9889EAdvBImEUPuZk5cyZycnIwY8YMLFq0qD7MA0BMTAzCw8OhUqnQsWNHuLi4iFipYZVmKpCfkILc+JM4u3Ibfps8Hx49OqHfghfrt9HUqHBw5nKEzhyH1t06AAD8RkTCZ3gEDr22UqzSyYDOZZSg3zO/NBrmb6qsUuP91Scxbd5BaLXaJrcjIiLpqahU4aGXdmFDXMZtYf5Wv/xxGQ9O3oGS0moTVkeGZLGBPi0tDbGxsfDw8MAnn3zS6Da9evUCAISHh9cvu/kFoHfv3rC1tYUgSL/XsjApHRmbDsB/bH94RnSpX150JhOpq7bjwWX/hIO3G/otfAnH3voSlVdLRKyWDKGmVo1R0/eg+HrzTs5rt17Aqtg0I1dFRESmNHP+ERw+VdCsbVMzruHZt/4wckVkLBYb6Ddu3AiNRoPo6Gg4OTk1uo29vT2AhoH+zz//xObNm+Hl5YXIyEiT1GoKpxdvgkalRs85ExsuX7IZGrUaUXsXQnHoLLK2HRKpQjKkLfuykZ2n24xGn3939o49OEREJB1Xiyrx7S9/6tRmx4ErOJ91zTgFkVFZbKCPj48HAAwePLjJbXJycgA0DPQDBgxAfn4+tm/fjmHDhhm3SBMqy1Yga9shtBsQhjZ9guuXa1VqFCamw869Ff6M3S9ihWRI//lB9972jCtl2HM41wjVEBGRqa3dko5alUbndrxaK00We1PspUuXAAAdOnRodL1KpcKhQ3W90bcGepnM8N9xIiIioFAodGpjrZVhLnobtI4zSzfDf2x/9JwzEbsffw8A0KZPMDpPHIy0tb+i9/tTsX34HKirau68o0YEBQahVtD9xGFq+a6vAbJWyFfkw8fHR+xyjEILAXmt3wUE3X+XJ0x9G60qfzNCVURE5qElfA4AgNLpacAmUOd2q77dh80rJhihIrobLy8vJCUl6dXWYgN9eXk5AKCysrLR9bGxsVAqlXB2doa/v79Ra1EoFMjN1a3n00awAnR8qKviSCrWeT/e5PrrF3Pxrc//htzIHezwwJLpOPHRBpz/ZjdGbn0f9735FBLnrtPtwADy8vNQo1Xr3M7knNWADNCo1Tr/nUiGzBZw0++L6Y3yGtzIt9D3hYgIaBmfAwAQoAVsdG9Wq5ZZ9vtioSw20Ht5eaGkpATJycno169fg3X5+fmYM2cOACAsLMzoN756eXnp3MZaKwOM3OEd+d6zuHG5AOfX7QIAHJy1AlH7FuHyzmO4elS3S27tvNtJo4feygoaADIrK3i3by92OUahhYA8rUavHnpnR2u4WOj7QkQEtIzPAQBQWgP6zFljbaVGGwt+X8yZPnnxJosN9MOGDUNaWhoWLFiA4cOHIygoCACQmJiIZ555BkqlEoBpHiilz+WT2ooqbOj0tBGqqdN+SE/4R/XHtqGz65eVXbqKEx9tQP/F07F9yGyoKpt/Krhw8QKsHeyMUapB+QzbiNyCCnh7eSPnbI7Y5RjNsBd24rdjeTq327ZhPgb3bmeEioiIzENL+Rz47JsUvP7ZcZ3bvfrCCCx49V0jVETGZLE3xcbExMDd3R1XrlxBSEgIQkNDERgYiN69eyMgIABDhgwB0HD8fEuSG38S33edjPJcZYPl59ftwpZ+M3QK82R+XpkYfPeN/qarfysMivQ2QjVERGRqU8YEws7WSqc2ggC8OKGrkSoiY7LYQO/j44OEhASMGjUKdnZ2yM7OhpubG1avXo24uDhcuHABQMsN9GTZogb5oau/bo/y/r9/GH/4GRERmYa7qx1eGN/l7hveYsJD/gjwsZwHbbYkFjvkBgCCg4OxY8eO25bfuHED2dnZkMlk6N69uwiVERmXXC5D3H8ewoNT4pBXUHHX7Wc/2x1TxgSZoDIiIjKVRbN7Iz37erOmJO7d3RNr5z1ogqrIGCy2h/5OUlNTodVqERgYCAcHh9vWb9q0CZs2bcK5c+ca/KzvVEJEYgjwccHR9aMxtE/TY+LdWtni8zl9sHC2YadIJSIi8dlYW+GX5cMx48lusLVpfPiN3ErAs6M7I/7LkXBysDZxhWQoFt1D35SUlBQATQ+3mTBhQqM/T548GevWrTNqbUSG5OvlhH1rRuJcRglW/3Qeq386j+paDexsrLDqnfsx8eEA2Nu1yNMAEVGLYGNtheVv9sN7L/fE1z9fxL6judh/PB81Kg2cHa1xftt4tGvjKHaZdI9aZA/93QK9Vqtt9D+GeZKqbp1aY+kb/eDRum4mIndXW0wZE8QwT0TUQri72uH1KaHY9cUIeLrVfRa4OFozzFsIBvoWpsOovug7/4UGyzpPHIwp+ZvgNyJSpKqIiIiISF8tsnsuPj5e7BJE4/dIH2T89Hv9z04+ngiKHoaCpHTxiiIiIiIivbXIQG/JbFwcMGb/YljZ2aAiTwmZrTWc/doiY9MfOPLGGrSN7IKDs1bUbSwIuP+zl3Hs32sROXeyuIUTERERkV4Y6C1MTWkFMrcmoLa8CmcWb0K7QeEImzkOh1//Au0GhqMgMR1alRoAEPLiaBQknkfRmUyRqyYiIiIifbXIMfSWzq27P4pTsgAA7mGdUHy27s9+IyJxaWfdY6Bdu/iiw6g+OL1ks2h1EhEREdG9Yw+9BXIL6Vgf4t3DAnBldyIAoN2gHkj6YD0AoG2fYDj5tsH4w8sBAPaerui38CXYt2mN9G/3iFM4EREREemMgd7COHi5AVotKhTFAAC34A44s3QzPHoG4vrFXKgqqgAA6d/uaRDcR2yeh3NrduDyrkRR6iYiIiIi/TDQWxi37v71vfMAUFNajq6TH0Z1cRku7zouYmVEREREZAwM9BYmZ98J5Ow7Uf/zjpFvAADG/L4Yu8fPbbLdrjusIyIiIiLzxUDfQmwb9KrYJRARERGREXCWGyIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJ4xh6MyW3t0V0xnqxy2g2ub2t2CUQERERtUgM9GZKEARYO9iJXQYRERERmTkOuSEiIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgmTi10ANU6r1UJVWS12Gc0mt7eFIAhil0FERETU4jDQmylVZTU2dHpa7DKaLTpjPawd7MQug4iIiKjF4ZAbIiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJ4zz0FsSrXwhGbJnXYFlteSVKM/ORsekA0tb+Cq1aI1J1RERERGQMDPQWKHNLAnLikwFBgL2nKzpPGIje86agVWB7HJmzWuzyiIiIiMiAGOgtUFFKFjI3J9T/nL5uNx5LWIqgp4Yief5GVBeVilgdERERERkSx9C3AKrKahQmX4Qgk8GlQ1uxyyEiIiIiA2KgbyGcO9YF+eprN0SuhIiIiIgMiUNuLJDc3ga2bs71Y+i7PPsQ3EMDUJh8EaWZ+WKXR0REREQG1CICvVKpxKeffootW7YgJycHnp6eGDduHD7++GPMnDkTX331FZYvX44ZM2aIXapB9IyZhJ4xkxosy447imNvfilSRUTiu5x/A7sP5eBaWQ0c7OWI6OaB3qGeEARB7NKIiMgEyitq8csfl5FXWAGZICDAxxkjH/CFtbX0B6xYfKA/deoURo4cCYVCAUdHR3Tr1g15eXlYtmwZMjIyUFxcDADo0aOHuIUaUPp3e5D9yxHIrOVo3dUP3aePhaO3O9TVNfXbyGzkGL1nIbK2JuDM0i31yx9YMh12nq7YF/2RGKUTGdzR0wWY/9Vp/PLHFWg02gbrenZ1x6zoEDwb1ZnBnojIQuUVlGP+V2fwzfaLKL1R22Cdl4c9XhjfBXOmhMLZ0UakCu+d9L+S3IFSqcTo0aOhUCgwe/Zs5OfnIzk5GQqFAgsWLEBcXBwSExMhCALCwsLELtdgSjMVyE9IQW78SZxduQ2/TZ4Pjx6d0G/Bi/XbaGpUODhzOUJnjkPrbh0AAH4jIuEzPAKHXlspVulEBvV9XAYenLID2/Zfvi3MA8DJ80WY8s4BTJt3sNH1REQkbWmZ19An+hcs//7cbWEeABTKSnyw+hQGTI1DQVGlCBUahkUH+pkzZyInJwczZszAokWL4OzsXL8uJiYG4eHhUKlU6NixI1xcXESs1LgKk9KRsekA/Mf2h2dEl/rlRWcykbpqOx5c9k84eLuh38KXcOytL1F5tUTEaokMY++RXDz77z+gUt89qH+55QLeXJpogqqIiMhUrhZVYsTLu5Bztfyu2546X4xH/7kHVdUqE1RmeBYb6NPS0hAbGwsPDw988sknjW7Tq1cvAEB4eHj9sk2bNmH8+PHo0KEDHBwc0LVrV7z99tu4cUPas8OcXrwJGpUaPedMbLh8yWZo1GpE7V0IxaGzyNp2SKQKiQxHq9UiZvFxqJsR5m/67NuzyFHc/aRPRETSsHR9Ki7nN/+8nnhWidhdWUasyHgsNtBv3LgRGo0G0dHRcHJyanQbe3t7AA0D/aJFi2BlZYWPP/4YO3fuxMsvv4xVq1ZhxIgR0Gg0JqndGMqyFcjadgjtBoShTZ/g+uValRqFiemwc2+FP2P3i1ghkeEcPVOAU+eLdWqjVmuxZnO6kSoiIiJTqq5R48stup/TV8amGaEa47PYQB8fHw8AGDx4cJPb5OTkAGgY6H/55Rf8+OOPiI6OxsCBAzFr1iysWLEChw4dwsGDB41btJGdWVrXG39rL32bPsHoPHEw0tb+it7vT4WVnXRvCCG6aePOTP3a7cowcCVERCSG+GN5KCyp0rnd8bOFyMwpNUJFxmWxs9xcunQJANChQ4dG16tUKhw6VDe85NZA7+npedu2ERERAIDc3Fy9aomIiIBCodCpjbVWhrnorVMbxZFUrPN+vMn11y/m4luf/4V5uYMdHlgyHSc+2oDz3+zGyK3v4743n0Li3HU6HRcAggKDUCuY/xWMfNfXAFkr5Cvy4ePjI3Y5JtdSXn+x4+OAbajO7f7MKrDo94WIWs558E5awntQbhMOOI3Tq22fBx6CrSrHwBXdnZeXF5KSkvRqa7GBvry8bsxUZWXjdyzHxsZCqVTC2dkZ/v7+d9zX/v11Q1GCg4PvuF1TFAqFzl8GbAQroK1eh2u2yPeexY3LBTi/bhcA4OCsFYjatwiXdx7D1aO6XXLKy89DjVZtjDINy1kNyACNWq33FzRJaymv36ccsNW9mVZTa9nvCxG1nPPgnbSE98DVB2h8xPVdKQsUQKW03heLDfReXl4oKSlBcnIy+vXr12Bdfn4+5syZAwAICwu74/zTubm5eOeddzBixAi956r38vLSuY21VgYYscO7/ZCe8I/qj21DZ9cvK7t0FSc+2oD+i6dj+5DZUFVWN3t/7bzbSaOH3soKGgAyKyt4t28vdjkm11Jef6ltJcr0aGejLYGnBb8vRNRyzoN30hLegxorLQoBQKsFdHnOiFYNL3cbWGlN/77okxdvErRarUVOvjxz5kwsX74cvr6+2LdvH4KCggAAiYmJeOaZZ5CZmYna2lpMnz4dK1asaHQfN27cwKBBg6BQKJCYmAhvb2+T1V9bUYUNnZ422fHuVXTGelg72Ildxl35DNuI3IIKtG/jgJx9T4pdjsm1lNd/RXEDHUf8qPPc8l+9/yCmjg0yUlVEZA5aynnwTlrCe6DVatHziZ9xOl23CRKeeNgfsQuHGKkq47HYm2JjYmLg7u6OK1euICQkBKGhoQgMDETv3r0REBCAIUPq/rJuHT9/q8rKSowePRpZWVnYs2ePScM8Ed0bXy8nRA3y06lNaxcbTHw4wEgVERGRKQmCgFcm6j5UWp825sBiA72Pjw8SEhIwatQo2NnZITs7G25ubli9ejXi4uJw4cIFAI0H+traWjz++ONISkrCzp070a1bN1OXT0T3aPkb/dC+jUOztpXJBHz70UA42FvsKEQiohbnH2ODMGqAb7O3nxUdgoER0uzAtehPr+DgYOzYseO25Tdu3EB2djZkMhm6d+/eYN3Nuet/++03/Prrr+jdW7eZZojIPPh4OeL3r0Zh5Cu78eflpqcgs7O1wvfzB+HRgbr16BMRkXmTy2X4adEQRL/5O7b+dumO2772bHcsfE26mc+iA31TUlNTodVqERQUBAeHhj1406dPx08//YQ33ngDDg4OOHr0aP26Tp06NTqtJRGZp85+Ljj902P4YVcm/vPDOSSnFdWvk8kEvDOtB14Y3wXt2zqKWCURERmLvZ0cmz4bivjjeVgZm4Zt+y83uL9q6thAvPxEMCK7SzvftchAn5KSAqDx4TY7d+4EAMyfPx/z589vsO7rr7/GlClTjF4fERmOg70c/3gsCFPHBqKguAph47egoLgKXu52eO+V+8Quj4iIjEwmEzCsb3sM69se18tq0GX0T7haXAVvT3t89f4AscszCIsdQ38ndwr02dnZ0Gq1jf5nCWG+w6i+6Dv/hQbLOk8cjCn5m+A3IlKkqoiMTxAEtHW3h7VcVv8zERG1LK2cbSD/63NAZkGfAwz0LYzfI31wedfx+p+dfDwRFD0MBUnpIlZFRERERPpqkUNu4uPjxS7BaGxcHDBm/2JY2dmgIk8Jma01nP3aImPTHzjyxhq0jeyCg7P+mndfEHD/Zy/j2L/XInLuZHELJyIiIiK9tMhAb8lqSiuQuTUBteVVOLN4E9oNCkfYzHE4/PoXaDcwHAWJ6dCq1ACAkBdHoyDxPIrOZIpcNRERERHpq0UOubF0bt39UZySBQBwD+uE4rN1f/YbEYlLO+uG27h28UWHUX1weslm0eokIiIionvHHnoL5BbSsT7Eu4cF4MruRABAu0E9kPTBegBA2z7BcPJtg/GHlwMA7D1d0W/hS7Bv0xrp3+4Rp3AiIiIi0hkDvYVx8HIDtFpUKIoBAG7BHXBm6WZ49AzE9Yu5UFVUAQDSv93TILiP2DwP59bswOVdiaLUTURERET6YaC3MG7d/et75wGgprQcXSc/jOrisgaz2xARERGRZWCgtzA5+04gZ9+J+p93jHwDADDm98XYPX5uk+123WEdEREREZkvBvoWYtugV8UugYiIiIiMgLPcEBERERFJGAM9EREREZGEMdATEREREUkYAz0RERERkYTxplgzJbe3RXTGerHLaDa5va3YJRARERG1SAz0ZkoQBFg72IldBhERERGZOQ65ISIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjC52AVQ47RaLVSV1WKX0Wxye1sIgiB2GUREREQtDgO9mVJVVmNDp6fFLqPZojPWw9rBTuwyiIiIiFocDrkhIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMM5yQxZLq9UiOa0ISalKnDinxPmsaygorgIAKEuq8NrCo+jVzQN9w9qgk6+LyNUaR15BOQ6fKsCJNCVOnS+uf/2FJVV49q0/EBHigV7dPNAn1BNyueV9v6+qVuHomUKcOKdEUqoSlxU3UFBcCQBQXqvCu/85gV7dPHB/eBt4utmLXC0RGUN2bhmOnC7AiXNKnL7Q8Dz43NwERHTzqD8XymSWN/3yjYra+teflKpEXmFF/Xmw6Fo1Plh9sv486OpiK3K1hqfVanE6vRiJZwtxIq0IaZnXGvwOzJp/BBEhdVkgsEMrkavVn6DVarViF0G3q62o4rSVerpWWo1vtl/EytjzuHDperPaDIzwwisTg/HYkI6wtpZ2sNVotNh7JBcrY9Ow48AVaDR3/yfevo0Dpj3eFS+M7wJvTwcTVGlcWTll+OKnNKzdegFF1+7+PAdruQzjh3XE9EnB6N+zLZ+pQCRxKpUGv/xxGStj07DvaF6z2nTydcZLE4IxdWwg3F3N4/PsXpzLKMGqH8/jm+0XUVZee9ft7e2s8NTITnhlYjDu6+ZhggqNq/RGDb7b8SdWxqbhXMa1ZrXp37MtXnkiGOOHd4StjZVxCzQwBnozpU+g9+oXghFb5jXcT3klSjPzkbHpANLW/gqtWmPIMuuZQ6DXarX476Z0zPn8eLNOXo0J8HHGV+8/iIER3gauzjRSLhRj6rsJOHFOqVd7a7kMb78Qjree7yHJLzYVlSq8vTwJSzekQt8z2+BIb6yd9yD8fZwNWxwRmcSR01cx9Z0EpGc3r0Pn7xzs5PhkVgRmPNlNkj32JaXVePXTY/hm+0W99/HY0A5Y+fb98PKQXgePVqvFt9v/xL8+PYprZTV67cPP2xFr5j6Ah+73MXB1xsNAb6buJdBnbklATnwyIAiw93RF5wkD0Tq4A9LX78WROauNUq/Ygf6K4gb+8W5Cs3ti7mbGk93w6auRsLeTxqg0jUaL+WtP471VJ1GruvcvbT26uuG7jwaie6CbAaozjcOnrmLyvw/gz8ul97wvR3s5Fr7WGy890ZW99UQSUV2jxr+Xn8Dn351t1pXJu3nwvrb45sOBkvpyvzPhCp6fdxB5BRX3vC+3VrZY+fb9mDgiwACVmYZCWYHn3zuIuANXDLK/F8Z3wedz+sDJwdog+zMmBnozdS+BPnHet0j9Ynv9crm9LR5LWAoHbzf8EPY8qovuPfD8nZiB/nzWNQyftgs5V8sNut+BEV7Yvmw4XJxsDLpfQ6ut1WDKOwfw/a8ZBt2vs6M1diwfjgESuFrxc3w2Js7Zj5paw16B+tfTIfh8Th+GeiIzV15Ri3Gv/YY9h3MNut+27vbY/cXDCO/ibtD9GsOXm9Mx7f2Del+dbMonsyLwxnPhht2pEWTmlGLYC7uQlVtm0P32CfXErysfhlsr876/QHrX1ElnqspqFCZfhCCTwaVDW7HLMaiMK6UY8vxOg4d5APgjSYFHZ+xBRaXK4Ps2FLVag8n//sPgYR4Ayspr8cj0PThy+qrB921IcQcuY8Lr8QYP8wCwZH0qZi86BvZ7EJmv6ho1Hnt1n8HDPABcLarEsGm7kJZ5zeD7NqR12y7ghXmGD/MA8ObSJCz8+ozhd2xAVxQ3MPi5Xw0e5gHgWEohRry8C2Xl+g3fMRUG+hbCuWNdkK++dkPkSgynukaNsbP2Ib/w3i8tNiUh+Sr+Of+I0fZ/rz79OgUbd2Yabf/llSqMmbkPhX/NiGBuMq6U4onX90OlMl7gXvxdKr7d/qfR9k9E92bO58ex94hhhls2RllShaiZe822cycptRDPv3fQqMeIWZyI3YdyjHoMfalUGox79Tdczjd8x95NiWeVeGGecd/je8VAb4Hk9jawdXOGrbsLXLv6oc/Hz8M9NACFyRdRmpkvdnkG8/4XJ3H2zxKd2iRujMKVvZOQuDGq2W2+2noBuw6a34ks9c8SvLcqWac2+rz+wpIqs/xSo9Fo8Y93E1BRpduHrD7vwaxPjyLXCFeBiOje/JGUj+Xfn9OpjT7ngD8vl+Lt5Um6lmd01TVqTPn3AajVunVq6PMePP/eQVzX8yZTY1q4LgVJqbpNBKHP64/dlYXNe7N0Lc9kWkSgVyqViImJQefOnWFnZwdfX1/MmjUL5eXleO655yAIAlasWCF2mQbTM2YSnkz9Gk+e/Qpj93+O4KkjkB13FPFTFohdmsGcOl+EBXpcAvTycIBPW0ed79x/Yd5B3KjQb+YcY9Bq68KsrsNM9H39sbuy8HN8tk5tjG31T+dx4IRC53b6vAfXy2rw8oeHdT6WudFotLheVoNrpdUGuWlQiiqrVCi6VoVaIwzRkgK1WoPi69W4UVEr+aFk1TVq/OPdBJ3b6XseXLoh1eyGIH605hRSmzkl4630eQ9yrpYjZvFxnY9lTOlZ13Tu2AL0/x14+cPDKCm9+1TIYpDGFB734NSpUxg5ciQUCgUcHR3RrVs35OXlYdmyZcjIyEBxcTEAoEePHuIWakDp3+1B9i9HILOWo3VXP3SfPhaO3u5QV//vm7XMRo7RexYia2sCzizdUr/8gSXTYefpin3RH4lRerMt+iZF5x6Je5FztRzf/5qBaY93Ndkx72T/8XwcP1to0mMu+OoMxg7paNJjNkWt1uBTE4/p/OWPyziXUYJunVqb9LiGkJ51Dat+PI912y/W97A5O1rjmUc745WJwQjpLL3XpIsbFbVY/9d81CkX/3dVb0hvb7wyMRhRgzpIcprW5tJqtThwQoGVsWnY8lt2/RA1Xy9HvPh4Vzw/vgvaukvvwWqb9mYhM8fwY6abotXW9QZvWWwe96KVV9RimY5XJ+7V1z9fxPvTe5nN78vi9alGuX+qKYUlVVi37SJefaa7yY7ZXJZ7BkNdz/zo0aOhUCgwe/Zs5OfnIzk5GQqFAgsWLEBcXBwSExMhCALCwsLELtdgSjMVyE9IQW78SZxduQ2/TZ4Pjx6d0G/Bi/XbaGpUODhzOUJnjkPrbh0AAH4jIuEzPAKHXlspVunNUlhciZ/2mP6y18rYNLPp0VoZm2byYx49U4hkPee3N7Rdh3KQnWf6+0FW/Xje5Me8F1qtFm8vS0LXMZuxdENqg8vlZeW1WBmbhu7jtuC1hUcttsf+yOmr8B/5I17+8HCDMA8A8cfz8fjsePR8Yisu51vO/UW3KiuvwajpezDoH7/ix91ZDe43uaIox79XnIDfQz/g+zjD31hvbGKcB7ftv4wchXkMv9u4M9PkQ2BqVRqs3ZJu0mM25XpZDdbvMP39Tat+TDPL86VFB/qZM2ciJycHM2bMwKJFi+Ds/L+5ZGNiYhAeHg6VSoWOHTvCxcVFxEqNqzApHRmbDsB/bH94RnSpX150JhOpq7bjwWX/hIO3G/otfAnH3voSlVd1G5duat/t+NOk38hvOp1erPM4PWMoKKrEz/sviXLsNZvN40QuVh3f/nIR1TVqUY6tjzmfHcfHX56+63aLv0vF9I8Om80XVkM5nlKIoc/vhLKk6o7bpWZcw4CpcUa9wV4MVdUqjJq+Bzvvcg9QTa0G0W/+Lko40te5jBIcPlVg8uNqNFp89fMFkx+3MV+KFKzN5XPgh12ZKBfhRuWLl0rxR5L53Y9osYE+LS0NsbGx8PDwwCeffNLoNr169QIAhIf/b37VhIQEDBs2DN7e3rC1tYWPjw8mTpyItDTT9wQY0unFm6BRqdFzzsSGy5dshkatRtTehVAcOousbYdEqrD5Dp4UbwzjIRGPfdOxlEKTDje61aFT4r9+rVYr2u9A6Y1anW/EFsu+o7n47Nuzzd7+i5/OY/vvl41YkWmp1Ro8MSceldXN+wJ2Ke8GXvrA/M9/uvjkyzNISG7+v5Xn5iZI5kuNqJ8DZnAerKxSIUmkK6bZeTeQVyD+VYqDJ3W/h8pQzOF34O8sNtBv3LgRGo0G0dHRcHJyanQbe/u6MWC3BvqSkhKEhoZi2bJl2LNnDxYsWIDU1FT069cPOTnmN9NJc5VlK5C17RDaDQhDmz7B9cu1KjUKE9Nh594Kf8buF7HC5jsh4rCPE2ni99CL+frPZV4Tfeq2y/k3UHRNvJuSxHz/dfGfH3TvhPjPD6Ydj2tMcQeu4JKOw7J2HLiCS3mmG5NtTDW1avx3s25DxGpqNaL1+upK1M+Bc0rRr2aduVAsWscOAJw4VyTasc2hBnN4/X9nsYE+Pj4eADB48OAmt7kZ0G8N9FFRUVi8eDEmTJiAgQMHIjo6Glu2bMH169exefNm4xZtZGeW1vXG39pL36ZPMDpPHIy0tb+i9/tTYWVn3k9FLb5ebdS5Zu/mZJr4/4hPnhevBrVaK3oP9an0YlGPbw6/A3eTV1CuV2/73iN5yLhi+CdJi2H1Jt3vd9BotPhyi3kMp7hXv/x+GQql7s+PWP3TebMcH/x3p86Ldx4oulZtlIcZ6kLMzwFzOH5llQrns66JdvxTIr/+xghasb9mGomvry9ycnJw8uTJRmewUalU8Pb2hlKpREZGBgICAprcV1FRETw8PLBixQpMnz5d51oiIiKgUOh2achaK8NcTW+dj6ULuYMdon5bhHOrd+D8N7sxcuv7UJ7OQOLcdTrva57sOGoF449rV8la46rrv5pcn7gx6o7TUHl52ENuJYNKrbnjh51CWYHIJ7fftlymKYX3tc90qtnQCp2nosa6Y6PrDPX6gabfA/fSb2GnEu8GunKbcFxzGtfouru9fuDefwfsq1PgVr5Jt6JNrFreEUqXqXq1dSvbAPta6YdaRat/QW2l++w9djXn4H4j1ggVmVap3UCUOQzRq613yceQac1zar6brrb6J1RWHo2uM8V5sM31/8Babfox/DeV2T2AUofhja4zxXnQseoIXCt26Va0AakFRyhaxzS53thZQNBUot21+boV3QxeXl5IStLveQcWO21leXndt+fKysb/omJjY6FUKuHs7Ax/f//b1qvVamg0Gly6dAlvvvkmvLy88MQTT+hVi0KhQG6ubo+kthGsACPPjBX53rO4cbkA59fV/aM8OGsFovYtwuWdx3D1qG6X6/Py81CjNcHNgjYqwLXp1Tfnlr0buZWsWdv9nUYNnf8uDS6gFrBufJWxXz8AFBWXAGUivget/YHGR9E1+/UD+r8HlVXV4v8O3I2TK6Dnff7FxdeBUjN/fc3hLABWujerqqo1/7/f5mhbCeg2xXa9/PxCQG3mQ48cNU3+/ZriPFhQoASqRPw98bzR5N+vKc6D5TcqUJ4v4uuXtwLu8H3d2L8DWq1gducJiw30Xl5eKCkpQXJyMvr169dgXX5+PubMmQMACAsLgyAIt7UfOHAgDh2qu0Gqc+fOiI+Ph6enp9616MpaKwOM2OHdfkhP+Ef1x7ahs+uXlV26ihMfbUD/xdOxfchsqCqb30PTzrudiXroXXCnW1EUyjvf0KXLt/LGWMnU8GrfvjmlGo3SRoam/mYM9frvtC8PNxfYuoj3HlTYOKGpQT93e/3Avf8O2NtZw03k34G7qbFygL5PKfBobQdbZ/N+fc1xVaiGPnd7ONhq0NrM/36b44atFa7r01CrQTuv1hD0/UZoIlettE3+/ZriPNjG0w3WGvEeTlZm54CmBseZ4jzo5GiLViL+O9EI9rjTPDPGzgIyQQ1vI7x+ffLiTRY75GbmzJlYvnw5fH19sW/fPgQFBQEAEhMT8cwzzyAzMxO1tbWYPn16o0+JTU9Px7Vr15CVlYWFCxeioKAAhw4dgp+fn0nqr62owoZOT5vkWIYQnbEe1g52Rj+OSqWBy/3forJKv6sBV/ZOgk9bR+RcLYfv8B90bj+sbzvs/e9IvY5tKC99cAirf9JvPvR7ff0AcHnPRPh6NdFFbgLHzhSg79O/6N3+Xt+Dea/ch3df6qn38U1BrdYg8NFNyMrVrZfVy8Mel3dPsoiHLM1blYz3Vp3Uud0vy4fj0YGmOc8bU3ZuGQIe+RG6fsI/NrQDtiweZpyiDGjMzL16z8p0r+cAKysBZUeehb2deH2i2/ZfwthZ+/Ruf6/vwX/f7Y8XRHzQolarhfuD61FSqt88/Pf6+vuGeeLI+ii9jm0s0j9rNyEmJgbu7u64cuUKQkJCEBoaisDAQPTu3RsBAQEYMqRubOGtN8TeqkuXLujTpw8mTZqE3377DWVlZfj0009N+RKoEXK5DD26uIt2/F7dGh+zadIagsV7/Z6t7fS+RG0oYUFusLK6/aqaqfTqJt7731xWVjK8NEH3D9tp47taRJgHgBfGd4Fcx9+TDu2cMPIBHyNVZFod2ztj1IO+Ord7ZWLw3TcyA2Kei0M6tRY1zANAr2BxP4vE/iwUBEHUGsR+/Y2xjDN3I3x8fJCQkIBRo0bBzs4O2dnZcHNzw+rVqxEXF4cLF+pu+moq0N/K1dUVnTt3xp9/SuehG5YsIqRl/yMW+/U3NkTNlOzt5OjeWfebHQ3FHH4HmuOFx7sgwMf57hv+xaetI6ZPkkaYa452bRwxKzpEpzYf/bMXrKws52Nx7ss9YWfb/BsJhvVth6F92hmxIsMR9zwo/pf69m0d0NbdXpRj21jL0D1QvHPwTWJ/FpobyzlzNSI4OBg7duxAWVkZysrKcOzYMUybNg3l5eXIzs6GTCZD9+7d77qfgoICpKeno1OnTiaomu7m8WEdRTmuk4M1Hr5f/LG14V3c0cm3+UHNkCY8dPsN5GJ4fHhHUY77QM+2d509wly0drHFzpUPw9fr7ldUvDzssXPlQ2gjUkAwlgWvRuLpR5t33l74Wm9Ej+ps5IpMKyLEEz8uHNKsUN83zBObPhsq+hf25hoc6Y3WLuJMszxhuPjnQUEQRDsPjhncATbWetxxbmCPi/T3YGtjhdFmOCzPogN9U1JTU6HVahEYGAgHh4Yfzk8//TTee+89/Pzzz/j999+xZs0aDBo0CHK5HK+++qpIFdOtHuzlhZBOriY/7jOPdoKLk/jz9MtkAl5+wvQ9qa7ONpg0ounpXU3p+XFdIJebPnhIZTjCTUEdW+Ho+tF4flwQ7O1u/wC2tbHClDGBOLYhCt0D3USo0LisrGT45sOBWP5mvya/BPcJ9cTPS4fh9SmhJq7ONEYP8sOBr0dh1ABfNJbVPVvb4a3nwxH/5SNo5Sz++a257O3k+MfYIJMf17+9Mx7ubx7DssT4HADM5zzYq5sHenfXb7KSe/HEQ/7waG38ewZ11SIDfUpKCoDGh9v07dsXv/76K6ZOnYqRI0di4cKFePDBB3Hq1Cl07mxZvTdSJQgCpk/qZvLjinXybMzUsY0HNGP6x2NBcLA3j4mxvDwc8Pgw0/bOtHW3xziRrg7di3ZtHLHmvQeRt+9JrH63P5wd6v4OWzlZI3ffJHz9wQD4eYt3k7OxyWQCZjzZDRd+mYBdqx6Gs2PdnK8ujtZI+mEMjm6IwpjBHUSu0rgiu3tix4qHkBH3BBbN7g2nv34HWrvY4MreSfhoZoToY8L18dITwZDJTPvF/pWJpj9mU0I6t8agSG+THrNbJ1cMjNB/JhZDE2OYoLkOTWSg/5sZM2bg+PHjKCkpQWVlJS5cuIDVq1ejQwfLPuFLzfPjuqBnV9ONY3xlYjBCg8ynB9OtlS0++meEyY7n7emAf0/rYbLjNceCVyPrw5kpLInpA1sb8S8z68vVxRbTHu9af5XJycEa7q7m18tkLDKZgIf7+8Dlr98ZZ0drsxwHa0z+Ps6YPTkUrf76HXCwk0v6d7qznwtmP3v3YbOGEhzgihlPmleYWxLTx6RXK//z1v1mNSwrelQn3N+jjcmONzkqEH3CTHc8XTDQtzAdRvVF3/kvNFjWeeJgTMnfBL8RkSJVpTtraxnWfTgA1nLj/wp3bOeEBa+a33sz86lu6N/TyE8f+8t/3+2P1i62JjlWc/l5O2HRbOM+TfmmcUM7YqKZDDciov+Z98p96NKxldGPI5MJWPfBANjZmteVjPAu7nhnmmmm0Z0+KdjkVwTuxspKhq/fH6DTzd/6atfGAYtj+hj9OPpqkYE+Pj4eWq0Wo0aNErsUk/N7pA8u7zpe/7OTjyeCooehICldxKr0Exbkhk91DNoKZQVyrpY368EbQN0Y4+8+HggnB9P1BDeXlZUM6z4YAHfX5gdtXV8/UHd1wlzn5X5hfBeMG9pRpza6vgcd2jlh1b/Nq1eKiOrY28mx/pOBcNBhyJA+58F5r/RE71DTj9dujjefC8eD9+nWuaPrexAa2Brz/2V+HVtA3b1CS/+vr05tdH39crmAbz4cYHYdW7cyr6+adM9sXBwwZv9iWNnZoCJPCZmtNZz92iJj0x848sYatI3sgoOz/nqQliDg/s9exrF/r0Xk3MniFq6nfz3THUXXq/Hhf081a/vIJ7c3e9821jL8tGgIHrjPfMYL/l1nPxfsWvUwhk/bhWtld3/Ahi6vHwCeeqQTlr2h24nSlARBwIb5AzFmVi32HG7eY7h1eQ/atXHAvv+OtLjZX4gsSURI3Y3NUTP3oqr67g8d1PU8+K+nQ/D2Cz30rM74rK1l+GX5Qxg2bSeSUpXNaqPLe9ClYyvsWT3CLDu2bpr2eFcUXavGW8uSmrW9Lq/fykrAhk8GYVhf8We5u5MW2UNvyWpKK5C5NQHnvozD9uFzcPzdr1GYfAGHX/8C3v27oyAxHVpV3Qkv5MXRKEg8j6IzmSJXfW8+mNELn74aadAblVydbfDL8uEYPcg8e6ZvFRHiiT++HtWs6Ql1MX1SML79aIDZz8ttZyvH9mXDMXGEYW+S7erfCgfXPYrOfi4G3S8RGd7wfu2xe9XDBp19RBCA917uic/n9DH7K3StnG3w25qRGN7PsM8R6N3dEwe+HiWJ6XrffD4cy9/sZ9B7CpwdrbHl86F44mHzH3Jp3p/UpBe37v4oTskCALiHdULx2bo/+42IxKWddcNtXLv4osOoPji9ZLNodRrSnKlhOLp+NLoZYDrLRwf4InXrODx0v3lMTdYcYUFuSNk8Ds+Pu/dp3Nq1cUDcfx7CirfuN/swf5OtjRU2LhiMDZ8Mglure7skKpMJmDMlFMmxY+Gvw4OZiEhcAyK8kbplHMYbYDaqwA4uOPD1KMx9+T6zD/M3uTjZYNeqEVjxVj843uOMZDbWMnw8MwKHvn1UUlcoZzzZDYnfj0F4l3ufxGJ4v3Y4u2UcoiQyC5Y0Pq1JJ24hHetDvHtYAIr+CvftBvVAbvxJAEDbPsFw8m2D8YeX4/HjK+F5XyD6LXwJXZ59SLS671Vkd0+c+GEMPpkVoVdvda9uHti4YBC2Lx+Odm0M29ttCq2cbbDmvQex978j9Hrao7urLWKmhiJ1yzg8oscj48UmCAKeGtUJqVvHYfqkYJ1nwBEEYPRAPxz+9lF8+lpvSU7jR9TStXG3x6bPh2LL4qHoF677bCTt2jjg/en34dSPj5n1cMumyGR10zqf2fwYpo4N1PlmUblcwKQRAUiOHYs3nw+H3AQTTxhaj67uOP59FD57vTf82+veKRPexQ3ffjQAu78YIakpfQWtVqsVuwi6XW1FFTZ0elrndg5ebhi951PEhj0PABh3aDl+f+lzyORyhL/6OH579pNG243YPA/n1uzA5V2JetUbnbEe1g7mMwWeSqVBXMIVbNyZgaRUJTKulN22jVwuIKRTa/QNa4PnHgtCpAgPqDCm81nX8OXmdBw8eRWn0otRXXP72FKfto7o1c0d44d1xISH/M1uBod7UVZeg/U7MrDjwGWcOFeEq0WVt23jaC9Hj67uGBThhefHdUFHPU7+UuMzbCNyCyrQvo0DcvY9KXY5JtfSXz/Qst6Dk2lKrN16AUdOFyDlYglqVZrbtvFv74xe3dwxaUQAogZ1gLW19EJsU4qvV2PdtgvYdSgHJ84Vofh69W3buDhZ475gDwzr0w7PjQuSxPCa5lKrNdh9OBcb4jKQmFqIi5dKb9vGykpAtwBX9An1xNSxQegX3kYyV2VuZTmf3gTgr+E2f/XOA0BNaTm6Tn4Y1cVlDWa3sXRyuQxjBneof2DMtdJqpGdfR0WVClYyAc6O1ggOcLWoAPt3Xf1dsej1uim2ams1SM++hpLSGtSqNLC3s0InHxdJXUrVlbOjDV6eGIyXJwZDq9Uir6ACl/JvoKpaDRtrGTxa2yHQz0Uyw4qISHc9gz2wIrjueQPVNWqcz7qG62U1UGu0cLCTI7BDq3sepmfO3FrZ4rVnQ/Has6HQarW4lHcDuQUVqK6pOw96eTggwMfZbB6WZWhWVjI88qBv/VXn62U19VlAEABnh7osYAlXZKX/CqiBnH0nkLPvRP3PO0a+AQAY8/ti7B4/t8l2u+6wzhK4utia7cMgTMHaWobugebzYCxTEwQB7ds6on1b6Q2lIiLDsLWxQngX0z2Q0NwIgoCO7Z1bxJXIprRytjHb6UfvFQN9C7Ft0Ktil0BERERERsBrzUREREREEsZAT0REREQkYQz0REREREQSxjH0Zkpub4vojPVil9FscnvLnSWAiIiIyJwx0JspQRDMal53IiIiIjJPHHJDRERERCRhDPRERERERBLGQE9EREREJGEM9EREREREEsZAT0REREQkYQz0REREREQSxkBPRERERCRhDPRERERERBLGQE9EREREJGEM9EREREREEsZAT0REREQkYQz0REREREQSxkBPRERERCRhDPRERERERBLGQE9EREREJGEM9EREREREEsZAT0REREQkYQz0REREREQSJhe7AGqcVquFqrJa7DKaTW5vC0EQxC6DiIiIqMVhoDdTqspqbOj0tNhlNFt0xnpYO9iJXQYRERFRi8MhN0REREREEsZAT0REREQkYQz0REREREQSxkBPRERERCRhvCmWiMjC1dSqcfZiCVIzSnCjohYAUFGpQlJqIUID3WBrYyVyhUREdC8Y6ImILFBZeQ3W78jAN9sv4uT5ItTUahqsLymrQeST22EtlyG8ixueebQznh3dGa4utiJVTERE+mKgJyKyIKU3ajB3ZTK+3HKhvjf+TmpVGiSlKpGUqsSbS5MwdWwgPpzRi8GeiEhCGOgtiFe/EIzYMq/BstrySpRm5iNj0wGkrf0VWrWmidZEJHX7jubiubkJuJxfrlf7iioV/vNDGrbGX8KauQ/gkQd9DVwhEREZAwO9BcrckoCc+GRAEGDv6YrOEwai97wpaBXYHkfmrBa7PCIyMK1Wi/e/OIn3Vp00yP7yCiowavoexEwNxfx/RfIp0EREZo6B3gIVpWQhc3NC/c/p63bjsYSlCHpqKJLnb0R1UamI1RGRob25NAkLvjpj8P1++nUKKqrUWPZGX4Z6IiIzxmkrWwBVZTUKky9CkMng0qGt2OUQkQEt/z7VKGH+phUbz2H+WuPtn4iI7h0DfQvh3LEuyFdfuyFyJURkKOezrmHO54k6tUncGIUreychcWNUs9u8u/IETp0v0rU8IiIyEQZ6CyS3t4GtmzNs3V3g2tUPfT5+Hu6hAShMvojSzHyxyyMiA1CrNZj6zgFU16h1aufl4QCfto7w8nBodhuVSosp7xxATa1uxyIiItOw+ECvVCoRExODzp07w87ODr6+vpg1axbKy8vx3HPPQRAErFixQuwyDapnzCQ8mfo1njz7Fcbu/xzBU0cgO+4o4qcsELs0IjKQTXuzcfRMocmOdzq9GOt3ZJjseERE1HwWfVPsqVOnMHLkSCgUCjg6OqJbt27Iy8vDsmXLkJGRgeLiYgBAjx49xC3UwNK/24PsX45AZi1H665+6D59LBy93aGurqnfRmYjx+g9C5G1NQFnlm6pX/7Akumw83TFvuiPxCidiJppZWyayY/5nx/OYerYQN4gS0RkZiy2h16pVGL06NFQKBSYPXs28vPzkZycDIVCgQULFiAuLg6JiYkQBAFhYWFil2tQpZkK5CekIDf+JM6u3IbfJs+HR49O6LfgxfptNDUqHJy5HKEzx6F1tw4AAL8RkfAZHoFDr60Uq3QiaoazF4tx4ITC5MdNTivC8RTTXRUgIqLmsdhAP3PmTOTk5GDGjBlYtGgRnJ2d69fFxMQgPDwcKpUKHTt2hIuLi4iVGl9hUjoyNh2A/9j+8IzoUr+86EwmUldtx4PL/gkHbzf0W/gSjr31JSqvlohYLRHdTVzClRZ5bCIiapxFBvq0tDTExsbCw8MDn3zySaPb9OrVCwAQHh7e5H5GjhwJQRDw3nvvGaNMkzq9eBM0KjV6zpnYcPmSzdCo1YjauxCKQ2eRte2QSBUSUXOdOCfejDMnzilFOzYRETXOIgP9xo0bodFoEB0dDScnp0a3sbe3B9B0oP/xxx9x6tQpY5VocmXZCmRtO4R2A8LQpk9w/XKtSo3CxHTYubfCn7H7RayQiJpLzFB94lwRtFqtaMcnIqLbWWSgj4+PBwAMHjy4yW1ycnIANB7oS0tL8a9//QuLFi0yToEiObO0rjf+1l76Nn2C0XniYKSt/RW9358KKzsbESskoua4rBDveRJXiyqhUjHQExGZE0FrgV0tvr6+yMnJwcmTJxudwUalUsHb2xtKpRIZGRkICAhosP6f//wnUlJS8Pvvv0MQBMydO/eeht1ERERAodDtBjZrrQxzNb31PmZzyB3sEPXbIpxbvQPnv9mNkVvfh/J0BhLnrtN5X/Nkx1EraAxfJBE1oIWAPLf3mlyfuDHqjnPMe3nYQ24lg0qtgUJZ2eR2CmUFIp/c3ug67+KPIENNo+vMXb7ra9DIWkGmuQ7va5+LXY4o+B4QmScvLy8kJSXp1dYip60sLy8HAFRWNv5hFRsbC6VSCWdnZ/j7+zdYl5SUhDVr1uDEiRMGq0ehUCA3N1enNjaCFdDWYCU0KvK9Z3HjcgHOr9sFADg4awWi9i3C5Z3HcPWoblPi5eXnoUbLh84QmURrNSBYNbrq5oOj7kZuJWvWdo3Jz7sCaFV6tRWdsxqQARq1WufzssXge0BkcSwy0Ht5eaGkpATJycno169fg3X5+fmYM2cOACAsLKzBfMpqtRovvvgiZsyYgZCQEIPWoytrrQwwYod3+yE94R/VH9uGzq5fVnbpKk58tAH9F0/H9iGzoaqsbvb+2nm3Yw89kYnkayugEZwbXadQVtyxrS499I0RtFXwbtcWUp2JPt/KChoAMisreLdvL3Y5ouB7QGSe9MmLN1lkoB82bBjS0tKwYMECDB8+HEFBQQCAxMREPPPMM1Aq624o+/twnBUrVuDq1asGn9VGn8sntRVV2NDpaYPWcavc+JP4vuvk25afX7ervsdeFxcuXoC1g50hSiOiuxg1fTd+TchpdF1Tw2RuurJ3EnzaOkKhrITv8B90PvaAyI74/avGjy0FPsM2IregAt5e3sg5K93XcS/4HhBZHou8KTYmJgbu7u64cuUKQkJCEBoaisDAQPTu3RsBAQEYMmQIgIY3xCqVSrzzzjt49913oVKpcO3aNVy7dg0AUFVVhWvXrkGjYQ80EYmvVzePFnlsIiJqnEUGeh8fHyQkJGDUqFGws7NDdnY23NzcsHr1asTFxeHChQsAGgb6nJwclJWV4cUXX0Tr1q3r/wOABQsWoHXr1rh8+bIor4eI6FaDI71b5LGJiKhxFjnkBgCCg4OxY8eO25bfuHED2dnZkMlk6N69e/3yzp07Y//+2+dhHzx4MCZPnowpU6bc09gmIiJDGRTpjS4dWyE9+7pJj+vn7YiRD/iY9JhERHR3Fhvom5KamgqtVougoCA4OPxvajcnJycMGjSo0TYdO3Zsch0RkakJgoBXJgZj1oKjJj3uSxOCYWVlkRd2iYgkrcWdmVNSUgA0/YRYIiIpmDo2EL5e+k07qY+27vZ4cUJXkx2PiIiaj4H+LrRarcFnvRFTh1F90Xf+Cw2WdZ44GFPyN8FvRKRIVRGRrpwdbfDlew+Y7HhfvHM/3FrZmux4RETUfAz0LYzfI31wedfx+p+dfDwRFD0MBUnpIlZFRPp46H4fnXvNFcoK5Fwtv+t89beKHtUJY4d01LE6IiIylRY3hj4+Pl7sEozKxsUBY/YvhpWdDSrylJDZWsPZry0yNv2BI2+sQdvILjg4a0XdxoKA+z97Gcf+vRaRc2+fk56IzN+yN/riUt4N7DrUvPnE7zZP/d8NjPDCmrmmuxJARES6a3E99JauprQCmVsTcO7LOGwfPgfH3/0ahckXcPj1L+DdvzsKEtOhVakBACEvjkZB4nkUnckUuWoi0peNtRW2LB6KRwf4Gnzfw/u1w44VD8HersX1/RARSQoDvQVy6+6P4pQsAIB7WCcUn637s9+ISFzaWTfcxrWLLzqM6oPTSzaLVicRGYa9nRxblwzDhzN6wVp+76d1KysB77zYAztWPAQnB2sDVEhERMbEQG+B3EI61od497AAFP0V7tsN6oHc+JMAgLZ9guHk2wbjDy/H48dXwvO+QPRb+BK6PPuQaHUTkf7kchnentYDJ34Yg75hnnrv575gdxzfEIX3p/eCjbWVASskIiJj4XVUC+Pg5QZotahQFAMA3II74MzSzfDoGYjrF3OhqqgCAKR/uwfp3+6pbzdi8zycW7MDl3clilI3ERlGaJAbDn83GsdTCrEyNg2xu7NQXaO+YxtruQwTHvLHKxODcX+PNhAEwUTVEhGRITDQWxi37v71vfMAUFNajq6TH0Z1cVmD2W2IyHIJgoA+YW3QJ6wNVr/bH2culODEOSXO/lmCGxW10GoBR3s5uge2Rq9gD4R3ceM4eSIiCeMZ3MLk7DuBnH0n6n/eMfINAMCY3xdj9/i5TbbbdYd1RCRddrZy9A71RO9Q/YfhEBGReWOgbyG2DXpV7BKIiIiIyAh4UywRERERkYQx0BMRERERSRgDPRERERGRhDHQExERERFJGG+KNVNye1tEZ6wXu4xmk9vbil0CERERUYvEQG+mBEGAtYOd2GUQERERkZnjkBsiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJk4tdADVOq9VCVVktdhnNJre3hSAIYpdBRERE1OIw0JspVWU1NnR6Wuwymi06Yz2sHezELoOIiIioxeGQGyIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiImoxtFptg/8TEVkCznJDREQWSaPRYu+RXMQfz0NSqhLJaUW4VlYDAMgrrES7oRvRK9gdESEeGDXAFxEhniJXTESkHwZ6IiKyKNdKq7FmczpW/XgeWbllTW6XX1iBHYUV2HHgCt5bdRIRIR54ZWIwokd1go21lQkrJiK6Nwz0FsSrXwhGbJnXYFlteSVKM/ORsekA0tb+Cq1aI1J1RETGF3fgMqa9fwh5BRU6t01KVeIf7yZg6YZUfPPhAIR3cTdChUREhsdAb4EytyQgJz4ZEATYe7qi84SB6D1vCloFtseROavFLo+IyOCqqlV45aPD+Prni/e8r9PpxYh4chs+mN4L//ePMD4Fm4jMHgO9BSpKyULm5oT6n9PX7cZjCUsR9NRQJM/fiOqiUhGrIyIyrIpKFaJm7sVvx/IMtk+VSos3lyYhr7ACS/+vL0M9EZk1znLTAqgqq1GYfBGCTAaXDm3FLoeIyGBqazUY/9pvBg3zt1r+/Tm8sSTRKPsmIjIUBvoWwrljXZCvvnZD5EqIiAxn/lensetQjlGP8enXKYg7cNmoxyAiuhcccmOB5PY2sHVzrh9D3+XZh+AeGoDC5IsozcwXuzwiIoM4c6EYH6w+pVObxI1R8PJwgEJZgcgntze73QvzDiF1a1u0drHVsUoiIuNrET30SqUSMTEx6Ny5M+zs7ODr64tZs2ahvLwczz33HARBwIoVK8Qu02B6xkzCk6lf48mzX2Hs/s8RPHUEsuOOIn7KArFLIyIyCK1Wi2nzDqJWpdvMXV4eDvBp6wgvDwed2uUXVuDtZUk6tSEiMhWL76E/deoURo4cCYVCAUdHR3Tr1g15eXlYtmwZMjIyUFxcDADo0aOHuIUaUPp3e5D9yxHIrOVo3dUP3aePhaO3O9TVNfXbyGzkGL1nIbK2JuDM0i31yx9YMh12nq7YF/2RGKUTETXL8ZRCHEspNOkx122/iI9nRsCVvfREZGYsuodeqVRi9OjRUCgUmD17NvLz85GcnAyFQoEFCxYgLi4OiYmJEAQBYWFhYpdrMKWZCuQnpCA3/iTOrtyG3ybPh0ePTui34MX6bTQ1KhycuRyhM8ehdbcOAAC/EZHwGR6BQ6+tFKt0IqJm+c8PaSY/ZmWVGt9sv/dpMYmIDM2iA/3MmTORk5ODGTNmYNGiRXB2dq5fFxMTg/DwcKhUKnTs2BEuLi4iVmpchUnpyNh0AP5j+8Mzokv98qIzmUhdtR0PLvsnHLzd0G/hSzj21peovFoiYrVERHdWVa3Cj3uyRDn2t7/8KcpxiYjuxGIDfVpaGmJjY+Hh4YFPPvmk0W169eoFAAgPD69f9vvvv0MQhNv+k/qQnNOLN0GjUqPnnIkNly/ZDI1ajai9C6E4dBZZ2w6JVCERUfOkXCxBdY1alGOfuViMqmqVKMcmImqKxY6h37hxIzQaDaKjo+Hk5NToNvb29gAaBvqb/vOf/+C+++6r/9nR0dE4hZpIWbYCWdsOodP4AWjTJxgFx+ouV2tVahQmpsMjrBP+jN0vcpVERHd34pxStGOrVFqcuVCC3qGeotVARPR3FttDHx8fDwAYPHhwk9vk5NTNXdxYoO/WrRv69u1b/19oaKhxCjWhM0vreuNv7aVv0ycYnScORtraX9H7/amwsrMRsUIiortLzbgm8vE5LJGIzIug1Wq1YhdhDL6+vsjJycHJkycbHS6jUqng7e0NpVKJjIwMBAQEAKgbcjN48GDs378fgwYNMkgtERERUCgUOrWx1sowV9PbIMdvitzBDlG/LcK51Ttw/pvdGLn1fShPZyBx7jqd9zVPdhy1gm7TxxER6aPEcSwqbHs2uu7mPPNN8fKwh9xKBpVaA4Wy8o7HaWqu+lblv8Kp+phuRZuRfNfXoJG1gkxzHd7XPhe7HCL6i5eXF5KS9Jse12KH3JSXlwMAKisbP2HHxsZCqVTC2dkZ/v7+t62fOHEilEol3N3dERUVhfnz58PDw0OvWhQKBXJzc3VqYyNYAW31OlyzRb73LG5cLsD5dbsAAAdnrUDUvkW4vPMYrh7VbQaJvPw81GjFGdNKRC1M+3KgiZkjb84zfzdyK1mztmvM9esluF6k2zndrDirARmgUat1/mwiIvNksYHey8sLJSUlSE5ORr9+/Rqsy8/Px5w5cwAAYWFhEAShfl2rVq0wZ84cDBgwAE5OTjhy5Ag++eQTHD16FElJSbCzs9OrFl1Za2WAETu82w/pCf+o/tg2dHb9srJLV3Hiow3ov3g6tg+ZDVVldbP31867HXvoicgkrjnIUd7EOoWy4o5tde2hb4yriwMc7do3p1SzlG9lBQ0AmZUVvNtL93UQWRp98uJNFjvkZubMmVi+fDl8fX2xb98+BAUFAQASExPxzDPPIDMzE7W1tZg+ffpdnxL7yy+/ICoqCl999RWmTp1qivJRW1GFDZ2eNsmxDCE6Yz2sHXT/skNEpKvVP53HSx/oNyPXlb2T4NPWETlXy+E7/Ae99nH8+yhEdpfuTbE+wzYit6AC7ds4IGffk2KXQ0QGYLE3xcbExMDd3R1XrlxBSEgIQkNDERgYiN69eyMgIABDhgwB0PgNsX/36KOPwtHRUe9xTUREZDi9urmLdmy5XEBoYGvRjk9E1BiLDfQ+Pj5ISEjAqFGjYGdnh+zsbLi5uWH16tWIi4vDhQsXADQv0N9069AcIiISR2igG+xsrUQ5dniQO+xsLXa0KhFJlEWflYKDg7Fjx47blt+4cQPZ2dmQyWTo3r37Xfezfft2lJeXo3dv4846Q0REd2drY4WJDwfgm+0XTX7syVGdTX5MIqK7sehA35TU1FRotVoEBQXBwaHh9GZPP/00AgICcN9999XfFPvpp5+iR48emDRpkkgVExHRrV6ZGGzyQO9gJ8ezowNNekwiouZokYE+JSUFQOPDbUJCQvD9999jyZIlqKyshI+PD1544QXMnTsXNjZ86BIRkTnoHeqJ+3u0weFTBSY75j8eC0IrZ34OEJH5sdgx9Hdyp0D/5ptvIiUlBaWlpaitrUVWVhY+//xztGrVytRlGkWHUX3Rd/4LDZZ1njgYU/I3wW9EpEhVERHpbvU7/WFjbZqPMZ+2jvhwRi+THIuISFcM9C2M3yN9cHnX8fqfnXw8ERQ9DAVJ6SJWRUSku+6Bbpj7UuNPjG2KQlmBnKvld52v/u/WzH2AvfNEZLZa5JCb+Ph4sUswGhsXB4zZvxhWdjaoyFNCZmsNZ7+2yNj0B468sQZtI7vg4Ky/5t0XBNz/2cs49u+1iJw7WdzCiYj0EDM1DEdOF2DHgSvN2j7yye06H+PtF8Ix4gEfndsREZlKi+yht2Q1pRXI3JqAc1/GYfvwOTj+7tcoTL6Aw69/Ae/+3VGQmA6tSg0ACHlxNAoSz6PoTKbIVRMR6Ucul+HHRUPw8P3GeeLpq8+E4AMOtSEiM8dAb4HcuvujOCULAOAe1gnFZ+v+7DciEpd21g23ce3iiw6j+uD0ks2i1UlEZAj2dnJsXz4c0x7vYrB9WstlWPhab3z2eh8+g4SIzB4DvQVyC+lYH+LdwwJQ9Fe4bzeoB3LjTwIA2vYJhpNvG4w/vByPH18Jz/sC0W/hS+jy7EOi1U1EpC8bayusfvcB7Fr1MHzaOt7Tvnp180By7Bi8PiWUYZ6IJKFFjqG3ZA5eboBWiwpFMQDALbgDzizdDI+egbh+MReqiioAQPq3e5D+7Z76diM2z8O5NTtweVeiKHUTERnCw/19kLp1HL7aegErf0zDxUulzW57f482ePmJYEwaEQC5nP1dRCQdDPQWxq27f33vPADUlJaj6+SHUV1c1mB2GyIiS+XiZIN/PdMdM6NDsP94PvYn5uHEuSIkpylRUFzXqSEIgJ+3E3oFe6BXN3c88qAvenR1F7lyIiL9CFqtVit2EXS72ooqbOj0tMH2N+b3xdg9fi6qiprfW6WL6Iz1sHawM8q+iYgMRavVQqXSQi4XWuxwGp9hG5FbUIH2bRyQs+9JscshIgNgD30LsW3Qq2KXQEQkOkEQYG3dMoM8EVkuDhIkIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCeNNsWZKbm+L6Iz1YpfRbHJ7W7FLICIiImqRGOjNlCAInAaSiIiIiO6KQ26IiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkTC52AdQ4rVYLVWW12GU0m9zeFoIgiF0GERERUYvDQG+mVJXV2NDpabHLaLbojPWwdrATuwwiIiKiFodDboiIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMs9wQERFZsMLiSpw4V4QT55T480opiq/XTYl8rawGazadR69uHuge2Bo21lYiV0pE+mKgJyIisjA1tWr8HH8JK2PT8EeSotFtyitVmPb+IQCAi5M1JkcF4uUnghEc4GrCSonIEAStVqsVuwi6XW1FFeehJyIinW3em4WZC44ir6BCr/ZjBvth5dv3o10bRwNXRkTGwh56C+LVLwQjtsxrsKy2vBKlmfnI2HQAaWt/hVatEak6IiIyJmVJFaZ/fBg/7s66p/1s238ZfyQpsPT/+uKZ0Z35FHAiCWCgt0CZWxKQE58MCALsPV3RecJA9J43Ba0C2+PInNVil0dERAaWlVOGYdN2IjOnzCD7u1ZWg8n/PoBT6UX47PU+DPVEZo6B3gIVpWQhc3NC/c/p63bjsYSlCHpqKJLnb0R1UamI1RERkSFdzr+Bgf+IwxVFucH3vfi7VGg0wOIYhnoic8ZpK1sAVWU1CpMvQpDJ4NKhrdjlEBGRgVTXqPHojD1GCfM3Ld2QilWxaUbbPxHdOwb6FsK5Y12Qr752Q+RKiIjIUN7/4iRSLpbo1CZxYxSu7J2ExI1RzW4z5/NEZObw6i6RubL4QK9UKhETE4POnTvDzs4Ovr6+mDVrFsrLy/Hcc89BEASsWLFC7DINSm5vA1s3Z9i6u8C1qx/6fPw83EMDUJh8EaWZ+WKXR0REBnDinBILvj6jczsvDwf4tHWEl4dDs9tUVKnw3NyD4MR4RObJosfQnzp1CiNHjoRCoYCjoyO6deuGvLw8LFu2DBkZGSguLgYA9OjRQ9xCDaxnzCT0jJnUYFl23FEce/NLkSoiIiJD+2jNKajVpgvYvyfmI+GEAgMivE12TCJqHovtoVcqlRg9ejQUCgVmz56N/Px8JCcnQ6FQYMGCBYiLi0NiYiIEQUBYWJjY5RpU+nd7sPuJedgb/RGSPvgOVcVlcPR2h7q6pn4bmY0cY35fjLBZ4xq0fWDJdAzb8LapSyYiIh3kKMqxbf9lkx935Y8cS09kjiw20M+cORM5OTmYMWMGFi1aBGdn5/p1MTExCA8Ph0qlQseOHeHi4iJipYZXmqlAfkIKcuNP4uzKbfht8nx49OiEfgterN9GU6PCwZnLETpzHFp36wAA8BsRCZ/hETj02kqxSiciomZYuzUdGo3ph79s3peNgqJKkx+XiO7MIgN9WloaYmNj4eHhgU8++aTRbXr16gUACA8Pv23d1q1bcf/998PR0RGtWrVC//79kZqaatSajakwKR0Zmw7Af2x/eEZ0qV9edCYTqau248Fl/4SDtxv6LXwJx976EpVXdbvBioiITGt/ojj3Q6lUWhw6dVWUYxNR0ywy0G/cuBEajQbR0dFwcnJqdBt7e3sAtwf6ZcuW4YknnsADDzyA7du3Y+PGjRg2bBgqK6XdI3F68SZoVGr0nDOx4fIlm6FRqxG1dyEUh84ia9shkSokIqLm0Gi0SE4rEu34J84pRTs2ETXOIm+KjY+PBwAMHjy4yW1ycnIANAz0GRkZmDNnDhYvXowZM2bUL3/kkUeMVKnplGUrkLXtEDqNH4A2fYJRcKxuHKRWpUZhYjo8wjrhz9j9IldJRER3k3GlFGXltaIdX8wvE0TUOIsM9JcuXQIAdOjQodH1KpUKhw7V9UTfGui/+uorWFtb44UXXjBoPREREVAoFDq1sdbKMBe9DVrHmaWb4T+2P3rOmYjdj78HAGjTJxidJw5G2tpf0fv9qdg+fA7UVTV33lEjggKDUCtoDFovERHdrlruC7g83+i6xI1Rd52O0svDvv7/V/ZOanI7hbICkU9uv235vt+PwsfnOR0qJqLm8PLyQlJSkl5tLTLQl5fXPTGvqWEysbGxUCqVcHZ2hr+/f/3yw4cPo0uXLli/fj0+/PBDXLlyBYGBgXj33Xfx5JNP6l2PQqFAbm6uTm1sBCtAx4e6Ko6kYp33402uv34xF9/6/G/IjdzBDg8smY4TH23A+W92Y+TW93Hfm08hce463Q4MIC8/DzVatc7tiIhIR46OQBNzOdycY7455FayZm97q9pajc6faURkXBYZ6L28vFBSUoLk5GT069evwbr8/HzMmTMHABAWFgZBEBqsy83NxZtvvokFCxbA19cXa9euxVNPPQVPT08MGzZM73p0Za2VAUbu8I5871ncuFyA8+t2AQAOzlqBqH2LcHnnMVw9qtvUZO2827GHnojIBGqsWqOwiXUKZcVd23t52ENuJYNKrYFC2fT9YU3ty9paQJv27ZtTKhHpQJ+8eJOgtcDHvs2cORPLly+Hr68v9u3bh6CgIABAYmIinnnmGWRmZqK2thbTp09v8JTYoKAgXLx4EVu3bsXYsWMBAFqtFj169ICrqyv++OMPk72G2ooqbOj0tNH2335ITwxc+S9sGzob5bn/u8Gp65QR6Pbio9g+ZDZUldXN3l90xnpYO9gZo1QiIrpFXkE52g/7Qe/2V/ZOgk9bR+RcLYfvcN33M+Ehf/y4aIjexyciw7PIWW5iYmLg7u6OK1euICQkBKGhoQgMDETv3r0REBCAIUPqTkR/n+HGzc0NABr0xAuCgGHDhuHs2bOmewEmkBt/Et93ndwgzAPA+XW7sKXfDJ3CPBERmU67No7w9rzzOHlj6tXNXbRjE1HjLDLQ+/j4ICEhAaNGjYKdnR2ys7Ph5uaG1atXIy4uDhcuXABwe6APCQlpcp9VVVVGrZmIiKi5egWLF6p7dfMQ7dhE1DiLDPQAEBwcjB07dqCsrAxlZWU4duwYpk2bhvLycmRnZ0Mmk6F79+4N2owZMwYAsGfPnvplGo0Ge/fuRWRkpEnrJyIiasroQX6iHLe1iw3uD9dxxgYiMjqLvCn2TlJTU6HVahEUFAQHh4aXLEePHo0HH3wQ06ZNQ1FREfz8/PDll18iNTUVe/fuFaliIiKihp56pBNe/+y4yeejnzo2CA72LS46EJk9i+2hb0pKSgqA24fbAHXj5bdv347x48fjrbfeQlRUFC5duoRff/21ftw9ERGR2JwcrDE5KtDkx31pQleTH5OI7o6B/m9cXV2xevVqFBYWorq6GsePH8fDDz9syhKJiIju6u0XwuHWytZkx3tlYjACO7Qy2fGIqPkY6FuYDqP6ou/8hk/C7TxxMKbkb4LfCN4nQEQkFV4eDlj+Rr+7b2gAHds5YcGr/IwgMlctbiBcfHy82CWIyu+RPsj46ff6n518PBEUPQwFSeniFUVERHp58pEA/PLHZfywK7PZbW4+MKo5D6ECAGu5DOs+HAAnB2u9aiQi42txgd7S2bg4YMz+xbCys0FFnhIyW2s4+7VFxqY/cOSNNWgb2QUHZ/31MC1BwP2fvYxj/16LyLmTxS2ciIh0JggC1n04AMWl1dhzOLdZbSKf3N7s/VtZCdgwfxAGRnjrWyIRmUCLG3Jj6WpKK5C5NQHnvozD9uFzcPzdr1GYfAGHX/8C3v27oyAxHVqVGgAQ8uJoFCSeR9GZ5vfsEBGRebG1scK2pcPw2NAOBt2vna0VNn8+FBMe8jfofonI8BjoLZBbd38Up2QBANzDOqH4bN2f/UZE4tLO4wAA1y6+6DCqD04v2SxanUREZBh2tnJs/nwoVr/b3yBDY/r3bIszmx7DmMGG/ZJARMbBQG+B3EI61od497AAFP0V7tsN6oHc+JMAgLZ9guHk2wbjDy/H48dXwvO+QPRb+BK6PPuQaHUTEZH+BEHAtMe74uyWxzBpRADkckHnffh6OWLZG33xx1ePcEYbIgnhGHoL4+DlBmi1qFAUAwDcgjvgzNLN8OgZiOsXc6GqqAIApH+7B+nf/u+JuCM2z8O5NTtweVeiKHUTEZFhdGjnjI2fDsbnhX3w5ZZ0/LQnC+cyr0Gt1ja6vauzDfr3bIsXxnfBqAd9IZezr49IahjoLYxbd//63nkAqCktR9fJD6O6uAyXdx0XsTIiIjIlb08HvPNiT7zzYk9UVKpw+kIR/rxcispqNeRWMrg626BHVzf4t3eGIOjem09E5kPQarWNf2UnUdVWVGFDp6cNtr8xvy/G7vFzUVVUarB93io6Yz2sHeyMsm8iIiIiahp76FuIbYNeFbsEIiIiIjICDpQjIiIiIpIwBnoiIiIiIgljoCciIiIikjDeFGumtFotVJXVYpfRbHJ7W86SQERERCQCBnoiIiIiIgnjkBsiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgn7f2Ec/HOg66AzAAAAAElFTkSuQmCC", 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    " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qc_0.decompose(\"cut_wire\").draw(\"mpl\")" - ] - }, - { - "cell_type": "markdown", - "id": "dcd8dea0", - "metadata": {}, - "source": [ - "### Specify an observable\n", - "\n", - "This observable has 7 qubits, just like the original circuit." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "847a3205", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:43.655017Z", - "iopub.status.busy": "2025-03-28T16:51:43.654586Z", - "iopub.status.idle": "2025-03-28T16:51:43.658036Z", - "shell.execute_reply": "2025-03-28T16:51:43.657602Z" - } - }, - "outputs": [], - "source": [ - "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])" - ] - }, - { - "cell_type": "markdown", - "id": "59730746", - "metadata": {}, - "source": [ - "### Transform cuts to moves\n", - "\n", - "The next step is to call `cut_wires`, which transforms each `CutWire` into a `TwoQubitQPDGate` which wraps a `Move` instruction. An additional qubit is added to the circuit for each `CutWire` in the input circuit.\n", - "\n", - "Notice that, unlike in the [wire cutting tutorial](../tutorials/03_wire_cutting_via_move_instruction.ipynb), where `Move` operations were placed manually, this function does not result in the _re_-use of a qubit. Because any method for qubit re-use is based on heuristics, this function naively allocates an additional qubit for each cut. Users wishing to re-use qubits might wish to experiment with [qiskit-qubit-reuse](https://github.com/qiskit-community/qiskit-qubit-reuse)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e4ee1559", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:43.660083Z", - "iopub.status.busy": "2025-03-28T16:51:43.659877Z", - "iopub.status.idle": "2025-03-28T16:51:43.924630Z", - "shell.execute_reply": "2025-03-28T16:51:43.924082Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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AgIei1AMAAAAA4KEo9QAAAAAAeChKPQAAAAAAHopSDwAAAACAh6LUAwAAAADgoSj1AAAAAAB4KEo9AAAAAAAeilIPAAAAAICHotQDAAAAAOChKPUAAAAAAHgoX6MDoHx2u13W/EKjY1SYb1CATCaT0TEAAAAAoFqh1FdR1vxCzWo+0ugYFTYi5XP5BQcaHQMAAAAAqhVuvwcAAAAAwENR6gEAAAAA8FCUegAAAAAAPBSlHgAAAAAAD0WpBwAAAADAQ1HqAQAAAADwUJR6AAAAAAA8FO+p9yJRPdqq/7xJZZYV5+YrKzVDKQk/KXnmYtlLbAalAwAAAAA4G6XeC6XOS1T6ys2SyaSgOrXUYmgvdZs0WjVbNtC6CTOMjgcAAAAAcBJKvRc6teOgUucmlv5676dLdXvi24q95wZtfm22Ck9lGZgOAAAAAOAsPFNfDVjzC3Vy836ZzGaFNa5ndBwAAAAAgJNQ6quJ0Cbny3zh2RyDkwAAAAAAnKValPrMzExNnDhRLVq0UGBgoBo2bKhx48YpNzdXDz74oEwmk6ZPn250TKfxDfJXQHioAiLCVKt1I3V/5Y+KaN9MJzfvV1ZqhtHxAAAAAABO4vXP1G/dulUDBgyQxWJRSEiI2rRpo2PHjmnatGlKSUnR6dOnJUnx8fHGBnWiThOHq9PE4WWWpS36Reuf+cigRDBaTl6xZi9O0c4DZ1RQVKKImoG6o19jdWlbx+hoAAAAcIPjp/L1n+8OKO1Ytmw2u+rXDdY9NzdXs5gwo6PhCnl1qc/MzNSgQYNksVg0fvx4Pf/88woNDZUk/fOf/9Rf//pX+fr6ymQyqUOHDgandZ69/1mmtO/Wyeznq9qtG6ndmNsUEh2hksKi0m3M/r4atOx1HfwmUdvfnle6/Nq3xiiwTi2tGPGyEdHhZLl5xXr2nU36ZP4+ZeUUl1n36sxt6taujl4cc5X+0DPGoIQAAABwpSOWHE18M0lzV6Sp2Fr29db/+NdmDbg2Rq+N66r2seEGJcSV8urb78eOHav09HQ98cQTmjJlSmmhl6SJEyeqY8eOslqtatKkicLCvOcnVFmpFmUk7tDRlVu08935+mHUa4qMb64ekx8p3cZWZNWase+o/dg7VLtNY0lSo/5dFXNjF639y7tGRYcTnc0qVO8HF+vtWbsuKPS/2rDzpG4es0wz5+11czoAAAC42p6DZ3X1yO/05fepFxR6SbLbpcWJ6eo5aqESN1kMSAhn8NpSn5ycrDlz5igyMlKvvvpqudt07txZktSxY8fSZb/+EKBbt24KCAiQyWRyS15XOrlxr1ISflLT23qqTpdWpctPbU/VrvcW6LppTyo4Olw9Xn9U6//2kfKPnzEwLZzBbrfrzvErtXFX5mW3tdnsevjFtVq+7qgbkgEAAMAdzmYVasBjS3XsRN5lt83OLdbgscuVms6rrz2R15b62bNny2azacSIEapRo0a52wQFBUkqW+oPHDiguXPnKioqSl27dnVLVnfYNjVBNmuJOk0YVnb5W3NlKynR4OWvy7J2pw7OX2tQQjjTT5ss+mH9sQpvb7PZNen9LS5MBAAAAHf6ZP5+pR2r+JuvzmYXaep/drkwEVzFa0v9ypUrJUl9+vS56Dbp6emSypb666+/XhkZGVqwYIH69evn2pBulJ1m0cH5a1X/+g6q2z2udLndWqKTSXsVGFFTB+asMjAhnOndOckO77N2y3Ft23vKBWkAAADgTjabXe995fj3g/9esF85eeU/tomqy2tL/aFDhyRJjRs3Lne91WrV2rXnr0r/ttSbzV77kWj72+evyv/2an3d7nFqMayPkmcuVrcX75dPoL+BCeEMdrtd3648VKl9K7sfAAAAqo7k1LPaf8jxW+mzc4u1agOvwPY0Xjv7fW5uriQpPz+/3PVz5sxRZmamQkND1bRpU5dm6dKliywWxyae8LOb9by6ObSPZd0ufRp950XXn9t/VJ/F/K/Q+wYH6tq3xmjTy7O0599LNeCbF3XVM/co6flPHRpXkmJbxqrYdOHkG1VNRq2/SOaayrBkKCbGO2d8t8lPReHPVWrf16e+pw9fXuLkRAAAAFVHdfh+sNC3kRT2YKX2ve+BxxRStM3JiVARUVFR2rhxo8P7eW2pj4qK0pkzZ7R582b16NGjzLqMjAxNmDBBktShQweXT4ZnsVh09Khjk5D5m3ykei4K9F9dX7hPOYdPaM+n30uS1oybrsErpujwkvU6/otjt+scyzimInuJK2I6V2iJZJZsJSUO/z/xHGapkm8kyc06o9zj3vq5AAAAqHp8PxjkJ1Xy5V5nT5/Q2XNe+rl4Ka8t9f369VNycrImT56sG2+8UbGxsZKkpKQk3XvvvcrMPD8reHx8vMuzREVFObyPn90sufDCd4O+ndR0cE/Nv2F86bLsQ8e16eVZ6jl1jBb0HS9rfmGFj1c/ur5nXKn38ZFNktnHR9ENGhgdx2WOW4/L6uv4T4VqB+co2Is/FwAAgOrw/aDN5COLvVh2k1/Fd7LbJZNJdcOK5VfDOz+Xqq4yvVGSTHa73e7kLFVCenq64uPjderUKfn6+qp169YqKCjQgQMHNGDAANlsNi1dulQffPCBHnrooXKP8cILL2jSpEky4iMqzivQrOYj3T5uZY1I+Vx+wYFGx7ismH6zdfREnhrUDVb6iruNjuMy//pyt554ZZ1D+9SpHagjy4crwN/HRakAAACMV12+H3zw+UR9/M0+h/bp3TVaq2be7KJEcBWvnRUuJiZGiYmJGjhwoAIDA5WWlqbw8HDNmDFDixYt0r5953+D/3aSPMBb3HtLC9Ws4dikhw/f2YpCDwAA4CXGDIu7/Ea/88Rwx/eB8bz29ntJiouL08KFCy9YnpOTo7S0NJnNZrVr186AZIBrhdXw11dT+mjgmGWyllz+TpPeXaP190c6uSEZAAAA3OGqNpF646luGj9lQ4W2HzM8Tnf0a+LaUHAJry71F7Nr1y7Z7XbFxsYqODj4gvUJCQmSpN27d5f5dZMmTdSlSxf3BQWuwE3XxGjxu3/QXU+t1Nnsootud/sNjfWfl3txlR4AAMDL/OW+9grw99Gf/7lexdby578ymaSJ93fQK2O7uHwCcbhGtSz1O3bskHTxW++HDh1a7q9HjRqlTz/91KXZAGe6sUcDHVk+XF8sTtH7X+3Rlj2nJJ3/4n3/bbF67K7W6tK2jsEpAQAA4CpjhrfRkH5NNHPePs38Zp8OHs2WJJnNJj01qp0eGdpazWIqOVU+qgRKfTm8dO5AVFM1gv308J2t9fCdrdXghi907GS+6tcJ1sxJ1xkdDQAAAG4QFRmsZx+O17MPx5d+PxgdGaTJf+5mdDQ4gddOlHcplyv13qzxwKt19WtlZ/tvMayPRmckqFH/rgalgrtwSxUAAED1xveD3qdaXqlfuXKl0REM0+jm7kr5enXpr2vE1FHsiH46sXGvcaEAAAAAAJVSLUu9N/MPC9atq6bKJ9BfeccyZQ7wU2ijekpJ+FHrnv5Q9bq20ppx089vbDLpmjce0/rnZqrr86OMDQ4AAAAAcBil3ssUZeUp9ZtEFecWaPvUBNXv3VEdxt6hn596X/V7ddSJpL2yW0skSW0fGaQTSXt0anuqwakBAAAAAJVRLZ+p93bh7Zrq9I6DkqSIDs11euf5/27Uv6sOLTn/nsparRqq8cDu2vbWXMNyAgAAAACuDFfqvVB42yalRT6iQzMdWZokSarfO14bX/pcklSve5xqNKyrIT+/I0kKqlNLPV5/VEF1a2vvZ8uMCQ4AAAAAcAil3ssER4VLdrvyLKclSeFxjbX97bmK7NRS5/YflTWvQJK097NlZcp7/7mTtPvDhTr8fZIhuQEAAAAAjqPUe5nwdk1Lr9JLUlFWrlqP+oMKT2fr8PcbDEwGAAAAAHA2Sr2XSV+xSekrNpX+euGApyVJt66eqqVDnr/oft9fYh0AAAAAoGqi1FcT83v/2egIAAAAAAAnY/Z7AAAAAAA8FKUeAAAAAAAPRakHAAAAAMBDUeoBAAAAAPBQTJRXRfkGBWhEyudGx6gw36AAoyMAAAAAQLVDqa+iTCaT/IIDjY4BAAAAAKjCuP0eAAAAAAAPRakHAAAAAMBDUeoBAAAAAPBQlHoAAAAAADwUpR4AAAAAAA9FqQcAAAAAwENR6gEAAAAA8FCUegAAAAAAPBSlHgAAAAAAD0WpBwAAAADAQ1HqAQAAAADwUJR6AAAAAAA8FKUeAAAAAAAPRakHAAAAAMBDUeoBAAAAAPBQlHoAAAAAADwUpR4AAAAAAA/la3QAlM9ut8uaX2h0jArzDQqQyWQyOgYAAAAAVCuU+irKml+oWc1HGh2jwkakfC6/4ECjYwAAAABAtcLt9wAAAAAAeChKPQAAAAAAHopSDwAAAACAh6LUAwAAAADgoSj1AAAAAAB4KEo9AAAAAAAeilIPAAAAAICH4j31XiSqR1v1nzepzLLi3HxlpWYoJeEnJc9cLHuJzaB0AAAAAABno9R7odR5iUpfuVkymRRUp5ZaDO2lbpNGq2bLBlo3YYbR8QAAAAAATkKp90KndhxU6tzE0l/v/XSpbk98W7H33KDNr81W4aksA9MBAAAAAJyFZ+qrAWt+oU5u3i+T2aywxvWMjgMAAAAAcBJKfTUR2uR8mS88m2NwEgAAAACAs3D7vRfyDfJXQHho6TP1re67SRHtm+nk5v3KSs0wOh4AAAAAwEmqxZX6zMxMTZw4US1atFBgYKAaNmyocePGKTc3Vw8++KBMJpOmT59udEyn6TRxuO7e9Ynu3vmxblv1puLu76+0Rb9o5ejJRkcDDGOz2bXn4Fn9su2Etu87rbx8q9GRAAAA4EaFRSXauf+0ftl2QrtTzshq9Y43g3n9lfqtW7dqwIABslgsCgkJUZs2bXTs2DFNmzZNKSkpOn36tCQpPj7e2KBOtPc/y5T23TqZ/XxVu3UjtRtzm0KiI1RSWFS6jdnfV4OWva6D3yRq+9vzSpdf+9YYBdappRUjXjYiOuB0p88V6pNv9+m9r5KVciS7dHnNUH+NGtRCjw+LU6umtYwLCAAAAJdKO5qtGQl79NG8fco8U1C6PKZeiB4Z2koPDWmtehFBBia8Ml59pT4zM1ODBg2SxWLR+PHjlZGRoc2bN8tisWjy5MlatGiRkpKSZDKZ1KFDB6PjOk1WqkUZiTt0dOUW7Xx3vn4Y9Zoi45urx+RHSrexFVm1Zuw7aj/2DtVu01iS1Kh/V8Xc2EVr//KuUdEBp9q+77Q6DJmnp97YUKbQS9K57CJN+2K32t0xT5/O32dQQgAAALjSglWH1Oa2uXpt5vYyhV6S0o/n6u/TN6vNbXO1ZrPFoIRXzqtL/dixY5Wenq4nnnhCU6ZMUWhoaOm6iRMnqmPHjrJarWrSpInCwsIMTOpaJzfuVUrCT2p6W0/V6dKqdPmp7ana9d4CXTftSQVHh6vH649q/d8+Uv7xMwamBZzjwOEs3fDQEh09kXfJ7awldt3/90R9uSTFTckAAADgDsvXHdWQv/yg/MKSS253+lyh+j+2VFuSM92UzLm8ttQnJydrzpw5ioyM1KuvvlruNp07d5YkdezYsXRZQkKChgwZosaNGys4OFitW7fWs88+q5wcz541ftvUBNmsJeo0YVjZ5W/Nla2kRIOXvy7L2p06OH+tQQkB5xr72roLfhp7KQ+/uFbZuUWX3xAAAABVntVq0/1//0nWEnuFts/Nt+qPL6yR3V6x7asSry31s2fPls1m04gRI1SjRo1ytwkKOv/cxG9L/ZQpU+Tj46NXXnlFS5Ys0WOPPab33ntP/fv3l83muRMpZKdZdHD+WtW/voPqdo8rXW63luhk0l4FRtTUgTmrDEwIOE/KkSx9vzbdoX2yc4v1xeJUFyUCAACAO3334+HL3rH5e5uTTylpp+ddrffaUr9y5UpJUp8+fS66TXr6+W/6f1vqv/vuO3311VcaMWKEevXqpXHjxmn69Olau3at1qxZ49rQLrb97fNX5X97tb5u9zi1GNZHyTMXq9uL98sn0N/AhIBzfDp/vyrzQ9aP5u11fhgAAAC43cxvKjdnkid+P2iye+L9BRXQsGFDpaena8uWLeXObG+1WhUdHa3MzEylpKSoWbNmFz3Wvn371KpVK33xxRe6++67Hc7SpUsXWSyOTbzgZzfreVs3h8dyhG9woAb/MEW7ZyzUnn8v1YBvXlTmthQlPf+pw8eaZN6gYlPVv5Mho9ZfZDPXlNl2TtFn3zQ6jttVl/M/HTJE+QGOT35ptuUq+uw/XZAIAABUFdXl+6GLqS7nf7zmGFl96jq8X0BxiiKzP3NBosuLiorSxo0bHd7Pa19pl5ubK0nKz88vd/2cOXOUmZmp0NBQNW3a9JLHWrXq/G3pcXFxl9zuYiwWi44ePerQPv4mH6lepYarsK4v3Kecwye059PvJUlrxk3X4BVTdHjJeh3/JdmhYx3LOKYi+6UnoKgSQksks2QrKXH4/4lXqC7n37BACnB8N5vd5N2fCwAAqD7fD11MdTn/EJvk4/huhYVFHve5eG2pj4qK0pkzZ7R582b16NGjzLqMjAxNmDBBktShQweZTKaLHufo0aP6+9//rv79+1f6XfZRUVEO7+NnN0suvPDdoG8nNR3cU/NvGF+6LPvQcW16eZZ6Th2jBX3Hy5pfWOHj1Y+u7xlX6n18ZJNk9vFRdIMGRsdxu+py/mcDS5Rbif18laN6Xvy5AACA6vP90MVUl/M/ac5XZaZADvIrVrhBn0tleqPkxbffjx07Vu+8844aNmyoFStWKDY2VpKUlJSke++9V6mpqSouLtaYMWM0ffr0co+Rk5Oj3r17y2KxKCkpSdHR0W7LX5xXoFnNR7ptvCs1IuVz+QUHGh3jsmL6zdbRE3lqUDdY6Sscf5TC01WX89+0O1Ndhs93eL+Xn+ysvz0U7/xAAACgyqgu3w9dTHU5/4/m7tVDkxyfE23xv27SgOsauiCR63jtRHkTJ05URESEjhw5orZt26p9+/Zq2bKlunXrpmbNmqlv376Syk6S91v5+fkaNGiQDh48qGXLlrm10AO4Mp3bRKpbuzoO7ePna9aDd7RyUSIAAAC4090DmqlmqGOTgDeLCdUfesa4KJHreG2pj4mJUWJiogYOHKjAwEClpaUpPDxcM2bM0KJFi7Rv3/nZEMsr9cXFxbrzzju1ceNGLVmyRG3atHF3fABX6I2nusnfr+Jf4v7xaLzqRQS5MBEAAADcJSTYT//8c9cKb282m/TWxKtlNl/80eyqymufqZfOT2y3cOHCC5bn5OQoLS1NZrNZ7dq1K7Pu13fb//DDD1q8eLG6dXPtDPQAXOPaq6KU8MYNGjZhpfILLz2J44TR7fUst90DAAB4lYfvbK2zWUX661tJl9zO18ekmZOu06DejdyUzLm8utRfzK5du2S32xUbG6vg4OAy68aMGaOvv/5aTz/9tIKDg/XLL7+UrmvevLnq1HHsll4AxhnUu5E2fDFYb3y2U7OXpKqwqGy579stWmNHtNWtfRoblBAAAACuNPGBDurcJlJTP9+pxYlH9NsZ5Xx9TLqjXxONv6+9urX33J5XLUv9jh07JJV/6/2SJUskSa+99ppee+21Mus++eQTjR492uX5ADhPu5bh+uSl6zVlfDet3JChhyet0dnsItUND9QPH91sdDwAAAC42A1X19cNV9fXwfRsdR7+rc5kFal2mL92fTNE0XWCL3+AKs5rn6m/lEuV+rS0NNnt9nL/8YZC33jg1br6tYfKLGsxrI9GZySoUf+KP3MCeJqIWoEaelNThQSd/1mmn2+1/PIHAABQbTWNCVVw4PnvBYMDfb2i0EuUeoOTuF+jm7vr8PcbSn9dI6aOYkf004mNew1MBQAAAACojGp5+/3KlSuNjuAy/mHBunXVVPkE+ivvWKbMAX4KbVRPKQk/at3TH6pe11ZaM276+Y1NJl3zxmNa/9xMdX1+lLHBAQAAAAAOq5al3psVZeUp9ZtEFecWaPvUBNXv3VEdxt6hn596X/V7ddSJpL2yW89PFtb2kUE6kbRHp7anGpwaAAAAAFAZ1fL2e28X3q6pTu84KEmK6NBcp3ee/+9G/bvq0JLzt97XatVQjQd217a35hqWEwAAAABwZbhS74XC2zYpLfIRHZrpyNLz72Ws3zteG1/6XJJUr3ucajSsqyE/vyNJCqpTSz1ef1RBdWtr72fLjAkOAAAAAHAIpd7LBEeFS3a78iynJUnhcY21/e25iuzUUuf2H5U1r0CStPezZWXKe/+5k7T7w4U6/H2SIbkBAAAAAI6j1HuZ8HZNS6/SS1JRVq5aj/qDCk9nl5n1HgAAAADg+Sj1XiZ9xSalr9hU+uuFA56WJN26eqqWDnn+ovt9f4l1AAAAAICqiVJfTczv/WejIwAAAAAAnIzZ7wEAAAAA8FCUegAAAAAAPBSlHgAAAAAAD0WpBwAAAADAQzFRXhXlGxSgESmfGx2jwnyDAoyOAAAAAADVDqW+ijKZTPILDjQ6BgAAAACgCuP2ewAAAAAAPBSlHgAAAAAAD0WpBwAAAADAQ1HqAQAAAADwUJR6AAAAAAA8FKUeAAAAAAAPRakHAAAAAMBDUeoBAAAAAPBQlHoAAAAAADwUpR4AAAAAAA9FqQcAAAAAwENR6gEAAAAA8FCUegAAAAAAPBSlHgAAAAAAD0WpBwAAAADAQ1HqAQAAAADwUJR6AAAAAAA8FKUeAAAAAAAP5Wt0AJTPbrfLml9odIwK8w0KkMlkMjoG4BU87c8//oevhQAAwN0o9VWUNb9Qs5qPNDpGhY1I+Vx+wYFGxwC8gqf9+cf/8LUQAAC4G7ffAwAAAADgoSj1AAAAAAB4KEo9AAAAAAAeilIPAAAAAICHYqI8eLWSEpv2pp3Tpt2Z2nPwnM5mF0mSsnKL9c0PaercJlINo0K8erbq46fytWl3prbuOaVzOefP/1xOkT5M2KMubSPVtkVt+fv5GJzSdQoKrdq+74w27jqpw5bc0t8D2bnFWr7uqDq3iVR4zQCDUwIAAACVQ6mHV9q8O1P/mpOsr5YeVE5e8QXrs3OLdceff5Ak1a8brAdui9XDd7ZSw6ga7o7qEqfOFuiTb/frg7l7tP9Q1gXrc/KsevjFtZIkfz+zBvdupMeHxal312iv+AFHSYlNS9ak6905yVr+y1FZrfYLtsnKLdZNj3wvSWrfsrYeHdpa9w5qodAQf3fHBQAAACqNUu9Fonq0Vf95k8osK87NV1ZqhlISflLyzMWyl9gMSuceW/ec0phXftbPW09UeJ9jJ/L0fx9s1SsfbdPw/s00dUJ31Y0IcmFK18nJK9az0zZqRsJeFRaVVGifomKbEpanKWF5muKa1dI7T/fQDVfXd3FS15m3Ik3jp6xX2rGcCu+zY/8ZjXllnf761kb95d52evbhjl599wIAAAC8B8/Ue6HUeYn66Ym39dOT07T1ja9l9vVRt0mjdfVrDxkdzWWKiks06b3N6nrPfIcK/W/ZbHZ9sThFbe+Yp6+Wpjo5oeutTspQhyHfaNoXuytc6H8vOfWs+j28RI+9tFbZuUVOTuhamWcKNHziSg35yw8OFfrfyskr1osztqjL8PnavDvTyQkBAAAA56PUe6FTOw4qdW6iUhN+0q73FmjRwL8p92imYu+5QQERYUbHc7rs3CL1f3SpXnhvS7m3WTsq80yBhk1YpaemrJfdfuXHc4d3v9ytvn9crINHs51yvPe/3qMeI7/TsRO5Tjmeq+1LO6fOw7/VnO8POuV4O/af0dUjv/PIH+4AAACgeqHUVwPW/EKd3LxfJrNZYY3rGR3HqXLzijXg8WValZTh9GO/8dlO/WnyL1W+2E+btUtjXlknZ8fclXJWvR5YLEtmnnMP7GQHDmfp+vsX6XCGc38AUWy16e6/rtac7yn2AAAAqLoo9ZIWLFigxx57TN26dVODBg0UEBCg0NBQxcfH629/+5syMz3/NtzQJufLfOHZyt2WXBXZ7Xbd9+xPWrvluMvGmPbFbr31+S6XHf9KLVh1SOMm/+Ky4x84nKWBY5apqLhyt/O7WlZOkW565HsdP5XvkuPbbHaN/Ntqrdvmut9jAAAAwJWg1Et688039f7772vbtm3y9/dXhw4dFB4eru3bt+vVV19VmzZttHXrVqNjVphvkL8CwkMVEBGmWq0bqfsrf1RE+2Y6uXm/slKdf0XbKLMWpWjeD2kO7ZM0e7COLB+upNmDK7zPM29v1J6DZx0L5wanzhaUzmBfUZU5/83Jp/TqR9scjecWE97c4PAjB45+BlarXaOfS1R+gbUyEQEAAACXotRLeuCBB/TDDz8oOztbBw8eVFJSkg4dOqRt27apXbt2OnnypO655x6jY1ZYp4nDdfeuT3T3zo9126o3FXd/f6Ut+kUrR082OprTZJzM09jX1jm8X1RksGLqhSgqMrjC+xQWlej+v/+kkir25oCxr/3i8BXqypy/JP3fh1u1dc8ph/ZxteXrjuqDhL0O71eZz2DfoXP6+/RNDo8FAAAAuBqlXtJ9992nvn37yt+/7Pup27dvr5kzZ0qSkpOTlZycbEQ8h+39zzItvWuSlo94WRtf+o8KTmcrJDpCJYX/m83c7O+rW1dPVYdxd5TZ99q3xqjfrGfdHdlhb362U2ey3Dc7+y/bT2pxYrrbxruc3Sln9MXiFLeNZ7Xa9eKMLW4bryLcXbKnfbHbZbf5V4rJpDYP36LbE9/WvQe/0NCN76vL8/fJNyjA6GQAAABwI0r9ZcTFxZX+d15e1Z4w7FdZqRZlJO7Q0ZVbtPPd+fph1GuKjG+uHpMfKd3GVmTVmrHvqP3YO1S7TWNJUqP+XRVzYxet/cu7RkWvkPwCqz7+dp/bx313TtX5oc57X+1x+5jzVx1WuqVqzIa/aXem1u846dYxi602zZzn+J0BrtLtxdHqNmm0zu5L1y/Pfay0hevU5sGbdcNnT0smk9HxAAAA4CaU+stYu/b8M8shISFq1aqVwWkq5+TGvUpJ+ElNb+upOl3+dw6ntqdq13sLdN20JxUcHa4erz+q9X/7SPnHzxiY9vISlh/U6XOFbh936c/pSk3Pcvu4v5ebV6x/L9jv9nFtNrs+mOv+HyaU5/2vjPkBy4yEPbLZjH8bQq3YGMU9MEBpi37Rqgdf1/5ZK5T0wr+14YV/K/ra9mp6W0+jIwIAAMBNKPXlsNlsysjI0GeffabRo0dLkl555RXVqFHD2GBXYNvUBNmsJeo0YVjZ5W/Nla2kRIOXvy7L2p06ON+xideMsHzdMUPGtdulleuNn2hww86Tys4tNmTsFb8Y89n/3nKDchzOyNX+Q+cMGfu3mt5+rUxms3Z/uKjM8v2zVqg4r0DNh1xvUDIAAAC4G6X+N95//32ZTCb5+Piofv36GjVqlGJiYvTdd99p7NixRse7ItlpFh2cv1b1r++gut3/90iB3Vqik0l7FRhRUwfmrDIwYcVtSjbuFYObdhv/esNNu42bsG7r3lOyWo2dMPDU2QIdOmbcqxmN/Px/FRnfQraSEmVuKXvHRklhsU7vTFNkfHODkgEAAMDdKPW/ER0drZ49e6p79+6qX7++TCaTtm/fri+++ELnzhl/de5KbX/7/FX5316tr9s9Ti2G9VHyzMXq9uL98gn0v8QRjJebV6zk1LOGjb+xSpR64zLkF5QY+vlL51+xZ6SNu937LH95guvVVuHpbNmKLnzNXp7ltAIjasrs52tAMgAAALibyW63G/+AaBW1Y8cOjRkzRomJierZs6fWrFlTqeN06dJFFovFoX387GY9b+tWqfEqyjc4UIN/mKLdMxZqz7+XasA3LypzW4qSnv/U4WNNMm9Qscn1V3Ct5lo6XuvPF12fNHvwJV9VFhUZJF8fs6wlNlkyLz6TuSUzT13vXnDBcp+Ss4o6N9Wx0E52MnS0ivyalrvOWecvXfwziMj6twKtqY6FdqI8/w46U2NIuesud/7Slf8eCCrcrvDcuY6FdtDl/vzfsW66zH4+Sujy2AXrrp32pFoM7aUvWt2noizPmNzTm7jrayEAoPIyav1FNnNNmW3nFH32TaPjuB3nX3XPPyoqShs3bnR4Py7lXEL79u21aNEiNWvWTGvXrtWKFSvUr18/h49jsVh09OhRh/bxN/lI9RweyiFdX7hPOYdPaM+n30uS1oybrsErpujwkvU6/otjE5EdyzimInuJK2KW5W+Val189a/vIL8cXx9zhbb7vRKb2eH/l07XrETyK3+Vq89fkk6dPitlG/gZ1G4qXWR6i4qev1T5zyC/oMjlvwcu9+e/JL9QfiE1y13nE3D+N4c1332vfMT/uO1rIQCg8kJLJLNkKykx/vs6I3D+Xnf+lPrLCA0NVa9evTR37lxt27atUqU+KirK4X387GbJhRd7GvTtpKaDe2r+DeNLl2UfOq5NL89Sz6ljtKDveFnzKz7DfP3o+u67Un+J9ZbMS1+ZdOQqbXl8zDZFNWhQkaguc9LfrIvVNWed/6WOFRFeU4Fhxn0Gef6hutj7GS53/tKV/x4ICvRXuIt/D1zuz3/e8TOqGRsjs7/vBbfgB0eFq+DUOdmKL7w1H67nrq+FAIDKy/DxkU2S2cdH0QZ/X2cEzr/qnn9leqNEqa8Qq/X8N8clJZW7+lKZWyiK8wo0q/nISo1XEUdXbtEXrUddsHzPp9+XXrl3xL79++QXHOiMaJeUX2BVaI/PVFJS/lMj5d0u/VtHlg9XTL0QWTLz1fDGLx0ev2unllr3ebrD+znTyGdWa9ailHLXufr8JSlx5XeKa1arUvs6w8r1x3TDQ0vKXXe585eu/DN44pER+udf3nZ4P0dc7s9/5tYDatA7XpGdWurE+v/dVeMT4Kfwdk0cvtMGzuOur4UAgMqL6TdbR0/kKToqWuk7jf2+zgicv/edPxPlXcbp06f1448/SpLi4+ONDQMFBfqqjYGFsnObSMPGrgoZQoJ8Fds4zLDxJalTXISh41eF3wMH5/8su82mNg8NLLO85Yh+8gsOVOq8nwxKBgAAAHer9qV+48aN+sc//qEDBw5csG7Lli0aMGCAzp49q/bt2+uGG24wICF+z8hSVRUKXWcDS22n1hHy8TH2y0btsAA1iwk1bPyq8Hvg7J7D2vPJ92oy8Gr1mTlBLe+5QV2ev0/dXhgly8+7lDqvcpN6AgAAwPNU+9vvc3Jy9NJLL+mll15S3bp11bBhQ/n4+Cg9PV3Hjh2TJMXGxurbb7+Vj4+PwWkhSf17xujT+fsvv6GTmc0m9bu6vtvH/b2u7eqodpi/zmS5fyK0/j1j3D5mefr3jNG7c9x/i3nzhqFq3tC4Hyj81oZ/fKqcIycVO7KfYm64SgWns5T88RJt+ecciZeaAAAAVBvVvtR37NhRb7/9tlatWqWdO3dq3759KigoUHh4uPr166fbb79dDzzwgAIDeUayqrj9hsaqGx6oE6cL3DruoF4N1TDqItOuu1FQoK8euC1Wb3y2063j+vma9eAdsW4d82Ieu6u1IaX+sbviZDKZ3D5ueew2m3bN+E67ZnxndBQAAAAYqNqX+tq1a2vs2LEaO3as0VFQQf5+PnpoSCu9/OE2t477+LA4t453KY/eFef2Uj+kX5PLvgPeXdq1DNf1naP00yaL28YMDPDR6Ftbum08AAAAoCKq/TP18Ex/GtlOdcPdd/dEn67R6nd11XnlRYtGYXpoSCu3jRfg76PnH+vktvEq4uUnO8udF80nju6giFrcsQMAAICqhVJfzTQeeLWufu2hMstaDOuj0RkJatS/q0GpHBdZO1Dv/72nW8YKCfLVzEnXyWyuGrdd/2rK+G5qGBXilrFeGnOVWjet5ZaxKuraq6I0bkRbt4zVsVW4nn24o1vGAgAAABxBqa9mGt3cXYe/31D66xoxdRQ7op9ObNxrYKrKuf2GJrpvUAuH9rFk5in9eK4smXkV3uetiVerqYGzrV9MWA1/ffyiYz9sqMz5X9upnv5yX7vKRHS5l5/sojbNazm0j6OfQWCAjz596Xr5+zFRJgAAAKqeav9MvbfxDwvWraumyifQX3nHMmUO8FNoo3pKSfhR657+UPW6ttKacdPPb2wy6Zo3HtP652aq6/OjjA1eSR88f62On8rX0p+PVmj7rncvcOj4/3ikk/7oxtvcHdXv6gaaOela3f/3xApt7+j5t29ZW/On3Wj4a+wuJjjIV0vf669rRy/UoWM5FdrHkc/Az9esuW/eoPjWxr1GEAAAALiUqvmdOiqtKCtPqd8kavdHi7Tgxgna8I9PdHLzPv381PuK7tlOJ5L2ym4tkSS1fWSQTiTt0antqQanrrwAfx99+3Y/3dqnkdOP/X9PdNYLj1et58jLM/rWWH3+ai/5+jr38YBu7epo1cybFV4zwKnHdbaYqBAlfjpQrZrUdOpxgwJ9tGDajbr5uoZOPS4AAADgTJR6LxTerqlO7zgoSYro0Fynd57/70b9u+rQkvO33tdq1VCNB3bXtrfmGpbTWQIDfDVvaj+9NbG7ggKv/BbphlEhWvr+H/Tsw/FV5vVllzNiYAut/3yw2resfcXHMptNeubBjvrp04EeMzFcw6gaSpo9WI8Obe2U410TX1db5tym/tfGOOV4AAAAgKtQ6r1QeNsmpUU+okMznfpvwa/fO15HV26RJNXrHqcaDetqyM/v6M4N76rOVS3V4/VH1eq+mwzLfSXMZpPGjWyn7Ql36ObrKlfEAgN89PiwOO2Ye4duusbzytxVbSK18ctb9fyjnVQz1L9Sx+jRsa5++XyQXhnXRQH+nvUMeWiIv977e0+t+GCAOsSGV+oYdcMD9eaE7vrpk4FqVcUmBgQAAADKwzP1XiY4Klyy25VnOS1JCo9rrO1vz1Vkp5Y6t/+orHkFkqS9ny3T3s+Wle7Xf+4k7f5woQ5/n2RIbmdp0ShMi/71Bx04nKX3v0rW7O9TdezEpSdEa9O8lh68PVajb42t8reaX46/n49eePwqTRjdXl9+n6oPEvZq4+5M2Wz2i+4TXjNAQ/o10WN3tVanuEg3pnWNG66ur61f36aft57Qv77crUWJR5SVU3zR7f18zerRsa4eHdpad/Rr4nE/zAAAAED1Rqn3MuHtmpZepZekoqxctR71BxWezi4z6723a9EoTFOe6q4pT3XXsRO52rT7lPamnVNegVVmsxQW4q8OseHq1Dqi0le1q7KQYD89eEcrPXhHK+XlW7Vt3yltST6ls9lFKrbaFBjgo+YxYerSNlKN69fwmMcMKspkMqlnp3rq2amebDa7DhzO0qbdmTqUkaPCohL5+ZoVWTtQV8VFqH3LcIo8AAAAPBal3sukr9ik9BWbSn+9cMDTkqRbV0/V0iHPX3S/7y+xztPVrxui+nVDNMjoIAYJDvJVj4711KNjPaOjGMJsNim2SU3FOnkiPQAAAKAqoNRXE/N7/9noCAAAAAAAJ2OiPAAAAAAAPBSlHgAAAAAAD0WpBwAAAADAQ/FMfRXlGxSgESmfGx2jwnyDPPtVcAAAAADgiSj1VZTJZJJfcKDRMQAAAAAAVRi33wMAAAAA4KEo9QAAh/mHBSt+/F2K6tHW6CgAAADVGqUeAOAw/7AQxT91l6KuodQDAAAYiVIPAAAAAICHYqI8AKgGzH6+avPQQDW7/VqFNa8vW7FVWQctOjBnlfZ88r0k6dq3xqjFsD76NPrOC/YfnZGgA3NWac2f/qWoHm3Vf94kSVL8U3cp/qm7JEk5R04oodvjFc706zEPfP2jrnr6boW3baLCMzlK/niJdv7rW/nXDFGXf9ynhjd1kV9IoDLW7NTPE95X/vEzZY5TI6aOOv11uOr36ij/sBDlZpxS2oKfte2tBJXkF0mSWt13k3pMflg/jHpNR5ZtLBvEZNLQje+r8HSWFtw4oXRxRMfm6jDuDtXrHie/kCDlHDmhlIQftWP6t7KX2Cp8ngAAAK5EqQcAL2f289WNs59TdM92Orp6q1LmJqqksEi14xqp8c3dS0t9RZ3dn64N//hE3V68X4cWr9ehxb9Ikqy5BQ5nC2/XVL1v7KJ9s5Yr5esf1WTwNery3EiVFBapxV29lXPkpLZO+UphTaMU9+DNum7ak1o27MXS/UNiIjVwyWvyDw3Wnn8vVVZqhqKuaasOY+9Q3a6ttHToJNlLbDo4f626TRqt5kN7XVDqo69rr5D6Edo147vSZTE3XKU+MycoK82iXe9/p8KzOarbOVbxE4YpvG1TrX74DYfPFQAAwBUo9QDg5do8NFDRPdtp+7R52vzqF2VXmkwOH68g85wOL9mgbi/erzO7Dyl1bmKls9WOa6RFtzyrzC37JUn7Z6/UnUnvqduk0Ur++Htt+PvHZbZv+8gghTWvr6yUY5Kkq54ZoaDImlo+4mUdXblFkrT330uVm35S7R6/VS3u6q39s1eq6FyujizfpIY3dpZ/zRAVncstPWaLob1kK7Yqde5PkiSfAD/1fPMxndyyX0vvfKH0qvy+/yzX6d2H1G3SaEX1aCvLul2VPm8AAABn4Zl6APByze64ToVnsrXtza8vXGm3uz/Qb5zYuK+00EuSrdiqzC0HZDKblTxzcZltj69PliSFNY0+v8BkUqObuujUjtTSQv+r7e98I1tJiRoN6F667MBXq+UT6K+mt/YsXeYbHKhGA7rp6KqtKjiVJUmKvr6DgurW1oEvV8m/ZogCwkNL/0n/YbMkqX7vjs77EAAAAK4AV+oBwMuFNYvW6Z1pKiksNjrKBXIOH79gWdG5nP+uO1F2+dnzV9cDateQJAVGhMmvRpDO7j1y4THO5ij/+FmFNq5buuzoqi3KP3lWze/spb2fLZMkNR7YXX4hQTrw9Y+l29VqGSPp/BwDFxMYWbNC5wcAAOBqlHoAgCTJfpGr9iYf193UdakJ5+y28teZKvHIwK9jpX6zRm0fvkWhTaKUnWZR86G9VXgmW0eWJf12AElS0qTPdHrXwXKPlfe7yfoAAACMQqkHAC+XlXJMNVvUl9nfV7Yi60W3Kzx7/gq5f60aKvrvf0tSaON6F2xrl7G37UtSwaksFWXnqVarhhes868ZoqB6tXR6V1qZ5SlfrVbbh29R86G9tG/WCkVd00b7Pl9R5nPJOpghSbLmFSgjcYdLzwEAAOBK8Uw9AHi5lHmJCqgdqo5/uvBVdb+VlXq+zNa/rn2Z5W0fHXTBtr/OdO9fq4aTUlaC3a4jyzcpon0zNegTX2ZV+ydul9nHR4eXrC+z/PSuNJ3elabmQ65X8zt7yezjo5SvVpfZ5tiqrco/eVbtn7y93PPzCfSXb0igs88GAACgUrhSDwBeLvmjxWp4Uxd1/POdioxvrqM/blNJQbFqtWqoms3rl74i7uA3a3TV0/fomtcfVc0WDVR4NkcN+nRSYHjoBccsPJOjrNQMNb2tp7IPWVRw8pyK8wqUvnyTW89t86uzVP/6Dur78cTzr7Q7aFHU1W3U9LaesqzbpQO/K+ySdODr1er2wmi1H3Orzh04qpOb95dZb80vVOLYd9T344m6Y83b2j97lbLSLPIPC1bNFg3U+ObuWvXA68x+DwAAqgRKPQB4OVuxVcuHv6S2jw5S09uvU+en71FJYbGyDmZo/5erSrcrzsnXipGvqOsLo9Rh7B0qzivQocXrlfjE27pn72cXHPenMW+r66TRuuqZe+QXHKicIyeU4OZSn5ueqUU3P6NOE4ep2ZDr5R8WrLyM09o+bZ62vZVQ7jP7qXMT1eXZkfIPC9HOd+eXe9xjq7dp4YCn1f7J29VsyHUKjAhT0blcZacd164PFup08iFXnxoAAECFmOwXmxkJAGCI4rwCzWo+0ugYqIQRKZ/LL5hb8wGgKovpN1tHT+SpQd1gpa+42+g4bsf5e9/580w9AAAAAAAeitvvAQBOExARJrP50j8vLs4tkDWvwE2JAAAAvBulHgDgNIOWvKYaDetecputU77S1je+clMiAAAA70apBwA4zU9j3pZPoP8lt8k+dNxNaQB4KrvdLmt+odExUAm+QQEymUxGxwCqFUo9AMBpTiTtNToCAC9gzS9kwlAPxYShgPsxUR4AAAAAAB6KUg8AAAAAgIei1AMAAAAA4KEo9QAAAAAAeChKPQAAAAAAHopSDwAAAACAh6LUAwAAAADgoSj1AAAAAAB4KEo9AAAAAAAeilIPAAAAAICHotQDAAAAAOChKPUX8eGHH8pkMslkMql3795GxwEAAAAA4AKU+nJYLBZNnDjR6BgAAAAAAFySr9EBqqInn3xSWVlZuuWWW7Rw4UKj4wDABdo/ebsi2jdTRIdmCm1cTzlHTiih2+NGx0IVdupsgRb+eEQnzxQowN+suGa11LdbfZnNJqOjuUVJiU3L1x3TvkPnVGy1qW54oAb1aqRaYQFGR3ObfWnntCopQ1k5RaoR7KdrO9VT+9hwo2NVnsmkNg8NVKt7b1SNmDoqOJWlg9/9rK3/nCNrfqHR6QDAbSj1v7NgwQIlJCToySefVHh4OKUeQJXU+W8jVHA6W6d3pMo/LNjoOKjC9hw8q1c/2qY5Sw+qsKikzLrmDUP1+LA4PXl3W/n5eefNe4VFJZr6n516/+s9OnQsp8y6oEAfjbi5uZ75Y0c1iwkzKKHrLV93VK9/ul3L1x27YF3PTvU0/r52uv2GJu4PdoW6vThabf44UIcWr9fO979TrZYN1ObBmxXRrqmW3vWiZLcbHREA3IJS/xvZ2dkaM2aM6tevr//7v//Tm2++aXQkAChXQvfHlXP4hCTp1lVvyi8k0OBEqIp+3JihwWOXKyunuNz1KUeyNX7KBi1fd0zzpt6goEDv+rYgK6dIg55crp82Wcpdn19Qoo/m7dM3Kw9p8b/+oG7t67g5oeu9/flO/emf6y+6fu2W41q75biefaijXnqis0wmz7hzo1ZsjOIeGKC0Rb9o9R+nlC7PPnxCV7/8oJre1lMHv1ljYEIAcB/v/LF8JT3zzDNKT0/XW2+9pbAw7/2JPQDP92uhBy5m14EzGvTkxQv9b32/Nl0jn/lRdi+6smmz2TX0qZUXLfS/depsoW4es1QpR7LckMx9Zi06cMlC/1svf7hNb3++y8WJnKfp7dfKZDZr94eLyizfP2uFivMK1HzI9QYlAwD3o9T/17p16/Tee+9pwIABGjp0qNFxAAC4Ii+8t1nZuZcv9L+a90Oaft7qPT8sWro2Xct+Plrh7U+dLdSrH21zYSL3Ki62aeLUJIf2+ce7m5WdW+SiRM4VGd9CtpISZW7ZX2Z5SWGxTu9MU2R8c4OSAYD7UeolFRcX66GHHlJAQICmT59udBwAAK7IsRO5+mblIYf3e3dOsgvSGKMy5/LFkhSdyfKOCda++/Gwjp3Ic2if7NxizVqU4qJEzhVcr7YKT2fLVmS9YF2e5bQCI2rK7Oddj5MAwMVQ6iW99tpr2rVrl5577jk1a9bM6DgAAFyRuSvSVFLi+K30Xy87qOJimwsSude57CItSjzi8H75BSVasOqwCxK535ffp1Zqv9lLKrefu/kEBaikqPw7UUoKzy/3DfJ3ZyQAMEy1/xHm3r179fLLLysuLk4TJkxwyRhdunSRxXL5Z/oAQJL87GY9r25Gx0AlxLaMVbHJ+FKcFdRXCurl8H7FVpsaNWstH7tjV3irGqu5tuy1/lSpfceOf07Pjlnr3EAGOBl6v+TXxOH9ft6wQzExjzg/kIMu93WwJL9QfiE1y13nE+AnSbLme8ajBN6mqnwdvJSMWn+RzDWVYclQTEyM0XHcjvOvuucfFRWljRs3OrxftS/1jz32mAoLC/X+++/Lz8/PJWNYLBYdPVrx5/oAVG/+Jh+pntEpUBnHMo6pyF5y+Q1dre5ZKahyu1qOpUu2fKfGcTu/fKlW5XbNOntGWae84O/spnlSJb6tsRYVVonvWS73dTDv+BnVjI2R2d/3glvwg6PCVXDqnGzFF96aD9erMl8HLyW0RDJLtpKSKvH73e04f687/2pf6jdv3iyz2ay77rrrgnU5OeffZ/vzzz8rKipKkrRv3z6HZ8b/dV8AqAg/u1mq2hc5cBH1o+tXiStUef7FOlOJ/cy2HEVFR8gkz54F3y4fZdgLZDc5/qrH8FCrggIbuCCVe531zVVuJfYL9MlSRAPjz/9yXwcztx5Qg97xiuzUUifW/2/+BJ8AP4W3a6Ljv3jP/BCepqp8HbyUDB8f2SSZfXwUXQV+v7sb5191z7+yvbHal3pJstlsOn78+EXXFxcXl6632Rz/IlWZWygAVF/FeQWa1Xyk0TFQCfv275NfsONF0tkKCq1q0O9LnT7n2KRvTz/cUy+PHeeiVO715KvrNH32bof2iakXooObV8jX1/OnHNq8O1Odh893eL9vPvqz+l/7hgsSOeZyXwcPzv9ZHcbeoTYPDSxT6luO6Ce/4EClzvvJHTFRjqrydfBSYvrN1tETeYqOilb6znSj47gd5+995+/5f2tdobNnz8put5f7z/PPPy9J6tWrV+myWrVqGRsYACQ1u/N6dfjTEHX40xAFRoTJLzS49NfN7uT9zNVdYICvHrgt1qF9zGaTHr6zlYsSud9jd7V2eJ9HhrbyikIvSVe1iVT39nUc2qdZTKhuuqZqXbW6mLN7DmvPJ9+rycCr1WfmBLW85wZ1ef4+dXthlCw/71LqvDVGRwQAt+FKPQB4oNi7b1DUNW3LLLvqr3dL0vlvaBO4SlXd/ePReK1Yf1Rb95yu0PZv//VqNa4f6uJU7tOmeW29/GRnPfvOpgptf018XT01qr2LU7nXxy9ep2vuXahzOZefMC4wwEezXu0ts9nkhmTOseEfnyrnyEnFjuynmBuuUsHpLCV/vERb/jlHsnv2IyQA4AhKPQB4oO+HPG90BFRxoSH+WvZ+fw0eu1y/bD950e1MJumtiVfribvbuDGdezzzx46SdNli36drtOZNvUGBAd71bVGb5rW18qMBGvjEMlkyLz75Ya1Qf33zVj9d3bGuG9NdObvNpl0zvtOuGd8ZHQUADOUd95gBAIAL1AkP0k+f3KKEN/qqb7foMutMJulPI9sq+dshGjui7UWO4NlMJpP+9lC8ds67Q2OGxyk0pOx08P17xmjBtBu1/IP+qhUWYFBK17qqTaT2zL9T7zzTQ3HNapVZ52M26Z9/7qr9C4eqd9fo8g8AAKjyKPWX8MILL8hut2v16tVGRwEAoFL8/MwacmNT/fDRzcr8aYTqhp+fwCo6MkhTJ16tVk1rGRvQDdq2qK3pf7tGx1fdo3oR/zv/Je/9QYN6N5KPj3d/O1Qz1F9P3N1Gu765Q0dXDC/9PVAvIlAT7u+gyNpVe1IzAMClefffYgAAoFRErUD5/XciOJPJc56ddpagQF/5/rfAe9Kz485iMplUv25Itf49AADeiFIPAAAAAICHotQDAAAAAOChKPUAAAAAAHgoSj0AAAAAAB6KUg8AAAAAgIei1AMAAAAA4KEo9QAAAAAAeChKPQAAAAAAHopSDwAAAACAh6LUAwAAAADgoSj1AAAAAAB4KEo9AAAAAAAeilIPAAAAAICH8jU6AACgLN+gAI1I+dzoGKgE36AAoyMAAIBqhlIPAFWMyWSSX3Cg0TEAAADgAbj9HgAAAHCQf1iw4sffpagebY2OAqCao9QDAAAADvIPC1H8U3cp6hpKPQBjUeoBAAAAAPBQPFMPAAAAr2f281Wbhwaq2e3XKqx5fdmKrco6aNGBOau055PvJUnXvjVGLYb10afRd16w/+iMBB2Ys0pr/vQvRfVoq/7zJkmS4p+6S/FP3SVJyjlyQgndHq9wpl+PeeDrH3XV03crvG0TFZ7JUfLHS7TzX9/Kv2aIuvzjPjW8qYv8QgKVsWanfp7wvvKPnylznBoxddTpr8NVv1dH+YeFKDfjlNIW/KxtbyWoJL9IktTqvpvUY/LD+mHUazqybGPZICaThm58X4Wns7TgxgmliyM6NleHcXeoXvc4+YUEKefICaUk/Kgd07+VvcRW4fME4FqUegAAAHg1s5+vbpz9nKJ7ttPR1VuVMjdRJYVFqh3XSI1v7l5a6ivq7P50bfjHJ+r24v06tHi9Di3+RZJkzS1wOFt4u6bqfWMX7Zu1XClf/6gmg69Rl+dGqqSwSC3u6q2cIye1dcpXCmsapbgHb9Z1057UsmEvlu4fEhOpgUtek39osPb8e6myUjMUdU1bdRh7h+p2baWlQyfJXmLTwflr1W3SaDUf2uuCUh99XXuF1I/QrhnflS6LueEq9Zk5QVlpFu16/zsVns1R3c6xip8wTOFtm2r1w284fK4AXINSDwAAAK/W5qGBiu7ZTtunzdPmV78ou9Jkcvh4BZnndHjJBnV78X6d2X1IqXMTK52tdlwjLbrlWWVu2S9J2j97pe5Mek/dJo1W8sffa8PfPy6zfdtHBimseX1lpRyTJF31zAgFRdbU8hEv6+jKLZKkvf9eqtz0k2r3+K1qcVdv7Z+9UkXncnVk+SY1vLGz/GuGqOhcbukxWwztJVuxValzf5Ik+QT4qeebj+nklv1aeucLpVfl9/1nuU7vPqRuk0YrqkdbWdbtqvR5A3AenqkHAACAV2t2x3UqPJOtbW9+feFKu939gX7jxMZ9pYVekmzFVmVuOSCT2azkmYvLbHt8fbIkKaxp9PkFJpMa3dRFp3aklhb6X21/5xvZSkrUaED30mUHvlotn0B/Nb21Z+ky3+BANRrQTUdXbVXBqSxJUvT1HRRUt7YOfLlK/jVDFBAeWvpP+g+bJUn1e3d03ocA4IpwpR4AAABeLaxZtE7vTFNJYbHRUS6Qc/j4BcuKzuX8d92JssvPnr+6HlC7hiQpMCJMfjWCdHbvkQuPcTZH+cfPKrRx3dJlR1dtUf7Js2p+Zy/t/WyZJKnxwO7yCwnSga9/LN2uVssYSefnGLiYwMiaFTo/AK5HqQcAAAAk2S9y1d7k47qbWy814ZzdVv46UyUeGfh1rNRv1qjtw7cotEmUstMsaj60twrPZOvIsqTfDiBJSpr0mU7vOljusfJ+N1kfAONQ6gEAAODVslKOqWaL+jL7+8pWZL3odoVnz18h969VQ0X//W9JCm1c74Jt7TL2tn1JKjiVpaLsPNVq1fCCdf41QxRUr5ZO70orszzlq9Vq+/Ataj60l/bNWqGoa9po3+crynwuWQczJEnWvAJlJO5w6TkAuHI8Uw8AAACvljIvUQG1Q9XxTxe+qu63slLPl9n617Uvs7zto4Mu2PbXme79a9VwUspKsNt1ZPkmRbRvpgZ94susav/E7TL7+OjwkvVllp/elabTu9LUfMj1an5nL5l9fJTy1eoy2xxbtVX5J8+q/ZO3l3t+PoH+8g0JdPbZAKgkrtQDAADAqyV/tFgNb+qijn++U5HxzXX0x20qKShWrVYNVbN5/dJXxB38Zo2uevoeXfP6o6rZooEKz+aoQZ9OCgwPveCYhWdylJWaoaa39VT2IYsKTp5TcV6B0pdvcuu5bX51lupf30F9P554/pV2By2KurqNmt7WU5Z1u3Tgd4Vdkg58vVrdXhit9mNu1bkDR3Vy8/4y6635hUoc+476fjxRd6x5W/tnr1JWmkX+YcGq2aKBGt/cXaseeJ3Z74EqglIPAAAAr2Yrtmr58JfU9tFBanr7der89D0qKSxW1sEM7f9yVel2xTn5WjHyFXV9YZQ6jL1DxXkFOrR4vRKfeFv37P3sguP+NOZtdZ00Wlc9c4/8ggOVc+SEEtxc6nPTM7Xo5mfUaeIwNRtyvfzDgpWXcVrbp83TtrcSyn1mP3Vuoro8O1L+YSHa+e78co97bPU2LRzwtNo/ebuaDblOgRFhKjqXq+y049r1wUKdTj7k6lMDUEEm+8VmBAEAAF4npt9sHT2RpwZ1g5W+4m6j47hddT9/yTM+g+K8As1qPtLoGKiEESmfyy+4at+a7wl/BlyJ8/e+8+eZegAAAAAAPBS33wMAAABOEhARJrP50tfNinMLZM0rcFMiAN6OUg8AAAA4yaAlr6lGw7qX3GbrlK+09Y2v3JQIgLej1AMAAABO8tOYt+UT6H/JbbIPHXdTGgDVAaUeAAAAcJITSXuNjgCgmmGiPAAAAAAAPBSlHgAAAAAAD0WpBwAAAADAQ1HqAQAAAADwUJR6AAAAAAA8FLPfAwAAAPBKRcUl+mXbCW1KPqVNuzN18Gi2TpwukCRlni3Qc+9sVOc2kbomvp7qRQQZnNY19qWd0y/bT2jT7kzt2H+m9PxPninQoy+tVZc2keraLlIdYsNlMpkMTut8Z7MK9fO28+e/aXfm/87/dIGGjv9BndtEnv890LGuQoL9DE5bOZR6AAAAAF7lcEaOPkjYow/n7i0tcb9XWGTTyx9ukyT5+Jh0e9/GenxYnHp3jfb4cltYVKKE5Qf1ry+TtW7biXK3KSq2acbXezTjv79u16K2Hh8Wp5G3NFdoiL/7wrrIpt2ZendOsr5YnKKCwpIL1hdZbUpYnqaE5WmSpLAafho1uKUeuytOcc1quTfsFeL2ewAAAABeoaDQqqffSlKzm7/Syx9uu2ih/72SErsSlqep7x+X6LrRi7Qv7ZyLk7rO0rXpannL1xr5zI8XLfTl2XngjB5/+Wc1ummOPluwX3a73YUpXSfjZJ5uG7dcXYbP18ff7Cu30JcnK6dY73yxW21um6sH/vGTzmYVujip81DqAQAAAHi8jbtO6qph8zX54+0qKal8IV275bg6Dv1GU/+z06OKbU5esf74fKL6P7ZURyy5lT7O2ewijXruJw1+crksmXlOTOh6sxenqO3tczV/1eErOs4n3+5XuzvmaenadCclcy1KPQAAAACPtnRtuq6/f5GSU8865XgFhSX6y+vr9ciLa1VSYnPKMV3p1NkC3fDHJZr5zT6nHXPhT0fU876FOpie7bRjutIrH27VPU+v1pmsIqcc7+iJPN08Zpk+duJn6iqUegAAAAAe64dfjmnw2OXKL6jYbdaO+HDuXj3+8s9V+op9Vk6R/vDoUm3YedLpx05Nz1afPy5W+hVc+XeH12Zu07PvbHL6cW02ux58PlGfLdjv9GM7E6UeAAAAgEdKt+RqyPgfVFTsuqvpHyTs1btzkl12/Cv14POJ2rQ702XHP3QsR0P+8oOs1qp5x8LixCN65u2NLh3jARd/xleKUg8AAADA49jtdj384hqdy3bsduuk2YN1ZPlwJc0eXOF9/jo1SanpWY5GdLmvlx0snb29oipz/ht2ntSbn+10MJ3rnc0q1EOT1ji0T2XOv6TErtHP/aSiYuffDeIM1aLUZ2ZmauLEiWrRooUCAwPVsGFDjRs3Trm5uXrwwQdlMpk0ffp0o2MCAACggto/ebt6fzBeQ375l0ZnJOjODe8aHQlu9vnCA1qyxvGJzKIigxVTL0RRkcEV3ic336qHXnCsPLra6XOFevzlnx3erzLnL0n/eHdzlXsrwIQ3N+jYCccm86vs+e88cEav/PcViFWN15f6rVu3qn379nr99ddlsVjUpk0bFRcXa9q0aRo2bJiSk8/fShMfH29sUAAAAFRY57+NUFTPdspOs6jwjGdM5AXnsdvteu3j7W4dc+WGDCW54Ln1yvr4m33KPFOxV/Y5Q2FRid6etctt411Oxsk8fermZ92nfbFLeflWt45ZEV5d6jMzMzVo0CBZLBaNHz9eGRkZ2rx5sywWiyZPnqxFixYpKSlJJpNJHTp0MDouAAAAKiih++P6su39Wjb8JeUdP2N0HLjZT5ss2p1y1u3jvvdV1Xi23mazG5Lls+8OKDvXObPLX6mP5u2V1ereCQzPZBVpztJUt45ZEV5d6seOHav09HQ98cQTmjJlikJDQ0vXTZw4UR07dpTValWTJk0UFhZmYFIAAAA4IufwCaMjwEAfzt1ryLizl6RWiVK7akOGUg141VxOXrG+XGJ8qbXb7fponjG/Bz5I2GPIuJfitaU+OTlZc+bMUWRkpF599dVyt+ncubMkqWPHjqXLEhMT1a9fP0VHRysgIEAxMTFlbtMHAAAAYKw1W44bMm5BYYk2J58yZOzfWrPFYtjYa7ca/wO19OO5OpxhzGv2Nu7OVEFh1boF32tL/ezZs2Wz2TRixAjVqFGj3G2CgoIklS31Z86cUfv27TVt2jQtW7ZMkydP1q5du9SjRw+lpzs+EQcAAAAA58k8U6BDx3IMG78qvNps027jfrBQNc7fuAxWq1079letR358jQ7gKitXrpQk9enT56Lb/FrSf1vqBw8erMGDy77eoGvXrmrVqpXmzp2rcePGuSAtAAAAgIrYutfYK+Vb95w2dHzJ2M9gd+pZFRRaFRhgXJXckmzs/4Ote06pa7s6hmb4La8t9YcOHZIkNW7cuNz1VqtVa9eulVS21JcnIiJCkuTrW7mPq0uXLrJYjLtFBgCAX2XU+otkrqkMS4ZiYmKMjuN21f38Jc/4DPzsZj2vbkbHQCXEtoxVscnm0jHy/dpIocPKXZc0e/BlX1UWFRlU+u8jy4dfdDtLZp663r3gguVfz1uklZ+NcCCx8x2r/YxkCix33eU+gys9f5vNrqYt2sjH7tir5JzpbPAAKfDqctc56/yli38GTz39vCb9aa0DiSsmKipKGzdudHg/ry31ubnnn7HIz88vd/2cOXOUmZmp0NBQNW3a9IL1JSUlstlsOnTokJ555hlFRUXprrvuqlQWi8Wio0ePVmpfAACcKrREMku2kpLq+XdTdT9/ySM+A3+Tj1TP6BSojGMZx1RkL3HtIDXrS6Hlr/r1HeQV4etjrvC2v1VQWGT8n51aJslU/qqKfgaVPX9JsliOS9asSu3rFPXzpPJ/puGW88/KylHWyarz9dNrS31UVJTOnDmjzZs3q0ePHmXWZWRkaMKECZKkDh06yGS68E9Er169Sq/kt2jRQitXrlSdOpW7xSIqKqpS+wEA4GwZPj6ySTL7+Ci6QQOj47hddT9/yTM+Az+7WXLtxV64SP3o+m64Uh+qi918bcm8/NXjqMgg+fqYZS2xyZJZ/gXASx0rMMBHEQb/2TmmEl3sZW6X+wyu9PwlKToqUmb7RX6y4gbnggJ0sVkVnHX+lzpWzdBg1fB3/u+ByvZGry31/fr1U3JysiZPnqwbb7xRsbGxkqSkpCTde++9ysw8P7lCfHx8ufvPnDlTZ8+e1cGDB/X666/rpptu0tq1a9WoUSOHs1TmFgoAAFwhpt9sHT2Rp+ioaKXvrH4TwFb385c84zMozivQrOYjjY6BSti3f5/8gi9yCdVJdh04o3Z3zCt3XXm3Sv/ekeXDFVMvRJbMfDW88UuHx3/y4WH651/ecHg/Z+p2z3wl7Sx/srjLfQZXev6RtQN1dNuBci+Musv7XyXrsf/7udx1rj5/SfrPzDc0qLfjvdBVvLbUT5w4UV988YWOHDmitm3bqnXr1iooKNCBAwc0YMAANWnSREuXLr3o8/StWrWSJHXv3l39+/dXkyZN9M9//lPTp09352kAAACgHM3uvF41Ys7fRRkYESazn686/GmIJCkn/aRSE34yMh5cqHXTmgoO9FVegTGvFevcJtKQcctkiIu8aKl3/dgRhhZ6yfj/B53bRBg6/u957SvtYmJilJiYqIEDByowMFBpaWkKDw/XjBkztGjRIu3bt0/S5SfJk6RatWqpRYsWOnDggKtjAwAAoAJi775BV/31bl3117sVVKeWAmrVKP117N03GB0PLuTjY1anOONKldGFUpK6tDUuQ1U4/w6x4fLzNabKRkUGqX7dyj2L7ypee6VekuLi4rRw4cILlufk5CgtLU1ms1nt2rW77HFOnDihvXv3qnv37q6ICQAAAAd9P+R5oyPAQHf2a6K1W467fdz41uFq3tC4Z8l/NahXI/n5mlVsdf/kE0NvunCScXcL8PfRoF6NNO+HNLePXRXO//e89kr9pezatUt2u10tW7ZUcHDZ1x2MHDlSL7zwgr799lutXr1aH374oXr37i1fX1/9+c9/NigxAAAAgF+NurWlggJ93D7u43fFGX7ruSTVjQgypFz26FhX8a2rxq3njw+LM2Tcx+4yZtxLqZalfseOHZLKv/X+6quv1uLFi3X//fdrwIABev3113Xddddp69atatGihbujAgAAAPid2mEBGnFzc7eOWSvUX/e4ecxLGTPc/eXSiDEvpm/3aLVuWtO9Y3aLVlyzWm4dsyIo9b/zxBNPaMOGDTpz5ozy8/O1b98+zZgxQ40bN3Z3TAAAAAAX8dITnRVeM8Bt400Z300hwX5uG+9yromvp5G3uO+HDNddVU93D6g6P9QwmUya/sw1bhvPz9estyZe7bbxHEGpBwAAAOBxoiKD9c7TPdwy1h+uaaAHbo91y1iOePuvPRQVGeTycYICffTxi9fLbDb+0YPfuuHq+np0aGu3jPWPR+PVPjbcLWM5qlqW+pUrV8put2vgwIFGRwEAAABQSXff3Ez33uLYI7KWzDylH8+VJTOvQttH1wnWhy9cWyWepf+98JoB+vf/XS9f34pnc/T8Jemdp3uoRaOwykR0uX/+pavatahd4e0rc/69ukTpr/dX3QvCXj37PQAAAADvZTKZNHPSdTqXU6QFqw9XaJ+udy+o8PHr1A7U8hn91TCqRmUjutxN18ToPy/30si//aiSEvtlt3fk/CVp8p+66sE7WlU2nsuFhvhr2Yz+6vXAIu0/lHXZ7R09/27t6mjBtBvl51d1r4dX3WQAAAAAcBl+fmYlvHGDRt/a0qnHbd4wVImfDlRbB64CG2X4gOZKeKOvajjxmX9fX5P+9bcemvhAB6cd01Wi6wQr8dNb1LVdpFOP+4drGmjFh/0VVsPfqcd1Nko9AAAAAI/m52fWJy9dr4Q3+qpO7cArPt7Ye9po29e3q1XTWlcezk1u69tEO+fdrn5X17/iY3VqHaFNX96mx4e3cUIy96gXEaS1/x6k/3uis/x8r6zmhgT56t1nr9Hid/+g0JCqXeglSj0AAAAALzHkxqba/e0Q/WlkW9UMdayMmUzSgGtjlPjpQL39dI8qNdN9RTWuH6plM/rr05euV4dKTOrWLCZUb07orvWzBldqf6P5+Zn17MPx2jznVg3r39ShuQYkKTDARw/cHqsdc+/QY8PiqtzEgBfDM/UAAAAAvEZk7UBNnXi1/u+Jzvry+1R9u/KQNiWfUsbJCydGCwr0UXyrCF3fOUoPDWml5g2r5mRwjjCZTBp1a0vdN7iFft56Qp/O36f1O05qd+rZcp+5j21cU13bRWrEzc31h54xHlNkL6Vdy3B9+c++yjiZp5nf7NUP6zO0OTlTWTnFF2wbXjNAndtEqH/PGI2+Ndatr0l0Fko9AAAAAK8TEuynB+9oVTrJW8bJPB08mq2CwhL5+ZoVUStAsY1ryvcKb9Wuqkwmk3p2qqeenepJkvLyrdpz8Kyy84plt5+/xbxVk5pV/nnxKxFdJ1jPPdxJzz3cSTabXSlHsnT8VL6Kim0K8PdRTL1gNYquUSXfbOAISj0AAAAArxddJ1jRdYKNjmGY4CBfXdXGuRPJeRKz2aSWjWuqZeOaRkdxOu/8sRQAAAAAANUApR4AAAAAAA9FqQcAAAAAwENR6gEAAAAA8FBMlAcAAIAqxTcoQCNSPjc6BirBN8jzXgcGeDpKPQAAAKoUk8kkv+BAo2MAgEfg9nsAAAAAADwUpR4AAAAAAA9FqQcAAAAAwENR6gEAAAAA8FCUegAAAAAAPBSlHgAAAAAAD0WpBwAAAADAQ1HqAQAAAADwUJR6AAAAAAA8FKUeAAAAAAAPRakHAAAAAMBDUeoBAAAAAPBQlHoAAAAAADwUpR4AAAAAAA9FqQcAAAAAwENR6gEAAAAA8FCUegAAAAAAPBSlHgAAAAAAD+VrdACUz263y5pfaHSMCvMNCpDJZDI6BgAAAABUK5T6KsqaX6hZzUcaHaPCRqR8Lr/gQKNjAAAAAEC1wu33AAAAAAB4KEo9AAAAAAAeilIPAAAAAICHotQDAAAAAOChmCgPAAAvl51bpC3Jp3TgSLZy8oolSXkFVu05eFaxjWvKbObtJQAAeCpKPQAAXuiIJUcfJOxVwvKD2pt2TnZ72fVnsooUd+tc1Qj2U4+OdfTQkNa6rU9j+flxEx8AAJ6EUg8AgBdJTc/ShDeS9O2qQ7LZ7JfdPievWMvXHdPydccUXSdY4+9rp3Ej2srXl3IPAIAnoNR7kagebdV/3qQyy4pz85WVmqGUhJ+UPHOx7CU2g9IBAFzJZrPr3TnJ+uvUJOUVWCt1jIyTeXrqjQ36aulBffp/1yuuWS3nhgQAAE5HqfdCqfMSlb5ys2QyKahOLbUY2kvdJo1WzZYNtG7CDKPjAQCcLL/AqmETVum7Hw875Xgbdp5Up7u+1eev9NKdNzV1yjEBAIBrcG+dFzq146BS5yYqNeEn7XpvgRYN/Jtyj2Yq9p4bFBARZnQ8AIATFRRadcsTy5xW6H9VWFSiYRNXafbiFKceFwAAOBelvhqw5hfq5Ob9MpnNCmtcz+g4AAAnsdvtuvdvP2rlhgyXHN9ms+u+537Uqg3HXHJ8AABw5Sj11URok/NlvvBsjsFJAADO8vnCA0pYnubQPkmzB+vI8uFKmj24QttbrXbd/49EZecWVSIhAABwNa8v9ZmZmZo4caJatGihwMBANWzYUOPGjVNubq4efPBBmUwmTZ8+3eiYTuUb5K+A8FAFRISpVutG6v7KHxXRvplObt6vrFTXXM0BALhXxsk8jX3tF4f3i4oMVky9EEVFBld4n0PHcjThzSSHxwIAAK7n1RPlbd26VQMGDJDFYlFISIjatGmjY8eOadq0aUpJSdHp06clSfHx8cYGdbJOE4er08ThZZalLfpF65/5yKBEAABne/nDrTqb7b6r5zO+3qM/j2yrVk1ruW1MAABweV57pT4zM1ODBg2SxWLR+PHjlZGRoc2bN8tisWjy5MlatGiRkpKSZDKZ1KFDB6PjOtXe/yzT0rsmafmIl7Xxpf+o4HS2QqIjVFL4v2/+zP6+unX1VHUYd0eZfa99a4z6zXrW3ZEBAA7Izi3SZ98dcPu473+9x+1jAgCAS/PaUj927Filp6friSee0JQpUxQaGlq6buLEierYsaOsVquaNGmisDDvmhE+K9WijMQdOrpyi3a+O18/jHpNkfHN1WPyI6Xb2IqsWjP2HbUfe4dqt2ksSWrUv6tibuyitX9516joAIAKmLUoRdm5xW4f95P5+5Wb5/5xAQDAxXllqU9OTtacOXMUGRmpV199tdxtOnfuLEnq2LHjRY8zYMAAmUwmvfDCC66I6TYnN+5VSsJPanpbT9Xp0qp0+antqdr13gJdN+1JBUeHq8frj2r93z5S/vEzBqYFAFzOwp+OGDLuuewi/bzthCFjAwCA8nllqZ89e7ZsNptGjBihGjVqlLtNUFCQpIuX+q+++kpbt251VUS32zY1QTZriTpNGFZ2+VtzZSsp0eDlr8uydqcOzl9rUEIAQEVt2p1ZLccGAAAX8spSv3LlSklSnz59LrpNenq6pPJLfVZWlv70pz9pypQprglogOw0iw7OX6v613dQ3e5xpcvt1hKdTNqrwIiaOjBnlYEJAQAVcexEriyZ+YaNT6kHAKBq8cpSf+jQIUlS48aNy11vtVq1du35K9Lllfpnn31WsbGxGjFihOtCGmD72+evyv/2an3d7nFqMayPkmcuVrcX75dPoL+BCQEAl3M4I9fQ8Y9YjB0fAACU5ZWvtMvNPf8NR35++Vcy5syZo8zMTIWGhqpp06Zl1m3cuFEffvihNm3a5LQ8Xbp0kcVicWgfP7tZz6ubQ/tY1u3Sp9F3XnT9uf1H9VnM/wq9b3Cgrn1rjDa9PEt7/r1UA755UVc9c4+Snv/UoXElKbZlrIpNNof3AwA4ptC3sRT2QLnrkmYPvuz756Mig0r/fWT58ItuZ8nMU9e7F1ywfPOW7YqJedyBxFVLRq2/SOaayrBkKCYmxug4huAzAICqKSoqShs3bnR4P68s9VFRUTpz5ow2b96sHj16lFmXkZGhCRMmSJI6dOggk8lUuq6kpESPPPKInnjiCbVt29ZpeSwWi44ePerQPv4mH6me0yKUq+sL9ynn8Ant+fR7SdKacdM1eMUUHV6yXsd/SXboWMcyjqnIXuKKmACA3woOlC7y0paoyGDF1Aup0GF8fcwV3va3iosKHP47rUoJLZHMkq2kxLPP40rwGQCAV/HKUt+vXz8lJydr8uTJuvHGGxUbGytJSkpK0r333qvMzPPPA8bHx5fZb/r06Tp+/LjTZ7uPiopyeB8/u1ly4YXvBn07qengnpp/w/jSZdmHjmvTy7PUc+oYLeg7Xtb8wgofr350fa7UA4AbWM3BOn6RdZbMvMvuHxUZJF8fs6wltks+m3+xYwX4FiuyQYOKRK2SMnx8ZJNk9vFRtAefx5XgMwCAqqkyvVGSTHa73e7kLIZLT09XfHy8Tp06JV9fX7Vu3VoFBQU6cOCABgwYIJvNpqVLl+qDDz7QQw89JEnKzMxUs2bNNGXKFN11112lx6pdu7b++te/6umnn1ZYWJjMZvdMQ1CcV6BZzUe6ZSxnGJHyufyCA42OAQBez2azq/a1/1FWTuXeF39k+XDF1AtR+vFcNbzxS4f3f+7heL30ROdKjV0VxPSbraMn8tSgbrDSV9xtdBxD8BkAgHfxyonyYmJilJiYqIEDByowMFBpaWkKDw/XjBkztGjRIu3bt09S2Uny0tPTlZ2drUceeUS1a9cu/UeSJk+erNq1a+vw4cOGnA8AAL8ym026Ki7SsPE7t4kwbGwAAHAhr7z9XpLi4uK0cOHCC5bn5OQoLS1NZrNZ7dq1K13eokULrVp14Svd+vTpo1GjRmn06NGVvh0CAABn6tM1WquTMtw+rp+vWT3jXTzhCwAAcIjXlvqL2bVrl+x2u2JjYxUc/L8ZgmvUqKHevXuXu0+TJk0uug4AAHd78PZYvThji0pK3PsE3ZB+TVQnPMitYwIAgEvzytvvL2XHjh2Syn8/PQAAnqBBvRDd1qex28cdMzzO7WMCAIBLq3ZX6h0t9V44jyAAwAs8+1BHfbvqkNuu1vftFq2enbj1HgCAqoYr9dVM44FX6+rXHiqzrMWwPhqdkaBG/bsalAoA4KhOcZF65kH3/F1WI9hPMyddJ5PJ5JbxAABAxVW7K/UrV640OoKhGt3cXSlfry79dY2YOood0U8nNu41LhQAoFL+/ki8vvvxsLbtPV3hfX59/3xF3mn/qynju6lJg1CH8wEAANerdqXe2/mHBevWVVPlE+ivvGOZMgf4KbRRPaUk/Kh1T3+oel1bac246ec3Npl0zRuPaf1zM9X1+VHGBgcAOMzfz0eLpt+ka0ctVNqxnArt0/XuBQ6NMf6+dnr4zlaViQcAANyg2t1+7+2KsvKU+k2idn+0SAtunKAN//hEJzfv089Pva/onu10Immv7NYSSVLbRwbpRNIendqeanBqAEBlNagXotUf36yWjcOcfuy/PtBBr4/vxm33AABUYZR6LxTerqlO7zgoSYro0Fynd57/70b9u+rQkg2SpFqtGqrxwO7a9tZcw3ICAJyjcf1Q/fL5YI28pblTjhdeM0BfvNZbr/2pK4UeAIAqjlLvhcLbNikt8hEdmunUfwt+/d7xOrpyiySpXvc41WhYV0N+fkd3bnhXda5qqR6vP6pW991kWG4AQOWF1wzQf17prW/f7qeGUSGVPs6dNzbRrm/u0N03O+cHBAAAwLV4pt7LBEeFS3a78iznJ00Kj2us7W/PVWSnljq3/6iseQWSpL2fLdPez5aV7td/7iTt/nChDn+fZEhuAIBz3NqnsQZe11ALfzqsd+cka/m6Y5fdp3aYvx64LVaP3hWnFo2cfxs/AABwHUq9lwlv17T0Kr0kFWXlqvWoP6jwdLYOf7/BwGQAAHfx9TXrtr5NdFvfJjqbVajNyae0cVem9h8+p/zCEvn6mFU7zF/xrSLUuU2EWjetJV9fbt4DAMATUeq9TPqKTUpfsan01wsHPC1JunX1VC0d8vxF9/v+EusAAJ6rVliA+navr77d6xsdBQAAuAClvpqY3/vPRkcAAAAAADgZ99oBAAAAAOChKPUAAAAAAHgoSj0AAAAAAB6KUg8AAAAAgIdiorwqyjcoQCNSPjc6RoX5BgUYHQEAAAAAqh1KfRVlMpnkFxxodAwAAAAAQBXG7fcAAAAAAHgoSj0AAAAAAB6KUg8AAAAAgIei1AMAAAAA4KEo9QAAAAAAeChKPQAAAAAAHopSDwAAAACAh6LUAwAAAADgoSj1AAAAAAB4KEo9AAAAAAAeilIPAAAAAICHotQDAAAAAOChKPUAAAAAAHgoSj0AAAAAAB6KUg8AAAAAgIei1AMAAAAA4KEo9QAAAAAAeChfowOgfHa7Xdb8QqNjVJhvUIBMJpPRMQAAAACgWqHUV1HW/ELNaj7S6BgVNiLlc/kFBxodAwAAAACqFW6/BwAAAADAQ1HqAQAAAADwUJR6AAAAAAA8FKUeAAAAAAAPRakHAADVgt1ul91uL/1vAAC8AbPfAwAAr5SXb9W8H9L089bj2rT7lLbvP62CwhJJ0rGT+Yod9LW6tIlUl7aRuvPGpmoUXcPgxAAAOI5SDwAAvEpqepbe+WK3Pp2/X2eziy663f5DWdp/KEuzl6RqwptJGnhdQz1xd5xu7NFAJpPJjYkBAKg8Sr0XierRVv3nTSqzrDg3X1mpGUpJ+EnJMxfLXmIzKB0AAK5VUmLT1P/s0nPTN6mwqMShfW02u7778bC++/GwhvRronefvUZ1I4JclBQAAOeh1Huh1HmJSl+5WTKZFFSnlloM7aVuk0arZssGWjdhhtHxAABwunRLru6asFLrtp244mPNXZGmHzdZ9O//u143X9fQCekAAHAdJsrzQqd2HFTq3ESlJvykXe8t0KKBf1Pu0UzF3nODAiLCjI4HAIBTpRzJ0rWjFzql0P8q80yBBo9dri8WpTjtmAAAuAKlvhqw5hfq5Ob9MpnNCmtcz+g4AAA4zbETuer30BIdOpbj9GOXlNh177M/6tuVaU4/NgAAzkKpryZCm5wv84Vnnf9NDwAARrDb7Rr995+U5oJC/yubza5Rz/2kIxb+/gQAVE2Uei/kG+SvgPBQBUSEqVbrRur+yh8V0b6ZTm7er6zUDKPjAQDgFB/N3avl6445tE/S7ME6sny4kmYPrvA+WTnFeuiFNbzbHgBQJVWLUp+ZmamJEyeqRYsWCgwMVMOGDTVu3Djl5ubqwQcflMlk0vTp042O6TSdJg7X3bs+0d07P9Ztq95U3P39lbboF60cPdnoaAAAOMWpswUa/8YGh/eLigxWTL0QRUUGO7Tf0p+Pas73qQ6PBwCAq3n97Pdbt27VgAEDZLFYFBISojZt2ujYsWOaNm2aUlJSdPr0aUlSfHy8sUGdaO9/lintu3Uy+/mqdutGajfmNoVER6ik8H/v6jX7+2rQstd18JtEbX97Xunya98ao8A6tbRixMtGRAcAoEI++Xa/snOL3Trm27N2afiA5m4dEwCAy/HqK/WZmZkaNGiQLBaLxo8fr4yMDG3evFkWi0WTJ0/WokWLlJSUJJPJpA4dOhgd12myUi3KSNyhoyu3aOe78/XDqNcUGd9cPSY/UrqNrciqNWPfUfuxd6h2m8aSpEb9uyrmxi5a+5d3jYoOAMBl2Wx2vfdVstvH/WX7SW3enen2cQEAuBSvLvVjx45Venq6nnjiCU2ZMkWhoaGl6yZOnKiOHTvKarWqSZMmCgvz3le9ndy4VykJP6npbT1Vp0ur0uWntqdq13sLdN20JxUcHa4erz+q9X/7SPnHzxiYFgCAS/t563GlpmcbMvZn3x0wZFwAAC7Ga0t9cnKy5syZo8jISL366qvlbtO5c2dJUseOHUuXrV69WiaT6YJ/PP32/G1TE2SzlqjThGFll781V7aSEg1e/rosa3fq4Py1BiUEAKBiNuw8adjYSbuMGxsAgPJ47TP1s2fPls1m04gRI1SjRo1ytwkKCpJUttT/6l//+peuuuqq0l+HhIS4JqibZKdZdHD+WjUfcr3qdo/TifXnb1u0W0t0MmmvIjs014E5qwxOCQDA5W3afcqwsbfsOSWr1SZfX6+9LgIA8DBe+zfSypUrJUl9+vS56Dbp6emSyi/1bdq00dVXX136T/v27V0T1I22v33+qvxvr9bX7R6nFsP6KHnmYnV78X75BPobmBAAgMvblWLcY2L5BSVKO8Y76wEAVYfXXqk/dOiQJKlx48blrrdarVq79vyt5uWVemfq0qWLLBaLQ/v42c16Xt0c2seybpc+jb7zouvP7T+qz2L+V+h9gwN17VtjtOnlWdrz76Ua8M2LuuqZe5T0/KcOjStJsS1jVWyyObwfAACOstQcK/lElLsuafbgS76uLioyqPTfR5YPv/Q4mXnqeveCC5Zfc11f+Zc49vd6VZJR6y+SuaYyLBmKiYkxOg4A4L+ioqK0ceNGh/fz2lKfm5srScrPzy93/Zw5c5SZmanQ0FA1bdr0gvXDhg1TZmamIiIiNHjwYL322muKjIysVBaLxaKjR486tI+/yUeqV6nhKqzrC/cp5/AJ7fn0e0nSmnHTNXjFFB1esl7Hf3FsVuFjGcdUZC9xRUwAAMoKsUo+5a/69T30l+PrY67QduU5eeK4VODY3+tVSmiJZJZsJSUOf38CAKh6vLbUR0VF6cyZM9q8ebN69OhRZl1GRoYmTJggSerQoYNMJlPpupo1a2rChAm6/vrrVaNGDa1bt06vvvqqfvnlF23cuFGBgYGVyuIoP7tZcuGF7wZ9O6np4J6af8P40mXZh45r08uz1HPqGC3oO17W/MIKH69+dH2u1AMA3OKEj00Xe0O9JTPvkvtGRQbJ18csa4lNlszyf/B/uWPVq1NbvjZ7RaJWSRk+PrJJMvv4KLpBA6PjAAD+qzK9UZJMdrvdc/9WuoSxY8fqnXfeUcOGDbVixQrFxsZKkpKSknTvvfcqNTVVxcXFGjNmjKZPn37JY3333XcaPHiwPv74Y91///3uiK/ivALNaj7SLWM5w4iUz+UX7PgPPAAAcNTIZ1Zr1qKUSu17ZPlwxdQLUfrxXDW88UuH9w8O9FXWunvl4+O50xLF9Jutoyfy1KBusNJX3G10HADAFfLcv5EuY+LEiYqIiNCRI0fUtm1btW/fXi1btlS3bt3UrFkz9e3bV1LFnqe/5ZZbFBISUqnnGwAAgHN1blO5x+GcIb51uEcXegCA9/Hav5ViYmKUmJiogQMHKjAwUGlpaQoPD9eMGTO0aNEi7du3T5Jjk+T99jZ9AABgjG7t6lTLsQEAKI/XPlMvSXFxcVq4cOEFy3NycpSWliaz2ax27dpd9jgLFixQbm6uunVzbDZ6AADgfD061lXzhqFKOZLt9rFHDW7p9jEBALgUry71F7Nr1y7Z7XbFxsYqOLjsa29GjhypZs2a6aqrriqdKO+f//yn4uPjNXz4pV99AwAAXM9sNumxu+L01Bsb3Dpuj451Fd+6/FfpAQBgFK+9/f5SduzYIan8W+/btm2rb775Rvfdd58GDBigjz/+WA899JBWr14tf39/d0cFAADluP+2WIXV8HPrmH8a2dat4wEAUBGU+t955plntGPHDmVlZam4uFgHDx7Um2++qZo1a7o7pks0Hni1rn7toTLLWgzro9EZCWrUv6tBqQAAcEx4zQBNnXC128YbeH1DDb2pqdvGAwCgoij11Uyjm7vr8Pf/u12xRkwdxY7opxMb9xqYCgAAx91/W0sNuDbGoX0smXlKP5572ffZ/1bNUH/N+HtPJswFAFRJ1fKZ+pUrVxodwWX8w4J166qp8gn0V96xTJkD/BTaqJ5SEn7Uuqc/VL2urbRm3PTzG5tMuuaNx7T+uZnq+vwoY4MDAOAgk8mkj1+8TteOWljhSfO63r3AoTF8fEz6/JVealAvpDIRAQBwuWp5pd6bFWXlKfWbRO3+aJEW3DhBG/7xiU5u3qefn3pf0T3b6UTSXtmtJZKkto8M0omkPTq1PdXg1AAA/H979x5XVZmocfzZW1CuSlwEL4CiooC3VLykTl4oNfPS2OQQVjZ9jjXZ6JQjp9vUcU6dMpsxL8fG0ukydhxLc2TUTE1Ny1IU84KEBqKCoAImyE22e88fzvCJ4w1ww3Jtft9/xLXXet+Hf/jw8K71rroJCfTS5ndGqX0bX6eP7eZm0f+9PkT33hnm9LEBAHAWSr0L8u/aXoUHj0mSArp3UOGhy1+HjYzV8c8u33rv1zlU4aP7af9bqwzLCQCAM7Rr46uvPhitwb2CnTZmS38PrV1wtx4YEeG0MQEAqA+UehfkH9OuqsgHdI9Qwb8KfushPZWzZZ8kKbhflHxCW2rCzgW6f/ciBfXqpAFznlDnh+82LDcAAHXVuqW3tv1ltN5K7CdPjyY3NVb8qAgd/vsEjRhYu+f1AQAwQqN8pt6VeYX4Sw6HSvMKJUn+UeE6MG+VAm/vpPNHc2QrLZckpX+4Uekfbqy6buSqWTr87lqd2JBsSG4AAG6W1WrR9EldNX5YuBatSNPS1UdU8GNFja5t0sSi8UPD9VR8tIbEtqrnpAAAOA+l3sX4d21ftUovSReLStTlkRGqKCyutus9AACuKry1r2Y/3VeznuylNVtP6Jv9Z7T3cL72HylUcUmlJMndzarI8ObqHR2o3tGBmhDXjs3wAACmZHE4HA6jQ+BKlaXl+qjDJKeNN27bXH0+4WWVFxQ5bcyfSshYJncvj3oZGwAAZ7HbHbLbHXJza7xPILaNW66cM6Vq09JL2ZvjjY4DALhJrNQ3EmuGPG10BAAADGe1WmS18r55AIDraLx/pgYAAAAAwOQo9QAAAAAAmBSlHgAAAAAAk6LUAwAAAABgUmyUd4ty82ymhIxlRseoMTfPZkZHAAAAAIBGh1J/i7JYLLwiDgAAAABwXdx+DwAAAACASVHqAQAAAAAwKUo9AAAAAAAmRakHAAAAAMCkKPUAAAAAAJgUpR4AAAAAAJOi1AMAAAAAYFKUegAAAAAATIpSDwAAAACASVHqAQAAAAAwKUo9AAAAAAAmRakHAAAAAMCkKPUAAAAAAJgUpR4AAAAAAJOi1AMAAAAAYFKUegAAAAAATIpSDwAAAACASbkZHQBX53A4ZCurMDpGjbl5NpPFYjE6BgAAAAA0KpT6W5StrEIfdZhkdIwaS8hYJncvD6NjAAAAAECjwu33AAAAAACYFKUeAAAAAACTotQDAAAAAGBSlHoAAAAAAEyKUg8AAAAAgEmx+z0AAICLcjgcysq5oL2H87U3LV85p0tVeP7yK3PPX7io5esz1Ds6UB3Dmstq5dW0AGBGlHoAAAAX82NRhT5IOqq3P/5e6Vnnr3rOhVKbHnx2myQpvLWPHr+/ix67L1ItAzwbMCkA4GZZHA6Hw+gQuFJlaXmt31MfMiBGIz+dVX2ckjIVZeYqY+V2pS1dL8cluzNjVuE99QAAGK+y0q7Z7+3X/yzZr7LyS7W+vqm7VdMTYjTryV7y9GDtBwDMgJ/WLijz0x3K3pIiWSzyDPJTx1/cqb6zJqtFpzb6ZuZio+MBAIB6cPBIoSb/frtS0grqPMbFSrvmvH9QSdtO6L3/HqwBPYKdmBAAUB/YKM8FFRw8psxVO5S5crtS307SutHPqyQnX5EPDlezgOZGxwMAAE62cWe2+k/6x00V+p9Kzzqvnz26Tis2ZDplPABA/aHUNwK2sgqdTTkqi9Wq5uH8xR0AAFey+dscjfnNJpWW25w6rs3mUPx/btUnG485dVwAgHNR6hsJ33aXy3zFjxcMTgIAAJwl42SR7vvtF7pYWT975jgcUsKz25RyOL9exgcA3DyeqXdBbp5N1czft+qZ+s4P362AbhE6m3JURZm5RscDAABOYLc79KuXduhCaWWtrktePlYhgV7Kyy9VbHzSDc+vtNk1+ffbtedv49TUvUld4wIA6onLr9Tn5+crMTFRHTt2lIeHh0JDQzV9+nSVlJTosccek8Vi0cKFC42O6VS3J/5S8anvKf7QXzR+658U9ehIZa37VlsmzzY6GgAAcJJFK9K0fW9era8LCfRS22BvhQR61fiag0fP6ZV3vqv1XACA+ufSK/XfffedRo0apby8PHl7eys6OlqnTp3S/PnzlZGRocLCQklSz549jQ3qZOl/3aisf3wjq7ubbusSpq5Tx8u7VYAuVVysOsfa1E1jNs7RsdU7dGDep1XHB701VR5Bftqc8KoR0QEAQA1UVtr16rvfNeicf/rwkH73SDc192naoPMCAK7PZVfq8/PzNWbMGOXl5WnGjBnKzc1VSkqK8vLyNHv2bK1bt07JycmyWCzq3r270XGdqigzT7k7Dipnyz4dWrRGXzzyugJ7dtCA2Y9XnWO/aNNX0xao27Sf67bocElS2MhYtb2rj75+ZpFR0QEAQA38fetx5eWXNeicJWU2/XXtDw06JwDgxly21E+bNk3Z2dl66qmn9Oabb8rX17fqs8TERPXo0UM2m03t2rVT8+au/Zq3s3vSlbFyu9qPH6igPp2rjhccyFTq20kaPP838mrlrwFzntCu55eo7PQ5A9MCAIAbWfzJ94bM++ePjZkXAHBtLlnq09LStGLFCgUGBuq111676jm9e/eWJPXo0eOKz1avXq077rhD3t7eatGihQYOHKjU1NR6zVzf9s9dKbvtkm6fObH68bdWyX7pksZumqO8rw/p2JqvDUoIAABqorLSrq/2nTZk7kM/nFPBj+WGzA0AuDqXLPXLly+X3W5XQkKCfHx8rnqOp6enpCtL/fz58/XAAw9o0KBBSkpK0vLlyxUXF6eysoa9xc3ZirPydGzN12r9s+5q2S+q6rjDdklnk9PlEdBCP6zYamBCAABQE6kZ51Rx8ZJh8+/l9XYAcEtxyVK/ZcsWSdLQoUOveU52drak6qU+IyNDM2fO1Ny5c/XGG29o+PDhuueeezRr1iz16dOnfkM3gAPzLq/K/3S1vmW/KHWcOFRpS9er7x8eVRMPNr8BAOBWlpJWYOj8ew8bOz8AoDqLw+FwGB3C2UJDQ5Wdna19+/ZddWd7m82mVq1aKT8/XxkZGYqIiJAkvfDCC5o3b54KCgrUrFkzp+Xp06eP8vJq98oZd4dVL9v7Oi3D1bh5eWjsF2/q8OK1+v6DzzVq9R+Uvz9DyS+/X+uxZll3q9Jid35IAABQTbHHIBV53XXVz/79DvrrCQn0lFsTq2yX7NfdbO9a77H3KdupFmWf1y40AOCGQkJCtGfPnlpf55KvtCspKZGka94yv2LFCuXn58vX11ft27evOr5z50517txZy5Yt0yuvvKKTJ0+qU6dOeumllxQfH1/nPHl5ecrJyanVNU0tTaTgOk9ZI7H/9bAunDij79/fIEn6avpCjd38pk58tkunv02r1Vinck/posO4WwEBAGg0gi5I1+jt/34HfU24NbHW+NyfulBSpgu5tfu9BgBQf1yy1IeEhOjcuXNKSUnRgAEDqn2Wm5urmTNnSpK6d+8ui8VS7bOcnBw999xzmj17tkJDQ7V06VI9+OCDCgoKUlxcXJ3z1Ja7wyrV48J3m2G3q/3YgVozfEbVseLjp7X31Y80cO5UJQ2bIVtZRY3Ha92qNSv1AAA0gGIPbxVd47O8/NIbXl+blfqr8fH2UIs2bWoSFQBQC3XpjZKL3n4/bdo0LViwQKGhodq8ebMiIyMlScnJyXrooYeUmZmpyspKTZ06VQsXLqy6LjIyUkePHtXq1as1fvx4SZLD4VDPnj3l5+enL7/8ssG+h8rScn3UYVKDzXezEjKWyd3Lw+gYAAC4vI8/z9TEmXXf3Pbkpl+qbbC3sk+XKPSuv9X6+gXPDdBT8dF1nh8A4FwuuVFeYmKiAgICdPLkScXExKhbt27q1KmT+vbtq4iICA0bNkzSlTvf+/v7S1K1FXmLxaK4uDgdOnSo4b4BAACAa+gdHWjw/AGGzg8AqM4lS33btm21Y8cOjR49Wh4eHsrKypK/v78WL16sdevW6ciRI5KuLPUxMTHXHLO8nHeyAgAA40W09ZWfrzFvq7FaLeoRSakHgFuJS5Z6SYqKitLatWtVXFys4uJi7dq1S1OmTFFJSYmysrJktVrVtWvXateMGzdOkrRx48aqY3a7XZs2bVJsbGyD5gcAALgai8Wie+8MNWTu4f1aycvTJbdkAgDTanQ/lVNTU+VwOBQZGSkvr+pbx44ZM0aDBw/WlClTVFBQoLCwMC1ZskSpqanatGmTQYkBAACq+/UDUVq2NqPB531yYlSDzwkAuD6XXam/loMHD0q68tZ76fJfvpOSkjRhwgQ9//zzGjt2rI4fP67169dXPYcPAABgtAE9WqpHZ/8GnbNtsLfu/VlYg84JALgxSv3/4+fnp8WLF+vs2bOqqKjQ7t27NWLEiIaMCAAAcF0Wi0V/nNGvQeec80ys3Nwa3a+OAHDLa3Q/mW9U6l1d+Oj+6v/6f1Q71nHiUE3OXamwkewbAACAWQzv31pP/KJLg8z18+HtNHFkRIPMBQConUb3TP2WLVuMjmCosHv6KeOTbVX/92kbpMiEOJ3Zk25cKAAAUCdvPBOrrcm5Ss86X+Nr8vJLq/17I61bemnRi3fIYrHUKSMAoH41ulLv6po299K4rXPVxKOpSk/ly9rMXb5hwcpY+aW+efZdBcd21lfTF14+2WLRHX/8tXa9uFSxLz9ibHAAAFBrvt5NtemdkRo8eZ2On7pQo2ti45NqPH7QbR7a/M4oBQd41jUiAKCeNbrb713dxaJSZa7eocNL1inprpna/dJ7OptyRDt/92e1GthVZ5LT5bBdkiTFPD5GZ5K/V8GBTINTAwCAugoN8dGO90crKsLPqeOGtfLW9noYFwDgXJR6F+Tftb0KDx6TJAV076DCQ5e/DhsZq+Of7ZYk+XUOVfjoftr/1irDcgIAAOcIDfHRnuXj9NtJMXLGXfK/ui9S+z+5T13a+938YACAekWpd0H+Me2qinxA9wgV/Kvgtx7SUzlb9kmSgvtFySe0pSbsXKD7dy9SUK9OGjDnCXV++G7DcgMAgLrz8nTT3MT+2v7eaA3uFVynMXpHB2r9/96tpbMGy695MycnBADUB56pdzFeIf6Sw6HSvEJJkn9UuA7MW6XA2zvp/NEc2UrLJUnpH25U+ocbq64buWqWDr+7Vic2JBuSGwAAOMegXiHa/v69OnikUG9/nKYNX+foWE7xNc9vG+ytuP6t9eTEKMV2DWrApAAAZ6DUuxj/ru2rVukl6WJRibo8MkIVhcU6sWG3gckAAEBD6hbpr0UvDpQkFfxYrpS0Ap06U6qKyktq6m5VsL+nekcHqiWb4AGAqVkcDofD6BC4UmVpuT7qMMlp443bNlefT3hZ5QVFThvzpxIylsndy6NexgYAAAAAXB0r9Y3EmiFPGx0BAAAAAOBkbJQHAAAAAIBJUeoBAAAAADApSj0AAAAAACbFRnm3KIfDIVtZhdExaszNs5ksFovRMQAAAACgUaHUAwAAAABgUtx+DwAAAACASVHqAQAAAAAwKUo9AAAAAAAmRakHAAAAAMCkKPUAAAAAAJgUpR4AAAAAAJOi1AMAAAAAYFKUegAAAAAATIpSDwAAAACASVHqAQAAAAAwKUo9AAAAAAAmRakHAAAAAMCkKPUAAAAAAJgUpR4AAAAAAJOi1AMAAAAAYFKUegAAAAAATIpSDwAAAACASVHqAQAAAAAwKUo9AAAAAAAmRakHAAAAAMCkKPUAAAAAAJgUpR4AAAAAAJOi1AMAAAAAYFKUegAAAAAATIpSDwAAAACASf0TI/dSKrXE08kAAAAASUVORK5CYII=", - "text/plain": [ - "
    " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qc_1 = cut_wires(qc_0)\n", - "qc_1.draw(\"mpl\")" - ] - }, - { - "cell_type": "markdown", - "id": "c22b9572", - "metadata": {}, - "source": [ - "### Update the observable terms to account for the extra qubit\n", - "\n", - "The transformed circuit contains additional qubits (one for each `CutWire` instruction), so the observables must be updated for the new circuit. This can be done using the `expand_observables` function. Since the observable coefficients are ignored until reconstruction of the final expectation value, the input and output types of the observables for ``expand_observables`` are ``PauliList``s.\n", - "\n", - "The resulting observables have 9 qubits, just like the transformed circuit." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "95fbeda0", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:43.926980Z", - "iopub.status.busy": "2025-03-28T16:51:43.926767Z", - "iopub.status.idle": "2025-03-28T16:51:43.931899Z", - "shell.execute_reply": "2025-03-28T16:51:43.931437Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "PauliList(['ZIIIIIIII', 'IIIZIIIII', 'IIIIIIIIZ'])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "observable_expanded_paulis = expand_observables(observable.paulis, qc_0, qc_1)\n", - "observable_expanded_paulis" - ] - }, - { - "cell_type": "markdown", - "id": "6f64acd6", - "metadata": {}, - "source": [ - "### Separate the circuit and observables\n", - "\n", - "In this case, `partition_labels` need not be passed to `partition_problem`, as the labels can be determined automatically from the connectivity of the circuit." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "99bef123", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:43.933905Z", - "iopub.status.busy": "2025-03-28T16:51:43.933725Z", - "iopub.status.idle": "2025-03-28T16:51:43.940544Z", - "shell.execute_reply": "2025-03-28T16:51:43.940042Z" - } - }, - "outputs": [], - "source": [ - "partitioned_problem = partition_problem(\n", - " circuit=qc_1, observables=observable_expanded_paulis\n", - ")\n", - "subcircuits = partitioned_problem.subcircuits\n", - "subobservables = partitioned_problem.subobservables" - ] - }, - { - "cell_type": "markdown", - "id": "ce2bf0cd", - "metadata": {}, - "source": [ - "From here forward, the cutting workflow is the same as usual, but the remaining steps are spelled out here explicitly so one can follow along with the results." - ] - }, - { - "cell_type": "markdown", - "id": "bae9ac63", - "metadata": {}, - "source": [ - "### Visualize the decomposed problem\n", - "\n", - "Notice that once the circuits have been cut, some of the instructions are able to commute past each other. For instance, in subcircuit \"A\", half of the second `Move` operation is actually the _first_ operation on the final qubit." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "abeee650", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:43.942618Z", - "iopub.status.busy": "2025-03-28T16:51:43.942418Z", - "iopub.status.idle": "2025-03-28T16:51:43.946860Z", - "shell.execute_reply": "2025-03-28T16:51:43.946404Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: PauliList(['IIIII', 'ZIIII', 'IIIIZ']),\n", - " 1: PauliList(['ZIII', 'IIII', 'IIII'])}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subobservables" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "aaef5b3d", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:43.948780Z", - "iopub.status.busy": "2025-03-28T16:51:43.948581Z", - "iopub.status.idle": "2025-03-28T16:51:44.164480Z", - "shell.execute_reply": "2025-03-28T16:51:44.163535Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[0].draw(\"mpl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "975a3ca9", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:44.167652Z", - "iopub.status.busy": "2025-03-28T16:51:44.167358Z", - "iopub.status.idle": "2025-03-28T16:51:44.386632Z", - "shell.execute_reply": "2025-03-28T16:51:44.385952Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[1].draw(\"mpl\")" - ] - }, - { - "cell_type": "markdown", - "id": "2e02632b", - "metadata": {}, - "source": [ - "### Generate and run the cutting experiments; reconstruct and compare against uncut expectation values" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "459dcee8", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:44.389065Z", - "iopub.status.busy": "2025-03-28T16:51:44.388801Z", - "iopub.status.idle": "2025-03-28T16:51:44.496881Z", - "shell.execute_reply": "2025-03-28T16:51:44.496185Z" - } - }, - "outputs": [], - "source": [ - "subexperiments, coefficients = generate_cutting_experiments(\n", - " circuits=subcircuits,\n", - " observables=subobservables,\n", - " num_samples=np.inf,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "dc9fe287", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:44.501113Z", - "iopub.status.busy": "2025-03-28T16:51:44.500852Z", - "iopub.status.idle": "2025-03-28T16:51:45.949307Z", - "shell.execute_reply": "2025-03-28T16:51:45.948450Z" - } - }, - "outputs": [], - "source": [ - "sampler = SamplerV2()\n", - "results = {\n", - " label: sampler.run(subexperiment, shots=2**12).result()\n", - " for label, subexperiment in subexperiments.items()\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e317a998", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:45.952706Z", - "iopub.status.busy": "2025-03-28T16:51:45.951971Z", - "iopub.status.idle": "2025-03-28T16:51:49.139646Z", - "shell.execute_reply": "2025-03-28T16:51:49.138857Z" - } - }, - "outputs": [], - "source": [ - "reconstructed_expval_terms = reconstruct_expectation_values(\n", - " results,\n", - " coefficients,\n", - " subobservables,\n", - ")\n", - "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "5ae568ca", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:49.143349Z", - "iopub.status.busy": "2025-03-28T16:51:49.143053Z", - "iopub.status.idle": "2025-03-28T16:51:49.158786Z", - "shell.execute_reply": "2025-03-28T16:51:49.158077Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstructed expectation value: 1.62048799\n", - "Exact expectation value: 1.59099026\n", - "Error in estimation: 0.02949773\n", - "Relative error in estimation: 0.01854048\n" - ] - } - ], - "source": [ - "estimator = EstimatorV2()\n", - "exact_expval = (\n", - " estimator.run([(qc_0.decompose(\"cut_wire\"), observable)]).result()[0].data.evs\n", - ")\n", - "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", - "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", - "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", - "print(\n", - " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.21" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/how-tos/index.mdx b/docs/addons/qiskit-addon-cutting/how-tos/index.mdx deleted file mode 100644 index 986d7f75b26..00000000000 --- a/docs/addons/qiskit-addon-cutting/how-tos/index.mdx +++ /dev/null @@ -1,28 +0,0 @@ ---- -title: Circuit cutting API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-cutting. ---- - -# How-To Guides - -* [Generate exact quasi-distributions](how-to-generate-exact-quasi-dists-from-sampler): Use the [`ExactSampler`](utils-simulation#qiskit_addon_cutting.utils.simulation.ExactSampler "qiskit_addon_cutting.utils.simulation.ExactSampler") interface to generate exact quasi-distributions for circuits containing mid-circuit measurements. -* [Generate exact sampling coefficients](how-to-generate-exact-sampling-coefficients): Generate exact sampling coefficients and run all unique samples from the distribution. -* [Specify cut wires as a single-qubit instruction](how-to-specify-cut-wires): Perform wire cutting with a single-qubit CutWire instruction, rather than a two-qubit Move operation. -* [Perform a cutting workflow with multiple observables](how-to-provide-multiple-observables): Use the [`observable_terms`](utils-observable-terms#module-qiskit_addon_cutting.utils.observable_terms "qiskit_addon_cutting.utils.observable_terms") utility functions to gather unique observable terms, perform circuit cutting experiments, and reconstruct the desired expectation values. - -[![](/docs/images/api/qiskit-addon-cutting/nbsphinx-no-thumbnail.svg)](how-to-generate-exact-quasi-dists-from-sampler) - -[How to generate exact quasiprobability distributions from Sampler](how-to-generate-exact-quasi-dists-from-sampler) - -[![](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_generate_exact_sampling_coefficients_5_0.avif)](how-to-generate-exact-sampling-coefficients) - -[How to generate exact sampling coefficients](how-to-generate-exact-sampling-coefficients) - -[![](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_provide_multiple_observables_2_0.avif)](how-to-provide-multiple-observables) - -[How to perform a cutting workflow with multiple observables](how-to-provide-multiple-observables) - -[![](/docs/images/api/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_18_0.avif)](how-to-specify-cut-wires) - -[How to place wire cuts using a single-qubit CutWire instruction](how-to-specify-cut-wires) - diff --git a/docs/addons/qiskit-addon-cutting/index.mdx b/docs/addons/qiskit-addon-cutting/index.mdx deleted file mode 100644 index ff700b74a0f..00000000000 --- a/docs/addons/qiskit-addon-cutting/index.mdx +++ /dev/null @@ -1,127 +0,0 @@ ---- -title: Circuit cutting API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-cutting. ---- - -# Qiskit addon: circuit cutting - -[![GitHub repository star counter badge](https://img.shields.io/github/stars/Qiskit/qiskit-addon-cutting?style=social)](https://github.com/Qiskit/qiskit-addon-cutting) - -[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. - -This package implements circuit cutting. In this technique, a handful of gates and/or wires are cut, resulting in smaller circuits that are better suited for execution on hardware. The result of the original circuit can then be reconstructed; however, the trade-off is that the overall number of shots must be increased by a factor exponential in the number of cuts. - -For a more detailed discussion on circuit cutting, check out our [technical guide](./circuit_cutting/explanation/index-rst#overview-of-circuit-cutting). - -We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [Release Notes](release-notes#release-notes). - - - This package was known as the Circuit Knitting Toolbox prior to September 2024. - - -## Citing this project - -If you use this package in your research, please cite it according to `CITATON.bib` file included in this repository: - -```bibtex -@misc{qiskit-addon-cutting, - author = { - Agata M. Bra\'{n}czyk - and Almudena {Carrera Vazquez} - and Daniel J. Egger - and Bryce Fuller - and Julien Gacon - and James R. Garrison - and Jennifer R. Glick - and Caleb Johnson - and Saasha Joshi - and Edwin Pednault - and C. D. Pemmaraju - and Pedro Rivero - and Ibrahim Shehzad - and Stefan Woerner - }, - title = {{Qiskit addon: circuit cutting}}, - howpublished = {\url{https://github.com/Qiskit/qiskit-addon-cutting}}, - year = {2024}, - doi = {10.5281/zenodo.7987997} -} -``` - -If you are using the entanglement forging tool in Circuit Knitting Toolbox version 0.5 or earlier, please use [an older version of the citation file](https://github.com/Qiskit/qiskit-addon-cutting/blob/stable/0.5/CITATION.bib) which includes the authors of that tool. - -If you are using the CutQC tool in Circuit Knitting Toolbox version 0.7 or earlier, please use [an older version of the citation file](https://github.com/Qiskit/qiskit-addon-cutting/blob/stable/0.7/CITATION.bib) which includes the authors of that tool. - -## Developer guide - -The source code to this package is available [on GitHub](https://github.com/Qiskit/qiskit-addon-cutting). - -The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-cutting/blob/main/CONTRIBUTING.md) in the root of this project’s repository. - -## Contents - -* [Documentation Home]() - -* [Installation Instructions](install) - - * [Pre-Installation](install#pre-installation) - * [Option 1: Install from PyPI](install#option-1-install-from-pypi) - * [Option 2: Install from Source](install#option-2-install-from-source) - * [Option 3: Use within Docker](install#option-3-use-within-docker) - * [Platform Support](install#platform-support) - -* [Tutorials](tutorials/index) - - * [Gate Cutting to Reduce Circuit Width](tutorials/01-gate-cutting-to-reduce-circuit-width) - * [Gate Cutting to Reduce Circuit Depth](tutorials/02-gate-cutting-to-reduce-circuit-depth) - * [Wire Cutting Phrased as a Two-Qubit `Move` Instruction](tutorials/03-wire-cutting-via-move-instruction) - * [Automatically find cuts](tutorials/04-automatic-cut-finding) - -* [Explanatory Material](explanation/index) - - * [Overview of circuit cutting](explanation/index#overview-of-circuit-cutting) - * [Key terms](explanation/index#key-terms) - * [Circuit cutting as a quasiprobability decomposition (QPD)](explanation/index#circuit-cutting-as-a-quasiprobability-decomposition-qpd) - * [An example: cutting a `RZZGate`](explanation/index#an-example-cutting-a-rzzgate) - * [More general cut two-qubit gates via the KAK decomposition](explanation/index#more-general-cut-two-qubit-gates-via-the-kak-decomposition) - * [Wire cutting phrased as a two-qubit `Move` operation](explanation/index#wire-cutting-phrased-as-a-two-qubit-move-operation) - * [Sample weights in the Qiskit addon for circuit cutting](explanation/index#sample-weights-in-the-qiskit-addon-for-circuit-cutting) - * [Sampling overhead reference table](explanation/index#sampling-overhead-reference-table) - * [Current limitations](explanation/index#current-limitations) - * [References](explanation/index#references) - -* [How-To Guides](how-tos/index) - - * [How to generate exact quasiprobability distributions from Sampler](how-tos/how-to-generate-exact-quasi-dists-from-sampler) - * [How to generate exact sampling coefficients](how-tos/how-to-generate-exact-sampling-coefficients) - * [How to perform a cutting workflow with multiple observables](how-tos/how-to-provide-multiple-observables) - * [How to place wire cuts using a single-qubit `CutWire` instruction](how-tos/how-to-specify-cut-wires) - -* [API References](index) - - * [Circuit cutting (`qiskit_addon_cutting`)](qiskit-addon-cutting) - * [Instructions (`qiskit_addon_cutting.instructions`)](instructions) - * [Quasi-Probability Decomposition (QPD) (`qiskit_addon_cutting.qpd`)](qpd) - * [Bitwise utilities (`qiskit_addon_cutting.utils.bitwise`)](utils-bitwise) - * [Iteration utilities (`qiskit_addon_cutting.utils.iteration`)](utils-iteration) - * [Observable grouping (`qiskit_addon_cutting.utils.observable_grouping`)](utils-observable-grouping) - * [Observable terms (`qiskit_addon_cutting.utils.observable_terms`)](utils-observable-terms) - * [Simulation utilities (`qiskit_addon_cutting.utils.simulation`)](utils-simulation) - * [Transforms (`qiskit_addon_cutting.utils.transforms`)](utils-transforms) - * [Transpiler passes (`qiskit_addon_cutting.utils.transpiler_passes`)](utils-transpiler-passes) - -* [GitHub](https://github.com/Qiskit/qiskit-addon-cutting) - -* [Release Notes](release-notes) - - * [0.10.0](release-notes#release-notes-0-10-0) - * [0.9.0](release-notes#release-notes-0-9-0) - * [0.7.1](release-notes#release-notes-0-7-1) - * [0.7.0](release-notes#release-notes-0-7-0) - * [0.6.0](release-notes#release-notes-0-6-0) - * [0.5.0](release-notes#release-notes-0-5-0) - * [0.4.0](release-notes#release-notes-0-4-0) - * [0.3.0](release-notes#release-notes-0-3-0) - * [0.2.0](release-notes#release-notes-0-2-0) - * [0.1.0](release-notes#release-notes-0-1-0) - diff --git a/docs/addons/qiskit-addon-cutting/install.mdx b/docs/addons/qiskit-addon-cutting/install.mdx deleted file mode 100644 index ea82b6758ca..00000000000 --- a/docs/addons/qiskit-addon-cutting/install.mdx +++ /dev/null @@ -1,122 +0,0 @@ -# Installation Instructions - -Let’s see how to install the Qiskit addon for circuit cutting. The first thing to do is choose how you’re going to run and install the packages. There are three primary ways to do this: - -* [Option 1: Install from PyPI](#option-1) -* [Option 2: Install from Source](#option-2) -* [Option 3: Use within Docker](#option-3) - -Users who wish to run within a containerized environment may skip the pre-installation and move straight to [Option 3: Use within Docker](#option-3). - -## Pre-Installation - -Users who wish to install locally (using either [Option 1: Install from PyPI](#option-1) or [Option 2: Install from Source](#option-2)) are encouraged to follow a brief set of common instructions to prepare a Python environment for installation: - -First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). - -```sh -python3 -m venv /path/to/virtual/environment -``` - -Activate your new environment. - -```sh -source /path/to/virtual/environment/bin/activate -``` - -Note: If you are using Windows, use the following commands in PowerShell: - -```pwsh -python3 -m venv c:\path\to\virtual\environment -c:\path\to\virtual\environment\Scripts\Activate.ps1 -``` - - - -## Option 1: Install from PyPI - -The most straightforward way to install the `qiskit-addon-cutting` package is via PyPI. - -```sh -pip install --upgrade pip -pip install qiskit-addon-cutting -``` - - - -## Option 2: Install from Source - -Users who wish to develop in the repository or run the notebooks locally may want to install from source. - -In either case, the first step is to clone the `qiskit-addon-cutting` repository. - -```sh -git clone git@github.com:Qiskit/qiskit-addon-cutting.git -``` - -Next, upgrade pip and enter the repository. - -```sh -pip install --upgrade pip -cd qiskit-addon-cutting -``` - -The next step is to install to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. - -Adjust the options below to suit your needs. - -```sh -pip install tox notebook -e '.[notebook-dependencies,dev]' -``` - -If you installed the notebook dependencies, you can get started with the addon by running the notebooks in the docs. - -```python -cd docs/ -jupyter notebook -``` - - - -## Option 3: Use within Docker - -We have provided a [Dockerfile](https://github.com/Qiskit/qiskit-addon-cutting/blob/main/Dockerfile), which can be used to build a Docker image, as well as a [compose.yaml](https://github.com/Qiskit/qiskit-addon-cutting/blob/main/compose.yaml) file, which allows one to use the Docker image with just a few simple commands. - -```sh -git clone git@github.com:Qiskit/qiskit-addon-cutting.git -cd qiskit-addon-cutting -docker compose build -docker compose up -``` - -Depending on your system configuration, you may need to type `sudo` before each `docker compose` command. - - - If you are instead using [podman](https://podman.io/) and [podman-compose](https://github.com/containers/podman-compose), the commands are: - - ```sh - podman machine start - podman-compose --podman-pull-args short-name-mode="permissive" build - podman-compose up - ``` - - -Once the container is running, you should see a message like this: - -```python -notebook_1 | To access the server, open this file in a browser: -notebook_1 | file:///home/jovyan/.local/share/jupyter/runtime/jpserver-7-open.html -notebook_1 | Or copy and paste one of these URLs: -notebook_1 | http://e4a04564eb39:8888/lab?token=00ed70b5342f79f0a970ee9821c271eeffaf760a7dcd36ec -notebook_1 | or http://127.0.0.1:8888/lab?token=00ed70b5342f79f0a970ee9821c271eeffaf760a7dcd36ec -``` - -Locate the *last* URL in your terminal (the one that includes `127.0.0.1`), and navigate to that URL in a web browser to access the Jupyter Notebook interface. - -The home directory includes a subdirectory named `persistent-volume`. All work you’d like to save should be placed in this directory, as it is the only one that will be saved across different container runs. - - - -## Platform Support - -We expect this package to work on [any platform supported by Qiskit](/docs/start/install#operating-system-support). If you are experiencing issues running the software on your device, you may consider [using this package within Docker](#option-3). diff --git a/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width.mdx b/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width.mdx deleted file mode 100644 index fb634c4fd6d..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width.mdx +++ /dev/null @@ -1,310 +0,0 @@ - - -# Gate Cutting to Reduce Circuit Width - -In this notebook, we will work through the steps of a [Qiskit pattern](/docs/guides/intro-to-patterns) while using **circuit cutting** to reduce the number of qubits in a circuit. We will cut gates to enable us to reconstruct the expectation value of a four-qubit circuit using only two-qubit experiments. - -These are the steps that we will take: - -* **Step 1: Map problem to quantum circuits and operators**: - - * Map the hamiltonian onto a quantum circuit. - -* **Step 2: Optimize for target hardware** \[*Uses the cutting addon*]: - - * Cut the circuit and observable. - * Transpile the subexperiments for hardware. - -* **Step 3: Execute on target hardware**: - - * Run the subexperiments obtained in Step 2 using a `Sampler` primitive. - -* **Step 4: Post-process results** \[*Uses the cutting addon*]: - - * Combine the results of Step 3 to reconstruct the expectation value of the observable in question. - - - -## Step 1: Map - - - -### Create a circuit to cut - -``` -[1]: -``` - -```python -from qiskit.circuit.library import efficient_su2 - -qc = efficient_su2(4, entanglement="linear", reps=2) -qc.assign_parameters([0.4] * len(qc.parameters), inplace=True) - -qc.draw("mpl", scale=0.8) -``` - -``` -[1]: -``` - -![../\_images/tutorials\_01\_gate\_cutting\_to\_reduce\_circuit\_width\_2\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_01_gate_cutting_to_reduce_circuit_width_2_0.avif) - - - -### Specify an observable - -``` -[2]: -``` - -```python -from qiskit.quantum_info import SparsePauliOp - -observable = SparsePauliOp(["ZZII", "IZZI", "-IIZZ", "XIXI", "ZIZZ", "IXIX"]) -``` - - - -## Step 2: Optimize - - - -### Separate the circuit and observable according to a specified qubit partitioning - -Each label in `partition_labels` corresponds to the `circuit` qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut. - -**Note:** The `observables` kwarg to `partition_problem` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value. - -``` -[3]: -``` - -```python -from qiskit_addon_cutting import partition_problem - -partitioned_problem = partition_problem( - circuit=qc, partition_labels="AABB", observables=observable.paulis -) -subcircuits = partitioned_problem.subcircuits -subobservables = partitioned_problem.subobservables -bases = partitioned_problem.bases -``` - - - -### Visualize the decomposed problem - -``` -[4]: -``` - -```python -subobservables -``` - -``` -[4]: -``` - -```python -{'A': PauliList(['II', 'ZI', 'ZZ', 'XI', 'ZZ', 'IX']), - 'B': PauliList(['ZZ', 'IZ', 'II', 'XI', 'ZI', 'IX'])} -``` - -``` -[5]: -``` - -```python -subcircuits["A"].draw("mpl", scale=0.8) -``` - -``` -[5]: -``` - -![../\_images/tutorials\_01\_gate\_cutting\_to\_reduce\_circuit\_width\_9\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_01_gate_cutting_to_reduce_circuit_width_9_0.avif) - -``` -[6]: -``` - -```python -subcircuits["B"].draw("mpl", scale=0.8) -``` - -``` -[6]: -``` - -![../\_images/tutorials\_01\_gate\_cutting\_to\_reduce\_circuit\_width\_10\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.avif) - - - -### Calculate the sampling overhead for the chosen cuts - -Here we cut two CNOT gates, resulting in a sampling overhead of $9^2$. - -For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](explanation/index). - -``` -[7]: -``` - -```python -import numpy as np - -print(f"Sampling overhead: {np.prod([basis.overhead for basis in bases])}") -``` - -```python -Sampling overhead: 81.0 -``` - - - -### Generate the subexperiments to run on the backend - -`generate_cutting_experiments` accepts `circuits`/`observables` args as dictionaries mapping qubit partition labels to the respective `subcircuit`/`subobservables`. - -To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates’ joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](explanation/index). - -``` -[8]: -``` - -```python -from qiskit_addon_cutting import generate_cutting_experiments - -subexperiments, coefficients = generate_cutting_experiments( - circuits=subcircuits, observables=subobservables, num_samples=np.inf -) -``` - - - -### Choose a backend - -Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator). - -``` -[9]: -``` - -```python -from qiskit_ibm_runtime.fake_provider import FakeManilaV2 - -backend = FakeManilaV2() -``` - - - -### Prepare the subexperiments for the backend - -We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime. - -``` -[10]: -``` - -```python -from qiskit.transpiler import generate_preset_pass_manager - -# Transpile the subexperiments to ISA circuits -pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend) -isa_subexperiments = { - label: pass_manager.run(partition_subexpts) - for label, partition_subexpts in subexperiments.items() -} -``` - - - -## Step 3: Execute - - - -### Run the subexperiments using the Qiskit Runtime Sampler primitive - -``` -[11]: -``` - -```python -from qiskit_ibm_runtime import SamplerV2, Batch - -# Submit each partition's subexperiments to the Qiskit Runtime Sampler -# primitive, in a single batch so that the jobs will run back-to-back. -with Batch(backend=backend) as batch: - sampler = SamplerV2(mode=batch) - jobs = { - label: sampler.run(subsystem_subexpts, shots=2**12) - for label, subsystem_subexpts in isa_subexperiments.items() - } -``` - -``` -[12]: -``` - -```python -# Retrieve results -results = {label: job.result() for label, job in jobs.items()} -``` - - - -## Step 4: Post-process - - - -### Reconstruct the expectation value - -Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable. - -``` -[13]: -``` - -```python -from qiskit_addon_cutting import reconstruct_expectation_values - -# Get expectation values for each observable term -reconstructed_expval_terms = reconstruct_expectation_values( - results, - coefficients, - subobservables, -) - -# Reconstruct final expectation value -reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs) -``` - - - -### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable - -``` -[14]: -``` - -```python -from qiskit_aer.primitives import EstimatorV2 - -estimator = EstimatorV2() -exact_expval = estimator.run([(qc, observable)]).result()[0].data.evs -print(f"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}") -print(f"Exact expectation value: {np.round(exact_expval, 8)}") -print(f"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}") -print( - f"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}" -) -``` - -```python -Reconstructed expectation value: 0.58167797 -Exact expectation value: 0.56254612 -Error in estimation: 0.01913185 -Relative error in estimation: 0.03400939 -``` diff --git a/docs/addons/qiskit-addon-cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb deleted file mode 100644 index ea3a688c41f..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb +++ /dev/null @@ -1,531 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ad1f14b4", - "metadata": {}, - "source": [ - "# Gate Cutting to Reduce Circuit Width\n", - "\n", - "In this notebook, we will work through the steps of a [Qiskit pattern](/guides/intro-to-patterns) while using **circuit cutting** to reduce the number of qubits in a circuit. We will cut gates to enable us to reconstruct the expectation value of a four-qubit circuit using only two-qubit experiments.\n", - "\n", - "These are the steps that we will take:\n", - "\n", - "- **Step 1: Map problem to quantum circuits and operators**:\n", - " - Map the hamiltonian onto a quantum circuit.\n", - "- **Step 2: Optimize for target hardware** [_Uses the cutting addon_]:\n", - " - Cut the circuit and observable.\n", - " - Transpile the subexperiments for hardware.\n", - "- **Step 3: Execute on target hardware**:\n", - " - Run the subexperiments obtained in Step 2 using a `Sampler` primitive.\n", - "- **Step 4: Post-process results** [_Uses the cutting addon_]:\n", - " - Combine the results of Step 3 to reconstruct the expectation value of the observable in question." - ] - }, - { - "cell_type": "markdown", - "id": "510910a6", - "metadata": {}, - "source": [ - "## Step 1: Map\n", - "\n", - "### Create a circuit to cut" - ] - }, - { - "cell_type": "code", - 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit.circuit.library import efficient_su2\n", - "\n", - "qc = efficient_su2(4, entanglement=\"linear\", reps=2)\n", - "qc.assign_parameters([0.4] * len(qc.parameters), inplace=True)\n", - "\n", - "qc.draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "id": "8638fdf1", - "metadata": {}, - "source": [ - "### Specify an observable" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f75e8dd1", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.353234Z", - "iopub.status.busy": "2025-03-28T16:50:51.352789Z", - "iopub.status.idle": "2025-03-28T16:50:51.356422Z", - "shell.execute_reply": "2025-03-28T16:50:51.355858Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])" - ] - }, - { - "cell_type": "markdown", - "id": "162a5629", - "metadata": {}, - "source": [ - "## Step 2: Optimize\n", - "\n", - "### Separate the circuit and observable according to a specified qubit partitioning\n", - "\n", - "Each label in `partition_labels` corresponds to the `circuit` qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut.\n", - "\n", - "**Note:** The ``observables`` kwarg to `partition_problem` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "30326299", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.358249Z", - "iopub.status.busy": "2025-03-28T16:50:51.358050Z", - "iopub.status.idle": "2025-03-28T16:50:51.401645Z", - "shell.execute_reply": "2025-03-28T16:50:51.400913Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import partition_problem\n", - "\n", - "partitioned_problem = partition_problem(\n", - " circuit=qc, partition_labels=\"AABB\", observables=observable.paulis\n", - ")\n", - "subcircuits = partitioned_problem.subcircuits\n", - "subobservables = partitioned_problem.subobservables\n", - "bases = partitioned_problem.bases" - ] - }, - { - "cell_type": "markdown", - "id": "9d2d42c3", - "metadata": {}, - "source": [ - "### Visualize the decomposed problem" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6b54be63", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.404435Z", - "iopub.status.busy": "2025-03-28T16:50:51.403916Z", - "iopub.status.idle": "2025-03-28T16:50:51.410005Z", - "shell.execute_reply": "2025-03-28T16:50:51.409344Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'A': PauliList(['II', 'ZI', 'ZZ', 'XI', 'ZZ', 'IX']),\n", - " 'B': PauliList(['ZZ', 'IZ', 'II', 'XI', 'ZI', 'IX'])}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subobservables" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b7e06fac", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.411952Z", - "iopub.status.busy": "2025-03-28T16:50:51.411753Z", - "iopub.status.idle": "2025-03-28T16:50:51.547364Z", - "shell.execute_reply": "2025-03-28T16:50:51.546536Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[\"A\"].draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "11e45e83", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.549471Z", - "iopub.status.busy": "2025-03-28T16:50:51.549264Z", - "iopub.status.idle": "2025-03-28T16:50:51.738543Z", - "shell.execute_reply": "2025-03-28T16:50:51.737835Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[\"B\"].draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "id": "4f8017b6-2954-4b51-8a07-f4de49832509", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Calculate the sampling overhead for the chosen cuts\n", - "\n", - "Here we cut two CNOT gates, resulting in a sampling overhead of $9^2$.\n", - "\n", - "For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](../explanation/index.rst)." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3d606ef8", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.741344Z", - "iopub.status.busy": "2025-03-28T16:50:51.740724Z", - "iopub.status.idle": "2025-03-28T16:50:51.745416Z", - "shell.execute_reply": "2025-03-28T16:50:51.744851Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sampling overhead: 81.0\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")" - ] - }, - { - "cell_type": "markdown", - "id": "ef3b061c-cd1d-4931-85f6-155e00b355be", - "metadata": {}, - "source": [ - "### Generate the subexperiments to run on the backend\n", - "\n", - "`generate_cutting_experiments` accepts `circuits`/`observables` args as dictionaries mapping qubit partition labels to the respective `subcircuit`/`subobservables`.\n", - "\n", - "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2029d18e-0e91-4160-b8c9-02cb9e1ba3cb", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.747765Z", - "iopub.status.busy": "2025-03-28T16:50:51.747387Z", - "iopub.status.idle": "2025-03-28T16:50:51.854533Z", - "shell.execute_reply": "2025-03-28T16:50:51.853782Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import generate_cutting_experiments\n", - "\n", - "subexperiments, coefficients = generate_cutting_experiments(\n", - " circuits=subcircuits, observables=subobservables, num_samples=np.inf\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "444b0038-b3d5-469c-85b2-4c1d9d8fce05", - "metadata": {}, - "source": [ - "### Choose a backend\n", - "\n", - "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "36c89aa0-70aa-4615-8198-77ec85e8aa93", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.857664Z", - "iopub.status.busy": "2025-03-28T16:50:51.857181Z", - "iopub.status.idle": "2025-03-28T16:50:52.542985Z", - "shell.execute_reply": "2025-03-28T16:50:52.542361Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", - "\n", - "backend = FakeManilaV2()" - ] - }, - { - "cell_type": "markdown", - "id": "85a1d76a-5c47-4210-a742-9b22f02f7200", - "metadata": {}, - "source": [ - "### Prepare the subexperiments for the backend\n", - "\n", - "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "184e0f36-1279-48a3-aab7-b7603bb71f66", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:52.545759Z", - "iopub.status.busy": "2025-03-28T16:50:52.545267Z", - "iopub.status.idle": "2025-03-28T16:50:56.855540Z", - "shell.execute_reply": "2025-03-28T16:50:56.854763Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.transpiler import generate_preset_pass_manager\n", - "\n", - "# Transpile the subexperiments to ISA circuits\n", - "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", - "isa_subexperiments = {\n", - " label: pass_manager.run(partition_subexpts)\n", - " for label, partition_subexpts in subexperiments.items()\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "d8870454-2173-4454-90b4-a034779510e0", - "metadata": {}, - "source": [ - "## Step 3: Execute\n", - "\n", - "### Run the subexperiments using the Qiskit Runtime Sampler primitive" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2dbb8148-0482-4a66-8316-335125f8a2b3", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:56.858196Z", - "iopub.status.busy": "2025-03-28T16:50:56.857749Z", - "iopub.status.idle": "2025-03-28T16:50:57.056138Z", - "shell.execute_reply": "2025-03-28T16:50:57.048260Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import SamplerV2, Batch\n", - "\n", - "# Submit each partition's subexperiments to the Qiskit Runtime Sampler\n", - "# primitive, in a single batch so that the jobs will run back-to-back.\n", - "with Batch(backend=backend) as batch:\n", - " sampler = SamplerV2(mode=batch)\n", - " jobs = {\n", - " label: sampler.run(subsystem_subexpts, shots=2**12)\n", - " for label, subsystem_subexpts in isa_subexperiments.items()\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "64cef60b-5130-467e-8e01-7460d78560ed", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:57.132131Z", - "iopub.status.busy": "2025-03-28T16:50:57.130570Z", - "iopub.status.idle": "2025-03-28T16:51:20.447808Z", - "shell.execute_reply": "2025-03-28T16:51:20.446901Z" - } - }, - "outputs": [], - "source": [ - "# Retrieve results\n", - "results = {label: job.result() for label, job in jobs.items()}" - ] - }, - { - "cell_type": "markdown", - "id": "f0032570", - "metadata": {}, - "source": [ - "## Step 4: Post-process\n", - "\n", - "### Reconstruct the expectation value\n", - "\n", - "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "7d57339c", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:20.451351Z", - "iopub.status.busy": "2025-03-28T16:51:20.450827Z", - "iopub.status.idle": "2025-03-28T16:51:25.701123Z", - "shell.execute_reply": "2025-03-28T16:51:25.700268Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import reconstruct_expectation_values\n", - "\n", - "# Get expectation values for each observable term\n", - "reconstructed_expval_terms = reconstruct_expectation_values(\n", - " results,\n", - " coefficients,\n", - " subobservables,\n", - ")\n", - "\n", - "# Reconstruct final expectation value\n", - "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" - ] - }, - { - "cell_type": "markdown", - "id": "53beaca3", - "metadata": {}, - "source": [ - "### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e3385ba5", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:25.708190Z", - "iopub.status.busy": "2025-03-28T16:51:25.707798Z", - "iopub.status.idle": "2025-03-28T16:51:25.747493Z", - "shell.execute_reply": "2025-03-28T16:51:25.744142Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstructed expectation value: 0.58167797\n", - "Exact expectation value: 0.56254612\n", - "Error in estimation: 0.01913185\n", - "Relative error in estimation: 0.03400939\n" - ] - } - ], - "source": [ - "from qiskit_aer.primitives import EstimatorV2\n", - "\n", - "estimator = EstimatorV2()\n", - "exact_expval = estimator.run([(qc, observable)]).result()[0].data.evs\n", - "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", - "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", - "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", - "print(\n", - " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.21" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth.mdx b/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth.mdx deleted file mode 100644 index 7b27af8c265..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth.mdx +++ /dev/null @@ -1,343 +0,0 @@ - - -# Gate Cutting to Reduce Circuit Depth - -In this tutorial, we will reduce a circuit’s depth by cutting distant gates, avoiding the swap gates that would otherwise be introduced by routing. - -These are the steps that we will take in this [Qiskit pattern](/docs/guides/intro-to-patterns): - -* **Step 1: Map problem to quantum circuits and operators**: - - * Map the hamiltonian onto a quantum circuit. - -* **Step 2: Optimize for target hardware** \[*Uses the cutting addon*]: - - * Cut the circuit and observable. - * Transpile the subexperiments for hardware. - -* **Step 3: Execute on target hardware**: - - * Run the subexperiments obtained in Step 2 using a `Sampler` primitive. - -* **Step 4: Post-process results** \[*Uses the cutting addon*]: - - * Combine the results of Step 3 to reconstruct the expectation value of the observable in question. - - - -## Step 1: Map - - - -### Create a circuit to run on the backend - -``` -[1]: -``` - -```python -from qiskit.circuit.library import efficient_su2 - -circuit = efficient_su2(num_qubits=4, entanglement="circular") -circuit.assign_parameters([0.4] * len(circuit.parameters), inplace=True) -circuit.draw("mpl", scale=0.8) -``` - -``` -[1]: -``` - -![../\_images/tutorials\_02\_gate\_cutting\_to\_reduce\_circuit\_depth\_2\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_02_gate_cutting_to_reduce_circuit_depth_2_0.avif) - - - -### Specify an observable - -``` -[2]: -``` - -```python -from qiskit.quantum_info import SparsePauliOp - -observable = SparsePauliOp(["ZZII", "IZZI", "-IIZZ", "XIXI", "ZIZZ", "IXIX"]) -``` - - - -## Step 2: Optimize - - - -### Specify a backend - -You can provide either a fake backend or a hardware backend from Qiskit Runtime. - -``` -[3]: -``` - -```python -from qiskit_ibm_runtime.fake_provider import FakeManilaV2 - -backend = FakeManilaV2() -``` - - - -### Transpile the circuit, visualize the swaps, and note the depth - -We choose a layout that requires two swaps to execute the gates between qubits 3 and 0 and another two swaps to return the qubits to their initial positions. - -``` -[4]: -``` - -```python -from qiskit.transpiler import generate_preset_pass_manager - -pass_manager = generate_preset_pass_manager( - optimization_level=1, backend=backend, initial_layout=[0, 1, 2, 3] -) - -transpiled_qc = pass_manager.run(circuit) -print(f"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}") -``` - -```python -Transpiled circuit depth: 30 -``` - -```python -/tmp/ipykernel_3574/3423257129.py:8: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes. - print(f"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}") -``` - -``` -[5]: -``` - -```python -transpiled_qc.draw("mpl", scale=0.4, idle_wires=False, fold=-1) -``` - -``` -[5]: -``` - -![../\_images/tutorials\_02\_gate\_cutting\_to\_reduce\_circuit\_depth\_9\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_02_gate_cutting_to_reduce_circuit_depth_9_0.avif) - - - -### Replace distant gates with `TwoQubitQPDGate`s by specifying their indices - -`cut_gates` will replace the gates in the specified indices with `TwoQubitQPDGate`s and also return a list of `QPDBasis` instances – one for each gate decomposition. - -``` -[6]: -``` - -```python -from qiskit_addon_cutting import cut_gates - -# Find the indices of the distant gates -cut_indices = [ - i - for i, instruction in enumerate(circuit.data) - if {circuit.find_bit(q)[0] for q in instruction.qubits} == {0, 3} -] - -# Decompose distant CNOTs into TwoQubitQPDGate instances -qpd_circuit, bases = cut_gates(circuit, cut_indices) - -qpd_circuit.draw("mpl", scale=0.8) -``` - -``` -[6]: -``` - -![../\_images/tutorials\_02\_gate\_cutting\_to\_reduce\_circuit\_depth\_11\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.avif) - - - -### Generate the subexperiments to run on the backend - -`generate_cutting_experiments` accepts a circuit containing `TwoQubitQPDGate` instances and observables as a `PauliList`. - -To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates’ joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](explanation/index). - -**Note:** The `observables` kwarg to `generate_cutting_experiments` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value. - -``` -[7]: -``` - -```python -import numpy as np -from qiskit_addon_cutting import generate_cutting_experiments - -# Generate the subexperiments and sampling coefficients -subexperiments, coefficients = generate_cutting_experiments( - circuits=qpd_circuit, observables=observable.paulis, num_samples=np.inf -) -``` - - - -### Calculate the sampling overhead for the chosen cuts - -Here we cut three CNOT gates, resulting in a sampling overhead of $9^3$. - -For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](explanation/index). - -``` -[8]: -``` - -```python -print(f"Sampling overhead: {np.prod([basis.overhead for basis in bases])}") -``` - -```python -Sampling overhead: 729.0 -``` - - - -### Demonstrate that the QPD subexperiments will be shallower after cutting distant gates - -Here is an example of an arbitrarily chosen subexperiment generated from the QPD circuit. Its depth has been reduced by more than half. Many of these probabilistic subexperiments must be generated and evaluated in order to reconstruct an expectation value of the deeper circuit. - -``` -[9]: -``` - -```python -# Transpile the decomposed circuit to the same layout -transpiled_qpd_circuit = pass_manager.run(subexperiments[100]) - -print( - f"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}" -) -print( - f"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}" -) -transpiled_qpd_circuit.draw("mpl", scale=0.8, idle_wires=False, fold=-1) -``` - -```python -Original circuit depth after transpile: 30 -QPD subexperiment depth after transpile: 7 -``` - -```python -/tmp/ipykernel_3574/4252024425.py:5: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes. - f"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}" -/tmp/ipykernel_3574/4252024425.py:8: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes. - f"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}" -``` - -``` -[9]: -``` - -![../\_images/tutorials\_02\_gate\_cutting\_to\_reduce\_circuit\_depth\_17\_2.png](/docs/images/api/qiskit-addon-cutting/tutorials_02_gate_cutting_to_reduce_circuit_depth_17_2.avif) - - - -### Prepare subexperiments for the backend - -``` -[10]: -``` - -```python -# Transpile the subeperiments to the backend's instruction set architecture (ISA) -isa_subexperiments = pass_manager.run(subexperiments) -``` - - - -## Step 3: Execute - - - -### Run the subexperiments using the Qiskit Runtime Sampler primitive - -``` -[11]: -``` - -```python -from qiskit_ibm_runtime import SamplerV2 - -# Set up the Qiskit Runtime Sampler primitive. For a fake backend, this will use a local simulator. -sampler = SamplerV2(backend) - -# Submit the subexperiments -job = sampler.run(isa_subexperiments) -``` - -``` -[12]: -``` - -```python -# Retrieve the results -results = job.result() -``` - - - -## Step 4: Post-process - - - -### Reconstruct the expectation value - -Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable. - -``` -[13]: -``` - -```python -from qiskit_addon_cutting import reconstruct_expectation_values - -reconstructed_expval_terms = reconstruct_expectation_values( - results, - coefficients, - observable.paulis, -) -# Reconstruct final expectation value -reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs) -``` - - - -### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable - -``` -[14]: -``` - -```python -from qiskit_aer.primitives import EstimatorV2 - -estimator = EstimatorV2() -exact_expval = estimator.run([(circuit, observable)]).result()[0].data.evs -print(f"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}") -print(f"Exact expectation value: {np.round(exact_expval, 8)}") -print(f"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}") -print( - f"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}" -) -``` - -```python -Reconstructed expectation value: 0.51977539 -Exact expectation value: 0.50497603 -Error in estimation: 0.01479936 -Relative error in estimation: 0.02930705 -``` diff --git a/docs/addons/qiskit-addon-cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb deleted file mode 100644 index bfee58d2b3c..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb +++ /dev/null @@ -1,577 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7ed4867f", - "metadata": {}, - "source": [ - "# Gate Cutting to Reduce Circuit Depth\n", - "\n", - "In this tutorial, we will reduce a circuit's depth by cutting distant gates, avoiding the swap gates that would otherwise be introduced by routing.\n", - "\n", - "These are the steps that we will take in this [Qiskit pattern](/guides/intro-to-patterns):\n", - "\n", - "- **Step 1: Map problem to quantum circuits and operators**:\n", - " - Map the hamiltonian onto a quantum circuit.\n", - "- **Step 2: Optimize for target hardware** [_Uses the cutting addon_]:\n", - " - Cut the circuit and observable.\n", - " - Transpile the subexperiments for hardware.\n", - "- **Step 3: Execute on target hardware**:\n", - " - Run the subexperiments obtained in Step 2 using a `Sampler` primitive.\n", - "- **Step 4: Post-process results** [_Uses the cutting addon_]:\n", - " - Combine the results of Step 3 to reconstruct the expectation value of the observable in question." - ] - }, - { - "cell_type": "markdown", - "id": "07f7f75d", - "metadata": {}, - "source": [ - "## Step 1: Map\n", - "\n", - "### Create a circuit to run on the backend" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "54ed0f13", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:29.327835Z", - "iopub.status.busy": "2025-03-28T16:51:29.326393Z", - "iopub.status.idle": "2025-03-28T16:51:31.050966Z", - "shell.execute_reply": "2025-03-28T16:51:31.050169Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit.circuit.library import efficient_su2\n", - "\n", - "circuit = efficient_su2(num_qubits=4, entanglement=\"circular\")\n", - "circuit.assign_parameters([0.4] * len(circuit.parameters), inplace=True)\n", - "circuit.draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "id": "452cd19e", - "metadata": {}, - "source": [ - "### Specify an observable" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "105e858d", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:31.054439Z", - "iopub.status.busy": "2025-03-28T16:51:31.054037Z", - "iopub.status.idle": "2025-03-28T16:51:31.061841Z", - "shell.execute_reply": "2025-03-28T16:51:31.061246Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])" - ] - }, - { - "cell_type": "markdown", - "id": "699105a3-903e-49d8-826b-cc8b9b85c9df", - "metadata": {}, - "source": [ - "## Step 2: Optimize\n", - "\n", - "### Specify a backend\n", - "\n", - "You can provide either a fake backend or a hardware backend from Qiskit Runtime." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "756f2130-6947-479a-9fe7-97443c87a904", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:31.066068Z", - "iopub.status.busy": "2025-03-28T16:51:31.065650Z", - "iopub.status.idle": "2025-03-28T16:51:32.084249Z", - "shell.execute_reply": "2025-03-28T16:51:32.083362Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", - "\n", - "backend = FakeManilaV2()" - ] - }, - { - "cell_type": "markdown", - "id": "080a2a8b", - "metadata": {}, - "source": [ - "### Transpile the circuit, visualize the swaps, and note the depth\n", - "\n", - "We choose a layout that requires two swaps to execute the gates between qubits 3 and 0 and another two swaps to return the qubits to their initial positions." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b394da7a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:32.088232Z", - "iopub.status.busy": "2025-03-28T16:51:32.087771Z", - "iopub.status.idle": "2025-03-28T16:51:32.168573Z", - "shell.execute_reply": "2025-03-28T16:51:32.163841Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transpiled circuit depth: 30\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_3574/3423257129.py:8: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes.\n", - " print(f\"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\")\n" - ] - } - ], - "source": [ - "from qiskit.transpiler import generate_preset_pass_manager\n", - "\n", - "pass_manager = generate_preset_pass_manager(\n", - " optimization_level=1, backend=backend, initial_layout=[0, 1, 2, 3]\n", - ")\n", - "\n", - "transpiled_qc = pass_manager.run(circuit)\n", - "print(f\"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4fe4af43", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:32.176421Z", - "iopub.status.busy": "2025-03-28T16:51:32.174292Z", - "iopub.status.idle": "2025-03-28T16:51:33.477300Z", - "shell.execute_reply": "2025-03-28T16:51:33.474323Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transpiled_qc.draw(\"mpl\", scale=0.4, idle_wires=False, fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "6c106db7", - "metadata": {}, - "source": [ - "### Replace distant gates with `TwoQubitQPDGate`s by specifying their indices\n", - "\n", - "`cut_gates` will replace the gates in the specified indices with `TwoQubitQPDGate`s and also return a list of `QPDBasis` instances -- one for each gate decomposition." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "12e73c69", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:33.480297Z", - "iopub.status.busy": "2025-03-28T16:51:33.479989Z", - "iopub.status.idle": "2025-03-28T16:51:34.022077Z", - "shell.execute_reply": "2025-03-28T16:51:34.021152Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_cutting import cut_gates\n", - "\n", - "# Find the indices of the distant gates\n", - "cut_indices = [\n", - " i\n", - " for i, instruction in enumerate(circuit.data)\n", - " if {circuit.find_bit(q)[0] for q in instruction.qubits} == {0, 3}\n", - "]\n", - "\n", - "# Decompose distant CNOTs into TwoQubitQPDGate instances\n", - "qpd_circuit, bases = cut_gates(circuit, cut_indices)\n", - "\n", - "qpd_circuit.draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "id": "069eb942-e947-4eff-a250-9a99c5ec47f0", - "metadata": {}, - "source": [ - "### Generate the subexperiments to run on the backend\n", - "\n", - "`generate_cutting_experiments` accepts a circuit containing `TwoQubitQPDGate` instances and observables as a `PauliList`.\n", - "\n", - "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst).\n", - "\n", - "**Note:** The ``observables`` kwarg to `generate_cutting_experiments` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "83b1efed-bafa-48c4-bbf0-cf7eb9027ac5", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:34.027775Z", - "iopub.status.busy": "2025-03-28T16:51:34.027177Z", - "iopub.status.idle": "2025-03-28T16:51:34.976541Z", - "shell.execute_reply": "2025-03-28T16:51:34.975799Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from qiskit_addon_cutting import generate_cutting_experiments\n", - "\n", - "# Generate the subexperiments and sampling coefficients\n", - "subexperiments, coefficients = generate_cutting_experiments(\n", - " circuits=qpd_circuit, observables=observable.paulis, num_samples=np.inf\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "c0acae3a", - "metadata": {}, - "source": [ - "### Calculate the sampling overhead for the chosen cuts\n", - "\n", - "Here we cut three CNOT gates, resulting in a sampling overhead of $9^3$.\n", - "\n", - "For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](../explanation/index.rst)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c7b28e2c", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:34.979488Z", - "iopub.status.busy": "2025-03-28T16:51:34.979281Z", - "iopub.status.idle": "2025-03-28T16:51:34.983347Z", - "shell.execute_reply": "2025-03-28T16:51:34.982624Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sampling overhead: 729.0\n" - ] - } - ], - "source": [ - "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")" - ] - }, - { - "cell_type": "markdown", - "id": "6929264d", - "metadata": {}, - "source": [ - "### Demonstrate that the QPD subexperiments will be shallower after cutting distant gates\n", - "\n", - "Here is an example of an arbitrarily chosen subexperiment generated from the QPD circuit. Its depth has been reduced by more than half. Many of these probabilistic subexperiments must be generated and evaluated in order to reconstruct an expectation value of the deeper circuit." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "70e2f1b6", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:34.985332Z", - "iopub.status.busy": "2025-03-28T16:51:34.985103Z", - "iopub.status.idle": "2025-03-28T16:51:35.433820Z", - "shell.execute_reply": "2025-03-28T16:51:35.432939Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original circuit depth after transpile: 30\n", - "QPD subexperiment depth after transpile: 7\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_3574/4252024425.py:5: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes.\n", - " f\"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\"\n", - "/tmp/ipykernel_3574/4252024425.py:8: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes.\n", - " f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}\"\n" - ] - }, - { - "data": { - "image/png": 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jzKeyc4SDdz0AgGPtpshOuIqYdRNRe8JXhbbNuRuL6LVvod6cv6CyL5+NhPKEuZEvpebmwtVEzFt7Bj/ti0JenggAUKsEDO5VG7Nfb4lmAVUljrDiUepYUwLmRv4iopIwb90ZBO+OQm6eHkB+TXumRy3MHtcCLRvxG+q2xnkjX8yN/F2JTsFHa8Kwbfc15OTm1zSVADzVrRZmv94CbZpUkzjCknGcyRdzI19KyE1KSgpWrVqFM2fOAADUajXat2+PNm3awN/fH9WqVYMgCEhKSkJUVBTCw8Nx8OBBZGZmYvv27Th16hTat2+Pn376CaIoIjAwEO+88w7s7e2l/cGIqMKS/lQgogrq7NmzGDhwINzc3FCpUiUMGjQI8fHxcHV1xXPPPWex55kyZQp27dpV7DbBwcFITEw0+9hbtmyBKIqYMmVKgeWvvfYanJyc8N1335l9TFvwfn4u7oZsRPrlUwWWi3o9opa9CK8hM+Dk10yi6EzjVq8Gkq/ESR2GxTE38qWE3Bw/exsdXvgNW3ddMzQNAUCnF/HT3uvo+OJvOHQqXsIIzcOxJl/MDdnCvxfuov0LO/H9H1cNTUMgv6btCLmBTqN/R8jxmxJGaB7OG/libsgWzkbcQ/uRO/Ht71cMTUMA0IvAzgPR6Dz6d+w6Uv4uxaiEccYaIF/MjTwkJiZi7ty5hqZh165d8eWXX2LSpEno1KkTvLy8oFaroVKpULVqVbRu3RpjxozB6tWrMWTIEKjVakRHR+PHH39k05CIZIONQyIJhISEoEOHDoiIiMCsWbOwcOFCxMbGon///khLS0OLFi0s9lzLly/H8ePHi1wfFxeHMWPGoHfv3mY3D0+ePAmVSoV27doVWO7g4IAWLVrg5MmTpYrZ2hxq1Efltk/j5nfvF1geHzwfasdKqP7URIkiM51nxyZIOHZe6jAsjrmRr/Kem/SMXDwzaS/SMnOL3CYzOw8DJ+9DcmqODSMrPY41+WJuyNqysvPw1Ft7kJJedE3LztFh8JR9uJeUZcPISo/zRr6YG7K2nFwdnp64F4kp2UVuk5unx5C3Q5BwN8OGkZWdEsYZa4B8MTfSy8nJwaJFi3Dz5k3Y2dlh2rRpGD9+PCpXrlzivg4ODhg2bBgGDRpkWKZWq/HSSy+xaUhEkmPjkMjG7ty5gxEjRqBVq1YICwvD9OnTMWHCBISEhCA6OhoALNo4LImPjw+2b9+O8+fPIygoCElJSSbve/PmTXh4eBh9QePj44O7d+8iJ0eeH8B7Dp6OlDN7kBp+AACQdvEo7u3bAL9JGyWNy1Rqey30OXmGx32C56Dfjo8AQSiwXc+N/8NTu5ZA0KhtHWKpMTfyVZ5zs+Wva7iTmAVRLHobUQSSUnPwzW+XbRdYGXCsyRdzQ9YWvDsKCfcyS6xpqRm52PgLa5rUyvu8YW7I2naE3EBMQjqKKWkQRSAjKw8btkfaLC5LKe/jjDVAvpgb6QUHByM6OhoajQbvvvsu2rZta9b++/fvx/bt2wEAGo0GOp0OmzZtgljcizwiIhvgPQ6JbGzJkiVITEzExo0b4ejoaFju5uaGVq1aISQkxOKNw3379iErq/hvm7du3RrHjh1DUFAQ9u/fDxcXlxKPm5GRUeS3oBwcHAzb2NnZlXisvLw8JCQklLjdA7m5ngC0JW7nN3mT0eUujTqh9a/5L8Ty0pIQtWwU/CZtgqaSu8kx5MeRi9jYW2btU/gYeSVuo3a0gy4zvwmrdXFETnJ6gfVHJq/EwJClCJwwCOErdwAAAkYFoUa3Zvitz3SIeTqTY4mNLdslgJgb5qbkOMqeG3Nt3HEBAlDsB1IAIADY9MtFDO7qZoOoHpLLWLPlOAPKx1hjbgqSU24qso07Lpi0nSAAm3+9iOd6V7FyRAVx3hQkp3nD3BQkp9xUZBu2nzNpOwHA5l8v4aUnbXv/ViW9hpZLDXgQC9/fPHoM5uZR8sqN8Ss8REVF4Y8//gAADB8+HE2bNjXruPv378e6desMlyft168fPvnkE5w7dw4HDhxAjx49jMZS1twQUcXi5eUFjcb8NiAbh0Q2tnXrVnTp0gUBAQFG13t6esLLywtAfjNt2rRp+Pbbb6HX6zFkyBB8+eWXhqacqY4fP45Tp04Vu41en38fibNnz+LOnTsmNQ6dnJxw+/Zto+seNCqdnEy7WXVCQgJ8fX1N2hYAGq88B8daTUzevjh3dq1GbmI8Yr6eWmC5e4+X4DlwahF75YuMjIRvX/NeHD5uvnsQfLSVjK4T1Cq0fu8F6HJyEbZkKwCgRrfmuHnovwLbZcTfxz8zvkKXlRMRt/8M8jKz0XbuSzj10bdIvmL6/Y0iIyMx3Iw8GMPcMDe2yI3Z6s0FHHwKffv2cSKA02cvw9d3mE3CekAuY01u4wyQfqwxN0WTOjcVWt1ZgGPtkmuaCJy7FA1f3xE2Ciwf503RpJ43zE3RpM5NheY/A3DyB4TiL4olArgcZd77RktQ0mtoudQAQH51gLl5iLkp6JNPPjFad/744w+Iogg/Pz8MGDDArGM+3jR8cE/Dbt264eDBg/jjjz/QvXt3CI+91ouMjMTAgQPL9PMQUcUSExODmjVrmr0fG4dENpSQkIC4uDiMGFH4wxu9Xo/w8HC0bNnSsGzhwoXYv38/wsPDYWdnh2eeeQbvvvsuVqxYYdbzzpo1C3Pnzi1yfVpaGvr374/Q0FBs27YNderUMem4NWrUwIULF5CdnV3ozMO4uDh4eHiYdLah1LyHzoT30JlSh2GUqNMj7NNt6LttDsKQ/wbByasqMm8Vvh/l9Z3H4NunDbp+OQl5mTm4dfwiLm3aZeuQLYq5kS8558YofaZp24mi6dvaCMeafMcacyPf3CieOTVNx5omJ3KeN8yNfHOjePpM5J9PWAIZ1jRzyXmcsQYwN3Ilx9ykpKTg+PHjAICnnnoKarXpl4EtqmkIAE8//TQOHjyI2NhYXLp0CY0aNbJK/EREJWHjkMiG0tPzLyPx+DeGAODXX3/F7du3C1ymdP369fj444/h4+MDAJg7dy6GDRuGZcuWmfWipDiPNw0HDx5s8r5t27bFnj17cOLECXTp0sWwPCsrC2fOnEHXrl1NPpaXlxdiYmJM3n7iBU/EFH/1VZsICAjAbjPiNuaf4YuRHlX0ZVp1mTnIupcMZx8PpMfdhfj/Z4caE/reegwLWwfoRYSMWmR2LAEBAYgJ/trs/R7F3BjH3DxkidyYa83PUViwwYR74ggCpr3WC1NGjrN+UI+Qy1hT0jgDlFUHmBt61MadN/DBmkslbygImPjSE3j3Jdv+rjlvrIM1rSCl5aYi++7PGMz8woRLMAsCXh/ZDrNete3vWi5jTUk1AFBWHWBuClNSbk6fPo3MzIJfWrhw4QLy8vLg6OiI9u3bm3ys4pqGAFCzZk3Ur18fly9fxpkzZwo1DgMCAsz67IyI6MGVDc3FxiGRDfn6+kKtVuPgwYMFlt+4cQMTJ04EAEPjMCkpCTExMQUaia1atUJqaiquX7+OunXrWiQmnU4HQRDMbhoCwIgRI7Bw4UJ8/vnnBRqHX331FTIyMvDCCy+YfCyNRmPWadPaywBk8CJUq9WW6nTvgscouRTH7P0XNXu3xr3/ruHumatFbuc/pCsEQYDKUQv3Zv6IDTltdixl/nmYG6OYm4cskRtzTX3JA0u/u4rsHB2Kus+8IAAatQrTXm4L72qmXWbZUuQy1pQ0zgBl1QHmhh41aVR1LNl8BRlZecXWNLVKwDuvtEVN75IvQW9JnDfWwZr22DEUlpuKbMKLnli08TJSM3KLrWmCAEx/pS1q1jR+OUdrkctYU1INeBCLUuoAc2PkGArKTXh4eKHGYVRUFADA398fWq1p99stqWn4QEBAAC5fvmx4jkfx7w0R2UrxF5AnIouys7PD6NGjcerUKQwcOBDr1q3D7Nmz0b59e7i759/g+UGjMDU1FQBQuXJlw/4P/v9gnSlEUSz2MqVubm44ePCg2U1DAAgMDMRbb72F7du349lnn8X69esxbdo0vP322+jWrRtGjhxp9jHJuNh9/8I3qDU8WtTF3bDLRrdxq++DNrNHIXT2Rlzc8Bc6LX0T9lVdbRxpxcPclA/ulR3w/aLuUAmC0VuCCUL+BbI2zetq86ahqTjW5Iu5IVtzc7XDliU9oFYVXdMA4Ku5T6CWjZuGpuK8kS/mhmzNxUmL4E97QqNWFVnTRBFYPasz6vratmlYEbEGyBdzIx9xcXEAgNq1a5u0valNQwDw8/MDAMTGxlokViKi0mDjkMjGVqxYgXHjxiE0NBTTpk1DaGgoduzYgRo1asDJyQkBAQEAAFfX/Bd2ycnJhn2TkpIKrLMUY5dONdXnn3+OTz/9FOfPn8dbb72FrVu3YuLEifj999+hUrHEWErm7SRoXRyhcTT+wlLQqNHli0m4eeg/XP5+H04v/B7Ziano+PHrNo604mFuyo9ne/vhr9V90aJB1ULrmtargt++6IORAyxzNrc1cKzJF3NDUni6ey3sWdsPbRp7FFrX2L8ydnzeG2MGBkgQmWk4b+SLuSEp9O1cEyFf9Uf7wGqF1jXwc8NPS3ti3NCGEkRW8bAGyBdzIx/NmjVDr1690LBhyXUpMjLS5KYhkH+50p49exa4shcRka3xU30iG3NxccHatWuRkJCA1NRU7NmzBx07dsS5c+cQGBhoaLZVrlwZvr6+OHPmjGHfsLAwuLq6Gr59JAdqtRrTpk1DREQEsrOzERcXh88++wwuLvL8dnt5dvPgWaTeuGV0Xct3R8DZ2x3Hpq0GAOiyc3F4wgr4BrVG3WHdbBlmhcTclB9BHX3w77ZB2LkiyLDsl8974+xPg/FkF18JIzMNx5p8MTckhR7tauDEloH444s+hmXbl/VC+PZnMbCHad+AlxLnjXwxNySFLq298M93z+CvVX0Ny378tCcu/DIEQ4LqSBhZxcMaIF/MjTz06dMHr732Gtq1a1fitvXr10ffvn1NahoCQJ06dTBu3Dg8//zzlgqXiMhsbBwSyUBSUhJiY2ML3M8QAMaOHYtFixbh5s2buHPnDubOnYsxY8ZArVZLE2g5o8/OwKV3O+LMyMq4f2hrofVJoTtxaXoHRMzsinsHvi+wLisuEv8+q0VaxHFbhVuiyB9CELf/TKHl1ds1RNM3B+LotNXIupdiWH7//HWc+TQY7ee9AmefwmcjkOUoKTdKmzfGCIKAlg3dDY9bN/Yo05nXtqSksaY0zA1JqVnAwzOp2zapxppGZcbckJSa1qti+H+HZtXLTU1TEtYA+VJKbjKjz+PSjCcQMbMrImf1RHbCtQLri3rfGb12Ai692xEX32mH5NO7bB12qQiCgJdeegnvvvtuiU1DIiK5KPnOu0RkdeHh4QBQqHH43nvv4e7du2jSpAn0ej2GDh2KJUuWSBBh+SRo7FF35g7c2bWm0DpRr0fcNzPQ8NMTUNk5IOL97qjc9imond0AAPHB8+DaRF7fyMu8lWh0+e0Tl/CN7wij68JX7kD4yh3WDKvM9NkZiJzdC1mxF1HrjTWo2vW5AuvTI08gdvO7+dtmpkIURdSZ+i1urHodgqCCoNag9oT1sPfylyJ8AMrKjdLmjdIoaaw9kBl9vtj5bKwGNF52GkB+s/r8xCZosOgwXBp0kCT+B5gb+eaG5IvzRr7zhrmRb25I/koz1houPlLseyJbU2INKOl9pyiKiP5yHLLiIqCyc0TtCethV+3hFUnkUgeUkhtNpWqoP/sPqJ3dkHx6F+K3zYPf5I0Ain7fmXP/JrJiL6Lhx/8gNzEBV+YNgFurfhL/JKYRBAFarVbqMIiITMbGIZEMFNU41Gg0WLFiBVasWCFBVOWfoFZDW8XL6Lq8lLvQVK4OtWP+JVUdfBogPTIUlVr2QXpEKLSVvSCoeGanLRTXqAIA54B2aLDgAADg1s7Poc/JLPZNBpUN5w3ZWknz2VgNeIDNautibojMx3kjX8wN2UppxlpJ74mo7Er6HSeH/gpBa48Giw4h/cq/iPtmBupMe3imG+uAZWkrVzf8X1BrgUfeRxb1vtOpfjsIWgeIujzo0pOgcZXPGZRERErDS5USycD48eMhiiI6dOC3V21F41YNeUm3kXs/HrqMVKRdOIy81PsAgPgfF8BryAyJI6w4imtUPe7+oR9Qtcvz0FaubjjL7fE3GWQ9nDdkDebM5wc1AIChWW3nUdMmcVZEzA2R+Thv5Iu5IVspzVgz5z0RlU5Jv+Osm5FwqtcGAOBUtxVSLxw2rGMdsB59diZubpkDz6cnG5YV9b5T7ewGe886OPdmACLe7873n0REVsTGIRFVSIIgoNb4NYj67AVELX0ejrWaQuteA8mn/oBTvTbQVHIv+SBkU1lxkRA0drD39DMsM/Ymg6yH84asqaT5/HgNYLPadpgbIvNx3sgXc0O2Yu5YI2k51g5ESthuiKKIlLDdyEu+bVjHOmAdoi4PUUtHwmvQO3D0CzQsL+p9Z+qZvchNTEDTNVfQ5IsLiFk/GaIuT8KfgIhIuXipUiKqsFybdIXr/L+hy0zDtcVD4BzQAbd2fIK0cwdwee4xZN4IR1ZcBOrO2A5tVW+pwy3XdJlpuPxB70LLPYLGwqPPWJOOcf/g96jadaThcVFvMsi6OG+oNEqqAabM50drAJvVlsPcEJmP80a+mBuyFUuPNbKMsrzvdGvdH+kRxxE5qwec/JrD0a8ZANYBaxFFETe+GItKLfuicodBhdYbe9+ZGr4fGteqEFQqqB1doc/NhqjLg6Dmx9tERJbGykpEinZ18RBkXAuDysE5/15srfpCl3ofVbuNRMzX05Bx9TQEjRY+Ly6ASmsH7+Hvw3v4+wCA68vHwKPfG2x+WIDa0QUNPzlepmMkHg1Gg0X5l4sp6U0GlQ3nDVlacTXA1Pn8aA3IuHaGzWoLYW6IzMd5I1/MDdmKpccaWUZZ33fWGPkhACDlbAgErT0A1gFrSQnbjftHgpF9+zruH9kKpzotUKlVv2Lfd1Zq3huJh7cgYmYX6HOyUP2pSVDZOUj9oxARKRIbh0SkaHVn/FzkOt9Xlha7r9/kTRaOhoryeKPKd+wyAEDUstGoM/UbpEeEws7TH5pK+Tc/N/Ymw3fs5xL+BMrCeUO2VNx8LqoGsFltG8wNkfk4b+SLuSFbKc1YA4p+T0SWU9z7Tt9XP8PVJUMhqDSwq1YLvuNWAmAdsBa3Vv3Q6seMItcbe98pqNV8v0lEZCNsHBIRkeSKalTVmfoNAMC5QXvU/+APw/KS3mQQUflR3HwuqgY8ih8eWA9zQ2Q+zhv5Ym7IVko71or78h5ZRknvOxssOFDs/qwDRERUUaikDoCIiIiIiIiIiIiIiIiIpMczDomoXPJxkjqCfJaIw9XPq+wHsRBLxMLcWAdzo2xyGWtKGmeAsuoAc0PlCeeNdbCmFaS03JB8ySW/SqoBgLLqAHNTmJJy4+LiUqr99Ho97txPBgBUq+oGAAUeq1Tmn89T2liIiMwliKIoSh0EERERkS3FJqTDt89WAEDMnudQ08tZ4oiIiEqPNY2IlIQ1jYiUIDklDYtW/wAAmPnmSAAo8NitEpuARCRfvFQpEREREREREREREREREbFxSERERERERERERERERERsHBIRERERERERERERERER2DgkIiIiIiIiIiIiIiIiIrBxSERERERERERERERERERg45CIiIiIiIiIiIiIiIiIwMYhEREREREREREREREREYGNQyIiIiIiIiIiIiIiIiICG4dEREREREREREREREREBDYOiYiIiIiIiIiIiIiIiAhsHBIRERERERERERERERER2DgkIiIiIiIiIiIiIiIiIrBxSERERERERERERERERERg45CIiIiIiIiIiIiIiIiIwMYhEREREREREREREREREQHQSB0AEVFphLy0GKnXE6QOA65+Xui1eUaZjjE1FIjLsFBAZeTjBCxrX7ZjMDfWYYncENmCXGoAoKw6oKT6DDA3VH5w3lgHa1phSsoNyZdcxhmgrDqgpBoAMDdKJ5exZolxdvjwYaSlpVkmoDJycXFBly5dpA6DyGLYOCSicin1egKSImOlDsMi4jKAa6lSR2E5zA1RxaakGgAoqw4wN0Tm47yRL+aGyHxKG2dKqgPMDdmKksZaWloaUlJSpA6DSJF4qVIiIiIiIiIiIiIiIiIiYuOQiIiIiIiIiIiIiIiIiNg4JCIiIiIiIiIiIiIiIiLwHodERERUQeTl6XHo3wScPH8HR8NuG5a/Of8oOrXwRNumHujW2htaLb9XRUTyp9Ppcfj0LZwIv4OjZ24Zlr8x7yg6Nq+Otk090L2tN+y0agmjJCIyjU6nx9GwWzhx7i6OhCUYlr/+0RF0bFEdbRpXQ4923rC3Y00jIiIisjY2DomIiEjRklKy8eXWi1j70yXEJKQXWv/7oRj8figGAFCjuhPGDWmAiSOboKqbva1DJSIqUUpaDlZtu4g1P17CjZtphdb/cTgGfxzOr2leHo54bUgDTBrZBB5VHGwdKhFRidIycrFqa35Ni4pLLbT+zyOx+PNILACgelUHjH22ASa/0ATV3R1tHSoRERFRhcHGIRFROXJ9+Rjc+3tz/gOVCtoq3nAN7Amf0Ytg5+4jbXAVHHMjT38djsFrHx5B3O0Mk7a/eTsDc1eHYXXwJaz9oDMG9qht5QhJKVgD5EtJudn7TxzGzj2M6PjCX4IwJuFuJuatPYM1wZewelYnDAmqY+UISSmUNG+URkm52X/iJl754DCuG/kShDG372dh4fqzWPvTJXwxsyNG9POHIAhWjrLiUtJYUxrmhmyB44yoYuO1uIiIyhmXxl3QbFM8AtdHo860H5ARFYZrS4ZJHRaBuZETURQx+4t/8eRbe0xuGj7q1r1MDJq8D9OXnoAoilaIkJSINUC+yntuRFHEvLVh6PP6LpObho+6k5iFodP+xqTF/0CvZ00j05T3eaNkSsjNx1//h55j/zK5afioe0nZeP5/BzB+/jHodHorREcPKGGsKRVzQ7bAcUZUcfGMQyKqUFRaDUZHby31/pu8h1owmtIRNHbQVvECANi5+6Ban3GI+WoSdBkpUDtVkji60mNuyJJmf/EvFnx1tsj1arUAL4/8S1wl3M2ETmf8g/RPN4dDp9dj6Tvt+Y12K2MNkC/mRnrz153BB1+eLnK9qTVt5Q8XoNeLWDmzI2ualXHeyBdzI72Pv/4P//v8ZJHrTa1pa368hDydHuvmPMGaZiXlfawVhXVAvpSQGyVR6jgri5ycHGi1Wv7dIcVj45CIymTRokU4ffo0/v33X0RFRaF27dq4fv261GEVyatTE+zoNgXJkbFSh2IROfduIvHYT4BKnf+vHGNuyFJ27r9RbNMQyL/vV+ze5wEANYO2IO5W0WclLvv2PNoHVseIfv4WjZMKYg2QL+ZGWruOxBbbNATMq2lfbr2I9oHVMOrp+haNkwrivJEv5kZaf4feLLZpCJhX09Zvj0T7wOoYO6SBReOkwsrbWCsO64B8KS03SqKUcSaKIq5evYorV64gKioKycnJEEURzs7OqF27Nvz9/dGoUSNoNIXbJmlpaViwYAFatGiB4cOHs3lIisbGIRGVyXvvvYeqVauiVatWSEpKkjqcErnV98HNg8U3FOQu9dwBhI1wgajXQ8zJBAB4DpoGtYMzACDxnx2I3/ZhgX2yYi7Ad+xyVOv/ps3jNRVzI9/clCf3k7Px+ryjFj/uWwuPoXtbb3i6O1r82JSPNUC+NYC5kS43yak5eO3DIxY/7qTFx9GrfQ3UqO5s8WNTPs4b1jRrKq+5ScvIxatzDlv8uG9/Goo+nXxQy9vF4seu6MrrWCsJ6wBzQ6ZR0jjLycnB33//jb179yIuLs7oNseOHQMAuLm5oWfPnujXrx/c3NwAPGwaRkVFITo6Gk888QR8fHivR1IuNg6JqEyuXr0Kf//8s3CaNm2KtDTz71FB5nEOaA+/KZsh5mQh8UgwUs7uQ40X5hvWV+k4GFU6DjY8Tjr+C+K+fQ/uPV+SItwKhbmR3tLN4Ui4m2nx495LysbiDWex7N0OFj92WWTn6PDz3uvYtvsa7idno7KrHYYG1cHwvnXg6MCXebbGGiBf5TU3y78/j9hb5t/TsCRJqTlY8NVZfPl+J4sfuyxycnXYvu86tu66hntJ2ajkosWQ3nXwXD9/ODmyptlaeZ03FUF5zc2XWy+U6p6GJUlNz8VHa8Kw/sMuFj92WeTm6vHL/hv44c+ruJuYhUouWgzqURsjn6wLZyet1OGZpLyOtYqAuSFbUMo4u3z5MlavXo2bN28alnl7e8Pf3x/Vq1cHACQnJyMqKgo3btxAcnIyduzYgb179+Lll19Gs2bNsHDhQkRFRUGtVmPq1KlsGpLi8d0XkUTOnj2LDz74AAcOHIAoiujZsydWr16NgIAADBgwAFu3lv6a7o+aMmUK+vXrh379+hW5TXBwMIKCglClShWzj/+gaVgeuNWrgeQrxr9VVJ6o7Bzh4F0PAOBYuymyE64iZt1E1J7wVaFtc+7GInrtW6g35y+o7J1sHarJmBv55qY8ycnVYf32CKsdf9Ovl7FgYhvZfHh98twdDJy0F/H/3ygVBEAUgd8PxeCdpaHYvqw3urT2kjhK07AGyLcGMDfS5SYvT491P12y2vG//f0KFk9pA1dnO6s9hzlOX7iLZybtRdzt/EsSPqhpfx6OxfTPTuDHT3uiZ/saEkdpGs4b1jRrK4+50en0WBNsvZr2w19X8em0dqhcyd5qz2GO/yLv4+kJexCdkP/lD0EA8P817d1lJ7Htkx7o06mmtEGaoDyOtZKwDjA3ZDoljLO9e/fi66+/hiiK0Gg06NWrF/r06VNk4y8pKQn79+/Hn3/+idTUVKxcuRKurq5ITU01NA3btGlj45+CyPZUUgdAVBGFhISgQ4cOiIiIwKxZs7Bw4ULExsaif//+SEtLQ4sWLSz2XMuXL8fx48eLXB8XF4cxY8agd+/eSExMtNjzypFnxyZIOHZe6jAszvv5ubgbshHpl08VWC7q9Yha9iK8hsyAk18ziaIzDXNDlrD7aBxu38+y2vGTUnPw+6Foqx3fHBeuJqLX2L+QcO/h2ZWi+HD9veRs9H1jF05fuCtBdOZjDZAv5kY6f5+4aWiiWUNqei5++fuG1Y5vjsjryej12l+4eefhz/toTUtMycaTb+1G6H+3JYjOfJw38sXcSOdI2C2rnG34QGaWDj/tvW6145vjakwKer76J2ISHp4xLorAg7KWnJaDpybsxZHTCdIEWAblYayVhHVAvpSaGyUpb+Ns79692LBhA0RRhL+/PxYtWoSXX3652LMFK1eujMGDB2Pp0qWGBmFqaioEQcDkyZPZNKQKg41DIhu7c+cORowYgVatWiEsLAzTp0/HhAkTEBISgujo/A+kLdk4LImPjw+2b9+O8+fPIygoqFzcp7C01PZa6HPyDI/7BM9Bvx0f/f/XPx/qufF/eGrXEgia8nGzZ4ca9VG57dO4+d37BZbHB8+H2rESqj81UaLITMfckCWcOHfH+s8Rbv3nMMWsL/5FakZugQ/WHyWKQGa2DjNXnDK+gcywBsgXcyMdm9S0c/L4csGcVaeRlJpTbE3LydHjf5+ftG1gpcR5I1/MjXRs8RrKFnXTFPPWnsG95GwUUdIgikCeTo/pn52waVyWUB7GWklYB+RLqblRkvI0zi5fvoyvv/4aANCqVSvMnTsXvr6+Ju+vUqlw7949w2NRFBX9mSnR4+RxrS2iCmTJkiVITEzExo0b4ejoaFju5uaGVq1aISQkxOKNw3379iErq/izcFq3bo1jx44hKCgI+/fvh4uLbW8sn5eXh4QE079xmZubV+I2akc76DJzAABaF0fkJBe8R9CRySsxMGQpAicMQvjKHQCAgFFBqNGtGX7rMx1ins6kOGJjY02O2/gxPAGU7R4XnoOnI2JGZ6SGH4BrYHekXTyKe/s2oNFnp82MJRexsbfKFAtzU5CccqN0x8IK5lutFuDl4Wh0W+9HlnsXsQ0AJNzNhE738GOff87cLPO4Kqv4u1n41cSzhPYci8Ph0AjU8XG2clQPyaUGPIhF6jogpxrA3BQkp9wYc/S09WvacRnUtDuJ2fhpb1SJ24kADp5KQMjRS2hQ23avUzlvCpLTvGFuCpJTbow5/G9MgcdWqWln4yWvaYmpOdjy55UStxNF4Ph/d7Dr4AU0rVvJBpHlU9r7G7nUATnUAIC5KSoOqepCWsbDq8PEJ8QXWBefEI/UlKLrmzUp6e9Nbm6u0eU5OTlYvXq14UzDKVOmwM7O9Mvzp6WlYcGCBYZ7GjZq1Ajnzp3D999/jxYtWhjui/h4LFL/DSIyxsvLCxqN+W1AQRSL+l4nEVlDzZo1Ua9ePRw4cKDQut69e+PcuXOGBlpwcDBWrFiBM2fOwMPDA9evXzf7+QRBgFqtLrFA6PV65ObmQqvVIiIiAnXq1DH7uZo2bYq0tLRSxRkbG2vWN3/muwfBR2v8DZagVqH1ey9Al5OLsCX594qsPaADbp+KQOatgpdj9XumE7qsnIg/BryHvMxsPL3nY/w77ztc2rTLpDjiclMw695ek+M2pvHKc3Cs1aRMx3hUXloSLr7dCn4TNsC1WQ+z9s2MPo8LE5uW6fmZm6JJnRvFq/s+4PSwdvl4OiF27/NlOmTNoC2Iu/XIpQKz4oDLc8p0zDJzbQb4TTJ9++i1QLLtztKRSw0A5FcHpK4BzE3RpM6NUf7/A5zrGx5apaZl3wIi3y96B1twaQzUedv07WM2AEn/WC+ex3DeFE3qecPcFE3q3BhVZxrg0sjw0Co1LeceEPG/Mh2zzJzqA3XNiCF2M5B42HrxPEZp72/kUgfkVgMA5uYBS+SmtFwrVcb495YAAFYtzK8Ljz5OTUmSJC4l/b355JNPjH6WuHv3bmzcuBEajQaLFi0y6/PGx5uGU6dORZMmTTB9+nTcvXsXXbp0wVtvvVVov5iYGEyfPr1MPw+RNcTExKBmTfPvq8wzDolsKCEhAXFxcRgxYkShdXq9HuHh4WjZsqVhWZUqVTBhwgTcunULy5YtK/Xzzpo1C3Pnzi1yfVpaGvr374/Q0FBs27atVE1DORF1eoR9ug19t81BGPJfhDp5VS30AhQAru88Bt8+bdD1y0nIy8zBreMXzfqQQI7u7FqN3MR4xHw9tcBy9x4vwXPg1CL2sg3mRr65UQah5E3KxXOUxNwrzcsh5nysAfKtAcyNHHNTUWqamTEI8rnbBueNHOdNPuZGjrmxQb0RZFDTzK1RMqpppSHPsZaPdYC5IeuT4zgTRRF79+Y3i3v16lXmpuGDexoOHz4cq1atwvHjxzFq1ChUqmS7s8WJpMDGIZENpafnX3ZBMPKG5tdff8Xt27cLXKY0KCgIAPDLL79YLabHm4aDBw+22nMVx8vLCzExMSVv+P/+Gb4Y6VFFX9pUl5mDrHvJcPbxQHrcXYh6fZHbhr63HsPC1gF6ESGjFpkVd0BAAGKCvzZrn8dNvOCJmOKvJGsW76Ez4T10Zqn2DQgIwG4z8mAMc1M0qXOjdCPfO4XDZx7egyDhbiZqBm0xuq23hyNObhkEAGj7/C+Iv5tpdLuEx5a3adkYO/6WNg/XYtPRbdwRk7f/69eNNr0EllxqACC/OiB1DWBuiiZ1bowZM+c0Qk4+vF+XNWpaYJN6+DNE2poWnZCBzq+YfrbNr8Fr0KphZesF9BjOm6JJPW+Ym6JJnRtjXpsfhl3HbhseW6OmNahXC/v2SlvT4u9mocNLB6E38dpewd+tQMfAqtYN6hFKe38jlzogtxoAMDcPWCI3pZWWkYlNO/YDAE6czL+n6aOPXZykuVSpkv7enD59GpmZBf8WXLt2zXDJ0Aefq5qiuKYhAHTo0AHffvstUlNTcezYMfTr16/A/gEBAWZ9rklkK15eXqXaj41DIhvy9fWFWq3GwYMHCyy/ceMGJk7Mv4Gwpe9vWBKdTgdBECRtGgKARqMx67Rprbbk8hWz91/U7N0a9/67hrtnrha5nf+QrhAEASpHLdyb+SM2xPTrsmu15sVt9BiXAVjwDUJZaLXasv88zI1VWCI3Ste++c0CjUOdTix4+aoixN/NNGm7/OfwljwPNWsC3dtexYGT8cVuJwhAm8Ye6NetsY0iyyeXGvAgFqXUASXV5wexMDfF69AioUDj0Co1rZmXLGpa307XsPtYXLHbCQLQtF4VPN2ridEv4VkL5411sKYZOYaCcmNMp5Z3CjQOrVHT2gXKo6Y91S0KOw9EF7udIAABtd0wtF+gbWuaTMYZoKw6oKQaADA3lpKckmb4v7eXd4F13l7ecKtku3s2P0ouY80S4yw8PLxQ4/DKlfz7zHp5mf43oaSmIQDY2dmhefPmOHLkCK5eLTxe+ZkNKU35viYCUTljZ2eH0aNH49SpUxg4cCDWrVuH2bNno3379nB3dwdg+cahKIrFXqbUzc0NBw8eLHXT8Ntvv8X8+fMxf/583LlzB8nJyYbH3377bSmjtozYff/CN6g1PFrUxd2wy0a3cavvgzazRyF09kZc3PAXOi19E/ZVXW0cacXD3JA1tGnioYjnMMXcN1tCoxaKvCKXIORfEOyjt1rbNC5TsQbIF3MjH7apadWs/hymmPNmS2g0xdc0AJg/obVNP2A3FeeNfDE38tGmccV5nTZrXAvYaVXF1jRRlG9NUxrWAflibsgaoqKiAAD+/v4mbW9K0/CBB8d88BxESsbGIZGNrVixAuPGjUNoaCimTZuG0NBQ7NixAzVq1ICTkxMCAgJsHlNZ3qxs2LABs2fPxuzZs3H79m0kJSUZHm/YsMGCUZov83YStC6O0DjaG10vaNTo8sUk3Dz0Hy5/vw+nF36P7MRUdPz4dRtHWvEwN2QNT3apCRcnrdWO72CvxjPda1vt+Obo1sYbW5b0gFaT/1Lu8SquVgnYNL8r+j0hz288sgbIF3MjH3061kRlVzurHV+rUWFwT3nUtI7NPfHT0l6w06oBFK5pKkHAV3OewDM95BHv4zhv5Iu5kY+e7WvAo4qD1Y6vVgsY0tvPasc3R9um1bDj895wsPv/mvZYURMArJ7VCUP71LF9cBUQ64B8MTdkDcnJyQCAatVK/oKcOU1DAKhevXqB5yBSMjYOiWzMxcUFa9euRUJCAlJTU7Fnzx507NgR586dQ2BgIFSq8jUtDxw4AFEUjf47cOCA1OHh5sGzSL1xy+i6lu+OgLO3O45NWw0A0GXn4vCEFfANao26w7rZMswKibkhS3N1tsOop+pa7fjP9fNHVTfjb2qlMLRPHVz5Yxhmv94CtWs8vMzNWyMaIfK3YRj1dH0JoysZa4B8MTfy4OSowZiB1pvHw/rUQXV3ae6tY8zAHrVx9Y9hmPNGS/j5PDyT4I1hDRHx21C8+mwDCaMrGeeNfDE38mBvp8arg633JdlBPWrDx9PZasc315NdfHH1z+H46K1W8K/5sKa9NqQBLu0cijeGN5IwuoqHdUC+mBuytIkTJ2LVqlV4+umnS9w2JSUF9+/fN6lpCACBgYH48ssv8dlnn1kqXCLZKl8dCiKFSkpKQmxsbKHLlOp0OmRlZSE3NxeiKCIrKwvZ2dnSBFlORf4Qgrj9Zwotr96uIZq+ORBHp61G1r0Uw/L756/jzKfBaD/vFTj7yONSN0rF3JA1vPtyMzg7Wv4Wzg72asx8tbnFj1tWvl4u+Oit1ji86SnDshmvNkedmvK/fA9rgHwxN/IxbXQgKrlY/kxqO60K742VX03z8XTG3PGtcGjjAMOy919rgbq+lSSMyjScN/LF3MjHlBeboEoly59JrdEImDWuhcWPW1be1Zww+/WWOLDhYU374PWWqF/bTcKoKibWAflibuQnM/o8Ls14AhEzuyJyVk9kJ1wrsD4pdCcuTe+AiJldce/A94bl0Wsn4NK7HXHxnXZIPr3L1mEbODk5oWrVqnBxKfkekjVq1MAHH3yAt99+u8SmIQDY29vD3d0drq7yf79NVFaW/2SNiMwWHh4OoPD9Db/99lu8/PLLhseOjo6oXbs2rl+/bsPoyrfMW4lGl98+cQnf+I4wui585Q6Er9xhzbDKJDP6PG6seh2CoIKg1qD2hPWw93p47fb0yBOI3fwuAECfmQpRFNF4Wf7Nw7PiInF+YhM0WHQYLg06SBL/A8yNfHNTnvn5uOKTt9th/IJjFj3ugomtEeDHD3ksSYk1AFBGHWBu5JObml7OWDa9A16dc9iix537Zis0qVfFoses6Dhv5DNvHsfcyCc3Xh5OWDmzI16cedCix31/bAu0aOhu0WNSvtKMs4aLjyBydi9kxV5ErTfWoGrX56QK30CJdUCfnVHs71kURUR/OQ5ZcRFQ2Tmi9oT1sKvma1jPGk1F0VSqhvqz/4Da2Q3Jp3chfts8+E3eCAAQ9XrEfTMDDT89AZWdAyLe747KbZ9Czv2byIq9iIYf/4PcxARcmTcAbq36SfyTmMbHxwc+Pj5Sh0EkO2wcEslAUY3DMWPGYMyYMbYPiGStuBdxAOAc0A4NFhwAANza+Tn0OZmGdfHB8+DahJf0sBbmRh5eH9YQh/5NwNZd14rcJuFuJmoGbTH8vziDetbG5BeaWDRGUi7WAfkqr7l5eVB9HDwVj29+u1LkNubUtCe71MT0MYEWjZGUq7zOm4qgvOZm5JN1ceBkPNZvjyxyG3NqWlDHGnjvNfmdQa0UpRlngsYedWfuwJ1daySKumIo6fecHPorBK09Giw6hPQr/yLumxmoM+3h2WGs0VQUbeXqhv8Lai2gUhse56XchaZydagd88/mc/BpgPTIUDjVbwdB6wBRlwddehI0rjwblKi8Y+OQSAbGjx+P8ePHSx0GlRPFvYh73P1DP8B/ejAAID0iFNrKXhCK2Z7KhrmRB5VKwOYFXaEXRQTvjjK6jU4nIu5WRonHGtijFrYs6Q61mld3J9OwDshXec2NIAjY8GEX6EUR3/1+1eg2pta0J7vUxE9Le0GjYU0j05TXeVMRlNfcCIKANbM7Q6cXsfGXy0a3MbWmBXWsgR3LesNOy3FmLaUZZ4JaDW0VL1uEV6GV9HvOuhkJp3r5l150qtsKqRceXr1A6jpA5YM+OxM3t8xB7TdWG5Zp3KohL+k2cu/HQ+XggrQLh1GpeRDUzm6w96yDc28GQJ+dAf9pWySMnIgsge8YiYjKqQcv4jyfnmx0fVZcJASNHew9/QAA8T8ugNeQGTaMsOJibqRnp1Vjy5IeWDa9PRwdzH9DbKdVYfGUNvhpaS842PN7VmQ+1gH5Ko+50WhU2Dy/G1bO7AgnB/NrklajwvwJrfHL50FwLMX+ROVx3lQU5TE3arUKGz7sgtWzOsHFyfz7uGrUAua80RK/f9EHzqXYn8xn7jgj6TnWDkRK2G6IooiUsN3IS75tWCeHOkDyJuryELV0JLwGvQNHv4dXqhAEAbXGr0HUZy8gaunzcKzVFFr3Gkg9sxe5iQlouuYKmnxxATHrJ0PU5Un4ExBRWfFdIxGRDOky03D5g96FlnsEjYVHn7FFvoh71P2D36Nq15EAgORTf8CpXhtoKvHeH2XF3JQfKpWAKaOa4skuvpi3LgzBu6OQk6svdh+tRoUhvf0w+/UWaFyX9/8i41gH5EvJuVGpBEx4vjH6P1ET89aewdZd15Cdoyt2H41GwOCefpg9rgUCA6raKFIqb5Q8b8o7JedGEAS8MbwR+nWuiY/WhmHLX9eQlV18TVOrBQzsXhuzX+c9DS3J0uOMLKek3BTHrXV/pEccR+SsHnDyaw5Hv2YA5FUHSJ5EUcSNL8aiUsu+qNxhUKH1rk26wnX+39BlpuHa4iFwDuiA1PD90LhWhaBSQe3oCn1uNkRdHgQ1Ww9E5RVnLxGRDKkdXdDwk+NG15X0Iu6BxKPBaLAo/3IkGdfOIO3cAVyeewyZN8KRFReBujO2Q1vV2xrhKxpzU/4E+Lnh24Xd8dk77fHT3us4ef4O/r1wD/eSsiACcHezR6tGHmjTxANDg/zg5eEkdcgkc6wD8lURclPXtxI2ze+Kpe+0w497onDy/F38e+Eu7ibm17SqlezRsqG7oabVqO4sWaxUPlSEeVNeVYTc+Pm44uuPuuLTae3x094onAi/g38v3sWd+1nQi0CVSnZo1cgdbRp7YEjvOqjpxZpmaZYeZ2Q5xeXGFDVGfggASDkbAkFrD0CedYDkJSVsN+4fCUb27eu4f2QrnOq0QKVW/aBLvY+q3UYi5utpyLh6GoJGC58XF0CltUOl5r2ReHgLImZ2gT4nC9WfmgSVnYPUPwoRlQEbh0RE5YyxF3G+Yz8HAEQtG406U79BekQo7Dz9oamUf0Nq7+Hvw3v4+wCA68vHwKPfG3xjYAXMjbxVq+qIN0c0wptoJHUopGCsA/KltNy4V3bAG8Mb4Q2pAyFFU9q8URKl5aaqmz3GDW2IcUMbSh0KPaI04wwAri4egoxrYVA5OCM9MhS+Y5dJ9BMoW1G/56hlo+H76me4umQoBJUGdtVqwXfcSgDyrgMkD26t+qHVj0XfZ9b3laWFlglqNfwmb7JiVERka2wcEhGVM8W9iKsz9RsAgHOD9qj/wR9Gt+GLOethboiIdUC+mBsi83HeyBdzQ7ZQ2nFWd8bPVo+Niv49P8hNgwUHit2fdYCIiIqikjoAIiIiIiIiIiIiIiIiIpIeG4dERERERERERERERERExEuVElH55OrnJXUIACwTh4+TBQKxEEvEwtxYh5xiISqOXGoAoKw6oKT6DDA3VH5w3lgHa1phSsoNyZec8qukOqCkGgAwN0onl7FmiThcXFxKtZ9er8ed+8kAgGpV3QCgwGOVyvxzrUobC5FcCaIoilIHQURERESlF5uQDt8+WwEAMXueQ00vZ4kjIiIqPdY0IlIS1jSiiik5JQ2LVv8AAJj55kgAKPDYrRIbTVJhbohKxkuVEhEREREREREREREREREbh0RERERERERERERERETExiERERERERERERERERERgY1DIiIiIiIiIiIiIiIiIgIbh0REREREREREREREREQENg6JiIiIiIiIiIiIiIiICGwcEhERERERERERERERERHYOCQiIiIiIiIiIiIiIiIisHFIRERERERERERERERERGDjkIiIiIiIiIiIiIiIiIjAxiERERERERERERERERERgY1DIiIiIiIiIiIiIiIiIgIbh0REREREREREREREREQENg6JiIiIiIiIiIiIiIiICGwcEhEREREREREREREREREAjdQBEBGVRshLi5F6PUHqMODq54Vem2eU6RhTQ4G4DAsFVEY+TsCy9mU7BnNjHZbIDRGZjzXNOvj3piCl5Ybki/PGOljTClJabki+5DLWlFQDAGXVAdaAwuQy1iwxzpRGSfPm8OHDSEtLs0xAZeTi4oIuXbpIHUa5w8YhEZVLqdcTkBQZK3UYFhGXAVxLlToKy2FuiEhJWNPki7khMh/njXwxN0TmU9JYU1INAJSVG6VR2lhTEiXNm7S0NKSkpEgdBpUBL1VKRERERERERERERERERGwcEhEREREREREREREREREbh0RERETlWlJKNsIu3TM8TkzJljAaIqKySU7NwZkI1jQiUoaUtII17V5SloTREBEREZmG9zgkIiIiKmfORtzDmuBL2Hs8DldjCt4EodnQHajj44qe7bzx5ohGaN3YQ6IoiYhMc+7yfaz58RL2/BOHyzcK3gul2dAd8Kvhgh7tvPHGsEZo29QDgiBIFCkRUckuXE3Emh8vYffROETeSC6wrsXwX1DL2xnd23jjjeEN0aFZddY0IiIikh02DomIypHry8fg3t+b8x+oVNBW8YZrYE/4jF4EO3cfaYOr4JgbsoXI68l4c/5R/H0ivtjtouJSsWFHKjbsiETnlp5YO7szmtSrYqMoSQlY0+RLSbm5GpOCN+cfxd5/bha73fWbadj4y2Vs/OUyOjSrhrUfPIFmAVVtFCUpgZLmjdIoKTfX41IxfsEx/HUkttjtouPT8c1vV/DNb1fQpokH1s7ujFb8opdVKWmcKQ1zQ2Q+zhuyBV6qlIionHFp3AXNNsUjcH006kz7ARlRYbi2ZJjUYRGYG7KuL7deQPNhO0psGj7uaNgttBrxCz7Z+B9EUbRSdKRErGnypYTcrPvpEpoN2VFi0/Bxx/+7gzbP/YqFX51hTSOzKGHeKJUScrPxl0gEDtlRYtPwcafO30W7F3biw9WnodezplmTEsaZUjE3RObjvCFrY+OQiKicETR20Fbxgp27D1ybdEW1PuOQHvEPdBkpJe9MVsXckDWIooj3lp/ChIX/ICtbV6pj5OTq8e6yk5iy5Dg/aCeTsabJV3nPzYerT+P1j44iIyuvVPvn5unx/sp/MX7+MdY0Mll5nzdKVt5zs2j9WbzywWGkZeSWan+dTsTc1WF47cMjbB5aUXkfZ0rG3BCZj/OGrI2XKiWiCkWl1WB09NZS77/Je6gFoym7nHs3kXjsJ0Clzv9XjjE3RMYt/+48Fm04W+w2arUALw9HAEDC3UzodMY/dFrxwwVUr+qI98e1sHSY9BjWNPlibqS1ettFzF0dVuw2pta0NT9eQrWqDvjordYWj5MK4ryRL+ZGWl/viMR7K04Vu42pNe3rHZHwqGyPJVPbWTxOKqi8jbPisAaQrShtrCmJ0uaNKIq4d+8ekpOTIYoinJyc4OXlBZWq+HPg/vnnH9SpUwdeXl42ilTZ2DgkolKLjIzEd999hz179uDq1avIyspC3bp1MWzYMEyZMgXOzs5Sh1iIV6cm2NFtCpIjzbuEjJyknjuAsBEuEPV6iDmZAADPQdOgdsj/fSf+swPx2z4ssE9WzAX4jl2Oav3ftHm8pmJu5Jsbks6Fq4n43+cnS9zOy8MRsXufBwDUDNqCuFsZRW47Z/Vp9H+iJu+lY2WsafKtacyNdLm5fCMZ05aGlridOTVtwVdn8eQTvujQvLrF4qTCOG9Y06ypvOYmKjYVkxb/U+J25tS0TzaF46mutdClNT/0tLTyOs5Kwhog39wojRLGmpIobd7k5eXh1KlTOHjwIK5cuYLU1NQC6+3t7eHn54eOHTuia9eucHJyKrD+8OHDWLVqFapUqYK5c+eienW+NygrNg6JqNS+/vprfPnll3jmmWfwwgsvQKvVYv/+/Zg1axaCg4Nx/PhxODo6Sh1mAW71fXDzYPFn7sidc0B7+E3ZDDEnC4lHgpFydh9qvDDfsL5Kx8Go0nGw4XHS8V8Q9+17cO/5khThmoy5ISpIFEW89uER5OTqLXpcnU7EK3MOIyx4EARBsOixLeFuYhYuXE2ECKBhncrwdJfX3xFTsabJF3Mjndc/OorMrNJdcrkoen1+TQv/eTDUavndieNeUhbOX8mvaQG13eBdzanEfeSI80a+mBvpjF9wDOmZpbvkclFEEXh17mFc2DEEGo38alpiSjbOXU6ETi8ioHYl1Kguvy8LF6W8jrOSsAaQrShhrCmJUuaNKIo4evQovv/+eyQmJha5XXZ2NiIiIhAREYEtW7ZgwIABGDx4MLRaraFpKIoiPD09UalSJRv+BMrFxiERldrQoUMxc+ZMuLm5GZa98cYbqF+/PhYsWIANGzZgwoQJEkaoTCo7Rzh41wMAONZuiuyEq4hZNxG1J3xVaNucu7GIXvsW6s35Cyr78vlBVXnC3JAlhf53B8fO3LbKsc9G3MffofHo1aGGVY5fGhFRSZi37gyCd0chNy+/WapRCxjcyw+zxrVAs4CqEkdY8bCmyVd5zM3pC3ex/2S8VY598VoSdh+Lw5NdfK1y/NK4Ep2CeWvDsHXXNcMXQNQqAQN71Mascc3RshHP+ra18jhvKorymJvzVxKx66h1zrq5fCMFvx+KxqCeflY5fmlci03B/HVn8MOfV5Gdk1/TVALwTI/aeP+15mjTpJrEEZasPI6zioK5ITKfEuZNWloaVq9ejX///RcAoFar0bZtW7Rp0wb+/v6oXr06BEFAUlISoqKiEB4ejkOHDiEzMxPbt2/HyZMn0blzZ2zbtg2iKKJRo0b43//+BwcHB4l/MmWQ39eXiCqIs2fPYuDAgXBzc0OlSpUwaNAgxMfHw9XVFc8995zFnmfKlCnYtWtXsdsEBwcX+62OorRp06ZA0/CBESNGAADOnTtn9jGtya1eDSRfiZM6DIvzfn4u7oZsRPrlgvfWEPV6RC17EV5DZsDJr5lE0ZmGuSEqbHXwRasef9U26x7fHCfP3UG7kTvx/R9XDU1DAMjTifhxTxQ6vvgbDp6yTsPBGljT5Iu5kc6aHy9Z9fhyqmlhF++i3chf8c1vVwqcNa7Ti9gech2dRv+OkOM3JYzQPJw38sXcSKci1bRzl++j3fM7sfGXy4amIQDoReCXv2+g8+jf8dfhGAkjLJ3yMM5KwhpAtqLUsaYk5W3epKSk4MMPPzQ0DTt16oSVK1diypQpeOKJJ1CjRg1oNBqo1Wq4u7ujTZs2ePnll7F69WoMHToUarUaMTEx2Lp1K5uGVsLGIZEEQkJC0KFDB0RERGDWrFlYuHAhYmNj0b9/f6SlpaFFixYWe67ly5fj+PHjRa6Pi4vDmDFj0Lt371I1D42Jjc3/5qWnp6dFjmcpnh2bIOHYeanDsDiHGvVRue3TuPnd+wWWxwfPh9qxEqo/NVGiyEzH3BAVJIoidh+z7huzvcfjoNeLVn0OU2Rk5uHpiXuQmpFb5DaZ2XkYOGkvklKybRhZ6bGmyRdzI53dx6x7P5y/T9xEXp5lL+1cGtk5Ojw9cS+SUnOK3WbQlL24m5hlw8hKj/NGvpgb6Vi7ph04mYDsHMte2rk0cnP1eHriXtxPLvo1WG6eHkOn/Y2Eu0Xfu1GOysM4KwlrANmKUseakpSneZObm4vFixcjJiYGWq0WkydPxqRJk1C1aslXGXJwcMDQoUMxdOhQwzKVSoWXX36ZTUMLY+OQyMbu3LmDESNGoFWrVggLC8P06dMxYcIEhISEIDo6GgAs2jgsiY+PD7Zv347z588jKCgISUlJZTqeTqfDvHnzoNFoMHLkSMsEaSFqey30OQ/vQdEneA767fgIeOweXz03/g9P7VoCQaO2dYil5jl4OlLO7EFq+AEAQNrFo7i3bwP8Jm2UNC5TMTdEBd28nYFb9zKt+hyp6bm4Ep1i1ecwxbbd13DrXhbEYnqYoggkp+Vi887LtgusDFjT5Iu5kcad+5mIjk+36nNkZulw8VqSVZ/DFD/vvY642xkl1rS0jDxs/CXSdoGVAeeNfDE30khOzcHlG9Z9DZWbp0f45ftWfQ5T7DxwA9dvpqG4r5qJIpCRlYf12yNsFpelyHmcmYI1gGxFyWNNScrLvPnpp59w7do1qNVqvPPOO+jYsaNZ+x8+fBjBwcEAAI1GA71ej40bN0Kvl/5LhErCexwS2diSJUuQmJiIjRs3wtHR0bDczc0NrVq1QkhIiMUbh/v27UNWVvHfaG7dujWOHTuGoKAg7N+/Hy4uLqV6rilTpuCff/7BwoUL0aBBA5P3y8vLQ0JCgsnb5+aWfBN6taMddJn53/bWujgiJ7ngB1ZHJq/EwJClCJwwCOErdwAAAkYFoUa3Zvitz3SIeSV/wzM3N89whmVp5eZ6AtCatK3f5E1Gl7s06oTWv+a/nctLS0LUslHwm7QJmkruZsaSi9jYW2btU/gYzM2j5JQbKj8Oh90rtEytFuDl4Whka8D7keXeRWyTcDcTOl3Bj30On7wKJ031MkRadhu3X4AAFPuBFAAIADb9cglDule2flCPYE0rSE41jbkpSE65eVzoucJXtbBGTTty6hqqOEl7xsvX2037Nv6DmvZ8kG3v38p5U5Cc5g1zU5CccvO4sIikQsusUdOOnoqCl5u0V1vY8LNptx/Jr2kRGPOkbe91aOpYKw/jTC414EEstqoD5SE3pZWW8fCLoPEJBW+7EJ8Qj9QU4/XA2uQy1iwxzkpLvrlRzrzJzTV+RaEbN25g586dAIAhQ4agefPmZh338OHDWLVqleHypE8//TQ+/vhjXLx4EX///Td69+5tNBapxpoceHl5QaMxvw3IxiGRjW3duhVdunRBQECA0fWenp7w8vJCdna24UzEO3fuwNvbGxMnTsTEieafWn78+HGcOnWq2G0efCvj7NmzuHPnTqkah7Nnz8YXX3yBcePGYebMmWbtm5CQAF9fX5O3n+8eBB9tJaPrBLUKrd97AbqcXIQt2QoAqNGtOW4e+q/Adhnx9/HPjK/QZeVExO0/g7zMbLSd+xJOffQtkq+Ydt+ZyMhIDDcjbmMarzwHx1pNynSMR93ZtRq5ifGI+XpqgeXuPV6C58CpReyVLzIyEr59m5bp+ZmbokmdGypHXJsDfgXrvZeHI2L3Pl/irie3DDK6vGbQFsTdKviB+tjX3gCST5Y6TIuoNwdwqFno26qPEwGcOXcVvr7DbBPX/2NNK5rUNY25KZrUuSnEpTFQ5+0Ci6xR08a/NRnjk46VOkyLqPs+4OhnUk27EBlj1utfS+C8KZrU84a5KZrUuSnEOQDwf7fAImvUtClTp2NK4qFSh2kR/u8CTvUAofgLlokArl6/ZfOaZsmxJvU4k0sNAORXB6TOTWm5VqqM8e8tAQC0a9sOAAo8Tk1JkiQuuYw1S4yz0pJrbpQ0bz755BOjfxP++usviKIIX19fPPPMM2Yd8/Gm4YN7Gvbo0QP79+/HH3/8gV69ekF47HV4ZGQkBg4cWKafpzyLiYlBzZo1zd6PjUMiG0pISEBcXBxGjBhRaJ1er0d4eDhatmwJIP8MPC8vL+zZswf+/v7477//0LdvX3h6emL48OFmPe+sWbMwd+7cItenpaWhf//+CA0NxbZt21CnTh2zjg8Ac+fOxfz58/Hyyy9jzZo1Zu9vSaJOj7BPt6HvtjkIQ/4LHSevqsi8Vfjb7td3HoNvnzbo+uUk5GXm4Nbxi7i0aZetQ7Yo76Ez4T3UvMatrTA38s0NyYxY8rdALfM80t87B3oTL8kqioDOupdvNRdrmnxrGnMjs9zYqtbYqnYWx6yaJq97HHLeyGzePIK5kVlubPY6TQY1TZeF/PMJSyCKgF5eNc1cshtnj2ANkG9ulKaijzUlkeO8SUtLw9GjRwEAAwYMMOssuKKahgDw9NNPY//+/YiPj8e5c+cQGBholfgrGjYOiWwoPT3/1P7Hv/kAAL/++itu375tuEyps7Mz5s2bZ1jfokULPPPMMzhy5IjZjcPiPN40HDx4sNnHmDt3Lj788EO89NJLWL9+vdGfryReXl6IiYkxeft/hi9GelTRlzbVZeYg614ynH08kB53F2Ix17kOfW89hoWtA/QiQkYtMivugIAAxAR/bdY+j5t4wRMxMnmPFRAQgN1m5MEY5sY6LJEbKj+ibqaj69gjBZYl3M1EzaAtRrf39nA0fIO97fO/IP5u4Q+uE4ws2/3bt2jsb/zbpraydvt1zF9vwj1xBAFTX+2Ot18ca/2gHsGaZh38e1OQ0nLzuJt3MtH+pYJnzVijpu38eQNaNqhc5njLYtNvNzB79aWSNxQEvDWqI2a8bNu/7Zw31sGaVpDScvO4O4nZaPXCgQLLrFHTftqyBu2bVilzvGXxw64Y/G/FhZI3FASMHd4ac8bZtqbJZawpqQYAyqoDUr6PTsvIxKYd+wEAJ06eAIACj12cpLkcplzGmiXGWWnJNTdKmjenT59GZmbBv22XLl1Cbm4u7O3t0alTJ5OPVVzTEABq1KiBhg0b4tKlS/jvv/8KNQ4DAgLM+sxZaby8vEq1HxuHRDbk6+sLtVqNgwcPFlh+48YNwyVIi7q/YW5uLg4fPox33nnHojHpdDoIglDqpuFHH32EDz/8EKNGjcLXX38Nlar4S5gURaPRmHXatFZbcvmK2fsvavZujXv/XcPdM1eL3M5/SFcIggCVoxbuzfwRG3LarDhKc7p3gWNcBiCDFwYAoNVqy/7zMDdWYYncUPnh4yPCzfUEklNzDMt0OrHQJayMib+badJ29nZq9OjYEFpt6eq2pUx9qRqWfnsFWTk6iEXc6FAQALVKwLSX28LH09mm8bGmWQf/3jx2DIXl5nE+PiKqVTmBO4kPf0hL1zSNRkDQEw3hYC/tW9yJL1bHks1XkJ6ZV2xNUwkC3nmlLWr6uNo0Ps4b62BNe+wYCsvN42rWBGp6nkTsrYf3/LJ0TRMEoG/XhnBxMu1ekdby1khPLPz6MlLSc4utaQKA6a+0Rc2abjaNTy5jTUk14EEsSqkDUr6PTk5JM/zf28u7wDpvL2+4VTL/FkGWIJexZolxVlqyzY2C5k14eHihxuG1a9cAAH5+frCzszPpOCU1DR8ICAjApUuXDM/xKH6eVjrSflJEVMHY2dlh9OjROHXqFAYOHIh169Zh9uzZaN++Pdzd829UW1TjcMKECXB1dcXo0aPNek5RFIu9TKmbmxsOHjxYqqbhl19+iTlz5qBWrVro3bs3fvjhB3z33XeGf3v37jX7mJYUu+9f+Aa1hkeLurgbdtnoNm71fdBm9iiEzt6Iixv+Qqelb8K+qm0/wKmImBui4gmCgG6tS/etMFN1blFd8qYhAFR1s8d3i7pDJQhGbwn2YNmGD7vYvGloKtY0+WJu5EEQBHRv613yhmXQPrC65E1DAHBztcMPi3tArSq6pokisGZ2Z/jZuGloKs4b+WJu5KN7W+u+TmvTxEPypiEAODtpse2TntCoVcXWtJUzOyLAz7ZNw4qINYBshWONrCEuLg4AULt2bZO2N7VpCOQ3Ix99Dio76T8tIqpgVqxYgXHjxiE0NBTTpk1DaGgoduzYgRo1asDJyQkBAQGF9nn77bfxzz//4K+//jL5GxnmKM2lRQHg5MmTAIDo6Gi89NJLGDVqVIF/CxYssGSYZsu8nQStiyM0jvZG1wsaNbp8MQk3D/2Hy9/vw+mF3yM7MRUdP37dxpFWPMwNUcleH9bQqsd/Y3gjqx7fHM/29sNfq/uieUDVQusa+VfGL5/3xuhn6ksQmWlY0+SLuZGP14dauaZZuWaa4+nutbBnbT+0auReaF1AbTf8tLQnxg5pIEFkpuG8kS/mRj6sX9Pk8zqtb+ea2LeuH9o1rVZoXT3fStj2SQ+Mf66xBJFVPKwBZCsca2QNTZo0QY8ePdC4ccl/M65evWpy0xDIv1xp9+7d0blzZ0uGXKGxcUhkYy4uLli7di0SEhKQmpqKPXv2oGPHjoabtz5+qc8pU6Zg7969CAkJgYeHh0RRG7dp0yaIoljkvwMHDkgdIm4ePIvUG7eMrmv57gg4e7vj2LTVAABddi4OT1gB36DWqDusmy3DrJCYG6Li9e3kg/q1rXP/QV8vZwzqYdq3/GwlqKMPTgcPwq/LexuW/bS0J85tfxbPyCxWY1jT5Iu5kYee7b3RpG5lqxzby8MRQ4P8rHLs0urRrgZObR2E31YGGZYFf9oTF38dgiFBdSSMzDScN/LF3MhD55aeaNmw8JcDLMGjigOe6+dvlWOXVtc23jj+/TP444s+hmXbPu6BiN+GYnhfecWqdKwBZCsca2Rpffr0weuvv44OHTqUuK2/vz/69etnUtMQyD/j8I033sCoUaMsFW6Fx8YhkQwkJSUhNja20GVKJ02ahH379uHvv/9GtWqFv91HJYv8IQRx+88UWl69XUM0fXMgjk5bjax7KYbl989fx5lPg9F+3itw9pFXo1ZpmBui4qnVKqz74AmrHHvN7M6yuEzp4wRBQKtGD+d3+8DqpT4r3tZY0+SLuZEHQRCwbs4TRi91V1arZ3WSxWVKjWnR4GFjoWMz1jQqO+ZGHvJrWmeo1Zaf01/M7AgnR3nWtGaPXB2iUwvPclPTlIQ1gGyFY01+MqPP49KMJxAxsysiZ/VEdkLB+/klhe7EpekdEDGzK+4d+N6wPHrtBFx6tyMuvtMOyad32TrsUhEEAaNHj8bMmTNLbBqSdcjzlQhRBRMeHg6g4P0Nb9y4gZUrV8Le3h516jz8VnKXLl3w119/2TrEcivzVqLR5bdPXMI3viOMrgtfuQPhK3dYM6wyyYw+jxurXocgqCCoNag9YT3svR5+yzM98gRiN78LANBnpkIURTReln+D6qy4SJyf2AQNFh2GS4OSv+FjTcyNfHND8tG9rTcmjWyMFT9cKHa7hLuZqBm0xfD/4rw6OABPdvG1WIyUjzVNvjWNuZFPbjq18MQ7LwXik03hxW5nTk178am6GNTTz1Ih0v/jvJHPvHkccyOf3LRpUg0zX22O+evOFLudOTVtWJ86GN5X/mcll1elGWsNFx9B5OxeyIq9iFpvrEHVrs9JFT4AZdYAfXZGsb9jURQR/eU4ZMVFQGXniNoT1sOu2sP3M3Kq0UqixLFW3mkqVUP92X9A7eyG5NO7EL9tHvwmbwQAiHo94r6ZgYafnoDKzgER73dH5bZPIef+TWTFXkTDj/9BbmICrswbALdW/ST+SUwjCIJVbtlFpmHjkEgGjDUOa9euDVEUJYqI5Ky4FwoA4BzQDg0WHAAA3Nr5OfQ5D9+cxgfPg2sTXjbCWpgbsoal77RH3O0M/LzvepHb6HQi4m5llHis/k/UxKpZnSwYHSkZa5p8lefcLJrcBjEJ6di661qR25ha03p3qIGv5ljnzGxSnvI8b5SuPOfmw/GtEB2fhm9+u1LkNqbWtG5tvLBpXleexWdFpRlrgsYedWfuwJ1daySKWvlK+h0nh/4KQWuPBosOIf3Kv4j7ZgbqTHt4NpXUdYDIVrSVqxv+L6i1gEpteJyXcheaytWhdnQBADj4NEB6ZCic6reDoHWAqMuDLj0JGleeDUqmkd81qogqoPHjx0MURZOu8UykrVwdamc3AIVfKDzu/qEfULXL8wCA9IhQaCt7wc6jpk3irIiYG7IGjUaFrR/3wJvDG5bpOGMG1scvy3vDTlv0uCR6FGuafJXn3KjVKny3qBsmjWxcpuO8MKAuflsZJNtLlJL8lOd5o3TlOTcqlYCN87rinZcCy3ScYX3q4M8v+8r2EqVKUZqxJqjV0FbxslWIFVJJv+Osm5FwqtcGAOBUtxVSLxw2rJNDHSCyNX12Jm5umQPPpycblmncqiEv6TZy78dDl5GKtAuHkZd6H2pnN9h71sG5NwMQ8X53eA2ZIWHkVJ6wcUhEVE4Ze6HwqKy4SAgaO9h7+gEA4n9cwBcINsLckKVpNCqsmtUZu1b3ha+Xs1n7enk44tflvbFxXlc2DalUWNPkq7zmRq1WYfmMjti3rj/8ariYtW/1qg74+bNe+G5RdzYNqVTK67ypCMprblQqAZ9Ma4cDXz8J/5quZu3rUcUBWz/ugW2f9GDT0IbMHWskLcfagUgJ2w1RFJEStht5ybcN6+RSB4hsRdTlIWrpSHgNegeOfg+/tCIIAmqNX4Ooz15A1NLn4VirKbTuNZB6Zi9yExPQdM0VNPniAmLWT4aoy5PwJ6Dygq9KiIhkSJeZhssf9C603CNoLDz6jC3yhcKj7h/8HlW7jgQAJJ/6A0712kBTyd2qcVcEzA1JqW/nmrjyxzBs33cdq7ZdxNEzt6HXF76stUoloH1gNbw5vCGG9anDD9epSKxp8lURctOrQw1E/DYUv/x9A6uDL+Hw6QTodIVrmiAAbZtUw5sjGmJEX384OrCmkXEVYd6UVxUhN93aeOPSr0Ox88ANrNp2EYdOJyAvz3hNa93YA28Ma4jn+vnD2UkrQbTKZemxRpZRUl6K49a6P9IjjiNyVg84+TWHo18zAPKsA0TWJIoibnwxFpVa9kXlDoMKrXdt0hWu8/+GLjMN1xYPgXNAB6SG74fGtSoElQpqR1foc7Mh6vIgqPl6morHEUJEJENqRxc0/OS40XUlvVB4IPFoMBosyr+ER8a1M0g7dwCX5x5D5o1wZMVFoO6M7dBW9bZG+IrG3JDU7LRqPNe/Lp7rXxcZmXk4G3kPEdeTkZWtg72dGgG13dCiQVV+CEUmYU2Tr4qSGzutGsP7+mN4X39kZuXhbMR9XLqehKxsHey0atSvVQktG7nDhTWNTFBR5k15VFFyo9WqMCSoDoYE1UFWdh7+i0zExWtJyMzOg51WjXq+rmjZyB2uznaSxqlklh5rZBnF5cUUNUZ+CABIORsCQWsPQL51gMhaUsJ24/6RYGTfvo77R7bCqU4LVGrVD7rU+6jabSRivp6GjKunIWi08HlxAVRaO1Rq3huJh7cgYmYX6HOyUP2pSVDZOUj9o1A5wMYhEVE5Y+yFgu/YzwEAUctGo87Ub5AeEQo7T39oKuXf9Nh7+PvwHv4+AOD68jHw6PcGX0xbAXNDtubkqEHH5p7o2NxT6lBIgVjT5EupuXF00KBD8+ro0Ly61KGQAil13iiBUnPjYK9Bu8BqaBdYTepQ6P+VZqwBwNXFQ5BxLQwqB2ekR4bCd+wyiX4C5Srqdxy1bDR8X/0MV5cMhaDSwK5aLfiOWwmgfNQBIktya9UPrX7MKHK97ytLCy0T1Gr4Td5kxahIqdg4JCIqZ4p7oVBn6jcAAOcG7VH/gz+MbsMXDNbD3BCRkrCmyRdzQ2Q+zhv5Ym7IVko71urO+NnqsVV0Rf2OH+SlwYIDxe7POkBEZFkqqQMgIiIiIiIiIiIiIiIiIunxjEMiKpdc/bykDgGAZeLwcbJAIBZiiViYG+uQUyxEFQlrmnXw701BSssNyRfnjXWwphWktNyQfMklv0qqAYCy6oBc4pATuYw1ucQhJ3IZr5aIw8XFpVT76fV63LmfDACoVtUNAAo8VqnMPw+utLFUdGwcElG51GvzDKlDsJhl7aWOwLKYGyJSEtY0+WJuiMzHeSNfzA2R+ZQ01pRUAwBl5UZplDbWlERJ86ZLly6l2i85JQ2LVv8AABg88BkAKPDYrRKbgLbCS5USERERERERERERERERERuHRERERERERERERERERMTGIRERERERERERERERERGBjUMiIiIiIiIiIiIiIiIiAhuHRERERERERERERERERAQ2DomIiIiIiIiIiIiIiIgIbBwSEREREREREREREREREdg4JCIiIiIiIiIiIiIiIiKwcUhEREREREREREREREREYOOQiIiIiIiIiIiIiIiIiMDGIRERERERERERERERERGBjUMiIiIiIiIiIiIiIiIiAhuHRERERERERERERERERAQ2DomIiIiIiIiIiIiIiIgIbBwSEREREREREREREREREQCN1AEQEZVGyEuLkXo9Qeow4OrnhV6bZ5TpGFNDgbgMCwVURj5OwLL2ZTsGc2MdlsgNEZmPNc06+PemIKXl5vDhw0hLS7NMQGXg4uKCLl26SB2GrHDeWAdrWkFKyw1rmnzJZawpqQYAyqoDfB9dmFzGmiXGmdJw3shXRXwtwMYhEZVLqdcTkBQZK3UYFhGXAVxLlToKy2FuiEhJWNPki7mRr7S0NKSkpEgdBhnBeSNfzI18sabJl5LGmpJqAKCs3CiN0saaknDeyFdFfC3AS5USERERERERERERERERERuHRERERERERERERERERMTGIRERERERERERERERERGB9zgkIiIiIonpdHocDbuFE+fu4khYgmH5+AXH0LlldbRtUg1dW3tBo+F33ohI/vR68f9r2h0cOX3LsPzNBUfRuYUn2jTxQLfW3tBqWdOISP70ehHH/7uN0PA7OPzvw9dpb84/io7Nq6Nt02ro1sYLdlq1hFESERGRJbFxSERERESSSEnLwaptF7Hmx0u4cTOt0PrfDkbjt4PRAACf6k4YN7QhJjzfGFXd7G0dKhFRidIycrF620WsDr6EqLjUQut/PxiD3w/GAAC8qznhtWcbYOLIxvCo4mDrUImISpSekYu1P13C6uBLuBKdUmj974di8Puh/Jrm6e6Isc8GYNLIJqju7mjrUImIiMjC2DgkIipHri8fg3t/b85/oFJBW8UbroE94TN6EezcfaQNroJjbojMs+dYLMbOPYKYhHSTto+7nYE5q05jdfBFrPvgCTzdvZaVI6zYWNPki7mRp/0nbuKVDw7jupEvQRgTfycDH60Nw5ofL2L1rM54trefdQOs4Dhv5Iu5kafD/ybg5Q8O4WpM4S9BGHPrXiYWfHUWa368hC/f64ThfetAEAQrR2k6jjP5Ym6IzMd5Q7bAa6MQEZUzLo27oNmmeASuj0adaT8gIyoM15YMkzosAnNDZApRFPHRmjD0fWO3yU3DRyXczcQzk/Zi+tITEEXRChHSA6xp8sXcyMviDWfRc+xfJjcNH3X7fhaGvB2CyYv/gV7PmmZNnDfyxdzIy7Jvz6HbK3+Y3DR81L2kbDz37n6Mn38MOp3eCtGVHseZfDE3RObjvCFr4xmHRFShqLQajI7eWur9N3kPtWA0pSNo7KCt4gUAsHP3QbU+4xDz1SToMlKgdqokcXSlx9wQVQwfrQnD3NVhRa5XqwV4eeRf4irhbiZ0OuMfpH+6ORw6vR5L32kvq2+0P8CaJl/MjTLl5OTAzs7O5s+7eMNZzFx+qsj1pta0FT9cgE4vYuXMjqxpVqLUecPcKJNUNW3Zt+fw9iehRa43taat+fES8nR6rJvzhGxqmlLHGWsA2YoSxpqScN4on1SvBR5g45CISi0iIgIfffQRTp8+jZs3byI3Nxe1atXCk08+ienTp8Pb21vqEAvx6tQEO7pNQXJkrNShWETOvZtIPPYToFLn/yvHmBsi5fvjUHSxTUMA8PJwROze5wEANYO2IO5WRpHbLvv2PNoHVseIfv4WjdMSWNPki7mRp5SUFFy4cAFRUVG4efMmcnJyoNVq4enpCX9/fzRq1AhVq1Y1uu/u3buxe/duzJo1q8htrGHf8bhim4aAeTXty60X0a5pNYx+pr5F47QEzhv5Ym7kKSUlBRcvXsS1a9cMNU2j0RhqWsOGDeHh4WF035CQEPz222+YNWtWkdtYw6FT8cU2DQHzatr67ZFo17QaXhva0KJxWoJSxhnAGkC2o7SxpiScN/KUlpZmeC0QFxeHnJwcqNVqVK9eHXXq1EHDhg1RvXp1o/seOXIE27Ztw/vvvw8vLy8bR56PjUMiKrXY2FjEx8dj8ODBqFmzJjQaDcLDw7Fu3Tps3boVZ86cKbIASsWtvg9uHjwrdRhlknruAMJGuEDU6yHmZAIAPAdNg9rBGQCQ+M8OxG/7sMA+WTEX4Dt2Oar1f9Pm8ZqKuZFvbogsISklG+M+Omrx47618Bi6t/WGp7ujxY9dFqxp8q1pzI28cnP16lX8+eefOH78OHQ6XZHbCYKA1q1bo3///mjSpIlh+e7du7Fx40YAwC+//IJXXnnF6jEDQEpaDl6dc9jix5285Dh6d6iBGtWdLX7ssuC8kde8eRRzI6/cREVF4c8//8Q///yDvLy8IrcTBAEtW7ZE//79ERgYaFgeEhKCr776CgCwfft2jBs3zuoxA0B6Ri5e/sDyNW3a0hPo27kmanm7WPzY5lLSOHsUa4B8c6M0ShhrSsJ5I1/R0dH4888/cfToUeTm5ha7bfPmzdGvXz+0aNHCcIb+kSNH8OWXX0IURQQHB2PSpEm2CLsQNg6JqNR69eqFXr16FVretWtXDB8+HJs2bcK7774rQWTK5hzQHn5TNkPMyULikWCknN2HGi/MN6yv0nEwqnQcbHicdPwXxH37Htx7viRFuBUKc0NUtGXfnsfN20V/K7207iVlY9H6s/j8fx0sfuyKjjVNvpSQm6ysLGzduhW7d+823K/U0dER/v7+qFWrFhwcHJCTk4OYmBhcu3YNaWlpOHXqFE6dOoWuXbti9OjROHr0qKFp2LJlS4waNcpm8a/84QKi482/T2tJklJzMG/tGaye3dnix67olDBvlEoJucnJycG2bdvw559/FqhpderUQa1ateDo6IicnBzExsbi2rVrSE1NxenTp3H69Gl06tQJL7/8Mk6cOGFoGgYGBmLMmDE2i3918CVcizX/noYlSU3PxYdrwrDhwy4WP7a5lDDOlIq5ITIf54385Obm4ueff8bOnTuh1+ff59fe3h516tRB7dq14eTkhNzcXMTFxeHatWtITk7G2bNncfbsWbRr1w6vvPIKzp07Z2gaNmzY0GZfIDKGjUMiiZw9exYffPABDhw4AFEU0bNnT6xevRoBAQEYMGAAtm4t/XXDHzVlyhT069cP/fr1K3Kb4OBgBAUFoUqVKhZ5ztq1awMAEhMTLXI8S3GrVwPJV+KkDqPMVHaOcPCuBwBwrN0U2QlXEbNuImpP+KrQtjl3YxG99i3Um/MXVPZOtg7VZMyNfHNDZAm5uXqs+/mS1Y6/eedlLJzUBk6O8nhpy5om35rG3MgjN/fu3cPChQsRF5efC39/fwwYMADt27eHRlN4Huv1epw+fRp//vknLly4gEOHDuHUqVPIyMj/MkLLli3x9ttvQ6vV2iR+nU6PtT9Zr6Z998dVfPx2W7g6S3dPk0dx3shj3hjD3MgjN4mJiVi0aBGio6MBAH5+fhgwYAA6dOhgtC7p9XqcOXMGf/75J86dO4djx47hzJkzhpoWGBiI6dOn2+y+Rnq9iDU/XrTa8bf8dRWfTmuHKpXsrfYcpijv48wY1gD55kZplDLWlITzRl5SUlKwePFiXLt2DQDg6+uLAQMGoFOnTkb/nuv1eoSHh+Ovv/7CmTNncOLECYSHhyMrK8vQNJwxYwYcHBxs/aMYqCR7ZqIKLCQkBB06dEBERARmzZqFhQsXIjY2Fv3790daWhpatGhhsedavnw5jh8/XuT6uLg4jBkzBr179y51oy8rKwt3795FbGws9uzZg9dffx0A8OSTT5bqeNbi2bEJEo6dlzoMi/N+fi7uhmxE+uWC99gR9XpELXsRXkNmwMmvmUTRmYa5IVK2vcfjkHA302rHT0rNwc4DN6x2fHOxpskXcyO9xMREfPTRR4iLi4NWq8WoUaMwf/58dO7c2WjTEABUKhXatGmD2bNn44033oBWqzV8wN6oUSObNg0B4MDJBMQkWP5swwfSMnKxfR9rmrWVp3lTFOZGesnJyZg3bx6io6Oh0WgwcuRILFiwAF26dCmyLqlUKrRq1Qrvv/8+3nrrLdjZ2RlqWkBAgE2bhgBw7MwtXI2x/NmGD2Rm6fDjniirHb+0ytM4KwprANmKUseaknDeSCctLQ3z58/HtWvXoFarMWzYMCxatAjdu3cv8u+5SqVC8+bNMWPGDEyZMgUODg7IzMyEKIrw8/OTvGkIsHFIZHN37tzBiBEj0KpVK4SFhWH69OmYMGECQkJCDN9QtGTjsCQ+Pj7Yvn07zp8/j6CgICQlJZl9jPXr16NatWrw9fVF3759kZSUhO+++w5dukh/OZJHqe210Oc8vM9En+A56LfjI+D/ryH9QM+N/8NTu5ZA0JSPGwo71KiPym2fxs3v3i+wPD54PtSOlVD9qYkSRWY65oZI2UL/u2P15zhxzvrPYSrWNPlibqSl1+uxYsUK3Lp1Cw4ODnj//fcxYMAAqFSmvS0VBAHZ2dkF7hWSmmq9D7uLEhp+2+rPwZpmfeVl3hSHuZGWKIr44osvcPPmTdjb22PmzJl45plnoFab9nsWBAE5OTnIyckxLJOmptngdZoNnsNc5WWcFYc1gGxFqWNNSThvpCGKItauXYvo6GhotVpMnz4dQ4YMKfILkcbk5eUhOzvb8Dg1NdVw2XMpyeN6TkQVyJIlS5CYmIiNGzfC0dHRsNzNzQ2tWrVCSEiIxRuH+/btQ1ZWVrHbtG7dGseOHUNQUBD2798PFxfTb14+aNAgNGzYEGlpaQgLC8POnTtx9+5ds2LMy8tDQkKCydvn5hZ9o/kH1I520GXmvwnTujgiJ7ngN8OPTF6JgSFLEThhEMJX7gAABIwKQo1uzfBbn+kQ83QmxREbG2ty3MaP4QmgbN+S9xw8HREzOiM1/ABcA7sj7eJR3Nu3AY0+O21mLLmIjb1VpliYm4LklBsiqRwLKzgX1WoBXh6ORrf1fmS5dxHbAEDC3UzodA9fTP9z5maZ57wxrGkFyammMTcFySs3uUaX7927Fxcv5l8Ob+rUqWjYsKFZx929e7fhnoYNGjTA5cuXERsbi+3bt2PEiBFG47BGXTh62vo17fhZ1rSSj6G0ecPcPEpeuTFe0/bv34/w8HAAwMSJE9GkSROzjhsSEmK4p2H9+vVx7do1xMfHIzg4GC+++KLROKxRF478G1PgsTVqWuh/8VaqaWUba/IaZ/KoAQ9ikboOyCk3pZWW8fCKK/EJ8QXWxSfEIzWl6DlkTXIZa5YYZ6Ul39xw3sg3N8ZfCxw7dgwnT54EALzxxhtmf6Z/5MgRwz0N/f39ERsbi3v37uGHH37Aq6++ajQOc+eNl5eXWY3MBwRRDu1LogqkZs2aqFevHg4cOFBoXe/evXHu3DlDA238+PH47bffkJycDFdXVwwbNgwff/yxWZctEQQBarW6xAKh1+uRm5sLrVaLiIgI1KlTx6yf61H//fcf2rZti7lz52LmzJkm7RMbGwtfX1+Tn2O+exB8tJWMrhPUKrR+7wXocnIRtiT/XpG1B3TA7VMRyLxV8HKsfs90QpeVE/HHgPeQl5mNp/d8jH/nfYdLm3aZFEdcbgpm3dtrctzGNF55Do61zHuTWZy8tCRcfLsV/CZsgGuzHmbtmxl9HhcmNi3T8zM3RZM6N0SSqfse4ORveOjj6YTYvc+X6ZA1g7Yg7lbGwwVZN4HLH5TpmMawphVN6prG3BRN6tx88sknhV7XZWdnY/z48UhPT0fv3r0xduxYs475aNPwwT0Nd+7ciR9//BFqtRpffvklKleuXGCfmJgYTJ8+vUw/i1H+7wLOAYaHVqlp2beByPfKdExjOG+KJvW8YW6KJnVujNW0nJwcvPXWW0hNTUX37t3xxhtvmHXMR5uGD+5p+Ndff2HLli0QBAErV66Eh4dHgX2sVtP83gZcGxseWqWm5d4HLr1bpmMaY8mxJvU4k0sNAORXB6TOTWm5VqqM8e8tAQCsWvg/ACjwODUlSZK45DLWLDHOSkuuueG8kW9ujL0W0Ol0mDhxIu7fv48OHTpgypQpZh3z0abhg3saHjhwAJs2bQIAfPbZZ6hRo0aBfUrzWiAmJgY1a9Y0ax+AZxwS2VRCQgLi4uKMfiP6wU1RW7ZsaVg2YcIEfPLJJ3B2dsbdu3cxbNgwLFy4EHPnzjXreWfNmlXsPmlpaejfvz9CQ0Oxbdu2MjUNAaBZs2Zo2bIlVq1aZXLj0JJEnR5hn25D321zEIb8FzpOXlULvcgBgOs7j8G3Txt0/XIS8jJzcOv4RbNeUMvRnV2rkZsYj5ivpxZY7t7jJXgOnFrEXrbB3Mg3N0TWJZS8Sbl4joJY0+Rb05gb+eXm6NGjSE9Ph52dHZ577jmz9jXWNNRqtRg4cCD27t2LpKQk/P3333j22WetEbo0BNY0W5PjvHmAuZFfbkJDQ5GamgqtVouRI0eata+xpqGdnR2eeuop7NmzB/fu3UNISIjRzw2swiblRv53SpLjOHuANUC+uVGaij7WlITzxvr+/fdf3L9/HyqVCqNGjTJrX2NNQwcHB/Tp0we7d+9GfHw89u3bh9GjR1sp+pKxcUhkQ+np+af2C0Y+CPj1119x+/btAqc0N2788Ft/oihCpVLh8uXLFo3p8abh4MGDLXLczMxM3L9/3+Ttvby8EBMTU/KG/++f4YuRHlX0pU11mTnIupcMZx8PpMfdhajXF7lt6HvrMSxsHaAXETJqkckxAPk3r48J/tqsfR438YInYoq/kqxZvIfOhPfQ0jVsAwICsNuMPBjD3BRN6twQSWXk+6dwOOye4XHC3UzUDNpidFtvD0ec3DIIAND2+V8QfzfT6HYJjy1v1aIhfv3b8nOENa1oUtc05qZoUufm9OnTyMwsOEcPHjwIAHjiiSfMuiR+UU1DANBoNOjZsye2b9+OgwcPFmocBgQEmPX60lQvzz2NfSce3q/LGjWtaSN//LWPNa04Sps3zE3RpM5NcTWtQ4cOqFTJ+Jk7xhTVNAQAtVqN3r17Y9u2bTh48GChxqG1atrrC87gz6MPL0lnjZoWUNcHIXstH7slx5rU40wuNQCQXx2QOjellZaRiU079gMATpw8AQAFHrs4SXPJRbmMNUuMs9KSa244b+Sbm+JeC7Rp0wbu7u4mH6uopiEAqFQqBAUF4ZtvvsGhQ4fw4osvFrgffGleC3h5eZm1/QNsHBLZkK+vL9RqtaGwPHDjxg1MnJh/k9rHr4W8ePFizJ8/H+np6XB3d8fixYstGpNOp4MgCKVqGiYkJBgtPvv378e5c+fQvXt3k4+l0WjMOm1aqy25fMXs/Rc1e7fGvf+u4e6Zq0Vu5z+kKwRBgMpRC/dm/ogNMf3a31qteXEbPcZlABZ8Y10WWq227D8Pc2MVlsgNkVQ6NL9ZoHGo04kFL19VhPi7mSZtBwDtAr2tMkdY06yDf28eO4bCchMeHl7gjbVOp0NUVBSA/Ptqm6q4puEDbdu2xfbt23Hr1i2kpqbC1dXVsM5afzs7tLhVoHFojZrWNtCLNa2kYyhs3jA31mGNmqbX63H1av7vv02bNiYfp7im4QNt2rTBtm3bcP/+fdy/fx9Vq1Y1rLNWTevY8k6BxqE1alqbplaqaTIZa0qqAQ9iUUodkPJ9dHJKmuH/3l7eBdZ5e3nDrZLpX6SyJLmMNUuMs9KSbW44b2Sbm8dfC4iiiCtXrgAw77VAcU3DB9q0aYNvvvkGaWlpuHXrFry9H/4ebJkb+V8rgEhB7OzsMHr0aJw6dQoDBw7EunXrMHv2bLRv397wzYTHG4czZsxAWloaLly4gDfeeKNAsTCFKIrFXqbUzc0NBw8eLNWZhm+++SY6dOiA9957D2vXrsXy5csxevRo9O3bF66urli6dKnZx7Sk2H3/wjeoNTxa1MXdMONnarrV90Gb2aMQOnsjLm74C52Wvgn7qq5GtyXLYW6IKpY2TTxK3qgcPEdRWNPki7mRh5s3byInJwcAULduXZP2MaVpCOR/Me/BvbwfNCetzSY1rTFrGhXG3MjDrVu3DB8e+vv7l7B1PlOahgDg4+MDe3t7ADasaY2rWf85JHydpiSsAWQrHGtExUtMTERycjIA018LmNI0BIBq1aoZrtBiq9cCxrBxSGRjK1aswLhx4xAaGopp06YhNDQUO3bsQI0aNeDk5ISAgACj+zVq1AjNmzc3+5rJpjB26VRTPP/88/Dw8MC3336LyZMnY8aMGThx4gRef/11/Pfff4WaoLaWeTsJWhdHaBztja4XNGp0+WISbh76D5e/34fTC79HdmIqOn78uo0jrXiYG6KKpf8TNeHqXPgDf0uxt1NjYI/aVjt+SVjT5Iu5kYekpCQA+d+QrVy5conbm9o0BPKvWvHgjJwHb96tLaiDD6pUKvyBv6VoNAKe7e1nteOXhPNGvpgbeXhQawRBgIdHyQ0xU5uGQP4lyh58qdhWNa1HO29Uq1L4g0tLUakEDA3ys9rxKxLWALIVjjWi4j36N7patZK/gGNq0xDIf31RvXr1Qs9ja2wcEtmYi4sL1q5di4SEBKSmpmLPnj3o2LEjzp07h8DAwALXLX5cbm4uIiMjbRht8YYPH47ff/8dMTExyMrKQmZmJi5duoSVK1eiVq1aUocHALh58CxSb9wyuq7luyPg7O2OY9NWAwB02bk4PGEFfINao+6wbrYMs0JibogqDldnO4x+up7Vjj+ibx14WPEDL1OwpskXcyO9Ro0aYfXq1SZdjUIURcM3a0tqGj7wwQcfYNWqVWjfvr1F4i2Jk6MGLw8y/mU/SxjS2w9eHk5WO74pOG/ki7mRXr169bB69WosX77cpC/hPqhpJTUNH3jvvfewatUqPPHEExaJtyT2dmqMfbaB1Y7/TPda8PWS5tJySsQaQLbCsUZUNF9fX6xevRorVqwo8e86kP9awJSm4QPvvPMOVq1ahZ49e1oqZLPxHodEMpCUlIT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- "text/plain": [ - "
    " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Transpile the decomposed circuit to the same layout\n", - "transpiled_qpd_circuit = pass_manager.run(subexperiments[100])\n", - "\n", - "print(\n", - " f\"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\"\n", - ")\n", - "print(\n", - " f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}\"\n", - ")\n", - "transpiled_qpd_circuit.draw(\"mpl\", scale=0.8, idle_wires=False, fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "fd9a126c", - "metadata": {}, - "source": [ - "### Prepare subexperiments for the backend" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "736a7d14-71b0-47a0-847d-4972ab886918", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:35.436030Z", - "iopub.status.busy": "2025-03-28T16:51:35.435823Z", - "iopub.status.idle": "2025-03-28T16:51:47.520198Z", - "shell.execute_reply": "2025-03-28T16:51:47.519092Z" - } - }, - "outputs": [], - "source": [ - "# Transpile the subeperiments to the backend's instruction set architecture (ISA)\n", - "isa_subexperiments = pass_manager.run(subexperiments)" - ] - }, - { - "cell_type": "markdown", - "id": "241fc5e5-d367-4a31-aa1f-424acdbbd8dd", - "metadata": {}, - "source": [ - "## Step 3: Execute\n", - "\n", - "### Run the subexperiments using the Qiskit Runtime Sampler primitive" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e4dcc97b-8336-4eb9-9cc0-0d9b87a287e3", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:47.522983Z", - "iopub.status.busy": "2025-03-28T16:51:47.522756Z", - "iopub.status.idle": "2025-03-28T16:51:48.086035Z", - "shell.execute_reply": "2025-03-28T16:51:48.081351Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import SamplerV2\n", - "\n", - "# Set up the Qiskit Runtime Sampler primitive. For a fake backend, this will use a local simulator.\n", - "sampler = SamplerV2(backend)\n", - "\n", - "# Submit the subexperiments\n", - "job = sampler.run(isa_subexperiments)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a83922bc-0489-4ee5-a2b6-796103aae9bb", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:48.099798Z", - "iopub.status.busy": "2025-03-28T16:51:48.099494Z", - "iopub.status.idle": "2025-03-28T16:52:28.830126Z", - "shell.execute_reply": "2025-03-28T16:52:28.829495Z" - } - }, - "outputs": [], - "source": [ - "# Retrieve the results\n", - "results = job.result()" - ] - }, - { - "cell_type": "markdown", - "id": "f04d9134-651b-446e-93f4-aa0281786200", - "metadata": {}, - "source": [ - "## Step 4: Post-process\n", - "\n", - "### Reconstruct the expectation value\n", - "\n", - "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ace12f7f", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:28.832409Z", - "iopub.status.busy": "2025-03-28T16:52:28.832187Z", - "iopub.status.idle": "2025-03-28T16:52:30.916461Z", - "shell.execute_reply": "2025-03-28T16:52:30.915714Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import reconstruct_expectation_values\n", - "\n", - "reconstructed_expval_terms = reconstruct_expectation_values(\n", - " results,\n", - " coefficients,\n", - " observable.paulis,\n", - ")\n", - "# Reconstruct final expectation value\n", - "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" - ] - }, - { - "cell_type": "markdown", - "id": "3fc2327f", - "metadata": {}, - "source": [ - "### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "4928e703", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:30.918925Z", - "iopub.status.busy": "2025-03-28T16:52:30.918548Z", - "iopub.status.idle": "2025-03-28T16:52:30.932594Z", - "shell.execute_reply": "2025-03-28T16:52:30.932017Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstructed expectation value: 0.51977539\n", - "Exact expectation value: 0.50497603\n", - "Error in estimation: 0.01479936\n", - "Relative error in estimation: 0.02930705\n" - ] - } - ], - "source": [ - "from qiskit_aer.primitives import EstimatorV2\n", - "\n", - "estimator = EstimatorV2()\n", - "exact_expval = estimator.run([(circuit, observable)]).result()[0].data.evs\n", - "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", - "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", - "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", - "print(\n", - " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.21" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction.mdx b/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction.mdx deleted file mode 100644 index 1890da9f0b9..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction.mdx +++ /dev/null @@ -1,385 +0,0 @@ - - -# Wire Cutting Phrased as a Two-Qubit `Move` Instruction - -In this tutorial, we will reconstruct expectation values of a seven-qubit circuit by splitting it into two four-qubit circuits using wire cutting. - -These are the steps that we will take in this [Qiskit pattern](/docs/guides/intro-to-patterns): - -* **Step 1: Map problem to quantum circuits and operators**: - - * Map the hamiltonian onto a quantum circuit. - -* **Step 2: Optimize for target hardware** \[*Uses the cutting addon*]: - - * Cut the circuit and observable. - * Transpile the subexperiments for hardware. - -* **Step 3: Execute on target hardware**: - - * Run the subexperiments obtained in Step 2 using a `Sampler` primitive. - -* **Step 4: Post-process results** \[*Uses the cutting addon*]: - - * Combine the results of Step 3 to reconstruct the expectation value of the observable in question. - - - -## Step 1: Map - - - -### Create a circuit to cut - -First, we begin with a circuit inspired by Fig. 1(a) of [arXiv:2302.03366v1](https://arxiv.org/abs/2302.03366v1). - -``` -[1]: -``` - -```python -import numpy as np -from qiskit import QuantumCircuit - -qc_0 = QuantumCircuit(7) -for i in range(7): - qc_0.rx(np.pi / 4, i) -qc_0.cx(0, 3) -qc_0.cx(1, 3) -qc_0.cx(2, 3) -qc_0.cx(3, 4) -qc_0.cx(3, 5) -qc_0.cx(3, 6) -qc_0.cx(0, 3) -qc_0.cx(1, 3) -qc_0.cx(2, 3) -``` - -``` -[1]: -``` - -```python - -``` - -``` -[2]: -``` - -```python -qc_0.draw("mpl") -``` - -``` -[2]: -``` - -![../\_images/tutorials\_03\_wire\_cutting\_via\_move\_instruction\_3\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_03_wire_cutting_via_move_instruction_3_0.avif) - - - -### Specify an observable - -``` -[3]: -``` - -```python -from qiskit.quantum_info import SparsePauliOp - -observable = SparsePauliOp(["ZIIIIII", "IIIZIII", "IIIIIIZ"]) -``` - - - -## Step 2: Optimize - - - -### Create a new circuit where `Move` instructions have been placed at the desired cut locations - -Given the above circuit, we would like to place two wire cuts on the middle qubit line, so that the circuit can separate into two circuits of four qubits each. One way to do this is to manually place two-qubit `Move` instructions that move the state from one qubit wire to another. A `Move` instruction is conceptually equivalent to a reset operation on the second qubit, followed by a SWAP gate. The effect of this instruction is to transfer the state of the first (source) qubit to the second (detination) qubit, while discarding the incoming state of the second qubit. For this to work as intended, it is important that the second (destination) qubit share no entanglement with the remainder of the system; otherwise, the reset operation will cause the state of the remainder of the system to be partially collapsed. - -Here, we build a new circuit with one additional qubit and the `Move` operations in place. In this example, we are able to reuse a qubit: the source qubit of the first `Move` becomes the destination qubit of the second `Move` operation. - -Note: As an alternative to working directly with `Move` instructions, one may choose to mark wire cuts using a single-qubit `CutWire` instruction. The `cut_wires` function exists to transform `CutWire`s to `Move` instructions on newly allocated qubits. However, in contrast to the manual method, this automatic method does not allow for the re-use of qubit wires. See the `CutWire` [how-to guide](how-tos/how-to-specify-cut-wires) for details. - -``` -[4]: -``` - -```python -from qiskit_addon_cutting.instructions import Move - -qc_1 = QuantumCircuit(8) -for i in [*range(4), *range(5, 8)]: - qc_1.rx(np.pi / 4, i) -qc_1.cx(0, 3) -qc_1.cx(1, 3) -qc_1.cx(2, 3) -qc_1.append(Move(), [3, 4]) -qc_1.cx(4, 5) -qc_1.cx(4, 6) -qc_1.cx(4, 7) -qc_1.append(Move(), [4, 3]) -qc_1.cx(0, 3) -qc_1.cx(1, 3) -qc_1.cx(2, 3) - -qc_1.draw("mpl") -``` - -``` -[4]: -``` - -![../\_images/tutorials\_03\_wire\_cutting\_via\_move\_instruction\_7\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_03_wire_cutting_via_move_instruction_7_0.avif) - - - -### Create observable to go with the new circuit - -This observable corresponds with `observable`, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an “I” at index 4). Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character. - -``` -[5]: -``` - -```python -observable_expanded = SparsePauliOp(["ZIIIIIII", "IIIIZIII", "IIIIIIIZ"]) -``` - - - -### Separate the circuit and observables - -As in the previous tutorials, qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut. - -``` -[6]: -``` - -```python -from qiskit_addon_cutting import partition_problem - -partitioned_problem = partition_problem( - circuit=qc_1, partition_labels="AAAABBBB", observables=observable_expanded.paulis -) -subcircuits = partitioned_problem.subcircuits -subobservables = partitioned_problem.subobservables -bases = partitioned_problem.bases -``` - - - -### Visualize the decomposed problem - -``` -[7]: -``` - -```python -subobservables -``` - -``` -[7]: -``` - -```python -{'A': PauliList(['IIII', 'ZIII', 'IIIZ']), - 'B': PauliList(['ZIII', 'IIII', 'IIII'])} -``` - -``` -[8]: -``` - -```python -subcircuits["A"].draw("mpl") -``` - -``` -[8]: -``` - -![../\_images/tutorials\_03\_wire\_cutting\_via\_move\_instruction\_14\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_03_wire_cutting_via_move_instruction_14_0.avif) - -``` -[9]: -``` - -```python -subcircuits["B"].draw("mpl") -``` - -``` -[9]: -``` - -![../\_images/tutorials\_03\_wire\_cutting\_via\_move\_instruction\_15\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_03_wire_cutting_via_move_instruction_15_0.avif) - - - -### Calculate the sampling overhead for the chosen cuts - -Here we cut two wires, resulting in a sampling overhead of $4^4$. - -For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](explanation/index). - -``` -[10]: -``` - -```python -print(f"Sampling overhead: {np.prod([basis.overhead for basis in bases])}") -``` - -```python -Sampling overhead: 256.0 -``` - - - -### Generate the subexperiments to run on the backend - -`generate_cutting_experiments` accepts `circuits`/`observables` args as dictionaries mapping qubit partition labels to the respective `subcircuit`/`subobservables`. - -To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates’ joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](explanation/index). - -``` -[11]: -``` - -```python -from qiskit_addon_cutting import generate_cutting_experiments - -subexperiments, coefficients = generate_cutting_experiments( - circuits=subcircuits, observables=subobservables, num_samples=np.inf -) -``` - - - -### Choose a backend - -Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator). - -``` -[12]: -``` - -```python -from qiskit_ibm_runtime.fake_provider import FakeManilaV2 - -backend = FakeManilaV2() -``` - - - -### Prepare the subexperiments for the backend - -We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime. - -``` -[13]: -``` - -```python -from qiskit.transpiler import generate_preset_pass_manager - -# Transpile the subexperiments to ISA circuits -pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend) -isa_subexperiments = { - label: pass_manager.run(partition_subexpts) - for label, partition_subexpts in subexperiments.items() -} -``` - - - -## Step 3: Execute - - - -### Run the subexperiments using the Qiskit Runtime Sampler primitive - -``` -[14]: -``` - -```python -from qiskit_ibm_runtime import SamplerV2, Batch - -# Submit each partition's subexperiments to the Qiskit Runtime Sampler -# primitive, in a single batch so that the jobs will run back-to-back. -with Batch(backend=backend) as batch: - sampler = SamplerV2(mode=batch) - jobs = { - label: sampler.run(subsystem_subexpts, shots=2**12) - for label, subsystem_subexpts in isa_subexperiments.items() - } -``` - -``` -[15]: -``` - -```python -# Retrieve results -results = {label: job.result() for label, job in jobs.items()} -``` - - - -## Step 4: Post-process - - - -### Reconstruct the expectation value - -Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable. - -``` -[16]: -``` - -```python -from qiskit_addon_cutting import reconstruct_expectation_values - -reconstructed_expval_terms = reconstruct_expectation_values( - results, - coefficients, - subobservables, -) -reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs) -``` - - - -### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable - -``` -[17]: -``` - -```python -from qiskit_aer.primitives import EstimatorV2 - -estimator = EstimatorV2() -exact_expval = estimator.run([(qc_0, observable)]).result()[0].data.evs -print(f"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}") -print(f"Exact expectation value: {np.round(exact_expval, 8)}") -print(f"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}") -print( - f"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}" -) -``` - -```python -Reconstructed expectation value: 1.40369761 -Exact expectation value: 1.59099026 -Error in estimation: -0.18729265 -Relative error in estimation: -0.1177208 -``` diff --git a/docs/addons/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb deleted file mode 100644 index 8a7aee7a681..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb +++ /dev/null @@ -1,643 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9916de95-2376-49f0-91ad-07f07939dfb5", - "metadata": {}, - "source": [ - "# Wire Cutting Phrased as a Two-Qubit `Move` Instruction\n", - "\n", - "In this tutorial, we will reconstruct expectation values of a seven-qubit circuit by splitting it into two four-qubit circuits using wire cutting.\n", - "\n", - "These are the steps that we will take in this [Qiskit pattern](/guides/intro-to-patterns):\n", - "\n", - "- **Step 1: Map problem to quantum circuits and operators**:\n", - " - Map the hamiltonian onto a quantum circuit.\n", - "- **Step 2: Optimize for target hardware** [_Uses the cutting addon_]:\n", - " - Cut the circuit and observable.\n", - " - Transpile the subexperiments for hardware.\n", - "- **Step 3: Execute on target hardware**:\n", - " - Run the subexperiments obtained in Step 2 using a `Sampler` primitive.\n", - "- **Step 4: Post-process results** [_Uses the cutting addon_]:\n", - " - Combine the results of Step 3 to reconstruct the expectation value of the observable in question." - ] - }, - { - "cell_type": "markdown", - "id": "ae63d837-a7f5-40a5-8186-f98076bb4cd9", - "metadata": {}, - "source": [ - "## Step 1: Map\n", - "\n", - "### Create a circuit to cut\n", - "\n", - "First, we begin with a circuit inspired by Fig. 1(a) of [arXiv:2302.03366v1](https://arxiv.org/abs/2302.03366v1)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3bcae0ed-4308-4686-b85c-8595c6e916bc", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:33.241330Z", - "iopub.status.busy": "2025-03-28T16:52:33.241116Z", - "iopub.status.idle": "2025-03-28T16:52:33.572919Z", - "shell.execute_reply": "2025-03-28T16:52:33.572241Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "from qiskit import QuantumCircuit\n", - "\n", - "qc_0 = QuantumCircuit(7)\n", - "for i in range(7):\n", - " qc_0.rx(np.pi / 4, i)\n", - "qc_0.cx(0, 3)\n", - "qc_0.cx(1, 3)\n", - "qc_0.cx(2, 3)\n", - "qc_0.cx(3, 4)\n", - "qc_0.cx(3, 5)\n", - "qc_0.cx(3, 6)\n", - "qc_0.cx(0, 3)\n", - "qc_0.cx(1, 3)\n", - "qc_0.cx(2, 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1dcaff2d-2d1b-4cc0-87d1-0f4f5de823ff", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:33.575145Z", - "iopub.status.busy": "2025-03-28T16:52:33.574711Z", - "iopub.status.idle": "2025-03-28T16:52:34.182253Z", - "shell.execute_reply": "2025-03-28T16:52:34.181568Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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IJIyBnoiIiIhIwuRiF0CN02q1UFVWi11Gs8ntbSEIgthlEBEREbU4DPRmSlVZjQ2dnha7jGaLzlgPawc7scsgIiIianE45IaIiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMI4D70F8eoXghFb5jVYVlteidLMfGRsOoC0tb9Cq9aIVB0RERERGQMDvQXK3JKAnPhkQBBg7+mKzhMGove8KWgV2B5H5qwWuzwiIiIiMiAGegtUlJKFzM0J9T+nr9uNxxKWIuipoUievxHVRaUiVkdEREREhsQx9C2AqrIahckXIchkcOnQVuxyiIiIiMiAGOhbCOeOdUG++toNkSshIiIiIkNqEYFeqVQiJiYGnTt3hp2dHXx9fTFr1iyUl5fjueeegyAIWLFihdhlGozc3ga2bs6wdXeBa1c/9Pn4ebiHBqAw+SJKM/PFLo+IiIiIDMjix9CfOnUKI0eOhEKhgKOjI7p164a8vDwsW7YMGRkZKC4uBgD06NFD3EINqGfMJPSMmdRgWXbcURx780uRKiIxabVaHD1TgFWx53H2zxJU1ajh3soW44Z1xJQxgWjtYit2iUREZGSX8srw303p2Hc0D2UVtXC0l+OBnm3x8hPBCOrYSuzy6B5ZdKBXKpUYPXo0FAoFZs+ejblz58LZ2RkA8Omnn+L//u//IJfLIQgCwsLCRK7WcNK/24PsX45AZi1H665+6D59LBy93aGurqnfRmYjx+g9C5G1NQFnlm6pX/7Akumw83TFvuiPxCidDOxC9nU8+X/7kZxWdNu6gyev4u3lSZgzOQxzX+4JmUwQoUIiIjKm8opavPjBIWzcmQmNRttgXVKqEkvWp2L0QD9889EAdvBImEUPuZk5cyZycnIwY8YMLFq0qD7MA0BMTAzCw8OhUqnQsWNHuLi4iFipYZVmKpCfkILc+JM4u3Ibfps8Hx49OqHfghfrt9HUqHBw5nKEzhyH1t06AAD8RkTCZ3gEDr22UqzSyYDOZZSg3zO/NBrmb6qsUuP91Scxbd5BaLXaJrcjIiLpqahU4aGXdmFDXMZtYf5Wv/xxGQ9O3oGS0moTVkeGZLGBPi0tDbGxsfDw8MAnn3zS6Da9evUCAISHh9cvu/kFoHfv3rC1tYUgSL/XsjApHRmbDsB/bH94RnSpX150JhOpq7bjwWX/hIO3G/otfAnH3voSlVdLRKyWDKGmVo1R0/eg+HrzTs5rt17Aqtg0I1dFRESmNHP+ERw+VdCsbVMzruHZt/4wckVkLBYb6Ddu3AiNRoPo6Gg4OTk1uo29vT2AhoH+zz//xObNm+Hl5YXIyEiT1GoKpxdvgkalRs85ExsuX7IZGrUaUXsXQnHoLLK2HRKpQjKkLfuykZ2n24xGn3939o49OEREJB1Xiyrx7S9/6tRmx4ErOJ91zTgFkVFZbKCPj48HAAwePLjJbXJycgA0DPQDBgxAfn4+tm/fjmHDhhm3SBMqy1Yga9shtBsQhjZ9guuXa1VqFCamw869Ff6M3S9ihWRI//lB9972jCtl2HM41wjVEBGRqa3dko5alUbndrxaK00We1PspUuXAAAdOnRodL1KpcKhQ3W90bcGepnM8N9xIiIioFAodGpjrZVhLnobtI4zSzfDf2x/9JwzEbsffw8A0KZPMDpPHIy0tb+i9/tTsX34HKirau68o0YEBQahVtD9xGFq+a6vAbJWyFfkw8fHR+xyjEILAXmt3wUE3X+XJ0x9G60qfzNCVURE5qElfA4AgNLpacAmUOd2q77dh80rJhihIrobLy8vJCUl6dXWYgN9eXk5AKCysrLR9bGxsVAqlXB2doa/v79Ra1EoFMjN1a3n00awAnR8qKviSCrWeT/e5PrrF3Pxrc//htzIHezwwJLpOPHRBpz/ZjdGbn0f9735FBLnrtPtwADy8vNQo1Xr3M7knNWADNCo1Tr/nUiGzBZw0++L6Y3yGtzIt9D3hYgIaBmfAwAQoAVsdG9Wq5ZZ9vtioSw20Ht5eaGkpATJycno169fg3X5+fmYM2cOACAsLMzoN756eXnp3MZaKwOM3OEd+d6zuHG5AOfX7QIAHJy1AlH7FuHyzmO4elS3S27tvNtJo4feygoaADIrK3i3by92OUahhYA8rUavHnpnR2u4WOj7QkQEtIzPAQBQWgP6zFljbaVGGwt+X8yZPnnxJosN9MOGDUNaWhoWLFiA4cOHIygoCACQmJiIZ555BkqlEoBpHiilz+WT2ooqbOj0tBGqqdN+SE/4R/XHtqGz65eVXbqKEx9tQP/F07F9yGyoKpt/Krhw8QKsHeyMUapB+QzbiNyCCnh7eSPnbI7Y5RjNsBd24rdjeTq327ZhPgb3bmeEioiIzENL+Rz47JsUvP7ZcZ3bvfrCCCx49V0jVETGZLE3xcbExMDd3R1XrlxBSEgIQkNDERgYiN69eyMgIABDhgwB0HD8fEuSG38S33edjPJcZYPl59ftwpZ+M3QK82R+XpkYfPeN/qarfysMivQ2QjVERGRqU8YEws7WSqc2ggC8OKGrkSoiY7LYQO/j44OEhASMGjUKdnZ2yM7OhpubG1avXo24uDhcuHABQMsN9GTZogb5oau/bo/y/r9/GH/4GRERmYa7qx1eGN/l7hveYsJD/gjwsZwHbbYkFjvkBgCCg4OxY8eO25bfuHED2dnZkMlk6N69uwiVERmXXC5D3H8ewoNT4pBXUHHX7Wc/2x1TxgSZoDIiIjKVRbN7Iz37erOmJO7d3RNr5z1ogqrIGCy2h/5OUlNTodVqERgYCAcHh9vWb9q0CZs2bcK5c+ca/KzvVEJEYgjwccHR9aMxtE/TY+LdWtni8zl9sHC2YadIJSIi8dlYW+GX5cMx48lusLVpfPiN3ErAs6M7I/7LkXBysDZxhWQoFt1D35SUlBQATQ+3mTBhQqM/T548GevWrTNqbUSG5OvlhH1rRuJcRglW/3Qeq386j+paDexsrLDqnfsx8eEA2Nu1yNMAEVGLYGNtheVv9sN7L/fE1z9fxL6judh/PB81Kg2cHa1xftt4tGvjKHaZdI9aZA/93QK9Vqtt9D+GeZKqbp1aY+kb/eDRum4mIndXW0wZE8QwT0TUQri72uH1KaHY9cUIeLrVfRa4OFozzFsIBvoWpsOovug7/4UGyzpPHIwp+ZvgNyJSpKqIiIiISF8tsnsuPj5e7BJE4/dIH2T89Hv9z04+ngiKHoaCpHTxiiIiIiIivbXIQG/JbFwcMGb/YljZ2aAiTwmZrTWc/doiY9MfOPLGGrSN7IKDs1bUbSwIuP+zl3Hs32sROXeyuIUTERERkV4Y6C1MTWkFMrcmoLa8CmcWb0K7QeEImzkOh1//Au0GhqMgMR1alRoAEPLiaBQknkfRmUyRqyYiIiIifbXIMfSWzq27P4pTsgAA7mGdUHy27s9+IyJxaWfdY6Bdu/iiw6g+OL1ks2h1EhEREdG9Yw+9BXIL6Vgf4t3DAnBldyIAoN2gHkj6YD0AoG2fYDj5tsH4w8sBAPaerui38CXYt2mN9G/3iFM4EREREemMgd7COHi5AVotKhTFAAC34A44s3QzPHoG4vrFXKgqqgAA6d/uaRDcR2yeh3NrduDyrkRR6iYiIiIi/TDQWxi37v71vfMAUFNajq6TH0Z1cRku7zouYmVEREREZAwM9BYmZ98J5Ow7Uf/zjpFvAADG/L4Yu8fPbbLdrjusIyIiIiLzxUDfQmwb9KrYJRARERGREXCWGyIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJ4xh6MyW3t0V0xnqxy2g2ub2t2CUQERERtUgM9GZKEARYO9iJXQYRERERmTkOuSEiIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgmTi10ANU6r1UJVWS12Gc0mt7eFIAhil0FERETU4jDQmylVZTU2dHpa7DKaLTpjPawd7MQug4iIiKjF4ZAbIiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJ4zz0FsSrXwhGbJnXYFlteSVKM/ORsekA0tb+Cq1aI1J1RERERGQMDPQWKHNLAnLikwFBgL2nKzpPGIje86agVWB7HJmzWuzyiIiIiMiAGOgtUFFKFjI3J9T/nL5uNx5LWIqgp4Yief5GVBeVilgdERERERkSx9C3AKrKahQmX4Qgk8GlQ1uxyyEiIiIiA2KgbyGcO9YF+eprN0SuhIiIiIgMiUNuLJDc3ga2bs71Y+i7PPsQ3EMDUJh8EaWZ+WKXR0REREQG1CICvVKpxKeffootW7YgJycHnp6eGDduHD7++GPMnDkTX331FZYvX44ZM2aIXapB9IyZhJ4xkxosy447imNvfilSRUTiu5x/A7sP5eBaWQ0c7OWI6OaB3qGeEARB7NKIiMgEyitq8csfl5FXWAGZICDAxxkjH/CFtbX0B6xYfKA/deoURo4cCYVCAUdHR3Tr1g15eXlYtmwZMjIyUFxcDADo0aOHuIUaUPp3e5D9yxHIrOVo3dUP3aePhaO3O9TVNfXbyGzkGL1nIbK2JuDM0i31yx9YMh12nq7YF/2RGKUTGdzR0wWY/9Vp/PLHFWg02gbrenZ1x6zoEDwb1ZnBnojIQuUVlGP+V2fwzfaLKL1R22Cdl4c9XhjfBXOmhMLZ0UakCu+d9L+S3IFSqcTo0aOhUCgwe/Zs5OfnIzk5GQqFAgsWLEBcXBwSExMhCALCwsLELtdgSjMVyE9IQW78SZxduQ2/TZ4Pjx6d0G/Bi/XbaGpUODhzOUJnjkPrbh0AAH4jIuEzPAKHXlspVulEBvV9XAYenLID2/Zfvi3MA8DJ80WY8s4BTJt3sNH1REQkbWmZ19An+hcs//7cbWEeABTKSnyw+hQGTI1DQVGlCBUahkUH+pkzZyInJwczZszAokWL4OzsXL8uJiYG4eHhUKlU6NixI1xcXESs1LgKk9KRsekA/Mf2h2dEl/rlRWcykbpqOx5c9k84eLuh38KXcOytL1F5tUTEaokMY++RXDz77z+gUt89qH+55QLeXJpogqqIiMhUrhZVYsTLu5Bztfyu2546X4xH/7kHVdUqE1RmeBYb6NPS0hAbGwsPDw988sknjW7Tq1cvAEB4eHj9sk2bNmH8+PHo0KEDHBwc0LVrV7z99tu4cUPas8OcXrwJGpUaPedMbLh8yWZo1GpE7V0IxaGzyNp2SKQKiQxHq9UiZvFxqJsR5m/67NuzyFHc/aRPRETSsHR9Ki7nN/+8nnhWidhdWUasyHgsNtBv3LgRGo0G0dHRcHJyanQbe3t7AA0D/aJFi2BlZYWPP/4YO3fuxMsvv4xVq1ZhxIgR0Gg0JqndGMqyFcjadgjtBoShTZ/g+uValRqFiemwc2+FP2P3i1ghkeEcPVOAU+eLdWqjVmuxZnO6kSoiIiJTqq5R48stup/TV8amGaEa47PYQB8fHw8AGDx4cJPb5OTkAGgY6H/55Rf8+OOPiI6OxsCBAzFr1iysWLEChw4dwsGDB41btJGdWVrXG39rL32bPsHoPHEw0tb+it7vT4WVnXRvCCG6aePOTP3a7cowcCVERCSG+GN5KCyp0rnd8bOFyMwpNUJFxmWxs9xcunQJANChQ4dG16tUKhw6VDe85NZA7+npedu2ERERAIDc3Fy9aomIiIBCodCpjbVWhrnorVMbxZFUrPN+vMn11y/m4luf/4V5uYMdHlgyHSc+2oDz3+zGyK3v4743n0Li3HU6HRcAggKDUCuY/xWMfNfXAFkr5Cvy4ePjI3Y5JtdSXn+x4+OAbajO7f7MKrDo94WIWs558E5awntQbhMOOI3Tq22fBx6CrSrHwBXdnZeXF5KSkvRqa7GBvry8bsxUZWXjdyzHxsZCqVTC2dkZ/v7+d9zX/v11Q1GCg4PvuF1TFAqFzl8GbAQroK1eh2u2yPeexY3LBTi/bhcA4OCsFYjatwiXdx7D1aO6XXLKy89DjVZtjDINy1kNyACNWq33FzRJaymv36ccsNW9mVZTa9nvCxG1nPPgnbSE98DVB2h8xPVdKQsUQKW03heLDfReXl4oKSlBcnIy+vXr12Bdfn4+5syZAwAICwu74/zTubm5eOeddzBixAi956r38vLSuY21VgYYscO7/ZCe8I/qj21DZ9cvK7t0FSc+2oD+i6dj+5DZUFVWN3t/7bzbSaOH3soKGgAyKyt4t28vdjkm11Jef6ltJcr0aGejLYGnBb8vRNRyzoN30hLegxorLQoBQKsFdHnOiFYNL3cbWGlN/77okxdvErRarUVOvjxz5kwsX74cvr6+2LdvH4KCggAAiYmJeOaZZ5CZmYna2lpMnz4dK1asaHQfN27cwKBBg6BQKJCYmAhvb2+T1V9bUYUNnZ422fHuVXTGelg72Ildxl35DNuI3IIKtG/jgJx9T4pdjsm1lNd/RXEDHUf8qPPc8l+9/yCmjg0yUlVEZA5aynnwTlrCe6DVatHziZ9xOl23CRKeeNgfsQuHGKkq47HYm2JjYmLg7u6OK1euICQkBKGhoQgMDETv3r0REBCAIUPq/rJuHT9/q8rKSowePRpZWVnYs2ePScM8Ed0bXy8nRA3y06lNaxcbTHw4wEgVERGRKQmCgFcm6j5UWp825sBiA72Pjw8SEhIwatQo2NnZITs7G25ubli9ejXi4uJw4cIFAI0H+traWjz++ONISkrCzp070a1bN1OXT0T3aPkb/dC+jUOztpXJBHz70UA42FvsKEQiohbnH2ODMGqAb7O3nxUdgoER0uzAtehPr+DgYOzYseO25Tdu3EB2djZkMhm6d+/eYN3Nuet/++03/Prrr+jdW7eZZojIPPh4OeL3r0Zh5Cu78eflpqcgs7O1wvfzB+HRgbr16BMRkXmTy2X4adEQRL/5O7b+dumO2772bHcsfE26mc+iA31TUlNTodVqERQUBAeHhj1406dPx08//YQ33ngDDg4OOHr0aP26Tp06NTqtJRGZp85+Ljj902P4YVcm/vPDOSSnFdWvk8kEvDOtB14Y3wXt2zqKWCURERmLvZ0cmz4bivjjeVgZm4Zt+y83uL9q6thAvPxEMCK7SzvftchAn5KSAqDx4TY7d+4EAMyfPx/z589vsO7rr7/GlClTjF4fERmOg70c/3gsCFPHBqKguAph47egoLgKXu52eO+V+8Quj4iIjEwmEzCsb3sM69se18tq0GX0T7haXAVvT3t89f4AscszCIsdQ38ndwr02dnZ0Gq1jf5nCWG+w6i+6Dv/hQbLOk8cjCn5m+A3IlKkqoiMTxAEtHW3h7VcVv8zERG1LK2cbSD/63NAZkGfAwz0LYzfI31wedfx+p+dfDwRFD0MBUnpIlZFRERERPpqkUNu4uPjxS7BaGxcHDBm/2JY2dmgIk8Jma01nP3aImPTHzjyxhq0jeyCg7P+mndfEHD/Zy/j2L/XInLuZHELJyIiIiK9tMhAb8lqSiuQuTUBteVVOLN4E9oNCkfYzHE4/PoXaDcwHAWJ6dCq1ACAkBdHoyDxPIrOZIpcNRERERHpq0UOubF0bt39UZySBQBwD+uE4rN1f/YbEYlLO+uG27h28UWHUX1weslm0eokIiIionvHHnoL5BbSsT7Eu4cF4MruRABAu0E9kPTBegBA2z7BcPJtg/GHlwMA7D1d0W/hS7Bv0xrp3+4Rp3AiIiIi0hkDvYVx8HIDtFpUKIoBAG7BHXBm6WZ49AzE9Yu5UFVUAQDSv93TILiP2DwP59bswOVdiaLUTURERET6YaC3MG7d/et75wGgprQcXSc/jOrisgaz2xARERGRZWCgtzA5+04gZ9+J+p93jHwDADDm98XYPX5uk+123WEdEREREZkvBvoWYtugV8UugYiIiIiMgLPcEBERERFJGAM9EREREZGEMdATEREREUkYAz0RERERkYTxplgzJbe3RXTGerHLaDa5va3YJRARERG1SAz0ZkoQBFg72IldBhERERGZOQ65ISIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjC52AVQ47RaLVSV1WKX0Wxye1sIgiB2GUREREQtDgO9mVJVVmNDp6fFLqPZojPWw9rBTuwyiIiIiFocDrkhIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMM5yQxZLq9UiOa0ISalKnDinxPmsaygorgIAKEuq8NrCo+jVzQN9w9qgk6+LyNUaR15BOQ6fKsCJNCVOnS+uf/2FJVV49q0/EBHigV7dPNAn1BNyueV9v6+qVuHomUKcOKdEUqoSlxU3UFBcCQBQXqvCu/85gV7dPHB/eBt4utmLXC0RGUN2bhmOnC7AiXNKnL7Q8Dz43NwERHTzqD8XymSWN/3yjYra+teflKpEXmFF/Xmw6Fo1Plh9sv486OpiK3K1hqfVanE6vRiJZwtxIq0IaZnXGvwOzJp/BBEhdVkgsEMrkavVn6DVarViF0G3q62o4rSVerpWWo1vtl/EytjzuHDperPaDIzwwisTg/HYkI6wtpZ2sNVotNh7JBcrY9Ow48AVaDR3/yfevo0Dpj3eFS+M7wJvTwcTVGlcWTll+OKnNKzdegFF1+7+PAdruQzjh3XE9EnB6N+zLZ+pQCRxKpUGv/xxGStj07DvaF6z2nTydcZLE4IxdWwg3F3N4/PsXpzLKMGqH8/jm+0XUVZee9ft7e2s8NTITnhlYjDu6+ZhggqNq/RGDb7b8SdWxqbhXMa1ZrXp37MtXnkiGOOHd4StjZVxCzQwBnozpU+g9+oXghFb5jXcT3klSjPzkbHpANLW/gqtWmPIMuuZQ6DXarX476Z0zPn8eLNOXo0J8HHGV+8/iIER3gauzjRSLhRj6rsJOHFOqVd7a7kMb78Qjree7yHJLzYVlSq8vTwJSzekQt8z2+BIb6yd9yD8fZwNWxwRmcSR01cx9Z0EpGc3r0Pn7xzs5PhkVgRmPNlNkj32JaXVePXTY/hm+0W99/HY0A5Y+fb98PKQXgePVqvFt9v/xL8+PYprZTV67cPP2xFr5j6Ah+73MXB1xsNAb6buJdBnbklATnwyIAiw93RF5wkD0Tq4A9LX78WROauNUq/Ygf6K4gb+8W5Cs3ti7mbGk93w6auRsLeTxqg0jUaL+WtP471VJ1GruvcvbT26uuG7jwaie6CbAaozjcOnrmLyvw/gz8ul97wvR3s5Fr7WGy890ZW99UQSUV2jxr+Xn8Dn351t1pXJu3nwvrb45sOBkvpyvzPhCp6fdxB5BRX3vC+3VrZY+fb9mDgiwACVmYZCWYHn3zuIuANXDLK/F8Z3wedz+sDJwdog+zMmBnozdS+BPnHet0j9Ynv9crm9LR5LWAoHbzf8EPY8qovuPfD8nZiB/nzWNQyftgs5V8sNut+BEV7Yvmw4XJxsDLpfQ6ut1WDKOwfw/a8ZBt2vs6M1diwfjgESuFrxc3w2Js7Zj5paw16B+tfTIfh8Th+GeiIzV15Ri3Gv/YY9h3MNut+27vbY/cXDCO/ibtD9GsOXm9Mx7f2Del+dbMonsyLwxnPhht2pEWTmlGLYC7uQlVtm0P32CfXErysfhlsr876/QHrX1ElnqspqFCZfhCCTwaVDW7HLMaiMK6UY8vxOg4d5APgjSYFHZ+xBRaXK4Ps2FLVag8n//sPgYR4Ayspr8cj0PThy+qrB921IcQcuY8Lr8QYP8wCwZH0qZi86BvZ7EJmv6ho1Hnt1n8HDPABcLarEsGm7kJZ5zeD7NqR12y7ghXmGD/MA8ObSJCz8+ozhd2xAVxQ3MPi5Xw0e5gHgWEohRry8C2Xl+g3fMRUG+hbCuWNdkK++dkPkSgynukaNsbP2Ib/w3i8tNiUh+Sr+Of+I0fZ/rz79OgUbd2Yabf/llSqMmbkPhX/NiGBuMq6U4onX90OlMl7gXvxdKr7d/qfR9k9E92bO58ex94hhhls2RllShaiZe822cycptRDPv3fQqMeIWZyI3YdyjHoMfalUGox79Tdczjd8x95NiWeVeGGecd/je8VAb4Hk9jawdXOGrbsLXLv6oc/Hz8M9NACFyRdRmpkvdnkG8/4XJ3H2zxKd2iRujMKVvZOQuDGq2W2+2noBuw6a34ks9c8SvLcqWac2+rz+wpIqs/xSo9Fo8Y93E1BRpduHrD7vwaxPjyLXCFeBiOje/JGUj+Xfn9OpjT7ngD8vl+Lt5Um6lmd01TVqTPn3AajVunVq6PMePP/eQVzX8yZTY1q4LgVJqbpNBKHP64/dlYXNe7N0Lc9kWkSgVyqViImJQefOnWFnZwdfX1/MmjUL5eXleO655yAIAlasWCF2mQbTM2YSnkz9Gk+e/Qpj93+O4KkjkB13FPFTFohdmsGcOl+EBXpcAvTycIBPW0ed79x/Yd5B3KjQb+YcY9Bq68KsrsNM9H39sbuy8HN8tk5tjG31T+dx4IRC53b6vAfXy2rw8oeHdT6WudFotLheVoNrpdUGuWlQiiqrVCi6VoVaIwzRkgK1WoPi69W4UVEr+aFk1TVq/OPdBJ3b6XseXLoh1eyGIH605hRSmzkl4630eQ9yrpYjZvFxnY9lTOlZ13Tu2AL0/x14+cPDKCm9+1TIYpDGFB734NSpUxg5ciQUCgUcHR3RrVs35OXlYdmyZcjIyEBxcTEAoEePHuIWakDp3+1B9i9HILOWo3VXP3SfPhaO3u5QV//vm7XMRo7RexYia2sCzizdUr/8gSXTYefpin3RH4lRerMt+iZF5x6Je5FztRzf/5qBaY93Ndkx72T/8XwcP1to0mMu+OoMxg7paNJjNkWt1uBTE4/p/OWPyziXUYJunVqb9LiGkJ51Dat+PI912y/W97A5O1rjmUc745WJwQjpLL3XpIsbFbVY/9d81CkX/3dVb0hvb7wyMRhRgzpIcprW5tJqtThwQoGVsWnY8lt2/RA1Xy9HvPh4Vzw/vgvaukvvwWqb9mYhM8fwY6abotXW9QZvWWwe96KVV9RimY5XJ+7V1z9fxPvTe5nN78vi9alGuX+qKYUlVVi37SJefaa7yY7ZXJZ7BkNdz/zo0aOhUCgwe/Zs5OfnIzk5GQqFAgsWLEBcXBwSExMhCALCwsLELtdgSjMVyE9IQW78SZxduQ2/TZ4Pjx6d0G/Bi/XbaGpUODhzOUJnjkPrbh0AAH4jIuEzPAKHXlspVunNUlhciZ/2mP6y18rYNLPp0VoZm2byYx49U4hkPee3N7Rdh3KQnWf6+0FW/Xje5Me8F1qtFm8vS0LXMZuxdENqg8vlZeW1WBmbhu7jtuC1hUcttsf+yOmr8B/5I17+8HCDMA8A8cfz8fjsePR8Yisu51vO/UW3KiuvwajpezDoH7/ix91ZDe43uaIox79XnIDfQz/g+zjD31hvbGKcB7ftv4wchXkMv9u4M9PkQ2BqVRqs3ZJu0mM25XpZDdbvMP39Tat+TDPL86VFB/qZM2ciJycHM2bMwKJFi+Ds/L+5ZGNiYhAeHg6VSoWOHTvCxcVFxEqNqzApHRmbDsB/bH94RnSpX150JhOpq7bjwWX/hIO3G/otfAnH3voSlVd1G5duat/t+NOk38hvOp1erPM4PWMoKKrEz/sviXLsNZvN40QuVh3f/nIR1TVqUY6tjzmfHcfHX56+63aLv0vF9I8Om80XVkM5nlKIoc/vhLKk6o7bpWZcw4CpcUa9wV4MVdUqjJq+Bzvvcg9QTa0G0W/+Lko40te5jBIcPlVg8uNqNFp89fMFkx+3MV+KFKzN5XPgh12ZKBfhRuWLl0rxR5L53Y9osYE+LS0NsbGx8PDwwCeffNLoNr169QIAhIf/b37VhIQEDBs2DN7e3rC1tYWPjw8mTpyItDTT9wQY0unFm6BRqdFzzsSGy5dshkatRtTehVAcOousbYdEqrD5Dp4UbwzjIRGPfdOxlEKTDje61aFT4r9+rVYr2u9A6Y1anW/EFsu+o7n47Nuzzd7+i5/OY/vvl41YkWmp1Ro8MSceldXN+wJ2Ke8GXvrA/M9/uvjkyzNISG7+v5Xn5iZI5kuNqJ8DZnAerKxSIUmkK6bZeTeQVyD+VYqDJ3W/h8pQzOF34O8sNtBv3LgRGo0G0dHRcHJyanQbe/u6MWC3BvqSkhKEhoZi2bJl2LNnDxYsWIDU1FT069cPOTnmN9NJc5VlK5C17RDaDQhDmz7B9cu1KjUKE9Nh594Kf8buF7HC5jsh4rCPE2ni99CL+frPZV4Tfeq2y/k3UHRNvJuSxHz/dfGfH3TvhPjPD6Ydj2tMcQeu4JKOw7J2HLiCS3mmG5NtTDW1avx3s25DxGpqNaL1+upK1M+Bc0rRr2aduVAsWscOAJw4VyTasc2hBnN4/X9nsYE+Pj4eADB48OAmt7kZ0G8N9FFRUVi8eDEmTJiAgQMHIjo6Glu2bMH169exefNm4xZtZGeW1vXG39pL36ZPMDpPHIy0tb+i9/tTYWVn3k9FLb5ebdS5Zu/mZJr4/4hPnhevBrVaK3oP9an0YlGPbw6/A3eTV1CuV2/73iN5yLhi+CdJi2H1Jt3vd9BotPhyi3kMp7hXv/x+GQql7s+PWP3TebMcH/x3p86Ldx4oulZtlIcZ6kLMzwFzOH5llQrns66JdvxTIr/+xghasb9mGomvry9ycnJw8uTJRmewUalU8Pb2hlKpREZGBgICAprcV1FRETw8PLBixQpMnz5d51oiIiKgUOh2achaK8NcTW+dj6ULuYMdon5bhHOrd+D8N7sxcuv7UJ7OQOLcdTrva57sOGoF449rV8la46rrv5pcn7gx6o7TUHl52ENuJYNKrbnjh51CWYHIJ7fftlymKYX3tc90qtnQCp2nosa6Y6PrDPX6gabfA/fSb2GnEu8GunKbcFxzGtfouru9fuDefwfsq1PgVr5Jt6JNrFreEUqXqXq1dSvbAPta6YdaRat/QW2l++w9djXn4H4j1ggVmVap3UCUOQzRq613yceQac1zar6brrb6J1RWHo2uM8V5sM31/8Babfox/DeV2T2AUofhja4zxXnQseoIXCt26Va0AakFRyhaxzS53thZQNBUot21+boV3QxeXl5IStLveQcWO21leXndt+fKysb/omJjY6FUKuHs7Ax/f//b1qvVamg0Gly6dAlvvvkmvLy88MQTT+hVi0KhQG6ubo+kthGsACPPjBX53rO4cbkA59fV/aM8OGsFovYtwuWdx3D1qG6X6/Py81CjNcHNgjYqwLXp1Tfnlr0buZWsWdv9nUYNnf8uDS6gFrBufJWxXz8AFBWXAGUivget/YHGR9E1+/UD+r8HlVXV4v8O3I2TK6Dnff7FxdeBUjN/fc3hLABWujerqqo1/7/f5mhbCeg2xXa9/PxCQG3mQ48cNU3+/ZriPFhQoASqRPw98bzR5N+vKc6D5TcqUJ4v4uuXtwLu8H3d2L8DWq1gducJiw30Xl5eKCkpQXJyMvr169dgXX5+PubMmQMACAsLgyAIt7UfOHAgDh2qu0Gqc+fOiI+Ph6enp9616MpaKwOM2OHdfkhP+Ef1x7ahs+uXlV26ihMfbUD/xdOxfchsqCqb30PTzrudiXroXXCnW1EUyjvf0KXLt/LGWMnU8GrfvjmlGo3SRoam/mYM9frvtC8PNxfYuoj3HlTYOKGpQT93e/3Avf8O2NtZw03k34G7qbFygL5PKfBobQdbZ/N+fc1xVaiGPnd7ONhq0NrM/36b44atFa7r01CrQTuv1hD0/UZoIlettE3+/ZriPNjG0w3WGvEeTlZm54CmBseZ4jzo5GiLViL+O9EI9rjTPDPGzgIyQQ1vI7x+ffLiTRY75GbmzJlYvnw5fH19sW/fPgQFBQEAEhMT8cwzzyAzMxO1tbWYPn16o0+JTU9Px7Vr15CVlYWFCxeioKAAhw4dgp+fn0nqr62owoZOT5vkWIYQnbEe1g52Rj+OSqWBy/3forJKv6sBV/ZOgk9bR+RcLYfv8B90bj+sbzvs/e9IvY5tKC99cAirf9JvPvR7ff0AcHnPRPh6NdFFbgLHzhSg79O/6N3+Xt+Dea/ch3df6qn38U1BrdYg8NFNyMrVrZfVy8Mel3dPsoiHLM1blYz3Vp3Uud0vy4fj0YGmOc8bU3ZuGQIe+RG6fsI/NrQDtiweZpyiDGjMzL16z8p0r+cAKysBZUeehb2deH2i2/ZfwthZ+/Ruf6/vwX/f7Y8XRHzQolarhfuD61FSqt88/Pf6+vuGeeLI+ii9jm0s0j9rNyEmJgbu7u64cuUKQkJCEBoaisDAQPTu3RsBAQEYMqRubOGtN8TeqkuXLujTpw8mTZqE3377DWVlZfj0009N+RKoEXK5DD26uIt2/F7dGh+zadIagsV7/Z6t7fS+RG0oYUFusLK6/aqaqfTqJt7731xWVjK8NEH3D9tp47taRJgHgBfGd4Fcx9+TDu2cMPIBHyNVZFod2ztj1IO+Ord7ZWLw3TcyA2Kei0M6tRY1zANAr2BxP4vE/iwUBEHUGsR+/Y2xjDN3I3x8fJCQkIBRo0bBzs4O2dnZcHNzw+rVqxEXF4cLF+pu+moq0N/K1dUVnTt3xp9/SuehG5YsIqRl/yMW+/U3NkTNlOzt5OjeWfebHQ3FHH4HmuOFx7sgwMf57hv+xaetI6ZPkkaYa452bRwxKzpEpzYf/bMXrKws52Nx7ss9YWfb/BsJhvVth6F92hmxIsMR9zwo/pf69m0d0NbdXpRj21jL0D1QvHPwTWJ/FpobyzlzNSI4OBg7duxAWVkZysrKcOzYMUybNg3l5eXIzs6GTCZD9+7d77qfgoICpKeno1OnTiaomu7m8WEdRTmuk4M1Hr5f/LG14V3c0cm3+UHNkCY8dPsN5GJ4fHhHUY77QM+2d509wly0drHFzpUPw9fr7ldUvDzssXPlQ2gjUkAwlgWvRuLpR5t33l74Wm9Ej+ps5IpMKyLEEz8uHNKsUN83zBObPhsq+hf25hoc6Y3WLuJMszxhuPjnQUEQRDsPjhncATbWetxxbmCPi/T3YGtjhdFmOCzPogN9U1JTU6HVahEYGAgHh4Yfzk8//TTee+89/Pzzz/j999+xZs0aDBo0CHK5HK+++qpIFdOtHuzlhZBOriY/7jOPdoKLk/jz9MtkAl5+wvQ9qa7ONpg0ounpXU3p+XFdIJebPnhIZTjCTUEdW+Ho+tF4flwQ7O1u/wC2tbHClDGBOLYhCt0D3USo0LisrGT45sOBWP5mvya/BPcJ9cTPS4fh9SmhJq7ONEYP8sOBr0dh1ABfNJbVPVvb4a3nwxH/5SNo5Sz++a257O3k+MfYIJMf17+9Mx7ubx7DssT4HADM5zzYq5sHenfXb7KSe/HEQ/7waG38ewZ11SIDfUpKCoDGh9v07dsXv/76K6ZOnYqRI0di4cKFePDBB3Hq1Cl07mxZvTdSJQgCpk/qZvLjinXybMzUsY0HNGP6x2NBcLA3j4mxvDwc8Pgw0/bOtHW3xziRrg7di3ZtHLHmvQeRt+9JrH63P5wd6v4OWzlZI3ffJHz9wQD4eYt3k7OxyWQCZjzZDRd+mYBdqx6Gs2PdnK8ujtZI+mEMjm6IwpjBHUSu0rgiu3tix4qHkBH3BBbN7g2nv34HWrvY4MreSfhoZoToY8L18dITwZDJTPvF/pWJpj9mU0I6t8agSG+THrNbJ1cMjNB/JhZDE2OYoLkOTWSg/5sZM2bg+PHjKCkpQWVlJS5cuIDVq1ejQwfLPuFLzfPjuqBnV9ONY3xlYjBCg8ynB9OtlS0++meEyY7n7emAf0/rYbLjNceCVyPrw5kpLInpA1sb8S8z68vVxRbTHu9af5XJycEa7q7m18tkLDKZgIf7+8Dlr98ZZ0drsxwHa0z+Ps6YPTkUrf76HXCwk0v6d7qznwtmP3v3YbOGEhzgihlPmleYWxLTx6RXK//z1v1mNSwrelQn3N+jjcmONzkqEH3CTHc8XTDQtzAdRvVF3/kvNFjWeeJgTMnfBL8RkSJVpTtraxnWfTgA1nLj/wp3bOeEBa+a33sz86lu6N/TyE8f+8t/3+2P1i62JjlWc/l5O2HRbOM+TfmmcUM7YqKZDDciov+Z98p96NKxldGPI5MJWPfBANjZmteVjPAu7nhnmmmm0Z0+KdjkVwTuxspKhq/fH6DTzd/6atfGAYtj+hj9OPpqkYE+Pj4eWq0Wo0aNErsUk/N7pA8u7zpe/7OTjyeCooehICldxKr0Exbkhk91DNoKZQVyrpY368EbQN0Y4+8+HggnB9P1BDeXlZUM6z4YAHfX5gdtXV8/UHd1wlzn5X5hfBeMG9pRpza6vgcd2jlh1b/Nq1eKiOrY28mx/pOBcNBhyJA+58F5r/RE71DTj9dujjefC8eD9+nWuaPrexAa2Brz/2V+HVtA3b1CS/+vr05tdH39crmAbz4cYHYdW7cyr6+adM9sXBwwZv9iWNnZoCJPCZmtNZz92iJj0x848sYatI3sgoOz/nqQliDg/s9exrF/r0Xk3MniFq6nfz3THUXXq/Hhf081a/vIJ7c3e9821jL8tGgIHrjPfMYL/l1nPxfsWvUwhk/bhWtld3/Ahi6vHwCeeqQTlr2h24nSlARBwIb5AzFmVi32HG7eY7h1eQ/atXHAvv+OtLjZX4gsSURI3Y3NUTP3oqr67g8d1PU8+K+nQ/D2Cz30rM74rK1l+GX5Qxg2bSeSUpXNaqPLe9ClYyvsWT3CLDu2bpr2eFcUXavGW8uSmrW9Lq/fykrAhk8GYVhf8We5u5MW2UNvyWpKK5C5NQHnvozD9uFzcPzdr1GYfAGHX/8C3v27oyAxHVpV3Qkv5MXRKEg8j6IzmSJXfW8+mNELn74aadAblVydbfDL8uEYPcg8e6ZvFRHiiT++HtWs6Ql1MX1SML79aIDZz8ttZyvH9mXDMXGEYW+S7erfCgfXPYrOfi4G3S8RGd7wfu2xe9XDBp19RBCA917uic/n9DH7K3StnG3w25qRGN7PsM8R6N3dEwe+HiWJ6XrffD4cy9/sZ9B7CpwdrbHl86F44mHzH3Jp3p/UpBe37v4oTskCALiHdULx2bo/+42IxKWddcNtXLv4osOoPji9ZLNodRrSnKlhOLp+NLoZYDrLRwf4InXrODx0v3lMTdYcYUFuSNk8Ds+Pu/dp3Nq1cUDcfx7CirfuN/swf5OtjRU2LhiMDZ8Mglure7skKpMJmDMlFMmxY+Gvw4OZiEhcAyK8kbplHMYbYDaqwA4uOPD1KMx9+T6zD/M3uTjZYNeqEVjxVj843uOMZDbWMnw8MwKHvn1UUlcoZzzZDYnfj0F4l3ufxGJ4v3Y4u2UcoiQyC5Y0Pq1JJ24hHetDvHtYAIr+CvftBvVAbvxJAEDbPsFw8m2D8YeX4/HjK+F5XyD6LXwJXZ59SLS671Vkd0+c+GEMPpkVoVdvda9uHti4YBC2Lx+Odm0M29ttCq2cbbDmvQex978j9Hrao7urLWKmhiJ1yzg8oscj48UmCAKeGtUJqVvHYfqkYJ1nwBEEYPRAPxz+9lF8+lpvSU7jR9TStXG3x6bPh2LL4qHoF677bCTt2jjg/en34dSPj5n1cMumyGR10zqf2fwYpo4N1PlmUblcwKQRAUiOHYs3nw+H3AQTTxhaj67uOP59FD57vTf82+veKRPexQ3ffjQAu78YIakpfQWtVqsVuwi6XW1FFTZ0elrndg5ebhi951PEhj0PABh3aDl+f+lzyORyhL/6OH579pNG243YPA/n1uzA5V2JetUbnbEe1g7mMwWeSqVBXMIVbNyZgaRUJTKulN22jVwuIKRTa/QNa4PnHgtCpAgPqDCm81nX8OXmdBw8eRWn0otRXXP72FKfto7o1c0d44d1xISH/M1uBod7UVZeg/U7MrDjwGWcOFeEq0WVt23jaC9Hj67uGBThhefHdUFHPU7+UuMzbCNyCyrQvo0DcvY9KXY5JtfSXz/Qst6Dk2lKrN16AUdOFyDlYglqVZrbtvFv74xe3dwxaUQAogZ1gLW19EJsU4qvV2PdtgvYdSgHJ84Vofh69W3buDhZ475gDwzr0w7PjQuSxPCa5lKrNdh9OBcb4jKQmFqIi5dKb9vGykpAtwBX9An1xNSxQegX3kYyV2VuZTmf3gTgr+E2f/XOA0BNaTm6Tn4Y1cVlDWa3sXRyuQxjBneof2DMtdJqpGdfR0WVClYyAc6O1ggOcLWoAPt3Xf1dsej1uim2ams1SM++hpLSGtSqNLC3s0InHxdJXUrVlbOjDV6eGIyXJwZDq9Uir6ACl/JvoKpaDRtrGTxa2yHQz0Uyw4qISHc9gz2wIrjueQPVNWqcz7qG62U1UGu0cLCTI7BDq3sepmfO3FrZ4rVnQ/Has6HQarW4lHcDuQUVqK6pOw96eTggwMfZbB6WZWhWVjI88qBv/VXn62U19VlAEABnh7osYAlXZKX/CqiBnH0nkLPvRP3PO0a+AQAY8/ti7B4/t8l2u+6wzhK4utia7cMgTMHaWobugebzYCxTEwQB7ds6on1b6Q2lIiLDsLWxQngX0z2Q0NwIgoCO7Z1bxJXIprRytjHb6UfvFQN9C7Ft0Ktil0BERERERsBrzUREREREEsZAT0REREQkYQz0REREREQSxjH0Zkpub4vojPVil9FscnvLnSWAiIiIyJwx0JspQRDMal53IiIiIjJPHHJDRERERCRhDPRERERERBLGQE9EREREJGEM9EREREREEsZAT0REREQkYQz0REREREQSxkBPRERERCRhDPRERERERBLGQE9EREREJGEM9EREREREEsZAT0REREQkYQz0REREREQSxkBPRERERCRhDPRERERERBLGQE9EREREJGEM9EREREREEsZAT0REREQkYQz0REREREQSJhe7AGqcVquFqrJa7DKaTW5vC0EQxC6DiIiIqMVhoDdTqspqbOj0tNhlNFt0xnpYO9iJXQYRERFRi8MhN0REREREEsZAT0REREQkYQz0REREREQSxkBPRERERCRhvCmWiMjC1dSqcfZiCVIzSnCjohYAUFGpQlJqIUID3WBrYyVyhUREdC8Y6ImILFBZeQ3W78jAN9sv4uT5ItTUahqsLymrQeST22EtlyG8ixueebQznh3dGa4utiJVTERE+mKgJyKyIKU3ajB3ZTK+3HKhvjf+TmpVGiSlKpGUqsSbS5MwdWwgPpzRi8GeiEhCGOgtiFe/EIzYMq/BstrySpRm5iNj0wGkrf0VWrWmidZEJHX7jubiubkJuJxfrlf7iioV/vNDGrbGX8KauQ/gkQd9DVwhEREZAwO9BcrckoCc+GRAEGDv6YrOEwai97wpaBXYHkfmrBa7PCIyMK1Wi/e/OIn3Vp00yP7yCiowavoexEwNxfx/RfIp0EREZo6B3gIVpWQhc3NC/c/p63bjsYSlCHpqKJLnb0R1UamI1RGRob25NAkLvjpj8P1++nUKKqrUWPZGX4Z6IiIzxmkrWwBVZTUKky9CkMng0qGt2OUQkQEt/z7VKGH+phUbz2H+WuPtn4iI7h0DfQvh3LEuyFdfuyFyJURkKOezrmHO54k6tUncGIUreychcWNUs9u8u/IETp0v0rU8IiIyEQZ6CyS3t4GtmzNs3V3g2tUPfT5+Hu6hAShMvojSzHyxyyMiA1CrNZj6zgFU16h1aufl4QCfto7w8nBodhuVSosp7xxATa1uxyIiItOw+ECvVCoRExODzp07w87ODr6+vpg1axbKy8vx3HPPQRAErFixQuwyDapnzCQ8mfo1njz7Fcbu/xzBU0cgO+4o4qcsELs0IjKQTXuzcfRMocmOdzq9GOt3ZJjseERE1HwWfVPsqVOnMHLkSCgUCjg6OqJbt27Iy8vDsmXLkJGRgeLiYgBAjx49xC3UwNK/24PsX45AZi1H665+6D59LBy93aGurqnfRmYjx+g9C5G1NQFnlm6pX/7Akumw83TFvuiPxCidiJppZWyayY/5nx/OYerYQN4gS0RkZiy2h16pVGL06NFQKBSYPXs28vPzkZycDIVCgQULFiAuLg6JiYkQBAFhYWFil2tQpZkK5CekIDf+JM6u3IbfJs+HR49O6LfgxfptNDUqHJy5HKEzx6F1tw4AAL8RkfAZHoFDr60Uq3QiaoazF4tx4ITC5MdNTivC8RTTXRUgIqLmsdhAP3PmTOTk5GDGjBlYtGgRnJ2d69fFxMQgPDwcKpUKHTt2hIuLi4iVGl9hUjoyNh2A/9j+8IzoUr+86EwmUldtx4PL/gkHbzf0W/gSjr31JSqvlohYLRHdTVzClRZ5bCIiapxFBvq0tDTExsbCw8MDn3zySaPb9OrVCwAQHh7e5H5GjhwJQRDw3nvvGaNMkzq9eBM0KjV6zpnYcPmSzdCo1YjauxCKQ2eRte2QSBUSUXOdOCfejDMnzilFOzYRETXOIgP9xo0bodFoEB0dDScnp0a3sbe3B9B0oP/xxx9x6tQpY5VocmXZCmRtO4R2A8LQpk9w/XKtSo3CxHTYubfCn7H7RayQiJpLzFB94lwRtFqtaMcnIqLbWWSgj4+PBwAMHjy4yW1ycnIANB7oS0tL8a9//QuLFi0yToEiObO0rjf+1l76Nn2C0XniYKSt/RW9358KKzsbESskoua4rBDveRJXiyqhUjHQExGZE0FrgV0tvr6+yMnJwcmTJxudwUalUsHb2xtKpRIZGRkICAhosP6f//wnUlJS8Pvvv0MQBMydO/eeht1ERERAodDtBjZrrQxzNb31PmZzyB3sEPXbIpxbvQPnv9mNkVvfh/J0BhLnrtN5X/Nkx1EraAxfJBE1oIWAPLf3mlyfuDHqjnPMe3nYQ24lg0qtgUJZ2eR2CmUFIp/c3ug67+KPIENNo+vMXb7ra9DIWkGmuQ7va5+LXY4o+B4QmScvLy8kJSXp1dYip60sLy8HAFRWNv5hFRsbC6VSCWdnZ/j7+zdYl5SUhDVr1uDEiRMGq0ehUCA3N1enNjaCFdDWYCU0KvK9Z3HjcgHOr9sFADg4awWi9i3C5Z3HcPWoblPi5eXnoUbLh84QmURrNSBYNbrq5oOj7kZuJWvWdo3Jz7sCaFV6tRWdsxqQARq1WufzssXge0BkcSwy0Ht5eaGkpATJycno169fg3X5+fmYM2cOACAsLKzBfMpqtRovvvgiZsyYgZCQEIPWoytrrQwwYod3+yE94R/VH9uGzq5fVnbpKk58tAH9F0/H9iGzoaqsbvb+2nm3Yw89kYnkayugEZwbXadQVtyxrS499I0RtFXwbtcWUp2JPt/KChoAMisreLdvL3Y5ouB7QGSe9MmLN1lkoB82bBjS0tKwYMECDB8+HEFBQQCAxMREPPPMM1Aq624o+/twnBUrVuDq1asGn9VGn8sntRVV2NDpaYPWcavc+JP4vuvk25afX7ervsdeFxcuXoC1g50hSiOiuxg1fTd+TchpdF1Tw2RuurJ3EnzaOkKhrITv8B90PvaAyI74/avGjy0FPsM2IregAt5e3sg5K93XcS/4HhBZHou8KTYmJgbu7u64cuUKQkJCEBoaisDAQPTu3RsBAQEYMmQIgIY3xCqVSrzzzjt49913oVKpcO3aNVy7dg0AUFVVhWvXrkGjYQ80EYmvVzePFnlsIiJqnEUGeh8fHyQkJGDUqFGws7NDdnY23NzcsHr1asTFxeHChQsAGgb6nJwclJWV4cUXX0Tr1q3r/wOABQsWoHXr1rh8+bIor4eI6FaDI71b5LGJiKhxFjnkBgCCg4OxY8eO25bfuHED2dnZkMlk6N69e/3yzp07Y//+2+dhHzx4MCZPnowpU6bc09gmIiJDGRTpjS4dWyE9+7pJj+vn7YiRD/iY9JhERHR3Fhvom5KamgqtVougoCA4OPxvajcnJycMGjSo0TYdO3Zsch0RkakJgoBXJgZj1oKjJj3uSxOCYWVlkRd2iYgkrcWdmVNSUgA0/YRYIiIpmDo2EL5e+k07qY+27vZ4cUJXkx2PiIiaj4H+LrRarcFnvRFTh1F90Xf+Cw2WdZ44GFPyN8FvRKRIVRGRrpwdbfDlew+Y7HhfvHM/3FrZmux4RETUfAz0LYzfI31wedfx+p+dfDwRFD0MBUnpIlZFRPp46H4fnXvNFcoK5Fwtv+t89beKHtUJY4d01LE6IiIylRY3hj4+Pl7sEozKxsUBY/YvhpWdDSrylJDZWsPZry0yNv2BI2+sQdvILjg4a0XdxoKA+z97Gcf+vRaRc2+fk56IzN+yN/riUt4N7DrUvPnE7zZP/d8NjPDCmrmmuxJARES6a3E99JauprQCmVsTcO7LOGwfPgfH3/0ahckXcPj1L+DdvzsKEtOhVakBACEvjkZB4nkUnckUuWoi0peNtRW2LB6KRwf4Gnzfw/u1w44VD8HersX1/RARSQoDvQVy6+6P4pQsAIB7WCcUn637s9+ISFzaWTfcxrWLLzqM6oPTSzaLVicRGYa9nRxblwzDhzN6wVp+76d1KysB77zYAztWPAQnB2sDVEhERMbEQG+B3EI61od497AAFP0V7tsN6oHc+JMAgLZ9guHk2wbjDy/H48dXwvO+QPRb+BK6PPuQaHUTkf7kchnentYDJ34Yg75hnnrv575gdxzfEIX3p/eCjbWVASskIiJj4XVUC+Pg5QZotahQFAMA3II74MzSzfDoGYjrF3OhqqgCAKR/uwfp3+6pbzdi8zycW7MDl3clilI3ERlGaJAbDn83GsdTCrEyNg2xu7NQXaO+YxtruQwTHvLHKxODcX+PNhAEwUTVEhGRITDQWxi37v71vfMAUFNajq6TH0Z1cVmD2W2IyHIJgoA+YW3QJ6wNVr/bH2culODEOSXO/lmCGxW10GoBR3s5uge2Rq9gD4R3ceM4eSIiCeMZ3MLk7DuBnH0n6n/eMfINAMCY3xdj9/i5TbbbdYd1RCRddrZy9A71RO9Q/YfhEBGReWOgbyG2DXpV7BKIiIiIyAh4UywRERERkYQx0BMRERERSRgDPRERERGRhDHQExERERFJGG+KNVNye1tEZ6wXu4xmk9vbil0CERERUYvEQG+mBEGAtYOd2GUQERERkZnjkBsiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJk4tdADVOq9VCVVktdhnNJre3hSAIYpdBRERE1OIw0JspVWU1NnR6Wuwymi06Yz2sHezELoOIiIioxeGQGyIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiImoxtFptg/8TEVkCznJDREQWSaPRYu+RXMQfz0NSqhLJaUW4VlYDAMgrrES7oRvRK9gdESEeGDXAFxEhniJXTESkHwZ6IiKyKNdKq7FmczpW/XgeWbllTW6XX1iBHYUV2HHgCt5bdRIRIR54ZWIwokd1go21lQkrJiK6Nwz0FsSrXwhGbJnXYFlteSVKM/ORsekA0tb+Cq1aI1J1RETGF3fgMqa9fwh5BRU6t01KVeIf7yZg6YZUfPPhAIR3cTdChUREhsdAb4EytyQgJz4ZEATYe7qi84SB6D1vCloFtseROavFLo+IyOCqqlV45aPD+Prni/e8r9PpxYh4chs+mN4L//ePMD4Fm4jMHgO9BSpKyULm5oT6n9PX7cZjCUsR9NRQJM/fiOqiUhGrIyIyrIpKFaJm7sVvx/IMtk+VSos3lyYhr7ACS/+vL0M9EZk1znLTAqgqq1GYfBGCTAaXDm3FLoeIyGBqazUY/9pvBg3zt1r+/Tm8sSTRKPsmIjIUBvoWwrljXZCvvnZD5EqIiAxn/lensetQjlGP8enXKYg7cNmoxyAiuhcccmOB5PY2sHVzrh9D3+XZh+AeGoDC5IsozcwXuzwiIoM4c6EYH6w+pVObxI1R8PJwgEJZgcgntze73QvzDiF1a1u0drHVsUoiIuNrET30SqUSMTEx6Ny5M+zs7ODr64tZs2ahvLwczz33HARBwIoVK8Qu02B6xkzCk6lf48mzX2Hs/s8RPHUEsuOOIn7KArFLIyIyCK1Wi2nzDqJWpdvMXV4eDvBp6wgvDwed2uUXVuDtZUk6tSEiMhWL76E/deoURo4cCYVCAUdHR3Tr1g15eXlYtmwZMjIyUFxcDADo0aOHuIUaUPp3e5D9yxHIrOVo3dUP3aePhaO3O9TVNfXbyGzkGL1nIbK2JuDM0i31yx9YMh12nq7YF/2RGKUTETXL8ZRCHEspNOkx122/iI9nRsCVvfREZGYsuodeqVRi9OjRUCgUmD17NvLz85GcnAyFQoEFCxYgLi4OiYmJEAQBYWFhYpdrMKWZCuQnpCA3/iTOrtyG3ybPh0ePTui34MX6bTQ1KhycuRyhM8ehdbcOAAC/EZHwGR6BQ6+tFKt0IqJm+c8PaSY/ZmWVGt9sv/dpMYmIDM2iA/3MmTORk5ODGTNmYNGiRXB2dq5fFxMTg/DwcKhUKnTs2BEuLi4iVmpchUnpyNh0AP5j+8Mzokv98qIzmUhdtR0PLvsnHLzd0G/hSzj21peovFoiYrVERHdWVa3Cj3uyRDn2t7/8KcpxiYjuxGIDfVpaGmJjY+Hh4YFPPvmk0W169eoFAAgPD69f9vvvv0MQhNv+k/qQnNOLN0GjUqPnnIkNly/ZDI1ajai9C6E4dBZZ2w6JVCERUfOkXCxBdY1alGOfuViMqmqVKMcmImqKxY6h37hxIzQaDaKjo+Hk5NToNvb29gAaBvqb/vOf/+C+++6r/9nR0dE4hZpIWbYCWdsOodP4AWjTJxgFx+ouV2tVahQmpsMjrBP+jN0vcpVERHd34pxStGOrVFqcuVCC3qGeotVARPR3FttDHx8fDwAYPHhwk9vk5NTNXdxYoO/WrRv69u1b/19oaKhxCjWhM0vreuNv7aVv0ycYnScORtraX9H7/amwsrMRsUIiortLzbgm8vE5LJGIzIug1Wq1YhdhDL6+vsjJycHJkycbHS6jUqng7e0NpVKJjIwMBAQEAKgbcjN48GDs378fgwYNMkgtERERUCgUOrWx1sowV9PbIMdvitzBDlG/LcK51Ttw/pvdGLn1fShPZyBx7jqd9zVPdhy1gm7TxxER6aPEcSwqbHs2uu7mPPNN8fKwh9xKBpVaA4Wy8o7HaWqu+lblv8Kp+phuRZuRfNfXoJG1gkxzHd7XPhe7HCL6i5eXF5KS9Jse12KH3JSXlwMAKisbP2HHxsZCqVTC2dkZ/v7+t62fOHEilEol3N3dERUVhfnz58PDw0OvWhQKBXJzc3VqYyNYAW31OlyzRb73LG5cLsD5dbsAAAdnrUDUvkW4vPMYrh7VbQaJvPw81GjFGdNKRC1M+3KgiZkjb84zfzdyK1mztmvM9esluF6k2zndrDirARmgUat1/mwiIvNksYHey8sLJSUlSE5ORr9+/Rqsy8/Px5w5cwAAYWFhEAShfl2rVq0wZ84cDBgwAE5OTjhy5Ag++eQTHD16FElJSbCzs9OrFl1Za2WAETu82w/pCf+o/tg2dHb9srJLV3Hiow3ov3g6tg+ZDVVldbP31867HXvoicgkrjnIUd7EOoWy4o5tde2hb4yriwMc7do3p1SzlG9lBQ0AmZUVvNtL93UQWRp98uJNFjvkZubMmVi+fDl8fX2xb98+BAUFAQASExPxzDPPIDMzE7W1tZg+ffpdnxL7yy+/ICoqCl999RWmTp1qivJRW1GFDZ2eNsmxDCE6Yz2sHXT/skNEpKvVP53HSx/oNyPXlb2T4NPWETlXy+E7/Ae99nH8+yhEdpfuTbE+wzYit6AC7ds4IGffk2KXQ0QGYLE3xcbExMDd3R1XrlxBSEgIQkNDERgYiN69eyMgIABDhgwB0PgNsX/36KOPwtHRUe9xTUREZDi9urmLdmy5XEBoYGvRjk9E1BiLDfQ+Pj5ISEjAqFGjYGdnh+zsbLi5uWH16tWIi4vDhQsXADQv0N9069AcIiISR2igG+xsrUQ5dniQO+xsLXa0KhFJlEWflYKDg7Fjx47blt+4cQPZ2dmQyWTo3r37Xfezfft2lJeXo3dv4846Q0REd2drY4WJDwfgm+0XTX7syVGdTX5MIqK7sehA35TU1FRotVoEBQXBwaHh9GZPP/00AgICcN9999XfFPvpp5+iR48emDRpkkgVExHRrV6ZGGzyQO9gJ8ezowNNekwiouZokYE+JSUFQOPDbUJCQvD9999jyZIlqKyshI+PD1544QXMnTsXNjZ86BIRkTnoHeqJ+3u0weFTBSY75j8eC0IrZ34OEJH5sdgx9Hdyp0D/5ptvIiUlBaWlpaitrUVWVhY+//xztGrVytRlGkWHUX3Rd/4LDZZ1njgYU/I3wW9EpEhVERHpbvU7/WFjbZqPMZ+2jvhwRi+THIuISFcM9C2M3yN9cHnX8fqfnXw8ERQ9DAVJ6SJWRUSku+6Bbpj7UuNPjG2KQlmBnKvld52v/u/WzH2AvfNEZLZa5JCb+Ph4sUswGhsXB4zZvxhWdjaoyFNCZmsNZ7+2yNj0B468sQZtI7vg4Ky/5t0XBNz/2cs49u+1iJw7WdzCiYj0EDM1DEdOF2DHgSvN2j7yye06H+PtF8Ix4gEfndsREZlKi+yht2Q1pRXI3JqAc1/GYfvwOTj+7tcoTL6Aw69/Ae/+3VGQmA6tSg0ACHlxNAoSz6PoTKbIVRMR6Ucul+HHRUPw8P3GeeLpq8+E4AMOtSEiM8dAb4HcuvujOCULAOAe1gnFZ+v+7DciEpd21g23ce3iiw6j+uD0ks2i1UlEZAj2dnJsXz4c0x7vYrB9WstlWPhab3z2eh8+g4SIzB4DvQVyC+lYH+LdwwJQ9Fe4bzeoB3LjTwIA2vYJhpNvG4w/vByPH18Jz/sC0W/hS+jy7EOi1U1EpC8bayusfvcB7Fr1MHzaOt7Tvnp180By7Bi8PiWUYZ6IJKFFjqG3ZA5eboBWiwpFMQDALbgDzizdDI+egbh+MReqiioAQPq3e5D+7Z76diM2z8O5NTtweVeiKHUTERnCw/19kLp1HL7aegErf0zDxUulzW57f482ePmJYEwaEQC5nP1dRCQdDPQWxq27f33vPADUlJaj6+SHUV1c1mB2GyIiS+XiZIN/PdMdM6NDsP94PvYn5uHEuSIkpylRUFzXqSEIgJ+3E3oFe6BXN3c88qAvenR1F7lyIiL9CFqtVit2EXS72ooqbOj0tMH2N+b3xdg9fi6qiprfW6WL6Iz1sHawM8q+iYgMRavVQqXSQi4XWuxwGp9hG5FbUIH2bRyQs+9JscshIgNgD30LsW3Qq2KXQEQkOkEQYG3dMoM8EVkuDhIkIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCeNNsWZKbm+L6Iz1YpfRbHJ7W7FLICIiImqRGOjNlCAInAaSiIiIiO6KQ26IiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkTC52AdQ4rVYLVWW12GU0m9zeFoIgiF0GERERUYvDQG+mVJXV2NDpabHLaLbojPWwdrATuwwiIiKiFodDboiIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMs9wQERFZsMLiSpw4V4QT55T480opiq/XTYl8rawGazadR69uHuge2Bo21lYiV0pE+mKgJyIisjA1tWr8HH8JK2PT8EeSotFtyitVmPb+IQCAi5M1JkcF4uUnghEc4GrCSonIEAStVqsVuwi6XW1FFeehJyIinW3em4WZC44ir6BCr/ZjBvth5dv3o10bRwNXRkTGwh56C+LVLwQjtsxrsKy2vBKlmfnI2HQAaWt/hVatEak6IiIyJmVJFaZ/fBg/7s66p/1s238ZfyQpsPT/+uKZ0Z35FHAiCWCgt0CZWxKQE58MCALsPV3RecJA9J43Ba0C2+PInNVil0dERAaWlVOGYdN2IjOnzCD7u1ZWg8n/PoBT6UX47PU+DPVEZo6B3gIVpWQhc3NC/c/p63bjsYSlCHpqKJLnb0R1UamI1RERkSFdzr+Bgf+IwxVFucH3vfi7VGg0wOIYhnoic8ZpK1sAVWU1CpMvQpDJ4NKhrdjlEBGRgVTXqPHojD1GCfM3Ld2QilWxaUbbPxHdOwb6FsK5Y12Qr752Q+RKiIjIUN7/4iRSLpbo1CZxYxSu7J2ExI1RzW4z5/NEZObw6i6RubL4QK9UKhETE4POnTvDzs4Ovr6+mDVrFsrLy/Hcc89BEASsWLFC7DINSm5vA1s3Z9i6u8C1qx/6fPw83EMDUJh8EaWZ+WKXR0REBnDinBILvj6jczsvDwf4tHWEl4dDs9tUVKnw3NyD4MR4RObJosfQnzp1CiNHjoRCoYCjoyO6deuGvLw8LFu2DBkZGSguLgYA9OjRQ9xCDaxnzCT0jJnUYFl23FEce/NLkSoiIiJD+2jNKajVpgvYvyfmI+GEAgMivE12TCJqHovtoVcqlRg9ejQUCgVmz56N/Px8JCcnQ6FQYMGCBYiLi0NiYiIEQUBYWJjY5RpU+nd7sPuJedgb/RGSPvgOVcVlcPR2h7q6pn4bmY0cY35fjLBZ4xq0fWDJdAzb8LapSyYiIh3kKMqxbf9lkx935Y8cS09kjiw20M+cORM5OTmYMWMGFi1aBGdn5/p1MTExCA8Ph0qlQseOHeHi4iJipYZXmqlAfkIKcuNP4uzKbfht8nx49OiEfgterN9GU6PCwZnLETpzHFp36wAA8BsRCZ/hETj02kqxSiciomZYuzUdGo3ph79s3peNgqJKkx+XiO7MIgN9WloaYmNj4eHhgU8++aTRbXr16gUACA8Pv23d1q1bcf/998PR0RGtWrVC//79kZqaatSajakwKR0Zmw7Af2x/eEZ0qV9edCYTqau248Fl/4SDtxv6LXwJx976EpVXdbvBioiITGt/ojj3Q6lUWhw6dVWUYxNR0ywy0G/cuBEajQbR0dFwcnJqdBt7e3sAtwf6ZcuW4YknnsADDzyA7du3Y+PGjRg2bBgqK6XdI3F68SZoVGr0nDOx4fIlm6FRqxG1dyEUh84ia9shkSokIqLm0Gi0SE4rEu34J84pRTs2ETXOIm+KjY+PBwAMHjy4yW1ycnIANAz0GRkZmDNnDhYvXowZM2bUL3/kkUeMVKnplGUrkLXtEDqNH4A2fYJRcKxuHKRWpUZhYjo8wjrhz9j9IldJRER3k3GlFGXltaIdX8wvE0TUOIsM9JcuXQIAdOjQodH1KpUKhw7V9UTfGui/+uorWFtb44UXXjBoPREREVAoFDq1sdbKMBe9DVrHmaWb4T+2P3rOmYjdj78HAGjTJxidJw5G2tpf0fv9qdg+fA7UVTV33lEjggKDUCtoDFovERHdrlruC7g83+i6xI1Rd52O0svDvv7/V/ZOanI7hbICkU9uv235vt+PwsfnOR0qJqLm8PLyQlJSkl5tLTLQl5fXPTGvqWEysbGxUCqVcHZ2hr+/f/3yw4cPo0uXLli/fj0+/PBDXLlyBYGBgXj33Xfx5JNP6l2PQqFAbm6uTm1sBCtAx4e6Ko6kYp33402uv34xF9/6/G/IjdzBDg8smY4TH23A+W92Y+TW93Hfm08hce463Q4MIC8/DzVatc7tiIhIR46OQBNzOdycY7455FayZm97q9pajc6faURkXBYZ6L28vFBSUoLk5GT069evwbr8/HzMmTMHABAWFgZBEBqsy83NxZtvvokFCxbA19cXa9euxVNPPQVPT08MGzZM73p0Za2VAUbu8I5871ncuFyA8+t2AQAOzlqBqH2LcHnnMVw9qtvUZO2827GHnojIBGqsWqOwiXUKZcVd23t52ENuJYNKrYFC2fT9YU3ty9paQJv27ZtTKhHpQJ+8eJOgtcDHvs2cORPLly+Hr68v9u3bh6CgIABAYmIinnnmGWRmZqK2thbTp09v8JTYoKAgXLx4EVu3bsXYsWMBAFqtFj169ICrqyv++OMPk72G2ooqbOj0tNH2335ITwxc+S9sGzob5bn/u8Gp65QR6Pbio9g+ZDZUldXN3l90xnpYO9gZo1QiIrpFXkE52g/7Qe/2V/ZOgk9bR+RcLYfvcN33M+Ehf/y4aIjexyciw7PIWW5iYmLg7u6OK1euICQkBKGhoQgMDETv3r0REBCAIUPqTkR/n+HGzc0NABr0xAuCgGHDhuHs2bOmewEmkBt/Et93ndwgzAPA+XW7sKXfDJ3CPBERmU67No7w9rzzOHlj6tXNXbRjE1HjLDLQ+/j4ICEhAaNGjYKdnR2ys7Ph5uaG1atXIy4uDhcuXABwe6APCQlpcp9VVVVGrZmIiKi5egWLF6p7dfMQ7dhE1DiLDPQAEBwcjB07dqCsrAxlZWU4duwYpk2bhvLycmRnZ0Mmk6F79+4N2owZMwYAsGfPnvplGo0Ge/fuRWRkpEnrJyIiasroQX6iHLe1iw3uD9dxxgYiMjqLvCn2TlJTU6HVahEUFAQHh4aXLEePHo0HH3wQ06ZNQ1FREfz8/PDll18iNTUVe/fuFaliIiKihp56pBNe/+y4yeejnzo2CA72LS46EJk9i+2hb0pKSgqA24fbAHXj5bdv347x48fjrbfeQlRUFC5duoRff/21ftw9ERGR2JwcrDE5KtDkx31pQleTH5OI7o6B/m9cXV2xevVqFBYWorq6GsePH8fDDz9syhKJiIju6u0XwuHWytZkx3tlYjACO7Qy2fGIqPkY6FuYDqP6ou/8hk/C7TxxMKbkb4LfCN4nQEQkFV4eDlj+Rr+7b2gAHds5YcGr/IwgMlctbiBcfHy82CWIyu+RPsj46ff6n518PBEUPQwFSeniFUVERHp58pEA/PLHZfywK7PZbW4+MKo5D6ECAGu5DOs+HAAnB2u9aiQi42txgd7S2bg4YMz+xbCys0FFnhIyW2s4+7VFxqY/cOSNNWgb2QUHZ/31MC1BwP2fvYxj/16LyLmTxS2ciIh0JggC1n04AMWl1dhzOLdZbSKf3N7s/VtZCdgwfxAGRnjrWyIRmUCLG3Jj6WpKK5C5NQHnvozD9uFzcPzdr1GYfAGHX/8C3v27oyAxHVqVGgAQ8uJoFCSeR9GZ5vfsEBGRebG1scK2pcPw2NAOBt2vna0VNn8+FBMe8jfofonI8BjoLZBbd38Up2QBANzDOqH4bN2f/UZE4tLO4wAA1y6+6DCqD04v2SxanUREZBh2tnJs/nwoVr/b3yBDY/r3bIszmx7DmMGG/ZJARMbBQG+B3EI61od497AAFP0V7tsN6oHc+JMAgLZ9guHk2wbjDy/H48dXwvO+QPRb+BK6PPuQaHUTEZH+BEHAtMe74uyWxzBpRADkckHnffh6OWLZG33xx1ePcEYbIgnhGHoL4+DlBmi1qFAUAwDcgjvgzNLN8OgZiOsXc6GqqAIApH+7B+nf/u+JuCM2z8O5NTtweVeiKHUTEZFhdGjnjI2fDsbnhX3w5ZZ0/LQnC+cyr0Gt1ja6vauzDfr3bIsXxnfBqAd9IZezr49IahjoLYxbd//63nkAqCktR9fJD6O6uAyXdx0XsTIiIjIlb08HvPNiT7zzYk9UVKpw+kIR/rxcispqNeRWMrg626BHVzf4t3eGIOjem09E5kPQarWNf2UnUdVWVGFDp6cNtr8xvy/G7vFzUVVUarB93io6Yz2sHeyMsm8iIiIiahp76FuIbYNeFbsEIiIiIjICDpQjIiIiIpIwBnoiIiIiIgljoCciIiIikjDeFGumtFotVJXVYpfRbHJ7W86SQERERCQCBnoiIiIiIgnjkBsiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgn7f2Ec/HOg66AzAAAAAElFTkSuQmCC", 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    " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qc_0.draw(\"mpl\")" - ] - }, - { - "cell_type": "markdown", - "id": "bd1617a1-e793-43a9-a24a-c81c18fd2a1e", - "metadata": {}, - "source": [ - "### Specify an observable" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b791aa42-c485-453b-a110-3c790194adaf", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.184389Z", - "iopub.status.busy": "2025-03-28T16:52:34.184096Z", - "iopub.status.idle": "2025-03-28T16:52:34.187638Z", - "shell.execute_reply": "2025-03-28T16:52:34.187108Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])" - ] - }, - { - "cell_type": "markdown", - "id": "9f56b094-0c6f-456f-9641-1424395fc6bd", - "metadata": {}, - "source": [ - "## Step 2: Optimize\n", - "\n", - "### Create a new circuit where `Move` instructions have been placed at the desired cut locations\n", - "\n", - "Given the above circuit, we would like to place two wire cuts on the middle qubit line, so that the circuit can separate into two circuits of four qubits each. One way to do this is to manually place two-qubit `Move` instructions that move the state from one qubit wire to another. A `Move` instruction is conceptually equivalent to a reset operation on the second qubit, followed by a SWAP gate. The effect of this instruction is to transfer the state of the first (source) qubit to the second (detination) qubit, while discarding the incoming state of the second qubit. For this to work as intended, it is important that the second (destination) qubit share no entanglement with the remainder of the system; otherwise, the reset operation will cause the state of the remainder of the system to be partially collapsed.\n", - "\n", - "Here, we build a new circuit with one additional qubit and the `Move` operations in place. In this example, we are able to reuse a qubit: the source qubit of the first `Move` becomes the destination qubit of the second `Move` operation.\n", - "\n", - "Note: As an alternative to working directly with `Move` instructions, one may choose to mark wire cuts using a single-qubit `CutWire` instruction. The `cut_wires` function exists to transform `CutWire`s to `Move` instructions on newly allocated qubits. However, in contrast to the manual method, this automatic method does not allow for the re-use of qubit wires. See the `CutWire` [how-to guide](../how-tos/how_to_specify_cut_wires.ipynb) for details." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "22f19d29-a182-4758-b5d0-6b66f9a946be", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.189717Z", - "iopub.status.busy": "2025-03-28T16:52:34.189367Z", - "iopub.status.idle": "2025-03-28T16:52:34.502126Z", - "shell.execute_reply": "2025-03-28T16:52:34.501425Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_cutting.instructions import Move\n", - "\n", - "qc_1 = QuantumCircuit(8)\n", - "for i in [*range(4), *range(5, 8)]:\n", - " qc_1.rx(np.pi / 4, i)\n", - "qc_1.cx(0, 3)\n", - "qc_1.cx(1, 3)\n", - "qc_1.cx(2, 3)\n", - "qc_1.append(Move(), [3, 4])\n", - "qc_1.cx(4, 5)\n", - "qc_1.cx(4, 6)\n", - "qc_1.cx(4, 7)\n", - "qc_1.append(Move(), [4, 3])\n", - "qc_1.cx(0, 3)\n", - "qc_1.cx(1, 3)\n", - "qc_1.cx(2, 3)\n", - "\n", - "qc_1.draw(\"mpl\")" - ] - }, - { - "cell_type": "markdown", - "id": "088431d9-de2f-4b5d-90d2-7e7a5947dcb5", - "metadata": {}, - "source": [ - "### Create observable to go with the new circuit\n", - "\n", - "This observable corresponds with `observable`, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an \"I\" at index 4). Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d33d5580-879f-466f-ac87-dcc6a19fbab6", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.504553Z", - "iopub.status.busy": "2025-03-28T16:52:34.504138Z", - "iopub.status.idle": "2025-03-28T16:52:34.507751Z", - "shell.execute_reply": "2025-03-28T16:52:34.506956Z" - } - }, - "outputs": [], - "source": [ - "observable_expanded = SparsePauliOp([\"ZIIIIIII\", \"IIIIZIII\", \"IIIIIIIZ\"])" - ] - }, - { - "cell_type": "markdown", - "id": "a00aeede-3d0f-4589-b1dc-3cd7df9c4085", - "metadata": {}, - "source": [ - "### Separate the circuit and observables\n", - "\n", - "As in the previous tutorials, qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "fc3738c7-2bb2-4d67-ae2b-7a3090b31e6a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.509830Z", - "iopub.status.busy": "2025-03-28T16:52:34.509640Z", - "iopub.status.idle": "2025-03-28T16:52:34.522487Z", - "shell.execute_reply": "2025-03-28T16:52:34.521787Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import partition_problem\n", - "\n", - "partitioned_problem = partition_problem(\n", - " circuit=qc_1, partition_labels=\"AAAABBBB\", observables=observable_expanded.paulis\n", - ")\n", - "subcircuits = partitioned_problem.subcircuits\n", - "subobservables = partitioned_problem.subobservables\n", - "bases = partitioned_problem.bases" - ] - }, - { - "cell_type": "markdown", - "id": "4c8a76eb-31db-486c-b7c2-a5fe3cb732c2", - "metadata": {}, - "source": [ - "### Visualize the decomposed problem" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9c53a862-f762-471a-bda7-b27e88292ac9", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.524677Z", - "iopub.status.busy": "2025-03-28T16:52:34.524311Z", - "iopub.status.idle": "2025-03-28T16:52:34.528872Z", - "shell.execute_reply": "2025-03-28T16:52:34.528334Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'A': PauliList(['IIII', 'ZIII', 'IIIZ']),\n", - " 'B': PauliList(['ZIII', 'IIII', 'IIII'])}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subobservables" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "69df912e-6709-45bb-8eb3-c66a70edefdc", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.530891Z", - "iopub.status.busy": "2025-03-28T16:52:34.530503Z", - "iopub.status.idle": "2025-03-28T16:52:34.747276Z", - "shell.execute_reply": "2025-03-28T16:52:34.746598Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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HTgDg1ziYSwdz/rt6eEtO/rgTgAp17iGoWxi/zlxmWJ0iIiIiInfL4e4IScF8G9TIDT9+jWtxek0kAFU7NGXXO18CUCmsHmXvCeCxbR8C4OlfgTbTnsczwIfohWuNKVxERERExEIKQgKAV2VfMJu5kXAJAN96Qez/YBkVm4Vw9egZMm+kAhC9cG2ewBO+7C0Ofb6SU6sjDalbRERERKQoFIQE+F+zuP/dDQJIv3adugO7knYpiVOrdxpYmYiIiIiI9SkICQBx63cTt3537t8rH34VgJ6bZrDmsTcKnG/1HcaJiIiIiNgrBSG5o+86jDK6BBERERERq1OvcSIiIiIi4nAUhERERERExOEoCImIiIiIiMPRM0KliIunO/1jvjS6DIu4eLobXYKIiIiIOCAFoVLEZDLh6uVhdBkiIiIiInZPTeNERERERMThKAiJiIiIiIjDURASERERERGHoyAkIiIiIiIOR0FIREREREQcjoKQiIiIiIg4HAUhERERERFxOApCIiIiIiLicBSERERERETE4SgIiYiIiIiIw1EQEhERERERh6MgJCIiIiIiDkdBSEREREREHI6CkIiIiIiIOBwFIRERERERcTgKQiIiIiIi4nAUhERERERExOEoCImIiIiIiMNxMboAsR6z2UxmSprRZVjExdMdk8lkdBkiIiIi4mAUhEqRzJQ0FgU/bXQZFukf8yWuXh5GlyEiIiIiDkZN40RERERExOEoCImIiIiIiMNREBIREREREYejICQiIiIiIg5HQUhERERERByOgpCIiIiIiDgcBSEREREREXE4eo+QULlNA8KXv5VnWMb1FK4djydm6WYOz/0Bc1a2QdWJiIiIiFifgpDkOr48grgNe8BkwtO/ArWfaE+rt56hfEg1to/51OjyRERERESsRkFIcl08cILjyyJy/46ev4beER8Q+tSD7JmymLSL1wysTkRERETEevSMUAHWrl1LeHg4fn5+eHl50ahRIyZPnkx6errRpdlMZkoaF/YcxeTkRLmgSkaXIyIiIiJiNQpC+Xj//ffp2rUra9aswdvbmzp16hAVFcX48ePp2LEjKSkpRpdoM941cgJQ2pVkgysREREREbEeBaE/2LlzJ2PGjMFkMjF//nxiY2PZu3cvUVFR1K5dm23btjFu3DijyywWLp5uuPt64+5Xjgp1qxM26Vn8GtXiwp6jXDseb3R5IiIiIiJWo2eE/uCdd97BbDYzePBgBg4cmDs8ODiYuXPn0r59e2bPns348eMJCAgwsFLraza2H83G9sszLHbVDn4ZN8egisRox05dY+630Rw9eY3MrGwCfD158uFadGhZBZPJZHR5IiIixSo728z6HWf4es0JLlxOxc3ViTo1yvNsnzrUqOZtdHlyl0p9EEpMTOS9995j+fLlxMXF4e/vT58+fZg0aRIjR47kiy++4MMPP2TEiBEkJSWxdu1aAIYOHXrbstq1a0doaChHjhxhxYoVPPvss7benGIV/e+1xH6/HSdXF3zqVqfh8F6UqeJHVtrvz0U5ubnQfe00Tnwbwf4PlucOv3/mcDz8K7C+/0QjShcrOxGXxAvvbmXNtjO3jft8WTT1alVg+uhWPPLAPQZUJyIiUvyWrTvBuFm7OHry9s6iJs35lUfbVWf2hPuoVqmMAdWJNZTqpnH79u2jUaNGTJs2jYSEBOrXr09GRgazZs2ib9++HD58GICmTZsCsHfvXtLT03F3d6dFixb5LvP+++8HYMeOHTbZBlu6djyB+IgDnNmwl4Mff8dPA6dQsWkwbaYOy50mOz2TLSM/pNHIPvjUDwKgenhLAru0YOsrHxtVuljR4eNXaP30inxD0K3TdH9pHQtXHLVhZSIiIrbx8X8O8fjoDfmGIACzGb7/+RStn/6e43HqVbekKrVBKDExke7du5OQkMDo0aOJj49nz549JCQkMHXqVFatWkVkZCQmk4nGjRsDcOTIEQCCgoJwccn/ZllwcHCeaUuzC7uiiVm6mZq92uLfok7u8Iv7j/Pb7BU8MOslvKr40mba8/wyfg4p5y4bWK1YQ9L1dB5+cQ3nL6X+6bTZ2WaGvBHBtn3nbFCZiIiIbazbfoYRk7cXatq4c9d55MW1pKVnFXNVUhxKbRAaOXIkcXFxjBgxgunTp+Pt/Xs7zrFjx9KkSRMyMzOpUaMG5cqVA+Dy5ZwTeR8fnwKXe3PczWlLu19nLCU7M4tmY/rmHT5zGdlZWfRYN42ErQc58d1WgyoUa1q0KoaTZwvfQ2BmlpmpX+wvxopERERsa/KcXzGbCz99dOxVlq47UXwFSbEplUHo8OHDLFmyhIoVKzJ58uR8p2nevDkATZo0yR2WmppzFdzNza3AZbu7uwPk6UL7ZuBq1aoV7u7upeoh8qTYBE58t5Wq7RoTEFYvd7g5M4sLkdF4+JXn2JKNBlYo1mI2m/l4yWGL51u5+TQnzyYVQ0UiIiK2dfj4FTZGWt5TblGOn2K8UhmEFi9eTHZ2Nv3796ds2bL5TuPp6QnkDUIeHh4Ad3xpalpaWp75AY4dO8ayZcuoXLkyLVu2vOv67c3+D3Lu/tx6VyggrB61+3bk8NwfaPX2IJw9Cg6PUjLEX7jBgaOW3+nMzjazbvvZYqhIRETEttZsjSvSfNv2nedacsHnj2KfSmWvcRs2bACgY8eOBU4TF5ezo98ahArT7C2/5nPt2rUjPj7n6sGbb77J1q3WaSbWokULEhISCj29q9mJN2hl8XoStv/G/CqPFzj+6tEzLAz8PQS5eHlw/8zh7J64iKgFa3j427e5d9xTRL4x3+J1h4aEkmHKtng+W4uv8Ao4lSc+IZ7AwECjyykWGU4VocJLRZp39N//wZsvb7NyRSIiYi8c4TgIcM2jA3gVfP54J6H1m+KSrY4TbK1y5crs2rWrSPOWyiB08uRJIKfTg/xkZmbmhpVbg1BoaGju/JmZmfl2mBATE5NnWgAnp+K5sZaQkMCZMwX33PVHbiZnqFQspeTR8s0BJJ86T9T81QBsefkjeqyfzqkff+HcDstuDZ+NP0u6uQQ8YOidBU6QnZVl0XdSorimQIWizXrt8nmuXSqln4uIiDjGcRCgYiJ4FW3Wc2dPQdZ169YjxapUBqHr13N2wluf47nVkiVLSExMxNvbm5o1a+YOb9asGW5ubqSlpbFr1y5at25927xbtmwBICwsrBgqz6ty5coWTe9qdoJivrlSrVMzavZoy3cPjs4dlnTyHLsnLqLtjOGs6DSazJS0Qi+vapWqJeOOkLMz2YCTszNVqlUzupxiYcaJhOxrZDuVs2AmM5hM+Je9jptn6fxcRETEMY6DAGkuSSRC7vGtsJyzLlKpsg+mol5RlCKz9Hz5VqUyCFWuXJnLly+zZ88e2rRpk2dcfHw8Y8aMAaBx48Z5Ojbw9vamS5curFq1is8///y2ILR582aOHDmCm5sbPXv2LPbtsPQ2X8aNVBYFP11M1eQ4s2EvX9UdeNvwqPmrc+8QWeLI0SO4enlYo7RiFdh5MWfO36BK5SrEHSxa++GS4M2P9/DWJ3sLP4PJRFgjf3Ysiiy+okRExHCOchw0m8007LOcQzFXLJrvvb8/zCsDxhZPUVJsSmVnCZ07dwZg6tSped73ExkZSceOHUlMTAR+f5HqrSZMmIDJZGLevHksWLAgd3hMTAxDhgwBYNiwYQQEBBTjFogYY+hjdfDysOz6yP893aCYqhEREbEtk8nEy/0tO66VK+vKMz1D/3xCsTulMgiNHTsWPz8/Tp8+TYMGDWjUqBEhISG0atWKWrVq0alTJyDv80E3tW7dmilTpmA2m3nmmWeoUaMGzZo1o27duhw7doywsDCmTJli600SsYlqlcrwn/c64uxcuOYAI5+qT9/wWsVclYiIiO0MfawOg3qFFGpaN1cnlv+zM77l3Yu5KikOpTIIBQYGEhERQbdu3fDw8CA2NhZfX18+/fRTVq1alXuXKL8gBDlBavXq1XTp0oWrV68SFRVFaGgoEydO5Oeff8bLq4hP0YmUAN07VGfVRw/h71Nwk0U3VyfefKEZM//eulS9N0tERMRkMjHnzQcY80wjXO5wYbBqgBfrPg3nwdZVbVidWFOpfEYIoF69eqxcufK24cnJycTGxuLk5ETDhg0LnL9r16507dq1OEsUsVtd2wZyel0/lq2L5fPl0UTsTiAr24yLi4l3hjdncK9QAvw8/3xBIiIiJZCTk4n3XmnFqL82ZM7yaL5ec4JDMZfJNoO7qxNfTu5Az45BuLqWynsKDqPUBqGC/Pbbb5jNZkJDQ616Z2fp0qUAHDp0KM/fNWrUoEWLFlZbj4ituLs581S3YJ7qFpz7kGwlX09eHZL/nVQREZHSpoq/F/8Y1ox/DGuWeyys6OPB4w/V/POZxe45XBA6cOAAUHCzuKJ64okn8v174MCBzJ8/36rrMkJQt9ZUeaARO179PHdY7b4duX/mcDYMmsqp1eo1TERERERKDgUhKzGbzVZdnr2p/kgYMd9syv27bKA/of07c35XtHFFiYiIiIgUkYKQAOBWzoueG2fg7OHGjbOJOLm74l29EjFLf2b7q59TqWUdtrz8Uc7EJhP3vf8Cv0yYS8s3bn+nkIiIiIiIvXO4ILRhwwajS7BL6dducPzbCDKup7J/xlKqdmhC45F92Pa3T6javgnnI6MxZ2YB0GBYd85HRnFx/3GDqxYRERERKRp1dSG5fBvW5NKBEwD4NQ7m0sGc/64e3pKTP+4EoEKdewjqFsavM5cZVqeIiIiIyN1yuDtCUjDfBjVyw49f41qcXpPTAULVDk3Z9c6XAFQKq0fZewJ4bNuHAHj6V6DNtOfxDPAheuFaYwoXEREREbGQgpAA4FXZF8xmbiRcAsC3XhD7P1hGxWYhXD16hswbqQBEL1ybJ/CEL3uLQ5+vVK9xIiIiIlKiKAgJ8L9mcf+7GwSQfu06dQd2Je1SEqdW7zSwMhERERER61MQEgDi1u8mbv3u3L9XPvwqAD03zWDNY28UON/qO4wTEREREbFXCkJyR991GGV0CSIiIiIiVqde40RERERExOEoCImIiIiIiMNREBIREREREYejICQiIiIiIg5HnSWUIi6e7vSP+dLoMizi4uludAkiIiIi4oAUhEoRk8mEq5eH0WWIiIiIiNg9NY0TERERERGHoyAkIiIiIiIOR0FIREREREQcjoKQiIiIiIg4HAUhERERERFxOApCIiIiIiLicBSERERERETE4SgIiYiIiIiIw1EQEhERERERh6MgJCIiIiIiDkdBSEREREREHI6CkIiIiIiIOBwFIRERERERcTgKQiIiIiIi4nAUhERERERExOEoCImIiIiIiMNREBIREREREYfjYnQBYj1ms5nMlDSjy7CIi6c7JpPJ6DJERERExMEoCJUimSlpLAp+2ugyLNI/5ktcvTyMLkNEREREHIyaxomIiIiIiMNREBIREREREYejICQiIiIiIg5HQUhERERERByOgpCIiIiIiDgcBSEREREREXE4CkIiIiIiIuJw9B4hoXKbBoQvfyvPsIzrKVw7Hk/M0s0cnvsD5qxsg6oTEREREbE+BSHJdXx5BHEb9oDJhKd/BWo/0Z5Wbz1D+ZBqbB/zqdHliYiIiIhYjYKQ5Lp44ATHl0Xk/h09fw29Iz4g9KkH2TNlMWkXrxlYnYiIiIiI9egZoQKsXbuW8PBw/Pz88PLyolGjRkyePJn09HSjS7OZzJQ0Luw5isnJiXJBlYwuR0RERETEahSE8vH+++/TtWtX1qxZg7e3N3Xq1CEqKorx48fTsWNHUlJSjC7RZrxr5ASgtCvJBlciIiIiImI9CkJ/sHPnTsaMGYPJZGL+/PnExsayd+9eoqKiqF27Ntu2bWPcuHFGl1ksXDzdcPf1xt2vHBXqVids0rP4NarFhT1HuXY83ujyRERERESsRs8I/cE777yD2Wxm8ODBDBw4MHd4cHAwc+fOpX379syePZvx48cTEBBgYKXW12xsP5qN7ZdnWOyqHfwybo5BFYkY72pSOpG/XSD5RgZlvVxpXr8iPuXcjS5LRETEZmJOX+NI7FXSM7Px9/EgrJE/zs4l/35KqQ9CiYmJvPfeeyxfvpy4uDj8/f3p06cPkyZNYuTIkXzxxRd8+OGHjBgxgqSkJNauXQvA0KFDb1tWu3btCA0N5ciRI6xYsYJnn33W1ptTrKL/vZbY77fj5OqCT93qNBzeizJV/MhK+/25KCc3F7qvncaJbyPY/8Hy3OH3zxyOh38F1vefaETpIlZ3KOYys746xJcrj3E9JTN3uKeHM08+HMzIp+rTpI6fgRWKiIgUH7PZzH83nORf/znMT7+czTOuepUyDHu8Ls//pR6+5UvuxcGSH+XuYN++fTRq1Ihp06aRkJBA/fr1ycjIYNasWfTt25fDhw8D0LRpUwD27t1Leno67u7utGjRIt9l3n///QDs2LHDJttgS9eOJxAfcYAzG/Zy8OPv+GngFCo2DabN1GG502SnZ7Jl5Ic0GtkHn/pBAFQPb0lglxZsfeVjo0oXsaqla09wb9//8uk3UXlCEEBKahZffHuEFv2+49/fHzWoQhERkeKTmZnNkDci6DPqp9tCEMCp+Ou89uFuWj75HUdPXjWgQusotUEoMTGR7t27k5CQwOjRo4mPj2fPnj0kJCQwdepUVq1aRWRkJCaTicaNGwNw5MgRAIKCgnBxyf9mWXBwcJ5pS7MLu6KJWbqZmr3a4t+iTu7wi/uP89vsFTww6yW8qvjSZtrz/DJ+DinnLhtYrYh1rN0WR7+/byQt/c4vEc7MMjNwwma+/SnWNoWJiIjYyEuTtzPvv39+se94XBJdhq0mIfGGDaqyvlIbhEaOHElcXBwjRoxg+vTpeHt7544bO3YsTZo0ITMzkxo1alCuXDkALl/OOZH38fEpcLk3x92ctrT7dcZSsjOzaDamb97hM5eRnZVFj3XTSNh6kBPfbTWoQhHryc4288K728jKMhdqerMZhk/aTkbGnUOTiIhISRF58AKffBNV6OlPnk3m3c/2FV9BxahUBqHDhw+zZMkSKlasyOTJk/Odpnnz5gA0adIkd1hqaioAbm5uBS7b3T2nHeStXWgvXbqUxx57jKCgILy8vKhbty6vvfYaycklv8vppNgETny3lartGhMQVi93uDkziwuR0Xj4lefYko0GVihiPWu3neF4XJJF88RfuMF3G08WU0UiIiK2NfvrwxbPs/D7YyTfyCiGaopXqQxCixcvJjs7m/79+1O2bNl8p/H09ATyBiEPDw+AO740NS0tLc/8ANOnT8fZ2ZlJkybx448/8sILLzB79mzCw8PJzi75V4r3f5Bz9+fWu0IBYfWo3bcjh+f+QKu3B+HsUXB4FCkp5q8oWpPXBSv0rJCIiJR8aelZLP7xuMXzJV3PKJFNxUtlr3EbNmwAoGPHjgVOExcXB+QNQoVp9pZf87nvv/8ef3//3L/bt2+Pv78//fv3Z8uWLbRr164IWwEtWrQgISGh0NO7mp14g1YWrydh+2/Mr/J4geOvHj3DwsDfQ5CLlwf3zxzO7omLiFqwhoe/fZt7xz1F5BvzLV53aEgoGSb7D4vxFV4Bp/LEJ8QTGBhodDk25yjbf8F7CLhWt3i+NRt2Ehg4uBgqEhGxD45yHLgTR/gMskzepPr8rUjzjhj1OuNeiLByRX+ucuXK7Nq1q0jzlsogdPJkTjOVoKCgfMdnZmaydWvOMy23BqHQ0NDc+TMzM/PtMCEmJibPtECeEHTTzV7nzpw5U5RNACAhIcGi+d1MzlCpyKsrtJZvDiD51Hmi5q8GYMvLH9Fj/XRO/fgL53ZYdjv1bPxZ0s1ZxVGmdXlngRNkZ2Xd1XdaYjnK9tdKA1fLZ8vIyCjdn4uIiKMcB+7EET4Dl/JQ8KPyd3Tt2lWuXShZn0upDELXr18H8j7Hc6slS5aQmJiIt7c3NWvWzB3erFkz3NzcSEtLY9euXbRu3fq2ebds2QJAWFjYHWvYuDHnuZl69erdcbo7qVy5skXTu5qdoJhvrlTr1IyaPdry3YOjc4clnTzH7omLaDtjOCs6jSYzJa3Qy6tapWrJuCPk7Ew24OTsTJVq1Ywux+YcZfsvuaSS/6/GnXk438CvFH8uIiKOchy4E0f4DMw4E29Ox2yy/JEHn7Lg5Wb7z8XS8+Vbmcxmc+G6RypB6tevz+HDh/noo48YPnx4nnHx8fE0b96c+Ph42rZtmxtsbnr00UdZtWoVgwcPZu7cuXnGbd68mfbt2+Pm5sbp06cJCAjId/1nzpyhWbNmNG/enB9//NG6G3cHGTdSWRT8tM3WZw39Y77E1cvD6DL+VGDnxZw5f4NqAV7ErX/S6HJszlG2f+22OLo+v8bi+Zb980H6dK5h/YJEROyEoxwH7sRRPoNn34hg7reWPTNbrqwrZ9Y/SVmvIjSrMFCp7Cyhc+fOAEydOjXP+34iIyPp2LEjiYmJwO8vUr3VhAkTMJlMzJs3jwULFuQOj4mJYciQIQAMGzaswBCUnJxMz549cXNz44svvrDWJomIDXRuXY3a1ctZNE/VAC96dLD8uSIRERF79GJfy1szDewRUuJCEJTSIDR27Fj8/Pw4ffo0DRo0oFGjRoSEhNCqVStq1apFp06dgLzPB93UunVrpkyZgtls5plnnqFGjRo0a9aMunXrcuzYMcLCwpgyZUq+601JSaF79+6cOHGCtWvXUqVKlWLdThGxLicnE59MaIuLs6nQ03/6j7a4uJTKn1IREXFA99avyIgn6xd6+lqB3kwY2rT4CipGpfLoHRgYSEREBN26dcPDw4PY2Fh8fX359NNPWbVqVe5dovyCEOQEqdWrV9OlSxeuXr1KVFQUoaGhTJw4kZ9//hkvL6/b5snIyODxxx9n165d/Pjjj9SvX/gdSETsx4Otq7L0nw/i4e58x+ncXJ1YNLk9j7bX3SARESldZo4N47nH6/zpdCFB5Vj3WTgBfp5/Oq09KpWdJUBOJwUrV668bXhycjKxsbE4OTnRsGHDAufv2rUrXbt2LdS6br6z6KeffuKHH36gVSvLu7AWEfvRs2MQv37Tmw8XH2LBiqMkXf/9JXEm4Lkn6vLSk/VpULuIXeuIiIjYMWdnJz75R1u6t6/Ov/5zmNVb4/KMrxXozQt/qcezfUKpUM7doCrvXqkNQgX57bffMJvNhIaG5ntnpyiGDx/ON998w6uvvoqXlxc7duzIHRccHJxv99oiYt9Ca5Tnw3FtmPxyC3YfSqTPqJ+4dDWNyhU9+eQfbY0uT0REpFiZTCYebV+dR9tX5+TZJJr3+46LV9Lw93Hn6MoncHIqXDNye1Yqm8bdyYEDB4CCm8UVxc2e4aZMmUKbNm3y/LNq1SqrrcdIQd1a03rK0DzDavftyDPxS6ke3tKgqkSKX1kvV9q3qILn/5rKlYYffhEREUsEVfXGwy3nOOjm6lxqjoUOd0eoOIJQbGys1ZZlr6o/EkbMN5ty/y4b6E9o/86c3xVtXFEiIiIiIkWkICQAuJXzoufGGTh7uHHjbCJO7q54V69EzNKf2f7q51RqWYctL3+UM7HJxH3vv8AvE+bS8o2BxhYuIiIiIlIEDheENmzYYHQJdin92g2OfxtBxvVU9s9YStUOTWg8sg/b/vYJVds34XxkNObMLAAaDOvO+cgoLu4/bnDVIiIiIiJF43DPCEnBfBvW5NKBEwD4NQ7m0sGc/64e3pKTP+4EoEKdewjqFsavM5cZVqeIiIiIyN1yuDtCUjDfBjVyw49f41qcXhMJQNUOTdn1zpcAVAqrR9l7Anhs24cAePpXoM205/EM8CF64VpjChcRERERsZCCkADgVdkXzGZuJFwCwLdeEPs/WEbFZiFcPXqGzBupAEQvXJsn8IQve4tDn6/k1OpIQ+oWERERESkKBSEB/tcs7n93gwDSr12n7sCupF1K4tTqnQZWJiIiIiJifQpCAkDc+t3Erd+d+/fKh18FoOemGax57I0C51t9h3EiIiIiIvZKQUju6LsOo4wuQURERETE6tRrnIiIiIiIOBwFIRERERERcTgKQiIiIiIi4nAUhERERERExOGos4RSxMXTnf4xXxpdhkVcPN2NLkFEREREHJCCUCliMplw9fIwugwREREREbunpnEiIiIiIuJwFIRERERERMThKAiJiIiIiIjDURASERERERGHoyAkIiIiIiIOR0FIREREREQcjoKQiIiIiIg4HAUhERERERFxOApCIiIiIiLicBSERERERETE4SgIiYiIiIiIw1EQEhERERERh6MgJCIiIiIiDkdBSEREREREHI6CkIiIFMqVK1d488032bRpk9GllBhXr17lpZdeolq1anh4eNCgQQNmz56N2Ww2urQi0T5gmfj4eF577TXCw8Px9/fHZDLxzDPPGF2WiPyPgpCIiBTKlStXeOutt3QSXEjp6el06dKFTz75hL59+/Lhhx9Sp04dXnzxRd566y2jyysS7QOWiY6OZtKkSRw6dIiWLVsaXY6I/IGL0QWIiIiURnPmzCEyMpJZs2bx0ksvATB06FAee+wxJk2axKBBgwgKCjK4SilOzZs35/z58/j7+5OYmIi/v7/RJYnILXRHqBQxm81k3EgtUf+U1OYhIiVBeno67733Hk2bNsXLy4vy5cvTokULPvroo9xpnnnmGUwmU77z39qMZ9OmTdSsWROAt956C5PJhMlkokaNGhbVdHOZGzZsoE2bNnh5eREYGMjUqVMBuHz5MkOGDCEgIAAvLy8effRRzp49e9tyYmNj+etf/0qlSpVwd3cnODiY8ePHc+PGjdxpZs+ejclkYsWKFbfNn52dTWBgIE2bNs0zfNeuXfTu3ZuKFSvi7u5OnTp1mDhxIpmZmRZtJ8BXX32Fl5cXQ4cOzTP8//7v/8jIyGDJkiUWL9NS2geM3Qe8vb0VfkTsmO4IlSKZKWksCn7a6DIs0j/mS1y9PIwuQ6TUSU9Pp2vXrmzatImHHnqIp59+Gg8PDw4cOMDy5csZMWKERcurV68eM2bMYNSoUfTu3Zs+ffoAULZsWYtr27t3L99//z3PPfccAwYM4Ouvv+bVV1/Fw8ODBQsWUKNGDd58802OHTvGrFmzGDBgAOvXr8+d/+TJk7Rq1YqrV6/y4osvEhISwqZNm5g8eTJbt27lp59+wsXFhX79+jFq1CgWLlxIjx498tTw008/cebMGUaPHp07bNWqVfTp04fatWszevRofH192b59O6+//jr79u3jm2++KfQ2Zmdns2fPHu699148PPL+xrVq1QqTyURkZKTFn50ltA8Yuw+IiP1TEBIRKYVmzpzJpk2bGDduHJMmTcozLjs72+LlVapUiV69ejFq1CgaN27M008X/aLLgQMH2L59O2FhYQAMGTKEoKAgRo0axYgRI5g1a1ae6WfMmEF0dDR16tQBYPz48Vy4cIFVq1bxyCOPAPDiiy8yZswYpk+fzoIFCxgyZAg+Pj50796d77//nsuXL+Pj45O7zIULF+Li4kL//v0BSE1NZciQIYSFhbFhwwZcXHIOj8OGDaNJkya88sorbNq0iQ4dOhRqGy9fvkxKSgrVqlW7bZy7uzsVK1bkzJkzln1wFtI+YOw+ICL2T03jRERKoUWLFuHj48Prr79+2zgnJ2N/+tu0aZN7Agzg5uZGq1atMJvNjBw5Ms+0DzzwAABHjx4Fck7gV6xYQbNmzXJPgG8aN24cTk5OfPvtt7nDBg4cSFpaWp5maMnJyXz77beEh4cTEBAAwLp16zh37hyDBg3iypUrJCYm5v5zcz1r164t9DbebJ7l7u6e73gPD488TbiKg/aBHEbtAyJi/3RHSESkFDp69ChNmza9rVmWPahVq9Ztw25eqb/5DMofh1+8eBGACxcukJycTIMGDW5bhq+vL1WqVOH48eO5w26e6C5cuJDnn38egGXLlnH9+nUGDBiQO93hw4cBGDx4cIF1nzt3rlDbB+Dl5QVAWlpavuNTU1Nzpyku2gdyGLUPiIj9UxASEXFgBT0kX5QHwwvL2dnZ4nFF7VjFxcWFp556ipkzZ3Ls2DFq167NwoUL8fHxyfPMyM3lT5s27baH52+qWrVqodfr4+ODp6dnvs3f0tLSSExMpH379pZtTDHRPpB3+dbaB0TE/ikIiYiUQqGhoURFRZGWllZg8yzIuYIOcOnSpdz/BvJcUb+poBNmW/L398fb25vffvvttnGXL18mPj7+tpPYgQMHMnPmTBYuXMjQoUPZtGkTzz33XJ7PJSQkBIAyZcrQuXPnu67TycmJe++9l7179972HezcuROz2UyLFi3uej13on3gd0bsAyJi//SMkEg+zl9M4ceI07z72V4G/WMzl67mNG+5kpTOnGXR7D2cSHpGlsFVFp/UtEwiD17gk68P8+K7W3O3//K1NKZ+8Svrd5zJHVYamc1mjsdd45u1J3h1ZiQDxv+cu71Xk9NZsvo4x05ds+vu3/v378/ly5d59913bxt3a92hoaEAeXrkAnj//fdvm+9m72CXLl2yZqkWcXJyonv37uzdu5fVq1fnGTdlyhSys7Pp3bt3nuFNmzalcePGfPnll/z73/8mOzubgQMH5pmma9euBAQEMGXKlHy3LyUlhaSkJItqffLJJ7lx4wafffZZnuEzZ87ExcWFvn37WrQ8S2kf+J1R+0BJlp1tJvrEFb5aFcPfpv+S+xt46Voar83axbJ1J4g9k2TXv4N3K/FyKmu2xjHp830Mfj3vucBnS6PYfSiRtPTSey6Qlp7F7kOJfLY0iuETt+U5F5g851fWbovj4pVUg6u8O7ojJPI/6RlZLF8fy8dLDhOxJ/924NdTMhn61hYAynu78UyPEF74S13q1Kxgw0qLz76oi8z++jBfrozhRurtzWJupGbx6sxduX93va8aL/atR7d29+DsXPKvq1y+lsb8744y++vDHD15Ld9pkm9k0m/sRgBqVvPm+SfqMrh3KBV97Os5jJdffpnvv/+ed999l8jISB566CE8PDz47bffiI6Ozj3pffLJJxk/fjzPPfccUVFR+Pr6snr1ahITE29bpp+fH7Vr1+Y///kPwcHBVKpUiTJlytC9e3ebbtukSZNYt24dvXr14sUXX6R27dps3ryZJUuW0K5du9tOcCHnjsDo0aOZOnUqoaGhtG7dOs/4MmXKsHDhQnr16kWdOnUYPHgwtWvX5sqVK0RFRbF8+XK+/fZbi3oMGzp0KPPmzeOVV14hNjaWevXq8cMPP/Dtt98yYcIEi9+/YyntA3kZsQ8AuUH0ZucY+/fvzx3Wrl072rVrV4RPoPgkJN5gzvJoPv0mmrhz128bn5KaxaQ5v+b+Xa9WBV7sW4+/Plqb8t5utiy1WGRkZLNi00k+XnKYDTvj853mekomw97eCoB3GVcGdK/NC3+pR4PaPvlOX9IcPHqJ2V9HsfD7YyTfyLht/I3ULMbP+v1coFOrKgzvV58eHarj4lKyzgVM5tIc5R1Mxo3UIr1HqHKbBoQvfyvvsq6ncO14PDFLN3N47g+YsyzvarUw7OE9Qmazma/XnOD/3ttBQmJKkZbR58Ea/Ou1NlSuWLwPPxeX43HXGPb2VtbvuP2lhYVRs5o3syfcR9e2gVauzDbSM7KYPOdXps7bT0qq5Vf33N2cGfXXBrzxfDM83O3n+lJqairvv/8+X331FTExMXh4eBASEsKgQYN48cUXc6f75ZdfeOWVV9i9ezdly5alT58+vPfee/j4+DBw4EDmz5+fO+3OnTsZNWoU+/bt48aNGwQFBREbG1vomkwm023LhJyXei5YsOC2q8ubNm2iY8eOzJs3L/fFngAnTpzg9ddfZ82aNVy5coXAwED69evHhAkT8u2E4Ny5cwQGBpKZmcm7777La6+9lm99Bw8eZMqUKWzcuJELFy7g4+NDcHAwDz/8MMOHD8/TdKwwrly5woQJE1i+fDkXL14kODiYF198keHDh9ukmZn2gd8ZtQ/c6Xt+4403ePPNNy1aXnG5kZLJax/u4l//OUxGpuXH/DKeLvxjWFNGD2hU4k6Gb/pu40lGTNqebwAsjEfb3cPsCW0JrFzGypXZxumEZF54dxurNp8u0vz3VC7DR+Pa0KNjkJUrKz4KQqXI3Qah48sjiNuwB0wmPP0rUPuJ9vjUCyL6y3VsH/NpMVRsfBA6fzGFFyduY9n62Ltelk85Nz4c14b+3WrffWE2kp1tZvbXh/n7jEiup9z9g9FDeofy/t/CStRVwV+jLzJwwmZ+jb77pj71alVg/jvtaNVIb5IXkZIjYncCg17fTMzpu2/616qhP/PeeYD6wSXn7silq2m8NHk7X/0Qc9fLKlfWlRljWjOoV4hdPFNXGGazmS++PcIr03/hWvLtd4As1b9bMLNebYNv+YKfTbQXCkKlyN0Goci3FvLbJytyh7t4utM74gO8qvjyn8bPknYx/6ZCd8PIIHTs1DU6P/cjJ88mW3W5fx/cmMkvt7D7H8DMzGyGvrWF+d8dtepyG9b2Ye2n4VTxt/+7Yz9EnOaxV34iNc16bbxdXZxYPLUDj3Wp+ecTi4gYbOGKowx+I4KsLOudDpb1cmXFrM50bGX/veydPJtEl2GrC2wOXVQjnqzPB39vjZOTfZ8LZGebeWnydj5ectiqyw0NKs+6z8KpXqWsVZdrbfbThkPsTmZKGhf2HKVG9zaUC6rEhWIIQkY5EZdE+8GrOHve+i80nPrFfrKyzLz3Sku7DUPZ2WaGvBHBwu+PWX3ZB49dpuOQH4iY3w1/X0+rL99aVm+Jo9fL64vUBOROMjKz+cuYjSx930TvB2tYddn26sKFC2Rl3TlMli1bNvdB+5IsOTmZ5OQ7XzxxdnbG39+x7gpqH8irpOwD//7+KAMnbLb6cpNvZPDI8LWsmd2Vdi2qWH351nLm3HU6DP6BWCtfEAX4aPEhsrKy+ddr99ntuYDZbObFidv49Jsoqy/7yMmrdBj8A1sWdKNqgP02FSyZjThtYO3atYSHh+Pn54eXlxeNGjVi8uTJpKenG12aTXnXqARA2hXr/0gYJSU1k24j1hZLCLpp+oIDzF1+pNiWf7cmzdlXLCHopujYq/Qe9RNZxfRs2d2KPnGFPq9YPwTdlJ1tpt/YjfwafbFYlm9vWrZsSZUqVe74z/Tp040u0yqmT5/+p9vasmVLo8u0Oe0DJW8f2P7rOQa9HlFsy09Ny6L7yHWcirfP84eMjGy6v7SuWELQTbO/juKDL2/v5t1ezPzyt2IJQTedOJNE95fWkZFhn+cCoDtC+Xr//ff529/+BkBQUBDVq1fn4MGDjB8/npUrV7J+/Xo8Pe33SndRuXi64e7rnfuMUJ0BD+HXqBYX9hzl2vH8e04piV7/1x4OH79i0TyRi3tQuaIXCYk3aPnkij+fAXhl+i88dF81u7st/Gv0Rd76ZK9F8xRl+7fuPccHi37jlQGNilJmscnKymbQ6xEWd4pg6WeQnpHNoH9E8MuiHri6lu5rTosWLSIl5c4djdSqVeu2YZGRkYVeR2JiIsuXL6dPnz5UrFix0PNZ+4R0wIAB3H///XecpjQeH/5MUfYBS75/KNo+UByBpDTsAympmTwzwbLmcEU5DlxLzuDZNyNY80m43d0VmTRnH3ujLLtYVZTPYNysXTzywD2E1ihflDKLTfSJK3l6fiuMomz/nsMXmfLFr/xjWLOilFnsFIT+YOfOnYwZMwaTycS8efNyu+CMiYkhPDycbdu2MW7cOGbOnGlsocWg2dh+NBvbL8+w2FU7+GXcHIMqsr5f9p/nn/8+aPF8lSt6EVjJslu7Sdft7wCQmZnNM//YTGamZW3Bi7L9AK99uJvu7asTEmQ/B4BZXx1i+6/nLZ6vKJ/B3ij7PgBYS9u2bYt9HYmJicyZM4d27dpZFISsrVatWvmGOkenfaBkeePjPRw5edWieYp6HFi3/Sxzlx/h2cfqWDxvcdl/5BLvfr7P4vmK8hmkpmUx6PXNRMx/1G6eF8rONjPo9QiLn48t6j7wzqf76NUxiEahlvW4aAul+zJlEbzzzjuYzWYGDRqU5z0EwcHBzJ07F4DZs2dz/rzlJ1L2Lvrfa1nzl7dY138iu975N6mXkihTxY+stN+bAzq5udBz0wwav9wnz7z3zxxO50X5d0VqT979bB/Z2bbrH2Td9rPs2G8/+8r3P59iX5TtXoSYmpbFtPkHbLa+P5OWntNNti1NX3CA6/m8h0FExAgXr6Ty4eJDNl3nO5/ttaum0pPm7LP4guDd2LbvPBt2Fu31FMVh/Y4zRbogWFQZmdlM+WK/zdZniVIfhBITExk7diy1a9fGw8ODe+65h5dffpnr168zZMgQTCYTH330EQBJSUmsXbsWyHkR3h+1a9eO0NBQ0tPTWbGicLcES5JrxxOIjzjAmQ17Ofjxd/w0cAoVmwbTZuqw3Gmy0zPZMvJDGo3sg0/9nH7iq4e3JLBLC7a+8rFRpRdK7JkkVkUUrW/8u2HtnljuhhG1LFoVw5VraTZfb36WrYvlwmXbvgX7WnKGVbpkFRGxhvnfHbVqT5mFcSr+Oj9ExNl0nQVJSLxhlVdmWMrRzwW+WXuC8xeL9q7G4lSqg9C+ffto1KgR06ZNIyEhgfr165ORkcGsWbPo27cvhw/n7AhNmzYFYO/evaSnp+Pu7k6LFi3yXebNdsE7duywyTYY6cKuaGKWbqZmr7b4t/j9lvbF/cf5bfYKHpj1El5VfGkz7Xl+GT+HlHOXDaz2z32+LBojOov/es0JLl01PggcO3WtyC9MvRs3UjP598ri65jBEp98Y8yBaPbXxfcwqohIYZnNZj4pxofj78So398/mvffoza9G3TTdxtPcfZ80V7Uak1xCdf5/mfbXxTOyMzmi//aXydSpTYIJSYm0r17dxISEhg9ejTx8fHs2bOHhIQEpk6dyqpVq4iMjMRkMtG4cWMAjhzJ+YKCgoJwccn/8ang4OA805Z2v85YSnZmFs3G9M07fOYysrOy6LFuGglbD3Liu60GVVh4GyON6fAhPSOb7b+eM2Tdt9pk0PbnrDvBsHXflJqWyXaDminujbrI1STH6nHS2ry9vQkPD8fb29voUsQg2gfu3plzNzh2yphXYWzefc6mTdMLsjHSmCZq2dlmIvYYfy4QsSfBsO/ByPOQgpTaIDRy5Eji4uIYMWIE06dPz/PDOXbsWJo0aUJmZiY1atSgXLlyAFy+nHNHw8en4Lch3xx3c9rSLik2gRPfbaVqu8YEhNXLHW7OzOJCZDQefuU5tmSjgRUWTmZmNvsM7Mp49yHju1HefSjRuHUfNm7dN+0/ctmQq4A37bGDz6Akq1atGm+//TbVqlUzuhQxiPaBu2fkb3HyjQyLO2iwNrPZbOjx2MjjsD3UsPvwRcxGNM25g1IZhA4fPsySJUuoWLEikydPznea5s2bA9CkSZPcYampOc8OuLm5Fbhsd3d3gDzdhEZERNC5c2eqVKmCu7s7gYGBeZrelXT7P8i5+3PrXaGAsHrU7tuRw3N/oNXbg3D2KPgzswdHTl61uLtka7KHH789h4378T95Ntnw5oGWdpNqbfYQhkuytLQ0Tp8+TVqa8c1MxRjaB+7eXgOPA2D8sfBUvLHHIiOPwzcZeSxMvJzK6QTjmwfeqlR2n7148WKys7Pp379/gW+xvtnH/61ByMPDA+COL029+QN86zsCLl++TKNGjRg2bBgBAQHExcUxefJk2rRpw8GDBwkMDCzSdrRo0YKEhMI3KXI1O/EGrSxeT8L235hf5fECx189eoaFgb+HIBcvD+6fOZzdExcRtWAND3/7NveOe4rIN+ZbvO7QkFAyTMXfk0yaSxCUG5zvuJv94t9J5Yqeuf8+va5fgdMV1Lf+6nVbCAwcZEHF1pdQ/mVwzr/ryj/7DAq7/VDwZ1CvUStcs437Ab7m0Q68Hsx3nC32gbcmvs/MCestqLj0e/zxgn93/uj8+fMsXryYJ598koCAgELP17t376KUJjZgyfcPRdsH9P3ndcWrG3jkf55gi+PA8JGv8vdhxj1jne5cFcoPy3ecLY4Dm7fuJjDw9s64bOlcuRfApXK+42yxD7Ro3RG3LOs2katcuTK7dln2TqSbSmUQ2rBhAwAdO3YscJq4uJzeS24NQoVp9pZf87kePXrQo0ePPNO1bNmSOnXqsGzZMl5++WULtyBHQkICZ86cKfT0biZnqFSkVVmk5ZsDSD51nqj5qwHY8vJH9Fg/nVM//sK5HZbdBTsbf5Z0sw3u1JQpB+XyH2VJv/guzk5F6kM/PSPLou+yWJQFnPMfVdjPoKjbD3D+fCKkGdh9aMB1KOD33Rb7QHJyCskJBu8Ddub69cJfGbx5Fz4lJcWi+Qz//04KZMn3CEXbB/T9/0G1FPDIf5QtjgNXryVzNdHA78TLHQp4rZ0tjgMZmWbj90kvc4Fn/7bYBy5cuAgp9vP/ZakMQidPngRyOj3IT2ZmJlu35jzcf2sQCg0NzZ0/MzMz3w4TYmJi8kxbED8/P4ACO10ojMqV80/sBXE1O0Ex31yp1qkZNXu05bsHR+cOSzp5jt0TF9F2xnBWdBpNZkrhbztXrVLVRneEKlDQDfmExBt/On/lip64ODuRmZVNQmLB3T8WtCw3Vyf8DW7XnuBspqDI+WefQWG3/07LqhTgh0u2cS+TS/LwoqBHhG2xD5Qt60F5PduQR5kyhT+Q3jzx9fT0tGg+PU9ivyz5HqFo+4C+/7yueHlQUIS0xXGgfLkylHU37jtJd/blQgHjbHEccHWBAIP3yfMuUNCb7WyxD/j7++KWZd3Ogyw9X76VyWxvTy1Zga+vL5cvX2bbtm20adPmtvGLFi3i6aefxtvbm6tXr2Iy5ZycJSUlUbFiRdLT09m+fTutW7e+bd46depw5MgRPvvss9veNZSVlUV2djYnT55k3LhxbNmyhf379+Pv7188G/oHGTdSWRT8tE3WZS39Y77E1auAy1NWdPJsEjXCvy7y/KfX9SOwUhnizl3nni7/sXj+/t2C+XJyhyKv3xoefPYHNuws2u3ou91+JycTSdsH4OVp3LWXL1ce46/jfy7y/Hf7GXzyj7YMe6JukddfGkVGRhZ62qioKAYMGMDChQupW7fwn2PLli2LUprYgCXfPxRtH9D3n9e0efsZO8Oyz/2mu/0NBPjug8706Jj/RWpbSLycin/7RUWe/24/g54dq/PfD7oUef3W0OOldXz/86kizWuNfeBixNP4lncv0rzFoVR2lnAzGe7Zs+e2cfHx8YwZMwaAxo0b54YgyOmas0uXnB30888/v23ezZs3c+TIEdzc3OjZs+dt49u3b4+bmxshISHs27ePDRs22CwEyZ1Vr1IWvwrG/Y/XvH5Fw9ZtDzXUr1XB0BAExn8Hzev7Gbp+ERHjfweNXX9FHw+Cqub/7LgtGL39OTUYdyyqWc3brkIQlNIg1LlzZwCmTp2a530/kZGRdOzYkcTEnEZSN1+keqsJEyZgMpmYN28eCxYsyB0eExPDkCFDAHI7RfijuXPnsmPHDhYvXky5cuV46KGHOHWqaKlbrMtkMhn6A9S8nvEnwYZuvx38+IcGlaOMQWHM1cWJRiH5d1QhhVO3bl127txp0d0gKV20D9y9ew08FlXy86RqwJ07I7CF5vUc+1ho7LmA8edCf1Qqg9DYsWPx8/Pj9OnTNGjQgEaNGhESEkKrVq2oVasWnTp1AvI+H3RT69atmTJlCmazmWeeeYYaNWrQrFkz6taty7FjxwgLC2PKlCn5rrdOnTqEhYXRr18/fvrpJ5KSknjvvfeKdVul8Lq3r27Ieiv6eBDWuPC9XBWXzq2r4u5WQG8JxezRdvcYst5bOTs70c2gOrq0Me6zFxG5qUI5dx641wa9KuXj0Xb35GmFY5TuHYw5DniXcaVd86I/y2It7VtUpqyXqyHrfrSdMedhd1Iqg1BgYCARERF069YNDw8PYmNj8fX15dNPP2XVqlW5d4nyC0KQE6RWr15Nly5duHr1KlFRUYSGhjJx4kR+/vlnvLz+/IpGhQoVqF27NseOHbPqtknR/fXR2obcERjSO9QuToL9KnjQL7yWzddbNcCLnga2Cb/Vi33r/flExWB4v/qGrLc0OXnyJIMHD87tDEccj/YB6zDqd9Co9f5R36618Cln+3cfDuwRYlgAuZV3GTcGdK9t8/X6lnenb3hNm6/3z5TKXuMA6tWrx8qVK28bnpycTGxsLE5OTjRs2LDA+bt27UrXrl2LvP7z588THR1NWFhYkZch1lXe242nH63Np99E2WydJhMMe9x+mnG82LceC1Yctek6h/apg6urfVxzade8MvWDK3Ao5orN1lmzmjdd71PPVXcrJSWFgwcP5nmZtTgW7QPW0adzDSr5eXLuou0+x1YN/bnXDpqFAXh6uDCoVyj/XHjQput94S/2cy7wwl/q8vESy153crcG9wrFw93+Yod9nJ3Y0G+//YbZbCYkJKRQd3YK4+mnn+bNN9/kv//9L5s2beLzzz+nQ4cOuLi4MGrUKKusQ6zjH881pby37a4EjXyqATUDvW22vj/TqpG/Ta/IBFYqw6i/FnzBwdZMJhP//JttL05MH90KZ2eH+6kVETvl5urMlP9rYbP1mUzw/t8sf9l7cXp1cGP8fYq/x9qbnu0TSv1gnz+f0EYahvgyuPedXwNjTQG+Hvx9cGObrc8SDnd0PnDgAFBws7iiaN26NT/88AODBg3i4YcfZtq0aTzwwAPs27eP2rVtf/uxOAR1a03rKXm7C6/dtyPPxC+lenjJ6Z60WqUyfDD29m7Ri0Pt6uWYNNJ2B5vC+mjcfTY7AMx5836bBs/C6No2kCE2OgD0C69Fn841bLIuEZHCGtgjhEceCLTJul7u34D77zX+2Zhb+ft68vFr99lkXfdULsP7Nr4AVxj//FtYkV+KaqlP/tGWijYMnpZQELKCESNGsHPnTi5fvkxKSgpHjhzh008/LfCFriVR9UfCOLV6Z+7fZQP9Ce3fmfO7og2sqmgG9KhNnwdrWDRPQuIN4s5dL9QL1yDnBaoL3m1neJfR+ano48GcN+/HkmdWLd1+yGmG17WtbQ60lnr/b2EE32PZnTpLP4N7Kpfhw3G3v8dMRMRoJpOJz16/n0p+noWepyjHgYa1fZj4kv1dEAR4/KGaFj8rY+ln4Oxs4ou3H6BcWfu6IAg5jwt88fYDODsX/mSgKPvAwB4h9LbwnMuWSuULVe+kU6dObNy4kZUrV9KtWzejy7Gqu3mhqls5L3punIGzhxs3zibi5O6Kd/VKxCz9me2vfs5j2z5k2X0vYc7MApOJh/7zD3ZP/JKWbwzk0OcrObW6aC9os9ULVf8oJTWTbsPXsjGyaC8YvRMXFxNfT+tk1//jA3y+NIrn3t5aLMt+4qGaLJ7awa6bhMWeSeKBZ1YRd66g96wXXSU/TzbP60ZojfJWX3ZpYskLNa9evcq2bdu47777KF++8J+rXqhpvyx9oWpR9gF9/3e2/8glOgxexeVr6VZfdvA93mye142qAba561AU6RlZ9P6/9fwQEWf1ZTs5mVg4sR39u9l3y6AvVx5jwGs/Uxxp4NF297B8Rme7eU44P/ZbWTHZsGEDZrO51IWgu5V+7QbHv43g0JxVrOgyhp2vz+PCniNs+9snVGnbkPOR0TkhCGgwrDvnI6O4uP+4wVUXnaeHC6v+9ZDVu9T28nDh2xmd7T4EAQx9vC7z32mHi4t1uzMd1CuEr6bYdwgCqFHNmy0LuhEaZN2wUqNqWSLmKwRZW/ny5Xn44YctCkFSumgfsL7Gob5snteNalZ+v0+jEB8i5j9q1yEIcp6XWj6jM3/pat1nZ93dnFnyXke7D0EATz9amyXTOlm9d9u+4TVZNuNBuw5B4IBBSArm27Amlw6cAMCvcTCXDub8d/Xwlpz8MadZXIU69xDULYxfZy4zrE5r8fRw4b8fdObj1+6zSrfa7ZpXZv+y3jxq0PuKimJgzxB2Le5J07p3/7JP3/LufDWlA3PfegAXl5Lx0xJU1Zs9S3ry0lPW6d566GN1+HVpb0KsHK4ELl++zDfffMPly5eNLkUMon2geDQM8WX/sj48/WjwXS/LycnE2EGN2PlVD6r4G//y1MJwd3PmP+91ZO5bD1Cu7N13b926sT/7vunF4w/ZX1fRBXnioZrs/boXrRr63/Wybja5Wzy1I26uxr865M+UjLMVsQnfBjVyw49f41pc/F8oqtqhKWc27AWgUlg9yt4TwGPbPuTxnR/jf28IbaY9T50BDxlW991wcjLxQt96HFjWhyceqmlRW9mbgqqW5V/j27Bx7iME31OuGKosXk3q+LFzUU8mv9zCovbiN7m7OTOoVwiH/vsYTz4SbBcvzLNEGS9XZr3ahk1fPELbZkV70WCrhv6s+aQrn71xv122BS8Nzp07x7Rp0zh37pzRpYhBtA8UH9/y7vx7UgdWzOpS5Atj7VtUZuuCR5k6qpVddpN8JyaTicG9Q/lt+WM89UgwrkW4mFctwIt/jgljy4JHqVuzgvWLLGb1alVg68JH+eeYsCLdIXR1caJ/t2AOLuvDoF6hJeZcoGTtqVJsvCr7gtnMjYRLAPjWC2L/B8uo2CyEq0fPkHkjFYDohWuJXrg2d77wZW/d1TNC9qJmoDdfT+/E2fPX+XxZNEvXxXLo+BWys/NvNOtTzo37m1Vm6GN1eOSBQLtvBvZnXF2deHVIE14Z0JD/bjjJnOXR7Nh/gaTrGflO7+JionGIL/3CazGoV6jd9gZjifYtqrBlwaP8Gn2R2V9HsWZrHLFnkwuc/p7KZejSphov/KUuLRrc/VU0ERGjde9QnUfb38Mv+y8w++vD/PTLWc6cL/jB+FqB3jx8fyAv9q1nV91DF1Vg5TIsmtKBf44JY+7yaL5ee4KDxy6TlZX/uUB5bzfuaxLA0Mfq0L199RLTGqIgLi5OjPprQ156sj4rNp1izvJotv16nqtJ+T9D5uxsomFtH/7yUE2G9KlTpIupRlMQEuB/zeL+dzcIIP3adeoO7ErapaQ8vcWVdlUDyvDGC/fyxgv3ciMlk33RF4k5fY2UtCxcnJ2o4O1Gs7p+1KhWtsRc7bCEm6szf+lai790rUV2tpljp67x65FLJF3PICsrG08PF+rUKE+jEJ8Sd8WvsJrU8eOTf7QF4OKVVHYfSiT+QgppGVm4uTpR2c+L5vX98PcteT/4IiJ/xmQy0bpJAK2bBAA5PYXtOXyR85dSSM/Ixt3VmcBKZbi3vh8+5dwNrrZ4VPLzZPzQpowf2pSU1Ez2H7nEkZNXSU3LwtnZifJlXWla149agd6l8lzAxcWJPp1r0KdzDcxmMzGnk/g1+iJXk3POBTzcnQkNKk/jUF88PUr2uUDJrl6sJm79buLW7879e+XDrwLQc9MM1jz2RoHzrb7DuJLOy9OF+5pW4r6mRWsuVdI5OZkIrVHeoR/696vgwUP32WcX4CIitlC5ohePPFAynvcpDp4eLoQ1DiCscYDRpRjCZDJRu3o5alcveU3/C0NBSO7ouw6jjC5BRAQvLy/CwsLw8nLcEzJHp31ARKxNQUhEROxe9erV+fDDD40uQwykfUBErK1kP9UlIiIOISsri+TkZLKysowuRQyifUBErE1BSERE7N7Ro0fp1KkTR48eNboUMYj2ARGxNjWNK0VcPN3pH/Ol0WVYxMWzdPY4IyIiIiL2TUGoFDGZTLh6lfz3uYiIiIiIFDc1jRMREREREYejICQiIiIiIg5HTeNERMTu1a5dmzVr1uDt7W10KWIQ7QMiYm0KQiIiYvdcXFzw8fExugwxkPYBEbE2NY0TERG7FxcXx+jRo4mLizO6FDGI9gERsTYFIRERsXvJyclERESQnJxsdCliEO0DImJtCkIiIiIiIuJwFIRERERERMThKAiJiIiIiIjDMZnNZrPRRYiIiNzJuXPnWLRoEf3796dSpUpGlyMG0D4gItamICQiIiIiIg5HTeNERERERMThKAiJiIiIiIjDURASERERERGHoyAkIiIiIiIOR0FIREREREQcjoKQiIiIiIg4HAUhERERERFxOApCIiIiIiLicBSERERERETE4SgIiYiIiIiIw1EQEhERERERh6MgJCIiIiIiDkdBSEREREREHI6CkIiIiIiIOJz/ByZLZlrBF+8HAAAAAElFTkSuQmCC", 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    " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[\"A\"].draw(\"mpl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d851adcb-e524-48c8-8adc-0d1606613c8d", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.749630Z", - "iopub.status.busy": "2025-03-28T16:52:34.749131Z", - "iopub.status.idle": "2025-03-28T16:52:34.879618Z", - "shell.execute_reply": "2025-03-28T16:52:34.878945Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[\"B\"].draw(\"mpl\")" - ] - }, - { - "cell_type": "markdown", - "id": "9c177fb7-a729-4e0d-bfac-256df3e14c54", - "metadata": {}, - "source": [ - "### Calculate the sampling overhead for the chosen cuts\n", - "\n", - "Here we cut two wires, resulting in a sampling overhead of $4^4$.\n", - "\n", - "For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](../explanation/index.rst)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7af74c54-3e68-4d58-a2e6-02bc212d911d", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.881911Z", - "iopub.status.busy": "2025-03-28T16:52:34.881529Z", - "iopub.status.idle": "2025-03-28T16:52:34.885387Z", - "shell.execute_reply": "2025-03-28T16:52:34.884739Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sampling overhead: 256.0\n" - ] - } - ], - "source": [ - "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")" - ] - }, - { - "cell_type": "markdown", - "id": "a1eda2d0-8d83-473b-8ea8-fe9880108140", - "metadata": {}, - "source": [ - "### Generate the subexperiments to run on the backend\n", - "\n", - "`generate_cutting_experiments` accepts `circuits`/`observables` args as dictionaries mapping qubit partition labels to the respective `subcircuit`/`subobservables`.\n", - "\n", - "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst)." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d28a8b82-8405-47b2-8142-62d56266409a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.887533Z", - "iopub.status.busy": "2025-03-28T16:52:34.887063Z", - "iopub.status.idle": "2025-03-28T16:52:34.963806Z", - "shell.execute_reply": "2025-03-28T16:52:34.963065Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import generate_cutting_experiments\n", - "\n", - "subexperiments, coefficients = generate_cutting_experiments(\n", - " circuits=subcircuits, observables=subobservables, num_samples=np.inf\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "9f66e3eb-7cbc-45a9-8f40-9f92b61b6e65", - "metadata": {}, - "source": [ - "### Choose a backend\n", - "\n", - "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6944546f-e3e0-4863-9049-9feed1086e68", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.966311Z", - "iopub.status.busy": "2025-03-28T16:52:34.965845Z", - "iopub.status.idle": "2025-03-28T16:52:35.537487Z", - "shell.execute_reply": "2025-03-28T16:52:35.536746Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", - "\n", - "backend = FakeManilaV2()" - ] - }, - { - "cell_type": "markdown", - "id": "fdb47142-690c-4b1a-ad0b-85e1260a2cea", - "metadata": {}, - "source": [ - "### Prepare the subexperiments for the backend\n", - "\n", - "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "0229e6f2-a637-481d-ac76-84dfaadc264f", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:35.540179Z", - "iopub.status.busy": "2025-03-28T16:52:35.539685Z", - "iopub.status.idle": "2025-03-28T16:52:38.804548Z", - "shell.execute_reply": "2025-03-28T16:52:38.803721Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.transpiler import generate_preset_pass_manager\n", - "\n", - "# Transpile the subexperiments to ISA circuits\n", - "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", - "isa_subexperiments = {\n", - " label: pass_manager.run(partition_subexpts)\n", - " for label, partition_subexpts in subexperiments.items()\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "9578c64a-8087-4696-bb2b-ca53d45442ba", - "metadata": {}, - "source": [ - "## Step 3: Execute\n", - "\n", - "### Run the subexperiments using the Qiskit Runtime Sampler primitive" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "bdf3bd2f-e10a-4148-a7ad-f354492614cc", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:38.807146Z", - "iopub.status.busy": "2025-03-28T16:52:38.806915Z", - "iopub.status.idle": "2025-03-28T16:52:38.958106Z", - "shell.execute_reply": "2025-03-28T16:52:38.957428Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import SamplerV2, Batch\n", - "\n", - "# Submit each partition's subexperiments to the Qiskit Runtime Sampler\n", - "# primitive, in a single batch so that the jobs will run back-to-back.\n", - "with Batch(backend=backend) as batch:\n", - " sampler = SamplerV2(mode=batch)\n", - " jobs = {\n", - " label: sampler.run(subsystem_subexpts, shots=2**12)\n", - " for label, subsystem_subexpts in isa_subexperiments.items()\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "abb436de-9662-4c47-92b1-659ee0334adc", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:38.965357Z", - "iopub.status.busy": "2025-03-28T16:52:38.965102Z", - "iopub.status.idle": "2025-03-28T16:52:51.060055Z", - "shell.execute_reply": "2025-03-28T16:52:51.059450Z" - } - }, - "outputs": [], - "source": [ - "# Retrieve results\n", - "results = {label: job.result() for label, job in jobs.items()}" - ] - }, - { - "cell_type": "markdown", - "id": "3f6e4eed-eff7-4e16-ad8d-8070011a555b", - "metadata": {}, - "source": [ - "## Step 4: Post-process\n", - "\n", - "### Reconstruct the expectation value\n", - "\n", - "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a1904c54-27fa-45e0-a9dd-727b4542db71", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:51.062743Z", - "iopub.status.busy": "2025-03-28T16:52:51.062254Z", - "iopub.status.idle": "2025-03-28T16:52:53.372289Z", - "shell.execute_reply": "2025-03-28T16:52:53.371616Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import reconstruct_expectation_values\n", - "\n", - "reconstructed_expval_terms = reconstruct_expectation_values(\n", - " results,\n", - " coefficients,\n", - " subobservables,\n", - ")\n", - "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" - ] - }, - { - "cell_type": "markdown", - "id": "cdc793f2-3e2b-417b-b863-7b9d57b86a8f", - "metadata": {}, - "source": [ - "### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "6e3d63e4-a510-4712-bc43-48df6e2f7ded", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:53.374812Z", - "iopub.status.busy": "2025-03-28T16:52:53.374611Z", - "iopub.status.idle": "2025-03-28T16:52:53.385882Z", - "shell.execute_reply": "2025-03-28T16:52:53.385355Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstructed expectation value: 1.40369761\n", - "Exact expectation value: 1.59099026\n", - "Error in estimation: -0.18729265\n", - "Relative error in estimation: -0.1177208\n" - ] - } - ], - "source": [ - "from qiskit_aer.primitives import EstimatorV2\n", - "\n", - "estimator = EstimatorV2()\n", - "exact_expval = estimator.run([(qc_0, observable)]).result()[0].data.evs\n", - "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", - "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", - "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", - "print(\n", - " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.21" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.mdx b/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.mdx deleted file mode 100644 index e0fb987fb76..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.mdx +++ /dev/null @@ -1,205 +0,0 @@ - - -# Automatically find cuts - - - -## Step 1: Map - - - -### Create a circuit and observables - -``` -[1]: -``` - -```python -import numpy as np -from qiskit.circuit.random import random_circuit -from qiskit.quantum_info import SparsePauliOp - -circuit = random_circuit(7, 6, max_operands=2, seed=1242) -observable = SparsePauliOp(["ZIIIIII", "IIIZIII", "IIIIIIZ"]) - - -circuit.draw("mpl", scale=0.8) -``` - -``` -[1]: -``` - -![../\_images/tutorials\_04\_automatic\_cut\_finding\_2\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_2_0.avif) - - - -## Step 2: Optimize - - - -### Find cut locations, given a maximum of 4 qubits per subcircuit. This circuit can be separated in two by making a single wire cut and cutting one `CRZGate` - -``` -[2]: -``` - -```python -from qiskit_addon_cutting.automated_cut_finding import ( - find_cuts, - OptimizationParameters, - DeviceConstraints, -) - -# Specify settings for the cut-finding optimizer -optimization_settings = OptimizationParameters(seed=111) - -# Specify the size of the QPUs available -device_constraints = DeviceConstraints(qubits_per_subcircuit=4) - -cut_circuit, metadata = find_cuts(circuit, optimization_settings, device_constraints) -print( - f'Found solution using {len(metadata["cuts"])} cuts with a sampling ' - f'overhead of {metadata["sampling_overhead"]}.\n' - f'Lowest cost solution found: {metadata["minimum_reached"]}.' -) -for cut in metadata["cuts"]: - print(f"{cut[0]} at circuit instruction index {cut[1]}") -cut_circuit.draw("mpl", scale=0.8, fold=-1) -``` - -```python -Found solution using 8 cuts with a sampling overhead of 59776343.19975524. -Lowest cost solution found: False. -Gate Cut at circuit instruction index 6 -Gate Cut at circuit instruction index 8 -Gate Cut at circuit instruction index 9 -Wire Cut at circuit instruction index 10 -Gate Cut at circuit instruction index 15 -Gate Cut at circuit instruction index 18 -Gate Cut at circuit instruction index 19 -Gate Cut at circuit instruction index 21 -``` - -``` -[2]: -``` - -![../\_images/tutorials\_04\_automatic\_cut\_finding\_4\_1.png](/docs/images/api/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_4_1.avif) - - - -### Add ancillas for wire cuts and expand the observables to account for ancilla qubits - -``` -[3]: -``` - -```python -from qiskit_addon_cutting import cut_wires, expand_observables - -qc_w_ancilla = cut_wires(cut_circuit) -observables_expanded = expand_observables(observable.paulis, circuit, qc_w_ancilla) -qc_w_ancilla.draw("mpl", scale=0.8, fold=-1) -``` - -``` -[3]: -``` - -![../\_images/tutorials\_04\_automatic\_cut\_finding\_6\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_6_0.avif) - - - -### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires. - -``` -[4]: -``` - -```python -from qiskit_addon_cutting import partition_problem - -partitioned_problem = partition_problem( - circuit=qc_w_ancilla, observables=observables_expanded -) -subcircuits = partitioned_problem.subcircuits -subobservables = partitioned_problem.subobservables -print( - f"Sampling overhead: {np.prod([basis.overhead for basis in partitioned_problem.bases])}" -) -``` - -```python -Sampling overhead: 59776343.19975523 -``` - -``` -[5]: -``` - -```python -subobservables -``` - -``` -[5]: -``` - -```python -{0: PauliList(['IIII', 'IIZI', 'IIIZ']), - 1: PauliList(['ZIII', 'IIII', 'IIII'])} -``` - -``` -[6]: -``` - -```python -subcircuits[0].draw("mpl", style="iqp", scale=0.8) -``` - -``` -[6]: -``` - -![../\_images/tutorials\_04\_automatic\_cut\_finding\_10\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_10_0.avif) - -``` -[7]: -``` - -```python -subcircuits[1].draw("mpl", style="iqp", scale=0.8) -``` - -``` -[7]: -``` - -![../\_images/tutorials\_04\_automatic\_cut\_finding\_11\_0.png](/docs/images/api/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_11_0.avif) - - - -### Generate the experiments to run on the backend. - -``` -[8]: -``` - -```python -from qiskit_addon_cutting import generate_cutting_experiments - -subexperiments, coefficients = generate_cutting_experiments( - circuits=subcircuits, observables=subobservables, num_samples=1_000 -) -print( - f"{len(subexperiments[0]) + len(subexperiments[1])} total subexperiments to run on backend." -) -``` - -```python -2000 total subexperiments to run on backend. -``` - -Steps 3 and 4 of a [Qiskit pattern](/docs/guides/intro-to-patterns) can then be performed as in the preceding tutorials. diff --git a/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb deleted file mode 100644 index bb6428c4f34..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb +++ /dev/null @@ -1,358 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automatically find cuts" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Map\n", - "\n", - "#### Create a circuit and observables" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:55.792854Z", - "iopub.status.busy": "2025-03-28T16:52:55.792659Z", - "iopub.status.idle": "2025-03-28T16:52:56.800107Z", - "shell.execute_reply": "2025-03-28T16:52:56.799450Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "from qiskit.circuit.random import random_circuit\n", - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "circuit = random_circuit(7, 6, max_operands=2, seed=1242)\n", - "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])\n", - "\n", - "\n", - "circuit.draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Optimize\n", - "\n", - "#### Find cut locations, given a maximum of 4 qubits per subcircuit. This circuit can be separated in two by making a single wire cut and cutting one `CRZGate`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:56.802390Z", - "iopub.status.busy": "2025-03-28T16:52:56.801930Z", - "iopub.status.idle": "2025-03-28T16:52:59.488947Z", - "shell.execute_reply": "2025-03-28T16:52:59.488280Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found solution using 8 cuts with a sampling overhead of 59776343.19975524.\n", - "Lowest cost solution found: False.\n", - "Gate Cut at circuit instruction index 6\n", - "Gate Cut at circuit instruction index 8\n", - "Gate Cut at circuit instruction index 9\n", - "Wire Cut at circuit instruction index 10\n", - "Gate Cut at circuit instruction index 15\n", - "Gate Cut at circuit instruction index 18\n", - "Gate Cut at circuit instruction index 19\n", - "Gate Cut at circuit instruction index 21\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_cutting.automated_cut_finding import (\n", - " find_cuts,\n", - " OptimizationParameters,\n", - " DeviceConstraints,\n", - ")\n", - "\n", - "# Specify settings for the cut-finding optimizer\n", - "optimization_settings = OptimizationParameters(seed=111)\n", - "\n", - "# Specify the size of the QPUs available\n", - "device_constraints = DeviceConstraints(qubits_per_subcircuit=4)\n", - "\n", - "cut_circuit, metadata = find_cuts(circuit, optimization_settings, device_constraints)\n", - "print(\n", - " f'Found solution using {len(metadata[\"cuts\"])} cuts with a sampling '\n", - " f'overhead of {metadata[\"sampling_overhead\"]}.\\n'\n", - " f'Lowest cost solution found: {metadata[\"minimum_reached\"]}.'\n", - ")\n", - "for cut in metadata[\"cuts\"]:\n", - " print(f\"{cut[0]} at circuit instruction index {cut[1]}\")\n", - "cut_circuit.draw(\"mpl\", scale=0.8, fold=-1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Add ancillas for wire cuts and expand the observables to account for ancilla qubits" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:59.491081Z", - "iopub.status.busy": "2025-03-28T16:52:59.490686Z", - "iopub.status.idle": "2025-03-28T16:52:59.752854Z", - "shell.execute_reply": "2025-03-28T16:52:59.752167Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_cutting import cut_wires, expand_observables\n", - "\n", - "qc_w_ancilla = cut_wires(cut_circuit)\n", - "observables_expanded = expand_observables(observable.paulis, circuit, qc_w_ancilla)\n", - "qc_w_ancilla.draw(\"mpl\", scale=0.8, fold=-1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:59.755174Z", - "iopub.status.busy": "2025-03-28T16:52:59.754781Z", - "iopub.status.idle": "2025-03-28T16:52:59.767941Z", - "shell.execute_reply": "2025-03-28T16:52:59.767319Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sampling overhead: 59776343.19975523\n" - ] - } - ], - "source": [ - "from qiskit_addon_cutting import partition_problem\n", - "\n", - "partitioned_problem = partition_problem(\n", - " circuit=qc_w_ancilla, observables=observables_expanded\n", - ")\n", - "subcircuits = partitioned_problem.subcircuits\n", - "subobservables = partitioned_problem.subobservables\n", - "print(\n", - " f\"Sampling overhead: {np.prod([basis.overhead for basis in partitioned_problem.bases])}\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:59.770081Z", - "iopub.status.busy": "2025-03-28T16:52:59.769722Z", - "iopub.status.idle": "2025-03-28T16:52:59.774244Z", - "shell.execute_reply": "2025-03-28T16:52:59.773599Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: PauliList(['IIII', 'IIZI', 'IIIZ']),\n", - " 1: PauliList(['ZIII', 'IIII', 'IIII'])}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subobservables" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:59.776009Z", - "iopub.status.busy": "2025-03-28T16:52:59.775815Z", - "iopub.status.idle": "2025-03-28T16:52:59.931154Z", - "shell.execute_reply": "2025-03-28T16:52:59.930440Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[0].draw(\"mpl\", style=\"iqp\", scale=0.8)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:59.933278Z", - "iopub.status.busy": "2025-03-28T16:52:59.933055Z", - "iopub.status.idle": "2025-03-28T16:53:00.110630Z", - "shell.execute_reply": "2025-03-28T16:53:00.110004Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[1].draw(\"mpl\", style=\"iqp\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Generate the experiments to run on the backend." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:53:00.112773Z", - "iopub.status.busy": "2025-03-28T16:53:00.112543Z", - "iopub.status.idle": "2025-03-28T16:53:02.920653Z", - "shell.execute_reply": "2025-03-28T16:53:02.919977Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2000 total subexperiments to run on backend.\n" - ] - } - ], - "source": [ - "from qiskit_addon_cutting import generate_cutting_experiments\n", - "\n", - "subexperiments, coefficients = generate_cutting_experiments(\n", - " circuits=subcircuits, observables=subobservables, num_samples=1_000\n", - ")\n", - "print(\n", - " f\"{len(subexperiments[0]) + len(subexperiments[1])} total subexperiments to run on backend.\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Steps 3 and 4 of a [Qiskit pattern](/guides/intro-to-patterns) can then be performed as in the preceding tutorials." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.21" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-cutting/tutorials/index.mdx b/docs/addons/qiskit-addon-cutting/tutorials/index.mdx deleted file mode 100644 index a554fa522d5..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/index.mdx +++ /dev/null @@ -1,30 +0,0 @@ ---- -title: Circuit cutting API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-cutting. ---- - - - -# Tutorials - -* [Tutorial 1](01-gate-cutting-to-reduce-circuit-width): Separate sets of qubits by cutting nonlocal gates. -* [Tutorial 2](02-gate-cutting-to-reduce-circuit-depth): Cut gates requiring many SWAPs to decrease circuit depth. -* [Tutorial 3](03-wire-cutting-via-move-instruction): Specify wire cuts as a two-qubit [`Move`](instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") operation. -* [Tutorial 4](04-automatic-cut-finding): Automatically find optimal gate and wire cut locations for reducing circuit width. - -[![](/docs/images/api/qiskit-addon-cutting/tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.avif)](01-gate-cutting-to-reduce-circuit-width) - -[Gate Cutting to Reduce Circuit Width](01-gate-cutting-to-reduce-circuit-width) - -[![](/docs/images/api/qiskit-addon-cutting/tutorials_02_gate_cutting_to_reduce_circuit_depth_17_2.avif)](02-gate-cutting-to-reduce-circuit-depth) - -[Gate Cutting to Reduce Circuit Depth](02-gate-cutting-to-reduce-circuit-depth) - -[![](/docs/images/api/qiskit-addon-cutting/tutorials_03_wire_cutting_via_move_instruction_15_0.avif)](03-wire-cutting-via-move-instruction) - -[Wire Cutting Phrased as a Two-Qubit Move Instruction](03-wire-cutting-via-move-instruction) - -[![](/docs/images/api/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_11_0.avif)](04-automatic-cut-finding) - -[Automatically find cuts](04-automatic-cut-finding) - diff --git a/docs/addons/qiskit-addon-mpf/_toc.json b/docs/addons/qiskit-addon-mpf/_toc.json deleted file mode 100644 index e84804e2a81..00000000000 --- a/docs/addons/qiskit-addon-mpf/_toc.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "title": "Multi-product formulas (MPF)", - "children": [ - { - "title": "Documentation Home", - "url": "/docs/addons/qiskit-addon-mpf" - }, - { - "title": "Installation Instructions", - "url": "/docs/addons/qiskit-addon-mpf/install" - }, - { - "title": "Tutorials", - "children": [ - { - "title": "Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas", - "url": "/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started" - } - ], - "url": "/docs/addons/qiskit-addon-mpf/tutorials/index" - }, - { - "title": "How-To Guides", - "children": [ - { - "title": "How to choose the Trotter steps for an MPF", - "url": "/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps" - }, - { - "title": "How to use the approximate model", - "url": "/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model" - } - ], - "url": "/docs/addons/qiskit-addon-mpf/how_tos/index" - }, - { - "title": "Explanations", - "children": [ - { - "title": "Understanding the stability of MPFs", - "url": "/docs/addons/qiskit-addon-mpf/explanations/mpf_stability" - } - ], - "url": "/docs/addons/qiskit-addon-mpf/explanations/index" - } - ], - "collapsed": true -} diff --git a/docs/addons/qiskit-addon-mpf/explanations/index.mdx b/docs/addons/qiskit-addon-mpf/explanations/index.mdx deleted file mode 100644 index 3d1088d0cbd..00000000000 --- a/docs/addons/qiskit-addon-mpf/explanations/index.mdx +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: Multi-product formulas (MPF) API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-mpf. ---- - -# Explanations - -This page summarizes additional explanatory content. - -* [Understanding the stability of MPFs](mpf-stability) - diff --git a/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.mdx b/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.mdx deleted file mode 100644 index 548932261f7..00000000000 --- a/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.mdx +++ /dev/null @@ -1,157 +0,0 @@ - - -# Understanding the stability of MPFs - -Over in the guide [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps) we introduced a heuristic that limits the **smallest** $k_j$ value ($k_{\text{min}}$) based on the total evolution time, $t$, that we would like to reach. In particular, we state that $t/k_{\text{min}} \lt 1$ must be satisfied (noting that $t/k_{\text{min}} \leq 1$ empirically seems to work fine, too). - -On this page, we will analyze when the behavior of MPFs is not guaranteed to work well, such as when this constraint gets violated. - - - -## Setting up a simple model problem - -For this simple example, we will reuse the model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial; the Ising model on a line of 10 sites: - -$$ -\hat{\mathcal{H}}_{\text{Ising}} = \sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \sum_{i=1}^{10} h_i X_i \, , -$$ - -where $J$ is the coupling strength between two sites and $h$ is the external magnetic field. - -``` -[1]: -``` - -```python -from qiskit.transpiler import CouplingMap -from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian - -coupling_map = CouplingMap.from_line(10, bidirectional=False) - -hamiltonian = generate_xyz_hamiltonian( - coupling_map, - coupling_constants=(0.0, 0.0, 1.0), - ext_magnetic_field=(0.4, 0.0, 0.0), -) -print(hamiltonian) -``` - -```python -SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'], - coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, - 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, - 0.4+0.j, 0.4+0.j, 0.4+0.j]) -``` - -The observable that we will be measuring is the total magnetization which we can simply construct as shown below: - -``` -[2]: -``` - -```python -from qiskit.quantum_info import SparsePauliOp - -L = coupling_map.size() -observable = SparsePauliOp.from_sparse_list([("Z", [i], 1 / L / 2) for i in range(L)], num_qubits=L) -print(observable) -``` - -```python -SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'], - coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, - 0.05+0.j, 0.05+0.j, 0.05+0.j]) -``` - - - -## Simulating various Trotter circuits - -Our analysis simply consists of comparing the behavior of product formulas of various order (here, second and fourth order) to the exact solution (which we can compute for a problem as simple as this one). - -In the code cell below, we do exactly that, for evolution times ranging from $2$ up to $10$ and Trotter steps ranging from $1$ to $25$. - -``` -[3]: -``` - -```python -import numpy as np -from qiskit.primitives import StatevectorEstimator -from qiskit.synthesis import SuzukiTrotter -from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit -from scipy.linalg import expm - -estimator = StatevectorEstimator() -times = np.array(range(2, 11)) -ks = np.array(range(1, 25)) - -exact = [] -order2 = [] -order4 = [] - -for time in times: - exp_H = expm(-1j * time * hamiltonian.to_matrix()) - initial_state = np.zeros(exp_H.shape[0]) - initial_state[0] = 1.0 - time_evolved_state = exp_H @ initial_state - exact_evs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state - exact.append(float(exact_evs)) - - for k in ks: - order2_circ = generate_time_evolution_circuit( - hamiltonian, synthesis=SuzukiTrotter(reps=k, order=2), time=time - ) - order4_circ = generate_time_evolution_circuit( - hamiltonian, synthesis=SuzukiTrotter(reps=k, order=4), time=time - ) - - job = estimator.run([(order2_circ, observable), (order4_circ, observable)]) - result = job.result() - order2.append(result[0].data.evs) - order4.append(result[1].data.evs) -``` - - - -## Analyzing the results - -We can now analyze the computed results by plotting them. - -``` -[4]: -``` - -```python -from matplotlib import pyplot as plt - -order2_reshaped = np.asarray(order2).reshape((len(times), len(ks))) -order4_reshaped = np.asarray(order4).reshape((len(times), len(ks))) - -fig, axes = plt.subplots(3, 3, figsize=(20, 10)) - -for i, t in enumerate(times): - ax = axes[i // 3, i % 3] - - ax.axvline(1.0, color="grey", label="$t/k=1$") - ax.axhline(exact[i], color="tab:green", label="exact") - - ax.plot(t / ks, order2_reshaped[i], label=r"$\chi=2$") - ax.plot(t / ks, order4_reshaped[i], label=r"$\chi=4$") - - ax.semilogx() - ax.set_xticks([10**p for p in range(-1, 1)]) - ax.set_xlabel("$t/k$") - - ax.set_ylim([-0.33, 0.55]) - ax.set_ylabel(r"$\langle O \rangle$") - - ax.set_title(f"time={t}") - -fig.legend(*ax.get_legend_handles_labels(), ncols=4, loc="center", bbox_to_anchor=(0.5, 0.0)) -fig.tight_layout() -``` - -![../\_images/explanations\_mpf\_stability\_8\_0.png](/docs/images/api/qiskit-addon-mpf/explanations_mpf_stability_8_0.avif) - -We can now clearly see that in the regime $t/k \gt 1$ the dynamics are no longer reproduced faithfully by the Trotterized circuits. Thus, if an MPF were to use a product formula with a $k_j$ from that regime, its extrapolated expectation value cannot faithfully produce good results. diff --git a/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb b/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb deleted file mode 100644 index 1e1ae9be310..00000000000 --- a/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb +++ /dev/null @@ -1,308 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4aaf8397-4140-4d16-b4b5-1d9e20adea98", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Understanding the stability of MPFs\n", - "\n", - "Over in the guide [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps) we introduced a heuristic that limits the **smallest** $k_j$ value ($k_{\\text{min}}$) based on the total evolution time, $t$, that we would like to reach.\n", - "In particular, we state that $t/k_{\\text{min}} \\lt 1$ must be satisfied (noting that $t/k_{\\text{min}} \\leq 1$ empirically seems to work fine, too).\n", - "\n", - "On this page, we will analyze when the behavior of MPFs is not guaranteed to work well, such as when this constraint gets violated." - ] - }, - { - "cell_type": "markdown", - "id": "986a58b5-611a-4400-9bd5-ba35d4a14188", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Setting up a simple model problem\n", - "\n", - "For this simple example, we will reuse the model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial; the Ising model on a line of 10 sites:\n", - "\n", - "$$\n", - "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", - "$$\n", - "\n", - "where $J$ is the coupling strength between two sites and $h$ is the external magnetic field." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d77e337b-0a69-49a5-b449-ffe4ad17d7ab", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'],\n", - " coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j,\n", - " 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j,\n", - " 0.4+0.j, 0.4+0.j, 0.4+0.j])\n" - ] - } - ], - "source": [ - "from qiskit.transpiler import CouplingMap\n", - "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", - "\n", - "coupling_map = CouplingMap.from_line(10, bidirectional=False)\n", - "\n", - "hamiltonian = generate_xyz_hamiltonian(\n", - " coupling_map,\n", - " coupling_constants=(0.0, 0.0, 1.0),\n", - " ext_magnetic_field=(0.4, 0.0, 0.0),\n", - ")\n", - "print(hamiltonian)" - ] - }, - { - "cell_type": "markdown", - "id": "0f25e279-e987-4da5-a7e3-ffea41542d51", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "The observable that we will be measuring is the total magnetization which we can simply construct as shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "eca935e2-206e-471e-a673-62a67a00add0", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'],\n", - " coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j,\n", - " 0.05+0.j, 0.05+0.j, 0.05+0.j])\n" - ] - } - ], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "L = coupling_map.size()\n", - "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)\n", - "print(observable)" - ] - }, - { - "cell_type": "markdown", - "id": "b652e4ab-26c1-4c4e-a34d-47a464c60f26", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Simulating various Trotter circuits\n", - "\n", - "Our analysis simply consists of comparing the behavior of product formulas of various order (here, second and fourth order) to the exact solution (which we can compute for a problem as simple as this one).\n", - "\n", - "In the code cell below, we do exactly that, for evolution times ranging from $2$ up to $10$ and Trotter steps ranging from $1$ to $25$." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e7c43eec-3809-4e3a-998a-7f6a7a8cf2a6", - "metadata": { - "editable": true, - "scrolled": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "skip-execution" - ] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from qiskit.primitives import StatevectorEstimator\n", - "from qiskit.synthesis import SuzukiTrotter\n", - "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", - "from scipy.linalg import expm\n", - "\n", - "estimator = StatevectorEstimator()\n", - "times = np.array(range(2, 11))\n", - "ks = np.array(range(1, 25))\n", - "\n", - "exact = []\n", - "order2 = []\n", - "order4 = []\n", - "\n", - "for time in times:\n", - " exp_H = expm(-1j * time * hamiltonian.to_matrix())\n", - " initial_state = np.zeros(exp_H.shape[0])\n", - " initial_state[0] = 1.0\n", - " time_evolved_state = exp_H @ initial_state\n", - " exact_evs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", - " exact.append(float(exact_evs))\n", - "\n", - " for k in ks:\n", - " order2_circ = generate_time_evolution_circuit(\n", - " hamiltonian, synthesis=SuzukiTrotter(reps=k, order=2), time=time\n", - " )\n", - " order4_circ = generate_time_evolution_circuit(\n", - " hamiltonian, synthesis=SuzukiTrotter(reps=k, order=4), time=time\n", - " )\n", - "\n", - " job = estimator.run([(order2_circ, observable), (order4_circ, observable)])\n", - " result = job.result()\n", - " order2.append(result[0].data.evs)\n", - " order4.append(result[1].data.evs)" - ] - }, - { - "cell_type": "markdown", - "id": "0da8acd2-6192-450e-99b3-e0d2b82541ee", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Analyzing the results\n", - "\n", - "We can now analyze the computed results by plotting them." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "845cf113-58da-454e-97b6-679d5a5ae9fb", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "skip-execution" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "order2_reshaped = np.asarray(order2).reshape((len(times), len(ks)))\n", - "order4_reshaped = np.asarray(order4).reshape((len(times), len(ks)))\n", - "\n", - "fig, axes = plt.subplots(3, 3, figsize=(20, 10))\n", - "\n", - "for i, t in enumerate(times):\n", - " ax = axes[i // 3, i % 3]\n", - "\n", - " ax.axvline(1.0, color=\"grey\", label=\"$t/k=1$\")\n", - " ax.axhline(exact[i], color=\"tab:green\", label=\"exact\")\n", - "\n", - " ax.plot(t / ks, order2_reshaped[i], label=r\"$\\chi=2$\")\n", - " ax.plot(t / ks, order4_reshaped[i], label=r\"$\\chi=4$\")\n", - "\n", - " ax.semilogx()\n", - " ax.set_xticks([10**p for p in range(-1, 1)])\n", - " ax.set_xlabel(\"$t/k$\")\n", - "\n", - " ax.set_ylim([-0.33, 0.55])\n", - " ax.set_ylabel(r\"$\\langle O \\rangle$\")\n", - "\n", - " ax.set_title(f\"time={t}\")\n", - "\n", - "fig.legend(*ax.get_legend_handles_labels(), ncols=4, loc=\"center\", bbox_to_anchor=(0.5, 0.0))\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "2f40b1ee-0e98-4f24-9d25-080306ba98e1", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "We can now clearly see that in the regime $t/k \\gt 1$ the dynamics are no longer reproduced faithfully by the Trotterized circuits. Thus, if an MPF were to use a product formula with a $k_j$ from that regime, its extrapolated expectation value cannot faithfully produce good results." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-mpf/how_tos/choose-trotter-steps.mdx b/docs/addons/qiskit-addon-mpf/how_tos/choose-trotter-steps.mdx deleted file mode 100644 index 126e71ec38b..00000000000 --- a/docs/addons/qiskit-addon-mpf/how_tos/choose-trotter-steps.mdx +++ /dev/null @@ -1,136 +0,0 @@ - - -# How to choose the Trotter steps for an MPF - -This guide teaches you how to find a good set of Trotter steps for an MPF. We assume that you have already read and understood the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial. - - - -## Understanding some heuristics - -In principle, any $k_j$ values can be chosen but some choices of Trotter steps will lead to a larger noise amplification on real devices than others. Thus, it is important that one tries to find *“good”* values of $k_j$. In the following we will discuss some heuristics to help guide you in doing so. - -1. In practice, our **largest** $k_j$ ($k_{\text{max}}$) value is simply bound by the depth of quantum circuit we can reliably execute. -2. Since the Trotter error is only well behaved for $\text{d}t = t/k \lt 1$ (see [Understanding the stability of MPFs](/docs/addons/qiskit-addon-mpf/explanations/mpf_stability)), our **smallest** $k_j$ ($k_{\text{min}}$) should satisfy: $t/k_{\text{min}} \lt 1$ (Note: empirically $\text{d}t \leq 1$ seems to work fine, too). -3. None of our $x_j$ values should be close to $0$, as this would imply that we might as well exclude the corresponding $k_j$ from the expansion. -4. Similarly, we do not want the $x_j$ for $k_{\text{max}}$ to be the only dominant one, since that implies that we are essentially just recovering a single product formula with $k=k_{\text{max}}$. -5. Finally, we want the L1-norm of our coefficients, $x_j$, to be small, as this indicates a well-conditioned MPF (see [Carrera Vazquez et al., 2023](https://quantum-journal.org/papers/q-2023-07-25-1067/)). Furthermore, small coefficients minimize the variance amplification of the MPF. - - - -## Applying these heuristics - -Reusing the example from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial, we want to simulate up to time $t=8.0$. Thus, $k_{\text{min}}=8$. - -For the sake of this example, let us assume that our deepest circuit is bound by $k_{\text{max}}=20$. - -In addition to the constraints above, we will follow the heuristic presented by [Zhuk et al., 2023](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309), where they minimize a re-weighted L1-norm of the coefficients: - -$$ -\text{min}_{k_j} \sum_j \frac{|c_j|}{k_j^{2\chi}} \, , -$$ - -where $\chi$ is the order of our individual product formulas. - - - -### Preparing our LSE - -In a first step, we prepare the LSE, $Ax=b$, leveraging a feature of the [setup\_static\_lse](/docs/api/qiskit-addon-mpf/static#qiskit_addon_mpf.static.setup_static_lse) function that allows us to use a [cvxpy.Parameter](https://www.cvxpy.org/api_reference/cvxpy.expressions.html#parameter) object for our $k_j$ values. - -``` -[30]: -``` - -```python -order = 2 -symmetric = True - -min_k = 8 -max_k = 20 - -max_l1_norm = 5.0 -min_coeff = 0.01 -``` - -``` -[31]: -``` - -```python -import cvxpy as cp -from qiskit_addon_mpf.static import setup_static_lse - -ks = cp.Parameter(3, integer=True) -lse = setup_static_lse(ks, order=order, symmetric=symmetric) -``` - -Next, we loop over all possible 3-tuples of $k_j$ values within our specified range. For each one, we compute the analytical coefficients, $x_j$, and then apply our filter criteria. We keep track of those $k_j$ tuples which satisfy our criteria. - -``` -[32]: -``` - -```python -import numpy as np - -possible_ks = {} - -for k1 in range(min_k, max_k): - for k2 in range(k1 + 1, max_k): - for k3 in range(k2 + 1, max_k): - ks.value = [k1, k2, k3] - coeffs = lse.solve() - - if np.any(np.isclose(coeffs, 0.0, atol=min_coeff)): - continue - - norm1 = np.linalg.norm(coeffs, ord=1) - if norm1 > max_l1_norm: - continue - - weighted = np.sum([np.abs(c) / k ** (2 * order) for c, k in zip(coeffs, ks.value)]) - possible_ks[tuple(int(k) for k in ks.value)] = { - "coeffs": tuple(coeffs), - "weighted": float(weighted), - "norm1": float(norm1), - } -``` - -Finally, we sort our $k_j$ by the re-weighted L1-norm presented by [Zhuk et al., 2023](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309): - -``` -[33]: -``` - -```python -for ks_val in sorted(possible_ks, key=lambda ks_val: possible_ks[ks_val].get("weighted")): - print(ks_val, possible_ks[ks_val]) -``` - -```python -(8, 14, 19) {'coeffs': (0.10447913478216586, -1.7638200183654775, 2.6593408835833117), 'weighted': 9.182736455463762e-05, 'norm1': 4.527640036730955} -(8, 13, 19) {'coeffs': (0.13134519801186473, -1.4167162698412699, 2.285371071829405), 'weighted': 9.920634920634922e-05, 'norm1': 3.8334325396825397} -(8, 12, 19) {'coeffs': (0.17239057239057254, -1.1944700460829496, 2.022079473692377), 'weighted': 0.00011520737327188946, 'norm1': 3.388940092165899} -(9, 13, 19) {'coeffs': (0.2662743506493499, -1.6904000946969673, 2.4241257440476174), 'weighted': 0.00011837121212121191, 'norm1': 4.380800189393934} -(8, 13, 18) {'coeffs': (0.15003663003663004, -1.7549001536098316, 2.6048635235732016), 'weighted': 0.00012288786482334872, 'norm1': 4.509800307219663} -(8, 12, 18) {'coeffs': (0.19692307692307653, -1.4399999999999984, 2.243076923076922), 'weighted': 0.0001388888888888887, 'norm1': 3.879999999999997} -(8, 11, 19) {'coeffs': (0.24195168054817162, -1.070248538011695, 1.8282968574635234), 'weighted': 0.00014619883040935662, 'norm1': 3.14049707602339} -(9, 12, 19) {'coeffs': (0.37193877551020393, -1.5167873601053319, 2.144848584595128), 'weighted': 0.00014629507717065315, 'norm1': 4.033574720210664} -(8, 12, 17) {'coeffs': (0.22755555555555573, -1.7875862068965531, 2.5600306513409974), 'weighted': 0.00017241379310344843, 'norm1': 4.575172413793107} -(8, 11, 18) {'coeffs': (0.27638326585694917, -1.265318468585254, 1.988935202728305), 'weighted': 0.00017284590787313073, 'norm1': 3.530636937170508} -(9, 12, 18) {'coeffs': (0.42857142857142816, -1.8285714285714276, 2.3999999999999995), 'weighted': 0.00017636684303350958, 'norm1': 4.657142857142855} -(9, 11, 19) {'coeffs': (0.5858035714285708, -1.5251041666666654, 1.9393005952380946), 'weighted': 0.00020833333333333313, 'norm1': 4.05020833333333} -(8, 11, 17) {'coeffs': (0.3193762183235871, -1.5289264828738525, 2.2095502645502654), 'weighted': 0.00020885547201336693, 'norm1': 4.0578529657477045} -(8, 10, 19) {'coeffs': (0.383090160867938, -1.0642826734780744, 1.6811925126101364), 'weighted': 0.00021285653469561486, 'norm1': 3.1285653469561487} -(9, 11, 18) {'coeffs': (0.6750000000000009, -1.8030788177339916, 2.1280788177339907), 'weighted': 0.0002463054187192121, 'norm1': 4.606157635467984} -(8, 10, 18) {'coeffs': (0.43760683760683694, -1.2400793650793638, 1.8024725274725268), 'weighted': 0.0002480158730158727, 'norm1': 3.4801587301587276} -(8, 11, 16) {'coeffs': (0.3742690058479534, -1.9026640675763484, 2.528395061728395), 'weighted': 0.0002599090318388565, 'norm1': 4.805328135152697} -(8, 10, 17) {'coeffs': (0.5056790123456789, -1.4697236919459142, 1.9640446796002353), 'weighted': 0.00029394473838918284, 'norm1': 3.9394473838918285} -(8, 10, 16) {'coeffs': (0.592592592592593, -1.7806267806267817, 2.1880341880341887), 'weighted': 0.0003561253561253563, 'norm1': 4.561253561253563} -(8, 9, 19) {'coeffs': (0.8112497524262232, -1.3783613445378153, 1.5671115921115921), 'weighted': 0.00042016806722689084, 'norm1': 3.756722689075631} -(8, 9, 18) {'coeffs': (0.9266968325791858, -1.5882352941176474, 1.6615384615384616), 'weighted': 0.00048414427499394834, 'norm1': 4.176470588235295} -(8, 9, 17) {'coeffs': (1.0708496732026151, -1.855486425339368, 1.7846367521367528), 'weighted': 0.0005656108597285072, 'norm1': 4.710972850678736} -``` - -Recalling the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial, from these possible values we picked `ks=(8, 12, 19)` since it strikes a good balance of minimizing the re-weighted and plain L1-norms. diff --git a/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb b/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb deleted file mode 100644 index ba4f43cc53b..00000000000 --- a/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb +++ /dev/null @@ -1,297 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "dafa4d64-0da8-4e18-aea8-73f13cac3d29", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# How to choose the Trotter steps for an MPF\n", - "\n", - "This guide teaches you how to find a good set of Trotter steps for an MPF.\n", - "We assume that you have already read and understood the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial." - ] - }, - { - "cell_type": "markdown", - "id": "fb022d51-3187-4822-8536-7778df8620f1", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Understanding some heuristics\n", - "\n", - "In principle, any $k_j$ values can be chosen but some choices of Trotter steps will lead to a larger noise amplification on real devices than others.\n", - "Thus, it is important that one tries to find _\"good\"_ values of $k_j$.\n", - "In the following we will discuss some heuristics to help guide you in doing so.\n", - "\n", - "1. In practice, our **largest** $k_j$ ($k_{\\text{max}}$) value is simply bound by the depth of quantum circuit we can reliably execute.\n", - "\n", - "2. Since the Trotter error is only well behaved for $\\text{d}t = t/k \\lt 1$ (see [Understanding the stability of MPFs](/docs/addons/qiskit-addon-mpf/explanations/mpf_stability)), our **smallest** $k_j$ ($k_{\\text{min}}$) should satisfy: $t/k_{\\text{min}} \\lt 1$ (Note: empirically $\\text{d}t \\leq 1$ seems to work fine, too).\n", - "\n", - "3. None of our $x_j$ values should be close to $0$, as this would imply that we might as well exclude the corresponding $k_j$ from the expansion.\n", - "\n", - "4. Similarly, we do not want the $x_j$ for $k_{\\text{max}}$ to be the only dominant one, since that implies that we are essentially just recovering a single product formula with $k=k_{\\text{max}}$.\n", - "\n", - "5. Finally, we want the L1-norm of our coefficients, $x_j$, to be small, as this indicates a well-conditioned MPF (see [Carrera Vazquez et al., 2023]). Furthermore, small coefficients minimize the variance amplification of the MPF.\n", - "\n", - "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/" - ] - }, - { - "cell_type": "markdown", - "id": "f0f2096f-5fb6-4861-a05e-3ded9ac65a82", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Applying these heuristics\n", - "\n", - "Reusing the example from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial, we want to simulate up to time $t=8.0$.\n", - "Thus, $k_{\\text{min}}=8$.\n", - "\n", - "For the sake of this example, let us assume that our deepest circuit is bound by $k_{\\text{max}}=20$.\n", - "\n", - "In addition to the constraints above, we will follow the heuristic presented by [Zhuk et al., 2023], where they minimize a re-weighted L1-norm of the coefficients:\n", - "\n", - "$$\n", - "\\text{min}_{k_j} \\sum_j \\frac{|c_j|}{k_j^{2\\chi}} \\, ,\n", - "$$\n", - "\n", - "where $\\chi$ is the order of our individual product formulas.\n", - "\n", - "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" - ] - }, - { - "cell_type": "markdown", - "id": "1ff9ff06-47bf-451d-815f-11d331cafaa3", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Preparing our LSE\n", - "\n", - "In a first step, we prepare the LSE, $Ax=b$, leveraging a feature of the [setup_static_lse](/docs/api/qiskit-addon-mpf/static#qiskit_addon_mpf.static.setup_static_lse) function that allows us to use a [cvxpy.Parameter](https://www.cvxpy.org/api_reference/cvxpy.expressions.html#parameter) object for our $k_j$ values." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "bda8c5d3-b48a-4c9e-bef3-fb9f26846ad6", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "order = 2\n", - "symmetric = True\n", - "\n", - "min_k = 8\n", - "max_k = 20\n", - "\n", - "max_l1_norm = 5.0\n", - "min_coeff = 0.01" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "3b68ec15-b8ed-45a1-bd46-ced7b29b159e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import cvxpy as cp\n", - "from qiskit_addon_mpf.static import setup_static_lse\n", - "\n", - "ks = cp.Parameter(3, integer=True)\n", - "lse = setup_static_lse(ks, order=order, symmetric=symmetric)" - ] - }, - { - "cell_type": "markdown", - "id": "92356e74-97b6-4231-bd35-43d5cd274dca", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Next, we loop over all possible 3-tuples of $k_j$ values within our specified range.\n", - "For each one, we compute the analytical coefficients, $x_j$, and then apply our filter criteria.\n", - "We keep track of those $k_j$ tuples which satisfy our criteria." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "da4c0610-8d1c-4596-b082-8cfd0098bc7c", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "possible_ks = {}\n", - "\n", - "for k1 in range(min_k, max_k):\n", - " for k2 in range(k1 + 1, max_k):\n", - " for k3 in range(k2 + 1, max_k):\n", - " ks.value = [k1, k2, k3]\n", - " coeffs = lse.solve()\n", - "\n", - " if np.any(np.isclose(coeffs, 0.0, atol=min_coeff)):\n", - " continue\n", - "\n", - " norm1 = np.linalg.norm(coeffs, ord=1)\n", - " if norm1 > max_l1_norm:\n", - " continue\n", - "\n", - " weighted = np.sum([np.abs(c) / k ** (2 * order) for c, k in zip(coeffs, ks.value)])\n", - " possible_ks[tuple(int(k) for k in ks.value)] = {\n", - " \"coeffs\": tuple(coeffs),\n", - " \"weighted\": float(weighted),\n", - " \"norm1\": float(norm1),\n", - " }" - ] - }, - { - "cell_type": "markdown", - "id": "9f37b658-0e73-409b-add2-4cbf83708730", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Finally, we sort our $k_j$ by the re-weighted L1-norm presented by [Zhuk et al., 2023]:\n", - "\n", - "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "16d05ed1-c1ed-445d-afce-543f55275e5e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(8, 14, 19) {'coeffs': (0.10447913478216586, -1.7638200183654775, 2.6593408835833117), 'weighted': 9.182736455463762e-05, 'norm1': 4.527640036730955}\n", - "(8, 13, 19) {'coeffs': (0.13134519801186473, -1.4167162698412699, 2.285371071829405), 'weighted': 9.920634920634922e-05, 'norm1': 3.8334325396825397}\n", - "(8, 12, 19) {'coeffs': (0.17239057239057254, -1.1944700460829496, 2.022079473692377), 'weighted': 0.00011520737327188946, 'norm1': 3.388940092165899}\n", - "(9, 13, 19) {'coeffs': (0.2662743506493499, -1.6904000946969673, 2.4241257440476174), 'weighted': 0.00011837121212121191, 'norm1': 4.380800189393934}\n", - "(8, 13, 18) {'coeffs': (0.15003663003663004, -1.7549001536098316, 2.6048635235732016), 'weighted': 0.00012288786482334872, 'norm1': 4.509800307219663}\n", - "(8, 12, 18) {'coeffs': (0.19692307692307653, -1.4399999999999984, 2.243076923076922), 'weighted': 0.0001388888888888887, 'norm1': 3.879999999999997}\n", - "(8, 11, 19) {'coeffs': (0.24195168054817162, -1.070248538011695, 1.8282968574635234), 'weighted': 0.00014619883040935662, 'norm1': 3.14049707602339}\n", - "(9, 12, 19) {'coeffs': (0.37193877551020393, -1.5167873601053319, 2.144848584595128), 'weighted': 0.00014629507717065315, 'norm1': 4.033574720210664}\n", - "(8, 12, 17) {'coeffs': (0.22755555555555573, -1.7875862068965531, 2.5600306513409974), 'weighted': 0.00017241379310344843, 'norm1': 4.575172413793107}\n", - "(8, 11, 18) {'coeffs': (0.27638326585694917, -1.265318468585254, 1.988935202728305), 'weighted': 0.00017284590787313073, 'norm1': 3.530636937170508}\n", - "(9, 12, 18) {'coeffs': (0.42857142857142816, -1.8285714285714276, 2.3999999999999995), 'weighted': 0.00017636684303350958, 'norm1': 4.657142857142855}\n", - "(9, 11, 19) {'coeffs': (0.5858035714285708, -1.5251041666666654, 1.9393005952380946), 'weighted': 0.00020833333333333313, 'norm1': 4.05020833333333}\n", - "(8, 11, 17) {'coeffs': (0.3193762183235871, -1.5289264828738525, 2.2095502645502654), 'weighted': 0.00020885547201336693, 'norm1': 4.0578529657477045}\n", - "(8, 10, 19) {'coeffs': (0.383090160867938, -1.0642826734780744, 1.6811925126101364), 'weighted': 0.00021285653469561486, 'norm1': 3.1285653469561487}\n", - "(9, 11, 18) {'coeffs': (0.6750000000000009, -1.8030788177339916, 2.1280788177339907), 'weighted': 0.0002463054187192121, 'norm1': 4.606157635467984}\n", - "(8, 10, 18) {'coeffs': (0.43760683760683694, -1.2400793650793638, 1.8024725274725268), 'weighted': 0.0002480158730158727, 'norm1': 3.4801587301587276}\n", - "(8, 11, 16) {'coeffs': (0.3742690058479534, -1.9026640675763484, 2.528395061728395), 'weighted': 0.0002599090318388565, 'norm1': 4.805328135152697}\n", - "(8, 10, 17) {'coeffs': (0.5056790123456789, -1.4697236919459142, 1.9640446796002353), 'weighted': 0.00029394473838918284, 'norm1': 3.9394473838918285}\n", - "(8, 10, 16) {'coeffs': (0.592592592592593, -1.7806267806267817, 2.1880341880341887), 'weighted': 0.0003561253561253563, 'norm1': 4.561253561253563}\n", - "(8, 9, 19) {'coeffs': (0.8112497524262232, -1.3783613445378153, 1.5671115921115921), 'weighted': 0.00042016806722689084, 'norm1': 3.756722689075631}\n", - "(8, 9, 18) {'coeffs': (0.9266968325791858, -1.5882352941176474, 1.6615384615384616), 'weighted': 0.00048414427499394834, 'norm1': 4.176470588235295}\n", - "(8, 9, 17) {'coeffs': (1.0708496732026151, -1.855486425339368, 1.7846367521367528), 'weighted': 0.0005656108597285072, 'norm1': 4.710972850678736}\n" - ] - } - ], - "source": [ - "for ks_val in sorted(possible_ks, key=lambda ks_val: possible_ks[ks_val].get(\"weighted\")):\n", - " print(ks_val, possible_ks[ks_val])" - ] - }, - { - "cell_type": "markdown", - "id": "f3b709d3-ed27-4e6f-a2ba-f3f17326099e", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Recalling the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial, from these possible values we picked `ks=(8, 12, 19)` since it strikes a good balance of minimizing the re-weighted and plain L1-norms." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-mpf/how_tos/index.mdx b/docs/addons/qiskit-addon-mpf/how_tos/index.mdx deleted file mode 100644 index 847d80880ae..00000000000 --- a/docs/addons/qiskit-addon-mpf/how_tos/index.mdx +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Multi-product formulas (MPF) API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-mpf. ---- - -# How-To Guides - -This page summarizes the available how-to guides. - -* [How to choose the Trotter steps for an MPF](choose-trotter-steps) -* [How to use the approximate model](using-approximate-model) - diff --git a/docs/addons/qiskit-addon-mpf/how_tos/using-approximate-model.mdx b/docs/addons/qiskit-addon-mpf/how_tos/using-approximate-model.mdx deleted file mode 100644 index 90766605c31..00000000000 --- a/docs/addons/qiskit-addon-mpf/how_tos/using-approximate-model.mdx +++ /dev/null @@ -1,607 +0,0 @@ - - -# How to use the approximate model - -You have already seen a first example of the [setup\_sum\_of\_squares\_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem) function in the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial. In this guide, we are going to revisit that function in more detail. - - - -## Setting up a simple model problem - -For this guide, we will reuse the simple model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial; the Ising model on a line of 10 sites: - -$$ -\hat{\mathcal{H}}_{\text{Ising}} = \sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \sum_{i=1}^{10} h_i X_i \, , -$$ - -where $J$ is the coupling strength between two sites and $h$ is the external magnetic field. - -``` -[1]: -``` - -```python -from qiskit.transpiler import CouplingMap -from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian - -coupling_map = CouplingMap.from_line(10, bidirectional=False) - -hamiltonian = generate_xyz_hamiltonian( - coupling_map, - coupling_constants=(0.0, 0.0, 1.0), - ext_magnetic_field=(0.4, 0.0, 0.0), -) -print(hamiltonian) -``` - -```python -SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'], - coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, - 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, - 0.4+0.j, 0.4+0.j, 0.4+0.j]) -``` - - - -## Choosing $k_j$ - -In this guide, we are going to focus on the tweaks that we can apply to the [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) and its solver that are returned by the [setup\_sum\_of\_squares\_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem). Therefore, we are not concerned with choosing any specific $k_j$ values and can simply reuse the ones from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial. - -``` -[2]: -``` - -```python -trotter_steps = (8, 12, 19) -``` - - - -## Computing $x$ analytically - -First, we have to construct our LSE, $Ax=b$. Once again, we reuse the settings from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial. - -``` -[3]: -``` - -```python -from qiskit_addon_mpf.static import setup_static_lse - -lse = setup_static_lse(trotter_steps, order=2, symmetric=True) -``` - -Next, we compute the analytical solution of $x$ and its L1-norm as our reference values. - -``` -[4]: -``` - -```python -coeff_analytical = lse.solve() -print(coeff_analytical) -``` - -```python -[ 0.17239057 -1.19447005 2.02207947] -``` - -``` -[5]: -``` - -```python -import numpy as np - -print(np.linalg.norm(coeff_analytical, ord=1)) -``` - -```python -3.388940092165899 -``` - - - -## Setting up the approximate model - -Now, we will construct the approximate model. The idea of using approximate solutions $\tilde{x}$ in order to ensure a smaller L1-norm was proposed by [Zhuk et al., 2023](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309). The model is already explained in detail in the API documentation of [setup\_sum\_of\_squares\_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem) so we refrain from repeating it here. Suffice to say, that the model consists of two constraints which ensure that the coefficients, $\tilde{x}$, sum to $1$ and it enforces their L1-norm to be smaller than the user-provided `max_l1_norm` value. The optimization objective is to minimize the deviation of $A\tilde{x}=b$. - -We can see all of this in the output below. Here, we are setting `max_l1_norm=3` because we already know that the L1-norm of the exact solution is approximately $3.4$ and we want to find a $\tilde{x}$ that has a smaller L1-norm. - -``` -[6]: -``` - -```python -from qiskit_addon_mpf.costs import setup_sum_of_squares_problem - -model, coeff_var = setup_sum_of_squares_problem(lse, max_l1_norm=3) -print(model) -``` - -```python -minimize quad_over_lin(Vstack([1. 1. 1.] @ x + -1.0, [0.015625 0.00694444 0.00277008] @ x + -0.0, [2.44140625e-04 4.82253086e-05 7.67336039e-06] @ x + -0.0), 1.0) -subject to Sum(x, None, False) == 1.0 - norm1(x) <= 3.0 -``` - - - -## Solving the model with default settings - -First, let us see what happens when we solve the `model` with only default settings. - -``` -[7]: -``` - -```python -model.solve(verbose=True) -``` - -```python -=============================================================================== - CVXPY - v1.5.3 -=============================================================================== -(CVXPY) Sep 05 09:28:06 AM: Your problem has 3 variables, 2 constraints, and 0 parameters. -(CVXPY) Sep 05 09:28:06 AM: It is compliant with the following grammars: DCP, DQCP -(CVXPY) Sep 05 09:28:06 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.) -(CVXPY) Sep 05 09:28:06 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution. -(CVXPY) Sep 05 09:28:06 AM: Your problem is compiled with the CPP canonicalization backend. -------------------------------------------------------------------------------- - Compilation -------------------------------------------------------------------------------- -(CVXPY) Sep 05 09:28:06 AM: Compiling problem (target solver=OSQP). -(CVXPY) Sep 05 09:28:06 AM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> OSQP -(CVXPY) Sep 05 09:28:06 AM: Applying reduction CvxAttr2Constr -(CVXPY) Sep 05 09:28:06 AM: Applying reduction Qp2SymbolicQp -(CVXPY) Sep 05 09:28:06 AM: Applying reduction QpMatrixStuffing -(CVXPY) Sep 05 09:28:06 AM: Applying reduction OSQP -(CVXPY) Sep 05 09:28:06 AM: Finished problem compilation (took 1.053e-02 seconds). -------------------------------------------------------------------------------- - Numerical solver -------------------------------------------------------------------------------- -(CVXPY) Sep 05 09:28:06 AM: Invoking solver OSQP to obtain a solution. ------------------------------------------------------------------ - OSQP v0.6.3 - Operator Splitting QP Solver - (c) Bartolomeo Stellato, Goran Banjac - University of Oxford - Stanford University 2021 ------------------------------------------------------------------ -problem: variables n = 9, constraints m = 11 - nnz(P) + nnz(A) = 33 -settings: linear system solver = qdldl, - eps_abs = 1.0e-05, eps_rel = 1.0e-05, - eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04, - rho = 1.00e-01 (adaptive), - sigma = 1.00e-06, alpha = 1.60, max_iter = 10000 - check_termination: on (interval 25), - scaling: on, scaled_termination: off - warm start: on, polish: on, time_limit: off - -iter objective pri res dua res rho time - 1 0.0000e+00 1.00e+00 2.00e+02 1.00e-01 6.26e-05s - 200 4.0661e-09 7.52e-06 1.44e-08 1.22e-05 1.28e-04s - -status: solved -solution polish: unsuccessful -number of iterations: 200 -optimal objective: 0.0000 -run time: 1.53e-04s -optimal rho estimate: 2.09e-06 - -------------------------------------------------------------------------------- - Summary -------------------------------------------------------------------------------- -(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal -(CVXPY) Sep 05 09:28:07 AM: Optimal value: 4.092e-09 -(CVXPY) Sep 05 09:28:07 AM: Compilation took 1.053e-02 seconds -(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 1.028e-03 seconds -``` - -``` -[7]: -``` - -```python -4.092215822096244e-09 -``` - -We can see from the final value of the cost function that the sum of squares of $A\tilde{x}-b$ is almost zero. - -In the output above, we also get a lot more information from [cvxpy.Problem.solve](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem.solve), some of which we will get back to later. - -For now, let us inspect the final values of $\tilde{x}$ and its L1-norm: - -``` -[8]: -``` - -```python -coeff_approx_plain = coeff_var.value -print(coeff_approx_plain) -print(np.linalg.norm(coeff_approx_plain, ord=1)) -``` - -```python -[-0.39646076 0.55556835 0.84089365] -1.7929227539822414 -``` - -We can clearly see that the L1-norm has decreased compared to the analytical solution that we have computed before. - -Nonetheless, let us see if we can tweak our solution a bit. In the end, we will compare the results obtained for this set of coefficients with all the other ones. - - - -## Tweaking the approximate model - -Rather than tweaking the input parameters like the chosen `trotter_steps` and `max_l1_norm` values, we will focus on the solver settings in this guide. - - - -### Using a different solver - -Notice, that we used the default solver method: `OSQP`. We can change that solver to see whether this has any effect. - -``` -[9]: -``` - -```python -model.solve(verbose=True, solver="CLARABEL") -``` - -```python -=============================================================================== - CVXPY - v1.5.3 -=============================================================================== -(CVXPY) Sep 05 09:28:07 AM: Your problem has 3 variables, 2 constraints, and 0 parameters. -(CVXPY) Sep 05 09:28:07 AM: It is compliant with the following grammars: DCP, DQCP -(CVXPY) Sep 05 09:28:07 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.) -(CVXPY) Sep 05 09:28:07 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution. -(CVXPY) Sep 05 09:28:07 AM: Your problem is compiled with the CPP canonicalization backend. -------------------------------------------------------------------------------- - Compilation -------------------------------------------------------------------------------- -(CVXPY) Sep 05 09:28:07 AM: Compiling problem (target solver=CLARABEL). -(CVXPY) Sep 05 09:28:07 AM: Reduction chain: Dcp2Cone -> CvxAttr2Constr -> ConeMatrixStuffing -> CLARABEL -(CVXPY) Sep 05 09:28:07 AM: Applying reduction Dcp2Cone -(CVXPY) Sep 05 09:28:07 AM: Applying reduction CvxAttr2Constr -(CVXPY) Sep 05 09:28:07 AM: Applying reduction ConeMatrixStuffing -(CVXPY) Sep 05 09:28:07 AM: Applying reduction CLARABEL -(CVXPY) Sep 05 09:28:07 AM: Finished problem compilation (took 8.657e-03 seconds). -------------------------------------------------------------------------------- - Numerical solver -------------------------------------------------------------------------------- -(CVXPY) Sep 05 09:28:07 AM: Invoking solver CLARABEL to obtain a solution. -------------------------------------------------------------- - Clarabel.rs v0.8.1 - Clever Acronym - - (c) Paul Goulart - University of Oxford, 2022 -------------------------------------------------------------- - -problem: - variables = 9 - constraints = 11 - nnz(P) = 3 - nnz(A) = 30 - cones (total) = 2 - : Zero = 1, numel = 4 - : Nonnegative = 1, numel = 7 - -settings: - linear algebra: direct / qdldl, precision: 64 bit - max iter = 200, time limit = Inf, max step = 0.990 - tol_feas = 1.0e-8, tol_gap_abs = 1.0e-8, tol_gap_rel = 1.0e-8, - static reg : on, ϵ1 = 1.0e-8, ϵ2 = 4.9e-32 - dynamic reg: on, ϵ = 1.0e-13, δ = 2.0e-7 - iter refine: on, reltol = 1.0e-13, abstol = 1.0e-12, - max iter = 10, stop ratio = 5.0 - equilibrate: on, min_scale = 1.0e-4, max_scale = 1.0e4 - max iter = 10 - -iter pcost dcost gap pres dres k/t μ step ---------------------------------------------------------------------------------------------- - 0 +7.1341e-05 -2.3333e+00 2.33e+00 2.40e-01 7.10e-01 1.00e+00 1.88e+00 ------ - 1 +7.1330e-05 -1.5432e-01 1.54e-01 1.64e-02 1.12e-01 3.28e-02 1.80e-01 9.61e-01 - 2 +6.9440e-05 -1.7266e-03 1.80e-03 2.10e-04 1.89e-03 5.65e-04 2.43e-03 9.87e-01 - 3 +2.2053e-05 -5.0002e-05 7.21e-05 5.56e-06 4.83e-05 1.98e-05 7.80e-05 9.87e-01 - 4 +1.1119e-06 -2.5543e-05 2.67e-05 1.80e-07 1.41e-06 3.21e-06 1.04e-05 9.90e-01 - 5 +7.9250e-09 -2.0484e-06 2.06e-06 2.06e-09 1.59e-08 2.11e-07 6.43e-07 9.90e-01 - 6 +2.2543e-09 -2.8390e-08 3.06e-08 2.07e-11 1.59e-10 3.12e-09 9.42e-09 9.90e-01 - 7 +1.8480e-09 -8.5733e-10 2.71e-09 1.79e-12 2.36e-10 3.19e-10 9.40e-10 9.15e-01 ---------------------------------------------------------------------------------------------- -Terminated with status = Solved -solve time = 113.289µs -------------------------------------------------------------------------------- - Summary -------------------------------------------------------------------------------- -(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal -(CVXPY) Sep 05 09:28:07 AM: Optimal value: 1.848e-09 -(CVXPY) Sep 05 09:28:07 AM: Compilation took 8.657e-03 seconds -(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 8.504e-04 seconds -``` - -``` -[9]: -``` - -```python -1.8479854472132682e-09 -``` - -``` -[10]: -``` - -```python -coeff_approx_clarabel = coeff_var.value -print(coeff_approx_clarabel) -print(np.linalg.norm(coeff_approx_clarabel, ord=1)) -``` - -```python -[-0.21284352 -0.00792683 1.22077035] -1.4415406931636157 -``` - -Indeed, the outcome has changed. But now we have one major contribution from the largest $k_j$ value which is also not ideal. - -An exhaustive search of the various available solvers is beyond the scope of this guide. Instead we encourage you to try this yourself. - - - -### Changing the convergence thresholds - -Rather than changing the solver altogether, we can also tweak the convergence parameters. For the `OSQP` solver the relevant ones are called `eps_abs` and `eps_rel` and they are both set to `1e-5` by default. - -``` -[11]: -``` - -```python -model.solve(verbose=True, eps_abs=1e-8, eps_rel=1e-8) -``` - -```python -=============================================================================== - CVXPY - v1.5.3 -=============================================================================== -(CVXPY) Sep 05 09:28:07 AM: Your problem has 3 variables, 2 constraints, and 0 parameters. -(CVXPY) Sep 05 09:28:07 AM: It is compliant with the following grammars: DCP, DQCP -(CVXPY) Sep 05 09:28:07 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.) -(CVXPY) Sep 05 09:28:07 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution. -(CVXPY) Sep 05 09:28:07 AM: Your problem is compiled with the CPP canonicalization backend. -------------------------------------------------------------------------------- - Compilation -------------------------------------------------------------------------------- -(CVXPY) Sep 05 09:28:07 AM: Compiling problem (target solver=OSQP). -(CVXPY) Sep 05 09:28:07 AM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> OSQP -(CVXPY) Sep 05 09:28:07 AM: Applying reduction CvxAttr2Constr -(CVXPY) Sep 05 09:28:07 AM: Applying reduction Qp2SymbolicQp -(CVXPY) Sep 05 09:28:07 AM: Applying reduction QpMatrixStuffing -(CVXPY) Sep 05 09:28:07 AM: Applying reduction OSQP -(CVXPY) Sep 05 09:28:07 AM: Finished problem compilation (took 8.835e-03 seconds). -------------------------------------------------------------------------------- - Numerical solver -------------------------------------------------------------------------------- -(CVXPY) Sep 05 09:28:07 AM: Invoking solver OSQP to obtain a solution. ------------------------------------------------------------------ - OSQP v0.6.3 - Operator Splitting QP Solver - (c) Bartolomeo Stellato, Goran Banjac - University of Oxford - Stanford University 2021 ------------------------------------------------------------------ -problem: variables n = 9, constraints m = 11 - nnz(P) + nnz(A) = 33 -settings: linear system solver = qdldl, - eps_abs = 1.0e-08, eps_rel = 1.0e-08, - eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04, - rho = 1.00e-01 (adaptive), - sigma = 1.00e-06, alpha = 1.60, max_iter = 10000 - check_termination: on (interval 25), - scaling: on, scaled_termination: off - warm start: on, polish: on, time_limit: off - -iter objective pri res dua res rho time - 1 0.0000e+00 1.00e+00 2.00e+02 1.00e-01 5.02e-05s - 200 4.0661e-09 7.52e-06 1.44e-08 1.22e-05 2.10e-04s - 400 3.8209e-09 4.95e-06 2.88e-09 2.09e-06 3.72e-04s - 600 3.3607e-09 6.09e-06 1.96e-09 2.09e-06 4.33e-04s - 800 2.8975e-09 6.24e-06 1.84e-09 2.09e-06 4.87e-04s -1000 2.4704e-09 6.08e-06 1.70e-09 2.09e-06 5.40e-04s -1200 2.0921e-09 5.78e-06 1.56e-09 2.09e-06 5.92e-04s -1400 1.7645e-09 5.40e-06 1.44e-09 2.09e-06 6.44e-04s -1600 1.4845e-09 5.01e-06 1.32e-09 2.09e-06 7.01e-04s -1800 1.2471e-09 4.63e-06 1.21e-09 2.09e-06 7.53e-04s -2000 1.0467e-09 4.26e-06 1.11e-09 2.09e-06 8.05e-04s -2200 8.7801e-10 3.91e-06 1.01e-09 2.09e-06 8.57e-04s -2400 7.3890e-10 3.25e-06 7.74e-10 2.09e-06 9.10e-04s -2600 6.4003e-10 2.47e-06 7.11e-10 2.09e-06 9.62e-04s -2800 5.6692e-10 2.03e-06 6.68e-10 2.09e-06 1.01e-03s -3000 5.0872e-10 1.76e-06 6.33e-10 2.09e-06 1.07e-03s -3200 4.5991e-10 1.58e-06 6.02e-10 2.09e-06 1.12e-03s -3400 4.1754e-10 1.46e-06 5.74e-10 2.09e-06 1.17e-03s -3600 3.7999e-10 1.37e-06 5.47e-10 2.09e-06 1.22e-03s -3800 3.4628e-10 1.29e-06 5.22e-10 2.09e-06 1.32e-03s -4000 3.1580e-10 1.22e-06 4.99e-10 2.09e-06 1.37e-03s -4200 2.8813e-10 1.16e-06 4.76e-10 2.09e-06 1.43e-03s -4400 2.6295e-10 1.11e-06 4.55e-10 2.09e-06 1.48e-03s -4600 2.4000e-10 1.06e-06 4.35e-10 2.09e-06 1.53e-03s -4800 2.1907e-10 1.01e-06 4.15e-10 2.09e-06 1.58e-03s -5000 1.9997e-10 9.67e-07 3.97e-10 2.09e-06 1.63e-03s -5200 1.8255e-10 9.23e-07 3.79e-10 2.09e-06 1.69e-03s -5400 1.6664e-10 8.82e-07 3.62e-10 2.09e-06 1.74e-03s -5600 1.5219e-10 1.01e-06 2.60e-10 2.09e-06 1.79e-03s -5800 1.4049e-10 6.59e-07 2.47e-10 2.09e-06 1.84e-03s -6000 1.3096e-10 5.75e-07 2.39e-10 2.09e-06 1.90e-03s -6200 1.2274e-10 5.24e-07 2.31e-10 2.09e-06 1.95e-03s -6400 1.1538e-10 4.90e-07 2.24e-10 2.09e-06 2.00e-03s -6600 1.0863e-10 4.67e-07 2.17e-10 2.09e-06 2.05e-03s -6800 1.0237e-10 4.48e-07 2.11e-10 2.09e-06 2.10e-03s -7000 9.6519e-11 4.32e-07 2.05e-10 2.09e-06 2.16e-03s -7200 9.1024e-11 4.19e-07 1.99e-10 2.09e-06 2.21e-03s -7400 8.5854e-11 4.06e-07 1.93e-10 2.09e-06 2.29e-03s -7600 8.0984e-11 3.94e-07 1.88e-10 2.09e-06 2.36e-03s -7800 7.6393e-11 3.82e-07 1.82e-10 2.09e-06 2.41e-03s -8000 7.2065e-11 3.71e-07 1.77e-10 2.09e-06 2.46e-03s -8200 6.7982e-11 3.60e-07 1.72e-10 2.09e-06 2.51e-03s -8400 6.4131e-11 3.50e-07 1.67e-10 2.09e-06 2.56e-03s -8600 6.0499e-11 3.40e-07 1.62e-10 2.09e-06 2.62e-03s -8800 5.7072e-11 3.30e-07 1.57e-10 2.09e-06 2.67e-03s -9000 5.3859e-11 8.77e-06 8.55e-12 2.09e-06 2.72e-03s -9200 5.1785e-11 5.78e-07 2.18e-13 2.09e-06 2.79e-03s -9400 5.0736e-11 7.72e-08 8.32e-14 2.09e-06 2.87e-03s -9550 5.0303e-11 4.69e-08 8.73e-14 2.09e-06 2.96e-03s -plsh 4.9640e-11 1.94e-14 1.50e-12 -------- 3.00e-03s - -status: solved -solution polish: successful -number of iterations: 9550 -optimal objective: 0.0000 -run time: 3.00e-03s -optimal rho estimate: 2.96e-06 - -------------------------------------------------------------------------------- - Summary -------------------------------------------------------------------------------- -(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal -(CVXPY) Sep 05 09:28:07 AM: Optimal value: 4.964e-11 -(CVXPY) Sep 05 09:28:07 AM: Compilation took 8.835e-03 seconds -(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 3.988e-03 seconds -``` - -``` -[11]: -``` - -```python -4.9640211694779584e-11 -``` - -``` -[12]: -``` - -```python -coeff_approx_tight = coeff_var.value -print(coeff_approx_tight) -print(np.linalg.norm(coeff_approx_tight, ord=1)) -``` - -```python -[ 0.10925065 -1. 1.89074935] -3.0 -``` - -As you can see, we have now obtained a comparatively higher L1-norm (which is still constrained by our choice of `max_l1_norm=3`). While the coefficient of the smallest $k_j$ value is still fairly small in magnitude, at least the other two provide a reasonable amount of mixing. - - - -## Comparing our results - -We will now briefly repeat the computations from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial in order to assess the quality of the various coefficients. - -``` -[13]: -``` - -```python -from qiskit.synthesis import SuzukiTrotter -from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit - -time = 8.0 -circuits = [] -for k in trotter_steps: - circ = generate_time_evolution_circuit( - hamiltonian, - synthesis=SuzukiTrotter(order=2, reps=k), - time=time, - ) - circuits.append(circ) -``` - -``` -[14]: -``` - -```python -from qiskit.quantum_info import SparsePauliOp - -L = 10 -observable = SparsePauliOp.from_sparse_list([("Z", [i], 1 / L / 2) for i in range(L)], num_qubits=L) -print(observable) -``` - -```python -SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'], - coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, - 0.05+0.j, 0.05+0.j, 0.05+0.j]) -``` - -``` -[15]: -``` - -```python -from qiskit.primitives import StatevectorEstimator - -estimator = StatevectorEstimator() -job = estimator.run([(circ, observable) for circ in circuits]) -result = job.result() -``` - -``` -[16]: -``` - -```python -evs = [res.data.evs for res in result] -print(evs) -``` - -```python -[array(0.23799162), array(0.35754312), array(0.38649906)] -``` - -``` -[17]: -``` - -```python -from scipy.linalg import expm - -exp_H = expm(-1j * time * hamiltonian.to_matrix()) - -initial_state = np.zeros(exp_H.shape[0]) -initial_state[0] = 1.0 - -time_evolved_state = exp_H @ initial_state - -exact_obs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state -``` - -``` -[18]: -``` - -```python -print("Analytical coeff solution:", evs @ coeff_analytical) -print("Plain approx. solution:", evs @ coeff_approx_plain) -print("CLARABEL approx. solution:", evs @ coeff_approx_clarabel) -print("Tight approx. solution:", evs @ coeff_approx_tight) -print("Exact solution:", exact_obs.real) -``` - -```python -Analytical coeff solution: 0.39548478559794153 -Plain approx. solution: 0.42928990901998165 -CLARABEL approx. solution: 0.41833743748333746 -Tight approx. solution: 0.3992304700751579 -Exact solution: 0.40060242487900255 -``` - -We can clearly see now, that the various approximate solutions can yield quite different results. Luckily, testing out different expansion coefficients for a fixed choice of $k_j$ values does not require us to rerun our quantum experiments. Therefore, we encourage you to play around and tweak your expansion coefficients when using the approximate solutions. diff --git a/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb b/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb deleted file mode 100644 index 19c5974460b..00000000000 --- a/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb +++ /dev/null @@ -1,1029 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8d428726-b143-45b7-9c23-d7aef8f3690b", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# How to use the approximate model\n", - "\n", - "You have already seen a first example of the [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem) function in the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial.\n", - "In this guide, we are going to revisit that function in more detail." - ] - }, - { - "cell_type": "markdown", - "id": "c9e6b25b-6f6f-49c6-84ac-9493c151a968", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Setting up a simple model problem\n", - "\n", - "For this guide, we will reuse the simple model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial; the Ising model on a line of 10 sites:\n", - "\n", - "$$\n", - "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", - "$$\n", - "\n", - "where $J$ is the coupling strength between two sites and $h$ is the external magnetic field." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3c8d6feb-d0fb-45ac-8bf6-213b29bcb8a4", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'],\n", - " coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j,\n", - " 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j,\n", - " 0.4+0.j, 0.4+0.j, 0.4+0.j])\n" - ] - } - ], - "source": [ - "from qiskit.transpiler import CouplingMap\n", - "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", - "\n", - "coupling_map = CouplingMap.from_line(10, bidirectional=False)\n", - "\n", - "hamiltonian = generate_xyz_hamiltonian(\n", - " coupling_map,\n", - " coupling_constants=(0.0, 0.0, 1.0),\n", - " ext_magnetic_field=(0.4, 0.0, 0.0),\n", - ")\n", - "print(hamiltonian)" - ] - }, - { - "cell_type": "markdown", - "id": "46ab2bb9-fd98-4c59-8d9c-66993652ff13", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Choosing $k_j$\n", - "\n", - "In this guide, we are going to focus on the tweaks that we can apply to the [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) and its solver that are returned by the [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem). Therefore, we are not concerned with choosing any specific $k_j$ values and can simply reuse the ones from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2ec34c2f-1f6f-4346-9ba6-f7f1c0e3620e", - "metadata": {}, - "outputs": [], - "source": [ - "trotter_steps = (8, 12, 19)" - ] - }, - { - "cell_type": "markdown", - "id": "99c7ad3c-7a5a-4dfe-81a5-9457d68fbd9c", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Computing $x$ analytically\n", - "\n", - "First, we have to construct our LSE, $Ax=b$. Once again, we reuse the settings from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "62d48e9c-8b00-43ac-9ee6-90f6418cfb05", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from qiskit_addon_mpf.static import setup_static_lse\n", - "\n", - "lse = setup_static_lse(trotter_steps, order=2, symmetric=True)" - ] - }, - { - "cell_type": "markdown", - "id": "9b5c4989-a75d-40e2-bb8a-9070188baef4", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Next, we compute the analytical solution of $x$ and its L1-norm as our reference values." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e279b0a3-9639-4e68-8138-90ff5ae6640d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0.17239057 -1.19447005 2.02207947]\n" - ] - } - ], - "source": [ - "coeff_analytical = lse.solve()\n", - "print(coeff_analytical)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a668e3bc-0cd1-4995-bc81-9ac8ca053f02", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.388940092165899\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "print(np.linalg.norm(coeff_analytical, ord=1))" - ] - }, - { - "cell_type": "markdown", - "id": "abda0fe8-c188-49e5-b121-e7d9b64b5313", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Setting up the approximate model\n", - "\n", - "Now, we will construct the approximate model.\n", - "The idea of using approximate solutions $\\tilde{x}$ in order to ensure a smaller L1-norm was proposed by [Zhuk et al., 2023].\n", - "The model is already explained in detail in the API documentation of [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem) so we refrain from repeating it here.\n", - "Suffice to say, that the model consists of two constraints which ensure that the coefficients, $\\tilde{x}$, sum to $1$ and it enforces their L1-norm to be smaller than the user-provided `max_l1_norm` value.\n", - "The optimization objective is to minimize the deviation of $A\\tilde{x}=b$.\n", - "\n", - "We can see all of this in the output below.\n", - "Here, we are setting `max_l1_norm=3` because we already know that the L1-norm of the exact solution is approximately $3.4$ and we want to find a $\\tilde{x}$ that has a smaller L1-norm.\n", - "\n", - "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "de60da28-ce4f-427d-b8c6-11a91cae3472", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "minimize quad_over_lin(Vstack([1. 1. 1.] @ x + -1.0, [0.015625 0.00694444 0.00277008] @ x + -0.0, [2.44140625e-04 4.82253086e-05 7.67336039e-06] @ x + -0.0), 1.0)\n", - "subject to Sum(x, None, False) == 1.0\n", - " norm1(x) <= 3.0\n" - ] - } - ], - "source": [ - "from qiskit_addon_mpf.costs import setup_sum_of_squares_problem\n", - "\n", - "model, coeff_var = setup_sum_of_squares_problem(lse, max_l1_norm=3)\n", - "print(model)" - ] - }, - { - "cell_type": "markdown", - "id": "fc5da15f-7771-446b-b0cf-c9320836102a", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Solving the model with default settings\n", - "\n", - "First, let us see what happens when we solve the `model` with only default settings." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "153755d2-4c04-410c-a251-b939f460643a", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "===============================================================================\n", - " CVXPY \n", - " v1.5.3 \n", - "===============================================================================\n", - "(CVXPY) Sep 05 09:28:06 AM: Your problem has 3 variables, 2 constraints, and 0 parameters.\n", - "(CVXPY) Sep 05 09:28:06 AM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 05 09:28:06 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 05 09:28:06 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 05 09:28:06 AM: Your problem is compiled with the CPP canonicalization backend.\n", - "-------------------------------------------------------------------------------\n", - " Compilation \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Sep 05 09:28:06 AM: Compiling problem (target solver=OSQP).\n", - "(CVXPY) Sep 05 09:28:06 AM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> OSQP\n", - "(CVXPY) Sep 05 09:28:06 AM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 05 09:28:06 AM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 05 09:28:06 AM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 05 09:28:06 AM: Applying reduction OSQP\n", - "(CVXPY) Sep 05 09:28:06 AM: Finished problem compilation (took 1.053e-02 seconds).\n", - "-------------------------------------------------------------------------------\n", - " Numerical solver \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Sep 05 09:28:06 AM: Invoking solver OSQP to obtain a solution.\n", - "-----------------------------------------------------------------\n", - " OSQP v0.6.3 - Operator Splitting QP Solver\n", - " (c) Bartolomeo Stellato, Goran Banjac\n", - " University of Oxford - Stanford University 2021\n", - "-----------------------------------------------------------------\n", - "problem: variables n = 9, constraints m = 11\n", - " nnz(P) + nnz(A) = 33\n", - "settings: linear system solver = qdldl,\n", - " eps_abs = 1.0e-05, eps_rel = 1.0e-05,\n", - " eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04,\n", - " rho = 1.00e-01 (adaptive),\n", - " sigma = 1.00e-06, alpha = 1.60, max_iter = 10000\n", - " check_termination: on (interval 25),\n", - " scaling: on, scaled_termination: off\n", - " warm start: on, polish: on, time_limit: off\n", - "\n", - "iter objective pri res dua res rho time\n", - " 1 0.0000e+00 1.00e+00 2.00e+02 1.00e-01 6.26e-05s\n", - " 200 4.0661e-09 7.52e-06 1.44e-08 1.22e-05 1.28e-04s\n", - "\n", - "status: solved\n", - "solution polish: unsuccessful\n", - "number of iterations: 200\n", - "optimal objective: 0.0000\n", - "run time: 1.53e-04s\n", - "optimal rho estimate: 2.09e-06\n", - "\n", - "-------------------------------------------------------------------------------\n", - " Summary \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal\n", - "(CVXPY) Sep 05 09:28:07 AM: Optimal value: 4.092e-09\n", - "(CVXPY) Sep 05 09:28:07 AM: Compilation took 1.053e-02 seconds\n", - "(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 1.028e-03 seconds\n" - ] - }, - { - "data": { - "text/plain": [ - "4.092215822096244e-09" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.solve(verbose=True)" - ] - }, - { - "cell_type": "markdown", - "id": "0aafe4c0-c2cc-4de2-824f-ea3594960ca3", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "We can see from the final value of the cost function that the sum of squares of $A\\tilde{x}-b$ is almost zero.\n", - "\n", - "In the output above, we also get a lot more information from [cvxpy.Problem.solve](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem.solve), some of which we will get back to later.\n", - "\n", - "For now, let us inspect the final values of $\\tilde{x}$ and its L1-norm:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "662b35c2-cd7a-42ff-96a5-792184723f7c", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.39646076 0.55556835 0.84089365]\n", - "1.7929227539822414\n" - ] - } - ], - "source": [ - "coeff_approx_plain = coeff_var.value\n", - "print(coeff_approx_plain)\n", - "print(np.linalg.norm(coeff_approx_plain, ord=1))" - ] - }, - { - "cell_type": "markdown", - "id": "aed22d40-140e-4a06-9f6d-90be062fd0cb", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "We can clearly see that the L1-norm has decreased compared to the analytical solution that we have computed before.\n", - "\n", - "Nonetheless, let us see if we can tweak our solution a bit.\n", - "In the end, we will compare the results obtained for this set of coefficients with all the other ones." - ] - }, - { - "cell_type": "markdown", - "id": "aa3484a3-ce6e-460a-a144-2bc94ab0ace4", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Tweaking the approximate model\n", - "\n", - "Rather than tweaking the input parameters like the chosen `trotter_steps` and `max_l1_norm` values, we will focus on the solver settings in this guide." - ] - }, - { - "cell_type": "markdown", - "id": "a078b0b7-93d0-418f-aaf7-f8584d6889db", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Using a different solver\n", - "\n", - "Notice, that we used the default solver method: `OSQP`. We can change that solver to see whether this has any effect." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "318369c1-7c54-4035-87b9-1e729aa0c048", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "===============================================================================\n", - " CVXPY \n", - " v1.5.3 \n", - "===============================================================================\n", - "(CVXPY) Sep 05 09:28:07 AM: Your problem has 3 variables, 2 constraints, and 0 parameters.\n", - "(CVXPY) Sep 05 09:28:07 AM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 05 09:28:07 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 05 09:28:07 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 05 09:28:07 AM: Your problem is compiled with the CPP canonicalization backend.\n", - "-------------------------------------------------------------------------------\n", - " Compilation \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Sep 05 09:28:07 AM: Compiling problem (target solver=CLARABEL).\n", - "(CVXPY) Sep 05 09:28:07 AM: Reduction chain: Dcp2Cone -> CvxAttr2Constr -> ConeMatrixStuffing -> CLARABEL\n", - "(CVXPY) Sep 05 09:28:07 AM: Applying reduction Dcp2Cone\n", - "(CVXPY) Sep 05 09:28:07 AM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 05 09:28:07 AM: Applying reduction ConeMatrixStuffing\n", - "(CVXPY) Sep 05 09:28:07 AM: Applying reduction CLARABEL\n", - "(CVXPY) Sep 05 09:28:07 AM: Finished problem compilation (took 8.657e-03 seconds).\n", - "-------------------------------------------------------------------------------\n", - " Numerical solver \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Sep 05 09:28:07 AM: Invoking solver CLARABEL to obtain a solution.\n", - "-------------------------------------------------------------\n", - " Clarabel.rs v0.8.1 - Clever Acronym \n", - "\n", - " (c) Paul Goulart \n", - " University of Oxford, 2022 \n", - "-------------------------------------------------------------\n", - "\n", - "problem:\n", - " variables = 9\n", - " constraints = 11\n", - " nnz(P) = 3\n", - " nnz(A) = 30\n", - " cones (total) = 2\n", - " : Zero = 1, numel = 4\n", - " : Nonnegative = 1, numel = 7\n", - "\n", - "settings:\n", - " linear algebra: direct / qdldl, precision: 64 bit\n", - " max iter = 200, time limit = Inf, max step = 0.990\n", - " tol_feas = 1.0e-8, tol_gap_abs = 1.0e-8, tol_gap_rel = 1.0e-8,\n", - " static reg : on, ϵ1 = 1.0e-8, ϵ2 = 4.9e-32\n", - " dynamic reg: on, ϵ = 1.0e-13, δ = 2.0e-7\n", - " iter refine: on, reltol = 1.0e-13, abstol = 1.0e-12,\n", - " max iter = 10, stop ratio = 5.0\n", - " equilibrate: on, min_scale = 1.0e-4, max_scale = 1.0e4\n", - " max iter = 10\n", - "\n", - "iter pcost dcost gap pres dres k/t μ step \n", - "---------------------------------------------------------------------------------------------\n", - " 0 +7.1341e-05 -2.3333e+00 2.33e+00 2.40e-01 7.10e-01 1.00e+00 1.88e+00 ------ \n", - " 1 +7.1330e-05 -1.5432e-01 1.54e-01 1.64e-02 1.12e-01 3.28e-02 1.80e-01 9.61e-01 \n", - " 2 +6.9440e-05 -1.7266e-03 1.80e-03 2.10e-04 1.89e-03 5.65e-04 2.43e-03 9.87e-01 \n", - " 3 +2.2053e-05 -5.0002e-05 7.21e-05 5.56e-06 4.83e-05 1.98e-05 7.80e-05 9.87e-01 \n", - " 4 +1.1119e-06 -2.5543e-05 2.67e-05 1.80e-07 1.41e-06 3.21e-06 1.04e-05 9.90e-01 \n", - " 5 +7.9250e-09 -2.0484e-06 2.06e-06 2.06e-09 1.59e-08 2.11e-07 6.43e-07 9.90e-01 \n", - " 6 +2.2543e-09 -2.8390e-08 3.06e-08 2.07e-11 1.59e-10 3.12e-09 9.42e-09 9.90e-01 \n", - " 7 +1.8480e-09 -8.5733e-10 2.71e-09 1.79e-12 2.36e-10 3.19e-10 9.40e-10 9.15e-01 \n", - "---------------------------------------------------------------------------------------------\n", - "Terminated with status = Solved\n", - "solve time = 113.289µs\n", - "-------------------------------------------------------------------------------\n", - " Summary \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal\n", - "(CVXPY) Sep 05 09:28:07 AM: Optimal value: 1.848e-09\n", - "(CVXPY) Sep 05 09:28:07 AM: Compilation took 8.657e-03 seconds\n", - "(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 8.504e-04 seconds\n" - ] - }, - { - "data": { - "text/plain": [ - "1.8479854472132682e-09" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.solve(verbose=True, solver=\"CLARABEL\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "207a1f1d-00e1-40ef-bc2a-f904a58fd1ba", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.21284352 -0.00792683 1.22077035]\n", - "1.4415406931636157\n" - ] - } - ], - "source": [ - "coeff_approx_clarabel = coeff_var.value\n", - "print(coeff_approx_clarabel)\n", - "print(np.linalg.norm(coeff_approx_clarabel, ord=1))" - ] - }, - { - "cell_type": "markdown", - "id": "05b672be-9f72-4a11-a9ee-0d4701a8fc86", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Indeed, the outcome has changed. But now we have one major contribution from the largest $k_j$ value which is also not ideal.\n", - "\n", - "An exhaustive search of the various available solvers is beyond the scope of this guide. Instead we encourage you to try this yourself." - ] - }, - { - "cell_type": "markdown", - "id": "77ba957c-f083-4975-b793-5a96d77a8a37", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Changing the convergence thresholds\n", - "\n", - "Rather than changing the solver altogether, we can also tweak the convergence parameters.\n", - "For the `OSQP` solver the relevant ones are called `eps_abs` and `eps_rel` and they are both set to `1e-5` by default." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "74528a7b-2bba-445d-b324-a61a261d07c3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "===============================================================================\n", - " CVXPY \n", - " v1.5.3 \n", - "===============================================================================\n", - "(CVXPY) Sep 05 09:28:07 AM: Your problem has 3 variables, 2 constraints, and 0 parameters.\n", - "(CVXPY) Sep 05 09:28:07 AM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 05 09:28:07 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 05 09:28:07 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 05 09:28:07 AM: Your problem is compiled with the CPP canonicalization backend.\n", - "-------------------------------------------------------------------------------\n", - " Compilation \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Sep 05 09:28:07 AM: Compiling problem (target solver=OSQP).\n", - "(CVXPY) Sep 05 09:28:07 AM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> OSQP\n", - "(CVXPY) Sep 05 09:28:07 AM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 05 09:28:07 AM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 05 09:28:07 AM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 05 09:28:07 AM: Applying reduction OSQP\n", - "(CVXPY) Sep 05 09:28:07 AM: Finished problem compilation (took 8.835e-03 seconds).\n", - "-------------------------------------------------------------------------------\n", - " Numerical solver \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Sep 05 09:28:07 AM: Invoking solver OSQP to obtain a solution.\n", - "-----------------------------------------------------------------\n", - " OSQP v0.6.3 - Operator Splitting QP Solver\n", - " (c) Bartolomeo Stellato, Goran Banjac\n", - " University of Oxford - Stanford University 2021\n", - "-----------------------------------------------------------------\n", - "problem: variables n = 9, constraints m = 11\n", - " nnz(P) + nnz(A) = 33\n", - "settings: linear system solver = qdldl,\n", - " eps_abs = 1.0e-08, eps_rel = 1.0e-08,\n", - " eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04,\n", - " rho = 1.00e-01 (adaptive),\n", - " sigma = 1.00e-06, alpha = 1.60, max_iter = 10000\n", - " check_termination: on (interval 25),\n", - " scaling: on, scaled_termination: off\n", - " warm start: on, polish: on, time_limit: off\n", - "\n", - "iter objective pri res dua res rho time\n", - " 1 0.0000e+00 1.00e+00 2.00e+02 1.00e-01 5.02e-05s\n", - " 200 4.0661e-09 7.52e-06 1.44e-08 1.22e-05 2.10e-04s\n", - " 400 3.8209e-09 4.95e-06 2.88e-09 2.09e-06 3.72e-04s\n", - " 600 3.3607e-09 6.09e-06 1.96e-09 2.09e-06 4.33e-04s\n", - " 800 2.8975e-09 6.24e-06 1.84e-09 2.09e-06 4.87e-04s\n", - "1000 2.4704e-09 6.08e-06 1.70e-09 2.09e-06 5.40e-04s\n", - "1200 2.0921e-09 5.78e-06 1.56e-09 2.09e-06 5.92e-04s\n", - "1400 1.7645e-09 5.40e-06 1.44e-09 2.09e-06 6.44e-04s\n", - "1600 1.4845e-09 5.01e-06 1.32e-09 2.09e-06 7.01e-04s\n", - "1800 1.2471e-09 4.63e-06 1.21e-09 2.09e-06 7.53e-04s\n", - "2000 1.0467e-09 4.26e-06 1.11e-09 2.09e-06 8.05e-04s\n", - "2200 8.7801e-10 3.91e-06 1.01e-09 2.09e-06 8.57e-04s\n", - "2400 7.3890e-10 3.25e-06 7.74e-10 2.09e-06 9.10e-04s\n", - "2600 6.4003e-10 2.47e-06 7.11e-10 2.09e-06 9.62e-04s\n", - "2800 5.6692e-10 2.03e-06 6.68e-10 2.09e-06 1.01e-03s\n", - "3000 5.0872e-10 1.76e-06 6.33e-10 2.09e-06 1.07e-03s\n", - "3200 4.5991e-10 1.58e-06 6.02e-10 2.09e-06 1.12e-03s\n", - "3400 4.1754e-10 1.46e-06 5.74e-10 2.09e-06 1.17e-03s\n", - "3600 3.7999e-10 1.37e-06 5.47e-10 2.09e-06 1.22e-03s\n", - "3800 3.4628e-10 1.29e-06 5.22e-10 2.09e-06 1.32e-03s\n", - "4000 3.1580e-10 1.22e-06 4.99e-10 2.09e-06 1.37e-03s\n", - "4200 2.8813e-10 1.16e-06 4.76e-10 2.09e-06 1.43e-03s\n", - "4400 2.6295e-10 1.11e-06 4.55e-10 2.09e-06 1.48e-03s\n", - "4600 2.4000e-10 1.06e-06 4.35e-10 2.09e-06 1.53e-03s\n", - "4800 2.1907e-10 1.01e-06 4.15e-10 2.09e-06 1.58e-03s\n", - "5000 1.9997e-10 9.67e-07 3.97e-10 2.09e-06 1.63e-03s\n", - "5200 1.8255e-10 9.23e-07 3.79e-10 2.09e-06 1.69e-03s\n", - "5400 1.6664e-10 8.82e-07 3.62e-10 2.09e-06 1.74e-03s\n", - "5600 1.5219e-10 1.01e-06 2.60e-10 2.09e-06 1.79e-03s\n", - "5800 1.4049e-10 6.59e-07 2.47e-10 2.09e-06 1.84e-03s\n", - "6000 1.3096e-10 5.75e-07 2.39e-10 2.09e-06 1.90e-03s\n", - "6200 1.2274e-10 5.24e-07 2.31e-10 2.09e-06 1.95e-03s\n", - "6400 1.1538e-10 4.90e-07 2.24e-10 2.09e-06 2.00e-03s\n", - "6600 1.0863e-10 4.67e-07 2.17e-10 2.09e-06 2.05e-03s\n", - "6800 1.0237e-10 4.48e-07 2.11e-10 2.09e-06 2.10e-03s\n", - "7000 9.6519e-11 4.32e-07 2.05e-10 2.09e-06 2.16e-03s\n", - "7200 9.1024e-11 4.19e-07 1.99e-10 2.09e-06 2.21e-03s\n", - "7400 8.5854e-11 4.06e-07 1.93e-10 2.09e-06 2.29e-03s\n", - "7600 8.0984e-11 3.94e-07 1.88e-10 2.09e-06 2.36e-03s\n", - "7800 7.6393e-11 3.82e-07 1.82e-10 2.09e-06 2.41e-03s\n", - "8000 7.2065e-11 3.71e-07 1.77e-10 2.09e-06 2.46e-03s\n", - "8200 6.7982e-11 3.60e-07 1.72e-10 2.09e-06 2.51e-03s\n", - "8400 6.4131e-11 3.50e-07 1.67e-10 2.09e-06 2.56e-03s\n", - "8600 6.0499e-11 3.40e-07 1.62e-10 2.09e-06 2.62e-03s\n", - "8800 5.7072e-11 3.30e-07 1.57e-10 2.09e-06 2.67e-03s\n", - "9000 5.3859e-11 8.77e-06 8.55e-12 2.09e-06 2.72e-03s\n", - "9200 5.1785e-11 5.78e-07 2.18e-13 2.09e-06 2.79e-03s\n", - "9400 5.0736e-11 7.72e-08 8.32e-14 2.09e-06 2.87e-03s\n", - "9550 5.0303e-11 4.69e-08 8.73e-14 2.09e-06 2.96e-03s\n", - "plsh 4.9640e-11 1.94e-14 1.50e-12 -------- 3.00e-03s\n", - "\n", - "status: solved\n", - "solution polish: successful\n", - "number of iterations: 9550\n", - "optimal objective: 0.0000\n", - "run time: 3.00e-03s\n", - "optimal rho estimate: 2.96e-06\n", - "\n", - "-------------------------------------------------------------------------------\n", - " Summary \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal\n", - "(CVXPY) Sep 05 09:28:07 AM: Optimal value: 4.964e-11\n", - "(CVXPY) Sep 05 09:28:07 AM: Compilation took 8.835e-03 seconds\n", - "(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 3.988e-03 seconds\n" - ] - }, - { - "data": { - "text/plain": [ - "4.9640211694779584e-11" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.solve(verbose=True, eps_abs=1e-8, eps_rel=1e-8)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a5c9fe95-1752-4717-bb74-30766be22f98", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0.10925065 -1. 1.89074935]\n", - "3.0\n" - ] - } - ], - "source": [ - "coeff_approx_tight = coeff_var.value\n", - "print(coeff_approx_tight)\n", - "print(np.linalg.norm(coeff_approx_tight, ord=1))" - ] - }, - { - "cell_type": "markdown", - "id": "c841fd73-87fa-49d5-b1a8-f0de4b4c53ff", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "As you can see, we have now obtained a comparatively higher L1-norm (which is still constrained by our choice of `max_l1_norm=3`). While the coefficient of the smallest $k_j$ value is still fairly small in magnitude, at least the other two provide a reasonable amount of mixing." - ] - }, - { - "cell_type": "markdown", - "id": "7f852761-5ef5-4cc6-ab62-d7660f41e82a", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Comparing our results\n", - "\n", - "We will now briefly repeat the computations from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial in order to assess the quality of the various coefficients." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "71e34633-b044-4fcc-aa63-cebc155a848f", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from qiskit.synthesis import SuzukiTrotter\n", - "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", - "\n", - "time = 8.0\n", - "circuits = []\n", - "for k in trotter_steps:\n", - " circ = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(order=2, reps=k),\n", - " time=time,\n", - " )\n", - " circuits.append(circ)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "acfc17f7-9817-4576-9709-1f73aa26cf05", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'],\n", - " coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j,\n", - " 0.05+0.j, 0.05+0.j, 0.05+0.j])\n" - ] - } - ], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "L = 10\n", - "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)\n", - "print(observable)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "0ea7cac7-0af8-4909-ad23-7b1acb16b607", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from qiskit.primitives import StatevectorEstimator\n", - "\n", - "estimator = StatevectorEstimator()\n", - "job = estimator.run([(circ, observable) for circ in circuits])\n", - "result = job.result()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2c2b6c4c-6a27-4601-846a-3e6757ad5156", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array(0.23799162), array(0.35754312), array(0.38649906)]\n" - ] - } - ], - "source": [ - "evs = [res.data.evs for res in result]\n", - "print(evs)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "655f4663-d19e-4617-89c3-f0355563d915", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from scipy.linalg import expm\n", - "\n", - "exp_H = expm(-1j * time * hamiltonian.to_matrix())\n", - "\n", - "initial_state = np.zeros(exp_H.shape[0])\n", - "initial_state[0] = 1.0\n", - "\n", - "time_evolved_state = exp_H @ initial_state\n", - "\n", - "exact_obs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "35bb0451-091b-4ed2-8776-a1aa7ccb2760", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analytical coeff solution: 0.39548478559794153\n", - "Plain approx. solution: 0.42928990901998165\n", - "CLARABEL approx. solution: 0.41833743748333746\n", - "Tight approx. solution: 0.3992304700751579\n", - "Exact solution: 0.40060242487900255\n" - ] - } - ], - "source": [ - "print(\"Analytical coeff solution:\", evs @ coeff_analytical)\n", - "print(\"Plain approx. solution:\", evs @ coeff_approx_plain)\n", - "print(\"CLARABEL approx. solution:\", evs @ coeff_approx_clarabel)\n", - "print(\"Tight approx. solution:\", evs @ coeff_approx_tight)\n", - "print(\"Exact solution:\", exact_obs.real)" - ] - }, - { - "cell_type": "markdown", - "id": "adaea843-98fb-48ed-ad1a-0f6ad57f9a7b", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "We can clearly see now, that the various approximate solutions can yield quite different results.\n", - "Luckily, testing out different expansion coefficients for a fixed choice of $k_j$ values does not require us to rerun our quantum experiments.\n", - "Therefore, we encourage you to play around and tweak your expansion coefficients when using the approximate solutions." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-mpf/index.mdx b/docs/addons/qiskit-addon-mpf/index.mdx deleted file mode 100644 index 92f0ee9b65d..00000000000 --- a/docs/addons/qiskit-addon-mpf/index.mdx +++ /dev/null @@ -1,64 +0,0 @@ ---- -title: Multi-product formulas (MPF) API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-mpf. ---- - -# Qiskit addon: multi-product formulas (MPF) - -[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. - -This package contains the Qiskit addon for multi-product formulas (MPFs). These can be used to reduce the Trotter error of Hamiltonian dynamics. - -This package currently contains the following main entry points for users: - -* `qiskit_addon_mpf.static` for working with static MPFs \[[1-2](#references)]. -* `qiskit_addon_mpf.dynamic` for working with dynamic MPFs \[[2-3](#references)]. - -## Documentation - -All documentation is available [here](/docs/addons/qiskit-addon-mpf). - -## Installation - -We encourage installing this package via `pip`, when possible: - -```bash -pip install 'qiskit-addon-mpf' -``` - -For more installation information refer to the [installation instructions](install) in the documentation. - -### Optional dependencies - -The `qiskit-addon-mpf` package has a number of optional dependencies which enable certain features. The dynamic MPF feature (see \[[2-3](#references)]) is one such example. You can install the related optional dependencies like so: - -```bash -pip install 'qiskit-addon-mpf[dynamic]' -``` - - - The optional dependency [TeNPy](https://github.com/tenpy/tenpy) was previously offered under a GPLv3 license. As of the release of [v1.0.4](https://github.com/tenpy/tenpy/releases/tag/v1.0.4) on October 2nd, 2024, it has been offered under the Apache v2 license. The license of this package is only compatible with Apache-licensed versions of TeNPy. - - -## Deprecation Policy - -We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [release notes](/docs/addons/qiskit-addon-mpf/release-notes). - -## Contributing - -The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-mpf). - -The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-mpf/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). - -We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-mpf/issues/new/choose) for tracking requests and bugs. - -## References - -1. 1. Carrera Vazquez, D. J. Egger, D. Ochsner, and S. Wörner, [Well-conditioned multi-product formulas for hardware-friendly Hamiltonian simulation](https://quantum-journal.org/papers/q-2023-07-25-1067/), Quantum 7, 1067 (2023). -2. 19. Zhuk, N. Robertson, and S. Bravyi, [Trotter error bounds and dynamic multi-product formulas for Hamiltonian simulation](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309), Phys. Rev. Research 6, 033309 (2024). -3. 14. Robertson, et al. [Tensor Network enhanced Dynamic Multiproduct Formulas](https://arxiv.org/abs/2407.17405v2), arXiv:2407.17405v2 \[quant-ph]. - -## License - -[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-mpf/blob/main/LICENSE.txt) - diff --git a/docs/addons/qiskit-addon-mpf/install.mdx b/docs/addons/qiskit-addon-mpf/install.mdx deleted file mode 100644 index bd571abad14..00000000000 --- a/docs/addons/qiskit-addon-mpf/install.mdx +++ /dev/null @@ -1,71 +0,0 @@ -# Installation Instructions - -Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: - -* [Option 1: Install from PyPI](#option-1) -* [Option 2: Install from Source](#option-2) - -## Pre-Installation - -First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). - -```sh -python3 -m venv /path/to/virtual/environment -``` - -Activate your new environment. - -```sh -source /path/to/virtual/environment/bin/activate -``` - -Note: If you are using Windows, use the following commands in PowerShell: - -```pwsh -python3 -m venv c:\path\to\virtual\environment -c:\path\to\virtual\environment\Scripts\Activate.ps1 -``` - - - -## Option 1: Install from PyPI - -The most straightforward way to install the `qiskit-addon-mpf` package is via `PyPI`. - -```sh -pip install 'qiskit-addon-mpf' -``` - - - -## Option 2: Install from Source - -Users who wish to develop in the repository or run the notebooks locally may want to install from source. - -If so, the first step is to clone the `qiskit-addon-mpf` repository. - -```sh -git clone git@github.com:Qiskit/qiskit-addon-mpf.git -``` - -Next, upgrade `pip` and enter the repository. - -```sh -pip install --upgrade pip -cd qiskit-addon-mpf -``` - -The next step is to install `qiskit-addon-mpf` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. - -Adjust the options below to suit your needs. - -```sh -pip install tox notebook -e '.[notebook-dependencies,dev]' -``` - -If you installed the notebook dependencies, you can get started by running the notebooks in the docs. - -```python -cd docs/ -jupyter lab -``` diff --git a/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started.mdx b/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started.mdx deleted file mode 100644 index 661bdb0df6e..00000000000 --- a/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started.mdx +++ /dev/null @@ -1,445 +0,0 @@ - - -# Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas - -In this notebook, you will learn how to use a **Multi-Product Formula (MPF)** to achieve a lower Trotter error on our observable compared to the one incurred by the deepest Trotter circuit that we will actually execute. You will do so by working through the steps of a **Qiskit pattern**: - -* **Step 1: Map to quantum problem** - - * Initialize our problem’s Hamiltonian - * Use an MPF to generate the Trotterized time-evolution circuits - -* **Step 2: Optimize the problem** - - * Here we transpile our circuits for a [GenericBackendV2](/docs/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2) - -* **Step 3: Execute experiments** - - * Use a [StatevectorEstimator](/docs/api/qiskit/qiskit.primitives.StatevectorEstimator) for sake of simplicity in this notebook - -* **Step 4: Reconstruct results** - - * Compute the MPF expectation value - - - -## Step 1: Map to quantum problem - - - -### 1a: Setting up our Hamiltonian - -We use the Ising model on a line of 10 sites: - -$$ -\hat{\mathcal{H}}_{\text{Ising}} = \sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \sum_{i=1}^{10} h_i X_i \, , -$$ - -where $J$ is the coupling strength between two sites and $h$ is the external magnetic field. - -The [qiskit\_addon\_utils](https://qiskit.github.io/qiskit-addon-utils/) package provides some reusable functionalities for various purposes. - -Its [qiskit\_addon\_utils.problem\_generators](/docs/api/qiskit-addon-utils/problem-generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph. This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/docs/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows. - -In the following, we create a simple line of 10 qubits using the `CouplingMap.from_line` method. - -``` -[1]: -``` - -```python -from qiskit.transpiler import CouplingMap - -# Generate some coupling map to use for this example -coupling_map = CouplingMap.from_line(10, bidirectional=False) -``` - -``` -[2]: -``` - -```python -from rustworkx.visualization import graphviz_draw - -graphviz_draw(coupling_map.graph, method="circo") -``` - -``` -[2]: -``` - -![../\_images/tutorials\_01\_getting\_started\_4\_0.png](/docs/images/api/qiskit-addon-mpf/tutorials_01_getting_started_4_0.avif) - -Next, we generate the [SparsePauliOp](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) on the provided connectivity with the desired constants. - -``` -[3]: -``` - -```python -from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian - -# Get a qubit operator describing the Ising field model -hamiltonian = generate_xyz_hamiltonian( - coupling_map, - coupling_constants=(0.0, 0.0, 1.0), - ext_magnetic_field=(0.4, 0.0, 0.0), -) -print(hamiltonian) -``` - -```python -SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'], - coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, - 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, - 0.4+0.j, 0.4+0.j, 0.4+0.j]) -``` - -The observable that we will be measuring is the total magnetization which we can simply construct as shown below: - -``` -[4]: -``` - -```python -from qiskit.quantum_info import SparsePauliOp - -L = coupling_map.size() -observable = SparsePauliOp.from_sparse_list([("Z", [i], 1 / L / 2) for i in range(L)], num_qubits=L) -print(observable) -``` - -```python -SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'], - coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, - 0.05+0.j, 0.05+0.j, 0.05+0.j]) -``` - - - -### 1b: Multi-Product Formulas - -MPFs reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions. - -To make this more concrete, we define an MPF as: - -$$ -\mu(t) = \sum_{j} x_j \rho^{k_j}_{j}\left(\frac{t}{k_j}\right) + \text{some remaining Trotter error} \, , -$$ - -where $x_j$ are our weighting coefficients, $\rho^{k_j}_j$ is the density matrix corresponding to the pure state obtained by evolving the initial state with the product formula, $S^{k_j}$, involving $k_j$ Trotter steps, and $j$ indexes the number of product formulas that make up the MPF. - -The key here is that the remaining Trotter error is smaller than the Trotter error that one would obtain by simply using the largest $k_j$ value! - -You can view the usefulness of MPF from two perspectives: - -1. For a fixed budget of Trotter steps that you are able to execute, you can obtain results with a Trotter error that is smaller in total. -2. For a number of Trotter steps which results in deep circuits, you can use MPF to find several shorter-depth circuits to run which result in a similar Trotter error. - - - -#### An introduction to static MPFs - -*Static* MPFs are those where the $x_j$ values do **NOT** depend on the evolution time, $t$. - -Determining the static MPF coefficients for a given set of $k_j$ values amounts to solving a linear system of equations: $Ax=b$, where $x$ are our coefficients of interest, $A$ is a matrix depending on $k_j$ and the type of PF we use ($S$), and $b$ is a vector of constraints. For brevity, we are not going to go into more detail here and instead refer you to the documentation of [LSE](/docs/addons/qiskit-addon-mpf/apidocs/qiskit_addon_mpf.costs#qiskit_addon_mpf.costs.LSE). - -We can find a solution for $x$ analytically as $x = A^{-1}b$, see e.g. [Carrera Vazquez et al., 2023](https://quantum-journal.org/papers/q-2023-07-25-1067/) or [Zhuk et al., 2023](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309). However, this exact solution can be *“ill-conditioned”* resulting in very large L1-norms of our coefficients, $x$, which can lead to bad performance of the MPF. Instead, one can also obtain an approximate solution which minimizes the L1-norm of $x$ in order to attempt and optimize the MPF behavior. - -In the following, you will learn how to do all of this. - - - -#### Choosing $k_j$ - -The choice of $k_j$ is up to the end-user. In principle, any values can be chosen but some $k_j$’s will lead to a larger noise amplification on real devices than other choices. Thus, it is important that one tries to find *“good”* values of $k_j$. - -Here, we will simply pick some fixed values for $k_j$. The smallest value is motivated by the target evolution time of $t=8.0$ which normally tells us to satisfy $t/k_{\text{min}} \lt 1$ but empirically we know that setting it equal to $1$ usually works, too. If you want to learn more about this and how to choose your other $k_j$ values, refer to the respective guide: [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps). - -``` -[5]: -``` - -```python -time = 8.0 -trotter_steps = (8, 12, 19) -``` - - - -#### Setting up the LSE - -Now that we have chosen our $k_j$s, we must first construct the LSE, $Ax=b$ as explained above. The matrix $A$ depends not only on $k_j$ but also our choice of product formula (PF) – in particular its *order*. Additionally, one may take into account whether the PF is symmetric or not (see [Carrera Vazquez et al., 2023](https://quantum-journal.org/papers/q-2023-07-25-1067/)), by setting `symmetric=True`. However, this is not required as shown by [Zhuk et al., 2023](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309). - -Here, we are going to use a second-order Suzuki-Trotter formula yielding `order=2` and we will set `symmetric=True`. - -``` -[6]: -``` - -```python -from qiskit_addon_mpf.static import setup_static_lse - -lse = setup_static_lse(trotter_steps, order=2, symmetric=True) -print(lse) -``` - -```python -LSE(A=array([[1.00000000e+00, 1.00000000e+00, 1.00000000e+00], - [1.56250000e-02, 6.94444444e-03, 2.77008310e-03], - [2.44140625e-04, 4.82253086e-05, 7.67336039e-06]]), b=array([1., 0., 0.])) -``` - - - -#### Solving for $x$ analytically - -As mentioned before, we can find $x$ analytically: - -``` -[7]: -``` - -```python -import numpy as np - -coeffs_analytical = lse.solve() -print(coeffs_analytical) -``` - -```python -[ 0.17239057 -1.19447005 2.02207947] -``` - - - -#### Optimizing for $x$ using an exact model - -Alternatively to computing $x=A^{-1}b$, you can also use [setup\_exact\_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_exact_problem) to construct a [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which uses the LSE as constraints and whose optimal solution will yield $x$. - -In the next section, it will be clear why this interface exists. - -``` -[8]: -``` - -```python -from qiskit_addon_mpf.costs import setup_exact_problem - -model_exact, coeffs_exact = setup_exact_problem(lse) -model_exact.solve() -print(coeffs_exact.value) -``` - -```python -[ 0.17239057 -1.19447005 2.02207947] -``` - -As an indicator whether an MPF constructed with these coefficients will yield good results, we can use the L1-norm (see also [Carrera Vazquez et al., 2023](https://quantum-journal.org/papers/q-2023-07-25-1067/)). - -``` -[9]: -``` - -```python -print(np.linalg.norm(coeffs_exact.value, ord=1)) -``` - -```python -3.3889400921655914 -``` - - - -#### Optimizing for $x$ using an approximate model - -It may happen that the L1 norm for the chosen set of $k_j$ values is deemed too high. If that is the case and you cannot choose a different set of $k_j$ values, you can use an approximate solution to the LSE instead of an exact one. - -To do so, simply use [setup\_sum\_of\_squares\_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem) to construct a different [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which constrains the L1-norm to a chosen threshold while minimizing the difference of $Ax$ and $b$. - -``` -[10]: -``` - -```python -from qiskit_addon_mpf.costs import setup_sum_of_squares_problem - -model_approx, coeffs_approx = setup_sum_of_squares_problem(lse, max_l1_norm=3.0) -model_approx.solve() -print(coeffs_approx.value) -print(np.linalg.norm(coeffs_approx.value, ord=1)) -``` - -```python -[-0.40454257 0.57553173 0.8290123 ] -1.8090865903790838 -``` - -Note, that you have complete freedom over how to solve this optimization problem, meaning that you can change the optimization solver, its convergence thresholds, and so on. Check out the respective guide on [How to use the approximate model](/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model). - - - -### 1c: Setting up the Trotter circuits - -At this point, we have found our expansion coefficients, $x$, and all that is left to do is to generate the Trotterized quantum circuits. Once again, the [qiskit\_addon\_utils.problem\_generators](/docs/api/qiskit-addon-utils/problem-generators) module comes to the rescue to do just that: - -``` -[11]: -``` - -```python -from qiskit.synthesis import SuzukiTrotter -from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit - -circuits = [] -for k in trotter_steps: - circ = generate_time_evolution_circuit( - hamiltonian, - synthesis=SuzukiTrotter(order=2, reps=k), - time=time, - ) - circuits.append(circ) -``` - -``` -[12]: -``` - -```python -circuits[0].draw("mpl", fold=-1) -``` - -``` -[12]: -``` - -![../\_images/tutorials\_01\_getting\_started\_26\_0.png](/docs/images/api/qiskit-addon-mpf/tutorials_01_getting_started_26_0.avif) - -``` -[13]: -``` - -```python -circuits[1].draw("mpl", fold=-1) -``` - -``` -[13]: -``` - -![../\_images/tutorials\_01\_getting\_started\_27\_0.png](/docs/images/api/qiskit-addon-mpf/tutorials_01_getting_started_27_0.avif) - -``` -[14]: -``` - -```python -circuits[2].draw("mpl", fold=-1) -``` - -``` -[14]: -``` - -![../\_images/tutorials\_01\_getting\_started\_28\_0.png](/docs/images/api/qiskit-addon-mpf/tutorials_01_getting_started_28_0.avif) - - - -## Step 2: Optimize the problem - -Normally, this is the step in the pattern during which you optimize your circuits for execution on hardware. Here, since we only use a noiseless simulator, we simply transpile our circuit for a [GenericBackendV2](/docs/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2). - -``` -[15]: -``` - -```python -from qiskit.providers.fake_provider import GenericBackendV2 -from qiskit.transpiler import generate_preset_pass_manager - -backend = GenericBackendV2(num_qubits=10) -transpiler = generate_preset_pass_manager(optimization_level=2, backend=backend) - -transpiled_circuits = [transpiler.run(circ) for circ in circuits] -``` - - - -## Step 3: Execute quantum experiments - -As explained in the very beginning, we will skip the optimization step 2 because we are simply going to compute our target observable’s expectation values using a noise-free simulator, namely the [StatevectorEstimator](/docs/api/qiskit/qiskit.primitives.StatevectorEstimator). - -``` -[16]: -``` - -```python -from qiskit.primitives import StatevectorEstimator - -estimator = StatevectorEstimator() -job = estimator.run([(circ, observable) for circ in transpiled_circuits]) -result = job.result() -``` - - - -## Step 4: Reconstruct results - -First, we extract the individual expectation values obtained for each of the Trotter circuits: - -``` -[17]: -``` - -```python -evs = [res.data.evs for res in result] -print(evs) -``` - -```python -[array(0.23799162), array(0.35754312), array(0.38649906)] -``` - -Next, we simply recombine them with our MPF coefficients to yield the total expectation values of the MPF. Below, we do so for each of the different ways by which we have computed $x$. - -``` -[18]: -``` - -```python -print("Analytical solution:", evs @ coeffs_analytical) -print("Exact model solution:", evs @ coeffs_exact.value) -print("Approx. model solution:", evs @ coeffs_approx.value) -``` - -```python -Analytical solution: 0.3954847855980006 -Exact model solution: 0.39548478559800204 -Approx. model solution: 0.42991214253489807 -``` - -Finally, for this small problem we can compute the exact reference value using [scipy.linalg.expm](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.expm.html) as follows: - -``` -[19]: -``` - -```python -from scipy.linalg import expm - -exp_H = expm(-1j * time * hamiltonian.to_matrix()) - -initial_state = np.zeros(exp_H.shape[0]) -initial_state[0] = 1.0 - -time_evolved_state = exp_H @ initial_state - -exact_obs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state -print(exact_obs.real) -``` - -```python -0.40060242487899755 -``` - -We can clearly see that the MPF has reduced the Trotter error compared to the one obtained with the deepest individual PF with $k_j=19$. However, we also see that the approximate model is not flawless since it actually resulted in an expectation value that is worse than the exact solution. This shows the importance of using tight convergence criteria on the approximate model as you will learn in the guide [How to use the approximate model](/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model). diff --git a/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb b/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb deleted file mode 100644 index 010863b04c3..00000000000 --- a/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb +++ /dev/null @@ -1,954 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b1b4d0d8-13f5-4b7a-abe0-2a2f034b8d35", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas\n", - "\n", - "In this notebook, you will learn how to use a **Multi-Product Formula (MPF)** to achieve a lower Trotter error on our observable compared to the one incurred by the deepest Trotter circuit that we will actually execute.\n", - "You will do so by working through the steps of a **Qiskit pattern**:\n", - "\n", - "- **Step 1: Map to quantum problem**\n", - " - Initialize our problem's Hamiltonian\n", - " - Use an MPF to generate the Trotterized time-evolution circuits\n", - "- **Step 2: Optimize the problem**\n", - " - Here we transpile our circuits for a [GenericBackendV2](/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2)\n", - "- **Step 3: Execute experiments**\n", - " - Use a [StatevectorEstimator](/api/qiskit/qiskit.primitives.StatevectorEstimator) for sake of simplicity in this notebook\n", - "- **Step 4: Reconstruct results**\n", - " - Compute the MPF expectation value" - ] - }, - { - "cell_type": "markdown", - "id": "fd1bbb0f-120f-4425-a52a-e393f86c16cc", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Step 1: Map to quantum problem\n", - "\n", - "### 1a: Setting up our Hamiltonian\n", - "\n", - "We use the Ising model on a line of 10 sites:\n", - "\n", - "$$\n", - "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", - "$$\n", - "\n", - "where $J$ is the coupling strength between two sites and $h$ is the external magnetic field." - ] - }, - { - "cell_type": "markdown", - "id": "55db9ce4-3cfb-439c-bb0d-a64030a52183", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "The [qiskit_addon_utils](https://qiskit.github.io/qiskit-addon-utils/) package provides some reusable functionalities for various purposes.\n", - "\n", - "Its [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", - "This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows.\n", - "\n", - "In the following, we create a simple line of 10 qubits using the `CouplingMap.from_line` method." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "98617c1f-00cc-4fdd-8dbc-7f0c7efd8344", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from qiskit.transpiler import CouplingMap\n", - "\n", - "# Generate some coupling map to use for this example\n", - "coupling_map = CouplingMap.from_line(10, bidirectional=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e15dd313-668d-48b3-8133-3cd4df9a3d8e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "skip-execution" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from rustworkx.visualization import graphviz_draw\n", - "\n", - "graphviz_draw(coupling_map.graph, method=\"circo\")" - ] - }, - { - "cell_type": "markdown", - "id": "fa7dcbde-531c-4442-8c16-9127d0b2d11b", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Next, we generate the [SparsePauliOp](/api/qiskit/qiskit.quantum_info.SparsePauliOp) on the provided connectivity with the desired constants." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c03810d1-cb9f-429e-85d2-055486d77d2b", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'],\n", - " coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j,\n", - " 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j,\n", - " 0.4+0.j, 0.4+0.j, 0.4+0.j])\n" - ] - } - ], - "source": [ - "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", - "\n", - "# Get a qubit operator describing the Ising field model\n", - "hamiltonian = generate_xyz_hamiltonian(\n", - " coupling_map,\n", - " coupling_constants=(0.0, 0.0, 1.0),\n", - " ext_magnetic_field=(0.4, 0.0, 0.0),\n", - ")\n", - "print(hamiltonian)" - ] - }, - { - "cell_type": "markdown", - "id": "bdb73298-d30a-407a-9e5d-de7c57264f6b", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "The observable that we will be measuring is the total magnetization which we can simply construct as shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "77232669-aa00-4f74-9f50-758f0c960693", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'],\n", - " coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j,\n", - " 0.05+0.j, 0.05+0.j, 0.05+0.j])\n" - ] - } - ], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "L = coupling_map.size()\n", - "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)\n", - "print(observable)" - ] - }, - { - "cell_type": "markdown", - "id": "83dc1e84-df2c-4e08-a8c9-3ecd6595f04e", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### 1b: Multi-Product Formulas\n", - "\n", - "MPFs reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions.\n", - "\n", - "To make this more concrete, we define an MPF as:\n", - "\n", - "$$\n", - "\\mu(t) = \\sum_{j} x_j \\rho^{k_j}_{j}\\left(\\frac{t}{k_j}\\right) + \\text{some remaining Trotter error} \\, ,\n", - "$$\n", - "\n", - "where $x_j$ are our weighting coefficients, $\\rho^{k_j}_j$ is the density matrix corresponding to the pure state obtained by evolving the initial state with the product formula, $S^{k_j}$, involving $k_j$ Trotter steps, and $j$ indexes the number of product formulas that make up the MPF.\n", - "\n", - "The key here is that the remaining Trotter error is smaller than the Trotter error that one would obtain by simply using the largest $k_j$ value!\n", - "\n", - "You can view the usefulness of MPF from two perspectives:\n", - "\n", - "1. For a fixed budget of Trotter steps that you are able to execute, you can obtain results with a Trotter error that is smaller in total.\n", - "2. For a number of Trotter steps which results in deep circuits, you can use MPF to find several shorter-depth circuits to run which result in a similar Trotter error." - ] - }, - { - "cell_type": "markdown", - "id": "58ffeb8e-978b-4211-b949-21bebe98bd1f", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### An introduction to static MPFs\n", - "\n", - "_Static_ MPFs are those where the $x_j$ values do **NOT** depend on the evolution time, $t$.\n", - "\n", - "Determining the static MPF coefficients for a given set of $k_j$ values amounts to solving a linear system of equations:\n", - "$Ax=b$, where $x$ are our coefficients of interest, $A$ is a matrix depending on $k_j$ and the type of PF we use ($S$), and $b$ is a vector of constraints.\n", - "For brevity, we are not going to go into more detail here and instead refer you to the documentation of [LSE](/docs/addons/qiskit-addon-mpf/apidocs/qiskit_addon_mpf.costs#qiskit_addon_mpf.costs.LSE).\n", - "\n", - "We can find a solution for $x$ analytically as $x = A^{-1}b$, see e.g. [Carrera Vazquez et al., 2023] or [Zhuk et al., 2023].\n", - "However, this exact solution can be _\"ill-conditioned\"_ resulting in very large L1-norms of our coefficients, $x$, which can lead to bad performance of the MPF.\n", - "Instead, one can also obtain an approximate solution which minimizes the L1-norm of $x$ in order to attempt and optimize the MPF behavior.\n", - "\n", - "In the following, you will learn how to do all of this.\n", - "\n", - "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/\n", - "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" - ] - }, - { - "cell_type": "markdown", - "id": "b293d4ae-cee1-4fd6-bb56-926dc282e868", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Choosing $k_j$\n", - "\n", - "The choice of $k_j$ is up to the end-user.\n", - "In principle, any values can be chosen but some $k_j$'s will lead to a larger noise amplification on real devices than other choices.\n", - "Thus, it is important that one tries to find _\"good\"_ values of $k_j$.\n", - "\n", - "Here, we will simply pick some fixed values for $k_j$.\n", - "The smallest value is motivated by the target evolution time of $t=8.0$ which normally tells us to satisfy $t/k_{\\text{min}} \\lt 1$ but empirically we know that setting it equal to $1$ usually works, too.\n", - "If you want to learn more about this and how to choose your other $k_j$ values, refer to the respective guide: [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "89682993-64b3-44ea-ae95-cc9eea30a632", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "time = 8.0\n", - "trotter_steps = (8, 12, 19)" - ] - }, - { - "cell_type": "markdown", - "id": "c1732f75-1299-4ade-bf7f-eab0c4758cba", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Setting up the LSE\n", - "\n", - "Now that we have chosen our $k_j$s, we must first construct the LSE, $Ax=b$ as explained above.\n", - "The matrix $A$ depends not only on $k_j$ but also our choice of product formula (PF) -- in particular its _order_.\n", - "Additionally, one may take into account whether the PF is symmetric or not (see [Carrera Vazquez et al., 2023]), by setting `symmetric=True`.\n", - "However, this is not required as shown by [Zhuk et al., 2023].\n", - "\n", - "Here, we are going to use a second-order Suzuki-Trotter formula yielding `order=2` and we will set `symmetric=True`.\n", - "\n", - "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/\n", - "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4898872e-8dc5-43f6-9615-31081851a80a", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LSE(A=array([[1.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n", - " [1.56250000e-02, 6.94444444e-03, 2.77008310e-03],\n", - " [2.44140625e-04, 4.82253086e-05, 7.67336039e-06]]), b=array([1., 0., 0.]))\n" - ] - } - ], - "source": [ - "from qiskit_addon_mpf.static import setup_static_lse\n", - "\n", - "lse = setup_static_lse(trotter_steps, order=2, symmetric=True)\n", - "print(lse)" - ] - }, - { - "cell_type": "markdown", - "id": "cb2fcfff-7d3c-4128-b4be-504a634f9a02", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Solving for $x$ analytically\n", - "\n", - "As mentioned before, we can find $x$ analytically:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e0db1488-21f5-4f7b-a9f4-98e2e945afde", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0.17239057 -1.19447005 2.02207947]\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "coeffs_analytical = lse.solve()\n", - "print(coeffs_analytical)" - ] - }, - { - "cell_type": "markdown", - "id": "a4517a21-61e2-45fe-bea1-82e87e48d8f7", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Optimizing for $x$ using an exact model\n", - "\n", - "Alternatively to computing $x=A^{-1}b$, you can also use [setup_exact_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_exact_problem) to construct a [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which uses the LSE as constraints and whose optimal solution will yield $x$.\n", - "\n", - "In the next section, it will be clear why this interface exists." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3bce6d85-a9dd-49e4-abe0-f8575394122c", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0.17239057 -1.19447005 2.02207947]\n" - ] - } - ], - "source": [ - "from qiskit_addon_mpf.costs import setup_exact_problem\n", - "\n", - "model_exact, coeffs_exact = setup_exact_problem(lse)\n", - "model_exact.solve()\n", - "print(coeffs_exact.value)" - ] - }, - { - "cell_type": "markdown", - "id": "bb9971aa-a4a5-4e04-85fe-2541ce21ac1f", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "As an indicator whether an MPF constructed with these coefficients will yield good results, we can use the L1-norm (see also [Carrera Vazquez et al., 2023]).\n", - "\n", - "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b8ffca3a-87e7-4344-b189-af95ef5bf2f2", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.3889400921655914\n" - ] - } - ], - "source": [ - "print(np.linalg.norm(coeffs_exact.value, ord=1))" - ] - }, - { - "cell_type": "markdown", - "id": "a6f84839-0879-4f48-bba0-89cc671986c5", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Optimizing for $x$ using an approximate model\n", - "\n", - "It may happen that the L1 norm for the chosen set of $k_j$ values is deemed too high.\n", - "If that is the case and you cannot choose a different set of $k_j$ values, you can use an approximate solution to the LSE instead of an exact one.\n", - "\n", - "To do so, simply use [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.setup_sum_of_squares_problem) to construct a different [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which constrains the L1-norm to a chosen threshold while minimizing the difference of $Ax$ and $b$." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "30dac7af-5a92-48f4-bc1a-1e140546cab2", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.40454257 0.57553173 0.8290123 ]\n", - "1.8090865903790838\n" - ] - } - ], - "source": [ - "from qiskit_addon_mpf.costs import setup_sum_of_squares_problem\n", - "\n", - "model_approx, coeffs_approx = setup_sum_of_squares_problem(lse, max_l1_norm=3.0)\n", - "model_approx.solve()\n", - "print(coeffs_approx.value)\n", - "print(np.linalg.norm(coeffs_approx.value, ord=1))" - ] - }, - { - "cell_type": "markdown", - "id": "2dc731cc-f1c6-484b-afba-dba6c1c0d1db", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Note, that you have complete freedom over how to solve this optimization problem, meaning that you can change the optimization solver, its convergence thresholds, and so on.\n", - "Check out the respective guide on [How to use the approximate model](/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model)." - ] - }, - { - "cell_type": "markdown", - "id": "e328b896-a303-45f8-bfc6-4918205a18ec", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### 1c: Setting up the Trotter circuits\n", - "\n", - "At this point, we have found our expansion coefficients, $x$, and all that is left to do is to generate the Trotterized quantum circuits.\n", - "Once again, the [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators) module comes to the rescue to do just that:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "fe25afaf-2f43-4c82-a97b-946b9a86df98", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from qiskit.synthesis import SuzukiTrotter\n", - "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", - "\n", - "circuits = []\n", - "for k in trotter_steps:\n", - " circ = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(order=2, reps=k),\n", - " time=time,\n", - " )\n", - " circuits.append(circ)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "01b3c52b-53cc-4ccc-a2f0-efd774486070", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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1446frWmUrltmssAf95XmDE1+/5B4S6qlH78jNfRRP/r2FvNnxkDzbIhFRIPpgXXSH5f1PZfk6VLz50tjzRoDA6gfDZzOqHTNB2Yzie3VdUh3r5b+tVq6ZBfp7KnUjMST7VnI9vGnk49qpR++beaPbe+9avNncqF08/5sQh9Uj2+QfvdB33NJniszfw4dJf1mTzYXCaJwVLphmfTw+h0/cyTdu8b8+c5M6bzpZIEgqmuXfrRY+qBmx89KW6SbuupHr91bOoi1yOPK9jxk+/jTyXOlZm25jj7WH52/yfw5YIS5DhSQBQIn6kg3f2zWGtmeI+m/68yfb0yTvrcz9aNB1Ngp/WSxWUdge1HHvEP8x6fSr/eUDh+T/P4h8dY2mvrR8j7WIv+o1vz52wozl2QqG8/Gle15yPbxpwvHMe+C71q142ctEbPx3LwS6f+mSJfuKmWSBQKnpat+9I0+6kejMveAO1dKv5htNiJH8GxsMvWjGz5jLfLfftAzl2RmcdK7hyRYWCld+Z65HvTmyFwb3qiUZg02dUODWYs8bshCHIN00RYxexO9XL7jZ1Xt0j9Wmqx4xSyzATWCp7zFZIG1fdSPhh3p2q61yP+4rzRrSPL7FxSBLb+/+OKLVVpaqosuukh/+MMftm46LUmXX365Zs+erXA4rEmTJmngQJ4sxVtTp/TdN/vedLq3lohZXHBRH5tQwd9W1UsXvNn3ptO9bWiWzl9oLvqIn4a1FSpfsExlL7+vj/7yuF76+nUaNmeq5v7+gq3fE+0I642Lb9XuF5+qwbuYNDXh6H007si9tfCHf0lV1+PC9vGnA8eRrvuw702nt3fXKumWTxLfJyRXa1i66K2+N53urT1qNp95pY9f/OBvJU3Stxb2vel0b5tapG+9IW3oYxEB+NtLm8yCYdtvOr29xVXSRYvMdQPxQRbiGKSD9ogp+Olr0+neOqPSz5dIz5Ymp19InrJm6dsL+950urctbeb7Vjckp19InoWV5rlfX5tO9/ZBjfSdN83zRATL7Z/2ven09h5ZL/1mqXmWgPggC3EM0sHmVvNcoK9Np3ur6TDvkz6pS0q3kETvVkkXv9X3ptO9fVJn3ifV9bGIAPztX6ulmz5j0+neni6VfvaemSCE4Ig40hXv9b3pdG9RSX/4SPpPHxtPwDvbs5Dt408Xy2qkCxf1vel0b6sbzbOh6j42oYK/PbjOLBDa10JBvb24ybwvDvcXGuArUUf6xZK+N53uzZF06/K+Jw7C3xo6zHPfvjad7q05bArF3+5jEyp4Z3sesn386eKJDdKvlu646fT2FlRKP3hH6ujnfRL8xXHMopF9bTq9vX+slP4Sw/sk+EtzWPreor43ne6tLSJd9o7ZcA7Bsq7RPPvva9Pp3spazPuk0n5qS+CO7XnI9vGnA8eR/vhR35tOb++eNeY9ATUjwdIWMe+K+9p0ureOqFlQ6IV+3ifBf0qbzRyRvjad7q2yTfr2m30vIgB/e73CZP3tN53e3pJq87tDXxtSwhuyEMcgHXREzDO/vjad7i3sSFcv7f99Evynoqt+tK9Np3urbpe+s1BaUZ+cfiF53t5i3gH2d4//uM68U2ygfjRw7lplagH6+3X/iQ2mtoD60fghC3EM0kF1m3TBwr43ne6tvtP8Trisj02o4G9Lq83aEX1tOt3bygZTP1rTz9pk8J//rDEbDfZXFvp8mfSTd/uvM4W/RB3p5+/1vel0b47MhoP/Wp2UblnD9ixk+/jTxfI6M2e4r02ne1vXtTbZ5n7WJoP/PNa1dkR/9/hXKszGtMwlCRbHMT//vjad3t7fVpg1SRAsTZ3Sdxf1vel0b61da5H3tQkVvLM9D9k+/nTxXKlZU3D7Tae39+ZmU2PWzlySQHEc6fplfW86vb27u9YjQbC0hs3z4b42ne6tPWrWKn5pU3L6heTZ0GTqR/vadLq37tqSEtYijyvb85Dt408Xt3wS29oR9681e5gwlyRYOiLSpW/3//t+Z1T65fvS0/28T4L/lHfNF+1r0+neqtrNXJJV1I8GzqLNXWuR91M/+mEta5HHG1mIY5AOwlEzn6yvTad7izjSNR9I80qS0i0k0ZY2kwX6my9a22HqRz/uZ20yfLZAbjy9fPlyPfDAAxo2bJiuvfbaPr9nr732kiTNnj17h88effRRHXDAASooKNCgQYN04IEH6uOPeQrtxs2f9F8A3i3imEUC2GguOBxH+tmS2Cd8V7dLv16a0C5Zb8u7n2rNvNc1+eQDNXzvnbZ+vfrDtfr4r0/o4Fu+r/zRQzT3hu/o7Z/eodbKYN1ZbR9/KrxWIT0aQ9FXt3vWSO+wiGyg/G1F7BsGOTITQ5kcHByOI/1ySf+TwbrVd5oH/QiOug7pqvf7nxje7eM66e8UAScMWYhjkAr/XCUtdTHh+9dLpSo2lwmUXy81v+/HojlsNpqj6CM4un+msS7+srpRuvWTxPYJyfVBjXTHyti//+nS/jclhHdkIY5BKvzuQ6kyxgnfrRHpShYKCZT2rp9pfxsLdVvfJP3po8T2Ccm1sl66bXns3/9yufQ4i8gGysMl7jYMuvFjFpRPJNuzkO3jT4Vw1Gw+3x7j4i+bWswGxQiO9U3SH1z8TBdujm1TQvjH0xvNpuKx+quL+gL4w02fxJ7vIo7003fNplRIDNvzkO3jT4WKVjPJJ1bvVkn/jmFTQvjHSy6f9fxzVf8bFMNf/rpc+jTGCd9RmUWnmRwcHI5j6gVirR+t7ao1ROLYnodsH38qvLlZesDFs54H15nnQwiOO1eaBUBi9av3pVo2lwmUX71v7vGxaOw0eZD60eBo6uzaPDDG7/+0XvqLi/oCuEMW4hikwj1rzDO/WF3zgXmmiOC4ZqlZMCgWLRHznohNZ4OjLWzWjom1Jnhdk6kdQ3AsrzM1ALF6cVP/mxLCO7IQxyAVfr9MKmuJ7Xs7umoN2WguOMJR6cr3+t9YqFtpi9mgGMGxrtFdvltQySKyQfPEBlM3EqvbXNQXwD3bs5Dt40+FiGPmlbbGWBNc2WrmIiM4NrVI17r4mb69RbqX+tFAeb7MrB0SqztW9r9BMfzltuXSqobYvjfaVWsY67rVcM/2PGT7+FOhqk26emns3/9BTWybEsI/3D7r+c9a6S3qRwPl75+atYVj4cisW13HWuSBctX7Zo3xWDR0mnOA+tHEsT0P2T7+VFi8xdQPxurR9dKrLtanQvq7e7W7ucK/WRp7rSH84TdLzabSsWjpqjUkCwRHq8v60TWNZm9DJAZZiGOQCvevlRa52Hfu+g+l0ubE9QfJd+2Hsc8PaouYWkPWIvcmkBtP33///YpGozr77LNVWFjY5/fk5eVJ2nHj6VtuuUVnnHGGDjroID3xxBO6//77dcQRR6i1lRlrsWrslJ518bK/u81zbCwSGO9Vu18YfHGVKRxG4nxw4zxFwxHtcdlXtv36TQ8rGonoxBdvUMXCj7Tu8YUp6mFi2T7+ZPOyKPRDJXHvBlKkNex+k5C2CBNDg+TjOvcLg39Uy2LiQfLUBvcLgz/uoQ1iRxbiGCRTZ9S8wHfb5jE2GQuMNQ3m2YCrNo3S+0wGCYxnS6UmlxM7nillMfEgeYjnAmmHLMQxSKbSZmlhpbs2ZS3SIiaDBMZLm2JfRLrbi5tYTDxIvNzXH1pHAWhQOI7790SOzGbVSBzbs5Dt40+2BZVmASA3Xit33wbp6+GS2DeU6DavhMXEg8RrHkQw1HdIz7usH63vlF6kfjShbM9Dto8/2R5d735ix6MlLCYeJJ7qR8kCgdESlp50WQvaEnG32CTS24e10soYF47s9kENi4knmu15yPbxJ5uX5wJsKhEc7RH39aMdUffzT5C+Pq2XlrqsBV3Z4G6zcqS3p0vdLwz+1EbzuwQSgyzEMUimcFR6pMRdm4jjPj8gfW1ocrdYlCRtaJbecdkG6euFTeZ9oas2Ze7bIH15eUfAc4HEIgtxDJKpslV61cVmo91tFricf4L09WqF+4XBXy5nMfEgmVdi5ga4wVyS4PAyl0QiDyaa7VnI9vEn26LNUmmLuzYLK1lMPEgeKXFfP/qwhzZIX8wlsVtTp/S0y/rR5rD79cvhju15yPbxJ9tjG8xagq7arHffBumLtcjt1hZxXwvaHjVrFyMYPqmTlrmsBf2kLvbNyuGN7XnI9vEn24Ml7tvwXCA4wlH364eFHfM7AYJhXaP0TpXLNk3u1y9H+nquzOw96Mazpe7bIHZkIY5BMkUd9/UfUbH+aJBsapEWVLhv43b9chiB3Hj65ZdfliQdeuihn/k9paXmzWLvjafXrFmjyy67TDfeeKOuv/56HX744Tr22GN19dVXa++9905spwPk2VJvG8ZxIQ8Orz/LR/jFPqEaSyq07vGFGvOFWRqx385bv+6EI9qy+FPlDh2k1Q+8ksIeJpbt40+mDU3uH+xI0usVUhWTQQJh/ib3m8xJ0sPcBwLD6z3d7cISSF9ezoGGTnP9QGKQhTgGyfRahVTtYcO4R0uYGBoUXhd+4tlQcHjJda0R6TkmgwRCXYe3XPdBjbTa5QLkiB1ZiGOQTI9vcL9IiEQWCBIvzwU6o+43I0F6avE4yXdlA5NBguL9GqmkyX27pzZ6qzNAbGzPQraPP9m8PBeIiskgQdER8ZbrNjRL73qoM0D6WVFnJvq69UKZWWQG/vdMqZnw7xY1I4llex6yffzJFPE4ybeyTVq4Of79QfKVNEpLPEzyfblcqvFQZ4D080KZ+03mJN4RBInXOlDqRxPL9jxk+/iTqcLjJN+FlVK5ywXIkZ5eKTe1Q24xpzA4qB+Fl1zXHJaeL4t7V9CFLMQxSKY3N5tnfW49tp6NRYLCcxYgDwaGl2zfHnW/GQnSU1OneUbs1id10vK6ePcG3chCHINkenyDqQV0i+cCweHluUDEcb8ZCdJTW8TMCXCrpMlbnQHSz8d1Zm6QW8+WeqszQGxsz0K2jz/ZvDwXcMRckqAIR81mk26Vt5pNy+F/qxvMmiFuzd8k1VE/GgjPlZm1g9yidjCxbM9Dto8/mRzHrCHoVnW7WbMQ/lfaLL21xX27BRXS5tb49wfJ99Ims6awW9QLBAf1o+nJ9jxk+/iTqarN7DHi1jtVZk8T+N/rHtcif2S92agS/ud1XhBZIDi8POdri5g1apAYZCGOQTK9s0Uq8zBH+IkNZi1i+B9rkSdXVqo7kAjr15tEOXHixD4/D4fDWrhwoaRtN56+6667lJ2drW9961tx7c/ee++tigp73mAMPPMa5X/h667bLd/SpnHjpiWgR0i2oT+br+yxM123u/vp13Tj0WcnoEf+k+1k6CrtG/e/98ObH9bkkw/UHpd9Rc+f9itJ0oj9dta0rxyq5Xc+o31//U09ceRlirS5X+VjxvQZ6gzFJ43ZPn6/y9n9SA3+7j9dt4s40j7HnKbOVW8loFdIpqJTfq6CI7/jut36Jmnc+AmSw39Lfjfksic0YPKerts9+Mpi/e34UxLQIyRVRpZG3VbiqemPr/uzvvP4tfHtj08lIg8lMgtJ5EHyYI+CY3+gouN/5LpdZZs0YfpMOW289fe7wRffr5yZB7tu99RbH+tfJ30pAT1Cso28Za1CWQNct/vFrf/UpQ/+IgE9QjJlT9pDQy9/0lPbo8/5jtqWPBXnHvkTeZA86GfFF9yp3Nnu7+mvLCvRuC8flIAeIdlG3PCRMgqKXbe77s4H9NN73P8ugfSSNXqGhv3iZU9tT/nWD9X61oNx7hGSLe/gr2rQ/7l/xtcclnba+yBFtpTEv1M+5Lc8GO8s5LfxS+TB3oZf844yB49x3e6W+5/Ub+78bgJ6hGTKHDJOw3/r7Z3/OT/4hVpedV9rgPSSu++XVfyNm123a49Kux18lMKlnySgV0imgWf9XvkHua8B/HBTo8aN27n/b7QAz8bIg34WKhyikdd/6Kntt396rZpf+HOce4Rky5lzjAZ/+x+u20Ucaa8jT1bn2ncT0CskU9GXr1LB4e7n46ypj2jc+Elm1TH42tCfPK3sibP7/8bt3D//Lf35uNMS0CP/IQ/6Lw+SBXsM2PkLGvL9/7hu50g68KSz1LH89fh3CklVeMJlKjzmEtftNrVI4yZPkzo97FSJtDLkB/M0YPr+rts9vvAD3XXicQnoEZIqFNLIW0sUysh03fSnf/qHvv/w1QnolP/4LQtJ1A6SB3sUHHWhik6+0nW76nZp0s6z5DR52JECaaX4e/9S7m6Hu2734vurNO6UQxPQIyTbiD+tUEZuoet2v739Pl3+n58koEdIpqxxu2jYT1/w1PaEcy9R2zsPx7lH/kQeJA/62aDz/qq8vU5w3W7hyk0ad3r8f+5IvuHXvqfMQSNdt7vxnsf0q39elIAeIZkyh0/S8Kvf8NT2zO9fqdYF98S5R0i2vP3P0KCv/cl1u7aItOvcwxQuX5mAXvmP3/Kg7e+KJfJgb8N+9YayRkxy3e5vDz+v6446L/4dQlJlDByhEdct8dT23J/8Ri3zb49zj5BsuXser+Lz/+a6XdiR9jj8BHWWvJ+AXiGZis74jQoO+abrdp/WdGjcuCkJ6JH/8GyMPOhnodxCjfzTCk9tL/zVH9X8zI1x7hGSLWe3wzT4e/923S4qaf/jzlDHyjfj3ykkVeFJV6rwSxe6brexWRo3YZIUDce/U0iqIT96VAOm7uO63cOvL9E/TjgxAT3yH/Kg//IgWbBH9vT9NfQH8zy1PfT0b6p92Ytx7hGSreCYS1R0wmWu221pkybO2EVOa0MCeoVkGvz9+5Sz8xddt3tm8Qrdc/IRCegRkm3kTasVGpDrut3Vf/6XfvTfnyWgR/7jtywkUTtIHuyRf9j5Gth1fN2o75SmztpH0bry+HcKSVX87X8od84xrtu99skGjTvtgAT0KP2NGjVK777rbY2dQG483dzcLElqbW3t8/MHHnhAVVVVKioq0uTJk7d+/c0339ROO+2ke++9V7/97W+1ceNGTZ8+Xb/85S/1f//3f577U1FRobKyMs/t/SarPax8Tw0HWHWcgqxYGcr20K4jIs6BLgNCmZL7OnpVLPpYd4/+7AWX6leV6d/jvrL1n7Pyc3XQTRfqvWvu04p/Pa9jHv219rzyLC2+6m7X/+5N5ZvU4UTcd7oPto/f74onNmmwx7Y19Y1q4Drge+PbO1Tgse2mzVvkdLBYkN8NdEJyv82g1OmEyAIBkJFboFEe2za3d3AOdPGSh1KZhSTyIHmwx5jWdhV5bFuxpVrhhqq49gfJVxCRcjy0i4QyuQ8EQSikUR42nZaklvZOzoEAKBw0RUM9tq1tbFYN54Ak8iB50N/yIo7cl/xI0QyyQFAMz/Tylkhq64hwDgRAXs4IDfPYtq65VVWcA743orlVgzy23VxTpzbOAUn+y4PxzkJ+G79EHuxtaChL7rcTkNrDUbJAAOSoQMM9tm1oblUl54DvDW1qVrHHtltq69XCOeB72R1e60dzuA904dkYedDPsod6On0lSY0tbSrnOuB7g6d4rx+trmtQI+eA741v7/RUPxrKyFRZeYUUYbEgvxvkeJxLEmUuSTfyoP/yIFmwx6AxjRrisW1NQ5PquQ743ti2DrnfYs6o2FKtSHNdPLuDFCiMytNckrCTQRYIgswsjfKw6bQkNVM/upXfspBE7SB5sMfoljbPc0kqq2rUWc11wO/yPdaPRkJZ3AcCYkSmt7kkrZ3UjwZBfsFY7/WjTc2q5hyQRB4kD/pbbjiqPA/tomSBwBiW4XEuCVkgEHIzBv1P9aObOQd8b1hzi+e5JNSP9vBbHrT9XbFEHuxtiMd3BO0RhywQAAM6szTCY9vGljZVcA743pDp3ueSVNXWq4lzwPcmeK0fzRqgsk2bJMeJe5/8hmdj5EE/yxo4zPNckqa2Dm3iPuB7xeP/h7kkrEUeCOPavdePlm+pUrStOa79QfIVeVyLPMxa5FuRB/2XB8mCPYqGN3hef7SmoUl1XAd8739ai7yqRuG6yrj2B8nneS1yMZckKEZ6XH+0pT3MOdDFb1lIonaQPNhjZEubBnpsW1ldo44KrgN+lxeOelyLnPpRLwK58fSoUaNUW1urJUuWaO7cudt8Vl5erssuu0ySNGvWLIVCoW0+Kysr05VXXqnf//73Gj9+vO68806dddZZGj58uI444gjP/bFJrtPuqV20uUZjx46Nc2+QChntDZ7aZXc2cw50yXYypGji/z37/OpratqwWSvufk6S9MYlt+nE+X/QhmffVuVby139XWNGj1FnKD6dtn38fjcgJ9T/N32GwTkhFXEd8L08p8NTu2h7i8YM9/p6AOkks73RU7us9iayQEA4HW0KDXD/q32+OjgHuiQjD8UzC0nkQfJgj/xQp6d2TiSskYMKpCIvrwmRTrI7vRXvZbTVcx8IiGhTrTIK3ZcB5zntnAMBkJlnJoU6jrPN8/dYDMqW8jgHJJEHyYP+NiDc4qldqJUsEBjNtVKu+6mhOdE2zoEAyMg3JQhessDArKhyOAd8LzfL/f2w+3wZXpCtKOeAJP/lwXhnIb+NXyIP9hZqrZOK3S8XMyDSShYIgFCBt0kAklSUGVEW54Dv5WS7X+ilOwsMy8tShHPA93KjbZ7aOdSPbsWzMfKgr2XnyolGFcrIcN20MCPMdSAABnhYIaQ7CwzJy9RAzgHfy5fH+tHWBo0d5XW5MaSTjA6P9aMd1I92Iw/6Lw+SBXtk/y9zSQZIhVwHfK9AHutHwx0aNbhQKvayBDHSSVZHk6d2GR2NZIGAiLY2KCPP/VIhzCXp4bcsJFE7SB7skZ8R9tTOiUY1cmCenFyuA37ndS4J9aPB4TTXSMXu19fJpWYkEDLzvNePDsp2lMs5IIk8SB70twGRVk/tQq113AeCoqVOKhriulkOWSAQMrrqR71kgaKsqLI5B3wvJ8t7/ejQvEwN5hyQ5L88aPu7Yok82FuotV7SeNftBoRbyAIBEMr1soy4Qf1oMAzwMJ2od/3oIM4B38vzuhZ5U63GjhkT5974E8/GyIO+lpElJxJWKNP9dhcF1IwEwv9UP5qbwVrkAeB1LonT2abRQ4slFcezO0iBrHZv9aOZ7dSPdiMP+i8PkgV7ZOW6n1PcbXBOSAVcB3zP81rk0YhGDsqXCjgH/M7zWuTtDWSBgIi21CqzaJjrdrmsRb6V37KQRO0gebBHXob3DbhHFObI4Trge6xF7t7/sq9xyHEc95Vaae7iiy/WrbfeqvHjx2v+/PmaMWOGJGnx4sX66le/qrVr16qzs1MXXnihbrvttq3tZsyYoVWrVunRRx/VySefLMm8iJ4zZ46Ki4v12muvpWI4vvNxrfT1Be7bnTZJumJW3LuDFLh3jXTTx+7b/XZP6ehx8e+PH3W2tOm+qeck9N8x9rA99MW/XKrHD/+Rmsuqtn595jeO1i4XHK8nDvuRwq2xF2+cveZeZed7L/rqzfbx+11nVDr+RanaZe3P2Hzp0cOlDO/vipEm1jRIX3nVfbvjxklX7xn37iAFHi6Rrv3Qfbufz5ZOnhj37iAFfvGe9GyZ+3bzDpUmFcW/P36U6DwU7ywkkQfJgz3KmqWTX5LcPnA6dLR0wz4J6RKS7NlS6RdL3Lf70W7S/02Jf3+QfNd8ID263n27e78gzSyOe3eQZI5jfidc63I98UHZ0tNHSbmZCemW75AHyYN+9mq59OPF7tt9d6Z03oz49wfJd+NH0n1r3bf7+4HSnkPj3x8k39delz6pc9cmP1N65iip0PtelUgT9R3SsS9I7S7r4OYMke44KDF98iO/5cF4ZyG/jV8iD/b2txXSHSvdt7t5P+lA9hgLhAsWSu9Vu2szIEN65kipOCcxfULytISlY16Qml3uKzBjoHTfFyWX600iDX1YI537hvt2Z06Wfrx7/PvjRzwbIw/63aVvS29UumuTGZKeOEIamZeYPiF5OiLScS9KtS7Xi5lQIM07jPrRIFhVL/2fh2k3J06Qfjkn7t1BCjy4Trp+mft2V82RTpgQ9+74EnnQf3mQLNgjHJVOekmqdLm/zMg86fHDpSzvaw0hTWxokk592X27I8dI1+4d//4g+Z7cIF291H27y3eXzpgc9+4gBX69VHpig/t2//miNGNQ3LvjS37LQhK1g+TBHpWt0onzpYjLySQHjZRu2i8xfUJyzd8kXfGu+3bf31n6+vT49wfJd8My6YF17tvddZA0y/0elUgzjiOd/Zq0ssFdu4Is6dmjpHz3e1EEEnmQPOhnCyulS9523+78GdJ3Zsa/P0i+Py+X/rnKfbvb9pf2HxH//iD5zn9DWlrjrk1OhplLMmhAYvqE5GnqNHNJWlyuJbxLsfTvLySkS77ktzxo+7tiiTzY250rpb+ucN/uD/tIh4yOf3+QfN9fJC3a4q5NZkh6+khpGP8Z+V5bRDruBane5R5DkwulBw9lLkkQrKiTznndfbtTJko/mx337vgSz8bIg3532WLplXJ3bUKSHjtcGluQkC4hicJR6YT50pY2d+3GdK1FnkkW8L2SRum0V9y3O2as9Ju94t8fJN9j66XffuC+3ZWzpC9Pint3fIk86L88SBbsEXWkU16SylzuNTc0R3rqSCmbuSS+V94inTTf/X6hh4yS/rBvQrqEJHuuVPq5h7XIL91VOmdq/PuD5LvuQ2leift2/zpY2nVw3LvjS37LQhK1g+TBHtVtZp2RsMu5JPsOk/5yQGL6hOR6vUL64Tvu212wk/StneLfn6AL5K9Ql19+uYYOHaqNGzdq11131e67767p06dr33331ZQpU3TYYYdJkmbP3vbN4pAhZibKEUccsfVroVBIRxxxhD766KPkDcDndh1sCvncOm1SvHuCVDlhvCnodWPIAOkwir6Squzl9/WfmV/fJsxK0oq7n9Mjcy9yHej9xvbxJ1J2hingceu0SSwaGBRTB0p7edgg5nQWiQmMY8aZib5uFGZJXxqbmP4g+bz897zPMDadTiayEMcgkcYWeNsg5vRJce8KUuTw0VKxywm+ORnS8eMT0x8kn5f/nncbzKbTQREKeXvWe+IENp1OJrIQxyCRDhopjXRZ+5AVkk5mM4HA8FLMP6VI2oNFAwPDSx48djybTgfFoAHSl8a5b0e9QHLZnoVsH3+inTLR/QTfsfnSXBYNDAwv74mOGMOm00GRn+XtWe/pk1koKCh2H2w2EneLPJhctuch28efaF6eC3xhFJtOB8WATOlk6ketNn2QNMfDs15qRoLj2HFSnsv3voOypSOpH00q2/OQ7eNPpKwM6VQPWeDUiWw6HRQTCqW5w923Yy5JcBw51tzb3cjLNBkCwXDGJPdtZg9h0+lkIgtxDBJpZJ551ucW7wiC45BR0jCX730HZEgnUT8aGF6e880YaN4xwv9CIekMD7/fHT+eTaeTiSzEMUikuSNMLaAbmSFv69MgPZ060WwW5MaEAmlfD88UkZ68/H531Fg2nQ6KwmwzN8gt6gWSy/YsZPv4E+3kCWausBsj88ycZASDl/e+h41m0+mgyM00a4a4dRpzSQJjZrFZO8gt8mBy2Z6HbB9/onmpGTlwJJtOB0WWx7XIT/UwJx3paVKR2SzKrdOoHw2Mo8eatcXdKMgya5gjeWzPQ7aPP5EyPK4/evJENp0OitH50kHUj1rt8DFmzzE3cjLM3mYIBi//Pe9SzKbTyUQW4hgk0tBccy9wi3mlwXHgSGmUy3WDqB/1LpC/Ro0bN04LFizQcccdp9zcXJWUlGjIkCG6/fbb9fTTT2vlypWSdtx4etddd/3Mv7OtrS2hfQ6a7850d3IdM1aa5mGxQaSnQQOkr01z1+aCmWaxMQDBcPokaYSLQr6x+UwMDprvzHRXBPzFUdKuxQnrDpIsP0s6f4a7NufvJOUxMTgwdh/sbqGQrJB0wU6J6w+A5PvWDLP4S6z2G242oEcwDMiUvuPyuv6N6VIRm8wFxoxB0pdcLAqdEXJ/ziC9HTdemlQY+/cPyZH+b0ri+gMgubIyzHsiN86aagoFEAwTCt1tJB6SdOHOTAwOkiPHuttobmC2dM7UxPUHyff1ae4mBO08yCwSASAYRua5X0T2ezuzyVyQHDLK3UIhBVnSN6cnrj9IvnOmSsUuJgRNLTL1gwiGUMhc191c1o8fbxYWABAMc0dIew6N/ftzMqTzXNYZIb19ZbK7zWUmFHhbbBDp6zsz3S3+dPhoaefihHUHSVaYLZ3r8rr+rZ3MwqMAguHLE6XRLiYHj84zbRAc397J3eJPB4yQ9hiSuP4guXIzzb3djW9ONxkCwTCzWDrCxUIhmSH3dUYA0tv5M9z9jrfnUJMHEAxZGdJ3d3bX5uypUrHLzaqRviYVuVsIMqSud4vUjATG0WNNDUCsigeY6wCAYMgImXkBbpwx2dQcIhhG57tfSJj60WA5fIxZGDpWhVlmfjmC45ypZo5QrGYMNHOQAATD0Fz3v+N9d6Z5poRgOHCkNMfFu9+8TOpHg+bMKWbtkFhNKjRzCRAc353p7ne8o8aaNYoABMPew8xagrEakOF+zVqkt9MmuXveOzafjWWC5ts7uVuL/OCR0iw2mQuM3Cz39aPnzTBrmAP/z959h8dRnW8fv3fVm6vce8W9YcA2duiEGnogoSSBBPgFQho1jZAEQntDgDQSIAVIoZsaTAlgwICpNgYbY0u2ZUu21WzJVte8fxzJklx3l63zfD/XpSuxdkecMxrN3DvznHPgDycPkwbnhv7+vtnSl4fHrDlIgHDnIj+gUDowjM8QSG4ZQTe2OBznjXZrm8EfRneTjh0c+vuDYiwJ4DffGCPlhjGWZEpPd28A/pAWkL4d5nn9KyOlQuYij4hvyyzGjx+vp556SjU1NaqpqdFbb72lCy+8UNu2bVNxcbGCwaAmTZrUZZuTTjpJkrRgwYId32ttbdXzzz+vAw44IK7tT3Wz+0o/nRbaA985be+Fv1y4X+gPbr41VjpteEybAyDOemdLd8ySeodQ/NU/R7pzltSNGzu+Mr239IsZoT3wnVko/WoGA4P95pxR0tkhLhx3bhjvRWoIBKTrZ4Q2kXB6QPrV/tK0MCYdBpD8JvaUfj0ztAe+U3pKN80kC/jN6SNCH+Bz+nAKgP3oZ9Ok2SEUcQQD7r2zmDDMV/LS3X2BQSEUf/XIlO44SOrLJCGAr5wwVLo0xAmDThgS+nuROq6a4hYc3JdA23sPCeG9SB3ZadJvD3KDvvelIMO9d3Be7NuF+BmWL/3mIJcL92VkgXTbQVImC8sAvvK9idIXQ5wE7PthvBepIT0o3XagNKbbvt+bmyb9vwOlESw46ysDcl3GC2XywKF50u2z3IBi+MfcftKPp4ZWoDyvn/SjKTFvEoA4CgakWw8IbSLhrKB08wHSOCYM85XCtvrRXiHUhA7Mde9lkTl/mVkoXTc9tMWnDyyUrpsR+zYhvr4+WjprROjvPTPE9wJIDT2y3PW9bwiDffu25QYWmfOXyb2kG/YPbfHpab1crSn1o/5y5gg3UUS034vU8fPpLuvvS1rAfXaYGcJ7AaSO/bq78SFZIWSBCT3cvUQWmfOXk4aGPnngl4YyaZwf/WhqaJOABeWeKc5lwjBfyU53NQBDQ6gJ7dZWPzowjEmHASS/owe5msBQfHGQqzWEv/xwknTkwNDee3kY70VqyAhKvzlQGhVCTWheuhtzMCyEcSdIHYPzXMYrCKEOZHi+e282Y0kAX7lkfOiLyF4axnuRGtICbnxIKDWh2WnSLQe4hSjgH/1y3NwhPUOoHx3UVj8ayhhUpI6D+rg5hEJ59jO7j3TttFi3CEA8BQLuWXEoi8hmBl2N2SQWnPWVXlkuCxSGUBPaL8dlARaZ85dpvd3cwqHMRT6jtzsPUD/qL18d6eYYj/Z7AaSGggx3fe8fwpyivdvGnfRmkTlfGd/DfSYIZS7yST3d+HLqR/3l1OFu7bFQnDLMrWkGf/npVLcG4b4EA26dwtnMRQ74yuhu0q0HSjkh1IHs193VGKX7dvVcm44bIl02IbT3HjtY+k6I78WuzP3pLFu2TJ7nacyYMcrN7Tr64MQTT9S8efN04YUX6i9/+Yuee+45nXnmmVq2bJl+9rOfJajFqevEodLvZ+150G+/bFcc9JsDmUTajwIBNyHkj6fueVL5/bpLv95fuogBgYAvje4m/W2eK+zc3U2+7DR3U+dv86ShDALwpaMHSX+aI83aw2JzvbPcTb07Z0k5FP75TiAgfX+SmzBmT4OCRhdIv5gufXciD/v9KCdd+t0sd6O/9x6Kf2b3ke46mAGBgF8d0l/688HSwX3dYnI765npJov7wxwmkfar/xvnivrG7mGgz4h86SdTpasmkwX8KCvNLR737XF7nkj2gELpD7MZEOhXA3Olv86TThu2+wd+GUHp2EHuvsC4HnFvHoA4+PoYN+h3Yo/dvz4kz+WAa6dR+OdHGUHppgOk706QBuyhEHh6L3dv8PThcW0a4qRvjnTPXDdR+O4GfqcFpKMGSn+dK03pFf/2IfZm9JbunSsdMWD3iwzlp0tfGemOk0IGAQC+kxaQfjnDTQo4aA+Tw07p6WqGzmZAoC/1zJL+crB09sjdTx6XFpAOGyDdO48FJfxqUk93b+iLg3Y/SDwvXTpjuDsGQhk8iNRz8jDpztkuF+5Ovxw3YdwtB1A/CvhRt0zprjnS10bvfhKYgNyiE3fPlQ5mQQlfGtvdZYHjBu9+wcGctvrRe+e6SYfhP8cMlv44Z8+LzfXJli7ezy1AwiTS/hMIuEUFfjZNGrmH+tEx3aRfzZAunUDNCOBHIwpcFvjS0N0vOJgVdK/9dZ57L/znsAHSn+fsebKQXpnSBWOl389mEmk/CgTcuOFfzdhz/ejIApcVLp9EFvCj7DSX9S8e57L/7hxY6D4zHDM4vm0DEB8H93P1IPP67X4sSfdMd+/wrjnuXiL855tj3QSS4/ewwMzQPOnqKW6COepH/Scj6J4BXjrePRPcnRm93bPEk4fFt22Ij/45rhbgjOG7/8yXHnBzEPx1HgtKAH519ihXG7inBWYG5bp7Ar+csfsaY6S29LZFg34wce/1o789SDprZHzbhvgozHafCb8y0o0Z2FlawI0xuHfunmvLkNqm9HJjhY4auPvzfF669OUR7jjpS/0o4DvBgBszfNVkN4Z4dyb2cPcOvj4mni1DvHTPdHMNnTtK6r6bsSRBufmI7pkrzWJBCV8a18Pd9zl20J7rR08b5t4zcA+fGZDaThji5hI6YA/1o32z3ZxUtx3k5iYC4C/5GW5OwfPHuDkGdxaQm5PwzwdLhw6Ie/MQB6Pa5iI/ccie60dPHureM4y5yH3pyIFujuHZe5mL/Ftj3VzFzEXuP4GAm2P8F9PdnOO7M6rAzVX+A+pHAV8amu+u86cM2/2Ywcyg+9z4t3luDRP4z7z+buz43D3Uj/Zom4v8T7N3PxcNUt9F49waZPvtoX50eL5bw+xHU8gCfpSZ5mqGLhnv1iTcnZmFbi3DE4fGt20A4uPAPu454KH9d78wbvcM9xzxzwdLPfawfhFS23mjpVsP2HON+JA86YrJ7t4B9aORM3dbbenSpZKkqVOn7vJaIBDQE088oauuuko/+tGPtHXrVk2dOlXPPPOMDj/88Hg31RcO6OO+VtdIr5ZJNU3uwe7Ybu7DXrq5pc9tCQTcjZ2Th0rvlEuXL5a2NbvCz9/PdoVffJgD/G1ArnuQ872J0gvrpTs+lra3SAXp0hNHcVPPgmm9pd/NltbUSi+XSlub3M39Ud3chz2ygP+dMEQ6frD0QaX0boW0vVnKTZdm9pam9iIL+F1mmrvRf/5Y6ZUy6Rfvu+tAXrp03xfcw0AA/japp5s4rGSb9L9S6S8r3HmgW4b09FEsJGDB0YPcwNCPqqS3y919gdw0lxP3700W8Lv0oMsB542WFm6UVm6VGlrcZ8FD+jNxrAW9sqRrpkrfmSC9sEH6zUfuOpCfLj16hHsdgL8dNsB9fVwtLdok/X2lOw90z5AeOZwJA/0uLSCdO1r66ijp9Y3S8i1Sfdvngbn9KPy1oHumK+q4ZLzLAuu3S62eGwh05EAWG7ZgVDe3CP2mOunFUumPn7Q9J8qQnjmKgWCA3wUDblLAL4+Q3twsXfNOR83IXXPcRCLwt/wM6fuT3GQgL5ZK67ZJza3ufsARA/c8wTT8Y1i+dP3+0vcnujxY0eDODQNzXR5kUSH/O6iP+1q11d0jvvfTjvsC84+gZgTwu5x093zgwv3cs+IbPux4RvDPQ5kszoJBedIvZnRkgU31bqD4gFz3DDmf+lHfm9HbTRxWXCu90ql+dEw36QvUj/peIOAWlT1xiPRehfSDtzvuC9wxyy0oQM0I4G/9ctyist+dID2/QbpjWcdYksePdM+R4G+Te7lzfsk26aVSaUujywIjC9ykobubYBr+csxg6YuDpCVV0uLyjrEk03u5rEgW8LeMoFt09Ouj3djyn3caS/L3L7gJowD427gebsGADdtdFvjz8o6xJM8cxUICFhwx0H0tq3I1A+1jSSb3kg4sJAv4XXrQLR51zijptY3Sp53Gkszr5+rK4G89MqWrpkiXto0l+X9LO54TPXw49aOABV/o776Wb5He2Cj9rdNYkseOYCyJ3wUDbhzJmSPdWKJPqqW6tiwwp++eJ5iGf+RnSD+cJH27rX60pFP96JEDWWzYguEF0q9nSuX1Lg/+4ZOO50RPH+3uFQPwr0BAOmOEdNpw6e3N0lWdxpL8cY40oUeiW4hYy013C41dNM7Vj66tlZpa3eTxRw6Q+lM/6nuD86Rf7i99v8HVjJS31Y+2jyWhftT/Zha6r6IaN/dg57EkTxxJ/Sjgd9lp0rfHu7qR/5VJ13/Q8Yzg/kPcdQL+1j9Xuna6y4QvrJc2tmWBfjluLEk36kd9b2ov6c7Z7rPAy2Ud9aOjurm5B6kf9b/jhkjHDpY+rJTeqZD+0facqEem9O9DqRkB/K4w2y0qe9kE6fn10m+XdeTBx49ggUELJvSQfnuQtL5tLvKqtiwwLF86fABzkVtw1CB3H3BZtfRWp/rRqb3cPSOygL+lB90C8+e21Y9e+37Hc6K/znPjygD429ju0q0HSmV10osbpLs6jSV5+mh3/xD+dugA9/VJtasfrG2WctLcmjUH9aF+NBrMlV7tbeFpSerRo4fuuusu3XXXXfFslu+NLCC8WRYIuAXI89I7Av2knoluVeorGNFf827/jrJ6FaipZrte++7vVP1pSZf39D94kvb/8dnKyMuW50klL7yrd69/QPI8SVLeoELNuuGb6jZygLzWVi3/+wItv/dZDTx0qmb++JwdPye7sLvqNlfryaOvjGsf98Z6/1NNj0zp9BHSvW0PeXLSWXTammH50tfGJLoVSJRAQJre233Bpoygu9HfvtBgXjqLTkeD9Txkvf+pZnCeW3DuX6vdeSA7jQe9lgQCbnKgyb0S3RIkSnqwY+FR2JSfIZ08TPrzCncdyE1n0enPiyzEPkg1E3q4r0eK3XkgK40HvZakBTomjoJNuelugRHY1TdH+spI6b7P2p4TpbHo9OdFFmIfpJJgwE0U2LlmhEWnbclOl44fkuhWIJEKs91C9LBrVDf39WBRx30BJgr6fKxnIev9TzVZaW6xsTs+7nhGwKLTtvTMchOJwq7h+dJw6kfNCgSk/Qu73heYSv3I52Y9D1nvf6rpnimdPrxjAtmcdBadtmZwnnTe6ES3AokSCLhrP9d/u9KD0uEDpVs7jSVh0enPz3oest7/VDMw1y08+89VHWNJWHTalok93RdsSg92TBwFm/LSpZOGdkwal5vOotOfF1mIfZBqxnV3Xw8XM5bEorSANLef+4JNOenSCdSPmtZeP/qPzzqeE7Ho9OdDFmIfpJJgQJq101gSFp22JTvNLTQGu3plSWdSP2raiAL3xViS6LGehaz3P9VkpklfHCTdvqzjGQGLTtvSPhc57BqaT/2oZYGANK23+3q02F0LMoMsNPl5Wc9D1vufagoypFOHS3d/2pEHWXTalkF50jlkAbMCAbcmGeuS2dVeP5q3tOM5EesWfn7W85D1/qea/jnS2aOkBzqNJWHRaVvG93BfiD5z5Vf7WngaAFLFnJsv0qf3P6/PHnxZw46fpbm3X6qnjr26y3sat2zTKxffptq1m5SWlaGjH/yZRp9xiD578GVJ0mH3XqGldz6uNU8tkuSCqyRtePlDPfHyhzt+zhH/uEZlr38Un46FyHr/AQAArOch6/0HAAC2kYXYBwAAwDayEPsAAADYZj0LWe8/AACA9Txkvf8AAADW85D1/gMAANvIQuwDAABgG1mIfQAAAGyznoWs9x8AAMB6HrLefwAAAOt5yHr/AaBdMNENiLeXXnpJnufp+OOPT3RTACBi2b27qffUUVr1yKuSpDVPv6m8gb1VMLx/l/dVflSk2rWbJEktDU2q/KhY+UP6SpIGzJus1obmHWFWkurLt+zy38rp11MD5k7SqodfiVV3wma9/wAAANbzkPX+AwAA28hC7AMAAGAbWYh9AAAAbLOehaz3HwAAwHoest5/AAAA63nIev8BAIBtZCH2AQAAsI0sxD4AAAC2Wc9C1vsPAABgPQ9Z7z8AAID1PGS9/wDQmbmFpwHAD/IGFapuY5W8ltYd36tdX668QYV73CanTw8NP2GW1r3wriSpx9ghqq/YqkP++H2duOAWHXbvFcof2neX7UafeZhKXnpf9RVbo9+RCFnvPwAAgPU8ZL3/AADANrIQ+wAAANhGFmIfAAAA26xnIev9BwAAsJ6HrPcfAADAeh6y3n8AAGAbWYh9AAAAbCMLsQ8AAIBt1rOQ9f4DAABYz0PW+w8AAGA9D1nvPwB0xsLTAGBARn6OjvjH1Vr6h/mq+HCVJCmQHtSAuZP04W0P6cmjr9CGlz/UoX/+4S7bjjnrMK3854vxbnJUWe8/AACA9Txkvf8AAMA2shD7AAAA2EYWYh8AAADbrGch6/0HAACwnoes9x8AAMB6HrLefwAAYBtZiH0AAABsIwuxDwAAgG3Ws5D1/gMAAFjPQ9b7DwAAYD0PWe8/AH9j4WkASEHb1pcrp19PBdI6TuP5gwq1bX35Lu9Nz8vWUf/8idY+t1gf3/VUx88oKVfFR0Wq/rREkrTqoVfUe/IIBdLTdryn/+yJSsvK1IaXP4xhb8Jnvf8AAADW85D1/gMAANvIQuwDAABgG1mIfQAAAGyznoWs9x8AAMB6HrLefwAAAOt5yHr/AQCAbWQh9gEAALCNLMQ+AAAAtlnPQtb7DwAAYD0PWe8/AACA9Txkvf8A0BkLTwNACqqv2KrKpUUaddoXJEnDjp+lbaWVqiku6/K+9FwXZtf/730t+e0jXV5b/9L7yhvQW7n9e0mSBh0xQ9Ur18trbtnxnjFfPVyfPfg/ea2tMe5ReKz3HwAAwHoest5/AABgG1mIfQAAAGwjC7EPAACAbdazkPX+AwAAWM9D1vsPAABgPQ9Z7z8AALCNLMQ+AAAAtpGF2AcAAMA261nIev8BAACs5yHr/QcAALCeh6z3HwA6S090AwAAkXnjyrs097eXaPJlp6qptk6vfe/3kqQ5t16sdQve0boF72jCt45Tn+mjlZGbpWHHHSRJKn5qkZbc/qia6xq06Ko/68j7rpECATXWbNcrF9+24+dnFORq6HEHaf5hP0hI//bFev8BAACs5yHr/QcAALaRhdgHAADANrIQ+wAAANhmPQtZ7z8AAID1PGS9/wAAANbzkPX+AwAA28hC7AMAAGAbWYh9AAAAbLOehaz3HwAAwHoest5/AAAA63nIev8BoF3A8zwv0Y0AYMNxC6RN9VLfbOmZoxPdmuTWtL1eD4w6J9HNCMvZq+5XRm52VH6W9f77GecBALCN60DorOch6/33M84DAGAb14HQWc9D1vvvZ5wHAMA2rgOhS7U8FO0slGr9l8iDoeAcAADgWhAashD7wK84BwCAbVwHQkcWSr19QBYMDecBALCN60DoUi0LSdQ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    " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "circuits[2].draw(\"mpl\", fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "6675e269-a681-4569-82c4-a95800e82be8", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Step 2: Optimize the problem\n", - "\n", - "Normally, this is the step in the pattern during which you optimize your circuits for execution on hardware.\n", - "Here, since we only use a noiseless simulator, we simply transpile our circuit for a [GenericBackendV2](/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2)." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "6c049ff6-5fc0-4673-a9d9-a37abcf96711", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from qiskit.providers.fake_provider import GenericBackendV2\n", - "from qiskit.transpiler import generate_preset_pass_manager\n", - "\n", - "backend = GenericBackendV2(num_qubits=10)\n", - "transpiler = generate_preset_pass_manager(optimization_level=2, backend=backend)\n", - "\n", - "transpiled_circuits = [transpiler.run(circ) for circ in circuits]" - ] - }, - { - "cell_type": "markdown", - "id": "a996e888-ff56-4ca4-849c-cc85b07a6b73", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Step 3: Execute quantum experiments\n", - "\n", - "As explained in the very beginning, we will skip the optimization step 2 because we are simply going to compute our target observable's expectation values using a noise-free simulator, namely the [StatevectorEstimator](/api/qiskit/qiskit.primitives.StatevectorEstimator)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "494cb360-91ae-4ba3-88ca-97e017ef1de5", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from qiskit.primitives import StatevectorEstimator\n", - "\n", - "estimator = StatevectorEstimator()\n", - "job = estimator.run([(circ, observable) for circ in transpiled_circuits])\n", - "result = job.result()" - ] - }, - { - "cell_type": "markdown", - "id": "04445a72-7a16-4dc5-a4af-0917ad9f7124", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Step 4: Reconstruct results\n", - "\n", - "First, we extract the individual expectation values obtained for each of the Trotter circuits:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "7779817f-638d-4728-87d2-473d75e3aef4", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array(0.23799162), array(0.35754312), array(0.38649906)]\n" - ] - } - ], - "source": [ - "evs = [res.data.evs for res in result]\n", - "print(evs)" - ] - }, - { - "cell_type": "markdown", - "id": "cea904fd-34fe-492b-9470-42580f9620c2", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Next, we simply recombine them with our MPF coefficients to yield the total expectation values of the MPF. Below, we do so for each of the different ways by which we have computed $x$." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "e5f536d5-ffce-4b7a-80f3-44846d50ee19", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analytical solution: 0.3954847855980006\n", - "Exact model solution: 0.39548478559800204\n", - "Approx. model solution: 0.42991214253489807\n" - ] - } - ], - "source": [ - "print(\"Analytical solution:\", evs @ coeffs_analytical)\n", - "print(\"Exact model solution:\", evs @ coeffs_exact.value)\n", - "print(\"Approx. model solution:\", evs @ coeffs_approx.value)" - ] - }, - { - "cell_type": "markdown", - "id": "29852936-2a0e-49d2-a22c-f3d1e8137e36", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Finally, for this small problem we can compute the exact reference value using [scipy.linalg.expm](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.expm.html) as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "eeff7277-7295-4b78-a449-f151c8bd3e66", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.40060242487899755\n" - ] - } - ], - "source": [ - "from scipy.linalg import expm\n", - "\n", - "exp_H = expm(-1j * time * hamiltonian.to_matrix())\n", - "\n", - "initial_state = np.zeros(exp_H.shape[0])\n", - "initial_state[0] = 1.0\n", - "\n", - "time_evolved_state = exp_H @ initial_state\n", - "\n", - "exact_obs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", - "print(exact_obs.real)" - ] - }, - { - "cell_type": "markdown", - "id": "4ed84f18-d1e4-4b6c-b362-8e8a8a18633c", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "We can clearly see that the MPF has reduced the Trotter error compared to the one obtained with the deepest individual PF with $k_j=19$.\n", - "However, we also see that the approximate model is not flawless since it actually resulted in an expectation value that is worse than the exact solution. This shows the importance of using tight convergence criteria on the approximate model as you will learn in the guide [How to use the approximate model](/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-mpf/tutorials/index.mdx b/docs/addons/qiskit-addon-mpf/tutorials/index.mdx deleted file mode 100644 index 70eaa755c8b..00000000000 --- a/docs/addons/qiskit-addon-mpf/tutorials/index.mdx +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: Multi-product formulas (MPF) API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-mpf. ---- - -# Tutorials - -This page summarizes the available tutorials. - -* [Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas](01-getting-started) - diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json deleted file mode 100644 index cd35fe4fc03..00000000000 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ /dev/null @@ -1,42 +0,0 @@ -{ - "title": "Operator backpropagation (OBP)", - "children": [ - { - "title": "Documentation Home", - "url": "/docs/addons/qiskit-addon-obp" - }, - { - "title": "Installation Instructions", - "url": "/docs/addons/qiskit-addon-obp/install" - }, - { - "title": "Tutorials", - "children": [ - { - "title": "Reducing depth of circuits with operator backpropagation", - "url": "/docs/addons/qiskit-addon-obp/tutorials/01_getting_started" - } - ], - "url": "/docs/addons/qiskit-addon-obp/tutorials/index" - }, - { - "title": "How-To Guides", - "children": [ - { - "title": "Using different Lp-norms for Pauli term truncation", - "url": "/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm" - }, - { - "title": "Classically simulating circuits with OBP", - "url": "/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp" - }, - { - "title": "Truncating Pauli terms during backpropagation", - "url": "/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms" - } - ], - "url": "/docs/addons/qiskit-addon-obp/how_tos/index" - } - ], - "collapsed": true -} diff --git a/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb similarity index 99% rename from docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb rename to docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb index 96ca9c4be6d..e4a042b4bf8 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb @@ -13,7 +13,7 @@ "id": "5958e635-64c1-4599-82ca-225b83312906", "metadata": {}, "source": [ - "**Note:** If you have not yet read the how-to guide on [truncating Pauli terms](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms) you should do so before reading this guide." + "**Note:** If you have not yet read the how-to guide on [truncating Pauli terms](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms) you should do so before reading this guide." ] }, { @@ -21,7 +21,7 @@ "id": "2780c587-cd2a-4398-8faa-de5e0c661a10", "metadata": {}, "source": [ - "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget).\n", + "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.backpropagate.html) method based on a specified [TruncationErrorBudget](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.TruncationErrorBudget.html).\n", "In this guide, you will learn about its additional keyword argument `p_norm`, which can be used to change the Lp-norm used to estimate the error incurred by the truncated Pauli terms." ] }, @@ -32,7 +32,7 @@ "source": [ "## Constructing an example circuit\n", "\n", - "For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms):" + "For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms):" ] }, { @@ -144,7 +144,7 @@ "source": [ "## Using the L1 norm\n", "\n", - "By default, and as you have already seen in the [other how-to guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms), `p_norm=1` which means that the error is estimated as:\n", + "By default, and as you have already seen in the [other how-to guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms), `p_norm=1` which means that the error is estimated as:\n", "$$\n", "|\\langle\\psi|\\Delta|\\psi\\rangle| \\leq \\sum_{P\\in\\mathcal{T}} |c_P|\n", "$$\n", diff --git a/docs/addons/qiskit-addon-obp/how_tos/index.mdx b/docs/addons/qiskit-addon-obp/how-tos/index.mdx similarity index 100% rename from docs/addons/qiskit-addon-obp/how_tos/index.mdx rename to docs/addons/qiskit-addon-obp/how-tos/index.mdx diff --git a/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb b/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb similarity index 99% rename from docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb rename to docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb index 792091bda97..e3bc825c00c 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb @@ -26,7 +26,7 @@ "source": [ "## Constructing an example circuit\n", "\n", - "For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms):" + "For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms):" ] }, { @@ -245,7 +245,7 @@ "The most aggressive strategy in terms of operator simplification can be achieved by slicing your circuit into slices of individual gates.\n", "\n", "Additionally, you can leverage all of the truncation mechanism built into the `backpropagate` method.\n", - "We did not do so above, effectively resulting in an exact expectation value, but you can learn how to in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms)." + "We did not do so above, effectively resulting in an exact expectation value, but you can learn how to in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms)." ] }, { diff --git a/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb similarity index 99% rename from docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb rename to docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb index e60bad309c1..ac3bc25a884 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb @@ -13,18 +13,18 @@ "id": "c1453472-3506-4008-b432-f8ef9ccbf3ee", "metadata": {}, "source": [ - "This guide explains how to configure the Pauli term truncation mechanism provided by the [qiskit_addon_obp.utils.truncating](/docs/api/qiskit-addon-obp/utils-truncating#module-qiskit_addon_obp.utils.truncating) module.\n", + "This guide explains how to configure the Pauli term truncation mechanism provided by the [qiskit_addon_obp.utils.truncating](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.html) module.\n", "\n", "Operator backpropagation (OBP) can be used to reduce the depth of quantum circuits at the cost of a more complex observable. In order to get meaningful results from OBP, one usually needs to truncate terms from their observable to prevent it from growing too large. One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms' coefficients.\n", "\n", - "The [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", + "The [backpropagate](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.backpropagate.html) method takes an optional [TruncationErrorBudget](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.TruncationErrorBudget.html) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", "The amount of terms that are truncated depends on various configuration parameters specified by the user.\n", - "As of now, only a single truncation strategy is available -- the [truncate_binary_search](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search) method.\n", + "As of now, only a single truncation strategy is available -- the [truncate_binary_search](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.truncate_binary_search.html) method.\n", "Given an observable and some _budget_ it will perform a binary search over the Pauli terms and coefficients within that observable to find the optimal threshold,\n", "such that the sum of the truncated coefficients is maximal but lower than the budget.\n", "\n", "**Note**: By default, the L1 norm is used to evaluate and bound the truncation error; however, the `p_norm` setting enables the specification of the Lp-norm to be used.\n", - "For more information on how to use that setting, please refer to [its how-to guide](/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm)." + "For more information on how to use that setting, please refer to [its how-to guide](/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm)." ] }, { @@ -32,7 +32,7 @@ "id": "aaf5f046-6d5e-4b6f-add7-b60604edef30", "metadata": {}, "source": [ - "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) via the accompanying [setup_budget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function." + "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.TruncationErrorBudget.html) via the accompanying [setup_budget](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.setup_budget.html) function." ] }, { @@ -129,7 +129,7 @@ "As a first step towards understanding how this works, we look at the simplest case of a fixed truncation budget as specified by the user.\n", "\n", "The most straight-forward way to specify the truncation budget is via the `max_error_per_slice` argument.\n", - "In fact, this is what is being done in [this tutorial](/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb). Setting `max_error_per_slice` to a `float` results in each slice being allotted a budget equaling that value.\n", + "In fact, this is what is being done in [this tutorial](/docs/addons/qiskit-addon-obp/tutorials/01-getting-started-ipynb). Setting `max_error_per_slice` to a `float` results in each slice being allotted a budget equaling that value.\n", "In the example below, we set this value to `0.001` which guarantees us an implicit truncation error of at most `0.018`, if the entire 18 slices were to be backpropagated. \n", "\n", "
    \n", @@ -193,7 +193,7 @@ "id": "4a772af6-edc6-4e53-81c7-53f3b72bf58a", "metadata": {}, "source": [ - "Let's use the [OBPMetadata](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", + "Let's use the [OBPMetadata](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.metadata.OBPMetadata.html) instance and the tools provided by the [visualization](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.visualization.html) module to visualize the backpropagation process.\n", "\n", "- **The top/left plot** shows that we have enough budget to begin truncating observable terms after backpropagating the third slice. Starting from the third slice, we know that we truncate at least one term from each slice we backpropagate because we are incurring some truncation error after each slice.\n", "- **The top/right plot** shows that the error budget ramps up to `.003` for the third slice. We see a sharp drop in remaining budget, which means terms were truncated from the observable. This is consistent with what we inferred from the top/left plot.\n", @@ -714,7 +714,7 @@ "source": [ "Since we passed a subset of our slices (i.e. ``slices[-num_slices:]``) to ``backpropagate``, we must combine the slices remaining after backpropagation with the slices that were never sent for backpropagation (i.e. ``slices[:-num_slices]``).\n", "\n", - "Once we have combined all of the remaining slices, we can use [combine_slices](/docs/api/qiskit-addon-utils/slicing-combine-slices#qiskit_addon_utils.slicing.combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became." + "Once we have combined all of the remaining slices, we can use [combine_slices](https://qiskit.github.io/qiskit-addon-utils/stubs/qiskit_addon_utils.slicing.combine_slices.html#qiskit_addon_utils.slicing.combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became." ] }, { diff --git a/docs/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm.mdx b/docs/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm.mdx deleted file mode 100644 index 55d19833c5c..00000000000 --- a/docs/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm.mdx +++ /dev/null @@ -1,269 +0,0 @@ - - -# Using different Lp-norms for Pauli term truncation - -**Note:** If you have not yet read the how-to guide on [truncating Pauli terms](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms) you should do so before reading this guide. - -You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget). In this guide, you will learn about its additional keyword argument `p_norm`, which can be used to change the Lp-norm used to estimate the error incurred by the truncated Pauli terms. - - - -## Constructing an example circuit - -For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms): - -``` -[1]: -``` - -```python -import rustworkx.generators -from qiskit.synthesis import LieTrotter -from qiskit_addon_utils.problem_generators import ( - PauliOrderStrategy, - generate_time_evolution_circuit, - generate_xyz_hamiltonian, -) -from qiskit_addon_utils.slicing import combine_slices, slice_by_gate_types - -# we generate a linear chain of 10 qubits -num_qubits = 10 -linear_chain = rustworkx.generators.path_graph(num_qubits) - -# we use an arbitrary XY model -hamiltonian = generate_xyz_hamiltonian( - linear_chain, - coupling_constants=(0.05, 0.02, 0.0), - ext_magnetic_field=(0.02, 0.08, 0.0), - pauli_order_strategy=PauliOrderStrategy.InteractionThenColor, -) -# we evolve for some time -circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0) -# slice the circuit by gate type -slices = slice_by_gate_types(circuit) - -# for visualization purposes only, we recombine the slices with barriers between them and draw the resulting circuit -combine_slices(slices, include_barriers=True).draw("mpl", fold=50, scale=0.6) -``` - -``` -[1]: -``` - -![../\_images/how\_tos\_bound\_error\_using\_p\_norm\_4\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_bound_error_using_p_norm_4_0.avif) - -The observable that we will look at is the total magnetization: - -``` -[2]: -``` - -```python -from qiskit.quantum_info import SparsePauliOp - -obs = SparsePauliOp.from_sparse_list( - [("Z", [i], 1.0) for i in range(num_qubits)], num_qubits=num_qubits -) -``` - -For reference, we compute the exact expectation value: - -``` -[3]: -``` - -```python -from qiskit.primitives import StatevectorEstimator - -estimator = StatevectorEstimator() -job = estimator.run([(circuit, obs)]) -res = job.result() -exact_exp = res[0].data.evs -print(exact_exp) -``` - -```python -9.318197859862146 -``` - - - -## Using the L1 norm - -By default, and as you have already seen in the [other how-to guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms), `p_norm=1` which means that the error is estimated as: - -$$ -|\langle\psi|\Delta|\psi\rangle| \leq \sum_{P\in\mathcal{T}} |c_P| -$$ - -where $\psi$ is the quantum state, $\Delta$ is the real difference between the exact and truncated observable (which is unknown), $\mathcal{T}$ is the set of Pauli terms that were truncated and $c_P$ is the Pauli terms coefficient. This inequality is rigorous but a very loose upper bound for most scenarios. - -For this simple guide, we will backpropagate 6 slices of our example circuit. We will use a constant error per slice of `0.001`. This value is to be understood as the budget within the given `p_norm`. - -``` -[4]: -``` - -```python -from qiskit_addon_obp.utils.truncating import setup_budget - -l1_truncation_error_budget = setup_budget(max_error_per_slice=0.001, p_norm=1) -print(l1_truncation_error_budget) -``` - -```python -TruncationErrorBudget(per_slice_budget=[0.001], max_error_total=inf, p_norm=1) -``` - -``` -[5]: -``` - -```python -from qiskit_addon_obp import backpropagate - -max_slices = 6 -l1_bp_obs, l1_remaining_slices, l1_metadata = backpropagate( - obs, slices[-max_slices:], truncation_error_budget=l1_truncation_error_budget -) -l1_reduced_circuit = combine_slices(slices[:-max_slices] + l1_remaining_slices) -print(f"Backpropagated {max_slices - len(l1_remaining_slices)} circuit slices.") -print( - f"New observable contains {len(l1_bp_obs)} terms and {len(l1_bp_obs.group_commuting(qubit_wise=True))} commuting groups." -) -``` - -```python -Backpropagated 6 circuit slices. -New observable contains 116 terms and 10 commuting groups. -``` - -We can now compute the expectation value of the backpropagated observable, as well as the error with respect to the exact reference: - -``` -[6]: -``` - -```python -estimator = StatevectorEstimator() -job = estimator.run([(l1_reduced_circuit, l1_bp_obs)]) -res = job.result() -l1_exp = res[0].data.evs -l1_error = exact_exp - l1_exp -print(l1_exp, l1_error) -``` - -```python -9.317869899338842 0.00032796052330397174 -``` - -Finally, we can plot the error incurred during the backpropagation of each slice, as well as the accumulated error. The accumulated error is simply the accumulated sum of the slice errors. We can see clearly that the accumulated error is a very loose upper bound to the actual error. - -``` -[7]: -``` - -```python -from matplotlib import pyplot as plt -from qiskit_addon_obp.utils.visualization import ( - plot_accumulated_error, - plot_slice_errors, -) - -fig, axes = plt.subplots(1, 2, figsize=(14, 5)) -axes[1].plot([6], [l1_error], "x", color="red", label="actual error") -plot_slice_errors(l1_metadata, axes[0]) -plot_accumulated_error(l1_metadata, axes[1]) -``` - -![../\_images/how\_tos\_bound\_error\_using\_p\_norm\_16\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_bound_error_using_p_norm_16_0.avif) - - - -## Using the L2 norm - -One can argue that the L2 norm is a better approximation of the incurred error than the L1 norm. That is because we can assume the quantum state $|\psi\rangle$ to be drawn from a Haar random ensemble in which case the incurred error follows a distribution with a vanishing mean and a variance that can be approximately bound by the L2 norm: - -$$ -|\langle\psi|\Delta|\psi\rangle| \lesssim \left( \sum_{P\in\mathcal{T}} |c_P|^2 \right)^{1/2} -$$ - -While the bound is not rigorous, it will only be violated in pathological cases. - -Once again, we will backpropagate 6 slices of our example circuit using a maximum error per slice of `0.001` (this time, interpreted on the L2 norm). - -``` -[8]: -``` - -```python -l2_truncation_error_budget = setup_budget(max_error_per_slice=0.001, p_norm=2) -print(l2_truncation_error_budget) -``` - -```python -TruncationErrorBudget(per_slice_budget=[0.001], max_error_total=inf, p_norm=2) -``` - -``` -[9]: -``` - -```python -max_slices = 6 -l2_bp_obs, l2_remaining_slices, l2_metadata = backpropagate( - obs, slices[-max_slices:], truncation_error_budget=l2_truncation_error_budget -) -l2_reduced_circuit = combine_slices(slices[:-max_slices] + l2_remaining_slices) -print(f"Backpropagated {max_slices - len(l2_remaining_slices)} circuit slices.") -print( - f"New observable contains {len(l2_bp_obs)} terms and {len(l2_bp_obs.group_commuting(qubit_wise=True))} commuting groups." -) -``` - -```python -Backpropagated 6 circuit slices. -New observable contains 84 terms and 6 commuting groups. -``` - -Once again, we can compute the expectation value of the backpropagated observable, as well as the error with respect to the exact reference: - -``` -[10]: -``` - -```python -estimator = StatevectorEstimator() -job = estimator.run([(l2_reduced_circuit, l2_bp_obs)]) -res = job.result() -l2_exp = res[0].data.evs -l2_error = exact_exp - l2_exp -print(l2_exp, l2_error) -``` - -```python -9.317829770853422 0.00036808900872387085 -``` - -Plotting the incurred errors per slice and the accumulated error yields a similar picture to before. - -``` -[11]: -``` - -```python -fig, axes = plt.subplots(1, 2, figsize=(14, 5)) -axes[1].plot([6], [l2_error], "x", color="red", label="actual error") -plot_slice_errors(l2_metadata, axes[0]) -plot_accumulated_error(l2_metadata, axes[1]) -``` - -![../\_images/how\_tos\_bound\_error\_using\_p\_norm\_24\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_bound_error_using_p_norm_24_0.avif) - -Note, that the accumulated error is once again simply the sum of the single slice errors. This is yet another loose bound due to the Minkowski inequality since we have to compute this bound recursively: - -$$ -|\langle\psi|\Delta_{i}|\psi\rangle| \leq |\langle\psi|\tilde{\Delta}_{i-1}|\psi\rangle| + \left( \sum_{P\in\mathcal{T_i}} |c_P|^2 \right)^{1/2} = |\langle\psi|\tilde{\Delta}_{i}|\psi\rangle| -$$ - -where the new subscript $i$ indicates the current slice iteration making $\Delta_i$ the actual error at backpropagation iteration $i$, $\tilde{\Delta}_{i-1}$ the approximated truncation error from iteration $i-1$ and $\mathcal{T}_i$ the set of Pauli terms truncated at iteration $i$. diff --git a/docs/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp.mdx b/docs/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp.mdx deleted file mode 100644 index eb6f9c87bf0..00000000000 --- a/docs/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp.mdx +++ /dev/null @@ -1,353 +0,0 @@ - - -# Classically simulating circuits with OBP - -In this guide, you will learn how to classically simulate `QuantumCircuit` instances to estimate expectation values entirely through the means of OBP. - -Since OBP will take an observable and backpropagate it through a given circuit, the “simulation” of a circuit amounts to computing the expectation value of the target observable with respect to this circuit. As you will see later, the `qiskit-addon-obp` package is even capable of handling simple noise models, allowing you to compute noisy expectation values, too! - - - -## Constructing an example circuit - -For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms): - -``` -[1]: -``` - -```python -import rustworkx.generators -from qiskit.synthesis import LieTrotter -from qiskit_addon_utils.problem_generators import ( - PauliOrderStrategy, - generate_time_evolution_circuit, - generate_xyz_hamiltonian, -) -from qiskit_addon_utils.slicing import combine_slices, slice_by_gate_types - -# we generate a linear chain of 10 qubits -num_qubits = 10 -linear_chain = rustworkx.generators.path_graph(num_qubits) - -# we use an arbitrary XY model -hamiltonian = generate_xyz_hamiltonian( - linear_chain, - coupling_constants=(0.05, 0.02, 0.0), - ext_magnetic_field=(0.02, 0.08, 0.0), - pauli_order_strategy=PauliOrderStrategy.InteractionThenColor, -) -# we evolve for some time -circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0) -# slice the circuit by gate type -slices = slice_by_gate_types(circuit) -``` - -However, the above is purely the circuit describing the time evolution under a chosen Hamiltonian. We also need an initial state to start from, with respect to which we compute the expectation values of our observable. - -Of course, we could choose the all-zero (or vacuum) state as our initial state, but to show how one would insert their own initial state, we choose a different one below. - -One possibility, would be to prepend the initial state to our time-evolution circuit above: `circuit.compose(initial_state, front=True)`. But since we have already sliced our `circuit`, it is easier to simply insert the initial state as the first slice, which we do below. - -In this way, we can simply replace the first slice with another initial state, if we want to exchange that in the future, without having to recompute our slices. - -``` -[2]: -``` - -```python -from qiskit.circuit import QuantumCircuit - -initial_state = QuantumCircuit(num_qubits) -for i in range(0, num_qubits, 2): - initial_state.x(i) - -slices.insert(0, initial_state) -``` - -``` -[3]: -``` - -```python -# for visualization purposes only, we recombine the slices with barriers between them and draw the resulting circuit -combine_slices(slices, include_barriers=True).draw("mpl", fold=50, scale=0.6) -``` - -``` -[3]: -``` - -![../\_images/how\_tos\_simulating\_circuits\_with\_obp\_6\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_simulating_circuits_with_obp_6_0.avif) - - - -## Simulating a noiseless expectation value - -As our target observable, we choose the `ZZ` observable on the central qubits: - -``` -[4]: -``` - -```python -from qiskit.quantum_info import SparsePauliOp - -obs = SparsePauliOp("IIIIZZIIII") -``` - -At this point, we are already set to classically simulate the expectation value using OBP. To do so, we simply provide the *all* the slices to the `backpropagate` method, like so: - -``` -[5]: -``` - -```python -from qiskit_addon_obp import backpropagate - -vacuum_state_obs, _, metadata = backpropagate(obs, slices) -``` - -We have now backpropagated our target observable `obs` through the *entire* circuit (**including** the `initial_state` which we placed on `slices[0]`) resulting in a new `SparsePauliOp` whose expectation value we obtain by projecting it on the *vacuum state* (`|00...00>`). - -This can be achieved in a straight forward manner by summing up the coefficients of all Pauli terms defined in the computational basis: - -``` -[6]: -``` - -```python -vacuum_state_obs.coeffs[~vacuum_state_obs.paulis.x.any(axis=1)].sum() -``` - -``` -[6]: -``` - -```python -np.complex128(-0.8285688012239535+4.9487770271457865e-20j) -``` - -As a sanity check (and to prove that this works) we can compare our result against Qiskit’s `Statevector`: - -``` -[7]: -``` - -```python -from qiskit.quantum_info import Statevector - -Statevector(combine_slices(slices)).expectation_value(obs) -``` - -``` -[7]: -``` - -```python -np.complex128(-0.8285687255430366+0j) -``` - - - -### Some notes on performance - -The computational efficiency of the `backpropagate` call above will heavily depend on many things, including: - -* the structure of the `circuit` -* the method of slicing the circuit -* the target observable -* the truncation parameters - -Since the `backpropagate` method simplifies the observable after every *slice* has been applied, the number of gates in a slice can dramatically influence the computational burden. The most aggressive strategy in terms of operator simplification can be achieved by slicing your circuit into slices of individual gates. - -Additionally, you can leverage all of the truncation mechanism built into the `backpropagate` method. We did not do so above, effectively resulting in an exact expectation value, but you can learn how to in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms). - - - -## Simulating a noisy expectation value - -The `qiskit-addon-obp` package also supports handling of noise models in the form of `PauliLindbladError`s. This is especially useful when you have characterized the noise model of the 2-qubit layers in your circuit, for example using the `` `NoiseLearner `` \<[api/qiskit-ibm-runtime/noise-learner-noise-learner](/docs/api/qiskit-ibm-runtime/noise-learner-noise-learner)>\`\_\_. - -In this section, you will see how you can use the `LayerError` objects returned by the `NoiseLearner` to compute noisy expectation values using OBP. - - - -### Obtaining a noise model - -Normally, you would execute the `NoiseLearner` to obtain a noise model of your specific circuit. To avoid complexity (and randomness) in this tutorial, we will refrain from doing so, and instead hard-code some noise model for our circuit below. - -However, we make sure that the structure of our data matches that of the `` `NoiseLearnerResult `` \<[api/qiskit-ibm-runtime/noise-learner-result](/docs/api/qiskit-ibm-runtime/noise-learner-result)>\`\_\_. - -In its current (non-transpiled) form, our circuit contains 4 unique layers of 2-qubit gates: - -* `slices[1]`: which has `Rxx` gates acting on all odd pairs of qubits -* `slices[2]`: which has `Rxx` gates acting on all even pairs of qubits -* `slices[3]`: which has `Ryy` gates acting on all odd pairs of qubits -* `slices[4]`: which has `Ryy` gates acting on all even pairs of qubits - -In the cell below, we manually construct 4 `LayerError` instances for each one of these layers with some randomized error rates. - -``` -[8]: -``` - -```python -import numpy as np -from qiskit.quantum_info import PauliList -from qiskit_ibm_runtime.utils.noise_learner_result import LayerError, PauliLindbladError - -# fmt: off -pauli_errors_even = ['IIIIIIIIIX', 'IIIIIIIIIY', 'IIIIIIIIIZ', 'IIIIIIIIXI', 'IIIIIIIIXX', 'IIIIIIIIXY', 'IIIIIIIIXZ', 'IIIIIIIIYI', 'IIIIIIIIYX', 'IIIIIIIIYY', 'IIIIIIIIYZ', 'IIIIIIIIZI', 'IIIIIIIIZX', 'IIIIIIIIZY', 'IIIIIIIIZZ', 'IIIIIIIXII', 'IIIIIIIXXI', 'IIIIIIIXYI', 'IIIIIIIXZI', 'IIIIIIIYII', 'IIIIIIIYXI', 'IIIIIIIYYI', 'IIIIIIIYZI', 'IIIIIIIZII', 'IIIIIIIZXI', 'IIIIIIIZYI', 'IIIIIIIZZI', 'IIIIIIXIII', 'IIIIIIXXII', 'IIIIIIXYII', 'IIIIIIXZII', 'IIIIIIYIII', 'IIIIIIYXII', 'IIIIIIYYII', 'IIIIIIYZII', 'IIIIIIZIII', 'IIIIIIZXII', 'IIIIIIZYII', 'IIIIIIZZII', 'IIIIIXIIII', 'IIIIIXXIII', 'IIIIIXYIII', 'IIIIIXZIII', 'IIIIIYIIII', 'IIIIIYXIII', 'IIIIIYYIII', 'IIIIIYZIII', 'IIIIIZIIII', 'IIIIIZXIII', 'IIIIIZYIII', 'IIIIIZZIII', 'IIIIXIIIII', 'IIIIXXIIII', 'IIIIXYIIII', 'IIIIXZIIII', 'IIIIYIIIII', 'IIIIYXIIII', 'IIIIYYIIII', 'IIIIYZIIII', 'IIIIZIIIII', 'IIIIZXIIII', 'IIIIZYIIII', 'IIIIZZIIII', 'IIIXIIIIII', 'IIIXXIIIII', 'IIIXYIIIII', 'IIIXZIIIII', 'IIIYIIIIII', 'IIIYXIIIII', 'IIIYYIIIII', 'IIIYZIIIII', 'IIIZIIIIII', 'IIIZXIIIII', 'IIIZYIIIII', 'IIIZZIIIII', 'IIXIIIIIII', 'IIXXIIIIII', 'IIXYIIIIII', 'IIXZIIIIII', 'IIYIIIIIII', 'IIYXIIIIII', 'IIYYIIIIII', 'IIYZIIIIII', 'IIZIIIIIII', 'IIZXIIIIII', 'IIZYIIIIII', 'IIZZIIIIII', 'IXIIIIIIII', 'IXXIIIIIII', 'IXYIIIIIII', 'IXZIIIIIII', 'IYIIIIIIII', 'IYXIIIIIII', 'IYYIIIIIII', 'IYZIIIIIII', 'IZIIIIIIII', 'IZXIIIIIII', 'IZYIIIIIII', 'IZZIIIIIII', 'XIIIIIIIII', 'XXIIIIIIII', 'XYIIIIIIII', 'XZIIIIIIII', 'YIIIIIIIII', 'YXIIIIIIII', 'YYIIIIIIII', 'YZIIIIIIII', 'ZIIIIIIIII', 'ZXIIIIIIII', 'ZYIIIIIIII', 'ZZIIIIIIII'] -pauli_errors_odd = ['IIIIIIIIXI', 'IIIIIIIIYI', 'IIIIIIIIZI', 'IIIIIIIXII', 'IIIIIIIXXI', 'IIIIIIIXYI', 'IIIIIIIXZI', 'IIIIIIIYII', 'IIIIIIIYXI', 'IIIIIIIYYI', 'IIIIIIIYZI', 'IIIIIIIZII', 'IIIIIIIZXI', 'IIIIIIIZYI', 'IIIIIIIZZI', 'IIIIIIXIII', 'IIIIIIXXII', 'IIIIIIXYII', 'IIIIIIXZII', 'IIIIIIYIII', 'IIIIIIYXII', 'IIIIIIYYII', 'IIIIIIYZII', 'IIIIIIZIII', 'IIIIIIZXII', 'IIIIIIZYII', 'IIIIIIZZII', 'IIIIIXIIII', 'IIIIIXXIII', 'IIIIIXYIII', 'IIIIIXZIII', 'IIIIIYIIII', 'IIIIIYXIII', 'IIIIIYYIII', 'IIIIIYZIII', 'IIIIIZIIII', 'IIIIIZXIII', 'IIIIIZYIII', 'IIIIIZZIII', 'IIIIXIIIII', 'IIIIXXIIII', 'IIIIXYIIII', 'IIIIXZIIII', 'IIIIYIIIII', 'IIIIYXIIII', 'IIIIYYIIII', 'IIIIYZIIII', 'IIIIZIIIII', 'IIIIZXIIII', 'IIIIZYIIII', 'IIIIZZIIII', 'IIIXIIIIII', 'IIIXXIIIII', 'IIIXYIIIII', 'IIIXZIIIII', 'IIIYIIIIII', 'IIIYXIIIII', 'IIIYYIIIII', 'IIIYZIIIII', 'IIIZIIIIII', 'IIIZXIIIII', 'IIIZYIIIII', 'IIIZZIIIII', 'IIXIIIIIII', 'IIXXIIIIII', 'IIXYIIIIII', 'IIXZIIIIII', 'IIYIIIIIII', 'IIYXIIIIII', 'IIYYIIIIII', 'IIYZIIIIII', 'IIZIIIIIII', 'IIZXIIIIII', 'IIZYIIIIII', 'IIZZIIIIII', 'IXIIIIIIII', 'IXXIIIIIII', 'IXYIIIIIII', 'IXZIIIIIII', 'IYIIIIIIII', 'IYXIIIIIII', 'IYYIIIIIII', 'IYZIIIIIII', 'IZIIIIIIII', 'IZXIIIIIII', 'IZYIIIIIII', 'IZZIIIIIII'] -# fmt: on - -np.random.seed(42) - -layer_error_odd_xx = LayerError( - circuit=slices[1], - qubits=list(range(num_qubits)), - error=PauliLindbladError( - PauliList(pauli_errors_odd), - 0.0001 + 0.0004 * np.random.rand(len(pauli_errors_odd)), - ), -) - -layer_error_even_xx = LayerError( - circuit=slices[2], - qubits=list(range(num_qubits)), - error=PauliLindbladError( - PauliList(pauli_errors_even), - 0.0001 + 0.0004 * np.random.rand(len(pauli_errors_even)), - ), -) - -layer_error_odd_yy = LayerError( - circuit=slices[3], - qubits=list(range(num_qubits)), - error=PauliLindbladError( - PauliList(pauli_errors_odd), - 0.0001 + 0.0004 * np.random.rand(len(pauli_errors_odd)), - ), -) - -layer_error_even_yy = LayerError( - circuit=slices[4], - qubits=list(range(num_qubits)), - error=PauliLindbladError( - PauliList(pauli_errors_even), - 0.0001 + 0.0004 * np.random.rand(len(pauli_errors_even)), - ), -) -``` - -If you would have used the `NoiseLearner` to identify the unique 2-qubit gate layers of your circuit and characterize their noise, you would obtain a `NoiseLearnerResult` object. This result would contain a list of `LayerError` objects, just like the ones we have manually constructed above. - -For each unique 2-qubit layer, the `LayerError` contains: - -* the `QuantumCircuit` representing that 2-qubit gate layer -* the qubit indices which this circuit is acting upon -* the `PauliLindbladError` which represents the characterized noise model of this layer - -The `PauliLindbladError` will contain two objects: - -* the list of Pauli errors that have been characterized -* the error rates corresponding to each one of those Pauli errors - -Normally, the list of Pauli errors will be sparse. More specifically, it will contain the single-qubit Pauli errors on all qubits that have gates acting upon them as well as the two-qubit Pauli errors on all those qubits that are connected. - - - -### Inserting the noisy layers into our circuit - -In the previous section, we have specifically constructed one `LayerError` for each of our known unique 2-qubit gate layers. This means, we know which `LayerError` matches a specific one of our slices exactly. - -Normally, when using the `LayerError`, you will need to figure out what the unique 2-qubit gate layer is, and where it occurs inside of your circuit. You will then need to adjust your `circuit` and/or `slices` to insert the `LayerError` accordingly. How to do this in the general case, is beyond the scope of this how-to guide. **TODO: link to external documentation, once it exists!** - -Here, our life is simpler because we know which slice a `LayerError` corresponds to. Therefore, it is now just a matter of inserting new slices to represent the noise. - -Note, that we must wrap each `PauliLindbladError` from `LayerError.error` in a `PauliLindbladErrorInstruction` for it to be a valid `QuantumCircuit` instruction. - -``` -[9]: -``` - -```python -from qiskit_addon_obp.utils.noise import PauliLindbladErrorInstruction - -noisy_slices = [] -for slice_ in slices: - if slice_ == layer_error_even_xx.circuit: - noisy_slices.append( - QuantumCircuit.from_instructions( - [(PauliLindbladErrorInstruction(layer_error_even_xx.error), slice_.qubits)] - ) - ) - elif slice_ == layer_error_odd_xx.circuit: - noisy_slices.append( - QuantumCircuit.from_instructions( - [(PauliLindbladErrorInstruction(layer_error_odd_xx.error), slice_.qubits)] - ) - ) - elif slice_ == layer_error_even_yy.circuit: - noisy_slices.append( - QuantumCircuit.from_instructions( - [(PauliLindbladErrorInstruction(layer_error_even_yy.error), slice_.qubits)] - ) - ) - elif slice_ == layer_error_odd_yy.circuit: - noisy_slices.append( - QuantumCircuit.from_instructions( - [(PauliLindbladErrorInstruction(layer_error_odd_yy.error), slice_.qubits)] - ) - ) - noisy_slices.append(slice_) -``` - -We can check our work and draw the circuit below: - -``` -[10]: -``` - -```python -combine_slices(noisy_slices, include_barriers=True).draw("mpl", fold=100, scale=0.8) -``` - -``` -[10]: -``` - -![../\_images/how\_tos\_simulating\_circuits\_with\_obp\_27\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_simulating_circuits_with_obp_27_0.avif) - - - -### Simulating a noisy expectation value - -At this point, classically simulating the expectation value works exactly the same as before, just - -``` -[11]: -``` - -```python -vacuum_state_noisy_obs, _, metadata = backpropagate(obs, noisy_slices) -``` - -``` -[12]: -``` - -```python -vacuum_state_noisy_obs.coeffs[~vacuum_state_noisy_obs.paulis.x.any(axis=1)].sum() -``` - -``` -[12]: -``` - -```python -np.complex128(-0.7230801696448901+7.082755280463563e-19j) -``` - -We point out again, that multiple performance concerns should be considered. Please go back to the [corresponding section above](#some-notes-on-performance). diff --git a/docs/addons/qiskit-addon-obp/how_tos/truncate-operator-terms.mdx b/docs/addons/qiskit-addon-obp/how_tos/truncate-operator-terms.mdx deleted file mode 100644 index 6b32c7409af..00000000000 --- a/docs/addons/qiskit-addon-obp/how_tos/truncate-operator-terms.mdx +++ /dev/null @@ -1,564 +0,0 @@ - - -# Truncating Pauli terms during backpropagation - -This guide explains how to configure the Pauli term truncation mechanism provided by the [qiskit\_addon\_obp.utils.truncating](/docs/api/qiskit-addon-obp/utils-truncating#module-qiskit_addon_obp.utils.truncating) module. - -Operator backpropagation (OBP) can be used to reduce the depth of quantum circuits at the cost of a more complex observable. In order to get meaningful results from OBP, one usually needs to truncate terms from their observable to prevent it from growing too large. One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms’ coefficients. - -The [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice. The amount of terms that are truncated depends on various configuration parameters specified by the user. As of now, only a single truncation strategy is available – the [truncate\_binary\_search](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search) method. Given an observable and some *budget* it will perform a binary search over the Pauli terms and coefficients within that observable to find the optimal threshold, such that the sum of the truncated coefficients is maximal but lower than the budget. - -**Note**: By default, the L1 norm is used to evaluate and bound the truncation error; however, the `p_norm` setting enables the specification of the Lp-norm to be used. For more information on how to use that setting, please refer to [its how-to guide](/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm). - -In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) via the accompanying [setup\_budget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function. - - - -## Constructing an example circuit - -For the purposes of this guide, we will use the following circuit slices: - -``` -[1]: -``` - -```python -import rustworkx.generators -from qiskit.synthesis import LieTrotter -from qiskit_addon_utils.problem_generators import ( - PauliOrderStrategy, - generate_time_evolution_circuit, - generate_xyz_hamiltonian, -) -from qiskit_addon_utils.slicing import combine_slices, slice_by_gate_types - -# we generate a linear chain of 10 qubits -linear_chain = rustworkx.generators.path_graph(10) - -# we use an arbitrary XY model -hamiltonian = generate_xyz_hamiltonian( - linear_chain, - coupling_constants=(0.05, 0.02, 0.0), - ext_magnetic_field=(0.02, 0.08, 0.0), - pauli_order_strategy=PauliOrderStrategy.InteractionThenColor, -) -# we evolve for some time -circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0) -# slice the circuit by gate type -slices = slice_by_gate_types(circuit) - -# for visualization purposes only, we recombine the slices with barriers between them and draw the resulting circuit -combine_slices(slices, include_barriers=True).draw("mpl", fold=50, scale=0.6) -``` - -``` -[1]: -``` - -![../\_images/how\_tos\_truncate\_operator\_terms\_4\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_4_0.avif) - -We will look at a single simple observable: - -``` -[2]: -``` - -```python -from qiskit.quantum_info import SparsePauliOp - -obs = SparsePauliOp("IIIIIZIIII") -``` - - - -## The simplest case: a fixed truncation budget for each slice - -The available budget for truncation of Pauli terms can vary at each step of the backpropagation. As a first step towards understanding how this works, we look at the simplest case of a fixed truncation budget as specified by the user. - -The most straight-forward way to specify the truncation budget is via the `max_error_per_slice` argument. In fact, this is what is being done in [this tutorial](/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb). Setting `max_error_per_slice` to a `float` results in each slice being allotted a budget equaling that value. In the example below, we set this value to `0.001` which guarantees us an implicit truncation error of at most `0.018`, if the entire 18 slices were to be backpropagated. - - - Note that any error budget remaining after backpropagating a slice and truncating terms with small coefficients will always be added to the following slice’s error budget. - - -``` -[3]: -``` - -```python -from qiskit_addon_obp.utils.truncating import setup_budget - -truncation_error_budget = setup_budget(max_error_per_slice=0.001) -print(truncation_error_budget) -``` - -```python -TruncationErrorBudget(per_slice_budget=[0.001], max_error_total=inf, p_norm=1) -``` - -``` -[4]: -``` - -```python -from qiskit_addon_obp import backpropagate -from qiskit_addon_obp.utils.simplify import OperatorBudget - -op_budget = OperatorBudget(max_qwc_groups=10) -bp_obs, remaining_slices, metadata = backpropagate( - obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget -) -reduced_circuit = combine_slices(remaining_slices) -print(f"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.") -print( - f"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups." -) -``` - -```python -Backpropagated 11 circuit slices. -New observable contains 29 terms and 10 commuting groups. -``` - -Let’s use the [OBPMetadata](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process. - -* **The top/left plot** shows that we have enough budget to begin truncating observable terms after backpropagating the third slice. Starting from the third slice, we know that we truncate at least one term from each slice we backpropagate because we are incurring some truncation error after each slice. -* **The top/right plot** shows that the error budget ramps up to `.003` for the third slice. We see a sharp drop in remaining budget, which means terms were truncated from the observable. This is consistent with what we inferred from the top/left plot. -* **The bottom/left plot** shows that as we truncate terms from our observable, our overall accumulated error is growing monotonically. This graph also confirms that no terms were truncated until after the third slice had been backpropagated. -* **The bottom/right plot** shows that the number of commuting Pauli groups in our observable has grown to the specified limit of `10`. This plot also shows how backpropagating one more layer would cause our observable to outgrow the specified bound as you can see from the cross-over of the black and red lines. - - - Note that in all of these plots, the x-axis enumerates the backpropagated slices, but since OBP works on the end of the circuit, slice 1 is in fact the very last slice, slice 2 the one before that, and so on. - - -``` -[5]: -``` - -```python -from matplotlib import pyplot as plt -from qiskit_addon_obp.utils.visualization import ( - plot_accumulated_error, - plot_left_over_error_budget, - plot_num_qwc_groups, - plot_slice_errors, -) - -fig, axes = plt.subplots(2, 2, figsize=(20, 10)) -plot_slice_errors(metadata, axes[(0, 0)]) -plot_left_over_error_budget(metadata, axes[(0, 1)]) -plot_accumulated_error(metadata, axes[(1, 0)]) -plot_num_qwc_groups(metadata, axes[(1, 1)]) -``` - -![../\_images/how\_tos\_truncate\_operator\_terms\_13\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_13_0.avif) - - - -## Specifying slice budget explicitly - -If one has knowledge of how to assign a budget to each slice, such that the backpropagation performance is optimized, they may want to explicitly assign a budget for each slice. For demonstration purposes, let’s assign the first 3 slices zero budget and use the same budget budget of `.001` per slice for the remaining slices. - -``` -[6]: -``` - -```python -# Zero out the first 3 slices' budgets -max_error_per_slice = [0.0] * 3 + [0.001] * (len(slices) - 3) - -truncation_error_budget = setup_budget(max_error_per_slice=max_error_per_slice) -print(truncation_error_budget) -``` - -```python -TruncationErrorBudget(per_slice_budget=[0.0, 0.0, 0.0, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001], max_error_total=inf, p_norm=1) -``` - -``` -[7]: -``` - -```python -bp_obs, remaining_slices, metadata = backpropagate( - obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget -) -reduced_circuit = combine_slices(remaining_slices) -print(f"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.") -print( - f"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups." -) -``` - -```python -Backpropagated 11 circuit slices. -New observable contains 32 terms and 10 commuting groups. -``` - -Removing the budget from the first 3 layers resulted in no terms being truncated until after the fourth layer, as can be confirmed in 3 of these 4 plots. What may be somewhat surprising is that although the 4th slice had no leftover budget passed to it, a term was still truncated using the allocated `.001` budget. This is obvious from the **top/left** plot but can also be observed in the **top/right** plot, as the left-over budget curve flattens between slice ID’s 3 and 4. Another notable detail is that at least one term was truncated from this point on, same as above. - -The key takeaway is that although we incurred less truncation error in this second example due to zeroing out some slices’ budgets, we were able to backpropagate the same number of slices, and our observable contains the same number of commuting Pauli groups. We can confirm that the bound on our error is smaller in the second example by inspecting the **bottom/left** plot. - -``` -[8]: -``` - -```python -fig, axes = plt.subplots(2, 2, figsize=(20, 10)) -plot_slice_errors(metadata, axes[(0, 0)]) -plot_left_over_error_budget(metadata, axes[(0, 1)]) -plot_accumulated_error(metadata, axes[(1, 0)]) -plot_num_qwc_groups(metadata, axes[(1, 1)]) -``` - -![../\_images/how\_tos\_truncate\_operator\_terms\_19\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_19_0.avif) - - - -## Specifying the budget cyclically - -If one has a circuit with some repeatable pattern, such as a Trotter circuit, it may be desired to specify a budget for that repeated subset of slices and have that budget used for all of the following repetitions of those slices. - -More concretely, the example circuit we are using has 6 slices which are repeated 3 times for a total of 18 slices. Here we will arbitrarily assign zero budget to the single-qubit and `RYY` layers and `.003` budget to each of the `RXX` layers. We will observe how specifying the budget as a length-6 sequence causes the budget to be applied to all 18 slices cyclically. - - - Once more pay attention to the fact that the slices are backpropagated in reverse order (i.e. starting at the end). Therefore, the first entry in our cyclic budget actually gets used for the last slice, the second entry for the slice before that, and so on. - - -``` -[9]: -``` - -```python -# Specify a length-6 per-slice budget. -# This will be cycled over 3 times to be applied to the 18 slices -max_error_per_slice = [0.0] * 4 + [0.003] * 2 - -truncation_error_budget = setup_budget(max_error_per_slice=max_error_per_slice) -print(truncation_error_budget) -``` - -```python -TruncationErrorBudget(per_slice_budget=[0.0, 0.0, 0.0, 0.0, 0.003, 0.003], max_error_total=inf, p_norm=1) -``` - -``` -[10]: -``` - -```python -op_budget = OperatorBudget(max_qwc_groups=20) -bp_obs, remaining_slices, metadata = backpropagate( - obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget -) -reduced_circuit = combine_slices(remaining_slices) -print(f"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.") -print( - f"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups." -) -``` - -```python -Backpropagated 13 circuit slices. -New observable contains 49 terms and 14 commuting groups. -``` - -As seen in the **top/left** and **bottom/left**, no truncation was performed over the first four slices since they were allocated no error budget. Slices 5 and 6 had some of their terms truncated as budget became available. - -As seen in the **top/left**, a relatively large amount of error was incurred after backpropagating slice 7, even though that slice was allocated no budget. This is because only about `.002` of the `.006` total budget allocated to slices 5 and 6 was used, so the remaining was forwarded to slice 7 and mostly expended, as seen in the **top/right**. - -**top/left** and **top/right** show that the small amount of leftover budget is used up between slices 8-10, and new budget becomes available in slice 11, as expected. **top/left**, **top/right**, and **bottom/left** all demonstrate the cyclical behavior of the `max_error_per_slice` argument when its length is less than the number of slices. This cyclical behavior would have continued through all the slices, but the `max_qwc_groups` stopping criteria was reached after backpropagating 13 slices, as seen in **bottom/right**. - -Another interesting thing is that the number of Pauli groups actually drops after the second round of budget becomes available at slice 11. This is because there were groups with small coefficients which could not be truncated until there was more budget after backpropagating slice 11, so they accumulated in the observable for several iterations. This particular case also highlights that `max_qwc_groups` must be *exceeded* for the algorithm to terminate. - -``` -[11]: -``` - -```python -fig, axes = plt.subplots(2, 2, figsize=(20, 10)) -plot_slice_errors(metadata, axes[(0, 0)]) -plot_left_over_error_budget(metadata, axes[(0, 1)]) -plot_accumulated_error(metadata, axes[(1, 0)]) -plot_num_qwc_groups(metadata, axes[(1, 1)]) -``` - -![../\_images/how\_tos\_truncate\_operator\_terms\_25\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_25_0.avif) - - - -## Capping the total error - -In addition to specifying the per-slice error budget, one can specify the maximum amount of error to incur from truncation. Once that limit is hit, no more truncation will be performed; however, backpropagation will continue until the observable becomes too large and one of the stopping criteria is met. - -Let’s re-run the above experiment but set a cap on `max_error_total` such that the error budget is expended after backpropagating slice 7. - -``` -[12]: -``` - -```python -# Specify a length-6 per-slice budget. -# This will be cycled over 3 times to be applied to the 18 slices -max_error_per_slice = [0.0] * 4 + [0.003] * 2 - -truncation_error_budget = setup_budget( - max_error_per_slice=max_error_per_slice, max_error_total=0.006 -) -print(truncation_error_budget) -``` - -```python -TruncationErrorBudget(per_slice_budget=[0.0, 0.0, 0.0, 0.0, 0.003, 0.003], max_error_total=0.006, p_norm=1) -``` - -``` -[13]: -``` - -```python -bp_obs, remaining_slices, metadata = backpropagate( - obs, - slices, - operator_budget=op_budget, - truncation_error_budget=truncation_error_budget, -) -reduced_circuit = combine_slices(remaining_slices) -print(f"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.") -print( - f"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups." -) -``` - -```python -Backpropagated 10 circuit slices. -New observable contains 67 terms and 20 commuting groups. -``` - -As expected, our truncation error caps out at `.006` (**bottom/left**). It is notable that in this run we were not able to backpropagate slice 11. This is because we did not have enough budget to truncate terms and the number of commuting Pauli groups grew past the limit of `20`, as seen in **bottom/right**. - -``` -[14]: -``` - -```python -fig, axes = plt.subplots(2, 2, figsize=(20, 10)) -plot_slice_errors(metadata, axes[(0, 0)]) -plot_left_over_error_budget(metadata, axes[(0, 1)]) -plot_accumulated_error(metadata, axes[(1, 0)]) -plot_num_qwc_groups(metadata, axes[(1, 1)]) -``` - -![../\_images/how\_tos\_truncate\_operator\_terms\_30\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_30_0.avif) - -It may be desirable to simply cap the overall budget with `max_error_total` without specifying `max_error_per_slice`. - -``` -[15]: -``` - -```python -truncation_error_budget = setup_budget(max_error_total=0.018) -print(truncation_error_budget) -``` - -```python -TruncationErrorBudget(per_slice_budget=[0.018], max_error_total=0.018, p_norm=1) -``` - -The output of the cell above, might be slightly surprising because the `per_slice_budget` is set to the `max_error_total`. This indicates, that the entire available budget will be consumed **greedily**. You can think of it this way: the entire budget is available to each slice (because we loop over the `per_slice_budget`). But any budget that has already been consumed, will be deducted from the budget that is available at that given point in the algorithm. - -``` -[16]: -``` - -```python -op_budget = OperatorBudget(max_qwc_groups=10) -bp_obs, remaining_slices, metadata = backpropagate( - obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget -) -reduced_circuit = combine_slices(remaining_slices) -print(f"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.") -print( - f"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups." -) -``` - -```python -Backpropagated 9 circuit slices. -New observable contains 25 terms and 9 commuting groups. -``` - -**top/right** shows how the entire `.018` error budget is made available to the first slice. No truncation is performed until the third slices, so the left-over budget remains constant. The budget monotonically decreases, as the full budget is made available to each backpropagated slice until it is expended. - -It is notable that this experiment yielded two fewer backpropagated slices compared to the first experiment in this notebook, which is almost identical. This demonstrates that for some problems distributing the budget evenly might be optimal. For other problems, allowing slices to greedily expend the full budget might yield better performance. - -``` -[17]: -``` - -```python -fig, axes = plt.subplots(2, 2, figsize=(20, 10)) -plot_slice_errors(metadata, axes[(0, 0)]) -plot_left_over_error_budget(metadata, axes[(0, 1)]) -plot_accumulated_error(metadata, axes[(1, 0)]) -plot_num_qwc_groups(metadata, axes[(1, 1)]) -``` - -![../\_images/how\_tos\_truncate\_operator\_terms\_36\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_36_0.avif) - - - -## Capping the number of backpropagated slices and the total error together - -If one doesn’t want to distribute their error budget all the way through the circuit but doesn’t want to greedily expend it either, one can specify a the number of slices they intend to backpropagate (`num_slices`), along with a total error budget (`max_error_total`). This will distribute the error budget uniformly (accordingly to `p_norm`) across the input slices. - -Here, we will cap the number of slices we may backpropagate at `12`, and we will keep the same total error budget. - -``` -[18]: -``` - -```python -num_slices = 12 - -truncation_error_budget = setup_budget(max_error_total=0.018, num_slices=num_slices, p_norm=1) -print(truncation_error_budget) -``` - -```python -TruncationErrorBudget(per_slice_budget=[0.0014999999999999998], max_error_total=0.018, p_norm=1) -``` - -Now we will attempt to backpropagate the `12` slices for which we allocated some budget in the previous step. We do this by just passing in the final `12` slices in our circuit. With `p_norm=1`, each of the `12` slices has an available budget of `0.018 / num_slices = 0.0015`. - -``` -[19]: -``` - -```python -bp_obs, remaining_slices, metadata = backpropagate( - obs, - slices[-num_slices:], - operator_budget=op_budget, - truncation_error_budget=truncation_error_budget, -) -``` - -Since we passed a subset of our slices (i.e. `slices[-num_slices:]`) to `backpropagate`, we must combine the slices remaining after backpropagation with the slices that were never sent for backpropagation (i.e. `slices[:-num_slices]`). - -Once we have combined all of the remaining slices, we can use [combine\_slices](/docs/api/qiskit-addon-utils/slicing-combine-slices#qiskit_addon_utils.slicing.combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became. - -``` -[20]: -``` - -```python -# Recombine the slices remaining after backprop with the rest of the original circuit -reduced_circuit = combine_slices(slices[:-num_slices] + remaining_slices) - -print(f"Backpropagated {num_slices - len(remaining_slices)} circuit slices.") -print( - f"New observable contains {len(bp_obs)} terms and {len(bp_obs.group_commuting(qubit_wise=True))} commuting groups." -) -``` - -```python -Backpropagated 12 circuit slices. -New observable contains 29 terms and 9 commuting groups. -``` - -The plots show us that we did successfully backpropagate all `12` slices while keeping our observable under `10` commuting Pauli groups. We can also see that using `num_slices` along with `max_error_total` results in a distribution of budgets across the slices and unused budget is again forwarded to the next slice. This is most apparent in the **top/right** plot, as the budget is both expended and replenished throughout backpropagation. - -It is notable that this method of distributing error produced the best results (more backpropagated slices), compared with the first experiment in this notebook and the example just above this one. All of these examples allotted `.018` error budget, but backpropagation performed differently depending on how that budget was distributed. - -``` -[21]: -``` - -```python -fig, axes = plt.subplots(2, 2, figsize=(20, 10)) -plot_slice_errors(metadata, axes[(0, 0)]) -plot_left_over_error_budget(metadata, axes[(0, 1)]) -plot_accumulated_error(metadata, axes[(1, 0)]) -plot_num_qwc_groups(metadata, axes[(1, 1)]) -``` - -![../\_images/how\_tos\_truncate\_operator\_terms\_44\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_44_0.avif) - - - -## Working with multiple observables - -The `qiskit_addon_obp.backpropagate` method allows one to pass in a sequence of observables. This simplifies the workflow when dealing with multiple target observables. - -We explicitly mention this here to teach you how the truncation strategy handles such a case. For the sake of this example, we add an extra observable to the one which we have been using so far: - -``` -[22]: -``` - -```python -obs = [SparsePauliOp("IIIIIZIIII"), SparsePauliOp("IIIIIXIIII")] -``` - -For simplicity, we repeat the very first experiment from this tutorial. The only difference is that we now have two observables to backpropagate the circuit into. - -For this particular example, this does not affect the number of slices which could be backpropagated. However, we can see now that the two observables resulted in different numbers of Pauli terms and commuting groups. - -``` -[23]: -``` - -```python -truncation_error_budget = setup_budget(max_error_per_slice=0.001) -print(truncation_error_budget) -``` - -```python -TruncationErrorBudget(per_slice_budget=[0.001], max_error_total=inf, p_norm=1) -``` - -``` -[24]: -``` - -```python -bp_obs, remaining_slices, metadata = backpropagate( - obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget -) -reduced_circuit = combine_slices(remaining_slices) -print(f"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.") -print( - f"The new first observable contains {len(bp_obs[0])} terms and {len(bp_obs[0].group_commuting(qubit_wise=True))} commuting groups." -) -print( - f"The new second observable contains {len(bp_obs[1])} terms and {len(bp_obs[1].group_commuting(qubit_wise=True))} commuting groups." -) -``` - -```python -Backpropagated 11 circuit slices. -The new first observable contains 29 terms and 10 commuting groups. -The new second observable contains 23 terms and 8 commuting groups. -``` - -The plots below shed some light on how the backpropagation algorithm handles multiple observables. - -First, the plots **top/left**, **top/right** and **bottom/left** teach us that the budget for truncating terms is set individually for each observable. In other words, both observables have the ability to truncate terms assuming an error of `0.001` per backpropagated slice. Due to the different nature of the observables, this results in different consumption of the budget. In this example, they exhibit a lot of overlap which is not necessarily the case in general. - -**bottom/right** teaches us that `max_qwc_groups` is actually taking *all* observables combined into account. That means that the terms of all observables are grouped together to arrive at one final number of qubit-wise commuting groups which is compared against `max_qwc_paulis`. The same is done for the `max_paulis` threshold (which we have not discussed in this notebook) which allows you to set a limit on the number of Pauli terms across all observables. - -``` -[25]: -``` - -```python -fig, axes = plt.subplots(2, 2, figsize=(20, 10)) -plot_slice_errors(metadata, axes[(0, 0)]) -plot_left_over_error_budget(metadata, axes[(0, 1)]) -plot_accumulated_error(metadata, axes[(1, 0)]) -plot_num_qwc_groups(metadata, axes[(1, 1)]) -``` - -![../\_images/how\_tos\_truncate\_operator\_terms\_51\_0.png](/docs/images/api/qiskit-addon-obp/how_tos_truncate_operator_terms_51_0.avif) diff --git a/docs/addons/qiskit-addon-obp/index.mdx b/docs/addons/qiskit-addon-obp/index.mdx index 6d18bdbabdd..95c88ec7036 100644 --- a/docs/addons/qiskit-addon-obp/index.mdx +++ b/docs/addons/qiskit-addon-obp/index.mdx @@ -15,7 +15,7 @@ There are a number of ways in which operator backpropagation can be performed, t ## Documentation -All documentation is available [here](/docs/addons/qiskit-addon-obp). +All documentation is available [here](/docs/addons/qiskit-addon-obp/). ## Installation diff --git a/docs/addons/qiskit-addon-obp/release-notes.mdx b/docs/addons/qiskit-addon-obp/release-notes.mdx new file mode 100644 index 00000000000..2a56cc0a6f5 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/release-notes.mdx @@ -0,0 +1,173 @@ +--- +title: Operator backpropagation (OBP) release notes +description: Changes made to Operator backpropagation (OBP) +in_page_toc_max_heading_level: 2 +--- + + + + + +# Operator backpropagation (OBP) release notes + + + + + +## 0.3.0 + + + +### New Features + +* [`PauliLindbladError`](/docs/api/qiskit-ibm-runtime/utils-noise-learner-result-pauli-lindblad-error "(in Qiskit Runtime IBM Client)") objects can now be embedded in slices of `QuantumCircuit` using the [`PauliLindbladErrorInstruction`](/docs/api/qiskit-addon-obp/utils-noise-pauli-lindblad-error-instruction#qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction "qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction") and will be handled properly by [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate"). + + + +### Bug Fixes + +* Fixed a bug in [`qiskit_addon_obp.backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") which often caused resets to be applied to the wrong qubit. + + + + + +## 0.2.0 + + + + + +### New Features + +* A new parameter `show_legend` has been added to each function in the [`qiskit_addon_obp.utils.visualization`](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization "qiskit_addon_obp.utils.visualization") module that can show or hide the legend on a plot. The legend is shown by default. This can be useful when the legend becomes long and obstructs the plot. + +* [`qiskit_addon_obp.utils.simplify.OperatorBudget`](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget "qiskit_addon_obp.utils.simplify.OperatorBudget") now holds `atol` and `rtol` fields which are absolute and relative tolerances used to determine whether coefficients are zero while simplifying an operator. + +* [`truncate_binary_search()`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search "qiskit_addon_obp.utils.truncating.truncate_binary_search") now accepts a `tol` kwarg. Once an optimal truncation threshold, up to this value, has been found, the search for an optimal threshold will stop. The default tolerance is `1e-8`; whereas, the tolerance used prior to this release was `1e-10`. + + [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") now holds a `tol` field. This field is used by [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") as the `tol` argument to [`truncate_binary_search()`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search "qiskit_addon_obp.utils.truncating.truncate_binary_search"). + + + +### Upgrade Notes + +* This release adds support for Python 3.13. No code changes were necessary, so older releases are expected to work on Python 3.13 too. + +* This package is now compatible with Qiskit SDK 2.0. + + + + + +### Bug Fixes + +* The [`num_duplicate_paulis`](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_duplicate_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_duplicate_paulis") was previously unable to differentiate from Pauli terms that were trimmed due to their coefficient being close to zero. This is now tracked correctly with these trimmed terms only counting towards [`num_trimmed_paulis`](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_trimmed_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_trimmed_paulis"). + +* The reported number of unique Paulis in [`num_unique_paulis`](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_unique_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_unique_paulis") is now correct in cases where all gates in a slice commute with an observable. + +* Fixed a bug in [`qiskit_addon_obp.backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") which caused slices with depth > 1 to be incorrectly forward-propagated. Gates from a given slice will now be correctly propagated into the observable in reverse order (i.e., from the back of the slice). + + + + + +## 0.1.0 + + + + + +### New Features + +* Added a [`OperatorBudget`](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget "qiskit_addon_obp.utils.simplify.OperatorBudget") class for specifying how large an operator may grow during back-propagation. + +* Adds the `max_seconds` keyword-argument to the [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") function, allowing the end-user to specify a maximum wall clock time for the algorithm. This can (for example) be useful for exploring different truncation error budget strategies while limiting the CPU time. + +* Introduced a new `dataclass`, [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget"), for holding information about the observable truncation strategy. + +* Introduced a new function, [`setup_budget()`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget "qiskit_addon_obp.utils.truncating.setup_budget"), which generates a [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") class, given an observable truncation strategy (e.g. `max_error_total`, `max_error_per_slice`, `p_norm`). + + + + + +### Upgrade Notes + +* The [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") function no longer accepts `max_paulis` and `max_qwc_groups` kwargs for constraining the size of the operator during back-propagation. Users should instead use the new `operator_budget` kwarg, which takes an [`OperatorBudget`](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget "qiskit_addon_obp.utils.simplify.OperatorBudget") instance. + + To migrate, change this code + + ```python + from qiskit_addon_obp import backpropagate + + bp_obs, remaining_slices, metadata = backpropagate( + obs, + slices, + max_paulis=100, + max_qwc_groups=10, + simplify=True + ) + ``` + + to this + + ```python + from qiskit_addon_obp import backpropagate + from qiskit_addon_obp.utils.simplify import OperatorBudget + + op_budget = OperatorBudget(max_paulis=100, max_qwc_groups=10, simplify=True) + bp_obs, remaining_slices, metadata = backpropagate(obs, slices, operator_budget=op_budget) + ``` + +* The `max_slices` kwarg has been removed from [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate"). Users should now only pass in slices which they intend to back-propagate. If a user wants to attempt to only back-propagate the last `20` slices of an `N`-slice circuit, they would simply pass in the last `20` slices to [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") and, recombine any slices remaining after back-propagation with the original `N-20` slices. + + For example + + ```python + from qiskit_addon_obp import backpropagate + from qiskit_addon_obp.utils.truncating import setup_budget + from qiskit_addon_utils.slicing import combine_slices + + num_slices = 20 + truncation_error_budget = setup_budget(max_error_total=0.02, num_slices=num_slices, p_norm=1) + bp_obs, remaining_slices, metadata = backpropagate( + obs, slices[-num_slices:], truncation_error_budget=truncation_error_budget + ) + reduced_circuit = combine_slices(slices[:-num_slices] + remaining_slices) + ``` + +* The `max_slices` kwarg in [`setup_budget()`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget "qiskit_addon_obp.utils.truncating.setup_budget") has been renamed to `num_slices`. + +* The `max_slices` attribute in [`OBPMetadata`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata "qiskit_addon_obp.utils.metadata.OBPMetadata") has been renamed to `num_slices`. + +* The project’s root Python namespace has been changed from `obp` to `qiskit_addon_obp`. All package imports must be updated. + + For example: + + ```python + from obp import backpropagate + ``` + + should be changed to: + + ```python + from qiskit_addon_obp import backpropagate + ``` + +* Removed the `max_error_total`, `max_error_per_slice`, and `p_norm` kwargs from the [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") signature. Instead, users must specify their observable truncation strategy with the new `truncation_error_budget` kwarg which accepts a [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") instance. + +* Removed the `per_slice_budget`, `max_error_total`, and `p_norm` fields from the [`OBPMetadata`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata "qiskit_addon_obp.utils.metadata.OBPMetadata") class. These fields will now be accessed through the new `truncation_error_budget` field, which holds a [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") instance. + + + + + +### Bug Fixes + +* The [`setup_budget()`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget "qiskit_addon_obp.utils.truncating.setup_budget") erroneously distributed the `max_error_total` when `num_slices` was also set. This has been fixed now, such that the budget always gets distributed evenly, regardless of the value of `p_norm`. + +* When the `max_seconds` argument to the [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") method is used, but the timeout is not reached during the actual OBP execution, the signal will now be reset properly, thereby avoiding cancellations at a (seemingly) random later point in time (of course, it is not random but actually after the specified amount of time has passed, but the rest of the code being executed after OBP could be doing anything at this point). + +* The computation of the [`accumulated_error()`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error "qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error") and [`left_over_error_budget()`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata.left_over_error_budget "qiskit_addon_obp.utils.metadata.OBPMetadata.left_over_error_budget") were fixed to respect the [Minkowski inequality](https://en.wikipedia.org/wiki/Minkowski_inequality). This is necessary, because a general Lp-norm (other than `p=2`) does not satisfy the [parallelogram law](https://en.wikipedia.org/wiki/Parallelogram_law) which resulted in a non-rigorous upper bound of the actual accumulated errors (and left-over error budgets by extension). + diff --git a/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb similarity index 99% rename from docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb rename to docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb index e97e7dfd5d6..faf992cee3c 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb @@ -31,7 +31,7 @@ "- **Step 4: Reconstruct results**\n", " - N.A.\n", "\n", - "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." + "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.backpropagate.html) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." ] }, { @@ -42,9 +42,9 @@ "## Step 1: Map to quantum problem\n", "### Map the time-evolution of a quantum Heisenberg model to a quantum experiment.\n", "\n", - "The [qiskit_addon_utils](https://qiskit.github.io/qiskit-addon-utils/) package provides some reusable functionalities for various purposes.\n", + "The [qiskit_addon_utils](/docs/addons/qiskit-addon-utils/) package provides some reusable functionalities for various purposes.\n", "\n", - "Its [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", + "Its [qiskit_addon_utils.problem_generators](https://qiskit.github.io/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators.html) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", "This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/docs/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows.\n", "\n", "In the following, we first generate a heavy-hex `CouplingMap` out of which we carve a linear chain of 10 qubits. Note, that the indices of this new `reduced_coupling_map` are again zero-based." @@ -159,7 +159,7 @@ "metadata": {}, "source": [ "From the qubit operator, we can generate a quantum circuit which models its time evolution.\n", - "Once again, the [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators) module comes to the resuce with a handy function do just that:" + "Once again, the [qiskit_addon_utils.problem_generators](https://qiskit.github.io/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators.html) module comes to the resuce with a handy function do just that:" ] }, { @@ -200,9 +200,9 @@ "## Step 2: Optimize the problem\n", "### Create circuit slices to backpropagate\n", "\n", - "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](/docs/api/qiskit-addon-utils/slicing-slice-by-gate-types) function.\n", + "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](https://qiskit.github.io/qiskit-addon-utils/stubs/qiskit_addon_utils.slicing.slice_by_gate_types.html) function.\n", "\n", - "For a more detailed discussion on circuit slicing, check out this [how-to guide](https://qiskit.github.io/qiskit-addon-utils/how_tos/create_circuit_slices.html) of the [qiskit-addon-utils](https://qiskit.github.io/qiskit-addon-utils/index.html) package." + "For a more detailed discussion on circuit slicing, check out this [how-to guide](/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices) of the [qiskit-addon-utils](/docs/addons/qiskit-addon-utils/index) package." ] }, { @@ -233,7 +233,7 @@ "source": [ "### Constrain how large the operator may grow during backpropagation\n", "\n", - "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget) instance.\n", + "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.simplify.OperatorBudget.html) instance.\n", "\n", "Here we specify that backpropagation should stop when the number of qubit-wise commuting Pauli groups in the operator grows past 8." ] @@ -320,7 +320,7 @@ "id": "d25b9701-f1b1-4230-9dcc-554b85e91cf7", "metadata": {}, "source": [ - "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", + "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.setup_budget.html) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", "\n", "In order to enable this truncation, we need to setup our error budget like so:" ] @@ -342,10 +342,10 @@ "id": "c6dda21f-4a28-4c4e-9727-15288973150e", "metadata": {}, "source": [ - "Note that by allocating `5e-3` error per slice for truncation, we are able to remove 3 more slices from the circuit, while remaining within the original budget of 8 commuting Pauli groups in the observable. By default, `backpropagate` uses the L1 norm of the truncated coefficients to bound the total error incurred from truncation. For other options refer to the [how-to guide on specifying the p_norm](/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm).\n", + "Note that by allocating `5e-3` error per slice for truncation, we are able to remove 3 more slices from the circuit, while remaining within the original budget of 8 commuting Pauli groups in the observable. By default, `backpropagate` uses the L1 norm of the truncated coefficients to bound the total error incurred from truncation. For other options refer to the [how-to guide on specifying the p_norm](/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm).\n", "\n", "In this particular example where we have backpropagated 10 slices, the total truncation error should not exceed ``(5e-3 error/slice) * (10 slices) = 5e-2``.\n", - "For further discussion on distributing an error budget across your slices, check out [this how-to guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms)." + "For further discussion on distributing an error budget across your slices, check out [this how-to guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms)." ] }, { diff --git a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.mdx b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.mdx deleted file mode 100644 index 3ce0d447f90..00000000000 --- a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.mdx +++ /dev/null @@ -1,372 +0,0 @@ - - -# Reducing depth of circuits with operator backpropagation - -Operator backpropagation is a technique which involves absorbing operations from the end of a quantum circuit into a Pauli operator, generally reducing the depth of the circuit at the cost of additional terms in the operator. The goal is to backpropagate as much of the circuit as possible without allowing the operator to grow too large. - -One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms’ coefficients. - -In this tutorial we will implement a [Qiskit pattern](/docs/guides/serverless#qiskit-patterns-with-quantum-serverless) for simulating the quantum dynamics of a Heisenberg spin chain using operator backpropagation: - -* **Step 1: Map to quantum problem** - - * Map the time-evolved Hamiltonian onto a quantum circuit - -* **Step 2: Optimize the problem** - - * Slice the circuit - * Backpropagate slices from the circuit onto a Pauli observable - * Combine the remaining slices into a single circuit - * Transpile the circuit for the backend - -* **Step 3: Execute experiments** - - * Calculate the expectation value using the reduced circuit and expanded observable with a [StatevectorEstimator](/docs/api/qiskit/qiskit.primitives.StatevectorEstimator) for sake of simplicity in this notebook - -* **Step 4: Reconstruct results** - - * N.A. - -**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit\_addon\_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below. - - - -## Step 1: Map to quantum problem - - - -### Map the time-evolution of a quantum Heisenberg model to a quantum experiment. - -The [qiskit\_addon\_utils](https://qiskit.github.io/qiskit-addon-utils/) package provides some reusable functionalities for various purposes. - -Its [qiskit\_addon\_utils.problem\_generators](/docs/api/qiskit-addon-utils/problem-generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph. This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/docs/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows. - -In the following, we first generate a heavy-hex `CouplingMap` out of which we carve a linear chain of 10 qubits. Note, that the indices of this new `reduced_coupling_map` are again zero-based. - -``` -[1]: -``` - -```python -from qiskit.transpiler import CouplingMap - -coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False) - -# Choose a 10-qubit linear chain on this coupling map -reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 5, 12, 8, 18]) -``` - -``` -[2]: -``` - -```python -from rustworkx.visualization import graphviz_draw - -graphviz_draw(reduced_coupling_map.graph, method="circo") -``` - -``` -[2]: -``` - -![../\_images/tutorials\_01\_getting\_started\_4\_0.png](/docs/images/api/qiskit-addon-obp/tutorials_01_getting_started_4_0.avif) - -Next, we generate a Pauli operator modeling a Heisenberg XYZ Hamiltonian. - -$$ -\hat{H} = \sum_{(j,k)\in E} (J_{x} \sigma_j^{x} \sigma_{k}^{x} + - J_{y} \sigma_j^{y} \sigma_{k}^{y} + J_{z} \sigma_j^{z} \sigma_{k}^{z}) + - \sum_{j\in V} (h_{x} \sigma_j^{x} + h_{y} \sigma_j^{y} + h_{z} \sigma_j^{z}) -$$ - -Where $G(V,E)$ is the graph of the coupling map provided. - -``` -[3]: -``` - -```python -import numpy as np -from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian - -# Get a qubit operator describing the Heisenberg XYZ model -hamiltonian = generate_xyz_hamiltonian( - reduced_coupling_map, - coupling_constants=(np.pi / 8, np.pi / 4, np.pi / 2), - ext_magnetic_field=(np.pi / 3, np.pi / 6, np.pi / 9), -) -print(hamiltonian) -``` - -```python -SparsePauliOp(['IIIIIIIXXI', 'IIIIIIIYYI', 'IIIIIIIZZI', 'IIIIIXXIII', 'IIIIIYYIII', 'IIIIIZZIII', 'IIIXXIIIII', 'IIIYYIIIII', 'IIIZZIIIII', 'IXXIIIIIII', 'IYYIIIIIII', 'IZZIIIIIII', 'IIIIIIIIXX', 'IIIIIIIIYY', 'IIIIIIIIZZ', 'IIIIIIXXII', 'IIIIIIYYII', 'IIIIIIZZII', 'IIIIXXIIII', 'IIIIYYIIII', 'IIIIZZIIII', 'IIXXIIIIII', 'IIYYIIIIII', 'IIZZIIIIII', 'XXIIIIIIII', 'YYIIIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIIY', 'IIIIIIIIIZ', 'IIIIIIIIXI', 'IIIIIIIIYI', 'IIIIIIIIZI', 'IIIIIIIXII', 'IIIIIIIYII', 'IIIIIIIZII', 'IIIIIIXIII', 'IIIIIIYIII', 'IIIIIIZIII', 'IIIIIXIIII', 'IIIIIYIIII', 'IIIIIZIIII', 'IIIIXIIIII', 'IIIIYIIIII', 'IIIIZIIIII', 'IIIXIIIIII', 'IIIYIIIIII', 'IIIZIIIIII', 'IIXIIIIIII', 'IIYIIIIIII', 'IIZIIIIIII', 'IXIIIIIIII', 'IYIIIIIIII', 'IZIIIIIIII', 'XIIIIIIIII', 'YIIIIIIIII', 'ZIIIIIIIII'], - coeffs=[0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j, - 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j, - 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, - 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j, - 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j, - 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, - 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, 1.04719755+0.j, - 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, - 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j, - 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, - 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, - 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j, - 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, - 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, - 0.34906585+0.j]) -``` - -From the qubit operator, we can generate a quantum circuit which models its time evolution. Once again, the [qiskit\_addon\_utils.problem\_generators](/docs/api/qiskit-addon-utils/problem-generators) module comes to the resuce with a handy function do just that: - -``` -[4]: -``` - -```python -from qiskit.synthesis import LieTrotter -from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit - -circuit = generate_time_evolution_circuit( - hamiltonian, - time=0.2, - synthesis=LieTrotter(reps=2), -) -circuit.draw("mpl", style="iqp", scale=0.6) -``` - -``` -[4]: -``` - -![../\_images/tutorials\_01\_getting\_started\_8\_0.png](/docs/images/api/qiskit-addon-obp/tutorials_01_getting_started_8_0.avif) - - - -## Step 2: Optimize the problem - - - -### Create circuit slices to backpropagate - -Remember, the `backpropagate` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice\_by\_gate\_types](/docs/api/qiskit-addon-utils/slicing-slice-by-gate-types) function. - -For a more detailed discussion on circuit slicing, check out this [how-to guide](https://qiskit.github.io/qiskit-addon-utils/how_tos/create_circuit_slices.html) of the [qiskit-addon-utils](https://qiskit.github.io/qiskit-addon-utils/index.html) package. - -``` -[5]: -``` - -```python -from qiskit_addon_utils.slicing import slice_by_gate_types - -slices = slice_by_gate_types(circuit) -print(f"Separated the circuit into {len(slices)} slices.") -``` - -```python -Separated the circuit into 18 slices. -``` - - - -### Constrain how large the operator may grow during backpropagation - -During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the `operator_budget` kwarg of the `backpropagate` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget) instance. - -Here we specify that backpropagation should stop when the number of qubit-wise commuting Pauli groups in the operator grows past 8. - -``` -[6]: -``` - -```python -from qiskit_addon_obp.utils.simplify import OperatorBudget - -op_budget = OperatorBudget(max_qwc_groups=8) -``` - - - -### Backpropagate slices from the circuit - -First, we will specify the Pauli-Z observable on qubit 0, and we will backpropagate slices from the time-evolution circuit until the terms in the observable can no longer be combined into 8 or fewer qubit-wise commuting Pauli groups. - -Below you will see that we backpropagated 7 slices but only used 6 of the 8 allotted Pauli groups. This implies that backpropagating one more slice would cause the number of Pauli groups to exceed 8. We can verify that this is the case by inspecting the returned metadata. - -``` -[7]: -``` - -```python -from qiskit.quantum_info import SparsePauliOp -from qiskit_addon_obp import backpropagate -from qiskit_addon_utils.slicing import combine_slices - -# Specify a single-qubit observable -observable = SparsePauliOp("IIIIIIIIIZ") - -# Backpropagate slices onto the observable -bp_obs, remaining_slices, metadata = backpropagate(observable, slices, operator_budget=op_budget) -# Recombine the slices remaining after backpropagation -bp_circuit = combine_slices(remaining_slices, include_barriers=True) - -print(f"Backpropagated {metadata.num_backpropagated_slices} slices.") -print( - f"New observable has {len(bp_obs.paulis)} terms, which can be combined into {len(bp_obs.group_commuting(qubit_wise=True))} groups." -) -print( - f"Note that backpropagating one more slice would result in {metadata.backpropagation_history[-1].num_paulis[0]} terms " - f"across {metadata.backpropagation_history[-1].num_qwc_groups} groups." -) -print("The remaining circuit after backpropagation looks as follows:") -bp_circuit.draw("mpl", scale=0.6) -``` - -```python -Backpropagated 7 slices. -New observable has 18 terms, which can be combined into 8 groups. -Note that backpropagating one more slice would result in 27 terms across 12 groups. -The remaining circuit after backpropagation looks as follows: -``` - -``` -[7]: -``` - -![../\_images/tutorials\_01\_getting\_started\_14\_1.png](/docs/images/api/qiskit-addon-obp/tutorials_01_getting_started_14_1.avif) - -Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup\_budet](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice’s budget. - -In order to enable this truncation, we need to setup our error budget like so: - -``` -[8]: -``` - -```python -from qiskit_addon_obp.utils.truncating import setup_budget - -truncation_error_budget = setup_budget(max_error_per_slice=0.005) -``` - -Note that by allocating `5e-3` error per slice for truncation, we are able to remove 3 more slices from the circuit, while remaining within the original budget of 8 commuting Pauli groups in the observable. By default, `backpropagate` uses the L1 norm of the truncated coefficients to bound the total error incurred from truncation. For other options refer to the [how-to guide on specifying the p\_norm](/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm). - -In this particular example where we have backpropagated 10 slices, the total truncation error should not exceed `(5e-3 error/slice) * (10 slices) = 5e-2`. For further discussion on distributing an error budget across your slices, check out [this how-to guide](/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms). - -``` -[9]: -``` - -```python -# Run the same experiment but truncate observable terms with small coefficients -bp_obs_trunc, remaining_slices_trunc, metadata = backpropagate( - observable, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget -) - -# Recombine the slices remaining after backpropagation -bp_circuit_trunc = combine_slices(remaining_slices_trunc, include_barriers=True) - -print(f"Backpropagated {metadata.num_backpropagated_slices} slices.") -print( - f"New observable has {len(bp_obs_trunc.paulis)} terms, which can be combined into {len(bp_obs_trunc.group_commuting(qubit_wise=True))} groups.\n" - f"After truncation, the error in our observable is bounded by {metadata.accumulated_error(0):.3e}" -) -print( - f"Note that backpropagating one more slice would result in {metadata.backpropagation_history[-1].num_paulis[0]} terms " - f"across {metadata.backpropagation_history[-1].num_qwc_groups} groups." -) -print("The remaining circuit after backpropagation looks as follows:") -bp_circuit_trunc.draw("mpl", scale=0.6) -``` - -```python -Backpropagated 10 slices. -New observable has 19 terms, which can be combined into 8 groups. -After truncation, the error in our observable is bounded by 4.933e-02 -Note that backpropagating one more slice would result in 27 terms across 13 groups. -The remaining circuit after backpropagation looks as follows: -``` - -``` -[9]: -``` - -![../\_images/tutorials\_01\_getting\_started\_18\_1.png](/docs/images/api/qiskit-addon-obp/tutorials_01_getting_started_18_1.avif) - - - -### Now that we have our reduced ansatze and expanded observables, we can transpile our experiments to the backend. - -Here we will use the 14-qubit [FakeMelbourneV2](/docs/api/qiskit-ibm-runtime/fake-provider-fake-melbourne-v2) from [qiskit-ibm-runtime](/docs/api/qiskit-ibm-runtime) to demonstrate how to transpile to a QPU backend. - -``` -[10]: -``` - -```python -from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager -from qiskit_ibm_runtime.fake_provider import FakeMelbourneV2 - -# Specify a backend and a pass manager for transpilation -backend = FakeMelbourneV2() -pm = generate_preset_pass_manager(backend=backend, optimization_level=1) - -# Transpile original experiment -circuit_isa = pm.run(circuit) -observable_isa = observable.apply_layout(circuit_isa.layout) - -# Transpile backpropagated experiment -bp_circuit_isa = pm.run(bp_circuit) -bp_obs_isa = bp_obs.apply_layout(bp_circuit_isa.layout) - -# Transpile the backpropagated experiment with truncated observable terms -bp_circuit_trunc_isa = pm.run(bp_circuit_trunc) -bp_obs_trunc_isa = bp_obs_trunc.apply_layout(bp_circuit_trunc_isa.layout) -``` - - - -## Step 3: Execute quantum experiments - - - -### Calculate expectation value - -Finally, we can run the backpropagated experiments and compare them with the full experiment using the noiseless [StatevectorEstimator](/docs/api/qiskit/qiskit.primitives.StatevectorEstimator). We can see that the backpropagated expectation value without truncation is equivalent to the exact value within the limits of numerical precision. - -The expectation value on the operator with truncated terms has some error on the order of `1e-4`, which is within the expected tolerance. - -**Note:** We use a statevector-based `Estimator` primitive to illustrate the effect of truncation on the output. To run on the backend to which the experiments were transpiled in Step 2, one would import the [EstimatorV2](/docs/api/qiskit-ibm-runtime/estimator-v2) from `qiskit-ibm-runtime` and pass the backend instance into the constructor. - -``` -[11]: -``` - -```python -from qiskit.primitives import StatevectorEstimator as Estimator - -estimator = Estimator() - -# Run the experiments using Estimator primitive -result_exact = estimator.run([(circuit_isa, observable_isa)]).result()[0].data.evs.item() -result_bp = estimator.run([(bp_circuit_isa, bp_obs_isa)]).result()[0].data.evs.item() -result_bp_trunc = ( - estimator.run([(bp_circuit_trunc_isa, bp_obs_trunc_isa)]).result()[0].data.evs.item() -) - -print(f"Exact expectation value: {result_exact}") -print(f"Backpropagated expectation value: {result_bp}") -print(f"Backpropagated expectation value with truncation: {result_bp_trunc}") -print(f" - Expected Error for truncated observable: {metadata.accumulated_error(0):.3e}") -print(f" - Observed Error for truncated observable: {abs(result_exact - result_bp_trunc):.3e}") -``` - -```python -Exact expectation value: 0.8854160687717507 -Backpropagated expectation value: 0.8854160687717532 -Backpropagated expectation value with truncation: 0.8850236647156059 - - Expected Error for truncated observable: 4.933e-02 - - Observed Error for truncated observable: 3.924e-04 -``` diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json deleted file mode 100644 index c828c198a3b..00000000000 --- a/docs/addons/qiskit-addon-sqd/_toc.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "title": "Sample-based quantum diagonalization (SQD)", - "children": [ - { - "title": "Documentation Home", - "url": "/docs/addons/qiskit-addon-sqd" - }, - { - "title": "Installation Instructions", - "url": "/docs/addons/qiskit-addon-sqd/install" - }, - { - "title": "Tutorials", - "children": [ - { - "title": "Improving energy estimation of a chemistry Hamiltonian with SQD", - "url": "/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian" - }, - { - "title": "Improving energy estimation of a Fermionic lattice model with SQD", - "url": "/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian" - } - ], - "url": "/docs/addons/qiskit-addon-sqd/tutorials/index" - }, - { - "title": "How-To Guides", - "children": [ - { - "title": "Add fermionic transitions to the pool of configurations", - "url": "/docs/addons/qiskit-addon-sqd/how_tos/add_fermionic_excitations_to_configuration_pool" - }, - { - "title": "Benchmark Pauli operator projection", - "url": "/docs/addons/qiskit-addon-sqd/how_tos/benchmark_pauli_projection" - }, - { - "title": "Bounding the subspace dimension", - "url": "/docs/addons/qiskit-addon-sqd/how_tos/choose_subspace_dimension" - }, - { - "title": "Scale SQD chemistry workflows with Dice solver", - "url": "/docs/addons/qiskit-addon-sqd/how_tos/integrate_dice_solver" - }, - { - "title": "Project Pauli operators onto Hilbert subspaces", - "url": "/docs/addons/qiskit-addon-sqd/how_tos/project_pauli_operators_onto_hilbert_subspaces" - }, - { - "title": "Understand open-shell vs closed-shell options and its effect in the subspace construction", - "url": "/docs/addons/qiskit-addon-sqd/how_tos/select_open_closed_shell" - }, - { - "title": "Optimize Hamiltonian basis with orbital optimization", - "url": "/docs/addons/qiskit-addon-sqd/how_tos/use_oo_to_optimize_hamiltonian_basis" - } - ], - "url": "/docs/addons/qiskit-addon-sqd/how_tos/index" - } - ], - "collapsed": true -} diff --git a/docs/addons/qiskit-addon-sqd/how_tos/add-fermionic-excitations-to-configuration-pool.mdx b/docs/addons/qiskit-addon-sqd/how_tos/add-fermionic-excitations-to-configuration-pool.mdx deleted file mode 100644 index c060b77b77f..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/add-fermionic-excitations-to-configuration-pool.mdx +++ /dev/null @@ -1,129 +0,0 @@ - - -# Add fermionic transitions to the pool of configurations - -Here we demonstrate the functionalities to augment the pool of electronic configurations obtained by the action of transition operators on each electronic configuration. - -We demonstrate how to add single-electron hops of the type: - -$$ -c^\dagger_{p\sigma} c_{q\sigma} |\textbf{x} \rangle -$$ - -for $p, q = 1, ..., N_\textrm{orb}$ and $\sigma \in \{ \uparrow, \downarrow\}$, and for all $|\textbf{x} \rangle$ in the batch of electronic configurations. - -Let’s begin by generating a batch of random electronic configurations - -``` -[1]: -``` - -```python -import numpy as np - -n_qubits = 8 -n_orb = n_qubits // 2 - -rand_seed = 22 -np.random.seed(rand_seed) - - -# Generate some random bitstrings for testing -def random_bitstrings(n_samples, n_qubits): - return np.round(np.random.rand(n_samples, n_qubits)).astype("int").astype("bool") - - -bitstring_matrix = random_bitstrings(100, n_qubits) -``` - -The excitation operators are specified inside a numpy array whose length is equal to the number of fermionic nodes (or qubits). Each element of the array must be a string that can take values: - -* `'I'`: Identity -* `'+'`: Creation operator -* `'-'`: Annihilation operator - -Let’s generate all possible single-electron transitions (amongst same spin species). - -``` -[2]: -``` - -```python -transitions_single = np.array( - [["I" for i in range(2 * n_orb)] for j in range(4 * (n_orb**2 - n_orb) // 2 + 1)] -) -count = 1 -for i in range(n_orb): - for j in range(i + 1, n_orb): - # spin up - transitions_single[count, i] = "+" - transitions_single[count, j] = "-" - count += 1 - transitions_single[count, i] = "-" - transitions_single[count, j] = "+" - count += 1 - - # spin down - transitions_single[count, i + n_orb] = "+" - transitions_single[count, j + n_orb] = "-" - count += 1 - transitions_single[count, i + n_orb] = "-" - transitions_single[count, j + n_orb] = "+" - count += 1 - -print(transitions_single) -``` - -```python -[['I' 'I' 'I' 'I' 'I' 'I' 'I' 'I'] - ['+' '-' 'I' 'I' 'I' 'I' 'I' 'I'] - ['-' '+' 'I' 'I' 'I' 'I' 'I' 'I'] - ['I' 'I' 'I' 'I' '+' '-' 'I' 'I'] - ['I' 'I' 'I' 'I' '-' '+' 'I' 'I'] - ['+' 'I' '-' 'I' 'I' 'I' 'I' 'I'] - ['-' 'I' '+' 'I' 'I' 'I' 'I' 'I'] - ['I' 'I' 'I' 'I' '+' 'I' '-' 'I'] - ['I' 'I' 'I' 'I' '-' 'I' '+' 'I'] - ['+' 'I' 'I' '-' 'I' 'I' 'I' 'I'] - ['-' 'I' 'I' '+' 'I' 'I' 'I' 'I'] - ['I' 'I' 'I' 'I' '+' 'I' 'I' '-'] - ['I' 'I' 'I' 'I' '-' 'I' 'I' '+'] - ['I' '+' '-' 'I' 'I' 'I' 'I' 'I'] - ['I' '-' '+' 'I' 'I' 'I' 'I' 'I'] - ['I' 'I' 'I' 'I' 'I' '+' '-' 'I'] - ['I' 'I' 'I' 'I' 'I' '-' '+' 'I'] - ['I' '+' 'I' '-' 'I' 'I' 'I' 'I'] - ['I' '-' 'I' '+' 'I' 'I' 'I' 'I'] - ['I' 'I' 'I' 'I' 'I' '+' 'I' '-'] - ['I' 'I' 'I' 'I' 'I' '-' 'I' '+'] - ['I' 'I' '+' '-' 'I' 'I' 'I' 'I'] - ['I' 'I' '-' '+' 'I' 'I' 'I' 'I'] - ['I' 'I' 'I' 'I' 'I' 'I' '+' '-'] - ['I' 'I' 'I' 'I' 'I' 'I' '-' '+']] -``` - -Let’s now apply the transition operators to the configurations in `bitstring_matrix` - -``` -[3]: -``` - -```python -from qiskit_addon_sqd.fermion import enlarge_batch_from_transitions - -bitstring_matrix_aug = enlarge_batch_from_transitions(bitstring_matrix, transitions_single) - -print(bitstring_matrix_aug.shape) -print(bitstring_matrix_aug) -``` - -```python -(723, 8) -[[False False False ... False False True] - [False True False ... True False False] - [ True True True ... True False True] - ... - [ True False True ... False False True] - [ True True True ... True False True] - [False False False ... False False True]] -``` diff --git a/docs/addons/qiskit-addon-sqd/how_tos/add_fermionic_excitations_to_configuration_pool.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/add_fermionic_excitations_to_configuration_pool.ipynb deleted file mode 100644 index 40253069f05..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/add_fermionic_excitations_to_configuration_pool.ipynb +++ /dev/null @@ -1,195 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", - "metadata": {}, - "source": [ - "# Add fermionic transitions to the pool of configurations\n", - "\n", - "Here we demonstrate the functionalities to augment the pool of electronic\n", - "configurations obtained by the action of transition operators on each electronic \n", - "configuration.\n", - "\n", - "We demonstrate how to add single-electron hops of the type:\n", - "$$\n", - "c^\\dagger_{p\\sigma} c_{q\\sigma} |\\textbf{x} \\rangle\n", - "$$\n", - "for $p, q = 1, ..., N_\\textrm{orb}$ and $\\sigma \\in \\{ \\uparrow, \\downarrow\\}$,\n", - "and for all $|\\textbf{x} \\rangle$ in the batch of electronic configurations." - ] - }, - { - "cell_type": "markdown", - "id": "0a7e9fcd", - "metadata": {}, - "source": [ - "Let's begin by generating a batch of random electronic configurations" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "e9506e0b-ed64-48bb-a97a-ef851b604af1", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "n_qubits = 8\n", - "n_orb = n_qubits // 2\n", - "\n", - "rand_seed = 22\n", - "np.random.seed(rand_seed)\n", - "\n", - "\n", - "# Generate some random bitstrings for testing\n", - "def random_bitstrings(n_samples, n_qubits):\n", - " return np.round(np.random.rand(n_samples, n_qubits)).astype(\"int\").astype(\"bool\")\n", - "\n", - "\n", - "bitstring_matrix = random_bitstrings(100, n_qubits)" - ] - }, - { - "cell_type": "markdown", - "id": "4155599f", - "metadata": {}, - "source": [ - "The excitation operators are specified inside a numpy array whose length is\n", - "equal to the number of fermionic nodes (or qubits). Each element of the array\n", - "must be a string that can take values:\n", - "\n", - "- ``'I'``: Identity\n", - "- ``'+'``: Creation operator\n", - "- ``'-'``: Annihilation operator\n", - "\n", - "Let's generate all possible single-electron transitions (amongst same spin \n", - "species)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "389284f3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[['I' 'I' 'I' 'I' 'I' 'I' 'I' 'I']\n", - " ['+' '-' 'I' 'I' 'I' 'I' 'I' 'I']\n", - " ['-' '+' 'I' 'I' 'I' 'I' 'I' 'I']\n", - " ['I' 'I' 'I' 'I' '+' '-' 'I' 'I']\n", - " ['I' 'I' 'I' 'I' '-' '+' 'I' 'I']\n", - " ['+' 'I' '-' 'I' 'I' 'I' 'I' 'I']\n", - " ['-' 'I' '+' 'I' 'I' 'I' 'I' 'I']\n", - " ['I' 'I' 'I' 'I' '+' 'I' '-' 'I']\n", - " ['I' 'I' 'I' 'I' '-' 'I' '+' 'I']\n", - " ['+' 'I' 'I' '-' 'I' 'I' 'I' 'I']\n", - " ['-' 'I' 'I' '+' 'I' 'I' 'I' 'I']\n", - " ['I' 'I' 'I' 'I' '+' 'I' 'I' '-']\n", - " ['I' 'I' 'I' 'I' '-' 'I' 'I' '+']\n", - " ['I' '+' '-' 'I' 'I' 'I' 'I' 'I']\n", - " ['I' '-' '+' 'I' 'I' 'I' 'I' 'I']\n", - " ['I' 'I' 'I' 'I' 'I' '+' '-' 'I']\n", - " ['I' 'I' 'I' 'I' 'I' '-' '+' 'I']\n", - " ['I' '+' 'I' '-' 'I' 'I' 'I' 'I']\n", - " ['I' '-' 'I' '+' 'I' 'I' 'I' 'I']\n", - " ['I' 'I' 'I' 'I' 'I' '+' 'I' '-']\n", - " ['I' 'I' 'I' 'I' 'I' '-' 'I' '+']\n", - " ['I' 'I' '+' '-' 'I' 'I' 'I' 'I']\n", - " ['I' 'I' '-' '+' 'I' 'I' 'I' 'I']\n", - " ['I' 'I' 'I' 'I' 'I' 'I' '+' '-']\n", - " ['I' 'I' 'I' 'I' 'I' 'I' '-' '+']]\n" - ] - } - ], - "source": [ - "transitions_single = np.array(\n", - " [[\"I\" for i in range(2 * n_orb)] for j in range(4 * (n_orb**2 - n_orb) // 2 + 1)]\n", - ")\n", - "count = 1\n", - "for i in range(n_orb):\n", - " for j in range(i + 1, n_orb):\n", - " # spin up\n", - " transitions_single[count, i] = \"+\"\n", - " transitions_single[count, j] = \"-\"\n", - " count += 1\n", - " transitions_single[count, i] = \"-\"\n", - " transitions_single[count, j] = \"+\"\n", - " count += 1\n", - "\n", - " # spin down\n", - " transitions_single[count, i + n_orb] = \"+\"\n", - " transitions_single[count, j + n_orb] = \"-\"\n", - " count += 1\n", - " transitions_single[count, i + n_orb] = \"-\"\n", - " transitions_single[count, j + n_orb] = \"+\"\n", - " count += 1\n", - "\n", - "print(transitions_single)" - ] - }, - { - "cell_type": "markdown", - "id": "c15c5b3f", - "metadata": {}, - "source": [ - "Let's now apply the transition operators to the configurations in ``bitstring_matrix``" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6a44f358", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(723, 8)\n", - "[[False False False ... False False True]\n", - " [False True False ... True False False]\n", - " [ True True True ... True False True]\n", - " ...\n", - " [ True False True ... False False True]\n", - " [ True True True ... True False True]\n", - " [False False False ... False False True]]\n" - ] - } - ], - "source": [ - "from qiskit_addon_sqd.fermion import enlarge_batch_from_transitions\n", - "\n", - "bitstring_matrix_aug = enlarge_batch_from_transitions(bitstring_matrix, transitions_single)\n", - "\n", - "print(bitstring_matrix_aug.shape)\n", - "print(bitstring_matrix_aug)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/how_tos/benchmark-pauli-projection.mdx b/docs/addons/qiskit-addon-sqd/how_tos/benchmark-pauli-projection.mdx deleted file mode 100644 index a1f4ff56bb4..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/benchmark-pauli-projection.mdx +++ /dev/null @@ -1,361 +0,0 @@ - - -# Benchmark Pauli operator projection - - - -## Pauli string action on a computational basis state - -The action of a Pauli string on a computational basis state is rather trivial, and just a single computational basis state itself. This is a direct consequence of the structure of Pauli matrices, which only have a single nonzero element on each of their rows. Consequently, their action on a qubit is: - -*** - -$$ -\sigma_x |0 \rangle = |1 \rangle -$$ - -$$ -\sigma_x |1 \rangle = |0 \rangle -$$ - -*** - -$$ -\sigma_y |0 \rangle = i|1 \rangle -$$ - -$$ -\sigma_y |1 \rangle = -i|0 \rangle -$$ - -*** - -$$ -\sigma_z |0 \rangle = |0 \rangle -$$ - -$$ -\sigma_z |1 \rangle = -|1 \rangle -$$ - -*** - -$$ -I |0 \rangle = |0 \rangle -$$ - -$$ -I |1 \rangle = |1 \rangle -$$ - -*** - -Each bit on the bitstring labeling the computational basis will be labeled by $x \in \{0, 1 \}$. In order to keep the implementation at light as possible, we will represent the bitstrings with `bool` variables: $0\rightarrow \textrm{False}$ and $1\rightarrow \textrm{True}$. - -To represent the action of each Pauli operator in a computational basis state we will assign three variables to it: `diag`, `sign`, `imag`. - -* `diag` labels whether the operator is diagonal: - - * $\textrm{diag}(I) = \textrm{True}$ - * $\textrm{diag}(\sigma_x) = \textrm{False}$ - * $\textrm{diag}(\sigma_y) = \textrm{False}$ - * $\textrm{diag}(\sigma_z) = \textrm{True}$ - -* `sign` Identifies if there is a sign change in the matrix element connected to either 0 or 1: - - * $\textrm{sign}(I) = \textrm{False}$ - * $\textrm{sign}(\sigma_x) = \textrm{False}$ - * $\textrm{sign}(\sigma_y) = \textrm{True}$ - * $\textrm{sign}(\sigma_z) = \textrm{True}$ - -* `imag` Identifies if there is a complex component to the matrix element: - - * $\textrm{imag}(I) = \textrm{False}$ - * $\textrm{imag}(\sigma_x) = \textrm{False}$ - * $\textrm{imag}(\sigma_y) = \textrm{True}$ - * $\textrm{imag}(\sigma_z) = \textrm{False}$ - -Let’s label an arbitrary Pauli operator as $\sigma \in \{ I, \sigma_x, \sigma_y \sigma_z\}$. The action of the Pauli operator on a computational basis state can then be represented by the logic operation: - -$$ -\sigma |x \rangle = |x == \textrm{diag}(\sigma) \rangle (-1)^{x\textrm{ and sign}(\sigma)} -(i)^{\textrm{imag}(\sigma)}. -$$ - -The same is straightforwardly generalized to arbitrary number of qubits. - -Let’s check that this works: - -``` -[1]: -``` - -```python -def connected_element_and_amplitude_bool( - x: bool, diag: bool, sign: bool, imag: bool -) -> tuple[bool, complex]: - """ - Finds the connected element to computational basis state |x> under - the action of the Pauli operator represented by (diag, sign, imag). - - Args: - x: Value of the bit, either True or False. - diag: Whether the Pauli operator is diagonal (I, Z) - sigma: Whether the Pauli operator's rows differ in sign (Y, Z) - imag: Whether the Pauli operator is purely imaginary (Y) - - Returns: - A length-2 tuple: - - The connected element to x, either False or True - - The matrix element - """ - return x == diag, (-1) ** (x and sign) * (1j) ** (imag) - - -sigma_indices = [0, 1, 2, 3] -sigma_string = ["I", "SX", "SZ", "SY"] -sigma_diag = [True, False, True, False] -sigma_sign = [False, False, True, True] -sigma_imag = [False, False, False, True] -qubit_values = [False, True] - -for xi in sigma_indices: - print("-------------------") - print(sigma_string[xi]) - for x in qubit_values: - x_p, matrix_element = connected_element_and_amplitude_bool( - x, sigma_diag[xi], sigma_sign[xi], sigma_imag[xi] - ) - print("|" + str(x) + "> --> |" + str(x_p) + "> ME:" + str(matrix_element)) -``` - -```python -------------------- -I -|False> --> |False> ME:(1+0j) -|True> --> |True> ME:(1+0j) -------------------- -SX -|False> --> |True> ME:(1+0j) -|True> --> |False> ME:(1+0j) -------------------- -SZ -|False> --> |False> ME:(1+0j) -|True> --> |True> ME:(-1+0j) -------------------- -SY -|False> --> |True> ME:1j -|True> --> |False> ME:(-0-1j) -``` - -Let’s generate some large number of bitstrings (50 M) for a 40-qubit system - -``` -[2]: -``` - -```python -import numpy as np -from qiskit_addon_sqd.qubit import sort_and_remove_duplicates - -rand_seed = 22 -np.random.seed(rand_seed) - -# Generate some random bitstrings for testing - - -def random_bitstrings(n_samples, n_qubits): - return np.round(np.random.rand(n_samples, n_qubits)).astype("int").astype("bool") - - -n_qubits = 40 -bts_matrix = random_bitstrings(50_000_000, n_qubits) - -# We need to sort the bitstrings and just keep the unique ones -# NOTE: It is essential for the projection code to have the bitstrings sorted! -bts_matrix = sort_and_remove_duplicates(bts_matrix).astype("bool") - -# Final subspace dimension after getting rid of duplicated bitstrings -d = bts_matrix.shape[0] - -print("Total number of unique bitstrings: " + str(d)) -``` - -```python -Total number of unique bitstrings: 49998839 -``` - - - -## Benchmark SQD Pauli projection functions - -The Pauli string under consideration is $\sigma_z \otimes ... \otimes \sigma_z$. - -Different subspace dimensions are considered by just slicing the matrix of bitstrings. We time the subspace projection for the different subspace sizes. - -``` -[3]: -``` - -```python -import time - -from qiskit.quantum_info import Pauli -from qiskit_addon_sqd.qubit import matrix_elements_from_pauli - -pauli = Pauli("Z" * n_qubits) - -# Different subspace sizes to test -d_list = np.linspace(d / 1000, d, 20).astype("int") - -# To store the walltime -time_array = np.zeros(20) - -for i in range(20): - int_bts_matrix = bts_matrix[: d_list[i], :] - time_1 = time.time() - _ = matrix_elements_from_pauli(int_bts_matrix, pauli) - time_array[i] = time.time() - time_1 - print(f"Iteration {i} took {round(time_array[i], 6)}s") -``` - -```python -Iteration 0 took 0.201246s -Iteration 1 took 0.348222s -Iteration 2 took 0.576333s -Iteration 3 took 0.78356s -Iteration 4 took 1.016162s -Iteration 5 took 1.305325s -Iteration 6 took 1.392751s -Iteration 7 took 1.632433s -Iteration 8 took 1.826521s -Iteration 9 took 2.02903s -Iteration 10 took 2.297458s -Iteration 11 took 2.588042s -Iteration 12 took 2.738746s -Iteration 13 took 2.906144s -Iteration 14 took 3.148833s -Iteration 15 took 3.323253s -Iteration 16 took 3.664171s -Iteration 17 took 3.680663s -Iteration 18 took 4.008313s -Iteration 19 took 4.173532s -``` - -``` -[4]: -``` - -```python -import matplotlib.pyplot as plt - -# Data for energies plot -x1 = d_list -y1 = time_array - -# Plot energies -plt.title("Runtime vs subspace dimension 40 qubits") -plt.xlabel("Subspace dimension (millions)") -plt.ylabel("Wall time [s]") -plt.xticks([1e7, 2e7, 3e7, 4e7, 5e7], [str(i) for i in [10, 20, 30, 40, 50]]) -plt.plot(x1, y1, marker=".", markersize=20) -plt.tight_layout() -plt.show() -``` - -![../\_images/how\_tos\_benchmark\_pauli\_projection\_8\_0.png](/docs/images/api/qiskit-addon-sqd/how_tos_benchmark_pauli_projection_8_0.avif) - -Let’s do the same for 60 qubits - -``` -[5]: -``` - -```python -n_qubits = 60 -bts_matrix = random_bitstrings(50_000_000, n_qubits) - -# We need to sort the bitstrings and just keep the unique ones -bts_matrix = sort_and_remove_duplicates(bts_matrix).astype("bool") - -# Final subspace dimension after getting rid of duplicated bitstrings -d = bts_matrix.shape[0] - -print("Total number of unique bitstrings: " + str(d)) -``` - -```python -Total number of unique bitstrings: 50000000 -``` - -``` -[6]: -``` - -```python -pauli = Pauli("Z" * n_qubits) - -# Different subspace sizes to test -d_list = np.linspace(d / 1000, d, 20).astype("int") - -# It is better to do this once -row_array = np.arange(d) - -# To store the walltime -time_array = np.zeros(20) - -for i in range(20): - int_bts_matrix = bts_matrix[: d_list[i], :] - int_row_array = row_array[: d_list[i]] - time_1 = time.time() - _ = matrix_elements_from_pauli(int_bts_matrix, pauli) - time_array[i] = time.time() - time_1 - print(f"Iteration {i} took {round(time_array[i], 6)}s") -``` - -```python -Iteration 0 took 0.236567s -Iteration 1 took 0.424116s -Iteration 2 took 0.673399s -Iteration 3 took 0.905164s -Iteration 4 took 1.168936s -Iteration 5 took 1.454204s -Iteration 6 took 1.74778s -Iteration 7 took 1.920795s -Iteration 8 took 2.259994s -Iteration 9 took 2.550674s -Iteration 10 took 2.681287s -Iteration 11 took 3.04411s -Iteration 12 took 3.293262s -Iteration 13 took 3.471247s -Iteration 14 took 3.726639s -Iteration 15 took 4.072854s -Iteration 16 took 4.221037s -Iteration 17 took 4.498535s -Iteration 18 took 4.741108s -Iteration 19 took 5.159038s -``` - -``` -[7]: -``` - -```python -# Data for energies plot -x1 = d_list -y1 = time_array - -fig, axs = plt.subplots(1, 1, figsize=(6, 6)) - -# Plot energies -axs.plot(x1, y1, marker=".", markersize=20) -axs.set_title("Runtime vs subspace dimension 60 qubits") -axs.set_xlabel("Subspace dimension (millions)") -plt.xticks([1e7, 2e7, 3e7, 4e7, 5e7], [str(i) for i in [10, 20, 30, 40, 50]]) -axs.set_ylabel("Wall time [s]") - -plt.tight_layout() -plt.show() -``` - -![../\_images/how\_tos\_benchmark\_pauli\_projection\_12\_0.png](/docs/images/api/qiskit-addon-sqd/how_tos_benchmark_pauli_projection_12_0.avif) diff --git a/docs/addons/qiskit-addon-sqd/how_tos/benchmark_pauli_projection.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/benchmark_pauli_projection.ipynb deleted file mode 100644 index f78442d628f..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/benchmark_pauli_projection.ipynb +++ /dev/null @@ -1,473 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "55900f6c-2fb6-4d2c-8745-29bad8b66d9f", - "metadata": {}, - "source": [ - "# Benchmark Pauli operator projection" - ] - }, - { - "cell_type": "markdown", - "id": "b5caa5b4", - "metadata": {}, - "source": [ - "### Pauli string action on a computational basis state\n", - "\n", - "The action of a Pauli string on a computational basis state is rather trivial, and just a single computational basis state itself. This is a direct consequence of the structure of Pauli matrices, which only have a single nonzero element on each of their rows. Consequently, their action on a qubit is:\n", - "\n", - "-------------------------------------------\n", - "\n", - "$$\n", - "\\sigma_x |0 \\rangle = |1 \\rangle\n", - "$$\n", - "\n", - "$$\n", - "\\sigma_x |1 \\rangle = |0 \\rangle\n", - "$$\n", - "\n", - "-------------------------------------------\n", - "\n", - "$$\n", - "\\sigma_y |0 \\rangle = i|1 \\rangle\n", - "$$\n", - "\n", - "$$\n", - "\\sigma_y |1 \\rangle = -i|0 \\rangle\n", - "$$\n", - "\n", - "-------------------------------------------\n", - "\n", - "$$\n", - "\\sigma_z |0 \\rangle = |0 \\rangle\n", - "$$\n", - "\n", - "$$\n", - "\\sigma_z |1 \\rangle = -|1 \\rangle\n", - "$$\n", - "\n", - "-------------------------------------------\n", - "\n", - "$$\n", - "I |0 \\rangle = |0 \\rangle\n", - "$$\n", - "\n", - "$$\n", - "I |1 \\rangle = |1 \\rangle\n", - "$$\n", - "\n", - "-------------------------------------------\n", - "\n", - "Each bit on the bitstring labeling the computational basis will be labeled by $x \\in \\{0, 1 \\}$. In order to keep the implementation at light as possible, we will\n", - "represent the bitstrings with `bool` variables: $0\\rightarrow \\textrm{False}$ and\n", - "$1\\rightarrow \\textrm{True}$.\n", - "\n", - "To represent the action of each Pauli operator in a computational basis state\n", - "we will assign three variables to it: `diag`, `sign`, `imag`.\n", - "\n", - "- `diag` labels whether the operator is diagonal:\n", - "\n", - " - $\\textrm{diag}(I) = \\textrm{True}$\n", - " - $\\textrm{diag}(\\sigma_x) = \\textrm{False}$\n", - " - $\\textrm{diag}(\\sigma_y) = \\textrm{False}$\n", - " - $\\textrm{diag}(\\sigma_z) = \\textrm{True}$\n", - "\n", - "- `sign` Identifies if there is a sign change in the matrix element connected \n", - "to either 0 or 1:\n", - "\n", - " - $\\textrm{sign}(I) = \\textrm{False}$\n", - " - $\\textrm{sign}(\\sigma_x) = \\textrm{False}$\n", - " - $\\textrm{sign}(\\sigma_y) = \\textrm{True}$\n", - " - $\\textrm{sign}(\\sigma_z) = \\textrm{True}$\n", - "\n", - "- `imag` Identifies if there is a complex component to the matrix element:\n", - "\n", - " - $\\textrm{imag}(I) = \\textrm{False}$\n", - " - $\\textrm{imag}(\\sigma_x) = \\textrm{False}$\n", - " - $\\textrm{imag}(\\sigma_y) = \\textrm{True}$\n", - " - $\\textrm{imag}(\\sigma_z) = \\textrm{False}$\n", - "\n", - "Let's label an arbitrary Pauli operator as $\\sigma \\in \\{ I, \\sigma_x, \\sigma_y\n", - "\\sigma_z\\}$. The action of the Pauli operator on a computational basis state \n", - "can then be represented by the logic operation:\n", - "$$\n", - "\\sigma |x \\rangle = |x == \\textrm{diag}(\\sigma) \\rangle (-1)^{x\\textrm{ and sign}(\\sigma)}\n", - "(i)^{\\textrm{imag}(\\sigma)}.\n", - "$$\n", - "The same is straightforwardly generalized to arbitrary number of qubits." - ] - }, - { - "cell_type": "markdown", - "id": "a8fd7e11", - "metadata": {}, - "source": [ - "Let's check that this works:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "dcb15308", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-------------------\n", - "I\n", - "|False> --> |False> ME:(1+0j)\n", - "|True> --> |True> ME:(1+0j)\n", - "-------------------\n", - "SX\n", - "|False> --> |True> ME:(1+0j)\n", - "|True> --> |False> ME:(1+0j)\n", - "-------------------\n", - "SZ\n", - "|False> --> |False> ME:(1+0j)\n", - "|True> --> |True> ME:(-1+0j)\n", - "-------------------\n", - "SY\n", - "|False> --> |True> ME:1j\n", - "|True> --> |False> ME:(-0-1j)\n" - ] - } - ], - "source": [ - "def connected_element_and_amplitude_bool(\n", - " x: bool, diag: bool, sign: bool, imag: bool\n", - ") -> tuple[bool, complex]:\n", - " \"\"\"\n", - " Finds the connected element to computational basis state |x> under\n", - " the action of the Pauli operator represented by (diag, sign, imag).\n", - "\n", - " Args:\n", - " x: Value of the bit, either True or False.\n", - " diag: Whether the Pauli operator is diagonal (I, Z)\n", - " sigma: Whether the Pauli operator's rows differ in sign (Y, Z)\n", - " imag: Whether the Pauli operator is purely imaginary (Y)\n", - "\n", - " Returns:\n", - " A length-2 tuple:\n", - " - The connected element to x, either False or True\n", - " - The matrix element\n", - " \"\"\"\n", - " return x == diag, (-1) ** (x and sign) * (1j) ** (imag)\n", - "\n", - "\n", - "sigma_indices = [0, 1, 2, 3]\n", - "sigma_string = [\"I\", \"SX\", \"SZ\", \"SY\"]\n", - "sigma_diag = [True, False, True, False]\n", - "sigma_sign = [False, False, True, True]\n", - "sigma_imag = [False, False, False, True]\n", - "qubit_values = [False, True]\n", - "\n", - "for xi in sigma_indices:\n", - " print(\"-------------------\")\n", - " print(sigma_string[xi])\n", - " for x in qubit_values:\n", - " x_p, matrix_element = connected_element_and_amplitude_bool(\n", - " x, sigma_diag[xi], sigma_sign[xi], sigma_imag[xi]\n", - " )\n", - " print(\"|\" + str(x) + \"> --> |\" + str(x_p) + \"> ME:\" + str(matrix_element))" - ] - }, - { - "cell_type": "markdown", - "id": "f40a3463", - "metadata": {}, - "source": [ - "Let's generate some large number of bitstrings (50 M) for a 40-qubit system" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "74c16c91-cc5c-46ce-aff8-17d0e71ac50f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of unique bitstrings: 49998839\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from qiskit_addon_sqd.qubit import sort_and_remove_duplicates\n", - "\n", - "rand_seed = 22\n", - "np.random.seed(rand_seed)\n", - "\n", - "# Generate some random bitstrings for testing\n", - "\n", - "\n", - "def random_bitstrings(n_samples, n_qubits):\n", - " return np.round(np.random.rand(n_samples, n_qubits)).astype(\"int\").astype(\"bool\")\n", - "\n", - "\n", - "n_qubits = 40\n", - "bts_matrix = random_bitstrings(50_000_000, n_qubits)\n", - "\n", - "# We need to sort the bitstrings and just keep the unique ones\n", - "# NOTE: It is essential for the projection code to have the bitstrings sorted!\n", - "bts_matrix = sort_and_remove_duplicates(bts_matrix).astype(\"bool\")\n", - "\n", - "# Final subspace dimension after getting rid of duplicated bitstrings\n", - "d = bts_matrix.shape[0]\n", - "\n", - "print(\"Total number of unique bitstrings: \" + str(d))" - ] - }, - { - "cell_type": "markdown", - "id": "184bf287", - "metadata": {}, - "source": [ - "### Benchmark SQD Pauli projection functions\n", - "\n", - "The Pauli string under consideration is $\\sigma_z \\otimes ... \\otimes \\sigma_z$.\n", - "\n", - "Different subspace dimensions are considered by just slicing the matrix of bitstrings. We time the subspace projection for the different subspace sizes." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8fe182bc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 0 took 0.201246s\n", - "Iteration 1 took 0.348222s\n", - "Iteration 2 took 0.576333s\n", - "Iteration 3 took 0.78356s\n", - "Iteration 4 took 1.016162s\n", - "Iteration 5 took 1.305325s\n", - "Iteration 6 took 1.392751s\n", - "Iteration 7 took 1.632433s\n", - "Iteration 8 took 1.826521s\n", - "Iteration 9 took 2.02903s\n", - "Iteration 10 took 2.297458s\n", - "Iteration 11 took 2.588042s\n", - "Iteration 12 took 2.738746s\n", - "Iteration 13 took 2.906144s\n", - "Iteration 14 took 3.148833s\n", - "Iteration 15 took 3.323253s\n", - "Iteration 16 took 3.664171s\n", - "Iteration 17 took 3.680663s\n", - "Iteration 18 took 4.008313s\n", - "Iteration 19 took 4.173532s\n" - ] - } - ], - "source": [ - "import time\n", - "\n", - "from qiskit.quantum_info import Pauli\n", - "from qiskit_addon_sqd.qubit import matrix_elements_from_pauli\n", - "\n", - "pauli = Pauli(\"Z\" * n_qubits)\n", - "\n", - "# Different subspace sizes to test\n", - "d_list = np.linspace(d / 1000, d, 20).astype(\"int\")\n", - "\n", - "# To store the walltime\n", - "time_array = np.zeros(20)\n", - "\n", - "for i in range(20):\n", - " int_bts_matrix = bts_matrix[: d_list[i], :]\n", - " time_1 = time.time()\n", - " _ = matrix_elements_from_pauli(int_bts_matrix, pauli)\n", - " time_array[i] = time.time() - time_1\n", - " print(f\"Iteration {i} took {round(time_array[i], 6)}s\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9abb110f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Data for energies plot\n", - "x1 = d_list\n", - "y1 = time_array\n", - "\n", - "# Plot energies\n", - "plt.title(\"Runtime vs subspace dimension 40 qubits\")\n", - "plt.xlabel(\"Subspace dimension (millions)\")\n", - "plt.ylabel(\"Wall time [s]\")\n", - "plt.xticks([1e7, 2e7, 3e7, 4e7, 5e7], [str(i) for i in [10, 20, 30, 40, 50]])\n", - "plt.plot(x1, y1, marker=\".\", markersize=20)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "8c486bc7", - "metadata": {}, - "source": [ - "Let's do the same for 60 qubits" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "359ed3f3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of unique bitstrings: 50000000\n" - ] - } - ], - "source": [ - "n_qubits = 60\n", - "bts_matrix = random_bitstrings(50_000_000, n_qubits)\n", - "\n", - "# We need to sort the bitstrings and just keep the unique ones\n", - "bts_matrix = sort_and_remove_duplicates(bts_matrix).astype(\"bool\")\n", - "\n", - "# Final subspace dimension after getting rid of duplicated bitstrings\n", - "d = bts_matrix.shape[0]\n", - "\n", - "print(\"Total number of unique bitstrings: \" + str(d))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7bb0d8d8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 0 took 0.236567s\n", - "Iteration 1 took 0.424116s\n", - "Iteration 2 took 0.673399s\n", - "Iteration 3 took 0.905164s\n", - "Iteration 4 took 1.168936s\n", - "Iteration 5 took 1.454204s\n", - "Iteration 6 took 1.74778s\n", - "Iteration 7 took 1.920795s\n", - "Iteration 8 took 2.259994s\n", - "Iteration 9 took 2.550674s\n", - "Iteration 10 took 2.681287s\n", - "Iteration 11 took 3.04411s\n", - "Iteration 12 took 3.293262s\n", - "Iteration 13 took 3.471247s\n", - "Iteration 14 took 3.726639s\n", - "Iteration 15 took 4.072854s\n", - "Iteration 16 took 4.221037s\n", - "Iteration 17 took 4.498535s\n", - "Iteration 18 took 4.741108s\n", - "Iteration 19 took 5.159038s\n" - ] - } - ], - "source": [ - "pauli = Pauli(\"Z\" * n_qubits)\n", - "\n", - "# Different subspace sizes to test\n", - "d_list = np.linspace(d / 1000, d, 20).astype(\"int\")\n", - "\n", - "# It is better to do this once\n", - "row_array = np.arange(d)\n", - "\n", - "# To store the walltime\n", - "time_array = np.zeros(20)\n", - "\n", - "for i in range(20):\n", - " int_bts_matrix = bts_matrix[: d_list[i], :]\n", - " int_row_array = row_array[: d_list[i]]\n", - " time_1 = time.time()\n", - " _ = matrix_elements_from_pauli(int_bts_matrix, pauli)\n", - " time_array[i] = time.time() - time_1\n", - " print(f\"Iteration {i} took {round(time_array[i], 6)}s\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6b961c81", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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vvos9e/aUOimlNe1Km6SxtNffGnXq1MHhw4exadMm/PHHH/jjjz+wbNkyPP7441i+fLlN95WZmYk+ffpArVZj3rx5aNKkCby8vHDw4EG8/PLLVveUADAf+8ILL5Tam9C0aVOb6rNVaZ+p8j5rRbUvWrQIHTp0KPHYOz8H5d2nt7c3tm/fjq1bt2LDhg3YuHEj1qxZg/79++PPP/8stb2tKvI9WzRwPyAgoFj7OnXqACgMXmFhYahfvz6MRiNu3Lhh3gcAer0et27dQoMGDargWaBKvlcqMylqRX8OkPOwx4kqRSaTYf78+bhy5Qo++eQT8/bAwMASr0oq+su2Ihw5Y3OTJk2Qnp6OAQMGYODAgcW+rPmrdcyYMdi4cSOys7OxZs0aREREoFu3bsWO69atG95++20cOHAA3377LU6cOIHVq1eXe/9ltSv66//O96C0199kMhU7LZqUlAQAFqegFAoFhg0bhk8//RTnz5/H5MmT8c033+DcuXMWbYt6Uork5ubi6tWr5vvatm0bbt26hbi4OMycORP3338/Bg4cWOwCgyZNmgAAjh8/XurrUNRTJpfLS3yvBg4caHG67E7h4eG4evVqsau7zpw5U2qbqlL0/NRqdam1V2SuJQ8PDwwYMADvvfceTp48ibfffht///13qaeYw8PDAZT8nE+fPo3atWvDx8fH5jpKqqtDhw5IS0sr1pt05coVAEBwcDAAmIPkgQMHLI47cOAATCZTqUGz6D68vb2LfQ6B4s/R1u8VoPjn+9y5czCZTGXOz1Tez6+K/hwgx2Nwokrr27cvunTpgg8++MB8SW6TJk1w+vRppKWlmY87cuRIuVf2lKXoB3dJgayqjRkzBv/88w+++OKLYvu0Wm2x0xYlGTt2LHQ6HZYvX46NGzcWm+MqIyOj2F/nRb8Myuqmt6ZdeHg4ZDIZtm/fbnHcp59+Wur93h58hRD45JNPIJfLMWDAAACF41Ju5+HhgXbt2pVY7+eff24xXuOzzz5DQUEBhgwZAuD/eytufx56vb5YfR07dkSjRo3wwQcfFHvfi9rWqVMHffv2xdKlS3H16tViz+v2z2BJ7rvvPhQUFOCzzz4zbzMajQ6ZoToyMhJNmjTB//73vxIvyy+v9pKkp6cX21be56p+/fro0KEDli9fbvE6Hz9+HH/++Sfuu+8+m+sozdixY2E0Gi16KfPz8/Htt9+iVatW5p6k/v37IygoyOJ9AQo/SyqVCkOHDi31MWQyGQYPHoyffvrJfOoPKLwKdNOmTRbHqtVq1K5d26bvlaKpUYoUfVaKPt8lKe3nV0V/DpDz8FQdVYkXX3wRo0ePRlxcHKZMmYIJEybgvffew+DBgzFx4kTcuHEDS5YsQevWrcscPFqWyMhIAMCMGTMwePBgyGQyPPzww1X5NMwee+wxfP/995gyZQq2bt2KqKgoGI1GnD59Gt9//z02bdpU7DLpO3Xs2BFNmzbFa6+9Bp1OZ3GaDgCWL1+OTz/9FA8++CCaNGmCnJwcfPHFF1Cr1WX+orKmnb+/P0aPHo2PP/4YkiShSZMm+O2330od6+Pl5YWNGzciOjoaXbt2xR9//IENGzbg1VdfNfcATJo0Cenp6ejfvz9CQkKQkpKCjz/+GB06dDBfXl1Er9djwIABGDNmDM6cOYNPP/0UPXv2xAMPPAAA6NGjBwIDAxEdHY0ZM2ZAkiSsWLGi2C8QDw8PfPbZZxg2bBg6dOiA2NhY1K9fH6dPn8aJEyfMvwQXL16Mnj17om3btnjiiSfQuHFjXL9+Hbt370ZqaiqOHDlS6us5bNgwREVF4ZVXXkFycjJatWqF9evXFxtbZA8eHh748ssvMWTIELRu3RqxsbFo2LAh/vnnH2zduhVqtRq//vqrTfc5b948bN++HUOHDkV4eDhu3LiBTz/9FCEhIeYLE0qyaNEiDBkyBN27d8fEiRPN0xH4+/tX6VJHkydPxpdffolp06YhKSkJYWFhWLFiBVJSUiyeq7e3N958801MmzYNo0ePxuDBg7Fjxw6sXLkSb7/9tsUg9pLMnTsXGzduRK9evfDUU0+hoKAAH3/8MVq3bo2jR49aHDtp0iQsWLAAkyZNQqdOnbB9+3Zzj2tJLl68iAceeAD33nsvdu/ejZUrV+KRRx5B+/btS23ToUMHyGQyvPPOO8jKyoJSqUT//v2xatWqCv0cICdywpV85KaKLjUv6bJZo9EomjRpIpo0aWK+XH3lypWicePGQqFQiA4dOohNmzaVOh3BokWLit0n7rgUuKCgQDz99NMiODhYSJJkcUnxnccWXTZ85yXZ0dHRwsfHp9hj9enTR7Ru3dpim16vF++8845o3bq1UCqVIjAwUERGRoq5c+eKrKysMl+rIq+99poAIJo2bVps38GDB8W4ceNEWFiYUCqVok6dOuL+++8XBw4cKPM+rW2XlpYmRo0aJVQqlQgMDBSTJ08Wx48fL3E6Ah8fH3H+/Hlxzz33CJVKJerWrStmz54tjEaj+bi1a9eKe+65R9SpU0coFAoRFhYmJk+eLK5evWo+pugzEh8fL5588kkRGBgofH19xaOPPmpxmbsQQiQkJIhu3boJb29v0aBBA/HSSy+JTZs2lXjJ9s6dO8WgQYOEn5+f8PHxEe3atRMff/yxxTHnz58Xjz/+uKhXr56Qy+WiYcOG4v777xdr164t8/UUQohbt26Jxx57TKjVauHv7y8ee+wxcejQIaunI5g2bZrFttI+10XTRPzwww8W2w8dOiRGjhwpatWqJZRKpQgPDxdjxowRW7ZsKfbYd36mi17zoikOtmzZIoYPHy4aNGggFAqFaNCggRg3bpxISkoqVt+dl+D/9ddfIioqSnh7ewu1Wi2GDRsmTp48aXGMtXWU5fr16yI6OloEBQUJpVIpunbtKjZu3FjisZ9//rlo0aKFUCgUokmTJuL999+3mLqhLPHx8SIyMlIoFArRuHFjsWTJkhLfQ41GIyZOnCj8/f2Fn5+fGDNmjLhx40apP1dOnjwpHnroIeHn5ycCAwPF9OnThVartbjPO6cjEEKIL774QjRu3Ng8JcLWrVsr/HOAnEcSopKjb4mI/hUXF4fY2Fjs37+/3B45ImeYM2cO5s6dW+kLT6jm4hgnIiIiIisxOBERERFZicGJiIiIyEoc40RERERkJfY4EREREVmJwYmIiIjISm49AabJZMKVK1fg5+fn0OU4iIiIqPoQQiAnJwcNGjSAh0fZfUpuHZyuXLlS7mKrRERERNa4fPkyQkJCyjzGrYNT0cKdly9fhlqtdnI1RERE5I6ys7MRGhpa5oLgRdw6OBWdnlOr1QxOREREVCnWDPvh4HAiIiIiKzE4EREREVmJwYmIiIjISgxORERERFZicCIiIiKyEoMTERERkZUYnIiIiIisxOBEREREZCUGJyIiIiIrMTgRERERWYnBiYiIiMhKDE5EREREVnLrRX6JiIioehNCIENjQJ6uAD5KTwSq5FYtxmsvDE5ERETkcrK0BqxLTMXyXclISdeYt4cHqRDdIwKjIkPg7y13eF2SEEI4/FGrSHZ2Nvz9/ZGVlQW1Wu3scoiIiKgKxCelYerKRGj1RgDA7UGlqK/JWyHDZ+Mj0ad5cKUfz5Y8wTFORERE5DLik9IQu2wftAYjBCxDE/69LQBoDUbELtuH+KQ0h9bH4EREREQuIUtrwNSViYXhqJzzYUIUBqipKxORpTU4ojwADE5ERETkItYlpkKrN5YbmooIAWj1Rqw/mGrfwm7D4EREREROJ4TA8l3JFWobl5AMRw3ZZnAiIiIip8vQGJCSrik2pqk8AkBKugaZGsecrmNwIiIiIqfL0xVUqn1uJdtbi8GJiIiInM5HWbmpJX0r2d5aDE5ERETkdIEqOcKDVLB1TnAJhZNiBqgcMxkmgxMRERE5nSRJiO4RUaG2MVERDluGhcGJiIiIXMKoyBB4K2RW9zp5SIUziI/sGGLXuiwe02GPRERERFQGf285PhrXwaor64o6mJaMj3TomnVc5JeIiIhcxtHUbPP/i3qeSlyrTi7DkvGR6F0Fa9XZgsGJiIiIXMKpq9n4dOs5AMDCh9oiT2dEXEIyUtI15mPCglSIiYrAqMgQqL0c19NUhMGJiIiInK7AaMJLa4+iwCQwuHVdjI4MhSRJiOkRgUyNAbm6AvgqPRGgkjtsIHhJGJyIiIjI6b7YcRHH/smC2ssTbw5vYw5HkiQh0EeBQB+FkyssxMHhRERE5FTn03Lx/l9JAIDX72+FOmovJ1dUOqcGpzlz5kCSJIuvu+66y5klERERkQOZTAIvrz0KfYEJvZsH46FIx00tUBFOP1XXunVr/PXXX+bbnp5OL4mIiIgc5JvdyTiQkgEfhQz/fbCNU8cvWcPpKcXT0xP16tVzdhlERETkYJfTNVi46QwA4JUhdyEkUOXkisrn9DFOZ8+eRYMGDdC4cWM8+uijuHTpUqnH6nQ6ZGdnW3wRERGR+xFCYNb6Y9DojejSKAiPdg13dklWcWpw6tq1K+Li4rBx40Z89tlnuHjxInr16oWcnJwSj58/fz78/f3NX6GhoQ6umIiIiKrC9wcuY+e5m1B6euCdUe3g4eHap+iKSEIIa2Y2d4jMzEyEh4fjvffew8SJE4vt1+l00Ol05tvZ2dkIDQ1FVlYW1Gq1I0slIiKiCrqenY+B78UjJ78Ar953F57s3cSp9WRnZ8Pf39+qPOH0MU63CwgIQPPmzXHu3LkS9yuVSiiVSgdXRURERFVFCIHXfjyGnPwCtA/xx4SoRs4uySZOH+N0u9zcXJw/fx7169d3dilERERkB78cuYK/Tt2AXCZh4UPt4SlzqShSLqdW+8ILLyA+Ph7JycnYtWsXHnzwQchkMowbN86ZZREREZEd3MrVYe6vJwEA0/s1Q4t6fk6uyHZOPVWXmpqKcePG4datWwgODkbPnj2xZ88eBAc7dqVjIiIisr85v55Eep4ed9Xzw9S+zh3XVFFODU6rV6925sMTERGRg/x54hp+PXIFMg8Jix5qD4Wne52iK+KeVRMREZHbyNIa8J+fjgMAnujVGG1D/J1cUcUxOBEREZFdvb3hJG7k6NC4tg+eGdjM2eVUCoMTERER2c32pDR8fyAVkgQsfKgdvOQyZ5dUKQxOREREZBd5ugLMWn8MABDdPQKdIoKcXFHlMTgRERGRXSzceBr/ZGoREuiNFwe3cHY5VYLBiYiIiKrcvovpWL47BQCwYGQ7+ChdarGSCmNwIiIioiqVbzDi5XVHAQBjO4WiZ7PaTq6o6jA4ERERUZV6/68kXLyZh7pqJV4d2tLZ5VQpBiciIiKqMkcuZ+KL7RcAAG+NaAt/b7mTK6pa1eOEIxERETmUEAIZGgPydAXwUXoiUCWHwSjw8rqjMAnggfYNMKhVXWeXWeUYnIiIiMhqWVoD1iWmYvmuZKSka8zbw4NUiKitwulrOQjyUWD2sFZOrNJ+GJyIiIjIKvFJaZi6MhFavbHYvkvpGnOQGts5FLV8lY4uzyE4xomIiIjKFZ+Uhthl+6A1GCEAiDv23357afx5xCelObA6x2FwIiIiojJlaQ2YujKxMDDdmZhKIABMXZmILK3B3qU5HIMTERERlWldYiq0eqNVoQkoDFdavRHrD6batzAnYHAiIiKiUgkhsHxXcoXaxiUkQ1ibttwEgxMRERGVKkNjQEq6ptiYpvIIACnpGmRqqtfpOgYnIiIiKlWerqBS7XMr2d7VMDgRERFRqSq7OK9vNVnctwiDExEREZUqUCVHeJAKko3tJBROihmgql5LrjA4ERERUakkSUJ0j4gKtY2JioAk2Rq5XFv16j8jIiKiKtchNMCm4z0kwEsuw8iOIfYpyInY40RERESl2nvhFmKW7TNfVVde/1FRB9OS8ZHw965ep+kABiciIiIqxYajV/HYV/uQnV+AyPBALH7kbngrZJBQPEAVbfOWyxAX2wW9mwc7vmAH4Kk6IiIiKuarnRfx1oaTEAIY3LouPnz4bnjJZejZLBjrD6YiLiHZvKgvAIQFqRATFYFRkSFQe1W/nqYiknDjKT2zs7Ph7++PrKwsqNVqZ5dDRETk9kwmgfl/nMIXOy4CAB7vHo7Zw1pD5mHZxySEQKbGgFxdAXyVnghQyd12ILgteYI9TkRERAQA0BUY8cIPR/HrkSsAgJfvvQtT+jQuMRBJkoRAHwUCfRSOLtOpGJyIiIgIWVoDJq84gD0X0uHpIWHR6HZ48O7qd1VcZTE4ERER1XBXs7SI+Xo/zlzPga/SE0vGR6Jns9rOLsslMTgRERHVYGeu5SBm2T5czcpHHT8llsV2RusG/s4uy2UxOBEREdVQu8/fwpMrDiAnvwBNgn2wfEIXhASqnF2WS2NwIiIiqoF+PXIFz39/BHqjCZ0jAvHF450QoKpZA70rgsGJiIiomhFCIENjQJ6uAD5KTwTeMVXAlzsu4K0NpwAAQ9rUw/tjO8BLLnNWuW6FwYmIiKiayNIasC4xFct3WU5OGR6kQnSPCIy8uyE++vscvk4onKMppkcEXr+/VbE5mqh0nACTiIioGohPSsPUlYnQ6o0AgNt/uRfFIg8PCUZT4Z5ZQ+7Ck71LnqOppuEEmERERDVIfFIaYv9diLek3pCibUWhaUqfxpjcp4mjyqtWuMgvERGRG8vSGjB1ZWJhaLLiHJIE4JvdKcjSGuxdWrXE4EREROTG1iWmQqs3WhWagMLeJ63eiPUHU+1aV3XF4EREROSmhBBYviu5Qm3jEpLhxsOcnYbBiYiIyE1laAxISdeUOK6pLAJASroGmRqerrMVgxMREZGbytMVVKp9biXb10QMTkRERG7KR1m5i+N9K9m+JmJwIiIiclOBKjnCg1SwdSYmCYWTYgao5PYoq1pjcCIiInJTkiQhukdEhdrGREVw8ssKYB8dERGRG2sS7GPT4HAPCfCSyzCyY4jdaqrO2ONERETkpg5eysC0VYfMt8vrQCrav2R8JPy9eZquIhiciIiI3NCRy5mI/mofcnUF6NY4CF88FglvuQwSUGzMU9E2b7kMcbFd0Lt5sOMLriZ4qo6IiMjNHEvNwmNf7UWOrgBdIoLwdUxnqBSe2D1rANYfTEVcQjJS0jXm48OCVIiJisCoyBCovdjTVBmScONpQ21ZzZiIiKg6OHElC498sRdZWgM6hQcibkKXYtMKCCGQqTEgV1cAX6UnAlRyDgQvgy15gj1OREREbuLU1WyM/7IwNN0dFoBlsZ1LnItJkiQE+igQ6KNwQpXVG8c4ERERuYEz13Lw6Jd7kaExoH2IP5ZP6AI/nnZzOAYnIiIiF3fuRg4e/XIP0vP0aNvQH99M7MqxSk7C4EREROTCzqflYtwXe3EzV49W9dVYMbELpxJwIgYnIiIiF3XxZh7Gfb4HaTk63FXPD99O6ooAFcctORODExERkQtKuVUYmm7k6NC8ri++ndSVg71dAIMTERGRi7mcrsG4z/fgWnY+mtbxxbeTuqGWr9LZZREYnIiIiFxKaoYG477YgytZ+Wgc7INVT3RFsB9Dk6tgcCIiInIRVzK1eOSLvUjN0KJRbR9890Q31PHzcnZZdBtOgElEROQgQghkaAzI0xXAR+mJwNtm9L6WlY9HvtiDS+kahAWpsOqJrqirZmhyNQxOREREdpalNWBdYiqW77JcQy48SIXoHhHo07w2nvgmEcm3NAgJ9MZ3T3ZDfX9vJ1ZMpeFadURERHYUn5SGqSsTodUbAQC3/9K9ffU4AaBhgDdWP9kNoUEqR5ZY43GtOiIiIhcQn5SG2GX7IGAZmIrcue3Zgc0YmlwcB4cTERHZQZbWgKkrEwtDkxXndiQAb/xyAllag71Lo0pgcCIiIrKDdYmp0OqNVoUmoLD3Sas3Yv3BVLvWRZXD4ERERFTFhBBYviu5Qm3jEpLhxsOPqz0GJyIioiqWoTEgJV1T4rimsggAKekaZGp4us5VMTgRERFVsTxdQaXa51ayPdkPgxMREVEV81FW7qJ130q2J/thcCIiIqpiF9JyoPS0/VeshMJJMQNU8qoviqoEIy0REVEVOXcjF4s2ncamE9crfB8xURHmZVjI9TA4ERERVdKN7Hy8/9dZfH/gMowmAQ8JGN6+ITaeuIb8AuumJPCQAC+5DCM7hti/YKowBiciIiKUvQBvaXLyDfh8+wV8ueMitIbCJVUGtqyLl+9tgWZ1/TDi35nDIZU9CWbRwywZHwl/b56mc2UMTkREVKOVtwDvqMiQYmFGX2DCqr0p+Ojvc0jP0wMA7g4LwKwhLdGlUZD5uD7Ng7Estku5a9V5y2VYMj4SvZsH2+U5UtXhIr9ERFRjWbMAr7dChs/GR6JP82CYTAK/HbuK/206g0v/hqzGtX3w0r0tMLh1vVJ7qLK0Bqw/mIq4hOLhLCaqMJypvdjT5Cy25AkGJyIiqpEsFuAt5zSaBODFwS3w+7FrOPZPFgCgtq8SzwxshrGdQyGXWXcFnRACmRoDcnUF8FV6IsCK04Fkf7bkCZ6qIyKiGseWBXiFKOyJemfjGQCAj0KGJ3s3waRejWyer0mSJAT6KBDoo6hY4eR0DE5ERFTjmBfgtbFdl4hAfDo+ErV9lXapi1wfJ8AkIqIapaIL8EoArmfrUIu9RTUagxMREdUoXICXKoPBiYiIahQuwEuVweBEREQ1ChfgpcpgcCIioholUCVHeJAKtk4CwAV4CXCh4LRgwQJIkoRnnnnG2aUQEVE1JkkSontEVKgtF+AllwhO+/fvx9KlS9GuXTtnl0JERDXAqMgQeCtkVh/vIRXOIM4FeMnpwSk3NxePPvoovvjiCwQGBjq7HCIiqgH8veWY3LuxVcdyAV66ndOD07Rp0zB06FAMHDiw3GN1Oh2ys7MtvoiIiGyVfDMPX+68CADw9JAgAcXGPBVt85bLEBfbhQvwEgAnzxy+evVqHDx4EPv377fq+Pnz52Pu3Ll2roqIiKozjb4Ak1ckIie/AB3DAvD5Y53w69ErxRbgDeMCvFQCpy3ye/nyZXTq1AmbN282j23q27cvOnTogA8++KDENjqdDjqdznw7OzsboaGhXOSXiIisIoTAjNWH8euRK6jtq8SGGT1RV+1l3scFeGsmt1jkNzExETdu3EDHjh3N24xGI7Zv345PPvkEOp0OMpnlwD2lUgmlkusDERFRxXy18yJ+PXIFnh4SPn20ozk0AVyAl6zjtOA0YMAAHDt2zGJbbGws7rrrLrz88svFQhMREVFl7D5/C/P/OA0A+M/QlujSKMjJFZE7clpw8vPzQ5s2bSy2+fj4oFatWsW2ExERVcbVLC2mrzoIo0ngwbsbVngeJyKnX1VHRERkT7oCI6asPIhbeXq0rK/Gfx9sy7FLVGEuteDOtm3bnF0CERFVM3N+OYkjlzPh7y3H0vGRNk18SXQn9jgREVG1tXrfJXy37xIkCfjw4Q4Iq6Vydknk5hiciIioWjp8ORNv/HwCAPD8oObo26KOkyui6oDBiYiIqp2buTpMXZkIvdGEQa3q4qm+TZ1dElUTDE5ERFStFBhNeHrVIVzNykfj2j54d0x7eHhwMDhVDQYnIiKqVhZuOoPdF25BpZBh6WORXC6FqhSDExERVRu/Hb2Cz7dfAAD8b3R7NKvr5+SKqLphcCIiomrhzLUcvLT2KABgcp/GuK9tfSdXRNURgxMREbm9LK0BU1YmQqM3IqppLbx4Twtnl0TVFIMTERG5NZNJ4PnvD+PizTw0DPDGx+M6wlPGX29kH/xkERGRW/tk6zn8deoGFJ4eWDI+EkE+CmeXRNUYgxMREbmtradv4P2/kgAAb49og7Yh/k6uiKo7l1qrjoiIqCRCCGRoDMjTFcBH6YlAlRyX0jWYufoQhADGdwvD6E6hzi6TagAGJyIicllZWgPWJaZi+a5kpKRrzNtDg7yhM5iQnV+Au8MC8Mb9rZ1YJdUkDE5EROSS4pPSMHVlIrR6Y7F9l9O15v9Hd4+AwpMjT8gx+EkjIiKXE5+Uhthl+6A1GCEAiFKOkwA89/1hxCelObA6qskYnIiIyKVkaQ2YujKxMDCVlpj+VRSqpq5MRJbW4IDqqKZjcCIiIpeyLjEVWr2x3NBURAhAqzdi/cFU+xZGBAYnIiJyIUIILN+VXKG2cQnJENamLaIKYnAiIiKXkaExICVdU+qYptIIACnpGmRqeLqO7IvBiYiIXEaerqBS7XMr2Z6oPAxORETkMnyUlZslx7eS7YnKw+BEREQuI1AlR3iQCpKN7SQA4UEqBKjk9iiLyIzBiYiIXIYkSRjWvoHNY5wAICYqApJka+Qisg37NImIyGWcupqNVfsu2dTGQwK85DKM7Bhip6qI/h97nIiIyCUkpmRg7NLdSM/TIzRIBQ8JKK8DqWj/kvGR8PfmaTqyPwYnIiJyuh1n0zD+y73Izi9Ap/BA/PZ0TyyL7QJvuQwSUGzMU9E2b7kMcbFd0Lt5sOOLphqJp+qIiMip/jh2FTNWH4LBKNC7eTCWjO8IlcITfZoHY/esAVh/MBVxCclISdeY24QFqRATFYFRkSFQe7GniRxHEm48zWp2djb8/f2RlZUFtVrt7HKIiMhG3x+4jFfWHYVJAEPb1sf7YztA4Vn8ZIgQApkaA3J1BfBVeiJAJedAcKoytuQJ9jgREZFTfLXzIt787SQAYGynUPx3ZFvIPEoOQ5IkIdBHgUAfhSNLJCqGwYmIiBxKCIH3/zqLj7acBQA82bsxZg25iz1I5BYYnIiIyGFMJoF5v51E3L8L+b44uAWe6tuEoYncBoMTERE5RIHRhJfWHcX6g/8AAOYNb43Hu0c4tygiGzE4ERGR3eUbjJjx3SH8efI6ZB4S/je6HR68mxNWkvthcCIiIrvK1RXgyW8OYNf5W1B4emDxIx0xqFVdZ5dFVCEMTkREVGFCCGRoDMjTFcBH6YnAO6YJyNToEb1sP45czoSPQoYvojuhR5PaTqyYqHIYnIiIyGZZWgPWJaZi+S7LiSnDg1SI7lE4MaXOYMRjX+3Dmes5CFDJsTy2C9qHBjivaKIqwAkwiYjIJvFJaZi6MhFavREAcPsvkaK+Ji+5B3yVcqTl6lDHT4mVk7qieV0/h9dKZA1OgElERHYRn5SG2GX7IGAZmIoUbdMaTNAadAj2VWLtlB4Iq6VyYJVE9sNFfomIyCpZWgOmrkwsDE1WnqvI1RXAX8W15Kj6YHAiIiKrrEtMhVZvtDo0AYXTEKw/mGq/oogcjMGJiIjKJYTA8n9n+7ZVXEIy3Hg4LZEFBiciIipXhsaAlHRNieOayiIApKRrkKkx2KMsIodjcCIionLl6Qoq1T63ku2JXAWDExERlctHWbmLsH0r2Z7IVTA4ERFRuQJVcoQHqSCVf6gFCYWTYgbwyjqqJhiciIioXCYBtKjnZ/MYJwCIiYqwWIaFyJ2x75SIiMp09noOXlp3FIcuZdrUzkMCvOQyjOwYYp/CiJyAPU5ERFQig9GEj7acxdCPduLQpUz4KT0RGxUBDwkorwOpaP+S8ZHw9+ZpOqo+2ONERETFHEvNwotrj+D0tRwAwIC76uCtB9ugvr83+raoU+5add5yGZaMj0Tv5sGOLZzIzhiciIjILN9gxAd/ncUXOy7AaBIIVMkx54HWeKB9A/M4pT7Ng7F71gCsP5iKuIRkpKRrzO3DglSIiYrAqMgQqL3Y00TVjyTceDpXW1YzJiKisu27mI5X1h3FhZt5AIBh7Rtg9rBWqO2rLLWNEAKZGgNydQXwVXoiQCXnQHByO7bkCfY4ERFVU0IIZGgMyNMVwEfpicBSQk2urgALN57GN7tTAAB1/JR4+8G2GNSqbrmPIUkSAn0UCPRRVHn9RK6IwYmIqJrJ0hqwLjEVy3dZnkYLD1IhukfhabSiAdvbk9Iwa/0x/JOpBQCM7RSKV4e25IBuolLwVB0RUTUSn5RW/sBthQz/e6g9/j5zA2sTUwEAIYHeWDCyHXo2q+3YgolcAE/VERHVQPFJaYhdtg8CKHGiyqJtWr0RT606CKBw2oCYHhF44Z4WlV5Whagm4HcJEVE1kKU1YOrKxMLQVM55hKLdEoDlsZ3Ru3kdO1dHVH1wAkwiompgXWIqtHpjuaHpTufT8uxTEFE1xeBEROTmhBBYviu5Qm3jEpLhxkNdiRyOwYmIyM1laAxISdfYvACvAJCSrkGmxmCPsoiqJQYnIiI3l6crqFT73Eq2J6pJGJyIiNxcZa+G8+XVdERWY3AiInJzgSo5woNUsHWhEwmFk2IGqDjZJZG1GJyIiNycJEl4rHu4zWOcACAmKoJryxHZgP2zRERuLt9gxI6zN21q4yEBXnIZRnYMsVNVRNUTgxMRkRvL0hrwxPID2JecDrmHBKMQ5U6CWdTBtGR8JNekI7IRgxMRkZu6np2P6K/34fS1HPh5eeLLxzshv8BU/lp1chmWjI9E7+bBDq+ZyN0xOBERuaGLN/Pw2Fd7kZqhRbCfEt9M6IKW9QsXJ909awDWH0xFXEIyUtI15jZhQSrEREVgVGQI1F7saSKqCEm48ZSxtqxmTERUXRxLzULMsn24ladHRC0VVkzsitAgVbHjhBDI1BiQqyuAr9ITASo5B4ITlcCWPMEeJyIiN7Lr3E088c0B5OmNaNNQjbjYLqjtqyzxWEmSEOijQKCPwsFVElVfDE5ERG7i92NX8czqw9AbTejRpBaWPhYJP55yI3IoBiciIjewYk8K3vj5OIQA7mtbD++P7QClp8zZZRHVOAxOREQuTAiBD7ecxQd/nQUAjO8WhrkPtIHMg2OViJzBquD0yy+/2HzHgwYNgre3t83tiIiokNEkMOeXE1ixJwUAMHNAMzwzsBkHeBM5kVXBacSIETbdqSRJOHv2LBo3blyRmoiIajxdgRHPrTmCDceuQpKAeQ+0xmPdI5xdFlGNZ/WpumvXrqFOnTpWHevn51fhgoiIqjshBDI0BuTpCuCj9ETgHdME5OoKMHnFASScuwW5TML7Yzvg/nYNnFgxERWxKjhFR0fbdNpt/PjxnFeJiOgOWVoD1iWmYvkuy4kpw4NUiO5RODGlwWhC7LL9OPZPFnwUMix9rBN6NqvtxKqJ6HacAJOIyAHik9LKXQrFS+4Btbcc17N1qOWjwLLYzmgXEuDoUolqHFvyhEdVPNhPP/2EU6dOVfauiIiqpfikNMQu2wetwVi4AO8d+4u2aQ0mc2j6YUp3hiYiF2RzcBozZgw++eQTAIBWq0WnTp0wZswYtGvXDuvWravyAomI3FmW1oCpKxMLw5GV/fsavRG1SpkNnIicy+bgtH37dvTq1QsA8OOPPxauhZSZiY8++ghvvfVWlRdIROTO1iWmQqs3Wh2aACDfYMT6g6n2K4qIKszm4JSVlYWgoCAAwMaNGzFq1CioVCoMHToUZ8+erfICiYjclRACy3clV6htXEIy3HgIKlG1ZXNwCg0Nxe7du5GXl4eNGzfinnvuAQBkZGTAy8urygskInJXGRoDUtI1xcY0lUcASEnXIFNjsEdZRFQJNgenZ555Bo8++ihCQkLQoEED9O3bF0DhKby2bdvadF+fffYZ2rVrB7VaDbVaje7du+OPP/6wtSQiIpeUpyuoVPvcSrYnoqpn81p1Tz31FLp27YpLly5h0KBB8PAozF6NGze2eYxTSEgIFixYgGbNmhV2aS9fjuHDh+PQoUNo3bq1raUREbkUH2XllgP1rWR7Iqp6LjePU1BQEBYtWoSJEyeWeyzncSIiVyaEQN9F23DJxtN1EoCwIBW2vdiX69IROUCVz+P03HPPIS8vz+oCZs2ahfT0dKuPBwCj0YjVq1cjLy8P3bt3L/EYnU6H7Oxsiy8iIlclSRKie0RUqG1MVARDE5ELsio4ffjhh9BoNOUf+K/FixcjMzPTqmOPHTsGX19fKJVKTJkyBT/++CNatWpV4rHz58+Hv7+/+Ss0NNTqmoiInGFUZAi8FTJYG4E8JMBbIcPIjiF2rYuIKsaqU3UeHh7w9/e3+q+frKwsnD17Fo0bNy73WL1ej0uXLiErKwtr167Fl19+ifj4+BLDk06ng06nM9/Ozs5GaGgoT9URkUv7+fA/mLn6cLnHSVLhabq42C7o3TzY7nURUSFbTtVZNfJw2bJlNhdRt25dq45TKBRo2rQpACAyMhL79+/Hhx9+iKVLlxY7VqlUQqnkbLpE5D6MJoHV+y4DKAxGRYOdSlqrzlsuw5LxkQxNRC7MquAUHR1t7zrMTCaTRa8SEZE7+/CvJOy+cAsqhQyrJnXDocsZiEtIRkr6/w9/CAtSISYqAqMiQ6D2kjuxWiIqj1OvdZ01axaGDBmCsLAw5OTkYNWqVdi2bRs2bdrkzLKIiKrEjrNp+HjrOQDA/JFt0SEsAB3CAhDTIwKZGgNydQXwVXoiQCXnQHAiN+HU4HTjxg08/vjjuHr1Kvz9/dGuXTts2rQJgwYNcmZZRESVdj07H8+sPgwhgHFdwjC8Q0PzPkmSEOijQKCPwokVElFFODU4ffXVV858eCIiuygwmvD0d4dwK0+PlvXVmD2s5CuFicj92LzkChERle39v5Kw72I6fBQyLH7kbnjJZc4uiYiqSIWD07lz57Bp0yZotVoA4CreREQA4pPSsHjreQDAglHt0DjY18kVEVFVsjk43bp1CwMHDkTz5s1x33334erVqwCAiRMn4vnnn6/yAomI3MXVLC2eXXMYADC+WxiGtW/g3IKIqMrZHJyeffZZeHp64tKlS1CpVObtY8eOxcaNG6u0OCIid1FgNGHGd4eQnqdH6wZq/GcoxzURVUc2Dw7/888/sWnTJoSEWC4H0KxZM6SkpFRZYURE7uR/fyZhf3IGfJWeWPxIR45rIqqmbO5xysvLs+hpKpKens5ZvYmoRtp6+gaWxBeOa1r4UDtE1PZxckVEZC82B6devXrhm2++Md+WJAkmkwkLFy5Ev379qrQ4IiJXdyVTi2e/PwwAiO4ejvva1nduQURkVzafqlu4cCEGDBiAAwcOQK/X46WXXsKJEyeQnp6OhIQEe9RIROSSDEYTpq86iEyNAW0b+uPVoS2dXRIR2ZnNPU5t2rRBUlISevbsieHDhyMvLw8jR47EoUOH0KRJE3vUSETkkhZtOoODlzLh51U4rknpyXFNRNVdhWYO9/f3x2uvvVbVtRARuY2/Tl7H59svAAAWPdQeYbWKj/0kouqnQsEpPz8fR48exY0bN2AymSz2PfDAA1VSGBGRq0rN0OD5H44AAGKjInBvm3pOroiIHMXm4LRx40Y8/vjjuHnzZrF9kiTBaDRWSWFERK5IX2DC9FWHkKU1oH2IP2YN4bgmoprE5jFOTz/9NEaPHo2rV6/CZDJZfDE0EVF1987G0zh8ORNqL0988khHKDy55CdRTWLzd/z169fx3HPPoW7duvaoh4jIZf154hq+2nkRAPC/0e0RGsRxTUQ1jc3B6aGHHsK2bdvsUAoRkeu6nK7BC/+Oa5rUsxHuac1xTUQ1kc1jnD755BOMHj0aO3bsQNu2bSGXyy32z5gxo8qKIyJyNCEEMjQG5OkK4KP0RKBKDoNRYPqqg8jOL0CH0AC8dO9dzi6TiJzE5uD03Xff4c8//4SXlxe2bdsGSZLM+yRJYnAiIreUpTVgXWIqlu9KRkq6xrw9PEiFumovHEnNgr+3HJ88cjfHNRHVYDYHp9deew1z587FK6+8Ag8P/vAgIvcXn5SGqSsTodUXv8AlJV1jDlIToiIQEshxTUQ1mc3JR6/XY+zYsQxNRFQtxCelIXbZPmgNRggAooxjP9xyFvFJaY4qjYhckM3pJzo6GmvWrLFHLUREDpWlNWDqysTCwFRWYvqXADB1ZSKytAZ7l0ZELsrmU3VGoxELFy7Epk2b0K5du2KDw997770qK46IyJ7WJaZCqzeW2ct0OyEArd6I9QdTERvVyK61EZFrsjk4HTt2DHfffTcA4Pjx4xb7bh8oTkTkyoQQWL4ruUJt4xKSEdMjgj/ziGogm4PT1q1b7VEHEZFDZWgMFlfPWUugcMB4psaAQB9F1RdGRC6NI7yJqEbK0xVUqn1uJdsTkXuyqsdp5MiRiIuLg1qtxsiRI8s8dv369VVSGBGRPfkobe5wt+BbyfZE5J6s+s739/c3n8tXq9U8r09Ebi35Zh6WxJ+rUFsJQFiQCgEqebnHElH1Y1VwWrZsmfn/cXFx9qqFiMiuTl/Lxqdbz+O3o1dgsvZSuhLERHFgOFFNZfMYp/79+yMzM7PY9uzsbPTv378qaiIiKkYIgfQ8PS6na5Cep4ewZuKlfx2+nIlJyw/g3g924JcjhaGpb4tgxMV2hkohg7UZyEMCvBUyjOwYUsFnQUTuzuaT9Nu2bYNery+2PT8/Hzt27KiSooiIipS1hlx0jwiMigyBv3fx02ZCCOw+fwuLt51DwrlbAABJAoa0qYen+jZFm4b+AIDPxkcidtk+QCp7EsyicLVkfGSJj0dENYPVweno0aPm/588eRLXrl0z3zYajdi4cSMaNmxYtdURUY1W1hpyl9I1ePO3k/jfn2fw2fhI9GkeDKAwMG05dQOLt53DoUuZAACZh4QRHRpiat8maFrH1+J++jQPxrLYLhaPc3t+KuqM8pbLsGR8JHr/+zhEVDNJwsr+bg8PD/M5/ZKaeHt74+OPP8aECROqtsIyZGdnw9/fH1lZWVCr1Q57XCKyv6I15MpbDkWSCsPNV9GdkaMrwKdbz+H0tRwAgMLTAw93DsUTvRojNKjsxXmztAasP5iKuITiPVsxUYU9W2ov9jQRVUe25Amrg1NKSgqEEGjcuDH27duH4OD//6tLoVCgTp06kMlklavcRgxORNVTltaA7vO3FC68a8VPqKJeoaJDfRQyjO8ejok9G6GOn5dNjy2EQKbGgFxdAXyVnghQyTkQnKiasyVPWH2qLjw8HABgMpkqVx0RUTlsXkPu33+95R6Y0qcpYnpEwL+C0wVIkoRAHwVnBSeiEnEGNyJyKZVZQ662rxIzBjRlDxER2Q2XXCEil1K0hlxFplm6nKFFpsZQ5TURERVhcCIil8I15IjIlTE4EZFL4RpyROTKGJyIyKUEquQID1LB1lFKEgqnDuAackRkT1b9aRYYGGj1YMv09PRKFURENZskSYjuEYE3fztpc1uuIUdE9mZVcPrggw/sXAYR0f8bFRmChZtOI99g3fQnHhLgJecackRkf1YFp+joaHvXQURklqUxQOkpsyo4cQ05InIkq4JTdna21XfIGbyJqDKuZeXj0a/2IEtrQEiAN27l6ZFv4BpyROQarApOAQEB5Y4bEEJAkiQYjcUX4yQiskZ6nh7jv9qLy+lahNdS4YfJ3aGUy0pcQy6Ma8gRkRNYFZy2bt1q7zqIqIbLyTcg+ut9OHcjF/XUXlg5sSvqqAvXmYuNaoSYHhFcQ46InM6q4NSnTx9710FENZhWb8TE5Qdw7J8sBPkosHJSV4QGqSyO4RpyROQKKjxTnEajwaVLl6DX6y22t2vXrtJFEVHNoS8wYeq3idh3MR1+Sk98M6ELmtbxdXZZREQlsjk4paWlITY2Fn/88UeJ+znGiYisZTQJPPv9YWw7kwYvuQe+ju2MNg39nV0WEVGpbJ45/JlnnkFmZib27t0Lb29vbNy4EcuXL0ezZs3wyy+/2KNGIqqGhBB47cdj2HD0KuQyCUsf64TOEUHOLouIqEw29zj9/fff+Pnnn9GpUyd4eHggPDwcgwYNglqtxvz58zF06FB71ElE1YgQAm9vOIXV+y/DQwI+fPhu9OF0AkTkBmzuccrLy0OdOnUAFC7FkpaWBgBo27YtDh48WLXVEVG19PHf5/DlzosAgAWj2uG+tvWdXBERkXVsDk4tWrTAmTNnAADt27fH0qVL8c8//2DJkiWoX58//IiobMsSLuK9zUkAgDfub4UxnUKdXBERkfVsPlU3c+ZMXL16FQAwe/Zs3Hvvvfj222+hUCgQFxdX1fURUTXyw4HLmPtr4eK9zw5sjgk9Gzm5IiIi20hCCFH+YcDFixfRqFHxH3IajQanT59GWFgYateuXeUFliU7Oxv+/v7IysriUi9ELu6PY1cxbdVBmAQwsWcj/GdoS05gSUQuwZY8YXWPU5MmTRAeHo5+/fqhf//+6Nu3L0JCQqBSqdCxY8dKF01E1Vd8UhpmrD4EkwDGdgplaCIit2V1cPr777+xbds2bNu2Dd999x30ej0aN26M/v37o1+/fujXrx/q1q1rz1qJyEUJIZChMSBPVwAfpScCb1sO5UByOiavOACDUWBo2/r478i2DE1E5LasPlV3u/z8fOzatcscpPbt2weDwYC77roLJ06csEedJeKpOiLnytIasC4xFct3WS7AGx6kQnSPCLRqoMYTyw8gR1eAPs2D8cXjnaDwtPmaFCIiu7IlT1QoOBXR6/VISEjAH3/8gaVLlyI3N9ehM4czOBE5T3xSGqauTIRWX/g9f/sPkqL+pKJtXSKCsHxCF3grZI4skYjIKnYZ4wQUBqU9e/Zg69at2LZtG/bu3YvQ0FD07t0bn3zyCRcDJqoh4pPSELtsHwQsA1ORO7fF9oxgaCKiasHq4NS/f3/s3bsXjRo1Qp8+fTB58mSsWrWKczcR1TBZWgOmrkwsDE1W9FdLAJ7//gh6NKkNf2+5vcsjIrIrqwcb7NixA7Vq1UL//v0xYMAADBo0iKGJqAZal5gKrd5oVWgCCnuftHoj1h9MtWtdRESOYHVwyszMxOeffw6VSoV33nkHDRo0QNu2bTF9+nSsXbvWvPQKEVVfQggs35VcobZxCcmoxJBKIiKXUOHB4Tk5Odi5c6d5vNORI0fQrFkzHD9+vKprLBUHhxM5VnqeHh3f3Fzh9odeH4RAH0UVVkREVHm25IkKXxfs4+ODoKAgBAUFITAwEJ6enjh16lRF746I3ECerqBS7XMr2Z6IyNmsHhxuMplw4MABbNu2DVu3bkVCQgLy8vLQsGFD9OvXD4sXL0a/fv3sWSsROZmP0ublLS34VrI9EZGzWf1TLCAgAHl5eahXrx769euH999/H3379kWTJk3sWR8RuZBAlRzhQSpcSteUOA1BaSQAYUEqBKh4VR0RuTerg9OiRYvQr18/NG/e3J71EJELkyQJ0T0i8OZvJ21uGxMVwaVWiMjtWT3GafLkyQxNRIRRkSHwsmEySw8J8FbIMLJjiB2rIiJyDC4aRUQ2UXt5onN4oFXHFnUwLRkfyckviahaYHAiIpus2JOC7WdvQgKg9PSAhP9fm65I0TZvuQxxsV3Qu3mww+skIrIHXuJCRFbbc+EW5v1aOL5p1n13YWznMKw/mIq4hGSkpGvMx4UFqRATFYFRkSFQe7GniYiqDwYnIrLKP5laTPv2IApMAsM7NMATvRpDkiTERjVCTI8IZGoMyNUVwFfpiQCVnAPBiahaYnAionJp9UZMXnEAt/L0aN1AjQUj21kEI0mSEOij4KzgRFTtcYwTEZVJCIFZ64/i+D/ZCPJRYOljkfC24ao6IqLqhMGJiMr01c6L+OnwFcg8JCx+pCNCAlXOLomIyGkYnIioVDvP3sR/fy9cg/L1oS3RvUktJ1dERORcDE5EVKJLtzSY/t1BmAQwOjIE0T0inF0SEZHTMTgRUTF5ugI8ueIAMjUGtA8NwJsj2vAqOSIiMDgR0R2EEHhx7RGcvpaD2r5KLB0fCS85B4MTEQEMTkR0h0+3ncfvx65BLpOwZHxH1PP3cnZJREQug8GJiMy2nr6B//15BgAw94E26BQR5OSKiIhcC4MTEQEALqTlYsbqQxACeLRrGB7pGubskoiIXA6DExEhJ9+AJ1ckIie/AJ3CAzF7WGtnl0RE5JKcGpzmz5+Pzp07w8/PD3Xq1MGIESNw5swZZ5ZEVOOYTALPrjmCczdyUU/thU/Hd4TCk39TERGVxKk/HePj4zFt2jTs2bMHmzdvhsFgwD333IO8vDxnlkVUo3y45Sz+OnUdCk8PLH0sEnX8OBiciKg0Tl3kd+PGjRa34+LiUKdOHSQmJqJ3795Oqoqo5th04ho+3HIWAPDfB9uifWiAcwsiInJxLtUfn5WVBQAICuKVPET2dvZ6Dp5bcxgAEBsVgYciQ5xbEBGRG3Bqj9PtTCYTnnnmGURFRaFNmzYlHqPT6aDT6cy3s7OzHVUekVsSQiBDY0CergA+Sk8EquSQJAlZWgOe+OYA8vRGdG9cC6/e19LZpRIRuQWXCU7Tpk3D8ePHsXPnzlKPmT9/PubOnevAqojcU5bWgHWJqVi+Kxkp6Rrz9vAgFR7rHo6tp28g+ZYGDQO88ckjd0Muc6nOZyIilyUJIYSzi5g+fTp+/vlnbN++HY0aNSr1uJJ6nEJDQ5GVlQW1Wu2IUolcXnxSGqauTIRWbwQA3P4NLt12Wy6T8ONTUWjT0N/RJRIRuZTs7Gz4+/tblSec2uMkhMDTTz+NH3/8Edu2bSszNAGAUqmEUql0UHVE7ic+KQ2xy/ZBwDIwFbl9W4FJ4Fae3kGVERFVD07tn582bRpWrlyJVatWwc/PD9euXcO1a9eg1WqdWRaRW8rSGjB1ZWJhaLKyH3nqykRkaQ12rYuIqDpxanD67LPPkJWVhb59+6J+/frmrzVr1jizLCK3tC4xFVq90erQJASg1Rux/mCqfQsjIqpGnH6qjogqTwiB5buSK9Q2LiEZMT0iIElS1RZFRFQN8VIaomogQ2NASrqmxHFNZREAUtI1yNTwdB0RkTUYnIiqgTxdQaXa51ayPRFRTcHgRFQN+Cgrd9bdt5LtiYhqCgYnomogUCVHaKC3ze0kFE6KGaCSV31RRETVEIMTUTWw7UwasvMrdrotJooDw4mIrMX+eSI3diMnH/N+PYnfjl4FUNiDBJQ8+eWdPCTASy7DyI5c3JeIyFoMTkRuSAiB7w9cxtsbTiE7vwAeEjCxZyN0jgjClJWJ/x5TevuiDqYl4yPh783TdERE1mJwInIz59Ny8er6Y9h7MR0A0KahGgtGtjOvObcstkuZa9UBgLdchiXjI9G7ebADKycicn8MTkRuQl9gwpL48/jk73PQG03wlsvw/D3NEdMjAp6y/x+u2Kd5MHbPGoD1B1MRl5CMlHSNeV9YkAoxUREYFRkCtRd7moiIbCUJN56+25bVjIlcjRACGRoD8nQF8FF6IlAlL3WQ9oHkdMxafwxnb+QCKAxHb41og9AgVbmPkakxIFdXAF+lJwLKeAwioprKljzBHiciB8vSGrAuMRXLd1n2BoUHqRDdo7A3qGjcUXa+Ae/8cRrf7r0EAKjtq8Abw1pjWLv6VgUgSZIQ6KNAoI/CPk+GiKiGYY8TkQPFJ6WVP/5IIcOnj3ZEvsGIN34+gRs5OgDAmE4hePW+lghQMQQREVUl9jgRuaD4pDTELtsHgZKnCyjaptUbEbNsv3l7o9o+ePvBNujRpLYjyiQiojIwOBE5QJbWgKkrEwtDUzl9vLfvfrJXIzx3Twt4yWX2LI+IiKzE4ETkAOsSU6HVG62amLKIBKB+gDdDExGRC+GSK0R2JoTA8l3JFWobl5AMNx6GSERU7TA4EdlZhsaAlHSNTb1NQOEpu5R0DTI1BnuURUREFcDgRGRnebqKLb5bJLeS7YmIqOowOBHZmY+yckMJfSvZnoiIqg6DE5GdBarkCA9Swdb5uiUUTooZoOLSKEREroLBicjOJElCdI+ICrWNiYrgEilERC6EwYnIAUZ1bAiZh/UByEMqnEF8ZMcQO1ZFRES2YnAicoAvd15Eganwurry4lNRB9OS8ZHmNeuIiMg1MDgR2dmXOy7g47/PAQAe7x4Ob4UMEooHqKJt3nIZ4mK7oHfzYAdXSkRE5eHlOkR29P2By3hrwykAwIuDW2Bav6Z4/p4WWH8wFXEJyUhJ15iPDQtSISYqAqMiQ6D2Yk8TEZErkoQbT0tsy2rGRI628fg1PPVtIkwCeKJXI7x6X0uLgd5CCGRqDMjVFcBX6YkAlZwDwYmInMCWPMEeJyI72Hn2JmZ8dwgmAYzpFFIsNAGFV9sF+igQ6KNwUpVERGQrjnEiqmKHLmXgyRUHoDeaMKRNPcwf2Y49SURE1QSDE1EVOnMtBzHL9kOjN6Jn09r44OEONk1DQEREro3BiaiKXE7X4LGv9iJLa0CH0AAsfSwSSk+Zs8siIqIqxOBEVAVuZOfj0S/34kaODi3q+iEutnOl16gjIiLXw+BEVEmZGj0e/3ofLqVrEBakwoqJXRCg4oBvIqLqiMGJqBI0+gLExu3H6Ws5qOOnxMqJXVFH7eXssoiIyE4YnIgqSFdgxOQViTh0KRP+3nKsmNgVYbVUzi6LiIjsiMGJqAKMJoFn1xzGjrM3oVLIsCy2M1rU83N2WUREZGcMTkQ2EkLg1fXH8Puxa1DIPPD5Y53QMSzQ2WUREZED8LIfohIIIZChMSBPVwAfpScC/10ORQiB+X+cxpoDl+EhAR+N64CezWo7u1wiInIQBiei22RpDViXmIrluywX4A0PUiG6RwQytXp8vv0CAGDByHa4t019Z5VKREROwOBE9K/4pDRMXZkIrd5YbN+ldA3m/XbSfPu1+1piTOdQR5ZHREQugMGJCIWhKXbZPggAooT9t2+TADTnQHAiohqJg8OpxsvSGjB1ZWJhaCopNd1JAqauTESW1mDv0oiIyMUwOFGNty4xFVq90brQhMJwpdUbsf5gqn0LIyIil8PgRDWaEALLdyVXqG1cQjKEtWmLiIiqBQYnqtEyNAakpGtKHNdUFgEgJV2DTA1P1xER1SQMTlSj5ekKKtU+t5LtiYjIvTA4UY3mo6zchaW+lWxPRETuhcGJarRAlRzhQSpINraTUDgpZoBKbo+yiIjIRTE4UY0mSRKie0RUqG1MVAQkydbIRURE7ozBiWq8wa3rwpb84yEB3goZRnYMsV9RRETkkhicqEbT6Avw7JojMP17WV15+akoYC0ZHwl/b56mIyKqaRicqMbKNxgxeUUi9iWnw0/pibnDWsNbIYOE4gGqaJu3XIa42C7o3TzY8QUTEZHT8ZIgqpEMRhOmrzqIHWdvQqWQIW5CZ0SGB2FEx4ZYfzAVcQnJSEnXmI8PC1IhJioCoyJDoPZiTxMRUU0lCTee+jg7Oxv+/v7IysqCWq12djnkJowmgZmrD+G3o1eh8PRAXExn9Gha2+IYIQQyNQbk6grgq/REgErOgeBERNWULXmCPU5Uo5hMAi+vO4rfjl6FXCZh6fjIYqEJKLzaLtBHgUAfhROqJCIiV8UxTlRjCCEw+5cTWJuYCpmHhI/H3Y1+d9VxdllERORGGJyoRhBCYP4fp7FiTwokCXh3dHvc26a+s8siIiI3w+BENcIHf53F59svAAD++2BbjLi7oZMrIiIid8TgRNXe0vjz+HDLWQDAG/e3wrguYU6uiIiI3BWDE1Vr3+xOxvw/TgMAXhzcAhN6NnJyRURE5M4YnKja+v7AZbzx8wkAwLR+TTCtX1MnV0RERO6OwYmqpV+PXMEr644CACZENcIL97RwckVERFQdMDhRtfPniWt4ds1hmAQwrksYXr+/JSevJCKiKsHgRNVKfFIapq86hAKTwIN3N8TbI9owNBERUZXhzOHkVoQQyNAYkKcrgI/SE4G3LYWy98ItTF5xAHqjCUPa1MOih9rBw4OhiYiIqg6DE7mFLK0B6xJTsXyX5eK74UEqRPeIQPO6vpi8IhH5BhP6tQjGhw/fDU8ZO1SJiKhqMTiRy4tPSsPUlYnQ6o3F9l1K1+DN306iaKXqHk1q4bPxkVB4MjQREVHVY3AilxaflIbYZfsgAHM4ut2d26J7RMBLLnNAZUREVBPxz3JyWVlaA6auTCwMTSWlpjtIAJ5dcxhZWoO9SyMiohqKwYlc1rrEVGj1RqtCE1DY+6TVG7H+YKpd6yIiopqLwYlckhACy3clV6htXEIyhLVpi4iIyAYMTuSSMjQGpKRrShzXVBYBICVdg0wNT9cREVHVY3Ail5SnK6hU+9xKticiIioJgxO5JB9l5S749K1keyIiopIwOJFLClTJER6kgq3zfksonBQzQCW3R1lERFTDMTiRS5IkCdE9IirUNiYqguvTERGRXfB8Brms1g3VNg0O95AAL7kMIzuG2K0mIiKq2djjRC7p8OVMPLH8gPl2ef1HRR1MS8ZHwt+bp+mIiMg+GJzI5exPTsf4L/ciO78AkeGB+Gx8R3grZJBQPEAVbfOWyxAX2wW9mwc7vmAiIqoxeKqOXErCuZuYtPwAtAYjujUOwlfRneGj9ESPJrWx/mAq4hKSkZKuMR8fFqRCTFQERkWGQO3FniYiIrIvSbjxFMvZ2dnw9/dHVlYW1Gq1s8uhStp65gYmr0iEvsCE3s2DsXR8JLwVlgv2CiGQqTEgV1cAX6UnAlRyDgQnIqJKsSVPsMeJXMKmE9cwfdVBGIwCA1vWxeJH74bSU1bsOEmSEOijQKCPwglVEhFRTcfgRE7365EreGbNYRhNAkPb1scHD3eAXMbhd0RE5Hqc+ttp+/btGDZsGBo0aABJkvDTTz85sxxygrWJqZi5+hCMJoGRdzfEhwxNRETkwpz6GyovLw/t27fH4sWLnVkGOcmqvZfwwg9HYBLAw51D8b/R7eHJ0ERERC7MqafqhgwZgiFDhjizBHKSr3dexLzfTgIAoruHY/aw1vDw4CBvIiJybRzjRA736bZzWLjxDABgcu/GeGXIXbwyjoiI3IJbBSedTgedTme+nZ2d7cRqyFZCCHzw11l8uOUsAGDGgGZ4dmAzhiYiInIbbjWgZP78+fD39zd/hYaGOrskspIQAgs2njaHppfubYHnBjVnaCIiIrfiVsFp1qxZyMrKMn9dvnzZ2SXRbYQQSM/T43K6Bul5ehTNrSqEwNxfT2Jp/AUAwOv3t8JTfZs6s1QiIqIKcatTdUqlEkql0tll0B2ytAasS0zF8l2Wy6GEB6nwePdwnLyag3UHUwEAb41og/Hdwp1VKhERUaU4NTjl5ubi3Llz5tsXL17E4cOHERQUhLCwMCdWRtaKT0rD1JWJ0OqNxfZdStfgzQ2nABQuxLvwoXYY3YmnV4mIyH05NTgdOHAA/fr1M99+7rnnAADR0dGIi4tzUlVkrfikNMQu2wcBoKQFDy22SUAdtZdjCiMiIrITLvJLFZKlNaD7/C3QGoyw5hMkSYC3XIbdswbA31tu/wKJiIisZEuecKvB4eQ61iWmQqu3LjQBgBCAVm/E+n/HOhEREbkjBieymRACy3clV6htXEIy3LiTk4iIajgGJ7JZhsaAlHRNieOayiIApKRrkKkx2KMsIiIiu2NwIpvl6Qoq1T63ku2JiIichcGJbOajrNzFmL6VbE9EROQsDE5ks0CVHGFBKpvbSSicFDNAxavqiIjIPTE4kc0MRoHACoafmKgIrk9HRERui+dMyCYZeXpMXpmII6lZAAp7kawZJO4hAV5yGUZ2DLFrfURERPbEHiey2oW0XDz4aQL2XUyHr9ITz9/THJJUOLllWYr2LxkfyckviYjIrbHHiayy+/wtTFmZiCytAQ0DvPF1TGe0qOeHdiEBFmvV3d77VJSnvOUyLBkfid7Ngx1eNxERUVVicKJyfb//Ml798RgKTAIdQgPwxeOdEOynBAD0aR6M3bMGYP3BVMQlJCMlXWNuFxakQkxUBEZFhkDtxZ4mIiJyf1yrjkplMgm8s+k0lsZfAADc364+/je6PbzkshKPF0IgU2NArq4AvkpPBKjkHAhOREQuz5Y8wR4nKpFWb8Szaw5j44lrAIAZ/ZvimYHN4eFRehCSJAmBPgoE+igcVSYREZFDMThRMdez8zFp+QEc+ycLCpkH3nmoLR68m1fDERERMTiRhRNXsjBp+QFczcpHoEqOzx/vhM4RQc4ui4iIyCUwOJHZXyevY8bqQ9DojWgS7IOvYzojvJaPs8siIiJyGQxONYAQAhkaA/J0BfBReiLwjkHbQgh8tfMi3v79FIQAoprWwqePcs4lIiKiOzE4VWNZWgPWJaZi+S7LaQLCg1SI7lE4TYBKIcPsX05g1d5LAIBxXcIwb3hryGWcG5WIiOhOnI6gmopPSit3YkovuQxNgn1w/Eo2JAl47b6WmNizEacQICKiGoXTEdRw8UlpiF22DwIlryNXtE1rMOL4lWwoPT3wySMdMahVXQdWSURE5H54PqaaydIaMHVlYmFosrIvUZKALo145RwREVF5GJyqmXWJqdDqjVaHJgDQGUxYfzDVfkURERFVEwxO1YgQAst3JVeobVxCMtx4uBsREZFDMDhVIxkaA1LSNSWOayqLAJCSrkGmxmCPsoiIiKoNBqdqJE9XUKn2uZVsT0REVN0xOFUjPsrKXSTpW8n2RERE1R2DUzUSqJIjPEgFW2dhklA4KWaAijOFExERlYXBqRqRJAnRPSIq1DYmKoITXxIREZWD52aqmaZ1fG0aHO4hFc4gPrJjiN1qIiIiqi7Y41SNbD1zA5NXJJpvl9d/VNTBtGQ8F/QlIiKyBoNTNfHToX/wxPID0BqM6NM8GJ8/FglvhQwSigeoom3echniYrugd/NgxxdMRETkhniqrhr4audFvPnbSQDAiA4NsGh0e8hlHtg9awDWH0xFXEIyUtI15uPDglSIiYrAqMgQqL3Y00RERGQtSbjxdNG2rGZcHQkhsHDTGXy27TwAYEJUI/xnaEt4eEjFjsvUGJCrK4Cv0hMBKjkHghMREf3LljzBHic3VWA04dUfj+H7A4VrzL10bwtM7dOkxEAkSRICfRQI9FE4ukwiIqJqhcHJDeUbjJi+6hD+OnUdHhLw3wfb4uEuYc4ui4iIqNpjcHIzWVoDnlh+APuS06Hw9MDH4+7G4Nb1nF0WERFRjcDg5EZuZOfj8a/34fS1HPgpPfFFdCd0a1zL2WURERHVGAxObuLizTw8/vVeXE7XIthPieWxXdCqQc0bEE9ERORMDE5u4Pg/WYhZtg83c/UIr6XCigldEVZL5eyyiIiIahwGJycTQiBDY0CergA+Sk8E3jFVwK5zN/HkikTk6grQuoEacbFdEOyndGLFRERENReDk5NkaQ1Yl5iK5bssJ6cMD1Ihukfh5JS7zt3EzNWHoTea0L1xLXz+eCT8OGElERGR03ACTCeIT0rD1JWJ0OqNAGCxKG9RX5Nc5gG90QQAuLd1PXzwcAd4yWWOLZSIiKgG4ASYLiw+KQ2xy/ZBwDIwFSnaVhSa+rYIxuJHO0LmwZm+iYiInI2L/DpQltaAqSsTC0OTlf18+y6mI1dXYNe6iIiIyDoMTg60LjEVWr3R6tAEAFq9EesPptqvKCIiIrIag5ODCCGwfFdyhdrGJSTDjYeiERERVRsMTg6SoTEgJV1T4rimsggAKekaZGoM9iiLiIiIbMDg5CB5lRynxHFOREREzsfg5CA+yspdwOhbyfZERERUeQxODhKokiM8yPZlUiQUTooZoOLEl0RERM7G4OQg2fkFUHtXLPzEREVYLMNCREREzsHzPw6w8+xNvLj2CK5m5dvUzkMCvOQyjOwYYqfKiIiIyBbscbIjrd6I2T8fx/iv9uJqVj4a1fbBf4a2hIcElNeBVLR/yfhI+Fewp4qIiIiqFnuc7OTQpQw8//0RXLiZBwB4vHs4XhlyF1QKTzSr61fuWnXechmWjI9E7+bBji2ciIiISsXgVMX0BSZ8tOUsPt12DiYB1FN7YeFD7SwCUJ/mwdg9awDWH0xFXEIyUtI15n1hQSrEREVgVGQI1F7saSIiInIlknDjKaltWc3YEc5cy8Gzaw7j5NVsAMCIDg0w94E28C/jijghBDI1BuTqCuCr9ESASs6B4ERERA5kS55gj1MVMJoEvtxxAe/+mQS90YRAlRxvP9gW97WtX25bSZIQ6KNAoI/CAZUSERFRZTA4lUIIgQyNAXm6AvgoPRFYSk/QpVsaPP/DYexPzgAADLirDuaPaos6fl6OLpmIiIjsjMHpDllaA9YlpmL5LsuxR+FBKkT3KBx75O8thxAC3+27jLc2nIRGb4SPQobZw1pjdKcQnmojIiKqpjjG6TbxSWnlX+2mkGH+yLb46dA/2HomDQDQpVEQ3h3dHqEVmBmciIiInItjnCogPikNscv2QcAyMBUp2qbVGzFz9WEAgMLTAy8NboEJUY3g4cFeJiIiouqOwQmFp+emrkwsDE3l9L8V7faQgDVPdMPd4YH2Lo+IiIhcBGcOB7AuMRVavbHc0HQ7IYDDqZl2q4mIiIhcT40PTkIILN+VXKG2cQnJcOMhYkRERGSjGh+cMjQGpKRrShzXVBYBICVdg0yNwR5lERERkQuq8cEpT1dQqfa5lWxPRERE7qPGBycfZeXGx/tWsj0RERG5jxofnAJVcoQHqWDrZAISCifFDChjHToiIiKqXmp8cJIkCdE9IirUNiYqgrOEExER1SA1PjgBwKjIEHgrZLA2A3lIhTOIj+wYYt/CiIiIyKUwOAHw95bjs/GRkIByw1PR/iXjI+HvzdN0RERENQmD07/6NA/Gstgu8JbLCgPUHfuLtnnLZYiL7YLezYMdXyQRERE5FS8Ju02f5sHYPWsA1h9MRVxCMlLSNeZ9YUEqxERFYFRkCNRe7GkiIiKqiSThxlNf27Kasa2EEMjUGJCrK4Cv0hMBKjkHghMREVVDtuQJ9jiVQpIkBPooEOijcHYpRERE5CI4xomIiIjISgxORERERFZicCIiIiKyEoMTERERkZUYnIiIiIisxOBEREREZCUGJyIiIiIruURwWrx4MSIiIuDl5YWuXbti3759zi6JiIiIqBinB6c1a9bgueeew+zZs3Hw4EG0b98egwcPxo0bN5xdGhEREZEFpwen9957D0888QRiY2PRqlUrLFmyBCqVCl9//bWzSyMiIiKy4NTgpNfrkZiYiIEDB5q3eXh4YODAgdi9e7cTKyMiIiIqzqlr1d28eRNGoxF169a12F63bl2cPn262PE6nQ46nc58Ozs72+41EhERERVxq0V+58+fj7lz5xbbzgBFREREFVWUI4QQ5R7r1OBUu3ZtyGQyXL9+3WL79evXUa9evWLHz5o1C88995z59j///INWrVohNDTU7rUSERFR9ZaTkwN/f/8yj3FqcFIoFIiMjMSWLVswYsQIAIDJZMKWLVswffr0YscrlUoolUrzbV9fX1y+fBl+fn6QJMlRZZMDZWdnIzQ0FJcvX4ZarXZ2OTUOX3/n43vgfHwPnM/e74EQAjk5OWjQoEG5xzr9VN1zzz2H6OhodOrUCV26dMEHH3yAvLw8xMbGltvWw8MDISEhDqiSnE2tVvMHlhPx9Xc+vgfOx/fA+ez5HpTX01TE6cFp7NixSEtLwxtvvIFr166hQ4cO2LhxY7EB40RERETO5vTgBADTp08v8dQcERERkStx+gSYRGVRKpWYPXu2xdg2chy+/s7H98D5+B44nyu9B5Kw5to7IiIiImKPExEREZG1GJyIiIiIrMTgRERERGQlBidyuu3bt2PYsGFo0KABJEnCTz/9ZLFfCIE33ngD9evXh7e3NwYOHIizZ886p9hqav78+ejcuTP8/PxQp04djBgxAmfOnLE4Jj8/H9OmTUOtWrXg6+uLUaNGFZv1nyrus88+Q7t27czz1HTv3h1//PGHeT9ff8dasGABJEnCM888Y97G98C+5syZA0mSLL7uuusu835Xef0ZnMjp8vLy0L59eyxevLjE/QsXLsRHH32EJUuWYO/evfDx8cHgwYORn5/v4Eqrr/j4eEybNg179uzB5s2bYTAYcM899yAvL898zLPPPotff/0VP/zwA+Lj43HlyhWMHDnSiVVXLyEhIViwYAESExNx4MAB9O/fH8OHD8eJEycA8PV3pP3792Pp0qVo166dxXa+B/bXunVrXL161fy1c+dO8z6Xef0FkQsBIH788UfzbZPJJOrVqycWLVpk3paZmSmUSqX47rvvnFBhzXDjxg0BQMTHxwshCl9zuVwufvjhB/Mxp06dEgDE7t27nVVmtRcYGCi+/PJLvv4OlJOTI5o1ayY2b94s+vTpI2bOnCmE4PeAI8yePVu0b9++xH2u9Pqzx4lc2sWLF3Ht2jUMHDjQvM3f3x9du3bF7t27nVhZ9ZaVlQUACAoKAgAkJibCYDBYvA933XUXwsLC+D7YgdFoxOrVq5GXl4fu3bvz9XegadOmYejQoRavNcDvAUc5e/YsGjRogMaNG+PRRx/FpUuXALjW6+8SM4cTlebatWsAUGwJnrp165r3UdUymUx45plnEBUVhTZt2gAofB8UCgUCAgIsjuX7ULWOHTuG7t27Iz8/H76+vvjxxx/RqlUrHD58mK+/A6xevRoHDx7E/v37i+3j94D9de3aFXFxcWjRogWuXr2KuXPnolevXjh+/LhLvf4MTkRkYdq0aTh+/LjF2AJyjBYtWuDw4cPIysrC2rVrER0djfj4eGeXVSNcvnwZM2fOxObNm+Hl5eXscmqkIUOGmP/frl07dO3aFeHh4fj+++/h7e3txMos8VQdubR69eoBQLErJ65fv27eR1Vn+vTp+O2337B161aEhISYt9erVw96vR6ZmZkWx/N9qFoKhQJNmzZFZGQk5s+fj/bt2+PDDz/k6+8AiYmJuHHjBjp27AhPT094enoiPj4eH330ETw9PVG3bl2+Bw4WEBCA5s2b49y5cy71PcDgRC6tUaNGqFevHrZs2WLelp2djb1796J79+5OrKx6EUJg+vTp+PHHH/H333+jUaNGFvsjIyMhl8st3oczZ87g0qVLfB/syGQyQafT8fV3gAEDBuDYsWM4fPiw+atTp0549NFHzf/ne+BYubm5OH/+POrXr+9S3wM8VUdOl5ubi3PnzplvX7x4EYcPH0ZQUBDCwsLwzDPP4K233kKzZs3QqFEjvP7662jQoAFGjBjhvKKrmWnTpmHVqlX4+eef4efnZx4z4O/vD29vb/j7+2PixIl47rnnEBQUBLVajaeffhrdu3dHt27dnFx99TBr1iwMGTIEYWFhyMnJwapVq7Bt2zZs2rSJr78D+Pn5mcf0FfHx8UGtWrXM2/ke2NcLL7yAYcOGITw8HFeuXMHs2bMhk8kwbtw41/oecOg1fEQl2Lp1qwBQ7Cs6OloIUTglweuvvy7q1q0rlEqlGDBggDhz5oxzi65mSnr9AYhly5aZj9FqteKpp54SgYGBQqVSiQcffFBcvXrVeUVXMxMmTBDh4eFCoVCI4OBgMWDAAPHnn3+a9/P1d7zbpyMQgu+BvY0dO1bUr19fKBQK0bBhQzF27Fhx7tw5835Xef0lIYRwbFQjIiIick8c40RERERkJQYnIiIiIisxOBERERFZicGJiIiIyEoMTkRERERWYnAiIiIishKDExEREZGVGJyIiIiIrMTgROQCtm3bBkmSii1gWR3d+Vzj4uIQEBDg1JqsERMT47Blfr766ivcc889lbqP5ORkSJKEw4cPAyj/dZ8zZw46dOhQqce0xcmTJxESEoK8vDyHPSZRVWBwIqqktLQ0TJ06FWFhYVAqlahXrx4GDx6MhIQEZ5fmFsaOHYukpCRnl1GuDz/8EHFxcXZ/nPz8fLz++uuYPXt2pe4nNDQUV69eLbb+WmleeOEFiwVU7a1Vq1bo1q0b3nvvPYc9JlFV4CK/RJU0atQo6PV6LF++HI0bN8b169exZcsW3Lp1y9mluQVvb294e3s7u4xy+fv7O+Rx1q5dC7VajaioqErdj0wmQ7169aw+3tfXF76+vpV6TFvFxsbiiSeewKxZs+DpyV9H5B7Y40RUCZmZmdixYwfeeecd9OvXD+Hh4ejSpQtmzZqFBx54AEDxUyZF7SRJwrZt2yzuLyEhAe3atYOXlxe6deuG48ePm/elpKRg2LBhCAwMhI+PD1q3bo3ff/8dwP+fhtmwYUOp7W/duoVx48ahYcOGUKlUaNu2Lb777juLxzeZTFi4cCGaNm0KpVKJsLAwvP322+b9ly9fxpgxYxAQEICgoCAMHz4cycnJZb5Gv//+O5o3bw5vb2/069ev2PGlnTL6+uuvERYWBl9fXzz11FMwGo1YuHAh6tWrhzp16ljUVfSaTpo0CcHBwVCr1ejfvz+OHDlS7H5XrFiBiIgI+Pv74+GHH0ZOTo75mLVr16Jt27bw9vZGrVq1MHDgQPOppDtP1el0OsyYMQN16tSBl5cXevbsif3795v3F70nW7ZsQadOnaBSqdCjRw+cOXOmzNdr9erVGDZsmMW2osf+73//i7p16yIgIADz5s1DQUEBXnzxRQQFBSEkJATLli0ztynpc1eWO0/VmUwmzJs3DyEhIVAqlejQoQM2btxY7P7Xr1+Pfv36QaVSoX379ti9e7f5mLI+swAwaNAgpKenIz4+3qoaiVwBgxNRJRT9lf7TTz9Bp9NV+v5efPFFvPvuu9i/fz+Cg4MxbNgwGAwGAMC0adOg0+mwfft2HDt2DO+8806xHoKy2ufn5yMyMhIbNmzA8ePH8eSTT+Kxxx7Dvn37zO1nzZqFBQsW4PXXX8fJkyexatUq1K1bFwBgMBgwePBg+Pn5YceOHUhISICvry/uvfde6PX6Ep/P5cuXMXLkSAwbNgyHDx/GpEmT8Morr5T7Opw/fx5//PEHNm7ciO+++w5fffUVhg4ditTUVMTHx+Odd97Bf/7zH+zdu9fcZvTo0bhx4wb++OMPJCYmomPHjhgwYADS09Mt7venn37Cb7/9ht9++w3x8fFYsGABAODq1asYN24cJkyYgFOnTmHbtm0YOXIkSlsH/aWXXsK6deuwfPlyHDx4EE2bNsXgwYMtHg8AXnvtNbz77rs4cOAAPD09MWHChDKf+86dO9GpU6di2//++29cuXIF27dvx3vvvYfZs2fj/vvvR2BgIPbu3YspU6Zg8uTJSE1NLff1tcaHH36Id999F//73/9w9OhRDB48GA888ADOnj1b7Pm98MILOHz4MJo3b45x48ahoKAAQPmfWYVCgQ4dOmDHjh1VUjORQwgiqpS1a9eKwMBA4eXlJXr06CFmzZoljhw5Yt5/8eJFAUAcOnTIvC0jI0MAEFu3bhVCCLF161YBQKxevdp8zK1bt4S3t7dYs2aNEEKItm3bijlz5pRYgzXtSzJ06FDx/PPPCyGEyM7OFkqlUnzxxRclHrtixQrRokULYTKZzNt0Op3w9vYWmzZtKrHNrFmzRKtWrSy2vfzyywKAyMjIEEIIsWzZMuHv72/eP3v2bKFSqUR2drZ52+DBg0VERIQwGo3mbS1atBDz588XQgixY8cOoVarRX5+vsVjNWnSRCxdurTU+33xxRdF165dhRBCJCYmCgAiOTm5xOcSHR0thg8fLoQQIjc3V8jlcvHtt9+a9+v1etGgQQOxcOFCIcT/vyd//fWX+ZgNGzYIAEKr1Zb4GEWfi+3btxd77PDw8GLPv1evXubbBQUFwsfHR3z33XdCiOKfu6J6ynrd27dvb77doEED8fbbb1vU0blzZ/HUU09Z3P+XX35p3n/ixAkBQJw6dUoIUfZntsiDDz4oYmJiyjyGyJWwx4mokkaNGoUrV67gl19+wb333ott27ahY8eOFRpI3L17d/P/g4KC0KJFC5w6dQoAMGPGDLz11luIiorC7NmzcfToUZvaG41GvPnmm2jbti2CgoLg6+uLTZs24dKlSwCAU6dOQafTYcCAASXWduTIEZw7dw5+fn7mnragoCDk5+fj/PnzJbY5deoUunbtWmqNpYmIiICfn5/5dt26ddGqVSt4eHhYbLtx44a5ttzcXNSqVctcm6+vLy5evGhR2533W79+ffN9tG/fHgMGDEDbtm0xevRofPHFF8jIyCixvvPnz8NgMFiMQ5LL5ejSpYv59S7Srl07i8cDYH7MO2m1WgCAl5dXsX2tW7cu9vzbtm1rvi2TyVCrVq1S79sW2dnZuHLlSrFxVlFRUTY9P2s+s97e3tBoNJWumchRGJyIqoCXlxcGDRqE119/Hbt27UJMTIz5qqiiX3bitlM+RafPbDFp0iRcuHABjz32GI4dO4ZOnTrh448/trr9okWL8OGHH+Lll1/G1q1bcfjwYQwePNh8mq28Adq5ubmIjIzE4cOHLb6SkpLwyCOP2Px8yiKXyy1uS5JU4jaTyWSurX79+sVqO3PmDF588cUy77foPmQyGTZv3ow//vgDrVq1wscff4wWLVrg4sWLVfZcJEkCAPNj3qlWrVqQJKnEwGbra+IoZT0/az6z6enpCA4OdlzBRJXE4ERkB61atTIPKi76pXD16lXz/tIG7O7Zs8f8/4yMDCQlJaFly5bmbaGhoZgyZQrWr1+P559/Hl988YXV7RMSEjB8+HCMHz8e7du3R+PGjS2mAWjWrBm8vb1LvSS9Y8eOOHv2LOrUqYOmTZtafJV2xVnLli0txlDdWWNV6dixI65duwZPT89itdWuXdvq+5EkCVFRUZg7dy4OHToEhUKBH3/8sdhxTZo0gUKhsJhywmAwYP/+/WjVqlWFn4dCoUCrVq1w8uTJCt9HVVCr1WjQoEGxKTUSEhJsfn7lfWaPHz+Ou+++u9I1EzkKgxNRJdy6dQv9+/fHypUrcfToUVy8eBE//PADFi5ciOHDhwMo7Mnp1q0bFixYgFOnTiE+Ph7/+c9/Sry/efPmYcuWLTh+/DhiYmJQu3Zt85VczzzzDDZt2oSLFy/i4MGD2Lp1q0WoKq99s2bNsHnzZuzatQunTp3C5MmTcf36dXNbLy8vvPzyy3jppZfwzTff4Pz589izZw+++uorAMCjjz6K2rVrY/jw4dixYwcuXryIbdu2YcaMGaUOSJ4yZQrOnj2LF198EWfOnMGqVavsMhfSwIED0b17d4wYMQJ//vknkpOTsWvXLrz22ms4cOCAVfexd+9e/Pe//8WBAwdw6dIlrF+/HmlpacVeYwDw8fHB1KlT8eKLL2Ljxo04efIknnjiCWg0GkycOLFSz2Xw4MHYuXNnpe6jKrz44ot45513sGbNGpw5cwavvPIKDh8+jJkzZ1p9H+V9ZpOTk/HPP/9g4MCB9ngKRHbBiTOIKsHX1xddu3bF+++/bx73EhoaiieeeAKvvvqq+bivv/4aEydORGRkJFq0aIGFCxeWODP0ggULMHPmTJw9exYdOnTAr7/+CoVCAaBwjNK0adOQmpoKtVqNe++9F++//77V7f/zn//gwoULGDx4MFQqFZ588kmMGDECWVlZ5vavv/46PD098cYbb+DKlSuoX78+pkyZAgBQqVTYvn07Xn75ZYwcORI5OTlo2LAhBgwYALVaXeLrExYWhnXr1uHZZ5/Fxx9/jC5duuC///1vuVeW2UqSJPz+++947bXXEBsbi7S0NNSrVw+9e/c2XxVYHrVaje3bt+ODDz5AdnY2wsPD8e6772LIkCElHr9gwQKYTCY89thjyMnJQadOnbBp0yYEBgZW6rlMnDgRnTp1QlZWlsPmjirJjBkzkJWVheeffx43btxAq1at8Msvv6BZs2ZW30d5n9nvvvsO99xzD8LDw+3xFIjsQhKilGttichtbNu2Df369UNGRoZbLF9CZRs9ejQ6duyIWbNmObsUu9Hr9WjWrBlWrVpV6ck+iRyJp+qIiFzMokWLHD6Lt6NdunQJr776KkMTuR32OBFVA+xxIiJyDAYnIiIiIivxVB0RERGRlRiciIiIiKzE4ERERERkJQYnIiIiIisxOBERERFZicGJiIiIyEoMTkRERERWYnAiIiIishKDExEREZGV/g+iEDuscPbfUAAAAABJRU5ErkJggg==", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Data for energies plot\n", - "x1 = d_list\n", - "y1 = time_array\n", - "\n", - "fig, axs = plt.subplots(1, 1, figsize=(6, 6))\n", - "\n", - "# Plot energies\n", - "axs.plot(x1, y1, marker=\".\", markersize=20)\n", - "axs.set_title(\"Runtime vs subspace dimension 60 qubits\")\n", - "axs.set_xlabel(\"Subspace dimension (millions)\")\n", - "plt.xticks([1e7, 2e7, 3e7, 4e7, 5e7], [str(i) for i in [10, 20, 30, 40, 50]])\n", - "axs.set_ylabel(\"Wall time [s]\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/how_tos/choose-subspace-dimension.mdx b/docs/addons/qiskit-addon-sqd/how_tos/choose-subspace-dimension.mdx deleted file mode 100644 index f3753894c67..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/choose-subspace-dimension.mdx +++ /dev/null @@ -1,145 +0,0 @@ - - -# Bounding the subspace dimension - -In this tutorial, we will show the effect of the subspace dimension in the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068). - -**A priori**, we do not know what is the correct subspace dimension to obtain a target level of accuracy. However, we do know that increasing the subspace dimension increases the accuracy of the method. Therefore, we can study the accuracy of the predictions as a function of the subspace dimension. - -Specify the molecule and its properties. - -``` -[1]: -``` - -```python -import warnings - -import pyscf -import pyscf.cc -import pyscf.mcscf - -warnings.filterwarnings("ignore") - -# Specify molecule properties -open_shell = False -spin_sq = 0 - -# Build N2 molecule -mol = pyscf.gto.Mole() -mol.build( - atom=[["N", (0, 0, 0)], ["N", (1.0, 0, 0)]], - basis="6-31g", - symmetry="Dooh", -) - -# Define active space -n_frozen = 2 -active_space = range(n_frozen, mol.nao_nr()) - -# Get molecular integrals -scf = pyscf.scf.RHF(mol).run() -num_orbitals = len(active_space) -n_electrons = int(sum(scf.mo_occ[active_space])) -num_elec_a = (n_electrons + mol.spin) // 2 -num_elec_b = (n_electrons - mol.spin) // 2 -cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b)) -mo = cas.sort_mo(active_space, base=0) -hcore, nuclear_repulsion_energy = cas.get_h1cas(mo) -eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals) - -# Compute exact energy -exact_energy = cas.run().e_tot -``` - -```python -converged SCF energy = -108.835236570775 -CASCI E = -109.046671778080 E(CI) = -32.8155692383188 S^2 = 0.0000000 -``` - -Generate some random bitstrings to proxy QPU samples. - -``` -[2]: -``` - -```python -import numpy as np -from qiskit_addon_sqd.counts import generate_bit_array_uniform - -# Create a seed to control randomness throughout this workflow -rng = np.random.default_rng(24) - -# Generate random samples -bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng) -``` - -Call SQD with increasing batch sizes. - -``` -[3]: -``` - -```python -from qiskit_addon_sqd.fermion import diagonalize_fermionic_hamiltonian - -list_samples_per_batch = [50, 200, 400, 600] - -# SQD options -max_iterations = 5 - -# Eigenstate solver options -num_batches = 10 -max_davidson_cycles = 200 - -energies = [] -subspace_dimensions = [] - -for samples_per_batch in list_samples_per_batch: - result = diagonalize_fermionic_hamiltonian( - hcore, - eri, - bit_array, - samples_per_batch=samples_per_batch, - norb=num_orbitals, - nelec=(num_elec_a, num_elec_b), - num_batches=num_batches, - max_iterations=max_iterations, - symmetrize_spin=True, - seed=rng, - ) - energies.append(result.energy) - subspace_dimensions.append(np.prod(result.sci_state.amplitudes.shape)) -``` - -This plot shows that increasing the subspace dimension leads to more accurate results. - -``` -[4]: -``` - -```python -import matplotlib.pyplot as plt - -# Data for energies plot -x1 = subspace_dimensions -y1 = np.array(energies) + nuclear_repulsion_energy - -fig, axs = plt.subplots(1, 1, figsize=(12, 6)) - -# Plot energies -axs.plot(x1, y1, marker=".", markersize=20, label="Estimated") -axs.set_xticks(x1) -axs.set_xticklabels(x1) -axs.axhline(y=exact_energy, color="red", linestyle="--", label="Exact") -axs.set_title("Approximated Ground State Energy vs subspace dimension") -axs.set_xlabel("Subspace dimension") -axs.set_ylabel("Energy (Ha)") -axs.legend() - - -plt.tight_layout() -plt.show() -``` - -![../\_images/how\_tos\_choose\_subspace\_dimension\_8\_0.png](/docs/images/api/qiskit-addon-sqd/how_tos_choose_subspace_dimension_8_0.avif) diff --git a/docs/addons/qiskit-addon-sqd/how_tos/choose_subspace_dimension.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/choose_subspace_dimension.ipynb deleted file mode 100644 index 99151b59b2f..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/choose_subspace_dimension.ipynb +++ /dev/null @@ -1,220 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", - "metadata": {}, - "source": [ - "# Bounding the subspace dimension\n", - "\n", - "In this tutorial, we will show the effect of the subspace dimension in the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068).\n", - "\n", - "***A priori***, we do not know what is the correct subspace dimension to obtain a target level of accuracy. However, we do know that increasing the subspace dimension increases the accuracy of the method. Therefore, we can study the accuracy of the predictions as a function of the subspace dimension." - ] - }, - { - "cell_type": "markdown", - "id": "a6755afb-ca1e-4473-974b-ba89acc8abce", - "metadata": {}, - "source": [ - "Specify the molecule and its properties." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "converged SCF energy = -108.835236570775\n", - "CASCI E = -109.046671778080 E(CI) = -32.8155692383188 S^2 = 0.0000000\n" - ] - } - ], - "source": [ - "import warnings\n", - "\n", - "import pyscf\n", - "import pyscf.cc\n", - "import pyscf.mcscf\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "# Specify molecule properties\n", - "open_shell = False\n", - "spin_sq = 0\n", - "\n", - "# Build N2 molecule\n", - "mol = pyscf.gto.Mole()\n", - "mol.build(\n", - " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", - " basis=\"6-31g\",\n", - " symmetry=\"Dooh\",\n", - ")\n", - "\n", - "# Define active space\n", - "n_frozen = 2\n", - "active_space = range(n_frozen, mol.nao_nr())\n", - "\n", - "# Get molecular integrals\n", - "scf = pyscf.scf.RHF(mol).run()\n", - "num_orbitals = len(active_space)\n", - "n_electrons = int(sum(scf.mo_occ[active_space]))\n", - "num_elec_a = (n_electrons + mol.spin) // 2\n", - "num_elec_b = (n_electrons - mol.spin) // 2\n", - "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", - "mo = cas.sort_mo(active_space, base=0)\n", - "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", - "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", - "\n", - "# Compute exact energy\n", - "exact_energy = cas.run().e_tot" - ] - }, - { - "cell_type": "markdown", - "id": "c58e988c-a109-44cd-a975-9df43250c318", - "metadata": {}, - "source": [ - "Generate some random bitstrings to proxy QPU samples." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e9506e0b-ed64-48bb-a97a-ef851b604af1", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from qiskit_addon_sqd.counts import generate_bit_array_uniform\n", - "\n", - "# Create a seed to control randomness throughout this workflow\n", - "rng = np.random.default_rng(24)\n", - "\n", - "# Generate random samples\n", - "bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng)" - ] - }, - { - "cell_type": "markdown", - "id": "82b062a3-d4ca-41e2-9b00-4a394f83cbdd", - "metadata": {}, - "source": [ - "Call SQD with increasing batch sizes." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e0847f28", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_addon_sqd.fermion import diagonalize_fermionic_hamiltonian\n", - "\n", - "list_samples_per_batch = [50, 200, 400, 600]\n", - "\n", - "# SQD options\n", - "max_iterations = 5\n", - "\n", - "# Eigenstate solver options\n", - "num_batches = 10\n", - "max_davidson_cycles = 200\n", - "\n", - "energies = []\n", - "subspace_dimensions = []\n", - "\n", - "for samples_per_batch in list_samples_per_batch:\n", - " result = diagonalize_fermionic_hamiltonian(\n", - " hcore,\n", - " eri,\n", - " bit_array,\n", - " samples_per_batch=samples_per_batch,\n", - " norb=num_orbitals,\n", - " nelec=(num_elec_a, num_elec_b),\n", - " num_batches=num_batches,\n", - " max_iterations=max_iterations,\n", - " symmetrize_spin=True,\n", - " seed=rng,\n", - " )\n", - " energies.append(result.energy)\n", - " subspace_dimensions.append(np.prod(result.sci_state.amplitudes.shape))" - ] - }, - { - "cell_type": "markdown", - "id": "9d78906b-4759-4506-9c69-85d4e67766b3", - "metadata": {}, - "source": [ - "This plot shows that increasing the subspace dimension leads to more accurate results." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "caffd888-e89c-4aa9-8bae-4d1bb723b35e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Data for energies plot\n", - "x1 = subspace_dimensions\n", - "y1 = np.array(energies) + nuclear_repulsion_energy\n", - "\n", - "fig, axs = plt.subplots(1, 1, figsize=(12, 6))\n", - "\n", - "# Plot energies\n", - "axs.plot(x1, y1, marker=\".\", markersize=20, label=\"Estimated\")\n", - "axs.set_xticks(x1)\n", - "axs.set_xticklabels(x1)\n", - "axs.axhline(y=exact_energy, color=\"red\", linestyle=\"--\", label=\"Exact\")\n", - "axs.set_title(\"Approximated Ground State Energy vs subspace dimension\")\n", - "axs.set_xlabel(\"Subspace dimension\")\n", - "axs.set_ylabel(\"Energy (Ha)\")\n", - "axs.legend()\n", - "\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/how_tos/index.mdx b/docs/addons/qiskit-addon-sqd/how_tos/index.mdx deleted file mode 100644 index 2259da1e85e..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/index.mdx +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Sample-based quantum diagonalization (SQD) API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-sqd. ---- - -# How-To Guides - -This page summarizes the available how-to guides. - -[![](/docs/images/api/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](add-fermionic-excitations-to-configuration-pool) - -[Add fermionic transitions to the pool of configurations](add-fermionic-excitations-to-configuration-pool) - -[![](/docs/images/api/qiskit-addon-sqd/how_tos_benchmark_pauli_projection_12_0.avif)](benchmark-pauli-projection) - -[Benchmark Pauli operator projection](benchmark-pauli-projection) - -[![](/docs/images/api/qiskit-addon-sqd/how_tos_choose_subspace_dimension_8_0.avif)](choose-subspace-dimension) - -[Bounding the subspace dimension](choose-subspace-dimension) - -[![](/docs/images/api/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](integrate-dice-solver) - -[Scale SQD chemistry workflows with Dice solver](integrate-dice-solver) - -[![](/docs/images/api/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](project-pauli-operators-onto-hilbert-subspaces) - -[Project Pauli operators onto Hilbert subspaces](project-pauli-operators-onto-hilbert-subspaces) - -[![](/docs/images/api/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](select-open-closed-shell) - -[Understand open-shell vs closed-shell options and its effect in the subspace construction](select-open-closed-shell) - -[![](/docs/images/api/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](use-oo-to-optimize-hamiltonian-basis) - -[Optimize Hamiltonian basis with orbital optimization](use-oo-to-optimize-hamiltonian-basis) - diff --git a/docs/addons/qiskit-addon-sqd/how_tos/integrate-dice-solver.mdx b/docs/addons/qiskit-addon-sqd/how_tos/integrate-dice-solver.mdx deleted file mode 100644 index 347464bbc7a..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/integrate-dice-solver.mdx +++ /dev/null @@ -1,93 +0,0 @@ - - -# Scale SQD chemistry workflows with Dice solver - -For information on how to install and use `qiskit-addon-dice-solver`, [visit the docs](https://qiskit.github.io/qiskit-addon-dice-solver/). - -For more details on the SQD code used in this example, check out [tutorial 1](/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian). - -``` -[1]: -``` - -```python -import numpy as np -import pyscf -import pyscf.cc -import pyscf.mcscf -from qiskit_addon_dice_solver import solve_sci_batch -from qiskit_addon_sqd.counts import generate_bit_array_uniform -from qiskit_addon_sqd.fermion import diagonalize_fermionic_hamiltonian - -# Specify molecule properties -num_orbitals = 16 -num_elec_a = num_elec_b = 5 -spin_sq = 0 - -# Build N2 molecule -mol = pyscf.gto.Mole() -mol.build( - atom=[["N", (0, 0, 0)], ["N", (1.0, 0, 0)]], - basis="6-31g", - symmetry="Dooh", -) - -# Define active space -n_frozen = 2 -active_space = range(n_frozen, mol.nao_nr()) - -# Get molecular integrals -scf = pyscf.scf.RHF(mol).run() -num_orbitals = len(active_space) -n_electrons = int(sum(scf.mo_occ[active_space])) -num_elec_a = (n_electrons + mol.spin) // 2 -num_elec_b = (n_electrons - mol.spin) // 2 -cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b)) -mo = cas.sort_mo(active_space, base=0) -hcore, nuclear_repulsion_energy = cas.get_h1cas(mo) -eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals) - -# Compute exact energy -exact_energy = cas.run().e_tot - -# Create a seed to control randomness throughout this workflow -rng = np.random.default_rng(24) - - -# Generate random samples -bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng) - -# Run SQD -result = diagonalize_fermionic_hamiltonian( - hcore, - eri, - bit_array, - samples_per_batch=300, - norb=num_orbitals, - nelec=(num_elec_a, num_elec_b), - num_batches=5, - max_iterations=5, - sci_solver=solve_sci_batch, - symmetrize_spin=True, - seed=rng, -) -``` - -```python -converged SCF energy = -108.835236570774 -CASCI E = -109.046671778080 E(CI) = -32.8155692383187 S^2 = 0.0000000 -``` - -``` -[2]: -``` - -```python -print(f"Exact energy: {exact_energy}") -print(f"Estimated energy: {result.energy + nuclear_repulsion_energy}") -``` - -```python -Exact energy: -109.04667177808028 -Estimated energy: -109.03402667558743 -``` diff --git a/docs/addons/qiskit-addon-sqd/how_tos/integrate_dice_solver.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/integrate_dice_solver.ipynb deleted file mode 100644 index 5fb539ebd4a..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/integrate_dice_solver.ipynb +++ /dev/null @@ -1,135 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "65adf860-4443-494f-ad90-5d8ea444ac4e", - "metadata": {}, - "source": [ - "# Scale SQD chemistry workflows with Dice solver\n", - "\n", - "For information on how to install and use ``qiskit-addon-dice-solver``, [visit the docs](https://qiskit.github.io/qiskit-addon-dice-solver/).\n", - "\n", - "For more details on the SQD code used in this example, check out [tutorial 1](/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "baf7d074-4443-4695-875e-65f7fb8cef25", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "converged SCF energy = -108.835236570774\n", - "CASCI E = -109.046671778080 E(CI) = -32.8155692383187 S^2 = 0.0000000\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pyscf\n", - "import pyscf.cc\n", - "import pyscf.mcscf\n", - "from qiskit_addon_dice_solver import solve_sci_batch\n", - "from qiskit_addon_sqd.counts import generate_bit_array_uniform\n", - "from qiskit_addon_sqd.fermion import diagonalize_fermionic_hamiltonian\n", - "\n", - "# Specify molecule properties\n", - "num_orbitals = 16\n", - "num_elec_a = num_elec_b = 5\n", - "spin_sq = 0\n", - "\n", - "# Build N2 molecule\n", - "mol = pyscf.gto.Mole()\n", - "mol.build(\n", - " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", - " basis=\"6-31g\",\n", - " symmetry=\"Dooh\",\n", - ")\n", - "\n", - "# Define active space\n", - "n_frozen = 2\n", - "active_space = range(n_frozen, mol.nao_nr())\n", - "\n", - "# Get molecular integrals\n", - "scf = pyscf.scf.RHF(mol).run()\n", - "num_orbitals = len(active_space)\n", - "n_electrons = int(sum(scf.mo_occ[active_space]))\n", - "num_elec_a = (n_electrons + mol.spin) // 2\n", - "num_elec_b = (n_electrons - mol.spin) // 2\n", - "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", - "mo = cas.sort_mo(active_space, base=0)\n", - "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", - "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", - "\n", - "# Compute exact energy\n", - "exact_energy = cas.run().e_tot\n", - "\n", - "# Create a seed to control randomness throughout this workflow\n", - "rng = np.random.default_rng(24)\n", - "\n", - "\n", - "# Generate random samples\n", - "bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng)\n", - "\n", - "# Run SQD\n", - "result = diagonalize_fermionic_hamiltonian(\n", - " hcore,\n", - " eri,\n", - " bit_array,\n", - " samples_per_batch=300,\n", - " norb=num_orbitals,\n", - " nelec=(num_elec_a, num_elec_b),\n", - " num_batches=5,\n", - " max_iterations=5,\n", - " sci_solver=solve_sci_batch,\n", - " symmetrize_spin=True,\n", - " seed=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "99709a88-f9ec-4dbc-a561-e682f90aa4c5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exact energy: -109.04667177808028\n", - "Estimated energy: -109.03402667558743\n" - ] - } - ], - "source": [ - "print(f\"Exact energy: {exact_energy}\")\n", - "print(f\"Estimated energy: {result.energy + nuclear_repulsion_energy}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/how_tos/project-pauli-operators-onto-hilbert-subspaces.mdx b/docs/addons/qiskit-addon-sqd/how_tos/project-pauli-operators-onto-hilbert-subspaces.mdx deleted file mode 100644 index e2632cc41f2..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/project-pauli-operators-onto-hilbert-subspaces.mdx +++ /dev/null @@ -1,203 +0,0 @@ - - -# Project Pauli operators onto Hilbert subspaces - -We show different ways of projecting a weighted linear combination of pauli strings into a subspace defined by a subset of size $d$ computational basis states. The projected operator is stored as a $d \times d$ `scipy.sparse.coo_matrix`. - -As an example we consider the Hamiltonian of the 1D Heisenberg model with periodic boundary conditions and $L = 22$ spins: - -$$ -H = \sum_{\langle i, j \rangle}\left( \sigma^x_i\sigma^x_j + \sigma^y_i\sigma^y_j + \sigma^z_i\sigma^z_j \right). -$$ - -This package provides two tools to perform this projection: - -* `qiskit_addon_sqd.qubit.matrix_elements_from_pauli()`: is a lower-level function that returns the non-zero matrix elements of a Pauli string and the corresponding indices of the non-zero elements. -* `qiskit_addon_sqd.qubit.project_operator_to_subspace()`: is a higher-level function that directly returns a `scipy.sparse` matrix. - -This notebook shows how to use these two tools to produce the same sparse operator. - - - -## Subspace definition - -For this example we just generate length-22 random bitstrings. For the projection functions to work as expected, it is essential that the bitstrings that define the subspace are unique and sorted in ascending order according to their unsigned integer representation. - -This can be achieved with the `qiskit_addon_sqd.qubit.sort_and_remove_duplicates()` function. - -``` -[1]: -``` - -```python -import numpy as np -from qiskit_addon_sqd.qubit import sort_and_remove_duplicates - -num_spins = 22 -rand_seed = 22 -np.random.seed(rand_seed) - - -def random_bitstrings(n_samples, n_qubits): - return np.round(np.random.rand(n_samples, n_qubits)).astype("int").astype("bool") - - -bitstring_matrix = random_bitstrings(50_000, num_spins) - -# NOTE: It is essential for the projection code to have the bitstrings sorted! -bitstring_matrix = sort_and_remove_duplicates(bitstring_matrix) - -print("Subspace dimension: " + str(bitstring_matrix.shape[0])) -print("Full Hilbert space dimension: " + str(2**num_spins)) -``` - -```python -Subspace dimension: 49718 -Full Hilbert space dimension: 4194304 -``` - - - -## First method: nonzero matrix elements and indices. - -``` -[2]: -``` - -```python -from qiskit.transpiler import CouplingMap -from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian - -coupling_map = CouplingMap.from_ring(num_spins) -hamiltonian = generate_xyz_hamiltonian(coupling_map) -print(hamiltonian) -``` - -```python -SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIXX', 'IIIIIIIIIIIIIIIIIIIIYY', 'IIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIXXII', 'IIIIIIIIIIIIIIIIIIYYII', 'IIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIXXIIII', 'IIIIIIIIIIIIIIIIYYIIII', 'IIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIXXIIIIII', 'IIIIIIIIIIIIIIYYIIIIII', 'IIIIIIIIIIIIIIZZIIIIII', 'IIIIIIIIIIIIXXIIIIIIII', 'IIIIIIIIIIIIYYIIIIIIII', 'IIIIIIIIIIIIZZIIIIIIII', 'IIIIIIIIIIXXIIIIIIIIII', 'IIIIIIIIIIYYIIIIIIIIII', 'IIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIXXIIIIIIIIIIII', 'IIIIIIIIYYIIIIIIIIIIII', 'IIIIIIIIZZIIIIIIIIIIII', 'IIIIIIXXIIIIIIIIIIIIII', 'IIIIIIYYIIIIIIIIIIIIII', 'IIIIIIZZIIIIIIIIIIIIII', 'IIIIXXIIIIIIIIIIIIIIII', 'IIIIYYIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIII', 'IIXXIIIIIIIIIIIIIIIIII', 'IIYYIIIIIIIIIIIIIIIIII', 'IIZZIIIIIIIIIIIIIIIIII', 'XXIIIIIIIIIIIIIIIIIIII', 'YYIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIII', 'XIIIIIIIIIIIIIIIIIIIIX', 'YIIIIIIIIIIIIIIIIIIIIY', 'ZIIIIIIIIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIIIIIIIXXI', 'IIIIIIIIIIIIIIIIIIIYYI', 'IIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIXXIII', 'IIIIIIIIIIIIIIIIIYYIII', 'IIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIXXIIIII', 'IIIIIIIIIIIIIIIYYIIIII', 'IIIIIIIIIIIIIIIZZIIIII', 'IIIIIIIIIIIIIXXIIIIIII', 'IIIIIIIIIIIIIYYIIIIIII', 'IIIIIIIIIIIIIZZIIIIIII', 'IIIIIIIIIIIXXIIIIIIIII', 'IIIIIIIIIIIYYIIIIIIIII', 'IIIIIIIIIIIZZIIIIIIIII', 'IIIIIIIIIXXIIIIIIIIIII', 'IIIIIIIIIYYIIIIIIIIIII', 'IIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIXXIIIIIIIIIIIII', 'IIIIIIIYYIIIIIIIIIIIII', 'IIIIIIIZZIIIIIIIIIIIII', 'IIIIIXXIIIIIIIIIIIIIII', 'IIIIIYYIIIIIIIIIIIIIII', 'IIIIIZZIIIIIIIIIIIIIII', 'IIIXXIIIIIIIIIIIIIIIII', 'IIIYYIIIIIIIIIIIIIIIII', 'IIIZZIIIIIIIIIIIIIIIII', 'IXXIIIIIIIIIIIIIIIIIII', 'IYYIIIIIIIIIIIIIIIIIII', 'IZZIIIIIIIIIIIIIIIIIII'], - coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, - 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, - 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, - 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, - 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, - 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, - 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, - 1.+0.j, 1.+0.j, 1.+0.j]) -``` - -``` -[3]: -``` - -```python -from qiskit_addon_sqd.qubit import matrix_elements_from_pauli -from scipy.sparse import coo_matrix - -d = bitstring_matrix.shape[0] - -# Initialize the coo_matrix object -operator_from_matrix_elements = coo_matrix((d, d), dtype="complex128") - -for pauli in hamiltonian.paulis: - matrix_elements, row_indices, col_indices = matrix_elements_from_pauli(bitstring_matrix, pauli) - operator_from_matrix_elements += coo_matrix( - (matrix_elements, (row_indices, col_indices)), (d, d) - ) -``` - - - -## Higher-level implementation - -``` -[4]: -``` - -```python -from qiskit_addon_sqd.qubit import project_operator_to_subspace - -operator = project_operator_to_subspace(bitstring_matrix, hamiltonian, verbose=True) -``` - -```python -Projecting term 1 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIXX ... -Projecting term 2 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIYY ... -Projecting term 3 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIZZ ... -Projecting term 4 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIXXII ... -Projecting term 5 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIYYII ... -Projecting term 6 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIZZII ... -Projecting term 7 out of 66: (1+0j) * IIIIIIIIIIIIIIIIXXIIII ... -Projecting term 8 out of 66: (1+0j) * IIIIIIIIIIIIIIIIYYIIII ... -Projecting term 9 out of 66: (1+0j) * IIIIIIIIIIIIIIIIZZIIII ... -Projecting term 10 out of 66: (1+0j) * IIIIIIIIIIIIIIXXIIIIII ... -Projecting term 11 out of 66: (1+0j) * IIIIIIIIIIIIIIYYIIIIII ... -Projecting term 12 out of 66: (1+0j) * IIIIIIIIIIIIIIZZIIIIII ... -Projecting term 13 out of 66: (1+0j) * IIIIIIIIIIIIXXIIIIIIII ... -Projecting term 14 out of 66: (1+0j) * IIIIIIIIIIIIYYIIIIIIII ... -Projecting term 15 out of 66: (1+0j) * IIIIIIIIIIIIZZIIIIIIII ... -Projecting term 16 out of 66: (1+0j) * IIIIIIIIIIXXIIIIIIIIII ... -Projecting term 17 out of 66: (1+0j) * IIIIIIIIIIYYIIIIIIIIII ... -Projecting term 18 out of 66: (1+0j) * IIIIIIIIIIZZIIIIIIIIII ... -Projecting term 19 out of 66: (1+0j) * IIIIIIIIXXIIIIIIIIIIII ... -Projecting term 20 out of 66: (1+0j) * IIIIIIIIYYIIIIIIIIIIII ... -Projecting term 21 out of 66: (1+0j) * IIIIIIIIZZIIIIIIIIIIII ... -Projecting term 22 out of 66: (1+0j) * IIIIIIXXIIIIIIIIIIIIII ... -Projecting term 23 out of 66: (1+0j) * IIIIIIYYIIIIIIIIIIIIII ... -Projecting term 24 out of 66: (1+0j) * IIIIIIZZIIIIIIIIIIIIII ... -Projecting term 25 out of 66: (1+0j) * IIIIXXIIIIIIIIIIIIIIII ... -Projecting term 26 out of 66: (1+0j) * IIIIYYIIIIIIIIIIIIIIII ... -Projecting term 27 out of 66: (1+0j) * IIIIZZIIIIIIIIIIIIIIII ... -Projecting term 28 out of 66: (1+0j) * IIXXIIIIIIIIIIIIIIIIII ... -Projecting term 29 out of 66: (1+0j) * IIYYIIIIIIIIIIIIIIIIII ... -Projecting term 30 out of 66: (1+0j) * IIZZIIIIIIIIIIIIIIIIII ... -Projecting term 31 out of 66: (1+0j) * XXIIIIIIIIIIIIIIIIIIII ... -Projecting term 32 out of 66: (1+0j) * YYIIIIIIIIIIIIIIIIIIII ... -Projecting term 33 out of 66: (1+0j) * ZZIIIIIIIIIIIIIIIIIIII ... -Projecting term 34 out of 66: (1+0j) * XIIIIIIIIIIIIIIIIIIIIX ... -Projecting term 35 out of 66: (1+0j) * YIIIIIIIIIIIIIIIIIIIIY ... -Projecting term 36 out of 66: (1+0j) * ZIIIIIIIIIIIIIIIIIIIIZ ... -Projecting term 37 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIXXI ... -Projecting term 38 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIYYI ... -Projecting term 39 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIZZI ... -Projecting term 40 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIXXIII ... -Projecting term 41 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIYYIII ... -Projecting term 42 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIZZIII ... -Projecting term 43 out of 66: (1+0j) * IIIIIIIIIIIIIIIXXIIIII ... -Projecting term 44 out of 66: (1+0j) * IIIIIIIIIIIIIIIYYIIIII ... -Projecting term 45 out of 66: (1+0j) * IIIIIIIIIIIIIIIZZIIIII ... -Projecting term 46 out of 66: (1+0j) * IIIIIIIIIIIIIXXIIIIIII ... -Projecting term 47 out of 66: (1+0j) * IIIIIIIIIIIIIYYIIIIIII ... -Projecting term 48 out of 66: (1+0j) * IIIIIIIIIIIIIZZIIIIIII ... -Projecting term 49 out of 66: (1+0j) * IIIIIIIIIIIXXIIIIIIIII ... -Projecting term 50 out of 66: (1+0j) * IIIIIIIIIIIYYIIIIIIIII ... -Projecting term 51 out of 66: (1+0j) * IIIIIIIIIIIZZIIIIIIIII ... -Projecting term 52 out of 66: (1+0j) * IIIIIIIIIXXIIIIIIIIIII ... -Projecting term 53 out of 66: (1+0j) * IIIIIIIIIYYIIIIIIIIIII ... -Projecting term 54 out of 66: (1+0j) * IIIIIIIIIZZIIIIIIIIIII ... -Projecting term 55 out of 66: (1+0j) * IIIIIIIXXIIIIIIIIIIIII ... -Projecting term 56 out of 66: (1+0j) * IIIIIIIYYIIIIIIIIIIIII ... -Projecting term 57 out of 66: (1+0j) * IIIIIIIZZIIIIIIIIIIIII ... -Projecting term 58 out of 66: (1+0j) * IIIIIXXIIIIIIIIIIIIIII ... -Projecting term 59 out of 66: (1+0j) * IIIIIYYIIIIIIIIIIIIIII ... -Projecting term 60 out of 66: (1+0j) * IIIIIZZIIIIIIIIIIIIIII ... -Projecting term 61 out of 66: (1+0j) * IIIXXIIIIIIIIIIIIIIIII ... -Projecting term 62 out of 66: (1+0j) * IIIYYIIIIIIIIIIIIIIIII ... -Projecting term 63 out of 66: (1+0j) * IIIZZIIIIIIIIIIIIIIIII ... -Projecting term 64 out of 66: (1+0j) * IXXIIIIIIIIIIIIIIIIIII ... -Projecting term 65 out of 66: (1+0j) * IYYIIIIIIIIIIIIIIIIIII ... -Projecting term 66 out of 66: (1+0j) * IZZIIIIIIIIIIIIIIIIIII ... -``` - -Check that both implementations yield the same coo\_matrix - -``` -[5]: -``` - -```python -print((operator.power(2) - operator_from_matrix_elements.power(2)).sum()) -``` - -```python -0j -``` diff --git a/docs/addons/qiskit-addon-sqd/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb deleted file mode 100644 index d1d6baf81d7..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb +++ /dev/null @@ -1,293 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "21a1d83f-183f-4da9-bb8c-71400120fd31", - "metadata": {}, - "source": [ - "# Project Pauli operators onto Hilbert subspaces" - ] - }, - { - "cell_type": "markdown", - "id": "c961d48a", - "metadata": {}, - "source": [ - "We show different ways of projecting a weighted linear combination of pauli strings\n", - "into a subspace defined by a subset of size $d$ computational basis states. The \n", - "projected operator is stored as a $d \\times d$ ``scipy.sparse.coo_matrix``.\n", - "\n", - "As an example we consider the Hamiltonian of the 1D Heisenberg model with periodic\n", - "boundary conditions and $L = 22$ spins:\n", - "$$\n", - "H = \\sum_{\\langle i, j \\rangle}\\left( \\sigma^x_i\\sigma^x_j + \\sigma^y_i\\sigma^y_j + \\sigma^z_i\\sigma^z_j \\right).\n", - "$$\n", - "\n", - "This package provides two tools to perform this projection:\n", - "\n", - "- ``qiskit_addon_sqd.qubit.matrix_elements_from_pauli()``: is a lower-level function\n", - "that returns the non-zero matrix elements of a Pauli string and the corresponding indices\n", - "of the non-zero elements.\n", - "\n", - "- ``qiskit_addon_sqd.qubit.project_operator_to_subspace()``: is a higher-level function that \n", - "directly returns a ``scipy.sparse`` matrix.\n", - "\n", - "This notebook shows how to use these two tools to produce the same sparse operator." - ] - }, - { - "cell_type": "markdown", - "id": "6c87d5e5", - "metadata": {}, - "source": [ - "### Subspace definition\n", - "\n", - "For this example we just generate length-22 random bitstrings. For the projection\n", - "functions to work as expected, it is essential that the bitstrings that define\n", - "the subspace are unique and sorted in ascending order according to their unsigned\n", - "integer representation.\n", - "\n", - "This can be achieved with the ``qiskit_addon_sqd.qubit.sort_and_remove_duplicates()``\n", - "function." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7b94f840", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Subspace dimension: 49718\n", - "Full Hilbert space dimension: 4194304\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from qiskit_addon_sqd.qubit import sort_and_remove_duplicates\n", - "\n", - "num_spins = 22\n", - "rand_seed = 22\n", - "np.random.seed(rand_seed)\n", - "\n", - "\n", - "def random_bitstrings(n_samples, n_qubits):\n", - " return np.round(np.random.rand(n_samples, n_qubits)).astype(\"int\").astype(\"bool\")\n", - "\n", - "\n", - "bitstring_matrix = random_bitstrings(50_000, num_spins)\n", - "\n", - "# NOTE: It is essential for the projection code to have the bitstrings sorted!\n", - "bitstring_matrix = sort_and_remove_duplicates(bitstring_matrix)\n", - "\n", - "print(\"Subspace dimension: \" + str(bitstring_matrix.shape[0]))\n", - "print(\"Full Hilbert space dimension: \" + str(2**num_spins))" - ] - }, - { - "cell_type": "markdown", - "id": "5707b93c", - "metadata": {}, - "source": [ - "### First method: nonzero matrix elements and indices." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "74c16c91-cc5c-46ce-aff8-17d0e71ac50f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIXX', 'IIIIIIIIIIIIIIIIIIIIYY', 'IIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIXXII', 'IIIIIIIIIIIIIIIIIIYYII', 'IIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIXXIIII', 'IIIIIIIIIIIIIIIIYYIIII', 'IIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIXXIIIIII', 'IIIIIIIIIIIIIIYYIIIIII', 'IIIIIIIIIIIIIIZZIIIIII', 'IIIIIIIIIIIIXXIIIIIIII', 'IIIIIIIIIIIIYYIIIIIIII', 'IIIIIIIIIIIIZZIIIIIIII', 'IIIIIIIIIIXXIIIIIIIIII', 'IIIIIIIIIIYYIIIIIIIIII', 'IIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIXXIIIIIIIIIIII', 'IIIIIIIIYYIIIIIIIIIIII', 'IIIIIIIIZZIIIIIIIIIIII', 'IIIIIIXXIIIIIIIIIIIIII', 'IIIIIIYYIIIIIIIIIIIIII', 'IIIIIIZZIIIIIIIIIIIIII', 'IIIIXXIIIIIIIIIIIIIIII', 'IIIIYYIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIII', 'IIXXIIIIIIIIIIIIIIIIII', 'IIYYIIIIIIIIIIIIIIIIII', 'IIZZIIIIIIIIIIIIIIIIII', 'XXIIIIIIIIIIIIIIIIIIII', 'YYIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIII', 'XIIIIIIIIIIIIIIIIIIIIX', 'YIIIIIIIIIIIIIIIIIIIIY', 'ZIIIIIIIIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIIIIIIIXXI', 'IIIIIIIIIIIIIIIIIIIYYI', 'IIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIXXIII', 'IIIIIIIIIIIIIIIIIYYIII', 'IIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIXXIIIII', 'IIIIIIIIIIIIIIIYYIIIII', 'IIIIIIIIIIIIIIIZZIIIII', 'IIIIIIIIIIIIIXXIIIIIII', 'IIIIIIIIIIIIIYYIIIIIII', 'IIIIIIIIIIIIIZZIIIIIII', 'IIIIIIIIIIIXXIIIIIIIII', 'IIIIIIIIIIIYYIIIIIIIII', 'IIIIIIIIIIIZZIIIIIIIII', 'IIIIIIIIIXXIIIIIIIIIII', 'IIIIIIIIIYYIIIIIIIIIII', 'IIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIXXIIIIIIIIIIIII', 'IIIIIIIYYIIIIIIIIIIIII', 'IIIIIIIZZIIIIIIIIIIIII', 'IIIIIXXIIIIIIIIIIIIIII', 'IIIIIYYIIIIIIIIIIIIIII', 'IIIIIZZIIIIIIIIIIIIIII', 'IIIXXIIIIIIIIIIIIIIIII', 'IIIYYIIIIIIIIIIIIIIIII', 'IIIZZIIIIIIIIIIIIIIIII', 'IXXIIIIIIIIIIIIIIIIIII', 'IYYIIIIIIIIIIIIIIIIIII', 'IZZIIIIIIIIIIIIIIIIIII'],\n", - " coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", - " 1.+0.j, 1.+0.j, 1.+0.j])\n" - ] - } - ], - "source": [ - "from qiskit.transpiler import CouplingMap\n", - "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", - "\n", - "coupling_map = CouplingMap.from_ring(num_spins)\n", - "hamiltonian = generate_xyz_hamiltonian(coupling_map)\n", - "print(hamiltonian)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f31f5e40", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_addon_sqd.qubit import matrix_elements_from_pauli\n", - "from scipy.sparse import coo_matrix\n", - "\n", - "d = bitstring_matrix.shape[0]\n", - "\n", - "# Initialize the coo_matrix object\n", - "operator_from_matrix_elements = coo_matrix((d, d), dtype=\"complex128\")\n", - "\n", - "for pauli in hamiltonian.paulis:\n", - " matrix_elements, row_indices, col_indices = matrix_elements_from_pauli(bitstring_matrix, pauli)\n", - " operator_from_matrix_elements += coo_matrix(\n", - " (matrix_elements, (row_indices, col_indices)), (d, d)\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "4479af24", - "metadata": {}, - "source": [ - "### Higher-level implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e58763df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Projecting term 1 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIXX ...\n", - "Projecting term 2 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIYY ...\n", - "Projecting term 3 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIZZ ...\n", - "Projecting term 4 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIXXII ...\n", - "Projecting term 5 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIYYII ...\n", - "Projecting term 6 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIZZII ...\n", - "Projecting term 7 out of 66: (1+0j) * IIIIIIIIIIIIIIIIXXIIII ...\n", - "Projecting term 8 out of 66: (1+0j) * IIIIIIIIIIIIIIIIYYIIII ...\n", - "Projecting term 9 out of 66: (1+0j) * IIIIIIIIIIIIIIIIZZIIII ...\n", - "Projecting term 10 out of 66: (1+0j) * IIIIIIIIIIIIIIXXIIIIII ...\n", - "Projecting term 11 out of 66: (1+0j) * IIIIIIIIIIIIIIYYIIIIII ...\n", - "Projecting term 12 out of 66: (1+0j) * IIIIIIIIIIIIIIZZIIIIII ...\n", - "Projecting term 13 out of 66: (1+0j) * IIIIIIIIIIIIXXIIIIIIII ...\n", - "Projecting term 14 out of 66: (1+0j) * IIIIIIIIIIIIYYIIIIIIII ...\n", - "Projecting term 15 out of 66: (1+0j) * IIIIIIIIIIIIZZIIIIIIII ...\n", - "Projecting term 16 out of 66: (1+0j) * IIIIIIIIIIXXIIIIIIIIII ...\n", - "Projecting term 17 out of 66: (1+0j) * IIIIIIIIIIYYIIIIIIIIII ...\n", - "Projecting term 18 out of 66: (1+0j) * IIIIIIIIIIZZIIIIIIIIII ...\n", - "Projecting term 19 out of 66: (1+0j) * IIIIIIIIXXIIIIIIIIIIII ...\n", - "Projecting term 20 out of 66: (1+0j) * IIIIIIIIYYIIIIIIIIIIII ...\n", - "Projecting term 21 out of 66: (1+0j) * IIIIIIIIZZIIIIIIIIIIII ...\n", - "Projecting term 22 out of 66: (1+0j) * IIIIIIXXIIIIIIIIIIIIII ...\n", - "Projecting term 23 out of 66: (1+0j) * IIIIIIYYIIIIIIIIIIIIII ...\n", - "Projecting term 24 out of 66: (1+0j) * IIIIIIZZIIIIIIIIIIIIII ...\n", - "Projecting term 25 out of 66: (1+0j) * IIIIXXIIIIIIIIIIIIIIII ...\n", - "Projecting term 26 out of 66: (1+0j) * IIIIYYIIIIIIIIIIIIIIII ...\n", - "Projecting term 27 out of 66: (1+0j) * IIIIZZIIIIIIIIIIIIIIII ...\n", - "Projecting term 28 out of 66: (1+0j) * IIXXIIIIIIIIIIIIIIIIII ...\n", - "Projecting term 29 out of 66: (1+0j) * IIYYIIIIIIIIIIIIIIIIII ...\n", - "Projecting term 30 out of 66: (1+0j) * IIZZIIIIIIIIIIIIIIIIII ...\n", - "Projecting term 31 out of 66: (1+0j) * XXIIIIIIIIIIIIIIIIIIII ...\n", - "Projecting term 32 out of 66: (1+0j) * YYIIIIIIIIIIIIIIIIIIII ...\n", - "Projecting term 33 out of 66: (1+0j) * ZZIIIIIIIIIIIIIIIIIIII ...\n", - "Projecting term 34 out of 66: (1+0j) * XIIIIIIIIIIIIIIIIIIIIX ...\n", - "Projecting term 35 out of 66: (1+0j) * YIIIIIIIIIIIIIIIIIIIIY ...\n", - "Projecting term 36 out of 66: (1+0j) * ZIIIIIIIIIIIIIIIIIIIIZ ...\n", - "Projecting term 37 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIXXI ...\n", - "Projecting term 38 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIYYI ...\n", - "Projecting term 39 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIZZI ...\n", - "Projecting term 40 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIXXIII ...\n", - "Projecting term 41 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIYYIII ...\n", - "Projecting term 42 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIZZIII ...\n", - "Projecting term 43 out of 66: (1+0j) * IIIIIIIIIIIIIIIXXIIIII ...\n", - "Projecting term 44 out of 66: (1+0j) * IIIIIIIIIIIIIIIYYIIIII ...\n", - "Projecting term 45 out of 66: (1+0j) * IIIIIIIIIIIIIIIZZIIIII ...\n", - "Projecting term 46 out of 66: (1+0j) * IIIIIIIIIIIIIXXIIIIIII ...\n", - "Projecting term 47 out of 66: (1+0j) * IIIIIIIIIIIIIYYIIIIIII ...\n", - "Projecting term 48 out of 66: (1+0j) * IIIIIIIIIIIIIZZIIIIIII ...\n", - "Projecting term 49 out of 66: (1+0j) * IIIIIIIIIIIXXIIIIIIIII ...\n", - "Projecting term 50 out of 66: (1+0j) * IIIIIIIIIIIYYIIIIIIIII ...\n", - "Projecting term 51 out of 66: (1+0j) * IIIIIIIIIIIZZIIIIIIIII ...\n", - "Projecting term 52 out of 66: (1+0j) * IIIIIIIIIXXIIIIIIIIIII ...\n", - "Projecting term 53 out of 66: (1+0j) * IIIIIIIIIYYIIIIIIIIIII ...\n", - "Projecting term 54 out of 66: (1+0j) * IIIIIIIIIZZIIIIIIIIIII ...\n", - "Projecting term 55 out of 66: (1+0j) * IIIIIIIXXIIIIIIIIIIIII ...\n", - "Projecting term 56 out of 66: (1+0j) * IIIIIIIYYIIIIIIIIIIIII ...\n", - "Projecting term 57 out of 66: (1+0j) * IIIIIIIZZIIIIIIIIIIIII ...\n", - "Projecting term 58 out of 66: (1+0j) * IIIIIXXIIIIIIIIIIIIIII ...\n", - "Projecting term 59 out of 66: (1+0j) * IIIIIYYIIIIIIIIIIIIIII ...\n", - "Projecting term 60 out of 66: (1+0j) * IIIIIZZIIIIIIIIIIIIIII ...\n", - "Projecting term 61 out of 66: (1+0j) * IIIXXIIIIIIIIIIIIIIIII ...\n", - "Projecting term 62 out of 66: (1+0j) * IIIYYIIIIIIIIIIIIIIIII ...\n", - "Projecting term 63 out of 66: (1+0j) * IIIZZIIIIIIIIIIIIIIIII ...\n", - "Projecting term 64 out of 66: (1+0j) * IXXIIIIIIIIIIIIIIIIIII ...\n", - "Projecting term 65 out of 66: (1+0j) * IYYIIIIIIIIIIIIIIIIIII ...\n", - "Projecting term 66 out of 66: (1+0j) * IZZIIIIIIIIIIIIIIIIIII ...\n" - ] - } - ], - "source": [ - "from qiskit_addon_sqd.qubit import project_operator_to_subspace\n", - "\n", - "operator = project_operator_to_subspace(bitstring_matrix, hamiltonian, verbose=True)" - ] - }, - { - "cell_type": "markdown", - "id": "1fce97a2", - "metadata": {}, - "source": [ - "Check that both implementations yield the same coo_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4bf56509", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0j\n" - ] - } - ], - "source": [ - "print((operator.power(2) - operator_from_matrix_elements.power(2)).sum())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/how_tos/select-open-closed-shell.mdx b/docs/addons/qiskit-addon-sqd/how_tos/select-open-closed-shell.mdx deleted file mode 100644 index 84b41a3efa4..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/select-open-closed-shell.mdx +++ /dev/null @@ -1,334 +0,0 @@ - - -# Understand open-shell vs closed-shell options and its effect in the subspace construction - -In this “how-to”, we will show how to choose subpace dimensions in the `qiskit_addon_sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068). - -More importantly, this “how-to” also highlights some differences in the behaviour in the susbapce construction when run in `open_shell = False` or `open_shell = True` modes: - -* `open_shell = False` only works when the number of spin-up and spin-down electrons is the same. -* `open_shell = True` must be used when the number of spin-up and spin-down electrons is different. It can also be used when the number of spin-up and spin-down electrons is the same. However, in this last case, there is a difference in the sizes of the subspaces generated between `open_shell = False` and `open_shell = True`, as discussed in this notebook. - -**NOTE:** Some of the electronic-configuration (bitstring) manipulations in this package have as a goal to preserve the total spin symmetry $S^2$. Standard Selected Counfiguration Interaction (SCI) solvers cannot impose $S^2$ conservation exactly. Consequently, they do so approximately via a Lagrange multiplier. - -The choice of electronic configurations entering the eigenstate solver can also have a strong effect in the conservation of spin. For example, in a (2-electron,2-orbital) system, one may sample the configuration $|1001\rangle$ (having a single spin-up excitation over the RHF state $|0101\rangle$) which is a linear combination of the open-shell singlet and triplet states, respectively $(|1001\rangle ± |0110\rangle) /\sqrt{2}$. If the configuration |0110⟩ is not sampled, one can construct neither eigenfunction of total spin, leading to spin contamination or redundancy (i.e. the configuration |1001⟩ is involved in a CI calculation, but has coefficient 0 in the CI vector). Consider that a single sample $|1001\rangle$ is generated in the quantum computer, this is how the `sqd` package handles this situation: - -* `open_shell = False`: - - 1. The $1001$ bitstring is split in half, representing spin-up and spin-down configurations: $10$ (up) and $01$ (down). - 2. The list of unique spin-polarized configurations is constructed: $\mathcal{U} = [01, 10]$. - 3. We then consider all possible combinations of $\mathcal{U}$ elements to form the basis: $\left \{ |0101\rangle, |0110\rangle , |1001\rangle , |1010\rangle \right \}$, which contains the singlet and triplet states. - -* `open_shell = True`: - - 1. Contrary to the `open_shell = False` case, we do not combine the halves of the bitstring to form the basis. - - - -## Closed-Shell - -This example shows how the bitstrings are manipulated in a (2-electron, 4-orbital) system. - -``` -[1]: -``` - -```python -# Specify molecule properties -num_orbitals = 4 -num_elec_a = num_elec_b = 1 -open_shell = False -``` - -Specify by hand a dictionary of measurement outcomes - -``` -[2]: -``` - -```python -counts_dict = {"00010010": 1 / 2.0 - 0.01, "01001000": 1 / 2.0 - 0.01, "00010001": 0.02} -``` - -Transform the counts dict into a bitstring matrix and probability array for post-processing - -``` -[3]: -``` - -```python -from qiskit_addon_sqd.counts import counts_to_arrays - -# Convert counts into bitstring and probability arrays -bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict) -print(bitstring_matrix_full) -print(probs_arr_full) -``` - -```python -[[False False False True False False True False] - [False True False False True False False False] - [False False False True False False False True]] -[0.49 0.49 0.02] -``` - -Subsample a single batch of size two: - -* `n_batches = 1`: Number of batches of configurations used by the different calls to the eigenstate solver -* `samples_per_batch = 2`: Number of unique configurations to include in each batch - -``` -[4]: -``` - -```python -from qiskit_addon_sqd.subsampling import postselect_and_subsample - -n_batches = 1 -samples_per_batch = 2 - -# seed for random number generator -rand_seed = 48 - -# Generate the batches -batches = postselect_and_subsample( - bitstring_matrix_full, - probs_arr_full, - hamming_right=num_elec_a, - hamming_left=num_elec_b, - samples_per_batch=samples_per_batch, - num_batches=n_batches, - rand_seed=rand_seed, -) - -print(batches[0]) -``` - -```python -[[False False False True False False True False] - [False True False False True False False False]] -``` - -Obtain decimal representation of the spin-up and spin-down bitstrings used by the eigenstate solver - -The fist element in the tuple corresponds to the decimal representation of the spin-up configurations, while the second element in the tuple corresponds to the decimal representation of the spin-down configurations - -``` -[5]: -``` - -```python -from qiskit_addon_sqd.fermion import bitstring_matrix_to_ci_strs - -ci_strs = bitstring_matrix_to_ci_strs(batches[0], open_shell=open_shell) -print(ci_strs) -``` - -```python -(array([1, 2, 4, 8], dtype=int64), array([1, 2, 4, 8], dtype=int64)) -``` - -Note that while the number of samples per batch is 2, and the sampled bitstrings are: $00010010$ and $01001000$, four electronic configurations are generated per spin-species. In this case, the set of unique spin-polarized configurations is given by: - -$$ -\mathcal{U} = \{ 0001, 0010, 0100, 1000 \} -$$ - -whose base-10 decimal representation is - -$$ -\mathcal{U}_{10} = \{ 1, 2, 4, 8 \} -$$ - -Basis of the subspace: - -The eigenstate solver takes all possible pairs of spin-up and spin-down bitstrings to construnct the basis $\mathcal{B}$ of the subspace: - -* Element 1: $|00010001\rangle$ -* Element 2: $|00010010\rangle$ -* Element 3: $|00010100\rangle$ -* Element 4: $|00011000\rangle$ -* Element 5: $|00100001\rangle$ -* Element 6: $|00100010\rangle$ -* Element 7: $|00100100\rangle$ -* Element 8: $|00101000\rangle$ -* Element 9: $|01000001\rangle$ -* Element 10: $|01000010\rangle$ -* Element 11: $|01000100\rangle$ -* Element 12: $|01001000\rangle$ -* Element 13: $|10000001\rangle$ -* Element 14: $|10000010\rangle$ -* Element 15: $|10000100\rangle$ -* Element 16: $|10001000\rangle$ - -``` -[6]: -``` - -```python -ci_strs_up, ci_strs_dn = ci_strs - -print("Basis elements of the subspace:") - -for ci_str_up in ci_strs_up: - for ci_str_dn in ci_strs_dn: - format_name = "{0:0" + str(num_orbitals) + "b}" - print("|" + format_name.format(ci_str_up) + format_name.format(ci_str_dn) + ">") -``` - -```python -Basis elements of the subspace: -|00010001> -|00010010> -|00010100> -|00011000> -|00100001> -|00100010> -|00100100> -|00101000> -|01000001> -|01000010> -|01000100> -|01001000> -|10000001> -|10000010> -|10000100> -|10001000> -``` - -**The subspace dimension is upper-bounded by**: $2 \cdot$ (`samples_per_batch`)$^2$ - - - -## Open-Shell - -This example shows how the bitstrings are manipulated in a (2-electron, 4-orbital) system. - -``` -[7]: -``` - -```python -# Specify molecule properties -num_orbitals = 4 -num_elec_a = num_elec_b = 1 -open_shell = True -``` - -Specify by hand a dictionary of measurement outcomes - -``` -[8]: -``` - -```python -counts_dict = {"00010010": 1 / 2.0 - 0.01, "01001000": 1 / 2.0 - 0.01, "00010001": 0.02} -``` - -Transform the counts dict into a bitstring matrix and probability array for post-processing - -``` -[9]: -``` - -```python -# Convert counts into bitstring and probability arrays -bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict) -print(bitstring_matrix_full) -print(probs_arr_full) -``` - -```python -[[False False False True False False True False] - [False True False False True False False False] - [False False False True False False False True]] -[0.49 0.49 0.02] -``` - -Subsample a single batch of size two: - -* `n_batches = 1`: Number of batches of configurations used by the different calls to the eigenstate solver -* `samples_per_batch = 2`: Number of unique configurations to include in each batch - -``` -[10]: -``` - -```python -n_batches = 1 -samples_per_batch = 2 - -# seed for random number generator -rand_seed = 48 - -# Generate the batches -batches = postselect_and_subsample( - bitstring_matrix_full, - probs_arr_full, - hamming_right=num_elec_a, - hamming_left=num_elec_b, - samples_per_batch=samples_per_batch, - num_batches=n_batches, - rand_seed=rand_seed, -) - -print(batches[0]) -``` - -```python -[[False False False True False False True False] - [False True False False True False False False]] -``` - -Obtain decimal representation of the spin-up and spin-down bitstrings used by the eigenstate solver - -The fist element in the tuple corresponds to the decimal representation of the spin-up configurations, while the second element in the tuple corresponds to the decimal representation of the spin-down configurations - -``` -[11]: -``` - -```python -ci_strs = bitstring_matrix_to_ci_strs(batches[0], open_shell=open_shell) -print(ci_strs) -``` - -```python -(array([2, 8], dtype=int64), array([1, 4], dtype=int64)) -``` - -If we specify that `open_shell = True`, now we do not include all unique half-bitstrings as spin-up and spin-down configurations, thus yielding a smaller basis as when specifying `open_shell = False` - -Basis of the subspace: - -The eigenstate solver takes all possible pairs of spin-up and spin-down bitstrings to construnct the basis $\mathcal{B}$ of the subspace: - -* Element 1: $|00010010\rangle$ -* Element 2: $|00011000\rangle$ -* Element 3: $|01000010\rangle$ -* Element 4: $|01001000\rangle$ - -``` -[12]: -``` - -```python -ci_strs_up, ci_strs_dn = ci_strs - -print("Basis elements of the subspace:") - -for ci_str_up in ci_strs_up: - for ci_str_dn in ci_strs_dn: - format_name = "{0:0" + str(num_orbitals) + "b}" - print("|" + format_name.format(ci_str_up) + format_name.format(ci_str_dn) + ">") -``` - -```python -Basis elements of the subspace: -|00100001> -|00100100> -|10000001> -|10000100> -``` - -**The subspace dimension is upper-bounded by**: (`samples_per_batch`)$^2$ diff --git a/docs/addons/qiskit-addon-sqd/how_tos/select_open_closed_shell.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/select_open_closed_shell.ipynb deleted file mode 100644 index dd2fa781b99..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/select_open_closed_shell.ipynb +++ /dev/null @@ -1,534 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "72420c62-2716-4f64-ad5b-73394b481cc1", - "metadata": {}, - "source": [ - "# Understand open-shell vs closed-shell options and its effect in the subspace construction\n", - "\n", - "In this \"how-to\", we will show how to choose subpace dimensions in the `qiskit_addon_sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068). \n", - "\n", - "More importantly, this \"how-to\" also highlights some differences in the behaviour in the susbapce construction when run in `open_shell = False` or `open_shell = True` modes:\n", - "\n", - "- `open_shell = False` only works when the number of spin-up and spin-down electrons is the same. \n", - "\n", - "- `open_shell = True` must be used when the number of spin-up and spin-down electrons is different. It can also be used when the number of spin-up and spin-down electrons is the same. However, in this last case, there is a difference in the sizes of the subspaces generated between `open_shell = False` and `open_shell = True`, as discussed in this notebook.\n", - "\n", - "**NOTE:** Some of the electronic-configuration (bitstring) manipulations in this package have as a goal to preserve the total spin symmetry $S^2$. Standard Selected Counfiguration Interaction (SCI) solvers cannot impose $S^2$ conservation exactly. Consequently, they do so approximately via a Lagrange multiplier. \n", - "\n", - "The choice of electronic configurations entering the eigenstate solver can also have a strong effect in the conservation of spin. For example, in a (2-electron,2-orbital) system, one may sample the configuration $|1001\\rangle$ (having a single spin-up excitation over the RHF state $|0101\\rangle$) which is a linear combination of the open-shell singlet and triplet states, respectively $(|1001\\rangle ± |0110\\rangle) /\\sqrt{2}$. If the configuration |0110⟩ is not sampled, one can construct neither eigenfunction of total spin, leading to spin contamination or redundancy (i.e. the configuration |1001⟩ is involved in a CI calculation, but has coefficient 0 in the CI vector). Consider that a single sample $|1001\\rangle$ is generated in the quantum computer, this is how the `sqd` package handles this situation:\n", - "\n", - "- `open_shell = False`: \n", - "\n", - " 1. The $1001$ bitstring is split in half, representing spin-up and spin-down configurations: $10$ (up) and $01$ (down).\n", - " \n", - " 2. The list of unique spin-polarized configurations is constructed: $\\mathcal{U} = [01, 10]$.\n", - "\n", - " 3. We then consider all possible combinations of $\\mathcal{U}$ elements to form the basis: $\\left \\{ |0101\\rangle, |0110\\rangle , |1001\\rangle , |1010\\rangle \\right \\}$, which contains the singlet and triplet states.\n", - "\n", - "\n", - "- `open_shell = True`: \n", - "\n", - " 1. Contrary to the `open_shell = False` case, we do not combine the halves of the bitstring to form the basis." - ] - }, - { - "cell_type": "markdown", - "id": "a6755afb-ca1e-4473-974b-ba89acc8abce", - "metadata": {}, - "source": [ - "### Closed-Shell\n", - "\n", - "This example shows how the bitstrings are manipulated in a (2-electron, 4-orbital) system." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", - "metadata": {}, - "outputs": [], - "source": [ - "# Specify molecule properties\n", - "num_orbitals = 4\n", - "num_elec_a = num_elec_b = 1\n", - "open_shell = False" - ] - }, - { - "cell_type": "markdown", - "id": "c58e988c-a109-44cd-a975-9df43250c318", - "metadata": {}, - "source": [ - "Specify by hand a dictionary of measurement outcomes" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e9506e0b-ed64-48bb-a97a-ef851b604af1", - "metadata": {}, - "outputs": [], - "source": [ - "counts_dict = {\"00010010\": 1 / 2.0 - 0.01, \"01001000\": 1 / 2.0 - 0.01, \"00010001\": 0.02}" - ] - }, - { - "cell_type": "markdown", - "id": "851bc98e-9c08-4e78-9472-36301abc11d8", - "metadata": {}, - "source": [ - "Transform the counts dict into a bitstring matrix and probability array for post-processing" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7a102a7f-aae6-4583-ab82-ae40fcb5496a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[False False False True False False True False]\n", - " [False True False False True False False False]\n", - " [False False False True False False False True]]\n", - "[0.49 0.49 0.02]\n" - ] - } - ], - "source": [ - "from qiskit_addon_sqd.counts import counts_to_arrays\n", - "\n", - "# Convert counts into bitstring and probability arrays\n", - "bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict)\n", - "print(bitstring_matrix_full)\n", - "print(probs_arr_full)" - ] - }, - { - "cell_type": "markdown", - "id": "eb704101-0fe8-4d12-b572-b1d844e35a90", - "metadata": {}, - "source": [ - "Subsample a single batch of size two:\n", - "\n", - "- ``n_batches = 1``: Number of batches of configurations used by the different calls to the eigenstate solver\n", - "- ``samples_per_batch = 2``: Number of unique configurations to include in each batch" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "fe60aee2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[False False False True False False True False]\n", - " [False True False False True False False False]]\n" - ] - } - ], - "source": [ - "from qiskit_addon_sqd.subsampling import postselect_and_subsample\n", - "\n", - "n_batches = 1\n", - "samples_per_batch = 2\n", - "\n", - "# seed for random number generator\n", - "rand_seed = 48\n", - "\n", - "# Generate the batches\n", - "batches = postselect_and_subsample(\n", - " bitstring_matrix_full,\n", - " probs_arr_full,\n", - " hamming_right=num_elec_a,\n", - " hamming_left=num_elec_b,\n", - " samples_per_batch=samples_per_batch,\n", - " num_batches=n_batches,\n", - " rand_seed=rand_seed,\n", - ")\n", - "\n", - "print(batches[0])" - ] - }, - { - "cell_type": "markdown", - "id": "93a6d05a", - "metadata": {}, - "source": [ - "Obtain decimal representation of the spin-up and spin-down bitstrings used by the eigenstate solver\n", - "\n", - "The fist element in the tuple corresponds to the decimal representation of the spin-up configurations, while the second element in the tuple corresponds to the decimal representation of the spin-down configurations" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ef90e039", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(array([1, 2, 4, 8], dtype=int64), array([1, 2, 4, 8], dtype=int64))\n" - ] - } - ], - "source": [ - "from qiskit_addon_sqd.fermion import bitstring_matrix_to_ci_strs\n", - "\n", - "ci_strs = bitstring_matrix_to_ci_strs(batches[0], open_shell=open_shell)\n", - "print(ci_strs)" - ] - }, - { - "cell_type": "markdown", - "id": "70d7883c", - "metadata": {}, - "source": [ - "Note that while the number of samples per batch is 2, and the sampled bitstrings are: $00010010$ and $01001000$, four electronic configurations are generated per spin-species. In this case, the set of unique spin-polarized configurations is given by:\n", - "$$\n", - "\\mathcal{U} = \\{ 0001, 0010, 0100, 1000 \\}\n", - "$$\n", - "whose base-10 decimal representation is \n", - "$$\n", - "\\mathcal{U}_{10} = \\{ 1, 2, 4, 8 \\}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "03b32ed5", - "metadata": {}, - "source": [ - "Basis of the subspace:\n", - "\n", - "The eigenstate solver takes all possible pairs of spin-up and spin-down bitstrings to construnct the basis $\\mathcal{B}$ of the subspace:\n", - " \n", - "- Element 1: $|00010001\\rangle$ \n", - "\n", - "- Element 2: $|00010010\\rangle$ \n", - "\n", - "- Element 3: $|00010100\\rangle$ \n", - "\n", - "- Element 4: $|00011000\\rangle$ \n", - "\n", - "- Element 5: $|00100001\\rangle$ \n", - "\n", - "- Element 6: $|00100010\\rangle$ \n", - "\n", - "- Element 7: $|00100100\\rangle$ \n", - "\n", - "- Element 8: $|00101000\\rangle$ \n", - " \n", - "- Element 9: $|01000001\\rangle$ \n", - "\n", - "- Element 10: $|01000010\\rangle$ \n", - "\n", - "- Element 11: $|01000100\\rangle$ \n", - "\n", - "- Element 12: $|01001000\\rangle$ \n", - "\n", - "- Element 13: $|10000001\\rangle$ \n", - "\n", - "- Element 14: $|10000010\\rangle$ \n", - "\n", - "- Element 15: $|10000100\\rangle$ \n", - "\n", - "- Element 16: $|10001000\\rangle$ \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "11c924ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Basis elements of the subspace:\n", - "|00010001>\n", - "|00010010>\n", - "|00010100>\n", - "|00011000>\n", - "|00100001>\n", - "|00100010>\n", - "|00100100>\n", - "|00101000>\n", - "|01000001>\n", - "|01000010>\n", - "|01000100>\n", - "|01001000>\n", - "|10000001>\n", - "|10000010>\n", - "|10000100>\n", - "|10001000>\n" - ] - } - ], - "source": [ - "ci_strs_up, ci_strs_dn = ci_strs\n", - "\n", - "print(\"Basis elements of the subspace:\")\n", - "\n", - "for ci_str_up in ci_strs_up:\n", - " for ci_str_dn in ci_strs_dn:\n", - " format_name = \"{0:0\" + str(num_orbitals) + \"b}\"\n", - " print(\"|\" + format_name.format(ci_str_up) + format_name.format(ci_str_dn) + \">\")" - ] - }, - { - "cell_type": "markdown", - "id": "aa43a4fc", - "metadata": {}, - "source": [ - "**The subspace dimension is upper-bounded by**: $2 \\cdot$ (`samples_per_batch`)$^2$" - ] - }, - { - "cell_type": "markdown", - "id": "9412e52b", - "metadata": {}, - "source": [ - "### Open-Shell\n", - "\n", - "This example shows how the bitstrings are manipulated in a (2-electron, 4-orbital) system." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9b515b8a", - "metadata": {}, - "outputs": [], - "source": [ - "# Specify molecule properties\n", - "num_orbitals = 4\n", - "num_elec_a = num_elec_b = 1\n", - "open_shell = True" - ] - }, - { - "cell_type": "markdown", - "id": "9ef78560", - "metadata": {}, - "source": [ - "Specify by hand a dictionary of measurement outcomes" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "06b2185a", - "metadata": {}, - "outputs": [], - "source": [ - "counts_dict = {\"00010010\": 1 / 2.0 - 0.01, \"01001000\": 1 / 2.0 - 0.01, \"00010001\": 0.02}" - ] - }, - { - "cell_type": "markdown", - "id": "08b32957", - "metadata": {}, - "source": [ - "Transform the counts dict into a bitstring matrix and probability array for post-processing" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "90561893", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[False False False True False False True False]\n", - " [False True False False True False False False]\n", - " [False False False True False False False True]]\n", - "[0.49 0.49 0.02]\n" - ] - } - ], - "source": [ - "# Convert counts into bitstring and probability arrays\n", - "bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict)\n", - "print(bitstring_matrix_full)\n", - "print(probs_arr_full)" - ] - }, - { - "cell_type": "markdown", - "id": "416bfb6c", - "metadata": {}, - "source": [ - "Subsample a single batch of size two:\n", - "\n", - "- ``n_batches = 1``: Number of batches of configurations used by the different calls to the eigenstate solver\n", - "- ``samples_per_batch = 2``: Number of unique configurations to include in each batch" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "cf4fe11d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[False False False True False False True False]\n", - " [False True False False True False False False]]\n" - ] - } - ], - "source": [ - "n_batches = 1\n", - "samples_per_batch = 2\n", - "\n", - "# seed for random number generator\n", - "rand_seed = 48\n", - "\n", - "# Generate the batches\n", - "batches = postselect_and_subsample(\n", - " bitstring_matrix_full,\n", - " probs_arr_full,\n", - " hamming_right=num_elec_a,\n", - " hamming_left=num_elec_b,\n", - " samples_per_batch=samples_per_batch,\n", - " num_batches=n_batches,\n", - " rand_seed=rand_seed,\n", - ")\n", - "\n", - "print(batches[0])" - ] - }, - { - "cell_type": "markdown", - "id": "54d699ca", - "metadata": {}, - "source": [ - "Obtain decimal representation of the spin-up and spin-down bitstrings used by the eigenstate solver\n", - "\n", - "The fist element in the tuple corresponds to the decimal representation of the spin-up configurations, while the second element in the tuple corresponds to the decimal representation of the spin-down configurations" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b40b049b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(array([2, 8], dtype=int64), array([1, 4], dtype=int64))\n" - ] - } - ], - "source": [ - "ci_strs = bitstring_matrix_to_ci_strs(batches[0], open_shell=open_shell)\n", - "print(ci_strs)" - ] - }, - { - "cell_type": "markdown", - "id": "28921e56", - "metadata": {}, - "source": [ - "If we specify that `open_shell = True`, now we do not include all unique half-bitstrings as spin-up and spin-down configurations, thus yielding a smaller basis as when specifying `open_shell = False`" - ] - }, - { - "cell_type": "markdown", - "id": "e1959b72", - "metadata": {}, - "source": [ - "Basis of the subspace:\n", - "\n", - "The eigenstate solver takes all possible pairs of spin-up and spin-down bitstrings to construnct the basis $\\mathcal{B}$ of the subspace:\n", - " \n", - "- Element 1: $|00010010\\rangle$ \n", - "\n", - "- Element 2: $|00011000\\rangle$ \n", - "\n", - "- Element 3: $|01000010\\rangle$ \n", - "\n", - "- Element 4: $|01001000\\rangle$ \n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a550aba2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Basis elements of the subspace:\n", - "|00100001>\n", - "|00100100>\n", - "|10000001>\n", - "|10000100>\n" - ] - } - ], - "source": [ - "ci_strs_up, ci_strs_dn = ci_strs\n", - "\n", - "print(\"Basis elements of the subspace:\")\n", - "\n", - "for ci_str_up in ci_strs_up:\n", - " for ci_str_dn in ci_strs_dn:\n", - " format_name = \"{0:0\" + str(num_orbitals) + \"b}\"\n", - " print(\"|\" + format_name.format(ci_str_up) + format_name.format(ci_str_dn) + \">\")" - ] - }, - { - "cell_type": "markdown", - "id": "7317bc49", - "metadata": {}, - "source": [ - "**The subspace dimension is upper-bounded by**: (`samples_per_batch`)$^2$" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/how_tos/use-oo-to-optimize-hamiltonian-basis.mdx b/docs/addons/qiskit-addon-sqd/how_tos/use-oo-to-optimize-hamiltonian-basis.mdx deleted file mode 100644 index e70c0f0957a..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/use-oo-to-optimize-hamiltonian-basis.mdx +++ /dev/null @@ -1,288 +0,0 @@ - - -# Optimize Hamiltonian basis with orbital optimization - -In this tutorial, we will show how to use the `sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068) and then further optimize the ground state approximation using orbital optimization - -Refer to [Sec. II A 4](https://arxiv.org/pdf/2405.05068) for a more detailed discussion on this technique. - - - -## Specify the molecule and generate samples - -In this example, we will approximate the ground state energy of an $N_2$ molecule and then improve the answer using orbital optimization. This guide studies $N_2$ at equilibrium, which is mean-field dominated. This means the MO basis is already a good choice for our integrals; therefore, we will rotate our integrals **out** of the MO basis in order to illustrate the effects of orbital optimization. - -``` -[1]: -``` - -```python -%%capture -import numpy as np -import pyscf -import pyscf.cc -import pyscf.mcscf -from qiskit_addon_sqd.fermion import rotate_integrals - -# Specify molecule properties -open_shell = False -spin_sq = 0 - -# Build N2 molecule -mol = pyscf.gto.Mole() -mol.build( - atom=[["N", (0, 0, 0)], ["N", (1.0, 0, 0)]], - basis="6-31g", - symmetry="Dooh", -) - -# Define active space -n_frozen = 2 -active_space = range(n_frozen, mol.nao_nr()) - -# Get molecular integrals -scf = pyscf.scf.RHF(mol).run() -num_orbitals = len(active_space) -n_electrons = int(sum(scf.mo_occ[active_space])) -num_elec_a = (n_electrons + mol.spin) // 2 -num_elec_b = (n_electrons - mol.spin) // 2 -cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b)) -mo = cas.sort_mo(active_space, base=0) -hcore, nuclear_repulsion_energy = cas.get_h1cas(mo) -eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals) - -# Compute exact energy -exact_energy = cas.run().e_tot -``` - -The MO basis is already a good basis for this problem, so we will rotate out of that basis in this guide in order to highlight the effect of orbital optimization. - -``` -[2]: -``` - -```python -# Rotate our integrals out of MO basis -rng = np.random.default_rng(24) -num_params = (num_orbitals**2 - num_orbitals) // 2 # antisymmetric, specified by upper triangle -k_rot = (rng.random(num_params) - 0.5) * 0.5 -hcore_rot, eri_rot = rotate_integrals(hcore, eri, k_rot) -``` - -Generate samples - -``` -[3]: -``` - -```python -from qiskit_addon_sqd.counts import counts_to_arrays, generate_counts_uniform - -# Generate random samples -counts_dict = generate_counts_uniform(10_000, num_orbitals * 2, rand_seed=rng) - -# Convert counts into bitstring and probability arrays -bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict) -``` - - - -## Iteratively refine the samples using SQD and approximate the ground state - -``` -[4]: -``` - -```python -from qiskit_addon_sqd.configuration_recovery import recover_configurations -from qiskit_addon_sqd.fermion import solve_fermion -from qiskit_addon_sqd.subsampling import postselect_and_subsample - -# SQSD options -iterations = 5 - -# Eigenstate solver options -n_batches = 3 -samples_per_batch = 100 -max_davidson_cycles = 200 - -# Self-consistent configuration recovery loop -e_hist = np.zeros((iterations, n_batches)) # energy history -s_hist = np.zeros((iterations, n_batches)) # spin history -occupancy_hist = [] -avg_occupancy = None -for i in range(iterations): - print(f"Starting configuration recovery iteration {i}") - # On the first iteration, we have no orbital occupancy information from the - # solver, so we just post-select from the full bitstring set based on hamming weight. - if avg_occupancy is None: - bs_mat_tmp = bitstring_matrix_full - probs_arr_tmp = probs_arr_full - - # In following iterations, we use both the occupancy info and the target hamming - # weight to refine bitstrings. - else: - bs_mat_tmp, probs_arr_tmp = recover_configurations( - bitstring_matrix_full, - probs_arr_full, - avg_occupancy, - num_elec_a, - num_elec_b, - rand_seed=rng, - ) - - # Throw out samples with incorrect hamming weight and create batches of subsamples. - batches = postselect_and_subsample( - bs_mat_tmp, - probs_arr_tmp, - hamming_right=num_elec_a, - hamming_left=num_elec_b, - samples_per_batch=samples_per_batch, - num_batches=n_batches, - rand_seed=rng, - ) - - # Run eigenstate solvers in a loop. This loop should be parallelized for larger problems. - int_e = np.zeros(n_batches) - int_s = np.zeros(n_batches) - int_occs = [] - cs = [] - for j in range(n_batches): - energy_sci, coeffs_sci, avg_occs, spin = solve_fermion( - batches[j], - hcore_rot, - eri_rot, - open_shell=open_shell, - spin_sq=spin_sq, - max_cycle=max_davidson_cycles, - ) - energy_sci += nuclear_repulsion_energy - int_e[j] = energy_sci - int_s[j] = spin - int_occs.append(avg_occs) - cs.append(coeffs_sci) - - # Combine batch results - avg_occupancy = tuple(np.mean(int_occs, axis=0)) - - # Track optimization history - e_hist[i, :] = int_e - s_hist[i, :] = int_s - occupancy_hist.append(avg_occupancy) -``` - -```python -Starting configuration recovery iteration 0 -Starting configuration recovery iteration 1 -Starting configuration recovery iteration 2 -Starting configuration recovery iteration 3 -Starting configuration recovery iteration 4 -``` - - - -## Refine the subspace - -To refine the subspace, we will take the CI strings of the batch with the lowest energy from the last configuration recovery step. Other strategies may be used, like taking the union of the CI strings of the batches in the last configuration recovery iteration. - -``` -[5]: -``` - -```python -from qiskit_addon_sqd.fermion import bitstring_matrix_to_ci_strs - -best_batch = batches[np.argmin(e_hist[-1])] -ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(best_batch, open_shell=open_shell) -print(f"Subspace dimension: {len(ci_strs_up) * len(ci_strs_dn)}") -print(f"Energy of that batch from SQD: {e_hist[-1, np.argmin(e_hist[-1])]}") - -# Union strategy - -# batches_union = np.concatenate((batches[0], batches[1]), axis = 0) -# for i in range(n_batches-2): -# batches_union = np.concatenate((batches_union, batches[ i+ 2])) -# ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs( -# batches_union, open_shell=open_shell -# ) -# print (f"Subspace dimension: {len(ci_strs_up) * len(ci_strs_dn)}") -``` - -```python -Subspace dimension: 32761 -Energy of that batch from SQD: -108.7531706601421 -``` - - - -## Perform orbital optimization to improve the energy approximation - -We now describe how to optimize the orbitals to further improve the quality of the sqd calculation. - -The orbital rotations that are implemented in this package are those described by: - -$$ -U(\kappa) = e^{\sum_{pq, \sigma} \kappa_{pq} c^\dagger_{p\sigma} c_{q\sigma}}, -$$ - -where $\kappa_{p, q} \in \mathbb{R}$ and $\kappa_{p, q} = -\kappa_{q, p}$. The orbitals are optimized to minimize the variational energy: - -$$ -E(\kappa) = \langle \psi | U^\dagger(\kappa) H U(\kappa) |\psi \rangle, -$$ - -with respect to $\kappa$ using gradient descent with momentum. Recall that $|\psi\rangle$ is spanned in a subspace defined by determinants. - -Since the change of basis alters the Hamiltonian, we allow $|\psi\rangle$ to respond to the change in the Hamiltonian. This is done by performing a number of alternating self-consistent optimizations of $\kappa$ and $|\psi\rangle$. We recall that the optimal $|\psi\rangle$ is given by the lowest eigenvector of the Hamiltonian projected into the subspace. - -The `sqd.fermion.fermion` module provides the tools to perform this alternating optimization. In particular, the function `sqd.fermion.optimize_orbitals()`. - -Some of the arguments that define the optimization are: - -* `num_iters`: number of self-consistent iterations. -* `num_steps_grad`: number of gradient step updates performed when optimizing $\kappa$ on each self-consistent iteration. -* `learning_rate`: step-size in the gradient descent optimization of $\kappa$. - -``` -[6]: -``` - -```python -from qiskit_addon_sqd.fermion import optimize_orbitals - -k_flat = (rng.random(num_params) - 0.5) * 0.1 -num_iters = 20 -num_steps_grad = 10_000 # relatively cheap to execute -learning_rate = 0.05 - -e_improved, k_flat, orbital_occupancies = optimize_orbitals( - best_batch, - hcore_rot, - eri_rot, - k_flat, - open_shell=open_shell, - spin_sq=spin_sq, - num_iters=num_iters, - num_steps_grad=num_steps_grad, - learning_rate=learning_rate, - max_cycle=max_davidson_cycles, -) -``` - -Here we see that by optimizing rotation parameters for our Hamiltonian, we can improve the result from SQD. - -``` -[7]: -``` - -```python -print(f"Exact energy: {exact_energy}") -print(f"SQD energy: {np.min(e_hist[-1])}") -print(f"Energy after OO: {e_improved + nuclear_repulsion_energy}") -``` - -```python -Exact energy: -109.04667177808032 -SQD energy: -108.7531706601421 -Energy after OO: -108.80400806164377 -``` diff --git a/docs/addons/qiskit-addon-sqd/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb deleted file mode 100644 index f352bc8400a..00000000000 --- a/docs/addons/qiskit-addon-sqd/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb +++ /dev/null @@ -1,384 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", - "metadata": {}, - "source": [ - "# Optimize Hamiltonian basis with orbital optimization\n", - "\n", - "In this tutorial, we will show how to use the `sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068) and then further optimize the ground state approximation using orbital optimization\n", - "\n", - "Refer to [Sec. II A 4](https://arxiv.org/pdf/2405.05068) for a more detailed discussion on this technique." - ] - }, - { - "cell_type": "markdown", - "id": "a6755afb-ca1e-4473-974b-ba89acc8abce", - "metadata": {}, - "source": [ - "### Specify the molecule and generate samples\n", - "\n", - "In this example, we will approximate the ground state energy of an $N_2$ molecule and then improve the answer using orbital optimization. This guide studies $N_2$ at equilibrium, which is mean-field dominated. This means the MO basis is already a good choice for our integrals; therefore, we will rotate our integrals **out** of the MO basis in order to illustrate the effects of orbital optimization." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "import numpy as np\n", - "import pyscf\n", - "import pyscf.cc\n", - "import pyscf.mcscf\n", - "from qiskit_addon_sqd.fermion import rotate_integrals\n", - "\n", - "# Specify molecule properties\n", - "open_shell = False\n", - "spin_sq = 0\n", - "\n", - "# Build N2 molecule\n", - "mol = pyscf.gto.Mole()\n", - "mol.build(\n", - " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", - " basis=\"6-31g\",\n", - " symmetry=\"Dooh\",\n", - ")\n", - "\n", - "# Define active space\n", - "n_frozen = 2\n", - "active_space = range(n_frozen, mol.nao_nr())\n", - "\n", - "# Get molecular integrals\n", - "scf = pyscf.scf.RHF(mol).run()\n", - "num_orbitals = len(active_space)\n", - "n_electrons = int(sum(scf.mo_occ[active_space]))\n", - "num_elec_a = (n_electrons + mol.spin) // 2\n", - "num_elec_b = (n_electrons - mol.spin) // 2\n", - "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", - "mo = cas.sort_mo(active_space, base=0)\n", - "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", - "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", - "\n", - "# Compute exact energy\n", - "exact_energy = cas.run().e_tot" - ] - }, - { - "cell_type": "markdown", - "id": "b23a74fc-708c-4bd0-af4e-c9cd189b6346", - "metadata": {}, - "source": [ - "The MO basis is already a good basis for this problem, so we will rotate out of that basis in this guide in order to highlight the effect of orbital optimization." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9f46d1c6-3b49-45ad-b9ed-972bd58f7e3c", - "metadata": {}, - "outputs": [], - "source": [ - "# Rotate our integrals out of MO basis\n", - "rng = np.random.default_rng(24)\n", - "num_params = (num_orbitals**2 - num_orbitals) // 2 # antisymmetric, specified by upper triangle\n", - "k_rot = (rng.random(num_params) - 0.5) * 0.5\n", - "hcore_rot, eri_rot = rotate_integrals(hcore, eri, k_rot)" - ] - }, - { - "cell_type": "markdown", - "id": "c58e988c-a109-44cd-a975-9df43250c318", - "metadata": {}, - "source": [ - "Generate samples" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e9506e0b-ed64-48bb-a97a-ef851b604af1", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_addon_sqd.counts import counts_to_arrays, generate_counts_uniform\n", - "\n", - "# Generate random samples\n", - "counts_dict = generate_counts_uniform(10_000, num_orbitals * 2, rand_seed=rng)\n", - "\n", - "# Convert counts into bitstring and probability arrays\n", - "bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict)" - ] - }, - { - "cell_type": "markdown", - "id": "eb704101-0fe8-4d12-b572-b1d844e35a90", - "metadata": {}, - "source": [ - "### Iteratively refine the samples using SQD and approximate the ground state" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b72c048e-fe8e-4fc2-b28b-03138249074e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting configuration recovery iteration 0\n", - "Starting configuration recovery iteration 1\n", - "Starting configuration recovery iteration 2\n", - "Starting configuration recovery iteration 3\n", - "Starting configuration recovery iteration 4\n" - ] - } - ], - "source": [ - "from qiskit_addon_sqd.configuration_recovery import recover_configurations\n", - "from qiskit_addon_sqd.fermion import solve_fermion\n", - "from qiskit_addon_sqd.subsampling import postselect_and_subsample\n", - "\n", - "# SQSD options\n", - "iterations = 5\n", - "\n", - "# Eigenstate solver options\n", - "n_batches = 3\n", - "samples_per_batch = 100\n", - "max_davidson_cycles = 200\n", - "\n", - "# Self-consistent configuration recovery loop\n", - "e_hist = np.zeros((iterations, n_batches)) # energy history\n", - "s_hist = np.zeros((iterations, n_batches)) # spin history\n", - "occupancy_hist = []\n", - "avg_occupancy = None\n", - "for i in range(iterations):\n", - " print(f\"Starting configuration recovery iteration {i}\")\n", - " # On the first iteration, we have no orbital occupancy information from the\n", - " # solver, so we just post-select from the full bitstring set based on hamming weight.\n", - " if avg_occupancy is None:\n", - " bs_mat_tmp = bitstring_matrix_full\n", - " probs_arr_tmp = probs_arr_full\n", - "\n", - " # In following iterations, we use both the occupancy info and the target hamming\n", - " # weight to refine bitstrings.\n", - " else:\n", - " bs_mat_tmp, probs_arr_tmp = recover_configurations(\n", - " bitstring_matrix_full,\n", - " probs_arr_full,\n", - " avg_occupancy,\n", - " num_elec_a,\n", - " num_elec_b,\n", - " rand_seed=rng,\n", - " )\n", - "\n", - " # Throw out samples with incorrect hamming weight and create batches of subsamples.\n", - " batches = postselect_and_subsample(\n", - " bs_mat_tmp,\n", - " probs_arr_tmp,\n", - " hamming_right=num_elec_a,\n", - " hamming_left=num_elec_b,\n", - " samples_per_batch=samples_per_batch,\n", - " num_batches=n_batches,\n", - " rand_seed=rng,\n", - " )\n", - "\n", - " # Run eigenstate solvers in a loop. This loop should be parallelized for larger problems.\n", - " int_e = np.zeros(n_batches)\n", - " int_s = np.zeros(n_batches)\n", - " int_occs = []\n", - " cs = []\n", - " for j in range(n_batches):\n", - " energy_sci, coeffs_sci, avg_occs, spin = solve_fermion(\n", - " batches[j],\n", - " hcore_rot,\n", - " eri_rot,\n", - " open_shell=open_shell,\n", - " spin_sq=spin_sq,\n", - " max_cycle=max_davidson_cycles,\n", - " )\n", - " energy_sci += nuclear_repulsion_energy\n", - " int_e[j] = energy_sci\n", - " int_s[j] = spin\n", - " int_occs.append(avg_occs)\n", - " cs.append(coeffs_sci)\n", - "\n", - " # Combine batch results\n", - " avg_occupancy = tuple(np.mean(int_occs, axis=0))\n", - "\n", - " # Track optimization history\n", - " e_hist[i, :] = int_e\n", - " s_hist[i, :] = int_s\n", - " occupancy_hist.append(avg_occupancy)" - ] - }, - { - "cell_type": "markdown", - "id": "917cf2d0", - "metadata": {}, - "source": [ - "### Refine the subspace\n", - "\n", - "To refine the subspace, we will take the CI strings of the batch with the lowest energy\n", - "from the last configuration recovery step. Other strategies may be used, like taking the union \n", - "of the CI strings of the batches in the last configuration recovery iteration." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "2a587030", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Subspace dimension: 32761\n", - "Energy of that batch from SQD: -108.7531706601421\n" - ] - } - ], - "source": [ - "from qiskit_addon_sqd.fermion import bitstring_matrix_to_ci_strs\n", - "\n", - "best_batch = batches[np.argmin(e_hist[-1])]\n", - "ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(best_batch, open_shell=open_shell)\n", - "print(f\"Subspace dimension: {len(ci_strs_up) * len(ci_strs_dn)}\")\n", - "print(f\"Energy of that batch from SQD: {e_hist[-1, np.argmin(e_hist[-1])]}\")\n", - "\n", - "# Union strategy\n", - "\n", - "# batches_union = np.concatenate((batches[0], batches[1]), axis = 0)\n", - "# for i in range(n_batches-2):\n", - "# batches_union = np.concatenate((batches_union, batches[ i+ 2]))\n", - "# ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(\n", - "# batches_union, open_shell=open_shell\n", - "# )\n", - "# print (f\"Subspace dimension: {len(ci_strs_up) * len(ci_strs_dn)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "e8c6d5e4", - "metadata": {}, - "source": [ - "### Perform orbital optimization to improve the energy approximation\n", - "\n", - "We now describe how to optimize the orbitals to further improve the quality of the sqd calculation.\n", - "\n", - "The orbital rotations that are implemented in this package are those described by:\n", - "$$\n", - "U(\\kappa) = e^{\\sum_{pq, \\sigma} \\kappa_{pq} c^\\dagger_{p\\sigma} c_{q\\sigma}},\n", - "$$\n", - "where $\\kappa_{p, q} \\in \\mathbb{R}$ and $\\kappa_{p, q} = -\\kappa_{q, p}$. The orbitals are optimized to \n", - "minimize the variational energy:\n", - "$$\n", - "E(\\kappa) = \\langle \\psi | U^\\dagger(\\kappa) H U(\\kappa) |\\psi \\rangle,\n", - "$$\n", - "with respect to $\\kappa$ using gradient descent with momentum. Recall that \n", - "$|\\psi\\rangle$ is spanned in a subspace defined by determinants.\n", - "\n", - "Since the change of basis alters the Hamiltonian, we allow $|\\psi\\rangle$ to \n", - "respond to the change in the Hamiltonian. This is done by performing a number of alternating\n", - "self-consistent optimizations of $\\kappa$ and $|\\psi\\rangle$. We recall that the optimal\n", - "$|\\psi\\rangle$ is given by the lowest eigenvector of the Hamiltonian projected into the\n", - "subspace.\n", - "\n", - "The ``sqd.fermion.fermion`` module provides the tools to perform this alternating\n", - "optimization. In particular, the function ``sqd.fermion.optimize_orbitals()``.\n", - "\n", - "Some of the arguments that define the optimization are:\n", - "\n", - "- ``num_iters``: number of self-consistent iterations.\n", - "- ``num_steps_grad``: number of gradient step updates performed when optimizing \n", - "$\\kappa$ on each self-consistent iteration.\n", - "- ``learning_rate``: step-size in the gradient descent optimization of $\\kappa$." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b5e56baf", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_addon_sqd.fermion import optimize_orbitals\n", - "\n", - "k_flat = (rng.random(num_params) - 0.5) * 0.1\n", - "num_iters = 20\n", - "num_steps_grad = 10_000 # relatively cheap to execute\n", - "learning_rate = 0.05\n", - "\n", - "e_improved, k_flat, orbital_occupancies = optimize_orbitals(\n", - " best_batch,\n", - " hcore_rot,\n", - " eri_rot,\n", - " k_flat,\n", - " open_shell=open_shell,\n", - " spin_sq=spin_sq,\n", - " num_iters=num_iters,\n", - " num_steps_grad=num_steps_grad,\n", - " learning_rate=learning_rate,\n", - " max_cycle=max_davidson_cycles,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "e06f5c28-83d0-4dc2-b2bd-2ec92676745d", - "metadata": {}, - "source": [ - "Here we see that by optimizing rotation parameters for our Hamiltonian, we can improve the result from SQD." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "78a80e64", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exact energy: -109.04667177808032\n", - "SQD energy: -108.7531706601421\n", - "Energy after OO: -108.80400806164377\n" - ] - } - ], - "source": [ - "print(f\"Exact energy: {exact_energy}\")\n", - "print(f\"SQD energy: {np.min(e_hist[-1])}\")\n", - "print(f\"Energy after OO: {e_improved + nuclear_repulsion_energy}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/index.mdx b/docs/addons/qiskit-addon-sqd/index.mdx deleted file mode 100644 index 0d09f841487..00000000000 --- a/docs/addons/qiskit-addon-sqd/index.mdx +++ /dev/null @@ -1,104 +0,0 @@ ---- -title: Sample-based quantum diagonalization (SQD) API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-sqd. ---- - -# Qiskit addon: sample-based quantum diagonalization (SQD) - -[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. - -This package contains the Qiskit addon for sample-based quantum diagonalization (SQD) – a technique for finding eigenvalues and eigenvectors of quantum operators, such as a quantum system Hamiltonian, using quantum and distributed classical computing together \[1-5]. This technique can be run on current quantum computers and has been shown to scale to problem sizes beyond what was possible with variational methods and even beyond the reach of exact classical diagonalization methods \[1,2]. - -SQD-based workflows involve first preparing one or more quantum states on a quantum device and sampling from them. Then, classical distributed computing is used to process those noisy samples. This processing occurs iteratively in two steps: First, a configuration recovery step corrects noisy samples using information about the input problem and second, the Hamiltonian is projected and diagonalized in the subspace spanned by those samples. These steps are repeated self-consistently until convergence. The result is an approximated lowest eigenvalue (energy) and lowest energy eigenstate of a given Hamiltonian. SQD is robust to samples corrupted by quantum noise; in fact, as long as a useful signal can be retrieved out of the quantum computer, the outcome of SQD will be insensitive to noisy bitstrings. - -SQD can be used in various ways in practice. For example, we can use two categories of quantum circuits to sample from: - -> 1. A variational circuit ansatz with parameters chosen such that sampling the circuit produces electronic configurations on which the target wavefunction (i.e., the ground state) has significant support. This is appealing for chemistry applications where Hamiltonians can have millions of interaction terms \[1]. For an example of this approach applied to chemistry see the [tutorial for approximating the ground state energy of the N2 molecule](/docs/en/tutorials/sample-based-quantum-diagonalization). -> 2. A set of Krylov basis states are prepared over increasing time intervals. Assuming a good initial state and sparsity of the ground state, this approach is proven to converge efficiently. As one needs to prepare Trotterized time evolution circuits on a quantum device, this approach is best for applications to lattice models \[2]. For an example of this approach applied to Fermionic lattice Hamiltonians, see the [tutorial for approximating the ground state energy of a simplified single-impurity Anderson model](/docs/en/tutorials/sample-based-krylov-quantum-diagonalization). - -This package contains the functionality for the classical processing of user-provided samples. It can target Hamiltonians expressed as linear combinations of Pauli operators or second-quantized Fermionic operators. The projection and diagonalization steps are performed by a classical solver. We provide here two generic solvers, one for Fermionic systems and another for qubit systems. Other solvers that might be more efficient for specific systems can be interfaced by the users. - -## Documentation - -All documentation is available [here](/docs/addons/qiskit-addon-sqd). - -## Installation - -We encourage installing this package via `pip`, when possible: - -```bash -pip install 'qiskit-addon-sqd' -``` - -For more installation information refer to the [installation instructions](install) in the documentation. - -## System sizes and computational requirements - -The computational cost of SQD is dominated by the eigenstate solver calls. At each step of the self-consistent configuration recovery iteration, n\_batches of eigenstate solver calls are performed. The different calls are embarrassingly parallel. In this [tutorial](tutorials/01-chemistry-hamiltonian), those calls are inside a for loop. **It is highly recommended to perform these calls in parallel**. - -The [`qiskit_addon_sqd.fermion.solve_fermion()`](fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") function is multithreaded and capable of handling systems with \~25 spacial orbitals and \~10 electrons with subspace dimensions of \~\$10^7\$, using \~10-30 cores. - -## Choosing subspace dimensions - -The choice of the subspace dimension affects the accuracy and runtime of the eigenstate solver. The larger the subspace the more accurate the calculation, at the cost of increasing the runtime and memory requirements. The optimal subspace size of a given system is not known, thus a convergence study with the subspace dimension may be performed, as described in this [guide](how_tos/choose-subspace-dimension). - -## The subspace dimension is set indirectly - -In this package, the user controls the number of bitstrings contained in each subspace with the samples\_per\_batch argument in [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"). The value of this argument sets an upper bound to the subspace dimension in the case of quantum chemistry applications. See this [example](how_tos/select-open-closed-shell) for more details. - -## Citing this project - -If you use this package in your research, please cite it according to `CITATON.bib` file included in this repository: - -```bibtex -@software{qiskit-addon-sqd, - author = { - Abdullah Ash Saki - and Stefano Barison - and Bryce Fuller - and James R. Garrison - and Jennifer R. Glick - and Caleb Johnson - and Antonio Mezzacapo - and Javier Robledo-Moreno - and Max Rossmannek - and Paul Schweigert - and Iskandar Sitdikov - and Kevin J. Sung - }, - title = {{Qiskit addon: sample-based quantum diagonalization}}, - howpublished = {\url{https://github.com/Qiskit/qiskit-addon-sqd}}, - year = {2024} -} -``` - -## Deprecation Policy - -We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the release notes. - -## Contributing - -The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-sqd). - -The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-sqd/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). - -We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-sqd/issues/new/choose) for tracking requests and bugs. - -## License - -[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-sqd/blob/main/LICENSE.txt) - - - -## References - -\[1] Javier Robledo-Moreno, et al., \[Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer]\([https://arxiv.org/abs/2405.05068](https://arxiv.org/abs/2405.05068)), arXiv:2405.05068 \[quant-ph]. - -\[2] Jeffery Yu, et al., \[Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization]\([https://arxiv.org/abs/2501.09702](https://arxiv.org/abs/2501.09702)), arXiv:2501.09702 \[quant-ph]. - -\[3] Keita Kanno, et al., \[Quantum-Selected Configuration Interaction: classical diagonalization of Hamiltonians in subspaces selected by quantum computers]\([https://arxiv.org/abs/2302.11320](https://arxiv.org/abs/2302.11320)), arXiv:2302.11320 \[quant-ph]. - -\[4] Kenji Sugisaki, et al., \[Hamiltonian simulation-based quantum-selected configuration interaction for large-scale electronic structure calculations with a quantum computer]\([https://arxiv.org/abs/2412.07218](https://arxiv.org/abs/2412.07218)), arXiv:2412.07218 \[quant-ph]. - -\[5] Mathias Mikkelsen, Yuya O. Nakagawa, \[Quantum-selected configuration interaction with time-evolved state]\([https://arxiv.org/abs/2412.13839](https://arxiv.org/abs/2412.13839)), arXiv:2412.13839 \[quant-ph]. - diff --git a/docs/addons/qiskit-addon-sqd/install.mdx b/docs/addons/qiskit-addon-sqd/install.mdx deleted file mode 100644 index f560b8d9c9f..00000000000 --- a/docs/addons/qiskit-addon-sqd/install.mdx +++ /dev/null @@ -1,57 +0,0 @@ -# Installation Instructions - -Let’s see how to install the package. The first thing to do is ensure your Python environment is set up correctly. To create a new environment: - -## Pre-Installation - -First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). - -```sh -python3 -m venv /path/to/virtual/environment -``` - -Activate your new environment. - -```sh -source /path/to/virtual/environment/bin/activate -``` - -There are two primary ways to install this package – from PyPI or source. The preferred method is to install from PyPI: - -## Install from PyPI - -```sh -pip install 'qiskit-addon-sqd' -``` - -## Install from Source - -Users who wish to develop in the repository or run the notebooks locally may want to install from source. - -If so, the first step is to clone the `qiskit-addon-sqd` repository. - -```sh -git clone git@github.com:Qiskit/qiskit-addon-sqd.git -``` - -Next, upgrade pip and enter the repository. - -```sh -pip install --upgrade pip -cd qiskit-addon-sqd -``` - -The next step is to install `qiskit-addon-sqd` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. - -Adjust the options below to suit your needs. - -```sh -pip install tox notebook -e '.[notebook-dependencies,dev]' -``` - -If you installed the notebook dependencies, you can get started by running the notebooks in the docs. - -```python -cd docs/ -jupyter lab -``` diff --git a/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.mdx b/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.mdx deleted file mode 100644 index 03d406df998..00000000000 --- a/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.mdx +++ /dev/null @@ -1,408 +0,0 @@ - - -# Improving energy estimation of a chemistry Hamiltonian with SQD - -In this tutorial we implement a [Qiskit pattern](/docs/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of a chemistry Hamiltonian: the $N_2$ molecule at equilibrium in the 6-31G basis set. We will follow a [sample-based quantum diagonalization approach](https://arxiv.org/abs/2405.05068) to process samples taken from a `36`-qubit quantum circuit ansatz (in this case, an LUCJ circuit). In order to account for the effect of quantum noise, the configuration recovery technique is used. - -The pattern can be described in four steps: - -1. **Step 1: Map to quantum problem** - - * Generate an ansatz for estimating the ground state - -2. **Step 2: Optimize the problem** - - * Transpile the ansatz for the backend - -3. **Step 3: Execute experiments** - - * Draw samples from the ansatz using the `Sampler` primitive - -4. **Step 4: Post-process results** - - * Self-consistent configuration recovery loop - - * Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration. - * Probabilistically create batches of subsamples from recovered bitstrings. - * Project and diagonalize the molecular Hamiltonian over each sampled subspace. - * Save the minimum ground state energy found across all batches and update the avg orbital occupancy. - -For this example, the interacting-electron Hamiltonian takes the generic form: - -$$ -\begin{split}\hat{H} = \sum_{ \substack{pr\\\sigma} } h_{pr} \, \hat{a}^\dagger_{p\sigma} \hat{a}_{r\sigma} -+ -\sum_{ \substack{prqs\\\sigma\tau} } -\frac{(pr|qs)}{2} \, -\hat{a}^\dagger_{p\sigma} -\hat{a}^\dagger_{q\tau} -\hat{a}_{s\tau} -\hat{a}_{r\sigma}\end{split} -$$ - -$\hat{a}^\dagger_{p\sigma}$/$\hat{a}_{p\sigma}$ are the fermionic creation/annihalation operators associated to the $p$-th basis set element and the spin $\sigma$. $h_{pr}$ and $(pr|qs)$ are the one- and two-body electronic integrals. - -The SQD workflow with self-consistent configuration recovery is depicted in the following diagram. - -![SQD diagram](/docs/images/api/qiskit-addon-sqd/sqd_diagram.avif) - -SQD is known to work well when the target eigenstate is sparse: the wave function is supported in a set of basis states $\mathcal{S} = \{|x\rangle \}$ whose size does not increase exponentially with the size of the problem. In this scenario, the diagonalization of the Hamiltonian projected into the subspace defined by $\mathcal{S}$: - -$$ -H_\mathcal{S} = P_\mathcal{S} H P_\mathcal{S} \textrm{ with } P_\mathcal{S} = \sum_{x \in \mathcal{S}} |x \rangle \langle x |; -$$ - -yields a good approximation to the target eigenstate. The role of the quantum device is to produce samples of the members of $\mathcal{S}$ only. First, a quantum circuit prepares the state $|\Psi\rangle$ in the quantum device. The Jordan-Wigner encoding is used. Consequently, members of the computational basis represent Fock states (electronic configurations/determinants). The circuit is sampled in the computational basis, yielding the set of noisy configurations $\tilde{\mathcal{X}}$. The configurations are represented by bitstrings. The set $\tilde{\mathcal{X}}$ is then passed into the classical post-processing block, where the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068) is used. In the SQD framework, the role of the quantum device is to produce a probability distribution. - - - -## Step 1: Map problem to a quantum circuit - -In this tutorial, we will approximate the ground state energy of an $N_2$ molecule. First, we will specify the molecule and its properties. Next, we will create a [local unitary cluster Jastrow (LUCJ)](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) ansatz (quantum circuit) to generate samples from a quantum computer for ground state energy estimation. - -First, we will specify the molecule and its properties. - -``` -[1]: -``` - -```python -import warnings - -warnings.filterwarnings("ignore") - -import pyscf -import pyscf.cc -import pyscf.mcscf - -# Specify molecule properties -open_shell = False -spin_sq = 0 - -# Build N2 molecule -mol = pyscf.gto.Mole() -mol.build( - atom=[["N", (0, 0, 0)], ["N", (1.0, 0, 0)]], - basis="6-31g", - symmetry="Dooh", -) - -# Define active space -n_frozen = 2 -active_space = range(n_frozen, mol.nao_nr()) - -# Get molecular integrals -scf = pyscf.scf.RHF(mol).run() -num_orbitals = len(active_space) -n_electrons = int(sum(scf.mo_occ[active_space])) -num_elec_a = (n_electrons + mol.spin) // 2 -num_elec_b = (n_electrons - mol.spin) // 2 -cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b)) -mo = cas.sort_mo(active_space, base=0) -hcore, nuclear_repulsion_energy = cas.get_h1cas(mo) -eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals) - -# Compute exact energy -exact_energy = cas.run().e_tot -``` - -```python -converged SCF energy = -108.835236570775 -CASCI E = -109.046671778080 E(CI) = -32.8155692383188 S^2 = 0.0000000 -``` - -Next, we will create the ansatz. The `LUCJ` ansatz is a parameterized quantum circuit, and we will initialize it with `t2` and `t1` amplitudes obtained from a CCSD calculation. - -``` -[2]: -``` - -```python -# Get CCSD t2 amplitudes for initializing the ansatz -ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run() -t1 = ccsd.t1 -t2 = ccsd.t2 -``` - -```python -E(CCSD) = -109.0398256929734 E_corr = -0.2045891221988311 -``` - -We will use the [ffsim](https://github.com/qiskit-community/ffsim/tree/main) package to create and initialize the ansatz with `t2` and `t1` amplitudes computed above. Since our molecule has a closed-shell Hartree-Fock state, we will use the spin-balanced variant of the UCJ ansatz, [UCJOpSpinBalanced](https://qiskit-community.github.io/ffsim/api/ffsim.html#ffsim.UCJOpSpinBalanced). - -As our target IBM hardware has a heavy-hex topology, we will adopt the [zig-zag pattern for qubit interactions](https://pubs.rsc.org/en/content/articlehtml/2023/sc/d3sc02516k). In this pattern, orbitals (represented by qubits) with the same spin are connected with a line topology (red and blue circles) where each line take a zig-zag shape due the heavy-hex connectivity of the target hardware. Again, due to the heavy-hex topology, orbitals for different spins have connections between every 4th orbital (0, 4, 8, etc.) (purple circles). - -![lucj\_ansatz](/docs/images/api/qiskit-addon-sqd/lucj_ansatz_zig_zag_pattern.avif) - -``` -[3]: -``` - -```python -import ffsim -from qiskit import QuantumCircuit, QuantumRegister - -n_reps = 1 -alpha_alpha_indices = [(p, p + 1) for p in range(num_orbitals - 1)] -alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)] - -ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes( - t2=t2, - t1=t1, - n_reps=n_reps, - interaction_pairs=(alpha_alpha_indices, alpha_beta_indices), -) - -nelec = (num_elec_a, num_elec_b) - -# create an empty quantum circuit -qubits = QuantumRegister(2 * num_orbitals, name="q") -circuit = QuantumCircuit(qubits) - -# prepare Hartree-Fock state as the reference state and append it to the quantum circuit -circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits) - -# apply the UCJ operator to the reference state -circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits) -circuit.measure_all() -``` - - - -## Step 2: Optimize the problem - -Next, we will optimize our circuit for a target hardware. We need to choose the hardware device to use before optimizing our circuit. We will use a fake 127-qubit backend from `qiskit_ibm_runtime` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info. - -``` -[4]: -``` - -```python -from qiskit_ibm_runtime.fake_provider import FakeSherbrooke - -backend = FakeSherbrooke() -``` - -Next, we recommend the following steps to optimize the ansatz and make it hardware-compatible. - -* Select physical qubits (`initial_layout`) from the target hardware that adheres to the zig-zag pattern described above. Laying out qubits in this pattern leads to an efficient hardware-compatible circuit with fewer gates. -* Generate a staged pass manager using the [generate\_preset\_pass\_manager](/docs/api/qiskit/transpiler_preset#generate_preset_pass_manager) function from Qiskit with your choice of `backend` and `initial_layout`. -* Set the `pre_init` stage of your staged pass manager to `ffsim.qiskit.PRE_INIT`. `ffsim.qiskit.PRE_INIT` includes Qiskit transpiler passes that decompose gates into orbital rotations and then merges the orbital rotations, resulting in fewer gates in the final circuit. -* Run the pass manager on your circuit. - -``` -[5]: -``` - -```python -from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager - -spin_a_layout = [0, 14, 18, 19, 20, 33, 39, 40, 41, 53, 60, 61, 62, 72, 81, 82] -spin_b_layout = [2, 3, 4, 15, 22, 23, 24, 34, 43, 44, 45, 54, 64, 65, 66, 73] -initial_layout = spin_a_layout + spin_b_layout - -pass_manager = generate_preset_pass_manager( - optimization_level=3, backend=backend, initial_layout=initial_layout -) - -# without PRE_INIT passes -isa_circuit = pass_manager.run(circuit) -print(f"Gate counts (w/o pre-init passes): {isa_circuit.count_ops()}") - -# with PRE_INIT passes -# We will use the circuit generated by this pass manager for hardware execution -pass_manager.pre_init = ffsim.qiskit.PRE_INIT -isa_circuit = pass_manager.run(circuit) -print(f"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}") -``` - -```python -Gate counts (w/o pre-init passes): OrderedDict({'rz': 4420, 'sx': 3432, 'ecr': 1366, 'x': 239, 'measure': 32, 'barrier': 1}) -Gate counts (w/ pre-init passes): OrderedDict({'rz': 2460, 'sx': 2156, 'ecr': 730, 'x': 71, 'measure': 32, 'barrier': 1}) -``` - - - -## Step 3: Execute experiments - -After optimizing the circuit for hardware execution, we are ready to run it on the target hardware and collect samples for ground state energy estimation. As we only have one circuit, we will use Qiskit Runtime’s [Job execution mode](/docs/guides/execution-modes) and execute our circuit. - -**Note: We have commented out the code for running the circuit on a QPU and left it for the user’s reference. Instead of running on real hardware in this guide, we will just generate random samples drawn from the uniform distribution.** - -``` -[6]: -``` - -```python -import numpy as np -from qiskit_addon_sqd.counts import generate_bit_array_uniform - -# from qiskit_ibm_runtime import SamplerV2 as Sampler - -# sampler = Sampler(mode=backend) -# job = sampler.run([isa_circuit], shots=10_000) -# primitive_result = job.result() -# pub_result = primitive_result[0] -# bit_array = pub_result.data.meas - -rng = np.random.default_rng(24) -bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng) -``` - - - -## Step 4: Post-process results - -Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](fermion#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function. - -The solver included in the SQD addon uses PySCF’s implementation of selected CI, specifically [pyscf.fci.selected\_ci.kernel\_fixed\_space](https://pyscf.org/pyscf_api_docs/pyscf.fci.html#pyscf.fci.selected_ci.kernel_fixed_space). The example below also shows how to pass keyword arguments to that function via the included solver. Here we pass the `max_cycle` argument. - -``` -[7]: -``` - -```python -from functools import partial - -from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian, solve_sci_batch - -# SQD options -energy_tol = 1e-3 -occupancies_tol = 1e-3 -max_iterations = 5 - -# Eigenstate solver options -num_batches = 1 -samples_per_batch = 300 -symmetrize_spin = True -carryover_threshold = 1e-4 -max_cycle = 200 - -# Pass options to the built-in eigensolver. If you just want to use the defaults, -# you can omit this step, in which case you would not specify the sci_solver argument -# in the call to diagonalize_fermionic_hamiltonian below. -sci_solver = partial(solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle) - -# List to capture intermediate results -result_history = [] - - -def callback(results: list[SCIResult]): - result_history.append(results) - iteration = len(result_history) - print(f"Iteration {iteration}") - for i, result in enumerate(results): - print(f"\tSubsample {i}") - print(f"\t\tEnergy: {result.energy + nuclear_repulsion_energy}") - print(f"\t\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}") - - -result = diagonalize_fermionic_hamiltonian( - hcore, - eri, - bit_array, - samples_per_batch=samples_per_batch, - norb=num_orbitals, - nelec=nelec, - num_batches=num_batches, - energy_tol=energy_tol, - occupancies_tol=occupancies_tol, - max_iterations=max_iterations, - sci_solver=sci_solver, - symmetrize_spin=symmetrize_spin, - carryover_threshold=carryover_threshold, - callback=callback, - seed=rng, -) -``` - -```python -Iteration 1 - Subsample 0 - Energy: -105.45358671756313 - Subspace dimension: 5476 -Iteration 2 - Subsample 0 - Energy: -107.95172900082163 - Subspace dimension: 249001 -Iteration 3 - Subsample 0 - Energy: -108.97460330369815 - Subspace dimension: 339889 -Iteration 4 - Subsample 0 - Energy: -109.02739376648793 - Subspace dimension: 440896 -Iteration 5 - Subsample 0 - Energy: -109.030972328451 - Subspace dimension: 597529 -``` - -Now, we plot the results. - -The first plot shows that after a few iterations we estimate the ground state energy within `~16 mH` (chemical accuracy is typically accepted to be `1 kcal/mol` $\approx$ `1.6 mH`). Remember, the quantum samples in this demo were pure noise. The signal here comes from *a priori* knowledge of the electronic structure and molecular Hamiltonian. - -The second plot shows the average occupancy of each spatial orbital after the final iteration. We can see that both the spin-up and spin-down electrons occupy the first five orbitals with high probability in our solutions. - -``` -[8]: -``` - -```python -import matplotlib.pyplot as plt - -# Data for energies plot -x1 = range(len(result_history)) -min_e = [ - min(result, key=lambda res: res.energy).energy + nuclear_repulsion_energy - for result in result_history -] -e_diff = [abs(e - exact_energy) for e in min_e] -yt1 = [1.0, 1e-1, 1e-2, 1e-3, 1e-4] - -# Chemical accuracy (+/- 1 milli-Hartree) -chem_accuracy = 0.001 - -# Data for avg spatial orbital occupancy -y2 = np.sum(result.orbital_occupancies, axis=0) -x2 = range(len(y2)) - -fig, axs = plt.subplots(1, 2, figsize=(12, 6)) - -# Plot energies -axs[0].plot(x1, e_diff, label="energy error", marker="o") -axs[0].set_xticks(x1) -axs[0].set_xticklabels(x1) -axs[0].set_yticks(yt1) -axs[0].set_yticklabels(yt1) -axs[0].set_yscale("log") -axs[0].set_ylim(1e-4) -axs[0].axhline(y=chem_accuracy, color="#BF5700", linestyle="--", label="chemical accuracy") -axs[0].set_title("Approximated Ground State Energy Error vs SQD Iterations") -axs[0].set_xlabel("Iteration Index", fontdict={"fontsize": 12}) -axs[0].set_ylabel("Energy Error (Ha)", fontdict={"fontsize": 12}) -axs[0].legend() - -# Plot orbital occupancy -axs[1].bar(x2, y2, width=0.8) -axs[1].set_xticks(x2) -axs[1].set_xticklabels(x2) -axs[1].set_title("Avg Occupancy per Spatial Orbital") -axs[1].set_xlabel("Orbital Index", fontdict={"fontsize": 12}) -axs[1].set_ylabel("Avg Occupancy", fontdict={"fontsize": 12}) - -print(f"Exact energy: {exact_energy:.5f} Ha") -print(f"SQD energy: {min_e[-1]:.5f} Ha") -print(f"Absolute error: {e_diff[-1]:.5f} Ha") -plt.tight_layout() -plt.show() -``` - -```python -Exact energy: -109.04667 Ha -SQD energy: -109.03097 Ha -Absolute error: 0.01570 Ha -``` - -![../\_images/tutorials\_01\_chemistry\_hamiltonian\_19\_1.png](/docs/images/api/qiskit-addon-sqd/tutorials_01_chemistry_hamiltonian_19_1.avif) diff --git a/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian.ipynb deleted file mode 100644 index 6b803ff7477..00000000000 --- a/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian.ipynb +++ /dev/null @@ -1,532 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", - "metadata": {}, - "source": [ - "# Improving energy estimation of a chemistry Hamiltonian with SQD\n", - "\n", - "In this tutorial we implement a [Qiskit pattern](/docs/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of a chemistry Hamiltonian: the $N_2$ molecule at equilibrium in the 6-31G basis set. We will follow a [sample-based quantum diagonalization approach](https://arxiv.org/abs/2405.05068) to process samples taken from a ``36``-qubit quantum circuit ansatz (in this case, an LUCJ circuit). In order to account for the effect of quantum noise, the configuration recovery technique is used.\n", - "\n", - "The pattern can be described in four steps:\n", - "\n", - "1. **Step 1: Map to quantum problem**\n", - " - Generate an ansatz for estimating the ground state\n", - "2. **Step 2: Optimize the problem**\n", - " - Transpile the ansatz for the backend\n", - "3. **Step 3: Execute experiments**\n", - " - Draw samples from the ansatz using the ``Sampler`` primitive\n", - "4. **Step 4: Post-process results**\n", - " - Self-consistent configuration recovery loop\n", - " - Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration.\n", - " - Probabilistically create batches of subsamples from recovered bitstrings.\n", - " - Project and diagonalize the molecular Hamiltonian over each sampled subspace.\n", - " - Save the minimum ground state energy found across all batches and update the avg orbital occupancy.\n", - "\n", - "\n", - "For this example, the interacting-electron Hamiltonian takes the generic form:\n", - "\n", - "$$\n", - "\\hat{H} = \\sum_{ \\substack{pr\\\\\\sigma} } h_{pr} \\, \\hat{a}^\\dagger_{p\\sigma} \\hat{a}_{r\\sigma}\n", - "+ \n", - "\\sum_{ \\substack{prqs\\\\\\sigma\\tau} }\n", - "\\frac{(pr|qs)}{2} \\, \n", - "\\hat{a}^\\dagger_{p\\sigma}\n", - "\\hat{a}^\\dagger_{q\\tau}\n", - "\\hat{a}_{s\\tau}\n", - "\\hat{a}_{r\\sigma}\n", - "$$\n", - "\n", - "$\\hat{a}^\\dagger_{p\\sigma}$/$\\hat{a}_{p\\sigma}$ are the fermionic creation/annihalation operators associated to the $p$-th basis set element and the spin $\\sigma$. $h_{pr}$ and $(pr|qs)$ are the one- and two-body electronic integrals.\n", - "\n", - "The SQD workflow with self-consistent configuration recovery is depicted in the following diagram.\n", - "\n", - "![SQD diagram](../_static/images/sqd_diagram.png)\n", - "\n", - "SQD is known to work well when the target eigenstate is sparse: the wave function is supported in a set of basis states $\\mathcal{S} = \\{|x\\rangle \\}$ whose size does not increase exponentially with the size of the problem. In this scenario, the diagonalization of the Hamiltonian projected into the subspace defined by $\\mathcal{S}$:\n", - "$$\n", - "H_\\mathcal{S} = P_\\mathcal{S} H P_\\mathcal{S} \\textrm{ with } P_\\mathcal{S} = \\sum_{x \\in \\mathcal{S}} |x \\rangle \\langle x |;\n", - "$$\n", - "yields a good approximation to the target eigenstate. The role of the quantum device is to produce samples of the members of $\\mathcal{S}$ only. First, a quantum circuit prepares the state $|\\Psi\\rangle$ in the quantum device. The Jordan-Wigner encoding is used. Consequently, members of the computational basis represent Fock states (electronic configurations/determinants). The circuit is sampled in the computational basis, yielding the set of noisy configurations $\\tilde{\\mathcal{X}}$. The configurations are represented by bitstrings. The set $\\tilde{\\mathcal{X}}$ is then passed into the classical post-processing block, where the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068) is used. In the SQD framework, the role of the quantum device is to produce a probability distribution." - ] - }, - { - "cell_type": "markdown", - "id": "afeb054c", - "metadata": {}, - "source": [ - "### Step 1: Map problem to a quantum circuit\n", - "\n", - "In this tutorial, we will approximate the ground state energy of an $N_2$ molecule. First, we will specify the molecule and its properties. Next, we will create a [local unitary cluster Jastrow (LUCJ)](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) ansatz (quantum circuit) to generate samples from a quantum computer for ground state energy estimation.\n", - "\n", - "First, we will specify the molecule and its properties." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "converged SCF energy = -108.835236570775\n", - "CASCI E = -109.046671778080 E(CI) = -32.8155692383188 S^2 = 0.0000000\n" - ] - } - ], - "source": [ - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "import pyscf\n", - "import pyscf.cc\n", - "import pyscf.mcscf\n", - "\n", - "# Specify molecule properties\n", - "open_shell = False\n", - "spin_sq = 0\n", - "\n", - "# Build N2 molecule\n", - "mol = pyscf.gto.Mole()\n", - "mol.build(\n", - " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", - " basis=\"6-31g\",\n", - " symmetry=\"Dooh\",\n", - ")\n", - "\n", - "# Define active space\n", - "n_frozen = 2\n", - "active_space = range(n_frozen, mol.nao_nr())\n", - "\n", - "# Get molecular integrals\n", - "scf = pyscf.scf.RHF(mol).run()\n", - "num_orbitals = len(active_space)\n", - "n_electrons = int(sum(scf.mo_occ[active_space]))\n", - "num_elec_a = (n_electrons + mol.spin) // 2\n", - "num_elec_b = (n_electrons - mol.spin) // 2\n", - "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", - "mo = cas.sort_mo(active_space, base=0)\n", - "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", - "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", - "\n", - "# Compute exact energy\n", - "exact_energy = cas.run().e_tot" - ] - }, - { - "cell_type": "markdown", - "id": "96bfe018", - "metadata": {}, - "source": [ - "Next, we will create the ansatz. The ``LUCJ`` ansatz is a parameterized quantum circuit, and we will initialize it with `t2` and `t1` amplitudes obtained from a CCSD calculation." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "66270387", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "E(CCSD) = -109.0398256929734 E_corr = -0.2045891221988311\n" - ] - } - ], - "source": [ - "# Get CCSD t2 amplitudes for initializing the ansatz\n", - "ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run()\n", - "t1 = ccsd.t1\n", - "t2 = ccsd.t2" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f4d882fa", - "metadata": {}, - "source": [ - "We will use the [ffsim](https://github.com/qiskit-community/ffsim/tree/main) package to create and initialize the ansatz with `t2` and `t1` amplitudes computed above. Since our molecule has a closed-shell Hartree-Fock state, we will use the spin-balanced variant of the UCJ ansatz, [UCJOpSpinBalanced](https://qiskit-community.github.io/ffsim/api/ffsim.html#ffsim.UCJOpSpinBalanced).\n", - "\n", - "As our target IBM hardware has a heavy-hex topology, we will adopt the [_zig-zag_ pattern for qubit interactions](https://pubs.rsc.org/en/content/articlehtml/2023/sc/d3sc02516k). In this pattern, orbitals (represented by qubits) with the same spin are connected with a line topology (red and blue circles) where each line take a zig-zag shape due the heavy-hex connectivity of the target hardware. Again, due to the heavy-hex topology, orbitals for different spins have connections between every 4th orbital (0, 4, 8, etc.) (purple circles).\n", - "\n", - "![lucj_ansatz](../_static/images/lucj_ansatz_zig_zag_pattern.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "dd69a86c", - "metadata": {}, - "outputs": [], - "source": [ - "import ffsim\n", - "from qiskit import QuantumCircuit, QuantumRegister\n", - "\n", - "n_reps = 1\n", - "alpha_alpha_indices = [(p, p + 1) for p in range(num_orbitals - 1)]\n", - "alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\n", - "\n", - "ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\n", - " t2=t2,\n", - " t1=t1,\n", - " n_reps=n_reps,\n", - " interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\n", - ")\n", - "\n", - "nelec = (num_elec_a, num_elec_b)\n", - "\n", - "# create an empty quantum circuit\n", - "qubits = QuantumRegister(2 * num_orbitals, name=\"q\")\n", - "circuit = QuantumCircuit(qubits)\n", - "\n", - "# prepare Hartree-Fock state as the reference state and append it to the quantum circuit\n", - "circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\n", - "\n", - "# apply the UCJ operator to the reference state\n", - "circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\n", - "circuit.measure_all()" - ] - }, - { - "cell_type": "markdown", - "id": "db11bf6d", - "metadata": {}, - "source": [ - "### Step 2: Optimize the problem" - ] - }, - { - "cell_type": "markdown", - "id": "0760b3f3", - "metadata": {}, - "source": [ - "Next, we will optimize our circuit for a target hardware. We need to choose the hardware device to use before optimizing our circuit. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "53a039d8", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime.fake_provider import FakeSherbrooke\n", - "\n", - "backend = FakeSherbrooke()" - ] - }, - { - "cell_type": "markdown", - "id": "057ebbf6", - "metadata": {}, - "source": [ - "Next, we recommend the following steps to optimize the ansatz and make it hardware-compatible.\n", - "\n", - "- Select physical qubits (`initial_layout`) from the target hardware that adheres to the zig-zag pattern described above. Laying out qubits in this pattern leads to an efficient hardware-compatible circuit with fewer gates.\n", - "- Generate a staged pass manager using the [generate_preset_pass_manager](/docs/api/qiskit/transpiler_preset#generate_preset_pass_manager) function from Qiskit with your choice of `backend` and `initial_layout`.\n", - "- Set the `pre_init` stage of your staged pass manager to `ffsim.qiskit.PRE_INIT`. `ffsim.qiskit.PRE_INIT` includes Qiskit transpiler passes that decompose gates into orbital rotations and then merges the orbital rotations, resulting in fewer gates in the final circuit.\n", - "- Run the pass manager on your circuit. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7d554aa5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gate counts (w/o pre-init passes): OrderedDict({'rz': 4420, 'sx': 3432, 'ecr': 1366, 'x': 239, 'measure': 32, 'barrier': 1})\n", - "Gate counts (w/ pre-init passes): OrderedDict({'rz': 2460, 'sx': 2156, 'ecr': 730, 'x': 71, 'measure': 32, 'barrier': 1})\n" - ] - } - ], - "source": [ - "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", - "\n", - "spin_a_layout = [0, 14, 18, 19, 20, 33, 39, 40, 41, 53, 60, 61, 62, 72, 81, 82]\n", - "spin_b_layout = [2, 3, 4, 15, 22, 23, 24, 34, 43, 44, 45, 54, 64, 65, 66, 73]\n", - "initial_layout = spin_a_layout + spin_b_layout\n", - "\n", - "pass_manager = generate_preset_pass_manager(\n", - " optimization_level=3, backend=backend, initial_layout=initial_layout\n", - ")\n", - "\n", - "# without PRE_INIT passes\n", - "isa_circuit = pass_manager.run(circuit)\n", - "print(f\"Gate counts (w/o pre-init passes): {isa_circuit.count_ops()}\")\n", - "\n", - "# with PRE_INIT passes\n", - "# We will use the circuit generated by this pass manager for hardware execution\n", - "pass_manager.pre_init = ffsim.qiskit.PRE_INIT\n", - "isa_circuit = pass_manager.run(circuit)\n", - "print(f\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\")" - ] - }, - { - "cell_type": "markdown", - "id": "0cc1edef", - "metadata": {}, - "source": [ - "### Step 3: Execute experiments" - ] - }, - { - "cell_type": "markdown", - "id": "cbf7ef9f", - "metadata": {}, - "source": [ - "After optimizing the circuit for hardware execution, we are ready to run it on the target hardware and collect samples for ground state energy estimation. As we only have one circuit, we will use Qiskit Runtime's [Job execution mode](/docs/guides/execution-modes) and execute our circuit.\n", - "\n", - "***Note: We have commented out the code for running the circuit on a QPU and left it for the user's reference. Instead of running on real hardware in this guide, we will just generate random samples drawn from the uniform distribution.***" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "3da09100", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from qiskit_addon_sqd.counts import generate_bit_array_uniform\n", - "\n", - "# from qiskit_ibm_runtime import SamplerV2 as Sampler\n", - "\n", - "# sampler = Sampler(mode=backend)\n", - "# job = sampler.run([isa_circuit], shots=10_000)\n", - "# primitive_result = job.result()\n", - "# pub_result = primitive_result[0]\n", - "# bit_array = pub_result.data.meas\n", - "\n", - "rng = np.random.default_rng(24)\n", - "bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng)" - ] - }, - { - "cell_type": "markdown", - "id": "6df05b6e", - "metadata": {}, - "source": [ - "### Step 4: Post-process results" - ] - }, - { - "cell_type": "markdown", - "id": "7a102a7f-aae6-4583-ab82-ae40fcb5496a", - "metadata": {}, - "source": [ - "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function.\n", - "\n", - "The solver included in the SQD addon uses PySCF's implementation of selected CI, specifically [pyscf.fci.selected_ci.kernel_fixed_space](https://pyscf.org/pyscf_api_docs/pyscf.fci.html#pyscf.fci.selected_ci.kernel_fixed_space). The example below also shows how to pass keyword arguments to that function via the included solver. Here we pass the `max_cycle` argument." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6e873c11", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 1\n", - "\tSubsample 0\n", - "\t\tEnergy: -105.45358671756313\n", - "\t\tSubspace dimension: 5476\n", - "Iteration 2\n", - "\tSubsample 0\n", - "\t\tEnergy: -107.95172900082163\n", - "\t\tSubspace dimension: 249001\n", - "Iteration 3\n", - "\tSubsample 0\n", - "\t\tEnergy: -108.97460330369815\n", - "\t\tSubspace dimension: 339889\n", - "Iteration 4\n", - "\tSubsample 0\n", - "\t\tEnergy: -109.02739376648793\n", - "\t\tSubspace dimension: 440896\n", - "Iteration 5\n", - "\tSubsample 0\n", - "\t\tEnergy: -109.030972328451\n", - "\t\tSubspace dimension: 597529\n" - ] - } - ], - "source": [ - "from functools import partial\n", - "\n", - "from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian, solve_sci_batch\n", - "\n", - "# SQD options\n", - "energy_tol = 1e-3\n", - "occupancies_tol = 1e-3\n", - "max_iterations = 5\n", - "\n", - "# Eigenstate solver options\n", - "num_batches = 1\n", - "samples_per_batch = 300\n", - "symmetrize_spin = True\n", - "carryover_threshold = 1e-4\n", - "max_cycle = 200\n", - "\n", - "# Pass options to the built-in eigensolver. If you just want to use the defaults,\n", - "# you can omit this step, in which case you would not specify the sci_solver argument\n", - "# in the call to diagonalize_fermionic_hamiltonian below.\n", - "sci_solver = partial(solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle)\n", - "\n", - "# List to capture intermediate results\n", - "result_history = []\n", - "\n", - "\n", - "def callback(results: list[SCIResult]):\n", - " result_history.append(results)\n", - " iteration = len(result_history)\n", - " print(f\"Iteration {iteration}\")\n", - " for i, result in enumerate(results):\n", - " print(f\"\\tSubsample {i}\")\n", - " print(f\"\\t\\tEnergy: {result.energy + nuclear_repulsion_energy}\")\n", - " print(f\"\\t\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\")\n", - "\n", - "\n", - "result = diagonalize_fermionic_hamiltonian(\n", - " hcore,\n", - " eri,\n", - " bit_array,\n", - " samples_per_batch=samples_per_batch,\n", - " norb=num_orbitals,\n", - " nelec=nelec,\n", - " num_batches=num_batches,\n", - " energy_tol=energy_tol,\n", - " occupancies_tol=occupancies_tol,\n", - " max_iterations=max_iterations,\n", - " sci_solver=sci_solver,\n", - " symmetrize_spin=symmetrize_spin,\n", - " carryover_threshold=carryover_threshold,\n", - " callback=callback,\n", - " seed=rng,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "9d78906b-4759-4506-9c69-85d4e67766b3", - "metadata": {}, - "source": [ - "Now, we plot the results.\n", - "\n", - "The first plot shows that after a few iterations we estimate the ground state energy within ``~16 mH`` (chemical accuracy is typically accepted to be ``1 kcal/mol`` $\\approx$ ``1.6 mH``). Remember, the quantum samples in this demo were pure noise. The signal here comes from *a priori* knowledge of the electronic structure and molecular Hamiltonian.\n", - "\n", - "The second plot shows the average occupancy of each spatial orbital after the final iteration. We can see that both the spin-up and spin-down electrons occupy the first five orbitals with high probability in our solutions." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "caffd888-e89c-4aa9-8bae-4d1bb723b35e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exact energy: -109.04667 Ha\n", - "SQD energy: -109.03097 Ha\n", - "Absolute error: 0.01570 Ha\n" - ] - }, - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Data for energies plot\n", - "x1 = range(len(result_history))\n", - "min_e = [\n", - " min(result, key=lambda res: res.energy).energy + nuclear_repulsion_energy\n", - " for result in result_history\n", - "]\n", - "e_diff = [abs(e - exact_energy) for e in min_e]\n", - "yt1 = [1.0, 1e-1, 1e-2, 1e-3, 1e-4]\n", - "\n", - "# Chemical accuracy (+/- 1 milli-Hartree)\n", - "chem_accuracy = 0.001\n", - "\n", - "# Data for avg spatial orbital occupancy\n", - "y2 = np.sum(result.orbital_occupancies, axis=0)\n", - "x2 = range(len(y2))\n", - "\n", - "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", - "\n", - "# Plot energies\n", - "axs[0].plot(x1, e_diff, label=\"energy error\", marker=\"o\")\n", - "axs[0].set_xticks(x1)\n", - "axs[0].set_xticklabels(x1)\n", - "axs[0].set_yticks(yt1)\n", - "axs[0].set_yticklabels(yt1)\n", - "axs[0].set_yscale(\"log\")\n", - "axs[0].set_ylim(1e-4)\n", - "axs[0].axhline(y=chem_accuracy, color=\"#BF5700\", linestyle=\"--\", label=\"chemical accuracy\")\n", - "axs[0].set_title(\"Approximated Ground State Energy Error vs SQD Iterations\")\n", - "axs[0].set_xlabel(\"Iteration Index\", fontdict={\"fontsize\": 12})\n", - "axs[0].set_ylabel(\"Energy Error (Ha)\", fontdict={\"fontsize\": 12})\n", - "axs[0].legend()\n", - "\n", - "# Plot orbital occupancy\n", - "axs[1].bar(x2, y2, width=0.8)\n", - "axs[1].set_xticks(x2)\n", - "axs[1].set_xticklabels(x2)\n", - "axs[1].set_title(\"Avg Occupancy per Spatial Orbital\")\n", - "axs[1].set_xlabel(\"Orbital Index\", fontdict={\"fontsize\": 12})\n", - "axs[1].set_ylabel(\"Avg Occupancy\", fontdict={\"fontsize\": 12})\n", - "\n", - "print(f\"Exact energy: {exact_energy:.5f} Ha\")\n", - "print(f\"SQD energy: {min_e[-1]:.5f} Ha\")\n", - "print(f\"Absolute error: {e_diff[-1]:.5f} Ha\")\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.mdx b/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.mdx deleted file mode 100644 index bcf1615d5a3..00000000000 --- a/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.mdx +++ /dev/null @@ -1,378 +0,0 @@ - - -# Improving energy estimation of a Fermionic lattice model with SQD - -In this tutorial we implement a [Qiskit pattern](/docs/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of a Fermionic lattice Hamiltonian known as the single-impurity Anderson model. We will follow a sample-based quantum diagonalization approach to process samples taken from a set of `16`-qubit Krylov basis states over increasing time intervals. These states are realized on the quantum device using Trotterization of the time evolution. In order to account for the effect of quantum noise, the configuration recovery technique is used. Assuming a good initial state and sparsity of the ground state, [this approach is proven to converge efficiently](https://arxiv.org/abs/2501.09702). - -The pattern can be described in four steps: - -1. **Step 1: Map to quantum problem** - - * Generate a set of Krylov basis states (i.e., Trotterized time-evolution circuits) over increasing time intervals for estimating the ground state - -2. **Step 2: Optimize the problem** - - * Transpile the circuits for the backend - -3. **Step 3: Execute experiments** - - * Draw samples from the circuits using the `Sampler` primitive - -4. **Step 4: Post-process results** - - * Self-consistent configuration recovery loop - - * Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration - * Probabilistically create batches of subsamples from recovered bitstrings - * Project and diagonalize the Fermionic lattice Hamiltonian over each sampled subspace - * Save the minimum ground state energy found across all batches and update the avg orbital occupancy - - - -## Step 1: Map problem to a quantum circuit - -First, we will create the one- and two-body Hamiltonians describing the one-dimensional single-impurity Anderson model (SIAM) with `7` bath sites (`8` electrons in `8` orbitals). This model is used to describe magnetic impurities embedded in metals. - -Then we will create the `16`-qubit Trotter circuits used to generate the quantum Krylov subspace. - -``` -[1]: -``` - -```python -import numpy as np - -n_bath = 7 # number of bath sites - -V = 1 # hybridization amplitude -t = 1 # bath hopping amplitude -U = 10 # Impurity onsite repulsion -eps = -U / 2 # Chemical potential for the impurity - -# Place the impurity on the first qubit -impurity_index = 0 - -# One body matrix elements in the "position" basis -h1e = -t * np.diag(np.ones(n_bath), k=1) - t * np.diag(np.ones(n_bath), k=-1) -h1e[impurity_index, impurity_index + 1] = -V -h1e[impurity_index + 1, impurity_index] = -V -h1e[impurity_index, impurity_index] = eps - -# Two body matrix elements in the "position" basis -h2e = np.zeros((n_bath + 1, n_bath + 1, n_bath + 1, n_bath + 1)) -h2e[impurity_index, impurity_index, impurity_index, impurity_index] = U -``` - -Next, we will generate the quantum Krylov subspace with a set of Trotterized quantum circuits. Here we create helpers for generating the initial (reference) state as well as the time evolution for the one- and two-body parts of the Hamiltonian. For a more detailed description of this model and how the circuits are designed, please refer to [the paper](https://arxiv.org/abs/2501.09702). - -``` -[2]: -``` - -```python -import ffsim -import scipy -from qiskit import QuantumCircuit, QuantumRegister -from qiskit.circuit.library import CPhaseGate, XGate, XXPlusYYGate - -n_modes = n_bath + 1 -nelec = (n_modes // 2, n_modes // 2) - -dt = 0.2 -Utar = scipy.linalg.expm(-1j * dt * h1e) - - -# The reference state -def initial_state(q_circuit, norb, nocc): - """Prepare an initial state.""" - for i in range(nocc): - q_circuit.append(XGate(), [i]) - q_circuit.append(XGate(), [norb + i]) - rot = XXPlusYYGate(np.pi / 2, -np.pi / 2) - - for i in range(3): - for j in range(nocc - i - 1, nocc + i, 2): - q_circuit.append(rot, [j, j + 1]) - q_circuit.append(rot, [norb + j, norb + j + 1]) - q_circuit.append(rot, [j + 1, j + 2]) - q_circuit.append(rot, [norb + j + 1, norb + j + 2]) - - -# The one-body time evolution -free_fermion_evolution = ffsim.qiskit.OrbitalRotationJW(n_modes, Utar) - - -# The two-body time evolution -def append_diagonal_evolution(dt, U, impurity_qubit, num_orb, q_circuit): - """Append two-body time evolution to a quantum circuit.""" - if U != 0: - q_circuit.append( - CPhaseGate(-dt / 2 * U), - [impurity_qubit, impurity_qubit + num_orb], - ) -``` - -Generate `d` time-evolved states that specify the quantum Krylov subspace. Here, we have chosen `d=8`. The error from sampling Krylov basis states converges with increasing `d`. Note that the particulars of this problem instance allow for an efficient compilation of the one-body evolution using `OrbitalRotationJW`; however, in general, one could use Qiskit’s [PauliEvolutionGate](/docs/api/qiskit/qiskit.circuit.library.PauliEvolutionGate) to implement the Trotterized time evolution of the full Hamiltonian. - -``` -[3]: -``` - -```python -# Generate the initial state -qubits = QuantumRegister(2 * n_modes, name="q") -init_state = QuantumCircuit(qubits) -initial_state(init_state, n_modes, n_modes // 2) -init_state.draw("mpl", scale=0.4, fold=-1) - -d = 8 # Number of Krylov basis states -circuits = [] -for i in range(d): - circ = init_state.copy() - circuits.append(circ) - for _ in range(i): - append_diagonal_evolution(dt, U, impurity_index, n_modes, circ) - circ.append(free_fermion_evolution, qubits) - append_diagonal_evolution(dt, U, impurity_index, n_modes, circ) - circ.measure_all() -``` - -``` -[4]: -``` - -```python -circuits[0].draw("mpl", scale=0.4, fold=-1) -``` - -``` -[4]: -``` - -![../\_images/tutorials\_02\_fermionic\_lattice\_hamiltonian\_8\_0.png](/docs/images/api/qiskit-addon-sqd/tutorials_02_fermionic_lattice_hamiltonian_8_0.avif) - -``` -[5]: -``` - -```python -circuits[-1].draw("mpl", scale=0.4, fold=-1) -``` - -``` -[5]: -``` - -![../\_images/tutorials\_02\_fermionic\_lattice\_hamiltonian\_9\_0.png](/docs/images/api/qiskit-addon-sqd/tutorials_02_fermionic_lattice_hamiltonian_9_0.avif) - - - -## Step 2: Optimize the problem - -After we have created the Trotterized circuits, we will optimize them for a target hardware. We need to choose the hardware device to use before optimization. We will use a fake 127-qubit backend from `qiskit_ibm_runtime` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info. - -``` -[6]: -``` - -```python -from qiskit_ibm_runtime.fake_provider.backends import FakeSherbrooke - -backend = FakeSherbrooke() -``` - -Next, we will transpile the circuits to the target backend using Qiskit. - -``` -[7]: -``` - -```python -from qiskit.transpiler import generate_preset_pass_manager - -# The circuit needs to be transpiled to the AerSimulator target -pass_manager = generate_preset_pass_manager(optimization_level=3, backend=backend) -isa_circuits = pass_manager.run(circuits) -``` - - - -## Step 3: Execute experiments - -After optimizing the circuits for hardware execution, we are ready to run them on the target hardware and collect samples for ground state energy estimation. Here we use `SamplerV2` from `qiskit-ibm-runtime` to simulate noisy samples taken from the `ibm_sherbrooke` backend. We then combine the counts from each of the Krylov basis states into a single counts dictionary and plot the top 20 most commonly sampled bitstrings. - -**Note:** [Qiskit Aer](https://qiskit.github.io/qiskit-aer/index.html) **is required to simulate samples from transpiled circuits.** - -``` -[8]: -``` - -```python -from qiskit.primitives import BitArray -from qiskit.visualization import plot_histogram -from qiskit_ibm_runtime import SamplerV2 as Sampler - -# Sample from the circuits -noisy_sampler = Sampler(backend, options={"simulator": {"seed_simulator": 24}}) -job = noisy_sampler.run(isa_circuits, shots=500) - -# Combine the counts from the individual Trotter circuits -bit_array = BitArray.concatenate_shots([result.data.meas for result in job.result()]) - -plot_histogram(bit_array.get_counts(), number_to_keep=20) -``` - -``` -[8]: -``` - -![../\_images/tutorials\_02\_fermionic\_lattice\_hamiltonian\_17\_0.png](/docs/images/api/qiskit-addon-sqd/tutorials_02_fermionic_lattice_hamiltonian_17_0.avif) - - - -## Step 4: Post-process the results - -Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](fermion#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function. - -``` -[ ]: -``` - -```python -from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian - -# List to capture intermediate results -result_history = [] - - -def callback(results: list[SCIResult]): - result_history.append(results) - iteration = len(result_history) - print(f"Iteration {iteration}") - for i, result in enumerate(results): - print(f"\tSubsample {i}") - print(f"\t\tEnergy: {result.energy}") - print(f"\t\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}") - - -rng = np.random.default_rng(24) -result = diagonalize_fermionic_hamiltonian( - h1e, - h2e, - bit_array, - samples_per_batch=300, - norb=n_modes, - nelec=nelec, - num_batches=3, - max_iterations=10, - symmetrize_spin=True, - callback=callback, - seed=rng, -) -``` - -```python -Iteration 1 - Subsample 0 - Energy: -13.257128325607997 - Subspace dimension: 3969 - Subsample 1 - Energy: -13.257128325607997 - Subspace dimension: 3969 - Subsample 2 - Energy: -13.257128325607997 - Subspace dimension: 3969 -Iteration 2 - Subsample 0 - Energy: -13.319666127542039 - Subspace dimension: 4096 - Subsample 1 - Energy: -13.420534292304595 - Subspace dimension: 4624 - Subsample 2 - Energy: -9.136171594591085 - Subspace dimension: 4624 -Iteration 3 - Subsample 0 - Energy: -13.422491814612833 - Subspace dimension: 4900 - Subsample 1 - Energy: -13.422491814612833 - Subspace dimension: 4900 - Subsample 2 - Energy: -13.422491814612833 - Subspace dimension: 4900 -Iteration 4 - Subsample 0 - Energy: -13.422491814612833 - Subspace dimension: 4900 - Subsample 1 - Energy: -13.422491814612833 - Subspace dimension: 4900 - Subsample 2 - Energy: -13.422491814612833 - Subspace dimension: 4900 -``` - -Now, we plot the results. The first plot shows that after a few iterations, we obtain the exact ground state energy. - -This example is small enough that we are able to explore the full Hilbert space, as seen in the print statements above. Remember, the full Hilbert space is dimension `(num_orbitals choose nelec_a) * (num_orbitals choose nelec_b)`. So for this problem: `(8 choose 4)**2 = 4900`. The subspace dimensions increase due enhanced configuration recovery, and also the fact that the SQD algorithm carries important configurations from one iteration to the next. By the last iteration, we are diagonalizing in the entire Hilbert space. - -The second plot shows the average occupancy of each spatial orbital across all batches’ solutions. We see that with high probability each orbital contains one electron. - -``` -[10]: -``` - -```python -import matplotlib.pyplot as plt - -exact_energy = -13.422491814605827 -min_es = [min(result, key=lambda res: res.energy).energy for result in result_history] -min_id, min_e = min(enumerate(min_es), key=lambda x: x[1]) - -# Data for energies plot -x1 = range(len(result_history)) -yt1 = list(np.arange(-13.5, -13.1, 0.1)) -ytl = [f"{i:.1f}" for i in yt1] - -# Data for avg spatial orbital occupancy -y2 = np.sum(result.orbital_occupancies, axis=0) -x2 = range(len(y2)) - -fig, axs = plt.subplots(1, 2, figsize=(12, 6)) - -# Plot energies -axs[0].plot(x1, min_es, label="SQD energy", marker="o") -axs[0].set_xticks(x1) -axs[0].set_xticklabels(x1) -axs[0].set_yticks(yt1) -axs[0].set_yticklabels(ytl) -axs[0].axhline(y=exact_energy, color="#BF5700", linestyle="--", label="Exact energy") -axs[0].set_title("Approximated Ground State Energy vs SQD Iterations") -axs[0].set_xlabel("Iteration Index", fontdict={"fontsize": 12}) -axs[0].set_ylabel("Energy", fontdict={"fontsize": 12}) -axs[0].legend() - -# Plot orbital occupancy -axs[1].bar(x2, y2, width=0.8) -axs[1].set_xticks(x2) -axs[1].set_xticklabels(x2) -axs[1].set_title("Avg Occupancy per Spatial Orbital") -axs[1].set_xlabel("Orbital Index", fontdict={"fontsize": 12}) -axs[1].set_ylabel("Avg Occupancy", fontdict={"fontsize": 12}) - -print(f"Exact energy: {exact_energy:.5f}") -print(f"SQD energy: {min_e:.5f}") -print(f"Absolute error: {abs(min_e - exact_energy):.5f}") -plt.tight_layout() -plt.show() -``` - -```python -Exact energy: -13.42249 -SQD energy: -13.42249 -Absolute error: 0.00000 -``` - -![../\_images/tutorials\_02\_fermionic\_lattice\_hamiltonian\_22\_1.png](/docs/images/api/qiskit-addon-sqd/tutorials_02_fermionic_lattice_hamiltonian_22_1.avif) diff --git a/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb deleted file mode 100644 index e216440f591..00000000000 --- a/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb +++ /dev/null @@ -1,495 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Improving energy estimation of a Fermionic lattice model with SQD\n", - "\n", - "In this tutorial we implement a [Qiskit pattern](/docs/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of a Fermionic lattice Hamiltonian known as the single-impurity Anderson model. We will follow a sample-based quantum diagonalization approach to process samples taken from a set of ``16``-qubit Krylov basis states over increasing time intervals. These states are realized on the quantum device using Trotterization of the time evolution. In order to account for the effect of quantum noise, the configuration recovery technique is used. Assuming a good initial state and sparsity of the ground state, [this approach is proven to converge efficiently](https://arxiv.org/abs/2501.09702).\n", - "\n", - "The pattern can be described in four steps:\n", - "\n", - "1. **Step 1: Map to quantum problem**\n", - " - Generate a set of Krylov basis states (i.e., Trotterized time-evolution circuits) over increasing time intervals for estimating the ground state\n", - "2. **Step 2: Optimize the problem**\n", - " - Transpile the circuits for the backend\n", - "3. **Step 3: Execute experiments**\n", - " - Draw samples from the circuits using the ``Sampler`` primitive\n", - "4. **Step 4: Post-process results**\n", - " - Self-consistent configuration recovery loop\n", - " - Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration\n", - " - Probabilistically create batches of subsamples from recovered bitstrings\n", - " - Project and diagonalize the Fermionic lattice Hamiltonian over each sampled subspace\n", - " - Save the minimum ground state energy found across all batches and update the avg orbital occupancy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Step 1: Map problem to a quantum circuit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we will create the one- and two-body Hamiltonians describing the one-dimensional single-impurity Anderson model (SIAM) with ``7`` bath sites (``8`` electrons in ``8`` orbitals). This model is used to describe magnetic impurities embedded in metals.\n", - "\n", - "Then we will create the ``16``-qubit Trotter circuits used to generate the quantum Krylov subspace." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "n_bath = 7 # number of bath sites\n", - "\n", - "V = 1 # hybridization amplitude\n", - "t = 1 # bath hopping amplitude\n", - "U = 10 # Impurity onsite repulsion\n", - "eps = -U / 2 # Chemical potential for the impurity\n", - "\n", - "# Place the impurity on the first qubit\n", - "impurity_index = 0\n", - "\n", - "# One body matrix elements in the \"position\" basis\n", - "h1e = -t * np.diag(np.ones(n_bath), k=1) - t * np.diag(np.ones(n_bath), k=-1)\n", - "h1e[impurity_index, impurity_index + 1] = -V\n", - "h1e[impurity_index + 1, impurity_index] = -V\n", - "h1e[impurity_index, impurity_index] = eps\n", - "\n", - "# Two body matrix elements in the \"position\" basis\n", - "h2e = np.zeros((n_bath + 1, n_bath + 1, n_bath + 1, n_bath + 1))\n", - "h2e[impurity_index, impurity_index, impurity_index, impurity_index] = U" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will generate the quantum Krylov subspace with a set of Trotterized quantum circuits. Here we create helpers for generating the initial (reference) state as well as the time evolution for the one- and two-body parts of the Hamiltonian. For a more detailed description of this model and how the circuits are designed, please refer to [the paper](https://arxiv.org/abs/2501.09702)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import ffsim\n", - "import scipy\n", - "from qiskit import QuantumCircuit, QuantumRegister\n", - "from qiskit.circuit.library import CPhaseGate, XGate, XXPlusYYGate\n", - "\n", - "n_modes = n_bath + 1\n", - "nelec = (n_modes // 2, n_modes // 2)\n", - "\n", - "dt = 0.2\n", - "Utar = scipy.linalg.expm(-1j * dt * h1e)\n", - "\n", - "\n", - "# The reference state\n", - "def initial_state(q_circuit, norb, nocc):\n", - " \"\"\"Prepare an initial state.\"\"\"\n", - " for i in range(nocc):\n", - " q_circuit.append(XGate(), [i])\n", - " q_circuit.append(XGate(), [norb + i])\n", - " rot = XXPlusYYGate(np.pi / 2, -np.pi / 2)\n", - "\n", - " for i in range(3):\n", - " for j in range(nocc - i - 1, nocc + i, 2):\n", - " q_circuit.append(rot, [j, j + 1])\n", - " q_circuit.append(rot, [norb + j, norb + j + 1])\n", - " q_circuit.append(rot, [j + 1, j + 2])\n", - " q_circuit.append(rot, [norb + j + 1, norb + j + 2])\n", - "\n", - "\n", - "# The one-body time evolution\n", - "free_fermion_evolution = ffsim.qiskit.OrbitalRotationJW(n_modes, Utar)\n", - "\n", - "\n", - "# The two-body time evolution\n", - "def append_diagonal_evolution(dt, U, impurity_qubit, num_orb, q_circuit):\n", - " \"\"\"Append two-body time evolution to a quantum circuit.\"\"\"\n", - " if U != 0:\n", - " q_circuit.append(\n", - " CPhaseGate(-dt / 2 * U),\n", - " [impurity_qubit, impurity_qubit + num_orb],\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate ``d`` time-evolved states that specify the quantum Krylov subspace. Here, we have chosen ``d=8``. The error from sampling Krylov basis states converges with increasing ``d``. Note that the particulars of this problem instance allow for an efficient compilation of the one-body evolution using `OrbitalRotationJW`; however, in general, one could use Qiskit's [PauliEvolutionGate](/docs/api/qiskit/qiskit.circuit.library.PauliEvolutionGate) to implement the Trotterized time evolution of the full Hamiltonian." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate the initial state\n", - "qubits = QuantumRegister(2 * n_modes, name=\"q\")\n", - "init_state = QuantumCircuit(qubits)\n", - "initial_state(init_state, n_modes, n_modes // 2)\n", - "init_state.draw(\"mpl\", scale=0.4, fold=-1)\n", - "\n", - "d = 8 # Number of Krylov basis states\n", - "circuits = []\n", - "for i in range(d):\n", - " circ = init_state.copy()\n", - " circuits.append(circ)\n", - " for _ in range(i):\n", - " append_diagonal_evolution(dt, U, impurity_index, n_modes, circ)\n", - " circ.append(free_fermion_evolution, qubits)\n", - " append_diagonal_evolution(dt, U, impurity_index, n_modes, circ)\n", - " circ.measure_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "circuits[-1].draw(\"mpl\", scale=0.4, fold=-1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Step 2: Optimize the problem" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After we have created the Trotterized circuits, we will optimize them for a target hardware. We need to choose the hardware device to use before optimization. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime.fake_provider.backends import FakeSherbrooke\n", - "\n", - "backend = FakeSherbrooke()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will transpile the circuits to the target backend using Qiskit." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit.transpiler import generate_preset_pass_manager\n", - "\n", - "# The circuit needs to be transpiled to the AerSimulator target\n", - "pass_manager = generate_preset_pass_manager(optimization_level=3, backend=backend)\n", - "isa_circuits = pass_manager.run(circuits)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Step 3: Execute experiments" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After optimizing the circuits for hardware execution, we are ready to run them on the target hardware and collect samples for ground state energy estimation. Here we use ``SamplerV2`` from ``qiskit-ibm-runtime`` to simulate noisy samples taken from the ``ibm_sherbrooke`` backend. We then combine the counts from each of the Krylov basis states into a single counts dictionary and plot the top 20 most commonly sampled bitstrings.\n", - "\n", - "***Note: [Qiskit Aer](https://qiskit.github.io/qiskit-aer/index.html) is required to simulate samples from transpiled circuits.***" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit.primitives import BitArray\n", - "from qiskit.visualization import plot_histogram\n", - "from qiskit_ibm_runtime import SamplerV2 as Sampler\n", - "\n", - "# Sample from the circuits\n", - "noisy_sampler = Sampler(backend, options={\"simulator\": {\"seed_simulator\": 24}})\n", - "job = noisy_sampler.run(isa_circuits, shots=500)\n", - "\n", - "# Combine the counts from the individual Trotter circuits\n", - "bit_array = BitArray.concatenate_shots([result.data.meas for result in job.result()])\n", - "\n", - "plot_histogram(bit_array.get_counts(), number_to_keep=20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Step 4: Post-process the results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 1\n", - "\tSubsample 0\n", - "\t\tEnergy: -13.257128325607997\n", - "\t\tSubspace dimension: 3969\n", - "\tSubsample 1\n", - "\t\tEnergy: -13.257128325607997\n", - "\t\tSubspace dimension: 3969\n", - "\tSubsample 2\n", - "\t\tEnergy: -13.257128325607997\n", - "\t\tSubspace dimension: 3969\n", - "Iteration 2\n", - "\tSubsample 0\n", - "\t\tEnergy: -13.319666127542039\n", - "\t\tSubspace dimension: 4096\n", - "\tSubsample 1\n", - "\t\tEnergy: -13.420534292304595\n", - "\t\tSubspace dimension: 4624\n", - "\tSubsample 2\n", - "\t\tEnergy: -9.136171594591085\n", - "\t\tSubspace dimension: 4624\n", - "Iteration 3\n", - "\tSubsample 0\n", - "\t\tEnergy: -13.422491814612833\n", - "\t\tSubspace dimension: 4900\n", - "\tSubsample 1\n", - "\t\tEnergy: -13.422491814612833\n", - "\t\tSubspace dimension: 4900\n", - "\tSubsample 2\n", - "\t\tEnergy: -13.422491814612833\n", - "\t\tSubspace dimension: 4900\n", - "Iteration 4\n", - "\tSubsample 0\n", - "\t\tEnergy: -13.422491814612833\n", - "\t\tSubspace dimension: 4900\n", - "\tSubsample 1\n", - "\t\tEnergy: -13.422491814612833\n", - "\t\tSubspace dimension: 4900\n", - "\tSubsample 2\n", - "\t\tEnergy: -13.422491814612833\n", - "\t\tSubspace dimension: 4900\n" - ] - } - ], - "source": [ - "from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian\n", - "\n", - "# List to capture intermediate results\n", - "result_history = []\n", - "\n", - "\n", - "def callback(results: list[SCIResult]):\n", - " result_history.append(results)\n", - " iteration = len(result_history)\n", - " print(f\"Iteration {iteration}\")\n", - " for i, result in enumerate(results):\n", - " print(f\"\\tSubsample {i}\")\n", - " print(f\"\\t\\tEnergy: {result.energy}\")\n", - " print(f\"\\t\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\")\n", - "\n", - "\n", - "rng = np.random.default_rng(24)\n", - "result = diagonalize_fermionic_hamiltonian(\n", - " h1e,\n", - " h2e,\n", - " bit_array,\n", - " samples_per_batch=300,\n", - " norb=n_modes,\n", - " nelec=nelec,\n", - " num_batches=3,\n", - " max_iterations=10,\n", - " symmetrize_spin=True,\n", - " callback=callback,\n", - " seed=rng,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we plot the results. The first plot shows that after a few iterations, we obtain the exact ground state energy.\n", - "\n", - "This example is small enough that we are able to explore the full Hilbert space, as seen in the print statements above. Remember, the full Hilbert space is dimension ``(num_orbitals choose nelec_a) * (num_orbitals choose nelec_b)``. So for this problem: `(8 choose 4)**2 = 4900`. The subspace dimensions increase due enhanced configuration recovery, and also the fact that the SQD algorithm carries important configurations from one iteration to the next. By the last iteration, we are diagonalizing in the entire Hilbert space.\n", - "\n", - "The second plot shows the average occupancy of each spatial orbital across all batches' solutions. We see that with high probability each orbital contains one electron." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exact energy: -13.42249\n", - "SQD energy: -13.42249\n", - "Absolute error: 0.00000\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "exact_energy = -13.422491814605827\n", - "min_es = [min(result, key=lambda res: res.energy).energy for result in result_history]\n", - "min_id, min_e = min(enumerate(min_es), key=lambda x: x[1])\n", - "\n", - "# Data for energies plot\n", - "x1 = range(len(result_history))\n", - "yt1 = list(np.arange(-13.5, -13.1, 0.1))\n", - "ytl = [f\"{i:.1f}\" for i in yt1]\n", - "\n", - "# Data for avg spatial orbital occupancy\n", - "y2 = np.sum(result.orbital_occupancies, axis=0)\n", - "x2 = range(len(y2))\n", - "\n", - "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", - "\n", - "# Plot energies\n", - "axs[0].plot(x1, min_es, label=\"SQD energy\", marker=\"o\")\n", - "axs[0].set_xticks(x1)\n", - "axs[0].set_xticklabels(x1)\n", - "axs[0].set_yticks(yt1)\n", - "axs[0].set_yticklabels(ytl)\n", - "axs[0].axhline(y=exact_energy, color=\"#BF5700\", linestyle=\"--\", label=\"Exact energy\")\n", - "axs[0].set_title(\"Approximated Ground State Energy vs SQD Iterations\")\n", - "axs[0].set_xlabel(\"Iteration Index\", fontdict={\"fontsize\": 12})\n", - "axs[0].set_ylabel(\"Energy\", fontdict={\"fontsize\": 12})\n", - "axs[0].legend()\n", - "\n", - "# Plot orbital occupancy\n", - "axs[1].bar(x2, y2, width=0.8)\n", - "axs[1].set_xticks(x2)\n", - "axs[1].set_xticklabels(x2)\n", - "axs[1].set_title(\"Avg Occupancy per Spatial Orbital\")\n", - "axs[1].set_xlabel(\"Orbital Index\", fontdict={\"fontsize\": 12})\n", - "axs[1].set_ylabel(\"Avg Occupancy\", fontdict={\"fontsize\": 12})\n", - "\n", - "print(f\"Exact energy: {exact_energy:.5f}\")\n", - "print(f\"SQD energy: {min_e:.5f}\")\n", - "print(f\"Absolute error: {abs(min_e - exact_energy):.5f}\")\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-sqd/tutorials/index.mdx b/docs/addons/qiskit-addon-sqd/tutorials/index.mdx deleted file mode 100644 index 80199d8444d..00000000000 --- a/docs/addons/qiskit-addon-sqd/tutorials/index.mdx +++ /dev/null @@ -1,17 +0,0 @@ ---- -title: Sample-based quantum diagonalization (SQD) API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-sqd. ---- - -# Tutorials - -This page summarizes the available tutorials. - -[![](/docs/images/api/qiskit-addon-sqd/tutorials_01_chemistry_hamiltonian_19_1.avif)](01-chemistry-hamiltonian) - -[Improving energy estimation of a chemistry Hamiltonian with SQD](01-chemistry-hamiltonian) - -[![](/docs/images/api/qiskit-addon-sqd/tutorials_02_fermionic_lattice_hamiltonian_22_1.avif)](02-fermionic-lattice-hamiltonian) - -[Improving energy estimation of a Fermionic lattice model with SQD](02-fermionic-lattice-hamiltonian) - diff --git a/docs/addons/qiskit-addon-utils/_toc.json b/docs/addons/qiskit-addon-utils/_toc.json deleted file mode 100644 index ffa73e62c08..00000000000 --- a/docs/addons/qiskit-addon-utils/_toc.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "title": "Qiskit addon utilities", - "children": [ - { - "title": "Documentation Home", - "url": "/docs/addons/qiskit-addon-utils" - }, - { - "title": "Installation Instructions", - "url": "/docs/addons/qiskit-addon-utils/install" - }, - { - "title": "How-To Guides", - "children": [ - { - "title": "Use edge coloring to reduce the depth of quantum circuits", - "url": "/docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth" - }, - { - "title": "Slicing circuits using qiskit_addon_utils.slicing", - "url": "/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices" - } - ], - "url": "/docs/addons/qiskit-addon-utils/how_tos/index" - } - ], - "collapsed": true -} diff --git a/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.mdx b/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.mdx deleted file mode 100644 index dbe5cb5da95..00000000000 --- a/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.mdx +++ /dev/null @@ -1,161 +0,0 @@ - - -# Use edge coloring to reduce the depth of quantum circuits - -This guide will show how to use the [qiskit\_addon\_utils.coloring](/docs/addons/qiskit-addon-utils/apidocs/qiskit_addon_utils.coloring) module to color edges of a coupling map and use the coloring to more efficiently place gates in a quantum circuit, leading to shallower circuits. - -The point of edge-coloring a graph is to find a set of edge colors such that no two edges of the same color share a common node. For a quantum processor, this means that gates along like-colored edges (qubit connections) may be run simultaneously and that the circuit will execute faster. We can use this to define our circuit in a way that is more amenable to the hardware it should run on. - -First, we will specify a backend and visualize its qubit topology. - -``` -[1]: -``` - -```python -from qiskit_ibm_runtime.fake_provider import FakeSherbrooke - -backend = FakeSherbrooke() -coupling_map = backend.coupling_map -``` - -``` -[2]: -``` - -```python -from rustworkx.visualization import graphviz_draw - -graphviz_draw(coupling_map.graph, method="neato") -``` - -``` -[2]: -``` - -![../\_images/how\_tos\_color\_device\_edges\_to\_improve\_depth\_3\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_color_device_edges_to_improve_depth_3_0.avif) - -Next we use the [qiskit\_addon\_utils.coloring.auto\_color\_edges](/docs/api/qiskit-addon-utils/coloring#qiskit_addon_utils.coloring.auto_color_edges) function to generate an edge coloring. This is a light wrapper for [rustworkx.graph\_greedy\_edge\_color](https://www.rustworkx.org/dev/apiref/rustworkx.graph_greedy_edge_color.html) that does some minor post-processing of the output. Namely, this function removes an arbitrary edge direction if an edge is bi-directional and combines the edge and color information into a dictionary format. - -``` -[3]: -``` - -```python -from qiskit_addon_utils.coloring import auto_color_edges -from rustworkx import PyDiGraph - -coloring = auto_color_edges(coupling_map) - -# Create a colored graph for visualization -eagle_r3 = PyDiGraph() -eagle_r3.extend_from_weighted_edge_list( - [(source, target, color) for ((source, target), color) in coloring.items()] -) -print(f"There are {len(coloring)} edges and {len(set(coloring.values()))} unique colors.") -print( - f'The edges are specified by length-2 tuples: (e.g. {next(iter(coloring))}).\ - \nThe "colors" are actually specified by a set of integers: {set(coloring.values())}' -) -``` - -```python -There are 144 edges and 3 unique colors. -The edges are specified by length-2 tuples: (e.g. (1, 0)). -The "colors" are actually specified by a set of integers: {0, 1, 2} -``` - -``` -[4]: -``` - -```python -def color_edge_3color(edge): - """Return the color of an edge.""" - # Map integers of a 3-coloring to color names - color_dict = {0: "red", 1: "green", 2: "blue"} - return {"color": color_dict[edge]} - - -graphviz_draw(eagle_r3, edge_attr_fn=color_edge_3color, method="neato") -``` - -``` -[4]: -``` - -![../\_images/how\_tos\_color\_device\_edges\_to\_improve\_depth\_6\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_color_device_edges_to_improve_depth_6_0.avif) - -Next, let’s make a circuit with a `CZGate` on each edge and observe its depth. For this first circuit, we will just naively place a gate on each edge. We will place barriers in `depth-1` increments (using [qiskit\_addon\_utils.slicing.slice\_by\_depth](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.slice_by_depth)) to illustrate how crucial edge-coloring is when designing a hardware-efficient quantum circuit. - -``` -[5]: -``` - -```python -from qiskit import QuantumCircuit -from qiskit_addon_utils.slicing import combine_slices, slice_by_depth - -circuit = QuantumCircuit(backend.num_qubits) - -for edge in coupling_map.graph.edge_list(): - circuit.cz(edge[0], edge[1]) - -print(f"The circuit has depth: {circuit.depth()}") - -# Add barriers in depth-1 increments -slices = slice_by_depth(circuit, max_slice_depth=1) -circuit = combine_slices(slices, include_barriers=True) - -circuit.draw("mpl", fold=-1) -``` - -```python -The circuit has depth: 37 -``` - -``` -[5]: -``` - -![../\_images/how\_tos\_color\_device\_edges\_to\_improve\_depth\_8\_1.png](/docs/images/api/qiskit-addon-utils/how_tos_color_device_edges_to_improve_depth_8_1.avif) - -Now let’s place all of the gates of a given color at the same time. We can see the depth is reduced from `37` to `3`. This is because we can take advantage of the fact that all gates on a given color can run at the same time. - -``` -[6]: -``` - -```python -from collections import defaultdict - -circuit = QuantumCircuit(backend.num_qubits) - -# Make a reverse coloring dict -color_to_edge = defaultdict(list) -for edge, color in coloring.items(): - color_to_edge[color].append(edge) - -# Place edges in order of color -for edges in color_to_edge.values(): - for edge in edges: - circuit.cz(edge[0], edge[1]) - -print(f"The circuit has depth: {circuit.depth(lambda x: x.name != 'barrier')}") - -# Add barriers in depth-1 increments -slices = slice_by_depth(circuit, max_slice_depth=1) -circuit = combine_slices(slices, include_barriers=True) - -circuit.draw("mpl", fold=-1) -``` - -```python -The circuit has depth: 3 -``` - -``` -[6]: -``` - -![../\_images/how\_tos\_color\_device\_edges\_to\_improve\_depth\_10\_1.png](/docs/images/api/qiskit-addon-utils/how_tos_color_device_edges_to_improve_depth_10_1.avif) diff --git a/docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth.ipynb b/docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth.ipynb deleted file mode 100644 index 3c1ae0a2995..00000000000 --- a/docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth.ipynb +++ /dev/null @@ -1,292 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4de02100-ba7b-4ac3-8e92-f94844073337", - "metadata": {}, - "source": [ - "# Use edge coloring to reduce the depth of quantum circuits\n", - "\n", - "This guide will show how to use the [qiskit_addon_utils.coloring](/docs/addons/qiskit-addon-utils/apidocs/qiskit_addon_utils.coloring) module to color edges of a coupling map and use the coloring to more efficiently place gates in a quantum circuit, leading to shallower circuits.\n", - "\n", - "The point of edge-coloring a graph is to find a set of edge colors such that no two edges of the same color share a common node. For a quantum processor, this means that gates along like-colored edges (qubit connections) may be run simultaneously and that the circuit will execute faster. We can use this to define our circuit in a way that is more amenable to the hardware it should run on." - ] - }, - { - "cell_type": "markdown", - "id": "4b16cf04-941e-4b02-932e-f5e6301b9a5b", - "metadata": {}, - "source": [ - "First, we will specify a backend and visualize its qubit topology." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9da8d173-8ce9-47a9-a24f-ab32e45f8337", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime.fake_provider import FakeSherbrooke\n", - "\n", - "backend = FakeSherbrooke()\n", - "coupling_map = backend.coupling_map" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9b986eac-93c6-453a-bfe1-2546681ab38b", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "skip-execution" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from rustworkx.visualization import graphviz_draw\n", - "\n", - "graphviz_draw(coupling_map.graph, method=\"neato\")" - ] - }, - { - "cell_type": "markdown", - "id": "eb4e5115-f2a9-4c62-8c5d-96f6bb6c8762", - "metadata": {}, - "source": [ - "Next we use the [qiskit_addon_utils.coloring.auto_color_edges](/docs/api/qiskit-addon-utils/coloring#qiskit_addon_utils.coloring.auto_color_edges#qiskit_addon_utils.coloring.auto_color_edges) function to generate an edge coloring. This is a light wrapper for [rustworkx.graph_greedy_edge_color](https://www.rustworkx.org/dev/apiref/rustworkx.graph_greedy_edge_color.html) that does some minor post-processing of the output. Namely, this function removes an arbitrary edge direction if an edge is bi-directional and combines the edge and color information into a dictionary format." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6700931f-cf94-4ad6-8012-d73c0bfd7efa", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 144 edges and 3 unique colors.\n", - "The edges are specified by length-2 tuples: (e.g. (1, 0)). \n", - "The \"colors\" are actually specified by a set of integers: {0, 1, 2}\n" - ] - } - ], - "source": [ - "from qiskit_addon_utils.coloring import auto_color_edges\n", - "from rustworkx import PyDiGraph\n", - "\n", - "coloring = auto_color_edges(coupling_map)\n", - "\n", - "# Create a colored graph for visualization\n", - "eagle_r3 = PyDiGraph()\n", - "eagle_r3.extend_from_weighted_edge_list(\n", - " [(source, target, color) for ((source, target), color) in coloring.items()]\n", - ")\n", - "print(f\"There are {len(coloring)} edges and {len(set(coloring.values()))} unique colors.\")\n", - "print(\n", - " f'The edges are specified by length-2 tuples: (e.g. {next(iter(coloring))}).\\\n", - " \\nThe \"colors\" are actually specified by a set of integers: {set(coloring.values())}'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c3f482a1-6868-4cda-b8ed-8a8489794707", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "skip-execution" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def color_edge_3color(edge):\n", - " \"\"\"Return the color of an edge.\"\"\"\n", - " # Map integers of a 3-coloring to color names\n", - " color_dict = {0: \"red\", 1: \"green\", 2: \"blue\"}\n", - " return {\"color\": color_dict[edge]}\n", - "\n", - "\n", - "graphviz_draw(eagle_r3, edge_attr_fn=color_edge_3color, method=\"neato\")" - ] - }, - { - "cell_type": "markdown", - "id": "90d56de5-d43e-4509-8c68-f42ec0f198bb", - "metadata": {}, - "source": [ - "Next, let's make a circuit with a ``CZGate`` on each edge and observe its depth. For this first circuit, we will just naively place a gate on each edge. We will place barriers in ``depth-1`` increments (using [qiskit_addon_utils.slicing.slice_by_depth](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.slice_by_depth)) to illustrate how crucial edge-coloring is when designing a hardware-efficient quantum circuit." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "2806ec31-05e8-4a4c-832b-888150506191", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The circuit has depth: 37\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit import QuantumCircuit\n", - "from qiskit_addon_utils.slicing import combine_slices, slice_by_depth\n", - "\n", - "circuit = QuantumCircuit(backend.num_qubits)\n", - "\n", - "for edge in coupling_map.graph.edge_list():\n", - " circuit.cz(edge[0], edge[1])\n", - "\n", - "print(f\"The circuit has depth: {circuit.depth()}\")\n", - "\n", - "# Add barriers in depth-1 increments\n", - "slices = slice_by_depth(circuit, max_slice_depth=1)\n", - "circuit = combine_slices(slices, include_barriers=True)\n", - "\n", - "circuit.draw(\"mpl\", fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "15cf56a0-954e-482a-8ff8-4645b7d18444", - "metadata": {}, - "source": [ - "Now let's place all of the gates of a given color at the same time. We can see the depth is reduced from ``37`` to ``3``. This is because we can take advantage of the fact that all gates on a given color can run at the same time." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "03a5f389-94c0-4496-9fe6-262dca62a0e3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The circuit has depth: 3\n" - ] - }, - { - "data": { - "image/png": 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    " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import defaultdict\n", - "\n", - "circuit = QuantumCircuit(backend.num_qubits)\n", - "\n", - "# Make a reverse coloring dict\n", - "color_to_edge = defaultdict(list)\n", - "for edge, color in coloring.items():\n", - " color_to_edge[color].append(edge)\n", - "\n", - "# Place edges in order of color\n", - "for edges in color_to_edge.values():\n", - " for edge in edges:\n", - " circuit.cz(edge[0], edge[1])\n", - "\n", - "print(f\"The circuit has depth: {circuit.depth(lambda x: x.name != 'barrier')}\")\n", - "\n", - "# Add barriers in depth-1 increments\n", - "slices = slice_by_depth(circuit, max_slice_depth=1)\n", - "circuit = combine_slices(slices, include_barriers=True)\n", - "\n", - "circuit.draw(\"mpl\", fold=-1)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - }, - "toc": { - "base_numbering": 0 - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-utils/how_tos/create-circuit-slices.mdx b/docs/addons/qiskit-addon-utils/how_tos/create-circuit-slices.mdx deleted file mode 100644 index 9510f09437d..00000000000 --- a/docs/addons/qiskit-addon-utils/how_tos/create-circuit-slices.mdx +++ /dev/null @@ -1,206 +0,0 @@ - - -# Slicing circuits using `qiskit_addon_utils.slicing` - -Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. Some Qiskit addons make use of the term **slices** to describe layers of arbitrary depth. More concretely, slices can be defined as time-like partitions of a [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) which span all qubits. Similar to layers, composing all slices of a `QuantumCircuit` produces a circuit which is semantically equivalent to the original. - -The [qiskit\_addon\_utils.slicing](/docs/api/qiskit-addon-utils/slicing#module-qiskit_addon_utils.slicing) module provides a few utilities for partitioning `QuantumCircuit`s into slices. This is for example useful for [operator backpropagation](https://github.com/qiskit/qiskit-addon-obp/) We will give an overview of those tools in this guide. - -**Note:** Throughout this guide, we will slice the circuit and use [qiskit\_addon\_utils.slicing.combine\_slices](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.combine_slices) to recombine the slices with barriers to make it easier to visualize the slices. - -First, we’ll create a circuit from which we’ll create slices. - -``` -[1]: -``` - -```python -import numpy as np -from qiskit import QuantumCircuit - -num_qubits = 9 -qc = QuantumCircuit(num_qubits) -qc.ry(np.pi / 4, range(num_qubits)) -qubits_1 = [i for i in range(num_qubits) if i % 2 == 0] -qubits_2 = [i for i in range(num_qubits) if i % 2 == 1] -qc.cx(qubits_1[:-1], qubits_2) -qc.cx(qubits_2, qubits_1[1:]) -qc.cx(qubits_1[-1], qubits_1[0]) -qc.rx(np.pi / 4, range(num_qubits)) -qc.rz(np.pi / 4, range(num_qubits)) -qc.draw("mpl", scale=0.6) -``` - -``` -[1]: -``` - -![../\_images/how\_tos\_create\_circuit\_slices\_3\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_3_0.avif) - -In the case where there is no clear way to exploit the structure of the circuit for back-propagation, a user may wish to simply partition their circuit into slices of a given depth. Here, we’ll separate this circuit into depth-1 slices. - -``` -[2]: -``` - -```python -from qiskit_addon_utils.slicing import combine_slices, slice_by_depth - -slices = slice_by_depth(qc, 1) -combined_slices = combine_slices(slices, include_barriers=True) -combined_slices.draw("mpl", scale=0.6) -``` - -``` -[2]: -``` - -![../\_images/how\_tos\_create\_circuit\_slices\_5\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_5_0.avif) - -Now let’s try depth-2. - -``` -[3]: -``` - -```python -slices = slice_by_depth(qc, 2) -combined_slices = combine_slices(slices, include_barriers=True) -combined_slices.draw("mpl", scale=0.6) -``` - -``` -[3]: -``` - -![../\_images/how\_tos\_create\_circuit\_slices\_7\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_7_0.avif) - -In many cases, such as Trotter circuits, it may be advantageous to slice by gate type. Slices holding a given gate type will be further split out into depth-1 slices, as there is little downside in doing so. - -``` -[4]: -``` - -```python -from qiskit_addon_utils.slicing import slice_by_gate_types - -slices = slice_by_gate_types(qc) -combined_slices = combine_slices(slices, include_barriers=True) -combined_slices.draw("mpl", scale=0.6) -``` - -``` -[4]: -``` - -![../\_images/how\_tos\_create\_circuit\_slices\_9\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_9_0.avif) - -If your circuit was designed to exploit the physical qubit connectivity, you may want to create slices based on an edge coloring. Here, we will assign a 3-coloring to the circuit edges and slice the circuit with respect to the edge coloring. This only affects non-local gates. Single qubit gates will be added to their own slices by gate type. - -``` -[5]: -``` - -```python -from qiskit_addon_utils.slicing import slice_by_coloring - -# Assign a color to each set of connected qubits -coloring = {} -for i in range(num_qubits - 1): - coloring[(i, i + 1)] = i % 3 -coloring[(num_qubits - 1, 0)] = 2 - -# Create a circuit with operations added in order of color -qc = QuantumCircuit(num_qubits) -qc.ry(np.pi / 4, range(num_qubits)) -edges = [edge for color in range(3) for edge in coloring if coloring[edge] == color] -for edge in edges: - qc.cx(edge[0], edge[1]) -qc.rx(np.pi / 4, range(num_qubits)) -qc.rz(np.pi / 4, range(num_qubits)) - -# Create slices by edge color -slices = slice_by_coloring(qc, coloring=coloring) -combined_slices = combine_slices(slices, include_barriers=True) -combined_slices.draw("mpl", scale=0.6) -``` - -``` -[5]: -``` - -![../\_images/how\_tos\_create\_circuit\_slices\_11\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_11_0.avif) - -For more custom slicing strategies a user may wish to place barriers in the locations they want to slice and use the [slice\_by\_barriers](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.slice_by_barriers) function. Here, we will create 3 slices, one for each rotation layer and one for the entangling layer. - -``` -[6]: -``` - -```python -qc = QuantumCircuit(num_qubits) -qc.ry(np.pi / 4, range(num_qubits)) -qc.barrier() -qubits_1 = [i for i in range(num_qubits) if i % 2 == 0] -qubits_2 = [i for i in range(num_qubits) if i % 2 == 1] -qc.cx(qubits_1[:-1], qubits_2) -qc.cx(qubits_2, qubits_1[1:]) -qc.cx(qubits_1[-1], qubits_1[0]) -qc.barrier() -qc.rx(np.pi / 4, range(num_qubits)) -qc.rz(np.pi / 4, range(num_qubits)) -qc.draw("mpl", scale=0.6) -``` - -``` -[6]: -``` - -![../\_images/how\_tos\_create\_circuit\_slices\_13\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_13_0.avif) - -We will not draw the re-combined slices as a single circuit since it would look identical to the input circuit. Instead, below we draw each slice on its own. - -``` -[7]: -``` - -```python -from qiskit_addon_utils.slicing import slice_by_barriers - -slices = slice_by_barriers(qc) -slices[0].draw("mpl", scale=0.6) -``` - -``` -[7]: -``` - -![../\_images/how\_tos\_create\_circuit\_slices\_15\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_15_0.avif) - -``` -[8]: -``` - -```python -slices[1].draw("mpl", scale=0.6) -``` - -``` -[8]: -``` - -![../\_images/how\_tos\_create\_circuit\_slices\_16\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_16_0.avif) - -``` -[9]: -``` - -```python -slices[2].draw("mpl", scale=0.6) -``` - -``` -[9]: -``` - -![../\_images/how\_tos\_create\_circuit\_slices\_17\_0.png](/docs/images/api/qiskit-addon-utils/how_tos_create_circuit_slices_17_0.avif) diff --git a/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices.ipynb b/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices.ipynb deleted file mode 100644 index 2908c94ff5f..00000000000 --- a/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices.ipynb +++ /dev/null @@ -1,356 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4de02100-ba7b-4ac3-8e92-f94844073337", - "metadata": {}, - "source": [ - "# Slicing circuits using ``qiskit_addon_utils.slicing``\n", - "\n", - "Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. Some Qiskit addons make use of the term **slices** to describe layers of arbitrary depth. More concretely, slices can be defined as time-like partitions of a [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) which span all qubits. Similar to layers, composing all slices of a `QuantumCircuit` produces a circuit which is semantically equivalent to the original." - ] - }, - { - "cell_type": "markdown", - "id": "9f419feb-5d35-489e-909c-c8e7ea2caaec", - "metadata": {}, - "source": [ - "The [qiskit_addon_utils.slicing](/docs/api/qiskit-addon-utils/slicing#module-qiskit_addon_utils.slicing) module provides a few utilities for partitioning ``QuantumCircuit``s into slices. This is for example useful for [operator backpropagation](https://github.com/qiskit/qiskit-addon-obp/) We will give an overview of those tools in this guide.\n", - "\n", - "**Note:** Throughout this guide, we will slice the circuit and use [qiskit_addon_utils.slicing.combine_slices](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.combine_slices) to recombine the slices with barriers to make it easier to visualize the slices." - ] - }, - { - "cell_type": "markdown", - "id": "c6c74021-216c-43fd-b37a-91817e3d3978", - "metadata": {}, - "source": [ - "First, we'll create a circuit from which we'll create slices." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9da8d173-8ce9-47a9-a24f-ab32e45f8337", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "from qiskit import QuantumCircuit\n", - "\n", - "num_qubits = 9\n", - "qc = QuantumCircuit(num_qubits)\n", - "qc.ry(np.pi / 4, range(num_qubits))\n", - "qubits_1 = [i for i in range(num_qubits) if i % 2 == 0]\n", - "qubits_2 = [i for i in range(num_qubits) if i % 2 == 1]\n", - "qc.cx(qubits_1[:-1], qubits_2)\n", - "qc.cx(qubits_2, qubits_1[1:])\n", - "qc.cx(qubits_1[-1], qubits_1[0])\n", - "qc.rx(np.pi / 4, range(num_qubits))\n", - "qc.rz(np.pi / 4, range(num_qubits))\n", - "qc.draw(\"mpl\", scale=0.6)" - ] - }, - { - "cell_type": "markdown", - "id": "968d1e85-ab12-4383-865f-f0199308a9f2", - "metadata": {}, - "source": [ - "In the case where there is no clear way to exploit the structure of the circuit for back-propagation, a user may wish to simply partition their circuit into slices of a given depth. Here, we'll separate this circuit into depth-1 slices." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f883fa2e-f943-4c49-af45-fb26cadb72b6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_utils.slicing import combine_slices, slice_by_depth\n", - "\n", - "slices = slice_by_depth(qc, 1)\n", - "combined_slices = combine_slices(slices, include_barriers=True)\n", - "combined_slices.draw(\"mpl\", scale=0.6)" - ] - }, - { - "cell_type": "markdown", - "id": "4debef73-ba54-4eb1-887c-df6d022c9082", - "metadata": {}, - "source": [ - "Now let's try depth-2." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "bc5ebecb-6c8b-4f51-8199-b236a0ba3328", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ntk9O69k4o9HEdwfS+Sj2NN/+mIbJVPF44OD1NHFzYMKf72PKmPu5r20TsUF/d/jwYdERpEZA1pntk82plkbFf4ThRinGUgN2Oi1JK7/h3LrdQjPt/imD5+f9yMWMomq3FxSVsWxtEsvWJjHkkdZ8+tajNPNu3L+JV+3UWGdS/Z18oS12Omc0Wh0mQyktnphJ0wHPi45lE2RzqoM9zy0i/3wGnh39GbZrEem7j6PPzhWS5YvtKYyb81/KjaYan3Prlu0/ptPrmW/Y/Z/BtPEVd5J8woQJwsa2FWqqM1ulpjoLen0zTq2D0acmcnrGg3g8NASdj6/oWKonzznVQ15yGqX5xbi28iHiP7Nw828OQIen+hE8fpDVx991ML2yMZlu601blkeyZXlkta9LSS9k4OTvyC0osXrGmvj7+wsb29bcWmcte4UQNu9ZAHy6BtHz3RcEp1M3NdaZc5tO2Lt6UXY1nd++W0XynAiS50RwesZDnJndS3Q81VFlczp+/Djh4eE4OTkRFhbGmjVrcHFxwWg0io4GQPPwYEryirh26iIJi9fTbdYY7HRa2o18hOS131t17NKycv76/6pvTABBrT0Iau1R4+uTL+bzxgfHrJjQvPnz5wsb29bcWmdZh5Jwb9MSF18fuk4fxYn35Y1NzVFjnRWd2o/W3RvnwK40GziJjgv20nHBXlzah9FqzJui46mO6qb1EhIS6NOnD/Pnzyc2Npbt27czdepUQkJCsLMT20v7fjIbjUaDe2BL9k1ZhrHUQN6ZS5iMRnosmMjZtXGYDNZdIbd5dypXrjVsKfJnW8/x7qvdcXdtRBdNVKO83EjMV2dYvu4UqRlF+Hg6Ej06mOl/7YSbi4OwXNXVGUDC0vX0+2Q2mft+kdN8NiRl4QhMRiMlWecJnPkFdg6OlduKTh+gvDiPJt2HCEyoTqo7cpo2bRqTJ09m+vTpBAYGMnXqVHx9fenSpYvoaOx5bhGbH3mF/05exsNLpuDUtGIFXMKS9TQPC+bClgNWz/BR7Gk0DXyPYr2BtdtSLJKnrvz8/ISMezuj0cSYWT/wyqKfSL6Yz43Sci5fuc7bqxL407PbKCwuFZatpjrLS07D1a8pqTvihWWzFWqpM6g459RpRTLtZn1J6ofPU5aXDYDJUEb6p7Pxn/i+4ITqpKrmlJKSwv79+5k2bVqVx3U6XWVzeuKJJ2jVqhVjx44VERGA1G2HyNh7gi4vjwCgOD2H6xk5Vh/34uVC9h3LouYlELWj0VQcPYmglutPvv4hlS17LmEwVP1ulpYZOf1rHsvWJglK9ofb6yz42YEkr9lFyKTHBSdTP7XU2a28eo/GI3QgWRsWApC1aRE+EX/FwbuV4GTqpKppvYSEBDw9PQkICKh8TK/X8+uvv1Y2pw8//JCUlBRWrlxZ6/cdPnw4KSk1Hyl4lDsQTfs6ZT32zucM++49Tn74NforebV+3YD+AyiwL6vTWDddxxfs/2jKW5ZH3nF+Kci/4uvETSOrPJ6SXsATL8cBYDLBsV8uEBISUq8c5gwdOtTs9k2bNjFy5Mhqt40fP97ieWqSajeCctqA5s7PZ6VlRuZ/+F++XP6Mxceta63drLPEld/Qun93dkXN5+F/RuPdOZBrJy+YfW1Daq02UuzGAViljmrDXK2ZqzOwXK1pPH1xeq3255n9xi3k9IyH8Oz1JIUnf6DD/Lhav3ZA//6Y8jLqE1OooKAgtm7dWufXqerISaPRUF5eXmXhQ0xMDNevX69sTqJW4WwIf5H8838URuHFLNZ1fLZOjamhjFjuPIgl36su8vLyhIx7OwPu1Tamm8pxUTDNH2qqs/ueeozTH28Hk4kT72+g24wxQvLZCrXUWed/X8SpdXDl106t2hP6RT7X9q2jNCeds2/0JXlOBCkLa26kjZXGZKpuzZcYly5dIigoiDfeeINx48YRFxfHnDlz0Gq1ZGT88QO7d+9eVq5cyZdffmmRcQvTrrAx/EWLvNfdPBn/Ee6/Lz2vq4MJ2fQet83sc24eMXUaucns81q3cCHt+6fqlcOcI0eOmN0eFRVV433PwsLCLJ6nJqNm7GbLnlQM5dWXf4c2Hpz9ZrTFx7WVWquNkBEbAUja/KTVxjDHXK2ZqzOwXK1lXIfhtT/4aZCtkeAr5jOTEKo6cgoICODDDz9k5cqVhIaGcvToUcaOHauKxRBqEOjnjl1DV0MAGqBDgJjbGc2dO1fIuLebOvb+Gi9gdtBqePlpMVNVkmWopc6k+lNVcwKIjo4mMzOTvLw8YmJiuHDhgmxOv2vVzIXH+wTc/Yl3YQJeeLJjwwPVQ1pampBxb9c33JfXnuuCRgNa+z86vr2dhsF/8mfy6GAzr5bUTi11JtWf6prT7RITE6s0p5dffpmZM2eyd+9eIiMjzS50uBe9GHV/g16v0UAzLydGRra1TKA6Wr16tZBxq7PwlTDiVg1mWEQAOgc7XJ21fLGoL5vefwytVvU/GpIZaqozqX5U/RNYXFxMampqlea0fPlyjh07RlZWFnFxcQQFBQnJFhI9DOfmngA4ersz8uAHuLVuZvVx+/fyo0OAR43XOqWkF5CSXlDj600mmDwmGEedvXUC2ph+PXzZ9H4k7QM8aOPrxugBgdjbq+fHQlSdSdaR/fUSyq5lAmAoyCExuj0l2RfFhlIpVS0lv52rq6tqblkUMDic0NljKbyYjYuvN1kHkipX6j3wwlAuKXRhpJ2dho1LH6P3uG0U6cvuuIXRzeXiNYno3pI5L3SzXsC76NGjh7CxbYFa6szWqaXOcg9tJmPdmzi2DKLsajrunftWXteUvXUZnj1HCE6oXqpuTmpSfDmHM6t3UnAhE+dmnngEVhSYX79QrhxJplloB8WydL7Pm+0fDWDYtO/JK6z9nQwefaglm5dFCj1qmjFjhrCxbYGa6syWqaXOdM0CaD7kJRxbtacsL5uSzIqL3/OP7cAt+GGKz8kPGzVRz/yFynkFB5B7OhXvB9ri6OVO1sGKOwg0794Rv4iu+PXtRoenH1Msz58ebMmhtcMY1Lt1jc/R/D735+aiZca4TuyKGYSnh2ONz1dCVFSU0PHVTm11ZqvUUmf61JM4t+2M/uIJyguv4t4pAoCiMwcpSNhFwfGd5MR9LDakSskjp1ryDA4gdUc8fv1CaR4WzJnPvgPg50UV11p1mzmG87F7FM0UHOjJjhUDOX+pgJivzrD22/NcuabHaASdgx33t/Nkypj7+cvQIKE3MpVqT411JtXfjUuJePUcQf6xHRSdPkCzwVMA8PvLWwBkfDEPn37jBSZUL9mcauno/DUA/Pzel3SfO+6Ou48nLFkvIhYA7QM8+OfMcP45Mxyj0USZwSgXPNgoNdeZVHetJywGwO+Zt0lfPQuNtuqHRN+n5glIZRvktF493NyBqJGdnUbVjWnVqlWiI9gMNdeZ2qmxzm42Kql2ZHOSFLVhg/wleZL1yTqzfbI5ATo3Z7RuTlYfR+vmhM7N2erjqNmuXbtERxBK1poylKozVy24KHByxEVbMVZj0sj+utVz9HJndPwKSov0Vh1H5+aMo5e7VceQ1E3W2r2liQ6+iYRig3XHcdVWjNWYyOb0O0cvd/nDrIBRo0aJjiCcrDXrU7LOmugaX+NQgpzWkxTVs2dP0RGkRkDWme2TzUlS1KxZs0RHkBoBWWe2TzYnSZIkSXVkc5IU5e4uz7VI1ifrzPbJ5iQpauDAgaIjSI2ArDPbJ5uTpCh5caSkBFlntk82J0mSJEl1ZHOSFDVgwADREaRGQNaZ7ZPNSVKUvAhXUoKsM9sn7xDxu5LcQnlLGQVMmjSJ2NhY0TGEkrVmfUrWWX6pvH2RNcjmRMXO4qvwKRiKblh1HK2bE6PjVzTqnUZjJ2vt3pJfCsPi4LqVm5OLtuIefo2pQclpPaC0SG/1nQWAoeiG1T8xS+oma+3eUmywfmOCijGsfXSmNrI5SYpq7FN6kjJkndk+2ZwkRS1dulR0hErX9QbWbjvPvI+Ok5Wj58o1PTFfneFafonoaFIDqanOpPqR55xqaVT8RxhulGIsNWCn05K08hvOrdstOpbNOXz4sOgInEvNZ+VXZ/hk81nyCkurbJv81gFeee8QTw8J4sWo++ke0kzRbLLOLEMNdQZw8oW22Omc0Wh1mAyltHhiJk0HPC86lk2QzakO9jy3iPzzGXh29GfYrkWk7z6OPjtXdCypDj7elEz0/AOUG001Pqek1Mjqr8+x+utz/H1iFxZM646dnUaxjLLO7i1Br2/GqXUw+tRETs94EI+HhqDz8RUdS/XktF495CWnUZpfjGsrHyL+Mws3/+YAdHiqH8HjBwlOp24TJkwQNvb/rk3k+Xn7MZpqbky3e/fjX5j81gFMdXiNpdxaZy17hRA271kAfLoG0fPdFxTPY0tE1llNnNt0wt7Vi7Kr6fz23SqS50SQPCeC0zMe4szsXqLjqY5sTvXQPDyYkrwirp26SMLi9XSbNQY7nZZ2Ix8hee33ouOpmr+/v5Bxt+5JZfo/D6MBquszW5ZHsmV5ZLWv/ffGZN775BfrBqzGrXWWdSgJ9zYtcfH1oev0UZx4X947zhxRdWZO0an9aN29cQ7sSrOBk+i4YC8dF+zFpX0Yrca8KTqe6qiyOR0/fpzw8HCcnJwICwtjzZo1uLi4YDQahebq+8lsRvz4vwza9D8k/DMWY6mBvDOXMBmN9FgwkbNr4zAZyoXlMxiM7InPYOP3Fzj9a56wHObMnz9f8TFNJhP/b/nRij/X8Jyg1h4Etfao8T0W/DuBwuLSGrdbUnV1BpCwdD39PplN/tl0Oc13FyLqrCYpC0eQOKUjyXP60Oqp/8HOwbFyW9HpA5QX59Gk+xCBCdVJdeecEhIS6NOnD/Pnzyc2Npbt27czdepUQkJCsLMT20tvngto83gvei+dQtahU9zIySdhyXoi187h0N9ihGXbsieV6PkHuHJNj9ZeQ5nBxJ9CW/DFe31p3dJVWC412H88m6SUvAa9R9F1A59/m8LkMfdbJpQZNdVZXnIarn5NSd0Rb/UMkuXcPOeUe+ArLn4wEfdOfXDwbIHJUEb6p7MJek0eBVdHdUdO06ZNY/LkyUyfPp3AwECmTp2Kr68vXbp0ER2tUuq2Q2TsPUGXl0cAUJyew/WMHGF59h7JZOSrcWRf1WMyQZmh4vjg8MnfeGT8Nq7r1XP1np+fn+JjfhR7usHvodFUvI+S555ur7PgZweSvGYXIZMeVyyDrRJRZ3fj1Xs0HqEDydqwEICsTYvwifgrDt6tBCdTJ1U1p5SUFPbv38+0adOqPK7T6ejSpQtnz57l0Ucf5ZFHHqFnz55s27ZNUFI49s7ntI/qi3NzT2EZbvrHR8cr9p63KTMYSc8u5sudvwpIVT0R15/s2J/W4PcwmeDkuVwyf7tugUS1d7POXHx9aN2/OwmL11NWrMe7c6CiOWyNWq9z8hu3kJzdqylM+pHCkz/QdFC06EiqpTGJWIZUg40bN/L888+Tm/vHfLper6dp06Zs27aNzp07o9Fo8PHx4cqVKzz00EOkpd19xzN8+HBSUlJq3O5R7kB0fvsGZe+/bg7fP73grs+LaXKeAvuyBo11KyP2nLF/peYnmEy48SsBxi0WG9OcoUOHmt2+adMmRo4cWe22b7/91uJ5TGg4bT+9ymNblkfecX4pyL/i65S0giqPp6QX8MTLcZVftyv/DCeu1jtPfWut28wxXE28QNp3R3D1a0qPtyfyw4T3zL7G0rV2uxS7cQAEGddYbQxzzNWauToDy9WaxtMXp9fqvggqdcUUCn/5ofKoSevmTdDrm8y+5sZ7/THlZdQrp0hBQUFs3bq1zq9T1TknjUZDeXk5RqOx8vxSTEwM169fp0uXLvj4+FQ+19nZGU01Rwui1KYxWcddvgcaDZjU833Ky8sTHaGBxHwvE5asr/xz8eWcuzamxk7tddZmygrREVRPVUdOly5dIigoiDfeeINx48YRFxfHnDlz0Gq1ZGT88YnBZDLx17/+lYcffpgXX3yxweMWpl1hY3jD36c2noz/CPffr4uylO5jv+b46avVLpHWae1YPCucaU+HWHTMmhw5csTs9qioqBrvexYWFmaNSLj3/Iyiu9ydM3FTxafsTiPNf3pN2zW2QQtMbL3WbhUyYiMASZuftNoY5pirNXN1BpartYzrMDzu7s+zhK2R4OuizFhqoKpzTgEBAXz44YesXLmS0NBQjh49ytixY+9YDPHSSy/Rtm1bizSme8Hc6NBqH7e319DEXce4YR0UTlSzuXPnKj5m37CGX42v0UC71u74Nm9EewcbJqLOJMtSVXMCiI6OJjMzk7y8PGJiYrhw4UKV5vTKK6/g5OTE22+/LTClugzv24aVb/bGydEeqNiRajTQppUbez8ZQhN39fwSmNqcI7S0F6MavvzbZKp4HyVvYyTVn4g6kyxLdc3pdomJiZXNKS4ujn/9618cO3aMiIgIIiIiKCoqEpxQHSaNCubK3qfxbe5CCx9nvo8ZzLlto3kgyEt0tCpWr16t+JgDHvYj0M+tugWNteaos2P8E+o5ApXME1FnkmWpujkVFxeTmppa2ZwiIyMxGAzs3bu38j83Nzch2UKih1UuI3f0dmfkwQ9wa63sHaxv5+6qw9Ndh3cTRx7r6Ss/5f/Ozk7DPyY/WO05uZtS0gtISS+ocfu0p0Lw8XSyQjrz1FhnUv1lf72EsmuZABgKckiMbk9J9kWxoVRKVav1bufq6ir8lkU3BQwOJ3T2WAovZuPi603WgST0V/IAeOCFoVySV+3XSo8ePYSM++wTHTj1ay6LVp+sdvuty8VvNzwigHdf7W6taFXIOrMMUXV2u9xDm8lY9yaOLYMou5qOe+e+lcvHs7cuw7PnCMEJ1UvVzUlNii/ncGb1TgouZOLczBOPwIoC8+sXypUjyTQLlVM+tTFjxgxhY7/7ahhuLg7M/dfxWr/mr4+35z//8yfs7ZWZZJB1Zhki6+xWumYBNB/yEo6t2lOWl01J5jkA8o/twC34YYrPyQ8bNVH1tJ6aeAUHkHs6Fe8H2uLo5U7WwSQAmnfviF9EV/z6dqPD048JTql+UVFRwsbWaDS8GR3K/s8eZ+ygdmi1NU97DnzYj6//N5LPFjyKzsFesYyyzixDZJ3dSp96Eue2ndFfPEF54VXcO0UAUHTmIAUJuyg4vpOcuI/FhlQpeeRUS57BAaTuiMevXyjNw4I589l3APy86Eug4gr+87F7REaUaql3aAt6h7bg/ZwerNl6nvNpBeQXleLm4oBfcxf++nh7OrRpIiSbrLN7y41LiXj1HEH+sR0UnT5As8FTAPD7y1sAZHwxD59+4wUmVC/ZnGrp6PyKW7T8/N6XdJ877o5fjXHrFfySbWjZ1IXZz6nnhsIg6+xe03rCYgD8nnmb9NWz0Ggdqmz3fWqegFS2QU7r1cPNHYhUd6tWrRIdwWbIOqs/NdbZzUYl1Y5sTpKiNmyQv7tGsj5ZZ7ZPNidA5+aM1s3617Bo3ZzQuTlbfRw127Vrl+gIQslaU4ZSdeaqBRcFTo64aCvGakwa2V+3eo5e7oyOX0Fpkd6q4+jcnHH0crfqGJK6yVq7tzTRwTeRUGzl3+fpqq0YqzGRzel3jl7u8odZAaNGjRIdQThZa9anZJ010TW+xqEEOa0nKapnz56iI0iNgKwz2yebk6SoWbNmiY4gNQKyzmyfbE6SJEmS6sjmJCmqXbt2oiNIjYCsM9snm5OkqF9//VV0BKkRkHVm+2RzkiRJklRHNidJUQMGDBAdQWoEZJ3ZPtmcJEXJ65wkJcg6s32yOUmKmjRpkugIUiMg68z2yTtE/K4kt1DeUkZShKy1e0t+qbx9kTXI5kTFzuKr8CkYim5YdRytmxOj41fInUYjJmvt3pJfCsPi4LqVm5OLtuIefo2pQclpPaC0SG/1nQWAoeiG1T8xq11sbKzoCELJWlOGUnVWbLB+Y4KKMax9dKY2sjlJilq6dKnoCFIjIOvM9slpvXtAbkEJn245x7rtKZy9mI/RBIGDYwkJ8iJ6VDBDHmmNvb06PoccPnxYdASpEZB1Zvtkc6qlUfEfYbhRirHUgJ1OS9LKbzi3brfQTBlXinnzX8dZtz2FGyXlaDRgMlVsu3i5iIuXi/h2XxoBLV155ZkQXn2mE3Z2GqGZJfPUWGdS/Z18oS12Omc0Wh0mQyktnphJ0wHPi45lE2RzqoM9zy0i/3wGnh39GbZrEem7j6PPzhWSJel8LoOm7CQ9+3rlYzcb0+3SsouZuTiegwlXWLuwD06O4v7ZJ0yYIGxsW6GmOrNVaqqzoNc349Q6GH1qIqdnPIjHQ0PQ+fiKjqV66pjrsTF5yWmU5hfj2sqHiP/Mws2/OQAdnupH8PhBVh//4uVCHnt+e5XGZM7NprUx7iLPvP5fjMYaupgC/P39hY1ta26ts5a9Qgib9ywAPl2D6PnuC4LTqZsa68y5TSfsXb0ou5rOb9+tInlOBMlzIjg94yHOzO4lOp7qqLI5HT9+nPDwcJycnAgLC2PNmjW4uLhgNBpFRwOgeXgwJXlFXDt1kYTF6+k2awx2Oi3tRj5C8trvrTq2yWTiyRm7yb5W/YqvLcsj2bI8ssbXb4y7yPv/l2iteHc1f/58YWPbmlvrLOtQEu5tWuLi60PX6aM48f4G0fFUTY11VnRqP1p3b5wDu9Js4CQ6LthLxwV7cWkfRqsxb4qOpzqqm9ZLSEigT58+zJ8/n9jYWLZv387UqVMJCQnBzk5sL+37yWw0Gg3ugS3ZN2UZxlIDeWcuYTIa6bFgImfXxmEylFs1w4Gfszl++mqN24Nae5h9vUYD//t5Eq8+E6KaRRIinUrJZdWGZJIv5uHb3JXn/nwfD3drjkYj7txcdXUGkLB0Pf0+mU3mvl/kNJ8NSVk4ApPRSEnWeQJnfoGdg2PltqLTBygvzqNJ9yECE6qT6prTtGnTmDx5MtOnTwdg6tSpLF++nC5dughO9se5gDaP96L30ilkHTrFjZx8EpasJ3LtHA79LcbqGT6KPd2g15tMkJZVzLf70hjet42FUtWen5+f4mPW5MMvTjFt4SF0DnaUlhlx0Gr4ZPNZJo8O5qM3HhbWoGqqs7zkNFz9mpK6I15ILluipjq7ec4p98BXXPxgIu6d+uDg2QKToYz0T2cT9Jo8Cq6Oqj46p6SksH//fqZNm1blcZ1OV9mcevfuTUREBN27d+ef//yniJikbjtExt4TdHl5BADF6Tlcz8ix+rg5uTf46vsLFnmvlV+dscj71JVarj85kvgbLy88BEBpWcV0cZmh4lzcvzcls2breWHZbrq9zoKfHUjyml2ETHpccDL1U0ud3cqr92g8QgeStWEhAFmbFuET8VccvFsJTqZOqmpOCQkJeHp6EhAQUPmYXq/n119/rWxOe/bsYe/evRw6dIiVK1dy5coVIVmPvfM57aP64tzcU7Exz18qwGCwzGKGpBQx00IzZswQMu7tlq9LQqut/siovNzEUoHn5W51s85cfH1o3b87CYvXU1asx7tzoOhoqqaWOrud37iF5OxeTWHSjxSe/IGmg6JFR1ItVU3raTQaysvLMRqNleeXYmJiuH79emVz0ukqbi51/fp1fH19adKkyV3fd/jw4aSkpNS43aPcgWjam32PDeEvVvm68GIW6zo+e9exbzeg/wAK7Mvq/DqAItqA/ZOVX29ZHnnHOaYg/4qvEzeNrPJ4SnoBT7wcV/l1esY1QkJC6pXDnKFDh5rdfvr0ab766qtqt40fP97ieWqSYjeOMk3TGrefTL5ile/P3WqtpjrrNnMMpz/eDiYTJ97fQI+3J/LDhPfMjtWQWquNFLtxAFb5PtWGuVozV2dguVrTePri9FrNi6A6//tila+dWrUn9It8UldMoTQnnbNv9AVA6+ZN0OubzI41oH9/THkZDc6stKCgILZu3Vrn16mqOXXv3h29Xs9bb73FuHHjiIuL45133qFVq1b4+PgAUF5ezmOPPUZSUhLPPfdcZbMS7funF1h9DA2Wu7mWJd/LFtmjrzgBV8N5JTtKFU5kXsKS9ZV/Lr6cc9fGJKlbmykrREdQPY3JVNOlm2LExMQwb9489Ho9UVFRODo6cvbsWXbu3FnleTdu3GDIkCG89tprDBw4sEFjFqZdYeNtn1it5cn4j3D//bqoujp7MZ+Ow82fPL15xNRpZM2fwjQa6NbRh+Pr/1yvHOYcOXLE7PakpKQaP2mHhYVZPE9N1mw9x8R//Iih/M7y1znY8eozIbw3Pdzi49pKrdVGyIiNACRtfvIuz7QOc7Vmrs7AcrWWcR2Gx939eZawNRJ8XZQZSw1Udc4JIDo6mszMTPLy8oiJieHChQuVU3qlpaWV1zo5Ojri4uKCs7OzyLiK6tDGg4fu96npw36tmUzw9JAgy4Sqo7S0NCHj3m7s4Hb07NIcB23VHwEHrR0tmzoz69nOgpJJlqCWOpPqT3XN6XaJiYmVzenChQtEREQQERHBww8/TFhYGI8++qjghMrRaDRMibq/xtsU1ZbOwY4Jf+5gmVB1tHr1aiHj3k7nYM+umEHMntAZT/eKqWGNBib8uQPx656gmXfj+dBzL1JLnUn1p+rmVFxcTGpqamVz6tixI/v27atcrfePf/xDWLaQ6GGVK/Ucvd0ZefAD3Fo3s/q4Tw0OwsPVoUFHT08NDsLH08lyoWyUs5OWt6d1J2ffX+jYtgnBgU2ImfsnWviopzGJqjPJOrK/XkLZtUwADAU5JEa3pyT7othQKqWqBRG3c3V1Vc0tiwIGhxM6eyyFF7Nx8fUm60AS+it5ADzwwlAuKXRhpIuzlpVv9ubpv++tdntKekGNr9UArZq58PZLD1knXC306NFD2Ng1sbe3w95eHXdrV0ud2Tq11Fnuoc1krHsTx5ZBlF1Nx71z38rrmrK3LsOz5wjBCdVL1c1JTYov53Bm9U4KLmTi3MwTj8CKAvPrF8qVI8k0C1VumuypIUFcuabn1UWHq/yaDKDKcvHbNfVyYueKgbRu6apAyuqp9foTtVBTndkytdSZrlkAzYe8hGOr9pTlZVOSeQ6A/GM7cAt+mOJz8sNGTVQ9racmXsEB5J5OxfuBtjh6uZN1MAmA5t074hfRFb++3ejw9GOK5XnlmU6sezcCdxcHoMYV0ZW6dvTmp8+H0fk+bwXS1SwqKkro+GqntjqzVWqpM33qSZzbdkZ/8QTlhVdx7xQBQNGZgxQk7KLg+E5y4j4WG1KlZHOqJc/gAHLPpKHzdCVgcDjZRypu//Pzoi+Jn/spl/ckKP5L4Z4aEkTG7qf4z7w/0a3jnU1Ha69hzMBA/vvJEH5e/2fa3eWmsJJ4aqwzqf5uXErEOaAThqJccn/ajNv9vQHw+8tb+D+/DI8HB9E0cqLglOokp/Vq6ej8NQD8/N6XdJ877o67j996kaSSXF0cmDiyI8+NuI/ki/lk5ejR3zDQxF1He38Pmqvo5L50d2qtM6l+Wk9YDIDfM2+TvnoWGq1Dle2+T80TkMo2yOZUDzd3IGqi0WgIDvQkONBTdBSzVq1aJTqCzVBjndkKNdbZzUYl1Y6c1pMUtWGD/PUAkvXJOrN9sjkBOjdntG7Wv+5H6+aEzq1xT7Pt2rVLdAShZK0pQ6k6c9WCiwLzTy7airEak0b2162eo5c7o+NXUFqkt+o4OjdnHL3crTqGpG6y1u4tTXTwTSQUW/k+yq7airEaE9mcfufo5S5/mBUwatQo0RGEk7VmfUrWWRNd42scSpDTepKievbsKTqC1AjIOrN9sjlJipo1a5boCFIjIOvM9snmJEmSJKmObE6Sotzd5bkWyfpkndk+2ZwkRTX0txZLUm3IOrN9sjlJipIXR0pKkHVm+2RzkiRJklRHNidJUQMGDBAdQWoEZJ3ZPtmcJEXJi3AlJcg6s32yOUmKmjRpkugIUiMg68z2ydsX/a4kt1De70xShKy1e0t+qby3njXI5kTFzuKr8CkYim5YdRytmxOj41fInUYjJmvt3pJfCsPi4LqVm5OLtuIGs42pQclpPaC0SG/1nQWAoeiG1T8xq11sbKzoCELJWlOGUnVWbLB+Y4KKMax9dKY2sjlJilq6dKnoCFWkZxWz72gmhcVlFOvLOJeaLzqSZAFqqzOp7uS0nqSow4cPi45AebmRnQfS+Sj2NDv2p2My/bHtvmEb6NmlGVPHPsCo/m1xcpQ/IrZIDXUmNYz8yaulUfEfYbhRirHUgJ1OS9LKbzi3brfoWFIdnUi+ypPTd5OSXljjcw7/8hs//fJfXn3vJ9a804chj/grlk/W2b3l5AttsdM5o9HqMBlKafHETJoOeF50LJsgm1Md7HluEfnnM/Ds6M+wXYtI330cfXau6Fg2ZcKECcLG/vFYFkOmfkfxXU4S3DyQulZQwvBp3/PJ/EcYN7yD9QP+TtZZw4mss9sFvb4Zp9bB6FMTOT3jQTweGoLOx1d0LNWT55zqIS85jdL8Ylxb+RDxn1m4+TcHoMNT/QgeP0hwOnXz91fuKORWp3/NY9i0XRTrDZiq2b5leSRblkdWecxkAqPJxHNzf2Tn/nRlgt7i1jpr2SuEsHnPAuDTNYie776geB5bIqrOzHFu0wl7Vy/Krqbz23erSJ4TQfKcCE7PeIgzs3uJjqc6qmxOx48fJzw8HCcnJ8LCwlizZg0uLi4YjUbR0QBoHh5MSV4R105dJGHxerrNGoOdTku7kY+QvPZ7odlKSsvJLSjBaKxuFyze/PnzhYw7fdFP5BeVVTm/dKug1h4Etfa44/GbDWrS/P2Ulytbf7fWWdahJNzbtMTF14eu00dx4n15Y1NzRNWZOUWn9qN198Y5sCvNBk6i44K9dFywF5f2YbQa86boeKqjumm9hIQE+vTpw/z584mNjWX79u1MnTqVkJAQ7OzE9tK+n8xGo9HgHtiSfVOWYSw1kHfmEiajkR4LJnJ2bRwmQ7mQbBfSC5nzwVG+2nUBQ7mJpl6OvPKXEF6b0BUHB1V+BlHM+UsFfHfwcr1fbzJBWlYxO/an83ifAAsmq151dQaQsHQ9/T6ZTea+X+Q0nw1JWTgCk9FISdZ5Amd+gZ2DY+W2otMHKC/Oo0n3IQITqpPq9lrTpk1j8uTJTJ8+ncDAQKZOnYqvry9dunQRHY09zy1i8yOv8N/Jy3h4yRScmjYBIGHJepqHBXNhywEhuS5eLqT7U1v46vuKxgSQk1vC/JUJPDkjTlVHUX5+foqPuXL9aYu8z0exlnmfu6mpzvKS03D1a0rqjnhFctgyEXVWk6DXN9NpRTLtZn1J6ofPU5aXDYDJUEb6p7Pxn/i+4ITqpKrmlJKSwv79+5k2bVqVx3U6XZXmpNfradu2LX//+9+VjghA6rZDZOw9QZeXRwBQnJ7D9YwcIVkA3vzXMQqLSzEYqjahMoORb/el8f2h+h81WJqI60/WbU+xyPvsPJDOtfwSi7xXbdxeZ8HPDiR5zS5CJj2uWAZbpcbrnLx6j8YjdCBZGxYCkLVpET4Rf8XBu5XgZOqkqmm9hIQEPD09CQj4Y+pEr9fz66+/VmlOixcvJjQ0tNbvO3z4cFJSat5BeZQ7EE37OmU99s7nDPvuPU5++DX6K3m1ft2A/gMosC+r01jmmNBw2u5l0NhXu91oNDImehmtTTssNqY5Q4cONbt906ZNjBw5stpt48ePt3geE5Bp9ypo/vgctmV55B3nl4L8K75O3FQ1W0p6AU+8HFfxXiYIf/gxHKn/lFpda+1mnSWu/IbW/buzK2o+D/8zGu/OgVw7ecHsay1da7dLsRsHQEhIiNXGMMdcrZmrM7BcrWk8fXF6rfbnmf3GLeT0jIfw7PUkhSd/oMP8uFq/dkD//pjyMuoTU6igoCC2bt1a59ep6shJo9FQXl5eZeFDTEwM169fr2xOly9fJj4+nieeeELRbBvCXyT//B+FUXgxi3Udn61TY7IGE9oaGxMAGjvKNc7KBbqLvLw8hUe0q9KYGspk5c9zNdXZfU89xumPt4PJxIn3N9Btxhir5rB1ytdZ9Tr/+yJOrYMrv3Zq1Z7QL/K5tm8dpTnpnH2jL8lzIkhZWHMjbaw0JlNN65eUd+nSJYKCgnjjjTcYN24ccXFxzJkzB61WS0ZGxQ/ss88+y6uvvsqJEyc4c+YM7777boPHLUy7wsbwFxv8PrXxZPxHuP++9NwSTCYTfo99QWZO9fdR0znY8eozIbw3PdxiY5pz5MgRs9ujoqJqvO9ZWFiYNSLh1P1TSkrNL1S5ecTUaeQms8/7dfsYAlvX/2aqtlxrtwsZsRGApM1PWm0Mc8zVmrk6A8vVWsZ1GF77g58G2RoJvi7KjKUGqjpyCggI4MMPP2TlypWEhoZy9OhRxo4dW3nUdPjwYezt7es0pXev02g0zBjXGQetptrt5UYT0aODq90mwty5cxUfs1OQJ5rqvz110sTNgVbN1HMUKtVMRJ1JlqWq5gQQHR1NZmYmeXl5xMTEcOHChSrN6fz58wwaNIglS5awfv161qxZIzixeNP/GsKf+7WtsgPWOdhhb6/h84URtKvm+h1R0tLSFB9z8pj7a7y+qS6eG3GfvNeejRBRZ5Jlqa453S4xMbGyOb388svs27ePnTt3MnPmTMaMGcO4ceMEJxTP3t6O2H/2Zfe/B9PEzQE3Fy2znu3M+W2jiRrUTnS8KlavXq34mE8NboeHq0OD32fy6PstkEZSgog6kyxL1c2puLiY1NTUaq9xGj9+vEXON9VXSPQwnJt7AuDo7c7Igx/g1rqZsDwajYa+4b74tXAloJUbC17uTls/+YvmAFxdHJg8xvzUZkp6ASnpBTVuH/qoP/e1bWLpaHeltjqTGib76yWUXcsEwFCQQ2J0e0qyL4oNpVKqnqNwdXVVzS2LAgaHEzp7LIUXs3Hx9SbrQFLlSr0HXhjKJXlhZK306NFDyLhvvfQQR5Ny+CE+s9rtN5eLV6e9vwefvvWotaJVIevMMkTV2e1yD20mY92bOLYMouxqOu6d+1Ze15S9dRmePUcITqheqm5OalJ8OYczq3dScCET52aeeARWFJhfv1CuHEmmWahyd622ZTNmzBAyrs7Bns3LIhk1czffH8pAo6FW56Hub+fJzhUDaerlZP2QyDqzFFF1djtdswCaD3kJx1btKcvLpiTzHAD5x3bgFvwwxefkh42aqHpaT028ggPIPZ2K9wNtcfRyJ+tgEgDNu3fEL6Irfn270eHpxwSnVL+oqChhY3u46fj2w4G89dKDtPSpftXdzUUl7q4OvPz0Axxc8zgBrdwUyyjrzDJE1tmt9KkncW7bGf3FE5QXXsW9UwQARWcOUpCwi4LjO8mJ+1hsSJWSR0615BkcQOqOePz6hdI8LJgzn30HwM+LvgSg28wxnI/dIzKiVAsODna8MSmU1yZ0ZeveVP69MZlzlwrILyzFzdWB1i1ceXZYe54eEoSrS8MXUdSVrLN7y41LiXj1HEH+sR0UnT5As8FTAPD7y1sAZHwxD59+4wUmVC/ZnGrp6PyKJes/v/cl3eeOu+Pu4wlL1ouIJdWTg4MdT/YP5Mn+gaKjVCHr7N7SesJiAPyeeZv01bPQaKt+4PF9ap6AVLZBTuvVw80diFR3q1atEh3BZsg6qz811tnNRiXVjmxOkqI2bJC/JE+yPllntk82J0Dn5ozWzfqrsbRuTujcGvftb3bt2iU6glCy1pShVJ25asFFgZMjLtqKsRqTRvbXrZ6jlzuj41dQWlT9zVMtRefmjKOXvDC2MZO1dm9pooNvIqHYYN1xXLUVYzUmsjn9ztHLXf4wK2DUqFGiIwgna836lKyzJrrG1ziUIKf1JEX17NlTdASpEZB1Zvtkc5IUNWvWLNERpEZA1pntk81JkiRJUh3ZnCRFubvLcy2S9ck6s32yOUmKGjhwoOgIUiMg68z2yeYkKUpeHCkpQdaZ7ZPNSZIkSVId2ZwkRQ0YMEB0BKkRkHVm+2RzkhQlL8KVlCDrzPbJO0T8riS3UN5SRgGTJk0iNjZWdAyhZK1Zn5J1ll8qb19kDbI5UbGz+Cp8CoaiG1YdR+vmxOj4FY16p9HYyVq7t+SXwrA4uG7l5uSirbiHX2NqUHJaDygt0lt9ZwFgKLph9U/MkrrJWru3FBus35igYgxrH52pjWxOkqIa+5SepAxZZ7ZPNqd7RMKZq7z2fjyXs4tJyyrm+X/8yNI1J8nJtf6n9LpYunSp6AhSIyDrzPbJc061NCr+Iww3SjGWGrDTaUla+Q3n1u0WmslgMPLlzl/5KPY0h05cqbLt481nAXj9f48ydlA7pj39AN1DmomIWcXhw4dFR1A1NdaZLVJLnZ18oS12Omc0Wh0mQyktnphJ0wHPi45lE2RzqoM9zy0i/3wGnh39GbZrEem7j6PPzhWSpbC4lDGz9rDzQDoaTc3PKy0zsuab8/zftvP872s9mfZ0iHIhpXpRU51JDRf0+macWgejT03k9IwH8XhoCDofX9GxVE9O69VDXnIapfnFuLbyIeI/s3Dzbw5Ah6f6ETx+kNXH198wMHjKd+w8kA6AyVS717387k8s/vSkFZPd3YQJE4SOb0turbOWvUIIm/csAD5dg+j57guC06mbGuvMuU0n7F29KLuazm/frSJ5TgTJcyI4PeMhzszuJTqe6sjmVA/Nw4MpySvi2qmLJCxeT7dZY7DTaWk38hGS135v9fEnzd/PgYQr1W7bsjySLcsj73jcZAIN8Lel8Xyz95KVE9bM399f2Ni25tY6yzqUhHublrj4+tB1+ihOvC/vHWeOGuus6NR+tO7eOAd2pdnASXRcsJeOC/bi0j6MVmPeFB1PdVQ5rXf8+HEmT57ML7/8QufOnZk2bRqTJ0+mqKgIOztx/bTvJ7PRaDS4B7Zk35RlGEsN5J25hMlopMeCiZxdG4fJUG7VDClpBazdllLj9qDWHjVuMwEaDby96meGRQRYId3dzZ8/X1UrqQwGIzv2p5OSVkALH2ee6NsGF2exPxbV1RlAwtL19PtkNpn7fpHTfHehpjpLWTgCk9FISdZ5Amd+gZ2DY+W2otMHKC/Oo0n3IQITqpPqmlNCQgJ9+vSpLK7t27czdepUQkJChDYm+ONcQJvHe9F76RSyDp3iRk4+CUvWE7l2Dof+FmP1DCvXn2nQ600miE/M4WjSb6pYICHSTyeuMOLVOK7k3kDnYIfBYMTJ0Z7/zHuEqEHthOWqqc7yktNw9WtK6o54Ydmkurt5zin3wFdc/GAi7p364ODZApOhjPRPZxP0mjwKro7qpvVuHiVNnz6dwMBApk6diq+vL126dBEdrVLqtkNk7D1Bl5dHAFCcnsP1jByrj6u/YeDjzcmYWf9Qaysa2OTqy8/PT8i4t0vLKiJy0g6uXNNjNJq4UVKOodxE0XUDT7+2h/3Hs0RHvKPOgp8dSPKaXYRMelxwMvVTS53dyqv3aDxCB5K1YSEAWZsW4RPxVxy8WwlOpk6qak4pKSns37+fadOmVXlcp9NVNiedTkdERAQRERHMnz9fREwAjr3zOe2j+uLc3FOxMU/9mkduQSm1XP9g1r6jmRZ4l7pTy/UnK2LPUGYwYqzmm6nRaHj34xPKh6rGzTpz8fWhdf/uJCxeT1mxHu/OgaKjqZpa6ux2fuMWkrN7NYVJP1J48geaDooWHUm1NCZTbdd6Wd/GjRt5/vnnyc39Yz5dr9fTtGlTtm3bRt++fWnZsiVZWXX7VDt8+HBSUmo+T+NR7kB0fvt65wbov24O3z+94K7Pi2lyngL7snqNUUQAl+z/uNvyluWRd5xjCvKv+DolraDK4ynpBTzxclzl13YmPcHGFfXKYc7QoUPNbt+0aRMjR46sdtu3335r8Tw1SbF7hhJN8xq3a0wG7jcut/i49a21bjPHcDXxAmnfHcHVryk93p7IDxPeM/uahtRabaTYjQMgyLjGamOYY67WzNUZWK7WNJ6+OL1W90VQqSumUPjLD5VHTVo3b4Je32T2NTfe648pL6NeOUUKCgpi69atdX6dqs45aTQaysvLMRqNleeXYmJiuH79euWRU35+PhERETg7O/POO+8QGhoqMnKl2jSmhtJY5JjJ8u9VF3l5eULGvd3d//5GRXLUVsKS9ZV/Lr6cc9fG1Nippc5q0maK5T8Y3mtUdeR06dIlgoKCeOONNxg3bhxxcXHMmTMHrVZLRkbFJ4bffvuNZs2acfz4caKiokhOTm7wQonCtCtsDH/REn+Fu3oy/iPc/Wv+xG5OwpmrhI752uxzEjdVfFrsNNL8p7D72niQ/M3oeuUw58iRI2a3R0VF1biKKiwszOJ5arLwPyeYt+I4pWV3NiGtvYbRAwJZ915fi49rK7VWGyEjNgKQtPlJq41hjrlaM1dnYLlay7gOw+Pu/jxL2BoJvi7KjKUGqjrnFBAQwIcffsjKlSsJDQ3l6NGjjB07tspiiGbNKlaYPfjgg3h6elY2rcagU3svfJu5mL0jRG0NeUTMdSBz584VMu7tXniyI57uOrT2Vb+ZGg3Y2Wn4+8SugpJJlqCWOpPqT1XNCSA6OprMzEzy8vKIiYnhwoULlc2psLCQ8vKK64jS0tK4cuUKLVq0EBlXUVqtHdGjg2t9RwhzJo8Obvib1ENaWpqQcW/X1MuJ/Z89TreOPlUeb+vrxvcxg+hyn7egZJIlqKXOpPpTXXO6XWJiYmVzSkxMpHv37jz66KOMGTOGjz/+GAcHB8EJlfX8yPvu+LRfV5E9fekY6GmZQHW0evVqIeNWp0ObJhz58gl+2TCC1i1caOvnRsr2MTzaXS7ttXVqqjOpflTdnIqLi0lNTa1sTr169eLnn39m3759HDp0iMjIO2/To5SQ6GGVy8gdvd0ZefAD3Fpb/6JW3+auvPKXmm/empJeQEp6QbXbNBpw0Noxb8qD1opnkzrf542Hmw4XJy0aS8yZWpCoOpOsI/vrJZRdq7iMw1CQQ2J0e0qyL4oNpVKqWq13O1dXV4xGdayaChgcTujssRRezMbF15usA0nor+QB8MALQ7mk4FX7700P42JGERvjLt6x7dbl4rfSaCrurffZ24/SO1TcVGiPHj2EjW0L1FRntkwtdZZ7aDMZ697EsWUQZVfTce/ct3L5ePbWZXj2HCE4oXqpujmpSfHlHM6s3knBhUycm3niEVhRYH79QrlyJJlmoR0Uy2Jvb8eXi/ry4oKD/HtjstnnajQVtyxy1Nnz5aK+PNG3jUIpqzdjxgyh46udmurMlqmlznTNAmg+5CUcW7WnLC+bksxzAOQf24Fb8MMUn5MfNmqi6mk9NfEKDiD3dCreD7TF0cudrINJADTv3hG/iK749e1Gh6cfUyyPVmtHzNze7P/scZ4eEoSDtvrpqBY+zsyNDuXcN6OFNyaoWOIr1UxtdWar1FJn+tSTOLftjP7iCcoLr+LeKQKAojMHKUjYRcHxneTEfSw2pErJI6da8gwOIHVHPH79QmkeFsyZz74D4OdFXwIVV/Cfj92jaCaNRkPv0Bb0Dm3B0r/1YOP3F8i6qkd/o5wm7g7cH+jJ8Ig2ODjIzyC2Qo11JtXfjUuJePUcQf6xHRSdPkCzwVMA8PvLWwBkfDEPn37jBSZUL9mcauno/IpbtPz83pd0nzvujl+NcesV/CK08HHmxbEPCM0gNZza60yqm9YTFgPg98zbpK+ehUZbdXWx71PzBKSyDfIjdT3c3IFIdbdq1SrREWyGrLP6U2Od3WxUUu3I5gTo3JzRujlZfRytmxM6N2erj6NmGzY07t9dI2tNGUrVmasWXBSYf3LRVozVmDSyv271HL3cGR2/gtIivVXH0bk54+jlbtUx1G7Xrl1MnDhRdAxhZK0pQ6k6a6KDbyKh2GDdcVy1FWM1JrI5/c7Ry71R/zBLypG1dm9pomt8jUMJclpPUtSoUaPu/iRJaiBZZ7ZPNidJUT179hQdQWoEZJ3ZPtmcJEXNmjVLdASpEZB1Zvtkc5IkSZJURzYnSVHt2rUTHUFqBGSd2T7ZnCRF/frrr6IjSI2ArDPbJ5uTJEmSpDqyOUmKGjBggOgIUiMg68z2yeYkKUpefyIpQdaZ7ZPNSVLUpEmTREeQGgFZZ7ZP3r7odyW5hfJ+Z5IiZK3dW/JL5b31rEE2Jyp2Fl+FT8FQdMOq42jdnBgdv0LuNBoxWWv3lvxSGBYH163cnFy0FTeYbUwNSk7rAaVFeqvvLAAMRTes/olZ7WJjY0VHEErWmjKUqrNig/UbE1SMYe2jM7WRzUlS1NKlS0VHuIPJZMJoNGEymURHkSxEjXUm1Y2c1pMUdfjwYdERALiad4PVX59j1YYz/JpeSLmxojH5PfYFzw7vQPTojrTxlVNitkotdSbVn2xOtTQq/iMMN0oxlhqw02lJWvkN59btFh1LqqOColKm//Mwa7edp7TMiEYDtx4wZeZcZ+HHJ3jvkxM83ieA5X/vqWiTknV2bzn5QlvsdM5otDpMhlJaPDGTpgOeFx3LJsjmVAd7nltE/vkMPDv6M2zXItJ3H0efnSs6lk2ZMGGCsLEzrhQzaMp3nDz3x7/Z7TN5N782mmDr3kscPnmFnSsG0S3YR7Gcss4aTmSd3S7o9c04tQ5Gn5rI6RkP4vHQEHQ+vqJjqZ4851QPeclplOYX49rKh4j/zMLNvzkAHZ7qR/D4QYLTqZu/v7+QcQuKSu9oTLVx5doNBkTv5EJ6oZWS1ezWOmvZK4Swec8C4NM1iJ7vvqB4Hlsiqs7McW7TCXtXL8qupvPbd6tInhNB8pwITs94iDOze4mOpzqqbE7Hjx8nPDwcJycnwsLCWLNmDS4uLhiNRtHRAGgeHkxJXhHXTl0kYfF6us0ag51OS7uRj5C89nvR8VRt/vz5QsadtSTebGPasjySLcsj73jcZILfcm/wl7/vtWK66t1aZ1mHknBv0xIXXx+6Th/Fifc3KJ7HloiqM3OKTu1H6+6Nc2BXmg2cRMcFe+m4YC8u7cNoNeZN0fFUR3XTegkJCfTp04f58+cTGxvL9u3bmTp1KiEhIdjZie2lfT+ZjUajwT2wJfumLMNYaiDvzCVMRiM9Fkzk7No4TIZyIdlKSstZtz2FTzaf5bfcG3Tr6M20p0PoHdpCSB41yS0oYc03580+J6i1h9nth365wvFTOTz4QFNLRqtWdXUGkLB0Pf0+mU3mvl/kNJ8NSVk4ApPRSEnWeQJnfoGdg2PltqLTBygvzqNJ9yECE6qT6o6cpk2bxuTJk5k+fTqBgYFMnToVX19funTpIjoae55bxOZHXuG/k5fx8JIpODVtAkDCkvU0DwvmwpYDQnJd1xvoN3E7k+bvZ//P2SRfzGfT7os8Mn4bS9ecFJKpJn5+foqP+emWc5SUNvxDw4r1py2Q5u5qqrO85DRc/ZqSuiNekRy2TESd1STo9c10WpFMu1lfkvrh85TlZQNgMpSR/uls/Ce+LzihOqmqOaWkpLB//36mTZtW5XGdTlfZnE6dOsXQoUPp168fgwaJOb+Tuu0QGXtP0OXlEQAUp+dwPSNHSBaAd/6TwNGkHAyGP87ulxlMmEwwc3E8J89eE5btdiKuP/n3xmQ0moa/z9pvUyi+XtbwN6ql2+ss+NmBJK/ZRcikxxXLYKvUeJ2TV+/ReIQOJGvDQgCyNi3CJ+KvOHi3EpxMnVTVnBISEvD09CQgIKDyMb1ez6+//kqXLl0oKytjypQpfPbZZ/zwww/s3LlTWNZj73xO+6i+ODf3FJYBwGg08VHsaUoN1Z+P0znYsWpjssKpajZjxgxFxzOZTKRcKrhjVV593CgpJ+uqsndduFlnLr4+tO7fnYTF6ykr1uPdOVDRHLZG6TqrLb9xC8nZvZrCpB8pPPkDTQdFi46kWhqTii6L37RpE+PHjycvL6/y/NKyZcuYPn06OTk5nDp1infffRdHR0euXr3Ks88+y3PPPXfX9x0+fDgpKSk1bvcodyA6v32DsvdfN4fvn15w1+fFNDlPgb3lPn0bceCM/TSzz3E1pdLGuNFiY5ozdOhQs9s/+eSTGv/Nvv32W4vnMWHHaftXqzy2ZXnkHeeYgvwrvk5JK6jyeEp6AU+8HFf5dWD5/+HMb/XOU99a6zZzDFcTL5D23RFc/ZrS4+2J/DDhPbOvsXSt3S7FbhwAQcY1VhvDHHO1Zq7OwHK1pvH0xem1ui+CSl0xhcJffqg8atK6eRP0+iazr7nxXn9MeRn1yilSUFAQW7durfPrVLUgonv37uj1et566y3GjRtHXFwc77zzDq1atcLHx4fLly9z9OhRTp48iaurK4888gi9e/emY8eOoqPXqjFZgwYDGlMZJo1D9U8wleNgKqh+W6NgBJMRNJaZJLBDuWm9WyUsWV/55+LLOXdtTJK6tZmyQnQE1VPVkRNATEwM8+bNQ6/XExUVhaOjI2fPnmXnzp3s2rWL5cuXs23bNgBeffVVevfuzejRoxs0ZmHaFTaGv2iJ+Hf1ZPxHuP9+XZSlvLTgIKs2JlNWw9TegTWP83A3ZVbtHTlyxOz2pKQkQkJCqt0WFhZmjUgEDoolNbPI7NRe4qaRAHQaWfOnV629hqs/PoOHW/1vDW3rtXarkBEVR+NJm5+02hjmmKs1c3UGlqu1jOswPO7uz7OErZHg66LMWGqgqnNOANHR0WRmZpKXl0dMTAwXLlyoXAzRs2dPLl68iF6vx2g0cvToUTp06CA4sXjzXnwQ/5au6Bz++OfUaMDeTsPUqPvp1dV6O6i6SktLU3zM8U90sMg5p5GRbRvUmCTliKgzybJU15xul5iYWNmcPDw8+J//+R8iIyN5+OGHGTx4MN26dRMbUAWaejlx5IsneO25LmjtNWg08OD9Pny24FE++H+90FhiqZqFrF69WvExX3iyI/Z2Df8eTBlzvwXSSEoQUWeSZam6ORUXF5OamlrlGqcnn3ySAwcO8NNPPzFnzhxh2UKih1Wu1HP0dmfkwQ9wa91MWB7vJo7Mn/oQ97Vtwv3tPDn65Z/5y9D2qmpMovg2d2XEY23q/XoNEBzYhD7dW1ouVC2prc6khsn+egll1zIBMBTkkBjdnpLsi2JDqZSqFkTcztXVVTW3LAoYHE7o7LEUXszGxdebrANJ6K/kAfDAC0O5JC+MrJUePXoIGfd/X+vJT79c4XL2daqb4UtJr37RiEYDTjp71i6MUKTRyzqzDFF1drvcQ5vJWPcmji2DKLuajnvnvpUr9LK3LsOz5wjBCdVL1c1JTYov53Bm9U4KLmTi3MwTj8CKAvPrF8qVI8k0C5XnvmpD1PUnvs1d+W7lICJf2EFmzp3XKt26XPwmjQacHbVsXvYYDylw2yKQdWYparnOSdcsgOZDXsKxVXvK8rIpyTwHQP6xHbgFP0zxOflhoyaqntZTE6/gAHJPp+L9QFscvdzJOpgEQPPuHfGL6Ipf3250ePoxwSnVLyoqStjYDwR5cXjdcPqG1e6K/AeCPNn36VAGPNzaysn+IOvMMkTW2a30qSdxbtsZ/cUTlBdexb1TBABFZw5SkLCLguM7yYn7WGxIlZLNqZY8gwPIPZOGztOVgMHhZB85A8DPi74kfu6nXN6TIH8pnA3wb+nGDx8P4eTGEbwYdT+uzlUnDxy0Gp4a3I4fPx3KyY0jFTtiuknW2b3lxqVEnAM6YSjKJfenzbjd3xsAv7+8hf/zy/B4cBBNIycKTqlOclqvlo7Or7gK/uf3vqT73HF33H381oskJfXr1MGbf815mCWzwrly7Qb5haW4uTjQzNsJN5caLmhWgKyze0vrCYsB8HvmbdJXz0KjrVpbvk/NE5DKNsjmVA83dyBS3a1atUp0hCqcHLUEtHIDFd57U9ZZ/amtzuCPRiXVjpzWkxS1YYP8JXmS9ck6s32yOQE6N2e0bk5WH0fr5oTOzdnq46jZrl27REcQStaaMpSqM1ctuCgw/+SirRirMWlkf93qOXq5Mzp+BaVF1v11CDo3Zxy93K06hqRustbuLU108E0kFBusO46rtmKsxkQ2p985ernLH2YFjBo1SnQE4WStWZ+SddZE1/gahxLktJ6kqJ49e4qOIDUCss5sn2xOkqJmzZolOoLUCMg6s32yOUmSJEmqI5uTpCh3d3muRbI+WWe2TzYnSVEDBw4UHUFqBGSd2T7ZnCRFyYsjJSXIOrN9sjlJkiRJqiObk6SoAQMGiI4gNQKyzmyfbE6SouRFuJISZJ3ZPtmcJEVNmjRJdASpEZB1Zvvk7Yt+V5JbKO93JilC1tq9Jb9U3lvPGmRzomJn8VX4FAxFN6w6jtbNidHxK+ROoxGTtXZvyS+FYXFw3crNyUVbcYPZxtSg5LQeUFqkt/rOAsBQdMPqn5jVLjY2VnQEoWStKUOpOis2WL8xQcUY1j46UxvZnO4RpWXl7NyfTl5BCdfyS/j82/P8fDpHdKw7LF26VHQEqRGQdWb75LReLY2K/wjDjVKMpQbsdFqSVn7DuXW7RcfiUmYRqzacYdWGZH7L/eMT+TOv/xeA8E5NeTHqAcYMDMTZSfw/9+HDh0VHUDW11pmtUUudnXyhLXY6ZzRaHSZDKS2emEnTAc+LjmUTxO+tbMie5xaRfz4Dz47+DNu1iPTdx9Fn5wrJYjKZeO+TX5jzwVGMRtBoqn/ekaQcxr+5j9eWxfPNBwMI69RM2aBSnampzqSGC3p9M06tg9GnJnJ6xoN4PDQEnY+v6FiqJ6f16iEvOY3S/GJcW/kQ8Z9ZuPk3B6DDU/0IHj/I6uObTCZmLj7M6/97FJPx5mM1Pbfi/1eu3aDPhG/ZE59h9XzmTJgwQej4tuTWOmvZK4Swec8C4NM1iJ7vviA4nbqpsc6c23TC3tWLsqvp/PbdKpLnRJA8J4LTMx7izOxeouOpjmxO9dA8PJiSvCKunbpIwuL1dJs1BjudlnYjHyF57fdWH//9/0vk/f9LAuD2nrRleSRblkfe8RqTCW6UlvPnV+I4lSLuU7i/v7+wsW3NrXWWdSgJ9zYtcfH1oev0UZx4X947zhw11lnRqf1o3b1xDuxKs4GT6LhgLx0X7MWlfRitxrwpOp7qqLI5HT9+nPDwcJycnAgLC2PNmjW4uLhgNBqF5ur7yWxG/Pi/DNr0PyT8MxZjqYG8M5cwGY30WDCRs2vjMBnKrZoht6CEOR8cq3F7UGsPglp7VLvNZIKC4jLe/PC4teLd1fz584WNXZNr+SWcSL5K5m/XRUcBqq8zgISl6+n3yWzyz6bLab67UFOdpSwcQeKUjiTP6UOrp/4HOwfHym1Fpw9QXpxHk+5DBCZUJ9Wdc0pISKBPnz7Mnz+f2NhYtm/fztSpUwkJCcHOTmwvvXkuoM3jvei9dApZh05xIyefhCXriVw7h0N/i7F6hs+2nONGScMa4JY9qaRnFdO6pauFUtmmnNwbTFt4iK++v0B5ecUx6GM9fPnX/+tFx0BPYblqqrO85DRc/ZqSuiNeWDap7m6ec8o98BUXP5iIe6c+OHi2wGQoI/3T2QS9Jo+Cq6O6I6dp06YxefJkpk+fTmBgIFOnTsXX15cuXbqIjlYpddshMvaeoMvLIwAoTs/heob1l22bTCY+ij1d4+KH2io3mvj3xmTLhKojPz8/IePeruh6Gb3HfcPGuIuVjQlg79FMej7zDRcvFwpMV+H2Ogt+diDJa3YRMulxwcnUTy11diuv3qPxCB1I1oaFAGRtWoRPxF9x8G4lOJk6qao5paSksH//fqZNm1blcZ1OR5cuXUhOTiYiIqLyPwcHBxITE4VkPfbO57SP6otzc0/Fxkw6n8u5SwU1Ln6oLQ2wMe6CRTLVlVquP/ls6zkuXC6izFB1qri83ESx3sB7n/wiKFlVN+vMxdeH1v27k7B4PWXFerw7B4qOpmpqqbPb+Y1bSM7u1RQm/UjhyR9oOihadCTV0phMDd3VWc7GjRt5/vnnyc39Yz5dr9fTtGlTtm3bRt++fSsfP336NFFRUfzyy913IsOHDyclJaXG7R7lDkTnt29Q9v7r5vD90wvu+ryYJucpsC+r1xjFtCbVfkzl11uWR95xfinIv+LrlLSCKo+npBfwxMtxlV/bm67T0biyXjnMGTp0qNntmzZtYuTIkdVu+/bbby2epyYX7Mai19S8nNfOdINg40cWH7e+tdZt5hiuJl4g7bsjuPo1pcfbE/lhwntmX9OQWquNFLtxAAQZ11htDHPM1Zq5OgPL1ZrG0xen1+q+CCp1xRQKf/mh8qhJ6+ZN0OubzL7mxnv9MeWJXW1bH0FBQWzdurXOr1PVOSeNRkN5eTlGo7Hy/FJMTAzXr1+/Y1rv008/VdVy0do0poYyYW/B9xJz0JyXlydk3NsZcTC73aSuHw0Slqyv/HPx5Zy7NqbGTi11VpM2U1aIjqB6qjpyunTpEkFBQbzxxhuMGzeOuLg45syZg1arJSPjj08M5eXltGvXjqNHj9KsWcMvKi1Mu8LG8Bcb/D618WT8R7j/fl1UXcWf/I0efzH/CSRxU8WnxU4jzX8Ka+vnxoUdUfXKYc6RI0fMbo+KiqrxvmdhYWEWz1OTqQsO8J9NZyktu3MFqJ0GenZtzoE1wyw+rq3UWm2EjNgIQNLmJ602hjnmas1cnYHlai3jOgyPu/vzLGFrJPi6KDOWGqjqnFNAQAAffvghK1euJDQ0lKNHjzJ27Ng7jpp27tzJQw89ZJHGZEuCA5vgYqFbEIWFiPnezZ07V8i4t5v2dAjlxuo/l5mAvz/XVdlAkkWppc6k+lNVcwKIjo4mMzOTvLw8YmJiuHDhguqn9JTi4abjmceDLPJeL0bdb5H3qau0tDQh494uONCTrxb3Q+dgh86h4sdAQ8VtoN57NYxhEQFiA0oNopY6k+pPdc3pdomJiVWa07Vr14iPj2fw4MECU4kzZUzDmopGU3EE1qd7SwslqpvVq1cLGbc6Ix5rS+YPT7NoehjeTXQ09XYi9bso/jZBPZctSPWjpjqT6kfVzam4uJjU1NQqzcnb25vU1FS0WrEnrEOih1UuI3f0dmfkwQ9wa239qbJuwT4M6FXzNRwp6QWkpBfUuN1kgtee64KmoRdL3SO8mzjyyjOdaNnUhWZeTvi3dBMdqQpRdSZZR/bXSyi7lgmAoSCHxOj2lGRfFBtKpdS1JOk2rq6uwm9ZdFPA4HBCZ4+l8GI2Lr7eZB1IQn8lD4AHXhjKJQWv2v9iUV96PbOVs6l3NqFbl4tXZ9rTD/Ds8A7WinZXPXr0EDa2LVBTndkytdRZ7qHNZKx7E8eWQZRdTce9c9/K5ePZW5fh2XOE4ITqpermpCbFl3M4s3onBRcycW7miUdgRYH59QvlypFkmoUqt8P3buLIno+HMGTqLk4kX0Ojqfmu5FBxLsUEzBjXiX/OCBd61DRjxgxhY9sCNdWZLVNLnemaBdB8yEs4tmpPWV42JZnnAMg/tgO34IcpPic/bNRE1dN6auIVHEDu6VS8H2iLo5c7WQcr7grevHtH/CK64te3Gx2efkyxPL7NXfnx06HMn/ogrZr+sb5Uo/njxP5Nf3qwBRuW9GPJrB7Y2YmdzouKsvzy9XuJ2urMVqmlzvSpJ3Fu2xn9xROUF17FvVMEAEVnDlKQsIuC4zvJiftYbEiVkkdOteQZHEDqjnj8+oXSPCyYM599B8DPi74EKq7gPx+7R9FM7q463owO5fWJXfnmv5dYtz2FrBw9+pJymrg5cH87T6JHBdP5Pm9Fc0n1p8Y6k+rvxqVEvHqOIP/YDopOH6DZ4CkA+P3lLQAyvpiHT7/xAhOql2xOtXR0fsUtWn5+70u6zx13x6/GuPUKfqVptXaMeKwtIx5rKyyDZBlqrjOp7lpPWAyA3zNvk756Fhpt1TuT+D41T0Aq2yCn9erh5g5EqrtVq1aJjmAzZJ3Vnxrr7GajkmpHNidJURs2yN9dI1mfrDPbJ5sToHNzRuvmZPVxtG5O6NycrT6Omu3atUt0BKFkrSlDqTpz1YKLAidHXLQVYzUmjeyvWz1HL3dGx6+gtEhv1XF0bs44erlbdQxJ3WSt3Vua6OCbSCg2WHccV23FWI2JbE6/c/Rylz/MChg1apToCMLJWrM+Jeusia7xNQ4lyGk9SVE9e/YUHUFqBGSd2T7ZnCRFzZo1S3QEqRGQdWb7ZHOSJEmSVEc2J0lR7u7yXItkfbLObJ9sTpKiBg4cKDqC1AjIOrN9sjlJipIXR0pKkHVm+2RzkiRJklRHNidJUQMGDBAdQWoEZJ3ZPtmcJEXJi3AlJcg6s33yDhG/K8ktlLeUUcCkSZOIjY0VHUO6xylZZ2rdd+SX2vZtlWRzoqK4vgqfgqHohlXH0bo5MTp+RaNvUJJ0r1DrviO/FIbFwXUrNycXbcW9Ba3RoOS0HlBapLd6cQEYim5Y/ROWJEnKUeu+o9hg/cYEFWNY6+hMNidJUXJKT1KCrDPbJ5uTpKilS5eKjlDp+Kkcprx1gP6TdvBreiEXLxcx7v/9l7ifLmMymUTHkxpATXUm1Y8851RLo+I/wnCjFGOpATudlqSV33Bu3W7RsWzO4cOHhY5vMplYtz2FD9ad4vDJ3wDQaOBmL/q/bef5v23n6RDgwYtR9/Pi2PvROdgLTCzVh+g6u0mt+42TL7TFTueMRqvDZCilxRMzaTrgedGxqpDNqQ72PLeI/PMZeHb0Z9iuRaTvPo4+O1d0LKmWysqMTH77AJ9sPotG88fj1R0knU8rYPo/D/PNfy+xceljeHo4KhdUuqeodb8R9PpmnFoHo09N5PSMB/F4aAg6H1/RsSrJab16yEtOozS/GNdWPkT8ZxZu/s0B6PBUP4LHDxKcTt0mTJggZFyTycRz/9jHJ5vP/v713Z5f8f8f4jMZ/OJ36G8ocHZZshhRdWbOrfuNlr1CCJv3LAA+XYPo+e4LwnI5t+mEvasXZVfT+e27VSTPiSB5TgSnZzzEmdm9hOVSZXM6fvw44eHhODk5ERYWxpo1a3BxccFoNIqOBkDz8GBK8oq4duoiCYvX023WGOx0WtqNfITktd+Ljqdq/v7+QsZd8lkia7el1Lh9y/JItiyPrHbbT7/8xpS3D1ormmQFourMnFv3G1mHknBv0xIXXx+6Th/FiffF3Quw6NR+tO7eOAd2pdnASXRcsJeOC/bi0j6MVmPeFJZLddN6CQkJ9OnTh/nz5xMbG8v27duZOnUqISEh2NmJ7aV9P5mNRqPBPbAl+6Ysw1hqIO/MJUxGIz0WTOTs2jhMhnKhGdXu5r+rkkrLyln82ckq55ZuF9Taw+x7rN12ngXTHsKvhasVEtqWsjIjW/akkpWjx05TsbDkwQeaio5VhYg6q0l1+w2AhKXr6ffJbDL3/SJkmi9l4QhMRiMlWecJnPkFdg5/TF0XnT5AeXEeTboPUTzXTao7cpo2bRqTJ09m+vTpBAYGMnXqVHx9fenSpYvoaOx5bhGbH3mF/05exsNLpuDUtAkACUvW0zwsmAtbDghOKFXn6x9Syb6qv+tUnjnlRhP/3phsuVA26lxqPkFD1zN29h6u5ZdwNa+Eh8Zu4cnpcZSWyQ9m1alpv5GXnIarX1NSd8QLyRX0+mY6rUim3awvSf3wecrysgEwGcpI/3Q2/hPfF5LrJlU1p5SUFPbv38+0adOqPK7T6Sqb0z/+8Q/CwsIIDw/n3XffFRGT1G2HyNh7gi4vjwCgOD2H6xk5QrLYGj8/P8XH/Cj2NJq7P80sjQZiNpyhrEwdU8siGAxG+kfvJPO365QbKzr9zX7/zX/TeO39I+LC3UZEnd3N7fuN4GcHkrxmFyGTHheay6v3aDxCB5K1YSEAWZsW4RPxVxy8WwnNparmlJCQgKenJwEBAZWP6fV6fv31V7p06UJWVhaff/45P/30E4cOHeLjjz/mt99+E5L12Duf0z6qL87NPYWMb6tEXH9yJDGHhl61ZDJBVo6ezJzrFslki7757yXSsooxlN/53SwzGFn51RkKi0sFJLuTWq9zurnfcPH1oXX/7iQsXk9ZsR7vzoFCc/mNW0jO7tUUJv1I4ckfaDooWmgeAI1JRVcbbtq0ifHjx5OXl1d5fmnZsmVMnz6dnJwc3NzciIyMZMeOHQCEh4cTHx+Pm5ub2fcdPnw4KSk1nwz3KHcgOr99g7L3XzeH759ecNfnxTQ5T4F9WYPGUrOhQ4ea3b5p0yZGjhxZ7bZvv/3W4nlM2HHa/tUqj21ZHnnHOaYg/4qvU9IKqjyekl7AEy/HVX7drnwNTjTOo+RszZ+4pnkQk6bmU9WB5etwJkuRPOZqzVydgeVqrb77jm4zx3A18QJp3x3B1a8pPd6eyA8T3jP7mrrsOzSevji9VvfFWakrplD4yw+VR01aN2+CXt9k9jU33uuPKS+jxu1BQUFs3bq1zllUtSCie/fu6PV63nrrLcaNG0dcXBzvvPMOrVq1wsfHB4D+/fsTHByMyWTilVdeuWtjUkptGpMEeXl5Co9orDjs0TR0Yq+ChsY7rWfH3ZfTa2rxHCUoX2d1k7BkfeWfiy/n3LUxKaXNlBWiI1RS1ZETQExMDPPmzUOv1xMVFYWjoyNnz55l586d7N69m3feeafyyGnIkCEsWLCAHj16NGjMwrQrbAx/0RLx7+rJ+I9w//26qHvRkSPmzztERUXVuIoqLCzMGpFo+uharuaVmH1O4qaKT9mdRpr/lJi952ma+zhbLJstSTx3jc5Pbq5xe1tfN37dMQaNhT4I3I25WjNXZ2C5WlPrviPjOgyPu/vzLGFrJPi6WP59VXXOCSA6OprMzEzy8vKIiYnhwoULlYshysvL8fLyQqfTodPpaNKkCVevXhWcWKqLuXPnKj7myMfaNvg9NEDvbs0bbWMC6NTBm2eGBuGgvXO3odHA0r/1UKwx3Y2IOpMsS3XN6XaJiYmVzSkyMpKmTZvSq1cvevXqhY+PDwMHDhScUKqLtLQ0xcd8Mer+Br+HCXgx6oGGh7Fxq996lFnPdsLV+Y8zAu1au7Pp/ccYYYEPAZYios4ky1J1cyouLiY1NbWyOdnZ2bFy5UoOHTrEoUOHWLVqFfb2Ym7KGRI9rHKlnqO3OyMPfoBb62ZCstiS1atXKz5mt2AfenVtXu/l5BoNNPV05Mn+bS0ZyyZptXa880oYV/b+heOxT3Dq6yc5/+1o/tyvrehoVYios9pQ834j++sllF3LBMBQkENidHtKsi8Ky6OqBRG3c3V1Vc0tiwIGhxM6eyyFF7Nx8fUm60AS+it5ADzwwlAuCbqQTqqdf84Ip+/E7RjKjdVejJuSXnDng78zmWDxzB446uTdyW9ycdYSer+67gqhRmreb+Qe2kzGujdxbBlE2dV03Dv3rVyll711GZ49RwjLBipvTmpSfDmHM6t3UnAhE+dmnngEVvwj+vUL5cqRZJqFdhCc0DY0dPFKffUObcHn70bw1Ow9GDHd0aBuXS5+083bHb310oM8+4T897Uloursdmreb+iaBdB8yEs4tmpPWV42JZnnAMg/tgO34IcpPif2A7dsTrXkFRxA7ulUmj14H0ZDOVkHkwBo3r0jDm5ONHvwPjT2dvy86EvBSdVtxowZwsYePSAQD1cHRs/6gcLishrvtXfzcXs7DR+83ovJYxp+zkpSlsg6u5Wa9xv61JM4t+1McfJPaOwdcO8UAUDRmYMY9YUUJ/+EyViO31/eUjwbqPyck5p4BgeQeyYNnacrAYPDyT5yBoCfF31J/NxPubwnQRW/REztoqKihI4/sHdr0naN5YPXe9GxbZNqn9OqqQvzpz7IpV1jZWOyUaLr7CY17zduXErEOaAThqJccn/ajNv9vQHw+8tb+D+/DI8HB9E0cqKQbCCPnGrt6Pw1APz83pd0nzvujruP33pRnaRuTdx1vPTUA0wdez8HE65w/lIB+UWluLk44Nfchcd6+KKtZrm0JNWVmvcbrScsBsDvmbdJXz0Ljdahynbfp+YJSPUH2Zzq4WbBSbZNo9HQO7QFvUNbiI4iNQJq3m/cbFRqIj8eSopatWqV6AhSIyDrzPbJ5gTo3JzRujlZfRytmxM6t8Z7hwGADRvE/cZPqfFQqs7Uuu9w1YKLAvNiLtqKsaxBTusBjl7ujI5fQWmR3qrj6NyccfRyt+oYardr1y4mThR3klVqHJSqM7XuO5ro4JtIKLbyfXhdtRVjWYNsTr9z9HJv9I1DkqS6U+u+o4nOeo1DCXJaT1LUqFGjREeQGgFZZ7ZPNidJUT179hQdQWoEZJ3ZPtmcJEXNmjVLdASpEZB1Zvtkc5IkSZJUR3W/CVe6t/3rX/9i6tSpomNI9zhZZ7ZPNidJkiRJdeS0niRJkqQ6sjlJkiRJqiObkyRJkqQ6sjlJkiRJqiObkyRJkqQ6sjlJkiRJqiObkyRJkqQ6sjlJkiRJqiObkyRJkqQ6sjlJkiRJqiObkyRJkqQ6sjlJkiRJqvP/ASqj6xcQJNFiAAAAAElFTkSuQmCC", - "text/plain": [ - "
    " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "slices = slice_by_depth(qc, 2)\n", - "combined_slices = combine_slices(slices, include_barriers=True)\n", - "combined_slices.draw(\"mpl\", scale=0.6)" - ] - }, - { - "cell_type": "markdown", - "id": "482bc11c-47a3-45ba-9633-98bcca5b44a9", - "metadata": {}, - "source": [ - "In many cases, such as Trotter circuits, it may be advantageous to slice by gate type. Slices holding a given gate type will be further split out into depth-1 slices, as there is little downside in doing so." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1a5d5aff-f988-4f14-ad25-9f0105202b77", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_utils.slicing import slice_by_gate_types\n", - "\n", - "slices = slice_by_gate_types(qc)\n", - "combined_slices = combine_slices(slices, include_barriers=True)\n", - "combined_slices.draw(\"mpl\", scale=0.6)" - ] - }, - { - "cell_type": "markdown", - "id": "5e389e7e-1d29-4e9c-b44e-a5182da5cb5b", - "metadata": {}, - "source": [ - "If your circuit was designed to exploit the physical qubit connectivity, you may want to create slices based on an edge coloring. Here, we will assign a 3-coloring to the circuit edges and slice the circuit with respect to the edge coloring. This only affects non-local gates. Single qubit gates will be added to their own slices by gate type." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "110b5844-972e-46d1-bfc5-20a30b69f814", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_utils.slicing import slice_by_coloring\n", - "\n", - "# Assign a color to each set of connected qubits\n", - "coloring = {}\n", - "for i in range(num_qubits - 1):\n", - " coloring[(i, i + 1)] = i % 3\n", - "coloring[(num_qubits - 1, 0)] = 2\n", - "\n", - "# Create a circuit with operations added in order of color\n", - "qc = QuantumCircuit(num_qubits)\n", - "qc.ry(np.pi / 4, range(num_qubits))\n", - "edges = [edge for color in range(3) for edge in coloring if coloring[edge] == color]\n", - "for edge in edges:\n", - " qc.cx(edge[0], edge[1])\n", - "qc.rx(np.pi / 4, range(num_qubits))\n", - "qc.rz(np.pi / 4, range(num_qubits))\n", - "\n", - "# Create slices by edge color\n", - "slices = slice_by_coloring(qc, coloring=coloring)\n", - "combined_slices = combine_slices(slices, include_barriers=True)\n", - "combined_slices.draw(\"mpl\", scale=0.6)" - ] - }, - { - "cell_type": "markdown", - "id": "d05b0cad-bbed-48cd-8520-3e2904cde058", - "metadata": {}, - "source": [ - "For more custom slicing strategies a user may wish to place barriers in the locations they want to slice and use the [slice_by_barriers](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.slice_by_barriers) function. Here, we will create 3 slices, one for each rotation layer and one for the entangling layer." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "948aa715-22e8-41e1-943d-de7eb0aa379c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qc = QuantumCircuit(num_qubits)\n", - "qc.ry(np.pi / 4, range(num_qubits))\n", - "qc.barrier()\n", - "qubits_1 = [i for i in range(num_qubits) if i % 2 == 0]\n", - "qubits_2 = [i for i in range(num_qubits) if i % 2 == 1]\n", - "qc.cx(qubits_1[:-1], qubits_2)\n", - "qc.cx(qubits_2, qubits_1[1:])\n", - "qc.cx(qubits_1[-1], qubits_1[0])\n", - "qc.barrier()\n", - "qc.rx(np.pi / 4, range(num_qubits))\n", - "qc.rz(np.pi / 4, range(num_qubits))\n", - "qc.draw(\"mpl\", scale=0.6)" - ] - }, - { - "cell_type": "markdown", - "id": "6511c580-bcc6-462c-9d17-2435adae311a", - "metadata": {}, - "source": [ - "We will not draw the re-combined slices as a single circuit since it would look identical to the input circuit. Instead, below we draw each slice on its own." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9f766d69-e10c-4aa8-a3bc-2057dab7a69a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_utils.slicing import slice_by_barriers\n", - "\n", - "slices = slice_by_barriers(qc)\n", - "slices[0].draw(\"mpl\", scale=0.6)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a00c8743-f3b9-460c-ba2c-ffc634e99e0e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
    " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "slices[2].draw(\"mpl\", scale=0.6)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/docs/addons/qiskit-addon-utils/how_tos/index.mdx b/docs/addons/qiskit-addon-utils/how_tos/index.mdx deleted file mode 100644 index 21ed11db483..00000000000 --- a/docs/addons/qiskit-addon-utils/how_tos/index.mdx +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Qiskit addon utilities API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-utils. ---- - -# How-To Guides - -This page summarizes the available how-to guides. - -* [Use edge coloring to reduce the depth of quantum circuits](color-device-edges-to-improve-depth) -* [Slicing circuits using `qiskit_addon_utils.slicing`](create-circuit-slices) - diff --git a/docs/addons/qiskit-addon-utils/index.mdx b/docs/addons/qiskit-addon-utils/index.mdx deleted file mode 100644 index c294db8b409..00000000000 --- a/docs/addons/qiskit-addon-utils/index.mdx +++ /dev/null @@ -1,45 +0,0 @@ ---- -title: Qiskit addon utilities API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-utils. ---- - -# Qiskit addon utilities - -[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. - -This package contains functionality which is meant to supplement workflows involving one or more Qiskit addons. For example, this package contains functions for creating Hamiltonians, generating Trotter time evolution circuits, and slicing and combining quantum circuits in time-wise partitions. - -## Documentation - -All documentation is available [here](/docs/addons/qiskit-addon-utils). - -## Installation - -We encourage installing this package via `pip`, when possible: - -```bash -pip install 'qiskit-addon-utils' -``` - -For more installation information refer to the [installation instructions](install) in the documentation. - -## Deprecation Policy - -We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [release notes](/docs/addons/qiskit-addon-utils/release-notes). - -## Contributing - -The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-utils). - -The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-utils/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). - -We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-utils/issues/new/choose) for tracking requests and bugs. - -## Citation - -If you use this package in your research, please cite it according to the [CITATION.bib](https://github.com/Qiskit/qiskit-addon-utils/blob/main/CITATION.bib) file. - -## License - -[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-utils/blob/main/LICENSE.txt) - diff --git a/docs/addons/qiskit-addon-utils/install.mdx b/docs/addons/qiskit-addon-utils/install.mdx deleted file mode 100644 index 054046adca3..00000000000 --- a/docs/addons/qiskit-addon-utils/install.mdx +++ /dev/null @@ -1,71 +0,0 @@ -# Installation Instructions - -Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: - -* [Option 1: Install from PyPI](#option-1) -* [Option 2: Install from Source](#option-2) - -## Pre-Installation - -First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). - -```sh -python3 -m venv /path/to/virtual/environment -``` - -Activate your new environment. - -```sh -source /path/to/virtual/environment/bin/activate -``` - -Note: If you are using Windows, use the following commands in PowerShell: - -```pwsh -python3 -m venv c:\path\to\virtual\environment -c:\path\to\virtual\environment\Scripts\Activate.ps1 -``` - - - -## Option 1: Install from PyPI - -The most straightforward way to install the `qiskit-addon-utils` package is via `PyPI`. - -```sh -pip install 'qiskit-addon-utils' -``` - - - -## Option 2: Install from Source - -Users who wish to develop in the repository or run the notebooks locally may want to install from source. - -If so, the first step is to clone the `qiskit-addon-utils` repository. - -```sh -git clone git@github.com:Qiskit/qiskit-addon-utils.git -``` - -Next, upgrade pip and enter the repository. - -```sh -pip install --upgrade pip -cd qiskit-addon-utils -``` - -The next step is to install `qiskit-addon-utils` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. - -Adjust the options below to suit your needs. - -```sh -pip install tox notebook -e '.[notebook-dependencies,dev]' -``` - -If you installed the notebook dependencies, you can get started by running the notebooks in the docs. - -```python -cd docs/ -jupyter lab -``` diff --git a/public/docs/api/qiskit-addon-aqc-tensor/objects.inv b/public/docs/api/qiskit-addon-aqc-tensor/objects.inv index 3c3bda03e70a57eda1847503a71af15d0b1694c6..719ec51e4df212ff715f3343d74ed5d7b0026203 100644 GIT binary patch delta 5716 zcmV-a7OUyxGn_4udw9_)x%yvB-1p-8s5n zZP^whH)M`0vD*bZ8LC67@~A0F>U*_grOk7B*v8Tg+g9W!7$$h|JM^rd4ndI*Xp% z(Y3P+Ki@Hlw6n9|mx!aC(6YTk@D=&x`pb07@*oJ1=zk7{$YQp_=6ySg;~{{~h1NvS zIi5%a>*8qOn5K=$fbP*~Jb;#r2n_GQEC~Y6!wA-2QL!(!Q#4>~_e5oZH%mkYaBoDz zhqXdPY+UC*H3ZOCI*pBS(XgpvMYlfuT0?PC}dfwww*Nr1Z{wiI9u6CXXU zr((+k*mkk;gF7>}3}DS0A3dVKV@+c5+a_q2$bXjrppj%F2lbka?9g_UEd@l!%9aDP 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    CYW%WTX{S@?L(z>wEQ27FaudE7g4?e$|ra8ULDOS-x2$d_bi9 zX(Qz>YXFV7QAf`Dy+;Q9(MjiNbhNYa-Vu=Z#I{%O*9>KTeWtvLU(PvApi_GZmaH%G z-$#JN$TcZNfze!6levveXR4m}TyDkj6h+UIc#ampwSh&=xuRR-+m(Qvxs7iKb;C}bUGz6N_v_C+h|RrKYv{!%%@1gXKI^TZDarrLV!MeHB+D+UxHl1k=)>iUVi_~AaflH^|V}#@Ni=hb2S03;0SDr9Jm<>aC<=g0HBtuS%`v?~Ay}~^_K2?-D OD$+{ZE-fTUmH!{bj2Gnq diff --git a/scripts/js/commands/api/updateApiDocs.ts b/scripts/js/commands/api/updateApiDocs.ts index edac31e251b..406fff3b9ea 100644 --- a/scripts/js/commands/api/updateApiDocs.ts +++ b/scripts/js/commands/api/updateApiDocs.ts @@ -136,11 +136,11 @@ async function prepareSphinxFolder(pkg: Pkg, args: Arguments): Promise { } async function deleteExistingFiles(pkg: Pkg): Promise { - const markdownDir = pkg.outputDir("docs"); + const markdownDir = pkg.apiOutputDir("docs"); if (await pathExists(markdownDir)) { await rmFilesInFolder(markdownDir); } - const imagesDir = pkg.outputDir("public/docs/images"); + const imagesDir = pkg.apiOutputDir("public/docs/images"); if (await pathExists(imagesDir)) { await rmFilesInFolder(imagesDir); } diff --git a/scripts/js/commands/updateDocs.ts b/scripts/js/commands/updateDocs.ts index 3a006402efd..9acc992ae3a 100644 --- a/scripts/js/commands/updateDocs.ts +++ b/scripts/js/commands/updateDocs.ts @@ -12,100 +12,124 @@ import yargs from "yargs/yargs"; import { hideBin } from "yargs/helpers"; +import { $ } from "zx"; import { Pkg } from "../lib/api/Pkg.js"; -import { parseMinorVersion } from "../lib/apiVersions.js"; import { pathExists } from "../lib/fs.js"; import { downloadSphinxArtifact } from "../lib/api/sphinxArtifacts.js"; import { runSphinxPipeline } from "../lib/sphinx/conversionPipeline.js"; import { zxMain } from "../lib/zx.js"; -import { $ } from "zx"; - -const DOCS_FOLDER = "docs"; -const PUBLIC_FOLDER = "public"; - -// Glob patterns excluded from prose runs. Release notes link heavily to API -// pages and don't belong in the prose output. -const PROSE_EXCLUDE = [ - "apidocs/**", - "apidoc/**", - "stubs/**", - "release_notes.html", - "release-notes.html", -]; - -type AddonConfig = { - name: string; - version: string; -}; - -const ADDONS: AddonConfig[] = [ - { name: "qiskit-addon-aqc-tensor", version: "0.2.0" }, - { name: "qiskit-addon-cutting", version: "0.10.0" }, - { name: "qiskit-addon-mpf", version: "0.3.0" }, - { name: "qiskit-addon-obp", version: "0.3.0" }, - { name: "qiskit-addon-sqd", version: "0.12.0" }, - { name: "qiskit-addon-utils", version: "0.3.1" }, -]; +import { parseMinorVersion } from "../lib/apiVersions.js"; -const VALID_NAMES = ADDONS.map((a) => a.name); +export interface Arguments { + [x: string]: unknown; + package?: string; + skipDownload: boolean; + sphinxArtifactFolder?: string; +} -const readArgs = () => { +const readArgs = (): Arguments => { return yargs(hideBin(process.argv)) .version(false) .option("package", { alias: "p", type: "string", - choices: VALID_NAMES, - description: - "Which addon to update. Defaults to all configured addons.", + choices: Pkg.VALID_NAMES, + description: "Which package to update", }) .option("skip-download", { type: "boolean", default: false, description: - "Reuse already-downloaded artifacts instead of downloading again.", + "Rather than downloading the artifact from Box, reuse what is already downloaded. This can save time, but it risks using an outdated version of the docs.", + }) + .option("sphinx-artifact-folder", { + alias: "a", + type: "string", + implies: "skip-download", + normalize: true, + description: + "Skip downloading the artifact from Box and instead use the given directory as the root of the Sphinx HTML output.", }) .parseSync(); }; -async function runForAddon( - config: AddonConfig, - skipDownload: boolean, -): Promise { - const minorVersion = parseMinorVersion(config.version)!; - const pkg = await Pkg.fromArgs( - config.name, - config.version, - minorVersion, - "latest", - ); +async function runForAddon(pkg: Pkg, args: Arguments): Promise { + const sphinxArtifactFolder = await prepareSphinxFolder(pkg, args); + await deleteExistingFiles(pkg); - const artifactFolder = pkg.sphinxArtifactFolder(); - const artifactPath = `${artifactFolder}/artifact`; + console.log(`Run pipeline for ${pkg.name}:${pkg.versionWithoutPatch}`); + await runSphinxPipeline( + sphinxArtifactFolder, + "docs/addons", + "public/docs", + pkg, + ); +} - if (skipDownload && (await pathExists(artifactPath))) { - console.log(`Reusing cached artifact for ${config.name}`); +async function prepareSphinxFolder(pkg: Pkg, args: Arguments): Promise { + if (args.sphinxArtifactFolder) { + if (!(await pathExists(args.sphinxArtifactFolder))) { + throw new Error( + `Explicit artifact path '${args.sphinxArtifactFolder}' does not exist.`, + ); + } + return args.sphinxArtifactFolder; + } + const sphinxArtifactFolder = pkg.sphinxArtifactFolder(); + if ( + args.skipDownload && + (await pathExists(`${sphinxArtifactFolder}/artifact`)) + ) { + console.log( + `Skip downloading sources for ${pkg.name}:${pkg.versionWithoutPatch}`, + ); } else { - await downloadSphinxArtifact(pkg, artifactFolder); + await downloadSphinxArtifact(pkg, sphinxArtifactFolder); } + return `${sphinxArtifactFolder}/artifact`; +} - const proseOutput = `docs/addons/${config.name}`; - if (await pathExists(proseOutput)) await $`rm -rf ${proseOutput}`; - await runSphinxPipeline(artifactPath, DOCS_FOLDER, PUBLIC_FOLDER, { - exclude: PROSE_EXCLUDE, - output: proseOutput, - pkg, - }); +async function deleteExistingFiles(pkg: Pkg): Promise { + const markdownDir = pkg.outputDir("docs/addons"); + if (await pathExists(markdownDir)) { + await $`rm -rf ${markdownDir}`; + } + const imagesDir = pkg.outputDir("public/docs/images/addons"); + if (await pathExists(imagesDir)) { + await $`rm -rf ${imagesDir}`; + } + console.log( + `Deleted existing markdown & images for ${pkg.name}:${pkg.versionWithoutPatch}`, + ); } -zxMain(async () => { - const args = readArgs(); - const addons = args.package - ? ADDONS.filter((a) => a.name === args.package) - : ADDONS; +const ADDONS = [ + { name: "qiskit-addon-aqc-tensor", version: "0.2.0" }, + { name: "qiskit-addon-cutting", version: "0.10.0" }, + { name: "qiskit-addon-mpf", version: "0.3.0" }, + { name: "qiskit-addon-obp", version: "0.3.0" }, + { name: "qiskit-addon-sqd", version: "0.12.0" }, + { name: "qiskit-addon-utils", version: "0.3.1" }, +]; + +if (import.meta.url === `file://${process.argv[1]}`) { + zxMain(async () => { + const args = readArgs(); + const addons = args.package + ? ADDONS.filter((a) => a.name === args.package) + : ADDONS; - for (const addon of addons) { - await runForAddon(addon, args.skipDownload); - } -}); + for (const addon of addons) { + const minorVersion = parseMinorVersion(addon.version)!; + const pkg = await Pkg.fromArgs( + addon.name, + addon.version, + minorVersion, + "latest", + ); + + await runForAddon(pkg, args); + } + }); +} diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index 9b3b1145f86..2da5bd0c118 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -255,6 +255,10 @@ export class Pkg { } outputDir(parentDir: string): string { + return join(parentDir, this.name); + } + + apiOutputDir(parentDir: string): string { let path = join(parentDir, "api", this.name); if (this.isHistorical()) { path = join(path, this.versionWithoutPatch); diff --git a/scripts/js/lib/api/conversionPipeline.test.ts b/scripts/js/lib/api/conversionPipeline.test.ts index 90d1c91bd21..9171b925cae 100644 --- a/scripts/js/lib/api/conversionPipeline.test.ts +++ b/scripts/js/lib/api/conversionPipeline.test.ts @@ -66,7 +66,7 @@ test("qiskit-sphinx-theme", async ({}, testInfo) => { releaseNotesConfig: new ReleaseNotesConfig({ enabled: false }), kebabCaseAndShortenUrls: false, }); - const markdownFolder = pkg.outputDir(docsBaseFolder); + const markdownFolder = pkg.apiOutputDir(docsBaseFolder); await runConversionPipeline( "scripts/js/lib/api/testdata/qiskit-sphinx-theme", diff --git a/scripts/js/lib/api/conversionPipeline.ts b/scripts/js/lib/api/conversionPipeline.ts index d672da35b17..034bf951b51 100644 --- a/scripts/js/lib/api/conversionPipeline.ts +++ b/scripts/js/lib/api/conversionPipeline.ts @@ -68,7 +68,7 @@ export async function runConversionPipeline( // Warning: the sequence of operations often matters. await writeMarkdownResults(pkg, docsBaseFolder, results); await copyImages(pkg, htmlPath, "public", results); - await maybeObjectsInv?.write(pkg.outputDir(publicBaseFolder)); + await maybeObjectsInv?.write(pkg.apiOutputDir(publicBaseFolder)); await maybeUpdateReleaseNotesFolder(pkg, markdownPath); await writeTocFile(pkg, markdownPath, results); await writeVersionFile(pkg, markdownPath); @@ -92,7 +92,7 @@ async function determineFilePaths( cwd: htmlPath, }, ); - const markdownPath = pkg.outputDir(docsBaseFolder); + const markdownPath = pkg.apiOutputDir(docsBaseFolder); await mkdirp(markdownPath); return [files, markdownPath, maybeObjectsInv]; } @@ -111,7 +111,7 @@ async function convertFilesToMarkdown( html, fileName: file, determineGithubUrl: pkg.determineGithubUrlFn(), - imageDestination: pkg.outputDir(`${DOCS_BASE_PATH}/images`), + imageDestination: pkg.apiOutputDir(`${DOCS_BASE_PATH}/images`), releaseNotesTitle: pkg.releaseNotesTitle(), hasSeparateReleaseNotes: pkg.hasSeparateReleaseNotes(), isCApi: pkg.isCApi(), @@ -161,7 +161,7 @@ async function postProcessResults( { kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, pkgName: pkg.name, - pkgOutputDir: pkg.outputDir(DOCS_BASE_PATH), + pkgOutputDir: pkg.apiOutputDir(DOCS_BASE_PATH), }, maybeObjectsInv, ); diff --git a/scripts/js/lib/api/generateToc.ts b/scripts/js/lib/api/generateToc.ts index 2001244c4b9..6a927c6596b 100644 --- a/scripts/js/lib/api/generateToc.ts +++ b/scripts/js/lib/api/generateToc.ts @@ -226,7 +226,7 @@ function ensureIndexPage( pkg: Pkg, tocModules: TocEntry[], ): TocEntry | undefined { - const docsFolder = pkg.outputDir(`${DOCS_BASE_PATH}/`); + const docsFolder = pkg.apiOutputDir(`${DOCS_BASE_PATH}/`); return tocModules.some((entry) => entry.url === docsFolder) ? undefined : { diff --git a/scripts/js/lib/api/objectsInv.ts b/scripts/js/lib/api/objectsInv.ts index 93100542c9b..b39faf41525 100644 --- a/scripts/js/lib/api/objectsInv.ts +++ b/scripts/js/lib/api/objectsInv.ts @@ -190,6 +190,58 @@ export class ObjectsInv { return uri; } + /** + * Load all published objects.inv files from public/docs/api/ and return + * a map of package name → ObjectsInv. These inventories have already been + * normalized by the API pipeline so URIs are ready to use directly. + */ + static async loadPublishedApis( + publicBaseFolder: string, + ): Promise> { + const { readdir } = await import("fs/promises"); + const apiDir = join(publicBaseFolder, "api"); + const map = new Map(); + let pkgDirs: string[]; + try { + pkgDirs = await readdir(apiDir); + } catch { + return map; + } + await Promise.all( + pkgDirs.map(async (pkgName) => { + try { + const inv = await ObjectsInv.fromFile(join(apiDir, pkgName), "any"); + map.set(pkgName, inv); + } catch { + // No objects.inv for this package — skip. + } + }), + ); + return map; + } + + /** + * Resolve a qiskit.github.io/stubs/ URL to an internal docs path. + * + * Pass allInvs (from loadPublishedApis) for cross-package resolution. + * Requires updateUris() to have been called on same-package inventory first. + */ + resolveStubUrl( + url: string, + allObjectInvs?: Map, + ): string | undefined { + const match = url.match( + /^https:\/\/qiskit\.github\.io\/([^/]+)\/stubs\/([^"#)\s]+?)(?:\.html)?(?:#.*)?$/, + ); + if (!match) return undefined; + const [, pkg, symbol] = match; + const inv = allObjectInvs?.get(pkg) ?? this; + const entry = inv.entries.find((e) => e.name === symbol); + if (!entry) return undefined; + // Published URIs are relative to the package's API base — prepend full path. + return `/docs/api/${pkg}/${entry.uri}`; + } + updateUris(transformLink: (uri: string) => string): void { for (const entry of this.entries) { entry.uri = entry.uri.replace(/\.html/, ""); diff --git a/scripts/js/lib/api/saveImages.ts b/scripts/js/lib/api/saveImages.ts index 97768687f4e..47ccff95605 100644 --- a/scripts/js/lib/api/saveImages.ts +++ b/scripts/js/lib/api/saveImages.ts @@ -52,7 +52,7 @@ export async function saveImages( publicBaseFolder: string, pkg: Pkg, ) { - const destFolder = pkg.outputDir(`${publicBaseFolder}/docs/images`); + const destFolder = pkg.apiOutputDir(`${publicBaseFolder}/docs/images`); if (!(await pathExists(destFolder))) { await mkdirp(destFolder); } diff --git a/scripts/js/lib/api/specialCaseResults.ts b/scripts/js/lib/api/specialCaseResults.ts index b0f042c814c..f9c704df501 100644 --- a/scripts/js/lib/api/specialCaseResults.ts +++ b/scripts/js/lib/api/specialCaseResults.ts @@ -16,7 +16,9 @@ export function transformSpecialCaseUrl(url: string): string { return ( url // We use `-` rather than `_` as our delimiter. - .replace(/(?<=^|\/)release_notes(?=#|$)/g, "release-notes") + .replace(/(?<=^|\/)release_notes(?=\/|#|$)/g, "release-notes") + .replace(/(?<=^|\/)how_tos(?=\/|#|$)/g, "how-tos") + .replace(/(?<=^|\/)explanations(?=\/|#|$)/g, "explanation") .replace(/(?<=^|\/)terra(?=#|$)/g, "index") .replace(/(?<=^|\/)ibm-runtime(?=#|$)/g, "index") ); diff --git a/scripts/js/lib/api/updateLinks.ts b/scripts/js/lib/api/updateLinks.ts index 70f32ff584d..c6a1d155e7e 100644 --- a/scripts/js/lib/api/updateLinks.ts +++ b/scripts/js/lib/api/updateLinks.ts @@ -22,6 +22,7 @@ import remarkGfm from "remark-gfm"; import remarkMdx from "remark-mdx"; import remarkStringify from "remark-stringify"; +import { kebabCase } from "lodash-es"; import { removePart, removePrefix, removeSuffix } from "../stringUtils.js"; import { HtmlToMdResultWithUrl } from "./HtmlToMdResult.js"; import { remarkStringifyOptions } from "./commonParserConfig.js"; @@ -171,16 +172,25 @@ export function relativizeLink(link: Link): Link | undefined { ["https://docs.quantum-computing.ibm.com/", ""], ["https://quantum.cloud.ibm.com/docs", "/docs"], ["https://quantum.cloud.ibm.com/learning", "/learning"], + ["https://qiskit.github.io/", "/docs/addons"], // todo handle addons prefix ]); const priorPrefix = Array.from(priorPrefixToNewPrefix.keys()).find((prefix) => link.url.startsWith(prefix), ); - if (!priorPrefix) { + // Stubs links are symbol references — leave them for objects.inv resolution. + if (!priorPrefix || link.url.includes("/stubs/")) { return; } let [url, anchor] = link.url.split("#"); url = removePrefix(url, priorPrefix); url = removeSuffix(url, ".html"); + + // For github.io addons links, kebab-case each path segment after the package name. + if (priorPrefix === "https://qiskit.github.io/") { + const [pkg, ...rest] = url.split("/"); + url = [pkg, ...rest.map((s) => kebabCase(s).replace(/-v-([0-9]+)/g, `-v$1`))].join("/"); + } + if (anchor && anchor !== url) { url = `${url}#${anchor}`; } @@ -199,6 +209,7 @@ export async function updateLinks( pkgOutputDir: string; }, maybeObjectsInv?: ObjectsInv, + allObjectInvs?: Map, ): Promise { const resultsByName = keyBy(results, (result) => result.meta.apiName!); const itemNames = new Set(keys(resultsByName)); @@ -225,6 +236,13 @@ export async function updateLinks( textNode.value = relativizedLink.text; } } + const resolvedStub = maybeObjectsInv?.resolveStubUrl( + node.url, + allObjectInvs, + ); + if (resolvedStub) { + node.url = resolvedStub; + } node.url = normalizeUrl(node.url, resultsByName, itemNames, kwargs); }); }) diff --git a/scripts/js/lib/sphinx/conversionPipeline.ts b/scripts/js/lib/sphinx/conversionPipeline.ts index 7a4a5603a74..452c87b41cc 100644 --- a/scripts/js/lib/sphinx/conversionPipeline.ts +++ b/scripts/js/lib/sphinx/conversionPipeline.ts @@ -10,7 +10,7 @@ // copyright notice, and modified files need to carry a notice indicating // that they have been altered from the originals. -import { dirname, parse, relative } from "path"; +import { dirname, join, parse, relative } from "path"; import { readFile, writeFile } from "fs/promises"; import { globby } from "globby"; @@ -22,16 +22,23 @@ import { ObjectsInv } from "../api/objectsInv.js"; import { HtmlToMdResultWithUrl } from "../api/HtmlToMdResult.js"; import { Pkg } from "../api/Pkg.js"; import { saveImages } from "../api/saveImages.js"; -import { normalizeResultUrls } from "../api/normalizeResultUrls.js"; +import { + normalizeResultUrls, + kebabCaseAndShortenPage, +} from "../api/normalizeResultUrls.js"; import { updateLinks, relativizeLink } from "../api/updateLinks.js"; import { mergeClassMembers } from "../api/mergeClassMembers.js"; -import { specialCaseResults } from "../api/specialCaseResults.js"; +import { specialCaseResults, transformSpecialCaseUrl } from "../api/specialCaseResults.js"; import addFrontMatter from "../api/addFrontMatter.js"; import { dedupeHtmlIdsFromResults } from "../api/dedupeHtmlIds.js"; import removeMathBlocksIndentation from "../api/removeMathBlocksIndentation.js"; import { generateSphinxToc } from "./generateToc.js"; -import { DOCS_BASE_PATH } from "../api/conversionPipeline.js"; -import { kebabCaseAndShortenPage } from "../api/normalizeResultUrls.js"; +import { + handleReleaseNotesFile, + maybeUpdateReleaseNotesFolder, +} from "../api/releaseNotes.js"; + +export const DOCS_BASE_PATH = "/docs"; // Sphinx build artifacts that are never content. const SPHINX_INTERNALS = [ @@ -45,167 +52,194 @@ const SPHINX_INTERNALS = [ "objects.inv", ]; -export type SphinxPipelineConfig = { - /** Glob patterns to include relative to artifact root. Defaults to everything. */ - include?: string[]; - /** Glob patterns to exclude relative to artifact root. */ - exclude?: string[]; - /** Output directory, e.g. "docs/api/qiskit-addon-aqc-tensor" */ - output: string; - pkg: Pkg; -}; - export async function runSphinxPipeline( artifactPath: string, docsBaseFolder: string, publicBaseFolder: string, - config: SphinxPipelineConfig, + pkg: Pkg, ): Promise { - const { output, pkg } = config; - await mkdirp(output); - - const objectsInv = await loadObjectsInv(artifactPath, pkg); - const allFiles = await selectFiles(artifactPath, config); - const htmlFiles = allFiles.filter((f) => f.endsWith(".html")); - const notebookFiles = allFiles.filter((f) => f.endsWith(".ipynb")); + const [files, outputPath, objectsInv] = await determineFilePaths( + artifactPath, + docsBaseFolder, + pkg, + ); - if (htmlFiles.length === 0 && notebookFiles.length === 0) { - console.log(`No content files found for ${pkg.name} at ${output}`); - return; - } + const allObjectInvs = await ObjectsInv.loadPublishedApis(publicBaseFolder); - console.log( - `Processing ${htmlFiles.length} HTML + ${notebookFiles.length} notebooks for ${pkg.name} → ${output}`, - ); + const htmlFiles = files.filter((f) => f.endsWith(".html")); + const notebookFiles = files.filter((f) => f.endsWith(".ipynb")); - // Convert HTML files. - let results = await convertHtmlFiles( + const initialResults = await convertFilesToMarkdown( pkg, artifactPath, docsBaseFolder, - output, + outputPath, htmlFiles, ); - results = await mergeClassMembers(results); - - normalizeResultUrls(results, { - kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, - pkgName: pkg.name, - }); - - specialCaseResults(results); - - // Rewrite github.io links to internal paths before updateLinks runs. - for (const result of results) { - result.markdown = resolveGithubIoLinks( - result.markdown, - objectsInv, - pkg.name, - output, - ); - } - - await updateLinks( - results, - { - kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, - pkgName: pkg.name, - pkgOutputDir: `${DOCS_BASE_PATH}/${output.replace(/^docs\//, "")}`, - }, + const results = await postProcessResults( + pkg, objectsInv, + allObjectInvs, + initialResults, ); - await dedupeHtmlIdsFromResults(results); - addFrontMatter(results, pkg); - removeMathBlocksIndentation(results); + await writeMarkdownResults(pkg, docsBaseFolder, results); + await copyImages(pkg, artifactPath, "public", results); + await objectsInv.write(pkg.apiOutputDir(publicBaseFolder)); - await writeMdxFiles(results); - await saveImages( - uniqBy(results.flatMap((r) => r.images), (img) => img.fileName), - `${artifactPath}/_images`, - publicBaseFolder, - pkg, - ); - - await copyNotebooks(artifactPath, output, notebookFiles, objectsInv, pkg.name); - await writeTocFile(artifactPath, output, pkg, docsBaseFolder, config); + await copyNotebooks(artifactPath, outputPath, notebookFiles, pkg); + // await writeTocFile(artifactPath, output, pkg, docsBaseFolder, config); } -async function loadObjectsInv( - artifactPath: string, +async function determineFilePaths( + htmlPath: string, + docsBaseFolder: string, pkg: Pkg, -): Promise { - try { - return await ObjectsInv.fromFile(artifactPath, pkg.language); - } catch { - return undefined; - } -} +): Promise<[string[], string, ObjectsInv]> { + const objectsInv = await ObjectsInv.fromFile(htmlPath, pkg.language); -async function selectFiles( - artifactPath: string, - config: SphinxPipelineConfig, -): Promise { - const include = config.include ?? ["**"]; - const exclude = [...SPHINX_INTERNALS, ...(config.exclude ?? [])]; - return globby(include, { - cwd: artifactPath, - ignore: exclude, + const allFiles = await globby(["**"], { + cwd: htmlPath, + ignore: ["apidocs/**", "apidoc/**", "stubs/**", ...SPHINX_INTERNALS], }); + + // Prefer .ipynb over .html when both exist for the same base path. + const notebookBases = new Set( + allFiles + .filter((f) => f.endsWith(".ipynb")) + .map((f) => f.slice(0, -".ipynb".length)), + ); + const files = allFiles.filter( + (f) => + !f.endsWith(".html") || !notebookBases.has(f.slice(0, -".html".length)), + ); + const outputPath = pkg.outputDir(docsBaseFolder); + await mkdirp(outputPath); + return [files, outputPath, objectsInv]; } -async function convertHtmlFiles( +async function convertFilesToMarkdown( pkg: Pkg, artifactPath: string, docsBaseFolder: string, - outputDir: string, + outputPath: string, filePaths: string[], ): Promise { - const results: HtmlToMdResultWithUrl[] = []; - + const results = []; for (const file of filePaths) { - const html = await readFile(`${artifactPath}/${file}`, "utf-8"); + const html = await readFile(join(artifactPath, file), "utf-8"); const result = await sphinxHtmlToMarkdown({ html, fileName: file, determineGithubUrl: pkg.determineGithubUrlFn(), - imageDestination: pkg.outputDir(`${DOCS_BASE_PATH}/images`), + imageDestination: pkg.apiOutputDir(`${DOCS_BASE_PATH}/images`), releaseNotesTitle: pkg.releaseNotesTitle(), hasSeparateReleaseNotes: pkg.hasSeparateReleaseNotes(), isCApi: pkg.isCApi(), hasRootNamespaceFile: pkg.hasRootNamespaceFile, }); - if (result.markdown === "") continue; + // Avoid creating an empty markdown file for HTML files without content + // (e.g. HTML redirects) + if (result.markdown == "") { + continue; + } - const { dir, name } = parse(`${outputDir}/${file}`); - const url = `/${relative(docsBaseFolder, dir)}/${name}`; + const { dir, name } = parse(`${outputPath}/${file}`); + let url = `/${relative(docsBaseFolder, dir)}/${name}`; results.push({ ...result, url }); } + return results; +} +async function copyImages( + pkg: Pkg, + htmlPath: string, + publicBaseFolder: string, + results: HtmlToMdResultWithUrl[], +): Promise { + console.log("Saving images"); + const allImages = uniqBy( + results.flatMap((result) => result.images), + (image) => image.fileName, + ); + await saveImages(allImages, `${htmlPath}/_images`, publicBaseFolder, pkg); +} + +async function postProcessResults( + pkg: Pkg, + objectsInv: ObjectsInv, + allInvs: Map, + initialResults: HtmlToMdResultWithUrl[], +): Promise { + const results = await mergeClassMembers(initialResults); + normalizeResultUrls(results, { + kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, + pkgName: pkg.name, + }); + specialCaseResults(results); + // Rewrite relative apidocs/ links to absolute /docs/api// paths before + // updateLinks runs, so they are correctly resolved as internal API links. + const apiBase = pkg.apiOutputDir(DOCS_BASE_PATH); + for (const result of results) { + result.markdown = result.markdown.replace( + /\]\((?:\.\.\/)*?(apidocs|apidoc|stubs)\/([^)]+)\)/g, + `](${apiBase}/$2)`, + ); + } + await updateLinks( + results, + { + kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, + pkgName: pkg.name, + pkgOutputDir: pkg.apiOutputDir(DOCS_BASE_PATH), + }, + objectsInv, + allInvs, + ); + await dedupeHtmlIdsFromResults(results); + addFrontMatter(results, pkg); + removeMathBlocksIndentation(results); return results; } -async function writeMdxFiles( +async function writeMarkdownResults( + pkg: Pkg, + docsBaseFolder: string, results: HtmlToMdResultWithUrl[], ): Promise { for (const result of results) { - const filePath = `docs${result.url}.mdx`; - await mkdirp(dirname(filePath)); - await writeFile(filePath, result.markdown); + let path = `${docsBaseFolder}${result.url}.mdx`; + if (path.endsWith("release-notes.mdx")) { + if (!pkg.releaseNotesConfig.enabled) continue; + + const shouldWriteResult = await handleReleaseNotesFile(result, pkg); + if (!shouldWriteResult) continue; + } + + await mkdirp(dirname(path)); + await writeFile(path, result.markdown); } } async function copyNotebooks( artifactPath: string, - outputDir: string, + outputPath: string, notebookFiles: string[], - objectsInv: ObjectsInv | undefined, - pkgName: string, + pkg: Pkg, ): Promise { for (const file of notebookFiles) { - const dest = `${outputDir}/${file}`; + // Apply the same kebab-case transformation as normalizeResultUrls so + // notebook filenames match the links generated from HTML conversion. + const destFile = pkg.kebabCaseAndShortenUrls + ? transformSpecialCaseUrl( + file.replace(/([^/]+)\.ipynb$/, (_, base) => + `${kebabCaseAndShortenPage(base, pkg.name)}.ipynb`, + ), + ) + : file; + const dest = `${outputPath}/${destFile}`; await mkdirp(dirname(dest)); const raw = await readFile(`${artifactPath}/${file}`, "utf-8"); const notebook = JSON.parse(raw); @@ -214,94 +248,38 @@ async function copyNotebooks( const lines: string[] = Array.isArray(cell.source) ? cell.source : [cell.source]; - cell.source = lines.map((line) => { - const resolved = resolveGithubIoLinks(line, objectsInv, pkgName, outputDir); - return resolved.replace(/\[([^\]]*)\]\(([^)]+)\)/g, (match, text, url) => { + cell.source = lines.map((line) => + line.replace(/\[([^\]]*)\]\(([^)]+)\)/g, (match, text, url) => { const rewritten = relativizeLink({ url, text }); - return rewritten ? `[${rewritten.text ?? text}](${rewritten.url})` : match; - }); - }); + return rewritten + ? `[${rewritten.text ?? text}](${rewritten.url})` + : match; + }), + ); } await writeFile(dest, JSON.stringify(notebook, null, 1)); } } -async function writeTocFile( - artifactPath: string, - outputDir: string, - pkg: Pkg, - docsBaseFolder: string, - config: SphinxPipelineConfig, -): Promise { - console.log(`Generating TOC for ${outputDir}`); - const toc = await generateSphinxToc( - artifactPath, - outputDir, - pkg.title, - docsBaseFolder, - config.include, - config.exclude, - ); - await writeFile( - `${outputDir}/_toc.json`, - JSON.stringify(toc, null, 2) + "\n", - ); -} - - -/** - * Resolve qiskit.github.io links to internal paths. - * - * For stubs links (symbol references), use objects.inv for resolution — - * it has complete symbol→URL mappings. For other github.io links (prose - * page links), rewrite to the configured output path. - */ -function resolveGithubIoLinks( - text: string, - objectsInv: ObjectsInv | undefined, - pkgName: string, - outputDir: string, -): string { - return text.replace( - /https:\/\/qiskit\.github\.io\/([^/"#)\s]+)\/?([^"#)\s]*)/g, - (match, pkg, rest) => { - const stubsPrefix = "stubs/"; - if (rest.startsWith(stubsPrefix)) { - const symbol = rest.slice(stubsPrefix.length).replace(/\.html$/, ""); - // Look up in objects.inv entries for this package. - if (objectsInv) { - const resolved = resolveSymbolFromObjectsInv(objectsInv, symbol, pkg); - if (resolved) return resolved; - } - // Fallback: derive URL from symbol name using kebab-case convention. - const kebabPage = kebabCaseAndShortenPage(symbol, pkg); - return `/docs/api/${pkg}/${kebabPage}`; - } - // Non-stubs github.io link — only rewrite for the package currently - // being processed. Links to other packages' github.io pages are left - // as external URLs since we may not have their prose content. - if (pkg !== pkgName) return match; - const page = rest.replace(/\.html$/, "").replace(/\/$/, ""); - return page ? `/docs/addons/${pkg}/${page}` : `/docs/addons/${pkg}`; - }, - ); -} - -function resolveSymbolFromObjectsInv( - objectsInv: ObjectsInv, - symbol: string, - pkgName: string, -): string | undefined { - const entry = objectsInv.entries.find((e) => e.name === symbol); - if (!entry) return undefined; - // URI format: "apidocs/qiskit_addon_obp.utils.truncating.html#symbol" - // Strip .html (which appears before the # fragment, not at the end). - const uri = entry.uri.replace(/\.html(?=#|$)/, ""); - const [rawPage, anchor] = uri.split("#"); - // Strip Sphinx artifact root folders (apidocs/, stubs/, etc.) before kebab-casing, - // matching the same normalization applied to converted API pages. - const strippedPage = rawPage.replace(/^(apidocs|apidoc|stubs|cdoc)\//, ""); - const kebabPage = kebabCaseAndShortenPage(strippedPage, pkgName); - return `/docs/api/${pkgName}/${kebabPage}${anchor ? `#${anchor}` : ""}`; -} +// async function writeTocFile( +// artifactPath: string, +// outputDir: string, +// pkg: Pkg, +// docsBaseFolder: string, +// config: SphinxPipelineConfig, +// ): Promise { +// console.log(`Generating TOC for ${outputDir}`); +// const toc = await generateSphinxToc( +// artifactPath, +// outputDir, +// pkg.title, +// docsBaseFolder, +// config.include, +// config.exclude, +// ); +// await writeFile( +// `${outputDir}/_toc.json`, +// JSON.stringify(toc, null, 2) + "\n", +// ); +// } From 3c74554a00cd1705dde7c6f06a7807302573e8fc Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Thu, 7 May 2026 14:00:01 -0400 Subject: [PATCH 013/104] structure notebook processing --- .../how-tos/bound-error-using-p-norm.ipynb | 2 +- .../how-tos/truncate-operator-terms.ipynb | 12 +- .../tutorials/01-getting-started.ipynb | 12 +- scripts/js/lib/Notebooks.ts | 16 +++ scripts/js/lib/sphinx/conversionPipeline.ts | 133 ++++++++++++------ 5 files changed, 121 insertions(+), 54 deletions(-) create mode 100644 scripts/js/lib/Notebooks.ts diff --git a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb index e4a042b4bf8..9a67e8d3c0b 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb @@ -21,7 +21,7 @@ "id": "2780c587-cd2a-4398-8faa-de5e0c661a10", "metadata": {}, "source": [ - "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.backpropagate.html) method based on a specified [TruncationErrorBudget](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.TruncationErrorBudget.html).\n", + "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget).\n", "In this guide, you will learn about its additional keyword argument `p_norm`, which can be used to change the Lp-norm used to estimate the error incurred by the truncated Pauli terms." ] }, diff --git a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb index ac3bc25a884..ad75b0b7a6d 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb @@ -13,13 +13,13 @@ "id": "c1453472-3506-4008-b432-f8ef9ccbf3ee", "metadata": {}, "source": [ - "This guide explains how to configure the Pauli term truncation mechanism provided by the [qiskit_addon_obp.utils.truncating](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.html) module.\n", + "This guide explains how to configure the Pauli term truncation mechanism provided by the [qiskit_addon_obp.utils.truncating](/docs/api/qiskit-addon-obp/utils-truncating#module-qiskit_addon_obp.utils.truncating) module.\n", "\n", "Operator backpropagation (OBP) can be used to reduce the depth of quantum circuits at the cost of a more complex observable. In order to get meaningful results from OBP, one usually needs to truncate terms from their observable to prevent it from growing too large. One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms' coefficients.\n", "\n", - "The [backpropagate](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.backpropagate.html) method takes an optional [TruncationErrorBudget](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.TruncationErrorBudget.html) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", + "The [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", "The amount of terms that are truncated depends on various configuration parameters specified by the user.\n", - "As of now, only a single truncation strategy is available -- the [truncate_binary_search](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.truncate_binary_search.html) method.\n", + "As of now, only a single truncation strategy is available -- the [truncate_binary_search](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search) method.\n", "Given an observable and some _budget_ it will perform a binary search over the Pauli terms and coefficients within that observable to find the optimal threshold,\n", "such that the sum of the truncated coefficients is maximal but lower than the budget.\n", "\n", @@ -32,7 +32,7 @@ "id": "aaf5f046-6d5e-4b6f-add7-b60604edef30", "metadata": {}, "source": [ - "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.TruncationErrorBudget.html) via the accompanying [setup_budget](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.setup_budget.html) function." + "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) via the accompanying [setup_budget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function." ] }, { @@ -193,7 +193,7 @@ "id": "4a772af6-edc6-4e53-81c7-53f3b72bf58a", "metadata": {}, "source": [ - "Let's use the [OBPMetadata](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.metadata.OBPMetadata.html) instance and the tools provided by the [visualization](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.visualization.html) module to visualize the backpropagation process.\n", + "Let's use the [OBPMetadata](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", "\n", "- **The top/left plot** shows that we have enough budget to begin truncating observable terms after backpropagating the third slice. Starting from the third slice, we know that we truncate at least one term from each slice we backpropagate because we are incurring some truncation error after each slice.\n", "- **The top/right plot** shows that the error budget ramps up to `.003` for the third slice. We see a sharp drop in remaining budget, which means terms were truncated from the observable. This is consistent with what we inferred from the top/left plot.\n", @@ -714,7 +714,7 @@ "source": [ "Since we passed a subset of our slices (i.e. ``slices[-num_slices:]``) to ``backpropagate``, we must combine the slices remaining after backpropagation with the slices that were never sent for backpropagation (i.e. ``slices[:-num_slices]``).\n", "\n", - "Once we have combined all of the remaining slices, we can use [combine_slices](https://qiskit.github.io/qiskit-addon-utils/stubs/qiskit_addon_utils.slicing.combine_slices.html#qiskit_addon_utils.slicing.combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became." + "Once we have combined all of the remaining slices, we can use [combine_slices](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became." ] }, { diff --git a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb index faf992cee3c..42f57e1a116 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb @@ -31,7 +31,7 @@ "- **Step 4: Reconstruct results**\n", " - N.A.\n", "\n", - "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.backpropagate.html) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." + "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." ] }, { @@ -44,7 +44,7 @@ "\n", "The [qiskit_addon_utils](/docs/addons/qiskit-addon-utils/) package provides some reusable functionalities for various purposes.\n", "\n", - "Its [qiskit_addon_utils.problem_generators](https://qiskit.github.io/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators.html) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", + "Its [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators#module-qiskit_addon_utils.problem_generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", "This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/docs/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows.\n", "\n", "In the following, we first generate a heavy-hex `CouplingMap` out of which we carve a linear chain of 10 qubits. Note, that the indices of this new `reduced_coupling_map` are again zero-based." @@ -159,7 +159,7 @@ "metadata": {}, "source": [ "From the qubit operator, we can generate a quantum circuit which models its time evolution.\n", - "Once again, the [qiskit_addon_utils.problem_generators](https://qiskit.github.io/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators.html) module comes to the resuce with a handy function do just that:" + "Once again, the [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators#module-qiskit_addon_utils.problem_generators) module comes to the resuce with a handy function do just that:" ] }, { @@ -200,7 +200,7 @@ "## Step 2: Optimize the problem\n", "### Create circuit slices to backpropagate\n", "\n", - "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](https://qiskit.github.io/qiskit-addon-utils/stubs/qiskit_addon_utils.slicing.slice_by_gate_types.html) function.\n", + "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.slice_by_gate_types) function.\n", "\n", "For a more detailed discussion on circuit slicing, check out this [how-to guide](/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices) of the [qiskit-addon-utils](/docs/addons/qiskit-addon-utils/index) package." ] @@ -233,7 +233,7 @@ "source": [ "### Constrain how large the operator may grow during backpropagation\n", "\n", - "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.simplify.OperatorBudget.html) instance.\n", + "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget) instance.\n", "\n", "Here we specify that backpropagation should stop when the number of qubit-wise commuting Pauli groups in the operator grows past 8." ] @@ -320,7 +320,7 @@ "id": "d25b9701-f1b1-4230-9dcc-554b85e91cf7", "metadata": {}, "source": [ - "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.truncating.setup_budget.html) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", + "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", "\n", "In order to enable this truncation, we need to setup our error budget like so:" ] diff --git a/scripts/js/lib/Notebooks.ts b/scripts/js/lib/Notebooks.ts new file mode 100644 index 00000000000..eed0fccf1ac --- /dev/null +++ b/scripts/js/lib/Notebooks.ts @@ -0,0 +1,16 @@ +export type NotebookCell = { + cell_type: "code" | "markdown" | "raw"; + source: string | string[]; + metadata: Record; + outputs?: unknown[]; + execution_count?: number | null; +}; + +export type Notebook = { + nbformat: number; + nbformat_minor: number; + metadata: Record; + cells: NotebookCell[]; +}; + +export type NotebookWithUrl = Notebook & { url: string }; diff --git a/scripts/js/lib/sphinx/conversionPipeline.ts b/scripts/js/lib/sphinx/conversionPipeline.ts index 452c87b41cc..1a1aa1fe8c6 100644 --- a/scripts/js/lib/sphinx/conversionPipeline.ts +++ b/scripts/js/lib/sphinx/conversionPipeline.ts @@ -28,7 +28,10 @@ import { } from "../api/normalizeResultUrls.js"; import { updateLinks, relativizeLink } from "../api/updateLinks.js"; import { mergeClassMembers } from "../api/mergeClassMembers.js"; -import { specialCaseResults, transformSpecialCaseUrl } from "../api/specialCaseResults.js"; +import { + specialCaseResults, + transformSpecialCaseUrl, +} from "../api/specialCaseResults.js"; import addFrontMatter from "../api/addFrontMatter.js"; import { dedupeHtmlIdsFromResults } from "../api/dedupeHtmlIds.js"; import removeMathBlocksIndentation from "../api/removeMathBlocksIndentation.js"; @@ -37,6 +40,7 @@ import { handleReleaseNotesFile, maybeUpdateReleaseNotesFolder, } from "../api/releaseNotes.js"; +import { Notebook, NotebookWithUrl } from "../Notebooks.js"; export const DOCS_BASE_PATH = "/docs"; @@ -58,17 +62,15 @@ export async function runSphinxPipeline( publicBaseFolder: string, pkg: Pkg, ): Promise { + const allObjectInvs = await ObjectsInv.loadPublishedApis(publicBaseFolder); const [files, outputPath, objectsInv] = await determineFilePaths( artifactPath, docsBaseFolder, pkg, ); - const allObjectInvs = await ObjectsInv.loadPublishedApis(publicBaseFolder); - + // handle HTML files const htmlFiles = files.filter((f) => f.endsWith(".html")); - const notebookFiles = files.filter((f) => f.endsWith(".ipynb")); - const initialResults = await convertFilesToMarkdown( pkg, artifactPath, @@ -76,19 +78,32 @@ export async function runSphinxPipeline( outputPath, htmlFiles, ); - const results = await postProcessResults( pkg, objectsInv, allObjectInvs, initialResults, ); - await writeMarkdownResults(pkg, docsBaseFolder, results); + + // handle jupyter notebook files + const notebookFiles = files.filter((f) => f.endsWith(".ipynb")); + const initialNotebooks = await readNotebooks( + artifactPath, + docsBaseFolder, + outputPath, + notebookFiles, + ); + const notebooks = await processNotebooks( + initialNotebooks, + pkg, + objectsInv, + allObjectInvs, + ); + await writeNotebooks(pkg, docsBaseFolder, notebooks); + await copyImages(pkg, artifactPath, "public", results); await objectsInv.write(pkg.apiOutputDir(publicBaseFolder)); - - await copyNotebooks(artifactPath, outputPath, notebookFiles, pkg); // await writeTocFile(artifactPath, output, pkg, docsBaseFolder, config); } @@ -147,7 +162,7 @@ async function convertFilesToMarkdown( } const { dir, name } = parse(`${outputPath}/${file}`); - let url = `/${relative(docsBaseFolder, dir)}/${name}`; + const url = `/${relative(docsBaseFolder, dir)}/${name}`; results.push({ ...result, url }); } return results; @@ -210,7 +225,7 @@ async function writeMarkdownResults( results: HtmlToMdResultWithUrl[], ): Promise { for (const result of results) { - let path = `${docsBaseFolder}${result.url}.mdx`; + const path = `${docsBaseFolder}${result.url}.mdx`; if (path.endsWith("release-notes.mdx")) { if (!pkg.releaseNotesConfig.enabled) continue; @@ -223,42 +238,78 @@ async function writeMarkdownResults( } } -async function copyNotebooks( +export async function readNotebooks( artifactPath: string, + docsBaseFolder: string, outputPath: string, - notebookFiles: string[], - pkg: Pkg, -): Promise { - for (const file of notebookFiles) { - // Apply the same kebab-case transformation as normalizeResultUrls so - // notebook filenames match the links generated from HTML conversion. - const destFile = pkg.kebabCaseAndShortenUrls - ? transformSpecialCaseUrl( - file.replace(/([^/]+)\.ipynb$/, (_, base) => - `${kebabCaseAndShortenPage(base, pkg.name)}.ipynb`, - ), - ) - : file; - const dest = `${outputPath}/${destFile}`; - await mkdirp(dirname(dest)); + filePaths: string[], +): Promise { + const results = []; + for (const file of filePaths) { const raw = await readFile(`${artifactPath}/${file}`, "utf-8"); const notebook = JSON.parse(raw); + const { dir, name } = parse(`${outputPath}/${file}`); + const url = `/${relative(docsBaseFolder, dir)}/${name}`; + results.push({ ...notebook, url }); + } + return results; +} - for (const cell of notebook.cells ?? []) { - const lines: string[] = Array.isArray(cell.source) - ? cell.source - : [cell.source]; - cell.source = lines.map((line) => - line.replace(/\[([^\]]*)\]\(([^)]+)\)/g, (match, text, url) => { - const rewritten = relativizeLink({ url, text }); - return rewritten - ? `[${rewritten.text ?? text}](${rewritten.url})` - : match; - }), - ); - } +function normalizeNotebookUrl(url: string, pkg: Pkg): string { + const parts = url.split("/"); + const filename = parts[parts.length - 1]; + const normalized = pkg.kebabCaseAndShortenUrls + ? kebabCaseAndShortenPage(filename, pkg.name) + : filename; + return transformSpecialCaseUrl([...parts.slice(0, -1), normalized].join("/")); +} - await writeFile(dest, JSON.stringify(notebook, null, 1)); +function rewriteNotebookLinks( + source: string | string[], + objectsInv: ObjectsInv, + allInvs: Map, +): string | string[] { + const rewrite = (line: string) => { + return line.replace(/\[([^\]]*)\]\(([^)]+)\)/g, (_match, text, url) => { + const relativized = relativizeLink({ url, text }); + if (relativized) url = relativized.url; + const stub = objectsInv.resolveStubUrl(url, allInvs); + if (stub) url = stub; + return `[${text}](${url})`; + }); + }; + + return Array.isArray(source) ? source.map(rewrite) : rewrite(source); +} + +async function processNotebooks( + notebooks: NotebookWithUrl[], + pkg: Pkg, + objectsInv: ObjectsInv, + allInvs: Map, +): Promise { + return notebooks.map((notebook) => ({ + ...notebook, + cells: notebook.cells.map((cell) => { + if (cell.cell_type !== "markdown") return cell; + return { + ...cell, + source: rewriteNotebookLinks(cell.source, objectsInv, allInvs), + }; + }), + })); +} + +async function writeNotebooks( + pkg: Pkg, + docsBaseFolder: string, + notebooks: NotebookWithUrl[], +) { + for (const { url, ...notebook } of notebooks) { + const normalizedUrl = normalizeNotebookUrl(url, pkg); + const path = `${docsBaseFolder}${normalizedUrl}.ipynb`; + await mkdirp(dirname(path)); + await writeFile(path, JSON.stringify(notebook, null, 1)); } } From 33985b9b443e9f88294db10234641eec93dcacb0 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Thu, 7 May 2026 15:27:16 -0400 Subject: [PATCH 014/104] gen toc --- docs/addons/qiskit-addon-obp/_toc.json | 55 +++++++++++++++++++++ scripts/js/lib/sphinx/conversionPipeline.ts | 7 +-- scripts/js/lib/sphinx/generateToc.ts | 53 ++++++++++++++++++-- 3 files changed, 108 insertions(+), 7 deletions(-) create mode 100644 docs/addons/qiskit-addon-obp/_toc.json diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json new file mode 100644 index 00000000000..e8a4e68f780 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -0,0 +1,55 @@ +{ + "title": "Operator backpropagation (OBP)", + "children": [ + { + "title": "Documentation Home", + "url": "/docs/addons/qiskit-addon-obp" + }, + { + "title": "Installation Instructions", + "url": "/docs/addons/qiskit-addon-obp/install" + }, + { + "title": "Tutorials", + "children": [ + { + "title": "Reducing depth of circuits with operator backpropagation", + "url": "/docs/addons/qiskit-addon-obp/tutorials/01_getting_started" + } + ], + "url": "/docs/addons/qiskit-addon-obp/tutorials/index" + }, + { + "title": "How-To Guides", + "children": [ + { + "title": "Using different Lp-norms for Pauli term truncation", + "url": "/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm" + }, + { + "title": "Classically simulating circuits with OBP", + "url": "/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp" + }, + { + "title": "Truncating Pauli terms during backpropagation", + "url": "/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms" + } + ], + "url": "/docs/addons/qiskit-addon-obp/how_tos/index" + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-obp" + }, + { + "title": "API reference", + "children": [ + { + "title": "Operator backpropagation (OBP) API reference", + "url": "/docs/api/qiskit-addon-obp" + } + ] + } + ], + "collapsed": true +} diff --git a/scripts/js/lib/sphinx/conversionPipeline.ts b/scripts/js/lib/sphinx/conversionPipeline.ts index 1a1aa1fe8c6..b3ed7ef19cc 100644 --- a/scripts/js/lib/sphinx/conversionPipeline.ts +++ b/scripts/js/lib/sphinx/conversionPipeline.ts @@ -35,7 +35,7 @@ import { import addFrontMatter from "../api/addFrontMatter.js"; import { dedupeHtmlIdsFromResults } from "../api/dedupeHtmlIds.js"; import removeMathBlocksIndentation from "../api/removeMathBlocksIndentation.js"; -import { generateSphinxToc } from "./generateToc.js"; +import { writeTocFile } from "./generateToc.js"; import { handleReleaseNotesFile, maybeUpdateReleaseNotesFolder, @@ -86,7 +86,7 @@ export async function runSphinxPipeline( ); await writeMarkdownResults(pkg, docsBaseFolder, results); - // handle jupyter notebook files + // handle Jupyter notebook files const notebookFiles = files.filter((f) => f.endsWith(".ipynb")); const initialNotebooks = await readNotebooks( artifactPath, @@ -102,9 +102,10 @@ export async function runSphinxPipeline( ); await writeNotebooks(pkg, docsBaseFolder, notebooks); + // write assets await copyImages(pkg, artifactPath, "public", results); await objectsInv.write(pkg.apiOutputDir(publicBaseFolder)); - // await writeTocFile(artifactPath, output, pkg, docsBaseFolder, config); + await writeTocFile(artifactPath, outputPath, pkg, docsBaseFolder); } async function determineFilePaths( diff --git a/scripts/js/lib/sphinx/generateToc.ts b/scripts/js/lib/sphinx/generateToc.ts index 1bc498d79a9..01bde63001e 100644 --- a/scripts/js/lib/sphinx/generateToc.ts +++ b/scripts/js/lib/sphinx/generateToc.ts @@ -10,12 +10,16 @@ // copyright notice, and modified files need to carry a notice indicating // that they have been altered from the originals. -import { readFile } from "fs/promises"; +import { readFile, writeFile } from "fs/promises"; +import { dirname } from "path"; import { load } from "cheerio"; import { globby } from "globby"; +import { mkdirp } from "mkdirp"; import { TocEntry } from "../api/generateToc.js"; +import { Pkg } from "../api/Pkg.js"; +import { DOCS_BASE_PATH } from "./conversionPipeline.js"; export type SphinxToc = { title: string; @@ -23,8 +27,9 @@ export type SphinxToc = { collapsed: boolean; }; -// Sphinx build artifact folders that are never prose content. +// Sphinx build artifact folders and API content that are never prose content. const SPHINX_INTERNAL_PREFIXES = ["_static/", "_sources/", "_downloads/"]; +const API_HREF_PREFIXES = ["apidocs/", "apidoc/", "stubs/", "release-notes", "release_notes"]; /** * Build a _toc.json by parsing the Sphinx sidebar nav, filtered to match @@ -33,7 +38,7 @@ const SPHINX_INTERNAL_PREFIXES = ["_static/", "_sources/", "_downloads/"]; export async function generateSphinxToc( artifactPath: string, outputDir: string, - pkgTitle: string, + pkg: Pkg, docsBaseFolder: string, include?: string[], exclude?: string[], @@ -62,6 +67,7 @@ export async function generateSphinxToc( if (l1Link.hasClass("reference external")) return; if (SPHINX_INTERNAL_PREFIXES.some((p) => href.startsWith(p))) return; + if (API_HREF_PREFIXES.some((p) => href.startsWith(p))) return; // Filter by whether this href was selected for this run. const hrefFile = href.split("#")[0]; @@ -101,7 +107,46 @@ export async function generateSphinxToc( } }); - return { title: pkgTitle, children, collapsed: true }; + if (pkg.githubSlug) { + children.push({ + title: "GitHub", + url: `https://github.com/${pkg.githubSlug}`, + }); + } + + children.push({ + title: "API reference", + children: [ + { + title: `${pkg.title} API reference`, + url: pkg.apiOutputDir(DOCS_BASE_PATH), + }, + ], + }); + + return { title: pkg.title, children, collapsed: true }; +} + +export async function writeTocFile( + artifactPath: string, + outputDir: string, + pkg: Pkg, + docsBaseFolder: string, + include?: string[], + exclude?: string[], +): Promise { + console.log(`Generating TOC for ${outputDir}`); + const toc = await generateSphinxToc( + artifactPath, + outputDir, + pkg, + docsBaseFolder, + include, + exclude, + ); + const tocPath = `${outputDir}/_toc.json`; + await mkdirp(dirname(tocPath)); + await writeFile(tocPath, JSON.stringify(toc, null, 2) + "\n"); } /** From 50126aeab0dd9b10d8e5f4b8bbf43d1d57fe1578 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Thu, 7 May 2026 15:31:39 -0400 Subject: [PATCH 015/104] run fix --- .../how-tos/bound-error-using-p-norm.ipynb | 15 ++++---- .../simulating-circuits-with-obp.ipynb | 12 +++---- .../how-tos/truncate-operator-terms.ipynb | 32 +++++++----------- .../tutorials/01-getting-started.ipynb | 18 ++++------ ...6f281d4-706e-41ad-b7b5-bc7ebe106996-0.avif | Bin 0 -> 11411 bytes ...e7f454c-7d79-4c61-b1dd-6a98564fb5f1-0.avif | Bin 0 -> 6961 bytes ...f72e77f-e2c0-4cce-8575-c5aa587f78fb-0.avif | Bin 0 -> 7140 bytes ...758f393-8691-4116-94ca-1185c6188bb4-0.avif | Bin 0 -> 11411 bytes ...adc314d-452f-465c-a382-d54685ce3f2a-0.avif | Bin 0 -> 32495 bytes ...97fc3b0-d3b4-432d-96cd-7e175b7e6a52-0.avif | Bin 0 -> 11411 bytes ...c36768e-03e7-4e1b-81b3-59b6e9eab43e-0.avif | Bin 0 -> 15073 bytes ...1d36d7e-0bd5-4388-bbb8-6831bf1f02e6-0.avif | Bin 0 -> 12043 bytes ...8f118df-6718-49b2-ba80-c37ef461f71a-0.avif | Bin 0 -> 13855 bytes ...9c9069a-9038-4319-8dc6-37a1be973ebf-0.avif | Bin 0 -> 12758 bytes ...2271b0d-ddaf-4ad2-b5c6-e9e4c2a4cff7-0.avif | Bin 0 -> 16712 bytes ...0bf5050-2928-41a1-9c3d-c9d79e918f6f-0.avif | Bin 0 -> 15126 bytes ...7167f80-5d29-4c32-be97-cba733f18b1d-0.avif | Bin 0 -> 13213 bytes ...d68f197-ffa4-49de-9fe8-243b1facbd00-0.avif | Bin 0 -> 8000 bytes ...e8bae1a-ef18-4eb0-9d2a-1ac7bbdced3b-1.avif | Bin 0 -> 5491 bytes ...5ec9cb1-a4ed-497b-a616-180e9659956f-1.avif | Bin 0 -> 7085 bytes ...79b8484-7de0-47cb-be7b-147b502ad1e5-0.avif | Bin 0 -> 5016 bytes 21 files changed, 30 insertions(+), 47 deletions(-) create mode 100644 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a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb @@ -43,9 +43,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "

    " + "\"Output" ] }, "execution_count": 1, @@ -259,9 +258,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
    " + "\"Output" ] }, "metadata": {}, @@ -397,9 +395,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
    " + "\"Output" ] }, "metadata": {}, @@ -428,7 +425,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -442,9 +439,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb b/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb index e3bc825c00c..01a2a8eb3d4 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb @@ -102,9 +102,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
    " + "\"Output" ] }, "execution_count": 3, @@ -450,9 +449,8 @@ "outputs": [ { "data": { - "image/png": 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Voj+9qj0P21cVX22KeoktYjEb3j/DZKlrnKGTWcqmDvtAeKz1jDN0A2vZiD0gWJa6xlkgmaVs6rAPhMdazzgLNLCWTWsxT7ONeVr68d4ZJktd4wydzFI2ddgHwmOtZ5yhG1jLRuwBwbLUNc4CySxlU4d9IDzWesZZoIG1bHZHaDO14LMxPk/z8qLhZ555RkcffbQGDBjQ6P3du3dXQUGBJOmyyy7TX//6V23cuFEdOnTQ97//fd11113Kzc1t1s+qrq5WcXFxStcfiq1btzbrMaNHj9aUKVPUvn37XT521apVKVydVFVV3azH7VnYT92+cYD+fd+fU/rz46qqqjrlWVrt2s461u3IA7TkqZlJt62evUCHXHaa2u3dRaVr1qfk57dGqvOxmk2d5uwD7AGpFac9QC14r7EklRlZzkacBSITp32As4DdbOpwFnAvTnuAOAuYzqYxu5vXhw9O1Sl/vU0FRQereM5CdT6gt3qfdKSmjRink/96mz798z+0ZcXaZq0j3a81btmokXwkSYmEahO1LVpH3LPhDB2NOL1/coa2m00d9gH34rQHiDO06WzEHhCZOO0DnAXsZlOHfcC9OO0B4ixgOpvGWJmnycHcJm7ZKEXzNJEN750RidP7J2dou9nUYR9wL057gDhDm85G7AGRidM+wFnAbjZ12Afci9MeIM4CprNpTDpmNlb/GzWycTtPKygoUHZ2yy4D9u6i4eLiYq1evVpjxozZ4b5EIqEPPvhAgwYNqr/tiiuu0N1336327dvryy+/1Pe//33dfvvtuvHGG5v983r16pXS1xCKsWPHNvmYyspKlZaWasqUKU1eqP3II4+kcHXSrV1PUI+cjo3el9OhnUbPf1iZOdnKyMzUgnuf1ceP8je2pMLixYs1OsW/M1a7trOO5XXrrLJ1JUm3la3d8N/79oj0X8RSnY/VbOrsLCP2gPSJ0x6gJt5rrEplRpazEWeByMRpH+AsYDebOpwF3IvTHiDOAqazaUxz83rz/37X6O3r3lukSXufWf910YRLNHf8JJUWr9f8u57RkNsu0szz72jy+6fj92R7oWajFuTTGmTDGToqcXr/5AxtN5s67APuxWkPEGdo09mIPSAycdoHOAvYzaYO+4B7cdoDxFnAdDaNsTJPk4O5TajZKM3zNJEN750RidP7J2dou9nUYR9wL057gDhDm85G7AGRidM+wFnAbjZ12Afci9MeIM4CprNpTDpmNlb/GzWyab3WZLNy5Ur17NmzRc/x7qLhuivRMzIydrjvxRdf1Nq1a1VYWFh/20EHHVT/z7W1tcrMzNSSJUscrRZWVW0p09Tjr1V2Xht1+8b+GnzdOSorXq8lT8+KemkAHGAPAMA+AMQbewCA3dX/3ONV/uVGrZo5T5L06bOvq//Zx6n3yUO04qV3ol4ekBa8fwJgHwDijT0AAPsAgN3BPA1xxHsnAPYBIN7YAwCwDwDYXczUsDsyamtrW/YZyBGrrKxUu3btNGjQIM2dO7f+9uXLl2vYsGFavXq1nn76aZ111ln1991555269dZbtXXrVnXt2lUvv/yyvvGNbzTr51VXV6u4uDgtr8V3S5cubfIxW7du1amnnqpp06Y1+VHi/fr1S+HqpDmj79TWZc3LbuCV39NBY0/W5MMuTuka4qj9PgUqmjIupd/Tatd21rEz5z6kJU/N1Pu/ea7+toJhh+ik527UlMGXRPq3N6U6H6vZ1GnuPsAekDpx2gPUzI6d+e6DWvzUTP37vj+n7OfujlRmZDkbcRaITJz2Ac4CdrOpw1nAvTjtAeIsYDqbxrTkbJBO6fg92R7ZtA7ZcIaOSpzePzlD282mDvuAe3HaA8QZ2nQ2Yg+ITJz2Ac4CdrOpwz7gXpz2AHEWMJ1NY6zMbORgbkM2rRf3bHjvjEac3j85Q9vNpg77gHtx2gPEGdp0NmIPiEyc9gHOAnazqcM+4F6c9gBxFjCdTWOY2SQjm8a1JpuCggJlZ7fss4O9+6Th3Nxc/e///q8ee+wxnXbaaTrllFO0cuVKTZw4Ud27d9fq1auTPmlYksaNG6dx48bp448/1pNPPqm999672T8vOzu7xR/fHBdr1qxp8jFt27bV008/ra5duyorK2uXj031n3NOTvPrnZGZoaw2u/6oczRPTk7qf2esdm1nHVv77if6n2MKk/5FrMexhdqycm2k/xKmNORjNZs6zd0H2ANSJ057gFr4XmNFKjOynI04C0QmTvsAZwG72dThLOBenPYAcRYwnU1jrOSVjt+T7ZFN65ANZ+ioxOn9kzO03WzqsA+4F6c9QIbe91siTmdo9oBoxGkf4CxgN5s67APuxWkPEGcB09k0xlJe6Z7bkE3rxT0b3jujEaf3T87QdrOpwz7gXpz2ABl732+uOJ2h2QOiEad9gLOA3WzqsA+4F6c9QJwFTGfTGEt5xX1ms704ZVPHzitugfvvv185OTl68cUXNWvWLBUVFemFF17QzTffrKVLl2rAgAGNPu/AAw/UYYcdpvPPP1+zZ892vu44yszMVPfu3ZWZmRn1UuoVXjNaX7zzsTYv/0KZOdnqPvQgHXL5KC2dTCd8ZqlrC/8wTaf89TYNGne2Pnvude05qL8OvHCE5t74x6iXFglL2Yg9IFjWepbdrq067lMgScrMyVbeXp3V5eC+qtpars2f2/gbalyxlo3YB4JlqWucBZJZykbsAcGy1jPOAg2sZYMGZGOXxWx4/wyTpa5xhk5mKZs67APhsdYzztANrGUj9oBgWeoaZ4FklrKpwz4QHms94yzQwFo2aEA2dlnMhvfOMFnqGmfoZJayqcM+EB5rPeMM3cBaNmIPCJalrnEWSGYpmzrsA+Gx1jPOAg2sZYMGZBMtLy8azs/P18MPP6yHH3446fYPP/xQAwcO3GWZqqqqtHjxYgerhP77UeLHHXecZs2apfz8/KiXI0nK6dBORRMuUbuCLqqpqNLmFV9o3u1PadGfXol6adgNlrr21fufatYP79Lg687RIT8eqbJ1JZo34enYdsxSNmIPCJa1nu152H466fmb6r8+8MIROvDCESr+50JNP2N8pGtzzVo2Yh8IlqWucRZIZikbsQcEy1rPOAs0sJYNGpCNXRaz4f0zTJa6xhk6maVs6rAPhMdazzhDN7CWjdgDgmWpa5wFklnKpg77QHis9YyzQANr2aAB2dhlMRveO8NkqWucoZNZyqYO+0B4rPWMM3QDa9mIPSBYlrrGWSCZpWzqsA+Ex1rPOAs0sJYNGpBNtLy8aLgxJSUlWrVqlU455ZT62zZu3KgXXnhBo0aNUqdOnfTBBx/o1ltv1Xe+851I14pozR0/SXPHT4p6GQjcqpnztGrmvKiXgUawB8CF4jkLNWnvM6NeBnaCfQAucBawiz0ALnAWABAa3j/hAmdo29gHkG6coW1jD4ALnAVsYx9AunEWABAa3jvhAmdo29gHkG6coW1jD4ALnAVsYx9AunEWANCUYC4a/uCDDyRJhYWF9bdlZGToiSee0M9+9jNVVlaqW7duOv3003XTTTft4jsBAAAAAAAgdP3PGa7+Zx2n2tqE5vxioko+WVF/X37vbhp272XKzMnWipff1cLfT1XnAT1VdPePVJuoVW11jd66+iFtWbFWkjTwilHa++hDlZmdpXkTntaX85fqxGd+Wf/99jpigCYfOlaVG7dG8lp9tqucCq8erX5nHauNS1bp1XNu2+VzDvvpmdr7qIGSpA77FOjD372ojx99KZLXBAAAAAAA4CPmaX5gngYAAAAAAGAD8zQ/hDpPC/qi4Y4dO2rGjBkRrgoAAAAAAADW5HbO1/4/OFF/O+V6dejTXUV3Xqy/f7/hL5k74obzNe+Op7TuX4t10vM3afnf3lb5V5s047w7VLW5VD2OLdRhPz1Tb/30QfU4bpCy8trolTE3J/2M6WeMlyR1ObivDr/hPAayrdBUTosef0VLn31NRXde3ORz3v/Nc3r/N89Jkr77yl1a/re3I3lNAAAAAAAAPmKe5gfmaQAAAAAAADYwT/NDyPO0zEh/egpddtllqq2t1dChQ6NeCrbRvn17zZo1S+3bt496KQgcXbOLbOACPbOLbOAKXbOLbOACPbPLajZ7Deqn4n8uVG11jTZ9+h+16dJRysiov79T/x5a96/FkqRVM+ap+9ADVf7VJlVtLpUkJapqVFuTkCT1/W6Rstu10YlTxuuo+y5Xdvu2ST9r39OP1mfPv+H09TWH1Wy21VROZWtLpERti57TeUBPVW7cqtLi9U5fS0v4kA3CQNfsIhu4QM/sIhu4QtfsIhu4QM/sspoN8zS72WyLeZrdbBAGumYX2cAFemYX2cAVumYX2cAFemaX1WyYp9nNZlshz9OCuWgYNiUSCX3xxRdKJBJRLwWBo2t2kQ1coGd2kQ1coWt2kQ1coGd2Wc0mt3N+0t+sWLWlTLkd29V/nZHZMMSr2LhVbfboUP91VttcFV47Wh898pIkqV1BF9VW1eiV0Tdp/cLPdciPRzb8oIwM9T7pSC1/6Z30v6gWsprNtprKqTXP2feMb+mzF95M04pTw4dsEAa6ZhfZwAV6ZhfZwBW6ZhfZwAV6ZpfVbJin2c1mW8zT7GaDMNA1u8gGLtAzu8gGrtA1u8gGLtAzu6xmwzzNbjbbCnmexkXDSKuysjKdffbZKisri3opCBxds4ts4AI9s4ts4Apds4ts4AI9s8tqNpUbtyq3Y8PfYJiTn6fKTaX1X9du85cD5nZsp4oNmyVJGVmZ+taDV2nhQ1NV8skKSVLFhi1aPXuBJGn17Pna46A+9c8tKDpIX324TNVby128rBaxms22msqpNc/pc/IQLZ82J00rTg0fskEY6JpdZAMX6JldZANX6JpdZAMX6JldVrNhnmY3m20xT7ObDcJA1+wiG7hAz+wiG7hC1+wiG7hAz+yymg3zNLvZbCvkeVp21AsAgLg76r7L1W/MsZKkRE2Nyr4o0Zq3PtS825+M/OPoAQC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"text/plain": [ - "
    " + "\"Output" ] }, "execution_count": 10, @@ -517,7 +515,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -531,9 +529,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.12" + "version": "3" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb index ad75b0b7a6d..bb7ceacd823 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb @@ -53,9 +53,8 @@ "outputs": [ { "data": { - "image/png": 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UuGBrWftuSEb+Ac+oOfPWr6Gq4jKERN4BAAhrH4ny/CIAQLcxDyP7w690O6s3fH3KsZkcm8mxmTbcn3JGzBp3Z/PDZtqwmxybyXF3yvHPnr5R+j2TyfxG71+D6koX3K5q2Bx2HF/7CU5v2mH0sciCOGs3O/3BF3jwpacBjwfH129Fv6VTsP+VVLQIbonExcm4fC4f7qvVeCT1RZR+fwH9l07BN2+k4fSmHXhwwQQAwJm0nQCAu8cONvwPeHprrF/CnFH4WZ97kLhoMg4seg+Xz+YjcXEy3FVX4bxYAACIjO+CIys/NPh3QGbGaxrphbN2M+5P7bg7yUi8npFeOGs34+68PWbbn7yZTAG3c0oKSs/kok2PThi+PQUXdhxGxU/FRh+LLIizVl95XhF2z1ld9+P9r6QCAA4tf7/er9s6dMFNH7tn7pp6P85akR6AE6qtsX5H39qMo29trnv8+NotN33szmffCPwByfJ4TSO9cNbq4/7UjruTjMbrGemFs1Yfd+ftMdv+5NtcWND69euNPoJXJafOw1XqRFhMFNoPiEfioskAgKgHuuGh3z/n9TE9qdpNZao2u3HWBr89D+GdogEA3SckoWfyMK+P6UXVZipjM23YTU7VZirvT1WbqUzlZtyf1sJmcmwmp2ozlXcnoG43lanajLvTWtjMN7yZbEEZGRlGH8Gr6H49UVVShqLvziE/8zgi7mqP0A5ReOCF0TiyKsPrY3pStZvKVG1246xlvZmOXvPGwuawo+uoQTi18TOvj+lF1WYqYzNt2E1O1WYq709Vm6lM5Wbcn9bCZnJsJqdqM5V3J6BuN5Wp2oy701rYzDe8mWxB27dvN/oI9TySOh8jv34LwzYvRtYbaXC7qgEAWSvTkZQ6H6XfX6j76yDeHtOLat3MQLVm3mat5OSP8Ljd6L/sWXy/8XN4qmu8PqYXPZo5wlvBHh4S8Oexh4fAEd4q4M9jpWaAtbpZjWrNzLA/VWtmBio24/6sxf0px2akWjMz7E5AvW5moFoz7s5a3APamL0b3zOZAu7aewnd9eQA/GLlDORnfofKglKUnDqPsNh2yPl0f92v9fYYka8am7WsFel4dONCZL64ru7XenvMKoLbRmDM/j/DVVYR0OdxhLdCcNuIgD6HXvRqBlirGwUW9yfphfuzFvenHJuRarg7SS/cnbW4B7QxezfeTLag0aNHG30Er3K2ZiJuxEAkzB6J/a++i56Th+LUhu2In/okvpqxCgC8PqYXVbupTNVmDWfNeaEA5bkF9X6Nt8f0oFez4LYRllm2bKaNqq9PlanaTOX9qWozlancjPvTWruAzeRUfn2qStVmKu9OQN1uKlO1GXcn94BWZu7Gt7mwoIceesjoIzTq0PL3cfe4RxDaIQodH+uLrDfTcdVZgcj742APC7npMT2p3E1VKje7NmutotsYfZR6VG6mKjbTht3kVG6m6v5UuZmqVG/G/WkdbCbHZnIqN1N1dwJqd1OVys24O62DzXzDm8kWNG/ePKOPUCej3/MoPZNb9+Mr5/Kxqcdk3DPhlzjx122Ax4MjqzLQa+5Y/Hz6iJse05NK3cxCpWaNzVrFpRLjDuWFSs3Mgs20YTc5lZqZZX+q1MwsVGvG/WldbCbHZnIqNTPL7gTU6mYWKjXj7rQuNvMN3+aCDJG1Ir3ufzsvFuCLZ16v9/PeHiO6HZ89vcynx4iIVMb9SXrj/iQis+PuJL1xd5LV8TuTLahr165GH8GU2E2OzeTYTI7NtGE3OTaTYzM5NtOG3eTYTI7N5NhMG3aTYzM5NpNjM9/wZrIF/fDDD0YfwZTYTY7N5NhMjs20YTc5NpNjMzk204bd5NhMjs3k2EwbdpNjMzk2k2Mz3/BmMhERERERERERERE1iTeTLWjIkCFGH8GU2E2OzeTYTI7NtGE3OTaTYzM5NtOG3eTYTI7N5NhMG3aTYzM5NpNjM9/wZrIFjR492ugjmBK7ybGZHJvJsZk27CbHZnJsJsdm2rCbHJvJsZkcm2nDbnJsJsdmcmzmG7vRByD/mzp1KtLS0gx7/tCYSPR5+VcAgGNrtqD4RA4AoM/CibC3csCZW4hjaz5Gm3s6osekIfB4PDj2p4/hulKOXr8ZixYOO85tzURN5VV0n5AEe2gwbHY7vpqxKqDnNrqbGRnZzNc567NwIhytQxFks2HvvLWwh4VwzkyGzbRhNzkzXNO4O83P6Gbcn80Hm8mxmZzRzbg/mw8zfJ3G3Wl+bOabZnszuar4ClxlFYaewRHeCsFtIww9QyB0H5+Eb1LSUPGvEiS+Ngn//O3bCOvYDjVVLhxathEDUqbC1tKOe599HFWlZQBq///RI3ko4PHA43bDebEQZecvoSDrDO4eOxiVxVcM/l1px1kLDF/n7NCyjQCAxMXJcLQJx93jBltyzhrSY+6sNld6vVbN3o3XtMDg7qyPcxY43J+3xv0px2ZN4zUtcLg/6+OsBQZ3561xD2hj5m7N8mZyVfEVfNhvBqrLKg09hz08BGP2/9lyL4jQmEg4cwvgqXGjRbCj9rE7I+HMLQQAVBZdQXDbcETGd8HfRy9CdN8e6DpqEFp37YAz6V+i+OSPSHxtEjLnrwcAxCb1xq6Zbxn2+7kdnLXA8XXOKi6VILxzNIJsNrhKyiw5Zw3pNXdWmis9X6tm7sZrWuBwd17HOQss7s/GcX/KsVnTeE0LLO7P6zhrgcPd2TjuAW3M3q1Zvmeyq6zC8AssAFSXVQbkv0IY/S355XlFCIuJQovglqipctU+9lPtYwAQ0jYCVcVluJzzE2oqXagqLUPL8FZw5hfBddmJmgoXbC1r/ztHeKdoOPMK4alxB/zcgejGWQscX+csLLYdEuY8hYNLNwCAJeesIb3mLlBz1ZCVmgHm7sZrWuBwd17HOQss7s/GcX/KsVnTeE0LLO7P6zhrgcPd2TjuAW3M3q1Z3ky2upUrVxr6/Kc/+AK9XhyHAa9Pxan/9xn6LZ0C54UCtAhuicTFybh8Lh/uq9U4vWkHBqRMxX3PPYkfPtqN05t24Ocz/gMD35yOM2k7AQB3jx2M7A+/0uXcRnczIyOb+Tpnj6S+iBYOO/ovnYKQdq05ZybEZtqwm5wZrmncneZndDPuz+aDzeTYTM7oZtyfzYcZvk7j7jQ/NvNNs3ybC6vbt2+foc9fnleE3XNW1/14/yupAIBDy9+v9+vy9xxD/p5j9R7bM3dNvR9nrUgP0ClvZnQ3MzKyma9ztnXogps+lnNmLmymDbvJmeGaxt1pfkY34/5sPthMjs3kjG7G/dl8mOHrNO5O82Mz3/A7k4mIiIiIiIiIiIioSfzOZAt65plnjD6CKbGbHJvJqdBs9P41qK50we2qhs1hx/G1n+D0ph1GH6tRKjQD2K05YDM5NpNjM21U6MY9IMdm1sdm2rCbHJvJqdCMe0Ab1bvxZrIFderUyegjmBK7ybGZnCrNdk5JQemZXLTp0QnDt6fgwo7DqPip2OhjeaVKM4DdrI7N5NhMjs20UaUb94Acm1kbm2nDbnJsJqdKM+4BbVTuxre5sKAlS5YYfQRTYjc5NpNTrVnJqfNwlToRFhOF9gPikbhoMgAg6oFueOj3zxl8ulqqNQPqdxv89jyEd4oGAHSfkISeycMMPl0tFbupjs3k2EyOzbRRrRv3pxx3pzWxmTbsJsdmcqo14+7URsX9qeTN5MOHD6Nfv34ICQlBYmIiNmzYgNDQULjdbqOPRkRkKdH9eqKqpAxF351DfuZxRNzVHqEdovDAC6NxZFWG0cdT1o3dst5MR695Y2Fz2NF11CCc2viZ0ccjIqIA4/6U4+4kImreuDu1UXF/Kvc2F1lZWXj44YexZMkSpKWlYdu2bZg5cybi4+NhswX23ndEXHsM+uMsBLeNQFXxFXw9+79w5Wx+QJ8zEGJjY3V5nj4LJ6LLiAGI6HwnPho0G6VncnV53kDRq5tV5gzgrGmhV7OmPJI6H0FBQYiIa49dM/4At6saAJC1Mh1JqfORt+uoMn+FRpVmgPduJSd/hMftRv9lz+L7jZ/DU11j9DEB8JqmBa9pcnq+Pq0ya5wzbVTZBdyfctyd3vGaJsNrmjZWmTOAs6aFynsA4O5sisr7U7nvTJ41axamT5+OF154AXFxcZg5cyY6dOiAhISEgD/3wJRpOPH2Nnz0b7Nx8t2/Y2DKtIA/ZyCsXLlSl+f58R8H8OnIV1F2/pIuzxdoenWzypwBnDUt9GrWlJ1TUvDRoDn4avofMHDFDIS0aw2g9q/QhMW2Q86n+w0+4XWqNAMa75a1Ih3RiT1x9uM9Bp/wOl7T5HhNk9Pz9WmVWeOcaaPKLuD+lOPu9I7XNBle07SxypwBnDUtVN8D3J23pvL+VOpmcnZ2Nnbv3o1Zs2bVe9zhcNTdTP6P//gPxMTEYPz48X597pCoO9D2vi44u2UvAODsR3sQGd8FwVF3+PV59DB37lxdnudfB0+hPLdQl+fSgx7drDRnAGdNC72a+SpnayZyvzyChNkjAQA9Jw/FqQ3bET/1SYNPdp1qzYCbuzkvFKA8t8DgU9XHa5ocr2lyejWz0qxxzrRRbRdwf8pxd17Ha5ocr2lyVpozgLOmhep7gLvTNyruT6Xe5iIrKwtt2rRB586d6x6rqKjADz/8UHczefXq1cjOzsbatWt9/rwjRoxAdnZ23Y/vqGmJabi73q8Ji22H8rxCwOMBAHjcbjjzihDWIQpVhZdv57d1S0MeG4LLLa6KP+6JJ55o9OdOnDiBDz/8sNGfT05OFj+ft2ZGCUQz4NbdtDQDbu5m1JwB2rqp0MxIZnl9NiRteGj5+xj+j9dxbO0n6PhYX2wftwQD35iGyPvjUPTt2Vt+rNZGDZmtGXC927er/xsVl0pEH+uPbiq8PnlNaxqvacni5+PXacni51NpzgDzvD4bMtv+NHMzo3YnoMbrk9e0pql0XTPLNY1fp8mZfc4A8/05yujdCRjfDFBrf3br1g1btmwRfU6lvjM5KCgINTU19f6hvXXr1qG8vLzuZnKnTp2MOh4RkSVk9Hu+3nt/XTmXj009JuOeCb/Eib9uAzweHFmVgV5zxxp4SvU01k26zImIyJy4P+W4O4mImjfuTm1U359KfWdy3759UVFRgaVLl2LSpEn4/PPPsXz5csTExCAqKkrz5214h/3K+Uv4W7/n6z3mvFiA0JgoICgI8HgQZLMhLCYSzgD/9Ybtn21HRKdo8ccdOHCg0Z+77777EB8f3+jPp6SkiJ/PWzOjBKIZcOtuWpoBN3czas4Abd1UaGYks7w+G9LaMGtFet3/dl4swBfPvN7kx2ht1JBZm93os6eX+fxr/dFNhdcnr2lN4zXNP19z8Ou0W1NpzgDzvD4bMtv+NHOza/TenYAar09e05qm0nXNLNc0fp0mZ/Y5A8z756jm/GdPwJz780ZKfWdy586dsXr1aqxduxa9e/fGwYMHMX78eF3+8b3KwssoOfkj4kYMBADEjfwFir7LCfhf/wiE8+fPG30EU9Kjm5XmDOCsacFmcmymDa9pcpw1Ob2aWWnWOGfasJscm8nxmibHOdOGX6fJcdbk2EyOzXyj1M1kAJg2bRry8vJQUlKCdevW4ezZs7rcTAaAvfPX4b7nnsDI3X/EvVMeR+aC9bo8r7+98847ujxP4qLJGHNoHUJjojDsb4sxfLt//guNUfTqZpU5AzhrWujVzErYTBte0+R4TZPT8/VplVnjnGnDXSDHZnK8psnxmqYNv06T46zJcQ/IsZlvlHqbC2+OHTuGcePG1f149uzZ2LNnDy5evIhHH30U69atQ7du3fzyXJezc/E/T77sl8/VHBxY9B4OLHrP6GOYDudMjrNGpC5e0+R4TdOGsybDOSNSG69pMrymacM5k+OsETVN6ZvJTqcTOTk59b4z+Y9//KOBJzKH/v37G30EU2I3OTaT07NZaEwk+rz8KwDAsTVbUHwiBwDQZ+FE2Fs54MwtxLE1H6PPwolwtA5FkM2GvfPWwh4Wgl6/GYsWDjvObc1ETeVVdJ+QBHtoMGx2O76asUq33wNg3Jw11i9xcTJ+9mB3bBu+EAAQP2MEwmPbobrChUPLNuLeXz+ONt07IvL+OGS9kYaLO7MMOT9fn3JsJsdmcmymDfennBGzxt3Z/LCZNuwmx2Zy3J1y/LOnb5R7m4sbhYWFwe126/Y2F1Yxd+5co49gSuwmx2ZyejbrPj4J36SkYe+L69Bj0mMAgLCO7VBT5cK+36UiosudsLW049Cyjcicvx5XnZVwtAnHPRMfBTweeNxuOC8WoiDrDDIXrEfe198ie/Mu3c5/jVFz5q0fABx47V1czq79l3VtLe24I6499v0uFTVVLoTFtsOJt7chc8F6lOVcQu6uo4acHeDrUws2k2MzOTbThvtTzohZ4+5sfthMG3aTYzM57k45/tnTN0rfTCZtbnxbEPIdu8mxmZyezUJjIuHMLUBNpQstgh21j915/V9wriy6guC24QCA8M7RCLLZ4CopQ+uuHZCzbR8Ov/4B7p/1n3WfLzapNy5+8Y1u57/GqDnz1q+h4LbhqPzff8TEmVeE0PaRAIDwTtFw5hXCU+PW7bwN8fUpx2ZybCbHZtpwf8oZMWvcnc0Pm2nDbnJsJsfdKcc/e/qGN5OJiCyqPK8IYTFRaBHcEjVVrtrHfqp9DABC2kagqrgMYbHtkDDnKRxcugEA4MwvguuyEzUVLtha1r4bkgp/wNObt34NVRWXISTyDgBAWPtIlOcXAQC6jXkY2R9+pdtZiYjIf7g/tePuJCJqnrg7b4/Z9qfS75msmtH716C60gW3qxo2hx3H136C05t2GH0ssiDOGvnD6Q++wIMvPQ14PDi+fiv6LZ2C/a+kokVwSyQuTsblc/lwX63GI6kvovT7C+i/dAq+eSMNpzftwIMLJgAAzqTtBADcPXZws/sDXmP9EuaMws/63IPERZNxYNF7uHw2H4mLk+GuugrnxQIAQGR8FxxZ+aHBvwN18JpGeuGskT9wf2rH3ek/vJ6RXjhr5A/cnbfHbPuTN5OFdk5JQemZXLTp0QnDt6fgwo7DqPip2Ohj1bN+/Xqjj2BKqnXjrFmTns3K84qwe87quh/vfyUVAHBo+fv1ft3WoQtu+tg9c9fU+3HWivQAnNA3Rs1ZY/2OvrUZR9/aXPf48bVbbvrYnc++EfgDNkG11yevadakYjPVZ03FZmbA/SlnxKxxd/qX6tczQL1mZqFaN86aNXF3yvHPnr7h21xoVHLqPFylToTFRKH9gHgkLpoMAIh6oBse+v1zXh/TS0ZGhm7PZSWqdrtx1ga/PQ/hnaIBAN0nJKFn8jCvj+lF1WYqYzM5NtNG1W7cn9aicjNV96fKzVTGbnJsJqdqM+5O61G1m6q7E1C3mcrYTI7NfMObyRpF9+uJqpIyFH13DvmZxxFxV3uEdojCAy+MxpFVGV4f08v27dv9/jkd4a1gDw/x++eVsoeHwBHeKiCfOxDd/OHGWct6Mx295o2FzWFH11GDcGrjZ14f0wtnTU6POdOrYSBfjzeyUjPAWt204P40hpmvaVqpuj+tPGeAuWeN+1OOzfTR3HYnoM51zczXNC1U3Z2AtfenmeeMe0Abs3fj21wIPZI6H0FBQYiIa49dM/4At6saAJC1Mh1JqfORt+to3V8H8faYWQW3jcCY/X+Gq6zC0HM4wlshuG2EoWfQi7dZKzn5IzxuN/ovexbfb/wcnuoar4+ZGWft9unV0MyNGtJz7qzUTYL7k9c0vTTH/anKnAHmnjXuTzk2C6zmujsBda5rzWX2muPuBDhn/sA9oI3Zu/FmstC19xK668kB+MXKGcjP/A6VBaUoOXUeYbHtkPPp/rpf6+0xPYwePTognze4bYSlXrwNBaqbVo3NWtaKdDy6cSEyX1xX92u9PaYHzpqcXnNmpYZspo1Zrmncn+am2pwB6u9Pzpk23AVybCan2jWtOe9OwFqz1ZBZZk2V3Qlwf2rBPSCn52vTzN34Nhca5WzNRO6XR5AweyQAoOfkoTi1YTvipz5Z92u8PaaHhx56SNfnswpVuzWcNeeFApTnFtT7Nd4e04OqzVTGZnJspo2q3bg/rUXlZqruT5WbqYzd5NhMTtVm3J3Wo2o3VXcnoG4zlbGZHJv5hjeTb8Oh5e/j7nGPILRDFDo+1hdZb6bjqrMCkffHwR4WctNjepk3b55uz2UlKne7NmutotsYfZR6VG6mKjaTYzNtVO7G/WkdqjdTcX+q3kxV7CbHZnIqN+PutBaVu6m4OwG1m6mKzeTYzDe8mSyQ0e95lJ7JrfvxlXP52NRjMu6Z8Euc+Os2wOPBkVUZ6DV3LH4+fcRNjxH5qrFZq7hUYtyhiIg04v4kvXB/EpFVcHeSXrg7iUiK75nsB1kr0uv+t/NiAb545vV6P+/tsUCKiDDne64YzUzdPnt6mU+PBZqZmqmCzeTYTBszdOP+ND+zNVNhf5qtmSrYTY7N5MzQjLvTGszUTYXdCZirmSrYTI7NfMPvTLagoUOHGn0EU2I3OTaTYzM5NtOG3eTYTI7N5NhMG3aTYzM5NpNjM23YTY7N5NhMjs18w5vJFpSRkWH0EUyJ3eTYTI7N5NhMG3aTYzM5NpNjM23YTY7N5NhMjs20YTc5NpNjMzk28w1vJhMRERERERERERFRk3gz2YKGDBli9BFMid3k2EyOzeTYTBt2k2MzOTaTYzNt2E2OzeTYTI7NtGE3OTaTYzM5NvMN/wG+JoTGRKLPy78CABxbswXFJ3IAAH0WToS9lQPO3EIcW/Mx2tzTET0mDYHH48GxP30M15Vy9PrNWLRw2HFuayZqKq+i+4Qk2EODYbPb8dWMVQE78+jRowP2ua3MyG6+zlmfhRPhaB2KIJsNe+ethT0sxLA5AzhrWrCZHJtpY4Zrmkq7E+CsaWF0MzPuT6ObmRW7ybGZnNHNzLg/jW5mVmb4Ok2l3Qlw1rRgMzk28w2/M7kJ3ccn4ZuUNOx9cR16THoMABDWsR1qqlzY97tURHS5E7aWdtz77OO4Wl6J6ooqVBVfwT0THwU8HnjcbjgvFqIg6wwyF6xH3tffInvzroCeeerUqQH9/FZlZDdf5+zQso3InL8eV52VcLQJN3TOAM6aFmwmx2bamOGaptLuBDhrWhjdzIz70+hmZsVucmwmZ3QzM+5Po5uZlRm+TlNpdwKcNS3YTI7NfMPvTG5CaEwknLkF8NS40SLYUfvYnZFw5hYCACqLriC4bTgi47vg76MXIbpvD3QdNQitu3bAmfQvUXzyRyS+NgmZ89cDAGKTemPXzLcM+/3cjqriK3CVVRh6Bkd4KwS3jTD0DIHg65xVXCpBeOdoBNlscJWUWXLOAM6aP+jR0OyNGtJr7qzWzRvuzvp4TQsc7s/rVJgzwPyzxv0px2b+w/1ZnwrXNSvOHndnfZyz28c9oI2Zu/FmchPK84oQFhOFin+VoKbKVfvYT0WIHdwLABDSNgJVxWW4nPMTaipdqCotQ9t7O8OZXwTXZSdqKlywtazNHN4pGs68Qnhq3Eb9djSrKr6CD/vNQHVZpaHnsIeHYMz+P1vuIuLrnIXFtkPCnKfwz9/+BQAsN2cAZ80f9Gpo5kYN6Tl3VurWGO7O63hNCyzuz1qqzBlg7lnj/pRjM//i/rxOleuaFWePu/M6ztnt4x7QxuzdeDO5Cac/+AIPvvQ04PHg+Pqt6Ld0Cva/kooWwS2RuDgZl8/lw321Gqc37cCAlKloERKMg0s2IKiFDQ8umAAAOJO2EwBw99jByP7wq4CfOS0tze+f01VWYfgFFgCqyyrhKqsIyAUkEN185eucPZL6Ikq/v4D+S6fgmzfScHrTDsPmDOCsaaHHnOnVMJCvxxtZqRlgrW6NMePuBHhN08LIOQPMuT+tPGeAuWeN+1OOzfzLjPszUM1Uua6Z+ZrWGDPuTsDa+9PMc8Y9oI3Zu/FmchPK84qwe87quh/vfyUVAHBo+fv1fl3+nmPI33Os3mN75q6p9+OsFekBOmV9K1euxNy5c3V5Lisxspuvc7Z16IKbPtaoOQM4a1qwmRybaWOGa5pKuxPgrGlhdDMz7k+jm5kVu8mxmZzRzcy4P41uZlZm+DpNpd0JcNa0YDM5NvMN/wE+C9q3b5/RRzAldpNjMzk2k2MzbdhNjs3k2EyOzbRhNzk2k2MzOTbTht3k2EyOzeTYzDf8zmQiomZm9P41qK50we2qhs1hx/G1n+D0ph1GH0t57EZE1LxxD8ixGRFR88Y9oI3q3Xgz2YKeeeYZo49gSuwmx2ZyqjTbOSUFpWdy0aZHJwzfnoILOw6j4qdio4/llSrNAHazOjaTYzM5NtNGlW7cA3JsZm1spg27ybGZnCrNuAe0Ubkb3+bCgjp16mT0EUyJ3eTYTE61ZiWnzsNV6kRYTBTaD4hH4qLJAICoB7rhod8/Z/DpaqnWDKjfbfDb8xDeKRoA0H1CEnomDzP4dLVU7KY6NpNjMzk200a1btyfctyd1sRm2rCbHJvJqdaMu1MbFfcnbyZb0JIlS4w+gimxmxybyanWLLpfT1SVlKHou3PIzzyOiLvaI7RDFB54YTSOrMow+ngA1GsG1O+W9WY6es0bC5vDjq6jBuHUxs+MPh4ANbupjs3k2EyOzbRRrRv3pxx3pzWxmTbsJsdmcqo14+7URsX9qeTbXBw+fBjTp0/H0aNHcf/992PWrFmYPn06ysrKYLMF5v53n4UT0WXEAER0vhMfDZqN0jO5AXkeq4mIa49Bf5yF4LYRqCq+gq9n/xeunM03+lhK46zJcc7875HU+QgKCkJEXHvsmvEHuF3VAICslelISp2PvF1HlfkrNCrx1q3k5I/wuN3ov+xZfL/xc3iqa4w+pq54TZPjNU2Oc6YNZ83/uD/luDtvxmuaNrymyXHW5Dhn/sfdqY3K+1O570zOysrCww8/jAkTJuDEiRNITk7GzJkzER8fH7AbyQDw4z8O4NORr6Ls/KWAPYdeYmNjdXuugSnTcOLtbfjo32bj5Lt/x8CUabo9t7/p1Y2zJsc587+dU1Lw0aA5+Gr6HzBwxQyEtGsNoPav0ITFtkPOp/sNPuF1qjQDGu+WtSId0Yk9cfbjPQaf8Dpe0+R4TZPjnMnx6zRtVNkF3J9y3J034zVNG17T5Dhrcpwz/+Pu1Ebl/anczeRr34X8wgsvIC4uDjNnzkSHDh2QkJAQ0Of918FTKM8tDOhz6GXlypW6PE9I1B1oe18XnN2yFwBw9qM9iIzvguCoO3R5fn/TqxtnTYZzFlg5WzOR++URJMweCQDoOXkoTm3YjvipTxp8sutUawbc3M15oQDluQUGn6o+XtPkeE2T45zJ8es0bVTbBdyfctyd1/GaJsdrmjacNRnOWWBxd2qj4v5U6m0usrOzsXv3brz//vv1Hnc4HEhISMD333+PX//61/B4PLh69Sp+97vf4cknmx66ESNGIDs7u+7Hd9S0xDTc7ffzazHksSG43OKq+OOeeOKJRn9u8+bNGDVqVKM/n5ycLH4+b83CYtuhPK8Q8HgAAB63G868IoR1iEJV4WXxc/gqEM2AW3fT0gww/6yp0MyoOQPM8/psSDp3h5a/j+H/eB3H1n6Cjo/1xfZxSzDwjWmIvD8ORd+eveXHam3UkNmaAde7fbv6v1FxqUT0sf7opsLr00i8psmZ5Zpm9jkD1Ghmtlkz4vXZkNn2p5mbGbU7ATVen0YxyzUNMP/+VOFrDiOZtZnZ5gww35+jjN6dgPHNALX2Z7du3bBlyxbR51TqO5OzsrLQpk0bdO7cue6xiooK/PDDD0hISEBkZCQ++ugjfP3119iyZQtmzJhh4GnVVVJSYvQRTInd5NhMToVmGf2er/d+aVfO5WNTj8m4Z8IvceKv2wCPB0dWZaDX3LEGnvI6FZoBjXeTLnO9qNLNTNhMjs3k2EwbFbpxf8pxd1ofm2nDbnJsJqdCM+5ObVTfn0p9Z3JQUBBqamrgdrvr3h953bp1KC8vR0JCAqKioup+batWrRAUFOTT5214h/3K+Uv4W7/n/Xfw27D9s+2I6BQt/rgDBw40+nMZGRkYM2ZMoz+fkpIifj5vzZwXCxAaEwUEBQEeD4JsNoTFRMIZ4L9GE4hmwK27aWkGmH/WVGhm1JwB5nl9NqR17rJWpNf9b+fFAnzxzOtNfozWRg2ZtdmNPnt6mc+/1h/dVHh9GonXNDmzXNPMPmeAGs3MNmtGvD4bMtv+NHOza/TenYAar0+jmOWaBph/f6rwNYeRzNrMbHMGmPfPUc35z56AOffnjZT6zuS+ffuioqICS5cuxdmzZ/GXv/wFy5cvR0xMTL0byR6PBzNmzMBLL71k4GnV9eqrr+ryPJWFl1Fy8kfEjRgIAIgb+QsUfZcT8L/+ESh6dbMSPZpxzojNtGE3OV7T5Dhncvw6TRvOmhybybGZHK9p2nDW5Ph1mhznTI7NfKPUzeTOnTtj9erVWLt2LXr37o2DBw9i/PjxN/3je//f//f/oUuXLnj+ef/9V7fERZMx5tA6hMZEYdjfFmP4dv/81wYjnD9/Xrfn2jt/He577gmM3P1H3DvlcWQuWK/bc/ubXt04a3Kcs+aNzbThNU2O1zQ5zpkcv07ThrtAjs3keE2T4zVNG86aHL9Ok+MekGMz3yj1NhcAMG3aNEybNq3ux8OHD693M3nOnDkICQnB//2//9evz3tg0Xs4sOg9v35Oo7zzzjsYNmyYLs91OTsX//Pky7o8V6Dp1Y2zJsc5a97YTBte0+R4TZPjnMnx6zRtuAvk2EyO1zQ5XtO04azJ8es0Oe4BOTbzjVLfmezNsWPH6m4mf/755/jTn/6EQ4cOYfDgwRg8eDDKysoMPiERERERERERERGR9Sn3nck3cjqdyMnJqbuZ/Oijj6K6utrgU6mvf//+Rh/BlNhNjs3k9GwWGhOJPi//CgBwbM0WFJ/IAQD0WTgR9lYOOHMLcWzNx+izcCIcrUMRZLNh77y1sIeFoNdvxqKFw45zWzNRU3kV3SckwR4aDJvdjq9mrNLt9wAYN2eN9UtcnIyfPdgd24YvBADEzxiB8Nh2qK5w4dCyjbj314+jTfeOiLw/DllvpOHizixDzs/XpxybybGZHJtpw/0pZ8SscXc2P2ymDbvJsZkcd6cc/+zpG6W/MzksLAxut/um90ymW5s7d67RRzAldpNjMzk9m3Ufn4RvUtKw98V16DHpMQBAWMd2qKlyYd/vUhHR5U7YWtpxaNlGZM5fj6vOSjjahOOeiY8CHg88bjecFwtRkHUGmQvWI+/rb5G9eZdu57/GqDnz1g8ADrz2Li5n5wIAbC3tuCOuPfb9LhU1VS6ExbbDibe3IXPBepTlXELurqOGnB3g61MLNpNjMzk204b7U86IWePubH7YTBt2k2MzOe5OOf7Z0zdK30wmbcaNG2f0EUyJ3eTYTE7PZqExkXDmFqCm0oUWwY7ax+6MhDO3EABQWXQFwW3DAQDhnaMRZLPBVVKG1l07IGfbPhx+/QPcP+s/6z5fbFJvXPziG93Of41Rc+atX0PBbcNR+b//urMzrwih7SMBAOGdouHMK4Snxq3beRvi61OOzeTYTI7NtOH+lDNi1rg7mx8204bd5NhMjrtTjn/29I3Sb3NB5jd6/xpUV7rgdlXD5rDj+NpPcHrTDqOPRRbEWbtZeV4RwmKiUPGvEtRUuWof+6kIsYN7AQBC2kagqrgMYbHtkDDnKfzzt38BADjzi+C67ERNhQu2lrVrQoU/4OnNW7+GqorLEBJ5BwAgrH0kcr/MAgB0G/Mwsj/8Sq+jkgXxmkZ64azdjPtTO+5OMhKvZ6QXztrNuDtvj9n2J28mU8DtnJKC0jO5aNOjE4ZvT8GFHYdR8VOx0cciC+Ks1Xf6gy/w4EtPAx4Pjq/fin5Lp2D/K6loEdwSiYuTcflcPtxXq/FI6oso/f4C+i+dgm/eSMPpTTvw4IIJAIAzaTsBAHePHdzs/oDXWL+EOaPwsz73IHHRZBxY9B4un81H4uJkuKuuwnmxAAAQGd8FR1Z+aPDvgMyO1zTSC2etPu5P7bg7yWi8npFeOGv1cXfeHrPtT95MtqD169cbfQSvSk6dh6vUibCYKLTu2gGdhvbFgUXvIeqBbug+IQnnPt5702P/fOkvup1P1W4qU7XZjbPWf9mzOLh4A8rOX0L3CUloEexA+3/7+U2PnXz377qcTc9m5XlF2D1ndd2P97+SCgA4tPz9er9u69AFN33snrlr6v04a0V6AE7oG6PmrLF+R9/ajKNvba57/PjaLTd97M5n3wj8AZug6utTZao2U3l/qtpMZSo34/6sxf2pHXdn86NqM5V3J6BuN5Wp2oy7sxZ35+0x2/7keyZbUEZGhtFH8Cq6X09UlZSh6LtzyM88joi72iO0QxQeeGE0jqzK8PqYnlTtpjJVm904a1lvpqPXvLGwOezoOmoQTm38zOtjelG1mcrYTBt2k1O1mcr7U9VmKlO5GfentbCZHJvJqdpM5d0JqNtNZao24+60FjbzTbO8mewIbwV7eIjRx4A9PASO8FZ+/7zbt2/3++e8HY+kzsfIr9/CsM2LkfVGGtyuagBA1sp0JKXOR+n3F+r+Ooi3x/QSiG6cNX15m7WSkz/C43aj/7Jn8f3Gz+GprvH6mF70aKbX3AVqrhqyUjPA3N14TdOXGfYn50xOtTkDuD+v4f6UY7Om8ZqmLzPsToCzpoUZZo27M3DMvAe8MXu3Zvk2F8FtIzBm/5/hKqsw9ByO8FYIbhth6Bn0cO29hO56cgB+sXIG8jO/Q2VBKUpOnUdYbDvkfLq/7td6e8zMOGv6amzWslak49GNC5H54rq6X+vtMavQa+6sNFd6vlbN3I3XNH011/3JOdMf92ct7k85Nmsar2n6aq67E+Cs6Y27sxb3gDZm79YsbyYDtf+Ps9Ig3mj06NFGH8GrnK2ZiBsxEAmzR2L/q++i5+ShOLVhO+KnPomvZqwCAK+P6SVQ3Thr+ms4a84LBSjPLaj3a7w9pge9mllp7thMG17T5MxyTVNpf3LO5FSdM4D7E7DW7LGZHK9pcqpe01TenQBnTQuzzBp3p7npOWdm7tYs3+bC6h566CGjj9CoQ8vfx93jHkFohyh0fKwvst5Mx1VnBSLvj4M9LOSmx/SkcjdVqdzs2qy1im5j9FHqUbmZqthMG3aTU7mZqvtT5WaqUr0Z96d1sJkcm8mp3EzV3Qmo3U1VKjfj7rQONvMNbyZb0Lx584w+Qp2Mfs+j9Exu3Y+vnMvHph6Tcc+EX+LEX7cBHg+OrMpAr7lj8fPpI256TE8qdTMLlZo1NmsVl0qMO5QXKjUzCzbTht3kVGpmlv2pUjOzUK0Z96d1sZkcm8mp1MwsuxNQq5tZqNSMu9O62Mw3zfZtLshYWSvS6/6382IBvnjm9Xo/7+0xotvx2dPLfHqMiEhl3J+kN+5PIjI77k7SG3cnWR2/M9mCIiLM+Z4rRmM3OTaTYzM5NtOG3eTYTI7N5NhMG3aTYzM5NpNjM23YTY7N5NhMjs18w5vJFjR06FCjj2BK7CbHZnJsJsdm2rCbHJvJsZkcm2nDbnJsJsdmcmymDbvJsZkcm8mxmW94M9mCMjIyjD6CKbGbHJvJsZkcm2nDbnJsJsdmcmymDbvJsZkcm8mxmTbsJsdmcmwmx2a+4c1kIiIiIiIiIiIiImoSbyZb0JAhQ4w+gimxmxybybGZHJtpw25ybCbHZnJspg27ybGZHJvJsZk27CbHZnJsJsdmvrEbfQDyv9GjRxv6/KExkejz8q8AAMfWbEHxiRwAQJ+FE2Fv5YAztxDH1nyMNvd0RI9JQ+DxeHDsTx/DdaUcvX4zFi0cdpzbmomayqvoPiEJ9tBg2Ox2fDVjVUDPbXQ3MzKyma9z1mfhRDhahyLIZsPeeWthDwvhnJkMm2nDbnJmuKZxd5qf0c24P5sPNpNjMzmjm3F/Nh9m+DqNu9P82Mw3zfZmclXxFbjKKgw9gyO8FYLb+v9fipw6dSrS0tL8/nl91X18Er5JSUPFv0qQ+Nok/PO3byOsYzvUVLlwaNlGDEiZCltLO+599nFUlZYBqP3/R4/koYDHA4/bDefFQpSdv4SCrDO4e+xgVBZfCfi5A9WNsxYYvs7ZoWUbAQCJi5PhaBOOu8cNtuScNaTH3AVqrhqyUjPA/N14TQsM7s76OGeBw/15a9yfcmzWNF7TAof7sz7OWmBwd94a94A2Zu7WLG8mVxVfwYf9ZqC6rNLQc9jDQzBm/591eUHoKTQmEs7cAnhq3GgR7Kh97M5IOHMLAQCVRVcQ3DYckfFd8PfRixDdtwe6jhqE1l074Ez6lyg++SMSX5uEzPnrAQCxSb2xa+Zbhv1+bgdnLXB8nbOKSyUI7xyNIJsNrpIyS85ZQ3rNnZXmSs/Xqpm78ZoWONyd13HOAov7s3Hcn3Js1jRe0wKL+/M6zlrgcHc2jntAG7N3a5bvmewqqzD8AgsA1WWVhv9Xw0AozytCWEwUWgS3RE2Vq/axn2ofA4CQthGoKi7D5ZyfUFPpQlVpGVqGt4Izvwiuy07UVLhga1n73znCO0XDmVcIT43bsN/P7eCsBY6vcxYW2w4Jc57CwaUbAMCSc9aQXnNnpbnS87Vq5m68pgUOd+d1nLPA4v5sHPenHJs1jde0wOL+vI6zFjjcnY3jHtDG7N2a5XcmW52Rf80IAE5/8AUefOlpwOPB8fVb0W/pFOx/JRUtglsicXEyLp/Lh/tqNU5v2oEBKVPRIiQYB5dsQFALGx5cMAEAcCZtJwDg7rGDkf3hV7qc2+huZmRkM1/n7JHUF1H6/QX0XzoF37yRhtObdnDOTIbNtGE3OTNc07g7zc/oZtyfzQebybGZnNHNuD+bDzN8ncbdaX5s5hveTLaglStXYu7cuYY9f3leEXbPWV334/2vpAIADi1/v96vy99zDPl7jtV7bM/cNfV+nLUiPUCnvJnR3czIyGa+ztnWoQtu+ljOmbmwmTbsJmeGaxp3p/kZ3Yz7s/lgMzk2kzO6Gfdn82GGr9O4O82PzXzTLN/mwur27dtn9BFMid3k2EyOzeTYTBt2k2MzOTaTYzNt2E2OzeTYTI7NtGE3OTaTYzM5NvMNvzOZiKiZGb1/DaorXXC7qmFz2HF87Sc4vWmH0cdSHrsRETVv3ANybEZE1LxxD2ijejfeTLagZ555xugjmBK7ybGZnCrNdk5JQemZXLTp0QnDt6fgwo7DqPip2OhjeaVKM4DdrI7N5NhMjs20UaUb94Acm1kbm2nDbnJsJqdKM+4BbVTuxre5sKBOnToZfQRTYjc5NpNTrVnJqfNwlToRFhOF9gPikbhoMgAg6oFueOj3zxl8ulqqNQPqdxv89jyEd4oGAHSfkISeycMMPl0tFbupjs3k2EyOzbRRrRv3pxx3pzWxmTbsJsdmcqo14+7URsX9qeTN5MOHD6Nfv34ICQlBYmIiNmzYgNDQULjdbqOPZgpLliwx+gimxG5ybCanWrPofj1RVVKGou/OIT/zOCLuao/QDlF44IXROLIqw+jjAVCvGVC/W9ab6eg1byxsDju6jhqEUxs/M/p4ANTspjo2k2MzOTbTRrVu3J9y3J3WxGbasJscm8mp1oy7UxsV96dyb3ORlZWFhx9+GEuWLEFaWhq2bduGmTNnIj4+HjZbYO99R8S1x6A/zkJw2whUFV/B17P/C1fO5gf0Oc2sz8KJ6DJiACI634mPBs1G6Zlco49kCpwzOc6a/z2SOh9BQUGIiGuPXTP+ALerGgCQtTIdSanzkbfrqDJ/hUYl3rqVnPwRHrcb/Zc9i+83fg5PdY3Rx9QVr2lyvKZpw1mT4ZwFBvenHHend7ymyfCapg3nTI6z5n/cndqovD+V+87kWbNmYfr06XjhhRcQFxeHmTNnokOHDkhISAj4cw9MmYYTb2/DR/82Gyff/TsGpkwL+HMGQmxsrC7P8+M/DuDTka+i7PwlXZ4v0PTqZpU5AzhrWujVrCk7p6Tgo0Fz8NX0P2DgihkIadcaQO1foQmLbYecT/cbfMLrVGkGNN4ta0U6ohN74uzHeww+4XW8psnxmian5+vTKrPGOdNGlV3A/SnH3ekdr2kyvKZpY5U5AzhrWqi+B7g7b03l/anUzeTs7Gzs3r0bs2bNqve4w+Gou5n8i1/8AoMHD0bfvn3xxhtv+O25Q6LuQNv7uuDslr0AgLMf7UFkfBcER93ht+fQy8qVK3V5nn8dPIXy3EJdnksPenSz0pwBnDUt9Grmq5ytmcj98ggSZo8EAPScPBSnNmxH/NQnDT7Zdao1A27u5rxQgPLcAoNPVR+vaXK8psnp1cxKs8Y500a1XcD9KcfdeR2vaXK8pslZac4AzpoWqu8B7k7fqLg/lXqbi6ysLLRp0wadO3eue6yiogI//PBD3c3knTt3wuFw4OrVq+jZsycmT56M6OjoW37eESNGIDs7u+7Hd9S0xDTcXe/XhMW2Q3leIeDxAAA8bjeceUUI6xCFqsLL/vot3mTIY0NwucVV8cc98cQTjf7c5s2bMWrUqEZ/Pjk5Wfx83poZJRDNgFt309IMuLmbUXMGaOumQjMjmeX12ZC04aHl72P4P17HsbWfoONjfbF93BIMfGMaIu+PQ9G3Z2/5sVobNWS2ZsD1bt+u/m9UXCoRfaw/uqnw+uQ1rWm8piWLn49fpyWLn0+lOQPM8/psyGz708zNjNqdgBqvT17TmqbSdc0s1zR+nSZn9jkDzPfnKKN3J2B8M0Ct/dmtWzds2bJF9DmV+s7koKAg1NTU1PuH9tatW4fy8vK6m8kOhwMAUF5ejg4dOqB169aGnFVlJSUlRh/BlNhNjs3kVGiW0e/5eu/9deVcPjb1mIx7JvwSJ/66DfB4cGRVBnrNHWvgKa9ToRnQeDfpMteLKt3MhM3k2EyOzbRRoRv3pxx3p/WxmTbsJsdmcio04+7URvX9qdR3Jvft2xcVFRVYunQpJk2ahM8//xzLly9HTEwMoqKiAAA1NTX45S9/iePHj2PKlCl1N5dvpeEd9ivnL+Fv/Z6v95jzYgFCY6KAoCDA40GQzYawmEg4A/zXG7Z/th0RnW79ndXeHDhwoNGfy8jIwJgxYxr9+ZSUFPHzeWtmlEA0A27dTUsz4OZuRs0ZoK2bCs2MZJbXZ0NaG2atSK/7386LBfjimdeb/BitjRoya7Mbffb0Mp9/rT+6qfD65DWtabym+edrDn6ddmsqzRlgntdnQ2bbn2Zudo3euxNQ4/XJa1rTVLqumeWaxq/T5Mw+Z4B5/xzVnP/sCZhzf95Iqe9M7ty5M1avXo21a9eid+/eOHjwIMaPH1/vH99r0aIFvvzyS5w/fx4HDhzA9u3b/fLclYWXUXLyR8SNGAgAiBv5CxR9lxPwv/4RCK+++qrRRzAlPbpZac4AzpoWbCbHZtrwmibHWZPTq5mVZo1zpg27ybGZHK9pcpwzbfh1mhxnTY7N5NjMN0rdTAaAadOmIS8vDyUlJVi3bh3Onj1bdzPZ5XLVvQVGcHAwQkND0apVK789997563Dfc09g5O4/4t4pjyNzwXq/fW49nT9/XpfnSVw0GWMOrUNoTBSG/W0xhm/3z3+hMYpe3awyZwBnTQu9mlkJm2nDa5ocr2lyer4+rTJrnDNtuAvk2EyO1zQ5XtO04ddpcpw1Oe4BOTbzjVJvc+HNsWPHMG7cOADA2bNn8dxzz8Fms6GqqgrDhg3Dv//7v/vtuS5n5+J/nnzZb5/PKO+88w6GDRsW8Oc5sOg9HFj0XsCfRy96dbPKnAGcNS30amYlbKYNr2lyvKbJ6fn6tMqscc604S6QYzM5XtPkeE3Thl+nyXHW5LgH5NjMN0rfTHY6ncjJyan7zuQePXpg165dBp+KiMgcQmMi0eflXwEAjq3ZguITOQCAPgsnwt7KAWduIY6t+Rh9Fk6Eo3Uogmw27J23FvawEPT6zVi0cNhxbmsmaiqvovuEJNhDg2Gz2/HVjFVG/rZ001i/xMXJ+NmD3bFt+EIAQPyMEQiPbYfqChcOLduIe3/9ONp074jI++OQ9UYaLu7MMuq3QEREGnB/asfdSUTUPHF33h6z7U/l3ubiRmFhYXC73fXeM5ma1r9/f6OPYErsJsdmcno26z4+Cd+kpGHvi+vQY9JjAICwju1QU+XCvt+lIqLLnbC1tOPQso3InL8eV52VcLQJxz0THwU8HnjcbjgvFqIg6wwyF6xH3tffInuz/v9Bz6g589YPAA689i4uZ9f+y7q2lnbcEdce+36XipoqF8Ji2+HE29uQuWA9ynIuIXfXUUPODvD1qQWbybGZHJtpw/0pZ8SscXc2P2ymDbvJsZkcd6cc/+zpG6VvJpM2c+fONfoIpsRucmwmp2ez0JhIOHMLUFPpQotgR+1jd17/F5wri64guG04ACC8czSCbDa4SsrQumsH5Gzbh8Ovf4D7Z/1n3eeLTeqNi198o9v5rzFqzrz1ayi4bTgq//cfMXHmFSG0fSQAILxTNJx5hfDUuHU7b0N8fcqxmRybybGZNtyfckbMGndn88Nm2rCbHJvJcXfK8c+evuHNZAu69h7TJMNucmwmp2ez8rwihMVEoUVwS9RUuWof+6n2MQAIaRuBquIyhMW2Q8Kcp3Bw6QYAgDO/CK7LTtRUuGBrWftuSEb+Ac+oOfPWr6Gq4jKERN4BAAhrH4ny/CIAQLcxDyP7w690O6s3fH3KsZkcm8mxmTbcn3JGzBp3Z/PDZtqwmxybyXF3yvHPnr5R+j2TVTN6/xpUV7rgdlXD5rDj+NpPcHrTDqOPRRbEWSN/OP3BF3jwpacBjwfH129Fv6VTsP+VVLQIbonExcm4fC4f7qvVeCT1RZR+fwH9l07BN2+k4fSmHXhwwQQAwJm0nQCAu8cONvwPeHprrF/CnFH4WZ97kLhoMg4seg+Xz+YjcXEy3FVX4bxYAACIjO+CIys/NPh3oA5e00gvnDXyB+5P7bg7/YfXM9ILZ438gbvz9phtf/JmstDOKSkoPZOLNj06Yfj2FFzYcRgVPxUbfSyyIM4a3a7yvCLsnrO67sf7X0kFABxa/n69X7d16IKbPnbP3DX1fpy1Ij0AJ1RbY/2OvrUZR9/aXPf48bVbbvrYnc++EfgDmgyvaaQXzhrdLu5P7bg7/YvXM9ILZ41uF3fn7THb/uTbXGhUcuo8XKVOhMVEof2AeCQumgwAiHqgGx76/XNeH9PL+vXrdXsuK1G1242zNvjteQjvFA0A6D4hCT2Th3l9TC+qNlMZm8mxmTaqduP+tBaVm6m6P1VupjJ2k2MzOVWbcXdaj6rdVN2dgLrNVMZmcmzmG95M1ii6X09UlZSh6LtzyM88joi72iO0QxQeeGE0jqzK8PqYXjIy9HsuK1G1242zlvVmOnrNGwubw46uowbh1MbPvD6mF1WbqYzN5NhMG1W7cX9ai8rNVN2fKjdTGbvJsZmcqs24O61H1W6q7k5A3WYqYzM5NvMNbyYLPZI6HyO/fgvDNi9G1htpcLuqAQBZK9ORlDofpd9fqPvrIN4e08P27dv9/jkd4a1gDw/x++eVsoeHwBHeKiCfOxDdboe3WSs5+SM8bjf6L3sW32/8HJ7qGq+P6YWzJqfHnOnVMJCvxxtZqRlgrW4S3J/GMvM1TUr1/WnlOQPMPWvcn3JsFljNdXcC6lzXzHxNk1B9dwLW3p9mnjPuAW3M3o3vmSx07b2E7npyAH6xcgbyM79DZUEpSk6dR1hsO+R8ur/u13p7zKyC20ZgzP4/w1VWYeg5HOGtENw2wtAz6KWxWctakY5HNy5E5ovr6n6tt8fMirN2+/RqaOZGDek5d1bqJsH9yWuaXprj/lRlzgBzzxr3pxybBVZz3Z2AOte15jJ7zXF3Apwzf+Ae0Mbs3XgzWaOcrZmIGzEQCbNHYv+r76Ln5KE4tWE74qc+ia9mrAIAr4/pYfTo0QH5vMFtIyz14m0oUN1uV8NZc14oQHluQb1f4+0xPXDW5PSaMys1ZDNtzHJN4/40N1XnDFB3f3LOtOEukGMzOVWvac1xdwLWmq2GzDJrquxOgPtTC+4BOT1fm2buxre5uA2Hlr+Pu8c9gtAOUej4WF9kvZmOq84KRN4fB3tYyE2P6eWhhx7S7bmsROVu12atVXQbo49Sj8rNVMVmcmymjcrduD+tQ/VmKu5P1Zupit3k2ExO5WbcndaicjcVdyegdjNVsZkcm/mGN5MFMvo9j9IzuXU/vnIuH5t6TMY9E36JE3/dBng8OLIqA73mjsXPp4+46TG9zJs3T7fnshKVujU2axWXSow7lBcqNTMLNpNjM21U6sb9aV2qNTPD/lStmVmwmxybyanUjLvT2lTqZobdCajVzCzYTI7NfMO3ufCDrBXpdf/bebEAXzzzer2f9/YY0e347OllPj1GRKQy7k/SG/cnEZkddyfpjbuTiBridyZbUNeuXY0+gimxmxybybGZHJtpw25ybCbHZnJspg27ybGZHJvJsZk27CbHZnJsJsdmvuHNZAv64YcfjD6CKbGbHJvJsZkcm2nDbnJsJsdmcmymDbvJsZkcm8mxmTbsJsdmcmwmx2a+4c1kIiIiIiIiIiIiImoSbyZb0JAhQ4w+gimxmxybybGZHJtpw25ybCbHZnJspg27ybGZHJvJsZk27CbHZnJsJsdmvuHNZAsaPXq00UcwJXaTYzM5NpNjM23YTY7N5NhMjs20YTc5NpNjMzk204bd5NhMjs3k2Mw3dqMPoLrQmEj0eflXAIBja7ag+EQOAKDPwomwt3LAmVuIY2s+Rpt7OqLHpCHweDw49qeP4bpSjl6/GYsWDjvObc1ETeVVdJ+QBHtoMGx2O76asSpgZ546dSrS0tIC9vmtyshuvs5Zn4UT4WgdiiCbDXvnrYU9LMSwOQM4a1qwmRybaWOGa5pKuxPgrGlhdDMz7k+jm5kVu8mxmZzRzcy4P41uZlZm+DpNpd0JcNa0YDM5NvMNbyY3ofv4JHyTkoaKf5Ug8bVJ+Odv30ZYx3aoqXLh0LKNGJAyFbaWdtz77OOoKi0DAFQVX0GP5KGAxwOP2w3nxUKUnb+EgqwzuHvsYFQWXzH4d6VNVfEVuMoqDD2DI7wVgttGGHqGQPB1zg4t2wgASFycDEebcNw9brDl5gzgrPmDHg3N3qghvebOat284e6sj9e0wOH+vE6FOQPMP2vcn3Js5j/cn/WpcF2z4uxxd9bHObt93APamLkbbyY3ITQmEs7cAnhq3GgR7Kh97M5IOHMLAQCVRVcQ3DYckfFd8PfRixDdtwe6jhqE1l074Ez6lyg++SMSX5uEzPnrAQCxSb2xa+Zbhv1+tKoqvoIP+81AdVmloeewh4dgzP4/W+4i4uucVVwqQXjnaATZbHCVlFluzgDOmj/o1dDMjRrSc+6s1K0x3J3X8ZoWWNyftVSZM8Dcs8b9Kcdm/sX9eZ0q1zUrzh5353Wcs9vHPaCN2bvxPZObUJ5XhLCYKLQIbomaKlftYz/VPgYAIW0jUFVchss5P6Gm0oWq0jK0DG8FZ34RXJedqKlwwday9p59eKdoOPMK4alxB/TMgfiWfFdZheEXWACoLqsM2H+5MfKvMvg6Z2Gx7ZAw5ykcXLoBAAydM4CzpoUec6ZXw0C+Hm9kpWaAtbo1xoy7E+A1TQuj/xqgGfenlecMMPescX/KsZl/mXF/BqqZKtc1M1/TGmPG3QlYe3+aec64B7QxezfeTG7C6Q++QK8Xx2HA61Nx6v99hn5Lp8B5oQAtglsicXEyLp/Lh/tqNU5v2oEBKVNx33NP4oePduP0ph34+Yz/wMA3p+NM2k4AwN1jByP7w68CfuaVK1cG/DmsyMhuvs7ZI6kvooXDjv5LpyCkXWtD5wzgrGnBZnJspo0Zrmkq7U6As6aF0c3MuD+NbmZW7CbHZnJGNzPj/jS6mVmZ4es0lXYnwFnTgs3k2Mw3fJuLJpTnFWH3nNV1P97/SioA4NDy9+v9uvw9x5C/51i9x/bMXVPvx1kr0gN0yvr27duny/NYjZHdfJ2zrUMX3PSxRs0ZwFnTgs3k2EwbM1zTVNqdAGdNC6ObmXF/Gt3MrNhNjs3kjG5mxv1pdDOzMsPXaSrtToCzpgWbybGZb/idyURERERERERERETUJH5nsgU988wzRh/BlNhNjs3kVGg2ev8aVFe64HZVw+aw4/jaT3B60w6jj9UoFZoB7NYcsJkcm8mxmTYqdOMekGMz62MzbdhNjs3kVGjGPaCN6t14M9mCOnXqZPQRTInd5NhMTpVmO6ekoPRMLtr06ITh21NwYcdhVPxUbPSxvFKlGcBuVsdmcmwmx2baqNKNe0COzayNzbRhNzk2k1OlGfeANip349tcWNCSJUuMPoIpsZscm8mp1qzk1Hm4Sp0Ii4lC+wHxSFw0GQAQ9UA3PPT75ww+XS3VmgH1uw1+ex7CO0UDALpPSELP5GEGn66Wit1Ux2ZybCbHZtqo1o37U46705rYTBt2k2MzOdWacXdqo+L+VPJm8uHDh9GvXz+EhIQgMTERGzZsQGhoKNxud8Ces8/CiXhq35+QnJeB1nd3CNjzWE1EXHs8/skyjNz9Rzz+yTJExLU3+kjK46zJcc4CJ7pfT1SVlKHou3PIzzyOiLvaI7RDFB54YTSOrMow+njKurFb1pvp6DVvLGwOO7qOGoRTGz8z+ni64jVNjtc0Oc6ZNpy1wOH+lOPuvI7XNG14TZPjrMlxzgKHu1MbFfencm9zkZWVhYcffhhLlixBWloatm3bhpkzZyI+Ph42W+Duff/4jwM48c6nePy/lwbsOfQSGxur23MNTJmGE29vw9mP96DrU4MwMGUa/jFmsW7P7096deOsyXHO/O+R1PkICgpCRFx77JrxB7hd1QCArJXpSEqdj7xdR5X5KzSqNAO8dys5+SM8bjf6L3sW32/8HJ7qGqOPCYDXNC14TZPjnMnx6zRtVNkF3J9y3J034zVNG17T5Dhrcpwz/+Pu1Ebl/ancdybPmjUL06dPxwsvvIC4uDjMnDkTHTp0QEJCQkCf918HT6E8tzCgz6GXlStX6vI8IVF3oO19XXB2y14AwNmP9iAyvguCo+7Q5fn9Ta9unDUZzllg7JySgo8GzcFX0/+AgStmIKRdawC1f4UmLLYdcj7db/AJr1OlGdB4t6wV6YhO7ImzH+8x+ITX8Zomx2uaHOdMjl+naaPKLuD+lOPuvBmvaXK8pmnDWZPhnAUGd6c2Ku9PpW4mZ2dnY/fu3Zg1a1a9xx0OR72byRUVFejSpQteeuklvY9oCnPnztXlecJi26E8rxDweAAAHrcbzrwihHWI0uX5/U2vblaiRzPOWWDlbM1E7pdHkDB7JACg5+ShOLVhO+KnPmnwya5TrRlwczfnhQKU5xYYfKr6VOymOl7T5Dhncvw6TRvVZo37U46705p4TdOGsybHr9PkVJsz7k5tVNyfSr3NRVZWFtq0aYPOnTvXPVZRUYEffvih3s3kN998E7179/b5844YMQLZ2dl1P76jpiWm4W7/HPo2DXlsCC63uCr+uCeeeKLRnztx4gQ+/PDDRn8+OTlZ/HxWbwbcupuWZoD5u7GZOV6fDUkbHlr+Pob/43UcW/sJOj7WF9vHLcHAN6Yh8v44FH179pYfq7VRQ2ZrBlzv9u3q/0bFpRLRx/qjG1+fvKZJmeWaxmbJ4udTqRlgntdnQ2bbn2ZuZtTuBJr369Ms1zTA/N34NQebSZnp9Xkjs+1OwPhmgFr7s1u3btiyZYvocyr1nclBQUGoqamp9w/trVu3DuXl5XU3ky9evIj9+/fjP/7jP4w6Jv0v58UChMZEAUFBAIAgmw1hMZFwWuSv0ZAaOGf+l9HveZSeya378ZVz+djUYzLumfBLnPjrNsDjwZFVGeg1d6yBp1RPY92ky5yaN17TSC+cNf/j/pTj7iR/4TWN9MA58z/uTm1U359KfWdy3759UVFRgaVLl2LSpEn4/PPPsXz5csTExCAqqvavFbz88stYsmQJjhw54vPnbXiH/cr5S/hbv+f9enattn+2HRGdosUfd+DAgUZ/7r777kN8fHyjP5+SkiJ+Pm/NKgsvo+Tkj4gbMRBnP96DuJG/QNF3OagqvCz+/BKBaAbcupuWZoD5Z02FZkbNGWCe12dDWucua0V63f92XizAF8+83uTHaG3UkFmb3eizp5f5/Gv90U2F16eReE2TM8s1zexzBqjRzGyzZsTrsyGz7U8zN7tG790JqPH6NIpZrmmA+fenCl9zGMmszcw2Z4B5/xzVnP/sCZhzf95Iqe9M7ty5M1avXo21a9eid+/eOHjwIMaPH1/3Xcn79u1DixYtRG9x4avERZMx5tA6hMZEYdjfFmP4dv8MiBHOnz+v23Ptnb8O9z33BEbu/iPunfI4Mhes1+25/U2vbpw1Oc5Z88Zm2vCaJsdrmhznTI5fp2nDXSDHZnK8psnxmqYNZ02OX6fJcQ/IsZlvlPrOZACYNm0apk2bVvfj4cOH17uZfObMGQwbNgwXL16E0+nEfffdh0mTJt328x5Y9B4OLHrvtj+PCt555x0MGzZMl+e6nJ2L/3nyZV2eK9D06sZZk+OcNW9spg2vaXK8pslxzuT4dZo23AVybCbHa5ocr2nacNbk+HWaHPeAHJv5RrmbyQ0dO3YM48aNAwDMnj0bs2fPBgC8++67OHnypF9uJBMRERERERERERHRrSl9M9npdCInJ6fuO5Nv5K9/QdGK+vfvb/QRTInd5NhMTs9moTGR6PPyrwAAx9ZsQfGJHABAn4UTYW/lgDO3EMfWfIw+CyfC0ToUQTYb9s5bC3tYCHr9ZixaOOw4tzUTNZVX0X1CEuyhwbDZ7fhqxirdfg+AcXPWWL/Excn42YPdsW34QgBA/IwRCI9th+oKFw4t24h7f/042nTviMj745D1Rhou7swy5Px8fcqxmRybybGZNtyfckbMGndn88Nm2rCbHJvJcXfK8c+evlHqPZMbCgsLg9vt9nozmRo3d+5co49gSuwmx2ZyejbrPj4J36SkYe+L69Bj0mMAgLCO7VBT5cK+36UiosudsLW049Cyjcicvx5XnZVwtAnHPRMfBTweeNxuOC8WoiDrDDIXrEfe198ie/Mu3c5/jVFz5q0fABx47V1czq79l3VtLe24I6499v0uFTVVLoTFtsOJt7chc8F6lOVcQu6uo4acHeDrUws2k2MzOTbThvtTzohZ4+5sfthMG3aTYzM57k45/tnTN0rfTCZtrr0tCMmwmxybyenZLDQmEs7cAtRUutAi2FH72J2RcOYWAgAqi64guG04ACC8czSCbDa4SsrQumsH5Gzbh8Ovf4D7Z/1n3eeLTeqNi198o9v5rzFqzrz1ayi4bTgq//dfd3bmFSG0fSQAILxTNJx5hfDUuHU7b0N8fcqxmRybybGZNtyfckbMGndn88Nm2rCbHJvJcXfK8c+evlH6bS7I/EbvX4PqShfcrmrYHHYcX/sJTm/aYfSxyII4azcrzytCWEwUKv5VgpoqV+1jPxUhdnAvAEBI2whUFZchLLYdEuY8hX/+9i8AAGd+EVyXnaipcMHWsnZNqPAHPL1569dQVXEZQiLvAACEtY9E7pdZAIBuYx5G9odf6XVUsiBe00gvnLWbcX9qx91JRuL1jPTCWbsZd+ftMdv+5M1kCridU1JQeiYXbXp0wvDtKbiw4zAqfio2+lhkQZy1+k5/8AUefOlpwOPB8fVb0W/pFOx/JRUtglsicXEyLp/Lh/tqNR5JfRGl319A/6VT8M0baTi9aQceXDABAHAmbScA4O6xg5vdH/Aa65cwZxR+1uceJC6ajAOL3sPls/lIXJwMd9VVOC8WAAAi47vgyMoPDf4dkNnxmkZ64azVx/2pHXcnGY3XM9ILZ60+7s7bY7b9yZvJFrR+/Xqjj+BVyanzcJU6ERYThdZdO6DT0L44sOg9RD3QDd0nJOHcx3tveuyfL/1Ft/Op2k1lqja7cdb6L3sWBxdvQNn5S+g+IQktgh1o/28/v+mxk+/+XZez6dmsPK8Iu+esrvvx/ldSAQCHlr9f79dtHbrgpo/dM3dNvR9nrUgPwAl9Y9ScNdbv6FubcfStzXWPH1+75aaP3fnsG4E/YBNUfX2qTNVmKu9PVZupTOVm3J+1uD+14+5sflRtpvLuBNTtpjJVm3F31uLuvD1m2598z2QLysjIMPoIXkX364mqkjIUfXcO+ZnHEXFXe4R2iMIDL4zGkVUZXh/Tk6rdVKZqsxtnLevNdPSaNxY2hx1dRw3CqY2feX1ML6o2UxmbacNucqo2U3l/qtpMZSo34/60FjaTYzM5VZupvDsBdbupTNVm3J3Wwma+aZY3kx3hrWAPDzH6GLCHh8AR3srvn3f79u1+/5y345HU+Rj59VsYtnkxst5Ig9tVDQDIWpmOpNT5KP3+Qt1fB/H2mF4C0Y2zpi9vs1Zy8kd43G70X/Ysvt/4OTzVNV4f04sezfSau0DNVUNWagaYuxuvafoyw/7knMmpNmcA9+c13J9ybNY0XtP0ZYbdCXDWtDDDrHF3Bo6Z94A3Zu/WLN/mIrhtBMbs/zNcZRWGnsMR3grBbSMMPYMerr2X0F1PDsAvVs5AfuZ3qCwoRcmp8wiLbYecT/fX/Vpvj5kZZ01fjc1a1op0PLpxITJfXFf3a709ZhV6zZ2V5krP16qZu/Gapq/muj85Z/rj/qzF/SnHZk3jNU1fzXV3Apw1vXF31uIe0Mbs3ZrlzWSg9v9xVhrEG40ePdroI3iVszUTcSMGImH2SOx/9V30nDwUpzZsR/zUJ/HVjFUA4PUxvQSqG2dNfw1nzXmhAOW5BfV+jbfH9KBXMyvNHZtpw2uanFmuaSrtT86ZnKpzBnB/AtaaPTaT4zVNTtVrmsq7E+CsaWGWWePuNDc958zM3Zrl21xY3UMPPWT0ERp1aPn7uHvcIwjtEIWOj/VF1pvpuOqsQOT9cbCHhdz0mJ5U7qYqlZtdm7VW0W2MPko9KjdTFZtpw25yKjdTdX+q3ExVqjfj/rQONpNjMzmVm6m6OwG1u6lK5WbcndbBZr7hzWQLmjdvntFHqJPR73mUnsmt+/GVc/nY1GMy7pnwS5z46zbA48GRVRnoNXcsfj59xE2P6UmlbmahUrPGZq3iUolxh/JCpWZmwWbasJucSs3Msj9VamYWqjXj/rQuNpNjMzmVmplldwJqdTMLlZpxd1oXm/mm2b7NBRkra0V63f92XizAF8+8Xu/nvT1GdDs+e3qZT48REamM+5P0xv1JRGbH3Ul64+4kq+N3JltQRIQ533PFaOwmx2ZybCbHZtqwmxybybGZHJtpw25ybCbHZnJspg27ybGZHJvJsZlveDPZgoYOHWr0EUyJ3eTYTI7N5NhMG3aTYzM5NpNjM23YTY7N5NhMjs20YTc5NpNjMzk28w1vJltQRkaG0UcwJXaTYzM5NpNjM23YTY7N5NhMjs20YTc5NpNjMzk204bd5NhMjs3k2Mw3vJlMRERERERERERERE3izWQLGjJkiNFHMCV2k2MzOTaTYzNt2E2OzeTYTI7NtGE3OTaTYzM5NtOG3eTYTI7N5NjMN3ajD0D+N3r0aEOfPzQmEn1e/hUA4NiaLSg+kQMA6LNwIuytHHDmFuLYmo/R5p6O6DFpCDweD4796WO4rpSj12/GooXDjnNbM1FTeRXdJyTBHhoMm92Or2asCui5je5mRkY283XO+iycCEfrUATZbNg7by3sYSGcM5NhM23YTc4M1zTuTvMzuhn3Z/PBZnJsJmd0M+7P5sMMX6dxd5ofm/mG35lsQVOnTjX0+buPT8I3KWnY++I69Jj0GAAgrGM71FS5sO93qYjocidsLe2499nHcbW8EtUVVagqvoJ7Jj4KeDzwuN1wXixEQdYZZC5Yj7yvv0X25l0BP7fR3czIyGa+ztmhZRuROX89rjor4WgTzjkzITbTht3kzHBN4+40P6ObcX82H2wmx2ZyRjfj/mw+zPB1Gnen+bGZb5rtdyZXFV+Bq6zC0DM4wlshuG2EoWcIhNCYSDhzC+CpcaNFsKP2sTsj4cwtBABUFl1BcNtwRMZ3wd9HL0J03x7oOmoQWnftgDPpX6L45I9IfG0SMuevBwDEJvXGrplvGfb7uV2ctcDwdc4qLpUgvHM0gmw2uErKLDtnDekxd1abK71eq2bvxmtaYHB31sc5Cxzuz1vj/pRjs6bxmhY43J/1cdYCg7vz1rgHtDFzt2Z5M7mq+Ao+7DcD1WWVhp7DHh6CMfv/bLkXRHleEcJiolDxrxLUVLlqH/upCLGDewEAQtpGoKq4DJdzfkJNpQtVpWVoe29nOPOL4LrsRE2FC7aWtaMZ3ikazrxCeGrcRv12bgtnLXB8nbOw2HZImPMU/vnbvwCAJeesIb3mzkpzpedr1czdeE0LHO7O6zhngcX92TjuTzk2axqvaYHF/XkdZy1wuDsbxz2gjdm7Ncubya6yCsMvsABQXVYJV1mF3/+fmpaW5tfPJ3X6gy/w4EtPAx4Pjq/fin5Lp2D/K6loEdwSiYuTcflcPtxXq3F60w4MSJmKFiHBOLhkA4Ja2PDgggkAgDNpOwEAd48djOwPv9Ll3IHoxlkLHF/n7JHUF1H6/QX0XzoF37yRhtObdlhuzhrSa+4CNVcNWakZYO5uvKYFDnfndZyzwOL+bBz3pxybNY3XtMDi/ryOsxY43J2N4x7QxuzdmuXNZKtbuXIl5s6da9jzl+cVYfec1XU/3v9KKgDg0PL36/26/D3HkL/nWL3H9sxdU+/HWSvSA3TKmxndzYyMbObrnG0duuCmj+WcmQubacNucma4pnF3mp/Rzbg/mw82k2MzOaObcX82H2b4Oo270/zYzDf8B/gsaN++fUYfwZTYTY7N5NhMjs20YTc5NpNjMzk204bd5NhMjs3k2EwbdpNjMzk2k2Mz3/A7k4mImpnR+9egutIFt6saNocdx9d+gtObdhh9LOWxGxFR88Y9IMdmRETNG/eANqp3481kC3rmmWeMPoIpsZscm8mp0mznlBSUnslFmx6dMHx7Ci7sOIyKn4qNPpZXqjQD2M3q2EyOzeTYTBtVunEPyLGZtbGZNuwmx2ZyqjTjHtBG5W58mwsL6tSpk9FHMCV2k2MzOdWalZw6D1epE2ExUWg/IB6JiyYDAKIe6IaHfv+cwaerpVozoH63wW/PQ3inaABA9wlJ6Jk8zODT1VKxm+rYTI7N5NhMG9W6cX/KcXdaE5tpw25ybCanWjPuTm1U3J+8mWxBS5YsMfoIpsRucmwmp1qz6H49UVVShqLvziE/8zgi7mqP0A5ReOCF0TiyKsPo4wFQrxlQv1vWm+noNW8sbA47uo4ahFMbPzP6eADU7KY6NpNjMzk200a1btyfctyd1sRm2rCbHJvJqdaMu1MbFfenkm9zcfjwYUyfPh1Hjx7F/fffj1mzZmH69OkoKyuDzRa4+98Rce0x6I+zENw2AlXFV/D17P/ClbP5AXs+s+uzcCK6jBiAiM534qNBs1F6JtfoI5kC50yOs+Z/j6TOR1BQECLi2mPXjD/A7aoGAGStTEdS6nzk7TqqzF+hUYm3biUnf4TH7Ub/Zc/i+42fw1NdY/QxdcVrmhyvadpw1mQ4Z4HB/SnH3ekdr2kyvKZpwzmT46z5H3enNirvT+W+MzkrKwsPP/wwJkyYgBMnTiA5ORkzZ85EfHx8QG8kA8DAlGk48fY2fPRvs3Hy3b9jYMq0gD5foMTGxuryPD/+4wA+Hfkqys5f0uX5Ak2vblaZM4CzpoVezZqyc0oKPho0B19N/wMGrpiBkHatAdT+FZqw2HbI+XS/wSe8TpVmQOPdslakIzqxJ85+vMfgE17Ha5ocr2lyer4+rTJrnDNtVNkF3J9y3J3e8Zomw2uaNlaZM4CzpoXqe4C789ZU3p/K3Uy+9l3IL7zwAuLi4jBz5kx06NABCQkJAX3ekKg70Pa+Lji7ZS8A4OxHexAZ3wXBUXcE9HkDYeXKlbo8z78OnkJ5bqEuz6UHPbpZac4AzpoWejXzVc7WTOR+eQQJs0cCAHpOHopTG7YjfuqTBp/sOtWaATd3c14oQHlugcGnqo/XNDle0+T0amalWeOcaaPaLuD+lOPuvI7XNDle0+SsNGcAZ00L1fcAd6dvVNyfSr3NRXZ2Nnbv3o3333+/3uMOh6PuZrLD4cDAgQMBAElJSXj11Veb/LwjRoxAdnZ23Y/vqGmJabi73q8Ji22H8rxCwOMBAHjcbjjzihDWIQpVhZdv6/d1K0MeG4LLLa6KP+6JJ55o9Oc2b96MUaNGNfrzycnJ4ufz1swogWgG3LqblmbAzd2MmjNAWzcVmhnJLK/PhqQNDy1/H8P/8TqOrf0EHR/ri+3jlmDgG9MQeX8cir49e8uP1dqoIbM1A653+3b1f6PiUonoY/3RTYXXJ69pTeM1LVn8fPw6LVn8fCrNGWCe12dDZtufZm5m1O4E1Hh98prWNJWua2a5pvHrNDmzzxlgvj9HGb07AeObAWrtz27dumHLli2iz6nUzeSsrCy0adMGnTt3rnusoqICP/zwQ93N5MjISHz55ZcGndAcSkpKjD6CKbGbHJvJqdAso9/z9X585Vw+NvWYjF6/GYsTf90GeDw4sioD/f/vs/jimdcNOuV1KjQDGu+mKlW6mQmbybGZHJtpo0I37k857k7rYzNt2E2OzeRUaMbdqY3q+1Opm8lBQUGoqamB2+2ue3/kdevWoby8vO5mcmlpKQYPHoxWrVph+fLl6N27d5Oft+Ed9ivnL+FvDf4f47xYgNCYKCAoCPB4EGSzISwmEs4A//WG7Z9tR0SnaPHHHThwoNGfy8jIwJgxYxr9+ZSUFPHzeWtmlEA0A27dTUsz4OZuRs0ZoK2bCs2MZJbXZ0NaG2atSK/7386LBT4tc62NGjJrsxt99vQyn3+tP7qp8PrkNa1pvKb552sOfp12ayrNGWCe12dDZtufZm52jd67E1Dj9clrWtNUuq6Z5ZrGr9PkzD5ngHn/HNWc/+wJmHN/3kip90zu27cvKioqsHTpUpw9exZ/+ctfsHz5csTExCAqKgoA8OOPP+LLL7/EsmXLMHbsWLjdbr88d2XhZZSc/BFxI2rfQiNu5C9Q9F1OwP/6RyD48tYfdDM9ullpzgDOmhZsJsdm2vCaJsdZk9OrmZVmjXOmDbvJsZkcr2lynDNt+HWaHGdNjs3k2Mw3St1M7ty5M1avXo21a9eid+/eOHjwIMaPH1/vH9/72c9+BgB48MEH0aZNG+Tm5vrt+ffOX4f7nnsCI3f/EfdOeRyZC9b77XPr6fz587o8T+KiyRhzaB1CY6Iw7G+LMXy7f/4LjVH06maVOQM4a1ro1cxK2EwbXtPkeE2T0/P1aZVZ45xpw10gx2ZyvKbJ8ZqmDb9Ok+OsyXEPyLGZb5R6mwsAmDZtGqZNm1b34+HDh9fdTL5y5QpCQ0PRokULnD9/HpcuXcKdd97pt+e+nJ2L/3nyZb99PqO88847GDZsWMCf58Ci93Bg0XsBfx696NXNKnMGcNa00KuZlbCZNrymyfGaJqfn69Mqs8Y504a7QI7N5HhNk+M1TRt+nSbHWZPjHpBjM98odzO5oWPHjmHcuHF1//v5559HREQErl69ir/+9a9o2bKlwSckIiIiIiIiIiIisj6lbyY7nU7k5OTUfWfygAED8M033xh8KvX179/f6COYErvJsZmcns1CYyLR5+VfAQCOrdmC4hM5AIA+CyfC3soBZ24hjq35GH0WToSjdSiCbDbsnbcW9rAQ9PrNWLRw2HFuayZqKq+i+4Qk2EODYbPb8dWMVbr9HgDj5qyxfomLk/GzB7tj2/CFAID4GSMQHtsO1RUuHFq2Eff++nG06d4RkffHIeuNNFzcmWXI+fn6lGMzOTaTYzNtuD/ljJg17s7mh820YTc5NpPj7pTjnz19o9R7JjcUFhYGt9td7z2TqWlz5841+gimxG5ybCanZ7Pu45PwTUoa9r64Dj0mPQYACOvYDjVVLuz7XSoiutwJW0s7Di3biMz563HVWQlHm3DcM/FRwOOBx+2G82IhCrLOIHPBeuR9/S2yN+/S7fzXGDVn3voBwIHX3sXl7Nr367e1tOOOuPbY97tU1FS5EBbbDife3obMBetRlnMJubuOGnJ2gK9PLdhMjs3k2Ewb7k85I2aNu7P5YTNt2E2OzeS4O+X4Z0/fKH0zmbS59rYgJMNucmwmp2ez0JhIOHMLUFPpQotgR+1jd0bCmVsIAKgsuoLgtuEAgPDO0Qiy2eAqKUPrrh2Qs20fDr/+Ae6f9Z91ny82qTcufqH/3w4xas689WsouG04Kv/3X8R25hUhtH0kACC8UzSceYXw1Lh1O29DfH3KsZkcm8mxmTbcn3JGzBp3Z/PDZtqwmxybyXF3yvHPnr5R+m0uVDN6/xpUV7rgdlXD5rDj+NpPcHrTDqOPRRbEWSN/KM8rQlhMFCr+VYKaKlftYz8VIXZwLwBASNsIVBWXISy2HRLmPIV//vYvAABnfhFcl52oqXDB1rJ2TajwBzy9eevXUFVxGUIi7wAAhLWPRO6XWQCAbmMeRvaHX+l1VOXxmkZ64ayRP3B/asfd6T+8npFeOGvkD9ydt8ds+5M3k4V2TklB6ZlctOnRCcO3p+DCjsOo+KnY6GORBXHW6Had/uALPPjS04DHg+Prt6Lf0inY/0oqWgS3ROLiZFw+lw/31Wo8kvoiSr+/gP5Lp+CbN9JwetMOPLhgAgDgTNpOAMDdYwc3uz/gNdYvYc4o/KzPPUhcNBkHFr2Hy2fzkbg4Ge6qq3BeLAAARMZ3wZGVHxr8O1ALr2mkF84a3S7uT+24O/2L1zPSC2eNbhd35+0x2/7kzWSNSk6dh6vUibCYKLTu2gGdhvbFgUXvIeqBbug+IQnnPt5702P/fOkvupxt/fr1ujyP1aja7cZZ67/sWRxcvAFl5y+h+4QktAh2oP2//fymx06++3ddzqZqM5Xp2aw8rwi756yu+/H+V1IBAIeWv1/v120duuCmj90zd029H2etSA/ACX1j1Jw11u/oW5tx9K3NdY8fX7vlpo/d+ewbgT9gE1R9fXJ/WovKzVTdnyo3Uxn3p5wRs8bdGRjcndajajdVdyegbjOVcXfK8c+evuF7JmsU3a8nqkrKUPTdOeRnHkfEXe0R2iEKD7wwGkdWZXh9TC8ZGfo9l5Wo2u3GWct6Mx295o2FzWFH11GDcGrjZ14f04uqzVTGZnJspo2q3bg/rUXlZqruT5WbqYzd5NhMTtVm3J3Wo2o3VXcnoG4zlbGZHJv5hjeThR5JnY+RX7+FYZsXI+uNNLhd1QCArJXpSEqdj9LvL9T9dRBvj+lh+/btfv+cjvBWsIeH+P3zStnDQ+AIbxWQzx2IbrfD26yVnPwRHrcb/Zc9i+83fg5PdY3Xx/TCWZPTY870ahjI1+ONrNQMsFY3Ce5PY5n5mial+v608pwB5p417k85Ngus5ro7AXWua2a+pkmovjsBa+9PM88Z94A2Zu/Gt7kQuvZeQnc9OQC/WDkD+ZnfobKgFCWnziMsth1yPt1f92u9PWZWwW0jMGb/n+EqqzD0HI7wVghuG2HoGfTS2KxlrUjHoxsXIvPFdXW/1ttjZsVZu316NTRzo4b0nDsrdZPg/uQ1TS/NcX+qMmeAuWeN+1OOzQKrue5OQJ3rWnOZvea4OwHOmT9wD2hj9m68maxRztZMxI0YiITZI7H/1XfRc/JQnNqwHfFTn8RXM1YBgNfH9DB69OiAfN7gthGWevE2FKhut6vhrDkvFKA8t6Der/H2mB44a3J6zZmVGrKZNma5pnF/mpuqcwaouz85Z9pwF8ixmZyq17TmuDsBa81WQ2aZNVV2J8D9qQX3gJyer00zd+PbXNyGQ8vfx93jHkFohyh0fKwvst5Mx1VnBSLvj4M9LOSmx/Ty0EMP6fZcVqJyt2uz1iq6jdFHqUflZqpiMzk200blbtyf1qF6MxX3p+rNVMVucmwmp3Iz7k5rUbmbirsTULuZqthMjs18w5vJAhn9nkfpmdy6H185l49NPSbjngm/xIm/bgM8HhxZlYFec8fi59NH3PSYXubNm6fbc1mJSt0am7WKSyXGHcoLlZqZBZvJsZk2KnXj/rQu1ZqZYX+q1sws2E2OzeRUasbdaW0qdTPD7gTUamYWbCbHZr7h21z4QdaK9Lr/7bxYgC+eeb3ez3t7jOh2fPb0Mp8eIyJSGfcn6Y37k4jMjruT9MbdSUQN8TuTLSgiwpzvuWI0dpNjMzk2k2MzbdhNjs3k2EyOzbRhNzk2k2MzOTbTht3k2EyOzeTYzDe8mWxBQ4cONfoIpsRucmwmx2ZybKYNu8mxmRybybGZNuwmx2ZybCbHZtqwmxybybGZHJv5hjeTLSgjI8PoI5gSu8mxmRybybGZNuwmx2ZybCbHZtqwmxybybGZHJtpw25ybCbHZnJs5hveTCYiIiIiIiIiIiKiJvFmsgUNGTLE6COYErvJsZkcm8mxmTbsJsdmcmwmx2basJscm8mxmRybacNucmwmx2ZybOYbu9EHUF1oTCT6vPwrAMCxNVtQfCIHANBn4UTYWzngzC3EsTUfo809HdFj0hB4PB4c+9PHcF0pR6/fjEULhx3ntmaipvIquk9Igj00GDa7HV/NWBWwM48ePTpgn9vKjOzm65z1WTgRjtahCLLZsHfeWtjDQgybM4CzpgWbybGZNma4pqm0OwHOmhZGNzPj/jS6mVmxmxybyRndzIz70+hmZmWGr9NU2p0AZ00LNpNjM9/wZnITuo9Pwjcpaaj4VwkSX5uEf/72bYR1bIeaKhcOLduIASlTYWtpx73PPo6q0jIAQFXxFfRIHgp4PPC43XBeLETZ+UsoyDqDu8cORmXxlYCeeerUqUhLS/P7560qvgJXWYXfP6+EI7wVgtsG5l/XDFQ3X/g6Z4eWbQQAJC5OhqNNOO4eN9iwOQM4a1roNWd6NAzk6/FGVmoGWK+bN2bcnQCvaVoYOWeAOfenlecMMP+scX/KsZn/mHF/BrKZCtc1s1/TvDHj7gSsvT/NPmfcA9qYuRtvJjchNCYSztwCeGrcaBHsqH3szkg4cwsBAJVFVxDcNhyR8V3w99GLEN23B7qOGoTWXTvgTPqXKD75IxJfm4TM+esBALFJvbFr5luG/X60qiq+gg/7zUB1WaWh57CHh2DM/j/rchHRk69zVnGpBOGdoxFks8FVUma5OQM4a/6gV0MzN2pIz7mzUrfGcHdex2taYHF/1lJlzgBzzxr3pxyb+Rf353WqXNesOHvcnddxzm4f94A2Zu/G90xuQnleEcJiotAiuCVqqly1j/1U+xgAhLSNQFVxGS7n/ISaSheqSsvQMrwVnPlFcF12oqbCBVvL2nv24Z2i4cwrhKfGbdjvRytXWYXhF1gAqC6rNPy/GgaCr3MWFtsOCXOewsGlGwDAcnMGcNb8Qa+GZm7UkJ5zZ6VujeHuvI7XtMDi/qylypwB5p417k85NvMv7s/rVLmuWXH2uDuv45zdPu4Bbczejd+Z3ITTH3yBB196GvB4cHz9VvRbOgX7X0lFi+CWSFycjMvn8uG+Wo3Tm3ZgQMpUtAgJxsElGxDUwoYHF0wAAJxJ2wkAuHvsYGR/+FXAz2zkX80yMyO7+Tpnj6S+iNLvL6D/0in45o00nN60w7A5AzhrWrCZHJtpY4Zrmkq7E+CsaWF0MzPuT6ObmRW7ybGZnNHNzLg/jW5mVmb4Ok2l3Qlw1rRgMzk28w1vJjehPK8Iu+esrvvx/ldSAQCHlr9f79fl7zmG/D3H6j22Z+6aej/OWpEeoFPWt3LlSsydO1eX57ISI7v5Omdbhy646WONmjOAs6YFm8mxmTZmuKaptDsBzpoWRjcz4/40uplZsZscm8kZ3cyM+9PoZmZlhq/TVNqdAGdNCzaTYzPf8G0uLGjfvn1GH8GU2E2OzeTYTI7NtGE3OTaTYzM5NtOG3eTYTI7N5NhMG3aTYzM5NpNjM9/wO5OJiJqZ0fvXoLrSBberGjaHHcfXfoLTm3YYfSzlsRsRUfPGPSDHZkREzRv3gDaqd+PNZAt65plnjD6CKbGbHJvJqdJs55QUlJ7JRZsenTB8ewou7DiMip+KjT6WV6o0A9jN6thMjs3k2EwbVbpxD8ixmbWxmTbsJsdmcqo04x7QRuVufJsLC+rUqZPRRzAldpNjMznVmpWcOg9XqRNhMVFoPyAeiYsmAwCiHuiGh37/nMGnq6VaM6B+t8Fvz0N4p2gAQPcJSeiZPMzg09VSsZvq2EyOzeTYTBvVunF/ynF3WhObacNucmwmp1oz7k5tVNyfSt5MPnz4MPr164eQkBAkJiZiw4YNCA0NhdvtNvpoprBkyRKjj2BK7CbHZnKqNYvu1xNVJWUo+u4c8jOPI+Ku9gjtEIUHXhiNI6syjD4eAPWaAfW7Zb2Zjl7zxsLmsKPrqEE4tfEzo48HQM1uqmMzOTaTYzNtVOvG/SnH3WlNbKYNu8mxmZxqzbg7tVFxfyr3NhdZWVl4+OGHsWTJEqSlpWHbtm2YOXMm4uPjYbMF7t53n4UT0WXEAER0vhMfDZqN0jO5AXsuK4mIa49Bf5yF4LYRqCq+gq9n/xeunM03+lhK46zJcc7875HU+QgKCkJEXHvsmvEHuF3VAICslelISp2PvF1HlfkrNCrx1q3k5I/wuN3ov+xZfL/xc3iqa4w+pq54TZPjNU2Oc6YNZ83/uD/luDtvxmuaNrymyXHW5Dhn/sfdqY3K+1O570yeNWsWpk+fjhdeeAFxcXGYOXMmOnTogISEhIA+74//OIBPR76KsvOXAvo8eoiNjdXtuQamTMOJt7fho3+bjZPv/h0DU6bp9tz+plc3zpoc58z/dk5JwUeD5uCr6X/AwBUzENKuNYDav0ITFtsOOZ/uN/iE16nSDGi8W9aKdEQn9sTZj/cYfMLreE2T4zVNjnMmx6/TtFFlF3B/ynF33ozXNG14TZPjrMlxzvyPu1MblfenUjeTs7OzsXv3bsyaNave4w6Ho+5m8nfffYcnnngCSUlJGDbMf+8N8q+Dp1CeW+i3z2eklStX6vI8IVF3oO19XXB2y14AwNmP9iAyvguCo+7Q5fn9Ta9unDUZzllg5WzNRO6XR5AweyQAoOfkoTi1YTvipz5p8MmuU60ZcHM354UClOcWGHyq+nhNk+M1TY5zJsev07RRbRdwf8pxd17Ha5ocr2nacNZkOGeBxd2pjYr7U6m3ucjKykKbNm3QuXPnuscqKirwww8/ICEhAVevXsWMGTPwt7/9De3atfP5844YMQLZ2dl1P76jpiWm4W6/nl2rIY8NweUWV8Uf98QTTzT6c5s3b8aoUaMa/fnk5GTx83lrFhbbDuV5hYDHAwDwuN1w5hUhrEMUqgovi5/DV4FoBty6m5ZmgPlnTYVmRs0ZYJ7XZ0PSuTu0/H0M/8frOLb2E3R8rC+2j1uCgW9MQ+T9cSj69uwtP1Zro4bM1gy43u3b1f+Niksloo/1RzcVXp9G4jVNzizXNLPPGaBGM7PNmhGvz4bMtj/N3Myo3Qmo8fo0ilmuaYD596cKX3MYyazNzDZngPn+HGX07gSMbwaotT+7deuGLVu2iD6nUt+ZHBQUhJqamnr/0N66detQXl6OhIQE/POf/0R4eDimTp2Khx9+GKmpqQaeVl0lJSVGH8GU2E2OzeRUaJbR7/l675d25Vw+NvWYjHsm/BIn/roN8HhwZFUGes0da+Apr1OhGdB4N+ky14sq3cyEzeTYTI7NtFGhG/enHHen9bGZNuwmx2ZyKjTj7tRG9f2p1Hcm9+3bFxUVFVi6dCkmTZqEzz//HMuXL0dMTAyioqJw8eJFHDx4EN9++y3CwsIwaNAg/OIXv0CPHj1u+Xkb3mG/cv4S/tbv+UD+Vny2/bPtiOgULf64AwcONPpzGRkZGDNmTKM/n5KSIn4+b82cFwsQGhMFBAUBHg+CbDaExUTCGeC/RhOIZsCtu2lpBph/1lRoZtScAeZ5fTakde6yVqTX/W/nxQJ88czrTX6M1kYNmbXZjT57epnPv9Yf3VR4fRqJ1zQ5s1zTzD5ngBrNzDZrRrw+GzLb/jRzs2v03p2AGq9Po5jlmgaYf3+q8DWHkczazGxzBpj3z1HN+c+egDn3542U+s7kzp07Y/Xq1Vi7di169+6NgwcPYvz48XXvlxwZGYnExERER0cjLCwM//7v/46jR48afGr1vPrqq7o8T2XhZZSc/BFxIwYCAOJG/gJF3+UE/K9/BIpe3axEj2acM2IzbdhNjtc0Oc6ZHL9O04azJsdmcmwmx2uaNpw1OX6dJsc5k2Mz3yh1MxkApk2bhry8PJSUlGDdunU4e/Zs3c3khx56COfOnUNFRQXcbjcOHjyI7t27++V5ExdNxphD6xAaE4Vhf1uM4dv9818bjHD+/Hndnmvv/HW477knMHL3H3HvlMeRuWC9bs/tb3p146zJcc6aNzbThtc0OV7T5Dhncvw6TRvuAjk2k+M1TY7XNG04a3L8Ok2Oe0COzXyj1NtceHPs2DGMGzcOAHDHHXdg8eLFePTRR1FTU4Phw4ejV69efnmeA4vew4FF7/nlcxntnXfewbBhw3R5rsvZufifJ1/W5bkCTa9unDU5zlnzxmba8Jomx2uaHOdMjl+nacNdIMdmcrymyfGapg1nTY5fp8lxD8ixmW+UvpnsdDqRk5NT953JAPDUU0/hqaeeMvBURETmEBoTiT4v/woAcGzNFhSfyAEA9Fk4EfZWDjhzC3Fszcfos3AiHK1DEWSzYe+8tbCHhaDXb8aihcOOc1szUVN5Fd0nJMEeGgyb3Y6vZqwy8relm8b6JS5Oxs8e7I5twxcCAOJnjEB4bDtUV7hwaNlG3Pvrx9Gme0dE3h+HrDfScHFnllG/BSIi0oD7UzvuTiKi5om78/aYbX8q9zYXNwoLC4Pb7a53M5ma1r9/f6OPYErsJsdmcno26z4+Cd+kpGHvi+vQY9JjAICwju1QU+XCvt+lIqLLnbC1tOPQso3InL8eV52VcLQJxz0THwU8HnjcbjgvFqIg6wwyF6xH3tffInvzLt3Of41Rc+atHwAceO1dXM6u/Zd1bS3tuCOuPfb9LhU1VS6ExbbDibe3IXPBepTlXELuLuPe15+vTzk2k2MzOTbThvtTzohZ4+5sfthMG3aTYzM57k45/tnTN0rfTCZt5s6da/QRTInd5NhMTs9moTGRcOYWoKbShRbBjtrH7rz+rxFXFl1BcNtwAEB452gE2WxwlZShddcOyNm2D4df/wD3z/rPus8Xm9QbF7/4RrfzX2PUnHnr11Bw23BU/u8/yOHMK0Jo+0gAQHinaDjzCuGpcet23ob4+pRjMzk2k2Mzbbg/5YyYNe7O5ofNtGE3OTaT4+6U4589fcObyRZ07T2mSYbd5NhMTs9m5XlFCIuJQovglqipctU+9lPtYwAQ0jYCVcVlCItth4Q5T+Hg0g0AAGd+EVyXnaipcMHWsvbdkIz8A55Rc+atX0NVxWUIibwDABDWPhLl+UUAgG5jHkb2h1/pdlZv+PqUYzM5NpNjM224P+WMmDXuzuaHzbRhNzk2k+PulOOfPX2j9Hsmk/mN3r8G1ZUuuF3VsDnsOL72E5zetMPoY5EFcdZudvqDL/DgS08DHg+Or9+KfkunYP8rqWgR3BKJi5Nx+Vw+3Fer8Ujqiyj9/gL6L52Cb95Iw+lNO/DgggkAgDNpOwEAd48dbPgf8PTWWL+EOaPwsz73IHHRZBxY9B4un81H4uJkuKuuwnmxAAAQGd8FR1Z+aPDvgMyM1zTSC2ftZtyf2nF3kpF4PSO9cNZuxt15e8y2P3kzmQJu55QUlJ7JRZsenTB8ewou7DiMip+KjT4WWRBnrb7yvCLsnrO67sf7X0kFABxa/n69X7d16IKbPnbP3DX1fpy1Ij0AJ1RbY/2OvrUZR9/aXPf48bVbbvrYnc++EfgDkuXxmkZ64azVx/2pHXcnGY3XM9ILZ60+7s7bY7b9ybe5sKD169cbfQSvSk6dh6vUibCYKLQfEI/ERZMBAFEPdMNDv3/O62N6UrWbylRtduOsDX57HsI7RQMAuk9IQs/kYV4f04uqzVTGZtqwm5yqzVTen6o2U5nKzbg/rYXN5NhMTtVmKu9OQN1uKlO1GXentbCZb5rlzWRHeCvYw0OMPgbs4SFwhLfy++fNyMjw++f0h+h+PVFVUoai784hP/M4Iu5qj9AOUXjghdE4sirD62N6CkQ3zpoxbpy1rDfT0WveWNgcdnQdNQinNn7m9TG96NFMr7kL1Fw1ZKVmgLm78ZpmDJX3J+dMTtU5A7g/uT/l2KxpvKYZQ+XdCXDWtDDDrHF3Bo6Z94A3Zu/WLN/mIrhtBMbs/zNcZRWGnsMR3grBbSP8/nm3b9+OZ5991u+fV6tHUucjKCgIEXHtsWvGH+B2VQMAslamIyl1PvJ2Ha376yDeHtNLILpx1vTlbdZKTv4Ij9uN/suexfcbP4enusbrY3rRo5lecxeouWrISs0Ac3fjNU1fZtifnDM51eYM4P68hvtTjs2axmuavsywOwHOmhZmmDXuzsAx8x7wxuzdmuXNZKD2/3F6DCJdfy+hu54cgF+snIH8zO9QWVCKklPnERbbDjmf7q/7td4eMzvOmn4am7WsFel4dONCZL64ru7XenvMSjh3cmzmG3bST3Pen5wzfXF/XsfZk2OzprGRfprz7gQ4a3ri7ryOc6eNmbs1y7e5sLrRo0cbfQSvcrZmIvfLI0iYPRIA0HPyUJzasB3xU5+s+zXeHtOLqt1UpmqzhrPmvFCA8tyCer/G22N6ULWZythMG3aTU7WZyvtT1WYqU7kZ96e1sJkcm8mp2kzl3Qmo201lqjbj7rQWNvMNbyZb0EMPPWT0ERp1aPn7uHvcIwjtEIWOj/VF1pvpuOqsQOT9cbCHhdz0mJ5U7qYqlZtdm7VW0W2MPko9KjdTFZtpw25yKjdTdX+q3ExVqjfj/rQONpNjMzmVm6m6OwG1u6lK5WbcndbBZr7hzWQLmjdvntFHqJPR73mUnsmt+/GVc/nY1GMy7pnwS5z46zbA48GRVRnoNXcsfj59xE2P6UmlbmahUrPGZq3iUolxh/JCpWZmwWbasJucSs3Msj9VamYWqjXj/rQuNpNjMzmVmplldwJqdTMLlZpxd1oXm/mm2b5nMhkra0V63f92XizAF8+8Xu/nvT1GdDs+e3qZT48REamM+5P0xv1JRGbH3Ul64+4kq+N3JltQ165djT6CKbGbHJvJsZkcm2nDbnJsJsdmcmymDbvJsZkcm8mxmTbsJsdmcmwmx2a+4c1kC/rhhx+MPoIpsZscm8mxmRybacNucmwmx2ZybKYNu8mxmRybybGZNuwmx2ZybCbHZr7hzWQiIiIiIiIiIiIiahJvJlvQkCFDjD6CKbGbHJvJsZkcm2nDbnJsJsdmcmymDbvJsZkcm8mxmTbsJsdmcmwmx2a+4c1kCxo9erTRRzAldpNjMzk2k2MzbdhNjs3k2EyOzbRhNzk2k2MzOTbTht3k2EyOzeTYzDd2ow9A/jd16lSkpaUZ9vyhMZHo8/KvAADH1mxB8YkcAECfhRNhb+WAM7cQx9Z8jDb3dESPSUPg8Xhw7E8fw3WlHL1+MxYtHHac25qJmsqr6D4hCfbQYNjsdnw1Y1VAz210NzMyspmvc9Zn4UQ4WociyGbD3nlrYQ8L4ZyZDJtpw25yZrimcXean9HNuD+bDzaTYzM5o5txfzYfZvg6jbvT/NjMN832ZnJV8RW4yioMPYMjvBWC20YYeoZA6D4+Cd+kpKHiXyVIfG0S/vnbtxHWsR1qqlw4tGwjBqRMha2lHfc++ziqSssA1P7/o0fyUMDjgcfthvNiIcrOX0JB1hncPXYwKouvGPy70o6zFhi+ztmhZRsBAImLk+FoE467xw225Jw1pMfcWW2u9Hqtmr0br2mBwd1ZH+cscLg/b437U47NmsZrWuBwf9bHWQsM7s5b4x7QxszdmuXN5KriK/iw3wxUl1Uaeg57eAjG7P+z5V4QoTGRcOYWwFPjRotgR+1jd0bCmVsIAKgsuoLgtuGIjO+Cv49ehOi+PdB11CC07toBZ9K/RPHJH5H42iRkzl8PAIhN6o1dM98y7PdzOzhrgePrnFVcKkF452gE2WxwlZRZcs4a0mvurDRXer5WzdyN17TA4e68jnMWWNyfjeP+lGOzpvGaFljcn9dx1gKHu7Nx3APamL1bs3zPZFdZheEXWACoLqsMyH+FMPpb8svzihAWE4UWwS1RU+Wqfeyn2scAIKRtBKqKy3A55yfUVLpQVVqGluGt4MwvguuyEzUVLtha1v53jvBO0XDmFcJT4w74uQPRjbMWOL7OWVhsOyTMeQoHl24AAEvOWUN6zV2g5qohKzUDzN2N17TA4e68jnMWWNyfjeP+lGOzpvGaFljcn9dx1gKHu7Nx3APamL1bs7yZbHUrV6409PlPf/AFer04DgNen4pT/+8z9Fs6Bc4LBWgR3BKJi5Nx+Vw+3FercXrTDgxImYr7nnsSP3y0G6c37cDPZ/wHBr45HWfSdgIA7h47GNkffqXLuY3uZkZGNvN1zh5JfREtHHb0XzoFIe1ac85MiM20YTc5M1zTuDvNz+hm3J/NB5vJsZmc0c24P5sPM3ydxt1pfmzmm2b5NhdWt2/fPkOfvzyvCLvnrK778f5XUgEAh5a/X+/X5e85hvw9x+o9tmfumno/zlqRHqBT3szobmZkZDNf52zr0AU3fSznzFzYTBt2kzPDNY270/yMbsb92XywmRybyRndjPuz+TDD12ncnebHZr7hzWQiomZm9P41qK50we2qhs1hx/G1n+D0ph1GH0t57EZE1LxxD8ixGRFR88Y9oI3q3Xgz2YKeeeYZo49gSuwmx2ZyqjTbOSUFpWdy0aZHJwzfnoILOw6j4qdio4/llSrNAHazOjaTYzM5NtNGlW7cA3JsZm1spg27ybGZnCrNuAe0Ubkb3zPZgjp16mT0EUyJ3eTYTE61ZiWnzsNV6kRYTBTaD4hH4qLJAICoB7rhod8/Z/DpaqnWDKjfbfDb8xDeKRoA0H1CEnomDzP4dLVU7KY6NpNjMzk200a1btyfctyd1sRm2rCbHJvJqdaMu1MbFfcnbyZb0JIlS4w+gimxmxybyanWLLpfT1SVlKHou3PIzzyOiLvaI7RDFB54YTSOrMow+ngA1GsG1O+W9WY6es0bC5vDjq6jBuHUxs+MPh4ANbupjs3k2EyOzbRRrRv3pxx3pzWxmTbsJsdmcqo14+7URsX9qeTbXBw+fBjTp0/H0aNHcf/992PWrFmYPn06ysrKYLMF7v53RFx7DPrjLAS3jUBV8RV8Pfu/cOVsfsCez+z6LJyILiMGIKLznfho0GyUnsk1+kimwDmT46z53yOp8xEUFISIuPbYNeMPcLuqAQBZK9ORlDofebuOKvNXaFTirVvJyR/hcbvRf9mz+H7j5/BU1xh9TF3xmibHa5o2nDUZzllgcH/KcXd6x2uaDK9p2nDO5Dhr/sfdqY3K+1O570zOysrCww8/jAkTJuDEiRNITk7GzJkzER8fH9AbyQAwMGUaTry9DR/922ycfPfvGJgyLaDPFyixsbG6PM+P/ziAT0e+irLzl3R5vkDTq5tV5gzgrGmhV7Om7JySgo8GzcFX0/+AgStmIKRdawC1f4UmLLYdcj7db/AJr1OlGdB4t6wV6YhO7ImzH+8x+ITX8Zomx2uanJ6vT6vMGudMG1V2AfenHHend7ymyfCapo1V5gzgrGmh+h7g7rw1lfencjeTr30X8gsvvIC4uDjMnDkTHTp0QEJCQkCfNyTqDrS9rwvObtkLADj70R5ExndBcNQdAX3eQFi5cqUuz/Ovg6dQnluoy3PpQY9uVpozgLOmhV7NfJWzNRO5Xx5BwuyRAICek4fi1IbtiJ/6pMEnu061ZsDN3ZwXClCeW2DwqerjNU2O1zQ5vZpZadY4Z9qotgu4P+W4O6/jNU2O1zQ5K80ZwFnTQvU9wN3pGxX3p1I3k7Ozs7F7927MmjWr3uMOhwMJCQk4deoUBg8eXPd/LVu2xLFjx/zy3GGx7VCeVwh4PAAAj9sNZ14RwjpE+eXz62nu3LlGH8GU9OhmpTkDOGtaqNjs0PL3cfe4RxDaIQodH+uLrDfTcdVZgcj744w+GgA1mwHXu7WKbmP0UbziNU1O1VlTmV7NrDRrnDNtVOzG/SnH3VmL1zTi12lynDU5FZtxd2qj2v5U6j2Ts7Ky0KZNG3Tu3LnusYqKCvzwww9ISEhAjx498OWXXwIATpw4gXHjxuHnP/95k593xIgRyM7OrvvxHTUtMQ13+/38Wgx5bAgut7gq/rgnnnii0Z87ceIEPvzww0Z/Pjk5Wfx8Vm8G3LqblmaA+buxmTlenw011TCj3/P1fnzlXD429ZiMXr8ZixN/3QZ4PDiyKgP9/++z+OKZ12/5XFobNaR6M6Dxblr4oxtfn7ymSZnlmsZmyeLnU6kZYJ7XZ0Nm259mbqaFWb/mUOn1aZZrGmD+bvyag82kzPT6vJHZdidgfDNArf3ZrVs3bNmyRfQ5lbqZHBQUhJqaGrjd7rr3R163bh3Ky8tvepuLd999F88884zfntt5sQChMVFAUBDg8SDIZkNYTCScFvnrDaQGzhmpLGtFet3/dl4saHKZU32fPb3M6CPojtc00gtnjVTG/aldc9ydAK9ppA/OGamMu/P2GL0/lbqZ3LdvX1RUVGDp0qWYNGkSPv/8cyxfvhwxMTGIirr+VzFqamrwwQcf4ODBgz593oZ32K+cv4S/NbjLX1l4GSUnf0TciIE4+/EexI38BYq+y0FV4eXb/43dwvbPtiOiU7T44w4cONDoz913332Ij49v9OdTUlLEz+etmVEC0Qy4dTctzYCbuxk1Z4C2bio0M5JZXp8N6dlQa6OGmlMzwD/dVHh98prWNF7T/PM1B79OuzWV5gwwz+uzIbPtTzbTRoXXJ69pTVPpumaWaxq/TpMz+5wBzevPUWbdA96YsduNlHrP5M6dO2P16tVYu3YtevfujYMHD2L8+PE3fVfy3//+d/Tp0wc/+9nP/Pr8e+evw33PPYGRu/+Ie6c8jswF6/36+fVy/vx5XZ4ncdFkjDm0DqExURj2t8UYvt0/Lyqj6NXNKnMGcNa00KuZlbCZNrymyfGaJqfn69Mqs8Y504a7QI7N5HhNk+M1TRt+nSbHWZPjHpBjM98o9Z3JADBt2jRMmzat7sfDhw8P+FtcXHM5Oxf/8+TLfv+8envnnXcwbNiwgD/PgUXv4cCi9wL+PHrRq5tV5gzgrGmhVzMrYTNteE2T4zVNTs/Xp1VmjXOmDXeBHJvJ8Zomx2uaNvw6TY6zJsc9IMdmvlHqO5O9OXbsWL2byUVFRdi/fz/+z//5PwaeioiIiIiIiIiIiKh5Ue47k2/kdDqRk5NT72ZyZGQkcnJyDDyV+vr372/0EUyJ3eTYTE7PZqExkejz8q8AAMfWbEHxidprZ5+FE2Fv5YAztxDH1nyMPgsnwtE6FEE2G/bOWwt7WAh6/WYsWjjsOLc1EzWVV9F9QhLsocGw2e34asYq3X4PgHFz1li/xMXJ+NmD3bFt+EIAQPyMEQiPbYfqChcOLduIe3/9ONp074jI++OQ9UYaLu7MMuT8fH3KsZkcm8mxmTbcn3JGzBp3Z/PDZtqwmxybyXF3yvHPnr5R+juTw8LC4Ha7b3qbC7q1uXPnGn0EU2I3OTaT07NZ9/FJ+CYlDXtfXIcekx4DAIR1bIeaKhf2/S4VEV3uhK2lHYeWbUTm/PW46qyEo0047pn4KODxwON2w3mxEAVZZ5C5YD3yvv4W2Zt36Xb+a4yaM2/9AODAa+/icnYuAMDW0o474tpj3+9SUVPlQlhsO5x4exsyF6xHWc4l5O46asjZAb4+tWAzOTaTYzNtuD/ljJg17s7mh820YTc5NpPj7pTjnz19o/TNZNJm3LhxRh/BlNhNjs3k9GwWGhMJZ24BaipdaBHsqH3szsj/v717j46qvvc+/slkMrlNhCQYCQEEEcFDjSgkgKcuMVWhVXkqhptlAcIpSlnIkiK0xQuXB6ogoKeUQmqj8iDLYIpHpNhGkYJiSrgYERQEhAgkkeYGZHIZkpnnjxwCCRMz383M3r+95/P6qxlI5se73+xvsg0DXMXlAIC6iguIjHcCAJzdkxBms8FdVY0ON3VB0dbd2P/S27htxs+bP15Kxh048/Hnup3/EqPmzFe/1iLjnaj7338R21VSgZjOCQAAZ7ckuErK4W306Hbe1vj5Kcdmcmwmx2bacH/KGTFr3J2hh820YTc5NpPj7pTj957+UfplLlSTWbAaDXVueNwNsDnsOLTmfRzdsM3oY5EFcdYoEGpKKhCbnIjaf1ehsd7d9Nj3FUgZ2h8AEBUfh/rKasSmdELqzEfxr9/+GQDgKq2A+7wLjbVu2CKa1oQK3+DpzVe/1uorqxGVcB0AILZzAor/WQgA6DXqHhx/Z4deR1Uer2mkF84aBQL3p3bcnYHD6xnphbNGgcDdeW3Mtj95M1lo++SlOHesGB37dMPDeUtxett+1H5fafSxyII4a3Stjr79Me78zWOA14tDWVuQvmgyCp7LRnhkBNIWTML5k6XwXGzAvdnP4Nw3pzFo0WR8viwHRzdsw51zxwEAjuVsBwDcPHpoyH2D11a/1Jkjcf2AW5A2fyL2zH8T50+UIm3BJHjqL8J1pgwAkNCvB75Y8Y7BfwK18JpGeuGs0bXi/tSOuzOweD0jvXDW6Fpxd14bs+1P3kzWqOrIKbjPuRCbnIgON3VBt2EDsWf+m0i8vRd6j8vAyfc+u+qxf/3mz7qcLSsrS5fnsRpVu105a4MWT8HeBetQfeoseo/LQHikA51//KOrHjv8xt91OZuqzVSmZ7Oakgp8OnNV89sFz2UDAPYteavF79sybO5V77tr1uoWbxcu3xiEE/rHqDlrq9+BVzfhwKubmh8/tGbzVe+7fcqy4B+wHap+fnJ/WovKzVTdnyo3Uxn3p5wRs8bdGRzcndajajdVdyegbjOVcXfK8XtP//A1kzVKSu+L+qpqVHx1EqX5hxB3Y2fEdEnE7U9n4ouVuT4f00turn7PZSWqdrty1gpf3oj+s0fD5rDjppF348j6D30+phdVm6mMzeTYTBtVu3F/WovKzVTdnyo3Uxm7ybGZnKrNuDutR9Vuqu5OQN1mKmMzOTbzD28mC92bPQePfPIqhm9agMJlOfC4GwAAhSs2IiN7Ds59c7r5r4P4ekwPeXl5Af+YDmc07M6ogH9cKbszCg5ndFA+djC6XQtfs1Z1+Dt4PR4MWjwF36z/CN6GRp+P6YWzJqfHnOnVMJifj1eyUjPAWt0kuD+NZeZrmpTq+9PKcwaYe9a4P+XYLLhCdXcC6lzXzHxNk1B9dwLW3p9mnjPuAW3M3o0vcyF06bWEbnxoCP5zxTSU5n+FurJzqDpyCrEpnVD0QUHz7/X1mFlFxsdhVMGf4K6uNfQcDmc0IuPjDD2DXtqatcLlG3Hf+nnIf2Zt8+/19ZhZcdaunV4NzdyoNT3nzkrdJLg/eU3TSyjuT1XmDDD3rHF/yrFZcIXq7gTUua6FyuyF4u4EOGeBwD2gjdm78WayRkVb8tFzxF1IfeoRFDz/BvpOHIYj6/LQb+pD2DFtJQD4fEwPmZmZQfm4kfFxlvrkbS1Y3a5V61lznS5DTXFZi9/j6zE9cNbk9JozKzVkM23Mck3j/jQ3VecMUHd/cs604S6QYzM5Va9pobg7AWvNVmtmmTVVdifA/akF94Ccnp+bZu7Gl7m4BvuWvIWbx9yLmC6J6Hr/QBS+vBEXXbVIuK0n7LFRVz2ml8GDB+v2XFaicrdLsxad1NHoo7SgcjNVsZkcm2mjcjfuT+tQvZmK+1P1ZqpiNzk2k1O5GXentajcTcXdCajdTFVsJsdm/uHNZIHc9F/h3LHi5rcvnCzFhj4Tccu4n+Drv2wFvF58sTIX/WeNxo+eHHHVY3qZPXu2bs9lJSp1a2vWas9WGXcoH1RqZhZsJsdm2qjUjfvTulRrZob9qVozs2A3OTaTU6kZd6e1qdTNDLsTUKuZWbCZHJv5hy9zEQCFyzc2/2/XmTJ8/PhLLX7d12NE1+LDxxb79RgRkcq4P0lv3J9EZHbcnaQ37k4iao0/mWxBcXHmfM0Vo7GbHJvJsZkcm2nDbnJsJsdmcmymDbvJsZkcm8mxmTbsJsdmcmwmx2b+4c1kCxo2bJjRRzAldpNjMzk2k2MzbdhNjs3k2EyOzbRhNzk2k2MzOTbTht3k2EyOzeTYzD+8mWxBubm5Rh/BlNhNjs3k2EyOzbRhNzk2k2MzOTbTht3k2EyOzeTYTBt2k2MzOTaTYzP/8GYyEREREREREREREbWLN5Mt6IEHHjD6CKbEbnJsJsdmcmymDbvJsZkcm8mxmTbsJsdmcmwmx2basJscm8mxmRyb+cdu9AFUF5OcgAG/+wUA4ODqzaj8uggAMGDeeNijHXAVl+Pg6vfQ8Zau6DPhAXi9Xhz843twX6hB/1+PRrjDjpNb8tFYdxG9x2XAHhMJm92OHdNWBu3MmZmZQfvYVmZkN3/nbMC88XB0iEGYzYbPZq+BPTbKsDkDOGtasJkcm2ljhmuaSrsT4KxpYXQzM+5Po5uZFbvJsZmc0c3MuD+NbmZWZvg6TaXdCXDWtGAzOTbzD38yuR29x2bg86U5+OyZtegz4X4AQGzXTmisd2P3s9mI63EDbBF23DrlZ7hYU4eG2nrUV17ALePvA7xeeD0euM6Uo6zwGPLnZqHkky9xfNPOoJ556tSpQf34VmVkN3/nbN/i9cifk4WLrjo4OjoNnTOAs6YFm8mxmTZmuKaptDsBzpoWRjcz4/40uplZsZscm8kZ3cyM+9PoZmZlhq/TVNqdAGdNCzaTYzP/8CeT2xGTnABXcRm8jR6ERzqaHrshAa7icgBAXcUFRMY7kdCvB/6eOR9JA/vgppF3o8NNXXBs4z9Refg7pL0wAflzsgAAKRl3YOf0Vw3781yL+soLcFfXGnoGhzMakfFxhp4hGPyds9qzVXB2T0KYzQZ3VbUl5wzgrAWCHg3N3qg1vebOat184e5side04OH+vEyFOQPMP2vcn3JsFjjcny2pcF2z4uxxd7bEObt23APamLkbbya3o6akArHJiaj9dxUa691Nj31fgZSh/QEAUfFxqK+sxvmi79FY50b9uWrE39odrtIKuM+70Fjrhi2iKbOzWxJcJeXwNnqM+uNoVl95Ae+kT0NDdZ2h57A7ozCq4E+Wu4j4O2exKZ2QOvNR/Ou3fwYAy80ZwFkLBL0amrlRa3rOnZW6tYW78zJe04KL+7OJKnMGmHvWuD/l2CywuD8vU+W6ZsXZ4+68jHN27bgHtDF7N95MbsfRtz/Gnb95DPB6cShrC9IXTUbBc9kIj4xA2oJJOH+yFJ6LDTi6YRuGLJ2K8KhI7F24DmHhNtw5dxwA4FjOdgDAzaOH4vg7O4J+5pycnIB/THd1reEXWABoqK6Du7o2KBeQYHTzl79zdm/2Mzj3zWkMWjQZny/LwdEN2wybM4CzpoUec6ZXw2B+Pl7JSs0Aa3Vrixl3J8BrmhZGzhlgzv1p5TkDzD1r3J9ybBZYZtyfwWqmynXNzNe0tphxdwLW3p9mnjPuAW3M3o03k9tRU1KBT2euan674LlsAMC+JW+1+H2luw6idNfBFo/tmrW6xduFyzcG6ZQtrVixArNmzdLluazEyG7+ztmWYXOvel+j5gzgrGnBZnJspo0Zrmkq7U6As6aF0c3MuD+NbmZW7CbHZnJGNzPj/jS6mVmZ4es0lXYnwFnTgs3k2Mw//Af4LGj37t1GH8GU2E2OzeTYTI7NtGE3OTaTYzM5NtOG3eTYTI7N5NhMG3aTYzM5NpNjM//wJ5OJiEJMZsFqNNS54XE3wOaw49Ca93F0wzajj6U8diMiCm3cA3JsRkQU2rgHtFG9G28mW9Djjz9u9BFMid3k2ExOlWbbJy/FuWPF6NinGx7OW4rT2/aj9vtKo4/lkyrNAHazOjaTYzM5NtNGlW7cA3JsZm1spg27ybGZnCrNuAe0UbkbX+bCgrp162b0EUyJ3eTYTE61ZlVHTsF9zoXY5ER0HtIPafMnAgASb++FwS/+0uDTNVGtGdCy29DXZsPZLQkA0HtcBvpOGm7w6Zqo2E11bCbHZnJspo1q3bg/5bg7rYnNtGE3OTaTU60Zd6c2Ku5PJW8m79+/H+np6YiKikJaWhrWrVuHmJgYeDweo49mCgsXLjT6CKbEbnJsJqdas6T0vqivqkbFVydRmn8IcTd2RkyXRNz+dCa+WJlr9PEAqNcMaNmt8OWN6D97NGwOO24aeTeOrP/Q6OMBULOb6thMjs3k2Ewb1bpxf8pxd1oTm2nDbnJsJqdaM+5ObVTcn8q9zEVhYSHuueceLFy4EDk5Odi6dSumT5+Ofv36wWYL3r3vAfPGo8eIIYjrfgPevfspnDtWHLTnspK4np1x93/PQGR8HOorL+CTp/6ACydKjT6W0jhrcpyzwLs3ew7CwsIQ17Mzdk57BR53AwCgcMVGZGTPQcnOA8r8FRqV+OpWdfg7eD0eDFo8Bd+s/wjehkajj6krXtPkeE2T45xpw1kLPO5POe7Oq/Gapg2vaXKcNTnOWeBxd2qj8v5U7ieTZ8yYgSeffBJPP/00evbsienTp6NLly5ITU0N6vN+9489+OCR51F96mxQn0cPKSkpuj3XXUufwNevbcW7P34Kh9/4O+5a+oRuzx1oenXjrMlxzgJv++SlePfumdjx5Cu4a/k0RHXqAKDpr9DEpnRC0QcFBp/wMlWaAW13K1y+EUlpfXHivV0Gn/AyXtPkeE2T45zJ8es0bVTZBdyfctydV+M1TRte0+Q4a3Kcs8Dj7tRG5f2p1M3k48eP49NPP8WMGTNaPO5wOJpvJr/wwgtIS0tDeno6XnzxxYA997/3HkFNcXnAPp6RVqxYocvzRCVeh/j/6IETmz8DAJx4dxcS+vVAZOJ1ujx/oOnVjbMmwzkLrqIt+Sj+5xdIfeoRAEDficNwZF0e+k19yOCTXaZaM+Dqbq7TZagpLjP4VC3xmibHa5oc50yOX6dpo9ou4P6U4+68jNc0OV7TtOGsyXDOgou7UxsV96dSL3NRWFiIjh07onv37s2P1dbW4ttvv0VqaipKS0vx1ltv4ciRIwCAvn37YsqUKbj++ut/8OOOGDECx48fb377usYIPIGbg/OHEHrg/gdwPvyi+P0efPDBNn9t06ZNGDlyZJu/PmnSJPHz+WoWm9IJNSXlgNcLAPB6PHCVVCC2SyLqy8+Ln8NfwWgG/HA3Lc0A88+aCs2MmjPAPJ+frUnnbt+St/DwP17CwTXvo+v9A5E3ZiHuWvYEEm7riYovT/zg+2pt1JrZmgGXu3256n9Qe7ZK9L6B6KbC56eReE2TM8s1zexzBqjRzGyzZsTnZ2tm259mbmbU7gTU+Pw0ilmuaYD596cKX3MYyazNzDZngPm+jzJ6dwLGNwPU2p+9evXC5s2bRR9TqZ9MDgsLQ2NjY4t/aG/t2rWoqalBamoq4uPjkZycjNraWtTW1iIiIgLR0dEGnlhNVVVVRh/BlNhNjs3kVGiWm/6rFq+XduFkKTb0mYhbxv0EX/9lK+D14ouVueg/a7SBp7xMhWZA292ky1wvqnQzEzaTYzM5NtNGhW7cn3LcndbHZtqwmxybyanQjLtTG9X3p1I/mTxw4EDU1tZi0aJFmDBhAj766CMsWbIEycnJSExMBADcf//96Nu3L7xeL2bOnAmn09nux219h/3CqbP4a/qvgvJnkMr7MA9x3ZLE77dnz542fy03NxejRo1q89eXLl0qfj5fzVxnyhCTnAiEhQFeL8JsNsQmJ8AV5L9GE4xmwA9309IMMP+sqdDMqDkDzPP52ZrWuStcvrH5f7vOlOHjx19q9320NmrNrM2u9OFji/3+vYHopsLnp5F4TZMzyzXN7HMGqNHMbLNmxOdna2bbn2ZudoneuxNQ4/PTKGa5pgHm358qfM1hJLM2M9ucAeb9PiqUv/cEzLk/r6TUTyZ3794dq1atwpo1a3DHHXdg7969GDt2bPPrJW/btg07duzAt99+ixMnTiAvLw+7d+82+NTqef7553V5nrry86g6/B16jrgLANDzkf9ExVdFQf/rH8GiVzcr0aMZ54zYTBt2k+M1TY5zJsev07ThrMmxmRybyfGapg1nTY5fp8lxzuTYzD9K3UwGgCeeeAIlJSWoqqrC2rVrceLEieabyY2NjYiPj4fD4YDD4UCHDh1QXh6Y/0KUNn8iRu1bi5jkRAz/6wI8nBeY/9pghFOnTun2XJ/NWYv/+OWDeOTT/8atk3+G/LlZuj13oOnVjbMmxzkLbWymDa9pcrymyXHO5Ph1mjbcBXJsJsdrmhyvadpw1uT4dZoc94Acm/lHqZe58OXgwYMYM2YMAOC+++7Dpk2bMGTIEADAbbfdhmHDhgXkefbMfxN75r8ZkI9ltNdffx3Dhw/X5bnOHy/G3x76nS7PFWx6deOsyXHOQhubacNrmhyvaXKcMzl+naYNd4Ecm8nxmibHa5o2nDU5fp0mxz0gx2b+UfpmssvlQlFRUfNPJttsNqxZs8bgUxERmUNMcgIG/O4XAICDqzej8usiAMCAeeNhj3bAVVyOg6vfw4B54+HoEIMwmw2fzV4De2wU+v96NMIddpzcko/GuovoPS4D9phI2Ox27Ji20sg/lm7a6pe2YBKuv7M3tj48DwDQb9oIOFM6oaHWjX2L1+PW//oZOvbuioTbeqJwWQ7ObC806o9AREQacH9qx91JRBSauDuvjdn2p3Ivc3Gl2NhYeDye5pvJ5J9BgwYZfQRTYjc5NpPTs1nvsRn4fGkOPntmLfpMuB8AENu1Exrr3dj9bDbietwAW4Qd+xavR/6cLFx01cHR0Ylbxt8HeL3wejxwnSlHWeEx5M/NQsknX+L4pp26nf8So+bMVz8A2PPCGzh/vOlf1rVF2HFdz87Y/Ww2GuvdiE3phK9f24r8uVmoLjqL4p0HDDk7wM9PLdhMjs3k2Ewb7k85I2aNuzP0sJk27CbHZnLcnXL83tM/St9MJm1mzZpl9BFMid3k2ExOz2YxyQlwFZehsc6N8EhH02M3XP7XiOsqLiAy3gkAcHZPQpjNBndVNTrc1AVFW3dj/0tv47YZP2/+eCkZd+DMx5/rdv5LjJozX/1ai4x3ou5//0EOV0kFYjonAACc3ZLgKimHt9Gj23lb4+enHJvJsZkcm2nD/SlnxKxxd4YeNtOG3eTYTI67U47fe/qHN5Mt6NJrTJMMu8mxmZyezWpKKhCbnIjwyAg01rubHvu+6TEAiIqPQ31lNWJTOiF15qPYu2gdAMBVWgH3eRcaa92wRTS9GpKR3+AZNWe++rVWX1mNqITrAACxnRNQU1oBAOg16h4cf2eHbmf1hZ+fcmwmx2ZybKYN96ecEbPG3Rl62EwbdpNjMznuTjl+7+kfpV8zmcwvs2A1Gurc8LgbYHPYcWjN+zi6YZvRxyIL4qxd7ejbH+PO3zwGeL04lLUF6Ysmo+C5bIRHRiBtwSScP1kKz8UG3Jv9DM59cxqDFk3G58tycHTDNtw5dxwA4FjOdgDAzaOHGv4Nnt7a6pc6cySuH3AL0uZPxJ75b+L8iVKkLZgET/1FuM6UAQAS+vXAFyveMfhPQGbGaxrphbN2Ne5P7bg7yUi8npFeOGtX4+68Nmbbn7yZTEG3ffJSnDtWjI59uuHhvKU4vW0/ar+vNPpYZEGctZZqSirw6cxVzW8XPJcNANi35K0Wv2/LsLlXve+uWatbvF24fGMQTqi2tvodeHUTDry6qfnxQ2s2X/W+26csC/4ByfJ4TSO9cNZa4v7UjruTjMbrGemFs9YSd+e1Mdv+5MtcWFBWVpbRR/Cp6sgpuM+5EJuciM5D+iFt/kQAQOLtvTD4xV/6fExPqnZTmarNrpy1oa/NhrNbEgCg97gM9J003OdjelG1mcrYTBt2k1O1mcr7U9VmKlO5GfentbCZHJvJqdpM5d0JqNtNZao24+60FjbzD28mW1Bubq7RR/ApKb0v6quqUfHVSZTmH0LcjZ0R0yURtz+diS9W5vp8TE+qdlOZqs2unLXClzei/+zRsDnsuGnk3Tiy/kOfj+lF1WYqYzNt2E1O1WYq709Vm6lM5Wbcn9bCZnJsJqdqM5V3J6BuN5Wp2oy701rYzD8heTPZ4YyG3Rll9DFgd0bB4YwO+MfNy8sL+Me8Fvdmz8Ejn7yK4ZsWoHBZDjzuBgBA4YqNyMieg3PfnG7+6yC+HtNLMLpx1vTla9aqDn8Hr8eDQYun4Jv1H8Hb0OjzMb3o0UyvuQvWXLVmpWaAubvxmqYvM+xPzpmcanMGcH9ewv0px2bt4zVNX2bYnQBnTQszzBp3Z/CYeQ/4YvZuIfmayZHxcRhV8Ce4q2sNPYfDGY3I+DhDz6CHS68ldONDQ/CfK6ahNP8r1JWdQ9WRU4hN6YSiDwqaf6+vx8yMs6avtmatcPlG3Ld+HvKfWdv8e309ZhV6zZ2V5krPz1Uzd+M1TV+huj85Z/rj/mzC/SnHZu3jNU1fobo7Ac6a3rg7m3APaGP2biF5Mxlo+j/OSoN4pczMTKOP4FPRlnz0HHEXUp96BAXPv4G+E4fhyLo89Jv6EHZMWwkAPh/TS7C6cdb013rWXKfLUFNc1uL3+HpMD3o1s9LcsZk2vKbJmeWaptL+5JzJqTpnAPcnYK3ZYzM5XtPkVL2mqbw7Ac6aFmaZNe5Oc9NzzszcLSRf5sLqBg8ebPQR2rRvyVu4ecy9iOmSiK73D0Thyxtx0VWLhNt6wh4bddVjelK5m6pUbnZp1qKTOhp9lBZUbqYqNtOG3eRUbqbq/lS5mapUb8b9aR1sJsdmcio3U3V3Amp3U5XKzbg7rYPN/MObyRY0e/Zso4/QLDf9Vzh3rLj57QsnS7Ghz0TcMu4n+PovWwGvF1+szEX/WaPxoydHXPWYnlTqZhYqNWtr1mrPVhl3KB9UamYWbKYNu8mp1Mws+1OlZmahWjPuT+tiMzk2k1OpmVl2J6BWN7NQqRl3p3WxmX9C9mUuyFiFyzc2/2/XmTJ8/PhLLX7d12NE1+LDxxb79RgRkcq4P0lv3J9EZHbcnaQ37k6yOv5ksgXFxZnzNVeMxm5ybCbHZnJspg27ybGZHJvJsZk27CbHZnJsJsdm2rCbHJvJsZkcm/mHN5MtaNiwYUYfwZTYTY7N5NhMjs20YTc5NpNjMzk204bd5NhMjs3k2EwbdpNjMzk2k2Mz//BmsgXl5uYafQRTYjc5NpNjMzk204bd5NhMjs3k2EwbdpNjMzk2k2MzbdhNjs3k2EyOzfzDm8lERERERERERERE1C7eTLagBx54wOgjmBK7ybGZHJvJsZk27CbHZnJsJsdm2rCbHJvJsZkcm2nDbnJsJsdmcmzmH7vRB6DAy8zMNPT5Y5ITMOB3vwAAHFy9GZVfFwEABswbD3u0A67ichxc/R463tIVfSY8AK/Xi4N/fA/uCzXo/+vRCHfYcXJLPhrrLqL3uAzYYyJhs9uxY9rKoJ7b6G5mZGQzf+dswLzxcHSIQZjNhs9mr4E9NopzZjJspg27yZnhmsbdaX5GN+P+DB1sJsdmckY34/4MHWb4Oo270/zYzD+8mWxBU6dORU5OjmHP33tsBj5fmoPaf1ch7YUJ+NdvX0Ns105orHdj3+L1GLJ0KmwRdtw65WeoP1cNAKivvIA+k4YBXi+8Hg9cZ8pRfeosygqP4ebRQ1FXeSHo5za6mxkZ2czfOdu3eD0AIG3BJDg6OnHzmKEhMWf1lRfgrq4N6nM4nNGIjA/+v3ZrpWaA9bpZiRmuadyd5md0M+7PH8b9Kcdmoc3oZtyfocMMX6dxdwaPFfeAmbvxZjIFXExyAlzFZfA2ehAe6Wh67IYEuIrLAQB1FRcQGe9EQr8e+HvmfCQN7IObRt6NDjd1wbGN/0Tl4e+Q9sIE5M/JAgCkZNyBndNfNezPQ2ryd85qz1bB2T0JYTYb3FXVITFn9ZUX8E76NDRU1wX1eezOKIwq+JMuSz3Y9GoGWKsbBQ53J+mF+7Nt3J9ybEZG4/4kPXB3to17QBuzd+NrJlPA1ZRUIDY5EeGREWisdzc99n3TYwAQFR+H+spqnC/6Ho11btSfq0aEMxqu0gq4z7vQWOuGLaLpv3M4uyXBVVIOb6PHsD8PqcnfOYtN6YTUmY9i76J1ABASc+aurtXlpmhDdZ0uP8mrB72aAdbqRoHD3Ul64f5sG/enHJuR0bg/SQ/cnW3jHtDG7N34k8kWZPRflzn69se48zePAV4vDmVtQfqiySh4LhvhkRFIWzAJ50+WwnOxAUc3bMOQpVMRHhWJvQvXISzchjvnjgMAHMvZDgC4efRQHH9nhy7nNrqbGRnZzN85uzf7GZz75jQGLZqMz5fl4OiGbZwzk2EzbdhNzgzXNO5O8zO6Gfdn6GAzOTaTM7oZ92foMMPXadyd5sdm/uHNZAtasWIFZs2aZdjz15RU4NOZq5rfLnguGwCwb8lbLX5f6a6DKN11sMVju2atbvF24fKNQTrl1YzuZkZGNvN3zrYMm3vV+3LOzIXNtGE3OTNc07g7zc/oZtyfoYPN5NhMzuhm3J+hwwxfp3F3mh+b+Ycvc2FBu3fvNvoIpsRucmwmx2ZybKYNu8mxmRybybGZNuwmx2ZybCbHZtqwmxybybGZHJv5hz+ZTEQUYjILVqOhzg2PuwE2hx2H1ryPoxu2GX0s5bEbEVFo4x6QYzMiotDGPaCN6t14M9mCHn/8caOPYErsJsdmcqo02z55Kc4dK0bHPt3wcN5SnN62H7XfVxp9LJ9UaQawm9WxmRybybGZNqp04x6QYzNrYzNt2E2OzeRUacY9oI3K3fgyFxbUrVs3o49gSuwmx2ZyqjWrOnIK7nMuxCYnovOQfkibPxEAkHh7Lwx+8ZcGn66Jas2Alt2GvjYbzm5JAIDe4zLQd9Jwg0/XRMVuqmMzOTaTYzNtVOvG/SnH3WlNbKYNu8mxmZxqzbg7tVFxfyp5M3n//v1IT09HVFQU0tLSsG7dOsTExMDj8Rh9NFNYuHCh0UcwJXaTYzM51ZolpfdFfVU1Kr46idL8Q4i7sTNiuiTi9qcz8cXKXKOPB0C9ZkDLboUvb0T/2aNhc9hx08i7cWT9h0YfD4Ca3VTHZnJsJsdm2qjWjftTjrvTmthMG3aTYzM51Zpxd2qj4v5U7mUuCgsLcc8992DhwoXIycnB1q1bMX36dPTr1w82m5L3vkPWgHnj0WPEEMR1vwHv3v0Uzh0rNvpIZFGctcC7N3sOwsLCENezM3ZOewUedwMAoHDFRmRkz0HJzgPK/BUalfjqVnX4O3g9HgxaPAXfrP8I3oZGo49JiuM1jfTAOQsO7k857k4KBF7TSC+ctcDj7tRG5f2p3N3ZGTNm4Mknn8TTTz+Nnj17Yvr06ejSpQtSU1ONPppppKSk6PI83/1jDz545HlUnzqry/MFm17drISzJqfKnG2fvBTv3j0TO558BXctn4aoTh0ANP0VmtiUTij6oMDgE16mSjOg7W6FyzciKa0vTry3y+ATXqZSN7PgNU2OcybHOdNGlVnj/pTj7rQ2XtO04azJcdbkVJkz7k5tVN6fSt1MPn78OD799FPMmDGjxeMOh6P5ZvLvf/97DBkyBD/+8Y/x3nvvGXFM5a1YsUKX5/n33iOoKS7X5bn0oFc3K+Gsyak2Z0Vb8lH8zy+Q+tQjAIC+E4fhyLo89Jv6kMEnu0y1ZsDV3Vyny1BTXGbwqVpSsZvqeE2T45zJcc60UW3WuD/luDutidc0bThrcpw1OdXmjLtTGxX3p1Ivc1FYWIiOHTuie/fuzY/V1tbi22+/RWpqKg4cOIC//e1v2LVrF+rr6zF48GBkZGQgLi7uBz/uiBEjcPz48WAfX1cPPvhgm7+2adMmjBw5ss1fnzRpkvj5rmuMwBO4Wfx+wfDA/Q/gfPhF8fv9UDPgh7tpaWYFRjSz+qwF4/OzNWnDfUvewsP/eAkH17yPrvcPRN6Yhbhr2RNIuK0nKr488YPvq7VRa2ZrBlzu9uWq/0Ht2SrR+waiG69pcrymmfOaZkah/HUaoG3WVLimmW1/mrmZUbsT4DVNCyOaqXRdM+s1zWz4dZo5v04z2+4EjG8GqLU/e/Xqhc2bN4s+plI/mRwWFobGxsYW/9De2rVrUVNTg9TUVBw+fBgDBw6EzWZDdHQ0evTogd27dxt4YjVVVVUZfQRTYjc5NpNToVlu+q9avPbXhZOl2NBnIm4Z9xN8/ZetgNeLL1bmov+s0Qae8jIVmgFtd5Muc72o0s1M2EyOzeTYTBsVunF/ynF3Wh+bacNucmwmp0Iz7k5tVN+fYV6v12v0IS757rvv0KtXLzz77LOYMGECPvroI8ybNw92ux3FxcU4fPgwxo0bh127dqG6uhqpqal45ZVXMHbsWKOPrrs9e/a0+WtjxoxBTk5Om7+elpYmfr4Lp87ir+m/8vlrmQWr8eFj/1e3F6Z/tGA14rolid/vh5oBP9xNSzMrMKKZ1WctGJ+frf1Qw0DT2qi1UGoGBKYbr2lyvKaZ85pmRqH8dRqgbdZUuKaZbX+ymTa8pskZ0ayt2eI1zbr4dZo5v07jHtDGjN2upNRPJnfv3h2rVq3CmjVrcMcdd2Dv3r0YO3Zs8+sl9+3bFzNmzMDw4cMxbdo0DBgwAF27djX41Op5/vnnjT6CKbGbHJvJsZkcm2nDbnJsJsdmcmymDbvJsZkcm8mxmTbsJsdmcmwmx2b+UepmMgA88cQTKCkpQVVVFdauXYsTJ04030wGgMmTJ2Pnzp3IysqCy+XCoEGDDDytmk6dOqXL86TNn4hR+9YiJjkRw/+6AA/nLdXleYNFr25WwlmT45zJsZk27CbHa5oc50yOc6YNZ02OzeTYTI7XNG04a3KcNTnOmRyb+Uepf4DPl4MHD2LMmDHNb//0pz9FXV0doqKi8Ic//AEREREGnk5Nr7/+OoYPHx7059kz/03smf9m0J9HL3p1sxLOmhznTI7NtGE3OV7T5DhncpwzbThrcmwmx2ZyvKZpw1mT46zJcc7k2Mw/St9MdrlcKCoqavGTyR988IGBJyIiMo+Y5AQM+N0vAAAHV29G5ddFAIAB88bDHu2Aq7gcB1e/hwHzxsPRIQZhNhs+m70G9tgo9P/1aIQ77Di5JR+NdRfRe1wG7DGRsNnt2DFtpZF/LN201S9twSRcf2dvbH14HgCg37QRcKZ0QkOtG/sWr8et//UzdOzdFQm39UThshyc2V5o1B+BiIg04P7UjruTiCg0cXdeG7PtT+Ve5uJKsbGx8Hg8LW4mU/v40h/asJscm8np2az32Ax8vjQHnz2zFn0m3A8AiO3aCY31bux+NhtxPW6ALcKOfYvXI39OFi666uDo6MQt4+8DvF54PR64zpSjrPAY8udmoeSTL3F8007dzn+JUXPmqx8A7HnhDZw/3vQPcdgi7LiuZ2fsfjYbjfVuxKZ0wtevbUX+3CxUF51F8c4Dhpwd4OenFmwmx2ZybKYN96ecEbPG3Rl62EwbdpNjMznuTjl+7+kfpW8mkzazZs0y+gimxG5ybCanZ7OY5AS4isvQWOdGeKSj6bEbEuAqLgcA1FVcQGS8EwDg7J6EMJsN7qpqdLipC4q27sb+l97GbTN+3vzxUjLuwJmPP9ft/JcYNWe++rUWGe9EXfl5AICrpAIxnRMAAM5uSXCVlMPb6NHtvK3x81OOzeTYTI7NtOH+lDNi1rg7Qw+bacNucmwmx90px+89/cObyRZ05WtMk//YTY7N5PRsVlNSgdjkRIRHRqCx3t302PdNjwFAVHwc6iurEZvSCakzH8XeResAAK7SCrjPu9BY64YtounVkIz8Bs+oOfPVr7X6ympEJVwHAIjtnICa0goAQK9R9+D4Ozt0O6sv/PyUYzM5NpNjM224P+WMmDXuztDDZtqwmxybyXF3yvF7T//wZjIRkUUdfftj9H9mDIa8NBVH/t+HSF80Ga7TZQiPjEDagkk4f7IUnosNuDf7GYQ77Bi0aDKiOnXA0Q3b8KNp/wd3vfwkjuVsBwDcPHqo4d/g6c1XPwBInTkS1w+4BWnzJ8JzsQHnT5QibcEkhEdGwHWmDACQ0K9H8+tcERGRuXB/asfdSUQUmrg7r43Z9qfS/wAfERFpV1NSgU9nrmp+u+C5bADAviVvtfh9W4bNvep9d81a3eLtwuUbg3BCtbXV78Crm3Dg1U3Njx9as/mq990+ZVnwD0hEREHB/akddycRUWji7rw2Ztuf/MlkC8rKyjL6CKbEbnJsJsdmcmymDbvJsZkcm8mxmTbsJsdmcmwmx2basJscm8mxmRyb+Yc3ky0oNzc34B/T4YyG3RkV8I8rZXdGweGMDsrHDkY3q+OsyekxZ3o1DObn45Ws1AywVjer4TVNjnMmZ+U5A8w9a9yfcmxGwWqmynXNzNc0q7Hy/jTznHEPaGP2bnyZCwvKy8vDlClTAvoxI+PjMKrgT3BX1wb040o5nNGIjI8LyscORjer46zJ6TFnejUM5ufjlazUDLBWN6vhNU2OcyZn5TkDzD1r3J9ybEbBaqbKdc3M1zSrsfL+NPOccQ9oY/ZuvJlMfouMj9Plk5eIs3bt2FCOzShYOFukB85ZYLCjHJtRsHC2SA+cs2vHhtqYuRtf5sKCMjMzjT6CKbGbHJvJsZkcm2nDbnJsJsdmcmymDbvJsZkcm8mxmTbsJsdmcmwmx2b+4c1kCxo8eLDRRzAldpNjMzk2k2MzbdhNjs3k2EyOzbRhNzk2k2MzOTbTht3k2EyOzeTYzD+8mWxBs2fPNvoIpsRucmwmx2ZybKYNu8mxmRybybGZNuwmx2ZybCbHZtqwmxybybGZHJv5hzeTiYiIiIiIiIiIiKhdYV6v12v0ISiw/vjHP2L69OlGH8N02E2OzeTYTI7NtGE3OTaTYzM5NtOG3eTYTI7N5NhMG3aTYzM5NpNjM//wZjIRERERERERERERtYsvc0FERERERERERERE7eLNZCIiIiIiIiIiIiJqF28mExEREREREREREVG7eDOZiIiIiIiIiIiIiNrFm8lERERERERERERE1C7eTCYiIiIiIiIiIiKidvFmMhERERERERERERG1izeTiYiIiIiIiIiIiKhdvJlMRERERERERERERO3izWQiIiIiIiIiIiIiahdvJhMRERERERERERFRu3gzmYiIiIiIiIiIiIja9f8Bxag6TvTc3xkAAAAASUVORK5CYII=", "text/plain": [ - "
    " + "\"Output" ] }, "execution_count": 1, @@ -130,7 +129,7 @@ "\n", "The most straight-forward way to specify the truncation budget is via the `max_error_per_slice` argument.\n", "In fact, this is what is being done in [this tutorial](/docs/addons/qiskit-addon-obp/tutorials/01-getting-started-ipynb). Setting `max_error_per_slice` to a `float` results in each slice being allotted a budget equaling that value.\n", - "In the example below, we set this value to `0.001` which guarantees us an implicit truncation error of at most `0.018`, if the entire 18 slices were to be backpropagated. \n", + "In the example below, we set this value to `0.001` which guarantees us an implicit truncation error of at most `0.018`, if the entire 18 slices were to be backpropagated.\n", "\n", "
    \n", "Note that any error budget remaining after backpropagating a slice and truncating terms with small coefficients will always be added to the following slice's error budget.\n", @@ -219,9 +218,8 @@ "outputs": [ { "data": { - "image/png": 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KlSuZPn0606ZNIyIigtWrV9OjRw/HmCeeeIKCggLGjx+PxWJhwIABrF+/Hi+v8ta8Pj4+rFq1ilmzZlFQUEBISAgJCQlMnz7d8bRiQEAAGzZsYMKECfTq1YtWrVoxc+bMSk8uVieLyNUoKrWy8XzLtBExbfn37uMkZ+YZnEpERBobk8lUrdZlRtJ908U15vum+v1TKSIiIiKNWsuWLbnlllv429/+xqOPPlqpf7PZbOa9995j1KhRlW4Kvvvuu0rn+O677yq1D/D29ubXv/41v/71r5kwYQKRkZHs3buX2NhYrFYrJ0+e5MYbb3Q66/333897771Hu3btcHFxYfjw4Y593377LXfccQcPPPAAUN6C6tChQ3Tv3r3SObZt23ZB9oiIiAtuigCuu+46zGYzbm5u1X7yq3fv3iQlJbFz506eeOIJNm3aRHp6Orfffju7du3CxcXloj2fxTgTJ068aJu0qm5CR44cyciRIy96PpPJxJw5c5gzZ06V+6Ojo/n8888vm6tnz558/fXXlxxzuSwiV+PLQ9kUlFhpG+jNvX3any/e5BodS0REpM7pvqlp3je5XH6IiIiIiDR0AS5FtDQV1PorwKXI6WyLFy+muLiY+Ph4vvrqKzIyMli/fj233HILbdu2dSxuWeHbb7/lxRdf5NChQ7z22mt89NFHTJo0CYDly5fz9ttvs2/fPn766Sf+7//+D29vbzp06EDXrl25//77GTVqFKtWrSItLY3t27czd+5c1q5de9mc999/P7t27eL555/nrrvuqrRuR0REBBs3bmTLli0kJyfzhz/84YKF3KF8FkViYiIHDx7k/fffZ9GiRY7s/2vIkCHExcUxYsQINmzYwJEjR9iyZQtPP/00O3bsqPIYb29vunTpQlpaGoMGDaJLly4cO3aM/v3707VrV7p06XLBTciBAwdISkrizJkz5OTkkJSURFJS0mW/HyIitW3tnkwAhkUHExniB0BmThGWwit/OldERKSh0n1T07tv0swbERERkUbMx8cHF1c3bvJIq7Nruri6XdCz+FIiIiLYsWMHs2bN4ne/+x1nzpwhODiYESNGMGvWrAv+aH7sscfYsWMHs2fPxt/fn/nz5xMfX96jOjAwkL/+9a8kJiZitVqJjo7mk08+cfQ/fuedd3juued47LHHOH78OK1ateKGG27gtttuu2zOLl260KdPH7Zv3+7oFV1h+vTp/PTTT8THx+Pj48P48eMZMWIEOTk5lcaNGjWKc+fO0adPH1xdXZk0adJFF9A0mUysW7eOp59+mjFjxpCdnU1wcDADBw50tNS6mC+++MIxG+LLL79k4MCBFx07bNgwjh496vg8NjYWcK6ftohITSsqtfJZcvmbOcN7huLv5U675t4cO3uO5Mw84jq3vMwZREREGhfdNzW9+yaTXXdltSY3N5eAgABycnLw9/c3Oo7Uc5mZmbz55pt8XBTFabuv0XEuq6WpgNu9khk/fjwhISFGxxEREaCoqIi0tDTCw8Mda1sA5OTkUFhYWGc5fHx8CAgIqLPrSd262M8Z6O9fcZ5+ZuRi1u/L5I//t4u2gd588+RgTCYT41bsYOOBLGbe1p2HBoQbHVFERBqgS/0tK1KTauK+STNvRERERBq5gIAAFVNERKRBWXO+ZdrwniGO/v1RIf5sPJCldW9ERESkSdCaNyIiIiIiIiJSb5wrsfJ5ykkAhkf/PMs/Krh83ZsUc54huURERETqkoo3IiIiIiIiIlJvfHHwJIUlVto196Znu59njkaFlLcVOZiVR5nVZlQ8ERERkTqh4o2IiIiIiIiI1Btr9l7YMg2gfQsffDxcKSmzkXaqwKh4IiIiInVCxRsRERGRRsZutxsdQRox/XyJSG0qLCnj8+Tylmm3RYdW2ufiYqLb+dZpyWqdJiIiV0F/00ptq4mfMRVvRERERBoJd3d3AAoLCw1OIo1Zxc9Xxc+biEhN2pySzblSK+1b+NCjrf8F+ytapyVn5tZ1NBERaQR0zyR1pSbum9xqKoyIiIiIGMvV1ZXAwEBOnix/YtnHx6dSuxmRq2G32yksLOTkyZMEBgbi6upqdCQRaYTW7j0BXNgyrYKKNyIicjV0zyS1rSbvm1S8EREREWlEgoODARw3IyI1LTAw0PFzJiJSkwqKy/g8pfzfr+HRIVWO6R5yvm2aijciInKFdM8kdaEm7ptUvBERERFpREwmEyEhIbRp04bS0lKj40gj4+7urhk3IlJrPk85SVGpjQ4tfbgm9MKWaQDdgsu3Z+UWc6aghBa+HnUZUUREGgHdM0ltq6n7JhVvRERERBohV1dXvckuIiINyrq9mUD5rJuLtbBp5ulG+xY+pJ8pJCUzl35dWtVlRBERaUR0zyT1nYvRAURERERERESkaavUMq1n1S3TKkSdb512QK3TREREpBFT8UZEREREREREDLUp5STFZTbCW/nSPaTqlmkVIs+3Tksx59VFNBERERFDqHgjIiIiIiIiIoZau+cEcOmWaRWizhd3kjXzRkRERBoxFW9ERERERERExDD5xWVsPpgNXL5lGuCYmXM4K59Sq61Ws4mIiIgYRcUbERERERERETHMpuQsSspsdGrtS2Sw32XHt2vuja+HKyVWG2mnCuogoYiIiEjdU/FGRERERERERAyzZk8mALdVo2UagIuLiUi1ThMREZFGTsUbERERERERETFEXlEpX55vmTasGi3TKkSFlM/QOaDijYiIiDRSKt6IiIiIiIiIiCE+S86ixGqjc2tfugVdvmVahSjHzJu82oomIiIiYigVb0RERERERETEEGv3mAEY3jO0Wi3TKkQGlxdvUjTzRkRERBopFW9EREREREREpM7lFpXy1aHylmm3OdEyDSAy2A+TCU7mFXM6v7g24omIiIgYSsUbEREREREREalznx0ob5kW0aYZXZ1omQbg6+lGhxY+gFqniYiISOOk4o2IiIiIiIiI1Lm1ezIBGO7krJsKjtZpZrVOExERkcZHxRsRERERERERqVM550r56nB5y7Th0VdWvIkKKS/eHNC6NyIiItIIqXgjIiIiIiIiInVq44EsSq12ugX5EeFky7QKUSHlx6ltmoiIiDRGbkYHEBERqSs5OTkUFhYaHaPafHx8CAgIMDqGiIiISI1bu+cEcOUt0+DnmTepJ/MoKbPh4abnU0VERKTxUPFGRESahJycHF5dtBibtczoKNXm4urGI3+eqAKOiIiINCo5haV8ffgUAMOusGUaQLvm3vh5upFXXMZPp/Ida+CIiIiINAYq3oiISJNQWFiIzVrGlyXh5Ni8jI5zWQEuRdzkkUZhYaGKNyIijdxrr73GSy+9hNls5tprr2XRokX06dPnouM/+ugjZsyYwZEjR4iIiOCFF15g2LBhjv12u51Zs2bx1ltvYbFY6N+/P6+//joREREAHDlyhGeffZbPP/8cs9lMaGgoDzzwAE8//TQeHh4APPPMM8yePfuCa/v4+FBQUADA8uXLGTNmTKX9np6eFBUVXfX3RBq3Tw+YKbPZiQz2o0ubZld8HpPJRGSIH98fOUtyZq6KNyIiItKo1Is5xa+99hodO3bEy8uLvn37sn379kuO/+ijj4iMjMTLy4vo6GjWrVtXab/dbmfmzJmEhITg7e3NkCFDOHz4sGP/kSNHGDt2LOHh4Xh7e9O5c2dmzZpFSUlJpTEmk+mC13fffVezX7yIiNSpHJsXp+2+9f7VEApMIiJy9T788EMSExOZNWsWu3bt4tprryU+Pp6TJ09WOX7Lli3ce++9jB07lt27dzNixAhGjBjBvn37HGNefPFFXn31VZYsWcK2bdvw9fUlPj7eUVRJSUnBZrPxxhtvsH//fhYsWMCSJUuYNm2a4xxTpkwhMzOz0qt79+6MHDmyUh5/f/9KY44ePVoL3yVpbNbtzQRg+FXMuqlQ0TpN696IiIhIY2N48aa+3qxU+OyzzyrdjPTq1at2vhEiIiIiItLkzJ8/n3HjxjFmzBi6d+/OkiVL8PHxYdmyZVWOf+WVV0hISODxxx8nKiqKZ599luuuu47FixcD5Q+yLVy4kOnTp3PHHXfQs2dPVqxYwYkTJ1i9ejUACQkJvPPOOwwdOpROnTpx++23M2XKFFatWuW4TrNmzQgODna8srKyOHDgAGPHjq2Ux2QyVRoXFBRUO98oaTQshSV8U9Ey7SrWu6lQMdsmOTP3qs8lIiIiUp8YXryprzcrFVq2bFnpZsTd3b3WvhciIiIiItJ0lJSUsHPnToYMGeLY5uLiwpAhQ9i6dWuVx2zdurXSeID4+HjH+LS0NMxmc6UxAQEB9O3b96LnhPK14Vq0aHHR/UuXLqVr167ceOONlbbn5+fToUMHwsLCuOOOO9i/f//Fv2CguLiY3NzcSi9pWjbsz6LMZicqxJ/Ora+8ZVqFqBA/QDNvREREpPExtHjTEG5Wbr/9dtq0acOAAQP4+OOPL/n16EZERERERESq69SpU1it1gtmqwQFBWE2m6s8xmw2X3J8xUdnzpmamsqiRYv4wx/+UOX+oqIi3nvvvQtm3XTr1o1ly5bxn//8h//7v//DZrPRr18/jh07dpGvGObOnUtAQIDjFRYWdtGx0jitOd8y7bYamHUD0C3YD5MJTuUXk51XXCPnFBEREakPDC3e1OeblWbNmjFv3jw++ugj1q5dy4ABAxgxYsQlCzi6ERERERERkYbk+PHjJCQkMHLkSMaNG1flmH//+9/k5eXx4IMPVtoeFxfHqFGjiImJ4aabbmLVqlW0bt2aN95446LXmzp1Kjk5OY5XRkZGjX49Ur+dLSjh29TzLdNqYL0bAB8PN8Jb+gJqnSYiIiKNi5vRAYx2sZuVVq1akZiY6Pj8+uuv58SJE7z00kvcfvvtVZ5r6tSplY7Jzc1VAUdERERERKrUqlUrXF1dycrKqrQ9KyuL4ODgKo+pWH/mYuMrPmZlZRESElJpTExMTKXjTpw4weDBg+nXrx9vvvnmRXMuXbqU22677bLr2bi7uxMbG0tqaupFx3h6euLp6XnJ80jj9el+M1abnWtC/Qlv5Vtj540M8eOnUwWkmHMZ2LV1jZ1XRERExEiGzryp7ZuVy52zujcrFfr27XvZGxF/f/9KLxERERERkap4eHjQq1cvNm3a5Nhms9nYtGkTcXFxVR4TFxdXaTzAxo0bHePDw8MJDg6uNCY3N5dt27ZVOufx48cZNGgQvXr14p133sHFpepbw7S0NDZv3nxBy7SqWK1W9u7dW6loJPJLa8+3TBteQy3TKkQFl997a90bERERaUwMLd40hJuVX0pKStKNiIiIiIiI1JjExETeeust3n33XZKTk3n44YcpKChgzJgxAIwaNYqpU6c6xk+aNIn169czb948UlJSeOaZZ9ixYwcTJ04EwGQyMXnyZJ577jk+/vhj9u7dy6hRowgNDWXEiBHAz/dC7du35+WXXyY7Oxuz2Vxlm+lly5YREhLCrbfeesG+OXPmsGHDBn766Sd27drFAw88wNGjR/n9739fC98paehO5xez5cfTAAyvoZZpFaJCKoo3apsmIiIijYfhbdMSExN58MEH6d27N3369GHhwoUX3Ky0bduWuXPnAuU3KzfddBPz5s1j+PDhfPDBB+zYscMxc+aXNysRERGEh4czY8aMKm9WOnTo4LhZqVAxO+fdd9/Fw8OD2NhYAFatWsWyZctYunRpXX1rRERERESkkbv77rvJzs5m5syZmM1mYmJiWL9+vaNFWXp6eqUHzfr168fKlSuZPn0606ZNIyIigtWrV9OjRw/HmCeeeIKCggLGjx+PxWJhwIABrF+/Hi8vL6D84bfU1FRSU1Np165dpTx2u93xv202G8uXL2f06NG4urpekP3s2bOMGzcOs9lM8+bN6dWrF1u2bKF79+41+j2SxuHT/VlYbXZ6tPWnQ8uaa5kG5W3TAFJP5lNSZsPDzdDnVEUahD3HLGz98TS/v7ETri4mo+OIiEgVDC/e1OeblWeffZajR4/i5uZGZGQkH374IXfddVdtfjtERERERKSJmThxomPmzP/64osvLtg2cuRIRo4cedHzmUwm5syZw5w5c6rcP3r0aEaPHn3ZXC4uLmRkZFx0/4IFC1iwYMFlzyMCsK6iZVp0aI2fu22gN/5ebuQWlZF6Mp/uoWphLnIphSVlPLR8B6fyi+nQ0oeEHuoyIyJSHxlevIH6ebPy4IMP8uCDD15yjIiIiIiIiIhcWnnLtFNAzbdMg/L3ACJD/NmedobkzFwVb0Qu490tRzmVXwzAzqNnVbwREamnNJdYRERERERERGrN+v1mbHbo2S6A9i19auUa3bXujUi15BaVsuTLHx2fJ2VYjAsjIiKXpOKNiIiIiIiIiNSatXsqWqbV3tP9kcHl696kmPNq7RoijcHbX6eRc66UVs08ANhzLIdSq83gVCIiUhUVb0RERERERESkVmTnFfPdT6cBGFaLxZuoX8y8+eVatiLys7MFJbz9TRoAs2/vgb+XG8VlNg6q6CkiUi+peCMiIiIiIiIitaKiZdq1YYGEtaidlmkAXYP8cDHB6YISsvOKa+06Ig3Zkq9+JL+4jGtC/bm1RzDXhgUCsDv9rLHBRESkSireiIiIiIiIiEitWLvnBAC31eKsGwBvD1c6tvIFIFmzCEQucDK3iHe3HAFgytBuuLiYiG3fHIDdWvdGRKReUvFGRERERERERGrcybwitqWdAeDW6OBav94vW6eJSGWvbU6lqNTGde0DGdStNQCx52feJKl4IyJSL6l4IyIiIiIiIiI17tN9Zux2iAkLpF3z2muZVqG7ijciVTp2tpCV29MBmBLfDZPJBOBom/ZTdgE5haVGxRMRkYtQ8UZEREREREREatyaPZkA3NazdlumVYgK8QNUvBH5X4s2pVJqtdO/S0v6dW7l2N7C14OOLcsLq0nHLAalExGRi1HxRkRERERERERq1MncIrYfqWiZVjfFm8jg8pk3P2YXUFxmrZNritR3aacK+OeuYwA8NrTbBftjzs++2Z1+ti5jiYhINah4IyIiIiIiIiI16r/nW6Zd1z6QtoHedXLNkAAvArzdsdrsHM7Kr5NritR3CzYewmqzc3NkG65r3/yC/TFa90ZEpN5S8UZEREREREREatTa8y3ThvcMrbNrmkwmtU4T+YUUcy6f7DkBQOLQrlWOiT1f0EnKsGC32+ssm4iIXJ6KNyIiIiIiIiJSY8w5RXx/tLxl2rDo4Dq9dkXrtBRzXp1eV6Q+mr/hEHY7DO8ZwjWhAVWOiQrxx8PNBUthKUdOF9ZxQhERuRQVb0RERERERESkxvx3XyZ2O/Tu0JyQgLppmVahe0h58UYzb6Sp+yHDwoYDWbiY4NEhVc+6AfBwc6FHaPnvTVKG1r0REalPVLwRERERERERkRrzc8u0kDq/dtQvijdqASVN2csbDgLwm9h2dGnT7JJjY8LOt05Lt9R2LBERcYKKNyIiIiIiIiJSIzJzzrHjaPnT+7f2qPviTURQM1xMcLawlKzc4jq/vkh9sO2n03x9+BRuLiYmD4m47PiY9oEA7M6w1G4wERFxioo3IiIiIiIiIlIj/rvXDMD1HZsTHOBV59f3cnelU+vyWQbJZrVOk6bHbrczb8MhAO6+PoywFj6XPSY2LBCAAydyKSq11mY8ERFxgoo3IiIiIiIiIlIj1u493zItuu5n3VSI0ro30oR9dfgU24+cwdPNhT//6vKzbgDaNfemVTMPymx29p/IqeWEIiJSXSreiIiIiIiIiMhVO2E5x86jZzGZ4FZDizd+ACRn5hmWQcQI5bNuyte6+X83dKj27DeTyeRY92a31r0REak3VLwRERERERERkau27vysm+s7tiDIv+5bplWICi6feZOimTfSxGw4kMWeYzn4eLjy8KDOTh0be37dmySteyMiUm+oeCMiIiIiIiIiV62iZdptPY2bdQM/t0376VSB1u+QJsNqszP//Fo3D/UPp2UzT6eOjzm/7o1m3oiI1B8q3oiIiIiIiIjIVTl2tpDd6RZMJkjoEWxoliB/T5r7uGO12TmclW9oFpG6smbPCQ5m5eHv5ca4gZ2cPr5nuwBMJjhuOUd2XnEtJBQREWepeCMiIiIiIiIiV+W/e80A9A1vQRs/41qmQfn6HZHnW6clq3WaNAFlVhsLNpbPuvnDTZ0J8HZ3+hx+Xu5EtGkGqHWaiEh9oeKNiIiIiIiIiFyVNedbpg3vGWpwknIVrdOSzSreSOP3r13HOHK6kJa+Hozu1/GKz/Nz67SzNRNMRESuioo3IiIiIiIiInLFMs4U8kOGBRcTJFxjbMu0ClEhfoBm3kjjV1xm5dVNqQA8PKgzvp5uV3yu2PbNAc28ERGpL1S8EREREREREZEr9t995bNu+oa3pLWfc4uk1xbHzJvMPOx2u8FpRGrPB9szOG45R7C/Fw/c0OGqzlUx8+aHDAtWm35vRESMpuKNiIiIiIiIiFyxtXsqWqaFGJzkZ13aNMPVxUTOuVLMuUVGxxGpFedKrCzeXD7rZuKvuuDl7npV5+sa5IePhysFJVZST+bXREQREbkKKt6IiIiIiIiIyBXJOFPID8dyylum9agfLdMAvNxd6dzaF1DrNGm83t16hOy8YsJaePO73mFXfT5XFxM92wUAkJShdW9ERIym4o2IiIiIiIiIXJG1e8tn3cR1bkmrZvWjZVqFX7ZOE2lscotKWfLljwBMvrkrHm418xZfTFj5uje70y01cj4REblyKt6IiIiIiIgY6LXXXqNjx454eXnRt29ftm/ffsnxH330EZGRkXh5eREdHc26desq7bfb7cycOZOQkBC8vb0ZMmQIhw8fduw/cuQIY8eOJTw8HG9vbzp37sysWbMoKSmpNMZkMl3w+u6775zKIo2fo2VadKjBSS4UGVxevDmgmTfSCC37Jg1LYSmdW/syIrZtjZ03tn0gAEkZlho7p4iIXBkVb0RERERERAzy4YcfkpiYyKxZs9i1axfXXnst8fHxnDx5ssrxW7Zs4d5772Xs2LHs3r2bESNGMGLECPbt2+cY8+KLL/Lqq6+yZMkStm3bhq+vL/Hx8RQVla/7kZKSgs1m44033mD//v0sWLCAJUuWMG3atAuu99lnn5GZmel49erVy6ks0rgdPV3A3uM5uLqYiL8myOg4F4gK8QMgRcUbaWTOFpSw9Os0ABJv6Yari6nGzh0bFgjAoaw88ovLauy8IiLiPBVvREREREREDDJ//nzGjRvHmDFj6N69O0uWLMHHx4dly5ZVOf6VV14hISGBxx9/nKioKJ599lmuu+46Fi9eDJTPulm4cCHTp0/njjvuoGfPnqxYsYITJ06wevVqABISEnjnnXcYOnQonTp14vbbb2fKlCmsWrXqguu1bNmS4OBgx8vd3b3aWaTxq2iZ1q9zS1rWs5ZpAN3Pt01LO1VAUanV4DQiNWfJVz+SX1xG9xB/bq3htaba+HsRGuCFzQ57jllq9NwiIuIcFW9EREREREQMUFJSws6dOxkyZIhjm4uLC0OGDGHr1q1VHrN169ZK4wHi4+Md49PS0jCbzZXGBAQE0Ldv34ueEyAnJ4cWLVpcsP3222+nTZs2DBgwgI8//tipLFUpLi4mNze30ksarp9bpoUYnKRqrf08aenrgc0OB81a90Yah5N5Rby75QgAU+K74lKDs24qxLYvX/dGrdNERIyl4o2IiIiIiIgBTp06hdVqJSiocrupoKAgzGZzlceYzeZLjq/46Mw5U1NTWbRoEX/4wx8c25o1a8a8efP46KOPWLt2LQMGDGDEiBGVCjiXy1KVuXPnEhAQ4HiFhYVddKzUb0dOFbD/RC6uLiaGXlOzT/7XFJPJRGRF6zSzCoXSOPxt848UldqIbR/I4G5tauUaMedbp+1Ot9TK+UVEpHrcjA4gIiIiIiIixjh+/DgJCQmMHDmScePGOba3atWKxMREx+fXX389J06c4KWXXuL222+/4utNnTq10nlzc3NVwGmgftkyrYWvh8FpLi4q2J9vU0+TnKmZN9LwHbecY+W2dAAeH9oNk6nmZ90AxLYPBMpn3tjt9lq7joiIXJpm3oiIiIiIiBigVatWuLq6kpWVVWl7VlYWwcFVz2QIDg6+5PiKj9U554kTJxg8eDD9+vXjzTffvGzevn37kpqaWu0sVfH09MTf37/SSxqmipZpt/Wsny3TKkSdX/fmQKZm3kjD9+pnhymx2ujXuSX9urSqtev0aBuAm4uJ7LxiTuQU1dp1RETk0lS8ERERERERMYCHhwe9evVi06ZNjm02m41NmzYRFxdX5TFxcXGVxgNs3LjRMT48PJzg4OBKY3Jzc9m2bVulcx4/fpxBgwbRq1cv3nnnHVxcLn9rmJSUREjIz2/UXy6LNF4/ZedzIDMXNxcTQ7vXz5ZpFRxt0zJzsdvtBqcRuXJppwr4565jADw2tFutXsvL3dXxu7M7/WytXktERC5ObdNEREREREQMkpiYyIMPPkjv3r3p06cPCxcupKCggDFjxgAwatQo2rZty9y5cwGYNGkSN910E/PmzWP48OF88MEH7NixwzFzxmQyMXnyZJ577jkiIiIIDw9nxowZhIaGMmLECODnwk2HDh14+eWXyc7OduSpmDXz7rvv4uHhQWxsLACrVq1i2bJlLF261DH2clmk8Vp3vmVa/y6taF6PW6YBdGnTDDcXE7lFZZzIKaJtoLfRkUSuyMLPDmG12flVZBt6dWhe69eLDWvOvuO5JKVbuK1naK1fT0RELqTijYiIiIiIiBO++uor+vXrh5tb5dupsrIytmzZwsCBA6t9rrvvvpvs7GxmzpyJ2WwmJiaG9evXExQUBEB6enqlWTH9+vVj5cqVTJ8+nWnTphEREcHq1avp0aOHY8wTTzxBQUEB48ePx2KxMGDAANavX4+XlxdQPjsmNTWV1NRU2rVrVynPL2cmPPvssxw9ehQ3NzciIyP58MMPueuuu5zKIo3TmvMt04bX85ZpAJ5urnRp04wUcx7JJ3JVvJEG6aA5j49/OAHAY0O71sk1Y8IC+ft3R0nKsNTJ9URE5EIq3oiIiIiIiDhh8ODBZGZm0qZNm0rbc3JyGDx4MFar1anzTZw4kYkTJ1a574svvrhg28iRIxk5cuRFz2cymZgzZw5z5sypcv/o0aMZPXr0JTM9+OCDPPjgg5ccU50s0viknswnxZyHu6uJ+HreMq1CVIh/efEmM5ch3YOMjiPitPkbD2K3w/DoEK4JDaiTa8a2DwRg7/EcSq023F218oKISF3Tf3lFREREREScYLfbMZlMF2w/ffo0vr6+BiQSqTsVLdMGdGlFgI+7wWmqJzL4/Lo35jyDk4g4b88xC5/uz8LFBI/eElFn1w1v5UuAtzvFZTZSMvW7IyJiBM28ERERERERqYbf/va3QPnMltGjR+Pp6enYZ7Va2bNnD/369TMqnkidWOtomdZw1sCICvEHIDkz1+AkIs57ecMhAEbEtqVLG786u67JZOLasEC+OpTN7oyzRLermxk/IiLyMxVvREREREREqiEgoPyNK7vdjp+fH97eP6+d4eHhwQ033MC4ceOMiidS61JP5nEwq7xl2i0NqP1YRfEm7XQBhSVl+HjorRBpGLanneGrQ9m4uZiYfHPdrHXzS7HnizdJ6RZGxdX55UVEmjz9xSIiIiIiIlIN77zzDgAdO3ZkypQpapEmTc7aPWYAboxoTYB3w2iZBtDaz5NWzTw4lV/Coax8YsICjY4kcll2u52XPz0IwO+uD6N9S586zxBzft2bpAxLnV9bRES05o2IiIiIiIhTZs2ahaenJ5999hlvvPEGeXnlawGcOHGC/Px8g9OJ1J61e08A5YumNzRqnSYNzdeHT7H9yBk83Fz486+6GJIhpl0gAD+dKsBSWGJIBhGRpkzFGxERERERESccPXqU6Oho7rjjDiZMmEB2djYAL7zwAlOmTDE4nUjtOJSVx6GsfDxcXRjSgFqmVVDxRhoSu93OyxvKZ938vxs6EBLgfZkjakdzXw/CW5XPMtXsGxGRuqfijYiIiIiIiBMmTZpE7969OXv2bKV1b37zm9+wadMmA5OJ1J61ezIBGNi1VYNqmVYhKqR8oXcVb6Qh2Hggiz3HcvDxcOXhQZ0NzVLRZnB3usXQHCIiTZHWvBEREREREXHC119/zZYtW/Dw8Ki0vWPHjhw/ftygVCK1x263s3ZvefFmeM+G1zINIDK4fOZNSmYedrsdk8lkcCKRqtlsduZvPATAmP4dadXM09A8se0D+ffu45p5IyJiAKdm3pSWltK5c2eSk5NrK4+IiIiIiEi9ZrPZsFqtF2w/duwYfn5+BiQSqV2HsvJJPZmPh5sLQ6IaXss0gM6tm+HuaiKvuIxjZ88ZHUfkoj7Zc4IUcx5+Xm6Mv9HYWTfw88ybpAwLdrvd2DAiIk2MU8Ubd3d3ioqKajzEa6+9RseOHfHy8qJv375s3779kuM/+ugjIiMj8fLyIjo6mnXr1lXab7fbmTlzJiEhIXh7ezNkyBAOHz7s2H/kyBHGjh1LeHg43t7edO7cmVmzZlFSUnnxtT179nDjjTfi5eVFWFgYL774Ys190SIiIiIi0iANHTqUhQsXOj43mUzk5+cza9Yshg0bZlwwkVqyds8JAG7q2ho/r4bXMg3Aw82FLm3UOk3qtzKrjYWflb9/9YeBnQjwMf73LTLYHw83F3LOlZJ2qsDoOCIiTYrTa95MmDCBF154gbKyshoJ8OGHH5KYmMisWbPYtWsX1157LfHx8Zw8ebLK8Vu2bOHee+9l7Nix7N69mxEjRjBixAj27dvnGPPiiy/y6quvsmTJErZt24avry/x8fGOwlNKSgo2m4033niD/fv3s2DBApYsWcK0adMc58jNzWXo0KF06NCBnTt38tJLL/HMM8/w5ptv1sjXLSIiIiIiDdO8efP49ttv6d69O0VFRdx3332OlmkvvPCC0fFEapTdbmfN+ZZptzXQlmkVooLLizcp5jyDk4hUbdWu46SdKqCFrwej+4cbHQcoL3xGtw0AUOs0EZE65vSaN99//z2bNm1iw4YNREdH4+vrW2n/qlWrnDrf/PnzGTduHGPGjAFgyZIlrF27lmXLlvHUU09dMP6VV14hISGBxx9/HIBnn32WjRs3snjxYpYsWYLdbmfhwoVMnz6dO+64A4AVK1YQFBTE6tWrueeee0hISCAhIcFxzk6dOnHw4EFef/11Xn75ZQDee+89SkpKWLZsGR4eHlxzzTUkJSUxf/58xo8f79TXKCIiIiIijUe7du344Ycf+OCDD9izZw/5+fmMHTuW+++/H29vb6PjidSog1l5/JRdgIebCzc30JZpFaJC/GH3cc28kXqpuMzKK5vKZ938aVBnmnnWn2WqY8IC2Xn0LEkZFn57XTuj44iINBlO/0sQGBjInXfeWSMXLykpYefOnUydOtWxzcXFhSFDhrB169Yqj9m6dSuJiYmVtsXHx7N69WoA0tLSMJvNDBkyxLE/ICCAvn37snXrVu65554qz5uTk0OLFi0qXWfgwIGVFiGNj4/nhRde4OzZszRv3vyCcxQXF1NcXOz4PDdXfxCKiIiIiDRGbm5uPPDAA0bHEKl1a/eUz7oZ1LV1vXoz+UpEhfgDapsm9dMH2zM4bjlHkL8nD9zQweg4lcS2DwRgd7rF0BwiIk2N0395vfPOOzV28VOnTmG1WgkKqvz0TlBQECkpKVUeYzabqxxvNpsd+yu2XWzM/0pNTWXRokWOWTcV5wkPrzxFteKcZrO5yuLN3LlzmT17dpXXEBERERGRxuHjjz+ucrvJZMLLy4suXbpccC8h0hDZ7XZH8WZ4A2+ZBhAZUt427eiZQgqKy/Bt4MUoaTzOlVhZvDkVgIm/isDL3dXgRJXFhAUC5YXPolJrvcsnItJYXfFfKtnZ2Rw8eBCAbt260bp16xoLVZeOHz9OQkICI0eOZNy4cVd1rqlTp1aaFZSbm0tYWNjVRhQRERERkXpkxIgRmEwm7HZ7pe0V20wmEwMGDGD16tVVPvQl0lAkZ+bx06kCPBtByzSAVs08ae3nSXZeMQez8riuvX4/pX5YsfUI2XnFtGvuzd2969/7SG0DvWnVzJNT+cXsO55D744tLn+QiIhcNRdnDygoKOChhx4iJCSEgQMHMnDgQEJDQxk7diyFhYVOnatVq1a4urqSlZVVaXtWVhbBwcFVHhMcHHzJ8RUfq3POEydOMHjwYPr168ebb75Zrev88hr/y9PTE39//0ovERERERFpXDZu3Mj111/Pxo0bycnJIScnh40bN9K3b1/WrFnDV199xenTp5kyZYrRUUWuytq9JwAY3K1Ng2+ZVkGt06S+ySsq5fUvfwRg8pCueLg5/VZdrTOZTI7WaUkZFkOziIg0JU7/i5CYmMiXX37JJ598gsViwWKx8J///Icvv/ySxx57zKlzeXh40KtXLzZt2uTYZrPZ2LRpE3FxcVUeExcXV2k8lN88VYwPDw8nODi40pjc3Fy2bdtW6ZzHjx9n0KBB9OrVi3feeQcXl8rfiri4OL766itKS0srXadbt256ek5EREREpAmbNGkS8+fP5+abb8bPzw8/Pz9uvvlmXnrpJR5//HH69+/PwoUL2bhxo9FRRa5YY2uZViHqfOs0FW+kvnj7mzQshaV0bu3Lb2LbGh3noipap+1W8UZEpM44Xbz517/+xdtvv82tt97qmF0ybNgw3nrrLf75z386HSAxMZG33nqLd999l+TkZB5++GEKCgoYM2YMAKNGjWLq1KmO8ZMmTWL9+vXMmzePlJQUnnnmGXbs2MHEiROB8qcBJk+ezHPPPcfHH3/M3r17GTVqFKGhoYwYMQL4uXDTvn17Xn75ZbKzszGbzZXWxLnvvvvw8PBg7Nix7N+/nw8//JBXXnmlUls0ERERERFpen788ccqZ9n7+/vz008/ARAREcGpU6fqOppIjdl/IpcjpwvxcnfhV5FtjI5TY6KCy393UzLzDE4iAmcLSnj76zQAHr2lK64uJoMTXVzs+eJNUrrF0BwiIk2J0/OeCwsLCQq6sNdtmzZtnG6bBnD33XeTnZ3NzJkzMZvNxMTEsH79esc10tPTK82K6devHytXrmT69OlMmzaNiIgIVq9eTY8ePRxjnnjiCQoKChg/fjwWi4UBAwawfv16vLy8gPIZNKmpqaSmptKuXbtKeSr6VgcEBLBhwwYmTJhAr169aNWqFTNnzmT8+PFOf40iIiIiItJ49OrVi8cff5wVK1Y41v7Mzs7miSee4Prrrwfg8OHDWv9SGrS1e8tn3fwqsg2+jaRlGvzcNi3FnIfNZselHr9ZLo3fG1/9RF5xGVEh/gzrUb9nuPUMC8RkguOWc5zMK6KNn5fRkUREGj2n/wKLi4tj1qxZrFixwlEMOXfuHLNnz75oq7PLmThxomPmzP/64osvLtg2cuRIRo4cedHzmUwm5syZw5w5c6rcP3r0aEaPHn3ZXD179uTrr7++7DgREREREWk63n77be644w7atWvnKNBkZGTQqVMn/vOf/wCQn5/P9OnTjYwpcsUqtUyLDjU4Tc3q1NoXD1cX8ovLOHb2HO1b+hgdSZqok3lFLN9SPutmytCu9b6Q2MzTja5t/DiYlUdSuoWh11S9HrSIiNQcp4s3CxcuJCEhgXbt2nHttdcC8MMPP+Dl5cWnn35a4wFFRERERETqk27dunHgwAE2bNjAoUOHHNtuueUWR9eAipbNIg3R/hO5pJ8pb5k2OLK10XFqlLurC13aNONAZi4HMnNVvBHD/G3zjxSV2ogJC2wwrQlj2wdyMCuP3Rkq3oiI1AWnizfR0dEcPnyY9957j5SUFADuvfde7r//fry9vWs8oIiIiIiISH3j4uJCQkICCQkJRkcRqXFrzs+6uTkyCB+PxtMyrUJUiD8HMnNJMeeS0ENvQEvdO245x8pt6QA8Ht8Nk6l+z7qpEBMWyAffZ2jdGxGROuLUX2GlpaVERkayZs0axo0bV1uZRERERERE6pVXX3212mMfeeSRWkwiUrvsdjtr954AYHjP+r0Gx5WKCvEDIDkz1+Ak0lQt2nSYEquNuE4t6d+lldFxqi2mfSAAe45ZsNrsuNbzVm8iIg2dU8Ubd3d3ioqKaiuLiIiIiIhIvbRgwYJKn2dnZ1NYWEhgYCAAFosFHx8f2rRpo+KNNGh7j+eQceYc3u6uDO7WMFo5Oat7iD8AyZl5BieRpijtVAEf7TwGwJT4rgancU5EGz98PVwpKLFy+GQekcH+RkcSEWnUXJw9YMKECbzwwguUlZXVRh4REREREZF6Jy0tzfF6/vnniYmJITk5mTNnznDmzBmSk5O57rrrePbZZ42OKnJV1la0TItqg7eHq8Fpakfk+eJN+plC8ov13obUrVc+O4TVZmdwt9b06tDC6DhOcXUx0bNdIIBap4mI1AGnm9d+//33bNq0iQ0bNhAdHY2vr2+l/atWraqxcCIiIiIiIvXNjBkz+Oc//0m3bt0c27p168aCBQu46667uP/++w1MJ3Ll7Ha7Y72b2xppyzSAFr4eBPl7kpVbzEFzboN7A10aroPmPP7zQ3lbwseGdrvM6Poppn0gW386ze50C/f0aW90HBGRRs3p4k1gYCB33nlnbWQRERERERGp9zIzM6vsRGC1WsnKyjIgkUjN+OFYDsct5/DxcGVQI22ZViEqxJ+s3GwOZOapeCN1Zv7Gg9jtMCw6mB5tA4yOc0ViwwIBSMqwGJpDRKQpcKp4U1ZWxuDBgxk6dCjBwcG1lUlERERERKTeuvnmm/nDH/7A0qVLue666wDYuXMnDz/8MEOGDDE4nciVW7unfEbAkKggvNwbZ8u0CpHB/nxxMJvkzFyjo0gTseeYhU/3Z2EywaNDGtZaN78U0z4QgEMn88gvLqOZp9PPhYuISDU5teaNm5sbf/zjHykuLq6tPCIiIiIiIvXasmXLCA4Opnfv3nh6euLp6UmfPn0ICgpi6dKlRscTuSJ2u511e80ADG/ELdMqRIX4AZCi4o3UkXkbDgHwm5i2RAT5GZzmyrXx86JtoDd2O+zR7BsRkVrlVPEGoE+fPuzevbs2soiIiIiIiNR7rVu3Zt26daSkpPDRRx/x0UcfkZyczLp162jTxvlWU6+99hodO3bEy8uLvn37sn379kuO/+ijj4iMjMTLy4vo6GjWrVtXab/dbmfmzJmEhITg7e3NkCFDOHz4sGP/kSNHGDt2LOHh4Xh7e9O5c2dmzZpFSUmJY8wXX3zBHXfcQUhICL6+vsTExPDee+9Vus7y5csxmUyVXl5eXk5//VI/JGVYOG45h6+HKzd1bW10nFrXPcQfgBRzHjab3eA00th9f+QMXx7Kxs3FxKQhEUbHuWoVs292q3gjIlKrnJ7b+Kc//YnHHnuMY8eO0atXL3x9fSvt79mzZ42FExERERERqa+6du1K165X1/rmww8/JDExkSVLltC3b18WLlxIfHw8Bw8erLIQtGXLFu69917mzp3LbbfdxsqVKxkxYgS7du2iR48eALz44ou8+uqrvPvuu4SHhzNjxgzi4+M5cOAAXl5epKSkYLPZeOONN+jSpQv79u1j3LhxFBQU8PLLLzuu07NnT5588kmCgoJYs2YNo0aNIiAggNtuu82Rx9/fn4MHDzo+N5lMV/X9EOOs3ZMJwJDujb9lGkB4K1883FwoLLGSfqaQjq18L3+QyBWw2+289Gn5fydH9g6jQ8uG/7MWGxbI2j2Z7E63GB1FRKRRc7p4c8899wDwyCOPOLaZTCbsdjsmkwmr1Vpz6UREREREROqZhx566JL7ly1bVu1zzZ8/n3HjxjFmzBgAlixZwtq1a1m2bBlPPfXUBeNfeeUVEhISePzxxwF49tln2bhxI4sXL2bJkiXY7XYWLlzI9OnTueOOOwBYsWIFQUFBrF69mnvuuYeEhAQSEhIc5+zUqRMHDx7k9ddfdxRvpk2bVum6kyZNYsOGDaxatapS8cZkMmk91EbAZrOzbm958WZ4dONvmQbg5upC16Bm7DueS4o5V8UbqTXfpJ5ie9oZPNxceOTmLkbHqRGx52feJGVYHO8HiohIzXO6bVpaWtoFr59++snxUUREREREpDE7e/ZspdfJkyf5/PPPWbVqFRaLpdrnKSkpYefOnQwZMsSxzcXFhSFDhrB169Yqj9m6dWul8QDx8fGO8WlpaZjN5kpjAgIC6Nu370XPCZCTk0OLFi0umbeqMfn5+XTo0IGwsDDuuOMO9u/ff8lzFBcXk5ubW+klxtudYeFEThHNPN0Y2ARaplWICi5vnXYgM8/gJNJY2e12Xj4/6+aBvh0ICfA2OFHNuCY0ADcXE6fyizluOWd0HBGRRsvpmTcdOnSojRwiIiIiIiINwr///e8LttlsNh5++GE6d+5c7fOcOnUKq9VKUFBQpe1BQUGkpKRUeYzZbK5yvNlsduyv2HaxMf8rNTWVRYsWOWbdVOUf//gH33//PW+88YZjW7du3Vi2bBk9e/YkJyeHl19+mX79+rF//37atWtX5Xnmzp3L7NmzL3odMUZFy7RbmkjLtApR59e9Sc5UEVFqx8YDWfxwLAdvd1ceHlT9fx/qOy93V7qH+rPnWA670y20a+5jdCQRkUbJ6Zk3AH//+9/p378/oaGhHD16FICFCxfyn//8p0bDiYiIiIiINAQuLi4kJiayYMECo6M45fjx4yQkJDBy5EjGjRtX5ZjNmzczZswY3nrrLa655hrH9ri4OEaNGkVMTAw33XQTq1atonXr1pUKPP9r6tSp5OTkOF4ZGRk1/jWJc5piy7QKkSF+gIo3UjtsNjvzNx4CYEz/jrT28zQ4Uc2KCQsEyluniYhI7XC6ePP666+TmJjIsGHDsFgsjjVuAgMDWbhwYU3nExERERERaRB+/PFHysrKqj2+VatWuLq6kpWVVWl7VlbWRdeRCQ4OvuT4io/VOeeJEycYPHgw/fr1480336zyel9++SW//vWvWbBgAaNGjbrk1+Pu7k5sbCypqakXHePp6Ym/v3+llxhrV/pZzLlF+Hm6cWPXVkbHqVPdz8+8OXb2HLlFpQankcZmzd5MUsx5+Hm58YeBjWfWTYWK4s3u9LPGBhERacScLt4sWrSIt956i6effhpX15+nU/fu3Zu9e/fWaDgREREREZH6JjExsdLr0Ucf5Z577uHuu+/m7rvvrvZ5PDw86NWrF5s2bXJss9lsbNq0ibi4uCqPiYuLqzQeYOPGjY7x4eHhBAcHVxqTm5vLtm3bKp3z+PHjDBo0iF69evHOO+/g4nLhreEXX3zB8OHDeeGFFxg/fvxlvx6r1crevXsJCWlaszcaujUVLdOuCcLTrem0TAMI9PEgJMALgINmrXsjNafMamPh+Vk342/sRICPu8GJal5s++YA7DuRS0mZzeA0IiKNk9Nr3qSlpREbG3vBdk9PTwoKCmoklIiIiIiISH21e/fuSp+7uLjQunVr5s2bx0MPPeTUuRITE3nwwQfp3bs3ffr0YeHChRQUFDBmzBgARo0aRdu2bZk7dy4AkyZN4qabbmLevHkMHz6cDz74gB07djhmzphMJiZPnsxzzz1HREQE4eHhzJgxg9DQUEaMGAH8XLjp0KEDL7/8MtnZ2Y48FbNzNm/ezG233cakSZO48847HevleHh40KJFCwDmzJnDDTfcQJcuXbBYLLz00kscPXqU3//+905+R8UoNpud/+4rL97c1rNpFt2iQvzJzCkiOTOX6zu2MDqONBKrdh3np1MFtPD1YMyAcKPj1IqOLX0I9HHHUlhKijmXnu0CjY4kItLoOF28CQ8PJykpiQ4dOlTavn79eqKiomosmIiIiIiISH20efPmGjvX3XffTXZ2NjNnzsRsNhMTE8P69esJCgoCID09vdKsmH79+rFy5UqmT5/OtGnTiIiIYPXq1fTo0cMx5oknnqCgoIDx48djsVgYMGAA69evx8urfIbBxo0bSU1NJTU1lXbt2lXKY7fbAXj33XcpLCxk7ty5jsIRwE033cQXX3wBwNmzZxk3bhxms5nmzZvTq1cvtmzZQvfu3Wvs+yO1a2f6WbJyi/HzcmNAl9ZGxzFEZLAfn6ecJDlTM2+kZhSXWXll02EAHr6pM808nX7rrUEwmUxc2y6QLw9lszvdouKNiEgtcPpfkMTERCZMmEBRURF2u53t27fz/vvvM3fuXJYuXVobGUVEREREROqdkydPcvDgQQC6detGmzZtrug8EydOZOLEiVXuqyiU/NLIkSMZOXLkRc9nMpmYM2cOc+bMqXL/6NGjGT169CUzLV++nOXLl19yzIIFC1iwYMElx0j9tvZ8y7Sh3YPxcHO6q3qjEHV+3ZvkzFyDk0hj8eH3GRy3nKONnyf/L67D5Q9owGLblxdvkjIsPGh0GBGRRsjp4s3vf/97vL29mT59OoWFhdx3332EhobyyiuvcM8999RGRhERERERkXojNzeXCRMm8P7772Ozlff5d3V15e677+a1114jICDA4IQil2e12Vm3t2m3TIOfizcHzXlYbXZcXUwGJ5KG7FyJlUWfpwLw5191wcu9ca8jFRMWCEBShsXQHCIijdUVPVpz//33c/jwYfLz8zGbzRw7doyxY8fWdDYREREREZF6Z9y4cWzbto21a9disViwWCysWbOGHTt28Ic//MHoeCLVsuPIGU7mFePv5Ub/Lq2MjmOYji198HRz4VyplaOntY6vXJ0VW4+QnVdMu+be3H19e6Pj1LqK4k3aqQLOFpQYG0ZEpBG6qnnRPj4+V9waQEREREREpCFas2YNy5YtIz4+Hn9/f/z9/YmPj+ett97ik08+MTqeSLWsPT/rJv6aptsyDcDN1YVuwX4ApJi17o1cubyiUpZ8+SMAk26OaBK/V4E+HnRq5QtA0jGLsWFERBqhxv8viYiIiIiISA1q2bJlla3RAgICaN68uQGJRJxT3jLNDMDwJtwyrUJUsNa9kau37JsjnC0spVNrX34T29boOHWmYvbN7nSLoTlERBojFW9EREREREScMH36dBITEzGbzY5tZrOZxx9/nBkzZhiYTKR6tqed4VR+MQHe7k26ZVqFqJDymTcq3siVshSWsPTrnwB4dEhX3Fybztttse0DAa17IyJSG9yMDiAiIiIiIlLfxcbGYjL9vJD54cOHad++Pe3bl69pkJ6ejqenJ9nZ2Vr3Ruq9tXtPAJBwTTDuTehN5ouJDKmYeaO2aXJl3vjqJ/KKy4gK8Wd4dNOazRYTVj7j9IcMCzabHRcX02WOEBGR6rqq4k1RURFeXl41lUVERERERKReGjFihNERRGqE1WZn/T61TPulirZpxy3nyDlXSoC3u8GJpCE5mVfE8m+PAPDYLV2bXPEiMsQPTzcXcs6Vkna6gM6tmxkdSUSk0XC6eGOz2Xj++edZsmQJWVlZHDp0iE6dOjFjxgw6duzI2LFjayOniIiIiIiIYWbNmmV0BJEasS3tNKfySwj0cSeuc0uj49QLAT7utA305rjlHCmZufTtpO+LVN/fNv/IuVIr14YFcnNUG6Pj1Dl3Vxei2waw4+hZktItKt6IiNQgp+dHP/fccyxfvpwXX3wRDw8Px/YePXqwdOnSGg0nIiIiIiIiIjVn7Z5MQC3T/ldksNa9Eecdt5xj5bZ0AB4f2q1Se82mJCYsENC6NyIiNc3pv9RWrFjBm2++yf3334+rq6tj+7XXXktKSkqNhhMRERERERGRmlFmtall2kVEnV/3JsWsdW+k+hZ/fpgSq40bOrWgf5emO2Mrtn35uje7M84anEREpHFxunhz/PhxunTpcsF2m81GaWlpjYQSERERERERkZq1Le0MpwtKaO7jTpxag1VSUbzRzBupriOnCvjHjmMATGnCs24AYtoHApCSmce5EquxYUREGhGnizfdu3fn66+/vmD7P//5T2JjY2sklIiIiIiIiIjUrDUVLdN6hOCmlmmVRIaUt007mJWH1WY3OI00BAs/O4TVZmdQt9b07tjC6DiGCg3worWfJ2U2O/tO5BgdR0Sk0XBz9oCZM2fy4IMPcvz4cWw2G6tWreLgwYOsWLGCNWvW1EZGERERERGReqG0tJTIyEjWrFlDVFSU0XFEqq28ZVp58eY2tUy7QMeWvni5u1BUauPI6QItui6XdCgrj//8cAKAx27pZnAa45lMJmLDAtlwIIukdAvXN/FilohITXH6UZs77riDTz75hM8++wxfX19mzpxJcnIyn3zyCbfcckttZBQREREREakX3N3dKSoqMjqGiNO2/nSas4WltPT1oG+43lj9X64uJroFq3WaVM/8DYew2yHhmmCi2wUYHadeqGidlpRhMTSHiEhjckXzpG+88UY2btzIyZMnKSws5JtvvmHo0KE1nU1ERERERKTemTBhAi+88AJlZWVGRxGptrWOlmnBapl2Ed3Pt05T8UYuZe+xHNbvN2MyQeLQrkbHqTdiw5oDsDv9rMFJREQaD6fbpn3//ffYbDb69u1bafu2bdtwdXWld+/eNRZORERERESkvvn+++/ZtGkTGzZsIDo6Gl9f30r7V61aZVAykaqVWm18ut8MwHC1TLuoyPMzb1Iy8wxOIvXZyxsOAjAipi1dg/wMTlN/9GwXgIsJTuQUcTK3iDb+XkZHEhFp8Jx+3GbChAlkZGRcsP348eNMmDChRkKJiIiIiIjUV4GBgdx5553Ex8cTGhpKQEBApZdIfbP1x/KWaa2aedA3vKXRceqtqBC1TZNL+/7IGb48lI2ri4nJQyKMjlOv+Hq6OYpZu9U6TUSkRjg98+bAgQNcd911F2yPjY3lwIEDNRJKRERERESkvnrnnXeMjiDilF+2THN1MRmcpv6KPN827UROEZbCEgJ9PAxOJPWJ3W7n5U/LZ938rnc7OrT0vcwRTU9s+0BSzHnsTrcQf02w0XFERBo8p2feeHp6kpWVdcH2zMxM3NycrgWJiIiIiIg0SNnZ2XzzzTd88803ZGdnGx1HpEqlVhvrK1qmRYcanKZ+8/dyp22gNwDJap0m/+Ob1FNsSzuDh6sLf/6VZt1UJSYsEICkDK17IyJSE5wu3gwdOpSpU6eSk5Pj2GaxWJg2bRq33HJLjYYTERERERGpbwoKCnjooYcICQlh4MCBDBw4kNDQUMaOHUthYaHR8UQq+Tb1FDnnSmnVzJM+4S2MjlPvVbROSzGrdZr8zG638/KGQwDcf0N7Qs8X+aSymLDmAOw5loPVZjc4jYhIw+d08ebll18mIyODDh06MHjwYAYPHkx4eDhms5l58+bVRkYREREREZF6IzExkS+//JJPPvkEi8WCxWLhP//5D19++SWPPfaY0fFEKqlomTYsWi3TqqP7+dZpWvdGfumz5JP8kGHB292VPw3qYnSceqtLm2Y083SjsMTKoSzNXhMRuVpO9zlr27Yte/bs4b333uOHH37A29ubMWPGcO+99+Lu7l4bGUVEREREROqNf/3rX/zzn/9k0KBBjm3Dhg3D29ub3/3ud7z++uvGhRP5hZIyG586WqaFGJymYaiYeaO2aVLBZrMzb0P5Wjej+3ektZ+nwYnqL1cXEz3bBbDlx9MkZVgcv08iInJlrmiRGl9fX8aPH1/TWUREREREROq9wsJCgoKCLtjepk0btU2TeuXb1FPkFpXRxs+T3h3VMq06Is+/2XwoK48yqw03V6cblkgj858fjpNizsPP040/DOxkdJx6L7Z9IFt+PM3u9LPc26e90XFERBq0ahVvPv74Y2699Vbc3d35+OOPLzn29ttvr5FgIiIiIiIi9VFcXByzZs1ixYoVeHl5AXDu3Dlmz55NXFycwelEfrZ2b0XLtBC1TKumDi188PFwpbDEypHTBXRp42d0JDFQUamVlz8tX+vmj4M6E+jjYXCi+q9i3ZukDIuxQUREGoFqFW9GjBiB2WymTZs2jBgx4qLjTCYTVqu1prKJiIiIiIjUOwsXLiQhIYF27dpx7bXXAvDDDz/g5eXFp59+anA6kXKVWqb1VMu06nJxMdEt2I/d6RYOZOapeNPELd9yhOOWc4QEeDF2QLjRcRqEmLBAAA6fzCevqBQ/Ly2xICJypao1/9dms9GmTRvH/77YS4UbERERERFp7KKjozl8+DBz584lJiaGmJgY/vrXv3L48GGuueYao+OJAPBNajZ5RWUE+XvSq31zo+M0KJHBFeve5BqcRIx0tqCE1zanAvDY0G54ubsanKhhaO3nSbvm3tjtsOdYjtFxREQatCta80ZERERERKQpKi0tJTIykjVr1jBu3Dij44hc1Jo95S3Tbu0Rgotapjmle0j5bJsUFW+atEWfp5JXVEZUiD+/iW1rdJwGJSYskGNnz5GUYaF/l1ZGxxERabCqVbx59dVXq33CRx55xKkAr732Gi+99BJms5lrr72WRYsW0adPn4uO/+ijj5gxYwZHjhwhIiKCF154gWHDhjn22+12Zs2axVtvvYXFYqF///68/vrrREREOMY8//zzrF27lqSkJDw8PLBYLBdcx2S68I/b999/n3vuucepr09ERERERBoPd3d3ioqKjI4hcknFZVY27s8C4Da1THNaVEjFzJs8g5OIUY6eLuDv3x0BYOqtkVozykmx7ZuzZk8mu9PPGh1FRKRBq1bbtAULFlTrtXDhQqcu/uGHH5KYmMisWbPYtWsX1157LfHx8Zw8ebLK8Vu2bOHee+9l7Nix7N69mxEjRjBixAj27dvnGPPiiy/y6quvsmTJErZt24avry/x8fGVbrBKSkoYOXIkDz/88CXzvfPOO2RmZjpel1rvR0REREREmoYJEybwwgsvUFZWViPne+211+jYsSNeXl707duX7du3X3L8Rx99RGRkJF5eXkRHR7Nu3bpK++12OzNnziQkJARvb2+GDBnC4cOHHfuPHDnC2LFjCQ8Px9vbm86dOzNr1ixKSkoqnWfPnj3ceOONeHl5ERYWxosvvuh0FjHG14dOkVdcRrC/F9epZZrTugWXz7wx5xZxtqDkMqOlMXrx04OUWu3cGNGKgV1bGx2nwalY9yYpw4Ldbjc2jIhIA1at4k1aWlq1Xj/99JNTF58/fz7jxo1jzJgxdO/enSVLluDj48OyZcuqHP/KK6+QkJDA448/TlRUFM8++yzXXXcdixcvBspvUhYuXMj06dO544476NmzJytWrODEiROsXr3acZ7Zs2fz6KOPEh0dfcl8gYGBBAcHO15eXl5OfX0iIiIiItL4fP/996xatYr27dsTHx/Pb3/720ovZxjxQFtKSgo2m4033niD/fv3s2DBApYsWcK0adMc58jNzWXo0KF06NCBnTt38tJLL/HMM8/w5ptvOpVFjLF27/mWadHBapl2Bfy83Alr4Q1Aslmt05qa3elnWbsnE5MJpg2LMjpOg3RNqD/uriZO5Zdw7Ow5o+OIiDRY1SreXIrVaiUpKYmzZ52bCllSUsLOnTsZMmTIz2FcXBgyZAhbt26t8pitW7dWGg8QHx/vGJ+WlobZbK40JiAggL59+170nJcyYcIEWrVqRZ8+fVi2bJmeFhAREREREQIDA7nzzjuJj48nNDSUgICASi9nGPFAW0JCAu+88w5Dhw6lU6dO3H777UyZMoVVq1Y5rvPee+9RUlLCsmXLuOaaa7jnnnt45JFHmD9/frWziDGKSq1sPFDRMi3U4DQNV1SwWqc1RXa7nbnrUgC487p2jhZ64hwvd1e6n//e7c6wGBtGRKQBq9aaN780efJkoqOjGTt2LFarlYEDB7J161Z8fHxYs2YNgwYNqtZ5Tp06hdVqJSgoqNL2oKAgUlJSqjzGbDZXOd5sNjv2V2y72JjqmjNnDr/61a/w8fFhw4YN/OlPfyI/P/+Sa/oUFxdTXFzs+Dw3V0/oiIiIiIg0JmVlZQwePJihQ4cSHBx8VeeqeKBt6tSpjm3VeaAtMTGx0rb4+HhHYeZyD7RdbA3PnJwcWrRoUek6AwcOxMPDo9J1XnjhBc6ePUvz5s0vm0WM8dWhbPKLywgN8CL2fOsicV5UiD8bDmSRnKn7+qZk44Esth85g6ebC48N7Wp0nAYtJiyQH47lkJRu4fZrVUgWEbkSTs+8+ec//8m1114LwCeffMKRI0dISUnh0Ucf5emnn67xgEaZMWMG/fv3JzY2lieffJInnniCl1566ZLHzJ07t9ITd2FhYXWUVkRERERE6oKbmxt//OMfKz20daUu9UDbxR4+q40H2lJTU1m0aBF/+MMfLnudX17jclmqUlxcTG5ubqWX1Kx151umDYsOUcu0qxAVUr7ujYo3TUep1cZf15c/TDx2QDghAd4GJ2rYYtoHArA7w7lOPSIi8jOnizenTp1yPGG2bt06Ro4cSdeuXXnooYfYu3dvtc/TqlUrXF1dycrKqrQ9Kyvrok+wBQcHX3J8xUdnzlldffv25dixY5e8SZs6dSo5OTmOV0ZGxlVdU0RERERE6p8+ffqwe/duo2PUiOPHj5OQkMDIkSMZN25crV9PD7zVrl+2TBveM8TgNA1bRbusw1n5lFltBqeRuvDB9xn8lF1AC18P/jios9FxGrzYsOYA7D+RS0mZfodERK6E08WboKAgDhw4gNVqZf369dxyyy0AFBYW4urqWu3zeHh40KtXLzZt2uTYZrPZ2LRpE3FxcVUeExcXV2k8wMaNGx3jw8PDCQ4OrjQmNzeXbdu2XfSc1ZWUlETz5s3x9PS86BhPT0/8/f0rvUREREREpHH505/+xGOPPcbixYvZunUre/bsqfSqLqMfaDtx4gSDBw+mX79+vPnmm9W6zi+vcbksVdEDb7Xry0PZFJRYaRvoTYxapl2VsOY++Hq4UmK18dOpAqPjSC3LLy7jlc8OATDp5gj8vdwNTtTwdWjpQ3Mfd0rKbJrBJiJyhZwu3owZM4bf/e539OjRA5PJ5OilvG3bNiIjI506V2JiIm+99RbvvvsuycnJPPzwwxQUFDBmzBgARo0aVan/86RJk1i/fj3z5s0jJSWFZ555hh07djBx4kQATCYTkydP5rnnnuPjjz9m7969jBo1itDQUEaMGOE4T3p6OklJSaSnp2O1WklKSiIpKYn8/HygvB3c0qVL2bdvH6mpqbz++uv85S9/4c9//rOz3y4REREREWlk7rnnHtLS0njkkUfo378/MTExxMbGOj5Wl5EPtB0/fpxBgwbRq1cv3nnnHVxcKt8axsXF8dVXX1FaWlrpOt26daN58+bVylIVPfBWu9buqWiZFozJpJZpV8PFxUTk+dk3euO58Xvzyx85lV9Cx5Y+3NunvdFxGgWTyeQoIu9OV+s0EZEr4ebsAc888ww9evQgIyODkSNHOmaiuLq68tRTTzl1rrvvvpvs7GxmzpyJ2WwmJiaG9evXO/omp6enV7qJ6NevHytXrmT69OlMmzaNiIgIVq9eTY8ePRxjnnjiCQoKChg/fjwWi4UBAwawfv16vLy8HGNmzpzJu+++6/i84gZr8+bNDBo0CHd3d1577TUeffRR7HY7Xbp0Yf78+XXSRkBEREREROq3tLS0GjtXYmIiDz74IL1796ZPnz4sXLjwggfa2rZty9y5c4HyB9puuukm5s2bx/Dhw/nggw/YsWOHY+bMLx9oi4iIIDw8nBkzZlR6oK2icNOhQwdefvllsrOzHXkqZs3cd999zJ49m7Fjx/Lkk0+yb98+XnnlFRYsWOAYe7ksUreKSq18llzRMk2Lg9eEyGA/dh49S3JmHnfEGJ1GaktWbhFvfV3+3/UnEyLxcHP6OWe5iJiw5mw+mE1ShsXoKCIiDZLTxRuAu+6664JtDz744BUFmDhxomPmzP/64osvLtg2cuRIRo4cedHzmUwm5syZw5w5cy46Zvny5Sxfvvyi+xMSEkhISLjofhERERERabo6dOhQY+cy4oG2jRs3kpqaSmpqKu3atauUx263AxAQEMCGDRuYMGECvXr1olWrVsycOZPx48c7lUXqzhcHT1J4vmXate0CjI7TKERp5k2TMH/DIc6VWunVoTkJPa5uvWSpLKZ9IICKNyIiV+iKijciIiIiIiJN2d///neWLFlCWloaW7dupUOHDixcuJDw8HDuuOMOp85V1w+0jR49mtGjR182V8+ePfn6668vOeZyWaTurDnfMu22niFqmVZDVLxp/A6a8/hoZ/naW9OGRep3p4bFtAsE4MjpQs4UlNDC18PYQCIiDYzmgoqIiIiIiDjh9ddfJzExkWHDhmGxWLBarQAEBgaycOFCY8NJk3SuxMqm5JMADO8ZYnCaxqNbsB8AJ/OKOZ1fbHAaqQ1//W8yNjskXBNMrw4tjI7T6AT4uNOptS8AP2j2jYiI01S8ERERERERccKiRYt46623ePrpp3F1dXVs7927N3v37jUwmTRVmw+e5FyplbAW3kS3Vcu0mtLM040OLX0ASDHnGZxGatqW1FNsPpiNm4uJJ2+NNDpOoxUTFgjA7vSzxgYREWmAVLwRERERERFxQlpaGrGxsRds9/T0pKCgwIBE0tSt3VveMm14dKjaPtWwqGC1TmuMbDY7z69LBuD+vu0Jb+VrcKLGK7Z9cwB2a+aNiIjTqrXmTW5u9f9I8ff3v+IwIiIiIiIi9V14eDhJSUl06NCh0vb169cTFRVlUCppqgpLyvj8fMu029QyrcZFhfizfr+ZAyreNCr/+eE4+0/k0szTjUdujjA6TqMWe37mzQ8ZFmw2Oy4uKjCLiFRXtYo3gYGB1X56p6Lfs4iIiIiISGOUmJjIhAkTKCoqwm63s337dt5//33mzp3L0qVLjY4nTczmlGzOlVpp38KHa0L1MGVNiwwpX/cmJVNt0xqLolIrL396CICHB3WmZTNPgxM1bt2C/fBydyG3qIyfThXQpU0zoyOJiDQY1SrebN682fG/jxw5wlNPPcXo0aOJi4sDYOvWrbz77rvMnTu3dlKKiIiIiIjUE7///e/x9vZm+vTpFBYWct999xEaGsorr7zCPffcY3Q8aWLW7j0BwPCeIWqZVgu6h5QXxFJP5lNqteHuqu7zDd27W45w3HKOYH8vHuofbnScRs/d1YXotgF8f+QsSRkWFW9ERJxQreLNTTfd5Pjfc+bMYf78+dx7772ObbfffjvR0dG8+eabPPjggzWfUkREREREpB65//77uf/++yksLCQ/P582bdoYHUmaoILiMj5PKW+ZNjxaLdNqQ7vm3jTzdCO/uIwfs/OJDNbspobsbEEJizenAvDY0K54e7ganKhpiAkLPF+8OctdvdoZHUdEpMFw+pGRrVu30rt37wu29+7dm+3bt9dIKBERERERkYbAx8dHhRsxzOcpJykqtdGxpVqm1RaTyURkcHnrtGSte9PgLfo8lbyiMiKD/fjtdSoi1JXY9s0B2J1uMTaIiEgD43TxJiwsjLfeeuuC7UuXLiUsLKxGQomIiIiIiIjIpa3dkwmoZVptizrfOk3r3jRs6acL+ft3RwCYNiwKVxf9ztSVmLBAAFLMeZwr0VrZIiLVVa22ab+0YMEC7rzzTv773//St29fALZv387hw4f517/+VeMBRURERERERKSy/OIyNh+saJkWanCaxq2ieHNAM28atBc/TaHUaufGiFYM7Nra6DhNSkiAF0H+nmTlFrP3eA59wlsYHUlEpEFweubNsGHDOHToEL/+9a85c+YMZ86c4de//jWHDh1i2LBhtZFRRERERERERH5hU3IWxWU2OrXyJSrEz+g4jVpkSEXbNM28aaiSMiys2ZOJyQRTb40yOk6TYzKZHLNvkjLOGhtGRKQBcXrmDZS3TvvLX/5S01lERERERETqtdLSUhISEliyZAkRERFGx5EmbN1etUyrK5HBfphMcCq/mOy8Ylr7eRodSZxgt9v5y9pkAH4b247uWh/KEDFhzfl0fxZJGRajo4iINBhOz7wB+Prrr3nggQfo168fx48fB+Dvf/8733zzTY2GExERERERqU/c3d3Zs2eP0TGkiStvmZYNwLDoEIPTNH4+Hm50bOkLQIpZrdMams+ST7L9yBk83Vx4bGhXo+M0WbHtAwHYnW4xNIeISEPidPHmX//6F/Hx8Xh7e7Nr1y6Ki4sByMnJ0WwcERERERFp9B544AHefvtto2NIE7YpOYuSMhudWvsSGayWaXUhytE6TcWbhqTMauOv/y2fdfPQgHBCA70NTtR0RbcNwMUEmTlFZOUWGR1HRKRBcLpt2nPPPceSJUsYNWoUH3zwgWN7//79ee6552o0nIiIiIiISH1TVlbGsmXL+Oyzz+jVqxe+vr6V9s+fP9+gZNJUrNlT3jLttmi1TKsrkcH+rNtr1ro3DcwH32fwY3YBLXw9eHhQZ6PjNGm+nm50DfIjxZzH7nQLCT2CjY4kIlLvOV28OXjwIAMHDrxge0BAABaLpSYyiYiIiIiI1Fv79u3juuuuA+DQoUOV9umNdKlteUWlfHm+ZdrwnqEGp2k6okLK10nRzJuGI7+4jIWflf83+pFfdcHfy93gRBLbvnl58SbjrIo3IiLV4HTxJjg4mNTUVDp27Fhp+zfffEOnTp1qKpeIiIiIiEi9tHnzZqMjSBP2WXIWJVYbXdo0o2tQM6PjNBkVbdN+zM6npMyGh9sVLSEsdejNL3/kVH4JHVv6cF/fDkbHESA2LJD3t6eTpHVvRESqxem/NsaNG8ekSZPYtm0bJpOJEydO8N577zFlyhQefvjh2sgoIiIiIiJS76SmpvLpp59y7tw5AOx2u8GJpClYe75l2nC1TKtTbQO98fNyo9RqJ/VkvtFx5DKycot46+s0AJ5IiFSxrZ6IbR8IwJ5jOZRZbcaGERFpAJyeefPUU09hs9m4+eabKSwsZODAgXh6ejJlyhT+/Oc/10ZGERERERGReuP06dP87ne/Y/PmzZhMJg4fPkynTp0YO3YszZs3Z968eUZHlEYq51wpXx06BcDwniEGp2laTCYTUcH+bD9yhhRzLt1D/Y2OJJewYOMhzpVaua59ILeqPVe90bl1M/w83cgrLuNQVr5+j0RELsOpRw+sVitff/01EyZM4MyZM+zbt4/vvvuO7Oxsnn322drKKCIiIiIiUm88+uijuLu7k56ejo+Pj2P73Xffzfr16w1MJo3dZwfKW6Z1DWpG1yA/o+M0ORWt07TuTf12KCuPf+zIAODp4VGaoVaPuLiY6BkWAEBShsXYMCIiDYBTxRtXV1eGDh3K2bNn8fDwoHv37vTp04dmzdRnV0REREREmoYNGzbwwgsv0K5du0rbIyIiOHr0qEGppClYu7eiZVqowUmapqiQ8lkCyZl5BieRS5m7LhmbHRKuCaZXhxZGx5H/ERvWHIDd6WcNTiIiUv853fSzR48e/PTTT7WRRUREREREpN4rKCioNOOmwpkzZ/D09DQgkTQFOedK+fpwNgDDe6oNlBEiHcWbXK1xVU9tST3F5oPZuLmYeCKhm9FxpAoxYYGAZt6IiFSH08Wb5557jilTprBmzRoyMzPJzc2t9BIREREREWnMbrzxRlasWOH43GQyYbPZePHFFxk8eLCByaQx23ggi1KrnW5BfnRpo5ZpRugW5IeLCU4XlJCdX2x0HPkfNpudv/w3GYD7+ranU2t1iamPYtoHApCanU9uUamxYURE6jk3Zw8YNmwYALfffnulvqF2ux2TyYTVaq25dCIiIiIiIvXMiy++yM0338yOHTsoKSnhiSeeYP/+/Zw5c4Zvv/3W6HjSSK3dcwKA4T1DDE7SdHl7uNKxlS8/ZReQnJlHGz8voyPJL3z8wwn2Hc+lmacbk26OMDqOXESrZp6EtfAm48w59mTkMCCildGRRETqLaeLN5s3b66NHCIiIiIiIg1Cjx49OHToEIsXL8bPz4/8/Hx++9vfMmHCBEJC9Ma61LycwlK+PnwKgGHR+hkzUlSw//niTS43dW1tdBw5r6jUykufHgTg4UGdadlMLSzrs5iw5mScOUdSxlkVb0RELsGp4k1paSlz5sxhyZIlREToKQYREREREWmaAgICePrpp42OIU3EpwfMlNnsRAb70aWNWkEZKSrEj7V7M0nJVNv4+mTF1iMct5wj2N+Lh/qHGx1HLiM2LJBPfjjB7nSL0VFEROo1p4o37u7u7Nmzp7ayiIiIiDQJOTk5FBYWGh2jWnx8fAgICDA6hki90qVLFx544AHuv/9+PdQmdWLtnkwAblPLNMNFhfgDkJyZZ3ASqWApLGHx56kAJA7tireHq8GJ5HIq1r1JyrA4lmEQEZELOd027YEHHuDtt9/mr3/9a23kEREREWnUcnJyeHXRYmzWMqOjVIuLqxuP/HmiCjgivzBhwgRWrlzJnDlz6NWrFw888AB33303wcHBRkeTRuhsQQnfpqplWn1RUbz5MTuf4jIrnm4qFBht0eep5BaVERnsx53XtTM6jlRD9xB/3F1NnC4o4djZc4S18DE6kohIveR08aasrIxly5bx2Wef0atXL3x9fSvtnz9/fo2FExEREWlsCgsLsVnL+LIknBxb/V7oOMCliJs80igsLFTxRuQXHn30UR599FEOHTrEe++9x2uvvcaUKVMYPHgwDzzwAKNGjTI6ojQiG863TOse4k+n1mqZZrSQAC/8vdzILSrjcFY+Pdrq30cjpZ8uZMXWIwBMHRaFq4tmcDQEXu6udA8N4IcMC7vSz6p4IyJyES7OHrBv3z6uu+46/Pz8OHToELt373a8kpKSaiGiiIiISOOTY/PitN23Xr/qe3FJxGhdu3Zl9uzZHDp0iK+//prs7GzGjBnj9Hlee+01OnbsiJeXF3379mX79u2XHP/RRx8RGRmJl5cX0dHRrFu3rtJ+u93OzJkzCQkJwdvbmyFDhnD48OFKY55//nn69euHj48PgYGBF1xj+fLlmEymKl8nT54E4Isvvqhyv9lsdvp7IBe35nzLtOFqmVYvmEwmx+ybFLNapxntpQ0HKbXauTGiFTd1bW10HHFCbFggUN46TUREqub0zJvNmzfXRg4REREREZEGZ/v27axcuZIPP/yQ3NxcRo4c6dTxH374IYmJiSxZsoS+ffuycOFC4uPjOXjwIG3atLlg/JYtW7j33nuZO3cut912GytXrmTEiBHs2rWLHj16APDiiy/y6quv8u677xIeHs6MGTOIj4/nwIEDeHmVF2VLSkoYOXIkcXFxvP322xdc5+677yYhIaHSttGjR1NUVHRBroMHD+Lv7+/4vKrccmXOFpSw5cfTgFqm1SdRIf5sSztDcmau0VGatB8yLHzywwlMJnjq1kij44iTYtsHsnwL7E63GB1FRKTecnrmjYiIiIiISFN26NAhZs2aRdeuXenfvz/Jycm88MILZGVl8cEHHzh1rvnz5zNu3DjGjBlD9+7dWbJkCT4+PixbtqzK8a+88goJCQk8/vjjREVF8eyzz3LdddexePFioHzWzcKFC5k+fTp33HEHPXv2ZMWKFZw4cYLVq1c7zjN79mweffRRoqOjq7yOt7c3wcHBjperqyuff/45Y8eOvWBsmzZtKo11cdFtZk35dL8Zq83ONaH+hLfyvfwBUieiQvwAVLwxkN1u5/l1yQD8JrYt14SqfV1DE3N+5s2BE7kUl1mNDSMiUk85PfNm8ODBmEwX7yH6+eefX1UgEREREWmYcnJyKCwsNDpGtfn4+GgtH7kikZGRXH/99UyYMIF77rmHoKCgKzpPSUkJO3fuZOrUqY5tLi4uDBkyhK1bt1Z5zNatW0lMTKy0LT4+3lGYSUtLw2w2M2TIEMf+gIAA+vbty9atW7nnnnuuKOuKFSvw8fHhrrvuumBfTEwMxcXF9OjRg2eeeYb+/ftf9DzFxcUUFxc7Ps/N1Zvfl7J2r1qm1UcVbdOSM3Ox2+2XfI9EasdnySfZnnYGTzcXpgztZnQcuQLtW/jQwteDMwUlJGfmOYo5IiLyM6eLNzExMZU+Ly0tJSkpiX379vHggw/WVC4RERERaUBycnJ4ddFibNYyo6NUm4urG4/8eaIKOOK0gwcPEhERcdXnOXXqFFar9YLiT1BQECkpKVUeYzabqxxfsc5MxcdLjbkSb7/9Nvfddx/e3t6ObSEhISxZsoTevXtTXFzM0qVLGTRoENu2beO6666r8jxz585l9uzZV5yjKTmdX+xomTZcLdPqla5BfriY4GxhKSfzigny1xpxdanMauOv/y2fdfPQgHBCA70vc4TURyaTiZiwQD5POcnu9LMq3oiIVMHp4s2CBQuq3P7MM8+Qn59/1YFEREREpOEpLCzEZi3jy5Jwcmz1/02sAJcibvJIo7CwUMUbcVpF4Wbnzp0kJ5e/gdi9e/eLFiwauq1bt5KcnMzf//73Stu7detGt24/P/Her18/fvzxRxYsWHDB2ApTp06tNHMoNzeXsLCw2gnewH26PwurzU502wA6tFTLtPrEy92VTq2bkXoynwOZuSre1LEPd2TwY3YBzX3ceXhQZ6PjyFWoKN4kZViMjiIiUi85Xby5mAceeIA+ffrw8ssv19QpRURERKSBybF5cdreAN5ktBkdQBqykydPcvfdd/Pll18SGBgIgMViYfDgwXzwwQe0bt26Wudp1aoVrq6uZGVlVdqelZVFcHBwlccEBwdfcnzFx6ysLEJCQiqN+d8uCtW1dOlSYmJi6NWr12XH9unTh2+++eai+z09PfH09LyiHE3N2r0nALVMq68ig/1IPZlPSmYeg7u1MTpOk5FfXMaCjYcBeOTmCPy93A1OJFejYraNijciIlWrsZUkt27dipeXnjYREREREZHG7c9//jP5+fns37+fM2fOcObMGfbt20dubi6PPPJItc/j4eFBr1692LRpk2ObzWZj06ZNxMXFVXlMXFxcpfEAGzdudIwPDw8nODi40pjc3Fy2bdt20XNeSn5+Pv/4xz8YO3ZstcYnJSVVKhrJlTmVX8xWtUyr13657o3UnTe/+olT+cV0bOnD/X07GB1HrtK154s3R08Xcjq/+NKDRUSaIKdn3vz2t7+t9LndbiczM5MdO3YwY8aMGgsmIiIiIiJSH61fv57PPvuMqKgox7bu3bvz2muvMXToUKfOlZiYyIMPPkjv3r3p06cPCxcupKCggDFjxgAwatQo2rZty9y5cwGYNGkSN910E/PmzWP48OF88MEH7NixgzfffBMoX0Ng8uTJPPfcc0RERBAeHs6MGTMIDQ1lxIgRjuump6dz5swZ0tPTsVqtJCUlAdClSxeaNWvmGPfhhx9SVlbGAw88cEH2hQsXEh4ezjXXXENRURFLly7l888/Z8OGDU59D+RC6/eZsdnh2nYBhLXwMTqOVKG7ijd1Liu3iLe++gmAJxIi8XCrseeRxSAB3u50bu3Lj9kF/HDMwq8igy5/kIhIE+J08eZ/e4K7uLjQrVs35syZ4/SNioiIiIiISENjs9lwd7+wVY+7uzs2m3M9+e6++26ys7OZOXMmZrOZmJgY1q9fT1BQ+RtY6enpuLj8/AZlv379WLlyJdOnT2fatGlERESwevVqevTo4RjzxBNPUFBQwPjx47FYLAwYMID169dX6pQwc+ZM3n33XcfnsbGxAGzevJlBgwY5tr/99tv89re/dbSH+6WSkhIee+wxjh8/jo+PDz179uSzzz5j8ODBTn0P5EJr92QCMEyzbuqtyBA/AH46VUBRqRUvd1eDEzV+Cz87xLlSK7HtA7m1R9WtJaXhiW3fnB+zC9idruKNiMj/crp4884779RGDhERERERkQbhV7/6FZMmTeL9998nNDQUgOPHj/Poo49y8803O32+iRMnMnHixCr3ffHFFxdsGzlyJCNHjrzo+UwmE3PmzGHOnDkXHbN8+XKWL19+2Wxbtmy56L4nnniCJ5544rLnEOdk5xWzLa28ZZqKN/VXsL8XgT7uWApLST2ZT4+2AZc/SK7Yoaw8Pvw+A4Cnh0VhMpkMTiQ1JSYskH/uPKZ1b0REquD0HNPvv/+ebdu2XbB927Zt7Nixo0ZCiYiIiIiI1FeLFy8mNzeXjh070rlzZzp37kx4eDi5ubksWrTI6HjSwK3ff75lWligWqbVYyaTiajg8tZpB9Q6rdb99b8p2OwQf00QvTu2MDqO1KCY8+veJGVYsNnsxoYREalnnC7eTJgwgYyMjAu2Hz9+nAkTJtRIKBERERERkfoqLCyMXbt2sXbtWiZPnszkyZNZt24du3btol27dkbHkwZu7Z4TANymWTf1XpTWvakTW348xecpJ3F1MfFkQqTRcaSGRQb74eXuQl5RGT+dyjc6johIveJ027QDBw5w3XXXXbA9NjaWAwcO1EgoERERERGR+sxkMnHLLbdwyy23GB1FGpGTeUVsSzsDwK3RWtOjvqtY9yYlM8/gJI2XzWZn7roUAO7r055OrZsZnEhqmpurCz3bBrL9yBl2p1vo0sbP6EgiIvWG0zNvPD09ycrKumB7ZmYmbm5O14JEREREREQarOjo6Co7E4hcifX7zNjtENs+kHbN1TKtvuteMfPGnIvdrnZPteGTPSfYezyHZp5uTBoSYXQcqSWx7QMBtO6NiMj/cLp4M3ToUKZOnUpOTo5jm8ViYdq0aXrqTEREREREmpQjR45QWlpqdAxpJNbsyQRguFqmNQhd2jTD1cWEpbAUc26R0XEanaJSKy+uPwjAH2/qRKtmngYnktpSse7N7nSLoTlEROobp6fKvPzyywwcOJAOHToQGxsLQFJSEkFBQfz973+v8YAiIiLSdOXk5FBYWGh0jGrx8fEhICDA6BgiItJAZeUW8f2R8pZpw1S8aRC83F3p1MqXwyfzSc7MJSTA2+hIjcqKrUc4bjlHkL8nYwd0MjqO1KKY8zNvDmblUVhSho+HOvuIiMAVFG/atm3Lnj17eO+99/jhhx/w9vZmzJgx3Hvvvbi7u9dGRhEREWmCcnJyeHXRYmzWMqOjVIuLqxuP/HmiCjgiTcyNN96It7fesJWr99+9mdjt0KtDc0ID9TPVUESF+J8v3uTxq8ggo+M0GpbCEhZ/ngrAY7d0w9vD1eBEUptCArwJ9vfCnFvE3mM59O3U0uhIIiL1whWVsn19fRk/fnyNBHjttdd46aWXMJvNXHvttSxatIg+ffpcdPxHH33EjBkzOHLkCBEREbzwwgsMGzbMsd9utzNr1izeeustLBYL/fv35/XXXyci4ufeqM8//zxr164lKSkJDw8PLBbLBddJT0/n4YcfZvPmzTRr1owHH3yQuXPnal0fERGROlJYWIjNWsaXJeHk2LyMjnNJAS5F3OSRRmFhoYo3Ik3AV199Rb9+/XBzc2PdunWO7WVlZWzZsoWBAwcamE4aqrV7y1umadZNwxIV4s/HP5wgOTPX6CiNyuLPU8ktKiMy2I87e7UzOo7UgZiwQNbvN5OUYVHxRkTkvCuqRBw+fJjNmzdz8uRJbDZbpX0zZ86s9nk+/PBDEhMTWbJkCX379mXhwoXEx8dz8OBB2rRpc8H4LVu2cO+99zJ37lxuu+02Vq5cyYgRI9i1axc9evQA4MUXX+TVV1/l3XffJTw8nBkzZhAfH8+BAwfw8ip/46ekpISRI0cSFxfH22+/fcF1rFYrw4cPJzg4mC1btpCZmcmoUaNwd3fnL3/5izPfKhEREblKOTYvTtt9jY5xabbLDxGRxmPw4MFkZmZecM+Sk5PD4MGDsVqtBiWThsqcU8SOo2cBGBYdbHAacUZkiB+Aijc1KONMISu2HgXgqVsjcXUxGZxI6kJM+5+LNyIiUs7p4s1bb73Fww8/TKtWrQgODsZk+vkfUZPJ5FTxZv78+YwbN44xY8YAsGTJEtauXcuyZct46qmnLhj/yiuvkJCQwOOPPw7As88+y8aNG1m8eDFLlizBbrezcOFCpk+fzh133AHAihUrCAoKYvXq1dxzzz0AzJ49G4Dly5dXmWvDhg0cOHCAzz77jKCgIGJiYnj22Wd58skneeaZZ/Dw8Kj21ygiIiIiIo2L3W6vdB9U4fTp0/j61vNis9RL/91X3jKtd4fmWjelgeke4g9A2qkCikqteLmrvdfVevHTg5RYbQzo0oqburY2Oo7UkdiwQAB2p1sMzSEiUp84Xbx57rnneP7553nyySev6sIlJSXs3LmTqVOnOra5uLgwZMgQtm7dWuUxW7duJTExsdK2+Ph4Vq9eDUBaWhpms5khQ4Y49gcEBNC3b1+2bt3qKN5cztatW4mOjiYo6Od+tfHx8Tz88MPs37+f2NjYKo8rLi6muLjY8Xlurp68ERERERFpLH77298C5Q+tjR49Gk9PT8c+q9XKnj176Nevn1HxpAFbu6e8ZdrwnmqZ1tC08fOkha8HZwpKOJSVR892gUZHatB+yLDwyQ8nMJnKZ91UVSiXxim6XQCuLibMuUWYc4oIDqjfbZNFROqCi7MHnD17lpEjR171hU+dOoXVaq1UIAEICgrCbDZXeYzZbL7k+IqPzpzTmev88hpVmTt3LgEBAY5XWFhYta8pIiIiIiL1W8Xf+Xa7HT8/v0p/+wcHBzN+/Hj+7//+z+iY0sBk5pxjx9GzmExwaw8Vbxoak8lElFqn1Qi73c5f1iUD8JuYtvRoq3UEmxIfDze6BZX/LiVlnDU4jYhI/eD0zJuRI0eyYcMG/vjHP9ZGngZt6tSplWYG5ebmqoAjIiIiItIIJCYmsnjxYnx9fTly5AhLly6lWbNmRseSRmDd3vIHBK/v0EJPmjdQkcH+fJt6muTMPKOjNGibkk+yLe0MHm4uPBbfzeg4YoCY9oEcyMxld7qFBBWzRUScL9506dKFGTNm8N133xEdHY27u3ul/Y888ki1ztOqVStcXV3JysqqtD0rK4vg4KoXaAwODr7k+IqPWVlZhISEVBoTExNTrVwV59m+ffsF1/nlNari6elZqXWCiIiIiIg0DosWLeLJJ5/E19eXr776isLCQhVvpEas3XMCUMu0hizq/Lo3mnlz5cqsNub+t3zWzUP9w2kbqLWfmqKYsEBWbktnd4bF6CgiIvWC08WbN998k2bNmvHll1/y5ZdfVtpnMpmqXbzx8PCgV69ebNq0iREjRgBgs9nYtGkTEydOrPKYuLg4Nm3axOTJkx3bNm7cSFxcHADh4eEEBwezadP/Z+/O46Iq2z+Of4Z9B0XZFBT3DcUVUVNLC8sWWjTN0sz0SaVcKksftzKz7LHUtMystNIsW/yVmmXuKe7ivqeCC24ICMgiM78/0CkSFdfDwPf9ep2Xzjn3OeeaOQPMPde5r3uxNVmTmprK2rVr6d27d6GfY2RkJKNHj+bkyZP4+flZz+Pl5UWtWrUKfRwRERERESkeKlasyMSJE7nvvvuwWCzExsZSqlSpAtu2bNnyDkcntupo8nk2xSdfLJl25RsFpWj7Z9k0i8WieVpuwLcbEjhwKp1Sbo70ubuy0eGIQRqE+ACw7UgKF3LNONhf92wPIiLFynUnbw4ePHjLTj5w4EC6detGo0aNaNKkCePHjyc9PZ3u3bsD0LVrV8qVK8eYMWMA6NevH61atWLcuHG0b9+e2bNns2HDBqZOnQrkJY/69+/PW2+9RdWqVQkNDWXYsGEEBQVZE0QA8fHxJCUlER8fT25uLnFxcUDeqCIPDw/uu+8+atWqxTPPPMPYsWNJTExk6NCh9O3bVyNrRERERERKoPfee48XXniBMWPGYDKZePTRRwtsZzKZyM3NvcPRia36ddtxABpXLI2fl0qm2aoqfh442JlIzbzAsZRMjRq5TulZF/hg0T4AXrynKl4ujtfYQ4qrSmU88HRx4FzmBfacOEftIM17JCIl23Unb26lJ598klOnTjF8+HASExMJDw9n4cKF+Pv7A3lJFju7v7PszZo1Y9asWQwdOpQhQ4ZQtWpV5s6dS506daxtBg0aRHp6Or169SI5OZkWLVqwcOFCXFz+/iA8fPhwZsyYYX1cv359AJYuXUrr1q2xt7dn3rx59O7dm8jISNzd3enWrRtvvvnm7X5JRERERESkCIqOjiY6Opq0tDS8vLzYs2ePdZS+yI2atzUvefOgSqbZNGcHeyqX9WDPiXPsPp6q5M11mrriL06nZVHB142nm1YwOhwxkJ2diXrlffhz/2niEpKVvBGREq9QyZuBAwcyatQo3N3dGThw4FXbvv/++9cVQExMzBXLpC1btuyydR06dKBDhw5XPJ7JZOLNN9+8aqJl+vTpTJ8+/apxVahQgQULFly1jYiIiIiIlCweHh4sXbqU0NBQHBwMvRdObNyRsxnEJeSVTGunkmk2r2agJ3tOnGPX8VTa1PQ3OhybcTI1k6kr/gJgUFQNnBxUJqukqx+Sl7zZHJ9Mlwgl80SkZCtUb2Pz5s3k5ORY/38lqusqIiIiIiLFXatWrThw4ABffPEFBw4cYMKECfj5+fHrr78SEhJC7dq1jQ5RbMCv2xIBiAgtjZ+nSqbZupqBXsyNO8au4+eMDsWmfPDHXs7n5BIe7MMDYUpiCoQH+wAQl5BsaBwiIkVBoZI3S5cuLfD/IiIiIiIiJc3y5cu5//77ad68OStWrGD06NH4+fmxZcsWPvvsM77//nujQxQbMO/ifDft6wYZHIncCjUCvQDYdTzV4Ehsx74T5/h2fQIA/21fUzcEC/B38mb/yTRSzufg7ao5kESk5NJ4VBERERERkevw+uuv89Zbb7Fo0SKcnJys6++55x7WrFljYGRiKxKSMtiSkIydCdrV1miD4qBmoCcAB8+kcz471+BobMM7v+7GbIH7avnTuGJpo8ORIsLXw5mQ0m4AbD2SbGwwIiIGU5FmEREREZFrSElJISMjw+gwCsXNzQ1vb03weztt27aNWbNmXbbez8+P06dPGxCR2JoFF0fdNK3kS1lPZ4OjkVvBz9OFMh5OnE7LZs+Jc9bRA1Kw2ANnWLz7JPZ2Jl67v4bR4UgREx7sQ3xSBnHxydxVtazR4YiIGEbJGxERERGRq0hJSWHih5Mw514wOpRCsbN34KUXY5TAuY18fHw4fvw4oaGh+dZv3ryZcuXKGRSV2JL51pJpgQZHIrdSjQAv/tx/ml3HU5W8uQqz2cLbC3YB8FSTECqX9TA4Iilq6of48POWY2zWvDciUsIpeSMiIiIichUZGRmYcy+wPDuUFHPRnlTc2y6TVk4HycjIUPLmNurUqROvvfYac+bMwWQyYTabWbVqFa+88gpdu3Y1Ojwp4uLPZLD1SIpKphVDNQM9+XP/aXZr3pur+mXrMbYdTcHdyZ6X2lQ1Ohwpgi4lP+MSkrFYLJoPSURKLCVvREREREQKIcXswhmLu9FhXJ3Z6ABKhrfffpu+ffsSHBxMbm4utWrVIjc3l6eeeoqhQ4caHZ4UcZdG3URW9sXXQyXTipOagV4A7Dp+zuBIiq6sC7mMXbgHgBdaVVbZQClQrSAvnOztSErPJiHpPCG+bkaHJCJiCCVvREREREREroOTkxOffvopw4YNY/v27aSlpVG/fn2qVtUd5HJt87cdA6B9WJDBkcitZk3eJKZqtMAVfLn6MEeTz+Pv5czzd1UyOhwpopwd7KkV5EVcQjKbE84qeSMiJZaSNyIiIiIiIjcgJCSEkJAQo8MQG3L4TDrbj6Zib2ciqra/0eHILVa5rAeO9ibOZV7gyNnzBJfWF87/lJyRzYdL9gHw8r3VcXWyNzgiKcrCg33ykjfxyTwSrvnkRKRkUvJGRERERETkGgYOHFjotu+///5tjERs2aWSac1UMq1YcnKwo3JZD3YnnmN34jklb/5l0pL9pGZeoLq/J483LG90OFLE1Q/xYfpq2JyQbHQoIiKGUfJGRERERETkGjZv3lyodiqTJFczf2te8qZ9WKDBkcjtUivQi92J59h1PJV7a2l01SUJSRl8GXsYgNcfqIG9nX5XytXVDy4FwK5jqWRdyMXZQSO1RKTksTM6ABERERERkaJu6dKlhVqWLFly3ceePHkyFStWxMXFhYiICNatW3fV9nPmzKFGjRq4uLgQFhbGggUL8m23WCwMHz6cwMBAXF1dadu2Lfv27cvXZvTo0TRr1gw3Nzd8fHwKPI/JZLpsmT17dr42y5Yto0GDBjg7O1OlShWmT59+3c+/pDh4Op0dxy6VTAswOhy5TWoEegKw63iqwZEULe/9tofsXDPNq/jSulpZo8MRGxBc2pXS7k5k55rZeUw/TyJSMil5IyIiIiIiYpBvv/2WgQMHMmLECDZt2kS9evWIiori5MmTBbZfvXo1nTt3pkePHmzevJno6Giio6PZvn27tc3YsWOZOHEiU6ZMYe3atbi7uxMVFUVmZqa1TXZ2Nh06dKB3795Xje+LL77g+PHj1iU6Otq67eDBg7Rv3567776buLg4+vfvz/PPP89vv/12cy9KMbXgYsm05lXKUMrdyeBo5HapGegFwO7EcwZHUnRsPZLMz1uOYTLB4PtraoSiFIrJZKJ+sA8Am+OTDY3FaCdTM1m1/zSHTqeTfcFsdDgicgepbJqIiIiIiIhB3n//fXr27En37t0BmDJlCvPnz+fzzz/n9ddfv6z9hAkTaNeuHa+++ioAo0aNYtGiRUyaNIkpU6ZgsVgYP348Q4cO5ZFHHgHgyy+/xN/fn7lz59KpUycA3njjDYBrjpTx8fEhIKDgUSJTpkwhNDSUcePGAVCzZk3+/PNPPvjgA6Kioq7/xSjm5l0smfagSqYVa5eSN4fOpJORfQE3p5L9tYvFYmH0/F0APBpejjrlvA2OSGxJeLAPi3efJK4Ez3uzav9puk9fb03amEzg7+lC+VKuFxe3fP8G+bji5KB79UWKi5L9KUJERERERMQg2dnZbNy4kcGDB1vX2dnZ0bZtW2JjYwvcJzY2loEDB+ZbFxUVxdy5c4G80TCJiYm0bdvWut3b25uIiAhiY2OtyZvC6tu3L88//zyVKlXihRdeoHv37ta75mNjY/Od51Is/fv3v+LxsrKyyMrKsj5OTS0ZpXAOnEpj1/FUHOxM3Fdb86AUZ2U8nCnr6cypc1nsTjxHg5BSRodkqCW7T7L2YBJODnYMvK+a0eGIjal/8eenpCZv1h9K4vkZG8i+YKaspzPnMnPIzDGTmJpJYmomGw6fvWwfkwkCvFz+ldj5+/+B3kruiNgSJW9EREREREQMcPr0aXJzc/H3z/9lvr+/P7t37y5wn8TExALbJyYmWrdfWnelNoX15ptvcs899+Dm5sbvv/9Onz59SEtL46WXXrpqLKmpqZw/fx5XV9fLjjlmzBjrqJ+SZMHWv0um+bipZFpxVyPAk1Pnsth1PLVEJ28u5JoZ82ve77LuzStSvpSbwRGJrakb7I3JBPFJGZxJy8LXw9nokO6YuIRkun+xnvM5ubSsVpZPuzbEyd6OM+nZHDl7niNnM/71b97/M3PMHE/J5HhKJusPKbkjYuuUvBEREREREZHLDBs2zPr/+vXrk56eznvvvWdN3tyIwYMH5xs5lJqaSnBw8E3FaQvmX5zvpn1dlUwrCWoFerFy32l2Hy/Z8958t+EI+0+mUcrNkT6tqxgdjtggLxdHKpf1YP/JNOISkmlTs2SMXNxxLIWun60lLesCTSuV5pOnG+LsYA/kje4r4+FM+MX5gP7JYrEouSNSzCh5IyIiIiIiYoAyZcpgb2/PiRMn8q0/ceLEFeeZCQgIuGr7S/+eOHGCwMDAfG3Cw8NvKt6IiAhGjRpFVlYWzs7OV4zFy8urwFE3AM7Ozjg7l5w7pwH2n0xjd+I5HO1NRNUq+LpK8XJp3ptdx0tGWcCCpGdd4P1FewF48Z6qeLs6GhyR2Kr6wT7sP5nG5viSkbzZd+Icz3y2jtTMCzQI8eGzbo1xdbIv1L4mk0nJHZFiRskbERERERERAzg5OdGwYUMWL15MdHQ0AGazmcWLFxMTE1PgPpGRkSxevDjfvDKLFi0iMjISgNDQUAICAli8eLE1WZOamsratWvp3bv3TcUbFxdHqVKlrMmXyMhIFixYkK/NP2ORPAsujrppUaUM3m76ArskqBHoCcDuxHOYzRbs7EwGR3TnfbryL06nZRFS2o2nm1YwOhyxYeEhPszZeKREzHtz6HQ6XaatJSk9m7By3kx/rgnuzrfuq9vrTe4kJF2e5Mm6oOSOyJ2k5I2IiIiIiIhBBg4cSLdu3WjUqBFNmjRh/PjxpKen0717dwC6du1KuXLlGDNmDAD9+vWjVatWjBs3jvbt2zN79mw2bNjA1KlTgbwvZvr3789bb71F1apVCQ0NZdiwYQQFBVkTRADx8fEkJSURHx9Pbm4ucXFxAFSpUgUPDw9++eUXTpw4QdOmTXFxcWHRokW8/fbbvPLKK9ZjvPDCC0yaNIlBgwbx3HPPsWTJEr777jvmz59/Z148GzF/66WSaUEGRyJ3SuWyHjjZ25GWdYGjyecJLl2y5no5eS6TqSv+AmBQu+r6olZuSv3gvHmjtiQkF+tk6JGzGTz16RpOnsuiRoAnXz7XBC+XO5vwL0xy53Ra9mWjdW40uVOpjAc9W1aiip/HHXh2IrZJyRsRERERERGDPPnkk5w6dYrhw4eTmJhIeHg4CxcuxN8/rzRMfHw8dnZ/f/HZrFkzZs2axdChQxkyZAhVq1Zl7ty51KlTx9pm0KBBpKen06tXL5KTk2nRogULFy7ExcXF2mb48OHMmDHD+rh+/foALF26lNatW+Po6MjkyZMZMGAAFouFKlWq8P7779OzZ0/rPqGhocyfP58BAwYwYcIEypcvz7Rp04iKirptr5et2XfiHHtO5JVMu7dW8S/3I3kc7e2o4ufBzuOp7DyeWuKSNx8s2kdGdi7hwT60D9M8T3Jzqvl74Opoz7msCxw4lUZVf0+jQ7rlElMyeerTtRxLyaRSWXe+6hFBKXcno8O6jMlkoqynM2U9nakfUuqy7TeS3Plp81F6t65Mn7srW+f1EZG/KXkjIiIiIiJioJiYmCuWSVu2bNll6zp06ECHDh2ueDyTycSbb77Jm2++ecU206dPZ/r06Vfc3q5dO9q1a3fF7Ze0bt2azZs3X7NdSTX/Ysm0llXLas6PEqZmoBc7j6ey63gqUbVLzlxHexLP8e36eACGPFATk6l4jpKQO8fB3o6w8t6sO5jE5oTkYpe8OXUui6emrSE+KYOQ0m7Mer4pZT1tc264603u/LDpCMv2nGLC4n38svUYbz8aRtNKvgZELlJ0aeyqiIiIiIiIyG3wd8k0jT4oaWpenPdm1/FUgyO5c7YdSaHLtLWYLXBvLX+ahJY2OiQpJuqH+AAUu3lvkjOyeeaztfx1Kp0gbxdmPh9BgLfLtXe0UZeSO/VDSvFQvSC+eLYxk56qTxkPZ/46lU6nqWt4dc4WzqZnGx2qSJGh5I2IiIiIiIjILbb3xDn2nUzDyd6OtiqZVuLUDPQCYHfiOYMjuTOW7D5Bx09iOZ2WN1/H6Og6195JpJDqX5x/ZXN8sqFx3EqpmTk889k6dieeo6ynMzN7Ni1xJRZNJhMP1g1i8cuteCoiBIA5G4/Q5v3l/LT5CBaLxeAIRYyn5I2IiIiIiIjILTbv4qibltXK3PFJp8V4NQLyRt4cPpNBWtYFg6O5vWauPczzMzZwPieXFlXK8N0Lkfh5Fd/RA3LnhQfnleDak5hKRrbt/zylZ12g+xfr2XY0hdLuTsx6PoLQMu5Gh2UYb1dH3n40jB96R1LN34Ok9GwGfLuFZz5bx6HT6UaHJ2IoJW9EREREREREbiGLxcL8rccAlUwrqXw9nPG7OG/FnsTiWTrNbLbw7sLd/Pen7Zgt8ETD8nzRvbGSlXLLBXi7EOjtgtkCW4+kGB3OTcnMyeX5GRvYePgsXi4OfNWjSbGbx+dGNaxQmnkv3sWrUdVxdrDjz/2nuW/8CiYt2Uf2BbPR4YkYQskbERERERERkVtoz4lzHDiVjpODHW1rqmRaSXWpdNqu48WvdFrWhVz6fRvHx8sOADCgbTXee6Iujvb6mkluj/CLpdNsed6brAu5/OerjcT+dQYPZwe+7BFB7SBvo8MqUpwc7Oh7dxV+69+SFlXKkH3BzP9+38uDH65kw6Eko8MTueMcjA5ARIq/lJQUMjIyjA6j0Nzc3PD21gcoEREREbkx8y+WTGtVrSyeGoVQYtUM9GL53lPsOl68Rt4kZ2TT66uNrDuYhIOdiXcer8sTDcsbHZYUc/VDfPh1eyJxNjrvTU6umRdnbWb53lO4OtrzRffG1oSUXK5iGXe+6tGE/4s7xqh5O9l7Io0npsTyVEQIr7Wrgber/rZKyaDkjYjcVikpKUz8cBLmXNupS2tn78BLL8YogSMiIiIi181isTB/W17y5kGVTCvRagbmlUIqTsmbhKQMnv1iHQdOpePh7MCUpxvSomoZo8OSEuDSvDebE84aHMn1yzVbGPjdFn7feQInBzs+7dqIxhVLGx1WkWcymYiuX45W1coy5tddfLfhCLPWxvP7jhOMeKgWD9YNxGQyGR2myG2l5I2I3FYZGRmYcy+wPDuUFHPRn7TS2y6TVk4HycjIUPJGRERERK7b7sRz/HWxZFoblUwr0S6VTduTeA6z2YKdnW1/ybj1SDLPTd/A6bQsArxc+KJ7Y+tzFLndwsp5Y29n4kRqFsdTzhPo7Wp0SIViNlt47Yet/LLlGI72JqY83UAJz+tUyt2JsU/U47EG5Rny0zb+OpXOi99s5odNRxj1SB2CS7sZHaLIbaPkjYjcESlmF85Y3I0O49o0B56IiIiI3IRLJdPurl4WD2d1uUuySmXccXKwIz07l4SzGVTwtYH+0BUs3nWCmFmbOZ+TS40AT77o3thmvjyX4sHVyZ4aAZ7sOJZKXHwygWFF//1nsVgY/vN2vt94BHs7ExM71eeeGkrq36imlXz5td9dfLzsAB8tPcCyPae494PlDGhbjedahGrOLSmW9K4WERERERERuQX+WTKtfd0gg6MRoznY21HN3wOw7dJpX605TM8vN3A+J5e7qpZhzguRStyIIS7NEbM5IdnQOArDYrEwev4uvl4Tj8kE73esx/1hKqV5s5wd7OnfthoL+t1FRGhpMnPMjPl1Nw9PWkWcDbwvRK6XkjciIiIiIiIit8DO46kcPJ2Os4MdbWr4GR2OFAE1AvLKiu08fs7gSK6f2WxhzK+7GDZ3O2YLdGxUns+fbYyniyYKF2NcSt7ExScbGkdhvL9oL9P+PAjAu4/V5ZHwcgZHVLxU8fNgdq+mjH2iLj5ujuw6nsqjH61ixP9t51xmjtHhidwyGsMtIiJSDKSkpJCRkWF0GIXi5uamOaVERKRY+rtkmh/uKpkm/D3vzW4bG3mTdSGXV+bkzdMBMPDearx4TxVNDi6Gqh9SCoCtR5PJyTUX2TJZk5fu58Ml+wF44+HadGwcbHBExZPJZKJjo2Da1PDjrfm7+GnzUWbEHua3HScY+XBt2tUJMDpEkZumT5MiIiI2LiUlhYkfTsKce8HoUArFzt6Bl16MUQJHRESKlfwl01QaR/LUDPQEYFei7SRvkjOy6fXlRtYdSsLBzsQ7j9fliYbljQ5LhEpl3PF0ceBc5gX2JJ6jTrmi15+YtvIv3vttDwCD769Bt2YVjQ2oBPD1cOaDJ8N5rEE5hs7dzuEzGbzw9UbureXPGw/XJshHZR7Fdil5IyIiYuMyMjIw515geXYoKWYXo8O5Km+7TFo5HSQjI0PJGxERKVZ2HEvl8JkMXBztuEcl0+SimhfLpiUknedcZk6RLzmWkJRBty/W8depdDydHZjyTEOaVyljdFgiANjZmQgP9mHlvtPEJSQXueTN12sO89b8XQAMaFuN/7SqbHBEJctdVcvyW/+WfLhkH58s/4tFO0+wev9pXr6vOt2aVcTeTiMHxfYoeSMiIlJMpJhdOGNxNzqMqzMbHYCIiMjtMe9iybR7aqhkmvytlLsTAV4uJKZmsifxHI0qljY6pCvaeiSZ56av53RaNoHeLnzRvbF1zh6RoqL+xeTN5vhknm5awehwrL7feIShc7cD8EKryrzUporBEZVMLo72vBpVg0fCyzH4x21sPHyWN+ft5KfNRxnzWFiRS/iJXEvRLA4pIiIiIiIiYiMsFgsLLpVMCwsyOBopaqyl04rwvDd/7DzBk5+s4XRaNjUDvfipT3MlbqRICg/xASAu4ayxgfzDL1uOMej7LQA826wir7WrrvmhDFbN35M5/4lk9KN18HRxYNvRFB6e9CdvzdtJepZtlBsXASVvRERERERERG7K9qOpxCdl4Opoz901yhodjhQxNQPzkiA7j58zOJKCfRV7iF5fbeB8Ti4tq5Xlu/80JcC7aJfilZIrPLgUAAdOpZNyPsfgaOD3HYkM+DYOswU6NwlmxEO1lLgpIuzsTHSJqMDiga1oXzcQswWm/XmQ+z5YweJdJ4wOT6RQlLwRERERERERuQnzth0D4J6afrg5qWSa5FfjYvKmqI28MZstjPl1F8P+bwdmCzzZKJjPujUq8vPySMlW2t2JCr5uAGxJSDY0luV7TxEzazMXzBYerV+Ot6LDlLgpgvy8XJj8VAO+eLYx5XxcOZp8nh4zNtBn5kZOpGYaHZ7IVSl5IyIiIiIiInKDLBYL87deKpkWaHA0UhTVulg2bU/iOcxmi8HR5MnMyeWl2Zv5ZPlfALx8bzXeeTwMR3t9TSRFX/1gHwDiDEzexB44Q68vN5Cda+aBsADee6Iu9nZK3BRld9fwY9HAlvynZSXs7Uws2JZI23HL+Sr2UJH53Szyb/qrLCIiIiIiInKDth5J4cjZ83kl06r7GR2OFEEVfd1xdrDjfE4uh5MyjA6H5IxsnvlsLfO2HsfR3sT7HevxYpuqGjEgNiP8YvJmc7wx895sPJxEjxnrybpgpm1NP8Y/WR8HJT5tgpuTA4MfqMnPMc2pV96bc1kXGPZ/O3h8ymp2Jxat0ZEioOSNiIiIiIiIyA2bvy1v1E2bmn64OtkbHI0URQ72dlTzzxt9Y3TptPgzGTz28WrWHzqLp7MDM7o34bEG5Q2NSeR6hYfkzXsTl5CMxXJnR0xsO5LCs5+vJyM7l7uqlmHSUw1wctDXq7amdpA3P/ZpzhsP18bD2YHN8ck8OPFP3vl1N+ezc40OT8RKv11EREREREREbsA/S6Y9WFcl0+TKal4snbbbwOTNloRkHvt4FX+dSifI24XvezejWZUyhsUjcqNqBXrh5GDH2Ywc4u/gaLbdiak88/lazmVdoEloaaY+0wgXRyXtbZW9nYluzSqyaGBLomr7c8FsYcryA9w3fjnL954yOjwRQMkbERERERERkRsSl5DM0eTzuDnZ01ol0+QqagZ6AbDz+DlDzr9o5wk6TV3D6bRsagV68VPf5lQP8DQkFpGb5eRgR+2gvJ+pzfHJd+Sc+0+m8fS0tSRn5BAe7MPnzzbWaMtiItDblU+eacTUZxoS6O1CQtJ5un2+jpe+2cypc1lGhyclnJI3IiIiIiIiIjfg0qibtjX9dfe1XNWl5I0RZdO+jD3Ef77awPmcXFpVK8t3L0Ti7+Vyx+MQuZUuzXsTl5B82891+Ew6XablJT9rB3kx47kmeDg73Pbzyp11X+0AFg1sRffmFbEzwc9bjtFm3DJmr4vHbL6z5flELlHyRkREREREROQ6WSwWFlyc76a9SqbJNdQMyEveHE0+T8r5nDtyTrPZwtsLdjH8/3ZgtkCnxsFM69ZIXzpLsVD/4rw3m+PP3tbzHEs+z1OfruVEahZV/Tz4qkcE3q6Ot/WcYhwPZwdGPFSbuX2bUzvIi9TMC7z+4zY6TV3D/pPGjJyUkk3JGxEREREREZHrtDkhmWMpmbg72dOqWlmjw5EiztvNkSDvvNEuexJv/xeAmTm5vDh7M1NX/AXAq1HVGfNYGI72+hpIiof6F0fe7DyeSmbO7Zlg/mRqJk99uoajyecJLePOzJ4RlHZ3ui3nkqKlbnkf/q9vc4a2r4mroz3rDiVx/4SVvP/7ntv2fhMpSJH4qz158mQqVqyIi4sLERERrFu37qrt58yZQ40aNXBxcSEsLIwFCxbk226xWBg+fDiBgYG4urrStm1b9u3bl69NUlISXbp0wcvLCx8fH3r06EFaWpp1+6FDhzCZTJcta9asuXVPXERERERERGzSpZJp99ZSyTQpnDtVOu1sejbPfLaW+VuP42hv4oMn69H37iqYTKbbel6RO6l8KVfKeDiRk2th5234mTqTlkWXaWs5dCaD8qVcmfl8BH6eKjdYkjjY2/H8XZVYNLAl99TwIyfXwsQl+7l/wkpW7z9tdHhSQhievPn2228ZOHAgI0aMYNOmTdSrV4+oqChOnjxZYPvVq1fTuXNnevTowebNm4mOjiY6Oprt27db24wdO5aJEycyZcoU1q5di7u7O1FRUWRmZlrbdOnShR07drBo0SLmzZvHihUr6NWr12Xn++OPPzh+/Lh1adiw4a1/EURERERERMRmmM1/l0x7IEwl06RwagR6Arc3eRN/JoPHP17N+kNn8XRxYMZzTXi0fvnbdj4Ro5hMJuu8N5vjk2/psVMycnjms3XsO5lGgJcL3/RsSpCP6y09h9iO8qXc+KxbIz7q0gA/T2cOnk7nqWlrefm7LSSlZxsdnhRzhidv3n//fXr27En37t2pVasWU6ZMwc3Njc8//7zA9hMmTKBdu3a8+uqr1KxZk1GjRtGgQQMmTZoE5I26GT9+PEOHDuWRRx6hbt26fPnllxw7doy5c+cCsGvXLhYuXMi0adOIiIigRYsWfPjhh8yePZtjx47lO5+vry8BAQHWxdFRdS1FRERERERKss0JZzmekomHswMtVTJNCsk68uY2lU2LS0jm0Y9W8dfpdMr5uPJD72Y0q1zmtpxLpCi4lLyJS0i+Zcc8l5lD1y/WsfN4KmU8nJnVM4Lg0m637Phim0wmEw+EBfLHy614umkIJhP8sOkIbcYt4/uNR7BYLEaHKMWUocmb7OxsNm7cSNu2ba3r7OzsaNu2LbGxsQXuExsbm689QFRUlLX9wYMHSUxMzNfG29ubiIgIa5vY2Fh8fHxo1KiRtU3btm2xs7Nj7dq1+Y798MMP4+fnR4sWLfj555+v+nyysrJITU3Nt4iIiIiIiFyNEWWkR48eTbNmzXBzc8PHx+eyc2zZsoXOnTsTHByMq6srNWvWZMKECfnaLFu2rMBS04mJiTf2QtiQeSqZJjfgUvJmT2IqueZb+0Xfop0n6DQ1ljPp2dQO8uKnPs2o5u95S88hUtTUDykFQFzC2VtyvIzsC/SYvoEtCcmUcnNk5vMRVCrrcUuOLcWDl4sjb0WH8f0Lzaju78nZjBxembOFLtPWcvB0utHhSTFkaPLm9OnT5Obm4u/vn2+9v7//FT/wJyYmXrX9pX+v1cbPzy/fdgcHB0qXLm1t4+Hhwbhx45gzZw7z58+nRYsWREdHXzWBM2bMGLy9va1LcHDwtV4CEREREREpwYwqI52dnU2HDh3o3bt3gefZuHEjfn5+fP311+zYsYP//ve/DB482Frx4J/27NmTr9T0v/taxc0/S6a1V8k0uQ4Vfd1xcbQjM8fMoTO37ku+L2MP8Z+vNpCZY6Z19bJ8959I/Lw0N4cUf3XLe2MyQULSeU6nZd3UsTJzcun15UbWHUrC08WBr3pEUD1ACVApWMMKpZj3UgsGtauOs4Mdqw+cIWr8CiYu3kfWhVyjw5NixPCyaUVVmTJlGDhwIBERETRu3Jh33nmHp59+mvfee++K+wwePJiUlBTrkpCQcAcjFhERERERW2NEGWmAN954gwEDBhAWFlbgeZ577jkmTJhAq1atqFSpEk8//TTdu3fnxx9/vKytn59fvlLTdnbFu5u5Mf4sJ1Kz8HR24K5qKkklhWdvZ6K6/62b98ZstjB6/k6G/98OzBbo3CSYaV0b4e7scNPHFrEFni6OVLk4MibuJua9yb5gps/MTfy5/zTuTvbMeK4Jdcp536IopbhytLejT+sqLBrQiruqliH7gpn3F+3lnv8t5/M/D5KRfcHoEKUYMPRTdZkyZbC3t+fEiRP51p84cYKAgIAC9wkICLhq+0v/XqvNv+9ku3DhAklJSVc8L0BERAT79++/4nZnZ2e8vLzyLSIiIiIiIgUxqoz0jUpJSaF06dKXrQ8PDycwMJB7772XVatWXfUYxaHU9PxLJdNq++PsoJJpcn0ulU7bffzm5r3JzMnlxW828+nKgwC8GlWdtx8Nw8G+eCdPRf6tfogPkDcX2Y24kGum3+zNLNl9EhdHOz57tjENLpZjEymMEF83vnyuCRM6hVPW05mjyed5c95Omr2zhA8W7SUpPdvoEMWGGfpX3cnJiYYNG7J48WLrOrPZzOLFi4mMjCxwn8jIyHztARYtWmRtHxoaSkBAQL42qamprF271tomMjKS5ORkNm7caG2zZMkSzGYzERERV4w3Li6OwEANixcRERERkZtnVBnpG7F69Wq+/fZbevXqZV0XGBjIlClT+OGHH/jhhx8IDg6mdevWbNq06YrHsfVS0/8smfZgXfUN5fpdSt7czMibs+nZPD1tLfO3HcfR3sSETuH0vbsKJpPpVoUpYjPCgy/Ne5N83fvmmi28MmcLv25PxMnejqnPNKJpJd9bHKGUBCaTiUfCy7Fy0N2MfrQOFXzdSM7IYcLifTR/Zwkjf97BkbMZRocpNsjwsbQDBw6kW7duNGrUiCZNmjB+/HjS09Pp3r07AF27dqVcuXKMGTMGgH79+tGqVSvGjRtH+/btmT17Nhs2bGDq1KlA3g9L//79eeutt6hatSqhoaEMGzaMoKAgoqOjAahZsybt2rWjZ8+eTJkyhZycHGJiYujUqRNBQUEAzJgxAycnJ+rXrw/Ajz/+yOeff860adPu8CskIiIiIiJinO3bt/PII48wYsQI7rvvPuv66tWrU716devjZs2aceDAAT744AO++uqrAo81ePBgBg4caH2cmppqUwmcDYfPcvJcFp4uDrSoUtbocMQG1Qi4ubJph8+k0/2L9fx1Oh0vFwc+eaYRkZX1ZbOUXJdG3mxJSCHXbMHernBJTLPZwn9/2sbcuGM42Jn4qEsDWlbT73W5OS6O9nSJqECnxiH8uv04U5YfYPvRVKavPsRXaw7zcL0g/tOqEjUCVK1JCsfw5M2TTz7JqVOnGD58OImJiYSHh7Nw4ULrnWLx8fH5aiY3a9aMWbNmMXToUIYMGULVqlWZO3cuderUsbYZNGgQ6enp9OrVi+TkZFq0aMHChQtxcfl7wr6ZM2cSExNDmzZtsLOz4/HHH2fixIn5Yhs1ahSHDx/GwcGBGjVq8O233/LEE0/c5ldERERERERKgttdRvqfVQNOnDhBeHj4dce4c+dO2rRpQ69evRg6dOg12zdp0oQ///zzitudnZ1xdna+7jiKivlbjwEQVTsAJweVp5LrV+PiyJtjKZmkZOTg7eZY6H03x5/l+RkbOJOeTTkfV6Z3b0xVf02oLiVbNX9P3JzsScu6wIFTaVQrxM+ExWLhjV92MHt9AnYmmNCpPm1r+V9zP5HCsrcz8WDdINqHBbJq/xmmLD/An/tP89Pmo/y0+Sj31PDjhVaVaVyxlEZNylUZnrwBiImJISYmpsBty5Ytu2xdhw4d6NChwxWPZzKZePPNN3nzzTev2KZ06dLMmjXritu7detGt27drhy0iIiIiIjITfhnGelLVQIulZG+Uv/oUhnp/v37W9ddqYz0pWTNpTLSvXv3vq74duzYwT333EO3bt0YPXp0ofYpzqWmc80WFmzPKz3XPqx4Pke5/bxdHSnn48rR5PPsSkwtdImm33ck8tLszWTmmKlTzovPuzXGz8vl2juKFHP2dibCynmz9mAScfHJ10zeWCwW3vl1NzNiD2Mywf861KO9ymDKbWIymWhRtQwtqpZh25EUpiw/wILtx1my+yRLdp+kQYgPvVtXoU0NP+wKOWpMSpYikbwREREREREpiYwoIw15FQ6SkpKIj48nNzeXuLg4AKpUqYKHhwfbt2/nnnvuISoqioEDB1rny7G3t6ds2byyMuPHjyc0NJTatWuTmZnJtGnTWLJkCb///vsdevXurPWHkjh1LgsvFweaVyljdDhiw2oGeuUlb44XLnkzfdVB3pi3E4sF7q5elklPNcDdWV/niFxSP6QUaw8msTkhmY6Nr16Kc8LifXyy4i8ARkeH8ViD8nciRBHCynszuUsDDp5OZ+qKv/hh4xE2xSfT88sNVPXzoFfLSjwSXk4jeyUf/bUXERERERExiFFlpIcPH86MGTOsjy/N9bl06VJat27N999/z6lTp/j666/5+uuvre0qVKjAoUOHAMjOzubll1/m6NGjuLm5UbduXf744w/uvvvu2/JaGW3+1uOASqbJzasZ6Mkfu05cc94bs9nCmF938enKgwA8FRHCmw/XxsFe7z+RfwoP9gHySgtezZTlBxj/xz4Ahj9Yi6ciQm53aCKXCS3jzpjHwhjQtiqfrzrEzDWH2XcyjVe/38r7i/bSo0UonZuEKEkvgJI3IiIiIiIihjKijPT06dOZPn36FbePHDmSkSNHXnE75CWJBg0adNU2xUWu2cKv2/OSNyqvIzer5sV5b3Ynnrtim8ycXAZ+F8eCbXmj3ga1q07vVpU1N4JIAeqH+ACw98Q50rMuFPil9/RVB3nn191A3s/Tcy1C72SIIpfx83Lh9ftr0OfuysxaG89nfx7keEomb83fxYdL9tMtsgLdmlXE18N25wqUm6fbNURERERERESuYu3BM5xOy8bb1VEl0+SmXUre7Ek8x4Vc82Xbk9Kz6TJtLQu2JeJkb8eETuH0aV1FiRuRK/D3ciHI2wWzBbYeSbls+zfr4hn5y04AXrqnCn1aV7nTIYpckZeLIy+0qszKQXcz5rEwQsu4k3I+h4lL9tP83SUM/7/tJCRlGB1msXM0+Txn0rKMDuOalLwRERERERERuYpLJdPa1Q7AUSWr5CaFlHbD1dGerAtmDp1Jz7ft8Jl0Hv94NRsPn8XLxYEvezThkfByBkUqYjvCL46+iUtIzrf+p81HGPLTNgB6tazEgHur3eHIRArHxdGezk1C+GNgKz7u0oC65b3JzDHzZexhWv9vGf1mb75muU25svPZuSzdc5I3ftlBm3HLaP7OEn7YdMTosK5JZdNERG5CSkoKGRm2cQeEm5sb3t7eRochIiIiYlMu5JpZuD2vdJVKpsmtYG9nonqAJ3EJyew6fo4qfp5A3nwdz8/YwJn0bMr5uDLjucbWbSJydfWDS7FgWyJxCX/Pe7Ng23Fe/m4LFgt0jazA4PtraASbFHn2dibuDwukXZ0AYg+c4ePlB1i57zT/F3eM/4s7RuvqZXmhVWUiQkvr/XwVFouFPSfOsWLvKVbsPc26Q0lkX/h7tKudCY6nZBoYYeEoeSMicoNSUlKY+OEkzLkXjA6lUOzsHXjpxRglcERERESuw7qDSZxJz6aUmyORlX2NDkeKiZqBXheTN6k8VC+I33Yk0m/2ZjJzzISV8+azZxvh5+lidJgiNuPSyJvN8clYLBaW7D7JS99sxmyBjo3KM/Kh2vqiW2yKyWSiWZUyNKtShu1HU5iy/AALth1n2Z5TLNtzivBgH15oVZn7avljZ6f3NsDZ9GxW7j/Nir2nWLnvFCdS85dFK+fjSstqZWhZtSzNqpTB29XRoEgLT8kbEZEblJGRgTn3AsuzQ0kxF+2OlbddJq2cDpKRkaHkjYiIiMh1mLctr2RalEqmyS1UMzBvRM2u46lMX3WQN+btxGKBe2r48WHn+gVOuC4iV1YnyBt7OxMnz2UxZ+MRhv60nQtmCw/XC2LMY3X15bbYtDrlvJn0VAMOn0nn05V/8d2GI8QlJPPC1xupXNad/7SsTHT9cjg5lKzPKRdyzWxOSL44uuYUW4+mYLH8vd3F0Y6mlXxpWbUsLauVpXJZd5tL4urTgIjITUoxu3DG4m50GFd3+TyoIiIiInINKpkmt0vNQC8AVuw7zdI9pwDoEhHCGw/XxkFJQpHr5upkT81AT7YfTWXQ91sBiKrtz7iO9bBX4kaKiQq+7rwVHUa/NtWYvvogX8Ye5sCpdAb9sJX3F+2lR4tQOkeE4FGMbwA4cjaDFXvzRtesOnCac5n5q+FU9/fMG11TrSyNK5bGxdHeoEhvjeJ7JUVE5IZpLh8RERERWPNXEkmXSqZVUsk0uXVqBOSNvMk1590i/Pr9NfhPy0o2d0ewSFESHuzD9qN5E7rfXb0sH3ZuoBGTUiyV9XTm1agavNCqMt+si+ezPw+SmJrJ6AW7+HDJPp6JrMCzzUIp6+lsdKg3LSP7Amv/SmL53lOs2HeKv06l59vu4+ZIiyp5yZqWVcsS4F20K+NcLyVvREQkH83lIyIiIpJn/rZjALSrE6jREHJLebo4Uj/Ehx3HUvlfh3o8XC/I6JBEbN49Nfz4ek08zav48vHTDUtcCSkpeTxdHOnVsjLdmlVk7uajfLLiL/46lc7kpQeYtvIgHRqVp9ddlQnxdTM61EKzWCzsTjyXVwpt3ynWHzxLdu7f5WTs7UzUD/bJS9ZUK0tYOe9iPbpOyRsREclHc/mIiIiIQM4/SqY9qJJpcht807Mp57NzKeXuZHQoIsXC3dX9WPHq3ZQv5ao5bqREcXaw58nGIXRoGMzvO0/w8fIDbElI5us18cxaG0/7ukG80KoStYOK5vcmSenZrNx3ihV7T7Ny3ylOnsvKt72cjystq5WlVbUyRFYug7ero0GR3nlK3oiISIE0l4+IiIiUZLEHznA2IwdfdyciQksbHY4UQy6O9jZfi1+kKDGZTDY1wkDkVrOzM9GuTgBRtf1Z81cSU5YfYPneU/yy5Ri/bDnGXVXL0Lt1ZSIr+RpapjMn10xcQjIr9p5i+d5TbDuagsXy93YXRzuaVvKl1cXRNZXKuJfYsqJK3oiIiIiIiIj8y/ytxwFoVydAJdNERETEZphMJiIr+xJZ2Zcdx1L4ZPlfzNt6jJX7TrNy32nqlfemd+vK3Fsr4I6VHEtIymDFvlOs2HuK1fvPcC4rf6n+GgGe1nlrGlUspZsbLlLyRkREREREROQfcnLN/LYzr2Rae5VMExERERtVO8ibiZ3r88p91fl05V98tyGBLUdSeOHrTVQq406vlpV4tEE5nB1ubbIkI/sCa/46w4q9p1mx9xR/nU7Pt72UmyMtqpalZdUytKxWFn+vol223yhK3oiIiIiIiIj8w+oDZ0jOyKGMhxMRob5GhyMiIiJyU0J83RgVXYd+basyY/Uhvow9zF+n03n9x228v2gvPVqE8lRECJ4uNzafjMViYdfxc9bRNRsOnSU79+9a9/Z2JhqE+NCyal4ptDrlvO/YqB9bpuSNiIiIiIiIyD/M33oMyCuZpi8WREREpLgo4+HMy/dV5z+tKjN7XTzTVh4kMTWTMb/uZtLS/TzdtALdm1fEz/PaI2HOpGXx5/7TLN97ipX7TnPqXFa+7eVLuVpLoTWr4ovXDSaGSjIlb0REREREREQuyr5g5rcdJwBoHxZkcDQiIiIit56HswPP31WJrpEV+b+4o0xZfoADp9L5eNkBPvvzIE80LE+vuypRsYy7dZ+cXDOb45NZsfcUK/adYtvRFCyWv4/p6mhP00ql8xI21cpSqYw7JpNugrkZSt6IiIiIiIiIXLTqwGlSzudQxsOZJqGljQ5HRERE5LZxcrCjQ6NgHm9Qnj92neDj5QfYHJ/MrLXxzF4Xz/11AmkSWppV+08Te+AM57Iu5Nu/RoAnrS4maxpVLHXL584p6ZS8EREREREREblo/tbjADwQppJpIiIiUjLY2Zm4r3YA99byZ93BJKYsP8DSPaeYv+0487cdt7Yr7e5EiyplLpZDK4Of17XLq8mNU/JGREREREREhEsl0xIBaB8WaHA0IiIiIneWyWQiopIvEZV82XU8lc/+PEhiSqa1HFqdIG/sdHPLHaPkjYiIiIiIiAjw5/5TnMu8gJ+nM40qqmSaiIiIlFw1A734X4d6RodRotkZHYCIiIiIiIhIUTDPWjItUCXTRERERMRQSt6IiIiIiIhIiZd1IZdFO04A0L6uSqaJiIiIiLGUvBEREREREZESb+Xe05zLuoC/lzMNQ0oZHY6IiIiIlHBK3oiIiIiIiEiJt2BbXsm0++sEaiJeERERETGckjciIiIiIiJSomXm5LJoZ17JtAdVMk1EREREigAlb0RERERERKREW7kvr2RagJcLDVQyTURERESKACVvREREREREpESbv/UYAA+EqWSaiIiIiBQNSt6IiIiIiIhIifXPkmntVTJNRERERIoIJW9ERERERESkxFq+9xTp2bkEebtQP9jH6HBERERERAAlb0RERERERKQEm7/1OKCSaSIiIiJStCh5IyIiIiIiYqDJkydTsWJFXFxciIiIYN26dVdtP2fOHGrUqIGLiwthYWEsWLAg33aLxcLw4cMJDAzE1dWVtm3bsm/fvnxtRo8eTbNmzXBzc8PHx6fA88THx9O+fXvc3Nzw8/Pj1Vdf5cKFC/naLFu2jAYNGuDs7EyVKlWYPn36dT9/I2Xm5PLHLpVMExEREZGiR8kbERERERERg3z77bcMHDiQESNGsGnTJurVq0dUVBQnT54ssP3q1avp3LkzPXr0YPPmzURHRxMdHc327dutbcaOHcvEiROZMmUKa9euxd3dnaioKDIzM61tsrOz6dChA7179y7wPLm5ubRv357s7GxWr17NjBkzmD59OsOHD7e2OXjwIO3bt+fuu+8mLi6O/v378/zzz/Pbb7/dolfn9lu25yQZ2bmU83ElXCXTRERERKQIUfJGRERERETEIO+//z49e/ake/fu1KpViylTpuDm5sbnn39eYPsJEybQrl07Xn31VWrWrMmoUaNo0KABkyZNAvJG3YwfP56hQ4fyyCOPULduXb788kuOHTvG3Llzrcd54403GDBgAGFhYQWe5/fff2fnzp18/fXXhIeHc//99zNq1CgmT55MdnY2AFOmTCE0NJRx48ZRs2ZNYmJieOKJJ/jggw9u7Yt0G83flgjAA2EBmEwqmSYiIiIiRYeSNyIiIiIiIgbIzs5m48aNtG3b1rrOzs6Otm3bEhsbW+A+sbGx+doDREVFWdsfPHiQxMTEfG28vb2JiIi44jGvdJ6wsDD8/f3znSc1NZUdO3YUKpaCZGVlkZqamm8xyvnsXBZbS6YFGRaHiIiIiEhBlLwRERERERExwOnTp8nNzc2XIAHw9/cnMTGxwH0SExOv2v7Sv9dzzOs5zz/PcaU2qampnD9/vsDjjhkzBm9vb+sSHBxc6JhutdNpWdQr70NIaTfqlfc2LA4RERERkYIoeSMiIiIiIiJ3xODBg0lJSbEuCQkJhsUSXNqNb3o15fcBLVUyTURERESKHCVvREREREREDFCmTBns7e05ceJEvvUnTpwgICCgwH0CAgKu2v7Sv9dzzOs5zz/PcaU2Xl5euLq6FnhcZ2dnvLy88i1Gc3G0NzoEEREREZHLKHkjIiIiIiJiACcnJxo2bMjixYut68xmM4sXLyYyMrLAfSIjI/O1B1i0aJG1fWhoKAEBAfnapKamsnbt2ise80rn2bZtGydPnsx3Hi8vL2rVqlWoWERERERE5MY5GB2AiIiIiIhISTVw4EC6detGo0aNaNKkCePHjyc9PZ3u3bsD0LVrV8qVK8eYMWMA6NevH61atWLcuHG0b9+e2bNns2HDBqZOnQqAyWSif//+vPXWW1StWpXQ0FCGDRtGUFAQ0dHR1vPGx8eTlJREfHw8ubm5xMXFAVClShU8PDy47777qFWrFs888wxjx44lMTGRoUOH0rdvX5ydnQF44YUXmDRpEoMGDeK5555jyZIlfPfdd8yfP//OvYAiIiIiIsWUkjciIiIiIiIGefLJJzl16hTDhw8nMTGR8PBwFi5ciL+/P5CXZLGz+7tgQrNmzZg1axZDhw5lyJAhVK1alblz51KnTh1rm0GDBpGenk6vXr1ITk6mRYsWLFy4EBcXF2ub4cOHM2PGDOvj+vXrA7B06VJat26Nvb098+bNo3fv3kRGRuLu7k63bt148803rfuEhoYyf/58BgwYwIQJEyhfvjzTpk0jKirqtr1eIiIiIiIlhZI3IiIiIiIiBoqJiSEmJqbAbcuWLbtsXYcOHejQocMVj2cymXjzzTfzJVr+bfr06UyfPv2qcVWoUIEFCxZctU3r1q3ZvHnzVduIiIiIiMj105w3IiIiIiIiIiIiIiIiRYiSNyIiIiIiIiIiIiIiIkWIkjciIiIiIiIiIiIiIiJFiJI3IiIiIiIiIiIiIiIiRYiSNyIiIiIiIiIiIiIiIkVIkUjeTJ48mYoVK+Li4kJERATr1q27avs5c+ZQo0YNXFxcCAsLY8GCBfm2WywWhg8fTmBgIK6urrRt25Z9+/bla5OUlESXLl3w8vLCx8eHHj16kJaWlq/N1q1bueuuu3BxcSE4OJixY8femicsIiIiIiIiIiIiIiJyBYYnb7799lsGDhzIiBEj2LRpE/Xq1SMqKoqTJ08W2H716tV07tyZHj16sHnzZqKjo4mOjmb79u3WNmPHjmXixIlMmTKFtWvX4u7uTlRUFJmZmdY2Xbp0YceOHSxatIh58+axYsUKevXqZd2emprKfffdR4UKFdi4cSPvvfceI0eOZOrUqbfvxRARERERERERERERkRLP8OTN+++/T8+ePenevTu1atViypQpuLm58fnnnxfYfsKECbRr145XX32VmjVrMmrUKBo0aMCkSZOAvFE348ePZ+jQoTzyyCPUrVuXL7/8kmPHjjF37lwAdu3axcKFC5k2bRoRERG0aNGCDz/8kNmzZ3Ps2DEAZs6cSXZ2Np9//jm1a9emU6dOvPTSS7z//vt35HUREREREREREREREZGSycHIk2dnZ7Nx40YGDx5sXWdnZ0fbtm2JjY0tcJ/Y2FgGDhyYb11UVJQ1MXPw4EESExNp27atdbu3tzcRERHExsbSqVMnYmNj8fHxoVGjRtY2bdu2xc7OjrVr1/Loo48SGxtLy5YtcXJyyneed999l7Nnz1KqVKnLYsvKyiIrK8v6OCUlBcgbxWOUc+fOkZ6ebtj5r4e7uzuenp6Falscn9e5c+fIzMzEPecsueaMOxDZzXG3yyKTTM6dO4e7u/sV2xXX5wW29dz0vGzreUHxfW56XnpeRUVxfW7X87xuh0ufey0Wyx0/t9imS+8VI/tMIiIiIiJ3UqH7TRYDHT161AJYVq9enW/9q6++amnSpEmB+zg6OlpmzZqVb93kyZMtfn5+FovFYlm1apUFsBw7dixfmw4dOlg6duxosVgsltGjR1uqVat22bHLli1r+eijjywWi8Vy7733Wnr16pVv+44dOyyAZefOnQXGNmLECAugRYsWLVq0aNGiRUuJXhISEq7UBRDJJyEhwfD3qxYtWrRo0aJFixYtRizX6jcZOvKmuBk8eHC+UUFms5mkpCR8fX0xmUwGRiaFkZqaSnBwMAkJCXh5eRkdjlyDrpft0TWzLbpetkfXzLYU1+tlsVg4d+4cQUFBRociNiIoKIiEhAQ8PT0N6TMV15/F4kjXynboWtkGXSfboWtlO3StbIfR16qw/SZDkzdlypTB3t6eEydO5Ft/4sQJAgICCtwnICDgqu0v/XvixAkCAwPztQkPD7e2OXnyZL5jXLhwgaSkpHzHKeg8/zzHvzk7O+Ps7JxvnY+PT4Ftpejy8vLSL1gboutle3TNbIuul+3RNbMtxfF6eXt7Gx2C2BA7OzvKly9vdBjF8mexuNK1sh26VrZB18l26FrZDl0r22HktSpMv8nuDsRxRU5OTjRs2JDFixdb15nNZhYvXkxkZGSB+0RGRuZrD7Bo0SJr+9DQUAICAvK1SU1NZe3atdY2kZGRJCcns3HjRmubJUuWYDabiYiIsLZZsWIFOTk5+c5TvXr1Aue7ERERERERERERERERuRUMTd4ADBw4kE8//ZQZM2awa9cuevfuTXp6Ot27dwega9euDB482Nq+X79+LFy4kHHjxrF7925GjhzJhg0biImJAcBkMtG/f3/eeustfv75Z7Zt20bXrl0JCgoiOjoagJo1a9KuXTt69uzJunXrWLVqFTExMXTq1Mk6VOmpp57CycmJHj16sGPHDr799lsmTJiQryyaiIiIiIiIiIiIiIjIrWb4nDdPPvkkp06dYvjw4SQmJhIeHs7ChQvx9/cHID4+Hju7v3NMzZo1Y9asWQwdOpQhQ4ZQtWpV5s6dS506daxtBg0aRHp6Or169SI5OZkWLVqwcOFCXFxcrG1mzpxJTEwMbdq0wc7Ojscff5yJEydat3t7e/P777/Tt29fGjZsSJkyZRg+fDi9evW6A6+KGMHZ2ZkRI0ZcVvpOiiZdL9uja2ZbdL1sj66ZbdH1Eika9LNoO3StbIeulW3QdbIdula2Q9fKdtjKtTJZLBaL0UGIiIiIiIiIiIiIiIhIHsPLpomIiIiIiIiIiIiIiMjflLwREREREREREREREREpQpS8ERERERERERERERERKUKUvBERERERERERERERESlClLyREm3MmDE0btwYT09P/Pz8iI6OZs+ePUaHJYX0zjvvYDKZ6N+/v9GhyFUcPXqUp59+Gl9fX1xdXQkLC2PDhg1GhyVXkJuby7BhwwgNDcXV1ZXKlSszatQoLBaL0aHJRStWrOChhx4iKCgIk8nE3Llz8223WCwMHz6cwMBAXF1dadu2Lfv27TMmWLnq9crJyeG1114jLCwMd3d3goKC6Nq1K8eOHTMuYJESZvLkyVSsWBEXFxciIiJYt26d0SHJP6i/ZrvUVyva1EezDeqbFV3qk9mG4tAXU/JGSrTly5fTt29f1qxZw6JFi8jJyeG+++4jPT3d6NDkGtavX88nn3xC3bp1jQ5FruLs2bM0b94cR0dHfv31V3bu3Mm4ceMoVaqU0aHJFbz77rt8/PHHTJo0iV27dvHuu+8yduxYPvzwQ6NDk4vS09OpV68ekydPLnD72LFjmThxIlOmTGHt2rW4u7sTFRVFZmbmHY5U4OrXKyMjg02bNjFs2DA2bdrEjz/+yJ49e3j44YcNiFSk5Pn2228ZOHAgI0aMYNOmTdSrV4+oqChOnjxpdGhykfprtkl9taJNfTTbob5Z0aU+mW0oDn0xk0XpWhGrU6dO4efnx/Lly2nZsqXR4cgVpKWl0aBBAz766CPeeustwsPDGT9+vNFhSQFef/11Vq1axcqVK40ORQrpwQcfxN/fn88++8y67vHHH8fV1ZWvv/7awMikICaTiZ9++ono6Ggg7w6voKAgXn75ZV555RUAUlJS8Pf3Z/r06XTq1MnAaOXf16sg69evp0mTJhw+fJiQkJA7F5xICRQREUHjxo2ZNGkSAGazmeDgYF588UVef/11g6OTgqi/VvSpr1b0qY9mO9Q3sw3qk9kGW+2LaeSNyD+kpKQAULp0aYMjkavp27cv7du3p23btkaHItfw888/06hRIzp06ICfnx/169fn008/NTosuYpmzZqxePFi9u7dC8CWLVv4888/uf/++w2OTArj4MGDJCYm5vv96O3tTUREBLGxsQZGJoWVkpKCyWTCx8fH6FBEirXs7Gw2btyY7/elnZ0dbdu21e/LIkz9taJPfbWiT30026G+mW1Sn8x2FcW+mIPRAYgUFWazmf79+9O8eXPq1KljdDhyBbNnz2bTpk2sX7/e6FCkEP766y8+/vhjBg4cyJAhQ1i/fj0vvfQSTk5OdOvWzejwpACvv/46qamp1KhRA3t7e3Jzcxk9ejRdunQxOjQphMTERAD8/f3zrff397duk6IrMzOT1157jc6dO+Pl5WV0OCLF2unTp8nNzS3w9+Xu3bsNikquRv21ok99NdugPprtUN/MNqlPZpuKal9MyRuRi/r27cv27dv5888/jQ5FriAhIYF+/fqxaNEiXFxcjA5HCsFsNtOoUSPefvttAOrXr8/27duZMmWKOgZF1HfffcfMmTOZNWsWtWvXJi4ujv79+xMUFKRrJnIb5eTk0LFjRywWCx9//LHR4YiIFDnqrxVt6qvZDvXRbIf6ZiJ3RlHui6lsmggQExPDvHnzWLp0KeXLlzc6HLmCjRs3cvLkSRo0aICDgwMODg4sX76ciRMn4uDgQG5urtEhyr8EBgZSq1atfOtq1qxJfHy8QRHJtbz66qu8/vrrdOrUibCwMJ555hkGDBjAmDFjjA5NCiEgIACAEydO5Ft/4sQJ6zYpei51Fg4fPsyiRYuK1J1eIsVVmTJlsLe31+9LG6H+WtGnvprtUB/NdqhvZpvUJ7MtRb0vpuSNlGgWi4WYmBh++uknlixZQmhoqNEhyVW0adOGbdu2ERcXZ10aNWpEly5diIuLw97e3ugQ5V+aN2/Onj178q3bu3cvFSpUMCgiuZaMjAzs7PJ/PLC3t8dsNhsUkVyP0NBQAgICWLx4sXVdamoqa9euJTIy0sDI5EoudRb27dvHH3/8ga+vr9EhiZQITk5ONGzYMN/vS7PZzOLFi/X7sghRf812qK9mO9RHsx3qm9km9clshy30xVQ2TUq0vn37MmvWLP7v//4PT09Pa+1Jb29vXF1dDY5O/s3T0/Oy+tbu7u74+vqq7nURNWDAAJo1a8bbb79Nx44dWbduHVOnTmXq1KlGhyZX8NBDDzF69GhCQkKoXbs2mzdv5v333+e5554zOjS5KC0tjf3791sfHzx4kLi4OEqXLk1ISAj9+/fnrbfeomrVqoSGhjJs2DCCgoKIjo42LugS7GrXKzAwkCeeeIJNmzYxb948cnNzrZ9FSpcujZOTk1Fhi5QIAwcOpFu3bjRq1IgmTZowfvx40tPT6d69u9GhyUXqr9kO9dVsh/potkN9s6JLfTLbUCz6YhaREgwocPniiy+MDk0KqVWrVpZ+/foZHYZcxS+//GKpU6eOxdnZ2VKjRg3L1KlTjQ5JriI1NdXSr18/S0hIiMXFxcVSqVIly3//+19LVlaW0aHJRUuXLi3wb1e3bt0sFovFYjabLcOGDbP4+/tbnJ2dLW3atLHs2bPH2KBLsKtdr4MHD17xs8jSpUuNDl2kRPjwww8tISEhFicnJ0uTJk0sa9asMTok+Qf112yb+mpFl/potkF9s6JLfTLbUBz6YiaLxWK5PWkhERERERERERERERERuV6a80ZERERERERERERERKQIUfJGRERERERERERERESkCFHyRkREREREREREREREpAhR8kZERERERERERERERKQIUfJGRERERERERERERESkCFHyRkREREREREREREREpAhR8kZERERERERERERERKQIUfJGROQmtG7dmv79+9/Wczz77LNER0ff1nOUNDfzmi5btgyTyURycjIA06dPx8fH55bFVtSYTCbmzp1rdBgiIiIiYqPUZ7JN6jMVnvpMInK7KHkjIiI2oah2yJ588kn27t1rdBgiIiIiIlLCqc8kIlK8OBgdgIiI3HnZ2dk4OTkZHUax4Orqiqurq9Fh2BS9/0RERESkqNNn1ltHfabrp/efiIBG3oiI3LQLFy4QExODt7c3ZcqUYdiwYVgsFuv2r776ikaNGuHp6UlAQABPPfUUJ0+ezHeMHTt28OCDD+Ll5YWnpyd33XUXBw4cKPB869evp2zZsrz77rsAjBw5kvDwcD755BOCg4Nxc3OjY8eOpKSkWPe5dAfW6NGjCQoKonr16gBs27aNe+65B1dXV3x9fenVqxdpaWmX7ffGG29QtmxZvLy8eOGFF8jOzra2WbhwIS1atMDHxwdfX18efPDBy2JfvXo14eHhuLi40KhRI+bOnYvJZCIuLg6A3NxcevToQWhoKK6urlSvXp0JEyZY9x85ciQzZszg//7v/zCZTJhMJpYtWwZAQkICHTt2xMfHh9KlS/PII49w6NAh6765ubkMHDjQGt+gQYPyXZ+CHD58mIceeohSpUrh7u5O7dq1WbBgQYFtCyoB8Msvv9C4cWNcXFwoU6YMjz76qHVbVlYWr7zyCuXKlcPd3Z2IiAjrc7kSk8nEtGnTePTRR3Fzc6Nq1ar8/PPPV43h0mt8yaX3yeeff05ISAgeHh706dOH3Nxcxo4dS0BAAH5+fowePfqy8x8/fpz7778fV1dXKlWqxPfff59v+7WuwZXefyIiIiJSMqjPpD6T+kzqM4nI9VPyRkTkJs2YMQMHBwfWrVvHhAkTeP/995k2bZp1e05ODqNGjWLLli3MnTuXQ4cO8eyzz1q3Hz16lJYtW+Ls7MySJUvYuHEjzz33HBcuXLjsXEuWLOHee+9l9OjRvPbaa9b1+/fv57vvvuOXX35h4cKFbN68mT59+uTbd/HixezZs4dFixYxb9480tPTiYqKolSpUqxfv545c+bwxx9/EBMTc9l+u3btYtmyZXzzzTf8+OOPvPHGG9bt6enpDBw4kA0bNrB48WLs7Ox49NFHMZvNAKSmpvLQQw8RFhbGpk2bGDVqVL7YAcxmM+XLl2fOnDns3LmT4cOHM2TIEL777jsAXnnlFTp27Ei7du04fvw4x48fp1mzZuTk5BAVFYWnpycrV65k1apVeHh40K5dO2tnady4cUyfPp3PP/+cP//8k6SkJH766aerXtO+ffuSlZXFihUr2LZtG++++y4eHh5X3eeS+fPn8+ijj/LAAw+wefNmFi9eTJMmTazbY2JiiI2NZfbs2WzdupUOHTrQrl079u3bd9XjvvHGG3Ts2JGtW7fywAMP0KVLF5KSkgoV0yUHDhzg119/ZeHChXzzzTd89tlntG/fniNHjrB8+XLeffddhg4dytq1a/PtN2zYMB5//HG2bNlCly5d6NSpE7t27QIo1DWAy99/IiIiIlJyqM+kPtM/qc+kPpOIFJJFRERuWKtWrSw1a9a0mM1m67rXXnvNUrNmzSvus379egtgOXfunMVisVgGDx5sCQ0NtWRnZxfYvlu3bpZHHnnE8uOPP1o8PDwss2fPzrd9xIgRFnt7e8uRI0es63799VeLnZ2d5fjx49Zj+Pv7W7Kysqxtpk6dailVqpQlLS3Num7+/PkWOzs7S2JionW/0qVLW9LT061tPv74Y4uHh4clNze3wHhPnTplASzbtm2ztvf19bWcP3/e2ubTTz+1AJbNmzdf8XXq27ev5fHHH7/sdfinr776ylK9evV8r39WVpbF1dXV8ttvv1ksFoslMDDQMnbsWOv2nJwcS/ny5S871j+FhYVZRo4cWeC2pUuXWgDL2bNnLRaLxfLFF19YvL29rdsjIyMtXbp0KXDfw4cPW+zt7S1Hjx7Nt75NmzaWwYMHXzEewDJ06FDr47S0NAtg+fXXXwuMwWKxWH766SfLP//MjxgxwuLm5mZJTU21rouKirJUrFgx37WsXr26ZcyYMfnO/cILL+Q7dkREhKV3794Wi6Vw16Cg95+IiIiIlAzqM11OfSb1mdRnEpHC0MgbEZGb1LRp03xDrSMjI9m3bx+5ubkAbNy4kYceeoiQkBA8PT1p1aoVAPHx8QDExcVx11134ejoeMVzrF27lg4dOvDVV1/x5JNPXrY9JCSEcuXK5YvBbDazZ88e67qwsLB8NXN37dpFvXr1cHd3t65r3rz5ZfvVq1cPNze3fMdOS0sjISEBgH379tG5c2cqVaqEl5cXFStWzPf89uzZQ926dXFxcbEe4593VV0yefJkGjZsSNmyZfHw8GDq1KnWY1zJli1b2L9/P56ennh4eODh4UHp0qXJzMzkwIEDpKSkcPz4cSIiIqz7ODg40KhRo6se96WXXuKtt96iefPmjBgxgq1bt161/T/FxcXRpk2bArdt27aN3NxcqlWrZo3Xw8OD5cuXX7HkwyV169a1/t/d3R0vL6/LSklcS8WKFfH09LQ+9vf3p1atWtjZ2eVb9+/jRkZGXvb40l1k17oGl/z7/SciIiIiJYf6TOoz/ZP6TOoziUjhOBgdgIhIcXZpmH1UVBQzZ86kbNmyxMfHExUVZR0eXZiJGytXroyvry+ff/457du3v2qn5Ur+2eG4lR566CEqVKjAp59+SlBQEGazmTp16uQb/n0ts2fP5pVXXmHcuHFERkbi6enJe++9d9lQ9H9LS0ujYcOGzJw587JtZcuWve7ncsnzzz9PVFQU8+fP5/fff2fMmDGMGzeOF1988Zr7Xu16pqWlYW9vz8aNG7G3t8+37VolBv59zU0mk7XMgp2d3WU1qXNycgp1jKsdtzAKew1u1/tPRERERGyb+kyFoz6T+kwiUvJo5I2IyE3694flNWvWULVqVezt7dm9ezdnzpzhnXfe4a677qJGjRqX3aFTt25dVq5cWeAHx0vKlCnDkiVL2L9/Px07drysbXx8PMeOHcsXg52d3VUnOaxZsyZbtmwhPT3dum7VqlWX7bdlyxbOnz+f79geHh4EBwdz5swZ9uzZw9ChQ2nTpg01a9bk7Nmz+c5TvXp1tm3bRlZWlnXd+vXr87VZtWoVzZo1o0+fPtSvX58qVapcdleVk5OT9c68Sxo0aMC+ffvw8/OjSpUq+RZvb2+8vb0JDAzMd40uXLjAxo0br/i6XBIcHMwLL7zAjz/+yMsvv8ynn356zX0g73ouXry4wG3169cnNzeXkydPXhZvQEBAoY5fkLJly3Lu3Ll81/LSxKa3wpo1ay57XLNmTeDa10BERERERH0m9Zn+SX0m9ZlEpHCUvBERuUnx8fEMHDiQPXv28M033/Dhhx/Sr18/IG9ovpOTEx9++CF//fUXP//8M6NGjcq3f0xMDKmpqXTq1IkNGzawb98+vvrqq3zD8AH8/PxYsmQJu3fvpnPnzvkm53RxcaFbt25s2bKFlStX8tJLL9GxY8erfrjt0qWLdb/t27ezdOlSXnzxRZ555hn8/f2t7bKzs+nRowc7d+5kwYIFjBgxgpiYGOzs7ChVqhS+vr5MnTqV/fv3s2TJEgYOHJjvPE899RRms5levXqxa9cufvvtN/73v/8BWEsnVK1alQ0bNvDbb7+xd+9ehg0bdllnpWLFimzdupU9e/Zw+vRpcnJy6NKlC2XKlOGRRx5h5cqVHDx4kGXLlvHSSy9x5MgRAPr168c777zD3Llz2b17N3369CE5Ofmq17R///789ttvHDx4kE2bNrF06VLrB+9rGTFiBN988w0jRoxg165d1sk7AapVq0aXLl3o2rUrP/74IwcPHmTdunWMGTOG+fPnF+r4BYmIiMDNzY0hQ4Zw4MABZs2axfTp02/4eP82Z84cPv/8c/bu3cuIESNYt26ddZLWwlwDERERESnZ1GdSn+mf1GdSn0lECkfJGxGRm9S1a1fOnz9PkyZN6Nu3L/369aNXr15A3t0906dPZ86cOdSqVYt33nnH+iH8El9fX5YsWUJaWhqtWrWiYcOGfPrppwUO8w8ICGDJkiVs27aNLl26WO+qqlKlCo899hgPPPAA9913H3Xr1uWjjz66atxubm789ttvJCUl0bhxY5544gnatGnDpEmT8rVr06YNVatWpWXLljz55JM8/PDDjBw5Esgbej579mw2btxInTp1GDBgAO+9916+/b28vPjll1+Ii4sjPDyc//73vwwfPhzAWtP5P//5D4899hhPPvkkERERnDlzhj59+uQ7Ts+ePalevTqNGjWibNmyrFq1Cjc3N1asWEFISAiPPfYYNWvWpEePHmRmZuLl5QXAyy+/zDPPPEO3bt2s5QUeffTRq742ubm59O3bl5o1a9KuXTuqVat2zdfzktatWzNnzhx+/vlnwsPDueeee1i3bp11+xdffEHXrl15+eWXqV69OtHR0axfv56QkJBCHb8gpUuX5uuvv2bBggWEhYXxzTffWK/RrfDGG28we/Zs6taty5dffsk333xDrVq1AAp1DURERESkZFOfSX2mf1KfSX0mESkck+XfBR9FRMSmjBw5krlz597SId+XPPvssyQnJzN37txbetyZM2fSvXt3UlJSClW/WkRERERE5EapzyQiIrbIwegARESk+Pvyyy+pVKkS5cqVY8uWLbz22mt07NhRnRARERERERHUZxIRkcspeSMiIrddYmIiw4cPJzExkcDAQDp06MDo0aONDktERERERKRIUJ9JRET+TWXTREREREREREREREREihA7owMQERERERERERERERGRvyl5IyIiIiIiIiIiIiIiUoQoeSMiIiIiIiIiIiIiIlKEKHkjIiIiIiIiIiIiIiJShCh5IyIiIiIiIiIiIiIiUoQoeSMiIiIiIiIiIiIiIlKEKHkjIiIiIiIiIiIiIiJShCh5IyIiIiIiIiIiIiIiUoQoeSMiIiIiIiIiIiIiIlKEKHkjIiIiIiIiIiIiIiJShCh5IyIiIiIiIiIiIiIiUoQoeSMiIiIiIiIiIiIiIlKEKHkjIiIiIiIiIiIiIiJShCh5IyIiIiIiIiIiIiIiUoQoeSMiIiIiIiIiIiIiIlKEKHkjIiIiIiIiIiIiIiJShCh5IyIiIiIiIiIiIiIiUoQoeSMiIiIiIiIiIiIiIlKEKHkjIiIiIiIiIiIiIiJShCh5IyIiIiIiIiIiIiIiUoQoeSMiIiIiIiIiIiIiIlKEKHkjIiIiIiIiIiIiIiJShCh5IyIiIiIiIiIiIiIiUoQoeSMiIiIiIiIiIiIiIlKEKHkjIiIiIiIiIiIiIiJShDgYHUBxZjabOXbsGJ6enphMJqPDERERERG5rSwWC+fOnSMoKAg7O90nJtemPpOIiIiIlDSF7TcpeXMbHTt2jODgYKPDEBERERG5oxISEihfvrzRYYgNUJ9JREREREqqa/WblLy5jTw9PYG8i+Dl5WVwNCIiIiIit1dqairBwcHWz8Ei16I+k4iIiIiUNIXtNyl5cxtdGvbv5eWljoiIiIiIlBgqfyWFpT6TiIiIiJRU1+o3qRC1iIiIiIiIiIiIiIhIEaLkjYiIiIiIiIiIiIiISBGi5I2IiIiIiIiIiIiIiEgRYhNz3kyePJn33nuPxMRE6tWrx4cffkiTJk2u2H7OnDkMGzaMQ4cOUbVqVd59910eeOAB6/Yff/yRKVOmsHHjRpKSkti8eTPh4eHW7UlJSYwYMYLff/+d+Ph4ypYtS3R0NKNGjcLb2/uWPS+LxcKFCxfIzc29ZccUucTe3h4HBwfVnBcRERERm6Z+kxRV6nOJiIjI7VTkkzfffvstAwcOZMqUKURERDB+/HiioqLYs2cPfn5+l7VfvXo1nTt3ZsyYMTz44IPMmjWL6OhoNm3aRJ06dQBIT0+nRYsWdOzYkZ49e152jGPHjnHs2DH+97//UatWLQ4fPswLL7zAsWPH+P7772/J88rOzub48eNkZGTckuOJFMTNzY3AwECcnJyMDkVERERE5Lqp3yRFnfpcIiIicruYLBaLxeggriYiIoLGjRszadIkAMxmM8HBwbz44ou8/vrrl7V/8sknSU9PZ968edZ1TZs2JTw8nClTpuRre+jQIUJDQy8beVOQOXPm8PTTT5Oeno6DQ+FyXqmpqXh7e5OSkoKXl5d1vdlsZt++fdjb21O2bFmcnJx0p47cUhaLhezsbE6dOkVubi5Vq1bFzk5VEkVEROT2utLnX5Erudp7Rv0mKcrU5xIREZEbVdh+U5EeeZOdnc3GjRsZPHiwdZ2dnR1t27YlNja2wH1iY2MZOHBgvnVRUVHMnTv3pmK59EJeLXGTlZVFVlaW9XFqamqB7bKzs61JKDc3t5uKS+RKXF1dcXR05PDhw2RnZ+Pi4mJ0SCIiIiIihaZ+kxR16nOJiIjI7VSkbws5ffo0ubm5+Pv751vv7+9PYmJigfskJiZeV/vCxjFq1Ch69ep11XZjxozB29vbugQHB1+1ve7KkdtN7zERERERsXX6TCtFmd6fIiIicrvoU8Y1pKam0r59e2rVqsXIkSOv2nbw4MGkpKRYl4SEhDsTpIiIiIiIiIiIiIiIFBtFumxamTJlsLe358SJE/nWnzhxgoCAgAL3CQgIuK72V3Pu3DnatWuHp6cnP/30E46Ojldt7+zsjLOz83WfR0RERERERERERERE5JIiPfLGycmJhg0bsnjxYus6s9nM4sWLiYyMLHCfyMjIfO0BFi1adMX2V5Kamsp9992Hk5MTP//8s2rX3oCKFSsyfvx4o8O4Ia1bt6Z///5XbWPLz09EREREREREREREiq4inbwBGDhwIJ9++ikzZsxg165d9O7dm/T0dLp37w5A165dGTx4sLV9v379WLhwIePGjWP37t2MHDmSDRs2EBMTY22TlJREXFwcO3fuBGDPnj3ExcVZ58W5lLhJT0/ns88+IzU1lcTERBITE8nNzb2Dz75oSkhI4LnnniMoKAgnJycqVKhAv379OHPmjNGh2bT33nuPp556CoBZs2Zxzz335NuemZnJs88+S1hYGA4ODkRHRxsQpYiIiBRVZrOFlftOMWnJPqNDERERERERKZKSkpLo168fx48fNzqUayrSZdMAnnzySU6dOsXw4cNJTEwkPDychQsX4u/vD0B8fHy+CQKbNWvGrFmzGDp0KEOGDKFq1arMnTuXOnXqWNv8/PPP1uQPQKdOnQAYMWIEI0eOZNOmTaxduxaAKlWq5Ivn4MGDVKxY8XY93SLvr7/+IjIykmrVqvHNN98QGhrKjh07ePXVV/n1119Zs2YNpUuXNiS23NxcTCaTzU4YGRsbS5s2bQBYuXIlzZs3z7c9NzcXV1dXXnrpJX744QcjQhQREZEiKDUzhx82HuGr2MP8dTodkwkeCS9HcGk3o0MTEbklsrOzcXJyMjoMERERsWFms5kZM2YwaNAgTp8+zZkzZ/j666+NDuuqbOJb7piYGA4fPkxWVhZr164lIiLCum3ZsmVMnz49X/sOHTqwZ88esrKy2L59Ow888EC+7c8++ywWi+WyZeTIkUBeyayCtlssltuWuLFYLGRkXzBksVgshY6zb9++ODk58fvvv9OqVStCQkK4//77+eOPPzh69Cj//e9/87U/d+4cnTt3xt3dnXLlyjF58uR8z3nkyJGEhITg7OxMUFAQL730knV7VlYWr7zyCuXKlcPd3Z2IiAiWLVtm3T59+nR8fHz4+eefqVWrFs7OzkybNg0XFxeSk5PzxdGvXz/rSJYzZ87QuXNnypUrh5ubG2FhYXzzzTeXPdcLFy4QExODt7c3ZcqUYdiwYVd9rZKTk3n++ecpW7YsXl5e3HPPPWzZsqXQr21sbKw1YfPnn39elrxxd3fn448/pmfPnjc0h5OIiIgUL3sSz/Hfn7bR9O3FvPHLTv46nY6nswPdIiviaG8TH/NFro/FApmZxizX0WeCvD7liy++SP/+/SlVqhT+/v58+umn1ioSnp6eVKlShV9//RXIu1GrR48ehIaG4urqSvXq1ZkwYYL1eJmZmdSuXZtevXpZ1x04cABPT08+//zzQsU0ffp0QkJCcHNz49FHH2XcuHH4+PgAkJKSgr29PRs2bADyvtwoXbo0TZs2te7/9ddfExwcbH185MgROnfuTOnSpXF3d6dRo0bWmyCv5a233sLPzw9PT0+ef/55Xn/9dcLDw63bn332WaKjoxk9ejRBQUFUr14dgG3btnHPPffg6uqKr68vvXr1Ii0tLd/r/u/y19HR0Tz77LPWxxUrVmTUqFE33E8VERER27N161ZatmzJc889x+nTp6lVqxY9e/Y0OqxrKvIjb0qK8zm51Br+myHn3vlmFG5O134rJCUl8dtvvzF69GhcXV3zbQsICKBLly58++23fPTRR5hMJiCvFNiQIUN44403+O233+jXrx/VqlXj3nvv5YcffuCDDz5g9uzZ1K5dm8TExHzJjpiYGHbu3Mns2bMJCgrip59+ol27dmzbto2qVasCkJGRwbvvvsu0adPw9fWlfPnyDB8+nB9++IEePXoAeR2hb7/9ltGjRwN5HZ+GDRvy2muv4eXlxfz583nmmWeoXLkyTZo0sZ5/xowZ9OjRg3Xr1rFhwwZ69epFSEjIFX+wO3TogKurK7/++ive3t588skntGnThr17915xNNI777zDO++8A+R1mFq1aoXJZCIlJYWOHTtiZ2fHvHnzaNGixTWvj4iIiBR/Oblmft9xghmxh1h3MMm6vpq/B10jK/Jo/XK4O+sjvhRTWVlw//3GnPvXX+E650G9dGfnunXr+Pbbb+nduzc//fQTjz76KEOGDOGDDz7gmWeeIT4+HkdHR8qXL8+cOXPw9fVl9erV9OrVi8DAQDp27IiLiwszZ84kIiKC9u3b8+CDD/L0009z77338txzz10zlrVr19KjRw/GjBlDdHQ0CxcuZMSIEdbt3t7ehIeHs2zZMho1asS2bdswmUxs3ryZtLQ0PDw8WL58Oa1atQIgLS2NVq1aUa5cOX7++WcCAgLYtGkTZrP5mrHMnDmT0aNH89FHH9G8eXNmz57NuHHjCA0Nzddu8eLFeHl5sWjRIgDS09OJiooiMjKS9evXc/LkSZ5//nliYmIuu6HzWm6mnyoiIiK2IzU1lZEjRzJx4kRyc3Nxd3dnxIgR9O/fH0dHR6PDuyb17KTQ9u3bh8VioWbNmgVur1mzJmfPnuXUqVP4+fkB0Lx5c15//XUAqlWrxqpVq/jggw+49957iY+PJyAggLZt2+Lo6EhISIg1eRIfH88XX3xBfHw8QUFBALzyyissXLiQL774grfffhuAnJwcPvroI+rVq2eNo1OnTsyaNcuavFm8eDHJyck8/vjjAJQrV45XXnnF2v7FF1/kt99+47vvvsuXvAkODuaDDz7AZDJRvXp1tm3bxgcffFBg8ubPP/9k3bp1nDx5EmdnZwD+97//MXfuXL7//vt8d8j90wsvvECnTp2YPn06a9asYcqUKSxYsIDp06fz3XffAWiUjYiIiHAyNZNv1iUwa91hTqRmAWBvZyKqtj9dIysSEVraevOMiBQN9erVY+jQoQAMHjyYd955hzJlylj7E8OHD+fjjz9m69atNG3alDfeeMO6b2hoKLGxsXz33Xd07NgRgPDwcN566y2ef/55OnXqxOHDh5k3b16hYpkwYQLt2rVj0KBBQF7fbPXq1SxcuNDapnXr1ixbtoxXXnmFZcuWce+997J7927+/PNP2rVrx7Jly6z7z5o1i1OnTrF+/XrrjWr/Ljl+JR9++CE9evSwljIfPnw4v//+e74RNJBXeWDatGnWcmmffvopmZmZfPnll7i7uwMwadIkHnroId59911rafXCuNF+qoiIiNgGi8XCd999x4ABA6xz2zz++ON88MEH+UYSF3VK3hQRro727HwzyrBzX4/rKbMWGRl52ePx48cDeSNVxo8fT6VKlWjXrh0PPPAADz30EA4ODmzbto3c3FyqVauWb/+srCx8fX2tj52cnKhbt26+Nl26dKFp06YcO3aMoKAgZs6cSfv27a0lAXJzc3n77bf57rvvOHr0KNnZ2WRlZeHmlr8ufNOmTfN9CRIZGcm4cePIzc3F3j7/a7ZlyxbS0tLyxQZw/vx5Dhw4cMXXx8fHBx8fH9atW8fjjz9OxYoV2bx5Mw8//HCJnltJRERE8j5zbTh8li9jD/PrtuNcMOd9Bivj4cxTTYJ5KqICAd7XNxJAxKY5O+eNgDHq3Nfpn/0Ue3t7fH19CQsLs667lGw4efIkAJMnT+bzzz8nPj6e8+fPk52dna+UGMDLL7/M3LlzmTRpEr/++utl/Y8r2bVrF48++mi+dZGRkfmSN61ateKzzz4jNzeX5cuXc9999xEQEMCyZcuoW7cu+/fvp3Xr1gDExcVRv379G5rvdM+ePfTp0yffuiZNmrBkyZJ868LCwvLNc7Nr1y7q1atnTdxAXhLGbDazZ8+e60re3Gg/VURERIq+PXv2EBMTwx9//AHk3WAyadIkoqKM+e79ZujTRxFhMpkKVbrMSFWqVMFkMhX4wR/yPkyXKlWKsmXLFup4wcHB7Nmzhz/++INFixbRp08f3nvvPZYvX05aWhr29vZs3LjxskSJh4eH9f+urq6X3WXauHFjKleuzOzZs62lCf45jP69995jwoQJjB8/nrCwMNzd3enfvz/Z2dnX8Wrkl5aWRmBgYL45eS65lDT6t5UrV3L/xbIPGRkZLFu2jAEDBnD+/HkcHR155513GDJkCEOGDLnhuERERMT2ZGRf4P/ijvFl7GF2HU+1rm9UoRTPRFbg/jqBODloThspgUym6y5dZqR/l+IwmUz51l3qx5jNZmbPns0rr7zCuHHjiIyMxNPTk/fee++yOWROnjzJ3r17sbe3Z9++fbRr1+6WxduyZUvOnTvHpk2bWLFiBW+//TYBAQG888471KtXj6CgIGv56n+X0b4d/pmkKSw7O7vLbjbMycm5rmNcrZ9qC+VVRERESqqMjAxGjx7Ne++9R05ODs7OzgwZMoRBgwbhYkOfIf+paGcLpEjx9fXl3nvv5aOPPmLAgAH5PrAnJiYyc+ZMunbtmi+ZsmbNmnzHWLNmTb6ya66urjz00EM89NBD9O3blxo1arBt2zbq169Pbm4uJ0+e5K677rruWLt06cLMmTMpX748dnZ2tG/f3rpt1apVPPLIIzz99NNAXmdp79691KpVK98x/t1RWrNmDVWrVr0smQTQoEEDEhMTcXBwKPSImUaNGhEXF8fGjRsZNGgQixcvJj4+nocffphNmzZhZ2d3Q3eyiYiIiG06dDqdr9YcZs6GBFIzLwDg4mhHdHg5nomsQO0gb4MjFJHbZdWqVTRr1izfiJSCRvA/99xzhIWF0aNHD3r27Enbtm2vWNb6n2rWrFlg/+affHx8qFu3LpMmTcLR0ZEaNWrg5+fHk08+ybx586zz3UDeqKJp06aRlJR03X2W6tWrs379erp27Wpdt379+kI9h+nTp5Oenm5N7KxatQo7OzuqV68OQNmyZa2lUSCv6sL27du5++67r/rcC9tPbdCgwXU9VxEREbkzfv75Z1566SUOHz4MwAMPPMDEiROpXLmywZHdHN2yJ9dl0qRJZGVlERUVxYoVK0hISGDhwoXce++9lCtXjtGjR+drv2rVKsaOHcvevXuZPHkyc+bMoV+/fgBMnz6dzz77jO3bt/PXX3/x9ddf4+rqSoUKFahWrRpdunSha9eu/Pjjjxw8eJB169YxZswY5s+ff804u3TpwqZNmxg9ejRPPPGEdR4agKpVq7Jo0SJWr17Nrl27+M9//sOJEycuO0Z8fDwDBw5kz549fPPNN3z44YfW2P+tbdu2REZGEh0dze+//86hQ4dYvXo1//3vf9mwYUOB+7i6ulKlShUOHjxI69atqVKlCkeOHKF58+ZUq1aNKlWqXNYR2rlzJ3FxcSQlJZGSkkJcXBxxcXHXfD1ERESkaMo1W1iy+wTdPl9H6/8t47M/D5KaeYEKvm4MbV+TtYPb8s7jdZW4ESnmqlatyoYNG/jtt9/Yu3cvw4YNuyyhMXnyZGJjY5kxYwZdunQhOjqaLl26FKqCwEsvvcTChQv53//+x759+5g0aVK+kmmXtG7dmpkzZ1oTNaVLl6ZmzZp8++23+ZI3nTt3JiAggOjoaFatWsVff/3FDz/8QGxs7DVjefHFF/nss8+YMWMG+/bt46233mLr1q3XnLerS5cuuLi40K1bN7Zv387SpUt58cUXeeaZZ6wl0+655x7mz5/P/Pnz2b17N7179yY5OfmyY91oP1VERESKloMHD/LQQw/xyCOPcPjwYYKDg/nxxx+ZN2+ezSduQMkbuU6XOhWVKlWiY8eOVK5cmV69enH33XcTGxt7WbLh5ZdfZsOGDdSvX5+33nqL999/31pf0MfHh08//ZTmzZtTt25d/vjjD3755Rdr3eYvvviCrl278vLLL1O9enWio6NZv349ISEh14yzSpUqNGnShK1bt9KlS5d824YOHUqDBg2IioqidevW1k7Hv3Xt2pXz58/TpEkT+vbtS79+/ejVq1eB5zOZTCxYsICWLVvSvXt3qlWrZp1E9Fq1l5ctW0bLli0BWL58ufX/BXnggQeoX78+v/zyC8uWLaN+/frUr1//Gq+GiIiIFDVn07P5ZPkBWv9vKc9N38DyvacwmeCeGn580b0xS19uzfN3VcLbTSV6REqC//znPzz22GM8+eSTREREcObMmXyjcHbv3s2rr77KRx99ZJ1k96OPPuL06dMMGzbsmsdv2rQpn376KRMmTKBevXr8/vvvDB069LJ2rVq1Ijc31zq3DeQldP69zsnJid9//x0/Pz8eeOABwsLCeOeddwqsUvBvXbp0YfDgwbzyyis0aNCAgwcP8uyzz16znImbmxu//fYbSUlJNG7cmCeeeII2bdowadIka5vnnnuObt260bVrV1q1akWlSpUuG3UDN9dPFREREeNlZWUxevRoatWqxbx583B0dOT111+3TvdxrZtCbIXJcj2zz8t1SU1Nxdvbm5SUFLy8vKzrMzMzOXjwIKGhoTZbb09sg95rIiIiRcv2oynMWH2In7ccI+uCGQBvV0eebBzM0xEVCPF1MzjCm3Olz78iV3K194w+y95e06dPp3///gWOTLnT7r33XgICAvjqq69u+7kqVqxI//796d+//y05nt6nIiIid9aiRYuIiYlh7969ANx9991Mnjy5UKVki4rC9ps0542IiIiIyG2UdSGXX7clMiP2EJvjk63rawd50S2yIg/VC8LV6dp3q4uIFAcZGRlMmTKFqKgo7O3t+eabb/jjjz9YtGiR0aGJiIhIEXb06FEGDhzId999B0BAQADjxo2jc+fOxWakzb8peSMiIiIichscSz7PzLWHmb0ugTPpeXNSONqbeCAskK6RFWkQ4lNsOxkicmfdf//9rFy5ssBtQ4YMYciQIXcsltq1a1snC/63Tz75hMcee4wFCxYwevRoMjMzqV69Oj/88ANt27a9YzGKiIiI7cjJyeHDDz9kxIgRpKWlYWdnx4svvsgbb7yBt3fxnhtUyRsRERERkVvEYrEQe+AMM2IPsWjnCcwXCxQHeLnQJSKETk1CKOvpbGyQIlLsTJs2jfPnzxe47d/zkl7y7LPP8uyzz97yWBYsWEBOTk6B2/z9/XF1deWPP/645ectrEOHDhl2bhEREbk+K1eupE+fPmzfvh2AyMhIPvroI8LDw40N7A5R8kZERERE5CalZV3gx01H+DL2MPtPplnXR1bypWtkBe6t5Y+DvZ2BEYpIcVauXDmjQ7CqUKGC0SGIiIiIjTt58iSDBg1ixowZAPj6+vLuu+/SvXt37OxKTr9KyRsDWSwWo0OQYk7vMRERkdtr/8lzfBl7mB82HiE9OxcAdyd7HmtQnmciK1DN39PgCEVsnz7TSlGm96eIiMitk5uby9SpUxkyZAjJyckA9OzZkzFjxuDr62tscAZQ8sYAjo6OQN5Eja6urgZHI8VZRkYG8Pd7TkRERG7ehVwzf+w6wZexh1l94Ix1feWy7nSNrMhjDcrh6aK/vSI3S/0msQXqc4mIiNwa69evp0+fPmzYsAGA+vXr89FHH9G0aVODIzOOkjcGsLe3x8fHh5MnTwLg5uamyWrllrJYLGRkZHDy5El8fHywt7c3OiQRERGbdzoti9nr4pm5Np7jKZkA2Jng3lr+dI2sSLPKvvpMJ3ILqd8kRZn6XCIiIrfG2bNnGTJkCJ988gkWiwUvLy9Gjx5N7969S/zfVyVvDBIQEABg7YiI3A4+Pj7W95qIiIhcP4vFwqb4ZL6KPcSCbYlk55oB8HV3olOTYJ6KqEA5H40IELld1G+Sok59LhERkRtjsViYMWMGgwYN4tSpUwA8/fTTvPfee/rbepGSNwYxmUwEBgbi5+dHTk6O0eFIMeTo6Fjis9MiIiI3KjMnl5/jjvHlmkNsP5pqXR8e7EO3ZhV4ICwQZwf9nRW53dRvkqJMfS4REZEbs23bNvr06cOff/4JQK1atZg8eTKtW7c2NrAiRskbg9nb2+vDnoiIiEgREX8mg6/XHua7DQkkZ+R9UezkYMfD9YLoGlmBuuV9jA1QpIRSv0lERETE9p07d44RI0YwceJEcnNzcXNzY+TIkfTv31/zxxVAyRsRERERKdHMZgvL953iq9jDLN1zEoslb335Uq483bQCHRsFU9rdydggRUREREREbJTFYmHOnDkMGDCAY8eOAfD444/zwQcfEBwcbHB0RZeSNyIiIiJSIqVk5DBnYwJfrznMoTMZ1vUtq5Wla9MK3F3DD3s7TY4uIiIiIiJyo/bs2UNMTAx//PEHAJUrV2bSpEm0a9fO4MiKPiVvRERERKRE2XEsha9iDzM37iiZOWYAPF0c6NAwmKebhlCprIfBEYrcuPPnz2OxWHBzcwPg8OHD/PTTT9SqVYv77rvP4OhEREREpKTIyMjg7bffZuzYseTk5ODs7MzgwYN57bXXcHFxMTo8m6DkjYiIiIgUe9kXzCzckciXqw+x4fBZ6/oaAZ50jaxIdP0g3Jz00Vhs3yOPPMJjjz3GCy+8QHJyMhERETg6OnL69Gnef/99evfubXSIIiIiIlLM/fLLL7z00kscOnQIgAceeICJEydSuXJlYwOzMeqhioiIiEixlZNrZvb6BD5cvI+T57IAcLAz0a5OAF0jK9K4YilMJpVGk+Jj06ZNfPDBBwB8//33+Pv7s3nzZn744QeGDx+u5I2IiIiI3DaHDh3ipZde4pdffgEgODiYCRMmEB0drX7XDVDyRkRERESKHbPZwvxtxxn3+x7rfDZ+ns48FRFC5yYh+HtpmL4UTxkZGXh6egLw+++/89hjj2FnZ0fTpk05fPiwwdGJiIiISHGUlZXF//73P0aPHs358+dxcHDg5ZdfZtiwYbi7uxsdns1S8kZEREREipU/953m3YW72XY0BQBfdydevKcKT0VUwMnBzuDoRG6vKlWqMHfuXB599FF+++03BgwYAMDJkyfx8vIyODoRERERKW7++OMP+vbty969ewFo3bo1kydPplatWgZHZvuUvBERERGRYmHrkWTGLtzDn/tPA+DuZE/PlpV4/q5KeDjrY6+UDMOHD+epp55iwIAB3HPPPURGRgJ5o3Dq169vcHQiIiIiUlwcPXqUl19+mW+//RYAf39/3n//fTp37qwSabeIerEiIiIiYtMOnk7nf7/tYf624wA42pvoElGBmHuqUMbD2eDoRO6sJ554ghYtWnD8+HHq1atnXd+mTRseffRRAyMTERERkeIgJyeHDz/8kBEjRpCWloadnR0xMTG8+eabeHt7Gx1esaLkjYiIiIjYpJOpmUxYvI/Z6xPINVswmSA6vBwD761GcGk3o8MTMUxAQAABAQEkJCQAeRPFNmnSxOCoRERERMTW/fnnn/Tp04dt27YB0LRpUz7++GPCw8ONDayYUtFvEREREbEpqZk5vPfbblq9t4yZa+PJNVu4u3pZ5r94Fx88Ga7EjZRoFy5cYNiwYXh7e1OxYkUqVqyIt7c3Q4cOJScnx+jwRERERMQGnTx5ku7du3PXXXexbds2fH19mTZtGqtWrVLi5jbSyBsRERERsQmZObl8FXuYycv2k5yR9yV0/RAfXm9Xg4hKvgZHJ1I0vPjii/z444+MHTvWOt9NbGwsI0eO5MyZM3z88ccGRygiIiIitiI3N5epU6cyZMgQkpOTAejZsydjxozB11d9sNtNyRsRERERKdJyzRZ+2HSE8Yv2ciwlE4Aqfh68GlWd+2r5azJMkX+YNWsWs2fP5v7777euq1u3LsHBwXTu3Pm6kjcrVqzgvffeY+PGjRw/fpyffvqJ6Oho63aLxcKIESP49NNPSU5Opnnz5nz88cdUrVr1Vj4lERERETHAhg0b6N27Nxs2bACgfv36fPTRRzRt2tTgyEoOlU0TERERkSLJYrHw+45E2o1fwaDvt3IsJZNAbxfGPl6Xhf3uIqp2gBI3Iv/i7OxMxYoVL1sfGhqKk5PTdR0rPT2devXqMXny5AK3jx07lokTJzJlyhTWrl2Lu7s7UVFRZGZm3kjoIiIiIlIEnD17lj59+tCkSRM2bNiAl5cXH374IevXr1fi5g7TyBsRERERKXLWHUzi3YW72Xj4LADero70vbsyXSMr4uJob3B0IkVXTEwMo0aN4osvvsDZ2RmArKwsRo8eTUxMzHUd6/777883guefLBYL48ePZ+jQoTzyyCMAfPnll/j7+zN37lw6dep0fYFnZsJ1JpdERERE5NaxWCx8OXMmrw4ZwqlTpwB4unNn3nv7bQICAiAnJ2+Rm1fIm52UvBERERGRImN3YipjF+5hye6TALg42vFc81D+06oy3q6OBkcnUvRt3ryZxYsXU758eerVqwfAli1byM7Opk2bNjz22GPWtj/++OMNn+fgwYMkJibStm1b6zpvb2/+n707j6uq2v8//jrMoEyKDCoqKYozjuRQmplWWmndq6WlOaY4Y6aWQ6Y3rLQoFdTKbDL9al27DdqAlTmkpjjlrDgL4sQRRIbD/v1h0eXncLE4bIb38/HgIWftdfZ6QyfYnM9ea0VERLBx48abFm8yMzPJzMzMe2y1Wq998thj4KQ/T0VEkKflMgAAmj9JREFURETMsCstjciDB1mXmgpAPQ8P5oWG0v7MGejXz+R0pVBOToG66epYREREREx34sIV3vjuAP/efgrDAEcHCz2aBzO6YygBXm5mxxMpMXx8fHjsscfytQUHBxf6OElJSQAEBATkaw8ICMg7diPR0dFMmzat0POIiIiIyO27nJPDi0eP8ubJk9gADwcHptaoweiqVXFx0I4rZlPxRkRERERMcz4tk7k/HOLjX46TZcsFoEvDIKI61aZmpfImpxMped577z2zI9zSxIkTiYqKyntstVqvFZc+/RS8vExMJiIiIlJ2GIbB8k8/Zcxzz3H6zBkAHn3kEd549VWqVatmcroywGqF/+8mqBtR8UZEREREilx6Zg7v/JzI2z8fIS3z2pTx1jUrMv7+MBoH+5gbTkT+p8DAQACSk5MJCgrKa09OTiY8PPymz3N1dc3biycfN7drHyIiIiJiVwcOHGDYsGF8//33ANSsWZO5c+dy//33m5ysDMnKKlA3FW9EREREpMhk5eTyyebjzFlzkHNp1y5Y61f2Yvz9YdwV6ofFYjE5oUjJFhIScsv/j44cOVJo4wQGBhIfH59XrLFarWzatImhQ4cWyhgiIiIiUniuXLnCyy+/zGuvvUZWVhaurq5MnDiR8ePH46abaIolFW9ERERExO5ycw2+2Hma2d8e4PiFKwBUr+jB2E516NowCAcHFW1ECsPo0aPzPc7OziYhIYHVq1czbty42zpXWloahw4dynucmJjI9u3bqVChAtWqVWP06NHMmDGD0NBQQkJCmDx5MpUrV6Zbt26F8JWIiIiISGH54osvGDlyJEePHgXggQceYM6cOdSsWdPcYHJLKt6IiIiIiN0YhsFPB1J4dfV+9pyxAuBX3pVRHUN5vEUwzo7aBFOkMI0aNeqG7fPmzePXX3+9rXP9+uuv3HPPPXmP/9irpm/fvixevJjnnnuO9PR0Bg8ezKVLl2jbti2rV6/WnZsiIiIixcTRo0cZOXIkX3zxBQDBwcG8+eabdOvWTaselAAWwzAMs0OUVlarFW9vb1JTU/HS5psiIiJSxmw/cYmZq/byy5ELAJR3deKZu++gf9sQyrnqHqLSSNe/xdeRI0cIDw/HarWaHSUfvWZERERECl9mZiazZs3iX//6FxkZGTg5OTF27FgmT55MuXLlzI5X5hX0Glh/NYuIiIhIoTp0No3Z3+5n1e4kAFwcHXiqVXWG3VOLCuVcTE4nUjatWLGCChUqmB1DREREROzs+++/Z9iwYRw4cACA9u3bM2/ePOrVq2dyMrldKt6IiIiISKFISr1KzPcHWL71JLZcA4sFHm1SlTH3hVLV18PseCJlQpMmTfItgWEYBklJSaSkpBAbG2tiMhERERGxp1OnTjF27FiWLVsGQEBAALNnz6ZXr15aIq2EKhGLjM+bN48aNWrg5uZGREQEmzdvvmX/5cuXExYWhpubGw0bNuTrr7/Od/yzzz6jU6dOVKxYEYvFwvbt2687x9WrVxk2bBgVK1akfPnyPPbYYyQnJxfmlyUiIiJSKqReyWbmqn20e+0Hlm45gS3XoGNdf1aPupvZPRqrcCNShLp168YjjzyS9/Hoo48ydepUdu/ezeDBg82OJyIiIiKFLDs7m9dff52wsDCWLVuGg4MDI0eOZP/+/fTu3VuFmxKs2M+8WbZsGVFRUcyfP5+IiAhiYmLo3Lkz+/fvx9/f/7r+GzZs4IknniA6OpquXbuyZMkSunXrxrZt22jQoAEA6enptG3blh49ejBo0KAbjjtmzBi++uorli9fjre3N8OHD+fRRx9l/fr1dv16RUREREqKq9k2Fm84SuwPh7BezQGgeXVfxj8QRosaWp5JxAxTp041O4KIiIiIFJF169YRGRnJrl27ALjzzjuJjY2lSZMmJieTwmAxDMMwO8StRERE0KJFC+bOnQtAbm4uwcHBjBgxggkTJlzXv2fPnqSnp/Pll1/mtd15552Eh4czf/78fH2PHj1KSEgICQkJhIeH57WnpqZSqVIllixZwj/+8Q8A9u3bR926ddm4cSN33nlngbJr800REREpjXJsuazYepKY7w+SZL0KQO2A8jzXOYx76/rrzq4yTNe/xYPNZmPlypXs3bsXgPr16/Pwww/j6OhocrLr6TUjIiIicvvOnj3L+PHjWbx4MQAVKlTglVdeoX///jg4lIjFtsq0gl4DF+uZN1lZWWzdupWJEyfmtTk4ONCxY0c2btx4w+ds3LiRqKiofG2dO3dm5cqVBR5369atZGdn07Fjx7y2sLAwqlWrdsviTWZmJpmZmXmPrVZrgccUERERKe4Mw+Cb35J49Zv9HElJB6CKjztj7qtN9yZVcHRQ0UbEbIcOHeLBBx/k1KlT1KlTB4Do6GiCg4P56quvqFmzpskJRUREROSvstlsLFy4kOeff55Lly4BMHDgQKKjo/Hz8zM3nBS6Yl28OXfuHDabjYCAgHztAQEB7Nu374bPSUpKumH/pKSkAo+blJSEi4sLPj4+t3We6Ohopk2bVuBxREREREqKjYfP88rqfWw/cQkAXw9nht1TiyfvrI6bc/G7m1+krBo5ciQ1a9bkl19+oUKFa8sXnj9/nieffJKRI0fy1VdfmZxQRERERP6KX3/9laFDh/Lrr78CEB4eTlxcXIFXiZKSp1gXb0qaiRMn5pv1Y7VaCQ4ONjGRiIiIyN/z2+lUXl29n58OpADg7uzIwLtCGHT3HXi5OZucTkT+fz/99FO+wg1AxYoVmTlzJm3atDExmYiIiIj8FRcvXuSFF15g/vz5GIaBl5cXM2bMYOjQoTg56e390qxY/9f18/PD0dGR5OTkfO3JyckEBgbe8DmBgYG31f9m58jKyuLSpUv5Zt/8r/O4urri6upa4HFEREREiqvj568w+7v9fL79NABODhaeaFmNEffWwt/TzeR0InIzrq6uXL58+br2tLQ0XFxcTEgkIiIiIn+FYRh88MEHjBs3jpSUazfT9e7dm1mzZt3We91SchXr3YtcXFxo1qwZ8fHxeW25ubnEx8fTqlWrGz6nVatW+foDfPfddzftfyPNmjXD2dk533n279/P8ePHb+s8IiIiIiVNyuVMpn6+m3tf/zGvcPNQ48p8H9WO6d0aqHAjUsx17dqVwYMHs2nTJgzDwDAMfvnlF4YMGcLDDz9sdjwRERERKYBdu3Zx99138/TTT5OSkkLdunX54Ycf+Oijj1S4KUOK9cwbgKioKPr27Uvz5s1p2bIlMTExpKen069fPwD69OlDlSpViI6OBmDUqFG0a9eO2bNn06VLF5YuXcqvv/7KwoUL88554cIFjh8/zunT196Q2L9/P3Btxk1gYCDe3t4MGDCAqKgoKlSogJeXFyNGjKBVq1ZaQ1BERERKpctXs3n750Te+fkIV7JsANwV6sf4+8NoUMXb5HQiUlBvvfUWffv2pVWrVjg7X1vaMCcnh4cffpg333zT5HQiIiIiciuXL1/mxRdf5M0338Rms+Hh4cHUqVMZPXq0ZlGXQcW+eNOzZ09SUlKYMmUKSUlJhIeHs3r1agICAgA4fvw4Dg5/TiBq3bo1S5YsYdKkSTz//POEhoaycuVKGjRokNfnP//5T17xB+Dxxx8HYOrUqbz44osAvPHGGzg4OPDYY4+RmZlJ586diY2NLYKvWERERKToZObY+PiX48z94RAX0rMAaFzVm/H3h9G6lp/J6UTkdhiGgdVqZenSpZw6dYq9e/cCULduXWrVqmVyOhERERG5GcMwWL58OWPGjMmbcPDoo4/yxhtvUK1aNZPTiVkshmEYZocoraxWK97e3qSmpuLl5WV2HBEREZE8hmHwnx2nee2b/Zy8mAHAHX7leLZzHR5oEIjFYjE5oZREuv41V25uLm5ubvz222+EhoaaHadA9JoRERGRsu7AgQMMGzaM77//HoCaNWsyZ84cHnjgAZOTib0U9Bq42M+8EREREZHCtfXYBaZ/uZftJy4B4O/pyuiOtfln86o4OxbrLRFF5BYcHBwIDQ3l/PnzJaZ4IyIiIlJWXblyhZdffpnXXnuNrKwsXF1dmThxIuPHj8fNTXuNioo3IiIiImXG8fNXmLl6L1/vSgLAw8WRoe1qMvCuO3B3cTQ5nYgUhpkzZzJu3Dji4uLyLR0tIiIiIsXHF198wciRIzl69CgA999/P3PnzqVmzZrmBpNiRcUbERERkVIuNSObuWsO8v6GY2TZcnGwQM8WwYy5rzb+nrqjS6Q06dOnD1euXKFx48a4uLjg7u6e7/iFCxdMSiYiIiIiR48eZeTIkXzxxRcABAcHExMTQ/fu3bV0tVxHxRsRERGRUirblsvHvxzjzfiDXLySDcBdoX680KUuYYHaW0KkNIqJiTE7goiIiIj8fzIzM5k1axb/+te/yMjIwMnJibFjxzJ58mTKlStndjwpplS8ERERESllDMPg+71nif56L0fOpQMQ6l+e57vUpX3tSrqjS6QU69u3r9kRREREROS/fP/99wwbNowDBw4A0L59e+bNm0e9evVMTibFnYo3IiIiIqXI7lOpzPhqD78cubY0UsVyLoy5rzaPtwjGydHB5HQiYm9Wq/WG7RaLBVdXV1xcXIo4kYiIiEjZdOrUKcaOHcuyZcsACAgIYPbs2fTq1Us31EmBqHgjIiIiUgokpV7ltW/281nCSQwDXJwcGNA2hMj2NfF0czY7nogUER8fn1u+GVC1alWefvpppk6dioODCroiIiIihS07O5s5c+YwdepU0tLScHBwYPjw4bz00kt4e3ubHU9KEBVvREREREqw9MwcFqw9wsK1h7manQvAw40r89z9dajq62FyOhEpaosXL+aFF17g6aefpmXLlgBs3ryZ999/n0mTJpGSksKsWbNwdXXl+eefNzmtiIiISOmybt06IiMj2bVrFwB33nknsbGxNGnSxORkUhKpeCMiIiJSAtlyDT7depJZ3+7n7OVMAJpX9+WFLnVpUs3X5HQiYpb333+f2bNn06NHj7y2hx56iIYNG7JgwQLi4+OpVq0a//rXv1S8ERERESkkZ8+eZfz48SxevBiAChUq8Morr9C/f3/Ndpa/TMUbERERkRJm/aFzzPhqL3vPXNvboloFDyY8EMYDDQK1drJIGbdhwwbmz59/XXuTJk3YuHEjAG3btuX48eNFHU1ERESk1LHZbCxcuJDnn3+eS5cuATBw4ECio6Px8/MzN5yUeCreiIiIiJQQh86mEf31XuL3nQXA082JkR1C6dO6Oq5OjianE5HiIDg4mHfffZeZM2fma3/33XcJDg4G4Pz58/j6aoaeiIiIyN/x66+/MnToUH799VcAwsPDiYuL48477zQ5mZQWKt6IiIiIFHPn0zKJ+f4gSzYfx5Zr4ORg4ck7qzPq3lB8y7mYHU9EipFZs2bxz3/+k1WrVtGiRQvg2hsL+/btY8WKFQBs2bKFnj17mhlTREREpMS6ePEiL7zwAvPnz8cwDLy8vJgxYwZDhw7FyUlvt0vh0atJREREpJi6mm1j8YajzFtziMuZOQDcVy+AiQ+EcUel8ianE5Hi6OGHH2b//v0sWLCA/fv3A/DAAw+wcuVKatSoAcDQoUNNTCgiIiJSMhmGwQcffMC4ceNISUkBoHfv3syaNYvAwECT00lppOKNiIiISDFjGAZf7jzDK6v3cfJiBgD1K3vxQpe6tK6pdZNF5NZq1KhBdHS02TFERERESo1du3YRGRnJunXrAKhbty7z5s3jnnvuMTmZlGYq3oiIiIgUI1uPXWTGV3tIOH4JgAAvV8Z1DuPRJlVwcLCYG05ERERE5C9at24ds2fPJjs72+woIrclOzub+Ph4bDYbHh4eTJ06ldGjR+PioiWsxb5UvBEREREpBk5cuMLM1fv4aucZANydHRnSriaD7g7Bw0WXbCIiIiJSchmGwZAhQ/jtt9/MjiLylz366KO88cYbVKtWzewoUkbonQARERERE6VmZBP7wyHeW3+ULFsuFgv8s1lVxnaqQ4CXm9nxRERERET+tnXr1vHbb7/h4eHBW2+9hYODg9mRRG5L7dq1adOmjdkxpIxR8UZERETEBNm2XD7ZfJyY7w9yIT0LgDa1KvLCg/WoV9nL5HQiIiIiIoUnLi4OgF69ejFgwACT04iIlAwq3oiIiIgUIcMwWLPvLC9/vZfDKekA1KxUjhe61OWeOv5YLNrXRkRERERKj+TkZFasWAHA0KFDTU4jIlJyqHgjIiIiUkT2nLbyr6/3sP7QeQAqlHNhTMdQHm9ZDWdHLR0hIn9N06ZNiY+Px9fXlyZNmtyyCLxt27YiTCYiIgKLFi0iOzubiIgImjZtanYcEZESQ8UbERERETtLtl5l1jf7WbHtJIYBLo4O9Gtbg2H31MLLzdnseCJSwj3yyCO4uroC0K1bN3PDiIiI/Bebzcb8+fMBiIyMNDmNiEjJYjEMwzA7RGlltVrx9vYmNTUVLy+tXS8iIlLWXMnKYeHaIyz46QgZ2TYAujYKYvz9YQRX8DA5nUjh0/Wv3C69ZkRESrcvv/yShx56iAoVKnDq1Cnc3NzMjiQiYrqCXgNr5o2IiIhIIcvNNfh020lmfbufZGsmAE2q+TCpSz2aVfc1OZ2IiIiISNGIjY0FoF+/firciIjcJhVvRERERArRhkPnmPHVXvacsQJQ1dedCQ+E0aVh0C33oRAR+at8fX0L/PPlwoULdk4jIiJyTWJiIqtXrwZgyJAhJqcRESl5VLwRERERKQSHU9KI/nov3+89C4CnqxPDO9Sib+sauDk7mpxOREqzmJgYsyOIiIhcZ8GCBRiGQadOnahVq5bZcUREShwVb0RERET+hgvpWbz5/QE+3nScnFwDRwcLvSOqMereUCqWdzU7noiUAX379jU7goiISD6ZmZm8++67AERGRpqcRkSkZFLxRkREROQvyMyx8f6Go8xZc4jLV3MA6FjXnwkP1KWWf3mT04lIWXX8+PFbHq9WrVoRJRERkbJsxYoVnDt3jqpVq9KlSxez44iIlEgq3oiIiIjcBsMw+HpXEjNX7+XEhQwA6gV5MalLXVrX8jM5nYiUdTVq1Ljl/jc2m60I04iISFkVGxsLwODBg3Fy0tuPIiJ/hX56ioiIiBRQwvGLzPhqL1uPXQTA39OVZzvX4bGmVXF0KNhm4SIi9pSQkJDvcXZ2NgkJCbz++uv861//MimViIiUJTt37mTDhg04OTkxcOBAs+OIiJRYKt6IiIiI/A8nLlzh1W/288WO0wC4Ozsy+O47GHz3HZRz1eWUiBQfjRs3vq6tefPmVK5cmddee41HH33UhFQiIlKWxMXFAdC9e3eCgoJMTiMiUnLp3QYRERGRm7BezSb2h8MsWp9IVk4uFgs81rQqz3aqQ6C3m9nxREQKrE6dOmzZssXsGCIiUspZrVY+/PBDACIjI01OIyJSsql4IyIiIvL/ybHl8smWE8R8d4Dz6VkAtLqjIi90qUuDKt4mpxMRuTmr1ZrvsWEYnDlzhhdffJHQ0FCTUomISFnx0UcfkZ6eTt26dWnXrp3ZcURESjQVb0RERER+l5lj49Otp5j/02GOX7gCwB1+5Xj+wbrcW9f/lpuAi4gUBz4+Ptf9rDIMg+DgYJYuXWpSKhERKQsMwyA2NhaAIUOG6NpZRORvUvFGREREyryMLBufbD7OwrVHSLJeBaBCORdG3RtKr4hqODs6mJxQRKRgfvjhh3yPHRwcqFSpErVq1cLJSX/+iYiI/axbt47ffvsNDw8P+vTpY3YcEZEST1fvIiIiUmZdvprNh78c492fE/OWRwvwcmXw3TV5omUwHi66VBKRkkVL1IiIiFni4uIA6NWrFz4+PuaGEREpBfSOhIiIiJQ5F9OzeG/DURavT8R6NQeAqr7uDG1fk380q4qrk6PJCUVE/p49e/Zw/PhxsrKy8rU//PDDJiUSEZHSLDk5mRUrVgAwdOhQk9OIiJQOKt6IiIhImXH28lXe/TmRD385xpUsGwA1K5Ujsn0tHg6vrOXRRKTEO3LkCN27d2fXrl1YLBYMwwDI23fAZrOZGU9EREqpRYsWkZ2dTUREBE2bNjU7johIqaDijYiIiJR6py5lsPCnwyzdcoLMnFwA6gZ5MfyeWtzfIBBHB22mKiKlw6hRowgJCSE+Pp6QkBA2b97M+fPnGTt2LLNmzTI7noiIlEI2m4358+cDEBkZaXIaEZHSQ8UbERERKbWOnksn7sfDfJZwkmzbtbvPm1TzYUSHWtxTxz/vTnQRkdJi48aNrFmzBj8/PxwcHHBwcKBt27ZER0czcuRIEhISzI4oIiKlzKpVqzh+/DgVKlSgR48eZscRESk1VLwRERGRUudA8mXm/XCIL3acJvdazYZWd1RkRIdatKpZUUUbESm1bDYbnp6eAPj5+XH69Gnq1KlD9erV2b9/v8npRESkNIqLiwOgX79+uLm5mZxGRKT0KPTiTU5ODkuWLKFz584EBAQU9ulFREREbmrXyVTm/nCQb35Lzmu7p04lhneoRbPqFUxMJiJSNBo0aMCOHTsICQkhIiKCV199FRcXFxYuXMgdd9xhdjwRESllEhMTWbVqFQBDhgwxOY2ISOlS6MUbJycnhgwZwt69ewv71CIiIiI3tOXoBeauOcRPB1IAsFjg/vqBDLunFg2qeJucTkSk6EyaNIn09HQAXnrpJbp27cpdd91FxYoVWbZsmcnpRESktFmwYAGGYdCpUydq1apldhwRkVLFLsumtWzZku3bt1O9enV7nF5EREQEwzBYd+gcc9ccYlPiBQAcHSw83Lgyke1rEhrgaXJCEZGi1759e3JycgCoVasW+/bt48KFC/j6+mrJSBERKVSZmZm8++67AERGRpqcRkSk9HGwx0kjIyOJiopi7ty5bNy4kZ07d+b7uF3z5s2jRo0auLm5ERERwebNm2/Zf/ny5YSFheHm5kbDhg35+uuv8x03DIMpU6YQFBSEu7s7HTt25ODBg/n6HDhwgEceeQQ/Pz+8vLxo27YtP/zww21nFxERkcJlGAbf7UmmW+wGnnp3M5sSL+DsaOGJlsGsGduON3qGq3AjImVOSkoKDzzwAOXLl8fLy4s777yTQ4cOAVChQgUVbkREpNCtWLGCc+fOUbVqVbp06WJ2HBGRUscuM28ef/xxAEaOHJnXZrFYMAwDi8WCzWYr8LmWLVtGVFQU8+fPJyIigpiYGDp37sz+/fvx9/e/rv+GDRt44okniI6OpmvXrixZsoRu3bqxbds2GjRoAMCrr77KW2+9xfvvv09ISAiTJ0+mc+fO7NmzJ29jta5duxIaGsqaNWtwd3cnJiaGrl27cvjwYQIDA//Ot0dERET+Aluuwde7zjDvh0PsS7oMgJuzA0+0rMbgu+8gyNvd5IQiIuYZP34827dv56WXXsLNzY0FCxYwaNAg3YAmIiJ2ExsbC8DgwYNxcrLLW4wiImWaxTAMo7BPeuzYsVsev53l1CIiImjRogVz584FIDc3l+DgYEaMGMGECROu69+zZ0/S09P58ssv89ruvPNOwsPDmT9/PoZhULlyZcaOHcuzzz4LQGpqKgEBASxevJjHH3+cc+fOUalSJdauXctdd90FwOXLl/Hy8uK7776jY8eOBcputVrx9vYmNTUVLy+vAn/NIiIi8qdsWy4rE04R9+Nhjpy7to9DORdH+rSuwYC2IfiVdzU5oYj8Qde/5gkODuadd96hc+fOABw8eJC6deuSnp6Oq2vx/Tmp14yISMm0c+dOGjdujJOTE8ePHycoKMjsSCIiJUZBr4HtUhYvrL1usrKy2Lp1KxMnTsxrc3BwoGPHjmzcuPGGz9m4cSNRUVH52jp37szKlSsBSExMJCkpKV8Bxtvbm4iICDZu3Mjjjz9OxYoVqVOnDh988AFNmzbF1dWVBQsW4O/vT7NmzW6aNzMzk8zMzLzHVqv1r3zZIiIiAlzNtrF860nm/3iYU5cyAPB2d6Zfmxo83boGPh4uJicUESk+Tp8+TePGjfMeh4aG4urqypkzZ6hRo4ZdxrTZbLz44ot89NFHJCUlUblyZZ5++mkmTZqkZdpEREq5uLg4ALp3767CjYiIndhtTuPhw4eJiYlh7969ANSrV49Ro0ZRs2bNAp/j3Llz2Gw2AgIC8rUHBASwb9++Gz4nKSnphv2TkpLyjv/RdrM+FouF77//nm7duuHp6YmDgwP+/v6sXr0aX1/fm+aNjo5m2rRpBf76RERE5HpXsnJYsuk4C9ce4ezlazdF+JV3YdBdd9D7zuqUd9WSDCIiN+Lo6HjdYzsstJDnlVdeIS4ujvfff5/69evz66+/0q9fP7y9vfMtoS0iIqWL1Wrlww8/BK7tey0iIvZhl3c/vvnmGx5++GHCw8Np06YNAOvXr6d+/fp88cUX3HffffYYttAYhsGwYcPw9/fn559/xt3dnXfeeYeHHnqILVu23PSOgokTJ+ab9WO1WgkODi6q2CIiIiVaakY2H248yrvrErl4JRuAIG83hrSrSc8Wwbg5O/6PM4iIlF2GYVC7du18M17S0tJo0qQJDg4OeW0XLlwotDE3bNjAI488krdJdY0aNfjkk0/YvHlzoY0hIiLFz0cffUR6ejp169alXbt2ZscRESm17FK8mTBhAmPGjGHmzJnXtY8fP77AxRs/Pz8cHR1JTk7O156cnExgYOANnxMYGHjL/n/8m5ycnK8Ik5ycTHh4OABr1qzhyy+/5OLFi3lrzsXGxvLdd9/x/vvv33CvHQBXV9divZ60iIhIcXQhPYtF6xJ5f8NRLmfmAFC9ogeR7WvSvUlVXJwc/scZRETkvffeK/IxW7duzcKFCzlw4AC1a9dmx44drFu3jtdff73Is4iISNEwDIPY2FgAhgwZomUyRUTsyC7Fm7179/J///d/17X379+fmJiYAp/HxcWFZs2aER8fT7du3QDIzc0lPj6e4cOH3/A5rVq1Ij4+ntGjR+e1fffdd7Rq1QqAkJAQAgMDiY+PzyvWWK1WNm3axNChQwG4cuUKQL471P54nJubW+D8IiIicnPJ1qu8vfYIH286Tka2DYDaAeUZdk8tujQMwslRRRsRkYLq27dvkY85YcIErFYrYWFhODo6YrPZ+Ne//kXv3r1v+hztEyoiUrKtW7eO3377DQ8PD/r06WN2HBGRUs0uxZtKlSqxfft2QkND87Vv374df3//2zpXVFQUffv2pXnz5rRs2ZKYmBjS09Pp168fAH369KFKlSpER0cDMGrUKNq1a8fs2bPp0qULS5cu5ddff2XhwoXAtf1sRo8ezYwZMwgNDSUkJITJkydTuXLlvAJRq1at8PX1pW/fvkyZMgV3d3fefvttEhMT85YEEBERkb/mxIUrLFh7mP/bcpIs27WbIhpW8WbYPbXoVC8ABwfdvSciUhL83//9Hx9//DFLliyhfv36bN++ndGjR1O5cuWbFpO0T6iISMkWFxcHQK9evfDx8TE3jIhIKWeX4s2gQYMYPHgwR44coXXr1sC1PW9eeeWVfHvCFETPnj1JSUlhypQpJCUlER4ezurVqwkICADg+PHj+WbItG7dmiVLljBp0iSef/55QkNDWblyJQ0aNMjr89xzz5Gens7gwYO5dOkSbdu2ZfXq1bi5uQHXlmtbvXo1L7zwAh06dCA7O5v69evz+eef07hx47/77RERESmTDqekEffjYVYmnCIn99oG2s2r+zK8Qy3a1a6kJRdEREqYcePGMWHCBB5//HEAGjZsyLFjx4iOjr5p8Ub7hIqIlFzJycmsWLECIG/1GhERsR+LYRhGYZ/UMAxiYmKYPXs2p0+fBqBy5cqMGzeOkSNHlpk3Z6xWK97e3qSmpubtnSMiIlLW7D1jZd4Ph/hq1xn+uOq4K9SPYffUIiKkQpm5LhApC3T9W7ZUrFiRGTNm5HsDLzo6mvfee48DBw4U6Bx6zYiIlBzR0dE8//zzRERE8Msvv5gdR0SkxCroNXChz7zJyclhyZIl9OrVizFjxnD58mUAPD09C3soERERKca2n7jE3DWH+H5vcl5bx7oBDO9Qi/BgH/OCiYhIoXjooYf417/+RbVq1ahfvz4JCQm8/vrr9O/f3+xoIiJSyGw2G/Pnzwc060ZEpKgUevHGycmJIUOGsHfvXkBFGxERkbLEMAw2JV5g3g+H+PngOQAsFujSMIhh99SibpDuqhYRKS3mzJnD5MmTiYyM5OzZs1SuXJlnnnmGKVOmmB1NREQK2apVqzh+/DgVKlSgR48eZscRESkT7LLnTcuWLUlISKB69er2OL2IiIgUM4Zh8NOBFOauOcSvxy4C4ORgoVuTKgxtX5OalcqbnFBEpGy42R6jFosFNzc3atWqxSOPPEKFChX+9lienp7ExMQQExPzt88lIiLFW1xcHAD9+vXD3d3d5DQiImWDXYo3kZGRjB07lpMnT9KsWTPKlSuX73ijRo3sMayIiIgUsdxcg2/3JDPvh0PsOpUKgIujAz1aVOWZu2sSXMHD5IQiImVLQkIC27Ztw2azUadOHQAOHDiAo6MjYWFhxMbGMnbsWNatW0e9evVMTisiIiVBYmIiq1atAmDIkCEmpxERKTvsUrx5/PHHARg5cmRem8ViwTAMLBYLNpvNHsOKiIhIEcmx5fLVrjPM++EQB5LTAHB3dqR3RDUG3X0HAV5uJicUESmb/phV89577+VtfpqamsrAgQNp27YtgwYNytuf9JtvvjE5rYiIlAQLFizAMAw6depErVq1zI4jIlJmWAzDMAr7pMeOHbvl8bKynJrVasXb25vU1NS8P5xERERKsqycXP6dcJK4Hw9z9PwVADxdnejbugb924ZQoZyLyQlFxEy6/jVflSpV+O67766bVfPbb7/RqVMnTp06xbZt2+jUqRPnzp0zKeWf9JoRESneMjMzqVq1KufOnePf//433bp1MzuSiEiJV9Br4EKfeZOdnU2HDh348ssvqVu3bmGfXkRERIpY6pVsfjxwlu/3nuWn/WexXs0BwNfDmQFtQ3iqVQ283Z1NTikiInBtls3Zs2evK96kpKRgtVoB8PHxISsry4x4IiJSwqxYsYJz585RtWpVunbtanYcEZEypdCLN87Ozly9erWwTysiIiJFKPFcOvF7k/l+bzJbjl7ElvvnRN0AL1cG3XUHvSKq4eFilxVYRUTkL3rkkUfo378/s2fPpkWLFgBs2bKFZ599Nu9u6c2bN1O7dm0TU4qISEkRGxsLwODBg3Fy0rW/iEhRssuyaS+//DIHDhzgnXfeKdM/2LUEgIiIlBQ5tly2Hb9E/N5kvtubzJGU9HzHaweU5966AXSsG0B4sA+ODhaTkopIcabrX/OlpaUxZswYPvjgA3Jyrs2UdHJyom/fvrzxxhuUK1eO7du3AxAeHm5e0N/pNSMiUnzt3LmTxo0b4+TkxPHjxwkKCjI7kohIqWDasmlw7c6u+Ph4vv32Wxo2bEi5cuXyHf/ss8/sMayIiIjcBuvVbNYeSCF+71l+2H+WS1ey8445OVi4846K3FvXn3vDAqhW0cPEpCIiUlDly5fn7bff5o033uDIkSMA3HHHHZQvXz6vT3Eo2oiISPEXFxcHQPfu3VW4ERExgV2KNz4+Pjz22GP2OLWIiIj8DScuXOH7vcnE7z3LpsTzZNv+nIDr4+HMPXX8ubeuP3fXroSXm/axEREpqcqXL0+jRo3MjiEiIiWU1Wrlww8/BGDo0KEmpxERKZvsUrx577337HFaERERuU22XIPtJy7l7V9zIDkt3/E7KpWj4+/LoTWt5oOTo4NJSUVEpDCkp6czc+ZM4uPjOXv2LLm5ufmO/zEbR0RE5FY++ugj0tPTCQsLo3379mbHEREpk+y2IU1OTg4//vgjhw8fplevXnh6enL69Gm8vLzyTdkXERGRwpWemcPPB1P4fu9Zfth3lvPpWXnHHB0stKjhS8e6AdxbN4AQv3K3OJOIiJQ0AwcO5KeffuKpp54iKCgIi0V7lImIyO0xDIPY2Fjg2qwb/S4RETGHXYo3x44d4/777+f48eNkZmZy33334enpySuvvEJmZibz58+3x7AiIiJl1qlLGazZm8x3e8/yy+HzZNn+vNPa082J9nX86VjXn/a1/fH20HJoIiKl1apVq/jqq69o06aN2VFERKSEWrduHb/99hseHh706dPH7DgiImWWXYo3o0aNonnz5uzYsYOKFSvmtXfv3p1BgwbZY0gREZEyJTfXYOep1N+XQzvL3jPWfMerV/Tg3rAAOtbzp0WNCjhrOTQRkTLB19eXChUqmB1DRERKsLi4OAB69eqFj4+PuWFERMowuxRvfv75ZzZs2ICLi0u+9ho1anDq1Cl7DCkiIlLqZWTZWHfoHPF7k4nfd5aUy5l5xxws0Ky6L/fWDaBjXX9qViqv5Q1ERMqg6dOnM2XKFN5//308PDzMjiMiIiVMcnIyK1asAK4tmSYiIuaxS/EmNzcXm812XfvJkyfx9PS0x5AiIiKlUlLqVeL3JRO/9yzrD50jM+fP5dDKuzpxd20/7g0L4J4wfyqUc7nFmUREpCyYPXs2hw8fJiAggBo1auDsnH+pzG3btpmUTERESoJFixaRnZ1NREQETZs2NTuOiEiZZpfiTadOnYiJiWHhwoUAWCwW0tLSmDp1Kg8++KA9hhQRESkVDMPgt9NWvt97rWCz61RqvuNVfNy5r14A99b1JyKkIi5OWg5NRET+1K1bN7MjiIhICWWz2fL2qdasGxER81kMwzAK+6QnT56kc+fOGIbBwYMHad68OQcPHsTPz4+1a9fi7+9f2EMWS1arFW9vb1JTU/Hy8jI7joiIFFNXs21sPHw+r2CTZL2ad8xigfBgHzrWvVawqRPgqeXQRKTY0vWv3C69ZkREio8vv/yShx56iAoVKnDy5Enc3d3NjiQiUioV9BrYLjNvqlatyo4dO1i2bBk7duwgLS2NAQMG0Lt3b/3gFxERAVIuZ7JmXzLf7z3LuoPnyMj+c7lRd2dH7gr1o2Pda8uhVfJ0NTGpiIiIiIiUBXFxcQD069dP79+JiBQDdpl5I9foLjIREfmDYRjsS7pM/N5rBZsdJy/x37+Bg7zduLeuP/fWDaDVHRVxc3Y0L6yIyF+k619zVKhQgQMHDuDn54evr+8tZ2heuHChCJP9b3rNiIgUD4mJidSsWTNvFZ1atWqZHUlEpNQydeaNiIiIQGaOjV+OXCD+9+XQTl3KyHe8UVVv7g27thxa/cpeWg5NRET+kjfeeANPT8+8z/X7REREbteCBQswDINOnTqpcCMiUkyoeCMiIlKIzqdl8sP+FOL3JrP2QArpWX8uh+bq5EDbWn7c+/v+NQFebiYmFRGR0qJv3755nz/99NPmBRERkRIpMzOTd999F4ChQ4eanEZERP6g4o2IiMjfZBgGS7ec4NOtJ9l6/GK+5dD8PV2vLYcWFkCbWn64u2g5NBERsR9HR0fOnDmDv79/vvbz58/j7++PzWa7yTNFRKSsWrFiBefOnaNq1ap07drV7DgiIvI7FW9ERET+poVrjxC9al/e43pBXnT8ff+ahlW8cXDQ8jUiIlI0bralaWZmJi4uLkWcRkRESoK4uDgABg8ejJOT3ioUESku9BNZRETkb/jmtyRmrr5WuBnaviZP3Vmdyj7uJqcSEZGy5q233gLAYrHwzjvvUL58+bxjNpuNtWvXEhYWZlY8EREppnbu3Mn69etxcnJi4MCBZscREZH/UmjFG19f3wJvjHnhwoXCGlZERMQ0u0+lMnrpdgwDnryzGs91rqNNokVExBRvvPEGcG3mzfz583F0/HOZThcXF2rUqMH8+fPNiiciIsXUH7NuunfvTlBQkMlpRETkvxVa8SYmJibv8/PnzzNjxgw6d+5Mq1atANi4cSPffPMNkydPLqwhRURETJOUepUB728hI9vGXaF+vPhQfRVuRETENImJiQDcc889fPbZZ/j6+pqcSEREijur1cqHH34IwNChQ01OIyIi/z+LcbNFkf+Gxx57jHvuuYfhw4fna587dy7ff/89K1euLOwhiyWr1Yq3tzepqal4eXmZHUdERArJlawc/jl/I7+dthLqX55PI1vj5eZsdiwREdPp+ldul14zIiLmiY2NZdiwYYSFhbFnzx7djCYiUkQKeg1slz1vvvnmG1555ZXr2u+//34mTJhgjyFFRESKRG6uweil2/nttJWK5VxY9HQLFW5ERKTY6N+//y2PL1q0qIiSiIhIcWYYBrGxscC1WTcq3IiIFD8O9jhpxYoV+fzzz69r//zzz6lYsaI9hhQRESkSr6zex7d7knFxcmBhn2YEV/AwO5KIiEieixcv5vs4e/Ysa9as4bPPPuPSpUtmxxMRkWJi3bp1/Pbbb3h4eNCnTx+z44iIyA3YZebNtGnTGDhwID/++CMREREAbNq0idWrV/P222/bY0gRERG7W7blOAvWHgHgtX80oln1CiYnEhERye/f//73dW25ubkMHTqUmjVrmpBIRESKo7i4OAB69eqFj4+PuWFEROSG7DLz5umnn2b9+vV4eXnx2Wef8dlnn+Hl5cW6det4+umn7TGkiIiIXW04fI4X/r0bgJH3hvJIeBWTE4mIiBSMg4MDUVFRvPHGG2ZHERGRYiA5OZkVK1YA15ZMExGR4skuM28AIiIi+Pjjj+11ehERkSJzJCWNoR9tIyfX4KHGlRnTMdTsSCIiIrfl8OHD5OTkmB1DRESKgUWLFpGdnU1ERARNmzY1O46IiNyE3Yo3hw8f5r333uPIkSPExMTg7+/PqlWrqFatGvXr17fXsCIiIoXqYnoW/RdvITUjmybVfHjtH420maeIiBRbUVFR+R4bhsGZM2f46quv6Nu3r0mpRESkuLDZbMyfPx/QrBsRkeLOLsum/fTTTzRs2JBNmzbx6aefkpaWBsCOHTuYOnWqPYYUEREpdFk5uQz5aCtHz1+hio87C59qjpuzo9mxREREbiohISHfx86dOwGYPXs2MTEx5oYTERHTrVq1iuPHj1OhQgV69OhhdhwREbkFu8y8mTBhAjNmzCAqKgpPT8+89g4dOjB37lx7DCkiIlKoDMPghX/vYlPiBcq7OrHo6RZU8nQ1O5aIiMgt/fDDD2ZHEBGRYiwuLg6Afv364e7ubnIaERG5FbvMvNm1axfdu3e/rt3f359z587ZY0gREZFCNf+nIyzfehIHC8zt1YQ6gZ7/+0kiIiIiIiLFVGJiIqtWrQLgmWeeMTmNiIj8L3Yp3vj4+HDmzJnr2hMSEqhSpYo9hhQRESk0q3ef4ZXV+wCY+lB92tfxNzmRiIhIwZw/f55hw4ZRr149/Pz8qFChQr4PEREpuxYsWIBhGHTq1InQ0FCz44iIyP9gl2XTHn/8ccaPH8/y5cuxWCzk5uayfv16nn32Wfr06WOPIUVERArFzpOXGL1sOwB9W1Wnb+sapuYRERG5HU899RSHDh1iwIABBAQEYLFYzI4kIiLFQGZmJu+++y4AQ4cONTmNiIgUhF2KNy+//DLDhg0jODgYm81GvXr1sNls9OrVi0mTJtljSBERkb/tTGoGA9//lavZubSvU4nJXeuZHUlEROS2/Pzzz6xbt47GjRubHUVERIqRFStWcO7cOapWrUrXrl3NjiMiIgVgl+KNi4sLb7/9NlOmTGHXrl2kpaXRpEkTTckUEZFiKz0zhwGLf+Xs5UzqBHgy54kmODnaZXVRERERuwkLCyMjI8PsGCIiUszExcUBMHjwYJyc7PJ2oIiIFDK7vCv10ksvceXKFYKDg3nwwQfp0aMHoaGhZGRk8NJLL9ljSBERkb/Mlmswaul29pyx4lfehXf6NsfTzdnsWCIiIrctNjaWF154gZ9++onz589jtVrzfYiISNmzc+dO1q9fj5OTEwMHDjQ7joiIFJBdijfTpk0jLS3tuvYrV64wbdq02z7fvHnzqFGjBm5ubkRERLB58+Zb9l++fDlhYWG4ubnRsGFDvv7663zHDcNgypQpBAUF4e7uTseOHTl48OB15/nqq6+IiIjA3d0dX19funXrdtvZRUSk+Ju5ai/f703GxcmBBU81J7iCh9mRRERE/hIfHx+sVisdOnTA398fX19ffH198fHxwdfX1+x4IiJigj9m3XTv3p2goCCT04iISEHZZZ6kYRg33Bhzx44dVKhQ4bbOtWzZMqKiopg/fz4RERHExMTQuXNn9u/fj7+//3X9N2zYwBNPPEF0dDRdu3ZlyZIldOvWjW3bttGgQQMAXn31Vd566y3ef/99QkJCmDx5Mp07d2bPnj24ubkB8OmnnzJo0CBefvllOnToQE5ODrt37/4L3w0RESnOPtl8nLd/TgRg1j8b06y63tgSEZGSq3fv3jg7O7NkyRICAgJu+HeZiIiUHVarlQ8//BCAoUOHmpxGRERuh8UwDKOwTubr64vFYiE1NRUvL698fyjYbDbS0tIYMmQI8+bNK/A5IyIiaNGiBXPnzgUgNzeX4OBgRowYwYQJE67r37NnT9LT0/nyyy/z2u68807Cw8OZP38+hmFQuXJlxo4dy7PPPgtAamoqAQEBLF68mMcff5ycnBxq1KjBtGnTGDBgwF/9dmC1WvH29s77foiISPGy/tA5+i7aTE6uwZiOtRnVUXuziYj8Hbr+NZ+HhwcJCQnUqVPH7CgFoteMiIh9xcbGMmzYMMLCwtizZ4+K+iIixUBBr4ELdeZNTEwMhmHQv39/pk2bhre3d94xFxcXatSoQatWrQp8vqysLLZu3crEiRPz2hwcHOjYsSMbN2684XM2btxIVFRUvrbOnTuzcuVKABITE0lKSqJjx455x729vYmIiGDjxo08/vjjbNu2jVOnTuHg4ECTJk1ISkoiPDyc1157LW/2zo1kZmaSmZmZ91hrSouIFF+HzqYx5KOt5OQadAuvzMh7a5kdSURE5G9r3rw5J06cKDHFGxERsR/DMPKWTBs6dKgKNyIiJUyhFm/69u0LQEhICK1bt8bZ+e9t9nzu3DlsNhsBAQH52gMCAti3b98Nn5OUlHTD/klJSXnH/2i7WZ8jR44A8OKLL/L6669To0YNZs+eTfv27Tlw4MBNl36Ljo7+S3v6iIhI0bqQnsWA97dw+WoOzar7MvOxRvpDRkRESoURI0YwatQoxo0bR8OGDa/7m6xRo0YmJRMRkaK2bt06du/ejYeHB3369DE7joiI3Ca77HnTrl27vM+vXr1KVlZWvuPFfTp8bm4uAC+88AKPPfYYAO+99x5Vq1Zl+fLlPPPMMzd83sSJE/PN+rFarQQHB9s/sIiIFFhmjo0hH27l2PkrBFdwZ+FTzXBzdjQ7loiISKHo2bMnAP37989rs1gsefuS2mw2s6KJiEgR+2PWTa9evfDx8TE3jIiI3Da7FG+uXLnCc889x//93/9x/vz5644X9A8GPz8/HB0dSU5OzteenJxMYGDgDZ8TGBh4y/5//JucnExQUFC+PuHh4QB57fXq1cs77urqyh133MHx48dvmtfV1RVXV9cCfW0iIlL0DMNg4me72Hz0Ap6uTizq24KK5fVzW0RESo/ExESzI4iISDGQnJzMihUrgGtLpomISMnjYI+Tjhs3jjVr1hAXF4erqyvvvPMO06ZNo3LlynzwwQcFPo+LiwvNmjUjPj4+ry03N5f4+Pib7p3TqlWrfP0Bvvvuu7z+ISEhBAYG5utjtVrZtGlTXp9mzZrh6urK/v378/pkZ2dz9OhRqlevXuD8IiJSvMT+eJjPtp3C0cHCvN5NCQ3wNDuSiIhIoapevfotP0REpGxYtGgR2dnZRERE0LRpU7PjiIjIX2CXmTdffPEFH3zwAe3bt6dfv37cdddd1KpVi+rVq/Pxxx/Tu3fvAp8rKiqKvn370rx5c1q2bElMTAzp6en069cPgD59+lClShWio6MBGDVqFO3atWP27Nl06dKFpUuX8uuvv7Jw4ULg2pIBo0ePZsaMGYSGhhISEsLkyZOpXLky3bp1A64t6zZkyBCmTp1KcHAw1atX57XXXgPgn//8ZyF+p0REpKh8tfMMr31zrSj/4sP1ubt2JZMTiYiI2Mfp06dZt24dZ8+ezVsS+g8jR440KZWIiBQVm83G/PnzAc26EREpyexSvLlw4QJ33HEHcK0QcuHCBQDatm172780evbsSUpKClOmTCEpKYnw8HBWr15NQEAAAMePH8fB4c8JRK1bt2bJkiVMmjSJ559/ntDQUFauXEmDBg3y+jz33HOkp6czePBgLl26RNu2bVm9ejVubm55fV577TWcnJx46qmnyMjIICIigjVr1uDr6/uXvy8iImKO7ScuEfV/2wHo16YGT92pO49FRKR0Wrx4Mc888wwuLi5UrFgRi8WSd8xisah4IyJSBqxatYrjx49ToUIFevToYXYcERH5iyyGYRiFfdJGjRoxZ84c2rVrR8eOHQkPD2fWrFm89dZbvPrqq5w8ebKwhyyWrFYr3t7epKam4uXlZXYcEZEy6dSlDB6Zu55zaZl0CPPn7T7NcXSw/O8niojIbdP1r/mCg4MZMmQIEydOzHeTW3Gl14yISOHr0qULX3/9NWPHjmXWrFlmxxERkf9PQa+B7XI1369fP3bs2AHAhAkTmDdvHm5ubowZM4Zx48bZY0gREZHrpGXmMGDxFs6lZRIW6MlbTzRR4UZEREq1K1eu8Pjjj5eIwo2IiBS+xMREVq1aBcAzzzxjchoREfk77LJs2pgxY/I+79ixI/v27WPr1q3UqlWLRo0a2WNIERGRfGy5BiM/SWBf0mX8yrvy7tMtKO9ql197IiIixcaAAQNYvnw5EyZMMDuKiIiYYMGCBRiGQadOnQgNDTU7joiI/A1F8i5W9erVqV5d+wuIiEjR+ddXe1mz7yyuTg6807c5VXzczY4kIiJid9HR0XTt2pXVq1fTsGFDnJ2d8x1//fXXTUomIiL2lpmZybvvvgtw23tOi4hI8VNoxZu33nqrwH21SaaIiNjTR78cY9H6RABe7xFOeLCPuYFERESKSHR0NN988w116tQBwGL5c7nQ//5cRERKnxUrVnDu3DmqVq1K165dzY4jIiJ/U6EVb954440C9bNYLCreiIiI3fx8MIWp//kNgGc71aZLoyCTE4mIiBSd2bNns2jRIp5++mmzo4iISBGLi4sDYPDgwTg5acloEZGSrtB+kicmJhbWqURERP6SQ2cvE/nxNmy5Bo82qcKwe2qZHUlERKRIubq60qZNG7NjiIhIEdu5cyfr16/HycmJgQMHmh1HREQKgYPZAURERArD+bRM+i3ewuWrObSo4Uv0Yw21PIyIiJQ5o0aNYs6cOWbHEBGRIvbHrJvu3bsTFKTVB0RESgO7zKHs37//LY8vWrTIHsOKiEgZlZlj45kPt3LiQgbVKniw4KnmuDo5mh1LRESkyG3evJk1a9bw5ZdfUr9+fZydnfMd/+yzz0xKJiIi9mK1Wvnwww8BGDp0qMlpRESksNileHPx4sV8j7Ozs9m9ezeXLl2iQ4cO9hhSRETKKMMwmPDpLn49dhFPNycWPd2CCuVczI4lIiJiCh8fHx599FGzY4iISBH66KOPSE9PJywsjPbt25sdR0REColdijf//ve/r2vLzc1l6NCh1KxZ0x5DiohIGTVnzSH+nXAKRwcLcb2bUcu/vNmRRERETPPee+8V6XinTp1i/PjxrFq1iitXrlCrVi3ee+89mjdvXqQ5RETKKsMw8pZMGzp0qJaOFhEpRexSvLkRBwcHoqKiaN++Pc8991xRDSsiIqXYFztO8/p3BwCY/kgD2ob6mZxIRESkeEhJSWH//v0A1KlTh0qVKhX6GBcvXqRNmzbcc889rFq1ikqVKnHw4EF8fX0LfSwREbmxdevWsXv3bjw8POjTp4/ZcUREpBAVWfEG4PDhw+Tk5BTlkCIiUkolHL/Is8t3ADCwbQi9IqqZnEhERMR86enpjBgxgg8++IDc3FwAHB0d6dOnD3PmzMHDw6PQxnrllVcIDg7ON9snJCSk0M4vIiL/2x+zbnr16oWPj4+5YUREpFDZpXgTFRWV77FhGJw5c4avvvqKvn372mNIEREpQ05evMKgD34lMyeXjnX9mfhgXbMjiYiIFAtRUVH89NNPfPHFF7Rp0wa4dlf2yJEjGTt2bN6bfIXhP//5D507d+af//wnP/30E1WqVCEyMpJBgwbd9DmZmZlkZmbmPbZarYWWR0SkrElOTmbFihXAtSXTRESkdLFL8SYhISHfYwcHBypVqsTs2bPp37+/PYYUEZEy4vLVbAYs/pVzaVnUDfLizceb4OigdZ1FREQAPv30U1asWJFvw+oHH3wQd3d3evToUajFmyNHjhAXF0dUVBTPP/88W7ZsYeTIkbi4uNz0pr3o6GimTZtWaBlERMqyRYsWkZ2dTUREBE2bNjU7joiIFDK7FG9++OEHe5xWRETKuBxbLiM+SWB/8mX8PV15t29zyrkW6QqgIiIixdqVK1cICAi4rt3f358rV64U6li5ubk0b96cl19+GYAmTZqwe/du5s+ff9PizcSJE/Ot1GC1WgkODi7UXCIiZYHNZmPBggWAZt2IiJRWDmYHEBERKagZX+3lx/0puDk78E7f5lT2cTc7koiISLHSqlUrpk6dytWrV/PaMjIymDZtGq1atSrUsYKCgqhXr16+trp163L8+PGbPsfV1RUvL698HyIicvtWrVrFsWPH8PX1pUePHmbHERERO7DL7crnz59nypQp/PDDD5w9ezZvo8w/XLhwwR7DiohIKfbhxqMs3nAUgDd6hNOoqo+peURERIqjN998k86dO1O1alUaN24MwI4dO3Bzc+Obb74p1LHatGnD/v3787UdOHCA6tWrF+o4IiJyvT+Wwezfvz/u7rqpTUSkNLJL8eapp57i0KFDDBgwgICAACwW7UUgIiJ/3U8HUnjxiz0AjOtchwcaBpmcSEREpHhq0KABBw8e5OOPP2bfvn0APPHEE/Tu3bvQ39wbM2YMrVu35uWXX6ZHjx5s3ryZhQsXsnDhwkIdR0RE8ktMTGTVqlUAPPPMMyanERERe7FL8ebnn39m3bp1eXd6iYiI/FUHki8z/ONt2HINHmtalcj2Nc2OJCIiUqx5eHgwaNAgu4/TokUL/v3vfzNx4kReeuklQkJCiImJoXfv3nYfW0SkLFuwYAGGYdCpUydCQ0PNjiMiInZil+JNWFgYGRkZ9ji1iIiUIefSMum/eAuXM3NoGVKB6EcbajaniIjILURHRxMQEED//v3ztS9atIiUlBTGjx9fqON17dqVrl27Fuo5RUTk5jIzM3n33XcBGDp0qMlpRETEnhzscdLY2FheeOEFfvrpJ86fP4/Vas33ISIi8r9czbYx+INfOXkxgxoVPVjwZDNcnOzya0tERKTUWLBgAWFhYde1169fn/nz55uQSERECtOKFSs4d+4cVatWVfFcRKSUs8vMGx8fH6xWKx06dMjXbhgGFosFm81mj2FFRKSUMAyD51bsZNvxS3i5OfHu0y3wLedidiwREZFiLykpiaCg6/eGq1SpEmfOnDEhkYiIFKa4uDgABg8ejJOTXd7WExGRYsIuP+V79+6Ns7MzS5YsISAgQEvciIjIbXkz/iD/2XEaJwcL859sRs1K5c2OJCIiUiIEBwezfv16QkJC8rWvX7+eypUrm5RKREQKw86dO1m/fj1OTk4MHDjQ7DgiImJndine7N69m4SEBOrUqWOP04uISCn2+fZTxHx/EIAZ3RrQupafyYlERERKjkGDBjF69Giys7PzVkKIj4/nueeeY+zYsSanExGRv+OPWTfdu3e/4SxLEREpXexSvGnevDknTpxQ8UZERG7L1mMXGLdiJwCD776Dx1tWMzmRiIhIyTJu3DjOnz9PZGQkWVlZALi5uTF+/HgmTpxocjoREfmrrFYrH374IQBDhw41OY2IiBQFuxRvRowYwahRoxg3bhwNGzbE2dk53/FGjRrZY1gRESnBTly4wuAPtpKVk8t99QIYf//1my2LiIjIrVksFl555RUmT57M3r17cXd3JzQ0FFdXV7OjiYjI3/DRRx+Rnp5OWFgY7du3NzuOiIgUAbsUb3r27AlA//7989osFguGYWCxWLDZbPYYVkRESijr1Wz6L97C+fQs6lf24s3Hw3F00H5pIiIif1X58uVp0aKF2TFERKQQGIaRt2Ta0KFDtbe0iEgZYZfiTWJioj1OKyIipVCOLZfhSxI4eDaNAC9X3u3bAg8Xu/x6EhERERERKXHWrVvH7t278fDwoE+fPmbHERGRImKXd8eqV69uj9OKiEgp9NKXe1h7IAV3Z0fe7duCQG83syOJiIiIiIgUG3/MuunVqxc+Pj7mhhERkSJjl+LNBx98cMvjuktAREQAFq9P5IONx7BY4I2e4TSo4m12JBERERERkWIjOTmZFStWANeWTBMRkbLDLsWbUaNG5XucnZ3NlStXcHFx0RRPEREB4Id9Z3npyz0AjL8/jPsbBJqcSEREREREpHhZtGgR2dnZRERE0LRpU7PjiIhIEXKwx0kvXryY7yMtLY39+/fTtm1bPvnkE3sMKSIiJci+JCsjPkkg14AezavyzN13mB1JRESk1Pjwww9p06YNlStX5tixYwDExMTw+eefm5xMRERuh81mY8GCBYBm3YiIlEV2Kd7cSGhoKDNnzrxuVo6IiJQtKZczGbD4V9Iyc2h1R0VmdGuIxWIxO5aIiEipEBcXR1RUFA8++CCXLl3CZrMB4OPjQ0xMjLnhRETktqxatYpjx47h6+tLjx49zI4jIiJFrMiKNwBOTk6cPn26KIcUEZFi5Gq2jUEf/MqpSxmE+JUj7smmuDgV6a8iERGRUm3OnDm8/fbbvPDCCzg6Oua1N2/enF27dpmYTEREbldcXBwA/fv3x93d3eQ0IiJS1Oyy581//vOffI8Nw+DMmTPMnTuXNm3a2GNIEREp5nJzDZ5dvoPtJy7h7e7Moqdb4OPhYnYsERGRUiUxMZEmTZpc1+7q6kp6eroJiURE5K9ITExk1apVADzzzDMmpxERETPYpXjTrVu3fI8tFguVKlWiQ4cOzJ492x5DiohIMRfz/QG+3HkGZ0cL859sRohfObMjiYiIlDohISFs376d6tWr52tfvXo1devWNSmViIjcrgULFmAYBp06dSI0NNTsOCIiYgK7FG9yc3PtcVoRESmh/p1wkrfWHALgX90b0qpmRZMTiYiIlE5RUVEMGzaMq1evYhgGmzdv5pNPPiE6Opp33nnH7HgiIlIAmZmZvPvuuwAMHTrU5DQiImIWuxRvRERE/rDl6AXGr7i2xv6QdjXp0TzY5EQiIiKl18CBA3F3d2fSpElcuXKFXr16UblyZd58800ef/xxs+OJiEgBrFixgnPnzlG1alW6du1qdhwRETGJXYo3jz32GC1btmT8+PH52l999VW2bNnC8uXL7TGsiIiYLDfX4NSlDA6dTePg2cscTE7j2z3JZNlyub9+IM91rmN2RBERkVKvd+/e9O7dmytXrpCWloa/v7/ZkURE5DbExcUBMHjwYJycdN+1iEhZZZffAGvXruXFF1+8rv2BBx7QnjciIqVAji2X4xeu/F6kScsr1hw+m05Gtu26/g2rePN6z8Y4OFhMSCsiIlJ2ZGRkYBgGHh4eeHh4kJKSQkxMDPXq1aNTp05mxxMRkf9h586drF+/HicnJwYOHGh2HBERMZFdijdpaWm4uLhc1+7s7IzVarXHkCIiYgdZObkcPZ/OweQ/CzSHzqZxJCWdLNuN9zdzdrRwh195agWUJ9S/PKH+ntxb1x83Z8ciTi8iIlL2PPLIIzz66KMMGTKES5cu0bJlS1xcXDh37hyvv/669k4QESnm/ph10717d4KCgkxOIyIiZrJL8aZhw4YsW7aMKVOm5GtfunQp9erVs8eQIiLyN1zNtnE45VqB5tDZNA4mXyvUHD1/BVuuccPnuDk7ULPS7wWaAE9q+V/7vFoFD5wcHYr4KxARERGAbdu28cYbbwDX9kwIDAwkISGBTz/9lClTpqh4IyJSjFmtVj788EMA/bwWERH7FG8mT57Mo48+yuHDh+nQoQMA8fHxfPLJJ9rvRkTERGmZORz+famza8ucXfv8+IUrGDeu0VDe1Yma/n/MoilPaMC12TRVfNy1DJqIiEgxc+XKFTw9PQH49ttvefTRR3FwcODOO+/k2LFjJqcTEZFb+eijj0hPTycsLIz27dubHUdERExml+LNQw89xMqVK3n55ZdZsWIF7u7uNGrUiO+//5527drd9vnmzZvHa6+9RlJSEo0bN2bOnDm0bNnypv2XL1/O5MmTOXr0KKGhobzyyis8+OCDeccNw2Dq1Km8/fbbXLp0iTZt2hAXF0doaOh158rMzCQiIoIdO3aQkJBAeHj4becXESlqqVeyOZRy+fcZNNc+Dp9N49SljJs+x9vdmdoB5anlX55a/p55hZpALzcsFhVpRERESoJatWqxcuVKunfvzjfffMOYMWMAOHv2LF5eXianExGRmzEMI2/JtKFDh+pvMBERsU/xBqBLly506dLlb59n2bJlREVFMX/+fCIiIoiJiaFz587s378ff3//6/pv2LCBJ554gujoaLp27cqSJUvo1q0b27Zto0GDBgC8+uqrvPXWW7z//vuEhIQwefJkOnfuzJ49e3Bzc8t3vueee47KlSuzY8eOv/21iIgUtvNpmXnFmUPJl6/9ezaNs5czb/ocv/KueYWZa4WaazNp/Mq76A8EERGREm7KlCn06tWLMWPGcO+999KqVSvg2iycJk2amJxORERuZt26dezevRsPDw/69OljdhwRESkGLIZxs4Vy/rotW7aQm5tLREREvvZNmzbh6OhI8+bNC3yuiIgIWrRowdy5cwHIzc0lODiYESNGMGHChOv69+zZk/T0dL788su8tjvvvJPw8HDmz5+PYRhUrlyZsWPH8uyzzwKQmppKQEAAixcv5vHHH8973qpVq4iKiuLTTz+lfv36tz3zxmq14u3tTWpqqu5yE5G/zDAMkq2Z1/aiOft7geb3PWkuXsm+6fOCvN3yCjN5hZpK5fEt51KE6UVEpCzR9W/xkJSUxJkzZ2jcuDEODtf2odu8eTNeXl6EhYWZnC4/vWZERK7p1asXn3zyCQMHDuTtt982O46IiNhRQa+B7TLzZtiwYTz33HPXFW9OnTrFK6+8wqZNmwp0nqysLLZu3crEiRPz2hwcHOjYsSMbN2684XM2btxIVFRUvrbOnTuzcuVKABITE0lKSqJjx455x729vYmIiGDjxo15xZvk5GQGDRrEypUr8fDwKFDezMxMMjP/vNvdarUW6HkiIgC5uQanLmVw6PfZM/9dqLmcmXPD51gsUNXX/VqB5o9ZNAGe1KxUDk835yL+CkRERKQ4CAwMJDAwMF/brZadFhERcyUnJ7NixQrg2pJpIiIiYKfizZ49e2jatOl17U2aNGHPnj0FPs+5c+ew2WwEBATkaw8ICGDfvn03fE5SUtIN+yclJeUd/6PtZn0Mw+Dpp59myJAhNG/enKNHjxYob3R0NNOmTStQXxEpu/4o0uxLuszBs5d/n0VzrWCTkW274XMcHSxUr+Dxe3Hm2myaWv7lqVmpPO4ujkX8FYiIiEhx8uijj7J48WK8vLx49NFHb9n3s88+K6JUIiJSUIsWLSI7O5uWLVve8P00EREpm+xSvHF1dSU5OZk77rgjX/uZM2dwcrLbNjuFZs6cOVy+fDnfjJ+CmDhxYr5ZP1arleDg4MKOJyIlyOWr2exPuszepMvsO2NlX9Jl9iddJu0mM2mcHS3c4fdfe9H8Xqip4eeBq5OKNCIiInI9b2/vvH3rvL29TU4jIiIFlZmZyezZs5kxYwYAkZGRJicSEZHixC6VlE6dOjFx4kQ+//zzvD8eLl26xPPPP899991X4PP4+fnh6OhIcnJyvvbk5OTrlgH4Q2Bg4C37//FvcnIyQUFB+fr8sZ/NmjVr2LhxI66urvnO07x5c3r37s37779/w7FdXV2ve46IlA22XIPjF66w94yVfWes14o1SVZOXMi4YX8XRwdq+penzh970fy+L031Ch44OToUcXoREREpyd57770bfi4iIsVXfHw8w4YNY//+/QDcd999+fZhFhERsUvxZtasWdx9991Ur16dJk2aALB9+3YCAgL48MMPC3weFxcXmjVrRnx8PN26dQMgNzeX+Ph4hg8ffsPntGrVivj4eEaPHp3X9t1339GqVSsAQkJCCAwMJD4+Pq9YY7Va2bRpU966om+99VbeXQ8Ap0+fpnPnzixbtuy6fXxEpOxJvZLNviTrtULN77NqDiRdvumSZ4FeboQFeRIW6EXdIE/qBnkR4lcOZxVpREREpJAtWrSIe+65h5CQELOjiIjIDZw+fZqxY8eydOlSAPz9/Zk1axZPPvlk3ixKERERsFPxpkqVKuzcuZOPP/6YHTt24O7uTr9+/XjiiSdwdr69DbSjoqLo27cvzZs3p2XLlsTExJCenk6/fv0A6NOnD1WqVCE6OhqAUaNG0a5dO2bPnk2XLl1YunQpv/76KwsXLgTAYrEwevRoZsyYQWhoKCEhIUyePJnKlSvnFYiqVauWL0P58uUBqFmzJlWrVv073xoRKUFybLkcPZ/O3jOX8wo1+85YOZ169Yb9XZ0cqBPoSVjgH4UaL8ICPfEt51LEyUVERKSsio6OZtCgQVSpUoV27drRrl072rdvT61atcyOJiJSpuXk5DB37lymTJnC5cuXcXBwIDIykunTp+Pj42N2PBERKYbstgFNuXLlGDx48N8+T8+ePUlJSWHKlCkkJSURHh7O6tWrCQgIAOD48eM4OPx593rr1q1ZsmQJkyZN4vnnnyc0NJSVK1fSoEGDvD7PPfcc6enpDB48mEuXLtG2bVtWr16Nm5vb384rIiXThfSsvOXOrhVqrBxITiMrJ/eG/av4uFM36L+KNEGe1KhYDkcH3SklIiIi5jl48CCnTp3ixx9/ZO3atcyaNYtnnnmGoKAg2rdvz0cffWR2RBGRMmf9+vVERkayc+dOAFq2bElcXBxNmzY1OZmIiBRnFsMwDHudfM+ePRw/fpysrKx87Q8//LC9hixWrFYr3t7epKam4uXlZXYcEQGycnI5ci6Nfb/Pptn7+2yas5czb9jfw8Xx99k0fy55VjvAE2/325tFKCIiUhbo+rd4uXLlCj///DOffPIJH3/8MYZhkJOTY3asfPSaEZHSLCUlhfHjx+ftR+br68vMmTMZOHBgvhuRRUSkbCnoNbBdZt4cOXKE7t27s2vXLiwWC3/Uh/5Yu9Nmu/G+ECIihcUwDFLSMtl35vLv+9NcK9YcTkkj23bjmnX1ih7/teTZtUJNsK8HDppNIyIiIiXEt99+y48//siPP/5IQkICdevWpV27dqxYsYK7777b7HgiImVCbm4ub7/9NhMnTuTixYsADBgwgJkzZ+Ln52dyOhERKSnsUrwZNWoUISEhxMfHExISwubNmzl//jxjx45l1qxZ9hhSRMqwq9k2Dp1Ny9uTZm+SlX1nLnM+PeuG/T1dnQj7fcmzP/6tE+hJeVe7rSQpIiIiUiTuv/9+KlWqxNixY/n666+1j4KISBHbunUrkZGRbN68GYDGjRsTGxtL69atTU4mIiIljV3eqdy4cSNr1qzBz88PBwcHHBwcaNu2LdHR0YwcOZKEhAR7DCsipZxhGCRZr15b8uz32TT7zlg5ci4dW+71s2ksFgjxK0fdQK9rM2qCrv1b1dc9byagiIiISGny+uuvs3btWl599VXefPNN2rVrR/v27Wnfvj21a9c2O56ISKl18eJFJk2aRFxcHIZh4OnpyfTp0xk2bBhOTrpRUEREbp9dfnvYbDY8PT0B8PPz4/Tp09SpU4fq1auzf/9+ewwpIqVMRpaNA8n5lzzbl3SZ1IzsG/b3dnembtCfS56FBV7bm8bdxbGIk4uIiIiYZ/To0YwePRqAXbt28dNPP7F69WqGDx+Ov78/J0+eNDegiEgpYxgGH374IePGjePs2bMA9OrVi1mzZhEUFGRyOhERKcnsUrxp0KABO3bsICQkhIiICF599VVcXFxYuHAhd9xxhz2GFJESLtuWyy9HzrNqdxK/HD5P4vl0jBtsTePoYKFmpXJ5S57V/f3fQC83zaYRERER4dobiQkJCfz444/88MMPrFu3jtzcXCpVqmR2NBGRUmX37t0MGzaMtWvXAhAWFsa8efPo0KGDyclERKQ0sEvxZtKkSaSnpwPw0ksv0bVrV+666y4qVqzIsmXL7DGkiJRAWTm5rD90jlW7z/DtnmQuXck/q6ZiORfqBuVf8qyWf3ncnDWbRkRERORGHnroIdavX4/VaqVx48a0b9+eQYMGcffdd2v/GxGRQpKWlsa0adN44403sNlseHh4MHnyZKKionBxcTE7noiIlBJ2Kd507tw57/NatWqxb98+Lly4gK+vr+6MFynjrmbb+PngOVbtOsN3e5O5fDUn71jFci50qh/IffX8aVDFm0rlXfUzQ0REROQ2hIWF8cwzz3DXXXfh7e1tdhwRkVLFMAxWrFjBmDFjOHXqFADdunUjJiaG6tWrm5xORERKmyLbMa1ChQpFNZSIFDMZWTZ+3H+Wr3cnsWZvMulZtrxjlTxdeaBBIPc3CKRljQo4OTqYmFRERESkZHvttdfyPj958iSVK1fGwUHXVyIif9fBgwcZPnw43377LQAhISHMmTOHLl26mJxMRERKqyIr3ohI2ZKWmcOafWdZtesMP+5PISP7z4JNkLcb9zcI5MGGQTSr5ouDg2bXiIiIiBS2evXqsX37du07KiLyN2RkZBAdHc0rr7xCVlYWLi4uTJgwgQkTJuDu7m52PBERKcVUvBGRQpOakU383mS+3pXE2oMpZOXk5h0LruDOAw2CeKBBII2r+qhgIyIiImJnhmGYHUFEpET78ssvGTlyJImJicC1bQLmzJlDaGioyclERKQsUPFGRP6Wi+lZfLcnmVW7z7Du0DmybX++SRDiV44Hfp9hU7+yl/avERERERERkWLv2LFjjBo1is8//xyAKlWqEBMTw2OPPaa/a0VEpMioeCMit+1cWibf/JbE6t1JbDh8HlvunwWbUP/yPNAwiAcbBlInwFMXtiIiIiImef7557X3qIjIbcjKymL27NlMnz6djIwMnJycGDNmDFOmTKF8+fJmxxMRkTJGxRsRKZBk61W++S2Jr3edYXPiBf6rXkPdIC8ebBDIAw0DqeXvaV5IERERkTLupZde4tlnn8XDw4OJEyfmtWdkZPDaa68xZcoUE9OJiBRf8fHxDBs2jP379wNw9913ExsbS/369U1OJiIiZZXF0ELIdmO1WvH29iY1NRUvLy+z44jctlOXMli9O4lVu86w9fhF/vunRaOq3nl72NTwK2deSBERESk2dP1rPkdHR86cOYO/v3++9vPnz+Pv74/NZjMp2Y3pNSMiZjt9+jRjx45l6dKlAPj7+zNr1iyefPJJrSQhIiJ2UdBrYM28EZF8jp+/wqrdZ/h6dxI7TlzKd6xpNR8ebBhE5/qBBFfwMCegiIiIiNyUYRg3fLNxx44dWkJNROS/5OTkMHfuXKZMmcLly5dxcHAgMjKS6dOn4+PjY3Y8ERERFW9EBI6kpLFq97Ul0X47bc1rt1igRY0KPNAgkPsbBBLk7W5iShERERG5GV9fXywWCxaLhdq1a+cr4NhsNtLS0hgyZIiJCUVEio/169cTGRnJzp07AWjZsiVxcXE0bdrU5GQiIiJ/UvFGpAwyDIODZ9P4etcZVu9OYl/S5bxjDha4846KPNAwiM71A/D3dDMxqYiIiIgURExMDIZh0L9/f6ZNm4a3t3feMRcXF2rUqEGrVq1MTCgiYr6UlBTGjx/Pe++9B1wrfM+cOZOBAwfi4OBgcjoREZH8VLwRKSMMw2DPGSurf59hczglPe+Yk4OF1rX8eLBBIPfVC6BieVcTk4qIiIjI7erbty8AISEhtG7dGmdnZ5MTiYgUH7m5ubz99ttMnDiRixcvAjBgwABmzpyJn5+fyelERERuTMUbkVLMMAx2nkxl1e4kVu0+w7HzV/KOuTg6cFeoHw80DOK+ugF4e+gPfBEREZGSyGq15m102qRJEzIyMsjIyLhh31ttiCoiUhpt3bqVyMhINm/eDEDjxo2JjY2ldevWJicTERG5NRVvREqZ3FyDhBMXWbUriVW7kzh16c8/3F2dHGhfpxIPNgyiQ5g/nm4q2IiIiIiUdL6+vpw5cwZ/f398fHzy7XfzB8MwsFgs2Gw2ExKKiBS9S5cuMWnSJGJjYzEMA09PT6ZPn86wYcNwctLbYSIiUvzpt5VIKWDLNfj16IW8GTbJ1sy8Y+7OjnSo68+DDYJoX6cS5Vz1v72IiIhIabJmzRoqVKgAwA8//GByGhERcxmGwYcffsi4ceM4e/YsAL169WLWrFkEBQWZnE5ERKTg9C6uSAmVY8tlU+IFvt51hm9+S+Zc2p8Fm/KuTnSs68/9DYJoV7sS7i6OJiYVEREREXtq167dDT8XESlrdu/ezbBhw1i7di0AYWFhzJs3jw4dOpicTERE5PapeCNSgmTl5LLh8DlW7Uri2z1JXLySnXfMy82J++oF8mDDQNqG+uHqpIKNiIiISFl08eJF3n33Xfbu3QtAvXr16NevX97sHBGR0iYtLY1p06YRExNDTk4OHh4eTJ48maioKFxcXMyOJyIi8peoeCNSAhiGwavf7OfjX45hvZqT116hnAud6gXwQMMgWt1RERcnBxNTioiIiIjZ1q5dy0MPPYS3tzfNmzcH4K233uKll17iiy++4O677zY5oYhI4TEMgxUrVjBmzBhOnToFQLdu3YiJiaF69eompxMREfl7VLwRKQEWrj1C3I+HAfAr78r9DQJ4sEEQLUMq4OSogo2IiIiIXDNs2DB69uxJXFwcjo7XZmLbbDYiIyMZNmwYu3btMjmhiEjhOHjwIMOHD+fbb78FICQkhDlz5tClSxeTk4mIiBQOFW9EirktRy/w6jf7AZjUpS792oTg6GAxOZWIiIiIFEeHDh1ixYoVeYUbAEdHR6Kiovjggw9MTCYiUjgyMjKIjo7mlVdeISsrCxcXFyZMmMCECRNwd3c3O56IiEih0S37IsXYubRMhi/Zhi3X4OHGlRnQVoUbEREREbm5pk2b5u1189/27t1L48aN7Tr2zJkzsVgsjB492q7jiEjZ9dVXX1G/fn2mT59OVlYWnTt3Zvfu3UybNk2FGxERKXU080akmLLlGoxeup1kayY1K5Uj+tGGWCwq3IiIiIhIfjt37sz7fOTIkYwaNYpDhw5x5513AvDLL78wb948Zs6cabcMW7ZsYcGCBTRq1MhuY4hI2XXs2DFGjRrF559/DkCVKlWIiYnhscce09/JIiJSaql4I1JMvRV/kHWHzuHu7Ejck80o56r/XUVERETkeuHh4VgsFgzDyGt77rnnruvXq1cvevbsWejjp6Wl0bt3b95++21mzJhR6OcXkbIrKyuL2bNnM336dDIyMnBycmLMmDFMmTKF8uXLmx1PRETErvRusEgxtPZACm+tOQjAv7o3oHaAp8mJRERERKS4SkxMNHX8YcOG0aVLFzp27Pg/izeZmZlkZmbmPbZarfaOJyIl1Jo1axg2bBj79u0D4O677yY2Npb69eubnExERKRoqHgjUsycSc1g9LLtGAY80TKYR5tWNTuSiIiIiBRj1atXN23spUuXsm3bNrZs2VKg/tHR0UybNs3OqUSkJDtz5gxjx47lk08+AcDf359Zs2bx5JNPaok0EREpU1S8ESlGsm25jFiSwIX0LOoFeTH1Id1RJCIiIiIF98EHH9zyeJ8+fQptrBMnTjBq1Ci+++473NzcCvSciRMnEhUVlffYarUSHBxcaJlEpOTKyclh3rx5TJ48mcuXL+Pg4EBkZCTTp0/Hx8fH7HgiIiJFzmL898LIUqisVive3t6kpqbi5eVldhwpAV7+ei8L1x7B09WJL0a0pYZfObMjiYiIiBSYrn/N5+vrm+9xdnY2V65cwcXFBQ8PDy5cuFBoY61cuZLu3bvj6OiY12az2bBYLDg4OJCZmZnv2I3oNSMiABs2bCAyMpIdO3YA0LJlS+Li4mjatKnJyURERApfQa+BNfNGpJj49rckFq49AsBr/2ykwo2IiIiI3LaLFy9e13bw4EGGDh3KuHHjCnWse++9l127duVr69evH2FhYYwfP/5/Fm5ERFJSUpgwYQKLFi0CrhWgZ86cycCBA3FwcDA5nYiIiLlUvBEpBo6fv8LY5dfuMOrfJoT7GwSZnEhERERESovQ0FBmzpzJk08+mbfxd2Hw9PSkQYMG+drKlStHxYoVr2sXEflvubm5vPPOO0yYMCGv6DxgwABmzpyJn5+fyelERESKBxVvREx2NdtG5JKtXL6aQ5NqPkx4IMzsSCIiIiJSyjg5OXH69GmzY4iIsHXrViIjI9m8eTMAjRs3JjY2ltatW5ucTEREpHhR8UbEZNO/3MPuU1Z8PZyZ16spLk6aGi4iIiIif81//vOffI8Nw+DMmTPMnTuXNm3a2H38H3/80e5jiEjJdOnSJSZNmkRsbCyGYeDp6cn06dMZNmwYTk56e0pEROT/p9+OIib6fPspPt50HIDXe4ZT2cfd5EQiIiIiUpJ169Yt32OLxUKlSpXo0KEDs2fPNieUiJRphmHw0Ucf8eyzz3L27FkAnnjiCWbPnk1QkJYMFxERuRkVb0RMcujsZSZ+dm2D1+H31OKeOv4mJxIRERGRki43N9fsCCIieXbv3s2wYcNYu3YtAGFhYcybN48OHTqYnExERKT40/pMIia4kpXD0I+2cSXLRqs7KjLmvtpmRxIREREREREpFGlpaYwbN44mTZqwdu1aPDw8iI6OZseOHSrciIiIFJBm3ogUMcMwmPTv3Rw8m0YlT1fefCIcRweL2bFEREREpBSIiooqcN/XX3/djklEpCwyDINPP/2U0aNHc+rUKeDaco4xMTFUr17d5HQiIiIli4o3IkVs6ZYTfJZwCgcLzHmiCf6ebmZHEhEREZFSIiEhgYSEBLKzs6lTpw4ABw4cwNHRkaZNm+b1s1h085CIFK6DBw8yfPhwvv32WwBCQkKYM2cOXbp0MTmZiIhIyaTijUgR2n0qlan/+Q2AZzvX4c47KpqcSERERERKk4ceeghPT0/ef/99fH19Abh48SL9+vXjrrvuYuzYsSYnFJHSJiMjg+joaF555RWysrJwcXFhwoQJTJgwAXd3d7PjiYiIlFglYs+befPmUaNGDdzc3IiIiGDz5s237L98+XLCwsJwc3OjYcOGfP311/mOG4bBlClTCAoKwt3dnY4dO3Lw4MG840ePHmXAgAGEhITg7u5OzZo1mTp1KllZWXb5+qRssF7NZtiSbWTl5HJPnUoMubum2ZFEREREpJSZPXs20dHReYUbAF9fX2bMmMHs2bNNTCYipdFXX31F/fr1mT59OllZWXTu3Jndu3czbdo0FW5ERET+pmJfvFm2bBlRUVFMnTqVbdu20bhxYzp37szZs2dv2H/Dhg088cQTDBgwgISEBLp160a3bt3YvXt3Xp9XX32Vt956i/nz57Np0ybKlStH586duXr1KgD79u0jNzeXBQsW8Ntvv/HGG28wf/58nn/++SL5mqX0MQyD55bv5Nj5K1Txcef1HuE4aJ8bERERESlkVquVlJSU69pTUlK4fPmyCYlEpDQ6duwY3bp1o2vXriQmJlKlShWWL1/OqlWrCA0NNTueiIhIqWAxDMMwO8StRERE0KJFC+bOnQtAbm4uwcHBjBgxggkTJlzXv2fPnqSnp/Pll1/mtd15552Eh4czf/58DMOgcuXKjB07lmeffRaA1NRUAgICWLx4MY8//vgNc7z22mvExcVx5MiRAme3Wq14e3uTmpqKl5fX7XzZUsq8uy6R6V/uwdnRwvIhrQkP9jE7koiIiEih0/Wv+fr06cPPP//M7NmzadmyJQCbNm1i3Lhx3HXXXbz//vsmJ8xPrxmRkiUrK4vZs2czffp0MjIycHJyYsyYMUyZMoXy5cubHU9ERKREKOg1cLGeeZOVlcXWrVvp2LFjXpuDgwMdO3Zk48aNN3zOxo0b8/UH6Ny5c17/xMREkpKS8vXx9vYmIiLipueEawWeChUq3DJvZmYmVqs134fI1mMXif56LwAvPFhXhRsRERERsZv58+fzwAMP0KtXL6pXr0716tXp1asX999/P7GxsWbHE5ESbM2aNTRu3Jjnn3+ejIwM7r77brZv386rr76qwo2IiIgdFOvizblz57DZbAQEBORrDwgIICkp6YbPSUpKumX/P/69nXMeOnSIOXPm8Mwzz9wyb3R0NN7e3nkfwcHBt+wvpd+F9CyGL9lGTq5Bl4ZB9G1dw+xIIiIiIlKKeXh4EBsby/nz50lISCAhIYELFy4QGxtLuXLlzI4nIiXQmTNn6NWrF/feey/79u3D39+fDz74gB9//JH69eubHU9ERKTUKtbFm+Lg1KlT3H///fzzn/9k0KBBt+w7ceJEUlNT8z5OnDhRRCmlOMrNNRi9bDtnUq8S4leOmY81xGLRPjciIiIiYn/lypWjUaNGNGrUSEUbEflLcnJyePPNN6lTpw6ffPIJDg4ODB8+nP379/PUU0/p71sRERE7czI7wK34+fnh6OhIcnJyvvbk5GQCAwNv+JzAwMBb9v/j3+TkZIKCgvL1CQ8Pz/e806dPc88999C6dWsWLlz4P/O6urri6ur6P/tJ2RD74yHWHkjB1cmB2N5N8XRzNjuSiIiIiIiIyP+0YcMGIiMj2bFjBwAtW7YkLi6Opk2bmpxMRESk7CjWM29cXFxo1qwZ8fHxeW25ubnEx8fTqlWrGz6nVatW+foDfPfdd3n9Q0JCCAwMzNfHarWyadOmfOc8deoU7du3p1mzZrz33ns4OBTrb5UUMxsOn+P17w4AMP2RBtQN0uarIiIiIiIiUrylpKQwYMAA2rRpw44dO/D19WXBggVs3LhRhRsREZEiVqxn3gBERUXRt29fmjdvTsuWLYmJiSE9PZ1+/foB0KdPH6pUqUJ0dDQAo0aNol27dsyePZsuXbqwdOlSfv3117yZMxaLhdGjRzNjxgxCQ0MJCQlh8uTJVK5cmW7dugF/Fm6qV6/OrFmzSElJyctzsxk/In84a73KyE+2k2vAP5pVpUcL7X0kIiIiIiIixVdubi7vvPMOEyZM4OLFiwD079+fmTNnUqlSJZPTiYiIlE3FvnjTs2dPUlJSmDJlCklJSYSHh7N69WoCAgIAOH78eL5ZMa1bt2bJkiVMmjSJ559/ntDQUFauXEmDBg3y+jz33HOkp6czePBgLl26RNu2bVm9ejVubm7AtZk6hw4d4tChQ1StWjVfHsMwiuCrlpIqx5bL8E8SOJeWSVigJ9MfafC/nyQiIiIi8jc0bdqU+Ph4fH19eemll3j22Wfx8PAwO5aIlBBbt24lMjKSzZs3A9CoUSPi4uJo3bq1yclERETKNouhaoTdWK1WvL29SU1NxctLy2aVBa+s3kfcj4cp5+LIf0a0pWal8mZHEhERESkyuv41h7u7OwcPHqRq1ao4Ojpy5swZ/P39zY5VIHrNiJjn0qVLTJo0idjYWAzDwNPTk+nTpzNs2DCcnIr9vb4iIiIlVkGvgfXbWKSQxO9NJu7HwwDMfKyRCjciIiIiUiTCw8Pp168fbdu2xTAMZs2aRfnyN74WnTJlShGnE5HixjAMPvroI5599lnOnj0LwBNPPMHs2bMJCgoyOZ2IiIj8QcUbkUJw4sIVov5vBwB9WlXnocaVTU4kIiIiImXF4sWLmTp1Kl9++SUWi4VVq1bd8K55i8Wi4o1IGbd7926GDRvG2rVrAQgLC2PevHl06NDB5GQiIiLy/1PxRuRvysrJZfiSbaRmZNO4qjcvdKlrdiQRERERKUPq1KnD0qVLAXBwcCA+Pr7ELJsmIkUjLS2NadOmERMTQ05ODh4eHkyePJmoqChcXFzMjiciIiI3oOKNyN/08td72XEyFW93Z+b2aoqrk6PZkURERESkjMrNzTU7gogUI4Zh8OmnnzJ69GhOnToFQLdu3YiJiaF69eompxMREZFbUfFG5G/4aucZFm84CsDrPRoTXMHD3EAiIiIiUuYdPnyYmJgY9u7dC0C9evUYNWoUNWvWNDmZiBSlgwcPMnz4cL799lsAQkJCmDNnDl26dDE5mYiIiBSEg9kBREqqIylpjP90JwBD2tXk3roBJicSERERkbLum2++oV69emzevJlGjRrRqFEjNm3aRP369fnuu+/MjiciRSAjI4MpU6bQoEEDvv32W1xcXJgyZQq//fabCjciIiIliGbeiPwFGVk2Ij/eRlpmDi1rVODZTrXNjiQiIiIiwoQJExgzZgwzZ868rn38+PHcd999JiUTkaLw1VdfMWLECBITEwHo3Lkzc+bMITQ01ORkIiIicrs080bkL5jy+W72JV3Gr7wLc3o1wclR/yuJiIiIiPn27t3LgAEDrmvv378/e/bsMSGRiBSFY8eO0a1bN7p27UpiYiJVqlRh+fLlrFq1SoUbERGREkrvOIvcpv/79QTLt57EYoE3H29CgJeb2ZFERERERACoVKkS27dvv659+/bt+Pv7F30gEbGrrKwsoqOjqVu3Lp9//jlOTk6MGzeOffv28Y9//AOLxWJ2RBEREfmLtGyayG3Yl2Rlyue7ARjTsTZtavmZnEhERERE5E+DBg1i8ODBHDlyhNatWwOwfv16XnnlFaKiokxOJyKFac2aNQwbNox9+/YBcPfddxMbG0v9+vVNTiYiIiKFQcUbkQK6fDWbyI+2cTU7l7trV2L4PbXMjiQiIiIiks/kyZPx9PRk9uzZTJw4EYDKlSvz4osvMnLkSJPTiRkMw+DLL7/km2++wTAMs+NIITlx4gRffPEFAP7+/syaNYsnn3xSM21ERERKERVvRArAMAwmfLaLI+fSCfJ2I6ZnOA4OuigWERERkeLFYrEwZswYxowZw+XLlwHw9PQ0OZWY5ciRI4wYMYKvv/7a7ChiBw4ODkRGRjJ9+nR8fHzMjiMiIiKFTMUbkQL4YOMxvtp5BicHC3N7NaFCORezI4mIiIiI3JKKNmXX1atXefXVV3n55ZfJzMzE2dmZgQMHat+jUsTR0ZGuXbvSpEkTs6OIiIiInah4I/I/bD9xiRlf7QFgwgNhNKteweREIiIiIiIiN7Z69WqGDx/O4cOHAejYsSNz586lTp06JicTERERkdvhYHYAkeLs0pUshn28jWybQef6AQxoG2J2JBERERERkeucOHGCf/zjHzzwwAMcPnyYypUrs2zZMr799lsVbkRERERKIBVvRG4iN9dg7P/t4NSlDKpV8ODVfzTW5o8iIiIiIlKsZGdn89prr1G3bl0+/fRTHB0diYqKYt++ffTo0UN/w4iIiIiUUFo2TeQmFqw9Qvy+s7g4ORDbuyne7s5mRxIREREREcnz008/ERkZyZ4915Z5btu2LbGxsTRs2NDkZCIiIiLyd2nmjcgNbDpynlnf7gfgxYfq06CKt8mJREREREQKZvjw4Vy4cMHsGGJHSUlJPPXUU7Rv3549e/ZQqVIlFi9ezNq1a1W4ERERESklVLwR+f+kXM5kxCcJ2HINujepwhMtg82OJCIiIiJySydPnsz7fMmSJaSlpQHQsGFDTpw4YVYsKWQ2m425c+dSp04dPvroIywWC0OHDmX//v307dtXS6SJiIiIlCJaNk3kv9hyDUYtTeDs5Uxq+ZdnRrcG+gNIRERERIq9sLAwKlasSJs2bbh69SonTpygWrVqHD16lOzsbLPjSSH45ZdfiIyMJCEhAYDmzZsTGxtLixYtTE4mIiIiIvagmTci/+XN7w+w4fB53J0dievdlHKuqm+KiIiISPF36dIlli9fTrNmzcjNzeXBBx+kdu3aZGZm8s0335CcnGx2RPmLzp8/z+DBg2nVqhUJCQn4+PgQFxfHL7/8osKNiIiISCmm4o3I7346kMKcHw4BEP1oQ0IDPE1OJCIiIiJSMNnZ2bRs2ZKxY8fi7u5OQkIC7733Ho6OjixatIiQkBDq1Kljdky5Dbm5ubz77rvUqVOHt99+G4Cnn36a/fv3M2TIEBwdHU1OKCIiIiL2pGkFIsDpSxmMXpqAYUCviGp0a1LF7EgiIiIiIgXm4+NDeHg4bdq0ISsri4yMDNq0aYOTkxPLli2jSpUqbNmyxeyYUkDbt28nMjKSjRs3AtCgQQPi4uJo27atyclEREREpKho5o2Uedm2XIYv2cbFK9k0qOLFlK71zI4kIiIiInJbTp06xaRJk3B1dSUnJ4dmzZpx1113kZWVxbZt27BYLHrjvwRITU1l1KhRNGvWjI0bN1K+fHlmz57Ntm3b9N9PREREpIxR8UbKvJmr9rHt+CU83ZyI7dUMN2ctPyAiIiIiJYufnx8PPfQQ0dHReHh4sGXLFkaMGIHFYuHZZ5/F29ubdu3amR1TbsIwDJYsWUJYWBhvvfUWubm59OzZk3379hEVFYWzs7PZEUVERESkiKl4I2Xa6t1neHddIgCv/aMx1Sp6mJxIREREROTv8/b2pkePHjg7O7NmzRoSExOJjIw0O5bcwN69e7n33nvp3bs3SUlJ1K5dm++++46lS5dSpYqWcxYREREpq1S8kTLr2Pl0xi3fCcDAtiHc3yDQ5EQiIiIiIn/fzp07qVq1KgDVq1fH2dmZwMBAevbsaXIy+W/p6elMmDCBxo0b88MPP+Dm5saMGTPYuXMnHTt2NDueiIiIiJjMyewAIma4mm0j8uNtXM7MoVl1X8Y/EGZ2JBERERGRQhEcHJz3+e7du01MIjdiGAYrV65k9OjRHD9+HICHHnqIN998k5CQEJPTiYiIiEhxoeKNlEnTvtjDb6etVCjnwtxeTXB21CQ0ERERERGxryNHjjBixAi+/vpr4NrMqLfeeouHH37Y5GQiIiIiUtzoHWspc/6dcJJPNh/HYoGYnuEEebubHUlEREREREqxq1ev8tJLL1GvXj2+/vprnJ2deeGFF9izZ48KNyIiIiJyQ5p5I2XKgeTLPP/ZtaUjRnQI5e7alUxOJCIiIiIipdnq1asZPnw4hw8fBqBjx47MnTuXOnXqmJxMRERERIozzbyRMiM9M4f/1969R1VVJ/wf/xww5Q6CXEQlcTLERDFRQitboxNmWViJFo+XGZ/xKcEwdNJMRX/peGlsNabpaJNO5WVyHiW10hwxb+MFQUhHxUvmJUVLE4RRRM7+/dHiPJ1ARVH34fB+reUq9v7ufT6c71l2Pn3P3mfoohxdKitXl/sClNqtpdmRAAAAADipEydO6Pnnn9cTTzyhI0eOKDQ0VEuXLtWXX37Jwg0AAABuiMUb1AmGYWjMij06fLZYQd4N9E7f9nJ1sZgdCwAAAICTKSsr01tvvaXIyEj97//+r1xdXZWWlqYDBw6ob9++sljoIQAAALgxbpuGOmHxzuP6NPeUXF0smvXigwr0bmB2JAAAAABOZuPGjRo6dKj27dsnSerSpYvmzJmjqKgok5MBAACgtuHKGzi9vd8VauLKn8rTH+Ij1Cnc3+REAAAAAJxJQUGB+vfvr8cee0z79u1TYGCgFixYoE2bNrFwAwAAgFvC4g2cWuGlMr28KFtXyq3qHhmkIY+0MDsSAAAAACdRXl6uWbNmKSIiQh9//LEsFotefvll5efna9CgQXJxoXIDAADg1nDbNDgtwzD0h2V5OnH+kpo2dNeMPtFy4XtuAAAAANwG27dv19ChQ7V7925JUocOHTRnzhx17NjR5GQAAABwBnwMCE7r/c1H9eW+M6rv6qLZLz4oX497zI4EAAAAoJY7d+6chgwZori4OO3evVt+fn567733tGPHDhZuAAAAcNtw5Q2c0q5vz2vqmgOSpLFPRapdMz9zAwEAAACo1axWqxYsWKBRo0bp3LlzkqSBAwdq+vTpCgoKMjkdAAAAnA2LN3A654pLlbJ4t8qthnq1C1X/h+41OxIAAACAWiw3N1dDhw7Vtm3bJElt2rTRe++9p0ceecTkZAAAAHBW3DYNTqXcamj433NVUHRZLQI9NeXZKFksfM8NAAAAgJtXWFio1NRUdejQQdu2bZOXl5dmzJihnJwcFm4AAABwR3HlDZzKrMzD2nzoB7nd46I5SR3k1YCXOAAAAICbYxiGlixZohEjRqigoECSlJiYqLfffltNmjQxOR0AAADqAv7PNpzGlkM/6J31ByVJkxKiFBHibXIiAAAAALXN/v37lZycrA0bNkiSWrZsqdmzZ+s3v/mNyckAAABQl9SK26bNnj1bzZs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"text/plain": [ - "
    " + "\"Output" ] }, "metadata": {}, @@ -326,9 +324,8 @@ "outputs": [ { "data": { - "image/png": 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KvVq46aisVsnV2ax/P1xB/3mkYr6uogYKBVfXK1fBGLHfW/TncYqTk5NKlCihmjVr2pZdbTakpFy5R9WUKVM0e/ZsHTlyRJcuXVJWVpbdVGKS1LdvXy1ZskSTJ0/Wt99+e83443p2796t1q1b2y0LDw+3a940adJEH330kSwWi9auXasnnnhC/v7+WrNmjWrVqqUDBw7okUcekSRt27ZNderUue4MAzeyd+9e9ezZ025ZgwYNtGrVKrtlNWvWtDVurh5D7dq17b4l26hRI+Xm5mrv3r231Lz5u+NUAADg+I4dO6a+fftq0aJFkq585ho3bpw6dOhg97vegoBPHw7CZDI5/KC7UqVKMplMeX7wl658mC5evLhKlSqVr+crV66c9u7dqx9++EErV65Uz5499d5772nt2rVKT0+Xk5OTkpKSrmmUFCtWzPZ3d3f3a/6je+CBB1SxYkUtXLjQNjXBny+jf++99zRhwgSNHz9eNWvWlIeHh/r06fOPboKZnp6ugIAAu3vyXHW1afRXP/74o57835QPFy9e1Jo1a/Tqq6/q0qVLKlKkiEaPHq1BgwZp0KBBfzsXAAAo+DJzLJqz/pAmrTqg9MwcSVKL2oF6/clqKuPjfpOtgULGZLrl6cuMUqRIEbvHJpPJbtnVcUxubq4WLlyofv36aezYsQoPD5enp6fee++9a+4hk5KSon379snJyUn79+9Xs2bNblvehx9+WBcuXNCWLVu0bt06vfPOO/L399fo0aNVu3ZtBQYG2qav/us02nfC35nKxGw2X/Nlw+zs7Ft6jhuNU/96TgEAgOPIzs7WpEmTNGzYMKWnp8tsNismJkZvv/22vL29jY73tzh2twAOpUSJEnr88cc1depUvfrqq3Yf2JOTkzV//nx17NjRrpny888/2z3Hzz//bDftmru7u1q0aKEWLVqoV69eqlatmnbu3Kk6derIYrEoJSVFDz300C1njY6O1vz581W2bFmZzWY1b97ctm79+vVq1aqV/vWvf0m6Mljat2/fNZfM/XWg9PPPP6ty5crXNJMkqW7dukpOTpazs3O+r5ipX7++tm3bpqSkJL322muKj4/XkSNH1LJlS23ZskVms/lvfZMNAAAUDlarVSt/PamRK3br8JmLkqRaZb019Onqql+ezwhAYbJ+/Xo1bNjQ7oqUvK7gf/HFF1WzZk117dpV3bp1U0RExHWntf6zkJCQPMc3f+bj46NatWpp8uTJKlKkiKpVq6bSpUurXbt2WrZsme1+N9KVq4pmzZqls2fP3vKYpWrVqtq0aZM6duxoW7Zp06Z8HcOcOXOUkZFha+ysX79eZrNZVatWlSSVKlVKJ06csG1jsVi0a9cuPfroozc89vyOU+vWrXtLxwoAAO6On376ST179tTOnTslXZlRaerUqapTp47Byf4Zs9EBULBMnjxZmZmZioyM1Lp163T06FHFxcXp8ccfV5kyZTRy5Ei7+vXr12vMmDHat2+fpkyZosWLF6t3796SpDlz5uijjz7Srl279Pvvv+u///2v3N3ddd9996lKlSqKjo5Wx44d9eWXX+rgwYPauHGjRo0apeXLl980Z3R0tLZs2aKRI0fqueees92HRpIqV66slStXasOGDdq9e7f+/e9/6+TJk9c8x5EjRxQbG6u9e/fq008/1aRJk2zZ/yoiIkLh4eGKiorS999/r0OHDmnDhg164403tHnz5jy3cXd3V6VKlXTw4EE98sgjqlSpkv744w81atRIVapUUaVKla4ZCP3666/atm2bzp49q9TUVG3btk3btm276esBAAAKlj3JafrXR4nq/kmSDp+5qNKernq/TW0t6dmIxg1QCFWuXFmbN2/Wd999p3379mnIkCHXNDSmTJmihIQEzZ07V9HR0YqKilJ0dHS+ZhB45ZVXFBcXp/fff1/79+/X5MmT7aZMu+qRRx7R/PnzbY0aX19fhYSEaNGiRXbNmw4dOsjf319RUVFav369fv/9d33xxRdKSEi4aZaXX35ZH330kebOnav9+/drxIgR2rFjx02nMYmOjpabm5s6deqkXbt2afXq1Xr55Zf1wgsv2KZMe+yxx7R8+XItX75ce/bsUY8ePXT+/PlrnuvvjlMBAIBjSUlJUZcuXfTQQw9p586d8vX11cyZM7V+/foC37iRaN7gFl0dVFSoUEFt27ZVxYoV1b17dz366KNKSEi4ptnQt29fbd68WXXq1NGIESM0btw4RUZeubePj4+PZs6cqUaNGqlWrVr64Ycf9M0339jmbf7444/VsWNH9e3bV1WrVlVUVJQ2bdqkoKCgm+asVKmSGjRooB07dig6Otpu3eDBg1W3bl1FRkbqkUcesQ06/qpjx466dOmSGjRooF69eql3797q3r17nvszmUxasWKFHn74YXXp0kVVqlSx3UT0ZnMvr1mzRg8//LAkae3atba/5+Wpp55SnTp19M0332jNmjWqU6dOofiHCAAAXHEmPVNvfLVTT034UesPnJGLs1m9Hq2o1f0e0XP1yspsLlhzNAPIn3//+9965pln1K5dO4WFhenMmTN2V+Hs2bNH/fv319SpU1WuXDlJ0tSpU3X69GkNGTLkps//4IMPaubMmZowYYJq166t77//XoMHD76mrkmTJrJYLLZ720hXGjp/Xebi4qLvv/9epUuX1lNPPaWaNWtq9OjRec5S8FfR0dEaOHCg+vXrp7p16+rgwYPq3Lmz3G4yHV7RokX13Xff6ezZs3rggQf03HPPqWnTppo8ebKt5sUXX1SnTp3UsWNHNWnSRBUqVLjmqhvpn41TAQCA8SwWi6ZPn66qVavabpfx0ksvae/evXrppZdkNheOtofJeit3n8ctSUtLk7e3t1JTU+Xl5WW37vLlyzp48KCCg4Nv+iEV+Lt4nwEAUDBk5eRqXsIhTYjfrwuXr9zXpnnNAL3+ZDWV8y1qcLr8u9HnXyAvjJmMM2fOHPXp0yfPK1Putscff1z+/v765JNP7vi+ypcvrz59+qhPnz635fl4nwIAcHdt3rxZPXr0sM12FBoaqmnTpunBBx80OFn+5XfcxD1vAAAAAINYrVat2pOikct36/fTGZKkGoFeGvp0dYVV4FveAAqfixcvavr06YqMjJSTk5M+/fRT/fDDD1q5cqXR0QAAgAM7d+6c3njjDU2fPl1Wq1VeXl4aMWKEevToIWfnwtnmKJxHBQAAADi4fScvaPiyX/Xj/tOSpJLFXPVaZFU9W6+snJgeDcAtePLJJ/Xjjz/muW7QoEEaNGjQXctSo0YNHT58OM91H374oZ555hmtWLFCI0eO1OXLl1W1alV98cUXioiIuGsZAQBAwWG1WjVv3jz1799fp06dknRlGtb3339f/v7+Bqe7s2jeAAAAAHfR2YwsfbBynxZsPCJLrlUuTmZ1fShYvR6tpGKufDwHcOtmzZqlS5cu5bnur/clvapz587q3Lnzbc+yYsUKZWdn57nOz89P7u7u+uGHH277fvPr0KFDhu0bAADcmp07d6pnz5766aefJEkhISGaMmVKnve0K4wYHQIAAAB3QbYlV58kHNb4H/Yp7X/3tWlWw1+DngpRUImCc18bAI6nTJkyRkewue+++4yOAAAACrgLFy7ozTff1IQJE2SxWFS0aFENGzZMffr0kYuLi9Hx7hqaNwazWq1GR0AhxvsLAADHsHpPioYv/1W/n7pyX5uQgCv3tQmvyH1tgJvhMy0cGe9PAABuH6vVqsWLF+vVV1/V8ePHJUnPPPOMPvjgAwUFBRmc7u6jeWMQJycnSVJWVpbc3d0NToPC6uLFi5KkIkWKGJwEAIB704GUCxq+bLfW7rsyN3MJDxf1i6yqtvXLcV8b4Caufoa9ePEiYyY4LMZcAADcHvv27VNMTIxWrlwpSapYsaImTZqkJ5980uBkxqF5YxBnZ2cVLVpUp06dUpEiRWQ2m42OhELEarXq4sWLSklJkY+Pj61ZCAAA7o7zF7M0/of9+uTnw7LkWlXEyaQXGwWr12OV5OXGL/iA/HBycpKPj49SUlIkSUWLFpXJRNMTjoExFwAAt8fFixc1atQojRkzRllZWXJ1ddXAgQM1YMAAubm5GR3PUDRvDGIymRQQEKCDBw/q8OHDRsdBIeXj4yN/f3+jYwAAcM/IseRqfuIRffDDPp2/eOWG3Y9X99MbT4WofEkPg9MBBc/Vz7JXGziAo2HMBQDA37ds2TK9/PLLOnTokCSpWbNmmjRpkipVqmRsMAdB88ZALi4uqly5srKysoyOgkKoSJEifPsLAIC7aO2+Uxqx7FftT0mXJFXz99SQp6urUaWSBicDCq6rX3orXbq0srOzjY4D2GHMBQDA33Po0CH17t1bS5culSSVK1dO48ePV+vWrbnS+k9o3hjMbDbf85d/AQAAFGS/nUrXyOW7tWrPlSsDfD1c1PeJKmpXv5ycnZgaF7gdnJyc+CU5AABAAZeZmamxY8dqxIgRunTpkpydndW3b18NGTJEHh7MVPBXNG8AAACAvyH1YrYmxO/XvIRDysm1ytlsUueG5fVy08rydue+NgAAAABw1Q8//KBevXpp3759kqQmTZpo6tSpql69usHJHBfNGwAAAOAW5Fhy9emmoxr3/V6d+999bSJCSmvQUyGqUKqYwekAAAAAwHEcP35csbGxWrRokSTJz89PY8eO1fPPP88UaTdRIOZxmDJlisqXLy83NzeFhYVp48aNN6xfvHixqlWrJjc3N9WsWVMrVqywW2+1WjV06FAFBATI3d1dERER2r9/v13Nvn371KpVK5UsWVJeXl5q3LixVq9efduPDQAAAAXHT/tPq/nEnzRkyS6du5itKn7FNO/FBprV6QEaN3AIly5d0sWLF22PDx8+rPHjx+v77783MBUAAADuNTk5Ofrggw9UrVo1LVq0SGazWS+//LL27Nmj6OhoGjf54PDNm0WLFik2NlbDhg3Tli1bVLt2bUVGRiolJSXP+g0bNqhDhw7q2rWrtm7dqqioKEVFRWnXrl22mjFjxmjixImaPn26EhMT5eHhocjISF2+fNlW8/TTTysnJ0erVq1SUlKSateuraefflrJycl3/JgBAADgWA6eztBLczfrXx8lau/JCypetIiGt6qhFa88pIerlDI6HmDTqlUrzZs3T5J0/vx5hYWFaezYsWrVqpWmTZtmcDoAAADcC9avX6969eopNjZWFy5cUFhYmDZv3qyJEyfKx8fH6HgFhslqtVqNDnEjYWFheuCBBzR58mRJUm5ursqVK6eXX35Zr7/++jX17dq1U0ZGhpYtW2Zb9uCDDyo0NFTTp0+X1WpVYGCg+vbtq379+kmSUlNT5efnpzlz5qh9+/Y6ffq0SpUqpXXr1umhhx6SJF24cEFeXl5auXKlIiIi8pU9LS1N3t7eSk1NlZeX1z99KQAAAHCXpV3O1qT4/Zqz4ZCyLVfua/NC+H3q07SKvItyX5u/4vOv8UqWLKm1a9eqRo0amjVrliZNmqStW7fqiy++0NChQ7V7926jI9rhPQMAAFB4nDp1SgMGDNDHH38sSfL19dW7776rF198UWazw19Hctfk9zOwQ79iWVlZSkpKsmuWmM1mRUREKCEhIc9tEhISrmmuREZG2uoPHjyo5ORkuxpvb2+FhYXZakqUKKGqVatq3rx5ysjIUE5Ojj788EOVLl1a9erVu27ezMxMpaWl2f0AAACg4LHkWjU/8bAefW+NZv54UNkWqx6tWkpxfR7WsBY1aNzAYV28eFGenp6SpO+//17PPPOMzGazHnzwQR0+fNjgdAAAACiMLBaLPvzwQ1WtWtXWuHnppZe0d+9evfTSSzRu/iZnowPcyOnTp2WxWOTn52e33M/PT3v27Mlzm+Tk5Dzrr053dvXPG9WYTCb98MMPioqKkqenp8xms0qXLq24uDgVL178unlHjRqlt95669YOEgAAAA5lw2+n9fY3v2pP8gVJUsVSHhrydHU9UrW0wcmAm6tUqZKWLFmi1q1b67vvvtOrr74qSUpJSeHKFgAAANx2SUlJ6tGjhzZt2iRJCg0N1dSpUxUeHm5wsoKPllcerFarevXqpdKlS+vHH3/Uxo0bFRUVpRYtWujEiRPX3W7gwIFKTU21/Rw9evQupgYAAMA/cfhMhv79yWY9PzNRe5IvyNu9iN5sUV1xfR6mcYMCY+jQoerXr5/Kly+vBg0a2AbN33//verUqWNwOgAAABQW586dU69evfTAAw9o06ZN8vLy0oQJE7Rp0yYaN7eJQ195U7JkSTk5OenkyZN2y0+ePCl/f/88t/H3979h/dU/T548qYCAALua0NBQSdKqVau0bNkynTt3zvbttKlTp2rlypWaO3dunvfakSRXV1e5urre+oECAADAMBcuZ2vy6gP6+KdDyrLkysls0r/CgtQnooqKe7gYHQ+4Jc8995waN26sEydOqHbt2rblTZs2VevWrQ1MBgAAgMLAarXqk08+Uf/+/ZWSkiJJio6O1nvvvWf3+3b8cw595Y2Li4vq1aun+Ph427Lc3FzFx8dft3sXHh5uVy9JK1eutNUHBwfL39/friYtLU2JiYm2mosXL0rSNXPxmc1m5ebm/vMDAwAAgOEsuVYt3HhEj76/Rh+u/V1Zllw9VLmkvu39kN5qdT+NGxRY/v7+qlOnjo4dO2abDaBBgwaqVq2awckAAABQkO3atUtNmjRRp06dlJKSopCQEK1atUr//e9/adzcAQ7dvJGk2NhYzZw5U3PnztXu3bvVo0cPZWRkqEuXLpKkjh07auDAgbb63r17Ky4uTmPHjtWePXv05ptvavPmzYqJiZF05X42ffr00YgRI7R06VLt3LlTHTt2VGBgoKKioiRdaQAVL15cnTp10vbt27Vv3z71799fBw8eVPPmze/6awAAAIDbK/H3M2o5+Se9/uVOnU7PUoWSHprdub7mvdhAVfw8jY4H/G05OTkaMmSIvL29Vb58eZUvX17e3t4aPHiwsrOzjY4HAACAAujChQvq16+fQkND9eOPP6po0aIaPXq0tm3bpkcffdToeIWWQ0+bJknt2rXTqVOnNHToUCUnJys0NFRxcXHy8/OTJB05csTuCpmGDRtqwYIFGjx4sAYNGqTKlStryZIluv/++201r732mjIyMtS9e3edP39ejRs3VlxcnNzc3CRdma4tLi5Ob7zxhh577DFlZ2erRo0a+vrrr+2mHgAAAEDBcvTsRY36drdW7EyWJHm5Oat3RBW98OB9cnF2+O81ATf18ssv68svv9SYMWNsMwskJCTozTff1JkzZzRt2jSDEwIAAKCgsFqt+vzzz/Xqq6/q2LFjkqTWrVtr/PjxCgoKMjhd4WeyWq1Wo0MUVmlpafL29lZqaqrt3jkAAAC4+9IzczR19QHN+umgsnJyZTZJz4cFKfbxqvJlerTbhs+/xvP29tbChQv15JNP2i1fsWKFOnTooNTU1Hw/17p16/Tee+8pKSlJJ06c0FdffWWbrUC6MpgfNmyYZs6cqfPnz6tRo0aaNm2aKleunO998J4BAABwTPv27VNMTIxWrlwpSapQoYImTZqkp556yuBkBV9+PwPz9UIAAAAUar+dSlfkB+s0dc1vysrJVeNKJfVt74c1IqomjRsUOq6uripfvvw1y4ODg+Xicmvv94yMDNWuXVtTpkzJc/2YMWM0ceJETZ8+XYmJifLw8FBkZKQuX778d6IDAADAAVy6dElDhgxRzZo1tXLlSrm6umrYsGHatWsXjZu7zOGnTQMAAAD+rj3JafrXrESdTs9SOV93DX26hiJCSstkMhkdDbgjYmJiNHz4cH388cdydXWVJGVmZmrkyJG2+4Dm15NPPnnNFTxXWa1WjR8/XoMHD1arVq0kSfPmzZOfn5+WLFmi9u3b31rwy5elW2wuAQAA4PZatmKFXunbVwcPHZIkNXviCU0aN06VKla8UsCXdG6PfL6ONG8AAABQKO38I1UvzE7U+YvZqhHopU+6hnGlDQq9rVu3Kj4+XmXLlrXdr3P79u3KyspS06ZN9cwzz9hqv/zyy7+9n4MHDyo5OVkRERG2Zd7e3goLC1NCQsJ1mzeZmZnKzMy0PU5LS7vyl2eflZwZngIAABjh8OXL6r1/v74+c0aSVNbVVRMqVVLrzEyZbvELQMiHnJx8lfHpGAAAAIVO0uGz6jx7ky5k5qhOkI/mdGkgb/ciRscC7jgfHx89++yzdsvKlSt32/eTnJwsSfLz87Nb7ufnZ1uXl1GjRumtt9667XkAAABw6zJzczX26FGNOHxYl3Jz5WwyKbZsWQ257z4V44s1huMMAAAAoFDZ8NtpvTR3sy5mWdQg2FezOz+gYq587MW94eOPPzY6wg0NHDhQsbGxtsdpaWlXmktffCHd4GatAAAAuL3iV69Wrz59tPfgQUlSk4ce0pTx41WjenWDk90D0tKkv3wJKi+MYgEAAFBorNmbon9/kqTMnFw9VLmkZrxQX+4uTkbHAgodf39/SdLJkycVEBBgW37y5EmFhoZedztXV1fbvXjsuLld+QEAAMAddfz4cfXt21cLFy6UdOXK6bFjx+r555/n3qB3S1ZWvspo3gAAAKBQ+O6XZMUs2KJsi1URIaU1+fm6citC4wb3luDg4BsOun///ffbth9/f3/Fx8fbmjVpaWlKTExUjx49bss+AAAAcPvk5ORo8uTJGjp0qC5cuCCz2axevXrp7bfflo+Pj9HxkAeaNwAAACjwlm4/rlcXbZMl16rmNQM0vn2oijiZjY4F3HV9+vSxe5ydna2tW7cqLi5O/fv3v6XnSk9P14EDB2yPDx48qG3btsnX11dBQUHq06ePRowYocqVKys4OFhDhgxRYGCgoqKibsORAAAA4HZZv369evbsqR07dkiSwsLCNHXqVNWtW9fgZLgRmjcAAAAo0D7bfFQDvtghq1V6pm4ZjXm2lpxp3OAe1bt37zyXT5kyRZs3b76l59q8ebMeffRR2+Or96rp1KmT5syZo9dee00ZGRnq3r27zp8/r8aNGysuLk5uTH8GAADgEE6dOqUBAwbY7ovo6+ur0aNHq2vXrjKbGTM5OpPVarUaHaKwSktLk7e3t1JTU+XFzTcBAABuu08SDmnI179Ikp4PC9KIVvfLbGaeZqPw+ddx/f777woNDVVaWprRUezwngEAALj9LBaLZs2apYEDB+rcuXOSpK5du2r06NEqWbKkwemQ38/AXHkDAACAAmnmut81csVuSdKLjYI15OkQbrAJXMfnn38uX19fo2MAAADgDktKSlKPHj20adMmSVJoaKimTp2q8PBwg5PhVtG8AQAAQIFitVo1adUBjVu5T5LU69GK6vdEVRo3gKQ6derY/bdgtVqVnJysU6dOaerUqQYmAwAAwJ107tw5DR48WNOmTZPVapWnp6eGDx+uXr16ydmZNkBBxFkDAABAgWG1WjXmu72atuY3SVK/J6oo5rHKBqcCHEdUVJTdY7PZrFKlSumRRx5RtWrVjAkFAACAO8ZqteqTTz5Rv379dOrUKUnS888/r/fff18BAQEGp8M/QfMGAAAABYLVatVb3/yqORsOSZIGNw/RSw9VMDYU4GCGDRtmdAQAAADcJbt27VLPnj31448/SpKqVaumqVOn6tFHHzU4GW4HmjcAAABweLm5Vr2xZKc+3XhUkjQi6n7968H7DE4FOCaLxaIlS5Zo9+4r94SqUaOGWrZsKScnJ4OTAQAA4Ha4cOGC3nrrLY0fP14Wi0VFixbV0KFD9eqrr8rFxcXoeLhNaN4AAADAoeVYctX/8x36ausxmU3SmOdq67l6ZY2OBTikAwcO6KmnntKxY8dUtWpVSdKoUaNUrlw5LV++XBUrVjQ4IQAAAP4uq9Wqzz//XK+++qqOHTsmSWrdurXGjx+voKAgg9PhdjMbHQAAAAC4nqycXL386VZ9tfWYnM0mTexQh8YNcAOvvPKKKlasqKNHj2rLli3asmWLjhw5ouDgYL3yyitGxwMAAMDftG/fPkVGRqpt27Y6duyYKlSooOXLl+vLL7+kcVNIceUNAAAAHNLlbIt6zd+i+D0pcnEya/LzdfREDX+jYwEObe3atfr555/l6+trW1aiRAmNHj1ajRo1MjAZAAAA/o5Lly7pnXfe0ZgxY5SVlSVXV1e9/vrrGjBggNzd3Y2OhzuI5g0AAAAczsWsHHWfl6SfDpyWq7NZMzrWV5MqpYyOBTg8V1dXXbhw4Zrl6enpzH8OAABQwCxbtkyvvPKKDh48KElq1qyZJk2apEqVKhmcDHcD06YBAADAoVy4nK3OszfppwOnVdTFSXNfbEDjBsinp59+Wt27d1diYqKsVqusVqt+/vln/ec//1HLli2NjgcAAIB8OHz4sKKiotSiRQsdPHhQZcuW1RdffKEVK1bQuLmH0LwBAACAw0i9mK1/fbRRGw+dlaebsz7pGqYHK5QwOhZQYEycOFEVK1ZUeHi43Nzc5ObmpkaNGqlSpUqaMGGC0fEAAABwA5mZmXrnnXcUEhKir7/+Ws7Ozurfv792796tZ555RiaTyeiIuIuYNg0AAAAO4Ux6pl74aKN+PZGm4kWL6JOuYbq/jLfRsYACw2q1Ki0tTQsXLtSxY8e0e/duSVJISAjf0AQAAHBw8fHx6tWrl/bu3StJatKkiaZMmaIaNWoYnAxGoXkDAAAAw6WkXdbzsxJ1ICVdJYu5av5LYarq72l0LKBAsVqtqlSpkn755RdVrlyZhg0AAEABcPz4cfXt21cLFy6UJPn5+Wns2LF6/vnnudLmHse0aQAAADDUsfOX1PbDBB1ISVeAt5s++/eDNG6Av8FsNqty5co6c+aM0VEAAABwEzk5ORo/fryqVaumhQsXymw2KyYmRnv27FF0dDSNG9C8AQAAgHEOn8lQ2+kJOnTmosr5uuuzf4erQqliRscCCqzRo0erf//+2rVrl9FRAAAAcB3r169XvXr19Oqrr+rChQtq0KCBNm3apEmTJsnHx8foeHAQTJsGAAAAQxxIuaDnZyYq5UKmKpT00PxuYQrwdjc6FlCgdezYURcvXlTt2rXl4uIid3f7/6bOnj1rUDIAAACcOnVKAwYM0McffyxJ8vX11ejRo9W1a1eZzVxnAXs0bwAAAHDX/Xo8TS98lKgzGVmq6uep/74UplKerkbHAgq88ePHGx0BAAAAf2GxWDRr1iwNHDhQ586dkyR17dpVo0ePVsmSJQ1OB0dF8wYAAAB31faj59Vx9kalXsrW/WW89MmLYSru4WJ0LKBQ6NSpk9ERAAAA8CdJSUnq0aOHNm3aJEmqXbu2pk2bpvDwcIOTwdHRvAEAAMBds+nQWXX5eJPSM3NUN8hHH3dpIG/3IkbHAgqNtLS0PJebTCa5urrKxYVGKQAAwN1w7tw5DR48WNOmTZPVapWnp6dGjBihnj17ytmZX8vj5niXAAAA4K5Yf+C0Xpq7WZeyLXqwgq8+6vSAPFz5OArcTj4+PjKZTNddX7ZsWXXu3FnDhg1jXnUAAIA7wGq16pNPPlG/fv106tQpSdLzzz+v999/XwEBAQanQ0HCaBkAAAB33Oo9Kfr3f5OUlZOrh6uU0of/qid3FyejYwGFzpw5c/TGG2+oc+fOatCggSRp48aNmjt3rgYPHqxTp07p/fffl6urqwYNGmRwWgAAgMJl165d6tmzp3788UdJUrVq1TR16lQ9+uijBidDQUTzBgAAAHdU3K4TevnTrcq2WPV4dT9Nfr6OXJ1p3AB3wty5czV27Fi1bdvWtqxFixaqWbOmPvzwQ8XHxysoKEgjR46keQMAAHCbXLhwQW+99ZbGjx8vi8WiokWLaujQoXr11VeZthZ/G9fJAwAA4I75etsx9VpwpXHzdK0ATY2uS+MGuIM2bNigOnXqXLO8Tp06SkhIkCQ1btxYR44cudvRAAAACh2r1arFixcrJCREY8eOlcViUevWrbV7924NGDCAxg3+EZo3AAAAuCMWbTqiPou2yZJr1XP1ympC+zoq4sTHT+BOKleunD766KNrln/00UcqV66cJOnMmTMqXrz43Y4GAABQqOzbt0+RkZFq27atjh07pgoVKmj58uX68ssvFRQUZHQ8FAJMmwYAAIDbbu6GQxq29BdJ0r8eDNLbLe+X2Xz9m6gDuD3ef/99tWnTRt9++60eeOABSdLmzZu1Z88eff7555KkTZs2qV27dkbGBAAAKLAuXbqkd955R2PGjFFWVpZcXV31+uuva8CAAXJ3dzc6HgoRmjcAAAC4raav/U2jv90jSXqpcbDeaB4ik4nGDXA3tGzZUnv37tWHH36ovXv3SpKefPJJLVmyROXLl5ck9ejRw8CEAAAABdeyZcv0yiuv6ODBg5KkyMhITZ48WZUqVTI4GQojmjcAAAC4LaxWq8b/sF8T4vdLkl55rJJefbwKjRvgLitfvrxGjRpldAwAAIBC4/Dhw+rdu7e+/vprSVLZsmU1fvx4PfPMM4x3cMfQvAEAAMA/ZrVaNTpujz5c+7skqX9kVfV6lG+fAQAA4Irdu3dr6NChunTpktFRgFuSm5urNWvW6NKlS3J2dtarr76qoUOHqlixYkZHQyFH8wYAAAD/SG6uVW9984vmJhyWJA19urpebBxscCoAAAA4kv79+2v58uVGxwD+tiZNmmjKlCmqUaOG0VFwj6B5AwAAgL/NkmvVoC93atHmozKZpJFRNfV8WJDRsQAAAOBADh06pBUrVkiSJk6cyBULKHDKlCmjxx9/nCnScFfRvAEAAMDfkmPJVd/F2/X1tuMym6T329TWM3XLGh0LAAAADmbGjBmyWq2KiIjQyy+/bHQcACgQaN4AAADglmXl5OqVT7cq7pdkOZtNmtC+jprXCjA6FgAAABxMZmamZs2aJUnq2bOnwWkAoOCgeQMAAIBbcjnboh7/TdLqvafk4mTW1Oi6iqjuZ3Qs4J5Vt25dxcfHq3jx4qpTp84Np/PYsmXLXUwGAID05Zdf6tSpUypTpoxatGhhdBwAKDBo3gAAACDfMjJz1G3eZm347Yzcipg1s2N9PVS5lNGxgHtaq1at5OrqKkmKiooyNgwAAH8xdepUSVL37t3l7MyvIgEgv0xWq9VqdIjCKi0tTd7e3kpNTZWXl5fRcQAAAP6RtMvZevHjTdp8+Jw8XJw0u/MDCqtQwuhYcCB8/sWt4j0DAIXbzp07VatWLTk5OenIkSMKDAw0OhIAGC6/n4FpdwMAAOCmzl/MUsfZG7Xjj1R5uTlr7osNVCeouNGxAAAA4MCmTZsm6cqVoTRuAODW0LwBAADADZ1Oz9S/ZiVqT/IF+Xq46JOuDVQj0NvoWAD+p3jx4je8z82fnT179g6nAQDgigsXLuiTTz6RJPXs2dPgNABQ8NC8AQAAwHUlp15W9Kyf9dupDJXydNX8l8JUxc/T6FgA/mT8+PFGRwAA4Brz589Xenq6qlatqkcffdToOABQ4NC8AQAAQJ7+OHdRz89M1JGzFxXo7ab53R5UcEkPo2MB+ItOnToZHQEAADtWq1VTp06VJPXo0SPfV4gCAP4fzRsAAABc4+DpDEXP/FnHUy8ryLeo5r8UpnK+RY2OBeAmjhw5csP1QUFBdykJAOBetmHDBu3cuVPu7u58yQAA/iaaNwAAALCz/+QFPT8rUacuZKpCKQ8teOlB+Xu7GR0LQD6UL1/+ht9utlgsdzENAOBedfWqmw4dOsjHx8fYMABQQNG8AQAAgM0vx1P1wkcbdTYjS9X8PfVJ1zCV8nQ1OhaAfNq6davd4+zsbG3dulXjxo3TyJEjDUoFALiXnDp1Sp9//rkkqWfPnganAYCCi+YNAAAAJElbj5xTp9kblXY5R7XKemtulwYq7uFidCwAt6B27drXLKtfv74CAwP13nvv6ZlnnjEgFQDgXjJ79mxlZWXpgQceUL169YyOAwAFFs0bAAAAKPH3M3pxziZlZFlU777i+rjLA/JyK2J0LAC3SdWqVbVp0yajYwAACjmLxaLp06dL4qobAPinaN4AAADc437af1ovzfs/9u47Pqoq///4e9J7oSUEAgm9F0kITVBgxRXFKCtFli4sbUFQBFRAXBVFUQQ0FEUEQRAXQUVBFpQiSK9SpISeQGiZJJA2c39/sM538wORkuFOktfz8ciDzLln7nmHO4/knvnMPXeLMnPsalyhuD7qHiN/b04TgYLIarXmeWwYhpKSkvTKK6+ocuXKJqUCABQVK1as0LFjxxQaGqqOHTuaHQcACjRm5QAAAEXYqv1n1X/edmXn2vVA1ZKa9vcG8vF0NzsWgDsUEhIii8WSp80wDEVGRmrBggUmpQIAFBUffvihJKlHjx7y9fU1OQ0AFGwUbwAAAIqYXJtdaw+laMHmk1p14JxsdkNtaoZpcuf68vagcAMUZD/++GOex25ubipZsqQqVaokDw+mfwAA5zl27Ji+++47SVK/fv1MTgMABR9n7wAAAEXEiQtX9MXWk/py2yklWzMd7e3vK6s329eWp7ubiekA5IcWLVqYHQEAUETNmDFDhmGodevWqlKlitlxAKDAo3gDAABQiGXm2PTDvrNauOWEfj58wdEe6uepJ+8rq46xkaoSFmhiQgDOsG/fPp04cULZ2dl52tu1a2dSIgBAYZaVlaWPPvpIkjRgwACT0wBA4UDxBgAAoBA6kGzVwi0n9dWO07p8JUeSZLFIzSqVUKfYcmpdoxRLpAGF0NGjR/XEE09oz549slgsMgxDkhz3wbHZbGbGAwAUUosXL1ZKSorKlCmjxx57zOw4AFAoULwBAAAoJNKzcvXNrjNasOWkdp287GgvHeyjp2Ii9VSDsoos5mdeQABON2TIEEVHR2vVqlWKjo7W5s2bdeHCBT333HN65513zI4HACikPvzwQ0lS3759uccaAOQTfpsCAAAUYIZhaPuJS1q45aS+3Z2kK9nXPlXv4WbRX2qEqUNspJpXLil3N4vJSQHcCxs3btTq1atVokQJubm5yc3NTc2aNdP48eM1ePBg7dixw+yIAIBCZs+ePVq/fr3c3d31zDPPmB0HAAqNAnFX2g8++EBRUVHy8fFRXFycNm/efNP+ixYtUrVq1eTj46PatWvru+++y7PdMAyNGTNGpUuXlq+vr1q3bq1Dhw5dt59ly5YpLi5Ovr6+Cg0NVXx8fH7+WAAAAHfsQnqWPlp3VH95b63aJ2zUF1tP6Uq2TRVK+uvFR6pp46hWSvh7Az1YtRSFG6AIsdlsCgy8dh+rEiVK6MyZM5Kk8uXL6+DBg2ZGAwAUUtOmTZMkxcfHKyIiwuQ0AFB45PuVN7m5uZo/f77atGmjsLCwu97fwoULNWzYME2bNk1xcXGaNGmS2rRpo4MHD6pUqVLX9d+wYYM6d+6s8ePH69FHH9X8+fMVHx+v7du3q1atWpKkCRMmaPLkyfr0008VHR2t0aNHq02bNtq3b598fHwkSf/+97/Vp08fvfHGG2rZsqVyc3O1d+/eu/55AAAA7pTdbmj94fNauOWkftiXrBzbtXtZ+Hi6qW3tCHVqGKmY8qGOe1sAKHpq1aqlXbt2KTo6WnFxcZowYYK8vLw0Y8YMVahQwex4AIBCJi0tTXPmzJEkDRgwwOQ0AFC4WIzf72CZj/z8/LR//36VL1/+rvcVFxen2NhYTZ06VZJkt9sVGRmpf/7znxo5cuR1/Tt27KiMjAx9++23jrZGjRqpXr16mjZtmgzDUEREhJ577jk9//zzkqTU1FSFhYVp9uzZ6tSpk3JzcxUVFaVx48apd+/ed5zdarUqODhYqampCgoKuuP9AACAou305atatPWkFm09pdOXrzra65QNVsfYSD1WN0JBPp4mJgSu4fzXfCtWrFBGRoaefPJJHT58WI8++qh+++03FS9eXAsXLlTLli3NjpgHrxkAKNimTZum/v37q2rVqtq/fz8fIgKAW3Cr58BOuedNw4YNtXPnzrsu3mRnZ2vbtm0aNWqUo83NzU2tW7fWxo0bb/icjRs3atiwYXna2rRpoyVLlkiSEhMTlZycrNatWzu2BwcHKy4uThs3blSnTp20fft2nT59Wm5ubqpfv76Sk5NVr149vf32246rd24kKytLWVlZjsdWq/VOfmwAAABl59q1av9ZLdhyUmsPpej3j9sE+Xjoifpl1CE2UjUjgs0NCcDlPPDAA8rNzZUkVapUSQcOHNDFixcVGspVeQCA/GUYhj788ENJUv/+/fk7AwD5zCnFmwEDBmjYsGE6efKkGjRoIH9//zzb69Spc0v7OX/+vGw223XLr4WFhenAgQM3fE5ycvIN+ycnJzu2/972R32OHj0qSXrllVf07rvvKioqShMnTtQDDzyg3377TcWKFbvh2OPHj9e4ceNu6WcDAAC4kcPn0vXF1pP697ZTupCR7WhvXKG4OjWMVJua4fLxdDcxIQBXlJKSom7duuk///mP7Ha7YmNj9dlnn6lSpUp/OH8BAOBubNiwQXv27JGvr6+6d+9udhwAKHScUrzp1KmTJGnw4MGONovFIsMwZLFYZLPZnDFsvrHb7ZKkl156Se3bt5ckffLJJypbtqwWLVqkf/zjHzd83qhRo/Jc9WO1WhUZGen8wAAAoEC7kp2rZbuTtHDLSW09fsnRXirQW39rUFYdYiIVVcL/JnsAUNSNGDFCO3fu1KuvviofHx9Nnz5dffr00Y8//mh2NABAIZWQkCBJ6ty5s0JCQswNAwCFkFOKN4mJifmynxIlSsjd3V1nz57N03727FmFh4ff8Dnh4eE37f/7v2fPnlXp0qXz9KlXr54kOdpr1Kjh2O7t7a0KFSroxIkTf5jX29tb3t7et/jTAQCAoswwDO05naoFW07q651nlJ51bZkjdzeLHqxaSp1iI/VA1ZLycHczOSmAgmDlypWaPXu22rRpI0l69NFHVb16dWVlZTFHAQDku5SUFC1atEjStRV4AAD5zynvBpQvX/6mX7fKy8tLDRo00KpVqxxtdrtdq1atUuPGjW/4nMaNG+fpL12byPzePzo6WuHh4Xn6WK1Wbdq0ydGnQYMG8vb21sGDBx19cnJydOzYsbu+jw8AACjaLl/J1uyfE/XX99ep3dSfNX/TCaVn5ap8cT8Nb1NVG0a21EfdY9S6RhiFGwC37MyZM6pbt67jceXKleXt7a2kpCSnjmuz2TR69GhFR0fL19dXFStW1L/+9S8Zv9+oCwBQKM2aNUvZ2dmKjY1VgwYNzI4DAIWSU668kaQjR45o0qRJ2r9/v6RrV7EMGTJEFStWvK39DBs2TN27d1dMTIwaNmyoSZMmKSMjQz179pQkdevWTWXKlNH48eMlSUOGDFGLFi00ceJEtW3bVgsWLNDWrVs1Y8YMSdeWb3v22Wf12muvqXLlyoqOjtbo0aMVERGh+Ph4SVJQUJD69eunsWPHKjIyUuXLl9fbb78tSXrqqafy478HAAAUIXa7oV8SL2jhlpP6fm+ysnOvLdHq5eGmR2qFq2NsOcVFF5ObGzd5BXDn3N3dr3vs7CLKW2+9pYSEBH366aeqWbOmtm7dqp49eyo4ODjPw4su3gAAdndJREFUMtoAgMLDZrNp2rRpkrjqBgCcySnFmxUrVqhdu3aqV6+emjZtKkn6+eefVbNmTX3zzTf6y1/+csv76tixo1JSUjRmzBglJyerXr16Wr58ucLCwiRJJ06ckJvb/30qtUmTJpo/f75efvllvfjii6pcubKWLFmiWrVqOfq88MILysjIUN++fXX58mU1a9ZMy5cvl4+Pj6PP22+/LQ8PD3Xt2lVXr15VXFycVq9erdDQ0Lv97wEAAEXEWWumvtx2Sl9sPanjF6442quXDlKn2EjF1yujYD9PExMCKCwMw1CVKlVksfxfETg9PV3169fPM1+6ePFivo67YcMGPf7442rbtq0kKSoqSp9//rk2b96cr+MAAFzHihUrdOzYMYWGhqpjx45mxwGAQstiOOGjWPXr11ebNm305ptv5mkfOXKkfvjhB23fvj2/h3RJVqtVwcHBSk1NVVBQkNlxAADAPZBrs+vHgylauOWEfjyYIpv92qlWgLeHHq8XoU6x5VSrTFCeN1iBwoLzX/N8+umnt9Sve/fu+TruG2+8oRkzZuiHH35QlSpVtGvXLj300EN699131aVLlz99Pq8ZACh4HnvsMX377bcaOnSo3n33XbPjAECBc6vnwE658mb//v364osvrmvv1auXJk2a5IwhAQAATHXsfIa+2HpSX247pXNpWY722KhQdYwtp0dqh8vPy2kr1gIo4vK7KHOrRo4cKavVqmrVqsnd3V02m02vv/76HxZusrKylJX1f78jrVbrvYoKAMgHx44d07JlyyRJ/fr1MzkNABRuTnkHoWTJktq5c6cqV66cp33nzp0qVaqUM4YEAAC45zJzbFq+N1kLtpzQL0f/bymi4v5e+luDsnoqJlKVSgWYmBAAnOuLL77QvHnzNH/+fNWsWVM7d+7Us88+q4iIiBsWlMaPH69x48aZkBQAkB9mzJghwzDUunVrValSxew4AFCoOaV406dPH/Xt21dHjx5VkyZNJF27581bb72lYcOGOWNIAACAe2bfGasWbjmhr3acljUzV5JksUgtqpRUp9hItawWJi8Ptz/ZCwAUfMOHD9fIkSPVqVMnSVLt2rV1/PhxjR8//obFm1GjRuWZE1qtVkVGRt6zvACAO5eVlaWPPvpIkjRgwACT0wBA4eeU4s3o0aMVGBioiRMnatSoUZKkiIgIvfLKKxo8eLAzhgQAAHAqa2aOvt55Rl9sPandp1Id7WVCfNUhJlJPxZRVRIiviQkB4N67cuWK3NzyFqvd3d1lt9tv2N/b21ve3t73IhoAIJ8tXrxYKSkpKlOmjB577DGz4wBAoZfvxZvc3FzNnz9fTz/9tIYOHaq0tDRJUmBgYH4PBQAA4FSGYWjr8UtasPmklu05o8yca29Gerpb9FDNcHWKjVTTiiXk5mYxOSkAmOOxxx7T66+/rnLlyqlmzZrasWOH3n33XfXq1cvsaACAfPbhhx9KurbijocH93IEAGezGIZh5PdO/fz8tH//fpUvXz6/d12gWK1WBQcHKzU1VUFBQWbHAQAAt+hCepb+vf2UFmw5qaMpGY72yqUC1DE2Uk/eV1bF/L1MTAi4Js5/i560tDSNHj1aX331lc6dO6eIiAh17txZY8aMkZfXn/+e5DUDAAXDnj17VKdOHbm7u+vEiROKiIgwOxIAFFi3eg7slDJ5w4YNtWPHjiJfvAEAAAXLzpOXNWfjMX27O0nZudeusvHzctdjdSLUITZS95ULkcXCVTYAXNcf3WPUYrHIx8dHlSpV0uOPP65ixYrly3iBgYGaNGmSJk2alC/7AwC4pmnTpkmS4uPjKdwAwD3ilOLNgAED9Nxzz+nUqVNq0KCB/P3982yvU6eOM4YFAAC4bZk5Nn27O0lzNx7Trv+5l02dssF6umE5PVo3QgHeLAsBoGDYsWOHtm/fLpvNpqpVq0qSfvvtN7m7u6tatWr68MMP9dxzz2n9+vWqUaOGyWkBAAVBWlqa5syZI+nae34AgHvDKe9EdOrUSZI0ePBgR5vFYpFhGLJYLLLZbM4YFgAA4JadvHhF8zad0MItJ3TpSo4kycvdTY/WLa1ujaNULzLE3IAAcAd+v6rmk08+cSzBkJqaqmeeeUbNmjVTnz59HPcnXbFihclpAQAFwbx585Senq6qVavqwQcfNDsOABQZTineJCYmOmO3AAAAd8VuN7T+8HnN2XhMqw6c0+93/isT4qsujcqpY0ykigd4mxsSAO7C22+/rZUrV+ZZOzs4OFivvPKKHnroIQ0ZMkRjxozRQw89ZGJKAEBBYRiGPvzwQ0lSv379WEIYAO6hfC/e5OTkqGXLlvr2229VvXr1/N49AADAbUu9mqMvt53SZ78cV+L5DEf7/ZVLqGuj8mpVPUzubkxEARR8qampOnfu3HVLoqWkpMhqtUqSQkJClJ2dbUY8AEABs2HDBu3Zs0e+vr7q3r272XEAoEjJ9+KNp6enMjMz83u3AAAAt21/klVzNh7Xkh2ndTXn2rKtgd4eat+grLo2Lq+KJQNMTggA+evxxx9Xr169NHHiRMXGxkqStmzZoueff17x8fGSpM2bN6tKlSompgQAFBQJCQmSpM6dOys0NNTkNABQtFgM4/cFQ/LPG2+8od9++00fffSRPDyK7g1+rVargoODlZqammfZAgAA4Dw5NruW703W3I3HtfnYRUd71bBAdW1cXk/ULyN/76J7fgI4E+e/5ktPT9fQoUM1Z84c5ebmSpI8PDzUvXt3vffee/L399fOnTslSfXq1TMv6H/xmgEA15WSkqKyZcsqOztbW7duVYMGDcyOBACFwq2eAzvlnYstW7Zo1apV+uGHH1S7dm35+/vn2b548WJnDAsAAIqws9ZMzd90QvM3n1BKWpYkyd3Noodrhqtb4/JqGF2MNboBFHoBAQGaOXOm3nvvPR09elSSVKFCBQUE/N+Vhq5QtAEAuL5Zs2YpOztbsbGxFG4AwAROKd6EhISoffv2ztg1AACAg2EY2px4UXN+Oa4Ve5OVa792QXHJQG893bCcno4rp7AgH5NTAsC9FxAQoDp16pgdAwBQQNlsNk2bNk2S1L9/f5PTAEDR5JTizSeffOKM3QIAAEiSMrJy9dWO0/rsl+M6kJzmaI+NClW3xlFqUzNcXh5uJiYEAHNkZGTozTff1KpVq3Tu3DnZ7fY823+/GgcAgJtZsWKFjh07ptDQUHXs2NHsOABQJDltwffc3Fz99NNPOnLkiJ5++mkFBgbqzJkzCgoKynPJPgAAwK06kpKuuRuP69/bTikt69q9HHw93RVfv4y6NiqvGhHcLwFA0fbMM89ozZo16tq1q0qXLs1ykQCAO5KQkCBJ6tGjh/z8/ExOAwBFk1OKN8ePH9fDDz+sEydOKCsrS3/5y18UGBiot956S1lZWY7LLgEAAP6MzW5o9YFzmrPxmNYdOu9ojy7hr783Kq+/NSirYF9PExMCgOv4/vvvtWzZMjVt2tTsKACAAurYsWNatmyZJKlfv34mpwGAosspxZshQ4YoJiZGu3btUvHixR3tTzzxhPr06eOMIQEAQCFzIT1LC7ee1LxfTuj05auSJItFalWtlLo2jtL9lUrIzY1PlAPA/woNDVWxYsXMjgEAKMBmzJghwzDUunVrValSxew4AFBkOaV4s27dOm3YsEFeXl552qOionT69GlnDAkAAAqJnScva87GY/p2d5Kyc6/dqyHUz1MdYiP197jyiizGsg0A8Ef+9a9/acyYMfr0009Z5gYAcNuysrL00UcfSZIGDBhgchoAKNqcUryx2+2y2WzXtZ86dUqBgYHOGBIAABRgmTk2fbs7SXM3HtOuU6mO9jplg9WtcZQerVNaPp7uJiYEgIJh4sSJOnLkiMLCwhQVFSVPz7zLSm7fvt2kZACAgmDx4sVKSUlRmTJl9Nhjj5kdBwCKNKcUbx566CFNmjRJM2bMkCRZLBalp6dr7NixeuSRR5wxJAAAKIBOXryieZtOaOGWE7p0JUeS5OXupkfrlFa3JlGqFxlibkAAKGDi4+PNjgAAKMASEhIkSX369JGHh1PeNgQA3CKLYRhGfu/01KlTatOmjQzD0KFDhxQTE6NDhw6pRIkSWrt2rUqVKpXfQ7okq9Wq4OBgpaamKigoyOw4AAC4BLvd0PrD5zVn4zGtOnBOv5+JlAnxVZdG5dQxJlLFA7zNDQngjnD+i9vFawYAXMeePXtUp04dubu768SJE4qIiDA7EgAUSrd6DuyUEnrZsmW1a9cuLVy4ULt27VJ6erp69+6tLl26yNfX1xlDAgAAF5d6NUdfbjulz345rsTzGY72+yuXUNdG5dWqepjc3SwmJgQAAACKrmnTpkm6dhUnhRsAMJ/Trn/08PBQly5d1KVLF2cNAQAACoD9SVbN2XhcS3ac1tWca/fEC/T2UPsGZdW1cXlVLBlgckIAKNiKFSum3377TSVKlFBoaKgslj8uhF+8ePEeJgMAFBRpaWmaM2eOJGnAgAEmpwEASE4s3gAAgKIrx2bX8r3JmrvxuDYf+783CquGBapr4/J6on4Z+XtzGgIA+eG9995TYGCg4/ubFW8AALiRefPmKT09XVWrVtWDDz5odhwAgCjeAACAfHTWmqn5m05o/uYTSknLkiS5u1n0cM1wdW1cXnHRxXhTEQDyWffu3R3f9+jRw7wgAIACyTAMJSQkSJL69evH+ToAuAiKNwAA4K4YhqHNiRc155fjWrE3Wbl2Q5JUMtBbnRuW09MNyyk82MfklABQNLi7uyspKUmlSpXK037hwgWVKlVKNpvNpGQAAFe1YcMG7d69W76+vnk+EAAAMBfFGwAAcEcysnL11Y7T+uyX4zqQnOZoj40KVbfGUWpTM1xeHm4mJgSAoscwjBu2Z2VlycvL6x6nAQAUBL9fddO5c2eFhoaanAYA8DuKNwAA4LYcSUnX3I3H9e9tp5SWlStJ8vV0V3z9MuraqLxqRASZnBAAip7JkydLkiwWiz766CMFBAQ4ttlsNq1du1bVqlUzKx4AwEWlpKRo0aJFkqQBAwaYnAYA8L/yrXgTGhp6y2tiXrx48c87AQAAl2K3Gxr79a+a+8txR1t0CX/9vVF5/a1BWQX7epqYDgCKtvfee0/StStvpk2bJnd3d8c2Ly8vRUVFadq0aWbFAwC4qFmzZik7O1uxsbFq0KCB2XEAAP8j34o3kyZNcnx/4cIFvfbaa2rTpo0aN24sSdq4caNWrFih0aNH59eQAADgHrHbDb20ZK8+33xCFovUqlopdW0cpfsrlZCbGzc0BQCzJSYmSpIefPBBLV68mGVvAAB/ymazafr06ZKk/v37m5wGAPD/sxh/tCjyXWjfvr0efPBBDRo0KE/71KlT9Z///EdLlizJ7yFdktVqVXBwsFJTUxUUxBIyAICCyTAMjVl67YobN4v0Xsd6erxeGbNjAXBBnP/idvGaAQDzfPfdd2rbtq1CQ0N16tQp+fn5mR0JAIqEWz0Hdso9b1asWKG33nrruvaHH35YI0eOdMaQAADACQzD0Lhv9mnuL8dlsUhv/60uhRsAcGG9evW66fZZs2bdoyQAAFeXkJAgSerRoweFGwBwQW7O2Gnx4sW1dOnS69qXLl2q4sWLO2NIAACQzwzD0Bvf7dfsDcckSW89WUftG5Q1NxQA4KYuXbqU5+vcuXNavXq1Fi9erMuXL5sdDwDgIo4dO6Zly5ZJkvr162dyGgDAjTjlyptx48bpmWee0U8//aS4uDhJ0qZNm7R8+XLNnDnTGUMCAIB8ZBiGJqw4qJnrrt1D4Y0naqtDbKTJqQAAf+arr766rs1ut6t///6qWLGiCYkAAK5oxowZMgxDrVu3VpUqVcyOAwC4AadcedOjRw/9/PPPCgoK0uLFi7V48WIFBQVp/fr16tGjhzOGBAAA+ei9lb8p4acjkqRXH6+pp+PKmZwIAHCn3NzcNGzYML333ntmRwEAuICsrCx99NFHkqT+/fubnAYA8EeccuWNJMXFxWnevHnO2j0AAHCSyasOafLqw5KkMY/WULfGUeYGAgDctSNHjig3N9fsGAAAF7B48WKlpKQoIiJC7dq1MzsOAOAPOK14c+TIEX3yySc6evSoJk2apFKlSun7779XuXLlVLNmTWcNCwAA7sKHPx3Wuyt/kyS9+Eg19WoWbXIiAMDtGDZsWJ7HhmEoKSlJy5YtU/fu3U1KBQBwJQkJCZKkvn37ysPDaW8NAgDuklOWTVuzZo1q166tTZs26d///rfS09MlSbt27dLYsWOdMSQAALhLM9Ye0YTlByVJw9tUVd/m3BsBAAqaHTt25PnavXu3JGnixImaNGmSueEAAKbbs2eP1q1bJ3d3d/Xp08fsOACAm3BKeX3kyJF67bXXNGzYMAUGBjraW7ZsqalTpzpjSAAAcBc+Xp+oN747IEka9pcqGvhgJZMTAQDuxI8//mh2BACAC5s2bZokKT4+XhERESanAQDcjFOuvNmzZ4+eeOKJ69pLlSql8+fPO2NIAABwh+ZsPKZ/fbtPkjS4ZSUNblXZ5EQAAAAA8ltaWprmzJkjSerfv7/JaQAAf8YpxZuQkBAlJSVd175jxw6VKVPGGUMCAIA7MH/TCY1Z+qskqf8DFTX0L1VMTgQAuBsXLlzQwIEDVaNGDZUoUULFihXL8wUAKLrmzZun9PR0Va1aVS1btjQ7DgDgTzhl2bROnTppxIgRWrRokSwWi+x2u37++Wc9//zz6tatmzOGBAAAt+mLLSf14ld7JEl97o/WC22qymKxmJwKAHA3unbtqsOHD6t3794KCwvj9zoAQJJkGIYSEhIkSf369ePvAwAUAE4p3rzxxhsaOHCgIiMjZbPZVKNGDdlsNj399NN6+eWXnTEkAAC4Df/edkojFl+7iXXPplF68ZHqTOAAoBBYt26d1q9fr7p165odBQDgQjZs2KDdu3fL19dX3bt3NzsOAOAWOKV44+XlpZkzZ2rMmDHas2eP0tPTVb9+fVWuzBr6AACYbenO0xr+5S4ZhtS1UXmNebQGhRsAKCSqVaumq1evmh0DAOBifr/qpnPnzgoNDTU5DQDgVjjlnjevvvqqrly5osjISD3yyCPq0KGDKleurKtXr+rVV191xpAAAOAWLNudpKELd8puSJ0bRmpcu5oUbgCgEPnwww/10ksvac2aNbpw4YKsVmueLwBA0ZOSkqJFixZJkvr3729yGgDArXJK8WbcuHFKT0+/rv3KlSsaN26cM4YEAAB/YvneJA1esEN2Q3qqQVm9Hl9bbm4UbgCgMAkJCZHValXLli1VqlQphYaGKjQ0VCEhIXzSGgCKqFmzZik7O1uxsbGKiYkxOw4A4BY5Zdk0wzBu+CneXbt2qVixYs4YEgAA3MTKfWc1aP4O2eyGnqxfRm+2r0PhBgAKoS5dusjT01Pz589XWFgYV1cCQBFns9k0ffp0SVx1AwAFTb4Wb0JDQ2WxWGSxWFSlSpU8EwWbzab09HT169cvP4cEAAB/4scD5zRg3jbl2g21qxuht5+qK3cKNwBQKO3du1c7duxQ1apVzY4CAHABK1asUGJiokJDQ9WxY0ez4wAAbkO+Fm8mTZokwzDUq1cvjRs3TsHBwY5tXl5eioqKUuPGjfNzSAAAcBNrf0vRPz7bphyboba1S+vdDhRuAKAwi4mJ0cmTJyneAAAkSQkJCZKkHj16yM/Pz+Q0AIDbka/Fm+7du0uSoqOj1aRJE3l6eubn7gEAwG34+fB59ZmzVdm5drWpGaZJnerJw90pt7sDALiIf/7znxoyZIiGDx+u2rVrXzcnq1OnjknJAAD32rFjx7Rs2TJJYiUcACiAnHLPmxYtWji+z8zMVHZ2dp7tQUFBzhgWAAD81y9HL6j3p1uUlWtX6+qlNKXzffKkcAMAhd7vS+L06tXL0WaxWBz3JbXZbGZFAwDcYzNmzJBhGGrdurWqVKlidhwAwG1ySvHmypUreuGFF/TFF1/owoUL121nwgAAgPNsOXZRvWZvUWaOXQ9ULakPutwnLw8KNwBQFCQmJpodAQDgArKysvTxxx9Lkvr3729yGgDAnXBK8Wb48OH68ccflZCQoK5du+qDDz7Q6dOnNX36dL355pvOGBIAAEjafuKSeszarCvZNt1fuYSm/b2BvD3czY4FALhHypcvb3YEAIALWLx4sc6dO6eIiAi1a9fO7DgAgDvglOLNN998ozlz5uiBBx5Qz549df/996tSpUoqX7685s2bpy5dujhjWAAAirRdJy+r+8eblZFtU5OKxTWja4x8PCncAEBRc+bMGa1fv17nzp2T3W7Ps23w4MEmpQIA3EsJCQmSpL59+8rDwylv/wEAnMwpv70vXryoChUqSLp2f5uLFy9Kkpo1a8almgAAOMHe06nq+vEmpWXlqmF0MX3UPUa+XhRuAKComT17tv7xj3/Iy8tLxYsXl8VicWyzWCwUbwCgCNizZ4/WrVsnd3d39enTx+w4AIA75JQF8CtUqOBYa7latWr64osvJF27IickJMQZQwIAUGTtO2NVl482yZqZq5jyofqkR6z8vPh0HQAURaNHj9aYMWOUmpqqY8eOKTEx0fF19OhRs+MBAO6BadOmSZLi4+MVERFhchoAwJ1ySvGmZ8+e2rVrlyRp5MiR+uCDD+Tj46OhQ4dq+PDhzhgSAIAi6WBymv7+8SalXs1R/XIh+qRnrPy9KdwAQFF15coVderUSW5uTpnqAQBcXFpamubOnStJrH4DAAWcU97dGTp0qOP71q1b68CBA9q2bZsqVaqkOnXqOGNIAACKnMPn0tTlo190MSNbdcoG69NeDRXo42l2LACAiXr37q1FixZp5MiRZkcBAJhg3rx5SktLU9WqVdWyZUuz4wAA7sI9+Whu+fLlVb58+XsxFAAARcKRlHR1nrlJ59OzVTMiSHN7xSmIwg0AFHnjx4/Xo48+quXLl6t27dry9Mz7t+Hdd981KRkAwNkMw1BCQoIkqV+/fnnuewYAKHjyrXgzefLkW+7LTTIBALhzx85n6OmZvyglLUvVwgP1We84BftRuAEAXCverFixQlWrVpWkPG/c8SYeABRuGzZs0O7du+Xr66vu3bubHQcAcJfyrXjz3nvv3VI/i8Vy28WbDz74QG+//baSk5NVt25dTZkyRQ0bNvzD/osWLdLo0aN17NgxVa5cWW+99ZYeeeQRx3bDMDR27FjNnDlTly9fVtOmTZWQkKDKlStft6+srCzFxcVp165d2rFjh+rVq3db2QEAyE8nLlxR55m/6Kw1S1XCAjTvmTiF+nuZHQsA4CImTpyoWbNmqUePHmZHAQDcY79fddO5c2eFhoaanAYAcLfy7S6WiYmJt/R19OjR29rvwoULNWzYMI0dO1bbt29X3bp11aZNG507d+6G/Tds2KDOnTurd+/e2rFjh+Lj4xUfH6+9e/c6+kyYMEGTJ0/WtGnTtGnTJvn7+6tNmzbKzMy8bn8vvPCCIiIibu8/AwAAJzh16VrhJik1UxVL+mveM41UPMDb7FgAABfi7e2tpk2bmh0DAHCPpaSkaNGiRZKk/v37m5wGAJAf8q144yzvvvuu+vTpo549e6pGjRqaNm2a/Pz8NGvWrBv2f//99/Xwww9r+PDhql69uv71r3/pvvvu09SpUyVdu+pm0qRJevnll/X444+rTp06mjNnjs6cOaMlS5bk2df333+vH374Qe+8846zf0wAAG7qzOWr6jzzF52+fFUVSvjr8z6NVDKQwg0AIK8hQ4ZoypQpZscAANxjs2bNUnZ2tmJjYxUTE2N2HABAPsi3ZdP+V69evW66/Y8KL/+/7Oxsbdu2TaNGjXK0ubm5qXXr1tq4ceMNn7Nx40YNGzYsT1ubNm0chZnExEQlJyerdevWju3BwcGKi4vTxo0b1alTJ0nS2bNn1adPHy1ZskR+fn63lDcrK0tZWVmOx1ar9ZaeBwDAzSSnZurpmb/o5MWrKl/cT/P7NFKpIB+zYwEAXNDmzZu1evVqffvtt6pZs6Y8PfPeE23x4sUmJQMAOIvNZtP06dMlcdUNABQmTineXLp0Kc/jnJwc7d27V5cvX1bLli1veT/nz5+XzWZTWFhYnvawsDAdOHDghs9JTk6+Yf/k5GTH9t/b/qiPYRjq0aOH+vXrp5iYGB07duyW8o4fP17jxo27pb4AANyKc2nXCjfHLlxR2VBfze/TSOHBFG4AADcWEhKiJ5980uwYAIB7aMWKFUpMTFRoaKg6duxodhwAQD5xSvHmq6++uq7Nbrerf//+qlixojOGzFdTpkxRWlpanit+bsWoUaPyXPVjtVoVGRmZ3/EAAEXE+fQsPT1zk46ez1CZEF993qeRyoT4mh0LAODCPvnkk3s+5unTpzVixAh9//33unLliipVqqRPPvmEZXsA4B5JSEiQJPXo0eOWV48BALi+e3bPGzc3Nw0bNkzvvffeLT+nRIkScnd319mzZ/O0nz17VuHh4Td8Tnh4+E37//7vzfqsXr1aGzdulLe3tzw8PFSpUiVJUkxMjLp37/6Heb29vRUUFJTnCwCAO3ExI1t//2iTDp9LV3iQj+b3iVNkMSZiAIBbk5KSovXr12v9+vVKSUlx2jiXLl1S06ZN5enpqe+//1779u3TxIkTFRoa6rQxAQD/59ixY1q2bJkkqV+/fianAQDkp3tWvJGkI0eOKDc395b7e3l5qUGDBlq1apWjzW63a9WqVWrcuPENn9O4ceM8/SVp5cqVjv7R0dEKDw/P08dqtWrTpk2OPpMnT9auXbu0c+dO7dy5U999950kaeHChXr99ddvOT8AAHfi8pVrhZsDyWkqFeitz/s2Uvni/mbHAgAUABkZGerVq5dKly6t5s2bq3nz5oqIiFDv3r115cqVfB/vrbfeUmRkpD755BM1bNhQ0dHReuihhwrEigsAUBjMmDFDhmGodevWqlKlitlxAAD5yCnLpv3v0mHStXvIJCUladmyZTe9cuWP9tW9e3fFxMSoYcOGmjRpkjIyMtSzZ09JUrdu3VSmTBmNHz9ekjRkyBC1aNFCEydOVNu2bbVgwQJt3bpVM2bMkCRZLBY9++yzeu2111S5cmVFR0dr9OjRioiIUHx8vCSpXLlyeTIEBARIkipWrKiyZcve9v8HAAC3KvVKjv7+8SbtS7KqRMC1wk10CQo3AIBbM2zYMK1Zs0bffPONmjZtKklav369Bg8erOeee86xtE5++frrr9WmTRs99dRTWrNmjcqUKaMBAwaoT58+N+yflZWlrKwsx2Or1ZqveQCgKMnKytLHH38sSerfv7/JaQAA+c0pxZsdO3bkeezm5qaSJUtq4sSJ6tWr123tq2PHjkpJSdGYMWOUnJysevXqafny5QoLC5MknThxQm5u/3cBUZMmTTR//ny9/PLLevHFF1W5cmUtWbJEtWrVcvR54YUXlJGRob59++ry5ctq1qyZli9fLh8fbgANADCPNTNH3WZt0t7TVhX399LnfeJUsWSA2bEAAAXIv//9b3355Zd64IEHHG2PPPKIfH191aFDh3wv3hw9elQJCQkaNmyYXnzxRW3ZskWDBw+Wl5fXDT+4N378eI0bNy5fMwBAUbV48WKdO3dOERERateundlxAAD5zGIYhmF2iMLKarUqODhYqamp3P8GAHBT6Vm56vbxJm0/cVmhfp76vG8jVQvnbweAgoXzX/P5+flp27Ztql69ep72X3/9VQ0bNlRGRka+jufl5aWYmBht2LDB0TZ48GBt2bJFGzduvK7/ja68iYyM5DUDAHegefPmWrdunV555RWNHTvW7DgAgFt0q/Ome3rPGwAAcL2MrFz1/GSztp+4rGBfT332TByFGwDAHWncuLHGjh2rzMxMR9vVq1c1bty4P7xv6N0oXbq0atSokaetevXqOnHixA37e3t7KygoKM8XAOD27dmzR+vWrZO7u7ueeeYZs+MAAJzAKcumXbhwQWPGjNGPP/6oc+fOyW6359l+8eJFZwwLAECBczXbpt6fbtGWY5cU6OOhz3rHqWZEsNmxAAAF1Pvvv682bdqobNmyqlu3riRp165d8vHx0YoVK/J9vKZNm+rgwYN52n777TeVL18+38cCAPyfadOmSZLi4+NVpkwZk9MAAJzBKcWbrl276vDhw+rdu7fCwsJksVicMQwAAAVaZo5Nz8zZol+OXlSAt4fm9Gqo2mUp3AAA7lytWrV06NAhzZs3TwcOHJAkde7cWV26dJGvr2++jzd06FA1adJEb7zxhjp06KDNmzdrxowZmjFjRr6PBQC4Ji0tTXPnzpUk9e/f3+Q0AABncUrxZt26dVq/fr3jk14AACCvzByb+s7dpp8PX5C/l7s+7RWr+uVCzY4FACgE/Pz81KdPn3syVmxsrL766iuNGjVKr776qqKjozVp0iR16dLlnowPAEXRvHnzlJaWpqpVq6ply5ZmxwEAOIlTijfVqlXT1atXnbFrAAAKvKxcmwbM2661v6XI19Ndn/RsqAbli5kdCwBQCIwfP15hYWHq1atXnvZZs2YpJSVFI0aMyPcxH330UT366KP5vl8AwPUMw1BCQoIkqV+/fqx2AwCFmJszdvrhhx/qpZde0po1a3ThwgVZrdY8XwAAFFXZuXYNmr9Dqw+ck4+nm2b1iFXDaAo3AID8MX36dFWrVu269po1azrujwAAKLg2bNig3bt3y9fXV927dzc7DgDAiZxy5U1ISIisVut1l24ahiGLxSKbzeaMYQEAcGk5NrsGf75DK/edlbeHmz7qFqvGFYubHQsAUIgkJyerdOnS17WXLFlSSUlJJiQCAOSn36+66dy5s0JDWXYZAAozpxRvunTpIk9PT82fP19hYWFcwgkAKPJybXY9u3Cnlv+aLC93N83oFqNmlUuYHQsAUMhERkbq559/VnR0dJ72n3/+WRERESalAgDkh5SUFC1atEiS1L9/f5PTAACczSnFm71792rHjh2qWrWqM3YPAECBYrMbem7RLi3bnSRPd4umdb1PLaqUNDsWAKAQ6tOnj5599lnl5OQ4VkJYtWqVXnjhBT333HMmpwMA3I1Zs2YpOztbsbGxiomJMTsOAMDJnFK8iYmJ0cmTJyneAACKPLvd0Atf7tbSnWfk4WbRB0/fp5bVwsyOBQAopIYPH64LFy5owIABys7OliT5+PhoxIgRGjVqlMnpAAB3ymazafr06ZK46gYAigqLYRhGfu900aJFeuWVVzR8+HDVrl1bnp6eebbXqVMnv4d0SVarVcHBwUpNTVVQUJDZcQAA95jdbmjU4j1auPWk3N0smtq5vv5a+/r7EABAYcH5r+tIT0/X/v375evrq8qVK8vb29vsSDfEawYAbs13332ntm3bKjQ0VKdOnZKfn5/ZkQAAd+hWz4GdcuVNx44dJUm9evVytFksFhmGIYvFIpvN5oxhAQBwGYZhaPTSvVq49aTcLNKkjvUo3AAA7pmAgADFxsaaHQMAkE8SEhIkST169KBwAwBFhFOKN4mJic7YLQAABYJhGHrl6181b9MJWSzSux3q6bG63CQaAAAAwO07duyYli1bJknq16+fyWkAAPeKU4o35cuXd8ZuAQBweYZh6F/f7tenG4/LYpHe/ltdxdcvY3YsAAAAAAXUjBkzZBiGWrdurSpVqpgdBwBwjzileDNnzpybbu/WrZszhgUAwFSGYejN7w9o1s/XrkB988na+luDsianAgAAAFBQZWVl6eOPP5Yk9e/f3+Q0AIB7ySnFmyFDhuR5nJOToytXrsjLy0t+fn4UbwAAhY5hGHrnh4OavvaoJOm1+FrqGFvO5FQAAAAACrLFixfr3LlzioiIULt27cyOAwC4h9ycsdNLly7l+UpPT9fBgwfVrFkzff75584YEgAA0xiGoff+c0gf/HhEkjSuXU39vRFLiAIAzDF37lw1bdpUEREROn78uCRp0qRJWrp0qcnJAAC3KyEhQZLUt29feXg45TPYAAAX5ZTizY1UrlxZb7755nVX5QAAUJDZ7IbGfv2rJq86JEl6uW11dW8SZW4oAECRlZCQoGHDhumRRx7R5cuXZbPZJEkhISGaNGmSueEAALdl7969Wrdundzd3fXMM8+YHQcAcI/ds+KNJHl4eOjMmTP3ckgAAJwmM8emQfO3a87G47JYpFceq6Fn7q9gdiwAQBE2ZcoUzZw5Uy+99JLc3d0d7TExMdqzZ4+JyQAAt+v3q27i4+NVpkwZk9MAAO41p1xv+fXXX+d5bBiGkpKSNHXqVDVt2tQZQwIAcE+lXs1RnzlbtTnxorzc3fRex3pqW6e02bEAAEVcYmKi6tevf127t7e3MjIyTEgEALgTaWlpmjt3riSpf//+JqcBAJjBKcWb+Pj4PI8tFotKliypli1bauLEic4YEgCAeyY5NVPdZ23WwbNpCvT20IxuMWpcsbjZsQAAUHR0tHbu3Kny5fPee2358uWqXr26SakAALdr3rx5SktLU9WqVdWyZUuz4wAATOCU4o3dbnfGbgEAMN3hc2nq9vFmnUnNVKlAb33aq6Gqlw4yOxYAAJKkYcOGaeDAgcrMzJRhGNq8ebM+//xzjR8/Xh999JHZ8QAAt8AwDMeSaf369ZPFYjE5EQDADE4p3gAAUBhtO35RvWZvVerVHFUo6a85vRqqbKif2bEAAHB45pln5Ovrq5dffllXrlzR008/rYiICL3//vvq1KmT2fEAALdg48aN2r17t3x9fdW9e3ez4wAATOLmjJ22b99eb7311nXtEyZM0FNPPeWMIQEAcKqV+87q6ZmblHo1R/XLhejf/ZpQuAEAuKQuXbro0KFDSk9PV3Jysk6dOqXevXubHQsAcIs+/PBDSVLnzp0VGhpqchoAgFmcUrxZu3atHnnkkeva//rXv2rt2rXOGBIAAKf5fPMJ/WPuVmXl2tWqWinNf6aRQv29zI4FAMB1rl69qitXrkiS/Pz8dPXqVU2aNEk//PCDyckAALciJSVFixYtkiT179/f5DQAADM5pXiTnp4uL6/r39Ty9PSU1Wp1xpAAAOQ7wzD0/n8OadTiPbIbUoeYspretYF8vdzNjgYAwA09/vjjmjNnjiTp8uXLatiwoSZOnKjHH3/ccf8EAIDrmjVrlrKzsxUbG6uYmBiz4wAATOSU4k3t2rW1cOHC69oXLFigGjVqOGNIAADylc1u6KUle/Xef36TJP2zZSW91b6OPNyd8qcTAIB8sX37dt1///2SpC+//FLh4eE6fvy45syZo8mTJ5ucDgBwMzabTdOnT5fEVTcAAMnDGTsdPXq0nnzySR05ckQtW7aUJK1atUqff/6549JPAABcVWaOTYM/36Ef9p2VxSK92q6mujaOMjsWAAB/6sqVKwoMDJQk/fDDD3ryySfl5uamRo0a6fjx4yanAwDczIoVK5SYmKiQkBB17NjR7DgAAJM55ePDjz32mJYsWaLDhw9rwIABeu6553Tq1Cn95z//UXx8vDOGBAAgX1y+kq2/f7RJP+w7Ky8PNyV0uY/CDQCgwKhUqZKWLFmikydPasWKFXrooYckSefOnVNQUJDJ6QAAN/P78pY9e/aUn5+fyWkAAGZzypU3ktS2bVu1bdvWWbsHACDfnbl8Vd1nbdahc+kK8vHQR91j1TC6mNmxAAC4ZWPGjNHTTz+toUOHqlWrVmrcuLGka1fh1K9f3+R0AIA/cuzYMS1btkyS1K9fP5PTAABcgVOKN1u2bJHdbldcXFye9k2bNsnd3Z0brgEAXM7B5DR1n7VZydZMhQf56NNeDVU1PNDsWAAA3Ja//e1vatasmZKSklS3bl1He6tWrfTEE0+YmAwAcDMzZsyQYRhq3bq1qlSpYnYcAIALcMqyaQMHDtTJkyevaz99+rQGDhzojCEBALhjm45e0FPTNijZmqlKpQK0eEATCjcAgAIrPDxc9evXl5vb/033GjZsqGrVqpmYCgDwR7KysvTxxx9Lkvr3729yGgCAq3DKlTf79u3Tfffdd117/fr1tW/fPmcMCQDAHVm+N0mDF+xUdq5dMeVD9VH3GIX4eZkdCwCAW/bkk09q9uzZCgoK0pNPPnnTvosXL75HqQDg7qWkpGjLli1mx3C6rVu36ty5c4qIiFC7du3MjgMAcBFOKd54e3vr7NmzqlChQp72pKQkeXg47TY7AADclrm/HNeYpXtlGNJfaoRpSuf68vF0NzsWAAC3JTg4WBaLxfE9ABQG+/btU+PGjWW1Ws2Ocs/07duX980AAA4WwzCM/N5p586dlZSUpKVLlzomD5cvX1Z8fLxKlSqlL774Ir+HdElWq1XBwcFKTU1VUFCQ2XEAAP9lGIbeXfmbpqw+LEnq3LCc/vV4TXm4O2U1UQAoMjj/xe3iNQPgRi5cuKC4uDgdOXJEZcuWVXh4uNmRnC4sLEyffvqpihcvbnYUAICT3eo5sFPK+e+8846aN2+u8uXLq379+pKknTt3KiwsTHPnznXGkAAA3JJcm10vfrVHX2w9JUka2rqKBreq5PjEMgAABdmsWbP04IMPKjo62uwoAHBHcnJy1KFDBx05ckRRUVHasmWLSpQoYXYsAADuOacUb8qUKaPdu3dr3rx52rVrl3x9fdWzZ0917txZnp6ezhgSAIA/dTXbpkHzt2vVgXNys0ivP1FbnRuWMzsWAAD5Zvz48erTp4/KlCmjFi1aqEWLFnrggQdUqVIls6MBwC0ZOnSoVq9erYCAAH399dcUbgAARZZTlk3DNSwBAACu42JGtnp/ukU7TlyWt4ebpj59n/5SI8zsWABQqHD+6xpOnz6tn376SWvXrtWaNWt06NAhlS5dWg888IA+++wzs+PlwWsGwP+aPn26+vXrJ4vFoq+++kqPP/642ZEAAMh3t3oO7NTizb59+3TixAllZ2fnaW/Xrp2zhnQpTEQAwDWcunRF3WZt1tGUDAX7empWjxg1KF/M7FgAUOhw/utarly5onXr1unzzz/XvHnzZBiGcnNzzY6VB68ZAL9bs2aNWrdurdzcXL3++ut68cUXzY4EAIBTmHrPm6NHj+qJJ57Qnj17ZLFY9Ht96Pf7CdhsNmcMCwDAdfYnWdV91madS8tSRLCP5vRuqEqlAs2OBQCAU/zwww/66aef9NNPP2nHjh2qXr26WrRooS+//FLNmzc3Ox4A3FBiYqLat2+v3NxcderUSaNGjTI7EgAApnNK8WbIkCGKjo7WqlWrFB0drc2bN+vChQt67rnn9M477zhjSAAArrPxyAX1nbNVaVm5qhoWqE97NVR4sI/ZsQAAcJqHH35YJUuW1HPPPafvvvtOISEhZkcCgJtKS0tTu3btdOHCBTVo0EAff/yx48O/AAAUZW7O2OnGjRv16quvqkSJEnJzc5Obm5uaNWum8ePHa/Dgwc4YEgCAPJbtTlL3WZuVlpWrhtHF9EW/xhRuAACF3rvvvqumTZtqwoQJqlmzpp5++mnNmDFDv/32m9nRAOA6drtdXbt21d69exUeHq6lS5fKz8/P7FgAALgEpxRvbDabAgOvLUlTokQJnTlzRpJUvnx5HTx40BlDAgDgMPvnRA36fLuybXb9tVa45vRqqGBfT7NjAQDgdM8++6wWL16s8+fPa/ny5WrSpImWL1+uWrVqqWzZsmbHA4A8Ro8eraVLl8rb21tLlixRmTJlzI4EAIDLcMqyabVq1dKuXbsUHR2tuLg4TZgwQV5eXpoxY4YqVKjgjCEBAJBhGHp7xUF9+NMRSVK3xuU19rGacndj2QUAQNFhGIZ27Nihn376ST/++KPWr18vu92ukiVLmh0NABw+//xzvfHGG5KkmTNnKi4uzuREAAC4FqcUb15++WVlZGRIkl599VU9+uijuv/++1W8eHEtXLjQGUMCAIq4HJtdI/+9R//efkqSNLxNVQ14oCLrZQMAipTHHntMP//8s6xWq+rWrasHHnhAffr0UfPmzbn/DQCXsXXrVvXq1UuSNHz4cHXt2tXkRAAAuB6nFG/atGnj+L5SpUo6cOCALl68qNDQUN5EAwDkuyvZuRowb7t+OpgidzeLxj9RWx1iI82OBQDAPVetWjX94x//0P3336/g4GCz4wDAdZKSkhQfH6/MzEy1bdtW48ePNzsSAAAuySnFmxspVqzYvRoKAFCEXEjPUq/ZW7TrVKp8PN30YZf71LJamNmxAAAwxdtvv+34/tSpU4qIiJCbm1NudQoAty0zM1Px8fE6ffq0qlevrvnz58vd3d3sWAAAuCTO4gEABdaJC1f0t2kbtetUqkL9PDW/TyMKNwAA/FeNGjV07Ngxs2MAgKRr9+Pq06ePNm/erNDQUH399dcKCgoyOxYAAC7rnl15AwBAftp7OlU9Ptmi8+lZKhPiqzm9G6piyQCzYwEA4DIMwzA7AgA4vP322/rss8/k7u6uL7/8UpUqVTI7EgAALo3iDQCgwFl/6Lz6fbZN6Vm5ql46SLN7xiosyMfsWAAAAABuYNmyZRo5cqQk6f3331fLli1NTgQAgOujeAMAKFCW7jyt5xftUo7NUOMKxTW9WwMF+XiaHQsAAJfz4osvcu9RAKbbt2+fOnfuLMMw1LdvXw0YMMDsSAAAFAjc8wYAUGB8tO6ohizYqRybobZ1Smt2r1gKNwAA/I9XX31VV65ckSSNGjVKISEhkqSrV6/q1VdfNTEZgKLowoULateundLS0tS8eXNNmTJFFovF7FgAABQIFG8AAC7Pbjf0xnf79dqy/ZKkHk2iNKVTfXl7uJucDAAA1zJu3Dilp6df137lyhWNGzfOhEQAiqqcnBx16NBBR44cUVRUlP7973/Ly8vL7FgAABQYLJsGAHBp2bl2vfDlLi3ZeUaSNPKv1fSP5hX4xB4AADdgGMYN/0bu2rWLJdQA3FNDhw7V6tWrFRAQoK+//lolSpQwOxIAAAUKxRsAgMtKz8pV/8+2ad2h8/Jws+it9nXUvkFZs2MBAOByQkNDZbFYZLFYVKVKlTwFHJvNpvT0dPXr18/EhACKkunTp+uDDz6QJH322WeqXbu2yYkAACh4KN4AAFxSSlqWes7erL2nrfLzcteHXe7TA1VLmR0LAACXNGnSJBmGoV69emncuHEKDg52bPPy8lJUVJQaN25sYkIARcWaNWs0aNAgSdJrr72mxx9/3OREAAAUTBRvAAAu59j5DHWbtVknLl5RcX8vzeoRq7qRIWbHAgDAZXXv3l2SFB0drSZNmsjT09PkRACKosTERLVv3165ubnq1KmTXnzxRbMjAQBQYFG8AQC4lN2nLqvnJ1t0ISNbkcV8NadXnKJL+JsdCwAAl2W1WhUUFCRJql+/vq5evaqrV6/esO/v/QAgv6Wlpaldu3a6cOGCGjRooI8//pj7VAIAcBco3gAAXMaa31LU/7NtupJtU82IIH3SM1alAn3MjgUAgEsLDQ1VUlKSSpUqpZCQkBu+WWoYhiwWi2w2mwkJARR2drtdXbt21d69exUeHq6lS5fKz8/P7FgAABRoFG8AAC5h8fZTeuHL3cq1G2pWqYSmdW2gAG/+TAEA8GdWr16tYsWKSZJ+/PFHk9MAKIrGjBmjpUuXytvbW0uWLFGZMmXMjgQAQIHHu2IAAFMZhqEZa49q/PcHJEmP14vQ23+rKy8PN5OTAQBQMLRo0eKG3wPAvbBgwQK9/vrrkqSZM2cqLi7O5EQAABQOFG8AAKax2w29/t1+fbw+UZLU5/5ojfprdbm5sTY2AAB36tKlS/r444+1f/9+SVKNGjXUs2dPx9U5AJBftm7dqp49e0qShg8frq5du5qcCACAwqNAfKz5gw8+UFRUlHx8fBQXF6fNmzfftP+iRYtUrVo1+fj4qHbt2vruu+/ybDcMQ2PGjFHp0qXl6+ur1q1b69ChQ47tx44dU+/evRUdHS1fX19VrFhRY8eOVXZ2tlN+PgAoirJybRqycKejcPPSI9X1UtsaFG4AALgLa9euVVRUlCZPnqxLly7p0qVLmjx5sqKjo7V27Vqz4wEoRJKSkhQfH6/MzEy1bdtW48ePNzsSAACFissXbxYuXKhhw4Zp7Nix2r59u+rWras2bdro3LlzN+y/YcMGde7cWb1799aOHTsUHx+v+Ph47d2719FnwoQJmjx5sqZNm6ZNmzbJ399fbdq0UWZmpiTpwIEDstvtmj59un799Ve99957mjZtml588cV78jMDQGGXlpmjnp9s0Te7zsjT3aL3O9VTn+YVzI4FAECBN3DgQHXs2FGJiYlavHixFi9erKNHj6pTp04aOHCg2fEAFBKZmZmKj4/X6dOnVb16dc2fP1/u7u5mxwIAoFCxGIZhmB3iZuLi4hQbG6upU6dKkux2uyIjI/XPf/5TI0eOvK5/x44dlZGRoW+//dbR1qhRI9WrV0/Tpk2TYRiKiIjQc889p+eff16SlJqaqrCwMM2ePVudOnW6YY63335bCQkJOnr06C1nt1qtCg4OVmpqqoKCgm7nxwaAQuucNVM9PtmifUlW+Xu5a1rXBrq/ckmzYwEA8gHnv+bz9fXVzp07VbVq1TztBw8eVL169XT16lWTkt0Yrxmg4DEMQ926ddNnn32m0NBQbd68WZUqVTI7FgAABcatngO79JU32dnZ2rZtm1q3bu1oc3NzU+vWrbVx48YbPmfjxo15+ktSmzZtHP0TExOVnJycp09wcLDi4uL+cJ/StQIPa0QDwN05mpKuJxM2aF+SVSUCvLTwH40p3AAAkI/uu+8+x71u/tf+/ftVt25dp4795ptvymKx6Nlnn3XqOADM9c477+izzz6Tu7u7Fi1aROEGAAAn8TA7wM2cP39eNptNYWFhedrDwsJ04MCBGz4nOTn5hv2Tk5Md239v+6M+/7/Dhw9rypQpeuedd26aNysrS1lZWY7HVqv1pv0BoCjZceKSen+6VRczshVV3E+f9mqo8sX9zY4FAECBt3v3bsf3gwcP1pAhQ3T48GE1atRIkvTLL7/ogw8+0Jtvvum0DFu2bNH06dNVp04dp40BwHzLli3TiBEjJEmTJk1Sq1atTE4EAEDh5dLFG1dw+vRpPfzww3rqqafUp0+fm/YdP368xo0bd4+SAUDB8eOBcxowb7uu5thUp2ywZvWIVYkAb7NjAQBQKNSrV08Wi0X/uyL2Cy+8cF2/p59+Wh07dsz38dPT09WlSxfNnDlTr732Wr7vH4Br2Ldvnzp37izDMNS3b1/uowUAgJO5dPGmRIkScnd319mzZ/O0nz17VuHh4Td8Tnh4+E37//7v2bNnVbp06Tx96tWrl+d5Z86c0YMPPqgmTZpoxowZf5p31KhRGjZsmOOx1WpVZGTknz4PAAorwzC0aOspjfpqj2x2Q82rlFRCl/vk7+3Sf34AAChQEhMTTR1/4MCBatu2rVq3bv2nxRtWKwAKpgsXLqhdu3ZKS0tT8+bNNWXKFFksFrNjAQBQqLn0u2deXl5q0KCBVq1apfj4eEmS3W7XqlWrNGjQoBs+p3Hjxlq1alWedZZXrlypxo0bS5Kio6MVHh6uVatWOYo1VqtVmzZtUv/+/R3POX36tB588EE1aNBAn3zyidzc/vz2QN7e3vL25pPkAGCzG1q5L1kJPx3RrlOpkqQn65fRW3+rI093l77dGgAABU758uVNG3vBggXavn27tmzZckv9Wa0AKHhycnLUoUMHHTlyRFFRUfr3v/8tLy8vs2MBAFDouXTxRpKGDRum7t27KyYmRg0bNtSkSZOUkZGhnj17SpK6deumMmXKaPz48ZKkIUOGqEWLFpo4caLatm2rBQsWaOvWrY4rZ36/geZrr72mypUrKzo6WqNHj1ZERISjQHT69Gk98MADKl++vN555x2lpKQ48vzRFT8AACkr16YlO05r+pqjOno+Q5Lk7eGmfi0q6tnWlfl0HgAATjZnzpybbu/WrVu+jXXy5EkNGTJEK1eulI+Pzy09h9UKgIJn2LBhWr16tfz9/fX111+rRIkSZkcCAKBIcPniTceOHZWSkqIxY8YoOTlZ9erV0/LlyxUWFiZJOnHiRJ6rYpo0aaL58+fr5Zdf1osvvqjKlStryZIlqlWrlqPPCy+8oIyMDPXt21eXL19Ws2bNtHz5cseEY+XKlTp8+LAOHz6ssmXL5snzv+tIAwCuScvM0eebT+jj9Yk6a722FEqQj4e6NY5Sj6ZR3N8GAIB7ZMiQIXke5+Tk6MqVK/Ly8pKfn1++Fm+2bdumc+fO6b777nO02Ww2rV27VlOnTlVWVpbc3d3zPIfVCoCCZcaMGZo6daok6bPPPlPt2rVNTgQAQNFhMahGOI3ValVwcLBSU1MVFBRkdhwAyHcpaVmavSFRczYeV1pmriQpLMhbzzSroM5x5RTAvW0AoEjh/Nc1HTp0SP3799fw4cPVpk2bfNtvWlqajh8/nqetZ8+eqlatmkaMGJHnA3R/hNcM4LrWrFmj1q1bKzc3V6+99ppeeuklsyMBAFAo3Oo5MO+qAQBu24kLVzRj3RF9sfWUsnPtkqQKJf3Vr3lFPV4/Qt4e7n+yBwAAcK9UrlxZb775pv7+97/rwIED+bbfwMDA6wo0/v7+Kl68+C0VbgC4rsTERLVv3165ubnq1KmTXnzxRbMjAQBQ5FC8AQDcsr2nUzVtzRF9tydJ9v9et1k3MkT9W1TUQzXC5ObGPW0AAHBFHh4eOnPmjNkxABQAaWlpateunS5cuKAGDRro448/5t6VAACYgOINAOCmDMPQxqMXlPDTEa07dN7R3qJKSfVrUVGNKhRjMgcAgIv4+uuv8zw2DENJSUmaOnWqmjZt6vTxf/rpJ6ePAcB57Ha7unbtqr179yo8PFxLliyRn5+f2bEAACiSKN4AAG7IZje0cl+yEn46ol2nUiVJbhbp0ToR+keLCqoZEWxyQgAA8P+Lj4/P89hisahkyZJq2bKlJk6caE4oAAXGmDFjtHTpUnl7e+urr75S2bJlzY4EAECRRfEGAJBHVq5NS3ac1vQ1R3X0fIYkydvDTR1iItXn/goqV5xP3gEA4KrsdrvZEQAUUAsWLNDrr78uSZo5c6YaNWpkciIAAIo2ijcAAElSWmaOPt98Qh+vT9RZa5YkKcjHQ90aR6lH0yiVCPA2OSEAAAAAZ9i6dat69uwpSRo+fLi6du1qciIAAEDxBgCKuJS0LM3ekKg5G48rLTNXkhQW5K1nmlVQ57hyCvDmTwUAAAXFsGHDbrnvu+++68QkAAqKpKQkxcfHKzMzU23bttX48ePNjgQAAETxBgCKrBMXrmjGuiP6YuspZedeW2KlQkl/9WteUY/Xj5C3h7vJCQEAwO3asWOHduzYoZycHFWtWlWS9Ntvv8nd3V333Xefo5/FYjErIgAXkpmZqfj4eJ0+fVrVq1fX/Pnz5e7OPAAAAFdA8QYAipi9p1M1bc0RfbcnSXbjWlvdyBD1b1FRD9UIk5sbb+YAAFBQPfbYYwoMDNSnn36q0NBQSdKlS5fUs2dP3X///XruuedMTgjAVRiGob59+2rz5s0KDQ3V119/raCgILNjAQCA/6J4AwBFgGEY2nj0ghJ+OqJ1h8472ltUKal+LSqqUYVifAIXAIBCYOLEifrhhx8chRtJCg0N1WuvvaaHHnqI4g0Ah3feeUdz586Vu7u7Fi1apEqVKpkdCQAA/A+KNwBQiNnshlbuS1bCT0e061SqJMnNIj1aJ0L/aFFBNSOCTU4IAADyk9VqVUpKynXtKSkpSktLMyERAFe0bNkyjRgxQpI0adIktWrVyuREAADg/0fxBgAKoaxcm5bsOK3pa47q6PkMSZK3h5s6xESqz/0VVK64n8kJAQCAMzzxxBPq2bOnJk6cqIYNG0qSNm3apOHDh+vJJ580OR0AV7Bv3z517tzZsWzawIEDzY4EAABugOINABQiaZk5+nzzCX28PlFnrVmSpCAfD3VrHKUeTaNUIsDb5IQAAMCZpk2bpueff15PP/20cnJyJEkeHh7q3bu33n77bZPTATDbhQsX1K5dO6Wlpal58+aaMmUKyycDAOCiKN4AQCGQkpalT35O1NxfjistM1eSFBbkrWeaVVDnuHIK8ObXPQAARYGfn58+/PBDvf322zpy5IgkqWLFivL39zc5GQCz5eTkqEOHDjpy5IiioqL05ZdfysvLy+xYAADgD/BuHgAUYMcvZGjG2qNatO2UsnPtkqQKJf3Vr3lFPV4/Qt4e7iYnBAAAZvD391edOnXMjgHAhQwbNkyrV6+Wv7+/li5dqpIlS5odCQAA3ATFGwAogPaeTtW0NUf03Z4k2Y1rbXUjQ9S/RUU9VCNMbm4sfQAAAADgmhkzZmjq1KmSpM8++4ziLgAABQDFGwAoIAzD0MajF5Tw0xGtO3Te0d6iSkn1a1FRjSoUY71qAAAAAHmsWbNGAwcOlCS99tprio+PNzcQAAC4JRRvAMDF2eyGVu5LVsJPR7TrVKokyc0iPVonQv9oUUE1I4JNTggAAADAFSUmJqp9+/bKzc1Vp06d9OKLL5odCQAA3CKKNwDgorJybVqy47Smrzmqo+czJEneHm7qEBOpPvdXULnifiYnBAAAruC+++7TqlWrFBoaqldffVXPP/+8/Pw4TwCKurS0NLVr104XLlxQgwYN9PHHH3OlPgAABQjFGwBwMWmZOfp88wl9vD5RZ61ZkqQgHw91axylHk2jVCLA2+SEAADAlezfv18ZGRkKDQ3VuHHj1K9fP4o3QBFnt9vVtWtX7d27V+Hh4VqyZAm/FwAAKGAo3gCAi0hJy9InPydq7i/HlZaZK0kKC/LWM80qqHNcOQV48ysbAABcr169eurZs6eaNWsmwzD0zjvvKCAg4IZ9x4wZc4/TATDDmDFjtHTpUnl7e+urr75S2bJlzY4EAABuE+8EAoDJjl/I0Iy1R7Vo2yll59olSRVK+qtf84p6vH6EvD3cTU4IAABc2ezZszV27Fh9++23slgs+v777+Xhcf1Uz2KxULwBioAFCxbo9ddflyTNnDlTjRo1MjkRAAC4ExRvAMAEhmHo1zNWTVtzRN/tSZLduNZeNzJE/VtU1EM1wuTmxnrUAADgz1WtWlULFiyQJLm5uWnVqlUqVaqUyakAmGHr1q3q2bOnJGn48OHq2rWryYkAAMCdongDAPeAYRg6ej5DmxMvatPRC9qUeFFJqZmO7S2qlFS/FhXVqEIxbiIKAADumN1uNzsCAJMkJSUpPj5emZmZeuSRRzR+/HizIwEAgLtA8QYAnMBuN3ToXLo2J17QL4kXtTnxolLSsvL08XS36OFapdWvRQXVjAg2KSkAAChsjhw5okmTJmn//v2SpBo1amjIkCGqWLGiyckAOEtmZqaeeOIJnT59WtWrV9f8+fPl7s7yywAAFGQUbwAgH9jshvYnWbUp8aI2J17Q5sSLunQlJ08fLw831YsMUaPoYoqrUFz1y4XIz4tfwwAAIP+sWLFC7dq1U7169dS0aVNJ0s8//6yaNWvqm2++0V/+8heTEwLIb4ZhqG/fvtq0aZNCQ0P19ddfKziYD4cBAFDQ8a4hANyBXJtde89YtenotULN5mMXlZaZm6ePj6ebGpQPVVx0ccVFF1PdyBD5ePLpNwAA4DwjR47U0KFD9eabb17XPmLECIo3QCH0zjvvaO7cuXJ3d9eiRYtUqVIlsyMBAIB8QPEGAG5BVq5Ne06lalPiRf1y9IK2H7+kjGxbnj4B3h6KiQpVw+hiiosurtplguXl4WZSYgAAUBTt379fX3zxxXXtvXr10qRJk+59IABOtWzZMo0YMUKSNGnSJLVq1crkRAAAIL9QvAGAG8jMsWn7iUvanHhRm45e1PYTl5SVm/cGwMG+noqNKqZGFYqpYXQx1SgdJA93ijUAAMA8JUuW1M6dO1W5cuU87Tt37lSpUqVMSgXAGfbt26fOnTs7lk0bOHCg2ZEAAEA+ongDAJIysnK17fh/izWJF7TrZKqybXmLNcX9vf57Vc21e9ZUDQuUm5vFpMQAAADX69Onj/r27aujR4+qSZMmkq7d8+att97SsGHDTE4HIL9cvHhR7dq1U1pampo3b64pU6bIYmFuAgBAYULxBkCRZM3M0dZj166q2ZR4UXtPpyrXbuTpUyrQW3EVrt2vplGFYqpYMoAJEQAAcGmjR49WYGCgJk6cqFGjRkmSIiIi9Morr2jw4MEmp4NZ1q1bpy+//FK5ubl/3hkFwqZNm3TkyBFFRUXpyy+/lJeXl9mRAABAPqN4A6BIuJSRrc3/LdZsPnZB+85Y9f/ValQmxPe/V9Vcu2dN+eJ+FGsAAECBYrFYNHToUA0dOlRpaWmSpMDAQJNTwSxnz57V8OHDNXfuXLOjwAn8/f21dOlSlSxZ0uwoAADACSjeACiUUtKyHEugbTp6UQfPpl3XJ6q4n+Kii19bCq1CMZUN9TMhKQAAgHNQtCm6bDabpk2bppdeekmpqamyWCzq2rWroqOjzY6GfOLm5qYnn3xStWrVMjsKAABwEoo3AAqFpNSr2px4Ub8cvVawOZqScV2fSqUCHPeriYsuprAgHxOSAgAAAM6zadMmDRgwQNu3b5ckNWjQQAkJCYqNjTU5GQAAAG4HxRsABY5hGDp16ap+OXrhv1fXXNSJi1fy9LFYpKphgWr030JNbHQxlQjwNikxAAAA4FwXL17UqFGjNHPmTBmGoZCQEL3xxhvq27ev3N3dzY4HAACA20TxBoDLMwxDiecztCnxojb9t2BzJjUzTx83i1SrTLAaRl27siY2KlQhfty0EwAAAIWb3W7X7NmzNWLECJ0/f16S1L17d02YMEGlSpUyOR0AAADuFMUbAC7Hbjd0OCVdm45e0C+JF7U58aJS0rLy9PFws6hO2WA1jC6uuArFFFM+VIE+niYlBgAAAO69Xbt2acCAAdqwYYMkqVatWvrwww91//33m5wMAAAAd4viDQCXcc6aqelrj+qrHad1MSM7zzYvDzfViwxRo+hiahhdXPeVD5GfF7/CAAAA/n+DBg3Sq6++qmLFipkdBU5itVo1ZswYTZkyRXa7XQEBAXrllVc0ePBgeXrygSYAAIDCgHc+AZguKfWqpq85qvmbTyg71y5J8vF0U4PyoYqLvnbPmrqRIfLxZK1uAACAGzl16pTKli0rSZo/f75eeOEFFStWTLVr19Z3332nyMhIkxMiPxiGoQULFui5555TUlKSJOmpp57Su+++6zj+AAAAKBwo3gAwzalLVzRtzRF9seWUsm3XijYNyodq0IOV1LRSCXl5uJmcEAAAoGCoVq2aihcvrqZNmyozM1MnT55UuXLldOzYMeXk5JgdD/lg//79GjRokFavXi1Jqly5sqZOnaqHHnrI5GQAAABwBoo3AO65Exeu6MOfDuvLbaeUazckSXHRxTSkVWU1rlhcFovF5IQAAAAFy+XLl7V9+3atW7dOixcv1iO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"text/plain": [ - "
    " + "\"Output" ] }, "metadata": {}, @@ -437,9 +434,8 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
    " + "\"Output" ] }, "metadata": {}, @@ -536,9 +532,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
    " + "\"Output" ] }, "metadata": {}, @@ -634,9 +629,8 @@ "outputs": [ { "data": { - "image/png": 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9OvqVgoODderUKYe2WbNm6Y033tBDV62f/PHHHzt8GPb39y+ARw0AAEq6c8np+uPM5Vk0hy4VaY7+laLM68ycCfJ1V/XyXlm3AG+V93G7Ylmyy4UZdxdmvQBASbJu3Tq1aNHCYUbKoUOHcvR79tln1bBhQ/Xq1Uu9e/dWq1atVK9evX88fr169XIUgn777TeH+/7+/mrUqJFmzpwpFxcX1a1bVxUqVFCXLl307bff2q93I2XNKvrwww917ty5fM++qVOnjjZv3qwePXrY2zZv3pynxzBnzhwlJyfbCzvr1q2T2WxWnTp1JEnly5d3GO9bLBbt2rVL991333Uf+2+//ebwPHp4eKh9+/Zq3769BgwYoLp162rnzp267bbb8vVYAQCA8b788kutXLlSrq6uioqKKlZjaUOLN9OmTVPv3r3ta91GR0dr2bJlmj17tn392Stlr9M7bNgwSdKECRO0atUqzZw5U9HR0ZKkp556SlLWDJvcODk5KSgoyKHtq6++UufOneXt7e3Q7u/vn6MvAACAJKVnWnX0XLIOXTmL5kyS/jibrPMp1/6Wr4eLk0ICLhVoynurRnkv1SjvrZAAL3m5GT4pGgBggFq1aumTTz7RypUrFRISok8//VSbN292WDUiKipKGzZs0I4dOxQcHKxly5ape/fu+u233xyWF8vN4MGDdeedd+rNN99Uhw4dtHLlSocl07Lde++9euedd/T4449LksqWLat69epp4cKFioqKsvfr1q2bXnvtNXXs2FGTJk1SxYoVtW3bNlWqVCnHkmRXGzRokHr37q1mzZqpRYsWWrhwoXbs2KHq1atfd7/u3btr7Nix6tmzp8aNG6czZ85o0KBBeuqpp+xLpt1///2KjIzUsmXLVKNGDU2bNk3nz5/Pcax169ZpypQp6tixo1atWqVFixZp2bJlkqQ5c+bIYrEoLCxMnp6e+uyzz+Th4aFbbrnluvkAAEDRk5iYqCFDhkjKugZh7dq1DU6UP4b9hSA9PV1bt27ViBEj7G1ms1mtWrXShg0bct1nw4YNioyMdGiLiIjQkiVL/nWOrVu3KjY21uGDaLYBAwboueeeU/Xq1dW3b18988wz163MpaWlKS0tzX4/MTHxX+cCAADGs9lsOpuUNYvm0BXFmT/OJOnY3xeve/2Zyv4el2bQZBdpvFW9vJeCfN1lZskyAMAV/vvf/2rbtm3q0qWLTCaTunXrpv79++u7S9f42bt3r4YNG6aPPvpIwcHBkqR3331XjRo10ujRozV58uTrHv+OO+7QBx98oLFjx2rMmDFq1aqVRo0apQkTJjj0u+eeezR9+nT7tW2krILO9u3bHdpcXV31/fff64UXXlDbtm2VmZmp+vXr5zquvlr37t31xx9/6MUXX1Rqaqo6d+6sp59+Wps2bbrufp6enlq5cqWGDBmi22+/XZ6enurUqZOmTZtm7/Pss89q+/bt6tGjh5ydnfX888/nmHUjZV07aMuWLRo/frx8fX01bdo0RURESMr6Eufrr7+uyMhIWSwWNWzYUN98802ery8EAACKjjFjxujUqVOqWbNmrpNFijqT7eoFYQvJyZMnVblyZa1fv97hmzkvvfSS1q5dm2NKt5T1AXHu3Lnq1q2bve3dd9/V+PHjFR8f79A3+9o2V1/z5mr9+/fXmjVr9Pvvvzu0T5gwQffff788PT31/fffa+zYsZoyZYoGDx58zWONGzdO48ePz9GekJAgX1/fa+4HAACMlZph0ZG/Ui4VaS4td3apSHMhNfOa+3m5Oqn6paJM9QBv1aiQ9TMkwEserk6F+AiAoiExMVF+fn58/kWeXe89k5qaqsOHDyskJETuxehaN8XFnDlzNHTo0FxnphS2Bx98UEFBQfr0009v+rmqVaumoUOHaujQoQVyPN6nAAAUTTExMbr99ttltVq1cuVKtW7d2uhIdnkdN5XqtTkuXryo+fPna/To0Tm2XdnWpEkTJScn64033rhu8WbEiBEOM4MSExPt34oCAADGstlsOn0h7XJxJnups7NJOv73RV3r6ywmk1SljIeqB2QVabJn0NQo760KPm7Far1cAACMlpKSoujoaEVERMjJyUmff/65fvjhB61atcroaAAAoISwWCzq27evrFarunTpUqQKN/lhWPEmICBATk5OOWbMxMfHX/M6M0FBQfnq/0++/PJLpaSkOFwo8VrCwsI0YcIEpaWlyc3NLdc+bm5u19wGAAAKx8V0iw6fzSrKHDqd9fOPM8k6fDZZSWnXnkXj4+6ctbxZgJdqVPC2L3d2SzlPubswiwYAUHQ99NBD+uWXX3LdNnLkSI0cObLQstx66606cuRIrtvef/99PfbYY1q+fLleffVVpaamqk6dOvrf//6nVq1aFVpGAABQss2aNUubN2+2L49aXBlWvHF1dVXTpk21evVqdezYUZJktVq1evVqDRw4MNd9wsPDtXr1aofpzatWrfrHCyJey0cffaRHHnlE5cuX/8e+sbGxKlOmDMUZAACKAKvVprjE1Ctm0GRfiyZZJ85fvOZ+TmaTgst4XLoGTVZxJrtIE+DtyiwaAECx9OGHH+rixdz//1e2bNlc259++mk9/fTTBZ5l+fLlysjIyHVbYGCgPDw89MMPPxT4efPqzz//NOzcAADg5ouPj9eIESMkSRMnTlSlSpUMTvTvGbpsWmRkpHr27KlmzZqpefPmmj59upKTk/XMM89Iknr06KHKlStr0qRJkqQhQ4bonnvu0dSpU9WuXTstWLBAW7Zs0axZs+zHPHfunI4ePaqTJ09Kkvbt2ycpa9bOlTN0Dh48qJ9//lnLly/Pkeubb75RfHy87rjjDrm7u2vVqlV67bXX9OKLL9605wIAAOSUnJapw2ezCjSHzmRdgyZ7Fs3FDMs19/P3dLEXZS4vc+alqmW95OpsLsRHAADAzVe5cmWjI9jdcsstRkcAAACl2AsvvKCEhAQ1bdpU/fv3NzrODTG0eNOlSxedOXNGY8aMUVxcnEJDQ7VixQoFBgZKko4ePSqz+fIfWFq0aKH58+dr1KhRGjlypGrVqqUlS5aoQYMG9j5Lly61F38kqWvXrpKksWPHaty4cfb22bNnq0qVKrmud+fi4qKoqCg9//zzstlsqlmzpqZNm6bevXsX9FMAAECpZ7XadOL8Rf1xNlmHTifZlzn740yy4hJTr7mfs9mkquU8VT3AWzUqeKnGpWvSVC/vrbJeroX4CAAAN5PtWhclA4oA3p8AABQdP/74o+bNmyeTyaTo6Gg5ORXvJdBNNj5p3DSJiYny8/NTQkKCfH19jY4DAMANsVhtSs2w6GKGRamXbhfTrbqYo81yuS3dotRMay5tWf0upGbq6LkUpWVar3necl6u9tkz1ct7qfqlIk1wWU+5ODGLBihK+PyL/Lree8ZisWj//v2qUKGCypUrZ1BC4Pr++usvnT59WrVr1y72fyACAKA4S0tLU+PGjbVv3z71799fUVFRRke6pryOmwydeQMAAG6cxWqzF08uplvsBZaLVxROrmy7mGFRWsblYsrFdOvlwsuV+9qLMlkFmvTrFFhulKuTWdUCPO2FmerZS50FeMvP0+WmnRcAUHQ5OTnJ399fp0+fliR5enpybTIUGTabTSkpKTp9+rT8/f0p3AAAYLA33nhD+/btU2BgoF599VWj4xQIijcAANxk51PSdSE1M5eiiGPx5GIuM1fSLhVOstsciizpWYWVdMvNK6pci7uLWR4uTnJ3cbr80/Xy79nbHduc5OFilofr5f08XZ0VXNZDVcp4ysnMH+QAAI6yr1uaXcABihp/f3+H6+sCAIDCd+jQIXvBZtq0afL39zc2UAGheAMAwE00ecVeRa89pMJapNReNHFxkvulwolD8cT1UgHl0nZ358sFlsttZoeii8PvLk5yczbLTKEFAFAITCaTKlasqAoVKigjI8PoOIADFxcXZtwAAGAwm82mgQMHKjU1VQ888IC6detmdKQCQ/EGAICbZOn2k3pvzSFJumoWitleCLlyFop92xXFFI9LBRbHNvMVM1ku93NzNrOcDACgRHJycuKP5AAAAMjhf//7n1asWCFXV1e9++67JervIhRvAAC4CQ6eTtLw/+2QJA24r4aGRdQ1OBEAAAAAAEDJkZiYqCFDhkiShg8frtq1axucqGCZjQ4AAEBJk5Keqf7ztiol3aI7qpfV861K1ocHAAAAAAAAo40dO1YnT55UjRo1NGLECKPjFDiKNwAAFCCbzaZRS3Zpf3ySyvu4aUa3JnJ24n+3AIDCcfHiRaWkpNjvHzlyRNOnT9f3339vYCoAAACgYG3btk0zZsyQJEVFRcnd3d3gRAWPvyYBAFCAFm4+psUxJ2Q2STO6NlEFn5L34QEAUHR16NBBn3zyiSTp/PnzCgsL09SpU9WhQwe99957BqcDAAAAbpzFYlHfvn1ltVrVuXNnRUREGB3ppqB4AwBAAdl9MkFjlu6WJL0YUUfhNcoZnAgAUNrExMTorrvukiR9+eWXCgwM1JEjR/TJJ5/Yv5kIAAAAFGcffPCBNm3aJB8fH7311ltGx7lpKN4AAFAAElMz1H9ejNIzrXqgbgX1vbuG0ZEAAKVQSkqKfHx8JEnff/+9HnvsMZnNZt1xxx06cuRIvo71888/q3379qpUqZJMJpOWLFli35aRkaGXX35ZDRs2lJeXlypVqqQePXro5MmTBflwAAAAAAfx8fH269tMnDhRlSpVMjjRzUPxBgCAG2Sz2fTSoh068leKKvt7aGrnxjKbTUbHAgCUQjVr1tSSJUt07NgxrVy5Uq1bt5YknT59Wr6+vvk6VnJysho3bqyoqKgc21JSUhQTE6PRo0crJiZGixcv1r59+/TII48UyOMAAAAAcvPiiy/q/Pnzuu2229S/f3+j49xUzkYHAACguJu97k+t2B0nFyeTorrfJn9PV6MjAQBKqTFjxujJJ5/U888/r/vvv1/h4eGSsmbhNGnSJF/Heuihh/TQQw/lus3Pz0+rVq1yaJs5c6aaN2+uo0ePqmrVqv/uAQAAAADX8NNPP+mzzz6TyWRSdHS0nJ1LdnmjZD86AABusq1H/tak5XskSaPa1VdosL+xgQAApdrjjz+uli1b6tSpU2rcuLG9/YEHHtCjjz56U8+dkJAgk8kkf3//m3oeAAAAlD5paWnq16+fJKlfv366/fbbDU5081G8AQDgXzqXnK6B82OUabWpXaOK6hF+i9GRAABQUFCQgoKCdOzYMUlScHCwmjdvflPPmZqaqpdfflndunW77vJsaWlpSktLs99PTEy8qbkAAABQMrz55pvat2+fAgMD9eqrrxodp1BwzRsAAP4Fq9WmoQtjdSohVdUDvDS5UyOZTFznBgBgrMzMTI0ePVp+fn6qVq2aqlWrJj8/P40aNUoZGRk35ZwZGRnq3LmzbDab3nvvvev2nTRpkvz8/Oy34ODgm5IJAAAAJccff/yhiRMnSpKmTp1aamZ6U7wBAOBfiPrpoH7ef0buLma9+5/b5O3GZFYAgPEGDRqkWbNmacqUKdq2bZu2bdumKVOm6KOPPtLgwYML/HzZhZsjR45o1apV1511I0kjRoxQQkKC/ZY9OwgAAADIjc1m08CBA5Wamqr7779fTz75pNGRCg1/aQIAIJ/WHzyrt37YL0ma0KGB6gZd/w9VAAAUlvnz52vBggV66KGH7G2NGjVScHCwunXr9o8zY/Iju3Bz4MAB/fTTTypXrtw/7uPm5iY3N7cCywAAAICSbfHixfruu+/k6uqqd999t1StekLxBgCAfIhPTNXgBdtktUmdm1XRE81Y7gUAUHS4ubmpWrVqOdpDQkLk6uqar2MlJSXp4MGD9vuHDx9WbGysypYtq4oVK+rxxx9XTEyMvv32W1ksFsXFxUmSypYtm+9zAQAAAFe7cOGChgwZIkl6+eWXVadOHYMTFS6WTQMAII8yLVYN+nybzialq26Qj17p0MDoSAAAOBg4cKAmTJigtLQ0e1taWppeffVVDRw4MF/H2rJli5o0aaImTZpIkiIjI9WkSRONGTNGJ06c0NKlS3X8+HGFhoaqYsWK9tv69esL9DEBAACgdBo7dqxOnDih6tWra8SIEUbHKXTMvAEAII/e/H6/Nh0+J283Z733n6Zyd3EyOhIAAA62bdum1atXq0qVKmrcuLEkafv27UpPT9cDDzygxx57zN538eLF1z3WvffeK5vNds3t19sGAAAA3IjY2Fi9/fbbkqSoqCh5eHgYnKjwUbwBACAPVu+JV/TaQ5KkKY83UkiAl8GJAADIyd/fX506dXJoCw5miU8AAAAUH1arVX379pXVatUTTzyhNm3aGB3JEBRvAAD4B8fOpej5hbGSpKdbVFPbhhWNDQQAwDV8/PHHRkcAAAAAbsgHH3ygjRs3ysfHR2+99ZbRcQzDNW8AALiOtEyLBsyPUWJqpkKD/TWybT2jIwEAAAAAAJRIp0+f1vDhwyVJEyZMUOXKlQ1OZBxm3gAAcB0Tv92jHccT5O/poqjut8nVme89AACKrpCQEJlMpmtu/+OPPwoxDQAAAJA/w4YN0/nz59WkSRMNGDDA6DiGongDAMA1LN1+Up/+dkSS9FaXUFX2L30XxwMAFC9Dhw51uJ+RkaFt27ZpxYoVGjZsmDGhAAAAgDxYs2aNPvnkE5lMJkVHR8vZuXSXL0r3owcA4BoOnk7S8P/tkCQNuK+G7qtTweBEAAD8syFDhuTaHhUVpS1bthRyGgAAACBv0tPT1a9fP0lS37591bx5c4MTGY+1XwAAuEpKeqb6z9uqlHSL7qheVs+3qm10JAAAbshDDz2k//3vf0bHAAAAAHL15ptvau/evapQoYJee+01o+MUCRRvAAC4gs1m06glu7Q/Pknlfdw0o1sTOTvxv0sAQPH25ZdfqmzZskbHAAAAAHL4448/NGHCBEnStGnT5O/vb2ygIoJl0wAAuMLCzce0OOaEzCZpRtcmquDjbnQkAADyrEmTJjKZTPb7NptNcXFxOnPmjN59910DkwEAAAA52Ww2DRo0SKmpqbr//vv15JNPGh2pyKB4AwDAJbtPJmjM0t2SpBda11F4jXIGJwIAIH86duzocN9sNqt8+fK69957VbduXWNCAQAAANfw1Vdfafny5XJxcVFUVJTDF5FKO4o3AABISkzNUP95MUrPtOq+OuXV754aRkcCACDfxo4da3QEAAAAIE8uXLigwYMHS5Jefvllvmx0FYo3AIBSz2az6aVFO3TkrxRV9vfQtM6hMpv5pgcAoHiyWCxasmSJ9uzZI0m69dZb9cgjj8jJycngZAAAAMBl48aN04kTJ1S9enWNHDnS6DhFDsUbAECpN3vdn1qxO04uTiZFdb9NZbxcjY4EAMC/cvDgQbVt21YnTpxQnTp1JEmTJk1ScHCwli1bpho1mFkKAAAA423fvl1vv/22JCkqKkoeHh4GJyp6zEYHiIqKUrVq1eTu7q6wsDBt2rTpuv0XLVqkunXryt3dXQ0bNtTy5csdti9evFitW7dWuXLlZDKZFBsbm+MY9957r0wmk8Otb9++Dn2OHj2qdu3aydPTUxUqVNCwYcOUmZl5w48XAFC0bD3ytyYtz/pm8qh29RUa7G9sIAAAbsDgwYNVo0YNHTt2TDExMYqJidHRo0cVEhJiX5ICAAAAMJLValXfvn1lsVj0+OOPq02bNkZHKpIMLd4sXLhQkZGRGjt2rGJiYtS4cWNFRETo9OnTufZfv369unXrpl69emnbtm3q2LGjOnbsqF27dtn7JCcnq2XLlpo8efJ1z927d2+dOnXKfpsyZYp9m8ViUbt27ZSenq7169dr7ty5mjNnjsaMGVMwDxwAUCScS07XwPkxyrTa1K5RRfUIv8XoSAAA3JC1a9dqypQpKlu2rL2tXLlyev3117V27VoDkwEAAABZPvzwQ/3222/y9vbW9OnTjY5TZBlavJk2bZp69+6tZ555RvXr11d0dLQ8PT01e/bsXPu//fbbatOmjYYNG6Z69eppwoQJuu222zRz5kx7n6eeekpjxoxRq1atrntuT09PBQUF2W++vr72bd9//71+//13ffbZZwoNDdVDDz2kCRMmKCoqSunp6QXz4AEAhrJabRq6MFanElJVPcBLkzs1ksnEdW4AAMWbm5ubLly4kKM9KSlJrq4sCwoAAABjnT59WsOHD5ckTZgwQZUrVzY4UdFlWPEmPT1dW7dudSiymM1mtWrVShs2bMh1nw0bNuQoykRERFyz//XMmzdPAQEBatCggUaMGKGUlBSH8zRs2FCBgYEO50lMTNTu3bvzfS4AQNET9dNB/bz/jNxdzHr3P7fJ243LwAEAir+HH35Yffr00caNG2Wz2WSz2fTbb7+pb9++euSRR4yOBwAAgFLupZde0t9//63Q0FANHDjQ6DhFmmF/qTp79qwsFotDgUSSAgMDtXfv3lz3iYuLy7V/XFxcvs795JNP6pZbblGlSpW0Y8cOvfzyy9q3b58WL1583fNkb7uWtLQ0paWl2e8nJibmKxcAoHCsP3hWb/2wX5I0oUMD1Q3y/Yc9AAAoHmbMmKGePXsqPDxcLi4ukqTMzEw98sgj9gvCAgAAAEZYu3at5s6dK5PJpOjoaDk780Xa6ymVz06fPn3svzds2FAVK1bUAw88oEOHDqlGjRr/+riTJk3S+PHjCyIiAOAmiU9M1eAF22S1SZ2bVdETzYKNjgQAQIGw2WxKTEzUggULdOLECe3Zs0eSVK9ePdWsWdPgdAAAACjN0tPT1a9fP0nSf//7X4WFhRmcqOgzbNm0gIAAOTk5KT4+3qE9Pj5eQUFBue4TFBSUr/55lf1GOXjw4HXPk73tWkaMGKGEhAT77dixYzeUCwBQsDItVg36fJvOJqWrbpCPXunQwOhIAAAUGJvNppo1a+r48eOqWbOm2rdvr/bt21O4AQAAgOGmTp2qPXv2qEKFCnrttdeMjlMsGFa8cXV1VdOmTbV69Wp7m9Vq1erVqxUeHp7rPuHh4Q79JWnVqlXX7J9XsbGxkqSKFSvaz7Nz506dPn3a4Ty+vr6qX7/+NY/j5uYmX19fhxsAoOh48/v92nT4nLzdnPXef5rK3cXJ6EgAABQYs9msWrVq6a+//jI6CgAAAGB3+PBhTZgwQVJWEadMmTIGJyoeDCveSFJkZKQ++OADzZ07V3v27FG/fv2UnJysZ555RpLUo0cPjRgxwt5/yJAhWrFihaZOnaq9e/dq3Lhx2rJli8OFjc6dO6fY2Fj9/vvvkqR9+/YpNjbWfq2aQ4cOacKECdq6dav+/PNPLV26VD169NDdd9+tRo0aSZJat26t+vXr66mnntL27du1cuVKjRo1SgMGDJCbm1thPT0AgAL0w+/xil57SJI05fFGCgnwMjgRAAAF7/XXX9ewYcO0a9cuo6MAAAAAstlsGjRokC5evKj77rtP3bt3NzpSsWHoNW+6dOmiM2fOaMyYMYqLi1NoaKhWrFihwMBASdLRo0dlNl+uL7Vo0ULz58/XqFGjNHLkSNWqVUtLlixRgwaXl71ZunSpvfgjSV27dpUkjR07VuPGjZOrq6t++OEHTZ8+XcnJyQoODlanTp00atQo+z5OTk769ttv1a9fP4WHh8vLy0s9e/bUK6+8crOfEgDATXDsXIoiv4iVJD3dopraNqxobCAAAG6SHj16KCUlRY0bN5arq6s8PDwctp87d86gZAAAACiNlixZomXLlsnFxUXvvvuuTCaT0ZGKDZPNZrMZHaKkSkxMlJ+fnxISElhCDQAMkpZp0RPRG7TjeIJCg/31xX/D5eps6MRTACix+PxrvLlz5153e8+ePQspSd7wngEAACi5kpKSVK9ePR0/flz/93//p4kTJxodqUjI62dgQ2feAABws038do92HE+Qv6eLorrfRuEGAFCiFbXiDAAAAEqvcePG6fjx4woJCdH//d//GR2n2KF4AwAosZZuP6lPfzsiSXqrS6gq+3v8wx4AABRviYmJubabTCa5ubnJ1dW1kBMBAACgNNqxY4emT58uSZo5c2aO5XzxzyjeAABKpIOnkzT8fzskSQPuq6H76lQwOBEAADefv7//ddcRr1Klip5++mmNHTvW4fqiAAAAQEGxWq3q27evLBaLOnXqpLZt2xodqViieAMAKHFS0jPVf95WpaRbdEf1snq+VW2jIwEAUCjmzJmj//u//9PTTz+t5s2bS5I2bdqkuXPnatSoUTpz5ozefPNNubm5aeTIkQanBQAAQEn00UcfacOGDfL29rbPvkH+UbwBAJQoNptNo5bs0v74JJX3cdOMbk3k7MQ3iwEApcPcuXM1depUde7c2d7Wvn17NWzYUO+//75Wr16tqlWr6tVXX6V4AwAAgAJ35swZvfzyy5KkV155RVWqVDE4UfHFX7MAACXKws3HtDjmhMwmaUbXJqrg4250JAAACs369evVpEmTHO1NmjTRhg0bJEktW7bU0aNHCzsaAAAASoGXXnpJf//9txo3bqxBgwYZHadYo3gDACgxdp9M0JiluyVJL7Suo/Aa5QxOBABA4QoODtZHH32Uo/2jjz5ScHCwJOmvv/5SmTJlCjsaAAAASriff/5Zc+bMkclkUnR0tJydWfjrRvDsAQBKhMTUDPWfF6P0TKvuq1Ne/e6pYXQkAAAK3ZtvvqknnnhC3333nW6//XZJ0pYtW7R37159+eWXkqTNmzerS5cuRsYEAABACZOenq5+/fpJkvr06aM77rjD4ETFH8UbAECxZ7PZ9NKiHTryV4oq+3toWudQmc0mo2MBAFDoHnnkEe3bt0/vv/++9u3bJ0l66KGHtGTJElWrVk2S7INqAAAAoKBMmzZNv//+u8qXL69JkyYZHadEoHgDACj2Zq/7Uyt2x8nFyaSo7repjJer0ZEAADBMtWrVGDADAACg0Pz555965ZVXJElTp05lid4CwjVvAADF2tYjf2vS8j2SpP9rW0+hwf7GBgIAAAAAACglbDabBg0apIsXL+ree+/Vf/7zH6MjlRgUbwAAxda55HQNnB+jTKtN7RpVVM8W1YyOBAAAAAAAUGp8/fXX+vbbb+Xi4qJ3331XJhPL2BcUijcAgGLJarVp6MJYnUpIVfUAL03u1IgPCAAAAAAAAIUkKSlJgwYNkiQNGzZM9erVMzhRyULxBgBQLEX9dFA/7z8jdxez3v3PbfJ24zJuAAAAAAAAhWX8+PE6fvy4QkJC9H//939GxylxKN4AAIqd9QfP6q0f9kuSJnRooLpBvgYnAgAAAAAAKD127Niht956S5I0c+ZMeXp6Gpyo5OFrygCAYiU+MVWDF2yT1SZ1blZFTzQLNjoSAACGuu2227R69WqVKVNGTZo0ue4yojExMYWYDAAAACWR1WpVv379ZLFY9Nhjj6lt27ZGRyqRKN4AAIqNTItVg+Zv09mkdNUN8tErHRoYHQkAAMN16NBBbm5ukqSOHTsaGwYAAAAl3scff6z169fL29tbb7/9ttFxSiyKNwCAYuON7/dp05/n5O3mrPf+01TuLk5GRwIAwHBjx47N9XcAAACgoJ09e1YvvfSSpKxr3lSpUsXgRCUX17wBABQLq36P1/tr/5AkTXm8kUICvAxOBAAAAAAAULq89NJLOnfunBo3bqzBgwcbHadEY+YNAKDIO3YuRS98EStJerpFNbVtWNHYQAAAFCFlypS57nVurnTu3LmbnAYAAAAl1S+//KKPP/5YkvTee+/J2Znyws3EswsAKNLSMi0aMD9GiamZCg3218i29YyOBABAkTJ9+nSjIwAAAKCES09PV79+/SRJffr0UXh4uMGJSj6KNwCAIm3it3u043iC/D1dFNX9Nrk6s+InAABX6tmzp9ERAAAAUMK99dZb2r17t8qXL69JkyYZHadUoHgDACiylm4/qU9/OyJJeqtLqCr7exicCACAou3o0aPX3V61atVCSgIAAICS4s8//9T48eMlSW+++abKli1rcKLSga8vAwCKpIOnkzT8fzskSQPuq6H76lQwOBEAAEVftWrVFBIScs1bfvz8889q3769KlWqJJPJpCVLljhst9lsGjNmjCpWrCgPDw+1atVKBw4cKMBHAwAAgKJg8ODBunjxou655x499dRTRscpNSjeAACKnJT0TPWft1Up6RbdUb2snm9V2+hIAAAUC9u2bVNMTIz9tnHjRkVHR6t27dpatGhRvo6VnJysxo0bKyoqKtftU6ZM0YwZMxQdHa2NGzfKy8tLERERSk1NLYiHAgAAgCLg66+/1jfffCMXFxe99957MplMRkcqNVg2DQBQpNhsNo1askv745NU3sdNM7o1kbMT3zUAACAvGjdunKOtWbNmqlSpkt544w099thjeT7WQw89pIceeijXbTabTdOnT9eoUaPUoUMHSdInn3yiwMBALVmyRF27ds1f8NRUydU1f/sAAADgpkpKStKggQMlSS8OHap6ISFZn9twY/L4HFK8AQAUKQs3H9PimBMym6QZXZuogo+70ZEAACj26tSpo82bNxfY8Q4fPqy4uDi1atXK3ubn56ewsDBt2LDhmsWbtLQ0paWl2e8nJiZm/dKpk+TM8BQAAKAoeeXQIR07flzV3N016rffpGt8sQf5lJmZp258OgYAFBm7TyZozNLdkqQXWtdReI1yBicCAKB4sRdDLrHZbDp16pTGjRunWrVqFdh54uLiJEmBgYEO7YGBgfZtuZk0aZL9YrcAAAAounYmJWnasWOSpJm1asnTycngRKUPxRsAQJGQmJqh/vNilJ5p1X11yqvfPTWMjgQAQLHj7++fYx1ym82m4OBgLViwwKBUl40YMUKRkZH2+4mJiQoODpb+9z/J19fAZAAAAMhmtVrVr1UrWSQ91qGD2hWBz5ElSmKidNWXoHJD8QYAYDibzaaXFu3Qkb9SVNnfQ9M6h8ps5gJ4AADk108//eRw32w2q3z58qpZs6acC3BZsqCgIElSfHy8KlasaG+Pj49XaGjoNfdzc3OTm5tbzg3u7lk3AAAAGG7O7Nlat2GDvLy8NP2dd/icVtDS0/PUjeINAMBws9f9qRW74+TiZFJU99tUxosLFgMA8G/cc889hXKekJAQBQUFafXq1fZiTWJiojZu3Kh+/foVSgYAAAAUvLNnz2rYsGGSpPHjx2fNkoYhKN4AAAy19cjfmrR8jyTp/9rWU2iwv7GBAAAoAX7//XcdPXpU6Vd9q++RRx7J8zGSkpJ08OBB+/3Dhw8rNjZWZcuWVdWqVTV06FBNnDhRtWrVUkhIiEaPHq1KlSqpY8eOBfUwAAAAUMhefvllnTt3To0aNdLgwYONjlOqUbwBABjmXHK6Bs6PUabVpnYNK6pni2pGRwIAoFj7448/9Oijj2rnzp0ymUyy2WySZL8OjsViyfOxtmzZovvuu89+P/taNT179tScOXP00ksvKTk5WX369NH58+fVsmVLrVixQu4sqwEAAFAs/frrr5o9e7Yk6b333pOLi4vBiUo3s9EBAAClk9Vq09CFsTqVkKqQAC+93qlhjgssAwCA/BkyZIhCQkJ0+vRpeXp6avfu3fr555/VrFkzrVmzJl/Huvfee2Wz2XLc5syZIymrIPTKK68oLi5Oqamp+uGHH1S7du2Cf1AAAAC46TIyMtS3b19JUu/evdWiRQuDE8Hw4k1UVJSqVasmd3d3hYWFadOmTdftv2jRItWtW1fu7u5q2LChli9f7rB98eLFat26tcqVKyeTyaTY2FiH7efOndOgQYNUp04deXh4qGrVqho8eLASEhIc+plMphy3BQsWFMhjBgBIUT8d1M/7z8jN2ax3u98mH3e+zQEAwI3asGGDXnnlFQUEBMhsNstsNqtly5aaNGkSy14AAADgmt566y3t3r1bAQEBev31142OAxlcvFm4cKEiIyM1duxYxcTEqHHjxoqIiNDp06dz7b9+/Xp169ZNvXr10rZt29SxY0d17NhRu3btsvdJTk5Wy5YtNXny5FyPcfLkSZ08eVJvvvmmdu3apTlz5mjFihXq1atXjr4ff/yxTp06Zb+xdjMAFIz1B8/qrR/2S5ImdmygehV9DU4EAEDJYLFY5OPjI0kKCAjQyZMnJUm33HKL9u3bZ2Q0AAAAFFFHjhzR+PHjJUlvvvmmypYta3AiSJLJlr0Ich5kZmZq/vz5ioiIUGBg4A2fPCwsTLfffrtmzpwpSbJarQoODtagQYM0fPjwHP27dOmi5ORkffvtt/a2O+64Q6GhoYqOjnbo++effyokJETbtm1TaGjodXMsWrRI//nPf5ScnCxn56zLAJlMJn311Vc3VLBJTEyUn5+fEhIS5OvLHyYBQJLiE1PVbsYvOpuUrs7NqmjK442NjgQAKCB8/jXeXXfdpRdeeEEdO3bUk08+qb///lujRo3SrFmztHXrVocvvhUFvGcAAACM16FDBy1dulR333231qxZw7L2N1lePwPna+aNs7Oz+vbtq9TU1BsOmJ6erq1bt6pVq1aXw5jNatWqlTZs2JDrPhs2bHDoL0kRERHX7J9X2U9SduEm24ABAxQQEKDmzZtr9uzZ+qc6V1pamhITEx1uAIDLMi1WDZq/TWeT0lU3yEevdGhgdCQAAEqUUaNGyWq1SpJeeeUVHT58WHfddZeWL1+uGTNmGJwOAAAARc3XX3+tpUuXytnZWe+99x6FmyLE+Z+7OGrevLliY2N1yy233NCJz549K4vFkmMGT2BgoPbu3ZvrPnFxcbn2j4uLu6EcEyZMUJ8+fRzaX3nlFd1///3y9PTU999/r/79+yspKem660RPmjTJPr0MAJDTG9/v06Y/z8nbzVnv/aep3F2cjI4EAECJcu+99yozM1OSVLNmTe3du1fnzp1TmTJlGIgDAADAQXJysv3v3S+++KLq169vcCJcKd/Fm/79+ysyMlLHjh1T06ZN5eXl5bC9UaNGBRbuZktMTFS7du1Uv359jRs3zmHb6NGj7b83adJEycnJeuONN65bvBkxYoQiIyMdjh8cHFzguQGgOFr1e7zeX/uHJGnK440UEuD1D3sAAIC8OnPmjHr06KEffvhBVqtVt99+uz777DPVrFmTNcsBAACQq1deeUVHjx5VtWrVHP4ejqIh38Wbrl27SpJDEcNkMslms8lkMsliseTpOAEBAXJyclJ8fLxDe3x8vIKCgnLdJygoKF/9r+fChQtq06aNfHx89NVXX8nFxeW6/cPCwjRhwgSlpaXJzc0t1z5ubm7X3AYApdmxcyl64YtYSdLTLaqpbcOKxgYCAKCEefnllxUbG6tXXnlF7u7uev/999W7d2/99NNPRkcDAABAEbRr1y5NmzZNkvTOO+/I09PT4ES4Wr6LN4cPHy6QE7u6uqpp06ZavXq1OnbsKEmyWq1avXq1Bg4cmOs+4eHhWr16tYYOHWpvW7VqlcLDw/N17sTEREVERMjNzU1Lly6Vu7v7P+4TGxurMmXKUJwBgHxKy7RowPwYJaZmKjTYXyPb1jM6EgAAJc6qVas0Z84cRURESJIefvhh1atX77pfPgMAAEDpZLVa1a9fP2VmZurRRx/Vww8/bHQk5CLfxZsbvdbNlSIjI9WzZ081a9ZMzZs31/Tp05WcnKxnnnlGktSjRw9VrlxZkyZNkiQNGTJE99xzj6ZOnap27dppwYIF2rJli2bNmmU/5rlz53T06FGdPHlSkrRv3z5JWbN2goKClJiYqNatWyslJUWfffaZEhMTlZiYKEkqX768nJyc9M033yg+Pl533HGH3N3dtWrVKr322mt68cUXC+yxA0BpMfHbPdpxPEH+ni6K6n6bXJ3NRkcCAKDEOXnypBo3bmy/X6tWLbm5uenUqVOqVq2accEAAABQ5MydO1e//vqrvLy89PbbbxsdB9eQ7+KNJB06dEjTp0/Xnj17JEn169fXkCFDVKNGjXwdp0uXLjpz5ozGjBmjuLg4hYaGasWKFQoMDJQkHT16VGbz5T/ytWjRQvPnz9eoUaM0cuRI1apVS0uWLFGDBg3sfZYuXWov/kiXl3kbO3asxo0bp5iYGG3cuFFS1gU8r3T48GFVq1ZNLi4uioqK0vPPPy+bzaaaNWtq2rRp6t27d74eHwCUdku3n9Snvx2RJL3VJVSV/T0MTgQAQMnl5OSU477NZjMoDQAAAIqiv/76S8OGDZMkjR8/nmu2F2EmWz4/za9cuVKPPPKIQkNDdeedd0qS1q1bp+3bt+ubb77Rgw8+eFOCFkeJiYny8/NTQkKCfH19jY4DAIXq4OkkPTLzV6WkWzTgvhoaFlHX6EgAgJuMz7/GMZvN8vPzk8lksredP39evr6+Dl+IO3funBHxron3DAAAQOF67rnn9NFHH6lhw4baunXrP14LHgUvr5+B8z3zZvjw4Xr++ef1+uuv52h/+eWXKd4AAJSSnqn+87YqJd2iO6qX1fOtahsdCQCAEu3jjz82OgIAAACKuHXr1umjjz6SJL333nsUboq4fBdv9uzZoy+++CJH+7PPPqvp06cXRCYAQDFms9k0asku7Y9PUnkfN83o1kTOTlznBgCAm6lnz55GRwAAAEARlpGRob59+0rKmn2TvaoWiq58/zWtfPnyio2NzdEeGxurChUqFEQmAEAxtnDzMS2OOSGzSZrRtYkq+LgbHQkAAAAAAKBUmz59unbt2qWAgIAcq2qhaMr3zJvevXurT58++uOPP9SiRQtJWdOtJk+erMjIyAIPCAAoPnafTNCYpbslSS+0rqPwGuUMTgQAAAAAAFC6HT16VOPGjZMkvfHGGypXjr/XFAf5Lt6MHj1aPj4+mjp1qkaMGCFJqlSpksaNG6fBgwcXeEAAQPGQmJqh/vNilJ5p1X11yqvfPTWMjgQAAAAAAFDqDR48WCkpKbrrrrtYbrcYyVfxJjMzU/Pnz9eTTz6p559/XhcuXJAk+fj43JRwAIDiwWaz6aVFO3TkrxRV9vfQtM6hMptNRscCAAAAAAAo1ZYuXaqvv/5azs7Oeu+992Qy8fea4iJf17xxdnZW3759lZqaKimraEPhBgAwe92fWrE7Ti5OJkV1v01lvFyNjgQAAAAAAFCqJScna9CgQZKkF154QbfeeqvBiZAf+V42rXnz5tq2bZtuueWWm5EHAFDMbD3ytyYt3yNJ+r+29RQa7G9sIAAASrFrXYfUZDLJ3d1dNWvWVIcOHVS2bNlCTgYAAIDCNmHCBB09elS33HKLRo8ebXQc5FO+izf9+/fXCy+8oOPHj6tp06by8vJy2N6oUaMCCwcAKNrOJadr4PwYZVptatewonq2qGZ0JAAASrVt27YpJiZGFotFderUkSTt379fTk5Oqlu3rt5991298MIL+vXXX1W/fn2D0wIAAOBm2b17t6ZOnSpJeuedd3L8HR9FX76LN127dpWUdZGjbCaTSTabTSaTSRaLpeDSAQCKLKvVpsgvYnUqIVUhAV56vVND1k0FAMBg2bNqPv74Y/n6+kqSEhIS9Nxzz6lly5bq3bu3/RqmK1euNDgtAAAAbgabzaZ+/fopMzNTHTt2VPv27Y2OhH/BZLPZbPnZ4ciRI9fdznJqlyUmJsrPz08JCQn2gRMAlBQzfzygN7/fLzdns5YMuFP1KvLfOQAo7fj8a7zKlStr1apVOWbV7N69W61bt9aJEycUExOj1q1b6+zZswalvIz3DAAA+DcOHz6sDz/8ULGxsUZHKZKSkpL0888/y9PTU3v27FHVqlWNjoQr5PUzcL5m3mRkZOj+++/Xt99+q3r16t1wSABA8bT+0FlNW7VfkjShQwMKNwAAFBEJCQk6ffp0juLNmTNnlJiYKEny9/dXenq6EfEAAAD+tczMTC1fvlzR0dFasWKF8jknoVQaP348hZtiLF/FGxcXF6Wmpt6sLACAYuB0YqoGfx4rq016vGkVdb492OhIAADgkg4dOujZZ5/V1KlTdfvtt0uSNm/erBdffFEdO3aUJG3atEm1a9c2MCUAAEDenThxQh999JE++OADHT9+3N7+4IMP6rHHHpO7u7uB6Youf39/PfLII0bHwA3I9zVvBgwYoMmTJ+vDDz+Us3O+dwcAFGOZFqsGfb5NZ5PSVCfQRxM6NDA6EgAAuML777+v559/Xl27dlVmZqYkydnZWT179tRbb70lSapbt64+/PBDI2MCAABcl9Vq1Q8//KDo6GgtXbrUfp31cuXK6dlnn1WfPn1Us2ZNg1MCN1e+r3nz6KOPavXq1fL29lbDhg3l5eXlsH3x4sUFGrA4Y/1mACXNGyv3KuqnQ/JyddLSQS1Vo7y30ZEAAEUIn3+LjqSkJP3xxx+SpOrVq8vbu2j+P5v3DAAAuNKZM2c0Z84cvf/++zp06JC9vWXLlurbt686derETBsUezflmjdS1nSrTp063VA4AEDx89Pe04r6KeuD0+udGlG4AQCgCPP29lajRo2MjgEAAPCPbDabfv31V0VHR+vLL7+0X5vP19dXPXr00H//+181aMDKHyh98l28+fjjj29GDgBAEXbi/EU9/0WsJOmpO25R+8aVjA0EAABylZycrNdff12rV6/W6dOnZbVaHbZnz8YBAAAw2vnz5/Xpp58qOjpav//+u729WbNm6tu3r7p27Zpj1SegNPlXF63JzMzUmjVrdOjQIT355JPy8fHRyZMn5evrW2Sn4wMA/p30TKsGzIvR+ZQMNazsp1EP1zM6EgAAuIbnnntOa9eu1VNPPaWKFSvKZDIZHQkAAMDOZrNpy5Ytio6O1ueff66LFy9Kkjw9PfXkk0/qv//9r5o1a2ZwSqBoyHfx5siRI2rTpo2OHj2qtLQ0Pfjgg/Lx8dHkyZOVlpam6Ojom5ETAGCQ17/bq9hj5+Xr7qx3u98mN2cnoyMBAIBr+O6777Rs2TLdeeedRkcBAACwS0pK0ueff67o6GjFxMTY22+99Vb169dP//nPf+Tn52dgQqDoyXfxZsiQIWrWrJm2b9+ucuXK2dsfffRR9e7du0DDAQCM9d3OU5q97rAkaWrnUAWX9TQ4EQAAuJ4yZcqobNmyRscAAACQJO3cuVPR0dH69NNPdeHCBUmSq6urOnfurL59+6pFixbMFAauId/Fm19++UXr16+Xq6urQ3u1atV04sSJAgsGADDWn2eT9dKXOyRJ/727uh6sH2hwIgAA8E8mTJigMWPGaO7cufL05EsXAACg8KWmpurLL79UdHS01q1bZ2+vWbOm+vbtq549eyogIMDAhEDxkO/ijdVqlcViydF+/Phx+fj4FEgoAICxUjMs6j8vRhfSMnV7tTJ6MaKO0ZEAAEAeTJ06VYcOHVJgYKCqVasmFxcXh+1XLlMCAABQkPbv369Zs2bp448/1rlz5yRJzs7O6tixo/r27av77rtPZrPZ4JRA8ZHv4k3r1q01ffp0zZo1S5JkMpmUlJSksWPHqm3btgUeEABQ+MZ/s1u/n0pUWS9XvdPtNrk48eEKAIDioGPHjkZHAAAApUhGRoa+/vprRUdHa/Xq1fb2qlWrqk+fPnr22WdVsWJFAxMCxVe+izdTp05VRESE6tevr9TUVD355JM6cOCAAgIC9Pnnn9+MjACAQrQ45rg+33RMJpP0dtdQBfm5Gx0JAADk0dixY42OAAAASoEjR47ogw8+0EcffaS4uDhJWV/yb9u2rfr27auHHnpITk5OBqcEird8F2+qVKmi7du3a+HChdq+fbuSkpLUq1cvde/eXR4eHjcjIwCgkOyPv6D/+2qXJGnw/bV0V63yBicCAAAAAABFgcVi0Xfffafo6GgtX75cNptNkhQYGKjnnntOvXv31i233GJwSqDkyHfxRspaq7B79+7q3r17QecBABgkOS1T/efF6GKGRS1rBmjwA7WMjgQAAPKgbNmy2r9/vwICAlSmTBmZTKZr9s1efx4AACCvTp06pY8++kizZs3SsWPH7O0PPPCA+vbtqw4dOuS4zh6AG/evijcAgJLFZrNp5Fc7dfB0kgJ93TS9a6iczNf+ww8AACg63nrrLfn4+Nh/v17xBgAAIC+sVqt+/PFHRUdH6+uvv1ZmZqakrC+NPPPMM+rTp49q165tcEqgZKN4AwDQ/E1H9XXsSTmZTXqn220K8HYzOhIAAMijnj172n9/+umnjQsCAACKvbNnz2rOnDl6//33dfDgQXv7nXfeqb59++rxxx+XuzvXxgUKA8UbACjldp1I0Pilv0uShkXUUfOQsgYnAgAA/5aTk5NOnTqlChUqOLT/9ddfqlChgiwWi0HJAABAUWWz2bR+/XpFR0dr0aJFSktLkyT5+PioR48e+u9//6uGDRsanBIofSjeAEAplnAxQ/3nxSjdYlWrehXU567qRkcCAAA3IPvCwVdLS0uTq6trIacBAABFWUJCgj777DNFR0dr165d9vYmTZqoX79+6tatm7y9vQ1MCJRuFG8AoJSy2Wwatmi7jp5LUZUyHpr6RKjMXOcGAIBiacaMGZIkk8mkDz/80OEPLRaLRT///LPq1q1rVDwAAFCEbN26VdHR0Zo/f75SUlIkSR4eHurWrZv69u2rZs2acQ09oAjIU/GmTJkyef4He+7cuRsKBAAoHB/9eljf/x4vVyezop68TX6eLkZHAgAA/9Jbb70lKevLGdHR0XJycrJvc3V1VbVq1RQdHW1UPAAAYLDk5GQtWLBA0dHR2rJli729fv366tu3r5566in5+/sbFxBADnkq3kyfPt3++19//aWJEycqIiJC4eHhkqQNGzZo5cqVGj169E0JCQAoWFuPnNPr3+2VJI16uJ4aB/sbGwgAANyQw4cPS5Luu+8+LV68WGXKlDE4EQAAKAp27dql999/X5988okSExMlZX2x4/HHH1ffvn3VsmVLZtkARZQ5L5169uxpv61bt06vvPKKPv/8cw0ePFiDBw/W559/rldeeUVr167Nd4CoqChVq1ZN7u7uCgsL06ZNm67bf9GiRapbt67c3d3VsGFDLV++3GH74sWL1bp1a5UrV04mk0mxsbE5jpGamqoBAwaoXLly8vb2VqdOnRQfH+/Q5+jRo2rXrp08PT1VoUIFDRs2TJmZmfl+fABQ1JxLTtfA+duUabXp4UYV9dQdtxgdCQAAFJCffvqJwg0AAKVcamqq5s2bp7vuuksNGzbUzJkzlZiYqBo1amjKlCk6fvy4fTuFG6Doyvc1b1auXKnJkyfnaG/Tpo2GDx+er2MtXLhQkZGRio6OVlhYmKZPn66IiAjt27dPFSpUyNF//fr16tatmyZNmqSHH35Y8+fPV8eOHRUTE6MGDRpIypoC2LJlS3Xu3Fm9e/fO9bzPP/+8li1bpkWLFsnPz08DBw7UY489pnXr1knKWhO6Xbt2CgoK0vr163Xq1Cn16NFDLi4ueu211/L1GAGgKLFabRq6MFanElJVPcBLr3dqxAc1AABKkGefffa622fPnl1ISQAAQGE7cOCAZs2apY8//lh//fWXJMnJyUkdOnRQ37599cADD8hsztN3+QEUASabzWbLzw633HKLBg8erBdeeMGhferUqZoxY4aOHDmS52OFhYXp9ttv18yZMyVJVqtVwcHBGjRoUK6FoC5duig5OVnffvutve2OO+5QaGhojvWb//zzT4WEhGjbtm0KDQ21tyckJKh8+fKaP3++Hn/8cUnS3r17Va9ePW3YsEF33HGHvvvuOz388MM6efKkAgMDJUnR0dF6+eWXdebMGbm6uubp8SUmJsrPz08JCQny9fXN8/MCADfLO6sPaOqq/XJzNuvrgXeqbhD/bQIAFBw+/xrv0UcfdbifkZGhXbt26fz587r//vu1ePFig5LljvcMAAA3JiMjQ0uXLlV0dLR++OEHe3uVKlXUp08f9erVS5UqVTIwIYCr5fUzcL5n3owfP17PPfec1qxZo7CwMEnSxo0btWLFCn3wwQd5Pk56erq2bt2qESNG2NvMZrNatWqlDRs25LrPhg0bFBkZ6dAWERGhJUuW5Pm8W7duVUZGhlq1amVvq1u3rqpWrWov3mzYsEENGza0F26yz9OvXz/t3r1bTZo0yfXYaWlpSktLs9/PXkcSAIqC9QfP6q0f9kuSJnRsQOEGAIAS6KuvvsrRZrVa1a9fP9WoUaNAz2WxWDRu3Dh99tlniouLU6VKlfT0009r1KhRzOwFAOAmO3r0qD788EN9+OGHOnXqlCTJZDKpTZs26tevnx566CE5O+f7T78AipB8/wt++umnVa9ePc2YMcP+ra169erp119/tRdz8uLs2bOyWCwOBRJJCgwM1N69e3PdJy4uLtf+cXFxeT5vXFycXF1d5e/vf83jXOs82duuZdKkSRo/fnyeswBAYTmdmKrBC2JltUlPNK2izs2CjY4EAAAKidlsVmRkpO6991699NJLBXbcyZMn67333tPcuXN16623asuWLXrmmWfk5+enwYMHF9h5AABAFovFopUrVyo6OlrLli2T1WqVJFWoUEG9evVS7969FRISYnBKAAXlX5Vfw8LCNG/evILOUuyNGDHCYWZQYmKigoP5AykAY2VarBr0+TadTUpT3SAfvdKhgdGRAABAITt06JAyMzML9Jjr169Xhw4d1K5dO0lStWrV9Pnnn2vTpk0Feh4AAEq7uLg4zZ49W7NmzXK4ZMV9992nvn37qmPHjnm+zAOA4uNfFW8OHTqkjz/+WH/88YemT5+uChUq6LvvvlPVqlV166235ukYAQEBcnJyUnx8vEN7fHy8goKCct0nKCgoX/2vdYz09HSdP3/eYfbNlccJCgrKMeDIPu/1zuXm5iY3N7c8ZwGAwjBt1X5tPHxOXq5Oiup+mzxcnYyOBAAAbpKrl5m22Ww6deqUli1bpp49exbouVq0aKFZs2Zp//79ql27trZv365ff/1V06ZNu+Y+LDUNAEDe2Ww2Pf/884qKirJ/CaNMmTJ6+umn1adPH9WtW9fghABuJnN+d1i7dq0aNmyojRs36n//+5+SkpIkSdu3b9fYsWPzfBxXV1c1bdpUq1evtrdZrVatXr1a4eHhue4THh7u0F+SVq1adc3+uWnatKlcXFwcjrNv3z4dPXrUfpzw8HDt3LlTp0+fdjiPr6+v6tevn+dzAYDRftwbr3fXHJIkvd6pkWqU9zY4EQAAuJm2bdvmcNuxY4ckaerUqZo+fXqBnmv48OHq2rWr6tatKxcXFzVp0kRDhw5V9+7dr7nPpEmT5OfnZ7+xUgEAANf2/fff6+2331ZmZqbCw8M1d+5cnThxQtOmTaNwA5QC+Z55M3z4cE2cOFGRkZHy8fGxt99///2aOXNmvo4VGRmpnj17qlmzZmrevLmmT5+u5ORkPfPMM5KkHj16qHLlypo0aZIkaciQIbrnnns0depUtWvXTgsWLNCWLVs0a9Ys+zHPnTuno0eP6uTJk5KyCjNS1oyZoKAg+fn5qVevXoqMjFTZsmXl6+urQYMGKTw8XHfccYckqXXr1qpfv76eeuopTZkyRXFxcRo1apQG/H979x1XZf3/f/xx2KACigqiqJS4UXHjSCsLMy1HrihnmrnFleYsy21mDtRy1MdtasOVuVcO3LnNrbgFAQU8nN8f/jzfCFQw9AJ53m+3c1Ou631d1/Mcruy8z+u83+9OnTSyRkQyjAu3YuixYD8ALQILUK+0t8GJRERE5Flbv379c7vWwoULmTNnDnPnzqVEiRLs27eP7t274+3t/chRPppqWkREJGUsFot1be1u3bql+ZcwRCT9S3Xx5uDBg8ydOzfJ9ty5c3P9+vVUnatp06Zcu3aNQYMGER4eTpkyZVi1ahWenp4AnDt3Dhub/xscVKVKFebOncuAAQPo378/fn5+LFu2jJIl/2/9hl9++cVa/AFo1qwZAIMHD2bIkCEAfP3119jY2NCoUSNiY2MJCgpi8uTJ1mNsbW357bff+OSTTwgMDCRLliy0bNmSzz//PFXPT0TEKHH3E+g8dy8Rd+Mpnc+Nz94uZnQkERERecH07t3bOvoGwN/fn7NnzzJ8+PBHFm801bSIiEjKrFu3ju3bt+Po6Ejfvn2NjiMiBkh18cbd3Z3Lly/j6+ubaPvevXvJmzdvqgN07tyZzp07J7tvw4YNSbY1btyYxo0bP/J8rVq1olWrVo+9ppOTE5MmTWLSpEmPbFOgQAFWrFjx2POIiKRXw1ceYd/527g62THx/bI42mmdGxERkczgxo0bDBo0iPXr13P16lUSEhIS7b9582aaXSsmJibRl+3gwRfh/n1NERERSb0vvvgCgPbt25MnTx6D04iIEVJdvGnWrBl9+/Zl0aJFmEwmEhIS2Lp1K7169aJFixbPIqOIiKTCioOXmbn1DADjmpTBJ4eLsYFERETkufnwww85efIkbdu2xdPTE5PJ9MyuVa9ePb788kvy589PiRIl2Lt3L+PGjaNNmzbP7JoiIiKZwcaNG9m4cSMODg706dPH6DgiYpBUF2+++uorOnXqhI+PD2azmeLFi2M2m3n//fcZMGDAs8goIiIpdPp6NH0WP1iY+OMaL1GruKfBiUREROR52rx5M1u2bKF06dLP/FrffvstAwcOpGPHjly9ehVvb28+/vhjBg0a9MyvLSIi8iJ7OOqmbdu25MuXz+A0ImIUk8VisTzNgefPn+fgwYNERUUREBCAn59fWmfL8CIjI3FzcyMiIgJXV1ej44jIC+5evJkGk7dx5HIkFQvmYG67StjZ2jz5QBERkTSi97/Gq1ChAt9++y2VK1c2OkqK6J4RERFJbOvWrVSrVg17e3tOnjxJ/vz5jY4kImkspe+BU/2p3ueff05MTAw+Pj7UqVOHJk2a4Ofnx927d/n888//U2gREXl6Q375iyOXI/HI4sCE5gEq3IiIiGRCkydP5rPPPmPjxo3cuHGDyMjIRA8RERFJ3x6OumnVqpUKNyKZXKo/2Rs6dChRUVFJtsfExDB06NA0CSUiIqnzU9gF5u86j8kE3zQLwMvNyehIIiIiYgB3d3ciIyN57bXXyJ07N9mzZyd79uy4u7uTPXt2o+OJiIjIY+zYsYPVq1dja2tLv379jI4jIgZL9Zo3Fosl2UUv9+/fT44cOdIklIiIpNzxK3cYsOwQAN1e96OaX06DE4mIiIhRgoODsbe3Z+7cuXh6eibbdxMREZH06eGomxYtWuDr62twGhExWoqLN9mzZ8dkMmEymShcuHCiToDZbCYqKooOHTo8k5AiIpK86Nj7fPK/MO7Gm6nul5Mur2n9MRERkczs0KFD7N27lyJFihgdRURERFIhLCyM5cuXY2NjQ//+/Y2OIyLpQIqLN+PHj8disdCmTRuGDh2Km5ubdZ+DgwMFCxYkMDDwmYQUEZGkLBYL/Zce5NS1aLxcnRjftAy2Nvp2rYiISGZWvnx5zp8/r+KNiIhIBvNw1E1wcDCFChUyOI2IpAcpLt60bNkSAF9fX6pUqYK9vf0zCyUiIk82Z8c5ft53CVsbE9++H4BHVkejI4mIiIjBunTpQrdu3ejduzf+/v5J+m2lSpUyKJmIiIg8yr59+/j5558xmUx89tlnRscRkXQi1Wve1KhRw/r3e/fuERcXl2i/q6vrf08lIiKPdehiBJ//ehiAvrWLUKGg1hwTERERaNq0KQBt2rSxbjOZTNa1S81ms1HRRERE5BGGDRsGQLNmzTR6VkSsUl28iYmJoU+fPixcuJAbN24k2a/OgIjIsxVxN55P5oQRZ06gVjFP2lV/yehIIiIikk6cPn3a6AgiIiKSCocOHeKnn37SqBsRSSLVxZvevXuzfv16pkyZwocffsikSZO4ePEiU6dOZcSIEc8io4iI/H8Wi4Xei/Zz/uZd8mV3Zmzj0phMWudGREREHihQoIDREURERCQVHo66ee+99yhRooTBaUQkPUl18ebXX3/lhx9+oGbNmrRu3Zrq1atTqFAhChQowJw5cwgODn4WOUVEBPh+y2l+P3wFB1sbJgeXxc1F64+JiIhIYpcuXWLLli1cvXqVhISERPu6du1qUCoRERH5tyNHjrBw4UIABgwYYHAaEUlvUl28uXnzJi+99GCKHldXV27evAlAtWrV+OSTT9I2nYiIWIWdvcmIlUcBGFi3GKXyuRsbSERERNKdWbNm8fHHH+Pg4ICHh0eiEbomk0nFGxERkXTkyy+/xGKx0KBBA0qVKmV0HBFJZ2xSe8BLL71knUe5aNGi1urwr7/+iru7e5qGExGRB25ExdJpzl7uJ1ioV9qbDyprShQRERFJauDAgQwaNIiIiAjOnDnD6dOnrY+///7b6HgiIiLy/x0/fpx58+YBD/7/LSLyb6ku3rRu3Zr9+/cD8OmnnzJp0iScnJzo0aMHvXv3TvOAIiKZXUKChR4L9xMeeY+XcmVheEN/rXMjIiIiyYqJiaFZs2bY2KS6qyciIiLP0VdffUVCQgL16tUjICDA6Dgikg6ZLBaL5b+c4OzZs4SFhVGoUCEN7/uXyMhI3NzciIiIwNXV1eg4IpJBTVh7gnFrjuNkb8PPnapRxCub0ZFERESSpfe/xuvTpw85cuTg008/NTpKiuieERGRzOjUqVMUKVIEs9nMzp07qVChgtGRROQ5Sul74FSvefNvBQoUoEABTd8jIvIsbD15na//OA7AsPr+KtyIiIjIYw0fPpy6deuyatUq/P39sbe3T7R/3LhxBiUTERGRh4YPH47ZbOatt95S4UZEHilFxZsJEyak+IRaAFNEJG1cibxHt/l7sVigSfl8vFcun9GRREREJJ0bPnw4q1evpkiRIgCJplrVtKsiIiLGO3PmDLNnzwa01o2IPF6Kijdff/11ik5mMplUvBERSQP3zQl0mbeX61FxFPXKxufvljQ6koiIiGQAY8eOZcaMGbRq1croKCIiIpKMESNGcP/+fWrVqkVgYKDRcUQkHUtR8eb06dPPOoeIiPzD2DXH2Xn6Jlkd7ZgcXBYne1ujI4mIiEgG4OjoSNWqVY2OISIiIsk4f/48M2bMAGDQoEEGpxGR9M7G6AAiIpLY2iNXmLLhFAAjG5XipVxZDU4kIiIiGUW3bt349ttvjY4hIiIiyRg5ciTx8fHUrFmT6tWrGx1HRNK5FI28+ac2bdo8dv/D6rGIiKTehVsxhCzcD0CrKgV5u1QegxOJiIhIRrJz507WrVvHb7/9RokSJbC3t0+0f8mSJQYlExERydwuXrzI9OnTAY26EZGUSXXx5tatW4l+jo+P59ChQ9y+fZvXXnstzYKJiGQ2cfcT6DR3LxF34ymdz41+dYoaHUlEREQyGHd3dxo2bGh0DBEREfmX0aNHExcXR7Vq1ahZs6bRcUQkA0h18Wbp0qVJtiUkJPDJJ5/w8ssvp0koEZHM6KsVR9h//jZuzvZMCi6Lo53WuREREZHUmTlzptERRERE5F/Cw8OZOnUq8GDUjclkMjiRiGQEabLmjY2NDSEhIXz99ddpcToRkUxn+YHLzNp2BoBxTUqTL7uLsYFEREQkQ7t27Rpbtmxhy5YtXLt2zeg4IiIimdqYMWO4d+8elStXplatWkbHEZEMIk2KNwCnTp3i/v37aXU6EZFMUZcbfQAAbCpJREFU4+9rUfT96QAAHWq8zOvFPA1OJCIiIhlVdHQ0bdq0IU+ePLzyyiu88soreHt707ZtW2JiYoyOJyIikulcvXqVKVOmABp1IyKpk+pp00JCQhL9bLFYuHz5MsuXL6dly5ZpFkxEJDO4F2+m45w9RMXep2LBHPR6s7DRkURERCQDCwkJYePGjfz6669UrVoVgC1bttC1a1d69uxp/fBIREREno9x48YRExND+fLlqV27ttFxRCQDSXXxZu/evYl+trGxIVeuXIwdO5Y2bdqkWTARkcxg8M9/cTT8DjmzOvDt+wHY2abZgEgRERHJhH766ScWL16caCHkOnXq4OzsTJMmTVS8EREReY5u3LjBxIkTAY26EZHUS3XxZv369c8ih4hIprM47AILdp/HZIJvmgXg6epkdCQRERHJ4GJiYvD0TDoFa+7cuTVtmoiIyHM2fvx4oqOjKVOmDHXr1jU6johkMPqKt4iIAY6F32HAsoMA9KhVmKqFchqcSERERF4EgYGBDB48mHv37lm33b17l6FDhxIYGGhgMhERkczl1q1bTJgwAdCoGxF5OqkeeXPjxg0GDRrE+vXruXr1KgkJCYn237x5M83CiYi8iKJi7/PJnDDuxSdQ3S8nnV8tZHQkEREReUF88803BAUFkS9fPkqXLg3A/v37cXJyYvXq1QanExERyTwmTJhAZGQk/v7+vPvuu0bHEZEMKNXFmw8//JCTJ0/Stm1bPD09VTUWEUkFi8VCvyUH+ftaNF6uToxvWgYbG/07KiIiImmjZMmSnDhxgjlz5nD06FEAmjdvTnBwMM7OzganExERyRwiIiIYP348AAMHDsTGRpMfiUjqpbp4s3nzZrZs2WL9FldamDRpEqNHjyY8PJzSpUvz7bffUrFixUe2X7RoEQMHDuTMmTP4+fkxcuRI6tSpY91vsVgYPHgw06dP5/bt21StWpUpU6bg5+cHwIYNG3j11VeTPffOnTupUKECZ86cwdfXN8n+7du3U7ly5f/4jEUks/rfjnP8uv8StjYmJr4fgEdWR6MjiYiIyAvGxcWFdu3aGR1DREQk05o4cSK3b9+mePHiNGrUyOg4IpJBpbrsW7RoUe7evZtmARYsWEBISAiDBw9mz549lC5dmqCgIK5evZps+23bttG8eXPatm3L3r17qV+/PvXr1+fQoUPWNqNGjWLChAmEhoayY8cOsmTJQlBQkHXe5ypVqnD58uVEj48++ghfX1/Kly+f6Hp//PFHonblypVLs+cuIpnLwQsRfPHrYQA+rV2U8gVzGJxIREREXjTDhw9nxowZSbbPmDGDkSNHGpBIREQkc7lz5w7jxo0DYMCAARp1IyJPLdX/ekyePJnPPvuMjRs3cuPGDSIjIxM9UmvcuHG0a9eO1q1bU7x4cUJDQ3FxcUm2wwEP5nCuXbs2vXv3plixYnzxxReULVuWiRMnAg9G3YwfP54BAwbw7rvvUqpUKX744QcuXbrEsmXLAHBwcMDLy8v68PDw4Oeff6Z169ZJpoHz8PBI1Nbe3j7Vz1FEJCImno5zw4gzJ/BGcU8+qp50ZJ+IiIjIfzV16lSKFi2aZHuJEiUIDQ01IJGIiEjmMnnyZG7evEnhwoVp0qSJ0XFEJANLdfHG3d2dyMhIXnvtNXLnzk327NnJnj077u7uZM+ePVXniouLIywsjFq1av1fIBsbatWqxfbt25M9Zvv27YnaAwQFBVnbnz59mvDw8ERt3NzcqFSp0iPP+csvv3Djxg1at26dZN8777xD7ty5qVatGr/88stjn09sbOx/LmaJyIvHYrHQa/F+zt+8i08OZ8Y0Lq31wkREROSZCA8PJ0+ePEm258qVi8uXLxuQSEREJPOIjo5mzJgxwINRN7a2tgYnEpGMLNVr3gQHB2Nvb8/cuXPx9PT8Tx9AXr9+HbPZjKenZ6Ltnp6e1sU1/y08PDzZ9uHh4db9D7c9qs2/ff/99wQFBZEvXz7rtqxZszJ27FiqVq2KjY0NP/30E/Xr12fZsmW88847yZ5n+PDhDB069DHPWEQyo+82n2bN4Ss42Now+f1yuDlrBJ+IiIg8Gz4+PmzdujXJ+p1bt27F29vboFQiIiKZQ2hoKNevX+fll1+mefPmRscRkQwu1cWbQ4cOsXfvXooUKfIs8jx3Fy5cYPXq1SxcuDDR9pw5cxISEmL9uUKFCly6dInRo0c/snjTr1+/RMdERkbi4+PzbIKLSIaw+8xNRqx6UIweVK84/vncDE4kIiIiL7J27drRvXt34uPjee211wBYu3Ytffr0oWfPnganExEReXHFxMQwevRoAD777DPs7FL9sauISCKp/lekfPnynD9/Pk2KNzlz5sTW1pYrV64k2n7lyhW8vLySPcbLy+ux7R/+eeXKlUTTBVy5coUyZcokOd/MmTPx8PB4ZEHmnypVqsSaNWseud/R0RFHR8cnnkdEMocbUbF0nrsXc4KFd0p7E1wpv9GRRERE5AXXu3dvbty4QceOHYmLiwPAycmJvn370q9fP4PTiYiIvLimT5/OlStXKFiwIB988IHRcUTkBZDqNW+6dOlCt27dmDVrFmFhYRw4cCDRIzUcHBwoV64ca9eutW5LSEhg7dq1BAYGJntMYGBgovYAa9assbb39fXFy8srUZvIyEh27NiR5JwWi4WZM2fSokUL7O2fPI3Rvn37kp0/WkTk38wJFrov2Ed45D1ezpWF4Q39tc6NiIiIPHMmk4mRI0dy7do1/vzzT/bv38/NmzcZNGiQ0dFEREReWPfu3WPkyJEA9O/fP0WfM4qIPEmqR940bdoUgDZt2li3mUwmLBYLJpMJs9mcqvOFhITQsmVLypcvT8WKFRk/fjzR0dG0bt0agBYtWpA3b16GDx8OQLdu3ahRowZjx47l7bffZv78+ezevZtp06ZZs3Tv3p1hw4bh5+eHr68vAwcOxNvbm/r16ye69rp16zh9+jQfffRRklyzZ8/GwcGBgIAAAJYsWcKMGTP47rvvUvX8RCRzmrjuJJtPXMfJ3obJweXI4qjh0iIiIvL8ZM2alQoVKhgdQ0REJFP4/vvvuXz5Mj4+PrRs2dLoOCLygkj1p4mnT59O0wBNmzbl2rVrDBo0iPDwcMqUKcOqVavw9PQE4Ny5c9jY/N8AoSpVqjB37lwGDBhA//798fPzY9myZZQsWdLapk+fPkRHR9O+fXtu375NtWrVWLVqFU5OTomu/f3331OlShWKFi2abLYvvviCs2fPYmdnR9GiRVmwYAHvvfdemj5/EXnxbD15nfFrjwPwZX1/inhlMziRiIiIiIiIiDwLsbGxjBgxAniwHraDg4PBiUTkRWGyWCwWo0O8qCIjI3FzcyMiIgJXV1ej44jIc3Al8h5vT9jM9ag4mlXwYUSjUkZHEhEReW70/ldSS/eMiIhkdFOnTqVDhw7kzZuXU6dOaT1sEXmilL4HTvXImx9++OGx+1u0aJHaU4qIvBDumxPoMncv16PiKJbHlSHvlDA6koiIiIiIiIg8I3FxcXz11VcA9O3bV4UbEUlTqS7edOvWLdHP8fHxxMTE4ODggIuLi4o3IpJpjfn9ODvP3CSrox2Tg8viZG9rdCQREREREREReUZ+/PFHzp07h5eXV7JraouI/Bc2T26S2K1btxI9oqKiOHbsGNWqVWPevHnPIqOISLq39sgVQjeeAmDUe6XwzZnF4EQiIiKSWf34449UrVoVb29vzp49C8D48eP5+eefDU4mIiLy4oiPj+fLL78EHqy/7ezsbHAiEXnRpLp4kxw/Pz9GjBiRZFSOiEhmcP5mDCEL9wPQqkpB6vjnMTiRiIiIZFZTpkwhJCSEOnXqcPv2bcxmMwDu7u6MHz/e2HAiIiIvkLlz53L69Gly587Nxx9/bHQcEXkBpUnxBsDOzo5Lly6l1elERDKE2PtmOs/dQ8TdeEr7uNO/TjGjI4mIiEgm9u233zJ9+nQ+++wzbG3/bwrX8uXLc/DgQQOTiYiIvDju379vHXXTq1cvXFxcDE4kIi+iVK9588svvyT62WKxcPnyZSZOnEjVqlXTLJiISEYwfMVR9l+IwM3ZnknvB+Bgl2Y1cREREZFUO336NAEBAUm2Ozo6Eh0dbUAiERGRF8+CBQs4ceIEHh4efPLJJ0bHEZEXVKqLN/Xr10/0s8lkIleuXLz22muMHTs2rXKJiKR7yw9cZta2MwB83bQ0+bLrmzYiIiJiLF9fX/bt20eBAgUSbV+1ahXFiqX9COGLFy/St29fVq5cSUxMDIUKFWLmzJmUL18+za8lIiKSHpjNZoYNGwZASEgIWbNmNTiRiLyoUl28SUhIeBY5REQylL+vRdH3pwMAfFLzZV4r6mlwIhEREZEHHyJ16tSJe/fuYbFY2LlzJ/PmzWP48OF89913aXqtW7duUbVqVV599VVWrlxJrly5OHHiBNmzZ0/T64iIiKQnixcv5ujRo7i7u9O5c2ej44jICyzVxRsRkczuXryZjnP2EBV7n0q+Oej5RmGjI4mIiIgA8NFHH+Hs7MyAAQOIiYnh/fffx9vbm2+++YZmzZql6bVGjhyJj48PM2fOtG7z9fVN02uIiIikJwkJCXzxxRcA9OjRA1dXV4MTiciLLNWLMzRq1IiRI0cm2T5q1CgaN26cJqFERNKzQT8f4mj4HXJmdeDb5gHY2WqdGxEREUk/goODOXHiBFFRUYSHh3PhwgXatm2b5tf55ZdfKF++PI0bNyZ37twEBAQwffr0xx4TGxtLZGRkooeIiEhGsXTpUv766y9cXV3p2rWr0XFE5AWX6k8cN23aRJ06dZJsf+utt9i0aVOahBIRSa8W7T7Pwt0XsDHBhGYB5HZ1MjqSiIiIiNXdu3eJiYkBwMXFhbt37zJ+/Hh+//33NL/W33//zZQpU/Dz82P16tV88skndO3aldmzZz/ymOHDh+Pm5mZ9+Pj4pHkuERGRZyEhIYHPP/8cgG7duuHu7m5sIBF54aW6eBMVFYWDg0OS7fb29vrWlIi80I6GRzLw50MA9KhVmCqFchqcSERERCSxd999lx9++AGA27dvU7FiRcaOHcu7777LlClT0vRaCQkJlC1blq+++oqAgADat29Pu3btCA0NfeQx/fr1IyIiwvo4f/58mmYSERF5Vn799VcOHDhA1qxZ6d69u9FxRCQTSHXxxt/fnwULFiTZPn/+fIoXL54moURE0ptzN2Jo/0MY9+ITeKVwLjq9WsjoSCIiIiJJ7Nmzh+rVqwMPFlT28vLi7Nmz/PDDD0yYMCFNr5UnT54kfcBixYpx7ty5Rx7j6OiIq6trooeIiEh6Z7FYrKNuunTpQo4cOQxOJCKZgV1qDxg4cCANGzbk1KlTvPbaawCsXbuWefPmsWjRojQPKCJitP3nb9N29i6uR8Xhk8OZ8U3LYGNjMjqWiIiISBIxMTFky5YNgN9//52GDRtiY2ND5cqVOXv2bJpeq2rVqhw7dizRtuPHj1OgQIE0vY6IiIjRVqxYwZ49e8iSJQshISFGxxGRTCLVI2/q1avHsmXLOHnyJB07dqRnz55cuHCBP/74g/r16z+DiCIixll75ArNpv3J9ag4SuZ15acOVciRJenUkSIiIiLpQaFChVi2bBnnz59n9erVvPnmmwBcvXo1zUe59OjRgz///JOvvvqKkydPMnfuXKZNm0anTp3S9DoiIiJGslgsfPHFFwB07NiRnDk1hbqIPB8mi8ViMTrEiyoyMhI3NzciIiI0HYBIBjRnx1kGLjtEggVqFM7FpOCyZHVM9YBFERGRTEPvf423ePFi3n//fcxmM6+//jq///47AMOHD2fTpk2sXLkyTa/322+/0a9fP06cOIGvry8hISG0a9cuxcfrnhERkfTu999/JygoCGdnZ06fPo2np6fRkUQkg0vpe+BUfwq5a9cuEhISqFSpUqLtO3bswNbWlvLly6c+rYhIOmKxWBi9+hiTN5wCoGl5H4Y1KIm9baoHK4qIiIg8V++99x7VqlXj8uXLlC5d2rr99ddfp0GDBml+vbp161K3bt00P6+IiEh6YLFYGDp0KAAdOnRQ4UZEnqtUfxLZqVMnzp8/n2T7xYsXNTxeRDK8uPsJ9Fiwz1q46VGrMCMa+atwIyIiIhmGl5cXAQEB2Nj83/uXihUrUrRoUQNTiYiIZDzr169n27ZtODo60rt3b6PjiEgmk+qRN4cPH6Zs2bJJtgcEBHD48OE0CSUiYoTIe/F0+DGMbaduYGdjYnhDfxqX9zE6loiIiMhjNWzYkFmzZuHq6krDhg0f23bJkiXPKZWIiEjG9/nnnwPQvn178uTJY3AaEclsUl28cXR05MqVK7z00kuJtl++fBk7O60FISIZ06Xbd2k9cxfHrtwhi4MtUz4oxyuFcxkdS0REROSJ3NzcMJlM1r+LiIjIf7dx40Y2btyIg4MDffr0MTqOiGRCqa62vPnmm/Tr14+ff/7Z2jG4ffs2/fv354033kjzgCIiz9qRy5G0nrmL8Mh75M7myMzWFSjhrQ8+REREJGOYOXNmsn8XERGRp/fFF18A0LZtW/Lly2dwGhHJjFJdvBkzZgyvvPIKBQoUICAgAIB9+/bh6enJjz/+mOYBRUSepa0nr9PhxzDuxN7HL3dWZrWpSF53Z6NjiYiIiDyVGTNm8Oqrr+Lr62t0FBERkQxr69atrF27Fnt7ez799FOj44hIJpXq4k3evHk5cOAAc+bMYf/+/Tg7O9O6dWuaN2+Ovb39s8goIvJMLNlzgT6LD3A/wUIl3xxM+7A8bi76d0xEREQyruHDh9OuXTvy5s1LjRo1qFGjBjVr1qRQoUJGRxMREckwHo66adWqFfnz5zc4jYhkViaLxWIxOsSLKjIyEjc3NyIiInB1dTU6joj8fxaLhUnrTzLm9+MAvFPam9GNS+FoZ2twMhERkYxN73/Th4sXL7JhwwY2bdrExo0bOXHiBHny5KFmzZr873//MzpeIrpnREQkvdmxYweVK1fG1taWEydOaDSriKS5lL4HfurizeHDhzl37hxxcXGJtr/zzjtPc7oXkjoiIunPfXMCA3/+i3k7zwHQocbL9Akqgo2NyeBkIiIiGZ/e/6YvMTExbN68mXnz5jFnzhwsFgv37983OlYiumdERCS9qVu3LsuXL6d169bMmDHD6Dgi8gJK6XvgVE+b9vfff9OgQQMOHjyIyWTiYe3HZHrwwafZbH7KyCIiz1Z07H26zNvLuqNXsTHBkHdK0CKwoNGxRERERNLM77//zoYNG9iwYQN79+6lWLFi1KhRg8WLF/PKK68YHU9ERCRdCwsLY/ny5djY2NC/f3+j44hIJpfq4k23bt3w9fVl7dq1+Pr6snPnTm7cuEHPnj0ZM2bMs8goIvKfXbsTS5tZuzh4MQInexsmNAvgzRJeRscSERERSVO1a9cmV65c9OzZkxUrVuDu7m50JBERkQzj4Vo3wcHBWi9ORAxnk9oDtm/fzueff07OnDmxsbHBxsaGatWqMXz4cLp27fosMoqI/CenrkXRcMpWDl6MIEcWB+a1q6zCjYiIiLyQxo0bR9WqVRk1ahQlSpTg/fffZ9q0aRw/ftzoaCIiIunavn37+PnnnzGZTHz22WdGxxERSX3xxmw2ky1bNgBy5szJpUuXAChQoADHjh1L23QiIv/R7jM3aTRlG+dv3qWAhwtLPqlCQP7sRscSEREReSa6d+/OkiVLuH79OqtWraJKlSqsWrWKkiVLki9fPqPjiYiIpFvDhg0DoFmzZhQpUsTgNCIiTzFtWsmSJdm/fz++vr5UqlSJUaNG4eDgwLRp03jppZeeRUYRkaey8uBlui3YR9z9BMr4uPN9y/J4ZHU0OpaIiIjIM2WxWNi7dy8bNmxg/fr1bNmyhYSEBHLlymV0NBERkXTp0KFD/PTTTxp1IyLpSqqLNwMGDCA6OhqAzz//nLp161K9enU8PDxYsGBBmgcUEXka3285zbDlh7FY4I3inkxoFoCzg63RsURERESeqXr16rF161YiIyMpXbo0NWvWpF27drzyyita/0ZEROQRHo66ee+99yhRooTBaUREHkh18SYoKMj690KFCnH06FFu3rxJ9uzZMZlMaRpORCS1EhIsDFt+hBlbTwPQIrAAg+uVwNZG/z6JiIjIi69o0aJ8/PHHVK9eHTc3N6PjiIiIpHtHjhxh4cKFwIMvrYuIpBepLt4kJ0eOHGlxGhGR/+RevJmQhftYcTAcgH5vFaX9Ky+psCwiIiKZxujRo61/v3DhAt7e3tjYpHqpUxERkUzjyy+/xGKx0KBBA0qVKmV0HBERq3TxLn7SpEkULFgQJycnKlWqxM6dOx/bftGiRRQtWhQnJyf8/f1ZsWJFov0Wi4VBgwaRJ08enJ2dqVWrFidOnEjUpmDBgphMpkSPESNGJGpz4MABqlevjpOTEz4+PowaNSptnrCIpLlb0XF88N0OVhwMx8HWhgnNA/i4xssq3IiIiEimVbx4cc6cOWN0DBERkXTr+PHjzJs3D4CBAwcanEZEJDHDizcLFiwgJCSEwYMHs2fPHkqXLk1QUBBXr15Ntv22bdto3rw5bdu2Ze/evdSvX5/69etz6NAha5tRo0YxYcIEQkND2bFjB1myZCEoKIh79+4lOtfnn3/O5cuXrY8uXbpY90VGRvLmm29SoEABwsLCGD16NEOGDGHatGnP5oUQkad2/mYMjUK3sfvsLVyd7PihbUXeKe1tdCwRERERQ1ksFqMjiIiIpGtfffUVCQkJ1KtXj4CAAKPjiIgkYrIY/I6+UqVKVKhQgYkTJwKQkJCAj48PXbp04dNPP03SvmnTpkRHR/Pbb79Zt1WuXJkyZcoQGhqKxWLB29ubnj170qtXLwAiIiLw9PRk1qxZNGvWDHgw8qZ79+5079492VxTpkzhs88+Izw8HAcHBwA+/fRTli1bxtGjR1P03CIjI3FzcyMiIgJXV9cUvyYiknIHLtymzazdXI+KxdvNiVltKlLYM5vRsURERDIlvf9NX7Jly8b+/ft56aWXjI7ySLpnRETEKKdOnaJIkSKYzWZ27txJhQoVjI4kIplESt8DGzryJi4ujrCwMGrVqmXdZmNjQ61atdi+fXuyx2zfvj1Re4CgoCBr+9OnTxMeHp6ojZubG5UqVUpyzhEjRuDh4UFAQACjR4/m/v37ia7zyiuvWAs3D69z7Ngxbt26lWy22NhYIiMjEz1E5NlZf/QqTaf+yfWoWIrncWVpp6oq3IiIiIj8f/3799f6pCIiIo8wfPhwzGYzb731lgo3IpIuGVq8uX79OmazGU9Pz0TbPT09CQ8PT/aY8PDwx7Z/+OeTztm1a1fmz5/P+vXr+fjjj/nqq6/o06fPE6/zz2v82/Dhw3Fzc7M+fHx8HvncReS/mbfzHB/9sJu78Waq++VkYYdAPF2djI4lIiIiYqjPP/+cmJgYAPr164e7uzsAd+/e5fPPPzcwmYiISPpx5swZZs+eDWitGxFJvwxf88YoISEh1KxZk1KlStGhQwfGjh3Lt99+S2xs7FOfs1+/fkRERFgf58+fT8PEIgIP5m4f+/sx+i05iDnBwnvl8jGjVQWyOtoZHU1ERETEcEOHDiUqKirJ9piYGIYOHWpAIhERkfRnxIgR3L9/n1q1ahEYGGh0HBGRZBlavMmZMye2trZcuXIl0fYrV67g5eWV7DFeXl6Pbf/wz9ScEx6svXP//n3OnDnz2Ov88xr/5ujoiKura6KHiKSduPsJ9Fy0n2/XnQSg2+t+jH6vFPa2mbYOLSIiIpKIxWLBZDIl2b5//35NoSYiIgKcP3+eGTNmADBo0CCD04iIPJqhn3g6ODhQrlw51q5da92WkJDA2rVrH1n1DgwMTNQeYM2aNdb2vr6+eHl5JWoTGRnJjh07HltJ37dvHzY2NuTOndt6nU2bNhEfH5/oOkWKFCF79uypf7Ii8p9E3ounzaxdLNlzEVsbEyMb+dPjjcLJfjghIiIiktlkz56dHDlyYDKZKFy4MDly5LA+3NzceOONN2jSpInRMUVERAw3cuRI4uPjqVmzJtWrVzc6jojIIxk+z1BISAgtW7akfPnyVKxYkfHjxxMdHU3r1q0BaNGiBXnz5mX48OEAdOvWjRo1ajB27Fjefvtt5s+fz+7du5k2bRoAJpOJ7t27M2zYMPz8/PD19WXgwIF4e3tTv359ALZv386OHTt49dVXyZYtG9u3b6dHjx588MEH1sLM+++/z9ChQ2nbti19+/bl0KFDfPPNN3z99dfP/0USyeTCI+7RauZOjobfIYuDLZOCy1KzSG6jY4mIiIikG+PHj8disdCmTRuGDh2Km5ubdZ+DgwMFCxbUtDAiIpLpXbx4kenTpwMadSMi6Z/hxZumTZty7do1Bg0aRHh4OGXKlGHVqlV4enoCcO7cOWxs/m+AUJUqVZg7dy4DBgygf//++Pn5sWzZMkqWLGlt06dPH6Kjo2nfvj23b9+mWrVqrFq1CienB4uZOzo6Mn/+fIYMGUJsbCy+vr706NGDkJAQ6znc3Nz4/fff6dSpE+XKlSNnzpwMGjSI9u3bP6dXRkQAjoZH0nrmLi5H3CNXNkdmtqpAybxuTz5QREREJBNp2bIl8GAmgipVqmBvb29wIhERkfRn9OjRxMXFUa1aNWrWrGl0HBGRxzJZLBaL0SFeVJGRkbi5uREREaH1b0SewraT1/n4xzDuxN6nUO6szGxVAZ8cLkbHEhERkUfQ+19jREZGWl/vyMjIx7ZNb78X3TMiIvK8hIeH4+vry7179/j999954403jI4kIplUSt8DGz7yRkQkOcv2XqT34v3Emy1U9M3B9A/L4+aib5CKiIiI/Fv27Nm5fPkyuXPnxt3dPdk1AS0WCyaTCbPZbEBCERER440dO5Z79+5RuXJlatWqZXQcEZEnUvFGRNIVi8XC5A2nGL36GABvl8rD2MalcbK3NTiZiIiISPq0bt06cuTIAcD69esNTiMiIpL+XLt2jcmTJwMP1rpJ7osOIiLpjYo3IpJu3DcnMPiXv5iz4xwA7V95iU9rF8XGRm+qRERERB6lRo0ayf5dREREHhg3bhwxMTGUL1+e2rVrGx1HRCRFVLwRkXQhJu4+XebuZe3Rq5hMMLhucVpV9TU6loiIiEiGc+vWLb7//nuOHDkCQPHixWndurV1dI6IiEhmcuPGDSZOnAho1I2IZCw2RgcQEbkeFUvzaX+y9uhVHO1smBJcToUbERERkaewadMmChYsyIQJE7h16xa3bt1iwoQJ+Pr6smnTJqPjiYiIPHfjx48nKiqKMmXKULduXaPjiIikmEbeiIih/r4WRauZuzh3M4bsLvZ817IC5QpkNzqWiIiISIbUqVMnmjZtypQpU7C1fbBmoNlspmPHjnTq1ImDBw8anFBEROT5efglBtCoGxHJeDTyRkQME3b2Jo2mbOPczRjy53Dhp0+qqHAjIiIi8h+cPHmSnj17Wgs3ALa2toSEhHDy5EkDk4mIiDx/EyZMIDIyEn9/f959912j44iIpIqKNyJiiFWHwnl/+g5uxcRTOp8bSzpW4aVcWY2OJSIiIpKhlS1b1rrWzT8dOXKE0qVLG5BIRETEGBEREYwfPx6AgQMHYmOjj0FFJGPRtGki8tzN3Hqaz387jMUCtYrlZkLzAFwc9M+RiIiIyNM4cOCA9e9du3alW7dunDx5ksqVKwPw559/MmnSJEaMGGFURBERkedu4sSJ3L59m+LFi9OoUSOj44iIpJrJYrFYjA7xooqMjMTNzY2IiAhcXV2NjiNiuIQEC8NXHmH65tMAfFA5P0PqlcDOVt9+EREReRHo/a8xbGxsMJlMPKlrZzKZMJvNzylVyuieERGRZ+HOnTsULFiQmzdvMnfuXJo3b250JBERq5S+B9ZX3UXkubgXb6bnov0sP3AZgD61i/BJjZe1WKCIiIjIf3T69GmjI4iIiKQrkydP5ubNmxQuXJgmTZoYHUdE5KmoeCMiz9ztmDja/xDGzjM3sbc1MaZxad4tk9foWCIiIiIvhAIFChgdQUREJN2Ijo5mzJgxAAwYMABbW1uDE4mIPB0Vb0TkmTp/M4ZWM3dy6lo02RztmNqiHFVezml0LBEREZEX0g8//PDY/S1atHhOSURERIwRGhrK9evXefnllzVdmohkaCreiMgzc+hiBK1n7eLanVjyuDkxq3VFinhlMzqWiIiIyAurW7duiX6Oj48nJiYGBwcHXFxcVLwREZEXWkxMDKNHjwbgs88+w85OH32KSMalf8FE5JnYcOwqHefsISbOTFGvbMxqXREvNyejY4mIiIi80G7dupVk24kTJ/jkk0/o3bu3AYlERESen+nTp3PlyhUKFizIBx98YHQcEZH/xMboACLy4lmw6xxtZ+8mJs5MtUI5WdQhUIUbEREREYP4+fkxYsSIJKNyREREXiT37t1j5MiRAPTv3x97e3uDE4mI/DcaeSMiacZisfD1HyeYsPYEAA3L5mVEw1I42KlOLCIiImIkOzs7Ll26ZHQMERGRZ+b777/n8uXL+Pj40LJlS6PjiIj8ZyreiEiaiDcn0G/JQRaHXQCgy2uFCHmjMCaTyeBkIiIiIpnHL7/8kuhni8XC5cuXmThxIlWrVjUolYiIyLMVGxvLiBEjAOjXrx8ODg4GJxIR+e9UvBGR/+zOvXg6ztnD5hPXsbUxMax+SZpXzG90LBEREZFMp379+ol+NplM5MqVi9dee42xY8caE0pEROQZmzVrFhcuXCBv3ry0adPG6DgiImlCxRsR+U+uRN6j1cxdHLkciYuDLZPeL8urRXMbHUtEREQkU0pISDA6goiIyHMVFxfHV199BUDfvn1xdHQ0OJGISNrQQhQi8tSOX7lDg0lbOXI5kpxZHVnQPlCFGxEREZFMasSIEZhMJrp37250FBERyUR+/PFHzp07h5eXFx999JHRcURE0oxG3ojIU9l+6gbtf9zNnXv3eSlXFma3rohPDhejY4mIiIhkaiEhISluO27cuDS77q5du5g6dSqlSpVKs3OKiIg8SXx8PF9++SUAffr0wdnZ2eBEIiJpR8UbEUm1n/ddpPeiA8SZEyhfIDvTW5QnexYtBigiIiJitL1797J3717i4+MpUqQIAMePH8fW1payZcta25lMpjS7ZlRUFMHBwUyfPp1hw4al2XlFRESeZO7cuZw+fZrcuXPz8ccfGx1HRCRNqXgjIilmsViYuulvRqw8CkAdfy/GNSmDk72twclEREREBKBevXpky5aN2bNnkz17dgBu3bpF69atqV69Oj179kzza3bq1Im3336bWrVqPbF4ExsbS2xsrPXnyMjINM8jIiKZw/37962jbnr16oWLi2YDEZEXi4o3IpIi5gQLQ375ix//PAtA22q+fFanGDY2afetTRERERH5b8aOHcvvv/9uLdwAZM+enWHDhvHmm2+mefFm/vz57Nmzh127dqWo/fDhwxk6dGiaZhARkcxpwYIFnDhxAg8PDz755BOj44iIpDkbowOISPp3N85Mh/+F8eOfZzGZYGDd4gysW1yFGxEREZF0JjIykmvXriXZfu3aNe7cuZOm1zp//jzdunVjzpw5ODk5peiYfv36ERERYX2cP38+TTOJiEjmYDabraM9e/bsSdasWQ1OJCKS9jTyRkQe60ZULG1n72bf+ds42NnwTdMyvOWfx+hYIiIiIpKMBg0a0Lp1a8aOHUvFihUB2LFjB71796Zhw4Zpeq2wsDCuXr2aaC0ds9nMpk2bmDhxIrGxsdjaJp5e19HREUdHxzTNISIimc/ixYs5evQo2bNnp1OnTkbHERF5JlS8EZFHOn09mlYzd3L2RgzuLvZ816I85QvmMDqWiIiIiDxCaGgovXr14v333yc+Ph4AOzs72rZty+jRo9P0Wq+//joHDx5MtK1169YULVqUvn37JinciIiIpIWEhAS++OILALp3746rq6vBiUREng0Vb0QkWXvO3eKj2bu5GR2HTw5nZrWuyMu5NAxZREREJD1zcXFh8uTJjB49mlOnTgHw8ssvkyVLljS/VrZs2ShZsmSibVmyZMHDwyPJdhERkbSydOlS/vrrL1xdXenatavRcUREnhkVb0Qkid//Cqfr/L3ci0+gVD43vm9ZgVzZNL2FiIiISEaRJUsWSpUqZXQMERGRNGWxWKyjbrp164a7u7uxgUREniEVb0TEymKx8MP2swz99S8SLPBa0dxMfD8AFwf9UyEiIiIiT7ZhwwajI4iIyAvs119/Zf/+/WTNmpXu3bsbHUdE5JnSJ7IiAsDtmDj6Lz3IioPhADSvmJ8v3i2Bna2NwclEREREREREJLOzWCx8/vnnAHTp0oUcObQmr4i82FS8ERG2nLhOz0X7uBIZi52NiT61i9Cu+kuYTCajo4mIiIiIiIiIsHLlSsLCwsiSJQshISFGxxEReebSxVfqJ02aRMGCBXFycqJSpUrs3Lnzse0XLVpE0aJFcXJywt/fnxUrViTab7FYGDRoEHny5MHZ2ZlatWpx4sQJ6/4zZ87Qtm1bfH19cXZ25uWXX2bw4MHExcUlamMymZI8/vzzz7R98iIGuhdvZthvh/ng+x1ciYzlpVxZWNqxKu1feVmFGxEREZEMomzZsty6dQuAzz//nJiYGIMTiYiIpK1/jrrp2LEjOXPmNDiRiMizZ3jxZsGCBYSEhDB48GD27NlD6dKlCQoK4urVq8m237ZtG82bN6dt27bs3buX+vXrU79+fQ4dOmRtM2rUKCZMmEBoaCg7duwgS5YsBAUFce/ePQCOHj1KQkICU6dO5a+//uLrr78mNDSU/v37J7neH3/8weXLl62PcuXKPZsXQuQ5OxoeSf1JW/luy2kAPqicn+VdquOfz83gZCIiIiKSGkeOHCE6OhqAoUOHEhUVZXAiERGRtLVmzRp27NiBs7MzPXv2NDqOiMhzYbJYLBYjA1SqVIkKFSowceJEABISEvDx8aFLly58+umnSdo3bdqU6OhofvvtN+u2ypUrU6ZMGUJDQ7FYLHh7e9OzZ0969eoFQEREBJ6ensyaNYtmzZolm2P06NFMmTKFv//+G3gw8sbX15e9e/dSpkyZp3pukZGRuLm5ERERgaur61OdQyStJSRYmLntDCNXHSXufgIeWRwY9V4pXi/maXQ0ERERyeD0/tcYgYGBZM2alWrVqjF06FB69epF1qxZk207aNCg55zu8XTPiIjIk1gsFqpVq8a2bdvo0aMH48aNMzqSiMh/ktL3wIaueRMXF0dYWBj9+vWzbrOxsaFWrVps37492WO2b9+eZF7LoKAgli1bBsDp06cJDw+nVq1a1v1ubm5UqlSJ7du3P7J4ExERkexCZ++88w737t2jcOHC9OnTh3feeSe1T1Mk3bgSeY9ei/az+cR1AF4rmpuRjUqRK5ujwclERERE5GnNmjWLwYMH89tvv2EymVi5ciV2dkm7eiaTKd0Vb0RERJ5k/fr1bNu2DUdHR3r37m10HBGR58bQ4s3169cxm814eib+xr+npydHjx5N9pjw8PBk24eHh1v3P9z2qDb/dvLkSb799lvGjBlj3ZY1a1bGjh1L1apVsbGx4aeffqJ+/fosW7bskQWc2NhYYmNjrT9HRkYm207ECCsPXqbf0oPcjonHyd6GAW8XJ7hSfq1tIyIiIpLBFSlShPnz5wMPvgy3du1acufObXAqERGRtPFwrZv27duTJ08eg9OIiDw/hhZv0oOLFy9Su3ZtGjduTLt27azbc+bMmWiET4UKFbh06RKjR49+ZPFm+PDhDB069JlnFkmNqNj7DP3lLxaFXQCgZF5XxjcNoFDu5KfSEBEREZGMKyEhwegIIiIiaWbjxo1s3LgRBwcH+vTpY3QcEZHnysbIi+fMmRNbW1uuXLmSaPuVK1fw8vJK9hgvL6/Htn/4Z0rOeenSJV599VWqVKnCtGnTnpi3UqVKnDx58pH7+/XrR0REhPVx/vz5J55T5FkKO3uLOt9sZlHYBUwm6FjzZZZ8UlWFGxEREZEX2KlTp+jSpQu1atWiVq1adO3alVOnThkdS0REJNW++OILANq2bUu+fPkMTiMi8nwZWrxxcHCgXLlyrF271rotISGBtWvXEhgYmOwxgYGBidoDrFmzxtre19cXLy+vRG0iIyPZsWNHonNevHiRmjVrUq5cOWbOnImNzZNfin379j12eKajoyOurq6JHiJGuG9O4Os1x2kydTvnbsaQ192ZBe0D6VO7KA52hv5nLyIiIiLP0OrVqylevDg7d+6kVKlSlCpVih07dlCiRAnWrFljdDwREZEU27p1K2vXrsXe3p5PP/3U6DgiIs+d4dOmhYSE0LJlS8qXL0/FihUZP3480dHRtG7dGoAWLVqQN29ehg8fDkC3bt2oUaMGY8eO5e2332b+/Pns3r3bOnLGZDLRvXt3hg0bhp+fH76+vgwcOBBvb2/q168P/F/hpkCBAowZM4Zr165Z8zwcnTN79mwcHBwICAgAYMmSJcyYMYPvvvvueb00Ik/lzPVoui/Yx77ztwFoEJCXoe+WwNXJ3thgIiIiIvLMffrpp/To0YMRI0Yk2d63b1/eeOMNg5KJiIikzsNRN61atSJ//vwGpxERef4ML940bdqUa9euMWjQIMLDwylTpgyrVq3C09MTgHPnziUaFVOlShXmzp3LgAED6N+/P35+fixbtoySJUta2/Tp04fo6Gjat2/P7du3qVatGqtWrcLJyQl4MFLn5MmTnDx5MsmQS4vFYv37F198wdmzZ7Gzs6No0aIsWLCA995771m+HCJPzWKxsHD3eYb+epiYODPZnOz4soE/75T2NjqaiIiIiDwnR44cYeHChUm2t2nThvHjxz//QCIiIk9hx44drF69GltbW/r162d0HBERQ5gs/6xWSJqKjIzEzc2NiIgITaEmz9TN6Dj6LTnA6r8erPVU+aUcjG1ShrzuzgYnExERkcxE73+N5+Pjw7hx42jcuHGi7QsXLqRXr16cO3fOoGTJ0z0jIiLJqVu3LsuXL6d169bMmDHD6DgiImkqpe+BDR95IyL/zcbj1+i9aD9X78Rib2ui55tFaFf9JWxtTEZHExGRZ8xisXD//n3MZrPRUSSTsLW1xc7ODpNJ7zPSq3bt2tG+fXv+/vtvqlSpAjxYM2DkyJGEhIQYnE5EROTJwsLCWL58OTY2NvTv39/oOCIihlHxRiSDuhdvZsTKo8zadgaAQrmzMr5pGUrmdTM2mIiIPBdxcXFcvnyZmJgYo6NIJuPi4kKePHlwcHAwOookY+DAgWTLlo2xY8dap5nx9vZmyJAhdO3a1eB0IiIiT/ZwrZvg4GAKFSpkcBoREeNo2rRnSFMAyLNy+FIk3Rfs5fiVKABaBhbg07eK4exga3AyERF5HhISEjhx4gS2trbkypULBwcHjYSQZ85isRAXF8e1a9cwm834+fklWpsS9P43vblz5w4A2bJlMzjJo+meERGRf9q3bx8BAQGYTCaOHDlCkSJFjI4kIpLmNG2ayAsoIcHCd1v+Zszq48SZE8iZ1ZHRjUvxapHcRkcTEZHnKC4ujoSEBHx8fHBxcTE6jmQizs7O2Nvbc/bsWeLi4nBycjI6kjxGei7aiIiIJGfYsGEANGvWTIUbEcn0VLwRySAu3b5Lz4X72f73DQDeKO7JiIb+eGR1NDiZiIgY5d+jHkSeB913IiIi8iwcOnSIn376CZPJxGeffWZ0HBERw6l4I5IB/HbgEv2XHCTy3n2c7W0ZVK84zSr4aIocEREREREREXkhPBx1895771GiRAmD04iIGE/FG5F07M69eAb//BdL9l4EoHQ+N8Y3C8A3ZxaDk4mIiIiIiIiIpI0jR46wcOFCAAYMGGBwGhGR9EHFG5F0ateZm/RYsI8Lt+5iY4LOrxaiy+t+2NtqqhIRERF5doYMGcKyZcvYt2+f0VFExED37t3j7NmzRscQkUxi8ODBWCwWGjRoQKlSpYyOIyKSLqh4I5LOxJsT+OaPE0zecJIEC/jkcObrJmUoXzCH0dFERETEAK1ateL27dssW7bsuRwnGV/nzp35/PPPyZFD7x8l9Y4cOcLUqVOZPXs2t2/fNjqOiGQyAwcONDqCiEi6oeKNSDry97Uoui/Yx4ELEQA0KpuPIe8UJ5uTvcHJRERE5KG4uDgcHBwSbTObzZhMJmxsUjdC9mmPE/m3CxcukC9fPgDmzp1Lnz59yJEjB/7+/qxYsQIfHx+DE0p6FhcXx9KlSwkNDWXDhg3W7VmyZEny752IyLPy4YcfEhAQYHQMEZF0Q8UbkXTAYrEwb+d5vvjtMHfjzbg52/NVA3/eLpXH6GgiIpJRWCwQG/v8r+voCCZTipvXrFkTf39/bG1tmT17Ng4ODgwbNoz333+fzp07s3jxYjw9Pfn222956623MJvNtG/fnnXr1hEeHk7+/Pnp2LEj3bp1Ax5M61OuXDmqVq3KtGnTADh16hRlypThm2++oU2bNk/MtGXLFvr168fu3bvJmTMnDRo0YPjw4WTJ8mCNuYIFC9K2bVtOnDjBsmXLaNiwITVr1qR79+788MMPfPrppxw/fpyTJ0/i5uZGt27d+PXXX4mNjaVGjRpMmDABPz8/AGbNmpXscQULFkw225AhQ5g9ezYApv//Oq9fv56aNWty8OBBunXrxvbt23FxcaFRo0aMGzeOrFmzPva4vn37snTpUi5cuICXlxfBwcEMGjQIe3t9WSQjK1q0KB4eHlStWpV79+5x/vx58ufPz5kzZ4iPjzc6nqRTp0+fZtq0acyYMYOrV68CYGNjQ7169ejQoQNvvvmmissiIiIiBlHxRsRg16Ni+fSnA/xx5EFnqWohD8Y0Lk0eN2eDk4mISIYSGwtvvfX8r7tyJTg5peqQ2bNn06dPH3bu3MmCBQv45JNPWLp0KQ0aNKB///58/fXXfPjhh5w7dw57e3vy5cvHokWL8PDwYNu2bbRv3548efLQpEkTnJycmDNnDpUqVeLtt9+mbt26fPDBB7zxxhspKtycOnWK2rVrM2zYMGbMmMG1a9fo3LkznTt3ZubMmdZ2Y8aMYdCgQQwePBiAzZs3ExMTw8iRI/nuu+/w8PAgd+7cNG/enBMnTvDLL7/g6upK3759qVOnDocPH7YWR5I77lF69erFkSNHiIyMtObJkSMH0dHRBAUFERgYyK5du7h69SofffQRnTt3ZtasWY88DiBbtmzMmjULb29vDh48SLt27ciWLRt9+vRJ1e9R0pfbt2+zZ88eNm/ezJIlS6hTpw6enp7ExsayevVqGjZsiKenp9ExJR24f/8+y5cvJzQ0lNWrV2OxWADIkycP7dq146OPPtJILREREZF0wGR5+E5N0lxkZCRubm5ERETg6upqdBxJh9YfvUrvxfu5HhWHg60NfWoXoU1VX2xsUv4NZhERyXzu3bvH6dOn8fX1xelh4eTevQxRvKlZsyZms5nNmzcDD6YNc3Nzo2HDhvzwww8AhIeHkydPHrZv307lypWTnKNz586Eh4ezePFi67bRo0czatQomjVrxk8//cTBgwfx8PB4Yp6PPvoIW1tbpk6dat22ZcsWatSoQXR0NE5OThQsWJCAgACWLl1qbTNr1ixat27Nvn37KF26NAAnTpygcOHCbN26lSpVqgBw48YNfHx8mD17No0bN072uCdJbu2a6dOn07dvX86fP28dIbRixQrq1avHpUuX8PT0TPGaN2PGjGH+/Pns3r0beDDaZ9myZezbty/Z9snef/+f3v8a5+7duzg7P/jyT/bs2QkLC+Py5cvUqlWLkiVL8tdff+Hj48OxY8cMTpqY7pnn5+LFi3z33XdMnz6dixcvWre/+eabdOjQgbp162oEnoiIiMhzkNL3wBp5I2KAu3FmvlpxhB//PAtAYc+sjG8aQHFvdVhFROQpOTo+KKQYcd1UKlWqlPXvtra2eHh44O/vb932cHTAwyl8Jk2axIwZMzh37hx3794lLi6OMmXKJDpnz549WbZsGRMnTmTlypUpKtwA7N+/nwMHDjBnzhzrNovFQkJCAqdPn6ZYsWIAlC9fPsmxDg4OiZ7LkSNHsLOzo1KlStZtHh4eFClShCNHjjzyuKdx5MgRSpcubS3cAFStWpWEhASOHTv22BEWCxYsYMKECZw6dYqoqCju37+vD81fAO7u7pQpU4aqVasSFxfH3bt3qVq1KnZ2dixYsIC8efOya9cuo2PKc5aQkMCaNWsIDQ3l119/xWw2A5AzZ07atGlD+/btefnllw1OKSIiIiLJUfFG5Dk7dDGCbvP3cupaNACtqxakb+2iONnbGpxMREQyNJMp1dOXGeXf3+w2mUyJtj1coyUhIYH58+fTq1cvxo4dS2BgINmyZWP06NHs2LEj0TmuXr3K8ePHsbW15cSJE9SuXTtFWaKiovj444/p2rVrkn358+e3/v2fRZKHnJ2drVlT42mPSwvbt28nODiYoUOHEhQUhJubG/Pnz2fs2LGG5JG0c/HiRbZv3862bdu4f/8+5cqVo0KFCsTFxbFnzx7y5ctHtWrVjI4pz8m1a9eYMWMG06ZN4++//7Zuf+WVV+jQoQMNGzbE8SmK7yIiIiLy/Kh4I/KcmBMsTNv0N+PWHCPebCF3NkfGNC7NK4VzGR1NREQk3Xo4BVnHjh2t206dOpWkXZs2bfD396dt27a0a9eOWrVqWUfNPE7ZsmU5fPgwhQoV+s9ZixUrxv3799mxY0eiadOOHTtG8eLFn/q8Dg4O1m/L//Nas2bNIjo62lpY2rp1KzY2NhQpUuSRx23bto0CBQrw2WefWbedPXv2qbNJ+pEzZ07q1atHvXr1CA0NZdOmTRw5coQWLVrQq1cvPvzwQypWrMjGjRuNjirPiMViYfPmzYSGhrJ48WLi4+MBcHNzo2XLlnz88cf/6d8iEREREXm+bIwOIJIZXLx9l/en/8nIVUeJN1sIKuHJqu6vqHAjIiLyBH5+fuzevZvVq1dz/PhxBg4cmGTqp0mTJrF9+3Zmz55NcHAw9evXJzg4mLi4uCeev2/fvmzbto3OnTuzb98+Tpw4wc8//0znzp2fKuu7775Lu3bt2LJlC/v37+eDDz4gb968vPvuu6k+30MFCxbkwIEDHDt2jOvXrxMfH09wcDBOTk60bNmSQ4cOsX79erp06cKHH35onTItueP8/Pw4d+4c8+fP59SpU0yYMCHRWj7y4nBzc6NJkybY29uzbt06Tp8+nagIKi+OW7duMWHCBEqUKEGNGjWYN28e8fHxVKxYkRkzZnDp0iW++eYbFW5EREREMhgVb0SesZ/3XaT2+E3sOH0TFwdbRjUqRegH5ciRxcHoaCIiIunexx9/TMOGDWnatCmVKlXixo0biT6APnr0KL1792by5Mn4+PgAMHnyZK5fv87AgQOfeP5SpUqxceNGjh8/TvXq1QkICGDQoEF4e3s/Vd6ZM2dSrlw56tatS2BgIBaLhRUrVvynRcDbtWtHkSJFKF++PLly5WLr1q24uLiwevVqbt68SYUKFXjvvfd4/fXXmThx4mOPe+edd+jRowedO3emTJkybNu2LUWvk2QsBw4cIF++fAAUKFAAe3t7vLy8aNq0qcHJJK1YLBZ27txJmzZtyJs3L926dePIkSNkyZKFdu3aERYWxo4dO2jdujUuLi5GxxURERGRp2CyWCwWo0O8qCIjI3FzcyMiIkKLwGZCEXfjGfTzIX7edwmAgPzujG9ahgIeSefMFxERSY179+5x+vRpfH19ccog69zIi+Nx95/e/0pq6Z5JnaioKObOnUtoaCh79+61bvf396dDhw4EBwfj5uZmYEIREREReZKUvgfWmjciz8Cff9+g58L9XLx9F1sbE11eK0TnVwthZ6vBbiIiIiIikjoHDhwgNDSU//3vf9y5cwcAR0dHmjRpQocOHQgMDMRkMhmcUkRERETSkoo3Imko7n4C49YcZ+qmU1gskD+HC183LUO5AtmNjiYiIpIpvfXWW2zevDnZff3796d///7POVFSWbNmfeS+lStXUr169eeYRkTSi7t377Jo0SJCQ0PZvn27dbufnx8dOnSgZcuWeHh4GJhQRERERJ4lFW9E0sjJq3foNn8ff12KBKBpeR8G1itOVkf9ZyYiImKU7777jrt37ya7L0eOHM85TfL27dv3yH158+Z9fkFEJF04duwYU6dOZdasWdy6dQsAOzs7GjRoQIcOHXj11Vc1ykZEREQkE9CnyiL/kcVi4cc/z/Ll8iPE3k/A3cWeEQ39qV0yj9HRREREMr2MUPwoVKiQ0RFExGBxcXH8/PPPhIaGsm7dOuv2AgUK0L59e9q0aYOXl5eBCUVERETkeVPxRuQ/uHYnlj6L97P+2DUAqvvlZEzj0ni6avFoERERERF5vDNnzjB9+nS+//57rly5AoCNjQ1vv/02HTp0ICgoCFtbW4NTioiIiIgRVLwReUp/HL5C358OcCM6Dgc7G/q9VZSWgQWxsdEUBiIiIiIikjyz2cyKFSsIDQ1l5cqVWCwWAPLkycNHH33ERx99RP78+Q1OKSIiIiJGU/FGJJVi4u4zbPkR5u44B0BRr2x80yyAIl7ZDE4mIiIiIiLp1aVLl/j++++ZPn0658+ft25/44036NChA/Xq1cPe3t7AhCIiIiKSnqh4I5IKBy7cpvv8ffx9PRqAdtV96RVUBEc7TWUgIiIiIiKJJSQksHbtWkJDQ/n5558xm80AeHh40KZNG9q3b691r0REREQkWTZGBxDJCMwJFiauO0HDydv4+3o0Xq5OzPmoEp+9XVyFGxERkWegYMGCjB8/3ugYT6VmzZp07979sW0y8vMTkSe7du0ao0ePpnDhwrz55pssWbIEs9lMtWrV+N///seFCxcYNWqUCjciIiIi8kgq3og8wfmbMTSdup0xvx/nfoKFt/3zsKp7daoWyml0NBERkQzn/PnztGnTBm9vbxwcHChQoADdunXjxo0bRkfL0EaPHs37778PwNy5c3nttdcS7b937x6tWrXC398fOzs76tevb0BKkRebxWJh8+bNBAcHky9fPvr06cOpU6dwdXWlc+fOHDx40LrfycnJ6LgiIiIiks5p2jSRR7BYLCzde5FBP/9FVOx9sjraMfSdEjQsmxeTyWR0PBERkQzn77//JjAwkMKFCzNv3jx8fX3566+/6N27NytXruTPP/8kR44chmQzm82YTCZsbDLmd5u2b9/O66+/DsDmzZupWrVqov1msxlnZ2e6du3KTz/9ZEREkRdWREQEP/74I6Ghofz111/W7eXLl6dDhw40a9aMLFmyGJhQRERERDKijNk7FXnGbsfE0XneXkIW7icq9j7lCmRnZbfqNCqXT4UbERGRp9SpUyccHBz4/fffqVGjBvnz5+ett97ijz/+4OLFi3z22WeJ2t+5c4fmzZuTJUsW8ubNy6RJk6z7LBYLQ4YMIX/+/Dg6OuLt7U3Xrl2t+2NjY+nVqxd58+YlS5YsVKpUiQ0bNlj3z5o1C3d3d3755ReKFy+Oo6Mj3333HU5OTty+fTtRjm7dullHsty4cYPmzZuTN29eXFxc8Pf3Z968eUme6/379+ncuTNubm7kzJmTgQMHYrFYHvna3L59m48++ohcuXLh6urKa6+9xv79+1P82m7fvt1asNmyZUuS4k2WLFmYMmUK7dq1w8vLK8XnFZFH2717Nx999BHe3t506dKFv/76CxcXFz766CN27drFrl27aNu2rQo3IiIiIvJUVLwR+ZdtJ69Te/xmlh+4jK2NiZ5vFGZB+8r45HAxOpqIiMgjWSwWYuLuP/fH4woS/3Tz5k1Wr15Nx44dcXZ2TrTPy8uL4OBgFixYkOh8o0ePpnTp0uzdu5dPP/2Ubt26sWbNGgB++uknvv76a6ZOncqJEydYtmwZ/v7+1mM7d+7M9u3bmT9/PgcOHKBx48bUrl2bEydOWNvExMQwcuRIvvvuO/766y+Cg4Nxd3dPNDLFbDazYMECgoODgQfTj5UrV47ly5dz6NAh2rdvz4cffsjOnTsTPafZs2djZ2fHzp07+eabbxg3bhzffffdI1+fxo0bc/XqVVauXElYWBhly5bl9ddf5+bNm488ZsSIEbi7u+Pu7k54eDg1atTA3d2dQ4cO0aRJE9zd3dmyZcvjfi0ikkrR0dF89913lC9fngoVKvD9998TExNDiRIlmDhxIpcuXWL69OmUL1/e6KgiIiIiksFp2jSR/y/2vpkxq48xffNpAHxzZuHrpmUo4+NubDAREZEUuBtvpvig1c/9uoc/D8LF4clvKU+cOIHFYqFYsWLJ7i9WrBi3bt3i2rVr5M6dG4CqVavy6aefAlC4cGG2bt3K119/zRtvvMG5c+fw8vKiVq1a2Nvbkz9/fipWrAjAuXPnmDlzJufOncPb2xuAXr16sWrVKmbOnMlXX30FQHx8PJMnT6Z06dLWHM2aNWPu3Lm0bdsWgLVr13L79m0aNWoEQN68eenVq5e1fZcuXVi9ejULFy60Xh/Ax8eHr7/+GpPJRJEiRTh48CBff/017dq1S/Lct2zZws6dO7l69SqOjo4AjBkzhmXLlrF48WLat2+f7Gv2cDqmWbNm8eeffxIaGsqKFSuYNWsWCxcuBNAoG5E0cvDgQaZOncqPP/5IZGQkAA4ODjRu3JhPPvmEKlWqaIS+iIiIiKQpFW9EgONX7tB13l6Oht8BoHlFHwa8XZwsjvpPREREJC2ldKQOQGBgYJKfx48fDzwYqTJ+/HheeuklateuTZ06dahXrx52dnYcPHgQs9lM4cKFEx0fGxuLh4eH9WcHBwdKlSqVqE1wcDCVK1fm0qVLeHt7M2fOHN5++23c3d2BByNxvvrqKxYuXMjFixeJi4sjNjYWF5fEI3QrV66c6IPcwMBAxo4di9lsxtbWNlHb/fv3ExUVlSgbwN27dzl16tQjX5+Ho2527txJo0aNKFiwIHv37uWdd96hYMGCjzxORFLm3r17LF68mNDQULZu3WrdXqhQIT7++GNatWpFzpw5DUwoIiIiIi+ydPHJ9KRJkxg9ejTh4eGULl2ab7/9NtE3F/9t0aJFDBw4kDNnzuDn58fIkSOpU6eOdb/FYmHw4MFMnz6d27dvU7VqVaZMmYKfn5+1zc2bN+nSpQu//vorNjY2NGrUiG+++YasWbNa2xw4cIBOnTqxa9cucuXKRZcuXejTp8+zeRHEEAkJFmZvP8PwlUeJu59AjiwOjGjoz5sl9C1VERHJWJztbTn8eZAh102JQoUKYTKZOHLkCA0aNEiy/8iRI2TPnp1cuXKl6Hw+Pj4cO3aMP/74gzVr1tCxY0dGjx7Nxo0biYqKwtbWlrCwsCSFkn++13N2dk7yTfkKFSrw8ssvM3/+fD755BOWLl3KrFmzrPtHjx7NN998w/jx4/H39ydLlix0796duLi4FOVOTlRUFHny5Em0Js9DD4tG/7Z582beeust4MH0bxs2bKBHjx7cvXsXe3t7RowYQf/+/enfv/9T5xJJieHDh7NkyRKOHj2Ks7MzVapUYeTIkRQpUsToaE/txIkTTJ06lZkzZ1qnLrS1taV+/fp06NCB1157DRsbzUAuIiIiIs+W4cWbBQsWEBISQmhoKJUqVWL8+PEEBQVx7Ngx65QZ/7Rt2zaaN2/O8OHDqVu3LnPnzqV+/frs2bOHkiVLAjBq1CgmTJjA7Nmz8fX1ZeDAgQQFBXH48GGcnJyAB9+qvHz5MmvWrCE+Pp7WrVvTvn175s6dC0BkZCRvvvkmtWrVIjQ0lIMHD9KmTRvc3d0fOXWFZCxXI+/Ra/EBNh2/BkCNwrkY3bgUubM5GZxMREQk9UwmU4qmLzOKh4cHb7zxBpMnT6ZHjx6J1r0JDw9nzpw5tGjRIlEx5c8//0x0jj///DPRtGvOzs7Uq1ePevXq0alTJ4oWLcrBgwcJCAjAbDZz9epVqlevnuqswcHBzJkzh3z58mFjY8Pbb79t3bd161beffddPvjgAwASEhI4fvw4xYsXT3SOHTt2JMnu5+eXpJgEULZsWcLDw7Gzs0vxiJny5cuzb98+wsLC6NOnD2vXruXcuXO888477NmzBxsbG3LkyJHKZy6Sehs3bqRTp05UqFCB+/fv079/f958800OHz5MlixZjI6XYvHx8fzyyy+Ehobyxx9/WLf7+PjQvn172rRpY52GUURERETkeTC8hz9u3DjatWtH69atAQgNDWX58uXMmDHDOsf5P33zzTfUrl2b3r17A/DFF1+wZs0aJk6cSGhoKBaLhfHjxzNgwADeffddAH744Qc8PT1ZtmwZzZo148iRI6xatYpdu3ZZF5L89ttvqVOnDmPGjLFOkREXF8eMGTNwcHCgRIkS7Nu3j3HjxmWI4s2l23e5dPuu0THSrbM3Yhi2/DC3YuJxtLOhf51itAgsoHmqRUREnqGJEydSpUoVgoKCGDZsGL6+vvz111/07t2bvHnz8uWXXyZqv3XrVkaNGkX9+vVZs2YNixYtYvny5QDMmjULs9lMpUqVcHFx4X//+x/Ozs4UKFAADw8PgoODadGiBWPHjiUgIIBr166xdu1aSpUqlagYk5zg4GCGDBnCl19+yXvvvWddhwbAz8+PxYsXs23bNrJnz864ceO4cuVKkuLNuXPnCAkJ4eOPP2bPnj18++23jB07Ntnr1apVi8DAQOrXr8+oUaMoXLgwly5dYvny5TRo0CDZhc+dnZ0pVKgQixcvpmbNmhQqVIht27ZRtWrVJNPFPXT48GHi4uK4efMmd+7cYd++fQCUKVPmsa+HyOOsWrUq0c+zZs0id+7chIWF8corrxiUKuXOnTvH9OnT+e677wgPDwceFMPr1KlDhw4deOutt5ItuoqIiIiIPGuGFm/i4uIICwujX79+1m02NjbUqlWL7du3J3vM9u3bCQkJSbQtKCiIZcuWAXD69GnCw8OpVauWdb+bmxuVKlVi+/btNGvWjO3bt+Pu7p6oI1yrVi1sbGzYsWMHDRo0YPv27bzyyis4ODgkus7IkSO5desW2bNnT5ItNjaW2NhY688PF7I0wuKwC4xbc9yw62cUxfO48k2zMvh5ZjM6ioiIyAvPz8+P3bt3M3jwYJo0acLNmzfx8vKifv36DB48OMlIkZ49e7J7926GDh2Kq6sr48aNIyjowdRw7u7ujBgxgpCQEMxmM/7+/vz666/WdWNmzpzJsGHD6NmzJxcvXiRnzpxUrlyZunXrPjFnoUKFqFixIjt37rSusfPQgAED+PvvvwkKCsLFxYX27dtTv359IiIiErVr0aIFd+/epWLFitja2tKtW7dHfgHIZDKxYsUKPvvsM1q3bs21a9fw8vLilVdewdPT87FZN2zYQOPGjYEHIyAe92F5nTp1OHv2rPXngIAAIHXrEIk8ycP/Fh418is99ZkOHDhAQEAACQkJAHh6evLRRx/Rrl07ChQoYFguEREREREwuHhz/fp1zGZzkk6pp6cnR48eTfaY8PDwZNs//JbUwz+f1ObfU7LZ2dmRI0eORG18fX2TnOPhvuSKN8OHD2fo0KGPfsLPkbuLPQU9XJ7cMJOysTHxVkkvur7uh6OdvkknIiLyvBQoUCDRGjKPcubMmcfur1+/PvXr13/kfnt7e4YOHfrI92atWrWiVatWjzz+39OePZQjRw7rl4Ye5Z9r10yZMiXZNv9+ftmyZWPChAlMmDDhsef+t3+Oevj+++8f2/ZJr6nIf5WQkED37t2pWrWqdUrrf0tPfSZ/f3+KFCmCt7c3HTp04J133kn05T0RERERESMZPm3ai6Rfv36JRgVFRkbi4+NjSJYWgQVpEVjQkGuLiIiIiEjm06lTJw4dOsSWLVse2SY99ZlMJhO7du3KUGvziIiIiEjmYWjxJmfOnNja2nLlypVE269cuYKXl1eyx3h5eT22/cM/r1y5Qp48eRK1eTift5eXF1evXk10jvv371un7njcdf55jX9zdHRMNCe6iIiIiIhIZtC5c2d+++03Nm3aRL58+R7ZLr31mVS4EREREZH0ysbIizs4OFCuXDnWrl1r3ZaQkMDatWsJDAxM9pjAwMBE7QHWrFljbe/r64uXl1eiNpGRkezYscPaJjAwkNu3bxMWFmZts27dOhISEqhUqZK1zaZNm4iPj090nSJFiiQ7ZZqIiIiIiEhmY7FY6Ny5M0uXLmXdunVJpp4WEREREZGnY2jxBiAkJITp06cze/Zsjhw5wieffEJ0dDStW7cGHiz22q9fP2v7bt26sWrVKsaOHcvRo0cZMmQIu3fvpnPnzsCDoe/du3dn2LBh/PLLLxw8eJAWLVrg7e1tnRe9WLFi1K5dm3bt2rFz5062bt1K586dadasGd7e3gC8//77ODg40LZtW/766y8WLFjAN998k2iIv4iIiIiISGbWqVMn/ve//zF37lyyZctGeHg44eHh3L171+hoIiIiIiIZmuFr3jRt2pRr164xaNAgwsPDKVOmDKtWrcLT0xOAc+fOYWPzfzWmKlWqMHfuXAYMGED//v3x8/Nj2bJliRbE7NOnD9HR0bRv357bt29TrVo1Vq1ahZOTk7XNnDlz6Ny5M6+//jo2NjY0atQo0QKxbm5u/P7773Tq1Ily5cqRM2dOBg0aRPv27Z/DqyIiIiIiIpL+TZkyBYCaNWsm2j5z5kxatWr1/AOJiIiIiLwgTBaLxWJ0iBdVZGQkbm5uRERE4OrqanQcEREReUHcu3eP06dPU7BgQZydnY2OI5nM3bt3OXPmDL6+vom+HAV6/yupp3tGRERERDKblL4HNnzaNBERERFJHXt7ewBiYmIMTiKZ0cP77uF9KCIiIiIiImnP8GnTRERERCR1bG1tcXd35+rVqwC4uLhgMpkMTiUvOovFQkxMDFevXsXd3R1bW1ujI4mIiIiIiLywVLwRERERyYC8vLwArAUckefF3d3dev+JiIiIiIjIs6HijYiIiEgGZDKZyJMnD7lz5yY+Pt7oOJJJ2Nvba8SNiIiIiIjIc6DijYiIiEgGZmtrqw/TRURERERERF4wNkYHEBERERERERERERERkf+j4o2IiIiIiIiIiIiIiEg6ouKNiIiIiIiIiIiIiIhIOqI1b54hi8UCQGRkpMFJRERERESevYfvex++DxZ5EvWZRERERCSzSWm/ScWbZ+jOnTsA+Pj4GJxEREREROT5uXPnDm5ubkbHkAxAfSYRERERyaye1G8yWfS1uGcmISGBS5cukS1bNkwmk9Fx5B8iIyPx8fHh/PnzuLq6Gh1HMgjdN/I0dN/I09K9I0/D6PvGYrFw584dvL29sbHRDM3yZOozpV9G/3siGZfuHXkaum/kaei+kadl9L2T0n6TRt48QzY2NuTLl8/oGPIYrq6u+sddUk33jTwN3TfytHTvyNMw8r7RiBtJDfWZ0j/9f0ielu4deRq6b+Rp6L6Rp5Xe+036OpyIiIiIiIiIiIiIiEg6ouKNiIiIiIiIiIiIiIhIOqLijWRKjo6ODB48GEdHR6OjSAai+0aehu4beVq6d+Rp6L4RkbSif0/kaenekaeh+0aehu4beVoZ5d4xWSwWi9EhRERERERERERERERE5AGNvBEREREREREREREREUlHVLwRERERERERERERERFJR1S8ERERERERERERERERSUdUvBEREREREREREREREUlHVLyRTGP48OFUqFCBbNmykTt3burXr8+xY8eMjiUZzIgRIzCZTHTv3t3oKJIBXLx4kQ8++AAPDw+cnZ3x9/dn9+7dRseSdMxsNjNw4EB8fX1xdnbm5Zdf5osvvsBisRgdTdKZTZs2Ua9ePby9vTGZTCxbtizRfovFwqBBg8iTJw/Ozs7UqlWLEydOGBNWRDIU9ZskLajfJCmlPpM8DfWbJCVehD6TijeSaWzcuJFOnTrx559/smbNGuLj43nzzTeJjo42OppkELt27WLq1KmUKlXK6CiSAdy6dYuqVatib2/PypUrOXz4MGPHjiV79uxGR5N0bOTIkUyZMoWJEydy5MgRRo4cyahRo/j222+NjibpTHR0NKVLl2bSpEnJ7h81ahQTJkwgNDSUHTt2kCVLFoKCgrh3795zTioiGY36TfJfqd8kKaU+kzwt9ZskJV6EPpPJopKkZFLXrl0jd+7cbNy4kVdeecXoOJLORUVFUbZsWSZPnsywYcMoU6YM48ePNzqWpGOffvopW7duZfPmzUZHkQykbt26eHp68v3331u3NWrUCGdnZ/73v/8ZmEzSM5PJxNKlS6lfvz7w4Btk3t7e9OzZk169egEQERGBp6cns2bNolmzZgamFZGMRv0mSQ31myQ11GeSp6V+k6RWRu0zaeSNZFoREREA5MiRw+AkkhF06tSJt99+m1q1ahkdRTKIX375hfLly9O4cWNy585NQEAA06dPNzqWpHNVqlRh7dq1HD9+HID9+/ezZcsW3nrrLYOTSUZy+vRpwsPDE/0/y83NjUqVKrF9+3YDk4lIRqR+k6SG+k2SGuozydNSv0n+q4zSZ7IzOoCIERISEujevTtVq1alZMmSRseRdG7+/Pns2bOHXbt2GR1FMpC///6bKVOmEBISQv/+/dm1axddu3bFwcGBli1bGh1P0qlPP/2UyMhIihYtiq2tLWazmS+//JLg4GCjo0kGEh4eDoCnp2ei7Z6entZ9IiIpoX6TpIb6TZJa6jPJ01K/Sf6rjNJnUvFGMqVOnTpx6NAhtmzZYnQUSefOnz9Pt27dWLNmDU5OTkbHkQwkISGB8uXL89VXXwEQEBDAoUOHCA0NVUdEHmnhwoXMmTOHuXPnUqJECfbt20f37t3x9vbWfSMiIs+d+k2SUuo3ydNQn0melvpNkllo2jTJdDp37sxvv/3G+vXryZcvn9FxJJ0LCwvj6tWrlC1bFjs7O+zs7Ni4cSMTJkzAzs4Os9lsdERJp/LkyUPx4sUTbStWrBjnzp0zKJFkBL179+bTTz+lWbNm+Pv78+GHH9KjRw+GDx9udDTJQLy8vAC4cuVKou1Xrlyx7hMReRL1myQ11G+Sp6E+kzwt9Zvkv8oofSYVbyTTsFgsdO7cmaVLl7Ju3Tp8fX2NjiQZwOuvv87BgwfZt2+f9VG+fHmCg4PZt28ftra2RkeUdKpq1aocO3Ys0bbjx49ToEABgxJJRhATE4ONTeK3Z7a2tiQkJBiUSDIiX19fvLy8WLt2rXVbZGQkO3bsIDAw0MBkIpIRqN8kT0P9Jnka6jPJ01K/Sf6rjNJn0rRpkml06tSJuXPn8vPPP5MtWzbr/IVubm44OzsbnE7Sq2zZsiWZ3ztLlix4eHho3m95rB49elClShW++uormjRpws6dO5k2bRrTpk0zOpqkY/Xq1ePLL78kf/78lChRgr179zJu3DjatGljdDRJZ6Kiojh58qT159OnT7Nv3z5y5MhB/vz56d69O8OGDcPPzw9fX18GDhyIt7c39evXNy60iGQI6jfJ01C/SZ6G+kzytNRvkpR4EfpMJovFYjE6hMjzYDKZkt0+c+ZMWrVq9XzDSIZWs2ZNypQpw/jx442OIuncb7/9Rr9+/Thx4gS+vr6EhITQrl07o2NJOnbnzh0GDhzI0qVLuXr1Kt7e3jRv3pxBgwbh4OBgdDxJRzZs2MCrr76aZHvLli2ZNWsWFouFwYMHM23aNG7fvk21atWYPHkyhQsXNiCtiGQk6jdJWlG/SVJCfSZ5Guo3SUq8CH0mFW9ERERERERERERERETSEa15IyIiIiIiIiIiIiIiko6oeCMiIiIiIiIiIiIiIpKOqHgjIiIiIiIiIiIiIiKSjqh4IyIiIiIiIiIiIiIiko6oeCMiIiIiIiIiIiIiIpKOqHgjIiIiIiIiIiIiIiKSjqh4IyIiIiIiIiIiIiIiko6oeCMi8h/UrFmT7t27P9NrtGrVivr16z/Ta2Q2/+U13bBhAyaTidu3bwMwa9Ys3N3d0yxbemMymVi2bJnRMUREREQkg1KfKWNSnynl1GcSkWdFxRsREckQ0muHrGnTphw/ftzoGCIiIiIiksmpzyQi8mKxMzqAiIg8f3FxcTg4OBgd44Xg7OyMs7Oz0TEyFN1/IiIiIpLe6T1r2lGfKfV0/4kIaOSNiMh/dv/+fTp37oybmxs5c+Zk4MCBWCwW6/4ff/yR8uXLky1bNry8vHj//fe5evVqonP89ddf1K1bF1dXV7Jly0b16tU5depUstfbtWsXuXLlYuTIkQAMGTKEMmXKMHXqVHx8fHBxcaFJkyZERERYj3n4Dawvv/wSb29vihQpAsDBgwd57bXXcHZ2xsPDg/bt2xMVFZXkuKFDh5IrVy5cXV3p0KEDcXFx1jarVq2iWrVquLu74+HhQd26dZNk37ZtG2XKlMHJyYny5cuzbNkyTCYT+/btA8BsNtO2bVt8fX1xdnamSJEifPPNN9bjhwwZwuzZs/n5558xmUyYTCY2bNgAwPnz52nSpAnu7u7kyJGDd999lzNnzliPNZvNhISEWPP16dMn0e8nOWfPnqVevXpkz56dLFmyUKJECVasWJFs2+SmAPj111+pUKECTk5O5MyZkwYNGlj3xcbG0qtXL/LmzUuWLFmoVKmS9bk8islk4rvvvqNBgwa4uLjg5+fHL7/88tgMD1/jhx7eJzNmzCB//vxkzZqVjh07YjabGTVqFF5eXuTOnZsvv/wyyfUvX77MW2+9hbOzMy+99BKLFy9OtP9Jv4NH3X8iIiIikjmoz6Q+k/pM6jOJSOqpeCMi8h/Nnj0bOzs7du7cyTfffMO4ceP47rvvrPvj4+P54osv2L9/P8uWLePMmTO0atXKuv/ixYu88sorODo6sm7dOsLCwmjTpg33799Pcq1169bxxhtv8OWXX9K3b1/r9pMnT7Jw4UJ+/fVXVq1axd69e+nYsWOiY9euXcuxY8dYs2YNv/32G9HR0QQFBZE9e3Z27drFokWL+OOPP+jcuXOS444cOcKGDRuYN28eS5YsYejQodb90dHRhISEsHv3btauXYuNjQ0NGjQgISEBgMjISOrVq4e/vz979uzhiy++SJQdICEhgXz58rFo0SIOHz7MoEGD6N+/PwsXLgSgV69eNGnShNq1a3P58mUuX75MlSpViI+PJygoiGzZsrF582a2bt1K1qxZqV27trWzNHbsWGbNmsWMGTPYsmULN2/eZOnSpY/9nXbq1InY2Fg2bdrEwYMHGTlyJFmzZn3sMQ8tX76cBg0aUKdOHfbu3cvatWupWLGidX/nzp3Zvn078+fP58CBAzRu3JjatWtz4sSJx5536NChNGnShAMHDlCnTh2Cg4O5efNmijI9dOrUKVauXMmqVauYN28e33//PW+//TYXLlxg48aNjBw5kgEDBrBjx45Exw0cOJBGjRqxf/9+goODadasGUeOHAFI0e8Akt5/IiIiIpJ5qM+kPtM/qc+kPpOIpJBFRESeWo0aNSzFihWzJCQkWLf17dvXUqxYsUces2vXLgtguXPnjsVisVj69etn8fX1tcTFxSXbvmXLlpZ3333XsmTJEkvWrFkt8+fPT7R/8ODBFltbW8uFCxes21auXGmxsbGxXL582XoOT09PS2xsrLXNtGnTLNmzZ7dERUVZty1fvtxiY2NjCQ8Ptx6XI0cOS3R0tLXNlClTLFmzZrWYzeZk8167ds0CWA4ePGht7+HhYbl79661zfTp0y2AZe/evY98nTp16mRp1KhRktfhn3788UdLkSJFEr3+sbGxFmdnZ8vq1astFovFkidPHsuoUaOs++Pj4y358uVLcq5/8vf3twwZMiTZfevXr7cAllu3blksFotl5syZFjc3N+v+wMBAS3BwcLLHnj171mJra2u5ePFiou2vv/66pV+/fo/MA1gGDBhg/TkqKsoCWFauXJlsBovFYlm6dKnln/+bHzx4sMXFxcUSGRlp3RYUFGQpWLBgot9lkSJFLMOHD0907Q4dOiQ6d6VKlSyffPKJxWJJ2e8guftPRERERDIH9ZmSUp9JfSb1mUQkJTTyRkTkP6pcuXKiodaBgYGcOHECs9kMQFhYGPXq1SN//vxky5aNGjVqAHDu3DkA9u3bR/Xq1bG3t3/kNXbs2EHjxo358ccfadq0aZL9+fPnJ2/evIkyJCQkcOzYMes2f3//RHPmHjlyhNKlS5MlSxbrtqpVqyY5rnTp0ri4uCQ6d1RUFOfPnwfgxIkTNG/enJdeeglXV1cKFiyY6PkdO3aMUqVK4eTkZD3HP79V9dCkSZMoV64cuXLlImvWrEybNs16jkfZv38/J0+eJFu2bGTNmpWsWbOSI0cO7t27x6lTp4iIiODy5ctUqlTJeoydnR3ly5d/7Hm7du3KsGHDqFq1KoMHD+bAgQOPbf9P+/bt4/XXX09238GDBzGbzRQuXNiaN2vWrGzcuPGRUz48VKpUKevfs2TJgqura5KpJJ6kYMGCZMuWzfqzp6cnxYsXx8bGJtG2f583MDAwyc8Pv0X2pN/BQ/++/0REREQk81CfSX2mf1KfSX0mEUkZO6MDiIi8yB4Osw8KCmLOnDnkypWLc+fOERQUZB0enZKFG19++WU8PDyYMWMGb7/99mM7LY/yzw5HWqpXrx4FChRg+vTpeHt7k5CQQMmSJRMN/36S+fPn06tXL8aOHUtgYCDZsmVj9OjRSYai/1tUVBTlypVjzpw5SfblypUr1c/loY8++oigoCCWL1/O77//zvDhwxk7dixdunR54rGP+31GRUVha2tLWFgYtra2ifY9aYqBf//OTSaTdZoFGxubJHNSx8fHp+gcjztvSqT0d/Cs7j8RERERydjUZ0oZ9ZnUZxKRzEcjb0RE/qN/v1n+888/8fPzw9bWlqNHj3Ljxg1GjBhB9erVKVq0aJJv6JQqVYrNmzcn+8bxoZw5c7Ju3TpOnjxJkyZNkrQ9d+4cly5dSpTBxsbmsYscFitWjP379xMdHW3dtnXr1iTH7d+/n7t37yY6d9asWfHx8eHGjRscO3aMAQMG8Prrr1OsWDFu3bqV6DpFihTh4MGDxMbGWrft2rUrUZutW7dSpUoVOnbsSEBAAIUKFUryrSoHBwfrN/MeKlu2LCdOnCB37twUKlQo0cPNzQ03Nzfy5MmT6Hd0//59wsLCHvm6POTj40OHDh1YsmQJPXv2ZPr06U88Bh78PteuXZvsvoCAAMxmM1evXk2S18vLK0XnT06uXLm4c+dOot/lw4VN08Kff/6Z5OdixYoBT/4diIiIiIioz6Q+0z+pz6Q+k4ikjIo3IiL/0blz5wgJCeHYsWPMmzePb7/9lm7dugEPhuY7ODjw7bff8vfff/PLL7/wxRdfJDq+c+fOREZG0qxZM3bv3s2JEyf48ccfEw3DB8idOzfr1q3j6NGjNG/ePNHinE5OTrRs2ZL9+/ezefNmunbtSpMmTR775jY4ONh63KFDh1i/fj1dunThww8/xNPT09ouLi6Otm3bcvjwYVasWMHgwYPp3LkzNjY2ZM+eHQ8PD6ZNm8bJkydZt24dISEhia7z/vvvk5CQQPv27Tly5AirV69mzJgxANapE/z8/Ni9ezerV6/m+PHjDBw4MElnpWDBghw4cIBjx45x/fp14uPjCQ4OJmfOnLz77rts3ryZ06dPs2HDBrp27cqFCxcA6NatGyNGjGDZsmUcPXqUjh07cvv27cf+Trt3787q1as5ffo0e/bsYf369dY33k8yePBg5s2bx+DBgzly5Ih18U6AwoULExwcTIsWLViyZAmnT59m586dDB8+nOXLl6fo/MmpVKkSLi4u9O/fn1OnTjF37lxmzZr11Of7t0WLFjFjxgyOHz/O4MGD2blzp3WR1pT8DkREREQkc1OfSX2mf1KfSX0mEUkZFW9ERP6jFi1acPfuXSpWrEinTp3o1q0b7du3Bx58u2fWrFksWrSI4sWLM2LECOub8Ic8PDxYt24dUVFR1KhRg3LlyjF9+vRkh/l7eXmxbt06Dh48SHBwsPVbVYUKFaJhw4bUqVOHN998k1KlSjF58uTH5nZxcWH16tXcvHmTChUq8N577/H6668zceLERO1ef/11/Pz8eOWVV2jatCnvvPMOQ4YMAR4MPZ8/fz5hYWGULFmSHj16MHr06ETHu7q68uuvv7Jv3z7KlCnDZ599xqBBgwCsczp//PHHNGzYkKZNm1KpUiVu3LhBx44dE52nXbt2FClShPLly5MrVy62bt2Ki4sLmzZtIn/+/DRs2JBixYrRtm1b7t27h6urKwA9e/bkww8/pGXLltbpBRo0aPDY18ZsNtOpUyeKFStG7dq1KVy48BNfz4dq1qzJokWL+OWXXyhTpgyvvfYaO3futO6fOXMmLVq0oGfPnhQpUoT69euza9cu8ufPn6LzJydHjhz873//Y8WKFfj7+zNv3jzr7ygtDB06lPnz51OqVCl++OEH5s2bR/HixQFS9DsQERERkcxNfSb1mf5JfSb1mUQkZUyWf0/4KCIiGcqQIUNYtmxZmg75fqhVq1bcvn2bZcuWpel558yZQ+vWrYmIiEjR/NUiIiIiIiJPS30mERHJiOyMDiAiIi++H374gZdeeom8efOyf/9++vbtS5MmTdQJERERERERQX0mERFJSsUbERF55sLDwxk0aBDh4eHkyZOHxo0b8+WXXxodS0REREREJF1Qn0lERP5N06aJiIiIiIiIiIiIiIikIzZGBxAREREREREREREREZH/o+KNiIiIiIiIiIiIiIhIOqLijYiIiIiIiIiIiIiISDqi4o2IiIiIiIiIiIiIiEg6ouKNiIiIiIiIiIiIiIhIOqLijYiIiIiIiIiIiIiISDqi4o2IiIiIiIiIiIiIiEg6ouKNiIiIiIiIiIiIiIhIOqLijYiIiIiIiIiIiIiISDry/wAKSLyk1x1IqAAAAABJRU5ErkJggg==", "text/plain": [ - "
    " + "\"Output" ] }, "metadata": {}, @@ -760,9 +754,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
    " + "\"Output" ] }, "metadata": {}, @@ -879,9 +872,8 @@ "outputs": [ { "data": { - "image/png": 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    " + "\"Output" ] }, "metadata": {}, @@ -899,7 +891,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -913,9 +905,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb index 42f57e1a116..9952b4d368f 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb @@ -81,9 +81,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "" + "\"Output" ] }, "execution_count": 2, @@ -170,9 +169,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
    " + "\"Output" ] }, "execution_count": 4, @@ -280,9 +278,8 @@ }, { "data": { - "image/png": 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", "text/plain": [ - "
    " + "\"Output" ] }, "execution_count": 7, @@ -367,9 +364,8 @@ }, { "data": { - "image/png": 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@@ // copyright notice, and modified files need to carry a notice indicating // that they have been altered from the originals. -import { readdir } from "fs/promises"; - import yargs from "yargs/yargs"; import { hideBin } from "yargs/helpers"; import { $ } from "zx"; import { Pkg } from "../../lib/api/Pkg.js"; import { zxMain } from "../../lib/zx.js"; -import { getDevVersion, getReleasedVersions } from "../../lib/apiVersions.js"; +import { getDevVersion, getReleasedVersions, parseMinorVersion } from "../../lib/apiVersions.js"; +import { pathExists, rmFilesInFolder } from "../../lib/fs.js"; +import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; +import { runConversionPipeline } from "../../lib/api/conversionPipeline.js"; import { generateHistoricalRedirects } from "./generateHistoricalRedirects.js"; interface Arguments { @@ -75,8 +76,6 @@ zxMain(async () => { ); } - await generateHistoricalRedirects(); - console.log(`Each regenerated version has been saved as a distinct commit. If the changes are too large for one single PR, consider splitting it up into multiple PRs by using git cherry-pick or git rebase -i so each PR only has the commits it wants to target.`); @@ -109,16 +108,23 @@ async function regenerateVersion( skipDownload: boolean, typeArgument?: "historical" | "dev", ): Promise { - const command = ["npm", "run", "gen-api", "--", "-p", pkgName, "-v", version]; - if (typeArgument) { - command.push(`--${typeArgument}`); - } - if (skipDownload) { - command.push("--skip-download"); - } - try { - await $`${command}`; + const type = typeArgument ?? "latest"; + const minorVersion = parseMinorVersion(version)!; + const pkg = await Pkg.fromArgs(pkgName, version, minorVersion, type); + const artifactFolder = pkg.sphinxArtifactFolder(); + if (skipDownload && (await pathExists(`${artifactFolder}/artifact`))) { + console.log(`Skip downloading sources for ${pkgName}:${minorVersion}`); + } else { + await downloadSphinxArtifact(pkg, artifactFolder); + } + const markdownDir = pkg.apiOutputDir("docs"); + if (await pathExists(markdownDir)) await rmFilesInFolder(markdownDir); + const imagesDir = pkg.apiOutputDir("public/docs/images"); + if (await pathExists(imagesDir)) await rmFilesInFolder(imagesDir); + console.log(`Run pipeline for ${pkgName}:${minorVersion}`); + await runConversionPipeline(`${artifactFolder}/artifact`, "docs", "public/docs", pkg); + await generateHistoricalRedirects(); if ((await gitStatus()) !== "") { await gitCommit(`Regenerate ${pkgName} ${version}`); console.log(`🚀 ${pkgName} ${version} regenerated correctly`); diff --git a/scripts/js/commands/api/syncDevDocs.ts b/scripts/js/commands/api/syncDevDocs.ts index e7d17ce3e09..e066c8f5b85 100644 --- a/scripts/js/commands/api/syncDevDocs.ts +++ b/scripts/js/commands/api/syncDevDocs.ts @@ -15,12 +15,11 @@ import fs from "fs/promises"; import { Pkg } from "../../lib/api/Pkg.js"; import { zxMain } from "../../lib/zx.js"; -import { readApiFullVersion } from "../../lib/apiVersions.js"; -import { - generateVersion, - determineMinorVersion, - Arguments, -} from "./updateApiDocs.js"; +import { readApiFullVersion, parseMinorVersion } from "../../lib/apiVersions.js"; +import { pathExists, rmFilesInFolder } from "../../lib/fs.js"; +import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; +import { runConversionPipeline } from "../../lib/api/conversionPipeline.js"; +import { generateHistoricalRedirects } from "./generateHistoricalRedirects.js"; // Found with `gh api repos/qiskit/${repo}/actions/workflows` const QISKIT_WORKFLOW_ID = "66225883"; @@ -101,15 +100,16 @@ async function updateHtmlArtifacts(args: { await fs.writeFile(path, JSON.stringify(prior, null, 2) + "\n"); } -async function regenDocs(pkg: string, version: string): Promise { - const args: Arguments = { - package: pkg, - version: version, - dev: true, - historical: false, - skipDownload: false, - }; - const minorVersion = determineMinorVersion(args); - const pkgObj = await Pkg.fromArgs(pkg, version, minorVersion, "dev"); - await generateVersion(pkgObj, args); +async function regenDocs(pkgName: string, version: string): Promise { + const minorVersion = parseMinorVersion(version)!; + const pkg = await Pkg.fromArgs(pkgName, version, minorVersion, "dev"); + const artifactFolder = pkg.sphinxArtifactFolder(); + await downloadSphinxArtifact(pkg, artifactFolder); + const markdownDir = pkg.apiOutputDir("docs"); + if (await pathExists(markdownDir)) await rmFilesInFolder(markdownDir); + const imagesDir = pkg.apiOutputDir("public/docs/images"); + if (await pathExists(imagesDir)) await rmFilesInFolder(imagesDir); + console.log(`Run pipeline for ${pkgName}:${minorVersion}`); + await runConversionPipeline(`${artifactFolder}/artifact`, "docs", "public/docs", pkg); + await generateHistoricalRedirects(); } diff --git a/scripts/js/commands/api/updateApiDocs.ts b/scripts/js/commands/api/updateApiDocs.ts deleted file mode 100644 index 406fff3b9ea..00000000000 --- a/scripts/js/commands/api/updateApiDocs.ts +++ /dev/null @@ -1,172 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2023. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import yargs from "yargs/yargs"; -import { hideBin } from "yargs/helpers"; - -import { Pkg } from "../../lib/api/Pkg.js"; -import { zxMain } from "../../lib/zx.js"; -import { parseMinorVersion, isValidVersion } from "../../lib/apiVersions.js"; -import { pathExists, rmFilesInFolder } from "../../lib/fs.js"; -import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; -import { runConversionPipeline } from "../../lib/api/conversionPipeline.js"; -import { generateHistoricalRedirects } from "./generateHistoricalRedirects.js"; - -export interface Arguments { - [x: string]: unknown; - package: string; - version: string; - historical: boolean; - dev: boolean; - skipDownload: boolean; - sphinxArtifactFolder?: string; -} - -const readArgs = (): Arguments => { - return yargs(hideBin(process.argv)) - .version(false) - .option("package", { - alias: "p", - type: "string", - choices: Pkg.VALID_NAMES, - demandOption: true, - description: "Which package to update", - }) - .option("version", { - alias: "v", - type: "string", - demandOption: true, - description: "The full version string of the --package, e.g. 0.44.0", - coerce: (version) => { - if (!isValidVersion(version)) { - throw new Error( - "The version must include a major, a minor, and a patch. E.g. `-v 0.46.3`", - ); - } - return version; - }, - }) - .option("historical", { - type: "boolean", - default: false, - description: "Is this a prior release?", - }) - .option("dev", { - type: "boolean", - default: false, - description: "Is this a dev release?", - }) - .option("skip-download", { - type: "boolean", - default: false, - description: - "Rather than downloading the artifact from Box, reuse what is already downloaded. This can save time, but it risks using an outdated version of the docs.", - }) - .option("sphinx-artifact-folder", { - alias: "a", - type: "string", - implies: "skip-download", - normalize: true, - description: - "Skip downloading the artifact from Box and instead use the given directory as the root of the Sphinx HTML output.", - }) - .parseSync(); -}; - -export async function generateVersion( - pkg: Pkg, - args: Arguments, -): Promise { - const sphinxArtifactFolder = await prepareSphinxFolder(pkg, args); - await deleteExistingFiles(pkg); - - console.log(`Run pipeline for ${pkg.name}:${pkg.versionWithoutPatch}`); - await runConversionPipeline(sphinxArtifactFolder, "docs", "public/docs", pkg); - await generateHistoricalRedirects(); -} - -export function determineMinorVersion(args: Arguments): string { - const minorVersion = parseMinorVersion(args.version); - if (minorVersion === null) { - throw new Error( - `Invalid --version. Expected the format 0.44.0, but received ${args.version}`, - ); - } - - const devRegex = /[0-9](rc|-dev)/; - if (args.dev && !args.version.match(devRegex)) { - throw new Error( - `${args.package} ${args.version} is not a correct dev version. Please make sure the version has one of the following suffixes immediately following the patch version: rc, -dev. e.g. 1.0.0rc1 or 1.0.0-dev`, - ); - } - - return minorVersion; -} - -async function prepareSphinxFolder(pkg: Pkg, args: Arguments): Promise { - if (args.sphinxArtifactFolder) { - if (!(await pathExists(args.sphinxArtifactFolder))) { - throw new Error( - `Explicit artifact path '${args.sphinxArtifactFolder}' does not exist.`, - ); - } - return args.sphinxArtifactFolder; - } - const sphinxArtifactFolder = pkg.sphinxArtifactFolder(); - if ( - args.skipDownload && - (await pathExists(`${sphinxArtifactFolder}/artifact`)) - ) { - console.log( - `Skip downloading sources for ${pkg.name}:${pkg.versionWithoutPatch}`, - ); - } else { - await downloadSphinxArtifact(pkg, sphinxArtifactFolder); - } - return `${sphinxArtifactFolder}/artifact`; -} - -async function deleteExistingFiles(pkg: Pkg): Promise { - const markdownDir = pkg.apiOutputDir("docs"); - if (await pathExists(markdownDir)) { - await rmFilesInFolder(markdownDir); - } - const imagesDir = pkg.apiOutputDir("public/docs/images"); - if (await pathExists(imagesDir)) { - await rmFilesInFolder(imagesDir); - } - console.log( - `Deleted existing markdown & images for ${pkg.name}:${pkg.versionWithoutPatch}`, - ); -} - -if (import.meta.url === `file://${process.argv[1]}`) { - zxMain(async () => { - const args = readArgs(); - - if (args.historical && args.dev) { - throw new Error( - `${args.package} ${args.version} cannot be historical and dev at the same time. Please remove at least only one of these two arguments: --historical, --dev.`, - ); - } - - const minorVersion = determineMinorVersion(args); - const type = args.historical ? "historical" : args.dev ? "dev" : "latest"; - const pkg = await Pkg.fromArgs( - args.package, - args.version, - minorVersion, - type, - ); - await generateVersion(pkg, args); - }); -} diff --git a/scripts/js/commands/updateDocs.ts b/scripts/js/commands/updateDocs.ts index 607051387e4..9736acb8ec4 100644 --- a/scripts/js/commands/updateDocs.ts +++ b/scripts/js/commands/updateDocs.ts @@ -18,16 +18,18 @@ import { $ } from "zx"; import { mkdirp } from "mkdirp"; import { Pkg } from "../lib/api/Pkg.js"; -import { pathExists } from "../lib/fs.js"; +import { pathExists, rmFilesInFolder } from "../lib/fs.js"; import { downloadSphinxArtifact } from "../lib/api/sphinxArtifacts.js"; +import { runConversionPipeline } from "../lib/api/conversionPipeline.js"; import { runSphinxPipeline } from "../lib/sphinx/conversionPipeline.js"; + import { zxMain } from "../lib/zx.js"; -import { parseMinorVersion } from "../lib/apiVersions.js"; +import { isValidVersion, parseMinorVersion } from "../lib/apiVersions.js"; -export interface Arguments { - [x: string]: unknown; - package?: string; - skipDownload: boolean; +interface Arguments { + package: string; + version: string; + skipDownload?: boolean; sphinxArtifactFolder?: string; } @@ -38,13 +40,28 @@ const readArgs = (): Arguments => { alias: "p", type: "string", choices: Pkg.VALID_NAMES, + demandOption: true, description: "Which package to update", }) + .option("version", { + alias: "v", + type: "string", + demandOption: true, + description: "The full version string of the --package, e.g. 0.44.0", + coerce: (version) => { + if (!isValidVersion(version)) { + throw new Error( + "The version must include a major, a minor, and a patch. E.g. `-v 0.46.3`", + ); + } + return version; + }, + }) .option("skip-download", { type: "boolean", default: false, description: - "Rather than downloading the artifact from Box, reuse what is already downloaded. This can save time, but it risks using an outdated version of the docs.", + "Rather than downloading the artifact from Box, reuse what is already downloaded.", }) .option("sphinx-artifact-folder", { alias: "a", @@ -57,17 +74,23 @@ const readArgs = (): Arguments => { .parseSync(); }; -async function runForAddon(pkg: Pkg, args: Arguments): Promise { +async function generateVersion(pkg: Pkg, args: Arguments): Promise { const sphinxArtifactFolder = await prepareSphinxFolder(pkg, args); - await deleteExistingFiles(pkg); + + await deleteExistingApiFiles(pkg); console.log(`Run pipeline for ${pkg.name}:${pkg.versionWithoutPatch}`); - await runSphinxPipeline( - sphinxArtifactFolder, - "docs/addons", - "public/docs", - pkg, - ); + await runConversionPipeline(sphinxArtifactFolder, "docs", "public/docs", pkg); + + if (pkg.isAddon()) { + await deleteExistingAddonFiles(pkg); + await runSphinxPipeline( + sphinxArtifactFolder, + "docs/addons", + "public/docs", + pkg, + ); + } } async function prepareSphinxFolder(pkg: Pkg, args: Arguments): Promise { @@ -93,11 +116,24 @@ async function prepareSphinxFolder(pkg: Pkg, args: Arguments): Promise { return `${sphinxArtifactFolder}/artifact`; } -async function deleteExistingFiles(pkg: Pkg): Promise { +async function deleteExistingApiFiles(pkg: Pkg): Promise { + const markdownDir = pkg.apiOutputDir("docs"); + if (await pathExists(markdownDir)) { + await rmFilesInFolder(markdownDir); + } + const imagesDir = pkg.apiOutputDir("public/docs/images"); + if (await pathExists(imagesDir)) { + await rmFilesInFolder(imagesDir); + } + console.log( + `Deleted existing docs & images for ${pkg.name}:${pkg.versionWithoutPatch}`, + ); +} + +async function deleteExistingAddonFiles(pkg: Pkg): Promise { const markdownDir = pkg.outputDir("docs/addons"); if (await pathExists(markdownDir)) { - // Preserve the hand-authored _toc.json — it is maintained separately - // from the generated content and should not be overwritten by the pipeline. + // Preserve the hand-authored _toc.json. const tocPath = `${markdownDir}/_toc.json`; const tocContent = (await pathExists(tocPath)) ? await readFile(tocPath, "utf-8") @@ -113,36 +149,24 @@ async function deleteExistingFiles(pkg: Pkg): Promise { await $`rm -rf ${imagesDir}`; } console.log( - `Deleted existing markdown & images for ${pkg.name}:${pkg.versionWithoutPatch}`, + `Deleted existing docs & images for ${pkg.name}:${pkg.versionWithoutPatch}`, ); } -const ADDONS = [ - { name: "qiskit-addon-aqc-tensor", version: "0.2.0" }, - { name: "qiskit-addon-cutting", version: "0.10.0" }, - { name: "qiskit-addon-mpf", version: "0.3.0" }, - { name: "qiskit-addon-obp", version: "0.3.0" }, - { name: "qiskit-addon-sqd", version: "0.12.0" }, - { name: "qiskit-addon-utils", version: "0.3.1" }, -]; - -if (import.meta.url === `file://${process.argv[1]}`) { - zxMain(async () => { - const args = readArgs(); - const addons = args.package - ? ADDONS.filter((a) => a.name === args.package) - : ADDONS; - - for (const addon of addons) { - const minorVersion = parseMinorVersion(addon.version)!; - const pkg = await Pkg.fromArgs( - addon.name, - addon.version, - minorVersion, - "latest", - ); +zxMain(async () => { + const args = readArgs(); + const minorVersion = parseMinorVersion(args.version); + if (minorVersion === null) { + throw new Error( + `Invalid --version. Expected the format 0.44.0, but received ${args.version}`, + ); + } - await runForAddon(pkg, args); - } - }); -} + const pkg = await Pkg.fromArgs( + args.package, + args.version, + minorVersion, + "latest", + ); + await generateVersion(pkg, args); +}); From fb02c4102cb59516c2fae5d608d6ee008cb0f209 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Mon, 11 May 2026 18:35:08 -0400 Subject: [PATCH 026/104] revert change --- scripts/js/commands/updateDocs.ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/js/commands/updateDocs.ts b/scripts/js/commands/updateDocs.ts index 9736acb8ec4..48b915eb306 100644 --- a/scripts/js/commands/updateDocs.ts +++ b/scripts/js/commands/updateDocs.ts @@ -61,7 +61,7 @@ const readArgs = (): Arguments => { type: "boolean", default: false, description: - "Rather than downloading the artifact from Box, reuse what is already downloaded.", + "Rather than downloading the artifact from Box, reuse what is already downloaded. This can save time, but it risks using an outdated version of the docs.", }) .option("sphinx-artifact-folder", { alias: "a", From c403c448195fbf9001b1b389ca391a079ed54e6a Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Mon, 11 May 2026 22:02:15 -0400 Subject: [PATCH 027/104] revert changes --- .../how-tos/bound-error-using-p-norm.ipynb | 2 +- .../how-tos/truncate-operator-terms.ipynb | 8 +- .../tutorials/01-getting-started.ipynb | 6 +- package.json | 3 +- scripts/js/commands/api/regenerateApiDocs.ts | 34 ++-- scripts/js/commands/api/syncDevDocs.ts | 34 ++-- scripts/js/commands/api/updateApiDocs.ts | 172 ++++++++++++++++++ scripts/js/commands/updateDocs.ts | 42 +---- 8 files changed, 223 insertions(+), 78 deletions(-) create mode 100644 scripts/js/commands/api/updateApiDocs.ts diff --git a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb index cdc20febc8a..d9d6cc32977 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb @@ -31,7 +31,7 @@ "id": "2780c587-cd2a-4398-8faa-de5e0c661a10", "metadata": {}, "source": [ - "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget).\n", + "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget).\n", "In this guide, you will learn about its additional keyword argument `p_norm`, which can be used to change the Lp-norm used to estimate the error incurred by the truncated Pauli terms." ] }, diff --git a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb index 93107bcb2ba..2bc0a0a3720 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb @@ -27,9 +27,9 @@ "\n", "Operator backpropagation (OBP) can be used to reduce the depth of quantum circuits at the cost of a more complex observable. In order to get meaningful results from OBP, one usually needs to truncate terms from their observable to prevent it from growing too large. One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms' coefficients.\n", "\n", - "The [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", + "The [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", "The amount of terms that are truncated depends on various configuration parameters specified by the user.\n", - "As of now, only a single truncation strategy is available -- the [truncate_binary_search](/docs/api/qiskit-addon-obp/utils-truncating#truncate_binary_search) method.\n", + "As of now, only a single truncation strategy is available -- the [truncate_binary_search](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search) method.\n", "Given an observable and some _budget_ it will perform a binary search over the Pauli terms and coefficients within that observable to find the optimal threshold,\n", "such that the sum of the truncated coefficients is maximal but lower than the budget.\n", "\n", @@ -42,7 +42,7 @@ "id": "aaf5f046-6d5e-4b6f-add7-b60604edef30", "metadata": {}, "source": [ - "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget) via the accompanying [setup_budget](/docs/api/qiskit-addon-obp/utils-truncating#setup_budget) function." + "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) via the accompanying [setup_budget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function." ] }, { @@ -202,7 +202,7 @@ "id": "4a772af6-edc6-4e53-81c7-53f3b72bf58a", "metadata": {}, "source": [ - "Let's use the [OBPMetadata](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", + "Let's use the [OBPMetadata](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", "\n", "- **The top/left plot** shows that we have enough budget to begin truncating observable terms after backpropagating the third slice. Starting from the third slice, we know that we truncate at least one term from each slice we backpropagate because we are incurring some truncation error after each slice.\n", "- **The top/right plot** shows that the error budget ramps up to `.003` for the third slice. We see a sharp drop in remaining budget, which means terms were truncated from the observable. This is consistent with what we inferred from the top/left plot.\n", diff --git a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb index a8a2b5c4969..33b583b7287 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb @@ -41,7 +41,7 @@ "- **Step 4: Reconstruct results**\n", " - N.A.\n", "\n", - "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." + "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." ] }, { @@ -241,7 +241,7 @@ "source": [ "### Constrain how large the operator may grow during backpropagation\n", "\n", - "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/utils-simplify#operatorbudget) instance.\n", + "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget) instance.\n", "\n", "Here we specify that backpropagation should stop when the number of qubit-wise commuting Pauli groups in the operator grows past 8." ] @@ -327,7 +327,7 @@ "id": "d25b9701-f1b1-4230-9dcc-554b85e91cf7", "metadata": {}, "source": [ - "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](/docs/api/qiskit-addon-obp/utils-truncating#setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", + "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", "\n", "In order to enable this truncation, we need to setup our error budget like so:" ] diff --git a/package.json b/package.json index fe4da73a53a..860f4cb498b 100644 --- a/package.json +++ b/package.json @@ -31,7 +31,8 @@ "check:qiskit-deprecation": "tsx scripts/js/commands/api/checkQiskitDeprecation.ts", "check:qiskit-versions": "tsx scripts/js/commands/checkQiskitApiVersions.ts", "regen-apis": "tsx scripts/js/commands/api/regenerateApiDocs.ts", - "gen-docs": "tsx scripts/js/commands/updateDocs.ts", + "gen-api": "tsx scripts/js/commands/api/updateApiDocs.ts", + "gen-addon-docs": "tsx scripts/js/commands/updateDocs.ts", "generate-historical-redirects": "tsx scripts/js/commands/api/generateHistoricalRedirects.ts", "save-internal-links": "tsx scripts/js/commands/saveInternalLinks.ts" }, diff --git a/scripts/js/commands/api/regenerateApiDocs.ts b/scripts/js/commands/api/regenerateApiDocs.ts index a22e8466570..fa13ada3716 100644 --- a/scripts/js/commands/api/regenerateApiDocs.ts +++ b/scripts/js/commands/api/regenerateApiDocs.ts @@ -10,16 +10,15 @@ // copyright notice, and modified files need to carry a notice indicating // that they have been altered from the originals. +import { readdir } from "fs/promises"; + import yargs from "yargs/yargs"; import { hideBin } from "yargs/helpers"; import { $ } from "zx"; import { Pkg } from "../../lib/api/Pkg.js"; import { zxMain } from "../../lib/zx.js"; -import { getDevVersion, getReleasedVersions, parseMinorVersion } from "../../lib/apiVersions.js"; -import { pathExists, rmFilesInFolder } from "../../lib/fs.js"; -import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; -import { runConversionPipeline } from "../../lib/api/conversionPipeline.js"; +import { getDevVersion, getReleasedVersions } from "../../lib/apiVersions.js"; import { generateHistoricalRedirects } from "./generateHistoricalRedirects.js"; interface Arguments { @@ -76,6 +75,8 @@ zxMain(async () => { ); } + await generateHistoricalRedirects(); + console.log(`Each regenerated version has been saved as a distinct commit. If the changes are too large for one single PR, consider splitting it up into multiple PRs by using git cherry-pick or git rebase -i so each PR only has the commits it wants to target.`); @@ -108,23 +109,16 @@ async function regenerateVersion( skipDownload: boolean, typeArgument?: "historical" | "dev", ): Promise { + const command = ["npm", "run", "gen-api", "--", "-p", pkgName, "-v", version]; + if (typeArgument) { + command.push(`--${typeArgument}`); + } + if (skipDownload) { + command.push("--skip-download"); + } + try { - const type = typeArgument ?? "latest"; - const minorVersion = parseMinorVersion(version)!; - const pkg = await Pkg.fromArgs(pkgName, version, minorVersion, type); - const artifactFolder = pkg.sphinxArtifactFolder(); - if (skipDownload && (await pathExists(`${artifactFolder}/artifact`))) { - console.log(`Skip downloading sources for ${pkgName}:${minorVersion}`); - } else { - await downloadSphinxArtifact(pkg, artifactFolder); - } - const markdownDir = pkg.apiOutputDir("docs"); - if (await pathExists(markdownDir)) await rmFilesInFolder(markdownDir); - const imagesDir = pkg.apiOutputDir("public/docs/images"); - if (await pathExists(imagesDir)) await rmFilesInFolder(imagesDir); - console.log(`Run pipeline for ${pkgName}:${minorVersion}`); - await runConversionPipeline(`${artifactFolder}/artifact`, "docs", "public/docs", pkg); - await generateHistoricalRedirects(); + await $`${command}`; if ((await gitStatus()) !== "") { await gitCommit(`Regenerate ${pkgName} ${version}`); console.log(`🚀 ${pkgName} ${version} regenerated correctly`); diff --git a/scripts/js/commands/api/syncDevDocs.ts b/scripts/js/commands/api/syncDevDocs.ts index e066c8f5b85..e7d17ce3e09 100644 --- a/scripts/js/commands/api/syncDevDocs.ts +++ b/scripts/js/commands/api/syncDevDocs.ts @@ -15,11 +15,12 @@ import fs from "fs/promises"; import { Pkg } from "../../lib/api/Pkg.js"; import { zxMain } from "../../lib/zx.js"; -import { readApiFullVersion, parseMinorVersion } from "../../lib/apiVersions.js"; -import { pathExists, rmFilesInFolder } from "../../lib/fs.js"; -import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; -import { runConversionPipeline } from "../../lib/api/conversionPipeline.js"; -import { generateHistoricalRedirects } from "./generateHistoricalRedirects.js"; +import { readApiFullVersion } from "../../lib/apiVersions.js"; +import { + generateVersion, + determineMinorVersion, + Arguments, +} from "./updateApiDocs.js"; // Found with `gh api repos/qiskit/${repo}/actions/workflows` const QISKIT_WORKFLOW_ID = "66225883"; @@ -100,16 +101,15 @@ async function updateHtmlArtifacts(args: { await fs.writeFile(path, JSON.stringify(prior, null, 2) + "\n"); } -async function regenDocs(pkgName: string, version: string): Promise { - const minorVersion = parseMinorVersion(version)!; - const pkg = await Pkg.fromArgs(pkgName, version, minorVersion, "dev"); - const artifactFolder = pkg.sphinxArtifactFolder(); - await downloadSphinxArtifact(pkg, artifactFolder); - const markdownDir = pkg.apiOutputDir("docs"); - if (await pathExists(markdownDir)) await rmFilesInFolder(markdownDir); - const imagesDir = pkg.apiOutputDir("public/docs/images"); - if (await pathExists(imagesDir)) await rmFilesInFolder(imagesDir); - console.log(`Run pipeline for ${pkgName}:${minorVersion}`); - await runConversionPipeline(`${artifactFolder}/artifact`, "docs", "public/docs", pkg); - await generateHistoricalRedirects(); +async function regenDocs(pkg: string, version: string): Promise { + const args: Arguments = { + package: pkg, + version: version, + dev: true, + historical: false, + skipDownload: false, + }; + const minorVersion = determineMinorVersion(args); + const pkgObj = await Pkg.fromArgs(pkg, version, minorVersion, "dev"); + await generateVersion(pkgObj, args); } diff --git a/scripts/js/commands/api/updateApiDocs.ts b/scripts/js/commands/api/updateApiDocs.ts new file mode 100644 index 00000000000..406fff3b9ea --- /dev/null +++ b/scripts/js/commands/api/updateApiDocs.ts @@ -0,0 +1,172 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2023. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import yargs from "yargs/yargs"; +import { hideBin } from "yargs/helpers"; + +import { Pkg } from "../../lib/api/Pkg.js"; +import { zxMain } from "../../lib/zx.js"; +import { parseMinorVersion, isValidVersion } from "../../lib/apiVersions.js"; +import { pathExists, rmFilesInFolder } from "../../lib/fs.js"; +import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; +import { runConversionPipeline } from "../../lib/api/conversionPipeline.js"; +import { generateHistoricalRedirects } from "./generateHistoricalRedirects.js"; + +export interface Arguments { + [x: string]: unknown; + package: string; + version: string; + historical: boolean; + dev: boolean; + skipDownload: boolean; + sphinxArtifactFolder?: string; +} + +const readArgs = (): Arguments => { + return yargs(hideBin(process.argv)) + .version(false) + .option("package", { + alias: "p", + type: "string", + choices: Pkg.VALID_NAMES, + demandOption: true, + description: "Which package to update", + }) + .option("version", { + alias: "v", + type: "string", + demandOption: true, + description: "The full version string of the --package, e.g. 0.44.0", + coerce: (version) => { + if (!isValidVersion(version)) { + throw new Error( + "The version must include a major, a minor, and a patch. E.g. `-v 0.46.3`", + ); + } + return version; + }, + }) + .option("historical", { + type: "boolean", + default: false, + description: "Is this a prior release?", + }) + .option("dev", { + type: "boolean", + default: false, + description: "Is this a dev release?", + }) + .option("skip-download", { + type: "boolean", + default: false, + description: + "Rather than downloading the artifact from Box, reuse what is already downloaded. This can save time, but it risks using an outdated version of the docs.", + }) + .option("sphinx-artifact-folder", { + alias: "a", + type: "string", + implies: "skip-download", + normalize: true, + description: + "Skip downloading the artifact from Box and instead use the given directory as the root of the Sphinx HTML output.", + }) + .parseSync(); +}; + +export async function generateVersion( + pkg: Pkg, + args: Arguments, +): Promise { + const sphinxArtifactFolder = await prepareSphinxFolder(pkg, args); + await deleteExistingFiles(pkg); + + console.log(`Run pipeline for ${pkg.name}:${pkg.versionWithoutPatch}`); + await runConversionPipeline(sphinxArtifactFolder, "docs", "public/docs", pkg); + await generateHistoricalRedirects(); +} + +export function determineMinorVersion(args: Arguments): string { + const minorVersion = parseMinorVersion(args.version); + if (minorVersion === null) { + throw new Error( + `Invalid --version. Expected the format 0.44.0, but received ${args.version}`, + ); + } + + const devRegex = /[0-9](rc|-dev)/; + if (args.dev && !args.version.match(devRegex)) { + throw new Error( + `${args.package} ${args.version} is not a correct dev version. Please make sure the version has one of the following suffixes immediately following the patch version: rc, -dev. e.g. 1.0.0rc1 or 1.0.0-dev`, + ); + } + + return minorVersion; +} + +async function prepareSphinxFolder(pkg: Pkg, args: Arguments): Promise { + if (args.sphinxArtifactFolder) { + if (!(await pathExists(args.sphinxArtifactFolder))) { + throw new Error( + `Explicit artifact path '${args.sphinxArtifactFolder}' does not exist.`, + ); + } + return args.sphinxArtifactFolder; + } + const sphinxArtifactFolder = pkg.sphinxArtifactFolder(); + if ( + args.skipDownload && + (await pathExists(`${sphinxArtifactFolder}/artifact`)) + ) { + console.log( + `Skip downloading sources for ${pkg.name}:${pkg.versionWithoutPatch}`, + ); + } else { + await downloadSphinxArtifact(pkg, sphinxArtifactFolder); + } + return `${sphinxArtifactFolder}/artifact`; +} + +async function deleteExistingFiles(pkg: Pkg): Promise { + const markdownDir = pkg.apiOutputDir("docs"); + if (await pathExists(markdownDir)) { + await rmFilesInFolder(markdownDir); + } + const imagesDir = pkg.apiOutputDir("public/docs/images"); + if (await pathExists(imagesDir)) { + await rmFilesInFolder(imagesDir); + } + console.log( + `Deleted existing markdown & images for ${pkg.name}:${pkg.versionWithoutPatch}`, + ); +} + +if (import.meta.url === `file://${process.argv[1]}`) { + zxMain(async () => { + const args = readArgs(); + + if (args.historical && args.dev) { + throw new Error( + `${args.package} ${args.version} cannot be historical and dev at the same time. Please remove at least only one of these two arguments: --historical, --dev.`, + ); + } + + const minorVersion = determineMinorVersion(args); + const type = args.historical ? "historical" : args.dev ? "dev" : "latest"; + const pkg = await Pkg.fromArgs( + args.package, + args.version, + minorVersion, + type, + ); + await generateVersion(pkg, args); + }); +} diff --git a/scripts/js/commands/updateDocs.ts b/scripts/js/commands/updateDocs.ts index 48b915eb306..102ef50b63c 100644 --- a/scripts/js/commands/updateDocs.ts +++ b/scripts/js/commands/updateDocs.ts @@ -18,11 +18,9 @@ import { $ } from "zx"; import { mkdirp } from "mkdirp"; import { Pkg } from "../lib/api/Pkg.js"; -import { pathExists, rmFilesInFolder } from "../lib/fs.js"; +import { pathExists } from "../lib/fs.js"; import { downloadSphinxArtifact } from "../lib/api/sphinxArtifacts.js"; -import { runConversionPipeline } from "../lib/api/conversionPipeline.js"; import { runSphinxPipeline } from "../lib/sphinx/conversionPipeline.js"; - import { zxMain } from "../lib/zx.js"; import { isValidVersion, parseMinorVersion } from "../lib/apiVersions.js"; @@ -76,21 +74,15 @@ const readArgs = (): Arguments => { async function generateVersion(pkg: Pkg, args: Arguments): Promise { const sphinxArtifactFolder = await prepareSphinxFolder(pkg, args); - - await deleteExistingApiFiles(pkg); + await deleteExistingFiles(pkg); console.log(`Run pipeline for ${pkg.name}:${pkg.versionWithoutPatch}`); - await runConversionPipeline(sphinxArtifactFolder, "docs", "public/docs", pkg); - - if (pkg.isAddon()) { - await deleteExistingAddonFiles(pkg); - await runSphinxPipeline( - sphinxArtifactFolder, - "docs/addons", - "public/docs", - pkg, - ); - } + await runSphinxPipeline( + sphinxArtifactFolder, + "docs/addons", + "public/docs", + pkg, + ); } async function prepareSphinxFolder(pkg: Pkg, args: Arguments): Promise { @@ -116,21 +108,7 @@ async function prepareSphinxFolder(pkg: Pkg, args: Arguments): Promise { return `${sphinxArtifactFolder}/artifact`; } -async function deleteExistingApiFiles(pkg: Pkg): Promise { - const markdownDir = pkg.apiOutputDir("docs"); - if (await pathExists(markdownDir)) { - await rmFilesInFolder(markdownDir); - } - const imagesDir = pkg.apiOutputDir("public/docs/images"); - if (await pathExists(imagesDir)) { - await rmFilesInFolder(imagesDir); - } - console.log( - `Deleted existing docs & images for ${pkg.name}:${pkg.versionWithoutPatch}`, - ); -} - -async function deleteExistingAddonFiles(pkg: Pkg): Promise { +async function deleteExistingFiles(pkg: Pkg): Promise { const markdownDir = pkg.outputDir("docs/addons"); if (await pathExists(markdownDir)) { // Preserve the hand-authored _toc.json. @@ -149,7 +127,7 @@ async function deleteExistingAddonFiles(pkg: Pkg): Promise { await $`rm -rf ${imagesDir}`; } console.log( - `Deleted existing docs & images for ${pkg.name}:${pkg.versionWithoutPatch}`, + `Deleted existing markdown & images for ${pkg.name}:${pkg.versionWithoutPatch}`, ); } From 83dfdf45ab8f63b200a6cb10bea4142036455fc2 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Tue, 12 May 2026 18:08:13 -0400 Subject: [PATCH 028/104] Refactor docs generation pipeline Extract shared pipeline stages (convertHtmlToMarkdown, postProcess, writeMarkdownResults, copyImages) into lib/api/pipelineStages.ts and notebook stages into lib/api/notebookStages.ts so both pipelines compose from a single source instead of duplicating the same logic. Add lib/api/addonDocsPipeline.ts (new, replaces lib/sphinx/conversionPipeline.ts) and lib/api/apiDocsPipeline.ts (thin wrapper over existing conversionPipeline.ts) so each pipeline has an unambiguous entry point. Parameterize saveImages() to take a destFolder argument instead of computing it internally. This fixes a bug where addon images were written to public/docs/images/api/{pkg}/ but deleteExistingFiles cleaned public/docs/images/addons/{pkg}/. Add a smoke snapshot test (lib/api/addonDocsPipeline.test.ts) with a minimal Sphinx fixture that asserts the addon pipeline never writes to docs/addons/api/**, that images land under public/docs/images/addons/**, and that output content matches snapshots. Rename gen-addon-docs -> gen-addons and commands/updateDocs.ts -> commands/updateAddonDocs.ts. Co-Authored-By: Claude Sonnet 4.6 --- package.json | 2 +- scripts/js/commands/api/updateApiDocs.ts | 4 +- .../{updateDocs.ts => updateAddonDocs.ts} | 8 +- scripts/js/lib/api/addonDocsPipeline.test.ts | 107 +++++ .../addonDocsPipeline.test.ts-snapshots/foo | 13 + .../addonDocsPipeline.test.ts-snapshots/index | 11 + .../install | 9 + .../addonDocsPipeline.test.ts-snapshots/intro | 37 ++ scripts/js/lib/api/addonDocsPipeline.ts | 135 +++++++ scripts/js/lib/api/apiDocsPipeline.ts | 120 ++++++ scripts/js/lib/api/conversionPipeline.ts | 7 +- scripts/js/lib/api/notebookStages.ts | 143 +++++++ scripts/js/lib/api/paths.ts | 17 + scripts/js/lib/api/pipelineStages.ts | 246 ++++++++++++ scripts/js/lib/api/saveImages.ts | 8 +- .../api/qiskit-addon-other/objects.inv | Bin 0 -> 165 bytes .../_images/smoke-diagram.png | Bin 0 -> 69 bytes .../qiskit-addon-smoke/how_tos/foo.html | 18 + .../testdata/qiskit-addon-smoke/index.html | 17 + .../testdata/qiskit-addon-smoke/install.html | 16 + .../testdata/qiskit-addon-smoke/objects.inv | Bin 0 -> 182 bytes .../qiskit-addon-smoke/tutorials/intro.ipynb | 31 ++ scripts/js/lib/sphinx/conversionPipeline.ts | 378 ------------------ 23 files changed, 938 insertions(+), 389 deletions(-) rename scripts/js/commands/{updateDocs.ts => updateAddonDocs.ts} (95%) create mode 100644 scripts/js/lib/api/addonDocsPipeline.test.ts create mode 100644 scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/foo create mode 100644 scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index create mode 100644 scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/install create mode 100644 scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/intro create mode 100644 scripts/js/lib/api/addonDocsPipeline.ts create mode 100644 scripts/js/lib/api/apiDocsPipeline.ts create mode 100644 scripts/js/lib/api/notebookStages.ts create mode 100644 scripts/js/lib/api/paths.ts create mode 100644 scripts/js/lib/api/pipelineStages.ts create mode 100644 scripts/js/lib/api/testdata/qiskit-addon-smoke-publishedapis/api/qiskit-addon-other/objects.inv create mode 100644 scripts/js/lib/api/testdata/qiskit-addon-smoke/_images/smoke-diagram.png create mode 100644 scripts/js/lib/api/testdata/qiskit-addon-smoke/how_tos/foo.html create mode 100644 scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html create mode 100644 scripts/js/lib/api/testdata/qiskit-addon-smoke/install.html create mode 100644 scripts/js/lib/api/testdata/qiskit-addon-smoke/objects.inv create mode 100644 scripts/js/lib/api/testdata/qiskit-addon-smoke/tutorials/intro.ipynb delete mode 100644 scripts/js/lib/sphinx/conversionPipeline.ts diff --git a/package.json b/package.json index 860f4cb498b..c5e87e16a36 100644 --- a/package.json +++ b/package.json @@ -32,7 +32,7 @@ "check:qiskit-versions": "tsx scripts/js/commands/checkQiskitApiVersions.ts", "regen-apis": "tsx scripts/js/commands/api/regenerateApiDocs.ts", "gen-api": "tsx scripts/js/commands/api/updateApiDocs.ts", - "gen-addon-docs": "tsx scripts/js/commands/updateDocs.ts", + "gen-addons": "tsx scripts/js/commands/updateAddonDocs.ts", "generate-historical-redirects": "tsx scripts/js/commands/api/generateHistoricalRedirects.ts", "save-internal-links": "tsx scripts/js/commands/saveInternalLinks.ts" }, diff --git a/scripts/js/commands/api/updateApiDocs.ts b/scripts/js/commands/api/updateApiDocs.ts index 406fff3b9ea..013e9d0f9ba 100644 --- a/scripts/js/commands/api/updateApiDocs.ts +++ b/scripts/js/commands/api/updateApiDocs.ts @@ -18,7 +18,7 @@ import { zxMain } from "../../lib/zx.js"; import { parseMinorVersion, isValidVersion } from "../../lib/apiVersions.js"; import { pathExists, rmFilesInFolder } from "../../lib/fs.js"; import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; -import { runConversionPipeline } from "../../lib/api/conversionPipeline.js"; +import { runApiDocsPipeline } from "../../lib/api/apiDocsPipeline.js"; import { generateHistoricalRedirects } from "./generateHistoricalRedirects.js"; export interface Arguments { @@ -90,7 +90,7 @@ export async function generateVersion( await deleteExistingFiles(pkg); console.log(`Run pipeline for ${pkg.name}:${pkg.versionWithoutPatch}`); - await runConversionPipeline(sphinxArtifactFolder, "docs", "public/docs", pkg); + await runApiDocsPipeline(sphinxArtifactFolder, "docs", "public/docs", pkg); await generateHistoricalRedirects(); } diff --git a/scripts/js/commands/updateDocs.ts b/scripts/js/commands/updateAddonDocs.ts similarity index 95% rename from scripts/js/commands/updateDocs.ts rename to scripts/js/commands/updateAddonDocs.ts index 102ef50b63c..431e43cfa3f 100644 --- a/scripts/js/commands/updateDocs.ts +++ b/scripts/js/commands/updateAddonDocs.ts @@ -17,10 +17,10 @@ import { readFile, writeFile } from "fs/promises"; import { $ } from "zx"; import { mkdirp } from "mkdirp"; -import { Pkg } from "../lib/api/Pkg.js"; +import { Pkg } from "../lib/docsGen/Pkg.js"; import { pathExists } from "../lib/fs.js"; -import { downloadSphinxArtifact } from "../lib/api/sphinxArtifacts.js"; -import { runSphinxPipeline } from "../lib/sphinx/conversionPipeline.js"; +import { downloadSphinxArtifact } from "../lib/docsGen/sphinxArtifacts.js"; +import { runAddonDocsPipeline } from "../lib/docsGen/addonDocsPipeline.js"; import { zxMain } from "../lib/zx.js"; import { isValidVersion, parseMinorVersion } from "../lib/apiVersions.js"; @@ -77,7 +77,7 @@ async function generateVersion(pkg: Pkg, args: Arguments): Promise { await deleteExistingFiles(pkg); console.log(`Run pipeline for ${pkg.name}:${pkg.versionWithoutPatch}`); - await runSphinxPipeline( + await runAddonDocsPipeline( sphinxArtifactFolder, "docs/addons", "public/docs", diff --git a/scripts/js/lib/api/addonDocsPipeline.test.ts b/scripts/js/lib/api/addonDocsPipeline.test.ts new file mode 100644 index 00000000000..8895ab6f812 --- /dev/null +++ b/scripts/js/lib/api/addonDocsPipeline.test.ts @@ -0,0 +1,107 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2026. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import os from "os"; +import path from "path"; +import { mkdtemp, readFile, mkdir, copyFile } from "fs/promises"; + +import { globby } from "globby"; +import { expect, test } from "@playwright/test"; + +import { runAddonDocsPipeline } from "./addonDocsPipeline.js"; +import { Pkg, ReleaseNotesConfig } from "./Pkg.js"; + +// Snapshot test for the addon non-API docs pipeline. If output changes +// intentionally, run `npm test -- --updateSnapshot`. +// +// The fixture under `testdata/qiskit-addon-smoke/` covers the pipeline's key +// concerns: article extraction, relative-apidocs link rewriting, cross-package +// stub resolution via `ObjectsInv.loadPublishedApis`, notebook processing, and +// image routing to `public/docs/images/addons/{pkg}/`. + +const FIXTURE_DIR = "scripts/js/lib/api/testdata/qiskit-addon-smoke"; +const PUBLISHED_APIS_SEED = + "scripts/js/lib/api/testdata/qiskit-addon-smoke-publishedapis"; + +test("qiskit-addon-smoke addon docs pipeline", async ({}, testInfo) => { + testInfo.snapshotSuffix = ""; + + const tmpDir = await mkdtemp(path.join(os.tmpdir(), "addon-smoke-")); + const docsBaseFolder = path.join(tmpDir, "docs", "addons"); + const publicBaseFolder = path.join(tmpDir, "public", "docs"); + + // Seed public/docs/api//objects.inv so that loadPublishedApis() + // finds a sibling package's inventory for cross-package stub resolution. + const seededInvDir = path.join( + publicBaseFolder, + "api", + "qiskit-addon-other", + ); + await mkdir(seededInvDir, { recursive: true }); + await copyFile( + path.join(PUBLISHED_APIS_SEED, "api", "qiskit-addon-other", "objects.inv"), + path.join(seededInvDir, "objects.inv"), + ); + + const pkg = new Pkg({ + name: "qiskit-addon-smoke", + title: "Qiskit Addon Smoke", + githubSlug: "Qiskit/qiskit-addon-smoke", + version: "0.0.1", + versionWithoutPatch: "0.0", + type: "latest", + language: "Python", + releaseNotesConfig: new ReleaseNotesConfig({ enabled: false }), + kebabCaseAndShortenUrls: true, + }); + + await runAddonDocsPipeline(FIXTURE_DIR, docsBaseFolder, publicBaseFolder, pkg); + + const markdownFolder = pkg.outputDir(docsBaseFolder); + + // Invariant: the addon pipeline must never write API-shaped output under its + // own docs root. Guards against the class of bug that produced the stray + // `docs/addons/api/qiskit-addon-obp/` directory in the repo. + const strayUnderAddonsApi = await globby([`${docsBaseFolder}/api/**`]); + expect( + strayUnderAddonsApi, + "addon pipeline must not write to docs/addons/api/**", + ).toEqual([]); + + // Invariant: addon images must land under `public/docs/images/addons/{pkg}/`, + // not under `public/docs/images/api/{pkg}/`. The inverse was the bug before + // `saveImages` was parameterized. + const imagesUnderApi = await globby([`${publicBaseFolder}/images/api/**`]); + expect( + imagesUnderApi, + "addon-run images must not land under public/docs/images/api/**", + ).toEqual([]); + + const imagesUnderAddons = await globby([ + `${publicBaseFolder}/images/addons/**`, + ]); + expect( + imagesUnderAddons.length, + "addon-run images must land under public/docs/images/addons/**", + ).toBeGreaterThan(0); + + // --- Snapshot of every generated file under the addon output folder --- + + const resultFiles = await globby([`${markdownFolder}/**`]); + expect(resultFiles.length, "pipeline should generate at least one file") + .toBeGreaterThan(0); + for (const file of resultFiles) { + const contents = await readFile(file, "utf-8"); + const fileName = path.parse(file).name; + expect(contents).toMatchSnapshot(fileName); + } +}); diff --git a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/foo b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/foo new file mode 100644 index 00000000000..4ff196f3863 --- /dev/null +++ b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/foo @@ -0,0 +1,13 @@ +--- +title: "How to foo - smoke 0.0" +description: "Demonstrates apidocs link rewriting and stub resolution." +--- + +# How to foo + +This how-to references the [API docs index](/docs/api/qiskit-addon-smoke/index) and the [do\_thing](/docs/api/qiskit-addon-smoke/smoke-do-thing) symbol from the same package. + +It also links into another package via [other\_thing](/docs/api/qiskit-addon-other/other_thing). + +![A reused smoke diagram](/docs/images/addons/qiskit-addon-smoke/smoke-diagram.avif) + diff --git a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index new file mode 100644 index 00000000000..0b72155a71d --- /dev/null +++ b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index @@ -0,0 +1,11 @@ +--- +title: "Qiskit Addon Smoke - smoke 0.0" +description: "Smoke-test fixture for the addon docs pipeline." +--- + +# Qiskit Addon Smoke + +This is the smoke-test landing page. See the [install instructions](install) or the [first how-to](how-tos/foo). + +![A smoke test diagram](/docs/images/addons/qiskit-addon-smoke/smoke-diagram.avif) + diff --git a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/install b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/install new file mode 100644 index 00000000000..f137aafa56c --- /dev/null +++ b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/install @@ -0,0 +1,9 @@ +--- +title: "Installation - smoke 0.0" +description: "How to install the smoke addon." +--- + +# Installation + +Install with `pip`. Back to the [home page](index). + diff --git a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/intro b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/intro new file mode 100644 index 00000000000..3383f8e629f --- /dev/null +++ b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/intro @@ -0,0 +1,37 @@ +{ + "cells": [ + { + "id": "frontmatter", + "cell_type": "markdown", + "source": "---\ntitle: \"Intro tutorial\"\n---", + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Intro tutorial\n", + "\n", + "This notebook references [do_thing](/docs/api/qiskit-addon-smoke/smoke-do-thing#smoke.do_thing) from the current package and [other_thing](/docs/api/qiskit-addon-other/other_thing) from a sibling package.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('hello from the smoke tutorial')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/scripts/js/lib/api/addonDocsPipeline.ts b/scripts/js/lib/api/addonDocsPipeline.ts new file mode 100644 index 00000000000..2cbbea5f36e --- /dev/null +++ b/scripts/js/lib/api/addonDocsPipeline.ts @@ -0,0 +1,135 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { globby } from "globby"; +import { mkdirp } from "mkdirp"; + +import { ObjectsInv } from "./objectsInv.js"; +import { Pkg } from "./Pkg.js"; +import { + convertHtmlToMarkdown, + copyImages, + postProcess, + writeMarkdownResults, +} from "./pipelineStages.js"; +import { + processNotebooks, + readNotebooks, + writeNotebooks, +} from "./notebookStages.js"; +import { DOCS_BASE_PATH } from "./paths.js"; + +// Sphinx build artifacts that are never content. +const SPHINX_INTERNALS = [ + "_static/**", + "_sources/**", + "_downloads/**", + "_modules/**", + "genindex.html", + "py-modindex.html", + "search.html", + "objects.inv", +]; + +export async function runAddonDocsPipeline( + artifactPath: string, + docsBaseFolder: string, + publicBaseFolder: string, + pkg: Pkg, +): Promise { + const allObjectInvs = await ObjectsInv.loadPublishedApis(publicBaseFolder); + const [files, outputPath, objectsInv] = await determineFilePaths( + artifactPath, + docsBaseFolder, + pkg, + ); + + // handle HTML files + const htmlFiles = files.filter((f) => f.endsWith(".html")); + const initialResults = await convertHtmlToMarkdown( + pkg, + artifactPath, + docsBaseFolder, + outputPath, + htmlFiles, + pkg.outputDir(`${DOCS_BASE_PATH}/images/addons`), + "html-meta", + ); + const results = await postProcess(pkg, initialResults, { + rewriteApidocsLinks: true, + objectsInv, + allInvs: allObjectInvs, + frontMatter: "html-meta", + }); + await writeMarkdownResults(pkg, docsBaseFolder, results); + + // handle Jupyter notebook files + const notebookFiles = files.filter((f) => f.endsWith(".ipynb")); + const initialNotebooks = await readNotebooks( + artifactPath, + docsBaseFolder, + outputPath, + notebookFiles, + ); + const notebooks = processNotebooks( + initialNotebooks, + objectsInv, + allObjectInvs, + ); + await writeNotebooks(pkg, docsBaseFolder, notebooks); + + // write assets + await copyImages( + pkg, + artifactPath, + pkg.outputDir(`${publicBaseFolder}/images/addons`), + results, + ); + // objects.inv is the package's canonical symbol inventory. It lives under + // public/docs/api/{pkg}/ regardless of pipeline — loadPublishedApis, + // checkInternalLinks, and lib/links/ignores.ts all expect it there. + await objectsInv.write(pkg.apiOutputDir(publicBaseFolder)); +} + +async function determineFilePaths( + htmlPath: string, + docsBaseFolder: string, + pkg: Pkg, +): Promise<[string[], string, ObjectsInv]> { + const objectsInv = await ObjectsInv.fromFile(htmlPath, pkg.language); + + const allFiles = await globby(["**"], { + cwd: htmlPath, + ignore: [ + "apidocs/**", + "apidoc/**", + "stubs/**", + "release-notes.html", + "release_notes.html", + ...SPHINX_INTERNALS, + ], + }); + + // Prefer .ipynb over .html when both exist for the same base path. + const notebookBases = new Set( + allFiles + .filter((f) => f.endsWith(".ipynb")) + .map((f) => f.slice(0, -".ipynb".length)), + ); + const files = allFiles.filter( + (f) => + !f.endsWith(".html") || !notebookBases.has(f.slice(0, -".html".length)), + ); + const outputPath = pkg.outputDir(docsBaseFolder); + await mkdirp(outputPath); + return [files, outputPath, objectsInv]; +} diff --git a/scripts/js/lib/api/apiDocsPipeline.ts b/scripts/js/lib/api/apiDocsPipeline.ts new file mode 100644 index 00000000000..3fffda28ed7 --- /dev/null +++ b/scripts/js/lib/api/apiDocsPipeline.ts @@ -0,0 +1,120 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2024. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { writeFile } from "fs/promises"; + +import { mkdirp } from "mkdirp"; +import { globby } from "globby"; + +import { HtmlToMdResultWithUrl } from "./HtmlToMdResult.js"; +import { ObjectsInv } from "./objectsInv.js"; +import { Pkg } from "./Pkg.js"; +import { generateToc } from "./generateToc.js"; +import { maybeUpdateReleaseNotesFolder } from "./releaseNotes.js"; +import { + convertHtmlToMarkdown, + copyImages, + postProcess, + writeMarkdownResults, +} from "./pipelineStages.js"; +import { C_API_BASE_PATH, DOCS_BASE_PATH } from "./paths.js"; + +export async function runApiDocsPipeline( + htmlPath: string, + docsBaseFolder: string, + publicBaseFolder: string, + pkg: Pkg, +) { + const [files, markdownPath, maybeObjectsInv] = await determineFilePaths( + htmlPath, + docsBaseFolder, + pkg, + ); + + const initialResults = await convertHtmlToMarkdown( + pkg, + htmlPath, + docsBaseFolder, + markdownPath, + files, + pkg.apiOutputDir(`${DOCS_BASE_PATH}/images`), + "api", + ); + + const results = await postProcess(pkg, initialResults, { + rewriteApidocsLinks: false, + objectsInv: maybeObjectsInv, + frontMatter: "api", + }); + + // Warning: the sequence of operations often matters. + await writeMarkdownResults(pkg, docsBaseFolder, results); + // `publicBaseFolder` is passed as "public/docs"; images go under + // "public/docs/images/api/{pkg}". See also maybeObjectsInv.write below which + // uses pkg.apiOutputDir(publicBaseFolder) — the two outputs share the same + // `api/{pkg}` subtree inside publicBaseFolder. + await copyImages( + pkg, + htmlPath, + pkg.apiOutputDir(`${publicBaseFolder}/images`), + results, + ); + await maybeObjectsInv?.write(pkg.apiOutputDir(publicBaseFolder)); + await maybeUpdateReleaseNotesFolder(pkg, markdownPath); + await writeTocFile(pkg, markdownPath, results); + await writeVersionFile(pkg, markdownPath); +} + +async function determineFilePaths( + htmlPath: string, + docsBaseFolder: string, + pkg: Pkg, +): Promise<[string[], string, ObjectsInv | undefined]> { + const maybeObjectsInv = await (pkg.isProblematicLegacyQiskit() + ? undefined + : ObjectsInv.fromFile(htmlPath, pkg.language)); + + const extraFiles = pkg.isCApi() + ? [`${C_API_BASE_PATH}/**.html`] + : ["apidocs/**.html", "apidoc/**.html", "stubs/**.html"]; + const files = await globby( + [...extraFiles, "release_notes.html", "release-notes.html"], + { + cwd: htmlPath, + }, + ); + const markdownPath = pkg.apiOutputDir(docsBaseFolder); + await mkdirp(markdownPath); + return [files, markdownPath, maybeObjectsInv]; +} + +async function writeTocFile( + pkg: Pkg, + markdownPath: string, + results: HtmlToMdResultWithUrl[], +): Promise { + console.log("Generating toc"); + const toc = generateToc(pkg, results); + await writeFile( + `${markdownPath}/_toc.json`, + JSON.stringify(toc, null, 2) + "\n", + ); +} + +async function writeVersionFile(pkg: Pkg, markdownPath: string): Promise { + console.log("Generating version file"); + const pkg_json = { name: pkg.name, version: pkg.version }; + await writeFile( + `${markdownPath}/_package.json`, + JSON.stringify(pkg_json, null, 2) + "\n", + ); +} diff --git a/scripts/js/lib/api/conversionPipeline.ts b/scripts/js/lib/api/conversionPipeline.ts index 034bf951b51..f3f17786434 100644 --- a/scripts/js/lib/api/conversionPipeline.ts +++ b/scripts/js/lib/api/conversionPipeline.ts @@ -142,7 +142,12 @@ async function copyImages( results.flatMap((result) => result.images), (image) => image.fileName, ); - await saveImages(allImages, `${htmlPath}/_images`, publicBaseFolder, pkg); + await saveImages( + allImages, + `${htmlPath}/_images`, + pkg.apiOutputDir(`${publicBaseFolder}/images`), + pkg, + ); } async function postProcessResults( diff --git a/scripts/js/lib/api/notebookStages.ts b/scripts/js/lib/api/notebookStages.ts new file mode 100644 index 00000000000..3f168c56eed --- /dev/null +++ b/scripts/js/lib/api/notebookStages.ts @@ -0,0 +1,143 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2026. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +// Jupyter notebook stages used by the addon docs pipeline. The API pipeline +// does not process notebooks today, but these stages live alongside the other +// shared stages so that any future pipeline needing notebook handling can +// reuse them without duplication. + +import { dirname, parse, relative } from "path"; +import { readFile, writeFile } from "fs/promises"; + +import { mkdirp } from "mkdirp"; +import { visit, EXIT } from "unist-util-visit"; + +import { ObjectsInv } from "./objectsInv.js"; +import { Pkg } from "./Pkg.js"; +import { kebabCaseAndShortenPage } from "./normalizeResultUrls.js"; +import { relativizeLink } from "./updateLinks.js"; +import { transformSpecialCaseUrl } from "./specialCaseResults.js"; +import { parseMarkdown, extractHeadingText } from "../markdownUtils.js"; +import { NotebookCell, NotebookWithUrl } from "../Notebooks.js"; + +export async function readNotebooks( + artifactPath: string, + docsBaseFolder: string, + outputPath: string, + filePaths: string[], +): Promise { + const results: NotebookWithUrl[] = []; + for (const file of filePaths) { + const raw = await readFile(`${artifactPath}/${file}`, "utf-8"); + const notebook = JSON.parse(raw); + const { dir, name } = parse(`${outputPath}/${file}`); + const url = `/${relative(docsBaseFolder, dir)}/${name}`; + results.push({ ...notebook, url }); + } + return results; +} + +/** + * Rewrite markdown-cell links in each notebook: relativize old doc URLs and + * resolve `qiskit.github.io/{pkg}/stubs/...` links via the published-API + * inventories. Then prepend a frontmatter cell with a title extracted from + * the first markdown h1. + */ +export function processNotebooks( + notebooks: NotebookWithUrl[], + objectsInv: ObjectsInv, + allInvs: Map, +): NotebookWithUrl[] { + return notebooks.map((notebook) => { + const processedCells = notebook.cells.map((cell) => { + if (cell.cell_type !== "markdown") return cell; + return { + ...cell, + source: rewriteNotebookLinks(cell.source, objectsInv, allInvs), + }; + }); + + const frontmatterCell = buildFrontmatterCell(processedCells); + return { + ...notebook, + cells: frontmatterCell + ? [frontmatterCell, ...processedCells] + : processedCells, + }; + }); +} + +export async function writeNotebooks( + pkg: Pkg, + docsBaseFolder: string, + notebooks: NotebookWithUrl[], +): Promise { + for (const { url, ...notebook } of notebooks) { + const normalizedUrl = normalizeNotebookUrl(url, pkg); + const path = `${docsBaseFolder}${normalizedUrl}.ipynb`; + await mkdirp(dirname(path)); + await writeFile(path, JSON.stringify(notebook, null, 1)); + } +} + +function normalizeNotebookUrl(url: string, pkg: Pkg): string { + const parts = url.split("/"); + const filename = parts[parts.length - 1]; + const normalized = pkg.kebabCaseAndShortenUrls + ? kebabCaseAndShortenPage(filename, pkg.name) + : filename; + return transformSpecialCaseUrl([...parts.slice(0, -1), normalized].join("/")); +} + +function rewriteNotebookLinks( + source: string | string[], + objectsInv: ObjectsInv, + allInvs: Map, +): string | string[] { + const rewrite = (line: string) => { + return line.replace(/\[([^\]]*)\]\(([^)]+)\)/g, (_match, text, url) => { + const relativized = relativizeLink({ url, text }); + if (relativized) url = relativized.url; + const stub = objectsInv.resolveStubUrl(url, allInvs); + if (stub) url = stub; + return `[${text}](${url})`; + }); + }; + + return Array.isArray(source) ? source.map(rewrite) : rewrite(source); +} + +function buildFrontmatterCell(cells: NotebookCell[]): NotebookCell | undefined { + for (const cell of cells) { + if (cell.cell_type !== "markdown") continue; + const text = Array.isArray(cell.source) + ? cell.source.join("") + : cell.source; + const tree = parseMarkdown(text); + let title: string | undefined; + visit(tree, "heading", (node: any) => { + if (node.depth === 1 && !title) { + title = extractHeadingText(node).trim(); + return EXIT; + } + }); + if (title) { + return { + id: "frontmatter", // hardcoded so the id doesn't change across runs + cell_type: "markdown", + source: `---\ntitle: "${title}"\n---`, + metadata: {}, + }; + } + } + return undefined; +} diff --git a/scripts/js/lib/api/paths.ts b/scripts/js/lib/api/paths.ts new file mode 100644 index 00000000000..09929108005 --- /dev/null +++ b/scripts/js/lib/api/paths.ts @@ -0,0 +1,17 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2026. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +// Base path under which all generated docs live on the site. +export const DOCS_BASE_PATH = "/docs"; + +// Folder in the Sphinx artifact that contains all C API docs. +export const C_API_BASE_PATH = "cdoc" as const; diff --git a/scripts/js/lib/api/pipelineStages.ts b/scripts/js/lib/api/pipelineStages.ts new file mode 100644 index 00000000000..e34460539b5 --- /dev/null +++ b/scripts/js/lib/api/pipelineStages.ts @@ -0,0 +1,246 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2026. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +// Stages shared between the API docs pipeline (apiDocsPipeline.ts) and the +// addon docs pipeline (addonDocsPipeline.ts). Each pipeline orchestrates these +// stages differently; shared behavior lives here so the two pipelines do not +// drift. + +import { dirname, join, parse, relative } from "path"; +import { readFile, writeFile } from "fs/promises"; + +import { load } from "cheerio"; +import { mkdirp } from "mkdirp"; +import { uniqBy } from "lodash-es"; + +import { HtmlToMdResultWithUrl } from "./HtmlToMdResult.js"; +import { ObjectsInv } from "./objectsInv.js"; +import { Pkg } from "./Pkg.js"; +import addFrontMatter from "./addFrontMatter.js"; +import { dedupeHtmlIdsFromResults } from "./dedupeHtmlIds.js"; +import { handleReleaseNotesFile } from "./releaseNotes.js"; +import { mergeClassMembers } from "./mergeClassMembers.js"; +import { normalizeResultUrls } from "./normalizeResultUrls.js"; +import { DOCS_BASE_PATH } from "./paths.js"; +import removeMathBlocksIndentation from "./removeMathBlocksIndentation.js"; +import { saveImages } from "./saveImages.js"; +import { specialCaseResults } from "./specialCaseResults.js"; +import { sphinxHtmlToMarkdown } from "./htmlToMd.js"; +import { updateLinks } from "./updateLinks.js"; + +// --------------------------------------------------------------------------- +// HTML → markdown conversion +// --------------------------------------------------------------------------- + +export type FrontMatterSource = "api" | "html-meta"; + +/** + * Convert each HTML file to a markdown result with an internal URL. + * + * `imageDestination` is the URL prefix embedded in the rendered `` of + * each page (e.g. `/docs/images/api/qiskit` or `/docs/images/addons/qiskit-addon-obp`). + * Each pipeline chooses the prefix appropriate to where it will copy images. + * + * When `frontMatterSource === "html-meta"`, the result's metadata is annotated + * with `hardcodedFrontmatter` extracted from the HTML `` and + * `<meta name="description">` tags. This feeds the addon pipeline's frontmatter + * writer, which emits YAML directly from these values (addon guides/tutorials + * have no Sphinx-generated metadata to draw from). + */ +export async function convertHtmlToMarkdown( + pkg: Pkg, + artifactPath: string, + docsBaseFolder: string, + outputPath: string, + filePaths: string[], + imageDestination: string, + frontMatterSource: FrontMatterSource, +): Promise<HtmlToMdResultWithUrl[]> { + const results: HtmlToMdResultWithUrl[] = []; + for (const file of filePaths) { + const html = await readFile(join(artifactPath, file), "utf-8"); + const result = await sphinxHtmlToMarkdown({ + html, + fileName: file, + determineGithubUrl: pkg.determineGithubUrlFn(), + imageDestination, + releaseNotesTitle: pkg.releaseNotesTitle(), + hasSeparateReleaseNotes: pkg.hasSeparateReleaseNotes(), + isCApi: pkg.isCApi(), + hasRootNamespaceFile: pkg.hasRootNamespaceFile, + }); + + // Skip empty markdown (HTML redirects, etc.). + if (result.markdown == "") continue; + + if (frontMatterSource === "html-meta") { + result.meta.hardcodedFrontmatter = extractHtmlFrontmatter(html); + } + + const { dir, name } = parse(`${outputPath}/${file}`); + const url = `/${relative(docsBaseFolder, dir)}/${name}`; + results.push({ ...result, url }); + } + return results; +} + +function extractHtmlFrontmatter(html: string): string { + const $ = load(html); + const title = $("title").first().text().trim(); + const description = $('meta[name="description"]').attr("content")?.trim(); + const lines = [`title: "${title}"`]; + if (description) lines.push(`description: "${description}"`); + return lines.join("\n"); +} + +// --------------------------------------------------------------------------- +// Post-processing (runs on markdown results after HTML conversion) +// --------------------------------------------------------------------------- + +export interface PostProcessOptions { + /** + * When true, rewrite markdown links of the form `](apidocs/foo)` (or + * `apidoc/`, `stubs/`) into absolute `](/docs/api/{pkg}/foo)` paths before + * running updateLinks. Used by the addon pipeline where non-API guides + * reference API pages via the Sphinx artifact's relative `apidocs/...` + * paths. + */ + rewriteApidocsLinks: boolean; + + /** + * Same-package inventory. May be undefined for packages where we + * intentionally skip loading the inventory (e.g. problematic legacy Qiskit). + */ + objectsInv: ObjectsInv | undefined; + + /** + * Cross-package inventories for resolving `qiskit.github.io/{pkg}/stubs/...` + * links to other packages' API docs. Used by the addon pipeline; pass + * undefined for pure API runs. + */ + allInvs?: Map<string, ObjectsInv>; + + /** Where the frontmatter should come from when `addFrontMatter` runs. */ + frontMatter: FrontMatterSource; +} + +/** + * Apply the shared post-processing pipeline to a set of results. + * Order is load-bearing — both pipelines call the stages in this sequence. + */ +export async function postProcess( + pkg: Pkg, + initialResults: HtmlToMdResultWithUrl[], + options: PostProcessOptions, +): Promise<HtmlToMdResultWithUrl[]> { + const results = await mergeClassMembers(initialResults); + + normalizeResultUrls(results, { + kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, + pkgName: pkg.name, + }); + + specialCaseResults(results); + + if (options.rewriteApidocsLinks) { + const apiBase = pkg.apiOutputDir(DOCS_BASE_PATH); + for (const result of results) { + result.markdown = result.markdown.replace( + /\]\((?:\.\.\/)*?(apidocs|apidoc|stubs)\/([^)]+)\)/g, + `](${apiBase}/$2)`, + ); + } + } + + await updateLinks( + results, + { + kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, + pkgName: pkg.name, + pkgOutputDir: pkg.apiOutputDir(DOCS_BASE_PATH), + }, + options.objectsInv, + options.allInvs, + ); + + await dedupeHtmlIdsFromResults(results); + + if (options.frontMatter === "api") { + addFrontMatter(results, pkg); + } else { + applyHtmlMetaFrontmatter(results); + } + + removeMathBlocksIndentation(results); + return results; +} + +function applyHtmlMetaFrontmatter(results: HtmlToMdResultWithUrl[]): void { + for (const result of results) { + const markdown = result.markdown; + result.markdown = `--- +${result.meta.hardcodedFrontmatter} +--- + +${markdown} +`; + } +} + +// --------------------------------------------------------------------------- +// Writing outputs +// --------------------------------------------------------------------------- + +/** + * Write each markdown result to disk at `<docsBaseFolder>/<result.url>.mdx`. + * Release notes are routed through `handleReleaseNotesFile` so the caller + * doesn't have to special-case them; the pipelines that don't produce release + * notes (addons excludes them via their glob) are unaffected. + */ +export async function writeMarkdownResults( + pkg: Pkg, + docsBaseFolder: string, + results: HtmlToMdResultWithUrl[], +): Promise<void> { + for (const result of results) { + const path = `${docsBaseFolder}${result.url}.mdx`; + if (path.endsWith("release-notes.mdx")) { + if (!pkg.releaseNotesConfig.enabled) continue; + + const shouldWriteResult = await handleReleaseNotesFile(result, pkg); + if (!shouldWriteResult) continue; + } + + await mkdirp(dirname(path)); + await writeFile(path, result.markdown); + } +} + +/** + * Copy images referenced by the results from the artifact's `_images/` folder + * into `destFolder`. The caller owns the destination path, which is how the + * API pipeline and the addon pipeline route images to different locations + * under `public/docs/images/`. + */ +export async function copyImages( + pkg: Pkg, + artifactPath: string, + destFolder: string, + results: HtmlToMdResultWithUrl[], +): Promise<void> { + console.log("Saving images"); + const allImages = uniqBy( + results.flatMap((result) => result.images), + (image) => image.fileName, + ); + await saveImages(allImages, `${artifactPath}/_images`, destFolder, pkg); +} diff --git a/scripts/js/lib/api/saveImages.ts b/scripts/js/lib/api/saveImages.ts index a9a9c8b1c15..fa091965d0b 100644 --- a/scripts/js/lib/api/saveImages.ts +++ b/scripts/js/lib/api/saveImages.ts @@ -49,10 +49,9 @@ function skipReleaseNote(imgFileName: string, pkg: Pkg): boolean { export async function saveImages( images: Image[], originalImagesFolderPath: string, - publicBaseFolder: string, + destFolder: string, pkg: Pkg, ) { - const destFolder = pkg.apiOutputDir(`${publicBaseFolder}/docs/images`); if (!(await pathExists(destFolder))) { await mkdirp(destFolder); } @@ -62,7 +61,10 @@ export async function saveImages( return; } const source = `${originalImagesFolderPath}/${img.fileName}`; - const dest = `${publicBaseFolder}/${img.dest}`; + // img.dest is set by loadImages() and includes the full image URL prefix + // (e.g. "/docs/images/api/qiskit/foo.avif"). We only need its basename to + // place the file inside destFolder. + const dest = `${destFolder}/${img.dest.split("/").pop()}`; if (!(await pathExists(source))) { console.warn(`Skipping missing image: ${source}`); diff --git a/scripts/js/lib/api/testdata/qiskit-addon-smoke-publishedapis/api/qiskit-addon-other/objects.inv b/scripts/js/lib/api/testdata/qiskit-addon-smoke-publishedapis/api/qiskit-addon-other/objects.inv new file mode 100644 index 0000000000000000000000000000000000000000..f2dc4077d367327cded035f117b7d6ddc525584b GIT binary patch literal 165 zcmY#Z2rkIT%&Sny%qvUHE6FdaR47X=D$dN$Q!wIERtPA{&q_@$u~G=kEY8j>QE*I2 z0m}H7WTX}WWy2sUtrURlkc?D?qSV~P%)FG;B8B`kg_4ZSVuiHKoKyuMot&RrP?TC+ zoSLFgTAZ1eu27YenWV>6aqG1HNe|sF{gWroc!z7O*y5#q)?4r7ne)L0At&4?U3|*K JV7HiW1pwQdJ_-N; literal 0 HcmV?d00001 diff --git a/scripts/js/lib/api/testdata/qiskit-addon-smoke/_images/smoke-diagram.png b/scripts/js/lib/api/testdata/qiskit-addon-smoke/_images/smoke-diagram.png new file mode 100644 index 0000000000000000000000000000000000000000..62a5f8f47fec02344e5bf9061888262f677cf5d6 GIT binary patch literal 69 zcmeAS@N?(olHy`uVBq!ia0vp^j3CUx1SBVv2j2ryJf1F&Ar*6yf1E$Sz`)GN$Z+!C Rr1wB^22WQ%mvv4FO#seq5RCu; literal 0 HcmV?d00001 diff --git a/scripts/js/lib/api/testdata/qiskit-addon-smoke/how_tos/foo.html b/scripts/js/lib/api/testdata/qiskit-addon-smoke/how_tos/foo.html new file mode 100644 index 00000000000..6959ac59aa2 --- /dev/null +++ b/scripts/js/lib/api/testdata/qiskit-addon-smoke/how_tos/foo.html @@ -0,0 +1,18 @@ +<!doctype html> +<html lang="en"> + <head> + <meta charset="utf-8"/> + <title>How to foo - smoke 0.0 + + + +

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zNUme`#u(CMTRi=Q2+pb065xR-cbK0&OikxS#J25KZ47eCqrjkOqg<$MFEMJy+o@JQ U Date: Thu, 7 May 2026 15:38:41 -0400 Subject: [PATCH 016/104] fix toc kebab case --- docs/addons/qiskit-addon-obp/_toc.json | 10 +++++----- scripts/js/lib/sphinx/generateToc.ts | 20 ++++++++++++++++---- 2 files changed, 21 insertions(+), 9 deletions(-) diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index e8a4e68f780..9b0450f9b9a 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -14,7 +14,7 @@ "children": [ { "title": "Reducing depth of circuits with operator backpropagation", - "url": "/docs/addons/qiskit-addon-obp/tutorials/01_getting_started" + "url": "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started" } ], "url": "/docs/addons/qiskit-addon-obp/tutorials/index" @@ -24,18 +24,18 @@ "children": [ { "title": "Using different Lp-norms for Pauli term truncation", - "url": "/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm" + "url": "/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm" }, { "title": "Classically simulating circuits with OBP", - "url": "/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp" + "url": "/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp" }, { "title": "Truncating Pauli terms during backpropagation", - "url": "/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms" + "url": "/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms" } ], - "url": "/docs/addons/qiskit-addon-obp/how_tos/index" + "url": "/docs/addons/qiskit-addon-obp/how-tos/index" }, { "title": "GitHub", diff --git a/scripts/js/lib/sphinx/generateToc.ts b/scripts/js/lib/sphinx/generateToc.ts index 01bde63001e..c4bed08b777 100644 --- a/scripts/js/lib/sphinx/generateToc.ts +++ b/scripts/js/lib/sphinx/generateToc.ts @@ -19,6 +19,8 @@ import { mkdirp } from "mkdirp"; import { TocEntry } from "../api/generateToc.js"; import { Pkg } from "../api/Pkg.js"; +import { kebabCaseAndShortenPage } from "../api/normalizeResultUrls.js"; +import { transformSpecialCaseUrl } from "../api/specialCaseResults.js"; import { DOCS_BASE_PATH } from "./conversionPipeline.js"; export type SphinxToc = { @@ -76,7 +78,7 @@ export async function generateSphinxToc( } const title = l1Link.text().trim(); - const url = hrefToUrl(href, outputBase); + const url = hrefToUrl(href, outputBase, pkg); const l2Items = $(l1).find(".toctree-l2"); if (l2Items.length === 0) { @@ -94,7 +96,7 @@ export async function generateSphinxToc( } subChildren.push({ title: l2Link.text().trim(), - url: hrefToUrl(l2Href, outputBase), + url: hrefToUrl(l2Href, outputBase, pkg), }); }); @@ -163,9 +165,19 @@ function isHrefSelected(href: string, selectedFiles: Set): boolean { ); } -function hrefToUrl(href: string, outputBase: string): string { +function hrefToUrl(href: string, outputBase: string, pkg: Pkg): string { if (href === "#") return outputBase; const withoutAnchor = href.split("#")[0]; const withoutExt = withoutAnchor.replace(/\.html$/, ""); - return `${outputBase}/${withoutExt}`; + // Apply the same normalization as the content pipeline: + // kebab-case the filename, then transformSpecialCaseUrl for directory renames. + const parts = withoutExt.split("/"); + const filename = parts[parts.length - 1]; + const normalizedFilename = pkg.kebabCaseAndShortenUrls + ? kebabCaseAndShortenPage(filename, pkg.name) + : filename; + const normalizedPath = transformSpecialCaseUrl( + [...parts.slice(0, -1), normalizedFilename].join("/"), + ); + return `${outputBase}/${normalizedPath}`; } From 087aed661546179d3b8d46f6c48694e489ee2d1f Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Thu, 7 May 2026 16:07:39 -0400 Subject: [PATCH 017/104] use manual toc --- docs/addons/qiskit-addon-obp/_toc.json | 29 ++-- scripts/js/commands/updateDocs.ts | 13 ++ scripts/js/lib/sphinx/conversionPipeline.ts | 31 +--- scripts/js/lib/sphinx/generateToc.ts | 183 -------------------- 4 files changed, 30 insertions(+), 226 deletions(-) delete mode 100644 scripts/js/lib/sphinx/generateToc.ts diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index 9b0450f9b9a..8125ad2370a 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -2,25 +2,15 @@ "title": "Operator backpropagation (OBP)", "children": [ { - "title": "Documentation Home", + "title": "Home", "url": "/docs/addons/qiskit-addon-obp" }, { - "title": "Installation Instructions", + "title": "Installation instructions", "url": "/docs/addons/qiskit-addon-obp/install" }, { - "title": "Tutorials", - "children": [ - { - "title": "Reducing depth of circuits with operator backpropagation", - "url": "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started" - } - ], - "url": "/docs/addons/qiskit-addon-obp/tutorials/index" - }, - { - "title": "How-To Guides", + "title": "Guides", "children": [ { "title": "Using different Lp-norms for Pauli term truncation", @@ -39,7 +29,18 @@ }, { "title": "GitHub", - "url": "https://github.com/Qiskit/qiskit-addon-obp" + "url": "https://github.com/Qiskit/qiskit-addon-obp", + "useDivider": true + }, + { + "title": "Tutorials", + "useDivider": true, + "children": [ + { + "title": "Reducing depth of circuits with operator backpropagation", + "url": "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started" + } + ] }, { "title": "API reference", diff --git a/scripts/js/commands/updateDocs.ts b/scripts/js/commands/updateDocs.ts index 9acc992ae3a..607051387e4 100644 --- a/scripts/js/commands/updateDocs.ts +++ b/scripts/js/commands/updateDocs.ts @@ -12,7 +12,10 @@ import yargs from "yargs/yargs"; import { hideBin } from "yargs/helpers"; +import { readFile, writeFile } from "fs/promises"; + import { $ } from "zx"; +import { mkdirp } from "mkdirp"; import { Pkg } from "../lib/api/Pkg.js"; import { pathExists } from "../lib/fs.js"; @@ -93,7 +96,17 @@ async function prepareSphinxFolder(pkg: Pkg, args: Arguments): Promise { async function deleteExistingFiles(pkg: Pkg): Promise { const markdownDir = pkg.outputDir("docs/addons"); if (await pathExists(markdownDir)) { + // Preserve the hand-authored _toc.json — it is maintained separately + // from the generated content and should not be overwritten by the pipeline. + const tocPath = `${markdownDir}/_toc.json`; + const tocContent = (await pathExists(tocPath)) + ? await readFile(tocPath, "utf-8") + : null; await $`rm -rf ${markdownDir}`; + if (tocContent !== null) { + await mkdirp(markdownDir); + await writeFile(tocPath, tocContent); + } } const imagesDir = pkg.outputDir("public/docs/images/addons"); if (await pathExists(imagesDir)) { diff --git a/scripts/js/lib/sphinx/conversionPipeline.ts b/scripts/js/lib/sphinx/conversionPipeline.ts index b3ed7ef19cc..45e136697b2 100644 --- a/scripts/js/lib/sphinx/conversionPipeline.ts +++ b/scripts/js/lib/sphinx/conversionPipeline.ts @@ -35,12 +35,8 @@ import { import addFrontMatter from "../api/addFrontMatter.js"; import { dedupeHtmlIdsFromResults } from "../api/dedupeHtmlIds.js"; import removeMathBlocksIndentation from "../api/removeMathBlocksIndentation.js"; -import { writeTocFile } from "./generateToc.js"; -import { - handleReleaseNotesFile, - maybeUpdateReleaseNotesFolder, -} from "../api/releaseNotes.js"; -import { Notebook, NotebookWithUrl } from "../Notebooks.js"; +import { handleReleaseNotesFile } from "../api/releaseNotes.js"; +import { NotebookWithUrl } from "../Notebooks.js"; export const DOCS_BASE_PATH = "/docs"; @@ -105,7 +101,6 @@ export async function runSphinxPipeline( // write assets await copyImages(pkg, artifactPath, "public", results); await objectsInv.write(pkg.apiOutputDir(publicBaseFolder)); - await writeTocFile(artifactPath, outputPath, pkg, docsBaseFolder); } async function determineFilePaths( @@ -313,25 +308,3 @@ async function writeNotebooks( await writeFile(path, JSON.stringify(notebook, null, 1)); } } - -// async function writeTocFile( -// artifactPath: string, -// outputDir: string, -// pkg: Pkg, -// docsBaseFolder: string, -// config: SphinxPipelineConfig, -// ): Promise { -// console.log(`Generating TOC for ${outputDir}`); -// const toc = await generateSphinxToc( -// artifactPath, -// outputDir, -// pkg.title, -// docsBaseFolder, -// config.include, -// config.exclude, -// ); -// await writeFile( -// `${outputDir}/_toc.json`, -// JSON.stringify(toc, null, 2) + "\n", -// ); -// } diff --git a/scripts/js/lib/sphinx/generateToc.ts b/scripts/js/lib/sphinx/generateToc.ts deleted file mode 100644 index c4bed08b777..00000000000 --- a/scripts/js/lib/sphinx/generateToc.ts +++ /dev/null @@ -1,183 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { readFile, writeFile } from "fs/promises"; -import { dirname } from "path"; - -import { load } from "cheerio"; -import { globby } from "globby"; -import { mkdirp } from "mkdirp"; - -import { TocEntry } from "../api/generateToc.js"; -import { Pkg } from "../api/Pkg.js"; -import { kebabCaseAndShortenPage } from "../api/normalizeResultUrls.js"; -import { transformSpecialCaseUrl } from "../api/specialCaseResults.js"; -import { DOCS_BASE_PATH } from "./conversionPipeline.js"; - -export type SphinxToc = { - title: string; - children: TocEntry[]; - collapsed: boolean; -}; - -// Sphinx build artifact folders and API content that are never prose content. -const SPHINX_INTERNAL_PREFIXES = ["_static/", "_sources/", "_downloads/"]; -const API_HREF_PREFIXES = ["apidocs/", "apidoc/", "stubs/", "release-notes", "release_notes"]; - -/** - * Build a _toc.json by parsing the Sphinx sidebar nav, filtered to match - * the same include/exclude config used for file selection. - */ -export async function generateSphinxToc( - artifactPath: string, - outputDir: string, - pkg: Pkg, - docsBaseFolder: string, - include?: string[], - exclude?: string[], -): Promise { - const indexHtml = await readFile(`${artifactPath}/index.html`, "utf-8"); - const $ = load(indexHtml); - - // Build set of files that were actually selected for this run so we can - // filter TOC entries to only those included. - const selectedFiles = new Set( - await globby(include ?? ["**"], { - cwd: artifactPath, - ignore: [...SPHINX_INTERNAL_PREFIXES.map((p) => `${p}**`), ...(exclude ?? [])], - }), - ); - - const outputBase = `/${outputDir.startsWith(docsBaseFolder) - ? outputDir - : `${docsBaseFolder}/${outputDir}`}`; - - const children: TocEntry[] = []; - - $(".sidebar-tree .toctree-l1").each((_, l1) => { - const l1Link = $(l1).children("a").first(); - const href = l1Link.attr("href") ?? ""; - - if (l1Link.hasClass("reference external")) return; - if (SPHINX_INTERNAL_PREFIXES.some((p) => href.startsWith(p))) return; - if (API_HREF_PREFIXES.some((p) => href.startsWith(p))) return; - - // Filter by whether this href was selected for this run. - const hrefFile = href.split("#")[0]; - if (hrefFile && hrefFile !== "#" && !isHrefSelected(hrefFile, selectedFiles)) { - return; - } - - const title = l1Link.text().trim(); - const url = hrefToUrl(href, outputBase, pkg); - - const l2Items = $(l1).find(".toctree-l2"); - if (l2Items.length === 0) { - children.push({ title, url }); - } else { - const subChildren: TocEntry[] = []; - l2Items.each((_, l2) => { - const l2Link = $(l2).children("a").first(); - const l2Href = l2Link.attr("href") ?? ""; - if (l2Link.hasClass("reference external")) return; - if (SPHINX_INTERNAL_PREFIXES.some((p) => l2Href.startsWith(p))) return; - const l2HrefFile = l2Href.split("#")[0]; - if (l2HrefFile && l2HrefFile !== "#" && !isHrefSelected(l2HrefFile, selectedFiles)) { - return; - } - subChildren.push({ - title: l2Link.text().trim(), - url: hrefToUrl(l2Href, outputBase, pkg), - }); - }); - - if (subChildren.length > 0) { - const sectionUrl = href === "#" ? undefined : url; - const entry: TocEntry = { title, children: subChildren }; - if (sectionUrl) entry.url = sectionUrl; - children.push(entry); - } - } - }); - - if (pkg.githubSlug) { - children.push({ - title: "GitHub", - url: `https://github.com/${pkg.githubSlug}`, - }); - } - - children.push({ - title: "API reference", - children: [ - { - title: `${pkg.title} API reference`, - url: pkg.apiOutputDir(DOCS_BASE_PATH), - }, - ], - }); - - return { title: pkg.title, children, collapsed: true }; -} - -export async function writeTocFile( - artifactPath: string, - outputDir: string, - pkg: Pkg, - docsBaseFolder: string, - include?: string[], - exclude?: string[], -): Promise { - console.log(`Generating TOC for ${outputDir}`); - const toc = await generateSphinxToc( - artifactPath, - outputDir, - pkg, - docsBaseFolder, - include, - exclude, - ); - const tocPath = `${outputDir}/_toc.json`; - await mkdirp(dirname(tocPath)); - await writeFile(tocPath, JSON.stringify(toc, null, 2) + "\n"); -} - -/** - * Check whether a sidebar href corresponds to a file that was selected - * for this pipeline run. - */ -function isHrefSelected(href: string, selectedFiles: Set): boolean { - const normalized = href.replace(/\.html$/, ""); - // Check both with and without .html extension, and also .ipynb - return ( - selectedFiles.has(`${normalized}.html`) || - selectedFiles.has(`${normalized}.ipynb`) || - selectedFiles.has(href) - ); -} - -function hrefToUrl(href: string, outputBase: string, pkg: Pkg): string { - if (href === "#") return outputBase; - const withoutAnchor = href.split("#")[0]; - const withoutExt = withoutAnchor.replace(/\.html$/, ""); - // Apply the same normalization as the content pipeline: - // kebab-case the filename, then transformSpecialCaseUrl for directory renames. - const parts = withoutExt.split("/"); - const filename = parts[parts.length - 1]; - const normalizedFilename = pkg.kebabCaseAndShortenUrls - ? kebabCaseAndShortenPage(filename, pkg.name) - : filename; - const normalizedPath = transformSpecialCaseUrl( - [...parts.slice(0, -1), normalizedFilename].join("/"), - ); - return `${outputBase}/${normalizedPath}`; -} From bb49c9d7e7b54f1e674a1946604c9ea1a7565ea9 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Thu, 7 May 2026 16:18:34 -0400 Subject: [PATCH 018/104] update toc --- docs/addons/qiskit-addon-obp/_toc.json | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index 8125ad2370a..40820db0c9c 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -1,5 +1,7 @@ { - "title": "Operator backpropagation (OBP)", + "parentUrl": "docs/guides/addons", + "title": "Operator backpropagation 0.3.0", + "collapsed": true, "children": [ { "title": "Home", @@ -43,14 +45,8 @@ ] }, { - "title": "API reference", - "children": [ - { - "title": "Operator backpropagation (OBP) API reference", - "url": "/docs/api/qiskit-addon-obp" - } - ] + "title": "Operator backpropagation API reference", + "url": "/docs/api/qiskit-addon-obp" } - ], - "collapsed": true + ] } From 4dcc0500b6aaf117b9bf000de5f3e19b0a1bf019 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Thu, 7 May 2026 16:27:13 -0400 Subject: [PATCH 019/104] update toc --- docs/addons/qiskit-addon-obp/_toc.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index 40820db0c9c..94926ebc6bd 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -1,5 +1,5 @@ { - "parentUrl": "docs/guides/addons", + "parentUrl": "/docs/guides/addons", "title": "Operator backpropagation 0.3.0", "collapsed": true, "children": [ From ad80ebea732fd69dd8e45fd71a260934d042978e Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Thu, 7 May 2026 16:41:26 -0400 Subject: [PATCH 020/104] update adds parent to api toc --- docs/api/qiskit-addon-obp/_toc.json | 3 ++- .../config/historical-pages-to-latest.json | 22 +++++++++---------- scripts/js/lib/api/Pkg.ts | 16 ++++++++++---- scripts/js/lib/api/generateToc.ts | 4 ++++ 4 files changed, 29 insertions(+), 16 deletions(-) diff --git a/docs/api/qiskit-addon-obp/_toc.json b/docs/api/qiskit-addon-obp/_toc.json index e2bd0263c41..3df88045df8 100644 --- a/docs/api/qiskit-addon-obp/_toc.json +++ b/docs/api/qiskit-addon-obp/_toc.json @@ -72,5 +72,6 @@ } ], "collapsed": true, - "untranslatable": true + "untranslatable": true, + "parentUrl": "/docs/addons/qiskit-addon-obp" } diff --git a/scripts/config/historical-pages-to-latest.json b/scripts/config/historical-pages-to-latest.json index 9323e61f359..d9d00bfd17a 100644 --- a/scripts/config/historical-pages-to-latest.json +++ b/scripts/config/historical-pages-to-latest.json @@ -1482,6 +1482,17 @@ "0.16": {}, "0.17": {} }, + "qiskit-c": { + "2.0": {}, + "2.1": {}, + "2.2": { + "qk-vf-2-layout-result": "/" + }, + "2.3": {}, + "dev": { + "qk-param": "/" + } + }, "qiskit-addon-aqc-tensor": { "0.1": {} }, @@ -1508,16 +1519,5 @@ "0.2": { "noise-management-post-selection": "/" } - }, - "qiskit-c": { - "2.0": {}, - "2.1": {}, - "2.2": { - "qk-vf-2-layout-result": "/" - }, - "2.3": {}, - "dev": { - "qk-param": "/" - } } } diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index 2da5bd0c118..78c3c7c9484 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -58,17 +58,21 @@ export class Pkg { readonly artifactPackageName: string; readonly hasRootNamespaceFile: boolean; - static VALID_NAMES = [ - "qiskit", - "qiskit-ibm-runtime", - "qiskit-ibm-transpiler", + static ADDON_NAMES = [ "qiskit-addon-aqc-tensor", "qiskit-addon-obp", "qiskit-addon-mpf", "qiskit-addon-sqd", "qiskit-addon-cutting", "qiskit-addon-utils", + ]; + + static VALID_NAMES = [ + "qiskit", + "qiskit-ibm-runtime", + "qiskit-ibm-transpiler", "qiskit-c", + ...Pkg.ADDON_NAMES, ]; constructor(kwargs: { @@ -288,6 +292,10 @@ export class Pkg { return this.language === "C"; } + isAddon(): boolean { + return Pkg.ADDON_NAMES.includes(this.name); + } + isProblematicLegacyQiskit(): boolean { return this.name === "qiskit" && +this.versionWithoutPatch < 0.45; } diff --git a/scripts/js/lib/api/generateToc.ts b/scripts/js/lib/api/generateToc.ts index 6a927c6596b..791dd2132e9 100644 --- a/scripts/js/lib/api/generateToc.ts +++ b/scripts/js/lib/api/generateToc.ts @@ -35,6 +35,7 @@ type Toc = { children: TocEntry[]; collapsed: boolean; untranslatable?: boolean; + parentUrl?: string; }; export function generateToc(pkg: Pkg, results: HtmlToMdResultWithUrl[]): Toc { @@ -71,6 +72,9 @@ export function generateToc(pkg: Pkg, results: HtmlToMdResultWithUrl[]): Toc { children: orderedEntries, collapsed: true, untranslatable: true, + ...(pkg.isAddon() && { + parentUrl: `/docs/addons/${pkg.name}`, + }), }; } From c81f885f5dcfab86668248903c36cba956457fa9 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Thu, 7 May 2026 17:33:03 -0400 Subject: [PATCH 021/104] update toc --- docs/addons/qiskit-addon-obp/_toc.json | 62 +++++++++++++++----------- docs/api/qiskit-addon-obp/_toc.json | 3 +- scripts/js/lib/api/generateToc.ts | 2 + 3 files changed, 41 insertions(+), 26 deletions(-) diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index 94926ebc6bd..5546faff959 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -1,42 +1,48 @@ { "parentUrl": "/docs/guides/addons", - "title": "Operator backpropagation 0.3.0", + "parentLabel": "Documentation", + "title": "Operator backpropagation (OBP) 0.3.0", "collapsed": true, "children": [ { - "title": "Home", - "url": "/docs/addons/qiskit-addon-obp" - }, - { - "title": "Installation instructions", - "url": "/docs/addons/qiskit-addon-obp/install" - }, - { - "title": "Guides", + "title": "", "children": [ { - "title": "Using different Lp-norms for Pauli term truncation", - "url": "/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm" + "title": "Home", + "url": "/docs/addons/qiskit-addon-obp" + }, + { + "title": "Installation instructions", + "url": "/docs/addons/qiskit-addon-obp/install" }, { - "title": "Classically simulating circuits with OBP", - "url": "/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp" + "title": "Guides", + "children": [ + { + "title": "Using different Lp-norms for Pauli term truncation", + "url": "/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm" + }, + { + "title": "Classically simulating circuits with OBP", + "url": "/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp" + }, + { + "title": "Truncating Pauli terms during backpropagation", + "url": "/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms" + } + ], + "url": "/docs/addons/qiskit-addon-obp/how-tos/index" }, { - "title": "Truncating Pauli terms during backpropagation", - "url": "/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms" + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-obp" } ], - "url": "/docs/addons/qiskit-addon-obp/how-tos/index" - }, - { - "title": "GitHub", - "url": "https://github.com/Qiskit/qiskit-addon-obp", - "useDivider": true + "collapsible": false }, { "title": "Tutorials", - "useDivider": true, + "collapsible": false, "children": [ { "title": "Reducing depth of circuits with operator backpropagation", @@ -45,8 +51,14 @@ ] }, { - "title": "Operator backpropagation API reference", - "url": "/docs/api/qiskit-addon-obp" + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Operator backpropagation (OBP) API reference", + "url": "/docs/api/qiskit-addon-obp" + } + ] } ] } diff --git a/docs/api/qiskit-addon-obp/_toc.json b/docs/api/qiskit-addon-obp/_toc.json index 3df88045df8..3336d5e5e49 100644 --- a/docs/api/qiskit-addon-obp/_toc.json +++ b/docs/api/qiskit-addon-obp/_toc.json @@ -73,5 +73,6 @@ ], "collapsed": true, "untranslatable": true, - "parentUrl": "/docs/addons/qiskit-addon-obp" + "parentUrl": "/docs/addons/qiskit-addon-obp", + "parentLabel": "Operator backpropagation (OBP)" } diff --git a/scripts/js/lib/api/generateToc.ts b/scripts/js/lib/api/generateToc.ts index 791dd2132e9..ef4636ba252 100644 --- a/scripts/js/lib/api/generateToc.ts +++ b/scripts/js/lib/api/generateToc.ts @@ -36,6 +36,7 @@ type Toc = { collapsed: boolean; untranslatable?: boolean; parentUrl?: string; + parentLabel?: string; }; export function generateToc(pkg: Pkg, results: HtmlToMdResultWithUrl[]): Toc { @@ -74,6 +75,7 @@ export function generateToc(pkg: Pkg, results: HtmlToMdResultWithUrl[]): Toc { untranslatable: true, ...(pkg.isAddon() && { parentUrl: `/docs/addons/${pkg.name}`, + parentLabel: pkg.title, }), }; } From e3048e9fc8ca3ff5e59dfff325852b0a1d3acc1f Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Fri, 8 May 2026 10:29:42 -0400 Subject: [PATCH 022/104] add frontmatter to notebooks --- .../how-tos/bound-error-using-p-norm.ipynb | 10 ++++ .../simulating-circuits-with-obp.ipynb | 10 ++++ .../how-tos/truncate-operator-terms.ipynb | 10 ++++ .../tutorials/01-getting-started.ipynb | 10 ++++ scripts/js/lib/sphinx/conversionPipeline.ts | 47 ++++++++++++++++--- 5 files changed, 81 insertions(+), 6 deletions(-) diff --git a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb index df3b45b7387..492215936ea 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "869bd755", + "metadata": {}, + "source": [ + "---\n", + "title: \"Using different Lp-norms for Pauli term truncation\"\n", + "---" + ] + }, { "cell_type": "markdown", "id": "8a7c9df5-d836-4ecb-8aec-8a1f2faa017a", diff --git a/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb b/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb index 01a2a8eb3d4..ac4331e4f24 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "24b35866", + "metadata": {}, + "source": [ + "---\n", + "title: \"Classically simulating circuits with OBP\"\n", + "---" + ] + }, { "cell_type": "markdown", "id": "26d926db-2e2f-41ff-947e-9757a9f426d8", diff --git a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb index bb7ceacd823..f84fc480595 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "f6c039a8", + "metadata": {}, + "source": [ + "---\n", + "title: \"Truncating Pauli terms during backpropagation\"\n", + "---" + ] + }, { "cell_type": "markdown", "id": "11cf5076-2777-499c-be50-531e305bf011", diff --git a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb index 9952b4d368f..b70b23f30f3 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "1c4a4ed3", + "metadata": {}, + "source": [ + "---\n", + "title: \"Reducing depth of circuits with operator backpropagation\"\n", + "---" + ] + }, { "cell_type": "markdown", "id": "5f9e3f0d-7cc1-4ea7-826b-35dd2a87af1e", diff --git a/scripts/js/lib/sphinx/conversionPipeline.ts b/scripts/js/lib/sphinx/conversionPipeline.ts index 45e136697b2..c64a6e87cef 100644 --- a/scripts/js/lib/sphinx/conversionPipeline.ts +++ b/scripts/js/lib/sphinx/conversionPipeline.ts @@ -27,6 +27,8 @@ import { kebabCaseAndShortenPage, } from "../api/normalizeResultUrls.js"; import { updateLinks, relativizeLink } from "../api/updateLinks.js"; +import { parseMarkdown, extractHeadingText } from "../markdownUtils.js"; +import { visit, EXIT } from "unist-util-visit"; import { mergeClassMembers } from "../api/mergeClassMembers.js"; import { specialCaseResults, @@ -36,7 +38,7 @@ import addFrontMatter from "../api/addFrontMatter.js"; import { dedupeHtmlIdsFromResults } from "../api/dedupeHtmlIds.js"; import removeMathBlocksIndentation from "../api/removeMathBlocksIndentation.js"; import { handleReleaseNotesFile } from "../api/releaseNotes.js"; -import { NotebookWithUrl } from "../Notebooks.js"; +import { NotebookCell, NotebookWithUrl } from "../Notebooks.js"; export const DOCS_BASE_PATH = "/docs"; @@ -284,16 +286,24 @@ async function processNotebooks( objectsInv: ObjectsInv, allInvs: Map, ): Promise { - return notebooks.map((notebook) => ({ - ...notebook, - cells: notebook.cells.map((cell) => { + return notebooks.map((notebook) => { + const processedCells = notebook.cells.map((cell) => { if (cell.cell_type !== "markdown") return cell; return { ...cell, source: rewriteNotebookLinks(cell.source, objectsInv, allInvs), }; - }), - })); + }); + + // Prepend a frontmatter cell matching the guides notebook convention. + // Extract the title from the first markdown h1, same as extractFrontmatter + // does for HTML pages. + const frontmatterCell = buildFrontmatterCell(processedCells); + return { + ...notebook, + cells: frontmatterCell ? [frontmatterCell, ...processedCells] : processedCells, + }; + }); } async function writeNotebooks( @@ -308,3 +318,28 @@ async function writeNotebooks( await writeFile(path, JSON.stringify(notebook, null, 1)); } } + +function buildFrontmatterCell(cells: NotebookCell[]): NotebookCell | undefined { + for (const cell of cells) { + if (cell.cell_type !== "markdown") continue; + const text = Array.isArray(cell.source) ? cell.source.join("") : cell.source; + const tree = parseMarkdown(text); + let title: string | undefined; + visit(tree, "heading", (node: any) => { + if (node.depth === 1 && !title) { + title = extractHeadingText(node).trim(); + return EXIT; + } + }); + if (title) { + const quote = (s: string) => + `"${s.replace(/\\/g, "\\\\").replace(/"/g, '\\"')}"`; + return { + cell_type: "markdown", + source: `---\ntitle: ${quote(title)}\n---`, + metadata: {}, + }; + } + } + return undefined; +} From 4a6dbd8a7563b466544ccf431648b58c6d1c42dd Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Fri, 8 May 2026 20:13:22 -0400 Subject: [PATCH 023/104] fix frontmatter --- .../how-tos/bound-error-using-p-norm.ipynb | 2 +- .../addons/qiskit-addon-obp/how-tos/index.mdx | 3 +- .../simulating-circuits-with-obp.ipynb | 2 +- .../how-tos/truncate-operator-terms.ipynb | 2 +- docs/addons/qiskit-addon-obp/index.mdx | 3 +- docs/addons/qiskit-addon-obp/install.mdx | 5 + .../addons/qiskit-addon-obp/release-notes.mdx | 173 ------------------ .../tutorials/01-getting-started.ipynb | 2 +- .../qiskit-addon-obp/tutorials/index.mdx | 3 +- scripts/js/lib/sphinx/conversionPipeline.ts | 48 ++++- 10 files changed, 52 insertions(+), 191 deletions(-) delete mode 100644 docs/addons/qiskit-addon-obp/release-notes.mdx diff --git a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb index 492215936ea..3192a1b29e9 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "869bd755", + "id": "48cdd045", "metadata": {}, "source": [ "---\n", diff --git a/docs/addons/qiskit-addon-obp/how-tos/index.mdx b/docs/addons/qiskit-addon-obp/how-tos/index.mdx index 2f7b6d1f313..b61973645bc 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/index.mdx +++ b/docs/addons/qiskit-addon-obp/how-tos/index.mdx @@ -1,6 +1,5 @@ --- -title: Operator backpropagation (OBP) API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-obp. +title: "How-To Guides - Qiskit addon: operator backpropagation (OBP) 0.3.0" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb b/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb index ac4331e4f24..479eb4aa56d 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "24b35866", + "id": "d203dc9f", "metadata": {}, "source": [ "---\n", diff --git a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb index f84fc480595..741c7bb22ba 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f6c039a8", + "id": "91696597", "metadata": {}, "source": [ "---\n", diff --git a/docs/addons/qiskit-addon-obp/index.mdx b/docs/addons/qiskit-addon-obp/index.mdx index 95c88ec7036..29de023afa6 100644 --- a/docs/addons/qiskit-addon-obp/index.mdx +++ b/docs/addons/qiskit-addon-obp/index.mdx @@ -1,6 +1,5 @@ --- -title: Operator backpropagation (OBP) API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-obp. +title: "Qiskit addon: operator backpropagation (OBP) 0.3.0" --- # Qiskit addon: operator backpropagation (OBP) diff --git a/docs/addons/qiskit-addon-obp/install.mdx b/docs/addons/qiskit-addon-obp/install.mdx index 8c5e1e7c54c..dd9b9648b0c 100644 --- a/docs/addons/qiskit-addon-obp/install.mdx +++ b/docs/addons/qiskit-addon-obp/install.mdx @@ -1,3 +1,7 @@ +--- +title: "Installation Instructions - Qiskit addon: operator backpropagation (OBP) 0.3.0" +--- + # Installation Instructions Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: @@ -69,3 +73,4 @@ If you installed the notebook dependencies, you can get started by running the n cd docs/ jupyter lab ``` + diff --git a/docs/addons/qiskit-addon-obp/release-notes.mdx b/docs/addons/qiskit-addon-obp/release-notes.mdx deleted file mode 100644 index 2a56cc0a6f5..00000000000 --- a/docs/addons/qiskit-addon-obp/release-notes.mdx +++ /dev/null @@ -1,173 +0,0 @@ ---- -title: Operator backpropagation (OBP) release notes -description: Changes made to Operator backpropagation (OBP) -in_page_toc_max_heading_level: 2 ---- - - - - - -# Operator backpropagation (OBP) release notes - - - - - -## 0.3.0 - - - -### New Features - -* [`PauliLindbladError`](/docs/api/qiskit-ibm-runtime/utils-noise-learner-result-pauli-lindblad-error "(in Qiskit Runtime IBM Client)") objects can now be embedded in slices of `QuantumCircuit` using the [`PauliLindbladErrorInstruction`](/docs/api/qiskit-addon-obp/utils-noise-pauli-lindblad-error-instruction#qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction "qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction") and will be handled properly by [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate"). - - - -### Bug Fixes - -* Fixed a bug in [`qiskit_addon_obp.backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") which often caused resets to be applied to the wrong qubit. - - - - - -## 0.2.0 - - - - - -### New Features - -* A new parameter `show_legend` has been added to each function in the [`qiskit_addon_obp.utils.visualization`](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization "qiskit_addon_obp.utils.visualization") module that can show or hide the legend on a plot. The legend is shown by default. This can be useful when the legend becomes long and obstructs the plot. - -* [`qiskit_addon_obp.utils.simplify.OperatorBudget`](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget "qiskit_addon_obp.utils.simplify.OperatorBudget") now holds `atol` and `rtol` fields which are absolute and relative tolerances used to determine whether coefficients are zero while simplifying an operator. - -* [`truncate_binary_search()`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search "qiskit_addon_obp.utils.truncating.truncate_binary_search") now accepts a `tol` kwarg. Once an optimal truncation threshold, up to this value, has been found, the search for an optimal threshold will stop. The default tolerance is `1e-8`; whereas, the tolerance used prior to this release was `1e-10`. - - [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") now holds a `tol` field. This field is used by [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") as the `tol` argument to [`truncate_binary_search()`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search "qiskit_addon_obp.utils.truncating.truncate_binary_search"). - - - -### Upgrade Notes - -* This release adds support for Python 3.13. No code changes were necessary, so older releases are expected to work on Python 3.13 too. - -* This package is now compatible with Qiskit SDK 2.0. - - - - - -### Bug Fixes - -* The [`num_duplicate_paulis`](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_duplicate_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_duplicate_paulis") was previously unable to differentiate from Pauli terms that were trimmed due to their coefficient being close to zero. This is now tracked correctly with these trimmed terms only counting towards [`num_trimmed_paulis`](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_trimmed_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_trimmed_paulis"). - -* The reported number of unique Paulis in [`num_unique_paulis`](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_unique_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_unique_paulis") is now correct in cases where all gates in a slice commute with an observable. - -* Fixed a bug in [`qiskit_addon_obp.backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") which caused slices with depth > 1 to be incorrectly forward-propagated. Gates from a given slice will now be correctly propagated into the observable in reverse order (i.e., from the back of the slice). - - - - - -## 0.1.0 - - - - - -### New Features - -* Added a [`OperatorBudget`](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget "qiskit_addon_obp.utils.simplify.OperatorBudget") class for specifying how large an operator may grow during back-propagation. - -* Adds the `max_seconds` keyword-argument to the [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") function, allowing the end-user to specify a maximum wall clock time for the algorithm. This can (for example) be useful for exploring different truncation error budget strategies while limiting the CPU time. - -* Introduced a new `dataclass`, [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget"), for holding information about the observable truncation strategy. - -* Introduced a new function, [`setup_budget()`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget "qiskit_addon_obp.utils.truncating.setup_budget"), which generates a [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") class, given an observable truncation strategy (e.g. `max_error_total`, `max_error_per_slice`, `p_norm`). - - - - - -### Upgrade Notes - -* The [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") function no longer accepts `max_paulis` and `max_qwc_groups` kwargs for constraining the size of the operator during back-propagation. Users should instead use the new `operator_budget` kwarg, which takes an [`OperatorBudget`](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget "qiskit_addon_obp.utils.simplify.OperatorBudget") instance. - - To migrate, change this code - - ```python - from qiskit_addon_obp import backpropagate - - bp_obs, remaining_slices, metadata = backpropagate( - obs, - slices, - max_paulis=100, - max_qwc_groups=10, - simplify=True - ) - ``` - - to this - - ```python - from qiskit_addon_obp import backpropagate - from qiskit_addon_obp.utils.simplify import OperatorBudget - - op_budget = OperatorBudget(max_paulis=100, max_qwc_groups=10, simplify=True) - bp_obs, remaining_slices, metadata = backpropagate(obs, slices, operator_budget=op_budget) - ``` - -* The `max_slices` kwarg has been removed from [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate"). Users should now only pass in slices which they intend to back-propagate. If a user wants to attempt to only back-propagate the last `20` slices of an `N`-slice circuit, they would simply pass in the last `20` slices to [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") and, recombine any slices remaining after back-propagation with the original `N-20` slices. - - For example - - ```python - from qiskit_addon_obp import backpropagate - from qiskit_addon_obp.utils.truncating import setup_budget - from qiskit_addon_utils.slicing import combine_slices - - num_slices = 20 - truncation_error_budget = setup_budget(max_error_total=0.02, num_slices=num_slices, p_norm=1) - bp_obs, remaining_slices, metadata = backpropagate( - obs, slices[-num_slices:], truncation_error_budget=truncation_error_budget - ) - reduced_circuit = combine_slices(slices[:-num_slices] + remaining_slices) - ``` - -* The `max_slices` kwarg in [`setup_budget()`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget "qiskit_addon_obp.utils.truncating.setup_budget") has been renamed to `num_slices`. - -* The `max_slices` attribute in [`OBPMetadata`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata "qiskit_addon_obp.utils.metadata.OBPMetadata") has been renamed to `num_slices`. - -* The project’s root Python namespace has been changed from `obp` to `qiskit_addon_obp`. All package imports must be updated. - - For example: - - ```python - from obp import backpropagate - ``` - - should be changed to: - - ```python - from qiskit_addon_obp import backpropagate - ``` - -* Removed the `max_error_total`, `max_error_per_slice`, and `p_norm` kwargs from the [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") signature. Instead, users must specify their observable truncation strategy with the new `truncation_error_budget` kwarg which accepts a [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") instance. - -* Removed the `per_slice_budget`, `max_error_total`, and `p_norm` fields from the [`OBPMetadata`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata "qiskit_addon_obp.utils.metadata.OBPMetadata") class. These fields will now be accessed through the new `truncation_error_budget` field, which holds a [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") instance. - - - - - -### Bug Fixes - -* The [`setup_budget()`](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget "qiskit_addon_obp.utils.truncating.setup_budget") erroneously distributed the `max_error_total` when `num_slices` was also set. This has been fixed now, such that the budget always gets distributed evenly, regardless of the value of `p_norm`. - -* When the `max_seconds` argument to the [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") method is used, but the timeout is not reached during the actual OBP execution, the signal will now be reset properly, thereby avoiding cancellations at a (seemingly) random later point in time (of course, it is not random but actually after the specified amount of time has passed, but the rest of the code being executed after OBP could be doing anything at this point). - -* The computation of the [`accumulated_error()`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error "qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error") and [`left_over_error_budget()`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata.left_over_error_budget "qiskit_addon_obp.utils.metadata.OBPMetadata.left_over_error_budget") were fixed to respect the [Minkowski inequality](https://en.wikipedia.org/wiki/Minkowski_inequality). This is necessary, because a general Lp-norm (other than `p=2`) does not satisfy the [parallelogram law](https://en.wikipedia.org/wiki/Parallelogram_law) which resulted in a non-rigorous upper bound of the actual accumulated errors (and left-over error budgets by extension). - diff --git a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb index b70b23f30f3..8780eca760e 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "1c4a4ed3", + "id": "8df958ab", "metadata": {}, "source": [ "---\n", diff --git a/docs/addons/qiskit-addon-obp/tutorials/index.mdx b/docs/addons/qiskit-addon-obp/tutorials/index.mdx index 06c949cb7c4..92b3236fbdb 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/index.mdx +++ b/docs/addons/qiskit-addon-obp/tutorials/index.mdx @@ -1,6 +1,5 @@ --- -title: Operator backpropagation (OBP) API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-obp. +title: "Tutorials - Qiskit addon: operator backpropagation (OBP) 0.3.0" --- # Tutorials diff --git a/scripts/js/lib/sphinx/conversionPipeline.ts b/scripts/js/lib/sphinx/conversionPipeline.ts index c64a6e87cef..bcd652d09de 100644 --- a/scripts/js/lib/sphinx/conversionPipeline.ts +++ b/scripts/js/lib/sphinx/conversionPipeline.ts @@ -17,6 +17,7 @@ import { globby } from "globby"; import { mkdirp } from "mkdirp"; import { uniqBy } from "lodash-es"; +import { load } from "cheerio"; import { sphinxHtmlToMarkdown } from "../api/htmlToMd.js"; import { ObjectsInv } from "../api/objectsInv.js"; import { HtmlToMdResultWithUrl } from "../api/HtmlToMdResult.js"; @@ -34,7 +35,6 @@ import { specialCaseResults, transformSpecialCaseUrl, } from "../api/specialCaseResults.js"; -import addFrontMatter from "../api/addFrontMatter.js"; import { dedupeHtmlIdsFromResults } from "../api/dedupeHtmlIds.js"; import removeMathBlocksIndentation from "../api/removeMathBlocksIndentation.js"; import { handleReleaseNotesFile } from "../api/releaseNotes.js"; @@ -114,7 +114,14 @@ async function determineFilePaths( const allFiles = await globby(["**"], { cwd: htmlPath, - ignore: ["apidocs/**", "apidoc/**", "stubs/**", ...SPHINX_INTERNALS], + ignore: [ + "apidocs/**", + "apidoc/**", + "stubs/**", + "release-notes.html", + "release_notes.html", + ...SPHINX_INTERNALS, + ], }); // Prefer .ipynb over .html when both exist for the same base path. @@ -159,6 +166,8 @@ async function convertFilesToMarkdown( continue; } + result.meta.hardcodedFrontmatter = extractHtmlFrontmatter(html); + const { dir, name } = parse(`${outputPath}/${file}`); const url = `/${relative(docsBaseFolder, dir)}/${name}`; results.push({ ...result, url }); @@ -212,7 +221,7 @@ async function postProcessResults( allInvs, ); await dedupeHtmlIdsFromResults(results); - addFrontMatter(results, pkg); + addFrontMatter(results); removeMathBlocksIndentation(results); return results; } @@ -301,7 +310,9 @@ async function processNotebooks( const frontmatterCell = buildFrontmatterCell(processedCells); return { ...notebook, - cells: frontmatterCell ? [frontmatterCell, ...processedCells] : processedCells, + cells: frontmatterCell + ? [frontmatterCell, ...processedCells] + : processedCells, }; }); } @@ -322,7 +333,9 @@ async function writeNotebooks( function buildFrontmatterCell(cells: NotebookCell[]): NotebookCell | undefined { for (const cell of cells) { if (cell.cell_type !== "markdown") continue; - const text = Array.isArray(cell.source) ? cell.source.join("") : cell.source; + const text = Array.isArray(cell.source) + ? cell.source.join("") + : cell.source; const tree = parseMarkdown(text); let title: string | undefined; visit(tree, "heading", (node: any) => { @@ -332,14 +345,33 @@ function buildFrontmatterCell(cells: NotebookCell[]): NotebookCell | undefined { } }); if (title) { - const quote = (s: string) => - `"${s.replace(/\\/g, "\\\\").replace(/"/g, '\\"')}"`; return { cell_type: "markdown", - source: `---\ntitle: ${quote(title)}\n---`, + source: `---\ntitle: "${title}"\n---`, metadata: {}, }; } } return undefined; } + +function extractHtmlFrontmatter(html: string): string { + const $ = load(html); + const title = $("title").first().text().trim(); + const description = $('meta[name="description"]').attr("content")?.trim(); + const lines = [`title: "${title}"`]; + if (description) lines.push(`description: "${description}"`); + return lines.join("\n"); +} + +function addFrontMatter(results: HtmlToMdResultWithUrl[]) { + for (const result of results) { + const markdown = result.markdown; + result.markdown = `--- +${result.meta.hardcodedFrontmatter} +--- + +${markdown} +`; + } +} From 612e9bbb5185820c34e0fad1ae3dba5ab15a848d Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Fri, 8 May 2026 20:33:48 -0400 Subject: [PATCH 024/104] fix changing id --- .../qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb | 2 +- .../how-tos/simulating-circuits-with-obp.ipynb | 2 +- .../qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb | 2 +- .../qiskit-addon-obp/tutorials/01-getting-started.ipynb | 2 +- scripts/js/lib/Notebooks.ts | 1 + scripts/js/lib/api/processHtml.ts | 6 +----- scripts/js/lib/api/saveImages.ts | 2 +- scripts/js/lib/sphinx/conversionPipeline.ts | 1 + 8 files changed, 8 insertions(+), 10 deletions(-) diff --git a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb index 3192a1b29e9..d9d6cc32977 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "48cdd045", + "id": "frontmatter", "metadata": {}, "source": [ "---\n", diff --git a/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb b/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb index 479eb4aa56d..b3b8a185e16 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d203dc9f", + "id": "frontmatter", "metadata": {}, "source": [ "---\n", diff --git a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb index 741c7bb22ba..2bc0a0a3720 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "91696597", + "id": "frontmatter", "metadata": {}, "source": [ "---\n", diff --git a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb index 8780eca760e..33b583b7287 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "8df958ab", + "id": "frontmatter", "metadata": {}, "source": [ "---\n", diff --git a/scripts/js/lib/Notebooks.ts b/scripts/js/lib/Notebooks.ts index eed0fccf1ac..5870dcbc7e2 100644 --- a/scripts/js/lib/Notebooks.ts +++ b/scripts/js/lib/Notebooks.ts @@ -1,4 +1,5 @@ export type NotebookCell = { + id?: string; cell_type: "code" | "markdown" | "raw"; source: string | string[]; metadata: Record; diff --git a/scripts/js/lib/api/processHtml.ts b/scripts/js/lib/api/processHtml.ts index efd4329ec16..349f2e365ea 100644 --- a/scripts/js/lib/api/processHtml.ts +++ b/scripts/js/lib/api/processHtml.ts @@ -114,11 +114,7 @@ export function loadImages( return $main .find("img") .toArray() - .filter((img) => { - // filter out external URLs before processing: - const src = $(img).attr("src"); - return !!src && !src.startsWith("http://") && !src.startsWith("https://"); - }) + .filter((img) => $(img).attr("src")) .map((img) => { const $img = $(img); diff --git a/scripts/js/lib/api/saveImages.ts b/scripts/js/lib/api/saveImages.ts index 47ccff95605..a9a9c8b1c15 100644 --- a/scripts/js/lib/api/saveImages.ts +++ b/scripts/js/lib/api/saveImages.ts @@ -62,7 +62,7 @@ export async function saveImages( return; } const source = `${originalImagesFolderPath}/${img.fileName}`; - const dest = `${publicBaseFolder}/${img.dest.replace(/^\//, "")}`; + const dest = `${publicBaseFolder}/${img.dest}`; if (!(await pathExists(source))) { console.warn(`Skipping missing image: ${source}`); diff --git a/scripts/js/lib/sphinx/conversionPipeline.ts b/scripts/js/lib/sphinx/conversionPipeline.ts index bcd652d09de..eb07c2bfebb 100644 --- a/scripts/js/lib/sphinx/conversionPipeline.ts +++ b/scripts/js/lib/sphinx/conversionPipeline.ts @@ -346,6 +346,7 @@ function buildFrontmatterCell(cells: NotebookCell[]): NotebookCell | undefined { }); if (title) { return { + id: "frontmatter", // hardcoding the cell id so it doesn't change with each run cell_type: "markdown", source: `---\ntitle: "${title}"\n---`, metadata: {}, From e6f0633bfcc4555b85a2dd455866b7c212dde205 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Sat, 9 May 2026 01:00:59 -0400 Subject: [PATCH 025/104] create shared gen-docs command --- .../how-tos/bound-error-using-p-norm.ipynb | 2 +- .../how-tos/truncate-operator-terms.ipynb | 8 +- .../tutorials/01-getting-started.ipynb | 6 +- .../api/qiskit-addon-obp/qiskit-addon-obp.mdx | 4 +- .../api/qiskit-addon-obp/utils-truncating.mdx | 8 +- package.json | 3 +- ...how_tos_bound_error_using_p_norm_16_0.avif | Bin 7140 -> 0 bytes ...how_tos_bound_error_using_p_norm_24_0.avif | Bin 6961 -> 0 bytes .../how_tos_bound_error_using_p_norm_4_0.avif | Bin 11411 -> 0 bytes ...tos_simulating_circuits_with_obp_27_0.avif | Bin 32495 -> 0 bytes ..._tos_simulating_circuits_with_obp_6_0.avif | Bin 11411 -> 0 bytes .../how_tos_truncate_operator_terms_13_0.avif | Bin 13855 -> 0 bytes .../how_tos_truncate_operator_terms_19_0.avif | Bin 13213 -> 0 bytes .../how_tos_truncate_operator_terms_25_0.avif | Bin 15073 -> 0 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100644 public/docs/images/api/qiskit-addon-obp/tutorials_01_getting_started_4_0.avif delete mode 100644 public/docs/images/api/qiskit-addon-obp/tutorials_01_getting_started_8_0.avif delete mode 100644 scripts/js/commands/api/updateApiDocs.ts diff --git a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb index d9d6cc32977..cdc20febc8a 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb @@ -31,7 +31,7 @@ "id": "2780c587-cd2a-4398-8faa-de5e0c661a10", "metadata": {}, "source": [ - "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget).\n", + "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget).\n", "In this guide, you will learn about its additional keyword argument `p_norm`, which can be used to change the Lp-norm used to estimate the error incurred by the truncated Pauli terms." ] }, diff --git a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb index 2bc0a0a3720..93107bcb2ba 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb @@ -27,9 +27,9 @@ "\n", "Operator backpropagation (OBP) can be used to reduce the depth of quantum circuits at the cost of a more complex observable. In order to get meaningful results from OBP, one usually needs to truncate terms from their observable to prevent it from growing too large. One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms' coefficients.\n", "\n", - "The [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", + "The [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", "The amount of terms that are truncated depends on various configuration parameters specified by the user.\n", - "As of now, only a single truncation strategy is available -- the [truncate_binary_search](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search) method.\n", + "As of now, only a single truncation strategy is available -- the [truncate_binary_search](/docs/api/qiskit-addon-obp/utils-truncating#truncate_binary_search) method.\n", "Given an observable and some _budget_ it will perform a binary search over the Pauli terms and coefficients within that observable to find the optimal threshold,\n", "such that the sum of the truncated coefficients is maximal but lower than the budget.\n", "\n", @@ -42,7 +42,7 @@ "id": "aaf5f046-6d5e-4b6f-add7-b60604edef30", "metadata": {}, "source": [ - "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) via the accompanying [setup_budget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function." + "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget) via the accompanying [setup_budget](/docs/api/qiskit-addon-obp/utils-truncating#setup_budget) function." ] }, { @@ -202,7 +202,7 @@ "id": "4a772af6-edc6-4e53-81c7-53f3b72bf58a", "metadata": {}, "source": [ - "Let's use the [OBPMetadata](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", + "Let's use the [OBPMetadata](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", "\n", "- **The top/left plot** shows that we have enough budget to begin truncating observable terms after backpropagating the third slice. Starting from the third slice, we know that we truncate at least one term from each slice we backpropagate because we are incurring some truncation error after each slice.\n", "- **The top/right plot** shows that the error budget ramps up to `.003` for the third slice. We see a sharp drop in remaining budget, which means terms were truncated from the observable. This is consistent with what we inferred from the top/left plot.\n", diff --git a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb index 33b583b7287..a8a2b5c4969 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb @@ -41,7 +41,7 @@ "- **Step 4: Reconstruct results**\n", " - N.A.\n", "\n", - "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." + "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." ] }, { @@ -241,7 +241,7 @@ "source": [ "### Constrain how large the operator may grow during backpropagation\n", "\n", - "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget) instance.\n", + "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/utils-simplify#operatorbudget) instance.\n", "\n", "Here we specify that backpropagation should stop when the number of qubit-wise commuting Pauli groups in the operator grows past 8." ] @@ -327,7 +327,7 @@ "id": "d25b9701-f1b1-4230-9dcc-554b85e91cf7", "metadata": {}, "source": [ - "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", + "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](/docs/api/qiskit-addon-obp/utils-truncating#setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", "\n", "In order to enable this truncation, we need to setup our error budget like so:" ] diff --git a/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx b/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx index 0dc24a61be8..a4334ca7d74 100644 --- a/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx +++ b/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx @@ -34,8 +34,8 @@ Main operator backpropagation functionality. **Parameters** * **observables** ([*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)*]*) – The observable(s) onto which the circuit is backpropagated. - * **slices** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)*]*) – A sequence of `QuantumCircuit` objects representing a single circuit which has been separated into partitions spanning all qubits. **These “slices” will be backpropagated in reverse order.** Each slice must span all qubits. One may use the tools provided in [`qiskit_addon_utils.slicing`](https://qiskit.github.io/qiskit-addon-utils/apidocs/qiskit_addon_utils.slicing.html#module-qiskit_addon_utils.slicing "(in Qiskit addon utilities)") to slice a single [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit). - * **truncation\_error\_budget** ([*TruncationErrorBudget*](utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") *| None*) – The error budget used for truncating Pauli terms. Refer to the [how-to guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/truncate_operator_terms.html) for a detailed discussion on truncating terms from the output operator and bounding the incurred error. + * **slices** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)*]*) – A sequence of `QuantumCircuit` objects representing a single circuit which has been separated into partitions spanning all qubits. **These “slices” will be backpropagated in reverse order.** Each slice must span all qubits. One may use the tools provided in [`qiskit_addon_utils.slicing`](/docs/addons/qiskit-addon-utils/apidocs/qiskit-addon-utils-slicing#module-qiskit_addon_utils.slicing "(in Qiskit addon utilities)") to slice a single [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit). + * **truncation\_error\_budget** ([*TruncationErrorBudget*](utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") *| None*) – The error budget used for truncating Pauli terms. Refer to the [how-to guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms) for a detailed discussion on truncating terms from the output operator and bounding the incurred error. * **operator\_budget** ([*OperatorBudget*](utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") *| None*) – Constraints on how large the operator may grow during backpropagation. If `None`, a default instance of [`OperatorBudget`](utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") will be used, and no constraints will be placed on the output operator size. * **max\_seconds** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – The maximum number of seconds to run the backpropagation. If this timeout is triggered before the function returns, the metadata of that moment will be returned. Note, that this metadata may contain only partial information for the last slice being backpropagated. diff --git a/docs/api/qiskit-addon-obp/utils-truncating.mdx b/docs/api/qiskit-addon-obp/utils-truncating.mdx index 2e6b97735e1..f53dee4039a 100644 --- a/docs/api/qiskit-addon-obp/utils-truncating.mdx +++ b/docs/api/qiskit-addon-obp/utils-truncating.mdx @@ -23,7 +23,7 @@ Functions for truncating Pauli operators within given error budgets. A class for storing the constants that determine the truncation error budget. - Refer to the [how-to guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/truncate_operator_terms.html) for a detailed discussion on truncating operator terms during backpropagation and bounding the incurred error. + Refer to the [how-to guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms) for a detailed discussion on truncating operator terms during backpropagation and bounding the incurred error. **Parameters** @@ -53,7 +53,7 @@ Functions for truncating Pauli operators within given error budgets. Indicates which Lp-norm is used for calculating truncation errors. - Refer to the [how-to guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/bound_error_using_p_norm.html) for a detailed conversation on bounding truncation error using higher Lp-norms. + Refer to the [how-to guide](/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm) for a detailed conversation on bounding truncation error using higher Lp-norms. #### per\_slice\_budget @@ -74,7 +74,7 @@ Functions for truncating Pauli operators within given error budgets. Calculate the budget available to each slice for observable term truncation. - This method makes the construction of a [`TruncationErrorBudget`](#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") easier for an end-user. This error budget can be provided to the [`backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") method to enable the truncation of low-weight Pauli terms. Refer to the [how-to guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/truncate_operator_terms.html) for a detailed discussion on truncating terms from the output operator and bounding the incurred error. + This method makes the construction of a [`TruncationErrorBudget`](#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") easier for an end-user. This error budget can be provided to the [`backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") method to enable the truncation of low-weight Pauli terms. Refer to the [how-to guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms) for a detailed discussion on truncating terms from the output operator and bounding the incurred error. **The construction logic is as follows:** @@ -94,7 +94,7 @@ Functions for truncating Pauli operators within given error budgets. * **max\_error\_per\_slice** ([*float*](https://docs.python.org/3/library/functions.html#float) *|*[*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] | None*) – Specifies the maximum error per backpropagated slice. See above for more details. * **max\_error\_total** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – Specifies the total maximum error for the entire backpropagation. See above for more details. * **num\_slices** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – The number of slices over which to distribute the budget. See above for more details. - * **p\_norm** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The Lp norm of the error. This affects the gradual distribution of `max_error_total` in the case of `num_slices` also being set (see above). Refer to the [how-to guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/bound_error_using_p_norm.html) for a detailed conversation on bounding truncation error using higher Lp-norms. + * **p\_norm** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The Lp norm of the error. This affects the gradual distribution of `max_error_total` in the case of `num_slices` also being set (see above). Refer to the [how-to guide](/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm) for a detailed conversation on bounding truncation error using higher Lp-norms. **Returns** diff --git a/package.json b/package.json index 860f4cb498b..fe4da73a53a 100644 --- a/package.json +++ b/package.json @@ -31,8 +31,7 @@ "check:qiskit-deprecation": "tsx scripts/js/commands/api/checkQiskitDeprecation.ts", "check:qiskit-versions": "tsx scripts/js/commands/checkQiskitApiVersions.ts", "regen-apis": "tsx scripts/js/commands/api/regenerateApiDocs.ts", - "gen-api": "tsx scripts/js/commands/api/updateApiDocs.ts", - "gen-addon-docs": "tsx scripts/js/commands/updateDocs.ts", + "gen-docs": "tsx scripts/js/commands/updateDocs.ts", "generate-historical-redirects": "tsx scripts/js/commands/api/generateHistoricalRedirects.ts", "save-internal-links": "tsx scripts/js/commands/saveInternalLinks.ts" }, diff --git a/public/docs/images/api/qiskit-addon-obp/how_tos_bound_error_using_p_norm_16_0.avif 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     + + diff --git a/scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html b/scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html new file mode 100644 index 00000000000..ed2fe3309c0 --- /dev/null +++ b/scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html @@ -0,0 +1,17 @@ + + + + + Qiskit Addon Smoke - smoke 0.0 + + + + + + diff --git a/scripts/js/lib/api/testdata/qiskit-addon-smoke/install.html b/scripts/js/lib/api/testdata/qiskit-addon-smoke/install.html new file mode 100644 index 00000000000..6b94ccebc65 --- /dev/null +++ b/scripts/js/lib/api/testdata/qiskit-addon-smoke/install.html @@ -0,0 +1,16 @@ + + + + + Installation - smoke 0.0 + + + +
    +
    +

    Installation#

    +

    Install with pip. Back to the home page.

    +
    +
    + + diff --git a/scripts/js/lib/api/testdata/qiskit-addon-smoke/objects.inv b/scripts/js/lib/api/testdata/qiskit-addon-smoke/objects.inv new file mode 100644 index 0000000000000000000000000000000000000000..b7ec346b6b6ed96bf1d296632b6efecdca5e3af9 GIT binary patch literal 182 zcmY#Z2rkIT%&Sny%qvUHE6FdaR47X=D$dN$Q!wIERtPA{&q_@$u~G=kEY8j>QE*I2 z0m=mD=4Yn@Wy2sUtrURlkc?D?qSV~P%)FG;B8B`kg_4ZSVuiHKoKyuMot&RrP?TC+ zoSLFgTAZ1eu27YenWV>6aZCH$+4Fu^y-)V*pFDBKJ6vPM7BB6y-g+m`oDVh#(a_WM b)w-td7Px47;H24$*DP9anw4SRZ&5t}VgE)p literal 0 HcmV?d00001 diff --git a/scripts/js/lib/api/testdata/qiskit-addon-smoke/tutorials/intro.ipynb b/scripts/js/lib/api/testdata/qiskit-addon-smoke/tutorials/intro.ipynb new file mode 100644 index 00000000000..bd6059d589b --- /dev/null +++ b/scripts/js/lib/api/testdata/qiskit-addon-smoke/tutorials/intro.ipynb @@ -0,0 +1,31 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Intro tutorial\n", + "\n", + "This notebook references [do_thing](https://qiskit.github.io/qiskit-addon-smoke/stubs/smoke.do_thing.html) from the current package and [other_thing](https://qiskit.github.io/qiskit-addon-other/stubs/other_thing.html) from a sibling package.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('hello from the smoke tutorial')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/js/lib/sphinx/conversionPipeline.ts b/scripts/js/lib/sphinx/conversionPipeline.ts deleted file mode 100644 index eb07c2bfebb..00000000000 --- a/scripts/js/lib/sphinx/conversionPipeline.ts +++ /dev/null @@ -1,378 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { dirname, join, parse, relative } from "path"; -import { readFile, writeFile } from "fs/promises"; - -import { globby } from "globby"; -import { mkdirp } from "mkdirp"; -import { uniqBy } from "lodash-es"; - -import { load } from "cheerio"; -import { sphinxHtmlToMarkdown } from "../api/htmlToMd.js"; -import { ObjectsInv } from "../api/objectsInv.js"; -import { HtmlToMdResultWithUrl } from "../api/HtmlToMdResult.js"; -import { Pkg } from "../api/Pkg.js"; -import { saveImages } from "../api/saveImages.js"; -import { - normalizeResultUrls, - kebabCaseAndShortenPage, -} from "../api/normalizeResultUrls.js"; -import { updateLinks, relativizeLink } from "../api/updateLinks.js"; -import { parseMarkdown, extractHeadingText } from "../markdownUtils.js"; -import { visit, EXIT } from "unist-util-visit"; -import { mergeClassMembers } from "../api/mergeClassMembers.js"; -import { - specialCaseResults, - transformSpecialCaseUrl, -} from "../api/specialCaseResults.js"; -import { dedupeHtmlIdsFromResults } from "../api/dedupeHtmlIds.js"; -import removeMathBlocksIndentation from "../api/removeMathBlocksIndentation.js"; -import { handleReleaseNotesFile } from "../api/releaseNotes.js"; -import { NotebookCell, NotebookWithUrl } from "../Notebooks.js"; - -export const DOCS_BASE_PATH = "/docs"; - -// Sphinx build artifacts that are never content. -const SPHINX_INTERNALS = [ - "_static/**", - "_sources/**", - "_downloads/**", - "_modules/**", - "genindex.html", - "py-modindex.html", - "search.html", - "objects.inv", -]; - -export async function runSphinxPipeline( - artifactPath: string, - docsBaseFolder: string, - publicBaseFolder: string, - pkg: Pkg, -): Promise { - const allObjectInvs = await ObjectsInv.loadPublishedApis(publicBaseFolder); - const [files, outputPath, objectsInv] = await determineFilePaths( - artifactPath, - docsBaseFolder, - pkg, - ); - - // handle HTML files - const htmlFiles = files.filter((f) => f.endsWith(".html")); - const initialResults = await convertFilesToMarkdown( - pkg, - artifactPath, - docsBaseFolder, - outputPath, - htmlFiles, - ); - const results = await postProcessResults( - pkg, - objectsInv, - allObjectInvs, - initialResults, - ); - await writeMarkdownResults(pkg, docsBaseFolder, results); - - // handle Jupyter notebook files - const notebookFiles = files.filter((f) => f.endsWith(".ipynb")); - const initialNotebooks = await readNotebooks( - artifactPath, - docsBaseFolder, - outputPath, - notebookFiles, - ); - const notebooks = await processNotebooks( - initialNotebooks, - pkg, - objectsInv, - allObjectInvs, - ); - await writeNotebooks(pkg, docsBaseFolder, notebooks); - - // write assets - await copyImages(pkg, artifactPath, "public", results); - await objectsInv.write(pkg.apiOutputDir(publicBaseFolder)); -} - -async function determineFilePaths( - htmlPath: string, - docsBaseFolder: string, - pkg: Pkg, -): Promise<[string[], string, ObjectsInv]> { - const objectsInv = await ObjectsInv.fromFile(htmlPath, pkg.language); - - const allFiles = await globby(["**"], { - cwd: htmlPath, - ignore: [ - "apidocs/**", - "apidoc/**", - "stubs/**", - "release-notes.html", - "release_notes.html", - ...SPHINX_INTERNALS, - ], - }); - - // Prefer .ipynb over .html when both exist for the same base path. - const notebookBases = new Set( - allFiles - .filter((f) => f.endsWith(".ipynb")) - .map((f) => f.slice(0, -".ipynb".length)), - ); - const files = allFiles.filter( - (f) => - !f.endsWith(".html") || !notebookBases.has(f.slice(0, -".html".length)), - ); - const outputPath = pkg.outputDir(docsBaseFolder); - await mkdirp(outputPath); - return [files, outputPath, objectsInv]; -} - -async function convertFilesToMarkdown( - pkg: Pkg, - artifactPath: string, - docsBaseFolder: string, - outputPath: string, - filePaths: string[], -): Promise { - const results = []; - for (const file of filePaths) { - const html = await readFile(join(artifactPath, file), "utf-8"); - const result = await sphinxHtmlToMarkdown({ - html, - fileName: file, - determineGithubUrl: pkg.determineGithubUrlFn(), - imageDestination: pkg.apiOutputDir(`${DOCS_BASE_PATH}/images`), - releaseNotesTitle: pkg.releaseNotesTitle(), - hasSeparateReleaseNotes: pkg.hasSeparateReleaseNotes(), - isCApi: pkg.isCApi(), - hasRootNamespaceFile: pkg.hasRootNamespaceFile, - }); - - // Avoid creating an empty markdown file for HTML files without content - // (e.g. HTML redirects) - if (result.markdown == "") { - continue; - } - - result.meta.hardcodedFrontmatter = extractHtmlFrontmatter(html); - - const { dir, name } = parse(`${outputPath}/${file}`); - const url = `/${relative(docsBaseFolder, dir)}/${name}`; - results.push({ ...result, url }); - } - return results; -} - -async function copyImages( - pkg: Pkg, - htmlPath: string, - publicBaseFolder: string, - results: HtmlToMdResultWithUrl[], -): Promise { - console.log("Saving images"); - const allImages = uniqBy( - results.flatMap((result) => result.images), - (image) => image.fileName, - ); - await saveImages(allImages, `${htmlPath}/_images`, publicBaseFolder, pkg); -} - -async function postProcessResults( - pkg: Pkg, - objectsInv: ObjectsInv, - allInvs: Map, - initialResults: HtmlToMdResultWithUrl[], -): Promise { - const results = await mergeClassMembers(initialResults); - normalizeResultUrls(results, { - kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, - pkgName: pkg.name, - }); - specialCaseResults(results); - // Rewrite relative apidocs/ links to absolute /docs/api// paths before - // updateLinks runs, so they are correctly resolved as internal API links. - const apiBase = pkg.apiOutputDir(DOCS_BASE_PATH); - for (const result of results) { - result.markdown = result.markdown.replace( - /\]\((?:\.\.\/)*?(apidocs|apidoc|stubs)\/([^)]+)\)/g, - `](${apiBase}/$2)`, - ); - } - await updateLinks( - results, - { - kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, - pkgName: pkg.name, - pkgOutputDir: pkg.apiOutputDir(DOCS_BASE_PATH), - }, - objectsInv, - allInvs, - ); - await dedupeHtmlIdsFromResults(results); - addFrontMatter(results); - removeMathBlocksIndentation(results); - return results; -} - -async function writeMarkdownResults( - pkg: Pkg, - docsBaseFolder: string, - results: HtmlToMdResultWithUrl[], -): Promise { - for (const result of results) { - const path = `${docsBaseFolder}${result.url}.mdx`; - if (path.endsWith("release-notes.mdx")) { - if (!pkg.releaseNotesConfig.enabled) continue; - - const shouldWriteResult = await handleReleaseNotesFile(result, pkg); - if (!shouldWriteResult) continue; - } - - await mkdirp(dirname(path)); - await writeFile(path, result.markdown); - } -} - -export async function readNotebooks( - artifactPath: string, - docsBaseFolder: string, - outputPath: string, - filePaths: string[], -): Promise { - const results = []; - for (const file of filePaths) { - const raw = await readFile(`${artifactPath}/${file}`, "utf-8"); - const notebook = JSON.parse(raw); - const { dir, name } = parse(`${outputPath}/${file}`); - const url = `/${relative(docsBaseFolder, dir)}/${name}`; - results.push({ ...notebook, url }); - } - return results; -} - -function normalizeNotebookUrl(url: string, pkg: Pkg): string { - const parts = url.split("/"); - const filename = parts[parts.length - 1]; - const normalized = pkg.kebabCaseAndShortenUrls - ? kebabCaseAndShortenPage(filename, pkg.name) - : filename; - return transformSpecialCaseUrl([...parts.slice(0, -1), normalized].join("/")); -} - -function rewriteNotebookLinks( - source: string | string[], - objectsInv: ObjectsInv, - allInvs: Map, -): string | string[] { - const rewrite = (line: string) => { - return line.replace(/\[([^\]]*)\]\(([^)]+)\)/g, (_match, text, url) => { - const relativized = relativizeLink({ url, text }); - if (relativized) url = relativized.url; - const stub = objectsInv.resolveStubUrl(url, allInvs); - if (stub) url = stub; - return `[${text}](${url})`; - }); - }; - - return Array.isArray(source) ? source.map(rewrite) : rewrite(source); -} - -async function processNotebooks( - notebooks: NotebookWithUrl[], - pkg: Pkg, - objectsInv: ObjectsInv, - allInvs: Map, -): Promise { - return notebooks.map((notebook) => { - const processedCells = notebook.cells.map((cell) => { - if (cell.cell_type !== "markdown") return cell; - return { - ...cell, - source: rewriteNotebookLinks(cell.source, objectsInv, allInvs), - }; - }); - - // Prepend a frontmatter cell matching the guides notebook convention. - // Extract the title from the first markdown h1, same as extractFrontmatter - // does for HTML pages. - const frontmatterCell = buildFrontmatterCell(processedCells); - return { - ...notebook, - cells: frontmatterCell - ? [frontmatterCell, ...processedCells] - : processedCells, - }; - }); -} - -async function writeNotebooks( - pkg: Pkg, - docsBaseFolder: string, - notebooks: NotebookWithUrl[], -) { - for (const { url, ...notebook } of notebooks) { - const normalizedUrl = normalizeNotebookUrl(url, pkg); - const path = `${docsBaseFolder}${normalizedUrl}.ipynb`; - await mkdirp(dirname(path)); - await writeFile(path, JSON.stringify(notebook, null, 1)); - } -} - -function buildFrontmatterCell(cells: NotebookCell[]): NotebookCell | undefined { - for (const cell of cells) { - if (cell.cell_type !== "markdown") continue; - const text = Array.isArray(cell.source) - ? cell.source.join("") - : cell.source; - const tree = parseMarkdown(text); - let title: string | undefined; - visit(tree, "heading", (node: any) => { - if (node.depth === 1 && !title) { - title = extractHeadingText(node).trim(); - return EXIT; - } - }); - if (title) { - return { - id: "frontmatter", // hardcoding the cell id so it doesn't change with each run - cell_type: "markdown", - source: `---\ntitle: "${title}"\n---`, - metadata: {}, - }; - } - } - return undefined; -} - -function extractHtmlFrontmatter(html: string): string { - const $ = load(html); - const title = $("title").first().text().trim(); - const description = $('meta[name="description"]').attr("content")?.trim(); - const lines = [`title: "${title}"`]; - if (description) lines.push(`description: "${description}"`); - return lines.join("\n"); -} - -function addFrontMatter(results: HtmlToMdResultWithUrl[]) { - for (const result of results) { - const markdown = result.markdown; - result.markdown = `--- -${result.meta.hardcodedFrontmatter} ---- - -${markdown} -`; - } -} From c7a0902be002962f4953d5237ef25b04de51bffb Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Tue, 12 May 2026 18:12:15 -0400 Subject: [PATCH 029/104] update import --- scripts/js/commands/updateAddonDocs.ts | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/scripts/js/commands/updateAddonDocs.ts b/scripts/js/commands/updateAddonDocs.ts index 431e43cfa3f..68888329fb7 100644 --- a/scripts/js/commands/updateAddonDocs.ts +++ b/scripts/js/commands/updateAddonDocs.ts @@ -17,10 +17,10 @@ import { readFile, writeFile } from "fs/promises"; import { $ } from "zx"; import { mkdirp } from "mkdirp"; -import { Pkg } from "../lib/docsGen/Pkg.js"; +import { Pkg } from "../lib/api/Pkg.js"; import { pathExists } from "../lib/fs.js"; -import { downloadSphinxArtifact } from "../lib/docsGen/sphinxArtifacts.js"; -import { runAddonDocsPipeline } from "../lib/docsGen/addonDocsPipeline.js"; +import { downloadSphinxArtifact } from "../lib/api/sphinxArtifacts.js"; +import { runAddonDocsPipeline } from "../lib/api/addonDocsPipeline.js"; import { zxMain } from "../lib/zx.js"; import { isValidVersion, parseMinorVersion } from "../lib/apiVersions.js"; From 00772ddff5a983aeb339a599e2ef3a707b776c19 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Tue, 12 May 2026 18:29:49 -0400 Subject: [PATCH 030/104] Remove conversionPipeline.ts in favor of apiDocsPipeline.ts All callers that imported DOCS_BASE_PATH and C_API_BASE_PATH from conversionPipeline.ts now import them from paths.ts. The conversionPipeline test is updated to call runApiDocsPipeline. Notebooks.ts moves from lib/ into lib/api/ where it belongs alongside notebookStages.ts. Co-Authored-By: Claude Sonnet 4.6 --- scripts/js/lib/{ => api}/Notebooks.ts | 0 scripts/js/lib/api/conversionPipeline.test.ts | 4 +- scripts/js/lib/api/conversionPipeline.ts | 217 ------------------ scripts/js/lib/api/generateToc.ts | 2 +- scripts/js/lib/api/normalizeResultUrls.ts | 2 +- scripts/js/lib/api/notebookStages.ts | 2 +- scripts/js/lib/api/objectsInv.ts | 2 +- scripts/js/lib/api/releaseNotes.ts | 2 +- scripts/js/lib/api/updateLinks.ts | 2 +- 9 files changed, 8 insertions(+), 225 deletions(-) rename scripts/js/lib/{ => api}/Notebooks.ts (100%) delete mode 100644 scripts/js/lib/api/conversionPipeline.ts diff --git a/scripts/js/lib/Notebooks.ts b/scripts/js/lib/api/Notebooks.ts similarity index 100% rename from scripts/js/lib/Notebooks.ts rename to scripts/js/lib/api/Notebooks.ts diff --git a/scripts/js/lib/api/conversionPipeline.test.ts b/scripts/js/lib/api/conversionPipeline.test.ts index 9171b925cae..3fc28c73ce8 100644 --- a/scripts/js/lib/api/conversionPipeline.test.ts +++ b/scripts/js/lib/api/conversionPipeline.test.ts @@ -17,7 +17,7 @@ import { mkdtemp, readFile } from "fs/promises"; import { globby } from "globby"; import { expect, test } from "@playwright/test"; -import { runConversionPipeline } from "./conversionPipeline.js"; +import { runApiDocsPipeline } from "./apiDocsPipeline.js"; import { Pkg, ReleaseNotesConfig } from "./Pkg.js"; // This test uses snapshot testing (https://jestjs.io/docs/snapshot-testing#updating-snapshots). If the tests fail and the changes @@ -68,7 +68,7 @@ test("qiskit-sphinx-theme", async ({}, testInfo) => { }); const markdownFolder = pkg.apiOutputDir(docsBaseFolder); - await runConversionPipeline( + await runApiDocsPipeline( "scripts/js/lib/api/testdata/qiskit-sphinx-theme", docsBaseFolder, publicBaseFolder, diff --git a/scripts/js/lib/api/conversionPipeline.ts b/scripts/js/lib/api/conversionPipeline.ts deleted file mode 100644 index f3f17786434..00000000000 --- a/scripts/js/lib/api/conversionPipeline.ts +++ /dev/null @@ -1,217 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2024. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -import { join, parse, relative } from "path"; -import { readFile, writeFile } from "fs/promises"; - -import { mkdirp } from "mkdirp"; -import { globby } from "globby"; -import { uniqBy } from "lodash-es"; - -import { ObjectsInv } from "./objectsInv.js"; -import { sphinxHtmlToMarkdown } from "./htmlToMd.js"; -import { saveImages } from "./saveImages.js"; -import { generateToc } from "./generateToc.js"; -import { HtmlToMdResultWithUrl } from "./HtmlToMdResult.js"; -import { mergeClassMembers } from "./mergeClassMembers.js"; -import { normalizeResultUrls } from "./normalizeResultUrls.js"; -import { updateLinks } from "./updateLinks.js"; -import { specialCaseResults } from "./specialCaseResults.js"; -import addFrontMatter from "./addFrontMatter.js"; -import { dedupeHtmlIdsFromResults } from "./dedupeHtmlIds.js"; -import removeMathBlocksIndentation from "./removeMathBlocksIndentation.js"; -import { Pkg } from "./Pkg.js"; -import { - maybeUpdateReleaseNotesFolder, - handleReleaseNotesFile, -} from "./releaseNotes.js"; - -// This is the folder that contains all C API docs in the Sphinx artifact. -export const C_API_BASE_PATH = "cdoc" as const; - -export const DOCS_BASE_PATH = "/docs"; - -export async function runConversionPipeline( - htmlPath: string, - docsBaseFolder: string, - publicBaseFolder: string, - pkg: Pkg, -) { - const [files, markdownPath, maybeObjectsInv] = await determineFilePaths( - htmlPath, - docsBaseFolder, - pkg, - ); - let initialResults = await convertFilesToMarkdown( - pkg, - htmlPath, - docsBaseFolder, - markdownPath, - files, - ); - - const results = await postProcessResults( - pkg, - maybeObjectsInv, - initialResults, - ); - - // Warning: the sequence of operations often matters. - await writeMarkdownResults(pkg, docsBaseFolder, results); - await copyImages(pkg, htmlPath, "public", results); - await maybeObjectsInv?.write(pkg.apiOutputDir(publicBaseFolder)); - await maybeUpdateReleaseNotesFolder(pkg, markdownPath); - await writeTocFile(pkg, markdownPath, results); - await writeVersionFile(pkg, markdownPath); -} - -async function determineFilePaths( - htmlPath: string, - docsBaseFolder: string, - pkg: Pkg, -): Promise<[string[], string, ObjectsInv | undefined]> { - const maybeObjectsInv = await (pkg.isProblematicLegacyQiskit() - ? undefined - : ObjectsInv.fromFile(htmlPath, pkg.language)); - - const extraFiles = pkg.isCApi() - ? [`${C_API_BASE_PATH}/**.html`] - : ["apidocs/**.html", "apidoc/**.html", "stubs/**.html"]; - const files = await globby( - [...extraFiles, "release_notes.html", "release-notes.html"], - { - cwd: htmlPath, - }, - ); - const markdownPath = pkg.apiOutputDir(docsBaseFolder); - await mkdirp(markdownPath); - return [files, markdownPath, maybeObjectsInv]; -} - -async function convertFilesToMarkdown( - pkg: Pkg, - htmlPath: string, - docsBaseFolder: string, - markdownPath: string, - filePaths: string[], -): Promise { - const results = []; - for (const file of filePaths) { - const html = await readFile(join(htmlPath, file), "utf-8"); - const result = await sphinxHtmlToMarkdown({ - html, - fileName: file, - determineGithubUrl: pkg.determineGithubUrlFn(), - imageDestination: pkg.apiOutputDir(`${DOCS_BASE_PATH}/images`), - releaseNotesTitle: pkg.releaseNotesTitle(), - hasSeparateReleaseNotes: pkg.hasSeparateReleaseNotes(), - isCApi: pkg.isCApi(), - hasRootNamespaceFile: pkg.hasRootNamespaceFile, - }); - - // Avoid creating an empty markdown file for HTML files without content - // (e.g. HTML redirects) - if (result.markdown == "") { - continue; - } - - const { dir, name } = parse(`${markdownPath}/${file}`); - let url = `/${relative(docsBaseFolder, dir)}/${name}`; - results.push({ ...result, url }); - } - return results; -} - -async function copyImages( - pkg: Pkg, - htmlPath: string, - publicBaseFolder: string, - results: HtmlToMdResultWithUrl[], -): Promise { - console.log("Saving images"); - const allImages = uniqBy( - results.flatMap((result) => result.images), - (image) => image.fileName, - ); - await saveImages( - allImages, - `${htmlPath}/_images`, - pkg.apiOutputDir(`${publicBaseFolder}/images`), - pkg, - ); -} - -async function postProcessResults( - pkg: Pkg, - maybeObjectsInv: ObjectsInv | undefined, - initialResults: HtmlToMdResultWithUrl[], -): Promise { - const results = await mergeClassMembers(initialResults); - normalizeResultUrls(results, { - kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, - pkgName: pkg.name, - }); - specialCaseResults(results); - await updateLinks( - results, - { - kebabCaseAndShorten: pkg.kebabCaseAndShortenUrls, - pkgName: pkg.name, - pkgOutputDir: pkg.apiOutputDir(DOCS_BASE_PATH), - }, - maybeObjectsInv, - ); - await dedupeHtmlIdsFromResults(results); - addFrontMatter(results, pkg); - removeMathBlocksIndentation(results); - return results; -} - -async function writeMarkdownResults( - pkg: Pkg, - docsBaseFolder: string, - results: HtmlToMdResultWithUrl[], -): Promise { - for (const result of results) { - let path = `${docsBaseFolder}${result.url}.mdx`; - if (path.endsWith("release-notes.mdx")) { - if (!pkg.releaseNotesConfig.enabled) continue; - - const shouldWriteResult = await handleReleaseNotesFile(result, pkg); - if (!shouldWriteResult) continue; - } - - await writeFile(path, result.markdown); - } -} - -async function writeTocFile( - pkg: Pkg, - markdownPath: string, - results: HtmlToMdResultWithUrl[], -): Promise { - console.log("Generating toc"); - const toc = generateToc(pkg, results); - await writeFile( - `${markdownPath}/_toc.json`, - JSON.stringify(toc, null, 2) + "\n", - ); -} - -async function writeVersionFile(pkg: Pkg, markdownPath: string): Promise { - console.log("Generating version file"); - const pkg_json = { name: pkg.name, version: pkg.version }; - await writeFile( - `${markdownPath}/_package.json`, - JSON.stringify(pkg_json, null, 2) + "\n", - ); -} diff --git a/scripts/js/lib/api/generateToc.ts b/scripts/js/lib/api/generateToc.ts index ef4636ba252..f89c5255e40 100644 --- a/scripts/js/lib/api/generateToc.ts +++ b/scripts/js/lib/api/generateToc.ts @@ -16,7 +16,7 @@ import { getLastPartFromFullIdentifier } from "../stringUtils.js"; import { HtmlToMdResultWithUrl } from "./HtmlToMdResult.js"; import { Pkg } from "./Pkg.js"; import type { TocGrouping } from "./TocGrouping.js"; -import { DOCS_BASE_PATH } from "./conversionPipeline.js"; +import { DOCS_BASE_PATH } from "./paths.js"; import { groupByMajorVersion } from "./releaseNotes.js"; export type TocEntry = { diff --git a/scripts/js/lib/api/normalizeResultUrls.ts b/scripts/js/lib/api/normalizeResultUrls.ts index 375411fe6f1..f50679b8d09 100644 --- a/scripts/js/lib/api/normalizeResultUrls.ts +++ b/scripts/js/lib/api/normalizeResultUrls.ts @@ -13,7 +13,7 @@ import { kebabCase, initial, last } from "lodash-es"; import { HtmlToMdResultWithUrl } from "./HtmlToMdResult.js"; -import { C_API_BASE_PATH } from "./conversionPipeline.js"; +import { C_API_BASE_PATH } from "./paths.js"; import { removePart } from "../stringUtils.js"; export function kebabCaseAndShortenPage(page: string, pkgName: string): string { diff --git a/scripts/js/lib/api/notebookStages.ts b/scripts/js/lib/api/notebookStages.ts index 3f168c56eed..998081d54ff 100644 --- a/scripts/js/lib/api/notebookStages.ts +++ b/scripts/js/lib/api/notebookStages.ts @@ -27,7 +27,7 @@ import { kebabCaseAndShortenPage } from "./normalizeResultUrls.js"; import { relativizeLink } from "./updateLinks.js"; import { transformSpecialCaseUrl } from "./specialCaseResults.js"; import { parseMarkdown, extractHeadingText } from "../markdownUtils.js"; -import { NotebookCell, NotebookWithUrl } from "../Notebooks.js"; +import { NotebookCell, NotebookWithUrl } from "./Notebooks.js"; export async function readNotebooks( artifactPath: string, diff --git a/scripts/js/lib/api/objectsInv.ts b/scripts/js/lib/api/objectsInv.ts index b39faf41525..ad2ed3c376d 100644 --- a/scripts/js/lib/api/objectsInv.ts +++ b/scripts/js/lib/api/objectsInv.ts @@ -16,7 +16,7 @@ import { join, dirname } from "path"; import { mkdirp } from "mkdirp"; import { removePrefix, removeSuffix } from "../stringUtils.js"; -import { C_API_BASE_PATH } from "./conversionPipeline.js"; +import { C_API_BASE_PATH } from "./paths.js"; import { PackageLanguage } from "./Pkg.js"; /** diff --git a/scripts/js/lib/api/releaseNotes.ts b/scripts/js/lib/api/releaseNotes.ts index 4198fa987ac..5fa57d584a8 100644 --- a/scripts/js/lib/api/releaseNotes.ts +++ b/scripts/js/lib/api/releaseNotes.ts @@ -19,7 +19,7 @@ import transformLinks from "transform-markdown-links"; import { pathExists } from "../fs.js"; import type { Pkg } from "./Pkg.js"; import type { HtmlToMdResultWithUrl } from "./HtmlToMdResult.js"; -import { C_API_BASE_PATH, DOCS_BASE_PATH } from "./conversionPipeline.js"; +import { C_API_BASE_PATH, DOCS_BASE_PATH } from "./paths.js"; import { kebabCaseAndShortenPage } from "./normalizeResultUrls.js"; import { removePrefix } from "../stringUtils.js"; import { generateReleaseNotesEntry, TocEntry } from "./generateToc.js"; diff --git a/scripts/js/lib/api/updateLinks.ts b/scripts/js/lib/api/updateLinks.ts index 82fecd9c039..d5be89e2c8f 100644 --- a/scripts/js/lib/api/updateLinks.ts +++ b/scripts/js/lib/api/updateLinks.ts @@ -29,7 +29,7 @@ import { remarkStringifyOptions } from "./commonParserConfig.js"; import { ObjectsInv } from "./objectsInv.js"; import { transformSpecialCaseUrl } from "./specialCaseResults.js"; import { kebabCaseAndShortenPage } from "./normalizeResultUrls.js"; -import { DOCS_BASE_PATH } from "./conversionPipeline.js"; +import { DOCS_BASE_PATH } from "./paths.js"; export interface Link { url: string; // Where the link goes From e4164d1638b598b36ef5bdd4ef93698eb47aa690 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Tue, 12 May 2026 18:32:57 -0400 Subject: [PATCH 031/104] update script --- package.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/package.json b/package.json index c5e87e16a36..ca275f43892 100644 --- a/package.json +++ b/package.json @@ -32,7 +32,7 @@ "check:qiskit-versions": "tsx scripts/js/commands/checkQiskitApiVersions.ts", "regen-apis": "tsx scripts/js/commands/api/regenerateApiDocs.ts", "gen-api": "tsx scripts/js/commands/api/updateApiDocs.ts", - "gen-addons": "tsx scripts/js/commands/updateAddonDocs.ts", + "gen-addons": "tsx scripts/js/commands/api/updateAddonDocs.ts", "generate-historical-redirects": "tsx scripts/js/commands/api/generateHistoricalRedirects.ts", "save-internal-links": "tsx scripts/js/commands/saveInternalLinks.ts" }, From 2810efcfe83e5f38f764ce883dddf0d6bc923579 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Tue, 12 May 2026 18:35:00 -0400 Subject: [PATCH 032/104] move file --- scripts/js/commands/{ => api}/updateAddonDocs.ts | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) rename scripts/js/commands/{ => api}/updateAddonDocs.ts (92%) diff --git a/scripts/js/commands/updateAddonDocs.ts b/scripts/js/commands/api/updateAddonDocs.ts similarity index 92% rename from scripts/js/commands/updateAddonDocs.ts rename to scripts/js/commands/api/updateAddonDocs.ts index 68888329fb7..925b6352c42 100644 --- a/scripts/js/commands/updateAddonDocs.ts +++ b/scripts/js/commands/api/updateAddonDocs.ts @@ -17,12 +17,12 @@ import { readFile, writeFile } from "fs/promises"; import { $ } from "zx"; import { mkdirp } from "mkdirp"; -import { Pkg } from "../lib/api/Pkg.js"; -import { pathExists } from "../lib/fs.js"; -import { downloadSphinxArtifact } from "../lib/api/sphinxArtifacts.js"; -import { runAddonDocsPipeline } from "../lib/api/addonDocsPipeline.js"; -import { zxMain } from "../lib/zx.js"; -import { isValidVersion, parseMinorVersion } from "../lib/apiVersions.js"; +import { Pkg } from "../../lib/api/Pkg.js"; +import { pathExists } from "../../lib/fs.js"; +import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; +import { runAddonDocsPipeline } from "../../lib/api/addonDocsPipeline.js"; +import { zxMain } from "../../lib/zx.js"; +import { isValidVersion, parseMinorVersion } from "../../lib/apiVersions.js"; interface Arguments { package: string; From 976970c827b2dba7d3f7cc0aa4c9eda69c1940b2 Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Tue, 12 May 2026 18:36:06 -0400 Subject: [PATCH 033/104] Rename conversionPipeline.test.ts -> apiDocsPipeline.test.ts Co-Authored-By: Claude Sonnet 4.6 --- .../api/{conversionPipeline.test.ts => apiDocsPipeline.test.ts} | 0 .../-package | 0 .../-toc | 0 .../api-example.Electron | 0 .../api-example.my_function1 | 0 .../index | 0 .../inline-classes | 0 .../module | 0 8 files changed, 0 insertions(+), 0 deletions(-) rename scripts/js/lib/api/{conversionPipeline.test.ts => apiDocsPipeline.test.ts} (100%) rename scripts/js/lib/api/{conversionPipeline.test.ts-snapshots => apiDocsPipeline.test.ts-snapshots}/-package (100%) rename scripts/js/lib/api/{conversionPipeline.test.ts-snapshots => apiDocsPipeline.test.ts-snapshots}/-toc (100%) rename scripts/js/lib/api/{conversionPipeline.test.ts-snapshots => apiDocsPipeline.test.ts-snapshots}/api-example.Electron (100%) rename scripts/js/lib/api/{conversionPipeline.test.ts-snapshots => apiDocsPipeline.test.ts-snapshots}/api-example.my_function1 (100%) rename scripts/js/lib/api/{conversionPipeline.test.ts-snapshots => apiDocsPipeline.test.ts-snapshots}/index (100%) rename scripts/js/lib/api/{conversionPipeline.test.ts-snapshots => apiDocsPipeline.test.ts-snapshots}/inline-classes (100%) rename scripts/js/lib/api/{conversionPipeline.test.ts-snapshots => apiDocsPipeline.test.ts-snapshots}/module (100%) diff --git a/scripts/js/lib/api/conversionPipeline.test.ts b/scripts/js/lib/api/apiDocsPipeline.test.ts similarity index 100% rename from scripts/js/lib/api/conversionPipeline.test.ts rename to scripts/js/lib/api/apiDocsPipeline.test.ts diff --git a/scripts/js/lib/api/conversionPipeline.test.ts-snapshots/-package b/scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/-package similarity index 100% rename from scripts/js/lib/api/conversionPipeline.test.ts-snapshots/-package rename to scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/-package diff --git a/scripts/js/lib/api/conversionPipeline.test.ts-snapshots/-toc b/scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/-toc similarity index 100% rename from scripts/js/lib/api/conversionPipeline.test.ts-snapshots/-toc rename to scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/-toc diff --git a/scripts/js/lib/api/conversionPipeline.test.ts-snapshots/api-example.Electron b/scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/api-example.Electron similarity index 100% rename from scripts/js/lib/api/conversionPipeline.test.ts-snapshots/api-example.Electron rename to scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/api-example.Electron diff --git a/scripts/js/lib/api/conversionPipeline.test.ts-snapshots/api-example.my_function1 b/scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/api-example.my_function1 similarity index 100% rename from scripts/js/lib/api/conversionPipeline.test.ts-snapshots/api-example.my_function1 rename to scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/api-example.my_function1 diff --git a/scripts/js/lib/api/conversionPipeline.test.ts-snapshots/index b/scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/index similarity index 100% rename from scripts/js/lib/api/conversionPipeline.test.ts-snapshots/index rename to scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/index diff --git a/scripts/js/lib/api/conversionPipeline.test.ts-snapshots/inline-classes b/scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/inline-classes similarity index 100% rename from scripts/js/lib/api/conversionPipeline.test.ts-snapshots/inline-classes rename to scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/inline-classes diff --git a/scripts/js/lib/api/conversionPipeline.test.ts-snapshots/module b/scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/module similarity index 100% rename from scripts/js/lib/api/conversionPipeline.test.ts-snapshots/module rename to scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/module From 6a3dcb5028d93dd8154a5be61ee73aea9f2c3a0e Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Tue, 12 May 2026 20:29:40 -0400 Subject: [PATCH 034/104] revert api/images changes --- .../aqc-compression.avif | Bin 29893 -> 0 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y{nLm|=93N7>@x#rVIAD!{KTuOZDM@�c$+1-G9@Xyf#ryk8dkHv(m~&i?@u^VK^5 diff --git a/public/docs/images/api/qiskit-addon-cutting/index-1.svg b/public/docs/images/api/qiskit-addon-cutting/index-1.svg deleted file mode 100644 index 54eda97e5d2..00000000000 --- a/public/docs/images/api/qiskit-addon-cutting/index-1.svg +++ /dev/null @@ -1,2865 +0,0 @@ - - - - - - - - 2025-03-28T16:50:49.198051 - image/svg+xml - - - Matplotlib v3.9.4, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - From 9cc6b541a327cf8341eacaff1bf473cea21b823c Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Tue, 12 May 2026 21:17:13 -0400 Subject: [PATCH 036/104] remove object.inv writing in addons --- .../how-tos/bound-error-using-p-norm.ipynb | 2 +- .../how-tos/truncate-operator-terms.ipynb | 10 +++++----- .../tutorials/01-getting-started.ipynb | 8 ++++---- package.json | 2 +- public/docs/api/qiskit-addon-obp/objects.inv | Bin 3439 -> 3689 bytes scripts/js/lib/api/addonDocsPipeline.ts | 4 ---- 6 files changed, 11 insertions(+), 15 deletions(-) diff --git a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb index d9d6cc32977..cdc20febc8a 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb @@ -31,7 +31,7 @@ "id": "2780c587-cd2a-4398-8faa-de5e0c661a10", "metadata": {}, "source": [ - "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget).\n", + "You are already familiar with the truncation of low-weight Pauli terms that is built into the [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate) method based on a specified [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget).\n", "In this guide, you will learn about its additional keyword argument `p_norm`, which can be used to change the Lp-norm used to estimate the error incurred by the truncated Pauli terms." ] }, diff --git a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb index 2bc0a0a3720..623eac084d0 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb @@ -27,9 +27,9 @@ "\n", "Operator backpropagation (OBP) can be used to reduce the depth of quantum circuits at the cost of a more complex observable. In order to get meaningful results from OBP, one usually needs to truncate terms from their observable to prevent it from growing too large. One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms' coefficients.\n", "\n", - "The [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", + "The [backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate) method takes an optional [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget) which configures the truncation of low-weight Pauli terms for each observable after the successful backpropagation of every slice.\n", "The amount of terms that are truncated depends on various configuration parameters specified by the user.\n", - "As of now, only a single truncation strategy is available -- the [truncate_binary_search](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search) method.\n", + "As of now, only a single truncation strategy is available -- the [truncate_binary_search](/docs/api/qiskit-addon-obp/utils-truncating#truncate_binary_search) method.\n", "Given an observable and some _budget_ it will perform a binary search over the Pauli terms and coefficients within that observable to find the optimal threshold,\n", "such that the sum of the truncated coefficients is maximal but lower than the budget.\n", "\n", @@ -42,7 +42,7 @@ "id": "aaf5f046-6d5e-4b6f-add7-b60604edef30", "metadata": {}, "source": [ - "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget) via the accompanying [setup_budget](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function." + "In the following examples, we will get to know various ways of building an [TruncationErrorBudget](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget) via the accompanying [setup_budget](/docs/api/qiskit-addon-obp/utils-truncating#setup_budget) function." ] }, { @@ -202,7 +202,7 @@ "id": "4a772af6-edc6-4e53-81c7-53f3b72bf58a", "metadata": {}, "source": [ - "Let's use the [OBPMetadata](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#qiskit_addon_obp.utils.metadata.OBPMetadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", + "Let's use the [OBPMetadata](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata) instance and the tools provided by the [visualization](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization) module to visualize the backpropagation process.\n", "\n", "- **The top/left plot** shows that we have enough budget to begin truncating observable terms after backpropagating the third slice. Starting from the third slice, we know that we truncate at least one term from each slice we backpropagate because we are incurring some truncation error after each slice.\n", "- **The top/right plot** shows that the error budget ramps up to `.003` for the third slice. We see a sharp drop in remaining budget, which means terms were truncated from the observable. This is consistent with what we inferred from the top/left plot.\n", @@ -718,7 +718,7 @@ "source": [ "Since we passed a subset of our slices (i.e. ``slices[-num_slices:]``) to ``backpropagate``, we must combine the slices remaining after backpropagation with the slices that were never sent for backpropagation (i.e. ``slices[:-num_slices]``).\n", "\n", - "Once we have combined all of the remaining slices, we can use [combine_slices](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became." + "Once we have combined all of the remaining slices, we can use [combine_slices](/docs/api/qiskit-addon-utils/slicing#combine_slices) to generate the reduced-depth [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html). Here we inspect how many slices were backpropagated vs. how large our observable became." ] }, { diff --git a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb index 33b583b7287..8f6e907f74d 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb @@ -41,7 +41,7 @@ "- **Step 4: Reconstruct results**\n", " - N.A.\n", "\n", - "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#qiskit_addon_obp.backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." + "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." ] }, { @@ -208,7 +208,7 @@ "## Step 2: Optimize the problem\n", "### Create circuit slices to backpropagate\n", "\n", - "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](/docs/api/qiskit-addon-utils/slicing#qiskit_addon_utils.slicing.slice_by_gate_types) function.\n", + "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](/docs/api/qiskit-addon-utils/slicing#slice_by_gate_types) function.\n", "\n", "For a more detailed discussion on circuit slicing, check out this [how-to guide](/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices) of the [qiskit-addon-utils](/docs/addons/qiskit-addon-utils/index) package." ] @@ -241,7 +241,7 @@ "source": [ "### Constrain how large the operator may grow during backpropagation\n", "\n", - "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/utils-simplify#qiskit_addon_obp.utils.simplify.OperatorBudget) instance.\n", + "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/utils-simplify#operatorbudget) instance.\n", "\n", "Here we specify that backpropagation should stop when the number of qubit-wise commuting Pauli groups in the operator grows past 8." ] @@ -327,7 +327,7 @@ "id": "d25b9701-f1b1-4230-9dcc-554b85e91cf7", "metadata": {}, "source": [ - "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](/docs/api/qiskit-addon-obp/utils-truncating#qiskit_addon_obp.utils.truncating.setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", + "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](/docs/api/qiskit-addon-obp/utils-truncating#setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", "\n", "In order to enable this truncation, we need to setup our error budget like so:" ] diff --git a/package.json b/package.json index ca275f43892..243b79c6283 100644 --- a/package.json +++ b/package.json @@ -32,7 +32,7 @@ "check:qiskit-versions": "tsx scripts/js/commands/checkQiskitApiVersions.ts", "regen-apis": "tsx scripts/js/commands/api/regenerateApiDocs.ts", "gen-api": "tsx scripts/js/commands/api/updateApiDocs.ts", - "gen-addons": "tsx scripts/js/commands/api/updateAddonDocs.ts", + "gen-addon": "tsx scripts/js/commands/api/updateAddonDocs.ts", "generate-historical-redirects": "tsx scripts/js/commands/api/generateHistoricalRedirects.ts", "save-internal-links": "tsx scripts/js/commands/saveInternalLinks.ts" }, diff --git a/public/docs/api/qiskit-addon-obp/objects.inv 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z2GLjJ_k!{c6rH%E#A+0j0{euFDmgD5%h!5h+hP=B|*RyL5b zEx#9(@QOB9fx&nlku(|Cth=>ijJ4ARP z<#p0QRFuq)_U}Ny?mFx+Dt7!{P~L~4_0`_N6s`HapoCYnzNS2gnl-mG!aGng)a#~j zd({;0;WblV6dy#tn%f!SJ%6a!-FF{I%FY2lFyTez^dh2ad;qaP6};zxC!FxQ3K|9N zrEC0NP{J$vic*rOrEK;0Y#3!H{P2qQp?43ej4`3H@inS*2cYRdPM4P&UWTdj`M8AB z=caofjW2~8^SNJ*Xn5_xnA+RbX2ys5j(zZP3ct+>eq#^39&9nYc0id Date: Tue, 12 May 2026 21:56:08 -0400 Subject: [PATCH 037/104] simplify frontmatter --- package.json | 1 + scripts/js/lib/api/addonDocsPipeline.ts | 13 ++-- scripts/js/lib/api/apiDocsPipeline.ts | 15 ++-- scripts/js/lib/api/pipelineStages.ts | 99 +++++-------------------- 4 files changed, 30 insertions(+), 98 deletions(-) diff --git a/package.json b/package.json index 243b79c6283..55c889bb2f3 100644 --- a/package.json +++ b/package.json @@ -33,6 +33,7 @@ "regen-apis": "tsx scripts/js/commands/api/regenerateApiDocs.ts", "gen-api": "tsx scripts/js/commands/api/updateApiDocs.ts", "gen-addon": "tsx scripts/js/commands/api/updateAddonDocs.ts", + "postgen-addon": "./fix", "generate-historical-redirects": "tsx scripts/js/commands/api/generateHistoricalRedirects.ts", "save-internal-links": "tsx scripts/js/commands/saveInternalLinks.ts" }, diff --git a/scripts/js/lib/api/addonDocsPipeline.ts b/scripts/js/lib/api/addonDocsPipeline.ts index 657d500cb5e..8fe6926a855 100644 --- a/scripts/js/lib/api/addonDocsPipeline.ts +++ b/scripts/js/lib/api/addonDocsPipeline.ts @@ -62,14 +62,15 @@ export async function runAddonDocsPipeline( outputPath, htmlFiles, pkg.outputDir(`${DOCS_BASE_PATH}/images/addons`), - "html-meta", + true, ); - const results = await postProcess(pkg, initialResults, { - rewriteApidocsLinks: true, + + const results = await postProcess( + pkg, + initialResults, objectsInv, - allInvs: allObjectInvs, - frontMatter: "html-meta", - }); + allObjectInvs, + ); await writeMarkdownResults(pkg, docsBaseFolder, results); // handle Jupyter notebook files diff --git a/scripts/js/lib/api/apiDocsPipeline.ts b/scripts/js/lib/api/apiDocsPipeline.ts index 3fffda28ed7..26f8ff38355 100644 --- a/scripts/js/lib/api/apiDocsPipeline.ts +++ b/scripts/js/lib/api/apiDocsPipeline.ts @@ -29,32 +29,27 @@ import { import { C_API_BASE_PATH, DOCS_BASE_PATH } from "./paths.js"; export async function runApiDocsPipeline( - htmlPath: string, + artifactPath: string, docsBaseFolder: string, publicBaseFolder: string, pkg: Pkg, ) { const [files, markdownPath, maybeObjectsInv] = await determineFilePaths( - htmlPath, + artifactPath, docsBaseFolder, pkg, ); const initialResults = await convertHtmlToMarkdown( pkg, - htmlPath, + artifactPath, docsBaseFolder, markdownPath, files, pkg.apiOutputDir(`${DOCS_BASE_PATH}/images`), - "api", ); - const results = await postProcess(pkg, initialResults, { - rewriteApidocsLinks: false, - objectsInv: maybeObjectsInv, - frontMatter: "api", - }); + const results = await postProcess(pkg, initialResults, maybeObjectsInv); // Warning: the sequence of operations often matters. await writeMarkdownResults(pkg, docsBaseFolder, results); @@ -64,7 +59,7 @@ export async function runApiDocsPipeline( // `api/{pkg}` subtree inside publicBaseFolder. await copyImages( pkg, - htmlPath, + artifactPath, pkg.apiOutputDir(`${publicBaseFolder}/images`), results, ); diff --git a/scripts/js/lib/api/pipelineStages.ts b/scripts/js/lib/api/pipelineStages.ts index e34460539b5..54f3158bb98 100644 --- a/scripts/js/lib/api/pipelineStages.ts +++ b/scripts/js/lib/api/pipelineStages.ts @@ -37,25 +37,6 @@ import { specialCaseResults } from "./specialCaseResults.js"; import { sphinxHtmlToMarkdown } from "./htmlToMd.js"; import { updateLinks } from "./updateLinks.js"; -// --------------------------------------------------------------------------- -// HTML → markdown conversion -// --------------------------------------------------------------------------- - -export type FrontMatterSource = "api" | "html-meta"; - -/** - * Convert each HTML file to a markdown result with an internal URL. - * - * `imageDestination` is the URL prefix embedded in the rendered `` of - * each page (e.g. `/docs/images/api/qiskit` or `/docs/images/addons/qiskit-addon-obp`). - * Each pipeline chooses the prefix appropriate to where it will copy images. - * - * When `frontMatterSource === "html-meta"`, the result's metadata is annotated - * with `hardcodedFrontmatter` extracted from the HTML `` and - * `<meta name="description">` tags. This feeds the addon pipeline's frontmatter - * writer, which emits YAML directly from these values (addon guides/tutorials - * have no Sphinx-generated metadata to draw from). - */ export async function convertHtmlToMarkdown( pkg: Pkg, artifactPath: string, @@ -63,7 +44,7 @@ export async function convertHtmlToMarkdown( outputPath: string, filePaths: string[], imageDestination: string, - frontMatterSource: FrontMatterSource, + extractfrontMatter?: boolean, ): Promise<HtmlToMdResultWithUrl[]> { const results: HtmlToMdResultWithUrl[] = []; for (const file of filePaths) { @@ -82,7 +63,8 @@ export async function convertHtmlToMarkdown( // Skip empty markdown (HTML redirects, etc.). if (result.markdown == "") continue; - if (frontMatterSource === "html-meta") { + if (extractfrontMatter) { + // extracts front matter from html source rather than generating it in addFrontMatter.js result.meta.hardcodedFrontmatter = extractHtmlFrontmatter(html); } @@ -102,37 +84,6 @@ function extractHtmlFrontmatter(html: string): string { return lines.join("\n"); } -// --------------------------------------------------------------------------- -// Post-processing (runs on markdown results after HTML conversion) -// --------------------------------------------------------------------------- - -export interface PostProcessOptions { - /** - * When true, rewrite markdown links of the form `](apidocs/foo)` (or - * `apidoc/`, `stubs/`) into absolute `](/docs/api/{pkg}/foo)` paths before - * running updateLinks. Used by the addon pipeline where non-API guides - * reference API pages via the Sphinx artifact's relative `apidocs/...` - * paths. - */ - rewriteApidocsLinks: boolean; - - /** - * Same-package inventory. May be undefined for packages where we - * intentionally skip loading the inventory (e.g. problematic legacy Qiskit). - */ - objectsInv: ObjectsInv | undefined; - - /** - * Cross-package inventories for resolving `qiskit.github.io/{pkg}/stubs/...` - * links to other packages' API docs. Used by the addon pipeline; pass - * undefined for pure API runs. - */ - allInvs?: Map<string, ObjectsInv>; - - /** Where the frontmatter should come from when `addFrontMatter` runs. */ - frontMatter: FrontMatterSource; -} - /** * Apply the shared post-processing pipeline to a set of results. * Order is load-bearing — both pipelines call the stages in this sequence. @@ -140,7 +91,8 @@ export interface PostProcessOptions { export async function postProcess( pkg: Pkg, initialResults: HtmlToMdResultWithUrl[], - options: PostProcessOptions, + objectsInv: ObjectsInv | undefined, + allInvs?: Map<string, ObjectsInv>, ): Promise<HtmlToMdResultWithUrl[]> { const results = await mergeClassMembers(initialResults); @@ -150,16 +102,7 @@ export async function postProcess( }); specialCaseResults(results); - - if (options.rewriteApidocsLinks) { - const apiBase = pkg.apiOutputDir(DOCS_BASE_PATH); - for (const result of results) { - result.markdown = result.markdown.replace( - /\]\((?:\.\.\/)*?(apidocs|apidoc|stubs)\/([^)]+)\)/g, - `](${apiBase}/$2)`, - ); - } - } + rewriteApiDocsLinks(results, pkg); await updateLinks( results, @@ -168,31 +111,23 @@ export async function postProcess( pkgName: pkg.name, pkgOutputDir: pkg.apiOutputDir(DOCS_BASE_PATH), }, - options.objectsInv, - options.allInvs, + objectsInv, + allInvs, ); + addFrontMatter(results, pkg); await dedupeHtmlIdsFromResults(results); - - if (options.frontMatter === "api") { - addFrontMatter(results, pkg); - } else { - applyHtmlMetaFrontmatter(results); - } - removeMathBlocksIndentation(results); return results; } -function applyHtmlMetaFrontmatter(results: HtmlToMdResultWithUrl[]): void { +function rewriteApiDocsLinks(results: HtmlToMdResultWithUrl[], pkg: Pkg) { + const apiBase = pkg.apiOutputDir(DOCS_BASE_PATH); for (const result of results) { - const markdown = result.markdown; - result.markdown = `--- -${result.meta.hardcodedFrontmatter} ---- - -${markdown} -`; + result.markdown = result.markdown.replace( + /\]\((?:\.\.\/)*?(apidocs|apidoc|stubs)\/([^)]+)\)/g, + `](${apiBase}/$2)`, + ); } } @@ -210,7 +145,7 @@ export async function writeMarkdownResults( pkg: Pkg, docsBaseFolder: string, results: HtmlToMdResultWithUrl[], -): Promise<void> { +) { for (const result of results) { const path = `${docsBaseFolder}${result.url}.mdx`; if (path.endsWith("release-notes.mdx")) { @@ -236,7 +171,7 @@ export async function copyImages( artifactPath: string, destFolder: string, results: HtmlToMdResultWithUrl[], -): Promise<void> { +) { console.log("Saving images"); const allImages = uniqBy( results.flatMap((result) => result.images), From 930b3dbec81f84ee2b3c8aa7c956465534d29ad8 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 12 May 2026 22:04:13 -0400 Subject: [PATCH 038/104] add shared script commands --- scripts/js/commands/api/updateAddonDocs.ts | 122 +++------------ scripts/js/commands/api/updateApiDocs.ts | 99 ++---------- scripts/js/commands/api/updateDocsShared.ts | 161 ++++++++++++++++++++ 3 files changed, 197 insertions(+), 185 deletions(-) create mode 100644 scripts/js/commands/api/updateDocsShared.ts diff --git a/scripts/js/commands/api/updateAddonDocs.ts b/scripts/js/commands/api/updateAddonDocs.ts index 925b6352c42..d0d6a3609d5 100644 --- a/scripts/js/commands/api/updateAddonDocs.ts +++ b/scripts/js/commands/api/updateAddonDocs.ts @@ -12,69 +12,35 @@ import yargs from "yargs/yargs"; import { hideBin } from "yargs/helpers"; -import { readFile, writeFile } from "fs/promises"; - -import { $ } from "zx"; -import { mkdirp } from "mkdirp"; import { Pkg } from "../../lib/api/Pkg.js"; -import { pathExists } from "../../lib/fs.js"; -import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; import { runAddonDocsPipeline } from "../../lib/api/addonDocsPipeline.js"; import { zxMain } from "../../lib/zx.js"; -import { isValidVersion, parseMinorVersion } from "../../lib/apiVersions.js"; - -interface Arguments { - package: string; - version: string; - skipDownload?: boolean; - sphinxArtifactFolder?: string; -} +import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { + addSharedOptions, + deleteOutputDirs, + prepareSphinxFolder, + SharedArguments, +} from "./updateDocsShared.js"; -const readArgs = (): Arguments => { - return yargs(hideBin(process.argv)) - .version(false) - .option("package", { - alias: "p", - type: "string", - choices: Pkg.VALID_NAMES, - demandOption: true, - description: "Which package to update", - }) - .option("version", { - alias: "v", - type: "string", - demandOption: true, - description: "The full version string of the --package, e.g. 0.44.0", - coerce: (version) => { - if (!isValidVersion(version)) { - throw new Error( - "The version must include a major, a minor, and a patch. E.g. `-v 0.46.3`", - ); - } - return version; - }, - }) - .option("skip-download", { - type: "boolean", - default: false, - description: - "Rather than downloading the artifact from Box, reuse what is already downloaded. This can save time, but it risks using an outdated version of the docs.", - }) - .option("sphinx-artifact-folder", { - alias: "a", - type: "string", - implies: "skip-download", - normalize: true, - description: - "Skip downloading the artifact from Box and instead use the given directory as the root of the Sphinx HTML output.", - }) - .parseSync(); +const readArgs = (): SharedArguments => { + return addSharedOptions( + yargs(hideBin(process.argv)).version(false), + ).parseSync() as unknown as SharedArguments; }; -async function generateVersion(pkg: Pkg, args: Arguments): Promise<void> { +async function generateVersion( + pkg: Pkg, + args: SharedArguments, +): Promise<void> { const sphinxArtifactFolder = await prepareSphinxFolder(pkg, args); - await deleteExistingFiles(pkg); + await deleteOutputDirs(pkg, { + markdownDir: pkg.outputDir("docs/addons"), + imagesDir: pkg.outputDir("public/docs/images/addons"), + recursive: true, + preserveFiles: ["_toc.json"], + }); console.log(`Run pipeline for ${pkg.name}:${pkg.versionWithoutPatch}`); await runAddonDocsPipeline( @@ -85,52 +51,6 @@ async function generateVersion(pkg: Pkg, args: Arguments): Promise<void> { ); } -async function prepareSphinxFolder(pkg: Pkg, args: Arguments): Promise<string> { - if (args.sphinxArtifactFolder) { - if (!(await pathExists(args.sphinxArtifactFolder))) { - throw new Error( - `Explicit artifact path '${args.sphinxArtifactFolder}' does not exist.`, - ); - } - return args.sphinxArtifactFolder; - } - const sphinxArtifactFolder = pkg.sphinxArtifactFolder(); - if ( - args.skipDownload && - (await pathExists(`${sphinxArtifactFolder}/artifact`)) - ) { - console.log( - `Skip downloading sources for ${pkg.name}:${pkg.versionWithoutPatch}`, - ); - } else { - await downloadSphinxArtifact(pkg, sphinxArtifactFolder); - } - return `${sphinxArtifactFolder}/artifact`; -} - -async function deleteExistingFiles(pkg: Pkg): Promise<void> { - const markdownDir = pkg.outputDir("docs/addons"); - if (await pathExists(markdownDir)) { - // Preserve the hand-authored _toc.json. - const tocPath = `${markdownDir}/_toc.json`; - const tocContent = (await pathExists(tocPath)) - ? await readFile(tocPath, "utf-8") - : null; - await $`rm -rf ${markdownDir}`; - if (tocContent !== null) { - await mkdirp(markdownDir); - await writeFile(tocPath, tocContent); - } - } - const imagesDir = pkg.outputDir("public/docs/images/addons"); - if (await pathExists(imagesDir)) { - await $`rm -rf ${imagesDir}`; - } - console.log( - `Deleted existing markdown & images for ${pkg.name}:${pkg.versionWithoutPatch}`, - ); -} - zxMain(async () => { const args = readArgs(); const minorVersion = parseMinorVersion(args.version); diff --git a/scripts/js/commands/api/updateApiDocs.ts b/scripts/js/commands/api/updateApiDocs.ts index 013e9d0f9ba..254c628ad98 100644 --- a/scripts/js/commands/api/updateApiDocs.ts +++ b/scripts/js/commands/api/updateApiDocs.ts @@ -15,46 +15,24 @@ import { hideBin } from "yargs/helpers"; import { Pkg } from "../../lib/api/Pkg.js"; import { zxMain } from "../../lib/zx.js"; -import { parseMinorVersion, isValidVersion } from "../../lib/apiVersions.js"; -import { pathExists, rmFilesInFolder } from "../../lib/fs.js"; -import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; +import { parseMinorVersion } from "../../lib/apiVersions.js"; import { runApiDocsPipeline } from "../../lib/api/apiDocsPipeline.js"; import { generateHistoricalRedirects } from "./generateHistoricalRedirects.js"; +import { + addSharedOptions, + deleteOutputDirs, + prepareSphinxFolder, + SharedArguments, +} from "./updateDocsShared.js"; -export interface Arguments { +export interface Arguments extends SharedArguments { [x: string]: unknown; - package: string; - version: string; historical: boolean; dev: boolean; - skipDownload: boolean; - sphinxArtifactFolder?: string; } const readArgs = (): Arguments => { - return yargs(hideBin(process.argv)) - .version(false) - .option("package", { - alias: "p", - type: "string", - choices: Pkg.VALID_NAMES, - demandOption: true, - description: "Which package to update", - }) - .option("version", { - alias: "v", - type: "string", - demandOption: true, - description: "The full version string of the --package, e.g. 0.44.0", - coerce: (version) => { - if (!isValidVersion(version)) { - throw new Error( - "The version must include a major, a minor, and a patch. E.g. `-v 0.46.3`", - ); - } - return version; - }, - }) + return addSharedOptions(yargs(hideBin(process.argv)).version(false)) .option("historical", { type: "boolean", default: false, @@ -65,21 +43,7 @@ const readArgs = (): Arguments => { default: false, description: "Is this a dev release?", }) - .option("skip-download", { - type: "boolean", - default: false, - description: - "Rather than downloading the artifact from Box, reuse what is already downloaded. This can save time, but it risks using an outdated version of the docs.", - }) - .option("sphinx-artifact-folder", { - alias: "a", - type: "string", - implies: "skip-download", - normalize: true, - description: - "Skip downloading the artifact from Box and instead use the given directory as the root of the Sphinx HTML output.", - }) - .parseSync(); + .parseSync() as unknown as Arguments; }; export async function generateVersion( @@ -87,7 +51,11 @@ export async function generateVersion( args: Arguments, ): Promise<void> { const sphinxArtifactFolder = await prepareSphinxFolder(pkg, args); - await deleteExistingFiles(pkg); + await deleteOutputDirs(pkg, { + markdownDir: pkg.apiOutputDir("docs"), + imagesDir: pkg.apiOutputDir("public/docs/images"), + recursive: false, + }); console.log(`Run pipeline for ${pkg.name}:${pkg.versionWithoutPatch}`); await runApiDocsPipeline(sphinxArtifactFolder, "docs", "public/docs", pkg); @@ -112,43 +80,6 @@ export function determineMinorVersion(args: Arguments): string { return minorVersion; } -async function prepareSphinxFolder(pkg: Pkg, args: Arguments): Promise<string> { - if (args.sphinxArtifactFolder) { - if (!(await pathExists(args.sphinxArtifactFolder))) { - throw new Error( - `Explicit artifact path '${args.sphinxArtifactFolder}' does not exist.`, - ); - } - return args.sphinxArtifactFolder; - } - const sphinxArtifactFolder = pkg.sphinxArtifactFolder(); - if ( - args.skipDownload && - (await pathExists(`${sphinxArtifactFolder}/artifact`)) - ) { - console.log( - `Skip downloading sources for ${pkg.name}:${pkg.versionWithoutPatch}`, - ); - } else { - await downloadSphinxArtifact(pkg, sphinxArtifactFolder); - } - return `${sphinxArtifactFolder}/artifact`; -} - -async function deleteExistingFiles(pkg: Pkg): Promise<void> { - const markdownDir = pkg.apiOutputDir("docs"); - if (await pathExists(markdownDir)) { - await rmFilesInFolder(markdownDir); - } - const imagesDir = pkg.apiOutputDir("public/docs/images"); - if (await pathExists(imagesDir)) { - await rmFilesInFolder(imagesDir); - } - console.log( - `Deleted existing markdown & images for ${pkg.name}:${pkg.versionWithoutPatch}`, - ); -} - if (import.meta.url === `file://${process.argv[1]}`) { zxMain(async () => { const args = readArgs(); diff --git a/scripts/js/commands/api/updateDocsShared.ts b/scripts/js/commands/api/updateDocsShared.ts new file mode 100644 index 00000000000..e4a2f30f7f3 --- /dev/null +++ b/scripts/js/commands/api/updateDocsShared.ts @@ -0,0 +1,161 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2026. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +// Shared helpers for updateApiDocs.ts and updateAddonDocs.ts — they both +// take --package/--version, download (or reuse) a Sphinx artifact, and wipe +// an output directory before running a pipeline. + +import { readFile, writeFile } from "fs/promises"; + +import { $ } from "zx"; +import { mkdirp } from "mkdirp"; +import type { Argv } from "yargs"; + +import { Pkg } from "../../lib/api/Pkg.js"; +import { pathExists, rmFilesInFolder } from "../../lib/fs.js"; +import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; +import { isValidVersion } from "../../lib/apiVersions.js"; + +/** Fields shared by both `updateApiDocs` and `updateAddonDocs` CLIs. */ +export interface SharedArguments { + package: string; + version: string; + skipDownload: boolean; + sphinxArtifactFolder?: string; +} + +/** + * Register the yargs options shared by both commands. Call on the builder + * before adding command-specific options. + */ +export function addSharedOptions(y: Argv): Argv { + return y + .option("package", { + alias: "p", + type: "string", + choices: Pkg.VALID_NAMES, + demandOption: true, + description: "Which package to update", + }) + .option("version", { + alias: "v", + type: "string", + demandOption: true, + description: "The full version string of the --package, e.g. 0.44.0", + coerce: (version) => { + if (!isValidVersion(version)) { + throw new Error( + "The version must include a major, a minor, and a patch. E.g. `-v 0.46.3`", + ); + } + return version; + }, + }) + .option("skip-download", { + type: "boolean", + default: false, + description: + "Rather than downloading the artifact from Box, reuse what is already downloaded. This can save time, but it risks using an outdated version of the docs.", + }) + .option("sphinx-artifact-folder", { + alias: "a", + type: "string", + implies: "skip-download", + normalize: true, + description: + "Skip downloading the artifact from Box and instead use the given directory as the root of the Sphinx HTML output.", + }); +} + +/** + * Resolve the Sphinx artifact folder: either a user-provided path, a reused + * prior download, or a fresh download from Box. + */ +export async function prepareSphinxFolder( + pkg: Pkg, + args: SharedArguments, +): Promise<string> { + if (args.sphinxArtifactFolder) { + if (!(await pathExists(args.sphinxArtifactFolder))) { + throw new Error( + `Explicit artifact path '${args.sphinxArtifactFolder}' does not exist.`, + ); + } + return args.sphinxArtifactFolder; + } + const sphinxArtifactFolder = pkg.sphinxArtifactFolder(); + if ( + args.skipDownload && + (await pathExists(`${sphinxArtifactFolder}/artifact`)) + ) { + console.log( + `Skip downloading sources for ${pkg.name}:${pkg.versionWithoutPatch}`, + ); + } else { + await downloadSphinxArtifact(pkg, sphinxArtifactFolder); + } + return `${sphinxArtifactFolder}/artifact`; +} + +export interface DeleteOptions { + /** Markdown output directory (contents removed before pipeline runs). */ + markdownDir: string; + /** Image output directory (removed entirely before pipeline runs). */ + imagesDir: string; + /** + * If true, recursively wipe `markdownDir`; otherwise only delete top-level + * files (so sibling historical-version subfolders are preserved). + */ + recursive: boolean; + /** + * Filenames under `markdownDir` to read into memory before deletion and + * restore after. Used to keep a hand-authored `_toc.json`. + */ + preserveFiles?: string[]; +} + +/** Wipe output directories in preparation for a fresh pipeline run. */ +export async function deleteOutputDirs( + pkg: Pkg, + { markdownDir, imagesDir, recursive, preserveFiles = [] }: DeleteOptions, +): Promise<void> { + if (await pathExists(markdownDir)) { + const preserved: Array<[string, string]> = []; + for (const name of preserveFiles) { + const path = `${markdownDir}/${name}`; + if (await pathExists(path)) { + preserved.push([name, await readFile(path, "utf-8")]); + } + } + if (recursive) { + await $`rm -rf ${markdownDir}`; + } else { + await rmFilesInFolder(markdownDir); + } + if (preserved.length > 0) { + await mkdirp(markdownDir); + for (const [name, content] of preserved) { + await writeFile(`${markdownDir}/${name}`, content); + } + } + } + if (await pathExists(imagesDir)) { + if (recursive) { + await $`rm -rf ${imagesDir}`; + } else { + await rmFilesInFolder(imagesDir); + } + } + console.log( + `Deleted existing docs & images for ${pkg.name}:${pkg.versionWithoutPatch}`, + ); +} From 4ddf7a77b2b915b334d7d571b323127f6984d453 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 12 May 2026 22:20:56 -0400 Subject: [PATCH 039/104] add ignores and handle release-notes --- docs/addons/qiskit-addon-obp/index.mdx | 2 +- scripts/js/lib/api/pipelineStages.ts | 22 +++++++++++++++++----- scripts/js/lib/links/ignores.ts | 8 ++++++++ 3 files changed, 26 insertions(+), 6 deletions(-) diff --git a/docs/addons/qiskit-addon-obp/index.mdx b/docs/addons/qiskit-addon-obp/index.mdx index 29de023afa6..3f7e4cc1ceb 100644 --- a/docs/addons/qiskit-addon-obp/index.mdx +++ b/docs/addons/qiskit-addon-obp/index.mdx @@ -28,7 +28,7 @@ For more installation information refer to the [installation instructions](insta ## Deprecation Policy -We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [release notes](/docs/addons/qiskit-addon-obp/release-notes). +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [release notes](/docs/api/qiskit-addon-obp/release-notes). ## Contributing diff --git a/scripts/js/lib/api/pipelineStages.ts b/scripts/js/lib/api/pipelineStages.ts index 54f3158bb98..898681fb712 100644 --- a/scripts/js/lib/api/pipelineStages.ts +++ b/scripts/js/lib/api/pipelineStages.ts @@ -115,19 +115,31 @@ export async function postProcess( allInvs, ); - addFrontMatter(results, pkg); await dedupeHtmlIdsFromResults(results); removeMathBlocksIndentation(results); + addFrontMatter(results, pkg); return results; } function rewriteApiDocsLinks(results: HtmlToMdResultWithUrl[], pkg: Pkg) { const apiBase = pkg.apiOutputDir(DOCS_BASE_PATH); + const githubIo = `https://qiskit.github.io/${pkg.name}`; for (const result of results) { - result.markdown = result.markdown.replace( - /\]\((?:\.\.\/)*?(apidocs|apidoc|stubs)\/([^)]+)\)/g, - `](${apiBase}/$2)`, - ); + result.markdown = result.markdown + .replace( + /\]\((?:\.\.\/)*?(apidocs|apidoc|stubs)\/([^)]+)\)/g, + `](${apiBase}/$2)`, + ) + // Release notes live under the API pipeline's output, even when + // referenced from addon guides/tutorials. Catches relative + // `release-notes.html`, github.io-absolute, and .html-less forms. + .replace( + new RegExp( + `\\]\\((?:${githubIo}/|(?:\\.\\./)*?)release[_-]notes(?:\\.html)?(#[^)]*)?\\)`, + "g", + ), + `](${apiBase}/release-notes$1)`, + ); } } diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index 1b445b34d58..b5ceec02ee8 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -217,6 +217,14 @@ const ALWAYS_IGNORED_URLS__SHOULD_FIX: string[] = [ // Other links that don't seem to exist any more "https://www.globaldataquantum.com/en/quantum-portfolio-optimizer/#form", + + // from qiskit-addon-obp + "/docs/guides/serverless#qiskit-patterns-with-quantum-serverless", + "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started-ipynb", + "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started-ipynb", + "/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html", + "/api/qiskit-ibm-runtime/noise-learner-noise-learner", + "/api/qiskit-ibm-runtime/noise-learner-result", ]; export const ALWAYS_IGNORED_URLS = new Set([ From 97339199a81e270ce8e200052ab89ee0a80f0236 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 12 May 2026 22:55:59 -0400 Subject: [PATCH 040/104] adds qiskit-addon-utils --- .../color-device-edges-to-improve-depth.ipynb | 298 +++++++++++++++ .../how-tos/create-circuit-slices.ipynb | 357 ++++++++++++++++++ .../qiskit-addon-utils/how-tos/index.mdx | 11 + docs/addons/qiskit-addon-utils/index.mdx | 44 +++ docs/addons/qiskit-addon-utils/install.mdx | 76 ++++ docs/api/qiskit-addon-utils/_toc.json | 4 +- docs/api/qiskit-addon-utils/exp-vals.mdx | 4 +- ...ement-post-selection-transpiler-passes.mdx | 10 +- docs/api/qiskit-addon-utils/release-notes.mdx | 18 +- .../slicing-transpiler-passes.mdx | 10 +- docs/api/qiskit-addon-utils/slicing.mdx | 6 +- .../docs/api/qiskit-addon-utils/objects.inv | Bin 3777 -> 3781 bytes ...3a5f389-94c0-4496-9fe6-262dca62a0e3-1.avif | Bin 0 -> 29995 bytes ...806ec31-05e8-4a4c-832b-888150506191-1.avif | Bin 0 -> 168928 bytes ...b986eac-93c6-453a-bfe1-2546681ab38b-0.avif | Bin 0 -> 75832 bytes ...3f482a1-6868-4cda-b8ed-8a8489794707-0.avif | Bin 0 -> 77450 bytes ...10b5844-972e-46d1-bfc5-20a30b69f814-0.avif | Bin 0 -> 4011 bytes ...a5d5aff-f988-4f14-ad25-9f0105202b77-0.avif | Bin 0 -> 4062 bytes ...91bd07b-739a-45ec-a3e9-2d068f8bd960-0.avif | Bin 0 -> 4711 bytes ...48aa715-22e8-41e1-943d-de7eb0aa379c-0.avif | Bin 0 -> 3584 bytes ...da8d173-8ce9-47a9-a24f-ab32e45f8337-0.avif | Bin 0 -> 3240 bytes ...f766d69-e10c-4aa8-a3bc-2057dab7a69a-0.avif | Bin 0 -> 2867 bytes ...00c8743-f3b9-460c-ba2c-ffc634e99e0e-0.avif | Bin 0 -> 2452 bytes ...c5ebecb-6c8b-4f51-8199-b236a0ba3328-0.avif | Bin 0 -> 4078 bytes ...883fa2e-f943-4c49-af45-fb26cadb72b6-0.avif | Bin 0 -> 4493 bytes ...skit_addon_utils-problem_generators-1.avif | Bin 4533 -> 7305 bytes scripts/js/lib/api/objectsInv.ts | 24 +- scripts/js/lib/api/updateLinks.ts | 11 +- 28 files changed, 838 insertions(+), 35 deletions(-) create mode 100644 docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb create mode 100644 docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices.ipynb create mode 100644 docs/addons/qiskit-addon-utils/how-tos/index.mdx create mode 100644 docs/addons/qiskit-addon-utils/index.mdx create mode 100644 docs/addons/qiskit-addon-utils/install.mdx create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth/extracted-outputs/03a5f389-94c0-4496-9fe6-262dca62a0e3-1.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth/extracted-outputs/2806ec31-05e8-4a4c-832b-888150506191-1.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth/extracted-outputs/9b986eac-93c6-453a-bfe1-2546681ab38b-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth/extracted-outputs/c3f482a1-6868-4cda-b8ed-8a8489794707-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/110b5844-972e-46d1-bfc5-20a30b69f814-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/1a5d5aff-f988-4f14-ad25-9f0105202b77-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/791bd07b-739a-45ec-a3e9-2d068f8bd960-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/948aa715-22e8-41e1-943d-de7eb0aa379c-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/9da8d173-8ce9-47a9-a24f-ab32e45f8337-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/9f766d69-e10c-4aa8-a3bc-2057dab7a69a-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/a00c8743-f3b9-460c-ba2c-ffc634e99e0e-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/bc5ebecb-6c8b-4f51-8199-b236a0ba3328-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/f883fa2e-f943-4c49-af45-fb26cadb72b6-0.avif diff --git a/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb b/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb new file mode 100644 index 00000000000..19e3e524c00 --- /dev/null +++ b/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb @@ -0,0 +1,298 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Use edge coloring to reduce the depth of quantum circuits\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "4de02100-ba7b-4ac3-8e92-f94844073337", + "metadata": {}, + "source": [ + "# Use edge coloring to reduce the depth of quantum circuits\n", + "\n", + "This guide will show how to use the [qiskit_addon_utils.coloring](/docs/api/qiskit-addon-utils/coloring) module to color edges of a coupling map and use the coloring to more efficiently place gates in a quantum circuit, leading to shallower circuits.\n", + "\n", + "The point of edge-coloring a graph is to find a set of edge colors such that no two edges of the same color share a common node. For a quantum processor, this means that gates along like-colored edges (qubit connections) may be run simultaneously and that the circuit will execute faster. We can use this to define our circuit in a way that is more amenable to the hardware it should run on." + ] + }, + { + "cell_type": "markdown", + "id": "4b16cf04-941e-4b02-932e-f5e6301b9a5b", + "metadata": {}, + "source": [ + "First, we will specify a backend and visualize its qubit topology." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9da8d173-8ce9-47a9-a24f-ab32e45f8337", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeSherbrooke\n", + "\n", + "backend = FakeSherbrooke()\n", + "coupling_map = backend.coupling_map" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9b986eac-93c6-453a-bfe1-2546681ab38b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth/extracted-outputs/9b986eac-93c6-453a-bfe1-2546681ab38b-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from rustworkx.visualization import graphviz_draw\n", + "\n", + "graphviz_draw(coupling_map.graph, method=\"neato\")" + ] + }, + { + "cell_type": "markdown", + "id": "eb4e5115-f2a9-4c62-8c5d-96f6bb6c8762", + "metadata": {}, + "source": [ + "Next we use the [qiskit_addon_utils.coloring.auto_color_edges](/docs/api/qiskit-addon-utils/coloring#auto_color_edges) function to generate an edge coloring. This is a light wrapper for [rustworkx.graph_greedy_edge_color](https://www.rustworkx.org/dev/apiref/rustworkx.graph_greedy_edge_color.html) that does some minor post-processing of the output. Namely, this function removes an arbitrary edge direction if an edge is bi-directional and combines the edge and color information into a dictionary format." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6700931f-cf94-4ad6-8012-d73c0bfd7efa", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 144 edges and 3 unique colors.\n", + "The edges are specified by length-2 tuples: (e.g. (1, 0)). \n", + "The \"colors\" are actually specified by a set of integers: {0, 1, 2}\n" + ] + } + ], + "source": [ + "from qiskit_addon_utils.coloring import auto_color_edges\n", + "from rustworkx import PyDiGraph\n", + "\n", + "coloring = auto_color_edges(coupling_map)\n", + "\n", + "# Create a colored graph for visualization\n", + "eagle_r3 = PyDiGraph()\n", + "eagle_r3.extend_from_weighted_edge_list(\n", + " [(source, target, color) for ((source, target), color) in coloring.items()]\n", + ")\n", + "print(f\"There are {len(coloring)} edges and {len(set(coloring.values()))} unique colors.\")\n", + "print(\n", + " f'The edges are specified by length-2 tuples: (e.g. {next(iter(coloring))}).\\\n", + " \\nThe \"colors\" are actually specified by a set of integers: {set(coloring.values())}'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c3f482a1-6868-4cda-b8ed-8a8489794707", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth/extracted-outputs/c3f482a1-6868-4cda-b8ed-8a8489794707-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def color_edge_3color(edge):\n", + " \"\"\"Return the color of an edge.\"\"\"\n", + " # Map integers of a 3-coloring to color names\n", + " color_dict = {0: \"red\", 1: \"green\", 2: \"blue\"}\n", + " return {\"color\": color_dict[edge]}\n", + "\n", + "\n", + "graphviz_draw(eagle_r3, edge_attr_fn=color_edge_3color, method=\"neato\")" + ] + }, + { + "cell_type": "markdown", + "id": "90d56de5-d43e-4509-8c68-f42ec0f198bb", + "metadata": {}, + "source": [ + "Next, let's make a circuit with a ``CZGate`` on each edge and observe its depth. For this first circuit, we will just naively place a gate on each edge. We will place barriers in ``depth-1`` increments (using [qiskit_addon_utils.slicing.slice_by_depth](/docs/api/qiskit-addon-utils/slicing#slice_by_depth)) to illustrate how crucial edge-coloring is when designing a hardware-efficient quantum circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2806ec31-05e8-4a4c-832b-888150506191", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The circuit has depth: 37\n" + ] + }, + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth/extracted-outputs/2806ec31-05e8-4a4c-832b-888150506191-1.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit\n", + "from qiskit_addon_utils.slicing import combine_slices, slice_by_depth\n", + "\n", + "circuit = QuantumCircuit(backend.num_qubits)\n", + "\n", + "for edge in coupling_map.graph.edge_list():\n", + " circuit.cz(edge[0], edge[1])\n", + "\n", + "print(f\"The circuit has depth: {circuit.depth()}\")\n", + "\n", + "# Add barriers in depth-1 increments\n", + "slices = slice_by_depth(circuit, max_slice_depth=1)\n", + "circuit = combine_slices(slices, include_barriers=True)\n", + "\n", + "circuit.draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "15cf56a0-954e-482a-8ff8-4645b7d18444", + "metadata": {}, + "source": [ + "Now let's place all of the gates of a given color at the same time. We can see the depth is reduced from ``37`` to ``3``. This is because we can take advantage of the fact that all gates on a given color can run at the same time." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "03a5f389-94c0-4496-9fe6-262dca62a0e3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The circuit has depth: 3\n" + ] + }, + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth/extracted-outputs/03a5f389-94c0-4496-9fe6-262dca62a0e3-1.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import defaultdict\n", + "\n", + "circuit = QuantumCircuit(backend.num_qubits)\n", + "\n", + "# Make a reverse coloring dict\n", + "color_to_edge = defaultdict(list)\n", + "for edge, color in coloring.items():\n", + " color_to_edge[color].append(edge)\n", + "\n", + "# Place edges in order of color\n", + "for edges in color_to_edge.values():\n", + " for edge in edges:\n", + " circuit.cz(edge[0], edge[1])\n", + "\n", + "print(f\"The circuit has depth: {circuit.depth(lambda x: x.name != 'barrier')}\")\n", + "\n", + "# Add barriers in depth-1 increments\n", + "slices = slice_by_depth(circuit, max_slice_depth=1)\n", + "circuit = combine_slices(slices, include_barriers=True)\n", + "\n", + "circuit.draw(\"mpl\", fold=-1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + }, + "toc": { + "base_numbering": 0 + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices.ipynb b/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices.ipynb new file mode 100644 index 00000000000..7a4a1e8f62f --- /dev/null +++ b/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices.ipynb @@ -0,0 +1,357 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Slicing circuits using qiskit_addon_utils.slicing\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "4de02100-ba7b-4ac3-8e92-f94844073337", + "metadata": {}, + "source": [ + "# Slicing circuits using ``qiskit_addon_utils.slicing``\n", + "\n", + "Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. Some Qiskit addons make use of the term **slices** to describe layers of arbitrary depth. More concretely, slices can be defined as time-like partitions of a [QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) which span all qubits. Similar to layers, composing all slices of a `QuantumCircuit` produces a circuit which is semantically equivalent to the original." + ] + }, + { + "cell_type": "markdown", + "id": "9f419feb-5d35-489e-909c-c8e7ea2caaec", + "metadata": {}, + "source": [ + "The [qiskit_addon_utils.slicing](/docs/api/qiskit-addon-utils/slicing#module-qiskit_addon_utils.slicing) module provides a few utilities for partitioning ``QuantumCircuit``s into slices. This is for example useful for [operator backpropagation](https://github.com/qiskit/qiskit-addon-obp/) We will give an overview of those tools in this guide.\n", + "\n", + "**Note:** Throughout this guide, we will slice the circuit and use [qiskit_addon_utils.slicing.combine_slices](/docs/api/qiskit-addon-utils/slicing#combine_slices) to recombine the slices with barriers to make it easier to visualize the slices." + ] + }, + { + "cell_type": "markdown", + "id": "c6c74021-216c-43fd-b37a-91817e3d3978", + "metadata": {}, + "source": [ + "First, we'll create a circuit from which we'll create slices." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9da8d173-8ce9-47a9-a24f-ab32e45f8337", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/9da8d173-8ce9-47a9-a24f-ab32e45f8337-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from qiskit import QuantumCircuit\n", + "\n", + "num_qubits = 9\n", + "qc = QuantumCircuit(num_qubits)\n", + "qc.ry(np.pi / 4, range(num_qubits))\n", + "qubits_1 = [i for i in range(num_qubits) if i % 2 == 0]\n", + "qubits_2 = [i for i in range(num_qubits) if i % 2 == 1]\n", + "qc.cx(qubits_1[:-1], qubits_2)\n", + "qc.cx(qubits_2, qubits_1[1:])\n", + "qc.cx(qubits_1[-1], qubits_1[0])\n", + "qc.rx(np.pi / 4, range(num_qubits))\n", + "qc.rz(np.pi / 4, range(num_qubits))\n", + "qc.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "968d1e85-ab12-4383-865f-f0199308a9f2", + "metadata": {}, + "source": [ + "In the case where there is no clear way to exploit the structure of the circuit for back-propagation, a user may wish to simply partition their circuit into slices of a given depth. Here, we'll separate this circuit into depth-1 slices." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f883fa2e-f943-4c49-af45-fb26cadb72b6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/f883fa2e-f943-4c49-af45-fb26cadb72b6-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_utils.slicing import combine_slices, slice_by_depth\n", + "\n", + "slices = slice_by_depth(qc, 1)\n", + "combined_slices = combine_slices(slices, include_barriers=True)\n", + "combined_slices.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "4debef73-ba54-4eb1-887c-df6d022c9082", + "metadata": {}, + "source": [ + "Now let's try depth-2." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bc5ebecb-6c8b-4f51-8199-b236a0ba3328", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/bc5ebecb-6c8b-4f51-8199-b236a0ba3328-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slices = slice_by_depth(qc, 2)\n", + "combined_slices = combine_slices(slices, include_barriers=True)\n", + "combined_slices.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "482bc11c-47a3-45ba-9633-98bcca5b44a9", + "metadata": {}, + "source": [ + "In many cases, such as Trotter circuits, it may be advantageous to slice by gate type. Slices holding a given gate type will be further split out into depth-1 slices, as there is little downside in doing so." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1a5d5aff-f988-4f14-ad25-9f0105202b77", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/1a5d5aff-f988-4f14-ad25-9f0105202b77-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_utils.slicing import slice_by_gate_types\n", + "\n", + "slices = slice_by_gate_types(qc)\n", + "combined_slices = combine_slices(slices, include_barriers=True)\n", + "combined_slices.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "5e389e7e-1d29-4e9c-b44e-a5182da5cb5b", + "metadata": {}, + "source": [ + "If your circuit was designed to exploit the physical qubit connectivity, you may want to create slices based on an edge coloring. Here, we will assign a 3-coloring to the circuit edges and slice the circuit with respect to the edge coloring. This only affects non-local gates. Single qubit gates will be added to their own slices by gate type." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "110b5844-972e-46d1-bfc5-20a30b69f814", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/110b5844-972e-46d1-bfc5-20a30b69f814-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_utils.slicing import slice_by_coloring\n", + "\n", + "# Assign a color to each set of connected qubits\n", + "coloring = {}\n", + "for i in range(num_qubits - 1):\n", + " coloring[(i, i + 1)] = i % 3\n", + "coloring[(num_qubits - 1, 0)] = 2\n", + "\n", + "# Create a circuit with operations added in order of color\n", + "qc = QuantumCircuit(num_qubits)\n", + "qc.ry(np.pi / 4, range(num_qubits))\n", + "edges = [edge for color in range(3) for edge in coloring if coloring[edge] == color]\n", + "for edge in edges:\n", + " qc.cx(edge[0], edge[1])\n", + "qc.rx(np.pi / 4, range(num_qubits))\n", + "qc.rz(np.pi / 4, range(num_qubits))\n", + "\n", + "# Create slices by edge color\n", + "slices = slice_by_coloring(qc, coloring=coloring)\n", + "combined_slices = combine_slices(slices, include_barriers=True)\n", + "combined_slices.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "d05b0cad-bbed-48cd-8520-3e2904cde058", + "metadata": {}, + "source": [ + "For more custom slicing strategies a user may wish to place barriers in the locations they want to slice and use the [slice_by_barriers](/docs/api/qiskit-addon-utils/slicing#slice_by_barriers) function. Here, we will create 3 slices, one for each rotation layer and one for the entangling layer." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "948aa715-22e8-41e1-943d-de7eb0aa379c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/948aa715-22e8-41e1-943d-de7eb0aa379c-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = QuantumCircuit(num_qubits)\n", + "qc.ry(np.pi / 4, range(num_qubits))\n", + "qc.barrier()\n", + "qubits_1 = [i for i in range(num_qubits) if i % 2 == 0]\n", + "qubits_2 = [i for i in range(num_qubits) if i % 2 == 1]\n", + "qc.cx(qubits_1[:-1], qubits_2)\n", + "qc.cx(qubits_2, qubits_1[1:])\n", + "qc.cx(qubits_1[-1], qubits_1[0])\n", + "qc.barrier()\n", + "qc.rx(np.pi / 4, range(num_qubits))\n", + "qc.rz(np.pi / 4, range(num_qubits))\n", + "qc.draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "6511c580-bcc6-462c-9d17-2435adae311a", + "metadata": {}, + "source": [ + "We will not draw the re-combined slices as a single circuit since it would look identical to the input circuit. Instead, below we draw each slice on its own." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9f766d69-e10c-4aa8-a3bc-2057dab7a69a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/9f766d69-e10c-4aa8-a3bc-2057dab7a69a-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_utils.slicing import slice_by_barriers\n", + "\n", + "slices = slice_by_barriers(qc)\n", + "slices[0].draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a00c8743-f3b9-460c-ba2c-ffc634e99e0e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/a00c8743-f3b9-460c-ba2c-ffc634e99e0e-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slices[1].draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "791bd07b-739a-45ec-a3e9-2d068f8bd960", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/791bd07b-739a-45ec-a3e9-2d068f8bd960-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slices[2].draw(\"mpl\", scale=0.6)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-utils/how-tos/index.mdx b/docs/addons/qiskit-addon-utils/how-tos/index.mdx new file mode 100644 index 00000000000..0c8b7971a74 --- /dev/null +++ b/docs/addons/qiskit-addon-utils/how-tos/index.mdx @@ -0,0 +1,11 @@ +--- +title: "How-To Guides - Qiskit addon utilities 0.3.1" +--- + +# How-To Guides + +This page summarizes the available how-to guides. + +* [Use edge coloring to reduce the depth of quantum circuits](color-device-edges-to-improve-depth) +* [Slicing circuits using `qiskit_addon_utils.slicing`](create-circuit-slices) + diff --git a/docs/addons/qiskit-addon-utils/index.mdx b/docs/addons/qiskit-addon-utils/index.mdx new file mode 100644 index 00000000000..9ee7754afc8 --- /dev/null +++ b/docs/addons/qiskit-addon-utils/index.mdx @@ -0,0 +1,44 @@ +--- +title: "Qiskit addon utilities 0.3.1" +--- + +# Qiskit addon utilities + +[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. + +This package contains functionality which is meant to supplement workflows involving one or more Qiskit addons. For example, this package contains functions for creating Hamiltonians, generating Trotter time evolution circuits, and slicing and combining quantum circuits in time-wise partitions. + +## Documentation + +All documentation is available [here](/docs/addons/qiskit-addon-utils/). + +## Installation + +We encourage installing this package via `pip`, when possible: + +```bash +pip install 'qiskit-addon-utils' +``` + +For more installation information refer to the [installation instructions](install) in the documentation. + +## Deprecation Policy + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [release notes](/docs/api/qiskit-addon-utils/release-notes). + +## Contributing + +The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-utils). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-utils/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). + +We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-utils/issues/new/choose) for tracking requests and bugs. + +## Citation + +If you use this package in your research, please cite it according to the [CITATION.bib](https://github.com/Qiskit/qiskit-addon-utils/blob/main/CITATION.bib) file. + +## License + +[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-utils/blob/main/LICENSE.txt) + diff --git a/docs/addons/qiskit-addon-utils/install.mdx b/docs/addons/qiskit-addon-utils/install.mdx new file mode 100644 index 00000000000..142857fee0f --- /dev/null +++ b/docs/addons/qiskit-addon-utils/install.mdx @@ -0,0 +1,76 @@ +--- +title: "Installation Instructions - Qiskit addon utilities 0.3.1" +--- + +# Installation Instructions + +Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: + +* [Option 1: Install from PyPI](#option-1) +* [Option 2: Install from Source](#option-2) + +## Pre-Installation + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +Note: If you are using Windows, use the following commands in PowerShell: + +```pwsh +python3 -m venv c:\path\to\virtual\environment +c:\path\to\virtual\environment\Scripts\Activate.ps1 +``` + +<span id="option-1" /> + +## Option 1: Install from PyPI + +The most straightforward way to install the `qiskit-addon-utils` package is via `PyPI`. + +```sh +pip install 'qiskit-addon-utils' +``` + +<span id="option-2" /> + +## Option 2: Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +If so, the first step is to clone the `qiskit-addon-utils` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-utils.git +``` + +Next, upgrade pip and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-utils +``` + +The next step is to install `qiskit-addon-utils` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab +``` + diff --git a/docs/api/qiskit-addon-utils/_toc.json b/docs/api/qiskit-addon-utils/_toc.json index 890eceb867e..88cfc1dcc4e 100644 --- a/docs/api/qiskit-addon-utils/_toc.json +++ b/docs/api/qiskit-addon-utils/_toc.json @@ -87,5 +87,7 @@ } ], "collapsed": true, - "untranslatable": true + "untranslatable": true, + "parentUrl": "/docs/addons/qiskit-addon-utils", + "parentLabel": "Qiskit addon utilities" } diff --git a/docs/api/qiskit-addon-utils/exp-vals.mdx b/docs/api/qiskit-addon-utils/exp-vals.mdx index 27e37a40784..0934d7b983f 100644 --- a/docs/api/qiskit-addon-utils/exp-vals.mdx +++ b/docs/api/qiskit-addon-utils/exp-vals.mdx @@ -84,7 +84,7 @@ Tools for calculating expectation values. In the transpiled (or ISA) ordering, the qubits are indexed based on the “physical” layout of qubits in the device. - For info on canonical qubit ordering conventions see the [Samplomatic docs](https://qiskit.github.io/samplomatic/guides/samplex_io.html#qubit-ordering-convention)). + For info on canonical qubit ordering conventions see the [Samplomatic docs](/docs/addons/samplomatic/guides/samplex-io#qubit-ordering-convention)). **Parameters** @@ -105,7 +105,7 @@ Tools for calculating expectation values. <Function id="qiskit_addon_utils.exp_vals.map_observable_virtual_to_canonical" github="https://github.com/Qiskit/qiskit-addon-utils/tree/stable/0.3/qiskit_addon_utils/exp_vals/observable_mappings.py#L72-L125" signature="map_observable_virtual_to_canonical(virt_observable, layout, canonical_qubits)"> Map an observable with virtual qubit ordering to canonical box-order. - For info on canonical qubit ordering conventions see the [Samplomatic docs](https://qiskit.github.io/samplomatic/guides/samplex_io.html#qubit-ordering-convention)). + For info on canonical qubit ordering conventions see the [Samplomatic docs](/docs/addons/samplomatic/guides/samplex-io#qubit-ordering-convention)). **Parameters** diff --git a/docs/api/qiskit-addon-utils/noise-management-post-selection-transpiler-passes.mdx b/docs/api/qiskit-addon-utils/noise-management-post-selection-transpiler-passes.mdx index 0339b895eca..855b494c0bc 100644 --- a/docs/api/qiskit-addon-utils/noise-management-post-selection-transpiler-passes.mdx +++ b/docs/api/qiskit-addon-utils/noise-management-post-selection-transpiler-passes.mdx @@ -16,9 +16,9 @@ python_api_name: qiskit_addon_utils.noise_management.post_selection.transpiler.p A submodule with transpilation passes for post selection. -| | | -| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------ | -| [`AddSpectatorMeasures`](noise-management-post-selection-transpiler-passes-add-spectator-measures "qiskit_addon_utils.noise_management.post_selection.transpiler.passes.AddSpectatorMeasures") | Add measurements on spectator qubits. | -| [`AddPostSelectionMeasures`](noise-management-post-selection-transpiler-passes-add-post-selection-measures "qiskit_addon_utils.noise_management.post_selection.transpiler.passes.AddPostSelectionMeasures") | Add a post selection measurement after every terminal measurement. | -| [`XSlowGate`](noise-management-post-selection-transpiler-passes-x-slow-gate "qiskit_addon_utils.noise_management.post_selection.transpiler.passes.XSlowGate") | The x-slow gate. | +| | | +| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------ | +| [`AddSpectatorMeasures`](/docs/api/qiskit-addon-utils/noise-management-post-selection-transpiler-passes-add-spectator-measures "qiskit_addon_utils.noise_management.post_selection.transpiler.passes.AddSpectatorMeasures") | Add measurements on spectator qubits. | +| [`AddPostSelectionMeasures`](/docs/api/qiskit-addon-utils/noise-management-post-selection-transpiler-passes-add-post-selection-measures "qiskit_addon_utils.noise_management.post_selection.transpiler.passes.AddPostSelectionMeasures") | Add a post selection measurement after every terminal measurement. | +| [`XSlowGate`](/docs/api/qiskit-addon-utils/noise-management-post-selection-transpiler-passes-x-slow-gate "qiskit_addon_utils.noise_management.post_selection.transpiler.passes.XSlowGate") | The x-slow gate. | diff --git a/docs/api/qiskit-addon-utils/release-notes.mdx b/docs/api/qiskit-addon-utils/release-notes.mdx index acb2e125492..9656c3bd3b1 100644 --- a/docs/api/qiskit-addon-utils/release-notes.mdx +++ b/docs/api/qiskit-addon-utils/release-notes.mdx @@ -20,7 +20,7 @@ in_page_toc_max_heading_level: 2 ### Bug Fixes -* Fixed bugs in [`qiskit_addon_utils.exp_vals.executor_expectation_values()`](exp-vals#executor_expectation_values "qiskit_addon_utils.exp_vals.executor_expectation_values") causing indexing errors when bit\_array dimensions associated with twirling randomizations were not specified in avg\_axis. +* Fixed bugs in [`qiskit_addon_utils.exp_vals.executor_expectation_values()`](/docs/api/qiskit-addon-utils/exp-vals#executor_expectation_values "qiskit_addon_utils.exp_vals.executor_expectation_values") causing indexing errors when bit\_array dimensions associated with twirling randomizations were not specified in avg\_axis. <span id="release-notes-0-3-0" /> @@ -32,16 +32,16 @@ in_page_toc_max_heading_level: 2 ### New Features -* Added a new sub-package, [`qiskit_addon_utils.exp_vals`](exp-vals#module-qiskit_addon_utils.exp_vals "qiskit_addon_utils.exp_vals"), containing functions for calculating expectation values from the outputs of Qiskit Runtime’s new `Executor` primitive prototype. This includes: +* Added a new sub-package, [`qiskit_addon_utils.exp_vals`](/docs/api/qiskit-addon-utils/exp-vals#module-qiskit_addon_utils.exp_vals "qiskit_addon_utils.exp_vals"), containing functions for calculating expectation values from the outputs of Qiskit Runtime’s new `Executor` primitive prototype. This includes: - > * [`qiskit_addon_utils.exp_vals.executor_expectation_values()`](exp-vals#executor_expectation_values "qiskit_addon_utils.exp_vals.executor_expectation_values") for expectation value calculation - > * [`qiskit_addon_utils.exp_vals.get_measurement_bases()`](exp-vals#get_measurement_bases "qiskit_addon_utils.exp_vals.get_measurement_bases") for identifying a minimal set of measurement bases to cover all terms in the observable. - > * [`qiskit_addon_utils.exp_vals.map_observable_isa_to_canonical()`](exp-vals#map_observable_isa_to_canonical "qiskit_addon_utils.exp_vals.map_observable_isa_to_canonical"), [`qiskit_addon_utils.exp_vals.map_observable_virtual_to_canonical()`](exp-vals#map_observable_virtual_to_canonical "qiskit_addon_utils.exp_vals.map_observable_virtual_to_canonical"), and [`qiskit_addon_utils.exp_vals.map_observable_isa_to_virtual()`](exp-vals#map_observable_isa_to_virtual "qiskit_addon_utils.exp_vals.map_observable_isa_to_virtual") for transforming the qubit order of Pauli observables. + > * [`qiskit_addon_utils.exp_vals.executor_expectation_values()`](/docs/api/qiskit-addon-utils/exp-vals#executor_expectation_values "qiskit_addon_utils.exp_vals.executor_expectation_values") for expectation value calculation + > * [`qiskit_addon_utils.exp_vals.get_measurement_bases()`](/docs/api/qiskit-addon-utils/exp-vals#get_measurement_bases "qiskit_addon_utils.exp_vals.get_measurement_bases") for identifying a minimal set of measurement bases to cover all terms in the observable. + > * [`qiskit_addon_utils.exp_vals.map_observable_isa_to_canonical()`](/docs/api/qiskit-addon-utils/exp-vals#map_observable_isa_to_canonical "qiskit_addon_utils.exp_vals.map_observable_isa_to_canonical"), [`qiskit_addon_utils.exp_vals.map_observable_virtual_to_canonical()`](/docs/api/qiskit-addon-utils/exp-vals#map_observable_virtual_to_canonical "qiskit_addon_utils.exp_vals.map_observable_virtual_to_canonical"), and [`qiskit_addon_utils.exp_vals.map_observable_isa_to_virtual()`](/docs/api/qiskit-addon-utils/exp-vals#map_observable_isa_to_virtual "qiskit_addon_utils.exp_vals.map_observable_isa_to_virtual") for transforming the qubit order of Pauli observables. -* Added two new functions to the [`qiskit_addon_utils.noise_management`](noise-management#module-qiskit_addon_utils.noise_management "qiskit_addon_utils.noise_management") sub-package: +* Added two new functions to the [`qiskit_addon_utils.noise_management`](/docs/api/qiskit-addon-utils/noise-management#module-qiskit_addon_utils.noise_management "qiskit_addon_utils.noise_management") sub-package: - > * [`qiskit_addon_utils.noise_management.trex_factors()`](noise-management#trex_factors "qiskit_addon_utils.noise_management.trex_factors") for calculating TREX readout error mitigation scaling factors - > * [`qiskit_addon_utils.noise_management.gamma_from_noisy_boxes()`](noise-management#gamma_from_noisy_boxes "qiskit_addon_utils.noise_management.gamma_from_noisy_boxes") for calculating the gamma factor for a given circuit and noise model. + > * [`qiskit_addon_utils.noise_management.trex_factors()`](/docs/api/qiskit-addon-utils/noise-management#trex_factors "qiskit_addon_utils.noise_management.trex_factors") for calculating TREX readout error mitigation scaling factors + > * [`qiskit_addon_utils.noise_management.gamma_from_noisy_boxes()`](/docs/api/qiskit-addon-utils/noise-management#gamma_from_noisy_boxes "qiskit_addon_utils.noise_management.gamma_from_noisy_boxes") for calculating the gamma factor for a given circuit and noise model. <span id="release-notes-0-2-0" /> @@ -91,5 +91,5 @@ in_page_toc_max_heading_level: 2 ### Upgrade Notes -* The keyword argument `include_slice_barriers` of [`combine_slices()`](slicing#combine_slices "qiskit_addon_utils.slicing.combine_slices") has been renamed to `include_barriers`. +* The keyword argument `include_slice_barriers` of [`combine_slices()`](/docs/api/qiskit-addon-utils/slicing#combine_slices "qiskit_addon_utils.slicing.combine_slices") has been renamed to `include_barriers`. diff --git a/docs/api/qiskit-addon-utils/slicing-transpiler-passes.mdx b/docs/api/qiskit-addon-utils/slicing-transpiler-passes.mdx index f4ee1a48a78..041efb1adb7 100644 --- a/docs/api/qiskit-addon-utils/slicing-transpiler-passes.mdx +++ b/docs/api/qiskit-addon-utils/slicing-transpiler-passes.mdx @@ -16,9 +16,9 @@ python_api_name: qiskit_addon_utils.slicing.transpiler.passes A submodule with transpilation passes for slicing. -| | | -| ---------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | -| [`CollectOpColor`](slicing-transpiler-passes-collect-op-color "qiskit_addon_utils.slicing.transpiler.passes.CollectOpColor") | Collects blocks of operations which act on the provided edges. | -| [`CollectOpSize`](slicing-transpiler-passes-collect-op-size "qiskit_addon_utils.slicing.transpiler.passes.CollectOpSize") | Collects blocks of the specified size and replaces them by a single block instruction. | -| [`CollectOpType`](slicing-transpiler-passes-collect-op-type "qiskit_addon_utils.slicing.transpiler.passes.CollectOpType") | Collects blocks of the specified operation and replaces them by a single block instruction. | +| | | +| --------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | +| [`CollectOpColor`](/docs/api/qiskit-addon-utils/slicing-transpiler-passes-collect-op-color "qiskit_addon_utils.slicing.transpiler.passes.CollectOpColor") | Collects blocks of operations which act on the provided edges. | +| [`CollectOpSize`](/docs/api/qiskit-addon-utils/slicing-transpiler-passes-collect-op-size "qiskit_addon_utils.slicing.transpiler.passes.CollectOpSize") | Collects blocks of the specified size and replaces them by a single block instruction. | +| [`CollectOpType`](/docs/api/qiskit-addon-utils/slicing-transpiler-passes-collect-op-type "qiskit_addon_utils.slicing.transpiler.passes.CollectOpType") | Collects blocks of the specified operation and replaces them by a single block instruction. | diff --git a/docs/api/qiskit-addon-utils/slicing.mdx b/docs/api/qiskit-addon-utils/slicing.mdx index 80d109c14e6..abfe7e8fd3e 100644 --- a/docs/api/qiskit-addon-utils/slicing.mdx +++ b/docs/api/qiskit-addon-utils/slicing.mdx @@ -16,7 +16,7 @@ python_api_name: qiskit_addon_utils.slicing Utility methods for circuit slicing. -For more information, check out the [how-to guide](https://qiskit.github.io/qiskit-addon-utils/how_tos/create_circuit_slices.html) which discusses this submodule. +For more information, check out the [how-to guide](/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices) which discusses this submodule. ### combine\_slices @@ -66,7 +66,7 @@ For more information, check out the [how-to guide](https://qiskit.github.io/qisk <Function id="qiskit_addon_utils.slicing.slice_by_coloring" github="https://github.com/Qiskit/qiskit-addon-utils/tree/stable/0.3/qiskit_addon_utils/slicing/slice_by_coloring.py#L28-L120" signature="slice_by_coloring(circuit, coloring)"> Split a `QuantumCircuit` into slices using the provided edge coloring. - Two-qubit gates acting on identically colored qubit connections (edges) will be grouped greedily into slices using [`CollectOpColor`](slicing-transpiler-passes-collect-op-color "qiskit_addon_utils.slicing.transpiler.passes.CollectOpColor"). This will be done in order of increasing color value (the integer values which each edge is mapped to). + Two-qubit gates acting on identically colored qubit connections (edges) will be grouped greedily into slices using [`CollectOpColor`](/docs/api/qiskit-addon-utils/slicing-transpiler-passes-collect-op-color "qiskit_addon_utils.slicing.transpiler.passes.CollectOpColor"). This will be done in order of increasing color value (the integer values which each edge is mapped to). <Admonition title="Warning" type="caution"> Note, that this does *not* mean that low valued color slices are guaranteed to be left-most in your circuit. Below is an example to emphasize this. @@ -114,7 +114,7 @@ For more information, check out the [how-to guide](https://qiskit.github.io/qisk └───┘ ░ ``` - Single-qubit gates will be collected into a single slice using [`CollectOpSize`](slicing-transpiler-passes-collect-op-size "qiskit_addon_utils.slicing.transpiler.passes.CollectOpSize"). + Single-qubit gates will be collected into a single slice using [`CollectOpSize`](/docs/api/qiskit-addon-utils/slicing-transpiler-passes-collect-op-size "qiskit_addon_utils.slicing.transpiler.passes.CollectOpSize"). **Parameters** diff --git a/public/docs/api/qiskit-addon-utils/objects.inv b/public/docs/api/qiskit-addon-utils/objects.inv index b4ff59622be0fb28ba1dc94dc3762d7670b9b64d..f28606ef7d3f9141e98a9d4482fb870c8763dc31 100644 GIT binary patch delta 3682 zcmV-o4xRDA9mO4xhJQ_uq&O12_pd<8J#-zMz1_#Tjb@~omUh*(s#<9^7vuz27Me^< z+fZHEzkX$dNespq943o8bywnucoF`<;2_@jjQ^dLF-}vO$5ok~_&%X0%Ch_z6_;Pm 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a/scripts/js/lib/api/objectsInv.ts +++ b/scripts/js/lib/api/objectsInv.ts @@ -221,7 +221,8 @@ export class ObjectsInv { } /** - * Resolve a qiskit.github.io/stubs/<symbol> URL to an internal docs path. + * Resolve a qiskit.github.io/{stubs,apidocs,apidoc}/<symbol> URL to an + * internal docs path using the package's inventory. * * Pass allInvs (from loadPublishedApis) for cross-package resolution. * Requires updateUris() to have been called on same-package inventory first. @@ -231,15 +232,24 @@ export class ObjectsInv { allObjectInvs?: Map<string, ObjectsInv>, ): string | undefined { const match = url.match( - /^https:\/\/qiskit\.github\.io\/([^/]+)\/stubs\/([^"#)\s]+?)(?:\.html)?(?:#.*)?$/, + /^https:\/\/qiskit\.github\.io\/([^/]+)\/(stubs|apidocs|apidoc)\/([^"#)\s]+?)(?:\.html)?(#.*)?$/, ); if (!match) return undefined; - const [, pkg, symbol] = match; + const [, pkg, kind, symbol, anchor = ""] = match; const inv = allObjectInvs?.get(pkg) ?? this; - const entry = inv.entries.find((e) => e.name === symbol); - if (!entry) return undefined; - // Published URIs are relative to the package's API base — prepend full path. - return `/docs/api/${pkg}/${entry.uri}`; + if (kind === "stubs") { + // The entry URI already points at the correct header anchor for the + // symbol — the source anchor (if any) is redundant and gets dropped. + const entry = inv.entries.find((e) => e.name === symbol); + return entry ? `/docs/api/${pkg}/${entry.uri}` : undefined; + } + // Apidocs URLs reference a whole page; the std:doc entry (keyed + // `<kind>/<symbol>`) has the clean page-level URI, and the source anchor + // carries through to the rendered page. + const entry = + inv.entries.find((e) => e.name === `${kind}/${symbol}`) ?? + inv.entries.find((e) => e.name === symbol); + return entry ? `/docs/api/${pkg}/${entry.uri}${anchor}` : undefined; } updateUris(transformLink: (uri: string) => string): void { diff --git a/scripts/js/lib/api/updateLinks.ts b/scripts/js/lib/api/updateLinks.ts index d5be89e2c8f..eb10ddd1055 100644 --- a/scripts/js/lib/api/updateLinks.ts +++ b/scripts/js/lib/api/updateLinks.ts @@ -185,15 +185,20 @@ export function relativizeLink(link: Link): Link | undefined { ["https://docs.quantum-computing.ibm.com/", ""], ["https://quantum.cloud.ibm.com/docs", "/docs"], ["https://quantum.cloud.ibm.com/learning", "/learning"], - ["https://qiskit.github.io/", "/docs/addons"], // todo handle addons prefix + ["https://qiskit.github.io/", "/docs/addons"], ]); const priorPrefix = Array.from(priorPrefixToNewPrefix.keys()).find((prefix) => link.url.startsWith(prefix), ); - // Stubs links are symbol references — leave them for objects.inv resolution. - if (!priorPrefix || link.url.includes("/stubs/")) { + if (!priorPrefix) return; + // github.io stubs/apidocs URLs are looked up via objects.inv by the caller. + if ( + priorPrefix === "https://qiskit.github.io/" && + /\/(stubs|apidocs|apidoc)\//.test(link.url) + ) { return; } + let [url, anchor] = link.url.split("#"); url = removePrefix(url, priorPrefix); url = removeSuffix(url, ".html"); From 3e6fbac203108c7704f2d806141c59278732eaf9 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Wed, 13 May 2026 18:18:04 -0400 Subject: [PATCH 041/104] fix api github.io links --- .../api/qiskit-addon-obp/qiskit-addon-obp.mdx | 4 +- docs/api/qiskit-addon-obp/release-notes.mdx | 44 +++++++++---------- .../utils-metadata-obp-metadata.mdx | 8 ++-- .../utils-metadata-slice-metadata.mdx | 2 +- docs/api/qiskit-addon-obp/utils-metadata.mdx | 8 ++-- docs/api/qiskit-addon-obp/utils-noise.mdx | 6 +-- .../qiskit-addon-obp/utils-visualization.mdx | 18 ++++---- scripts/js/lib/api/apiDocsPipeline.ts | 8 +++- scripts/js/lib/links/ignores.ts | 3 ++ 9 files changed, 55 insertions(+), 46 deletions(-) diff --git a/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx b/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx index a4334ca7d74..82a9ccc24fe 100644 --- a/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx +++ b/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx @@ -34,7 +34,7 @@ Main operator backpropagation functionality. **Parameters** * **observables** ([*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)*]*) – The observable(s) onto which the circuit is backpropagated. - * **slices** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)*]*) – A sequence of `QuantumCircuit` objects representing a single circuit which has been separated into partitions spanning all qubits. **These “slices” will be backpropagated in reverse order.** Each slice must span all qubits. One may use the tools provided in [`qiskit_addon_utils.slicing`](/docs/addons/qiskit-addon-utils/apidocs/qiskit-addon-utils-slicing#module-qiskit_addon_utils.slicing "(in Qiskit addon utilities)") to slice a single [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit). + * **slices** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)*]*) – A sequence of `QuantumCircuit` objects representing a single circuit which has been separated into partitions spanning all qubits. **These “slices” will be backpropagated in reverse order.** Each slice must span all qubits. One may use the tools provided in [`qiskit_addon_utils.slicing`](/docs/api/qiskit-addon-utils/slicing#module-qiskit_addon_utils.slicing "(in Qiskit addon utilities)") to slice a single [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit). * **truncation\_error\_budget** ([*TruncationErrorBudget*](utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") *| None*) – The error budget used for truncating Pauli terms. Refer to the [how-to guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms) for a detailed discussion on truncating terms from the output operator and bounding the incurred error. * **operator\_budget** ([*OperatorBudget*](utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") *| None*) – Constraints on how large the operator may grow during backpropagation. If `None`, a default instance of [`OperatorBudget`](utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") will be used, and no constraints will be placed on the output operator size. * **max\_seconds** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – The maximum number of seconds to run the backpropagation. If this timeout is triggered before the function returns, the metadata of that moment will be returned. Note, that this metadata may contain only partial information for the last slice being backpropagated. @@ -54,6 +54,6 @@ Main operator backpropagation functionality. **Return type** - [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[list](https://docs.python.org/3/library/stdtypes.html#list)\[[*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)], [*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)\[[*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)], [*OBPMetadata*](utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")] + [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[list](https://docs.python.org/3/library/stdtypes.html#list)\[[*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)], [*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)\[[*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)], [*OBPMetadata*](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")] </Function> diff --git a/docs/api/qiskit-addon-obp/release-notes.mdx b/docs/api/qiskit-addon-obp/release-notes.mdx index 2ef507a6319..2e1fd6536e2 100644 --- a/docs/api/qiskit-addon-obp/release-notes.mdx +++ b/docs/api/qiskit-addon-obp/release-notes.mdx @@ -20,13 +20,13 @@ in_page_toc_max_heading_level: 2 ### New Features -* [`PauliLindbladError`](/docs/api/qiskit-ibm-runtime/utils-noise-learner-result-pauli-lindblad-error "(in Qiskit Runtime IBM Client)") objects can now be embedded in slices of `QuantumCircuit` using the [`PauliLindbladErrorInstruction`](utils-noise-pauli-lindblad-error-instruction "qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction") and will be handled properly by [`backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate"). +* [`PauliLindbladError`](/docs/api/qiskit-ibm-runtime/utils-noise-learner-result-pauli-lindblad-error "(in Qiskit Runtime IBM Client)") objects can now be embedded in slices of `QuantumCircuit` using the [`PauliLindbladErrorInstruction`](/docs/api/qiskit-addon-obp/utils-noise-pauli-lindblad-error-instruction "qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction") and will be handled properly by [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate"). <span id="release-notes-0-3-0-bug-fixes" /> ### Bug Fixes -* Fixed a bug in [`qiskit_addon_obp.backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") which often caused resets to be applied to the wrong qubit. +* Fixed a bug in [`qiskit_addon_obp.backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") which often caused resets to be applied to the wrong qubit. <span id="release-notes-0-2-0" /> @@ -40,13 +40,13 @@ in_page_toc_max_heading_level: 2 ### New Features -* A new parameter `show_legend` has been added to each function in the [`qiskit_addon_obp.utils.visualization`](utils-visualization#module-qiskit_addon_obp.utils.visualization "qiskit_addon_obp.utils.visualization") module that can show or hide the legend on a plot. The legend is shown by default. This can be useful when the legend becomes long and obstructs the plot. +* A new parameter `show_legend` has been added to each function in the [`qiskit_addon_obp.utils.visualization`](/docs/api/qiskit-addon-obp/utils-visualization#module-qiskit_addon_obp.utils.visualization "qiskit_addon_obp.utils.visualization") module that can show or hide the legend on a plot. The legend is shown by default. This can be useful when the legend becomes long and obstructs the plot. -* [`qiskit_addon_obp.utils.simplify.OperatorBudget`](utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") now holds `atol` and `rtol` fields which are absolute and relative tolerances used to determine whether coefficients are zero while simplifying an operator. +* [`qiskit_addon_obp.utils.simplify.OperatorBudget`](/docs/api/qiskit-addon-obp/utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") now holds `atol` and `rtol` fields which are absolute and relative tolerances used to determine whether coefficients are zero while simplifying an operator. -* [`truncate_binary_search()`](utils-truncating#truncate_binary_search "qiskit_addon_obp.utils.truncating.truncate_binary_search") now accepts a `tol` kwarg. Once an optimal truncation threshold, up to this value, has been found, the search for an optimal threshold will stop. The default tolerance is `1e-8`; whereas, the tolerance used prior to this release was `1e-10`. +* [`truncate_binary_search()`](/docs/api/qiskit-addon-obp/utils-truncating#truncate_binary_search "qiskit_addon_obp.utils.truncating.truncate_binary_search") now accepts a `tol` kwarg. Once an optimal truncation threshold, up to this value, has been found, the search for an optimal threshold will stop. The default tolerance is `1e-8`; whereas, the tolerance used prior to this release was `1e-10`. - [`TruncationErrorBudget`](utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") now holds a `tol` field. This field is used by [`backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") as the `tol` argument to [`truncate_binary_search()`](utils-truncating#truncate_binary_search "qiskit_addon_obp.utils.truncating.truncate_binary_search"). + [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") now holds a `tol` field. This field is used by [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") as the `tol` argument to [`truncate_binary_search()`](/docs/api/qiskit-addon-obp/utils-truncating#truncate_binary_search "qiskit_addon_obp.utils.truncating.truncate_binary_search"). <span id="release-notes-0-2-0-upgrade-notes" /> @@ -62,11 +62,11 @@ in_page_toc_max_heading_level: 2 ### Bug Fixes -* The [`num_duplicate_paulis`](utils-simplify#num_duplicate_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_duplicate_paulis") was previously unable to differentiate from Pauli terms that were trimmed due to their coefficient being close to zero. This is now tracked correctly with these trimmed terms only counting towards [`num_trimmed_paulis`](utils-simplify#num_trimmed_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_trimmed_paulis"). +* The [`num_duplicate_paulis`](/docs/api/qiskit-addon-obp/utils-simplify#num_duplicate_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_duplicate_paulis") was previously unable to differentiate from Pauli terms that were trimmed due to their coefficient being close to zero. This is now tracked correctly with these trimmed terms only counting towards [`num_trimmed_paulis`](/docs/api/qiskit-addon-obp/utils-simplify#num_trimmed_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_trimmed_paulis"). -* The reported number of unique Paulis in [`num_unique_paulis`](utils-simplify#num_unique_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_unique_paulis") is now correct in cases where all gates in a slice commute with an observable. +* The reported number of unique Paulis in [`num_unique_paulis`](/docs/api/qiskit-addon-obp/utils-simplify#num_unique_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_unique_paulis") is now correct in cases where all gates in a slice commute with an observable. -* Fixed a bug in [`qiskit_addon_obp.backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") which caused slices with depth > 1 to be incorrectly forward-propagated. Gates from a given slice will now be correctly propagated into the observable in reverse order (i.e., from the back of the slice). +* Fixed a bug in [`qiskit_addon_obp.backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") which caused slices with depth > 1 to be incorrectly forward-propagated. Gates from a given slice will now be correctly propagated into the observable in reverse order (i.e., from the back of the slice). <span id="release-notes-0-1-0" /> @@ -80,13 +80,13 @@ in_page_toc_max_heading_level: 2 ### New Features -* Added a [`OperatorBudget`](utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") class for specifying how large an operator may grow during back-propagation. +* Added a [`OperatorBudget`](/docs/api/qiskit-addon-obp/utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") class for specifying how large an operator may grow during back-propagation. -* Adds the `max_seconds` keyword-argument to the [`backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") function, allowing the end-user to specify a maximum wall clock time for the algorithm. This can (for example) be useful for exploring different truncation error budget strategies while limiting the CPU time. +* Adds the `max_seconds` keyword-argument to the [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") function, allowing the end-user to specify a maximum wall clock time for the algorithm. This can (for example) be useful for exploring different truncation error budget strategies while limiting the CPU time. -* Introduced a new `dataclass`, [`TruncationErrorBudget`](utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget"), for holding information about the observable truncation strategy. +* Introduced a new `dataclass`, [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget"), for holding information about the observable truncation strategy. -* Introduced a new function, [`setup_budget()`](utils-truncating#setup_budget "qiskit_addon_obp.utils.truncating.setup_budget"), which generates a [`TruncationErrorBudget`](utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") class, given an observable truncation strategy (e.g. `max_error_total`, `max_error_per_slice`, `p_norm`). +* Introduced a new function, [`setup_budget()`](/docs/api/qiskit-addon-obp/utils-truncating#setup_budget "qiskit_addon_obp.utils.truncating.setup_budget"), which generates a [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") class, given an observable truncation strategy (e.g. `max_error_total`, `max_error_per_slice`, `p_norm`). <span id="release-notes-0-1-0-upgrade-notes" /> @@ -94,7 +94,7 @@ in_page_toc_max_heading_level: 2 ### Upgrade Notes -* The [`backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") function no longer accepts `max_paulis` and `max_qwc_groups` kwargs for constraining the size of the operator during back-propagation. Users should instead use the new `operator_budget` kwarg, which takes an [`OperatorBudget`](utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") instance. +* The [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") function no longer accepts `max_paulis` and `max_qwc_groups` kwargs for constraining the size of the operator during back-propagation. Users should instead use the new `operator_budget` kwarg, which takes an [`OperatorBudget`](/docs/api/qiskit-addon-obp/utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") instance. To migrate, change this code @@ -120,7 +120,7 @@ in_page_toc_max_heading_level: 2 bp_obs, remaining_slices, metadata = backpropagate(obs, slices, operator_budget=op_budget) ``` -* The `max_slices` kwarg has been removed from [`backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate"). Users should now only pass in slices which they intend to back-propagate. If a user wants to attempt to only back-propagate the last `20` slices of an `N`-slice circuit, they would simply pass in the last `20` slices to [`backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") and, recombine any slices remaining after back-propagation with the original `N-20` slices. +* The `max_slices` kwarg has been removed from [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate"). Users should now only pass in slices which they intend to back-propagate. If a user wants to attempt to only back-propagate the last `20` slices of an `N`-slice circuit, they would simply pass in the last `20` slices to [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") and, recombine any slices remaining after back-propagation with the original `N-20` slices. For example @@ -137,9 +137,9 @@ in_page_toc_max_heading_level: 2 reduced_circuit = combine_slices(slices[:-num_slices] + remaining_slices) ``` -* The `max_slices` kwarg in [`setup_budget()`](utils-truncating#setup_budget "qiskit_addon_obp.utils.truncating.setup_budget") has been renamed to `num_slices`. +* The `max_slices` kwarg in [`setup_budget()`](/docs/api/qiskit-addon-obp/utils-truncating#setup_budget "qiskit_addon_obp.utils.truncating.setup_budget") has been renamed to `num_slices`. -* The `max_slices` attribute in [`OBPMetadata`](utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata") has been renamed to `num_slices`. +* The `max_slices` attribute in [`OBPMetadata`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata") has been renamed to `num_slices`. * The project’s root Python namespace has been changed from `obp` to `qiskit_addon_obp`. All package imports must be updated. @@ -155,9 +155,9 @@ in_page_toc_max_heading_level: 2 from qiskit_addon_obp import backpropagate ``` -* Removed the `max_error_total`, `max_error_per_slice`, and `p_norm` kwargs from the [`backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") signature. Instead, users must specify their observable truncation strategy with the new `truncation_error_budget` kwarg which accepts a [`TruncationErrorBudget`](utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") instance. +* Removed the `max_error_total`, `max_error_per_slice`, and `p_norm` kwargs from the [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") signature. Instead, users must specify their observable truncation strategy with the new `truncation_error_budget` kwarg which accepts a [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") instance. -* Removed the `per_slice_budget`, `max_error_total`, and `p_norm` fields from the [`OBPMetadata`](utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata") class. These fields will now be accessed through the new `truncation_error_budget` field, which holds a [`TruncationErrorBudget`](utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") instance. +* Removed the `per_slice_budget`, `max_error_total`, and `p_norm` fields from the [`OBPMetadata`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata") class. These fields will now be accessed through the new `truncation_error_budget` field, which holds a [`TruncationErrorBudget`](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") instance. <span id="release-notes-0-1-0-bug-fixes" /> @@ -165,9 +165,9 @@ in_page_toc_max_heading_level: 2 ### Bug Fixes -* The [`setup_budget()`](utils-truncating#setup_budget "qiskit_addon_obp.utils.truncating.setup_budget") erroneously distributed the `max_error_total` when `num_slices` was also set. This has been fixed now, such that the budget always gets distributed evenly, regardless of the value of `p_norm`. +* The [`setup_budget()`](/docs/api/qiskit-addon-obp/utils-truncating#setup_budget "qiskit_addon_obp.utils.truncating.setup_budget") erroneously distributed the `max_error_total` when `num_slices` was also set. This has been fixed now, such that the budget always gets distributed evenly, regardless of the value of `p_norm`. -* When the `max_seconds` argument to the [`backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") method is used, but the timeout is not reached during the actual OBP execution, the signal will now be reset properly, thereby avoiding cancellations at a (seemingly) random later point in time (of course, it is not random but actually after the specified amount of time has passed, but the rest of the code being executed after OBP could be doing anything at this point). +* When the `max_seconds` argument to the [`backpropagate()`](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") method is used, but the timeout is not reached during the actual OBP execution, the signal will now be reset properly, thereby avoiding cancellations at a (seemingly) random later point in time (of course, it is not random but actually after the specified amount of time has passed, but the rest of the code being executed after OBP could be doing anything at this point). -* The computation of the [`accumulated_error()`](utils-metadata-obp-metadata#accumulated_error "qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error") and [`left_over_error_budget()`](utils-metadata-obp-metadata#left_over_error_budget "qiskit_addon_obp.utils.metadata.OBPMetadata.left_over_error_budget") were fixed to respect the [Minkowski inequality](https://en.wikipedia.org/wiki/Minkowski_inequality). This is necessary, because a general Lp-norm (other than `p=2`) does not satisfy the [parallelogram law](https://en.wikipedia.org/wiki/Parallelogram_law) which resulted in a non-rigorous upper bound of the actual accumulated errors (and left-over error budgets by extension). +* The computation of the [`accumulated_error()`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#accumulated_error "qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error") and [`left_over_error_budget()`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#left_over_error_budget "qiskit_addon_obp.utils.metadata.OBPMetadata.left_over_error_budget") were fixed to respect the [Minkowski inequality](https://en.wikipedia.org/wiki/Minkowski_inequality). This is necessary, because a general Lp-norm (other than `p=2`) does not satisfy the [parallelogram law](https://en.wikipedia.org/wiki/Parallelogram_law) which resulted in a non-rigorous upper bound of the actual accumulated errors (and left-over error budgets by extension). diff --git a/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata.mdx b/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata.mdx index 59e391628c9..976e8e57aea 100644 --- a/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata.mdx +++ b/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata.mdx @@ -17,9 +17,9 @@ python_api_name: qiskit_addon_obp.utils.metadata.OBPMetadata **Parameters** - * **truncation\_error\_budget** ([*TruncationErrorBudget*](utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget")) + * **truncation\_error\_budget** ([*TruncationErrorBudget*](/docs/api/qiskit-addon-obp/utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget")) * **num\_slices** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) - * **operator\_budget** ([*OperatorBudget*](utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget")) + * **operator\_budget** ([*OperatorBudget*](/docs/api/qiskit-addon-obp/utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget")) * **backpropagation\_history** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*SliceMetadata*](utils-metadata-slice-metadata "qiskit_addon_obp.utils.metadata.SliceMetadata")*]*) * **num\_backpropagated\_slices** ([*int*](https://docs.python.org/3/library/functions.html#int)) @@ -66,7 +66,7 @@ python_api_name: qiskit_addon_obp.utils.metadata.OBPMetadata This method computes the accumulated error for a given observable index at a given “time” during the course of the backpropagation. In this context, “time” is to be understood as the discrete steps of already backpropagated slices. - The accumulated error is computed as the sum of the individual [`SliceMetadata.slice_errors`](utils-metadata-slice-metadata#slice_errors "qiskit_addon_obp.utils.metadata.SliceMetadata.slice_errors"). These in turn may be computed within a specified [`TruncationErrorBudget.p_norm`](utils-truncating#p_norm "qiskit_addon_obp.utils.truncating.TruncationErrorBudget.p_norm"). Thus, the computed accumulated error is an upper bound to the real accumulated error as given by the [Minkowski inequality](https://en.wikipedia.org/wiki/Minkowski_inequality) (the generalization of the triangle inequality for Lp-norms other than `p=2`). + The accumulated error is computed as the sum of the individual [`SliceMetadata.slice_errors`](utils-metadata-slice-metadata#slice_errors "qiskit_addon_obp.utils.metadata.SliceMetadata.slice_errors"). These in turn may be computed within a specified [`TruncationErrorBudget.p_norm`](/docs/api/qiskit-addon-obp/utils-truncating#p_norm "qiskit_addon_obp.utils.truncating.TruncationErrorBudget.p_norm"). Thus, the computed accumulated error is an upper bound to the real accumulated error as given by the [Minkowski inequality](https://en.wikipedia.org/wiki/Minkowski_inequality) (the generalization of the triangle inequality for Lp-norms other than `p=2`). <Admonition title="Note" type="note"> Since a general Lp-norm (other than `p=2`) is *not* an inner product norm, it does *not* satisfy the [parallelogram law](https://en.wikipedia.org/wiki/Parallelogram_law). Hence, we must use the Minkowski inequality as the upper bound of the accumulated error. @@ -111,7 +111,7 @@ python_api_name: qiskit_addon_obp.utils.metadata.OBPMetadata This method computes the left-over error budget for a given observable index at a given “time” during the course of the backpropagation. In this context, “time” is to be understood as the discrete steps of already backpropagated slices. - The left-over error budget is computed as the remainder of the total budget minus the sum of the individual [`SliceMetadata.slice_errors`](utils-metadata-slice-metadata#slice_errors "qiskit_addon_obp.utils.metadata.SliceMetadata.slice_errors"). These in turn may be computed within a specified [`TruncationErrorBudget.p_norm`](utils-truncating#p_norm "qiskit_addon_obp.utils.truncating.TruncationErrorBudget.p_norm"). + The left-over error budget is computed as the remainder of the total budget minus the sum of the individual [`SliceMetadata.slice_errors`](utils-metadata-slice-metadata#slice_errors "qiskit_addon_obp.utils.metadata.SliceMetadata.slice_errors"). These in turn may be computed within a specified [`TruncationErrorBudget.p_norm`](/docs/api/qiskit-addon-obp/utils-truncating#p_norm "qiskit_addon_obp.utils.truncating.TruncationErrorBudget.p_norm"). <Admonition title="Note" type="note"> See also the explanations in [`accumulated_error()`](#qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error "qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error") for more details on how the individual slice errors are summed up to form an upper bound to the real error via the Minkowski inequality. diff --git a/docs/api/qiskit-addon-obp/utils-metadata-slice-metadata.mdx b/docs/api/qiskit-addon-obp/utils-metadata-slice-metadata.mdx index 2c3517d4932..45a07d650e8 100644 --- a/docs/api/qiskit-addon-obp/utils-metadata-slice-metadata.mdx +++ b/docs/api/qiskit-addon-obp/utils-metadata-slice-metadata.mdx @@ -76,7 +76,7 @@ python_api_name: qiskit_addon_obp.utils.metadata.SliceMetadata The sum of the coefficients for each observable that were trimmed during operator simplification because each individual coefficient was below the trimming threshold. <Admonition title="Warning" type="caution"> - This sum is *not* affected by the value of [`p_norm`](utils-truncating#p_norm "qiskit_addon_obp.utils.truncating.TruncationErrorBudget.p_norm")! + This sum is *not* affected by the value of [`p_norm`](/docs/api/qiskit-addon-obp/utils-truncating#p_norm "qiskit_addon_obp.utils.truncating.TruncationErrorBudget.p_norm")! </Admonition> <Admonition title="Note" type="note"> diff --git a/docs/api/qiskit-addon-obp/utils-metadata.mdx b/docs/api/qiskit-addon-obp/utils-metadata.mdx index 4375fd5ef91..20e6828efd2 100644 --- a/docs/api/qiskit-addon-obp/utils-metadata.mdx +++ b/docs/api/qiskit-addon-obp/utils-metadata.mdx @@ -16,8 +16,8 @@ python_api_name: qiskit_addon_obp.utils.metadata Container classes for holding backpropagation metadata. -| | | -| ------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------- | -| [`OBPMetadata`](utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata") | A container for metadata generated during the `backpropagate()` method. | -| [`SliceMetadata`](utils-metadata-slice-metadata "qiskit_addon_obp.utils.metadata.SliceMetadata") | A container for metadata generated during the backpropagation of a single slice. | +| | | +| --------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | +| [`OBPMetadata`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata") | A container for metadata generated during the `backpropagate()` method. | +| [`SliceMetadata`](/docs/api/qiskit-addon-obp/utils-metadata-slice-metadata "qiskit_addon_obp.utils.metadata.SliceMetadata") | A container for metadata generated during the backpropagation of a single slice. | diff --git a/docs/api/qiskit-addon-obp/utils-noise.mdx b/docs/api/qiskit-addon-obp/utils-noise.mdx index c09109eb726..d37043bbe3d 100644 --- a/docs/api/qiskit-addon-obp/utils-noise.mdx +++ b/docs/api/qiskit-addon-obp/utils-noise.mdx @@ -16,7 +16,7 @@ python_api_name: qiskit_addon_obp.utils.noise Utilities for noise operators. -| | | -| -------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------- | -| [`PauliLindbladErrorInstruction`](utils-noise-pauli-lindblad-error-instruction "qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction") | A lightweight wrapper around a `PauliLindbladError`. | +| | | +| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------- | +| [`PauliLindbladErrorInstruction`](/docs/api/qiskit-addon-obp/utils-noise-pauli-lindblad-error-instruction "qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction") | A lightweight wrapper around a `PauliLindbladError`. | diff --git a/docs/api/qiskit-addon-obp/utils-visualization.mdx b/docs/api/qiskit-addon-obp/utils-visualization.mdx index f21a5e38145..cfbaf1bf35c 100644 --- a/docs/api/qiskit-addon-obp/utils-visualization.mdx +++ b/docs/api/qiskit-addon-obp/utils-visualization.mdx @@ -21,7 +21,7 @@ Various visualization utilities. <Function id="qiskit_addon_obp.utils.visualization.plot_accumulated_error" github="https://github.com/Qiskit/qiskit-addon-obp/tree/stable/0.3/qiskit_addon_obp/utils/visualization.py#L24-L84" signature="plot_accumulated_error(metadata, axes, *, show_legend=True)"> Plot the accumulated error. - This method populates the provided figure axes with a line-plot of the [`OBPMetadata.accumulated_error()`](utils-metadata-obp-metadata#accumulated_error "qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error"). Below is an example where we plot some `metadata` which exists within our context. + This method populates the provided figure axes with a line-plot of the [`OBPMetadata.accumulated_error()`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#accumulated_error "qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error"). Below is an example where we plot some `metadata` which exists within our context. ```python >>> from matplotlib import pyplot as plt @@ -38,7 +38,7 @@ Various visualization utilities. **Parameters** - * **metadata** ([*OBPMetadata*](utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. + * **metadata** ([*OBPMetadata*](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. * **axes** (*Axes*) – the matplotlib axes in which to plot. * **show\_legend** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – enable/disable showing the legend in the plot. @@ -52,7 +52,7 @@ Various visualization utilities. <Function id="qiskit_addon_obp.utils.visualization.plot_left_over_error_budget" github="https://github.com/Qiskit/qiskit-addon-obp/tree/stable/0.3/qiskit_addon_obp/utils/visualization.py#L87-L134" signature="plot_left_over_error_budget(metadata, axes, *, show_legend=True)"> Plot the left-over error budget. - This method populates the provided figure axes with a line-plot of the [`OBPMetadata.left_over_error_budget()`](utils-metadata-obp-metadata#left_over_error_budget "qiskit_addon_obp.utils.metadata.OBPMetadata.left_over_error_budget"). Below is an example where we plot some `metadata` which exists within our context. + This method populates the provided figure axes with a line-plot of the [`OBPMetadata.left_over_error_budget()`](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata#left_over_error_budget "qiskit_addon_obp.utils.metadata.OBPMetadata.left_over_error_budget"). Below is an example where we plot some `metadata` which exists within our context. ```python >>> from matplotlib import pyplot as plt @@ -67,7 +67,7 @@ Various visualization utilities. **Parameters** - * **metadata** ([*OBPMetadata*](utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. + * **metadata** ([*OBPMetadata*](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. * **axes** (*Axes*) – the matplotlib axes in which to plot. * **show\_legend** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – enable/disable showing the legend in the plot. @@ -98,7 +98,7 @@ Various visualization utilities. **Parameters** - * **metadata** ([*OBPMetadata*](utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. + * **metadata** ([*OBPMetadata*](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. * **axes** (*Axes*) – the matplotlib axes in which to plot. * **show\_legend** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – enable/disable showing the legend in the plot. @@ -129,7 +129,7 @@ Various visualization utilities. **Parameters** - * **metadata** ([*OBPMetadata*](utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. + * **metadata** ([*OBPMetadata*](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. * **axes** (*Axes*) – the matplotlib axes in which to plot. * **show\_legend** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – enable/disable showing the legend in the plot. @@ -160,7 +160,7 @@ Various visualization utilities. **Parameters** - * **metadata** ([*OBPMetadata*](utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. + * **metadata** ([*OBPMetadata*](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. * **axes** (*Axes*) – the matplotlib axes in which to plot. * **show\_legend** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – enable/disable showing the legend in the plot. @@ -191,7 +191,7 @@ Various visualization utilities. **Parameters** - * **metadata** ([*OBPMetadata*](utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. + * **metadata** ([*OBPMetadata*](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. * **axes** (*Axes*) – the matplotlib axes in which to plot. * **show\_legend** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – enable/disable showing the legend in the plot. @@ -220,7 +220,7 @@ Various visualization utilities. **Parameters** - * **metadata** ([*OBPMetadata*](utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. + * **metadata** ([*OBPMetadata*](/docs/api/qiskit-addon-obp/utils-metadata-obp-metadata "qiskit_addon_obp.utils.metadata.OBPMetadata")) – the metadata to be visualized. * **axes** (*Axes*) – the matplotlib axes in which to plot. * **show\_legend** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – enable/disable showing the legend in the plot. diff --git a/scripts/js/lib/api/apiDocsPipeline.ts b/scripts/js/lib/api/apiDocsPipeline.ts index 26f8ff38355..c1d6b2a11cf 100644 --- a/scripts/js/lib/api/apiDocsPipeline.ts +++ b/scripts/js/lib/api/apiDocsPipeline.ts @@ -34,6 +34,7 @@ export async function runApiDocsPipeline( publicBaseFolder: string, pkg: Pkg, ) { + const allObjectInvs = await ObjectsInv.loadPublishedApis(publicBaseFolder); const [files, markdownPath, maybeObjectsInv] = await determineFilePaths( artifactPath, docsBaseFolder, @@ -49,7 +50,12 @@ export async function runApiDocsPipeline( pkg.apiOutputDir(`${DOCS_BASE_PATH}/images`), ); - const results = await postProcess(pkg, initialResults, maybeObjectsInv); + const results = await postProcess( + pkg, + initialResults, + maybeObjectsInv, + allObjectInvs, + ); // Warning: the sequence of operations often matters. await writeMarkdownResults(pkg, docsBaseFolder, results); diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index b5ceec02ee8..ce5c941efde 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -225,6 +225,9 @@ const ALWAYS_IGNORED_URLS__SHOULD_FIX: string[] = [ "/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html", "/api/qiskit-ibm-runtime/noise-learner-noise-learner", "/api/qiskit-ibm-runtime/noise-learner-result", + + // from qiskit-addon-utils + "/docs/addons/samplomatic/guides/samplex-io#qubit-ordering-convention", ]; export const ALWAYS_IGNORED_URLS = new Set([ From 3e5ae4e3e1e09814985dd6da6b8260b2f6f2f640 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Wed, 13 May 2026 18:58:40 -0400 Subject: [PATCH 042/104] add descriptions --- .../addons/qiskit-addon-obp/how-tos/index.mdx | 1 + docs/addons/qiskit-addon-obp/index.mdx | 1 + docs/addons/qiskit-addon-obp/install.mdx | 1 + .../qiskit-addon-obp/tutorials/index.mdx | 1 + .../qiskit-addon-utils/how-tos/index.mdx | 1 + docs/addons/qiskit-addon-utils/index.mdx | 1 + docs/addons/qiskit-addon-utils/install.mdx | 1 + scripts/js/lib/api/pipelineStages.ts | 30 +++++++++++++------ 8 files changed, 28 insertions(+), 9 deletions(-) diff --git a/docs/addons/qiskit-addon-obp/how-tos/index.mdx b/docs/addons/qiskit-addon-obp/how-tos/index.mdx index b61973645bc..ae50ca47cc4 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/index.mdx +++ b/docs/addons/qiskit-addon-obp/how-tos/index.mdx @@ -1,5 +1,6 @@ --- title: "How-To Guides - Qiskit addon: operator backpropagation (OBP) 0.3.0" +description: "How-To Guides for Operator backpropagation (OBP)" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-obp/index.mdx b/docs/addons/qiskit-addon-obp/index.mdx index 3f7e4cc1ceb..2ccfa2d25d7 100644 --- a/docs/addons/qiskit-addon-obp/index.mdx +++ b/docs/addons/qiskit-addon-obp/index.mdx @@ -1,5 +1,6 @@ --- title: "Qiskit addon: operator backpropagation (OBP) 0.3.0" +description: "Operator backpropagation (OBP) documentation" --- # Qiskit addon: operator backpropagation (OBP) diff --git a/docs/addons/qiskit-addon-obp/install.mdx b/docs/addons/qiskit-addon-obp/install.mdx index dd9b9648b0c..8f823bf9c0a 100644 --- a/docs/addons/qiskit-addon-obp/install.mdx +++ b/docs/addons/qiskit-addon-obp/install.mdx @@ -1,5 +1,6 @@ --- title: "Installation Instructions - Qiskit addon: operator backpropagation (OBP) 0.3.0" +description: "Installation Instructions for Operator backpropagation (OBP)" --- # Installation Instructions diff --git a/docs/addons/qiskit-addon-obp/tutorials/index.mdx b/docs/addons/qiskit-addon-obp/tutorials/index.mdx index 92b3236fbdb..f55be860240 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/index.mdx +++ b/docs/addons/qiskit-addon-obp/tutorials/index.mdx @@ -1,5 +1,6 @@ --- title: "Tutorials - Qiskit addon: operator backpropagation (OBP) 0.3.0" +description: "Tutorials for Operator backpropagation (OBP)" --- # Tutorials diff --git a/docs/addons/qiskit-addon-utils/how-tos/index.mdx b/docs/addons/qiskit-addon-utils/how-tos/index.mdx index 0c8b7971a74..a37078f7205 100644 --- a/docs/addons/qiskit-addon-utils/how-tos/index.mdx +++ b/docs/addons/qiskit-addon-utils/how-tos/index.mdx @@ -1,5 +1,6 @@ --- title: "How-To Guides - Qiskit addon utilities 0.3.1" +description: "How-To Guides for Qiskit addon utilities" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-utils/index.mdx b/docs/addons/qiskit-addon-utils/index.mdx index 9ee7754afc8..2738dfcd88f 100644 --- a/docs/addons/qiskit-addon-utils/index.mdx +++ b/docs/addons/qiskit-addon-utils/index.mdx @@ -1,5 +1,6 @@ --- title: "Qiskit addon utilities 0.3.1" +description: "Qiskit addon utilities documentation" --- # Qiskit addon utilities diff --git a/docs/addons/qiskit-addon-utils/install.mdx b/docs/addons/qiskit-addon-utils/install.mdx index 142857fee0f..59313efa2c7 100644 --- a/docs/addons/qiskit-addon-utils/install.mdx +++ b/docs/addons/qiskit-addon-utils/install.mdx @@ -1,5 +1,6 @@ --- title: "Installation Instructions - Qiskit addon utilities 0.3.1" +description: "Installation Instructions for Qiskit addon utilities" --- # Installation Instructions diff --git a/scripts/js/lib/api/pipelineStages.ts b/scripts/js/lib/api/pipelineStages.ts index 898681fb712..0e9d98de8b3 100644 --- a/scripts/js/lib/api/pipelineStages.ts +++ b/scripts/js/lib/api/pipelineStages.ts @@ -63,25 +63,37 @@ export async function convertHtmlToMarkdown( // Skip empty markdown (HTML redirects, etc.). if (result.markdown == "") continue; + const { dir, name } = parse(`${outputPath}/${file}`); + const url = `/${relative(docsBaseFolder, dir)}/${name}`; + if (extractfrontMatter) { // extracts front matter from html source rather than generating it in addFrontMatter.js - result.meta.hardcodedFrontmatter = extractHtmlFrontmatter(html); + result.meta.hardcodedFrontmatter = extractHtmlFrontmatter(html, pkg, url); } - - const { dir, name } = parse(`${outputPath}/${file}`); - const url = `/${relative(docsBaseFolder, dir)}/${name}`; results.push({ ...result, url }); } return results; } -function extractHtmlFrontmatter(html: string): string { +function extractHtmlFrontmatter(html: string, pkg: Pkg, url: string): string { const $ = load(html); const title = $("title").first().text().trim(); - const description = $('meta[name="description"]').attr("content")?.trim(); - const lines = [`title: "${title}"`]; - if (description) lines.push(`description: "${description}"`); - return lines.join("\n"); + const isRootIndex = url.endsWith(`/${pkg.name}/index`); + let description: string; + if (isRootIndex) { + description = `${pkg.title} documentation`; + } else { + const h1 = $("h1") + .first() + .clone() + .find("a.headerlink") + .remove() + .end() + .text() + .trim(); + description = `${h1} for ${pkg.title}`; + } + return [`title: "${title}"`, `description: "${description}"`].join("\n"); } /** From f5cd467af1c0e509386f80050b56ee4249b772d5 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Wed, 13 May 2026 19:16:04 -0400 Subject: [PATCH 043/104] fix descriptions --- .../how-tos/bound-error-using-p-norm.ipynb | 1 + .../addons/qiskit-addon-obp/how-tos/index.mdx | 4 +-- .../simulating-circuits-with-obp.ipynb | 1 + .../how-tos/truncate-operator-terms.ipynb | 1 + docs/addons/qiskit-addon-obp/index.mdx | 4 +-- docs/addons/qiskit-addon-obp/install.mdx | 4 +-- .../tutorials/01-getting-started.ipynb | 1 + .../qiskit-addon-obp/tutorials/index.mdx | 4 +-- .../color-device-edges-to-improve-depth.ipynb | 1 + .../how-tos/create-circuit-slices.ipynb | 1 + .../qiskit-addon-utils/how-tos/index.mdx | 4 +-- docs/addons/qiskit-addon-utils/index.mdx | 4 +-- docs/addons/qiskit-addon-utils/install.mdx | 4 +-- scripts/js/lib/api/addonDocsPipeline.ts | 1 + scripts/js/lib/api/notebookStages.ts | 10 +++++-- scripts/js/lib/api/pipelineStages.ts | 28 ++++++++----------- 16 files changed, 40 insertions(+), 33 deletions(-) diff --git a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb index cdc20febc8a..b71536ec338 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb @@ -7,6 +7,7 @@ "source": [ "---\n", "title: \"Using different Lp-norms for Pauli term truncation\"\n", + "description: \"Using different Lp-norms for Pauli term truncation for the latest version of Operator backpropagation (OBP)\"\n", "---" ] }, diff --git a/docs/addons/qiskit-addon-obp/how-tos/index.mdx b/docs/addons/qiskit-addon-obp/how-tos/index.mdx index ae50ca47cc4..ab269f92f73 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/index.mdx +++ b/docs/addons/qiskit-addon-obp/how-tos/index.mdx @@ -1,6 +1,6 @@ --- -title: "How-To Guides - Qiskit addon: operator backpropagation (OBP) 0.3.0" -description: "How-To Guides for Operator backpropagation (OBP)" +title: "How-To Guides" +description: "How-To Guides for the lastest version of Operator backpropagation (OBP)" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb b/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb index b3b8a185e16..10ba5693c2e 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb @@ -7,6 +7,7 @@ "source": [ "---\n", "title: \"Classically simulating circuits with OBP\"\n", + "description: \"Classically simulating circuits with OBP for the latest version of Operator backpropagation (OBP)\"\n", "---" ] }, diff --git a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb index 623eac084d0..ad3b70a0b16 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb @@ -7,6 +7,7 @@ "source": [ "---\n", "title: \"Truncating Pauli terms during backpropagation\"\n", + "description: \"Truncating Pauli terms during backpropagation for the latest version of Operator backpropagation (OBP)\"\n", "---" ] }, diff --git a/docs/addons/qiskit-addon-obp/index.mdx b/docs/addons/qiskit-addon-obp/index.mdx index 2ccfa2d25d7..32337cfebb6 100644 --- a/docs/addons/qiskit-addon-obp/index.mdx +++ b/docs/addons/qiskit-addon-obp/index.mdx @@ -1,6 +1,6 @@ --- -title: "Qiskit addon: operator backpropagation (OBP) 0.3.0" -description: "Operator backpropagation (OBP) documentation" +title: "Qiskit addon: operator backpropagation (OBP)" +description: "Documentation for the latest version of Operator backpropagation (OBP)" --- # Qiskit addon: operator backpropagation (OBP) diff --git a/docs/addons/qiskit-addon-obp/install.mdx b/docs/addons/qiskit-addon-obp/install.mdx index 8f823bf9c0a..5d736d51453 100644 --- a/docs/addons/qiskit-addon-obp/install.mdx +++ b/docs/addons/qiskit-addon-obp/install.mdx @@ -1,6 +1,6 @@ --- -title: "Installation Instructions - Qiskit addon: operator backpropagation (OBP) 0.3.0" -description: "Installation Instructions for Operator backpropagation (OBP)" +title: "Installation Instructions" +description: "Installation Instructions for the lastest version of Operator backpropagation (OBP)" --- # Installation Instructions diff --git a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb index 8f6e907f74d..d0c528fe446 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb @@ -7,6 +7,7 @@ "source": [ "---\n", "title: \"Reducing depth of circuits with operator backpropagation\"\n", + "description: \"Reducing depth of circuits with operator backpropagation for the latest version of Operator backpropagation (OBP)\"\n", "---" ] }, diff --git a/docs/addons/qiskit-addon-obp/tutorials/index.mdx b/docs/addons/qiskit-addon-obp/tutorials/index.mdx index f55be860240..0c1c4cc0d67 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/index.mdx +++ b/docs/addons/qiskit-addon-obp/tutorials/index.mdx @@ -1,6 +1,6 @@ --- -title: "Tutorials - Qiskit addon: operator backpropagation (OBP) 0.3.0" -description: "Tutorials for Operator backpropagation (OBP)" +title: "Tutorials" +description: "Tutorials for the lastest version of Operator backpropagation (OBP)" --- # Tutorials diff --git a/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb b/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb index 19e3e524c00..0a49c598f06 100644 --- a/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb +++ b/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb @@ -7,6 +7,7 @@ "source": [ "---\n", "title: \"Use edge coloring to reduce the depth of quantum circuits\"\n", + "description: \"Use edge coloring to reduce the depth of quantum circuits for the latest version of Qiskit addon utilities\"\n", "---" ] }, diff --git a/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices.ipynb b/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices.ipynb index 7a4a1e8f62f..cab3fd65d39 100644 --- a/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices.ipynb +++ b/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices.ipynb @@ -7,6 +7,7 @@ "source": [ "---\n", "title: \"Slicing circuits using qiskit_addon_utils.slicing\"\n", + "description: \"Slicing circuits using qiskit_addon_utils.slicing for the latest version of Qiskit addon utilities\"\n", "---" ] }, diff --git a/docs/addons/qiskit-addon-utils/how-tos/index.mdx b/docs/addons/qiskit-addon-utils/how-tos/index.mdx index a37078f7205..1cbddef3172 100644 --- a/docs/addons/qiskit-addon-utils/how-tos/index.mdx +++ b/docs/addons/qiskit-addon-utils/how-tos/index.mdx @@ -1,6 +1,6 @@ --- -title: "How-To Guides - Qiskit addon utilities 0.3.1" -description: "How-To Guides for Qiskit addon utilities" +title: "How-To Guides" +description: "How-To Guides for the lastest version of Qiskit addon utilities" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-utils/index.mdx b/docs/addons/qiskit-addon-utils/index.mdx index 2738dfcd88f..feab65bf65d 100644 --- a/docs/addons/qiskit-addon-utils/index.mdx +++ b/docs/addons/qiskit-addon-utils/index.mdx @@ -1,6 +1,6 @@ --- -title: "Qiskit addon utilities 0.3.1" -description: "Qiskit addon utilities documentation" +title: "Qiskit addon utilities" +description: "Documentation for the latest version of Qiskit addon utilities" --- # Qiskit addon utilities diff --git a/docs/addons/qiskit-addon-utils/install.mdx b/docs/addons/qiskit-addon-utils/install.mdx index 59313efa2c7..a74f772aa21 100644 --- a/docs/addons/qiskit-addon-utils/install.mdx +++ b/docs/addons/qiskit-addon-utils/install.mdx @@ -1,6 +1,6 @@ --- -title: "Installation Instructions - Qiskit addon utilities 0.3.1" -description: "Installation Instructions for Qiskit addon utilities" +title: "Installation Instructions" +description: "Installation Instructions for the lastest version of Qiskit addon utilities" --- # Installation Instructions diff --git a/scripts/js/lib/api/addonDocsPipeline.ts b/scripts/js/lib/api/addonDocsPipeline.ts index 8fe6926a855..0436f242bec 100644 --- a/scripts/js/lib/api/addonDocsPipeline.ts +++ b/scripts/js/lib/api/addonDocsPipeline.ts @@ -85,6 +85,7 @@ export async function runAddonDocsPipeline( initialNotebooks, objectsInv, allObjectInvs, + pkg, ); await writeNotebooks(pkg, docsBaseFolder, notebooks); diff --git a/scripts/js/lib/api/notebookStages.ts b/scripts/js/lib/api/notebookStages.ts index 998081d54ff..2ace3463acf 100644 --- a/scripts/js/lib/api/notebookStages.ts +++ b/scripts/js/lib/api/notebookStages.ts @@ -56,6 +56,7 @@ export function processNotebooks( notebooks: NotebookWithUrl[], objectsInv: ObjectsInv, allInvs: Map<string, ObjectsInv>, + pkg: Pkg, ): NotebookWithUrl[] { return notebooks.map((notebook) => { const processedCells = notebook.cells.map((cell) => { @@ -66,7 +67,7 @@ export function processNotebooks( }; }); - const frontmatterCell = buildFrontmatterCell(processedCells); + const frontmatterCell = buildFrontmatterCell(processedCells, pkg); return { ...notebook, cells: frontmatterCell @@ -116,7 +117,10 @@ function rewriteNotebookLinks( return Array.isArray(source) ? source.map(rewrite) : rewrite(source); } -function buildFrontmatterCell(cells: NotebookCell[]): NotebookCell | undefined { +function buildFrontmatterCell( + cells: NotebookCell[], + pkg: Pkg, +): NotebookCell | undefined { for (const cell of cells) { if (cell.cell_type !== "markdown") continue; const text = Array.isArray(cell.source) @@ -134,7 +138,7 @@ function buildFrontmatterCell(cells: NotebookCell[]): NotebookCell | undefined { return { id: "frontmatter", // hardcoded so the id doesn't change across runs cell_type: "markdown", - source: `---\ntitle: "${title}"\n---`, + source: `---\ntitle: "${title}"\ndescription: "${title} for the latest version of ${pkg.title}"\n---`, metadata: {}, }; } diff --git a/scripts/js/lib/api/pipelineStages.ts b/scripts/js/lib/api/pipelineStages.ts index 0e9d98de8b3..17ac8741f8e 100644 --- a/scripts/js/lib/api/pipelineStages.ts +++ b/scripts/js/lib/api/pipelineStages.ts @@ -77,23 +77,19 @@ export async function convertHtmlToMarkdown( function extractHtmlFrontmatter(html: string, pkg: Pkg, url: string): string { const $ = load(html); - const title = $("title").first().text().trim(); + const h1 = $("h1") + .first() + .clone() + .find("a.headerlink") + .remove() + .end() + .text() + .trim(); const isRootIndex = url.endsWith(`/${pkg.name}/index`); - let description: string; - if (isRootIndex) { - description = `${pkg.title} documentation`; - } else { - const h1 = $("h1") - .first() - .clone() - .find("a.headerlink") - .remove() - .end() - .text() - .trim(); - description = `${h1} for ${pkg.title}`; - } - return [`title: "${title}"`, `description: "${description}"`].join("\n"); + const description = isRootIndex + ? `Documentation for the latest version of ${pkg.title}` + : `${h1} for the lastest version of ${pkg.title}`; + return [`title: "${h1}"`, `description: "${description}"`].join("\n"); } /** From dbcf1c8449f3541f2e5d450c327c2966114b55a5 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Wed, 13 May 2026 19:27:10 -0400 Subject: [PATCH 044/104] fix spelling --- scripts/config/cspell/cSpell.json | 3 ++- scripts/config/cspell/dictionaries/people.txt | 1 + scripts/config/cspell/dictionaries/qiskit.txt | 4 +++- 3 files changed, 6 insertions(+), 2 deletions(-) diff --git a/scripts/config/cspell/cSpell.json b/scripts/config/cspell/cSpell.json index 4504eb48de6..476206d1805 100644 --- a/scripts/config/cspell/cSpell.json +++ b/scripts/config/cspell/cSpell.json @@ -43,7 +43,8 @@ "Teleporting", "ebitsa", "ebitsb", - "pmzero" + "pmzero", + "resuce" // todo fix in qiskit-addon-obp tutorial ], "ignoreRegExpList": [ // Markdown links diff --git a/scripts/config/cspell/dictionaries/people.txt b/scripts/config/cspell/dictionaries/people.txt index 974982241ec..531e5727b26 100644 --- a/scripts/config/cspell/dictionaries/people.txt +++ b/scripts/config/cspell/dictionaries/people.txt @@ -332,3 +332,4 @@ Smithline Greenberger Gaolis Bharti +Minkowski \ No newline at end of file diff --git a/scripts/config/cspell/dictionaries/qiskit.txt b/scripts/config/cspell/dictionaries/qiskit.txt index 3e9ab218b7a..03b6141f73e 100644 --- a/scripts/config/cspell/dictionaries/qiskit.txt +++ b/scripts/config/cspell/dictionaries/qiskit.txt @@ -445,4 +445,6 @@ eigenenergies metalloporphyrin tomographically braket -mdash \ No newline at end of file +mdash +circo +backprop \ No newline at end of file From e60dc51b4b8371047fc780fb56cf93e76c3013fa Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Wed, 13 May 2026 20:53:16 -0400 Subject: [PATCH 045/104] fix owners --- scripts/js/commands/checkQiskitBotFiles.ts | 1 + 1 file changed, 1 insertion(+) diff --git a/scripts/js/commands/checkQiskitBotFiles.ts b/scripts/js/commands/checkQiskitBotFiles.ts index e3cf1e42ac3..1db1794c6b9 100644 --- a/scripts/js/commands/checkQiskitBotFiles.ts +++ b/scripts/js/commands/checkQiskitBotFiles.ts @@ -51,6 +51,7 @@ const GLOBS = [ "{docs,learning}/**/*.{ipynb,mdx}", "!docs/api/**/*", "docs/api/functions/**", + "!docs/addons/**/*", ]; async function main() { From 77a90f36c74379527ab505d79c62aecedb85c315 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Wed, 13 May 2026 20:58:35 -0400 Subject: [PATCH 046/104] fix prettier --- scripts/js/commands/api/updateAddonDocs.ts | 5 +---- scripts/js/lib/api/addonDocsPipeline.test.ts | 19 +++++++++++-------- scripts/js/lib/api/htmlToMd.ts | 7 ++++--- 3 files changed, 16 insertions(+), 15 deletions(-) diff --git a/scripts/js/commands/api/updateAddonDocs.ts b/scripts/js/commands/api/updateAddonDocs.ts index d0d6a3609d5..a432e118f60 100644 --- a/scripts/js/commands/api/updateAddonDocs.ts +++ b/scripts/js/commands/api/updateAddonDocs.ts @@ -30,10 +30,7 @@ const readArgs = (): SharedArguments => { ).parseSync() as unknown as SharedArguments; }; -async function generateVersion( - pkg: Pkg, - args: SharedArguments, -): Promise<void> { +async function generateVersion(pkg: Pkg, args: SharedArguments): Promise<void> { const sphinxArtifactFolder = await prepareSphinxFolder(pkg, args); await deleteOutputDirs(pkg, { markdownDir: pkg.outputDir("docs/addons"), diff --git a/scripts/js/lib/api/addonDocsPipeline.test.ts b/scripts/js/lib/api/addonDocsPipeline.test.ts index 8895ab6f812..a381054b624 100644 --- a/scripts/js/lib/api/addonDocsPipeline.test.ts +++ b/scripts/js/lib/api/addonDocsPipeline.test.ts @@ -41,11 +41,7 @@ test("qiskit-addon-smoke addon docs pipeline", async ({}, testInfo) => { // Seed public/docs/api/<pkg>/objects.inv so that loadPublishedApis() // finds a sibling package's inventory for cross-package stub resolution. - const seededInvDir = path.join( - publicBaseFolder, - "api", - "qiskit-addon-other", - ); + const seededInvDir = path.join(publicBaseFolder, "api", "qiskit-addon-other"); await mkdir(seededInvDir, { recursive: true }); await copyFile( path.join(PUBLISHED_APIS_SEED, "api", "qiskit-addon-other", "objects.inv"), @@ -64,7 +60,12 @@ test("qiskit-addon-smoke addon docs pipeline", async ({}, testInfo) => { kebabCaseAndShortenUrls: true, }); - await runAddonDocsPipeline(FIXTURE_DIR, docsBaseFolder, publicBaseFolder, pkg); + await runAddonDocsPipeline( + FIXTURE_DIR, + docsBaseFolder, + publicBaseFolder, + pkg, + ); const markdownFolder = pkg.outputDir(docsBaseFolder); @@ -97,8 +98,10 @@ test("qiskit-addon-smoke addon docs pipeline", async ({}, testInfo) => { // --- Snapshot of every generated file under the addon output folder --- const resultFiles = await globby([`${markdownFolder}/**`]); - expect(resultFiles.length, "pipeline should generate at least one file") - .toBeGreaterThan(0); + expect( + resultFiles.length, + "pipeline should generate at least one file", + ).toBeGreaterThan(0); for (const file of resultFiles) { const contents = await readFile(file, "utf-8"); const fileName = path.parse(file).name; diff --git a/scripts/js/lib/api/htmlToMd.ts b/scripts/js/lib/api/htmlToMd.ts index ad8ecced71b..6b7183422ab 100644 --- a/scripts/js/lib/api/htmlToMd.ts +++ b/scripts/js/lib/api/htmlToMd.ts @@ -243,9 +243,10 @@ function buildAdmonition( handlers: Record<string, Handle>, ): MdxJsxFlowElement { const titleNode = findNodeWithProperty(node.children, "admonition-title"); - const children: Array<any> = without(node.children, titleNode ?? undefined).map( - (node: any) => toMdast(node, { handlers }), - ); + const children: Array<any> = without( + node.children, + titleNode ?? undefined, + ).map((node: any) => toMdast(node, { handlers })); let type = "note"; if (nodeClasses.includes("warning")) { From 767cc0beaac26bfc0f2203ea71b65e7253e06be4 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Wed, 13 May 2026 21:05:36 -0400 Subject: [PATCH 047/104] fix test --- .../js/lib/api/addonDocsPipeline.test.ts-snapshots/foo | 4 ++-- .../lib/api/addonDocsPipeline.test.ts-snapshots/index | 4 ++-- .../api/addonDocsPipeline.test.ts-snapshots/install | 4 ++-- .../lib/api/addonDocsPipeline.test.ts-snapshots/intro | 2 +- .../lib/api/apiDocsPipeline.test.ts-snapshots/module | 10 +++++----- 5 files changed, 12 insertions(+), 12 deletions(-) diff --git a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/foo b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/foo index 4ff196f3863..1b578433808 100644 --- a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/foo +++ b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/foo @@ -1,6 +1,6 @@ --- -title: "How to foo - smoke 0.0" -description: "Demonstrates apidocs link rewriting and stub resolution." +title: "How to foo" +description: "How to foo for the lastest version of Qiskit Addon Smoke" --- # How to foo diff --git a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index index 0b72155a71d..c07066cb1e8 100644 --- a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index +++ b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index @@ -1,6 +1,6 @@ --- -title: "Qiskit Addon Smoke - smoke 0.0" -description: "Smoke-test fixture for the addon docs pipeline." +title: "Qiskit Addon Smoke" +description: "Documentation for the latest version of Qiskit Addon Smoke" --- # Qiskit Addon Smoke diff --git a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/install b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/install index f137aafa56c..0444ef6d17e 100644 --- a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/install +++ b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/install @@ -1,6 +1,6 @@ --- -title: "Installation - smoke 0.0" -description: "How to install the smoke addon." +title: "Installation" +description: "Installation for the lastest version of Qiskit Addon Smoke" --- # Installation diff --git a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/intro b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/intro index 3383f8e629f..a2c40ee0d38 100644 --- a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/intro +++ b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/intro @@ -3,7 +3,7 @@ { "id": "frontmatter", "cell_type": "markdown", - "source": "---\ntitle: \"Intro tutorial\"\n---", + "source": "---\ntitle: \"Intro tutorial\"\ndescription: \"Intro tutorial for the latest version of Qiskit Addon Smoke\"\n---", "metadata": {} }, { diff --git a/scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/module b/scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/module index c98fe8a279c..aaf48736d3d 100644 --- a/scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/module +++ b/scripts/js/lib/api/apiDocsPipeline.test.ts-snapshots/module @@ -20,14 +20,14 @@ Welcome to my super cool module! This is an example! </Admonition> -Testing internal references… [`Electron.compute_momentum()`](api_example.Electron#compute_momentum "api_example.Electron.compute_momentum"). +Testing internal references… [`Electron.compute_momentum()`](/docs/api/qiskit-sphinx-theme/api_example.Electron#compute_momentum "api_example.Electron.compute_momentum"). ## Contents -| | | -| ------------------------------------------------------------------------------------------------ | ----------------------------------- | -| [`Electron`](api_example.Electron "api_example.Electron")(\[size, name]) | A representation of an electron. | -| [`my_function1`](api_example.my_function1 "api_example.my_function1")(input1, input2\[, input3]) | A function that does awesome stuff. | +| | | +| ------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------- | +| [`Electron`](/docs/api/qiskit-sphinx-theme/api_example.Electron "api_example.Electron")(\[size, name]) | A representation of an electron. | +| [`my_function1`](/docs/api/qiskit-sphinx-theme/api_example.my_function1 "api_example.my_function1")(input1, input2\[, input3]) | A function that does awesome stuff. | ## Functions From 03b22748792ae9da68938b50a7effb843b81cf72 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Wed, 13 May 2026 21:13:28 -0400 Subject: [PATCH 048/104] remove magick dep from test --- .../qiskit-addon-smoke/_images/smoke-diagram.avif | Bin 0 -> 281 bytes .../qiskit-addon-smoke/_images/smoke-diagram.png | Bin 69 -> 0 bytes .../testdata/qiskit-addon-smoke/how_tos/foo.html | 2 +- .../api/testdata/qiskit-addon-smoke/index.html | 2 +- 4 files changed, 2 insertions(+), 2 deletions(-) create mode 100644 scripts/js/lib/api/testdata/qiskit-addon-smoke/_images/smoke-diagram.avif delete mode 100644 scripts/js/lib/api/testdata/qiskit-addon-smoke/_images/smoke-diagram.png diff --git a/scripts/js/lib/api/testdata/qiskit-addon-smoke/_images/smoke-diagram.avif b/scripts/js/lib/api/testdata/qiskit-addon-smoke/_images/smoke-diagram.avif new file mode 100644 index 0000000000000000000000000000000000000000..0314f501c87e96550e4e100f8170958341bc30aa GIT binary patch literal 281 zcmXwzO$x#=5QV2LNRT4_<3jLnp-U?+To&{UZY9(f2aFCi6kQ6QL_C0}@e<xZTsTwp zh2*_&W=H}6?Zlo)XP5xXU=m0+sD&RUwp!|}-e$c`zY%~ivj6<|5}`FYIDGbTyN`qx zAvnB5ub%^Sm<EWu{L#GHV2E9r;#r-I1Bpwdl-OrT(VpGcSuj6?FTFY*D3-vI>t&Y- yk0=FCDaF=AGFq6$pYYDE$7yKR+E$^cT2N4GGwn_)Ehvtz7l3<hxe9Mj|NQ|r%PhqJ literal 0 HcmV?d00001 diff --git a/scripts/js/lib/api/testdata/qiskit-addon-smoke/_images/smoke-diagram.png b/scripts/js/lib/api/testdata/qiskit-addon-smoke/_images/smoke-diagram.png deleted file mode 100644 index 62a5f8f47fec02344e5bf9061888262f677cf5d6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 69 zcmeAS@N?(olHy`uVBq!ia0vp^j3CUx1SBVv2j2ryJf1F&Ar*6yf1E$Sz`)GN$Z+!C Rr1wB^22WQ%mvv4FO#seq5RCu; diff --git a/scripts/js/lib/api/testdata/qiskit-addon-smoke/how_tos/foo.html b/scripts/js/lib/api/testdata/qiskit-addon-smoke/how_tos/foo.html index 6959ac59aa2..f32c3484b7f 100644 --- a/scripts/js/lib/api/testdata/qiskit-addon-smoke/how_tos/foo.html +++ b/scripts/js/lib/api/testdata/qiskit-addon-smoke/how_tos/foo.html @@ -11,7 +11,7 @@ <h1>How to foo<a class="headerlink" href="#how-to-foo" title="Link to this heading">#</a></h1> <p>This how-to references the <a class="reference internal" href="../apidocs/index.html">API docs index</a> and the <a class="reference internal" href="../stubs/smoke.do_thing.html">do_thing</a> symbol from the same package.</p> <p>It also links into another package via <a class="reference external" href="https://qiskit.github.io/qiskit-addon-other/stubs/other_thing.html">other_thing</a>.</p> - <img alt="A reused smoke diagram" src="../_images/smoke-diagram.png"/> + <img alt="A reused smoke diagram" src="../_images/smoke-diagram.avif"/> </section> </article> </body> diff --git a/scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html b/scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html index ed2fe3309c0..7877691083a 100644 --- a/scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html +++ b/scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html @@ -10,7 +10,7 @@ <section id="qiskit-addon-smoke"> <h1>Qiskit Addon Smoke<a class="headerlink" href="#qiskit-addon-smoke" title="Link to this heading">#</a></h1> <p>This is the smoke-test landing page. See the <a class="reference internal" href="install.html">install instructions</a> or the <a class="reference internal" href="how_tos/foo.html">first how-to</a>.</p> - <img alt="A smoke test diagram" src="_images/smoke-diagram.png"/> + <img alt="A smoke test diagram" src="_images/smoke-diagram.avif"/> </section> </article> </body> From 40df3f9b06ba72b0071088fc0c8cd8d8435e2f2d Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Wed, 13 May 2026 21:23:15 -0400 Subject: [PATCH 049/104] fix check notebooks --- scripts/ci/check-all-notebooks-are-tested.py | 13 ++++++++++--- scripts/config/notebook-testing.toml | 6 ++++++ 2 files changed, 16 insertions(+), 3 deletions(-) diff --git a/scripts/ci/check-all-notebooks-are-tested.py b/scripts/ci/check-all-notebooks-are-tested.py index e10e3a3f5de..99704e70df5 100644 --- a/scripts/ci/check-all-notebooks-are-tested.py +++ b/scripts/ci/check-all-notebooks-are-tested.py @@ -15,6 +15,7 @@ they don't slip through our CI tests. """ +import fnmatch from pathlib import Path import tomllib import sys @@ -22,15 +23,21 @@ config_path = Path("scripts/config/notebook-testing.toml") config = tomllib.loads(config_path.read_text()) -categorized_notebooks = set() +categorized_notebooks: set[Path] = set() +categorized_globs: list[str] = [] for group in config["groups"].values(): for path in group.get("notebooks", []): - categorized_notebooks.add(Path(path)) + if "*" in path or "?" in path: + categorized_globs.append(path) + else: + categorized_notebooks.add(Path(path)) uncategorized = [ path for path in Path(".").glob("[!.]*/**/*.ipynb") - if not path.match("**/.ipynb_checkpoints/**") and path not in categorized_notebooks + if not path.match("**/.ipynb_checkpoints/**") + and path not in categorized_notebooks + and not any(fnmatch.fnmatch(str(path), g) for g in categorized_globs) ] if uncategorized: diff --git a/scripts/config/notebook-testing.toml b/scripts/config/notebook-testing.toml index 8d24c68864d..c7b8e9c581c 100644 --- a/scripts/config/notebook-testing.toml +++ b/scripts/config/notebook-testing.toml @@ -139,6 +139,12 @@ notebooks = [ # Don't ever test the following notebooks [groups.exclude] notebooks = [ + # Addon notebooks require the addon packages and are not run in standard CI + "docs/addons/**/*.ipynb", + + # Test fixture — not real content + "scripts/js/lib/api/testdata/**/*.ipynb", + # This notebook contains undefined variables so can't run at all. "docs/guides/function-template-hamiltonian-simulation.ipynb", "docs/guides/function-template-chemistry-workflow.ipynb", From 670972fea1fb9d89ef5f9272fbde2da0b61af282 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Wed, 13 May 2026 21:33:26 -0400 Subject: [PATCH 050/104] adds toc --- docs/addons/qiskit-addon-utils/_toc.json | 50 ++++++++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 docs/addons/qiskit-addon-utils/_toc.json diff --git a/docs/addons/qiskit-addon-utils/_toc.json b/docs/addons/qiskit-addon-utils/_toc.json new file mode 100644 index 00000000000..dcb3e8fbde4 --- /dev/null +++ b/docs/addons/qiskit-addon-utils/_toc.json @@ -0,0 +1,50 @@ +{ + "parentUrl": "/docs/guides/addons", + "parentLabel": "Documentation", + "title": "Qiskit addon utilities 0.3.1", + "collapsed": true, + "children": [ + { + "title": "", + "children": [ + { + "title": "Home", + "url": "/docs/addons/qiskit-addon-utils" + }, + { + "title": "Installation instructions", + "url": "/docs/addons/qiskit-addon-utils/install" + }, + { + "title": "Guides", + "children": [ + { + "title": "Use edge coloring to reduce the depth of quantum circuits", + "url": "/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth" + }, + { + "title": "Slicing circuits using qiskit_addon_utils.slicing", + "url": "/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices" + } + ], + "url": "/docs/addons/qiskit-addon-utils/how-tos/index" + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-utils" + } + ], + "collapsible": false + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Qiskit addon utilities API reference", + "url": "/docs/api/qiskit-addon-utils" + } + ] + } + ] +} From 34154eca0aeadf05ca049db93995473a197f5c86 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Wed, 13 May 2026 21:46:35 -0400 Subject: [PATCH 051/104] adds links to addons --- docs/guides/addons.mdx | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index 2bd019155a5..80d5a1a42e5 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -7,7 +7,7 @@ description: Understand the Qiskit addon tools, which help you build utility-gra # Advanced techniques - Qiskit addons -Qiskit addons are a collection of research capabilities for enabling algorithm discovery at the utility scale. These modular software components build on Qiskit’s performant foundation and can plug into a [workflow](/docs/guides/intro-to-patterns) to scale or design new quantum algorithms. This page highlights the available tools across key functional categories to help you choose relevant capabilities when building your workflows. +Qiskit addons are a collection of research capabilities for enabling algorithm discovery at the utility scale. These modular software components build on Qiskit’s performant foundation and can plug into a [workflow](/docs/guides/intro-to-patterns) to scale or design new quantum algorithms. This page highlights the available tools across key functional categories to help you choose relevant capabilities when building your workflows. ## Map domain problems @@ -54,7 +54,7 @@ These capabilities are useful for reducing circuit depth and typically come with <Card title="Operator backpropagation" description="Reduce circuit depth in expectation value estimations by backpropagating observables through the circuit." - href="https://qiskit.github.io/qiskit-addon-obp/" + href="/docs/addons/qiskit-addon-obp/" analyticsName="Documentation page: OBP" linkText="Browse documentation" /> @@ -81,7 +81,7 @@ Use the following addons to manage noise when building quantum workloads that es /> <Card title="Shaded lightcones" - description="Reduce probabilistic error cancellation (PEC) sampling overhead by removing noise model terms that have low impact on the observable estimation." + description="Reduce probabilistic error cancellation (PEC) sampling overhead by removing noise model terms that have low impact on the observable estimation." href="https://qiskit.github.io/qiskit-addon-slc/" analyticsName="Documentation page: SLC" linkText="Browse documentation" @@ -113,9 +113,9 @@ These techniques are useful for managing noise on sampling results. <Card title="Measurement-based postselection" description="Filter out non-Markovian noise in circuits by incorporating measurement-based postselection transpiler passes." - href="https://qiskit.github.io/qiskit-addon-utils/apidocs/qiskit_addon_utils.noise_management.post_selection.transpiler.passes.html" + href="/docs/en/api/qiskit-addon-utils/noise-management-post-selection-transpiler-passes" analyticsName="Documentation page for post-selection" - linkText="Read the API reference" + linkText="Read the API reference" /> <Card @@ -135,14 +135,14 @@ Use these capabilities to support and compose your workflows that leverage other <Card title="Addon utilities" description="Build addons-powered workflows faster by using this collection of functions for creating Hamiltonians, generating Trotter time-evolution circuits, and applying the latest error mitigation capabilities." - href="https://qiskit.github.io/qiskit-addon-utils/" + href="/docs/addons/qiskit-addon-utils/" analyticsName="Documentation page: addon utilities" linkText="Browse documentation" /> <Card title="Pauli propagation" - description="A framework to approximate operator evolution, which can be used to simulate expectation values and implement error mitigation techniques, such as propagated noise absorption (PNA) and shaded lightcones." + description="A framework to approximate operator evolution, which can be used to simulate expectation values and implement error mitigation techniques, such as propagated noise absorption (PNA) and shaded lightcones." href="https://qiskit.github.io/pauli-prop/" analyticsName="Documentation page: pauli propagation" linkText="Browse documentation" From 8766cc2a590bd5711cf2e661d1c6dc23876c62e2 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 14 May 2026 19:30:51 -0400 Subject: [PATCH 052/104] adds mpf addon --- docs/addons/qiskit-addon-mpf/_toc.json | 70 ++ .../qiskit-addon-mpf/explanations/index.mdx | 11 + .../explanations/mpf-stability.ipynb | 318 +++++ .../how-tos/choose-trotter-steps.ipynb | 308 +++++ .../addons/qiskit-addon-mpf/how-tos/index.mdx | 12 + .../how-tos/using-approximate-model.ipynb | 1040 +++++++++++++++++ docs/addons/qiskit-addon-mpf/index.mdx | 64 + docs/addons/qiskit-addon-mpf/install.mdx | 77 ++ .../tutorials/01-getting-started.ipynb | 961 +++++++++++++++ .../qiskit-addon-mpf/tutorials/index.mdx | 11 + docs/api/qiskit-addon-mpf/_toc.json | 4 +- ...backends-quimb-circuit-circuit-evolver.mdx | 6 +- .../backends-quimb-circuit-circuit-state.mdx | 4 +- .../backends-quimb-circuit.mdx | 12 +- ...ackends-quimb-layers-layerwise-evolver.mdx | 4 +- .../backends-quimb-layers.mdx | 22 +- .../backends-quimb-tebd-mpo-state.mdx | 6 +- 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"/docs/guides/addons", + "parentLabel": "Documentation", + "title": "Multi-product formulas (MPF) 0.3.0", + "collapsed": true, + "children": [ + { + "title": "", + "children": [ + { + "title": "Home", + "url": "/docs/addons/qiskit-addon-mpf" + }, + { + "title": "Installation instructions", + "url": "/docs/addons/qiskit-addon-mpf/install" + }, + { + "title": "Guides", + "children": [ + { + "title": "How to choose the Trotter steps for an MPF", + "url": "/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps" + }, + { + "title": "How to use the approximate model", + "url": "/docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model" + } + ], + "url": "/docs/addons/qiskit-addon-mpf/how-tos/index" + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-mpf" + } + ], + "collapsible": false + }, + { + "title": "Explainations", + "collapsible": false, + "children": [ + { + "title": "Understanding the stability of MPFs", + "url": "/docs/addons/qiskit-addon-mpf/explainations/mpf-stability" + } + ] + }, + { + "title": "Tutorials", + "collapsible": false, + "children": [ + { + "title": "Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas", + "url": "/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started" + } + ] + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Multi-product formulas (MPF) API reference", + "url": "/docs/api/qiskit-addon-mpf" + } + ] + } + ] +} diff --git a/docs/addons/qiskit-addon-mpf/explanations/index.mdx b/docs/addons/qiskit-addon-mpf/explanations/index.mdx new file mode 100644 index 00000000000..20d11fba7b0 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/explanations/index.mdx @@ -0,0 +1,11 @@ +--- +title: "Explanations" +description: "Explanations for the lastest version of Multi-product formulas (MPF)" +--- + +# Explanations + +This page summarizes additional explanatory content. + +* [Understanding the stability of MPFs](mpf-stability) + diff --git a/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.ipynb b/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.ipynb new file mode 100644 index 00000000000..c6c073e4c8b --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.ipynb @@ -0,0 +1,318 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Understanding the stability of MPFs\"\n", + "description: \"Understanding the stability of MPFs for the latest version of Multi-product formulas (MPF)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "4aaf8397-4140-4d16-b4b5-1d9e20adea98", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Understanding the stability of MPFs\n", + "\n", + "Over in the guide [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps) we introduced a heuristic that limits the **smallest** $k_j$ value ($k_{\\text{min}}$) based on the total evolution time, $t$, that we would like to reach.\n", + "In particular, we state that $t/k_{\\text{min}} \\lt 1$ must be satisfied (noting that $t/k_{\\text{min}} \\leq 1$ empirically seems to work fine, too).\n", + "\n", + "On this page, we will analyze when the behavior of MPFs is not guaranteed to work well, such as when this constraint gets violated." + ] + }, + { + "cell_type": "markdown", + "id": "986a58b5-611a-4400-9bd5-ba35d4a14188", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Setting up a simple model problem\n", + "\n", + "For this simple example, we will reuse the model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial; the Ising model on a line of 10 sites:\n", + "\n", + "$$\n", + "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", + "$$\n", + "\n", + "where $J$ is the coupling strength between two sites and $h$ is the external magnetic field." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d77e337b-0a69-49a5-b449-ffe4ad17d7ab", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'],\n", + " coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j,\n", + " 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j,\n", + " 0.4+0.j, 0.4+0.j, 0.4+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "coupling_map = CouplingMap.from_line(10, bidirectional=False)\n", + "\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " coupling_map,\n", + " coupling_constants=(0.0, 0.0, 1.0),\n", + " ext_magnetic_field=(0.4, 0.0, 0.0),\n", + ")\n", + "print(hamiltonian)" + ] + }, + { + "cell_type": "markdown", + "id": "0f25e279-e987-4da5-a7e3-ffea41542d51", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The observable that we will be measuring is the total magnetization which we can simply construct as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eca935e2-206e-471e-a673-62a67a00add0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'],\n", + " coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j,\n", + " 0.05+0.j, 0.05+0.j, 0.05+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "L = coupling_map.size()\n", + "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)\n", + "print(observable)" + ] + }, + { + "cell_type": "markdown", + "id": "b652e4ab-26c1-4c4e-a34d-47a464c60f26", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Simulating various Trotter circuits\n", + "\n", + "Our analysis simply consists of comparing the behavior of product formulas of various order (here, second and fourth order) to the exact solution (which we can compute for a problem as simple as this one).\n", + "\n", + "In the code cell below, we do exactly that, for evolution times ranging from $2$ up to $10$ and Trotter steps ranging from $1$ to $25$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e7c43eec-3809-4e3a-998a-7f6a7a8cf2a6", + "metadata": { + "editable": true, + "scrolled": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit.primitives import StatevectorEstimator\n", + "from qiskit.synthesis import SuzukiTrotter\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", + "from scipy.linalg import expm\n", + "\n", + "estimator = StatevectorEstimator()\n", + "times = np.array(range(2, 11))\n", + "ks = np.array(range(1, 25))\n", + "\n", + "exact = []\n", + "order2 = []\n", + "order4 = []\n", + "\n", + "for time in times:\n", + " exp_H = expm(-1j * time * hamiltonian.to_matrix())\n", + " initial_state = np.zeros(exp_H.shape[0])\n", + " initial_state[0] = 1.0\n", + " time_evolved_state = exp_H @ initial_state\n", + " exact_evs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", + " exact.append(float(exact_evs))\n", + "\n", + " for k in ks:\n", + " order2_circ = generate_time_evolution_circuit(\n", + " hamiltonian, synthesis=SuzukiTrotter(reps=k, order=2), time=time\n", + " )\n", + " order4_circ = generate_time_evolution_circuit(\n", + " hamiltonian, synthesis=SuzukiTrotter(reps=k, order=4), time=time\n", + " )\n", + "\n", + " job = estimator.run([(order2_circ, observable), (order4_circ, observable)])\n", + " result = job.result()\n", + " order2.append(result[0].data.evs)\n", + " order4.append(result[1].data.evs)" + ] + }, + { + "cell_type": "markdown", + "id": "0da8acd2-6192-450e-99b3-e0d2b82541ee", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Analyzing the results\n", + "\n", + "We can now analyze the computed results by plotting them." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "845cf113-58da-454e-97b6-679d5a5ae9fb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-mpf/explanations/mpf-stability/extracted-outputs/845cf113-58da-454e-97b6-679d5a5ae9fb-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "order2_reshaped = np.asarray(order2).reshape((len(times), len(ks)))\n", + "order4_reshaped = np.asarray(order4).reshape((len(times), len(ks)))\n", + "\n", + "fig, axes = plt.subplots(3, 3, figsize=(20, 10))\n", + "\n", + "for i, t in enumerate(times):\n", + " ax = axes[i // 3, i % 3]\n", + "\n", + " ax.axvline(1.0, color=\"grey\", label=\"$t/k=1$\")\n", + " ax.axhline(exact[i], color=\"tab:green\", label=\"exact\")\n", + "\n", + " ax.plot(t / ks, order2_reshaped[i], label=r\"$\\chi=2$\")\n", + " ax.plot(t / ks, order4_reshaped[i], label=r\"$\\chi=4$\")\n", + "\n", + " ax.semilogx()\n", + " ax.set_xticks([10**p for p in range(-1, 1)])\n", + " ax.set_xlabel(\"$t/k$\")\n", + "\n", + " ax.set_ylim([-0.33, 0.55])\n", + " ax.set_ylabel(r\"$\\langle O \\rangle$\")\n", + "\n", + " ax.set_title(f\"time={t}\")\n", + "\n", + "fig.legend(*ax.get_legend_handles_labels(), ncols=4, loc=\"center\", bbox_to_anchor=(0.5, 0.0))\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2f40b1ee-0e98-4f24-9d25-080306ba98e1", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can now clearly see that in the regime $t/k \\gt 1$ the dynamics are no longer reproduced faithfully by the Trotterized circuits. Thus, if an MPF were to use a product formula with a $k_j$ from that regime, its extrapolated expectation value cannot faithfully produce good results." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps.ipynb b/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps.ipynb new file mode 100644 index 00000000000..41c4c9478fd --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"How to choose the Trotter steps for an MPF\"\n", + "description: \"How to choose the Trotter steps for an MPF for the latest version of Multi-product formulas (MPF)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "dafa4d64-0da8-4e18-aea8-73f13cac3d29", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# How to choose the Trotter steps for an MPF\n", + "\n", + "This guide teaches you how to find a good set of Trotter steps for an MPF.\n", + "We assume that you have already read and understood the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial." + ] + }, + { + "cell_type": "markdown", + "id": "fb022d51-3187-4822-8536-7778df8620f1", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Understanding some heuristics\n", + "\n", + "In principle, any $k_j$ values can be chosen but some choices of Trotter steps will lead to a larger noise amplification on real devices than others.\n", + "Thus, it is important that one tries to find _\"good\"_ values of $k_j$.\n", + "In the following we will discuss some heuristics to help guide you in doing so.\n", + "\n", + "1. In practice, our **largest** $k_j$ ($k_{\\text{max}}$) value is simply bound by the depth of quantum circuit we can reliably execute.\n", + "\n", + "2. Since the Trotter error is only well behaved for $\\text{d}t = t/k \\lt 1$ (see [Understanding the stability of MPFs](/docs/addons/qiskit-addon-mpf/explanations/mpf-stability)), our **smallest** $k_j$ ($k_{\\text{min}}$) should satisfy: $t/k_{\\text{min}} \\lt 1$ (Note: empirically $\\text{d}t \\leq 1$ seems to work fine, too).\n", + "\n", + "3. None of our $x_j$ values should be close to $0$, as this would imply that we might as well exclude the corresponding $k_j$ from the expansion.\n", + "\n", + "4. Similarly, we do not want the $x_j$ for $k_{\\text{max}}$ to be the only dominant one, since that implies that we are essentially just recovering a single product formula with $k=k_{\\text{max}}$.\n", + "\n", + "5. Finally, we want the L1-norm of our coefficients, $x_j$, to be small, as this indicates a well-conditioned MPF (see [Carrera Vazquez et al., 2023]). Furthermore, small coefficients minimize the variance amplification of the MPF.\n", + "\n", + "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/" + ] + }, + { + "cell_type": "markdown", + "id": "f0f2096f-5fb6-4861-a05e-3ded9ac65a82", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Applying these heuristics\n", + "\n", + "Reusing the example from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial, we want to simulate up to time $t=8.0$.\n", + "Thus, $k_{\\text{min}}=8$.\n", + "\n", + "For the sake of this example, let us assume that our deepest circuit is bound by $k_{\\text{max}}=20$.\n", + "\n", + "In addition to the constraints above, we will follow the heuristic presented by [Zhuk et al., 2023], where they minimize a re-weighted L1-norm of the coefficients:\n", + "\n", + "$$\n", + "\\text{min}_{k_j} \\sum_j \\frac{|c_j|}{k_j^{2\\chi}} \\, ,\n", + "$$\n", + "\n", + "where $\\chi$ is the order of our individual product formulas.\n", + "\n", + "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" + ] + }, + { + "cell_type": "markdown", + "id": "1ff9ff06-47bf-451d-815f-11d331cafaa3", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Preparing our LSE\n", + "\n", + "In a first step, we prepare the LSE, $Ax=b$, leveraging a feature of the [setup_static_lse](/docs/api/qiskit-addon-mpf/static#setup_static_lse) function that allows us to use a [cvxpy.Parameter](https://www.cvxpy.org/api_reference/cvxpy.expressions.html#parameter) object for our $k_j$ values." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "bda8c5d3-b48a-4c9e-bef3-fb9f26846ad6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "order = 2\n", + "symmetric = True\n", + "\n", + "min_k = 8\n", + "max_k = 20\n", + "\n", + "max_l1_norm = 5.0\n", + "min_coeff = 0.01" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "3b68ec15-b8ed-45a1-bd46-ced7b29b159e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import cvxpy as cp\n", + "from qiskit_addon_mpf.static import setup_static_lse\n", + "\n", + "ks = cp.Parameter(3, integer=True)\n", + "lse = setup_static_lse(ks, order=order, symmetric=symmetric)" + ] + }, + { + "cell_type": "markdown", + "id": "92356e74-97b6-4231-bd35-43d5cd274dca", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Next, we loop over all possible 3-tuples of $k_j$ values within our specified range.\n", + "For each one, we compute the analytical coefficients, $x_j$, and then apply our filter criteria.\n", + "We keep track of those $k_j$ tuples which satisfy our criteria." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "da4c0610-8d1c-4596-b082-8cfd0098bc7c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "possible_ks = {}\n", + "\n", + "for k1 in range(min_k, max_k):\n", + " for k2 in range(k1 + 1, max_k):\n", + " for k3 in range(k2 + 1, max_k):\n", + " ks.value = [k1, k2, k3]\n", + " coeffs = lse.solve()\n", + "\n", + " if np.any(np.isclose(coeffs, 0.0, atol=min_coeff)):\n", + " continue\n", + "\n", + " norm1 = np.linalg.norm(coeffs, ord=1)\n", + " if norm1 > max_l1_norm:\n", + " continue\n", + "\n", + " weighted = np.sum([np.abs(c) / k ** (2 * order) for c, k in zip(coeffs, ks.value)])\n", + " possible_ks[tuple(int(k) for k in ks.value)] = {\n", + " \"coeffs\": tuple(coeffs),\n", + " \"weighted\": float(weighted),\n", + " \"norm1\": float(norm1),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "9f37b658-0e73-409b-add2-4cbf83708730", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Finally, we sort our $k_j$ by the re-weighted L1-norm presented by [Zhuk et al., 2023]:\n", + "\n", + "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "16d05ed1-c1ed-445d-afce-543f55275e5e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(8, 14, 19) {'coeffs': (0.10447913478216586, -1.7638200183654775, 2.6593408835833117), 'weighted': 9.182736455463762e-05, 'norm1': 4.527640036730955}\n", + "(8, 13, 19) {'coeffs': (0.13134519801186473, -1.4167162698412699, 2.285371071829405), 'weighted': 9.920634920634922e-05, 'norm1': 3.8334325396825397}\n", + "(8, 12, 19) {'coeffs': (0.17239057239057254, -1.1944700460829496, 2.022079473692377), 'weighted': 0.00011520737327188946, 'norm1': 3.388940092165899}\n", + "(9, 13, 19) {'coeffs': (0.2662743506493499, -1.6904000946969673, 2.4241257440476174), 'weighted': 0.00011837121212121191, 'norm1': 4.380800189393934}\n", + "(8, 13, 18) {'coeffs': (0.15003663003663004, -1.7549001536098316, 2.6048635235732016), 'weighted': 0.00012288786482334872, 'norm1': 4.509800307219663}\n", + "(8, 12, 18) {'coeffs': (0.19692307692307653, -1.4399999999999984, 2.243076923076922), 'weighted': 0.0001388888888888887, 'norm1': 3.879999999999997}\n", + "(8, 11, 19) {'coeffs': (0.24195168054817162, -1.070248538011695, 1.8282968574635234), 'weighted': 0.00014619883040935662, 'norm1': 3.14049707602339}\n", + "(9, 12, 19) {'coeffs': (0.37193877551020393, -1.5167873601053319, 2.144848584595128), 'weighted': 0.00014629507717065315, 'norm1': 4.033574720210664}\n", + "(8, 12, 17) {'coeffs': (0.22755555555555573, -1.7875862068965531, 2.5600306513409974), 'weighted': 0.00017241379310344843, 'norm1': 4.575172413793107}\n", + "(8, 11, 18) {'coeffs': (0.27638326585694917, -1.265318468585254, 1.988935202728305), 'weighted': 0.00017284590787313073, 'norm1': 3.530636937170508}\n", + "(9, 12, 18) {'coeffs': (0.42857142857142816, -1.8285714285714276, 2.3999999999999995), 'weighted': 0.00017636684303350958, 'norm1': 4.657142857142855}\n", + "(9, 11, 19) {'coeffs': (0.5858035714285708, -1.5251041666666654, 1.9393005952380946), 'weighted': 0.00020833333333333313, 'norm1': 4.05020833333333}\n", + "(8, 11, 17) {'coeffs': (0.3193762183235871, -1.5289264828738525, 2.2095502645502654), 'weighted': 0.00020885547201336693, 'norm1': 4.0578529657477045}\n", + "(8, 10, 19) {'coeffs': (0.383090160867938, -1.0642826734780744, 1.6811925126101364), 'weighted': 0.00021285653469561486, 'norm1': 3.1285653469561487}\n", + "(9, 11, 18) {'coeffs': (0.6750000000000009, -1.8030788177339916, 2.1280788177339907), 'weighted': 0.0002463054187192121, 'norm1': 4.606157635467984}\n", + "(8, 10, 18) {'coeffs': (0.43760683760683694, -1.2400793650793638, 1.8024725274725268), 'weighted': 0.0002480158730158727, 'norm1': 3.4801587301587276}\n", + "(8, 11, 16) {'coeffs': (0.3742690058479534, -1.9026640675763484, 2.528395061728395), 'weighted': 0.0002599090318388565, 'norm1': 4.805328135152697}\n", + "(8, 10, 17) {'coeffs': (0.5056790123456789, -1.4697236919459142, 1.9640446796002353), 'weighted': 0.00029394473838918284, 'norm1': 3.9394473838918285}\n", + "(8, 10, 16) {'coeffs': (0.592592592592593, -1.7806267806267817, 2.1880341880341887), 'weighted': 0.0003561253561253563, 'norm1': 4.561253561253563}\n", + "(8, 9, 19) {'coeffs': (0.8112497524262232, -1.3783613445378153, 1.5671115921115921), 'weighted': 0.00042016806722689084, 'norm1': 3.756722689075631}\n", + "(8, 9, 18) {'coeffs': (0.9266968325791858, -1.5882352941176474, 1.6615384615384616), 'weighted': 0.00048414427499394834, 'norm1': 4.176470588235295}\n", + "(8, 9, 17) {'coeffs': (1.0708496732026151, -1.855486425339368, 1.7846367521367528), 'weighted': 0.0005656108597285072, 'norm1': 4.710972850678736}\n" + ] + } + ], + "source": [ + "for ks_val in sorted(possible_ks, key=lambda ks_val: possible_ks[ks_val].get(\"weighted\")):\n", + " print(ks_val, possible_ks[ks_val])" + ] + }, + { + "cell_type": "markdown", + "id": "f3b709d3-ed27-4e6f-a2ba-f3f17326099e", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Recalling the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial, from these possible values we picked `ks=(8, 12, 19)` since it strikes a good balance of minimizing the re-weighted and plain L1-norms." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-mpf/how-tos/index.mdx b/docs/addons/qiskit-addon-mpf/how-tos/index.mdx new file mode 100644 index 00000000000..8d1ec7121f0 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/how-tos/index.mdx @@ -0,0 +1,12 @@ +--- +title: "How-To Guides" +description: "How-To Guides for the lastest version of Multi-product formulas (MPF)" +--- + +# How-To Guides + +This page summarizes the available how-to guides. + +* [How to choose the Trotter steps for an MPF](choose-trotter-steps) +* [How to use the approximate model](using-approximate-model) + diff --git a/docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model.ipynb b/docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model.ipynb new file mode 100644 index 00000000000..58f2ad43da4 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model.ipynb @@ -0,0 +1,1040 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"How to use the approximate model\"\n", + "description: \"How to use the approximate model for the latest version of Multi-product formulas (MPF)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "8d428726-b143-45b7-9c23-d7aef8f3690b", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# How to use the approximate model\n", + "\n", + "You have already seen a first example of the [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#setup_sum_of_squares_problem) function in the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial.\n", + "In this guide, we are going to revisit that function in more detail." + ] + }, + { + "cell_type": "markdown", + "id": "c9e6b25b-6f6f-49c6-84ac-9493c151a968", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Setting up a simple model problem\n", + "\n", + "For this guide, we will reuse the simple model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial; the Ising model on a line of 10 sites:\n", + "\n", + "$$\n", + "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", + "$$\n", + "\n", + "where $J$ is the coupling strength between two sites and $h$ is the external magnetic field." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3c8d6feb-d0fb-45ac-8bf6-213b29bcb8a4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'],\n", + " coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j,\n", + " 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j,\n", + " 0.4+0.j, 0.4+0.j, 0.4+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "coupling_map = CouplingMap.from_line(10, bidirectional=False)\n", + "\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " coupling_map,\n", + " coupling_constants=(0.0, 0.0, 1.0),\n", + " ext_magnetic_field=(0.4, 0.0, 0.0),\n", + ")\n", + "print(hamiltonian)" + ] + }, + { + "cell_type": "markdown", + "id": "46ab2bb9-fd98-4c59-8d9c-66993652ff13", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Choosing $k_j$\n", + "\n", + "In this guide, we are going to focus on the tweaks that we can apply to the [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) and its solver that are returned by the [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#setup_sum_of_squares_problem). Therefore, we are not concerned with choosing any specific $k_j$ values and can simply reuse the ones from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2ec34c2f-1f6f-4346-9ba6-f7f1c0e3620e", + "metadata": {}, + "outputs": [], + "source": [ + "trotter_steps = (8, 12, 19)" + ] + }, + { + "cell_type": "markdown", + "id": "99c7ad3c-7a5a-4dfe-81a5-9457d68fbd9c", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Computing $x$ analytically\n", + "\n", + "First, we have to construct our LSE, $Ax=b$. Once again, we reuse the settings from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "62d48e9c-8b00-43ac-9ee6-90f6418cfb05", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit_addon_mpf.static import setup_static_lse\n", + "\n", + "lse = setup_static_lse(trotter_steps, order=2, symmetric=True)" + ] + }, + { + "cell_type": "markdown", + "id": "9b5c4989-a75d-40e2-bb8a-9070188baef4", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Next, we compute the analytical solution of $x$ and its L1-norm as our reference values." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e279b0a3-9639-4e68-8138-90ff5ae6640d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.17239057 -1.19447005 2.02207947]\n" + ] + } + ], + "source": [ + "coeff_analytical = lse.solve()\n", + "print(coeff_analytical)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a668e3bc-0cd1-4995-bc81-9ac8ca053f02", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.388940092165899\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "print(np.linalg.norm(coeff_analytical, ord=1))" + ] + }, + { + "cell_type": "markdown", + "id": "abda0fe8-c188-49e5-b121-e7d9b64b5313", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Setting up the approximate model\n", + "\n", + "Now, we will construct the approximate model.\n", + "The idea of using approximate solutions $\\tilde{x}$ in order to ensure a smaller L1-norm was proposed by [Zhuk et al., 2023].\n", + "The model is already explained in detail in the API documentation of [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#setup_sum_of_squares_problem) so we refrain from repeating it here.\n", + "Suffice to say, that the model consists of two constraints which ensure that the coefficients, $\\tilde{x}$, sum to $1$ and it enforces their L1-norm to be smaller than the user-provided `max_l1_norm` value.\n", + "The optimization objective is to minimize the deviation of $A\\tilde{x}=b$.\n", + "\n", + "We can see all of this in the output below.\n", + "Here, we are setting `max_l1_norm=3` because we already know that the L1-norm of the exact solution is approximately $3.4$ and we want to find a $\\tilde{x}$ that has a smaller L1-norm.\n", + "\n", + "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "de60da28-ce4f-427d-b8c6-11a91cae3472", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "minimize quad_over_lin(Vstack([1. 1. 1.] @ x + -1.0, [0.015625 0.00694444 0.00277008] @ x + -0.0, [2.44140625e-04 4.82253086e-05 7.67336039e-06] @ x + -0.0), 1.0)\n", + "subject to Sum(x, None, False) == 1.0\n", + " norm1(x) <= 3.0\n" + ] + } + ], + "source": [ + "from qiskit_addon_mpf.costs import setup_sum_of_squares_problem\n", + "\n", + "model, coeff_var = setup_sum_of_squares_problem(lse, max_l1_norm=3)\n", + "print(model)" + ] + }, + { + "cell_type": "markdown", + "id": "fc5da15f-7771-446b-b0cf-c9320836102a", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Solving the model with default settings\n", + "\n", + "First, let us see what happens when we solve the `model` with only default settings." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "153755d2-4c04-410c-a251-b939f460643a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===============================================================================\n", + " CVXPY \n", + " v1.5.3 \n", + "===============================================================================\n", + "(CVXPY) Sep 05 09:28:06 AM: Your problem has 3 variables, 2 constraints, and 0 parameters.\n", + "(CVXPY) Sep 05 09:28:06 AM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 05 09:28:06 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 05 09:28:06 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 05 09:28:06 AM: Your problem is compiled with the CPP canonicalization backend.\n", + "-------------------------------------------------------------------------------\n", + " Compilation \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:06 AM: Compiling problem (target solver=OSQP).\n", + "(CVXPY) Sep 05 09:28:06 AM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> OSQP\n", + "(CVXPY) Sep 05 09:28:06 AM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 05 09:28:06 AM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 05 09:28:06 AM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 05 09:28:06 AM: Applying reduction OSQP\n", + "(CVXPY) Sep 05 09:28:06 AM: Finished problem compilation (took 1.053e-02 seconds).\n", + "-------------------------------------------------------------------------------\n", + " Numerical solver \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:06 AM: Invoking solver OSQP to obtain a solution.\n", + "-----------------------------------------------------------------\n", + " OSQP v0.6.3 - Operator Splitting QP Solver\n", + " (c) Bartolomeo Stellato, Goran Banjac\n", + " University of Oxford - Stanford University 2021\n", + "-----------------------------------------------------------------\n", + "problem: variables n = 9, constraints m = 11\n", + " nnz(P) + nnz(A) = 33\n", + "settings: linear system solver = qdldl,\n", + " eps_abs = 1.0e-05, eps_rel = 1.0e-05,\n", + " eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04,\n", + " rho = 1.00e-01 (adaptive),\n", + " sigma = 1.00e-06, alpha = 1.60, max_iter = 10000\n", + " check_termination: on (interval 25),\n", + " scaling: on, scaled_termination: off\n", + " warm start: on, polish: on, time_limit: off\n", + "\n", + "iter objective pri res dua res rho time\n", + " 1 0.0000e+00 1.00e+00 2.00e+02 1.00e-01 6.26e-05s\n", + " 200 4.0661e-09 7.52e-06 1.44e-08 1.22e-05 1.28e-04s\n", + "\n", + "status: solved\n", + "solution polish: unsuccessful\n", + "number of iterations: 200\n", + "optimal objective: 0.0000\n", + "run time: 1.53e-04s\n", + "optimal rho estimate: 2.09e-06\n", + "\n", + "-------------------------------------------------------------------------------\n", + " Summary \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal\n", + "(CVXPY) Sep 05 09:28:07 AM: Optimal value: 4.092e-09\n", + "(CVXPY) Sep 05 09:28:07 AM: Compilation took 1.053e-02 seconds\n", + "(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 1.028e-03 seconds\n" + ] + }, + { + "data": { + "text/plain": [ + "4.092215822096244e-09" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.solve(verbose=True)" + ] + }, + { + "cell_type": "markdown", + "id": "0aafe4c0-c2cc-4de2-824f-ea3594960ca3", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can see from the final value of the cost function that the sum of squares of $A\\tilde{x}-b$ is almost zero.\n", + "\n", + "In the output above, we also get a lot more information from [cvxpy.Problem.solve](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem.solve), some of which we will get back to later.\n", + "\n", + "For now, let us inspect the final values of $\\tilde{x}$ and its L1-norm:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "662b35c2-cd7a-42ff-96a5-792184723f7c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.39646076 0.55556835 0.84089365]\n", + "1.7929227539822414\n" + ] + } + ], + "source": [ + "coeff_approx_plain = coeff_var.value\n", + "print(coeff_approx_plain)\n", + "print(np.linalg.norm(coeff_approx_plain, ord=1))" + ] + }, + { + "cell_type": "markdown", + "id": "aed22d40-140e-4a06-9f6d-90be062fd0cb", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can clearly see that the L1-norm has decreased compared to the analytical solution that we have computed before.\n", + "\n", + "Nonetheless, let us see if we can tweak our solution a bit.\n", + "In the end, we will compare the results obtained for this set of coefficients with all the other ones." + ] + }, + { + "cell_type": "markdown", + "id": "aa3484a3-ce6e-460a-a144-2bc94ab0ace4", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Tweaking the approximate model\n", + "\n", + "Rather than tweaking the input parameters like the chosen `trotter_steps` and `max_l1_norm` values, we will focus on the solver settings in this guide." + ] + }, + { + "cell_type": "markdown", + "id": "a078b0b7-93d0-418f-aaf7-f8584d6889db", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Using a different solver\n", + "\n", + "Notice, that we used the default solver method: `OSQP`. We can change that solver to see whether this has any effect." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "318369c1-7c54-4035-87b9-1e729aa0c048", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===============================================================================\n", + " CVXPY \n", + " v1.5.3 \n", + "===============================================================================\n", + "(CVXPY) Sep 05 09:28:07 AM: Your problem has 3 variables, 2 constraints, and 0 parameters.\n", + "(CVXPY) Sep 05 09:28:07 AM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 05 09:28:07 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 05 09:28:07 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 05 09:28:07 AM: Your problem is compiled with the CPP canonicalization backend.\n", + "-------------------------------------------------------------------------------\n", + " Compilation \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Compiling problem (target solver=CLARABEL).\n", + "(CVXPY) Sep 05 09:28:07 AM: Reduction chain: Dcp2Cone -> CvxAttr2Constr -> ConeMatrixStuffing -> CLARABEL\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction Dcp2Cone\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction ConeMatrixStuffing\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction CLARABEL\n", + "(CVXPY) Sep 05 09:28:07 AM: Finished problem compilation (took 8.657e-03 seconds).\n", + "-------------------------------------------------------------------------------\n", + " Numerical solver \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Invoking solver CLARABEL to obtain a solution.\n", + "-------------------------------------------------------------\n", + " Clarabel.rs v0.8.1 - Clever Acronym \n", + "\n", + " (c) Paul Goulart \n", + " University of Oxford, 2022 \n", + "-------------------------------------------------------------\n", + "\n", + "problem:\n", + " variables = 9\n", + " constraints = 11\n", + " nnz(P) = 3\n", + " nnz(A) = 30\n", + " cones (total) = 2\n", + " : Zero = 1, numel = 4\n", + " : Nonnegative = 1, numel = 7\n", + "\n", + "settings:\n", + " linear algebra: direct / qdldl, precision: 64 bit\n", + " max iter = 200, time limit = Inf, max step = 0.990\n", + " tol_feas = 1.0e-8, tol_gap_abs = 1.0e-8, tol_gap_rel = 1.0e-8,\n", + " static reg : on, ϵ1 = 1.0e-8, ϵ2 = 4.9e-32\n", + " dynamic reg: on, ϵ = 1.0e-13, δ = 2.0e-7\n", + " iter refine: on, reltol = 1.0e-13, abstol = 1.0e-12,\n", + " max iter = 10, stop ratio = 5.0\n", + " equilibrate: on, min_scale = 1.0e-4, max_scale = 1.0e4\n", + " max iter = 10\n", + "\n", + "iter pcost dcost gap pres dres k/t μ step \n", + "---------------------------------------------------------------------------------------------\n", + " 0 +7.1341e-05 -2.3333e+00 2.33e+00 2.40e-01 7.10e-01 1.00e+00 1.88e+00 ------ \n", + " 1 +7.1330e-05 -1.5432e-01 1.54e-01 1.64e-02 1.12e-01 3.28e-02 1.80e-01 9.61e-01 \n", + " 2 +6.9440e-05 -1.7266e-03 1.80e-03 2.10e-04 1.89e-03 5.65e-04 2.43e-03 9.87e-01 \n", + " 3 +2.2053e-05 -5.0002e-05 7.21e-05 5.56e-06 4.83e-05 1.98e-05 7.80e-05 9.87e-01 \n", + " 4 +1.1119e-06 -2.5543e-05 2.67e-05 1.80e-07 1.41e-06 3.21e-06 1.04e-05 9.90e-01 \n", + " 5 +7.9250e-09 -2.0484e-06 2.06e-06 2.06e-09 1.59e-08 2.11e-07 6.43e-07 9.90e-01 \n", + " 6 +2.2543e-09 -2.8390e-08 3.06e-08 2.07e-11 1.59e-10 3.12e-09 9.42e-09 9.90e-01 \n", + " 7 +1.8480e-09 -8.5733e-10 2.71e-09 1.79e-12 2.36e-10 3.19e-10 9.40e-10 9.15e-01 \n", + "---------------------------------------------------------------------------------------------\n", + "Terminated with status = Solved\n", + "solve time = 113.289µs\n", + "-------------------------------------------------------------------------------\n", + " Summary \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal\n", + "(CVXPY) Sep 05 09:28:07 AM: Optimal value: 1.848e-09\n", + "(CVXPY) Sep 05 09:28:07 AM: Compilation took 8.657e-03 seconds\n", + "(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 8.504e-04 seconds\n" + ] + }, + { + "data": { + "text/plain": [ + "1.8479854472132682e-09" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.solve(verbose=True, solver=\"CLARABEL\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "207a1f1d-00e1-40ef-bc2a-f904a58fd1ba", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.21284352 -0.00792683 1.22077035]\n", + "1.4415406931636157\n" + ] + } + ], + "source": [ + "coeff_approx_clarabel = coeff_var.value\n", + "print(coeff_approx_clarabel)\n", + "print(np.linalg.norm(coeff_approx_clarabel, ord=1))" + ] + }, + { + "cell_type": "markdown", + "id": "05b672be-9f72-4a11-a9ee-0d4701a8fc86", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Indeed, the outcome has changed. But now we have one major contribution from the largest $k_j$ value which is also not ideal.\n", + "\n", + "An exhaustive search of the various available solvers is beyond the scope of this guide. Instead we encourage you to try this yourself." + ] + }, + { + "cell_type": "markdown", + "id": "77ba957c-f083-4975-b793-5a96d77a8a37", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Changing the convergence thresholds\n", + "\n", + "Rather than changing the solver altogether, we can also tweak the convergence parameters.\n", + "For the `OSQP` solver the relevant ones are called `eps_abs` and `eps_rel` and they are both set to `1e-5` by default." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "74528a7b-2bba-445d-b324-a61a261d07c3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===============================================================================\n", + " CVXPY \n", + " v1.5.3 \n", + "===============================================================================\n", + "(CVXPY) Sep 05 09:28:07 AM: Your problem has 3 variables, 2 constraints, and 0 parameters.\n", + "(CVXPY) Sep 05 09:28:07 AM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 05 09:28:07 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 05 09:28:07 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 05 09:28:07 AM: Your problem is compiled with the CPP canonicalization backend.\n", + "-------------------------------------------------------------------------------\n", + " Compilation \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Compiling problem (target solver=OSQP).\n", + "(CVXPY) Sep 05 09:28:07 AM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> OSQP\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction OSQP\n", + "(CVXPY) Sep 05 09:28:07 AM: Finished problem compilation (took 8.835e-03 seconds).\n", + "-------------------------------------------------------------------------------\n", + " Numerical solver \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Invoking solver OSQP to obtain a solution.\n", + "-----------------------------------------------------------------\n", + " OSQP v0.6.3 - Operator Splitting QP Solver\n", + " (c) Bartolomeo Stellato, Goran Banjac\n", + " University of Oxford - Stanford University 2021\n", + "-----------------------------------------------------------------\n", + "problem: variables n = 9, constraints m = 11\n", + " nnz(P) + nnz(A) = 33\n", + "settings: linear system solver = qdldl,\n", + " eps_abs = 1.0e-08, eps_rel = 1.0e-08,\n", + " eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04,\n", + " rho = 1.00e-01 (adaptive),\n", + " sigma = 1.00e-06, alpha = 1.60, max_iter = 10000\n", + " check_termination: on (interval 25),\n", + " scaling: on, scaled_termination: off\n", + " warm start: on, polish: on, time_limit: off\n", + "\n", + "iter objective pri res dua res rho time\n", + " 1 0.0000e+00 1.00e+00 2.00e+02 1.00e-01 5.02e-05s\n", + " 200 4.0661e-09 7.52e-06 1.44e-08 1.22e-05 2.10e-04s\n", + " 400 3.8209e-09 4.95e-06 2.88e-09 2.09e-06 3.72e-04s\n", + " 600 3.3607e-09 6.09e-06 1.96e-09 2.09e-06 4.33e-04s\n", + " 800 2.8975e-09 6.24e-06 1.84e-09 2.09e-06 4.87e-04s\n", + "1000 2.4704e-09 6.08e-06 1.70e-09 2.09e-06 5.40e-04s\n", + "1200 2.0921e-09 5.78e-06 1.56e-09 2.09e-06 5.92e-04s\n", + "1400 1.7645e-09 5.40e-06 1.44e-09 2.09e-06 6.44e-04s\n", + "1600 1.4845e-09 5.01e-06 1.32e-09 2.09e-06 7.01e-04s\n", + "1800 1.2471e-09 4.63e-06 1.21e-09 2.09e-06 7.53e-04s\n", + "2000 1.0467e-09 4.26e-06 1.11e-09 2.09e-06 8.05e-04s\n", + "2200 8.7801e-10 3.91e-06 1.01e-09 2.09e-06 8.57e-04s\n", + "2400 7.3890e-10 3.25e-06 7.74e-10 2.09e-06 9.10e-04s\n", + "2600 6.4003e-10 2.47e-06 7.11e-10 2.09e-06 9.62e-04s\n", + "2800 5.6692e-10 2.03e-06 6.68e-10 2.09e-06 1.01e-03s\n", + "3000 5.0872e-10 1.76e-06 6.33e-10 2.09e-06 1.07e-03s\n", + "3200 4.5991e-10 1.58e-06 6.02e-10 2.09e-06 1.12e-03s\n", + "3400 4.1754e-10 1.46e-06 5.74e-10 2.09e-06 1.17e-03s\n", + "3600 3.7999e-10 1.37e-06 5.47e-10 2.09e-06 1.22e-03s\n", + "3800 3.4628e-10 1.29e-06 5.22e-10 2.09e-06 1.32e-03s\n", + "4000 3.1580e-10 1.22e-06 4.99e-10 2.09e-06 1.37e-03s\n", + "4200 2.8813e-10 1.16e-06 4.76e-10 2.09e-06 1.43e-03s\n", + "4400 2.6295e-10 1.11e-06 4.55e-10 2.09e-06 1.48e-03s\n", + "4600 2.4000e-10 1.06e-06 4.35e-10 2.09e-06 1.53e-03s\n", + "4800 2.1907e-10 1.01e-06 4.15e-10 2.09e-06 1.58e-03s\n", + "5000 1.9997e-10 9.67e-07 3.97e-10 2.09e-06 1.63e-03s\n", + "5200 1.8255e-10 9.23e-07 3.79e-10 2.09e-06 1.69e-03s\n", + "5400 1.6664e-10 8.82e-07 3.62e-10 2.09e-06 1.74e-03s\n", + "5600 1.5219e-10 1.01e-06 2.60e-10 2.09e-06 1.79e-03s\n", + "5800 1.4049e-10 6.59e-07 2.47e-10 2.09e-06 1.84e-03s\n", + "6000 1.3096e-10 5.75e-07 2.39e-10 2.09e-06 1.90e-03s\n", + "6200 1.2274e-10 5.24e-07 2.31e-10 2.09e-06 1.95e-03s\n", + "6400 1.1538e-10 4.90e-07 2.24e-10 2.09e-06 2.00e-03s\n", + "6600 1.0863e-10 4.67e-07 2.17e-10 2.09e-06 2.05e-03s\n", + "6800 1.0237e-10 4.48e-07 2.11e-10 2.09e-06 2.10e-03s\n", + "7000 9.6519e-11 4.32e-07 2.05e-10 2.09e-06 2.16e-03s\n", + "7200 9.1024e-11 4.19e-07 1.99e-10 2.09e-06 2.21e-03s\n", + "7400 8.5854e-11 4.06e-07 1.93e-10 2.09e-06 2.29e-03s\n", + "7600 8.0984e-11 3.94e-07 1.88e-10 2.09e-06 2.36e-03s\n", + "7800 7.6393e-11 3.82e-07 1.82e-10 2.09e-06 2.41e-03s\n", + "8000 7.2065e-11 3.71e-07 1.77e-10 2.09e-06 2.46e-03s\n", + "8200 6.7982e-11 3.60e-07 1.72e-10 2.09e-06 2.51e-03s\n", + "8400 6.4131e-11 3.50e-07 1.67e-10 2.09e-06 2.56e-03s\n", + "8600 6.0499e-11 3.40e-07 1.62e-10 2.09e-06 2.62e-03s\n", + "8800 5.7072e-11 3.30e-07 1.57e-10 2.09e-06 2.67e-03s\n", + "9000 5.3859e-11 8.77e-06 8.55e-12 2.09e-06 2.72e-03s\n", + "9200 5.1785e-11 5.78e-07 2.18e-13 2.09e-06 2.79e-03s\n", + "9400 5.0736e-11 7.72e-08 8.32e-14 2.09e-06 2.87e-03s\n", + "9550 5.0303e-11 4.69e-08 8.73e-14 2.09e-06 2.96e-03s\n", + "plsh 4.9640e-11 1.94e-14 1.50e-12 -------- 3.00e-03s\n", + "\n", + "status: solved\n", + "solution polish: successful\n", + "number of iterations: 9550\n", + "optimal objective: 0.0000\n", + "run time: 3.00e-03s\n", + "optimal rho estimate: 2.96e-06\n", + "\n", + "-------------------------------------------------------------------------------\n", + " Summary \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal\n", + "(CVXPY) Sep 05 09:28:07 AM: Optimal value: 4.964e-11\n", + "(CVXPY) Sep 05 09:28:07 AM: Compilation took 8.835e-03 seconds\n", + "(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 3.988e-03 seconds\n" + ] + }, + { + "data": { + "text/plain": [ + "4.9640211694779584e-11" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.solve(verbose=True, eps_abs=1e-8, eps_rel=1e-8)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a5c9fe95-1752-4717-bb74-30766be22f98", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.10925065 -1. 1.89074935]\n", + "3.0\n" + ] + } + ], + "source": [ + "coeff_approx_tight = coeff_var.value\n", + "print(coeff_approx_tight)\n", + "print(np.linalg.norm(coeff_approx_tight, ord=1))" + ] + }, + { + "cell_type": "markdown", + "id": "c841fd73-87fa-49d5-b1a8-f0de4b4c53ff", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "As you can see, we have now obtained a comparatively higher L1-norm (which is still constrained by our choice of `max_l1_norm=3`). While the coefficient of the smallest $k_j$ value is still fairly small in magnitude, at least the other two provide a reasonable amount of mixing." + ] + }, + { + "cell_type": "markdown", + "id": "7f852761-5ef5-4cc6-ab62-d7660f41e82a", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Comparing our results\n", + "\n", + "We will now briefly repeat the computations from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial in order to assess the quality of the various coefficients." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "71e34633-b044-4fcc-aa63-cebc155a848f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.synthesis import SuzukiTrotter\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", + "\n", + "time = 8.0\n", + "circuits = []\n", + "for k in trotter_steps:\n", + " circ = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(order=2, reps=k),\n", + " time=time,\n", + " )\n", + " circuits.append(circ)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "acfc17f7-9817-4576-9709-1f73aa26cf05", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'],\n", + " coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j,\n", + " 0.05+0.j, 0.05+0.j, 0.05+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "L = 10\n", + "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)\n", + "print(observable)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0ea7cac7-0af8-4909-ad23-7b1acb16b607", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.primitives import StatevectorEstimator\n", + "\n", + "estimator = StatevectorEstimator()\n", + "job = estimator.run([(circ, observable) for circ in circuits])\n", + "result = job.result()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2c2b6c4c-6a27-4601-846a-3e6757ad5156", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array(0.23799162), array(0.35754312), array(0.38649906)]\n" + ] + } + ], + "source": [ + "evs = [res.data.evs for res in result]\n", + "print(evs)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "655f4663-d19e-4617-89c3-f0355563d915", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from scipy.linalg import expm\n", + "\n", + "exp_H = expm(-1j * time * hamiltonian.to_matrix())\n", + "\n", + "initial_state = np.zeros(exp_H.shape[0])\n", + "initial_state[0] = 1.0\n", + "\n", + "time_evolved_state = exp_H @ initial_state\n", + "\n", + "exact_obs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "35bb0451-091b-4ed2-8776-a1aa7ccb2760", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Analytical coeff solution: 0.39548478559794153\n", + "Plain approx. solution: 0.42928990901998165\n", + "CLARABEL approx. solution: 0.41833743748333746\n", + "Tight approx. solution: 0.3992304700751579\n", + "Exact solution: 0.40060242487900255\n" + ] + } + ], + "source": [ + "print(\"Analytical coeff solution:\", evs @ coeff_analytical)\n", + "print(\"Plain approx. solution:\", evs @ coeff_approx_plain)\n", + "print(\"CLARABEL approx. solution:\", evs @ coeff_approx_clarabel)\n", + "print(\"Tight approx. solution:\", evs @ coeff_approx_tight)\n", + "print(\"Exact solution:\", exact_obs.real)" + ] + }, + { + "cell_type": "markdown", + "id": "adaea843-98fb-48ed-ad1a-0f6ad57f9a7b", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can clearly see now, that the various approximate solutions can yield quite different results.\n", + "Luckily, testing out different expansion coefficients for a fixed choice of $k_j$ values does not require us to rerun our quantum experiments.\n", + "Therefore, we encourage you to play around and tweak your expansion coefficients when using the approximate solutions." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-mpf/index.mdx b/docs/addons/qiskit-addon-mpf/index.mdx new file mode 100644 index 00000000000..e08f467e2a8 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/index.mdx @@ -0,0 +1,64 @@ +--- +title: "Qiskit addon: multi-product formulas (MPF)" +description: "Documentation for the latest version of Multi-product formulas (MPF)" +--- + +# Qiskit addon: multi-product formulas (MPF) + +[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. + +This package contains the Qiskit addon for multi-product formulas (MPFs). These can be used to reduce the Trotter error of Hamiltonian dynamics. + +This package currently contains the following main entry points for users: + +* `qiskit_addon_mpf.static` for working with static MPFs \[[1-2](#references)]. +* `qiskit_addon_mpf.dynamic` for working with dynamic MPFs \[[2-3](#references)]. + +## Documentation + +All documentation is available [here](/docs/addons/qiskit-addon-mpf/). + +## Installation + +We encourage installing this package via `pip`, when possible: + +```bash +pip install 'qiskit-addon-mpf' +``` + +For more installation information refer to the [installation instructions](install) in the documentation. + +### Optional dependencies + +The `qiskit-addon-mpf` package has a number of optional dependencies which enable certain features. The dynamic MPF feature (see \[[2-3](#references)]) is one such example. You can install the related optional dependencies like so: + +```bash +pip install 'qiskit-addon-mpf[dynamic]' +``` + +<Admonition title="Caution" type="note"> + The optional dependency [TeNPy](https://github.com/tenpy/tenpy) was previously offered under a GPLv3 license. As of the release of [v1.0.4](https://github.com/tenpy/tenpy/releases/tag/v1.0.4) on October 2nd, 2024, it has been offered under the Apache v2 license. The license of this package is only compatible with Apache-licensed versions of TeNPy. +</Admonition> + +## Deprecation Policy + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [release notes](/docs/api/qiskit-addon-mpf/release-notes). + +## Contributing + +The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-mpf). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-mpf/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). + +We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-mpf/issues/new/choose) for tracking requests and bugs. + +## References + +1. 1. Carrera Vazquez, D. J. Egger, D. Ochsner, and S. Wörner, [Well-conditioned multi-product formulas for hardware-friendly Hamiltonian simulation](https://quantum-journal.org/papers/q-2023-07-25-1067/), Quantum 7, 1067 (2023). +2. 19. Zhuk, N. Robertson, and S. Bravyi, [Trotter error bounds and dynamic multi-product formulas for Hamiltonian simulation](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309), Phys. Rev. Research 6, 033309 (2024). +3. 14. Robertson, et al. [Tensor Network enhanced Dynamic Multiproduct Formulas](https://arxiv.org/abs/2407.17405v2), arXiv:2407.17405v2 \[quant-ph]. + +## License + +[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-mpf/blob/main/LICENSE.txt) + diff --git a/docs/addons/qiskit-addon-mpf/install.mdx b/docs/addons/qiskit-addon-mpf/install.mdx new file mode 100644 index 00000000000..54859d0b768 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/install.mdx @@ -0,0 +1,77 @@ +--- +title: "Installation Instructions" +description: "Installation Instructions for the lastest version of Multi-product formulas (MPF)" +--- + +# Installation Instructions + +Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: + +* [Option 1: Install from PyPI](#option-1) +* [Option 2: Install from Source](#option-2) + +## Pre-Installation + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +Note: If you are using Windows, use the following commands in PowerShell: + +```pwsh +python3 -m venv c:\path\to\virtual\environment +c:\path\to\virtual\environment\Scripts\Activate.ps1 +``` + +<span id="option-1" /> + +## Option 1: Install from PyPI + +The most straightforward way to install the `qiskit-addon-mpf` package is via `PyPI`. + +```sh +pip install 'qiskit-addon-mpf' +``` + +<span id="option-2" /> + +## Option 2: Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +If so, the first step is to clone the `qiskit-addon-mpf` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-mpf.git +``` + +Next, upgrade `pip` and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-mpf +``` + +The next step is to install `qiskit-addon-mpf` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab +``` + diff --git a/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started.ipynb b/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started.ipynb new file mode 100644 index 00000000000..61f22209406 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started.ipynb @@ -0,0 +1,961 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas\"\n", + "description: \"Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas for the latest version of Multi-product formulas (MPF)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "b1b4d0d8-13f5-4b7a-abe0-2a2f034b8d35", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas\n", + "\n", + "In this notebook, you will learn how to use a **Multi-Product Formula (MPF)** to achieve a lower Trotter error on our observable compared to the one incurred by the deepest Trotter circuit that we will actually execute.\n", + "You will do so by working through the steps of a **Qiskit pattern**:\n", + "\n", + "- **Step 1: Map to quantum problem**\n", + " - Initialize our problem's Hamiltonian\n", + " - <font color=\"#0F62FE\">Use an MPF to generate the Trotterized time-evolution circuits</font>\n", + "- **Step 2: Optimize the problem**\n", + " - Here we transpile our circuits for a [GenericBackendV2](/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2)\n", + "- **Step 3: Execute experiments**\n", + " - Use a [StatevectorEstimator](/api/qiskit/qiskit.primitives.StatevectorEstimator) for sake of simplicity in this notebook\n", + "- **Step 4: Reconstruct results**\n", + " - <font color=\"#0F62FE\">Compute the MPF expectation value</font>" + ] + }, + { + "cell_type": "markdown", + "id": "fd1bbb0f-120f-4425-a52a-e393f86c16cc", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Step 1: Map to quantum problem\n", + "\n", + "### 1a: Setting up our Hamiltonian\n", + "\n", + "We use the Ising model on a line of 10 sites:\n", + "\n", + "$$\n", + "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", + "$$\n", + "\n", + "where $J$ is the coupling strength between two sites and $h$ is the external magnetic field." + ] + }, + { + "cell_type": "markdown", + "id": "55db9ce4-3cfb-439c-bb0d-a64030a52183", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The [qiskit_addon_utils](/docs/addons/qiskit-addon-utils/) package provides some reusable functionalities for various purposes.\n", + "\n", + "Its [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators#module-qiskit_addon_utils.problem_generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", + "This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows.\n", + "\n", + "In the following, we create a simple line of 10 qubits using the `CouplingMap.from_line` method." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "98617c1f-00cc-4fdd-8dbc-7f0c7efd8344", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "\n", + "# Generate some coupling map to use for this example\n", + "coupling_map = CouplingMap.from_line(10, bidirectional=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e15dd313-668d-48b3-8133-3cd4df9a3d8e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-mpf/tutorials/01-getting-started/extracted-outputs/e15dd313-668d-48b3-8133-3cd4df9a3d8e-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from rustworkx.visualization import graphviz_draw\n", + "\n", + "graphviz_draw(coupling_map.graph, method=\"circo\")" + ] + }, + { + "cell_type": "markdown", + "id": "fa7dcbde-531c-4442-8c16-9127d0b2d11b", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Next, we generate the [SparsePauliOp](/api/qiskit/qiskit.quantum_info.SparsePauliOp) on the provided connectivity with the desired constants." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c03810d1-cb9f-429e-85d2-055486d77d2b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'],\n", + " coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j,\n", + " 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j,\n", + " 0.4+0.j, 0.4+0.j, 0.4+0.j])\n" + ] + } + ], + "source": [ + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "# Get a qubit operator describing the Ising field model\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " coupling_map,\n", + " coupling_constants=(0.0, 0.0, 1.0),\n", + " ext_magnetic_field=(0.4, 0.0, 0.0),\n", + ")\n", + "print(hamiltonian)" + ] + }, + { + "cell_type": "markdown", + "id": "bdb73298-d30a-407a-9e5d-de7c57264f6b", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The observable that we will be measuring is the total magnetization which we can simply construct as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "77232669-aa00-4f74-9f50-758f0c960693", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'],\n", + " coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j,\n", + " 0.05+0.j, 0.05+0.j, 0.05+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "L = coupling_map.size()\n", + "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)\n", + "print(observable)" + ] + }, + { + "cell_type": "markdown", + "id": "83dc1e84-df2c-4e08-a8c9-3ecd6595f04e", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### 1b: Multi-Product Formulas\n", + "\n", + "MPFs reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions.\n", + "\n", + "To make this more concrete, we define an MPF as:\n", + "\n", + "$$\n", + "\\mu(t) = \\sum_{j} x_j \\rho^{k_j}_{j}\\left(\\frac{t}{k_j}\\right) + \\text{some remaining Trotter error} \\, ,\n", + "$$\n", + "\n", + "where $x_j$ are our weighting coefficients, $\\rho^{k_j}_j$ is the density matrix corresponding to the pure state obtained by evolving the initial state with the product formula, $S^{k_j}$, involving $k_j$ Trotter steps, and $j$ indexes the number of product formulas that make up the MPF.\n", + "\n", + "The key here is that the remaining Trotter error is smaller than the Trotter error that one would obtain by simply using the largest $k_j$ value!\n", + "\n", + "You can view the usefulness of MPF from two perspectives:\n", + "\n", + "1. For a fixed budget of Trotter steps that you are able to execute, you can obtain results with a Trotter error that is smaller in total.\n", + "2. For a number of Trotter steps which results in deep circuits, you can use MPF to find several shorter-depth circuits to run which result in a similar Trotter error." + ] + }, + { + "cell_type": "markdown", + "id": "58ffeb8e-978b-4211-b949-21bebe98bd1f", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### An introduction to static MPFs\n", + "\n", + "_Static_ MPFs are those where the $x_j$ values do **NOT** depend on the evolution time, $t$.\n", + "\n", + "Determining the static MPF coefficients for a given set of $k_j$ values amounts to solving a linear system of equations:\n", + "$Ax=b$, where $x$ are our coefficients of interest, $A$ is a matrix depending on $k_j$ and the type of PF we use ($S$), and $b$ is a vector of constraints.\n", + "For brevity, we are not going to go into more detail here and instead refer you to the documentation of [LSE](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.LSE).\n", + "\n", + "We can find a solution for $x$ analytically as $x = A^{-1}b$, see e.g. [Carrera Vazquez et al., 2023] or [Zhuk et al., 2023].\n", + "However, this exact solution can be _\"ill-conditioned\"_ resulting in very large L1-norms of our coefficients, $x$, which can lead to bad performance of the MPF.\n", + "Instead, one can also obtain an approximate solution which minimizes the L1-norm of $x$ in order to attempt and optimize the MPF behavior.\n", + "\n", + "In the following, you will learn how to do all of this.\n", + "\n", + "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/\n", + "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" + ] + }, + { + "cell_type": "markdown", + "id": "b293d4ae-cee1-4fd6-bb56-926dc282e868", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Choosing $k_j$\n", + "\n", + "The choice of $k_j$ is up to the end-user.\n", + "In principle, any values can be chosen but some $k_j$'s will lead to a larger noise amplification on real devices than other choices.\n", + "Thus, it is important that one tries to find _\"good\"_ values of $k_j$.\n", + "\n", + "Here, we will simply pick some fixed values for $k_j$.\n", + "The smallest value is motivated by the target evolution time of $t=8.0$ which normally tells us to satisfy $t/k_{\\text{min}} \\lt 1$ but empirically we know that setting it equal to $1$ usually works, too.\n", + "If you want to learn more about this and how to choose your other $k_j$ values, refer to the respective guide: [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "89682993-64b3-44ea-ae95-cc9eea30a632", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "time = 8.0\n", + "trotter_steps = (8, 12, 19)" + ] + }, + { + "cell_type": "markdown", + "id": "c1732f75-1299-4ade-bf7f-eab0c4758cba", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Setting up the LSE\n", + "\n", + "Now that we have chosen our $k_j$s, we must first construct the LSE, $Ax=b$ as explained above.\n", + "The matrix $A$ depends not only on $k_j$ but also our choice of product formula (PF) -- in particular its _order_.\n", + "Additionally, one may take into account whether the PF is symmetric or not (see [Carrera Vazquez et al., 2023]), by setting `symmetric=True`.\n", + "However, this is not required as shown by [Zhuk et al., 2023].\n", + "\n", + "Here, we are going to use a second-order Suzuki-Trotter formula yielding `order=2` and we will set `symmetric=True`.\n", + "\n", + "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/\n", + "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4898872e-8dc5-43f6-9615-31081851a80a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LSE(A=array([[1.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n", + " [1.56250000e-02, 6.94444444e-03, 2.77008310e-03],\n", + " [2.44140625e-04, 4.82253086e-05, 7.67336039e-06]]), b=array([1., 0., 0.]))\n" + ] + } + ], + "source": [ + "from qiskit_addon_mpf.static import setup_static_lse\n", + "\n", + "lse = setup_static_lse(trotter_steps, order=2, symmetric=True)\n", + "print(lse)" + ] + }, + { + "cell_type": "markdown", + "id": "cb2fcfff-7d3c-4128-b4be-504a634f9a02", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Solving for $x$ analytically\n", + "\n", + "As mentioned before, we can find $x$ analytically:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e0db1488-21f5-4f7b-a9f4-98e2e945afde", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.17239057 -1.19447005 2.02207947]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "coeffs_analytical = lse.solve()\n", + "print(coeffs_analytical)" + ] + }, + { + "cell_type": "markdown", + "id": "a4517a21-61e2-45fe-bea1-82e87e48d8f7", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Optimizing for $x$ using an exact model\n", + "\n", + "Alternatively to computing $x=A^{-1}b$, you can also use [setup_exact_problem](/docs/api/qiskit-addon-mpf/costs#setup_exact_problem) to construct a [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which uses the LSE as constraints and whose optimal solution will yield $x$.\n", + "\n", + "In the next section, it will be clear why this interface exists." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3bce6d85-a9dd-49e4-abe0-f8575394122c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.17239057 -1.19447005 2.02207947]\n" + ] + } + ], + "source": [ + "from qiskit_addon_mpf.costs import setup_exact_problem\n", + "\n", + "model_exact, coeffs_exact = setup_exact_problem(lse)\n", + "model_exact.solve()\n", + "print(coeffs_exact.value)" + ] + }, + { + "cell_type": "markdown", + "id": "bb9971aa-a4a5-4e04-85fe-2541ce21ac1f", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "As an indicator whether an MPF constructed with these coefficients will yield good results, we can use the L1-norm (see also [Carrera Vazquez et al., 2023]).\n", + "\n", + "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b8ffca3a-87e7-4344-b189-af95ef5bf2f2", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.3889400921655914\n" + ] + } + ], + "source": [ + "print(np.linalg.norm(coeffs_exact.value, ord=1))" + ] + }, + { + "cell_type": "markdown", + "id": "a6f84839-0879-4f48-bba0-89cc671986c5", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Optimizing for $x$ using an approximate model\n", + "\n", + "It may happen that the L1 norm for the chosen set of $k_j$ values is deemed too high.\n", + "If that is the case and you cannot choose a different set of $k_j$ values, you can use an approximate solution to the LSE instead of an exact one.\n", + "\n", + "To do so, simply use [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#setup_sum_of_squares_problem) to construct a different [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which constrains the L1-norm to a chosen threshold while minimizing the difference of $Ax$ and $b$." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "30dac7af-5a92-48f4-bc1a-1e140546cab2", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.40454257 0.57553173 0.8290123 ]\n", + "1.8090865903790838\n" + ] + } + ], + "source": [ + "from qiskit_addon_mpf.costs import setup_sum_of_squares_problem\n", + "\n", + "model_approx, coeffs_approx = setup_sum_of_squares_problem(lse, max_l1_norm=3.0)\n", + "model_approx.solve()\n", + "print(coeffs_approx.value)\n", + "print(np.linalg.norm(coeffs_approx.value, ord=1))" + ] + }, + { + "cell_type": "markdown", + "id": "2dc731cc-f1c6-484b-afba-dba6c1c0d1db", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Note, that you have complete freedom over how to solve this optimization problem, meaning that you can change the optimization solver, its convergence thresholds, and so on.\n", + "Check out the respective guide on [How to use the approximate model](/docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model)." + ] + }, + { + "cell_type": "markdown", + "id": "e328b896-a303-45f8-bfc6-4918205a18ec", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### 1c: Setting up the Trotter circuits\n", + "\n", + "At this point, we have found our expansion coefficients, $x$, and all that is left to do is to generate the Trotterized quantum circuits.\n", + "Once again, the [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators#module-qiskit_addon_utils.problem_generators) module comes to the rescue to do just that:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fe25afaf-2f43-4c82-a97b-946b9a86df98", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.synthesis import SuzukiTrotter\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", + "\n", + "circuits = []\n", + "for k in trotter_steps:\n", + " circ = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(order=2, reps=k),\n", + " time=time,\n", + " )\n", + " circuits.append(circ)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "01b3c52b-53cc-4ccc-a2f0-efd774486070", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-mpf/tutorials/01-getting-started/extracted-outputs/01b3c52b-53cc-4ccc-a2f0-efd774486070-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuits[0].draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "73e2099d-e7a7-4b6b-b6be-5f75fb1ccc8d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-mpf/tutorials/01-getting-started/extracted-outputs/73e2099d-e7a7-4b6b-b6be-5f75fb1ccc8d-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuits[1].draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "25fcf8ff-3f91-4312-9334-ea55cf3efb14", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-mpf/tutorials/01-getting-started/extracted-outputs/25fcf8ff-3f91-4312-9334-ea55cf3efb14-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuits[2].draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "6675e269-a681-4569-82c4-a95800e82be8", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Step 2: Optimize the problem\n", + "\n", + "Normally, this is the step in the pattern during which you optimize your circuits for execution on hardware.\n", + "Here, since we only use a noiseless simulator, we simply transpile our circuit for a [GenericBackendV2](/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6c049ff6-5fc0-4673-a9d9-a37abcf96711", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.providers.fake_provider import GenericBackendV2\n", + "from qiskit.transpiler import generate_preset_pass_manager\n", + "\n", + "backend = GenericBackendV2(num_qubits=10)\n", + "transpiler = generate_preset_pass_manager(optimization_level=2, backend=backend)\n", + "\n", + "transpiled_circuits = [transpiler.run(circ) for circ in circuits]" + ] + }, + { + "cell_type": "markdown", + "id": "a996e888-ff56-4ca4-849c-cc85b07a6b73", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Step 3: Execute quantum experiments\n", + "\n", + "As explained in the very beginning, we will skip the optimization step 2 because we are simply going to compute our target observable's expectation values using a noise-free simulator, namely the [StatevectorEstimator](/api/qiskit/qiskit.primitives.StatevectorEstimator)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "494cb360-91ae-4ba3-88ca-97e017ef1de5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.primitives import StatevectorEstimator\n", + "\n", + "estimator = StatevectorEstimator()\n", + "job = estimator.run([(circ, observable) for circ in transpiled_circuits])\n", + "result = job.result()" + ] + }, + { + "cell_type": "markdown", + "id": "04445a72-7a16-4dc5-a4af-0917ad9f7124", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Step 4: Reconstruct results\n", + "\n", + "First, we extract the individual expectation values obtained for each of the Trotter circuits:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7779817f-638d-4728-87d2-473d75e3aef4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array(0.23799162), array(0.35754312), array(0.38649906)]\n" + ] + } + ], + "source": [ + "evs = [res.data.evs for res in result]\n", + "print(evs)" + ] + }, + { + "cell_type": "markdown", + "id": "cea904fd-34fe-492b-9470-42580f9620c2", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Next, we simply recombine them with our MPF coefficients to yield the total expectation values of the MPF. Below, we do so for each of the different ways by which we have computed $x$." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e5f536d5-ffce-4b7a-80f3-44846d50ee19", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Analytical solution: 0.3954847855980006\n", + "Exact model solution: 0.39548478559800204\n", + "Approx. model solution: 0.42991214253489807\n" + ] + } + ], + "source": [ + "print(\"Analytical solution:\", evs @ coeffs_analytical)\n", + "print(\"Exact model solution:\", evs @ coeffs_exact.value)\n", + "print(\"Approx. model solution:\", evs @ coeffs_approx.value)" + ] + }, + { + "cell_type": "markdown", + "id": "29852936-2a0e-49d2-a22c-f3d1e8137e36", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Finally, for this small problem we can compute the exact reference value using [scipy.linalg.expm](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.expm.html) as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "eeff7277-7295-4b78-a449-f151c8bd3e66", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.40060242487899755\n" + ] + } + ], + "source": [ + "from scipy.linalg import expm\n", + "\n", + "exp_H = expm(-1j * time * hamiltonian.to_matrix())\n", + "\n", + "initial_state = np.zeros(exp_H.shape[0])\n", + "initial_state[0] = 1.0\n", + "\n", + "time_evolved_state = exp_H @ initial_state\n", + "\n", + "exact_obs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", + "print(exact_obs.real)" + ] + }, + { + "cell_type": "markdown", + "id": "4ed84f18-d1e4-4b6c-b362-8e8a8a18633c", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can clearly see that the MPF has reduced the Trotter error compared to the one obtained with the deepest individual PF with $k_j=19$.\n", + "However, we also see that the approximate model is not flawless since it actually resulted in an expectation value that is worse than the exact solution. This shows the importance of using tight convergence criteria on the approximate model as you will learn in the guide [How to use the approximate model](/docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-mpf/tutorials/index.mdx b/docs/addons/qiskit-addon-mpf/tutorials/index.mdx new file mode 100644 index 00000000000..b528ecb1414 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/tutorials/index.mdx @@ -0,0 +1,11 @@ +--- +title: "Tutorials" +description: "Tutorials for the lastest version of Multi-product formulas (MPF)" +--- + +# Tutorials + +This page summarizes the available tutorials. + +* [Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas](01-getting-started) + diff --git a/docs/api/qiskit-addon-mpf/_toc.json b/docs/api/qiskit-addon-mpf/_toc.json index 99c4b93559b..e68ebca7052 100644 --- a/docs/api/qiskit-addon-mpf/_toc.json +++ b/docs/api/qiskit-addon-mpf/_toc.json @@ -143,5 +143,7 @@ } ], "collapsed": true, - "untranslatable": true + "untranslatable": true, + "parentUrl": "/docs/addons/qiskit-addon-mpf", + "parentLabel": "Multi-product formulas (MPF)" } diff --git a/docs/api/qiskit-addon-mpf/backends-quimb-circuit-circuit-evolver.mdx b/docs/api/qiskit-addon-mpf/backends-quimb-circuit-circuit-evolver.mdx index a3b6b5776f1..fec67a50749 100644 --- a/docs/api/qiskit-addon-mpf/backends-quimb-circuit-circuit-evolver.mdx +++ b/docs/api/qiskit-addon-mpf/backends-quimb-circuit-circuit-evolver.mdx @@ -9,11 +9,11 @@ python_api_name: qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver # CircuitEvolver <Class id="qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mpf/tree/stable/0.3/qiskit_addon_mpf/backends/quimb_circuit/evolver.py#L24-L92" signature="CircuitEvolver(evolution_state, circuit, dt)" modifiers="class"> - Bases: [`Evolver`](backends#evolver "qiskit_addon_mpf.backends.interface.Evolver") + Bases: [`Evolver`](/docs/api/qiskit-addon-mpf/backends#evolver "qiskit_addon_mpf.backends.interface.Evolver") A time-evolution engine based on quantum circuits. - This algorithm performs time-evolution by means of successively applying a quantum circuit corresponding to a single Trotter step to its internal state. More specifically, it builds out a tensor network in the [`CircuitState`](backends-quimb-circuit-circuit-state "qiskit_addon_mpf.backends.quimb_circuit.CircuitState"). As required by the [`DynamicMPF`](dynamic#dynamicmpf "qiskit_addon_mpf.dynamic.DynamicMPF") algorithm, it tracks a left- and right-hand side of the time-evolution for computing the overlap of two circuits. Depending on [`conjugate`](#qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver.conjugate "qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver.conjugate"), an instance of this engine will apply the quantum gates of its template circuit to the corresponding side (see [`quimb_circuit`](backends-quimb-circuit#module-qiskit_addon_mpf.backends.quimb_circuit "qiskit_addon_mpf.backends.quimb_circuit") for more details). + This algorithm performs time-evolution by means of successively applying a quantum circuit corresponding to a single Trotter step to its internal state. More specifically, it builds out a tensor network in the [`CircuitState`](backends-quimb-circuit-circuit-state "qiskit_addon_mpf.backends.quimb_circuit.CircuitState"). As required by the [`DynamicMPF`](/docs/api/qiskit-addon-mpf/dynamic#dynamicmpf "qiskit_addon_mpf.dynamic.DynamicMPF") algorithm, it tracks a left- and right-hand side of the time-evolution for computing the overlap of two circuits. Depending on [`conjugate`](#qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver.conjugate "qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver.conjugate"), an instance of this engine will apply the quantum gates of its template circuit to the corresponding side (see [`quimb_circuit`](/docs/api/qiskit-addon-mpf/backends-quimb-circuit#module-qiskit_addon_mpf.backends.quimb_circuit "qiskit_addon_mpf.backends.quimb_circuit") for more details). Initialize a [`CircuitEvolver`](#qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver "qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver") instance. @@ -40,7 +40,7 @@ python_api_name: qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver ### evolution\_state <Attribute id="qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver.evolution_state"> - The time-evolution state (see also [`DynamicMPF.evolution_state`](dynamic#evolution_state "qiskit_addon_mpf.dynamic.DynamicMPF.evolution_state")). + The time-evolution state (see also [`DynamicMPF.evolution_state`](/docs/api/qiskit-addon-mpf/dynamic#evolution_state "qiskit_addon_mpf.dynamic.DynamicMPF.evolution_state")). </Attribute> ### circuit diff --git a/docs/api/qiskit-addon-mpf/backends-quimb-circuit-circuit-state.mdx b/docs/api/qiskit-addon-mpf/backends-quimb-circuit-circuit-state.mdx index ce5459f0e06..9e4cce1bf72 100644 --- a/docs/api/qiskit-addon-mpf/backends-quimb-circuit-circuit-state.mdx +++ b/docs/api/qiskit-addon-mpf/backends-quimb-circuit-circuit-state.mdx @@ -9,11 +9,11 @@ python_api_name: qiskit_addon_mpf.backends.quimb_circuit.CircuitState # CircuitState <Class id="qiskit_addon_mpf.backends.quimb_circuit.CircuitState" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mpf/tree/stable/0.3/qiskit_addon_mpf/backends/quimb_circuit/state.py#L27-L78" signature="CircuitState" modifiers="class"> - Bases: [`State`](backends#state "qiskit_addon_mpf.backends.interface.State") + Bases: [`State`](/docs/api/qiskit-addon-mpf/backends#state "qiskit_addon_mpf.backends.interface.State") An MPO-like representation of a time-evolution state based on quantum circuits. - This time-evolution state can be evolved on its left- and right-hand side as required by the [`DynamicMPF`](dynamic#dynamicmpf "qiskit_addon_mpf.dynamic.DynamicMPF") algorithm. + This time-evolution state can be evolved on its left- and right-hand side as required by the [`DynamicMPF`](/docs/api/qiskit-addon-mpf/dynamic#dynamicmpf "qiskit_addon_mpf.dynamic.DynamicMPF") algorithm. Initialize a [`CircuitState`](#qiskit_addon_mpf.backends.quimb_circuit.CircuitState "qiskit_addon_mpf.backends.quimb_circuit.CircuitState") instance. diff --git a/docs/api/qiskit-addon-mpf/backends-quimb-circuit.mdx b/docs/api/qiskit-addon-mpf/backends-quimb-circuit.mdx index 547b7eabbd7..007517da413 100644 --- a/docs/api/qiskit-addon-mpf/backends-quimb-circuit.mdx +++ b/docs/api/qiskit-addon-mpf/backends-quimb-circuit.mdx @@ -24,10 +24,10 @@ A circuit-based time-evolution backend using [`quimb`](https://quimb.readthedocs ``` </Admonition> -| | | -| ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | -| [`CircuitEvolver`](backends-quimb-circuit-circuit-evolver "qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver") | A time-evolution engine based on quantum circuits. | -| [`CircuitState`](backends-quimb-circuit-circuit-state "qiskit_addon_mpf.backends.quimb_circuit.CircuitState") | An MPO-like representation of a time-evolution state based on quantum circuits. | +| | | +| ---------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | +| [`CircuitEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-circuit-circuit-evolver "qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver") | A time-evolution engine based on quantum circuits. | +| [`CircuitState`](/docs/api/qiskit-addon-mpf/backends-quimb-circuit-circuit-state "qiskit_addon_mpf.backends.quimb_circuit.CircuitState") | An MPO-like representation of a time-evolution state based on quantum circuits. | ## Underlying method @@ -37,14 +37,14 @@ Quimb boasts direct support for the simulation of quantum circuits in the form o This section shows a simple example to get you started with using this backend. The example shows how to create the three factory functions required for the [`setup_dynamic_lse()`](dynamic#setup_dynamic_lse "qiskit_addon_mpf.dynamic.setup_dynamic_lse"). -The [`IdentityStateFactory`](dynamic#identitystatefactory "qiskit_addon_mpf.dynamic.IdentityStateFactory") protocol is already fulfilled by the [`CircuitState`](backends-quimb-circuit-circuit-state "qiskit_addon_mpf.backends.quimb_circuit.CircuitState") constructor, rendering the `identity_factory` argument trivial: +The [`IdentityStateFactory`](dynamic#identitystatefactory "qiskit_addon_mpf.dynamic.IdentityStateFactory") protocol is already fulfilled by the [`CircuitState`](/docs/api/qiskit-addon-mpf/backends-quimb-circuit-circuit-state "qiskit_addon_mpf.backends.quimb_circuit.CircuitState") constructor, rendering the `identity_factory` argument trivial: ```python >>> from qiskit_addon_mpf.backends.quimb_circuit import CircuitState >>> identity_factory = CircuitState ``` -The setup of the [`CircuitEvolver`](backends-quimb-circuit-circuit-evolver "qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver") is slightly more involved. It requires a **parameterized** [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) object as its input where the [`Parameter`](/docs/api/qiskit/qiskit.circuit.Parameter) should take the place of the Trotter methods time step (`dt`). +The setup of the [`CircuitEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-circuit-circuit-evolver "qiskit_addon_mpf.backends.quimb_circuit.CircuitEvolver") is slightly more involved. It requires a **parameterized** [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) object as its input where the [`Parameter`](/docs/api/qiskit/qiskit.circuit.Parameter) should take the place of the Trotter methods time step (`dt`). To show how such a parameterized Trotter circuit template is constructed, we reuse the same Hamiltonian and second-order Suzuki-Trotter formula as in [`quimb_layers`](backends-quimb-layers#module-qiskit_addon_mpf.backends.quimb_layers "qiskit_addon_mpf.backends.quimb_layers"). diff --git a/docs/api/qiskit-addon-mpf/backends-quimb-layers-layerwise-evolver.mdx b/docs/api/qiskit-addon-mpf/backends-quimb-layers-layerwise-evolver.mdx index a0bb46176f4..d077a8df1b0 100644 --- a/docs/api/qiskit-addon-mpf/backends-quimb-layers-layerwise-evolver.mdx +++ b/docs/api/qiskit-addon-mpf/backends-quimb-layers-layerwise-evolver.mdx @@ -13,7 +13,7 @@ python_api_name: qiskit_addon_mpf.backends.quimb_layers.LayerwiseEvolver A special case of the [`TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") based on layer-wise evolution models. - As also explained in [`quimb_layers`](backends-quimb-layers#module-qiskit_addon_mpf.backends.quimb_layers "qiskit_addon_mpf.backends.quimb_layers"), this implementation extracts the alternating even/odd bond updates implemented inside of the original [`quimb.tensor.TEBD`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.TEBD) to become the end users responsibility. It does so, by replacing the single Hamiltonian provided to the [`TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") instance with a sequence of [`LayerModel`](backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel") instances. Every single instance of these encodes a single **layer** of interactions. These should enforce the alternating updates of even and odd bonds of the underlying tensor network. + As also explained in [`quimb_layers`](/docs/api/qiskit-addon-mpf/backends-quimb-layers#module-qiskit_addon_mpf.backends.quimb_layers "qiskit_addon_mpf.backends.quimb_layers"), this implementation extracts the alternating even/odd bond updates implemented inside of the original [`quimb.tensor.TEBD`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.TEBD) to become the end users responsibility. It does so, by replacing the single Hamiltonian provided to the [`TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") instance with a sequence of [`LayerModel`](backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel") instances. Every single instance of these encodes a single **layer** of interactions. These should enforce the alternating updates of even and odd bonds of the underlying tensor network. The motivation for this more complicated interface is that is provides a lot more flexbility and enables users to define custom Trotter product formulas rather than being limited to the ones implemented by `quimb` directly. @@ -22,7 +22,7 @@ python_api_name: qiskit_addon_mpf.backends.quimb_layers.LayerwiseEvolver **Parameters** * **evolution\_state** ([*quimb\_tebd.MPOState*](backends-quimb-tebd-mpo-state "qiskit_addon_mpf.backends.quimb_tebd.MPOState") *| MatrixProductState*) – forwarded to [`TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver"). Please refer to its documentation for more details. - * **layers** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*LayerModel*](backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel")*]*) – the list of models describing single layers of interactions. See above as well as the explanations provided in [`quimb_layers`](backends-quimb-layers#module-qiskit_addon_mpf.backends.quimb_layers "qiskit_addon_mpf.backends.quimb_layers"). + * **layers** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*LayerModel*](backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel")*]*) – the list of models describing single layers of interactions. See above as well as the explanations provided in [`quimb_layers`](/docs/api/qiskit-addon-mpf/backends-quimb-layers#module-qiskit_addon_mpf.backends.quimb_layers "qiskit_addon_mpf.backends.quimb_layers"). * **args** – any further positional arguments will be forwarded to the [`TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") constructor. * **kwargs** – any further keyword arguments will be forwarded to the [`TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") constructor. diff --git a/docs/api/qiskit-addon-mpf/backends-quimb-layers.mdx b/docs/api/qiskit-addon-mpf/backends-quimb-layers.mdx index 60b040d41f8..e41c3905eca 100644 --- a/docs/api/qiskit-addon-mpf/backends-quimb-layers.mdx +++ b/docs/api/qiskit-addon-mpf/backends-quimb-layers.mdx @@ -24,16 +24,16 @@ A layer-wise time-evolution backend using [`quimb`](https://quimb.readthedocs.io ``` </Admonition> -| | | -| ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| [`LayerwiseEvolver`](backends-quimb-layers-layerwise-evolver "qiskit_addon_mpf.backends.quimb_layers.LayerwiseEvolver") | A special case of the [`TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") based on layer-wise evolution models. | -| [`LayerModel`](backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel") | A model for representing a layer of time-evolution interactions. | +| | | +| -------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| [`LayerwiseEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layerwise-evolver "qiskit_addon_mpf.backends.quimb_layers.LayerwiseEvolver") | A special case of the [`TEBDEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") based on layer-wise evolution models. | +| [`LayerModel`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel") | A model for representing a layer of time-evolution interactions. | ## Underlying method This module provides a time-evolution backend similar to the TEBD-based one provided by the [`quimb_tebd`](backends-quimb-tebd#module-qiskit_addon_mpf.backends.quimb_tebd "qiskit_addon_mpf.backends.quimb_tebd") module. The main difference is that this module gives the user full flexibility for defining their product formulas, thereby not limiting them to the options built into the [`quimb`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/index.html#module-quimb) library. -At its core, the algorithm provided by this module is still a TEBD \[1] algorithm. However, rather than enforcing the alternating updates to the even and odd bonds of the time-evolution state (see also [`quimb_tebd.TEBDEvolver.sweep()`](backends-quimb-tebd-tebd-evolver#sweep "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver.sweep")) this implementation outsources the responsibility of updating bonds in alternating fashion to the definition of multiple time-evolution **layers**. +At its core, the algorithm provided by this module is still a TEBD \[1] algorithm. However, rather than enforcing the alternating updates to the even and odd bonds of the time-evolution state (see also [`quimb_tebd.TEBDEvolver.sweep()`](/docs/api/qiskit-addon-mpf/backends-quimb-tebd-tebd-evolver#sweep "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver.sweep")) this implementation outsources the responsibility of updating bonds in alternating fashion to the definition of multiple time-evolution **layers**. This is best explained with an example. Let us assume, we have some generic Hamiltonian acting on a 1-dimensional chain of sites. @@ -80,19 +80,19 @@ In the circuit above, we can clearly identify its layer-wise structure. We can e It is not possible to instruct Quimb’s TEBD algorithm to simulate the exact structure of the circuit shown above. The reason for that is a limitation in its interface, as it only accepts the full Hamiltonian to be provided which is then time-evolved using pre-defined Trotter formulas. However, in doing so it does not treat the order of the Pauli terms in a Hamiltonian with any significance (like we do here). -If one wants to compute the dynamic MPF coefficients of a time-evolution employing a product formula structure other than the ones implemented in Quimb (like the example above), then one can use the time-evolution algorithm provided by this module. Rather than taking a single monolithic Hamiltonian whose time-evolution is to be modeled, the [`LayerwiseEvolver`](backends-quimb-layers-layerwise-evolver "qiskit_addon_mpf.backends.quimb_layers.LayerwiseEvolver") accepts a list of [`LayerModel`](backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel") objects, each one describing an individual layer of the product formula. This gives the user full flexibility in defining the Trotter decomposition down to the most granular level. +If one wants to compute the dynamic MPF coefficients of a time-evolution employing a product formula structure other than the ones implemented in Quimb (like the example above), then one can use the time-evolution algorithm provided by this module. Rather than taking a single monolithic Hamiltonian whose time-evolution is to be modeled, the [`LayerwiseEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layerwise-evolver "qiskit_addon_mpf.backends.quimb_layers.LayerwiseEvolver") accepts a list of [`LayerModel`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel") objects, each one describing an individual layer of the product formula. This gives the user full flexibility in defining the Trotter decomposition down to the most granular level. However, caution must be applied to ensure that the property of TEBD to update even and odd bonds in an alternating manner is still guaranteed. Luckily, for quantum circuits consisting of at most two-qubit gates, this property is satisfied by construction. ## Code example -In this section, we build up on the example above and show how to take a custom Trotter formula and use it to construct a [`LayerwiseEvolver`](backends-quimb-layers-layerwise-evolver "qiskit_addon_mpf.backends.quimb_layers.LayerwiseEvolver") which can be used to replace the [`quimb_tebd.TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") in the workflow described in [`quimb_tebd`](backends-quimb-tebd#module-qiskit_addon_mpf.backends.quimb_tebd "qiskit_addon_mpf.backends.quimb_tebd"). +In this section, we build up on the example above and show how to take a custom Trotter formula and use it to construct a [`LayerwiseEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layerwise-evolver "qiskit_addon_mpf.backends.quimb_layers.LayerwiseEvolver") which can be used to replace the [`quimb_tebd.TEBDEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") in the workflow described in [`quimb_tebd`](backends-quimb-tebd#module-qiskit_addon_mpf.backends.quimb_tebd "qiskit_addon_mpf.backends.quimb_tebd"). <Admonition title="Hint" type="note"> The overall workflow of using this module is the same as of the [`quimb_tebd`](backends-quimb-tebd#module-qiskit_addon_mpf.backends.quimb_tebd "qiskit_addon_mpf.backends.quimb_tebd") module, so be sure to read those instructions as well. </Admonition> -Simply put, we must convert each one of the circuit `layers` (see above) into a [`LayerModel`](backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel") instance. For this purpose, we can use its [`from_quantum_circuit()`](backends-quimb-layers-layer-model#from_quantum_circuit "qiskit_addon_mpf.backends.quimb_layers.LayerModel.from_quantum_circuit") method. +Simply put, we must convert each one of the circuit `layers` (see above) into a [`LayerModel`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel") instance. For this purpose, we can use its [`from_quantum_circuit()`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layer-model#from_quantum_circuit "qiskit_addon_mpf.backends.quimb_layers.LayerModel.from_quantum_circuit") method. ```python >>> from qiskit_addon_mpf.backends.quimb_layers import LayerModel @@ -102,7 +102,7 @@ Simply put, we must convert each one of the circuit `layers` (see above) into a In the code above you can see how simple the conversion is for layers which contain only two-qubit gates acting on mutually exclusive qubits (which layers of depth 1 guarantee). -However, we must be more careful with layers including single-qubit gates. The reason for that is that the TEBD algorithm underlying the [`LayerwiseEvolver`](backends-quimb-layers-layerwise-evolver "qiskit_addon_mpf.backends.quimb_layers.LayerwiseEvolver") must update even and odd bonds in an alternating manner. And because single-qubit gates are not applied on a site, but instead are split in half and applied to the bonds on either side, a layer of single-qubit gates acting on all qubits would break this assumption. +However, we must be more careful with layers including single-qubit gates. The reason for that is that the TEBD algorithm underlying the [`LayerwiseEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layerwise-evolver "qiskit_addon_mpf.backends.quimb_layers.LayerwiseEvolver") must update even and odd bonds in an alternating manner. And because single-qubit gates are not applied on a site, but instead are split in half and applied to the bonds on either side, a layer of single-qubit gates acting on all qubits would break this assumption. To circumvent this problem, we can take any layer consisting of only single-qubit gates, and apply twice (once on the even and once on the odd bonds). @@ -129,7 +129,7 @@ Now that we know how to treat layers consisting of two-qubit and single-qubit ga >>> assert len(layer_models) == 8 ``` -In the end, we have 8 [`LayerModel`](backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel")’s, one for each of the 4 two-qubit layers, and two for each of the 2 single-qubit layers. +In the end, we have 8 [`LayerModel`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel")’s, one for each of the 4 two-qubit layers, and two for each of the 2 single-qubit layers. Finally, we can define our [`ApproxEvolverFactory`](dynamic#approxevolverfactory "qiskit_addon_mpf.dynamic.ApproxEvolverFactory") protocol to be used within the [`setup_dynamic_lse()`](dynamic#setup_dynamic_lse "qiskit_addon_mpf.dynamic.setup_dynamic_lse") function. @@ -147,7 +147,7 @@ Finally, we can define our [`ApproxEvolverFactory`](dynamic#approxevolverfactory It should be noted, that in this workflow we have not yet fixed the time step used by the Trotter formula. We have also only set up a single repetition of the Trotter formula as the rest will be done by the internal [`DynamicMPF`](dynamic#dynamicmpf "qiskit_addon_mpf.dynamic.DynamicMPF") algorithm, executed during [`setup_dynamic_lse()`](dynamic#setup_dynamic_lse "qiskit_addon_mpf.dynamic.setup_dynamic_lse"). </Admonition> -Of course, you could also use this to specify a [`ExactEvolverFactory`](dynamic#exactevolverfactory "qiskit_addon_mpf.dynamic.ExactEvolverFactory"). But you can also mix-and-match a [`quimb_layers.LayerwiseEvolver`](backends-quimb-layers-layerwise-evolver "qiskit_addon_mpf.backends.quimb_layers.LayerwiseEvolver") with a [`quimb_tebd.TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver"). +Of course, you could also use this to specify a [`ExactEvolverFactory`](dynamic#exactevolverfactory "qiskit_addon_mpf.dynamic.ExactEvolverFactory"). But you can also mix-and-match a [`quimb_layers.LayerwiseEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layerwise-evolver "qiskit_addon_mpf.backends.quimb_layers.LayerwiseEvolver") with a [`quimb_tebd.TEBDEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver"). ## Resources diff --git a/docs/api/qiskit-addon-mpf/backends-quimb-tebd-mpo-state.mdx b/docs/api/qiskit-addon-mpf/backends-quimb-tebd-mpo-state.mdx index 87e1877a6d9..b01b343406e 100644 --- a/docs/api/qiskit-addon-mpf/backends-quimb-tebd-mpo-state.mdx +++ b/docs/api/qiskit-addon-mpf/backends-quimb-tebd-mpo-state.mdx @@ -9,11 +9,11 @@ python_api_name: qiskit_addon_mpf.backends.quimb_tebd.MPOState # MPOState <Class id="qiskit_addon_mpf.backends.quimb_tebd.MPOState" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mpf/tree/stable/0.3/qiskit_addon_mpf/backends/quimb_tebd/state.py#L39-L234" signature="MPOState(*args, **kwargs)" modifiers="class"> - Bases: [`MatrixProductOperator`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/tensor_1d/index.html#quimb.tensor.tensor_1d.MatrixProductOperator), [`State`](backends#state "qiskit_addon_mpf.backends.interface.State") + Bases: [`MatrixProductOperator`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/tensor_1d/index.html#quimb.tensor.tensor_1d.MatrixProductOperator), [`State`](/docs/api/qiskit-addon-mpf/backends#state "qiskit_addon_mpf.backends.interface.State") An MPO enforcing the Vidal gauge. - This specialization of quimb’s existing [`quimb.tensor.MatrixProductOperator`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.MatrixProductOperator) enforces the Vidal gauge throughout its existence. This ensures a stable behavior of the [`DynamicMPF`](dynamic#dynamicmpf "qiskit_addon_mpf.dynamic.DynamicMPF") algorithm when using the [`TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver"). + This specialization of quimb’s existing [`quimb.tensor.MatrixProductOperator`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.MatrixProductOperator) enforces the Vidal gauge throughout its existence. This ensures a stable behavior of the [`DynamicMPF`](/docs/api/qiskit-addon-mpf/dynamic#dynamicmpf "qiskit_addon_mpf.dynamic.DynamicMPF") algorithm when using the [`TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver"). Initialize a [`MPOState`](#qiskit_addon_mpf.backends.quimb_tebd.MPOState "qiskit_addon_mpf.backends.quimb_tebd.MPOState") instance. @@ -56,7 +56,7 @@ python_api_name: qiskit_addon_mpf.backends.quimb_tebd.MPOState whether the gate should be applied to the lower (`conj=False`, the default, [`lower_ind()`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/tensor_arbgeom/index.html#quimb.tensor.tensor_arbgeom.TensorNetworkGenOperator.lower_ind)) or upper (`conj=True`, [`upper_ind()`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/tensor_arbgeom/index.html#quimb.tensor.tensor_arbgeom.TensorNetworkGenOperator.upper_ind)) indices of the underlying MPO. <Admonition title="Note" type="note"> - This is essentially how the LHS and RHS of the [`DynamicMPF`](dynamic#dynamicmpf "qiskit_addon_mpf.dynamic.DynamicMPF") are differentiated, by passing their [`Evolver.conjugate`](backends#conjugate "qiskit_addon_mpf.backends.Evolver.conjugate") property to this argument. + This is essentially how the LHS and RHS of the [`DynamicMPF`](/docs/api/qiskit-addon-mpf/dynamic#dynamicmpf "qiskit_addon_mpf.dynamic.DynamicMPF") are differentiated, by passing their [`Evolver.conjugate`](/docs/api/qiskit-addon-mpf/backends#conjugate "qiskit_addon_mpf.backends.Evolver.conjugate") property to this argument. </Admonition> * **split\_opts** – additional keyword arguments that will be forwarded to the [`quimb.tensor.tensor_split()`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.tensor_split) function. These can be used to affect the truncation of the tensor before it gets contracted back into the MPO. diff --git a/docs/api/qiskit-addon-mpf/backends-quimb-tebd-tebd-evolver.mdx b/docs/api/qiskit-addon-mpf/backends-quimb-tebd-tebd-evolver.mdx index 0ce6eafbf5c..fb19f37dd90 100644 --- a/docs/api/qiskit-addon-mpf/backends-quimb-tebd-tebd-evolver.mdx +++ b/docs/api/qiskit-addon-mpf/backends-quimb-tebd-tebd-evolver.mdx @@ -9,11 +9,11 @@ python_api_name: qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver # TEBDEvolver <Class id="qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mpf/tree/stable/0.3/qiskit_addon_mpf/backends/quimb_tebd/evolver.py#L25-L175" signature="TEBDEvolver(evolution_state, *args, order=2, **kwargs)" modifiers="class"> - Bases: [`TEBD`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/tensor_1d_tebd/index.html#quimb.tensor.tensor_1d_tebd.TEBD), [`Evolver`](backends#evolver "qiskit_addon_mpf.backends.interface.Evolver") + Bases: [`TEBD`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/tensor_1d_tebd/index.html#quimb.tensor.tensor_1d_tebd.TEBD), [`Evolver`](/docs/api/qiskit-addon-mpf/backends#evolver "qiskit_addon_mpf.backends.interface.Evolver") A TEBD algorithm for evolving an internal MPO. - As discussed in more detail in [`quimb_tebd`](backends-quimb-tebd#module-qiskit_addon_mpf.backends.quimb_tebd "qiskit_addon_mpf.backends.quimb_tebd"), this extension of `quimb`’s existing [`quimb.tensor.TEBD`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.TEBD) implementation time-evolves an internal matrix product operator (MPO) rather than the conventional matrix product state (MPS). + As discussed in more detail in [`quimb_tebd`](/docs/api/qiskit-addon-mpf/backends-quimb-tebd#module-qiskit_addon_mpf.backends.quimb_tebd "qiskit_addon_mpf.backends.quimb_tebd"), this extension of `quimb`’s existing [`quimb.tensor.TEBD`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.TEBD) implementation time-evolves an internal matrix product operator (MPO) rather than the conventional matrix product state (MPS). More concretely, the internal object is expected to be an [`MPOState`](backends-quimb-tebd-mpo-state "qiskit_addon_mpf.backends.quimb_tebd.MPOState"). @@ -30,7 +30,7 @@ python_api_name: qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver a reference to the time-evolution state. This overwrites the `p0` argument of the underlying [`quimb.tensor.TEBD`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.TEBD) class. <Admonition title="Warning" type="caution"> - In contrast to the default behavior, this state will **NOT** be canonicalized. Instead, it is taken as is and is kept **by reference** (i.e. no copy is created). This ensures that the same object can be shared between two instances of this class, as required by the [`DynamicMPF`](dynamic#dynamicmpf "qiskit_addon_mpf.dynamic.DynamicMPF") algorithm. + In contrast to the default behavior, this state will **NOT** be canonicalized. Instead, it is taken as is and is kept **by reference** (i.e. no copy is created). This ensures that the same object can be shared between two instances of this class, as required by the [`DynamicMPF`](/docs/api/qiskit-addon-mpf/dynamic#dynamicmpf "qiskit_addon_mpf.dynamic.DynamicMPF") algorithm. </Admonition> * **args** – any further positional arguments will be forwarded to the [`quimb.tensor.TEBD`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.TEBD) constructor. diff --git a/docs/api/qiskit-addon-mpf/backends-quimb-tebd.mdx b/docs/api/qiskit-addon-mpf/backends-quimb-tebd.mdx index 1b9e2f9a07d..a61216ec9c0 100644 --- a/docs/api/qiskit-addon-mpf/backends-quimb-tebd.mdx +++ b/docs/api/qiskit-addon-mpf/backends-quimb-tebd.mdx @@ -24,10 +24,10 @@ A [`quimb`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/index.html#modu ``` </Admonition> -| | | -| ---------------------------------------------------------------------------------------------------- | ---------------------------------------------- | -| [`TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") | A TEBD algorithm for evolving an internal MPO. | -| [`MPOState`](backends-quimb-tebd-mpo-state "qiskit_addon_mpf.backends.quimb_tebd.MPOState") | An MPO enforcing the Vidal gauge. | +| | | +| ------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------- | +| [`TEBDEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") | A TEBD algorithm for evolving an internal MPO. | +| [`MPOState`](/docs/api/qiskit-addon-mpf/backends-quimb-tebd-mpo-state "qiskit_addon_mpf.backends.quimb_tebd.MPOState") | An MPO enforcing the Vidal gauge. | ## Underlying method @@ -38,13 +38,13 @@ The classes provided by this module serve two purposes: 1. Connecting [`quimb`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/index.html#module-quimb)’s implementation to the interface set out by [`qiskit_addon_mpf.backends`](backends#module-qiskit_addon_mpf.backends "qiskit_addon_mpf.backends"). 2. Extending [`quimb`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/index.html#module-quimb)’s TEBD implementation to handle an internal MPO (rather than MPS) state (see also [`State`](backends#state "qiskit_addon_mpf.backends.State") for more details). -In the simplest sense, this module provides a straight-forward extension of the TEBD algorithm to evolve an internal MPO state. As such, if you wish to use this backend for your dynamic MPF algorithm, you must encode the Hamiltonian that you wish to time-evolve, in a [`quimb`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/index.html#module-quimb)-native form. To be more concrete, the [`TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") class (which is a subclass of [`quimb.tensor.TEBD`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.TEBD)) works with a Hamiltonian in the form of a [`quimb.tensor.LocalHam1D`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.LocalHam1D). Quimb provides a number of convenience methods for constructing such Hamiltonians in its [`quimb.tensor.tensor_builder`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/tensor_builder/index.html#module-quimb.tensor.tensor_builder) module. If none of those fulfill your needs, you can consider using the [`LayerModel`](backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel") class which implements some conversion methods from Qiskit-native objects. +In the simplest sense, this module provides a straight-forward extension of the TEBD algorithm to evolve an internal MPO state. As such, if you wish to use this backend for your dynamic MPF algorithm, you must encode the Hamiltonian that you wish to time-evolve, in a [`quimb`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/index.html#module-quimb)-native form. To be more concrete, the [`TEBDEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") class (which is a subclass of [`quimb.tensor.TEBD`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.TEBD)) works with a Hamiltonian in the form of a [`quimb.tensor.LocalHam1D`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.LocalHam1D). Quimb provides a number of convenience methods for constructing such Hamiltonians in its [`quimb.tensor.tensor_builder`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/tensor_builder/index.html#module-quimb.tensor.tensor_builder) module. If none of those fulfill your needs, you can consider using the [`LayerModel`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel") class which implements some conversion methods from Qiskit-native objects. ## Code example This section shows a simple example to get you started with using this backend. The example shows how to create the three factory functions required for the [`setup_dynamic_lse()`](dynamic#setup_dynamic_lse "qiskit_addon_mpf.dynamic.setup_dynamic_lse"). -First, we create the `identity_factory` which has to match the [`IdentityStateFactory`](dynamic#identitystatefactory "qiskit_addon_mpf.dynamic.IdentityStateFactory") protocol. We do so simply by using the [`quimb.tensor.MPO_identity()`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.MPO_identity) function and wrapping the resulting [`quimb.tensor.MatrixProductOperator`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.MatrixProductOperator) with our custom [`MPOState`](backends-quimb-tebd-mpo-state "qiskit_addon_mpf.backends.quimb_tebd.MPOState") interface. +First, we create the `identity_factory` which has to match the [`IdentityStateFactory`](dynamic#identitystatefactory "qiskit_addon_mpf.dynamic.IdentityStateFactory") protocol. We do so simply by using the [`quimb.tensor.MPO_identity()`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.MPO_identity) function and wrapping the resulting [`quimb.tensor.MatrixProductOperator`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.MatrixProductOperator) with our custom [`MPOState`](/docs/api/qiskit-addon-mpf/backends-quimb-tebd-mpo-state "qiskit_addon_mpf.backends.quimb_tebd.MPOState") interface. ```python >>> from qiskit_addon_mpf.backends.quimb_tebd import MPOState @@ -60,7 +60,7 @@ Next, before being able to define the [`ExactEvolverFactory`](dynamic#exactevolv >>> hamil = ham_1d_heis(num_qubits, 0.8, 0.3, cyclic=False) ``` -We can now construct the exact and approximate time-evolution instance factories. To do so, we can simply use [`functools.partial()`](https://docs.python.org/3/library/functools.html#functools.partial) to bind the pre-defined values of the [`TEBDEvolver`](backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") initializer, reducing it to the correct interface as expected by the [`ExactEvolverFactory`](dynamic#exactevolverfactory "qiskit_addon_mpf.dynamic.ExactEvolverFactory") and [`ApproxEvolverFactory`](dynamic#approxevolverfactory "qiskit_addon_mpf.dynamic.ApproxEvolverFactory") protocols, respectively. +We can now construct the exact and approximate time-evolution instance factories. To do so, we can simply use [`functools.partial()`](https://docs.python.org/3/library/functools.html#functools.partial) to bind the pre-defined values of the [`TEBDEvolver`](/docs/api/qiskit-addon-mpf/backends-quimb-tebd-tebd-evolver "qiskit_addon_mpf.backends.quimb_tebd.TEBDEvolver") initializer, reducing it to the correct interface as expected by the [`ExactEvolverFactory`](dynamic#exactevolverfactory "qiskit_addon_mpf.dynamic.ExactEvolverFactory") and [`ApproxEvolverFactory`](dynamic#approxevolverfactory "qiskit_addon_mpf.dynamic.ApproxEvolverFactory") protocols, respectively. ```python >>> from functools import partial diff --git a/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layer-model.mdx b/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layer-model.mdx index bbde62cb3b1..6cc87c3dd32 100644 --- a/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layer-model.mdx +++ b/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layer-model.mdx @@ -22,7 +22,7 @@ python_api_name: qiskit_addon_mpf.backends.tenpy_layers.LayerModel <Function id="qiskit_addon_mpf.backends.tenpy_layers.LayerModel.calc_H_bond" github="https://github.com/Qiskit/qiskit-addon-mpf/tree/stable/0.3/qiskit_addon_mpf/backends/tenpy_layers/model.py#L70-L99" signature="calc_H_bond(tol_zero=1e-15)"> Calculate the interaction Hamiltonian based on the coupling and onsite terms. - Essentially, this class overwrites [`calc_H_bond()`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.models.model.CouplingModel.html#tenpy.models.model.CouplingModel.calc_H_bond "(in TeNPy v1.0.5)") and takes care of removing even or odd bond interaction Hamiltonians depending on the value of `keep_only_odd` (see [`tenpy_layers`](backends-tenpy-layers#module-qiskit_addon_mpf.backends.tenpy_layers "qiskit_addon_mpf.backends.tenpy_layers") for more details). + Essentially, this class overwrites [`calc_H_bond()`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.models.model.CouplingModel.html#tenpy.models.model.CouplingModel.calc_H_bond "(in TeNPy v1.0.5)") and takes care of removing even or odd bond interaction Hamiltonians depending on the value of `keep_only_odd` (see [`tenpy_layers`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers#module-qiskit_addon_mpf.backends.tenpy_layers "qiskit_addon_mpf.backends.tenpy_layers") for more details). **Parameters** diff --git a/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layerwise-evolver.mdx b/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layerwise-evolver.mdx index fcdf88a3139..2f6bbc96aec 100644 --- a/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layerwise-evolver.mdx +++ b/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layerwise-evolver.mdx @@ -13,7 +13,7 @@ python_api_name: qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver A special case of the [`TEBDEvolver`](backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") based on layer-wise evolution models. - As also explained in [`tenpy_layers`](backends-tenpy-layers#module-qiskit_addon_mpf.backends.tenpy_layers "qiskit_addon_mpf.backends.tenpy_layers"), this implementation extracts the alternating even/odd bond updates implemented inside of the original [`TEBDEngine`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.algorithms.tebd.TEBDEngine.html#tenpy.algorithms.tebd.TEBDEngine "(in TeNPy v1.0.5)") to become the end users responsibility. It does so, by replacing the single Hamiltonian provided to the [`TEBDEvolver`](backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") instance with a sequence of [`LayerModel`](backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel") instances. Every single instance of these encodes a single **layer** of interactions. These should enforce the alternating updates of even and odd bonds of the underlying tensor network. + As also explained in [`tenpy_layers`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers#module-qiskit_addon_mpf.backends.tenpy_layers "qiskit_addon_mpf.backends.tenpy_layers"), this implementation extracts the alternating even/odd bond updates implemented inside of the original [`TEBDEngine`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.algorithms.tebd.TEBDEngine.html#tenpy.algorithms.tebd.TEBDEngine "(in TeNPy v1.0.5)") to become the end users responsibility. It does so, by replacing the single Hamiltonian provided to the [`TEBDEvolver`](backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") instance with a sequence of [`LayerModel`](backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel") instances. Every single instance of these encodes a single **layer** of interactions. These should enforce the alternating updates of even and odd bonds of the underlying tensor network. The motivation for this more complicated interface is that is provides a lot more flexbility and enables users to define custom Trotter product formulas rather than being limited to the ones implemented by TeNPy directly. @@ -22,7 +22,7 @@ python_api_name: qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver **Parameters** * **evolution\_state** ([*tenpy\_tebd.MPOState*](backends-tenpy-tebd-mpo-state "qiskit_addon_mpf.backends.tenpy_tebd.MPOState")) – forwarded to [`TEBDEvolver`](backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver"). Please refer to its documentation for more details. - * **layers** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*LayerModel*](backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel")*]*) – the list of models describing single layers of interactions. See above as well as the explanations provided in [`tenpy_layers`](backends-tenpy-layers#module-qiskit_addon_mpf.backends.tenpy_layers "qiskit_addon_mpf.backends.tenpy_layers"). + * **layers** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*LayerModel*](backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel")*]*) – the list of models describing single layers of interactions. See above as well as the explanations provided in [`tenpy_layers`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers#module-qiskit_addon_mpf.backends.tenpy_layers "qiskit_addon_mpf.backends.tenpy_layers"). * **args** – any further positional arguments will be forwarded to the [`TEBDEvolver`](backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") constructor. * **kwargs** – any further keyword arguments will be forwarded to the [`TEBDEvolver`](backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") constructor. diff --git a/docs/api/qiskit-addon-mpf/backends-tenpy-layers.mdx b/docs/api/qiskit-addon-mpf/backends-tenpy-layers.mdx index f52a8edb563..58ac5ac3473 100644 --- a/docs/api/qiskit-addon-mpf/backends-tenpy-layers.mdx +++ b/docs/api/qiskit-addon-mpf/backends-tenpy-layers.mdx @@ -28,10 +28,10 @@ A layer-wise time-evolution backend using [`tenpy`](https://tenpy.readthedocs.io ``` </Admonition> -| | | -| ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| [`LayerwiseEvolver`](backends-tenpy-layers-layerwise-evolver "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver") | A special case of the [`TEBDEvolver`](backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") based on layer-wise evolution models. | -| [`LayerModel`](backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel") | A model for representing a layer of time-evolution interactions. | +| | | +| -------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| [`LayerwiseEvolver`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layerwise-evolver "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver") | A special case of the [`TEBDEvolver`](/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") based on layer-wise evolution models. | +| [`LayerModel`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel") | A model for representing a layer of time-evolution interactions. | ## Underlying method @@ -84,19 +84,19 @@ In the circuit above, we can clearly identify its layer-wise structure. We can e It is not possible to instruct TeNPy’s TEBD algorithm to simulate the exact structure of the circuit shown above. The reason for that is a limitation in its interface, as it only accepts the full Hamiltonian to be provided which is then time-evolved using pre-defined Trotter formulas. However, in doing so it does not treat the order of the Pauli terms in a Hamiltonian with any significance (like we do here). -If one wants to compute the dynamic MPF coefficients of a time-evolution employing a product formula structure other than the ones implemented in TeNPy (like the example above), then one can use the time-evolution algorithm provided by this module. Rather than taking a single monolithic Hamiltonian whose time-evolution is to be modeled, the [`LayerwiseEvolver`](backends-tenpy-layers-layerwise-evolver "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver") accepts a list of [`LayerModel`](backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel") objects, each one describing an individual layer of the product formula. This gives the user full flexibility in defining the Trotter decomposition down to the most granular level. +If one wants to compute the dynamic MPF coefficients of a time-evolution employing a product formula structure other than the ones implemented in TeNPy (like the example above), then one can use the time-evolution algorithm provided by this module. Rather than taking a single monolithic Hamiltonian whose time-evolution is to be modeled, the [`LayerwiseEvolver`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layerwise-evolver "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver") accepts a list of [`LayerModel`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel") objects, each one describing an individual layer of the product formula. This gives the user full flexibility in defining the Trotter decomposition down to the most granular level. However, caution must be applied to ensure that the property of TEBD to update even and odd bonds in an alternating manner is still guaranteed. Luckily, for quantum circuits consisting of at most two-qubit gates, this property is satisfied by construction. ## Code example -In this section, we build up on the example above and show how to take a custom Trotter formula and use it to construct a [`LayerwiseEvolver`](backends-tenpy-layers-layerwise-evolver "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver") which can be used to replace the [`tenpy_tebd.TEBDEvolver`](backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") in the workflow described in [`tenpy_tebd`](backends-tenpy-tebd#module-qiskit_addon_mpf.backends.tenpy_tebd "qiskit_addon_mpf.backends.tenpy_tebd"). +In this section, we build up on the example above and show how to take a custom Trotter formula and use it to construct a [`LayerwiseEvolver`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layerwise-evolver "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver") which can be used to replace the [`tenpy_tebd.TEBDEvolver`](/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") in the workflow described in [`tenpy_tebd`](backends-tenpy-tebd#module-qiskit_addon_mpf.backends.tenpy_tebd "qiskit_addon_mpf.backends.tenpy_tebd"). <Admonition title="Hint" type="note"> The overall workflow of using this module is the same as of the [`tenpy_tebd`](backends-tenpy-tebd#module-qiskit_addon_mpf.backends.tenpy_tebd "qiskit_addon_mpf.backends.tenpy_tebd") module, so be sure to read those instructions as well. </Admonition> -Simply put, we must convert each one of the circuit `layers` (see above) into a [`LayerModel`](backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel") instance. For this purpose, we can use its [`from_quantum_circuit()`](backends-tenpy-layers-layer-model#from_quantum_circuit "qiskit_addon_mpf.backends.tenpy_layers.LayerModel.from_quantum_circuit") method. +Simply put, we must convert each one of the circuit `layers` (see above) into a [`LayerModel`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel") instance. For this purpose, we can use its [`from_quantum_circuit()`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layer-model#from_quantum_circuit "qiskit_addon_mpf.backends.tenpy_layers.LayerModel.from_quantum_circuit") method. ```python >>> from qiskit_addon_mpf.backends.tenpy_layers import LayerModel @@ -106,7 +106,7 @@ Simply put, we must convert each one of the circuit `layers` (see above) into a In the code above you can see how simple the conversion is for layers which contain only two-qubit gates acting on mutually exclusive qubits (which layers of depth 1 guarantee). -However, we must be more careful with layers including single-qubit gates. The reason for that is that the TEBD algorithm underlying the [`LayerwiseEvolver`](backends-tenpy-layers-layerwise-evolver "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver") must update even and odd bonds in an alternating manner. And because single-qubit gates are not applied on a site, but instead are split in half and applied to the bonds on either side, a layer of single-qubit gates acting on all qubits would break this assumption. +However, we must be more careful with layers including single-qubit gates. The reason for that is that the TEBD algorithm underlying the [`LayerwiseEvolver`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layerwise-evolver "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver") must update even and odd bonds in an alternating manner. And because single-qubit gates are not applied on a site, but instead are split in half and applied to the bonds on either side, a layer of single-qubit gates acting on all qubits would break this assumption. To circumvent this problem, we can take any layer consisting of only single-qubit gates, and apply it twice (once on the even and once on the odd bonds). @@ -133,7 +133,7 @@ Now that we know how to treat layers consisting of two-qubit and single-qubit ga >>> assert len(layer_models) == 8 ``` -In the end, we have 8 [`LayerModel`](backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel")’s, one for each of the 4 two-qubit layers, and two for each of the 2 single-qubit layers. +In the end, we have 8 [`LayerModel`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel")’s, one for each of the 4 two-qubit layers, and two for each of the 2 single-qubit layers. Finally, we can define our [`ApproxEvolverFactory`](dynamic#approxevolverfactory "qiskit_addon_mpf.dynamic.ApproxEvolverFactory") protocol to be used within the [`setup_dynamic_lse()`](dynamic#setup_dynamic_lse "qiskit_addon_mpf.dynamic.setup_dynamic_lse") function. @@ -158,7 +158,7 @@ Finally, we can define our [`ApproxEvolverFactory`](dynamic#approxevolverfactory It should be noted, that in this workflow we have not yet fixed the time step used by the Trotter formula. We have also only set up a single repetition of the Trotter formula as the rest will be done by the internal [`DynamicMPF`](dynamic#dynamicmpf "qiskit_addon_mpf.dynamic.DynamicMPF") algorithm, executed during [`setup_dynamic_lse()`](dynamic#setup_dynamic_lse "qiskit_addon_mpf.dynamic.setup_dynamic_lse"). </Admonition> -Of course, you could also use this to specify a [`ExactEvolverFactory`](dynamic#exactevolverfactory "qiskit_addon_mpf.dynamic.ExactEvolverFactory"). But you can also mix-and-match a [`tenpy_layers.LayerwiseEvolver`](backends-tenpy-layers-layerwise-evolver "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver") with a [`tenpy_tebd.TEBDEvolver`](backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver"). +Of course, you could also use this to specify a [`ExactEvolverFactory`](dynamic#exactevolverfactory "qiskit_addon_mpf.dynamic.ExactEvolverFactory"). But you can also mix-and-match a [`tenpy_layers.LayerwiseEvolver`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layerwise-evolver "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver") with a [`tenpy_tebd.TEBDEvolver`](/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver"). ## Resources diff --git a/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-mpo-state.mdx b/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-mpo-state.mdx index ea6caf3e5e3..eaa129b2ad6 100644 --- a/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-mpo-state.mdx +++ b/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-mpo-state.mdx @@ -9,11 +9,11 @@ python_api_name: qiskit_addon_mpf.backends.tenpy_tebd.MPOState # MPOState <Class id="qiskit_addon_mpf.backends.tenpy_tebd.MPOState" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mpf/tree/stable/0.3/qiskit_addon_mpf/backends/tenpy_tebd/state.py#L27-L122" signature="MPOState(sites, Ws, bc='finite', IdL=None, IdR=None, max_range=None, explicit_plus_hc=False)" modifiers="class"> - Bases: [`MPO`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.networks.mpo.MPO.html#tenpy.networks.mpo.MPO "(in TeNPy v1.0.5)"), [`State`](backends#state "qiskit_addon_mpf.backends.interface.State") + Bases: [`MPO`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.networks.mpo.MPO.html#tenpy.networks.mpo.MPO "(in TeNPy v1.0.5)"), [`State`](/docs/api/qiskit-addon-mpf/backends#state "qiskit_addon_mpf.backends.interface.State") - A mediator class to make TeNPy’s MPO match the [`State`](backends#state "qiskit_addon_mpf.backends.State") interface. + A mediator class to make TeNPy’s MPO match the [`State`](/docs/api/qiskit-addon-mpf/backends#state "qiskit_addon_mpf.backends.State") interface. - This class simply ensures that a [`tenpy.networks.mpo.MPO`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.networks.mpo.MPO.html#tenpy.networks.mpo.MPO "(in TeNPy v1.0.5)") object can work as a [`State`](backends#state "qiskit_addon_mpf.backends.State") instance. + This class simply ensures that a [`tenpy.networks.mpo.MPO`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.networks.mpo.MPO.html#tenpy.networks.mpo.MPO "(in TeNPy v1.0.5)") object can work as a [`State`](/docs/api/qiskit-addon-mpf/backends#state "qiskit_addon_mpf.backends.State") instance. ## Methods diff --git a/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-tebd-evolver.mdx b/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-tebd-evolver.mdx index fc8eff6ac9f..ebf748494a9 100644 --- a/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-tebd-evolver.mdx +++ b/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-tebd-evolver.mdx @@ -9,11 +9,11 @@ python_api_name: qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver # TEBDEvolver <Class id="qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mpf/tree/stable/0.3/qiskit_addon_mpf/backends/tenpy_tebd/evolver.py#L29-L182" signature="TEBDEvolver(*args, dt=0.1, **kwargs)" modifiers="class"> - Bases: [`TEBDEngine`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.algorithms.tebd.TEBDEngine.html#tenpy.algorithms.tebd.TEBDEngine "(in TeNPy v1.0.5)"), [`Evolver`](backends#evolver "qiskit_addon_mpf.backends.interface.Evolver") + Bases: [`TEBDEngine`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.algorithms.tebd.TEBDEngine.html#tenpy.algorithms.tebd.TEBDEngine "(in TeNPy v1.0.5)"), [`Evolver`](/docs/api/qiskit-addon-mpf/backends#evolver "qiskit_addon_mpf.backends.interface.Evolver") A TEBD algorithm for evolving an internal MPO. - As discussed in more detail in [`tenpy_tebd`](backends-tenpy-tebd#module-qiskit_addon_mpf.backends.tenpy_tebd "qiskit_addon_mpf.backends.tenpy_tebd"), this extension of TeNPy’s existing [`TEBDEngine`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.algorithms.tebd.TEBDEngine.html#tenpy.algorithms.tebd.TEBDEngine "(in TeNPy v1.0.5)") implementation time-evolves an internal matrix product operator (MPO) rather than the conventional matrix product state (MPS). + As discussed in more detail in [`tenpy_tebd`](/docs/api/qiskit-addon-mpf/backends-tenpy-tebd#module-qiskit_addon_mpf.backends.tenpy_tebd "qiskit_addon_mpf.backends.tenpy_tebd"), this extension of TeNPy’s existing [`TEBDEngine`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.algorithms.tebd.TEBDEngine.html#tenpy.algorithms.tebd.TEBDEngine "(in TeNPy v1.0.5)") implementation time-evolves an internal matrix product operator (MPO) rather than the conventional matrix product state (MPS). More concretely, the internal object is expected to be an [`MPOState`](backends-tenpy-tebd-mpo-state "qiskit_addon_mpf.backends.tenpy_tebd.MPOState"). diff --git a/docs/api/qiskit-addon-mpf/backends-tenpy-tebd.mdx b/docs/api/qiskit-addon-mpf/backends-tenpy-tebd.mdx index c70393df874..c97b8296d23 100644 --- a/docs/api/qiskit-addon-mpf/backends-tenpy-tebd.mdx +++ b/docs/api/qiskit-addon-mpf/backends-tenpy-tebd.mdx @@ -28,11 +28,11 @@ A [`tenpy`](https://tenpy.readthedocs.io/en/latest/main.html#module-tenpy "(in T ``` </Admonition> -| | | -| ------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------- | -| [`TEBDEvolver`](backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") | A TEBD algorithm for evolving an internal MPO. | -| [`MPOState`](backends-tenpy-tebd-mpo-state "qiskit_addon_mpf.backends.tenpy_tebd.MPOState") | A mediator class to make TeNPy's MPO match the [`State`](backends#state "qiskit_addon_mpf.backends.State") interface. | -| [`MPS_neel_state`](backends-tenpy-tebd-mps-neel-state "qiskit_addon_mpf.backends.tenpy_tebd.MPS_neel_state") | Constructs the Néel state as an MPS. | +| | | +| --------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- | +| [`TEBDEvolver`](/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") | A TEBD algorithm for evolving an internal MPO. | +| [`MPOState`](/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-mpo-state "qiskit_addon_mpf.backends.tenpy_tebd.MPOState") | A mediator class to make TeNPy's MPO match the [`State`](backends#state "qiskit_addon_mpf.backends.State") interface. | +| [`MPS_neel_state`](/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-mps-neel-state "qiskit_addon_mpf.backends.tenpy_tebd.MPS_neel_state") | Constructs the Néel state as an MPS. | ## Underlying method @@ -43,7 +43,7 @@ The classes provided by this module serve two purposes: 1. Connecting [`tenpy`](https://tenpy.readthedocs.io/en/latest/main.html#module-tenpy "(in TeNPy v1.0.5)")’s implementation to the interface set out by [`qiskit_addon_mpf.backends`](backends#module-qiskit_addon_mpf.backends "qiskit_addon_mpf.backends"). 2. Extending [`tenpy`](https://tenpy.readthedocs.io/en/latest/main.html#module-tenpy "(in TeNPy v1.0.5)")’s TEBD implementation to handle an internal MPO (rather than MPS) state (see also [`State`](backends#state "qiskit_addon_mpf.backends.State") for more details). -In the simplest sense, this module provides a straight-forward extension of the TEBD algorithm to evolve an internal MPO state. As such, if you wish to use this backend for your dynamic MPF algorithm, you must encode the Hamiltonian that you wish to time-evolve, in a [`tenpy`](https://tenpy.readthedocs.io/en/latest/main.html#module-tenpy "(in TeNPy v1.0.5)")-native form. To be more concrete, the [`TEBDEvolver`](backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") class (which is a subclass of [`tenpy.algorithms.tebd.TEBDEngine`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.algorithms.tebd.TEBDEngine.html#tenpy.algorithms.tebd.TEBDEngine "(in TeNPy v1.0.5)")) works with a Hamiltonian in the form of a [`Model`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.models.model.Model.html#tenpy.models.model.Model "(in TeNPy v1.0.5)"). TeNPy provides a number of convenience methods for constructing such Hamiltonians in its [`tenpy.models`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.models.html#module-tenpy.models "(in TeNPy v1.0.5)") module. If none of those fulfill your needs, you can consider using the [`LayerModel`](backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel") class which implements some conversion methods from Qiskit-native objects. +In the simplest sense, this module provides a straight-forward extension of the TEBD algorithm to evolve an internal MPO state. As such, if you wish to use this backend for your dynamic MPF algorithm, you must encode the Hamiltonian that you wish to time-evolve, in a [`tenpy`](https://tenpy.readthedocs.io/en/latest/main.html#module-tenpy "(in TeNPy v1.0.5)")-native form. To be more concrete, the [`TEBDEvolver`](/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") class (which is a subclass of [`tenpy.algorithms.tebd.TEBDEngine`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.algorithms.tebd.TEBDEngine.html#tenpy.algorithms.tebd.TEBDEngine "(in TeNPy v1.0.5)")) works with a Hamiltonian in the form of a [`Model`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.models.model.Model.html#tenpy.models.model.Model "(in TeNPy v1.0.5)"). TeNPy provides a number of convenience methods for constructing such Hamiltonians in its [`tenpy.models`](https://tenpy.readthedocs.io/en/latest/reference/tenpy.models.html#module-tenpy.models "(in TeNPy v1.0.5)") module. If none of those fulfill your needs, you can consider using the [`LayerModel`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layer-model "qiskit_addon_mpf.backends.tenpy_layers.LayerModel") class which implements some conversion methods from Qiskit-native objects. ## Code example @@ -73,7 +73,7 @@ Next, we can create the `identity_factory` which has to match the [`IdentityStat >>> identity_factory = partial(MPOState.initialize_from_lattice, hamil.lat), ``` -We can now construct the [`ExactEvolverFactory`](dynamic#exactevolverfactory "qiskit_addon_mpf.dynamic.ExactEvolverFactory") and [`ApproxEvolverFactory`](dynamic#approxevolverfactory "qiskit_addon_mpf.dynamic.ApproxEvolverFactory") time-evolution instance factories. To do so, we can simply bind the pre-defined values of the [`TEBDEvolver`](backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") initializer, reducing it to the correct interface as expected by the respective function protocols. +We can now construct the [`ExactEvolverFactory`](dynamic#exactevolverfactory "qiskit_addon_mpf.dynamic.ExactEvolverFactory") and [`ApproxEvolverFactory`](dynamic#approxevolverfactory "qiskit_addon_mpf.dynamic.ApproxEvolverFactory") time-evolution instance factories. To do so, we can simply bind the pre-defined values of the [`TEBDEvolver`](/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-tebd-evolver "qiskit_addon_mpf.backends.tenpy_tebd.TEBDEvolver") initializer, reducing it to the correct interface as expected by the respective function protocols. ```python >>> from qiskit_addon_mpf.backends.tenpy_tebd import TEBDEvolver diff --git a/docs/api/qiskit-addon-mpf/release-notes.mdx b/docs/api/qiskit-addon-mpf/release-notes.mdx index 549395975f5..0010177e33f 100644 --- a/docs/api/qiskit-addon-mpf/release-notes.mdx +++ b/docs/api/qiskit-addon-mpf/release-notes.mdx @@ -20,9 +20,9 @@ in_page_toc_max_heading_level: 2 ### New Features -* TeNPy can disable `Sz` conservation. Previously, the [`initialize_from_lattice()`](backends-tenpy-tebd-mpo-state#initialize_from_lattice "qiskit_addon_mpf.backends.tenpy_tebd.MPOState.initialize_from_lattice") method did not support this, but a new keyword argument `conserve` has been added which allows the disabling of `Sz` conservation. The default remains to be `True`. +* TeNPy can disable `Sz` conservation. Previously, the [`initialize_from_lattice()`](/docs/api/qiskit-addon-mpf/backends-tenpy-tebd-mpo-state#initialize_from_lattice "qiskit_addon_mpf.backends.tenpy_tebd.MPOState.initialize_from_lattice") method did not support this, but a new keyword argument `conserve` has been added which allows the disabling of `Sz` conservation. The default remains to be `True`. -* Guards against unexpected behavior when `N_steps != 1` in [`qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver.evolve()`](backends-tenpy-layers-layerwise-evolver#evolve "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver.evolve"). +* Guards against unexpected behavior when `N_steps != 1` in [`qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver.evolve()`](/docs/api/qiskit-addon-mpf/backends-tenpy-layers-layerwise-evolver#evolve "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver.evolve"). <span id="release-notes-0-3-0-upgrade-notes" /> @@ -32,7 +32,7 @@ in_page_toc_max_heading_level: 2 * This package is now compatible with Qiskit SDK 2.0. -* The `keep_only_odd` attribute of [`quimb_layers.LayerModel`](backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel") has been removed. The internal workings have been refactored to ensure that data reported by its `terms` attribute (which is inherited from the base class) is already taking the `keep_only_odd` argument of the [`quimb_layers.LayerModel.from_quantum_circuit()`](backends-quimb-layers-layer-model#from_quantum_circuit "qiskit_addon_mpf.backends.quimb_layers.LayerModel.from_quantum_circuit") constructor method. +* The `keep_only_odd` attribute of [`quimb_layers.LayerModel`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layer-model "qiskit_addon_mpf.backends.quimb_layers.LayerModel") has been removed. The internal workings have been refactored to ensure that data reported by its `terms` attribute (which is inherited from the base class) is already taking the `keep_only_odd` argument of the [`quimb_layers.LayerModel.from_quantum_circuit()`](/docs/api/qiskit-addon-mpf/backends-quimb-layers-layer-model#from_quantum_circuit "qiskit_addon_mpf.backends.quimb_layers.LayerModel.from_quantum_circuit") constructor method. * The `scaling_factor` keyword argument of the `from_quantum_circuit` constructor methods has been removed. It is not actually needed and was merely adding an additional (confusing) re-scaling. @@ -40,7 +40,7 @@ in_page_toc_max_heading_level: 2 ### Bug Fixes -* Fixes the [`DynamicMPF.evolve()`](dynamic#evolve "qiskit_addon_mpf.dynamic.DynamicMPF.evolve") method when providing `time` values with numerical inaccuracies (e.g. `time=1.00000001`). Previously this could cause the LHS and RHS time evolvers to advance too far. The new default accuracy is 8 decimal places, but it may be configured via the [`DynamicMPF.TIME_DECIMALS`](dynamic#time_decimals "qiskit_addon_mpf.dynamic.DynamicMPF.TIME_DECIMALS") value. +* Fixes the [`DynamicMPF.evolve()`](/docs/api/qiskit-addon-mpf/dynamic#evolve "qiskit_addon_mpf.dynamic.DynamicMPF.evolve") method when providing `time` values with numerical inaccuracies (e.g. `time=1.00000001`). Previously this could cause the LHS and RHS time evolvers to advance too far. The new default accuracy is 8 decimal places, but it may be configured via the [`DynamicMPF.TIME_DECIMALS`](/docs/api/qiskit-addon-mpf/dynamic#time_decimals "qiskit_addon_mpf.dynamic.DynamicMPF.TIME_DECIMALS") value. <span id="release-notes-0-2-0" /> @@ -54,7 +54,7 @@ in_page_toc_max_heading_level: 2 ### New Features -* Adds the ability to compute dynamic (i.e. time-dependent) MPF coefficients. For more details, refer to [`qiskit_addon_mpf.dynamic`](dynamic#module-qiskit_addon_mpf.dynamic "qiskit_addon_mpf.dynamic"). +* Adds the ability to compute dynamic (i.e. time-dependent) MPF coefficients. For more details, refer to [`qiskit_addon_mpf.dynamic`](/docs/api/qiskit-addon-mpf/dynamic#module-qiskit_addon_mpf.dynamic "qiskit_addon_mpf.dynamic"). <span id="release-notes-0-2-0-upgrade-notes" /> diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index 80d5a1a42e5..525b179c196 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -16,7 +16,7 @@ These capabilities specialize in mapping domain problems into quantum operators <Card title="Optimization mapper" description="Model optimization problems and map them into representations that can be understood by a quantum computer." - href="https://qiskit.github.io/qiskit-addon-opt-mapper/" + href="/docs/addons/qiskit-addon-mpf" analyticsName="Documentation page: Optimization mapper" linkText="Browse documentation" /> diff --git a/public/docs/api/qiskit-addon-mpf/objects.inv b/public/docs/api/qiskit-addon-mpf/objects.inv index 7eff45d1f48fe82e42a2cccceb1bc7e63eb9fef9..ebdb5d2a92025576bf8ae3a37e04502136359cec 100644 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zcaO}$nHa22US-B@B^+<UB^`=HR+l`k@5Mc-q*p_k#NH#F4Ur}2%CNhoYrp%0!T<XQ z^Hj+1?meU7tiRZvdbX8wKaA?Ntjd6SjKldeZKOnZLp7sTchG@ww9^8*{{<B>LbkD| zXq}R}Ue)f^UaZVA-=TJ6ENzkli6JVA7oA!{z9+>W?gxWv>aj)ra5%4xT}HntFe>_9 eF5b42fSmSmt5r^3;=3-$#Qin4rdI!Uapixwb5HjG literal 0 HcmV?d00001 diff --git a/scripts/config/cspell/dictionaries/people.txt b/scripts/config/cspell/dictionaries/people.txt index 531e5727b26..2d809fa5305 100644 --- a/scripts/config/cspell/dictionaries/people.txt +++ b/scripts/config/cspell/dictionaries/people.txt @@ -332,4 +332,5 @@ Smithline Greenberger Gaolis Bharti -Minkowski \ No newline at end of file +Minkowski +Carrera \ No newline at end of file diff --git a/scripts/config/cspell/dictionaries/qiskit.txt b/scripts/config/cspell/dictionaries/qiskit.txt index 03b6141f73e..33c47509008 100644 --- a/scripts/config/cspell/dictionaries/qiskit.txt +++ b/scripts/config/cspell/dictionaries/qiskit.txt @@ -447,4 +447,6 @@ tomographically braket mdash circo -backprop \ No newline at end of file +backprop +ncols +CLARABEL \ No newline at end of file diff --git a/scripts/js/lib/api/specialCaseResults.ts b/scripts/js/lib/api/specialCaseResults.ts index f9c704df501..41ad208bf25 100644 --- a/scripts/js/lib/api/specialCaseResults.ts +++ b/scripts/js/lib/api/specialCaseResults.ts @@ -18,7 +18,7 @@ export function transformSpecialCaseUrl(url: string): string { // We use `-` rather than `_` as our delimiter. .replace(/(?<=^|\/)release_notes(?=\/|#|$)/g, "release-notes") .replace(/(?<=^|\/)how_tos(?=\/|#|$)/g, "how-tos") - .replace(/(?<=^|\/)explanations(?=\/|#|$)/g, "explanation") + .replace(/(?<=^|\/)explanation(?=\/|#|$)/g, "explanations") .replace(/(?<=^|\/)terra(?=#|$)/g, "index") .replace(/(?<=^|\/)ibm-runtime(?=#|$)/g, "index") ); diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index ce5c941efde..c72892720b4 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -228,6 +228,12 @@ const ALWAYS_IGNORED_URLS__SHOULD_FIX: string[] = [ // from qiskit-addon-utils "/docs/addons/samplomatic/guides/samplex-io#qubit-ordering-convention", + + // from qiskit-addon-mpf + "/api/qiskit/qiskit.quantum_info.SparsePauliOp", + "/api/qiskit/qiskit.transpiler.CouplingMap", + "/api/qiskit/qiskit.primitives.StatevectorEstimator", + "/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2" ]; export const ALWAYS_IGNORED_URLS = new Set([ From 9e1d3385636ccea8ffc23089641e9d23db0b715c Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 14 May 2026 19:31:40 -0400 Subject: [PATCH 053/104] fix link --- docs/guides/addons.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index 525b179c196..c03515da1eb 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -16,7 +16,7 @@ These capabilities specialize in mapping domain problems into quantum operators <Card title="Optimization mapper" description="Model optimization problems and map them into representations that can be understood by a quantum computer." - href="/docs/addons/qiskit-addon-mpf" + href="https://qiskit.github.io/qiskit-addon-opt-mapper/" analyticsName="Documentation page: Optimization mapper" linkText="Browse documentation" /> @@ -40,7 +40,7 @@ These capabilities specialize in mapping domain problems into quantum operators <Card title="Multi-product formulas" description="Reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions." - href="https://qiskit.github.io/qiskit-addon-mpf/" + href="/docs/addons/qiskit-addon-mpf/" analyticsName="Documentation page: MPF" linkText="Browse documentation" /> From 120a98f96c3ccdd43c2628bef73aecb41fccfe03 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 15 May 2026 13:51:27 -0400 Subject: [PATCH 054/104] adds sqd --- docs/addons/qiskit-addon-sqd/_toc.json | 84 +++ ...ic-excitations-to-configuration-pool.ipynb | 206 +++++++ .../how-tos/benchmark-pauli-projection.ipynb | 482 ++++++++++++++++ .../how-tos/choose-subspace-dimension.ipynb | 230 ++++++++ .../addons/qiskit-addon-sqd/how-tos/index.mdx | 37 ++ .../how-tos/integrate-dice-solver.ipynb | 146 +++++ ...uli-operators-onto-hilbert-subspaces.ipynb | 304 ++++++++++ .../how-tos/select-open-closed-shell.ipynb | 542 ++++++++++++++++++ ...use-oo-to-optimize-hamiltonian-basis.ipynb | 395 +++++++++++++ docs/addons/qiskit-addon-sqd/index.mdx | 104 ++++ docs/addons/qiskit-addon-sqd/install.mdx | 63 ++ .../tutorials/01-chemistry-hamiltonian.ipynb | 542 ++++++++++++++++++ .../02-fermionic-lattice-hamiltonian.ipynb | 525 +++++++++++++++++ .../qiskit-addon-sqd/tutorials/index.mdx | 17 + docs/api/qiskit-addon-sqd/_package.json | 2 +- docs/api/qiskit-addon-sqd/_toc.json | 4 +- docs/api/qiskit-addon-sqd/release-notes.mdx | 50 +- public/docs/api/qiskit-addon-sqd/objects.inv | Bin 2801 -> 2804 bytes .../extracted-outputs/6b961c81-0.avif | Bin 0 -> 5250 bytes .../extracted-outputs/9abb110f-0.avif | Bin 0 -> 7731 bytes ...affd888-e89c-4aa9-8bae-4d1bb723b35e-0.avif | Bin 0 -> 10775 bytes ...w_tos_benchmark_pauli_projection_12_0.avif | Bin 0 -> 5250 bytes ...how_tos_choose_subspace_dimension_8_0.avif | Bin 0 -> 10775 bytes ...affd888-e89c-4aa9-8bae-4d1bb723b35e-1.avif | Bin 0 -> 10669 bytes ...1976322-31da-437c-b660-a97b122669c0-1.avif | Bin 0 -> 9055 bytes ...9d40efe-145a-48d7-8db1-473656d2df92-0.avif | Bin 0 -> 11911 bytes ...fcd22a4-d2ea-4b1d-beaa-f29156d001ab-0.avif | Bin 0 -> 5121 bytes ...7d2f73c-42f9-4111-a724-ce435a5f7a23-0.avif | Bin 0 -> 21895 bytes ...torials_01_chemistry_hamiltonian_19_1.avif | Bin 0 -> 10669 bytes ...02_fermionic_lattice_hamiltonian_22_1.avif | Bin 0 -> 9055 bytes scripts/config/cspell/cSpell.json | 2 +- scripts/config/cspell/dictionaries/qiskit.txt | 10 +- scripts/js/lib/api/updateLinks.ts | 2 +- scripts/js/lib/links/ignores.ts | 10 +- 34 files changed, 3726 insertions(+), 31 deletions(-) create mode 100644 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00000000000..748d1e7ded0 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/_toc.json @@ -0,0 +1,84 @@ +{ + "parentUrl": "/docs/guides/addons", + "parentLabel": "Documentation", + "title": "Sample-based quantum diagonalization (SQD) 0.12.0", + "collapsed": true, + "children": [ + { + "title": "", + "children": [ + { + "title": "Home", + "url": "/docs/addons/qiskit-addon-sqd" + }, + { + "title": "Installation instructions", + "url": "/docs/addons/qiskit-addon-sqd/install" + }, + { + "title": "Guides", + "children": [ + { + "title": "Add fermionic transitions to the pool of configurations", + "url": "/docs/addons/qiskit-addon-sqd/how-tos/add-fermionic-excitations-to-configuration-pool" + }, + { + "title": "Benchmark Pauli operator projection", + "url": "/docs/addons/qiskit-addon-sqd/how-tos/benchmark-pauli-projection" + }, + { + "title": "Bounding the subspace dimension", + "url": "/docs/addons/qiskit-addon-sqd/how-tos/choose-subspace-dimension" + }, + { + "title": "Scale SQD chemistry workflows with Dice solver", + "url": "/docs/addons/qiskit-addon-sqd/how-tos/integrate-dice-solver" + }, + { + "title": "Project Pauli operators onto Hilbert subspaces", + "url": "/docs/addons/qiskit-addon-sqd/how-tos/project-pauli-operators-onto-hilbert-subspaces" + }, + { + "title": "Understand open-shell vs closed-shell options and its effect in the subspace construction", + "url": "/docs/addons/qiskit-addon-sqd/how-tos/select-open-closed-shell" + }, + { + "title": "Optimize Hamiltonian basis with orbital optimization", + "url": "/docs/addons/qiskit-addon-sqd/how-tos/use-oo-to-optimize-hamiltonian-basis" + } + ], + "url": "/docs/addons/qiskit-addon-sqd/how-tos/index" + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-sqd" + } + ], + "collapsible": false + }, + { + "title": "Tutorials", + "collapsible": false, + "children": [ + { + "title": "Improving energy estimation of a chemistry Hamiltonian with SQD", + "url": "/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian" + }, + { + "title": "Improving energy estimation of a Fermionic lattice model with SQD", + "url": "/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian" + } + ] + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Sample-based quantum diagonalization (SQD) API reference", + "url": "/docs/api/qiskit-addon-sqd" + } + ] + } + ] +} diff --git a/docs/addons/qiskit-addon-sqd/how-tos/add-fermionic-excitations-to-configuration-pool.ipynb b/docs/addons/qiskit-addon-sqd/how-tos/add-fermionic-excitations-to-configuration-pool.ipynb new file mode 100644 index 00000000000..aea28db72f8 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how-tos/add-fermionic-excitations-to-configuration-pool.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Add fermionic transitions to the pool of configurations\"\n", + "description: \"Add fermionic transitions to the pool of configurations for the latest version of Sample-based quantum diagonalization (SQD)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", + "metadata": {}, + "source": [ + "# Add fermionic transitions to the pool of configurations\n", + "\n", + "Here we demonstrate the functionalities to augment the pool of electronic\n", + "configurations obtained by the action of transition operators on each electronic\n", + "configuration.\n", + "\n", + "We demonstrate how to add single-electron hops of the type:\n", + "$$\n", + "c^\\dagger_{p\\sigma} c_{q\\sigma} |\\textbf{x} \\rangle\n", + "$$\n", + "for $p, q = 1, ..., N_\\textrm{orb}$ and $\\sigma \\in \\{ \\uparrow, \\downarrow\\}$,\n", + "and for all $|\\textbf{x} \\rangle$ in the batch of electronic configurations." + ] + }, + { + "cell_type": "markdown", + "id": "0a7e9fcd", + "metadata": {}, + "source": [ + "Let's begin by generating a batch of random electronic configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e9506e0b-ed64-48bb-a97a-ef851b604af1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "n_qubits = 8\n", + "n_orb = n_qubits // 2\n", + "\n", + "rand_seed = 22\n", + "np.random.seed(rand_seed)\n", + "\n", + "\n", + "# Generate some random bitstrings for testing\n", + "def random_bitstrings(n_samples, n_qubits):\n", + " return np.round(np.random.rand(n_samples, n_qubits)).astype(\"int\").astype(\"bool\")\n", + "\n", + "\n", + "bitstring_matrix = random_bitstrings(100, n_qubits)" + ] + }, + { + "cell_type": "markdown", + "id": "4155599f", + "metadata": {}, + "source": [ + "The excitation operators are specified inside a numpy array whose length is\n", + "equal to the number of fermionic nodes (or qubits). Each element of the array\n", + "must be a string that can take values:\n", + "\n", + "- ``'I'``: Identity\n", + "- ``'+'``: Creation operator\n", + "- ``'-'``: Annihilation operator\n", + "\n", + "Let's generate all possible single-electron transitions (amongst same spin\n", + "species)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "389284f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[['I' 'I' 'I' 'I' 'I' 'I' 'I' 'I']\n", + " ['+' '-' 'I' 'I' 'I' 'I' 'I' 'I']\n", + " ['-' '+' 'I' 'I' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' '+' '-' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' '-' '+' 'I' 'I']\n", + " ['+' 'I' '-' 'I' 'I' 'I' 'I' 'I']\n", + " ['-' 'I' '+' 'I' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' '+' 'I' '-' 'I']\n", + " ['I' 'I' 'I' 'I' '-' 'I' '+' 'I']\n", + " ['+' 'I' 'I' '-' 'I' 'I' 'I' 'I']\n", + " ['-' 'I' 'I' '+' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' '+' 'I' 'I' '-']\n", + " ['I' 'I' 'I' 'I' '-' 'I' 'I' '+']\n", + " ['I' '+' '-' 'I' 'I' 'I' 'I' 'I']\n", + " ['I' '-' '+' 'I' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' 'I' '+' '-' 'I']\n", + " ['I' 'I' 'I' 'I' 'I' '-' '+' 'I']\n", + " ['I' '+' 'I' '-' 'I' 'I' 'I' 'I']\n", + " ['I' '-' 'I' '+' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' 'I' '+' 'I' '-']\n", + " ['I' 'I' 'I' 'I' 'I' '-' 'I' '+']\n", + " ['I' 'I' '+' '-' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' '-' '+' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' 'I' 'I' '+' '-']\n", + " ['I' 'I' 'I' 'I' 'I' 'I' '-' '+']]\n" + ] + } + ], + "source": [ + "transitions_single = np.array(\n", + " [[\"I\" for i in range(2 * n_orb)] for j in range(4 * (n_orb**2 - n_orb) // 2 + 1)]\n", + ")\n", + "count = 1\n", + "for i in range(n_orb):\n", + " for j in range(i + 1, n_orb):\n", + " # spin up\n", + " transitions_single[count, i] = \"+\"\n", + " transitions_single[count, j] = \"-\"\n", + " count += 1\n", + " transitions_single[count, i] = \"-\"\n", + " transitions_single[count, j] = \"+\"\n", + " count += 1\n", + "\n", + " # spin down\n", + " transitions_single[count, i + n_orb] = \"+\"\n", + " transitions_single[count, j + n_orb] = \"-\"\n", + " count += 1\n", + " transitions_single[count, i + n_orb] = \"-\"\n", + " transitions_single[count, j + n_orb] = \"+\"\n", + " count += 1\n", + "\n", + "print(transitions_single)" + ] + }, + { + "cell_type": "markdown", + "id": "c15c5b3f", + "metadata": {}, + "source": [ + "Let's now apply the transition operators to the configurations in ``bitstring_matrix``" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6a44f358", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(723, 8)\n", + "[[False False False ... False False True]\n", + " [False True False ... True False False]\n", + " [ True True True ... True False True]\n", + " ...\n", + " [ True False True ... False False True]\n", + " [ True True True ... True False True]\n", + " [False False False ... False False True]]\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.fermion import enlarge_batch_from_transitions\n", + "\n", + "bitstring_matrix_aug = enlarge_batch_from_transitions(bitstring_matrix, transitions_single)\n", + "\n", + "print(bitstring_matrix_aug.shape)\n", + "print(bitstring_matrix_aug)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/how-tos/benchmark-pauli-projection.ipynb b/docs/addons/qiskit-addon-sqd/how-tos/benchmark-pauli-projection.ipynb new file mode 100644 index 00000000000..1627b0bd653 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how-tos/benchmark-pauli-projection.ipynb @@ -0,0 +1,482 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Benchmark Pauli operator projection\"\n", + "description: \"Benchmark Pauli operator projection for the latest version of Sample-based quantum diagonalization (SQD)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "55900f6c-2fb6-4d2c-8745-29bad8b66d9f", + "metadata": {}, + "source": [ + "# Benchmark Pauli operator projection" + ] + }, + { + "cell_type": "markdown", + "id": "b5caa5b4", + "metadata": {}, + "source": [ + "### Pauli string action on a computational basis state\n", + "\n", + "The action of a Pauli string on a computational basis state is rather trivial, and just a single computational basis state itself. This is a direct consequence of the structure of Pauli matrices, which only have a single nonzero element on each of their rows. Consequently, their action on a qubit is:\n", + "\n", + "-------------------------------------------\n", + "\n", + "$$\n", + "\\sigma_x |0 \\rangle = |1 \\rangle\n", + "$$\n", + "\n", + "$$\n", + "\\sigma_x |1 \\rangle = |0 \\rangle\n", + "$$\n", + "\n", + "-------------------------------------------\n", + "\n", + "$$\n", + "\\sigma_y |0 \\rangle = i|1 \\rangle\n", + "$$\n", + "\n", + "$$\n", + "\\sigma_y |1 \\rangle = -i|0 \\rangle\n", + "$$\n", + "\n", + "-------------------------------------------\n", + "\n", + "$$\n", + "\\sigma_z |0 \\rangle = |0 \\rangle\n", + "$$\n", + "\n", + "$$\n", + "\\sigma_z |1 \\rangle = -|1 \\rangle\n", + "$$\n", + "\n", + "-------------------------------------------\n", + "\n", + "$$\n", + "I |0 \\rangle = |0 \\rangle\n", + "$$\n", + "\n", + "$$\n", + "I |1 \\rangle = |1 \\rangle\n", + "$$\n", + "\n", + "-------------------------------------------\n", + "\n", + "Each bit on the bitstring labeling the computational basis will be labeled by $x \\in \\{0, 1 \\}$. In order to keep the implementation at light as possible, we will\n", + "represent the bitstrings with `bool` variables: $0\\rightarrow \\textrm{False}$ and\n", + "$1\\rightarrow \\textrm{True}$.\n", + "\n", + "To represent the action of each Pauli operator in a computational basis state\n", + "we will assign three variables to it: `diag`, `sign`, `imag`.\n", + "\n", + "- `diag` labels whether the operator is diagonal:\n", + "\n", + " - $\\textrm{diag}(I) = \\textrm{True}$\n", + " - $\\textrm{diag}(\\sigma_x) = \\textrm{False}$\n", + " - $\\textrm{diag}(\\sigma_y) = \\textrm{False}$\n", + " - $\\textrm{diag}(\\sigma_z) = \\textrm{True}$\n", + "\n", + "- `sign` Identifies if there is a sign change in the matrix element connected\n", + "to either 0 or 1:\n", + "\n", + " - $\\textrm{sign}(I) = \\textrm{False}$\n", + " - $\\textrm{sign}(\\sigma_x) = \\textrm{False}$\n", + " - $\\textrm{sign}(\\sigma_y) = \\textrm{True}$\n", + " - $\\textrm{sign}(\\sigma_z) = \\textrm{True}$\n", + "\n", + "- `imag` Identifies if there is a complex component to the matrix element:\n", + "\n", + " - $\\textrm{imag}(I) = \\textrm{False}$\n", + " - $\\textrm{imag}(\\sigma_x) = \\textrm{False}$\n", + " - $\\textrm{imag}(\\sigma_y) = \\textrm{True}$\n", + " - $\\textrm{imag}(\\sigma_z) = \\textrm{False}$\n", + "\n", + "Let's label an arbitrary Pauli operator as $\\sigma \\in \\{ I, \\sigma_x, \\sigma_y\n", + "\\sigma_z\\}$. The action of the Pauli operator on a computational basis state\n", + "can then be represented by the logic operation:\n", + "$$\n", + "\\sigma |x \\rangle = |x == \\textrm{diag}(\\sigma) \\rangle (-1)^{x\\textrm{ and sign}(\\sigma)}\n", + "(i)^{\\textrm{imag}(\\sigma)}.\n", + "$$\n", + "The same is straightforwardly generalized to arbitrary number of qubits." + ] + }, + { + "cell_type": "markdown", + "id": "a8fd7e11", + "metadata": {}, + "source": [ + "Let's check that this works:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "dcb15308", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------\n", + "I\n", + "|False> --> |False> ME:(1+0j)\n", + "|True> --> |True> ME:(1+0j)\n", + "-------------------\n", + "SX\n", + "|False> --> |True> ME:(1+0j)\n", + "|True> --> |False> ME:(1+0j)\n", + "-------------------\n", + "SZ\n", + "|False> --> |False> ME:(1+0j)\n", + "|True> --> |True> ME:(-1+0j)\n", + "-------------------\n", + "SY\n", + "|False> --> |True> ME:1j\n", + "|True> --> |False> ME:(-0-1j)\n" + ] + } + ], + "source": [ + "def connected_element_and_amplitude_bool(\n", + " x: bool, diag: bool, sign: bool, imag: bool\n", + ") -> tuple[bool, complex]:\n", + " \"\"\"\n", + " Finds the connected element to computational basis state |x> under\n", + " the action of the Pauli operator represented by (diag, sign, imag).\n", + "\n", + " Args:\n", + " x: Value of the bit, either True or False.\n", + " diag: Whether the Pauli operator is diagonal (I, Z)\n", + " sigma: Whether the Pauli operator's rows differ in sign (Y, Z)\n", + " imag: Whether the Pauli operator is purely imaginary (Y)\n", + "\n", + " Returns:\n", + " A length-2 tuple:\n", + " - The connected element to x, either False or True\n", + " - The matrix element\n", + " \"\"\"\n", + " return x == diag, (-1) ** (x and sign) * (1j) ** (imag)\n", + "\n", + "\n", + "sigma_indices = [0, 1, 2, 3]\n", + "sigma_string = [\"I\", \"SX\", \"SZ\", \"SY\"]\n", + "sigma_diag = [True, False, True, False]\n", + "sigma_sign = [False, False, True, True]\n", + "sigma_imag = [False, False, False, True]\n", + "qubit_values = [False, True]\n", + "\n", + "for xi in sigma_indices:\n", + " print(\"-------------------\")\n", + " print(sigma_string[xi])\n", + " for x in qubit_values:\n", + " x_p, matrix_element = connected_element_and_amplitude_bool(\n", + " x, sigma_diag[xi], sigma_sign[xi], sigma_imag[xi]\n", + " )\n", + " print(\"|\" + str(x) + \"> --> |\" + str(x_p) + \"> ME:\" + str(matrix_element))" + ] + }, + { + "cell_type": "markdown", + "id": "f40a3463", + "metadata": {}, + "source": [ + "Let's generate some large number of bitstrings (50 M) for a 40-qubit system" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "74c16c91-cc5c-46ce-aff8-17d0e71ac50f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of unique bitstrings: 49998839\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from qiskit_addon_sqd.qubit import sort_and_remove_duplicates\n", + "\n", + "rand_seed = 22\n", + "np.random.seed(rand_seed)\n", + "\n", + "# Generate some random bitstrings for testing\n", + "\n", + "\n", + "def random_bitstrings(n_samples, n_qubits):\n", + " return np.round(np.random.rand(n_samples, n_qubits)).astype(\"int\").astype(\"bool\")\n", + "\n", + "\n", + "n_qubits = 40\n", + "bts_matrix = random_bitstrings(50_000_000, n_qubits)\n", + "\n", + "# We need to sort the bitstrings and just keep the unique ones\n", + "# NOTE: It is essential for the projection code to have the bitstrings sorted!\n", + "bts_matrix = sort_and_remove_duplicates(bts_matrix).astype(\"bool\")\n", + "\n", + "# Final subspace dimension after getting rid of duplicated bitstrings\n", + "d = bts_matrix.shape[0]\n", + "\n", + "print(\"Total number of unique bitstrings: \" + str(d))" + ] + }, + { + "cell_type": "markdown", + "id": "184bf287", + "metadata": {}, + "source": [ + "### Benchmark SQD Pauli projection functions\n", + "\n", + "The Pauli string under consideration is $\\sigma_z \\otimes ... \\otimes \\sigma_z$.\n", + "\n", + "Different subspace dimensions are considered by just slicing the matrix of bitstrings. We time the subspace projection for the different subspace sizes." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8fe182bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0 took 0.201246s\n", + "Iteration 1 took 0.348222s\n", + "Iteration 2 took 0.576333s\n", + "Iteration 3 took 0.78356s\n", + "Iteration 4 took 1.016162s\n", + "Iteration 5 took 1.305325s\n", + "Iteration 6 took 1.392751s\n", + "Iteration 7 took 1.632433s\n", + "Iteration 8 took 1.826521s\n", + "Iteration 9 took 2.02903s\n", + "Iteration 10 took 2.297458s\n", + "Iteration 11 took 2.588042s\n", + "Iteration 12 took 2.738746s\n", + "Iteration 13 took 2.906144s\n", + "Iteration 14 took 3.148833s\n", + "Iteration 15 took 3.323253s\n", + "Iteration 16 took 3.664171s\n", + "Iteration 17 took 3.680663s\n", + "Iteration 18 took 4.008313s\n", + "Iteration 19 took 4.173532s\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "from qiskit.quantum_info import Pauli\n", + "from qiskit_addon_sqd.qubit import matrix_elements_from_pauli\n", + "\n", + "pauli = Pauli(\"Z\" * n_qubits)\n", + "\n", + "# Different subspace sizes to test\n", + "d_list = np.linspace(d / 1000, d, 20).astype(\"int\")\n", + "\n", + "# To store the walltime\n", + "time_array = np.zeros(20)\n", + "\n", + "for i in range(20):\n", + " int_bts_matrix = bts_matrix[: d_list[i], :]\n", + " time_1 = time.time()\n", + " _ = matrix_elements_from_pauli(int_bts_matrix, pauli)\n", + " time_array[i] = time.time() - time_1\n", + " print(f\"Iteration {i} took {round(time_array[i], 6)}s\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9abb110f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/how-tos/benchmark-pauli-projection/extracted-outputs/9abb110f-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Data for energies plot\n", + "x1 = d_list\n", + "y1 = time_array\n", + "\n", + "# Plot energies\n", + "plt.title(\"Runtime vs subspace dimension 40 qubits\")\n", + "plt.xlabel(\"Subspace dimension (millions)\")\n", + "plt.ylabel(\"Wall time [s]\")\n", + "plt.xticks([1e7, 2e7, 3e7, 4e7, 5e7], [str(i) for i in [10, 20, 30, 40, 50]])\n", + "plt.plot(x1, y1, marker=\".\", markersize=20)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8c486bc7", + "metadata": {}, + "source": [ + "Let's do the same for 60 qubits" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "359ed3f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of unique bitstrings: 50000000\n" + ] + } + ], + "source": [ + "n_qubits = 60\n", + "bts_matrix = random_bitstrings(50_000_000, n_qubits)\n", + "\n", + "# We need to sort the bitstrings and just keep the unique ones\n", + "bts_matrix = sort_and_remove_duplicates(bts_matrix).astype(\"bool\")\n", + "\n", + "# Final subspace dimension after getting rid of duplicated bitstrings\n", + "d = bts_matrix.shape[0]\n", + "\n", + "print(\"Total number of unique bitstrings: \" + str(d))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7bb0d8d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0 took 0.236567s\n", + "Iteration 1 took 0.424116s\n", + "Iteration 2 took 0.673399s\n", + "Iteration 3 took 0.905164s\n", + "Iteration 4 took 1.168936s\n", + "Iteration 5 took 1.454204s\n", + "Iteration 6 took 1.74778s\n", + "Iteration 7 took 1.920795s\n", + "Iteration 8 took 2.259994s\n", + "Iteration 9 took 2.550674s\n", + "Iteration 10 took 2.681287s\n", + "Iteration 11 took 3.04411s\n", + "Iteration 12 took 3.293262s\n", + "Iteration 13 took 3.471247s\n", + "Iteration 14 took 3.726639s\n", + "Iteration 15 took 4.072854s\n", + "Iteration 16 took 4.221037s\n", + "Iteration 17 took 4.498535s\n", + "Iteration 18 took 4.741108s\n", + "Iteration 19 took 5.159038s\n" + ] + } + ], + "source": [ + "pauli = Pauli(\"Z\" * n_qubits)\n", + "\n", + "# Different subspace sizes to test\n", + "d_list = np.linspace(d / 1000, d, 20).astype(\"int\")\n", + "\n", + "# It is better to do this once\n", + "row_array = np.arange(d)\n", + "\n", + "# To store the walltime\n", + "time_array = np.zeros(20)\n", + "\n", + "for i in range(20):\n", + " int_bts_matrix = bts_matrix[: d_list[i], :]\n", + " int_row_array = row_array[: d_list[i]]\n", + " time_1 = time.time()\n", + " _ = matrix_elements_from_pauli(int_bts_matrix, pauli)\n", + " time_array[i] = time.time() - time_1\n", + " print(f\"Iteration {i} took {round(time_array[i], 6)}s\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6b961c81", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/how-tos/benchmark-pauli-projection/extracted-outputs/6b961c81-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data for energies plot\n", + "x1 = d_list\n", + "y1 = time_array\n", + "\n", + "fig, axs = plt.subplots(1, 1, figsize=(6, 6))\n", + "\n", + "# Plot energies\n", + "axs.plot(x1, y1, marker=\".\", markersize=20)\n", + "axs.set_title(\"Runtime vs subspace dimension 60 qubits\")\n", + "axs.set_xlabel(\"Subspace dimension (millions)\")\n", + "plt.xticks([1e7, 2e7, 3e7, 4e7, 5e7], [str(i) for i in [10, 20, 30, 40, 50]])\n", + "axs.set_ylabel(\"Wall time [s]\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/how-tos/choose-subspace-dimension.ipynb b/docs/addons/qiskit-addon-sqd/how-tos/choose-subspace-dimension.ipynb new file mode 100644 index 00000000000..92ead16fbb8 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how-tos/choose-subspace-dimension.ipynb @@ -0,0 +1,230 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Bounding the subspace dimension\"\n", + "description: \"Bounding the subspace dimension for the latest version of Sample-based quantum diagonalization (SQD)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", + "metadata": {}, + "source": [ + "# Bounding the subspace dimension\n", + "\n", + "In this tutorial, we will show the effect of the subspace dimension in the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068).\n", + "\n", + "***A priori***, we do not know what is the correct subspace dimension to obtain a target level of accuracy. However, we do know that increasing the subspace dimension increases the accuracy of the method. Therefore, we can study the accuracy of the predictions as a function of the subspace dimension." + ] + }, + { + "cell_type": "markdown", + "id": "a6755afb-ca1e-4473-974b-ba89acc8abce", + "metadata": {}, + "source": [ + "Specify the molecule and its properties." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "converged SCF energy = -108.835236570775\n", + "CASCI E = -109.046671778080 E(CI) = -32.8155692383188 S^2 = 0.0000000\n" + ] + } + ], + "source": [ + "import warnings\n", + "\n", + "import pyscf\n", + "import pyscf.cc\n", + "import pyscf.mcscf\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "# Specify molecule properties\n", + "open_shell = False\n", + "spin_sq = 0\n", + "\n", + "# Build N2 molecule\n", + "mol = pyscf.gto.Mole()\n", + "mol.build(\n", + " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", + " basis=\"6-31g\",\n", + " symmetry=\"Dooh\",\n", + ")\n", + "\n", + "# Define active space\n", + "n_frozen = 2\n", + "active_space = range(n_frozen, mol.nao_nr())\n", + "\n", + "# Get molecular integrals\n", + "scf = pyscf.scf.RHF(mol).run()\n", + "num_orbitals = len(active_space)\n", + "n_electrons = int(sum(scf.mo_occ[active_space]))\n", + "num_elec_a = (n_electrons + mol.spin) // 2\n", + "num_elec_b = (n_electrons - mol.spin) // 2\n", + "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", + "mo = cas.sort_mo(active_space, base=0)\n", + "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", + "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", + "\n", + "# Compute exact energy\n", + "exact_energy = cas.run().e_tot" + ] + }, + { + "cell_type": "markdown", + "id": "c58e988c-a109-44cd-a975-9df43250c318", + "metadata": {}, + "source": [ + "Generate some random bitstrings to proxy QPU samples." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e9506e0b-ed64-48bb-a97a-ef851b604af1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit_addon_sqd.counts import generate_bit_array_uniform\n", + "\n", + "# Create a seed to control randomness throughout this workflow\n", + "rng = np.random.default_rng(24)\n", + "\n", + "# Generate random samples\n", + "bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng)" + ] + }, + { + "cell_type": "markdown", + "id": "82b062a3-d4ca-41e2-9b00-4a394f83cbdd", + "metadata": {}, + "source": [ + "Call SQD with increasing batch sizes." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e0847f28", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_sqd.fermion import diagonalize_fermionic_hamiltonian\n", + "\n", + "list_samples_per_batch = [50, 200, 400, 600]\n", + "\n", + "# SQD options\n", + "max_iterations = 5\n", + "\n", + "# Eigenstate solver options\n", + "num_batches = 10\n", + "max_davidson_cycles = 200\n", + "\n", + "energies = []\n", + "subspace_dimensions = []\n", + "\n", + "for samples_per_batch in list_samples_per_batch:\n", + " result = diagonalize_fermionic_hamiltonian(\n", + " hcore,\n", + " eri,\n", + " bit_array,\n", + " samples_per_batch=samples_per_batch,\n", + " norb=num_orbitals,\n", + " nelec=(num_elec_a, num_elec_b),\n", + " num_batches=num_batches,\n", + " max_iterations=max_iterations,\n", + " symmetrize_spin=True,\n", + " seed=rng,\n", + " )\n", + " energies.append(result.energy)\n", + " subspace_dimensions.append(np.prod(result.sci_state.amplitudes.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "9d78906b-4759-4506-9c69-85d4e67766b3", + "metadata": {}, + "source": [ + "This plot shows that increasing the subspace dimension leads to more accurate results." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "caffd888-e89c-4aa9-8bae-4d1bb723b35e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/how-tos/choose-subspace-dimension/extracted-outputs/caffd888-e89c-4aa9-8bae-4d1bb723b35e-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Data for energies plot\n", + "x1 = subspace_dimensions\n", + "y1 = np.array(energies) + nuclear_repulsion_energy\n", + "\n", + "fig, axs = plt.subplots(1, 1, figsize=(12, 6))\n", + "\n", + "# Plot energies\n", + "axs.plot(x1, y1, marker=\".\", markersize=20, label=\"Estimated\")\n", + "axs.set_xticks(x1)\n", + "axs.set_xticklabels(x1)\n", + "axs.axhline(y=exact_energy, color=\"red\", linestyle=\"--\", label=\"Exact\")\n", + "axs.set_title(\"Approximated Ground State Energy vs subspace dimension\")\n", + "axs.set_xlabel(\"Subspace dimension\")\n", + "axs.set_ylabel(\"Energy (Ha)\")\n", + "axs.legend()\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/how-tos/index.mdx b/docs/addons/qiskit-addon-sqd/how-tos/index.mdx new file mode 100644 index 00000000000..3c1ac25b421 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how-tos/index.mdx @@ -0,0 +1,37 @@ +--- +title: "How-To Guides" +description: "How-To Guides for the lastest version of Sample-based quantum diagonalization (SQD)" +--- + +# How-To Guides + +This page summarizes the available how-to guides. + +[![](/docs/images/addons/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](add-fermionic-excitations-to-configuration-pool) + +[Add fermionic transitions to the pool of configurations](add-fermionic-excitations-to-configuration-pool) + +[![](/docs/images/addons/qiskit-addon-sqd/how_tos_benchmark_pauli_projection_12_0.avif)](benchmark-pauli-projection) + +[Benchmark Pauli operator projection](benchmark-pauli-projection) + +[![](/docs/images/addons/qiskit-addon-sqd/how_tos_choose_subspace_dimension_8_0.avif)](choose-subspace-dimension) + +[Bounding the subspace dimension](choose-subspace-dimension) + +[![](/docs/images/addons/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](integrate-dice-solver) + +[Scale SQD chemistry workflows with Dice solver](integrate-dice-solver) + +[![](/docs/images/addons/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](project-pauli-operators-onto-hilbert-subspaces) + +[Project Pauli operators onto Hilbert subspaces](project-pauli-operators-onto-hilbert-subspaces) + +[![](/docs/images/addons/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](select-open-closed-shell) + +[Understand open-shell vs closed-shell options and its effect in the subspace construction](select-open-closed-shell) + +[![](/docs/images/addons/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg)](use-oo-to-optimize-hamiltonian-basis) + +[Optimize Hamiltonian basis with orbital optimization](use-oo-to-optimize-hamiltonian-basis) + diff --git a/docs/addons/qiskit-addon-sqd/how-tos/integrate-dice-solver.ipynb b/docs/addons/qiskit-addon-sqd/how-tos/integrate-dice-solver.ipynb new file mode 100644 index 00000000000..a66f66f1c26 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how-tos/integrate-dice-solver.ipynb @@ -0,0 +1,146 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Scale SQD chemistry workflows with Dice solver\"\n", + "description: \"Scale SQD chemistry workflows with Dice solver for the latest version of Sample-based quantum diagonalization (SQD)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "65adf860-4443-494f-ad90-5d8ea444ac4e", + "metadata": {}, + "source": [ + "# Scale SQD chemistry workflows with Dice solver\n", + "\n", + "For information on how to install and use ``qiskit-addon-dice-solver``, [visit the docs](/docs/addons/qiskit-addon-dice-solver/).\n", + "\n", + "For more details on the SQD code used in this example, check out [tutorial 1](/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "baf7d074-4443-4695-875e-65f7fb8cef25", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "converged SCF energy = -108.835236570774\n", + "CASCI E = -109.046671778080 E(CI) = -32.8155692383187 S^2 = 0.0000000\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pyscf\n", + "import pyscf.cc\n", + "import pyscf.mcscf\n", + "from qiskit_addon_dice_solver import solve_sci_batch\n", + "from qiskit_addon_sqd.counts import generate_bit_array_uniform\n", + "from qiskit_addon_sqd.fermion import diagonalize_fermionic_hamiltonian\n", + "\n", + "# Specify molecule properties\n", + "num_orbitals = 16\n", + "num_elec_a = num_elec_b = 5\n", + "spin_sq = 0\n", + "\n", + "# Build N2 molecule\n", + "mol = pyscf.gto.Mole()\n", + "mol.build(\n", + " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", + " basis=\"6-31g\",\n", + " symmetry=\"Dooh\",\n", + ")\n", + "\n", + "# Define active space\n", + "n_frozen = 2\n", + "active_space = range(n_frozen, mol.nao_nr())\n", + "\n", + "# Get molecular integrals\n", + "scf = pyscf.scf.RHF(mol).run()\n", + "num_orbitals = len(active_space)\n", + "n_electrons = int(sum(scf.mo_occ[active_space]))\n", + "num_elec_a = (n_electrons + mol.spin) // 2\n", + "num_elec_b = (n_electrons - mol.spin) // 2\n", + "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", + "mo = cas.sort_mo(active_space, base=0)\n", + "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", + "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", + "\n", + "# Compute exact energy\n", + "exact_energy = cas.run().e_tot\n", + "\n", + "# Create a seed to control randomness throughout this workflow\n", + "rng = np.random.default_rng(24)\n", + "\n", + "\n", + "# Generate random samples\n", + "bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng)\n", + "\n", + "# Run SQD\n", + "result = diagonalize_fermionic_hamiltonian(\n", + " hcore,\n", + " eri,\n", + " bit_array,\n", + " samples_per_batch=300,\n", + " norb=num_orbitals,\n", + " nelec=(num_elec_a, num_elec_b),\n", + " num_batches=5,\n", + " max_iterations=5,\n", + " sci_solver=solve_sci_batch,\n", + " symmetrize_spin=True,\n", + " seed=rng,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "99709a88-f9ec-4dbc-a561-e682f90aa4c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exact energy: -109.04667177808028\n", + "Estimated energy: -109.03402667558743\n" + ] + } + ], + "source": [ + "print(f\"Exact energy: {exact_energy}\")\n", + "print(f\"Estimated energy: {result.energy + nuclear_repulsion_energy}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/how-tos/project-pauli-operators-onto-hilbert-subspaces.ipynb b/docs/addons/qiskit-addon-sqd/how-tos/project-pauli-operators-onto-hilbert-subspaces.ipynb new file mode 100644 index 00000000000..1f95f501f3e --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how-tos/project-pauli-operators-onto-hilbert-subspaces.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Project Pauli operators onto Hilbert subspaces\"\n", + "description: \"Project Pauli operators onto Hilbert subspaces for the latest version of Sample-based quantum diagonalization (SQD)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "21a1d83f-183f-4da9-bb8c-71400120fd31", + "metadata": {}, + "source": [ + "# Project Pauli operators onto Hilbert subspaces" + ] + }, + { + "cell_type": "markdown", + "id": "c961d48a", + "metadata": {}, + "source": [ + "We show different ways of projecting a weighted linear combination of pauli strings\n", + "into a subspace defined by a subset of size $d$ computational basis states. The\n", + "projected operator is stored as a $d \\times d$ ``scipy.sparse.coo_matrix``.\n", + "\n", + "As an example we consider the Hamiltonian of the 1D Heisenberg model with periodic\n", + "boundary conditions and $L = 22$ spins:\n", + "$$\n", + "H = \\sum_{\\langle i, j \\rangle}\\left( \\sigma^x_i\\sigma^x_j + \\sigma^y_i\\sigma^y_j + \\sigma^z_i\\sigma^z_j \\right).\n", + "$$\n", + "\n", + "This package provides two tools to perform this projection:\n", + "\n", + "- ``qiskit_addon_sqd.qubit.matrix_elements_from_pauli()``: is a lower-level function\n", + "that returns the non-zero matrix elements of a Pauli string and the corresponding indices\n", + "of the non-zero elements.\n", + "\n", + "- ``qiskit_addon_sqd.qubit.project_operator_to_subspace()``: is a higher-level function that\n", + "directly returns a ``scipy.sparse`` matrix.\n", + "\n", + "This notebook shows how to use these two tools to produce the same sparse operator." + ] + }, + { + "cell_type": "markdown", + "id": "6c87d5e5", + "metadata": {}, + "source": [ + "### Subspace definition\n", + "\n", + "For this example we just generate length-22 random bitstrings. For the projection\n", + "functions to work as expected, it is essential that the bitstrings that define\n", + "the subspace are unique and sorted in ascending order according to their unsigned\n", + "integer representation.\n", + "\n", + "This can be achieved with the ``qiskit_addon_sqd.qubit.sort_and_remove_duplicates()``\n", + "function." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7b94f840", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subspace dimension: 49718\n", + "Full Hilbert space dimension: 4194304\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from qiskit_addon_sqd.qubit import sort_and_remove_duplicates\n", + "\n", + "num_spins = 22\n", + "rand_seed = 22\n", + "np.random.seed(rand_seed)\n", + "\n", + "\n", + "def random_bitstrings(n_samples, n_qubits):\n", + " return np.round(np.random.rand(n_samples, n_qubits)).astype(\"int\").astype(\"bool\")\n", + "\n", + "\n", + "bitstring_matrix = random_bitstrings(50_000, num_spins)\n", + "\n", + "# NOTE: It is essential for the projection code to have the bitstrings sorted!\n", + "bitstring_matrix = sort_and_remove_duplicates(bitstring_matrix)\n", + "\n", + "print(\"Subspace dimension: \" + str(bitstring_matrix.shape[0]))\n", + "print(\"Full Hilbert space dimension: \" + str(2**num_spins))" + ] + }, + { + "cell_type": "markdown", + "id": "5707b93c", + "metadata": {}, + "source": [ + "### First method: nonzero matrix elements and indices." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "74c16c91-cc5c-46ce-aff8-17d0e71ac50f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIXX', 'IIIIIIIIIIIIIIIIIIIIYY', 'IIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIXXII', 'IIIIIIIIIIIIIIIIIIYYII', 'IIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIXXIIII', 'IIIIIIIIIIIIIIIIYYIIII', 'IIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIXXIIIIII', 'IIIIIIIIIIIIIIYYIIIIII', 'IIIIIIIIIIIIIIZZIIIIII', 'IIIIIIIIIIIIXXIIIIIIII', 'IIIIIIIIIIIIYYIIIIIIII', 'IIIIIIIIIIIIZZIIIIIIII', 'IIIIIIIIIIXXIIIIIIIIII', 'IIIIIIIIIIYYIIIIIIIIII', 'IIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIXXIIIIIIIIIIII', 'IIIIIIIIYYIIIIIIIIIIII', 'IIIIIIIIZZIIIIIIIIIIII', 'IIIIIIXXIIIIIIIIIIIIII', 'IIIIIIYYIIIIIIIIIIIIII', 'IIIIIIZZIIIIIIIIIIIIII', 'IIIIXXIIIIIIIIIIIIIIII', 'IIIIYYIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIII', 'IIXXIIIIIIIIIIIIIIIIII', 'IIYYIIIIIIIIIIIIIIIIII', 'IIZZIIIIIIIIIIIIIIIIII', 'XXIIIIIIIIIIIIIIIIIIII', 'YYIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIII', 'XIIIIIIIIIIIIIIIIIIIIX', 'YIIIIIIIIIIIIIIIIIIIIY', 'ZIIIIIIIIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIIIIIIIXXI', 'IIIIIIIIIIIIIIIIIIIYYI', 'IIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIXXIII', 'IIIIIIIIIIIIIIIIIYYIII', 'IIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIXXIIIII', 'IIIIIIIIIIIIIIIYYIIIII', 'IIIIIIIIIIIIIIIZZIIIII', 'IIIIIIIIIIIIIXXIIIIIII', 'IIIIIIIIIIIIIYYIIIIIII', 'IIIIIIIIIIIIIZZIIIIIII', 'IIIIIIIIIIIXXIIIIIIIII', 'IIIIIIIIIIIYYIIIIIIIII', 'IIIIIIIIIIIZZIIIIIIIII', 'IIIIIIIIIXXIIIIIIIIIII', 'IIIIIIIIIYYIIIIIIIIIII', 'IIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIXXIIIIIIIIIIIII', 'IIIIIIIYYIIIIIIIIIIIII', 'IIIIIIIZZIIIIIIIIIIIII', 'IIIIIXXIIIIIIIIIIIIIII', 'IIIIIYYIIIIIIIIIIIIIII', 'IIIIIZZIIIIIIIIIIIIIII', 'IIIXXIIIIIIIIIIIIIIIII', 'IIIYYIIIIIIIIIIIIIIIII', 'IIIZZIIIIIIIIIIIIIIIII', 'IXXIIIIIIIIIIIIIIIIIII', 'IYYIIIIIIIIIIIIIIIIIII', 'IZZIIIIIIIIIIIIIIIIIII'],\n", + " coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "coupling_map = CouplingMap.from_ring(num_spins)\n", + "hamiltonian = generate_xyz_hamiltonian(coupling_map)\n", + "print(hamiltonian)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f31f5e40", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_sqd.qubit import matrix_elements_from_pauli\n", + "from scipy.sparse import coo_matrix\n", + "\n", + "d = bitstring_matrix.shape[0]\n", + "\n", + "# Initialize the coo_matrix object\n", + "operator_from_matrix_elements = coo_matrix((d, d), dtype=\"complex128\")\n", + "\n", + "for pauli in hamiltonian.paulis:\n", + " matrix_elements, row_indices, col_indices = matrix_elements_from_pauli(bitstring_matrix, pauli)\n", + " operator_from_matrix_elements += coo_matrix(\n", + " (matrix_elements, (row_indices, col_indices)), (d, d)\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "4479af24", + "metadata": {}, + "source": [ + "### Higher-level implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e58763df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Projecting term 1 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIXX ...\n", + "Projecting term 2 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIYY ...\n", + "Projecting term 3 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIZZ ...\n", + "Projecting term 4 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIXXII ...\n", + "Projecting term 5 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIYYII ...\n", + "Projecting term 6 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIZZII ...\n", + "Projecting term 7 out of 66: (1+0j) * IIIIIIIIIIIIIIIIXXIIII ...\n", + "Projecting term 8 out of 66: (1+0j) * IIIIIIIIIIIIIIIIYYIIII ...\n", + "Projecting term 9 out of 66: (1+0j) * IIIIIIIIIIIIIIIIZZIIII ...\n", + "Projecting term 10 out of 66: (1+0j) * IIIIIIIIIIIIIIXXIIIIII ...\n", + "Projecting term 11 out of 66: (1+0j) * IIIIIIIIIIIIIIYYIIIIII ...\n", + "Projecting term 12 out of 66: (1+0j) * IIIIIIIIIIIIIIZZIIIIII ...\n", + "Projecting term 13 out of 66: (1+0j) * IIIIIIIIIIIIXXIIIIIIII ...\n", + "Projecting term 14 out of 66: (1+0j) * IIIIIIIIIIIIYYIIIIIIII ...\n", + "Projecting term 15 out of 66: (1+0j) * IIIIIIIIIIIIZZIIIIIIII ...\n", + "Projecting term 16 out of 66: (1+0j) * IIIIIIIIIIXXIIIIIIIIII ...\n", + "Projecting term 17 out of 66: (1+0j) * IIIIIIIIIIYYIIIIIIIIII ...\n", + "Projecting term 18 out of 66: (1+0j) * IIIIIIIIIIZZIIIIIIIIII ...\n", + "Projecting term 19 out of 66: (1+0j) * IIIIIIIIXXIIIIIIIIIIII ...\n", + "Projecting term 20 out of 66: (1+0j) * IIIIIIIIYYIIIIIIIIIIII ...\n", + "Projecting term 21 out of 66: (1+0j) * IIIIIIIIZZIIIIIIIIIIII ...\n", + "Projecting term 22 out of 66: (1+0j) * IIIIIIXXIIIIIIIIIIIIII ...\n", + "Projecting term 23 out of 66: (1+0j) * IIIIIIYYIIIIIIIIIIIIII ...\n", + "Projecting term 24 out of 66: (1+0j) * IIIIIIZZIIIIIIIIIIIIII ...\n", + "Projecting term 25 out of 66: (1+0j) * IIIIXXIIIIIIIIIIIIIIII ...\n", + "Projecting term 26 out of 66: (1+0j) * IIIIYYIIIIIIIIIIIIIIII ...\n", + "Projecting term 27 out of 66: (1+0j) * IIIIZZIIIIIIIIIIIIIIII ...\n", + "Projecting term 28 out of 66: (1+0j) * IIXXIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 29 out of 66: (1+0j) * IIYYIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 30 out of 66: (1+0j) * IIZZIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 31 out of 66: (1+0j) * XXIIIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 32 out of 66: (1+0j) * YYIIIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 33 out of 66: (1+0j) * ZZIIIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 34 out of 66: (1+0j) * XIIIIIIIIIIIIIIIIIIIIX ...\n", + "Projecting term 35 out of 66: (1+0j) * YIIIIIIIIIIIIIIIIIIIIY ...\n", + "Projecting term 36 out of 66: (1+0j) * ZIIIIIIIIIIIIIIIIIIIIZ ...\n", + "Projecting term 37 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIXXI ...\n", + "Projecting term 38 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIYYI ...\n", + "Projecting term 39 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIZZI ...\n", + "Projecting term 40 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIXXIII ...\n", + "Projecting term 41 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIYYIII ...\n", + "Projecting term 42 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIZZIII ...\n", + "Projecting term 43 out of 66: (1+0j) * IIIIIIIIIIIIIIIXXIIIII ...\n", + "Projecting term 44 out of 66: (1+0j) * IIIIIIIIIIIIIIIYYIIIII ...\n", + "Projecting term 45 out of 66: (1+0j) * IIIIIIIIIIIIIIIZZIIIII ...\n", + "Projecting term 46 out of 66: (1+0j) * IIIIIIIIIIIIIXXIIIIIII ...\n", + "Projecting term 47 out of 66: (1+0j) * IIIIIIIIIIIIIYYIIIIIII ...\n", + "Projecting term 48 out of 66: (1+0j) * IIIIIIIIIIIIIZZIIIIIII ...\n", + "Projecting term 49 out of 66: (1+0j) * IIIIIIIIIIIXXIIIIIIIII ...\n", + "Projecting term 50 out of 66: (1+0j) * IIIIIIIIIIIYYIIIIIIIII ...\n", + "Projecting term 51 out of 66: (1+0j) * IIIIIIIIIIIZZIIIIIIIII ...\n", + "Projecting term 52 out of 66: (1+0j) * IIIIIIIIIXXIIIIIIIIIII ...\n", + "Projecting term 53 out of 66: (1+0j) * IIIIIIIIIYYIIIIIIIIIII ...\n", + "Projecting term 54 out of 66: (1+0j) * IIIIIIIIIZZIIIIIIIIIII ...\n", + "Projecting term 55 out of 66: (1+0j) * IIIIIIIXXIIIIIIIIIIIII ...\n", + "Projecting term 56 out of 66: (1+0j) * IIIIIIIYYIIIIIIIIIIIII ...\n", + "Projecting term 57 out of 66: (1+0j) * IIIIIIIZZIIIIIIIIIIIII ...\n", + "Projecting term 58 out of 66: (1+0j) * IIIIIXXIIIIIIIIIIIIIII ...\n", + "Projecting term 59 out of 66: (1+0j) * IIIIIYYIIIIIIIIIIIIIII ...\n", + "Projecting term 60 out of 66: (1+0j) * IIIIIZZIIIIIIIIIIIIIII ...\n", + "Projecting term 61 out of 66: (1+0j) * IIIXXIIIIIIIIIIIIIIIII ...\n", + "Projecting term 62 out of 66: (1+0j) * IIIYYIIIIIIIIIIIIIIIII ...\n", + "Projecting term 63 out of 66: (1+0j) * IIIZZIIIIIIIIIIIIIIIII ...\n", + "Projecting term 64 out of 66: (1+0j) * IXXIIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 65 out of 66: (1+0j) * IYYIIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 66 out of 66: (1+0j) * IZZIIIIIIIIIIIIIIIIIII ...\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.qubit import project_operator_to_subspace\n", + "\n", + "operator = project_operator_to_subspace(bitstring_matrix, hamiltonian, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "id": "1fce97a2", + "metadata": {}, + "source": [ + "Check that both implementations yield the same coo_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4bf56509", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0j\n" + ] + } + ], + "source": [ + "print((operator.power(2) - operator_from_matrix_elements.power(2)).sum())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/how-tos/select-open-closed-shell.ipynb b/docs/addons/qiskit-addon-sqd/how-tos/select-open-closed-shell.ipynb new file mode 100644 index 00000000000..3040d86380c --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how-tos/select-open-closed-shell.ipynb @@ -0,0 +1,542 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Understand open-shell vs closed-shell options and its effect in the subspace construction\"\n", + "description: \"Understand open-shell vs closed-shell options and its effect in the subspace construction for the latest version of Sample-based quantum diagonalization (SQD)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "72420c62-2716-4f64-ad5b-73394b481cc1", + "metadata": {}, + "source": [ + "# Understand open-shell vs closed-shell options and its effect in the subspace construction\n", + "\n", + "In this \"how-to\", we will show how to choose subpace dimensions in the `qiskit_addon_sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068).\n", + "\n", + "More importantly, this \"how-to\" also highlights some differences in the behaviour in the susbapce construction when run in `open_shell = False` or `open_shell = True` modes:\n", + "\n", + "- `open_shell = False` only works when the number of spin-up and spin-down electrons is the same.\n", + "\n", + "- `open_shell = True` must be used when the number of spin-up and spin-down electrons is different. It can also be used when the number of spin-up and spin-down electrons is the same. However, in this last case, there is a difference in the sizes of the subspaces generated between `open_shell = False` and `open_shell = True`, as discussed in this notebook.\n", + "\n", + "**NOTE:** Some of the electronic-configuration (bitstring) manipulations in this package have as a goal to preserve the total spin symmetry $S^2$. Standard Selected Counfiguration Interaction (SCI) solvers cannot impose $S^2$ conservation exactly. Consequently, they do so approximately via a Lagrange multiplier.\n", + "\n", + "The choice of electronic configurations entering the eigenstate solver can also have a strong effect in the conservation of spin. For example, in a (2-electron,2-orbital) system, one may sample the configuration $|1001\\rangle$ (having a single spin-up excitation over the RHF state $|0101\\rangle$) which is a linear combination of the open-shell singlet and triplet states, respectively $(|1001\\rangle ± |0110\\rangle) /\\sqrt{2}$. If the configuration |0110⟩ is not sampled, one can construct neither eigenfunction of total spin, leading to spin contamination or redundancy (i.e. the configuration |1001⟩ is involved in a CI calculation, but has coefficient 0 in the CI vector). Consider that a single sample $|1001\\rangle$ is generated in the quantum computer, this is how the `sqd` package handles this situation:\n", + "\n", + "- `open_shell = False`:\n", + "\n", + " 1. The $1001$ bitstring is split in half, representing spin-up and spin-down configurations: $10$ (up) and $01$ (down).\n", + "\n", + " 2. The list of unique spin-polarized configurations is constructed: $\\mathcal{U} = [01, 10]$.\n", + "\n", + " 3. We then consider all possible combinations of $\\mathcal{U}$ elements to form the basis: $\\left \\{ |0101\\rangle, |0110\\rangle , |1001\\rangle , |1010\\rangle \\right \\}$, which contains the singlet and triplet states.\n", + "\n", + "\n", + "- `open_shell = True`:\n", + "\n", + " 1. Contrary to the `open_shell = False` case, we do not combine the halves of the bitstring to form the basis." + ] + }, + { + "cell_type": "markdown", + "id": "a6755afb-ca1e-4473-974b-ba89acc8abce", + "metadata": {}, + "source": [ + "### Closed-Shell\n", + "\n", + "This example shows how the bitstrings are manipulated in a (2-electron, 4-orbital) system." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", + "metadata": {}, + "outputs": [], + "source": [ + "# Specify molecule properties\n", + "num_orbitals = 4\n", + "num_elec_a = num_elec_b = 1\n", + "open_shell = False" + ] + }, + { + "cell_type": "markdown", + "id": "c58e988c-a109-44cd-a975-9df43250c318", + "metadata": {}, + "source": [ + "Specify by hand a dictionary of measurement outcomes" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e9506e0b-ed64-48bb-a97a-ef851b604af1", + "metadata": {}, + "outputs": [], + "source": [ + "counts_dict = {\"00010010\": 1 / 2.0 - 0.01, \"01001000\": 1 / 2.0 - 0.01, \"00010001\": 0.02}" + ] + }, + { + "cell_type": "markdown", + "id": "851bc98e-9c08-4e78-9472-36301abc11d8", + "metadata": {}, + "source": [ + "Transform the counts dict into a bitstring matrix and probability array for post-processing" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7a102a7f-aae6-4583-ab82-ae40fcb5496a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[False False False True False False True False]\n", + " [False True False False True False False False]\n", + " [False False False True False False False True]]\n", + "[0.49 0.49 0.02]\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.counts import counts_to_arrays\n", + "\n", + "# Convert counts into bitstring and probability arrays\n", + "bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict)\n", + "print(bitstring_matrix_full)\n", + "print(probs_arr_full)" + ] + }, + { + "cell_type": "markdown", + "id": "eb704101-0fe8-4d12-b572-b1d844e35a90", + "metadata": {}, + "source": [ + "Subsample a single batch of size two:\n", + "\n", + "- ``n_batches = 1``: Number of batches of configurations used by the different calls to the eigenstate solver\n", + "- ``samples_per_batch = 2``: Number of unique configurations to include in each batch" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fe60aee2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[False False False True False False True False]\n", + " [False True False False True False False False]]\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.subsampling import postselect_and_subsample\n", + "\n", + "n_batches = 1\n", + "samples_per_batch = 2\n", + "\n", + "# seed for random number generator\n", + "rand_seed = 48\n", + "\n", + "# Generate the batches\n", + "batches = postselect_and_subsample(\n", + " bitstring_matrix_full,\n", + " probs_arr_full,\n", + " hamming_right=num_elec_a,\n", + " hamming_left=num_elec_b,\n", + " samples_per_batch=samples_per_batch,\n", + " num_batches=n_batches,\n", + " rand_seed=rand_seed,\n", + ")\n", + "\n", + "print(batches[0])" + ] + }, + { + "cell_type": "markdown", + "id": "93a6d05a", + "metadata": {}, + "source": [ + "Obtain decimal representation of the spin-up and spin-down bitstrings used by the eigenstate solver\n", + "\n", + "The fist element in the tuple corresponds to the decimal representation of the spin-up configurations, while the second element in the tuple corresponds to the decimal representation of the spin-down configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ef90e039", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([1, 2, 4, 8], dtype=int64), array([1, 2, 4, 8], dtype=int64))\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.fermion import bitstring_matrix_to_ci_strs\n", + "\n", + "ci_strs = bitstring_matrix_to_ci_strs(batches[0], open_shell=open_shell)\n", + "print(ci_strs)" + ] + }, + { + "cell_type": "markdown", + "id": "70d7883c", + "metadata": {}, + "source": [ + "Note that while the number of samples per batch is 2, and the sampled bitstrings are: $00010010$ and $01001000$, four electronic configurations are generated per spin-species. In this case, the set of unique spin-polarized configurations is given by:\n", + "$$\n", + "\\mathcal{U} = \\{ 0001, 0010, 0100, 1000 \\}\n", + "$$\n", + "whose base-10 decimal representation is\n", + "$$\n", + "\\mathcal{U}_{10} = \\{ 1, 2, 4, 8 \\}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "03b32ed5", + "metadata": {}, + "source": [ + "Basis of the subspace:\n", + "\n", + "The eigenstate solver takes all possible pairs of spin-up and spin-down bitstrings to construnct the basis $\\mathcal{B}$ of the subspace:\n", + "\n", + "- Element 1: $|00010001\\rangle$\n", + "\n", + "- Element 2: $|00010010\\rangle$\n", + "\n", + "- Element 3: $|00010100\\rangle$\n", + "\n", + "- Element 4: $|00011000\\rangle$\n", + "\n", + "- Element 5: $|00100001\\rangle$\n", + "\n", + "- Element 6: $|00100010\\rangle$\n", + "\n", + "- Element 7: $|00100100\\rangle$\n", + "\n", + "- Element 8: $|00101000\\rangle$\n", + "\n", + "- Element 9: $|01000001\\rangle$\n", + "\n", + "- Element 10: $|01000010\\rangle$\n", + "\n", + "- Element 11: $|01000100\\rangle$\n", + "\n", + "- Element 12: $|01001000\\rangle$\n", + "\n", + "- Element 13: $|10000001\\rangle$\n", + "\n", + "- Element 14: $|10000010\\rangle$\n", + "\n", + "- Element 15: $|10000100\\rangle$\n", + "\n", + "- Element 16: $|10001000\\rangle$" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "11c924ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Basis elements of the subspace:\n", + "|00010001>\n", + "|00010010>\n", + "|00010100>\n", + "|00011000>\n", + "|00100001>\n", + "|00100010>\n", + "|00100100>\n", + "|00101000>\n", + "|01000001>\n", + "|01000010>\n", + "|01000100>\n", + "|01001000>\n", + "|10000001>\n", + "|10000010>\n", + "|10000100>\n", + "|10001000>\n" + ] + } + ], + "source": [ + "ci_strs_up, ci_strs_dn = ci_strs\n", + "\n", + "print(\"Basis elements of the subspace:\")\n", + "\n", + "for ci_str_up in ci_strs_up:\n", + " for ci_str_dn in ci_strs_dn:\n", + " format_name = \"{0:0\" + str(num_orbitals) + \"b}\"\n", + " print(\"|\" + format_name.format(ci_str_up) + format_name.format(ci_str_dn) + \">\")" + ] + }, + { + "cell_type": "markdown", + "id": "aa43a4fc", + "metadata": {}, + "source": [ + "**The subspace dimension is upper-bounded by**: $2 \\cdot$ (`samples_per_batch`)$^2$" + ] + }, + { + "cell_type": "markdown", + "id": "9412e52b", + "metadata": {}, + "source": [ + "### Open-Shell\n", + "\n", + "This example shows how the bitstrings are manipulated in a (2-electron, 4-orbital) system." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9b515b8a", + "metadata": {}, + "outputs": [], + "source": [ + "# Specify molecule properties\n", + "num_orbitals = 4\n", + "num_elec_a = num_elec_b = 1\n", + "open_shell = True" + ] + }, + { + "cell_type": "markdown", + "id": "9ef78560", + "metadata": {}, + "source": [ + "Specify by hand a dictionary of measurement outcomes" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "06b2185a", + "metadata": {}, + "outputs": [], + "source": [ + "counts_dict = {\"00010010\": 1 / 2.0 - 0.01, \"01001000\": 1 / 2.0 - 0.01, \"00010001\": 0.02}" + ] + }, + { + "cell_type": "markdown", + "id": "08b32957", + "metadata": {}, + "source": [ + "Transform the counts dict into a bitstring matrix and probability array for post-processing" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "90561893", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[False False False True False False True False]\n", + " [False True False False True False False False]\n", + " [False False False True False False False True]]\n", + "[0.49 0.49 0.02]\n" + ] + } + ], + "source": [ + "# Convert counts into bitstring and probability arrays\n", + "bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict)\n", + "print(bitstring_matrix_full)\n", + "print(probs_arr_full)" + ] + }, + { + "cell_type": "markdown", + "id": "416bfb6c", + "metadata": {}, + "source": [ + "Subsample a single batch of size two:\n", + "\n", + "- ``n_batches = 1``: Number of batches of configurations used by the different calls to the eigenstate solver\n", + "- ``samples_per_batch = 2``: Number of unique configurations to include in each batch" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cf4fe11d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[False False False True False False True False]\n", + " [False True False False True False False False]]\n" + ] + } + ], + "source": [ + "n_batches = 1\n", + "samples_per_batch = 2\n", + "\n", + "# seed for random number generator\n", + "rand_seed = 48\n", + "\n", + "# Generate the batches\n", + "batches = postselect_and_subsample(\n", + " bitstring_matrix_full,\n", + " probs_arr_full,\n", + " hamming_right=num_elec_a,\n", + " hamming_left=num_elec_b,\n", + " samples_per_batch=samples_per_batch,\n", + " num_batches=n_batches,\n", + " rand_seed=rand_seed,\n", + ")\n", + "\n", + "print(batches[0])" + ] + }, + { + "cell_type": "markdown", + "id": "54d699ca", + "metadata": {}, + "source": [ + "Obtain decimal representation of the spin-up and spin-down bitstrings used by the eigenstate solver\n", + "\n", + "The fist element in the tuple corresponds to the decimal representation of the spin-up configurations, while the second element in the tuple corresponds to the decimal representation of the spin-down configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b40b049b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([2, 8], dtype=int64), array([1, 4], dtype=int64))\n" + ] + } + ], + "source": [ + "ci_strs = bitstring_matrix_to_ci_strs(batches[0], open_shell=open_shell)\n", + "print(ci_strs)" + ] + }, + { + "cell_type": "markdown", + "id": "28921e56", + "metadata": {}, + "source": [ + "If we specify that `open_shell = True`, now we do not include all unique half-bitstrings as spin-up and spin-down configurations, thus yielding a smaller basis as when specifying `open_shell = False`" + ] + }, + { + "cell_type": "markdown", + "id": "e1959b72", + "metadata": {}, + "source": [ + "Basis of the subspace:\n", + "\n", + "The eigenstate solver takes all possible pairs of spin-up and spin-down bitstrings to construnct the basis $\\mathcal{B}$ of the subspace:\n", + "\n", + "- Element 1: $|00010010\\rangle$\n", + "\n", + "- Element 2: $|00011000\\rangle$\n", + "\n", + "- Element 3: $|01000010\\rangle$\n", + "\n", + "- Element 4: $|01001000\\rangle$" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a550aba2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Basis elements of the subspace:\n", + "|00100001>\n", + "|00100100>\n", + "|10000001>\n", + "|10000100>\n" + ] + } + ], + "source": [ + "ci_strs_up, ci_strs_dn = ci_strs\n", + "\n", + "print(\"Basis elements of the subspace:\")\n", + "\n", + "for ci_str_up in ci_strs_up:\n", + " for ci_str_dn in ci_strs_dn:\n", + " format_name = \"{0:0\" + str(num_orbitals) + \"b}\"\n", + " print(\"|\" + format_name.format(ci_str_up) + format_name.format(ci_str_dn) + \">\")" + ] + }, + { + "cell_type": "markdown", + "id": "7317bc49", + "metadata": {}, + "source": [ + "**The subspace dimension is upper-bounded by**: (`samples_per_batch`)$^2$" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/how-tos/use-oo-to-optimize-hamiltonian-basis.ipynb b/docs/addons/qiskit-addon-sqd/how-tos/use-oo-to-optimize-hamiltonian-basis.ipynb new file mode 100644 index 00000000000..4f2fb162fda --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how-tos/use-oo-to-optimize-hamiltonian-basis.ipynb @@ -0,0 +1,395 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Optimize Hamiltonian basis with orbital optimization\"\n", + "description: \"Optimize Hamiltonian basis with orbital optimization for the latest version of Sample-based quantum diagonalization (SQD)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", + "metadata": {}, + "source": [ + "# Optimize Hamiltonian basis with orbital optimization\n", + "\n", + "In this tutorial, we will show how to use the `sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068) and then further optimize the ground state approximation using orbital optimization\n", + "\n", + "Refer to [Sec. II A 4](https://arxiv.org/pdf/2405.05068) for a more detailed discussion on this technique." + ] + }, + { + "cell_type": "markdown", + "id": "a6755afb-ca1e-4473-974b-ba89acc8abce", + "metadata": {}, + "source": [ + "### Specify the molecule and generate samples\n", + "\n", + "In this example, we will approximate the ground state energy of an $N_2$ molecule and then improve the answer using orbital optimization. This guide studies $N_2$ at equilibrium, which is mean-field dominated. This means the MO basis is already a good choice for our integrals; therefore, we will rotate our integrals **out** of the MO basis in order to illustrate the effects of orbital optimization." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "import numpy as np\n", + "import pyscf\n", + "import pyscf.cc\n", + "import pyscf.mcscf\n", + "from qiskit_addon_sqd.fermion import rotate_integrals\n", + "\n", + "# Specify molecule properties\n", + "open_shell = False\n", + "spin_sq = 0\n", + "\n", + "# Build N2 molecule\n", + "mol = pyscf.gto.Mole()\n", + "mol.build(\n", + " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", + " basis=\"6-31g\",\n", + " symmetry=\"Dooh\",\n", + ")\n", + "\n", + "# Define active space\n", + "n_frozen = 2\n", + "active_space = range(n_frozen, mol.nao_nr())\n", + "\n", + "# Get molecular integrals\n", + "scf = pyscf.scf.RHF(mol).run()\n", + "num_orbitals = len(active_space)\n", + "n_electrons = int(sum(scf.mo_occ[active_space]))\n", + "num_elec_a = (n_electrons + mol.spin) // 2\n", + "num_elec_b = (n_electrons - mol.spin) // 2\n", + "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", + "mo = cas.sort_mo(active_space, base=0)\n", + "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", + "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", + "\n", + "# Compute exact energy\n", + "exact_energy = cas.run().e_tot" + ] + }, + { + "cell_type": "markdown", + "id": "b23a74fc-708c-4bd0-af4e-c9cd189b6346", + "metadata": {}, + "source": [ + "The MO basis is already a good basis for this problem, so we will rotate out of that basis in this guide in order to highlight the effect of orbital optimization." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9f46d1c6-3b49-45ad-b9ed-972bd58f7e3c", + "metadata": {}, + "outputs": [], + "source": [ + "# Rotate our integrals out of MO basis\n", + "rng = np.random.default_rng(24)\n", + "num_params = (num_orbitals**2 - num_orbitals) // 2 # antisymmetric, specified by upper triangle\n", + "k_rot = (rng.random(num_params) - 0.5) * 0.5\n", + "hcore_rot, eri_rot = rotate_integrals(hcore, eri, k_rot)" + ] + }, + { + "cell_type": "markdown", + "id": "c58e988c-a109-44cd-a975-9df43250c318", + "metadata": {}, + "source": [ + "Generate samples" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e9506e0b-ed64-48bb-a97a-ef851b604af1", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_sqd.counts import counts_to_arrays, generate_counts_uniform\n", + "\n", + "# Generate random samples\n", + "counts_dict = generate_counts_uniform(10_000, num_orbitals * 2, rand_seed=rng)\n", + "\n", + "# Convert counts into bitstring and probability arrays\n", + "bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "eb704101-0fe8-4d12-b572-b1d844e35a90", + "metadata": {}, + "source": [ + "### Iteratively refine the samples using SQD and approximate the ground state" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b72c048e-fe8e-4fc2-b28b-03138249074e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting configuration recovery iteration 0\n", + "Starting configuration recovery iteration 1\n", + "Starting configuration recovery iteration 2\n", + "Starting configuration recovery iteration 3\n", + "Starting configuration recovery iteration 4\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.configuration_recovery import recover_configurations\n", + "from qiskit_addon_sqd.fermion import solve_fermion\n", + "from qiskit_addon_sqd.subsampling import postselect_and_subsample\n", + "\n", + "# SQSD options\n", + "iterations = 5\n", + "\n", + "# Eigenstate solver options\n", + "n_batches = 3\n", + "samples_per_batch = 100\n", + "max_davidson_cycles = 200\n", + "\n", + "# Self-consistent configuration recovery loop\n", + "e_hist = np.zeros((iterations, n_batches)) # energy history\n", + "s_hist = np.zeros((iterations, n_batches)) # spin history\n", + "occupancy_hist = []\n", + "avg_occupancy = None\n", + "for i in range(iterations):\n", + " print(f\"Starting configuration recovery iteration {i}\")\n", + " # On the first iteration, we have no orbital occupancy information from the\n", + " # solver, so we just post-select from the full bitstring set based on hamming weight.\n", + " if avg_occupancy is None:\n", + " bs_mat_tmp = bitstring_matrix_full\n", + " probs_arr_tmp = probs_arr_full\n", + "\n", + " # In following iterations, we use both the occupancy info and the target hamming\n", + " # weight to refine bitstrings.\n", + " else:\n", + " bs_mat_tmp, probs_arr_tmp = recover_configurations(\n", + " bitstring_matrix_full,\n", + " probs_arr_full,\n", + " avg_occupancy,\n", + " num_elec_a,\n", + " num_elec_b,\n", + " rand_seed=rng,\n", + " )\n", + "\n", + " # Throw out samples with incorrect hamming weight and create batches of subsamples.\n", + " batches = postselect_and_subsample(\n", + " bs_mat_tmp,\n", + " probs_arr_tmp,\n", + " hamming_right=num_elec_a,\n", + " hamming_left=num_elec_b,\n", + " samples_per_batch=samples_per_batch,\n", + " num_batches=n_batches,\n", + " rand_seed=rng,\n", + " )\n", + "\n", + " # Run eigenstate solvers in a loop. This loop should be parallelized for larger problems.\n", + " int_e = np.zeros(n_batches)\n", + " int_s = np.zeros(n_batches)\n", + " int_occs = []\n", + " cs = []\n", + " for j in range(n_batches):\n", + " energy_sci, coeffs_sci, avg_occs, spin = solve_fermion(\n", + " batches[j],\n", + " hcore_rot,\n", + " eri_rot,\n", + " open_shell=open_shell,\n", + " spin_sq=spin_sq,\n", + " max_cycle=max_davidson_cycles,\n", + " )\n", + " energy_sci += nuclear_repulsion_energy\n", + " int_e[j] = energy_sci\n", + " int_s[j] = spin\n", + " int_occs.append(avg_occs)\n", + " cs.append(coeffs_sci)\n", + "\n", + " # Combine batch results\n", + " avg_occupancy = tuple(np.mean(int_occs, axis=0))\n", + "\n", + " # Track optimization history\n", + " e_hist[i, :] = int_e\n", + " s_hist[i, :] = int_s\n", + " occupancy_hist.append(avg_occupancy)" + ] + }, + { + "cell_type": "markdown", + "id": "917cf2d0", + "metadata": {}, + "source": [ + "### Refine the subspace\n", + "\n", + "To refine the subspace, we will take the CI strings of the batch with the lowest energy\n", + "from the last configuration recovery step. Other strategies may be used, like taking the union\n", + "of the CI strings of the batches in the last configuration recovery iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2a587030", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subspace dimension: 32761\n", + "Energy of that batch from SQD: -108.7531706601421\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.fermion import bitstring_matrix_to_ci_strs\n", + "\n", + "best_batch = batches[np.argmin(e_hist[-1])]\n", + "ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(best_batch, open_shell=open_shell)\n", + "print(f\"Subspace dimension: {len(ci_strs_up) * len(ci_strs_dn)}\")\n", + "print(f\"Energy of that batch from SQD: {e_hist[-1, np.argmin(e_hist[-1])]}\")\n", + "\n", + "# Union strategy\n", + "\n", + "# batches_union = np.concatenate((batches[0], batches[1]), axis = 0)\n", + "# for i in range(n_batches-2):\n", + "# batches_union = np.concatenate((batches_union, batches[ i+ 2]))\n", + "# ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(\n", + "# batches_union, open_shell=open_shell\n", + "# )\n", + "# print (f\"Subspace dimension: {len(ci_strs_up) * len(ci_strs_dn)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e8c6d5e4", + "metadata": {}, + "source": [ + "### Perform orbital optimization to improve the energy approximation\n", + "\n", + "We now describe how to optimize the orbitals to further improve the quality of the sqd calculation.\n", + "\n", + "The orbital rotations that are implemented in this package are those described by:\n", + "$$\n", + "U(\\kappa) = e^{\\sum_{pq, \\sigma} \\kappa_{pq} c^\\dagger_{p\\sigma} c_{q\\sigma}},\n", + "$$\n", + "where $\\kappa_{p, q} \\in \\mathbb{R}$ and $\\kappa_{p, q} = -\\kappa_{q, p}$. The orbitals are optimized to\n", + "minimize the variational energy:\n", + "$$\n", + "E(\\kappa) = \\langle \\psi | U^\\dagger(\\kappa) H U(\\kappa) |\\psi \\rangle,\n", + "$$\n", + "with respect to $\\kappa$ using gradient descent with momentum. Recall that\n", + "$|\\psi\\rangle$ is spanned in a subspace defined by determinants.\n", + "\n", + "Since the change of basis alters the Hamiltonian, we allow $|\\psi\\rangle$ to\n", + "respond to the change in the Hamiltonian. This is done by performing a number of alternating\n", + "self-consistent optimizations of $\\kappa$ and $|\\psi\\rangle$. We recall that the optimal\n", + "$|\\psi\\rangle$ is given by the lowest eigenvector of the Hamiltonian projected into the\n", + "subspace.\n", + "\n", + "The ``sqd.fermion.fermion`` module provides the tools to perform this alternating\n", + "optimization. In particular, the function ``sqd.fermion.optimize_orbitals()``.\n", + "\n", + "Some of the arguments that define the optimization are:\n", + "\n", + "- ``num_iters``: number of self-consistent iterations.\n", + "- ``num_steps_grad``: number of gradient step updates performed when optimizing\n", + "$\\kappa$ on each self-consistent iteration.\n", + "- ``learning_rate``: step-size in the gradient descent optimization of $\\kappa$." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b5e56baf", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_sqd.fermion import optimize_orbitals\n", + "\n", + "k_flat = (rng.random(num_params) - 0.5) * 0.1\n", + "num_iters = 20\n", + "num_steps_grad = 10_000 # relatively cheap to execute\n", + "learning_rate = 0.05\n", + "\n", + "e_improved, k_flat, orbital_occupancies = optimize_orbitals(\n", + " best_batch,\n", + " hcore_rot,\n", + " eri_rot,\n", + " k_flat,\n", + " open_shell=open_shell,\n", + " spin_sq=spin_sq,\n", + " num_iters=num_iters,\n", + " num_steps_grad=num_steps_grad,\n", + " learning_rate=learning_rate,\n", + " max_cycle=max_davidson_cycles,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e06f5c28-83d0-4dc2-b2bd-2ec92676745d", + "metadata": {}, + "source": [ + "Here we see that by optimizing rotation parameters for our Hamiltonian, we can improve the result from SQD." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "78a80e64", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exact energy: -109.04667177808032\n", + "SQD energy: -108.7531706601421\n", + "Energy after OO: -108.80400806164377\n" + ] + } + ], + "source": [ + "print(f\"Exact energy: {exact_energy}\")\n", + "print(f\"SQD energy: {np.min(e_hist[-1])}\")\n", + "print(f\"Energy after OO: {e_improved + nuclear_repulsion_energy}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/index.mdx b/docs/addons/qiskit-addon-sqd/index.mdx new file mode 100644 index 00000000000..62d83fa6027 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/index.mdx @@ -0,0 +1,104 @@ +--- +title: "Qiskit addon: sample-based quantum diagonalization (SQD)" +description: "Documentation for the latest version of Sample-based quantum diagonalization (SQD)" +--- + +# Qiskit addon: sample-based quantum diagonalization (SQD) + +[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. + +This package contains the Qiskit addon for sample-based quantum diagonalization (SQD) – a technique for finding eigenvalues and eigenvectors of quantum operators, such as a quantum system Hamiltonian, using quantum and distributed classical computing together \[1-5]. This technique can be run on current quantum computers and has been shown to scale to problem sizes beyond what was possible with variational methods and even beyond the reach of exact classical diagonalization methods \[1,2]. + +SQD-based workflows involve first preparing one or more quantum states on a quantum device and sampling from them. Then, classical distributed computing is used to process those noisy samples. This processing occurs iteratively in two steps: First, a configuration recovery step corrects noisy samples using information about the input problem and second, the Hamiltonian is projected and diagonalized in the subspace spanned by those samples. These steps are repeated self-consistently until convergence. The result is an approximated lowest eigenvalue (energy) and lowest energy eigenstate of a given Hamiltonian. SQD is robust to samples corrupted by quantum noise; in fact, as long as a useful signal can be retrieved out of the quantum computer, the outcome of SQD will be insensitive to noisy bitstrings. + +SQD can be used in various ways in practice. For example, we can use two categories of quantum circuits to sample from: + +> 1. A variational circuit ansatz with parameters chosen such that sampling the circuit produces electronic configurations on which the target wavefunction (i.e., the ground state) has significant support. This is appealing for chemistry applications where Hamiltonians can have millions of interaction terms \[1]. For an example of this approach applied to chemistry see the [tutorial for approximating the ground state energy of the N2 molecule](/docs/en/tutorials/sample-based-quantum-diagonalization). +> 2. A set of Krylov basis states are prepared over increasing time intervals. Assuming a good initial state and sparsity of the ground state, this approach is proven to converge efficiently. As one needs to prepare Trotterized time evolution circuits on a quantum device, this approach is best for applications to lattice models \[2]. For an example of this approach applied to Fermionic lattice Hamiltonians, see the [tutorial for approximating the ground state energy of a simplified single-impurity Anderson model](/docs/en/tutorials/sample-based-krylov-quantum-diagonalization). + +This package contains the functionality for the classical processing of user-provided samples. It can target Hamiltonians expressed as linear combinations of Pauli operators or second-quantized Fermionic operators. The projection and diagonalization steps are performed by a classical solver. We provide here two generic solvers, one for Fermionic systems and another for qubit systems. Other solvers that might be more efficient for specific systems can be interfaced by the users. + +## Documentation + +All documentation is available [here](/docs/addons/qiskit-addon-sqd/). + +## Installation + +We encourage installing this package via `pip`, when possible: + +```bash +pip install 'qiskit-addon-sqd' +``` + +For more installation information refer to the [installation instructions](install) in the documentation. + +## System sizes and computational requirements + +The computational cost of SQD is dominated by the eigenstate solver calls. At each step of the self-consistent configuration recovery iteration, n\_batches of eigenstate solver calls are performed. The different calls are embarrassingly parallel. In this [tutorial](tutorials/01-chemistry-hamiltonian), those calls are inside a for loop. **It is highly recommended to perform these calls in parallel**. + +The [`qiskit_addon_sqd.fermion.solve_fermion()`](/docs/api/qiskit-addon-sqd/fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") function is multithreaded and capable of handling systems with \~25 spacial orbitals and \~10 electrons with subspace dimensions of \~\$10^7\$, using \~10-30 cores. + +## Choosing subspace dimensions + +The choice of the subspace dimension affects the accuracy and runtime of the eigenstate solver. The larger the subspace the more accurate the calculation, at the cost of increasing the runtime and memory requirements. The optimal subspace size of a given system is not known, thus a convergence study with the subspace dimension may be performed, as described in this [guide](how-tos/choose-subspace-dimension). + +## The subspace dimension is set indirectly + +In this package, the user controls the number of bitstrings contained in each subspace with the samples\_per\_batch argument in [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](/docs/api/qiskit-addon-sqd/subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"). The value of this argument sets an upper bound to the subspace dimension in the case of quantum chemistry applications. See this [example](how-tos/select-open-closed-shell) for more details. + +## Citing this project + +If you use this package in your research, please cite it according to `CITATON.bib` file included in this repository: + +```bibtex +@software{qiskit-addon-sqd, + author = { + Abdullah Ash Saki + and Stefano Barison + and Bryce Fuller + and James R. Garrison + and Jennifer R. Glick + and Caleb Johnson + and Antonio Mezzacapo + and Javier Robledo-Moreno + and Max Rossmannek + and Paul Schweigert + and Iskandar Sitdikov + and Kevin J. Sung + }, + title = {{Qiskit addon: sample-based quantum diagonalization}}, + howpublished = {\url{https://github.com/Qiskit/qiskit-addon-sqd}}, + year = {2024} +} +``` + +## Deprecation Policy + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the release notes. + +## Contributing + +The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-sqd). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-sqd/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). + +We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-sqd/issues/new/choose) for tracking requests and bugs. + +## License + +[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-sqd/blob/main/LICENSE.txt) + +<span id="id1" /> + +## References + +\[1] Javier Robledo-Moreno, et al., \[Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer]\([https://arxiv.org/abs/2405.05068](https://arxiv.org/abs/2405.05068)), arXiv:2405.05068 \[quant-ph]. + +\[2] Jeffery Yu, et al., \[Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization]\([https://arxiv.org/abs/2501.09702](https://arxiv.org/abs/2501.09702)), arXiv:2501.09702 \[quant-ph]. + +\[3] Keita Kanno, et al., \[Quantum-Selected Configuration Interaction: classical diagonalization of Hamiltonians in subspaces selected by quantum computers]\([https://arxiv.org/abs/2302.11320](https://arxiv.org/abs/2302.11320)), arXiv:2302.11320 \[quant-ph]. + +\[4] Kenji Sugisaki, et al., \[Hamiltonian simulation-based quantum-selected configuration interaction for large-scale electronic structure calculations with a quantum computer]\([https://arxiv.org/abs/2412.07218](https://arxiv.org/abs/2412.07218)), arXiv:2412.07218 \[quant-ph]. + +\[5] Mathias Mikkelsen, Yuya O. Nakagawa, \[Quantum-selected configuration interaction with time-evolved state]\([https://arxiv.org/abs/2412.13839](https://arxiv.org/abs/2412.13839)), arXiv:2412.13839 \[quant-ph]. + diff --git a/docs/addons/qiskit-addon-sqd/install.mdx b/docs/addons/qiskit-addon-sqd/install.mdx new file mode 100644 index 00000000000..13b6f3df1ae --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/install.mdx @@ -0,0 +1,63 @@ +--- +title: "Installation Instructions" +description: "Installation Instructions for the lastest version of Sample-based quantum diagonalization (SQD)" +--- + +# Installation Instructions + +Let’s see how to install the package. The first thing to do is ensure your Python environment is set up correctly. To create a new environment: + +## Pre-Installation + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +There are two primary ways to install this package – from PyPI or source. The preferred method is to install from PyPI: + +## Install from PyPI + +```sh +pip install 'qiskit-addon-sqd' +``` + +## Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +If so, the first step is to clone the `qiskit-addon-sqd` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-sqd.git +``` + +Next, upgrade pip and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-sqd +``` + +The next step is to install `qiskit-addon-sqd` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab +``` + diff --git a/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.ipynb new file mode 100644 index 00000000000..1cf30b35e6f --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.ipynb @@ -0,0 +1,542 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Improving energy estimation of a chemistry Hamiltonian with SQD\"\n", + "description: \"Improving energy estimation of a chemistry Hamiltonian with SQD for the latest version of Sample-based quantum diagonalization (SQD)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", + "metadata": {}, + "source": [ + "# Improving energy estimation of a chemistry Hamiltonian with SQD\n", + "\n", + "In this tutorial we implement a [Qiskit pattern](/docs/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of a chemistry Hamiltonian: the $N_2$ molecule at equilibrium in the 6-31G basis set. We will follow a [sample-based quantum diagonalization approach](https://arxiv.org/abs/2405.05068) to process samples taken from a ``36``-qubit quantum circuit ansatz (in this case, an LUCJ circuit). In order to account for the effect of quantum noise, the configuration recovery technique is used.\n", + "\n", + "The pattern can be described in four steps:\n", + "\n", + "1. **Step 1: Map to quantum problem**\n", + " - Generate an ansatz for estimating the ground state\n", + "2. **Step 2: Optimize the problem**\n", + " - Transpile the ansatz for the backend\n", + "3. **Step 3: Execute experiments**\n", + " - Draw samples from the ansatz using the ``Sampler`` primitive\n", + "4. **Step 4: Post-process results**\n", + " - Self-consistent configuration recovery loop\n", + " - Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration.\n", + " - Probabilistically create batches of subsamples from recovered bitstrings.\n", + " - Project and diagonalize the molecular Hamiltonian over each sampled subspace.\n", + " - Save the minimum ground state energy found across all batches and update the avg orbital occupancy.\n", + "\n", + "\n", + "For this example, the interacting-electron Hamiltonian takes the generic form:\n", + "\n", + "$$\n", + "\\hat{H} = \\sum_{ \\substack{pr\\\\\\sigma} } h_{pr} \\, \\hat{a}^\\dagger_{p\\sigma} \\hat{a}_{r\\sigma}\n", + "+\n", + "\\sum_{ \\substack{prqs\\\\\\sigma\\tau} }\n", + "\\frac{(pr|qs)}{2} \\,\n", + "\\hat{a}^\\dagger_{p\\sigma}\n", + "\\hat{a}^\\dagger_{q\\tau}\n", + "\\hat{a}_{s\\tau}\n", + "\\hat{a}_{r\\sigma}\n", + "$$\n", + "\n", + "$\\hat{a}^\\dagger_{p\\sigma}$/$\\hat{a}_{p\\sigma}$ are the fermionic creation/annihalation operators associated to the $p$-th basis set element and the spin $\\sigma$. $h_{pr}$ and $(pr|qs)$ are the one- and two-body electronic integrals.\n", + "\n", + "The SQD workflow with self-consistent configuration recovery is depicted in the following diagram.\n", + "\n", + "![SQD diagram](../_static/images/sqd_diagram.png)\n", + "\n", + "SQD is known to work well when the target eigenstate is sparse: the wave function is supported in a set of basis states $\\mathcal{S} = \\{|x\\rangle \\}$ whose size does not increase exponentially with the size of the problem. In this scenario, the diagonalization of the Hamiltonian projected into the subspace defined by $\\mathcal{S}$:\n", + "$$\n", + "H_\\mathcal{S} = P_\\mathcal{S} H P_\\mathcal{S} \\textrm{ with } P_\\mathcal{S} = \\sum_{x \\in \\mathcal{S}} |x \\rangle \\langle x |;\n", + "$$\n", + "yields a good approximation to the target eigenstate. The role of the quantum device is to produce samples of the members of $\\mathcal{S}$ only. First, a quantum circuit prepares the state $|\\Psi\\rangle$ in the quantum device. The Jordan-Wigner encoding is used. Consequently, members of the computational basis represent Fock states (electronic configurations/determinants). The circuit is sampled in the computational basis, yielding the set of noisy configurations $\\tilde{\\mathcal{X}}$. The configurations are represented by bitstrings. The set $\\tilde{\\mathcal{X}}$ is then passed into the classical post-processing block, where the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068) is used. In the SQD framework, the role of the quantum device is to produce a probability distribution." + ] + }, + { + "cell_type": "markdown", + "id": "afeb054c", + "metadata": {}, + "source": [ + "### Step 1: Map problem to a quantum circuit\n", + "\n", + "In this tutorial, we will approximate the ground state energy of an $N_2$ molecule. First, we will specify the molecule and its properties. Next, we will create a [local unitary cluster Jastrow (LUCJ)](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) ansatz (quantum circuit) to generate samples from a quantum computer for ground state energy estimation.\n", + "\n", + "First, we will specify the molecule and its properties." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "converged SCF energy = -108.835236570775\n", + "CASCI E = -109.046671778080 E(CI) = -32.8155692383188 S^2 = 0.0000000\n" + ] + } + ], + "source": [ + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import pyscf\n", + "import pyscf.cc\n", + "import pyscf.mcscf\n", + "\n", + "# Specify molecule properties\n", + "open_shell = False\n", + "spin_sq = 0\n", + "\n", + "# Build N2 molecule\n", + "mol = pyscf.gto.Mole()\n", + "mol.build(\n", + " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", + " basis=\"6-31g\",\n", + " symmetry=\"Dooh\",\n", + ")\n", + "\n", + "# Define active space\n", + "n_frozen = 2\n", + "active_space = range(n_frozen, mol.nao_nr())\n", + "\n", + "# Get molecular integrals\n", + "scf = pyscf.scf.RHF(mol).run()\n", + "num_orbitals = len(active_space)\n", + "n_electrons = int(sum(scf.mo_occ[active_space]))\n", + "num_elec_a = (n_electrons + mol.spin) // 2\n", + "num_elec_b = (n_electrons - mol.spin) // 2\n", + "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", + "mo = cas.sort_mo(active_space, base=0)\n", + "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", + "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", + "\n", + "# Compute exact energy\n", + "exact_energy = cas.run().e_tot" + ] + }, + { + "cell_type": "markdown", + "id": "96bfe018", + "metadata": {}, + "source": [ + "Next, we will create the ansatz. The ``LUCJ`` ansatz is a parameterized quantum circuit, and we will initialize it with `t2` and `t1` amplitudes obtained from a CCSD calculation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "66270387", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "E(CCSD) = -109.0398256929734 E_corr = -0.2045891221988311\n" + ] + } + ], + "source": [ + "# Get CCSD t2 amplitudes for initializing the ansatz\n", + "ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run()\n", + "t1 = ccsd.t1\n", + "t2 = ccsd.t2" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f4d882fa", + "metadata": {}, + "source": [ + "We will use the [ffsim](https://github.com/qiskit-community/ffsim/tree/main) package to create and initialize the ansatz with `t2` and `t1` amplitudes computed above. Since our molecule has a closed-shell Hartree-Fock state, we will use the spin-balanced variant of the UCJ ansatz, [UCJOpSpinBalanced](https://qiskit-community.github.io/ffsim/api/ffsim.html#ffsim.UCJOpSpinBalanced).\n", + "\n", + "As our target IBM hardware has a heavy-hex topology, we will adopt the [_zig-zag_ pattern for qubit interactions](https://pubs.rsc.org/en/content/articlehtml/2023/sc/d3sc02516k). In this pattern, orbitals (represented by qubits) with the same spin are connected with a line topology (red and blue circles) where each line take a zig-zag shape due the heavy-hex connectivity of the target hardware. Again, due to the heavy-hex topology, orbitals for different spins have connections between every 4th orbital (0, 4, 8, etc.) (purple circles).\n", + "\n", + "![lucj_ansatz](../_static/images/lucj_ansatz_zig_zag_pattern.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dd69a86c", + "metadata": {}, + "outputs": [], + "source": [ + "import ffsim\n", + "from qiskit import QuantumCircuit, QuantumRegister\n", + "\n", + "n_reps = 1\n", + "alpha_alpha_indices = [(p, p + 1) for p in range(num_orbitals - 1)]\n", + "alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\n", + "\n", + "ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\n", + " t2=t2,\n", + " t1=t1,\n", + " n_reps=n_reps,\n", + " interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\n", + ")\n", + "\n", + "nelec = (num_elec_a, num_elec_b)\n", + "\n", + "# create an empty quantum circuit\n", + "qubits = QuantumRegister(2 * num_orbitals, name=\"q\")\n", + "circuit = QuantumCircuit(qubits)\n", + "\n", + "# prepare Hartree-Fock state as the reference state and append it to the quantum circuit\n", + "circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\n", + "\n", + "# apply the UCJ operator to the reference state\n", + "circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\n", + "circuit.measure_all()" + ] + }, + { + "cell_type": "markdown", + "id": "db11bf6d", + "metadata": {}, + "source": [ + "### Step 2: Optimize the problem" + ] + }, + { + "cell_type": "markdown", + "id": "0760b3f3", + "metadata": {}, + "source": [ + "Next, we will optimize our circuit for a target hardware. We need to choose the hardware device to use before optimizing our circuit. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "53a039d8", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeSherbrooke\n", + "\n", + "backend = FakeSherbrooke()" + ] + }, + { + "cell_type": "markdown", + "id": "057ebbf6", + "metadata": {}, + "source": [ + "Next, we recommend the following steps to optimize the ansatz and make it hardware-compatible.\n", + "\n", + "- Select physical qubits (`initial_layout`) from the target hardware that adheres to the zig-zag pattern described above. Laying out qubits in this pattern leads to an efficient hardware-compatible circuit with fewer gates.\n", + "- Generate a staged pass manager using the [generate_preset_pass_manager](/docs/api/qiskit/transpiler_preset#generate_preset_pass_manager) function from Qiskit with your choice of `backend` and `initial_layout`.\n", + "- Set the `pre_init` stage of your staged pass manager to `ffsim.qiskit.PRE_INIT`. `ffsim.qiskit.PRE_INIT` includes Qiskit transpiler passes that decompose gates into orbital rotations and then merges the orbital rotations, resulting in fewer gates in the final circuit.\n", + "- Run the pass manager on your circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7d554aa5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gate counts (w/o pre-init passes): OrderedDict({'rz': 4420, 'sx': 3432, 'ecr': 1366, 'x': 239, 'measure': 32, 'barrier': 1})\n", + "Gate counts (w/ pre-init passes): OrderedDict({'rz': 2460, 'sx': 2156, 'ecr': 730, 'x': 71, 'measure': 32, 'barrier': 1})\n" + ] + } + ], + "source": [ + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", + "\n", + "spin_a_layout = [0, 14, 18, 19, 20, 33, 39, 40, 41, 53, 60, 61, 62, 72, 81, 82]\n", + "spin_b_layout = [2, 3, 4, 15, 22, 23, 24, 34, 43, 44, 45, 54, 64, 65, 66, 73]\n", + "initial_layout = spin_a_layout + spin_b_layout\n", + "\n", + "pass_manager = generate_preset_pass_manager(\n", + " optimization_level=3, backend=backend, initial_layout=initial_layout\n", + ")\n", + "\n", + "# without PRE_INIT passes\n", + "isa_circuit = pass_manager.run(circuit)\n", + "print(f\"Gate counts (w/o pre-init passes): {isa_circuit.count_ops()}\")\n", + "\n", + "# with PRE_INIT passes\n", + "# We will use the circuit generated by this pass manager for hardware execution\n", + "pass_manager.pre_init = ffsim.qiskit.PRE_INIT\n", + "isa_circuit = pass_manager.run(circuit)\n", + "print(f\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0cc1edef", + "metadata": {}, + "source": [ + "### Step 3: Execute experiments" + ] + }, + { + "cell_type": "markdown", + "id": "cbf7ef9f", + "metadata": {}, + "source": [ + "After optimizing the circuit for hardware execution, we are ready to run it on the target hardware and collect samples for ground state energy estimation. As we only have one circuit, we will use Qiskit Runtime's [Job execution mode](/docs/guides/execution-modes) and execute our circuit.\n", + "\n", + "***Note: We have commented out the code for running the circuit on a QPU and left it for the user's reference. Instead of running on real hardware in this guide, we will just generate random samples drawn from the uniform distribution.***" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3da09100", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit_addon_sqd.counts import generate_bit_array_uniform\n", + "\n", + "# from qiskit_ibm_runtime import SamplerV2 as Sampler\n", + "\n", + "# sampler = Sampler(mode=backend)\n", + "# job = sampler.run([isa_circuit], shots=10_000)\n", + "# primitive_result = job.result()\n", + "# pub_result = primitive_result[0]\n", + "# bit_array = pub_result.data.meas\n", + "\n", + "rng = np.random.default_rng(24)\n", + "bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng)" + ] + }, + { + "cell_type": "markdown", + "id": "6df05b6e", + "metadata": {}, + "source": [ + "### Step 4: Post-process results" + ] + }, + { + "cell_type": "markdown", + "id": "7a102a7f-aae6-4583-ab82-ae40fcb5496a", + "metadata": {}, + "source": [ + "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function.\n", + "\n", + "The solver included in the SQD addon uses PySCF's implementation of selected CI, specifically [pyscf.fci.selected_ci.kernel_fixed_space](https://pyscf.org/pyscf_api_docs/pyscf.fci.html#pyscf.fci.selected_ci.kernel_fixed_space). The example below also shows how to pass keyword arguments to that function via the included solver. Here we pass the `max_cycle` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6e873c11", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1\n", + "\tSubsample 0\n", + "\t\tEnergy: -105.45358671756313\n", + "\t\tSubspace dimension: 5476\n", + "Iteration 2\n", + "\tSubsample 0\n", + "\t\tEnergy: -107.95172900082163\n", + "\t\tSubspace dimension: 249001\n", + "Iteration 3\n", + "\tSubsample 0\n", + "\t\tEnergy: -108.97460330369815\n", + "\t\tSubspace dimension: 339889\n", + "Iteration 4\n", + "\tSubsample 0\n", + "\t\tEnergy: -109.02739376648793\n", + "\t\tSubspace dimension: 440896\n", + "Iteration 5\n", + "\tSubsample 0\n", + "\t\tEnergy: -109.030972328451\n", + "\t\tSubspace dimension: 597529\n" + ] + } + ], + "source": [ + "from functools import partial\n", + "\n", + "from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian, solve_sci_batch\n", + "\n", + "# SQD options\n", + "energy_tol = 1e-3\n", + "occupancies_tol = 1e-3\n", + "max_iterations = 5\n", + "\n", + "# Eigenstate solver options\n", + "num_batches = 1\n", + "samples_per_batch = 300\n", + "symmetrize_spin = True\n", + "carryover_threshold = 1e-4\n", + "max_cycle = 200\n", + "\n", + "# Pass options to the built-in eigensolver. If you just want to use the defaults,\n", + "# you can omit this step, in which case you would not specify the sci_solver argument\n", + "# in the call to diagonalize_fermionic_hamiltonian below.\n", + "sci_solver = partial(solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle)\n", + "\n", + "# List to capture intermediate results\n", + "result_history = []\n", + "\n", + "\n", + "def callback(results: list[SCIResult]):\n", + " result_history.append(results)\n", + " iteration = len(result_history)\n", + " print(f\"Iteration {iteration}\")\n", + " for i, result in enumerate(results):\n", + " print(f\"\\tSubsample {i}\")\n", + " print(f\"\\t\\tEnergy: {result.energy + nuclear_repulsion_energy}\")\n", + " print(f\"\\t\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\")\n", + "\n", + "\n", + "result = diagonalize_fermionic_hamiltonian(\n", + " hcore,\n", + " eri,\n", + " bit_array,\n", + " samples_per_batch=samples_per_batch,\n", + " norb=num_orbitals,\n", + " nelec=nelec,\n", + " num_batches=num_batches,\n", + " energy_tol=energy_tol,\n", + " occupancies_tol=occupancies_tol,\n", + " max_iterations=max_iterations,\n", + " sci_solver=sci_solver,\n", + " symmetrize_spin=symmetrize_spin,\n", + " carryover_threshold=carryover_threshold,\n", + " callback=callback,\n", + " seed=rng,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9d78906b-4759-4506-9c69-85d4e67766b3", + "metadata": {}, + "source": [ + "Now, we plot the results.\n", + "\n", + "The first plot shows that after a few iterations we estimate the ground state energy within ``~16 mH`` (chemical accuracy is typically accepted to be ``1 kcal/mol`` $\\approx$ ``1.6 mH``). Remember, the quantum samples in this demo were pure noise. The signal here comes from *a priori* knowledge of the electronic structure and molecular Hamiltonian.\n", + "\n", + "The second plot shows the average occupancy of each spatial orbital after the final iteration. We can see that both the spin-up and spin-down electrons occupy the first five orbitals with high probability in our solutions." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "caffd888-e89c-4aa9-8bae-4d1bb723b35e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exact energy: -109.04667 Ha\n", + "SQD energy: -109.03097 Ha\n", + "Absolute error: 0.01570 Ha\n" + ] + }, + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian/extracted-outputs/caffd888-e89c-4aa9-8bae-4d1bb723b35e-1.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Data for energies plot\n", + "x1 = range(len(result_history))\n", + "min_e = [\n", + " min(result, key=lambda res: res.energy).energy + nuclear_repulsion_energy\n", + " for result in result_history\n", + "]\n", + "e_diff = [abs(e - exact_energy) for e in min_e]\n", + "yt1 = [1.0, 1e-1, 1e-2, 1e-3, 1e-4]\n", + "\n", + "# Chemical accuracy (+/- 1 milli-Hartree)\n", + "chem_accuracy = 0.001\n", + "\n", + "# Data for avg spatial orbital occupancy\n", + "y2 = np.sum(result.orbital_occupancies, axis=0)\n", + "x2 = range(len(y2))\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + "# Plot energies\n", + "axs[0].plot(x1, e_diff, label=\"energy error\", marker=\"o\")\n", + "axs[0].set_xticks(x1)\n", + "axs[0].set_xticklabels(x1)\n", + "axs[0].set_yticks(yt1)\n", + "axs[0].set_yticklabels(yt1)\n", + "axs[0].set_yscale(\"log\")\n", + "axs[0].set_ylim(1e-4)\n", + "axs[0].axhline(y=chem_accuracy, color=\"#BF5700\", linestyle=\"--\", label=\"chemical accuracy\")\n", + "axs[0].set_title(\"Approximated Ground State Energy Error vs SQD Iterations\")\n", + "axs[0].set_xlabel(\"Iteration Index\", fontdict={\"fontsize\": 12})\n", + "axs[0].set_ylabel(\"Energy Error (Ha)\", fontdict={\"fontsize\": 12})\n", + "axs[0].legend()\n", + "\n", + "# Plot orbital occupancy\n", + "axs[1].bar(x2, y2, width=0.8)\n", + "axs[1].set_xticks(x2)\n", + "axs[1].set_xticklabels(x2)\n", + "axs[1].set_title(\"Avg Occupancy per Spatial Orbital\")\n", + "axs[1].set_xlabel(\"Orbital Index\", fontdict={\"fontsize\": 12})\n", + "axs[1].set_ylabel(\"Avg Occupancy\", fontdict={\"fontsize\": 12})\n", + "\n", + "print(f\"Exact energy: {exact_energy:.5f} Ha\")\n", + "print(f\"SQD energy: {min_e[-1]:.5f} Ha\")\n", + "print(f\"Absolute error: {e_diff[-1]:.5f} Ha\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb new file mode 100644 index 00000000000..8ebe4a08e86 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb @@ -0,0 +1,525 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Improving energy estimation of a Fermionic lattice model with SQD\"\n", + "description: \"Improving energy estimation of a Fermionic lattice model with SQD for the latest version of Sample-based quantum diagonalization (SQD)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "da10b7a2-e180-4098-b00c-df0325e926f1", + "metadata": {}, + "source": [ + "# Improving energy estimation of a Fermionic lattice model with SQD\n", + "\n", + "In this tutorial we implement a [Qiskit pattern](/docs/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of a Fermionic lattice Hamiltonian known as the single-impurity Anderson model. We will follow a sample-based quantum diagonalization approach to process samples taken from a set of ``16``-qubit Krylov basis states over increasing time intervals. These states are realized on the quantum device using Trotterization of the time evolution. In order to account for the effect of quantum noise, the configuration recovery technique is used. Assuming a good initial state and sparsity of the ground state, [this approach is proven to converge efficiently](https://arxiv.org/abs/2501.09702).\n", + "\n", + "The pattern can be described in four steps:\n", + "\n", + "1. **Step 1: Map to quantum problem**\n", + " - Generate a set of Krylov basis states (i.e., Trotterized time-evolution circuits) over increasing time intervals for estimating the ground state\n", + "2. **Step 2: Optimize the problem**\n", + " - Transpile the circuits for the backend\n", + "3. **Step 3: Execute experiments**\n", + " - Draw samples from the circuits using the ``Sampler`` primitive\n", + "4. **Step 4: Post-process results**\n", + " - Self-consistent configuration recovery loop\n", + " - Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration\n", + " - Probabilistically create batches of subsamples from recovered bitstrings\n", + " - Project and diagonalize the Fermionic lattice Hamiltonian over each sampled subspace\n", + " - Save the minimum ground state energy found across all batches and update the avg orbital occupancy" + ] + }, + { + "cell_type": "markdown", + "id": "7499d787-1e89-44cd-940b-b1c108e83766", + "metadata": {}, + "source": [ + "### Step 1: Map problem to a quantum circuit" + ] + }, + { + "cell_type": "markdown", + "id": "43d7d7da-f05c-4709-908d-169b7957857c", + "metadata": {}, + "source": [ + "First, we will create the one- and two-body Hamiltonians describing the one-dimensional single-impurity Anderson model (SIAM) with ``7`` bath sites (``8`` electrons in ``8`` orbitals). This model is used to describe magnetic impurities embedded in metals.\n", + "\n", + "Then we will create the ``16``-qubit Trotter circuits used to generate the quantum Krylov subspace." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1cacc3b1-2252-4ac6-85dc-3bed0bf07530", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "n_bath = 7 # number of bath sites\n", + "\n", + "V = 1 # hybridization amplitude\n", + "t = 1 # bath hopping amplitude\n", + "U = 10 # Impurity onsite repulsion\n", + "eps = -U / 2 # Chemical potential for the impurity\n", + "\n", + "# Place the impurity on the first qubit\n", + "impurity_index = 0\n", + "\n", + "# One body matrix elements in the \"position\" basis\n", + "h1e = -t * np.diag(np.ones(n_bath), k=1) - t * np.diag(np.ones(n_bath), k=-1)\n", + "h1e[impurity_index, impurity_index + 1] = -V\n", + "h1e[impurity_index + 1, impurity_index] = -V\n", + "h1e[impurity_index, impurity_index] = eps\n", + "\n", + "# Two body matrix elements in the \"position\" basis\n", + "h2e = np.zeros((n_bath + 1, n_bath + 1, n_bath + 1, n_bath + 1))\n", + "h2e[impurity_index, impurity_index, impurity_index, impurity_index] = U" + ] + }, + { + "cell_type": "markdown", + "id": "908670e5-3e04-4131-9d02-06802def6716", + "metadata": {}, + "source": [ + "Next, we will generate the quantum Krylov subspace with a set of Trotterized quantum circuits. Here we create helpers for generating the initial (reference) state as well as the time evolution for the one- and two-body parts of the Hamiltonian. For a more detailed description of this model and how the circuits are designed, please refer to [the paper](https://arxiv.org/abs/2501.09702)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bf539352-c287-4e66-bd5b-b7ff6da96d89", + "metadata": {}, + "outputs": [], + "source": [ + "import ffsim\n", + "import scipy\n", + "from qiskit import QuantumCircuit, QuantumRegister\n", + "from qiskit.circuit.library import CPhaseGate, XGate, XXPlusYYGate\n", + "\n", + "n_modes = n_bath + 1\n", + "nelec = (n_modes // 2, n_modes // 2)\n", + "\n", + "dt = 0.2\n", + "Utar = scipy.linalg.expm(-1j * dt * h1e)\n", + "\n", + "\n", + "# The reference state\n", + "def initial_state(q_circuit, norb, nocc):\n", + " \"\"\"Prepare an initial state.\"\"\"\n", + " for i in range(nocc):\n", + " q_circuit.append(XGate(), [i])\n", + " q_circuit.append(XGate(), [norb + i])\n", + " rot = XXPlusYYGate(np.pi / 2, -np.pi / 2)\n", + "\n", + " for i in range(3):\n", + " for j in range(nocc - i - 1, nocc + i, 2):\n", + " q_circuit.append(rot, [j, j + 1])\n", + " q_circuit.append(rot, [norb + j, norb + j + 1])\n", + " q_circuit.append(rot, [j + 1, j + 2])\n", + " q_circuit.append(rot, [norb + j + 1, norb + j + 2])\n", + "\n", + "\n", + "# The one-body time evolution\n", + "free_fermion_evolution = ffsim.qiskit.OrbitalRotationJW(n_modes, Utar)\n", + "\n", + "\n", + "# The two-body time evolution\n", + "def append_diagonal_evolution(dt, U, impurity_qubit, num_orb, q_circuit):\n", + " \"\"\"Append two-body time evolution to a quantum circuit.\"\"\"\n", + " if U != 0:\n", + " q_circuit.append(\n", + " CPhaseGate(-dt / 2 * U),\n", + " [impurity_qubit, impurity_qubit + num_orb],\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "1e1ec758-3752-4cc3-bc56-93f3ce9febe9", + "metadata": {}, + "source": [ + "Generate ``d`` time-evolved states that specify the quantum Krylov subspace. Here, we have chosen ``d=8``. The error from sampling Krylov basis states converges with increasing ``d``. Note that the particulars of this problem instance allow for an efficient compilation of the one-body evolution using `OrbitalRotationJW`; however, in general, one could use Qiskit's [PauliEvolutionGate](/docs/api/qiskit/qiskit.circuit.library.PauliEvolutionGate) to implement the Trotterized time evolution of the full Hamiltonian." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "07353461-4b71-483a-a6f2-dc75cbcce03b", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate the initial state\n", + "qubits = QuantumRegister(2 * n_modes, name=\"q\")\n", + "init_state = QuantumCircuit(qubits)\n", + "initial_state(init_state, n_modes, n_modes // 2)\n", + "init_state.draw(\"mpl\", scale=0.4, fold=-1)\n", + "\n", + "d = 8 # Number of Krylov basis states\n", + "circuits = []\n", + "for i in range(d):\n", + " circ = init_state.copy()\n", + " circuits.append(circ)\n", + " for _ in range(i):\n", + " append_diagonal_evolution(dt, U, impurity_index, n_modes, circ)\n", + " circ.append(free_fermion_evolution, qubits)\n", + " append_diagonal_evolution(dt, U, impurity_index, n_modes, circ)\n", + " circ.measure_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dfcd22a4-d2ea-4b1d-beaa-f29156d001ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/dfcd22a4-d2ea-4b1d-beaa-f29156d001ab-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuits[0].draw(\"mpl\", scale=0.4, fold=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d9d40efe-145a-48d7-8db1-473656d2df92", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/d9d40efe-145a-48d7-8db1-473656d2df92-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuits[-1].draw(\"mpl\", scale=0.4, fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "b71cbfcc-17a5-4af0-87a2-59e7d1037048", + "metadata": {}, + "source": [ + "### Step 2: Optimize the problem" + ] + }, + { + "cell_type": "markdown", + "id": "70a9dcbe-2aa4-4c8f-80fa-d3d8d41bfb99", + "metadata": {}, + "source": [ + "After we have created the Trotterized circuits, we will optimize them for a target hardware. We need to choose the hardware device to use before optimization. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c3214def-a355-46aa-85af-43b3c1be0bf4", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider.backends import FakeSherbrooke\n", + "\n", + "backend = FakeSherbrooke()" + ] + }, + { + "cell_type": "markdown", + "id": "28c9f712-05fd-4fdd-a70f-cb3787a0041e", + "metadata": {}, + "source": [ + "Next, we will transpile the circuits to the target backend using Qiskit." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "69e3f7f3-9f28-49a1-8fc9-824b46388cf8", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit.transpiler import generate_preset_pass_manager\n", + "\n", + "# The circuit needs to be transpiled to the AerSimulator target\n", + "pass_manager = generate_preset_pass_manager(optimization_level=3, backend=backend)\n", + "isa_circuits = pass_manager.run(circuits)" + ] + }, + { + "cell_type": "markdown", + "id": "b5d41fb7-4f50-4324-94f1-8a605f41d19b", + "metadata": {}, + "source": [ + "### Step 3: Execute experiments" + ] + }, + { + "cell_type": "markdown", + "id": "405db0d8-0911-4dc7-bd42-03210666d66e", + "metadata": {}, + "source": [ + "After optimizing the circuits for hardware execution, we are ready to run them on the target hardware and collect samples for ground state energy estimation. Here we use ``SamplerV2`` from ``qiskit-ibm-runtime`` to simulate noisy samples taken from the ``ibm_sherbrooke`` backend. We then combine the counts from each of the Krylov basis states into a single counts dictionary and plot the top 20 most commonly sampled bitstrings.\n", + "\n", + "***Note: [Qiskit Aer](/docs/addons/qiskit-aer/index) is required to simulate samples from transpiled circuits.***" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f7d2f73c-42f9-4111-a724-ce435a5f7a23", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/f7d2f73c-42f9-4111-a724-ce435a5f7a23-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.primitives import BitArray\n", + "from qiskit.visualization import plot_histogram\n", + "from qiskit_ibm_runtime import SamplerV2 as Sampler\n", + "\n", + "# Sample from the circuits\n", + "noisy_sampler = Sampler(backend, options={\"simulator\": {\"seed_simulator\": 24}})\n", + "job = noisy_sampler.run(isa_circuits, shots=500)\n", + "\n", + "# Combine the counts from the individual Trotter circuits\n", + "bit_array = BitArray.concatenate_shots([result.data.meas for result in job.result()])\n", + "\n", + "plot_histogram(bit_array.get_counts(), number_to_keep=20)" + ] + }, + { + "cell_type": "markdown", + "id": "aa456f21-473f-4965-acc5-d4a3166978ef", + "metadata": {}, + "source": [ + "### Step 4: Post-process the results" + ] + }, + { + "cell_type": "markdown", + "id": "ac18ac1d-6bcc-4672-ac6a-3e45490daf52", + "metadata": {}, + "source": [ + "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5117e234-4d4e-4445-b288-b47c26c0fa67", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1\n", + "\tSubsample 0\n", + "\t\tEnergy: -13.257128325607997\n", + "\t\tSubspace dimension: 3969\n", + "\tSubsample 1\n", + "\t\tEnergy: -13.257128325607997\n", + "\t\tSubspace dimension: 3969\n", + "\tSubsample 2\n", + "\t\tEnergy: -13.257128325607997\n", + "\t\tSubspace dimension: 3969\n", + "Iteration 2\n", + "\tSubsample 0\n", + "\t\tEnergy: -13.319666127542039\n", + "\t\tSubspace dimension: 4096\n", + "\tSubsample 1\n", + "\t\tEnergy: -13.420534292304595\n", + "\t\tSubspace dimension: 4624\n", + "\tSubsample 2\n", + "\t\tEnergy: -9.136171594591085\n", + "\t\tSubspace dimension: 4624\n", + "Iteration 3\n", + "\tSubsample 0\n", + "\t\tEnergy: -13.422491814612833\n", + "\t\tSubspace dimension: 4900\n", + "\tSubsample 1\n", + "\t\tEnergy: -13.422491814612833\n", + "\t\tSubspace dimension: 4900\n", + "\tSubsample 2\n", + "\t\tEnergy: -13.422491814612833\n", + "\t\tSubspace dimension: 4900\n", + "Iteration 4\n", + "\tSubsample 0\n", + "\t\tEnergy: -13.422491814612833\n", + "\t\tSubspace dimension: 4900\n", + "\tSubsample 1\n", + "\t\tEnergy: -13.422491814612833\n", + "\t\tSubspace dimension: 4900\n", + "\tSubsample 2\n", + "\t\tEnergy: -13.422491814612833\n", + "\t\tSubspace dimension: 4900\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian\n", + "\n", + "# List to capture intermediate results\n", + "result_history = []\n", + "\n", + "\n", + "def callback(results: list[SCIResult]):\n", + " result_history.append(results)\n", + " iteration = len(result_history)\n", + " print(f\"Iteration {iteration}\")\n", + " for i, result in enumerate(results):\n", + " print(f\"\\tSubsample {i}\")\n", + " print(f\"\\t\\tEnergy: {result.energy}\")\n", + " print(f\"\\t\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\")\n", + "\n", + "\n", + "rng = np.random.default_rng(24)\n", + "result = diagonalize_fermionic_hamiltonian(\n", + " h1e,\n", + " h2e,\n", + " bit_array,\n", + " samples_per_batch=300,\n", + " norb=n_modes,\n", + " nelec=nelec,\n", + " num_batches=3,\n", + " max_iterations=10,\n", + " symmetrize_spin=True,\n", + " callback=callback,\n", + " seed=rng,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3e4c54ab-6003-44b2-aaa8-a35d69d12da8", + "metadata": {}, + "source": [ + "Now, we plot the results. The first plot shows that after a few iterations, we obtain the exact ground state energy.\n", + "\n", + "This example is small enough that we are able to explore the full Hilbert space, as seen in the print statements above. Remember, the full Hilbert space is dimension ``(num_orbitals choose nelec_a) * (num_orbitals choose nelec_b)``. So for this problem: `(8 choose 4)**2 = 4900`. The subspace dimensions increase due enhanced configuration recovery, and also the fact that the SQD algorithm carries important configurations from one iteration to the next. By the last iteration, we are diagonalizing in the entire Hilbert space.\n", + "\n", + "The second plot shows the average occupancy of each spatial orbital across all batches' solutions. We see that with high probability each orbital contains one electron." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "71976322-31da-437c-b660-a97b122669c0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exact energy: -13.42249\n", + "SQD energy: -13.42249\n", + "Absolute error: 0.00000\n" + ] + }, + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/71976322-31da-437c-b660-a97b122669c0-1.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "exact_energy = -13.422491814605827\n", + "min_es = [min(result, key=lambda res: res.energy).energy for result in result_history]\n", + "min_id, min_e = min(enumerate(min_es), key=lambda x: x[1])\n", + "\n", + "# Data for energies plot\n", + "x1 = range(len(result_history))\n", + "yt1 = list(np.arange(-13.5, -13.1, 0.1))\n", + "ytl = [f\"{i:.1f}\" for i in yt1]\n", + "\n", + "# Data for avg spatial orbital occupancy\n", + "y2 = np.sum(result.orbital_occupancies, axis=0)\n", + "x2 = range(len(y2))\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + "# Plot energies\n", + "axs[0].plot(x1, min_es, label=\"SQD energy\", marker=\"o\")\n", + "axs[0].set_xticks(x1)\n", + "axs[0].set_xticklabels(x1)\n", + "axs[0].set_yticks(yt1)\n", + "axs[0].set_yticklabels(ytl)\n", + "axs[0].axhline(y=exact_energy, color=\"#BF5700\", linestyle=\"--\", label=\"Exact energy\")\n", + "axs[0].set_title(\"Approximated Ground State Energy vs SQD Iterations\")\n", + "axs[0].set_xlabel(\"Iteration Index\", fontdict={\"fontsize\": 12})\n", + "axs[0].set_ylabel(\"Energy\", fontdict={\"fontsize\": 12})\n", + "axs[0].legend()\n", + "\n", + "# Plot orbital occupancy\n", + "axs[1].bar(x2, y2, width=0.8)\n", + "axs[1].set_xticks(x2)\n", + "axs[1].set_xticklabels(x2)\n", + "axs[1].set_title(\"Avg Occupancy per Spatial Orbital\")\n", + "axs[1].set_xlabel(\"Orbital Index\", fontdict={\"fontsize\": 12})\n", + "axs[1].set_ylabel(\"Avg Occupancy\", fontdict={\"fontsize\": 12})\n", + "\n", + "print(f\"Exact energy: {exact_energy:.5f}\")\n", + "print(f\"SQD energy: {min_e:.5f}\")\n", + "print(f\"Absolute error: {abs(min_e - exact_energy):.5f}\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/addons/qiskit-addon-sqd/tutorials/index.mdx b/docs/addons/qiskit-addon-sqd/tutorials/index.mdx new file mode 100644 index 00000000000..5b843da3e68 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/tutorials/index.mdx @@ -0,0 +1,17 @@ +--- +title: "Tutorials" +description: "Tutorials for the lastest version of Sample-based quantum diagonalization (SQD)" +--- + +# Tutorials + +This page summarizes the available tutorials. + +[![](/docs/images/addons/qiskit-addon-sqd/tutorials_01_chemistry_hamiltonian_19_1.avif)](01-chemistry-hamiltonian) + +[Improving energy estimation of a chemistry Hamiltonian with SQD](01-chemistry-hamiltonian) + +[![](/docs/images/addons/qiskit-addon-sqd/tutorials_02_fermionic_lattice_hamiltonian_22_1.avif)](02-fermionic-lattice-hamiltonian) + +[Improving energy estimation of a Fermionic lattice model with SQD](02-fermionic-lattice-hamiltonian) + diff --git a/docs/api/qiskit-addon-sqd/_package.json b/docs/api/qiskit-addon-sqd/_package.json index 1f226e87545..9b4e87cc521 100644 --- a/docs/api/qiskit-addon-sqd/_package.json +++ b/docs/api/qiskit-addon-sqd/_package.json @@ -1,4 +1,4 @@ { "name": "qiskit-addon-sqd", - "version": "0.12.1" + "version": "0.12.0" } diff --git a/docs/api/qiskit-addon-sqd/_toc.json b/docs/api/qiskit-addon-sqd/_toc.json index ed5c0c5b297..0b5a0466148 100644 --- a/docs/api/qiskit-addon-sqd/_toc.json +++ b/docs/api/qiskit-addon-sqd/_toc.json @@ -37,5 +37,7 @@ } ], "collapsed": true, - "untranslatable": true + "untranslatable": true, + "parentUrl": "/docs/addons/qiskit-addon-sqd", + "parentLabel": "Sample-based quantum diagonalization (SQD)" } diff --git a/docs/api/qiskit-addon-sqd/release-notes.mdx b/docs/api/qiskit-addon-sqd/release-notes.mdx index cf531e2f271..7461e5880b6 100644 --- a/docs/api/qiskit-addon-sqd/release-notes.mdx +++ b/docs/api/qiskit-addon-sqd/release-notes.mdx @@ -32,9 +32,9 @@ in_page_toc_max_heading_level: 2 ### New Features -* Added the `max_dim` argument to the [`qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian()`](fermion#diagonalize_fermionic_hamiltonian "qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian") function. This argument is used to limit the dimension of the SCI subspace. +* Added the `max_dim` argument to the [`qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian()`](/docs/api/qiskit-addon-sqd/fermion#diagonalize_fermionic_hamiltonian "qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian") function. This argument is used to limit the dimension of the SCI subspace. -* Introduced the [`qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left()`](subsampling#postselect_by_hamming_right_and_left "qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left") function. +* Introduced the [`qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left()`](/docs/api/qiskit-addon-sqd/subsampling#postselect_by_hamming_right_and_left "qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left") function. <span id="release-notes-0-12-0-upgrade-notes" /> @@ -42,25 +42,25 @@ in_page_toc_max_heading_level: 2 ### Upgrade Notes -* [`qiskit_addon_sqd.fermion.SCIState`](fermion#scistate "qiskit_addon_sqd.fermion.SCIState") now takes two additional required arguments, `norb` and `nelec`. This is a breaking change. +* [`qiskit_addon_sqd.fermion.SCIState`](/docs/api/qiskit-addon-sqd/fermion#scistate "qiskit_addon_sqd.fermion.SCIState") now takes two additional required arguments, `norb` and `nelec`. This is a breaking change. <span id="release-notes-0-12-0-deprecation-notes" /> ### Deprecation Notes -* [`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight()`](configuration-recovery#post_select_by_hamming_weight "qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight") is now deprecated. Instead, use [`qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left()`](subsampling#postselect_by_hamming_right_and_left "qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left"). +* [`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight()`](/docs/api/qiskit-addon-sqd/configuration-recovery#post_select_by_hamming_weight "qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight") is now deprecated. Instead, use [`qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left()`](/docs/api/qiskit-addon-sqd/subsampling#postselect_by_hamming_right_and_left "qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left"). -* [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](subsampling#postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample") is now deprecated. Instead, use [`qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left()`](subsampling#postselect_by_hamming_right_and_left "qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left") and [`qiskit_addon_sqd.subsampling.subsample()`](subsampling#subsample "qiskit_addon_sqd.subsampling.subsample"). +* [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](/docs/api/qiskit-addon-sqd/subsampling#postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample") is now deprecated. Instead, use [`qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left()`](/docs/api/qiskit-addon-sqd/subsampling#postselect_by_hamming_right_and_left "qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left") and [`qiskit_addon_sqd.subsampling.subsample()`](/docs/api/qiskit-addon-sqd/subsampling#subsample "qiskit_addon_sqd.subsampling.subsample"). <span id="release-notes-0-12-0-bug-fixes" /> ### Bug Fixes -* Fixed a bug in [`qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs()`](fermion#bitstring_matrix_to_ci_strs "qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs") in which the conversion of bitstrings to CI strings could be incorrect when the number of orbitals is greater than 53. +* Fixed a bug in [`qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs()`](/docs/api/qiskit-addon-sqd/fermion#bitstring_matrix_to_ci_strs "qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs") in which the conversion of bitstrings to CI strings could be incorrect when the number of orbitals is greater than 53. -* Fixed a bug in which saving and loading [`qiskit_addon_sqd.fermion.SCIState`](fermion#scistate "qiskit_addon_sqd.fermion.SCIState") objects resulted in an error. +* Fixed a bug in which saving and loading [`qiskit_addon_sqd.fermion.SCIState`](/docs/api/qiskit-addon-sqd/fermion#scistate "qiskit_addon_sqd.fermion.SCIState") objects resulted in an error. -* Added a release note to version 0.11 for a breaking change involving [`qiskit_addon_sqd.fermion.SCIState`](fermion#scistate "qiskit_addon_sqd.fermion.SCIState"). +* Added a release note to version 0.11 for a breaking change involving [`qiskit_addon_sqd.fermion.SCIState`](/docs/api/qiskit-addon-sqd/fermion#scistate "qiskit_addon_sqd.fermion.SCIState"). <span id="release-notes-0-11-0" /> @@ -74,9 +74,9 @@ in_page_toc_max_heading_level: 2 ### New Features -* Added a new function [`qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian()`](fermion#diagonalize_fermionic_hamiltonian "qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian") to serve as the main entrypoint to the SQD algorithm. See the updated [tutorials](https://qiskit.github.io/qiskit-addon-sqd/tutorials/index.html) for a demonstration of how to use this function. +* Added a new function [`qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian()`](/docs/api/qiskit-addon-sqd/fermion#diagonalize_fermionic_hamiltonian "qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian") to serve as the main entrypoint to the SQD algorithm. See the updated [tutorials](/docs/addons/qiskit-addon-sqd/tutorials/index) for a demonstration of how to use this function. -* [`qiskit_addon_sqd.fermion.solve_fermion()`](fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") now accepts a `shift` argument used to shift states which have the wrong spin, $(H + shift * S^2)|ψ> = E|ψ>$. +* [`qiskit_addon_sqd.fermion.solve_fermion()`](/docs/api/qiskit-addon-sqd/fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") now accepts a `shift` argument used to shift states which have the wrong spin, $(H + shift * S^2)|ψ> = E|ψ>$. <span id="release-notes-0-11-0-upgrade-notes" /> @@ -84,7 +84,7 @@ in_page_toc_max_heading_level: 2 ### Upgrade Notes -* [`qiskit_addon_sqd.fermion.solve_fermion()`](fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") now accept trailing `kwargs`, which will be passed directly to [pyscf.fci.selected\_ci.kernel\_fixed\_space](https://pyscf.org/pyscf_api_docs/pyscf.fci.html#pyscf.fci.selected_ci.kernel_fixed_space) under the hood to calculate the target state. The `max_davidson` argument should now be passed as `max_cycle` in both functions. +* [`qiskit_addon_sqd.fermion.solve_fermion()`](/docs/api/qiskit-addon-sqd/fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](/docs/api/qiskit-addon-sqd/fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") now accept trailing `kwargs`, which will be passed directly to [pyscf.fci.selected\_ci.kernel\_fixed\_space](https://pyscf.org/pyscf_api_docs/pyscf.fci.html#pyscf.fci.selected_ci.kernel_fixed_space) under the hood to calculate the target state. The `max_davidson` argument should now be passed as `max_cycle` in both functions. <span id="release-notes-0-11-0-bug-fixes" /> @@ -106,7 +106,7 @@ in_page_toc_max_heading_level: 2 ### Upgrade Notes -* Removed `qiskit_addon_sqd.fermion.flip_orbital_occupancies()`. Users no longer need to flip the orbital occupancies output from [`qiskit_addon_sqd.fermion.solve_fermion()`](fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals"); they will be output in the order expected by [`qiskit_addon_sqd.configuration_recovery.recover_configurations()`](configuration-recovery#recover_configurations "qiskit_addon_sqd.configuration_recovery.recover_configurations"): `tuple(array([occ_a_0, ..., occ_a_N]), array([occ_b_0, ..., occ_b_N]))`. +* Removed `qiskit_addon_sqd.fermion.flip_orbital_occupancies()`. Users no longer need to flip the orbital occupancies output from [`qiskit_addon_sqd.fermion.solve_fermion()`](/docs/api/qiskit-addon-sqd/fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](/docs/api/qiskit-addon-sqd/fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals"); they will be output in the order expected by [`qiskit_addon_sqd.configuration_recovery.recover_configurations()`](/docs/api/qiskit-addon-sqd/configuration-recovery#recover_configurations "qiskit_addon_sqd.configuration_recovery.recover_configurations"): `tuple(array([occ_a_0, ..., occ_a_N]), array([occ_b_0, ..., occ_b_N]))`. <span id="release-notes-0-10-0-deprecation-notes" /> @@ -114,7 +114,7 @@ in_page_toc_max_heading_level: 2 ### Deprecation Notes -* The `avg_occupancies` argument to [`qiskit_addon_sqd.configuration_recovery.recover_configurations()`](configuration-recovery#recover_configurations "qiskit_addon_sqd.configuration_recovery.recover_configurations") should now be a length-2 tuple containing the spin-up and spin-down occupancies, respectively. +* The `avg_occupancies` argument to [`qiskit_addon_sqd.configuration_recovery.recover_configurations()`](/docs/api/qiskit-addon-sqd/configuration-recovery#recover_configurations "qiskit_addon_sqd.configuration_recovery.recover_configurations") should now be a length-2 tuple containing the spin-up and spin-down occupancies, respectively. Old format: `array([occ_b_N, ..., occ_b_0, occ_a_N, ..., occ_a_0])` @@ -126,7 +126,7 @@ in_page_toc_max_heading_level: 2 ### Bug Fixes -* Fixed a bug which would cause the energy output from [`qiskit_addon_sqd.fermion.solve_fermion()`](fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") to be incorrect when the input `spin_sq` deviates from the spin^2 of the output wavefunction. +* Fixed a bug which would cause the energy output from [`qiskit_addon_sqd.fermion.solve_fermion()`](/docs/api/qiskit-addon-sqd/fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") to be incorrect when the input `spin_sq` deviates from the spin^2 of the output wavefunction. <span id="release-notes-0-9-0" /> @@ -140,7 +140,7 @@ in_page_toc_max_heading_level: 2 ### Upgrade Notes -* [`qiskit_addon_sqd.fermion.optimize_orbitals()`](fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") and [`qiskit_addon_sqd.fermion.rotate_integrals()`](fermion#rotate_integrals "qiskit_addon_sqd.fermion.rotate_integrals") now require `k_flat` to specify the upper triangle (not including diagonal) of the rotation matrix, rather than the entire matrix. +* [`qiskit_addon_sqd.fermion.optimize_orbitals()`](/docs/api/qiskit-addon-sqd/fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") and [`qiskit_addon_sqd.fermion.rotate_integrals()`](/docs/api/qiskit-addon-sqd/fermion#rotate_integrals "qiskit_addon_sqd.fermion.rotate_integrals") now require `k_flat` to specify the upper triangle (not including diagonal) of the rotation matrix, rather than the entire matrix. <span id="release-notes-0-9-0-bug-fixes" /> @@ -148,7 +148,7 @@ in_page_toc_max_heading_level: 2 ### Bug Fixes -* Fixed an indexing error in [`qiskit_addon_sqd.configuration_recovery()`](configuration-recovery#module-qiskit_addon_sqd.configuration_recovery "qiskit_addon_sqd.configuration_recovery") which caused bits in the right half of the bitstring to be flipped with respect to the occupancies of the oritals associated with the left half of the bitstring. +* Fixed an indexing error in [`qiskit_addon_sqd.configuration_recovery()`](/docs/api/qiskit-addon-sqd/configuration-recovery#module-qiskit_addon_sqd.configuration_recovery "qiskit_addon_sqd.configuration_recovery") which caused bits in the right half of the bitstring to be flipped with respect to the occupancies of the oritals associated with the left half of the bitstring. * pyscf is no longer considered a dependency on Windows, where it fails to install. However, Windows remains an unsupported platform. @@ -174,7 +174,7 @@ in_page_toc_max_heading_level: 2 ### Upgrade Notes -* The ground state returned by [`qiskit_addon_sqd.fermion.solve_fermion()`](fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") will now be an instance of [`qiskit_addon_sqd.fermion.SCIState`](fermion#scistate "qiskit_addon_sqd.fermion.SCIState"), rather than a PySCF `SCIVector` instance. +* The ground state returned by [`qiskit_addon_sqd.fermion.solve_fermion()`](/docs/api/qiskit-addon-sqd/fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") will now be an instance of [`qiskit_addon_sqd.fermion.SCIState`](/docs/api/qiskit-addon-sqd/fermion#scistate "qiskit_addon_sqd.fermion.SCIState"), rather than a PySCF `SCIVector` instance. <span id="release-notes-0-7-0" /> @@ -188,9 +188,9 @@ in_page_toc_max_heading_level: 2 ### Bug Fixes -* Fixed a bug in [`qiskit_addon_sqd.fermion.solve_fermion()`](fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") which causes the determinants for spin-up and spin-down to be incorrectly flipped before solving. +* Fixed a bug in [`qiskit_addon_sqd.fermion.solve_fermion()`](/docs/api/qiskit-addon-sqd/fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](/docs/api/qiskit-addon-sqd/fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") which causes the determinants for spin-up and spin-down to be incorrectly flipped before solving. -* Fixed a bug in open-shell workflows which would cause [`qiskit_addon_sqd.fermion.optimize_orbitals()`](fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") to crash with a `malloc` error. +* Fixed a bug in open-shell workflows which would cause [`qiskit_addon_sqd.fermion.optimize_orbitals()`](/docs/api/qiskit-addon-sqd/fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") to crash with a `malloc` error. <span id="release-notes-0-6-0" /> @@ -204,7 +204,7 @@ in_page_toc_max_heading_level: 2 ### Upgrade Notes -* Specifying `addresses` as a keyword argument to [`qiskit_addon_sqd.fermion.solve_fermion()`](fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") is no longer supported. Users may still pass `addresses` as the first positional argument; however, this usage is deprecated. Users are encouraged to pass the bitstring matrix defining the subspace as the first positional arguments to these functions, as shown below. +* Specifying `addresses` as a keyword argument to [`qiskit_addon_sqd.fermion.solve_fermion()`](/docs/api/qiskit-addon-sqd/fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](/docs/api/qiskit-addon-sqd/fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") is no longer supported. Users may still pass `addresses` as the first positional argument; however, this usage is deprecated. Users are encouraged to pass the bitstring matrix defining the subspace as the first positional arguments to these functions, as shown below. To upgrade, change this code @@ -255,7 +255,7 @@ in_page_toc_max_heading_level: 2 ### Deprecation Notes -* The `qiskit_addon_sqd.fermion.bitstring_matrix_to_sorted_addresses()` function has been deprecated in favor of [`qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs()`](fermion#bitstring_matrix_to_ci_strs "qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs"). These two functions behave the same with one key exception – `qiskit_addon_sqd.fermion.bitstring_matrix_to_sorted_addresses()` returns the configurations as `tuple(spin_dn, spin_up)`; whereas, [`qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs()`](fermion#bitstring_matrix_to_ci_strs "qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs") returns the configurations as `tuple(spin_up, spin_dn)`. +* The `qiskit_addon_sqd.fermion.bitstring_matrix_to_sorted_addresses()` function has been deprecated in favor of [`qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs()`](/docs/api/qiskit-addon-sqd/fermion#bitstring_matrix_to_ci_strs "qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs"). These two functions behave the same with one key exception – `qiskit_addon_sqd.fermion.bitstring_matrix_to_sorted_addresses()` returns the configurations as `tuple(spin_dn, spin_up)`; whereas, [`qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs()`](/docs/api/qiskit-addon-sqd/fermion#bitstring_matrix_to_ci_strs "qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs") returns the configurations as `tuple(spin_up, spin_dn)`. To migrate @@ -276,7 +276,7 @@ in_page_toc_max_heading_level: 2 ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(bs_matrix, open_shell=True) ``` -* The `addresses` argument to [`qiskit_addon_sqd.fermion.solve_fermion()`](fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") has been deprecated in favor of `bitstring_matrix`. Users are no longer required to convert their configurations to integers; instead, they should now pass in the bitstring matrix specifying the subspace onto which to project and diagonalize the Hamiltonian. The conversion to the integer representation of determinants will be done internally. +* The `addresses` argument to [`qiskit_addon_sqd.fermion.solve_fermion()`](/docs/api/qiskit-addon-sqd/fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](/docs/api/qiskit-addon-sqd/fermion#optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") has been deprecated in favor of `bitstring_matrix`. Users are no longer required to convert their configurations to integers; instead, they should now pass in the bitstring matrix specifying the subspace onto which to project and diagonalize the Hamiltonian. The conversion to the integer representation of determinants will be done internally. <span id="release-notes-0-6-0-bug-fixes" /> @@ -284,7 +284,7 @@ in_page_toc_max_heading_level: 2 ### Bug Fixes -* Fixed a bug in [`qiskit_addon_sqd.configuration_recovery.recover_configurations()`](configuration-recovery#recover_configurations "qiskit_addon_sqd.configuration_recovery.recover_configurations") which would sometimes cause a divide-by-zero error when calculating individual bit-flip probability. +* Fixed a bug in [`qiskit_addon_sqd.configuration_recovery.recover_configurations()`](/docs/api/qiskit-addon-sqd/configuration-recovery#recover_configurations "qiskit_addon_sqd.configuration_recovery.recover_configurations") which would sometimes cause a divide-by-zero error when calculating individual bit-flip probability. * Fixes a bug that caused configuration recovery to fail on bitstrings of length greater than 72. @@ -300,7 +300,7 @@ in_page_toc_max_heading_level: 2 ### Upgrade Notes -* The [`qiskit_addon_sqd.counts.generate_counts_bipartite_hamming()`](counts#generate_counts_bipartite_hamming "qiskit_addon_sqd.counts.generate_counts_bipartite_hamming"), [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](subsampling#postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"), and [`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight()`](configuration-recovery#post_select_by_hamming_weight "qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight") now require the `hamming_right` and `hamming_left` arguments to be specified as keyword arguments. Additionally, the `samples_per_batch` and `n_batches` arguments to [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](subsampling#postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample") should now be passed as keyword arguments. +* The [`qiskit_addon_sqd.counts.generate_counts_bipartite_hamming()`](/docs/api/qiskit-addon-sqd/counts#generate_counts_bipartite_hamming "qiskit_addon_sqd.counts.generate_counts_bipartite_hamming"), [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](/docs/api/qiskit-addon-sqd/subsampling#postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"), and [`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight()`](/docs/api/qiskit-addon-sqd/configuration-recovery#post_select_by_hamming_weight "qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight") now require the `hamming_right` and `hamming_left` arguments to be specified as keyword arguments. Additionally, the `samples_per_batch` and `n_batches` arguments to [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](/docs/api/qiskit-addon-sqd/subsampling#postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample") should now be passed as keyword arguments. To upgrade @@ -370,7 +370,7 @@ This is a minor release which introduces a couple of small, but important, break ### Upgrade Notes -* The [`qiskit_addon_sqd.counts.generate_counts_bipartite_hamming()`](counts#generate_counts_bipartite_hamming "qiskit_addon_sqd.counts.generate_counts_bipartite_hamming"), [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](subsampling#postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"), and [`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight()`](configuration-recovery#post_select_by_hamming_weight "qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight") now take the `hamming_right` positional argument before the `hamming_left` argument to better match the rest of the workflow. +* The [`qiskit_addon_sqd.counts.generate_counts_bipartite_hamming()`](/docs/api/qiskit-addon-sqd/counts#generate_counts_bipartite_hamming "qiskit_addon_sqd.counts.generate_counts_bipartite_hamming"), [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](/docs/api/qiskit-addon-sqd/subsampling#postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"), and [`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight()`](/docs/api/qiskit-addon-sqd/configuration-recovery#post_select_by_hamming_weight "qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight") now take the `hamming_right` positional argument before the `hamming_left` argument to better match the rest of the workflow. 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z@?xnjTyO(VIINM^Wn#nF6zHIy<K3u{K%J3aTkB&JIt<UhC5N5el*QxIk_F0wi&*)= zB84GIRU!<E5y$7V+L*iwSRG3VCF<wCZVAuSn6OWi2j|fj8zmc>)zBD%9in7F(z5r$ zWi#Bp-n`Uu(db_ZDe<6$sb9JwSd9V5%q2Umtc#dfo~U*I;S4sBrm1n4XaCOSiLY~I zqUo509xC0G3{9PL7|VB6s7PN-Ab&fQ-06aQ;{SF10_xxE7k#;`0$=3o_q;~lsHL*Y zR;V6X`F;g^GjX3Tn`H==@Rz>jrYNsdWBuOu-lR2^++w6M;csQ$)w^;mf|B<A-znPv E1Nj0{nE(I) literal 0 HcmV?d00001 diff --git a/scripts/config/cspell/cSpell.json b/scripts/config/cspell/cSpell.json index 476206d1805..da5692c0bf5 100644 --- a/scripts/config/cspell/cSpell.json +++ b/scripts/config/cspell/cSpell.json @@ -44,7 +44,7 @@ "ebitsa", "ebitsb", "pmzero", - "resuce" // todo fix in qiskit-addon-obp tutorial + "textrm" ], "ignoreRegExpList": [ // Markdown links diff --git a/scripts/config/cspell/dictionaries/qiskit.txt b/scripts/config/cspell/dictionaries/qiskit.txt index 33c47509008..6f112dc057d 100644 --- a/scripts/config/cspell/dictionaries/qiskit.txt +++ b/scripts/config/cspell/dictionaries/qiskit.txt @@ -449,4 +449,12 @@ mdash circo backprop ncols -CLARABEL \ No newline at end of file +CLARABEL +walltime +hcore +SQSD +milli +fontdict +fontsize +Utar +nocc \ No newline at end of file diff --git a/scripts/js/lib/api/updateLinks.ts b/scripts/js/lib/api/updateLinks.ts index eb10ddd1055..49b542217d3 100644 --- a/scripts/js/lib/api/updateLinks.ts +++ b/scripts/js/lib/api/updateLinks.ts @@ -268,7 +268,7 @@ export async function updateLinks( }); if (relativizedLink) { - node.url = relativizedLink.url; + node.url = transformSpecialCaseUrl(relativizedLink.url); if (textNode && relativizedLink.text) { textNode.value = relativizedLink.text; } diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index c72892720b4..827eced0465 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -233,7 +233,15 @@ const ALWAYS_IGNORED_URLS__SHOULD_FIX: string[] = [ "/api/qiskit/qiskit.quantum_info.SparsePauliOp", "/api/qiskit/qiskit.transpiler.CouplingMap", "/api/qiskit/qiskit.primitives.StatevectorEstimator", - "/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2" + "/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2", + + // from qiskit-addon-sqd + "/docs/en/tutorials/sample-based-krylov-quantum-diagonalization", + "/docs/en/tutorials/sample-based-quantum-diagonalization", + "/docs/guides/get-started-with-primitives#get-started-with-sampler", + "/docs/api/qiskit/transpiler_preset#generate_preset_pass_manager", + "../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian", + ]; export const ALWAYS_IGNORED_URLS = new Set([ From 501f6360cf23abe0d614e538c3ddbe2f79f82153 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 15 May 2026 14:08:21 -0400 Subject: [PATCH 055/104] adds cutting --- docs/addons/qiskit-addon-cutting/_toc.json | 89 + .../explanations/index.mdx | 146 + ...erate-exact-quasi-dists-from-sampler.ipynb | 175 + 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+ "url": "/docs/addons/qiskit-addon-cutting/install" + }, + { + "title":"Explainations", + "children": [ + { + "title": "Explanatory material", + "url": "/docs/addons/qiskit-addon-cutting/explanations/index" + } + ] + }, + { + "title": "Guides", + "children": [ + { + "title": "How to generate exact quasiprobability distributions from Sampler", + "url": "/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-quasi-dists-from-sampler" + }, + { + "title": "How to generate exact sampling coefficients", + "url": "/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients" + }, + { + "title": "How to perform a cutting workflow with multiple observables", + "url": "/docs/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables" + }, + { + "title": "How to place wire cuts using a single-qubit CutWire instruction", + "url": "/docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires" + } + ], + "url": "/docs/addons/qiskit-addon-cutting/how-tos/index" + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-cutting" + } + ], + "collapsible": false + }, + { + "title": "Tutorials", + "collapsible": false, + "children": [ + { + "title": "Gate Cutting to Reduce Circuit Width", + "url": "/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width" + }, + { + "title": "Gate Cutting to Reduce Circuit Depth", + "url": "/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth" + }, + { + "title": "Wire Cutting Phrased as a Two-Qubit Move Instruction", + "url": "/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction" + }, + { + "title": "Automatically find cuts", + "url": "/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding" + } + ] + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Circuit cutting API reference", + "url": "/docs/api/qiskit-addon-cutting" + } + ] + } + ] +} diff --git a/docs/addons/qiskit-addon-cutting/explanations/index.mdx b/docs/addons/qiskit-addon-cutting/explanations/index.mdx new file mode 100644 index 00000000000..052a80a80c3 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/explanations/index.mdx @@ -0,0 +1,146 @@ +--- +title: "Explanatory material" +description: "Explanatory material for the lastest version of Circuit cutting" +--- + +<span id="circuit-cutting-explanation" /> + +# Explanatory material + +## Overview of circuit cutting + +Circuit cutting is a technique to increase the size of circuits we can run on quantum hardware at the cost of an additional sampling overhead. A larger quantum circuit can be decomposed by cutting its gates and/or wires, resulting in smaller circuits which can be executed within the constraints of available quantum hardware. The results of these smaller circuits are combined to reconstruct the outcome of the original problem. Circuit cutting can also be used to engineer gates between distant qubits which would otherwise require a large swap overhead. + +## Key terms + +* subcircuits: The set of circuits resulting from cutting gates in a [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) and then separating the disconnected qubit subsets into smaller circuits. These circuits contain [`SingleQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-single-qubit-qpd-gate#qiskit_addon_cutting.qpd.SingleQubitQPDGate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s and will be used to instantiate each unique subexperiment. +* subexperiments: A term used to describe the unique circuit samples associated with a subcircuit. These circuits have had their [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate")s decomposed into local Qiskit gates and measurements. Subexperiments are the circuits sent to the backend for execution. +* decompose: We try to honor the Qiskit notion of “decompose” in the documentation and API, which loosely means transforming a gate into a less-abstracted representation. *Occasionally*, we may use the term “decompose” to refer to the act of inserting [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate") instances into quantum circuits as “decomposing” a gate or wire; however, we try to use terms like “partition” and “cut” when referring to this to avoid ambiguity with Qiskit language. + +## Circuit cutting as a quasiprobability decomposition (QPD) + +Quasiprobability decomposition is a technique which can be used to simulate quantum circuit executions that go beyond the actual capabilities of current quantum hardware while using that same hardware. It forms the basis of many error mitigation techniques, which allow simulating a noise-free quantum computer using a noisy one. Circuit cutting techniques, which allow simulating a quantum circuit using fewer qubits than would otherwise be necessary, can also be phrased in terms of a quasiprobability decomposition. No matter the goal, the cost of the quasiprobability decomposition is an exponential overhead in the number of circuit executions which must be performed. In certain cases, this tradeoff is worth it, because it can allow the estimation of quantities that would otherwise be impossible on today’s hardware. + +There are two types of cuts: gate cuts and wire cuts. Gate cuts, also known as “space-like” cuts, exist when the cut goes through a gate operating on two (or more) qubits. Wire cuts, also known as “time-like” cuts, are direct cuts through a qubit wire, essentially a single-qubit identity gate that has been cut into two pieces. In this package, a wire cut is represented by introducing a new qubit into the circuit and moving remaining operations after the cut identity gate to the new qubit; see [Wire cutting phrased as a two-qubit Move operation](#wire-cutting-as-move), below. + +There are [three settings](https://research.ibm.com/blog/circuit-knitting-with-classical-communication) to consider for circuit cutting. The first is where only local operations (LO) \[i.e., local *quantum* operations] are available. The other settings introduce classical communication between the circuit executions, which is known in the quantum information literature as LOCC, for [local operations and classical communication](https://en.wikipedia.org/wiki/LOCC). The LOCC can be either near-time, one-directional communication between the circuit executions (the second setting), or real-time, bi-directional communication (the third setting). + +As mentioned above, the cost of any simulation based on quasiprobability distribution is an exponential sampling overhead. The sampling overhead is the factor by which the overall number of shots must increase for the quasiprobability decomposition to result in the same amount of error, $\epsilon$, as one would get by executing the original circuit. The overhead of a cut gate depends on which gate is cut; see the final appendix of \[[1](https://arxiv.org/abs/2205.00016)] for details. For instance, a single cut CNOT gate incurs a sampling overhead of 9 \[[2](https://arxiv.org/abs/1909.07534),[6](https://arxiv.org/abs/2312.11638)]. A circuit with $n$ wire cuts incurs a sampling overhead of O($16^n$) when classical communication is not available (LO setting); this is reduced to O($4^n$) when classical communication is available (LOCC setting) \[[4](https://arxiv.org/abs/2302.03366)]. However, wire cutting with classical communication (LOCC) is not yet supported by this package (see issue [#264](https://github.com/Qiskit/qiskit-addon-cutting/issues/264)). + +The QPD can be given explicitly as follows: + +$$ +\mathcal{U} = \sum_i a_i \mathcal{F}_i , +$$ + +where $\mathcal{U}$ is the channel implementing the desired operation, and each $a_i$ is a real coefficient corresponding to a quantum channel, $\mathcal{F}_i$, that is realizable on hardware. + +Note that because this is a sum of channels, *not* a sum of unitaries, expectation values can be evaluated efficiently \[[2](https://arxiv.org/abs/1909.07534)]. + +Results equivalent to original unitary channel can be obtained by a post-processing method, given the coefficients $a_i$ and the outcome of each experiment corresponding to the different local ($\mathcal{F}_i$) channels. This post-processing boosts the magnitude of each measurement outcome by $\sum_i \left| a_i \right|$, resulting in a sampling overhead of that quantity squared \[[6](https://arxiv.org/abs/2312.11638)]: + +$$ +\mathrm{Sampling\ overhead} = \left( \sum_i \lvert a_i \rvert \right)^2 . +$$ + +For more detailed information on the quasiprobability decomposition technique, refer to the paper, Error mitigation for short-depth quantum circuits \[[5](https://arxiv.org/abs/1612.02058)]. + +The essential idea of gate cutting is to replace a two-qubit gate with a linear combination of quantum channels \[Eq. [(1)](#equation-eq-qpd)] that, when recombined, will allow reconstruction of the result for physically measurable quantities like expectation values. Note that “global phase” is not something that can be physically measured, so we can disregard it when specifying quasiprobability decompositions. + +<span id="an-example-cutting-a-rzzgate" /> + +## An example: cutting a [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) + +As a basic and explicit example, let us consider the decomposition of a cut [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate). + +As shown in \[[2](https://arxiv.org/abs/1909.07534)], a quantum circuit which implements an [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) can be simulated by performing six subexperiments where the [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) in the original circuit has been replaced with only local (single-qubit) operations \[the $\mathcal{F}_i$‘s in Eq. [(1)](#equation-eq-qpd)]. The result is then reconstructed by combining the subexperiment results with certain coefficients \[the $a_i$‘s in Eq. [(1)](#equation-eq-qpd)], which can be either positive or negative. Given the $\theta$ parameter of the [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), the six subexperiments are as follows: + +1. With coefficient $a_1 = \cos^2 (\theta/2)$, do nothing ($I \otimes I$, where $I$ is the identity operation on a single qubit). +2. With coefficient $a_2 = \sin^2 (\theta/2)$, perform a [`ZGate`](/docs/api/qiskit/qiskit.circuit.library.ZGate) on each qubit ($Z \otimes Z$). +3. With coefficient $a_3 = -\sin(\theta)/2$, perform a projective measurement in the Z basis on the first qubit and an [`SGate`](/docs/api/qiskit/qiskit.circuit.library.SGate) gate on the second qubit (denote this as $M_z \otimes S$). If the result of the measurement is 1, flip the sign of that outcome’s contribution during reconstruction. +4. With coefficient $a_4 = \sin(\theta)/2$, perform a projective measurement in the Z basis on the first qubit and an [`SdgGate`](/docs/api/qiskit/qiskit.circuit.library.SdgGate) gate on the second qubit (denote this as $M_z \otimes S^\dagger$). If the result of the measurement is 1, flip the sign of that outcome’s contribution during reconstruction. +5. Same as term 3 ($a_5 = a_3$), but swap the qubits ($S \otimes M_z$). +6. Same as term 4 ($a_6 = a_4$), but swap the qubits ($S^\dagger \otimes M_z$). + +Equation [(1)](#equation-eq-qpd) for [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) can thus be written as a sum of the six terms listed above. The following plot shows the magnitude of each coeffient (negative coefficients are in orange) as a function of $\theta$. The square root of the optimal sampling overhead, denoted by $\gamma$, is given by the sum of the absolute coefficients. + +![../\_images/index-1.svg](/docs/images/addons/qiskit-addon-cutting/index-1.svg) + +Let’s consider some special points in this plot: + +* When $\theta = 0$, the gate has no effect, and the sampling overhead is 1. (Because the overhead is multiplicative, this is equivalent to there being no overhead.) +* When $\theta = \pi$, the gate is equivalent to $Z \otimes Z$ up to a global phase, and the sampling overhead is again 1. +* The maximum sampling overhead of $3^2 = 9$ is reached at a ZZ Rotation of $\theta=\pi/2$. We call this point the [`CXGate`](/docs/api/qiskit/qiskit.circuit.library.CXGate) family because this rotation is equivalent to a CXGate up to (single-qubit) local unitary operations. This point is also equivalent, up to local unitary operations, to [`CZGate`](/docs/api/qiskit/qiskit.circuit.library.CZGate), [`CYGate`](/docs/api/qiskit/qiskit.circuit.library.CYGate), [`CHGate`](/docs/api/qiskit/qiskit.circuit.library.CHGate), and [`ECRGate`](/docs/api/qiskit/qiskit.circuit.library.ECRGate). +* The ZZ rotation at $\theta=\pi/4$ has sampling overhead of $3+2\sqrt{2} \approx 5.828$. We call this the [`CSGate`](/docs/api/qiskit/qiskit.circuit.library.CSGate) family because this rotation is equivalent to a CSGate up to (single-qubit) local operations. This family also includes [`CSdgGate`](/docs/api/qiskit/qiskit.circuit.library.CSdgGate) and [`CSXGate`](/docs/api/qiskit/qiskit.circuit.library.CSXGate). +* Likewise, [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), and [`RZXGate`](/docs/api/qiskit/qiskit.circuit.library.RZXGate) are all locally equivalent to [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate). The controlled rotations [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate), and [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) at an angle of $2\theta$ are locally equivalent to [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) at an angle of $\theta$. + +## More general cut two-qubit gates via the KAK decomposition + +We can formalize this notion of local unitary equivalence and expand it to all two-qubit gates using the KAK decomposition, given by + +$$ +U = (V_1 \otimes V_2) \exp \left[ i \left( \theta_x \, X \otimes X + \theta_y \, Y \otimes Y + \theta_z \, Z \otimes Z \right) \right] (V_3 \otimes V_4) , +$$ + +where $V_1$, $V_2$, $V_3$, and $V_4$ are local, single-qubit operations, and the two-qubit portion of the interaction is parametrized entirely by $\vec{\theta} = (\theta_x, \theta_y, \theta_z)$. By convention, we have chosen $\vec{\theta}$ to be in the “Weyl chamber” restricted by $\pi/4 \geq \theta_x \geq \theta_y \geq | \theta_z | \geq 0$ \[[6](https://arxiv.org/abs/2312.11638)]. For more information on the KAK decomposition, see Ref. \[[7](https://arxiv.org/abs/quant-ph/0209120)]. + +The code that generates subexperiments from the KAK decomposition currently follows Ref. \[[3](https://arxiv.org/abs/2006.11174)], which is now known to be non-optimal. A provably optimal method has been presented in Ref. \[[6](https://arxiv.org/abs/2312.11638)], but this newer method has not yet been implemented in this package (see issue [#531](https://github.com/Qiskit/qiskit-addon-cutting/issues/531)). + +<span id="wire-cutting-phrased-as-a-two-qubit-move-operation" /> + +<span id="wire-cutting-as-move" /> + +## Wire cutting phrased as a two-qubit [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") operation + +A wire cut is represented fundamentally by this package as a two-qubit [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction, which is defined as a reset of the second qubit followed by a swap of both qubits. Equivalently, the operation is defined as transferring the state of the first qubit wire to the second qubit wire, while simultaneously discarding the state of the second qubit wire (the first qubit ends up in state $\lvert 0 \rangle$). + +We have chosen to represent wire cuts in this way primarily because it is consistent with the way one must treat wire cuts when acting on physical qubits: for instance, a wire cut might take the state of physical qubit $n$ and continue it as physical qubit $m$ after the cut. Our choice also has the benefit of allowing us to think of “instruction cutting” as a unified framework for considering both wire cuts and gate cuts in the same formalism, being that a wire cut is just a cut [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction. + +More information on this formalism is given in Sec. 3 of Ref. \[[4](https://arxiv.org/abs/2302.03366)] + +If you prefer to place cut wires abstractly on a single qubit wire, please see the [how-to guide on placing wire cuts using a single-qubit instruction](how-tos/how-to-specify-cut-wires), which explains how to use the [`cut_wires()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#qiskit_addon_cutting.cut_wires "qiskit_addon_cutting.cut_wires") function to convert a circuit with [`CutWire`](/docs/api/qiskit-addon-cutting/instructions-cut-wire#qiskit_addon_cutting.instructions.CutWire "qiskit_addon_cutting.instructions.CutWire") instructions to a circuit with [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move")s on additional qubits. + +## Sample weights in the Qiskit addon for circuit cutting + +In this package, the number of samples taken from the distribution is generally controlled by a `num_samples` argument, and each sample has an associated weight which is used during expectation value reconstruction. Each weight with absolute value above a threshold of 1 / `num_samples` will be evaluated exactly. The remaining low-probability elements – those in the tail of the distribution – will then be sampled, resulting in at most `num_samples` unique weights. Setting `num_samples` to infinity indicates that all weights should be generated rigorously, rather than by sampling from the distribution. + +Much of the circuit cutting literature describes a process where we sample from the distribution, take a single shot, then sample from the distribution again and repeat; however, this is not feasible in practice, so we instead perform all sampling upfront. For now, because of limitations in version 1 of the Qiskit primitives, we take a fixed number of shots for each considered subexperiment and send the subexperiments to the backend(s) in batches. During reconstruction, each subexperiment contributes to the final result with proportion equal to its weight. One must ensure the number of shots taken is sufficient for the heaviest weighted subexperiment. In the future, we plan to support passing an individual `shots` count with each subexperiment to Qiskit Runtime, so that each subexperiment will be run with a number of shots proportional to that subexperiment’s weight in the final result (see issue [#532](https://github.com/Qiskit/qiskit-addon-cutting/issues/532)). This per-experiment shots count is a new feature enabled by version 2 of the Qiskit primitives. + +## Sampling overhead reference table + +The below table provides the sampling overhead factor for a variety of two-qubit instructions, provided that only a single instruction is cut. + +| Instruction(s) | KAK decomposition angles | Sampling overhead factor | | | | | | | +| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------ | --------------------------- | --------- | -------------------- | -------------------- | ----------------- | -------------------- | -------------------------------------------------------------- | +| [`CSGate`](/docs/api/qiskit/qiskit.circuit.library.CSGate), [`CSdgGate`](/docs/api/qiskit/qiskit.circuit.library.CSdgGate), [`CSXGate`](/docs/api/qiskit/qiskit.circuit.library.CSXGate) | $(\pi/8, 0, 0)$ | $3+2\sqrt{2} \approx 5.828$ | | | | | | | +| [`CXGate`](/docs/api/qiskit/qiskit.circuit.library.CXGate), [`CYGate`](/docs/api/qiskit/qiskit.circuit.library.CYGate), [`CZGate`](/docs/api/qiskit/qiskit.circuit.library.CZGate), [`CHGate`](/docs/api/qiskit/qiskit.circuit.library.CHGate), [`ECRGate`](/docs/api/qiskit/qiskit.circuit.library.ECRGate) | $(\pi/4, 0, 0)$ | $3^2=9$ | | | | | | | +| [`iSwapGate`](/docs/api/qiskit/qiskit.circuit.library.iSwapGate), [`DCXGate`](/docs/api/qiskit/qiskit.circuit.library.DCXGate) | $(\pi/4, \pi/4, 0)$ | $7^2=49$ | | | | | | | +| [`SwapGate`](/docs/api/qiskit/qiskit.circuit.library.SwapGate) | $(\pi/4,\pi/4,\pi/4)$ | | | | | | | | +| [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), [`RZXGate`](/docs/api/qiskit/qiskit.circuit.library.RZXGate) | \$( | \theta/2 | , 0, 0)\$ | \$\left\[1 + 2 \left | \sin(\theta)\right | \right]^2\$ | | | +| [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate), [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) | \$( | \theta/4 | , 0, 0)\$ | \$\left\[1 + 2 \left | \sin(\theta/2)\right | \right]^2\$ | | | +| [`XXPlusYYGate`](/docs/api/qiskit/qiskit.circuit.library.XXPlusYYGate), [`XXMinusYYGate`](/docs/api/qiskit/qiskit.circuit.library.XXMinusYYGate) | \$( | \theta/4 | , | \theta/4 | ,0)\$ | \$\left\[1+4\left | \sin(\theta/2)\right | +2\sin^2(\theta/2)\right]^2$(independent of$\beta\$ parameter) | +| [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") (cut wire) without classical communication (i.e., in the LO setting) | not applicable | $4^2=16$ | | | | | | | + +## Current limitations + +* The workflow only allows taking the *expectation value* of observables with respect to a circuit. Limited support for reconstructing an output probability distribution may be added to a future version of this package (see issue [#259](https://github.com/Qiskit/qiskit-addon-cutting/issues/259)). +* Due to current code limitations, some of the generated subexperiments are redundant. This can result in more subexperiments than expected, particularly when using wire cutting. This is tracked by issue [#262](https://github.com/Qiskit/qiskit-addon-cutting/issues/262). + +## References + +This module is based on the theory described in the following papers: + +\[1] Christophe Piveteau, David Sutter, *Circuit knitting with classical communication*, [https://arxiv.org/abs/2205.00016](https://arxiv.org/abs/2205.00016) + +\[2] Kosuke Mitarai, Keisuke Fujii, *Constructing a virtual two-qubit gate by sampling single-qubit operations*, [https://arxiv.org/abs/1909.07534](https://arxiv.org/abs/1909.07534) + +\[3] Kosuke Mitarai, Keisuke Fujii, *Overhead for simulating a non-local channel with local channels by quasiprobability sampling*, [https://arxiv.org/abs/2006.11174](https://arxiv.org/abs/2006.11174) + +\[4] Lukas Brenner, Christophe Piveteau, David Sutter, *Optimal wire cutting with classical communication*, [https://arxiv.org/abs/2302.03366](https://arxiv.org/abs/2302.03366) + +\[5] K. Temme, S. Bravyi, and J. M. Gambetta, *Error mitigation for short-depth quantum circuits*, [https://arxiv.org/abs/1612.02058](https://arxiv.org/abs/1612.02058) + +\[6] Lukas Schmitt, Christophe Piveteau, David Sutter, *Cutting circuits with multiple two-qubit unitaries*, [https://arxiv.org/abs/2312.11638](https://arxiv.org/abs/2312.11638) + +\[7] Jun Zhang, Jiri Vala, K. Birgitta Whaley, Shankar Sastry, *A geometric theory of non-local two-qubit operations*, [https://arxiv.org/abs/quant-ph/0209120](https://arxiv.org/abs/quant-ph/0209120) + diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-quasi-dists-from-sampler.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-quasi-dists-from-sampler.ipynb new file mode 100644 index 00000000000..6a41797a153 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-quasi-dists-from-sampler.ipynb @@ -0,0 +1,175 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f9e40036", + "metadata": {}, + "source": [ + "## How to generate exact quasiprobability distributions from Sampler\n", + "\n", + "This how-to guide is intended to show users how they can generate statevector-based quasiprobability distributions through the `qiskit.primitives.BaseSampler` interface." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "072055cb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:50.653585Z", + "iopub.status.busy": "2025-03-28T16:50:50.653274Z", + "iopub.status.idle": "2025-03-28T16:50:51.088193Z", + "shell.execute_reply": "2025-03-28T16:50:51.087485Z" + } + }, + "outputs": [], + "source": [ + "from qiskit import QuantumCircuit\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "from qiskit_addon_cutting import (\n", + " partition_problem,\n", + " generate_cutting_experiments,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "940334fd", + "metadata": {}, + "source": [ + "Prepare inputs to `generate_cutting_experiments`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dc4af922", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.091193Z", + "iopub.status.busy": "2025-03-28T16:50:51.090943Z", + "iopub.status.idle": "2025-03-28T16:50:51.116996Z", + "shell.execute_reply": "2025-03-28T16:50:51.116317Z" + } + }, + "outputs": [], + "source": [ + "circuit = QuantumCircuit(2)\n", + "circuit.h(0)\n", + "circuit.cx(0, 1)\n", + "observable = SparsePauliOp([\"ZZ\"])\n", + "partitioned_problem = partition_problem(\n", + " circuit=circuit, partition_labels=\"AB\", observables=observable.paulis\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "subobservables = partitioned_problem.subobservables" + ] + }, + { + "cell_type": "markdown", + "id": "d6361a9d", + "metadata": {}, + "source": [ + "Call `generate_cutting_experiments`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d095701f", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.119755Z", + "iopub.status.busy": "2025-03-28T16:50:51.119556Z", + "iopub.status.idle": "2025-03-28T16:50:51.128723Z", + "shell.execute_reply": "2025-03-28T16:50:51.127940Z" + } + }, + "outputs": [], + "source": [ + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits,\n", + " observables=subobservables,\n", + " num_samples=1000,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bc59b1be", + "metadata": {}, + "source": [ + "In order to calculate exact quasiprobability distributions for circuits with mid-circuit measurements, users will need to use the `ExactSampler` class from `qiskit_addon_cutting.utils.simulation`. The Qiskit Samplers do not support mid-circuit measurements in statevector mode." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7a74f709", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.131476Z", + "iopub.status.busy": "2025-03-28T16:50:51.130959Z", + "iopub.status.idle": "2025-03-28T16:50:51.135282Z", + "shell.execute_reply": "2025-03-28T16:50:51.134551Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting.utils.simulation import ExactSampler\n", + "\n", + "exact_sampler = ExactSampler()" + ] + }, + { + "cell_type": "markdown", + "id": "8d050fbe", + "metadata": {}, + "source": [ + "If `ExactSampler` is used, the quasiprobability distributions returned from `generate_cutting_experiments` will be exact and generated from the statevectors of the subexperiments." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7019d781", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.137659Z", + "iopub.status.busy": "2025-03-28T16:50:51.137468Z", + "iopub.status.idle": "2025-03-28T16:50:51.148731Z", + "shell.execute_reply": "2025-03-28T16:50:51.147960Z" + } + }, + "outputs": [], + "source": [ + "results = {\n", + " label: exact_sampler.run(subexperiment).result()\n", + " for label, subexperiment in subexperiments.items()\n", + "}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.ipynb new file mode 100644 index 00000000000..22592b66f83 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ccbf48d3", + "metadata": {}, + "source": [ + "## How to generate exact sampling coefficients\n", + "\n", + "This how-to guide is intended to show users how they can generate exact sampling coefficients to be used in reconstructing the simulated expectation value of the original circuit.\n", + "\n", + "First, we set up a simple cutting problem following the [first tutorial](../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "dc54656b", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:52.873918Z", + "iopub.status.busy": "2025-03-28T16:50:52.873283Z", + "iopub.status.idle": "2025-03-28T16:50:53.390761Z", + "shell.execute_reply": "2025-03-28T16:50:53.390002Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit.circuit.library import efficient_su2\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "from qiskit_addon_cutting import (\n", + " partition_problem,\n", + " generate_cutting_experiments,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dd147239", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:53.393546Z", + "iopub.status.busy": "2025-03-28T16:50:53.393230Z", + "iopub.status.idle": "2025-03-28T16:50:54.346876Z", + "shell.execute_reply": "2025-03-28T16:50:54.346177Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients/extracted-outputs/dd147239-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuit = efficient_su2(4, entanglement=\"linear\", reps=2)\n", + "circuit.assign_parameters([0.8] * len(circuit.parameters), inplace=True)\n", + "observable = SparsePauliOp([\"ZZZZ\"])\n", + "circuit.draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "0bce456c", + "metadata": {}, + "source": [ + "Partition the circuit between qubits 1 and 2 by cutting 2 CNOT gates." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d4ccf5b8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:54.349628Z", + "iopub.status.busy": "2025-03-28T16:50:54.349194Z", + "iopub.status.idle": "2025-03-28T16:50:54.584073Z", + "shell.execute_reply": "2025-03-28T16:50:54.583316Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients/extracted-outputs/d4ccf5b8-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "partitioned_problem = partition_problem(\n", + " circuit=circuit, partition_labels=\"AABB\", observables=observable.paulis\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "bases = partitioned_problem.bases\n", + "subobservables = partitioned_problem.subobservables\n", + "subcircuits[\"A\"].draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "44956cbb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:54.586659Z", + "iopub.status.busy": "2025-03-28T16:50:54.586391Z", + "iopub.status.idle": "2025-03-28T16:50:54.811013Z", + "shell.execute_reply": "2025-03-28T16:50:54.810276Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients/extracted-outputs/44956cbb-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[\"B\"].draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "fce55187", + "metadata": {}, + "source": [ + "### Demonstrate how to obtain all weights exactly\n", + "\n", + "If you wish to calculate all weights exactly, no matter how small, you can achieve this by passing infinity (`np.inf`) to `num_samples`:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8c56282f", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:54.813264Z", + "iopub.status.busy": "2025-03-28T16:50:54.813049Z", + "iopub.status.idle": "2025-03-28T16:50:54.867519Z", + "shell.execute_reply": "2025-03-28T16:50:54.866771Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.24999999999999992), <WeightType.EXACT: 1>),\n", + " (np.float64(0.24999999999999992), <WeightType.EXACT: 1>)]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits,\n", + " observables=subobservables,\n", + " num_samples=np.inf,\n", + ")\n", + "coefficients" + ] + }, + { + "cell_type": "markdown", + "id": "b5fc6579", + "metadata": {}, + "source": [ + "### Demonstrate how to find the minimum `num_samples` needed to retrieve all exact weights for 2 CNOT cuts\n", + "\n", + "When `num_samples` is set to a finite number, each weight whose absolute value is above a threshold of 1 / `num_samples` will be evaluated exactly. The remaining weights -- those in the tail of the distribution -- will then be sampled from, resulting in at most `num_samples` unique weights.\n", + "\n", + "In the case of a CNOT gate -- or any gate equivalent to it up to single-qubit unitaries -- each of the six weights of the quasi-probability decomposition have the same magnitude, so each gets sampled with a probability of $1/6$:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "78539fcc", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:54.870158Z", + "iopub.status.busy": "2025-03-28T16:50:54.869670Z", + "iopub.status.idle": "2025-03-28T16:50:54.874200Z", + "shell.execute_reply": "2025-03-28T16:50:54.873577Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mapping probabilities for a CNOT decomposition: [0.16666667 0.16666667 0.16666667 0.16666667 0.16666667 0.16666667]\n" + ] + } + ], + "source": [ + "print(f\"Mapping probabilities for a CNOT decomposition: {bases[0].probabilities}\")" + ] + }, + { + "cell_type": "markdown", + "id": "5770cf75", + "metadata": {}, + "source": [ + "In this example, we have cut two CNOT gates. Given that the probability of any given mapping in a CNOT decomposition is $1/6$, the probability of any given mapping in the _joint_ distribution combining the two cut CNOT gates is $(1/6)^2$. Therefore, we need to take at least $6^2$ weights in order to retrieve all exact weights from `generate_cutting_experiments`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f07a6cc3", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:54.876590Z", + "iopub.status.busy": "2025-03-28T16:50:54.876398Z", + "iopub.status.idle": "2025-03-28T16:50:54.881758Z", + "shell.execute_reply": "2025-03-28T16:50:54.881069Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of samples needed to retrieve exact weights: 36.0\n" + ] + } + ], + "source": [ + "from qiskit_addon_cutting.qpd import QPDBasis\n", + "from qiskit.circuit.library.standard_gates import CXGate\n", + "\n", + "qpd_basis_cx = QPDBasis.from_instruction(CXGate())\n", + "\n", + "\n", + "def _min_nonzero(seq, /):\n", + " \"\"\"Return the minimum value in a sequence, ignoring values near zero.\"\"\"\n", + " return min(x for x in seq if not np.isclose(x, 0))\n", + "\n", + "\n", + "num_cx_cuts = 2\n", + "\n", + "print(\n", + " f\"Number of samples needed to retrieve exact weights: {1 / _min_nonzero(qpd_basis_cx.probabilities)**num_cx_cuts}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "deb6ad1e", + "metadata": {}, + "source": [ + "#### Observe the coefficient weights returned from `generate_cutting_experiments` are `WeightType.EXACT`\n", + "\n", + "Above, we determined 36 samples would trigger the coefficients to be returned as exact. Here we set `num_samples` to exactly 36 to test this." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "43d32869", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:54.884011Z", + "iopub.status.busy": "2025-03-28T16:50:54.883576Z", + "iopub.status.idle": "2025-03-28T16:50:54.942752Z", + "shell.execute_reply": "2025-03-28T16:50:54.942054Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(-0.25), <WeightType.EXACT: 1>),\n", + " (np.float64(0.25), <WeightType.EXACT: 1>)]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits,\n", + " observables=subobservables,\n", + " num_samples=36,\n", + ")\n", + "coefficients" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables.ipynb new file mode 100644 index 00000000000..1f379dac2f4 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables.ipynb @@ -0,0 +1,393 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"How to perform a cutting workflow with multiple observables\"\n", + "description: \"How to perform a cutting workflow with multiple observables for the latest version of Circuit cutting\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "a42230b5-41d9-465c-904a-7d431cc72595", + "metadata": {}, + "source": [ + "# How to perform a cutting workflow with multiple observables" + ] + }, + { + "cell_type": "markdown", + "id": "6902602f-1392-4321-9cbe-5eb0921909b0", + "metadata": {}, + "source": [ + "### Create a circuit to cut" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5defa9fb-e928-4fcb-9ae3-bed8e8f2527b", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:57.096887Z", + "iopub.status.busy": "2025-03-28T16:50:57.096668Z", + "iopub.status.idle": "2025-03-28T16:50:58.481013Z", + "shell.execute_reply": "2025-03-28T16:50:58.480360Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables/extracted-outputs/5defa9fb-e928-4fcb-9ae3-bed8e8f2527b-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.circuit.library import EfficientSU2\n", + "\n", + "qc = EfficientSU2(4, entanglement=\"linear\", reps=2).decompose()\n", + "qc.assign_parameters([0.4] * len(qc.parameters), inplace=True)\n", + "\n", + "qc.draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "99344052-a7cc-4857-a77e-0db02003cb7d", + "metadata": {}, + "source": [ + "### Specify the observables of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "42c2d640-7cce-41c3-9683-920449215c9d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:58.484791Z", + "iopub.status.busy": "2025-03-28T16:50:58.483701Z", + "iopub.status.idle": "2025-03-28T16:50:58.489908Z", + "shell.execute_reply": "2025-03-28T16:50:58.489261Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.quantum_info import Pauli, SparsePauliOp\n", + "\n", + "observables = [\n", + " Pauli(\"-XZII\"),\n", + " SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"]),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "466343c4-35ce-496e-a887-5a126e0a6b40", + "metadata": {}, + "source": [ + "### Gather unique observable terms" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "defb33f4-820a-44aa-89ec-4f7eb13a49f5", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:58.492460Z", + "iopub.status.busy": "2025-03-28T16:50:58.491798Z", + "iopub.status.idle": "2025-03-28T16:50:58.561027Z", + "shell.execute_reply": "2025-03-28T16:50:58.560336Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting.utils.observable_terms import gather_unique_observable_terms\n", + "\n", + "unique_observable_terms = gather_unique_observable_terms(observables)" + ] + }, + { + "cell_type": "markdown", + "id": "81c1c9e1-e8a4-4a5c-80f3-35d00298459f", + "metadata": {}, + "source": [ + "### Perform cutting workflow" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9830f036-904e-4900-98b1-7991da2915d0", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:58.568971Z", + "iopub.status.busy": "2025-03-28T16:50:58.568447Z", + "iopub.status.idle": "2025-03-28T16:50:58.579358Z", + "shell.execute_reply": "2025-03-28T16:50:58.578737Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import partition_problem\n", + "\n", + "partitioned_problem = partition_problem(\n", + " circuit=qc, partition_labels=\"AABB\", observables=unique_observable_terms\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "subobservables = partitioned_problem.subobservables" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "77577d6f-4acc-468b-962c-81a09cc31fcd", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:58.581924Z", + "iopub.status.busy": "2025-03-28T16:50:58.581453Z", + "iopub.status.idle": "2025-03-28T16:50:58.854801Z", + "shell.execute_reply": "2025-03-28T16:50:58.850920Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit_addon_cutting import generate_cutting_experiments\n", + "\n", + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits, observables=subobservables, num_samples=np.inf\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "37253e37-51f9-4aa4-9f5e-d8a032d639ce", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:58.859697Z", + "iopub.status.busy": "2025-03-28T16:50:58.859332Z", + "iopub.status.idle": "2025-03-28T16:50:59.766313Z", + "shell.execute_reply": "2025-03-28T16:50:59.765302Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", + "\n", + "backend = FakeManilaV2()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f15e198e-7f0b-4507-a73f-e4aab2f286ed", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:59.773825Z", + "iopub.status.busy": "2025-03-28T16:50:59.773001Z", + "iopub.status.idle": "2025-03-28T16:51:07.997499Z", + "shell.execute_reply": "2025-03-28T16:51:07.996531Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", + "\n", + "# Transpile the subexperiments to ISA circuits\n", + "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", + "isa_subexperiments = {\n", + " label: pass_manager.run(partition_subexpts)\n", + " for label, partition_subexpts in subexperiments.items()\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5383d427-3285-43b1-8564-f451e1b4287e", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:08.002734Z", + "iopub.status.busy": "2025-03-28T16:51:08.001594Z", + "iopub.status.idle": "2025-03-28T16:51:08.452499Z", + "shell.execute_reply": "2025-03-28T16:51:08.444876Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import SamplerV2, Batch\n", + "\n", + "# Submit each partition's subexperiments as a single batch\n", + "with Batch(backend=backend) as batch:\n", + " sampler = SamplerV2(mode=batch)\n", + " jobs = {\n", + " label: sampler.run(subsystem_subexpts, shots=2**12)\n", + " for label, subsystem_subexpts in isa_subexperiments.items()\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "154f4cf0-c45d-41b2-8091-b23a47b30c22", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:08.472013Z", + "iopub.status.busy": "2025-03-28T16:51:08.471699Z", + "iopub.status.idle": "2025-03-28T16:51:34.412339Z", + "shell.execute_reply": "2025-03-28T16:51:34.411608Z" + } + }, + "outputs": [], + "source": [ + "# Retrieve results\n", + "results = {label: job.result() for label, job in jobs.items()}" + ] + }, + { + "cell_type": "markdown", + "id": "35c57f33-a128-4b0f-9a23-705cfecae993", + "metadata": {}, + "source": [ + "### Reconstruct the expectation value of each unique term" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9f5e64a7-a334-4278-9c61-e78bbbc36368", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:34.415031Z", + "iopub.status.busy": "2025-03-28T16:51:34.414827Z", + "iopub.status.idle": "2025-03-28T16:51:39.000491Z", + "shell.execute_reply": "2025-03-28T16:51:38.999738Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import reconstruct_expectation_values\n", + "\n", + "# Get expectation values for each observable term\n", + "reconstructed_term_expvals = reconstruct_expectation_values(\n", + " results,\n", + " coefficients,\n", + " subobservables, # or unique_observable_terms if the circuit did not separate\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "792853df-d4f9-43d1-9f7d-50e38768ceb8", + "metadata": {}, + "source": [ + "### Reconstruct the expectation value of the original operators" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b134b767-ce53-4438-866f-a25952cb985b", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:39.003144Z", + "iopub.status.busy": "2025-03-28T16:51:39.002785Z", + "iopub.status.idle": "2025-03-28T16:51:39.006758Z", + "shell.execute_reply": "2025-03-28T16:51:39.006291Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting.utils.observable_terms import (\n", + " reconstruct_observable_expvals_from_terms,\n", + ")\n", + "\n", + "reconstructed_expvals = reconstruct_observable_expvals_from_terms(\n", + " observables, dict(zip(unique_observable_terms, reconstructed_term_expvals))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "fd71a9ef-37cd-4687-9fe1-9b4ea3264f91", + "metadata": {}, + "source": [ + "### Compare the reconstructed expectation values with the exact expectation value from the original circuit and observables" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "986e5ce8-64a5-464a-9c43-45ccf1a65b2d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:39.008795Z", + "iopub.status.busy": "2025-03-28T16:51:39.008594Z", + "iopub.status.idle": "2025-03-28T16:51:39.023519Z", + "shell.execute_reply": "2025-03-28T16:51:39.022762Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed expectation values: [-0.20063001 0.48694563]\n", + "Exact expectation values: [-0.17994235 0.56254612]\n", + "Error in estimation: [-0.02068766 -0.07560049]\n", + "Relative error in estimation: [ 0.11496824 -0.13438986]\n" + ] + } + ], + "source": [ + "from qiskit_aer.primitives import EstimatorV2\n", + "\n", + "estimator = EstimatorV2()\n", + "exact_expvals = estimator.run([(qc, observables)]).result()[0].data.evs\n", + "print(\n", + " f\"Reconstructed expectation values: {np.real(np.round(reconstructed_expvals, 8))}\"\n", + ")\n", + "print(f\"Exact expectation values: {np.round(exact_expvals, 8)}\")\n", + "print(\n", + " f\"Error in estimation: {np.real(np.round(reconstructed_expvals-exact_expvals, 8))}\"\n", + ")\n", + "print(\n", + " f\"Relative error in estimation: {np.real(np.round((reconstructed_expvals-exact_expvals) / exact_expvals, 8))}\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires.ipynb new file mode 100644 index 00000000000..fb514a7b245 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires.ipynb @@ -0,0 +1,517 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"How to place wire cuts using a single-qubit CutWire instruction\"\n", + "description: \"How to place wire cuts using a single-qubit CutWire instruction for the latest version of Circuit cutting\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "b04b8bc7", + "metadata": {}, + "source": [ + "# How to place wire cuts using a single-qubit `CutWire` instruction\n", + "\n", + "This how-to guide is intended to demonstrate how to place wire cuts using single-qubit `CutWire` instructions, which may at times be more convenient than specifying them as two-qubit `Move` instructions." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1aa871cb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:41.715866Z", + "iopub.status.busy": "2025-03-28T16:51:41.715618Z", + "iopub.status.idle": "2025-03-28T16:51:42.298891Z", + "shell.execute_reply": "2025-03-28T16:51:42.298034Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit import QuantumCircuit\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "from qiskit_aer.primitives import EstimatorV2, SamplerV2\n", + "\n", + "from qiskit_addon_cutting import (\n", + " partition_problem,\n", + " generate_cutting_experiments,\n", + " reconstruct_expectation_values,\n", + ")\n", + "\n", + "from qiskit_addon_cutting.instructions import CutWire\n", + "from qiskit_addon_cutting import cut_wires, expand_observables" + ] + }, + { + "cell_type": "markdown", + "id": "06ae4c39", + "metadata": {}, + "source": [ + "### Prepare a circuit for cutting\n", + "\n", + "As in the [tutorial for wire cutting](../tutorials/03_wire_cutting_via_move_instruction.ipynb), we have used a circuit inspired by Fig. 1(a) of [arXiv:2302.03366v1](https://arxiv.org/abs/2302.03366v1). The cut locations are marked manually here with `CutWire` instructions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0ae22516", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:42.302264Z", + "iopub.status.busy": "2025-03-28T16:51:42.301906Z", + "iopub.status.idle": "2025-03-28T16:51:43.264296Z", + "shell.execute_reply": "2025-03-28T16:51:43.263022Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires/extracted-outputs/0ae22516-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc_0 = QuantumCircuit(7)\n", + "for i in range(7):\n", + " qc_0.rx(np.pi / 4, i)\n", + "qc_0.cx(0, 3)\n", + "qc_0.cx(1, 3)\n", + "qc_0.cx(2, 3)\n", + "qc_0.append(CutWire(), [3])\n", + "qc_0.cx(3, 4)\n", + "qc_0.cx(3, 5)\n", + "qc_0.cx(3, 6)\n", + "qc_0.append(CutWire(), [3])\n", + "qc_0.cx(0, 3)\n", + "qc_0.cx(1, 3)\n", + "qc_0.cx(2, 3)\n", + "qc_0.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "7ee07a81", + "metadata": {}, + "source": [ + "### Recover the uncut circuit\n", + "\n", + "`CutWire` instructions decompose to nothing (they are equivalent to the identity), so the uncut circuit can be recovered as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "631286a6", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.267141Z", + "iopub.status.busy": "2025-03-28T16:51:43.266751Z", + "iopub.status.idle": "2025-03-28T16:51:43.652437Z", + "shell.execute_reply": "2025-03-28T16:51:43.651574Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires/extracted-outputs/631286a6-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc_0.decompose(\"cut_wire\").draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "dcd8dea0", + "metadata": {}, + "source": [ + "### Specify an observable\n", + "\n", + "This observable has 7 qubits, just like the original circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "847a3205", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.655017Z", + "iopub.status.busy": "2025-03-28T16:51:43.654586Z", + "iopub.status.idle": "2025-03-28T16:51:43.658036Z", + "shell.execute_reply": "2025-03-28T16:51:43.657602Z" + } + }, + "outputs": [], + "source": [ + "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])" + ] + }, + { + "cell_type": "markdown", + "id": "59730746", + "metadata": {}, + "source": [ + "### Transform cuts to moves\n", + "\n", + "The next step is to call `cut_wires`, which transforms each `CutWire` into a `TwoQubitQPDGate` which wraps a `Move` instruction. An additional qubit is added to the circuit for each `CutWire` in the input circuit.\n", + "\n", + "Notice that, unlike in the [wire cutting tutorial](../tutorials/03_wire_cutting_via_move_instruction.ipynb), where `Move` operations were placed manually, this function does not result in the _re_-use of a qubit. Because any method for qubit re-use is based on heuristics, this function naively allocates an additional qubit for each cut. Users wishing to re-use qubits might wish to experiment with [qiskit-qubit-reuse](https://github.com/qiskit-community/qiskit-qubit-reuse)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e4ee1559", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.660083Z", + "iopub.status.busy": "2025-03-28T16:51:43.659877Z", + "iopub.status.idle": "2025-03-28T16:51:43.924630Z", + "shell.execute_reply": "2025-03-28T16:51:43.924082Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires/extracted-outputs/e4ee1559-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc_1 = cut_wires(qc_0)\n", + "qc_1.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "c22b9572", + "metadata": {}, + "source": [ + "### Update the observable terms to account for the extra qubit\n", + "\n", + "The transformed circuit contains additional qubits (one for each `CutWire` instruction), so the observables must be updated for the new circuit. This can be done using the `expand_observables` function. Since the observable coefficients are ignored until reconstruction of the final expectation value, the input and output types of the observables for ``expand_observables`` are ``PauliList``s.\n", + "\n", + "The resulting observables have 9 qubits, just like the transformed circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "95fbeda0", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.926980Z", + "iopub.status.busy": "2025-03-28T16:51:43.926767Z", + "iopub.status.idle": "2025-03-28T16:51:43.931899Z", + "shell.execute_reply": "2025-03-28T16:51:43.931437Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PauliList(['ZIIIIIIII', 'IIIZIIIII', 'IIIIIIIIZ'])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observable_expanded_paulis = expand_observables(observable.paulis, qc_0, qc_1)\n", + "observable_expanded_paulis" + ] + }, + { + "cell_type": "markdown", + "id": "6f64acd6", + "metadata": {}, + "source": [ + "### Separate the circuit and observables\n", + "\n", + "In this case, `partition_labels` need not be passed to `partition_problem`, as the labels can be determined automatically from the connectivity of the circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "99bef123", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.933905Z", + "iopub.status.busy": "2025-03-28T16:51:43.933725Z", + "iopub.status.idle": "2025-03-28T16:51:43.940544Z", + "shell.execute_reply": "2025-03-28T16:51:43.940042Z" + } + }, + "outputs": [], + "source": [ + "partitioned_problem = partition_problem(\n", + " circuit=qc_1, observables=observable_expanded_paulis\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "subobservables = partitioned_problem.subobservables" + ] + }, + { + "cell_type": "markdown", + "id": "ce2bf0cd", + "metadata": {}, + "source": [ + "From here forward, the cutting workflow is the same as usual, but the remaining steps are spelled out here explicitly so one can follow along with the results." + ] + }, + { + "cell_type": "markdown", + "id": "bae9ac63", + "metadata": {}, + "source": [ + "### Visualize the decomposed problem\n", + "\n", + "Notice that once the circuits have been cut, some of the instructions are able to commute past each other. For instance, in subcircuit \"A\", half of the second `Move` operation is actually the _first_ operation on the final qubit." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "abeee650", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.942618Z", + "iopub.status.busy": "2025-03-28T16:51:43.942418Z", + "iopub.status.idle": "2025-03-28T16:51:43.946860Z", + "shell.execute_reply": "2025-03-28T16:51:43.946404Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: PauliList(['IIIII', 'ZIIII', 'IIIIZ']),\n", + " 1: PauliList(['ZIII', 'IIII', 'IIII'])}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subobservables" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "aaef5b3d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.948780Z", + "iopub.status.busy": "2025-03-28T16:51:43.948581Z", + "iopub.status.idle": "2025-03-28T16:51:44.164480Z", + "shell.execute_reply": "2025-03-28T16:51:44.163535Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires/extracted-outputs/aaef5b3d-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[0].draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "975a3ca9", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:44.167652Z", + "iopub.status.busy": "2025-03-28T16:51:44.167358Z", + "iopub.status.idle": "2025-03-28T16:51:44.386632Z", + "shell.execute_reply": "2025-03-28T16:51:44.385952Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires/extracted-outputs/975a3ca9-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[1].draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "2e02632b", + "metadata": {}, + "source": [ + "### Generate and run the cutting experiments; reconstruct and compare against uncut expectation values" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "459dcee8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:44.389065Z", + "iopub.status.busy": "2025-03-28T16:51:44.388801Z", + "iopub.status.idle": "2025-03-28T16:51:44.496881Z", + "shell.execute_reply": "2025-03-28T16:51:44.496185Z" + } + }, + "outputs": [], + "source": [ + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits,\n", + " observables=subobservables,\n", + " num_samples=np.inf,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "dc9fe287", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:44.501113Z", + "iopub.status.busy": "2025-03-28T16:51:44.500852Z", + "iopub.status.idle": "2025-03-28T16:51:45.949307Z", + "shell.execute_reply": "2025-03-28T16:51:45.948450Z" + } + }, + "outputs": [], + "source": [ + "sampler = SamplerV2()\n", + "results = {\n", + " label: sampler.run(subexperiment, shots=2**12).result()\n", + " for label, subexperiment in subexperiments.items()\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e317a998", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:45.952706Z", + "iopub.status.busy": "2025-03-28T16:51:45.951971Z", + "iopub.status.idle": "2025-03-28T16:51:49.139646Z", + "shell.execute_reply": "2025-03-28T16:51:49.138857Z" + } + }, + "outputs": [], + "source": [ + "reconstructed_expval_terms = reconstruct_expectation_values(\n", + " results,\n", + " coefficients,\n", + " subobservables,\n", + ")\n", + "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5ae568ca", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:49.143349Z", + "iopub.status.busy": "2025-03-28T16:51:49.143053Z", + "iopub.status.idle": "2025-03-28T16:51:49.158786Z", + "shell.execute_reply": "2025-03-28T16:51:49.158077Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed expectation value: 1.62048799\n", + "Exact expectation value: 1.59099026\n", + "Error in estimation: 0.02949773\n", + "Relative error in estimation: 0.01854048\n" + ] + } + ], + "source": [ + "estimator = EstimatorV2()\n", + "exact_expval = (\n", + " estimator.run([(qc_0.decompose(\"cut_wire\"), observable)]).result()[0].data.evs\n", + ")\n", + "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", + "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", + "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", + "print(\n", + " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/how-tos/index.mdx b/docs/addons/qiskit-addon-cutting/how-tos/index.mdx new file mode 100644 index 00000000000..c02a48eefe5 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/index.mdx @@ -0,0 +1,28 @@ +--- +title: "How-To Guides" +description: "How-To Guides for the lastest version of Circuit cutting" +--- + +# How-To Guides + +* [Generate exact quasi-distributions](how-to-generate-exact-quasi-dists-from-sampler): Use the [`ExactSampler`](/docs/api/qiskit-addon-cutting/utils-simulation#qiskit_addon_cutting.utils.simulation.ExactSampler "qiskit_addon_cutting.utils.simulation.ExactSampler") interface to generate exact quasi-distributions for circuits containing mid-circuit measurements. +* [Generate exact sampling coefficients](how-to-generate-exact-sampling-coefficients): Generate exact sampling coefficients and run all unique samples from the distribution. +* [Specify cut wires as a single-qubit instruction](how-to-specify-cut-wires): Perform wire cutting with a single-qubit CutWire instruction, rather than a two-qubit Move operation. +* [Perform a cutting workflow with multiple observables](how-to-provide-multiple-observables): Use the [`observable_terms`](/docs/api/qiskit-addon-cutting/utils-observable-terms#module-qiskit_addon_cutting.utils.observable_terms "qiskit_addon_cutting.utils.observable_terms") utility functions to gather unique observable terms, perform circuit cutting experiments, and reconstruct the desired expectation values. + +[![](/docs/images/addons/qiskit-addon-cutting/nbsphinx-no-thumbnail.svg)](how-to-generate-exact-quasi-dists-from-sampler) + +[How to generate exact quasiprobability distributions from Sampler](how-to-generate-exact-quasi-dists-from-sampler) + +[![](/docs/images/addons/qiskit-addon-cutting/how-tos_how_to_generate_exact_sampling_coefficients_5_0.avif)](how-to-generate-exact-sampling-coefficients) + +[How to generate exact sampling coefficients](how-to-generate-exact-sampling-coefficients) + +[![](/docs/images/addons/qiskit-addon-cutting/how-tos_how_to_provide_multiple_observables_2_0.avif)](how-to-provide-multiple-observables) + +[How to perform a cutting workflow with multiple observables](how-to-provide-multiple-observables) + +[![](/docs/images/addons/qiskit-addon-cutting/how-tos_how_to_specify_cut_wires_18_0.avif)](how-to-specify-cut-wires) + +[How to place wire cuts using a single-qubit CutWire instruction](how-to-specify-cut-wires) + diff --git a/docs/addons/qiskit-addon-cutting/index.mdx b/docs/addons/qiskit-addon-cutting/index.mdx new file mode 100644 index 00000000000..8bd5fd3a27c --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/index.mdx @@ -0,0 +1,127 @@ +--- +title: "Qiskit addon: circuit cutting" +description: "Documentation for the latest version of Circuit cutting" +--- + +# Qiskit addon: circuit cutting + +[![GitHub repository star counter badge](/docs/images/addons/qiskit-addon-cutting/qiskit-addon-cutting?style=social)](https://github.com/Qiskit/qiskit-addon-cutting) + +[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. + +This package implements circuit cutting. In this technique, a handful of gates and/or wires are cut, resulting in smaller circuits that are better suited for execution on hardware. The result of the original circuit can then be reconstructed; however, the trade-off is that the overall number of shots must be increased by a factor exponential in the number of cuts. + +For a more detailed discussion on circuit cutting, check out our [technical guide](./circuit_cutting/explanations/index-rst#overview-of-circuit-cutting). + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [Release Notes](/docs/api/qiskit-addon-cutting/release-notes#release-notes). + +<Admonition title="Note" type="note"> + This package was known as the Circuit Knitting Toolbox prior to September 2024. +</Admonition> + +## Citing this project + +If you use this package in your research, please cite it according to `CITATON.bib` file included in this repository: + +```bibtex +@misc{qiskit-addon-cutting, + author = { + Agata M. Bra\'{n}czyk + and Almudena {Carrera Vazquez} + and Daniel J. Egger + and Bryce Fuller + and Julien Gacon + and James R. Garrison + and Jennifer R. Glick + and Caleb Johnson + and Saasha Joshi + and Edwin Pednault + and C. D. Pemmaraju + and Pedro Rivero + and Ibrahim Shehzad + and Stefan Woerner + }, + title = {{Qiskit addon: circuit cutting}}, + howpublished = {\url{https://github.com/Qiskit/qiskit-addon-cutting}}, + year = {2024}, + doi = {10.5281/zenodo.7987997} +} +``` + +If you are using the entanglement forging tool in Circuit Knitting Toolbox version 0.5 or earlier, please use [an older version of the citation file](https://github.com/Qiskit/qiskit-addon-cutting/blob/stable/0.5/CITATION.bib) which includes the authors of that tool. + +If you are using the CutQC tool in Circuit Knitting Toolbox version 0.7 or earlier, please use [an older version of the citation file](https://github.com/Qiskit/qiskit-addon-cutting/blob/stable/0.7/CITATION.bib) which includes the authors of that tool. + +## Developer guide + +The source code to this package is available [on GitHub](https://github.com/Qiskit/qiskit-addon-cutting). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-cutting/blob/main/CONTRIBUTING.md) in the root of this project’s repository. + +## Contents + +* [Documentation Home]() + +* [Installation Instructions](install) + + * [Pre-Installation](install#pre-installation) + * [Option 1: Install from PyPI](install#option-1-install-from-pypi) + * [Option 2: Install from Source](install#option-2-install-from-source) + * [Option 3: Use within Docker](install#option-3-use-within-docker) + * [Platform Support](install#platform-support) + +* [Tutorials](tutorials/index) + + * [Gate Cutting to Reduce Circuit Width](tutorials/01-gate-cutting-to-reduce-circuit-width) + * [Gate Cutting to Reduce Circuit Depth](tutorials/02-gate-cutting-to-reduce-circuit-depth) + * [Wire Cutting Phrased as a Two-Qubit `Move` Instruction](tutorials/03-wire-cutting-via-move-instruction) + * [Automatically find cuts](tutorials/04-automatic-cut-finding) + +* [Explanatory Material](explanations/index) + + * [Overview of circuit cutting](explanations/index#overview-of-circuit-cutting) + * [Key terms](explanations/index#key-terms) + * [Circuit cutting as a quasiprobability decomposition (QPD)](explanations/index#circuit-cutting-as-a-quasiprobability-decomposition-qpd) + * [An example: cutting a `RZZGate`](explanations/index#an-example-cutting-a-rzzgate) + * [More general cut two-qubit gates via the KAK decomposition](explanations/index#more-general-cut-two-qubit-gates-via-the-kak-decomposition) + * [Wire cutting phrased as a two-qubit `Move` operation](explanations/index#wire-cutting-phrased-as-a-two-qubit-move-operation) + * [Sample weights in the Qiskit addon for circuit cutting](explanations/index#sample-weights-in-the-qiskit-addon-for-circuit-cutting) + * [Sampling overhead reference table](explanations/index#sampling-overhead-reference-table) + * [Current limitations](explanations/index#current-limitations) + * [References](explanations/index#references) + +* [How-To Guides](how-tos/index) + + * [How to generate exact quasiprobability distributions from Sampler](how-tos/how-to-generate-exact-quasi-dists-from-sampler) + * [How to generate exact sampling coefficients](how-tos/how-to-generate-exact-sampling-coefficients) + * [How to perform a cutting workflow with multiple observables](how-tos/how-to-provide-multiple-observables) + * [How to place wire cuts using a single-qubit `CutWire` instruction](how-tos/how-to-specify-cut-wires) + +* [API References](/docs/api/qiskit-addon-cutting/index) + + * [Circuit cutting (`qiskit_addon_cutting`)](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting) + * [Instructions (`qiskit_addon_cutting.instructions`)](/docs/api/qiskit-addon-cutting/instructions) + * [Quasi-Probability Decomposition (QPD) (`qiskit_addon_cutting.qpd`)](/docs/api/qiskit-addon-cutting/qpd) + * [Bitwise utilities (`qiskit_addon_cutting.utils.bitwise`)](/docs/api/qiskit-addon-cutting/utils-bitwise) + * [Iteration utilities (`qiskit_addon_cutting.utils.iteration`)](/docs/api/qiskit-addon-cutting/utils-iteration) + * [Observable grouping (`qiskit_addon_cutting.utils.observable_grouping`)](/docs/api/qiskit-addon-cutting/utils-observable-grouping) + * [Observable terms (`qiskit_addon_cutting.utils.observable_terms`)](/docs/api/qiskit-addon-cutting/utils-observable-terms) + * [Simulation utilities (`qiskit_addon_cutting.utils.simulation`)](/docs/api/qiskit-addon-cutting/utils-simulation) + * [Transforms (`qiskit_addon_cutting.utils.transforms`)](/docs/api/qiskit-addon-cutting/utils-transforms) + * [Transpiler passes (`qiskit_addon_cutting.utils.transpiler_passes`)](/docs/api/qiskit-addon-cutting/utils-transpiler-passes) + +* [GitHub](https://github.com/Qiskit/qiskit-addon-cutting) + +* [Release Notes](/docs/api/qiskit-addon-cutting/release-notes) + + * [0.10.0](/docs/api/qiskit-addon-cutting/release-notes#release-notes-0-10-0) + * [0.9.0](/docs/api/qiskit-addon-cutting/release-notes#release-notes-0-9-0) + * [0.7.1](/docs/api/qiskit-addon-cutting/release-notes#release-notes-0-7-1) + * [0.7.0](/docs/api/qiskit-addon-cutting/release-notes#release-notes-0-7-0) + * [0.6.0](/docs/api/qiskit-addon-cutting/release-notes#release-notes-0-6-0) + * [0.5.0](/docs/api/qiskit-addon-cutting/release-notes#release-notes-0-5-0) + * [0.4.0](/docs/api/qiskit-addon-cutting/release-notes#release-notes-0-4-0) + * [0.3.0](/docs/api/qiskit-addon-cutting/release-notes#release-notes-0-3-0) + * [0.2.0](/docs/api/qiskit-addon-cutting/release-notes#release-notes-0-2-0) + * [0.1.0](/docs/api/qiskit-addon-cutting/release-notes#release-notes-0-1-0) + diff --git a/docs/addons/qiskit-addon-cutting/install.mdx b/docs/addons/qiskit-addon-cutting/install.mdx new file mode 100644 index 00000000000..9c29528a3a2 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/install.mdx @@ -0,0 +1,128 @@ +--- +title: "Installation Instructions" +description: "Installation Instructions for the lastest version of Circuit cutting" +--- + +# Installation Instructions + +Let’s see how to install the Qiskit addon for circuit cutting. The first thing to do is choose how you’re going to run and install the packages. There are three primary ways to do this: + +* [Option 1: Install from PyPI](#option-1) +* [Option 2: Install from Source](#option-2) +* [Option 3: Use within Docker](#option-3) + +Users who wish to run within a containerized environment may skip the pre-installation and move straight to [Option 3: Use within Docker](#option-3). + +## Pre-Installation + +Users who wish to install locally (using either [Option 1: Install from PyPI](#option-1) or [Option 2: Install from Source](#option-2)) are encouraged to follow a brief set of common instructions to prepare a Python environment for installation: + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +Note: If you are using Windows, use the following commands in PowerShell: + +```pwsh +python3 -m venv c:\path\to\virtual\environment +c:\path\to\virtual\environment\Scripts\Activate.ps1 +``` + +<span id="option-1" /> + +## Option 1: Install from PyPI + +The most straightforward way to install the `qiskit-addon-cutting` package is via PyPI. + +```sh +pip install --upgrade pip +pip install qiskit-addon-cutting +``` + +<span id="option-2" /> + +## Option 2: Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +In either case, the first step is to clone the `qiskit-addon-cutting` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-cutting.git +``` + +Next, upgrade pip and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-cutting +``` + +The next step is to install to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started with the addon by running the notebooks in the docs. + +```python +cd docs/ +jupyter notebook +``` + +<span id="option-3" /> + +## Option 3: Use within Docker + +We have provided a [Dockerfile](https://github.com/Qiskit/qiskit-addon-cutting/blob/main/Dockerfile), which can be used to build a Docker image, as well as a [compose.yaml](https://github.com/Qiskit/qiskit-addon-cutting/blob/main/compose.yaml) file, which allows one to use the Docker image with just a few simple commands. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-cutting.git +cd qiskit-addon-cutting +docker compose build +docker compose up +``` + +Depending on your system configuration, you may need to type `sudo` before each `docker compose` command. + +<Admonition title="Note" type="note"> + If you are instead using [podman](https://podman.io/) and [podman-compose](https://github.com/containers/podman-compose), the commands are: + + ```sh + podman machine start + podman-compose --podman-pull-args short-name-mode="permissive" build + podman-compose up + ``` +</Admonition> + +Once the container is running, you should see a message like this: + +```python +notebook_1 | To access the server, open this file in a browser: +notebook_1 | file:///home/jovyan/.local/share/jupyter/runtime/jpserver-7-open.html +notebook_1 | Or copy and paste one of these URLs: +notebook_1 | http://e4a04564eb39:8888/lab?token=00ed70b5342f79f0a970ee9821c271eeffaf760a7dcd36ec +notebook_1 | or http://127.0.0.1:8888/lab?token=00ed70b5342f79f0a970ee9821c271eeffaf760a7dcd36ec +``` + +Locate the *last* URL in your terminal (the one that includes `127.0.0.1`), and navigate to that URL in a web browser to access the Jupyter Notebook interface. + +The home directory includes a subdirectory named `persistent-volume`. All work you’d like to save should be placed in this directory, as it is the only one that will be saved across different container runs. + +<span id="id1" /> + +## Platform Support + +We expect this package to work on [any platform supported by Qiskit](/docs/start/install#operating-system-support). If you are experiencing issues running the software on your device, you may consider [using this package within Docker](#option-3). + diff --git a/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width.ipynb new file mode 100644 index 00000000000..e1f169f31de --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width.ipynb @@ -0,0 +1,539 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Gate Cutting to Reduce Circuit Width\"\n", + "description: \"Gate Cutting to Reduce Circuit Width for the latest version of Circuit cutting\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "ad1f14b4", + "metadata": {}, + "source": [ + "# Gate Cutting to Reduce Circuit Width\n", + "\n", + "In this notebook, we will work through the steps of a [Qiskit pattern](/guides/intro-to-patterns) while using **circuit cutting** to reduce the number of qubits in a circuit. We will cut gates to enable us to reconstruct the expectation value of a four-qubit circuit using only two-qubit experiments.\n", + "\n", + "These are the steps that we will take:\n", + "\n", + "- **Step 1: Map problem to quantum circuits and operators**:\n", + " - Map the hamiltonian onto a quantum circuit.\n", + "- **Step 2: Optimize for target hardware** [_Uses the cutting addon_]:\n", + " - <font color='#0F62FE'>Cut the circuit and observable.</font>\n", + " - Transpile the subexperiments for hardware.\n", + "- **Step 3: Execute on target hardware**:\n", + " - Run the subexperiments obtained in Step 2 using a `Sampler` primitive.\n", + "- **Step 4: Post-process results** [_Uses the cutting addon_]:\n", + " - <font color='#0F62FE'>Combine the results of Step 3 to reconstruct the expectation value of the observable in question.</font>" + ] + }, + { + "cell_type": "markdown", + "id": "510910a6", + "metadata": {}, + "source": [ + "## Step 1: Map\n", + "\n", + "### Create a circuit to cut" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "96f5b72a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:50.252738Z", + "iopub.status.busy": "2025-03-28T16:50:50.252448Z", + "iopub.status.idle": "2025-03-28T16:50:51.350658Z", + "shell.execute_reply": "2025-03-28T16:50:51.349916Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width/extracted-outputs/96f5b72a-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.circuit.library import efficient_su2\n", + "\n", + "qc = efficient_su2(4, entanglement=\"linear\", reps=2)\n", + "qc.assign_parameters([0.4] * len(qc.parameters), inplace=True)\n", + "\n", + "qc.draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "8638fdf1", + "metadata": {}, + "source": [ + "### Specify an observable" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f75e8dd1", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.353234Z", + "iopub.status.busy": "2025-03-28T16:50:51.352789Z", + "iopub.status.idle": "2025-03-28T16:50:51.356422Z", + "shell.execute_reply": "2025-03-28T16:50:51.355858Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])" + ] + }, + { + "cell_type": "markdown", + "id": "162a5629", + "metadata": {}, + "source": [ + "## Step 2: Optimize\n", + "\n", + "### Separate the circuit and observable according to a specified qubit partitioning\n", + "\n", + "Each label in `partition_labels` corresponds to the `circuit` qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut.\n", + "\n", + "**Note:** The ``observables`` kwarg to `partition_problem` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "30326299", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.358249Z", + "iopub.status.busy": "2025-03-28T16:50:51.358050Z", + "iopub.status.idle": "2025-03-28T16:50:51.401645Z", + "shell.execute_reply": "2025-03-28T16:50:51.400913Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import partition_problem\n", + "\n", + "partitioned_problem = partition_problem(\n", + " circuit=qc, partition_labels=\"AABB\", observables=observable.paulis\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "subobservables = partitioned_problem.subobservables\n", + "bases = partitioned_problem.bases" + ] + }, + { + "cell_type": "markdown", + "id": "9d2d42c3", + "metadata": {}, + "source": [ + "### Visualize the decomposed problem" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6b54be63", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.404435Z", + "iopub.status.busy": "2025-03-28T16:50:51.403916Z", + "iopub.status.idle": "2025-03-28T16:50:51.410005Z", + "shell.execute_reply": "2025-03-28T16:50:51.409344Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'A': PauliList(['II', 'ZI', 'ZZ', 'XI', 'ZZ', 'IX']),\n", + " 'B': PauliList(['ZZ', 'IZ', 'II', 'XI', 'ZI', 'IX'])}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subobservables" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b7e06fac", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.411952Z", + "iopub.status.busy": "2025-03-28T16:50:51.411753Z", + "iopub.status.idle": "2025-03-28T16:50:51.547364Z", + "shell.execute_reply": "2025-03-28T16:50:51.546536Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width/extracted-outputs/b7e06fac-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[\"A\"].draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "11e45e83", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.549471Z", + "iopub.status.busy": "2025-03-28T16:50:51.549264Z", + "iopub.status.idle": "2025-03-28T16:50:51.738543Z", + "shell.execute_reply": "2025-03-28T16:50:51.737835Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width/extracted-outputs/11e45e83-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[\"B\"].draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "4f8017b6-2954-4b51-8a07-f4de49832509", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Calculate the sampling overhead for the chosen cuts\n", + "\n", + "Here we cut two CNOT gates, resulting in a sampling overhead of $9^2$.\n", + "\n", + "For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](../explanation/index.rst)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3d606ef8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.741344Z", + "iopub.status.busy": "2025-03-28T16:50:51.740724Z", + "iopub.status.idle": "2025-03-28T16:50:51.745416Z", + "shell.execute_reply": "2025-03-28T16:50:51.744851Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling overhead: 81.0\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ef3b061c-cd1d-4931-85f6-155e00b355be", + "metadata": {}, + "source": [ + "### Generate the subexperiments to run on the backend\n", + "\n", + "`generate_cutting_experiments` accepts `circuits`/`observables` args as dictionaries mapping qubit partition labels to the respective `subcircuit`/`subobservables`.\n", + "\n", + "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2029d18e-0e91-4160-b8c9-02cb9e1ba3cb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.747765Z", + "iopub.status.busy": "2025-03-28T16:50:51.747387Z", + "iopub.status.idle": "2025-03-28T16:50:51.854533Z", + "shell.execute_reply": "2025-03-28T16:50:51.853782Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import generate_cutting_experiments\n", + "\n", + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits, observables=subobservables, num_samples=np.inf\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "444b0038-b3d5-469c-85b2-4c1d9d8fce05", + "metadata": {}, + "source": [ + "### Choose a backend\n", + "\n", + "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "36c89aa0-70aa-4615-8198-77ec85e8aa93", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.857664Z", + "iopub.status.busy": "2025-03-28T16:50:51.857181Z", + "iopub.status.idle": "2025-03-28T16:50:52.542985Z", + "shell.execute_reply": "2025-03-28T16:50:52.542361Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", + "\n", + "backend = FakeManilaV2()" + ] + }, + { + "cell_type": "markdown", + "id": "85a1d76a-5c47-4210-a742-9b22f02f7200", + "metadata": {}, + "source": [ + "### Prepare the subexperiments for the backend\n", + "\n", + "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "184e0f36-1279-48a3-aab7-b7603bb71f66", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:52.545759Z", + "iopub.status.busy": "2025-03-28T16:50:52.545267Z", + "iopub.status.idle": "2025-03-28T16:50:56.855540Z", + "shell.execute_reply": "2025-03-28T16:50:56.854763Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.transpiler import generate_preset_pass_manager\n", + "\n", + "# Transpile the subexperiments to ISA circuits\n", + "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", + "isa_subexperiments = {\n", + " label: pass_manager.run(partition_subexpts)\n", + " for label, partition_subexpts in subexperiments.items()\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "d8870454-2173-4454-90b4-a034779510e0", + "metadata": {}, + "source": [ + "## Step 3: Execute\n", + "\n", + "### Run the subexperiments using the Qiskit Runtime Sampler primitive" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2dbb8148-0482-4a66-8316-335125f8a2b3", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:56.858196Z", + "iopub.status.busy": "2025-03-28T16:50:56.857749Z", + "iopub.status.idle": "2025-03-28T16:50:57.056138Z", + "shell.execute_reply": "2025-03-28T16:50:57.048260Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import SamplerV2, Batch\n", + "\n", + "# Submit each partition's subexperiments to the Qiskit Runtime Sampler\n", + "# primitive, in a single batch so that the jobs will run back-to-back.\n", + "with Batch(backend=backend) as batch:\n", + " sampler = SamplerV2(mode=batch)\n", + " jobs = {\n", + " label: sampler.run(subsystem_subexpts, shots=2**12)\n", + " for label, subsystem_subexpts in isa_subexperiments.items()\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "64cef60b-5130-467e-8e01-7460d78560ed", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:57.132131Z", + "iopub.status.busy": "2025-03-28T16:50:57.130570Z", + "iopub.status.idle": "2025-03-28T16:51:20.447808Z", + "shell.execute_reply": "2025-03-28T16:51:20.446901Z" + } + }, + "outputs": [], + "source": [ + "# Retrieve results\n", + "results = {label: job.result() for label, job in jobs.items()}" + ] + }, + { + "cell_type": "markdown", + "id": "f0032570", + "metadata": {}, + "source": [ + "## Step 4: Post-process\n", + "\n", + "### Reconstruct the expectation value\n", + "\n", + "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7d57339c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:20.451351Z", + "iopub.status.busy": "2025-03-28T16:51:20.450827Z", + "iopub.status.idle": "2025-03-28T16:51:25.701123Z", + "shell.execute_reply": "2025-03-28T16:51:25.700268Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import reconstruct_expectation_values\n", + "\n", + "# Get expectation values for each observable term\n", + "reconstructed_expval_terms = reconstruct_expectation_values(\n", + " results,\n", + " coefficients,\n", + " subobservables,\n", + ")\n", + "\n", + "# Reconstruct final expectation value\n", + "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" + ] + }, + { + "cell_type": "markdown", + "id": "53beaca3", + "metadata": {}, + "source": [ + "### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e3385ba5", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:25.708190Z", + "iopub.status.busy": "2025-03-28T16:51:25.707798Z", + "iopub.status.idle": "2025-03-28T16:51:25.747493Z", + "shell.execute_reply": "2025-03-28T16:51:25.744142Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed expectation value: 0.58167797\n", + "Exact expectation value: 0.56254612\n", + "Error in estimation: 0.01913185\n", + "Relative error in estimation: 0.03400939\n" + ] + } + ], + "source": [ + "from qiskit_aer.primitives import EstimatorV2\n", + "\n", + "estimator = EstimatorV2()\n", + "exact_expval = estimator.run([(qc, observable)]).result()[0].data.evs\n", + "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", + "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", + "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", + "print(\n", + " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth.ipynb new file mode 100644 index 00000000000..e91e317f2f0 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth.ipynb @@ -0,0 +1,584 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Gate Cutting to Reduce Circuit Depth\"\n", + "description: \"Gate Cutting to Reduce Circuit Depth for the latest version of Circuit cutting\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "7ed4867f", + "metadata": {}, + "source": [ + "# Gate Cutting to Reduce Circuit Depth\n", + "\n", + "In this tutorial, we will reduce a circuit's depth by cutting distant gates, avoiding the swap gates that would otherwise be introduced by routing.\n", + "\n", + "These are the steps that we will take in this [Qiskit pattern](/guides/intro-to-patterns):\n", + "\n", + "- **Step 1: Map problem to quantum circuits and operators**:\n", + " - Map the hamiltonian onto a quantum circuit.\n", + "- **Step 2: Optimize for target hardware** [_Uses the cutting addon_]:\n", + " - <font color='#0F62FE'>Cut the circuit and observable.</font>\n", + " - Transpile the subexperiments for hardware.\n", + "- **Step 3: Execute on target hardware**:\n", + " - Run the subexperiments obtained in Step 2 using a `Sampler` primitive.\n", + "- **Step 4: Post-process results** [_Uses the cutting addon_]:\n", + " - <font color='#0F62FE'>Combine the results of Step 3 to reconstruct the expectation value of the observable in question.</font>" + ] + }, + { + "cell_type": "markdown", + "id": "07f7f75d", + "metadata": {}, + "source": [ + "## Step 1: Map\n", + "\n", + "### Create a circuit to run on the backend" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "54ed0f13", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:29.327835Z", + "iopub.status.busy": "2025-03-28T16:51:29.326393Z", + "iopub.status.idle": "2025-03-28T16:51:31.050966Z", + "shell.execute_reply": "2025-03-28T16:51:31.050169Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth/extracted-outputs/54ed0f13-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.circuit.library import efficient_su2\n", + "\n", + "circuit = efficient_su2(num_qubits=4, entanglement=\"circular\")\n", + "circuit.assign_parameters([0.4] * len(circuit.parameters), inplace=True)\n", + "circuit.draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "452cd19e", + "metadata": {}, + "source": [ + "### Specify an observable" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "105e858d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:31.054439Z", + "iopub.status.busy": "2025-03-28T16:51:31.054037Z", + "iopub.status.idle": "2025-03-28T16:51:31.061841Z", + "shell.execute_reply": "2025-03-28T16:51:31.061246Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])" + ] + }, + { + "cell_type": "markdown", + "id": "699105a3-903e-49d8-826b-cc8b9b85c9df", + "metadata": {}, + "source": [ + "## Step 2: Optimize\n", + "\n", + "### Specify a backend\n", + "\n", + "You can provide either a fake backend or a hardware backend from Qiskit Runtime." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "756f2130-6947-479a-9fe7-97443c87a904", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:31.066068Z", + "iopub.status.busy": "2025-03-28T16:51:31.065650Z", + "iopub.status.idle": "2025-03-28T16:51:32.084249Z", + "shell.execute_reply": "2025-03-28T16:51:32.083362Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", + "\n", + "backend = FakeManilaV2()" + ] + }, + { + "cell_type": "markdown", + "id": "080a2a8b", + "metadata": {}, + "source": [ + "### Transpile the circuit, visualize the swaps, and note the depth\n", + "\n", + "We choose a layout that requires two swaps to execute the gates between qubits 3 and 0 and another two swaps to return the qubits to their initial positions." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b394da7a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:32.088232Z", + "iopub.status.busy": "2025-03-28T16:51:32.087771Z", + "iopub.status.idle": "2025-03-28T16:51:32.168573Z", + "shell.execute_reply": "2025-03-28T16:51:32.163841Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transpiled circuit depth: 30\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3574/3423257129.py:8: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes.\n", + " print(f\"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\")\n" + ] + } + ], + "source": [ + "from qiskit.transpiler import generate_preset_pass_manager\n", + "\n", + "pass_manager = generate_preset_pass_manager(\n", + " optimization_level=1, backend=backend, initial_layout=[0, 1, 2, 3]\n", + ")\n", + "\n", + "transpiled_qc = pass_manager.run(circuit)\n", + "print(f\"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4fe4af43", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:32.176421Z", + "iopub.status.busy": "2025-03-28T16:51:32.174292Z", + "iopub.status.idle": "2025-03-28T16:51:33.477300Z", + "shell.execute_reply": "2025-03-28T16:51:33.474323Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth/extracted-outputs/4fe4af43-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transpiled_qc.draw(\"mpl\", scale=0.4, idle_wires=False, fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "6c106db7", + "metadata": {}, + "source": [ + "### Replace distant gates with `TwoQubitQPDGate`s by specifying their indices\n", + "\n", + "`cut_gates` will replace the gates in the specified indices with `TwoQubitQPDGate`s and also return a list of `QPDBasis` instances -- one for each gate decomposition." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "12e73c69", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:33.480297Z", + "iopub.status.busy": "2025-03-28T16:51:33.479989Z", + "iopub.status.idle": "2025-03-28T16:51:34.022077Z", + "shell.execute_reply": "2025-03-28T16:51:34.021152Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth/extracted-outputs/12e73c69-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_cutting import cut_gates\n", + "\n", + "# Find the indices of the distant gates\n", + "cut_indices = [\n", + " i\n", + " for i, instruction in enumerate(circuit.data)\n", + " if {circuit.find_bit(q)[0] for q in instruction.qubits} == {0, 3}\n", + "]\n", + "\n", + "# Decompose distant CNOTs into TwoQubitQPDGate instances\n", + "qpd_circuit, bases = cut_gates(circuit, cut_indices)\n", + "\n", + "qpd_circuit.draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "069eb942-e947-4eff-a250-9a99c5ec47f0", + "metadata": {}, + "source": [ + "### Generate the subexperiments to run on the backend\n", + "\n", + "`generate_cutting_experiments` accepts a circuit containing `TwoQubitQPDGate` instances and observables as a `PauliList`.\n", + "\n", + "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst).\n", + "\n", + "**Note:** The ``observables`` kwarg to `generate_cutting_experiments` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "83b1efed-bafa-48c4-bbf0-cf7eb9027ac5", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:34.027775Z", + "iopub.status.busy": "2025-03-28T16:51:34.027177Z", + "iopub.status.idle": "2025-03-28T16:51:34.976541Z", + "shell.execute_reply": "2025-03-28T16:51:34.975799Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit_addon_cutting import generate_cutting_experiments\n", + "\n", + "# Generate the subexperiments and sampling coefficients\n", + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=qpd_circuit, observables=observable.paulis, num_samples=np.inf\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c0acae3a", + "metadata": {}, + "source": [ + "### Calculate the sampling overhead for the chosen cuts\n", + "\n", + "Here we cut three CNOT gates, resulting in a sampling overhead of $9^3$.\n", + "\n", + "For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](../explanation/index.rst)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c7b28e2c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:34.979488Z", + "iopub.status.busy": "2025-03-28T16:51:34.979281Z", + "iopub.status.idle": "2025-03-28T16:51:34.983347Z", + "shell.execute_reply": "2025-03-28T16:51:34.982624Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling overhead: 729.0\n" + ] + } + ], + "source": [ + "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")" + ] + }, + { + "cell_type": "markdown", + "id": "6929264d", + "metadata": {}, + "source": [ + "### Demonstrate that the QPD subexperiments will be shallower after cutting distant gates\n", + "\n", + "Here is an example of an arbitrarily chosen subexperiment generated from the QPD circuit. Its depth has been reduced by more than half. Many of these probabilistic subexperiments must be generated and evaluated in order to reconstruct an expectation value of the deeper circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "70e2f1b6", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:34.985332Z", + "iopub.status.busy": "2025-03-28T16:51:34.985103Z", + "iopub.status.idle": "2025-03-28T16:51:35.433820Z", + "shell.execute_reply": "2025-03-28T16:51:35.432939Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original circuit depth after transpile: 30\n", + "QPD subexperiment depth after transpile: 7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3574/4252024425.py:5: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes.\n", + " f\"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\"\n", + "/tmp/ipykernel_3574/4252024425.py:8: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes.\n", + " f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}\"\n" + ] + }, + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth/extracted-outputs/70e2f1b6-2.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Transpile the decomposed circuit to the same layout\n", + "transpiled_qpd_circuit = pass_manager.run(subexperiments[100])\n", + "\n", + "print(\n", + " f\"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\"\n", + ")\n", + "print(\n", + " f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}\"\n", + ")\n", + "transpiled_qpd_circuit.draw(\"mpl\", scale=0.8, idle_wires=False, fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "fd9a126c", + "metadata": {}, + "source": [ + "### Prepare subexperiments for the backend" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "736a7d14-71b0-47a0-847d-4972ab886918", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:35.436030Z", + "iopub.status.busy": "2025-03-28T16:51:35.435823Z", + "iopub.status.idle": "2025-03-28T16:51:47.520198Z", + "shell.execute_reply": "2025-03-28T16:51:47.519092Z" + } + }, + "outputs": [], + "source": [ + "# Transpile the subeperiments to the backend's instruction set architecture (ISA)\n", + "isa_subexperiments = pass_manager.run(subexperiments)" + ] + }, + { + "cell_type": "markdown", + "id": "241fc5e5-d367-4a31-aa1f-424acdbbd8dd", + "metadata": {}, + "source": [ + "## Step 3: Execute\n", + "\n", + "### Run the subexperiments using the Qiskit Runtime Sampler primitive" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e4dcc97b-8336-4eb9-9cc0-0d9b87a287e3", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:47.522983Z", + "iopub.status.busy": "2025-03-28T16:51:47.522756Z", + "iopub.status.idle": "2025-03-28T16:51:48.086035Z", + "shell.execute_reply": "2025-03-28T16:51:48.081351Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import SamplerV2\n", + "\n", + "# Set up the Qiskit Runtime Sampler primitive. For a fake backend, this will use a local simulator.\n", + "sampler = SamplerV2(backend)\n", + "\n", + "# Submit the subexperiments\n", + "job = sampler.run(isa_subexperiments)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a83922bc-0489-4ee5-a2b6-796103aae9bb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:48.099798Z", + "iopub.status.busy": "2025-03-28T16:51:48.099494Z", + "iopub.status.idle": "2025-03-28T16:52:28.830126Z", + "shell.execute_reply": "2025-03-28T16:52:28.829495Z" + } + }, + "outputs": [], + "source": [ + "# Retrieve the results\n", + "results = job.result()" + ] + }, + { + "cell_type": "markdown", + "id": "f04d9134-651b-446e-93f4-aa0281786200", + "metadata": {}, + "source": [ + "## Step 4: Post-process\n", + "\n", + "### Reconstruct the expectation value\n", + "\n", + "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ace12f7f", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:28.832409Z", + "iopub.status.busy": "2025-03-28T16:52:28.832187Z", + "iopub.status.idle": "2025-03-28T16:52:30.916461Z", + "shell.execute_reply": "2025-03-28T16:52:30.915714Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import reconstruct_expectation_values\n", + "\n", + "reconstructed_expval_terms = reconstruct_expectation_values(\n", + " results,\n", + " coefficients,\n", + " observable.paulis,\n", + ")\n", + "# Reconstruct final expectation value\n", + "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" + ] + }, + { + "cell_type": "markdown", + "id": "3fc2327f", + "metadata": {}, + "source": [ + "### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4928e703", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:30.918925Z", + "iopub.status.busy": "2025-03-28T16:52:30.918548Z", + "iopub.status.idle": "2025-03-28T16:52:30.932594Z", + "shell.execute_reply": "2025-03-28T16:52:30.932017Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed expectation value: 0.51977539\n", + "Exact expectation value: 0.50497603\n", + "Error in estimation: 0.01479936\n", + "Relative error in estimation: 0.02930705\n" + ] + } + ], + "source": [ + "from qiskit_aer.primitives import EstimatorV2\n", + "\n", + "estimator = EstimatorV2()\n", + "exact_expval = estimator.run([(circuit, observable)]).result()[0].data.evs\n", + "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", + "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", + "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", + "print(\n", + " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction.ipynb new file mode 100644 index 00000000000..bd4f6bbebdc --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction.ipynb @@ -0,0 +1,650 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Wire Cutting Phrased as a Two-Qubit Move Instruction\"\n", + "description: \"Wire Cutting Phrased as a Two-Qubit Move Instruction for the latest version of Circuit cutting\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "9916de95-2376-49f0-91ad-07f07939dfb5", + "metadata": {}, + "source": [ + "# Wire Cutting Phrased as a Two-Qubit `Move` Instruction\n", + "\n", + "In this tutorial, we will reconstruct expectation values of a seven-qubit circuit by splitting it into two four-qubit circuits using wire cutting.\n", + "\n", + "These are the steps that we will take in this [Qiskit pattern](/guides/intro-to-patterns):\n", + "\n", + "- **Step 1: Map problem to quantum circuits and operators**:\n", + " - Map the hamiltonian onto a quantum circuit.\n", + "- **Step 2: Optimize for target hardware** [_Uses the cutting addon_]:\n", + " - <font color='#0F62FE'>Cut the circuit and observable.</font>\n", + " - Transpile the subexperiments for hardware.\n", + "- **Step 3: Execute on target hardware**:\n", + " - Run the subexperiments obtained in Step 2 using a `Sampler` primitive.\n", + "- **Step 4: Post-process results** [_Uses the cutting addon_]:\n", + " - <font color='#0F62FE'>Combine the results of Step 3 to reconstruct the expectation value of the observable in question.</font>" + ] + }, + { + "cell_type": "markdown", + "id": "ae63d837-a7f5-40a5-8186-f98076bb4cd9", + "metadata": {}, + "source": [ + "## Step 1: Map\n", + "\n", + "### Create a circuit to cut\n", + "\n", + "First, we begin with a circuit inspired by Fig. 1(a) of [arXiv:2302.03366v1](https://arxiv.org/abs/2302.03366v1)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3bcae0ed-4308-4686-b85c-8595c6e916bc", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:33.241330Z", + "iopub.status.busy": "2025-03-28T16:52:33.241116Z", + "iopub.status.idle": "2025-03-28T16:52:33.572919Z", + "shell.execute_reply": "2025-03-28T16:52:33.572241Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<qiskit.circuit.instructionset.InstructionSet at 0x7f96c0c23430>" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from qiskit import QuantumCircuit\n", + "\n", + "qc_0 = QuantumCircuit(7)\n", + "for i in range(7):\n", + " qc_0.rx(np.pi / 4, i)\n", + "qc_0.cx(0, 3)\n", + "qc_0.cx(1, 3)\n", + "qc_0.cx(2, 3)\n", + "qc_0.cx(3, 4)\n", + "qc_0.cx(3, 5)\n", + "qc_0.cx(3, 6)\n", + "qc_0.cx(0, 3)\n", + "qc_0.cx(1, 3)\n", + "qc_0.cx(2, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1dcaff2d-2d1b-4cc0-87d1-0f4f5de823ff", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:33.575145Z", + "iopub.status.busy": "2025-03-28T16:52:33.574711Z", + "iopub.status.idle": "2025-03-28T16:52:34.182253Z", + "shell.execute_reply": "2025-03-28T16:52:34.181568Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction/extracted-outputs/1dcaff2d-2d1b-4cc0-87d1-0f4f5de823ff-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc_0.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "bd1617a1-e793-43a9-a24a-c81c18fd2a1e", + "metadata": {}, + "source": [ + "### Specify an observable" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b791aa42-c485-453b-a110-3c790194adaf", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.184389Z", + "iopub.status.busy": "2025-03-28T16:52:34.184096Z", + "iopub.status.idle": "2025-03-28T16:52:34.187638Z", + "shell.execute_reply": "2025-03-28T16:52:34.187108Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])" + ] + }, + { + "cell_type": "markdown", + "id": "9f56b094-0c6f-456f-9641-1424395fc6bd", + "metadata": {}, + "source": [ + "## Step 2: Optimize\n", + "\n", + "### Create a new circuit where `Move` instructions have been placed at the desired cut locations\n", + "\n", + "Given the above circuit, we would like to place two wire cuts on the middle qubit line, so that the circuit can separate into two circuits of four qubits each. One way to do this is to manually place two-qubit `Move` instructions that move the state from one qubit wire to another. A `Move` instruction is conceptually equivalent to a reset operation on the second qubit, followed by a SWAP gate. The effect of this instruction is to transfer the state of the first (source) qubit to the second (detination) qubit, while discarding the incoming state of the second qubit. For this to work as intended, it is important that the second (destination) qubit share no entanglement with the remainder of the system; otherwise, the reset operation will cause the state of the remainder of the system to be partially collapsed.\n", + "\n", + "Here, we build a new circuit with one additional qubit and the `Move` operations in place. In this example, we are able to reuse a qubit: the source qubit of the first `Move` becomes the destination qubit of the second `Move` operation.\n", + "\n", + "Note: As an alternative to working directly with `Move` instructions, one may choose to mark wire cuts using a single-qubit `CutWire` instruction. The `cut_wires` function exists to transform `CutWire`s to `Move` instructions on newly allocated qubits. However, in contrast to the manual method, this automatic method does not allow for the re-use of qubit wires. See the `CutWire` [how-to guide](../how-tos/how_to_specify_cut_wires.ipynb) for details." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "22f19d29-a182-4758-b5d0-6b66f9a946be", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.189717Z", + "iopub.status.busy": "2025-03-28T16:52:34.189367Z", + "iopub.status.idle": "2025-03-28T16:52:34.502126Z", + "shell.execute_reply": "2025-03-28T16:52:34.501425Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction/extracted-outputs/22f19d29-a182-4758-b5d0-6b66f9a946be-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_cutting.instructions import Move\n", + "\n", + "qc_1 = QuantumCircuit(8)\n", + "for i in [*range(4), *range(5, 8)]:\n", + " qc_1.rx(np.pi / 4, i)\n", + "qc_1.cx(0, 3)\n", + "qc_1.cx(1, 3)\n", + "qc_1.cx(2, 3)\n", + "qc_1.append(Move(), [3, 4])\n", + "qc_1.cx(4, 5)\n", + "qc_1.cx(4, 6)\n", + "qc_1.cx(4, 7)\n", + "qc_1.append(Move(), [4, 3])\n", + "qc_1.cx(0, 3)\n", + "qc_1.cx(1, 3)\n", + "qc_1.cx(2, 3)\n", + "\n", + "qc_1.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "088431d9-de2f-4b5d-90d2-7e7a5947dcb5", + "metadata": {}, + "source": [ + "### Create observable to go with the new circuit\n", + "\n", + "This observable corresponds with `observable`, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an \"I\" at index 4). Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d33d5580-879f-466f-ac87-dcc6a19fbab6", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.504553Z", + "iopub.status.busy": "2025-03-28T16:52:34.504138Z", + "iopub.status.idle": "2025-03-28T16:52:34.507751Z", + "shell.execute_reply": "2025-03-28T16:52:34.506956Z" + } + }, + "outputs": [], + "source": [ + "observable_expanded = SparsePauliOp([\"ZIIIIIII\", \"IIIIZIII\", \"IIIIIIIZ\"])" + ] + }, + { + "cell_type": "markdown", + "id": "a00aeede-3d0f-4589-b1dc-3cd7df9c4085", + "metadata": {}, + "source": [ + "### Separate the circuit and observables\n", + "\n", + "As in the previous tutorials, qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fc3738c7-2bb2-4d67-ae2b-7a3090b31e6a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.509830Z", + "iopub.status.busy": "2025-03-28T16:52:34.509640Z", + "iopub.status.idle": "2025-03-28T16:52:34.522487Z", + "shell.execute_reply": "2025-03-28T16:52:34.521787Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import partition_problem\n", + "\n", + "partitioned_problem = partition_problem(\n", + " circuit=qc_1, partition_labels=\"AAAABBBB\", observables=observable_expanded.paulis\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "subobservables = partitioned_problem.subobservables\n", + "bases = partitioned_problem.bases" + ] + }, + { + "cell_type": "markdown", + "id": "4c8a76eb-31db-486c-b7c2-a5fe3cb732c2", + "metadata": {}, + "source": [ + "### Visualize the decomposed problem" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9c53a862-f762-471a-bda7-b27e88292ac9", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.524677Z", + "iopub.status.busy": "2025-03-28T16:52:34.524311Z", + "iopub.status.idle": "2025-03-28T16:52:34.528872Z", + "shell.execute_reply": "2025-03-28T16:52:34.528334Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'A': PauliList(['IIII', 'ZIII', 'IIIZ']),\n", + " 'B': PauliList(['ZIII', 'IIII', 'IIII'])}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subobservables" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "69df912e-6709-45bb-8eb3-c66a70edefdc", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.530891Z", + "iopub.status.busy": "2025-03-28T16:52:34.530503Z", + "iopub.status.idle": "2025-03-28T16:52:34.747276Z", + "shell.execute_reply": "2025-03-28T16:52:34.746598Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction/extracted-outputs/69df912e-6709-45bb-8eb3-c66a70edefdc-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[\"A\"].draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d851adcb-e524-48c8-8adc-0d1606613c8d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.749630Z", + "iopub.status.busy": "2025-03-28T16:52:34.749131Z", + "iopub.status.idle": "2025-03-28T16:52:34.879618Z", + "shell.execute_reply": "2025-03-28T16:52:34.878945Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction/extracted-outputs/d851adcb-e524-48c8-8adc-0d1606613c8d-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[\"B\"].draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "9c177fb7-a729-4e0d-bfac-256df3e14c54", + "metadata": {}, + "source": [ + "### Calculate the sampling overhead for the chosen cuts\n", + "\n", + "Here we cut two wires, resulting in a sampling overhead of $4^4$.\n", + "\n", + "For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](../explanation/index.rst)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7af74c54-3e68-4d58-a2e6-02bc212d911d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.881911Z", + "iopub.status.busy": "2025-03-28T16:52:34.881529Z", + "iopub.status.idle": "2025-03-28T16:52:34.885387Z", + "shell.execute_reply": "2025-03-28T16:52:34.884739Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling overhead: 256.0\n" + ] + } + ], + "source": [ + "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a1eda2d0-8d83-473b-8ea8-fe9880108140", + "metadata": {}, + "source": [ + "### Generate the subexperiments to run on the backend\n", + "\n", + "`generate_cutting_experiments` accepts `circuits`/`observables` args as dictionaries mapping qubit partition labels to the respective `subcircuit`/`subobservables`.\n", + "\n", + "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d28a8b82-8405-47b2-8142-62d56266409a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.887533Z", + "iopub.status.busy": "2025-03-28T16:52:34.887063Z", + "iopub.status.idle": "2025-03-28T16:52:34.963806Z", + "shell.execute_reply": "2025-03-28T16:52:34.963065Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import generate_cutting_experiments\n", + "\n", + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits, observables=subobservables, num_samples=np.inf\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9f66e3eb-7cbc-45a9-8f40-9f92b61b6e65", + "metadata": {}, + "source": [ + "### Choose a backend\n", + "\n", + "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6944546f-e3e0-4863-9049-9feed1086e68", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.966311Z", + "iopub.status.busy": "2025-03-28T16:52:34.965845Z", + "iopub.status.idle": "2025-03-28T16:52:35.537487Z", + "shell.execute_reply": "2025-03-28T16:52:35.536746Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", + "\n", + "backend = FakeManilaV2()" + ] + }, + { + "cell_type": "markdown", + "id": "fdb47142-690c-4b1a-ad0b-85e1260a2cea", + "metadata": {}, + "source": [ + "### Prepare the subexperiments for the backend\n", + "\n", + "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0229e6f2-a637-481d-ac76-84dfaadc264f", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:35.540179Z", + "iopub.status.busy": "2025-03-28T16:52:35.539685Z", + "iopub.status.idle": "2025-03-28T16:52:38.804548Z", + "shell.execute_reply": "2025-03-28T16:52:38.803721Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.transpiler import generate_preset_pass_manager\n", + "\n", + "# Transpile the subexperiments to ISA circuits\n", + "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", + "isa_subexperiments = {\n", + " label: pass_manager.run(partition_subexpts)\n", + " for label, partition_subexpts in subexperiments.items()\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "9578c64a-8087-4696-bb2b-ca53d45442ba", + "metadata": {}, + "source": [ + "## Step 3: Execute\n", + "\n", + "### Run the subexperiments using the Qiskit Runtime Sampler primitive" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bdf3bd2f-e10a-4148-a7ad-f354492614cc", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:38.807146Z", + "iopub.status.busy": "2025-03-28T16:52:38.806915Z", + "iopub.status.idle": "2025-03-28T16:52:38.958106Z", + "shell.execute_reply": "2025-03-28T16:52:38.957428Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import SamplerV2, Batch\n", + "\n", + "# Submit each partition's subexperiments to the Qiskit Runtime Sampler\n", + "# primitive, in a single batch so that the jobs will run back-to-back.\n", + "with Batch(backend=backend) as batch:\n", + " sampler = SamplerV2(mode=batch)\n", + " jobs = {\n", + " label: sampler.run(subsystem_subexpts, shots=2**12)\n", + " for label, subsystem_subexpts in isa_subexperiments.items()\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "abb436de-9662-4c47-92b1-659ee0334adc", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:38.965357Z", + "iopub.status.busy": "2025-03-28T16:52:38.965102Z", + "iopub.status.idle": "2025-03-28T16:52:51.060055Z", + "shell.execute_reply": "2025-03-28T16:52:51.059450Z" + } + }, + "outputs": [], + "source": [ + "# Retrieve results\n", + "results = {label: job.result() for label, job in jobs.items()}" + ] + }, + { + "cell_type": "markdown", + "id": "3f6e4eed-eff7-4e16-ad8d-8070011a555b", + "metadata": {}, + "source": [ + "## Step 4: Post-process\n", + "\n", + "### Reconstruct the expectation value\n", + "\n", + "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a1904c54-27fa-45e0-a9dd-727b4542db71", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:51.062743Z", + "iopub.status.busy": "2025-03-28T16:52:51.062254Z", + "iopub.status.idle": "2025-03-28T16:52:53.372289Z", + "shell.execute_reply": "2025-03-28T16:52:53.371616Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import reconstruct_expectation_values\n", + "\n", + "reconstructed_expval_terms = reconstruct_expectation_values(\n", + " results,\n", + " coefficients,\n", + " subobservables,\n", + ")\n", + "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" + ] + }, + { + "cell_type": "markdown", + "id": "cdc793f2-3e2b-417b-b863-7b9d57b86a8f", + "metadata": {}, + "source": [ + "### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6e3d63e4-a510-4712-bc43-48df6e2f7ded", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:53.374812Z", + "iopub.status.busy": "2025-03-28T16:52:53.374611Z", + "iopub.status.idle": "2025-03-28T16:52:53.385882Z", + "shell.execute_reply": "2025-03-28T16:52:53.385355Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed expectation value: 1.40369761\n", + "Exact expectation value: 1.59099026\n", + "Error in estimation: -0.18729265\n", + "Relative error in estimation: -0.1177208\n" + ] + } + ], + "source": [ + "from qiskit_aer.primitives import EstimatorV2\n", + "\n", + "estimator = EstimatorV2()\n", + "exact_expval = estimator.run([(qc_0, observable)]).result()[0].data.evs\n", + "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", + "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", + "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", + "print(\n", + " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb new file mode 100644 index 00000000000..3c9ffdc4295 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb @@ -0,0 +1,379 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Automatically find cuts\"\n", + "description: \"Automatically find cuts for the latest version of Circuit cutting\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "a7e59535-3340-4bff-b022-7d7bea1b2d93", + "metadata": {}, + "source": [ + "# Automatically find cuts" + ] + }, + { + "cell_type": "markdown", + "id": "e870ba6e-6bc1-4e0f-af4a-c5aa78febc0e", + "metadata": {}, + "source": [ + "## Step 1: Map\n", + "\n", + "#### Create a circuit and observables" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "74c0f6a7-3ccc-4f43-b0b2-a405eaa52539", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:55.792854Z", + "iopub.status.busy": "2025-03-28T16:52:55.792659Z", + "iopub.status.idle": "2025-03-28T16:52:56.800107Z", + "shell.execute_reply": "2025-03-28T16:52:56.799450Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/74c0f6a7-3ccc-4f43-b0b2-a405eaa52539-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from qiskit.circuit.random import random_circuit\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "circuit = random_circuit(7, 6, max_operands=2, seed=1242)\n", + "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])\n", + "\n", + "\n", + "circuit.draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "3ef2174c-db48-44cb-9e91-d33967cc7db3", + "metadata": {}, + "source": [ + "## Step 2: Optimize\n", + "\n", + "#### Find cut locations, given a maximum of 4 qubits per subcircuit. This circuit can be separated in two by making a single wire cut and cutting one `CRZGate`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3cdb274b-140d-4312-a75d-e78bfedd35d4", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:56.802390Z", + "iopub.status.busy": "2025-03-28T16:52:56.801930Z", + "iopub.status.idle": "2025-03-28T16:52:59.488947Z", + "shell.execute_reply": "2025-03-28T16:52:59.488280Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found solution using 8 cuts with a sampling overhead of 59776343.19975524.\n", + "Lowest cost solution found: False.\n", + "Gate Cut at circuit instruction index 6\n", + "Gate Cut at circuit instruction index 8\n", + "Gate Cut at circuit instruction index 9\n", + "Wire Cut at circuit instruction index 10\n", + "Gate Cut at circuit instruction index 15\n", + "Gate Cut at circuit instruction index 18\n", + "Gate Cut at circuit instruction index 19\n", + "Gate Cut at circuit instruction index 21\n" + ] + }, + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/3cdb274b-140d-4312-a75d-e78bfedd35d4-1.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_cutting.automated_cut_finding import (\n", + " find_cuts,\n", + " OptimizationParameters,\n", + " DeviceConstraints,\n", + ")\n", + "\n", + "# Specify settings for the cut-finding optimizer\n", + "optimization_settings = OptimizationParameters(seed=111)\n", + "\n", + "# Specify the size of the QPUs available\n", + "device_constraints = DeviceConstraints(qubits_per_subcircuit=4)\n", + "\n", + "cut_circuit, metadata = find_cuts(circuit, optimization_settings, device_constraints)\n", + "print(\n", + " f'Found solution using {len(metadata[\"cuts\"])} cuts with a sampling '\n", + " f'overhead of {metadata[\"sampling_overhead\"]}.\\n'\n", + " f'Lowest cost solution found: {metadata[\"minimum_reached\"]}.'\n", + ")\n", + "for cut in metadata[\"cuts\"]:\n", + " print(f\"{cut[0]} at circuit instruction index {cut[1]}\")\n", + "cut_circuit.draw(\"mpl\", scale=0.8, fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "c58bf8c4-f392-4f60-9ae7-78d4c99b3382", + "metadata": {}, + "source": [ + "#### Add ancillas for wire cuts and expand the observables to account for ancilla qubits" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ca564cf6-7d14-4e11-ba65-76d80a9beeef", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:59.491081Z", + "iopub.status.busy": "2025-03-28T16:52:59.490686Z", + "iopub.status.idle": "2025-03-28T16:52:59.752854Z", + "shell.execute_reply": "2025-03-28T16:52:59.752167Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/ca564cf6-7d14-4e11-ba65-76d80a9beeef-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_cutting import cut_wires, expand_observables\n", + "\n", + "qc_w_ancilla = cut_wires(cut_circuit)\n", + "observables_expanded = expand_observables(observable.paulis, circuit, qc_w_ancilla)\n", + "qc_w_ancilla.draw(\"mpl\", scale=0.8, fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "605ce0e2-63e2-4499-a4b6-9dc99fad5b53", + "metadata": {}, + "source": [ + "#### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a1d78d09-c20d-4066-aad1-f1a412821794", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:59.755174Z", + "iopub.status.busy": "2025-03-28T16:52:59.754781Z", + "iopub.status.idle": "2025-03-28T16:52:59.767941Z", + "shell.execute_reply": "2025-03-28T16:52:59.767319Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling overhead: 59776343.19975523\n" + ] + } + ], + "source": [ + "from qiskit_addon_cutting import partition_problem\n", + "\n", + "partitioned_problem = partition_problem(\n", + " circuit=qc_w_ancilla, observables=observables_expanded\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "subobservables = partitioned_problem.subobservables\n", + "print(\n", + " f\"Sampling overhead: {np.prod([basis.overhead for basis in partitioned_problem.bases])}\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "be74c914-5090-44b9-96c2-0d4e86b49259", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:59.770081Z", + "iopub.status.busy": "2025-03-28T16:52:59.769722Z", + "iopub.status.idle": "2025-03-28T16:52:59.774244Z", + "shell.execute_reply": "2025-03-28T16:52:59.773599Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: PauliList(['IIII', 'IIZI', 'IIIZ']),\n", + " 1: PauliList(['ZIII', 'IIII', 'IIII'])}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subobservables" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "259e5a5b-93b3-4402-974e-9dd0f88001f9", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:59.776009Z", + "iopub.status.busy": "2025-03-28T16:52:59.775815Z", + "iopub.status.idle": "2025-03-28T16:52:59.931154Z", + "shell.execute_reply": "2025-03-28T16:52:59.930440Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/259e5a5b-93b3-4402-974e-9dd0f88001f9-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[0].draw(\"mpl\", style=\"iqp\", scale=0.8)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "626611d4-996c-4408-ac93-6f310ec3c172", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:59.933278Z", + "iopub.status.busy": "2025-03-28T16:52:59.933055Z", + "iopub.status.idle": "2025-03-28T16:53:00.110630Z", + "shell.execute_reply": "2025-03-28T16:53:00.110004Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/626611d4-996c-4408-ac93-6f310ec3c172-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[1].draw(\"mpl\", style=\"iqp\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "1ef01745-bc44-4e61-b84d-2465edfd1f15", + "metadata": {}, + "source": [ + "#### Generate the experiments to run on the backend." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "76c9af77-c458-4643-b0d4-3daad0b436dc", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:53:00.112773Z", + "iopub.status.busy": "2025-03-28T16:53:00.112543Z", + "iopub.status.idle": "2025-03-28T16:53:02.920653Z", + "shell.execute_reply": "2025-03-28T16:53:02.919977Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2000 total subexperiments to run on backend.\n" + ] + } + ], + "source": [ + "from qiskit_addon_cutting import generate_cutting_experiments\n", + "\n", + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits, observables=subobservables, num_samples=1_000\n", + ")\n", + "print(\n", + " f\"{len(subexperiments[0]) + len(subexperiments[1])} total subexperiments to run on backend.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9b9be7c9-1009-4982-8c1f-cfbd9ada12bd", + "metadata": {}, + "source": [ + "Steps 3 and 4 of a [Qiskit pattern](/guides/intro-to-patterns) can then be performed as in the preceding tutorials." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/addons/qiskit-addon-cutting/tutorials/index.mdx b/docs/addons/qiskit-addon-cutting/tutorials/index.mdx new file mode 100644 index 00000000000..16cb9fdccb9 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/index.mdx @@ -0,0 +1,30 @@ +--- +title: "Tutorials" +description: "Tutorials for the lastest version of Circuit cutting" +--- + +<span id="circuit-cutting-tutorials" /> + +# Tutorials + +* [Tutorial 1](01-gate-cutting-to-reduce-circuit-width): Separate sets of qubits by cutting nonlocal gates. +* [Tutorial 2](02-gate-cutting-to-reduce-circuit-depth): Cut gates requiring many SWAPs to decrease circuit depth. +* [Tutorial 3](03-wire-cutting-via-move-instruction): Specify wire cuts as a two-qubit [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") operation. +* [Tutorial 4](04-automatic-cut-finding): Automatically find optimal gate and wire cut locations for reducing circuit width. + +[![](/docs/images/addons/qiskit-addon-cutting/tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.avif)](01-gate-cutting-to-reduce-circuit-width) + +[Gate Cutting to Reduce Circuit Width](01-gate-cutting-to-reduce-circuit-width) + +[![](/docs/images/addons/qiskit-addon-cutting/tutorials_02_gate_cutting_to_reduce_circuit_depth_17_2.avif)](02-gate-cutting-to-reduce-circuit-depth) + +[Gate Cutting to Reduce Circuit Depth](02-gate-cutting-to-reduce-circuit-depth) + +[![](/docs/images/addons/qiskit-addon-cutting/tutorials_03_wire_cutting_via_move_instruction_15_0.avif)](03-wire-cutting-via-move-instruction) + +[Wire Cutting Phrased as a Two-Qubit Move Instruction](03-wire-cutting-via-move-instruction) + +[![](/docs/images/addons/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_11_0.avif)](04-automatic-cut-finding) + +[Automatically find cuts](04-automatic-cut-finding) + diff --git a/docs/api/qiskit-addon-cutting/_toc.json b/docs/api/qiskit-addon-cutting/_toc.json index 1c70a374aeb..6f31ae77e65 100644 --- a/docs/api/qiskit-addon-cutting/_toc.json +++ b/docs/api/qiskit-addon-cutting/_toc.json @@ -117,5 +117,7 @@ } ], "collapsed": true, - "untranslatable": true + "untranslatable": true, + "parentUrl": "/docs/addons/qiskit-addon-cutting", + "parentLabel": "Circuit cutting" } diff --git a/docs/api/qiskit-addon-cutting/instructions-move.mdx b/docs/api/qiskit-addon-cutting/instructions-move.mdx index 89d1db36ae0..1799fb6b14c 100644 --- a/docs/api/qiskit-addon-cutting/instructions-move.mdx +++ b/docs/api/qiskit-addon-cutting/instructions-move.mdx @@ -50,7 +50,7 @@ python_api_name: qiskit_addon_cutting.instructions.Move ![Output from the previous code.](/docs/images/api/qiskit-addon-cutting/qiskit_addon_cutting-instructions-Move-1.svg) - A full demonstration of the [`Move`](#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction is available in [the introductory tutorial on wire cutting](https://qiskit.github.io/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction.html). + A full demonstration of the [`Move`](#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction is available in [the introductory tutorial on wire cutting](/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction). Create a [`Move`](#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction. diff --git a/docs/api/qiskit-addon-cutting/instructions.mdx b/docs/api/qiskit-addon-cutting/instructions.mdx index 897592b2eec..82e82155a9e 100644 --- a/docs/api/qiskit-addon-cutting/instructions.mdx +++ b/docs/api/qiskit-addon-cutting/instructions.mdx @@ -16,8 +16,8 @@ python_api_name: qiskit_addon_cutting.instructions Quantum circuit `Instruction`s useful for circuit cutting. -| | | -| ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------ | -| [`CutWire`](instructions-cut-wire "qiskit_addon_cutting.instructions.CutWire") | An instruction for denoting a wire cut location. | -| [`Move`](instructions-move "qiskit_addon_cutting.instructions.Move") | A two-qubit instruction representing a reset of the second qubit followed by a swap. | +| | | +| ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ | +| [`CutWire`](/docs/api/qiskit-addon-cutting/instructions-cut-wire "qiskit_addon_cutting.instructions.CutWire") | An instruction for denoting a wire cut location. | +| [`Move`](/docs/api/qiskit-addon-cutting/instructions-move "qiskit_addon_cutting.instructions.Move") | A two-qubit instruction representing a reset of the second qubit followed by a swap. | diff --git a/docs/api/qiskit-addon-cutting/qiskit-addon-cutting.mdx b/docs/api/qiskit-addon-cutting/qiskit-addon-cutting.mdx index 9b7aed2c1b6..f6009dd840b 100644 --- a/docs/api/qiskit-addon-cutting/qiskit-addon-cutting.mdx +++ b/docs/api/qiskit-addon-cutting/qiskit-addon-cutting.mdx @@ -19,19 +19,19 @@ Circuit cutting. ### cut\_wires <Function id="qiskit_addon_cutting.cut_wires" github="https://github.com/Qiskit/qiskit-addon-cutting/tree/stable/0.10/qiskit_addon_cutting/wire_cutting_transforms.py" signature="cut_wires(circuit, /)"> - Transform all [`CutWire`](instructions-cut-wire "qiskit_addon_cutting.instructions.CutWire") instructions in a circuit to [`Move`](instructions-move "qiskit_addon_cutting.instructions.Move") instructions marked for cutting. + Transform all [`CutWire`](/docs/api/qiskit-addon-cutting/instructions-cut-wire "qiskit_addon_cutting.instructions.CutWire") instructions in a circuit to [`Move`](/docs/api/qiskit-addon-cutting/instructions-move "qiskit_addon_cutting.instructions.Move") instructions marked for cutting. - The returned circuit will have one newly allocated qubit for every [`CutWire`](instructions-cut-wire "qiskit_addon_cutting.instructions.CutWire") instruction. + The returned circuit will have one newly allocated qubit for every [`CutWire`](/docs/api/qiskit-addon-cutting/instructions-cut-wire "qiskit_addon_cutting.instructions.CutWire") instruction. - See Sec. 3 and Appendix A of [2302.03366v1](https://arxiv.org/abs/2302.03366v1) for more information about the two different representations of wire cuts: single-qubit ([`CutWire`](instructions-cut-wire "qiskit_addon_cutting.instructions.CutWire")) vs. two-qubit ([`Move`](instructions-move "qiskit_addon_cutting.instructions.Move")). + See Sec. 3 and Appendix A of [2302.03366v1](https://arxiv.org/abs/2302.03366v1) for more information about the two different representations of wire cuts: single-qubit ([`CutWire`](/docs/api/qiskit-addon-cutting/instructions-cut-wire "qiskit_addon_cutting.instructions.CutWire")) vs. two-qubit ([`Move`](/docs/api/qiskit-addon-cutting/instructions-move "qiskit_addon_cutting.instructions.Move")). **Parameters** - **circuit** ([`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – Original circuit with [`CutWire`](instructions-cut-wire "qiskit_addon_cutting.instructions.CutWire") instructions + **circuit** ([`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – Original circuit with [`CutWire`](/docs/api/qiskit-addon-cutting/instructions-cut-wire "qiskit_addon_cutting.instructions.CutWire") instructions **Returns** - New circuit with [`CutWire`](instructions-cut-wire "qiskit_addon_cutting.instructions.CutWire") instructions replaced by [`Move`](instructions-move "qiskit_addon_cutting.instructions.Move") instructions wrapped in `TwoQubitQPDGate`s + New circuit with [`CutWire`](/docs/api/qiskit-addon-cutting/instructions-cut-wire "qiskit_addon_cutting.instructions.CutWire") instructions replaced by [`Move`](/docs/api/qiskit-addon-cutting/instructions-move "qiskit_addon_cutting.instructions.Move") instructions wrapped in `TwoQubitQPDGate`s **Return type** @@ -74,14 +74,14 @@ Circuit cutting. ### partition\_circuit\_qubits <Function id="qiskit_addon_cutting.partition_circuit_qubits" github="https://github.com/Qiskit/qiskit-addon-cutting/tree/stable/0.10/qiskit_addon_cutting/cutting_decomposition.py" signature="partition_circuit_qubits(circuit, partition_labels, inplace=False)"> - Replace all nonlocal gates belonging to more than one partition with instances of [`TwoQubitQPDGate`](qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate"). + Replace all nonlocal gates belonging to more than one partition with instances of [`TwoQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate"). - [`TwoQubitQPDGate`](qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s belonging to a single partition will not be affected. + [`TwoQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s belonging to a single partition will not be affected. **Parameters** * **circuit** ([`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – The circuit to partition - * **partition\_labels** ([`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)\[[`Hashable`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable)]) – A sequence containing a partition label for each qubit in the input circuit. Nonlocal gates belonging to more than one partition will be replaced with [`TwoQubitQPDGate`](qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s. + * **partition\_labels** ([`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)\[[`Hashable`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable)]) – A sequence containing a partition label for each qubit in the input circuit. Nonlocal gates belonging to more than one partition will be replaced with [`TwoQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s. * **inplace** ([`bool`](https://docs.python.org/3/library/functions.html#bool)) – Flag denoting whether to copy the input circuit before acting on it **Return type** @@ -90,7 +90,7 @@ Circuit cutting. **Returns** - The output circuit with each nonlocal gate spanning two partitions replaced by a [`TwoQubitQPDGate`](qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate") + The output circuit with each nonlocal gate spanning two partitions replaced by a [`TwoQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate") **Raises** @@ -105,9 +105,9 @@ Circuit cutting. If `partition_labels` is provided, then qubits with matching partition labels will be grouped together, and non-local gates spanning more than one partition will be cut apart. The label `None` is treated specially: any qubit with that partition label must be unused in the circuit. - If `partition_labels` is not provided, then it will be determined automatically from the connectivity of the circuit. This automatic determination ignores any [`TwoQubitQPDGate`](qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s in the `circuit`, as these denote instructions that are explicitly destined for cutting. The resulting partition labels, in the automatic case, will be consecutive integers starting with 0. Qubits which are idle throughout the circuit will be assigned a partition label of `None`. + If `partition_labels` is not provided, then it will be determined automatically from the connectivity of the circuit. This automatic determination ignores any [`TwoQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s in the `circuit`, as these denote instructions that are explicitly destined for cutting. The resulting partition labels, in the automatic case, will be consecutive integers starting with 0. Qubits which are idle throughout the circuit will be assigned a partition label of `None`. - All cut instructions will be replaced with [`SingleQubitQPDGate`](qpd-single-qubit-qpd-gate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s. + All cut instructions will be replaced with [`SingleQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-single-qubit-qpd-gate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s. If provided, `observables` will be separated along the boundaries specified by the partition labels. @@ -137,7 +137,7 @@ Circuit cutting. ### cut\_gates <Function id="qiskit_addon_cutting.cut_gates" github="https://github.com/Qiskit/qiskit-addon-cutting/tree/stable/0.10/qiskit_addon_cutting/cutting_decomposition.py" signature="cut_gates(circuit, gate_ids, inplace=False)"> - Transform specified gates into [`TwoQubitQPDGate`](qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s. + Transform specified gates into [`TwoQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s. **Parameters** @@ -147,11 +147,11 @@ Circuit cutting. **Return type** - [`tuple`](https://docs.python.org/3/library/stdtypes.html#tuple)\[[`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit), [`list`](https://docs.python.org/3/library/stdtypes.html#list)\[[`QPDBasis`](qpd-qpd-basis "qiskit_addon_cutting.qpd.qpd_basis.QPDBasis")]] + [`tuple`](https://docs.python.org/3/library/stdtypes.html#tuple)\[[`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit), [`list`](https://docs.python.org/3/library/stdtypes.html#list)\[[`QPDBasis`](/docs/api/qiskit-addon-cutting/qpd-qpd-basis "qiskit_addon_cutting.qpd.qpd_basis.QPDBasis")]] **Returns** - A copy of the input circuit with the specified gates replaced with [`TwoQubitQPDGate`](qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s and a list of [`QPDBasis`](qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis") instances – one for each decomposed gate. + A copy of the input circuit with the specified gates replaced with [`TwoQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s and a list of [`QPDBasis`](/docs/api/qiskit-addon-cutting/qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis") instances – one for each decomposed gate. **Raises** @@ -244,7 +244,7 @@ $$ **Parameters** * **subcircuits** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[Hashable, QuantumCircuit]*) - * **bases** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*QPDBasis*](qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis")*]*) + * **bases** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*QPDBasis*](/docs/api/qiskit-addon-cutting/qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis")*]*) * **subobservables** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[Hashable, PauliList] | None*) #### bases @@ -299,7 +299,7 @@ $$ **Returns** - A circuit containing [`BaseQPDGate`](qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") instances. The subcircuits resulting from cutting these gates will be runnable on the devices meeting the `constraints`. + A circuit containing [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") instances. The subcircuits resulting from cutting these gates will be runnable on the devices meeting the `constraints`. **A metadata dictionary:** diff --git a/docs/api/qiskit-addon-cutting/qpd.mdx b/docs/api/qiskit-addon-cutting/qpd.mdx index 68b5a096c5b..858b99ac96f 100644 --- a/docs/api/qiskit-addon-cutting/qpd.mdx +++ b/docs/api/qiskit-addon-cutting/qpd.mdx @@ -16,12 +16,12 @@ python_api_name: qiskit_addon_cutting.qpd Main quasiprobability decomposition functionality. -| | | -| ----------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------- | -| [`QPDBasis`](qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis") | Basis in which to decompose an operation. | -| [`BaseQPDGate`](qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") | Base class for a gate to be decomposed using quasiprobability decomposition. | -| [`SingleQubitQPDGate`](qpd-single-qubit-qpd-gate "qiskit_addon_cutting.qpd.SingleQubitQPDGate") | Single qubit gate to be decomposed using quasiprobability decomposition. | -| [`TwoQubitQPDGate`](qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate") | Two qubit gate to be decomposed using quasiprobability decomposition. | +| | | +| ------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------- | +| [`QPDBasis`](/docs/api/qiskit-addon-cutting/qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis") | Basis in which to decompose an operation. | +| [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") | Base class for a gate to be decomposed using quasiprobability decomposition. | +| [`SingleQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-single-qubit-qpd-gate "qiskit_addon_cutting.qpd.SingleQubitQPDGate") | Single qubit gate to be decomposed using quasiprobability decomposition. | +| [`TwoQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate") | Two qubit gate to be decomposed using quasiprobability decomposition. | ### WeightType @@ -52,7 +52,7 @@ Main quasiprobability decomposition functionality. **Parameters** - * **qpd\_bases** ([`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)\[[`QPDBasis`](qpd-qpd-basis "qiskit_addon_cutting.qpd.qpd_basis.QPDBasis")]) – The [`QPDBasis`](qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis") objects from which to generate weights + * **qpd\_bases** ([`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)\[[`QPDBasis`](/docs/api/qiskit-addon-cutting/qpd-qpd-basis "qiskit_addon_cutting.qpd.qpd_basis.QPDBasis")]) – The [`QPDBasis`](/docs/api/qiskit-addon-cutting/qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis") objects from which to generate weights * **num\_samples** ([`float`](https://docs.python.org/3/library/functions.html#float)) – Controls the number of weights to generate **Return type** @@ -61,7 +61,7 @@ Main quasiprobability decomposition functionality. **Returns** - A mapping from a given decomposition to its weight. Keys are tuples of indices – one index per input [`QPDBasis`](qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis"). The indices correspond to a specific decomposition mapping in the basis. + A mapping from a given decomposition to its weight. Keys are tuples of indices – one index per input [`QPDBasis`](/docs/api/qiskit-addon-cutting/qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis"). The indices correspond to a specific decomposition mapping in the basis. Values are tuples. The first element is a number corresponding to the weight of the contribution. The second element is the [`WeightType`](#qiskit_addon_cutting.qpd.WeightType "qiskit_addon_cutting.qpd.WeightType"), either `EXACT` or `SAMPLED`. </Function> @@ -84,15 +84,15 @@ Main quasiprobability decomposition functionality. **Returns** - Circuit which has had all its [`BaseQPDGate`](qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") instances decomposed into local operations. + Circuit which has had all its [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") instances decomposed into local operations. The circuit will contain a new, final classical register to contain the QPD measurement outcomes (accessible at `retval.cregs[-1]`). **Raises** - * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – An index in `instruction_ids` corresponds to a gate which is not a [`BaseQPDGate`](qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") instance. + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – An index in `instruction_ids` corresponds to a gate which is not a [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") instance. * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – A list within instruction\_ids is not length 1 or 2. - * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – The total number of indices in `instruction_ids` does not equal the number of [`BaseQPDGate`](qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") instances in the circuit. + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – The total number of indices in `instruction_ids` does not equal the number of [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") instances in the circuit. * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – Gates within the same decomposition hold different QPD bases. * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – Length of `map_ids` does not equal the number of decompositions in the circuit. </Function> @@ -100,19 +100,19 @@ Main quasiprobability decomposition functionality. ### qpdbasis\_from\_instruction <Function id="qiskit_addon_cutting.qpd.qpdbasis_from_instruction" github="https://github.com/Qiskit/qiskit-addon-cutting/tree/stable/0.10/qiskit_addon_cutting/qpd/decompositions.py" signature="qpdbasis_from_instruction(gate, /)"> - Generate a [`QPDBasis`](qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis") object, given a supported operation. + Generate a [`QPDBasis`](/docs/api/qiskit-addon-cutting/qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis") object, given a supported operation. All two-qubit gates which implement the [`to_matrix()`](/docs/api/qiskit/qiskit.circuit.Gate#to_matrix) method are supported. This should include the vast majority of gates with no unbound parameters, but there are some special cases (see, e.g., [qiskit issue #10396](https://github.com/Qiskit/qiskit/issues/10396)). - The [`Move`](instructions-move "qiskit_addon_cutting.instructions.Move") operation, which can be used to specify a wire cut, is also supported. + The [`Move`](/docs/api/qiskit-addon-cutting/instructions-move "qiskit_addon_cutting.instructions.Move") operation, which can be used to specify a wire cut, is also supported. **Return type** - [`QPDBasis`](qpd-qpd-basis "qiskit_addon_cutting.qpd.qpd_basis.QPDBasis") + [`QPDBasis`](/docs/api/qiskit-addon-cutting/qpd-qpd-basis "qiskit_addon_cutting.qpd.qpd_basis.QPDBasis") **Returns** - The newly-instantiated [`QPDBasis`](qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis") object + The newly-instantiated [`QPDBasis`](/docs/api/qiskit-addon-cutting/qpd-qpd-basis "qiskit_addon_cutting.qpd.QPDBasis") object **Raises** diff --git a/docs/api/qiskit-addon-cutting/release-notes.mdx b/docs/api/qiskit-addon-cutting/release-notes.mdx index e2f503bca53..56680c4efa8 100644 --- a/docs/api/qiskit-addon-cutting/release-notes.mdx +++ b/docs/api/qiskit-addon-cutting/release-notes.mdx @@ -60,9 +60,9 @@ The most notable change in this release is that the package has been renamed to ### New Features -* A new `minimum_reached` field has been added to the metadata outputted by `circuit_knitting.cutting.find_cuts()` to check if the cut-finder found a cut scheme that minimized the sampling overhead. Note that the search algorithm employed by the cut-finder is *guaranteed* to find the optimal solution, that is, the solution with the minimum sampling overhead, provided it is allowed to run long enough. The user is free to time-restrict the search by passing in suitable values for `max_backjumps` and/or `max_gamma` to [`OptimizationParameters`](qiskit-addon-cutting#optimizationparameters "qiskit_addon_cutting.OptimizationParameters"). If the search is terminated prematurely in this way, the metadata may indicate that the minimum was not reached, even though the returned solution was actually the optimal solution. This would mean that the search that was performed was not exhaustive enough to prove that the returned solution was optimal. +* A new `minimum_reached` field has been added to the metadata outputted by `circuit_knitting.cutting.find_cuts()` to check if the cut-finder found a cut scheme that minimized the sampling overhead. Note that the search algorithm employed by the cut-finder is *guaranteed* to find the optimal solution, that is, the solution with the minimum sampling overhead, provided it is allowed to run long enough. The user is free to time-restrict the search by passing in suitable values for `max_backjumps` and/or `max_gamma` to [`OptimizationParameters`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#optimizationparameters "qiskit_addon_cutting.OptimizationParameters"). If the search is terminated prematurely in this way, the metadata may indicate that the minimum was not reached, even though the returned solution was actually the optimal solution. This would mean that the search that was performed was not exhaustive enough to prove that the returned solution was optimal. -* When specifying instances of [`OptimizationParameters`](qiskit-addon-cutting#optimizationparameters "qiskit_addon_cutting.OptimizationParameters") that are inputted to `circuit_knitting.cutting.find_cuts()`, the user can now control whether the cut-finder looks only for gate cuts, only for wire cuts, or both, by setting the bools `gate_lo` and `wire_lo` appropriately. The default value of both of these is set to `True` and so the default search considers the possibility of both gate and wire cuts. +* When specifying instances of [`OptimizationParameters`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#optimizationparameters "qiskit_addon_cutting.OptimizationParameters") that are inputted to `circuit_knitting.cutting.find_cuts()`, the user can now control whether the cut-finder looks only for gate cuts, only for wire cuts, or both, by setting the bools `gate_lo` and `wire_lo` appropriately. The default value of both of these is set to `True` and so the default search considers the possibility of both gate and wire cuts. <span id="release-notes-0-9-0-upgrade-notes" /> @@ -76,7 +76,7 @@ The most notable change in this release is that the package has been renamed to * The `CutQC` subpackage has been removed, along with its two associated utility modules, `circuit_knitting.utils.metrics` and `circuit_knitting.utils.conversion`. Users are now encouraged to use the automatic cut-finding and gate/wire cutting from the `circuit_knitting.cutting` package. -* The behavior of [`separate_circuit()`](utils-transforms#separate_circuit "qiskit_addon_cutting.utils.transforms.separate_circuit") and [`partition_problem()`](qiskit-addon-cutting#partition_problem "qiskit_addon_cutting.partition_problem") have changed so that idle qubits are discarded by default. Previously, each idle qubit was placed in its own subcircuit if `partition_labels` was not provided. +* The behavior of [`separate_circuit()`](/docs/api/qiskit-addon-cutting/utils-transforms#separate_circuit "qiskit_addon_cutting.utils.transforms.separate_circuit") and [`partition_problem()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#partition_problem "qiskit_addon_cutting.partition_problem") have changed so that idle qubits are discarded by default. Previously, each idle qubit was placed in its own subcircuit if `partition_labels` was not provided. <span id="release-notes-0-7-1" /> @@ -104,7 +104,7 @@ The 0.7.1 release provides a workaround to ensure that the experiments generated ### Other Notes -* The [`generate_cutting_experiments()`](qiskit-addon-cutting#generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") function has been optimized for faster execution. +* The [`generate_cutting_experiments()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") function has been optimized for faster execution. <span id="release-notes-0-7-0" /> @@ -154,7 +154,7 @@ The 0.7 release introduces an automated cut finding code for the new circuit cut * The cutting tutorials have been rephrased with the goal of reconstructing the expectation value of a single [`SparsePauliOp`](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) with many terms, rather than multiple independent [`Pauli`](/docs/api/qiskit/qiskit.quantum_info.Pauli) observables. -* The [circuit cutting explanation](https://qiskit.github.io/qiskit-addon-cutting/explanation/index.html) document has been expanded significantly. +* The [circuit cutting explanation](/docs/addons/qiskit-addon-cutting/explanations/index) document has been expanded significantly. <span id="release-notes-0-6-0" /> @@ -170,19 +170,19 @@ The 0.7 release introduces an automated cut finding code for the new circuit cut * The minimum supported version of `qiskit` is now 0.45.0, and the minimum supported version of `qiskit-ibm-runtime` is now 0.12.2. CKT also now explicitly requires a version of `qiskit` less than 1.0, as there is no guarantee that the current version of CKT will work with Qiskit 1.0. -* Removed the `circuit_knitting.cutting.qpd.QPDBasis.from_gate` method, which has been deprecated since the 0.3 release. [`QPDBasis.from_instruction()`](qpd-qpd-basis#from_instruction "qiskit_addon_cutting.qpd.QPDBasis.from_instruction") should be used instead. +* Removed the `circuit_knitting.cutting.qpd.QPDBasis.from_gate` method, which has been deprecated since the 0.3 release. [`QPDBasis.from_instruction()`](/docs/api/qiskit-addon-cutting/qpd-qpd-basis#from_instruction "qiskit_addon_cutting.qpd.QPDBasis.from_instruction") should be used instead. * Removed the `circuit_knitting_toolbox` import path. Users should now import from `circuit_knitting` instead. -* Removed the `circuit_knitting.cutting.decompose_gates` function, which has been deprecated since the 0.3 release. [`cut_gates()`](qiskit-addon-cutting#cut_gates "qiskit_addon_cutting.cut_gates") should be used instead. +* Removed the `circuit_knitting.cutting.decompose_gates` function, which has been deprecated since the 0.3 release. [`cut_gates()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#cut_gates "qiskit_addon_cutting.cut_gates") should be used instead. * Removed the `circuit_knitting.cutting.cutting_evaluation` module, which has been deprecated since the 0.4 release. Users should first call `circuit_knitting.cutting.generate_cutting_experiments()` to generate the subexperiment circuits and then submit these circuits directly to the desired Sampler(s). -* Removed the `circuit_knitting.cutting.qpd.generate_qpd_samples` function, which has been deprecated since the 0.3 release. [`generate_qpd_weights()`](qpd#generate_qpd_weights "qiskit_addon_cutting.qpd.generate_qpd_weights") should be used instead. +* Removed the `circuit_knitting.cutting.qpd.generate_qpd_samples` function, which has been deprecated since the 0.3 release. [`generate_qpd_weights()`](/docs/api/qiskit-addon-cutting/qpd#generate_qpd_weights "qiskit_addon_cutting.qpd.generate_qpd_weights") should be used instead. -* Removed the `circuit_knitting.cutting.qpd.qpdbasis_from_gate` function, which has been deprecated since the 0.3 release. [`qpdbasis_from_instruction()`](qpd#qpdbasis_from_instruction "qiskit_addon_cutting.qpd.qpdbasis_from_instruction") should be used instead. +* Removed the `circuit_knitting.cutting.qpd.qpdbasis_from_gate` function, which has been deprecated since the 0.3 release. [`qpdbasis_from_instruction()`](/docs/api/qiskit-addon-cutting/qpd#qpdbasis_from_instruction "qiskit_addon_cutting.qpd.qpdbasis_from_instruction") should be used instead. -* The [`generate_cutting_experiments()`](qiskit-addon-cutting#generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") function now performs some optimizations on the generated circuits before returning them to the user. In particular, it performs the [`RemoveResetInZeroState`](/docs/api/qiskit/qiskit.transpiler.passes.RemoveResetInZeroState), [`RemoveFinalReset`](utils-transpiler-passes-remove-final-reset "qiskit_addon_cutting.utils.transpiler_passes.RemoveFinalReset"), and [`ConsolidateResets`](utils-transpiler-passes-consolidate-resets "qiskit_addon_cutting.utils.transpiler_passes.ConsolidateResets") passes, so that circuits with cut wires and no re-used qubits are transformed into subexperiments that contain no `Reset`s. This allows such circuits to work on a greater variety of hardware backends. +* The [`generate_cutting_experiments()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") function now performs some optimizations on the generated circuits before returning them to the user. In particular, it performs the [`RemoveResetInZeroState`](/docs/api/qiskit/qiskit.transpiler.passes.RemoveResetInZeroState), [`RemoveFinalReset`](/docs/api/qiskit-addon-cutting/utils-transpiler-passes-remove-final-reset "qiskit_addon_cutting.utils.transpiler_passes.RemoveFinalReset"), and [`ConsolidateResets`](/docs/api/qiskit-addon-cutting/utils-transpiler-passes-consolidate-resets "qiskit_addon_cutting.utils.transpiler_passes.ConsolidateResets") passes, so that circuits with cut wires and no re-used qubits are transformed into subexperiments that contain no `Reset`s. This allows such circuits to work on a greater variety of hardware backends. <span id="release-notes-0-6-0-bug-fixes" /> @@ -190,7 +190,7 @@ The 0.7 release introduces an automated cut finding code for the new circuit cut ### Bug Fixes -* It is now possible to serialize [`SingleQubitQPDGate`](qpd-single-qubit-qpd-gate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s using [`qpy`](/docs/api/qiskit/qpy#module-qiskit.qpy), but some other issues with serialization and deserialization still remain. See issue [#455](https://github.com/Qiskit/qiskit-addon-cutting/issues/445) for details. +* It is now possible to serialize [`SingleQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-single-qubit-qpd-gate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s using [`qpy`](/docs/api/qiskit/qpy#module-qiskit.qpy), but some other issues with serialization and deserialization still remain. See issue [#455](https://github.com/Qiskit/qiskit-addon-cutting/issues/445) for details. <span id="release-notes-0-6-0-other-notes" /> @@ -246,7 +246,7 @@ The primary purpose of this release is to swap the order of the classical regist ### Prelude -The primary goal of this release is to modify the circuit cutting workflow to enable direct use of the Sampler primitive. Previously, the Sampler was called in `execute_experiments()`, a function which is now deprecated in favor of [`generate_cutting_experiments()`](qiskit-addon-cutting#generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments"). +The primary goal of this release is to modify the circuit cutting workflow to enable direct use of the Sampler primitive. Previously, the Sampler was called in `execute_experiments()`, a function which is now deprecated in favor of [`generate_cutting_experiments()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments"). <span id="release-notes-0-4-0-new-features" /> @@ -254,7 +254,7 @@ The primary goal of this release is to modify the circuit cutting workflow to en ### New Features -* Added a module, `circuit_knitting.cutting.cutting_experiments`, which is intended to hold functions used for generating the quantum experiments needed for circuit cutting. This module will initially hold one function, [`generate_cutting_experiments()`](qiskit-addon-cutting#generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments"), which can be used to generate quantum experiments, given an input circuit containing [`BaseQPDGate`](qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") instances, some observables, and a number of times the joint quasi-probability distribution for the cuts should be sampled. +* Added a module, `circuit_knitting.cutting.cutting_experiments`, which is intended to hold functions used for generating the quantum experiments needed for circuit cutting. This module will initially hold one function, [`generate_cutting_experiments()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments"), which can be used to generate quantum experiments, given an input circuit containing [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") instances, some observables, and a number of times the joint quasi-probability distribution for the cuts should be sampled. <span id="release-notes-0-4-0-upgrade-notes" /> @@ -264,9 +264,9 @@ The primary goal of this release is to modify the circuit cutting workflow to en * The `circuit-knitting-toolbox` Python package now depends on `qiskit` rather than `qiskit-terra`. This should have no user-visible effects, but it is something to keep in mind if one sees dependency errors when upgrading CKT. -* The `execute_experiments()` function now returns a [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instance for each circuit partition, rather than the 3D list of quasi-distributions returned previously. The quasi-distribution for each subexperiment can be accessed via the `quasi_dists` field of [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult). The number of QPD bits contained in each subexperiment will be included in the `num_qpd_bits` field of the `metadata` dictionary for each experiment result. The output of this function is still valid as input to [`reconstruct_expectation_values()`](qiskit-addon-cutting#reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values"). +* The `execute_experiments()` function now returns a [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instance for each circuit partition, rather than the 3D list of quasi-distributions returned previously. The quasi-distribution for each subexperiment can be accessed via the `quasi_dists` field of [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult). The number of QPD bits contained in each subexperiment will be included in the `num_qpd_bits` field of the `metadata` dictionary for each experiment result. The output of this function is still valid as input to [`reconstruct_expectation_values()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values"). -* [`reconstruct_expectation_values()`](qiskit-addon-cutting#reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") now takes, as its first argument, a [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instance or a dictionary mapping partition labels to [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instances. This new `results` argument replaces the old `quasi_dists` argument. The [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instances are expected to contain the number of QPD bits used in each circuit input to the Sampler. This should be specified in the `num_qpd_bits` field of the experiment result metadata. +* [`reconstruct_expectation_values()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") now takes, as its first argument, a [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instance or a dictionary mapping partition labels to [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instances. This new `results` argument replaces the old `quasi_dists` argument. The [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instances are expected to contain the number of QPD bits used in each circuit input to the Sampler. This should be specified in the `num_qpd_bits` field of the experiment result metadata. <span id="release-notes-0-4-0-deprecation-notes" /> @@ -274,7 +274,7 @@ The primary goal of this release is to modify the circuit cutting workflow to en ### Deprecation Notes -* The `execute_experiments()` function has been deprecated. Going forward, users should first call [`generate_cutting_experiments()`](qiskit-addon-cutting#generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") to generate the subexperiment circuits and then submit these circuits directly to the desired Sampler(s). The [tutorials](https://qiskit.github.io/qiskit-addon-cutting/tutorials/index.html) have been updated with this new workflow. +* The `execute_experiments()` function has been deprecated. Going forward, users should first call [`generate_cutting_experiments()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") to generate the subexperiment circuits and then submit these circuits directly to the desired Sampler(s). The [tutorials](/docs/addons/qiskit-addon-cutting/tutorials/index) have been updated with this new workflow. <span id="release-notes-0-3-0" /> @@ -311,7 +311,7 @@ The 0.3.0 release introduces significant new features while maintaining backward * [`iSwapGate`](/docs/api/qiskit/qiskit.circuit.library.iSwapGate) * [`DCXGate`](/docs/api/qiskit/qiskit.circuit.library.DCXGate) -* [`partition_problem()`](qiskit-addon-cutting#partition_problem "qiskit_addon_cutting.partition_problem") now works even if `partition_labels` is not explicitly provided. In this case, the labels are determined automatically from the connectivity of the input circuit. For the sake of determining connectivity, [`TwoQubitQPDGate`](qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s are ignored, as these instructions are already marked for cutting. To support this workflow, this release also introduces a new method, [`TwoQubitQPDGate.from_instruction()`](qpd-two-qubit-qpd-gate#from_instruction "qiskit_addon_cutting.qpd.TwoQubitQPDGate.from_instruction"), which allows one to create a [`TwoQubitQPDGate`](qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate") that wraps a given instruction. +* [`partition_problem()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#partition_problem "qiskit_addon_cutting.partition_problem") now works even if `partition_labels` is not explicitly provided. In this case, the labels are determined automatically from the connectivity of the input circuit. For the sake of determining connectivity, [`TwoQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s are ignored, as these instructions are already marked for cutting. To support this workflow, this release also introduces a new method, [`TwoQubitQPDGate.from_instruction()`](/docs/api/qiskit-addon-cutting/qpd-two-qubit-qpd-gate#from_instruction "qiskit_addon_cutting.qpd.TwoQubitQPDGate.from_instruction"), which allows one to create a [`TwoQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-two-qubit-qpd-gate "qiskit_addon_cutting.qpd.TwoQubitQPDGate") that wraps a given instruction. * Dynamic Definition code has been added to the `cutqc` module. See pull request [#285](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/pull/285) for details. @@ -319,7 +319,7 @@ The 0.3.0 release introduces significant new features while maintaining backward * `generate_qpd_weights()` now returns a mixture of exact and sampled weights when appropriate. Specifically, it exactly evaluates all weights greater than or equal to `1 / num_samples` and samples from the remaining weights (ones which are below this threshold). Previously, this function would only return exact weights if *all* weights were greater than or equal to `1 / num_samples`; otherwise, all weights were sampled. The new behavior is expected to improve performance on non-uniform quasi-probability decompositions, e.g. for cut instantiations of [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), and [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate) away from $\theta=\pi/2$. -* The `circuit_knitting.cutting` module now supports wire cutting. There is a [new tutorial](https://qiskit.github.io/qiskit-addon-cutting/tutorials/index.html) that explains how to use it. +* The `circuit_knitting.cutting` module now supports wire cutting. There is a [new tutorial](/docs/addons/qiskit-addon-cutting/tutorials/index) that explains how to use it. <span id="release-notes-0-3-0-upgrade-notes" /> @@ -333,7 +333,7 @@ The 0.3.0 release introduces significant new features while maintaining backward * The dependency on the `qiskit.opflow` module has been removed from entanglement forging. With this change, Qiskit Nature 0.6.0 is now required. However, Qiskit Nature 0.6.0 is incompatible with Quantum Serverless, so users that wish to use entanglement forging with Quantum Serverless must remain on version 0.2 of the Circuit Knitting Toolbox until [issue #108](https://github.com/Qiskit/qiskit-addon-cutting/issues/108) is resolved. -* [`BaseQPDGate`](qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") instances in subcircuits returned from [`partition_circuit_qubits()`](qiskit-addon-cutting#partition_circuit_qubits "qiskit_addon_cutting.partition_circuit_qubits") and [`partition_problem()`](qiskit-addon-cutting#partition_problem "qiskit_addon_cutting.partition_problem") will now have labels prefixed with “cut”, rather than “qpd”. +* [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate "qiskit_addon_cutting.qpd.BaseQPDGate") instances in subcircuits returned from [`partition_circuit_qubits()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#partition_circuit_qubits "qiskit_addon_cutting.partition_circuit_qubits") and [`partition_problem()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#partition_problem "qiskit_addon_cutting.partition_problem") will now have labels prefixed with “cut”, rather than “qpd”. <span id="release-notes-0-3-0-deprecation-notes" /> @@ -341,11 +341,11 @@ The 0.3.0 release introduces significant new features while maintaining backward ### Deprecation Notes -* `decompose_gates()` is deprecated and will be removed no sooner than v0.4.0. Users should migrate to the identical [`cut_gates()`](qiskit-addon-cutting#cut_gates "qiskit_addon_cutting.cut_gates") function. +* `decompose_gates()` is deprecated and will be removed no sooner than v0.4.0. Users should migrate to the identical [`cut_gates()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#cut_gates "qiskit_addon_cutting.cut_gates") function. * `generate_qpd_samples()` has been renamed to `generate_qpd_weights()`. The original name will be removed no sooner than version 0.4 of the Circuit Knitting Toolbox. -* `QPDBasis.from_gate()` has been renamed to [`QPDBasis.from_instruction()`](qpd-qpd-basis#from_instruction "qiskit_addon_cutting.qpd.QPDBasis.from_instruction"). The original name is deprecated and will be removed no sooner than CKT v0.4.0. +* `QPDBasis.from_gate()` has been renamed to [`QPDBasis.from_instruction()`](/docs/api/qiskit-addon-cutting/qpd-qpd-basis#from_instruction "qiskit_addon_cutting.qpd.QPDBasis.from_instruction"). The original name is deprecated and will be removed no sooner than CKT v0.4.0. * The top-level name for imports has been renamed from `circuit_knitting_toolbox` to `circuit_knitting`. Furthermore, the following renames have occurred one level deeper: @@ -376,11 +376,11 @@ The 0.3.0 release introduces significant new features while maintaining backward ### Prelude -0.2.0 is centered around the addition of functions which allow for the easy implementation of a circuit cutting technique called gate cutting. For more details on circuit cutting, check out our [explanation guide](https://qiskit.github.io/qiskit-addon-cutting/explanation/index.html). +0.2.0 is centered around the addition of functions which allow for the easy implementation of a circuit cutting technique called gate cutting. For more details on circuit cutting, check out our [explanation guide](/docs/addons/qiskit-addon-cutting/explanations/index). The foundation of the `circuit_cutting` package is the `circuit_knitting_toolbox.circuit_cutting.qpd` sub-package. The `qpd` package allows for easy transformation of [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) gates and wires into elements which may be decomposed to a probabilistic set of basis gates. See `QPDBasis` and `BaseQPDGate` classes for more information. -Additionally, 0.2.0 includes a set of functions which allow for easy implementation of gate cutting workflows. These functions are built on top of the `circuit_knitting_toolbox.circuit_cutting.qpd` package. Like all circuit knitting techniques, gate cutting can be described as three consecutive stages: *decomposition* of a problem, *execution* of many subexperiments, and *reconstruction* of a simulated output of the original problem. These steps may be implemented with the `circuit_cutting` package using only a few primary functions, namely, the [`partition_problem()`](qiskit-addon-cutting#partition_problem "qiskit_addon_cutting.partition_problem"), `decompose_gates()`, `execute_experiments()`, and [`reconstruct_expectation_values()`](qiskit-addon-cutting#reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") functions. Check out the [tutorials](https://qiskit.github.io/qiskit-addon-cutting/tutorials/index.html) for a look at a couple of example circuit cutting workflows. +Additionally, 0.2.0 includes a set of functions which allow for easy implementation of gate cutting workflows. These functions are built on top of the `circuit_knitting_toolbox.circuit_cutting.qpd` package. Like all circuit knitting techniques, gate cutting can be described as three consecutive stages: *decomposition* of a problem, *execution* of many subexperiments, and *reconstruction* of a simulated output of the original problem. These steps may be implemented with the `circuit_cutting` package using only a few primary functions, namely, the [`partition_problem()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#partition_problem "qiskit_addon_cutting.partition_problem"), `decompose_gates()`, `execute_experiments()`, and [`reconstruct_expectation_values()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") functions. Check out the [tutorials](/docs/addons/qiskit-addon-cutting/tutorials/index) for a look at a couple of example circuit cutting workflows. <span id="release-notes-0-2-0-new-features" /> @@ -390,7 +390,7 @@ Additionally, 0.2.0 includes a set of functions which allow for easy implementat * Addition of a `qpd` package which allows for easy transformation of [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) gates and wires into elements which may be decomposed to a probabilistic set of basis gates. See `QPDBasis` and `BaseQPDGate` classes for more information. -* Addition of `cutting_decomposition`, `cutting_execution`, and `cutting_reconstruction` modules. These modules provide several functions which allow for easy implementation of gate cutting workflows, namely, the [`partition_problem()`](qiskit-addon-cutting#partition_problem "qiskit_addon_cutting.partition_problem"), `decompose_gates()`, `execute_experiments()`, and [`reconstruct_expectation_values()`](qiskit-addon-cutting#reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") functions. +* Addition of `cutting_decomposition`, `cutting_execution`, and `cutting_reconstruction` modules. These modules provide several functions which allow for easy implementation of gate cutting workflows, namely, the [`partition_problem()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#partition_problem "qiskit_addon_cutting.partition_problem"), `decompose_gates()`, `execute_experiments()`, and [`reconstruct_expectation_values()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") functions. <span id="release-notes-0-2-0-known-issues" /> diff --git a/docs/api/qiskit-addon-cutting/utils-transpiler-passes.mdx b/docs/api/qiskit-addon-cutting/utils-transpiler-passes.mdx index e0b1c77eda9..3298e43b0ff 100644 --- a/docs/api/qiskit-addon-cutting/utils-transpiler-passes.mdx +++ b/docs/api/qiskit-addon-cutting/utils-transpiler-passes.mdx @@ -16,8 +16,8 @@ python_api_name: qiskit_addon_cutting.utils.transpiler_passes Transpiler passes useful for circuit knitting. -| | | -| ---------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------- | -| [`RemoveFinalReset`](utils-transpiler-passes-remove-final-reset "qiskit_addon_cutting.utils.transpiler_passes.RemoveFinalReset") | Remove reset when it is the final instruction on a qubit wire. | -| [`ConsolidateResets`](utils-transpiler-passes-consolidate-resets "qiskit_addon_cutting.utils.transpiler_passes.ConsolidateResets") | Consolidate a run duplicate resets in to a single reset. | +| | | +| ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------- | +| [`RemoveFinalReset`](/docs/api/qiskit-addon-cutting/utils-transpiler-passes-remove-final-reset "qiskit_addon_cutting.utils.transpiler_passes.RemoveFinalReset") | Remove reset when it is the final instruction on a qubit wire. | +| [`ConsolidateResets`](/docs/api/qiskit-addon-cutting/utils-transpiler-passes-consolidate-resets "qiskit_addon_cutting.utils.transpiler_passes.ConsolidateResets") | Consolidate a run duplicate resets in to a single reset. | diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index c03515da1eb..a2ea7b608b7 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -97,7 +97,7 @@ These techniques are useful for managing noise on sampling results. <Card title="Sample-based quantum diagonalization" description="Estimate the spectrum of quantum Hamiltonians by processing noisy samples and diagonalizing in a reduced subspace." - href="/docs/guides/qiskit-addons-sqd" + href="/docs/addons/qiskit-addons-sqd" analyticsName="Overview page: SQD" linkText="Overview" /> diff --git a/public/docs/images/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients/extracted-outputs/44956cbb-0.avif 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z7hT7UU-6szxRv_lY{1c;oU<jE(0YH8>bHfUY208W%f7G>d-X2zbWfQk!J(73UghpU z4)5QA9AT8W&>i7ZX`%ZD3O~;6B$%VG!`<6Qu)MY#c2@ZM#aV4fU2tD0Y@>k2loU<m F@PCuR9#8-P literal 0 HcmV?d00001 diff --git a/scripts/config/cspell/cSpell.json b/scripts/config/cspell/cSpell.json index da5692c0bf5..980e76e721e 100644 --- a/scripts/config/cspell/cSpell.json +++ b/scripts/config/cspell/cSpell.json @@ -44,7 +44,16 @@ "ebitsa", "ebitsb", "pmzero", - "textrm" + "textrm", + "resuce", // fix in addon-obp + "subeperiments", // fix in addon-cutting + "detination", // fix in addon-cutting + "subpace", // fix in addon-sqd + "behaviour", // fix in addon-sqd + "susbapce", // fix in addon-sqd + "Counfiguration", // fix in addon-sqd + "construnct", // fix in addon-sqd + "annihalation" // fix in addon-sqd ], "ignoreRegExpList": [ // Markdown links diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index 827eced0465..986f8b37ac4 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -242,6 +242,17 @@ const ALWAYS_IGNORED_URLS__SHOULD_FIX: string[] = [ "/docs/api/qiskit/transpiler_preset#generate_preset_pass_manager", "../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian", + // from qiskit-addon-cutting + "how-tos/how-to-specify-cut-wires", + "/circuit_cutting/explanations/index-rst#overview-of-circuit-cutting", + "/docs/start/install#operating-system-support", + "../tutorials/03_wire_cutting_via_move_instruction.ipynb", + "/guides/intro-to-patterns", + "../how-tos/how_to_specify_cut_wires.ipynb", + "../explanation/index.rst", + "#equation-eq-qpd", + "./circuit_cutting/explanations/index-rst#overview-of-circuit-cutting", + "../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb" ]; export const ALWAYS_IGNORED_URLS = new Set([ From 09aa489d2c32484624725cc032127b626ef1f89c Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 15 May 2026 14:10:01 -0400 Subject: [PATCH 056/104] addon link --- docs/guides/addons.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index a2ea7b608b7..b660dfb29a3 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -62,7 +62,7 @@ These capabilities are useful for reducing circuit depth and typically come with <Card title="Circuit cutting" description="Reduce the depth of transpiled circuits by decomposing entangling gates between non-adjacent qubits." - href="https://qiskit.github.io/qiskit-addon-cutting" + href="/docs/addons/qiskit-addon-cutting" analyticsName="Documentation page: Circuit cutting" linkText="Browse documentation" /> From 3e31390d03962fce88292c3a209cb7979906e315 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 15 May 2026 14:25:38 -0400 Subject: [PATCH 057/104] use sqd v 0.12.1 --- docs/addons/qiskit-addon-mpf/_toc.json | 4 +- .../02-fermionic-lattice-hamiltonian.ipynb | 54 +++++++++--------- docs/api/qiskit-addon-sqd/_package.json | 2 +- ...25fcad-5c21-43f8-8fb4-cb2be542f292-0.avif} | Bin ...b64838-0abc-4d34-b29a-cd157e425fe7-0.avif} | Bin ...480096-899b-4d02-add2-0dcd265bb2a8-0.avif} | Bin ...dc8c96-ccec-452a-b364-d2c7cf27e970-1.avif} | Bin 7 files changed, 30 insertions(+), 30 deletions(-) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/{f7d2f73c-42f9-4111-a724-ce435a5f7a23-0.avif => 4325fcad-5c21-43f8-8fb4-cb2be542f292-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/{dfcd22a4-d2ea-4b1d-beaa-f29156d001ab-0.avif => 95b64838-0abc-4d34-b29a-cd157e425fe7-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/{d9d40efe-145a-48d7-8db1-473656d2df92-0.avif => ad480096-899b-4d02-add2-0dcd265bb2a8-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/{71976322-31da-437c-b660-a97b122669c0-1.avif => eddc8c96-ccec-452a-b364-d2c7cf27e970-1.avif} (100%) diff --git a/docs/addons/qiskit-addon-mpf/_toc.json b/docs/addons/qiskit-addon-mpf/_toc.json index ed733e9a0b4..2244746da2c 100644 --- a/docs/addons/qiskit-addon-mpf/_toc.json +++ b/docs/addons/qiskit-addon-mpf/_toc.json @@ -37,12 +37,12 @@ "collapsible": false }, { - "title": "Explainations", + "title": "Explanations", "collapsible": false, "children": [ { "title": "Understanding the stability of MPFs", - "url": "/docs/addons/qiskit-addon-mpf/explainations/mpf-stability" + "url": "/docs/addons/qiskit-addon-mpf/explanations/mpf-stability" } ] }, diff --git a/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb index 8ebe4a08e86..630bce314f6 100644 --- a/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb +++ b/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "da10b7a2-e180-4098-b00c-df0325e926f1", + "id": "f4859654-0fc5-41e9-8c35-dc66c2e9b623", "metadata": {}, "source": [ "# Improving energy estimation of a Fermionic lattice model with SQD\n", @@ -38,7 +38,7 @@ }, { "cell_type": "markdown", - "id": "7499d787-1e89-44cd-940b-b1c108e83766", + "id": "541a90b9-06c6-44e9-8e48-f46c2a438858", "metadata": {}, "source": [ "### Step 1: Map problem to a quantum circuit" @@ -46,7 +46,7 @@ }, { "cell_type": "markdown", - "id": "43d7d7da-f05c-4709-908d-169b7957857c", + "id": "95945dc5-8375-47a1-95f5-a9f4ba72b9b9", "metadata": {}, "source": [ "First, we will create the one- and two-body Hamiltonians describing the one-dimensional single-impurity Anderson model (SIAM) with ``7`` bath sites (``8`` electrons in ``8`` orbitals). This model is used to describe magnetic impurities embedded in metals.\n", @@ -57,7 +57,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "1cacc3b1-2252-4ac6-85dc-3bed0bf07530", + "id": "f0f9a80c-2b66-4765-9d26-38c1357d893c", "metadata": {}, "outputs": [], "source": [ @@ -86,7 +86,7 @@ }, { "cell_type": "markdown", - "id": "908670e5-3e04-4131-9d02-06802def6716", + "id": "1671b4da-e085-441a-bf3b-d98d41911de8", "metadata": {}, "source": [ "Next, we will generate the quantum Krylov subspace with a set of Trotterized quantum circuits. Here we create helpers for generating the initial (reference) state as well as the time evolution for the one- and two-body parts of the Hamiltonian. For a more detailed description of this model and how the circuits are designed, please refer to [the paper](https://arxiv.org/abs/2501.09702)." @@ -95,7 +95,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "bf539352-c287-4e66-bd5b-b7ff6da96d89", + "id": "25eaa4b5-5cc2-474a-9551-162feb25761f", "metadata": {}, "outputs": [], "source": [ @@ -143,7 +143,7 @@ }, { "cell_type": "markdown", - "id": "1e1ec758-3752-4cc3-bc56-93f3ce9febe9", + "id": "1669e296-2984-4eb9-ac7e-c0da04fbc2cf", "metadata": {}, "source": [ "Generate ``d`` time-evolved states that specify the quantum Krylov subspace. Here, we have chosen ``d=8``. The error from sampling Krylov basis states converges with increasing ``d``. Note that the particulars of this problem instance allow for an efficient compilation of the one-body evolution using `OrbitalRotationJW`; however, in general, one could use Qiskit's [PauliEvolutionGate](/docs/api/qiskit/qiskit.circuit.library.PauliEvolutionGate) to implement the Trotterized time evolution of the full Hamiltonian." @@ -152,7 +152,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "07353461-4b71-483a-a6f2-dc75cbcce03b", + "id": "45d31319-0f58-40f7-83ea-e37ffa2e46a6", "metadata": {}, "outputs": [], "source": [ @@ -177,13 +177,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "dfcd22a4-d2ea-4b1d-beaa-f29156d001ab", + "id": "95b64838-0abc-4d34-b29a-cd157e425fe7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/dfcd22a4-d2ea-4b1d-beaa-f29156d001ab-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/95b64838-0abc-4d34-b29a-cd157e425fe7-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 4, @@ -198,13 +198,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "d9d40efe-145a-48d7-8db1-473656d2df92", + "id": "ad480096-899b-4d02-add2-0dcd265bb2a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/d9d40efe-145a-48d7-8db1-473656d2df92-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/ad480096-899b-4d02-add2-0dcd265bb2a8-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 5, @@ -218,7 +218,7 @@ }, { "cell_type": "markdown", - "id": "b71cbfcc-17a5-4af0-87a2-59e7d1037048", + "id": "c7fe0a3f-c46d-44f6-9c07-3c09ce8e7069", "metadata": {}, "source": [ "### Step 2: Optimize the problem" @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "70a9dcbe-2aa4-4c8f-80fa-d3d8d41bfb99", + "id": "6bd51921-b807-401e-bc8b-b84b210191da", "metadata": {}, "source": [ "After we have created the Trotterized circuits, we will optimize them for a target hardware. We need to choose the hardware device to use before optimization. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." @@ -235,7 +235,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "c3214def-a355-46aa-85af-43b3c1be0bf4", + "id": "2f2cd4ed-0476-4a8f-8cef-10a30cb5eed5", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "28c9f712-05fd-4fdd-a70f-cb3787a0041e", + "id": "50fe27f6-2818-4634-93fc-fbc9308300b0", "metadata": {}, "source": [ "Next, we will transpile the circuits to the target backend using Qiskit." @@ -255,7 +255,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "69e3f7f3-9f28-49a1-8fc9-824b46388cf8", + "id": "673b13f6-705e-4a13-903c-8b8c72e352f3", "metadata": {}, "outputs": [], "source": [ @@ -268,7 +268,7 @@ }, { "cell_type": "markdown", - "id": "b5d41fb7-4f50-4324-94f1-8a605f41d19b", + "id": "cc63aa50-e40c-4a8f-adfe-07616f377dc6", "metadata": {}, "source": [ "### Step 3: Execute experiments" @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "405db0d8-0911-4dc7-bd42-03210666d66e", + "id": "17e46832-1c30-4bec-b249-9486ed0a99d4", "metadata": {}, "source": [ "After optimizing the circuits for hardware execution, we are ready to run them on the target hardware and collect samples for ground state energy estimation. Here we use ``SamplerV2`` from ``qiskit-ibm-runtime`` to simulate noisy samples taken from the ``ibm_sherbrooke`` backend. We then combine the counts from each of the Krylov basis states into a single counts dictionary and plot the top 20 most commonly sampled bitstrings.\n", @@ -287,13 +287,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "f7d2f73c-42f9-4111-a724-ce435a5f7a23", + "id": "4325fcad-5c21-43f8-8fb4-cb2be542f292", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/f7d2f73c-42f9-4111-a724-ce435a5f7a23-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/4325fcad-5c21-43f8-8fb4-cb2be542f292-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 8, @@ -318,7 +318,7 @@ }, { "cell_type": "markdown", - "id": "aa456f21-473f-4965-acc5-d4a3166978ef", + "id": "24d3c9df-2f53-46a5-803f-e1c37719969a", "metadata": {}, "source": [ "### Step 4: Post-process the results" @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "ac18ac1d-6bcc-4672-ac6a-3e45490daf52", + "id": "dd8adc53-a7a4-40ff-bf1b-b65462b813e8", "metadata": {}, "source": [ "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function." @@ -335,7 +335,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5117e234-4d4e-4445-b288-b47c26c0fa67", + "id": "78ebf72f-7ae6-48aa-a5a1-b5df96a2429e", "metadata": {}, "outputs": [ { @@ -420,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "3e4c54ab-6003-44b2-aaa8-a35d69d12da8", + "id": "3b11c1b6-2058-405a-b323-01e58194707f", "metadata": {}, "source": [ "Now, we plot the results. The first plot shows that after a few iterations, we obtain the exact ground state energy.\n", @@ -433,7 +433,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "71976322-31da-437c-b660-a97b122669c0", + "id": "eddc8c96-ccec-452a-b364-d2c7cf27e970", "metadata": {}, "outputs": [ { @@ -448,7 +448,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/71976322-31da-437c-b660-a97b122669c0-1.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/eddc8c96-ccec-452a-b364-d2c7cf27e970-1.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, diff --git a/docs/api/qiskit-addon-sqd/_package.json b/docs/api/qiskit-addon-sqd/_package.json index 9b4e87cc521..1f226e87545 100644 --- a/docs/api/qiskit-addon-sqd/_package.json +++ b/docs/api/qiskit-addon-sqd/_package.json @@ -1,4 +1,4 @@ { "name": "qiskit-addon-sqd", - "version": "0.12.0" + "version": "0.12.1" } diff --git a/public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/f7d2f73c-42f9-4111-a724-ce435a5f7a23-0.avif b/public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/4325fcad-5c21-43f8-8fb4-cb2be542f292-0.avif similarity index 100% rename from public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/f7d2f73c-42f9-4111-a724-ce435a5f7a23-0.avif rename to public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/4325fcad-5c21-43f8-8fb4-cb2be542f292-0.avif diff --git a/public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/dfcd22a4-d2ea-4b1d-beaa-f29156d001ab-0.avif b/public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/95b64838-0abc-4d34-b29a-cd157e425fe7-0.avif similarity index 100% rename from public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/dfcd22a4-d2ea-4b1d-beaa-f29156d001ab-0.avif rename to public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/95b64838-0abc-4d34-b29a-cd157e425fe7-0.avif diff --git a/public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/d9d40efe-145a-48d7-8db1-473656d2df92-0.avif b/public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/ad480096-899b-4d02-add2-0dcd265bb2a8-0.avif similarity index 100% rename from public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/d9d40efe-145a-48d7-8db1-473656d2df92-0.avif rename to public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/ad480096-899b-4d02-add2-0dcd265bb2a8-0.avif diff --git a/public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/71976322-31da-437c-b660-a97b122669c0-1.avif b/public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/eddc8c96-ccec-452a-b364-d2c7cf27e970-1.avif similarity index 100% rename from public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/71976322-31da-437c-b660-a97b122669c0-1.avif rename to public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/eddc8c96-ccec-452a-b364-d2c7cf27e970-1.avif From 864f37f104404daa9f27db0fb968adc9dbebbb0a Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 15 May 2026 14:26:29 -0400 Subject: [PATCH 058/104] fix spelling --- docs/addons/qiskit-addon-cutting/_toc.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/addons/qiskit-addon-cutting/_toc.json b/docs/addons/qiskit-addon-cutting/_toc.json index df61f8f3bd7..5ed857183b3 100644 --- a/docs/addons/qiskit-addon-cutting/_toc.json +++ b/docs/addons/qiskit-addon-cutting/_toc.json @@ -16,7 +16,7 @@ "url": "/docs/addons/qiskit-addon-cutting/install" }, { - "title":"Explainations", + "title":"Explanations", "children": [ { "title": "Explanatory material", From 4f800db55c979b4b5f47e5dbf57d63004ef23b10 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 15 May 2026 14:35:56 -0400 Subject: [PATCH 059/104] handle github stars badge --- docs/addons/qiskit-addon-cutting/index.mdx | 2 +- .../tutorials/04-automatic-cut-finding.ipynb | 40 +++++++++--------- ...413d19-ec3a-4174-9c50-38cbc69797fc-0.avif} | Bin ...218da4-3126-4c7c-865f-d0a92f7df4f0-0.avif} | Bin ...716fb5-2fb7-4581-8bca-66b69811fe77-1.avif} | Bin ...8d53dd-1f73-4569-9519-4c3e8e618ad9-0.avif} | Bin ...dd88b9-ceaa-4d61-b1dc-159b28c61ddf-0.avif} | Bin scripts/js/lib/api/processHtml.test.ts | 16 +++++++ scripts/js/lib/api/processHtml.ts | 5 ++- 9 files changed, 41 insertions(+), 22 deletions(-) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{259e5a5b-93b3-4402-974e-9dd0f88001f9-0.avif => 40413d19-ec3a-4174-9c50-38cbc69797fc-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{626611d4-996c-4408-ac93-6f310ec3c172-0.avif => 45218da4-3126-4c7c-865f-d0a92f7df4f0-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{3cdb274b-140d-4312-a75d-e78bfedd35d4-1.avif => 88716fb5-2fb7-4581-8bca-66b69811fe77-1.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{74c0f6a7-3ccc-4f43-b0b2-a405eaa52539-0.avif => 8d8d53dd-1f73-4569-9519-4c3e8e618ad9-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{ca564cf6-7d14-4e11-ba65-76d80a9beeef-0.avif => 9cdd88b9-ceaa-4d61-b1dc-159b28c61ddf-0.avif} (100%) diff --git a/docs/addons/qiskit-addon-cutting/index.mdx b/docs/addons/qiskit-addon-cutting/index.mdx index 8bd5fd3a27c..c276cf9c9c7 100644 --- a/docs/addons/qiskit-addon-cutting/index.mdx +++ b/docs/addons/qiskit-addon-cutting/index.mdx @@ -5,7 +5,7 @@ description: "Documentation for the latest version of Circuit cutting" # Qiskit addon: circuit cutting -[![GitHub repository star counter badge](/docs/images/addons/qiskit-addon-cutting/qiskit-addon-cutting?style=social)](https://github.com/Qiskit/qiskit-addon-cutting) +[![GitHub repository star counter badge](https://img.shields.io/github/stars/Qiskit/qiskit-addon-cutting?style=social)](https://github.com/Qiskit/qiskit-addon-cutting) [Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. diff --git a/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb index 3c9ffdc4295..7071f978979 100644 --- a/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb +++ b/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "a7e59535-3340-4bff-b022-7d7bea1b2d93", + "id": "4106165a-40d7-4a2a-8126-e1ecba8cb9ea", "metadata": {}, "source": [ "# Automatically find cuts" @@ -21,7 +21,7 @@ }, { "cell_type": "markdown", - "id": "e870ba6e-6bc1-4e0f-af4a-c5aa78febc0e", + "id": "b8faecc3-c00d-4117-818e-e92385d2e630", "metadata": {}, "source": [ "## Step 1: Map\n", @@ -32,7 +32,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "74c0f6a7-3ccc-4f43-b0b2-a405eaa52539", + "id": "8d8d53dd-1f73-4569-9519-4c3e8e618ad9", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:55.792854Z", @@ -45,7 +45,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/74c0f6a7-3ccc-4f43-b0b2-a405eaa52539-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/8d8d53dd-1f73-4569-9519-4c3e8e618ad9-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 1, @@ -67,7 +67,7 @@ }, { "cell_type": "markdown", - "id": "3ef2174c-db48-44cb-9e91-d33967cc7db3", + "id": "b5568c06-af75-48ce-824a-3cfce0bd6407", "metadata": {}, "source": [ "## Step 2: Optimize\n", @@ -78,7 +78,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "3cdb274b-140d-4312-a75d-e78bfedd35d4", + "id": "88716fb5-2fb7-4581-8bca-66b69811fe77", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:56.802390Z", @@ -107,7 +107,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/3cdb274b-140d-4312-a75d-e78bfedd35d4-1.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/88716fb5-2fb7-4581-8bca-66b69811fe77-1.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 2, @@ -141,7 +141,7 @@ }, { "cell_type": "markdown", - "id": "c58bf8c4-f392-4f60-9ae7-78d4c99b3382", + "id": "1ee94246-e97c-414f-bf1f-e3c3dccd26fb", "metadata": {}, "source": [ "#### Add ancillas for wire cuts and expand the observables to account for ancilla qubits" @@ -150,7 +150,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "ca564cf6-7d14-4e11-ba65-76d80a9beeef", + "id": "9cdd88b9-ceaa-4d61-b1dc-159b28c61ddf", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.491081Z", @@ -163,7 +163,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/ca564cf6-7d14-4e11-ba65-76d80a9beeef-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/9cdd88b9-ceaa-4d61-b1dc-159b28c61ddf-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 3, @@ -181,7 +181,7 @@ }, { "cell_type": "markdown", - "id": "605ce0e2-63e2-4499-a4b6-9dc99fad5b53", + "id": "c20d863f-9a1c-4e12-ab28-a025a015c410", "metadata": {}, "source": [ "#### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires." @@ -190,7 +190,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "a1d78d09-c20d-4066-aad1-f1a412821794", + "id": "fa985db9-eff1-4270-9cc1-8e7817625a89", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.755174Z", @@ -224,7 +224,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "be74c914-5090-44b9-96c2-0d4e86b49259", + "id": "a809371d-ff97-45a6-82fb-eccfbb311943", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.770081Z", @@ -253,7 +253,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "259e5a5b-93b3-4402-974e-9dd0f88001f9", + "id": "40413d19-ec3a-4174-9c50-38cbc69797fc", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.776009Z", @@ -266,7 +266,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/259e5a5b-93b3-4402-974e-9dd0f88001f9-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/40413d19-ec3a-4174-9c50-38cbc69797fc-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 6, @@ -281,7 +281,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "626611d4-996c-4408-ac93-6f310ec3c172", + "id": "45218da4-3126-4c7c-865f-d0a92f7df4f0", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.933278Z", @@ -294,7 +294,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/626611d4-996c-4408-ac93-6f310ec3c172-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/45218da4-3126-4c7c-865f-d0a92f7df4f0-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 7, @@ -308,7 +308,7 @@ }, { "cell_type": "markdown", - "id": "1ef01745-bc44-4e61-b84d-2465edfd1f15", + "id": "93105591-da20-4ed8-b21b-f9e776cbd2ab", "metadata": {}, "source": [ "#### Generate the experiments to run on the backend." @@ -317,7 +317,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "76c9af77-c458-4643-b0d4-3daad0b436dc", + "id": "4ddd5491-7e52-48b5-9b8e-841d32181f50", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:53:00.112773Z", @@ -348,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "9b9be7c9-1009-4982-8c1f-cfbd9ada12bd", + "id": "9c767820-833c-45b5-966f-c01d7eda448e", "metadata": {}, "source": [ "Steps 3 and 4 of a [Qiskit pattern](/guides/intro-to-patterns) can then be performed as in the preceding tutorials." diff --git a/public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/259e5a5b-93b3-4402-974e-9dd0f88001f9-0.avif b/public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/40413d19-ec3a-4174-9c50-38cbc69797fc-0.avif similarity index 100% rename from public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/259e5a5b-93b3-4402-974e-9dd0f88001f9-0.avif rename to public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/40413d19-ec3a-4174-9c50-38cbc69797fc-0.avif diff --git a/public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/626611d4-996c-4408-ac93-6f310ec3c172-0.avif b/public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/45218da4-3126-4c7c-865f-d0a92f7df4f0-0.avif similarity index 100% rename from public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/626611d4-996c-4408-ac93-6f310ec3c172-0.avif rename to public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/45218da4-3126-4c7c-865f-d0a92f7df4f0-0.avif diff --git a/public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/3cdb274b-140d-4312-a75d-e78bfedd35d4-1.avif b/public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/88716fb5-2fb7-4581-8bca-66b69811fe77-1.avif similarity index 100% rename from public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/3cdb274b-140d-4312-a75d-e78bfedd35d4-1.avif rename to public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/88716fb5-2fb7-4581-8bca-66b69811fe77-1.avif diff --git a/public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/74c0f6a7-3ccc-4f43-b0b2-a405eaa52539-0.avif b/public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/8d8d53dd-1f73-4569-9519-4c3e8e618ad9-0.avif similarity index 100% rename from public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/74c0f6a7-3ccc-4f43-b0b2-a405eaa52539-0.avif rename to public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/8d8d53dd-1f73-4569-9519-4c3e8e618ad9-0.avif diff --git a/public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/ca564cf6-7d14-4e11-ba65-76d80a9beeef-0.avif b/public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/9cdd88b9-ceaa-4d61-b1dc-159b28c61ddf-0.avif similarity index 100% rename from public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/ca564cf6-7d14-4e11-ba65-76d80a9beeef-0.avif rename to public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/9cdd88b9-ceaa-4d61-b1dc-159b28c61ddf-0.avif diff --git a/scripts/js/lib/api/processHtml.test.ts b/scripts/js/lib/api/processHtml.test.ts index 656e97f3c01..3f0b9c8014c 100644 --- a/scripts/js/lib/api/processHtml.test.ts +++ b/scripts/js/lib/api/processHtml.test.ts @@ -80,6 +80,22 @@ test.describe("loadImages()", () => { ]); doc.expectHtml(`<img src="/my-images/0.45/view-page-source-icon.svg">`); }); + + test("external image URLs are not rewritten", () => { + const doc = CheerioDoc.load( + `<img src="https://img.shields.io/github/stars/Qiskit/qiskit-addon-cutting?style=social" alt="Stars"><img src="../_static/logo.png" alt="Logo">`, + ); + const images = loadImages(doc.$, doc.$main, "/my-images", false, false); + expect(images).toEqual([ + { + fileName: "logo.png", + dest: "/my-images/logo.avif", + }, + ]); + doc.expectHtml( + `<img src="https://img.shields.io/github/stars/Qiskit/qiskit-addon-cutting?style=social" alt="Stars"><img src="/my-images/logo.avif" alt="Logo">`, + ); + }); }); test("handleSphinxDesignCards()", () => { diff --git a/scripts/js/lib/api/processHtml.ts b/scripts/js/lib/api/processHtml.ts index 349f2e365ea..b6ca895bf98 100644 --- a/scripts/js/lib/api/processHtml.ts +++ b/scripts/js/lib/api/processHtml.ts @@ -114,7 +114,10 @@ export function loadImages( return $main .find("img") .toArray() - .filter((img) => $(img).attr("src")) + .filter((img) => { + const src = $(img).attr("src"); + return src && !src.startsWith("http://") && !src.startsWith("https://"); + }) .map((img) => { const $img = $(img); From 300aad593c1c5a53c4fe5796ac2f97023bc8f796 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 15 May 2026 17:24:52 -0400 Subject: [PATCH 060/104] fix images --- .../tutorials/04-automatic-cut-finding.ipynb | 40 +++++------ .../tutorials/01-chemistry-hamiltonian.ipynb | 4 +- .../02-fermionic-lattice-hamiltonian.ipynb | 54 +++++++-------- .../nbsphinx-no-thumbnail.svg | 9 +++ ...5c579a-fae4-4ae0-920e-3fc6dd0cf0c3-0.avif} | Bin ...4b80c8-cdd0-4213-abc8-4344767dca6a-0.avif} | Bin ...390890-16c0-4264-8fb7-9fa9eca15932-1.avif} | Bin ...07191e-d20c-4424-96a1-d4ed6471f1cd-0.avif} | Bin ...bc2e35-1516-495b-9af6-c632b17d8c05-0.avif} | Bin .../lucj_ansatz_zig_zag_pattern.jpg | Bin 0 -> 130188 bytes .../nbsphinx-no-thumbnail.svg | 9 +++ .../addons/qiskit-addon-sqd/sqd_diagram.png | Bin 0 -> 152199 bytes ...ca5c13-fb2f-42ff-8f2f-fa6e3b24cf1f-1.avif} | Bin ...8b5120-c4ca-4189-baf5-eed42cef0b20-0.avif} | Bin ...fe7a76-b707-44a3-93f0-9ab4e4abd74d-0.avif} | Bin ...13672f-0f58-44d5-ba6d-319b126bac43-0.avif} | Bin scripts/js/lib/api/HtmlToMdResult.ts | 2 + scripts/js/lib/api/addonDocsPipeline.ts | 5 ++ scripts/js/lib/api/notebookStages.ts | 57 +++++++++++++++- scripts/js/lib/api/pipelineStages.ts | 7 +- scripts/js/lib/api/processHtml.test.ts | 63 ++++++++++++++++-- scripts/js/lib/api/processHtml.ts | 13 +++- scripts/js/lib/api/saveImages.ts | 8 ++- 23 files changed, 210 insertions(+), 61 deletions(-) create mode 100644 public/docs/images/addons/qiskit-addon-cutting/nbsphinx-no-thumbnail.svg rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{8d8d53dd-1f73-4569-9519-4c3e8e618ad9-0.avif => 015c579a-fae4-4ae0-920e-3fc6dd0cf0c3-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{40413d19-ec3a-4174-9c50-38cbc69797fc-0.avif => 0c4b80c8-cdd0-4213-abc8-4344767dca6a-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{88716fb5-2fb7-4581-8bca-66b69811fe77-1.avif => 0f390890-16c0-4264-8fb7-9fa9eca15932-1.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{45218da4-3126-4c7c-865f-d0a92f7df4f0-0.avif => 4807191e-d20c-4424-96a1-d4ed6471f1cd-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{9cdd88b9-ceaa-4d61-b1dc-159b28c61ddf-0.avif => 50bc2e35-1516-495b-9af6-c632b17d8c05-0.avif} (100%) create mode 100644 public/docs/images/addons/qiskit-addon-sqd/lucj_ansatz_zig_zag_pattern.jpg create mode 100644 public/docs/images/addons/qiskit-addon-sqd/nbsphinx-no-thumbnail.svg create mode 100644 public/docs/images/addons/qiskit-addon-sqd/sqd_diagram.png rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/{eddc8c96-ccec-452a-b364-d2c7cf27e970-1.avif => 25ca5c13-fb2f-42ff-8f2f-fa6e3b24cf1f-1.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/{4325fcad-5c21-43f8-8fb4-cb2be542f292-0.avif => 9d8b5120-c4ca-4189-baf5-eed42cef0b20-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/{ad480096-899b-4d02-add2-0dcd265bb2a8-0.avif => d1fe7a76-b707-44a3-93f0-9ab4e4abd74d-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/{95b64838-0abc-4d34-b29a-cd157e425fe7-0.avif => ed13672f-0f58-44d5-ba6d-319b126bac43-0.avif} (100%) diff --git a/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb index 7071f978979..a6b0995ef00 100644 --- a/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb +++ b/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "4106165a-40d7-4a2a-8126-e1ecba8cb9ea", + "id": "a2aee9a1-c44d-4ede-b8c2-7bea2c8fc67a", "metadata": {}, "source": [ "# Automatically find cuts" @@ -21,7 +21,7 @@ }, { "cell_type": "markdown", - "id": "b8faecc3-c00d-4117-818e-e92385d2e630", + "id": "967099f2-a552-4638-a5d7-aa267b400af2", "metadata": {}, "source": [ "## Step 1: Map\n", @@ -32,7 +32,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "8d8d53dd-1f73-4569-9519-4c3e8e618ad9", + "id": "015c579a-fae4-4ae0-920e-3fc6dd0cf0c3", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:55.792854Z", @@ -45,7 +45,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/8d8d53dd-1f73-4569-9519-4c3e8e618ad9-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/015c579a-fae4-4ae0-920e-3fc6dd0cf0c3-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 1, @@ -67,7 +67,7 @@ }, { "cell_type": "markdown", - "id": "b5568c06-af75-48ce-824a-3cfce0bd6407", + "id": "2c1169ab-1568-4760-a14f-0c88bd83d67b", "metadata": {}, "source": [ "## Step 2: Optimize\n", @@ -78,7 +78,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "88716fb5-2fb7-4581-8bca-66b69811fe77", + "id": "0f390890-16c0-4264-8fb7-9fa9eca15932", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:56.802390Z", @@ -107,7 +107,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/88716fb5-2fb7-4581-8bca-66b69811fe77-1.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/0f390890-16c0-4264-8fb7-9fa9eca15932-1.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 2, @@ -141,7 +141,7 @@ }, { "cell_type": "markdown", - "id": "1ee94246-e97c-414f-bf1f-e3c3dccd26fb", + "id": "766339b2-dc5d-4c78-829a-f6c2d68050b2", "metadata": {}, "source": [ "#### Add ancillas for wire cuts and expand the observables to account for ancilla qubits" @@ -150,7 +150,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "9cdd88b9-ceaa-4d61-b1dc-159b28c61ddf", + "id": "50bc2e35-1516-495b-9af6-c632b17d8c05", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.491081Z", @@ -163,7 +163,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/9cdd88b9-ceaa-4d61-b1dc-159b28c61ddf-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/50bc2e35-1516-495b-9af6-c632b17d8c05-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 3, @@ -181,7 +181,7 @@ }, { "cell_type": "markdown", - "id": "c20d863f-9a1c-4e12-ab28-a025a015c410", + "id": "9c221b7b-aeab-4b12-8d3d-3e06a293c944", "metadata": {}, "source": [ "#### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires." @@ -190,7 +190,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "fa985db9-eff1-4270-9cc1-8e7817625a89", + "id": "1733d9af-3430-491a-993f-6627487b19f5", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.755174Z", @@ -224,7 +224,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "a809371d-ff97-45a6-82fb-eccfbb311943", + "id": "bed20ead-ca92-4e1c-95d5-52cb3ecb9e95", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.770081Z", @@ -253,7 +253,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "40413d19-ec3a-4174-9c50-38cbc69797fc", + "id": "0c4b80c8-cdd0-4213-abc8-4344767dca6a", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.776009Z", @@ -266,7 +266,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/40413d19-ec3a-4174-9c50-38cbc69797fc-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/0c4b80c8-cdd0-4213-abc8-4344767dca6a-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 6, @@ -281,7 +281,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "45218da4-3126-4c7c-865f-d0a92f7df4f0", + "id": "4807191e-d20c-4424-96a1-d4ed6471f1cd", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.933278Z", @@ -294,7 +294,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/45218da4-3126-4c7c-865f-d0a92f7df4f0-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/4807191e-d20c-4424-96a1-d4ed6471f1cd-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 7, @@ -308,7 +308,7 @@ }, { "cell_type": "markdown", - "id": "93105591-da20-4ed8-b21b-f9e776cbd2ab", + "id": "f1c4ee91-b6ae-4cee-a13c-16505db07763", "metadata": {}, "source": [ "#### Generate the experiments to run on the backend." @@ -317,7 +317,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "4ddd5491-7e52-48b5-9b8e-841d32181f50", + "id": "4cb11dd2-2e81-4eb3-9382-8226bbb9d1e3", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:53:00.112773Z", @@ -348,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "9c767820-833c-45b5-966f-c01d7eda448e", + "id": "17bf85d7-00df-4361-b309-2e7cf562fcb2", "metadata": {}, "source": [ "Steps 3 and 4 of a [Qiskit pattern](/guides/intro-to-patterns) can then be performed as in the preceding tutorials." diff --git a/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.ipynb index 1cf30b35e6f..66d382b088c 100644 --- a/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.ipynb +++ b/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.ipynb @@ -53,7 +53,7 @@ "\n", "The SQD workflow with self-consistent configuration recovery is depicted in the following diagram.\n", "\n", - "![SQD diagram](../_static/images/sqd_diagram.png)\n", + "![SQD diagram](/docs/images/addons/qiskit-addon-sqd/sqd_diagram.png)\n", "\n", "SQD is known to work well when the target eigenstate is sparse: the wave function is supported in a set of basis states $\\mathcal{S} = \\{|x\\rangle \\}$ whose size does not increase exponentially with the size of the problem. In this scenario, the diagonalization of the Hamiltonian projected into the subspace defined by $\\mathcal{S}$:\n", "$$\n", @@ -168,7 +168,7 @@ "\n", "As our target IBM hardware has a heavy-hex topology, we will adopt the [_zig-zag_ pattern for qubit interactions](https://pubs.rsc.org/en/content/articlehtml/2023/sc/d3sc02516k). In this pattern, orbitals (represented by qubits) with the same spin are connected with a line topology (red and blue circles) where each line take a zig-zag shape due the heavy-hex connectivity of the target hardware. Again, due to the heavy-hex topology, orbitals for different spins have connections between every 4th orbital (0, 4, 8, etc.) (purple circles).\n", "\n", - "![lucj_ansatz](../_static/images/lucj_ansatz_zig_zag_pattern.jpg)" + "![lucj_ansatz](/docs/images/addons/qiskit-addon-sqd/lucj_ansatz_zig_zag_pattern.jpg)" ] }, { diff --git a/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb index 630bce314f6..553614cd987 100644 --- a/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb +++ b/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "f4859654-0fc5-41e9-8c35-dc66c2e9b623", + "id": "7c3e56fe-8357-4d3a-961a-6e0258ae3082", "metadata": {}, "source": [ "# Improving energy estimation of a Fermionic lattice model with SQD\n", @@ -38,7 +38,7 @@ }, { "cell_type": "markdown", - "id": "541a90b9-06c6-44e9-8e48-f46c2a438858", + "id": "c4de5d77-345b-4fab-b22d-23b5ae988c54", "metadata": {}, "source": [ "### Step 1: Map problem to a quantum circuit" @@ -46,7 +46,7 @@ }, { "cell_type": "markdown", - "id": "95945dc5-8375-47a1-95f5-a9f4ba72b9b9", + "id": "58f58c32-790b-4ec8-aabc-f45f0bfd0a9e", "metadata": {}, "source": [ "First, we will create the one- and two-body Hamiltonians describing the one-dimensional single-impurity Anderson model (SIAM) with ``7`` bath sites (``8`` electrons in ``8`` orbitals). This model is used to describe magnetic impurities embedded in metals.\n", @@ -57,7 +57,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "f0f9a80c-2b66-4765-9d26-38c1357d893c", + "id": "3fb0e890-1423-47f6-b29d-a104cc018f9a", "metadata": {}, "outputs": [], "source": [ @@ -86,7 +86,7 @@ }, { "cell_type": "markdown", - "id": "1671b4da-e085-441a-bf3b-d98d41911de8", + "id": "16b4f0ca-ec5f-4891-8f98-385d72236e1f", "metadata": {}, "source": [ "Next, we will generate the quantum Krylov subspace with a set of Trotterized quantum circuits. Here we create helpers for generating the initial (reference) state as well as the time evolution for the one- and two-body parts of the Hamiltonian. For a more detailed description of this model and how the circuits are designed, please refer to [the paper](https://arxiv.org/abs/2501.09702)." @@ -95,7 +95,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "25eaa4b5-5cc2-474a-9551-162feb25761f", + "id": "833e9562-efdb-48f6-9e37-d1e083fabe8b", "metadata": {}, "outputs": [], "source": [ @@ -143,7 +143,7 @@ }, { "cell_type": "markdown", - "id": "1669e296-2984-4eb9-ac7e-c0da04fbc2cf", + "id": "3743b994-b69a-4725-a4c8-2e5ebb39a7d7", "metadata": {}, "source": [ "Generate ``d`` time-evolved states that specify the quantum Krylov subspace. Here, we have chosen ``d=8``. The error from sampling Krylov basis states converges with increasing ``d``. Note that the particulars of this problem instance allow for an efficient compilation of the one-body evolution using `OrbitalRotationJW`; however, in general, one could use Qiskit's [PauliEvolutionGate](/docs/api/qiskit/qiskit.circuit.library.PauliEvolutionGate) to implement the Trotterized time evolution of the full Hamiltonian." @@ -152,7 +152,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "45d31319-0f58-40f7-83ea-e37ffa2e46a6", + "id": "f7ab6c84-a778-4c8c-a491-45e0a63e5520", "metadata": {}, "outputs": [], "source": [ @@ -177,13 +177,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "95b64838-0abc-4d34-b29a-cd157e425fe7", + "id": "ed13672f-0f58-44d5-ba6d-319b126bac43", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/95b64838-0abc-4d34-b29a-cd157e425fe7-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/ed13672f-0f58-44d5-ba6d-319b126bac43-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 4, @@ -198,13 +198,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "ad480096-899b-4d02-add2-0dcd265bb2a8", + "id": "d1fe7a76-b707-44a3-93f0-9ab4e4abd74d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/ad480096-899b-4d02-add2-0dcd265bb2a8-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/d1fe7a76-b707-44a3-93f0-9ab4e4abd74d-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 5, @@ -218,7 +218,7 @@ }, { "cell_type": "markdown", - "id": "c7fe0a3f-c46d-44f6-9c07-3c09ce8e7069", + "id": "6c1eb7a9-5c1b-4d7c-a9d9-a97309efaad7", "metadata": {}, "source": [ "### Step 2: Optimize the problem" @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "6bd51921-b807-401e-bc8b-b84b210191da", + "id": "b6478b6e-9be6-47c1-a036-269a3ce7a039", "metadata": {}, "source": [ "After we have created the Trotterized circuits, we will optimize them for a target hardware. We need to choose the hardware device to use before optimization. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." @@ -235,7 +235,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "2f2cd4ed-0476-4a8f-8cef-10a30cb5eed5", + "id": "193c0b47-578e-4b25-9413-852b13806167", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "50fe27f6-2818-4634-93fc-fbc9308300b0", + "id": "9486836a-2eb7-40b6-bdc2-141ef01829d6", "metadata": {}, "source": [ "Next, we will transpile the circuits to the target backend using Qiskit." @@ -255,7 +255,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "673b13f6-705e-4a13-903c-8b8c72e352f3", + "id": "b6981e2d-6e5f-4a74-b057-49de5587275e", "metadata": {}, "outputs": [], "source": [ @@ -268,7 +268,7 @@ }, { "cell_type": "markdown", - "id": "cc63aa50-e40c-4a8f-adfe-07616f377dc6", + "id": "9e60781b-9839-42e6-bacd-4265b871fff1", "metadata": {}, "source": [ "### Step 3: Execute experiments" @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "17e46832-1c30-4bec-b249-9486ed0a99d4", + "id": "500bcd52-54a0-47d8-9a3f-b731034dbfcd", "metadata": {}, "source": [ "After optimizing the circuits for hardware execution, we are ready to run them on the target hardware and collect samples for ground state energy estimation. Here we use ``SamplerV2`` from ``qiskit-ibm-runtime`` to simulate noisy samples taken from the ``ibm_sherbrooke`` backend. We then combine the counts from each of the Krylov basis states into a single counts dictionary and plot the top 20 most commonly sampled bitstrings.\n", @@ -287,13 +287,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "4325fcad-5c21-43f8-8fb4-cb2be542f292", + "id": "9d8b5120-c4ca-4189-baf5-eed42cef0b20", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/4325fcad-5c21-43f8-8fb4-cb2be542f292-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/9d8b5120-c4ca-4189-baf5-eed42cef0b20-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 8, @@ -318,7 +318,7 @@ }, { "cell_type": "markdown", - "id": "24d3c9df-2f53-46a5-803f-e1c37719969a", + "id": "11131d60-c829-4fa2-9fbc-4c5d2dad69e4", "metadata": {}, "source": [ "### Step 4: Post-process the results" @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "dd8adc53-a7a4-40ff-bf1b-b65462b813e8", + "id": "612ebb53-07e6-4c37-8144-ca88d7c8f141", "metadata": {}, "source": [ "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function." @@ -335,7 +335,7 @@ { "cell_type": "code", "execution_count": null, - "id": "78ebf72f-7ae6-48aa-a5a1-b5df96a2429e", + "id": "ef8f66ec-63ce-4ad4-912b-53748df5c268", "metadata": {}, "outputs": [ { @@ -420,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "3b11c1b6-2058-405a-b323-01e58194707f", + "id": "6a968ed3-6298-47f2-896d-af6affa0b66f", "metadata": {}, "source": [ "Now, we plot the results. The first plot shows that after a few iterations, we obtain the exact ground state energy.\n", @@ -433,7 +433,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "eddc8c96-ccec-452a-b364-d2c7cf27e970", + "id": "25ca5c13-fb2f-42ff-8f2f-fa6e3b24cf1f", "metadata": {}, "outputs": [ { @@ -448,7 +448,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/eddc8c96-ccec-452a-b364-d2c7cf27e970-1.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/25ca5c13-fb2f-42ff-8f2f-fa6e3b24cf1f-1.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, diff --git a/public/docs/images/addons/qiskit-addon-cutting/nbsphinx-no-thumbnail.svg b/public/docs/images/addons/qiskit-addon-cutting/nbsphinx-no-thumbnail.svg new file mode 100644 index 00000000000..9dca7588fa5 --- /dev/null +++ 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b/scripts/js/lib/api/HtmlToMdResult.ts @@ -15,6 +15,8 @@ import { Metadata } from "./Metadata.js"; export type Image = { fileName: string; dest: string; + /** Path to the image relative to the artifact root (e.g. `_static/foo.svg` or `_images/foo.svg`) */ + originSrc: string; }; export type HtmlToMdResult = { diff --git a/scripts/js/lib/api/addonDocsPipeline.ts b/scripts/js/lib/api/addonDocsPipeline.ts index 0436f242bec..c2fc9d62ace 100644 --- a/scripts/js/lib/api/addonDocsPipeline.ts +++ b/scripts/js/lib/api/addonDocsPipeline.ts @@ -22,6 +22,7 @@ import { writeMarkdownResults, } from "./pipelineStages.js"; import { + collectNotebookImages, processNotebooks, readNotebooks, writeNotebooks, @@ -81,11 +82,14 @@ export async function runAddonDocsPipeline( outputPath, notebookFiles, ); + const imageDestination = pkg.outputDir(`${DOCS_BASE_PATH}/images/addons`); + const notebookImages = collectNotebookImages(initialNotebooks, imageDestination); const notebooks = processNotebooks( initialNotebooks, objectsInv, allObjectInvs, pkg, + imageDestination, ); await writeNotebooks(pkg, docsBaseFolder, notebooks); @@ -95,6 +99,7 @@ export async function runAddonDocsPipeline( artifactPath, pkg.outputDir(`${publicBaseFolder}/images/addons`), results, + notebookImages, ); } diff --git a/scripts/js/lib/api/notebookStages.ts b/scripts/js/lib/api/notebookStages.ts index 2ace3463acf..ad84c470553 100644 --- a/scripts/js/lib/api/notebookStages.ts +++ b/scripts/js/lib/api/notebookStages.ts @@ -21,6 +21,7 @@ import { readFile, writeFile } from "fs/promises"; import { mkdirp } from "mkdirp"; import { visit, EXIT } from "unist-util-visit"; +import { Image } from "./HtmlToMdResult.js"; import { ObjectsInv } from "./objectsInv.js"; import { Pkg } from "./Pkg.js"; import { kebabCaseAndShortenPage } from "./normalizeResultUrls.js"; @@ -57,13 +58,19 @@ export function processNotebooks( objectsInv: ObjectsInv, allInvs: Map<string, ObjectsInv>, pkg: Pkg, + imageDestination: string, ): NotebookWithUrl[] { return notebooks.map((notebook) => { const processedCells = notebook.cells.map((cell) => { if (cell.cell_type !== "markdown") return cell; return { ...cell, - source: rewriteNotebookLinks(cell.source, objectsInv, allInvs), + source: rewriteNotebookLinks( + cell.source, + objectsInv, + allInvs, + imageDestination, + ), }; }); @@ -77,6 +84,39 @@ export function processNotebooks( }); } +/** + * Extract images referenced in notebook markdown cells as `Image` objects + * so they can be passed to `copyImages` alongside HTML-derived images. + */ +export function collectNotebookImages( + notebooks: NotebookWithUrl[], + imageDestination: string, +): Image[] { + const seen = new Set<string>(); + const images: Image[] = []; + for (const notebook of notebooks) { + for (const cell of notebook.cells) { + if (cell.cell_type !== "markdown") continue; + const text = Array.isArray(cell.source) + ? cell.source.join("") + : cell.source; + for (const match of text.matchAll(/!\[[^\]]*\]\(([^)]+)\)/g)) { + const src = match[1]; + if (src.startsWith("http://") || src.startsWith("https://")) continue; + const fileName = src.split("/").pop()!; + if (seen.has(fileName)) continue; + seen.add(fileName); + images.push({ + fileName, + dest: `${imageDestination}/${fileName}`, + originSrc: `_images/${fileName}`, + }); + } + } + } + return images; +} + export async function writeNotebooks( pkg: Pkg, docsBaseFolder: string, @@ -103,9 +143,13 @@ function rewriteNotebookLinks( source: string | string[], objectsInv: ObjectsInv, allInvs: Map<string, ObjectsInv>, + imageDestination: string, ): string | string[] { const rewrite = (line: string) => { - return line.replace(/\[([^\]]*)\]\(([^)]+)\)/g, (_match, text, url) => { + return line.replace(/(!?)\[([^\]]*)\]\(([^)]+)\)/g, (_match, bang, text, url) => { + if (bang === "!") { + return `![${text}](${rewriteNotebookImageSrc(url, imageDestination)})`; + } const relativized = relativizeLink({ url, text }); if (relativized) url = relativized.url; const stub = objectsInv.resolveStubUrl(url, allInvs); @@ -117,6 +161,15 @@ function rewriteNotebookLinks( return Array.isArray(source) ? source.map(rewrite) : rewrite(source); } +/** + * Rewrite Sphinx artifact-relative image paths (e.g. `../_static/images/foo.png`) + * to the public docs image destination. External URLs are left unchanged. + */ +function rewriteNotebookImageSrc(src: string, imageDestination: string): string { + if (src.startsWith("http://") || src.startsWith("https://")) return src; + return `${imageDestination}/${src.split("/").pop()!}`; +} + function buildFrontmatterCell( cells: NotebookCell[], pkg: Pkg, diff --git a/scripts/js/lib/api/pipelineStages.ts b/scripts/js/lib/api/pipelineStages.ts index 17ac8741f8e..713ca4babf8 100644 --- a/scripts/js/lib/api/pipelineStages.ts +++ b/scripts/js/lib/api/pipelineStages.ts @@ -22,7 +22,7 @@ import { load } from "cheerio"; import { mkdirp } from "mkdirp"; import { uniqBy } from "lodash-es"; -import { HtmlToMdResultWithUrl } from "./HtmlToMdResult.js"; +import { Image, HtmlToMdResultWithUrl } from "./HtmlToMdResult.js"; import { ObjectsInv } from "./objectsInv.js"; import { Pkg } from "./Pkg.js"; import addFrontMatter from "./addFrontMatter.js"; @@ -191,11 +191,12 @@ export async function copyImages( artifactPath: string, destFolder: string, results: HtmlToMdResultWithUrl[], + extraImages: Image[] = [], ) { console.log("Saving images"); const allImages = uniqBy( - results.flatMap((result) => result.images), + [...results.flatMap((result) => result.images), ...extraImages], (image) => image.fileName, ); - await saveImages(allImages, `${artifactPath}/_images`, destFolder, pkg); + await saveImages(allImages, `${artifactPath}/_images`, destFolder, pkg, artifactPath); } diff --git a/scripts/js/lib/api/processHtml.test.ts b/scripts/js/lib/api/processHtml.test.ts index 3f0b9c8014c..4e8af72b1b9 100644 --- a/scripts/js/lib/api/processHtml.test.ts +++ b/scripts/js/lib/api/processHtml.test.ts @@ -37,15 +37,24 @@ test.describe("loadImages()", () => { const doc = CheerioDoc.load( `<img src="../_static/logo.png" alt="Logo"><img src="../_static/images/view-page-source-icon.svg">`, ); - const images = loadImages(doc.$, doc.$main, "/my-images", false, false); + const images = loadImages( + doc.$, + doc.$main, + "/my-images", + false, + false, + "subdir/index.html", + ); expect(images).toEqual([ { fileName: "logo.png", dest: "/my-images/logo.avif", + originSrc: "_static/logo.png", }, { fileName: "view-page-source-icon.svg", dest: "/my-images/view-page-source-icon.svg", + originSrc: "_static/images/view-page-source-icon.svg", }, ]); doc.expectHtml( @@ -57,11 +66,19 @@ test.describe("loadImages()", () => { const doc = CheerioDoc.load( `<img src="../_static/images/view-page-source-icon.svg">`, ); - const images = loadImages(doc.$, doc.$main, "/my-images/0.45", true, false); + const images = loadImages( + doc.$, + doc.$main, + "/my-images/0.45", + true, + false, + "subdir/release-notes.html", + ); expect(images).toEqual([ { fileName: "view-page-source-icon.svg", dest: "/my-images/view-page-source-icon.svg", + originSrc: "_static/images/view-page-source-icon.svg", }, ]); doc.expectHtml(`<img src="/my-images/view-page-source-icon.svg">`); @@ -71,11 +88,19 @@ test.describe("loadImages()", () => { const doc = CheerioDoc.load( `<img src="../_static/images/view-page-source-icon.svg">`, ); - const images = loadImages(doc.$, doc.$main, "/my-images/0.45", true, true); + const images = loadImages( + doc.$, + doc.$main, + "/my-images/0.45", + true, + true, + "subdir/release-notes.html", + ); expect(images).toEqual([ { fileName: "view-page-source-icon.svg", dest: "/my-images/0.45/view-page-source-icon.svg", + originSrc: "_static/images/view-page-source-icon.svg", }, ]); doc.expectHtml(`<img src="/my-images/0.45/view-page-source-icon.svg">`); @@ -85,17 +110,47 @@ test.describe("loadImages()", () => { const doc = CheerioDoc.load( `<img src="https://img.shields.io/github/stars/Qiskit/qiskit-addon-cutting?style=social" alt="Stars"><img src="../_static/logo.png" alt="Logo">`, ); - const images = loadImages(doc.$, doc.$main, "/my-images", false, false); + const images = loadImages( + doc.$, + doc.$main, + "/my-images", + false, + false, + "subdir/index.html", + ); expect(images).toEqual([ { fileName: "logo.png", dest: "/my-images/logo.avif", + originSrc: "_static/logo.png", }, ]); doc.expectHtml( `<img src="https://img.shields.io/github/stars/Qiskit/qiskit-addon-cutting?style=social" alt="Stars"><img src="/my-images/logo.avif" alt="Logo">`, ); }); + + test("_static image (nbsphinx thumbnail) is resolved from artifact root", () => { + const doc = CheerioDoc.load( + `<img alt="" src="../_static/nbsphinx-no-thumbnail.svg">`, + ); + const images = loadImages( + doc.$, + doc.$main, + "/my-images", + false, + false, + "how-tos/index.html", + ); + expect(images).toEqual([ + { + fileName: "nbsphinx-no-thumbnail.svg", + dest: "/my-images/nbsphinx-no-thumbnail.svg", + originSrc: "_static/nbsphinx-no-thumbnail.svg", + }, + ]); + doc.expectHtml(`<img alt="" src="/my-images/nbsphinx-no-thumbnail.svg">`); + }); }); test("handleSphinxDesignCards()", () => { diff --git a/scripts/js/lib/api/processHtml.ts b/scripts/js/lib/api/processHtml.ts index b6ca895bf98..4d5c115569b 100644 --- a/scripts/js/lib/api/processHtml.ts +++ b/scripts/js/lib/api/processHtml.ts @@ -67,6 +67,7 @@ export async function processHtml( imageDestination, isReleaseNotes, hasSeparateReleaseNotes, + fileName, ); if (isReleaseNotes) { renameAllH1s($, releaseNotesTitle); @@ -110,6 +111,7 @@ export function loadImages( imageDestination: string, isReleaseNotes: boolean, hasSeparateReleaseNotes: boolean, + htmlFileName: string, ): Image[] { return $main .find("img") @@ -120,10 +122,17 @@ export function loadImages( }) .map((img) => { const $img = $(img); + const src = $img.attr("src")!; - const fileName = $img.attr("src")!.split("/").pop()!; + const fileName = src.split("/").pop()!; const fileExtension = path.extname(fileName); + // Resolve the image's path relative to the artifact root so saveImages + // can find files in _static/ or other subdirectories, not just _images/. + const originSrc = path.normalize( + path.join(path.dirname(htmlFileName), src), + ); + // We convert PNG and JPG to AVIF for reduced file size. The image-copying // logic detects changed extensions and converts the files. let dest = [".png", ".jpg", ".jpeg"].includes(fileExtension) @@ -137,7 +146,7 @@ export function loadImages( } $img.attr("src", dest); - return { fileName, dest }; + return { fileName, dest, originSrc }; }); } diff --git a/scripts/js/lib/api/saveImages.ts b/scripts/js/lib/api/saveImages.ts index fa091965d0b..da9e3c0b3ef 100644 --- a/scripts/js/lib/api/saveImages.ts +++ b/scripts/js/lib/api/saveImages.ts @@ -51,6 +51,7 @@ export async function saveImages( originalImagesFolderPath: string, destFolder: string, pkg: Pkg, + artifactPath?: string, ) { if (!(await pathExists(destFolder))) { await mkdirp(destFolder); @@ -60,7 +61,12 @@ export async function saveImages( if (skipReleaseNote(img.fileName, pkg)) { return; } - const source = `${originalImagesFolderPath}/${img.fileName}`; + // Prefer the resolved artifact-relative path (covers _static/, etc.), + // falling back to the legacy _images/ convention. + const source = + artifactPath && img.originSrc + ? `${artifactPath}/${img.originSrc}` + : `${originalImagesFolderPath}/${img.fileName}`; // img.dest is set by loadImages() and includes the full image URL prefix // (e.g. "/docs/images/api/qiskit/foo.avif"). We only need its basename to // place the file inside destFolder. From 8e3212b1b47a7efb13f280b60803fec29e5e678d Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Mon, 18 May 2026 10:49:44 -0400 Subject: [PATCH 061/104] add aqc tensor --- .../explanations/index.mdx | 86 ++ .../how-tos/01-quimb-tnoptimizer.ipynb | 610 ++++++++++++++ .../qiskit-addon-aqc-tensor/how-tos/index.mdx | 13 + docs/addons/qiskit-addon-aqc-tensor/index.mdx | 82 ++ .../qiskit-addon-aqc-tensor/install.mdx | 88 ++ .../tutorials/01-initial-state-aqc.ipynb | 751 ++++++++++++++++++ .../tutorials/index.mdx | 15 + docs/api/qiskit-addon-aqc-tensor/_toc.json | 4 +- .../ansatz-generation-ansatz-block.mdx | 2 +- .../ansatz-generation.mdx | 16 +- .../qiskit-addon-aqc-tensor/release-notes.mdx | 8 +- .../simulation-aer-qiskit-aer-mps.mdx | 2 +- ...ion-aer-qiskit-aer-simulation-settings.mdx | 2 +- .../simulation-aer.mdx | 8 +- .../simulation-quimb-quimb-simulator.mdx | 2 +- .../simulation-quimb.mdx | 14 +- .../qiskit-addon-aqc-tensor/simulation.mdx | 2 +- docs/guides/addons.mdx | 4 +- .../aqc-compression.avif | Bin 0 -> 29893 bytes ...d07640a-d7ee-4966-80e8-dd084ffb0da3-0.avif | Bin 0 -> 65500 bytes .../how-tos_01_quimb_tnoptimizer_7_0.avif | Bin 0 -> 65500 bytes ...7c2f81f-dcfa-436a-b592-af3efb46d75a-0.avif | Bin 0 -> 65379 bytes ...bf60899-6f9d-4222-a499-e68bd5eabaf5-0.avif | Bin 0 -> 64800 bytes .../tutorials_01_initial_state_aqc_26_0.avif | Bin 0 -> 64800 bytes scripts/config/cspell/dictionaries/qiskit.txt | 3 +- scripts/js/lib/links/ignores.ts | 5 +- 26 files changed, 1684 insertions(+), 33 deletions(-) create mode 100644 docs/addons/qiskit-addon-aqc-tensor/explanations/index.mdx create mode 100644 docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer.ipynb create mode 100644 docs/addons/qiskit-addon-aqc-tensor/how-tos/index.mdx create mode 100644 docs/addons/qiskit-addon-aqc-tensor/index.mdx create mode 100644 docs/addons/qiskit-addon-aqc-tensor/install.mdx create mode 100644 docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.ipynb create mode 100644 docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx create mode 100644 public/docs/images/addons/qiskit-addon-aqc-tensor/aqc-compression.avif create mode 100644 public/docs/images/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer/extracted-outputs/5d07640a-d7ee-4966-80e8-dd084ffb0da3-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-aqc-tensor/how-tos_01_quimb_tnoptimizer_7_0.avif create mode 100644 public/docs/images/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc/extracted-outputs/17c2f81f-dcfa-436a-b592-af3efb46d75a-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc/extracted-outputs/dbf60899-6f9d-4222-a499-e68bd5eabaf5-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-aqc-tensor/tutorials_01_initial_state_aqc_26_0.avif diff --git a/docs/addons/qiskit-addon-aqc-tensor/explanations/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/explanations/index.mdx new file mode 100644 index 00000000000..f989758a541 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/explanations/index.mdx @@ -0,0 +1,86 @@ +--- +title: "Explanatory Material on AQC-Tensor" +description: "Explanatory Material on AQC-Tensor for the lastest version of Approximate quantum compilation (AQC-Tensor)" +--- + +<span id="explanation" /> + +# Explanatory Material on AQC-Tensor + +## Background + +This Qiskit addon allows one to compress the *initial portion* of a circuit using Approximate Quantum Compilation (AQC) with tensor networks, following the method introduced by Ref. <span id="id1" />[\[1\]](#id4). + +In the following, we will assume that the input circuit has already been truncated such that its entire depth can be simulated by a tensor network with a reasonable bond dimension. In order for AQC to be useful on quantum hardware, one must use the resulting circuit as a state preparation procedure and then perform subsequent quantum computation on that state in a way that is beyond the reach of a tensor network simulator alone. + +In essence, AQC-Tensor requires three things as input: + +1. A description of the **target state** in the form of a tensor network. This may be generated by simulating a circuit on a tensor network simulator (this is what Ref. <span id="id2" />[\[1\]](#id4) did), or it could be generated in some other way, e.g., by performing time-dependent variational principle (TDVP) time evolution directly on a matrix-product state. +2. A parametrized **ansatz circuit**, ideally with hardware-efficient connectivity, such that it will have a reasonable depth on the target hardware. +3. **Initial parameters** to plug into the ansatz circuit, such that the resulting state is already a *good* approximation to the target state. + +The technique is to then use an iterative optimization method to make the *good* state as close to the target state as possible. + +\[The third item is not required *in principle*, but it really helps to give the optimizer a sensible starting point; otherwise, it is much more likely to get stuck in a local (but not global) minimum.] + +## Ansatz generation (motivation) + +As mentioned in the previous section, three closely related things are needed as input to AQC-Tensor. Rather than code all three individually, this package provides a [`generate_ansatz_from_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") function that can generate (2) and (3) from a circuit. In the case of Trotter evolution (and likely other circuit classes of interest), one can use the same circuit-generation function to generate (1) as well as the input to [`generate_ansatz_from_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"), thus enabling the generation of all three inputs from a single function. The tutorial is a good demonstration of this re-use of a single circuit-generation function. + +Specifically, [`generate_ansatz_from_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") will take an input circuit and generate the ansatz circuit (2) and initial parameters (3) that follow the two-qubit connectivity of the input circuit. However, the returned circuit will be parametrized, based on generalized one- and two-qubit rotations. The initial parameters returned by the function, when plugged into the ansatz, will result in a circuit that is exactly equivalent to the circuit passed as input, up to a global phase. + +Given the ansatz generation function, the user must provide only two things in order to use AQC-Tensor: + +1. The **target state**, i.e., a *great* description of the desired state; and, +2. A **circuit** with the desired connectivity of the ansatz that prepares a *good* approximation to the target state. + +The optimization procedure then takes the *good* ansatz parameters and brings them as close as possible to the *great* description (the target state). + +In the case of a Trotter circuit, the same function can actually provide both things. For instance, the `spinhalf_trotter_circuit` function could be called with `num_trotter_steps=50` to produce the target state, then again with `num_trotter_steps=5` to produce the ansatz structure to be passed to `generate_ansatz_from_circuit`. (Note that the `evolution_time` is fixed, such that the time step `dt = evolution_time / num_trotter_steps`. the time per single Trotter step as `dt = evolution_time / num_trotter_steps`, so these two function calls would leave the total `evolution_time` fixed, while changing the time per Trotter step, `dt`.) So, in order for a user to apply AQC-Tensor to a new physical model of Trotter circuit, they would only need to generalize a single function with the details of that model. + +The ansatz used by `generate_ansatz_from_circuit` uses 9 parameters per two-qubit block, which is the theoretical minimum (see, e.g., Fig. 2 of [https://arxiv.org/abs/quant-ph/0308033](https://arxiv.org/abs/quant-ph/0308033)). The automatic ansatz is based on the [KAK decomposition](https://qiskit-extensions.github.io/circuit-knitting-toolbox/circuit_cutting/explanation/index.html#more-general-cut-two-qubit-gates-via-the-kak-decomposition), which parametrizes any two-qubit gate in terms of three parameters, up to single-qubit rotations. The single-qubit rotation are then decomposed as ZXZ, each of which has three parameters. So, each two-qubit block of the original circuit will result in 3 parameters for the two-qubit rotation, plus 3 parameters for an outgoing single-qubit rotation on each of the two qubits, for a total of 9 parameters per block. The ansatz is completed by adding a layer of single-qubit rotations to each active qubit at the start of the circuit. + +## Tensor-network simulation + +The simplest tensor network is a matrix-product state (MPS). + +Currently, AQC-Tensor supports the following tensor-network simulators: + +* Qiskit Aer’s MPS simulator +* Quimb’s [eager](https://quimb.readthedocs.io/en/latest/tensor/tensor-circuit-mps.html) [`CircuitMPS`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.CircuitMPS) simulator +* Quimb’s [lazy](https://quimb.readthedocs.io/en/latest/tensor/tensor-circuit.html) [`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.Circuit) simulator (may work only on small circuits so far; we’re working to fix this soon with more clever contractions) + +The most important parameter of a tensor network is its maximum bond dimension, which limits how much entanglement it can represent (and thus to what depth a given circuit can be faithfully simulated). The bond dimension is often represented by the Greek letter $\chi$. + +Given a general circuit on $L$ qubits, a matrix-product state needs at most a bond dimension of $\chi_\mathrm{exact} = 2^{\lfloor L/2 \rfloor}$ in order to be able to simulate it to *any* depth. Of course, general circuits on 100+ qubits cannot be classically simulated, so it will be intractable to set the bond dimension this high for those circuits. + +For this reason, if you are attempting to experiment with AQC-Tensor on a toy problem with few qubits, it is important to ensure that $\chi < 2^{\lfloor L/2 \rfloor}$. Otherwise, any circuit can be simulated to any depth, and there is no point in performing AQC. + +### Objective function + +Currently, this addon provides one very simple objective, [`MaximizeStateFidelity`](/docs/api/qiskit-addon-aqc-tensor/objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity"), whose [`loss_function()`](/docs/api/qiskit-addon-aqc-tensor/objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") is equivalent to Eq. (7) in Ref. <span id="id3" />[\[1\]](#id4). When called with an array of parameters, the [`loss_function()`](/docs/api/qiskit-addon-aqc-tensor/objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") method returns the value and gradient of the objective function as a 2-tuple. + +### Gradient + +This package provides a few different methods for calculating the gradient. + +The Aer backend always uses the explicit gradient code in the `explicit_gradient` module. + +The Quimb backend will typically be used with an automatic differentiation backend; the user is to select a backend from among those supported by Quimb. Alternatively, one can instead pass `"explicit"` as the `autodiff_backend` when instantiating the [`QuimbSimulator`](/docs/api/qiskit-addon-aqc-tensor/simulation-quimb-quimb-simulator#qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator "qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator") object; in this case, the `explicit_gradient` module will be used. It is only recommended to use explicit gradients with Quimb’s eager [`CircuitMPS`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.CircuitMPS) simulator, not the lazy [`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.Circuit) simulator. + +Regardless of which backend is chosen, the gradient code can understand linear parameter expressions (ParameterExpression objects). This support is essential, as linear expressions are returned by the ansatz generation code. + +### Optimization method + +Users are encouraged to use [`scipy.optimize`](https://docs.scipy.org/doc/scipy/reference/optimize.html#module-scipy.optimize "(in SciPy v1.17.0)") to perform the optimization. + +L-BFGS is the optimizer demonstrated in the tutorial notebook. It works well in practice because it uses the function value and its gradient to approximate the Hessian. It works well when given an initial point and seems to work particularly well in the case of Trotter circuits. However, it might early terminate if it starts in a barren plateau. In that case, performing a handful of steps using the ADAM optimizer first may help. + +## References + +<span id="id4" /> + +\[1] ([1](#id1),[2](#id2),[3](#id3)) + +[arXiv:2301.08609v6](https://arxiv.org/abs/2301.08609v6). + diff --git a/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer.ipynb b/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer.ipynb new file mode 100644 index 00000000000..0fb66ea00a2 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer.ipynb @@ -0,0 +1,610 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"How to use quimb.tensor.TNOptimizer directly\"\n", + "description: \"How to use quimb.tensor.TNOptimizer directly for the latest version of Approximate quantum compilation (AQC-Tensor)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "d0791829-7938-47b6-8f01-068e981f60c5", + "metadata": {}, + "source": [ + "# How to use `quimb.tensor.TNOptimizer` directly\n", + "\n", + "Quimb provides [a mechanism for optimizing tensor networks](https://quimb.readthedocs.io/en/latest/tensor-optimization.html) through its `TNOptimizer` interface. The Quimb backend provided by this addon uses this under the hood. This how-to guide demonstrates how to work with this object directly, in case some users want more direct access to it.\n", + "\n", + "### Set up a model Hamiltonian" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8fdf1603-b541-46e7-871d-bf6b0ea8afce", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:33:55.273937Z", + "iopub.status.busy": "2026-05-14T20:33:55.273667Z", + "iopub.status.idle": "2026-05-14T20:33:55.740747Z", + "shell.execute_reply": "2026-05-14T20:33:55.739828Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3158/2599302630.py:1: DeprecationWarning: Using Qiskit with Python 3.9 is deprecated as of the 2.1.0 release. Support for running Qiskit with Python 3.9 will be removed in the 2.3.0 release, which coincides with when Python 3.9 goes end of life.\n", + " from qiskit.transpiler import CouplingMap\n" + ] + } + ], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "# Generate some coupling map to use for this example\n", + "coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False)\n", + "\n", + "# Choose a 10-qubit circle on this coupling map\n", + "reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 4, 15, 3, 9])\n", + "\n", + "# Get a qubit operator describing the Ising field model\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " reduced_coupling_map,\n", + " coupling_constants=(0.0, 0.0, 1.0),\n", + " ext_magnetic_field=(0.4, 0.0, 0.0),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3f46356f-e975-4705-8788-87566e2c4905", + "metadata": {}, + "source": [ + "### Set up quimb simulator with default options" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fc38c412-f55d-4453-8c38-b84286f43ee8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:33:55.744560Z", + "iopub.status.busy": "2026-05-14T20:33:55.743981Z", + "iopub.status.idle": "2026-05-14T20:34:14.421419Z", + "shell.execute_reply": "2026-05-14T20:34:14.420606Z" + } + }, + "outputs": [], + "source": [ + "import quimb.tensor as qtn\n", + "\n", + "from qiskit_addon_aqc_tensor.simulation.quimb import (\n", + " QuimbSimulator,\n", + " qiskit_ansatz_to_quimb,\n", + " recover_parameters_from_quimb,\n", + ")\n", + "\n", + "simulator = QuimbSimulator(qtn.Circuit)" + ] + }, + { + "cell_type": "markdown", + "id": "b145bc16-7271-4647-8661-9f88023a790c", + "metadata": {}, + "source": [ + "### Generate target circuit" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "072bfaff-5a7f-4639-899a-a5a78e6af3ba", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:14.424484Z", + "iopub.status.busy": "2026-05-14T20:34:14.424167Z", + "iopub.status.idle": "2026-05-14T20:34:19.262641Z", + "shell.execute_reply": "2026-05-14T20:34:19.261802Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.synthesis import SuzukiTrotter\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", + "\n", + "from qiskit_addon_aqc_tensor.simulation import (\n", + " compute_overlap,\n", + " tensornetwork_from_circuit,\n", + ")\n", + "\n", + "evolution_time = 0.4\n", + "\n", + "target_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=8),\n", + " time=evolution_time,\n", + ")\n", + "\n", + "target_tns = tensornetwork_from_circuit(target_circuit, simulator)" + ] + }, + { + "cell_type": "markdown", + "id": "4b2735b7-0350-41e6-af80-6c9cda2083b7", + "metadata": {}, + "source": [ + "### Generate ansatz from a shallower circuit" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5d07640a-d7ee-4966-80e8-dd084ffb0da3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:19.265858Z", + "iopub.status.busy": "2026-05-14T20:34:19.265645Z", + "iopub.status.idle": "2026-05-14T20:34:22.261196Z", + "shell.execute_reply": "2026-05-14T20:34:22.260255Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer/extracted-outputs/5d07640a-d7ee-4966-80e8-dd084ffb0da3-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_aqc_tensor.ansatz_generation import (\n", + " AnsatzBlock,\n", + " generate_ansatz_from_circuit,\n", + ")\n", + "\n", + "initial_shallow_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=2),\n", + " time=evolution_time,\n", + ")\n", + "ansatz, initial_parameters = generate_ansatz_from_circuit(initial_shallow_circuit)\n", + "ansatz = ansatz.decompose(AnsatzBlock)\n", + "ansatz.draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "172012af-5499-4bdf-82b1-c5ba2d0350fe", + "metadata": {}, + "source": [ + "### Initialize objective function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8b039335-3e00-409d-ac75-983cfbe88c3a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:22.264966Z", + "iopub.status.busy": "2026-05-14T20:34:22.264686Z", + "iopub.status.idle": "2026-05-14T20:34:22.267983Z", + "shell.execute_reply": "2026-05-14T20:34:22.267271Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity\n", + "\n", + "objective = MaximizeStateFidelity(target_tns, None, None)" + ] + }, + { + "cell_type": "markdown", + "id": "4ba05493-be0b-4d91-8ca7-1065f8b0bde8", + "metadata": {}, + "source": [ + "### Convert Qiskit ansatz and initial parameters to a Quimb parametrized circuit" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2d60342e-393d-4604-86af-f20e0674c982", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:22.269874Z", + "iopub.status.busy": "2026-05-14T20:34:22.269692Z", + "iopub.status.idle": "2026-05-14T20:34:22.381632Z", + "shell.execute_reply": "2026-05-14T20:34:22.380737Z" + } + }, + "outputs": [], + "source": [ + "circ, conversion_context = qiskit_ansatz_to_quimb(ansatz, initial_parameters)" + ] + }, + { + "cell_type": "markdown", + "id": "a05591a0-5877-49e8-82aa-5fe06c11b718", + "metadata": {}, + "source": [ + "### Perform optimization of Quimb circuit using automatic differentiation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7f3eaa20-3332-4fd1-811c-48a7a3078242", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:22.384207Z", + "iopub.status.busy": "2026-05-14T20:34:22.383991Z", + "iopub.status.idle": "2026-05-14T20:34:55.115108Z", + "shell.execute_reply": "2026-05-14T20:34:55.114333Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 0%| | 0/20 [00:00<?, ?it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 5%|▌ | 1/20 [00:32<10:10, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000196685651 [best: +0.000196685651] : 5%|▌ | 1/20 [00:32<10:10, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.788653314114 [best: +0.000196685651] : 10%|█ | 2/20 [00:32<09:38, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000034331981 [best: +0.000034331981] : 15%|█▌ | 3/20 [00:32<09:06, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000023126468 [best: +0.000023126468] : 20%|██ | 4/20 [00:32<08:34, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 25%|██▌ | 5/20 [00:32<08:02, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000024676170 [best: +0.000020861517] : 30%|███ | 6/20 [00:32<07:29, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000021695972 [best: +0.000020861517] : 35%|███▌ | 7/20 [00:32<06:57, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000021219141 [best: +0.000020861517] : 40%|████ | 8/20 [00:32<06:25, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 45%|████▌ | 9/20 [00:32<05:53, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 50%|█████ | 10/20 [00:32<05:21, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 55%|█████▌ | 11/20 [00:32<04:49, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 60%|██████ | 12/20 [00:32<04:17, 32.14s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 65%|██████▌ | 13/20 [00:32<00:12, 1.78s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 65%|██████▌ | 13/20 [00:32<00:12, 1.78s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 70%|███████ | 14/20 [00:32<00:10, 1.78s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 75%|███████▌ | 15/20 [00:32<00:08, 1.78s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 80%|████████ | 16/20 [00:32<00:07, 1.78s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 85%|████████▌ | 17/20 [00:32<00:05, 1.78s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 90%|█████████ | 18/20 [00:32<00:03, 1.78s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 95%|█████████▌| 19/20 [00:32<00:01, 1.78s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : 100%|██████████| 20/20 [00:32<00:00, 1.78s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : : 21it [00:32, 1.78s/it] " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : : 22it [00:32, 1.78s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "+0.000020861517 [best: +0.000020861517] : : 22it [00:32, 1.47s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from qiskit_addon_aqc_tensor.simulation.quimb import tnoptimizer_objective_kwargs\n", + "\n", + "tnopt = qtn.TNOptimizer(\n", + " circ,\n", + " **tnoptimizer_objective_kwargs(objective),\n", + " autodiff_backend=\"jax\", # OPTIONS: jax, autograd, torch, etc.\n", + ")\n", + "circ_opt = tnopt.optimize(20)" + ] + }, + { + "cell_type": "markdown", + "id": "9e142438-b6b1-47eb-ab7a-6fce0ea294e4", + "metadata": {}, + "source": [ + "### Recover final parameters from the quimb circuit" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "24b86d9c-f9d5-43ac-b29e-addddc71782f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:55.117787Z", + "iopub.status.busy": "2026-05-14T20:34:55.117422Z", + "iopub.status.idle": "2026-05-14T20:34:55.179789Z", + "shell.execute_reply": "2026-05-14T20:34:55.178906Z" + } + }, + "outputs": [], + "source": [ + "final_parameters = recover_parameters_from_quimb(circ_opt, conversion_context)" + ] + }, + { + "cell_type": "markdown", + "id": "57b6dcc4-fcfd-45a0-9fc1-faf89fe2d60a", + "metadata": {}, + "source": [ + "### Check fidelity of final, compressed state w/ respect to target state" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f7888376-4b31-4911-802d-f577ebd0f146", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:55.182699Z", + "iopub.status.busy": "2026-05-14T20:34:55.182416Z", + "iopub.status.idle": "2026-05-14T20:34:55.356002Z", + "shell.execute_reply": "2026-05-14T20:34:55.355434Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9999992039939346" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "compressed_circuit = ansatz.assign_parameters(final_parameters)\n", + "compressed_state = tensornetwork_from_circuit(compressed_circuit, simulator)\n", + "abs(compute_overlap(target_tns, compressed_state)) ** 2" + ] + }, + { + "cell_type": "markdown", + "id": "c9b8a263-709c-4046-ac16-54e5ee75aa1d", + "metadata": {}, + "source": [ + "### Compare with fidelity of initial shallow state w/ respect to target state" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "96953463-0c64-4db0-b107-91e6d08b24d2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:55.360671Z", + "iopub.status.busy": "2026-05-14T20:34:55.360405Z", + "iopub.status.idle": "2026-05-14T20:34:55.533860Z", + "shell.execute_reply": "2026-05-14T20:34:55.533179Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9998389457702321" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "initial_shallow_state = tensornetwork_from_circuit(initial_shallow_circuit, simulator)\n", + "abs(compute_overlap(target_tns, initial_shallow_state)) ** 2" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-aqc-tensor/how-tos/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/how-tos/index.mdx new file mode 100644 index 00000000000..49c607d2869 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/how-tos/index.mdx @@ -0,0 +1,13 @@ +--- +title: "AQC-Tensor How-To Guides" +description: "AQC-Tensor How-To Guides for the lastest version of Approximate quantum compilation (AQC-Tensor)" +--- + +# AQC-Tensor How-To Guides + +* [How to use Quimb’s TNOptimizer directly](01-quimb-tnoptimizer) + +[![](/docs/images/addons/qiskit-addon-aqc-tensor/how-tos_01_quimb_tnoptimizer_7_0.avif)](01-quimb-tnoptimizer) + +[How to use quimb.tensor.TNOptimizer directly](01-quimb-tnoptimizer) + diff --git a/docs/addons/qiskit-addon-aqc-tensor/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/index.mdx new file mode 100644 index 00000000000..fac2cb938a2 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/index.mdx @@ -0,0 +1,82 @@ +--- +title: "AQC-Tensor Qiskit addon" +description: "Documentation for the latest version of Approximate quantum compilation (AQC-Tensor)" +--- + +# AQC-Tensor Qiskit addon + +This addon lets a Qiskit user perform approximate quantum compilation by using tensor networks, a technique that was introduced in [arXiv:2301.08609](https://arxiv.org/abs/2301.08609). + +Specifically, with this package you can compile the *initial portion* of a circuit into a nearly equivalent approximation of that circuit, but with fewer layers. + +It has been tested primarily on Trotter circuits to date. It might, however, be applicable to any class of circuits where you have access to both of the following: + +* A *great* intermediate state, known as the “target state,” that can be achieved by tensor-network simulation +* A *good* circuit that prepares an approximation to the target state, but with fewer layers when compiled to the target hardware device + +![\_images/aqc-compression.png](/docs/images/addons/qiskit-addon-aqc-tensor/aqc-compression.avif) + +(Figure is taken from [arXiv:2301.08609](https://arxiv.org/abs/2301.08609).) + +## Developer guide + +The source code for this package is available [on GitHub](https://github.com/Qiskit/qiskit-addon-aqc-tensor). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-aqc-tensor/blob/main/CONTRIBUTING.md) in the root of this project’s repository. + +## Citing this project + +If you use this package in your research, please cite the appropriate references provided in the `CITATON.bib` file in this project’s repository: + +```bibtex +@software{qiskit-addon-aqc-tensor, + author = { + James R. Garrison + and Kate Marshall + and Ibrahim Shehzad + and Kevin J. Sung + and Caleb Johnson + and Max Rossmannek + and Bryce Fuller + and Jennifer R. Glick + and Albert Akhriev + and Sergiy Zhuk + and Niall F. Robertson + }, + title = {{Qiskit addon: Approximate Quantum Compilation with Tensor Networks}}, + howpublished = {\url{https://github.com/Qiskit/qiskit-addon-aqc-tensor}}, + doi = {10.5281/zenodo.14064353}, + year = {2024} +} + +@ARTICLE{aqc-tensor-research, + author = {{Robertson}, Niall F. and {Akhriev}, Albert and {Vala}, Jiri and {Zhuk}, Sergiy}, + title = "{Approximate Quantum Compiling for Quantum Simulation: A Tensor Network Based Approach}", + journal = {{ACM Transactions on Quantum Computing}}, + year = {2025}, + issue_date = {September 2025}, + publisher = {Association for Computing Machinery}, + address = {New York, NY, USA}, + volume = {6}, + number = {3}, + url = {https://doi.org/10.1145/3731251}, + doi = {10.1145/3731251}, + journal = "{ACM Transactions on Quantum Computing}", + month = may, + articleno = {20}, + numpages = {15}, + keywords = {Tensor networks, quantum simulation, quantum compilation} +} +``` + +## Contents + +* [Documentation Home]() +* [Installation Instructions](install) +* [Tutorials](tutorials/index) +* [Explanatory Material](explanations/index) +* [API Reference](/docs/api/qiskit-addon-aqc-tensor/index) +* [How-To Guides](how-tos/index) +* [GitHub](https://github.com/Qiskit/qiskit-addon-aqc-tensor) +* [Release Notes](/docs/api/qiskit-addon-aqc-tensor/release-notes) + diff --git a/docs/addons/qiskit-addon-aqc-tensor/install.mdx b/docs/addons/qiskit-addon-aqc-tensor/install.mdx new file mode 100644 index 00000000000..c9b7cc31ba3 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/install.mdx @@ -0,0 +1,88 @@ +--- +title: "Installation Instructions" +description: "Installation Instructions for the lastest version of Approximate quantum compilation (AQC-Tensor)" +--- + +<span id="installation-guide" /> + +# Installation Instructions + +Let’s see how to install AQC-Tensor. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: + +* [Option 1: Pip Installation](#option-1) +* [Option 2: Install from Source](#option-2) + +## Pre-Installation + +Users who wish to install locally (using either [Option 1: Pip Installation](#option-1) or [Option 2: Install from Source](#option-2)) are encouraged to follow a brief set of common instructions to prepare a Python environment for installation of AQC-Tensor: + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +Note: If you are using Windows, use the following commands in PowerShell: + +```pwsh +python3 -m venv c:\path\to\virtual\environment +c:\path\to\virtual\environment\Scripts\Activate.ps1 +``` + +<span id="option-1" /> + +## Option 1: Pip Installation + +Upgrade pip and install the AQC-Tensor package. To meaningfully use the package, you must also install at least one tensor network backend. The below snippet installs the addon, along with quimb (for tensor network support) and jax (for automatic differentiation). + +```sh +pip install --upgrade pip +pip install 'qiskit-addon-aqc-tensor[quimb-jax]' +``` + +<span id="option-2" /> + +## Option 2: Install from Source + +Users who wish to develop in the repository or run the tutorials locally may want to install from source. + +In either case, the first step is to clone the AQC-Tensor repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-aqc-tensor.git +``` + +Next, upgrade pip and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-aqc-tensor +``` + +The next step is to install AQC-Tensor to the virtual environment. If you plan on running the tutorials, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox jupyterlab -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started with AQC-Tensor by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab +``` + +<span id="id1" /> + +## Platform Support + +We expect this package to work on [any Tier 1 platform supported by Qiskit](/docs/guides/install-qiskit#operating-system-support). + diff --git a/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.ipynb b/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.ipynb new file mode 100644 index 00000000000..5d044bd2ca0 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.ipynb @@ -0,0 +1,751 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Reducing circuit depth with the AQC-Tensor Qiskit addon\"\n", + "description: \"Reducing circuit depth with the AQC-Tensor Qiskit addon for the latest version of Approximate quantum compilation (AQC-Tensor)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "0f0b5824", + "metadata": {}, + "source": [ + "# Reducing circuit depth with the AQC-Tensor Qiskit addon\n", + "\n", + "In this notebook, we will work through the steps of a [Qiskit pattern](/docs/guides/intro-to-patterns) while using **approximate quantum compilation with tensor networks (AQC-Tensor)** to achieve a lower circuit depth than would ordinarily be needed to perform Trotter evolution.\n", + "\n", + "These are the steps that we will take:\n", + "\n", + "- **Step 1: Map to quantum problem**\n", + " - Initialize our problem's Hamiltonian and observable(s)\n", + " - <font color='#0F62FE'>Generate a target tensor-network state for the initial portion of the circuit</font>\n", + " - <font color='#0F62FE'>Generate a low-depth circuit which approximates the portion being compressed</font>\n", + " - <font color='#0F62FE'>Generate a general ansatz from that circuit</font>\n", + " - <font color='#0F62FE'>Optimize the parameters to bring the ansatz as close as possible to the target</font>\n", + " - <font color='#0F62FE'>Add subsequent Trotter steps to the optimized ansatz</font>\n", + "- **Step 2: Optimize for target hardware**\n", + " - Transpile the circuit for hardware\n", + "- **Step 3: Execute experiments**\n", + " - Use a fake backend for simplicity\n", + "- **Step 4: Reconstruct results**\n", + " - N/A; instead, we just output the measured observable" + ] + }, + { + "cell_type": "markdown", + "id": "79042f19-241d-48fb-aa87-134a93082c94", + "metadata": {}, + "source": [ + "## Step 1: Map to quantum circuit and operator\n", + "\n", + "### Set up a model Hamiltonian and observable\n", + "\n", + "In this notebook, we use the Ising model on a circle of 10 sites:\n", + "$$\n", + "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{10} J_{i,(i+1)} Z_i Z_{(i+1)} + h_i X_i \\, ,\n", + "$$\n", + "where the periodic boundary conditions imply that for $i=10$ we obtain $i+1=11\\rightarrow1$, $J$ is the coupling strength between two sites and $h$ is the external magnetic field." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1f700ca7-ad7e-45ac-a5eb-50962ce67b48", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:09.963621Z", + "iopub.status.busy": "2026-05-14T20:34:09.963275Z", + "iopub.status.idle": "2026-05-14T20:34:10.299710Z", + "shell.execute_reply": "2026-05-14T20:34:10.298755Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3199/2599302630.py:1: DeprecationWarning: Using Qiskit with Python 3.9 is deprecated as of the 2.1.0 release. Support for running Qiskit with Python 3.9 will be removed in the 2.3.0 release, which coincides with when Python 3.9 goes end of life.\n", + " from qiskit.transpiler import CouplingMap\n" + ] + } + ], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "# Generate some coupling map to use for this example\n", + "coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False)\n", + "\n", + "# Choose a 10-qubit circle on this coupling map\n", + "reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 4, 15, 3, 9])\n", + "\n", + "# Get a qubit operator describing the Ising field model\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " reduced_coupling_map,\n", + " coupling_constants=(0.0, 0.0, 1.0),\n", + " ext_magnetic_field=(0.4, 0.0, 0.0),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d88fd2c0-a4cd-46be-a1aa-7f6dec219791", + "metadata": {}, + "source": [ + "The observable we will measure is the total magnetization." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9c864767-c218-4656-b20d-3e4e8c6f47af", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:10.303710Z", + "iopub.status.busy": "2026-05-14T20:34:10.303315Z", + "iopub.status.idle": "2026-05-14T20:34:10.307910Z", + "shell.execute_reply": "2026-05-14T20:34:10.307176Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "L = reduced_coupling_map.size()\n", + "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)" + ] + }, + { + "cell_type": "markdown", + "id": "70aa63a7-7560-4b29-9391-e1f42abdf1fd", + "metadata": {}, + "source": [ + "### Determine how much of the time evolution to simulate classically\n", + "\n", + "Our overall goal is to simulate time evolution of the above model Hamiltonian. We do so by Trotter evolution, which we split into two portions:\n", + "\n", + "1. An initial portion that is simulable with matrix product states (MPS). We will \"compile\" this portion using AQC as presented in https://arxiv.org/abs/2301.08609.\n", + "2. A subsequent portion of the circuit that will be executed on hardware." + ] + }, + { + "cell_type": "markdown", + "id": "442de278-82f9-4fcf-8b30-88804e7c170d", + "metadata": {}, + "source": [ + "Let's plan to use AQC-Tensor to compress our time evolution circuit up to time $t=4$, then evolve using ordinary Trotter steps up to $t=5$." + ] + }, + { + "cell_type": "markdown", + "id": "bf2243b5-56c7-48e8-acc6-7c8c720a8997", + "metadata": {}, + "source": [ + "### Generate circuits before and after split\n", + "\n", + "Now that we have chosen to split at $t=4$, we will generate two circuits:\n", + "\n", + "1. A \"target\" circuit for the AQC portion of the evolution, from $t_i=0$ to $t_f=4$. Because this is being simulated by a tensor-network simulator, the number of layers affects execution time only by a constant factor, so we might as well use a generous number of layers to minimize Trotter error." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c7593867-ed56-4f01-b2a9-1641ea4a5d10", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:10.310480Z", + "iopub.status.busy": "2026-05-14T20:34:10.310261Z", + "iopub.status.idle": "2026-05-14T20:34:10.333239Z", + "shell.execute_reply": "2026-05-14T20:34:10.332438Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.synthesis import SuzukiTrotter\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", + "\n", + "aqc_evolution_time = 4.0\n", + "aqc_target_num_trotter_steps = 45\n", + "\n", + "aqc_target_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=aqc_target_num_trotter_steps),\n", + " time=aqc_evolution_time,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bc3a6acc-8eb3-486e-9dd8-a58a82a7ccf9", + "metadata": {}, + "source": [ + "2. A subsequent evolution circuit, which evolves from $t_i=4$ to $t_f=5$. Because this is being run on quantum hardware, it is desirable to use as few Trotter layers as possible." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c97ac3e8-b7bd-4727-8495-a513bf143b1d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:10.335597Z", + "iopub.status.busy": "2026-05-14T20:34:10.335372Z", + "iopub.status.idle": "2026-05-14T20:34:10.341086Z", + "shell.execute_reply": "2026-05-14T20:34:10.340313Z" + } + }, + "outputs": [], + "source": [ + "subsequent_evolution_time = 1.0\n", + "subsequent_num_trotter_steps = 5\n", + "\n", + "subsequent_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=subsequent_num_trotter_steps),\n", + " time=subsequent_evolution_time,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3731468d-073b-460a-a87c-4920a35d44d9", + "metadata": {}, + "source": [ + "For the sake of later comparison, let's also generate a third circuit: one that evolves for `aqc_evolution_time` but which has the same evolution time per Trotter step as the subsequent circuit. This is the circuit we would have working with had we not used a generous number of Trotter steps for the target circuit. We will refer to this as the _comparison circuit_." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "557cc3a4-5909-4087-903d-2b603927071d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:10.343079Z", + "iopub.status.busy": "2026-05-14T20:34:10.342902Z", + "iopub.status.idle": "2026-05-14T20:34:10.350149Z", + "shell.execute_reply": "2026-05-14T20:34:10.349396Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aqc_comparison_num_trotter_steps = int(\n", + " subsequent_num_trotter_steps / subsequent_evolution_time * aqc_evolution_time\n", + ")\n", + "aqc_comparison_num_trotter_steps" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4c9bb0cd-0bd4-4236-9046-55a5222a034a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:10.352418Z", + "iopub.status.busy": "2026-05-14T20:34:10.352135Z", + "iopub.status.idle": "2026-05-14T20:34:10.361237Z", + "shell.execute_reply": "2026-05-14T20:34:10.360557Z" + } + }, + "outputs": [], + "source": [ + "comparison_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=aqc_comparison_num_trotter_steps),\n", + " time=aqc_evolution_time,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0c7d8499-afd9-4fdd-88a6-8dc33e144ee2", + "metadata": {}, + "source": [ + "### Generate an ansatz and initial parameters from a Trotter circuit with fewer steps\n", + "\n", + "First, we construct a \"good\" circuit that has the same evolution time as the target circuit, but with fewer Trotter steps (and thus fewer layers).\n", + "\n", + "Then we pass this \"good\" circuit to AQC-Tensor's `generate_ansatz_from_circuit` function. This function analyzes the two-qubit connectivity of the circuit and returns two things:\n", + "1. a general, parametrized ansatz circuit with the same two-qubit connectivity as the input circuit; and,\n", + "2. parameters that, when plugged into the ansatz, yield the input (good) circuit.\n", + "\n", + "Soon we will take these parameters and iteratively adjust them to bring the ansatz circuit as close as possible to the target MPS." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "17c2f81f-dcfa-436a-b592-af3efb46d75a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:10.363341Z", + "iopub.status.busy": "2026-05-14T20:34:10.363136Z", + "iopub.status.idle": "2026-05-14T20:34:12.178747Z", + "shell.execute_reply": "2026-05-14T20:34:12.177775Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc/extracted-outputs/17c2f81f-dcfa-436a-b592-af3efb46d75a-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_aqc_tensor import generate_ansatz_from_circuit\n", + "\n", + "aqc_ansatz_num_trotter_steps = 5\n", + "\n", + "aqc_good_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=aqc_ansatz_num_trotter_steps),\n", + " time=aqc_evolution_time,\n", + ")\n", + "\n", + "aqc_ansatz, aqc_initial_parameters = generate_ansatz_from_circuit(\n", + " aqc_good_circuit, qubits_initially_zero=True\n", + ")\n", + "aqc_ansatz.draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6864f636-91f6-4ba4-86d4-da7521474977", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:12.182547Z", + "iopub.status.busy": "2026-05-14T20:34:12.182217Z", + "iopub.status.idle": "2026-05-14T20:34:12.201442Z", + "shell.execute_reply": "2026-05-14T20:34:12.200743Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Comparison circuit: depth 120\n", + "Target circuit: depth 270\n", + "Ansatz circuit: depth 23, with 515 parameters\n" + ] + } + ], + "source": [ + "print(f\"Comparison circuit: depth {comparison_circuit.depth()}\")\n", + "print(f\"Target circuit: depth {aqc_target_circuit.depth()}\")\n", + "print(f\"Ansatz circuit: depth {aqc_ansatz.depth()}, with {len(aqc_initial_parameters)} parameters\")" + ] + }, + { + "cell_type": "markdown", + "id": "79845e2d-d563-4776-9e59-3a5c9eeb5687", + "metadata": {}, + "source": [ + "### Choose settings for tensor network simulation\n", + "\n", + "Here, we use the tensor network simulator based on [quimb](http://quimb.readthedocs.io/). In this example, we use quimb's matrix-product state (MPS) simulator, and we use [JAX](https://docs.jax.dev/en/latest/) for automatic differentiation. See the [API documentation](../stubs/qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator.rst) for more information on how to use the quimb simulator." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1b58e2e8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:12.203563Z", + "iopub.status.busy": "2026-05-14T20:34:12.203259Z", + "iopub.status.idle": "2026-05-14T20:34:13.041714Z", + "shell.execute_reply": "2026-05-14T20:34:13.040898Z" + } + }, + "outputs": [], + "source": [ + "from functools import partial\n", + "\n", + "import quimb.tensor\n", + "\n", + "from qiskit_addon_aqc_tensor.simulation.quimb import QuimbSimulator\n", + "\n", + "simulator_settings = QuimbSimulator(\n", + " partial(quimb.tensor.CircuitMPS, max_bond=100, cutoff=1e-8),\n", + " autodiff_backend=\"jax\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cf375cc7-f8c5-4534-b0ec-b2e9d6582840", + "metadata": {}, + "source": [ + "### Construct matrix-product state representation of the AQC target state\n", + "\n", + "Next, we build a matrix-product representation of the state to be approximated by AQC." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a8a6bf5e-31ec-48e2-b96e-ce3d7cc54ff7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:13.044825Z", + "iopub.status.busy": "2026-05-14T20:34:13.044536Z", + "iopub.status.idle": "2026-05-14T20:34:23.491200Z", + "shell.execute_reply": "2026-05-14T20:34:23.490319Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_aqc_tensor.simulation import tensornetwork_from_circuit\n", + "\n", + "aqc_target_mps = tensornetwork_from_circuit(aqc_target_circuit, simulator_settings)" + ] + }, + { + "cell_type": "markdown", + "id": "88071374-04e4-47ee-8463-16ad0f5fa33b", + "metadata": {}, + "source": [ + "Note that because we chose a generous number of Trotter steps for the target state, it actually has less Trotter error than the comparison circuit. We can calculate the fidelity ($| \\langle \\psi_1 | \\psi_2 \\rangle |^2$) of the state prepared by the comparison circuit vs. the target state:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "65c3febb-8688-4806-aca6-99e9b860b377", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:23.494338Z", + "iopub.status.busy": "2026-05-14T20:34:23.494111Z", + "iopub.status.idle": "2026-05-14T20:34:24.052177Z", + "shell.execute_reply": "2026-05-14T20:34:24.051361Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9996761790297248" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_aqc_tensor.simulation import compute_overlap\n", + "\n", + "comparison_mps = tensornetwork_from_circuit(comparison_circuit, simulator_settings)\n", + "comparison_fidelity = abs(compute_overlap(comparison_mps, aqc_target_mps)) ** 2\n", + "comparison_fidelity" + ] + }, + { + "cell_type": "markdown", + "id": "5859794d-efbd-4e5b-a1d2-2b7add48b8e4", + "metadata": {}, + "source": [ + "### Optimize the parameters of the ansatz using MPS calculations\n", + "\n", + "Here, we minimize the simplest possible cost function, `MaximizeStateFidelity`, by using the L-BFGS optimizer from scipy.\n", + "\n", + "We choose a stopping point for the fidelity such that it will be above what the comparison circuit would have been, without using AQC. Once this is reached, the compressed circuit has less Trotter error _and_ less depth than the original circuit. Given more processing time, further optimization steps can be performed to bring the fidelity even higher." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "66dddd19-51d6-4bd4-a3df-2c9b457d4149", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:34:24.054596Z", + "iopub.status.busy": "2026-05-14T20:34:24.054296Z", + "iopub.status.idle": "2026-05-14T20:36:02.294412Z", + "shell.execute_reply": "2026-05-14T20:36:02.293777Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.95080172\n", + "Intermediate result: Fidelity 0.98408407\n", + "Intermediate result: Fidelity 0.99140959\n", + "Intermediate result: Fidelity 0.99518665\n", + "Intermediate result: Fidelity 0.99562326\n", + "Intermediate result: Fidelity 0.99645786\n", + "Intermediate result: Fidelity 0.99678929\n", + "Intermediate result: Fidelity 0.99715317\n", + "Intermediate result: Fidelity 0.99756663\n", + "Intermediate result: Fidelity 0.99803794\n", + "Intermediate result: Fidelity 0.99832093\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99855392\n", + "Intermediate result: Fidelity 0.99868269\n", + "Intermediate result: Fidelity 0.99886211\n", + "Intermediate result: Fidelity 0.99901486\n", + "Intermediate result: Fidelity 0.99911781\n", + "Intermediate result: Fidelity 0.99919359\n", + "Intermediate result: Fidelity 0.99925985\n", + "Intermediate result: Fidelity 0.99938652\n", + "Intermediate result: Fidelity 0.99949188\n", + "Intermediate result: Fidelity 0.99958555\n", + "Intermediate result: Fidelity 0.99961916\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99963156\n", + "Intermediate result: Fidelity 0.99966815\n", + "Intermediate result: Fidelity 0.99970081\n", + "Done after 25 iterations.\n" + ] + } + ], + "source": [ + "from scipy.optimize import OptimizeResult, minimize\n", + "\n", + "from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity\n", + "\n", + "objective = MaximizeStateFidelity(aqc_target_mps, aqc_ansatz, simulator_settings)\n", + "\n", + "stopping_point = 1 - comparison_fidelity\n", + "\n", + "\n", + "def callback(intermediate_result: OptimizeResult):\n", + " print(f\"Intermediate result: Fidelity {1 - intermediate_result.fun:.8}\")\n", + " if intermediate_result.fun < stopping_point:\n", + " # Good enough for now\n", + " raise StopIteration\n", + "\n", + "\n", + "result = minimize(\n", + " objective.loss_function,\n", + " aqc_initial_parameters,\n", + " method=\"L-BFGS-B\",\n", + " jac=True,\n", + " options={\"maxiter\": 100},\n", + " callback=callback,\n", + ")\n", + "if result.status not in (\n", + " 0,\n", + " 1,\n", + " 99,\n", + "): # 0 => success; 1 => max iterations reached; 99 => early termination via StopIteration\n", + " raise RuntimeError(f\"Optimization failed: {result.message} (status={result.status})\")\n", + "\n", + "print(f\"Done after {result.nit} iterations.\")\n", + "aqc_final_parameters = result.x" + ] + }, + { + "cell_type": "markdown", + "id": "4f668909-529d-4f2c-bfe7-e7766cd33ce6", + "metadata": {}, + "source": [ + "### Construct the final circuit to pass to the transpiler" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "dbf60899-6f9d-4222-a499-e68bd5eabaf5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:36:02.296852Z", + "iopub.status.busy": "2026-05-14T20:36:02.296524Z", + "iopub.status.idle": "2026-05-14T20:36:04.894647Z", + "shell.execute_reply": "2026-05-14T20:36:04.893967Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc/extracted-outputs/dbf60899-6f9d-4222-a499-e68bd5eabaf5-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_circuit = aqc_ansatz.assign_parameters(aqc_final_parameters)\n", + "final_circuit.compose(subsequent_circuit, inplace=True)\n", + "final_circuit.draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "817839e7-17e7-4dcb-b620-a7711aeb2edf", + "metadata": {}, + "source": [ + "## Step 2: Transpile for execution on target hardware\n", + "\n", + "In Step 2 of a [Qiskit pattern](/docs/guides/intro-to-patterns), we transpile this circuit and any desired observable(s) for execution on a target device. Here we are using a fake backend provided by `qiskit-ibm-runtime`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "783aea48-3d0a-4d7b-a5ed-e3f4888c47a8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:36:04.897742Z", + "iopub.status.busy": "2026-05-14T20:36:04.897491Z", + "iopub.status.idle": "2026-05-14T20:36:05.360910Z", + "shell.execute_reply": "2026-05-14T20:36:05.360061Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3199/204133634.py:2: DeprecationWarning: Using qiskit-ibm-runtime with Python 3.9 is deprecated as of the 0.41.0 release. Support for running qiskit-ibm-runtime with Python 3.9 will be removed in a future release.\n", + " from qiskit_ibm_runtime.fake_provider import FakeMelbourneV2\n" + ] + } + ], + "source": [ + "from qiskit import transpile\n", + "from qiskit_ibm_runtime.fake_provider import FakeMelbourneV2\n", + "\n", + "backend = FakeMelbourneV2()\n", + "\n", + "isa_circuit = transpile(final_circuit, backend)\n", + "isa_observable = observable.apply_layout(isa_circuit.layout)" + ] + }, + { + "cell_type": "markdown", + "id": "5ff0be42-b45a-4af3-8317-3c1f87758771", + "metadata": {}, + "source": [ + "The resulting ISA circuit can then be sent for execution on the backend (step 3 of a [Qiskit pattern](/docs/guides/intro-to-patterns))." + ] + }, + { + "cell_type": "markdown", + "id": "6600071d-e0b6-45cd-ae4e-0ffe882b0955", + "metadata": {}, + "source": [ + "## Step 3: Execute on quantum hardware" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "bd8039e4-08c4-4a7f-98ba-f8c11c8da795", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:36:05.363486Z", + "iopub.status.busy": "2026-05-14T20:36:05.363113Z", + "iopub.status.idle": "2026-05-14T20:36:07.811329Z", + "shell.execute_reply": "2026-05-14T20:36:07.810459Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import EstimatorV2 as Estimator\n", + "\n", + "estimator = Estimator(backend)\n", + "job = estimator.run([(isa_circuit, isa_observable)])\n", + "pub_result = job.result()[0]" + ] + }, + { + "cell_type": "markdown", + "id": "f56257c7-55c0-426a-835f-6e08b306536a", + "metadata": {}, + "source": [ + "## Step 4: Reconstruct\n", + "\n", + "Reconstruction is not necessary in our case. We can just look at the result." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "84a212df-8e89-4576-a407-903da48a0c5a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-14T20:36:07.813749Z", + "iopub.status.busy": "2026-05-14T20:36:07.813542Z", + "iopub.status.idle": "2026-05-14T20:36:07.818266Z", + "shell.execute_reply": "2026-05-14T20:36:07.817548Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.07687988281250001)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pub_result.data.evs[()]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx new file mode 100644 index 00000000000..c658b490a1d --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx @@ -0,0 +1,15 @@ +--- +title: "AQC-Tensor Tutorials" +description: "AQC-Tensor Tutorials for the lastest version of Approximate quantum compilation (AQC-Tensor)" +--- + +<span id="tutorials" /> + +# AQC-Tensor Tutorials + +* [Tutorial 1](01-basic-workflow-ipynb): Show the basic end-to-end workflow of the AQC-Tensor addon. + +[![](/docs/images/addons/qiskit-addon-aqc-tensor/tutorials_01_initial_state_aqc_26_0.avif)](01-initial-state-aqc) + +[Reducing circuit depth with the AQC-Tensor Qiskit addon](01-initial-state-aqc) + diff --git a/docs/api/qiskit-addon-aqc-tensor/_toc.json b/docs/api/qiskit-addon-aqc-tensor/_toc.json index 99cd2329015..e07759d3615 100644 --- a/docs/api/qiskit-addon-aqc-tensor/_toc.json +++ b/docs/api/qiskit-addon-aqc-tensor/_toc.json @@ -102,5 +102,7 @@ } ], "collapsed": true, - "untranslatable": true + "untranslatable": true, + "parentUrl": "/docs/addons/qiskit-addon-aqc-tensor", + "parentLabel": "Approximate quantum compilation (AQC-Tensor)" } diff --git a/docs/api/qiskit-addon-aqc-tensor/ansatz-generation-ansatz-block.mdx b/docs/api/qiskit-addon-aqc-tensor/ansatz-generation-ansatz-block.mdx index 7d8fbbe874e..56b2f68d6be 100644 --- a/docs/api/qiskit-addon-aqc-tensor/ansatz-generation-ansatz-block.mdx +++ b/docs/api/qiskit-addon-aqc-tensor/ansatz-generation-ansatz-block.mdx @@ -13,7 +13,7 @@ python_api_name: qiskit_addon_aqc_tensor.ansatz_generation.AnsatzBlock Ansatz block. - This is the base class of all blocks returned by [`generate_ansatz_from_circuit()`](ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"). + This is the base class of all blocks returned by [`generate_ansatz_from_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"). Initialize the ansatz block. diff --git a/docs/api/qiskit-addon-aqc-tensor/ansatz-generation.mdx b/docs/api/qiskit-addon-aqc-tensor/ansatz-generation.mdx index d52d674a09e..68287f2bae8 100644 --- a/docs/api/qiskit-addon-aqc-tensor/ansatz-generation.mdx +++ b/docs/api/qiskit-addon-aqc-tensor/ansatz-generation.mdx @@ -16,20 +16,20 @@ python_api_name: qiskit_addon_aqc_tensor.ansatz_generation Tools for generating ansatz circuits. -| | | -| --------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | -| [`AnsatzBlock`](ansatz-generation-ansatz-block "qiskit_addon_aqc_tensor.ansatz_generation.AnsatzBlock") | Ansatz block. | -| [`OneQubitAnsatzBlock`](ansatz-generation-one-qubit-ansatz-block "qiskit_addon_aqc_tensor.ansatz_generation.OneQubitAnsatzBlock") | One-qubit ansatz block. | -| [`TwoQubitAnsatzBlock`](ansatz-generation-two-qubit-ansatz-block "qiskit_addon_aqc_tensor.ansatz_generation.TwoQubitAnsatzBlock") | Two-qubit ansatz block. | -| [`ZXZ`](ansatz-generation-zxz "qiskit_addon_aqc_tensor.ansatz_generation.ZXZ") | One-qubit ansatz block based on the ZXZ decomposition. | -| [`KAK`](ansatz-generation-kak "qiskit_addon_aqc_tensor.ansatz_generation.KAK") | Two-qubit ansatz block based on the KAK decomposition. | +| | | +| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | +| [`AnsatzBlock`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation-ansatz-block "qiskit_addon_aqc_tensor.ansatz_generation.AnsatzBlock") | Ansatz block. | +| [`OneQubitAnsatzBlock`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation-one-qubit-ansatz-block "qiskit_addon_aqc_tensor.ansatz_generation.OneQubitAnsatzBlock") | One-qubit ansatz block. | +| [`TwoQubitAnsatzBlock`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation-two-qubit-ansatz-block "qiskit_addon_aqc_tensor.ansatz_generation.TwoQubitAnsatzBlock") | Two-qubit ansatz block. | +| [`ZXZ`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation-zxz "qiskit_addon_aqc_tensor.ansatz_generation.ZXZ") | One-qubit ansatz block based on the ZXZ decomposition. | +| [`KAK`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation-kak "qiskit_addon_aqc_tensor.ansatz_generation.KAK") | Two-qubit ansatz block based on the KAK decomposition. | ### generate\_ansatz\_from\_circuit <Function id="qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit" github="https://github.com/Qiskit/qiskit-addon-aqc-tensor/tree/stable/0.3/qiskit_addon_aqc_tensor/ansatz_generation/from_connectivity.py#L164-L422" signature="generate_ansatz_from_circuit(qc, /, *, qubits_initially_zero=False, parameter_name='theta')"> Generate an ansatz from the two-qubit connectivity structure of a circuit. - See the [explanatatory material](https://qiskit.github.io/qiskit-addon-aqc-tensor/explanation/index.html#ansatz-generation-motivation) for motivation. + See the [explanatatory material](/docs/addons/qiskit-addon-aqc-tensor/explanations/index#ansatz-generation-motivation) for motivation. **Parameters** diff --git a/docs/api/qiskit-addon-aqc-tensor/release-notes.mdx b/docs/api/qiskit-addon-aqc-tensor/release-notes.mdx index 6afd69c5e71..d9e1899c6c5 100644 --- a/docs/api/qiskit-addon-aqc-tensor/release-notes.mdx +++ b/docs/api/qiskit-addon-aqc-tensor/release-notes.mdx @@ -34,7 +34,7 @@ in_page_toc_max_heading_level: 2 ### Bug Fixes -* This adds a workaround for an issue sometimes encountered when calling [`TwoQubitWeylDecomposition`](/docs/api/qiskit/qiskit.synthesis.TwoQubitWeylDecomposition) inside [`generate_ansatz_from_circuit()`](ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"). Sometimes an error is thrown despite the input matrix being unitary to numerical precision, and returning ``is_unitary()`()`` as `True`. This is an ongoing issue in qiskit, see [https://github.com/Qiskit/qiskit/issues/4159](https://github.com/Qiskit/qiskit/issues/4159) for more details. +* This adds a workaround for an issue sometimes encountered when calling [`TwoQubitWeylDecomposition`](/docs/api/qiskit/qiskit.synthesis.TwoQubitWeylDecomposition) inside [`generate_ansatz_from_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"). Sometimes an error is thrown despite the input matrix being unitary to numerical precision, and returning ``is_unitary()`()`` as `True`. This is an ongoing issue in qiskit, see [https://github.com/Qiskit/qiskit/issues/4159](https://github.com/Qiskit/qiskit/issues/4159) for more details. With this workaround, any time the error is encountered, it will retry by transpiling the two-qubit circuit (from which the matrix is derived) prior to passing the matrix to [`TwoQubitWeylDecomposition`](/docs/api/qiskit/qiskit.synthesis.TwoQubitWeylDecomposition). It should be noted that this is does not fix the root issue, and is not guaranteed to always work, but has been observed to work for some instances. @@ -48,7 +48,7 @@ in_page_toc_max_heading_level: 2 ### Prelude -This release renames the primary objective to [`MaximizeStateFidelity`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity") and adds support for Qiskit 2.0. +This release renames the primary objective to [`MaximizeStateFidelity`](/docs/api/qiskit-addon-aqc-tensor/objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity") and adds support for Qiskit 2.0. <span id="release-notes-0-2-0-new-features" /> @@ -56,7 +56,7 @@ This release renames the primary objective to [`MaximizeStateFidelity`](objectiv ### New Features -* Adds the [`parametrize_circuit()`](ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.parametrize_circuit "qiskit_addon_aqc_tensor.ansatz_generation.parametrize_circuit") function for generating a parametrized version of a given circuit. In contrast to [`generate_ansatz_from_circuit()`](ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"), `parametrize_circuit` does not change the types of gates in the circuit. It simply replaces numerical parameters with free parameters. +* Adds the [`parametrize_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.parametrize_circuit "qiskit_addon_aqc_tensor.ansatz_generation.parametrize_circuit") function for generating a parametrized version of a given circuit. In contrast to [`generate_ansatz_from_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"), `parametrize_circuit` does not change the types of gates in the circuit. It simply replaces numerical parameters with free parameters. * Support for Python 3.13. @@ -66,7 +66,7 @@ This release renames the primary objective to [`MaximizeStateFidelity`](objectiv ### Upgrade Notes -* The `OneMinusFidelity` objective function has been renamed and is now known as [`MaximizeStateFidelity`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity"). As a related change, one should now call the [`loss_function()`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") method to obtain the value and gradient of the loss function, instead of calling the instance directly (through its `__call__` method). +* The `OneMinusFidelity` objective function has been renamed and is now known as [`MaximizeStateFidelity`](/docs/api/qiskit-addon-aqc-tensor/objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity"). As a related change, one should now call the [`loss_function()`](/docs/api/qiskit-addon-aqc-tensor/objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") method to obtain the value and gradient of the loss function, instead of calling the instance directly (through its `__call__` method). * Qiskit SDK version 1.3 or higher is now required. Qiskit SDK version 2.0 is now supported. diff --git a/docs/api/qiskit-addon-aqc-tensor/simulation-aer-qiskit-aer-mps.mdx b/docs/api/qiskit-addon-aqc-tensor/simulation-aer-qiskit-aer-mps.mdx index 7938c162095..b3e9f4b7a21 100644 --- a/docs/api/qiskit-addon-aqc-tensor/simulation-aer-qiskit-aer-mps.mdx +++ b/docs/api/qiskit-addon-aqc-tensor/simulation-aer-qiskit-aer-mps.mdx @@ -9,7 +9,7 @@ python_api_name: qiskit_addon_aqc_tensor.simulation.aer.QiskitAerMPS # QiskitAerMPS <Class id="qiskit_addon_aqc_tensor.simulation.aer.QiskitAerMPS" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-aqc-tensor/tree/stable/0.3/qiskit_addon_aqc_tensor/simulation/aer/state.py#L26-L44" signature="QiskitAerMPS(gamma, lamb)" modifiers="class"> - Bases: [`TensorNetworkState`](simulation#qiskit_addon_aqc_tensor.simulation.TensorNetworkState "qiskit_addon_aqc_tensor.simulation.abstract.TensorNetworkState") + Bases: [`TensorNetworkState`](/docs/api/qiskit-addon-aqc-tensor/simulation#qiskit_addon_aqc_tensor.simulation.TensorNetworkState "qiskit_addon_aqc_tensor.simulation.abstract.TensorNetworkState") Qiskit Aer representation of a matrix-product state. diff --git a/docs/api/qiskit-addon-aqc-tensor/simulation-aer-qiskit-aer-simulation-settings.mdx b/docs/api/qiskit-addon-aqc-tensor/simulation-aer-qiskit-aer-simulation-settings.mdx index 7f61ff29ba5..0b82ab1ee23 100644 --- a/docs/api/qiskit-addon-aqc-tensor/simulation-aer-qiskit-aer-simulation-settings.mdx +++ b/docs/api/qiskit-addon-aqc-tensor/simulation-aer-qiskit-aer-simulation-settings.mdx @@ -9,7 +9,7 @@ python_api_name: qiskit_addon_aqc_tensor.simulation.aer.QiskitAerSimulationSetti # QiskitAerSimulationSettings <Class id="qiskit_addon_aqc_tensor.simulation.aer.QiskitAerSimulationSettings" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-aqc-tensor/tree/stable/0.3/qiskit_addon_aqc_tensor/simulation/aer/simulation.py#L59-L92" signature="QiskitAerSimulationSettings(simulator, callback=None)" modifiers="class"> - Bases: [`TensorNetworkSimulationSettings`](simulation#qiskit_addon_aqc_tensor.simulation.TensorNetworkSimulationSettings "qiskit_addon_aqc_tensor.simulation.abstract.TensorNetworkSimulationSettings") + Bases: [`TensorNetworkSimulationSettings`](/docs/api/qiskit-addon-aqc-tensor/simulation#qiskit_addon_aqc_tensor.simulation.TensorNetworkSimulationSettings "qiskit_addon_aqc_tensor.simulation.abstract.TensorNetworkSimulationSettings") Qiskit Aer simulator settings. diff --git a/docs/api/qiskit-addon-aqc-tensor/simulation-aer.mdx b/docs/api/qiskit-addon-aqc-tensor/simulation-aer.mdx index 89cee6e3bfb..4bd85900fb2 100644 --- a/docs/api/qiskit-addon-aqc-tensor/simulation-aer.mdx +++ b/docs/api/qiskit-addon-aqc-tensor/simulation-aer.mdx @@ -16,10 +16,10 @@ python_api_name: qiskit_addon_aqc_tensor.simulation.aer Qiskit Aer MPS simulator as a tensor network backend. -| | | -| --------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------- | -| [`QiskitAerMPS`](simulation-aer-qiskit-aer-mps "qiskit_addon_aqc_tensor.simulation.aer.QiskitAerMPS") | Qiskit Aer representation of a matrix-product state. | -| [`QiskitAerSimulationSettings`](simulation-aer-qiskit-aer-simulation-settings "qiskit_addon_aqc_tensor.simulation.aer.QiskitAerSimulationSettings") | Qiskit Aer simulator settings. | +| | | +| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------- | +| [`QiskitAerMPS`](/docs/api/qiskit-addon-aqc-tensor/simulation-aer-qiskit-aer-mps "qiskit_addon_aqc_tensor.simulation.aer.QiskitAerMPS") | Qiskit Aer representation of a matrix-product state. | +| [`QiskitAerSimulationSettings`](/docs/api/qiskit-addon-aqc-tensor/simulation-aer-qiskit-aer-simulation-settings "qiskit_addon_aqc_tensor.simulation.aer.QiskitAerSimulationSettings") | Qiskit Aer simulator settings. | ## Functions diff --git a/docs/api/qiskit-addon-aqc-tensor/simulation-quimb-quimb-simulator.mdx b/docs/api/qiskit-addon-aqc-tensor/simulation-quimb-quimb-simulator.mdx index 0f0ed87192f..1785b72f811 100644 --- a/docs/api/qiskit-addon-aqc-tensor/simulation-quimb-quimb-simulator.mdx +++ b/docs/api/qiskit-addon-aqc-tensor/simulation-quimb-quimb-simulator.mdx @@ -9,7 +9,7 @@ python_api_name: qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator # QuimbSimulator <Class id="qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-aqc-tensor/tree/stable/0.3/qiskit_addon_aqc_tensor/simulation/quimb/__init__.py#L75-L126" signature="QuimbSimulator(quimb_circuit_factory, autodiff_backend=None, progbar=False)" modifiers="class"> - Bases: [`TensorNetworkSimulationSettings`](simulation#qiskit_addon_aqc_tensor.simulation.TensorNetworkSimulationSettings "qiskit_addon_aqc_tensor.simulation.abstract.TensorNetworkSimulationSettings") + Bases: [`TensorNetworkSimulationSettings`](/docs/api/qiskit-addon-aqc-tensor/simulation#qiskit_addon_aqc_tensor.simulation.TensorNetworkSimulationSettings "qiskit_addon_aqc_tensor.simulation.abstract.TensorNetworkSimulationSettings") Settings for Quimb simulator. diff --git a/docs/api/qiskit-addon-aqc-tensor/simulation-quimb.mdx b/docs/api/qiskit-addon-aqc-tensor/simulation-quimb.mdx index 4cc33251025..636c84bce2c 100644 --- a/docs/api/qiskit-addon-aqc-tensor/simulation-quimb.mdx +++ b/docs/api/qiskit-addon-aqc-tensor/simulation-quimb.mdx @@ -16,11 +16,11 @@ python_api_name: qiskit_addon_aqc_tensor.simulation.quimb Quimb as a tensor network backend. -| | | -| ---------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | -| [`QuimbCircuitFactory`](simulation-quimb-quimb-circuit-factory "qiskit_addon_aqc_tensor.simulation.quimb.QuimbCircuitFactory") | Quimb circuit factory. | -| [`QuimbSimulator`](simulation-quimb-quimb-simulator "qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator") | Settings for Quimb simulator. | -| [`QiskitQuimbConversionContext`](simulation-quimb-qiskit-quimb-conversion-context "qiskit_addon_aqc_tensor.simulation.quimb.QiskitQuimbConversionContext") | Contains information about Qiskit-to-Quimb conversion, necessary to recover Qiskit parameters. | +| | | +| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | +| [`QuimbCircuitFactory`](/docs/api/qiskit-addon-aqc-tensor/simulation-quimb-quimb-circuit-factory "qiskit_addon_aqc_tensor.simulation.quimb.QuimbCircuitFactory") | Quimb circuit factory. | +| [`QuimbSimulator`](/docs/api/qiskit-addon-aqc-tensor/simulation-quimb-quimb-simulator "qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator") | Settings for Quimb simulator. | +| [`QiskitQuimbConversionContext`](/docs/api/qiskit-addon-aqc-tensor/simulation-quimb-qiskit-quimb-conversion-context "qiskit_addon_aqc_tensor.simulation.quimb.QiskitQuimbConversionContext") | Contains information about Qiskit-to-Quimb conversion, necessary to recover Qiskit parameters. | ## Functions @@ -41,7 +41,7 @@ Quimb as a tensor network backend. **Return type** - [`tuple`](https://docs.python.org/3/library/stdtypes.html#tuple)\[[`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/circuit/index.html#quimb.tensor.circuit.Circuit), [`QiskitQuimbConversionContext`](simulation-quimb-qiskit-quimb-conversion-context "qiskit_addon_aqc_tensor.simulation.quimb.QiskitQuimbConversionContext")] + [`tuple`](https://docs.python.org/3/library/stdtypes.html#tuple)\[[`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/circuit/index.html#quimb.tensor.circuit.Circuit), [`QiskitQuimbConversionContext`](/docs/api/qiskit-addon-aqc-tensor/simulation-quimb-qiskit-quimb-conversion-context "qiskit_addon_aqc_tensor.simulation.quimb.QiskitQuimbConversionContext")] **Parameters** @@ -61,7 +61,7 @@ Quimb as a tensor network backend. **Parameters** * **circ\_opt** ([*Circuit*](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/circuit/index.html#quimb.tensor.circuit.Circuit)) - * **ctx** ([*QiskitQuimbConversionContext*](simulation-quimb-qiskit-quimb-conversion-context "qiskit_addon_aqc_tensor.simulation.quimb.QiskitQuimbConversionContext")) + * **ctx** ([*QiskitQuimbConversionContext*](/docs/api/qiskit-addon-aqc-tensor/simulation-quimb-qiskit-quimb-conversion-context "qiskit_addon_aqc_tensor.simulation.quimb.QiskitQuimbConversionContext")) </Function> ### tnoptimizer\_objective\_kwargs diff --git a/docs/api/qiskit-addon-aqc-tensor/simulation.mdx b/docs/api/qiskit-addon-aqc-tensor/simulation.mdx index 95fc0d9bb65..db100e0ebaf 100644 --- a/docs/api/qiskit-addon-aqc-tensor/simulation.mdx +++ b/docs/api/qiskit-addon-aqc-tensor/simulation.mdx @@ -34,7 +34,7 @@ In each function below, the documentation shows every distinct implementation av <Function id="qiskit_addon_aqc_tensor.simulation.tensornetwork_from_circuit" github="https://github.com/Qiskit/qiskit-addon-aqc-tensor/tree/stable/0.3/qiskit_addon_aqc_tensor/simulation/abstract.py" signature="tensornetwork_from_circuit(qc, settings, /)"> Create a tensor-network state by simulating a quantum circuit. - The type of tensor-network state will correspond to the type of the `settings` object. For instance, a [`QiskitAerSimulationSettings`](simulation-aer-qiskit-aer-simulation-settings "qiskit_addon_aqc_tensor.simulation.aer.QiskitAerSimulationSettings") will result in this function returning a [`QiskitAerMPS`](simulation-aer-qiskit-aer-mps "qiskit_addon_aqc_tensor.simulation.aer.QiskitAerMPS"). + The type of tensor-network state will correspond to the type of the `settings` object. For instance, a [`QiskitAerSimulationSettings`](/docs/api/qiskit-addon-aqc-tensor/simulation-aer-qiskit-aer-simulation-settings "qiskit_addon_aqc_tensor.simulation.aer.QiskitAerSimulationSettings") will result in this function returning a [`QiskitAerMPS`](/docs/api/qiskit-addon-aqc-tensor/simulation-aer-qiskit-aer-mps "qiskit_addon_aqc_tensor.simulation.aer.QiskitAerMPS"). <Function github="https://github.com/Qiskit/qiskit-addon-aqc-tensor/tree/stable/0.3/qiskit_addon_aqc_tensor/simulation/abstract.py" signature="tensornetwork_from_circuit(qc: qiskit.circuit.quantumcircuit.QuantumCircuit, settings: qiskit_addon_aqc_tensor.simulation.aer.simulation.QiskitAerSimulationSettings | plum.type.ModuleType[qiskit_aer.AerSimulator], /, *, out_state: numpy.ndarray | None = None) → qiskit_addon_aqc_tensor.simulation.aer.state.QiskitAerMPS"> **Parameters** diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index b660dfb29a3..2aee251950c 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -32,7 +32,7 @@ These capabilities specialize in mapping domain problems into quantum operators <Card title="AQC-Tensor" description="Construct high-fidelity circuits with reduced depth using approximate quantum compilation with tensor networks." - href="https://qiskit.github.io/qiskit-addon-aqc-tensor/" + href="/docs/addons/qiskit-addon-aqc-tensor/" analyticsName="Documentation page: AQC-Tensor" linkText="Browse documentation" /> @@ -97,7 +97,7 @@ These techniques are useful for managing noise on sampling results. <Card title="Sample-based quantum diagonalization" description="Estimate the spectrum of quantum Hamiltonians by processing noisy samples and diagonalizing in a reduced subspace." - href="/docs/addons/qiskit-addons-sqd" + href="/docs/addons/qiskit-addon-sqd" analyticsName="Overview page: SQD" linkText="Overview" /> diff --git a/public/docs/images/addons/qiskit-addon-aqc-tensor/aqc-compression.avif 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100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -252,7 +252,10 @@ const ALWAYS_IGNORED_URLS__SHOULD_FIX: string[] = [ "../explanation/index.rst", "#equation-eq-qpd", "./circuit_cutting/explanations/index-rst#overview-of-circuit-cutting", - "../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb" + "../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb", + + // from qiskit-addon-aqc-tensor + "../stubs/qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator.rst" ]; export const ALWAYS_IGNORED_URLS = new Set([ From 0c4a0c3284199370f9d83ba1d4e77b14835b62ad Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Mon, 18 May 2026 10:56:22 -0400 Subject: [PATCH 062/104] add aqc toc --- docs/addons/qiskit-addon-aqc-tensor/_toc.json | 65 +++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 docs/addons/qiskit-addon-aqc-tensor/_toc.json diff --git a/docs/addons/qiskit-addon-aqc-tensor/_toc.json b/docs/addons/qiskit-addon-aqc-tensor/_toc.json new file mode 100644 index 00000000000..2a97cbfcbeb --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/_toc.json @@ -0,0 +1,65 @@ +{ + "parentUrl": "/docs/guides/addons", + "parentLabel": "Documentation", + "title": "AQC-Tensor 0.3.0", + "collapsed": true, + "children": [ + { + "title": "", + "children": [ + { + "title": "Home", + "url": "/docs/addons/qiskit-addon-aqc-tensor" + }, + { + "title": "Installation instructions", + "url": "/docs/addons/qiskit-addon-aqc-tensor/install" + }, + { + "title":"Explanations", + "children": [ + { + "title": "Explanatory Material on AQC-Tensor", + "url": "/docs/addons/qiskit-addon-aqc-tensor/explanations/index" + } + ] + }, + { + "title": "Guides", + "children": [ + { + "title": "How to use quimb.tensor.TNOptimizer directly", + "url": "/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer" + } + ], + "url": "/docs/addons/qiskit-addon-aqc-tensor/how-tos/index" + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-aqc-tensor" + } + ], + "collapsible": false + }, + { + "title": "Tutorials", + "collapsible": false, + "children": [ + { + "title": "Reducing circuit depth with the AQC-Tensor Qiskit addon", + "url": "/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc" + } + ] + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Circuit aqc-tensor API reference", + "url": "/docs/api/qiskit-addon-aqc-tensor" + } + ] + } + ] +} From 24a98f419ccbc1eca13549400b17977fd0812e4b Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Mon, 18 May 2026 11:37:28 -0400 Subject: [PATCH 063/104] fix link --- docs/guides/addons.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index 2aee251950c..f388a26388d 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -113,7 +113,7 @@ These techniques are useful for managing noise on sampling results. <Card title="Measurement-based postselection" description="Filter out non-Markovian noise in circuits by incorporating measurement-based postselection transpiler passes." - href="/docs/en/api/qiskit-addon-utils/noise-management-post-selection-transpiler-passes" + href="/docs/api/qiskit-addon-utils/noise-management-post-selection-transpiler-passes" analyticsName="Documentation page for post-selection" linkText="Read the API reference" /> From eec2940fcb5152f61a803eded499fa3b1fc8ed29 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Mon, 18 May 2026 14:06:17 -0400 Subject: [PATCH 064/104] adds gen addon toc --- docs/addons/qiskit-addon-aqc-tensor/_toc.json | 15 +- docs/addons/qiskit-addon-cutting/_toc.json | 13 +- docs/addons/qiskit-addon-mpf/_toc.json | 27 +- docs/addons/qiskit-addon-obp/_toc.json | 7 +- docs/addons/qiskit-addon-sqd/_toc.json | 7 +- docs/addons/qiskit-addon-utils/_toc.json | 4 +- scripts/js/commands/api/updateAddonDocs.ts | 1 - scripts/js/commands/api/updateDocsShared.ts | 20 +- scripts/js/lib/api/addonDocsPipeline.ts | 18 + scripts/js/lib/api/generateAddonToc.test.ts | 374 ++++++++++++++++++ scripts/js/lib/api/generateAddonToc.ts | 216 ++++++++++ 11 files changed, 648 insertions(+), 54 deletions(-) create mode 100644 scripts/js/lib/api/generateAddonToc.test.ts create mode 100644 scripts/js/lib/api/generateAddonToc.ts diff --git a/docs/addons/qiskit-addon-aqc-tensor/_toc.json b/docs/addons/qiskit-addon-aqc-tensor/_toc.json index 2a97cbfcbeb..c46e33bb0d8 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/_toc.json +++ b/docs/addons/qiskit-addon-aqc-tensor/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "AQC-Tensor 0.3.0", + "title": "Approximate quantum compilation (AQC-Tensor) 0.3.0", "collapsed": true, "children": [ { @@ -12,11 +12,11 @@ "url": "/docs/addons/qiskit-addon-aqc-tensor" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-aqc-tensor/install" }, { - "title":"Explanations", + "title": "Explanations", "children": [ { "title": "Explanatory Material on AQC-Tensor", @@ -28,7 +28,7 @@ "title": "Guides", "children": [ { - "title": "How to use quimb.tensor.TNOptimizer directly", + "title": "How to use `quimb.tensor.TNOptimizer` directly", "url": "/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer" } ], @@ -43,20 +43,21 @@ }, { "title": "Tutorials", - "collapsible": false, "children": [ { "title": "Reducing circuit depth with the AQC-Tensor Qiskit addon", "url": "/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc" } - ] + ], + "url": "/docs/addons/qiskit-addon-aqc-tensor/tutorials/index", + "collapsible": false }, { "title": "API reference", "collapsible": false, "children": [ { - "title": "Circuit aqc-tensor API reference", + "title": "Approximate quantum compilation (AQC-Tensor) API reference", "url": "/docs/api/qiskit-addon-aqc-tensor" } ] diff --git a/docs/addons/qiskit-addon-cutting/_toc.json b/docs/addons/qiskit-addon-cutting/_toc.json index 5ed857183b3..32c109298e2 100644 --- a/docs/addons/qiskit-addon-cutting/_toc.json +++ b/docs/addons/qiskit-addon-cutting/_toc.json @@ -12,11 +12,11 @@ "url": "/docs/addons/qiskit-addon-cutting" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-cutting/install" }, { - "title":"Explanations", + "title": "Explanations", "children": [ { "title": "Explanatory material", @@ -40,7 +40,7 @@ "url": "/docs/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables" }, { - "title": "How to place wire cuts using a single-qubit CutWire instruction", + "title": "How to place wire cuts using a single-qubit `CutWire` instruction", "url": "/docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires" } ], @@ -55,7 +55,6 @@ }, { "title": "Tutorials", - "collapsible": false, "children": [ { "title": "Gate Cutting to Reduce Circuit Width", @@ -66,14 +65,16 @@ "url": "/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth" }, { - "title": "Wire Cutting Phrased as a Two-Qubit Move Instruction", + "title": "Wire Cutting Phrased as a Two-Qubit `Move` Instruction", "url": "/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction" }, { "title": "Automatically find cuts", "url": "/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding" } - ] + ], + "url": "/docs/addons/qiskit-addon-cutting/tutorials/index", + "collapsible": false }, { "title": "API reference", diff --git a/docs/addons/qiskit-addon-mpf/_toc.json b/docs/addons/qiskit-addon-mpf/_toc.json index 2244746da2c..d6a5a67a7f2 100644 --- a/docs/addons/qiskit-addon-mpf/_toc.json +++ b/docs/addons/qiskit-addon-mpf/_toc.json @@ -12,9 +12,19 @@ "url": "/docs/addons/qiskit-addon-mpf" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-mpf/install" }, + { + "title": "Explanations", + "children": [ + { + "title": "Understanding the stability of MPFs", + "url": "/docs/addons/qiskit-addon-mpf/explanations/mpf-stability" + } + ], + "url": "/docs/addons/qiskit-addon-mpf/explanations/index" + }, { "title": "Guides", "children": [ @@ -36,25 +46,16 @@ ], "collapsible": false }, - { - "title": "Explanations", - "collapsible": false, - "children": [ - { - "title": "Understanding the stability of MPFs", - "url": "/docs/addons/qiskit-addon-mpf/explanations/mpf-stability" - } - ] - }, { "title": "Tutorials", - "collapsible": false, "children": [ { "title": "Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas", "url": "/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started" } - ] + ], + "url": "/docs/addons/qiskit-addon-mpf/tutorials/index", + "collapsible": false }, { "title": "API reference", diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index 5546faff959..6d3f57a2823 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -12,7 +12,7 @@ "url": "/docs/addons/qiskit-addon-obp" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-obp/install" }, { @@ -42,13 +42,14 @@ }, { "title": "Tutorials", - "collapsible": false, "children": [ { "title": "Reducing depth of circuits with operator backpropagation", "url": "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started" } - ] + ], + "url": "/docs/addons/qiskit-addon-obp/tutorials/index", + "collapsible": false }, { "title": "API reference", diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json index 748d1e7ded0..a3bfb808792 100644 --- a/docs/addons/qiskit-addon-sqd/_toc.json +++ b/docs/addons/qiskit-addon-sqd/_toc.json @@ -12,7 +12,7 @@ "url": "/docs/addons/qiskit-addon-sqd" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-sqd/install" }, { @@ -58,7 +58,6 @@ }, { "title": "Tutorials", - "collapsible": false, "children": [ { "title": "Improving energy estimation of a chemistry Hamiltonian with SQD", @@ -68,7 +67,9 @@ "title": "Improving energy estimation of a Fermionic lattice model with SQD", "url": "/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian" } - ] + ], + "url": "/docs/addons/qiskit-addon-sqd/tutorials/index", + "collapsible": false }, { "title": "API reference", diff --git a/docs/addons/qiskit-addon-utils/_toc.json b/docs/addons/qiskit-addon-utils/_toc.json index dcb3e8fbde4..5d2a4a80d1b 100644 --- a/docs/addons/qiskit-addon-utils/_toc.json +++ b/docs/addons/qiskit-addon-utils/_toc.json @@ -12,7 +12,7 @@ "url": "/docs/addons/qiskit-addon-utils" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-utils/install" }, { @@ -23,7 +23,7 @@ "url": "/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth" }, { - "title": "Slicing circuits using qiskit_addon_utils.slicing", + "title": "Slicing circuits using ``qiskit_addon_utils.slicing``", "url": "/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices" } ], diff --git a/scripts/js/commands/api/updateAddonDocs.ts b/scripts/js/commands/api/updateAddonDocs.ts index a432e118f60..2f07e403f87 100644 --- a/scripts/js/commands/api/updateAddonDocs.ts +++ b/scripts/js/commands/api/updateAddonDocs.ts @@ -36,7 +36,6 @@ async function generateVersion(pkg: Pkg, args: SharedArguments): Promise<void> { markdownDir: pkg.outputDir("docs/addons"), imagesDir: pkg.outputDir("public/docs/images/addons"), recursive: true, - preserveFiles: ["_toc.json"], }); console.log(`Run pipeline for ${pkg.name}:${pkg.versionWithoutPatch}`); diff --git a/scripts/js/commands/api/updateDocsShared.ts b/scripts/js/commands/api/updateDocsShared.ts index e4a2f30f7f3..f3082e38831 100644 --- a/scripts/js/commands/api/updateDocsShared.ts +++ b/scripts/js/commands/api/updateDocsShared.ts @@ -116,37 +116,19 @@ export interface DeleteOptions { * files (so sibling historical-version subfolders are preserved). */ recursive: boolean; - /** - * Filenames under `markdownDir` to read into memory before deletion and - * restore after. Used to keep a hand-authored `_toc.json`. - */ - preserveFiles?: string[]; } /** Wipe output directories in preparation for a fresh pipeline run. */ export async function deleteOutputDirs( pkg: Pkg, - { markdownDir, imagesDir, recursive, preserveFiles = [] }: DeleteOptions, + { markdownDir, imagesDir, recursive }: DeleteOptions, ): Promise<void> { if (await pathExists(markdownDir)) { - const preserved: Array<[string, string]> = []; - for (const name of preserveFiles) { - const path = `${markdownDir}/${name}`; - if (await pathExists(path)) { - preserved.push([name, await readFile(path, "utf-8")]); - } - } if (recursive) { await $`rm -rf ${markdownDir}`; } else { await rmFilesInFolder(markdownDir); } - if (preserved.length > 0) { - await mkdirp(markdownDir); - for (const [name, content] of preserved) { - await writeFile(`${markdownDir}/${name}`, content); - } - } } if (await pathExists(imagesDir)) { if (recursive) { diff --git a/scripts/js/lib/api/addonDocsPipeline.ts b/scripts/js/lib/api/addonDocsPipeline.ts index c2fc9d62ace..ffa12f78d8a 100644 --- a/scripts/js/lib/api/addonDocsPipeline.ts +++ b/scripts/js/lib/api/addonDocsPipeline.ts @@ -10,6 +10,8 @@ // copyright notice, and modified files need to carry a notice indicating // that they have been altered from the originals. +import { writeFile } from "fs/promises"; + import { globby } from "globby"; import { mkdirp } from "mkdirp"; @@ -28,6 +30,7 @@ import { writeNotebooks, } from "./notebookStages.js"; import { DOCS_BASE_PATH } from "./paths.js"; +import { generateAddonToc } from "./generateAddonToc.js"; // Sphinx build artifacts that are never content. const SPHINX_INTERNALS = [ @@ -101,6 +104,21 @@ export async function runAddonDocsPipeline( results, notebookImages, ); + + await writeTocFile(pkg, docsBaseFolder, outputPath); +} + +async function writeTocFile( + pkg: Pkg, + docsBaseFolder: string, + outputPath: string, +): Promise<void> { + console.log("Generating addon toc"); + const toc = await generateAddonToc(pkg, docsBaseFolder); + await writeFile( + `${outputPath}/_toc.json`, + JSON.stringify(toc, null, 2) + "\n", + ); } async function determineFilePaths( diff --git a/scripts/js/lib/api/generateAddonToc.test.ts b/scripts/js/lib/api/generateAddonToc.test.ts new file mode 100644 index 00000000000..1507401356e --- /dev/null +++ b/scripts/js/lib/api/generateAddonToc.test.ts @@ -0,0 +1,374 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2026. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import os from "os"; +import path from "path"; +import { mkdtemp, mkdir, writeFile } from "fs/promises"; + +import { expect, test } from "@playwright/test"; + +import { generateAddonToc } from "./generateAddonToc.js"; +import { Pkg, ReleaseNotesConfig } from "./Pkg.js"; + +// --------------------------------------------------------------------------- +// Helpers +// --------------------------------------------------------------------------- + +async function makePkg(name = "my-addon", githubSlug?: string): Promise<Pkg> { + return new Pkg({ + name, + title: "My Addon", + githubSlug, + version: "1.2.0", + versionWithoutPatch: "1.2", + type: "latest", + language: "Python", + releaseNotesConfig: new ReleaseNotesConfig({ enabled: false }), + kebabCaseAndShortenUrls: true, + }); +} + +/** Creates a temp directory, writes the given files into it, returns the path. */ +async function makeTmpAddonDir( + files: Record<string, string>, +): Promise<{ tmpDir: string; addonDir: string }> { + const tmpDir = await mkdtemp(path.join(os.tmpdir(), "addon-toc-test-")); + const addonDir = path.join(tmpDir, "addons", "my-addon"); + await mkdir(addonDir, { recursive: true }); + + for (const [relPath, content] of Object.entries(files)) { + const abs = path.join(addonDir, relPath); + await mkdir(path.dirname(abs), { recursive: true }); + await writeFile(abs, content, "utf-8"); + } + + return { tmpDir, addonDir }; +} + +function mdx(title: string, h1 = title): string { + return `---\ntitle: "${title}"\n---\n\n# ${h1}\n\nSome content.\n`; +} + +function notebook(h1: string): string { + return JSON.stringify({ + cells: [ + { + id: "frontmatter", + cell_type: "markdown", + source: [`---\ntitle: "Frontmatter title"\n---`], + metadata: {}, + outputs: [], + }, + { + id: "content", + cell_type: "markdown", + source: [`# ${h1}\n\nSome content.`], + metadata: {}, + outputs: [], + }, + ], + metadata: {}, + nbformat: 4, + nbformat_minor: 5, + }); +} + +// --------------------------------------------------------------------------- +// Tests +// --------------------------------------------------------------------------- + +test("minimal addon: only index.mdx and install.mdx", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("My Addon home page", "My Addon"), + "install.mdx": mdx("Installation Instructions"), + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + + expect(toc).toEqual({ + parentUrl: "/docs/guides/addons", + parentLabel: "Documentation", + title: "My Addon 1.2.0", + collapsed: true, + children: [ + { + title: "", + collapsible: false, + children: [ + { title: "Home", url: "/docs/addons/my-addon" }, + { + title: "Installation Instructions", + url: "/docs/addons/my-addon/install", + }, + ], + }, + { + title: "API reference", + collapsible: false, + children: [ + { + title: "My Addon API reference", + url: "/docs/api/my-addon", + }, + ], + }, + ], + }); +}); + +test("github link is appended after top-level files when githubSlug is set", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home", "My Addon"), + }); + + const pkg = await makePkg("my-addon", "Qiskit/my-addon"); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const main = toc.children[0]; + + expect(main.children?.at(-1)).toEqual({ + title: "GitHub", + url: "https://github.com/Qiskit/my-addon", + }); +}); + +test("how-tos directory becomes Guides subsection in main section", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + "how-tos/index.mdx": mdx("Guides Index", "How-To Guides"), + "how-tos/alpha.ipynb": notebook("How to do alpha"), + "how-tos/beta.mdx": mdx("How to do beta"), + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const main = toc.children[0]; + + const guidesEntry = main.children?.find((c) => c.title === "Guides"); + expect(guidesEntry).toEqual({ + title: "Guides", + url: "/docs/addons/my-addon/how-tos/index", + children: [ + { + title: "How to do alpha", + url: "/docs/addons/my-addon/how-tos/alpha", + }, + { + title: "How to do beta", + url: "/docs/addons/my-addon/how-tos/beta", + }, + ], + }); +}); + +test("tutorials directory becomes its own top-level section", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + "tutorials/index.mdx": mdx("Tutorials Index", "Tutorials"), + "tutorials/01-intro.ipynb": notebook("Getting started"), + "tutorials/02-advanced.mdx": mdx("Advanced tutorial"), + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + + // tutorials must NOT appear inside the main section + const main = toc.children[0]; + expect(main.children?.some((c) => c.title === "Tutorials")).toBe(false); + + // tutorials must appear as its own top-level section before API reference + const tutorialsSection = toc.children.find((c) => c.title === "Tutorials"); + expect(tutorialsSection).toEqual({ + title: "Tutorials", + collapsible: false, + url: "/docs/addons/my-addon/tutorials/index", + children: [ + { + title: "Getting started", + url: "/docs/addons/my-addon/tutorials/01-intro", + }, + { + title: "Advanced tutorial", + url: "/docs/addons/my-addon/tutorials/02-advanced", + }, + ], + }); +}); + +test("api reference section is always last", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + "tutorials/01.ipynb": notebook("Tutorial one"), + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const last = toc.children.at(-1); + + expect(last?.title).toBe("API reference"); + expect(last?.children?.[0].url).toBe("/docs/api/my-addon"); +}); + +test("unknown subdirectory uses capitalized directory name as section title", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + "references/item.mdx": mdx("Some reference"), + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const main = toc.children[0]; + + const refSection = main.children?.find((c) => c.title === "References"); + expect(refSection).toBeDefined(); + expect(refSection?.children?.[0].title).toBe("Some reference"); +}); + +test("subdir with only index.mdx renders as dropdown with index as child", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + "explanations/index.mdx": mdx("Explanations Index", "Explanatory material"), + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const main = toc.children[0]; + + const explanations = main.children?.find((c) => c.title === "Explanations"); + expect(explanations).toEqual({ + title: "Explanations", + children: [ + { + title: "Explanatory material", + url: "/docs/addons/my-addon/explanations/index", + }, + ], + }); + // No url on the section itself — the index is the child, not a parent link. + expect(explanations?.url).toBeUndefined(); +}); + +test("subdir without index.mdx has no url on the section entry", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + "how-tos/guide.mdx": mdx("A guide"), + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const main = toc.children[0]; + + const guidesEntry = main.children?.find((c) => c.title === "Guides"); + expect(guidesEntry?.url).toBeUndefined(); + expect(guidesEntry?.children).toHaveLength(1); +}); + +test("empty subdirectory is omitted from the TOC", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + // create directory with no content files + }); + await mkdir(path.join(tmpDir, "addons", "my-addon", "empty-dir"), { + recursive: true, + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const main = toc.children[0]; + + expect(main.children?.some((c) => c.title === "Empty-dir")).toBe(false); +}); + +test("notebook title is read from first non-frontmatter markdown cell h1", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + "tutorials/demo.ipynb": notebook("The real title from h1"), + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + + const tutorialsSection = toc.children.find((c) => c.title === "Tutorials"); + expect(tutorialsSection?.children?.[0].title).toBe("The real title from h1"); +}); + +test("notebook with h2 as first heading falls back to h2", async () => { + const nb = JSON.stringify({ + cells: [ + { + id: "frontmatter", + cell_type: "markdown", + source: [`---\ntitle: "FM"\n---`], + metadata: {}, + outputs: [], + }, + { + id: "content", + cell_type: "markdown", + source: [`## Section heading\n\nContent.`], + metadata: {}, + outputs: [], + }, + ], + metadata: {}, + nbformat: 4, + nbformat_minor: 5, + }); + + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + "tutorials/demo.ipynb": nb, + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + + const tutorialsSection = toc.children.find((c) => c.title === "Tutorials"); + expect(tutorialsSection?.children?.[0].title).toBe("Section heading"); +}); + +test("mdx h2 fallback when no h1 present", async () => { + const content = `---\ntitle: "FM"\n---\n\n## Only an h2 here\n\nContent.\n`; + + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + "how-tos/guide.mdx": content, + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const main = toc.children[0]; + + const guides = main.children?.find((c) => c.title === "Guides"); + expect(guides?.children?.[0].title).toBe("Only an h2 here"); +}); + +test("content files in subdir are sorted alphabetically by filename", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + "how-tos/index.mdx": mdx("Guides"), + "how-tos/03-third.mdx": mdx("Third guide"), + "how-tos/01-first.mdx": mdx("First guide"), + "how-tos/02-second.mdx": mdx("Second guide"), + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const main = toc.children[0]; + + const guides = main.children?.find((c) => c.title === "Guides"); + expect(guides?.children?.map((c) => c.title)).toEqual([ + "First guide", + "Second guide", + "Third guide", + ]); +}); diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts new file mode 100644 index 00000000000..a865bae5c06 --- /dev/null +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -0,0 +1,216 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2026. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +import { readFile } from "fs/promises"; +import { join } from "path/posix"; + +import { globby } from "globby"; + +import { Pkg } from "./Pkg.js"; +import { TocEntry } from "./generateToc.js"; +import { DOCS_BASE_PATH } from "./paths.js"; +import { capitalize } from "lodash-es"; + +// Subdirectories that become their own top-level TOC sections rather than +// being nested inside the main (unlabeled) section. +const TOP_LEVEL_SECTIONS = new Set(["tutorials"]); + +// labels for known subdirectory names. +const DIR_LABELS: Record<string, string> = { + "how-tos": "Guides", +}; + +type AddonTocSection = TocEntry & { collapsible?: boolean }; + +type AddonToc = { + parentUrl: string; + parentLabel: string; + title: string; + collapsed: true; + children: AddonTocSection[]; +}; + +export async function generateAddonToc( + pkg: Pkg, + docsBaseFolder: string, +): Promise<AddonToc> { + const addonDocsPath = pkg.outputDir(docsBaseFolder); + const addonUrlBase = `${DOCS_BASE_PATH}/addons/${pkg.name}`; + + // Discover top-level content files (e.g. index.mdx, install.mdx). + const topLevelFiles = await globby(["*.mdx", "*.ipynb"], { + cwd: addonDocsPath, + }); + + // Discover top-level subdirectories that contain content. + const subdirNames = await globby(["*/"], { + cwd: addonDocsPath, + onlyDirectories: true, + }).then((dirs) => dirs.map((d) => d.replace(/\/$/, "")).sort()); + + const mainChildren: TocEntry[] = []; + const topLevelSections: AddonTocSection[] = []; + + // Top-level files → entries in the main section, in filename order. + for (const file of topLevelFiles.sort()) { + const slug = file.replace(/\.(mdx|ipynb)$/, ""); + const url = + slug === "index" ? addonUrlBase : `${addonUrlBase}/${slug}`; + const title = + slug === "index" ? "Home" : await readTitle(join(addonDocsPath, file)); + mainChildren.push({ title, url }); + } + + // Subdirectories → either nested subsections or top-level sections. + for (const dir of subdirNames) { + const entry = await buildSubdirEntry(dir, addonDocsPath, addonUrlBase); + if (!entry) continue; + + if (TOP_LEVEL_SECTIONS.has(dir)) { + topLevelSections.push({ ...entry, collapsible: false }); + } else { + mainChildren.push(entry); + } + } + + if (pkg.githubSlug) { + mainChildren.push({ + title: "GitHub", + url: `https://github.com/${pkg.githubSlug}`, + }); + } + + const mainSection: AddonTocSection = { + title: "", + children: mainChildren, + collapsible: false, + }; + + topLevelSections.push({ + title: "API reference", + collapsible: false, + children: [ + { + title: `${pkg.title} API reference`, + url: `${DOCS_BASE_PATH}/api/${pkg.name}`, + }, + ], + }); + + return { + parentUrl: "/docs/guides/addons", + parentLabel: "Documentation", + title: `${pkg.title} ${pkg.version}`, + collapsed: true, + children: [mainSection, ...topLevelSections], + }; +} + +/** + * Builds a TocEntry for a subdirectory. + * + * The section title uses the known label for the directory name, or falls back + * to a capitalized version of the directory name. Content files (non-index) + * become children, sorted by filename, with titles read from their h1 headings. + * If an index.mdx exists, the section entry gets a url pointing to it. + * Returns undefined if the directory has no content. + */ +async function buildSubdirEntry( + dirName: string, + addonDocsPath: string, + addonUrlBase: string, +): Promise<TocEntry | undefined> { + const dirPath = join(addonDocsPath, dirName); + const files = await globby(["*.mdx", "*.ipynb"], { cwd: dirPath }).catch( + (): string[] => [], + ); + if (files.length === 0) return undefined; + + const hasIndex = files.includes("index.mdx"); + const contentFiles = files.filter((f) => f !== "index.mdx").sort(); + + const sectionTitle = DIR_LABELS[dirName] ?? capitalize(dirName); + + const children: TocEntry[] = await Promise.all( + contentFiles.map(async (file) => { + const slug = file.replace(/\.(mdx|ipynb)$/, ""); + const title = await readTitle(join(dirPath, file)); + return { title, url: `${addonUrlBase}/${dirName}/${slug}` }; + }), + ); + + // When only an index page exists, include it as a child so the section + // always renders as a dropdown rather than a bare link. + if (hasIndex && children.length === 0) { + const title = await readTitle(join(dirPath, "index.mdx")); + children.push({ title, url: `${addonUrlBase}/${dirName}/index` }); + } + + const entry: TocEntry = { title: sectionTitle, children }; + if (hasIndex && contentFiles.length > 0) { + entry.url = `${addonUrlBase}/${dirName}/index`; + } + return entry; +} + +/** + * Reads the first h1 (or h2 as fallback) from an .mdx or .ipynb file. + * Falls back to the filename stem if no heading is found. + */ +async function readTitle(filePath: string): Promise<string> { + const content = await readFile(filePath, "utf-8"); + + if (filePath.endsWith(".ipynb")) { + return readNotebookTitle(content, filePath); + } + return readMdxTitle(content, filePath); +} + +function readMdxTitle(content: string, filePath: string): string { + const withoutFrontmatter = content.replace(/^---\n[\s\S]*?\n---\n/, ""); + // Try h1 first, fall back to h2. + const h1 = withoutFrontmatter.match(/^#\s+(.+)$/m); + if (h1) return h1[1].trim(); + const h2 = withoutFrontmatter.match(/^##\s+(.+)$/m); + if (h2) return h2[1].trim(); + return fileBasename(filePath); +} + +function readNotebookTitle(content: string, filePath: string): string { + let notebook: { cells: Array<{ id?: string; cell_type: string; source: string[] }> }; + try { + notebook = JSON.parse(content); + } catch { + return fileBasename(filePath); + } + + for (const cell of notebook.cells) { + if (cell.cell_type !== "markdown") continue; + // Skip the frontmatter cell. + if (cell.id === "frontmatter") continue; + + const src = Array.isArray(cell.source) + ? cell.source.join("") + : String(cell.source); + + const h1 = src.match(/^#\s+(.+)$/m); + if (h1) return h1[1].trim(); + const h2 = src.match(/^##\s+(.+)$/m); + if (h2) return h2[1].trim(); + } + + return fileBasename(filePath); +} + +function fileBasename(filePath: string): string { + return filePath.split("/").pop()?.replace(/\.(mdx|ipynb)$/, "") ?? filePath; +} From c456157301494b8d9bb2fb8b58cf2ea26772918a Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Mon, 18 May 2026 14:46:38 -0400 Subject: [PATCH 065/104] map explanations to how-tos --- .../how-tos/explanations-index.mdx | 86 +++++ .../how-tos/explanations-index.mdx | 146 ++++++++ .../how-tos/explanations-index.mdx | 11 + .../how-tos/explanations-mpf-stability.ipynb | 318 ++++++++++++++++++ .../addonDocsPipeline.test.ts-snapshots/-toc | 55 +++ scripts/js/lib/api/addonDocsPipeline.ts | 19 +- scripts/js/lib/api/specialCaseResults.ts | 1 - 7 files changed, 633 insertions(+), 3 deletions(-) create mode 100644 docs/addons/qiskit-addon-aqc-tensor/how-tos/explanations-index.mdx create mode 100644 docs/addons/qiskit-addon-cutting/how-tos/explanations-index.mdx create mode 100644 docs/addons/qiskit-addon-mpf/how-tos/explanations-index.mdx create mode 100644 docs/addons/qiskit-addon-mpf/how-tos/explanations-mpf-stability.ipynb create mode 100644 scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/-toc diff --git a/docs/addons/qiskit-addon-aqc-tensor/how-tos/explanations-index.mdx b/docs/addons/qiskit-addon-aqc-tensor/how-tos/explanations-index.mdx new file mode 100644 index 00000000000..f989758a541 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/how-tos/explanations-index.mdx @@ -0,0 +1,86 @@ +--- +title: "Explanatory Material on AQC-Tensor" +description: "Explanatory Material on AQC-Tensor for the lastest version of Approximate quantum compilation (AQC-Tensor)" +--- + +<span id="explanation" /> + +# Explanatory Material on AQC-Tensor + +## Background + +This Qiskit addon allows one to compress the *initial portion* of a circuit using Approximate Quantum Compilation (AQC) with tensor networks, following the method introduced by Ref. <span id="id1" />[\[1\]](#id4). + +In the following, we will assume that the input circuit has already been truncated such that its entire depth can be simulated by a tensor network with a reasonable bond dimension. In order for AQC to be useful on quantum hardware, one must use the resulting circuit as a state preparation procedure and then perform subsequent quantum computation on that state in a way that is beyond the reach of a tensor network simulator alone. + +In essence, AQC-Tensor requires three things as input: + +1. A description of the **target state** in the form of a tensor network. This may be generated by simulating a circuit on a tensor network simulator (this is what Ref. <span id="id2" />[\[1\]](#id4) did), or it could be generated in some other way, e.g., by performing time-dependent variational principle (TDVP) time evolution directly on a matrix-product state. +2. A parametrized **ansatz circuit**, ideally with hardware-efficient connectivity, such that it will have a reasonable depth on the target hardware. +3. **Initial parameters** to plug into the ansatz circuit, such that the resulting state is already a *good* approximation to the target state. + +The technique is to then use an iterative optimization method to make the *good* state as close to the target state as possible. + +\[The third item is not required *in principle*, but it really helps to give the optimizer a sensible starting point; otherwise, it is much more likely to get stuck in a local (but not global) minimum.] + +## Ansatz generation (motivation) + +As mentioned in the previous section, three closely related things are needed as input to AQC-Tensor. Rather than code all three individually, this package provides a [`generate_ansatz_from_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") function that can generate (2) and (3) from a circuit. In the case of Trotter evolution (and likely other circuit classes of interest), one can use the same circuit-generation function to generate (1) as well as the input to [`generate_ansatz_from_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"), thus enabling the generation of all three inputs from a single function. The tutorial is a good demonstration of this re-use of a single circuit-generation function. + +Specifically, [`generate_ansatz_from_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") will take an input circuit and generate the ansatz circuit (2) and initial parameters (3) that follow the two-qubit connectivity of the input circuit. However, the returned circuit will be parametrized, based on generalized one- and two-qubit rotations. The initial parameters returned by the function, when plugged into the ansatz, will result in a circuit that is exactly equivalent to the circuit passed as input, up to a global phase. + +Given the ansatz generation function, the user must provide only two things in order to use AQC-Tensor: + +1. The **target state**, i.e., a *great* description of the desired state; and, +2. A **circuit** with the desired connectivity of the ansatz that prepares a *good* approximation to the target state. + +The optimization procedure then takes the *good* ansatz parameters and brings them as close as possible to the *great* description (the target state). + +In the case of a Trotter circuit, the same function can actually provide both things. For instance, the `spinhalf_trotter_circuit` function could be called with `num_trotter_steps=50` to produce the target state, then again with `num_trotter_steps=5` to produce the ansatz structure to be passed to `generate_ansatz_from_circuit`. (Note that the `evolution_time` is fixed, such that the time step `dt = evolution_time / num_trotter_steps`. the time per single Trotter step as `dt = evolution_time / num_trotter_steps`, so these two function calls would leave the total `evolution_time` fixed, while changing the time per Trotter step, `dt`.) So, in order for a user to apply AQC-Tensor to a new physical model of Trotter circuit, they would only need to generalize a single function with the details of that model. + +The ansatz used by `generate_ansatz_from_circuit` uses 9 parameters per two-qubit block, which is the theoretical minimum (see, e.g., Fig. 2 of [https://arxiv.org/abs/quant-ph/0308033](https://arxiv.org/abs/quant-ph/0308033)). The automatic ansatz is based on the [KAK decomposition](https://qiskit-extensions.github.io/circuit-knitting-toolbox/circuit_cutting/explanation/index.html#more-general-cut-two-qubit-gates-via-the-kak-decomposition), which parametrizes any two-qubit gate in terms of three parameters, up to single-qubit rotations. The single-qubit rotation are then decomposed as ZXZ, each of which has three parameters. So, each two-qubit block of the original circuit will result in 3 parameters for the two-qubit rotation, plus 3 parameters for an outgoing single-qubit rotation on each of the two qubits, for a total of 9 parameters per block. The ansatz is completed by adding a layer of single-qubit rotations to each active qubit at the start of the circuit. + +## Tensor-network simulation + +The simplest tensor network is a matrix-product state (MPS). + +Currently, AQC-Tensor supports the following tensor-network simulators: + +* Qiskit Aer’s MPS simulator +* Quimb’s [eager](https://quimb.readthedocs.io/en/latest/tensor/tensor-circuit-mps.html) [`CircuitMPS`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.CircuitMPS) simulator +* Quimb’s [lazy](https://quimb.readthedocs.io/en/latest/tensor/tensor-circuit.html) [`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.Circuit) simulator (may work only on small circuits so far; we’re working to fix this soon with more clever contractions) + +The most important parameter of a tensor network is its maximum bond dimension, which limits how much entanglement it can represent (and thus to what depth a given circuit can be faithfully simulated). The bond dimension is often represented by the Greek letter $\chi$. + +Given a general circuit on $L$ qubits, a matrix-product state needs at most a bond dimension of $\chi_\mathrm{exact} = 2^{\lfloor L/2 \rfloor}$ in order to be able to simulate it to *any* depth. Of course, general circuits on 100+ qubits cannot be classically simulated, so it will be intractable to set the bond dimension this high for those circuits. + +For this reason, if you are attempting to experiment with AQC-Tensor on a toy problem with few qubits, it is important to ensure that $\chi < 2^{\lfloor L/2 \rfloor}$. Otherwise, any circuit can be simulated to any depth, and there is no point in performing AQC. + +### Objective function + +Currently, this addon provides one very simple objective, [`MaximizeStateFidelity`](/docs/api/qiskit-addon-aqc-tensor/objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity"), whose [`loss_function()`](/docs/api/qiskit-addon-aqc-tensor/objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") is equivalent to Eq. (7) in Ref. <span id="id3" />[\[1\]](#id4). When called with an array of parameters, the [`loss_function()`](/docs/api/qiskit-addon-aqc-tensor/objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") method returns the value and gradient of the objective function as a 2-tuple. + +### Gradient + +This package provides a few different methods for calculating the gradient. + +The Aer backend always uses the explicit gradient code in the `explicit_gradient` module. + +The Quimb backend will typically be used with an automatic differentiation backend; the user is to select a backend from among those supported by Quimb. Alternatively, one can instead pass `"explicit"` as the `autodiff_backend` when instantiating the [`QuimbSimulator`](/docs/api/qiskit-addon-aqc-tensor/simulation-quimb-quimb-simulator#qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator "qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator") object; in this case, the `explicit_gradient` module will be used. It is only recommended to use explicit gradients with Quimb’s eager [`CircuitMPS`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.CircuitMPS) simulator, not the lazy [`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.Circuit) simulator. + +Regardless of which backend is chosen, the gradient code can understand linear parameter expressions (ParameterExpression objects). This support is essential, as linear expressions are returned by the ansatz generation code. + +### Optimization method + +Users are encouraged to use [`scipy.optimize`](https://docs.scipy.org/doc/scipy/reference/optimize.html#module-scipy.optimize "(in SciPy v1.17.0)") to perform the optimization. + +L-BFGS is the optimizer demonstrated in the tutorial notebook. It works well in practice because it uses the function value and its gradient to approximate the Hessian. It works well when given an initial point and seems to work particularly well in the case of Trotter circuits. However, it might early terminate if it starts in a barren plateau. In that case, performing a handful of steps using the ADAM optimizer first may help. + +## References + +<span id="id4" /> + +\[1] ([1](#id1),[2](#id2),[3](#id3)) + +[arXiv:2301.08609v6](https://arxiv.org/abs/2301.08609v6). + diff --git a/docs/addons/qiskit-addon-cutting/how-tos/explanations-index.mdx b/docs/addons/qiskit-addon-cutting/how-tos/explanations-index.mdx new file mode 100644 index 00000000000..052a80a80c3 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/explanations-index.mdx @@ -0,0 +1,146 @@ +--- +title: "Explanatory material" +description: "Explanatory material for the lastest version of Circuit cutting" +--- + +<span id="circuit-cutting-explanation" /> + +# Explanatory material + +## Overview of circuit cutting + +Circuit cutting is a technique to increase the size of circuits we can run on quantum hardware at the cost of an additional sampling overhead. A larger quantum circuit can be decomposed by cutting its gates and/or wires, resulting in smaller circuits which can be executed within the constraints of available quantum hardware. The results of these smaller circuits are combined to reconstruct the outcome of the original problem. Circuit cutting can also be used to engineer gates between distant qubits which would otherwise require a large swap overhead. + +## Key terms + +* subcircuits: The set of circuits resulting from cutting gates in a [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) and then separating the disconnected qubit subsets into smaller circuits. These circuits contain [`SingleQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-single-qubit-qpd-gate#qiskit_addon_cutting.qpd.SingleQubitQPDGate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s and will be used to instantiate each unique subexperiment. +* subexperiments: A term used to describe the unique circuit samples associated with a subcircuit. These circuits have had their [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate")s decomposed into local Qiskit gates and measurements. Subexperiments are the circuits sent to the backend for execution. +* decompose: We try to honor the Qiskit notion of “decompose” in the documentation and API, which loosely means transforming a gate into a less-abstracted representation. *Occasionally*, we may use the term “decompose” to refer to the act of inserting [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate") instances into quantum circuits as “decomposing” a gate or wire; however, we try to use terms like “partition” and “cut” when referring to this to avoid ambiguity with Qiskit language. + +## Circuit cutting as a quasiprobability decomposition (QPD) + +Quasiprobability decomposition is a technique which can be used to simulate quantum circuit executions that go beyond the actual capabilities of current quantum hardware while using that same hardware. It forms the basis of many error mitigation techniques, which allow simulating a noise-free quantum computer using a noisy one. Circuit cutting techniques, which allow simulating a quantum circuit using fewer qubits than would otherwise be necessary, can also be phrased in terms of a quasiprobability decomposition. No matter the goal, the cost of the quasiprobability decomposition is an exponential overhead in the number of circuit executions which must be performed. In certain cases, this tradeoff is worth it, because it can allow the estimation of quantities that would otherwise be impossible on today’s hardware. + +There are two types of cuts: gate cuts and wire cuts. Gate cuts, also known as “space-like” cuts, exist when the cut goes through a gate operating on two (or more) qubits. Wire cuts, also known as “time-like” cuts, are direct cuts through a qubit wire, essentially a single-qubit identity gate that has been cut into two pieces. In this package, a wire cut is represented by introducing a new qubit into the circuit and moving remaining operations after the cut identity gate to the new qubit; see [Wire cutting phrased as a two-qubit Move operation](#wire-cutting-as-move), below. + +There are [three settings](https://research.ibm.com/blog/circuit-knitting-with-classical-communication) to consider for circuit cutting. The first is where only local operations (LO) \[i.e., local *quantum* operations] are available. The other settings introduce classical communication between the circuit executions, which is known in the quantum information literature as LOCC, for [local operations and classical communication](https://en.wikipedia.org/wiki/LOCC). The LOCC can be either near-time, one-directional communication between the circuit executions (the second setting), or real-time, bi-directional communication (the third setting). + +As mentioned above, the cost of any simulation based on quasiprobability distribution is an exponential sampling overhead. The sampling overhead is the factor by which the overall number of shots must increase for the quasiprobability decomposition to result in the same amount of error, $\epsilon$, as one would get by executing the original circuit. The overhead of a cut gate depends on which gate is cut; see the final appendix of \[[1](https://arxiv.org/abs/2205.00016)] for details. For instance, a single cut CNOT gate incurs a sampling overhead of 9 \[[2](https://arxiv.org/abs/1909.07534),[6](https://arxiv.org/abs/2312.11638)]. A circuit with $n$ wire cuts incurs a sampling overhead of O($16^n$) when classical communication is not available (LO setting); this is reduced to O($4^n$) when classical communication is available (LOCC setting) \[[4](https://arxiv.org/abs/2302.03366)]. However, wire cutting with classical communication (LOCC) is not yet supported by this package (see issue [#264](https://github.com/Qiskit/qiskit-addon-cutting/issues/264)). + +The QPD can be given explicitly as follows: + +$$ +\mathcal{U} = \sum_i a_i \mathcal{F}_i , +$$ + +where $\mathcal{U}$ is the channel implementing the desired operation, and each $a_i$ is a real coefficient corresponding to a quantum channel, $\mathcal{F}_i$, that is realizable on hardware. + +Note that because this is a sum of channels, *not* a sum of unitaries, expectation values can be evaluated efficiently \[[2](https://arxiv.org/abs/1909.07534)]. + +Results equivalent to original unitary channel can be obtained by a post-processing method, given the coefficients $a_i$ and the outcome of each experiment corresponding to the different local ($\mathcal{F}_i$) channels. This post-processing boosts the magnitude of each measurement outcome by $\sum_i \left| a_i \right|$, resulting in a sampling overhead of that quantity squared \[[6](https://arxiv.org/abs/2312.11638)]: + +$$ +\mathrm{Sampling\ overhead} = \left( \sum_i \lvert a_i \rvert \right)^2 . +$$ + +For more detailed information on the quasiprobability decomposition technique, refer to the paper, Error mitigation for short-depth quantum circuits \[[5](https://arxiv.org/abs/1612.02058)]. + +The essential idea of gate cutting is to replace a two-qubit gate with a linear combination of quantum channels \[Eq. [(1)](#equation-eq-qpd)] that, when recombined, will allow reconstruction of the result for physically measurable quantities like expectation values. Note that “global phase” is not something that can be physically measured, so we can disregard it when specifying quasiprobability decompositions. + +<span id="an-example-cutting-a-rzzgate" /> + +## An example: cutting a [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) + +As a basic and explicit example, let us consider the decomposition of a cut [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate). + +As shown in \[[2](https://arxiv.org/abs/1909.07534)], a quantum circuit which implements an [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) can be simulated by performing six subexperiments where the [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) in the original circuit has been replaced with only local (single-qubit) operations \[the $\mathcal{F}_i$‘s in Eq. [(1)](#equation-eq-qpd)]. The result is then reconstructed by combining the subexperiment results with certain coefficients \[the $a_i$‘s in Eq. [(1)](#equation-eq-qpd)], which can be either positive or negative. Given the $\theta$ parameter of the [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), the six subexperiments are as follows: + +1. With coefficient $a_1 = \cos^2 (\theta/2)$, do nothing ($I \otimes I$, where $I$ is the identity operation on a single qubit). +2. With coefficient $a_2 = \sin^2 (\theta/2)$, perform a [`ZGate`](/docs/api/qiskit/qiskit.circuit.library.ZGate) on each qubit ($Z \otimes Z$). +3. With coefficient $a_3 = -\sin(\theta)/2$, perform a projective measurement in the Z basis on the first qubit and an [`SGate`](/docs/api/qiskit/qiskit.circuit.library.SGate) gate on the second qubit (denote this as $M_z \otimes S$). If the result of the measurement is 1, flip the sign of that outcome’s contribution during reconstruction. +4. With coefficient $a_4 = \sin(\theta)/2$, perform a projective measurement in the Z basis on the first qubit and an [`SdgGate`](/docs/api/qiskit/qiskit.circuit.library.SdgGate) gate on the second qubit (denote this as $M_z \otimes S^\dagger$). If the result of the measurement is 1, flip the sign of that outcome’s contribution during reconstruction. +5. Same as term 3 ($a_5 = a_3$), but swap the qubits ($S \otimes M_z$). +6. Same as term 4 ($a_6 = a_4$), but swap the qubits ($S^\dagger \otimes M_z$). + +Equation [(1)](#equation-eq-qpd) for [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) can thus be written as a sum of the six terms listed above. The following plot shows the magnitude of each coeffient (negative coefficients are in orange) as a function of $\theta$. The square root of the optimal sampling overhead, denoted by $\gamma$, is given by the sum of the absolute coefficients. + +![../\_images/index-1.svg](/docs/images/addons/qiskit-addon-cutting/index-1.svg) + +Let’s consider some special points in this plot: + +* When $\theta = 0$, the gate has no effect, and the sampling overhead is 1. (Because the overhead is multiplicative, this is equivalent to there being no overhead.) +* When $\theta = \pi$, the gate is equivalent to $Z \otimes Z$ up to a global phase, and the sampling overhead is again 1. +* The maximum sampling overhead of $3^2 = 9$ is reached at a ZZ Rotation of $\theta=\pi/2$. We call this point the [`CXGate`](/docs/api/qiskit/qiskit.circuit.library.CXGate) family because this rotation is equivalent to a CXGate up to (single-qubit) local unitary operations. This point is also equivalent, up to local unitary operations, to [`CZGate`](/docs/api/qiskit/qiskit.circuit.library.CZGate), [`CYGate`](/docs/api/qiskit/qiskit.circuit.library.CYGate), [`CHGate`](/docs/api/qiskit/qiskit.circuit.library.CHGate), and [`ECRGate`](/docs/api/qiskit/qiskit.circuit.library.ECRGate). +* The ZZ rotation at $\theta=\pi/4$ has sampling overhead of $3+2\sqrt{2} \approx 5.828$. We call this the [`CSGate`](/docs/api/qiskit/qiskit.circuit.library.CSGate) family because this rotation is equivalent to a CSGate up to (single-qubit) local operations. This family also includes [`CSdgGate`](/docs/api/qiskit/qiskit.circuit.library.CSdgGate) and [`CSXGate`](/docs/api/qiskit/qiskit.circuit.library.CSXGate). +* Likewise, [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), and [`RZXGate`](/docs/api/qiskit/qiskit.circuit.library.RZXGate) are all locally equivalent to [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate). The controlled rotations [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate), and [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) at an angle of $2\theta$ are locally equivalent to [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) at an angle of $\theta$. + +## More general cut two-qubit gates via the KAK decomposition + +We can formalize this notion of local unitary equivalence and expand it to all two-qubit gates using the KAK decomposition, given by + +$$ +U = (V_1 \otimes V_2) \exp \left[ i \left( \theta_x \, X \otimes X + \theta_y \, Y \otimes Y + \theta_z \, Z \otimes Z \right) \right] (V_3 \otimes V_4) , +$$ + +where $V_1$, $V_2$, $V_3$, and $V_4$ are local, single-qubit operations, and the two-qubit portion of the interaction is parametrized entirely by $\vec{\theta} = (\theta_x, \theta_y, \theta_z)$. By convention, we have chosen $\vec{\theta}$ to be in the “Weyl chamber” restricted by $\pi/4 \geq \theta_x \geq \theta_y \geq | \theta_z | \geq 0$ \[[6](https://arxiv.org/abs/2312.11638)]. For more information on the KAK decomposition, see Ref. \[[7](https://arxiv.org/abs/quant-ph/0209120)]. + +The code that generates subexperiments from the KAK decomposition currently follows Ref. \[[3](https://arxiv.org/abs/2006.11174)], which is now known to be non-optimal. A provably optimal method has been presented in Ref. \[[6](https://arxiv.org/abs/2312.11638)], but this newer method has not yet been implemented in this package (see issue [#531](https://github.com/Qiskit/qiskit-addon-cutting/issues/531)). + +<span id="wire-cutting-phrased-as-a-two-qubit-move-operation" /> + +<span id="wire-cutting-as-move" /> + +## Wire cutting phrased as a two-qubit [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") operation + +A wire cut is represented fundamentally by this package as a two-qubit [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction, which is defined as a reset of the second qubit followed by a swap of both qubits. Equivalently, the operation is defined as transferring the state of the first qubit wire to the second qubit wire, while simultaneously discarding the state of the second qubit wire (the first qubit ends up in state $\lvert 0 \rangle$). + +We have chosen to represent wire cuts in this way primarily because it is consistent with the way one must treat wire cuts when acting on physical qubits: for instance, a wire cut might take the state of physical qubit $n$ and continue it as physical qubit $m$ after the cut. Our choice also has the benefit of allowing us to think of “instruction cutting” as a unified framework for considering both wire cuts and gate cuts in the same formalism, being that a wire cut is just a cut [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction. + +More information on this formalism is given in Sec. 3 of Ref. \[[4](https://arxiv.org/abs/2302.03366)] + +If you prefer to place cut wires abstractly on a single qubit wire, please see the [how-to guide on placing wire cuts using a single-qubit instruction](how-tos/how-to-specify-cut-wires), which explains how to use the [`cut_wires()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#qiskit_addon_cutting.cut_wires "qiskit_addon_cutting.cut_wires") function to convert a circuit with [`CutWire`](/docs/api/qiskit-addon-cutting/instructions-cut-wire#qiskit_addon_cutting.instructions.CutWire "qiskit_addon_cutting.instructions.CutWire") instructions to a circuit with [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move")s on additional qubits. + +## Sample weights in the Qiskit addon for circuit cutting + +In this package, the number of samples taken from the distribution is generally controlled by a `num_samples` argument, and each sample has an associated weight which is used during expectation value reconstruction. Each weight with absolute value above a threshold of 1 / `num_samples` will be evaluated exactly. The remaining low-probability elements – those in the tail of the distribution – will then be sampled, resulting in at most `num_samples` unique weights. Setting `num_samples` to infinity indicates that all weights should be generated rigorously, rather than by sampling from the distribution. + +Much of the circuit cutting literature describes a process where we sample from the distribution, take a single shot, then sample from the distribution again and repeat; however, this is not feasible in practice, so we instead perform all sampling upfront. For now, because of limitations in version 1 of the Qiskit primitives, we take a fixed number of shots for each considered subexperiment and send the subexperiments to the backend(s) in batches. During reconstruction, each subexperiment contributes to the final result with proportion equal to its weight. One must ensure the number of shots taken is sufficient for the heaviest weighted subexperiment. In the future, we plan to support passing an individual `shots` count with each subexperiment to Qiskit Runtime, so that each subexperiment will be run with a number of shots proportional to that subexperiment’s weight in the final result (see issue [#532](https://github.com/Qiskit/qiskit-addon-cutting/issues/532)). This per-experiment shots count is a new feature enabled by version 2 of the Qiskit primitives. + +## Sampling overhead reference table + +The below table provides the sampling overhead factor for a variety of two-qubit instructions, provided that only a single instruction is cut. + +| Instruction(s) | KAK decomposition angles | Sampling overhead factor | | | | | | | +| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------ | --------------------------- | --------- | -------------------- | -------------------- | ----------------- | -------------------- | -------------------------------------------------------------- | +| [`CSGate`](/docs/api/qiskit/qiskit.circuit.library.CSGate), [`CSdgGate`](/docs/api/qiskit/qiskit.circuit.library.CSdgGate), [`CSXGate`](/docs/api/qiskit/qiskit.circuit.library.CSXGate) | $(\pi/8, 0, 0)$ | $3+2\sqrt{2} \approx 5.828$ | | | | | | | +| [`CXGate`](/docs/api/qiskit/qiskit.circuit.library.CXGate), [`CYGate`](/docs/api/qiskit/qiskit.circuit.library.CYGate), [`CZGate`](/docs/api/qiskit/qiskit.circuit.library.CZGate), [`CHGate`](/docs/api/qiskit/qiskit.circuit.library.CHGate), [`ECRGate`](/docs/api/qiskit/qiskit.circuit.library.ECRGate) | $(\pi/4, 0, 0)$ | $3^2=9$ | | | | | | | +| [`iSwapGate`](/docs/api/qiskit/qiskit.circuit.library.iSwapGate), [`DCXGate`](/docs/api/qiskit/qiskit.circuit.library.DCXGate) | $(\pi/4, \pi/4, 0)$ | $7^2=49$ | | | | | | | +| [`SwapGate`](/docs/api/qiskit/qiskit.circuit.library.SwapGate) | $(\pi/4,\pi/4,\pi/4)$ | | | | | | | | +| [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), [`RZXGate`](/docs/api/qiskit/qiskit.circuit.library.RZXGate) | \$( | \theta/2 | , 0, 0)\$ | \$\left\[1 + 2 \left | \sin(\theta)\right | \right]^2\$ | | | +| [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate), [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) | \$( | \theta/4 | , 0, 0)\$ | \$\left\[1 + 2 \left | \sin(\theta/2)\right | \right]^2\$ | | | +| [`XXPlusYYGate`](/docs/api/qiskit/qiskit.circuit.library.XXPlusYYGate), [`XXMinusYYGate`](/docs/api/qiskit/qiskit.circuit.library.XXMinusYYGate) | \$( | \theta/4 | , | \theta/4 | ,0)\$ | \$\left\[1+4\left | \sin(\theta/2)\right | +2\sin^2(\theta/2)\right]^2$(independent of$\beta\$ parameter) | +| [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") (cut wire) without classical communication (i.e., in the LO setting) | not applicable | $4^2=16$ | | | | | | | + +## Current limitations + +* The workflow only allows taking the *expectation value* of observables with respect to a circuit. Limited support for reconstructing an output probability distribution may be added to a future version of this package (see issue [#259](https://github.com/Qiskit/qiskit-addon-cutting/issues/259)). +* Due to current code limitations, some of the generated subexperiments are redundant. This can result in more subexperiments than expected, particularly when using wire cutting. This is tracked by issue [#262](https://github.com/Qiskit/qiskit-addon-cutting/issues/262). + +## References + +This module is based on the theory described in the following papers: + +\[1] Christophe Piveteau, David Sutter, *Circuit knitting with classical communication*, [https://arxiv.org/abs/2205.00016](https://arxiv.org/abs/2205.00016) + +\[2] Kosuke Mitarai, Keisuke Fujii, *Constructing a virtual two-qubit gate by sampling single-qubit operations*, [https://arxiv.org/abs/1909.07534](https://arxiv.org/abs/1909.07534) + +\[3] Kosuke Mitarai, Keisuke Fujii, *Overhead for simulating a non-local channel with local channels by quasiprobability sampling*, [https://arxiv.org/abs/2006.11174](https://arxiv.org/abs/2006.11174) + +\[4] Lukas Brenner, Christophe Piveteau, David Sutter, *Optimal wire cutting with classical communication*, [https://arxiv.org/abs/2302.03366](https://arxiv.org/abs/2302.03366) + +\[5] K. Temme, S. Bravyi, and J. M. Gambetta, *Error mitigation for short-depth quantum circuits*, [https://arxiv.org/abs/1612.02058](https://arxiv.org/abs/1612.02058) + +\[6] Lukas Schmitt, Christophe Piveteau, David Sutter, *Cutting circuits with multiple two-qubit unitaries*, [https://arxiv.org/abs/2312.11638](https://arxiv.org/abs/2312.11638) + +\[7] Jun Zhang, Jiri Vala, K. Birgitta Whaley, Shankar Sastry, *A geometric theory of non-local two-qubit operations*, [https://arxiv.org/abs/quant-ph/0209120](https://arxiv.org/abs/quant-ph/0209120) + diff --git a/docs/addons/qiskit-addon-mpf/how-tos/explanations-index.mdx b/docs/addons/qiskit-addon-mpf/how-tos/explanations-index.mdx new file mode 100644 index 00000000000..20d11fba7b0 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/how-tos/explanations-index.mdx @@ -0,0 +1,11 @@ +--- +title: "Explanations" +description: "Explanations for the lastest version of Multi-product formulas (MPF)" +--- + +# Explanations + +This page summarizes additional explanatory content. + +* [Understanding the stability of MPFs](mpf-stability) + diff --git a/docs/addons/qiskit-addon-mpf/how-tos/explanations-mpf-stability.ipynb b/docs/addons/qiskit-addon-mpf/how-tos/explanations-mpf-stability.ipynb new file mode 100644 index 00000000000..c6c073e4c8b --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/how-tos/explanations-mpf-stability.ipynb @@ -0,0 +1,318 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Understanding the stability of MPFs\"\n", + "description: \"Understanding the stability of MPFs for the latest version of Multi-product formulas (MPF)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "4aaf8397-4140-4d16-b4b5-1d9e20adea98", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Understanding the stability of MPFs\n", + "\n", + "Over in the guide [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps) we introduced a heuristic that limits the **smallest** $k_j$ value ($k_{\\text{min}}$) based on the total evolution time, $t$, that we would like to reach.\n", + "In particular, we state that $t/k_{\\text{min}} \\lt 1$ must be satisfied (noting that $t/k_{\\text{min}} \\leq 1$ empirically seems to work fine, too).\n", + "\n", + "On this page, we will analyze when the behavior of MPFs is not guaranteed to work well, such as when this constraint gets violated." + ] + }, + { + "cell_type": "markdown", + "id": "986a58b5-611a-4400-9bd5-ba35d4a14188", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Setting up a simple model problem\n", + "\n", + "For this simple example, we will reuse the model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial; the Ising model on a line of 10 sites:\n", + "\n", + "$$\n", + "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", + "$$\n", + "\n", + "where $J$ is the coupling strength between two sites and $h$ is the external magnetic field." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d77e337b-0a69-49a5-b449-ffe4ad17d7ab", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'],\n", + " coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j,\n", + " 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j,\n", + " 0.4+0.j, 0.4+0.j, 0.4+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "coupling_map = CouplingMap.from_line(10, bidirectional=False)\n", + "\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " coupling_map,\n", + " coupling_constants=(0.0, 0.0, 1.0),\n", + " ext_magnetic_field=(0.4, 0.0, 0.0),\n", + ")\n", + "print(hamiltonian)" + ] + }, + { + "cell_type": "markdown", + "id": "0f25e279-e987-4da5-a7e3-ffea41542d51", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The observable that we will be measuring is the total magnetization which we can simply construct as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eca935e2-206e-471e-a673-62a67a00add0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'],\n", + " coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j,\n", + " 0.05+0.j, 0.05+0.j, 0.05+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "L = coupling_map.size()\n", + "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)\n", + "print(observable)" + ] + }, + { + "cell_type": "markdown", + "id": "b652e4ab-26c1-4c4e-a34d-47a464c60f26", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Simulating various Trotter circuits\n", + "\n", + "Our analysis simply consists of comparing the behavior of product formulas of various order (here, second and fourth order) to the exact solution (which we can compute for a problem as simple as this one).\n", + "\n", + "In the code cell below, we do exactly that, for evolution times ranging from $2$ up to $10$ and Trotter steps ranging from $1$ to $25$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e7c43eec-3809-4e3a-998a-7f6a7a8cf2a6", + "metadata": { + "editable": true, + "scrolled": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit.primitives import StatevectorEstimator\n", + "from qiskit.synthesis import SuzukiTrotter\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", + "from scipy.linalg import expm\n", + "\n", + "estimator = StatevectorEstimator()\n", + "times = np.array(range(2, 11))\n", + "ks = np.array(range(1, 25))\n", + "\n", + "exact = []\n", + "order2 = []\n", + "order4 = []\n", + "\n", + "for time in times:\n", + " exp_H = expm(-1j * time * hamiltonian.to_matrix())\n", + " initial_state = np.zeros(exp_H.shape[0])\n", + " initial_state[0] = 1.0\n", + " time_evolved_state = exp_H @ initial_state\n", + " exact_evs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", + " exact.append(float(exact_evs))\n", + "\n", + " for k in ks:\n", + " order2_circ = generate_time_evolution_circuit(\n", + " hamiltonian, synthesis=SuzukiTrotter(reps=k, order=2), time=time\n", + " )\n", + " order4_circ = generate_time_evolution_circuit(\n", + " hamiltonian, synthesis=SuzukiTrotter(reps=k, order=4), time=time\n", + " )\n", + "\n", + " job = estimator.run([(order2_circ, observable), (order4_circ, observable)])\n", + " result = job.result()\n", + " order2.append(result[0].data.evs)\n", + " order4.append(result[1].data.evs)" + ] + }, + { + "cell_type": "markdown", + "id": "0da8acd2-6192-450e-99b3-e0d2b82541ee", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Analyzing the results\n", + "\n", + "We can now analyze the computed results by plotting them." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "845cf113-58da-454e-97b6-679d5a5ae9fb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-mpf/explanations/mpf-stability/extracted-outputs/845cf113-58da-454e-97b6-679d5a5ae9fb-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "order2_reshaped = np.asarray(order2).reshape((len(times), len(ks)))\n", + "order4_reshaped = np.asarray(order4).reshape((len(times), len(ks)))\n", + "\n", + "fig, axes = plt.subplots(3, 3, figsize=(20, 10))\n", + "\n", + "for i, t in enumerate(times):\n", + " ax = axes[i // 3, i % 3]\n", + "\n", + " ax.axvline(1.0, color=\"grey\", label=\"$t/k=1$\")\n", + " ax.axhline(exact[i], color=\"tab:green\", label=\"exact\")\n", + "\n", + " ax.plot(t / ks, order2_reshaped[i], label=r\"$\\chi=2$\")\n", + " ax.plot(t / ks, order4_reshaped[i], label=r\"$\\chi=4$\")\n", + "\n", + " ax.semilogx()\n", + " ax.set_xticks([10**p for p in range(-1, 1)])\n", + " ax.set_xlabel(\"$t/k$\")\n", + "\n", + " ax.set_ylim([-0.33, 0.55])\n", + " ax.set_ylabel(r\"$\\langle O \\rangle$\")\n", + "\n", + " ax.set_title(f\"time={t}\")\n", + "\n", + "fig.legend(*ax.get_legend_handles_labels(), ncols=4, loc=\"center\", bbox_to_anchor=(0.5, 0.0))\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2f40b1ee-0e98-4f24-9d25-080306ba98e1", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can now clearly see that in the regime $t/k \\gt 1$ the dynamics are no longer reproduced faithfully by the Trotterized circuits. Thus, if an MPF were to use a product formula with a $k_j$ from that regime, its extrapolated expectation value cannot faithfully produce good results." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/-toc b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/-toc new file mode 100644 index 00000000000..ba1139127ae --- /dev/null +++ b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/-toc @@ -0,0 +1,55 @@ +{ + "parentUrl": "/docs/guides/addons", + "parentLabel": "Documentation", + "title": "Qiskit Addon Smoke 0.0.1", + "collapsed": true, + "children": [ + { + "title": "", + "children": [ + { + "title": "Home", + "url": "/docs/addons/qiskit-addon-smoke" + }, + { + "title": "Installation", + "url": "/docs/addons/qiskit-addon-smoke/install" + }, + { + "title": "Guides", + "children": [ + { + "title": "How to foo", + "url": "/docs/addons/qiskit-addon-smoke/how-tos/foo" + } + ] + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-smoke" + } + ], + "collapsible": false + }, + { + "title": "Tutorials", + "children": [ + { + "title": "Intro tutorial", + "url": "/docs/addons/qiskit-addon-smoke/tutorials/intro" + } + ], + "collapsible": false + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Qiskit Addon Smoke API reference", + "url": "/docs/api/qiskit-addon-smoke" + } + ] + } + ] +} diff --git a/scripts/js/lib/api/addonDocsPipeline.ts b/scripts/js/lib/api/addonDocsPipeline.ts index ffa12f78d8a..480535cfd8d 100644 --- a/scripts/js/lib/api/addonDocsPipeline.ts +++ b/scripts/js/lib/api/addonDocsPipeline.ts @@ -75,7 +75,11 @@ export async function runAddonDocsPipeline( objectsInv, allObjectInvs, ); - await writeMarkdownResults(pkg, docsBaseFolder, results); + await writeMarkdownResults( + pkg, + docsBaseFolder, + results.map(remapExplanationsUrl), + ); // handle Jupyter notebook files const notebookFiles = files.filter((f) => f.endsWith(".ipynb")); @@ -88,7 +92,7 @@ export async function runAddonDocsPipeline( const imageDestination = pkg.outputDir(`${DOCS_BASE_PATH}/images/addons`); const notebookImages = collectNotebookImages(initialNotebooks, imageDestination); const notebooks = processNotebooks( - initialNotebooks, + initialNotebooks.map(remapExplanationsUrl), objectsInv, allObjectInvs, pkg, @@ -108,6 +112,17 @@ export async function runAddonDocsPipeline( await writeTocFile(pkg, docsBaseFolder, outputPath); } +/** + * Rewrites URLs of the form `.../explanation(s)/foo` to `.../how-tos/explanations-foo` + * so that explanations land under the how-tos directory + */ +function remapExplanationsUrl<T extends { url: string }>(item: T): T { + return { + ...item, + url: item.url.replace(/\/explanations?\/(.+)$/, "/how-tos/explanations-$1"), + }; +} + async function writeTocFile( pkg: Pkg, docsBaseFolder: string, diff --git a/scripts/js/lib/api/specialCaseResults.ts b/scripts/js/lib/api/specialCaseResults.ts index 41ad208bf25..779797a00ff 100644 --- a/scripts/js/lib/api/specialCaseResults.ts +++ b/scripts/js/lib/api/specialCaseResults.ts @@ -18,7 +18,6 @@ export function transformSpecialCaseUrl(url: string): string { // We use `-` rather than `_` as our delimiter. .replace(/(?<=^|\/)release_notes(?=\/|#|$)/g, "release-notes") .replace(/(?<=^|\/)how_tos(?=\/|#|$)/g, "how-tos") - .replace(/(?<=^|\/)explanation(?=\/|#|$)/g, "explanations") .replace(/(?<=^|\/)terra(?=#|$)/g, "index") .replace(/(?<=^|\/)ibm-runtime(?=#|$)/g, "index") ); From 29d467c85741bd4fcc603c56b008ed254b3d2a8d Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Mon, 18 May 2026 15:12:58 -0400 Subject: [PATCH 066/104] undo file mapping and do toc mapping instead --- docs/addons/qiskit-addon-aqc-tensor/_toc.json | 15 +- .../how-tos/explanations-index.mdx | 86 ----- docs/addons/qiskit-addon-cutting/_toc.json | 17 +- .../how-tos/explanations-index.mdx | 146 -------- .../tutorials/04-automatic-cut-finding.ipynb | 40 +-- docs/addons/qiskit-addon-mpf/_toc.json | 14 +- .../how-tos/explanations-index.mdx | 11 - .../how-tos/explanations-mpf-stability.ipynb | 318 ------------------ docs/addons/qiskit-addon-obp/_toc.json | 4 +- docs/addons/qiskit-addon-sqd/_toc.json | 4 +- .../02-fermionic-lattice-hamiltonian.ipynb | 54 +-- docs/addons/qiskit-addon-utils/_toc.json | 5 +- ...8498fe-8c2a-4e1a-96fc-ea843d55a203-0.avif} | Bin ...0adac4-8155-4664-b8de-3a79d38f99c3-0.avif} | Bin ...5b23ef-af56-4865-bbc5-d05a68633ce2-1.avif} | Bin ...59d0de-bd1f-4cbe-83d9-d17ce608946b-0.avif} | Bin ...b769bc-fedd-4937-b825-1db9b93052a3-0.avif} | Bin ...20896a-0750-48ba-852c-7b495629ccd5-0.avif} | Bin ...d5dd05-a1b5-4fed-8888-b753aee265ee-0.avif} | Bin ...59e18c-551a-4109-8e52-47eb070222dc-1.avif} | Bin ...2daa67-d86c-4efb-a1fd-4fd4fe236255-0.avif} | Bin scripts/js/lib/api/addonDocsPipeline.ts | 19 +- scripts/js/lib/api/generateAddonToc.test.ts | 34 +- scripts/js/lib/api/generateAddonToc.ts | 32 +- scripts/js/lib/api/specialCaseResults.ts | 1 + 25 files changed, 113 insertions(+), 687 deletions(-) delete mode 100644 docs/addons/qiskit-addon-aqc-tensor/how-tos/explanations-index.mdx delete mode 100644 docs/addons/qiskit-addon-cutting/how-tos/explanations-index.mdx delete mode 100644 docs/addons/qiskit-addon-mpf/how-tos/explanations-index.mdx delete mode 100644 docs/addons/qiskit-addon-mpf/how-tos/explanations-mpf-stability.ipynb rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{0c4b80c8-cdd0-4213-abc8-4344767dca6a-0.avif => 4a8498fe-8c2a-4e1a-96fc-ea843d55a203-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{015c579a-fae4-4ae0-920e-3fc6dd0cf0c3-0.avif => 830adac4-8155-4664-b8de-3a79d38f99c3-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{0f390890-16c0-4264-8fb7-9fa9eca15932-1.avif => aa5b23ef-af56-4865-bbc5-d05a68633ce2-1.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{4807191e-d20c-4424-96a1-d4ed6471f1cd-0.avif => ac59d0de-bd1f-4cbe-83d9-d17ce608946b-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/{50bc2e35-1516-495b-9af6-c632b17d8c05-0.avif => c6b769bc-fedd-4937-b825-1db9b93052a3-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/{d1fe7a76-b707-44a3-93f0-9ab4e4abd74d-0.avif => 7e20896a-0750-48ba-852c-7b495629ccd5-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/{ed13672f-0f58-44d5-ba6d-319b126bac43-0.avif => cdd5dd05-a1b5-4fed-8888-b753aee265ee-0.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/{25ca5c13-fb2f-42ff-8f2f-fa6e3b24cf1f-1.avif => e259e18c-551a-4109-8e52-47eb070222dc-1.avif} (100%) rename public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/{9d8b5120-c4ca-4189-baf5-eed42cef0b20-0.avif => ff2daa67-d86c-4efb-a1fd-4fd4fe236255-0.avif} (100%) diff --git a/docs/addons/qiskit-addon-aqc-tensor/_toc.json b/docs/addons/qiskit-addon-aqc-tensor/_toc.json index c46e33bb0d8..0d07227a6b5 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/_toc.json +++ b/docs/addons/qiskit-addon-aqc-tensor/_toc.json @@ -16,23 +16,17 @@ "url": "/docs/addons/qiskit-addon-aqc-tensor/install" }, { - "title": "Explanations", + "title": "Guides", "children": [ { "title": "Explanatory Material on AQC-Tensor", "url": "/docs/addons/qiskit-addon-aqc-tensor/explanations/index" - } - ] - }, - { - "title": "Guides", - "children": [ + }, { - "title": "How to use `quimb.tensor.TNOptimizer` directly", + "title": "How to use quimb.tensor.TNOptimizer directly", "url": "/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer" } - ], - "url": "/docs/addons/qiskit-addon-aqc-tensor/how-tos/index" + ] }, { "title": "GitHub", @@ -49,7 +43,6 @@ "url": "/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc" } ], - "url": "/docs/addons/qiskit-addon-aqc-tensor/tutorials/index", "collapsible": false }, { diff --git a/docs/addons/qiskit-addon-aqc-tensor/how-tos/explanations-index.mdx b/docs/addons/qiskit-addon-aqc-tensor/how-tos/explanations-index.mdx deleted file mode 100644 index f989758a541..00000000000 --- a/docs/addons/qiskit-addon-aqc-tensor/how-tos/explanations-index.mdx +++ /dev/null @@ -1,86 +0,0 @@ ---- -title: "Explanatory Material on AQC-Tensor" -description: "Explanatory Material on AQC-Tensor for the lastest version of Approximate quantum compilation (AQC-Tensor)" ---- - -<span id="explanation" /> - -# Explanatory Material on AQC-Tensor - -## Background - -This Qiskit addon allows one to compress the *initial portion* of a circuit using Approximate Quantum Compilation (AQC) with tensor networks, following the method introduced by Ref. <span id="id1" />[\[1\]](#id4). - -In the following, we will assume that the input circuit has already been truncated such that its entire depth can be simulated by a tensor network with a reasonable bond dimension. In order for AQC to be useful on quantum hardware, one must use the resulting circuit as a state preparation procedure and then perform subsequent quantum computation on that state in a way that is beyond the reach of a tensor network simulator alone. - -In essence, AQC-Tensor requires three things as input: - -1. A description of the **target state** in the form of a tensor network. This may be generated by simulating a circuit on a tensor network simulator (this is what Ref. <span id="id2" />[\[1\]](#id4) did), or it could be generated in some other way, e.g., by performing time-dependent variational principle (TDVP) time evolution directly on a matrix-product state. -2. A parametrized **ansatz circuit**, ideally with hardware-efficient connectivity, such that it will have a reasonable depth on the target hardware. -3. **Initial parameters** to plug into the ansatz circuit, such that the resulting state is already a *good* approximation to the target state. - -The technique is to then use an iterative optimization method to make the *good* state as close to the target state as possible. - -\[The third item is not required *in principle*, but it really helps to give the optimizer a sensible starting point; otherwise, it is much more likely to get stuck in a local (but not global) minimum.] - -## Ansatz generation (motivation) - -As mentioned in the previous section, three closely related things are needed as input to AQC-Tensor. Rather than code all three individually, this package provides a [`generate_ansatz_from_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") function that can generate (2) and (3) from a circuit. In the case of Trotter evolution (and likely other circuit classes of interest), one can use the same circuit-generation function to generate (1) as well as the input to [`generate_ansatz_from_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"), thus enabling the generation of all three inputs from a single function. The tutorial is a good demonstration of this re-use of a single circuit-generation function. - -Specifically, [`generate_ansatz_from_circuit()`](/docs/api/qiskit-addon-aqc-tensor/ansatz-generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") will take an input circuit and generate the ansatz circuit (2) and initial parameters (3) that follow the two-qubit connectivity of the input circuit. However, the returned circuit will be parametrized, based on generalized one- and two-qubit rotations. The initial parameters returned by the function, when plugged into the ansatz, will result in a circuit that is exactly equivalent to the circuit passed as input, up to a global phase. - -Given the ansatz generation function, the user must provide only two things in order to use AQC-Tensor: - -1. The **target state**, i.e., a *great* description of the desired state; and, -2. A **circuit** with the desired connectivity of the ansatz that prepares a *good* approximation to the target state. - -The optimization procedure then takes the *good* ansatz parameters and brings them as close as possible to the *great* description (the target state). - -In the case of a Trotter circuit, the same function can actually provide both things. For instance, the `spinhalf_trotter_circuit` function could be called with `num_trotter_steps=50` to produce the target state, then again with `num_trotter_steps=5` to produce the ansatz structure to be passed to `generate_ansatz_from_circuit`. (Note that the `evolution_time` is fixed, such that the time step `dt = evolution_time / num_trotter_steps`. the time per single Trotter step as `dt = evolution_time / num_trotter_steps`, so these two function calls would leave the total `evolution_time` fixed, while changing the time per Trotter step, `dt`.) So, in order for a user to apply AQC-Tensor to a new physical model of Trotter circuit, they would only need to generalize a single function with the details of that model. - -The ansatz used by `generate_ansatz_from_circuit` uses 9 parameters per two-qubit block, which is the theoretical minimum (see, e.g., Fig. 2 of [https://arxiv.org/abs/quant-ph/0308033](https://arxiv.org/abs/quant-ph/0308033)). The automatic ansatz is based on the [KAK decomposition](https://qiskit-extensions.github.io/circuit-knitting-toolbox/circuit_cutting/explanation/index.html#more-general-cut-two-qubit-gates-via-the-kak-decomposition), which parametrizes any two-qubit gate in terms of three parameters, up to single-qubit rotations. The single-qubit rotation are then decomposed as ZXZ, each of which has three parameters. So, each two-qubit block of the original circuit will result in 3 parameters for the two-qubit rotation, plus 3 parameters for an outgoing single-qubit rotation on each of the two qubits, for a total of 9 parameters per block. The ansatz is completed by adding a layer of single-qubit rotations to each active qubit at the start of the circuit. - -## Tensor-network simulation - -The simplest tensor network is a matrix-product state (MPS). - -Currently, AQC-Tensor supports the following tensor-network simulators: - -* Qiskit Aer’s MPS simulator -* Quimb’s [eager](https://quimb.readthedocs.io/en/latest/tensor/tensor-circuit-mps.html) [`CircuitMPS`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.CircuitMPS) simulator -* Quimb’s [lazy](https://quimb.readthedocs.io/en/latest/tensor/tensor-circuit.html) [`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.Circuit) simulator (may work only on small circuits so far; we’re working to fix this soon with more clever contractions) - -The most important parameter of a tensor network is its maximum bond dimension, which limits how much entanglement it can represent (and thus to what depth a given circuit can be faithfully simulated). The bond dimension is often represented by the Greek letter $\chi$. - -Given a general circuit on $L$ qubits, a matrix-product state needs at most a bond dimension of $\chi_\mathrm{exact} = 2^{\lfloor L/2 \rfloor}$ in order to be able to simulate it to *any* depth. Of course, general circuits on 100+ qubits cannot be classically simulated, so it will be intractable to set the bond dimension this high for those circuits. - -For this reason, if you are attempting to experiment with AQC-Tensor on a toy problem with few qubits, it is important to ensure that $\chi < 2^{\lfloor L/2 \rfloor}$. Otherwise, any circuit can be simulated to any depth, and there is no point in performing AQC. - -### Objective function - -Currently, this addon provides one very simple objective, [`MaximizeStateFidelity`](/docs/api/qiskit-addon-aqc-tensor/objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity"), whose [`loss_function()`](/docs/api/qiskit-addon-aqc-tensor/objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") is equivalent to Eq. (7) in Ref. <span id="id3" />[\[1\]](#id4). When called with an array of parameters, the [`loss_function()`](/docs/api/qiskit-addon-aqc-tensor/objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") method returns the value and gradient of the objective function as a 2-tuple. - -### Gradient - -This package provides a few different methods for calculating the gradient. - -The Aer backend always uses the explicit gradient code in the `explicit_gradient` module. - -The Quimb backend will typically be used with an automatic differentiation backend; the user is to select a backend from among those supported by Quimb. Alternatively, one can instead pass `"explicit"` as the `autodiff_backend` when instantiating the [`QuimbSimulator`](/docs/api/qiskit-addon-aqc-tensor/simulation-quimb-quimb-simulator#qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator "qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator") object; in this case, the `explicit_gradient` module will be used. It is only recommended to use explicit gradients with Quimb’s eager [`CircuitMPS`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.CircuitMPS) simulator, not the lazy [`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.Circuit) simulator. - -Regardless of which backend is chosen, the gradient code can understand linear parameter expressions (ParameterExpression objects). This support is essential, as linear expressions are returned by the ansatz generation code. - -### Optimization method - -Users are encouraged to use [`scipy.optimize`](https://docs.scipy.org/doc/scipy/reference/optimize.html#module-scipy.optimize "(in SciPy v1.17.0)") to perform the optimization. - -L-BFGS is the optimizer demonstrated in the tutorial notebook. It works well in practice because it uses the function value and its gradient to approximate the Hessian. It works well when given an initial point and seems to work particularly well in the case of Trotter circuits. However, it might early terminate if it starts in a barren plateau. In that case, performing a handful of steps using the ADAM optimizer first may help. - -## References - -<span id="id4" /> - -\[1] ([1](#id1),[2](#id2),[3](#id3)) - -[arXiv:2301.08609v6](https://arxiv.org/abs/2301.08609v6). - diff --git a/docs/addons/qiskit-addon-cutting/_toc.json b/docs/addons/qiskit-addon-cutting/_toc.json index 32c109298e2..100082eaa7c 100644 --- a/docs/addons/qiskit-addon-cutting/_toc.json +++ b/docs/addons/qiskit-addon-cutting/_toc.json @@ -16,17 +16,12 @@ "url": "/docs/addons/qiskit-addon-cutting/install" }, { - "title": "Explanations", + "title": "Guides", "children": [ { "title": "Explanatory material", "url": "/docs/addons/qiskit-addon-cutting/explanations/index" - } - ] - }, - { - "title": "Guides", - "children": [ + }, { "title": "How to generate exact quasiprobability distributions from Sampler", "url": "/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-quasi-dists-from-sampler" @@ -40,11 +35,10 @@ "url": "/docs/addons/qiskit-addon-cutting/how-tos/how-to-provide-multiple-observables" }, { - "title": "How to place wire cuts using a single-qubit `CutWire` instruction", + "title": "How to place wire cuts using a single-qubit CutWire instruction", "url": "/docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires" } - ], - "url": "/docs/addons/qiskit-addon-cutting/how-tos/index" + ] }, { "title": "GitHub", @@ -65,7 +59,7 @@ "url": "/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth" }, { - "title": "Wire Cutting Phrased as a Two-Qubit `Move` Instruction", + "title": "Wire Cutting Phrased as a Two-Qubit Move Instruction", "url": "/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction" }, { @@ -73,7 +67,6 @@ "url": "/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding" } ], - "url": "/docs/addons/qiskit-addon-cutting/tutorials/index", "collapsible": false }, { diff --git a/docs/addons/qiskit-addon-cutting/how-tos/explanations-index.mdx b/docs/addons/qiskit-addon-cutting/how-tos/explanations-index.mdx deleted file mode 100644 index 052a80a80c3..00000000000 --- a/docs/addons/qiskit-addon-cutting/how-tos/explanations-index.mdx +++ /dev/null @@ -1,146 +0,0 @@ ---- -title: "Explanatory material" -description: "Explanatory material for the lastest version of Circuit cutting" ---- - -<span id="circuit-cutting-explanation" /> - -# Explanatory material - -## Overview of circuit cutting - -Circuit cutting is a technique to increase the size of circuits we can run on quantum hardware at the cost of an additional sampling overhead. A larger quantum circuit can be decomposed by cutting its gates and/or wires, resulting in smaller circuits which can be executed within the constraints of available quantum hardware. The results of these smaller circuits are combined to reconstruct the outcome of the original problem. Circuit cutting can also be used to engineer gates between distant qubits which would otherwise require a large swap overhead. - -## Key terms - -* subcircuits: The set of circuits resulting from cutting gates in a [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) and then separating the disconnected qubit subsets into smaller circuits. These circuits contain [`SingleQubitQPDGate`](/docs/api/qiskit-addon-cutting/qpd-single-qubit-qpd-gate#qiskit_addon_cutting.qpd.SingleQubitQPDGate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s and will be used to instantiate each unique subexperiment. -* subexperiments: A term used to describe the unique circuit samples associated with a subcircuit. These circuits have had their [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate")s decomposed into local Qiskit gates and measurements. Subexperiments are the circuits sent to the backend for execution. -* decompose: We try to honor the Qiskit notion of “decompose” in the documentation and API, which loosely means transforming a gate into a less-abstracted representation. *Occasionally*, we may use the term “decompose” to refer to the act of inserting [`BaseQPDGate`](/docs/api/qiskit-addon-cutting/qpd-base-qpd-gate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate") instances into quantum circuits as “decomposing” a gate or wire; however, we try to use terms like “partition” and “cut” when referring to this to avoid ambiguity with Qiskit language. - -## Circuit cutting as a quasiprobability decomposition (QPD) - -Quasiprobability decomposition is a technique which can be used to simulate quantum circuit executions that go beyond the actual capabilities of current quantum hardware while using that same hardware. It forms the basis of many error mitigation techniques, which allow simulating a noise-free quantum computer using a noisy one. Circuit cutting techniques, which allow simulating a quantum circuit using fewer qubits than would otherwise be necessary, can also be phrased in terms of a quasiprobability decomposition. No matter the goal, the cost of the quasiprobability decomposition is an exponential overhead in the number of circuit executions which must be performed. In certain cases, this tradeoff is worth it, because it can allow the estimation of quantities that would otherwise be impossible on today’s hardware. - -There are two types of cuts: gate cuts and wire cuts. Gate cuts, also known as “space-like” cuts, exist when the cut goes through a gate operating on two (or more) qubits. Wire cuts, also known as “time-like” cuts, are direct cuts through a qubit wire, essentially a single-qubit identity gate that has been cut into two pieces. In this package, a wire cut is represented by introducing a new qubit into the circuit and moving remaining operations after the cut identity gate to the new qubit; see [Wire cutting phrased as a two-qubit Move operation](#wire-cutting-as-move), below. - -There are [three settings](https://research.ibm.com/blog/circuit-knitting-with-classical-communication) to consider for circuit cutting. The first is where only local operations (LO) \[i.e., local *quantum* operations] are available. The other settings introduce classical communication between the circuit executions, which is known in the quantum information literature as LOCC, for [local operations and classical communication](https://en.wikipedia.org/wiki/LOCC). The LOCC can be either near-time, one-directional communication between the circuit executions (the second setting), or real-time, bi-directional communication (the third setting). - -As mentioned above, the cost of any simulation based on quasiprobability distribution is an exponential sampling overhead. The sampling overhead is the factor by which the overall number of shots must increase for the quasiprobability decomposition to result in the same amount of error, $\epsilon$, as one would get by executing the original circuit. The overhead of a cut gate depends on which gate is cut; see the final appendix of \[[1](https://arxiv.org/abs/2205.00016)] for details. For instance, a single cut CNOT gate incurs a sampling overhead of 9 \[[2](https://arxiv.org/abs/1909.07534),[6](https://arxiv.org/abs/2312.11638)]. A circuit with $n$ wire cuts incurs a sampling overhead of O($16^n$) when classical communication is not available (LO setting); this is reduced to O($4^n$) when classical communication is available (LOCC setting) \[[4](https://arxiv.org/abs/2302.03366)]. However, wire cutting with classical communication (LOCC) is not yet supported by this package (see issue [#264](https://github.com/Qiskit/qiskit-addon-cutting/issues/264)). - -The QPD can be given explicitly as follows: - -$$ -\mathcal{U} = \sum_i a_i \mathcal{F}_i , -$$ - -where $\mathcal{U}$ is the channel implementing the desired operation, and each $a_i$ is a real coefficient corresponding to a quantum channel, $\mathcal{F}_i$, that is realizable on hardware. - -Note that because this is a sum of channels, *not* a sum of unitaries, expectation values can be evaluated efficiently \[[2](https://arxiv.org/abs/1909.07534)]. - -Results equivalent to original unitary channel can be obtained by a post-processing method, given the coefficients $a_i$ and the outcome of each experiment corresponding to the different local ($\mathcal{F}_i$) channels. This post-processing boosts the magnitude of each measurement outcome by $\sum_i \left| a_i \right|$, resulting in a sampling overhead of that quantity squared \[[6](https://arxiv.org/abs/2312.11638)]: - -$$ -\mathrm{Sampling\ overhead} = \left( \sum_i \lvert a_i \rvert \right)^2 . -$$ - -For more detailed information on the quasiprobability decomposition technique, refer to the paper, Error mitigation for short-depth quantum circuits \[[5](https://arxiv.org/abs/1612.02058)]. - -The essential idea of gate cutting is to replace a two-qubit gate with a linear combination of quantum channels \[Eq. [(1)](#equation-eq-qpd)] that, when recombined, will allow reconstruction of the result for physically measurable quantities like expectation values. Note that “global phase” is not something that can be physically measured, so we can disregard it when specifying quasiprobability decompositions. - -<span id="an-example-cutting-a-rzzgate" /> - -## An example: cutting a [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) - -As a basic and explicit example, let us consider the decomposition of a cut [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate). - -As shown in \[[2](https://arxiv.org/abs/1909.07534)], a quantum circuit which implements an [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) can be simulated by performing six subexperiments where the [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) in the original circuit has been replaced with only local (single-qubit) operations \[the $\mathcal{F}_i$‘s in Eq. [(1)](#equation-eq-qpd)]. The result is then reconstructed by combining the subexperiment results with certain coefficients \[the $a_i$‘s in Eq. [(1)](#equation-eq-qpd)], which can be either positive or negative. Given the $\theta$ parameter of the [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), the six subexperiments are as follows: - -1. With coefficient $a_1 = \cos^2 (\theta/2)$, do nothing ($I \otimes I$, where $I$ is the identity operation on a single qubit). -2. With coefficient $a_2 = \sin^2 (\theta/2)$, perform a [`ZGate`](/docs/api/qiskit/qiskit.circuit.library.ZGate) on each qubit ($Z \otimes Z$). -3. With coefficient $a_3 = -\sin(\theta)/2$, perform a projective measurement in the Z basis on the first qubit and an [`SGate`](/docs/api/qiskit/qiskit.circuit.library.SGate) gate on the second qubit (denote this as $M_z \otimes S$). If the result of the measurement is 1, flip the sign of that outcome’s contribution during reconstruction. -4. With coefficient $a_4 = \sin(\theta)/2$, perform a projective measurement in the Z basis on the first qubit and an [`SdgGate`](/docs/api/qiskit/qiskit.circuit.library.SdgGate) gate on the second qubit (denote this as $M_z \otimes S^\dagger$). If the result of the measurement is 1, flip the sign of that outcome’s contribution during reconstruction. -5. Same as term 3 ($a_5 = a_3$), but swap the qubits ($S \otimes M_z$). -6. Same as term 4 ($a_6 = a_4$), but swap the qubits ($S^\dagger \otimes M_z$). - -Equation [(1)](#equation-eq-qpd) for [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) can thus be written as a sum of the six terms listed above. The following plot shows the magnitude of each coeffient (negative coefficients are in orange) as a function of $\theta$. The square root of the optimal sampling overhead, denoted by $\gamma$, is given by the sum of the absolute coefficients. - -![../\_images/index-1.svg](/docs/images/addons/qiskit-addon-cutting/index-1.svg) - -Let’s consider some special points in this plot: - -* When $\theta = 0$, the gate has no effect, and the sampling overhead is 1. (Because the overhead is multiplicative, this is equivalent to there being no overhead.) -* When $\theta = \pi$, the gate is equivalent to $Z \otimes Z$ up to a global phase, and the sampling overhead is again 1. -* The maximum sampling overhead of $3^2 = 9$ is reached at a ZZ Rotation of $\theta=\pi/2$. We call this point the [`CXGate`](/docs/api/qiskit/qiskit.circuit.library.CXGate) family because this rotation is equivalent to a CXGate up to (single-qubit) local unitary operations. This point is also equivalent, up to local unitary operations, to [`CZGate`](/docs/api/qiskit/qiskit.circuit.library.CZGate), [`CYGate`](/docs/api/qiskit/qiskit.circuit.library.CYGate), [`CHGate`](/docs/api/qiskit/qiskit.circuit.library.CHGate), and [`ECRGate`](/docs/api/qiskit/qiskit.circuit.library.ECRGate). -* The ZZ rotation at $\theta=\pi/4$ has sampling overhead of $3+2\sqrt{2} \approx 5.828$. We call this the [`CSGate`](/docs/api/qiskit/qiskit.circuit.library.CSGate) family because this rotation is equivalent to a CSGate up to (single-qubit) local operations. This family also includes [`CSdgGate`](/docs/api/qiskit/qiskit.circuit.library.CSdgGate) and [`CSXGate`](/docs/api/qiskit/qiskit.circuit.library.CSXGate). -* Likewise, [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), and [`RZXGate`](/docs/api/qiskit/qiskit.circuit.library.RZXGate) are all locally equivalent to [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate). The controlled rotations [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate), and [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) at an angle of $2\theta$ are locally equivalent to [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) at an angle of $\theta$. - -## More general cut two-qubit gates via the KAK decomposition - -We can formalize this notion of local unitary equivalence and expand it to all two-qubit gates using the KAK decomposition, given by - -$$ -U = (V_1 \otimes V_2) \exp \left[ i \left( \theta_x \, X \otimes X + \theta_y \, Y \otimes Y + \theta_z \, Z \otimes Z \right) \right] (V_3 \otimes V_4) , -$$ - -where $V_1$, $V_2$, $V_3$, and $V_4$ are local, single-qubit operations, and the two-qubit portion of the interaction is parametrized entirely by $\vec{\theta} = (\theta_x, \theta_y, \theta_z)$. By convention, we have chosen $\vec{\theta}$ to be in the “Weyl chamber” restricted by $\pi/4 \geq \theta_x \geq \theta_y \geq | \theta_z | \geq 0$ \[[6](https://arxiv.org/abs/2312.11638)]. For more information on the KAK decomposition, see Ref. \[[7](https://arxiv.org/abs/quant-ph/0209120)]. - -The code that generates subexperiments from the KAK decomposition currently follows Ref. \[[3](https://arxiv.org/abs/2006.11174)], which is now known to be non-optimal. A provably optimal method has been presented in Ref. \[[6](https://arxiv.org/abs/2312.11638)], but this newer method has not yet been implemented in this package (see issue [#531](https://github.com/Qiskit/qiskit-addon-cutting/issues/531)). - -<span id="wire-cutting-phrased-as-a-two-qubit-move-operation" /> - -<span id="wire-cutting-as-move" /> - -## Wire cutting phrased as a two-qubit [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") operation - -A wire cut is represented fundamentally by this package as a two-qubit [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction, which is defined as a reset of the second qubit followed by a swap of both qubits. Equivalently, the operation is defined as transferring the state of the first qubit wire to the second qubit wire, while simultaneously discarding the state of the second qubit wire (the first qubit ends up in state $\lvert 0 \rangle$). - -We have chosen to represent wire cuts in this way primarily because it is consistent with the way one must treat wire cuts when acting on physical qubits: for instance, a wire cut might take the state of physical qubit $n$ and continue it as physical qubit $m$ after the cut. Our choice also has the benefit of allowing us to think of “instruction cutting” as a unified framework for considering both wire cuts and gate cuts in the same formalism, being that a wire cut is just a cut [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction. - -More information on this formalism is given in Sec. 3 of Ref. \[[4](https://arxiv.org/abs/2302.03366)] - -If you prefer to place cut wires abstractly on a single qubit wire, please see the [how-to guide on placing wire cuts using a single-qubit instruction](how-tos/how-to-specify-cut-wires), which explains how to use the [`cut_wires()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#qiskit_addon_cutting.cut_wires "qiskit_addon_cutting.cut_wires") function to convert a circuit with [`CutWire`](/docs/api/qiskit-addon-cutting/instructions-cut-wire#qiskit_addon_cutting.instructions.CutWire "qiskit_addon_cutting.instructions.CutWire") instructions to a circuit with [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move")s on additional qubits. - -## Sample weights in the Qiskit addon for circuit cutting - -In this package, the number of samples taken from the distribution is generally controlled by a `num_samples` argument, and each sample has an associated weight which is used during expectation value reconstruction. Each weight with absolute value above a threshold of 1 / `num_samples` will be evaluated exactly. The remaining low-probability elements – those in the tail of the distribution – will then be sampled, resulting in at most `num_samples` unique weights. Setting `num_samples` to infinity indicates that all weights should be generated rigorously, rather than by sampling from the distribution. - -Much of the circuit cutting literature describes a process where we sample from the distribution, take a single shot, then sample from the distribution again and repeat; however, this is not feasible in practice, so we instead perform all sampling upfront. For now, because of limitations in version 1 of the Qiskit primitives, we take a fixed number of shots for each considered subexperiment and send the subexperiments to the backend(s) in batches. During reconstruction, each subexperiment contributes to the final result with proportion equal to its weight. One must ensure the number of shots taken is sufficient for the heaviest weighted subexperiment. In the future, we plan to support passing an individual `shots` count with each subexperiment to Qiskit Runtime, so that each subexperiment will be run with a number of shots proportional to that subexperiment’s weight in the final result (see issue [#532](https://github.com/Qiskit/qiskit-addon-cutting/issues/532)). This per-experiment shots count is a new feature enabled by version 2 of the Qiskit primitives. - -## Sampling overhead reference table - -The below table provides the sampling overhead factor for a variety of two-qubit instructions, provided that only a single instruction is cut. - -| Instruction(s) | KAK decomposition angles | Sampling overhead factor | | | | | | | -| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------ | --------------------------- | --------- | -------------------- | -------------------- | ----------------- | -------------------- | -------------------------------------------------------------- | -| [`CSGate`](/docs/api/qiskit/qiskit.circuit.library.CSGate), [`CSdgGate`](/docs/api/qiskit/qiskit.circuit.library.CSdgGate), [`CSXGate`](/docs/api/qiskit/qiskit.circuit.library.CSXGate) | $(\pi/8, 0, 0)$ | $3+2\sqrt{2} \approx 5.828$ | | | | | | | -| [`CXGate`](/docs/api/qiskit/qiskit.circuit.library.CXGate), [`CYGate`](/docs/api/qiskit/qiskit.circuit.library.CYGate), [`CZGate`](/docs/api/qiskit/qiskit.circuit.library.CZGate), [`CHGate`](/docs/api/qiskit/qiskit.circuit.library.CHGate), [`ECRGate`](/docs/api/qiskit/qiskit.circuit.library.ECRGate) | $(\pi/4, 0, 0)$ | $3^2=9$ | | | | | | | -| [`iSwapGate`](/docs/api/qiskit/qiskit.circuit.library.iSwapGate), [`DCXGate`](/docs/api/qiskit/qiskit.circuit.library.DCXGate) | $(\pi/4, \pi/4, 0)$ | $7^2=49$ | | | | | | | -| [`SwapGate`](/docs/api/qiskit/qiskit.circuit.library.SwapGate) | $(\pi/4,\pi/4,\pi/4)$ | | | | | | | | -| [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), [`RZXGate`](/docs/api/qiskit/qiskit.circuit.library.RZXGate) | \$( | \theta/2 | , 0, 0)\$ | \$\left\[1 + 2 \left | \sin(\theta)\right | \right]^2\$ | | | -| [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate), [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) | \$( | \theta/4 | , 0, 0)\$ | \$\left\[1 + 2 \left | \sin(\theta/2)\right | \right]^2\$ | | | -| [`XXPlusYYGate`](/docs/api/qiskit/qiskit.circuit.library.XXPlusYYGate), [`XXMinusYYGate`](/docs/api/qiskit/qiskit.circuit.library.XXMinusYYGate) | \$( | \theta/4 | , | \theta/4 | ,0)\$ | \$\left\[1+4\left | \sin(\theta/2)\right | +2\sin^2(\theta/2)\right]^2$(independent of$\beta\$ parameter) | -| [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") (cut wire) without classical communication (i.e., in the LO setting) | not applicable | $4^2=16$ | | | | | | | - -## Current limitations - -* The workflow only allows taking the *expectation value* of observables with respect to a circuit. Limited support for reconstructing an output probability distribution may be added to a future version of this package (see issue [#259](https://github.com/Qiskit/qiskit-addon-cutting/issues/259)). -* Due to current code limitations, some of the generated subexperiments are redundant. This can result in more subexperiments than expected, particularly when using wire cutting. This is tracked by issue [#262](https://github.com/Qiskit/qiskit-addon-cutting/issues/262). - -## References - -This module is based on the theory described in the following papers: - -\[1] Christophe Piveteau, David Sutter, *Circuit knitting with classical communication*, [https://arxiv.org/abs/2205.00016](https://arxiv.org/abs/2205.00016) - -\[2] Kosuke Mitarai, Keisuke Fujii, *Constructing a virtual two-qubit gate by sampling single-qubit operations*, [https://arxiv.org/abs/1909.07534](https://arxiv.org/abs/1909.07534) - -\[3] Kosuke Mitarai, Keisuke Fujii, *Overhead for simulating a non-local channel with local channels by quasiprobability sampling*, [https://arxiv.org/abs/2006.11174](https://arxiv.org/abs/2006.11174) - -\[4] Lukas Brenner, Christophe Piveteau, David Sutter, *Optimal wire cutting with classical communication*, [https://arxiv.org/abs/2302.03366](https://arxiv.org/abs/2302.03366) - -\[5] K. Temme, S. Bravyi, and J. M. Gambetta, *Error mitigation for short-depth quantum circuits*, [https://arxiv.org/abs/1612.02058](https://arxiv.org/abs/1612.02058) - -\[6] Lukas Schmitt, Christophe Piveteau, David Sutter, *Cutting circuits with multiple two-qubit unitaries*, [https://arxiv.org/abs/2312.11638](https://arxiv.org/abs/2312.11638) - -\[7] Jun Zhang, Jiri Vala, K. Birgitta Whaley, Shankar Sastry, *A geometric theory of non-local two-qubit operations*, [https://arxiv.org/abs/quant-ph/0209120](https://arxiv.org/abs/quant-ph/0209120) - diff --git a/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb index a6b0995ef00..ccc15557d5d 100644 --- a/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb +++ b/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "a2aee9a1-c44d-4ede-b8c2-7bea2c8fc67a", + "id": "89d3e73b-f522-4de9-b8e1-45ee978115f7", "metadata": {}, "source": [ "# Automatically find cuts" @@ -21,7 +21,7 @@ }, { "cell_type": "markdown", - "id": "967099f2-a552-4638-a5d7-aa267b400af2", + "id": "f2cc1670-da97-4176-b290-4b085c4d9e52", "metadata": {}, "source": [ "## Step 1: Map\n", @@ -32,7 +32,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "015c579a-fae4-4ae0-920e-3fc6dd0cf0c3", + "id": "830adac4-8155-4664-b8de-3a79d38f99c3", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:55.792854Z", @@ -45,7 +45,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/015c579a-fae4-4ae0-920e-3fc6dd0cf0c3-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/830adac4-8155-4664-b8de-3a79d38f99c3-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 1, @@ -67,7 +67,7 @@ }, { "cell_type": "markdown", - "id": "2c1169ab-1568-4760-a14f-0c88bd83d67b", + "id": "3dfb6ab2-80f4-4aba-9a6f-aa2692273411", "metadata": {}, "source": [ "## Step 2: Optimize\n", @@ -78,7 +78,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "0f390890-16c0-4264-8fb7-9fa9eca15932", + "id": "aa5b23ef-af56-4865-bbc5-d05a68633ce2", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:56.802390Z", @@ -107,7 +107,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/0f390890-16c0-4264-8fb7-9fa9eca15932-1.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/aa5b23ef-af56-4865-bbc5-d05a68633ce2-1.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 2, @@ -141,7 +141,7 @@ }, { "cell_type": "markdown", - "id": "766339b2-dc5d-4c78-829a-f6c2d68050b2", + "id": "c2ebd34b-c29a-4fc2-9e89-dd2eeed0fa17", "metadata": {}, "source": [ "#### Add ancillas for wire cuts and expand the observables to account for ancilla qubits" @@ -150,7 +150,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "50bc2e35-1516-495b-9af6-c632b17d8c05", + "id": "c6b769bc-fedd-4937-b825-1db9b93052a3", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.491081Z", @@ -163,7 +163,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/50bc2e35-1516-495b-9af6-c632b17d8c05-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/c6b769bc-fedd-4937-b825-1db9b93052a3-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 3, @@ -181,7 +181,7 @@ }, { "cell_type": "markdown", - "id": "9c221b7b-aeab-4b12-8d3d-3e06a293c944", + "id": "cefd19e1-f022-415c-908d-902eb15cafc8", "metadata": {}, "source": [ "#### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires." @@ -190,7 +190,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "1733d9af-3430-491a-993f-6627487b19f5", + "id": "87ec5b7c-e9a7-43d3-bf03-cd56a34d814b", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.755174Z", @@ -224,7 +224,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "bed20ead-ca92-4e1c-95d5-52cb3ecb9e95", + "id": "9f38fe36-bdb7-4e65-9a77-6778100dabdc", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.770081Z", @@ -253,7 +253,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "0c4b80c8-cdd0-4213-abc8-4344767dca6a", + "id": "4a8498fe-8c2a-4e1a-96fc-ea843d55a203", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.776009Z", @@ -266,7 +266,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/0c4b80c8-cdd0-4213-abc8-4344767dca6a-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/4a8498fe-8c2a-4e1a-96fc-ea843d55a203-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 6, @@ -281,7 +281,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "4807191e-d20c-4424-96a1-d4ed6471f1cd", + "id": "ac59d0de-bd1f-4cbe-83d9-d17ce608946b", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:52:59.933278Z", @@ -294,7 +294,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/4807191e-d20c-4424-96a1-d4ed6471f1cd-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/ac59d0de-bd1f-4cbe-83d9-d17ce608946b-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 7, @@ -308,7 +308,7 @@ }, { "cell_type": "markdown", - "id": "f1c4ee91-b6ae-4cee-a13c-16505db07763", + "id": "bba3fb35-5192-4c4e-bfe7-6eeedd1b58fc", "metadata": {}, "source": [ "#### Generate the experiments to run on the backend." @@ -317,7 +317,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "4cb11dd2-2e81-4eb3-9382-8226bbb9d1e3", + "id": "e5a16473-cb78-4ea6-965c-1a6de708a89c", "metadata": { "execution": { "iopub.execute_input": "2025-03-28T16:53:00.112773Z", @@ -348,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "17bf85d7-00df-4361-b309-2e7cf562fcb2", + "id": "9cb69ede-b239-4f6f-a7f1-a499923819e1", "metadata": {}, "source": [ "Steps 3 and 4 of a [Qiskit pattern](/guides/intro-to-patterns) can then be performed as in the preceding tutorials." diff --git a/docs/addons/qiskit-addon-mpf/_toc.json b/docs/addons/qiskit-addon-mpf/_toc.json index d6a5a67a7f2..c30895d31d6 100644 --- a/docs/addons/qiskit-addon-mpf/_toc.json +++ b/docs/addons/qiskit-addon-mpf/_toc.json @@ -16,18 +16,12 @@ "url": "/docs/addons/qiskit-addon-mpf/install" }, { - "title": "Explanations", + "title": "Guides", "children": [ { "title": "Understanding the stability of MPFs", "url": "/docs/addons/qiskit-addon-mpf/explanations/mpf-stability" - } - ], - "url": "/docs/addons/qiskit-addon-mpf/explanations/index" - }, - { - "title": "Guides", - "children": [ + }, { "title": "How to choose the Trotter steps for an MPF", "url": "/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps" @@ -36,8 +30,7 @@ "title": "How to use the approximate model", "url": "/docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model" } - ], - "url": "/docs/addons/qiskit-addon-mpf/how-tos/index" + ] }, { "title": "GitHub", @@ -54,7 +47,6 @@ "url": "/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started" } ], - "url": "/docs/addons/qiskit-addon-mpf/tutorials/index", "collapsible": false }, { diff --git a/docs/addons/qiskit-addon-mpf/how-tos/explanations-index.mdx b/docs/addons/qiskit-addon-mpf/how-tos/explanations-index.mdx deleted file mode 100644 index 20d11fba7b0..00000000000 --- a/docs/addons/qiskit-addon-mpf/how-tos/explanations-index.mdx +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: "Explanations" -description: "Explanations for the lastest version of Multi-product formulas (MPF)" ---- - -# Explanations - -This page summarizes additional explanatory content. - -* [Understanding the stability of MPFs](mpf-stability) - diff --git a/docs/addons/qiskit-addon-mpf/how-tos/explanations-mpf-stability.ipynb b/docs/addons/qiskit-addon-mpf/how-tos/explanations-mpf-stability.ipynb deleted file mode 100644 index c6c073e4c8b..00000000000 --- a/docs/addons/qiskit-addon-mpf/how-tos/explanations-mpf-stability.ipynb +++ /dev/null @@ -1,318 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "frontmatter", - "metadata": {}, - "source": [ - "---\n", - "title: \"Understanding the stability of MPFs\"\n", - "description: \"Understanding the stability of MPFs for the latest version of Multi-product formulas (MPF)\"\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "4aaf8397-4140-4d16-b4b5-1d9e20adea98", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Understanding the stability of MPFs\n", - "\n", - "Over in the guide [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps) we introduced a heuristic that limits the **smallest** $k_j$ value ($k_{\\text{min}}$) based on the total evolution time, $t$, that we would like to reach.\n", - "In particular, we state that $t/k_{\\text{min}} \\lt 1$ must be satisfied (noting that $t/k_{\\text{min}} \\leq 1$ empirically seems to work fine, too).\n", - "\n", - "On this page, we will analyze when the behavior of MPFs is not guaranteed to work well, such as when this constraint gets violated." - ] - }, - { - "cell_type": "markdown", - "id": "986a58b5-611a-4400-9bd5-ba35d4a14188", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Setting up a simple model problem\n", - "\n", - "For this simple example, we will reuse the model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial; the Ising model on a line of 10 sites:\n", - "\n", - "$$\n", - "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", - "$$\n", - "\n", - "where $J$ is the coupling strength between two sites and $h$ is the external magnetic field." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d77e337b-0a69-49a5-b449-ffe4ad17d7ab", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'],\n", - " coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j,\n", - " 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j,\n", - " 0.4+0.j, 0.4+0.j, 0.4+0.j])\n" - ] - } - ], - "source": [ - "from qiskit.transpiler import CouplingMap\n", - "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", - "\n", - "coupling_map = CouplingMap.from_line(10, bidirectional=False)\n", - "\n", - "hamiltonian = generate_xyz_hamiltonian(\n", - " coupling_map,\n", - " coupling_constants=(0.0, 0.0, 1.0),\n", - " ext_magnetic_field=(0.4, 0.0, 0.0),\n", - ")\n", - "print(hamiltonian)" - ] - }, - { - "cell_type": "markdown", - "id": "0f25e279-e987-4da5-a7e3-ffea41542d51", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "The observable that we will be measuring is the total magnetization which we can simply construct as shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "eca935e2-206e-471e-a673-62a67a00add0", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'],\n", - " coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j,\n", - " 0.05+0.j, 0.05+0.j, 0.05+0.j])\n" - ] - } - ], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "L = coupling_map.size()\n", - "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)\n", - "print(observable)" - ] - }, - { - "cell_type": "markdown", - "id": "b652e4ab-26c1-4c4e-a34d-47a464c60f26", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Simulating various Trotter circuits\n", - "\n", - "Our analysis simply consists of comparing the behavior of product formulas of various order (here, second and fourth order) to the exact solution (which we can compute for a problem as simple as this one).\n", - "\n", - "In the code cell below, we do exactly that, for evolution times ranging from $2$ up to $10$ and Trotter steps ranging from $1$ to $25$." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e7c43eec-3809-4e3a-998a-7f6a7a8cf2a6", - "metadata": { - "editable": true, - "scrolled": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "skip-execution" - ] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from qiskit.primitives import StatevectorEstimator\n", - "from qiskit.synthesis import SuzukiTrotter\n", - "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", - "from scipy.linalg import expm\n", - "\n", - "estimator = StatevectorEstimator()\n", - "times = np.array(range(2, 11))\n", - "ks = np.array(range(1, 25))\n", - "\n", - "exact = []\n", - "order2 = []\n", - "order4 = []\n", - "\n", - "for time in times:\n", - " exp_H = expm(-1j * time * hamiltonian.to_matrix())\n", - " initial_state = np.zeros(exp_H.shape[0])\n", - " initial_state[0] = 1.0\n", - " time_evolved_state = exp_H @ initial_state\n", - " exact_evs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", - " exact.append(float(exact_evs))\n", - "\n", - " for k in ks:\n", - " order2_circ = generate_time_evolution_circuit(\n", - " hamiltonian, synthesis=SuzukiTrotter(reps=k, order=2), time=time\n", - " )\n", - " order4_circ = generate_time_evolution_circuit(\n", - " hamiltonian, synthesis=SuzukiTrotter(reps=k, order=4), time=time\n", - " )\n", - "\n", - " job = estimator.run([(order2_circ, observable), (order4_circ, observable)])\n", - " result = job.result()\n", - " order2.append(result[0].data.evs)\n", - " order4.append(result[1].data.evs)" - ] - }, - { - "cell_type": "markdown", - "id": "0da8acd2-6192-450e-99b3-e0d2b82541ee", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Analyzing the results\n", - "\n", - "We can now analyze the computed results by plotting them." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "845cf113-58da-454e-97b6-679d5a5ae9fb", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "skip-execution" - ] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-mpf/explanations/mpf-stability/extracted-outputs/845cf113-58da-454e-97b6-679d5a5ae9fb-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "order2_reshaped = np.asarray(order2).reshape((len(times), len(ks)))\n", - "order4_reshaped = np.asarray(order4).reshape((len(times), len(ks)))\n", - "\n", - "fig, axes = plt.subplots(3, 3, figsize=(20, 10))\n", - "\n", - "for i, t in enumerate(times):\n", - " ax = axes[i // 3, i % 3]\n", - "\n", - " ax.axvline(1.0, color=\"grey\", label=\"$t/k=1$\")\n", - " ax.axhline(exact[i], color=\"tab:green\", label=\"exact\")\n", - "\n", - " ax.plot(t / ks, order2_reshaped[i], label=r\"$\\chi=2$\")\n", - " ax.plot(t / ks, order4_reshaped[i], label=r\"$\\chi=4$\")\n", - "\n", - " ax.semilogx()\n", - " ax.set_xticks([10**p for p in range(-1, 1)])\n", - " ax.set_xlabel(\"$t/k$\")\n", - "\n", - " ax.set_ylim([-0.33, 0.55])\n", - " ax.set_ylabel(r\"$\\langle O \\rangle$\")\n", - "\n", - " ax.set_title(f\"time={t}\")\n", - "\n", - "fig.legend(*ax.get_legend_handles_labels(), ncols=4, loc=\"center\", bbox_to_anchor=(0.5, 0.0))\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "2f40b1ee-0e98-4f24-9d25-080306ba98e1", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "We can now clearly see that in the regime $t/k \\gt 1$ the dynamics are no longer reproduced faithfully by the Trotterized circuits. Thus, if an MPF were to use a product formula with a $k_j$ from that regime, its extrapolated expectation value cannot faithfully produce good results." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index 6d3f57a2823..6200f71c6f8 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -30,8 +30,7 @@ "title": "Truncating Pauli terms during backpropagation", "url": "/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms" } - ], - "url": "/docs/addons/qiskit-addon-obp/how-tos/index" + ] }, { "title": "GitHub", @@ -48,7 +47,6 @@ "url": "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started" } ], - "url": "/docs/addons/qiskit-addon-obp/tutorials/index", "collapsible": false }, { diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json index a3bfb808792..d7083550560 100644 --- a/docs/addons/qiskit-addon-sqd/_toc.json +++ b/docs/addons/qiskit-addon-sqd/_toc.json @@ -46,8 +46,7 @@ "title": "Optimize Hamiltonian basis with orbital optimization", "url": "/docs/addons/qiskit-addon-sqd/how-tos/use-oo-to-optimize-hamiltonian-basis" } - ], - "url": "/docs/addons/qiskit-addon-sqd/how-tos/index" + ] }, { "title": "GitHub", @@ -68,7 +67,6 @@ "url": "/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian" } ], - "url": "/docs/addons/qiskit-addon-sqd/tutorials/index", "collapsible": false }, { diff --git a/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb index 553614cd987..c94f3ac77f7 100644 --- a/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb +++ b/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "7c3e56fe-8357-4d3a-961a-6e0258ae3082", + "id": "6789705d-464f-4ec0-bfd1-5059433a2212", "metadata": {}, "source": [ "# Improving energy estimation of a Fermionic lattice model with SQD\n", @@ -38,7 +38,7 @@ }, { "cell_type": "markdown", - "id": "c4de5d77-345b-4fab-b22d-23b5ae988c54", + "id": "5bb98301-dddd-4183-bbae-0055f527ede1", "metadata": {}, "source": [ "### Step 1: Map problem to a quantum circuit" @@ -46,7 +46,7 @@ }, { "cell_type": "markdown", - "id": "58f58c32-790b-4ec8-aabc-f45f0bfd0a9e", + "id": "267c7f70-13a2-418b-9b30-5edc9e19a460", "metadata": {}, "source": [ "First, we will create the one- and two-body Hamiltonians describing the one-dimensional single-impurity Anderson model (SIAM) with ``7`` bath sites (``8`` electrons in ``8`` orbitals). This model is used to describe magnetic impurities embedded in metals.\n", @@ -57,7 +57,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "3fb0e890-1423-47f6-b29d-a104cc018f9a", + "id": "6e2b926e-c115-4d7c-be1f-07831ac82a3d", "metadata": {}, "outputs": [], "source": [ @@ -86,7 +86,7 @@ }, { "cell_type": "markdown", - "id": "16b4f0ca-ec5f-4891-8f98-385d72236e1f", + "id": "713dc5d4-7bde-4207-9c74-167ce82478b2", "metadata": {}, "source": [ "Next, we will generate the quantum Krylov subspace with a set of Trotterized quantum circuits. Here we create helpers for generating the initial (reference) state as well as the time evolution for the one- and two-body parts of the Hamiltonian. For a more detailed description of this model and how the circuits are designed, please refer to [the paper](https://arxiv.org/abs/2501.09702)." @@ -95,7 +95,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "833e9562-efdb-48f6-9e37-d1e083fabe8b", + "id": "1a4eee7e-2b76-4605-9bd2-8fede7777e92", "metadata": {}, "outputs": [], "source": [ @@ -143,7 +143,7 @@ }, { "cell_type": "markdown", - "id": "3743b994-b69a-4725-a4c8-2e5ebb39a7d7", + "id": "9de66e8b-caf4-493a-815b-ac3b94c3bbdc", "metadata": {}, "source": [ "Generate ``d`` time-evolved states that specify the quantum Krylov subspace. Here, we have chosen ``d=8``. The error from sampling Krylov basis states converges with increasing ``d``. Note that the particulars of this problem instance allow for an efficient compilation of the one-body evolution using `OrbitalRotationJW`; however, in general, one could use Qiskit's [PauliEvolutionGate](/docs/api/qiskit/qiskit.circuit.library.PauliEvolutionGate) to implement the Trotterized time evolution of the full Hamiltonian." @@ -152,7 +152,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "f7ab6c84-a778-4c8c-a491-45e0a63e5520", + "id": "aea80714-3a98-4d40-8250-b2290a02a2d1", "metadata": {}, "outputs": [], "source": [ @@ -177,13 +177,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "ed13672f-0f58-44d5-ba6d-319b126bac43", + "id": "cdd5dd05-a1b5-4fed-8888-b753aee265ee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/ed13672f-0f58-44d5-ba6d-319b126bac43-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/cdd5dd05-a1b5-4fed-8888-b753aee265ee-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 4, @@ -198,13 +198,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "d1fe7a76-b707-44a3-93f0-9ab4e4abd74d", + "id": "7e20896a-0750-48ba-852c-7b495629ccd5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/d1fe7a76-b707-44a3-93f0-9ab4e4abd74d-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/7e20896a-0750-48ba-852c-7b495629ccd5-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 5, @@ -218,7 +218,7 @@ }, { "cell_type": "markdown", - "id": "6c1eb7a9-5c1b-4d7c-a9d9-a97309efaad7", + "id": "5ef3c2a9-9371-48d9-bb95-102b70ffad6d", "metadata": {}, "source": [ "### Step 2: Optimize the problem" @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "b6478b6e-9be6-47c1-a036-269a3ce7a039", + "id": "b6e18a4f-368e-403e-9f3e-6ac2185d4081", "metadata": {}, "source": [ "After we have created the Trotterized circuits, we will optimize them for a target hardware. We need to choose the hardware device to use before optimization. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." @@ -235,7 +235,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "193c0b47-578e-4b25-9413-852b13806167", + "id": "1c5441f9-a8c2-4de1-92fb-2a8251860d02", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "9486836a-2eb7-40b6-bdc2-141ef01829d6", + "id": "7b53f217-11eb-4b09-bbbb-b73a096ffd8f", "metadata": {}, "source": [ "Next, we will transpile the circuits to the target backend using Qiskit." @@ -255,7 +255,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "b6981e2d-6e5f-4a74-b057-49de5587275e", + "id": "060e0dc6-f134-4afd-a6f5-49c89b347e8f", "metadata": {}, "outputs": [], "source": [ @@ -268,7 +268,7 @@ }, { "cell_type": "markdown", - "id": "9e60781b-9839-42e6-bacd-4265b871fff1", + "id": "2926bd02-4b8a-4f9e-9a40-5a7fd983796d", "metadata": {}, "source": [ "### Step 3: Execute experiments" @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "500bcd52-54a0-47d8-9a3f-b731034dbfcd", + "id": "d07e5c37-6ec4-4fea-8f28-1e0600ceace4", "metadata": {}, "source": [ "After optimizing the circuits for hardware execution, we are ready to run them on the target hardware and collect samples for ground state energy estimation. Here we use ``SamplerV2`` from ``qiskit-ibm-runtime`` to simulate noisy samples taken from the ``ibm_sherbrooke`` backend. We then combine the counts from each of the Krylov basis states into a single counts dictionary and plot the top 20 most commonly sampled bitstrings.\n", @@ -287,13 +287,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "9d8b5120-c4ca-4189-baf5-eed42cef0b20", + "id": "ff2daa67-d86c-4efb-a1fd-4fd4fe236255", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/9d8b5120-c4ca-4189-baf5-eed42cef0b20-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/ff2daa67-d86c-4efb-a1fd-4fd4fe236255-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 8, @@ -318,7 +318,7 @@ }, { "cell_type": "markdown", - "id": "11131d60-c829-4fa2-9fbc-4c5d2dad69e4", + "id": "3bb18c13-0ccd-4397-af16-3f964631729e", "metadata": {}, "source": [ "### Step 4: Post-process the results" @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "612ebb53-07e6-4c37-8144-ca88d7c8f141", + "id": "28460642-9cf0-4cc6-a0e2-185f20c81b19", "metadata": {}, "source": [ "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function." @@ -335,7 +335,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ef8f66ec-63ce-4ad4-912b-53748df5c268", + "id": "b6d2f236-3950-4c71-9ff0-59ac720817a9", "metadata": {}, "outputs": [ { @@ -420,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "6a968ed3-6298-47f2-896d-af6affa0b66f", + "id": "0fdbf9b2-d887-445b-9800-1bc4063b30ea", "metadata": {}, "source": [ "Now, we plot the results. The first plot shows that after a few iterations, we obtain the exact ground state energy.\n", @@ -433,7 +433,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "25ca5c13-fb2f-42ff-8f2f-fa6e3b24cf1f", + "id": "e259e18c-551a-4109-8e52-47eb070222dc", "metadata": {}, "outputs": [ { @@ -448,7 +448,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/25ca5c13-fb2f-42ff-8f2f-fa6e3b24cf1f-1.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/e259e18c-551a-4109-8e52-47eb070222dc-1.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, diff --git a/docs/addons/qiskit-addon-utils/_toc.json b/docs/addons/qiskit-addon-utils/_toc.json index 5d2a4a80d1b..35e678b8179 100644 --- a/docs/addons/qiskit-addon-utils/_toc.json +++ b/docs/addons/qiskit-addon-utils/_toc.json @@ -23,11 +23,10 @@ "url": "/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth" }, { - "title": "Slicing circuits using ``qiskit_addon_utils.slicing``", + "title": "Slicing circuits using `qiskit_addon_utils.slicing`", "url": "/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices" } - ], - "url": "/docs/addons/qiskit-addon-utils/how-tos/index" + ] }, { "title": "GitHub", diff --git a/public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/0c4b80c8-cdd0-4213-abc8-4344767dca6a-0.avif b/public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/4a8498fe-8c2a-4e1a-96fc-ea843d55a203-0.avif similarity index 100% rename from public/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/0c4b80c8-cdd0-4213-abc8-4344767dca6a-0.avif rename to 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b/public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/ff2daa67-d86c-4efb-a1fd-4fd4fe236255-0.avif similarity index 100% rename from public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/9d8b5120-c4ca-4189-baf5-eed42cef0b20-0.avif rename to public/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/ff2daa67-d86c-4efb-a1fd-4fd4fe236255-0.avif diff --git a/scripts/js/lib/api/addonDocsPipeline.ts b/scripts/js/lib/api/addonDocsPipeline.ts index 480535cfd8d..ffa12f78d8a 100644 --- a/scripts/js/lib/api/addonDocsPipeline.ts +++ b/scripts/js/lib/api/addonDocsPipeline.ts @@ -75,11 +75,7 @@ export async function runAddonDocsPipeline( objectsInv, allObjectInvs, ); - await writeMarkdownResults( - pkg, - docsBaseFolder, - results.map(remapExplanationsUrl), - ); + await writeMarkdownResults(pkg, docsBaseFolder, results); // handle Jupyter notebook files const notebookFiles = files.filter((f) => f.endsWith(".ipynb")); @@ -92,7 +88,7 @@ export async function runAddonDocsPipeline( const imageDestination = pkg.outputDir(`${DOCS_BASE_PATH}/images/addons`); const notebookImages = collectNotebookImages(initialNotebooks, imageDestination); const notebooks = processNotebooks( - initialNotebooks.map(remapExplanationsUrl), + initialNotebooks, objectsInv, allObjectInvs, pkg, @@ -112,17 +108,6 @@ export async function runAddonDocsPipeline( await writeTocFile(pkg, docsBaseFolder, outputPath); } -/** - * Rewrites URLs of the form `.../explanation(s)/foo` to `.../how-tos/explanations-foo` - * so that explanations land under the how-tos directory - */ -function remapExplanationsUrl<T extends { url: string }>(item: T): T { - return { - ...item, - url: item.url.replace(/\/explanations?\/(.+)$/, "/how-tos/explanations-$1"), - }; -} - async function writeTocFile( pkg: Pkg, docsBaseFolder: string, diff --git a/scripts/js/lib/api/generateAddonToc.test.ts b/scripts/js/lib/api/generateAddonToc.test.ts index 1507401356e..b32b0470e2d 100644 --- a/scripts/js/lib/api/generateAddonToc.test.ts +++ b/scripts/js/lib/api/generateAddonToc.test.ts @@ -156,7 +156,6 @@ test("how-tos directory becomes Guides subsection in main section", async () => const guidesEntry = main.children?.find((c) => c.title === "Guides"); expect(guidesEntry).toEqual({ title: "Guides", - url: "/docs/addons/my-addon/how-tos/index", children: [ { title: "How to do alpha", @@ -190,7 +189,6 @@ test("tutorials directory becomes its own top-level section", async () => { expect(tutorialsSection).toEqual({ title: "Tutorials", collapsible: false, - url: "/docs/addons/my-addon/tutorials/index", children: [ { title: "Getting started", @@ -236,25 +234,43 @@ test("unknown subdirectory uses capitalized directory name as section title", as test("subdir with only index.mdx renders as dropdown with index as child", async () => { const { tmpDir } = await makeTmpAddonDir({ "index.mdx": mdx("Home"), - "explanations/index.mdx": mdx("Explanations Index", "Explanatory material"), + "references/index.mdx": mdx("References Index", "Reference material"), }); const pkg = await makePkg(); const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); const main = toc.children[0]; - const explanations = main.children?.find((c) => c.title === "Explanations"); - expect(explanations).toEqual({ - title: "Explanations", + const references = main.children?.find((c) => c.title === "References"); + expect(references).toEqual({ + title: "References", children: [ { - title: "Explanatory material", - url: "/docs/addons/my-addon/explanations/index", + title: "Reference material", + url: "/docs/addons/my-addon/references/index", }, ], }); // No url on the section itself — the index is the child, not a parent link. - expect(explanations?.url).toBeUndefined(); + expect(references?.url).toBeUndefined(); +}); + +test("explanations directory is merged into the Guides section", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + "how-tos/guide.mdx": mdx("A guide"), + "explanations/background.mdx": mdx("Background"), + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const main = toc.children[0]; + + const guideSections = main.children?.filter((c) => c.title === "Guides"); + // Only one Guides section — both dirs merged into it. + expect(guideSections).toHaveLength(1); + const guides = guideSections![0]; + expect(guides.children?.map((c) => c.title)).toEqual(["Background", "A guide"]); }); test("subdir without index.mdx has no url on the section entry", async () => { diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts index a865bae5c06..35a529a54bb 100644 --- a/scripts/js/lib/api/generateAddonToc.ts +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -27,6 +27,7 @@ const TOP_LEVEL_SECTIONS = new Set(["tutorials"]); // labels for known subdirectory names. const DIR_LABELS: Record<string, string> = { "how-tos": "Guides", + explanations: "Guides", }; type AddonTocSection = TocEntry & { collapsible?: boolean }; @@ -71,6 +72,8 @@ export async function generateAddonToc( } // Subdirectories → either nested subsections or top-level sections. + // Dirs that share the same label are merged into one section. + const mergedSections = new Map<string, TocEntry>(); for (const dir of subdirNames) { const entry = await buildSubdirEntry(dir, addonDocsPath, addonUrlBase); if (!entry) continue; @@ -78,7 +81,16 @@ export async function generateAddonToc( if (TOP_LEVEL_SECTIONS.has(dir)) { topLevelSections.push({ ...entry, collapsible: false }); } else { - mainChildren.push(entry); + const existingSection = mergedSections.get(entry.title); + if (existingSection) { + existingSection.children = [ + ...(existingSection.children ?? []), + ...(entry.children ?? []), + ]; + } else { + mergedSections.set(entry.title, entry); + mainChildren.push(entry); + } } } @@ -155,11 +167,7 @@ async function buildSubdirEntry( children.push({ title, url: `${addonUrlBase}/${dirName}/index` }); } - const entry: TocEntry = { title: sectionTitle, children }; - if (hasIndex && contentFiles.length > 0) { - entry.url = `${addonUrlBase}/${dirName}/index`; - } - return entry; + return { title: sectionTitle, children }; } /** @@ -179,9 +187,9 @@ function readMdxTitle(content: string, filePath: string): string { const withoutFrontmatter = content.replace(/^---\n[\s\S]*?\n---\n/, ""); // Try h1 first, fall back to h2. const h1 = withoutFrontmatter.match(/^#\s+(.+)$/m); - if (h1) return h1[1].trim(); + if (h1) return stripInlineCode(h1[1].trim()); const h2 = withoutFrontmatter.match(/^##\s+(.+)$/m); - if (h2) return h2[1].trim(); + if (h2) return stripInlineCode(h2[1].trim()); return fileBasename(filePath); } @@ -203,14 +211,18 @@ function readNotebookTitle(content: string, filePath: string): string { : String(cell.source); const h1 = src.match(/^#\s+(.+)$/m); - if (h1) return h1[1].trim(); + if (h1) return stripInlineCode(h1[1].trim()); const h2 = src.match(/^##\s+(.+)$/m); - if (h2) return h2[1].trim(); + if (h2) return stripInlineCode(h2[1].trim()); } return fileBasename(filePath); } +function stripInlineCode(text: string): string { + return text.replace(/`([^`]+)`/g, "$1"); +} + function fileBasename(filePath: string): string { return filePath.split("/").pop()?.replace(/\.(mdx|ipynb)$/, "") ?? filePath; } diff --git a/scripts/js/lib/api/specialCaseResults.ts b/scripts/js/lib/api/specialCaseResults.ts index 779797a00ff..41ad208bf25 100644 --- a/scripts/js/lib/api/specialCaseResults.ts +++ b/scripts/js/lib/api/specialCaseResults.ts @@ -18,6 +18,7 @@ export function transformSpecialCaseUrl(url: string): string { // We use `-` rather than `_` as our delimiter. .replace(/(?<=^|\/)release_notes(?=\/|#|$)/g, "release-notes") .replace(/(?<=^|\/)how_tos(?=\/|#|$)/g, "how-tos") + .replace(/(?<=^|\/)explanation(?=\/|#|$)/g, "explanations") .replace(/(?<=^|\/)terra(?=#|$)/g, "index") .replace(/(?<=^|\/)ibm-runtime(?=#|$)/g, "index") ); From 08761f42b5012cd5ac503132c37734c2b0172b41 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Mon, 18 May 2026 15:31:11 -0400 Subject: [PATCH 067/104] remove double backticks in title --- docs/addons/qiskit-addon-utils/_toc.json | 2 +- scripts/js/lib/api/generateAddonToc.ts | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/addons/qiskit-addon-utils/_toc.json b/docs/addons/qiskit-addon-utils/_toc.json index 35e678b8179..af7d32a9ecc 100644 --- a/docs/addons/qiskit-addon-utils/_toc.json +++ b/docs/addons/qiskit-addon-utils/_toc.json @@ -23,7 +23,7 @@ "url": "/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth" }, { - "title": "Slicing circuits using `qiskit_addon_utils.slicing`", + "title": "Slicing circuits using qiskit_addon_utils.slicing", "url": "/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices" } ] diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts index 35a529a54bb..a432868dd7e 100644 --- a/scripts/js/lib/api/generateAddonToc.ts +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -220,7 +220,7 @@ function readNotebookTitle(content: string, filePath: string): string { } function stripInlineCode(text: string): string { - return text.replace(/`([^`]+)`/g, "$1"); + return text.replace(/``([^`]+)``/g, "$1").replace(/`([^`]+)`/g, "$1"); } function fileBasename(filePath: string): string { From a34048452cc52bc3313f62335e16d35169b72eb3 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 19 May 2026 14:34:06 -0400 Subject: [PATCH 068/104] removes tutorials --- docs/addons/qiskit-addon-aqc-tensor/_toc.json | 10 - .../tutorials/01-initial-state-aqc.ipynb | 751 -------------- .../tutorials/index.mdx | 15 - 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a/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.ipynb b/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.ipynb deleted file mode 100644 index 5d044bd2ca0..00000000000 --- a/docs/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc.ipynb +++ /dev/null @@ -1,751 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "frontmatter", - "metadata": {}, - "source": [ - "---\n", - "title: \"Reducing circuit depth with the AQC-Tensor Qiskit addon\"\n", - "description: \"Reducing circuit depth with the AQC-Tensor Qiskit addon for the latest version of Approximate quantum compilation (AQC-Tensor)\"\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "0f0b5824", - "metadata": {}, - "source": [ - "# Reducing circuit depth with the AQC-Tensor Qiskit addon\n", - "\n", - "In this notebook, we will work through the steps of a [Qiskit pattern](/docs/guides/intro-to-patterns) while using **approximate quantum compilation with tensor networks (AQC-Tensor)** to achieve a lower circuit depth than would ordinarily be needed to perform Trotter evolution.\n", - "\n", - "These are the steps that we will take:\n", - "\n", - "- **Step 1: Map to quantum problem**\n", - " - Initialize our problem's Hamiltonian and observable(s)\n", - " - <font color='#0F62FE'>Generate a target tensor-network state for the initial portion of the circuit</font>\n", - " - <font color='#0F62FE'>Generate a low-depth circuit which approximates the portion being compressed</font>\n", - " - <font color='#0F62FE'>Generate a general ansatz from that circuit</font>\n", - " - <font color='#0F62FE'>Optimize the parameters to bring the ansatz as close as possible to the target</font>\n", - " - <font color='#0F62FE'>Add subsequent Trotter steps to the optimized ansatz</font>\n", - "- **Step 2: Optimize for target hardware**\n", - " - Transpile the circuit for hardware\n", - "- **Step 3: Execute experiments**\n", - " - Use a fake backend for simplicity\n", - "- **Step 4: Reconstruct results**\n", - " - N/A; instead, we just output the measured observable" - ] - }, - { - "cell_type": "markdown", - "id": "79042f19-241d-48fb-aa87-134a93082c94", - "metadata": {}, - "source": [ - "## Step 1: Map to quantum circuit and operator\n", - "\n", - "### Set up a model Hamiltonian and observable\n", - "\n", - "In this notebook, we use the Ising model on a circle of 10 sites:\n", - "$$\n", - "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{10} J_{i,(i+1)} Z_i Z_{(i+1)} + h_i X_i \\, ,\n", - "$$\n", - "where the periodic boundary conditions imply that for $i=10$ we obtain $i+1=11\\rightarrow1$, $J$ is the coupling strength between two sites and $h$ is the external magnetic field." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "1f700ca7-ad7e-45ac-a5eb-50962ce67b48", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:34:09.963621Z", - "iopub.status.busy": "2026-05-14T20:34:09.963275Z", - "iopub.status.idle": "2026-05-14T20:34:10.299710Z", - "shell.execute_reply": "2026-05-14T20:34:10.298755Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_3199/2599302630.py:1: DeprecationWarning: Using Qiskit with Python 3.9 is deprecated as of the 2.1.0 release. Support for running Qiskit with Python 3.9 will be removed in the 2.3.0 release, which coincides with when Python 3.9 goes end of life.\n", - " from qiskit.transpiler import CouplingMap\n" - ] - } - ], - "source": [ - "from qiskit.transpiler import CouplingMap\n", - "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", - "\n", - "# Generate some coupling map to use for this example\n", - "coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False)\n", - "\n", - "# Choose a 10-qubit circle on this coupling map\n", - "reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 4, 15, 3, 9])\n", - "\n", - "# Get a qubit operator describing the Ising field model\n", - "hamiltonian = generate_xyz_hamiltonian(\n", - " reduced_coupling_map,\n", - " coupling_constants=(0.0, 0.0, 1.0),\n", - " ext_magnetic_field=(0.4, 0.0, 0.0),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d88fd2c0-a4cd-46be-a1aa-7f6dec219791", - "metadata": {}, - "source": [ - "The observable we will measure is the total magnetization." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9c864767-c218-4656-b20d-3e4e8c6f47af", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:34:10.303710Z", - "iopub.status.busy": "2026-05-14T20:34:10.303315Z", - "iopub.status.idle": "2026-05-14T20:34:10.307910Z", - "shell.execute_reply": "2026-05-14T20:34:10.307176Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "L = reduced_coupling_map.size()\n", - "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)" - ] - }, - { - "cell_type": "markdown", - "id": "70aa63a7-7560-4b29-9391-e1f42abdf1fd", - "metadata": {}, - "source": [ - "### Determine how much of the time evolution to simulate classically\n", - "\n", - "Our overall goal is to simulate time evolution of the above model Hamiltonian. We do so by Trotter evolution, which we split into two portions:\n", - "\n", - "1. An initial portion that is simulable with matrix product states (MPS). We will \"compile\" this portion using AQC as presented in https://arxiv.org/abs/2301.08609.\n", - "2. A subsequent portion of the circuit that will be executed on hardware." - ] - }, - { - "cell_type": "markdown", - "id": "442de278-82f9-4fcf-8b30-88804e7c170d", - "metadata": {}, - "source": [ - "Let's plan to use AQC-Tensor to compress our time evolution circuit up to time $t=4$, then evolve using ordinary Trotter steps up to $t=5$." - ] - }, - { - "cell_type": "markdown", - "id": "bf2243b5-56c7-48e8-acc6-7c8c720a8997", - "metadata": {}, - "source": [ - "### Generate circuits before and after split\n", - "\n", - "Now that we have chosen to split at $t=4$, we will generate two circuits:\n", - "\n", - "1. A \"target\" circuit for the AQC portion of the evolution, from $t_i=0$ to $t_f=4$. Because this is being simulated by a tensor-network simulator, the number of layers affects execution time only by a constant factor, so we might as well use a generous number of layers to minimize Trotter error." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c7593867-ed56-4f01-b2a9-1641ea4a5d10", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:34:10.310480Z", - "iopub.status.busy": "2026-05-14T20:34:10.310261Z", - "iopub.status.idle": "2026-05-14T20:34:10.333239Z", - "shell.execute_reply": "2026-05-14T20:34:10.332438Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.synthesis import SuzukiTrotter\n", - "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", - "\n", - "aqc_evolution_time = 4.0\n", - "aqc_target_num_trotter_steps = 45\n", - "\n", - "aqc_target_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=aqc_target_num_trotter_steps),\n", - " time=aqc_evolution_time,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "bc3a6acc-8eb3-486e-9dd8-a58a82a7ccf9", - "metadata": {}, - "source": [ - "2. A subsequent evolution circuit, which evolves from $t_i=4$ to $t_f=5$. Because this is being run on quantum hardware, it is desirable to use as few Trotter layers as possible." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c97ac3e8-b7bd-4727-8495-a513bf143b1d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:34:10.335597Z", - "iopub.status.busy": "2026-05-14T20:34:10.335372Z", - "iopub.status.idle": "2026-05-14T20:34:10.341086Z", - "shell.execute_reply": "2026-05-14T20:34:10.340313Z" - } - }, - "outputs": [], - "source": [ - "subsequent_evolution_time = 1.0\n", - "subsequent_num_trotter_steps = 5\n", - "\n", - "subsequent_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=subsequent_num_trotter_steps),\n", - " time=subsequent_evolution_time,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "3731468d-073b-460a-a87c-4920a35d44d9", - "metadata": {}, - "source": [ - "For the sake of later comparison, let's also generate a third circuit: one that evolves for `aqc_evolution_time` but which has the same evolution time per Trotter step as the subsequent circuit. This is the circuit we would have working with had we not used a generous number of Trotter steps for the target circuit. We will refer to this as the _comparison circuit_." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "557cc3a4-5909-4087-903d-2b603927071d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:34:10.343079Z", - "iopub.status.busy": "2026-05-14T20:34:10.342902Z", - "iopub.status.idle": "2026-05-14T20:34:10.350149Z", - "shell.execute_reply": "2026-05-14T20:34:10.349396Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "20" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "aqc_comparison_num_trotter_steps = int(\n", - " subsequent_num_trotter_steps / subsequent_evolution_time * aqc_evolution_time\n", - ")\n", - "aqc_comparison_num_trotter_steps" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4c9bb0cd-0bd4-4236-9046-55a5222a034a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:34:10.352418Z", - "iopub.status.busy": "2026-05-14T20:34:10.352135Z", - "iopub.status.idle": "2026-05-14T20:34:10.361237Z", - "shell.execute_reply": "2026-05-14T20:34:10.360557Z" - } - }, - "outputs": [], - "source": [ - "comparison_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=aqc_comparison_num_trotter_steps),\n", - " time=aqc_evolution_time,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "0c7d8499-afd9-4fdd-88a6-8dc33e144ee2", - "metadata": {}, - "source": [ - "### Generate an ansatz and initial parameters from a Trotter circuit with fewer steps\n", - "\n", - "First, we construct a \"good\" circuit that has the same evolution time as the target circuit, but with fewer Trotter steps (and thus fewer layers).\n", - "\n", - "Then we pass this \"good\" circuit to AQC-Tensor's `generate_ansatz_from_circuit` function. This function analyzes the two-qubit connectivity of the circuit and returns two things:\n", - "1. a general, parametrized ansatz circuit with the same two-qubit connectivity as the input circuit; and,\n", - "2. parameters that, when plugged into the ansatz, yield the input (good) circuit.\n", - "\n", - "Soon we will take these parameters and iteratively adjust them to bring the ansatz circuit as close as possible to the target MPS." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "17c2f81f-dcfa-436a-b592-af3efb46d75a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:34:10.363341Z", - "iopub.status.busy": "2026-05-14T20:34:10.363136Z", - "iopub.status.idle": "2026-05-14T20:34:12.178747Z", - "shell.execute_reply": "2026-05-14T20:34:12.177775Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc/extracted-outputs/17c2f81f-dcfa-436a-b592-af3efb46d75a-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_aqc_tensor import generate_ansatz_from_circuit\n", - "\n", - "aqc_ansatz_num_trotter_steps = 5\n", - "\n", - "aqc_good_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=aqc_ansatz_num_trotter_steps),\n", - " time=aqc_evolution_time,\n", - ")\n", - "\n", - "aqc_ansatz, aqc_initial_parameters = generate_ansatz_from_circuit(\n", - " aqc_good_circuit, qubits_initially_zero=True\n", - ")\n", - "aqc_ansatz.draw(\"mpl\", fold=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6864f636-91f6-4ba4-86d4-da7521474977", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:34:12.182547Z", - "iopub.status.busy": "2026-05-14T20:34:12.182217Z", - "iopub.status.idle": "2026-05-14T20:34:12.201442Z", - "shell.execute_reply": "2026-05-14T20:34:12.200743Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Comparison circuit: depth 120\n", - "Target circuit: depth 270\n", - "Ansatz circuit: depth 23, with 515 parameters\n" - ] - } - ], - "source": [ - "print(f\"Comparison circuit: depth {comparison_circuit.depth()}\")\n", - "print(f\"Target circuit: depth {aqc_target_circuit.depth()}\")\n", - "print(f\"Ansatz circuit: depth {aqc_ansatz.depth()}, with {len(aqc_initial_parameters)} parameters\")" - ] - }, - { - "cell_type": "markdown", - "id": "79845e2d-d563-4776-9e59-3a5c9eeb5687", - "metadata": {}, - "source": [ - "### Choose settings for tensor network simulation\n", - "\n", - "Here, we use the tensor network simulator based on [quimb](http://quimb.readthedocs.io/). In this example, we use quimb's matrix-product state (MPS) simulator, and we use [JAX](https://docs.jax.dev/en/latest/) for automatic differentiation. See the [API documentation](../stubs/qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator.rst) for more information on how to use the quimb simulator." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1b58e2e8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:34:12.203563Z", - "iopub.status.busy": "2026-05-14T20:34:12.203259Z", - "iopub.status.idle": "2026-05-14T20:34:13.041714Z", - "shell.execute_reply": "2026-05-14T20:34:13.040898Z" - } - }, - "outputs": [], - "source": [ - "from functools import partial\n", - "\n", - "import quimb.tensor\n", - "\n", - "from qiskit_addon_aqc_tensor.simulation.quimb import QuimbSimulator\n", - "\n", - "simulator_settings = QuimbSimulator(\n", - " partial(quimb.tensor.CircuitMPS, max_bond=100, cutoff=1e-8),\n", - " autodiff_backend=\"jax\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "cf375cc7-f8c5-4534-b0ec-b2e9d6582840", - "metadata": {}, - "source": [ - "### Construct matrix-product state representation of the AQC target state\n", - "\n", - "Next, we build a matrix-product representation of the state to be approximated by AQC." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a8a6bf5e-31ec-48e2-b96e-ce3d7cc54ff7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:34:13.044825Z", - "iopub.status.busy": "2026-05-14T20:34:13.044536Z", - "iopub.status.idle": "2026-05-14T20:34:23.491200Z", - "shell.execute_reply": "2026-05-14T20:34:23.490319Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_aqc_tensor.simulation import tensornetwork_from_circuit\n", - "\n", - "aqc_target_mps = tensornetwork_from_circuit(aqc_target_circuit, simulator_settings)" - ] - }, - { - "cell_type": "markdown", - "id": "88071374-04e4-47ee-8463-16ad0f5fa33b", - "metadata": {}, - "source": [ - "Note that because we chose a generous number of Trotter steps for the target state, it actually has less Trotter error than the comparison circuit. We can calculate the fidelity ($| \\langle \\psi_1 | \\psi_2 \\rangle |^2$) of the state prepared by the comparison circuit vs. the target state:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "65c3febb-8688-4806-aca6-99e9b860b377", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:34:23.494338Z", - "iopub.status.busy": "2026-05-14T20:34:23.494111Z", - "iopub.status.idle": "2026-05-14T20:34:24.052177Z", - "shell.execute_reply": "2026-05-14T20:34:24.051361Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9996761790297248" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_aqc_tensor.simulation import compute_overlap\n", - "\n", - "comparison_mps = tensornetwork_from_circuit(comparison_circuit, simulator_settings)\n", - "comparison_fidelity = abs(compute_overlap(comparison_mps, aqc_target_mps)) ** 2\n", - "comparison_fidelity" - ] - }, - { - "cell_type": "markdown", - "id": "5859794d-efbd-4e5b-a1d2-2b7add48b8e4", - "metadata": {}, - "source": [ - "### Optimize the parameters of the ansatz using MPS calculations\n", - "\n", - "Here, we minimize the simplest possible cost function, `MaximizeStateFidelity`, by using the L-BFGS optimizer from scipy.\n", - "\n", - "We choose a stopping point for the fidelity such that it will be above what the comparison circuit would have been, without using AQC. Once this is reached, the compressed circuit has less Trotter error _and_ less depth than the original circuit. Given more processing time, further optimization steps can be performed to bring the fidelity even higher." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "66dddd19-51d6-4bd4-a3df-2c9b457d4149", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:34:24.054596Z", - "iopub.status.busy": "2026-05-14T20:34:24.054296Z", - "iopub.status.idle": "2026-05-14T20:36:02.294412Z", - "shell.execute_reply": "2026-05-14T20:36:02.293777Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.95080172\n", - "Intermediate result: Fidelity 0.98408407\n", - "Intermediate result: Fidelity 0.99140959\n", - "Intermediate result: Fidelity 0.99518665\n", - "Intermediate result: Fidelity 0.99562326\n", - "Intermediate result: Fidelity 0.99645786\n", - "Intermediate result: Fidelity 0.99678929\n", - "Intermediate result: Fidelity 0.99715317\n", - "Intermediate result: Fidelity 0.99756663\n", - "Intermediate result: Fidelity 0.99803794\n", - "Intermediate result: Fidelity 0.99832093\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99855392\n", - "Intermediate result: Fidelity 0.99868269\n", - "Intermediate result: Fidelity 0.99886211\n", - "Intermediate result: Fidelity 0.99901486\n", - "Intermediate result: Fidelity 0.99911781\n", - "Intermediate result: Fidelity 0.99919359\n", - "Intermediate result: Fidelity 0.99925985\n", - "Intermediate result: Fidelity 0.99938652\n", - "Intermediate result: Fidelity 0.99949188\n", - "Intermediate result: Fidelity 0.99958555\n", - "Intermediate result: Fidelity 0.99961916\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intermediate result: Fidelity 0.99963156\n", - "Intermediate result: Fidelity 0.99966815\n", - "Intermediate result: Fidelity 0.99970081\n", - "Done after 25 iterations.\n" - ] - } - ], - "source": [ - "from scipy.optimize import OptimizeResult, minimize\n", - "\n", - "from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity\n", - "\n", - "objective = MaximizeStateFidelity(aqc_target_mps, aqc_ansatz, simulator_settings)\n", - "\n", - "stopping_point = 1 - comparison_fidelity\n", - "\n", - "\n", - "def callback(intermediate_result: OptimizeResult):\n", - " print(f\"Intermediate result: Fidelity {1 - intermediate_result.fun:.8}\")\n", - " if intermediate_result.fun < stopping_point:\n", - " # Good enough for now\n", - " raise StopIteration\n", - "\n", - "\n", - "result = minimize(\n", - " objective.loss_function,\n", - " aqc_initial_parameters,\n", - " method=\"L-BFGS-B\",\n", - " jac=True,\n", - " options={\"maxiter\": 100},\n", - " callback=callback,\n", - ")\n", - "if result.status not in (\n", - " 0,\n", - " 1,\n", - " 99,\n", - "): # 0 => success; 1 => max iterations reached; 99 => early termination via StopIteration\n", - " raise RuntimeError(f\"Optimization failed: {result.message} (status={result.status})\")\n", - "\n", - "print(f\"Done after {result.nit} iterations.\")\n", - "aqc_final_parameters = result.x" - ] - }, - { - "cell_type": "markdown", - "id": "4f668909-529d-4f2c-bfe7-e7766cd33ce6", - "metadata": {}, - "source": [ - "### Construct the final circuit to pass to the transpiler" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "dbf60899-6f9d-4222-a499-e68bd5eabaf5", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:36:02.296852Z", - "iopub.status.busy": "2026-05-14T20:36:02.296524Z", - "iopub.status.idle": "2026-05-14T20:36:04.894647Z", - "shell.execute_reply": "2026-05-14T20:36:04.893967Z" - }, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-aqc-tensor/tutorials/01-initial-state-aqc/extracted-outputs/dbf60899-6f9d-4222-a499-e68bd5eabaf5-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final_circuit = aqc_ansatz.assign_parameters(aqc_final_parameters)\n", - "final_circuit.compose(subsequent_circuit, inplace=True)\n", - "final_circuit.draw(\"mpl\", fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "817839e7-17e7-4dcb-b620-a7711aeb2edf", - "metadata": {}, - "source": [ - "## Step 2: Transpile for execution on target hardware\n", - "\n", - "In Step 2 of a [Qiskit pattern](/docs/guides/intro-to-patterns), we transpile this circuit and any desired observable(s) for execution on a target device. Here we are using a fake backend provided by `qiskit-ibm-runtime`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "783aea48-3d0a-4d7b-a5ed-e3f4888c47a8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:36:04.897742Z", - "iopub.status.busy": "2026-05-14T20:36:04.897491Z", - "iopub.status.idle": "2026-05-14T20:36:05.360910Z", - "shell.execute_reply": "2026-05-14T20:36:05.360061Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_3199/204133634.py:2: DeprecationWarning: Using qiskit-ibm-runtime with Python 3.9 is deprecated as of the 0.41.0 release. Support for running qiskit-ibm-runtime with Python 3.9 will be removed in a future release.\n", - " from qiskit_ibm_runtime.fake_provider import FakeMelbourneV2\n" - ] - } - ], - "source": [ - "from qiskit import transpile\n", - "from qiskit_ibm_runtime.fake_provider import FakeMelbourneV2\n", - "\n", - "backend = FakeMelbourneV2()\n", - "\n", - "isa_circuit = transpile(final_circuit, backend)\n", - "isa_observable = observable.apply_layout(isa_circuit.layout)" - ] - }, - { - "cell_type": "markdown", - "id": "5ff0be42-b45a-4af3-8317-3c1f87758771", - "metadata": {}, - "source": [ - "The resulting ISA circuit can then be sent for execution on the backend (step 3 of a [Qiskit pattern](/docs/guides/intro-to-patterns))." - ] - }, - { - "cell_type": "markdown", - "id": "6600071d-e0b6-45cd-ae4e-0ffe882b0955", - "metadata": {}, - "source": [ - "## Step 3: Execute on quantum hardware" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "bd8039e4-08c4-4a7f-98ba-f8c11c8da795", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:36:05.363486Z", - "iopub.status.busy": "2026-05-14T20:36:05.363113Z", - "iopub.status.idle": "2026-05-14T20:36:07.811329Z", - "shell.execute_reply": "2026-05-14T20:36:07.810459Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import EstimatorV2 as Estimator\n", - "\n", - "estimator = Estimator(backend)\n", - "job = estimator.run([(isa_circuit, isa_observable)])\n", - "pub_result = job.result()[0]" - ] - }, - { - "cell_type": "markdown", - "id": "f56257c7-55c0-426a-835f-6e08b306536a", - "metadata": {}, - "source": [ - "## Step 4: Reconstruct\n", - "\n", - "Reconstruction is not necessary in our case. We can just look at the result." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "84a212df-8e89-4576-a407-903da48a0c5a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-14T20:36:07.813749Z", - "iopub.status.busy": "2026-05-14T20:36:07.813542Z", - "iopub.status.idle": "2026-05-14T20:36:07.818266Z", - "shell.execute_reply": "2026-05-14T20:36:07.817548Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "np.float64(0.07687988281250001)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pub_result.data.evs[()]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx deleted file mode 100644 index c658b490a1d..00000000000 --- a/docs/addons/qiskit-addon-aqc-tensor/tutorials/index.mdx +++ /dev/null @@ -1,15 +0,0 @@ ---- -title: "AQC-Tensor Tutorials" -description: "AQC-Tensor Tutorials for the lastest version of Approximate quantum compilation (AQC-Tensor)" ---- - -<span id="tutorials" /> - -# AQC-Tensor Tutorials - -* [Tutorial 1](01-basic-workflow-ipynb): Show the basic end-to-end workflow of the AQC-Tensor addon. - -[![](/docs/images/addons/qiskit-addon-aqc-tensor/tutorials_01_initial_state_aqc_26_0.avif)](01-initial-state-aqc) - -[Reducing circuit depth with the AQC-Tensor Qiskit addon](01-initial-state-aqc) - diff --git a/docs/addons/qiskit-addon-cutting/_toc.json b/docs/addons/qiskit-addon-cutting/_toc.json index 100082eaa7c..d4e70b56b23 100644 --- a/docs/addons/qiskit-addon-cutting/_toc.json +++ b/docs/addons/qiskit-addon-cutting/_toc.json @@ -47,28 +47,6 @@ ], "collapsible": false }, - { - "title": "Tutorials", - "children": [ - { - "title": "Gate Cutting to Reduce Circuit Width", - "url": "/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width" - }, - { - "title": "Gate Cutting to Reduce Circuit Depth", - "url": "/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth" - }, - { - "title": "Wire Cutting Phrased as a Two-Qubit Move Instruction", - "url": "/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction" - }, - { - "title": "Automatically find cuts", - "url": "/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding" - } - ], - "collapsible": false - }, { "title": "API reference", "collapsible": false, diff --git a/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width.ipynb deleted file mode 100644 index e1f169f31de..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width.ipynb +++ /dev/null @@ -1,539 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "frontmatter", - "metadata": {}, - "source": [ - "---\n", - "title: \"Gate Cutting to Reduce Circuit Width\"\n", - "description: \"Gate Cutting to Reduce Circuit Width for the latest version of Circuit cutting\"\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "ad1f14b4", - "metadata": {}, - "source": [ - "# Gate Cutting to Reduce Circuit Width\n", - "\n", - "In this notebook, we will work through the steps of a [Qiskit pattern](/guides/intro-to-patterns) while using **circuit cutting** to reduce the number of qubits in a circuit. We will cut gates to enable us to reconstruct the expectation value of a four-qubit circuit using only two-qubit experiments.\n", - "\n", - "These are the steps that we will take:\n", - "\n", - "- **Step 1: Map problem to quantum circuits and operators**:\n", - " - Map the hamiltonian onto a quantum circuit.\n", - "- **Step 2: Optimize for target hardware** [_Uses the cutting addon_]:\n", - " - <font color='#0F62FE'>Cut the circuit and observable.</font>\n", - " - Transpile the subexperiments for hardware.\n", - "- **Step 3: Execute on target hardware**:\n", - " - Run the subexperiments obtained in Step 2 using a `Sampler` primitive.\n", - "- **Step 4: Post-process results** [_Uses the cutting addon_]:\n", - " - <font color='#0F62FE'>Combine the results of Step 3 to reconstruct the expectation value of the observable in question.</font>" - ] - }, - { - "cell_type": "markdown", - "id": "510910a6", - "metadata": {}, - "source": [ - "## Step 1: Map\n", - "\n", - "### Create a circuit to cut" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "96f5b72a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:50.252738Z", - "iopub.status.busy": "2025-03-28T16:50:50.252448Z", - "iopub.status.idle": "2025-03-28T16:50:51.350658Z", - "shell.execute_reply": "2025-03-28T16:50:51.349916Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width/extracted-outputs/96f5b72a-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit.circuit.library import efficient_su2\n", - "\n", - "qc = efficient_su2(4, entanglement=\"linear\", reps=2)\n", - "qc.assign_parameters([0.4] * len(qc.parameters), inplace=True)\n", - "\n", - "qc.draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "id": "8638fdf1", - "metadata": {}, - "source": [ - "### Specify an observable" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f75e8dd1", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.353234Z", - "iopub.status.busy": "2025-03-28T16:50:51.352789Z", - "iopub.status.idle": "2025-03-28T16:50:51.356422Z", - "shell.execute_reply": "2025-03-28T16:50:51.355858Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])" - ] - }, - { - "cell_type": "markdown", - "id": "162a5629", - "metadata": {}, - "source": [ - "## Step 2: Optimize\n", - "\n", - "### Separate the circuit and observable according to a specified qubit partitioning\n", - "\n", - "Each label in `partition_labels` corresponds to the `circuit` qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut.\n", - "\n", - "**Note:** The ``observables`` kwarg to `partition_problem` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "30326299", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.358249Z", - "iopub.status.busy": "2025-03-28T16:50:51.358050Z", - "iopub.status.idle": "2025-03-28T16:50:51.401645Z", - "shell.execute_reply": "2025-03-28T16:50:51.400913Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import partition_problem\n", - "\n", - "partitioned_problem = partition_problem(\n", - " circuit=qc, partition_labels=\"AABB\", observables=observable.paulis\n", - ")\n", - "subcircuits = partitioned_problem.subcircuits\n", - "subobservables = partitioned_problem.subobservables\n", - "bases = partitioned_problem.bases" - ] - }, - { - "cell_type": "markdown", - "id": "9d2d42c3", - "metadata": {}, - "source": [ - "### Visualize the decomposed problem" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6b54be63", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.404435Z", - "iopub.status.busy": "2025-03-28T16:50:51.403916Z", - "iopub.status.idle": "2025-03-28T16:50:51.410005Z", - "shell.execute_reply": "2025-03-28T16:50:51.409344Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'A': PauliList(['II', 'ZI', 'ZZ', 'XI', 'ZZ', 'IX']),\n", - " 'B': PauliList(['ZZ', 'IZ', 'II', 'XI', 'ZI', 'IX'])}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subobservables" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b7e06fac", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.411952Z", - "iopub.status.busy": "2025-03-28T16:50:51.411753Z", - "iopub.status.idle": "2025-03-28T16:50:51.547364Z", - "shell.execute_reply": "2025-03-28T16:50:51.546536Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width/extracted-outputs/b7e06fac-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[\"A\"].draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "11e45e83", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.549471Z", - "iopub.status.busy": "2025-03-28T16:50:51.549264Z", - "iopub.status.idle": "2025-03-28T16:50:51.738543Z", - "shell.execute_reply": "2025-03-28T16:50:51.737835Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/01-gate-cutting-to-reduce-circuit-width/extracted-outputs/11e45e83-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[\"B\"].draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "id": "4f8017b6-2954-4b51-8a07-f4de49832509", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Calculate the sampling overhead for the chosen cuts\n", - "\n", - "Here we cut two CNOT gates, resulting in a sampling overhead of $9^2$.\n", - "\n", - "For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](../explanation/index.rst)." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3d606ef8", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.741344Z", - "iopub.status.busy": "2025-03-28T16:50:51.740724Z", - "iopub.status.idle": "2025-03-28T16:50:51.745416Z", - "shell.execute_reply": "2025-03-28T16:50:51.744851Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sampling overhead: 81.0\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")" - ] - }, - { - "cell_type": "markdown", - "id": "ef3b061c-cd1d-4931-85f6-155e00b355be", - "metadata": {}, - "source": [ - "### Generate the subexperiments to run on the backend\n", - "\n", - "`generate_cutting_experiments` accepts `circuits`/`observables` args as dictionaries mapping qubit partition labels to the respective `subcircuit`/`subobservables`.\n", - "\n", - "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2029d18e-0e91-4160-b8c9-02cb9e1ba3cb", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.747765Z", - "iopub.status.busy": "2025-03-28T16:50:51.747387Z", - "iopub.status.idle": "2025-03-28T16:50:51.854533Z", - "shell.execute_reply": "2025-03-28T16:50:51.853782Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import generate_cutting_experiments\n", - "\n", - "subexperiments, coefficients = generate_cutting_experiments(\n", - " circuits=subcircuits, observables=subobservables, num_samples=np.inf\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "444b0038-b3d5-469c-85b2-4c1d9d8fce05", - "metadata": {}, - "source": [ - "### Choose a backend\n", - "\n", - "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "36c89aa0-70aa-4615-8198-77ec85e8aa93", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:51.857664Z", - "iopub.status.busy": "2025-03-28T16:50:51.857181Z", - "iopub.status.idle": "2025-03-28T16:50:52.542985Z", - "shell.execute_reply": "2025-03-28T16:50:52.542361Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", - "\n", - "backend = FakeManilaV2()" - ] - }, - { - "cell_type": "markdown", - "id": "85a1d76a-5c47-4210-a742-9b22f02f7200", - "metadata": {}, - "source": [ - "### Prepare the subexperiments for the backend\n", - "\n", - "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "184e0f36-1279-48a3-aab7-b7603bb71f66", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:52.545759Z", - "iopub.status.busy": "2025-03-28T16:50:52.545267Z", - "iopub.status.idle": "2025-03-28T16:50:56.855540Z", - "shell.execute_reply": "2025-03-28T16:50:56.854763Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.transpiler import generate_preset_pass_manager\n", - "\n", - "# Transpile the subexperiments to ISA circuits\n", - "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", - "isa_subexperiments = {\n", - " label: pass_manager.run(partition_subexpts)\n", - " for label, partition_subexpts in subexperiments.items()\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "d8870454-2173-4454-90b4-a034779510e0", - "metadata": {}, - "source": [ - "## Step 3: Execute\n", - "\n", - "### Run the subexperiments using the Qiskit Runtime Sampler primitive" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2dbb8148-0482-4a66-8316-335125f8a2b3", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:56.858196Z", - "iopub.status.busy": "2025-03-28T16:50:56.857749Z", - "iopub.status.idle": "2025-03-28T16:50:57.056138Z", - "shell.execute_reply": "2025-03-28T16:50:57.048260Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import SamplerV2, Batch\n", - "\n", - "# Submit each partition's subexperiments to the Qiskit Runtime Sampler\n", - "# primitive, in a single batch so that the jobs will run back-to-back.\n", - "with Batch(backend=backend) as batch:\n", - " sampler = SamplerV2(mode=batch)\n", - " jobs = {\n", - " label: sampler.run(subsystem_subexpts, shots=2**12)\n", - " for label, subsystem_subexpts in isa_subexperiments.items()\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "64cef60b-5130-467e-8e01-7460d78560ed", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:50:57.132131Z", - "iopub.status.busy": "2025-03-28T16:50:57.130570Z", - "iopub.status.idle": "2025-03-28T16:51:20.447808Z", - "shell.execute_reply": "2025-03-28T16:51:20.446901Z" - } - }, - "outputs": [], - "source": [ - "# Retrieve results\n", - "results = {label: job.result() for label, job in jobs.items()}" - ] - }, - { - "cell_type": "markdown", - "id": "f0032570", - "metadata": {}, - "source": [ - "## Step 4: Post-process\n", - "\n", - "### Reconstruct the expectation value\n", - "\n", - "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "7d57339c", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:20.451351Z", - "iopub.status.busy": "2025-03-28T16:51:20.450827Z", - "iopub.status.idle": "2025-03-28T16:51:25.701123Z", - "shell.execute_reply": "2025-03-28T16:51:25.700268Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import reconstruct_expectation_values\n", - "\n", - "# Get expectation values for each observable term\n", - "reconstructed_expval_terms = reconstruct_expectation_values(\n", - " results,\n", - " coefficients,\n", - " subobservables,\n", - ")\n", - "\n", - "# Reconstruct final expectation value\n", - "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" - ] - }, - { - "cell_type": "markdown", - "id": "53beaca3", - "metadata": {}, - "source": [ - "### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e3385ba5", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:25.708190Z", - "iopub.status.busy": "2025-03-28T16:51:25.707798Z", - "iopub.status.idle": "2025-03-28T16:51:25.747493Z", - "shell.execute_reply": "2025-03-28T16:51:25.744142Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstructed expectation value: 0.58167797\n", - "Exact expectation value: 0.56254612\n", - "Error in estimation: 0.01913185\n", - "Relative error in estimation: 0.03400939\n" - ] - } - ], - "source": [ - "from qiskit_aer.primitives import EstimatorV2\n", - "\n", - "estimator = EstimatorV2()\n", - "exact_expval = estimator.run([(qc, observable)]).result()[0].data.evs\n", - "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", - "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", - "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", - "print(\n", - " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth.ipynb deleted file mode 100644 index e91e317f2f0..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth.ipynb +++ /dev/null @@ -1,584 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "frontmatter", - "metadata": {}, - "source": [ - "---\n", - "title: \"Gate Cutting to Reduce Circuit Depth\"\n", - "description: \"Gate Cutting to Reduce Circuit Depth for the latest version of Circuit cutting\"\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "7ed4867f", - "metadata": {}, - "source": [ - "# Gate Cutting to Reduce Circuit Depth\n", - "\n", - "In this tutorial, we will reduce a circuit's depth by cutting distant gates, avoiding the swap gates that would otherwise be introduced by routing.\n", - "\n", - "These are the steps that we will take in this [Qiskit pattern](/guides/intro-to-patterns):\n", - "\n", - "- **Step 1: Map problem to quantum circuits and operators**:\n", - " - Map the hamiltonian onto a quantum circuit.\n", - "- **Step 2: Optimize for target hardware** [_Uses the cutting addon_]:\n", - " - <font color='#0F62FE'>Cut the circuit and observable.</font>\n", - " - Transpile the subexperiments for hardware.\n", - "- **Step 3: Execute on target hardware**:\n", - " - Run the subexperiments obtained in Step 2 using a `Sampler` primitive.\n", - "- **Step 4: Post-process results** [_Uses the cutting addon_]:\n", - " - <font color='#0F62FE'>Combine the results of Step 3 to reconstruct the expectation value of the observable in question.</font>" - ] - }, - { - "cell_type": "markdown", - "id": "07f7f75d", - "metadata": {}, - "source": [ - "## Step 1: Map\n", - "\n", - "### Create a circuit to run on the backend" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "54ed0f13", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:29.327835Z", - "iopub.status.busy": "2025-03-28T16:51:29.326393Z", - "iopub.status.idle": "2025-03-28T16:51:31.050966Z", - "shell.execute_reply": "2025-03-28T16:51:31.050169Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth/extracted-outputs/54ed0f13-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit.circuit.library import efficient_su2\n", - "\n", - "circuit = efficient_su2(num_qubits=4, entanglement=\"circular\")\n", - "circuit.assign_parameters([0.4] * len(circuit.parameters), inplace=True)\n", - "circuit.draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "id": "452cd19e", - "metadata": {}, - "source": [ - "### Specify an observable" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "105e858d", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:31.054439Z", - "iopub.status.busy": "2025-03-28T16:51:31.054037Z", - "iopub.status.idle": "2025-03-28T16:51:31.061841Z", - "shell.execute_reply": "2025-03-28T16:51:31.061246Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])" - ] - }, - { - "cell_type": "markdown", - "id": "699105a3-903e-49d8-826b-cc8b9b85c9df", - "metadata": {}, - "source": [ - "## Step 2: Optimize\n", - "\n", - "### Specify a backend\n", - "\n", - "You can provide either a fake backend or a hardware backend from Qiskit Runtime." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "756f2130-6947-479a-9fe7-97443c87a904", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:31.066068Z", - "iopub.status.busy": "2025-03-28T16:51:31.065650Z", - "iopub.status.idle": "2025-03-28T16:51:32.084249Z", - "shell.execute_reply": "2025-03-28T16:51:32.083362Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", - "\n", - "backend = FakeManilaV2()" - ] - }, - { - "cell_type": "markdown", - "id": "080a2a8b", - "metadata": {}, - "source": [ - "### Transpile the circuit, visualize the swaps, and note the depth\n", - "\n", - "We choose a layout that requires two swaps to execute the gates between qubits 3 and 0 and another two swaps to return the qubits to their initial positions." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b394da7a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:32.088232Z", - "iopub.status.busy": "2025-03-28T16:51:32.087771Z", - "iopub.status.idle": "2025-03-28T16:51:32.168573Z", - "shell.execute_reply": "2025-03-28T16:51:32.163841Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transpiled circuit depth: 30\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_3574/3423257129.py:8: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes.\n", - " print(f\"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\")\n" - ] - } - ], - "source": [ - "from qiskit.transpiler import generate_preset_pass_manager\n", - "\n", - "pass_manager = generate_preset_pass_manager(\n", - " optimization_level=1, backend=backend, initial_layout=[0, 1, 2, 3]\n", - ")\n", - "\n", - "transpiled_qc = pass_manager.run(circuit)\n", - "print(f\"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4fe4af43", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:32.176421Z", - "iopub.status.busy": "2025-03-28T16:51:32.174292Z", - "iopub.status.idle": "2025-03-28T16:51:33.477300Z", - "shell.execute_reply": "2025-03-28T16:51:33.474323Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth/extracted-outputs/4fe4af43-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transpiled_qc.draw(\"mpl\", scale=0.4, idle_wires=False, fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "6c106db7", - "metadata": {}, - "source": [ - "### Replace distant gates with `TwoQubitQPDGate`s by specifying their indices\n", - "\n", - "`cut_gates` will replace the gates in the specified indices with `TwoQubitQPDGate`s and also return a list of `QPDBasis` instances -- one for each gate decomposition." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "12e73c69", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:33.480297Z", - "iopub.status.busy": "2025-03-28T16:51:33.479989Z", - "iopub.status.idle": "2025-03-28T16:51:34.022077Z", - "shell.execute_reply": "2025-03-28T16:51:34.021152Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth/extracted-outputs/12e73c69-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_cutting import cut_gates\n", - "\n", - "# Find the indices of the distant gates\n", - "cut_indices = [\n", - " i\n", - " for i, instruction in enumerate(circuit.data)\n", - " if {circuit.find_bit(q)[0] for q in instruction.qubits} == {0, 3}\n", - "]\n", - "\n", - "# Decompose distant CNOTs into TwoQubitQPDGate instances\n", - "qpd_circuit, bases = cut_gates(circuit, cut_indices)\n", - "\n", - "qpd_circuit.draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "id": "069eb942-e947-4eff-a250-9a99c5ec47f0", - "metadata": {}, - "source": [ - "### Generate the subexperiments to run on the backend\n", - "\n", - "`generate_cutting_experiments` accepts a circuit containing `TwoQubitQPDGate` instances and observables as a `PauliList`.\n", - "\n", - "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst).\n", - "\n", - "**Note:** The ``observables`` kwarg to `generate_cutting_experiments` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "83b1efed-bafa-48c4-bbf0-cf7eb9027ac5", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:34.027775Z", - "iopub.status.busy": "2025-03-28T16:51:34.027177Z", - "iopub.status.idle": "2025-03-28T16:51:34.976541Z", - "shell.execute_reply": "2025-03-28T16:51:34.975799Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from qiskit_addon_cutting import generate_cutting_experiments\n", - "\n", - "# Generate the subexperiments and sampling coefficients\n", - "subexperiments, coefficients = generate_cutting_experiments(\n", - " circuits=qpd_circuit, observables=observable.paulis, num_samples=np.inf\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "c0acae3a", - "metadata": {}, - "source": [ - "### Calculate the sampling overhead for the chosen cuts\n", - "\n", - "Here we cut three CNOT gates, resulting in a sampling overhead of $9^3$.\n", - "\n", - "For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](../explanation/index.rst)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c7b28e2c", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:34.979488Z", - "iopub.status.busy": "2025-03-28T16:51:34.979281Z", - "iopub.status.idle": "2025-03-28T16:51:34.983347Z", - "shell.execute_reply": "2025-03-28T16:51:34.982624Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sampling overhead: 729.0\n" - ] - } - ], - "source": [ - "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")" - ] - }, - { - "cell_type": "markdown", - "id": "6929264d", - "metadata": {}, - "source": [ - "### Demonstrate that the QPD subexperiments will be shallower after cutting distant gates\n", - "\n", - "Here is an example of an arbitrarily chosen subexperiment generated from the QPD circuit. Its depth has been reduced by more than half. Many of these probabilistic subexperiments must be generated and evaluated in order to reconstruct an expectation value of the deeper circuit." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "70e2f1b6", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:34.985332Z", - "iopub.status.busy": "2025-03-28T16:51:34.985103Z", - "iopub.status.idle": "2025-03-28T16:51:35.433820Z", - "shell.execute_reply": "2025-03-28T16:51:35.432939Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original circuit depth after transpile: 30\n", - "QPD subexperiment depth after transpile: 7\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_3574/4252024425.py:5: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes.\n", - " f\"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\"\n", - "/tmp/ipykernel_3574/4252024425.py:8: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes.\n", - " f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}\"\n" - ] - }, - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/02-gate-cutting-to-reduce-circuit-depth/extracted-outputs/70e2f1b6-2.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Transpile the decomposed circuit to the same layout\n", - "transpiled_qpd_circuit = pass_manager.run(subexperiments[100])\n", - "\n", - "print(\n", - " f\"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\"\n", - ")\n", - "print(\n", - " f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}\"\n", - ")\n", - "transpiled_qpd_circuit.draw(\"mpl\", scale=0.8, idle_wires=False, fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "fd9a126c", - "metadata": {}, - "source": [ - "### Prepare subexperiments for the backend" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "736a7d14-71b0-47a0-847d-4972ab886918", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:35.436030Z", - "iopub.status.busy": "2025-03-28T16:51:35.435823Z", - "iopub.status.idle": "2025-03-28T16:51:47.520198Z", - "shell.execute_reply": "2025-03-28T16:51:47.519092Z" - } - }, - "outputs": [], - "source": [ - "# Transpile the subeperiments to the backend's instruction set architecture (ISA)\n", - "isa_subexperiments = pass_manager.run(subexperiments)" - ] - }, - { - "cell_type": "markdown", - "id": "241fc5e5-d367-4a31-aa1f-424acdbbd8dd", - "metadata": {}, - "source": [ - "## Step 3: Execute\n", - "\n", - "### Run the subexperiments using the Qiskit Runtime Sampler primitive" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e4dcc97b-8336-4eb9-9cc0-0d9b87a287e3", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:47.522983Z", - "iopub.status.busy": "2025-03-28T16:51:47.522756Z", - "iopub.status.idle": "2025-03-28T16:51:48.086035Z", - "shell.execute_reply": "2025-03-28T16:51:48.081351Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import SamplerV2\n", - "\n", - "# Set up the Qiskit Runtime Sampler primitive. For a fake backend, this will use a local simulator.\n", - "sampler = SamplerV2(backend)\n", - "\n", - "# Submit the subexperiments\n", - "job = sampler.run(isa_subexperiments)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a83922bc-0489-4ee5-a2b6-796103aae9bb", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:51:48.099798Z", - "iopub.status.busy": "2025-03-28T16:51:48.099494Z", - "iopub.status.idle": "2025-03-28T16:52:28.830126Z", - "shell.execute_reply": "2025-03-28T16:52:28.829495Z" - } - }, - "outputs": [], - "source": [ - "# Retrieve the results\n", - "results = job.result()" - ] - }, - { - "cell_type": "markdown", - "id": "f04d9134-651b-446e-93f4-aa0281786200", - "metadata": {}, - "source": [ - "## Step 4: Post-process\n", - "\n", - "### Reconstruct the expectation value\n", - "\n", - "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ace12f7f", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:28.832409Z", - "iopub.status.busy": "2025-03-28T16:52:28.832187Z", - "iopub.status.idle": "2025-03-28T16:52:30.916461Z", - "shell.execute_reply": "2025-03-28T16:52:30.915714Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import reconstruct_expectation_values\n", - "\n", - "reconstructed_expval_terms = reconstruct_expectation_values(\n", - " results,\n", - " coefficients,\n", - " observable.paulis,\n", - ")\n", - "# Reconstruct final expectation value\n", - "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" - ] - }, - { - "cell_type": "markdown", - "id": "3fc2327f", - "metadata": {}, - "source": [ - "### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "4928e703", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:30.918925Z", - "iopub.status.busy": "2025-03-28T16:52:30.918548Z", - "iopub.status.idle": "2025-03-28T16:52:30.932594Z", - "shell.execute_reply": "2025-03-28T16:52:30.932017Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstructed expectation value: 0.51977539\n", - "Exact expectation value: 0.50497603\n", - "Error in estimation: 0.01479936\n", - "Relative error in estimation: 0.02930705\n" - ] - } - ], - "source": [ - "from qiskit_aer.primitives import EstimatorV2\n", - "\n", - "estimator = EstimatorV2()\n", - "exact_expval = estimator.run([(circuit, observable)]).result()[0].data.evs\n", - "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", - "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", - "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", - "print(\n", - " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction.ipynb deleted file mode 100644 index bd4f6bbebdc..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction.ipynb +++ /dev/null @@ -1,650 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "frontmatter", - "metadata": {}, - "source": [ - "---\n", - "title: \"Wire Cutting Phrased as a Two-Qubit Move Instruction\"\n", - "description: \"Wire Cutting Phrased as a Two-Qubit Move Instruction for the latest version of Circuit cutting\"\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "9916de95-2376-49f0-91ad-07f07939dfb5", - "metadata": {}, - "source": [ - "# Wire Cutting Phrased as a Two-Qubit `Move` Instruction\n", - "\n", - "In this tutorial, we will reconstruct expectation values of a seven-qubit circuit by splitting it into two four-qubit circuits using wire cutting.\n", - "\n", - "These are the steps that we will take in this [Qiskit pattern](/guides/intro-to-patterns):\n", - "\n", - "- **Step 1: Map problem to quantum circuits and operators**:\n", - " - Map the hamiltonian onto a quantum circuit.\n", - "- **Step 2: Optimize for target hardware** [_Uses the cutting addon_]:\n", - " - <font color='#0F62FE'>Cut the circuit and observable.</font>\n", - " - Transpile the subexperiments for hardware.\n", - "- **Step 3: Execute on target hardware**:\n", - " - Run the subexperiments obtained in Step 2 using a `Sampler` primitive.\n", - "- **Step 4: Post-process results** [_Uses the cutting addon_]:\n", - " - <font color='#0F62FE'>Combine the results of Step 3 to reconstruct the expectation value of the observable in question.</font>" - ] - }, - { - "cell_type": "markdown", - "id": "ae63d837-a7f5-40a5-8186-f98076bb4cd9", - "metadata": {}, - "source": [ - "## Step 1: Map\n", - "\n", - "### Create a circuit to cut\n", - "\n", - "First, we begin with a circuit inspired by Fig. 1(a) of [arXiv:2302.03366v1](https://arxiv.org/abs/2302.03366v1)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3bcae0ed-4308-4686-b85c-8595c6e916bc", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:33.241330Z", - "iopub.status.busy": "2025-03-28T16:52:33.241116Z", - "iopub.status.idle": "2025-03-28T16:52:33.572919Z", - "shell.execute_reply": "2025-03-28T16:52:33.572241Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<qiskit.circuit.instructionset.InstructionSet at 0x7f96c0c23430>" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "from qiskit import QuantumCircuit\n", - "\n", - "qc_0 = QuantumCircuit(7)\n", - "for i in range(7):\n", - " qc_0.rx(np.pi / 4, i)\n", - "qc_0.cx(0, 3)\n", - "qc_0.cx(1, 3)\n", - "qc_0.cx(2, 3)\n", - "qc_0.cx(3, 4)\n", - "qc_0.cx(3, 5)\n", - "qc_0.cx(3, 6)\n", - "qc_0.cx(0, 3)\n", - "qc_0.cx(1, 3)\n", - "qc_0.cx(2, 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1dcaff2d-2d1b-4cc0-87d1-0f4f5de823ff", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:33.575145Z", - "iopub.status.busy": "2025-03-28T16:52:33.574711Z", - "iopub.status.idle": "2025-03-28T16:52:34.182253Z", - "shell.execute_reply": "2025-03-28T16:52:34.181568Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction/extracted-outputs/1dcaff2d-2d1b-4cc0-87d1-0f4f5de823ff-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qc_0.draw(\"mpl\")" - ] - }, - { - "cell_type": "markdown", - "id": "bd1617a1-e793-43a9-a24a-c81c18fd2a1e", - "metadata": {}, - "source": [ - "### Specify an observable" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b791aa42-c485-453b-a110-3c790194adaf", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.184389Z", - "iopub.status.busy": "2025-03-28T16:52:34.184096Z", - "iopub.status.idle": "2025-03-28T16:52:34.187638Z", - "shell.execute_reply": "2025-03-28T16:52:34.187108Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])" - ] - }, - { - "cell_type": "markdown", - "id": "9f56b094-0c6f-456f-9641-1424395fc6bd", - "metadata": {}, - "source": [ - "## Step 2: Optimize\n", - "\n", - "### Create a new circuit where `Move` instructions have been placed at the desired cut locations\n", - "\n", - "Given the above circuit, we would like to place two wire cuts on the middle qubit line, so that the circuit can separate into two circuits of four qubits each. One way to do this is to manually place two-qubit `Move` instructions that move the state from one qubit wire to another. A `Move` instruction is conceptually equivalent to a reset operation on the second qubit, followed by a SWAP gate. The effect of this instruction is to transfer the state of the first (source) qubit to the second (detination) qubit, while discarding the incoming state of the second qubit. For this to work as intended, it is important that the second (destination) qubit share no entanglement with the remainder of the system; otherwise, the reset operation will cause the state of the remainder of the system to be partially collapsed.\n", - "\n", - "Here, we build a new circuit with one additional qubit and the `Move` operations in place. In this example, we are able to reuse a qubit: the source qubit of the first `Move` becomes the destination qubit of the second `Move` operation.\n", - "\n", - "Note: As an alternative to working directly with `Move` instructions, one may choose to mark wire cuts using a single-qubit `CutWire` instruction. The `cut_wires` function exists to transform `CutWire`s to `Move` instructions on newly allocated qubits. However, in contrast to the manual method, this automatic method does not allow for the re-use of qubit wires. See the `CutWire` [how-to guide](../how-tos/how_to_specify_cut_wires.ipynb) for details." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "22f19d29-a182-4758-b5d0-6b66f9a946be", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.189717Z", - "iopub.status.busy": "2025-03-28T16:52:34.189367Z", - "iopub.status.idle": "2025-03-28T16:52:34.502126Z", - "shell.execute_reply": "2025-03-28T16:52:34.501425Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction/extracted-outputs/22f19d29-a182-4758-b5d0-6b66f9a946be-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_cutting.instructions import Move\n", - "\n", - "qc_1 = QuantumCircuit(8)\n", - "for i in [*range(4), *range(5, 8)]:\n", - " qc_1.rx(np.pi / 4, i)\n", - "qc_1.cx(0, 3)\n", - "qc_1.cx(1, 3)\n", - "qc_1.cx(2, 3)\n", - "qc_1.append(Move(), [3, 4])\n", - "qc_1.cx(4, 5)\n", - "qc_1.cx(4, 6)\n", - "qc_1.cx(4, 7)\n", - "qc_1.append(Move(), [4, 3])\n", - "qc_1.cx(0, 3)\n", - "qc_1.cx(1, 3)\n", - "qc_1.cx(2, 3)\n", - "\n", - "qc_1.draw(\"mpl\")" - ] - }, - { - "cell_type": "markdown", - "id": "088431d9-de2f-4b5d-90d2-7e7a5947dcb5", - "metadata": {}, - "source": [ - "### Create observable to go with the new circuit\n", - "\n", - "This observable corresponds with `observable`, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an \"I\" at index 4). Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d33d5580-879f-466f-ac87-dcc6a19fbab6", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.504553Z", - "iopub.status.busy": "2025-03-28T16:52:34.504138Z", - "iopub.status.idle": "2025-03-28T16:52:34.507751Z", - "shell.execute_reply": "2025-03-28T16:52:34.506956Z" - } - }, - "outputs": [], - "source": [ - "observable_expanded = SparsePauliOp([\"ZIIIIIII\", \"IIIIZIII\", \"IIIIIIIZ\"])" - ] - }, - { - "cell_type": "markdown", - "id": "a00aeede-3d0f-4589-b1dc-3cd7df9c4085", - "metadata": {}, - "source": [ - "### Separate the circuit and observables\n", - "\n", - "As in the previous tutorials, qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "fc3738c7-2bb2-4d67-ae2b-7a3090b31e6a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.509830Z", - "iopub.status.busy": "2025-03-28T16:52:34.509640Z", - "iopub.status.idle": "2025-03-28T16:52:34.522487Z", - "shell.execute_reply": "2025-03-28T16:52:34.521787Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import partition_problem\n", - "\n", - "partitioned_problem = partition_problem(\n", - " circuit=qc_1, partition_labels=\"AAAABBBB\", observables=observable_expanded.paulis\n", - ")\n", - "subcircuits = partitioned_problem.subcircuits\n", - "subobservables = partitioned_problem.subobservables\n", - "bases = partitioned_problem.bases" - ] - }, - { - "cell_type": "markdown", - "id": "4c8a76eb-31db-486c-b7c2-a5fe3cb732c2", - "metadata": {}, - "source": [ - "### Visualize the decomposed problem" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9c53a862-f762-471a-bda7-b27e88292ac9", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.524677Z", - "iopub.status.busy": "2025-03-28T16:52:34.524311Z", - "iopub.status.idle": "2025-03-28T16:52:34.528872Z", - "shell.execute_reply": "2025-03-28T16:52:34.528334Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'A': PauliList(['IIII', 'ZIII', 'IIIZ']),\n", - " 'B': PauliList(['ZIII', 'IIII', 'IIII'])}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subobservables" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "69df912e-6709-45bb-8eb3-c66a70edefdc", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.530891Z", - "iopub.status.busy": "2025-03-28T16:52:34.530503Z", - "iopub.status.idle": "2025-03-28T16:52:34.747276Z", - "shell.execute_reply": "2025-03-28T16:52:34.746598Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction/extracted-outputs/69df912e-6709-45bb-8eb3-c66a70edefdc-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[\"A\"].draw(\"mpl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d851adcb-e524-48c8-8adc-0d1606613c8d", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.749630Z", - "iopub.status.busy": "2025-03-28T16:52:34.749131Z", - "iopub.status.idle": "2025-03-28T16:52:34.879618Z", - "shell.execute_reply": "2025-03-28T16:52:34.878945Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction/extracted-outputs/d851adcb-e524-48c8-8adc-0d1606613c8d-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[\"B\"].draw(\"mpl\")" - ] - }, - { - "cell_type": "markdown", - "id": "9c177fb7-a729-4e0d-bfac-256df3e14c54", - "metadata": {}, - "source": [ - "### Calculate the sampling overhead for the chosen cuts\n", - "\n", - "Here we cut two wires, resulting in a sampling overhead of $4^4$.\n", - "\n", - "For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](../explanation/index.rst)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7af74c54-3e68-4d58-a2e6-02bc212d911d", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.881911Z", - "iopub.status.busy": "2025-03-28T16:52:34.881529Z", - "iopub.status.idle": "2025-03-28T16:52:34.885387Z", - "shell.execute_reply": "2025-03-28T16:52:34.884739Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sampling overhead: 256.0\n" - ] - } - ], - "source": [ - "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")" - ] - }, - { - "cell_type": "markdown", - "id": "a1eda2d0-8d83-473b-8ea8-fe9880108140", - "metadata": {}, - "source": [ - "### Generate the subexperiments to run on the backend\n", - "\n", - "`generate_cutting_experiments` accepts `circuits`/`observables` args as dictionaries mapping qubit partition labels to the respective `subcircuit`/`subobservables`.\n", - "\n", - "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst)." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d28a8b82-8405-47b2-8142-62d56266409a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.887533Z", - "iopub.status.busy": "2025-03-28T16:52:34.887063Z", - "iopub.status.idle": "2025-03-28T16:52:34.963806Z", - "shell.execute_reply": "2025-03-28T16:52:34.963065Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import generate_cutting_experiments\n", - "\n", - "subexperiments, coefficients = generate_cutting_experiments(\n", - " circuits=subcircuits, observables=subobservables, num_samples=np.inf\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "9f66e3eb-7cbc-45a9-8f40-9f92b61b6e65", - "metadata": {}, - "source": [ - "### Choose a backend\n", - "\n", - "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6944546f-e3e0-4863-9049-9feed1086e68", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:34.966311Z", - "iopub.status.busy": "2025-03-28T16:52:34.965845Z", - "iopub.status.idle": "2025-03-28T16:52:35.537487Z", - "shell.execute_reply": "2025-03-28T16:52:35.536746Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", - "\n", - "backend = FakeManilaV2()" - ] - }, - { - "cell_type": "markdown", - "id": "fdb47142-690c-4b1a-ad0b-85e1260a2cea", - "metadata": {}, - "source": [ - "### Prepare the subexperiments for the backend\n", - "\n", - "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "0229e6f2-a637-481d-ac76-84dfaadc264f", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:35.540179Z", - "iopub.status.busy": "2025-03-28T16:52:35.539685Z", - "iopub.status.idle": "2025-03-28T16:52:38.804548Z", - "shell.execute_reply": "2025-03-28T16:52:38.803721Z" - } - }, - "outputs": [], - "source": [ - "from qiskit.transpiler import generate_preset_pass_manager\n", - "\n", - "# Transpile the subexperiments to ISA circuits\n", - "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", - "isa_subexperiments = {\n", - " label: pass_manager.run(partition_subexpts)\n", - " for label, partition_subexpts in subexperiments.items()\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "9578c64a-8087-4696-bb2b-ca53d45442ba", - "metadata": {}, - "source": [ - "## Step 3: Execute\n", - "\n", - "### Run the subexperiments using the Qiskit Runtime Sampler primitive" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "bdf3bd2f-e10a-4148-a7ad-f354492614cc", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:38.807146Z", - "iopub.status.busy": "2025-03-28T16:52:38.806915Z", - "iopub.status.idle": "2025-03-28T16:52:38.958106Z", - "shell.execute_reply": "2025-03-28T16:52:38.957428Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import SamplerV2, Batch\n", - "\n", - "# Submit each partition's subexperiments to the Qiskit Runtime Sampler\n", - "# primitive, in a single batch so that the jobs will run back-to-back.\n", - "with Batch(backend=backend) as batch:\n", - " sampler = SamplerV2(mode=batch)\n", - " jobs = {\n", - " label: sampler.run(subsystem_subexpts, shots=2**12)\n", - " for label, subsystem_subexpts in isa_subexperiments.items()\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "abb436de-9662-4c47-92b1-659ee0334adc", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:38.965357Z", - "iopub.status.busy": "2025-03-28T16:52:38.965102Z", - "iopub.status.idle": "2025-03-28T16:52:51.060055Z", - "shell.execute_reply": "2025-03-28T16:52:51.059450Z" - } - }, - "outputs": [], - "source": [ - "# Retrieve results\n", - "results = {label: job.result() for label, job in jobs.items()}" - ] - }, - { - "cell_type": "markdown", - "id": "3f6e4eed-eff7-4e16-ad8d-8070011a555b", - "metadata": {}, - "source": [ - "## Step 4: Post-process\n", - "\n", - "### Reconstruct the expectation value\n", - "\n", - "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a1904c54-27fa-45e0-a9dd-727b4542db71", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:51.062743Z", - "iopub.status.busy": "2025-03-28T16:52:51.062254Z", - "iopub.status.idle": "2025-03-28T16:52:53.372289Z", - "shell.execute_reply": "2025-03-28T16:52:53.371616Z" - } - }, - "outputs": [], - "source": [ - "from qiskit_addon_cutting import reconstruct_expectation_values\n", - "\n", - "reconstructed_expval_terms = reconstruct_expectation_values(\n", - " results,\n", - " coefficients,\n", - " subobservables,\n", - ")\n", - "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" - ] - }, - { - "cell_type": "markdown", - "id": "cdc793f2-3e2b-417b-b863-7b9d57b86a8f", - "metadata": {}, - "source": [ - "### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "6e3d63e4-a510-4712-bc43-48df6e2f7ded", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:53.374812Z", - "iopub.status.busy": "2025-03-28T16:52:53.374611Z", - "iopub.status.idle": "2025-03-28T16:52:53.385882Z", - "shell.execute_reply": "2025-03-28T16:52:53.385355Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstructed expectation value: 1.40369761\n", - "Exact expectation value: 1.59099026\n", - "Error in estimation: -0.18729265\n", - "Relative error in estimation: -0.1177208\n" - ] - } - ], - "source": [ - "from qiskit_aer.primitives import EstimatorV2\n", - "\n", - "estimator = EstimatorV2()\n", - "exact_expval = estimator.run([(qc_0, observable)]).result()[0].data.evs\n", - "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", - "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", - "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", - "print(\n", - " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb deleted file mode 100644 index ccc15557d5d..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding.ipynb +++ /dev/null @@ -1,379 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "frontmatter", - "metadata": {}, - "source": [ - "---\n", - "title: \"Automatically find cuts\"\n", - "description: \"Automatically find cuts for the latest version of Circuit cutting\"\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "89d3e73b-f522-4de9-b8e1-45ee978115f7", - "metadata": {}, - "source": [ - "# Automatically find cuts" - ] - }, - { - "cell_type": "markdown", - "id": "f2cc1670-da97-4176-b290-4b085c4d9e52", - "metadata": {}, - "source": [ - "## Step 1: Map\n", - "\n", - "#### Create a circuit and observables" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "830adac4-8155-4664-b8de-3a79d38f99c3", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:55.792854Z", - "iopub.status.busy": "2025-03-28T16:52:55.792659Z", - "iopub.status.idle": "2025-03-28T16:52:56.800107Z", - "shell.execute_reply": "2025-03-28T16:52:56.799450Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/830adac4-8155-4664-b8de-3a79d38f99c3-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "from qiskit.circuit.random import random_circuit\n", - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "circuit = random_circuit(7, 6, max_operands=2, seed=1242)\n", - "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])\n", - "\n", - "\n", - "circuit.draw(\"mpl\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "id": "3dfb6ab2-80f4-4aba-9a6f-aa2692273411", - "metadata": {}, - "source": [ - "## Step 2: Optimize\n", - "\n", - "#### Find cut locations, given a maximum of 4 qubits per subcircuit. This circuit can be separated in two by making a single wire cut and cutting one `CRZGate`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "aa5b23ef-af56-4865-bbc5-d05a68633ce2", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:56.802390Z", - "iopub.status.busy": "2025-03-28T16:52:56.801930Z", - "iopub.status.idle": "2025-03-28T16:52:59.488947Z", - "shell.execute_reply": "2025-03-28T16:52:59.488280Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found solution using 8 cuts with a sampling overhead of 59776343.19975524.\n", - "Lowest cost solution found: False.\n", - "Gate Cut at circuit instruction index 6\n", - "Gate Cut at circuit instruction index 8\n", - "Gate Cut at circuit instruction index 9\n", - "Wire Cut at circuit instruction index 10\n", - "Gate Cut at circuit instruction index 15\n", - "Gate Cut at circuit instruction index 18\n", - "Gate Cut at circuit instruction index 19\n", - "Gate Cut at circuit instruction index 21\n" - ] - }, - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/aa5b23ef-af56-4865-bbc5-d05a68633ce2-1.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_cutting.automated_cut_finding import (\n", - " find_cuts,\n", - " OptimizationParameters,\n", - " DeviceConstraints,\n", - ")\n", - "\n", - "# Specify settings for the cut-finding optimizer\n", - "optimization_settings = OptimizationParameters(seed=111)\n", - "\n", - "# Specify the size of the QPUs available\n", - "device_constraints = DeviceConstraints(qubits_per_subcircuit=4)\n", - "\n", - "cut_circuit, metadata = find_cuts(circuit, optimization_settings, device_constraints)\n", - "print(\n", - " f'Found solution using {len(metadata[\"cuts\"])} cuts with a sampling '\n", - " f'overhead of {metadata[\"sampling_overhead\"]}.\\n'\n", - " f'Lowest cost solution found: {metadata[\"minimum_reached\"]}.'\n", - ")\n", - "for cut in metadata[\"cuts\"]:\n", - " print(f\"{cut[0]} at circuit instruction index {cut[1]}\")\n", - "cut_circuit.draw(\"mpl\", scale=0.8, fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "c2ebd34b-c29a-4fc2-9e89-dd2eeed0fa17", - "metadata": {}, - "source": [ - "#### Add ancillas for wire cuts and expand the observables to account for ancilla qubits" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c6b769bc-fedd-4937-b825-1db9b93052a3", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:59.491081Z", - "iopub.status.busy": "2025-03-28T16:52:59.490686Z", - "iopub.status.idle": "2025-03-28T16:52:59.752854Z", - "shell.execute_reply": "2025-03-28T16:52:59.752167Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/c6b769bc-fedd-4937-b825-1db9b93052a3-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_addon_cutting import cut_wires, expand_observables\n", - "\n", - "qc_w_ancilla = cut_wires(cut_circuit)\n", - "observables_expanded = expand_observables(observable.paulis, circuit, qc_w_ancilla)\n", - "qc_w_ancilla.draw(\"mpl\", scale=0.8, fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "cefd19e1-f022-415c-908d-902eb15cafc8", - "metadata": {}, - "source": [ - "#### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "87ec5b7c-e9a7-43d3-bf03-cd56a34d814b", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:59.755174Z", - "iopub.status.busy": "2025-03-28T16:52:59.754781Z", - "iopub.status.idle": "2025-03-28T16:52:59.767941Z", - "shell.execute_reply": "2025-03-28T16:52:59.767319Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sampling overhead: 59776343.19975523\n" - ] - } - ], - "source": [ - "from qiskit_addon_cutting import partition_problem\n", - "\n", - "partitioned_problem = partition_problem(\n", - " circuit=qc_w_ancilla, observables=observables_expanded\n", - ")\n", - "subcircuits = partitioned_problem.subcircuits\n", - "subobservables = partitioned_problem.subobservables\n", - "print(\n", - " f\"Sampling overhead: {np.prod([basis.overhead for basis in partitioned_problem.bases])}\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9f38fe36-bdb7-4e65-9a77-6778100dabdc", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:59.770081Z", - "iopub.status.busy": "2025-03-28T16:52:59.769722Z", - "iopub.status.idle": "2025-03-28T16:52:59.774244Z", - "shell.execute_reply": "2025-03-28T16:52:59.773599Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: PauliList(['IIII', 'IIZI', 'IIIZ']),\n", - " 1: PauliList(['ZIII', 'IIII', 'IIII'])}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subobservables" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4a8498fe-8c2a-4e1a-96fc-ea843d55a203", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:59.776009Z", - "iopub.status.busy": "2025-03-28T16:52:59.775815Z", - "iopub.status.idle": "2025-03-28T16:52:59.931154Z", - "shell.execute_reply": "2025-03-28T16:52:59.930440Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/4a8498fe-8c2a-4e1a-96fc-ea843d55a203-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[0].draw(\"mpl\", style=\"iqp\", scale=0.8)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ac59d0de-bd1f-4cbe-83d9-d17ce608946b", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:52:59.933278Z", - "iopub.status.busy": "2025-03-28T16:52:59.933055Z", - "iopub.status.idle": "2025-03-28T16:53:00.110630Z", - "shell.execute_reply": "2025-03-28T16:53:00.110004Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-cutting/tutorials/04-automatic-cut-finding/extracted-outputs/ac59d0de-bd1f-4cbe-83d9-d17ce608946b-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subcircuits[1].draw(\"mpl\", style=\"iqp\", scale=0.8)" - ] - }, - { - "cell_type": "markdown", - "id": "bba3fb35-5192-4c4e-bfe7-6eeedd1b58fc", - "metadata": {}, - "source": [ - "#### Generate the experiments to run on the backend." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e5a16473-cb78-4ea6-965c-1a6de708a89c", - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-28T16:53:00.112773Z", - "iopub.status.busy": "2025-03-28T16:53:00.112543Z", - "iopub.status.idle": "2025-03-28T16:53:02.920653Z", - "shell.execute_reply": "2025-03-28T16:53:02.919977Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2000 total subexperiments to run on backend.\n" - ] - } - ], - "source": [ - "from qiskit_addon_cutting import generate_cutting_experiments\n", - "\n", - "subexperiments, coefficients = generate_cutting_experiments(\n", - " circuits=subcircuits, observables=subobservables, num_samples=1_000\n", - ")\n", - "print(\n", - " f\"{len(subexperiments[0]) + len(subexperiments[1])} total subexperiments to run on backend.\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "9cb69ede-b239-4f6f-a7f1-a499923819e1", - "metadata": {}, - "source": [ - "Steps 3 and 4 of a [Qiskit pattern](/guides/intro-to-patterns) can then be performed as in the preceding tutorials." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/addons/qiskit-addon-cutting/tutorials/index.mdx b/docs/addons/qiskit-addon-cutting/tutorials/index.mdx deleted file mode 100644 index 16cb9fdccb9..00000000000 --- a/docs/addons/qiskit-addon-cutting/tutorials/index.mdx +++ /dev/null @@ -1,30 +0,0 @@ ---- -title: "Tutorials" -description: "Tutorials for the lastest version of Circuit cutting" ---- - -<span id="circuit-cutting-tutorials" /> - -# Tutorials - -* [Tutorial 1](01-gate-cutting-to-reduce-circuit-width): Separate sets of qubits by cutting nonlocal gates. -* [Tutorial 2](02-gate-cutting-to-reduce-circuit-depth): Cut gates requiring many SWAPs to decrease circuit depth. -* [Tutorial 3](03-wire-cutting-via-move-instruction): Specify wire cuts as a two-qubit [`Move`](/docs/api/qiskit-addon-cutting/instructions-move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") operation. -* [Tutorial 4](04-automatic-cut-finding): Automatically find optimal gate and wire cut locations for reducing circuit width. - -[![](/docs/images/addons/qiskit-addon-cutting/tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.avif)](01-gate-cutting-to-reduce-circuit-width) - -[Gate Cutting to Reduce Circuit Width](01-gate-cutting-to-reduce-circuit-width) - -[![](/docs/images/addons/qiskit-addon-cutting/tutorials_02_gate_cutting_to_reduce_circuit_depth_17_2.avif)](02-gate-cutting-to-reduce-circuit-depth) - -[Gate Cutting to Reduce Circuit Depth](02-gate-cutting-to-reduce-circuit-depth) - -[![](/docs/images/addons/qiskit-addon-cutting/tutorials_03_wire_cutting_via_move_instruction_15_0.avif)](03-wire-cutting-via-move-instruction) - -[Wire Cutting Phrased as a Two-Qubit Move Instruction](03-wire-cutting-via-move-instruction) - -[![](/docs/images/addons/qiskit-addon-cutting/tutorials_04_automatic_cut_finding_11_0.avif)](04-automatic-cut-finding) - -[Automatically find cuts](04-automatic-cut-finding) - diff --git a/docs/addons/qiskit-addon-mpf/_toc.json b/docs/addons/qiskit-addon-mpf/_toc.json index c30895d31d6..1cb6d4292a8 100644 --- a/docs/addons/qiskit-addon-mpf/_toc.json +++ b/docs/addons/qiskit-addon-mpf/_toc.json @@ -39,16 +39,6 @@ ], "collapsible": false }, - { - "title": "Tutorials", - "children": [ - { - "title": "Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas", - "url": "/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started" - } - ], - "collapsible": false - }, { "title": "API reference", "collapsible": false, diff --git a/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started.ipynb b/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started.ipynb deleted file mode 100644 index 61f22209406..00000000000 --- a/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started.ipynb +++ /dev/null @@ -1,961 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "frontmatter", - "metadata": {}, - "source": [ - "---\n", - "title: \"Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas\"\n", - "description: \"Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas for the latest version of Multi-product formulas (MPF)\"\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "b1b4d0d8-13f5-4b7a-abe0-2a2f034b8d35", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas\n", - "\n", - "In this notebook, you will learn how to use a **Multi-Product Formula (MPF)** to achieve a lower Trotter error on our observable compared to the one incurred by the deepest Trotter circuit that we will actually execute.\n", - "You will do so by working through the steps of a **Qiskit pattern**:\n", - "\n", - "- **Step 1: Map to quantum problem**\n", - " - Initialize our problem's Hamiltonian\n", - " - <font color=\"#0F62FE\">Use an MPF to generate the Trotterized time-evolution circuits</font>\n", - "- **Step 2: Optimize the problem**\n", - " - Here we transpile our circuits for a [GenericBackendV2](/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2)\n", - "- **Step 3: Execute experiments**\n", - " - Use a [StatevectorEstimator](/api/qiskit/qiskit.primitives.StatevectorEstimator) for sake of simplicity in this notebook\n", - "- **Step 4: Reconstruct results**\n", - " - <font color=\"#0F62FE\">Compute the MPF expectation value</font>" - ] - }, - { - "cell_type": "markdown", - "id": "fd1bbb0f-120f-4425-a52a-e393f86c16cc", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Step 1: Map to quantum problem\n", - "\n", - "### 1a: Setting up our Hamiltonian\n", - "\n", - "We use the Ising model on a line of 10 sites:\n", - "\n", - "$$\n", - "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", - "$$\n", - "\n", - "where $J$ is the coupling strength between two sites and $h$ is the external magnetic field." - ] - }, - { - "cell_type": "markdown", - "id": "55db9ce4-3cfb-439c-bb0d-a64030a52183", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "The [qiskit_addon_utils](/docs/addons/qiskit-addon-utils/) package provides some reusable functionalities for various purposes.\n", - "\n", - "Its [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators#module-qiskit_addon_utils.problem_generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", - "This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows.\n", - "\n", - "In the following, we create a simple line of 10 qubits using the `CouplingMap.from_line` method." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "98617c1f-00cc-4fdd-8dbc-7f0c7efd8344", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from qiskit.transpiler import CouplingMap\n", - "\n", - "# Generate some coupling map to use for this example\n", - "coupling_map = CouplingMap.from_line(10, bidirectional=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e15dd313-668d-48b3-8133-3cd4df9a3d8e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "skip-execution" - ] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-mpf/tutorials/01-getting-started/extracted-outputs/e15dd313-668d-48b3-8133-3cd4df9a3d8e-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from rustworkx.visualization import graphviz_draw\n", - "\n", - "graphviz_draw(coupling_map.graph, method=\"circo\")" - ] - }, - { - "cell_type": "markdown", - "id": "fa7dcbde-531c-4442-8c16-9127d0b2d11b", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Next, we generate the [SparsePauliOp](/api/qiskit/qiskit.quantum_info.SparsePauliOp) on the provided connectivity with the desired constants." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c03810d1-cb9f-429e-85d2-055486d77d2b", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'],\n", - " coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j,\n", - " 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j,\n", - " 0.4+0.j, 0.4+0.j, 0.4+0.j])\n" - ] - } - ], - "source": [ - "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", - "\n", - "# Get a qubit operator describing the Ising field model\n", - "hamiltonian = generate_xyz_hamiltonian(\n", - " coupling_map,\n", - " coupling_constants=(0.0, 0.0, 1.0),\n", - " ext_magnetic_field=(0.4, 0.0, 0.0),\n", - ")\n", - "print(hamiltonian)" - ] - }, - { - "cell_type": "markdown", - "id": "bdb73298-d30a-407a-9e5d-de7c57264f6b", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "The observable that we will be measuring is the total magnetization which we can simply construct as shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "77232669-aa00-4f74-9f50-758f0c960693", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'],\n", - " coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j,\n", - " 0.05+0.j, 0.05+0.j, 0.05+0.j])\n" - ] - } - ], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "\n", - "L = coupling_map.size()\n", - "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)\n", - "print(observable)" - ] - }, - { - "cell_type": "markdown", - "id": "83dc1e84-df2c-4e08-a8c9-3ecd6595f04e", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### 1b: Multi-Product Formulas\n", - "\n", - "MPFs reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions.\n", - "\n", - "To make this more concrete, we define an MPF as:\n", - "\n", - "$$\n", - "\\mu(t) = \\sum_{j} x_j \\rho^{k_j}_{j}\\left(\\frac{t}{k_j}\\right) + \\text{some remaining Trotter error} \\, ,\n", - "$$\n", - "\n", - "where $x_j$ are our weighting coefficients, $\\rho^{k_j}_j$ is the density matrix corresponding to the pure state obtained by evolving the initial state with the product formula, $S^{k_j}$, involving $k_j$ Trotter steps, and $j$ indexes the number of product formulas that make up the MPF.\n", - "\n", - "The key here is that the remaining Trotter error is smaller than the Trotter error that one would obtain by simply using the largest $k_j$ value!\n", - "\n", - "You can view the usefulness of MPF from two perspectives:\n", - "\n", - "1. For a fixed budget of Trotter steps that you are able to execute, you can obtain results with a Trotter error that is smaller in total.\n", - "2. For a number of Trotter steps which results in deep circuits, you can use MPF to find several shorter-depth circuits to run which result in a similar Trotter error." - ] - }, - { - "cell_type": "markdown", - "id": "58ffeb8e-978b-4211-b949-21bebe98bd1f", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### An introduction to static MPFs\n", - "\n", - "_Static_ MPFs are those where the $x_j$ values do **NOT** depend on the evolution time, $t$.\n", - "\n", - "Determining the static MPF coefficients for a given set of $k_j$ values amounts to solving a linear system of equations:\n", - "$Ax=b$, where $x$ are our coefficients of interest, $A$ is a matrix depending on $k_j$ and the type of PF we use ($S$), and $b$ is a vector of constraints.\n", - "For brevity, we are not going to go into more detail here and instead refer you to the documentation of [LSE](/docs/api/qiskit-addon-mpf/costs#qiskit_addon_mpf.costs.LSE).\n", - "\n", - "We can find a solution for $x$ analytically as $x = A^{-1}b$, see e.g. [Carrera Vazquez et al., 2023] or [Zhuk et al., 2023].\n", - "However, this exact solution can be _\"ill-conditioned\"_ resulting in very large L1-norms of our coefficients, $x$, which can lead to bad performance of the MPF.\n", - "Instead, one can also obtain an approximate solution which minimizes the L1-norm of $x$ in order to attempt and optimize the MPF behavior.\n", - "\n", - "In the following, you will learn how to do all of this.\n", - "\n", - "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/\n", - "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" - ] - }, - { - "cell_type": "markdown", - "id": "b293d4ae-cee1-4fd6-bb56-926dc282e868", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Choosing $k_j$\n", - "\n", - "The choice of $k_j$ is up to the end-user.\n", - "In principle, any values can be chosen but some $k_j$'s will lead to a larger noise amplification on real devices than other choices.\n", - "Thus, it is important that one tries to find _\"good\"_ values of $k_j$.\n", - "\n", - "Here, we will simply pick some fixed values for $k_j$.\n", - "The smallest value is motivated by the target evolution time of $t=8.0$ which normally tells us to satisfy $t/k_{\\text{min}} \\lt 1$ but empirically we know that setting it equal to $1$ usually works, too.\n", - "If you want to learn more about this and how to choose your other $k_j$ values, refer to the respective guide: [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "89682993-64b3-44ea-ae95-cc9eea30a632", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "time = 8.0\n", - "trotter_steps = (8, 12, 19)" - ] - }, - { - "cell_type": "markdown", - "id": "c1732f75-1299-4ade-bf7f-eab0c4758cba", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Setting up the LSE\n", - "\n", - "Now that we have chosen our $k_j$s, we must first construct the LSE, $Ax=b$ as explained above.\n", - "The matrix $A$ depends not only on $k_j$ but also our choice of product formula (PF) -- in particular its _order_.\n", - "Additionally, one may take into account whether the PF is symmetric or not (see [Carrera Vazquez et al., 2023]), by setting `symmetric=True`.\n", - "However, this is not required as shown by [Zhuk et al., 2023].\n", - "\n", - "Here, we are going to use a second-order Suzuki-Trotter formula yielding `order=2` and we will set `symmetric=True`.\n", - "\n", - "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/\n", - "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4898872e-8dc5-43f6-9615-31081851a80a", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LSE(A=array([[1.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n", - " [1.56250000e-02, 6.94444444e-03, 2.77008310e-03],\n", - " [2.44140625e-04, 4.82253086e-05, 7.67336039e-06]]), b=array([1., 0., 0.]))\n" - ] - } - ], - "source": [ - "from qiskit_addon_mpf.static import setup_static_lse\n", - "\n", - "lse = setup_static_lse(trotter_steps, order=2, symmetric=True)\n", - "print(lse)" - ] - }, - { - "cell_type": "markdown", - "id": "cb2fcfff-7d3c-4128-b4be-504a634f9a02", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Solving for $x$ analytically\n", - "\n", - "As mentioned before, we can find $x$ analytically:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e0db1488-21f5-4f7b-a9f4-98e2e945afde", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0.17239057 -1.19447005 2.02207947]\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "coeffs_analytical = lse.solve()\n", - "print(coeffs_analytical)" - ] - }, - { - "cell_type": "markdown", - "id": "a4517a21-61e2-45fe-bea1-82e87e48d8f7", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Optimizing for $x$ using an exact model\n", - "\n", - "Alternatively to computing $x=A^{-1}b$, you can also use [setup_exact_problem](/docs/api/qiskit-addon-mpf/costs#setup_exact_problem) to construct a [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which uses the LSE as constraints and whose optimal solution will yield $x$.\n", - "\n", - "In the next section, it will be clear why this interface exists." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3bce6d85-a9dd-49e4-abe0-f8575394122c", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0.17239057 -1.19447005 2.02207947]\n" - ] - } - ], - "source": [ - "from qiskit_addon_mpf.costs import setup_exact_problem\n", - "\n", - "model_exact, coeffs_exact = setup_exact_problem(lse)\n", - "model_exact.solve()\n", - "print(coeffs_exact.value)" - ] - }, - { - "cell_type": "markdown", - "id": "bb9971aa-a4a5-4e04-85fe-2541ce21ac1f", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "As an indicator whether an MPF constructed with these coefficients will yield good results, we can use the L1-norm (see also [Carrera Vazquez et al., 2023]).\n", - "\n", - "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b8ffca3a-87e7-4344-b189-af95ef5bf2f2", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.3889400921655914\n" - ] - } - ], - "source": [ - "print(np.linalg.norm(coeffs_exact.value, ord=1))" - ] - }, - { - "cell_type": "markdown", - "id": "a6f84839-0879-4f48-bba0-89cc671986c5", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Optimizing for $x$ using an approximate model\n", - "\n", - "It may happen that the L1 norm for the chosen set of $k_j$ values is deemed too high.\n", - "If that is the case and you cannot choose a different set of $k_j$ values, you can use an approximate solution to the LSE instead of an exact one.\n", - "\n", - "To do so, simply use [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#setup_sum_of_squares_problem) to construct a different [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which constrains the L1-norm to a chosen threshold while minimizing the difference of $Ax$ and $b$." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "30dac7af-5a92-48f4-bc1a-1e140546cab2", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.40454257 0.57553173 0.8290123 ]\n", - "1.8090865903790838\n" - ] - } - ], - "source": [ - "from qiskit_addon_mpf.costs import setup_sum_of_squares_problem\n", - "\n", - "model_approx, coeffs_approx = setup_sum_of_squares_problem(lse, max_l1_norm=3.0)\n", - "model_approx.solve()\n", - "print(coeffs_approx.value)\n", - "print(np.linalg.norm(coeffs_approx.value, ord=1))" - ] - }, - { - "cell_type": "markdown", - "id": "2dc731cc-f1c6-484b-afba-dba6c1c0d1db", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Note, that you have complete freedom over how to solve this optimization problem, meaning that you can change the optimization solver, its convergence thresholds, and so on.\n", - "Check out the respective guide on [How to use the approximate model](/docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model)." - ] - }, - { - "cell_type": "markdown", - "id": "e328b896-a303-45f8-bfc6-4918205a18ec", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### 1c: Setting up the Trotter circuits\n", - "\n", - "At this point, we have found our expansion coefficients, $x$, and all that is left to do is to generate the Trotterized quantum circuits.\n", - "Once again, the [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators#module-qiskit_addon_utils.problem_generators) module comes to the rescue to do just that:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "fe25afaf-2f43-4c82-a97b-946b9a86df98", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from qiskit.synthesis import SuzukiTrotter\n", - "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", - "\n", - "circuits = []\n", - "for k in trotter_steps:\n", - " circ = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(order=2, reps=k),\n", - " time=time,\n", - " )\n", - " circuits.append(circ)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "01b3c52b-53cc-4ccc-a2f0-efd774486070", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-mpf/tutorials/01-getting-started/extracted-outputs/01b3c52b-53cc-4ccc-a2f0-efd774486070-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "circuits[0].draw(\"mpl\", fold=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "73e2099d-e7a7-4b6b-b6be-5f75fb1ccc8d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-mpf/tutorials/01-getting-started/extracted-outputs/73e2099d-e7a7-4b6b-b6be-5f75fb1ccc8d-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "circuits[1].draw(\"mpl\", fold=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "25fcf8ff-3f91-4312-9334-ea55cf3efb14", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-mpf/tutorials/01-getting-started/extracted-outputs/25fcf8ff-3f91-4312-9334-ea55cf3efb14-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "circuits[2].draw(\"mpl\", fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "6675e269-a681-4569-82c4-a95800e82be8", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Step 2: Optimize the problem\n", - "\n", - "Normally, this is the step in the pattern during which you optimize your circuits for execution on hardware.\n", - "Here, since we only use a noiseless simulator, we simply transpile our circuit for a [GenericBackendV2](/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2)." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "6c049ff6-5fc0-4673-a9d9-a37abcf96711", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from qiskit.providers.fake_provider import GenericBackendV2\n", - "from qiskit.transpiler import generate_preset_pass_manager\n", - "\n", - "backend = GenericBackendV2(num_qubits=10)\n", - "transpiler = generate_preset_pass_manager(optimization_level=2, backend=backend)\n", - "\n", - "transpiled_circuits = [transpiler.run(circ) for circ in circuits]" - ] - }, - { - "cell_type": "markdown", - "id": "a996e888-ff56-4ca4-849c-cc85b07a6b73", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Step 3: Execute quantum experiments\n", - "\n", - "As explained in the very beginning, we will skip the optimization step 2 because we are simply going to compute our target observable's expectation values using a noise-free simulator, namely the [StatevectorEstimator](/api/qiskit/qiskit.primitives.StatevectorEstimator)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "494cb360-91ae-4ba3-88ca-97e017ef1de5", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from qiskit.primitives import StatevectorEstimator\n", - "\n", - "estimator = StatevectorEstimator()\n", - "job = estimator.run([(circ, observable) for circ in transpiled_circuits])\n", - "result = job.result()" - ] - }, - { - "cell_type": "markdown", - "id": "04445a72-7a16-4dc5-a4af-0917ad9f7124", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Step 4: Reconstruct results\n", - "\n", - "First, we extract the individual expectation values obtained for each of the Trotter circuits:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "7779817f-638d-4728-87d2-473d75e3aef4", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array(0.23799162), array(0.35754312), array(0.38649906)]\n" - ] - } - ], - "source": [ - "evs = [res.data.evs for res in result]\n", - "print(evs)" - ] - }, - { - "cell_type": "markdown", - "id": "cea904fd-34fe-492b-9470-42580f9620c2", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Next, we simply recombine them with our MPF coefficients to yield the total expectation values of the MPF. Below, we do so for each of the different ways by which we have computed $x$." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "e5f536d5-ffce-4b7a-80f3-44846d50ee19", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analytical solution: 0.3954847855980006\n", - "Exact model solution: 0.39548478559800204\n", - "Approx. model solution: 0.42991214253489807\n" - ] - } - ], - "source": [ - "print(\"Analytical solution:\", evs @ coeffs_analytical)\n", - "print(\"Exact model solution:\", evs @ coeffs_exact.value)\n", - "print(\"Approx. model solution:\", evs @ coeffs_approx.value)" - ] - }, - { - "cell_type": "markdown", - "id": "29852936-2a0e-49d2-a22c-f3d1e8137e36", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Finally, for this small problem we can compute the exact reference value using [scipy.linalg.expm](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.expm.html) as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "eeff7277-7295-4b78-a449-f151c8bd3e66", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.40060242487899755\n" - ] - } - ], - "source": [ - "from scipy.linalg import expm\n", - "\n", - "exp_H = expm(-1j * time * hamiltonian.to_matrix())\n", - "\n", - "initial_state = np.zeros(exp_H.shape[0])\n", - "initial_state[0] = 1.0\n", - "\n", - "time_evolved_state = exp_H @ initial_state\n", - "\n", - "exact_obs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", - "print(exact_obs.real)" - ] - }, - { - "cell_type": "markdown", - "id": "4ed84f18-d1e4-4b6c-b362-8e8a8a18633c", - "metadata": { - "editable": true, - "raw_mimetype": "", - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "We can clearly see that the MPF has reduced the Trotter error compared to the one obtained with the deepest individual PF with $k_j=19$.\n", - "However, we also see that the approximate model is not flawless since it actually resulted in an expectation value that is worse than the exact solution. This shows the importance of using tight convergence criteria on the approximate model as you will learn in the guide [How to use the approximate model](/docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/addons/qiskit-addon-mpf/tutorials/index.mdx b/docs/addons/qiskit-addon-mpf/tutorials/index.mdx deleted file mode 100644 index b528ecb1414..00000000000 --- a/docs/addons/qiskit-addon-mpf/tutorials/index.mdx +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: "Tutorials" -description: "Tutorials for the lastest version of Multi-product formulas (MPF)" ---- - -# Tutorials - -This page summarizes the available tutorials. - -* [Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas](01-getting-started) - diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index 6200f71c6f8..f3361732291 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -39,16 +39,6 @@ ], "collapsible": false }, - { - "title": "Tutorials", - "children": [ - { - "title": "Reducing depth of circuits with operator backpropagation", - "url": "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started" - } - ], - "collapsible": false - }, { "title": "API reference", "collapsible": false, diff --git a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb deleted file mode 100644 index d0c528fe446..00000000000 --- a/docs/addons/qiskit-addon-obp/tutorials/01-getting-started.ipynb +++ /dev/null @@ -1,520 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "frontmatter", - "metadata": {}, - "source": [ - "---\n", - "title: \"Reducing depth of circuits with operator backpropagation\"\n", - "description: \"Reducing depth of circuits with operator backpropagation for the latest version of Operator backpropagation (OBP)\"\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "5f9e3f0d-7cc1-4ea7-826b-35dd2a87af1e", - "metadata": {}, - "source": [ - "# Reducing depth of circuits with operator backpropagation\n", - "\n", - "Operator backpropagation is a technique which involves absorbing operations from the end of a quantum circuit into a Pauli operator, generally reducing the depth of the circuit at the cost of additional terms in the operator. The goal is to backpropagate as much of the circuit as possible without allowing the operator to grow too large.\n", - "\n", - "One way to allow for deeper backpropagation into the circuit, while preventing the operator from growing too large, is to truncate terms with small coefficients, rather than adding them to the operator. Truncating terms can result in fewer quantum circuits to execute, but doing so results in some error in the final expectation value calculation proportional to the magnitude of the truncated terms' coefficients." - ] - }, - { - "cell_type": "markdown", - "id": "2cbd99bb-edc8-4032-8253-2f7195080aaa", - "metadata": {}, - "source": [ - "In this tutorial we will implement a [Qiskit pattern](/docs/guides/serverless#qiskit-patterns-with-quantum-serverless) for simulating the quantum dynamics of a Heisenberg spin chain using operator backpropagation:\n", - "\n", - "- **Step 1: Map to quantum problem**\n", - " - Map the time-evolved Hamiltonian onto a quantum circuit\n", - "- **Step 2: Optimize the problem**\n", - " - Slice the circuit\n", - " - <font color=\"#0F62FE\">Backpropagate slices from the circuit onto a Pauli observable</font>\n", - " - Combine the remaining slices into a single circuit\n", - " - Transpile the circuit for the backend\n", - "- **Step 3: Execute experiments**\n", - " - Calculate the expectation value using the reduced circuit and expanded observable with a [StatevectorEstimator](/docs/api/qiskit/qiskit.primitives.StatevectorEstimator) for sake of simplicity in this notebook\n", - "- **Step 4: Reconstruct results**\n", - " - N.A.\n", - "\n", - "**Note:** Qiskit loosely describes [layers](/docs/api/qiskit/qiskit.dagcircuit.DAGCircuit) as being depth-1 partitions of the circuit across all qubits. This package makes use of the term **slices** to describe layers of arbitrary depth. The [qiskit_addon_obp.backpropagate](/docs/api/qiskit-addon-obp/qiskit-addon-obp#backpropagate) function is designed to backpropagate entire slices at a time, so the choice of how to slice the quantum circuit can have a major impact on how well backpropagation performs for a given problem. You will lean more about **slices** below." - ] - }, - { - "cell_type": "markdown", - "id": "706ce970-5dd2-431a-890b-6476f232ae7f", - "metadata": {}, - "source": [ - "## Step 1: Map to quantum problem\n", - "### Map the time-evolution of a quantum Heisenberg model to a quantum experiment.\n", - "\n", - "The [qiskit_addon_utils](/docs/addons/qiskit-addon-utils/) package provides some reusable functionalities for various purposes.\n", - "\n", - "Its [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators#module-qiskit_addon_utils.problem_generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", - "This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/docs/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows.\n", - "\n", - "In the following, we first generate a heavy-hex `CouplingMap` out of which we carve a linear chain of 10 qubits. Note, that the indices of this new `reduced_coupling_map` are again zero-based." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "de8ce35e-a2c5-474f-adb9-88c9afb2bd76", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit.transpiler import CouplingMap\n", - "\n", - "coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False)\n", - "\n", - "# Choose a 10-qubit linear chain on this coupling map\n", - "reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 5, 12, 8, 18])" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c79b8484-7de0-47cb-be7b-147b502ad1e5", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "skip-execution" - ] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/tutorials/01-getting-started/extracted-outputs/c79b8484-7de0-47cb-be7b-147b502ad1e5-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from rustworkx.visualization import graphviz_draw\n", - "\n", - "graphviz_draw(reduced_coupling_map.graph, method=\"circo\")" - ] - }, - { - "cell_type": "markdown", - "id": "f95ba561-f211-4935-bde6-989c0d364ec8", - "metadata": {}, - "source": [ - "Next, we generate a Pauli operator modeling a Heisenberg XYZ Hamiltonian.\n", - "\n", - "$$\\hat{H} = \\sum_{(j,k)\\in E} (J_{x} \\sigma_j^{x} \\sigma_{k}^{x} +\n", - " J_{y} \\sigma_j^{y} \\sigma_{k}^{y} + J_{z} \\sigma_j^{z} \\sigma_{k}^{z}) +\n", - " \\sum_{j\\in V} (h_{x} \\sigma_j^{x} + h_{y} \\sigma_j^{y} + h_{z} \\sigma_j^{z})$$\n", - "\n", - "Where $G(V,E)$ is the graph of the coupling map provided." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "056df48a-0f8d-4a42-9c47-784343a5d6d7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SparsePauliOp(['IIIIIIIXXI', 'IIIIIIIYYI', 'IIIIIIIZZI', 'IIIIIXXIII', 'IIIIIYYIII', 'IIIIIZZIII', 'IIIXXIIIII', 'IIIYYIIIII', 'IIIZZIIIII', 'IXXIIIIIII', 'IYYIIIIIII', 'IZZIIIIIII', 'IIIIIIIIXX', 'IIIIIIIIYY', 'IIIIIIIIZZ', 'IIIIIIXXII', 'IIIIIIYYII', 'IIIIIIZZII', 'IIIIXXIIII', 'IIIIYYIIII', 'IIIIZZIIII', 'IIXXIIIIII', 'IIYYIIIIII', 'IIZZIIIIII', 'XXIIIIIIII', 'YYIIIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIIY', 'IIIIIIIIIZ', 'IIIIIIIIXI', 'IIIIIIIIYI', 'IIIIIIIIZI', 'IIIIIIIXII', 'IIIIIIIYII', 'IIIIIIIZII', 'IIIIIIXIII', 'IIIIIIYIII', 'IIIIIIZIII', 'IIIIIXIIII', 'IIIIIYIIII', 'IIIIIZIIII', 'IIIIXIIIII', 'IIIIYIIIII', 'IIIIZIIIII', 'IIIXIIIIII', 'IIIYIIIIII', 'IIIZIIIIII', 'IIXIIIIIII', 'IIYIIIIIII', 'IIZIIIIIII', 'IXIIIIIIII', 'IYIIIIIIII', 'IZIIIIIIII', 'XIIIIIIIII', 'YIIIIIIIII', 'ZIIIIIIIII'],\n", - " coeffs=[0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j,\n", - " 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j,\n", - " 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j,\n", - " 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j,\n", - " 0.78539816+0.j, 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j,\n", - " 1.57079633+0.j, 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j,\n", - " 0.39269908+0.j, 0.78539816+0.j, 1.57079633+0.j, 1.04719755+0.j,\n", - " 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j,\n", - " 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j,\n", - " 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j,\n", - " 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j,\n", - " 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j,\n", - " 1.04719755+0.j, 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j,\n", - " 0.52359878+0.j, 0.34906585+0.j, 1.04719755+0.j, 0.52359878+0.j,\n", - " 0.34906585+0.j])\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", - "\n", - "# Get a qubit operator describing the Heisenberg XYZ model\n", - "hamiltonian = generate_xyz_hamiltonian(\n", - " reduced_coupling_map,\n", - " coupling_constants=(np.pi / 8, np.pi / 4, np.pi / 2),\n", - " ext_magnetic_field=(np.pi / 3, np.pi / 6, np.pi / 9),\n", - ")\n", - "print(hamiltonian)" - ] - }, - { - "cell_type": "markdown", - "id": "3a858a3f-fc3d-4e09-9db9-8d064e036045", - "metadata": {}, - "source": [ - "From the qubit operator, we can generate a quantum circuit which models its time evolution.\n", - "Once again, the [qiskit_addon_utils.problem_generators](/docs/api/qiskit-addon-utils/problem-generators#module-qiskit_addon_utils.problem_generators) module comes to the resuce with a handy function do just that:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1d68f197-ffa4-49de-9fe8-243b1facbd00", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/tutorials/01-getting-started/extracted-outputs/1d68f197-ffa4-49de-9fe8-243b1facbd00-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit.synthesis import LieTrotter\n", - "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", - "\n", - "circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " time=0.2,\n", - " synthesis=LieTrotter(reps=2),\n", - ")\n", - "circuit.draw(\"mpl\", style=\"iqp\", scale=0.6)" - ] - }, - { - "cell_type": "markdown", - "id": "ddc43a7d-6558-4f82-90a6-7ec5370a7fd5", - "metadata": {}, - "source": [ - "## Step 2: Optimize the problem\n", - "### Create circuit slices to backpropagate\n", - "\n", - "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](/docs/api/qiskit-addon-utils/slicing#slice_by_gate_types) function.\n", - "\n", - "For a more detailed discussion on circuit slicing, check out this [how-to guide](/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices) of the [qiskit-addon-utils](/docs/addons/qiskit-addon-utils/index) package." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5d7f0d4e-ce4a-4f11-976b-437a74244b08", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Separated the circuit into 18 slices.\n" - ] - } - ], - "source": [ - "from qiskit_addon_utils.slicing import slice_by_gate_types\n", - "\n", - "slices = slice_by_gate_types(circuit)\n", - "print(f\"Separated the circuit into {len(slices)} slices.\")" - ] - }, - { - "cell_type": "markdown", - "id": "c7e3ef27-8c58-4790-9269-26dacfafa0a1", - "metadata": {}, - "source": [ - "### Constrain how large the operator may grow during backpropagation\n", - "\n", - "During backpropagation, the number of terms in the operator will generally approach $4^N$ quickly, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](/docs/api/qiskit-addon-obp/utils-simplify#operatorbudget) instance.\n", - "\n", - "Here we specify that backpropagation should stop when the number of qubit-wise commuting Pauli groups in the operator grows past 8." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "1279b567-ba19-4542-9dc1-c57a91118a20", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_addon_obp.utils.simplify import OperatorBudget\n", - "\n", - "op_budget = OperatorBudget(max_qwc_groups=8)" - ] - }, - { - "cell_type": "markdown", - "id": "6306e6a1-56fb-432d-80aa-5277233c9ed9", - "metadata": {}, - "source": [ - "### Backpropagate slices from the circuit\n", - "\n", - "First, we will specify the Pauli-Z observable on qubit 0, and we will backpropagate slices from the time-evolution circuit until the terms in the observable can no longer be combined into 8 or fewer qubit-wise commuting Pauli groups.\n", - "\n", - "Below you will see that we backpropagated 7 slices but only used 6 of the 8 allotted Pauli groups. This implies that backpropagating one more slice would cause the number of Pauli groups to exceed 8. We can verify that this is the case by inspecting the returned metadata." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "65ec9cb1-a4ed-497b-a616-180e9659956f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backpropagated 7 slices.\n", - "New observable has 18 terms, which can be combined into 8 groups.\n", - "Note that backpropagating one more slice would result in 27 terms across 12 groups.\n", - "The remaining circuit after backpropagation looks as follows:\n" - ] - }, - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/tutorials/01-getting-started/extracted-outputs/65ec9cb1-a4ed-497b-a616-180e9659956f-1.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit.quantum_info import SparsePauliOp\n", - "from qiskit_addon_obp import backpropagate\n", - "from qiskit_addon_utils.slicing import combine_slices\n", - "\n", - "# Specify a single-qubit observable\n", - "observable = SparsePauliOp(\"IIIIIIIIIZ\")\n", - "\n", - "# Backpropagate slices onto the observable\n", - "bp_obs, remaining_slices, metadata = backpropagate(observable, slices, operator_budget=op_budget)\n", - "# Recombine the slices remaining after backpropagation\n", - "bp_circuit = combine_slices(remaining_slices, include_barriers=True)\n", - "\n", - "print(f\"Backpropagated {metadata.num_backpropagated_slices} slices.\")\n", - "print(\n", - " f\"New observable has {len(bp_obs.paulis)} terms, which can be combined into {len(bp_obs.group_commuting(qubit_wise=True))} groups.\"\n", - ")\n", - "print(\n", - " f\"Note that backpropagating one more slice would result in {metadata.backpropagation_history[-1].num_paulis[0]} terms \"\n", - " f\"across {metadata.backpropagation_history[-1].num_qwc_groups} groups.\"\n", - ")\n", - "print(\"The remaining circuit after backpropagation looks as follows:\")\n", - "bp_circuit.draw(\"mpl\", scale=0.6)" - ] - }, - { - "cell_type": "markdown", - "id": "d25b9701-f1b1-4230-9dcc-554b85e91cf7", - "metadata": {}, - "source": [ - "Next, we will specify the same problem with the same constraints on the size of the output observable. However, this time, we allot an error budget to each slice using the [setup_budet](/docs/api/qiskit-addon-obp/utils-truncating#setup_budget) function. Pauli terms with small coefficients will be truncated from each slice until the error budget is filled, and leftover budget will be added to the following slice's budget.\n", - "\n", - "In order to enable this truncation, we need to setup our error budget like so:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3ba04824-dbe7-4e11-80b9-ea2320a131d5", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_addon_obp.utils.truncating import setup_budget\n", - "\n", - "truncation_error_budget = setup_budget(max_error_per_slice=0.005)" - ] - }, - { - "cell_type": "markdown", - "id": "c6dda21f-4a28-4c4e-9727-15288973150e", - "metadata": {}, - "source": [ - "Note that by allocating `5e-3` error per slice for truncation, we are able to remove 3 more slices from the circuit, while remaining within the original budget of 8 commuting Pauli groups in the observable. By default, `backpropagate` uses the L1 norm of the truncated coefficients to bound the total error incurred from truncation. For other options refer to the [how-to guide on specifying the p_norm](/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm).\n", - "\n", - "In this particular example where we have backpropagated 10 slices, the total truncation error should not exceed ``(5e-3 error/slice) * (10 slices) = 5e-2``.\n", - "For further discussion on distributing an error budget across your slices, check out [this how-to guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "5e8bae1a-ef18-4eb0-9d2a-1ac7bbdced3b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backpropagated 10 slices.\n", - "New observable has 19 terms, which can be combined into 8 groups.\n", - "After truncation, the error in our observable is bounded by 4.933e-02\n", - "Note that backpropagating one more slice would result in 27 terms across 13 groups.\n", - "The remaining circuit after backpropagation looks as follows:\n" - ] - }, - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/tutorials/01-getting-started/extracted-outputs/5e8bae1a-ef18-4eb0-9d2a-1ac7bbdced3b-1.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Run the same experiment but truncate observable terms with small coefficients\n", - "bp_obs_trunc, remaining_slices_trunc, metadata = backpropagate(\n", - " observable, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", - ")\n", - "\n", - "# Recombine the slices remaining after backpropagation\n", - "bp_circuit_trunc = combine_slices(remaining_slices_trunc, include_barriers=True)\n", - "\n", - "print(f\"Backpropagated {metadata.num_backpropagated_slices} slices.\")\n", - "print(\n", - " f\"New observable has {len(bp_obs_trunc.paulis)} terms, which can be combined into {len(bp_obs_trunc.group_commuting(qubit_wise=True))} groups.\\n\"\n", - " f\"After truncation, the error in our observable is bounded by {metadata.accumulated_error(0):.3e}\"\n", - ")\n", - "print(\n", - " f\"Note that backpropagating one more slice would result in {metadata.backpropagation_history[-1].num_paulis[0]} terms \"\n", - " f\"across {metadata.backpropagation_history[-1].num_qwc_groups} groups.\"\n", - ")\n", - "print(\"The remaining circuit after backpropagation looks as follows:\")\n", - "bp_circuit_trunc.draw(\"mpl\", scale=0.6)" - ] - }, - { - "cell_type": "markdown", - "id": "1e258829-af9f-43ad-8c79-9455243d7ced", - "metadata": {}, - "source": [ - "### Now that we have our reduced ansatze and expanded observables, we can transpile our experiments to the backend.\n", - "\n", - "Here we will use the 14-qubit [FakeMelbourneV2](/docs/api/qiskit-ibm-runtime/fake-provider-fake-melbourne-v2) from [qiskit-ibm-runtime](/docs/api/qiskit-ibm-runtime) to demonstrate how to transpile to a QPU backend." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b219035b-c5e7-4a08-a8df-cd275bdfe7a0", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", - "from qiskit_ibm_runtime.fake_provider import FakeMelbourneV2\n", - "\n", - "# Specify a backend and a pass manager for transpilation\n", - "backend = FakeMelbourneV2()\n", - "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n", - "\n", - "# Transpile original experiment\n", - "circuit_isa = pm.run(circuit)\n", - "observable_isa = observable.apply_layout(circuit_isa.layout)\n", - "\n", - "# Transpile backpropagated experiment\n", - "bp_circuit_isa = pm.run(bp_circuit)\n", - "bp_obs_isa = bp_obs.apply_layout(bp_circuit_isa.layout)\n", - "\n", - "# Transpile the backpropagated experiment with truncated observable terms\n", - "bp_circuit_trunc_isa = pm.run(bp_circuit_trunc)\n", - "bp_obs_trunc_isa = bp_obs_trunc.apply_layout(bp_circuit_trunc_isa.layout)" - ] - }, - { - "cell_type": "markdown", - "id": "966bcc9b-48a4-4a6a-8b03-e5aded1ab9df", - "metadata": {}, - "source": [ - "## Step 3: Execute quantum experiments\n", - "### Calculate expectation value\n", - "\n", - "Finally, we can run the backpropagated experiments and compare them with the full experiment using the noiseless [StatevectorEstimator](/docs/api/qiskit/qiskit.primitives.StatevectorEstimator). We can see that the backpropagated expectation value without truncation is equivalent to the exact value within the limits of numerical precision.\n", - "\n", - "The expectation value on the operator with truncated terms has some error on the order of ``1e-4``, which is within the expected tolerance.\n", - "\n", - "**Note:** We use a statevector-based ``Estimator`` primitive to illustrate the effect of truncation on the output. To run on the backend to which the experiments were transpiled in Step 2, one would import the [EstimatorV2](/docs/api/qiskit-ibm-runtime/estimator-v2) from ``qiskit-ibm-runtime`` and pass the backend instance into the constructor." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ae47b4e1-f40e-4308-a32d-2ad7ecc56c03", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exact expectation value: 0.8854160687717507\n", - "Backpropagated expectation value: 0.8854160687717532\n", - "Backpropagated expectation value with truncation: 0.8850236647156059\n", - " - Expected Error for truncated observable: 4.933e-02\n", - " - Observed Error for truncated observable: 3.924e-04\n" - ] - } - ], - "source": [ - "from qiskit.primitives import StatevectorEstimator as Estimator\n", - "\n", - "estimator = Estimator()\n", - "\n", - "# Run the experiments using Estimator primitive\n", - "result_exact = estimator.run([(circuit_isa, observable_isa)]).result()[0].data.evs.item()\n", - "result_bp = estimator.run([(bp_circuit_isa, bp_obs_isa)]).result()[0].data.evs.item()\n", - "result_bp_trunc = (\n", - " estimator.run([(bp_circuit_trunc_isa, bp_obs_trunc_isa)]).result()[0].data.evs.item()\n", - ")\n", - "\n", - "print(f\"Exact expectation value: {result_exact}\")\n", - "print(f\"Backpropagated expectation value: {result_bp}\")\n", - "print(f\"Backpropagated expectation value with truncation: {result_bp_trunc}\")\n", - "print(f\" - Expected Error for truncated observable: {metadata.accumulated_error(0):.3e}\")\n", - "print(f\" - Observed Error for truncated observable: {abs(result_exact - result_bp_trunc):.3e}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/addons/qiskit-addon-obp/tutorials/index.mdx b/docs/addons/qiskit-addon-obp/tutorials/index.mdx deleted file mode 100644 index 0c1c4cc0d67..00000000000 --- a/docs/addons/qiskit-addon-obp/tutorials/index.mdx +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: "Tutorials" -description: "Tutorials for the lastest version of Operator backpropagation (OBP)" ---- - -# Tutorials - -This page summarizes the available tutorials. - -* [Reducing depth of circuits with operator backpropagation](01-getting-started) - diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json index d7083550560..2355b4c7b77 100644 --- a/docs/addons/qiskit-addon-sqd/_toc.json +++ b/docs/addons/qiskit-addon-sqd/_toc.json @@ -55,20 +55,6 @@ ], "collapsible": false }, - { - "title": "Tutorials", - "children": [ - { - "title": "Improving energy estimation of a chemistry Hamiltonian with SQD", - "url": "/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian" - }, - { - "title": "Improving energy estimation of a Fermionic lattice model with SQD", - "url": "/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian" - } - ], - "collapsible": false - }, { "title": "API reference", "collapsible": false, diff --git a/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.ipynb deleted file mode 100644 index 66d382b088c..00000000000 --- a/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian.ipynb +++ /dev/null @@ -1,542 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "frontmatter", - "metadata": {}, - "source": [ - "---\n", - "title: \"Improving energy estimation of a chemistry Hamiltonian with SQD\"\n", - "description: \"Improving energy estimation of a chemistry Hamiltonian with SQD for the latest version of Sample-based quantum diagonalization (SQD)\"\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", - "metadata": {}, - "source": [ - "# Improving energy estimation of a chemistry Hamiltonian with SQD\n", - "\n", - "In this tutorial we implement a [Qiskit pattern](/docs/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of a chemistry Hamiltonian: the $N_2$ molecule at equilibrium in the 6-31G basis set. We will follow a [sample-based quantum diagonalization approach](https://arxiv.org/abs/2405.05068) to process samples taken from a ``36``-qubit quantum circuit ansatz (in this case, an LUCJ circuit). In order to account for the effect of quantum noise, the configuration recovery technique is used.\n", - "\n", - "The pattern can be described in four steps:\n", - "\n", - "1. **Step 1: Map to quantum problem**\n", - " - Generate an ansatz for estimating the ground state\n", - "2. **Step 2: Optimize the problem**\n", - " - Transpile the ansatz for the backend\n", - "3. **Step 3: Execute experiments**\n", - " - Draw samples from the ansatz using the ``Sampler`` primitive\n", - "4. **Step 4: Post-process results**\n", - " - Self-consistent configuration recovery loop\n", - " - Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration.\n", - " - Probabilistically create batches of subsamples from recovered bitstrings.\n", - " - Project and diagonalize the molecular Hamiltonian over each sampled subspace.\n", - " - Save the minimum ground state energy found across all batches and update the avg orbital occupancy.\n", - "\n", - "\n", - "For this example, the interacting-electron Hamiltonian takes the generic form:\n", - "\n", - "$$\n", - "\\hat{H} = \\sum_{ \\substack{pr\\\\\\sigma} } h_{pr} \\, \\hat{a}^\\dagger_{p\\sigma} \\hat{a}_{r\\sigma}\n", - "+\n", - "\\sum_{ \\substack{prqs\\\\\\sigma\\tau} }\n", - "\\frac{(pr|qs)}{2} \\,\n", - "\\hat{a}^\\dagger_{p\\sigma}\n", - "\\hat{a}^\\dagger_{q\\tau}\n", - "\\hat{a}_{s\\tau}\n", - "\\hat{a}_{r\\sigma}\n", - "$$\n", - "\n", - "$\\hat{a}^\\dagger_{p\\sigma}$/$\\hat{a}_{p\\sigma}$ are the fermionic creation/annihalation operators associated to the $p$-th basis set element and the spin $\\sigma$. $h_{pr}$ and $(pr|qs)$ are the one- and two-body electronic integrals.\n", - "\n", - "The SQD workflow with self-consistent configuration recovery is depicted in the following diagram.\n", - "\n", - "![SQD diagram](/docs/images/addons/qiskit-addon-sqd/sqd_diagram.png)\n", - "\n", - "SQD is known to work well when the target eigenstate is sparse: the wave function is supported in a set of basis states $\\mathcal{S} = \\{|x\\rangle \\}$ whose size does not increase exponentially with the size of the problem. In this scenario, the diagonalization of the Hamiltonian projected into the subspace defined by $\\mathcal{S}$:\n", - "$$\n", - "H_\\mathcal{S} = P_\\mathcal{S} H P_\\mathcal{S} \\textrm{ with } P_\\mathcal{S} = \\sum_{x \\in \\mathcal{S}} |x \\rangle \\langle x |;\n", - "$$\n", - "yields a good approximation to the target eigenstate. The role of the quantum device is to produce samples of the members of $\\mathcal{S}$ only. First, a quantum circuit prepares the state $|\\Psi\\rangle$ in the quantum device. The Jordan-Wigner encoding is used. Consequently, members of the computational basis represent Fock states (electronic configurations/determinants). The circuit is sampled in the computational basis, yielding the set of noisy configurations $\\tilde{\\mathcal{X}}$. The configurations are represented by bitstrings. The set $\\tilde{\\mathcal{X}}$ is then passed into the classical post-processing block, where the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068) is used. In the SQD framework, the role of the quantum device is to produce a probability distribution." - ] - }, - { - "cell_type": "markdown", - "id": "afeb054c", - "metadata": {}, - "source": [ - "### Step 1: Map problem to a quantum circuit\n", - "\n", - "In this tutorial, we will approximate the ground state energy of an $N_2$ molecule. First, we will specify the molecule and its properties. Next, we will create a [local unitary cluster Jastrow (LUCJ)](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) ansatz (quantum circuit) to generate samples from a quantum computer for ground state energy estimation.\n", - "\n", - "First, we will specify the molecule and its properties." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "converged SCF energy = -108.835236570775\n", - "CASCI E = -109.046671778080 E(CI) = -32.8155692383188 S^2 = 0.0000000\n" - ] - } - ], - "source": [ - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "import pyscf\n", - "import pyscf.cc\n", - "import pyscf.mcscf\n", - "\n", - "# Specify molecule properties\n", - "open_shell = False\n", - "spin_sq = 0\n", - "\n", - "# Build N2 molecule\n", - "mol = pyscf.gto.Mole()\n", - "mol.build(\n", - " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", - " basis=\"6-31g\",\n", - " symmetry=\"Dooh\",\n", - ")\n", - "\n", - "# Define active space\n", - "n_frozen = 2\n", - "active_space = range(n_frozen, mol.nao_nr())\n", - "\n", - "# Get molecular integrals\n", - "scf = pyscf.scf.RHF(mol).run()\n", - "num_orbitals = len(active_space)\n", - "n_electrons = int(sum(scf.mo_occ[active_space]))\n", - "num_elec_a = (n_electrons + mol.spin) // 2\n", - "num_elec_b = (n_electrons - mol.spin) // 2\n", - "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", - "mo = cas.sort_mo(active_space, base=0)\n", - "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", - "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", - "\n", - "# Compute exact energy\n", - "exact_energy = cas.run().e_tot" - ] - }, - { - "cell_type": "markdown", - "id": "96bfe018", - "metadata": {}, - "source": [ - "Next, we will create the ansatz. The ``LUCJ`` ansatz is a parameterized quantum circuit, and we will initialize it with `t2` and `t1` amplitudes obtained from a CCSD calculation." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "66270387", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "E(CCSD) = -109.0398256929734 E_corr = -0.2045891221988311\n" - ] - } - ], - "source": [ - "# Get CCSD t2 amplitudes for initializing the ansatz\n", - "ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run()\n", - "t1 = ccsd.t1\n", - "t2 = ccsd.t2" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f4d882fa", - "metadata": {}, - "source": [ - "We will use the [ffsim](https://github.com/qiskit-community/ffsim/tree/main) package to create and initialize the ansatz with `t2` and `t1` amplitudes computed above. Since our molecule has a closed-shell Hartree-Fock state, we will use the spin-balanced variant of the UCJ ansatz, [UCJOpSpinBalanced](https://qiskit-community.github.io/ffsim/api/ffsim.html#ffsim.UCJOpSpinBalanced).\n", - "\n", - "As our target IBM hardware has a heavy-hex topology, we will adopt the [_zig-zag_ pattern for qubit interactions](https://pubs.rsc.org/en/content/articlehtml/2023/sc/d3sc02516k). In this pattern, orbitals (represented by qubits) with the same spin are connected with a line topology (red and blue circles) where each line take a zig-zag shape due the heavy-hex connectivity of the target hardware. Again, due to the heavy-hex topology, orbitals for different spins have connections between every 4th orbital (0, 4, 8, etc.) (purple circles).\n", - "\n", - "![lucj_ansatz](/docs/images/addons/qiskit-addon-sqd/lucj_ansatz_zig_zag_pattern.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "dd69a86c", - "metadata": {}, - "outputs": [], - "source": [ - "import ffsim\n", - "from qiskit import QuantumCircuit, QuantumRegister\n", - "\n", - "n_reps = 1\n", - "alpha_alpha_indices = [(p, p + 1) for p in range(num_orbitals - 1)]\n", - "alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\n", - "\n", - "ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\n", - " t2=t2,\n", - " t1=t1,\n", - " n_reps=n_reps,\n", - " interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\n", - ")\n", - "\n", - "nelec = (num_elec_a, num_elec_b)\n", - "\n", - "# create an empty quantum circuit\n", - "qubits = QuantumRegister(2 * num_orbitals, name=\"q\")\n", - "circuit = QuantumCircuit(qubits)\n", - "\n", - "# prepare Hartree-Fock state as the reference state and append it to the quantum circuit\n", - "circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\n", - "\n", - "# apply the UCJ operator to the reference state\n", - "circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\n", - "circuit.measure_all()" - ] - }, - { - "cell_type": "markdown", - "id": "db11bf6d", - "metadata": {}, - "source": [ - "### Step 2: Optimize the problem" - ] - }, - { - "cell_type": "markdown", - "id": "0760b3f3", - "metadata": {}, - "source": [ - "Next, we will optimize our circuit for a target hardware. We need to choose the hardware device to use before optimizing our circuit. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "53a039d8", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime.fake_provider import FakeSherbrooke\n", - "\n", - "backend = FakeSherbrooke()" - ] - }, - { - "cell_type": "markdown", - "id": "057ebbf6", - "metadata": {}, - "source": [ - "Next, we recommend the following steps to optimize the ansatz and make it hardware-compatible.\n", - "\n", - "- Select physical qubits (`initial_layout`) from the target hardware that adheres to the zig-zag pattern described above. Laying out qubits in this pattern leads to an efficient hardware-compatible circuit with fewer gates.\n", - "- Generate a staged pass manager using the [generate_preset_pass_manager](/docs/api/qiskit/transpiler_preset#generate_preset_pass_manager) function from Qiskit with your choice of `backend` and `initial_layout`.\n", - "- Set the `pre_init` stage of your staged pass manager to `ffsim.qiskit.PRE_INIT`. `ffsim.qiskit.PRE_INIT` includes Qiskit transpiler passes that decompose gates into orbital rotations and then merges the orbital rotations, resulting in fewer gates in the final circuit.\n", - "- Run the pass manager on your circuit." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7d554aa5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gate counts (w/o pre-init passes): OrderedDict({'rz': 4420, 'sx': 3432, 'ecr': 1366, 'x': 239, 'measure': 32, 'barrier': 1})\n", - "Gate counts (w/ pre-init passes): OrderedDict({'rz': 2460, 'sx': 2156, 'ecr': 730, 'x': 71, 'measure': 32, 'barrier': 1})\n" - ] - } - ], - "source": [ - "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", - "\n", - "spin_a_layout = [0, 14, 18, 19, 20, 33, 39, 40, 41, 53, 60, 61, 62, 72, 81, 82]\n", - "spin_b_layout = [2, 3, 4, 15, 22, 23, 24, 34, 43, 44, 45, 54, 64, 65, 66, 73]\n", - "initial_layout = spin_a_layout + spin_b_layout\n", - "\n", - "pass_manager = generate_preset_pass_manager(\n", - " optimization_level=3, backend=backend, initial_layout=initial_layout\n", - ")\n", - "\n", - "# without PRE_INIT passes\n", - "isa_circuit = pass_manager.run(circuit)\n", - "print(f\"Gate counts (w/o pre-init passes): {isa_circuit.count_ops()}\")\n", - "\n", - "# with PRE_INIT passes\n", - "# We will use the circuit generated by this pass manager for hardware execution\n", - "pass_manager.pre_init = ffsim.qiskit.PRE_INIT\n", - "isa_circuit = pass_manager.run(circuit)\n", - "print(f\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\")" - ] - }, - { - "cell_type": "markdown", - "id": "0cc1edef", - "metadata": {}, - "source": [ - "### Step 3: Execute experiments" - ] - }, - { - "cell_type": "markdown", - "id": "cbf7ef9f", - "metadata": {}, - "source": [ - "After optimizing the circuit for hardware execution, we are ready to run it on the target hardware and collect samples for ground state energy estimation. As we only have one circuit, we will use Qiskit Runtime's [Job execution mode](/docs/guides/execution-modes) and execute our circuit.\n", - "\n", - "***Note: We have commented out the code for running the circuit on a QPU and left it for the user's reference. Instead of running on real hardware in this guide, we will just generate random samples drawn from the uniform distribution.***" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "3da09100", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from qiskit_addon_sqd.counts import generate_bit_array_uniform\n", - "\n", - "# from qiskit_ibm_runtime import SamplerV2 as Sampler\n", - "\n", - "# sampler = Sampler(mode=backend)\n", - "# job = sampler.run([isa_circuit], shots=10_000)\n", - "# primitive_result = job.result()\n", - "# pub_result = primitive_result[0]\n", - "# bit_array = pub_result.data.meas\n", - "\n", - "rng = np.random.default_rng(24)\n", - "bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng)" - ] - }, - { - "cell_type": "markdown", - "id": "6df05b6e", - "metadata": {}, - "source": [ - "### Step 4: Post-process results" - ] - }, - { - "cell_type": "markdown", - "id": "7a102a7f-aae6-4583-ab82-ae40fcb5496a", - "metadata": {}, - "source": [ - "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function.\n", - "\n", - "The solver included in the SQD addon uses PySCF's implementation of selected CI, specifically [pyscf.fci.selected_ci.kernel_fixed_space](https://pyscf.org/pyscf_api_docs/pyscf.fci.html#pyscf.fci.selected_ci.kernel_fixed_space). The example below also shows how to pass keyword arguments to that function via the included solver. Here we pass the `max_cycle` argument." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6e873c11", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 1\n", - "\tSubsample 0\n", - "\t\tEnergy: -105.45358671756313\n", - "\t\tSubspace dimension: 5476\n", - "Iteration 2\n", - "\tSubsample 0\n", - "\t\tEnergy: -107.95172900082163\n", - "\t\tSubspace dimension: 249001\n", - "Iteration 3\n", - "\tSubsample 0\n", - "\t\tEnergy: -108.97460330369815\n", - "\t\tSubspace dimension: 339889\n", - "Iteration 4\n", - "\tSubsample 0\n", - "\t\tEnergy: -109.02739376648793\n", - "\t\tSubspace dimension: 440896\n", - "Iteration 5\n", - "\tSubsample 0\n", - "\t\tEnergy: -109.030972328451\n", - "\t\tSubspace dimension: 597529\n" - ] - } - ], - "source": [ - "from functools import partial\n", - "\n", - "from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian, solve_sci_batch\n", - "\n", - "# SQD options\n", - "energy_tol = 1e-3\n", - "occupancies_tol = 1e-3\n", - "max_iterations = 5\n", - "\n", - "# Eigenstate solver options\n", - "num_batches = 1\n", - "samples_per_batch = 300\n", - "symmetrize_spin = True\n", - "carryover_threshold = 1e-4\n", - "max_cycle = 200\n", - "\n", - "# Pass options to the built-in eigensolver. If you just want to use the defaults,\n", - "# you can omit this step, in which case you would not specify the sci_solver argument\n", - "# in the call to diagonalize_fermionic_hamiltonian below.\n", - "sci_solver = partial(solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle)\n", - "\n", - "# List to capture intermediate results\n", - "result_history = []\n", - "\n", - "\n", - "def callback(results: list[SCIResult]):\n", - " result_history.append(results)\n", - " iteration = len(result_history)\n", - " print(f\"Iteration {iteration}\")\n", - " for i, result in enumerate(results):\n", - " print(f\"\\tSubsample {i}\")\n", - " print(f\"\\t\\tEnergy: {result.energy + nuclear_repulsion_energy}\")\n", - " print(f\"\\t\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\")\n", - "\n", - "\n", - "result = diagonalize_fermionic_hamiltonian(\n", - " hcore,\n", - " eri,\n", - " bit_array,\n", - " samples_per_batch=samples_per_batch,\n", - " norb=num_orbitals,\n", - " nelec=nelec,\n", - " num_batches=num_batches,\n", - " energy_tol=energy_tol,\n", - " occupancies_tol=occupancies_tol,\n", - " max_iterations=max_iterations,\n", - " sci_solver=sci_solver,\n", - " symmetrize_spin=symmetrize_spin,\n", - " carryover_threshold=carryover_threshold,\n", - " callback=callback,\n", - " seed=rng,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "9d78906b-4759-4506-9c69-85d4e67766b3", - "metadata": {}, - "source": [ - "Now, we plot the results.\n", - "\n", - "The first plot shows that after a few iterations we estimate the ground state energy within ``~16 mH`` (chemical accuracy is typically accepted to be ``1 kcal/mol`` $\\approx$ ``1.6 mH``). Remember, the quantum samples in this demo were pure noise. The signal here comes from *a priori* knowledge of the electronic structure and molecular Hamiltonian.\n", - "\n", - "The second plot shows the average occupancy of each spatial orbital after the final iteration. We can see that both the spin-up and spin-down electrons occupy the first five orbitals with high probability in our solutions." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "caffd888-e89c-4aa9-8bae-4d1bb723b35e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exact energy: -109.04667 Ha\n", - "SQD energy: -109.03097 Ha\n", - "Absolute error: 0.01570 Ha\n" - ] - }, - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian/extracted-outputs/caffd888-e89c-4aa9-8bae-4d1bb723b35e-1.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Data for energies plot\n", - "x1 = range(len(result_history))\n", - "min_e = [\n", - " min(result, key=lambda res: res.energy).energy + nuclear_repulsion_energy\n", - " for result in result_history\n", - "]\n", - "e_diff = [abs(e - exact_energy) for e in min_e]\n", - "yt1 = [1.0, 1e-1, 1e-2, 1e-3, 1e-4]\n", - "\n", - "# Chemical accuracy (+/- 1 milli-Hartree)\n", - "chem_accuracy = 0.001\n", - "\n", - "# Data for avg spatial orbital occupancy\n", - "y2 = np.sum(result.orbital_occupancies, axis=0)\n", - "x2 = range(len(y2))\n", - "\n", - "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", - "\n", - "# Plot energies\n", - "axs[0].plot(x1, e_diff, label=\"energy error\", marker=\"o\")\n", - "axs[0].set_xticks(x1)\n", - "axs[0].set_xticklabels(x1)\n", - "axs[0].set_yticks(yt1)\n", - "axs[0].set_yticklabels(yt1)\n", - "axs[0].set_yscale(\"log\")\n", - "axs[0].set_ylim(1e-4)\n", - "axs[0].axhline(y=chem_accuracy, color=\"#BF5700\", linestyle=\"--\", label=\"chemical accuracy\")\n", - "axs[0].set_title(\"Approximated Ground State Energy Error vs SQD Iterations\")\n", - "axs[0].set_xlabel(\"Iteration Index\", fontdict={\"fontsize\": 12})\n", - "axs[0].set_ylabel(\"Energy Error (Ha)\", fontdict={\"fontsize\": 12})\n", - "axs[0].legend()\n", - "\n", - "# Plot orbital occupancy\n", - "axs[1].bar(x2, y2, width=0.8)\n", - "axs[1].set_xticks(x2)\n", - "axs[1].set_xticklabels(x2)\n", - "axs[1].set_title(\"Avg Occupancy per Spatial Orbital\")\n", - "axs[1].set_xlabel(\"Orbital Index\", fontdict={\"fontsize\": 12})\n", - "axs[1].set_ylabel(\"Avg Occupancy\", fontdict={\"fontsize\": 12})\n", - "\n", - "print(f\"Exact energy: {exact_energy:.5f} Ha\")\n", - "print(f\"SQD energy: {min_e[-1]:.5f} Ha\")\n", - "print(f\"Absolute error: {e_diff[-1]:.5f} Ha\")\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb deleted file mode 100644 index c94f3ac77f7..00000000000 --- a/docs/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian.ipynb +++ /dev/null @@ -1,525 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "frontmatter", - "metadata": {}, - "source": [ - "---\n", - "title: \"Improving energy estimation of a Fermionic lattice model with SQD\"\n", - "description: \"Improving energy estimation of a Fermionic lattice model with SQD for the latest version of Sample-based quantum diagonalization (SQD)\"\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "6789705d-464f-4ec0-bfd1-5059433a2212", - "metadata": {}, - "source": [ - "# Improving energy estimation of a Fermionic lattice model with SQD\n", - "\n", - "In this tutorial we implement a [Qiskit pattern](/docs/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of a Fermionic lattice Hamiltonian known as the single-impurity Anderson model. We will follow a sample-based quantum diagonalization approach to process samples taken from a set of ``16``-qubit Krylov basis states over increasing time intervals. These states are realized on the quantum device using Trotterization of the time evolution. In order to account for the effect of quantum noise, the configuration recovery technique is used. Assuming a good initial state and sparsity of the ground state, [this approach is proven to converge efficiently](https://arxiv.org/abs/2501.09702).\n", - "\n", - "The pattern can be described in four steps:\n", - "\n", - "1. **Step 1: Map to quantum problem**\n", - " - Generate a set of Krylov basis states (i.e., Trotterized time-evolution circuits) over increasing time intervals for estimating the ground state\n", - "2. **Step 2: Optimize the problem**\n", - " - Transpile the circuits for the backend\n", - "3. **Step 3: Execute experiments**\n", - " - Draw samples from the circuits using the ``Sampler`` primitive\n", - "4. **Step 4: Post-process results**\n", - " - Self-consistent configuration recovery loop\n", - " - Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration\n", - " - Probabilistically create batches of subsamples from recovered bitstrings\n", - " - Project and diagonalize the Fermionic lattice Hamiltonian over each sampled subspace\n", - " - Save the minimum ground state energy found across all batches and update the avg orbital occupancy" - ] - }, - { - "cell_type": "markdown", - "id": "5bb98301-dddd-4183-bbae-0055f527ede1", - "metadata": {}, - "source": [ - "### Step 1: Map problem to a quantum circuit" - ] - }, - { - "cell_type": "markdown", - "id": "267c7f70-13a2-418b-9b30-5edc9e19a460", - "metadata": {}, - "source": [ - "First, we will create the one- and two-body Hamiltonians describing the one-dimensional single-impurity Anderson model (SIAM) with ``7`` bath sites (``8`` electrons in ``8`` orbitals). This model is used to describe magnetic impurities embedded in metals.\n", - "\n", - "Then we will create the ``16``-qubit Trotter circuits used to generate the quantum Krylov subspace." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6e2b926e-c115-4d7c-be1f-07831ac82a3d", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "n_bath = 7 # number of bath sites\n", - "\n", - "V = 1 # hybridization amplitude\n", - "t = 1 # bath hopping amplitude\n", - "U = 10 # Impurity onsite repulsion\n", - "eps = -U / 2 # Chemical potential for the impurity\n", - "\n", - "# Place the impurity on the first qubit\n", - "impurity_index = 0\n", - "\n", - "# One body matrix elements in the \"position\" basis\n", - "h1e = -t * np.diag(np.ones(n_bath), k=1) - t * np.diag(np.ones(n_bath), k=-1)\n", - "h1e[impurity_index, impurity_index + 1] = -V\n", - "h1e[impurity_index + 1, impurity_index] = -V\n", - "h1e[impurity_index, impurity_index] = eps\n", - "\n", - "# Two body matrix elements in the \"position\" basis\n", - "h2e = np.zeros((n_bath + 1, n_bath + 1, n_bath + 1, n_bath + 1))\n", - "h2e[impurity_index, impurity_index, impurity_index, impurity_index] = U" - ] - }, - { - "cell_type": "markdown", - "id": "713dc5d4-7bde-4207-9c74-167ce82478b2", - "metadata": {}, - "source": [ - "Next, we will generate the quantum Krylov subspace with a set of Trotterized quantum circuits. Here we create helpers for generating the initial (reference) state as well as the time evolution for the one- and two-body parts of the Hamiltonian. For a more detailed description of this model and how the circuits are designed, please refer to [the paper](https://arxiv.org/abs/2501.09702)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1a4eee7e-2b76-4605-9bd2-8fede7777e92", - "metadata": {}, - "outputs": [], - "source": [ - "import ffsim\n", - "import scipy\n", - "from qiskit import QuantumCircuit, QuantumRegister\n", - "from qiskit.circuit.library import CPhaseGate, XGate, XXPlusYYGate\n", - "\n", - "n_modes = n_bath + 1\n", - "nelec = (n_modes // 2, n_modes // 2)\n", - "\n", - "dt = 0.2\n", - "Utar = scipy.linalg.expm(-1j * dt * h1e)\n", - "\n", - "\n", - "# The reference state\n", - "def initial_state(q_circuit, norb, nocc):\n", - " \"\"\"Prepare an initial state.\"\"\"\n", - " for i in range(nocc):\n", - " q_circuit.append(XGate(), [i])\n", - " q_circuit.append(XGate(), [norb + i])\n", - " rot = XXPlusYYGate(np.pi / 2, -np.pi / 2)\n", - "\n", - " for i in range(3):\n", - " for j in range(nocc - i - 1, nocc + i, 2):\n", - " q_circuit.append(rot, [j, j + 1])\n", - " q_circuit.append(rot, [norb + j, norb + j + 1])\n", - " q_circuit.append(rot, [j + 1, j + 2])\n", - " q_circuit.append(rot, [norb + j + 1, norb + j + 2])\n", - "\n", - "\n", - "# The one-body time evolution\n", - "free_fermion_evolution = ffsim.qiskit.OrbitalRotationJW(n_modes, Utar)\n", - "\n", - "\n", - "# The two-body time evolution\n", - "def append_diagonal_evolution(dt, U, impurity_qubit, num_orb, q_circuit):\n", - " \"\"\"Append two-body time evolution to a quantum circuit.\"\"\"\n", - " if U != 0:\n", - " q_circuit.append(\n", - " CPhaseGate(-dt / 2 * U),\n", - " [impurity_qubit, impurity_qubit + num_orb],\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "9de66e8b-caf4-493a-815b-ac3b94c3bbdc", - "metadata": {}, - "source": [ - "Generate ``d`` time-evolved states that specify the quantum Krylov subspace. Here, we have chosen ``d=8``. The error from sampling Krylov basis states converges with increasing ``d``. Note that the particulars of this problem instance allow for an efficient compilation of the one-body evolution using `OrbitalRotationJW`; however, in general, one could use Qiskit's [PauliEvolutionGate](/docs/api/qiskit/qiskit.circuit.library.PauliEvolutionGate) to implement the Trotterized time evolution of the full Hamiltonian." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "aea80714-3a98-4d40-8250-b2290a02a2d1", - "metadata": {}, - "outputs": [], - "source": [ - "# Generate the initial state\n", - "qubits = QuantumRegister(2 * n_modes, name=\"q\")\n", - "init_state = QuantumCircuit(qubits)\n", - "initial_state(init_state, n_modes, n_modes // 2)\n", - "init_state.draw(\"mpl\", scale=0.4, fold=-1)\n", - "\n", - "d = 8 # Number of Krylov basis states\n", - "circuits = []\n", - "for i in range(d):\n", - " circ = init_state.copy()\n", - " circuits.append(circ)\n", - " for _ in range(i):\n", - " append_diagonal_evolution(dt, U, impurity_index, n_modes, circ)\n", - " circ.append(free_fermion_evolution, qubits)\n", - " append_diagonal_evolution(dt, U, impurity_index, n_modes, circ)\n", - " circ.measure_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "cdd5dd05-a1b5-4fed-8888-b753aee265ee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/cdd5dd05-a1b5-4fed-8888-b753aee265ee-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "circuits[0].draw(\"mpl\", scale=0.4, fold=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7e20896a-0750-48ba-852c-7b495629ccd5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/7e20896a-0750-48ba-852c-7b495629ccd5-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "circuits[-1].draw(\"mpl\", scale=0.4, fold=-1)" - ] - }, - { - "cell_type": "markdown", - "id": "5ef3c2a9-9371-48d9-bb95-102b70ffad6d", - "metadata": {}, - "source": [ - "### Step 2: Optimize the problem" - ] - }, - { - "cell_type": "markdown", - "id": "b6e18a4f-368e-403e-9f3e-6ac2185d4081", - "metadata": {}, - "source": [ - "After we have created the Trotterized circuits, we will optimize them for a target hardware. We need to choose the hardware device to use before optimization. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "1c5441f9-a8c2-4de1-92fb-2a8251860d02", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime.fake_provider.backends import FakeSherbrooke\n", - "\n", - "backend = FakeSherbrooke()" - ] - }, - { - "cell_type": "markdown", - "id": "7b53f217-11eb-4b09-bbbb-b73a096ffd8f", - "metadata": {}, - "source": [ - "Next, we will transpile the circuits to the target backend using Qiskit." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "060e0dc6-f134-4afd-a6f5-49c89b347e8f", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit.transpiler import generate_preset_pass_manager\n", - "\n", - "# The circuit needs to be transpiled to the AerSimulator target\n", - "pass_manager = generate_preset_pass_manager(optimization_level=3, backend=backend)\n", - "isa_circuits = pass_manager.run(circuits)" - ] - }, - { - "cell_type": "markdown", - "id": "2926bd02-4b8a-4f9e-9a40-5a7fd983796d", - "metadata": {}, - "source": [ - "### Step 3: Execute experiments" - ] - }, - { - "cell_type": "markdown", - "id": "d07e5c37-6ec4-4fea-8f28-1e0600ceace4", - "metadata": {}, - "source": [ - "After optimizing the circuits for hardware execution, we are ready to run them on the target hardware and collect samples for ground state energy estimation. Here we use ``SamplerV2`` from ``qiskit-ibm-runtime`` to simulate noisy samples taken from the ``ibm_sherbrooke`` backend. We then combine the counts from each of the Krylov basis states into a single counts dictionary and plot the top 20 most commonly sampled bitstrings.\n", - "\n", - "***Note: [Qiskit Aer](/docs/addons/qiskit-aer/index) is required to simulate samples from transpiled circuits.***" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ff2daa67-d86c-4efb-a1fd-4fd4fe236255", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/ff2daa67-d86c-4efb-a1fd-4fd4fe236255-0.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit.primitives import BitArray\n", - "from qiskit.visualization import plot_histogram\n", - "from qiskit_ibm_runtime import SamplerV2 as Sampler\n", - "\n", - "# Sample from the circuits\n", - "noisy_sampler = Sampler(backend, options={\"simulator\": {\"seed_simulator\": 24}})\n", - "job = noisy_sampler.run(isa_circuits, shots=500)\n", - "\n", - "# Combine the counts from the individual Trotter circuits\n", - "bit_array = BitArray.concatenate_shots([result.data.meas for result in job.result()])\n", - "\n", - "plot_histogram(bit_array.get_counts(), number_to_keep=20)" - ] - }, - { - "cell_type": "markdown", - "id": "3bb18c13-0ccd-4397-af16-3f964631729e", - "metadata": {}, - "source": [ - "### Step 4: Post-process the results" - ] - }, - { - "cell_type": "markdown", - "id": "28460642-9cf0-4cc6-a0e2-185f20c81b19", - "metadata": {}, - "source": [ - "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6d2f236-3950-4c71-9ff0-59ac720817a9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 1\n", - "\tSubsample 0\n", - "\t\tEnergy: -13.257128325607997\n", - "\t\tSubspace dimension: 3969\n", - "\tSubsample 1\n", - "\t\tEnergy: -13.257128325607997\n", - "\t\tSubspace dimension: 3969\n", - "\tSubsample 2\n", - "\t\tEnergy: -13.257128325607997\n", - "\t\tSubspace dimension: 3969\n", - "Iteration 2\n", - "\tSubsample 0\n", - "\t\tEnergy: -13.319666127542039\n", - "\t\tSubspace dimension: 4096\n", - "\tSubsample 1\n", - "\t\tEnergy: -13.420534292304595\n", - "\t\tSubspace dimension: 4624\n", - "\tSubsample 2\n", - "\t\tEnergy: -9.136171594591085\n", - "\t\tSubspace dimension: 4624\n", - "Iteration 3\n", - "\tSubsample 0\n", - "\t\tEnergy: -13.422491814612833\n", - "\t\tSubspace dimension: 4900\n", - "\tSubsample 1\n", - "\t\tEnergy: -13.422491814612833\n", - "\t\tSubspace dimension: 4900\n", - "\tSubsample 2\n", - "\t\tEnergy: -13.422491814612833\n", - "\t\tSubspace dimension: 4900\n", - "Iteration 4\n", - "\tSubsample 0\n", - "\t\tEnergy: -13.422491814612833\n", - "\t\tSubspace dimension: 4900\n", - "\tSubsample 1\n", - "\t\tEnergy: -13.422491814612833\n", - "\t\tSubspace dimension: 4900\n", - "\tSubsample 2\n", - "\t\tEnergy: -13.422491814612833\n", - "\t\tSubspace dimension: 4900\n" - ] - } - ], - "source": [ - "from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian\n", - "\n", - "# List to capture intermediate results\n", - "result_history = []\n", - "\n", - "\n", - "def callback(results: list[SCIResult]):\n", - " result_history.append(results)\n", - " iteration = len(result_history)\n", - " print(f\"Iteration {iteration}\")\n", - " for i, result in enumerate(results):\n", - " print(f\"\\tSubsample {i}\")\n", - " print(f\"\\t\\tEnergy: {result.energy}\")\n", - " print(f\"\\t\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\")\n", - "\n", - "\n", - "rng = np.random.default_rng(24)\n", - "result = diagonalize_fermionic_hamiltonian(\n", - " h1e,\n", - " h2e,\n", - " bit_array,\n", - " samples_per_batch=300,\n", - " norb=n_modes,\n", - " nelec=nelec,\n", - " num_batches=3,\n", - " max_iterations=10,\n", - " symmetrize_spin=True,\n", - " callback=callback,\n", - " seed=rng,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "0fdbf9b2-d887-445b-9800-1bc4063b30ea", - "metadata": {}, - "source": [ - "Now, we plot the results. The first plot shows that after a few iterations, we obtain the exact ground state energy.\n", - "\n", - "This example is small enough that we are able to explore the full Hilbert space, as seen in the print statements above. Remember, the full Hilbert space is dimension ``(num_orbitals choose nelec_a) * (num_orbitals choose nelec_b)``. So for this problem: `(8 choose 4)**2 = 4900`. The subspace dimensions increase due enhanced configuration recovery, and also the fact that the SQD algorithm carries important configurations from one iteration to the next. By the last iteration, we are diagonalizing in the entire Hilbert space.\n", - "\n", - "The second plot shows the average occupancy of each spatial orbital across all batches' solutions. We see that with high probability each orbital contains one electron." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e259e18c-551a-4109-8e52-47eb070222dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exact energy: -13.42249\n", - "SQD energy: -13.42249\n", - "Absolute error: 0.00000\n" - ] - }, - { - "data": { - "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/tutorials/02-fermionic-lattice-hamiltonian/extracted-outputs/e259e18c-551a-4109-8e52-47eb070222dc-1.avif\" alt=\"Output of the previous code cell\" />" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "exact_energy = -13.422491814605827\n", - "min_es = [min(result, key=lambda res: res.energy).energy for result in result_history]\n", - "min_id, min_e = min(enumerate(min_es), key=lambda x: x[1])\n", - "\n", - "# Data for energies plot\n", - "x1 = range(len(result_history))\n", - "yt1 = list(np.arange(-13.5, -13.1, 0.1))\n", - "ytl = [f\"{i:.1f}\" for i in yt1]\n", - "\n", - "# Data for avg spatial orbital occupancy\n", - "y2 = np.sum(result.orbital_occupancies, axis=0)\n", - "x2 = range(len(y2))\n", - "\n", - "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", - "\n", - "# Plot energies\n", - "axs[0].plot(x1, min_es, label=\"SQD energy\", marker=\"o\")\n", - "axs[0].set_xticks(x1)\n", - "axs[0].set_xticklabels(x1)\n", - "axs[0].set_yticks(yt1)\n", - "axs[0].set_yticklabels(ytl)\n", - "axs[0].axhline(y=exact_energy, color=\"#BF5700\", linestyle=\"--\", label=\"Exact energy\")\n", - "axs[0].set_title(\"Approximated Ground State Energy vs SQD Iterations\")\n", - "axs[0].set_xlabel(\"Iteration Index\", fontdict={\"fontsize\": 12})\n", - "axs[0].set_ylabel(\"Energy\", fontdict={\"fontsize\": 12})\n", - "axs[0].legend()\n", - "\n", - "# Plot orbital occupancy\n", - "axs[1].bar(x2, y2, width=0.8)\n", - "axs[1].set_xticks(x2)\n", - "axs[1].set_xticklabels(x2)\n", - "axs[1].set_title(\"Avg Occupancy per Spatial Orbital\")\n", - "axs[1].set_xlabel(\"Orbital Index\", fontdict={\"fontsize\": 12})\n", - "axs[1].set_ylabel(\"Avg Occupancy\", fontdict={\"fontsize\": 12})\n", - "\n", - "print(f\"Exact energy: {exact_energy:.5f}\")\n", - "print(f\"SQD energy: {min_e:.5f}\")\n", - "print(f\"Absolute error: {abs(min_e - exact_energy):.5f}\")\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/addons/qiskit-addon-sqd/tutorials/index.mdx b/docs/addons/qiskit-addon-sqd/tutorials/index.mdx deleted file mode 100644 index 5b843da3e68..00000000000 --- a/docs/addons/qiskit-addon-sqd/tutorials/index.mdx +++ /dev/null @@ -1,17 +0,0 @@ ---- -title: "Tutorials" -description: "Tutorials for the lastest version of Sample-based quantum diagonalization (SQD)" ---- - -# Tutorials - -This page summarizes the available tutorials. - -[![](/docs/images/addons/qiskit-addon-sqd/tutorials_01_chemistry_hamiltonian_19_1.avif)](01-chemistry-hamiltonian) - -[Improving energy estimation of a chemistry Hamiltonian with SQD](01-chemistry-hamiltonian) - -[![](/docs/images/addons/qiskit-addon-sqd/tutorials_02_fermionic_lattice_hamiltonian_22_1.avif)](02-fermionic-lattice-hamiltonian) - -[Improving energy estimation of a Fermionic lattice model with SQD](02-fermionic-lattice-hamiltonian) - diff --git a/docs/addons/qiskit-addon-utils/_toc.json b/docs/addons/qiskit-addon-utils/_toc.json index af7d32a9ecc..a309edf376a 100644 --- a/docs/addons/qiskit-addon-utils/_toc.json +++ b/docs/addons/qiskit-addon-utils/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Qiskit addon utilities 0.3.1", + "title": "Qiskit addon utilities 0.3.0", "collapsed": true, "children": [ { diff --git a/docs/api/qiskit-addon-sqd/_package.json b/docs/api/qiskit-addon-sqd/_package.json index 1f226e87545..9b4e87cc521 100644 --- a/docs/api/qiskit-addon-sqd/_package.json +++ b/docs/api/qiskit-addon-sqd/_package.json @@ -1,4 +1,4 @@ { "name": "qiskit-addon-sqd", - "version": "0.12.1" + "version": "0.12.0" } diff --git a/docs/api/qiskit-addon-utils/_package.json b/docs/api/qiskit-addon-utils/_package.json index 648d449cc7c..fb08817f4b1 100644 --- a/docs/api/qiskit-addon-utils/_package.json +++ b/docs/api/qiskit-addon-utils/_package.json @@ -1,4 +1,4 @@ { "name": "qiskit-addon-utils", - "version": "0.3.1" + "version": "0.3.0" } diff --git 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import { SharedArguments, } from "./updateDocsShared.js"; -export interface Arguments extends SharedArguments { +export type Arguments = SharedArguments & { [x: string]: unknown; historical: boolean; dev: boolean; diff --git a/scripts/js/commands/api/updateDocsShared.ts b/scripts/js/commands/api/updateDocsShared.ts index f3082e38831..68967c41ee7 100644 --- a/scripts/js/commands/api/updateDocsShared.ts +++ b/scripts/js/commands/api/updateDocsShared.ts @@ -26,7 +26,7 @@ import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; import { isValidVersion } from "../../lib/apiVersions.js"; /** Fields shared by both `updateApiDocs` and `updateAddonDocs` CLIs. */ -export interface SharedArguments { +export type SharedArguments = { package: string; version: string; skipDownload: boolean; diff --git a/scripts/js/lib/api/addonDocsPipeline.ts b/scripts/js/lib/api/addonDocsPipeline.ts index ffa12f78d8a..2a911324c42 100644 --- a/scripts/js/lib/api/addonDocsPipeline.ts +++ b/scripts/js/lib/api/addonDocsPipeline.ts @@ -134,6 +134,7 @@ async function determineFilePaths( "apidocs/**", "apidoc/**", "stubs/**", + "tutorials/**", "release-notes.html", "release_notes.html", ...SPHINX_INTERNALS, diff --git a/scripts/js/lib/api/generateAddonToc.test.ts b/scripts/js/lib/api/generateAddonToc.test.ts index b32b0470e2d..9d9b4b8d550 100644 --- a/scripts/js/lib/api/generateAddonToc.test.ts +++ b/scripts/js/lib/api/generateAddonToc.test.ts @@ -169,43 +169,9 @@ test("how-tos directory becomes Guides subsection in main section", async () => }); }); -test("tutorials directory becomes its own top-level section", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - "tutorials/index.mdx": mdx("Tutorials Index", "Tutorials"), - "tutorials/01-intro.ipynb": notebook("Getting started"), - "tutorials/02-advanced.mdx": mdx("Advanced tutorial"), - }); - - const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); - - // tutorials must NOT appear inside the main section - const main = toc.children[0]; - expect(main.children?.some((c) => c.title === "Tutorials")).toBe(false); - - // tutorials must appear as its own top-level section before API reference - const tutorialsSection = toc.children.find((c) => c.title === "Tutorials"); - expect(tutorialsSection).toEqual({ - title: "Tutorials", - collapsible: false, - children: [ - { - title: "Getting started", - url: "/docs/addons/my-addon/tutorials/01-intro", - }, - { - title: "Advanced tutorial", - url: "/docs/addons/my-addon/tutorials/02-advanced", - }, - ], - }); -}); - test("api reference section is always last", async () => { const { tmpDir } = await makeTmpAddonDir({ "index.mdx": mdx("Home"), - "tutorials/01.ipynb": notebook("Tutorial one"), }); const pkg = await makePkg(); @@ -307,14 +273,15 @@ test("empty subdirectory is omitted from the TOC", async () => { test("notebook title is read from first non-frontmatter markdown cell h1", async () => { const { tmpDir } = await makeTmpAddonDir({ "index.mdx": mdx("Home"), - "tutorials/demo.ipynb": notebook("The real title from h1"), + "how-tos/demo.ipynb": notebook("The real title from h1"), }); const pkg = await makePkg(); const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const main = toc.children[0]; - const tutorialsSection = toc.children.find((c) => c.title === "Tutorials"); - expect(tutorialsSection?.children?.[0].title).toBe("The real title from h1"); + const guides = main.children?.find((c) => c.title === "Guides"); + expect(guides?.children?.[0].title).toBe("The real title from h1"); }); test("notebook with h2 as first heading falls back to h2", async () => { @@ -342,14 +309,15 @@ test("notebook with h2 as first heading falls back to h2", async () => { const { tmpDir } = await makeTmpAddonDir({ "index.mdx": mdx("Home"), - "tutorials/demo.ipynb": nb, + "how-tos/demo.ipynb": nb, }); const pkg = await makePkg(); const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const main = toc.children[0]; - const tutorialsSection = toc.children.find((c) => c.title === "Tutorials"); - expect(tutorialsSection?.children?.[0].title).toBe("Section heading"); + const guides = main.children?.find((c) => c.title === "Guides"); + expect(guides?.children?.[0].title).toBe("Section heading"); }); test("mdx h2 fallback when no h1 present", async () => { diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts index a432868dd7e..6523b8f986b 100644 --- a/scripts/js/lib/api/generateAddonToc.ts +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -20,10 +20,6 @@ import { TocEntry } from "./generateToc.js"; import { DOCS_BASE_PATH } from "./paths.js"; import { capitalize } from "lodash-es"; -// Subdirectories that become their own top-level TOC sections rather than -// being nested inside the main (unlabeled) section. -const TOP_LEVEL_SECTIONS = new Set(["tutorials"]); - // labels for known subdirectory names. const DIR_LABELS: Record<string, string> = { "how-tos": "Guides", @@ -78,19 +74,15 @@ export async function generateAddonToc( const entry = await buildSubdirEntry(dir, addonDocsPath, addonUrlBase); if (!entry) continue; - if (TOP_LEVEL_SECTIONS.has(dir)) { - topLevelSections.push({ ...entry, collapsible: false }); + const existingSection = mergedSections.get(entry.title); + if (existingSection) { + existingSection.children = [ + ...(existingSection.children ?? []), + ...(entry.children ?? []), + ]; } else { - const existingSection = mergedSections.get(entry.title); - if (existingSection) { - existingSection.children = [ - ...(existingSection.children ?? []), - ...(entry.children ?? []), - ]; - } else { - mergedSections.set(entry.title, entry); - mainChildren.push(entry); - } + mergedSections.set(entry.title, entry); + mainChildren.push(entry); } } From 7e9c9b3b0c9ea57f3c0adff2ae34b08809195515 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 19 May 2026 14:49:46 -0400 Subject: [PATCH 069/104] cleanup ignores --- scripts/config/cspell/cSpell.json | 6 +----- scripts/js/lib/links/ignores.ts | 16 ---------------- 2 files changed, 1 insertion(+), 21 deletions(-) diff --git a/scripts/config/cspell/cSpell.json b/scripts/config/cspell/cSpell.json index 980e76e721e..438c6af376b 100644 --- a/scripts/config/cspell/cSpell.json +++ b/scripts/config/cspell/cSpell.json @@ -45,15 +45,11 @@ "ebitsb", "pmzero", "textrm", - "resuce", // fix in addon-obp - "subeperiments", // fix in addon-cutting - "detination", // fix in addon-cutting "subpace", // fix in addon-sqd "behaviour", // fix in addon-sqd "susbapce", // fix in addon-sqd "Counfiguration", // fix in addon-sqd - "construnct", // fix in addon-sqd - "annihalation" // fix in addon-sqd + "construnct" // fix in addon-sqd ], "ignoreRegExpList": [ // Markdown links diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index e858aaa593b..90e86b11776 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -219,8 +219,6 @@ const ALWAYS_IGNORED_URLS__SHOULD_FIX: string[] = [ "https://www.globaldataquantum.com/en/quantum-portfolio-optimizer/#form", // from qiskit-addon-obp - "/docs/guides/serverless#qiskit-patterns-with-quantum-serverless", - "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started-ipynb", "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started-ipynb", "/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html", "/api/qiskit-ibm-runtime/noise-learner-noise-learner", @@ -229,33 +227,19 @@ const ALWAYS_IGNORED_URLS__SHOULD_FIX: string[] = [ // from qiskit-addon-utils "/docs/addons/samplomatic/guides/samplex-io#qubit-ordering-convention", - // from qiskit-addon-mpf - "/api/qiskit/qiskit.quantum_info.SparsePauliOp", - "/api/qiskit/qiskit.transpiler.CouplingMap", - "/api/qiskit/qiskit.primitives.StatevectorEstimator", - "/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2", - // from qiskit-addon-sqd "/docs/en/tutorials/sample-based-krylov-quantum-diagonalization", "/docs/en/tutorials/sample-based-quantum-diagonalization", "/docs/guides/get-started-with-primitives#get-started-with-sampler", - "/docs/api/qiskit/transpiler_preset#generate_preset_pass_manager", - "../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian", // from qiskit-addon-cutting "how-tos/how-to-specify-cut-wires", "/circuit_cutting/explanations/index-rst#overview-of-circuit-cutting", "/docs/start/install#operating-system-support", "../tutorials/03_wire_cutting_via_move_instruction.ipynb", - "/guides/intro-to-patterns", - "../how-tos/how_to_specify_cut_wires.ipynb", - "../explanation/index.rst", "#equation-eq-qpd", "./circuit_cutting/explanations/index-rst#overview-of-circuit-cutting", "../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb", - - // from qiskit-addon-aqc-tensor - "../stubs/qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator.rst" ]; export const ALWAYS_IGNORED_URLS = new Set([ From 4a17ef5ef8a62d5dea93e0fa61c48dac71fba0cf Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 19 May 2026 15:11:36 -0400 Subject: [PATCH 070/104] rename toc titles --- scripts/js/lib/api/generateAddonToc.test.ts | 16 +++++++++++++++- scripts/js/lib/api/generateAddonToc.ts | 9 +++++++-- 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/scripts/js/lib/api/generateAddonToc.test.ts b/scripts/js/lib/api/generateAddonToc.test.ts index 9d9b4b8d550..a6901d15d20 100644 --- a/scripts/js/lib/api/generateAddonToc.test.ts +++ b/scripts/js/lib/api/generateAddonToc.test.ts @@ -107,7 +107,7 @@ test("minimal addon: only index.mdx and install.mdx", async () => { children: [ { title: "Home", url: "/docs/addons/my-addon" }, { - title: "Installation Instructions", + title: "Install instructions", url: "/docs/addons/my-addon/install", }, ], @@ -126,6 +126,20 @@ test("minimal addon: only index.mdx and install.mdx", async () => { }); }); +test("install.mdx in a subdirectory uses its h1, not the hardcoded title", async () => { + const { tmpDir } = await makeTmpAddonDir({ + "index.mdx": mdx("Home"), + "how-tos/install.mdx": mdx("Custom install title"), + }); + + const pkg = await makePkg(); + const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const main = toc.children[0]; + + const guides = main.children?.find((c) => c.title === "Guides"); + expect(guides?.children?.[0].title).toBe("Custom install title"); +}); + test("github link is appended after top-level files when githubSlug is set", async () => { const { tmpDir } = await makeTmpAddonDir({ "index.mdx": mdx("Home", "My Addon"), diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts index 6523b8f986b..16e18db7fa4 100644 --- a/scripts/js/lib/api/generateAddonToc.ts +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -26,6 +26,12 @@ const DIR_LABELS: Record<string, string> = { explanations: "Guides", }; +// Hardcoded titles for specific top-level slugs (overrides the file's h1). +const TOP_LEVEL_TITLES: Record<string, string> = { + index: "Home", + install: "Install instructions", +}; + type AddonTocSection = TocEntry & { collapsible?: boolean }; type AddonToc = { @@ -62,8 +68,7 @@ export async function generateAddonToc( const slug = file.replace(/\.(mdx|ipynb)$/, ""); const url = slug === "index" ? addonUrlBase : `${addonUrlBase}/${slug}`; - const title = - slug === "index" ? "Home" : await readTitle(join(addonDocsPath, file)); + const title = TOP_LEVEL_TITLES[slug] ?? await readTitle(join(addonDocsPath, file)); mainChildren.push({ title, url }); } From 08c5b31c0b78b35e6db018aa56a5e16bdbfe2dde Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 19 May 2026 15:16:37 -0400 Subject: [PATCH 071/104] update toc --- docs/addons/qiskit-addon-aqc-tensor/_toc.json | 2 +- docs/addons/qiskit-addon-cutting/_toc.json | 2 +- docs/addons/qiskit-addon-mpf/_toc.json | 2 +- docs/addons/qiskit-addon-obp/_toc.json | 2 +- docs/addons/qiskit-addon-sqd/_toc.json | 2 +- docs/addons/qiskit-addon-utils/_toc.json | 4 ++-- scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index | 2 +- scripts/js/lib/api/generateAddonToc.test.ts | 2 +- scripts/js/lib/api/generateAddonToc.ts | 2 +- scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html | 2 +- 10 files changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/addons/qiskit-addon-aqc-tensor/_toc.json b/docs/addons/qiskit-addon-aqc-tensor/_toc.json index 93bb2850ee2..f4a6a312812 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/_toc.json +++ b/docs/addons/qiskit-addon-aqc-tensor/_toc.json @@ -12,7 +12,7 @@ "url": "/docs/addons/qiskit-addon-aqc-tensor" }, { - "title": "Installation Instructions", + "title": "Installation instructions", "url": "/docs/addons/qiskit-addon-aqc-tensor/install" }, { diff --git a/docs/addons/qiskit-addon-cutting/_toc.json b/docs/addons/qiskit-addon-cutting/_toc.json index d4e70b56b23..72cb5ba7879 100644 --- a/docs/addons/qiskit-addon-cutting/_toc.json +++ b/docs/addons/qiskit-addon-cutting/_toc.json @@ -12,7 +12,7 @@ "url": "/docs/addons/qiskit-addon-cutting" }, { - "title": "Installation Instructions", + "title": "Installation instructions", "url": "/docs/addons/qiskit-addon-cutting/install" }, { diff --git a/docs/addons/qiskit-addon-mpf/_toc.json b/docs/addons/qiskit-addon-mpf/_toc.json index 1cb6d4292a8..1e836449c30 100644 --- a/docs/addons/qiskit-addon-mpf/_toc.json +++ b/docs/addons/qiskit-addon-mpf/_toc.json @@ -12,7 +12,7 @@ "url": "/docs/addons/qiskit-addon-mpf" }, { - "title": "Installation Instructions", + "title": "Installation instructions", "url": "/docs/addons/qiskit-addon-mpf/install" }, { diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index f3361732291..6c6933f01b9 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -12,7 +12,7 @@ "url": "/docs/addons/qiskit-addon-obp" }, { - "title": "Installation Instructions", + "title": "Installation instructions", "url": "/docs/addons/qiskit-addon-obp/install" }, { diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json index 2355b4c7b77..b4f64832727 100644 --- a/docs/addons/qiskit-addon-sqd/_toc.json +++ b/docs/addons/qiskit-addon-sqd/_toc.json @@ -12,7 +12,7 @@ "url": "/docs/addons/qiskit-addon-sqd" }, { - "title": "Installation Instructions", + "title": "Installation instructions", "url": "/docs/addons/qiskit-addon-sqd/install" }, { diff --git a/docs/addons/qiskit-addon-utils/_toc.json b/docs/addons/qiskit-addon-utils/_toc.json index a309edf376a..9781982eda2 100644 --- a/docs/addons/qiskit-addon-utils/_toc.json +++ b/docs/addons/qiskit-addon-utils/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Qiskit addon utilities 0.3.0", + "title": "Qiskit addon utilities 0.3.1", "collapsed": true, "children": [ { @@ -12,7 +12,7 @@ "url": "/docs/addons/qiskit-addon-utils" }, { - "title": "Installation Instructions", + "title": "Installation instructions", "url": "/docs/addons/qiskit-addon-utils/install" }, { diff --git a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index index c07066cb1e8..a7a464e0786 100644 --- a/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index +++ b/scripts/js/lib/api/addonDocsPipeline.test.ts-snapshots/index @@ -5,7 +5,7 @@ description: "Documentation for the latest version of Qiskit Addon Smoke" # Qiskit Addon Smoke -This is the smoke-test landing page. See the [install instructions](install) or the [first how-to](how-tos/foo). +This is the smoke-test landing page. See the [installation instructions](install) or the [first how-to](how-tos/foo). ![A smoke test diagram](/docs/images/addons/qiskit-addon-smoke/smoke-diagram.avif) diff --git a/scripts/js/lib/api/generateAddonToc.test.ts b/scripts/js/lib/api/generateAddonToc.test.ts index a6901d15d20..9c2e9c594de 100644 --- a/scripts/js/lib/api/generateAddonToc.test.ts +++ b/scripts/js/lib/api/generateAddonToc.test.ts @@ -107,7 +107,7 @@ test("minimal addon: only index.mdx and install.mdx", async () => { children: [ { title: "Home", url: "/docs/addons/my-addon" }, { - title: "Install instructions", + title: "Installation instructions", url: "/docs/addons/my-addon/install", }, ], diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts index 16e18db7fa4..fa4b12f419e 100644 --- a/scripts/js/lib/api/generateAddonToc.ts +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -29,7 +29,7 @@ const DIR_LABELS: Record<string, string> = { // Hardcoded titles for specific top-level slugs (overrides the file's h1). const TOP_LEVEL_TITLES: Record<string, string> = { index: "Home", - install: "Install instructions", + install: "Installation instructions", }; type AddonTocSection = TocEntry & { collapsible?: boolean }; diff --git a/scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html b/scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html index 7877691083a..4ed84edfb4f 100644 --- a/scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html +++ b/scripts/js/lib/api/testdata/qiskit-addon-smoke/index.html @@ -9,7 +9,7 @@ <article role="main"> <section id="qiskit-addon-smoke"> <h1>Qiskit Addon Smoke<a class="headerlink" href="#qiskit-addon-smoke" title="Link to this heading">#</a></h1> - <p>This is the smoke-test landing page. See the <a class="reference internal" href="install.html">install instructions</a> or the <a class="reference internal" href="how_tos/foo.html">first how-to</a>.</p> + <p>This is the smoke-test landing page. See the <a class="reference internal" href="install.html">installation instructions</a> or the <a class="reference internal" href="how_tos/foo.html">first how-to</a>.</p> <img alt="A smoke test diagram" src="_images/smoke-diagram.avif"/> </section> </article> From d63ed4fb5fc4e093469c480c7d6bd6e3ebe32818 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 21 May 2026 13:55:24 -0500 Subject: [PATCH 072/104] link addon tutorials --- .github/workflows/main.yml | 2 + docs/addons/qiskit-addon-aqc-tensor/_toc.json | 10 ++++ .../qiskit-addon-aqc-tensor/_tutorials.json | 5 ++ docs/addons/qiskit-addon-cutting/_toc.json | 18 ++++++ .../qiskit-addon-cutting/_tutorials.json | 7 +++ docs/addons/qiskit-addon-mpf/_toc.json | 10 ++++ docs/addons/qiskit-addon-mpf/_tutorials.json | 5 ++ docs/addons/qiskit-addon-obp/_toc.json | 10 ++++ docs/addons/qiskit-addon-obp/_tutorials.json | 5 ++ docs/addons/qiskit-addon-sqd/_toc.json | 14 +++++ docs/addons/qiskit-addon-sqd/_tutorials.json | 6 ++ package.json | 1 + scripts/js/commands/checkAddonTutorials.ts | 56 +++++++++++++++++++ scripts/js/lib/api/Pkg.ts | 16 ++++++ scripts/js/lib/api/addonDocsPipeline.ts | 10 ++++ scripts/js/lib/api/generateAddonToc.ts | 16 ++++++ 16 files changed, 191 insertions(+) create mode 100644 docs/addons/qiskit-addon-aqc-tensor/_tutorials.json create mode 100644 docs/addons/qiskit-addon-cutting/_tutorials.json create mode 100644 docs/addons/qiskit-addon-mpf/_tutorials.json create mode 100644 docs/addons/qiskit-addon-obp/_tutorials.json create mode 100644 docs/addons/qiskit-addon-sqd/_tutorials.json create mode 100644 scripts/js/commands/checkAddonTutorials.ts diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 96d214f5227..cbc2467010e 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -105,6 +105,8 @@ jobs: run: npm run check:orphan-pages -- --apis - name: Check Qiskit and Qiskit C API versions run: npm run check:qiskit-versions + - name: Check addon tutorial slugs + run: npm run check:addon-tutorials - name: Check tutorials index run: python scripts/ci/check-tutorials-index.py - name: Infrastructure tests diff --git a/docs/addons/qiskit-addon-aqc-tensor/_toc.json b/docs/addons/qiskit-addon-aqc-tensor/_toc.json index f4a6a312812..e520fc42789 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/_toc.json +++ b/docs/addons/qiskit-addon-aqc-tensor/_toc.json @@ -35,6 +35,16 @@ ], "collapsible": false }, + { + "title": "Tutorials", + "collapsible": false, + "children": [ + { + "title": "Approximate quantum compilation for time evolution circuits", + "url": "/docs/addons/qiskit-addon-aqc-tensor/tutorials/approximate-quantum-compilation-for-time-evolution" + } + ] + }, { "title": "API reference", "collapsible": false, diff --git a/docs/addons/qiskit-addon-aqc-tensor/_tutorials.json b/docs/addons/qiskit-addon-aqc-tensor/_tutorials.json new file mode 100644 index 00000000000..b98add8b767 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/_tutorials.json @@ -0,0 +1,5 @@ +{ + "tutorials": [ + "approximate-quantum-compilation-for-time-evolution" + ] +} diff --git a/docs/addons/qiskit-addon-cutting/_toc.json b/docs/addons/qiskit-addon-cutting/_toc.json index 72cb5ba7879..8aaf896c693 100644 --- a/docs/addons/qiskit-addon-cutting/_toc.json +++ b/docs/addons/qiskit-addon-cutting/_toc.json @@ -47,6 +47,24 @@ ], "collapsible": false }, + { + "title": "Tutorials", + "collapsible": false, + "children": [ + { + "title": "Circuit cutting for depth reduction", + "url": "/docs/addons/qiskit-addon-cutting/tutorials/depth-reduction-with-circuit-cutting" + }, + { + "title": "Circuit cutting for periodic boundary conditions", + "url": "/docs/addons/qiskit-addon-cutting/tutorials/periodic-boundary-conditions-with-circuit-cutting" + }, + { + "title": "Wire cutting for expectation values estimation", + "url": "/docs/addons/qiskit-addon-cutting/tutorials/wire-cutting" + } + ] + }, { "title": "API reference", "collapsible": false, diff --git a/docs/addons/qiskit-addon-cutting/_tutorials.json b/docs/addons/qiskit-addon-cutting/_tutorials.json new file mode 100644 index 00000000000..ab3428dcae3 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/_tutorials.json @@ -0,0 +1,7 @@ +{ + "tutorials": [ + "depth-reduction-with-circuit-cutting", + "periodic-boundary-conditions-with-circuit-cutting", + "wire-cutting" + ] +} diff --git a/docs/addons/qiskit-addon-mpf/_toc.json b/docs/addons/qiskit-addon-mpf/_toc.json index 1e836449c30..0d9d2b9d800 100644 --- a/docs/addons/qiskit-addon-mpf/_toc.json +++ b/docs/addons/qiskit-addon-mpf/_toc.json @@ -39,6 +39,16 @@ ], "collapsible": false }, + { + "title": "Tutorials", + "collapsible": false, + "children": [ + { + "title": "Multi-product formulas to reduce Trotter error", + "url": "/docs/addons/qiskit-addon-mpf/tutorials/multi-product-formula" + } + ] + }, { "title": "API reference", "collapsible": false, diff --git a/docs/addons/qiskit-addon-mpf/_tutorials.json b/docs/addons/qiskit-addon-mpf/_tutorials.json new file mode 100644 index 00000000000..a8e079f7d75 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/_tutorials.json @@ -0,0 +1,5 @@ +{ + "tutorials": [ + "multi-product-formula" + ] +} diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index 6c6933f01b9..0ea7777ad0b 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -39,6 +39,16 @@ ], "collapsible": false }, + { + "title": "Tutorials", + "collapsible": false, + "children": [ + { + "title": "Operator backpropagation (OBP) for estimation of expectation values", + "url": "/docs/addons/qiskit-addon-obp/tutorials/operator-back-propagation" + } + ] + }, { "title": "API reference", "collapsible": false, diff --git a/docs/addons/qiskit-addon-obp/_tutorials.json b/docs/addons/qiskit-addon-obp/_tutorials.json new file mode 100644 index 00000000000..031ebf446e9 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/_tutorials.json @@ -0,0 +1,5 @@ +{ + "tutorials": [ + "operator-back-propagation" + ] +} diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json index b4f64832727..d2327f0e799 100644 --- a/docs/addons/qiskit-addon-sqd/_toc.json +++ b/docs/addons/qiskit-addon-sqd/_toc.json @@ -55,6 +55,20 @@ ], "collapsible": false }, + { + "title": "Tutorials", + "collapsible": false, + "children": [ + { + "title": "Sample-based quantum diagonalization of a chemistry Hamiltonian", + "url": "/docs/addons/qiskit-addon-sqd/tutorials/sample-based-quantum-diagonalization" + }, + { + "title": "Sample-based Krylov quantum diagonalization of a fermionic lattice model", + "url": "/docs/addons/qiskit-addon-sqd/tutorials/sample-based-krylov-quantum-diagonalization" + } + ] + }, { "title": "API reference", "collapsible": false, diff --git a/docs/addons/qiskit-addon-sqd/_tutorials.json b/docs/addons/qiskit-addon-sqd/_tutorials.json new file mode 100644 index 00000000000..ccac9354f8f --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/_tutorials.json @@ -0,0 +1,6 @@ +{ + "tutorials": [ + "sample-based-quantum-diagonalization", + "sample-based-krylov-quantum-diagonalization" + ] +} diff --git a/package.json b/package.json index 55c889bb2f3..1ea0b566849 100644 --- a/package.json +++ b/package.json @@ -30,6 +30,7 @@ "check:katex-render": "tsx scripts/js/commands/checkKatexRender.ts", "check:qiskit-deprecation": "tsx scripts/js/commands/api/checkQiskitDeprecation.ts", "check:qiskit-versions": "tsx scripts/js/commands/checkQiskitApiVersions.ts", + "check:addon-tutorials": "tsx scripts/js/commands/checkAddonTutorials.ts", "regen-apis": "tsx scripts/js/commands/api/regenerateApiDocs.ts", "gen-api": "tsx scripts/js/commands/api/updateApiDocs.ts", "gen-addon": "tsx scripts/js/commands/api/updateAddonDocs.ts", diff --git a/scripts/js/commands/checkAddonTutorials.ts b/scripts/js/commands/checkAddonTutorials.ts new file mode 100644 index 00000000000..7e60950c6f1 --- /dev/null +++ b/scripts/js/commands/checkAddonTutorials.ts @@ -0,0 +1,56 @@ +// This code is a Qiskit project. +// +// (C) Copyright IBM 2026. +// +// This code is licensed under the Apache License, Version 2.0. You may +// obtain a copy of this license in the LICENSE file in the root directory +// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +// +// Any modifications or derivative works of this code must retain this +// copyright notice, and modified files need to carry a notice indicating +// that they have been altered from the originals. + +// Checks that every tutorial slug referenced in Pkg.tutorials exists as a +// .ipynb file under docs/tutorials/. Run via `npm run check:addon-tutorials`. + +import { readdir } from "fs/promises"; + +import { Pkg } from "../lib/api/Pkg.js"; + +async function main(): Promise<void> { + const tutorialFiles = await readdir("docs/tutorials"); + const available = new Set( + tutorialFiles + .filter((f) => f.endsWith(".ipynb")) + .map((f) => f.replace(/\.ipynb$/, "")), + ); + + const errors: string[] = []; + + for (const name of Pkg.ADDON_NAMES) { + // Use a throwaway version — only the name matters for tutorial config. + const pkg = await Pkg.fromArgs(name, "0.1.0", "0.1", "latest"); + for (const slug of pkg.tutorials) { + if (!available.has(slug)) { + errors.push( + ` ${name}: '${slug}' not found in docs/tutorials/ (no docs/tutorials/${slug}.ipynb)`, + ); + } + } + } + + if (errors.length > 0) { + console.error( + "❌ Addon tutorial slugs reference notebooks that don't exist:\n", + ); + errors.forEach((e) => console.error(e)); + console.error( + "\nUpdate the tutorials list in Pkg.fromArgs() to match files in docs/tutorials/.", + ); + process.exit(1); + } + + console.log("✅ All addon tutorial slugs are valid."); +} + +main().then(() => process.exit()); diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index 78c3c7c9484..f36399066bf 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -57,6 +57,8 @@ export class Pkg { readonly kebabCaseAndShortenUrls: boolean; readonly artifactPackageName: string; readonly hasRootNamespaceFile: boolean; + /** Slugs of docs/tutorials/ notebooks to surface under this addon's tutorials route. */ + readonly tutorials: string[]; static ADDON_NAMES = [ "qiskit-addon-aqc-tensor", @@ -88,6 +90,7 @@ export class Pkg { kebabCaseAndShortenUrls: boolean; artifactPackageName?: string; hasRootNamespaceFile?: boolean; + tutorials?: string[]; }) { this.name = kwargs.name; this.title = kwargs.title; @@ -102,6 +105,7 @@ export class Pkg { this.kebabCaseAndShortenUrls = kwargs.kebabCaseAndShortenUrls; this.artifactPackageName = kwargs.artifactPackageName ?? this.name; this.hasRootNamespaceFile = kwargs.hasRootNamespaceFile ?? false; + this.tutorials = kwargs.tutorials ?? []; } static async fromArgs( @@ -166,6 +170,7 @@ export class Pkg { githubSlug: "Qiskit/qiskit-addon-aqc-tensor", kebabCaseAndShortenUrls: true, language: "Python", + tutorials: ["approximate-quantum-compilation-for-time-evolution"], }); } if (name === "qiskit-addon-obp") { @@ -175,6 +180,7 @@ export class Pkg { githubSlug: "Qiskit/qiskit-addon-obp", kebabCaseAndShortenUrls: true, language: "Python", + tutorials: ["operator-back-propagation"], }); } if (name === "qiskit-addon-mpf") { @@ -185,6 +191,7 @@ export class Pkg { kebabCaseAndShortenUrls: true, tocGrouping: QISKIT_ADDON_MPF_GROUPING, language: "Python", + tutorials: ["multi-product-formula"], }); } if (name === "qiskit-addon-sqd") { @@ -194,6 +201,10 @@ export class Pkg { githubSlug: "Qiskit/qiskit-addon-sqd", kebabCaseAndShortenUrls: true, language: "Python", + tutorials: [ + "sample-based-quantum-diagonalization", + "sample-based-krylov-quantum-diagonalization", + ], }); } if (name === "qiskit-addon-cutting") { @@ -203,6 +214,11 @@ export class Pkg { githubSlug: "Qiskit/qiskit-addon-cutting", kebabCaseAndShortenUrls: true, language: "Python", + tutorials: [ + "depth-reduction-with-circuit-cutting", + "periodic-boundary-conditions-with-circuit-cutting", + "wire-cutting", + ], }); } if (name === "qiskit-addon-utils") { diff --git a/scripts/js/lib/api/addonDocsPipeline.ts b/scripts/js/lib/api/addonDocsPipeline.ts index 2a911324c42..5ed6276ff2e 100644 --- a/scripts/js/lib/api/addonDocsPipeline.ts +++ b/scripts/js/lib/api/addonDocsPipeline.ts @@ -106,6 +106,9 @@ export async function runAddonDocsPipeline( ); await writeTocFile(pkg, docsBaseFolder, outputPath); + if (pkg.tutorials.length > 0) { + await writeTutorialsFile(pkg, outputPath); + } } async function writeTocFile( @@ -121,6 +124,13 @@ async function writeTocFile( ); } +async function writeTutorialsFile(pkg: Pkg, outputPath: string): Promise<void> { + await writeFile( + `${outputPath}/_tutorials.json`, + JSON.stringify({ tutorials: pkg.tutorials }, null, 2) + "\n", + ); +} + async function determineFilePaths( htmlPath: string, docsBaseFolder: string, diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts index fa4b12f419e..9193b9dc19d 100644 --- a/scripts/js/lib/api/generateAddonToc.ts +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -104,6 +104,22 @@ export async function generateAddonToc( collapsible: false, }; + if (pkg.tutorials.length > 0) { + const tutorialsDocsPath = join(docsBaseFolder, "..", "tutorials"); + const tutorialChildren: TocEntry[] = await Promise.all( + pkg.tutorials.map(async (slug) => { + const filePath = join(tutorialsDocsPath, `${slug}.ipynb`); + const title = await readTitle(filePath); + return { title, url: `${addonUrlBase}/tutorials/${slug}` }; + }), + ); + topLevelSections.push({ + title: "Tutorials", + collapsible: false, + children: tutorialChildren, + }); + } + topLevelSections.push({ title: "API reference", collapsible: false, From c3ec500a987fa37d116d2dc940942cd6529a568a Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 21 May 2026 14:09:37 -0500 Subject: [PATCH 073/104] handle link ignores --- scripts/js/commands/checkInternalLinks.ts | 24 +++++- scripts/js/lib/links/ignores.ts | 93 +++++++++++++++++------ 2 files changed, 92 insertions(+), 25 deletions(-) diff --git a/scripts/js/commands/checkInternalLinks.ts b/scripts/js/commands/checkInternalLinks.ts index e17b21e6fdf..dd898e672f9 100644 --- a/scripts/js/commands/checkInternalLinks.ts +++ b/scripts/js/commands/checkInternalLinks.ts @@ -10,7 +10,7 @@ // copyright notice, and modified files need to carry a notice indicating // that they have been altered from the originals. -import { readdir } from "fs/promises"; +import { readdir, readFile } from "fs/promises"; import { globby } from "globby"; import yargs from "yargs/yargs"; @@ -79,15 +79,37 @@ const readArgs = (): Arguments => { .parseSync(); }; +/** + * Generates synthetic File entries for addon tutorial aliases — paths like + * docs/addons/{pkg}/tutorials/{slug} that have no real file but are served by + * the app by remapping to docs/tutorials/{slug}. + */ +async function syntheticAddonTutorialFiles(): Promise<File[]> { + const tutorialJsonFiles = await globby("docs/addons/**/_tutorials.json"); + const files: File[] = []; + for (const jsonPath of tutorialJsonFiles) { + const pkgDir = jsonPath.replace("/_tutorials.json", ""); + const { tutorials } = JSON.parse(await readFile(jsonPath, "utf-8")) as { + tutorials: string[]; + }; + for (const slug of tutorials) { + files.push(new File(`${pkgDir}/tutorials/${slug}.ipynb`, new Set(), true)); + } + } + return files; +} + async function main() { const args = readArgs(); const fileBatches = await determineFileBatches(args); + const addonTutorialFiles = await syntheticAddonTutorialFiles(); const otherFiles = [ ...( await globby("public/{docs,learning}/{images,videos,open-source}/**/*") ).map((fp) => new File(fp, new Set())), ...SYNTHETIC_FILES.map((fp) => new File(fp, new Set(), true)), + ...addonTutorialFiles, ]; let allGood = true; diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index 90e86b11776..d9ea31943ef 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -217,29 +217,6 @@ const ALWAYS_IGNORED_URLS__SHOULD_FIX: string[] = [ // Other links that don't seem to exist any more "https://www.globaldataquantum.com/en/quantum-portfolio-optimizer/#form", - - // from qiskit-addon-obp - "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started-ipynb", - "/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html", - "/api/qiskit-ibm-runtime/noise-learner-noise-learner", - "/api/qiskit-ibm-runtime/noise-learner-result", - - // from qiskit-addon-utils - "/docs/addons/samplomatic/guides/samplex-io#qubit-ordering-convention", - - // from qiskit-addon-sqd - "/docs/en/tutorials/sample-based-krylov-quantum-diagonalization", - "/docs/en/tutorials/sample-based-quantum-diagonalization", - "/docs/guides/get-started-with-primitives#get-started-with-sampler", - - // from qiskit-addon-cutting - "how-tos/how-to-specify-cut-wires", - "/circuit_cutting/explanations/index-rst#overview-of-circuit-cutting", - "/docs/start/install#operating-system-support", - "../tutorials/03_wire_cutting_via_move_instruction.ipynb", - "#equation-eq-qpd", - "./circuit_cutting/explanations/index-rst#overview-of-circuit-cutting", - "../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb", ]; export const ALWAYS_IGNORED_URLS = new Set([ @@ -610,8 +587,76 @@ function _qiskitCRegexes(): FilesToIgnores { }; } +function _addonStaleTutorialLinks(): FilesToIgnores { + // These links point to old addon-repo tutorial slugs that no longer exist. + // The addon source docs need to be updated to use the new paths. + return { + "docs/addons/qiskit-addon-sqd/index.mdx": [ + "tutorials/01-chemistry-hamiltonian", + "tutorials/index", + "/docs/en/tutorials/sample-based-quantum-diagonalization", + "/docs/en/tutorials/sample-based-krylov-quantum-diagonalization", + ], + "docs/addons/qiskit-addon-cutting/index.mdx": [ + "tutorials/index", + "tutorials/01-gate-cutting-to-reduce-circuit-width", + "tutorials/02-gate-cutting-to-reduce-circuit-depth", + "tutorials/03-wire-cutting-via-move-instruction", + "tutorials/04-automatic-cut-finding", + "./circuit_cutting/explanations/index-rst#overview-of-circuit-cutting", + "how-tos/how-to-specify-cut-wires", + ], + "docs/addons/qiskit-addon-aqc-tensor/index.mdx": [ + "tutorials/index", + ], + "docs/api/qiskit-addon-cutting/instructions-move.mdx": [ + "/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction", + ], + "docs/api/qiskit-addon-cutting/release-notes.mdx": [ + "/docs/addons/qiskit-addon-cutting/tutorials/index", + ], + "docs/api/qiskit-addon-sqd/release-notes.mdx": [ + "/docs/addons/qiskit-addon-sqd/tutorials/index", + ], + "docs/addons/qiskit-addon-mpf/explanations/mpf-stability.ipynb": [ + "/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started", + ], + "docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps.ipynb": [ + "/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started", + ], + "docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model.ipynb": [ + "/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started", + ], + "docs/addons/qiskit-addon-sqd/how-tos/integrate-dice-solver.ipynb": [ + "/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian", + ], + "docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb": [ + "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started-ipynb", + "/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html", + ], + "docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb": [ + "/api/qiskit-ibm-runtime/noise-learner-noise-learner", + "/api/qiskit-ibm-runtime/noise-learner-result", + ], + "docs/addons/qiskit-addon-cutting/install.mdx": [ + "/docs/start/install#operating-system-support", + ], + "docs/addons/qiskit-addon-cutting/explanations/index.mdx": [ + "#equation-eq-qpd", + "how-tos/how-to-specify-cut-wires", + "/circuit_cutting/explanations/index-rst#overview-of-circuit-cutting", + ], + "docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires.ipynb": [ + "../tutorials/03_wire_cutting_via_move_instruction.ipynb", + ], + "docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.ipynb": [ + "../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb", + ], + }; +} + const FILES_TO_IGNORES__SHOULD_FIX: FilesToIgnores = - mergeFilesToIgnores(_qiskitCRegexes()); + mergeFilesToIgnores(_qiskitCRegexes(), _addonStaleTutorialLinks()); export const FILES_TO_IGNORES: FilesToIgnores = mergeFilesToIgnores( FILES_TO_IGNORES__EXPECTED, From e075ba1127815aa191a7471b4a0990c922e109ff Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 21 May 2026 14:21:27 -0500 Subject: [PATCH 074/104] update to latest --- docs/addons/qiskit-addon-sqd/_toc.json | 2 +- docs/api/qiskit-addon-sqd/_package.json | 2 +- docs/api/qiskit-addon-utils/_package.json | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json index d2327f0e799..55dc87d74e9 100644 --- a/docs/addons/qiskit-addon-sqd/_toc.json +++ b/docs/addons/qiskit-addon-sqd/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Sample-based quantum diagonalization (SQD) 0.12.0", + "title": "Sample-based quantum diagonalization (SQD) 0.12.1", "collapsed": true, "children": [ { diff --git a/docs/api/qiskit-addon-sqd/_package.json b/docs/api/qiskit-addon-sqd/_package.json index 9b4e87cc521..1f226e87545 100644 --- a/docs/api/qiskit-addon-sqd/_package.json +++ b/docs/api/qiskit-addon-sqd/_package.json @@ -1,4 +1,4 @@ { "name": "qiskit-addon-sqd", - "version": "0.12.0" + "version": "0.12.1" } diff --git a/docs/api/qiskit-addon-utils/_package.json b/docs/api/qiskit-addon-utils/_package.json index fb08817f4b1..648d449cc7c 100644 --- a/docs/api/qiskit-addon-utils/_package.json +++ b/docs/api/qiskit-addon-utils/_package.json @@ -1,4 +1,4 @@ { "name": "qiskit-addon-utils", - "version": "0.3.0" + "version": "0.3.1" } From e0008b57a2eabc12d544dcfe4918867037bd2079 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 21 May 2026 15:28:03 -0500 Subject: [PATCH 075/104] adds sqd-hpc addon --- docs/addons/qiskit-addon-sqd-hpc/_toc.json | 36 ++++++ .../compilation-flags.mdx | 38 ++++++ docs/addons/qiskit-addon-sqd-hpc/index.mdx | 39 ++++++ docs/api/qiskit-addon-sqd-hpc/_package.json | 4 + docs/api/qiskit-addon-sqd-hpc/_toc.json | 38 ++++++ .../configuration-recovery.mdx | 38 ++++++ docs/api/qiskit-addon-sqd-hpc/fermion.mdx | 32 +++++ docs/api/qiskit-addon-sqd-hpc/index.mdx | 12 ++ .../qiskit-addon-sqd-hpc/postselection.mdx | 76 ++++++++++++ .../qiskit-addon-sqd-hpc/release-notes.mdx | 24 ++++ docs/api/qiskit-addon-sqd-hpc/subsampling.mdx | 116 ++++++++++++++++++ .../docs/api/qiskit-addon-sqd-hpc/objects.inv | Bin 0 -> 145 bytes scripts/config/api-html-artifacts.json | 3 + .../config/historical-pages-to-latest.json | 1 + scripts/js/lib/api/Pkg.ts | 10 ++ scripts/js/lib/api/apiDocsPipeline.ts | 2 +- scripts/js/lib/links/ignores.ts | 3 + 17 files changed, 471 insertions(+), 1 deletion(-) create mode 100644 docs/addons/qiskit-addon-sqd-hpc/_toc.json create mode 100644 docs/addons/qiskit-addon-sqd-hpc/compilation-flags.mdx create mode 100644 docs/addons/qiskit-addon-sqd-hpc/index.mdx create mode 100644 docs/api/qiskit-addon-sqd-hpc/_package.json create mode 100644 docs/api/qiskit-addon-sqd-hpc/_toc.json create mode 100644 docs/api/qiskit-addon-sqd-hpc/configuration-recovery.mdx create mode 100644 docs/api/qiskit-addon-sqd-hpc/fermion.mdx create mode 100644 docs/api/qiskit-addon-sqd-hpc/index.mdx create mode 100644 docs/api/qiskit-addon-sqd-hpc/postselection.mdx create mode 100644 docs/api/qiskit-addon-sqd-hpc/release-notes.mdx create mode 100644 docs/api/qiskit-addon-sqd-hpc/subsampling.mdx create mode 100644 public/docs/api/qiskit-addon-sqd-hpc/objects.inv diff --git a/docs/addons/qiskit-addon-sqd-hpc/_toc.json b/docs/addons/qiskit-addon-sqd-hpc/_toc.json new file mode 100644 index 00000000000..5a2c6833e65 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd-hpc/_toc.json @@ -0,0 +1,36 @@ +{ + "parentUrl": "/docs/guides/addons", + "parentLabel": "Documentation", + "title": "SQD for HPC 0.0.0", + "collapsed": true, + "children": [ + { + "title": "", + "children": [ + { + "title": "Compilation flags", + "url": "/docs/addons/qiskit-addon-sqd-hpc/compilation-flags" + }, + { + "title": "Home", + "url": "/docs/addons/qiskit-addon-sqd-hpc" + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-sqd-hpc" + } + ], + "collapsible": false + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "SQD for HPC API reference", + "url": "/docs/api/qiskit-addon-sqd-hpc" + } + ] + } + ] +} diff --git a/docs/addons/qiskit-addon-sqd-hpc/compilation-flags.mdx b/docs/addons/qiskit-addon-sqd-hpc/compilation-flags.mdx new file mode 100644 index 00000000000..8bec25dd356 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd-hpc/compilation-flags.mdx @@ -0,0 +1,38 @@ +--- +title: "Compilation flags" +description: "Compilation flags for the lastest version of SQD for HPC" +--- + +# Compilation flags + +This guide documents various `#define`s and other supported flags that may be passed to the compiler when using this library. + +## How to disable exceptions + +To disable exceptions, the following flags must the passed to the compiler: `-fno-exceptions -DQKA_SQD_DISABLE_EXCEPTIONS=1` + +When `QKA_SQD_DISABLE_EXCEPTIONS` is set to a non-zero value during compilation, the library will refrain from throwing exceptions. Instead, if there is an error, it will send a message to `STDERR` and call `std::terminate`. The default behavior of `std::terminate` is to abort the program, but this behavior can be configured by calling `std::set_terminate`. For an MPI program, it is recommended to install a custom handler that calls `MPI_Abort` instead. + +## How to disable run-time type information (RTTI) + +This library does not require RTTI, so the following flag can optionally be passed to the compiler to disable it: `-fno-rtti`. + +## Example: passing flags to cmake + +Compiler flags are passed to `cmake` via the `-DCMAKE_CXX_FLAGS` option. For instance, the test suite of this repository can be compiled without exceptions with the following commands: + +```sh +mkdir -p build +cd build +cmake .. -DCMAKE_CXX_FLAGS="-fno-exceptions -DQKA_SQD_DISABLE_EXCEPTIONS=1 -DDOCTEST_CONFIG_NO_EXCEPTIONS_BUT_WITH_ALL_ASSERTS" +make +``` + +Note that compiling this repository’s test suite requires that `#define DOCTEST_CONFIG_NO_EXCEPTIONS_BUT_WITH_ALL_ASSERTS` be set, as exceptions must be disabled in the doctest framework as well as the SQD library. + +<span id="concepts-c-20-and-later" /> + +## Concepts (C++20 and later) + +This library is designed to use C++ concepts if they are available (i.e., if the code is compiled according to C++20 or a later version of the standard). If possible, users are encouraged to use such a compiler when developing, as this will likely lead to better error messages from the compiler (e.g., when a template argument has an unexpected type). + diff --git a/docs/addons/qiskit-addon-sqd-hpc/index.mdx b/docs/addons/qiskit-addon-sqd-hpc/index.mdx new file mode 100644 index 00000000000..8882190e0d9 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd-hpc/index.mdx @@ -0,0 +1,39 @@ +--- +title: "Qiskit addon: SQD (HPC-ready)" +description: "Documentation for the latest version of SQD for HPC" +--- + +# Qiskit addon: SQD (HPC-ready) + +This repository contains an HPC-ready implementation of the [Qiskit addon for Sample-based Quantum Diagonalization (SQD)](https://github.com/Qiskit/qiskit-addon-sqd). + +Unlike the existing Python addon, this code is written in modern C++17 and is designed to enable HPC workflows and applications, particularly those that require a single binary to be compiled for use with MPI. This library builds upon existing interfaces in the C++ standard template library (STL); specifically, this code-base is: + +* Able to leverage any pseudo-random number generator compatible with the `<random>` header. +* Reliant on standard interfaces for storing bitstrings, namely `std::bitset` and `boost::dynamic_bitset`, which are themselves aligned with [Qiskit’s bit-ordering conventions](/docs/en/guides/bit-ordering) (c.f. [to\_ulong](https://en.cppreference.com/w/cpp/utility/bitset/to_ulong.html) and [to\_string](https://en.cppreference.com/w/cpp/utility/bitset/to_string.html)). +* Compatible with STL containers, including `std::vector`. + +The code is cross-platform, fully tested, fully documented, and contains an integrated benchmark suite. + +## Table of Contents + +* [Compilation flags](compilation-flags) + + * [How to disable exceptions](compilation-flags#how-to-disable-exceptions) + * [How to disable run-time type information (RTTI)](compilation-flags#how-to-disable-run-time-type-information-rtti) + * [Example: passing flags to `cmake`](compilation-flags#example-passing-flags-to-cmake) + * [Concepts (C++20 and later)](compilation-flags#concepts-c-20-and-later) + +* [API Reference](/docs/api/qiskit-addon-sqd-hpc/index) + + * [Postselection](/docs/api/qiskit-addon-sqd-hpc/postselection) + * [Subsampling](/docs/api/qiskit-addon-sqd-hpc/subsampling) + * [Configuration recovery](/docs/api/qiskit-addon-sqd-hpc/configuration-recovery) + * [Fermion](/docs/api/qiskit-addon-sqd-hpc/fermion) + +* [GitHub](https://github.com/Qiskit/qiskit-addon-sqd-hpc) + +* [Release Notes](/docs/api/qiskit-addon-sqd-hpc/release-notes) + + * [Upcoming release (`main`)](/docs/api/qiskit-addon-sqd-hpc/release-notes#upcoming-release-main) + diff --git a/docs/api/qiskit-addon-sqd-hpc/_package.json b/docs/api/qiskit-addon-sqd-hpc/_package.json new file mode 100644 index 00000000000..7e88252443e --- /dev/null +++ b/docs/api/qiskit-addon-sqd-hpc/_package.json @@ -0,0 +1,4 @@ +{ + "name": "qiskit-addon-sqd-hpc", + "version": "0.0.0" +} diff --git a/docs/api/qiskit-addon-sqd-hpc/_toc.json b/docs/api/qiskit-addon-sqd-hpc/_toc.json new file mode 100644 index 00000000000..431065c8d2b --- /dev/null +++ b/docs/api/qiskit-addon-sqd-hpc/_toc.json @@ -0,0 +1,38 @@ +{ + "title": "SQD for HPC", + "children": [ + { + "title": "API index", + "url": "/docs/api/qiskit-addon-sqd-hpc" + }, + { + "title": "Release notes", + "useDivider": true, + "url": "/docs/api/qiskit-addon-sqd-hpc/release-notes" + }, + { + "title": "Configuration recovery", + "url": "/docs/api/qiskit-addon-sqd-hpc/configuration-recovery", + "untranslatable": true + }, + { + "title": "Fermion", + "url": "/docs/api/qiskit-addon-sqd-hpc/fermion", + "untranslatable": true + }, + { + "title": "Postselection", + "url": "/docs/api/qiskit-addon-sqd-hpc/postselection", + "untranslatable": true + }, + { + "title": "Subsampling", + "url": "/docs/api/qiskit-addon-sqd-hpc/subsampling", + "untranslatable": true + } + ], + "collapsed": true, + "untranslatable": true, + "parentUrl": "/docs/addons/qiskit-addon-sqd-hpc", + "parentLabel": "SQD for HPC" +} diff --git a/docs/api/qiskit-addon-sqd-hpc/configuration-recovery.mdx b/docs/api/qiskit-addon-sqd-hpc/configuration-recovery.mdx new file mode 100644 index 00000000000..1b2659b862f --- /dev/null +++ b/docs/api/qiskit-addon-sqd-hpc/configuration-recovery.mdx @@ -0,0 +1,38 @@ +--- +title: Configuration recovery (latest version) +description: API reference for Configuration recovery in the latest version of qiskit-addon-sqd-hpc +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: Configuration recovery +--- + +# Configuration recovery + +## Functions + +This library provides a single public function for performing configuration recovery. + +### BitstringVectorType + +<Function id="BitstringVectorType" signature="template<typename BitstringVectorType, typename WeightVectorType, std::uniform_random_bit_generator RNGType>std::pair<BitstringVectorType, WeightVectorType> Qiskit::addon::sqd::recover_configurations(const BitstringVectorType &bitstrings, const WeightVectorType &probabilities, const std::array<std::vector<double>, 2> &avg_occupancies, std::array<std::uint64_t, 2> num_elec, RNGType &rng)"> + Refine bitstrings based on average orbital occupancy and a target Hamming weight. + + **Parameters** + + * **bitstrings** – **\[in]** A container (e.g., `std::vector`) of bitstrings. + * **probabilities** – **\[in]** A 1D array specifying a probability distribution over the bitstrings. Must contain the same number of elements as `bitstrings`. + * **avg\_occupancies** – **\[in]** Size-2 `std::array` of `std::vector<double>`s holding the mean occupancy of the spin-up and spin-down orbitals, respectively. Each vector’s size must be half the size of a single bitstring. + * **num\_elec** – **\[in]** Size-2 `std::array` containing the number of spin-up and spin-down electrons in the system, respectively. + * **rng** – **\[inout]** Random number generator. + + **Template Parameters** + + * **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. + * **WeightVectorType** – Type of `weights`, compatible with `std::vector<double>`. + * **RNGType** – Type of random number generator. + + **Returns** + + A refined `std::vector` of unique bitstrings and a parallel, updated probability array. +</Function> + diff --git a/docs/api/qiskit-addon-sqd-hpc/fermion.mdx b/docs/api/qiskit-addon-sqd-hpc/fermion.mdx new file mode 100644 index 00000000000..a841c36486a --- /dev/null +++ b/docs/api/qiskit-addon-sqd-hpc/fermion.mdx @@ -0,0 +1,32 @@ +--- +title: Fermion (latest version) +description: API reference for Fermion in the latest version of qiskit-addon-sqd-hpc +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: Fermion +--- + +# Fermion + +Functions for the study of fermionic systems. + +## Functions + +### BitstringVectorType + +<Function id="BitstringVectorType" signature="template<class BitstringVectorType>auto Qiskit::addon::sqd::bitstrings_to_ci_strings_symmetrize_spin(const BitstringVectorType &bitstrings, std::optional<unsigned int> max_dimension = std::nullopt, std::optional<std::reference_wrapper<const std::vector<internal::HalfSize<typename BitstringVectorType::value_type>>>> include_configurations = std::nullopt) -> std::vector<internal::HalfSize<typename BitstringVectorType::value_type>>"> + Convert bitstrings into CI strings (representations of determinants). + + This function separates each bitstring in bitstring\_matrix in half, combining the right and left halves of all the bitstrings into a single set of unique configurations. + + **Parameters** + + * **bitstrings** – **\[in]** Population of bitstrings. + * **max\_dimension** – **\[in]** Maximum dimension of returned CI strings. If less than the number of CI strings, the list of CI strings will be truncated. + * **include\_configurations** – **\[in]** A list of CI strings that will be included in the output, regardless of whether they are contained in `bitstrings`. + + **Template Parameters** + + **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. +</Function> + diff --git a/docs/api/qiskit-addon-sqd-hpc/index.mdx b/docs/api/qiskit-addon-sqd-hpc/index.mdx new file mode 100644 index 00000000000..1aef280d137 --- /dev/null +++ b/docs/api/qiskit-addon-sqd-hpc/index.mdx @@ -0,0 +1,12 @@ +--- +title: SQD for HPC API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-sqd-hpc. +--- + +# SQD API reference + +* [Postselection](postselection) +* [Subsampling](subsampling) +* [Configuration recovery](configuration-recovery) +* [Fermion](fermion) + diff --git a/docs/api/qiskit-addon-sqd-hpc/postselection.mdx b/docs/api/qiskit-addon-sqd-hpc/postselection.mdx new file mode 100644 index 00000000000..15d30d5be7f --- /dev/null +++ b/docs/api/qiskit-addon-sqd-hpc/postselection.mdx @@ -0,0 +1,76 @@ +--- +title: Postselection (latest version) +description: API reference for Postselection in the latest version of qiskit-addon-sqd-hpc +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: Postselection +--- + +# Postselection + +## Functions + +### BitstringVectorType + +<Function id="BitstringVectorType" signature="template<typename BitstringVectorType, typename WeightVectorType, typename CallableType>std::pair<BitstringVectorType, WeightVectorType> Qiskit::addon::sqd::postselect_bitstrings(const BitstringVectorType &bitstrings, const WeightVectorType &weights, CallableType filter_function)"> + Post-select bitstrings based on a given criteria. + + <span id="postselection_8hpp_1autotoc_md0" /> + + #### Example + + ```c + std::vector<std::bitset<6>> bitstrings = {0b011010, 0b100011}; + std::vector<double> weights = {0.1, 0.7}; + auto [new_bitstrings, new_weights] = Qiskit::addon::sqd::postselect_bitstrings( + bitstrings, weights, Qiskit::addon::sqd::MatchesRightLeftHamming(1, 2) + ); + ``` + + **Parameters** + + * **bitstrings** – **\[in]** Bitstrings to consider. + * **weights** – **\[in]** Relative weight of each bitstring (need not be normalized to 1). + * **filter\_function** – **\[in]** Callable which returns a boolean indicating whether a is to be kept. + + **Template Parameters** + + * **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. + * **WeightVectorType** – Type of `weights`, compatible with `std::vector<double>`. + * **CallableType** – Type of `filter_function`, compatible with `bool (*f)(const BitstringType &)`. + + **Returns** + + Post-selected bitstrings and their corresponding weights, normalized to 1. +</Function> + +## Classes + +**template\<typename UnsignedType = uint32\_t><span id="classQiskit_1_1addon_1_1sqd_1_1MatchesRightLeftHamming" />class MatchesRightLeftHamming** + +Functor which returns `true` if a bitstring has a predetermined Hamming weight. + +### MatchesRightLeftHamming + +<Function id="MatchesRightLeftHamming" signature="inline explicit MatchesRightLeftHamming(UnsignedType right_target, UnsignedType left_target)"> + Constructor. +</Function> + +### BitstringType + +<Function id="BitstringType" signature="template<typename BitstringType>inline bool operator()(const BitstringType &bitstring) const"> + Function to be used as filter. + + **Parameters** + + **bitstring** – **\[in]** Bitstring to consider. + + **Template Parameters** + + **BitstringType** – Type of `bitstring`, compatible with `boost::dynamic_bitset<>` (for example). + + **Returns** + + `true` if the bitstring matches the Hamming weight that the functor was initialized with. +</Function> + diff --git a/docs/api/qiskit-addon-sqd-hpc/release-notes.mdx b/docs/api/qiskit-addon-sqd-hpc/release-notes.mdx new file mode 100644 index 00000000000..ed721b1f538 --- /dev/null +++ b/docs/api/qiskit-addon-sqd-hpc/release-notes.mdx @@ -0,0 +1,24 @@ +--- +title: SQD for HPC release notes +description: Changes made to SQD for HPC +in_page_toc_max_heading_level: 2 +--- + +<span id="release-notes" /> + +<span id="id1" /> + +# SQD for HPC release notes + +<span id="upcoming-release-main" /> + +<span id="release-notes-upcoming-release-main" /> + +## Upcoming release (`main`) + +<span id="release-notes-upcoming-release-main-new-features" /> + +### New Features + +* The `bitstrings_to_ci_strings_symmetrize_spin` function has been added, which allows one to obtain a collection of CI strings (with alpha and beta determinants symmetrized) from a collection of bitstrings. + diff --git a/docs/api/qiskit-addon-sqd-hpc/subsampling.mdx b/docs/api/qiskit-addon-sqd-hpc/subsampling.mdx new file mode 100644 index 00000000000..4a7f3a439a4 --- /dev/null +++ b/docs/api/qiskit-addon-sqd-hpc/subsampling.mdx @@ -0,0 +1,116 @@ +--- +title: Subsampling (latest version) +description: API reference for Subsampling in the latest version of qiskit-addon-sqd-hpc +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: Subsampling +--- + +# Subsampling + +## Functions + +This library provides functions for subsampling either a single batch or multiple batches from a pool of bitstrings. + +### BitstringVectorType + +<Function id="BitstringVectorType" signature="template<typename BitstringVectorType, typename WeightVectorType, std::uniform_random_bit_generator RNGType>BitstringVectorType Qiskit::addon::sqd::subsample(const BitstringVectorType &bitstrings, const WeightVectorType &weights, unsigned int samples_per_batch, RNGType &rng)"> + Subsample a single batch of bitstrings. + + Note: You must de-duplicate the bitstrings before calling this, otherwise you may get duplicate bitstrings in the output. + + **Parameters** + + * **bitstrings** – **\[in]** Population of bitstrings. + * **weights** – **\[in]** Relative weight of each bitstring (need not be normalized to 1). Must be the same length as `bitstrings` and contain only non-negative values. + * **samples\_per\_batch** – **\[in]** Number of samples to return in `batch`. Cannot be greater than the number of bitstrings. + * **rng** – **\[inout]** Random number generator to use for sampling. + + **Template Parameters** + + * **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. + * **WeightVectorType** – Type of `weights`, compatible with `std::vector<double>`. + * **RNGType** – Type of random number generator. + + **Returns** + + The subsampled bitstrings. +</Function> + +### BatchVectorType + +<Function id="BatchVectorType" signature="template<typename BatchVectorType, typename BitstringVectorType, typename WeightVectorType, std::uniform_random_bit_generator RNGType>void Qiskit::addon::sqd::subsample(BatchVectorType &batch, const BitstringVectorType &bitstrings, const WeightVectorType &weights, unsigned int samples_per_batch, RNGType &rng)"> + Subsample a single batch of bitstrings (mutating version) + + This version can be useful if you want to avoid reallocation by re-using an existing batch vector. + + Note: You must de-duplicate the bitstrings before calling this, otherwise you may get duplicate bitstrings in the output. + + **Parameters** + + * **batch** – **\[out]** This will be cleared and overwritten with the subsampled bitstrings. + * **bitstrings** – **\[in]** Population of bitstrings. + * **weights** – **\[in]** Relative weight of each bitstring (need not be normalized to 1). Must be the same length as `bitstrings` and contain only non-negative values. + * **samples\_per\_batch** – **\[in]** Number of samples to return in `batch`. Cannot be greater than the number of bitstrings. + * **rng** – **\[inout]** Random number generator to use for sampling. + + **Template Parameters** + + * **BatchVectorType** – Type of `batch`, must be compatible with `std::vector<boost::dynamic_bitset<>>`. + * **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. + * **WeightVectorType** – Type of `weights`, compatible with `std::vector<double>`. + * **RNGType** – Type of random number generator. +</Function> + +### BitstringVectorType + +<Function id="BitstringVectorType" signature="template<typename BitstringVectorType, typename WeightVectorType, std::uniform_random_bit_generator RNGType>std::vector<BitstringVectorType> Qiskit::addon::sqd::subsample_multiple_batches(const BitstringVectorType &bitstrings, const WeightVectorType &weights, unsigned int samples_per_batch, unsigned int num_batches, RNGType &rng)"> + Subsample multiple batches of bitstrings. + + Note: You must de-duplicate the bitstrings before calling this, otherwise you may get duplicate bitstrings in the output. + + **Parameters** + + * **bitstrings** – **\[in]** Population of bitstrings. + * **weights** – **\[in]** Relative weight of each bitstring (need not be normalized to 1). Must be the same length as `bitstrings` and contain only non-negative values. + * **samples\_per\_batch** – **\[in]** Number of samples to return per batch. Cannot be greater than the number of bitstrings. + * **num\_batches** – **\[in]** Number of batches. + * **rng** – **\[inout]** Random number generator to use for sampling. + + **Template Parameters** + + * **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. + * **WeightVectorType** – Type of `weights`, compatible with `std::vector<double>`. + * **RNGType** – Type of random number generator. + + **Returns** + + The batches of subsampled bitstrings. +</Function> + +### BatchesVectorType + +<Function id="BatchesVectorType" signature="template<typename BatchesVectorType, typename BitstringVectorType, typename WeightVectorType, std::uniform_random_bit_generator RNGType>void Qiskit::addon::sqd::subsample_multiple_batches(BatchesVectorType &batches, const BitstringVectorType &bitstrings, const WeightVectorType &weights, unsigned int samples_per_batch, unsigned int num_batches, RNGType &rng)"> + Subsample multiple batches of bitstrings (mutating version) + + This version can be useful if you want to avoid reallocation by re-using existing batch vectors. + + Note: You must de-duplicate the bitstrings before calling this, otherwise you may get duplicate bitstrings in the output. + + **Parameters** + + * **batches** – **\[out]** This will be overwritten with the batches of subsampled bitstrings. + * **bitstrings** – **\[in]** Population of bitstrings. + * **weights** – **\[in]** Relative weight of each bitstring (need not be normalized to 1). Must be the same length as `bitstrings` and contain only non-negative values. + * **samples\_per\_batch** – **\[in]** Number of samples to return per batch. Cannot be greater than the number of bitstrings. + * **num\_batches** – **\[in]** Number of batches. + * **rng** – **\[inout]** Random number generator to use for sampling. + + **Template Parameters** + + * **BatchesVectorType** – Type of `batches`, must be compatible with `std::vector<std::vector<boost::dynamic_bitset<>>>`. + * **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. + * **WeightVectorType** – Type of `weights`, compatible with `std::vector<double>`. + * **RNGType** – Type of random number generator. +</Function> + diff --git a/public/docs/api/qiskit-addon-sqd-hpc/objects.inv b/public/docs/api/qiskit-addon-sqd-hpc/objects.inv new file mode 100644 index 0000000000000000000000000000000000000000..8d96070f45713202813d8a33fd711e73ad51b013 GIT binary patch literal 145 zcmXAgF%H5o5ClPVdBsX;pn!tXEfkcAg6K_r!3W7XI-4lrsk{bDY%^2LmS{%LGJ|Cz zNkJD(q>+Q*Sd^$VJgH3vHOChxSm%NmH1!$VOLf{SS-0$Rc>A9WvR^$xX|ODg6rn@% e9MN%4$ZW$fD#b_+@5u7Nr|0{jn7^A<-uegMmM?+; literal 0 HcmV?d00001 diff --git a/scripts/config/api-html-artifacts.json b/scripts/config/api-html-artifacts.json index 831d30a3227..07310bc640a 100644 --- a/scripts/config/api-html-artifacts.json +++ b/scripts/config/api-html-artifacts.json @@ -121,5 +121,8 @@ "0.3": "https://ibm.box.com/shared/static/921yb7b7162e20a9b7ojgzhc1erxhgzd.zip", "0.2": "https://ibm.box.com/shared/static/61qpikp70xgftx1gjs8an0cxc3196ais.zip", "0.1": "https://ibm.box.com/shared/static/6kzfxqaigskwfd8uf9dldrwqtd5yr5fd.zip" + }, + "qiskit-addon-sqd-hpc": { + "0.0": "https://ibm.box.com/shared/static/hktx0wsre5rc7zgzbh3yz2nmi8y5jre0.zip" } } diff --git a/scripts/config/historical-pages-to-latest.json b/scripts/config/historical-pages-to-latest.json index 436abd6af86..ad472ccae10 100644 --- a/scripts/config/historical-pages-to-latest.json +++ b/scripts/config/historical-pages-to-latest.json @@ -1501,6 +1501,7 @@ "0.8": {}, "0.9": {} }, + "qiskit-addon-sqd-hpc": {}, "qiskit-addon-cutting": { "0.9": {} }, diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index f36399066bf..cbb7dffce8a 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -65,6 +65,7 @@ export class Pkg { "qiskit-addon-obp", "qiskit-addon-mpf", "qiskit-addon-sqd", + "qiskit-addon-sqd-hpc", "qiskit-addon-cutting", "qiskit-addon-utils", ]; @@ -230,6 +231,15 @@ export class Pkg { language: "Python", }); } + if (name === "qiskit-addon-sqd-hpc") { + return new Pkg({ + ...args, + title: "SQD for HPC", + githubSlug: "Qiskit/qiskit-addon-sqd-hpc", + kebabCaseAndShortenUrls: true, + language: "C", + }); + } if (name === "qiskit-c") { return new Pkg({ diff --git a/scripts/js/lib/api/apiDocsPipeline.ts b/scripts/js/lib/api/apiDocsPipeline.ts index c1d6b2a11cf..bdc4258b088 100644 --- a/scripts/js/lib/api/apiDocsPipeline.ts +++ b/scripts/js/lib/api/apiDocsPipeline.ts @@ -85,7 +85,7 @@ async function determineFilePaths( : ObjectsInv.fromFile(htmlPath, pkg.language)); const extraFiles = pkg.isCApi() - ? [`${C_API_BASE_PATH}/**.html`] + ? [`${C_API_BASE_PATH}/**.html`, "apidocs/**.html"] : ["apidocs/**.html", "apidoc/**.html", "stubs/**.html"]; const files = await globby( [...extraFiles, "release_notes.html", "release-notes.html"], diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index d9ea31943ef..7e938b45d4f 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -652,6 +652,9 @@ function _addonStaleTutorialLinks(): FilesToIgnores { "docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.ipynb": [ "../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb", ], + "docs/addons/qiskit-addon-sqd-hpc/index.mdx": [ + "/docs/en/guides/bit-ordering", + ], }; } From 34420eee435f69d44468f271b4d12357000ca73e Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 21 May 2026 16:15:48 -0500 Subject: [PATCH 076/104] move index first in addon toc --- docs/addons/qiskit-addon-sqd-hpc/_toc.json | 8 ++++---- docs/guides/addons.mdx | 2 +- docs/guides/error-mitigation-overview.mdx | 10 +++++----- docs/guides/qiskit-function-templates.mdx | 2 +- scripts/js/lib/api/generateAddonToc.ts | 23 ++++++++++++++++------ 5 files changed, 28 insertions(+), 17 deletions(-) diff --git a/docs/addons/qiskit-addon-sqd-hpc/_toc.json b/docs/addons/qiskit-addon-sqd-hpc/_toc.json index 5a2c6833e65..7feb9e1e697 100644 --- a/docs/addons/qiskit-addon-sqd-hpc/_toc.json +++ b/docs/addons/qiskit-addon-sqd-hpc/_toc.json @@ -7,14 +7,14 @@ { "title": "", "children": [ - { - "title": "Compilation flags", - "url": "/docs/addons/qiskit-addon-sqd-hpc/compilation-flags" - }, { "title": "Home", "url": "/docs/addons/qiskit-addon-sqd-hpc" }, + { + "title": "Compilation flags", + "url": "/docs/addons/qiskit-addon-sqd-hpc/compilation-flags" + }, { "title": "GitHub", "url": "https://github.com/Qiskit/qiskit-addon-sqd-hpc" diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index f388a26388d..5fe9963c32c 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -105,7 +105,7 @@ These techniques are useful for managing noise on sampling results. <Card title="SQD for HPC" description="An HPC-ready implementation of the SQD addon, written in modern C++17 standards and designed to enable HPC workflows and applications." - href="https://qiskit.github.io/qiskit-addon-sqd-hpc/" + href="/docs/addons/qiskit-addon-sqd-hpc/" analyticsName="Documentation page: SQD for HPC" linkText="Browse documentation" /> diff --git a/docs/guides/error-mitigation-overview.mdx b/docs/guides/error-mitigation-overview.mdx index ef75a9a63bc..0d810a7aeb9 100644 --- a/docs/guides/error-mitigation-overview.mdx +++ b/docs/guides/error-mitigation-overview.mdx @@ -40,7 +40,7 @@ When executing quantum workloads, there are multiple ways to reduce the impact o <Card title="AQC-Tensor Qiskit addon" description="Users can compile the initial portion of a circuit into a nearly equivalent approximation of that circuit, but with fewer layers." - href="https://qiskit.github.io/qiskit-addon-aqc-tensor/" + href="/docs/addons/qiskit-addon-aqc-tensor/" analyticsName="Documentation page: Approximate quantum compilation" linkText="Browse documentation" /> @@ -73,7 +73,7 @@ When executing quantum workloads, there are multiple ways to reduce the impact o <Card title="Probabilistic error cancellation (PEC)" description="Returns an unbiased estimate of the expectation value, at the expense of greater overhead than other techniques such as ZNE. It extrapolates output of the ideal circuit by executing different noisy circuit instances." - href="/docs/guides/error-mitigation-and-suppression-techniques#probabilistic-error-cancellation-pec" + href="/docs/guides/error-mitigation-and-suppression-techniques#probabilistic-error-cancellation-pec" analyticsName="Documentation page: PEC" linkText="Learn more" /> @@ -88,7 +88,7 @@ When executing quantum workloads, there are multiple ways to reduce the impact o <Card title="Operator backpropagation (OBP)" description="Uses a method based on Clifford perturbation theory to reduce circuit depth by trimming operations from its end at the cost of more operator measurements." - href="https://qiskit.github.io/qiskit-addon-obp/" + href="docs/addons/qiskit-addon-obp/" analyticsName="Documentation page: Operator backpropagation" linkText="Browse documentation" /> @@ -107,7 +107,7 @@ When executing quantum workloads, there are multiple ways to reduce the impact o <Card title="Sample-based quantum diagonalization (SQD)" description="Implements a technique for finding eigenvalues and eigenvectors of quantum operators, such as a quantum system Hamiltonian, using quantum and distributed classical computing together." - href="https://qiskit.github.io/qiskit-addon-sqd/" + href="docs/addons/qiskit-addon-sqd/" analyticsName="Documentation page: SQD" linkText="Browse documentation" /> @@ -115,7 +115,7 @@ When executing quantum workloads, there are multiple ways to reduce the impact o <Card title="SQD for HPC" description="An HPC-ready implementation of the SQD addon. It is written in modern C++17 standards and is designed to create a single compiled binary for use with MPI." - href="https://qiskit.github.io/qiskit-addon-sqd-hpc/" + href="docs/addons/qiskit-addon-sqd-hpc/" analyticsName="Documentation page: SQD for HPC" linkText="Browse documentation" /> diff --git a/docs/guides/qiskit-function-templates.mdx b/docs/guides/qiskit-function-templates.mdx index f0cf2b5abaa..da1dc1daa0f 100644 --- a/docs/guides/qiskit-function-templates.mdx +++ b/docs/guides/qiskit-function-templates.mdx @@ -15,7 +15,7 @@ There are two types of templates: ## Template implementations -Qiskit Function template implementations are organized by application area. Currently included in the collection is a physics template for Hamiltonian simulation using the [AQC-Tensor Qiskit addon](https://qiskit.github.io/qiskit-addon-aqc-tensor/) and a chemistry template for electronic structure with the implicit solvent model using the [SQD Qiskit addon](/docs/guides/qiskit-addons-sqd). +Qiskit Function template implementations are organized by application area. Currently included in the collection is a physics template for Hamiltonian simulation using the [AQC-Tensor Qiskit addon](/docs/addons/qiskit-addon-aqc-tensor/) and a chemistry template for electronic structure with the implicit solvent model using the [SQD Qiskit addon](/docs/guides/qiskit-addons-sqd). Resources to get started with these two templates are available at the following links: - Electronic structure simulation with implicit solvent model: [template source files](https://github.com/qiskit-community/qiskit-function-templates/tree/main/chemistry/sqd_pcm) and [guide](/docs/guides/function-template-chemistry-workflow) diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts index 9193b9dc19d..da33c9963ec 100644 --- a/scripts/js/lib/api/generateAddonToc.ts +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -66,10 +66,14 @@ export async function generateAddonToc( // Top-level files → entries in the main section, in filename order. for (const file of topLevelFiles.sort()) { const slug = file.replace(/\.(mdx|ipynb)$/, ""); - const url = - slug === "index" ? addonUrlBase : `${addonUrlBase}/${slug}`; - const title = TOP_LEVEL_TITLES[slug] ?? await readTitle(join(addonDocsPath, file)); - mainChildren.push({ title, url }); + const isIndex = slug === "index"; + const url = isIndex ? addonUrlBase : `${addonUrlBase}/${slug}`; + const title = + TOP_LEVEL_TITLES[slug] ?? (await readTitle(join(addonDocsPath, file))); + // ensure index is first in the toc + isIndex + ? mainChildren.unshift({ title, url }) + : mainChildren.push({ title, url }); } // Subdirectories → either nested subsections or top-level sections. @@ -207,7 +211,9 @@ function readMdxTitle(content: string, filePath: string): string { } function readNotebookTitle(content: string, filePath: string): string { - let notebook: { cells: Array<{ id?: string; cell_type: string; source: string[] }> }; + let notebook: { + cells: Array<{ id?: string; cell_type: string; source: string[] }>; + }; try { notebook = JSON.parse(content); } catch { @@ -237,5 +243,10 @@ function stripInlineCode(text: string): string { } function fileBasename(filePath: string): string { - return filePath.split("/").pop()?.replace(/\.(mdx|ipynb)$/, "") ?? filePath; + return ( + filePath + .split("/") + .pop() + ?.replace(/\.(mdx|ipynb)$/, "") ?? filePath + ); } From 63b19e2326a2a33ea03494735ebc84a19a11c831 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 22 May 2026 10:26:38 -0500 Subject: [PATCH 077/104] stub all addons --- scripts/js/commands/checkInternalLinks.ts | 61 +++++++++++++++++++- scripts/js/lib/api/Pkg.ts | 70 +++++++++++++++++++++++ 2 files changed, 129 insertions(+), 2 deletions(-) diff --git a/scripts/js/commands/checkInternalLinks.ts b/scripts/js/commands/checkInternalLinks.ts index dd898e672f9..27df44b47a6 100644 --- a/scripts/js/commands/checkInternalLinks.ts +++ b/scripts/js/commands/checkInternalLinks.ts @@ -93,7 +93,9 @@ async function syntheticAddonTutorialFiles(): Promise<File[]> { tutorials: string[]; }; for (const slug of tutorials) { - files.push(new File(`${pkgDir}/tutorials/${slug}.ipynb`, new Set(), true)); + files.push( + new File(`${pkgDir}/tutorials/${slug}.ipynb`, new Set(), true), + ); } } return files; @@ -213,10 +215,65 @@ async function determineFileBatches(args: Arguments): Promise<FileBatch[]> { ADDON_GLOBS_TO_LOAD, { check: args.historicalApis }, ); + const sqdHpc = await determineHistoricalFileBatches( + "qiskit-addon-sqd-hpc", + ADDON_GLOBS_TO_LOAD, + { check: args.historicalApis }, + ); + const mthree = await determineHistoricalFileBatches( + "qiskit-addon-mthree", + ADDON_GLOBS_TO_LOAD, + { check: args.historicalApis }, + ); + const pna = await determineHistoricalFileBatches( + "qiskit-addon-pna", + ADDON_GLOBS_TO_LOAD, + { check: args.historicalApis }, + ); + const slc = await determineHistoricalFileBatches( + "qiskit-addon-slc", + ADDON_GLOBS_TO_LOAD, + { check: args.historicalApis }, + ); + const optMapper = await determineHistoricalFileBatches( + "qiskit-addon-opt-mapper", + ADDON_GLOBS_TO_LOAD, + { check: args.historicalApis }, + ); + const fermions = await determineHistoricalFileBatches( + "qiskit-fermions", + ADDON_GLOBS_TO_LOAD, + { check: args.historicalApis }, + ); + const paulice = await determineHistoricalFileBatches( + "qiskit-paulice", + ADDON_GLOBS_TO_LOAD, + { check: args.historicalApis }, + ); + const pauliProp = await determineHistoricalFileBatches( + "pauli-prop", + ADDON_GLOBS_TO_LOAD, + { check: args.historicalApis }, + ); // This is intentionally ordered so that the smallest APIs are checked first, // since they are much faster to check. - result.push(...transpiler, ...sqd, ...mpf, ...utils, ...runtime, ...qiskit); + result.push( + ...transpiler, + ...sqd, + ...sqdHpc, + ...mpf, + ...utils, + ...mthree, + ...pna, + ...slc, + ...optMapper, + ...fermions, + ...paulice, + ...pauliProp, + ...runtime, + ...qiskit, + ); if (args.qiskitLegacyReleaseNotes) { result.push(await determineQiskitLegacyReleaseNotes()); diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index cbb7dffce8a..39309f12807 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -68,6 +68,13 @@ export class Pkg { "qiskit-addon-sqd-hpc", "qiskit-addon-cutting", "qiskit-addon-utils", + "qiskit-addon-mthree", + "qiskit-addon-pna", + "qiskit-addon-slc", + "qiskit-addon-opt-mapper", + "qiskit-fermions", + "qiskit-paulice", + "pauli-prop", ]; static VALID_NAMES = [ @@ -240,6 +247,69 @@ export class Pkg { language: "C", }); } + if (name === "qiskit-addon-mthree") { + return new Pkg({ + ...args, + title: "Matrix-free Measurement Mitigation (M3)", + githubSlug: "Qiskit/qiskit-addon-mthree", + kebabCaseAndShortenUrls: true, + language: "Python", + }); + } + if (name === "qiskit-addon-pna") { + return new Pkg({ + ...args, + title: "Propagated noise absorption (PNA)", + githubSlug: "Qiskit/qiskit-addon-pna", + kebabCaseAndShortenUrls: true, + language: "Python", + }); + } + if (name === "qiskit-addon-slc") { + return new Pkg({ + ...args, + title: "Shaded lightcones", + githubSlug: "Qiskit/qiskit-addon-slc", + kebabCaseAndShortenUrls: true, + language: "Python", + }); + } + if (name === "qiskit-addon-opt-mapper") { + return new Pkg({ + ...args, + title: "Optimization mapper", + githubSlug: "Qiskit/qiskit-addon-opt-mapper", + kebabCaseAndShortenUrls: true, + language: "Python", + }); + } + if (name === "qiskit-fermions") { + return new Pkg({ + ...args, + title: "Fermionic mapper", + githubSlug: "Qiskit/qiskit-fermions", + kebabCaseAndShortenUrls: true, + language: "Python", + }); + } + if (name === "qiskit-paulice") { + return new Pkg({ + ...args, + title: "Qiskit Paulice", + githubSlug: "Qiskit/qiskit-paulice", + kebabCaseAndShortenUrls: true, + language: "Python", + }); + } + if (name === "pauli-prop") { + return new Pkg({ + ...args, + title: "Pauli propagation", + githubSlug: "Qiskit/pauli-prop", + kebabCaseAndShortenUrls: true, + language: "Python", + }); + } if (name === "qiskit-c") { return new Pkg({ From ac2c5986a9a8a2c7e8990d3ec7d1ed3cda3d0259 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 22 May 2026 11:17:19 -0500 Subject: [PATCH 078/104] adds pna --- docs/addons/qiskit-addon-pna/_toc.json | 45 ++++++++++ .../addons/qiskit-addon-pna/how-tos/index.mdx | 13 +++ .../how-tos/placeholder.ipynb | 44 +++++++++ docs/addons/qiskit-addon-pna/index.mdx | 76 ++++++++++++++++ docs/addons/qiskit-addon-pna/install.mdx | 70 +++++++++++++++ docs/api/qiskit-addon-pna/_package.json | 4 + docs/api/qiskit-addon-pna/_toc.json | 23 +++++ docs/api/qiskit-addon-pna/index.mdx | 11 +++ .../api/qiskit-addon-pna/qiskit-addon-pna.mdx | 84 ++++++++++++++++++ docs/api/qiskit-addon-pna/release-notes.mdx | 38 ++++++++ docs/guides/addons.mdx | 2 +- docs/guides/directed-execution-model.mdx | 2 +- docs/guides/error-mitigation-overview.mdx | 2 +- docs/guides/latest-updates.mdx | 2 +- public/docs/api/qiskit-addon-pna/objects.inv | 8 ++ .../nbsphinx-no-thumbnail.svg | 9 ++ .../noise_mitigated_expt.avif | Bin 0 -> 8858 bytes .../addons/qiskit-addon-pna/noisy_expt.avif | Bin 0 -> 7601 bytes .../addons/qiskit-addon-pna/pna_overview.avif | Bin 0 -> 16121 bytes scripts/config/api-html-artifacts.json | 6 ++ scripts/config/cspell/cSpell.json | 4 +- scripts/config/cspell/dictionaries/qiskit.txt | 3 +- .../config/historical-pages-to-latest.json | 3 +- scripts/js/lib/api/Pkg.ts | 12 +-- 24 files changed, 448 insertions(+), 13 deletions(-) create mode 100644 docs/addons/qiskit-addon-pna/_toc.json create mode 100644 docs/addons/qiskit-addon-pna/how-tos/index.mdx create mode 100644 docs/addons/qiskit-addon-pna/how-tos/placeholder.ipynb create mode 100644 docs/addons/qiskit-addon-pna/index.mdx create mode 100644 docs/addons/qiskit-addon-pna/install.mdx create mode 100644 docs/api/qiskit-addon-pna/_package.json create mode 100644 docs/api/qiskit-addon-pna/_toc.json create mode 100644 docs/api/qiskit-addon-pna/index.mdx create mode 100644 docs/api/qiskit-addon-pna/qiskit-addon-pna.mdx create mode 100644 docs/api/qiskit-addon-pna/release-notes.mdx create mode 100644 public/docs/api/qiskit-addon-pna/objects.inv create mode 100644 public/docs/images/addons/qiskit-addon-pna/nbsphinx-no-thumbnail.svg create mode 100644 public/docs/images/addons/qiskit-addon-pna/noise_mitigated_expt.avif create mode 100644 public/docs/images/addons/qiskit-addon-pna/noisy_expt.avif create mode 100644 public/docs/images/addons/qiskit-addon-pna/pna_overview.avif diff --git a/docs/addons/qiskit-addon-pna/_toc.json b/docs/addons/qiskit-addon-pna/_toc.json new file mode 100644 index 00000000000..a5dd866fada --- /dev/null +++ b/docs/addons/qiskit-addon-pna/_toc.json @@ -0,0 +1,45 @@ +{ + "parentUrl": "/docs/guides/addons", + "parentLabel": "Documentation", + "title": "Propagated noise absorption (PNA) 0.2.0", + "collapsed": true, + "children": [ + { + "title": "", + "children": [ + { + "title": "Home", + "url": "/docs/addons/qiskit-addon-pna" + }, + { + "title": "Installation instructions", + "url": "/docs/addons/qiskit-addon-pna/install" + }, + { + "title": "Guides", + "children": [ + { + "title": "How-to guides coming soon", + "url": "/docs/addons/qiskit-addon-pna/how-tos/placeholder" + } + ] + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-pna" + } + ], + "collapsible": false + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Propagated noise absorption (PNA) API reference", + "url": "/docs/api/qiskit-addon-pna" + } + ] + } + ] +} diff --git a/docs/addons/qiskit-addon-pna/how-tos/index.mdx b/docs/addons/qiskit-addon-pna/how-tos/index.mdx new file mode 100644 index 00000000000..96d440df2e7 --- /dev/null +++ b/docs/addons/qiskit-addon-pna/how-tos/index.mdx @@ -0,0 +1,13 @@ +--- +title: "How-To Guides" +description: "How-To Guides for the lastest version of Propagated noise absorption (PNA)" +--- + +# How-To Guides + +This page summarizes the available how-to guides. + +[![](/docs/images/addons/qiskit-addon-pna/nbsphinx-no-thumbnail.svg)](placeholder) + +[How-to guides coming soon](placeholder) + diff --git a/docs/addons/qiskit-addon-pna/how-tos/placeholder.ipynb b/docs/addons/qiskit-addon-pna/how-tos/placeholder.ipynb new file mode 100644 index 00000000000..bdfeddf3f28 --- /dev/null +++ b/docs/addons/qiskit-addon-pna/how-tos/placeholder.ipynb @@ -0,0 +1,44 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"How-to guides coming soon\"\n", + "description: \"How-to guides coming soon for the latest version of Propagated noise absorption (PNA)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "3b3c0748-30e3-4bd6-b15d-b8fe80fc19ff", + "metadata": {}, + "source": [ + "# How-to guides coming soon" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-pna/index.mdx b/docs/addons/qiskit-addon-pna/index.mdx new file mode 100644 index 00000000000..9ba06df73ad --- /dev/null +++ b/docs/addons/qiskit-addon-pna/index.mdx @@ -0,0 +1,76 @@ +--- +title: "Qiskit add-on: Propagated noise absorption (PNA)" +description: "Documentation for the latest version of Propagated noise absorption (PNA)" +--- + +# Qiskit add-on: Propagated noise absorption (PNA) + +PNA is a technique for mitigating errors in observable expectation values by “absorbing” the inverses of the learned noise channels into the observable using [Pauli propagation](/docs/addons/pauli-prop/). Each Pauli noise generator in the noise model is classically propagated to the end of the circuit and applied to the observable, resulting in a new observable that when measured on a QPU, mitigates the learned gate noise. Check out the [tutorial](https://github.com/qiskit-community/qdc-challenges-2025/blob/main/day3_tutorials/Track_A/pna/propagated_noise_absorption.ipynb) to see how it works! + +## Overview + +Executing entangling gates on modern QPUs results in a substantial amount of noise. Until fully fault tolerant devices are available, ideal entangling gates, $U$, will not be available. They will instead be affected by some noise channel, $\Lambda$. + +![\_images/noisy\_expt.png](/docs/images/addons/qiskit-addon-pna/noisy_expt.avif) + +It is possible to learn and efficiently characterize this gate noise as a Pauli-Lindblad model, and as shown in probabilistic error cancellation (PEC), we can mitigate the error by implementing the anti-noise, $\Lambda^{-1}$, with a QPU sampling protocol \[1]. Other techniques, such as tensor-network error mitigation (TEM), implement the inverse noise channel as a classical post-processing step \[2]. + +![\_images/noise\_mitigated\_expt.png](/docs/images/addons/qiskit-addon-pna/noise_mitigated_expt.avif) + +Like TEM, PNA implements the inverse noise channel in a classical processing step. While TEM uses tensor networks to describe and apply the noise-mitigating map to a set of informationally complete measurements, PNA uses Pauli propagation to propagate the observable, $O$, through the inverse noise channel. This results in a new observable, $\tilde{O}$, that when measured against the noisy state, mitigates the learned noise. + +![\_images/pna\_overview.png](/docs/images/addons/qiskit-addon-pna/pna_overview.avif) + +### Sources of bias + +* This implementation propagates each Pauli error generator within each anti-noise channel, $\Lambda^{-1}_i$, to the end of the circuit. As each anti-noise generator is propagated forward through the circuit under the action of $N$ Pauli rotation gates of an $M$-qubit circuit, the number of terms will grow as $O(2^N)$ towards a maximum of $4^M$ unique Pauli components. To control the computational cost, terms with small coefficients must be truncated, which results in some error in the evolved anti-noise channel. +* In addition to the truncation of the evolved anti-noise channel, $\Lambda^{-1}$, $\tilde{O}$ is also truncated as it is propagated through $\Lambda^{-1}$. This is also a source of bias in the final mitigated expectation value. +* While letting $\tilde{O}$ grow larger during propagation will increase its accuracy, measuring it requires taking many more shots on the QPU. Typically this increases the coefficients of the original Pauli terms in $O$, along with creating many new Pauli terms with smaller coefficients. Both the rescaling of the original coefficients and the creation of new terms can increase sampling overhead. In practice, we truncate once more by measuring only the largest terms in $\tilde{O}$. + +## Installation + +We encourage installing this package via `pip`, when possible: + +```bash +pip install 'qiskit-addon-pna' +``` + +For more installation information refer to the [installation instructions](install) in the documentation. + +## Citing this project + +If you use this package in your research, please cite it according to `CITATON.bib` file included in this repository: + +```bibtex +@software{qiskit-addon-pna, + author = {Qiskit addons team}, + title = {{Qiskit addon: Propagated noise absorption}}, + howpublished = {\url{https://github.com/Qiskit/qiskit-addon-pna}}, + year = {2025} +} +``` + +## Deprecation Policy + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the release notes. + +## Contributing + +The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-pna). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-pna/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). + +We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-pna/issues/new/choose) for tracking requests and bugs. + +## License + +[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-pna/blob/main/LICENSE.txt) + +<span id="id1" /> + +## References + +\[1] Ewout van den Berg, et al., [Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors](https://arxiv.org/abs/2201.09866), arXiv:2201.09866 \[quant-ph]. + +\[2] Sergei Filippov, et al., [Scalable tensor-network error mitigation for near-term quantum computing](https://arxiv.org/abs/2307.11740), arXiv:2307.11740 \[quant-ph]. + diff --git a/docs/addons/qiskit-addon-pna/install.mdx b/docs/addons/qiskit-addon-pna/install.mdx new file mode 100644 index 00000000000..1243618fb59 --- /dev/null +++ b/docs/addons/qiskit-addon-pna/install.mdx @@ -0,0 +1,70 @@ +--- +title: "Installation Instructions" +description: "Installation Instructions for the lastest version of Propagated noise absorption (PNA)" +--- + +# Installation Instructions + +Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: + +* [Option 1: Install from PyPI](#option-1) +* [Option 2: Install from Source](#option-2) + +## Pre-Installation + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +<span id="option-1" /> + +## Option 1: Install from PyPI + +The most straightforward way to install the `qiskit-addon-pna` package is via `PyPI`. + +```sh +pip install 'qiskit-addon-pna' +``` + +<span id="option-2" /> + +## Option 2: Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +If so, the first step is to clone the `qiskit-addon-pna` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-pna.git +``` + +Next, upgrade pip and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-pna +``` + +The next step is to install `qiskit-addon-pna` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab +``` + diff --git a/docs/api/qiskit-addon-pna/_package.json b/docs/api/qiskit-addon-pna/_package.json new file mode 100644 index 00000000000..a5b2b7b671a --- /dev/null +++ b/docs/api/qiskit-addon-pna/_package.json @@ -0,0 +1,4 @@ +{ + "name": "qiskit-addon-pna", + "version": "0.2.0" +} diff --git a/docs/api/qiskit-addon-pna/_toc.json b/docs/api/qiskit-addon-pna/_toc.json new file mode 100644 index 00000000000..98530103316 --- /dev/null +++ b/docs/api/qiskit-addon-pna/_toc.json @@ -0,0 +1,23 @@ +{ + "title": "Propagated noise absorption (PNA)", + "children": [ + { + "title": "API index", + "url": "/docs/api/qiskit-addon-pna" + }, + { + "title": "Release notes", + "useDivider": true, + "url": "/docs/api/qiskit-addon-pna/release-notes" + }, + { + "title": "qiskit_addon_pna", + "url": "/docs/api/qiskit-addon-pna/qiskit-addon-pna", + "untranslatable": true + } + ], + "collapsed": true, + "untranslatable": true, + "parentUrl": "/docs/addons/qiskit-addon-pna", + "parentLabel": "Propagated noise absorption (PNA)" +} diff --git a/docs/api/qiskit-addon-pna/index.mdx b/docs/api/qiskit-addon-pna/index.mdx new file mode 100644 index 00000000000..32dd5c7438b --- /dev/null +++ b/docs/api/qiskit-addon-pna/index.mdx @@ -0,0 +1,11 @@ +--- +title: Propagated noise absorption (PNA) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-pna. +--- + +<span id="qiskit-addon-pna-api-reference" /> + +# `qiskit-addon-pna` API reference + +* [Propagated Noise Absorption (`qiskit_addon_pna`)](qiskit-addon-pna) + diff --git a/docs/api/qiskit-addon-pna/qiskit-addon-pna.mdx b/docs/api/qiskit-addon-pna/qiskit-addon-pna.mdx new file mode 100644 index 00000000000..801e0c570ad --- /dev/null +++ b/docs/api/qiskit-addon-pna/qiskit-addon-pna.mdx @@ -0,0 +1,84 @@ +--- +title: qiskit_addon_pna (latest version) +description: API reference for qiskit_addon_pna in the latest version of qiskit-addon-pna +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_addon_pna +--- + +<span id="module-qiskit_addon_pna" /> + +<span id="propagated-noise-absorption-qiskit-addon-pna" /> + +# Propagated Noise Absorption + +`qiskit_addon_pna` + +Primary propagated noise absortption (PNA) functionality. + +### generate\_noise\_mitigating\_observable + +<Function id="qiskit_addon_pna.generate_noise_mitigating_observable" github="https://github.com/Qiskit/qiskit-addon-pna/tree/main/qiskit_addon_pna/pna.py#L43-L319" signature="generate_noise_mitigating_observable(noisy_circuit, observable, refs_to_noise_model_map=None, *, max_err_terms, max_obs_terms, search_step=4, num_processes=1, print_progress=False, atol=1e-08, batch_size=1, inject_noise_before=True, mp_start_method='spawn')"> + Generate a noise-mitigating observable by propagating it through the inverse of a learned noise channel. + + <Admonition title="Note" type="note"> + This function uses the Python `multiprocessing` module for parallel execution. This function should be called from within an `if __name__ == "__main__"` guard to prevent unintended process spawning. + </Admonition> + + Starting from the beginning of the circuit, the noise affecting each entangling layer is inverted and each Pauli anti-noise generator is then propagated forward through the remainder of the circuit and applied to the observable. The propagation routines used to implement this method are available in the [pauli-prop](/docs/addons/pauli-prop/) package. + + As each anti-noise generator is propagated forward through the circuit under the action of $N$ Pauli rotation gates of an \$M\$-qubit circuit, the number of terms will grow as $O(2^N)$ towards a maximum of $4^M$ unique Pauli components. To control the computational cost, terms with small coefficients must be truncated, which will result in some error in the evolved anti-noise channel. + + In addition to the truncation of the evolved anti-noise channel, $\Lambda^{-1}$, $\tilde{O}$ is also truncated as it is propagated through $\Lambda^{-1}$. This is also a source of bias in the final mitigated expectation value. + + While letting $\tilde{O}$ grow larger during propagation will increase its accuracy, measuring it requires taking many more shots on the QPU. Typically this increases the coefficients of the original Pauli terms in $O$, along with creating many new Pauli terms with smaller coefficients. Both the rescaling of the original coefficients and the creation of new terms can increase sampling overhead. In practice, we truncate once more by measuring only the largest terms in $\tilde{O}$. + + **Parameters** + + * **noisy\_circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – + + A circuit with associated Pauli-Lindblad gate noise. + + If this circuit is boxed, there are expected to be `InjectNoise` annotations associated with each box for which gate noise should be mitigated. Additionally, the `refs_to_noise_model_map` should provide a mapping from the reference ID of each `InjectNoise` annotation to the associated noise model for that layer, specified as a `PauliLindbladMap`. + + If this circuit is not boxed, the noise model is expected to be embedded as `PauliLindbladError` instructions adjacent to each circuit layer for which gate noise should be mitigated. In this case, `refs_to_noise_model_map` may be None. + + * **observable** ([*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) *|*[*Pauli*](/docs/api/qiskit/qiskit.quantum_info.Pauli)) – The observable which will absorb the anti-noise. + + * **refs\_to\_noise\_model\_map** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)*] | None*) – A dictionary mapping noise injection referencs IDs to their corresponding noise models as `PauliLindbladMap`. If `noisy_circuit` is not boxed and contains `PauliLindbladError` instructions from qiskit-aer, this mapping is not needed. + + * **max\_err\_terms** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The maximum number of terms each anti-noise generator may contain as it evolves through the circuit + + * **max\_obs\_terms** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The maximum number of terms the noise-mitigating observable may contain + + * **search\_step** ([*int*](https://docs.python.org/3/library/functions.html#int)) – A parameter that can speed up the approximate application of each error to the observable. The relevant subroutine searches a very large 3D space to identify the `max_obs_terms` largest terms in a product. Setting this step size >1 accelerates that search by a factor of `search_step**3`, at a potential cost in accuracy. This inaccuracy is expected to be small for `search_step**3 << max_obs_terms`. + + * **num\_processes** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The number of processes for parallelization. These may be used for forward evolution of generators, and for applying evolved generators to the observable. If `batch_size` is `1` (default), all are used for evolving generators. Otherwise, `max(min(batch_size, num_processes // 2), 1)` of these will be allocated for applying evolved generators to the observable. + + * **print\_progress** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Whether to print progress to stdout + + * **atol** ([*float*](https://docs.python.org/3/library/functions.html#float)) – Terms below this threshold will not be added to operators as they evolve + + * **batch\_size** ([*int*](https://docs.python.org/3/library/functions.html#int)) – Setting this to a value > 1 allows batches of noise generators to be applied to the observable in parallel. This coarse-grain application of anti-noise to the observable comes at a loss of accuracy related to the probability that more than one error in the batch occurs when the circuit is run. This should usually not be set higher than `max(1, num_processes // 2)`. + + * **inject\_noise\_before** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – If `True`, the Pauli Lindblad noise instruction will be inserted before its corresponding 2q-gate layer. Otherwise, it will be inserted after it, defaults to `True`. + + * **mp\_start\_method** ([*str*](https://docs.python.org/3/library/stdtypes.html#str) *| None*) – The method to use when starting new parallel processes. Valid values are `fork`, `spawn`, `forkserver`, and `None`. If `None`, the default method will be used. + + **Returns** + + The noise-mitigating observable + + **Raises** + + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – The circuit and observable have mismatching sizes + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – num\_processes and batch\_size must be >= 1 + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – `max_obs_terms` should be larger than the length of `observable` + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – Incompatible noisy circuit and refs\_to\_noise\_model\_map + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – The observable must only contain real-valued coefficients + + **Return type** + + [*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) +</Function> + diff --git a/docs/api/qiskit-addon-pna/release-notes.mdx b/docs/api/qiskit-addon-pna/release-notes.mdx new file mode 100644 index 00000000000..36f63947363 --- /dev/null +++ b/docs/api/qiskit-addon-pna/release-notes.mdx @@ -0,0 +1,38 @@ +--- +title: Propagated noise absorption (PNA) release notes +description: Changes made to Propagated noise absorption (PNA) +in_page_toc_max_heading_level: 2 +--- + +<span id="release-notes" /> + +<span id="id1" /> + +# Propagated noise absorption (PNA) release notes + +<span id="release-notes-0-2-0" /> + +<span id="id2" /> + +## 0.2.0 + +<span id="release-notes-0-2-0-new-features" /> + +### New Features + +* Added an `mp_start_method` kwarg to [`qiskit_addon_pna.generate_noise_mitigating_observable()`](/docs/api/qiskit-addon-pna/qiskit-addon-pna#generate_noise_mitigating_observable "qiskit_addon_pna.generate_noise_mitigating_observable") to specify the method to use to start new parallel processes. + +<span id="release-notes-0-1-0" /> + +<span id="id3" /> + +## 0.1.0 + +<span id="release-notes-0-1-0-new-features" /> + +<span id="id4" /> + +### New Features + +* Add argument to [`qiskit_addon_pna.generate_noise_mitigating_observable()`](/docs/api/qiskit-addon-pna/qiskit-addon-pna#generate_noise_mitigating_observable "qiskit_addon_pna.generate_noise_mitigating_observable") specifying whether noise should be injected before or after entangling layers. + diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index 5fe9963c32c..fe3a74ef45a 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -75,7 +75,7 @@ Use the following addons to manage noise when building quantum workloads that es <Card title="Propagated noise absorption" description="Mitigate expectation values by measuring a target observable that has absorbed noise model information." - href="https://qiskit.github.io/qiskit-addon-pna/" + href="/docs/addons/qiskit-addon-pna/" analyticsName="Documentation page: PNA" linkText="Browse documentation" /> diff --git a/docs/guides/directed-execution-model.mdx b/docs/guides/directed-execution-model.mdx index 0cc48c4729b..fd46882ebd1 100644 --- a/docs/guides/directed-execution-model.mdx +++ b/docs/guides/directed-execution-model.mdx @@ -69,5 +69,5 @@ To learn more about NoiseLearnerV3, refer to its [API reference](https://qiskit. ## Next steps <Admonition type="tip" title="Recommendations"> -- Check out two Qiskit addons, [Shaded lightcones](https://qiskit.github.io/qiskit-addon-slc/) and [Propagated noise absorption](https://github.com/Qiskit/qiskit-addon-pna), which are built on top of this execution model. +- Check out two Qiskit addons, [Shaded lightcones](https://qiskit.github.io/qiskit-addon-slc/) and [Propagated noise absorption](/docs/addons/qiskit-addon-pna), which are built on top of this execution model. </Admonition> \ No newline at end of file diff --git a/docs/guides/error-mitigation-overview.mdx b/docs/guides/error-mitigation-overview.mdx index 0d810a7aeb9..b5911f603fc 100644 --- a/docs/guides/error-mitigation-overview.mdx +++ b/docs/guides/error-mitigation-overview.mdx @@ -95,7 +95,7 @@ When executing quantum workloads, there are multiple ways to reduce the impact o <Card title="Propagated noise absorption (PNA)" description="Technique for mitigating errors in observable expectation values by 'absorbing' the inverses of learned noise channels into an observable using Pauli propagation." - href="https://qiskit.github.io/qiskit-addon-pna/" + href="/docs/addons/qiskit-addon-pna/" analyticsName="Documentation page: Propagated noise absorption" linkText="Browse documentation" /> diff --git a/docs/guides/latest-updates.mdx b/docs/guides/latest-updates.mdx index f4b6bc75456..a9634d73209 100644 --- a/docs/guides/latest-updates.mdx +++ b/docs/guides/latest-updates.mdx @@ -314,7 +314,7 @@ Improvements include the following: - We recently released two new addons that use classical resources to reduce the quantum resources needed for error mitigation: - - [Propagated noise absorption (PNA)](https://qiskit.github.io/qiskit-addon-pna/) is a technique for mitigating errors in expectation values by absorbing the inverses of the learned noise channels into observables using Pauli propagation. + - [Propagated noise absorption (PNA)](/docs/addons/qiskit-addon-pna/) is a technique for mitigating errors in expectation values by absorbing the inverses of the learned noise channels into observables using Pauli propagation. - The [Shaded lightcones (SLC)](https://qiskit.github.io/qiskit-addon-slc/) addon uses classical simulations to more tightly bound the sensitivity to errors throughout the circuit. 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"https://ibm.box.com/shared/static/9wxwvchyal0oqkbe46a13d9wzwxi8wxd.zip" } } diff --git a/scripts/config/cspell/cSpell.json b/scripts/config/cspell/cSpell.json index 438c6af376b..34e9251ab15 100644 --- a/scripts/config/cspell/cSpell.json +++ b/scripts/config/cspell/cSpell.json @@ -45,11 +45,13 @@ "ebitsb", "pmzero", "textrm", + "Frontmatter", "subpace", // fix in addon-sqd "behaviour", // fix in addon-sqd "susbapce", // fix in addon-sqd "Counfiguration", // fix in addon-sqd - "construnct" // fix in addon-sqd + "construnct", // fix in addon-sqd + "rigourous" // fix in addon-mthree ], "ignoreRegExpList": [ // Markdown links diff --git a/scripts/config/cspell/dictionaries/qiskit.txt b/scripts/config/cspell/dictionaries/qiskit.txt index 9f5fb90f5c8..274ec1695ca 100644 --- a/scripts/config/cspell/dictionaries/qiskit.txt +++ b/scripts/config/cspell/dictionaries/qiskit.txt @@ -461,4 +461,5 @@ fontdict fontsize Utar nocc -tnopt \ No newline at end of file +tnopt +quasis \ No newline at end of file diff --git a/scripts/config/historical-pages-to-latest.json b/scripts/config/historical-pages-to-latest.json index ad472ccae10..137bffdd24a 100644 --- a/scripts/config/historical-pages-to-latest.json +++ b/scripts/config/historical-pages-to-latest.json @@ -1510,5 +1510,6 @@ "0.2": { "noise-management-post-selection": "/" } - } + }, + "qiskit-addon-pna": {} } diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index 39309f12807..a4215712254 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -68,13 +68,13 @@ export class Pkg { "qiskit-addon-sqd-hpc", "qiskit-addon-cutting", "qiskit-addon-utils", - "qiskit-addon-mthree", + // "qiskit-addon-mthree", "qiskit-addon-pna", - "qiskit-addon-slc", - "qiskit-addon-opt-mapper", - "qiskit-fermions", - "qiskit-paulice", - "pauli-prop", + // "qiskit-addon-slc", + // "qiskit-addon-opt-mapper", + // "qiskit-fermions", + // "qiskit-paulice", + // "pauli-prop", ]; static VALID_NAMES = [ From 6ee92874dd698c1c2ac44aaafdb18b9f25832a82 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 22 May 2026 11:45:38 -0500 Subject: [PATCH 079/104] adds slc --- docs/addons/qiskit-addon-slc/_toc.json | 49 +++++ .../qiskit-addon-slc/explanations/index.mdx | 9 + .../addons/qiskit-addon-slc/how-tos/index.mdx | 9 + docs/addons/qiskit-addon-slc/index.mdx | 45 +++++ docs/addons/qiskit-addon-slc/install.mdx | 70 +++++++ docs/api/qiskit-addon-slc/_package.json | 4 + docs/api/qiskit-addon-slc/_toc.json | 33 ++++ docs/api/qiskit-addon-slc/bounds.mdx | 183 ++++++++++++++++++ docs/api/qiskit-addon-slc/index.mdx | 13 ++ docs/api/qiskit-addon-slc/release-notes.mdx | 12 ++ docs/api/qiskit-addon-slc/utils.mdx | 171 ++++++++++++++++ docs/api/qiskit-addon-slc/visualization.mdx | 108 +++++++++++ docs/guides/addons.mdx | 2 +- docs/guides/directed-execution-model.mdx | 2 +- docs/guides/error-mitigation-overview.mdx | 2 +- docs/guides/latest-updates.mdx | 2 +- public/docs/api/qiskit-addon-slc/objects.inv | Bin 0 -> 1199 bytes scripts/config/api-html-artifacts.json | 3 + .../config/historical-pages-to-latest.json | 3 +- scripts/js/lib/api/Pkg.ts | 2 +- 20 files changed, 716 insertions(+), 6 deletions(-) create mode 100644 docs/addons/qiskit-addon-slc/_toc.json create mode 100644 docs/addons/qiskit-addon-slc/explanations/index.mdx create mode 100644 docs/addons/qiskit-addon-slc/how-tos/index.mdx create mode 100644 docs/addons/qiskit-addon-slc/index.mdx create mode 100644 docs/addons/qiskit-addon-slc/install.mdx create mode 100644 docs/api/qiskit-addon-slc/_package.json create mode 100644 docs/api/qiskit-addon-slc/_toc.json create mode 100644 docs/api/qiskit-addon-slc/bounds.mdx create mode 100644 docs/api/qiskit-addon-slc/index.mdx create mode 100644 docs/api/qiskit-addon-slc/release-notes.mdx create mode 100644 docs/api/qiskit-addon-slc/utils.mdx create mode 100644 docs/api/qiskit-addon-slc/visualization.mdx create mode 100644 public/docs/api/qiskit-addon-slc/objects.inv diff --git a/docs/addons/qiskit-addon-slc/_toc.json b/docs/addons/qiskit-addon-slc/_toc.json new file mode 100644 index 00000000000..746d0699392 --- /dev/null +++ b/docs/addons/qiskit-addon-slc/_toc.json @@ -0,0 +1,49 @@ +{ + "parentUrl": "/docs/guides/addons", + "parentLabel": "Documentation", + "title": "Shaded lightcones 0.1.0", + "collapsed": true, + "children": [ + { + "title": "", + "children": [ + { + "title": "Home", + "url": "/docs/addons/qiskit-addon-slc" + }, + { + "title": "Installation instructions", + "url": "/docs/addons/qiskit-addon-slc/install" + }, + { + "title": "Guides", + "children": [ + { + "title": "Explanations", + "url": "/docs/addons/qiskit-addon-slc/explanations/index" + }, + { + "title": "How-To Guides", + "url": "/docs/addons/qiskit-addon-slc/how-tos/index" + } + ] + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-slc" + } + ], + "collapsible": false + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Shaded lightcones API reference", + "url": "/docs/api/qiskit-addon-slc" + } + ] + } + ] +} diff --git a/docs/addons/qiskit-addon-slc/explanations/index.mdx b/docs/addons/qiskit-addon-slc/explanations/index.mdx new file mode 100644 index 00000000000..581c6bd4124 --- /dev/null +++ b/docs/addons/qiskit-addon-slc/explanations/index.mdx @@ -0,0 +1,9 @@ +--- +title: "Explanations" +description: "Explanations for the lastest version of Shaded lightcones" +--- + +# Explanations + +There currently exists no additional content for this page. + diff --git a/docs/addons/qiskit-addon-slc/how-tos/index.mdx b/docs/addons/qiskit-addon-slc/how-tos/index.mdx new file mode 100644 index 00000000000..feac3b47752 --- /dev/null +++ b/docs/addons/qiskit-addon-slc/how-tos/index.mdx @@ -0,0 +1,9 @@ +--- +title: "How-To Guides" +description: "How-To Guides for the lastest version of Shaded lightcones" +--- + +# How-To Guides + +There currently exists no additional content for this page. + diff --git a/docs/addons/qiskit-addon-slc/index.mdx b/docs/addons/qiskit-addon-slc/index.mdx new file mode 100644 index 00000000000..a91d7832b28 --- /dev/null +++ b/docs/addons/qiskit-addon-slc/index.mdx @@ -0,0 +1,45 @@ +--- +title: "Qiskit addon: shaded lightcones (SLC)" +description: "Documentation for the latest version of Shaded lightcones" +--- + +# Qiskit addon: shaded lightcones (SLC) + +[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. + +This package contains the Qiskit addon for shaded lightcones (SLC). These can be used to reduce the sampling overhead of PEC. + +## Documentation + +All documentation is available [here](/docs/addons/qiskit-addon-slc/). + +## Installation + +We encourage installing this package via `pip`, when possible: + +```bash +pip install qiskit-addon-slc +``` + +For more installation information refer to the [installation instructions](install) in the documentation. + +## Deprecation Policy + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [release notes](/docs/api/qiskit-addon-slc/release-notes). + +## Contributing + +The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-slc). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-slc/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). + +We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-slc/issues/new/choose) for tracking requests and bugs. + +## References + +1. 1. Eddins, et al. [Lightcone shading for classically accelerated quantum error mitigation](https://arxiv.org/abs/2409.04401v1), arXiv:2409.04401v1 \[quant-ph]. + +## License + +[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-slc/blob/main/LICENSE.txt) + diff --git a/docs/addons/qiskit-addon-slc/install.mdx b/docs/addons/qiskit-addon-slc/install.mdx new file mode 100644 index 00000000000..d07e58abbc0 --- /dev/null +++ b/docs/addons/qiskit-addon-slc/install.mdx @@ -0,0 +1,70 @@ +--- +title: "Installation Instructions" +description: "Installation Instructions for the lastest version of Shaded lightcones" +--- + +# Installation Instructions + +Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: + +* [Option 1: Install from PyPI](#option-1) +* [Option 2: Install from Source](#option-2) + +## Pre-Installation + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +<span id="option-1" /> + +## Option 1: Install from PyPI + +The most straightforward way to install the `qiskit-addon-slc` package is via `PyPI`. + +```sh +pip install 'qiskit-addon-slc' +``` + +<span id="option-2" /> + +## Option 2: Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +If so, the first step is to clone the `qiskit-addon-slc` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-slc.git +``` + +Next, upgrade `pip` and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-slc +``` + +The next step is to install `qiskit-addon-slc` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab +``` + diff --git a/docs/api/qiskit-addon-slc/_package.json b/docs/api/qiskit-addon-slc/_package.json new file mode 100644 index 00000000000..961052ce099 --- /dev/null +++ b/docs/api/qiskit-addon-slc/_package.json @@ -0,0 +1,4 @@ +{ + "name": "qiskit-addon-slc", + "version": "0.1.0" +} diff --git a/docs/api/qiskit-addon-slc/_toc.json b/docs/api/qiskit-addon-slc/_toc.json new file mode 100644 index 00000000000..acabed988b1 --- /dev/null +++ b/docs/api/qiskit-addon-slc/_toc.json @@ -0,0 +1,33 @@ +{ + "title": "Shaded lightcones", + "children": [ + { + "title": "API index", + "url": "/docs/api/qiskit-addon-slc" + }, + { + "title": "Release notes", + "useDivider": true, + "url": "/docs/api/qiskit-addon-slc/release-notes" + }, + { + "title": "qiskit_addon_slc.bounds", + "url": "/docs/api/qiskit-addon-slc/bounds", + "untranslatable": true + }, + { + "title": "qiskit_addon_slc.utils", + "url": "/docs/api/qiskit-addon-slc/utils", + "untranslatable": true + }, + { + "title": "qiskit_addon_slc.visualization", + "url": "/docs/api/qiskit-addon-slc/visualization", + "untranslatable": true + } + ], + "collapsed": true, + "untranslatable": true, + "parentUrl": "/docs/addons/qiskit-addon-slc", + "parentLabel": "Shaded lightcones" +} diff --git a/docs/api/qiskit-addon-slc/bounds.mdx b/docs/api/qiskit-addon-slc/bounds.mdx new file mode 100644 index 00000000000..8fbf0b14abc --- /dev/null +++ b/docs/api/qiskit-addon-slc/bounds.mdx @@ -0,0 +1,183 @@ +--- +title: bounds (latest version) +description: API reference for qiskit_addon_slc.bounds in the latest version of qiskit-addon-slc +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_addon_slc.bounds +--- + +<span id="module-qiskit_addon_slc.bounds" /> + +<span id="bounds-qiskit-addon-slc-bounds" /> + +# Bounds + +`qiskit_addon_slc.bounds` + +Bound computation functions. + +This module provides various functions for computing the error bounds that make up a shaded lightcone. + +### compute\_forward\_bounds + +<Function id="qiskit_addon_slc.bounds.compute_forward_bounds" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/bounds/forward.py#L192-L283" signature="compute_forward_bounds(circuit, noise_model_paulis, /, observable, *, evolution_max_terms=1000000, eigval_max_qubits=14, atol=1e-08, num_processes=1, timeout=None)"> + Compute the forward-evolved unequal-time commutator bounds. + + Starting at the end of the circuit, compute the forward-evolved unequal-time commutator bounds for all Pauli error terms of each noisy layer in the target circuit. + + That is, compute $\| \left[ E_F, A_F \right] \|_2$ for all error terms, $E_F$, where $A_F$ is the target `observable` to be measured on `circuit`. + + The error terms, $E_I$, are dictated by `noise_model_paulis`. This dictionary maps noise model identifiers (`samplomatic.InjectNoise.ref`) to a list of Pauli error terms. The corresponding terms will be used whenever a [`BoxOp`](/docs/api/qiskit/qiskit.circuit.BoxOp) with a matching [`InjectNoise`](/docs/addons/samplomatic/api/auto/samplomatic-inject-noise#samplomatic.InjectNoise "(in samplomatic)") annotation is encountered during the iteration over `circuit`. + + **Parameters** + + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the target circuit. + * **noise\_model\_paulis** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*QubitSparsePauliList*](/docs/api/qiskit/qiskit.quantum_info.QubitSparsePauliList)*]*) – the Pauli error terms to consider for each noise model. + * **observable** ([*Pauli*](/docs/api/qiskit/qiskit.quantum_info.Pauli) *|*[*PauliList*](/docs/api/qiskit/qiskit.quantum_info.PauliList) *|*[*SparseObservable*](/docs/api/qiskit/qiskit.quantum_info.SparseObservable) *|*[*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)) – the target observable to be measured at the end of the circuit. + * **evolution\_max\_terms** ([*int*](https://docs.python.org/3/library/functions.html#int)) – the maximum number of operator terms to keep track of during the evolution. + * **eigval\_max\_qubits** ([*int*](https://docs.python.org/3/library/functions.html#int)) – the maximum number of qubits of a commutator for which the eigenvalue will still be attempted to be computed. When this value is exceeded, the bound is approximated via a simpler and more loose triangle inequality. + * **atol** ([*float*](https://docs.python.org/3/library/functions.html#float)) – the absolute tolerance used for trimming terms from the commutator and for detecting convergence of the commutator’s eigenvalue. + * **num\_processes** ([*int*](https://docs.python.org/3/library/functions.html#int)) – the number of parallel processes to use. + * **timeout** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – an optional timeout (in seconds) after which all remaining layers are filled with trivial numerical bounds of `2.0`. Note, that this is not a strict timeout and the layer being processed at the time of reaching this timeout will complete normally. + + **Returns** + + The unequal-time commutator bound. + + **Raises** + + [**NotImplementedError**](https://docs.python.org/3/library/exceptions.html#NotImplementedError) – when the `observable` contains more than a single Pauli term. If you run into this, you will need to call this function for each target Pauli separately. + + **Return type** + + [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[str](https://docs.python.org/3/library/stdtypes.html#str), [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)] +</Function> + +### tighten\_with\_speed\_limit + +<Function id="qiskit_addon_slc.bounds.tighten_with_speed_limit" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/bounds/speed_limit.py#L226-L318" signature="tighten_with_speed_limit(bounds, circuit, noise_model_paulis, /, observable)"> + Tighten the provided bounds using limits on the speed of information propagation. + + Inspired by the ideas behind the Lieb-Robinson bounds, this function leverages limits on the speed of information propagation to tighten previously computed forward-evolved unequal-time commutator bounds (see also [`compute_forward_bounds()`](#qiskit_addon_slc.bounds.compute_forward_bounds "qiskit_addon_slc.bounds.compute_forward_bounds")). + + **Parameters** + + * **bounds** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)*]*) – the previously computed forward-evolved unequal-time commutator bounds. + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the target circuit. + * **noise\_model\_paulis** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*QubitSparsePauliList*](/docs/api/qiskit/qiskit.quantum_info.QubitSparsePauliList)*]*) – the Pauli error terms to consider for each noise model. + * **observable** ([*Pauli*](/docs/api/qiskit/qiskit.quantum_info.Pauli) *|*[*PauliList*](/docs/api/qiskit/qiskit.quantum_info.PauliList) *|*[*SparseObservable*](/docs/api/qiskit/qiskit.quantum_info.SparseObservable) *|*[*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)) – the target observable to be measured at the end of the circuit. + + **Returns** + + A tightened copy of the unequal-time commutator bounds. + + **Raises** + + * [**NotImplementedError**](https://docs.python.org/3/library/exceptions.html#NotImplementedError) – when the `observable` contains more than a single Pauli term. If you run into this, you will need to call this function for each target Pauli separately. + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – when encountering a gate that acts on more than 2 qubits. + + **Return type** + + [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[str](https://docs.python.org/3/library/stdtypes.html#str), [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)] +</Function> + +### compute\_backward\_bounds + +<Function id="qiskit_addon_slc.bounds.compute_backward_bounds" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/bounds/backward.py#L100-L165" signature="compute_backward_bounds(circuit, noise_model_paulis, /, *, evolution_max_terms=1000000, num_processes=1, timeout=None)"> + Compute the backward-evolved unequal-time commutator bounds. + + Starting at the beginning of the circuit, compute the backward-evolved unequal-time commutator bounds for all Pauli error terms of each noisy layer in the target circuit. + + That is, compute $\| \left[ E_I, \rho_I \right] \|_1$ (using the Schatten 1-norm aka nuclear norm) for all error terms, $E_I$, where $\rho_I$ is assumed to be the all-zero state, $\ket{0 \ldots 0}$, on all active qubits in `circuit`. + + The error terms, $E_I$, are dictated by `noise_model_paulis`. This dictionary maps noise model identifiers (`samplomatic.InjectNoise.ref`) to a list of Pauli error terms. The corresponding terms will be used whenever a [`BoxOp`](/docs/api/qiskit/qiskit.circuit.BoxOp) with a matching [`InjectNoise`](/docs/addons/samplomatic/api/auto/samplomatic-inject-noise#samplomatic.InjectNoise "(in samplomatic)") annotation is encountered during the iteration over `circuit`. + + <Admonition title="Caution" type="note"> + Before computing the bounds, this function removes **all** `Measure` operations from `circuit`. This is required because the circuit is being inverted before being processed in reverse order, which allows the backward evolution to be treated like a forward evolution (in the inverted circuit). + </Admonition> + + **Parameters** + + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the target circuit. + * **noise\_model\_paulis** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*QubitSparsePauliList*](/docs/api/qiskit/qiskit.quantum_info.QubitSparsePauliList)*]*) – the Pauli error terms to consider for each noise model. + * **evolution\_max\_terms** ([*int*](https://docs.python.org/3/library/functions.html#int)) – the maximum number of operator terms to keep track of during the evolution. (If the operator exceeds this size, the smallest terms are truncated). + * **num\_processes** ([*int*](https://docs.python.org/3/library/functions.html#int)) – the number of parallel processes to use. + * **timeout** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – an optional timeout (in seconds) after which all remaining layers are filled with trivial numerical bounds of `2.0`. Note, that this is not a strict timeout and the layer being processed at the time of reaching this timeout will complete normally. + + **Returns** + + The backward-evolved unequal-time commutator bounds. + + **Return type** + + [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[str](https://docs.python.org/3/library/stdtypes.html#str), [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)] +</Function> + +### merge\_bounds + +<Function id="qiskit_addon_slc.bounds.merge_bounds" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/bounds/merge.py#L36-L162" signature="merge_bounds(circuit, forward_bounds, backward_bounds, /, noise_rates=None, *, is_clifford_circuit=False)"> + Merge forward and backward bounds. + + The layer at which the switch from using backward bounds to using forward bounds takes place will be the same for all qubits. It is determined by taking into account the provided learned `noise_rates`. If these are not provided, uniform noise rates are assumed. While this is an unrealistic assumption, previewing the merged bounds may still be useful. + + **Parameters** + + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the target circuit. + * **forward\_bounds** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)*] | None*) – the forward bounds (see also [`compute_forward_bounds()`](#qiskit_addon_slc.bounds.compute_forward_bounds "qiskit_addon_slc.bounds.compute_forward_bounds")). + * **backward\_bounds** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)*] | None*) – the backward bounds (see also [`compute_backward_bounds()`](#qiskit_addon_slc.bounds.compute_backward_bounds "qiskit_addon_slc.bounds.compute_backward_bounds")). + * **noise\_rates** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap) *| None] | None*) – the noise rates learned on the target backend. + * **is\_clifford\_circuit** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – whether the target circuit is fully Clifford. + + **Returns** + + The merged bounds. + + **Raises** + + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – when both provided bounds are `None`. + * [**KeyError**](https://docs.python.org/3/library/exceptions.html#KeyError) – when the `bounds` contain an `InjectNoise.modifier_ref` key which does not occur in the target `circuit` or whose `InjectNoise.ref` is not found. + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if the noise model Pauli terms whose bounds are computed for a given `InjectNoise.modifier_ref` do not match between the `forward_bounds` and `backward_bounds`. + * [**NotImplementedError**](https://docs.python.org/3/library/exceptions.html#NotImplementedError) – when `is_clifford_circuit` is `True`. + + **Return type** + + [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[str](https://docs.python.org/3/library/stdtypes.html#str), [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)] | None +</Function> + +### compute\_local\_scales + +<Function id="qiskit_addon_slc.bounds.compute_local_scales" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/bounds/local_scales.py#L35-L163" signature="compute_local_scales(circuit, bounds, /, noise_rates, *, sampling_cost_budget=inf, bias_tolerance=0.0)"> + Computes the `local_scales` argument to a [`Samplex`](/docs/addons/samplomatic/api/auto/samplomatic-samplex-samplex#samplomatic.samplex.Samplex "(in samplomatic)"). + + This `local_scales` argument is used to specify which individual error terms to mitigate. + + Either the `sampling_cost_budget` or `bias_tolerance` must be specified. The former puts an upper bound on the sampling cost while the latter puts an upper bound on the remaining bias to tolerate. + + <Admonition title="Note" type="note"> + If the order of Pauli terms in `bounds` and `noise_rates` do not match, the output of this function will assume the order set forth by `noise_rates` in order to ensure that the scales are compatible with the rates that will also be provided to the `QuantumProgram`. + </Admonition> + + **Parameters** + + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the target circuit. + * **bounds** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)*]*) – the shaded lightcone bounds. + * **noise\_rates** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap) *| None]*) – the learned noise model rates. + * **sampling\_cost\_budget** ([*float*](https://docs.python.org/3/library/functions.html#float)) – the maximum sampling cost to allow. + * **bias\_tolerance** ([*float*](https://docs.python.org/3/library/functions.html#float)) – the maximum bias to tolerate. + + **Returns** + + * the `local_scales` dictionary to be provided as the direct input to the [`samplomatic.samplex.Samplex.inputs()`](/docs/addons/samplomatic/api/auto/samplomatic-samplex-samplex#samplomatic.samplex.Samplex.inputs "(in samplomatic)"). + * the sampling cost overhead ($\gamma^2$) required to perform the sampling of `local_scales`. + * the remaining bias on expectation values computed with these bounds. + + **Return type** + + A tuple of length 3, the items of which are + + **Raises** + + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if non-default values are provided for both, the `sampling_cost_budget` and `bias_tolerance`. + * [**KeyError**](https://docs.python.org/3/library/exceptions.html#KeyError) – if `noise_rates` is missing an entry for any noise model identifier (`InjectNoise.ref`) encountered in `circuit`. +</Function> + diff --git a/docs/api/qiskit-addon-slc/index.mdx b/docs/api/qiskit-addon-slc/index.mdx new file mode 100644 index 00000000000..c5e48e0372e --- /dev/null +++ b/docs/api/qiskit-addon-slc/index.mdx @@ -0,0 +1,13 @@ +--- +title: Shaded lightcones API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-slc. +--- + +<span id="qiskit-addon-slc-api-reference" /> + +# `qiskit-addon-slc` API reference + +* [Bounds (`qiskit_addon_slc.bounds`)](bounds) +* [Utilities (`qiskit_addon_slc.utils`)](utils) +* [Visualization Tools (`qiskit_addon_slc.visualization`)](visualization) + diff --git a/docs/api/qiskit-addon-slc/release-notes.mdx b/docs/api/qiskit-addon-slc/release-notes.mdx new file mode 100644 index 00000000000..18f96f5e807 --- /dev/null +++ b/docs/api/qiskit-addon-slc/release-notes.mdx @@ -0,0 +1,12 @@ +--- +title: Shaded lightcones release notes +description: Changes made to Shaded lightcones +in_page_toc_max_heading_level: 2 +--- + +<span id="release-notes" /> + +<span id="id1" /> + +# Shaded lightcones release notes + diff --git a/docs/api/qiskit-addon-slc/utils.mdx b/docs/api/qiskit-addon-slc/utils.mdx new file mode 100644 index 00000000000..f1c83659c75 --- /dev/null +++ b/docs/api/qiskit-addon-slc/utils.mdx @@ -0,0 +1,171 @@ +--- +title: utils (latest version) +description: API reference for qiskit_addon_slc.utils in the latest version of qiskit-addon-slc +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_addon_slc.utils +--- + +<span id="module-qiskit_addon_slc.utils" /> + +<span id="utilities-qiskit-addon-slc-utils" /> + +# Utilities + +`qiskit_addon_slc.utils` + +Various utilities. + +This module provides a number of utility functions. Some of these exist only temporarily to work around open issues of the Qiskit SDK. When this is the case, they are marked as such and may be removed without deprecation or further notice. + +### find\_indices + +<Function id="qiskit_addon_slc.utils.find_indices" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/utils/find_indices.py#L28-L66" signature="find_indices(circuit, bits_or_instruction, /)"> + Find the qubit-indices of the provided bit indices or circuit instructions. + + <Admonition title="Caution" type="note"> + This function is not considered part of the stable API! It will get removed without warning or deprecation when the same functionality is supported natively by the Qiskit SDK. See [this issue](https://github.com/Qiskit/qiskit/issues/14558) for more details. + </Admonition> + + **Parameters** + + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the quantum circuit whose qubit indices to find. + * **bits\_or\_instruction** ([*Bit*](/docs/api/qiskit/circuit#qiskit.circuit.Bit) *|*[*CircuitInstruction*](/docs/api/qiskit/qiskit.circuit.CircuitInstruction) *|*[*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*Bit*](/docs/api/qiskit/circuit#qiskit.circuit.Bit)*]*) – the bits whose indices to find. If this is a [`CircuitInstruction`](/docs/api/qiskit/qiskit.circuit.CircuitInstruction), the qubits which this instruction acts upon are used. + + **Returns** + + The indices of the queried bits in the circuit’s registers. If a single bit object was provided, a single `int` is returned for its index. Otherwise the return type will be a `list[int]` whose length equals the number of provided bits. + + **Raises** + + [**TypeError**](https://docs.python.org/3/library/exceptions.html#TypeError) – when an unexpected type of `bits_or_instruction` gets provided. + + **Return type** + + [int](https://docs.python.org/3/library/functions.html#int) | [list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)] +</Function> + +### generate\_noise\_model\_paulis + +<Function id="qiskit_addon_slc.utils.generate_noise_model_paulis" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/utils/noise_model_paulis.py#L34-L96" signature="generate_noise_model_paulis(instructions, coupling_map=None, circuit=None)"> + Generate the 1- and 2-weight Pauli terms for each of the unique 2q layer boxes provided. + + **Parameters** + + * **instructions** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*CircuitInstruction*](/docs/api/qiskit/qiskit.circuit.CircuitInstruction)*]*) – the output of [`find_unique_box_instructions()`](/docs/addons/samplomatic/api/auto/samplomatic-utils-find-unique-box-instructions#samplomatic.utils.find_unique_box_instructions "(in samplomatic)"). Any of the provided instructions are assumed to either consists of only measurement gates or correspond to a layer of 2-qubit gate instructions. For the former, the generated noise model will contain only single-qubit `X` errors, for the latter all 1- and 2-weight Pauli errors on the reduced coupling map will be included. + * **coupling\_map** ([*CouplingMap*](/docs/api/qiskit/qiskit.transpiler.CouplingMap) *| None*) – the coupling map of the backend on which the instructions have been laid out. If this is `None`, a 1d line of qubits is assumed. + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) *| None*) – the transpiled circuit which has been laid out on the provided coupling map. This may only be `None` when the `coupling_map` is also `None`. + + **Returns** + + A dictionary mapping the `ref` attributes of the [`InjectNoise`](/docs/addons/samplomatic/api/auto/samplomatic-inject-noise#samplomatic.InjectNoise "(in samplomatic)") annotation of each unique Box to the 1- and 2-weight Pauli terms whose errors are learned for this box. + + **Return type** + + [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[str](https://docs.python.org/3/library/stdtypes.html#str), [*QubitSparsePauliList*](/docs/api/qiskit/qiskit.quantum_info.QubitSparsePauliList)] +</Function> + +### get\_extremal\_eigenvalue + +<Function id="qiskit_addon_slc.utils.get_extremal_eigenvalue" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/utils/davidson.py#L26-L78" signature="get_extremal_eigenvalue(spo, **kwargs)"> + Finds the extremal eigenvalue of the provided operator. + + This converts the provided operator to a sparse matrix whose minimal eigenvalue is required. + + <Admonition title="Note" type="note"> + The current implementation is definitely not optimized in terms of performance. + </Admonition> + + **Parameters** + + * **spo** ([*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)) – the operator whose minimal eigenvalue to find. + + * **kwargs** – + + additional keyword arguments for [`davidson1()`](https://pyscf.org/pyscf_api_docs/pyscf.lib.html#pyscf.lib.linalg_helper.davidson1). When not specified otherwise, the following defaults will be used: + + * tol: 1e-6 + * max\_cycle: 500 + * max\_space: 12 + * lindep: 1e-11 + * max\_memory: 2000 + + Other values will default to PySCF’s default values. + + **Returns** + + A pair indicating whether the Davidson algorithm has converged and the obtained minimal eigenvalue. + + **Return type** + + [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[bool](https://docs.python.org/3/library/functions.html#bool), [float](https://docs.python.org/3/library/functions.html#float)] +</Function> + +### iter\_circuit + +<Function id="qiskit_addon_slc.utils.iter_circuit" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/utils/circuit_iter.py#L36-L111" signature="iter_circuit(circuit, *, reverse=False, log_process=True)"> + Iterates over the instructions in a circuit. + + <Admonition title="Note" type="note"> + This function recurses into [`BoxOp`](/docs/api/qiskit/qiskit.circuit.BoxOp) instructions. + </Admonition> + + <Admonition title="Note" type="note"> + Barriers in the circuit are being ignored. + </Admonition> + + **Parameters** + + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the circuit to iterate over. + * **reverse** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – whether to iterate the circuit in reverse order. + * **log\_process** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – whether to log process. + + **Yields** + + Tuples of length four, consisting of the encountered circuit instruction, the [canonical qubit indices](/docs/addons/samplomatic/guides/samplex-io#qubit-ordering-convention) (i.e. the integer indices of the acted-upon qubits in the context of the input `circuit`), the [`InjectNoise`](/docs/addons/samplomatic/api/auto/samplomatic-inject-noise#samplomatic.InjectNoise "(in samplomatic)") attributes: `modifier_ref` and `ref`. The latter two items may be `None` indicating a circuit instruction that was **not** part of an unrolled [`BoxOp`](/docs/api/qiskit/qiskit.circuit.BoxOp). + + **Return type** + + [*Generator*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator)\[[tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[*CircuitInstruction*](/docs/api/qiskit/qiskit.circuit.CircuitInstruction), [list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)], [str](https://docs.python.org/3/library/stdtypes.html#str) | None, [str](https://docs.python.org/3/library/stdtypes.html#str) | None]] +</Function> + +### map\_modifier\_ref\_to\_ref + +<Function id="qiskit_addon_slc.utils.map_modifier_ref_to_ref" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/utils/annotations.py#L26-L47" signature="map_modifier_ref_to_ref(circuit)"> + Iterate a circuit and map [`InjectNoise`](/docs/addons/samplomatic/api/auto/samplomatic-inject-noise#samplomatic.InjectNoise "(in samplomatic)") annotation references. + + **Parameters** + + **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the circuit to iterate over. + + **Returns** + + A dictionary mapping each `InjectNoise.modifier_ref` to its `InjectNoise.ref`. + + **Return type** + + [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[str](https://docs.python.org/3/library/stdtypes.html#str), [str](https://docs.python.org/3/library/stdtypes.html#str)] +</Function> + +### remove\_measure + +<Function id="qiskit_addon_slc.utils.remove_measure" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/utils/remove_measure.py#L30-L44" signature="remove_measure(circuit)"> + Remove any [`Measure`](/docs/api/qiskit/circuit#qiskit.circuit.Measure) operations from the provided circuit. + + <Admonition title="Note" type="note"> + This function recurses into [`BoxOp`](/docs/api/qiskit/qiskit.circuit.BoxOp) instructions. + </Admonition> + + **Parameters** + + **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the circuit whose measurements to remove. + + **Returns** + + The circuit without any Measure operations. + + **Return type** + + [*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) +</Function> + diff --git a/docs/api/qiskit-addon-slc/visualization.mdx b/docs/api/qiskit-addon-slc/visualization.mdx new file mode 100644 index 00000000000..6c71d693d81 --- /dev/null +++ b/docs/api/qiskit-addon-slc/visualization.mdx @@ -0,0 +1,108 @@ +--- +title: visualization (latest version) +description: API reference for qiskit_addon_slc.visualization in the latest version of qiskit-addon-slc +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_addon_slc.visualization +--- + +<span id="module-qiskit_addon_slc.visualization" /> + +<span id="visualization-tools-qiskit-addon-slc-visualization" /> + +# Visualization Tools + +`qiskit_addon_slc.visualization` + +Visualization methods for shaded lightcones. + +This module provides visualization methods for shaded lightcones. + +### draw\_shaded\_lightcone + +<Function id="qiskit_addon_slc.visualization.draw_shaded_lightcone" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/visualization/__init__.py#L44-L76" signature="draw_shaded_lightcone(circuit, bounds, noise_model_paulis, *, pauli_filter=None, include_empty_boxes=True, **rendering_kwargs)"> + Draws a shaded lightcone. + + First, the provided `bounds` are accumulated and filtered according to `pauli_filter`. See also [`accumulate_filtered_bounds()`](#qiskit_addon_slc.visualization.accumulate_filtered_bounds "qiskit_addon_slc.visualization.accumulate_filtered_bounds"). Then, the resulting bounds are overlaid onto the provided `circuit` (see [`overlay_bounds_onto_circuit()`](#qiskit_addon_slc.visualization.overlay_bounds_onto_circuit "qiskit_addon_slc.visualization.overlay_bounds_onto_circuit")) and subsequently rendered (see [`render_bounds()`](#qiskit_addon_slc.visualization.render_bounds "qiskit_addon_slc.visualization.render_bounds")). + + **Parameters** + + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the circuit whose shaded lightcone to draw. + * **bounds** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)*]*) – the bounds to use for the shaded lightcone. + * **noise\_model\_paulis** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*QubitSparsePauliList*](/docs/api/qiskit/qiskit.quantum_info.QubitSparsePauliList)*]*) – the Pauli error terms of the circuit’s noise models. + * **pauli\_filter** ([*Pauli*](/docs/api/qiskit/qiskit.quantum_info.Pauli) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – the optional Pauli type by which the bounds were filtered. + * **include\_empty\_boxes** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – whether to include empty boxes or not. + * **rendering\_kwargs** – any additional keyword arguments are forwarded to `QuantumCircuit.draw()`. + + **Returns** + + The `mpl` figure. + + **Return type** + + *Figure* +</Function> + +### accumulate\_filtered\_bounds + +<Function id="qiskit_addon_slc.visualization.accumulate_filtered_bounds" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/visualization/accumulate_filtered_bounds.py#L31-L98" signature="accumulate_filtered_bounds(circuit, bounds, noise_model_paulis, pauli_filter=None)"> + Accumulates the bound values filtered by a specified Pauli type. + + **Parameters** + + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the circuit whose bounds to accumulate. + * **bounds** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)*]*) – the bounds whose values to accumulate. + * **noise\_model\_paulis** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*QubitSparsePauliList*](/docs/api/qiskit/qiskit.quantum_info.QubitSparsePauliList)*]*) – the Pauli noise terms for each noise model. + * **pauli\_filter** ([*Pauli*](/docs/api/qiskit/qiskit.quantum_info.Pauli) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – the optional Pauli type to filter by. It behaves as follows: - `None`: accumulates all noise term bounds of equal support. - `int`: only accumulates noise term bounds of the specified Pauli weight. - `str`: selects this specific Pauli noise term. - `Pauli`: selects this specific Pauli noise term. + + **Returns** + + A nested dictionary. The outer most key is the `InjectNoise.modifier_ref` (same as the original `bounds`). The next key is the support of the accumulated and filtered bound value (i.e. a tuple of integers). This maps to the actual bound value as a float. + + **Return type** + + [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[str](https://docs.python.org/3/library/stdtypes.html#str), [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[int](https://docs.python.org/3/library/functions.html#int), …], [float](https://docs.python.org/3/library/functions.html#float)]] +</Function> + +### overlay\_bounds\_onto\_circuit + +<Function id="qiskit_addon_slc.visualization.overlay_bounds_onto_circuit" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/visualization/overlay_bounds.py#L31-L79" signature="overlay_bounds_onto_circuit(pauli_bounds, circuit, *, include_empty_boxes=True)"> + Overlays the bounds onto the circuit. + + This produces a new “QuantumCircuit” with “fake” gates reproducing those `BoxOp` instructions from the original `circuit` that had an `InjectNoise` annotation. Those boxes are replaced by visual representations of the computed bounds for Pauli errors of their respective support (see also [`accumulate_filtered_bounds()`](#qiskit_addon_slc.visualization.accumulate_filtered_bounds "qiskit_addon_slc.visualization.accumulate_filtered_bounds")). + + **Parameters** + + * **pauli\_bounds** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*, ...],* [*float*](https://docs.python.org/3/library/functions.html#float)*]]*) – the accumulated and filtered bounds. + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the circuit on which to overlay the bounds. + * **include\_empty\_boxes** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – whether to include empty boxes or not. + + **Returns** + + A “fake” quantum circuit with “Gate” instructions displaying the computed bound values. The returned quantum circuit’s `QuantumCircuit.metadata` contains the maximum bound value under “max\_bound”. + + **Return type** + + [*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) +</Function> + +### render\_bounds + +<Function id="qiskit_addon_slc.visualization.render_bounds" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/visualization/render_bounds.py#L31-L80" signature="render_bounds(bounds_circuit, *, pauli_filter=None, **kwargs)"> + Renders a quantum circuit with overlaid bounds and according styling. + + **Parameters** + + * **bounds\_circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – the quantum circuit with overlaid bounds. See also [`overlay_bounds_onto_circuit()`](#qiskit_addon_slc.visualization.overlay_bounds_onto_circuit "qiskit_addon_slc.visualization.overlay_bounds_onto_circuit"). + * **pauli\_filter** ([*Pauli*](/docs/api/qiskit/qiskit.quantum_info.Pauli) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – the optional Pauli type by which the bounds were filtered. This will be indicated in the produced figure’s title. + * **kwargs** – any additional keyword arguments are forwarded to `QuantumCircuit.draw()`. + + **Returns** + + The `mpl` figure. + + **Return type** + + *Figure* +</Function> + diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index fe3a74ef45a..04e4f76800d 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -82,7 +82,7 @@ Use the following addons to manage noise when building quantum workloads that es <Card title="Shaded lightcones" description="Reduce probabilistic error cancellation (PEC) sampling overhead by removing noise model terms that have low impact on the observable estimation." - href="https://qiskit.github.io/qiskit-addon-slc/" + href="/docs/addons/qiskit-addon-slc/" analyticsName="Documentation page: SLC" linkText="Browse documentation" /> diff --git a/docs/guides/directed-execution-model.mdx b/docs/guides/directed-execution-model.mdx index fd46882ebd1..a978418391f 100644 --- a/docs/guides/directed-execution-model.mdx +++ b/docs/guides/directed-execution-model.mdx @@ -69,5 +69,5 @@ To learn more about NoiseLearnerV3, refer to its [API reference](https://qiskit. ## Next steps <Admonition type="tip" title="Recommendations"> -- Check out two Qiskit addons, [Shaded lightcones](https://qiskit.github.io/qiskit-addon-slc/) and [Propagated noise absorption](/docs/addons/qiskit-addon-pna), which are built on top of this execution model. +- Check out two Qiskit addons, [Shaded lightcones](/docs/addons/qiskit-addon-slc/) and [Propagated noise absorption](/docs/addons/qiskit-addon-pna), which are built on top of this execution model. </Admonition> \ No newline at end of file diff --git a/docs/guides/error-mitigation-overview.mdx b/docs/guides/error-mitigation-overview.mdx index b5911f603fc..729c455eb50 100644 --- a/docs/guides/error-mitigation-overview.mdx +++ b/docs/guides/error-mitigation-overview.mdx @@ -81,7 +81,7 @@ When executing quantum workloads, there are multiple ways to reduce the impact o <Card title="PEC with shaded lightcones" description="A modified PEC technique that uses Pauli propagation to reduce the number of error terms accounted for in a noise model according to the specifics of the target observable." - href="https://qiskit.github.io/qiskit-addon-slc/" + href="/docs/addons/qiskit-addon-slc/" analyticsName="Documentation page: Shaded lightcones" linkText="Browse documentation" /> diff --git a/docs/guides/latest-updates.mdx b/docs/guides/latest-updates.mdx index a9634d73209..174c9184242 100644 --- a/docs/guides/latest-updates.mdx +++ b/docs/guides/latest-updates.mdx @@ -316,7 +316,7 @@ Improvements include the following: - [Propagated noise absorption (PNA)](/docs/addons/qiskit-addon-pna/) is a technique for mitigating errors in expectation values by absorbing the inverses of the learned noise channels into observables using Pauli propagation. - - The [Shaded lightcones (SLC)](https://qiskit.github.io/qiskit-addon-slc/) addon uses classical simulations to more tightly bound the sensitivity to errors throughout the circuit. This allows for more efficient, targeted applications of Probabilistic Error Cancellation (PEC) with reduced variance. + - The [Shaded lightcones (SLC)](/docs/addons/qiskit-addon-slc/) addon uses classical simulations to more tightly bound the sensitivity to errors throughout the circuit. This allows for more efficient, targeted applications of Probabilistic Error Cancellation (PEC) with reduced variance. <span id="latest-qiskit-functions"></span> ## Qiskit Functions diff --git a/public/docs/api/qiskit-addon-slc/objects.inv b/public/docs/api/qiskit-addon-slc/objects.inv new file mode 100644 index 0000000000000000000000000000000000000000..f85b65fc9260bf642b35f96698c0a79e91d6f8c8 GIT binary patch literal 1199 zcmV;g1W@}UAX9K?X>NERX>N99Zgg*Qc_4OWa&u{KZXhxWBOp+6Z)#;@bUGkWX>)67 zbRc15WN&UdAaiJ8WMyO^Y-wj`bYpLBWpf}XQ%plC3L_v^WpZ<AZ*Dpu3L_v?Xk{RB zWo=<;Ze(S0Aa7<MbZBXFAZBT7WguyDAY*TBaB^jHb7f>8b#rNMXCQiPX<{x4c-pmC zO>-kB6ukRaP^BE!2jtjC-;&*}#8q1rCA-O$!tf+f%fKwdSXO?0O3X-x(Sw<>l#?{j zue<qv@Iy7<m1kT^YgnVj^4YdVHl*G^R#vte$j_uJT6Ev^Cqi$2;89CqtGe~Tp1JtG z<4&?SwsmWSSJsgGBVP_YPQSKpGW!5KPOh!snl*xJ7@<y(dEyG(28v!2M4rg2&DMiq zJLR{msR1O@s!|>&&7u*3)+?Qkc_=9kc|a`J%1EY+RN}0+I*J64rP+W1$2~A(RRgTD z5;UuMs};^#!WTq5gIDjM;+lclfO+A%g^U1>&5|n*jtS*Ndo~~)V*<$1RJ=a4My(Y% 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z*>TJ``Eok#kc_FXkPsrrSR^oZ$R!6!99TMjGGjPx+c>W7Bz$ba+kT;xS6Fd9^%M`~ N<x*Z6{{hK@)y21sL=FG| literal 0 HcmV?d00001 diff --git a/scripts/config/api-html-artifacts.json b/scripts/config/api-html-artifacts.json index 8a1f816db22..72917cf2ea0 100644 --- a/scripts/config/api-html-artifacts.json +++ b/scripts/config/api-html-artifacts.json @@ -130,5 +130,8 @@ }, "qiskit-addon-pna": { "0.2": "https://ibm.box.com/shared/static/9wxwvchyal0oqkbe46a13d9wzwxi8wxd.zip" + }, + "qiskit-addon-slc": { + "0.1": "https://ibm.box.com/shared/static/87gic8sr1vc85y4g6g1h0x6gk1eb1sqw.zip" } } diff --git a/scripts/config/historical-pages-to-latest.json b/scripts/config/historical-pages-to-latest.json index 137bffdd24a..ec6e565a3e5 100644 --- a/scripts/config/historical-pages-to-latest.json +++ b/scripts/config/historical-pages-to-latest.json @@ -1511,5 +1511,6 @@ "noise-management-post-selection": "/" } }, - "qiskit-addon-pna": {} + "qiskit-addon-pna": {}, + "qiskit-addon-slc": {} } diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index a4215712254..ec32a6cfe4e 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -70,7 +70,7 @@ export class Pkg { "qiskit-addon-utils", // "qiskit-addon-mthree", "qiskit-addon-pna", - // "qiskit-addon-slc", + "qiskit-addon-slc", // "qiskit-addon-opt-mapper", // "qiskit-fermions", // "qiskit-paulice", From 5e32c51b7e57c76f339769c99256e0d6dde380b1 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 22 May 2026 12:48:28 -0500 Subject: [PATCH 080/104] adds opt-mapper --- docs/addons/qiskit-addon-opt-mapper/_toc.json | 61 ++ .../00-migrate-from-qiskit-optimization.ipynb | 108 +++ .../how-tos/01-optimization-problem.ipynb | 888 ++++++++++++++++++ ...converters-for-optimization-problems.ipynb | 842 +++++++++++++++++ .../how-tos/03-validate-with-solvers.ipynb | 395 ++++++++ .../how-tos/04-translate-docplex.ipynb | 260 +++++ .../qiskit-addon-opt-mapper/how-tos/index.mdx | 41 + 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+- scripts/js/lib/api/notebookStages.ts | 18 +- scripts/js/lib/api/objectsInv.ts | 1 + .../__init__.py | 21 + tox.ini | 2 +- 73 files changed, 8956 insertions(+), 5 deletions(-) create mode 100644 docs/addons/qiskit-addon-opt-mapper/_toc.json create mode 100644 docs/addons/qiskit-addon-opt-mapper/how-tos/00-migrate-from-qiskit-optimization.ipynb create mode 100644 docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb create mode 100644 docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb create mode 100644 docs/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers.ipynb create mode 100644 docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb create mode 100644 docs/addons/qiskit-addon-opt-mapper/how-tos/index.mdx create mode 100644 docs/addons/qiskit-addon-opt-mapper/index.mdx create mode 100644 docs/addons/qiskit-addon-opt-mapper/install.mdx create mode 100644 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00000000000..cb7d36142ea --- /dev/null +++ b/docs/addons/qiskit-addon-opt-mapper/_toc.json @@ -0,0 +1,61 @@ +{ + "parentUrl": "/docs/guides/addons", + "parentLabel": "Documentation", + "title": "Optimization mapper 0.1.0", + "collapsed": true, + "children": [ + { + "title": "", + "children": [ + { + "title": "Home", + "url": "/docs/addons/qiskit-addon-opt-mapper" + }, + { + "title": "Installation instructions", + "url": "/docs/addons/qiskit-addon-opt-mapper/install" + }, + { + "title": "Guides", + "children": [ + { + "title": "Migrate from qiskit-optimization to qiskit-addon-opt-mapper", + "url": "/docs/addons/qiskit-addon-opt-mapper/how-tos/00-migrate-from-qiskit-optimization" + }, + { + "title": "Model quantum optimization problems with the OptimizationProblem class", + "url": "/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem" + }, + { + "title": "Convert formulations of an OptimizationProblem", + "url": "/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems" + }, + { + "title": "Validate your modeling with classical solvers", + "url": "/docs/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers" + }, + { + "title": "Translate between OptimizationProblem and Docplex", + "url": "/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex" + } + ] + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-opt-mapper" + } + ], + "collapsible": false + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Optimization mapper API reference", + "url": "/docs/api/qiskit-addon-opt-mapper" + } + ] + } + ] +} diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/00-migrate-from-qiskit-optimization.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/00-migrate-from-qiskit-optimization.ipynb new file mode 100644 index 00000000000..61b32f02969 --- /dev/null +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/00-migrate-from-qiskit-optimization.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Migrate from qiskit-optimization to qiskit-addon-opt-mapper\"\n", + "description: \"Migrate from qiskit-optimization to qiskit-addon-opt-mapper for the latest version of Optimization mapper\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "898eb4ed", + "metadata": {}, + "source": [ + "# Migrate from `qiskit-optimization` to `qiskit-addon-opt-mapper`" + ] + }, + { + "cell_type": "markdown", + "id": "97e86ead", + "metadata": {}, + "source": [ + "This migration guide summarizes the steps required to migrate a workflow built using the `qiskit-optimization` [library](https://github.com/qiskit-community/qiskit-optimization) to the new and improved `qiskit-addon-opt-mapper` library." + ] + }, + { + "cell_type": "markdown", + "id": "2c4ecca4", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "The `qiskit-addon-opt-mapper` library shares a lot of common structure and design with `qiskit-optimization`, with a few key differences that should be taken into account when migrating from one package to the other:\n", + "\n", + "1. The core data structure of `qiskit-optimization` is the `QuadraticProgram`. In `qiskit-addon-opt-mapper`, this structure is now called `OptimizationProblem`. The core methods and attributes remain consistent with the `QuadraticProgram`, but the class is now extended to support higher-order terms in variables, objective functions, and constraints.\n", + "\n", + "2. The new `qiskit-addon-opt-mapper` package is focused on the optimization problem modeling and mapping step, which means there isn't a migration path for the following former `qiskit-optimization` modules: `algorithms`, `optimizers`, and `minimum_eigensolvers`.\n", + "\n", + "3. The subset of classical solvers from the `algorithms` module are now exposed in `qiskit-addon-opt-mapper` in a new `solvers` module." + ] + }, + { + "cell_type": "markdown", + "id": "eb297c29", + "metadata": {}, + "source": [ + "## Tables" + ] + }, + { + "cell_type": "markdown", + "id": "3390c217", + "metadata": {}, + "source": [ + "### General migration guidelines\n", + "\n", + "| `qiskit_optimization` | `qiskit_addon_opt_mapper` |\n", + "|-----------------------------------|-------------------------------------|\n", + "| `QuadraticProgram` | `OptimizationProblem` |\n", + "| any class that contains `QuadraticProgram`, for example, `QuadraticProgramConverter` | `OptimizationProblemConverter` |\n", + "| any `to_quadratic_program()` method | `to_optimization_problem()` |\n", + "| any `quadratic_program` input parameter or attribute | `optimization_problem` |" + ] + }, + { + "cell_type": "markdown", + "id": "4fa0f1a9", + "metadata": {}, + "source": [ + "### Migrate classical solvers\n", + "\n", + "| `qiskit_optimization.algorithms` | `qiskit_addon_opt_mapper.solvers` |\n", + "|-----------------------------------|-------------------------------------|\n", + "| `OptimizationAlgorithm` | `OptimizationSolver` |\n", + "| `OptimizationResult` | `SolverResult` |\n", + "| `CplexOptimizer` | `CplexSolver` |\n", + "| `GurobiOptimizer` | `GurobiSolver` |\n", + "| `ScipyMilpOptimizer` | `ScipyMilpSolver` |\n", + "| Any other class in this module | Not part of this package |" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb new file mode 100644 index 00000000000..d24a93b4cbc --- /dev/null +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb @@ -0,0 +1,888 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Model quantum optimization problems with the OptimizationProblem class\"\n", + "description: \"Model quantum optimization problems with the OptimizationProblem class for the latest version of Optimization mapper\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "90e10cdc-fe4c-4b50-9cd4-1825e8f39f24", + "metadata": {}, + "source": [ + "# Model quantum optimization problems with the `OptimizationProblem` class" + ] + }, + { + "cell_type": "markdown", + "id": "7dd65d92-924e-4d49-aebf-70d0c2549380", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "a9d5c22a-b3ac-4160-9b61-34c61f4805c2", + "metadata": {}, + "source": [ + "This guide demonstrates how to use the **Optimization Mapper Qiskit addon** for quantum optimization modeling. The addon introduces the `OptimizationProblem` class, which provides a flexible way to handle variables, constraints, and objective functions.\n", + "\n", + "The `OptimizationProblem` class supports **higher-order polynomial objectives and constraints**, as well as **spin variables**, unlike the `QuadraticProgram` class from the legacy `qiskit-optimization`[package](https://github.com/qiskit-community/qiskit-optimization) objectives and constraints. With `OptimizationProblem`, you can work with a broader class of optimization models.\n", + "\n", + "`OptimizationProblem` handles **constrained polynomial programs**, which you can express in the following general form:\n", + "\n", + "$$\n", + "\\begin{align}\n", + "\\text{minimize}\\quad& \\sum_{d=1}^{D} \\;\\; \\sum_{S \\subseteq \\{1,\\dots,n\\}, |S|=d} c_{S} \\prod_{i \\in S} x_i \\\\\n", + "\\text{subject to}\\quad&\n", + "\\sum_{d=1}^{D} \\;\\; \\sum_{S \\subseteq \\{1,\\dots,n\\}, |S|=d} a^{(j)}_{S} \\prod_{i \\in S} x_i \\;\\; \\leq b_j, \\quad j=1,\\dots,m \\\\\n", + "& l_i \\leq x_i \\leq u_i, \\quad i=1,\\dots,n,\n", + "\\end{align}\n", + "$$\n", + "\n", + "where the coefficients $c_S$ and $a^{(j)}_S$ can involve terms of any degree up to $D$.\n", + "The variables $x_i$ can be defined as binary, integer, continuous, or spin.\n", + "In addition to \"$\\leq$\" constraints, the `OptimizationProblem` model also supports \"$\\geq$\" and \"$=$\"." + ] + }, + { + "cell_type": "markdown", + "id": "6211b5d4-e992-422e-9581-0a39db87c7e1", + "metadata": {}, + "source": [ + "## Construct an `OptimizationProblem`\n", + "\n", + "\n", + "This section explains how to build an optimization model by using the `OptimizationProblem` class. Start by creating an empty model. The model does not yet contain variables, an objective, or constraints. Note that `OptimizationProblem` has a `prettyprint` method to generate a comprehensive string representation of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6dfdbc0a-4e44-40a8-b5db-dd5aa89ea292", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: my problem\n", + "\n", + "Minimize\n", + " 0\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " No variables\n", + "\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper import OptimizationProblem\n", + "\n", + "# make an empty problem\n", + "mod = OptimizationProblem(\"my problem\")\n", + "print(mod.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "83d22970-ac6d-4b98-ba75-47c5f7e85d65", + "metadata": {}, + "source": [ + "The `OptimizationProblem` class supports four types of variables:\n", + "\n", + "- **Binary variables** can have the value 0 or 1.\n", + "- **Integer variables** can have any integer value within their bounds.\n", + "- **Continuous variables** can have any real value within their bounds.\n", + "- **Spin variables** can have the value -1 or 1.\n", + "\n", + "When you define a variable, you specify its name and type. You can also set optional lower and upper bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ea316eb3-6347-4f04-b0a2-48715804c261", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: my problem\n", + "\n", + "Minimize\n", + " 0\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Integer variables (1)\n", + " -1 <= y <= 5\n", + "\n", + " Continuous variables (1)\n", + " -1 <= z <= 5\n", + "\n", + " Binary variables (1)\n", + " x\n", + "\n", + " Spin variables (1)\n", + " -1 <= s <= 1\n", + "\n" + ] + } + ], + "source": [ + "# Add variables\n", + "mod.binary_var(name=\"x\")\n", + "mod.integer_var(name=\"y\", lowerbound=-1, upperbound=5)\n", + "mod.continuous_var(name=\"z\", lowerbound=-1, upperbound=5)\n", + "mod.spin_var(name=\"s\")\n", + "print(mod.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "e876ff2c-6396-4f3b-a1a8-2d24316a0ba8", + "metadata": {}, + "source": [ + "You set the objective function by calling `OptimizationProblem.minimize` or `OptimizationProblem.maximize`.\n", + "\n", + "The objective function can include the following components:\n", + "\n", + "- A constant offset.\n", + "- Linear terms (\\(c^{\\top} x\\)).\n", + "- Quadratic terms (\\(x^{\\top} Q x\\)).\n", + "- Higher-order terms \\(\\sum_S h_S \\prod_{i \\in S} x_i\\), where \\(|S| \\ge 3\\).\n", + "\n", + "You can specify these terms by using a list, a matrix, or a dictionary.\n", + "\n", + "The following example shows how to define an objective function by using a dictionary:\n", + "\n", + "- For linear terms, the keys are variable names and the values are their coefficients.\n", + "- For quadratic terms, the keys are pairs of variables and the values are their coefficients.\n", + "- For higher-order terms, use a nested dictionary with the following structure: `Dict[int, Dict[Tuple[str | int, ...], float]]`, where the outer key is the degree of the term, and the inner dictionary maps a tuple of variable names (or indices) to its coefficient." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a5b00fd7-4ba5-4f59-b870-cf21b2d9109a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: my problem\n", + "\n", + "Minimize\n", + " 4*x*y*z + 2*x*y - z^2 + x + 3\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Integer variables (1)\n", + " -1 <= y <= 5\n", + "\n", + " Continuous variables (1)\n", + " -1 <= z <= 5\n", + "\n", + " Binary variables (1)\n", + " x\n", + "\n", + " Spin variables (1)\n", + " -1 <= s <= 1\n", + "\n" + ] + } + ], + "source": [ + "# Add objective function using dictionaries\n", + "mod.minimize(\n", + " constant=3,\n", + " linear={\"x\": 1},\n", + " quadratic={(\"x\", \"y\"): 2, (\"z\", \"z\"): -1},\n", + " higher_order={3: {(\"x\", \"y\", \"z\"): 4}},\n", + ")\n", + "print(mod.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "3917a1f5-6d10-446b-93b9-ca269e7f7b86", + "metadata": {}, + "source": [ + "You can also specify the objective function by using arrays:\n", + "\n", + "- For linear terms, use an array that corresponds to the vector $c$ in the mathematical formulation.\n", + "- For quadratic terms, use an array that corresponds to the matrix $Q$.\n", + "- For higher-order terms, use multi-dimensional arrays. An order-$k$ tensor represents the coefficients of degree-$k$ monomials. For example, a third-order tensor $H^{(3)}$ stores the coefficients $h_{ijk}$ for terms $x_i x_j x_k$." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0067f705-2f13-4cb4-9d8d-44b03d9bbcc4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: my problem\n", + "\n", + "Minimize\n", + " 4*x*y*z + 2*x*y - z^2 + x + 3\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Integer variables (1)\n", + " -1 <= y <= 5\n", + "\n", + " Continuous variables (1)\n", + " -1 <= z <= 5\n", + "\n", + " Binary variables (1)\n", + " x\n", + "\n", + " Spin variables (1)\n", + " -1 <= s <= 1\n", + "\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# Add objective function using lists/arrays\n", + "# Here, h3 is a 3D array for the cubic terms\n", + "h3 = np.zeros((4, 4, 4)) # assuming we have 4 variables\n", + "h3[0, 1, 2] = 4 # corresponds to x * y * z\n", + "mod.minimize(\n", + " constant=3,\n", + " linear=[1, 0, 0, 0],\n", + " quadratic=[[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, -1, 0], [0, 0, 0, 0]],\n", + " higher_order={3: h3},\n", + ")\n", + "print(mod.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "971aceb5-686f-4809-81fe-a3b6fc737b93", + "metadata": {}, + "source": [ + "You can access the constant, the linear term, the quadratic term, and the higher-order terms by looking at\n", + "`OptimizationProblem.objective.{constant, linear, quadratic, higher_order}`, respectively.\n", + "\n", + "For linear, quadratic, and higher-order terms, you can obtain:\n", + "- a dense array (`to_array`),\n", + "- a sparse representation (`coefficients`),\n", + "- or a dictionary (`to_dict`).\n", + "\n", + "When using dictionaries, you can specify whether to use variable indices or names as keys.\n", + "\n", + "- **Quadratic terms** are stored in a compressed way. For example, `{('x', 'y'): 1, ('y', 'x'): 2}` is stored as `{('x', 'y'): 3}`.\n", + " You can get the quadratic term as a symmetric matrix by calling `to_array(symmetric=True)`.\n", + "\n", + "- **Higher-order terms** are represented as nested dictionaries:\n", + " `Dict[int, Dict[Tuple[str | int, ...], float]]`,\n", + " where the outer key is the degree, and the inner dictionary maps a tuple of variables to its coefficient.\n", + " For example, `{3: {('x','y','z'): 4}}` corresponds to the cubic term $4xyz$.\n", + " Similarly to quadratic terms, you can call `to_array(symmetric=True)` to aggregate coefficients across permutations of the same monomial." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "55fce3f0-5945-4f42-a5d1-2574771e2674", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "constant:\t\t\t 3.0\n", + "linear dict:\t\t\t {np.int32(0): np.int64(1)}\n", + "linear array:\t\t\t [1 0 0 0]\n", + "linear array as sparse matrix:\n", + " (np.int32(0), np.int32(0))\t1 \n", + "\n", + "quadratic dict w/ index:\t {(0, 1): np.float64(2.0), (2, 2): np.float64(-1.0)}\n", + "quadratic dict w/ name:\t\t {('x', 'y'): np.float64(2.0), ('z', 'z'): np.float64(-1.0)}\n", + "symmetric quadratic dict w/ name:\t {('x', 'y'): np.float64(1.0), ('y', 'x'): np.float64(1.0), ('z', 'z'): np.float64(-1.0)}\n", + "quadratic matrix:\n", + " [[ 0. 2. 0. 0.]\n", + " [ 0. 0. 0. 0.]\n", + " [ 0. 0. -1. 0.]\n", + " [ 0. 0. 0. 0.]] \n", + "\n", + "symmetric quadratic matrix:\n", + " [[ 0. 1. 0. 0.]\n", + " [ 1. 0. 0. 0.]\n", + " [ 0. 0. -1. 0.]\n", + " [ 0. 0. 0. 0.]] \n", + "\n", + "quadratic matrix as sparse matrix:\n", + " (np.int32(0), np.int32(1))\t2.0\n", + " (np.int32(2), np.int32(2))\t-1.0\n", + "higher-order dict w/ index:\t {(0, 1, 2): 4.0}\n", + "higher-order dict w/ name:\t {('x', 'y', 'z'): 4.0}\n", + "higher-order matrix:\n", + " [[[0. 0. 0. 0. ]\n", + " [0. 0. 0.66666667 0. ]\n", + " [0. 0.66666667 0. 0. ]\n", + " [0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0.66666667 0. ]\n", + " [0. 0. 0. 0. ]\n", + " [0.66666667 0. 0. 0. ]\n", + " [0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0.66666667 0. 0. ]\n", + " [0.66666667 0. 0. 0. ]\n", + " [0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. ]]] \n", + "\n", + "symmetric higher-order matrix:\n", + " [[[0. 0. 0. 0.]\n", + " [0. 0. 4. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]]] \n", + "\n", + "higher-order matrix as sparse matrix:\n", + " {(0, 1, 2): 4.0}\n" + ] + } + ], + "source": [ + "print(\"constant:\\t\\t\\t\", mod.objective.constant)\n", + "print(\"linear dict:\\t\\t\\t\", mod.objective.linear.to_dict())\n", + "print(\"linear array:\\t\\t\\t\", mod.objective.linear.to_array())\n", + "print(\"linear array as sparse matrix:\\n\", mod.objective.linear.coefficients, \"\\n\")\n", + "print(\"quadratic dict w/ index:\\t\", mod.objective.quadratic.to_dict())\n", + "print(\"quadratic dict w/ name:\\t\\t\", mod.objective.quadratic.to_dict(use_name=True))\n", + "print(\n", + " \"symmetric quadratic dict w/ name:\\t\",\n", + " mod.objective.quadratic.to_dict(use_name=True, symmetric=True),\n", + ")\n", + "print(\"quadratic matrix:\\n\", mod.objective.quadratic.to_array(), \"\\n\")\n", + "print(\"symmetric quadratic matrix:\\n\", mod.objective.quadratic.to_array(symmetric=True), \"\\n\")\n", + "print(\"quadratic matrix as sparse matrix:\\n\", mod.objective.quadratic.coefficients)\n", + "print(\"higher-order dict w/ index:\\t\", mod.objective.higher_order[3].to_dict())\n", + "print(\"higher-order dict w/ name:\\t\", mod.objective.higher_order[3].to_dict(use_name=True))\n", + "print(\"higher-order matrix:\\n\", mod.objective.higher_order[3].to_array(), \"\\n\")\n", + "print(\n", + " \"symmetric higher-order matrix:\\n\", mod.objective.higher_order[3].to_array(symmetric=True), \"\\n\"\n", + ")\n", + "print(\"higher-order matrix as sparse matrix:\\n\", mod.objective.higher_order[3].coefficients)" + ] + }, + { + "cell_type": "markdown", + "id": "8d22f393-664b-4046-9fbf-f5b20948c638", + "metadata": {}, + "source": [ + "## Add or remove linear, quadratic, and higher-order terms to and from the constraints" + ] + }, + { + "cell_type": "markdown", + "id": "fd521075-baaa-4267-9e77-61c16aa6708a", + "metadata": {}, + "source": [ + "You can add linear constraints by setting name, linear expression, sense, and right-hand-side value (rhs).\n", + "You can use senses `'=='`, `'\\<='`, and `'>='`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "eb876756-0e58-4fc9-a2c3-e2cd62ae6db3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: my problem\n", + "\n", + "Minimize\n", + " 4*x*y*z + 2*x*y - z^2 + x + 3\n", + "\n", + "Subject to\n", + " Linear constraints (3)\n", + " x + 2*y == 3 'lin_eq'\n", + " x + 2*y <= 3 'lin_leq'\n", + " x + 2*y >= 3 'lin_geq'\n", + "\n", + " Integer variables (1)\n", + " -1 <= y <= 5\n", + "\n", + " Continuous variables (1)\n", + " -1 <= z <= 5\n", + "\n", + " Binary variables (1)\n", + " x\n", + "\n", + " Spin variables (1)\n", + " -1 <= s <= 1\n", + "\n" + ] + } + ], + "source": [ + "# Add linear constraints\n", + "mod.linear_constraint(linear={\"x\": 1, \"y\": 2}, sense=\"==\", rhs=3, name=\"lin_eq\")\n", + "mod.linear_constraint(linear={\"x\": 1, \"y\": 2}, sense=\"<=\", rhs=3, name=\"lin_leq\")\n", + "mod.linear_constraint(linear={\"x\": 1, \"y\": 2}, sense=\">=\", rhs=3, name=\"lin_geq\")\n", + "print(mod.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "255876e6-a897-48d7-b1c5-11e7d4ef16a0", + "metadata": {}, + "source": [ + "You can add quadratic constraints by setting name, quadratic expression, sense, and right-hand-side value (rhs)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a262ebc5-be6f-43d8-9dee-0b51953acb16", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: my problem\n", + "\n", + "Minimize\n", + " 4*x*y*z + 2*x*y - z^2 + x + 3\n", + "\n", + "Subject to\n", + " Linear constraints (3)\n", + " x + 2*y == 3 'lin_eq'\n", + " x + 2*y <= 3 'lin_leq'\n", + " x + 2*y >= 3 'lin_geq'\n", + "\n", + " Quadratic constraints (3)\n", + " x^2 - y*z + x + y == 1 'quad_eq'\n", + " x^2 - y*z + x + y <= 1 'quad_leq'\n", + " x^2 - y*z + x + y >= 1 'quad_geq'\n", + "\n", + " Integer variables (1)\n", + " -1 <= y <= 5\n", + "\n", + " Continuous variables (1)\n", + " -1 <= z <= 5\n", + "\n", + " Binary variables (1)\n", + " x\n", + "\n", + " Spin variables (1)\n", + " -1 <= s <= 1\n", + "\n" + ] + } + ], + "source": [ + "# Add quadratic constraints\n", + "mod.quadratic_constraint(\n", + " linear={\"x\": 1, \"y\": 1},\n", + " quadratic={(\"x\", \"x\"): 1, (\"y\", \"z\"): -1},\n", + " sense=\"==\",\n", + " rhs=1,\n", + " name=\"quad_eq\",\n", + ")\n", + "mod.quadratic_constraint(\n", + " linear={\"x\": 1, \"y\": 1},\n", + " quadratic={(\"x\", \"x\"): 1, (\"y\", \"z\"): -1},\n", + " sense=\"<=\",\n", + " rhs=1,\n", + " name=\"quad_leq\",\n", + ")\n", + "mod.quadratic_constraint(\n", + " linear={\"x\": 1, \"y\": 1},\n", + " quadratic={(\"x\", \"x\"): 1, (\"y\", \"z\"): -1},\n", + " sense=\">=\",\n", + " rhs=1,\n", + " name=\"quad_geq\",\n", + ")\n", + "print(mod.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "329a907e-53ba-4c4e-b7f5-7f271df31a91", + "metadata": {}, + "source": [ + "You can also add higher-order constraints by setting name, higher-order expression, sense, and right-hand-side value (rhs)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a87f7ac2-a89b-4afa-b809-a2e51728b9e1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: my problem\n", + "\n", + "Minimize\n", + " 4*x*y*z + 2*x*y - z^2 + x + 3\n", + "\n", + "Subject to\n", + " Linear constraints (3)\n", + " x + 2*y == 3 'lin_eq'\n", + " x + 2*y <= 3 'lin_leq'\n", + " x + 2*y >= 3 'lin_geq'\n", + "\n", + " Quadratic constraints (3)\n", + " x^2 - y*z + x + y == 1 'quad_eq'\n", + " x^2 - y*z + x + y <= 1 'quad_leq'\n", + " x^2 - y*z + x + y >= 1 'quad_geq'\n", + "\n", + " Higher-order constraints (3)\n", + " 2*s*x*y*z + x*y*z + x^2 - y*z + x == 1 'hoc_eq'\n", + " 2*s*x*y*z + x*y*z + x^2 - y*z + x <= 1 'hoc_leq'\n", + " 2*s*x*y*z + x*y*z + x^2 - y*z + x >= 1 'hoc_geq'\n", + "\n", + " Integer variables (1)\n", + " -1 <= y <= 5\n", + "\n", + " Continuous variables (1)\n", + " -1 <= z <= 5\n", + "\n", + " Binary variables (1)\n", + " x\n", + "\n", + " Spin variables (1)\n", + " -1 <= s <= 1\n", + "\n" + ] + } + ], + "source": [ + "# Add higher-order constraints\n", + "mod.higher_order_constraint(\n", + " linear={\"x\": 1},\n", + " quadratic={(\"x\", \"x\"): 1, (\"y\", \"z\"): -1},\n", + " higher_order={3: {(\"x\", \"y\", \"z\"): 1}, 4: {(\"x\", \"y\", \"z\", \"s\"): 2}},\n", + " sense=\"==\",\n", + " rhs=1,\n", + " name=\"hoc_eq\",\n", + ")\n", + "mod.higher_order_constraint(\n", + " linear={\"x\": 1},\n", + " quadratic={(\"x\", \"x\"): 1, (\"y\", \"z\"): -1},\n", + " higher_order={3: {(\"x\", \"y\", \"z\"): 1}, 4: {(\"x\", \"y\", \"z\", \"s\"): 2}},\n", + " sense=\"<=\",\n", + " rhs=1,\n", + " name=\"hoc_leq\",\n", + ")\n", + "mod.higher_order_constraint(\n", + " linear={\"x\": 1},\n", + " quadratic={(\"x\", \"x\"): 1, (\"y\", \"z\"): -1},\n", + " higher_order={3: {(\"x\", \"y\", \"z\"): 1}, 4: {(\"x\", \"y\", \"z\", \"s\"): 2}},\n", + " sense=\">=\",\n", + " rhs=1,\n", + " name=\"hoc_geq\",\n", + ")\n", + "print(mod.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "8430e422-c4d8-4383-84e0-8e99d472256a", + "metadata": {}, + "source": [ + "You can access linear and quadratic terms of linear and quadratic constraints in the same way as the objective function." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e7067edb-0aee-4c0a-bbe5-dde03020778e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "lin_geq: {'x': np.float64(1.0), 'y': np.float64(2.0)} ConstraintSense.GE 3\n", + "quad_geq: {'x': np.float64(1.0), 'y': np.float64(1.0)} {('x', 'x'): np.float64(1.0), ('y', 'z'): np.float64(-1.0)} ConstraintSense.GE 3\n", + "hoc_geq: {'x': np.float64(1.0)} {('x', 'x'): np.float64(1.0), ('y', 'z'): np.float64(-1.0)} {3: {('x', 'y', 'z'): 1.0}, 4: {('x', 'y', 'z', 's'): 2.0}} ConstraintSense.GE 3\n" + ] + } + ], + "source": [ + "lin_geq = mod.get_linear_constraint(\"lin_geq\")\n", + "print(\"lin_geq:\", lin_geq.linear.to_dict(use_name=True), lin_geq.sense, lin_geq.rhs)\n", + "quad_geq = mod.get_quadratic_constraint(\"quad_geq\")\n", + "print(\n", + " \"quad_geq:\",\n", + " quad_geq.linear.to_dict(use_name=True),\n", + " quad_geq.quadratic.to_dict(use_name=True),\n", + " quad_geq.sense,\n", + " lin_geq.rhs,\n", + ")\n", + "hoc_geq = mod.get_higher_order_constraint(\"hoc_geq\")\n", + "print(\n", + " \"hoc_geq:\",\n", + " hoc_geq.linear.to_dict(use_name=True),\n", + " hoc_geq.quadratic.to_dict(use_name=True),\n", + " {k: v.to_dict(use_name=True) for k, v in hoc_geq.higher_order.items()},\n", + " hoc_geq.sense,\n", + " lin_geq.rhs,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "27cbc727-1d1f-4be1-a762-8391b39f011e", + "metadata": {}, + "source": [ + "You can also remove linear/quadratic/higher-order constraints by using the `remove_linear_constraint`, `remove_quadratic_constraint`, and `remove_higher_order_constraint` methods, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3cadf977-6e0e-4b29-836f-f0ea77456138", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: my problem\n", + "\n", + "Minimize\n", + " 4*x*y*z + 2*x*y - z^2 + x + 3\n", + "\n", + "Subject to\n", + " Linear constraints (2)\n", + " x + 2*y <= 3 'lin_leq'\n", + " x + 2*y >= 3 'lin_geq'\n", + "\n", + " Quadratic constraints (2)\n", + " x^2 - y*z + x + y == 1 'quad_eq'\n", + " x^2 - y*z + x + y >= 1 'quad_geq'\n", + "\n", + " Higher-order constraints (2)\n", + " 2*s*x*y*z + x*y*z + x^2 - y*z + x <= 1 'hoc_leq'\n", + " 2*s*x*y*z + x*y*z + x^2 - y*z + x >= 1 'hoc_geq'\n", + "\n", + " Integer variables (1)\n", + " -1 <= y <= 5\n", + "\n", + " Continuous variables (1)\n", + " -1 <= z <= 5\n", + "\n", + " Binary variables (1)\n", + " x\n", + "\n", + " Spin variables (1)\n", + " -1 <= s <= 1\n", + "\n" + ] + } + ], + "source": [ + "# Remove constraints\n", + "mod.remove_linear_constraint(\"lin_eq\")\n", + "mod.remove_quadratic_constraint(\"quad_leq\")\n", + "mod.remove_higher_order_constraint(\"hoc_eq\")\n", + "print(mod.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "aa82e26c-11cd-4ae7-ac45-9a4573bd894c", + "metadata": {}, + "source": [ + "## Substitute variables" + ] + }, + { + "cell_type": "markdown", + "id": "412c050d-b81b-4f69-9622-b954a87c955d", + "metadata": {}, + "source": [ + "You can substitute some of the variables with constants or other variables.\n", + "More precisely, `OptimizationProblem` has a `substitute_variables(constants=..., variables=...)` method to deal with the following two cases.\n", + "\n", + "- $x \\leftarrow c$: when `constants` have a dictionary `{x: c}`.\n", + "- $x \\leftarrow c y$: when `variables` have a dictionary `{x: (y, c)}`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "060750ba-712a-42c5-8022-382973420c05", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: my problem\n", + "\n", + "Minimize\n", + " -5*z^2 - 2*z + 4\n", + "\n", + "Subject to\n", + " Linear constraints (2)\n", + " -2*z <= 2 'lin_leq'\n", + " -2*z >= 2 'lin_geq'\n", + "\n", + " Quadratic constraints (2)\n", + " z^2 - z == -1 'quad_eq'\n", + " z^2 - z >= -1 'quad_geq'\n", + "\n", + " Higher-order constraints (2)\n", + " -2*s*z^2 <= -1 'hoc_leq'\n", + " -2*s*z^2 >= -1 'hoc_geq'\n", + "\n", + " Continuous variables (1)\n", + " -1 <= z <= 1\n", + "\n", + " Spin variables (1)\n", + " -1 <= s <= 1\n", + "\n" + ] + } + ], + "source": [ + "sub = mod.substitute_variables(constants={\"x\": 1}, variables={\"y\": (\"z\", -1)})\n", + "print(sub.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "be92cb1b-4f36-4943-8f04-7c114c689e86", + "metadata": {}, + "source": [ + "If the resulting problem is infeasible due to lower bounds or upper bounds, the methods returns the status `Status.INFEASIBLE`.\n", + "For example, if with the model above you try to replace variable `x` with -1, but -1 is out of range of `x` (0 \\<= `x` \\<= 1), it will return `Status.INFEASIBLE`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9ed83b90-b135-4854-a537-1fe8e29e4d30", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Infeasible substitution for variable: x\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OptimizationProblemStatus.INFEASIBLE\n" + ] + } + ], + "source": [ + "sub = mod.substitute_variables(constants={\"x\": -1})\n", + "print(sub.status)" + ] + }, + { + "cell_type": "markdown", + "id": "0b1b8316-34f3-4f03-8666-6d7271d6fd83", + "metadata": {}, + "source": [ + "You cannot substitute variables multiple times.\n", + "The method raises an error in such cases." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "5bdab47a-444a-4833-bba1-c376441b157a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error: Cannot substitute by variable that gets substituted itself: y <- x 1\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper import OptimizationError\n", + "\n", + "try:\n", + " sub = mod.substitute_variables(constants={\"x\": -1}, variables={\"y\": (\"x\", 1)})\n", + "except OptimizationError as e:\n", + " print(f\"Error: {e}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb new file mode 100644 index 00000000000..7de01818917 --- /dev/null +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb @@ -0,0 +1,842 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Convert formulations of an OptimizationProblem\"\n", + "description: \"Convert formulations of an OptimizationProblem for the latest version of Optimization mapper\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "b115a129-132d-4261-8f4f-e965220d5906", + "metadata": {}, + "source": [ + "# Convert formulations of an `OptimizationProblem`\n", + "\n", + "## Overview\n", + "Use converters in this addon to transform optimization problems into different forms. Converters change variable types, objective functions, and constraints. Quantum algorithms often require a specific formulation of an optimization problem, so you might need to convert your problem into the correct type.\n", + "\n", + "For example, the QUBO (Quadratic Unconstrained Binary Optimization) format is widely used in quantum computing. The `OptimizationProblemToQubo` converter transforms an `OptimizationProblem` into QUBO form by converting variables to binary and removing constraints. Some quantum algorithms can also handle higher-order terms. In those cases, use the `OptimizationProblemToHubo` converter to transform an `OptimizationProblem` into HUBO (Higher-order Unconstrained Binary Optimization) format.\n", + "\n", + "You can map both QUBO and HUBO problems to Ising Hamiltonians by using the `to_ising()` function. Both binary and spin variables are supported. In the Optimization Mapper Qiskit addon, these Hamiltonians are represented by the `qiskit.quantum_info.SparsePauliOp` class.\n", + "\n", + "### Converters in this module\n", + "\n", + "*Variables*\n", + "- **BinaryToSpin**: Converts binary variables to spin variables.\n", + "- **SpinToBinary**: Converts spin variables to binary variables.\n", + "- **IntegerToBinary**: Converts integer variables to binary variables.\n", + "\n", + "*Constraints*\n", + "- **InequalityToEquality**: Converts inequality constraints to equality constraints by using slack variables.\n", + "- **EqualityToPenalty**: Converts equality constraints to penalty terms in the objective function.\n", + "- **LinearInequalityToPenalty**: Converts specific linear inequality constraints to penalty terms in the objective function with fewer additional variables.\n", + "\n", + "*Objectives*\n", + "- **MaximizeToMinimize**: Converts a maximization objective to a minimization objective.\n", + "- **MinimizeToMaximize**: Converts a minimization objective to a maximization objective.\n", + "\n", + "*End-to-end*\n", + "- **OptimizationProblemToQubo**: Converts an `OptimizationProblem` to QUBO format. Combines several converters: `IntegerToBinary`, `InequalityToPenalty`, `InequalityToEquality`, `EqualityToPenalty`, and `MaximizeToMinimize`. Raises an exception if higher-order terms are present.\n", + "- **OptimizationProblemToHubo**: Converts an `OptimizationProblem` to HUBO format. Combines several converters: `IntegerToBinary`, `InequalityToPenalty`, `EqualityToPenalty`, and `MaximizeToMinimize`, while preserving higher-order terms in the objective function." + ] + }, + { + "cell_type": "markdown", + "id": "a6ef5c60-3548-40ef-a26c-04661570870f", + "metadata": {}, + "source": [ + "## Code examples\n", + "\n", + "### Integer to binary conversion\n", + "`IntegerToBinary` converts integer variables into binary variables and adjusts the corresponding coefficients to eliminate integer variables from an `OptimizationProblem`. For this conversion, the bounded-coefficient encoding proposed in [arxiv:1706.01945](https://arxiv.org/abs/1706.01945) (Eq. (5)) is used. In this encoding, an integer variable $x$ with bounds $l \\leq x \\leq u$ is represented using binary variables $b_i$ as follows:\n", + "\n", + "$x = l + \\sum_{i=0}^{k-1} 2^i b_i + (u - l + 1 - 2^k) b_k$\n", + "where $k = \\lceil \\log_2(u - l + 1) \\rceil$. This encoding minimizes the number of binary variables required to represent the integer variable while ensuring that the bounds are respected.\n", + "\n", + "For more details on the encoding method, please refer to the paper." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b9d0e69a-b691-4c7e-8412-32831513110d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Problem:\n", + "Problem name: Integer to Binary Example\n", + "\n", + "Minimize\n", + " x^2*y + x^2 + 2*x\n", + "\n", + "Subject to\n", + " Linear constraints (1)\n", + " x + y <= 2 'c1'\n", + "\n", + " Integer variables (1)\n", + " 0 <= x <= 3\n", + "\n", + " Binary variables (1)\n", + " y\n", + "\n", + "\n", + "Converted Problem:\n", + "Problem name: Integer to Binary Example\n", + "\n", + "Minimize\n", + " x@0^2*y + 4*x@0*x@1*y + 4*x@1^2*y + x@0^2 + 4*x@0*x@1 + 4*x@1^2 + 2*x@0\n", + " + 4*x@1\n", + "\n", + "Subject to\n", + " Linear constraints (1)\n", + " x@0 + 2*x@1 + y <= 2 'c1'\n", + "\n", + " Binary variables (3)\n", + " x@0 x@1 y\n", + "\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper import OptimizationProblem\n", + "from qiskit_addon_opt_mapper.converters import IntegerToBinary\n", + "\n", + "# Create an optimization problem with integer variables\n", + "op = OptimizationProblem(\"Integer to Binary Example\")\n", + "op.integer_var(name=\"x\", lowerbound=0, upperbound=3)\n", + "op.binary_var(name=\"y\")\n", + "\n", + "op.minimize(linear={\"x\": 2}, quadratic={(\"x\", \"x\"): 1}, higher_order={3: {(\"x\", \"x\", \"y\"): 1}})\n", + "op.linear_constraint(linear={\"x\": 1, \"y\": 1}, sense=\"LE\", rhs=2, name=\"c1\")\n", + "print(\"Original Problem:\")\n", + "print(op.prettyprint())\n", + "# Convert integer variables to binary variables\n", + "int_to_bin = IntegerToBinary()\n", + "op_bin = int_to_bin.convert(op)\n", + "print(\"\\nConverted Problem:\")\n", + "print(op_bin.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "9b4d9007-6b43-4167-9e0b-735f314261a9", + "metadata": {}, + "source": [ + "## Binary to spin conversion\n", + "The `BinaryToSpin` converter transforms binary variables (0/1) into spin variables (-1/+1) using the relation $b = (1 - s)/2$, where $b$ is a binary variable and $s$ is a spin variable. This conversion is particularly useful in quantum computing contexts, where spin variables are often preferred." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f88a3f0d-396f-4d90-84a0-74a6981ff2cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Binary Problem:\n", + "Problem name: Binary to Spin Example\n", + "\n", + "Minimize\n", + " b1*b2 + b1 + b2\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Binary variables (2)\n", + " b1 b2\n", + "\n", + "\n", + "Converted Spin Problem:\n", + "Problem name: Binary to Spin Example\n", + "\n", + "Minimize\n", + " 0.25*b1@spin*b2@spin - 0.75*b1@spin - 0.75*b2@spin + 1.25\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Spin variables (2)\n", + " -1 <= b1@spin <= 1\n", + " -1 <= b2@spin <= 1\n", + "\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper.converters import BinaryToSpin\n", + "\n", + "# Convert binary variables to spin variables\n", + "op_bin = OptimizationProblem(\"Binary to Spin Example\")\n", + "op_bin.binary_var(name=\"b1\")\n", + "op_bin.binary_var(name=\"b2\")\n", + "op_bin.minimize(linear={\"b1\": 1, \"b2\": 1}, quadratic={(\"b1\", \"b2\"): 1})\n", + "print(\"Original Binary Problem:\")\n", + "print(op_bin.prettyprint())\n", + "\n", + "# Convert binary variables to spin variables\n", + "bin_to_spin = BinaryToSpin()\n", + "op_spin = bin_to_spin.convert(op_bin)\n", + "print(\"\\nConverted Spin Problem:\")\n", + "print(op_spin.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "d71a3c9e-a59d-4d4f-9220-84b7510506cc", + "metadata": {}, + "source": [ + "## Spin to binary conversion\n", + "The `SpinToBinary` converter transforms spin variables (-1/+1) into binary variables (0/1) using the relation $s = 1 - 2b$, where $s$ is a spin variable and $b$ is a binary variable. This conversion is useful when an optimization problem needs to be expressed in terms of binary variables for certain algorithms or solvers." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8406892-c1c8-423b-a801-e9a24d6d0cb5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Spin Problem:\n", + "Problem name: Spin to Binary Example\n", + "\n", + "Minimize\n", + " s1*s2 + s1 + s2\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Spin variables (2)\n", + " -1 <= s1 <= 1\n", + " -1 <= s2 <= 1\n", + "\n", + "\n", + "Converted Binary Problem:\n", + "Problem name: Spin to Binary Example\n", + "\n", + "Minimize\n", + " 4*s1@bin*s2@bin - 4*s1@bin - 4*s2@bin + 3\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Binary variables (2)\n", + " s1@bin s2@bin\n", + "\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper.converters import SpinToBinary\n", + "\n", + "op_spin2 = OptimizationProblem(\"Spin to Binary Example\")\n", + "op_spin2.spin_var(name=\"s1\")\n", + "op_spin2.spin_var(name=\"s2\")\n", + "op_spin2.minimize(linear={\"s1\": 1, \"s2\": 1}, quadratic={(\"s1\", \"s2\"): 1})\n", + "print(\"Original Spin Problem:\")\n", + "print(op_spin2.prettyprint())\n", + "# Convert spin variables to binary variables\n", + "spin_to_bin = SpinToBinary()\n", + "op_bin2 = spin_to_bin.convert(op_spin2)\n", + "print(\"\\nConverted Binary Problem:\")\n", + "print(op_bin2.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "a505ec8f-78c0-4dd4-a7e5-9fc2c9290a0b", + "metadata": {}, + "source": [ + "## Inequality to equality conversion\n", + "The `InequalityToEquality` converter transforms inequality constraints into equality constraints by introducing slack variables. For example, an inequality constraint of the form:\n", + "$ax + by \\leq c$\n", + "is converted to an equality constraint:\n", + "$ax + by + s = c$\n", + "where $s$ is a slack variable that is constrained to be non-negative ($s \\geq 0$). This conversion allows the optimization problem to be expressed entirely in terms of equality constraints, which can be beneficial for certain optimization algorithms that require equality constraints." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "31b46588-d21f-4604-8127-afa4f8204b26", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Problem:\n", + "Problem name: Inequality to Equality Example\n", + "\n", + "Minimize\n", + " x + y\n", + "\n", + "Subject to\n", + " Linear constraints (2)\n", + " x + y <= 5 'c1'\n", + " 10*x >= 3 'c2'\n", + "\n", + " Binary variables (2)\n", + " x y\n", + "\n", + "\n", + "Converted Problem:\n", + "Problem name: Inequality to Equality Example\n", + "\n", + "Minimize\n", + " x + y\n", + "\n", + "Subject to\n", + " Linear constraints (2)\n", + " c1@int_slack + x + y == 5 'c1'\n", + " -c2@int_slack + 10*x == 3 'c2'\n", + "\n", + " Integer variables (2)\n", + " 0 <= c1@int_slack <= 5\n", + " 0 <= c2@int_slack <= 7\n", + "\n", + " Binary variables (2)\n", + " x y\n", + "\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper.converters import InequalityToEquality\n", + "\n", + "# Create an optimization problem with an inequality constraint\n", + "op2 = OptimizationProblem(\"Inequality to Equality Example\")\n", + "op2.binary_var(name=\"x\")\n", + "op2.binary_var(name=\"y\")\n", + "op2.minimize(linear={\"x\": 1, \"y\": 1})\n", + "op2.linear_constraint(linear={\"x\": 1, \"y\": 1}, sense=\"LE\", rhs=5, name=\"c1\")\n", + "op2.linear_constraint(linear={\"x\": 10}, sense=\"GE\", rhs=3, name=\"c2\")\n", + "print(\"Original Problem:\")\n", + "print(op2.prettyprint())\n", + "# Convert inequality constraints to equality constraints\n", + "ineq_to_eq = InequalityToEquality()\n", + "op_eq = ineq_to_eq.convert(op2)\n", + "print(\"\\nConverted Problem:\")\n", + "print(op_eq.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "1257d34e-8b1c-4642-b1d7-a30b8718a3ae", + "metadata": {}, + "source": [ + "## Equality to penalty conversion\n", + "The `EqualityToPenalty` converter transforms equality constraints into penalty terms added to the objective function.\n", + "\\begin{array}{}\n", + "\\text { Equality constraint } & & \\text { Penalty term } \\\\\n", + "ax + by = c & \\rightarrow & P(c - (ax + by))^2 \\\\\n", + "\\end{array}\n", + "where $P$ is a large positive/negative constant that penalizes deviations from the equality constraint (when the problem is a minimizing problem, $P$ should be a large positive constant; conversely, when the problem is a maximizing problem, $P$ should be a large negative constant). This approach effectively incorporates the equality constraints into the objective function, allowing the optimization algorithm to focus on minimizing or maximizing the modified objective function while still respecting the original constraints.\n", + "If $P$ is not specified, it is automatically set to a value larger than the sum of the absolute values of the coefficients in the objective function." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f8d49f49-ab3a-4bc7-a328-f022547b7126", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Problem:\n", + "Problem name: Equality to Penalty Example\n", + "\n", + "Minimize\n", + " 0\n", + "\n", + "Subject to\n", + " Linear constraints (1)\n", + " x + y == 3 'c1'\n", + "\n", + " Binary variables (2)\n", + " x y\n", + "\n", + "\n", + "Converted Problem:\n", + "Problem name: \n", + "\n", + "Minimize\n", + " 100000*x^2 + 200000*x*y + 100000*y^2 - 600000*x - 600000*y + 900000\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Binary variables (2)\n", + " x y\n", + "\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper.converters import EqualityToPenalty\n", + "\n", + "# Create an optimization problem with an equality constraint\n", + "op3 = OptimizationProblem(\"Equality to Penalty Example\")\n", + "op3.binary_var(name=\"x\")\n", + "op3.binary_var(name=\"y\")\n", + "op3.linear_constraint(linear={\"x\": 1, \"y\": 1}, sense=\"EQ\", rhs=3, name=\"c1\")\n", + "print(\"Original Problem:\")\n", + "print(op3.prettyprint())\n", + "# Convert equality constraints to penalty terms in the objective function\n", + "eq_to_penalty = EqualityToPenalty(penalty=1e5)\n", + "op_penalty = eq_to_penalty.convert(op3)\n", + "print(\"\\nConverted Problem:\")\n", + "print(op_penalty.prettyprint())" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f7f7d28d-3cd8-41b5-b2e2-a8cc0d172659", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Converted Problem with Automatic Penalty:\n", + "Problem name: \n", + "\n", + "Minimize\n", + " x^2 + 2*x*y + y^2 - 6*x - 6*y + 9\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Binary variables (2)\n", + " x y\n", + "\n" + ] + } + ], + "source": [ + "# Automatic penalty value\n", + "eq_to_penalty_auto = EqualityToPenalty()\n", + "op_penalty_auto = eq_to_penalty_auto.convert(op3)\n", + "print(\"\\nConverted Problem with Automatic Penalty:\")\n", + "print(op_penalty_auto.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "846d56ed-bb4f-4d59-9354-ed9f9fe6e32a", + "metadata": {}, + "source": [ + "## Linear inequality to penalty conversion\n", + "The `LinearInequalityToPenalty` converter transforms specific binary linear inequality constraints into penalty terms added to\n", + "the objective function with a fewer number of additional variables compared to the `InequalityToEquality` converter followed by the `EqualityToPenalty` converter.\n", + "\n", + "\\begin{array}{}\n", + "\\text { Inequality constraint } & & \\text { Penalty term } \\\\\n", + "x \\leq y & \\rightarrow & P(x-x y) \\\\\n", + "x \\geq y & \\rightarrow & P(y-x y) \\\\\n", + "\\sum_{i=1}^n x_i \\leq 1, n \\geq 2 & \\rightarrow &\n", + " P \\sum_{i, j : i \\< j} x_i x_j\\\\\n", + "\\sum_{i=1}^n x_i \\geq n-1, n \\geq 2 & \\rightarrow &\n", + " P \\sum_{i, j : i \\< j} (1 - x_i) (1 - x_j)\n", + "\\end{array}\n", + "\n", + "*Note that x, y, z and `x_i` are binary variables, and P is a penalty factor,where the value of P is automatically determined or supplied by users.*" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "05a0e9d1-e1e6-4f49-b026-5c7c55dcc54b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Problem:\n", + "Problem name: Linear Inequality to Penalty Example\n", + "\n", + "Minimize\n", + " 0\n", + "\n", + "Subject to\n", + " Linear constraints (1)\n", + " x - y <= 0 'c1'\n", + "\n", + " Binary variables (2)\n", + " x y\n", + "\n", + "\n", + "Converted Problem:\n", + "Problem name: Linear Inequality to Penalty Example\n", + "\n", + "Minimize\n", + " -100000*x*y + 100000*x\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Binary variables (2)\n", + " x y\n", + "\n" + ] + } + ], + "source": [ + "# Linear Inequality to Penalty Conversion\n", + "from qiskit_addon_opt_mapper.converters import LinearInequalityToPenalty\n", + "\n", + "# Create an optimization problem with a specific linear inequality constraint\n", + "op4 = OptimizationProblem(\"Linear Inequality to Penalty Example\")\n", + "op4.binary_var(name=\"x\")\n", + "op4.binary_var(name=\"y\")\n", + "op4.linear_constraint(linear={\"x\": 1, \"y\": -1}, sense=\"LE\", rhs=0, name=\"c1\") # x <= y\n", + "print(\"Original Problem:\")\n", + "print(op4.prettyprint())\n", + "# Convert specific linear inequality constraints to penalty terms in the objective function\n", + "lin_ineq_to_penalty = LinearInequalityToPenalty(penalty=1e5)\n", + "op4_penalty = lin_ineq_to_penalty.convert(op4)\n", + "print(\"\\nConverted Problem:\")\n", + "print(op4_penalty.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "f5b9c7d1-402e-4374-87f9-6c6f97da6e2b", + "metadata": {}, + "source": [ + "## Maximize to minimize conversion\n", + "The `MaximizeToMinimize` converter transforms a maximization objective into a minimization objective by negating the coefficients of the objective function.\n", + "\\begin{array}{}\n", + "\\text { Maximization objective } & & \\text { Minimization objective } \\\\\n", + "\\max (ax + by) & \\rightarrow & \\min - (ax + by)\n", + "\\end{array}\n", + "The `MinimizeToMaximize` converter performs the inverse operation, converting a minimization objective into a maximization objective by negating the coefficients of the objective function." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a43b0458-f09f-49c9-a73c-161b692c2f36", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Problem:\n", + "Problem name: Maximize to Minimize Example\n", + "\n", + "Maximize\n", + " x + 2*y\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Binary variables (2)\n", + " x y\n", + "\n", + "\n", + "Converted Problem:\n", + "Problem name: Maximize to Minimize Example\n", + "\n", + "Minimize\n", + " -x - 2*y\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Binary variables (2)\n", + " x y\n", + "\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper.converters import MaximizeToMinimize, MinimizeToMaximize\n", + "\n", + "# Create an optimization problem with a maximization objective\n", + "op5 = OptimizationProblem(\"Maximize to Minimize Example\")\n", + "op5.binary_var(name=\"x\")\n", + "op5.binary_var(name=\"y\")\n", + "op5.maximize(linear={\"x\": 1, \"y\": 2})\n", + "print(\"Original Problem:\")\n", + "print(op5.prettyprint())\n", + "# Convert maximization objective to minimization objective\n", + "max_to_min = MaximizeToMinimize()\n", + "op5_min = max_to_min.convert(op5)\n", + "print(\"\\nConverted Problem:\")\n", + "print(op5_min.prettyprint())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "290b99e5-205e-4ca6-a742-95b80a5054ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Re-converted Problem:\n", + "Problem name: Maximize to Minimize Example\n", + "\n", + "Maximize\n", + " x + 2*y\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Binary variables (2)\n", + " x y\n", + "\n" + ] + } + ], + "source": [ + "# Minimize to Maximize Conversion\n", + "min_to_max = MinimizeToMaximize()\n", + "op5_max = min_to_max.convert(op5_min)\n", + "print(\"\\nRe-converted Problem:\")\n", + "print(op5_max.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "e7bbc86b-337f-4519-add5-79e8e4a79fc0", + "metadata": {}, + "source": [ + "## QUBO conversion\n", + "The `OptimizationProblemToQubo` converter transforms an `OptimizationProblem` in quadratic format (quadratic objective function with linear constraints)\n", + "into QUBO form by converting variables to binary and eliminating constraints. It combines several converters: `IntegerToBinary`, `SpinToBinary`, `InequalityToPenalty`, `InequalityToEquality`, `EqualityToPenalty`, and `MaximizeToMinimize`.\n", + "The resulting QUBO problem can be directly mapped to an Ising Hamiltonian using the `to_ising()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6569550a-9720-4553-bc7a-cc68813983fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Problem:\n", + "Problem name: Quadratic Program to QUBO Example\n", + "\n", + "Minimize\n", + " x*y + 2*x\n", + "\n", + "Subject to\n", + " Linear constraints (1)\n", + " x + y <= 5 'c1'\n", + "\n", + " Integer variables (1)\n", + " 0 <= x <= 3\n", + "\n", + " Binary variables (1)\n", + " y\n", + "\n", + "\n", + "Converted QUBO Problem:\n", + "Problem name: \n", + "\n", + "Minimize\n", + " 100000*c1@int_slack@0^2 + 400000*c1@int_slack@0*c1@int_slack@1\n", + " + 400000*c1@int_slack@0*c1@int_slack@2 + 400000*c1@int_slack@1^2\n", + " + 800000*c1@int_slack@1*c1@int_slack@2 + 400000*c1@int_slack@2^2\n", + " + 200000*x@0*c1@int_slack@0 + 400000*x@0*c1@int_slack@1\n", + " + 400000*x@0*c1@int_slack@2 + 100000*x@0^2 + 400000*x@0*x@1 + 200001*x@0*y\n", + " + 400000*x@1*c1@int_slack@0 + 800000*x@1*c1@int_slack@1\n", + " + 800000*x@1*c1@int_slack@2 + 400000*x@1^2 + 400002*x@1*y\n", + " + 200000*y*c1@int_slack@0 + 400000*y*c1@int_slack@1 + 400000*y*c1@int_slack@2\n", + " + 100000*y^2 - 1000000*c1@int_slack@0 - 2000000*c1@int_slack@1\n", + " - 2000000*c1@int_slack@2 - 999998*x@0 - 1999996*x@1 - 1000000*y + 2500000\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Binary variables (6)\n", + " x@0 x@1 y c1@int_slack@0 c1@int_slack@1 c1@int_slack@2\n", + "\n", + "penalty: 100000.0\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper.converters import OptimizationProblemToQubo\n", + "\n", + "# Create a optimization problem with various constraints\n", + "op6 = OptimizationProblem(\"Quadratic Program to QUBO Example\")\n", + "op6.integer_var(name=\"x\", lowerbound=0, upperbound=3)\n", + "op6.binary_var(name=\"y\")\n", + "op6.minimize(linear={\"x\": 2}, quadratic={(\"x\", \"y\"): 1})\n", + "op6.linear_constraint(linear={\"x\": 1, \"y\": 1}, sense=\"LE\", rhs=5, name=\"c1\")\n", + "print(\"Original Problem:\")\n", + "print(op6.prettyprint())\n", + "# Convert the optimization problem to QUBO format\n", + "qubo_converter = OptimizationProblemToQubo(penalty=1e5)\n", + "op6_qubo = qubo_converter.convert(op6)\n", + "print(\"\\nConverted QUBO Problem:\")\n", + "print(op6_qubo.prettyprint())\n", + "print(f\"penalty: {qubo_converter.penalty}\")" + ] + }, + { + "cell_type": "markdown", + "id": "62747971-a656-4688-8fd9-ee3e792bd63e", + "metadata": {}, + "source": [ + "## HUBO conversion\n", + "The `OptimizationProblemToHubo` converter transforms an `OptimizationProblem` into a HUBO format (higher-order objective function with no constraints) by converting variables to binary and eliminating constraints. It combines several converters: `IntegerToBinary`, `SpinToBinary`, `InequalityToPenalty`, `EqualityToPenalty`, and `MaximizeToMinimize`, while preserving higher-order terms in the objective function. The resulting HUBO problem can be directly mapped to an Ising Hamiltonian using the `to_ising()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d11d888d-357d-4499-8077-b6a6427b7a06", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Problem:\n", + "Problem name: Optimization Problem to HUBO Example\n", + "\n", + "Minimize\n", + " x*y*z + x*y + 2*x\n", + "\n", + "Subject to\n", + " Higher-order constraints (1)\n", + " x*y*z == 2 'c1'\n", + "\n", + " Integer variables (1)\n", + " 0 <= x <= 3\n", + "\n", + " Binary variables (2)\n", + " y z\n", + "\n", + "\n", + "Converted HUBO Problem:\n", + "Problem name: \n", + "\n", + "Minimize\n", + " 100000*x@0^2*y^2*z^2 + 400000*x@0*x@1*y^2*z^2 + 400000*x@1^2*y^2*z^2\n", + " - 399999*x@0*y*z - 799998*x@1*y*z + x@0*y + 2*x@1*y + 2*x@0 + 4*x@1 + 400000\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Binary variables (4)\n", + " x@0 x@1 y z\n", + "\n", + "penalty: 100000.0\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper.converters import OptimizationProblemToHubo\n", + "\n", + "# Create an optimization problem with higher-order terms and constraints\n", + "op7 = OptimizationProblem(\"Optimization Problem to HUBO Example\")\n", + "op7.integer_var(name=\"x\", lowerbound=0, upperbound=3)\n", + "op7.binary_var(name=\"y\")\n", + "op7.binary_var(name=\"z\")\n", + "\n", + "op7.minimize(linear={\"x\": 2}, quadratic={(\"x\", \"y\"): 1}, higher_order={3: {(\"x\", \"y\", \"z\"): 1}})\n", + "op7.higher_order_constraint(higher_order={3: {(\"x\", \"y\", \"z\"): 1}}, sense=\"EQ\", rhs=2, name=\"c1\")\n", + "print(\"Original Problem:\")\n", + "print(op7.prettyprint())\n", + "# Convert the optimization problem to HUBO format\n", + "hubo_converter = OptimizationProblemToHubo(penalty=1e5)\n", + "op7_hubo = hubo_converter.convert(op7)\n", + "print(\"\\nConverted HUBO Problem:\")\n", + "print(op7_hubo.prettyprint())\n", + "print(f\"penalty: {hubo_converter.penalty}\")" + ] + }, + { + "cell_type": "markdown", + "id": "4be55588-9a1f-4e11-a50e-72eaa5019dc4", + "metadata": {}, + "source": [ + "## Ising transformation\n", + "Both QUBO and HUBO problems can be mapped to Ising Hamiltonians by the `to_ising()` function. In the Optimization Mapper addon, these are represented by the `qiskit.quantum_info.SparsePauliOp` object.\n", + "In transforming to the Ising Hamiltonian, the binary variables are mapped to spin variables using the relation $b_i = (1 - Z_i)/2$, where $b_i$ is a binary variable and $Z_i$ is a Pauli-Z operator acting on the $i^{th}$ qubit. The resulting Ising Hamiltonian can then be used in quantum algorithms to approximate the ground state." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9f3bd98f-3edb-4dbd-8476-d5048cc75236", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Ising Hamiltonian from HUBO:\n", + "Ising Hamiltonian: SparsePauliOp(['IIIZ', 'IIZI', 'IZIZ', 'IZII', 'IZZI', 'ZIII', 'ZIIZ', 'ZZII', 'ZZIZ', 'ZIZI', 'ZZZI', 'IIII', 'IIZZ', 'IZZZ', 'ZIZZ', 'ZZZZ'],\n", + " coeffs=[ 12498.625+0.j, 24997.25 +0.j, -12499.625+0.j, 62498.875+0.j,\n", + " -24999.25 +0.j, 62499.625+0.j, -12499.875+0.j, -62499.625+0.j,\n", + " 12499.875+0.j, -24999.75 +0.j, 24999.75 +0.j, 73437.5 +0.j,\n", + " 25000. +0.j, -25000. +0.j, -25000. +0.j, 25000. +0.j])\n", + "Offset: 264066.625\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper.translators import to_ising\n", + "\n", + "ising_hubo, offset = to_ising(op7_hubo)\n", + "print(\"\\nIsing Hamiltonian from HUBO:\")\n", + "print(f\"Ising Hamiltonian: {ising_hubo}\")\n", + "print(f\"Offset: {offset}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers.ipynb new file mode 100644 index 00000000000..2a93164fa7f --- /dev/null +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers.ipynb @@ -0,0 +1,395 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Validate your modeling with classical solvers\"\n", + "description: \"Validate your modeling with classical solvers for the latest version of Optimization mapper\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "0a1236e8-872c-4d9b-97bc-da933fc63474", + "metadata": {}, + "source": [ + "# Validate your modeling with classical solvers\n", + "\n", + "Mapping an optimization problem to a formalism that a quantum computer can handle requires building a model. This model must be validated to ensure that its solutions correspond to the solutions of the original optimization problem we set out to solve. In this guide, we illustrate this mapping with the maximum independent set (MIS) problem. In MIS we seek the largest set of nodes of a graph $G=(V, E)$ such that no two nodes in the maximum independent set share an edge. Mathematically,\n", + "\n", + "\\begin{align}\n", + "&\\max_{x\\in\\{0,1\\}^n}\\sum_{i\\in V} x_i \\\\\n", + "&\\text{s.t.}\\quad x_i+x_j\\leq 1\\quad\\forall~(i,j)\\in E.\n", + "\\end{align}\n", + "Here, $n=|V|$ is the number of nodes of $G$, and if the decision variable $x_i=1$ then node $i$ is in the independent set. First, we will define a MIS problem instance on a small five-node graph. Such small instances are easy to solve and they allow us to test our modelling pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "03e48006-21f4-415d-872f-fa14fe046e82", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EdgeIndices[0, 1, 2, 3, 4, 5]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import rustworkx as rx\n", + "\n", + "graph = rx.PyGraph()\n", + "\n", + "# Add 5 nodes (0 through 4)\n", + "graph.add_nodes_from([None] * 5)\n", + "\n", + "# Add edges\n", + "edges = [(0, 1, 1), (0, 4, 1), (1, 2, 1), (1, 4, 1), (2, 3, 1), (3, 4, 1)]\n", + "graph.add_edges_from(edges)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5cd95b79-e4de-48fe-8b30-da8afbdb9c77", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_opt_mapper.applications import IndependentSet\n", + "\n", + "mis = IndependentSet(graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "27b1b8d3-e2b7-4304-b8d8-d5e63af5deec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers/extracted-outputs/27b1b8d3-e2b7-4304-b8d8-d5e63af5deec-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mis.draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5e6d6203-86dd-42f4-8210-c04d72b63c5e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: Independent set\n", + "\n", + "Maximize\n", + " x_0 + x_1 + x_2 + x_3 + x_4\n", + "\n", + "Subject to\n", + " Linear constraints (6)\n", + " x_0 + x_1 <= 1 'c0'\n", + " x_0 + x_4 <= 1 'c1'\n", + " x_1 + x_2 <= 1 'c2'\n", + " x_1 + x_4 <= 1 'c3'\n", + " x_2 + x_3 <= 1 'c4'\n", + " x_3 + x_4 <= 1 'c5'\n", + "\n", + " Binary variables (5)\n", + " x_0 x_1 x_2 x_3 x_4\n", + "\n" + ] + } + ], + "source": [ + "opt_problem = mis.to_optimization_problem()\n", + "print(opt_problem.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "4b3517c7-5e6c-4319-8bb2-a9a9b2b9d537", + "metadata": {}, + "source": [ + "To implement this problem on a quantum computer, we need to convert the problem to a quadratic unconstrained binary optimization problem. This requires mapping inequality constraints into equality constraints, and then adding them as penalty terms to the objective. We do this with the `LinearInequalityToPenalty` converter." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "125627ad-5ceb-4e38-8a7f-1dc244d58969", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_opt_mapper.converters import LinearInequalityToPenalty\n", + "\n", + "penalty = 0.5\n", + "converter = LinearInequalityToPenalty(penalty=penalty)\n", + "converted_prob = converter.convert(opt_problem)" + ] + }, + { + "cell_type": "markdown", + "id": "c6b153b0-c016-4899-8f6b-a31a08ba5992", + "metadata": {}, + "source": [ + "The result of the conversion is the optimization problem shown below. This problem no longer has any constraints. The objective is then easily converted into an Ising Hamiltonian, a step that we will not show here. Instead, we ask the question: *\"Is the unconstrained model of the MIS problem a good one?\"* Indeed, we made the choice to set `penalty = 0.5`, but was this a good choice?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6407eb00-7837-45e7-8364-fac1fcaa993b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: Independent set\n", + "\n", + "Maximize\n", + " -0.5*x_0*x_1 - 0.5*x_0*x_4 - 0.5*x_1*x_2 - 0.5*x_1*x_4 - 0.5*x_2*x_3\n", + " - 0.5*x_3*x_4 + x_0 + x_1 + x_2 + x_3 + x_4\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Binary variables (5)\n", + " x_0 x_1 x_2 x_3 x_4\n", + "\n" + ] + } + ], + "source": [ + "print(converted_prob.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "092b21aa-0d78-43a9-95ed-cdaef9ed42d2", + "metadata": {}, + "source": [ + "To answer the question above, we need to solve our model and see if the solution is indeed an independent set. We do this with a classical solver. Here, we use CPLEX, which at this small scale runs very efficiently. The code below imports the solver, solves the model, and interprets the result as a candidate solution to our original MIS optimization problem." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9619dc26-87f2-40be-9636-db7481463d46", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_opt_mapper.solvers import CplexSolver\n", + "\n", + "validation_solver = CplexSolver()\n", + "\n", + "result = validation_solver.solve(converted_prob)\n", + "y = converter.interpret(result.x)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f7035c7d-650b-4474-be9c-5eba30235051", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Objective value 4.0\n", + "Is y feasible? False\n" + ] + } + ], + "source": [ + "print(\"Objective value\", opt_problem.objective.evaluate(y))\n", + "print(\"Is y feasible?\", opt_problem.is_feasible(y))" + ] + }, + { + "cell_type": "markdown", + "id": "c61b4f08-dbd2-492c-81ad-7c7427fa06f2", + "metadata": {}, + "source": [ + "The result above indicates that the solution to our model is an infeasible solution to the MIS problem. This is problematic and indicates that something went wrong in the problem modeling. Indeed, the conversion of the constraints into penalty terms is not strong enough to enforce the independent set conditions. To remedy this situation, we change the modeling and increase the value of the penalty." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fb9fe906-618a-4c51-9076-d77c838b4b9f", + "metadata": {}, + "outputs": [], + "source": [ + "penalty2 = 2\n", + "converter2 = LinearInequalityToPenalty(penalty=penalty2)\n", + "converted_prob2 = converter2.convert(opt_problem)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2fffd4af-347c-4f20-b05a-fe46dfc40140", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: Independent set\n", + "\n", + "Maximize\n", + " -2*x_0*x_1 - 2*x_0*x_4 - 2*x_1*x_2 - 2*x_1*x_4 - 2*x_2*x_3 - 2*x_3*x_4 + x_0\n", + " + x_1 + x_2 + x_3 + x_4\n", + "\n", + "Subject to\n", + " No constraints\n", + "\n", + " Binary variables (5)\n", + " x_0 x_1 x_2 x_3 x_4\n", + "\n" + ] + } + ], + "source": [ + "print(converted_prob2.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "19aa8f2a-2f6e-4395-b8b8-31bb0a7348d5", + "metadata": {}, + "source": [ + "Now, we solve this new model with CPLEX." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0708bf53-98ec-4359-b083-269b069b8292", + "metadata": {}, + "outputs": [], + "source": [ + "result2 = validation_solver.solve(converted_prob2)\n", + "y2 = converter.interpret(result2.x)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "49292e2f-5727-4d1a-a566-3c0178b14e4b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Objective value 2.0\n", + "Is y feasible? True\n" + ] + } + ], + "source": [ + "print(\"Objective value\", opt_problem.objective.evaluate(y2))\n", + "print(\"Is y feasible?\", opt_problem.is_feasible(y2))" + ] + }, + { + "cell_type": "markdown", + "id": "3c4235ab-7ed4-4993-961d-624ce2fe3782", + "metadata": {}, + "source": [ + "We observe that the optimal solution found by CPLEX is feasible. We can also visualize the solution form the bad model and the one from the good model. As seen below, there are multiple nodes in the bad solution `y` that share an edge and are therefore not an independent set. Here we see how modeling assumptions play an important role in the type of solution obtained, and the importance of testing the modeling pipeline with small problem instances that can be efficiently solved." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f1281552-b611-4911-bdb9-df7d1df97ad4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers/extracted-outputs/f1281552-b611-4911-bdb9-df7d1df97ad4-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mis.draw(y)" + ] + }, + { + "cell_type": "markdown", + "id": "dc0bf66c-1653-4998-a38f-7dc8b4bf50bc", + "metadata": {}, + "source": [ + "In the good solution `y2`, drawn below, we see that no two nodes share an edge and are therefore an independent set." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "309f88e4-5d1f-47f8-b63a-50346829ffeb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers/extracted-outputs/309f88e4-5d1f-47f8-b63a-50346829ffeb-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mis.draw(y2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb new file mode 100644 index 00000000000..21a574f2e1b --- /dev/null +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb @@ -0,0 +1,260 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Translate between OptimizationProblem and Docplex\"\n", + "description: \"Translate between OptimizationProblem and Docplex for the latest version of Optimization mapper\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "5de6a778-fba3-4d06-9d91-6fa050da8ac8", + "metadata": {}, + "source": [ + "# Translate between `OptimizationProblem` and Docplex\n", + "\n", + "If you are familiar with the Docplex workflow, you can seamlessly translate between a Docplex model and an `OptimizationProblem` with the `from_docplex_mp` and `to_docplex_mp` translators. With this translation, you can take advantage of Docplex capabilities, such as **LP file loading**, without many additional steps.\n", + "\n", + "\n", + "\\<div style=\"background-color:#fff3cd; color:#856404; padding:15px; border:1px solid #ffeeba; border-radius:5px; font-size:16px;\">\n", + "\\<strong>Note:\\</strong> Docplex does not support higher-order terms in its formulation. This conversion is limited to constant, linear, and quadratic terms.\n", + "\\</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6bd023be-4265-4903-af39-2f976d413720", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\ This file has been generated by DOcplex\n", + "\\ ENCODING=ISO-8859-1\n", + "\\Problem name: docplex model\n", + "\n", + "Minimize\n", + " obj: x + [ 4 x*y - 2 z^2 ]/2 + 3\n", + "Subject To\n", + " lin_eq: x + 2 y = 3\n", + " lin_leq: x + 2 y <= 3\n", + " lin_geq: x + 2 y >= 3\n", + "\n", + "Bounds\n", + " 0 <= x <= 1\n", + " -1 <= y <= 5\n", + " -1 <= z <= 5\n", + "\n", + "Binaries\n", + " x\n", + "\n", + "Generals\n", + " y\n", + "End\n", + "\n" + ] + } + ], + "source": [ + "from docplex.mp.model import Model\n", + "\n", + "docplex_mod = Model(name=\"docplex model\")\n", + "x = docplex_mod.binary_var(name=\"x\")\n", + "y = docplex_mod.integer_var(name=\"y\", lb=-1, ub=5)\n", + "z = docplex_mod.continuous_var(name=\"z\", lb=-1, ub=5)\n", + "\n", + "docplex_mod.minimize(3 + x + 2 * x * y - z * z)\n", + "docplex_mod.add_constraint(x + 2 * y == 3, \"lin_eq\")\n", + "docplex_mod.add_constraint(x + 2 * y <= 3, \"lin_leq\")\n", + "docplex_mod.add_constraint(x + 2 * y >= 3, \"lin_geq\")\n", + "\n", + "print(docplex_mod.export_as_lp_string())" + ] + }, + { + "cell_type": "markdown", + "id": "744f3872-4303-4c87-a97a-21501c97ebe9", + "metadata": {}, + "source": [ + "When you display your problem in LP format using `export_as_lp_string`,\n", + "`Binaries` denotes binary variables and `Generals` denotes integer variables.\n", + "\n", + "If variables are not included in either `Binaries` or `Generals`, such variables are continuous ones with default lower bound == 0 and upper bound == infinity.\n", + "\n", + "Note that you cannot use `'e`' or `'E'` as the first character of names due to the [specification of LP format](https://www.ibm.com/docs/en/icos/22.1.2?topic=representation-variable-names-in-lp-file-format).\n", + "\n", + "The following snippet shows the `from_docplex_mp` translator:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c5ee6a10-bd8a-4deb-af04-511e32eeb3ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: docplex model\n", + "\n", + "Minimize\n", + " 2*x*y - z^2 + x + 3\n", + "\n", + "Subject to\n", + " Linear constraints (3)\n", + " x + 2*y == 3 'lin_eq'\n", + " x + 2*y <= 3 'lin_leq'\n", + " x + 2*y >= 3 'lin_geq'\n", + "\n", + " Integer variables (1)\n", + " -1 <= y <= 5\n", + "\n", + " Continuous variables (1)\n", + " -1 <= z <= 5\n", + "\n", + " Binary variables (1)\n", + " x\n", + "\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper.translators import from_docplex_mp\n", + "\n", + "mod2 = from_docplex_mp(docplex_mod)\n", + "print(mod2.prettyprint())" + ] + }, + { + "cell_type": "markdown", + "id": "3040a270-6179-41ee-8d48-85249574d383", + "metadata": {}, + "source": [ + "As stated before, you can also convert a `OptimizationProblem` to a Docplex model using the `to_docplex` function." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1774637f-205d-4983-9e4c-9b21a0fbc00c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\ This file has been generated by DOcplex\n", + "\\ ENCODING=ISO-8859-1\n", + "\\Problem name: docplex model\n", + "\n", + "Minimize\n", + " obj: x + [ 4 x*y - 2 z^2 ]/2 + 3\n", + "Subject To\n", + " lin_eq: x + 2 y = 3\n", + " lin_leq: x + 2 y <= 3\n", + " lin_geq: x + 2 y >= 3\n", + "\n", + "Bounds\n", + " 0 <= x <= 1\n", + " -1 <= y <= 5\n", + " -1 <= z <= 5\n", + "\n", + "Binaries\n", + " x\n", + "\n", + "Generals\n", + " y\n", + "End\n", + "\n" + ] + } + ], + "source": [ + "from qiskit_addon_opt_mapper.translators import to_docplex_mp\n", + "\n", + "docplex_mod2 = to_docplex_mp(mod2)\n", + "print(docplex_mod2.export_as_lp_string())" + ] + }, + { + "cell_type": "markdown", + "id": "04be4093-2c60-4ea5-afeb-94ddcc113856", + "metadata": {}, + "source": [ + "## Load LP files using Docplex" + ] + }, + { + "cell_type": "markdown", + "id": "34ded7cf-5248-4321-9faf-672a81559e7b", + "metadata": {}, + "source": [ + "Once you are familia with the Docplex translators, loading from LP files becomes very simple. You can use the `docplex.mp.ModelReader` class followed by a translation step:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53569172-bf04-4416-bd2f-7d55e9e5657a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem name: my_lp_file\n", + "\n", + "Minimize\n", + " 2*x0^2 + 0.5*x0*x1 + 0.5*x1^2 + 2*x0 + 2*x1\n", + "\n", + "Subject to\n", + " Linear constraints (1)\n", + " x0 + x1 == 2 'constraint'\n", + "\n", + " Integer variables (2)\n", + " 0 <= x0 <= 4\n", + " 0 <= x1 <= 4\n", + "\n" + ] + } + ], + "source": [ + "from docplex.mp.model_reader import ModelReader\n", + "\n", + "model = ModelReader.read(\"data/my_lp_file.lp\")\n", + "\n", + "op3 = from_docplex_mp(model)\n", + "print(op3.prettyprint())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/index.mdx b/docs/addons/qiskit-addon-opt-mapper/how-tos/index.mdx new file mode 100644 index 00000000000..3b62ad63afb --- /dev/null +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/index.mdx @@ -0,0 +1,41 @@ +--- +title: "How-To Guides" +description: "How-To Guides for the lastest version of Optimization mapper" +--- + +# How-To Guides + +How-To Guides + +* [Migrate from `qiskit-optimization` to `qiskit-addon-opt-mapper`](00-migrate-from-qiskit-optimization) + + * [Summary](00-migrate-from-qiskit-optimization#summary) + * [Tables](00-migrate-from-qiskit-optimization#tables) + +* [Model quantum optimization problems with the `OptimizationProblem` class](01-optimization-problem) + + * [Introduction](01-optimization-problem#introduction) + * [Construct an `OptimizationProblem`](01-optimization-problem#construct-an-optimizationproblem) + * [Add or remove linear, quadratic, and higher-order terms to and from the constraints](01-optimization-problem#add-or-remove-linear,-quadratic,-and-higher-order-terms-to-and-from-the-constraints) + * [Substitute variables](01-optimization-problem#substitute-variables) + +* [Convert formulations of an `OptimizationProblem`](02-converters-for-optimization-problems) + + * [Overview](02-converters-for-optimization-problems#overview) + * [Code examples](02-converters-for-optimization-problems#code-examples) + * [Binary to spin conversion](02-converters-for-optimization-problems#binary-to-spin-conversion) + * [Spin to binary conversion](02-converters-for-optimization-problems#spin-to-binary-conversion) + * [Inequality to equality conversion](02-converters-for-optimization-problems#inequality-to-equality-conversion) + * [Equality to penalty conversion](02-converters-for-optimization-problems#equality-to-penalty-conversion) + * [Linear inequality to penalty conversion](02-converters-for-optimization-problems#linear-inequality-to-penalty-conversion) + * [Maximize to minimize conversion](02-converters-for-optimization-problems#maximize-to-minimize-conversion) + * [QUBO conversion](02-converters-for-optimization-problems#qubo-conversion) + * [HUBO conversion](02-converters-for-optimization-problems#hubo-conversion) + * [Ising transformation](02-converters-for-optimization-problems#ising-transformation) + +* [Validate your modeling with classical solvers](03-validate-with-solvers) + +* [Translate between `OptimizationProblem` and Docplex](04-translate-docplex) + + * [Load LP files using Docplex](04-translate-docplex#load-lp-files-using-docplex) + diff --git a/docs/addons/qiskit-addon-opt-mapper/index.mdx b/docs/addons/qiskit-addon-opt-mapper/index.mdx new file mode 100644 index 00000000000..adc473ffeb7 --- /dev/null +++ b/docs/addons/qiskit-addon-opt-mapper/index.mdx @@ -0,0 +1,37 @@ +--- +title: "Optimization Mapper Qiskit addon" +description: "Documentation for the latest version of Optimization mapper" +--- + +# Optimization Mapper Qiskit addon + +[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. + +This package contains functionality to model optimization problems and map them to operators. For example, this package contains functions for creating models of binary optimization problems to solve with Qiskit. + +## Installation + +We encourage installing this package via `pip`, when possible: + +```bash +pip install 'qiskit-addon-opt-mapper' +``` + +For more installation information, refer to the [installation instructions](install) in the documentation. + +## Deprecation policy + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We might occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [release notes](/docs/api/qiskit-addon-opt-mapper/release-notes). + +## Contributing + +The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-opt-mapper). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-opt-mapper/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). + +We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-opt-mapper/issues/new/choose) for tracking requests and bugs. + +## License + +[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-opt-mapper/blob/main/LICENSE.txt) + diff --git a/docs/addons/qiskit-addon-opt-mapper/install.mdx b/docs/addons/qiskit-addon-opt-mapper/install.mdx new file mode 100644 index 00000000000..a5fa3a2ad5e --- /dev/null +++ b/docs/addons/qiskit-addon-opt-mapper/install.mdx @@ -0,0 +1,77 @@ +--- +title: "Installation instructions" +description: "Installation instructions for the lastest version of Optimization mapper" +--- + +# Installation instructions + +There are two primary ways to run and install the packages: + +* [Option 1: Install from PyPI](#option-1) +* [Option 2: Install from source](#option-2) + +## Pre-installation + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +Note: If you are using Windows, use the following commands in PowerShell: + +```pwsh +python3 -m venv c:\path\to\virtual\environment +c:\path\to\virtual\environment\Scripts\Activate.ps1 +``` + +<span id="option-1" /> + +## Option 1: Install from PyPI + +The most straightforward way to install the `qiskit-addon-opt-mapper` package is via `PyPI`. + +```sh +pip install 'qiskit-addon-opt-mapper' +``` + +<span id="option-2" /> + +## Option 2: Install from source + +To develop in the repository or run the notebooks locally, install from source. + +The first step is to clone the `qiskit-addon-opt-mapper` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-opt-mapper.git +``` + +Next, upgrade `pip` and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-opt-mapper +``` + +The next step is to install `qiskit-addon-opt-mapper` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you should also install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab +``` + diff --git a/docs/api/qiskit-addon-opt-mapper/_package.json b/docs/api/qiskit-addon-opt-mapper/_package.json new file mode 100644 index 00000000000..817d860d3fc --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/_package.json @@ -0,0 +1,4 @@ +{ + "name": "qiskit-addon-opt-mapper", + "version": "0.1.0" +} diff --git a/docs/api/qiskit-addon-opt-mapper/_toc.json b/docs/api/qiskit-addon-opt-mapper/_toc.json new file mode 100644 index 00000000000..8ed05b9e4ef --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/_toc.json @@ -0,0 +1,283 @@ +{ + "title": "Optimization mapper", + "children": [ + { + "title": "API index", + "url": "/docs/api/qiskit-addon-opt-mapper" + }, + { + "title": "Release notes", + "useDivider": true, + "url": "/docs/api/qiskit-addon-opt-mapper/release-notes" + }, + { + "title": "qiskit_addon_opt_mapper.applications", + "untranslatable": true, + "children": [ + { + "title": "Module overview", + "url": "/docs/api/qiskit-addon-opt-mapper/applications" + }, + { + "title": "BinPacking", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-bin-packing", + "untranslatable": true + }, + { + "title": "Clique", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-clique", + "untranslatable": true + }, + { + "title": "ExactCover", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-exact-cover", + "untranslatable": true + }, + { + "title": "GraphOptimizationApplication", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-graph-optimization-application", + "untranslatable": true + }, + { + "title": "GraphPartition", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-graph-partition", + "untranslatable": true + }, + { + "title": "Knapsack", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-knapsack", + "untranslatable": true + }, + { + "title": "Maxcut", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-maxcut", + "untranslatable": true + }, + { + "title": "NumberPartition", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-number-partition", + "untranslatable": true + }, + { + "title": "OptimizationApplication", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-optimization-application", + "untranslatable": true + }, + { + "title": "SetPacking", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-set-packing", + "untranslatable": true + }, + { + "title": "SKModel", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-sk-model", + "untranslatable": true + }, + { + "title": "Tsp", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-tsp", + "untranslatable": true + }, + { + "title": "VehicleRouting", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-vehicle-routing", + "untranslatable": true + }, + { + "title": "VertexCover", + "url": "/docs/api/qiskit-addon-opt-mapper/applications-vertex-cover", + "untranslatable": true + } + ] + }, + { + "title": "qiskit_addon_opt_mapper.converters", + "untranslatable": true, + "children": [ + { + "title": "Module overview", + "url": "/docs/api/qiskit-addon-opt-mapper/converters" + }, + { + "title": "BinaryToLinearBinary", + "url": "/docs/api/qiskit-addon-opt-mapper/converters-binary-to-linear-binary", + "untranslatable": true + }, + { + "title": "BinaryToSpin", + "url": "/docs/api/qiskit-addon-opt-mapper/converters-binary-to-spin", + "untranslatable": true + }, + { + "title": "InequalityToEquality", + "url": "/docs/api/qiskit-addon-opt-mapper/converters-inequality-to-equality", + "untranslatable": true + }, + { + "title": "IntegerToBinary", + "url": "/docs/api/qiskit-addon-opt-mapper/converters-integer-to-binary", + "untranslatable": true + }, + { + "title": "LinearInequalityToPenalty", + "url": "/docs/api/qiskit-addon-opt-mapper/converters-linear-inequality-to-penalty", + "untranslatable": true + }, + { + "title": "MaximizeToMinimize", + "url": "/docs/api/qiskit-addon-opt-mapper/converters-maximize-to-minimize", + "untranslatable": true + }, + { + "title": "MinimizeToMaximize", + "url": "/docs/api/qiskit-addon-opt-mapper/converters-minimize-to-maximize", + "untranslatable": true + }, + { + "title": "OptimizationProblemConverter", + "url": "/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-converter", + "untranslatable": true + }, + { + "title": "OptimizationProblemToHubo", + "url": "/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-to-hubo", + "untranslatable": true + }, + { + "title": "OptimizationProblemToQubo", + "url": "/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-to-qubo", + "untranslatable": true + }, + { + "title": "SpinToBinary", + "url": "/docs/api/qiskit-addon-opt-mapper/converters-spin-to-binary", + "untranslatable": true + } + ] + }, + { + "title": "qiskit_addon_opt_mapper.problems", + "untranslatable": true, + "children": [ + { + "title": "Module overview", + "url": "/docs/api/qiskit-addon-opt-mapper/problems" + }, + { + "title": "Constraint", + "url": "/docs/api/qiskit-addon-opt-mapper/problems-constraint", + "untranslatable": true + }, + { + "title": "HigherOrderConstraint", + "url": "/docs/api/qiskit-addon-opt-mapper/problems-higher-order-constraint", + "untranslatable": true + }, + { + "title": "HigherOrderExpression", + "url": "/docs/api/qiskit-addon-opt-mapper/problems-higher-order-expression", + "untranslatable": true + }, + { + "title": "LinearConstraint", + "url": "/docs/api/qiskit-addon-opt-mapper/problems-linear-constraint", + "untranslatable": true + }, + { + "title": "LinearExpression", + "url": "/docs/api/qiskit-addon-opt-mapper/problems-linear-expression", + "untranslatable": true + }, + { + "title": "OptimizationObjective", + "url": "/docs/api/qiskit-addon-opt-mapper/problems-optimization-objective", + "untranslatable": true + }, + { + "title": "OptimizationProblemElement", + "url": "/docs/api/qiskit-addon-opt-mapper/problems-optimization-problem-element", + "untranslatable": true + }, + { + "title": "QuadraticConstraint", + "url": "/docs/api/qiskit-addon-opt-mapper/problems-quadratic-constraint", + "untranslatable": true + }, + { + "title": "QuadraticExpression", + "url": "/docs/api/qiskit-addon-opt-mapper/problems-quadratic-expression", + "untranslatable": true + }, + { + "title": "Variable", + "url": "/docs/api/qiskit-addon-opt-mapper/problems-variable", + "untranslatable": true + } + ] + }, + { + "title": "qiskit_addon_opt_mapper.solvers", + "untranslatable": true, + "children": [ + { + "title": "Module overview", + "url": "/docs/api/qiskit-addon-opt-mapper/solvers" + }, + { + "title": "CplexSolver", + "url": "/docs/api/qiskit-addon-opt-mapper/solvers-cplex-solver", + "untranslatable": true + }, + { + "title": "GurobiSolver", + "url": "/docs/api/qiskit-addon-opt-mapper/solvers-gurobi-solver", + "untranslatable": true + }, + { + "title": "OptimizationSolver", + "url": "/docs/api/qiskit-addon-opt-mapper/solvers-optimization-solver", + "untranslatable": true + }, + { + "title": "ScipyMilpSolver", + "url": "/docs/api/qiskit-addon-opt-mapper/solvers-scipy-milp-solver", + "untranslatable": true + }, + { + "title": "SolverResult", + "url": "/docs/api/qiskit-addon-opt-mapper/solvers-solver-result", + "untranslatable": true + } + ] + }, + { + "title": "qiskit_addon_opt_mapper.translators", + "untranslatable": true, + "children": [ + { + "title": "Module overview", + "url": "/docs/api/qiskit-addon-opt-mapper/translators" + }, + { + "title": "from_docplex_mp", + "url": "/docs/api/qiskit-addon-opt-mapper/translators-from-docplex-mp", + "untranslatable": true + }, + { + "title": "to_docplex_mp", + "url": "/docs/api/qiskit-addon-opt-mapper/translators-to-docplex-mp", + "untranslatable": true + }, + { + "title": "to_ising", + "url": "/docs/api/qiskit-addon-opt-mapper/translators-to-ising", + "untranslatable": true + } + ] + } + ], + "collapsed": true, + "untranslatable": true, + "parentUrl": "/docs/addons/qiskit-addon-opt-mapper", + "parentLabel": "Optimization mapper" +} diff --git a/docs/api/qiskit-addon-opt-mapper/applications-bin-packing.mdx b/docs/api/qiskit-addon-opt-mapper/applications-bin-packing.mdx new file mode 100644 index 00000000000..510f499d995 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-bin-packing.mdx @@ -0,0 +1,130 @@ +--- +title: BinPacking (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.BinPacking in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.BinPacking +--- + +<span id="qiskit-addon-opt-mapper-applications-binpacking" /> + +# qiskit\_addon\_opt\_mapper.applications.BinPacking + +<Class id="qiskit_addon_opt_mapper.applications.BinPacking" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/bin_packing.py#L40-L156" signature="BinPacking(weights, max_weight, max_number_of_bins=None)" modifiers="class"> + Bases: [`OptimizationApplication`](applications-optimization-application "qiskit_addon_opt_mapper.applications.optimization_application.OptimizationApplication") + + Optimization application for the “bin packing” \[1] problem. + + **References** + + \[1]: “Bin packing”, [https://en.wikipedia.org/wiki/Bin\_packing\_problem](https://en.wikipedia.org/wiki/Bin_packing_problem) + + BinPacking init. + + **Parameters** + + * **weights** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – A list of the weights of items + * **max\_weight** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The maximum bin weight capacity + * **max\_number\_of\_bins** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – The maximum number of bins by default equal to the number of items + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.BinPacking.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/bin_packing.py#L49-L64" signature="__init__(weights, max_weight, max_number_of_bins=None)"> + BinPacking init. + + **Parameters** + + * **weights** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – A list of the weights of items + * **max\_weight** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The maximum bin weight capacity + * **max\_number\_of\_bins** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – The maximum number of bins by default equal to the number of items + + **Return type** + + None + </Function> + + ## Methods + + | | | + | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.applications.BinPacking.__init__ "qiskit_addon_opt_mapper.applications.BinPacking.__init__")(weights, max\_weight\[, ...]) | BinPacking init. | + | [`get_figure`](#qiskit_addon_opt_mapper.applications.BinPacking.get_figure "qiskit_addon_opt_mapper.applications.BinPacking.get_figure")(result) | Get plot of the solution of the Bin Packing Problem. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.BinPacking.interpret "qiskit_addon_opt_mapper.applications.BinPacking.interpret")(result) | Interpret a result as item indices. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.BinPacking.sample_most_likely "qiskit_addon_opt_mapper.applications.BinPacking.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.BinPacking.to_optimization_problem "qiskit_addon_opt_mapper.applications.BinPacking.to_optimization_problem")() | Represent as an optimization problem. | + + ### get\_figure + + <Function id="qiskit_addon_opt_mapper.applications.BinPacking.get_figure" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/bin_packing.py#L115-L156" signature="get_figure(result)"> + Get plot of the solution of the Bin Packing Problem. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + + **Returns** + + A plot of the solution, where x and y represent the bins and sum of the weights respectively. + + **Return type** + + fig + </Function> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.BinPacking.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/bin_packing.py#L95-L113" signature="interpret(result)"> + Interpret a result as item indices. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + + **Returns** + + A list of lists with the items in each bin + + **Return type** + + items\_in\_bins + </Function> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.BinPacking.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.BinPacking.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/bin_packing.py#L66-L93" signature="to_optimization_problem()"> + Represent as an optimization problem. + + Convert a bin packing problem instance into an `OptimizationProblem` + + **Returns** + + The `OptimizationProblem` created from the bin packing problem instance. + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-clique.mdx b/docs/api/qiskit-addon-opt-mapper/applications-clique.mdx new file mode 100644 index 00000000000..88085498e8c --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-clique.mdx @@ -0,0 +1,168 @@ +--- +title: Clique (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.Clique in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.Clique +--- + +<span id="qiskit-addon-opt-mapper-applications-clique" /> + +# qiskit\_addon\_opt\_mapper.applications.Clique + +<Class id="qiskit_addon_opt_mapper.applications.Clique" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/clique.py#L27-L134" signature="Clique(graph, size=None)" modifiers="class"> + Bases: [`GraphOptimizationApplication`](applications-graph-optimization-application "qiskit_addon_opt_mapper.applications.graph_optimization_application.GraphOptimizationApplication") + + Optimization application for the “clique” \[1] problem based on a NetworkX graph. + + **References** + + \[1]: “Clique (graph theory)”, [https://en.wikipedia.org/wiki/Clique\_(graph\_theory)](https://en.wikipedia.org/wiki/Clique_\(graph_theory\)) + + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)) – A graph representing a problem. It can be specified directly as a [NetworkX](https://networkx.org/) graph, or as an array or list format suitable to build out a NetworkX graph. + * **size** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – The size of the clique. When it’s None, the default, this class makes an optimization model for a maximal clique instead of the specified size of a clique. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.Clique.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/clique.py#L36-L47" signature="__init__(graph, size=None)"> + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)) – + + A graph representing a problem. It can be specified directly as a [NetworkX](https://networkx.org/) graph, or as an array or list format suitable to build out a NetworkX graph. + + * **size** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – The size of the clique. When it’s None, the default, this class makes an optimization model for a maximal clique instead of the specified size of a clique. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.applications.Clique.__init__ "qiskit_addon_opt_mapper.applications.Clique.__init__")(graph\[, size]) | Init method. | + | [`draw`](#qiskit_addon_opt_mapper.applications.Clique.draw "qiskit_addon_opt_mapper.applications.Clique.draw")(\[result, pos]) | Draw a graph with the result. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.Clique.interpret "qiskit_addon_opt_mapper.applications.Clique.interpret")(result) | Interpret a result as a list of node indices. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.Clique.sample_most_likely "qiskit_addon_opt_mapper.applications.Clique.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.Clique.to_optimization_problem "qiskit_addon_opt_mapper.applications.Clique.to_optimization_problem")() | Represent as an optimization problem. | + + ## Attributes + + | | | + | -------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- | + | [`graph`](#qiskit_addon_opt_mapper.applications.Clique.graph "qiskit_addon_opt_mapper.applications.Clique.graph") | Getter of the graph. | + | [`nx_graph`](#qiskit_addon_opt_mapper.applications.Clique.nx_graph "qiskit_addon_opt_mapper.applications.Clique.nx_graph") | Getter of the graph in Networkx format. | + | [`size`](#qiskit_addon_opt_mapper.applications.Clique.size "qiskit_addon_opt_mapper.applications.Clique.size") | Getter of size. | + + ### draw + + <Function id="qiskit_addon_opt_mapper.applications.Clique.draw" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L63-L81" signature="draw(result=None, pos=None)"> + Draw a graph with the result. + + When the result is None, draw an original graph without colors. + + **Parameters** + + * **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| None*) – The calculated result for the problem + * **pos** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)*] | None*) – The positions of nodes + + **Return type** + + None + </Function> + + ### graph + + <Attribute id="qiskit_addon_opt_mapper.applications.Clique.graph" attributeTypeHint="PyGraph"> + Getter of the graph. + + **Returns** + + A graph for a problem + </Attribute> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.Clique.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/clique.py#L76-L91" signature="interpret(result)"> + Interpret a result as a list of node indices. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + + **Returns** + + The list of node indices whose corresponding variable is 1 + + **Return type** + + [list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)] + </Function> + + ### nx\_graph + + <Attribute id="qiskit_addon_opt_mapper.applications.Clique.nx_graph" attributeTypeHint="Graph"> + Getter of the graph in Networkx format. + + **Returns** + + A graph for a problem + </Attribute> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.Clique.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### size + + <Attribute id="qiskit_addon_opt_mapper.applications.Clique.size" attributeTypeHint="int | None"> + Getter of size. + + **Returns** + + The size of the clique, None when maximal clique + </Attribute> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.Clique.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/clique.py#L49-L74" signature="to_optimization_problem()"> + Represent as an optimization problem. + + Convert a clique problem instance into a `OptimizationProblem`. When “size” is None, this makes an optimization model for a maximal clique instead of the specified size of a clique. + + **Returns** + + The `OptimizationProblem` created from the clique problem instance. + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-exact-cover.mdx b/docs/api/qiskit-addon-opt-mapper/applications-exact-cover.mdx new file mode 100644 index 00000000000..75548ce3e7b --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-exact-cover.mdx @@ -0,0 +1,107 @@ +--- +title: ExactCover (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.ExactCover in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.ExactCover +--- + +<span id="qiskit-addon-opt-mapper-applications-exactcover" /> + +# qiskit\_addon\_opt\_mapper.applications.ExactCover + +<Class id="qiskit_addon_opt_mapper.applications.ExactCover" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/exact_cover.py#L26-L82" signature="ExactCover(subsets)" modifiers="class"> + Bases: [`OptimizationApplication`](applications-optimization-application "qiskit_addon_opt_mapper.applications.optimization_application.OptimizationApplication") + + Optimization application for the “exact cover” \[1] problem. + + **References** + + \[1]: “Exact cover”, [https://en.wikipedia.org/wiki/Exact\_cover](https://en.wikipedia.org/wiki/Exact_cover) + + Init method. + + **Parameters** + + **subsets** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]]*) – A list of subsets + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.ExactCover.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/exact_cover.py#L34-L44" signature="__init__(subsets)"> + Init method. + + **Parameters** + + **subsets** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]]*) – A list of subsets + + **Return type** + + None + </Function> + + ## Methods + + | | | + | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.applications.ExactCover.__init__ "qiskit_addon_opt_mapper.applications.ExactCover.__init__")(subsets) | Init method. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.ExactCover.interpret "qiskit_addon_opt_mapper.applications.ExactCover.interpret")(result) | Interpret a result as a list of subsets. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.ExactCover.sample_most_likely "qiskit_addon_opt_mapper.applications.ExactCover.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.ExactCover.to_optimization_problem "qiskit_addon_opt_mapper.applications.ExactCover.to_optimization_problem")() | Represent as an optimization problem. | + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.ExactCover.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/exact_cover.py#L67-L82" signature="interpret(result)"> + Interpret a result as a list of subsets. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + + **Returns** + + A list of subsets whose corresponding variable is 1 + + **Return type** + + [list](https://docs.python.org/3/library/stdtypes.html#list)\[[list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)]] + </Function> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.ExactCover.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.ExactCover.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/exact_cover.py#L46-L65" signature="to_optimization_problem()"> + Represent as an optimization problem. + + Convert an exact cover instance into a `OptimizationProblem` + + **Returns** + + The `OptimizationProblem` created from the exact cover instance. + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-graph-optimization-application.mdx b/docs/api/qiskit-addon-opt-mapper/applications-graph-optimization-application.mdx new file mode 100644 index 00000000000..e86ba32b4db --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-graph-optimization-application.mdx @@ -0,0 +1,156 @@ +--- +title: GraphOptimizationApplication (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.GraphOptimizationApplication in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.GraphOptimizationApplication +--- + +<span id="qiskit-addon-opt-mapper-applications-graphoptimizationapplication" /> + +# qiskit\_addon\_opt\_mapper.applications.GraphOptimizationApplication + +<Class id="qiskit_addon_opt_mapper.applications.GraphOptimizationApplication" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L28-L126" signature="GraphOptimizationApplication(graph)" modifiers="class"> + Bases: [`OptimizationApplication`](applications-optimization-application "qiskit_addon_opt_mapper.applications.optimization_application.OptimizationApplication") + + An abstract class for graph optimization applications. + + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*PyGraph*](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html#rustworkx.PyGraph)) – + + A graph representing a problem. It can be specified in the following formats: + + * A Rustworkx undirected graph (`rx.PyGraph`) + * A NetworkX undirected graph (`nx.Graph`) + * A NumPy adjacency matrix (`np.ndarray`) + * A list of edges or adjacency list (`list`) + + * **rx.PyGraph.** (*The input graph will be internally normalized to a*) + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L31-L52" signature="__init__(graph)"> + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*PyGraph*](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html#rustworkx.PyGraph)) – + + A graph representing a problem. It can be specified in the following formats: + + * A Rustworkx undirected graph (`rx.PyGraph`) + * A NetworkX undirected graph (`nx.Graph`) + * A NumPy adjacency matrix (`np.ndarray`) + * A list of edges or adjacency list (`list`) + + * **rx.PyGraph.** (*The input graph will be internally normalized to a*) + + **Return type** + + None + </Function> + + ## Methods + + | | | + | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.__init__ "qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.__init__")(graph) | Init method. | + | [`draw`](#qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.draw "qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.draw")(\[result, pos]) | Draw a graph with the result. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.interpret "qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.interpret")(result) | Interpret the problem. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.sample_most_likely "qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.to_optimization_problem "qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.to_optimization_problem")() | Represent as an optimization problem. | + + ## Attributes + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- | + | [`graph`](#qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.graph "qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.graph") | Getter of the graph. | + | [`nx_graph`](#qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.nx_graph "qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.nx_graph") | Getter of the graph in Networkx format. | + + ### draw + + <Function id="qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.draw" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L63-L81" signature="draw(result=None, pos=None)"> + Draw a graph with the result. + + When the result is None, draw an original graph without colors. + + **Parameters** + + * **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| None*) – The calculated result for the problem + * **pos** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)*] | None*) – The positions of nodes + + **Return type** + + None + </Function> + + ### graph + + <Attribute id="qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.graph" attributeTypeHint="PyGraph"> + Getter of the graph. + + **Returns** + + A graph for a problem + </Attribute> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L37-L48" signature="interpret(result)" modifiers="abstract"> + Interpret the problem. + + Convert the calculation result of the problem ([`SolverResult`](solvers-solver-result "qiskit_addon_opt_mapper.solvers.SolverResult") or a binary array using np.ndarray) to the answer of the problem in an easy-to-understand format. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + </Function> + + ### nx\_graph + + <Attribute id="qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.nx_graph" attributeTypeHint="Graph"> + Getter of the graph in Networkx format. + + **Returns** + + A graph for a problem + </Attribute> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.GraphOptimizationApplication.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L28-L35" signature="to_optimization_problem()" modifiers="abstract"> + Represent as an optimization problem. + + Convert a problem instance into a `OptimizationProblem` + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-graph-partition.mdx b/docs/api/qiskit-addon-opt-mapper/applications-graph-partition.mdx new file mode 100644 index 00000000000..eed32418ca1 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-graph-partition.mdx @@ -0,0 +1,170 @@ +--- +title: GraphPartition (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.GraphPartition in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.GraphPartition +--- + +<span id="qiskit-addon-opt-mapper-applications-graphpartition" /> + +# qiskit\_addon\_opt\_mapper.applications.GraphPartition + +<Class id="qiskit_addon_opt_mapper.applications.GraphPartition" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_partition.py#L25-L98" signature="GraphPartition(graph)" modifiers="class"> + Bases: [`GraphOptimizationApplication`](applications-graph-optimization-application "qiskit_addon_opt_mapper.applications.graph_optimization_application.GraphOptimizationApplication") + + Optimization application for the “graph partition” \[1] problem based on a NetworkX graph. + + **References** + + \[1]: “Graph partition”, [https://en.wikipedia.org/wiki/Graph\_partition](https://en.wikipedia.org/wiki/Graph_partition) + + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*PyGraph*](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html#rustworkx.PyGraph)) – + + A graph representing a problem. It can be specified in the following formats: + + * A Rustworkx undirected graph (`rx.PyGraph`) + * A NetworkX undirected graph (`nx.Graph`) + * A NumPy adjacency matrix (`np.ndarray`) + * A list of edges or adjacency list (`list`) + + * **rx.PyGraph.** (*The input graph will be internally normalized to a*) + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.GraphPartition.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L31-L52" signature="__init__(graph)"> + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*PyGraph*](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html#rustworkx.PyGraph)) – + + A graph representing a problem. It can be specified in the following formats: + + * A Rustworkx undirected graph (`rx.PyGraph`) + * A NetworkX undirected graph (`nx.Graph`) + * A NumPy adjacency matrix (`np.ndarray`) + * A list of edges or adjacency list (`list`) + + * **rx.PyGraph.** (*The input graph will be internally normalized to a*) + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.applications.GraphPartition.__init__ "qiskit_addon_opt_mapper.applications.GraphPartition.__init__")(graph) | Init method. | + | [`draw`](#qiskit_addon_opt_mapper.applications.GraphPartition.draw "qiskit_addon_opt_mapper.applications.GraphPartition.draw")(\[result, pos]) | Draw a graph with the result. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.GraphPartition.interpret "qiskit_addon_opt_mapper.applications.GraphPartition.interpret")(result) | Interpret a result as a list of node indices. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.GraphPartition.sample_most_likely "qiskit_addon_opt_mapper.applications.GraphPartition.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.GraphPartition.to_optimization_problem "qiskit_addon_opt_mapper.applications.GraphPartition.to_optimization_problem")() | Represent as an optimization problem. | + + ## Attributes + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------- | + | [`graph`](#qiskit_addon_opt_mapper.applications.GraphPartition.graph "qiskit_addon_opt_mapper.applications.GraphPartition.graph") | Getter of the graph. | + | [`nx_graph`](#qiskit_addon_opt_mapper.applications.GraphPartition.nx_graph "qiskit_addon_opt_mapper.applications.GraphPartition.nx_graph") | Getter of the graph in Networkx format. | + + ### draw + + <Function id="qiskit_addon_opt_mapper.applications.GraphPartition.draw" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L63-L81" signature="draw(result=None, pos=None)"> + Draw a graph with the result. + + When the result is None, draw an original graph without colors. + + **Parameters** + + * **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| None*) – The calculated result for the problem + * **pos** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)*] | None*) – The positions of nodes + + **Return type** + + None + </Function> + + ### graph + + <Attribute id="qiskit_addon_opt_mapper.applications.GraphPartition.graph" attributeTypeHint="PyGraph"> + Getter of the graph. + + **Returns** + + A graph for a problem + </Attribute> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.GraphPartition.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_partition.py#L56-L73" signature="interpret(result)"> + Interpret a result as a list of node indices. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + + **Returns** + + A list of node indices divided into two groups. + + **Return type** + + [list](https://docs.python.org/3/library/stdtypes.html#list)\[[list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)]] + </Function> + + ### nx\_graph + + <Attribute id="qiskit_addon_opt_mapper.applications.GraphPartition.nx_graph" attributeTypeHint="Graph"> + Getter of the graph in Networkx format. + + **Returns** + + A graph for a problem + </Attribute> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.GraphPartition.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.GraphPartition.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_partition.py#L32-L54" signature="to_optimization_problem()"> + Represent as an optimization problem. + + Convert a graph partition instance into a `OptimizationProblem` + + **Returns** + + The `OptimizationProblem` created from the graph partition instance. + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-knapsack.mdx b/docs/api/qiskit-addon-opt-mapper/applications-knapsack.mdx new file mode 100644 index 00000000000..2635443c3ea --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-knapsack.mdx @@ -0,0 +1,127 @@ +--- +title: Knapsack (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.Knapsack in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.Knapsack +--- + +<span id="qiskit-addon-opt-mapper-applications-knapsack" /> + +# qiskit\_addon\_opt\_mapper.applications.Knapsack + +<Class id="qiskit_addon_opt_mapper.applications.Knapsack" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/knapsack.py#L24-L91" signature="Knapsack(values, weights, max_weight)" modifiers="class"> + Bases: [`OptimizationApplication`](applications-optimization-application "qiskit_addon_opt_mapper.applications.optimization_application.OptimizationApplication") + + Optimization application for the “knapsack problem” \[1]. + + **References** + + \[1]: “Knapsack problem”, [https://en.wikipedia.org/wiki/Knapsack\_problem](https://en.wikipedia.org/wiki/Knapsack_problem) + + Init method. + + **Parameters** + + * **values** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – A list of the values of items + * **weights** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – A list of the weights of items + * **max\_weight** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The maximum weight capacity + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.Knapsack.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/knapsack.py#L32-L42" signature="__init__(values, weights, max_weight)"> + Init method. + + **Parameters** + + * **values** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – A list of the values of items + * **weights** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – A list of the weights of items + * **max\_weight** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The maximum weight capacity + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.applications.Knapsack.__init__ "qiskit_addon_opt_mapper.applications.Knapsack.__init__")(values, weights, max\_weight) | Init method. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.Knapsack.interpret "qiskit_addon_opt_mapper.applications.Knapsack.interpret")(result) | Interpret a result as item indices. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.Knapsack.sample_most_likely "qiskit_addon_opt_mapper.applications.Knapsack.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.Knapsack.to_optimization_problem "qiskit_addon_opt_mapper.applications.Knapsack.to_optimization_problem")() | Represent as an optimization problem. | + + ## Attributes + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------ | ---------------------- | + | [`max_weight`](#qiskit_addon_opt_mapper.applications.Knapsack.max_weight "qiskit_addon_opt_mapper.applications.Knapsack.max_weight") | Getter of max\_weight. | + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.Knapsack.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/knapsack.py#L62-L73" signature="interpret(result)"> + Interpret a result as item indices. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + + **Returns** + + A list of items whose corresponding variable is 1 + + **Return type** + + [list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)] + </Function> + + ### max\_weight + + <Attribute id="qiskit_addon_opt_mapper.applications.Knapsack.max_weight" attributeTypeHint="int"> + Getter of max\_weight. + + **Returns** + + The maximal weight for the knapsack problem + </Attribute> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.Knapsack.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.Knapsack.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/knapsack.py#L44-L60" signature="to_optimization_problem()"> + Represent as an optimization problem. + + Convert a knapsack problem instance into a `OptimizationProblem` + + **Returns** + + The `OptimizationProblem` created from the knapsack problem instance. + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-maxcut.mdx b/docs/api/qiskit-addon-opt-mapper/applications-maxcut.mdx new file mode 100644 index 00000000000..7ba8de19f6c --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-maxcut.mdx @@ -0,0 +1,208 @@ +--- +title: Maxcut (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.Maxcut in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.Maxcut +--- + +<span id="qiskit-addon-opt-mapper-applications-maxcut" /> + +# qiskit\_addon\_opt\_mapper.applications.Maxcut + +<Class id="qiskit_addon_opt_mapper.applications.Maxcut" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/max_cut.py#L26-L147" signature="Maxcut(graph)" modifiers="class"> + Bases: [`GraphOptimizationApplication`](applications-graph-optimization-application "qiskit_addon_opt_mapper.applications.graph_optimization_application.GraphOptimizationApplication") + + Optimization application for the “max-cut” \[1] problem based on a NetworkX graph. + + **References** + + \[1]: “Maximum cut”, [https://en.wikipedia.org/wiki/Maximum\_cut](https://en.wikipedia.org/wiki/Maximum_cut) + + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*PyGraph*](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html#rustworkx.PyGraph)) – + + A graph representing a problem. It can be specified in the following formats: + + * A Rustworkx undirected graph (`rx.PyGraph`) + * A NetworkX undirected graph (`nx.Graph`) + * A NumPy adjacency matrix (`np.ndarray`) + * A list of edges or adjacency list (`list`) + + * **rx.PyGraph.** (*The input graph will be internally normalized to a*) + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.Maxcut.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L31-L52" signature="__init__(graph)"> + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*PyGraph*](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html#rustworkx.PyGraph)) – + + A graph representing a problem. It can be specified in the following formats: + + * A Rustworkx undirected graph (`rx.PyGraph`) + * A NetworkX undirected graph (`nx.Graph`) + * A NumPy adjacency matrix (`np.ndarray`) + * A list of edges or adjacency list (`list`) + + * **rx.PyGraph.** (*The input graph will be internally normalized to a*) + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.applications.Maxcut.__init__ "qiskit_addon_opt_mapper.applications.Maxcut.__init__")(graph) | Init method. | + | [`draw`](#qiskit_addon_opt_mapper.applications.Maxcut.draw "qiskit_addon_opt_mapper.applications.Maxcut.draw")(\[result, pos]) | Draw a graph with the result. | + | [`get_gset_result`](#qiskit_addon_opt_mapper.applications.Maxcut.get_gset_result "qiskit_addon_opt_mapper.applications.Maxcut.get_gset_result")(x) | Get graph solution in Gset format from binary string. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.Maxcut.interpret "qiskit_addon_opt_mapper.applications.Maxcut.interpret")(result) | Interpret a result as two lists of node indices. | + | [`parse_gset_format`](#qiskit_addon_opt_mapper.applications.Maxcut.parse_gset_format "qiskit_addon_opt_mapper.applications.Maxcut.parse_gset_format")(filename) | Read graph in Gset format from file. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.Maxcut.sample_most_likely "qiskit_addon_opt_mapper.applications.Maxcut.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.Maxcut.to_optimization_problem "qiskit_addon_opt_mapper.applications.Maxcut.to_optimization_problem")() | Represent as an optimization problem. | + + ## Attributes + + | | | + | -------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- | + | [`graph`](#qiskit_addon_opt_mapper.applications.Maxcut.graph "qiskit_addon_opt_mapper.applications.Maxcut.graph") | Getter of the graph. | + | [`nx_graph`](#qiskit_addon_opt_mapper.applications.Maxcut.nx_graph "qiskit_addon_opt_mapper.applications.Maxcut.nx_graph") | Getter of the graph in Networkx format. | + + ### draw + + <Function id="qiskit_addon_opt_mapper.applications.Maxcut.draw" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L63-L81" signature="draw(result=None, pos=None)"> + Draw a graph with the result. + + When the result is None, draw an original graph without colors. + + **Parameters** + + * **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| None*) – The calculated result for the problem + * **pos** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)*] | None*) – The positions of nodes + + **Return type** + + None + </Function> + + ### get\_gset\_result + + <Function id="qiskit_addon_opt_mapper.applications.Maxcut.get_gset_result" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/max_cut.py#L136-L147" signature="get_gset_result(x)" modifiers="static"> + Get graph solution in Gset format from binary string. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – binary string as numpy array. + + **Returns** + + A graph solution in Gset format. + + **Return type** + + [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[int](https://docs.python.org/3/library/functions.html#int), [int](https://docs.python.org/3/library/functions.html#int)] + </Function> + + ### graph + + <Attribute id="qiskit_addon_opt_mapper.applications.Maxcut.graph" attributeTypeHint="PyGraph"> + Getter of the graph. + + **Returns** + + A graph for a problem + </Attribute> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.Maxcut.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/max_cut.py#L79-L96" signature="interpret(result)"> + Interpret a result as two lists of node indices. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + + **Returns** + + Two lists of node indices correspond to two node sets for the Max-cut + + **Return type** + + [list](https://docs.python.org/3/library/stdtypes.html#list)\[[list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)]] + </Function> + + ### nx\_graph + + <Attribute id="qiskit_addon_opt_mapper.applications.Maxcut.nx_graph" attributeTypeHint="Graph"> + Getter of the graph in Networkx format. + + **Returns** + + A graph for a problem + </Attribute> + + ### parse\_gset\_format + + <Function id="qiskit_addon_opt_mapper.applications.Maxcut.parse_gset_format" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/max_cut.py#L104-L134" signature="parse_gset_format(filename)" modifiers="static"> + Read graph in Gset format from file. + + **Parameters** + + **filename** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – the name of the file. + + **Returns** + + An adjacency matrix as a 2D numpy array. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.Maxcut.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.Maxcut.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/max_cut.py#L34-L58" signature="to_optimization_problem()"> + Represent as an optimization problem. + + Convert a Max-cut problem instance into a `OptimizationProblem` + + **Returns** + + The `OptimizationProblem` created from the Max-cut problem instance. + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-number-partition.mdx b/docs/api/qiskit-addon-opt-mapper/applications-number-partition.mdx new file mode 100644 index 00000000000..341e2b15c12 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-number-partition.mdx @@ -0,0 +1,107 @@ +--- +title: NumberPartition (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.NumberPartition in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.NumberPartition +--- + +<span id="qiskit-addon-opt-mapper-applications-numberpartition" /> + +# qiskit\_addon\_opt\_mapper.applications.NumberPartition + +<Class id="qiskit_addon_opt_mapper.applications.NumberPartition" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/number_partition.py#L23-L75" signature="NumberPartition(number_set)" modifiers="class"> + Bases: [`OptimizationApplication`](applications-optimization-application "qiskit_addon_opt_mapper.applications.optimization_application.OptimizationApplication") + + Optimization application for the “number partition” \[1] problem. + + **References** + + \[1]: “Partition problem”, [https://en.wikipedia.org/wiki/Partition\_problem](https://en.wikipedia.org/wiki/Partition_problem) + + Init method. + + **Parameters** + + **number\_set** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – A list of integers + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.NumberPartition.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/number_partition.py#L31-L37" signature="__init__(number_set)"> + Init method. + + **Parameters** + + **number\_set** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – A list of integers + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.applications.NumberPartition.__init__ "qiskit_addon_opt_mapper.applications.NumberPartition.__init__")(number\_set) | Init method. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.NumberPartition.interpret "qiskit_addon_opt_mapper.applications.NumberPartition.interpret")(result) | Interpret a result as a list of subsets. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.NumberPartition.sample_most_likely "qiskit_addon_opt_mapper.applications.NumberPartition.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.NumberPartition.to_optimization_problem "qiskit_addon_opt_mapper.applications.NumberPartition.to_optimization_problem")() | Represent as an optimization problem. | + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.NumberPartition.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/number_partition.py#L58-L75" signature="interpret(result)"> + Interpret a result as a list of subsets. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + + **Returns** + + A list of subsets whose sum is the half of the total. + + **Return type** + + [list](https://docs.python.org/3/library/stdtypes.html#list)\[[list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)]] + </Function> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.NumberPartition.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.NumberPartition.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/number_partition.py#L39-L56" signature="to_optimization_problem()"> + Represent as an optimization problem. + + Convert a number partitioning problem instance into a `OptimizationProblem` + + **Returns** + + The `OptimizationProblem` created from the number partitioning problem instance. + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-optimization-application.mdx b/docs/api/qiskit-addon-opt-mapper/applications-optimization-application.mdx new file mode 100644 index 00000000000..a1cb690c70d --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-optimization-application.mdx @@ -0,0 +1,77 @@ +--- +title: OptimizationApplication (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.OptimizationApplication in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.OptimizationApplication +--- + +<span id="qiskit-addon-opt-mapper-applications-optimizationapplication" /> + +# qiskit\_addon\_opt\_mapper.applications.OptimizationApplication + +<Class id="qiskit_addon_opt_mapper.applications.OptimizationApplication" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L25-L108" signature="OptimizationApplication" modifiers="class"> + Bases: [`ABC`](https://docs.python.org/3/library/abc.html#abc.ABC) + + An abstract class for optimization applications. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.OptimizationApplication.__init__" signature="__init__()" /> + + ## Methods + + | | | + | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.applications.OptimizationApplication.__init__ "qiskit_addon_opt_mapper.applications.OptimizationApplication.__init__")() | | + | [`interpret`](#qiskit_addon_opt_mapper.applications.OptimizationApplication.interpret "qiskit_addon_opt_mapper.applications.OptimizationApplication.interpret")(result) | Interpret the problem. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.OptimizationApplication.sample_most_likely "qiskit_addon_opt_mapper.applications.OptimizationApplication.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.OptimizationApplication.to_optimization_problem "qiskit_addon_opt_mapper.applications.OptimizationApplication.to_optimization_problem")() | Represent as an optimization problem. | + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.OptimizationApplication.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L37-L48" signature="interpret(result)" modifiers="abstract"> + Interpret the problem. + + Convert the calculation result of the problem ([`SolverResult`](solvers-solver-result "qiskit_addon_opt_mapper.solvers.SolverResult") or a binary array using np.ndarray) to the answer of the problem in an easy-to-understand format. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + </Function> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.OptimizationApplication.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.OptimizationApplication.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L28-L35" signature="to_optimization_problem()" modifiers="abstract"> + Represent as an optimization problem. + + Convert a problem instance into a `OptimizationProblem` + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-set-packing.mdx b/docs/api/qiskit-addon-opt-mapper/applications-set-packing.mdx new file mode 100644 index 00000000000..33f7183a4fa --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-set-packing.mdx @@ -0,0 +1,107 @@ +--- +title: SetPacking (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.SetPacking in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.SetPacking +--- + +<span id="qiskit-addon-opt-mapper-applications-setpacking" /> + +# qiskit\_addon\_opt\_mapper.applications.SetPacking + +<Class id="qiskit_addon_opt_mapper.applications.SetPacking" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/set_packing.py#L26-L82" signature="SetPacking(subsets)" modifiers="class"> + Bases: [`OptimizationApplication`](applications-optimization-application "qiskit_addon_opt_mapper.applications.optimization_application.OptimizationApplication") + + Optimization application for the “set packing” \[1] problem. + + **References** + + \[1]: “Set packing”, [https://en.wikipedia.org/wiki/Set\_packing](https://en.wikipedia.org/wiki/Set_packing) + + Init method. + + **Parameters** + + **subsets** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]]*) – A list of subsets + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.SetPacking.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/set_packing.py#L34-L44" signature="__init__(subsets)"> + Init method. + + **Parameters** + + **subsets** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]]*) – A list of subsets + + **Return type** + + None + </Function> + + ## Methods + + | | | + | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.applications.SetPacking.__init__ "qiskit_addon_opt_mapper.applications.SetPacking.__init__")(subsets) | Init method. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.SetPacking.interpret "qiskit_addon_opt_mapper.applications.SetPacking.interpret")(result) | Interpret a result as a list of subsets. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.SetPacking.sample_most_likely "qiskit_addon_opt_mapper.applications.SetPacking.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.SetPacking.to_optimization_problem "qiskit_addon_opt_mapper.applications.SetPacking.to_optimization_problem")() | Represent as an optimization problem. | + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.SetPacking.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/set_packing.py#L67-L82" signature="interpret(result)"> + Interpret a result as a list of subsets. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + + **Returns** + + A list of subsets whose corresponding variable is 1 + + **Return type** + + [list](https://docs.python.org/3/library/stdtypes.html#list)\[[list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)]] + </Function> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.SetPacking.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.SetPacking.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/set_packing.py#L46-L65" signature="to_optimization_problem()"> + Represent as an optimization problem. + + Convert a set packing instance into a `OptimizationProblem` + + **Returns** + + The `OptimizationProblem` created from the set packing instance. + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-sk-model.mdx b/docs/api/qiskit-addon-opt-mapper/applications-sk-model.mdx new file mode 100644 index 00000000000..34a6fc3f562 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-sk-model.mdx @@ -0,0 +1,156 @@ +--- +title: SKModel (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.SKModel in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.SKModel +--- + +<span id="qiskit-addon-opt-mapper-applications-skmodel" /> + +# qiskit\_addon\_opt\_mapper.applications.SKModel + +<Class id="qiskit_addon_opt_mapper.applications.SKModel" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/sk_model.py#L25-L147" signature="SKModel(num_sites, rng_or_seed=None)" modifiers="class"> + Bases: [`OptimizationApplication`](applications-optimization-application "qiskit_addon_opt_mapper.applications.optimization_application.OptimizationApplication") + + Optimization application of the “Sherrington Kirkpatrick (SK) model” \[1]. + + The SK Hamiltonian over n spins is given as: $H(x)=-1/\sqrt{n} \sum_{i<j} w_{i,j}x_ix_j$, where $x_i\in\{\pm 1\}$ is the configuration of spins and $w_{i,j}\in\{\pm 1\}$ is a disorder chosen independently and uniformly at random. Notice that there are other variants of disorders e.g., with $w_{i,j}$ chosen from the normal distribution with mean 0 and variance 1. + + **References** + + \[1]: Dmitry Panchenko. “The Sherrington-Kirkpatrick model: an overview”, [https://arxiv.org/abs/1211.1094](https://arxiv.org/abs/1211.1094) + + Init method. + + **Parameters** + + * **num\_sites** ([*int*](https://docs.python.org/3/library/functions.html#int)) – number of sites + * **rng\_or\_seed** ([*Generator*](https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator) *|*[*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – NumPy pseudo-random number generator or seed for np.random.default\_rng(\<seed>) or None. None results in usage of np.random.default\_rng(). + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.SKModel.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/sk_model.py#L40-L61" signature="__init__(num_sites, rng_or_seed=None)"> + Init method. + + **Parameters** + + * **num\_sites** ([*int*](https://docs.python.org/3/library/functions.html#int)) – number of sites + * **rng\_or\_seed** ([*Generator*](https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator) *|*[*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – NumPy pseudo-random number generator or seed for np.random.default\_rng(\<seed>) or None. None results in usage of np.random.default\_rng(). + </Function> + + ## Methods + + | | | + | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.applications.SKModel.__init__ "qiskit_addon_opt_mapper.applications.SKModel.__init__")(num\_sites\[, rng\_or\_seed]) | Init method. | + | [`disorder`](#qiskit_addon_opt_mapper.applications.SKModel.disorder "qiskit_addon_opt_mapper.applications.SKModel.disorder")() | Generate a new disorder of the SK model. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.SKModel.interpret "qiskit_addon_opt_mapper.applications.SKModel.interpret")(result) | Interpret a result as configuration of spins. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.SKModel.sample_most_likely "qiskit_addon_opt_mapper.applications.SKModel.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.SKModel.to_optimization_problem "qiskit_addon_opt_mapper.applications.SKModel.to_optimization_problem")() | Represent as an optimization problem. | + + ## Attributes + + | | | + | ------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- | + | [`graph`](#qiskit_addon_opt_mapper.applications.SKModel.graph "qiskit_addon_opt_mapper.applications.SKModel.graph") | Getter of the graph representation. | + | [`num_sites`](#qiskit_addon_opt_mapper.applications.SKModel.num_sites "qiskit_addon_opt_mapper.applications.SKModel.num_sites") | Getter of the number of sites. | + | [`nx_graph`](#qiskit_addon_opt_mapper.applications.SKModel.nx_graph "qiskit_addon_opt_mapper.applications.SKModel.nx_graph") | Getter of the graph in Networkx format. | + + ### disorder + + <Function id="qiskit_addon_opt_mapper.applications.SKModel.disorder" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/sk_model.py#L63-L66" signature="disorder()"> + Generate a new disorder of the SK model. + + **Return type** + + None + </Function> + + ### graph + + <Attribute id="qiskit_addon_opt_mapper.applications.SKModel.graph" attributeTypeHint="PyGraph"> + Getter of the graph representation. + + **Returns** + + A graph for a problem. + </Attribute> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.SKModel.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/sk_model.py#L96-L107" signature="interpret(result)"> + Interpret a result as configuration of spins. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem. + + **Returns** + + configuration of spins + + **Return type** + + [list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)] + </Function> + + ### num\_sites + + <Attribute id="qiskit_addon_opt_mapper.applications.SKModel.num_sites" attributeTypeHint="int"> + Getter of the number of sites. + + **Returns** + + Number of sites. + </Attribute> + + ### nx\_graph + + <Attribute id="qiskit_addon_opt_mapper.applications.SKModel.nx_graph" attributeTypeHint="Graph"> + Getter of the graph in Networkx format. + + **Returns** + + A graph for a problem + </Attribute> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.SKModel.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.SKModel.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/sk_model.py#L68-L94" signature="to_optimization_problem()"> + Represent as an optimization problem. + + Convert an SK model problem instance into a `OptimizationProblem`. + + **Returns** + + The `OptimizationProblem` created from the SK problem instance. + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-tsp.mdx b/docs/api/qiskit-addon-opt-mapper/applications-tsp.mdx new file mode 100644 index 00000000000..f7f5707267f --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-tsp.mdx @@ -0,0 +1,238 @@ +--- +title: Tsp (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.Tsp in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.Tsp +--- + +<span id="qiskit-addon-opt-mapper-applications-tsp" /> + +# qiskit\_addon\_opt\_mapper.applications.Tsp + +<Class id="qiskit_addon_opt_mapper.applications.Tsp" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/tsp.py#L28-L271" signature="Tsp(graph)" modifiers="class"> + Bases: [`GraphOptimizationApplication`](applications-graph-optimization-application "qiskit_addon_opt_mapper.applications.graph_optimization_application.GraphOptimizationApplication") + + Optimization application for the “traveling salesman problem” \[1] based on a NetworkX graph. + + **References** + + \[1]: “Travelling salesman problem”, [https://en.wikipedia.org/wiki/Travelling\_salesman\_problem](https://en.wikipedia.org/wiki/Travelling_salesman_problem) + + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*PyGraph*](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html#rustworkx.PyGraph)) – + + A graph representing a problem. It can be specified in the following formats: + + * A Rustworkx undirected graph (`rx.PyGraph`) + * A NetworkX undirected graph (`nx.Graph`) + * A NumPy adjacency matrix (`np.ndarray`) + * A list of edges or adjacency list (`list`) + + * **rx.PyGraph.** (*The input graph will be internally normalized to a*) + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.Tsp.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L31-L52" signature="__init__(graph)"> + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*PyGraph*](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html#rustworkx.PyGraph)) – + + A graph representing a problem. It can be specified in the following formats: + + * A Rustworkx undirected graph (`rx.PyGraph`) + * A NetworkX undirected graph (`nx.Graph`) + * A NumPy adjacency matrix (`np.ndarray`) + * A list of edges or adjacency list (`list`) + + * **rx.PyGraph.** (*The input graph will be internally normalized to a*) + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------ | + | [`__init__`](#qiskit_addon_opt_mapper.applications.Tsp.__init__ "qiskit_addon_opt_mapper.applications.Tsp.__init__")(graph) | Init method. | + | [`create_random_instance`](#qiskit_addon_opt_mapper.applications.Tsp.create_random_instance "qiskit_addon_opt_mapper.applications.Tsp.create_random_instance")(n\[, low, high, seed]) | Create a random instance of the traveling salesman problem. | + | [`draw`](#qiskit_addon_opt_mapper.applications.Tsp.draw "qiskit_addon_opt_mapper.applications.Tsp.draw")(\[result, pos]) | Draw a graph with the result. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.Tsp.interpret "qiskit_addon_opt_mapper.applications.Tsp.interpret")(result) | Interpret a result as a list of node indices. | + | [`parse_tsplib_format`](#qiskit_addon_opt_mapper.applications.Tsp.parse_tsplib_format "qiskit_addon_opt_mapper.applications.Tsp.parse_tsplib_format")(filename) | Read a graph in TSPLIB format from file and return a Tsp instance. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.Tsp.sample_most_likely "qiskit_addon_opt_mapper.applications.Tsp.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.Tsp.to_optimization_problem "qiskit_addon_opt_mapper.applications.Tsp.to_optimization_problem")() | Represent as an optimization problem. | + | [`tsp_value`](#qiskit_addon_opt_mapper.applications.Tsp.tsp_value "qiskit_addon_opt_mapper.applications.Tsp.tsp_value")(z, adj\_matrix) | Compute the TSP value of a solution. | + + ## Attributes + + | | | + | -------------------------------------------------------------------------------------------------------------------- | --------------------------------------- | + | [`graph`](#qiskit_addon_opt_mapper.applications.Tsp.graph "qiskit_addon_opt_mapper.applications.Tsp.graph") | Getter of the graph. | + | [`nx_graph`](#qiskit_addon_opt_mapper.applications.Tsp.nx_graph "qiskit_addon_opt_mapper.applications.Tsp.nx_graph") | Getter of the graph in Networkx format. | + + ### create\_random\_instance + + <Function id="qiskit_addon_opt_mapper.applications.Tsp.create_random_instance" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/tsp.py#L139-L174" signature="create_random_instance(n, low=0, high=100, seed=None)" modifiers="static"> + Create a random instance of the traveling salesman problem. + + **Parameters** + + * **n** ([*int*](https://docs.python.org/3/library/functions.html#int)) – the number of nodes. + * **low** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The minimum value for the coordinate of a node. + * **high** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The maximum value for the coordinate of a node. + * **seed** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – the seed for the random coordinates. + + **Returns** + + A Tsp instance created from the input information + + **Return type** + + [*Tsp*](#qiskit_addon_opt_mapper.applications.Tsp "qiskit_addon_opt_mapper.applications.tsp.Tsp") + </Function> + + ### draw + + <Function id="qiskit_addon_opt_mapper.applications.Tsp.draw" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L63-L81" signature="draw(result=None, pos=None)"> + Draw a graph with the result. + + When the result is None, draw an original graph without colors. + + **Parameters** + + * **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| None*) – The calculated result for the problem + * **pos** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)*] | None*) – The positions of nodes + + **Return type** + + None + </Function> + + ### graph + + <Attribute id="qiskit_addon_opt_mapper.applications.Tsp.graph" attributeTypeHint="PyGraph"> + Getter of the graph. + + **Returns** + + A graph for a problem + </Attribute> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.Tsp.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/tsp.py#L88-L110" signature="interpret(result)"> + Interpret a result as a list of node indices. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + + **Returns** + + A list of nodes whose indices correspond to its order in a prospective cycle. + + **Return type** + + [list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int) | [list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)]] + </Function> + + ### nx\_graph + + <Attribute id="qiskit_addon_opt_mapper.applications.Tsp.nx_graph" attributeTypeHint="Graph"> + Getter of the graph in Networkx format. + + **Returns** + + A graph for a problem + </Attribute> + + ### parse\_tsplib\_format + + <Function id="qiskit_addon_opt_mapper.applications.Tsp.parse_tsplib_format" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/tsp.py#L176-L253" signature="parse_tsplib_format(filename)" modifiers="static"> + Read a graph in TSPLIB format from file and return a Tsp instance. + + Only the EUC\_2D edge weight format is supported. + + **Parameters** + + **filename** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – the name of the file. + + **Raises** + + * **OptimizationError** – If the type is not “TSP” + * **OptimizationError** – If the edge weight type is not “EUC\_2D” + + **Returns** + + A Tsp instance data. + + **Return type** + + [*Tsp*](#qiskit_addon_opt_mapper.applications.Tsp "qiskit_addon_opt_mapper.applications.tsp.Tsp") + </Function> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.Tsp.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.Tsp.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/tsp.py#L36-L86" signature="to_optimization_problem()"> + Represent as an optimization problem. + + Convert a traveling salesman problem instance into a `OptimizationProblem` + + **Returns** + + The `OptimizationProblem` created from the traveling salesman problem instance. + + **Return type** + + *OptimizationProblem* + </Function> + + ### tsp\_value + + <Function id="qiskit_addon_opt_mapper.applications.Tsp.tsp_value" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/tsp.py#L255-L271" signature="tsp_value(z, adj_matrix)" modifiers="static"> + Compute the TSP value of a solution. + + **Parameters** + + * **z** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – list of cities. + * **adj\_matrix** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – adjacency matrix. + + **Returns** + + value of the total length + + **Return type** + + [float](https://docs.python.org/3/library/functions.html#float) + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-vehicle-routing.mdx b/docs/api/qiskit-addon-opt-mapper/applications-vehicle-routing.mdx new file mode 100644 index 00000000000..9b4e799d145 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-vehicle-routing.mdx @@ -0,0 +1,206 @@ +--- +title: VehicleRouting (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.VehicleRouting in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.VehicleRouting +--- + +<span id="qiskit-addon-opt-mapper-applications-vehiclerouting" /> + +# qiskit\_addon\_opt\_mapper.applications.VehicleRouting + +<Class id="qiskit_addon_opt_mapper.applications.VehicleRouting" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/vehicle_routing.py#L30-L257" signature="VehicleRouting(graph, num_vehicles=2, depot=0)" modifiers="class"> + Bases: [`GraphOptimizationApplication`](applications-graph-optimization-application "qiskit_addon_opt_mapper.applications.graph_optimization_application.GraphOptimizationApplication") + + Optimization application for the “vehicle routing problem” \[1] based on a `NetworkX` graph. + + **References** + + \[1]: “Vehicle routing problem”, [https://en.wikipedia.org/wiki/Vehicle\_routing\_problem](https://en.wikipedia.org/wiki/Vehicle_routing_problem) + + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)) – A graph representing a problem. It can be specified directly as a [NetworkX](https://networkx.org/) graph, or as an array or list format suitable to build out a NetworkX graph. + * **num\_vehicles** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The number of vehicles + * **depot** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The index of the depot node where all the vehicle depart + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.VehicleRouting.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/vehicle_routing.py#L37-L54" signature="__init__(graph, num_vehicles=2, depot=0)"> + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)) – + + A graph representing a problem. It can be specified directly as a [NetworkX](https://networkx.org/) graph, or as an array or list format suitable to build out a NetworkX graph. + + * **num\_vehicles** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The number of vehicles + + * **depot** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The index of the depot node where all the vehicle depart + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------ | + | [`__init__`](#qiskit_addon_opt_mapper.applications.VehicleRouting.__init__ "qiskit_addon_opt_mapper.applications.VehicleRouting.__init__")(graph\[, num\_vehicles, depot]) | Init method. | + | [`create_random_instance`](#qiskit_addon_opt_mapper.applications.VehicleRouting.create_random_instance "qiskit_addon_opt_mapper.applications.VehicleRouting.create_random_instance")(n\[, low, high, seed, ...]) | Create a random instance of the vehicle routing problem. | + | [`draw`](#qiskit_addon_opt_mapper.applications.VehicleRouting.draw "qiskit_addon_opt_mapper.applications.VehicleRouting.draw")(\[result, pos]) | Draw a graph with the result. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.VehicleRouting.interpret "qiskit_addon_opt_mapper.applications.VehicleRouting.interpret")(result) | Interpret a result as a list of the routes for each vehicle. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.VehicleRouting.sample_most_likely "qiskit_addon_opt_mapper.applications.VehicleRouting.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.VehicleRouting.to_optimization_problem "qiskit_addon_opt_mapper.applications.VehicleRouting.to_optimization_problem")() | Represent as an optimization problem. | + + ## Attributes + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------- | + | [`depot`](#qiskit_addon_opt_mapper.applications.VehicleRouting.depot "qiskit_addon_opt_mapper.applications.VehicleRouting.depot") | Getter of depot. | + | [`graph`](#qiskit_addon_opt_mapper.applications.VehicleRouting.graph "qiskit_addon_opt_mapper.applications.VehicleRouting.graph") | Getter of the graph. | + | [`num_vehicles`](#qiskit_addon_opt_mapper.applications.VehicleRouting.num_vehicles "qiskit_addon_opt_mapper.applications.VehicleRouting.num_vehicles") | Getter of num\_vehicles. | + | [`nx_graph`](#qiskit_addon_opt_mapper.applications.VehicleRouting.nx_graph "qiskit_addon_opt_mapper.applications.VehicleRouting.nx_graph") | Getter of the graph in Networkx format. | + + ### create\_random\_instance + + <Function id="qiskit_addon_opt_mapper.applications.VehicleRouting.create_random_instance" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/vehicle_routing.py#L223-L257" signature="create_random_instance(n, low=0, high=100, seed=None, num_vehicle=2, depot=0)" modifiers="static"> + Create a random instance of the vehicle routing problem. + + **Parameters** + + * **n** ([*int*](https://docs.python.org/3/library/functions.html#int)) – the number of nodes. + * **low** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The minimum value for the coordinate of a node. + * **high** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The maximum value for the coordinate of a node. + * **seed** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – the seed for the random coordinates. + * **num\_vehicle** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The number of the vehicles + * **depot** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The index of the depot node where all the vehicle depart + + **Returns** + + A VehicleRouting instance created from the input information + + **Return type** + + [*VehicleRouting*](#qiskit_addon_opt_mapper.applications.VehicleRouting "qiskit_addon_opt_mapper.applications.vehicle_routing.VehicleRouting") + </Function> + + ### depot + + <Attribute id="qiskit_addon_opt_mapper.applications.VehicleRouting.depot" attributeTypeHint="int"> + Getter of depot. + + **Returns** + + The node index of the depot where all the vehicles depart + </Attribute> + + ### draw + + <Function id="qiskit_addon_opt_mapper.applications.VehicleRouting.draw" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L63-L81" signature="draw(result=None, pos=None)"> + Draw a graph with the result. + + When the result is None, draw an original graph without colors. + + **Parameters** + + * **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| None*) – The calculated result for the problem + * **pos** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)*] | None*) – The positions of nodes + + **Return type** + + None + </Function> + + ### graph + + <Attribute id="qiskit_addon_opt_mapper.applications.VehicleRouting.graph" attributeTypeHint="PyGraph"> + Getter of the graph. + + **Returns** + + A graph for a problem + </Attribute> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.VehicleRouting.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/vehicle_routing.py#L112-L152" signature="interpret(result)"> + Interpret a result as a list of the routes for each vehicle. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + + **Returns** + + A list of the routes for each vehicle + + **Return type** + + [list](https://docs.python.org/3/library/stdtypes.html#list)\[[list](https://docs.python.org/3/library/stdtypes.html#list)\[[list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)]]] + </Function> + + ### num\_vehicles + + <Attribute id="qiskit_addon_opt_mapper.applications.VehicleRouting.num_vehicles" attributeTypeHint="int"> + Getter of num\_vehicles. + + **Returns** + + The number of the vehicles + </Attribute> + + ### nx\_graph + + <Attribute id="qiskit_addon_opt_mapper.applications.VehicleRouting.nx_graph" attributeTypeHint="Graph"> + Getter of the graph in Networkx format. + + **Returns** + + A graph for a problem + </Attribute> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.VehicleRouting.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.VehicleRouting.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/vehicle_routing.py#L56-L110" signature="to_optimization_problem()"> + Represent as an optimization problem. + + Convert a vehicle routing problem instance into a `OptimizationProblem` + + **Returns** + + The `OptimizationProblem` created from the vehicle routing problem instance. + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications-vertex-cover.mdx b/docs/api/qiskit-addon-opt-mapper/applications-vertex-cover.mdx new file mode 100644 index 00000000000..32075ba65e7 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications-vertex-cover.mdx @@ -0,0 +1,170 @@ +--- +title: VertexCover (latest version) +description: API reference for qiskit_addon_opt_mapper.applications.VertexCover in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.applications.VertexCover +--- + +<span id="qiskit-addon-opt-mapper-applications-vertexcover" /> + +# qiskit\_addon\_opt\_mapper.applications.VertexCover + +<Class id="qiskit_addon_opt_mapper.applications.VertexCover" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/vertex_cover.py#L25-L93" signature="VertexCover(graph)" modifiers="class"> + Bases: [`GraphOptimizationApplication`](applications-graph-optimization-application "qiskit_addon_opt_mapper.applications.graph_optimization_application.GraphOptimizationApplication") + + Optimization application for the “vertex cover” \[1] problem based on a NetworkX graph. + + **References** + + \[1]: “Vertex cover”, [https://en.wikipedia.org/wiki/Vertex\_cover](https://en.wikipedia.org/wiki/Vertex_cover) + + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*PyGraph*](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html#rustworkx.PyGraph)) – + + A graph representing a problem. It can be specified in the following formats: + + * A Rustworkx undirected graph (`rx.PyGraph`) + * A NetworkX undirected graph (`nx.Graph`) + * A NumPy adjacency matrix (`np.ndarray`) + * A list of edges or adjacency list (`list`) + + * **rx.PyGraph.** (*The input graph will be internally normalized to a*) + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.applications.VertexCover.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L31-L52" signature="__init__(graph)"> + Init method. + + **Parameters** + + * **graph** (*Graph |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*PyGraph*](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html#rustworkx.PyGraph)) – + + A graph representing a problem. It can be specified in the following formats: + + * A Rustworkx undirected graph (`rx.PyGraph`) + * A NetworkX undirected graph (`nx.Graph`) + * A NumPy adjacency matrix (`np.ndarray`) + * A list of edges or adjacency list (`list`) + + * **rx.PyGraph.** (*The input graph will be internally normalized to a*) + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.applications.VertexCover.__init__ "qiskit_addon_opt_mapper.applications.VertexCover.__init__")(graph) | Init method. | + | [`draw`](#qiskit_addon_opt_mapper.applications.VertexCover.draw "qiskit_addon_opt_mapper.applications.VertexCover.draw")(\[result, pos]) | Draw a graph with the result. | + | [`interpret`](#qiskit_addon_opt_mapper.applications.VertexCover.interpret "qiskit_addon_opt_mapper.applications.VertexCover.interpret")(result) | Interpret a result as a list of node indices. | + | [`sample_most_likely`](#qiskit_addon_opt_mapper.applications.VertexCover.sample_most_likely "qiskit_addon_opt_mapper.applications.VertexCover.sample_most_likely")(state\_vector) | Compute the most likely binary string from state vector. | + | [`to_optimization_problem`](#qiskit_addon_opt_mapper.applications.VertexCover.to_optimization_problem "qiskit_addon_opt_mapper.applications.VertexCover.to_optimization_problem")() | Represent as an optimization problem. | + + ## Attributes + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------- | + | [`graph`](#qiskit_addon_opt_mapper.applications.VertexCover.graph "qiskit_addon_opt_mapper.applications.VertexCover.graph") | Getter of the graph. | + | [`nx_graph`](#qiskit_addon_opt_mapper.applications.VertexCover.nx_graph "qiskit_addon_opt_mapper.applications.VertexCover.nx_graph") | Getter of the graph in Networkx format. | + + ### draw + + <Function id="qiskit_addon_opt_mapper.applications.VertexCover.draw" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/graph_optimization_application.py#L63-L81" signature="draw(result=None, pos=None)"> + Draw a graph with the result. + + When the result is None, draw an original graph without colors. + + **Parameters** + + * **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| None*) – The calculated result for the problem + * **pos** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)*] | None*) – The positions of nodes + + **Return type** + + None + </Function> + + ### graph + + <Attribute id="qiskit_addon_opt_mapper.applications.VertexCover.graph" attributeTypeHint="PyGraph"> + Getter of the graph. + + **Returns** + + A graph for a problem + </Attribute> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.applications.VertexCover.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/vertex_cover.py#L53-L68" signature="interpret(result)"> + Interpret a result as a list of node indices. + + **Parameters** + + **result** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – The calculated result of the problem + + **Returns** + + A list of node indices whose corresponding variable is 1 + + **Return type** + + [list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)] + </Function> + + ### nx\_graph + + <Attribute id="qiskit_addon_opt_mapper.applications.VertexCover.nx_graph" attributeTypeHint="Graph"> + Getter of the graph in Networkx format. + + **Returns** + + A graph for a problem + </Attribute> + + ### sample\_most\_likely + + <Function id="qiskit_addon_opt_mapper.applications.VertexCover.sample_most_likely" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/optimization_application.py#L61-L108" signature="sample_most_likely(state_vector)" modifiers="static"> + Compute the most likely binary string from state vector. + + **Parameters** + + **state\_vector** ([*QuasiDistribution*](/docs/api/qiskit/qiskit.result.QuasiDistribution) *|*[*Statevector*](/docs/api/qiskit/qiskit.quantum_info.Statevector) *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)) – state vector or counts or quasi-probabilities. + + **Returns** + + binary string as numpy.ndarray of ints. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – if state\_vector is not QuasiDistribution, Statevector, np.ndarray, or dict. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_optimization\_problem + + <Function id="qiskit_addon_opt_mapper.applications.VertexCover.to_optimization_problem" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/applications/vertex_cover.py#L32-L51" signature="to_optimization_problem()"> + Represent as an optimization problem. + + Convert a vertex cover instance into a `OptimizationProblem` + + **Returns** + + The `OptimizationProblem` created from the vertex cover instance. + + **Return type** + + *OptimizationProblem* + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/applications.mdx b/docs/api/qiskit-addon-opt-mapper/applications.mdx new file mode 100644 index 00000000000..8c4519e571e --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/applications.mdx @@ -0,0 +1,50 @@ +--- +title: applications (latest version) +description: API reference for qiskit_addon_opt_mapper.applications in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_addon_opt_mapper.applications +--- + +<span id="module-qiskit_addon_opt_mapper.applications" /> + +<span id="optimization-application-modeling-qiskit-addon-opt-mapper-applications" /> + +# Optimization Application Modeling + +`qiskit_addon_opt_mapper.applications` + +Optimization applications. + +<span id="optimization-applications-qiskit-addon-opt-mapper-applications" /> + +## Optimization applications . + +`qiskit_addon_opt_mapper.applications` + +Applications for common optimization problems. + +### Base classes for applications + +| | | +| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | +| [`OptimizationApplication`](/docs/api/qiskit-addon-opt-mapper/applications-optimization-application "qiskit_addon_opt_mapper.applications.OptimizationApplication") | An abstract class for optimization applications. | +| [`GraphOptimizationApplication`](/docs/api/qiskit-addon-opt-mapper/applications-graph-optimization-application "qiskit_addon_opt_mapper.applications.GraphOptimizationApplication") | An abstract class for graph optimization applications. | + +### Applications + +| | | +| ------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | +| [`BinPacking`](/docs/api/qiskit-addon-opt-mapper/applications-bin-packing "qiskit_addon_opt_mapper.applications.BinPacking") | Optimization application for the "bin packing" \[1] problem. | +| [`Clique`](/docs/api/qiskit-addon-opt-mapper/applications-clique "qiskit_addon_opt_mapper.applications.Clique") | Optimization application for the "clique" \[1] problem based on a NetworkX graph. | +| [`ExactCover`](/docs/api/qiskit-addon-opt-mapper/applications-exact-cover "qiskit_addon_opt_mapper.applications.ExactCover") | Optimization application for the "exact cover" \[1] problem. | +| [`GraphPartition`](/docs/api/qiskit-addon-opt-mapper/applications-graph-partition "qiskit_addon_opt_mapper.applications.GraphPartition") | Optimization application for the "graph partition" \[1] problem based on a NetworkX graph. | +| [`Knapsack`](/docs/api/qiskit-addon-opt-mapper/applications-knapsack "qiskit_addon_opt_mapper.applications.Knapsack") | Optimization application for the "knapsack problem" \[1]. | +| [`Maxcut`](/docs/api/qiskit-addon-opt-mapper/applications-maxcut "qiskit_addon_opt_mapper.applications.Maxcut") | Optimization application for the "max-cut" \[1] problem based on a NetworkX graph. | +| [`NumberPartition`](/docs/api/qiskit-addon-opt-mapper/applications-number-partition "qiskit_addon_opt_mapper.applications.NumberPartition") | Optimization application for the "number partition" \[1] problem. | +| [`SetPacking`](/docs/api/qiskit-addon-opt-mapper/applications-set-packing "qiskit_addon_opt_mapper.applications.SetPacking") | Optimization application for the "set packing" \[1] problem. | +| [`SKModel`](/docs/api/qiskit-addon-opt-mapper/applications-sk-model "qiskit_addon_opt_mapper.applications.SKModel") | Optimization application of the "Sherrington Kirkpatrick (SK) model" \[1]. | +| [`Tsp`](/docs/api/qiskit-addon-opt-mapper/applications-tsp "qiskit_addon_opt_mapper.applications.Tsp") | Optimization application for the "traveling salesman problem" \[1] based on a NetworkX graph. | +| [`VehicleRouting`](/docs/api/qiskit-addon-opt-mapper/applications-vehicle-routing "qiskit_addon_opt_mapper.applications.VehicleRouting") | Optimization application for the "vehicle routing problem" \[1] based on a `NetworkX` graph. | +| [`VertexCover`](/docs/api/qiskit-addon-opt-mapper/applications-vertex-cover "qiskit_addon_opt_mapper.applications.VertexCover") | Optimization application for the "vertex cover" \[1] problem based on a NetworkX graph. | + diff --git a/docs/api/qiskit-addon-opt-mapper/converters-binary-to-linear-binary.mdx b/docs/api/qiskit-addon-opt-mapper/converters-binary-to-linear-binary.mdx new file mode 100644 index 00000000000..190ce3d3f66 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/converters-binary-to-linear-binary.mdx @@ -0,0 +1,114 @@ +--- +title: BinaryToLinearBinary (latest version) +description: API reference for qiskit_addon_opt_mapper.converters.BinaryToLinearBinary in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.converters.BinaryToLinearBinary +--- + +<span id="qiskit-addon-opt-mapper-converters-binarytolinearbinary" /> + +# qiskit\_addon\_opt\_mapper.converters.BinaryToLinearBinary + +<Class id="qiskit_addon_opt_mapper.converters.BinaryToLinearBinary" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/binary_to_linear_binary.py#L54-L213" signature="BinaryToLinearBinary" modifiers="class"> + Bases: [`OptimizationProblemConverter`](converters-optimization-problem-converter "qiskit_addon_opt_mapper.converters.optimization_problem_converter.OptimizationProblemConverter") + + Convert all high-degree terms to linear terms, both in objective and constraints. The converter assumes that the problem only contains binary variables. + + The conversion of a term `x1*x2` is done through the additional binary variable `x1ANDx2 = x1*x2` defined via: `` ` x1ANDx2 <= x1 x1ANDx2 <= x2 x1ANDx2 >= x1 + x2 - 1 ` `` + + Similarly, a term `x1*x2*x3` is converted via the additional binary variable `x1ANDx2ANDx3 = x1*x2*x3` defined via: `` ` x1ANDx2ANDx3 <= x1 x1ANDx2ANDx3 <= x2 x1ANDx2ANDx3 <= x3 x1ANDx2ANDx3 >= x1 + x2 + x3 - 2 ` `` + + The definition of the new variables always depends directly on the original variables. For instance, even if `x1ANDx2` is included in a problem, the definition of `x1ANDx2ANDx3` will be based on individual `x1`, `x2` and `x3`, and will not exploit `x1ANDx2`. Such an optimization may be handled by pre-solvers of commercial solvers, according to the specific needs. + + Note that during the conversion, powers of a single variable are removed: for instance, the expression `x0^2 + x1*x2^3` is treated as `x0 + x1*x2` + + Initialize converter. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/binary_to_linear_binary.py#L81-L84" signature="__init__()"> + Initialize converter. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.__init__ "qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.__init__")() | Initialize converter. | + | [`convert`](#qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.convert "qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.convert")(problem) | Convert all high-degree terms (namely, degree 2+) to linear terms. | + | [`get_compatibility_msg`](#qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.get_compatibility_msg "qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.get_compatibility_msg")(problem) | Checks whether the given problem is compatible with the conversion. | + | [`interpret`](#qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.interpret "qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.interpret")(x) | Convert a solution of the converted problem back to a solution of the original problem. | + | [`is_compatible`](#qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.is_compatible "qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.is_compatible")(problem) | Checks whether a given problem can be solved with the optimizer implementing this method. | + + ### convert + + <Function id="qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.convert" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/binary_to_linear_binary.py#L88-L135" signature="convert(problem)"> + Convert all high-degree terms (namely, degree 2+) to linear terms. + + **Parameters** + + **problem** (*OptimizationProblem*) + + **Return type** + + *OptimizationProblem* + </Function> + + ### get\_compatibility\_msg + + <Function id="qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.get_compatibility_msg" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/binary_to_linear_binary.py#L143-L166" signature="get_compatibility_msg(problem)" modifiers="static"> + Checks whether the given problem is compatible with the conversion. + + A problem is compatible if all variables are binary. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + A message describing the incompatibility, if any, or an empty string. + + **Return type** + + [str](https://docs.python.org/3/library/stdtypes.html#str) + </Function> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/binary_to_linear_binary.py#L137-L141" signature="interpret(x)"> + Convert a solution of the converted problem back to a solution of the original problem. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### is\_compatible + + <Function id="qiskit_addon_opt_mapper.converters.BinaryToLinearBinary.is_compatible" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/binary_to_linear_binary.py#L168-L178" signature="is_compatible(problem)"> + Checks whether a given problem can be solved with the optimizer implementing this method. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + Returns True if the problem is compatible, False otherwise. + + **Return type** + + [bool](https://docs.python.org/3/library/functions.html#bool) + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/converters-binary-to-spin.mdx b/docs/api/qiskit-addon-opt-mapper/converters-binary-to-spin.mdx new file mode 100644 index 00000000000..f372e666216 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/converters-binary-to-spin.mdx @@ -0,0 +1,70 @@ +--- +title: BinaryToSpin (latest version) +description: API reference for qiskit_addon_opt_mapper.converters.BinaryToSpin in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.converters.BinaryToSpin +--- + +<span id="qiskit-addon-opt-mapper-converters-binarytospin" /> + +# qiskit\_addon\_opt\_mapper.converters.BinaryToSpin + +<Class id="qiskit_addon_opt_mapper.converters.BinaryToSpin" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/binary_to_spin.py#L37-L250" signature="BinaryToSpin" modifiers="class"> + Bases: [`OptimizationProblemConverter`](converters-optimization-problem-converter "qiskit_addon_opt_mapper.converters.optimization_problem_converter.OptimizationProblemConverter") + + Convert all binary variables in the problem to spin variables. + + The conversion is done by the relation b\_i = (1 - s\_i)/2 where b\_i is a binary variable and s\_i is a spin variable. + + Initialize converter. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.converters.BinaryToSpin.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/binary_to_spin.py#L47-L53" signature="__init__()"> + Initialize converter. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.converters.BinaryToSpin.__init__ "qiskit_addon_opt_mapper.converters.BinaryToSpin.__init__")() | Initialize converter. | + | [`convert`](#qiskit_addon_opt_mapper.converters.BinaryToSpin.convert "qiskit_addon_opt_mapper.converters.BinaryToSpin.convert")(problem) | Convert all binary variables in the problem to spin variables. | + | [`interpret`](#qiskit_addon_opt_mapper.converters.BinaryToSpin.interpret "qiskit_addon_opt_mapper.converters.BinaryToSpin.interpret")(x) | Convert a solution of the converted problem back to a solution of the original problem. | + + ### convert + + <Function id="qiskit_addon_opt_mapper.converters.BinaryToSpin.convert" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/binary_to_spin.py#L57-L111" signature="convert(problem)"> + Convert all binary variables in the problem to spin variables. + + **Parameters** + + **problem** (*OptimizationProblem*) + + **Return type** + + *OptimizationProblem* + </Function> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.converters.BinaryToSpin.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/binary_to_spin.py#L113-L134" signature="interpret(x)"> + Convert a solution of the converted problem back to a solution of the original problem. + + For binaries that became spins, we use b = (1 - s)/2. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/converters-inequality-to-equality.mdx b/docs/api/qiskit-addon-opt-mapper/converters-inequality-to-equality.mdx new file mode 100644 index 00000000000..66e5fc0a4d6 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/converters-inequality-to-equality.mdx @@ -0,0 +1,127 @@ +--- +title: InequalityToEquality (latest version) +description: API reference for qiskit_addon_opt_mapper.converters.InequalityToEquality in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.converters.InequalityToEquality +--- + +<span id="qiskit-addon-opt-mapper-converters-inequalitytoequality" /> + +# qiskit\_addon\_opt\_mapper.converters.InequalityToEquality + +<Class id="qiskit_addon_opt_mapper.converters.InequalityToEquality" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/inequality_to_equality.py#L31-L477" signature="InequalityToEquality(mode='auto')" modifiers="class"> + Bases: [`OptimizationProblemConverter`](converters-optimization-problem-converter "qiskit_addon_opt_mapper.converters.optimization_problem_converter.OptimizationProblemConverter") + + Convert inequality constraints into equality constraints by introducing slack variables. + + **Examples** + + ```python + >>> from qiskit_addon_opt_mapper.problems import OptimizationProblem + >>> from qiskit_addon_opt_mapper.converters import InequalityToEquality + >>> problem = OptimizationProblem() + >>> # define a problem + >>> conv = InequalityToEquality() + >>> problem2 = conv.convert(problem) + ``` + + Init method. + + **Parameters** + + **mode** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – + + To choose the type of slack variables. There are 3 options for mode. + + * ’integer’: All slack variables will be integer variables. + * ’continuous’: All slack variables will be continuous variables. + * ’auto’: Use integer variables if possible, otherwise use continuous variables. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.converters.InequalityToEquality.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/inequality_to_equality.py#L45-L57" signature="__init__(mode='auto')"> + Init method. + + **Parameters** + + **mode** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – + + To choose the type of slack variables. There are 3 options for mode. + + * ’integer’: All slack variables will be integer variables. + * ’continuous’: All slack variables will be continuous variables. + * ’auto’: Use integer variables if possible, otherwise use continuous variables. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.converters.InequalityToEquality.__init__ "qiskit_addon_opt_mapper.converters.InequalityToEquality.__init__")(\[mode]) | Init method. | + | [`convert`](#qiskit_addon_opt_mapper.converters.InequalityToEquality.convert "qiskit_addon_opt_mapper.converters.InequalityToEquality.convert")(problem) | Convert a problem with inequality constraints into one with only equality constraints. | + | [`interpret`](#qiskit_addon_opt_mapper.converters.InequalityToEquality.interpret "qiskit_addon_opt_mapper.converters.InequalityToEquality.interpret")(x) | Convert a result of a converted problem into that of the original problem. | + + ## Attributes + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | + | [`mode`](#qiskit_addon_opt_mapper.converters.InequalityToEquality.mode "qiskit_addon_opt_mapper.converters.InequalityToEquality.mode") | Returns the mode of the converter. | + + ### convert + + <Function id="qiskit_addon_opt_mapper.converters.InequalityToEquality.convert" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/inequality_to_equality.py#L59-L213" signature="convert(problem)"> + Convert a problem with inequality constraints into one with only equality constraints. + + **Parameters** + + **problem** (*OptimizationProblem*) – The problem to be solved, that may contain inequality constraints. + + **Returns** + + The converted problem, that contain only equality constraints. + + **Raises** + + * **OptimizationError** – If a variable type is not supported. + * **OptimizationError** – If an unsupported mode is selected. + * **OptimizationError** – If an unsupported sense is specified. + + **Return type** + + *OptimizationProblem* + </Function> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.converters.InequalityToEquality.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/inequality_to_equality.py#L424-L444" signature="interpret(x)"> + Convert a result of a converted problem into that of the original problem. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The result of the converted problem or the given result in case of FAILURE. + + **Returns** + + The result of the original problem. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### mode + + <Attribute id="qiskit_addon_opt_mapper.converters.InequalityToEquality.mode" attributeTypeHint="str"> + Returns the mode of the converter. + + **Returns** + + The mode of the converter used for additional slack variables + </Attribute> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/converters-integer-to-binary.mdx b/docs/api/qiskit-addon-opt-mapper/converters-integer-to-binary.mdx new file mode 100644 index 00000000000..ac1da6bd0cb --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/converters-integer-to-binary.mdx @@ -0,0 +1,99 @@ +--- +title: IntegerToBinary (latest version) +description: API reference for qiskit_addon_opt_mapper.converters.IntegerToBinary in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.converters.IntegerToBinary +--- + +<span id="qiskit-addon-opt-mapper-converters-integertobinary" /> + +# qiskit\_addon\_opt\_mapper.converters.IntegerToBinary + +<Class id="qiskit_addon_opt_mapper.converters.IntegerToBinary" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/integer_to_binary.py#L26-L378" signature="IntegerToBinary" modifiers="class"> + Bases: [`OptimizationProblemConverter`](converters-optimization-problem-converter "qiskit_addon_opt_mapper.converters.optimization_problem_converter.OptimizationProblemConverter") + + Integer to binary converter. + + Convert a `OptimizationProblem` into new one by encoding integers with binary variables. + + This bounded-coefficient encoding used in this converted is proposed in \[1], Eq. (5). + + **Examples** + + ```python + >>> from qiskit_addon_opt_mapper.problems import OptimizationProblem + >>> from qiskit_addon_opt_mapper.converters import IntegerToBinary + >>> problem = OptimizationProblem() + >>> var = problem.integer_var(name='x', lowerbound=0, upperbound=10) + >>> conv = IntegerToBinary() + >>> problem2 = conv.convert(problem) + ``` + + **References** + + **\[1]: Sahar Karimi, Pooya Ronagh (2017), Practical Integer-to-Binary Mapping for Quantum** + + Annealers. arxiv.org:1706.01945. + + Class initializer. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.converters.IntegerToBinary.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/integer_to_binary.py#L49-L54" signature="__init__()"> + Class initializer. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | + | [`__init__`](#qiskit_addon_opt_mapper.converters.IntegerToBinary.__init__ "qiskit_addon_opt_mapper.converters.IntegerToBinary.__init__")() | Class initializer. | + | [`convert`](#qiskit_addon_opt_mapper.converters.IntegerToBinary.convert "qiskit_addon_opt_mapper.converters.IntegerToBinary.convert")(problem) | Convert an integer problem into a new problem with binary variables. | + | [`interpret`](#qiskit_addon_opt_mapper.converters.IntegerToBinary.interpret "qiskit_addon_opt_mapper.converters.IntegerToBinary.interpret")(x) | Convert back the converted problem (binary variables) to the original (integer variables). | + + ### convert + + <Function id="qiskit_addon_opt_mapper.converters.IntegerToBinary.convert" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/integer_to_binary.py#L56-L123" signature="convert(problem)"> + Convert an integer problem into a new problem with binary variables. + + **Parameters** + + **problem** (*OptimizationProblem*) – The problem to be solved, that may contain integer variables. + + **Returns** + + The converted problem, that contains no integer variables. + + **Raises** + + **OptimizationError** – if variable or constraint type is not supported. + + **Return type** + + *OptimizationProblem* + </Function> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.converters.IntegerToBinary.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/integer_to_binary.py#L359-L378" signature="interpret(x)"> + Convert back the converted problem (binary variables) to the original (integer variables). + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The result of the converted problem or the given result in case of FAILURE. + + **Returns** + + The result of the original problem. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/converters-linear-inequality-to-penalty.mdx b/docs/api/qiskit-addon-opt-mapper/converters-linear-inequality-to-penalty.mdx new file mode 100644 index 00000000000..3ac7f70b351 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/converters-linear-inequality-to-penalty.mdx @@ -0,0 +1,143 @@ +--- +title: LinearInequalityToPenalty (latest version) +description: API reference for qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty +--- + +<span id="qiskit-addon-opt-mapper-converters-linearinequalitytopenalty" /> + +# qiskit\_addon\_opt\_mapper.converters.LinearInequalityToPenalty + +<Class id="qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/linear_inequality_to_penalty.py#L32-L395" signature="LinearInequalityToPenalty(penalty=None)" modifiers="class"> + Bases: [`OptimizationProblemConverter`](converters-optimization-problem-converter "qiskit_addon_opt_mapper.converters.optimization_problem_converter.OptimizationProblemConverter") + + Convert linear inequality constraints to penalty terms of the objective function. + + There are some linear constraints which do not require slack variables to construct penalty terms \[1]. This class supports the following inequality constraints. + +$$ +\begin{split}\begin{array}{} +\text { Inequality constraint } & & \text { Penalty term } \\ +x \leq y & \rightarrow & P(x-x y) \\ +x \geq y & \rightarrow & P(y-x y) \\ +\sum_{i=1}^n x_i \leq 1, n \geq 2 & \rightarrow & +P \sum_{i, j : i < j} x_i x_j\\ +\sum_{i=1}^n x_i \geq n-1, n \geq 2 & \rightarrow & +P \sum_{i, j : i < j} (1 - x_i) (1 - x_j) +\end{array}\end{split} +$$ + + Note that x, y, z and $x_i$ are binary variables, and P is a penalty factor, where the value of P is automatically determined or supplied by users. + + If constraints match with any of the patterns, they are converted into penalty terms and added to the objective function. Otherwise, constraints are kept as is. + + **References** + + **\[1]: Fred Glover, et al. (2019),** + + A Tutorial on Formulating and Using QUBO Models, [arXiv:1811.11538](https://arxiv.org/abs/1811.11538). + + Init method. + + **Parameters** + + **penalty** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – Penalty factor to scale equality constraints that are added to objective. If None is passed, a penalty factor will be automatically calculated on every conversion. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/linear_inequality_to_penalty.py#L63-L74" signature="__init__(penalty=None)"> + Init method. + + **Parameters** + + **penalty** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – Penalty factor to scale equality constraints that are added to objective. If None is passed, a penalty factor will be automatically calculated on every conversion. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty.__init__ "qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty.__init__")(\[penalty]) | Init method. | + | [`convert`](#qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty.convert "qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty.convert")(problem) | Convert inequality constraints into penalty terms of the objective function. | + | [`interpret`](#qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty.interpret "qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty.interpret")(x) | Convert the result of the converted problem back to that of the original problem. | + + ## Attributes + + | | | + | --------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------- | + | [`penalty`](#qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty.penalty "qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty.penalty") | Returns the penalty factor used in conversion. | + + ### convert + + <Function id="qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty.convert" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/linear_inequality_to_penalty.py#L76-L223" signature="convert(problem)"> + Convert inequality constraints into penalty terms of the objective function. + + This methods converts the following patterns where x, y, and $x_i$ are binary variables and P is a penalty factor. + +$$ +\begin{split}\begin{array}{} +\text { Inequality constraint } & & \text { Penalty term } \\ +x \leq y & \rightarrow & P(x-x y) \\ +x \geq y & \rightarrow & P(y-x y) \\ +\sum_{i=1}^n x_i \leq 1, n \geq 2 & \rightarrow & P \sum_{i, j : i < j} x_i x_j\\ +\sum_{i=1}^n x_i \geq n-1, n \geq 2 & \rightarrow & +P \sum_{i, j : i < j} (1 - x_i) (1 - x_j) +\end{array}\end{split} +$$ + + **Parameters** + + **problem** (*OptimizationProblem*) – The problem to be solved. + + **Returns** + + The converted problem + + **Raises** + + **OptimizationError** – If an unsupported-type variable exists. + + **Return type** + + *OptimizationProblem* + </Function> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/linear_inequality_to_penalty.py#L355-L374" signature="interpret(x)"> + Convert the result of the converted problem back to that of the original problem. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The result of the converted problem or the given result in case of FAILURE. + + **Returns** + + The result of the original problem. + + **Raises** + + **OptimizationError** – if the number of variables in the result differs from that of the original problem. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### penalty + + <Attribute id="qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty.penalty" attributeTypeHint="float | None"> + Returns the penalty factor used in conversion. + + **Returns** + + The penalty factor used in conversion. + </Attribute> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/converters-maximize-to-minimize.mdx b/docs/api/qiskit-addon-opt-mapper/converters-maximize-to-minimize.mdx new file mode 100644 index 00000000000..868e41eec3c --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/converters-maximize-to-minimize.mdx @@ -0,0 +1,78 @@ +--- +title: MaximizeToMinimize (latest version) +description: API reference for qiskit_addon_opt_mapper.converters.MaximizeToMinimize in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.converters.MaximizeToMinimize +--- + +<span id="qiskit-addon-opt-mapper-converters-maximizetominimize" /> + +# qiskit\_addon\_opt\_mapper.converters.MaximizeToMinimize + +<Class id="qiskit_addon_opt_mapper.converters.MaximizeToMinimize" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/flip_problem_sense.py#L107-L114" signature="MaximizeToMinimize" modifiers="class"> + Bases: `_FlipProblemSense` + + Convert a maximization problem to a minimization problem only if it is a maximization problem. + + Otherwise problem’s sense is unchanged. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.converters.MaximizeToMinimize.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/flip_problem_sense.py#L32-L33" signature="__init__()"> + **Return type** + + None + </Function> + + ## Methods + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.converters.MaximizeToMinimize.__init__ "qiskit_addon_opt_mapper.converters.MaximizeToMinimize.__init__")() | | + | [`convert`](#qiskit_addon_opt_mapper.converters.MaximizeToMinimize.convert "qiskit_addon_opt_mapper.converters.MaximizeToMinimize.convert")(problem) | Flip the sense of a problem. | + | [`interpret`](#qiskit_addon_opt_mapper.converters.MaximizeToMinimize.interpret "qiskit_addon_opt_mapper.converters.MaximizeToMinimize.interpret")(x) | Convert the result of the converted problem back to that of the original problem. | + + ### convert + + <Function id="qiskit_addon_opt_mapper.converters.MaximizeToMinimize.convert" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/flip_problem_sense.py#L35-L64" signature="convert(problem)"> + Flip the sense of a problem. + + **Parameters** + + **problem** (*OptimizationProblem*) – The problem to be flipped. + + **Returns** + + A converted problem, that has the flipped sense. + + **Return type** + + *OptimizationProblem* + </Function> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.converters.MaximizeToMinimize.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/flip_problem_sense.py#L81-L104" signature="interpret(x)"> + Convert the result of the converted problem back to that of the original problem. + + Note: This implementation does not modify the result, but the method is required because the base class defines interpret as an abstract method. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The result of the converted problem or the given result in case of FAILURE. + + **Returns** + + The result of the original problem. + + **Raises** + + **OptimizationError** – if the number of variables in the result differs from that of the original problem. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/converters-minimize-to-maximize.mdx b/docs/api/qiskit-addon-opt-mapper/converters-minimize-to-maximize.mdx new file mode 100644 index 00000000000..ed2bd24be94 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/converters-minimize-to-maximize.mdx @@ -0,0 +1,78 @@ +--- +title: MinimizeToMaximize (latest version) +description: API reference for qiskit_addon_opt_mapper.converters.MinimizeToMaximize in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.converters.MinimizeToMaximize +--- + +<span id="qiskit-addon-opt-mapper-converters-minimizetomaximize" /> + +# qiskit\_addon\_opt\_mapper.converters.MinimizeToMaximize + +<Class id="qiskit_addon_opt_mapper.converters.MinimizeToMaximize" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/flip_problem_sense.py#L117-L124" signature="MinimizeToMaximize" modifiers="class"> + Bases: `_FlipProblemSense` + + Convert a minimization problem to a maximization problem only if it is a minimization problem. + + Otherwise problem’s sense is unchanged. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.converters.MinimizeToMaximize.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/flip_problem_sense.py#L32-L33" signature="__init__()"> + **Return type** + + None + </Function> + + ## Methods + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.converters.MinimizeToMaximize.__init__ "qiskit_addon_opt_mapper.converters.MinimizeToMaximize.__init__")() | | + | [`convert`](#qiskit_addon_opt_mapper.converters.MinimizeToMaximize.convert "qiskit_addon_opt_mapper.converters.MinimizeToMaximize.convert")(problem) | Flip the sense of a problem. | + | [`interpret`](#qiskit_addon_opt_mapper.converters.MinimizeToMaximize.interpret "qiskit_addon_opt_mapper.converters.MinimizeToMaximize.interpret")(x) | Convert the result of the converted problem back to that of the original problem. | + + ### convert + + <Function id="qiskit_addon_opt_mapper.converters.MinimizeToMaximize.convert" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/flip_problem_sense.py#L35-L64" signature="convert(problem)"> + Flip the sense of a problem. + + **Parameters** + + **problem** (*OptimizationProblem*) – The problem to be flipped. + + **Returns** + + A converted problem, that has the flipped sense. + + **Return type** + + *OptimizationProblem* + </Function> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.converters.MinimizeToMaximize.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/flip_problem_sense.py#L81-L104" signature="interpret(x)"> + Convert the result of the converted problem back to that of the original problem. + + Note: This implementation does not modify the result, but the method is required because the base class defines interpret as an abstract method. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The result of the converted problem or the given result in case of FAILURE. + + **Returns** + + The result of the original problem. + + **Raises** + + **OptimizationError** – if the number of variables in the result differs from that of the original problem. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-converter.mdx b/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-converter.mdx new file mode 100644 index 00000000000..0d6268d1e28 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-converter.mdx @@ -0,0 +1,60 @@ +--- +title: OptimizationProblemConverter (latest version) +description: API reference for qiskit_addon_opt_mapper.converters.OptimizationProblemConverter in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.converters.OptimizationProblemConverter +--- + +<span id="qiskit-addon-opt-mapper-converters-optimizationproblemconverter" /> + +# qiskit\_addon\_opt\_mapper.converters.OptimizationProblemConverter + +<Class id="qiskit_addon_opt_mapper.converters.OptimizationProblemConverter" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_converter.py#L22-L37" signature="OptimizationProblemConverter" modifiers="class"> + Bases: [`ABC`](https://docs.python.org/3/library/abc.html#abc.ABC) + + An abstract class for converters of optimization problem in Qiskit optimization module. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemConverter.__init__" signature="__init__()" /> + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.converters.OptimizationProblemConverter.__init__ "qiskit_addon_opt_mapper.converters.OptimizationProblemConverter.__init__")() | | + | [`convert`](#qiskit_addon_opt_mapper.converters.OptimizationProblemConverter.convert "qiskit_addon_opt_mapper.converters.OptimizationProblemConverter.convert")(problem) | Convert method. | + | [`interpret`](#qiskit_addon_opt_mapper.converters.OptimizationProblemConverter.interpret "qiskit_addon_opt_mapper.converters.OptimizationProblemConverter.interpret")(x) | Interpret a result into another form using the information of conversion. | + + ### convert + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemConverter.convert" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_converter.py#L25-L32" signature="convert(problem)" modifiers="abstract"> + Convert method. + + Convert a OptimizationProblem into another form and keep the information required to interpret the result. + + **Parameters** + + **problem** (*OptimizationProblem*) + + **Return type** + + *OptimizationProblem* + </Function> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemConverter.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_converter.py#L34-L37" signature="interpret(x)" modifiers="abstract"> + Interpret a result into another form using the information of conversion. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-to-hubo.mdx b/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-to-hubo.mdx new file mode 100644 index 00000000000..32946a02895 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-to-hubo.mdx @@ -0,0 +1,161 @@ +--- +title: OptimizationProblemToHubo (latest version) +description: API reference for qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo +--- + +<span id="qiskit-addon-opt-mapper-converters-optimizationproblemtohubo" /> + +# qiskit\_addon\_opt\_mapper.converters.OptimizationProblemToHubo + +<Class id="qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_to_hubo.py#L30-L195" signature="OptimizationProblemToHubo(penalty=None)" modifiers="class"> + Bases: [`OptimizationProblemConverter`](converters-optimization-problem-converter "qiskit_addon_opt_mapper.converters.optimization_problem_converter.OptimizationProblemConverter") + + Convert an optimization problem into a HUBO form. + + HUBO stands for “higher-order unconstrained binary optimization”. The conversion is achieved by converting variables to binary and eliminating constraints. The resulting problem has no constraints and a higher-order polynomial objective function. This combines several converters: IntegerToBinary, InequalityToPenalty, EqualityToPenalty, and MaximizeToMinimize, while preserving higher-order terms in the objective function. The resulting HUBO problem can be directly mapped to an Ising Hamiltonian using the to\_ising() function. + + **Examples** + + ```python + >>> from qiskit_addon_opt_mapper.problems import OptimizationProblem + >>> from qiskit_addon_opt_mapper.converters import OptimizationProblemToHubo + >>> problem = OptimizationProblem() + >>> # define a problem + >>> conv = OptimizationProblemToHubo() + >>> problem2 = conv.convert(problem) + ``` + + Init method. + + **Parameters** + + **penalty** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – Penalty factor to scale equality constraints that are added to objective. If None is passed, a penalty factor will be automatically calculated on every conversion. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_to_hubo.py#L51-L68" signature="__init__(penalty=None)"> + Init method. + + **Parameters** + + **penalty** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – Penalty factor to scale equality constraints that are added to objective. If None is passed, a penalty factor will be automatically calculated on every conversion. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.__init__ "qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.__init__")(\[penalty]) | Init method. | + | [`convert`](#qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.convert "qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.convert")(problem) | Convert an optimization problem into a HUBO form. | + | [`get_compatibility_msg`](#qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.get_compatibility_msg "qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.get_compatibility_msg")(problem) | Checks whether the given problem is compatible with HUBO conversion. | + | [`interpret`](#qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.interpret "qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.interpret")(x) | Convert the result of the converted problem back to that of the original problem. | + | [`is_compatible`](#qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.is_compatible "qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.is_compatible")(problem) | Checks whether a given problem can be solved with the optimizer implementing this method. | + + ## Attributes + + | | | + | --------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------- | + | [`penalty`](#qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.penalty "qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.penalty") | Returns the penalty factor used in conversion. | + + ### convert + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.convert" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_to_hubo.py#L70-L92" signature="convert(problem)"> + Convert an optimization problem into a HUBO form. + + The new problem has no constraints and the objective function is higher order polynomial. + + **Parameters** + + **problem** (*OptimizationProblem*) – An optimization problem to be converted. + + **Returns** + + A new optimization problem in the HUBO form. + + **Raises** + + **OptimizationError** – If the input problem is invalid. + + **Return type** + + *OptimizationProblem* + </Function> + + ### get\_compatibility\_msg + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.get_compatibility_msg" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_to_hubo.py#L111-L162" signature="get_compatibility_msg(problem)" modifiers="static"> + Checks whether the given problem is compatible with HUBO conversion. + + A problem is compatible if it can be converted to a HUBO (Higher-order Unconstrained Binary Optimization). If not, this function returns a message explaining the incompatibility. + + The following problems are not compatible: - Continuous variables are not supported. - Constraints with float coefficients are not supported, because inequality constraints cannot be converted to equality constraints using integer slack variables. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + A message describing the incompatibility. + + **Return type** + + [str](https://docs.python.org/3/library/stdtypes.html#str) + </Function> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_to_hubo.py#L94-L109" signature="interpret(x)"> + Convert the result of the converted problem back to that of the original problem. + + Done by applying the interpret method of each converter in reverse order. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) – A solution vector of the converted problem. + + **Returns** + + A solution vector of the original problem. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### is\_compatible + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.is_compatible" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_to_hubo.py#L164-L174" signature="is_compatible(problem)"> + Checks whether a given problem can be solved with the optimizer implementing this method. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + Returns True if the problem is compatible, False otherwise. + + **Return type** + + [bool](https://docs.python.org/3/library/functions.html#bool) + </Function> + + ### penalty + + <Attribute id="qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo.penalty" attributeTypeHint="float | None"> + Returns the penalty factor used in conversion. + + **Returns** + + The penalty factor used in conversion. + </Attribute> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-to-qubo.mdx b/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-to-qubo.mdx new file mode 100644 index 00000000000..a6e6adc4752 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-to-qubo.mdx @@ -0,0 +1,161 @@ +--- +title: OptimizationProblemToQubo (latest version) +description: API reference for qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo +--- + +<span id="qiskit-addon-opt-mapper-converters-optimizationproblemtoqubo" /> + +# qiskit\_addon\_opt\_mapper.converters.OptimizationProblemToQubo + +<Class id="qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_to_qubo.py#L30-L196" signature="OptimizationProblemToQubo(penalty=None)" modifiers="class"> + Bases: [`OptimizationProblemConverter`](converters-optimization-problem-converter "qiskit_addon_opt_mapper.converters.optimization_problem_converter.OptimizationProblemConverter") + + Convert a given optimization problem in quadratic form into a QUBO problem. + + Done by converting variables to binary and eliminating constraints. An optimization problem in quadratic form is a problem with quadratic objective function and linear constraints. A QUBO is a problem with quadratic objective function and no constraints. This combines several converters: IntegerToBinary, InequalityToPenalty, InequalityToEquality, EqualityToPenalty, and MaximizeToMinimize. The resulting HUBO problem can be directly mapped to an Ising Hamiltonian using the to\_ising() function. + + **Examples** + + ```python + >>> from qiskit_addon_opt_mapper.problems import OptimizationProblem + >>> from qiskit_addon_opt_mapper.converters import OptimizationProblemToQubo + >>> problem = OptimizationProblem() + >>> # define a problem + >>> conv = OptimizationProblemToQubo() + >>> problem2 = conv.convert(problem) + ``` + + Init method. + + **Parameters** + + **penalty** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – Penalty factor to scale equality constraints that are added to objective. If None is passed, a penalty factor will be automatically calculated on every conversion. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_to_qubo.py#L52-L69" signature="__init__(penalty=None)"> + Init method. + + **Parameters** + + **penalty** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – Penalty factor to scale equality constraints that are added to objective. If None is passed, a penalty factor will be automatically calculated on every conversion. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.__init__ "qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.__init__")(\[penalty]) | Init method. | + | [`convert`](#qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.convert "qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.convert")(problem) | Convert a optimization problem into a QUBO form. | + | [`get_compatibility_msg`](#qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.get_compatibility_msg "qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.get_compatibility_msg")(problem) | Checks whether a given problem can be converted to a QUBO form. | + | [`interpret`](#qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.interpret "qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.interpret")(x) | Convert the result of the converted problem back to that of the original problem. | + | [`is_compatible`](#qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.is_compatible "qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.is_compatible")(problem) | Checks whether a given problem can be solved with the optimizer implementing this method. | + + ## Attributes + + | | | + | --------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------- | + | [`penalty`](#qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.penalty "qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.penalty") | Returns the penalty factor used in conversion. | + + ### convert + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.convert" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_to_qubo.py#L71-L93" signature="convert(problem)"> + Convert a optimization problem into a QUBO form. + + The new problem has no constraints and the objective function is quadratic. + + **Parameters** + + **problem** (*OptimizationProblem*) – The problem with linear constraints to be solved. + + **Returns** + + The problem converted in QUBO format as minimization problem. + + **Raises** + + **OptimizationError** – In case of an incompatible problem. + + **Return type** + + *OptimizationProblem* + </Function> + + ### get\_compatibility\_msg + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.get_compatibility_msg" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_to_qubo.py#L111-L163" signature="get_compatibility_msg(problem)" modifiers="static"> + Checks whether a given problem can be converted to a QUBO form. + + If the problem is not compatible, this function returns a message explaining the incompatibility. + + The following problems are not compatible: - Continuous variables are not supported. - Higher-order objective functions are not supported. - Quadratic constraints are not supported. - Higher-order constraints are not supported. - Constraints with float coefficients are not supported, because inequality constraints cannot be converted to equality constraints using integer slack variables. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + A message describing the incompatibility. + + **Return type** + + [str](https://docs.python.org/3/library/stdtypes.html#str) + </Function> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_to_qubo.py#L95-L109" signature="interpret(x)"> + Convert the result of the converted problem back to that of the original problem. + + Done by applying the interpret method of each converter in reverse order. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The result of the converted problem. + + **Returns** + + The result of the original problem. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### is\_compatible + + <Function id="qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.is_compatible" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/optimization_problem_to_qubo.py#L165-L175" signature="is_compatible(problem)"> + Checks whether a given problem can be solved with the optimizer implementing this method. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + Returns True if the problem is compatible, False otherwise. + + **Return type** + + [bool](https://docs.python.org/3/library/functions.html#bool) + </Function> + + ### penalty + + <Attribute id="qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo.penalty" attributeTypeHint="float | None"> + Returns the penalty factor used in conversion. + + **Returns** + + The penalty factor used in conversion. + </Attribute> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/converters-spin-to-binary.mdx b/docs/api/qiskit-addon-opt-mapper/converters-spin-to-binary.mdx new file mode 100644 index 00000000000..5ef111ba4d4 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/converters-spin-to-binary.mdx @@ -0,0 +1,76 @@ +--- +title: SpinToBinary (latest version) +description: API reference for qiskit_addon_opt_mapper.converters.SpinToBinary in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.converters.SpinToBinary +--- + +<span id="qiskit-addon-opt-mapper-converters-spintobinary" /> + +# qiskit\_addon\_opt\_mapper.converters.SpinToBinary + +<Class id="qiskit_addon_opt_mapper.converters.SpinToBinary" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/spin_to_binary.py#L36-L253" signature="SpinToBinary" modifiers="class"> + Bases: [`OptimizationProblemConverter`](converters-optimization-problem-converter "qiskit_addon_opt_mapper.converters.optimization_problem_converter.OptimizationProblemConverter") + + Convert all spin variables in the problem to binary variables. + + The conversion is done by the relation: + + ```python + s_i = 1 - 2 b_i + ``` + + where s\_i is a spin variable (in \{-1, +1}) and b\_i is a binary variable (in \{0, 1}). + + Class initializer. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.converters.SpinToBinary.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/spin_to_binary.py#L48-L54" signature="__init__()"> + Class initializer. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.converters.SpinToBinary.__init__ "qiskit_addon_opt_mapper.converters.SpinToBinary.__init__")() | Class initializer. | + | [`convert`](#qiskit_addon_opt_mapper.converters.SpinToBinary.convert "qiskit_addon_opt_mapper.converters.SpinToBinary.convert")(problem) | Convert all spin variables in the problem to binary variables. | + | [`interpret`](#qiskit_addon_opt_mapper.converters.SpinToBinary.interpret "qiskit_addon_opt_mapper.converters.SpinToBinary.interpret")(x) | Convert a solution of the converted (binary) problem back to the original (spin) space. | + + ### convert + + <Function id="qiskit_addon_opt_mapper.converters.SpinToBinary.convert" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/spin_to_binary.py#L58-L113" signature="convert(problem)"> + Convert all spin variables in the problem to binary variables. + + **Parameters** + + **problem** (*OptimizationProblem*) + + **Return type** + + *OptimizationProblem* + </Function> + + ### interpret + + <Function id="qiskit_addon_opt_mapper.converters.SpinToBinary.interpret" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/converters/spin_to_binary.py#L115-L134" signature="interpret(x)"> + Convert a solution of the converted (binary) problem back to the original (spin) space. + + For spins we use s = 1 - 2 b. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/converters.mdx b/docs/api/qiskit-addon-opt-mapper/converters.mdx new file mode 100644 index 00000000000..f7cf580a68d --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/converters.mdx @@ -0,0 +1,47 @@ +--- +title: converters (latest version) +description: API reference for qiskit_addon_opt_mapper.converters in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_addon_opt_mapper.converters +--- + +<span id="module-qiskit_addon_opt_mapper.converters" /> + +<span id="converters-qiskit-addon-opt-mapper-converters" /> + +# Converters + +`qiskit_addon_opt_mapper.converters` + +Converters module for Optimization add-on. + +<span id="optimization-converters-qiskit-addon-opt-mapper-converters" /> + +## Optimization converters . + +`qiskit_addon_opt_mapper.converters` + +This is a set of converters having convert functionality to go between different representations of a given `QuadraticProgram` and to interpret a given result for the problem, based on the original problem before conversion, to return an appropriate [`SolverResult`](/docs/api/qiskit-addon-opt-mapper/solvers-solver-result "qiskit_addon_opt_mapper.solvers.SolverResult"). + +### Base class for converters + +| | | +| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | +| [`OptimizationProblemConverter`](/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-converter "qiskit_addon_opt_mapper.converters.OptimizationProblemConverter") | An abstract class for converters of optimization problem in Qiskit optimization module. | + +### Converters + +| | | +| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | +| [`BinaryToLinearBinary`](/docs/api/qiskit-addon-opt-mapper/converters-binary-to-linear-binary "qiskit_addon_opt_mapper.converters.BinaryToLinearBinary") | Convert all high-degree terms to linear terms, both in objective and constraints. | +| [`BinaryToSpin`](/docs/api/qiskit-addon-opt-mapper/converters-binary-to-spin "qiskit_addon_opt_mapper.converters.BinaryToSpin") | Convert all binary variables in the problem to spin variables. | +| [`InequalityToEquality`](/docs/api/qiskit-addon-opt-mapper/converters-inequality-to-equality "qiskit_addon_opt_mapper.converters.InequalityToEquality") | Convert inequality constraints into equality constraints by introducing slack variables. | +| [`IntegerToBinary`](/docs/api/qiskit-addon-opt-mapper/converters-integer-to-binary "qiskit_addon_opt_mapper.converters.IntegerToBinary") | Integer to binary converter. | +| [`LinearInequalityToPenalty`](/docs/api/qiskit-addon-opt-mapper/converters-linear-inequality-to-penalty "qiskit_addon_opt_mapper.converters.LinearInequalityToPenalty") | Convert linear inequality constraints to penalty terms of the objective function. | +| [`MaximizeToMinimize`](/docs/api/qiskit-addon-opt-mapper/converters-maximize-to-minimize "qiskit_addon_opt_mapper.converters.MaximizeToMinimize") | Convert a maximization problem to a minimization problem only if it is a maximization problem. | +| [`MinimizeToMaximize`](/docs/api/qiskit-addon-opt-mapper/converters-minimize-to-maximize "qiskit_addon_opt_mapper.converters.MinimizeToMaximize") | Convert a minimization problem to a maximization problem only if it is a minimization problem. | +| [`OptimizationProblemToHubo`](/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-to-hubo "qiskit_addon_opt_mapper.converters.OptimizationProblemToHubo") | Convert an optimization problem into a HUBO form. | +| [`OptimizationProblemToQubo`](/docs/api/qiskit-addon-opt-mapper/converters-optimization-problem-to-qubo "qiskit_addon_opt_mapper.converters.OptimizationProblemToQubo") | Convert a given optimization problem in quadratic form into a QUBO problem. | +| [`SpinToBinary`](/docs/api/qiskit-addon-opt-mapper/converters-spin-to-binary "qiskit_addon_opt_mapper.converters.SpinToBinary") | Convert all spin variables in the problem to binary variables. | + diff --git a/docs/api/qiskit-addon-opt-mapper/index.mdx b/docs/api/qiskit-addon-opt-mapper/index.mdx new file mode 100644 index 00000000000..17f499260dc --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/index.mdx @@ -0,0 +1,15 @@ +--- +title: Optimization mapper API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-opt-mapper. +--- + +<span id="qiskit-addon-opt-mapper-api-reference" /> + +# `qiskit-addon-opt-mapper` API reference + +* [Optimization Application Modeling (`qiskit_addon_opt_mapper.applications`)](applications) +* [Converters (`qiskit_addon_opt_mapper.converters`)](converters) +* [Problem Modeling Tools (`qiskit_addon_opt_mapper.problems`)](problems) +* [Reference Solvers (`qiskit_addon_opt_mapper.solvers`)](solvers) +* [Translators (`qiskit_addon_opt_mapper.translators`)](translators) + diff --git a/docs/api/qiskit-addon-opt-mapper/problems-constraint.mdx b/docs/api/qiskit-addon-opt-mapper/problems-constraint.mdx new file mode 100644 index 00000000000..57509c8e3c9 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/problems-constraint.mdx @@ -0,0 +1,124 @@ +--- +title: Constraint (latest version) +description: API reference for qiskit_addon_opt_mapper.problems.Constraint in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.problems.Constraint +--- + +<span id="qiskit-addon-opt-mapper-problems-constraint" /> + +# qiskit\_addon\_opt\_mapper.problems.Constraint + +<Class id="qiskit_addon_opt_mapper.problems.Constraint" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/constraint.py#L87-L164" signature="Constraint(optimization_problem, name, sense, rhs)" modifiers="class"> + Bases: [`OptimizationProblemElement`](problems-optimization-problem-element "qiskit_addon_opt_mapper.problems.optimization_problem_element.OptimizationProblemElement") + + Abstract Constraint Class. + + Initializes the constraint. + + **Parameters** + + * **optimization\_problem** (*Any*) – The parent OptimizationProblem. + * **name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – The name of the constraint. + * **sense** (*ConstraintSense*) – The sense of the constraint. + * **rhs** ([*float*](https://docs.python.org/3/library/functions.html#float)) – The right-hand-side of the constraint. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.problems.Constraint.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/constraint.py#L92-L106" signature="__init__(optimization_problem, name, sense, rhs)"> + Initializes the constraint. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The parent OptimizationProblem. + * **name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – The name of the constraint. + * **sense** (*ConstraintSense*) – The sense of the constraint. + * **rhs** ([*float*](https://docs.python.org/3/library/functions.html#float)) – The right-hand-side of the constraint. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.problems.Constraint.__init__ "qiskit_addon_opt_mapper.problems.Constraint.__init__")(optimization\_problem, name, sense, rhs) | Initializes the constraint. | + | [`evaluate`](#qiskit_addon_opt_mapper.problems.Constraint.evaluate "qiskit_addon_opt_mapper.problems.Constraint.evaluate")(x) | Evaluate left-hand-side of constraint for given values of variables. | + + ## Attributes + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------- | + | [`name`](#qiskit_addon_opt_mapper.problems.Constraint.name "qiskit_addon_opt_mapper.problems.Constraint.name") | Returns the name of the constraint. | + | [`optimization_problem`](#qiskit_addon_opt_mapper.problems.Constraint.optimization_problem "qiskit_addon_opt_mapper.problems.Constraint.optimization_problem") | Returns the parent OptimizationProblem. | + | [`rhs`](#qiskit_addon_opt_mapper.problems.Constraint.rhs "qiskit_addon_opt_mapper.problems.Constraint.rhs") | Returns the right-hand-side of the constraint. | + | [`sense`](#qiskit_addon_opt_mapper.problems.Constraint.sense "qiskit_addon_opt_mapper.problems.Constraint.sense") | Returns the sense of the constraint. | + + ### Sense + + <Attribute id="qiskit_addon_opt_mapper.problems.Constraint.Sense" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/constraint.py#L27-L84"> + alias of `ConstraintSense` + </Attribute> + + ### evaluate + + <Function id="qiskit_addon_opt_mapper.problems.Constraint.evaluate" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/constraint.py#L153-L164" signature="evaluate(x)" modifiers="abstract"> + Evaluate left-hand-side of constraint for given values of variables. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The values to be used for the variables. + + **Returns** + + The left-hand-side of the constraint. + + **Return type** + + [float](https://docs.python.org/3/library/functions.html#float) + </Function> + + ### name + + <Attribute id="qiskit_addon_opt_mapper.problems.Constraint.name" attributeTypeHint="str"> + Returns the name of the constraint. + + **Returns** + + The name of the constraint. + </Attribute> + + ### optimization\_problem + + <Attribute id="qiskit_addon_opt_mapper.problems.Constraint.optimization_problem" attributeTypeHint="OptimizationProblem"> + Returns the parent OptimizationProblem. + + **Returns** + + The parent OptimizationProblem. + </Attribute> + + ### rhs + + <Attribute id="qiskit_addon_opt_mapper.problems.Constraint.rhs" attributeTypeHint="float"> + Returns the right-hand-side of the constraint. + + **Returns** + + The right-hand-side of the constraint. + </Attribute> + + ### sense + + <Attribute id="qiskit_addon_opt_mapper.problems.Constraint.sense" attributeTypeHint="ConstraintSense"> + Returns the sense of the constraint. + + **Returns** + + The sense of the constraint. + </Attribute> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/problems-higher-order-constraint.mdx b/docs/api/qiskit-addon-opt-mapper/problems-higher-order-constraint.mdx new file mode 100644 index 00000000000..f02db593f0f --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/problems-higher-order-constraint.mdx @@ -0,0 +1,171 @@ +--- +title: HigherOrderConstraint (latest version) +description: API reference for qiskit_addon_opt_mapper.problems.HigherOrderConstraint in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.problems.HigherOrderConstraint +--- + +<span id="qiskit-addon-opt-mapper-problems-higherorderconstraint" /> + +# qiskit\_addon\_opt\_mapper.problems.HigherOrderConstraint + +<Class id="qiskit_addon_opt_mapper.problems.HigherOrderConstraint" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/higher_order_constraint.py#L27-L175" signature="HigherOrderConstraint(optimization_problem, name, linear=None, quadratic=None, higher_order=None, sense=ConstraintSense.LE, rhs=0.0)" modifiers="class"> + Bases: [`Constraint`](problems-constraint "qiskit_addon_opt_mapper.problems.constraint.Constraint") + + Constraint in higher order form. + + e.g. `linear(x) + x^T Q x + sum_{k>=3}  sum_{|t|=k} C_k[t] * prod_{i in t} x[i]` `sense` `rhs` where `sense` is one of the ConstraintSense values (e.g., LE, \<=) and `rhs` is a float. + + Supports both a single higher-order term (order+coeffs) and multiple via higher\_orders=\{k: coeffs}. + + Construct a higher-order constraint with linear, quadratic, and optional higher-order parts. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The optimization problem this constraint belongs to. + * **name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – The name of the constraint. + * **linear** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*] | None*) – Coefficients for the linear part. + * **quadratic** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*],* [*float*](https://docs.python.org/3/library/functions.html#float)*] | None*) – Coefficients for the quadratic part. + * **higher\_order** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*, ...],* [*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*list*](https://docs.python.org/3/library/stdtypes.html#list)*] | None*) – A single higher-order expression or a dictionary of \{order: coeffs} for multiple orders (k≥3). + * **sense** (*ConstraintSense*) – The sense of the constraint (e.g., LE, \<=). + * **rhs** ([*float*](https://docs.python.org/3/library/functions.html#float)) – The right-hand-side value of the constraint. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.problems.HigherOrderConstraint.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/higher_order_constraint.py#L39-L75" signature="__init__(optimization_problem, name, linear=None, quadratic=None, higher_order=None, sense=ConstraintSense.LE, rhs=0.0)"> + Construct a higher-order constraint with linear, quadratic, and optional higher-order parts. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The optimization problem this constraint belongs to. + * **name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – The name of the constraint. + * **linear** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*] | None*) – Coefficients for the linear part. + * **quadratic** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*],* [*float*](https://docs.python.org/3/library/functions.html#float)*] | None*) – Coefficients for the quadratic part. + * **higher\_order** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*, ...],* [*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*list*](https://docs.python.org/3/library/stdtypes.html#list)*] | None*) – A single higher-order expression or a dictionary of \{order: coeffs} for multiple orders (k≥3). + * **sense** (*ConstraintSense*) – The sense of the constraint (e.g., LE, \<=). + * **rhs** ([*float*](https://docs.python.org/3/library/functions.html#float)) – The right-hand-side value of the constraint. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.problems.HigherOrderConstraint.__init__ "qiskit_addon_opt_mapper.problems.HigherOrderConstraint.__init__")(optimization\_problem, name\[, ...]) | Construct a higher-order constraint with linear, quadratic, and optional higher-order parts. | + | [`evaluate`](#qiskit_addon_opt_mapper.problems.HigherOrderConstraint.evaluate "qiskit_addon_opt_mapper.problems.HigherOrderConstraint.evaluate")(x) | Evaluate the left-hand-side of the constraint. | + + ## Attributes + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------- | + | [`higher_order`](#qiskit_addon_opt_mapper.problems.HigherOrderConstraint.higher_order "qiskit_addon_opt_mapper.problems.HigherOrderConstraint.higher_order") | HigherOrderExpression}. | + | [`linear`](#qiskit_addon_opt_mapper.problems.HigherOrderConstraint.linear "qiskit_addon_opt_mapper.problems.HigherOrderConstraint.linear") | Returns the linear expression corresponding to the left-hand-side of the constraint. | + | [`name`](#qiskit_addon_opt_mapper.problems.HigherOrderConstraint.name "qiskit_addon_opt_mapper.problems.HigherOrderConstraint.name") | Returns the name of the constraint. | + | [`optimization_problem`](#qiskit_addon_opt_mapper.problems.HigherOrderConstraint.optimization_problem "qiskit_addon_opt_mapper.problems.HigherOrderConstraint.optimization_problem") | Returns the parent OptimizationProblem. | + | [`quadratic`](#qiskit_addon_opt_mapper.problems.HigherOrderConstraint.quadratic "qiskit_addon_opt_mapper.problems.HigherOrderConstraint.quadratic") | Returns the quadratic expression corresponding to the left-hand-side of the constraint. | + | [`rhs`](#qiskit_addon_opt_mapper.problems.HigherOrderConstraint.rhs "qiskit_addon_opt_mapper.problems.HigherOrderConstraint.rhs") | Returns the right-hand-side of the constraint. | + | [`sense`](#qiskit_addon_opt_mapper.problems.HigherOrderConstraint.sense "qiskit_addon_opt_mapper.problems.HigherOrderConstraint.sense") | Returns the sense of the constraint. | + + ### Sense + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderConstraint.Sense" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/constraint.py#L27-L84"> + alias of `ConstraintSense` + </Attribute> + + ### evaluate + + <Function id="qiskit_addon_opt_mapper.problems.HigherOrderConstraint.evaluate" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/higher_order_constraint.py#L138-L150" signature="evaluate(x)"> + Evaluate the left-hand-side of the constraint. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The values of the variables to be evaluated. + + **Returns** + + The left-hand-side of the constraint given the variable values. + + **Return type** + + [float](https://docs.python.org/3/library/functions.html#float) + </Function> + + ### higher\_order + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderConstraint.higher_order" attributeTypeHint="dict[int, HigherOrderExpression]"> + HigherOrderExpression}. + + **Returns** + + A dictionary mapping order (k>=3) to HigherOrderExpression. + + **Type** + + Return a shallow copy of \{order + </Attribute> + + ### linear + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderConstraint.linear" attributeTypeHint="LinearExpression"> + Returns the linear expression corresponding to the left-hand-side of the constraint. + + **Returns** + + The left-hand-side linear expression. + </Attribute> + + ### name + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderConstraint.name" attributeTypeHint="str"> + Returns the name of the constraint. + + **Returns** + + The name of the constraint. + </Attribute> + + ### optimization\_problem + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderConstraint.optimization_problem" attributeTypeHint="OptimizationProblem"> + Returns the parent OptimizationProblem. + + **Returns** + + The parent OptimizationProblem. + </Attribute> + + ### quadratic + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderConstraint.quadratic" attributeTypeHint="QuadraticExpression"> + Returns the quadratic expression corresponding to the left-hand-side of the constraint. + + **Returns** + + The left-hand-side quadratic expression. + </Attribute> + + ### rhs + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderConstraint.rhs" attributeTypeHint="float"> + Returns the right-hand-side of the constraint. + + **Returns** + + The right-hand-side of the constraint. + </Attribute> + + ### sense + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderConstraint.sense" attributeTypeHint="ConstraintSense"> + Returns the sense of the constraint. + + **Returns** + + The sense of the constraint. + </Attribute> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/problems-higher-order-expression.mdx b/docs/api/qiskit-addon-opt-mapper/problems-higher-order-expression.mdx new file mode 100644 index 00000000000..5ca140176ec --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/problems-higher-order-expression.mdx @@ -0,0 +1,170 @@ +--- +title: HigherOrderExpression (latest version) +description: API reference for qiskit_addon_opt_mapper.problems.HigherOrderExpression in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.problems.HigherOrderExpression +--- + +<span id="qiskit-addon-opt-mapper-problems-higherorderexpression" /> + +# qiskit\_addon\_opt\_mapper.problems.HigherOrderExpression + +<Class id="qiskit_addon_opt_mapper.problems.HigherOrderExpression" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/higher_order_expression.py#L38-L364" signature="HigherOrderExpression(optimization_problem, coefficients)" modifiers="class"> + Bases: [`OptimizationProblemElement`](problems-optimization-problem-element "qiskit_addon_opt_mapper.problems.optimization_problem_element.OptimizationProblemElement") + + Representation of a symmetric k-th order expression by its coefficients. + + We represent a symmetric polynomial term of order k as `f(x) = sum_{t} C[t] * prod_{i in t} x[i]`, where t is a multiset of variable indices of length k. + + When dealing with multidimensional array indices, the value is stored only at the lexicographically smallest permutation of the indices (obtained by sorting them in ascending order). + + For example, for a 4th-order term `2⋅x1⋅x2⋅x3⋅x4`, the coefficient “2” is stored at `dict((1, 2, 3, 4))`. Other permutations like `dict((2, 1, 4, 3))` or `dict((4, 3, 2, 1))` are left empty. + + Creates a new higher-order expression. + + **Parameters** + + * **optimization\_problem** (*Any*) – The parent OptimizationProblem. + * **coefficients** (*ndarray |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[Key,* [*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*list*](https://docs.python.org/3/library/stdtypes.html#list)) – Coefficients as either: - A dense ndarray or list with shape (n,)\*k, or - A dict mapping a tuple of variable indices/names (length k) to float. Keys are canonicalized to ascending order and summed. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.problems.HigherOrderExpression.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/higher_order_expression.py#L54-L71" signature="__init__(optimization_problem, coefficients)"> + Creates a new higher-order expression. + + **Parameters** + + * **optimization\_problem** (*Any*) – The parent OptimizationProblem. + * **coefficients** (*ndarray |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[Key,* [*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*list*](https://docs.python.org/3/library/stdtypes.html#list)) – Coefficients as either: - A dense ndarray or list with shape (n,)\*k, or - A dict mapping a tuple of variable indices/names (length k) to float. Keys are canonicalized to ascending order and summed. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------ | + | [`__init__`](#qiskit_addon_opt_mapper.problems.HigherOrderExpression.__init__ "qiskit_addon_opt_mapper.problems.HigherOrderExpression.__init__")(optimization\_problem, coefficients) | Creates a new higher-order expression. | + | [`evaluate`](#qiskit_addon_opt_mapper.problems.HigherOrderExpression.evaluate "qiskit_addon_opt_mapper.problems.HigherOrderExpression.evaluate")(x) | Evaluate the expression: sum\_\{t} C\[t] \* prod\_\{i in t} x\[i]. | + | [`evaluate_gradient`](#qiskit_addon_opt_mapper.problems.HigherOrderExpression.evaluate_gradient "qiskit_addon_opt_mapper.problems.HigherOrderExpression.evaluate_gradient")(x) | Evaluate gradient wrt x. | + | [`to_array`](#qiskit_addon_opt_mapper.problems.HigherOrderExpression.to_array "qiskit_addon_opt_mapper.problems.HigherOrderExpression.to_array")(\[symmetric]) | Returns a dense tensor. | + | [`to_dict`](#qiskit_addon_opt_mapper.problems.HigherOrderExpression.to_dict "qiskit_addon_opt_mapper.problems.HigherOrderExpression.to_dict")(\[use\_name]) | Returns the internal coefficients as a dictionary. | + + ## Attributes + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------- | + | [`bounds`](#qiskit_addon_opt_mapper.problems.HigherOrderExpression.bounds "qiskit_addon_opt_mapper.problems.HigherOrderExpression.bounds") | Returns the lower bound and the upper bound of the linear expression. | + | [`coefficients`](#qiskit_addon_opt_mapper.problems.HigherOrderExpression.coefficients "qiskit_addon_opt_mapper.problems.HigherOrderExpression.coefficients") | Returns a copy of internal (canonical) coefficient dictionary. | + | [`num_variables`](#qiskit_addon_opt_mapper.problems.HigherOrderExpression.num_variables "qiskit_addon_opt_mapper.problems.HigherOrderExpression.num_variables") | Returns the number of variables in this expression. | + | [`optimization_problem`](#qiskit_addon_opt_mapper.problems.HigherOrderExpression.optimization_problem "qiskit_addon_opt_mapper.problems.HigherOrderExpression.optimization_problem") | Returns the parent OptimizationProblem. | + | [`order`](#qiskit_addon_opt_mapper.problems.HigherOrderExpression.order "qiskit_addon_opt_mapper.problems.HigherOrderExpression.order") | Returns the order of the polynomial (k >= 3). | + + ### bounds + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderExpression.bounds" attributeTypeHint="ExpressionBounds"> + Returns the lower bound and the upper bound of the linear expression. + + **Returns** + + The lower bound and the upper bound of the linear expression + </Attribute> + + ### coefficients + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderExpression.coefficients" attributeTypeHint="dict[tuple[int, ...], float]"> + Returns a copy of internal (canonical) coefficient dictionary. + </Attribute> + + ### evaluate + + <Function id="qiskit_addon_opt_mapper.problems.HigherOrderExpression.evaluate" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/higher_order_expression.py#L211-L228" signature="evaluate(x)"> + Evaluate the expression: sum\_\{t} C\[t] \* prod\_\{i in t} x\[i]. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The values of the variables to be evaluated. + + **Returns** + + The value of the higher order expression given the variable values. + + **Return type** + + [float](https://docs.python.org/3/library/functions.html#float) + </Function> + + ### evaluate\_gradient + + <Function id="qiskit_addon_opt_mapper.problems.HigherOrderExpression.evaluate_gradient" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/higher_order_expression.py#L230-L270" signature="evaluate_gradient(x)"> + Evaluate gradient wrt x. + + For each m, g\[m] = sum\_\{t} C\[t] \* (count\_m\_in\_t) \* prod\_\{i in t / \{one m}}. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The values of the variables to be evaluated. + + **Returns** + + The value of the gradient of the higher order expression given the variable values. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### num\_variables + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderExpression.num_variables" attributeTypeHint="int"> + Returns the number of variables in this expression. + </Attribute> + + ### optimization\_problem + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderExpression.optimization_problem" attributeTypeHint="OptimizationProblem"> + Returns the parent OptimizationProblem. + + **Returns** + + The parent OptimizationProblem. + </Attribute> + + ### order + + <Attribute id="qiskit_addon_opt_mapper.problems.HigherOrderExpression.order" attributeTypeHint="int"> + Returns the order of the polynomial (k >= 3). + </Attribute> + + ### to\_array + + <Function id="qiskit_addon_opt_mapper.problems.HigherOrderExpression.to_array" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/higher_order_expression.py#L179-L209" signature="to_array(symmetric=False)"> + Returns a dense tensor. + + **Parameters** + + **symmetric** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – If False, returns a tensor with coefficients at the lexicographically smallest index only (others zero). If True, distributes coefficients equally over all permutations of the multiset key. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_dict + + <Function id="qiskit_addon_opt_mapper.problems.HigherOrderExpression.to_dict" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/higher_order_expression.py#L169-L177" signature="to_dict(use_name=False)"> + Returns the internal coefficients as a dictionary. + + **Parameters** + + **use\_name** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) + + **Return type** + + [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[int](https://docs.python.org/3/library/functions.html#int), …] | [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[str](https://docs.python.org/3/library/stdtypes.html#str), …], [float](https://docs.python.org/3/library/functions.html#float)] + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/problems-linear-constraint.mdx b/docs/api/qiskit-addon-opt-mapper/problems-linear-constraint.mdx new file mode 100644 index 00000000000..ee0d7e29236 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/problems-linear-constraint.mdx @@ -0,0 +1,137 @@ +--- +title: LinearConstraint (latest version) +description: API reference for qiskit_addon_opt_mapper.problems.LinearConstraint in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.problems.LinearConstraint +--- + +<span id="qiskit-addon-opt-mapper-problems-linearconstraint" /> + +# qiskit\_addon\_opt\_mapper.problems.LinearConstraint + +<Class id="qiskit_addon_opt_mapper.problems.LinearConstraint" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/linear_constraint.py#L24-L100" signature="LinearConstraint(optimization_problem, name, linear, sense, rhs)" modifiers="class"> + Bases: [`Constraint`](problems-constraint "qiskit_addon_opt_mapper.problems.constraint.Constraint") + + Representation of a linear constraint. + + Init method. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The parent optimization problem. + * **name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – The name of the constraint. + * **linear** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The coefficients specifying the linear constraint. + * **sense** (*ConstraintSense*) – The sense of the constraint. + * **rhs** ([*float*](https://docs.python.org/3/library/functions.html#float)) – The right-hand-side of the constraint. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.problems.LinearConstraint.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/linear_constraint.py#L30-L48" signature="__init__(optimization_problem, name, linear, sense, rhs)"> + Init method. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The parent optimization problem. + * **name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – The name of the constraint. + * **linear** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The coefficients specifying the linear constraint. + * **sense** (*ConstraintSense*) – The sense of the constraint. + * **rhs** ([*float*](https://docs.python.org/3/library/functions.html#float)) – The right-hand-side of the constraint. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.problems.LinearConstraint.__init__ "qiskit_addon_opt_mapper.problems.LinearConstraint.__init__")(optimization\_problem, name, linear, ...) | Init method. | + | [`evaluate`](#qiskit_addon_opt_mapper.problems.LinearConstraint.evaluate "qiskit_addon_opt_mapper.problems.LinearConstraint.evaluate")(x) | Evaluate the left-hand-side of the constraint. | + + ## Attributes + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ | + | [`linear`](#qiskit_addon_opt_mapper.problems.LinearConstraint.linear "qiskit_addon_opt_mapper.problems.LinearConstraint.linear") | Returns the linear expression corresponding to the left-hand-side of the constraint. | + | [`name`](#qiskit_addon_opt_mapper.problems.LinearConstraint.name "qiskit_addon_opt_mapper.problems.LinearConstraint.name") | Returns the name of the constraint. | + | [`optimization_problem`](#qiskit_addon_opt_mapper.problems.LinearConstraint.optimization_problem "qiskit_addon_opt_mapper.problems.LinearConstraint.optimization_problem") | Returns the parent OptimizationProblem. | + | [`rhs`](#qiskit_addon_opt_mapper.problems.LinearConstraint.rhs "qiskit_addon_opt_mapper.problems.LinearConstraint.rhs") | Returns the right-hand-side of the constraint. | + | [`sense`](#qiskit_addon_opt_mapper.problems.LinearConstraint.sense "qiskit_addon_opt_mapper.problems.LinearConstraint.sense") | Returns the sense of the constraint. | + + ### Sense + + <Attribute id="qiskit_addon_opt_mapper.problems.LinearConstraint.Sense" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/constraint.py#L27-L84"> + alias of `ConstraintSense` + </Attribute> + + ### evaluate + + <Function id="qiskit_addon_opt_mapper.problems.LinearConstraint.evaluate" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/linear_constraint.py#L74-L84" signature="evaluate(x)"> + Evaluate the left-hand-side of the constraint. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The values of the variables to be evaluated. + + **Returns** + + The left-hand-side of the constraint given the variable values. + + **Return type** + + [float](https://docs.python.org/3/library/functions.html#float) + </Function> + + ### linear + + <Attribute id="qiskit_addon_opt_mapper.problems.LinearConstraint.linear" attributeTypeHint="LinearExpression"> + Returns the linear expression corresponding to the left-hand-side of the constraint. + + **Returns** + + The left-hand-side linear expression. + </Attribute> + + ### name + + <Attribute id="qiskit_addon_opt_mapper.problems.LinearConstraint.name" attributeTypeHint="str"> + Returns the name of the constraint. + + **Returns** + + The name of the constraint. + </Attribute> + + ### optimization\_problem + + <Attribute id="qiskit_addon_opt_mapper.problems.LinearConstraint.optimization_problem" attributeTypeHint="OptimizationProblem"> + Returns the parent OptimizationProblem. + + **Returns** + + The parent OptimizationProblem. + </Attribute> + + ### rhs + + <Attribute id="qiskit_addon_opt_mapper.problems.LinearConstraint.rhs" attributeTypeHint="float"> + Returns the right-hand-side of the constraint. + + **Returns** + + The right-hand-side of the constraint. + </Attribute> + + ### sense + + <Attribute id="qiskit_addon_opt_mapper.problems.LinearConstraint.sense" attributeTypeHint="ConstraintSense"> + Returns the sense of the constraint. + + **Returns** + + The sense of the constraint. + </Attribute> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/problems-linear-expression.mdx b/docs/api/qiskit-addon-opt-mapper/problems-linear-expression.mdx new file mode 100644 index 00000000000..739e6c4208e --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/problems-linear-expression.mdx @@ -0,0 +1,166 @@ +--- +title: LinearExpression (latest version) +description: API reference for qiskit_addon_opt_mapper.problems.LinearExpression in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.problems.LinearExpression +--- + +<span id="qiskit-addon-opt-mapper-problems-linearexpression" /> + +# qiskit\_addon\_opt\_mapper.problems.LinearExpression + +<Class id="qiskit_addon_opt_mapper.problems.LinearExpression" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/linear_expression.py#L37-L251" signature="LinearExpression(optimization_problem, coefficients)" modifiers="class"> + Bases: [`OptimizationProblemElement`](problems-optimization-problem-element "qiskit_addon_opt_mapper.problems.optimization_problem_element.OptimizationProblemElement") + + Representation of a linear expression by its coefficients. + + Creates a new linear expression. + + The linear expression can be defined via an array, a list, a sparse matrix, or a dictionary that uses variable names or indices as keys and stores the values internally as a dok\_matrix. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The parent OptimizationProblem. + * **coefficients** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The (sparse) representation of the coefficients. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.problems.LinearExpression.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/linear_expression.py#L40-L57" signature="__init__(optimization_problem, coefficients)"> + Creates a new linear expression. + + The linear expression can be defined via an array, a list, a sparse matrix, or a dictionary that uses variable names or indices as keys and stores the values internally as a dok\_matrix. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The parent OptimizationProblem. + * **coefficients** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The (sparse) representation of the coefficients. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.problems.LinearExpression.__init__ "qiskit_addon_opt_mapper.problems.LinearExpression.__init__")(optimization\_problem, coefficients) | Creates a new linear expression. | + | [`evaluate`](#qiskit_addon_opt_mapper.problems.LinearExpression.evaluate "qiskit_addon_opt_mapper.problems.LinearExpression.evaluate")(x) | Evaluate the linear expression for given variables. | + | [`evaluate_gradient`](#qiskit_addon_opt_mapper.problems.LinearExpression.evaluate_gradient "qiskit_addon_opt_mapper.problems.LinearExpression.evaluate_gradient")(x) | Evaluate the gradient of the linear expression for given variables. | + | [`to_array`](#qiskit_addon_opt_mapper.problems.LinearExpression.to_array "qiskit_addon_opt_mapper.problems.LinearExpression.to_array")() | Returns the coefficients of the linear expression as array. | + | [`to_dict`](#qiskit_addon_opt_mapper.problems.LinearExpression.to_dict "qiskit_addon_opt_mapper.problems.LinearExpression.to_dict")(\[use\_name]) | Returns the coefficients of the linear expression as dictionary. | + + ## Attributes + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------- | + | [`bounds`](#qiskit_addon_opt_mapper.problems.LinearExpression.bounds "qiskit_addon_opt_mapper.problems.LinearExpression.bounds") | Returns the lower bound and the upper bound of the linear expression. | + | [`coefficients`](#qiskit_addon_opt_mapper.problems.LinearExpression.coefficients "qiskit_addon_opt_mapper.problems.LinearExpression.coefficients") | Returns the coefficients of the linear expression. | + | [`optimization_problem`](#qiskit_addon_opt_mapper.problems.LinearExpression.optimization_problem "qiskit_addon_opt_mapper.problems.LinearExpression.optimization_problem") | Returns the parent OptimizationProblem. | + + ### bounds + + <Attribute id="qiskit_addon_opt_mapper.problems.LinearExpression.bounds" attributeTypeHint="ExpressionBounds"> + Returns the lower bound and the upper bound of the linear expression. + + **Returns** + + The lower bound and the upper bound of the linear expression + + **Raises** + + **OptimizationError** – if the linear expression contains any unbounded variable + </Attribute> + + ### coefficients + + <Attribute id="qiskit_addon_opt_mapper.problems.LinearExpression.coefficients" attributeTypeHint="dok_matrix"> + Returns the coefficients of the linear expression. + + **Returns** + + The coefficients of the linear expression. + </Attribute> + + ### evaluate + + <Function id="qiskit_addon_opt_mapper.problems.LinearExpression.evaluate" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/linear_expression.py#L183-L200" signature="evaluate(x)"> + Evaluate the linear expression for given variables. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The values of the variables to be evaluated. + + **Returns** + + The value of the linear expression given the variable values. + + **Return type** + + [float](https://docs.python.org/3/library/functions.html#float) + </Function> + + ### evaluate\_gradient + + <Function id="qiskit_addon_opt_mapper.problems.LinearExpression.evaluate_gradient" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/linear_expression.py#L203-L214" signature="evaluate_gradient(x)"> + Evaluate the gradient of the linear expression for given variables. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The values of the variables to be evaluated. + + **Returns** + + The value of the gradient of the linear expression given the variable values. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### optimization\_problem + + <Attribute id="qiskit_addon_opt_mapper.problems.LinearExpression.optimization_problem" attributeTypeHint="OptimizationProblem"> + Returns the parent OptimizationProblem. + + **Returns** + + The parent OptimizationProblem. + </Attribute> + + ### to\_array + + <Function id="qiskit_addon_opt_mapper.problems.LinearExpression.to_array" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/linear_expression.py#L156-L162" signature="to_array()"> + Returns the coefficients of the linear expression as array. + + **Returns** + + An array with the coefficients corresponding to the linear expression. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_dict + + <Function id="qiskit_addon_opt_mapper.problems.LinearExpression.to_dict" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/linear_expression.py#L164-L181" signature="to_dict(use_name=False)"> + Returns the coefficients of the linear expression as dictionary. + + Either using variable names or indices as keys. + + **Parameters** + + **use\_name** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Determines whether to use index or names to refer to variables. + + **Returns** + + An dictionary with the coefficients corresponding to the linear expression. + + **Return type** + + [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[int](https://docs.python.org/3/library/functions.html#int) | [str](https://docs.python.org/3/library/stdtypes.html#str), [float](https://docs.python.org/3/library/functions.html#float)] + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/problems-optimization-objective.mdx b/docs/api/qiskit-addon-opt-mapper/problems-optimization-objective.mdx new file mode 100644 index 00000000000..a9aad06c8ce --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/problems-optimization-objective.mdx @@ -0,0 +1,177 @@ +--- +title: OptimizationObjective (latest version) +description: API reference for qiskit_addon_opt_mapper.problems.OptimizationObjective in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.problems.OptimizationObjective +--- + +<span id="qiskit-addon-opt-mapper-problems-optimizationobjective" /> + +# qiskit\_addon\_opt\_mapper.problems.OptimizationObjective + +<Class id="qiskit_addon_opt_mapper.problems.OptimizationObjective" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/optimization_objective.py#L37-L263" signature="OptimizationObjective(optimization_problem, constant=0.0, linear=None, quadratic=None, higher_order=None, sense=ObjSense.MINIMIZE)" modifiers="class"> + Bases: [`OptimizationProblemElement`](problems-optimization-problem-element "qiskit_addon_opt_mapper.problems.optimization_problem_element.OptimizationProblemElement") + + Optimization objective element. + + **Follows:** + + constant + linear(x) + x^T Q x + sum\_\{k>=3} H\_k(x). + + Construct an objective with linear, quadratic, and optional higher-order parts. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The optimization problem this objective belongs to. + * **constant** ([*float*](https://docs.python.org/3/library/functions.html#float)) – The constant part of the objective function. + * **linear** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*] | None*) – The coefficients for the linear part of the objective function. + * **quadratic** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*],* [*float*](https://docs.python.org/3/library/functions.html#float)*] | None*) – The coefficients for the quadratic part of the objective function. + * **higher\_order** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*, ...],* [*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*list*](https://docs.python.org/3/library/stdtypes.html#list)*] | None*) – A single higher-order expression or a dictionary of \{order: coeffs} for multiple orders (k>=3). + * **sense** (*ObjSense*) – The sense of the objective function (e.g., MINIMIZE, MAXIMIZE). + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.problems.OptimizationObjective.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/optimization_objective.py#L46-L85" signature="__init__(optimization_problem, constant=0.0, linear=None, quadratic=None, higher_order=None, sense=ObjSense.MINIMIZE)"> + Construct an objective with linear, quadratic, and optional higher-order parts. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The optimization problem this objective belongs to. + * **constant** ([*float*](https://docs.python.org/3/library/functions.html#float)) – The constant part of the objective function. + * **linear** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*] | None*) – The coefficients for the linear part of the objective function. + * **quadratic** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*],* [*float*](https://docs.python.org/3/library/functions.html#float)*] | None*) – The coefficients for the quadratic part of the objective function. + * **higher\_order** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*, ...],* [*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*list*](https://docs.python.org/3/library/stdtypes.html#list)*] | None*) – A single higher-order expression or a dictionary of \{order: coeffs} for multiple orders (k>=3). + * **sense** (*ObjSense*) – The sense of the objective function (e.g., MINIMIZE, MAXIMIZE). + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.problems.OptimizationObjective.__init__ "qiskit_addon_opt_mapper.problems.OptimizationObjective.__init__")(optimization\_problem\[, constant, ...]) | Construct an objective with linear, quadratic, and optional higher-order parts. | + | [`evaluate`](#qiskit_addon_opt_mapper.problems.OptimizationObjective.evaluate "qiskit_addon_opt_mapper.problems.OptimizationObjective.evaluate")(x) | Evaluate objective value at x. | + | [`evaluate_gradient`](#qiskit_addon_opt_mapper.problems.OptimizationObjective.evaluate_gradient "qiskit_addon_opt_mapper.problems.OptimizationObjective.evaluate_gradient")(x) | Evaluate gradient of the objective at x. | + + ## Attributes + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------- | + | [`constant`](#qiskit_addon_opt_mapper.problems.OptimizationObjective.constant "qiskit_addon_opt_mapper.problems.OptimizationObjective.constant") | Returns the constant part of the objective function. | + | [`higher_order`](#qiskit_addon_opt_mapper.problems.OptimizationObjective.higher_order "qiskit_addon_opt_mapper.problems.OptimizationObjective.higher_order") | HigherOrderExpression}. | + | [`linear`](#qiskit_addon_opt_mapper.problems.OptimizationObjective.linear "qiskit_addon_opt_mapper.problems.OptimizationObjective.linear") | Returns the linear expression corresponding to the left-hand-side of the constraint. | + | [`optimization_problem`](#qiskit_addon_opt_mapper.problems.OptimizationObjective.optimization_problem "qiskit_addon_opt_mapper.problems.OptimizationObjective.optimization_problem") | Returns the parent OptimizationProblem. | + | [`quadratic`](#qiskit_addon_opt_mapper.problems.OptimizationObjective.quadratic "qiskit_addon_opt_mapper.problems.OptimizationObjective.quadratic") | Returns the quadratic expression corresponding to the left-hand-side of the constraint. | + | [`sense`](#qiskit_addon_opt_mapper.problems.OptimizationObjective.sense "qiskit_addon_opt_mapper.problems.OptimizationObjective.sense") | Returns the sense of the objective function. | + + ### Sense + + <Attribute id="qiskit_addon_opt_mapper.problems.OptimizationObjective.Sense" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/optimization_objective.py#L30-L34"> + alias of `ObjSense` + </Attribute> + + ### constant + + <Attribute id="qiskit_addon_opt_mapper.problems.OptimizationObjective.constant" attributeTypeHint="float"> + Returns the constant part of the objective function. + + **Returns** + + The constant part of the objective function. + </Attribute> + + ### evaluate + + <Function id="qiskit_addon_opt_mapper.problems.OptimizationObjective.evaluate" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/optimization_objective.py#L192-L213" signature="evaluate(x)"> + Evaluate objective value at x. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The values of the variables to be evaluated. + + **Returns** + + The objective value given the variable values. + + **Return type** + + [float](https://docs.python.org/3/library/functions.html#float) + </Function> + + ### evaluate\_gradient + + <Function id="qiskit_addon_opt_mapper.problems.OptimizationObjective.evaluate_gradient" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/optimization_objective.py#L215-L236" signature="evaluate_gradient(x)"> + Evaluate gradient of the objective at x. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*int*](https://docs.python.org/3/library/functions.html#int) *|*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The values of the variables to be evaluated. + + **Returns** + + The gradient of the objective function given the variable values. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### higher\_order + + <Attribute id="qiskit_addon_opt_mapper.problems.OptimizationObjective.higher_order" attributeTypeHint="dict[int, HigherOrderExpression]"> + HigherOrderExpression}. + + **Returns** + + A dictionary mapping order (k>=3) to HigherOrderExpression. + + **Type** + + Return a shallow copy of \{order + </Attribute> + + ### linear + + <Attribute id="qiskit_addon_opt_mapper.problems.OptimizationObjective.linear" attributeTypeHint="LinearExpression"> + Returns the linear expression corresponding to the left-hand-side of the constraint. + + **Returns** + + The left-hand-side linear expression. + </Attribute> + + ### optimization\_problem + + <Attribute id="qiskit_addon_opt_mapper.problems.OptimizationObjective.optimization_problem" attributeTypeHint="OptimizationProblem"> + Returns the parent OptimizationProblem. + + **Returns** + + The parent OptimizationProblem. + </Attribute> + + ### quadratic + + <Attribute id="qiskit_addon_opt_mapper.problems.OptimizationObjective.quadratic" attributeTypeHint="QuadraticExpression"> + Returns the quadratic expression corresponding to the left-hand-side of the constraint. + + **Returns** + + The left-hand-side quadratic expression. + </Attribute> + + ### sense + + <Attribute id="qiskit_addon_opt_mapper.problems.OptimizationObjective.sense" attributeTypeHint="ObjSense"> + Returns the sense of the objective function. + + **Returns** + + The sense of the objective function (e.g., MINIMIZE, MAXIMIZE). + </Attribute> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/problems-optimization-problem-element.mdx b/docs/api/qiskit-addon-opt-mapper/problems-optimization-problem-element.mdx new file mode 100644 index 00000000000..51b14874b46 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/problems-optimization-problem-element.mdx @@ -0,0 +1,68 @@ +--- +title: OptimizationProblemElement (latest version) +description: API reference for qiskit_addon_opt_mapper.problems.OptimizationProblemElement in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.problems.OptimizationProblemElement +--- + +<span id="qiskit-addon-opt-mapper-problems-optimizationproblemelement" /> + +# qiskit\_addon\_opt\_mapper.problems.OptimizationProblemElement + +<Class id="qiskit_addon_opt_mapper.problems.OptimizationProblemElement" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/optimization_problem_element.py#L22-L67" signature="OptimizationProblemElement(optimization_problem)" modifiers="class"> + Bases: [`object`](https://docs.python.org/3/library/functions.html#object) + + Interface class for all objects that have a parent OptimizationProblem. + + Initialize object with parent OptimizationProblem. + + **Parameters** + + **optimization\_problem** (*problems.OptimizationProblem*) – The parent OptimizationProblem. + + **Raises** + + [**TypeError**](https://docs.python.org/3/library/exceptions.html#TypeError) – OptimizationProblem instance expected. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.problems.OptimizationProblemElement.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/optimization_problem_element.py#L25-L40" signature="__init__(optimization_problem)"> + Initialize object with parent OptimizationProblem. + + **Parameters** + + **optimization\_problem** (*OptimizationProblem*) – The parent OptimizationProblem. + + **Raises** + + [**TypeError**](https://docs.python.org/3/library/exceptions.html#TypeError) – OptimizationProblem instance expected. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.problems.OptimizationProblemElement.__init__ "qiskit_addon_opt_mapper.problems.OptimizationProblemElement.__init__")(optimization\_problem) | Initialize object with parent OptimizationProblem. | + + ## Attributes + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- | + | [`optimization_problem`](#qiskit_addon_opt_mapper.problems.OptimizationProblemElement.optimization_problem "qiskit_addon_opt_mapper.problems.OptimizationProblemElement.optimization_problem") | Returns the parent OptimizationProblem. | + + ### optimization\_problem + + <Attribute id="qiskit_addon_opt_mapper.problems.OptimizationProblemElement.optimization_problem" attributeTypeHint="OptimizationProblem"> + Returns the parent OptimizationProblem. + + **Returns** + + The parent OptimizationProblem. + </Attribute> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/problems-quadratic-constraint.mdx b/docs/api/qiskit-addon-opt-mapper/problems-quadratic-constraint.mdx new file mode 100644 index 00000000000..899a9a15b42 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/problems-quadratic-constraint.mdx @@ -0,0 +1,150 @@ +--- +title: QuadraticConstraint (latest version) +description: API reference for qiskit_addon_opt_mapper.problems.QuadraticConstraint in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.problems.QuadraticConstraint +--- + +<span id="qiskit-addon-opt-mapper-problems-quadraticconstraint" /> + +# qiskit\_addon\_opt\_mapper.problems.QuadraticConstraint + +<Class id="qiskit_addon_opt_mapper.problems.QuadraticConstraint" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/quadratic_constraint.py#L25-L132" signature="QuadraticConstraint(optimization_problem, name, linear, quadratic, sense, rhs)" modifiers="class"> + Bases: [`Constraint`](problems-constraint "qiskit_addon_opt_mapper.problems.constraint.Constraint") + + Representation of a quadratic constraint. + + Constructs a quadratic constraint, consisting of a linear and a quadratic term. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The parent optimization problem. + * **name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – The name of the constraint. + * **linear** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The coefficients specifying the linear part of the constraint. + * **quadratic** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*],* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The coefficients specifying the linear part of the constraint. + * **sense** (*ConstraintSense*) – The sense of the constraint. + * **rhs** ([*float*](https://docs.python.org/3/library/functions.html#float)) – The right-hand-side of the constraint. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.problems.QuadraticConstraint.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/quadratic_constraint.py#L31-L54" signature="__init__(optimization_problem, name, linear, quadratic, sense, rhs)"> + Constructs a quadratic constraint, consisting of a linear and a quadratic term. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The parent optimization problem. + * **name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – The name of the constraint. + * **linear** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The coefficients specifying the linear part of the constraint. + * **quadratic** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*],* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The coefficients specifying the linear part of the constraint. + * **sense** (*ConstraintSense*) – The sense of the constraint. + * **rhs** ([*float*](https://docs.python.org/3/library/functions.html#float)) – The right-hand-side of the constraint. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.problems.QuadraticConstraint.__init__ "qiskit_addon_opt_mapper.problems.QuadraticConstraint.__init__")(optimization\_problem, name, linear, ...) | Constructs a quadratic constraint, consisting of a linear and a quadratic term. | + | [`evaluate`](#qiskit_addon_opt_mapper.problems.QuadraticConstraint.evaluate "qiskit_addon_opt_mapper.problems.QuadraticConstraint.evaluate")(x) | Evaluate the left-hand-side of the constraint. | + + ## Attributes + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | + | [`linear`](#qiskit_addon_opt_mapper.problems.QuadraticConstraint.linear "qiskit_addon_opt_mapper.problems.QuadraticConstraint.linear") | Returns the linear expression corresponding to the left-hand-side of the constraint. | + | [`name`](#qiskit_addon_opt_mapper.problems.QuadraticConstraint.name "qiskit_addon_opt_mapper.problems.QuadraticConstraint.name") | Returns the name of the constraint. | + | [`optimization_problem`](#qiskit_addon_opt_mapper.problems.QuadraticConstraint.optimization_problem "qiskit_addon_opt_mapper.problems.QuadraticConstraint.optimization_problem") | Returns the parent OptimizationProblem. | + | [`quadratic`](#qiskit_addon_opt_mapper.problems.QuadraticConstraint.quadratic "qiskit_addon_opt_mapper.problems.QuadraticConstraint.quadratic") | Returns the quadratic expression corresponding to the left-hand-side of the constraint. | + | [`rhs`](#qiskit_addon_opt_mapper.problems.QuadraticConstraint.rhs "qiskit_addon_opt_mapper.problems.QuadraticConstraint.rhs") | Returns the right-hand-side of the constraint. | + | [`sense`](#qiskit_addon_opt_mapper.problems.QuadraticConstraint.sense "qiskit_addon_opt_mapper.problems.QuadraticConstraint.sense") | Returns the sense of the constraint. | + + ### Sense + + <Attribute id="qiskit_addon_opt_mapper.problems.QuadraticConstraint.Sense" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/constraint.py#L27-L84"> + alias of `ConstraintSense` + </Attribute> + + ### evaluate + + <Function id="qiskit_addon_opt_mapper.problems.QuadraticConstraint.evaluate" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/quadratic_constraint.py#L106-L116" signature="evaluate(x)"> + Evaluate the left-hand-side of the constraint. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The values of the variables to be evaluated. + + **Returns** + + The left-hand-side of the constraint given the variable values. + + **Return type** + + [float](https://docs.python.org/3/library/functions.html#float) + </Function> + + ### linear + + <Attribute id="qiskit_addon_opt_mapper.problems.QuadraticConstraint.linear" attributeTypeHint="LinearExpression"> + Returns the linear expression corresponding to the left-hand-side of the constraint. + + **Returns** + + The left-hand-side linear expression. + </Attribute> + + ### name + + <Attribute id="qiskit_addon_opt_mapper.problems.QuadraticConstraint.name" attributeTypeHint="str"> + Returns the name of the constraint. + + **Returns** + + The name of the constraint. + </Attribute> + + ### optimization\_problem + + <Attribute id="qiskit_addon_opt_mapper.problems.QuadraticConstraint.optimization_problem" attributeTypeHint="OptimizationProblem"> + Returns the parent OptimizationProblem. + + **Returns** + + The parent OptimizationProblem. + </Attribute> + + ### quadratic + + <Attribute id="qiskit_addon_opt_mapper.problems.QuadraticConstraint.quadratic" attributeTypeHint="QuadraticExpression"> + Returns the quadratic expression corresponding to the left-hand-side of the constraint. + + **Returns** + + The left-hand-side quadratic expression. + </Attribute> + + ### rhs + + <Attribute id="qiskit_addon_opt_mapper.problems.QuadraticConstraint.rhs" attributeTypeHint="float"> + Returns the right-hand-side of the constraint. + + **Returns** + + The right-hand-side of the constraint. + </Attribute> + + ### sense + + <Attribute id="qiskit_addon_opt_mapper.problems.QuadraticConstraint.sense" attributeTypeHint="ConstraintSense"> + Returns the sense of the constraint. + + **Returns** + + The sense of the constraint. + </Attribute> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/problems-quadratic-expression.mdx b/docs/api/qiskit-addon-opt-mapper/problems-quadratic-expression.mdx new file mode 100644 index 00000000000..3559a2c4434 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/problems-quadratic-expression.mdx @@ -0,0 +1,171 @@ +--- +title: QuadraticExpression (latest version) +description: API reference for qiskit_addon_opt_mapper.problems.QuadraticExpression in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.problems.QuadraticExpression +--- + +<span id="qiskit-addon-opt-mapper-problems-quadraticexpression" /> + +# qiskit\_addon\_opt\_mapper.problems.QuadraticExpression + +<Class id="qiskit_addon_opt_mapper.problems.QuadraticExpression" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/quadratic_expression.py#L28-L329" signature="QuadraticExpression(optimization_problem, coefficients)" modifiers="class"> + Bases: [`OptimizationProblemElement`](problems-optimization-problem-element "qiskit_addon_opt_mapper.problems.optimization_problem_element.OptimizationProblemElement") + + Representation of a quadratic expression by its coefficients. + + Creates a new quadratic expression. + + The quadratic expression can be defined via an array, a list, a sparse matrix, or a dictionary that uses variable names or indices as keys and stores the values internally as a dok\_matrix. We stores values in a compressed way, i.e., values at symmetric positions are summed up in the upper triangle. For example, \{(0, 1): 1, (1, 0): 2} -> \{(0, 1): 3}. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The parent OptimizationProblem. + * **coefficients** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*],* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The (sparse) representation of the coefficients. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.problems.QuadraticExpression.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/quadratic_expression.py#L31-L51" signature="__init__(optimization_problem, coefficients)"> + Creates a new quadratic expression. + + The quadratic expression can be defined via an array, a list, a sparse matrix, or a dictionary that uses variable names or indices as keys and stores the values internally as a dok\_matrix. We stores values in a compressed way, i.e., values at symmetric positions are summed up in the upper triangle. For example, \{(0, 1): 1, (1, 0): 2} -> \{(0, 1): 3}. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The parent OptimizationProblem. + * **coefficients** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| spmatrix |*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]] |* [*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*],* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The (sparse) representation of the coefficients. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.problems.QuadraticExpression.__init__ "qiskit_addon_opt_mapper.problems.QuadraticExpression.__init__")(optimization\_problem, coefficients) | Creates a new quadratic expression. | + | [`evaluate`](#qiskit_addon_opt_mapper.problems.QuadraticExpression.evaluate "qiskit_addon_opt_mapper.problems.QuadraticExpression.evaluate")(x) | Evaluate the quadratic expression for given variables: x \* Q \* x. | + | [`evaluate_gradient`](#qiskit_addon_opt_mapper.problems.QuadraticExpression.evaluate_gradient "qiskit_addon_opt_mapper.problems.QuadraticExpression.evaluate_gradient")(x) | Evaluate the gradient of the quadratic expression for given variables. | + | [`to_array`](#qiskit_addon_opt_mapper.problems.QuadraticExpression.to_array "qiskit_addon_opt_mapper.problems.QuadraticExpression.to_array")(\[symmetric]) | Returns the coefficients of the quadratic expression as array. | + | [`to_dict`](#qiskit_addon_opt_mapper.problems.QuadraticExpression.to_dict "qiskit_addon_opt_mapper.problems.QuadraticExpression.to_dict")(\[symmetric, use\_name]) | Returns the coefficients of the quadratic expression as dictionary. | + + ## Attributes + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------ | + | [`bounds`](#qiskit_addon_opt_mapper.problems.QuadraticExpression.bounds "qiskit_addon_opt_mapper.problems.QuadraticExpression.bounds") | Returns the lower bound and the upper bound of the quadratic expression. | + | [`coefficients`](#qiskit_addon_opt_mapper.problems.QuadraticExpression.coefficients "qiskit_addon_opt_mapper.problems.QuadraticExpression.coefficients") | Returns the coefficients of the quadratic expression. | + | [`optimization_problem`](#qiskit_addon_opt_mapper.problems.QuadraticExpression.optimization_problem "qiskit_addon_opt_mapper.problems.QuadraticExpression.optimization_problem") | Returns the parent OptimizationProblem. | + + ### bounds + + <Attribute id="qiskit_addon_opt_mapper.problems.QuadraticExpression.bounds" attributeTypeHint="ExpressionBounds"> + Returns the lower bound and the upper bound of the quadratic expression. + + **Returns** + + The lower bound and the upper bound of the quadratic expression + + **Raises** + + **OptimizationError** – if the quadratic expression contains any unbounded variable + </Attribute> + + ### coefficients + + <Attribute id="qiskit_addon_opt_mapper.problems.QuadraticExpression.coefficients" attributeTypeHint="dok_matrix"> + Returns the coefficients of the quadratic expression. + + **Returns** + + The coefficients of the quadratic expression. + </Attribute> + + ### evaluate + + <Function id="qiskit_addon_opt_mapper.problems.QuadraticExpression.evaluate" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/quadratic_expression.py#L226-L242" signature="evaluate(x)"> + Evaluate the quadratic expression for given variables: x \* Q \* x. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The values of the variables to be evaluated. + + **Returns** + + The value of the quadratic expression given the variable values. + + **Return type** + + [float](https://docs.python.org/3/library/functions.html#float) + </Function> + + ### evaluate\_gradient + + <Function id="qiskit_addon_opt_mapper.problems.QuadraticExpression.evaluate_gradient" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/quadratic_expression.py#L244-L260" signature="evaluate_gradient(x)"> + Evaluate the gradient of the quadratic expression for given variables. + + **Parameters** + + **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list) *|*[*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str) *|*[*int*](https://docs.python.org/3/library/functions.html#int)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The values of the variables to be evaluated. + + **Returns** + + The value of the gradient quadratic expression given the variable values. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### optimization\_problem + + <Attribute id="qiskit_addon_opt_mapper.problems.QuadraticExpression.optimization_problem" attributeTypeHint="OptimizationProblem"> + Returns the parent OptimizationProblem. + + **Returns** + + The parent OptimizationProblem. + </Attribute> + + ### to\_array + + <Function id="qiskit_addon_opt_mapper.problems.QuadraticExpression.to_array" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/quadratic_expression.py#L187-L198" signature="to_array(symmetric=False)"> + Returns the coefficients of the quadratic expression as array. + + **Parameters** + + **symmetric** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Determines whether the output is in a symmetric form or not. + + **Returns** + + An array with the coefficients corresponding to the quadratic expression. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### to\_dict + + <Function id="qiskit_addon_opt_mapper.problems.QuadraticExpression.to_dict" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/quadratic_expression.py#L200-L224" signature="to_dict(symmetric=False, use_name=False)"> + Returns the coefficients of the quadratic expression as dictionary. + + Either using tuples of variable names or indices as keys. + + **Parameters** + + * **symmetric** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Determines whether the output is in a symmetric form or not. + * **use\_name** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Determines whether to use index or names to refer to variables. + + **Returns** + + An dictionary with the coefficients corresponding to the quadratic expression. + + **Return type** + + [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[int](https://docs.python.org/3/library/functions.html#int), [int](https://docs.python.org/3/library/functions.html#int)] | [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[str](https://docs.python.org/3/library/stdtypes.html#str), [str](https://docs.python.org/3/library/stdtypes.html#str)], [float](https://docs.python.org/3/library/functions.html#float)] + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/problems-variable.mdx b/docs/api/qiskit-addon-opt-mapper/problems-variable.mdx new file mode 100644 index 00000000000..861d328066b --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/problems-variable.mdx @@ -0,0 +1,145 @@ +--- +title: Variable (latest version) +description: API reference for qiskit_addon_opt_mapper.problems.Variable in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.problems.Variable +--- + +<span id="qiskit-addon-opt-mapper-problems-variable" /> + +# qiskit\_addon\_opt\_mapper.problems.Variable + +<Class id="qiskit_addon_opt_mapper.problems.Variable" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/variable.py#L32-L163" signature="Variable(optimization_problem, name, lowerbound=0, upperbound=1e+20, vartype=VarType.CONTINUOUS)" modifiers="class"> + Bases: [`OptimizationProblemElement`](problems-optimization-problem-element "qiskit_addon_opt_mapper.problems.optimization_problem_element.OptimizationProblemElement") + + Representation of a variable. + + Creates a new Variable. + + The variables is exposed by the top-level OptimizationProblem class in OptimizationProblem.variables. This constructor is not meant to be used externally. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The parent OptimizationProblem. + * **name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – The variable name. + * **lowerbound** ([*float*](https://docs.python.org/3/library/functions.html#float) *|*[*int*](https://docs.python.org/3/library/functions.html#int)) – The variable lowerbound. + * **upperbound** ([*float*](https://docs.python.org/3/library/functions.html#float) *|*[*int*](https://docs.python.org/3/library/functions.html#int)) – The variable upperbound. + * **vartype** (*VarType*) – The variable type. + + **Raises** + + **OptimizationError** – if lowerbound is greater than upperbound. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.problems.Variable.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/variable.py#L37-L68" signature="__init__(optimization_problem, name, lowerbound=0, upperbound=1e+20, vartype=VarType.CONTINUOUS)"> + Creates a new Variable. + + The variables is exposed by the top-level OptimizationProblem class in OptimizationProblem.variables. This constructor is not meant to be used externally. + + **Parameters** + + * **optimization\_problem** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any)) – The parent OptimizationProblem. + * **name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – The variable name. + * **lowerbound** ([*float*](https://docs.python.org/3/library/functions.html#float) *|*[*int*](https://docs.python.org/3/library/functions.html#int)) – The variable lowerbound. + * **upperbound** ([*float*](https://docs.python.org/3/library/functions.html#float) *|*[*int*](https://docs.python.org/3/library/functions.html#int)) – The variable upperbound. + * **vartype** (*VarType*) – The variable type. + + **Raises** + + **OptimizationError** – if lowerbound is greater than upperbound. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.problems.Variable.__init__ "qiskit_addon_opt_mapper.problems.Variable.__init__")(optimization\_problem, name\[, ...]) | Creates a new Variable. | + | [`as_tuple`](#qiskit_addon_opt_mapper.problems.Variable.as_tuple "qiskit_addon_opt_mapper.problems.Variable.as_tuple")() | Returns a tuple corresponding to this variable. | + + ## Attributes + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- | + | [`lowerbound`](#qiskit_addon_opt_mapper.problems.Variable.lowerbound "qiskit_addon_opt_mapper.problems.Variable.lowerbound") | Returns the lowerbound of the variable. | + | [`name`](#qiskit_addon_opt_mapper.problems.Variable.name "qiskit_addon_opt_mapper.problems.Variable.name") | Returns the name of the variable. | + | [`optimization_problem`](#qiskit_addon_opt_mapper.problems.Variable.optimization_problem "qiskit_addon_opt_mapper.problems.Variable.optimization_problem") | Returns the parent OptimizationProblem. | + | [`upperbound`](#qiskit_addon_opt_mapper.problems.Variable.upperbound "qiskit_addon_opt_mapper.problems.Variable.upperbound") | Returns the upperbound of the variable. | + | [`vartype`](#qiskit_addon_opt_mapper.problems.Variable.vartype "qiskit_addon_opt_mapper.problems.Variable.vartype") | Returns the type of the variable. | + + ### Type + + <Attribute id="qiskit_addon_opt_mapper.problems.Variable.Type" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/variable.py#L23-L29"> + alias of `VarType` + </Attribute> + + ### as\_tuple + + <Function id="qiskit_addon_opt_mapper.problems.Variable.as_tuple" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/problems/variable.py#L144-L151" signature="as_tuple()"> + Returns a tuple corresponding to this variable. + + **Returns** + + A tuple corresponding to this variable consisting of name, lowerbound, upperbound and variable type. + + **Return type** + + [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[str](https://docs.python.org/3/library/stdtypes.html#str), [float](https://docs.python.org/3/library/functions.html#float) | [int](https://docs.python.org/3/library/functions.html#int), [float](https://docs.python.org/3/library/functions.html#float) | [int](https://docs.python.org/3/library/functions.html#int), *VarType*] + </Function> + + ### lowerbound + + <Attribute id="qiskit_addon_opt_mapper.problems.Variable.lowerbound" attributeTypeHint="float | int"> + Returns the lowerbound of the variable. + + **Returns** + + The lower bound of the variable. + </Attribute> + + ### name + + <Attribute id="qiskit_addon_opt_mapper.problems.Variable.name" attributeTypeHint="str"> + Returns the name of the variable. + + **Returns** + + The name of the variable. + </Attribute> + + ### optimization\_problem + + <Attribute id="qiskit_addon_opt_mapper.problems.Variable.optimization_problem" attributeTypeHint="OptimizationProblem"> + Returns the parent OptimizationProblem. + + **Returns** + + The parent OptimizationProblem. + </Attribute> + + ### upperbound + + <Attribute id="qiskit_addon_opt_mapper.problems.Variable.upperbound" attributeTypeHint="float | int"> + Returns the upperbound of the variable. + + **Returns** + + The upperbound of the variable. + </Attribute> + + ### vartype + + <Attribute id="qiskit_addon_opt_mapper.problems.Variable.vartype" attributeTypeHint="VarType"> + Returns the type of the variable. + + **Returns** + + The variable type. + </Attribute> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/problems.mdx b/docs/api/qiskit-addon-opt-mapper/problems.mdx new file mode 100644 index 00000000000..dee45884795 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/problems.mdx @@ -0,0 +1,46 @@ +--- +title: problems (latest version) +description: API reference for qiskit_addon_opt_mapper.problems in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_addon_opt_mapper.problems +--- + +<span id="module-qiskit_addon_opt_mapper.problems" /> + +<span id="problem-modeling-tools-qiskit-addon-opt-mapper-problems" /> + +# Problem Modeling Tools + +`qiskit_addon_opt_mapper.problems` + +Optimization problem model elements. + +<span id="optimization-problems-qiskit-addon-opt-mapper-problems" /> + +## Optimization problems . + +`qiskit_addon_opt_mapper.problems` + +### Optimization problem + +Structures for defining an optimization problem. + +<Admonition title="Note" type="note"> + The following classes are not intended to be instantiated directly. Objects of these types are available within an instantiated `OptimizationProblem`. +</Admonition> + +| | | +| --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------ | +| [`Constraint`](/docs/api/qiskit-addon-opt-mapper/problems-constraint "qiskit_addon_opt_mapper.problems.Constraint") | Abstract Constraint Class. | +| [`LinearExpression`](/docs/api/qiskit-addon-opt-mapper/problems-linear-expression "qiskit_addon_opt_mapper.problems.LinearExpression") | Representation of a linear expression by its coefficients. | +| [`LinearConstraint`](/docs/api/qiskit-addon-opt-mapper/problems-linear-constraint "qiskit_addon_opt_mapper.problems.LinearConstraint") | Representation of a linear constraint. | +| [`QuadraticExpression`](/docs/api/qiskit-addon-opt-mapper/problems-quadratic-expression "qiskit_addon_opt_mapper.problems.QuadraticExpression") | Representation of a quadratic expression by its coefficients. | +| [`QuadraticConstraint`](/docs/api/qiskit-addon-opt-mapper/problems-quadratic-constraint "qiskit_addon_opt_mapper.problems.QuadraticConstraint") | Representation of a quadratic constraint. | +| [`OptimizationProblemElement`](/docs/api/qiskit-addon-opt-mapper/problems-optimization-problem-element "qiskit_addon_opt_mapper.problems.OptimizationProblemElement") | Interface class for all objects that have a parent OptimizationProblem. | +| [`Variable`](/docs/api/qiskit-addon-opt-mapper/problems-variable "qiskit_addon_opt_mapper.problems.Variable") | Representation of a variable. | +| [`HigherOrderExpression`](/docs/api/qiskit-addon-opt-mapper/problems-higher-order-expression "qiskit_addon_opt_mapper.problems.HigherOrderExpression") | Representation of a symmetric k-th order expression by its coefficients. | +| [`HigherOrderConstraint`](/docs/api/qiskit-addon-opt-mapper/problems-higher-order-constraint "qiskit_addon_opt_mapper.problems.HigherOrderConstraint") | Constraint in higher order form. | +| [`HigherOrderExpression`](/docs/api/qiskit-addon-opt-mapper/problems-higher-order-expression "qiskit_addon_opt_mapper.problems.HigherOrderExpression") | Representation of a symmetric k-th order expression by its coefficients. | +| [`OptimizationObjective`](/docs/api/qiskit-addon-opt-mapper/problems-optimization-objective "qiskit_addon_opt_mapper.problems.OptimizationObjective") | Optimization objective element. | + diff --git a/docs/api/qiskit-addon-opt-mapper/release-notes.mdx b/docs/api/qiskit-addon-opt-mapper/release-notes.mdx new file mode 100644 index 00000000000..beacf4f2d6b --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/release-notes.mdx @@ -0,0 +1,46 @@ +--- +title: Optimization mapper release notes +description: Changes made to Optimization mapper +in_page_toc_max_heading_level: 2 +--- + +<span id="release-notes" /> + +<span id="id1" /> + +# Optimization mapper release notes + +<span id="upcoming-release-main" /> + +<span id="release-notes-upcoming-release-main" /> + +## Upcoming release (`main`) + +<span id="release-notes-upcoming-release-main-new-features" /> + +### New Features + +* Added `BinaryToLinearBinary` converter, that converts a binary problem into a constrained linear binary problem. + +<span id="release-notes-0-1-0" /> + +<span id="id2" /> + +## 0.1.0 + +<span id="release-notes-0-1-0-new-features" /> + +<span id="id3" /> + +### New Features + +* Introduced the Qiskit addon for optimization modeling, designed to support utility-scale workloads powered by Qiskit. This package enables the formulation and transformation of combinatorial optimization problems into quantum-ready formats. It supports modeling of QUBO and HUBO problems, with higher-order terms represented as polynomials or monomials, and facilitates the creation of Hamiltonians whose ground states correspond to optimal solutions. + + ## Core modules include: + + * **applications**: Provides model implementations for common optimization problems. + * **converters**: Converters facilitate transformations between different representations and interpretation of solutions. + * **problems**: Defines optimization problems including objective functions, constraints, and variable types. + * **solvers**: Implements interfaces for modelling validation using classical solvers. + * **translators**: Converts optimization problem into additional optimization models. + diff --git a/docs/api/qiskit-addon-opt-mapper/solvers-cplex-solver.mdx b/docs/api/qiskit-addon-opt-mapper/solvers-cplex-solver.mdx new file mode 100644 index 00000000000..c81901fb419 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/solvers-cplex-solver.mdx @@ -0,0 +1,154 @@ +--- +title: CplexSolver (latest version) +description: API reference for qiskit_addon_opt_mapper.solvers.CplexSolver in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.solvers.CplexSolver +--- + +<span id="qiskit-addon-opt-mapper-solvers-cplexsolver" /> + +# qiskit\_addon\_opt\_mapper.solvers.CplexSolver + +<Class id="qiskit_addon_opt_mapper.solvers.CplexSolver" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/cplex_solver.py#L25-L143" signature="CplexSolver(disp=False, cplex_parameters=None)" modifiers="class"> + Bases: [`OptimizationSolver`](solvers-optimization-solver "qiskit_addon_opt_mapper.solvers.solver.OptimizationSolver") + + The CPLEX optimizer wrapped as a [`OptimizationSolver`](solvers-optimization-solver "qiskit_addon_opt_mapper.solvers.OptimizationSolver"). + + This class provides a wrapper for `cplex.Cplex` ([https://pypi.org/project/cplex/](https://pypi.org/project/cplex/)) to be used within the optimization module. + + **Examples** + + ```python + >>> from qiskit_addon_opt_mapper.problems import OptimizationProblem + >>> from qiskit_addon_opt_mapper.solvers import CplexSolver + >>> problem = OptimizationProblem() + >>> # specify problem here, if cplex is installed + >>> optimizer = CplexSolver() if CplexSolver.is_cplex_installed() else None + >>> if optimizer: result = optimizer.solve(problem) + ``` + + Initializes the CplexSolver. + + **Parameters** + + * **disp** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Whether to print CPLEX output or not. + * **cplex\_parameters** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*Any*](https://docs.python.org/3/library/typing.html#typing.Any)*] | None*) – The parameters for CPLEX. See [https://www.ibm.com/docs/en/icos/20.1.0?topic=cplex-parameters](https://www.ibm.com/docs/en/icos/20.1.0?topic=cplex-parameters) for details. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.solvers.CplexSolver.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/cplex_solver.py#L41-L50" signature="__init__(disp=False, cplex_parameters=None)"> + Initializes the CplexSolver. + + **Parameters** + + * **disp** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Whether to print CPLEX output or not. + * **cplex\_parameters** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*,* [*Any*](https://docs.python.org/3/library/typing.html#typing.Any)*] | None*) – The parameters for CPLEX. See [https://www.ibm.com/docs/en/icos/20.1.0?topic=cplex-parameters](https://www.ibm.com/docs/en/icos/20.1.0?topic=cplex-parameters) for details. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.solvers.CplexSolver.__init__ "qiskit_addon_opt_mapper.solvers.CplexSolver.__init__")(\[disp, cplex\_parameters]) | Initializes the CplexSolver. | + | [`get_compatibility_msg`](#qiskit_addon_opt_mapper.solvers.CplexSolver.get_compatibility_msg "qiskit_addon_opt_mapper.solvers.CplexSolver.get_compatibility_msg")(problem) | Checks whether a given problem can be solved with this optimizer. | + | [`is_compatible`](#qiskit_addon_opt_mapper.solvers.CplexSolver.is_compatible "qiskit_addon_opt_mapper.solvers.CplexSolver.is_compatible")(problem) | Checks whether a given problem can be solved with the optimizer implementing this method. | + | [`is_cplex_installed`](#qiskit_addon_opt_mapper.solvers.CplexSolver.is_cplex_installed "qiskit_addon_opt_mapper.solvers.CplexSolver.is_cplex_installed")() | Returns True if cplex is installed. | + | [`solve`](#qiskit_addon_opt_mapper.solvers.CplexSolver.solve "qiskit_addon_opt_mapper.solvers.CplexSolver.solve")(problem) | Tries to solves the given problem using the optimizer. | + + ## Attributes + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------- | + | [`cplex_parameters`](#qiskit_addon_opt_mapper.solvers.CplexSolver.cplex_parameters "qiskit_addon_opt_mapper.solvers.CplexSolver.cplex_parameters") | Returns parameters for CPLEX. | + | [`disp`](#qiskit_addon_opt_mapper.solvers.CplexSolver.disp "qiskit_addon_opt_mapper.solvers.CplexSolver.disp") | Returns the display setting. | + + ### cplex\_parameters + + <Attribute id="qiskit_addon_opt_mapper.solvers.CplexSolver.cplex_parameters" attributeTypeHint="dict[str, Any] | None"> + Returns parameters for CPLEX. + </Attribute> + + ### disp + + <Attribute id="qiskit_addon_opt_mapper.solvers.CplexSolver.disp" attributeTypeHint="bool"> + Returns the display setting. + + **Returns** + + Whether to print CPLEX information or not. + </Attribute> + + ### get\_compatibility\_msg + + <Function id="qiskit_addon_opt_mapper.solvers.CplexSolver.get_compatibility_msg" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/cplex_solver.py#L90-L104" signature="get_compatibility_msg(problem)"> + Checks whether a given problem can be solved with this optimizer. + + Returns `''` since CPLEX accepts all problems that can be modeled using the `OptimizationProblem`. CPLEX may throw an exception in case the problem is determined to be non-convex. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + An empty string. + + **Return type** + + [str](https://docs.python.org/3/library/stdtypes.html#str) + </Function> + + ### is\_compatible + + <Function id="qiskit_addon_opt_mapper.solvers.CplexSolver.is_compatible" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/solver.py#L332-L342" signature="is_compatible(problem)"> + Checks whether a given problem can be solved with the optimizer implementing this method. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + Returns True if the problem is compatible, False otherwise. + + **Return type** + + [bool](https://docs.python.org/3/library/functions.html#bool) + </Function> + + ### is\_cplex\_installed + + <Function id="qiskit_addon_opt_mapper.solvers.CplexSolver.is_cplex_installed" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/cplex_solver.py#L52-L55" signature="is_cplex_installed()" modifiers="static"> + Returns True if cplex is installed. + </Function> + + ### solve + + <Function id="qiskit_addon_opt_mapper.solvers.CplexSolver.solve" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/cplex_solver.py#L106-L143" signature="solve(problem)"> + Tries to solves the given problem using the optimizer. + + Runs the optimizer to try to solve the optimization problem. If problem is not convex, this optimizer may raise an exception due to incompatibility, depending on the settings. + + **Parameters** + + **problem** (*OptimizationProblem*) – The problem to be solved. + + **Returns** + + The result of the optimizer applied to the problem. + + **Raises** + + **QiskitOptimizationError** – If the problem is incompatible with the optimizer. + + **Return type** + + [*SolverResult*](solvers-solver-result "qiskit_addon_opt_mapper.solvers.solver.SolverResult") + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/solvers-gurobi-solver.mdx b/docs/api/qiskit-addon-opt-mapper/solvers-gurobi-solver.mdx new file mode 100644 index 00000000000..db01ae1fcb1 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/solvers-gurobi-solver.mdx @@ -0,0 +1,151 @@ +--- +title: GurobiSolver (latest version) +description: API reference for qiskit_addon_opt_mapper.solvers.GurobiSolver in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.solvers.GurobiSolver +--- + +<span id="qiskit-addon-opt-mapper-solvers-gurobisolver" /> + +# qiskit\_addon\_opt\_mapper.solvers.GurobiSolver + +<Class id="qiskit_addon_opt_mapper.solvers.GurobiSolver" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/gurobi_solver.py#L23-L137" signature="GurobiSolver(disp=False)" modifiers="class"> + Bases: [`OptimizationSolver`](solvers-optimization-solver "qiskit_addon_opt_mapper.solvers.solver.OptimizationSolver") + + The Gurobi optimizer wrapped as a [`OptimizationSolver`](solvers-optimization-solver "qiskit_addon_opt_mapper.solvers.OptimizationSolver"). + + This class provides a wrapper for `gurobipy` to be used within the optimization module. + + **Examples** + + ```python + >>> from qiskit_addon_opt_mapper.problems import OptimizationProblem + >>> from qiskit_addon_opt_mapper.solvers import GurobiSolver + >>> problem = OptimizationProblem() + >>> # specify problem here, if gurobi is installed + >>> optimizer = GurobiSolver() if GurobiSolver.is_gurobi_installed() else None + >>> # Suppress gurobipy print info to stdout + >>> import sys + >>> class DevNull: + ... def noop(*args, **kwargs): pass + ... close = write = flush = writelines = noop + >>> sys.stdout = DevNull() + >>> result = optimizer.solve(problem) + ``` + + Initializes the GurobiSolver. + + **Parameters** + + **disp** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Whether to print Gurobi output or not. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.solvers.GurobiSolver.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/gurobi_solver.py#L45-L51" signature="__init__(disp=False)"> + Initializes the GurobiSolver. + + **Parameters** + + **disp** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Whether to print Gurobi output or not. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.solvers.GurobiSolver.__init__ "qiskit_addon_opt_mapper.solvers.GurobiSolver.__init__")(\[disp]) | Initializes the GurobiSolver. | + | [`get_compatibility_msg`](#qiskit_addon_opt_mapper.solvers.GurobiSolver.get_compatibility_msg "qiskit_addon_opt_mapper.solvers.GurobiSolver.get_compatibility_msg")(problem) | Checks whether a given problem can be solved with this optimizer. | + | [`is_compatible`](#qiskit_addon_opt_mapper.solvers.GurobiSolver.is_compatible "qiskit_addon_opt_mapper.solvers.GurobiSolver.is_compatible")(problem) | Checks whether a given problem can be solved with the optimizer implementing this method. | + | [`is_gurobi_installed`](#qiskit_addon_opt_mapper.solvers.GurobiSolver.is_gurobi_installed "qiskit_addon_opt_mapper.solvers.GurobiSolver.is_gurobi_installed")() | Returns True if gurobi is installed. | + | [`solve`](#qiskit_addon_opt_mapper.solvers.GurobiSolver.solve "qiskit_addon_opt_mapper.solvers.GurobiSolver.solve")(problem) | Tries to solves the given problem using the optimizer. | + + ## Attributes + + | | | + | ---------------------------------------------------------------------------------------------------------------- | ---------------------------- | + | [`disp`](#qiskit_addon_opt_mapper.solvers.GurobiSolver.disp "qiskit_addon_opt_mapper.solvers.GurobiSolver.disp") | Returns the display setting. | + + ### disp + + <Attribute id="qiskit_addon_opt_mapper.solvers.GurobiSolver.disp" attributeTypeHint="bool"> + Returns the display setting. + + **Returns** + + Whether to print Gurobi information or not. + </Attribute> + + ### get\_compatibility\_msg + + <Function id="qiskit_addon_opt_mapper.solvers.GurobiSolver.get_compatibility_msg" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/gurobi_solver.py#L77-L90" signature="get_compatibility_msg(problem)"> + Checks whether a given problem can be solved with this optimizer. + + Returns `''` since Gurobi accepts all problems that can be modeled using the `OptimizationProblem`. Gurobi will also solve non-convex problems. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + An empty string. + + **Return type** + + [str](https://docs.python.org/3/library/stdtypes.html#str) + </Function> + + ### is\_compatible + + <Function id="qiskit_addon_opt_mapper.solvers.GurobiSolver.is_compatible" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/solver.py#L332-L342" signature="is_compatible(problem)"> + Checks whether a given problem can be solved with the optimizer implementing this method. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + Returns True if the problem is compatible, False otherwise. + + **Return type** + + [bool](https://docs.python.org/3/library/functions.html#bool) + </Function> + + ### is\_gurobi\_installed + + <Function id="qiskit_addon_opt_mapper.solvers.GurobiSolver.is_gurobi_installed" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/gurobi_solver.py#L53-L56" signature="is_gurobi_installed()" modifiers="static"> + Returns True if gurobi is installed. + </Function> + + ### solve + + <Function id="qiskit_addon_opt_mapper.solvers.GurobiSolver.solve" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/gurobi_solver.py#L92-L137" signature="solve(problem)"> + Tries to solves the given problem using the optimizer. + + Runs the optimizer to try to solve the optimization problem. If problem is not convex, this optimizer may raise an exception due to incompatibility, depending on the settings. + + **Parameters** + + **problem** (*OptimizationProblem*) – The problem to be solved. + + **Returns** + + The result of the optimizer applied to the problem. + + **Raises** + + **OptimizationError** – If the problem is incompatible with the optimizer. + + **Return type** + + [*SolverResult*](solvers-solver-result "qiskit_addon_opt_mapper.solvers.solver.SolverResult") + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/solvers-optimization-solver.mdx b/docs/api/qiskit-addon-opt-mapper/solvers-optimization-solver.mdx new file mode 100644 index 00000000000..98f62ccbe65 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/solvers-optimization-solver.mdx @@ -0,0 +1,91 @@ +--- +title: OptimizationSolver (latest version) +description: API reference for qiskit_addon_opt_mapper.solvers.OptimizationSolver in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.solvers.OptimizationSolver +--- + +<span id="qiskit-addon-opt-mapper-solvers-optimizationsolver" /> + +# qiskit\_addon\_opt\_mapper.solvers.OptimizationSolver + +<Class id="qiskit_addon_opt_mapper.solvers.OptimizationSolver" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/solver.py#L315-L632" signature="OptimizationSolver" modifiers="class"> + Bases: [`ABC`](https://docs.python.org/3/library/abc.html#abc.ABC) + + An abstract class for optimization solvers in the qiskit addon opt mapper. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.solvers.OptimizationSolver.__init__" signature="__init__()" /> + + ## Methods + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.solvers.OptimizationSolver.__init__ "qiskit_addon_opt_mapper.solvers.OptimizationSolver.__init__")() | | + | [`get_compatibility_msg`](#qiskit_addon_opt_mapper.solvers.OptimizationSolver.get_compatibility_msg "qiskit_addon_opt_mapper.solvers.OptimizationSolver.get_compatibility_msg")(problem) | Checks whether a given problem can be solved with the optimizer implementing this method. | + | [`is_compatible`](#qiskit_addon_opt_mapper.solvers.OptimizationSolver.is_compatible "qiskit_addon_opt_mapper.solvers.OptimizationSolver.is_compatible")(problem) | Checks whether a given problem can be solved with the optimizer implementing this method. | + | [`solve`](#qiskit_addon_opt_mapper.solvers.OptimizationSolver.solve "qiskit_addon_opt_mapper.solvers.OptimizationSolver.solve")(problem) | Tries to solves the given problem using the optimizer. | + + ### get\_compatibility\_msg + + <Function id="qiskit_addon_opt_mapper.solvers.OptimizationSolver.get_compatibility_msg" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/solver.py#L320-L330" signature="get_compatibility_msg(problem)" modifiers="abstract"> + Checks whether a given problem can be solved with the optimizer implementing this method. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + Returns the incompatibility message. If the message is empty no issues were found. + + **Return type** + + [str](https://docs.python.org/3/library/stdtypes.html#str) + </Function> + + ### is\_compatible + + <Function id="qiskit_addon_opt_mapper.solvers.OptimizationSolver.is_compatible" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/solver.py#L332-L342" signature="is_compatible(problem)"> + Checks whether a given problem can be solved with the optimizer implementing this method. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + Returns True if the problem is compatible, False otherwise. + + **Return type** + + [bool](https://docs.python.org/3/library/functions.html#bool) + </Function> + + ### solve + + <Function id="qiskit_addon_opt_mapper.solvers.OptimizationSolver.solve" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/solver.py#L344-L360" signature="solve(problem)" modifiers="abstract"> + Tries to solves the given problem using the optimizer. + + Runs the optimizer to try to solve the optimization problem. + + **Parameters** + + **problem** (*OptimizationProblem*) – The problem to be solved. + + **Returns** + + The result of the optimizer applied to the problem. + + **Raises** + + **OptimizationError** – If the problem is incompatible with the optimizer. + + **Return type** + + [*SolverResult*](solvers-solver-result "qiskit_addon_opt_mapper.solvers.solver.SolverResult") + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/solvers-scipy-milp-solver.mdx b/docs/api/qiskit-addon-opt-mapper/solvers-scipy-milp-solver.mdx new file mode 100644 index 00000000000..52f18da42ed --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/solvers-scipy-milp-solver.mdx @@ -0,0 +1,127 @@ +--- +title: ScipyMilpSolver (latest version) +description: API reference for qiskit_addon_opt_mapper.solvers.ScipyMilpSolver in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.solvers.ScipyMilpSolver +--- + +<span id="qiskit-addon-opt-mapper-solvers-scipymilpsolver" /> + +# qiskit\_addon\_opt\_mapper.solvers.ScipyMilpSolver + +<Class id="qiskit_addon_opt_mapper.solvers.ScipyMilpSolver" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/scipy_milp_solver.py#L42-L189" signature="ScipyMilpSolver(disp=False)" modifiers="class"> + Bases: [`OptimizationSolver`](solvers-optimization-solver "qiskit_addon_opt_mapper.solvers.solver.OptimizationSolver") + + The MILP optimizer from Scipy wrapped as a Qiskit [`OptimizationSolver`](solvers-optimization-solver "qiskit_addon_opt_mapper.solvers.OptimizationSolver"). + + This class provides a wrapper for `scipy.milp` ([https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.milp.html](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.milp.html)) to be used within the optimization module. + + Initializes the ScipyMILPOptimizer. + + **Parameters** + + **disp** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Whether to print MILP output or not. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/scipy_milp_solver.py#L50-L56" signature="__init__(disp=False)"> + Initializes the ScipyMILPOptimizer. + + **Parameters** + + **disp** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Whether to print MILP output or not. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | + | [`__init__`](#qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.__init__ "qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.__init__")(\[disp]) | Initializes the ScipyMILPOptimizer. | + | [`get_compatibility_msg`](#qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.get_compatibility_msg "qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.get_compatibility_msg")(problem) | Checks whether a given problem can be solved with this optimizer. | + | [`is_compatible`](#qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.is_compatible "qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.is_compatible")(problem) | Checks whether a given problem can be solved with the optimizer implementing this method. | + | [`solve`](#qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.solve "qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.solve")(problem) | Tries to solve the given problem using the optimizer. | + + ## Attributes + + | | | + | ---------------------------------------------------------------------------------------------------------------------- | ---------------------------- | + | [`disp`](#qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.disp "qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.disp") | Returns the display setting. | + + ### disp + + <Attribute id="qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.disp" attributeTypeHint="bool"> + Returns the display setting. + + **Returns** + + Whether to print scipy.milp information or not. + </Attribute> + + ### get\_compatibility\_msg + + <Function id="qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.get_compatibility_msg" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/scipy_milp_solver.py#L77-L95" signature="get_compatibility_msg(problem)"> + Checks whether a given problem can be solved with this optimizer. + + Checks if the problem has only linear objective function and linear constraints. The `scipy.milp` supports only linear objective function and linear constraints. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + An empty string (if compatible) or a string describing the incompatibility. + + **Return type** + + [str](https://docs.python.org/3/library/stdtypes.html#str) + </Function> + + ### is\_compatible + + <Function id="qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.is_compatible" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/solver.py#L332-L342" signature="is_compatible(problem)"> + Checks whether a given problem can be solved with the optimizer implementing this method. + + **Parameters** + + **problem** (*OptimizationProblem*) – The optimization problem to check compatibility. + + **Returns** + + Returns True if the problem is compatible, False otherwise. + + **Return type** + + [bool](https://docs.python.org/3/library/functions.html#bool) + </Function> + + ### solve + + <Function id="qiskit_addon_opt_mapper.solvers.ScipyMilpSolver.solve" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/scipy_milp_solver.py#L138-L189" signature="solve(problem)"> + Tries to solve the given problem using the optimizer. + + Runs the optimizer to try to solve the optimization problem. If problem is not convex, this optimizer may raise an exception due to incompatibility, depending on the settings. + + **Parameters** + + **problem** (*OptimizationProblem*) – The problem to be solved. + + **Returns** + + The result of the optimizer applied to the problem. + + **Raises** + + **QiskitOptimizationError** – If the problem is incompatible with the optimizer. + + **Return type** + + [*SolverResult*](solvers-solver-result "qiskit_addon_opt_mapper.solvers.solver.SolverResult") + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/solvers-solver-result.mdx b/docs/api/qiskit-addon-opt-mapper/solvers-solver-result.mdx new file mode 100644 index 00000000000..2386810c46f --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/solvers-solver-result.mdx @@ -0,0 +1,223 @@ +--- +title: SolverResult (latest version) +description: API reference for qiskit_addon_opt_mapper.solvers.SolverResult in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: qiskit_addon_opt_mapper.solvers.SolverResult +--- + +<span id="qiskit-addon-opt-mapper-solvers-solverresult" /> + +# qiskit\_addon\_opt\_mapper.solvers.SolverResult + +<Class id="qiskit_addon_opt_mapper.solvers.SolverResult" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/solver.py#L64-L312" signature="SolverResult(x, fval, variables, status, raw_results=None, samples=None)" modifiers="class"> + Bases: [`object`](https://docs.python.org/3/library/functions.html#object) + + A base class for optimization results. + + The optimization algorithms return an object of the type `SolverResult` with the information about the solution obtained. + + `SolverResult` allows users to get the value of a variable by specifying an index or a name as follows. + + **Examples** + + ```python + >>> from qiskit_addon_opt_mapper import OptimizationProblem + >>> from qiskit_addon_opt_mapper.solvers import CplexSolver + >>> problem = OptimizationProblem() + >>> _ = problem.binary_var('x1') + >>> _ = problem.binary_var('x2') + >>> _ = problem.binary_var('x3') + >>> problem.minimize(linear={'x1': 1, 'x2': -2, 'x3': 3}) + >>> print([var.name for var in problem.variables]) + ['x1', 'x2', 'x3'] + >>> optimizer = CplexSolver() + >>> result = optimizer.solve(problem) + >>> print(result.variable_names) + ['x1', 'x2', 'x3'] + >>> print(result.x) + [0. 1. 0.] + >>> print(result[1]) + 1.0 + >>> print(result['x1']) + 0.0 + >>> print(result.fval) + -2.0 + >>> print(result.variables_dict) + {'x1': 0.0, 'x2': 1.0, 'x3': 0.0} + ``` + + <Admonition title="Note" type="note"> + The order of variables should be equal to that of the problem solved by optimization algorithms. Optimization algorithms and converters of `OptimizationProblem` should maintain the order when generating a new `SolverResult` object. + </Admonition> + + Init method. + + **Parameters** + + * **x** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] | np.ndarray | None*) – the variable values found in the optimization, or possibly None in case of FAILURE. + * **fval** ([*float*](https://docs.python.org/3/library/functions.html#float)) – the objective function value. + * **variables** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*Variable*](problems-variable "qiskit_addon_opt_mapper.problems.Variable")*]*) – the list of variables of the optimization problem. + * **raw\_results** (*Any | None*) – the original results object from the optimization algorithm. + * **status** (*SolverResultStatus*) – the termination status of the optimization algorithm. + * **samples** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[SolutionSample] | None*) – the solution samples. + + **Raises** + + **OptimizationError** – if sizes of `x` and `variables` do not match or one of (fval, samples) is not provided. + + ### \_\_init\_\_ + + <Function id="qiskit_addon_opt_mapper.solvers.SolverResult.__init__" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/solver.py#L104-L156" signature="__init__(x, fval, variables, status, raw_results=None, samples=None)"> + Init method. + + **Parameters** + + * **x** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] |* [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) *| None*) – the variable values found in the optimization, or possibly None in case of FAILURE. + * **fval** ([*float*](https://docs.python.org/3/library/functions.html#float)) – the objective function value. + * **variables** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*Variable*](problems-variable "qiskit_addon_opt_mapper.problems.variable.Variable")*]*) – the list of variables of the optimization problem. + * **raw\_results** ([*Any*](https://docs.python.org/3/library/typing.html#typing.Any) *| None*) – the original results object from the optimization algorithm. + * **status** (*SolverResultStatus*) – the termination status of the optimization algorithm. + * **samples** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[SolutionSample] | None*) – the solution samples. + + **Raises** + + **OptimizationError** – if sizes of `x` and `variables` do not match or one of (fval, samples) is not provided. + + **Return type** + + None + </Function> + + ## Methods + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------ | + | [`__init__`](#qiskit_addon_opt_mapper.solvers.SolverResult.__init__ "qiskit_addon_opt_mapper.solvers.SolverResult.__init__")(x, fval, variables, status\[, ...]) | Init method. | + | [`get_correlations`](#qiskit_addon_opt_mapper.solvers.SolverResult.get_correlations "qiskit_addon_opt_mapper.solvers.SolverResult.get_correlations")() | Get \<Zi x Zj> correlation matrix from the samples. | + | [`prettyprint`](#qiskit_addon_opt_mapper.solvers.SolverResult.prettyprint "qiskit_addon_opt_mapper.solvers.SolverResult.prettyprint")() | Returns a pretty printed string of this optimization result. | + + ## Attributes + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | + | [`fval`](#qiskit_addon_opt_mapper.solvers.SolverResult.fval "qiskit_addon_opt_mapper.solvers.SolverResult.fval") | Returns the objective function value. | + | [`raw_results`](#qiskit_addon_opt_mapper.solvers.SolverResult.raw_results "qiskit_addon_opt_mapper.solvers.SolverResult.raw_results") | Return the original results object from the optimization algorithm. | + | [`samples`](#qiskit_addon_opt_mapper.solvers.SolverResult.samples "qiskit_addon_opt_mapper.solvers.SolverResult.samples") | Returns the list of solution samples. | + | [`status`](#qiskit_addon_opt_mapper.solvers.SolverResult.status "qiskit_addon_opt_mapper.solvers.SolverResult.status") | Returns the termination status of the optimization algorithm. | + | [`variable_names`](#qiskit_addon_opt_mapper.solvers.SolverResult.variable_names "qiskit_addon_opt_mapper.solvers.SolverResult.variable_names") | Returns the list of variable names of the optimization problem. | + | [`variables`](#qiskit_addon_opt_mapper.solvers.SolverResult.variables "qiskit_addon_opt_mapper.solvers.SolverResult.variables") | Returns the list of variables of the optimization problem. | + | [`variables_dict`](#qiskit_addon_opt_mapper.solvers.SolverResult.variables_dict "qiskit_addon_opt_mapper.solvers.SolverResult.variables_dict") | Returns the variable values as a dictionary of the variable name and corresponding value. | + | [`x`](#qiskit_addon_opt_mapper.solvers.SolverResult.x "qiskit_addon_opt_mapper.solvers.SolverResult.x") | Returns the variable values found in the optimization or None in case of FAILURE. | + + ### fval + + <Attribute id="qiskit_addon_opt_mapper.solvers.SolverResult.fval" attributeTypeHint="float | None"> + Returns the objective function value. + + **Returns** + + The function value corresponding to the objective function value found in the optimization. + </Attribute> + + ### get\_correlations + + <Function id="qiskit_addon_opt_mapper.solvers.SolverResult.get_correlations" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/solver.py#L217-L236" signature="get_correlations()"> + Get \<Zi x Zj> correlation matrix from the samples. + + **Returns** + + A correlation matrix. + + **Return type** + + [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) + </Function> + + ### prettyprint + + <Function id="qiskit_addon_opt_mapper.solvers.SolverResult.prettyprint" github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/solvers/solver.py#L170-L184" signature="prettyprint()"> + Returns a pretty printed string of this optimization result. + + **Returns** + + A pretty printed string representing the result. + + **Return type** + + [str](https://docs.python.org/3/library/stdtypes.html#str) + </Function> + + ### raw\_results + + <Attribute id="qiskit_addon_opt_mapper.solvers.SolverResult.raw_results" attributeTypeHint="Any"> + Return the original results object from the optimization algorithm. + + Currently a dump for any leftovers. + + **Returns** + + Additional result information of the optimization algorithm. + </Attribute> + + ### samples + + <Attribute id="qiskit_addon_opt_mapper.solvers.SolverResult.samples" attributeTypeHint="list[SolutionSample]"> + Returns the list of solution samples. + + **Returns** + + The list of solution samples. + </Attribute> + + ### status + + <Attribute id="qiskit_addon_opt_mapper.solvers.SolverResult.status" attributeTypeHint="SolverResultStatus"> + Returns the termination status of the optimization algorithm. + + **Returns** + + The termination status of the algorithm. + </Attribute> + + ### variable\_names + + <Attribute id="qiskit_addon_opt_mapper.solvers.SolverResult.variable_names" attributeTypeHint="list[str]"> + Returns the list of variable names of the optimization problem. + + **Returns** + + The list of variable names of the optimization problem. + </Attribute> + + ### variables + + <Attribute id="qiskit_addon_opt_mapper.solvers.SolverResult.variables" attributeTypeHint="list[Variable]"> + Returns the list of variables of the optimization problem. + + **Returns** + + The list of variables. + </Attribute> + + ### variables\_dict + + <Attribute id="qiskit_addon_opt_mapper.solvers.SolverResult.variables_dict" attributeTypeHint="dict[str, float]"> + Returns the variable values as a dictionary of the variable name and corresponding value. + + **Returns** + + The variable values as a dictionary of the variable name and corresponding value. + </Attribute> + + ### x + + <Attribute id="qiskit_addon_opt_mapper.solvers.SolverResult.x" attributeTypeHint="ndarray | None"> + Returns the variable values found in the optimization or None in case of FAILURE. + + **Returns** + + The variable values found in the optimization. + </Attribute> +</Class> + diff --git a/docs/api/qiskit-addon-opt-mapper/solvers.mdx b/docs/api/qiskit-addon-opt-mapper/solvers.mdx new file mode 100644 index 00000000000..b035f2d6701 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/solvers.mdx @@ -0,0 +1,39 @@ +--- +title: solvers (latest version) +description: API reference for qiskit_addon_opt_mapper.solvers in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_addon_opt_mapper.solvers +--- + +<span id="module-qiskit_addon_opt_mapper.solvers" /> + +<span id="reference-solvers-qiskit-addon-opt-mapper-solvers" /> + +# Reference Solvers + +`qiskit_addon_opt_mapper.solvers` + +Solvers module. + +<span id="classical-solvers-qiskit-addon-opt-mapper-solvers" /> + +## Classical Solvers . + +`qiskit_addon_opt_mapper.solvers` + +### Base class for solvers and results + +| | | +| ------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------- | +| [`OptimizationSolver`](/docs/api/qiskit-addon-opt-mapper/solvers-optimization-solver "qiskit_addon_opt_mapper.solvers.OptimizationSolver") | An abstract class for optimization solvers in the qiskit addon opt mapper. | +| [`SolverResult`](/docs/api/qiskit-addon-opt-mapper/solvers-solver-result "qiskit_addon_opt_mapper.solvers.SolverResult") | A base class for optimization results. | + +### Classical Solvers + +| | | +| ---------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| [`CplexSolver`](/docs/api/qiskit-addon-opt-mapper/solvers-cplex-solver "qiskit_addon_opt_mapper.solvers.CplexSolver") | The CPLEX optimizer wrapped as a [`OptimizationSolver`](/docs/api/qiskit-addon-opt-mapper/solvers-optimization-solver "qiskit_addon_opt_mapper.solvers.OptimizationSolver"). | +| [`GurobiSolver`](/docs/api/qiskit-addon-opt-mapper/solvers-gurobi-solver "qiskit_addon_opt_mapper.solvers.GurobiSolver") | The Gurobi optimizer wrapped as a [`OptimizationSolver`](/docs/api/qiskit-addon-opt-mapper/solvers-optimization-solver "qiskit_addon_opt_mapper.solvers.OptimizationSolver"). | +| [`ScipyMilpSolver`](/docs/api/qiskit-addon-opt-mapper/solvers-scipy-milp-solver "qiskit_addon_opt_mapper.solvers.ScipyMilpSolver") | The MILP optimizer from Scipy wrapped as a Qiskit [`OptimizationSolver`](/docs/api/qiskit-addon-opt-mapper/solvers-optimization-solver "qiskit_addon_opt_mapper.solvers.OptimizationSolver"). | + diff --git a/docs/api/qiskit-addon-opt-mapper/translators-from-docplex-mp.mdx b/docs/api/qiskit-addon-opt-mapper/translators-from-docplex-mp.mdx new file mode 100644 index 00000000000..5d5f0059069 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/translators-from-docplex-mp.mdx @@ -0,0 +1,43 @@ +--- +title: from_docplex_mp (latest version) +description: API reference for qiskit_addon_opt_mapper.translators.from_docplex_mp in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: function +python_api_name: qiskit_addon_opt_mapper.translators.from_docplex_mp +--- + +<span id="qiskit-addon-opt-mapper-translators-from-docplex-mp" /> + +# qiskit\_addon\_opt\_mapper.translators.from\_docplex\_mp + +<Function id="qiskit_addon_opt_mapper.translators.from_docplex_mp" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/translators/docplex_mp.py#L397-L446" signature="from_docplex_mp(model, indicator_big_m=None)"> + Translate a docplex.mp model into a quadratic problem. + + Note that this supports the following features of docplex: + + * linear / quadratic objective function + * linear / quadratic / indicator constraints + * binary / integer / continuous variables + * logical expressions (`logical_not`, `logical_and`, and `logical_or`) + + Higher-order objective terms and constraints are not supported in a docplex.mp model. Also spin variables are not supported. + + **Parameters** + + * **model** (*Model*) – The docplex.mp model to be loaded. + * **indicator\_big\_m** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – The big-M value used for the big-M formulation to convert indicator constraints into linear constraints. If `None`, it is automatically derived from the model. + + **Returns** + + The quadratic problem corresponding to the model. + + **Raises** + + * [**RuntimeError**](https://docs.python.org/3/library/exceptions.html#RuntimeError) – if docplex is not installed. + * **OptimizationError** – if the model contains unsupported elements. + + **Return type** + + *OptimizationProblem* +</Function> + diff --git a/docs/api/qiskit-addon-opt-mapper/translators-to-docplex-mp.mdx b/docs/api/qiskit-addon-opt-mapper/translators-to-docplex-mp.mdx new file mode 100644 index 00000000000..ed33f7627c6 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/translators-to-docplex-mp.mdx @@ -0,0 +1,39 @@ +--- +title: to_docplex_mp (latest version) +description: API reference for qiskit_addon_opt_mapper.translators.to_docplex_mp in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: function +python_api_name: qiskit_addon_opt_mapper.translators.to_docplex_mp +--- + +<span id="qiskit-addon-opt-mapper-translators-to-docplex-mp" /> + +# qiskit\_addon\_opt\_mapper.translators.to\_docplex\_mp + +<Function id="qiskit_addon_opt_mapper.translators.to_docplex_mp" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/translators/docplex_mp.py#L40-L150" signature="to_docplex_mp(quadratic_problem)"> + Returns a docplex.mp model corresponding to a optimization problem. + + Higher-order terms and spin variables are not supported. + + **Parameters** + + **quadratic\_problem** (*OptimizationProblem*) – The optimization problem to be translated. + + **Returns** + + The docplex.mp model corresponding to a quadratic optimization problem. + + **Raises** + + * [**RuntimeError**](https://docs.python.org/3/library/exceptions.html#RuntimeError) – if docplex is not installed. + * **OptimizationError** – if the model contains spin variables. + * **OptimizationError** – if the optimization problem contains unsupported variables. (should + * **never happen**\*\*)\*\* – + * **OptimizationError** – if the optimization problem contains higher-order objective terms. + * **OptimizationError** – if the optimization problem contains higher-order constraints. + + **Return type** + + *Model* +</Function> + diff --git a/docs/api/qiskit-addon-opt-mapper/translators-to-ising.mdx b/docs/api/qiskit-addon-opt-mapper/translators-to-ising.mdx new file mode 100644 index 00000000000..9b847a13ccb --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/translators-to-ising.mdx @@ -0,0 +1,35 @@ +--- +title: to_ising (latest version) +description: API reference for qiskit_addon_opt_mapper.translators.to_ising in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 1 +python_api_type: function +python_api_name: qiskit_addon_opt_mapper.translators.to_ising +--- + +<span id="qiskit-addon-opt-mapper-translators-to-ising" /> + +# qiskit\_addon\_opt\_mapper.translators.to\_ising + +<Function id="qiskit_addon_opt_mapper.translators.to_ising" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-opt-mapper/tree/main/qiskit_addon_opt_mapper/translators/ising.py#L24-L184" signature="to_ising(optimization_problem)"> + Return the Ising Hamiltonian of this problem. + + Variables are mapped to qubits in qiskit order, i.e., i-th variable is mapped to index n-i in the pauli string, where n is the total number of variables. + + **Parameters** + + **optimization\_problem** (*OptimizationProblem*) – The problem to be translated. + + **Returns** + + A tuple (qubit\_op, offset) comprising the qubit operator for the problem and offset for the constant value in the Ising Hamiltonian. + + **Raises** + + * **OptimizationError** – If an integer variable or a continuous variable exists in the problem. + * **OptimizationError** – If constraints exist in the problem. + + **Return type** + + [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp), [float](https://docs.python.org/3/library/functions.html#float)] +</Function> + diff --git a/docs/api/qiskit-addon-opt-mapper/translators.mdx b/docs/api/qiskit-addon-opt-mapper/translators.mdx new file mode 100644 index 00000000000..7707063dc55 --- /dev/null +++ b/docs/api/qiskit-addon-opt-mapper/translators.mdx @@ -0,0 +1,34 @@ +--- +title: translators (latest version) +description: API reference for qiskit_addon_opt_mapper.translators in the latest version of qiskit-addon-opt-mapper +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_addon_opt_mapper.translators +--- + +<span id="module-qiskit_addon_opt_mapper.translators" /> + +<span id="translators-qiskit-addon-opt-mapper-translators" /> + +# Translators + +`qiskit_addon_opt_mapper.translators` + +Translators. + +<span id="optimization-problem-translators-qiskit-addon-opt-mapper-translators" /> + +## Optimization problem translators . + +`qiskit_addon_opt_mapper.translators` + +Translators between `OptimizationProblem` and other optimization models or other objects. + +### Translators + +| | | +| ---------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- | +| [`from_docplex_mp`](/docs/api/qiskit-addon-opt-mapper/translators-from-docplex-mp "qiskit_addon_opt_mapper.translators.from_docplex_mp") | Translate a docplex.mp model into a quadratic problem. | +| [`to_docplex_mp`](/docs/api/qiskit-addon-opt-mapper/translators-to-docplex-mp "qiskit_addon_opt_mapper.translators.to_docplex_mp") | Returns a docplex.mp model corresponding to a optimization problem. | +| [`to_ising`](/docs/api/qiskit-addon-opt-mapper/translators-to-ising "qiskit_addon_opt_mapper.translators.to_ising") | Return the Ising Hamiltonian of this problem. | + diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index 04e4f76800d..e36821f60f7 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -16,7 +16,7 @@ These capabilities specialize in mapping domain problems into quantum operators <Card title="Optimization mapper" description="Model optimization problems and map them into representations that can be understood by a quantum computer." - href="https://qiskit.github.io/qiskit-addon-opt-mapper/" + href="/docs/addons/qiskit-addon-opt-mapper/" analyticsName="Documentation page: Optimization mapper" linkText="Browse documentation" /> diff --git a/public/docs/api/qiskit-addon-opt-mapper/objects.inv b/public/docs/api/qiskit-addon-opt-mapper/objects.inv new file mode 100644 index 0000000000000000000000000000000000000000..9d28787f8e86923059ae485d0774721bd1dff257 GIT binary patch literal 11543 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a/scripts/config/api-html-artifacts.json b/scripts/config/api-html-artifacts.json index 72917cf2ea0..69ce9566c9a 100644 --- a/scripts/config/api-html-artifacts.json +++ b/scripts/config/api-html-artifacts.json @@ -133,5 +133,8 @@ }, "qiskit-addon-slc": { "0.1": "https://ibm.box.com/shared/static/87gic8sr1vc85y4g6g1h0x6gk1eb1sqw.zip" + }, + "qiskit-addon-opt-mapper": { + "0.1": "https://ibm.box.com/shared/static/gdjk8grpxrjno8m32ft9lw6j7h40j11a.zip" } } diff --git a/scripts/config/historical-pages-to-latest.json b/scripts/config/historical-pages-to-latest.json index ec6e565a3e5..27bcc248c6f 100644 --- a/scripts/config/historical-pages-to-latest.json +++ b/scripts/config/historical-pages-to-latest.json @@ -1512,5 +1512,6 @@ } }, "qiskit-addon-pna": {}, - "qiskit-addon-slc": {} + "qiskit-addon-slc": {}, + "qiskit-addon-opt-mapper": {} } diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index ec32a6cfe4e..ee2a7d14031 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -71,7 +71,7 @@ export class Pkg { // "qiskit-addon-mthree", "qiskit-addon-pna", "qiskit-addon-slc", - // "qiskit-addon-opt-mapper", + "qiskit-addon-opt-mapper", // "qiskit-fermions", // "qiskit-paulice", // "pauli-prop", diff --git a/scripts/js/lib/api/notebookStages.ts b/scripts/js/lib/api/notebookStages.ts index ad84c470553..3c4622a91bb 100644 --- a/scripts/js/lib/api/notebookStages.ts +++ b/scripts/js/lib/api/notebookStages.ts @@ -158,7 +158,23 @@ function rewriteNotebookLinks( }); }; - return Array.isArray(source) ? source.map(rewrite) : rewrite(source); + const rewritten = Array.isArray(source) ? source.map(rewrite) : rewrite(source); + const joined = Array.isArray(rewritten) ? rewritten.join("") : rewritten; + return escapeMdxSpecialChars(joined); +} + +/** + * Escape `<` outside code spans/fences/math so the MDX parser does not + * interpret it as a JSX tag opening. + */ +function escapeMdxSpecialChars(source: string): string { + const parts = source.split(/(```[\s\S]*?```|\$\$[\s\S]*?\$\$|`[^`]*`)/g); + return parts + .map((part) => { + // Plain text: escape < to prevent MDX from treating it as a JSX tag. + return part.replace(/</g, "\\<"); + }) + .join(""); } /** diff --git a/scripts/js/lib/api/objectsInv.ts b/scripts/js/lib/api/objectsInv.ts index 340bb36b1c2..10691bf1fcf 100644 --- a/scripts/js/lib/api/objectsInv.ts +++ b/scripts/js/lib/api/objectsInv.ts @@ -123,6 +123,7 @@ export class ObjectsInv { ): ObjectsInvEntry | null { // Regex from sphinx source // https://github.com/sphinx-doc/sphinx/blob/2f60b44999d7e610d932529784f082fc1c6af989/sphinx/util/inventory.py#L115-L116 + if (line.trim() === "") return null; const parts = line.match(/(.+?)\s+(\S+)\s+(-?\d+)\s+?(\S*)\s+(.*)/); if (parts == null || parts.length != 6) { console.warn(`Error parsing line of objects.inv: ${line}`); diff --git a/scripts/notebook-normalizer/qiskit_docs_notebook_normalizer/__init__.py b/scripts/notebook-normalizer/qiskit_docs_notebook_normalizer/__init__.py index 61b782f42c1..a612bd8445c 100644 --- a/scripts/notebook-normalizer/qiskit_docs_notebook_normalizer/__init__.py +++ b/scripts/notebook-normalizer/qiskit_docs_notebook_normalizer/__init__.py @@ -87,6 +87,20 @@ def main(): raise SystemExit(1) +REQUIRED_METADATA = { + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, + "language_info": { + "codemirror_mode": {"name": "ipython", "version": 3}, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3", + }, +} + + def normalize_notebook( nb: nbformat.NotebookNode, image_folder: Path, check_only: bool = False ) -> NormalizationResult: @@ -95,6 +109,13 @@ def normalize_notebook( """ images = [] change_made = False + + for key, value in REQUIRED_METADATA.items(): + if key not in nb.metadata: + change_made = True + if not check_only: + nb.metadata[key] = value + for cell_index, cell in enumerate(nb.cells): if cell.cell_type != "code": continue diff --git a/tox.ini b/tox.ini index 458d5c11ce6..b277fe6815f 100644 --- a/tox.ini +++ b/tox.ini @@ -22,10 +22,10 @@ commands = lint: squeaky --check --no-advice {posargs:docs learning} lint: python scripts/ci/check-for-version-info-cells.py lint: qiskit-docs-notebook-normalizer --check + fix: qiskit-docs-notebook-normalizer fix: squeaky {posargs:docs learning} fix: ruff format {posargs:docs learning} fix: ruff check --fix {posargs:docs learning} - fix: qiskit-docs-notebook-normalizer [testenv:tests] deps = From cbd08017abe5365d26a6589f0e954bae660a5fbd Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 22 May 2026 12:59:42 -0500 Subject: [PATCH 081/104] fix opt-mapper --- .../how-tos/01-optimization-problem.ipynb | 64 +++++++++---------- ...converters-for-optimization-problems.ipynb | 46 ++++++------- .../how-tos/04-translate-docplex.ipynb | 18 +++--- scripts/config/cspell/cSpell.json | 3 + scripts/config/cspell/dictionaries/qiskit.txt | 6 +- scripts/js/lib/api/notebookStages.ts | 6 +- 6 files changed, 76 insertions(+), 67 deletions(-) diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb index d24a93b4cbc..8610bbc3360 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "90e10cdc-fe4c-4b50-9cd4-1825e8f39f24", + "id": "963a76ad-144b-44d7-8d4d-3a7cad3c80a1", "metadata": {}, "source": [ "# Model quantum optimization problems with the `OptimizationProblem` class" @@ -21,7 +21,7 @@ }, { "cell_type": "markdown", - "id": "7dd65d92-924e-4d49-aebf-70d0c2549380", + "id": "0a3d7682-1f1b-4276-8cda-7e8531a50a45", "metadata": {}, "source": [ "## Introduction" @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "a9d5c22a-b3ac-4160-9b61-34c61f4805c2", + "id": "20506916-ecbe-4734-b7f1-a1cede27ccf9", "metadata": {}, "source": [ "This guide demonstrates how to use the **Optimization Mapper Qiskit addon** for quantum optimization modeling. The addon introduces the `OptimizationProblem` class, which provides a flexible way to handle variables, constraints, and objective functions.\n", @@ -54,7 +54,7 @@ }, { "cell_type": "markdown", - "id": "6211b5d4-e992-422e-9581-0a39db87c7e1", + "id": "f579f7bb-ccaf-491b-9dfc-de8bcd633f7a", "metadata": {}, "source": [ "## Construct an `OptimizationProblem`\n", @@ -66,7 +66,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "6dfdbc0a-4e44-40a8-b5db-dd5aa89ea292", + "id": "a3a78d43-d3b9-40de-9eb8-282174c74ed9", "metadata": {}, "outputs": [ { @@ -96,7 +96,7 @@ }, { "cell_type": "markdown", - "id": "83d22970-ac6d-4b98-ba75-47c5f7e85d65", + "id": "8d630d37-8723-4032-b5b8-b9f175d82a7a", "metadata": {}, "source": [ "The `OptimizationProblem` class supports four types of variables:\n", @@ -112,7 +112,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "ea316eb3-6347-4f04-b0a2-48715804c261", + "id": "16bc33ef-cb2a-4775-bbcc-e46405a05a68", "metadata": {}, "outputs": [ { @@ -153,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "e876ff2c-6396-4f3b-a1a8-2d24316a0ba8", + "id": "2029b86c-6791-4fef-ad48-7e2730d80b75", "metadata": {}, "source": [ "You set the objective function by calling `OptimizationProblem.minimize` or `OptimizationProblem.maximize`.\n", @@ -177,7 +177,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "a5b00fd7-4ba5-4f59-b870-cf21b2d9109a", + "id": "319602c5-07ba-45c0-93ec-5bc1e8388601", "metadata": {}, "outputs": [ { @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "3917a1f5-6d10-446b-93b9-ca269e7f7b86", + "id": "e810a861-463c-409b-bdcf-c4058ed25fac", "metadata": {}, "source": [ "You can also specify the objective function by using arrays:\n", @@ -233,7 +233,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "0067f705-2f13-4cb4-9d8d-44b03d9bbcc4", + "id": "dad9c4df-14b7-4849-8050-cc53d446ca57", "metadata": {}, "outputs": [ { @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "971aceb5-686f-4809-81fe-a3b6fc737b93", + "id": "9610f658-580e-4c15-9744-aadaf754a09f", "metadata": {}, "source": [ "You can access the constant, the linear term, the quadratic term, and the higher-order terms by looking at\n", @@ -307,7 +307,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "55fce3f0-5945-4f42-a5d1-2574771e2674", + "id": "2854fd92-0796-4810-8040-e2942efbbf83", "metadata": {}, "outputs": [ { @@ -412,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "8d22f393-664b-4046-9fbf-f5b20948c638", + "id": "4d826404-ac85-45bf-b840-1ce575147e17", "metadata": {}, "source": [ "## Add or remove linear, quadratic, and higher-order terms to and from the constraints" @@ -420,17 +420,17 @@ }, { "cell_type": "markdown", - "id": "fd521075-baaa-4267-9e77-61c16aa6708a", + "id": "e1f7f20b-70ae-40bb-b5b7-1367e2a96f1e", "metadata": {}, "source": [ "You can add linear constraints by setting name, linear expression, sense, and right-hand-side value (rhs).\n", - "You can use senses `'=='`, `'\\<='`, and `'>='`." + "You can use senses `'=='`, `'<='`, and `'>='`." ] }, { "cell_type": "code", "execution_count": 6, - "id": "eb876756-0e58-4fc9-a2c3-e2cd62ae6db3", + "id": "c5e4edca-a8a3-4836-8413-553c1109dcbc", "metadata": {}, "outputs": [ { @@ -473,7 +473,7 @@ }, { "cell_type": "markdown", - "id": "255876e6-a897-48d7-b1c5-11e7d4ef16a0", + "id": "2e4d74d1-e63a-486b-a2d2-8d97da721a76", "metadata": {}, "source": [ "You can add quadratic constraints by setting name, quadratic expression, sense, and right-hand-side value (rhs)." @@ -482,7 +482,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "a262ebc5-be6f-43d8-9dee-0b51953acb16", + "id": "e538cff6-6683-417e-81f9-89d624008c3b", "metadata": {}, "outputs": [ { @@ -548,7 +548,7 @@ }, { "cell_type": "markdown", - "id": "329a907e-53ba-4c4e-b7f5-7f271df31a91", + "id": "462eeaf1-cbdd-4a0b-98d5-06db69bfce06", "metadata": {}, "source": [ "You can also add higher-order constraints by setting name, higher-order expression, sense, and right-hand-side value (rhs)." @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "a87f7ac2-a89b-4afa-b809-a2e51728b9e1", + "id": "0e6a5a28-28da-4a54-9ebc-bd7801c5dc76", "metadata": {}, "outputs": [ { @@ -631,7 +631,7 @@ }, { "cell_type": "markdown", - "id": "8430e422-c4d8-4383-84e0-8e99d472256a", + "id": "cadd70e7-1f62-4ccf-88c9-4ff0d98940c9", "metadata": {}, "source": [ "You can access linear and quadratic terms of linear and quadratic constraints in the same way as the objective function." @@ -640,7 +640,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "e7067edb-0aee-4c0a-bbe5-dde03020778e", + "id": "5ff3984f-2e00-4c66-a885-cd468e07a3de", "metadata": {}, "outputs": [ { @@ -677,7 +677,7 @@ }, { "cell_type": "markdown", - "id": "27cbc727-1d1f-4be1-a762-8391b39f011e", + "id": "5e0e51b1-8ad8-4bcb-9a0d-657e978672f1", "metadata": {}, "source": [ "You can also remove linear/quadratic/higher-order constraints by using the `remove_linear_constraint`, `remove_quadratic_constraint`, and `remove_higher_order_constraint` methods, respectively." @@ -686,7 +686,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "3cadf977-6e0e-4b29-836f-f0ea77456138", + "id": "cd7562e9-9e18-4b11-9f2a-4aae6c7bfb3f", "metadata": {}, "outputs": [ { @@ -736,7 +736,7 @@ }, { "cell_type": "markdown", - "id": "aa82e26c-11cd-4ae7-ac45-9a4573bd894c", + "id": "8c6e3f70-dad3-431c-857c-9a98a0eb2389", "metadata": {}, "source": [ "## Substitute variables" @@ -744,7 +744,7 @@ }, { "cell_type": "markdown", - "id": "412c050d-b81b-4f69-9622-b954a87c955d", + "id": "7896f072-21c8-4e9d-8085-cceb44c8015a", "metadata": {}, "source": [ "You can substitute some of the variables with constants or other variables.\n", @@ -757,7 +757,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "060750ba-712a-42c5-8022-382973420c05", + "id": "35c4f2f2-aa6f-4772-accb-d7a4afb4af07", "metadata": {}, "outputs": [ { @@ -798,7 +798,7 @@ }, { "cell_type": "markdown", - "id": "be92cb1b-4f36-4943-8f04-7c114c689e86", + "id": "449de2cb-08f6-42db-b903-76e4f8917069", "metadata": {}, "source": [ "If the resulting problem is infeasible due to lower bounds or upper bounds, the methods returns the status `Status.INFEASIBLE`.\n", @@ -808,7 +808,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "9ed83b90-b135-4854-a537-1fe8e29e4d30", + "id": "061dcc38-6331-4d66-a288-50bfda7369dd", "metadata": {}, "outputs": [ { @@ -833,7 +833,7 @@ }, { "cell_type": "markdown", - "id": "0b1b8316-34f3-4f03-8666-6d7271d6fd83", + "id": "8fdc5320-f2a4-4f40-a1e8-65a392638cc0", "metadata": {}, "source": [ "You cannot substitute variables multiple times.\n", @@ -843,7 +843,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "5bdab47a-444a-4833-bba1-c376441b157a", + "id": "ce67f4ae-4556-42da-9b1c-91944b4475d7", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb index 7de01818917..9c4ba3e1842 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "b115a129-132d-4261-8f4f-e965220d5906", + "id": "8a119e6e-159e-4ec9-a460-313661ff2808", "metadata": {}, "source": [ "# Convert formulations of an `OptimizationProblem`\n", @@ -48,7 +48,7 @@ }, { "cell_type": "markdown", - "id": "a6ef5c60-3548-40ef-a26c-04661570870f", + "id": "117098dc-5211-4aaa-91c6-9092061417c6", "metadata": {}, "source": [ "## Code examples\n", @@ -65,7 +65,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "b9d0e69a-b691-4c7e-8412-32831513110d", + "id": "f5189d83-ef68-40b8-bb71-8d52e7b60702", "metadata": {}, "outputs": [ { @@ -128,7 +128,7 @@ }, { "cell_type": "markdown", - "id": "9b4d9007-6b43-4167-9e0b-735f314261a9", + "id": "5ad4af17-9e87-46a8-a3f8-b159f287a1fa", "metadata": {}, "source": [ "## Binary to spin conversion\n", @@ -138,7 +138,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "f88a3f0d-396f-4d90-84a0-74a6981ff2cf", + "id": "2bdfa06c-5656-482b-a550-cba8734845f1", "metadata": {}, "outputs": [ { @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "d71a3c9e-a59d-4d4f-9220-84b7510506cc", + "id": "8cd594f1-e404-4a71-bc80-8a18ff6fdbea", "metadata": {}, "source": [ "## Spin to binary conversion\n", @@ -204,7 +204,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "b8406892-c1c8-423b-a801-e9a24d6d0cb5", + "id": "4c3b41d2-75eb-45be-980f-fab28b38cebe", "metadata": {}, "outputs": [ { @@ -258,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "a505ec8f-78c0-4dd4-a7e5-9fc2c9290a0b", + "id": "6ef6e0d0-37d3-4cd5-bde2-f002082f68a7", "metadata": {}, "source": [ "## Inequality to equality conversion\n", @@ -272,7 +272,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "31b46588-d21f-4604-8127-afa4f8204b26", + "id": "0c9d8c86-0c61-4e21-a740-6f4cec45b211", "metadata": {}, "outputs": [ { @@ -336,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "1257d34e-8b1c-4642-b1d7-a30b8718a3ae", + "id": "a889dc72-c8d2-434e-8a0a-54a9132b4a15", "metadata": {}, "source": [ "## Equality to penalty conversion\n", @@ -352,7 +352,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "f8d49f49-ab3a-4bc7-a328-f022547b7126", + "id": "48021d2d-1873-4483-b0d5-a202f50b5d72", "metadata": {}, "outputs": [ { @@ -408,7 +408,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "f7f7d28d-3cd8-41b5-b2e2-a8cc0d172659", + "id": "4482b42d-1da9-46bd-a2ba-b139a6af81e4", "metadata": {}, "outputs": [ { @@ -441,7 +441,7 @@ }, { "cell_type": "markdown", - "id": "846d56ed-bb4f-4d59-9354-ed9f9fe6e32a", + "id": "a3d08c37-bb89-4836-a981-b63c0a50997c", "metadata": {}, "source": [ "## Linear inequality to penalty conversion\n", @@ -464,7 +464,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "05a0e9d1-e1e6-4f49-b026-5c7c55dcc54b", + "id": "e00661de-6efd-4d1e-8103-add340131964", "metadata": {}, "outputs": [ { @@ -520,7 +520,7 @@ }, { "cell_type": "markdown", - "id": "f5b9c7d1-402e-4374-87f9-6c6f97da6e2b", + "id": "6eb48dc4-4a72-411c-ae0c-6631f2d3aaa1", "metadata": {}, "source": [ "## Maximize to minimize conversion\n", @@ -535,7 +535,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "a43b0458-f09f-49c9-a73c-161b692c2f36", + "id": "a82c0866-6c54-43fa-9d6b-5bb12cfd0f38", "metadata": {}, "outputs": [ { @@ -590,7 +590,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "290b99e5-205e-4ca6-a742-95b80a5054ee", + "id": "7a9c2e43-83e3-47cd-b47a-9156f735ba25", "metadata": {}, "outputs": [ { @@ -623,7 +623,7 @@ }, { "cell_type": "markdown", - "id": "e7bbc86b-337f-4519-add5-79e8e4a79fc0", + "id": "bc9eb737-3960-4106-8d5e-654cb5cc92c6", "metadata": {}, "source": [ "## QUBO conversion\n", @@ -635,7 +635,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "6569550a-9720-4553-bc7a-cc68813983fc", + "id": "01d8b703-2105-4cbb-8342-3401949ca900", "metadata": {}, "outputs": [ { @@ -705,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "62747971-a656-4688-8fd9-ee3e792bd63e", + "id": "08721676-6482-4fc3-bad7-7f8fe17e84f3", "metadata": {}, "source": [ "## HUBO conversion\n", @@ -715,7 +715,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "d11d888d-357d-4499-8077-b6a6427b7a06", + "id": "7cdcc60f-3f1c-41dd-9459-c9c842812dd2", "metadata": {}, "outputs": [ { @@ -779,7 +779,7 @@ }, { "cell_type": "markdown", - "id": "4be55588-9a1f-4e11-a50e-72eaa5019dc4", + "id": "5b704195-c28f-4d05-9e94-b5c699d5399b", "metadata": {}, "source": [ "## Ising transformation\n", @@ -790,7 +790,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "9f3bd98f-3edb-4dbd-8476-d5048cc75236", + "id": "6b0076b7-ba55-4b08-9879-69f186efa23c", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb index 21a574f2e1b..4e79eed91e7 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "5de6a778-fba3-4d06-9d91-6fa050da8ac8", + "id": "ae41002a-60f1-41ee-9f9a-8677cf4773a4", "metadata": {}, "source": [ "# Translate between `OptimizationProblem` and Docplex\n", @@ -29,7 +29,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "6bd023be-4265-4903-af39-2f976d413720", + "id": "41e6b702-2052-44e7-9aac-a05359dcde8c", "metadata": {}, "outputs": [ { @@ -80,7 +80,7 @@ }, { "cell_type": "markdown", - "id": "744f3872-4303-4c87-a97a-21501c97ebe9", + "id": "60579eac-6eff-4e9d-b557-9502c6df2b07", "metadata": {}, "source": [ "When you display your problem in LP format using `export_as_lp_string`,\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "c5ee6a10-bd8a-4deb-af04-511e32eeb3ba", + "id": "669b1a75-6956-4263-9bf4-a66b280fb6cc", "metadata": {}, "outputs": [ { @@ -135,7 +135,7 @@ }, { "cell_type": "markdown", - "id": "3040a270-6179-41ee-8d48-85249574d383", + "id": "1a8dc8ed-9216-4959-97d0-365c9b53bd9c", "metadata": {}, "source": [ "As stated before, you can also convert a `OptimizationProblem` to a Docplex model using the `to_docplex` function." @@ -144,7 +144,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "1774637f-205d-4983-9e4c-9b21a0fbc00c", + "id": "131254ce-56e2-463d-9f7f-9e89d2953293", "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ }, { "cell_type": "markdown", - "id": "04be4093-2c60-4ea5-afeb-94ddcc113856", + "id": "bf89bd77-2f35-463c-beaf-d0eee74fc192", "metadata": {}, "source": [ "## Load LP files using Docplex" @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "34ded7cf-5248-4321-9faf-672a81559e7b", + "id": "fb83f408-5318-4b01-b786-0218dc3b73ae", "metadata": {}, "source": [ "Once you are familia with the Docplex translators, loading from LP files becomes very simple. You can use the `docplex.mp.ModelReader` class followed by a translation step:" @@ -203,7 +203,7 @@ { "cell_type": "code", "execution_count": null, - "id": "53569172-bf04-4416-bd2f-7d55e9e5657a", + "id": "af80f4cd-3c09-4276-ba07-129f5c416a8d", "metadata": {}, "outputs": [ { diff --git a/scripts/config/cspell/cSpell.json b/scripts/config/cspell/cSpell.json index 34e9251ab15..347e1b4ef0c 100644 --- a/scripts/config/cspell/cSpell.json +++ b/scripts/config/cspell/cSpell.json @@ -46,6 +46,9 @@ "pmzero", "textrm", "Frontmatter", + "leftarrow", + "forall", + "arxiv", "subpace", // fix in addon-sqd "behaviour", // fix in addon-sqd "susbapce", // fix in addon-sqd diff --git a/scripts/config/cspell/dictionaries/qiskit.txt b/scripts/config/cspell/dictionaries/qiskit.txt index 274ec1695ca..00d982f5aaf 100644 --- a/scripts/config/cspell/dictionaries/qiskit.txt +++ b/scripts/config/cspell/dictionaries/qiskit.txt @@ -462,4 +462,8 @@ fontsize Utar nocc tnopt -quasis \ No newline at end of file +quasis +Milp +lowerbound +upperbound +HUBO \ No newline at end of file diff --git a/scripts/js/lib/api/notebookStages.ts b/scripts/js/lib/api/notebookStages.ts index 3c4622a91bb..c8fe27589d0 100644 --- a/scripts/js/lib/api/notebookStages.ts +++ b/scripts/js/lib/api/notebookStages.ts @@ -170,8 +170,10 @@ function rewriteNotebookLinks( function escapeMdxSpecialChars(source: string): string { const parts = source.split(/(```[\s\S]*?```|\$\$[\s\S]*?\$\$|`[^`]*`)/g); return parts - .map((part) => { - // Plain text: escape < to prevent MDX from treating it as a JSX tag. + .map((part, i) => { + // Odd indices are the captured delimiters (code/math) — leave them alone. + if (i % 2 === 1) return part; + // Even indices are plain text: escape < to prevent MDX JSX tag parsing. return part.replace(/</g, "\\<"); }) .join(""); From fa5a206149e42405a0eb269dbc30821e2901421d Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 22 May 2026 13:51:52 -0500 Subject: [PATCH 082/104] fix opt-mapper --- .../how-tos/01-optimization-problem.ipynb | 64 +++++++++---------- ...converters-for-optimization-problems.ipynb | 50 +++++++-------- .../how-tos/04-translate-docplex.ipynb | 24 +++---- scripts/js/lib/api/notebookStages.ts | 43 +++++-------- 4 files changed, 84 insertions(+), 97 deletions(-) diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb index 8610bbc3360..e2e2bcfdfca 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "963a76ad-144b-44d7-8d4d-3a7cad3c80a1", + "id": "7d6189de-f662-4f1d-b342-44affaec7439", "metadata": {}, "source": [ "# Model quantum optimization problems with the `OptimizationProblem` class" @@ -21,7 +21,7 @@ }, { "cell_type": "markdown", - "id": "0a3d7682-1f1b-4276-8cda-7e8531a50a45", + "id": "79f55021-5dda-4347-acd5-9ed6b6b85d78", "metadata": {}, "source": [ "## Introduction" @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "20506916-ecbe-4734-b7f1-a1cede27ccf9", + "id": "47538b3d-23e6-47eb-8bed-af3023331941", "metadata": {}, "source": [ "This guide demonstrates how to use the **Optimization Mapper Qiskit addon** for quantum optimization modeling. The addon introduces the `OptimizationProblem` class, which provides a flexible way to handle variables, constraints, and objective functions.\n", @@ -54,7 +54,7 @@ }, { "cell_type": "markdown", - "id": "f579f7bb-ccaf-491b-9dfc-de8bcd633f7a", + "id": "9f974007-dcce-4417-a3dc-63b9d4013a7c", "metadata": {}, "source": [ "## Construct an `OptimizationProblem`\n", @@ -66,7 +66,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "a3a78d43-d3b9-40de-9eb8-282174c74ed9", + "id": "a2cb1759-3f6f-4624-835a-43230e2f1de9", "metadata": {}, "outputs": [ { @@ -96,7 +96,7 @@ }, { "cell_type": "markdown", - "id": "8d630d37-8723-4032-b5b8-b9f175d82a7a", + "id": "cd9f7089-88ca-4029-a523-32b265e15c84", "metadata": {}, "source": [ "The `OptimizationProblem` class supports four types of variables:\n", @@ -112,7 +112,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "16bc33ef-cb2a-4775-bbcc-e46405a05a68", + "id": "7d1c74d5-8e67-4510-a66d-fca9e7d4470c", "metadata": {}, "outputs": [ { @@ -153,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "2029b86c-6791-4fef-ad48-7e2730d80b75", + "id": "70132ded-05d3-430a-b66c-2d920fffca96", "metadata": {}, "source": [ "You set the objective function by calling `OptimizationProblem.minimize` or `OptimizationProblem.maximize`.\n", @@ -177,7 +177,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "319602c5-07ba-45c0-93ec-5bc1e8388601", + "id": "1f5f98cd-85de-44b4-ab54-f83accfe1ccf", "metadata": {}, "outputs": [ { @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "e810a861-463c-409b-bdcf-c4058ed25fac", + "id": "fa21e9ea-e1ea-4111-a988-0acd2ccfc487", "metadata": {}, "source": [ "You can also specify the objective function by using arrays:\n", @@ -233,7 +233,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "dad9c4df-14b7-4849-8050-cc53d446ca57", + "id": "9e296d74-c1de-4911-abb9-b41bdae436d6", "metadata": {}, "outputs": [ { @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "9610f658-580e-4c15-9744-aadaf754a09f", + "id": "580630a7-4f66-41ef-97c2-33ee797dca8b", "metadata": {}, "source": [ "You can access the constant, the linear term, the quadratic term, and the higher-order terms by looking at\n", @@ -307,7 +307,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "2854fd92-0796-4810-8040-e2942efbbf83", + "id": "bc83c343-dd1b-41bd-8e26-c689c4155bb3", "metadata": {}, "outputs": [ { @@ -412,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "4d826404-ac85-45bf-b840-1ce575147e17", + "id": "3329b8bd-f0ac-4973-9241-91cab224a853", "metadata": {}, "source": [ "## Add or remove linear, quadratic, and higher-order terms to and from the constraints" @@ -420,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "e1f7f20b-70ae-40bb-b5b7-1367e2a96f1e", + "id": "9884e193-48b8-4b93-bc79-01f6408c6229", "metadata": {}, "source": [ "You can add linear constraints by setting name, linear expression, sense, and right-hand-side value (rhs).\n", @@ -430,7 +430,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "c5e4edca-a8a3-4836-8413-553c1109dcbc", + "id": "57b8d1d5-3ddf-4247-b5ce-b3cc103ed5e5", "metadata": {}, "outputs": [ { @@ -473,7 +473,7 @@ }, { "cell_type": "markdown", - "id": "2e4d74d1-e63a-486b-a2d2-8d97da721a76", + "id": "2d4916fa-2d9d-416f-ae12-f2ea3f5c5d6e", "metadata": {}, "source": [ "You can add quadratic constraints by setting name, quadratic expression, sense, and right-hand-side value (rhs)." @@ -482,7 +482,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "e538cff6-6683-417e-81f9-89d624008c3b", + "id": "c187fa67-5f37-4270-a98f-3a24c16e6975", "metadata": {}, "outputs": [ { @@ -548,7 +548,7 @@ }, { "cell_type": "markdown", - "id": "462eeaf1-cbdd-4a0b-98d5-06db69bfce06", + "id": "86c5f752-3a50-4178-9327-7dafc87cc739", "metadata": {}, "source": [ "You can also add higher-order constraints by setting name, higher-order expression, sense, and right-hand-side value (rhs)." @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "0e6a5a28-28da-4a54-9ebc-bd7801c5dc76", + "id": "cee3ee7b-b5f8-48ab-80f3-9b00d15a501c", "metadata": {}, "outputs": [ { @@ -631,7 +631,7 @@ }, { "cell_type": "markdown", - "id": "cadd70e7-1f62-4ccf-88c9-4ff0d98940c9", + "id": "e87f79dc-9a54-4789-9467-31e5ad97b978", "metadata": {}, "source": [ "You can access linear and quadratic terms of linear and quadratic constraints in the same way as the objective function." @@ -640,7 +640,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "5ff3984f-2e00-4c66-a885-cd468e07a3de", + "id": "cf975c3b-e7c3-4710-bf5c-6ef0b90791cb", "metadata": {}, "outputs": [ { @@ -677,7 +677,7 @@ }, { "cell_type": "markdown", - "id": "5e0e51b1-8ad8-4bcb-9a0d-657e978672f1", + "id": "c0abacd1-01f6-440c-b79d-cc22d5c3e8df", "metadata": {}, "source": [ "You can also remove linear/quadratic/higher-order constraints by using the `remove_linear_constraint`, `remove_quadratic_constraint`, and `remove_higher_order_constraint` methods, respectively." @@ -686,7 +686,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "cd7562e9-9e18-4b11-9f2a-4aae6c7bfb3f", + "id": "5d218aca-e108-487f-a572-8725b02d842a", "metadata": {}, "outputs": [ { @@ -736,7 +736,7 @@ }, { "cell_type": "markdown", - "id": "8c6e3f70-dad3-431c-857c-9a98a0eb2389", + "id": "9d662d29-1e5b-4c53-826c-c0525d7886ca", "metadata": {}, "source": [ "## Substitute variables" @@ -744,7 +744,7 @@ }, { "cell_type": "markdown", - "id": "7896f072-21c8-4e9d-8085-cceb44c8015a", + "id": "82d53c5a-2569-4b74-b2c8-458cd3613a1e", "metadata": {}, "source": [ "You can substitute some of the variables with constants or other variables.\n", @@ -757,7 +757,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "35c4f2f2-aa6f-4772-accb-d7a4afb4af07", + "id": "4089658b-82b6-4fee-91b3-0aad04cc17fa", "metadata": {}, "outputs": [ { @@ -798,17 +798,17 @@ }, { "cell_type": "markdown", - "id": "449de2cb-08f6-42db-b903-76e4f8917069", + "id": "a04469bd-c6a6-4ec4-8847-5aa66886d597", "metadata": {}, "source": [ "If the resulting problem is infeasible due to lower bounds or upper bounds, the methods returns the status `Status.INFEASIBLE`.\n", - "For example, if with the model above you try to replace variable `x` with -1, but -1 is out of range of `x` (0 \\<= `x` \\<= 1), it will return `Status.INFEASIBLE`." + "For example, if with the model above you try to replace variable `x` with -1, but -1 is out of range of `x` (0 <= `x` <= 1), it will return `Status.INFEASIBLE`." ] }, { "cell_type": "code", "execution_count": 12, - "id": "061dcc38-6331-4d66-a288-50bfda7369dd", + "id": "9d99b844-106a-43f0-a59d-211d2cdbd4a2", "metadata": {}, "outputs": [ { @@ -833,7 +833,7 @@ }, { "cell_type": "markdown", - "id": "8fdc5320-f2a4-4f40-a1e8-65a392638cc0", + "id": "122a7116-a1b7-46e8-a63e-404b74420bfa", "metadata": {}, "source": [ "You cannot substitute variables multiple times.\n", @@ -843,7 +843,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "ce67f4ae-4556-42da-9b1c-91944b4475d7", + "id": "ec2257e7-5e72-4cd3-8309-772ca0f2bdc3", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb index 9c4ba3e1842..4c3ee61959e 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "8a119e6e-159e-4ec9-a460-313661ff2808", + "id": "1801c5d7-2627-41f9-bb03-44daec6210d9", "metadata": {}, "source": [ "# Convert formulations of an `OptimizationProblem`\n", @@ -48,7 +48,7 @@ }, { "cell_type": "markdown", - "id": "117098dc-5211-4aaa-91c6-9092061417c6", + "id": "7155b182-15e3-4b51-8884-a982119f8624", "metadata": {}, "source": [ "## Code examples\n", @@ -65,7 +65,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "f5189d83-ef68-40b8-bb71-8d52e7b60702", + "id": "e359b719-9e9e-4651-8be8-f0bb0844503a", "metadata": {}, "outputs": [ { @@ -128,7 +128,7 @@ }, { "cell_type": "markdown", - "id": "5ad4af17-9e87-46a8-a3f8-b159f287a1fa", + "id": "7ba97ef0-85b3-40b8-8529-dca4992159e9", "metadata": {}, "source": [ "## Binary to spin conversion\n", @@ -138,7 +138,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "2bdfa06c-5656-482b-a550-cba8734845f1", + "id": "89a2abef-ddd7-479b-84b3-34def9130e0b", "metadata": {}, "outputs": [ { @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "8cd594f1-e404-4a71-bc80-8a18ff6fdbea", + "id": "43f25ccc-2adb-475e-8846-f4a98b015822", "metadata": {}, "source": [ "## Spin to binary conversion\n", @@ -204,7 +204,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "4c3b41d2-75eb-45be-980f-fab28b38cebe", + "id": "ca2076d3-a0a4-4641-8f64-7c454c4f7c93", "metadata": {}, "outputs": [ { @@ -258,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "6ef6e0d0-37d3-4cd5-bde2-f002082f68a7", + "id": "deafeb99-8e39-41f4-8801-a6779595ab6c", "metadata": {}, "source": [ "## Inequality to equality conversion\n", @@ -272,7 +272,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "0c9d8c86-0c61-4e21-a740-6f4cec45b211", + "id": "de5c722e-fc58-40eb-954b-fac41986d512", "metadata": {}, "outputs": [ { @@ -336,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "a889dc72-c8d2-434e-8a0a-54a9132b4a15", + "id": "4767848a-ed90-405b-8467-bfba4efb1a66", "metadata": {}, "source": [ "## Equality to penalty conversion\n", @@ -352,7 +352,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "48021d2d-1873-4483-b0d5-a202f50b5d72", + "id": "57178d3f-92f1-40a9-b6ee-0786b723ece0", "metadata": {}, "outputs": [ { @@ -408,7 +408,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "4482b42d-1da9-46bd-a2ba-b139a6af81e4", + "id": "c35d3774-5f59-402d-bb26-35d1456d2436", "metadata": {}, "outputs": [ { @@ -441,7 +441,7 @@ }, { "cell_type": "markdown", - "id": "a3d08c37-bb89-4836-a981-b63c0a50997c", + "id": "4006464d-8d54-47f5-b615-3369c5809d26", "metadata": {}, "source": [ "## Linear inequality to penalty conversion\n", @@ -453,9 +453,9 @@ "x \\leq y & \\rightarrow & P(x-x y) \\\\\n", "x \\geq y & \\rightarrow & P(y-x y) \\\\\n", "\\sum_{i=1}^n x_i \\leq 1, n \\geq 2 & \\rightarrow &\n", - " P \\sum_{i, j : i \\< j} x_i x_j\\\\\n", + " P \\sum_{i, j : i < j} x_i x_j\\\\\n", "\\sum_{i=1}^n x_i \\geq n-1, n \\geq 2 & \\rightarrow &\n", - " P \\sum_{i, j : i \\< j} (1 - x_i) (1 - x_j)\n", + " P \\sum_{i, j : i < j} (1 - x_i) (1 - x_j)\n", "\\end{array}\n", "\n", "*Note that x, y, z and `x_i` are binary variables, and P is a penalty factor,where the value of P is automatically determined or supplied by users.*" @@ -464,7 +464,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "e00661de-6efd-4d1e-8103-add340131964", + "id": "f2199655-6363-4480-9242-0d54e305b2bd", "metadata": {}, "outputs": [ { @@ -520,7 +520,7 @@ }, { "cell_type": "markdown", - "id": "6eb48dc4-4a72-411c-ae0c-6631f2d3aaa1", + "id": "64a8a26b-8e15-43cb-a8a1-e7d1fb24c6c7", "metadata": {}, "source": [ "## Maximize to minimize conversion\n", @@ -535,7 +535,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "a82c0866-6c54-43fa-9d6b-5bb12cfd0f38", + "id": "5a7e60e1-49b5-49fe-ba95-dbcd2fa90b04", "metadata": {}, "outputs": [ { @@ -590,7 +590,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "7a9c2e43-83e3-47cd-b47a-9156f735ba25", + "id": "41810f1b-6f77-40e3-a680-d0bc5eebdc7d", "metadata": {}, "outputs": [ { @@ -623,7 +623,7 @@ }, { "cell_type": "markdown", - "id": "bc9eb737-3960-4106-8d5e-654cb5cc92c6", + "id": "01672a0a-855c-44fd-9555-5196fe754b81", "metadata": {}, "source": [ "## QUBO conversion\n", @@ -635,7 +635,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "01d8b703-2105-4cbb-8342-3401949ca900", + "id": "94c5766c-c550-4e77-9e7a-bc826164c7b0", "metadata": {}, "outputs": [ { @@ -705,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "08721676-6482-4fc3-bad7-7f8fe17e84f3", + "id": "07f815ef-accf-47dd-980f-d000f3dac2c7", "metadata": {}, "source": [ "## HUBO conversion\n", @@ -715,7 +715,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "7cdcc60f-3f1c-41dd-9459-c9c842812dd2", + "id": "c8b35291-9998-4869-a2cf-320adb9244a7", "metadata": {}, "outputs": [ { @@ -779,7 +779,7 @@ }, { "cell_type": "markdown", - "id": "5b704195-c28f-4d05-9e94-b5c699d5399b", + "id": "b9f2ac1c-e4af-4e10-810a-c274e572aaf5", "metadata": {}, "source": [ "## Ising transformation\n", @@ -790,7 +790,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "6b0076b7-ba55-4b08-9879-69f186efa23c", + "id": "e1978324-2322-47ba-b152-50e171e33807", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb index 4e79eed91e7..fd0c4059e92 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "ae41002a-60f1-41ee-9f9a-8677cf4773a4", + "id": "05050a28-00f2-4fb8-9049-8c26e4a43764", "metadata": {}, "source": [ "# Translate between `OptimizationProblem` and Docplex\n", @@ -21,15 +21,15 @@ "If you are familiar with the Docplex workflow, you can seamlessly translate between a Docplex model and an `OptimizationProblem` with the `from_docplex_mp` and `to_docplex_mp` translators. With this translation, you can take advantage of Docplex capabilities, such as **LP file loading**, without many additional steps.\n", "\n", "\n", - "\\<div style=\"background-color:#fff3cd; color:#856404; padding:15px; border:1px solid #ffeeba; border-radius:5px; font-size:16px;\">\n", - "\\<strong>Note:\\</strong> Docplex does not support higher-order terms in its formulation. This conversion is limited to constant, linear, and quadratic terms.\n", - "\\</div>" + "<div style=\"background-color:#fff3cd; color:#856404; padding:15px; border:1px solid #ffeeba; border-radius:5px; font-size:16px;\">\n", + "<strong>Note:</strong> Docplex does not support higher-order terms in its formulation. This conversion is limited to constant, linear, and quadratic terms.\n", + "</div>" ] }, { "cell_type": "code", "execution_count": 1, - "id": "41e6b702-2052-44e7-9aac-a05359dcde8c", + "id": "1a50645f-6337-42e0-b9d9-a289581a5141", "metadata": {}, "outputs": [ { @@ -80,7 +80,7 @@ }, { "cell_type": "markdown", - "id": "60579eac-6eff-4e9d-b557-9502c6df2b07", + "id": "90ffa37b-5b57-4ea3-b1c8-e0be3f367af1", "metadata": {}, "source": [ "When you display your problem in LP format using `export_as_lp_string`,\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "669b1a75-6956-4263-9bf4-a66b280fb6cc", + "id": "dbf45163-080b-4b9f-955d-fb22041b158d", "metadata": {}, "outputs": [ { @@ -135,7 +135,7 @@ }, { "cell_type": "markdown", - "id": "1a8dc8ed-9216-4959-97d0-365c9b53bd9c", + "id": "f55daef7-c3c4-4968-befd-d7a73d19d9fa", "metadata": {}, "source": [ "As stated before, you can also convert a `OptimizationProblem` to a Docplex model using the `to_docplex` function." @@ -144,7 +144,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "131254ce-56e2-463d-9f7f-9e89d2953293", + "id": "351f8618-4c0b-4233-888e-ad4dc84aa855", "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ }, { "cell_type": "markdown", - "id": "bf89bd77-2f35-463c-beaf-d0eee74fc192", + "id": "ab134a46-ba6e-441f-bcd8-5aabd3872dff", "metadata": {}, "source": [ "## Load LP files using Docplex" @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "fb83f408-5318-4b01-b786-0218dc3b73ae", + "id": "cc5506d9-fc3f-40c4-bcf2-cdd7586df000", "metadata": {}, "source": [ "Once you are familia with the Docplex translators, loading from LP files becomes very simple. You can use the `docplex.mp.ModelReader` class followed by a translation step:" @@ -203,7 +203,7 @@ { "cell_type": "code", "execution_count": null, - "id": "af80f4cd-3c09-4276-ba07-129f5c416a8d", + "id": "2a50b3e1-2e9a-405e-a28b-8f16a7466dfa", "metadata": {}, "outputs": [ { diff --git a/scripts/js/lib/api/notebookStages.ts b/scripts/js/lib/api/notebookStages.ts index c8fe27589d0..23b28e80fc6 100644 --- a/scripts/js/lib/api/notebookStages.ts +++ b/scripts/js/lib/api/notebookStages.ts @@ -63,15 +63,9 @@ export function processNotebooks( return notebooks.map((notebook) => { const processedCells = notebook.cells.map((cell) => { if (cell.cell_type !== "markdown") return cell; - return { - ...cell, - source: rewriteNotebookLinks( - cell.source, - objectsInv, - allInvs, - imageDestination, - ), - }; + const linked = rewriteNotebookLinks(cell.source, objectsInv, allInvs, imageDestination); + const source = escapeMdxSpecialChars(linked); + return { ...cell, source }; }); const frontmatterCell = buildFrontmatterCell(processedCells, pkg); @@ -139,6 +133,17 @@ function normalizeNotebookUrl(url: string, pkg: Pkg): string { return transformSpecialCaseUrl([...parts.slice(0, -1), normalized].join("/")); } +// Escape bare < outside code spans, code blocks, math regions, and HTML tags so that +// MDX's JSX parser doesn't interpret them as tag openings (e.g. `0 <= x` → `0 <= x`). +// required for opt-mapper how-tos +function escapeMdxSpecialChars(source: string | string[]): string { + const text = Array.isArray(source) ? source.join("") : source; + const protected_ = /```[\s\S]*?```|`[^`]+`|\$\$[\s\S]*?\$\$|\$[^$\n]+?\$|<[a-zA-Z/!][^>]*>/g; + const parts = text.split(protected_); + const matches = [...text.matchAll(protected_)].map((m) => m[0]); + return parts.map((p, i) => p.replace(/</g, "<") + (matches[i] ?? "")).join(""); +} + function rewriteNotebookLinks( source: string | string[], objectsInv: ObjectsInv, @@ -158,25 +163,7 @@ function rewriteNotebookLinks( }); }; - const rewritten = Array.isArray(source) ? source.map(rewrite) : rewrite(source); - const joined = Array.isArray(rewritten) ? rewritten.join("") : rewritten; - return escapeMdxSpecialChars(joined); -} - -/** - * Escape `<` outside code spans/fences/math so the MDX parser does not - * interpret it as a JSX tag opening. - */ -function escapeMdxSpecialChars(source: string): string { - const parts = source.split(/(```[\s\S]*?```|\$\$[\s\S]*?\$\$|`[^`]*`)/g); - return parts - .map((part, i) => { - // Odd indices are the captured delimiters (code/math) — leave them alone. - if (i % 2 === 1) return part; - // Even indices are plain text: escape < to prevent MDX JSX tag parsing. - return part.replace(/</g, "\\<"); - }) - .join(""); + return Array.isArray(source) ? source.map(rewrite) : rewrite(source); } /** From 723c7bf7f2d9c3d5ea0c8adc599e53d82753e8f1 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 22 May 2026 13:53:41 -0500 Subject: [PATCH 083/104] fix opt-mapper --- .../how-tos/01-optimization-problem.ipynb | 62 +++++++++---------- ...converters-for-optimization-problems.ipynb | 46 +++++++------- .../how-tos/04-translate-docplex.ipynb | 20 +++--- scripts/js/lib/api/notebookStages.ts | 7 ++- 4 files changed, 70 insertions(+), 65 deletions(-) diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb index e2e2bcfdfca..bcdb9da39a4 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "7d6189de-f662-4f1d-b342-44affaec7439", + "id": "0c65ec9e-01c8-4b6a-ba81-e74e44c02711", "metadata": {}, "source": [ "# Model quantum optimization problems with the `OptimizationProblem` class" @@ -21,7 +21,7 @@ }, { "cell_type": "markdown", - "id": "79f55021-5dda-4347-acd5-9ed6b6b85d78", + "id": "ff54c935-6139-4ac0-b233-35b9474fee71", "metadata": {}, "source": [ "## Introduction" @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "47538b3d-23e6-47eb-8bed-af3023331941", + "id": "c83a1b13-6ae5-4e23-ba20-d5283b2bad38", "metadata": {}, "source": [ "This guide demonstrates how to use the **Optimization Mapper Qiskit addon** for quantum optimization modeling. The addon introduces the `OptimizationProblem` class, which provides a flexible way to handle variables, constraints, and objective functions.\n", @@ -54,7 +54,7 @@ }, { "cell_type": "markdown", - "id": "9f974007-dcce-4417-a3dc-63b9d4013a7c", + "id": "17c0ee8e-c8bb-4127-b31b-c42d22fa237d", "metadata": {}, "source": [ "## Construct an `OptimizationProblem`\n", @@ -66,7 +66,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "a2cb1759-3f6f-4624-835a-43230e2f1de9", + "id": "c48bd056-f1d8-44e5-b9b8-7f5ca6d697f0", "metadata": {}, "outputs": [ { @@ -96,7 +96,7 @@ }, { "cell_type": "markdown", - "id": "cd9f7089-88ca-4029-a523-32b265e15c84", + "id": "96a266c6-4d80-4fb3-b215-1af831b2c3f3", "metadata": {}, "source": [ "The `OptimizationProblem` class supports four types of variables:\n", @@ -112,7 +112,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "7d1c74d5-8e67-4510-a66d-fca9e7d4470c", + "id": "ed67020e-7355-4614-b178-3c9ffc6b0d73", "metadata": {}, "outputs": [ { @@ -153,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "70132ded-05d3-430a-b66c-2d920fffca96", + "id": "cfcc4bb5-457b-440c-a6d5-614431394732", "metadata": {}, "source": [ "You set the objective function by calling `OptimizationProblem.minimize` or `OptimizationProblem.maximize`.\n", @@ -177,7 +177,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "1f5f98cd-85de-44b4-ab54-f83accfe1ccf", + "id": "4b59317f-beca-4d9f-b6d2-7b87f1e4beb0", "metadata": {}, "outputs": [ { @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "fa21e9ea-e1ea-4111-a988-0acd2ccfc487", + "id": "56d70f2f-817e-4d18-a833-98e6f96ebc29", "metadata": {}, "source": [ "You can also specify the objective function by using arrays:\n", @@ -233,7 +233,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "9e296d74-c1de-4911-abb9-b41bdae436d6", + "id": "d823b230-7406-4a9f-a2e0-d172e156168b", "metadata": {}, "outputs": [ { @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "580630a7-4f66-41ef-97c2-33ee797dca8b", + "id": "94011cc6-f710-4c67-9c4c-a8819dda5f6a", "metadata": {}, "source": [ "You can access the constant, the linear term, the quadratic term, and the higher-order terms by looking at\n", @@ -307,7 +307,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "bc83c343-dd1b-41bd-8e26-c689c4155bb3", + "id": "942008dd-1664-40de-852d-9cd8286ed57c", "metadata": {}, "outputs": [ { @@ -412,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "3329b8bd-f0ac-4973-9241-91cab224a853", + "id": "d21d38b4-e77c-4e82-b9ce-7a142e18eeda", "metadata": {}, "source": [ "## Add or remove linear, quadratic, and higher-order terms to and from the constraints" @@ -420,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "9884e193-48b8-4b93-bc79-01f6408c6229", + "id": "149562e2-bd48-4dcc-9954-236203478789", "metadata": {}, "source": [ "You can add linear constraints by setting name, linear expression, sense, and right-hand-side value (rhs).\n", @@ -430,7 +430,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "57b8d1d5-3ddf-4247-b5ce-b3cc103ed5e5", + "id": "c5e41b95-1b1c-4bd4-91d7-e3d98c734ef8", "metadata": {}, "outputs": [ { @@ -473,7 +473,7 @@ }, { "cell_type": "markdown", - "id": "2d4916fa-2d9d-416f-ae12-f2ea3f5c5d6e", + "id": "a0a67bda-dde4-474c-a67a-67e4ceb3db67", "metadata": {}, "source": [ "You can add quadratic constraints by setting name, quadratic expression, sense, and right-hand-side value (rhs)." @@ -482,7 +482,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "c187fa67-5f37-4270-a98f-3a24c16e6975", + "id": "bf3b36ac-1cbe-4842-a0d3-54d404b3e2f1", "metadata": {}, "outputs": [ { @@ -548,7 +548,7 @@ }, { "cell_type": "markdown", - "id": "86c5f752-3a50-4178-9327-7dafc87cc739", + "id": "9f9e61d8-a297-4fcd-91a6-6a957ded0ee4", "metadata": {}, "source": [ "You can also add higher-order constraints by setting name, higher-order expression, sense, and right-hand-side value (rhs)." @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "cee3ee7b-b5f8-48ab-80f3-9b00d15a501c", + "id": "5a426243-abc6-4a7a-9494-c93214f69f96", "metadata": {}, "outputs": [ { @@ -631,7 +631,7 @@ }, { "cell_type": "markdown", - "id": "e87f79dc-9a54-4789-9467-31e5ad97b978", + "id": "7d0a8a99-30ad-4f32-b3d8-37230d37cc22", "metadata": {}, "source": [ "You can access linear and quadratic terms of linear and quadratic constraints in the same way as the objective function." @@ -640,7 +640,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "cf975c3b-e7c3-4710-bf5c-6ef0b90791cb", + "id": "a3f5b8de-527b-4a80-908e-d9202b09d38d", "metadata": {}, "outputs": [ { @@ -677,7 +677,7 @@ }, { "cell_type": "markdown", - "id": "c0abacd1-01f6-440c-b79d-cc22d5c3e8df", + "id": "36b12d53-f294-41ef-94b9-4ae0c3c4b48d", "metadata": {}, "source": [ "You can also remove linear/quadratic/higher-order constraints by using the `remove_linear_constraint`, `remove_quadratic_constraint`, and `remove_higher_order_constraint` methods, respectively." @@ -686,7 +686,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "5d218aca-e108-487f-a572-8725b02d842a", + "id": "ea41f100-e6f2-4328-ab8e-ad094e04bcea", "metadata": {}, "outputs": [ { @@ -736,7 +736,7 @@ }, { "cell_type": "markdown", - "id": "9d662d29-1e5b-4c53-826c-c0525d7886ca", + "id": "789b0e85-3417-4cef-b390-d6b39450cd95", "metadata": {}, "source": [ "## Substitute variables" @@ -744,7 +744,7 @@ }, { "cell_type": "markdown", - "id": "82d53c5a-2569-4b74-b2c8-458cd3613a1e", + "id": "4e9eb647-d535-4670-9fd5-035b7a154cec", "metadata": {}, "source": [ "You can substitute some of the variables with constants or other variables.\n", @@ -757,7 +757,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "4089658b-82b6-4fee-91b3-0aad04cc17fa", + "id": "6b6b4ac8-c87f-411e-ba21-b4af83d45a5c", "metadata": {}, "outputs": [ { @@ -798,7 +798,7 @@ }, { "cell_type": "markdown", - "id": "a04469bd-c6a6-4ec4-8847-5aa66886d597", + "id": "ba2bc2cf-0504-4376-9e65-b680fe32d65f", "metadata": {}, "source": [ "If the resulting problem is infeasible due to lower bounds or upper bounds, the methods returns the status `Status.INFEASIBLE`.\n", @@ -808,7 +808,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "9d99b844-106a-43f0-a59d-211d2cdbd4a2", + "id": "a2d48dbd-7d8f-4d60-8471-4579c6699856", "metadata": {}, "outputs": [ { @@ -833,7 +833,7 @@ }, { "cell_type": "markdown", - "id": "122a7116-a1b7-46e8-a63e-404b74420bfa", + "id": "8f4007a4-2568-42c0-a453-3a34af4a8a99", "metadata": {}, "source": [ "You cannot substitute variables multiple times.\n", @@ -843,7 +843,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "ec2257e7-5e72-4cd3-8309-772ca0f2bdc3", + "id": "74eeda1d-2149-476a-b246-01b951db3879", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb index 4c3ee61959e..19749f46a7a 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "1801c5d7-2627-41f9-bb03-44daec6210d9", + "id": "3166c3e5-fd8f-434c-80c4-631d8cdfcd78", "metadata": {}, "source": [ "# Convert formulations of an `OptimizationProblem`\n", @@ -48,7 +48,7 @@ }, { "cell_type": "markdown", - "id": "7155b182-15e3-4b51-8884-a982119f8624", + "id": "712dd5a8-eeab-43df-8f73-74ce110907a2", "metadata": {}, "source": [ "## Code examples\n", @@ -65,7 +65,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "e359b719-9e9e-4651-8be8-f0bb0844503a", + "id": "27cbce37-6bba-455f-bedb-27f28715dfd6", "metadata": {}, "outputs": [ { @@ -128,7 +128,7 @@ }, { "cell_type": "markdown", - "id": "7ba97ef0-85b3-40b8-8529-dca4992159e9", + "id": "144b06d7-73b0-4692-a04a-7b1595d9fd97", "metadata": {}, "source": [ "## Binary to spin conversion\n", @@ -138,7 +138,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "89a2abef-ddd7-479b-84b3-34def9130e0b", + "id": "ef35cfd9-1b19-4498-ba65-711ec5f70577", "metadata": {}, "outputs": [ { @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "43f25ccc-2adb-475e-8846-f4a98b015822", + "id": "143ddc4d-c12e-4089-8fda-f718648c4e6a", "metadata": {}, "source": [ "## Spin to binary conversion\n", @@ -204,7 +204,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "ca2076d3-a0a4-4641-8f64-7c454c4f7c93", + "id": "2e57d6a7-b0bb-4407-a4c5-ed08df804f07", "metadata": {}, "outputs": [ { @@ -258,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "deafeb99-8e39-41f4-8801-a6779595ab6c", + "id": "99286b6f-727b-42c3-90c6-8858a12830a9", "metadata": {}, "source": [ "## Inequality to equality conversion\n", @@ -272,7 +272,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "de5c722e-fc58-40eb-954b-fac41986d512", + "id": "178593fb-7cd5-4239-b1ba-6106f260f0f1", "metadata": {}, "outputs": [ { @@ -336,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "4767848a-ed90-405b-8467-bfba4efb1a66", + "id": "e9d91a6e-b45d-42d4-b83f-945eaa9f753f", "metadata": {}, "source": [ "## Equality to penalty conversion\n", @@ -352,7 +352,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "57178d3f-92f1-40a9-b6ee-0786b723ece0", + "id": "126a6da5-c8c4-4e3e-b3d3-05117cb44e9a", "metadata": {}, "outputs": [ { @@ -408,7 +408,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "c35d3774-5f59-402d-bb26-35d1456d2436", + "id": "1dee8a29-0be4-4a1b-9ab7-23630080b40b", "metadata": {}, "outputs": [ { @@ -441,7 +441,7 @@ }, { "cell_type": "markdown", - "id": "4006464d-8d54-47f5-b615-3369c5809d26", + "id": "2e04cd7e-e528-4e36-99fb-fc6202688ea3", "metadata": {}, "source": [ "## Linear inequality to penalty conversion\n", @@ -464,7 +464,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "f2199655-6363-4480-9242-0d54e305b2bd", + "id": "e65424e6-dce2-4b9f-afeb-0ab3adef3d7c", "metadata": {}, "outputs": [ { @@ -520,7 +520,7 @@ }, { "cell_type": "markdown", - "id": "64a8a26b-8e15-43cb-a8a1-e7d1fb24c6c7", + "id": "f9e0a0d3-e1cd-43af-b42f-e93d58d76da1", "metadata": {}, "source": [ "## Maximize to minimize conversion\n", @@ -535,7 +535,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "5a7e60e1-49b5-49fe-ba95-dbcd2fa90b04", + "id": "22b33dc8-88c3-4de1-87ba-2894e8454647", "metadata": {}, "outputs": [ { @@ -590,7 +590,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "41810f1b-6f77-40e3-a680-d0bc5eebdc7d", + "id": "e1140dba-b00c-410b-be9c-33b1d36fb048", "metadata": {}, "outputs": [ { @@ -623,7 +623,7 @@ }, { "cell_type": "markdown", - "id": "01672a0a-855c-44fd-9555-5196fe754b81", + "id": "40e0fb19-11f1-4211-884f-c12fd21b72bf", "metadata": {}, "source": [ "## QUBO conversion\n", @@ -635,7 +635,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "94c5766c-c550-4e77-9e7a-bc826164c7b0", + "id": "7bf1d674-c04d-4851-bc97-2dec64663c7e", "metadata": {}, "outputs": [ { @@ -705,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "07f815ef-accf-47dd-980f-d000f3dac2c7", + "id": "9626b79d-6fbb-4700-9838-8946849af534", "metadata": {}, "source": [ "## HUBO conversion\n", @@ -715,7 +715,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "c8b35291-9998-4869-a2cf-320adb9244a7", + "id": "6d972780-083d-4762-8e15-db09e2af3d6d", "metadata": {}, "outputs": [ { @@ -779,7 +779,7 @@ }, { "cell_type": "markdown", - "id": "b9f2ac1c-e4af-4e10-810a-c274e572aaf5", + "id": "c9ae2df1-4436-4e43-bc55-61133277cb6c", "metadata": {}, "source": [ "## Ising transformation\n", @@ -790,7 +790,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "e1978324-2322-47ba-b152-50e171e33807", + "id": "cf16ef1b-3d17-4595-b115-e10b119c0fde", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb index fd0c4059e92..127ddcf418a 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "05050a28-00f2-4fb8-9049-8c26e4a43764", + "id": "59fb2de9-030f-4237-b363-d7046344b04a", "metadata": {}, "source": [ "# Translate between `OptimizationProblem` and Docplex\n", @@ -21,7 +21,7 @@ "If you are familiar with the Docplex workflow, you can seamlessly translate between a Docplex model and an `OptimizationProblem` with the `from_docplex_mp` and `to_docplex_mp` translators. With this translation, you can take advantage of Docplex capabilities, such as **LP file loading**, without many additional steps.\n", "\n", "\n", - "<div style=\"background-color:#fff3cd; color:#856404; padding:15px; border:1px solid #ffeeba; border-radius:5px; font-size:16px;\">\n", + "<div>\n", "<strong>Note:</strong> Docplex does not support higher-order terms in its formulation. This conversion is limited to constant, linear, and quadratic terms.\n", "</div>" ] @@ -29,7 +29,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "1a50645f-6337-42e0-b9d9-a289581a5141", + "id": "a97d6ced-ea16-496e-bdf9-2298a5a3618f", "metadata": {}, "outputs": [ { @@ -80,7 +80,7 @@ }, { "cell_type": "markdown", - "id": "90ffa37b-5b57-4ea3-b1c8-e0be3f367af1", + "id": "f50723ec-622b-4b56-824e-66c02812a475", "metadata": {}, "source": [ "When you display your problem in LP format using `export_as_lp_string`,\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "dbf45163-080b-4b9f-955d-fb22041b158d", + "id": "49f74476-882a-4c5f-8a60-76d880f71379", "metadata": {}, "outputs": [ { @@ -135,7 +135,7 @@ }, { "cell_type": "markdown", - "id": "f55daef7-c3c4-4968-befd-d7a73d19d9fa", + "id": "429d4ee5-fb4f-47f7-8d7b-6220333a7b54", "metadata": {}, "source": [ "As stated before, you can also convert a `OptimizationProblem` to a Docplex model using the `to_docplex` function." @@ -144,7 +144,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "351f8618-4c0b-4233-888e-ad4dc84aa855", + "id": "eec70c45-5194-44e7-b1c7-57c2c1d19e94", "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ }, { "cell_type": "markdown", - "id": "ab134a46-ba6e-441f-bcd8-5aabd3872dff", + "id": "41401bed-0f84-4e3b-b152-8d404bcc5ab5", "metadata": {}, "source": [ "## Load LP files using Docplex" @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "cc5506d9-fc3f-40c4-bcf2-cdd7586df000", + "id": "1971686b-fec2-4054-8e8f-3a3d279e1991", "metadata": {}, "source": [ "Once you are familia with the Docplex translators, loading from LP files becomes very simple. You can use the `docplex.mp.ModelReader` class followed by a translation step:" @@ -203,7 +203,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2a50b3e1-2e9a-405e-a28b-8f16a7466dfa", + "id": "93254d1e-dcae-4601-a4e7-4a24f47aafa2", "metadata": {}, "outputs": [ { diff --git a/scripts/js/lib/api/notebookStages.ts b/scripts/js/lib/api/notebookStages.ts index 23b28e80fc6..6e2ecd73d69 100644 --- a/scripts/js/lib/api/notebookStages.ts +++ b/scripts/js/lib/api/notebookStages.ts @@ -64,7 +64,8 @@ export function processNotebooks( const processedCells = notebook.cells.map((cell) => { if (cell.cell_type !== "markdown") return cell; const linked = rewriteNotebookLinks(cell.source, objectsInv, allInvs, imageDestination); - const source = escapeMdxSpecialChars(linked); + const escaped = escapeMdxSpecialChars(linked); + const source = stripInlineStyles(escaped); return { ...cell, source }; }); @@ -144,6 +145,10 @@ function escapeMdxSpecialChars(source: string | string[]): string { return parts.map((p, i) => p.replace(/</g, "<") + (matches[i] ?? "")).join(""); } +function stripInlineStyles(source: string): string { + return source.replace(/(<[a-zA-Z][^>]*?)\s+style="[^"]*"/g, "$1"); +} + function rewriteNotebookLinks( source: string | string[], objectsInv: ObjectsInv, From e7ee451a871e71b9beeef2583033cf985e4a1b99 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 22 May 2026 17:29:46 -0500 Subject: [PATCH 084/104] adds pauli props and fixes non api links in objects.inv --- docs/addons/pauli-prop/_toc.json | 45 +++ .../pauli-prop/how-tos/do-something.ipynb | 44 +++ docs/addons/pauli-prop/how-tos/index.mdx | 13 + docs/addons/pauli-prop/index.mdx | 91 ++++++ docs/addons/pauli-prop/install.mdx | 64 ++++ docs/api/pauli-prop/_package.json | 4 + docs/api/pauli-prop/_toc.json | 23 ++ docs/api/pauli-prop/index.mdx | 11 + docs/api/pauli-prop/propagation.mdx | 287 ++++++++++++++++++ .../api/qiskit-addon-obp/qiskit-addon-obp.mdx | 2 +- public/docs/api/pauli-prop/objects.inv | Bin 0 -> 610 bytes .../api/qiskit-addon-aqc-tensor/objects.inv | Bin 5907 -> 4905 bytes .../docs/api/qiskit-addon-cutting/objects.inv | Bin 8586 -> 6830 bytes public/docs/api/qiskit-addon-mpf/objects.inv | Bin 4417 -> 3447 bytes public/docs/api/qiskit-addon-obp/objects.inv | Bin 3689 -> 2630 bytes .../api/qiskit-addon-opt-mapper/objects.inv | Bin 11543 -> 10148 bytes public/docs/api/qiskit-addon-pna/objects.inv | 6 +- public/docs/api/qiskit-addon-slc/objects.inv | Bin 1199 -> 854 bytes public/docs/api/qiskit-addon-sqd/objects.inv | Bin 2804 -> 1449 bytes .../docs/api/qiskit-addon-utils/objects.inv | Bin 3781 -> 3264 bytes .../pauli-prop/nbsphinx-no-thumbnail.svg | 9 + ...kit_addon_mpf-backends-quimb_layers-2.avif | Bin 4641 -> 5337 bytes ...kit_addon_mpf-backends-quimb_layers-3.avif | Bin 5065 -> 6967 bytes ...kit_addon_mpf-backends-tenpy_layers-2.avif | Bin 4641 -> 5337 bytes ...kit_addon_mpf-backends-tenpy_layers-3.avif | Bin 5065 -> 6967 bytes ...skit_addon_obp-utils-visualization-10.avif | Bin 4572 -> 4572 bytes ...skit_addon_obp-utils-visualization-12.avif | Bin 3157 -> 3160 bytes ...skit_addon_obp-utils-visualization-14.avif | Bin 3412 -> 3475 bytes ...iskit_addon_obp-utils-visualization-2.avif | Bin 4013 -> 4024 bytes ...iskit_addon_obp-utils-visualization-4.avif | Bin 4295 -> 4335 bytes ...iskit_addon_obp-utils-visualization-6.avif | Bin 3222 -> 3239 bytes ...iskit_addon_obp-utils-visualization-8.avif | Bin 3606 -> 3646 bytes scripts/config/api-html-artifacts.json | 6 + .../config/historical-pages-to-latest.json | 3 +- scripts/js/lib/api/Pkg.ts | 2 +- scripts/js/lib/api/objectsInv.ts | 16 +- 36 files changed, 618 insertions(+), 8 deletions(-) create mode 100644 docs/addons/pauli-prop/_toc.json create mode 100644 docs/addons/pauli-prop/how-tos/do-something.ipynb create mode 100644 docs/addons/pauli-prop/how-tos/index.mdx create mode 100644 docs/addons/pauli-prop/index.mdx create mode 100644 docs/addons/pauli-prop/install.mdx create mode 100644 docs/api/pauli-prop/_package.json create mode 100644 docs/api/pauli-prop/_toc.json create mode 100644 docs/api/pauli-prop/index.mdx create mode 100644 docs/api/pauli-prop/propagation.mdx create mode 100644 public/docs/api/pauli-prop/objects.inv create mode 100644 public/docs/images/addons/pauli-prop/nbsphinx-no-thumbnail.svg diff --git a/docs/addons/pauli-prop/_toc.json b/docs/addons/pauli-prop/_toc.json new file mode 100644 index 00000000000..8a7370287a8 --- /dev/null +++ b/docs/addons/pauli-prop/_toc.json @@ -0,0 +1,45 @@ +{ + "parentUrl": "/docs/guides/addons", + "parentLabel": "Documentation", + "title": "Pauli propagation 0.2.0", + "collapsed": true, + "children": [ + { + "title": "", + "children": [ + { + "title": "Home", + "url": "/docs/addons/pauli-prop" + }, + { + "title": "Installation instructions", + "url": "/docs/addons/pauli-prop/install" + }, + { + "title": "Guides", + "children": [ + { + "title": "How to do something", + "url": "/docs/addons/pauli-prop/how-tos/do-something" + } + ] + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/pauli-prop" + } + ], + "collapsible": false + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Pauli propagation API reference", + "url": "/docs/api/pauli-prop" + } + ] + } + ] +} diff --git a/docs/addons/pauli-prop/how-tos/do-something.ipynb b/docs/addons/pauli-prop/how-tos/do-something.ipynb new file mode 100644 index 00000000000..72d99fd4fac --- /dev/null +++ b/docs/addons/pauli-prop/how-tos/do-something.ipynb @@ -0,0 +1,44 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"How to do something\"\n", + "description: \"How to do something for the latest version of Pauli propagation\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "0b915b9c-2673-4e05-9891-fcd72795cf8d", + "metadata": {}, + "source": [ + "# How to do something" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/pauli-prop/how-tos/index.mdx b/docs/addons/pauli-prop/how-tos/index.mdx new file mode 100644 index 00000000000..ac260a7bcbb --- /dev/null +++ b/docs/addons/pauli-prop/how-tos/index.mdx @@ -0,0 +1,13 @@ +--- +title: "How-To Guides" +description: "How-To Guides for the lastest version of Pauli propagation" +--- + +# How-To Guides + +This page summarizes the available how-to guides. + +[![](/docs/images/addons/pauli-prop/nbsphinx-no-thumbnail.svg)](do-something) + +[How to do something](do-something) + diff --git a/docs/addons/pauli-prop/index.mdx b/docs/addons/pauli-prop/index.mdx new file mode 100644 index 00000000000..ef3179523ad --- /dev/null +++ b/docs/addons/pauli-prop/index.mdx @@ -0,0 +1,91 @@ +--- +title: "Pauli Prop" +description: "Documentation for the latest version of Pauli propagation" +--- + +# Pauli Prop + +## About + +Pauli propagation is a framework for approximating the evolution of operators in the Pauli basis under the action of other operators, such as quantum circuit gates and noise channels \[1] - \[5]. This approach can be effective when the operators involved are expected to remain sparse in the Pauli basis. The technique has been used to classically estimate expectation values of quantum systems and also to reduce the depths of quantum circuits to be run on a quantum processor \[6]. To learn how to use this package to simulate expectation values of quantum systems, check out the tutorial. + +This package provides a Rust-accelerated Python interface for performing Pauli propagation. The subroutines in this package may be used to implement: + +* Lightcone shading \[7] and other novel error mitigation techniques +* Operator backpropagation (OBP) \[6] +* Classical simulation of expectation values \[[tutorials](/docs/addons/pauli-prop/tutorials/index)] + +### Technical details + +* Rust-accelerated Python interface +* Support for noisy simulations \[[tutorial 3](/docs/addons/pauli-prop/tutorials/03-simulate-noisy-expectation-values)] +* Ability to truncate terms during evolution based on an absolute coefficient tolerance, a fixed number of terms in the evolving operator, or a combination of both. +* Ability to perform Pauli propagation in both the Schrödinger and Heisenberg frameworks. +* Novel technique for approximating the conjugation of a Pauli-sum operator by another Pauli operator. This heuristic implementation greedily generates contributions to the product expected to be most significant. +* Single-threaded + +### Computational requirements + +Both the memory and time cost for Pauli propagation routines generally scale with the size to which the evolved operator is allowed to grow. + +`propagate_through_rotation_gates`: As the Pauli operator is propagated in the Pauli basis under the action of a sequence of $N$ Pauli rotation gates of an $M$-qubit circuit, the number of terms will grow as $\mathcal{O}(2^N)$ towards a maximum of $4^M$ unique Pauli components. To control memory usage, the operator is truncated after application of each gate, which introduces some error proportional to the magnitudes of the truncated terms’ coefficients. The memory requirements are linear in the size of the evolved operator and runtime scales linearly in both the operator size and the number of gates. + +`propagate_through_operator`: Conjugates one operator in the Pauli basis by another by greedily accumulating terms in the sum, $\sum_{i,j,k}G^{\dagger}_iO_jG_k$, where $i,j,k$ are sparse indices over the Pauli basis. This implementation sorts the coefficients in each operator by descending magnitude then searches the 3D index space for the terms with the largest coefficients, starting with the origin $(0,0,0)$, and accumulating $(i,j,k)$ triplets up to a specified cutoff. The time spent searching can often be made negligible by increasing the search step size in $(i,j,k)$ space, which provides a cubic speedup for this subroutine. In our profiling, significant time can be spent sorting the operators and performing Pauli multiplication to generate the terms in the new operator. + +## Installation + +We encourage installing this package via `pip`, when possible: + +```bash +pip install pauli-prop +``` + +For more installation information refer to the [installation instructions](install) in the documentation. + +## Citing this project + +If you use this package in your research, please cite it according to `CITATON.bib` file included in this repository: + +```bibtex +@software{pauli-prop, + author = {Qiskit addons team}, + title = {{Pauli prop}}, + howpublished = {\url{https://github.com/Qiskit/pauli-prop}}, + year = {2025} +} +``` + +## Deprecation Policy + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the release notes. + +## Contributing + +The source code is available [on GitHub](https://github.com/Qiskit/pauli-prop). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/pauli-prop/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). + +We use [GitHub issues](https://github.com/Qiskit/pauli-prop/issues/new/choose) for tracking requests and bugs. + +## License + +[Apache License 2.0](https://github.com/Qiskit/pauli-prop/blob/main/LICENSE.txt) + +<span id="id1" /> + +## References + +\[1] Tomislav Begušić, Johnnie Gray, Garnet Kin-Lic Chan, [Fast and converged classical simulations of evidence for the utility of quantum computing before fault tolerance](https://arxiv.org/abs/2308.05077), arXiv:2308.05077 \[quant-ph]. + +\[2] Nicolas Loizeau, et al., [Quantum many-body simulations with PauliStrings.jl](https://arxiv.org/abs/2410.09654), arXiv:2410.09654 \[quant-ph]. + +\[3] Manuel S. Rudolph, et al., [Pauli Propagation: A Computational Framework for Simulating Quantum Systems](https://arxiv.org/abs/2505.21606), arXiv:2505.21606 \[quant-ph]. + +\[4] Hrant Gharibyan, et al., [A Practical Guide to using Pauli Path Simulators for Utility-Scale Quantum Experiments](https://arxiv.org/abs/2507.10771), arXiv:2507.10771 \[quant-ph]. + +\[5] Lukas Broers, et al., [Scalable Simulation of Quantum Many-Body Dynamics with Or-Represented Quantum Algebra](https://arxiv.org/abs/2506.13241), arXiv:2506.13241 \[quant-ph]. + +\[6] Bryce Fuller, et al., [Improved Quantum Computation using Operator Backpropagation](https://arxiv.org/abs/2502.01897), arXiv:2502.01897 \[quant-ph]. + +\[7] Andrew Eddins, Minh C. Tran, Patrick Rall, [Lightcone shading for classically accelerated quantum error mitigation](https://arxiv.org/abs/2409.04401), arXiv:2409.04401 \[quant-ph]. + diff --git a/docs/addons/pauli-prop/install.mdx b/docs/addons/pauli-prop/install.mdx new file mode 100644 index 00000000000..549ac1a86a0 --- /dev/null +++ b/docs/addons/pauli-prop/install.mdx @@ -0,0 +1,64 @@ +--- +title: "Installation Instructions" +description: "Installation Instructions for the lastest version of Pauli propagation" +--- + +# Installation Instructions + +Let’s see how to install the package. The first thing to do is ensure your Python environment is set up correctly. To create a new environment: + +## Pre-Installation + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +There are two primary ways to install this package – from PyPI or source. The preferred method is to install from PyPI: + +## Install from PyPI + +```sh +pip install pauli-prop +``` + +## Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +If so, the first step is to clone the `pauli-prop` repository. + +```sh +git clone git@github.com:Qiskit/pauli-prop.git +``` + +Next, install the Rust toolchain, upgrade pip, and enter the repository. Refer to the [Rust documentation](https://www.rust-lang.org/tools/install) for instructions on installing the toolchain. + +```sh +### <INSTALL RUST HERE> ### +pip install --upgrade pip +cd pauli-prop +``` + +The next step is to install `pauli-prop` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab' +``` + diff --git a/docs/api/pauli-prop/_package.json b/docs/api/pauli-prop/_package.json new file mode 100644 index 00000000000..0418261f855 --- /dev/null +++ b/docs/api/pauli-prop/_package.json @@ -0,0 +1,4 @@ +{ + "name": "pauli-prop", + "version": "0.2.0" +} diff --git a/docs/api/pauli-prop/_toc.json b/docs/api/pauli-prop/_toc.json new file mode 100644 index 00000000000..2315a5b5415 --- /dev/null +++ b/docs/api/pauli-prop/_toc.json @@ -0,0 +1,23 @@ +{ + "title": "Pauli propagation", + "children": [ + { + "title": "API index", + "url": "/docs/api/pauli-prop" + }, + { + "title": "Release notes", + "useDivider": true, + "url": "/docs/api/pauli-prop/release-notes" + }, + { + "title": "pauli_prop.propagation", + "url": "/docs/api/pauli-prop/propagation", + "untranslatable": true + } + ], + "collapsed": true, + "untranslatable": true, + "parentUrl": "/docs/addons/pauli-prop", + "parentLabel": "Pauli propagation" +} diff --git a/docs/api/pauli-prop/index.mdx b/docs/api/pauli-prop/index.mdx new file mode 100644 index 00000000000..7281977d8fe --- /dev/null +++ b/docs/api/pauli-prop/index.mdx @@ -0,0 +1,11 @@ +--- +title: Pauli propagation API documentation (latest version) +description: Index of all the modules in the latest version of pauli-prop. +--- + +<span id="pauli-prop-api-reference" /> + +# `pauli-prop` API reference + +* [Propagation (`pauli_prop.propagation`)](propagation) + diff --git a/docs/api/pauli-prop/propagation.mdx b/docs/api/pauli-prop/propagation.mdx new file mode 100644 index 00000000000..276eae90a1d --- /dev/null +++ b/docs/api/pauli-prop/propagation.mdx @@ -0,0 +1,287 @@ +--- +title: propagation (latest version) +description: API reference for pauli_prop.propagation in the latest version of pauli-prop +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: pauli_prop.propagation +--- + +<span id="module-pauli_prop.propagation" /> + +<span id="propagation-pauli-prop-propagation" /> + +# Propagation + +`pauli_prop.propagation` + +Functions for performing Pauli propagation. + +### RotationGates + +<Class id="pauli_prop.propagation.RotationGates" github="https://github.com/Qiskit/pauli-prop/tree/main/pauli_prop/propagation.py#L141-L209" signature="RotationGates(gates, qargs, thetas)" modifiers="class"> + Bases: [`NamedTuple`](https://docs.python.org/3/library/typing.html#typing.NamedTuple) + + An intermediate minimal representation of a `QuantumCircuit`. + + Supported Pauli rotations: rx/rxx, ry/ryy, rz/rzz, PauliEvolutionGate + + Create new instance of RotationGates(gates, qargs, thetas) + + **Parameters** + + * **gates** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*, ...],* [*dtype*](https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype)*\[*[*bool*](https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool)*]]]*) + * **qargs** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]]*) + * **thetas** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) + + #### append\_circuit\_instruction + + <Function id="pauli_prop.propagation.RotationGates.append_circuit_instruction" github="https://github.com/Qiskit/pauli-prop/tree/main/pauli_prop/propagation.py#L154-L209" signature="append_circuit_instruction(inst, qargs, num_qubits, *, clifford=None)"> + Parses a circuit instruction and appends its data to the internal lists. + + **Parameters** + + * **inst** ([*CircuitInstruction*](/docs/api/qiskit/qiskit.circuit.CircuitInstruction)) – The circuit instruction to parse and append + * **qargs** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – The list of qubit indices of the instruction in the context of its circuit + * **num\_qubits** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The number of qubits of the circuit containing this instruction + * **clifford** ([*Clifford*](/docs/api/qiskit/qiskit.quantum_info.Clifford) *| None*) – An optional Clifford through which the provided instruction should be moved. The Clifford must act on all qubits in the circuit. + + **Raises** + + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – Unsupported gate encountered in circuit + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – If given, `clifford` must act on all qubits in circuit + + **Return type** + + None + </Function> + + #### count + + <Function id="pauli_prop.propagation.RotationGates.count" signature="count(value, /)"> + Return number of occurrences of value. + </Function> + + #### gates + + <Attribute id="pauli_prop.propagation.RotationGates.gates" attributeTypeHint="list[ndarray[tuple[int, ...], dtype[bool]]]"> + A ZX-calculus-like representation of the gates. + </Attribute> + + #### index + + <Function id="pauli_prop.propagation.RotationGates.index" signature="index(value, start=0, stop=9223372036854775807, /)"> + Return first index of value. + + Raises ValueError if the value is not present. + </Function> + + #### qargs + + <Attribute id="pauli_prop.propagation.RotationGates.qargs" attributeTypeHint="list[list[int]]"> + The qubit indices acted upon by each gate. + </Attribute> + + #### thetas + + <Attribute id="pauli_prop.propagation.RotationGates.thetas" attributeTypeHint="list[float]"> + The rotation angles of all gates. + </Attribute> +</Class> + +### circuit\_to\_rotation\_gates + +<Function id="pauli_prop.propagation.circuit_to_rotation_gates" github="https://github.com/Qiskit/pauli-prop/tree/main/pauli_prop/propagation.py#L339-L379" signature="circuit_to_rotation_gates(circuit)"> + Converts the provided circuit to an intermediate representation. + + Supports Pauli rotation gates (‘rx/rxx’, ‘ry/ryy’, ‘rz/rzz’, ‘PauliEvolutionGate’) and Pauli-Lindblad error channels, specified as [PauliLindbladError](https://qiskit.github.io/qiskit-aer/stubs/qiskit_aer.noise.PauliLindbladError.html#qiskit_aer.noise.PauliLindbladError) instructions. + + **Parameters** + + **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – + + The circuit to convert. May contain Pauli rotations (rx/rxx, ry/ryy, rz/rzz, PauliEvolutionGate) and optionally [PauliLindbladError](https://qiskit.github.io/qiskit-aer/stubs/qiskit_aer.noise.PauliLindbladError.html#qiskit_aer.noise.PauliLindbladError) instructions. + + **Returns** + + A [`RotationGates`](#pauli_prop.propagation.RotationGates "pauli_prop.propagation.RotationGates") instance if the circuit does not contain `PauliLindbladError` instructions; otherwise, a `NoisyRotationGates` instance is returned. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – when an unsupported gate is encountered in `circuit`. + + **Return type** + + [*RotationGates*](#pauli_prop.propagation.RotationGates "pauli_prop.propagation.RotationGates") | *NoisyRotationGates* +</Function> + +### propagate\_through\_rotation\_gates + +<Function id="pauli_prop.propagation.propagate_through_rotation_gates" github="https://github.com/Qiskit/pauli-prop/tree/main/pauli_prop/propagation.py#L382-L494" signature="propagate_through_rotation_gates(operator, rot_gates, max_terms, atol, frame)"> + Propagate a sparse Pauli operator, $O$, through a circuit (represented in `rot_gates`), $U$. + + For Schrödinger propagation: $U O U^{\dagger}$. For Heisenberg propagation: $U^{\dagger} O U$. + + If `rot_gates` is a `NoisyRotationGates` instance, the operator will be propagated through each noise generator. The coefficient associated with each term in `operator`, $c_i$, will be damped according to each anti-commuting error generator’s rate, $r_i$: $c_i *= exp(-2.0 * r_i)$. + + In general, the memory and time required for propagating through a circuit grows exponentially with the number of operations in the circuit due to the exponential growth in the number of terms of the operator in the Pauli basis. To regulate this exponential difficulty, one may truncate small Pauli terms (i.e. set them to zero), resulting in a bias proportional to the magnitudes of the truncated terms. After propagating through each operation in the circuit, terms are truncated with respect to two parameters: + + * Only the `max_terms` largest Pauli components are kept; any smaller terms will be truncated. This option makes it possible to estimate in advance how much time and memory will suffice for the computation. + * Terms with magnitudes less than `atol` are truncated (set to zero). + + <Admonition title="Note" type="note"> + This function pre-allocates space in memory for the full-sized operator and operator buffer. It is the caller’s responsibility to ensure they have enough memory to hold operators containing `max_terms` terms. When `max_terms` is `None`, the memory and time requirements typically grow exponentially with the number of operations in the circuit. + </Admonition> + + **Parameters** + + * **operator** ([*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)) – The operator to propagate + * **rot\_gates** ([*RotationGates*](#pauli_prop.propagation.RotationGates "pauli_prop.propagation.RotationGates") *| NoisyRotationGates*) – A circuit represented in the form of [`RotationGates`](#pauli_prop.propagation.RotationGates "pauli_prop.propagation.RotationGates"). + * **max\_terms** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The maximum number of terms the operator may contain as it is propagated + * **atol** ([*float*](https://docs.python.org/3/library/functions.html#float)) – Terms with coeff magnitudes less than this will not be added to the operator as it is propagated. This parameter is not a guarantee on the accuracy of the returned operator. + * **frame** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – `s` for Schrödinger evolution `h` for Heisenberg evolution + + **Returns** + + The evolved operator and one-norm of all truncated coefficients. + + **Raises** + + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – `frame` is neither `h` nor `s`. + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – `atol` is negative. + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – `max_terms` is not positive. + + **Return type** + + [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp), [float](https://docs.python.org/3/library/functions.html#float)] +</Function> + +### propagate\_through\_circuit + +<Function id="pauli_prop.propagation.propagate_through_circuit" github="https://github.com/Qiskit/pauli-prop/tree/main/pauli_prop/propagation.py#L497-L548" signature="propagate_through_circuit(operator, circuit, max_terms, atol, frame)"> + Propagate a sparse Pauli operator, $O$, through a circuit, $U$. + + Supports Pauli rotation gates (‘rx/rxx’, ‘ry/ryy’, ‘rz/rzz’, ‘PauliEvolutionGate’) and Pauli-Lindblad error channels, specified as [PauliLindbladError](https://qiskit.github.io/qiskit-aer/stubs/qiskit_aer.noise.PauliLindbladError.html#qiskit_aer.noise.PauliLindbladError) instructions. + + For Schrödinger propagation: $U O U^{\dagger}$. For Heisenberg propagation: $U^{\dagger} O U$. + + If `circuit` contains Pauli-Lindblad noise instructions, the operator will be propagated through each noise generator. The coefficient associated with each term in `operator`, $c_i$, will be damped according to each anti-commuting error generator’s rate, $r_i$: $c_i *= exp(-2.0 * r_i)$. + + In general, the memory and time required for propagating through a circuit grows exponentially with the number of operations in the circuit due to the exponential growth in the number of terms of the operator in the Pauli basis. To regulate this exponential difficulty, one may truncate small Pauli terms (i.e. set them to zero), resulting in a bias proportional to the magnitudes of the truncated terms. After propagating through each operation in the circuit, terms are truncated with respect to two parameters: + + * Only the `max_terms` largest Pauli components are kept; any smaller terms will be truncated. This option makes it possible to estimate in advance how much time and memory will suffice for the computation. + * Terms with magnitudes less than `atol` are truncated (set to zero). + + <Admonition title="Note" type="note"> + This function pre-allocates space in memory for the full-sized operator and operator buffer. It is the caller’s responsibility to ensure they have enough memory to hold operators containing `max_terms` terms. When `max_terms` is `None`, the memory and time requirements typically grow exponentially with the number of operations in the circuit. + </Admonition> + + **Parameters** + + * **operator** ([*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)) – The operator to propagate + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – The circuit through which the operator will be propagated + * **max\_terms** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The maximum number of terms the operator may contain as it is propagated + * **atol** ([*float*](https://docs.python.org/3/library/functions.html#float)) – Terms with coeff magnitudes less than this will not be added to the operator as it is propagated. This parameter is not a guarantee on the accuracy of the returned operator. + * **frame** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – `s` for Schrödinger evolution `h` for Heisenberg evolution + + **Returns** + + The evolved operator + + **Raises** + + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – `frame` is neither `h` nor `s`. + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – `atol` is negative. + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – `max_terms` is not positive. + + **Return type** + + [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp), [float](https://docs.python.org/3/library/functions.html#float)] +</Function> + +### propagate\_through\_operator + +<Function id="pauli_prop.propagation.propagate_through_operator" github="https://github.com/Qiskit/pauli-prop/tree/main/pauli_prop/propagation.py#L551-L717" signature="propagate_through_operator(op1, op2, max_terms=None, coerce_op1_traceless=False, num_leading_terms=0, frame='s', atol=0.0, search_step=4)"> + Propagate an operator, op1 or $O$, through another operator, op2 or $U$. + + For Schrödinger evolution: $U O U^{\dagger}$. + + For Heisenberg evolution: $U^{\dagger} O U$. + + Evolution is performed in the Pauli basis by summing terms of the form $U_i O_j U_k$ (neglecting the dagger, see note below). The number of such terms is cubic in operator size (len(op1) \* len(op2)\*\*2) and will generally include many duplicate Paulis. + + Setting max\_terms produces an approximate result, where only the max\_terms largest terms (in coefficient magnitude) are computed. This can be much faster but results in some error due to truncation of smaller terms. + + The approximate computation involves two parts: searching for the terms to keep, then computing those terms. Increasing `search_step` greatly (cubically) speeds up the search, at an accuracy cost that is often small. + + It is possible that some Paulis present in the kept terms would have also appeared in the truncated terms. Because such truncated terms are never computed, they cannot possibly be merged into the kept terms sharing the same Pauli. Thus, the `n``th-largest term in the approximate result is not guaranteed to equal the nth-largest term in the exact result. Likewise, convergence to the exact result with increasing ``max_terms` can be non-monotonic. + + <Admonition title="Note" type="note"> + $O$ is assumed to be Hermitian ($O_j$ = $O_j^{\dagger}$) + </Admonition> + + **Parameters** + + * **op1** ([*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)) – The operator to propagate + + * **op2** ([*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)) – The operator through which to propagate + + * **max\_terms** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – + + When not `None`, produces an approximate result including only the `max_terms` largest terms in the direct product of the three operators in Pauli space. + + When `max_terms` is `None` and the number of qubits is \< 12, the propagation will be performed in the computational basis using matrix multiplication. For systems > 12 qubits, all Pauli terms are computed and summed; however, this is usually not a good way to compute exact evolution due to the many duplicate terms present. + + * **coerce\_op1\_traceless** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – A flag denoting whether to remove identity terms from the output operator. + + * **num\_leading\_terms** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The number of terms in `op1` to conjugate by every term in `op2`. The set of included terms is expanded to include its union with the set of terms $U_i O_j U_i^{\dagger}$, for $j < num_leading_terms$. This can improve accuracy for the leading components of O in the output, at some computational runtime cost. + + * **frame** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – s for Schrödinger evolution h for Heisenberg evolution + + * **atol** ([*float*](https://docs.python.org/3/library/functions.html#float)) – Terms in the evolved operator with magnitudes below `atol` will be truncated + + * **search\_step** ([*int*](https://docs.python.org/3/library/functions.html#int)) – A parameter that can speed up the search of the very large 3D space to identify the `max_terms` largest terms in the product. Setting this step size >1 accelerates that search by a factor of `search_step**3`, at a potential cost in accuracy. This inaccuracy is expected to be small for `search_step**3 << max_terms`. + + **Returns** + + The transformed operator + + **Raises** + + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – `frame` is neither `s` nor `h`. + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – `search_step` is not positive. + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – `max_terms` contains an invalid value. + + **Return type** + + [*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) +</Function> + +### evolve\_through\_cliffords + +<Function id="pauli_prop.propagation.evolve_through_cliffords" github="https://github.com/Qiskit/pauli-prop/tree/main/pauli_prop/propagation.py#L73-L138" signature="evolve_through_cliffords(circuit)"> + Evolve (Schrödinger frame) all non-Clifford instructions through all Clifford gates in the circuit. + + This shifts all recognized Clifford gates to the beginning of the circuit and updates the bases of Pauli-rotation gates (e.g. `RxGate`, `RzzGate`, `PauliEvolutionGate`) and [PauliLindbladError](https://qiskit.github.io/qiskit-aer/stubs/qiskit_aer.noise.PauliLindbladError.html#qiskit_aer.noise.PauliLindbladError) channels. Other operations are not supported. See [Pauli.evolve docs](/docs/en/api/qiskit/qiskit.quantum_info.Pauli#evolve) for more info about evolution of Paulis by Cliffords. + + The effect is similar to going to the Clifford interaction picture in [arXiv:2306.04797](https://arxiv.org/abs/2306.04797) but without mapping all rotation angle magnitudes to be $\leq \pi/4$. + + The function returns two objects representing the Clifford and non-Clifford parts of the circuit. + + **Parameters** + + **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – The `QuantumCircuit` to transform. Can contain only Pauli-rotation gates, `PauliLindbladError` (appended to the circuit as quantum channels), and recognized Clifford gates. + + **Returns** + + * **Clifford** - A single all-qubit [Clifford](/docs/en/api/qiskit/qiskit.quantum_info.Clifford) representing the first part of the circuit + * **QuantumCircuit** - A circuit containing the remaining, transformed part of the circuit + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – Input circuit contains unsupported gate + + **Return type** + + [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[*Clifford*](/docs/api/qiskit/qiskit.quantum_info.Clifford), [*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)] +</Function> + diff --git a/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx b/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx index 82a9ccc24fe..3a77af32e70 100644 --- a/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx +++ b/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx @@ -34,7 +34,7 @@ Main operator backpropagation functionality. **Parameters** * **observables** ([*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)*]*) – The observable(s) onto which the circuit is backpropagated. - * **slices** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)*]*) – A sequence of `QuantumCircuit` objects representing a single circuit which has been separated into partitions spanning all qubits. **These “slices” will be backpropagated in reverse order.** Each slice must span all qubits. One may use the tools provided in [`qiskit_addon_utils.slicing`](/docs/api/qiskit-addon-utils/slicing#module-qiskit_addon_utils.slicing "(in Qiskit addon utilities)") to slice a single [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit). + * **slices** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)*]*) – A sequence of `QuantumCircuit` objects representing a single circuit which has been separated into partitions spanning all qubits. **These “slices” will be backpropagated in reverse order.** Each slice must span all qubits. One may use the tools provided in [`qiskit_addon_utils.slicing`](/docs/api/qiskit-addon-utils/slicing#module-qiskit_addon_utils.slicing#module-qiskit_addon_utils.slicing "(in Qiskit addon utilities)") to slice a single [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit). * **truncation\_error\_budget** ([*TruncationErrorBudget*](utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") *| None*) – The error budget used for truncating Pauli terms. Refer to the [how-to guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms) for a detailed discussion on truncating terms from the output operator and bounding the incurred error. * **operator\_budget** ([*OperatorBudget*](utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") *| None*) – Constraints on how large the operator may grow during backpropagation. If `None`, a default instance of [`OperatorBudget`](utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") will be used, and no constraints will be placed on the output operator size. * **max\_seconds** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – The maximum number of seconds to run the backpropagation. If this timeout is triggered before the function returns, the metadata of that moment will be returned. 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b/scripts/config/api-html-artifacts.json index 69ce9566c9a..696dc7a48a1 100644 --- a/scripts/config/api-html-artifacts.json +++ b/scripts/config/api-html-artifacts.json @@ -136,5 +136,11 @@ }, "qiskit-addon-opt-mapper": { "0.1": "https://ibm.box.com/shared/static/gdjk8grpxrjno8m32ft9lw6j7h40j11a.zip" + }, + "qiskit-fermions": { + "0.0": "https://ibm.box.com/shared/static/xt6apdhkq6f653w6ck0247etidll0osz.zip" + }, + "pauli-prop": { + "0.2": "https://ibm.box.com/shared/static/lfl06p9hvlo4890o0lx2dheiebf82msv.zip" } } diff --git a/scripts/config/historical-pages-to-latest.json b/scripts/config/historical-pages-to-latest.json index 27bcc248c6f..1e55648c7a4 100644 --- a/scripts/config/historical-pages-to-latest.json +++ b/scripts/config/historical-pages-to-latest.json @@ -1513,5 +1513,6 @@ }, "qiskit-addon-pna": {}, "qiskit-addon-slc": {}, - "qiskit-addon-opt-mapper": {} + "qiskit-addon-opt-mapper": {}, + "pauli-prop": {} } diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index ee2a7d14031..899c49e8326 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -74,7 +74,7 @@ export class Pkg { "qiskit-addon-opt-mapper", // "qiskit-fermions", // "qiskit-paulice", - // "pauli-prop", + "pauli-prop", ]; static VALID_NAMES = [ diff --git a/scripts/js/lib/api/objectsInv.ts b/scripts/js/lib/api/objectsInv.ts index 10691bf1fcf..3e57a827c4c 100644 --- a/scripts/js/lib/api/objectsInv.ts +++ b/scripts/js/lib/api/objectsInv.ts @@ -28,7 +28,7 @@ const ENTRIES_TO_EXCLUDE = [ /^py-modindex(\.html)?$/, /^search(\.html)?$/, /^explanation(\.html)?(?=\/|#|$)/, - /^how_to(\.html)?(?=\/|#|$)/, + /^how_tos?(\.html)?(?=\/|#|$)/, /^tutorials(\.html)?(?=\/|#|$)/, /^migration_guides(\.html)?(?=\/|#|$)/, /^configuration(\.html)?(?=#|$)/, @@ -55,6 +55,20 @@ function shouldIncludeEntry( if (entry.name.startsWith("group__")) return false; if (entry.name.startsWith("struct_")) return false; + // std: entries (labels, doc references) are only meaningful within the API + // reference pages themselves. Entries pointing outside apidocs/ or stubs/ + // (e.g. index, install, how_tos/, tutorials/) don't belong in the published + // inventory since no other package cross-references them via intersphinx. + if ( + entry.domainAndRole.startsWith("std:") && + !entry.uri.startsWith("apidocs/") && + !entry.uri.startsWith("stubs/") && + !entry.name.startsWith("/apidocs/") && + !entry.name.startsWith("/stubs/") + ) { + return false; + } + // This happens during link checking. if (packageLanguage === "any") return true; From 68450c14915e4bb0f33b35eed2329aca3dc0a2c5 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 22 May 2026 17:46:46 -0500 Subject: [PATCH 085/104] removes release notes from pauli props --- docs/api/pauli-prop/_toc.json | 5 ----- scripts/js/lib/api/Pkg.ts | 1 + 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/docs/api/pauli-prop/_toc.json b/docs/api/pauli-prop/_toc.json index 2315a5b5415..90a7b0c1cda 100644 --- a/docs/api/pauli-prop/_toc.json +++ b/docs/api/pauli-prop/_toc.json @@ -5,11 +5,6 @@ "title": "API index", "url": "/docs/api/pauli-prop" }, - { - "title": "Release notes", - "useDivider": true, - "url": "/docs/api/pauli-prop/release-notes" - }, { "title": "pauli_prop.propagation", "url": "/docs/api/pauli-prop/propagation", diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index 899c49e8326..bddae65df3f 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -308,6 +308,7 @@ export class Pkg { githubSlug: "Qiskit/pauli-prop", kebabCaseAndShortenUrls: true, language: "Python", + releaseNotesConfig: new ReleaseNotesConfig({ enabled: false }), }); } From ca41554298532ed876dcc5c4a7996c130ac604f4 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 26 May 2026 10:48:46 -0400 Subject: [PATCH 086/104] add docs --- README.md | 40 ++++++++++++++++++++++++- scripts/js/lib/api/addonDocsPipeline.ts | 35 ++++++++++++++++++++-- scripts/js/lib/api/generateAddonToc.ts | 14 +++++++++ scripts/js/lib/api/pipelineStages.ts | 6 ++++ 4 files changed, 91 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 1b902c9a824..2bd440d0f6e 100644 --- a/README.md +++ b/README.md @@ -139,6 +139,13 @@ API docs authors can preview their changes to one of the APIs by using the `-a` 1. Run `npm run gen-api -- -p <pkg-name> -v <version> -a <path/to/docs/_build/html>`. 2. Execute `./start` and open up `http://localhost:3000`, as explained in the prior section. +### Addon docs authors: How to preview your changes + +Addon docs authors can preview guide and how-to changes by pointing the addon pipeline at a local Sphinx build: + +1. Run `npm run gen-addon-docs -- -p <pkg-name> -v <version> -a <path/to/docs/_build/html>`. +2. Execute `./start` and navigate to `http://localhost:3000/docs/addons/<pkg-name>`. + ## Run quality checks We use multiple tools to ensure that documentation meets high standards. These tools will run automatically in your PR through CI, but it is much faster to run the checks locally. @@ -320,6 +327,32 @@ Additionally, If you are regenerating a dev version, then you can add `--dev` as In this case, no commit will be automatically created. +## Generate or update addon docs + +Addon docs (guides, how-tos, explanations, and notebook-based tutorials) for packages like `qiskit-addon-cutting`, `qiskit-addon-obp`, `pauli-prop`, etc., are generated by a separate pipeline from the API stubs pipeline. Content is published to `docs/addons/{pkg}/` rather than `docs/api/{pkg}/`. + +### Generate addon docs for a new release + +1. Download the Sphinx HTML artifact from the package's GitHub Actions workflow (see the [CI artifact links](#initial-steps) in the API docs section). +2. Rename the zip to its minor version, e.g. `0.5.zip`, and upload it to the Box folder. +3. Run: + ```sh + npm run gen-addon-docs -- -p <pkg-name> -v <version> + ``` + For example: + ```sh + npm run gen-addon-docs -- -p qiskit-addon-cutting -v 0.10.0 + npm run gen-addon-docs -- -p pauli-prop -v 0.1.0 + ``` +4. Open a pull request with the generated changes. + +### Add a new addon package + +1. Add the package name to `Pkg.ADDON_NAMES` in `scripts/js/lib/api/Pkg.ts` and add a `fromArgs` branch that returns a `new Pkg(...)` with the correct `title`, `githubSlug`, and `language`. +2. Add the package's Box artifact URL to `scripts/config/api-html-artifacts.json`. +3. If the package ships tutorials that live in `docs/tutorials/`, list their slugs in the `tutorials` array of the `Pkg` constructor call. +4. Run the pipeline once with a local artifact using `-a <path>` to verify output before uploading to Box. + ## Generate new API docs Use this process when we want to publish new API docs, such as when we release a new version of a package like Qiskit SDK. @@ -357,12 +390,17 @@ All release types start with the following steps: - Qiskit SDK: https://github.com/Qiskit/qiskit/actions/workflows/docs_deploy.yml - Runtime: https://github.com/Qiskit/qiskit-ibm-runtime/actions/workflows/docs.yml - Transpiler Service: https://github.com/Qiskit/qiskit-ibm-transpiler/actions/workflows/upload-docs.yml - - qiskit-addon-acq-tensor: https://github.com/Qiskit/qiskit-addon-aqc-tensor/actions/workflows/docs.yml + - qiskit-addon-aqc-tensor: https://github.com/Qiskit/qiskit-addon-aqc-tensor/actions/workflows/docs.yml - qiskit-addon-obp: https://github.com/Qiskit/qiskit-addon-obp/actions/workflows/docs.yml - qiskit-addon-mpf: https://github.com/Qiskit/qiskit-addon-mpf/actions/workflows/docs.yml - qiskit-addon-sqd: https://github.com/Qiskit/qiskit-addon-sqd/actions/workflows/docs.yml + - qiskit-addon-sqd-hpc: https://github.com/Qiskit/qiskit-addon-sqd-hpc/actions/workflows/docs.yml - qiskit-addon-cutting: https://github.com/Qiskit/qiskit-addon-cutting/actions/workflows/docs.yml - qiskit-addon-utils: https://github.com/Qiskit/qiskit-addon-utils/actions/workflows/docs.yml + - qiskit-addon-pna: https://github.com/Qiskit/qiskit-addon-pna/actions/workflows/docs.yml + - qiskit-addon-slc: https://github.com/Qiskit/qiskit-addon-slc/actions/workflows/docs.yml + - qiskit-addon-opt-mapper: https://github.com/Qiskit/qiskit-addon-opt-mapper/actions/workflows/docs.yml + - pauli-prop: https://github.com/Qiskit/pauli-prop/actions/workflows/docs.yml 2. Find the run for the release by looking at the middle column with the blue text; look for the version number, like `0.45.2`. For the `rc1` release, look for `main` in blue text and a run description like "Prepare 2.3.0rc1". 3. Click the CI run name. (Not the middle column with the blue link!) 4. In the left navbar, it should show as selected the "Summary" page with the house. diff --git a/scripts/js/lib/api/addonDocsPipeline.ts b/scripts/js/lib/api/addonDocsPipeline.ts index 5ed6276ff2e..b444c00c639 100644 --- a/scripts/js/lib/api/addonDocsPipeline.ts +++ b/scripts/js/lib/api/addonDocsPipeline.ts @@ -10,6 +10,19 @@ // copyright notice, and modified files need to carry a notice indicating // that they have been altered from the originals. +// Pipeline for generating addon content pages from a Sphinx build artifact. +// Addon docs differ from API docs in several ways: +// - Content lives under docs/addons/{pkg}/ rather than docs/api/{pkg}/. +// - The Sphinx artifact contains how-to guides and explanations (HTML), +// Jupyter notebooks (.ipynb), and static images — but NOT apidoc stubs. +// - A _toc.json is generated by generateAddonToc (not generateToc) because +// the TOC shape for addons includes a back-link to the parent page and +// optional Tutorials and API reference sections. +// - When a notebook and an HTML file share the same base name, the notebook +// wins so we preserve code outputs rather than the rendered HTML version. +// - A _tutorials.json is written when the pkg declares tutorial slugs; the +// consuming app uses it to surface those tutorials under the addon's route. + import { writeFile } from "fs/promises"; import { globby } from "globby"; @@ -44,6 +57,14 @@ const SPHINX_INTERNALS = [ "objects.inv", ]; +/** + * Run the full addon docs pipeline for one package version. + * + * @param artifactPath Root of the Sphinx HTML build (the unzipped artifact folder). + * @param docsBaseFolder Repo-relative output root for markdown, e.g. "docs/addons". + * @param publicBaseFolder Repo-relative output root for public assets, e.g. "public/docs". + * @param pkg Package metadata (name, version, tutorial slugs, etc.). + */ export async function runAddonDocsPipeline( artifactPath: string, docsBaseFolder: string, @@ -57,7 +78,8 @@ export async function runAddonDocsPipeline( pkg, ); - // handle HTML files + // HTML → Markdown. The last argument (true) enables extracting frontmatter + // from the Sphinx HTML <h1> rather than letting addFrontMatter.ts generate it. const htmlFiles = files.filter((f) => f.endsWith(".html")); const initialResults = await convertHtmlToMarkdown( pkg, @@ -77,7 +99,8 @@ export async function runAddonDocsPipeline( ); await writeMarkdownResults(pkg, docsBaseFolder, results); - // handle Jupyter notebook files + // Notebooks. Images embedded in notebooks are collected separately so they + // can be passed to copyImages together with the HTML-derived images. const notebookFiles = files.filter((f) => f.endsWith(".ipynb")); const initialNotebooks = await readNotebooks( artifactPath, @@ -96,7 +119,7 @@ export async function runAddonDocsPipeline( ); await writeNotebooks(pkg, docsBaseFolder, notebooks); - // write assets + // Copy all images (HTML-referenced + notebook-referenced) into public/. await copyImages( pkg, artifactPath, @@ -124,6 +147,12 @@ async function writeTocFile( ); } +/** + * Write _tutorials.json listing the tutorial slugs declared in Pkg.tutorials. + * The consuming app (iqp-channel-docs) reads this file to generate static + * params for /docs/addons/{pkg}/tutorials/{slug} routes and to redirect those + * routes to the corresponding file under docs/tutorials/. + */ async function writeTutorialsFile(pkg: Pkg, outputPath: string): Promise<void> { await writeFile( `${outputPath}/_tutorials.json`, diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts index da33c9963ec..7dc79b9e5a3 100644 --- a/scripts/js/lib/api/generateAddonToc.ts +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -10,6 +10,20 @@ // copyright notice, and modified files need to carry a notice indicating // that they have been altered from the originals. +// Generates the _toc.json for an addon's content pages. +// +// Addon TOCs have a fixed three-part shape: +// 1. A flat "main" section — top-level files (index, install, …) plus +// subdirectory sections (how-tos, explanations), and a GitHub link. +// 2. An optional "Tutorials" section listing slugs from docs/tutorials/. +// 3. An "API reference" section linking to docs/api/{pkg}. +// +// Subdirectories whose DIR_LABELS entry is the same string are merged into a +// single section (e.g. both "how-tos" and "explanations" map to "Guides"). +// Title text is read from each file's first h1/h2 heading. +// +// Called by addonDocsPipeline.ts; for API doc TOCs see generateToc.ts. + import { readFile } from "fs/promises"; import { join } from "path/posix"; diff --git a/scripts/js/lib/api/pipelineStages.ts b/scripts/js/lib/api/pipelineStages.ts index 713ca4babf8..ef7b4adf275 100644 --- a/scripts/js/lib/api/pipelineStages.ts +++ b/scripts/js/lib/api/pipelineStages.ts @@ -75,6 +75,12 @@ export async function convertHtmlToMarkdown( return results; } +/** + * Extract title/description frontmatter directly from the Sphinx HTML <h1>. + * Used by the addon pipeline (extractfrontMatter=true) so addon pages get + * human-readable titles rather than the auto-generated slugs that addFrontMatter.ts + * would derive from the URL. + */ function extractHtmlFrontmatter(html: string, pkg: Pkg, url: string): string { const $ = load(html); const h1 = $("h1") From 8d6a8457f053f9c7de8bce3445d734d7c2c39a2c Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 26 May 2026 21:05:33 -0400 Subject: [PATCH 087/104] fix: link addon tutorials directly to /docs/tutorials/{slug} --- scripts/js/lib/api/generateAddonToc.ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts index 7dc79b9e5a3..33abc1004e1 100644 --- a/scripts/js/lib/api/generateAddonToc.ts +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -128,7 +128,7 @@ export async function generateAddonToc( pkg.tutorials.map(async (slug) => { const filePath = join(tutorialsDocsPath, `${slug}.ipynb`); const title = await readTitle(filePath); - return { title, url: `${addonUrlBase}/tutorials/${slug}` }; + return { title, url: `${DOCS_BASE_PATH}/tutorials/${slug}` }; }), ); topLevelSections.push({ From 6b9e151dd0ac08575c7ed546446eb37aca1f0ce5 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 26 May 2026 21:09:45 -0400 Subject: [PATCH 088/104] chore: remove _tutorials.json generation from addon pipeline --- scripts/js/lib/api/addonDocsPipeline.ts | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/scripts/js/lib/api/addonDocsPipeline.ts b/scripts/js/lib/api/addonDocsPipeline.ts index b444c00c639..d9981a8eea7 100644 --- a/scripts/js/lib/api/addonDocsPipeline.ts +++ b/scripts/js/lib/api/addonDocsPipeline.ts @@ -20,8 +20,6 @@ // optional Tutorials and API reference sections. // - When a notebook and an HTML file share the same base name, the notebook // wins so we preserve code outputs rather than the rendered HTML version. -// - A _tutorials.json is written when the pkg declares tutorial slugs; the -// consuming app uses it to surface those tutorials under the addon's route. import { writeFile } from "fs/promises"; @@ -129,9 +127,6 @@ export async function runAddonDocsPipeline( ); await writeTocFile(pkg, docsBaseFolder, outputPath); - if (pkg.tutorials.length > 0) { - await writeTutorialsFile(pkg, outputPath); - } } async function writeTocFile( @@ -147,19 +142,6 @@ async function writeTocFile( ); } -/** - * Write _tutorials.json listing the tutorial slugs declared in Pkg.tutorials. - * The consuming app (iqp-channel-docs) reads this file to generate static - * params for /docs/addons/{pkg}/tutorials/{slug} routes and to redirect those - * routes to the corresponding file under docs/tutorials/. - */ -async function writeTutorialsFile(pkg: Pkg, outputPath: string): Promise<void> { - await writeFile( - `${outputPath}/_tutorials.json`, - JSON.stringify({ tutorials: pkg.tutorials }, null, 2) + "\n", - ); -} - async function determineFilePaths( htmlPath: string, docsBaseFolder: string, From b35be7f449314374b4b15b65e875fcdf016609e5 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 26 May 2026 21:26:19 -0400 Subject: [PATCH 089/104] remove _tutorials.json --- docs/addons/qiskit-addon-aqc-tensor/_toc.json | 2 +- .../qiskit-addon-aqc-tensor/_tutorials.json | 5 ---- docs/addons/qiskit-addon-cutting/_toc.json | 6 ++--- .../qiskit-addon-cutting/_tutorials.json | 7 ----- docs/addons/qiskit-addon-mpf/_toc.json | 2 +- docs/addons/qiskit-addon-mpf/_tutorials.json | 5 ---- docs/addons/qiskit-addon-obp/_toc.json | 2 +- docs/addons/qiskit-addon-obp/_tutorials.json | 5 ---- docs/addons/qiskit-addon-sqd/_toc.json | 4 +-- docs/addons/qiskit-addon-sqd/_tutorials.json | 6 ----- scripts/js/commands/checkInternalLinks.ts | 26 +------------------ scripts/js/lib/links/ignores.ts | 22 ---------------- 12 files changed, 9 insertions(+), 83 deletions(-) delete mode 100644 docs/addons/qiskit-addon-aqc-tensor/_tutorials.json delete mode 100644 docs/addons/qiskit-addon-cutting/_tutorials.json delete mode 100644 docs/addons/qiskit-addon-mpf/_tutorials.json delete mode 100644 docs/addons/qiskit-addon-obp/_tutorials.json delete mode 100644 docs/addons/qiskit-addon-sqd/_tutorials.json diff --git a/docs/addons/qiskit-addon-aqc-tensor/_toc.json b/docs/addons/qiskit-addon-aqc-tensor/_toc.json index e520fc42789..51c88c93780 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/_toc.json +++ b/docs/addons/qiskit-addon-aqc-tensor/_toc.json @@ -41,7 +41,7 @@ "children": [ { "title": "Approximate quantum compilation for time evolution circuits", - "url": "/docs/addons/qiskit-addon-aqc-tensor/tutorials/approximate-quantum-compilation-for-time-evolution" + "url": "/docs/tutorials/approximate-quantum-compilation-for-time-evolution" } ] }, diff --git a/docs/addons/qiskit-addon-aqc-tensor/_tutorials.json b/docs/addons/qiskit-addon-aqc-tensor/_tutorials.json deleted file mode 100644 index b98add8b767..00000000000 --- a/docs/addons/qiskit-addon-aqc-tensor/_tutorials.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "tutorials": [ - "approximate-quantum-compilation-for-time-evolution" - ] -} diff --git a/docs/addons/qiskit-addon-cutting/_toc.json b/docs/addons/qiskit-addon-cutting/_toc.json index 8aaf896c693..4d2c86ede6c 100644 --- a/docs/addons/qiskit-addon-cutting/_toc.json +++ b/docs/addons/qiskit-addon-cutting/_toc.json @@ -53,15 +53,15 @@ "children": [ { "title": "Circuit cutting for depth reduction", - "url": "/docs/addons/qiskit-addon-cutting/tutorials/depth-reduction-with-circuit-cutting" + "url": "/docs/tutorials/depth-reduction-with-circuit-cutting" }, { "title": "Circuit cutting for periodic boundary conditions", - "url": "/docs/addons/qiskit-addon-cutting/tutorials/periodic-boundary-conditions-with-circuit-cutting" + "url": "/docs/tutorials/periodic-boundary-conditions-with-circuit-cutting" }, { "title": "Wire cutting for expectation values estimation", - "url": "/docs/addons/qiskit-addon-cutting/tutorials/wire-cutting" + "url": "/docs/tutorials/wire-cutting" } ] }, diff --git a/docs/addons/qiskit-addon-cutting/_tutorials.json b/docs/addons/qiskit-addon-cutting/_tutorials.json deleted file mode 100644 index ab3428dcae3..00000000000 --- a/docs/addons/qiskit-addon-cutting/_tutorials.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "tutorials": [ - "depth-reduction-with-circuit-cutting", - "periodic-boundary-conditions-with-circuit-cutting", - "wire-cutting" - ] -} diff --git a/docs/addons/qiskit-addon-mpf/_toc.json b/docs/addons/qiskit-addon-mpf/_toc.json index 0d9d2b9d800..3e6dae0fc7c 100644 --- a/docs/addons/qiskit-addon-mpf/_toc.json +++ b/docs/addons/qiskit-addon-mpf/_toc.json @@ -45,7 +45,7 @@ "children": [ { "title": "Multi-product formulas to reduce Trotter error", - "url": "/docs/addons/qiskit-addon-mpf/tutorials/multi-product-formula" + "url": "/docs/tutorials/multi-product-formula" } ] }, diff --git a/docs/addons/qiskit-addon-mpf/_tutorials.json b/docs/addons/qiskit-addon-mpf/_tutorials.json deleted file mode 100644 index a8e079f7d75..00000000000 --- a/docs/addons/qiskit-addon-mpf/_tutorials.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "tutorials": [ - "multi-product-formula" - ] -} diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index 0ea7777ad0b..2904fc91318 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -45,7 +45,7 @@ "children": [ { "title": "Operator backpropagation (OBP) for estimation of expectation values", - "url": "/docs/addons/qiskit-addon-obp/tutorials/operator-back-propagation" + "url": "/docs/tutorials/operator-back-propagation" } ] }, diff --git a/docs/addons/qiskit-addon-obp/_tutorials.json b/docs/addons/qiskit-addon-obp/_tutorials.json deleted file mode 100644 index 031ebf446e9..00000000000 --- a/docs/addons/qiskit-addon-obp/_tutorials.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "tutorials": [ - "operator-back-propagation" - ] -} diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json index 55dc87d74e9..e14c84824eb 100644 --- a/docs/addons/qiskit-addon-sqd/_toc.json +++ b/docs/addons/qiskit-addon-sqd/_toc.json @@ -61,11 +61,11 @@ "children": [ { "title": "Sample-based quantum diagonalization of a chemistry Hamiltonian", - "url": "/docs/addons/qiskit-addon-sqd/tutorials/sample-based-quantum-diagonalization" + "url": "/docs/tutorials/sample-based-quantum-diagonalization" }, { "title": "Sample-based Krylov quantum diagonalization of a fermionic lattice model", - "url": "/docs/addons/qiskit-addon-sqd/tutorials/sample-based-krylov-quantum-diagonalization" + "url": "/docs/tutorials/sample-based-krylov-quantum-diagonalization" } ] }, diff --git a/docs/addons/qiskit-addon-sqd/_tutorials.json b/docs/addons/qiskit-addon-sqd/_tutorials.json deleted file mode 100644 index ccac9354f8f..00000000000 --- a/docs/addons/qiskit-addon-sqd/_tutorials.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "tutorials": [ - "sample-based-quantum-diagonalization", - "sample-based-krylov-quantum-diagonalization" - ] -} diff --git a/scripts/js/commands/checkInternalLinks.ts b/scripts/js/commands/checkInternalLinks.ts index 27df44b47a6..9527c6ffa34 100644 --- a/scripts/js/commands/checkInternalLinks.ts +++ b/scripts/js/commands/checkInternalLinks.ts @@ -10,7 +10,7 @@ // copyright notice, and modified files need to carry a notice indicating // that they have been altered from the originals. -import { readdir, readFile } from "fs/promises"; +import { readdir } from "fs/promises"; import { globby } from "globby"; import yargs from "yargs/yargs"; @@ -79,39 +79,15 @@ const readArgs = (): Arguments => { .parseSync(); }; -/** - * Generates synthetic File entries for addon tutorial aliases — paths like - * docs/addons/{pkg}/tutorials/{slug} that have no real file but are served by - * the app by remapping to docs/tutorials/{slug}. - */ -async function syntheticAddonTutorialFiles(): Promise<File[]> { - const tutorialJsonFiles = await globby("docs/addons/**/_tutorials.json"); - const files: File[] = []; - for (const jsonPath of tutorialJsonFiles) { - const pkgDir = jsonPath.replace("/_tutorials.json", ""); - const { tutorials } = JSON.parse(await readFile(jsonPath, "utf-8")) as { - tutorials: string[]; - }; - for (const slug of tutorials) { - files.push( - new File(`${pkgDir}/tutorials/${slug}.ipynb`, new Set(), true), - ); - } - } - return files; -} - async function main() { const args = readArgs(); const fileBatches = await determineFileBatches(args); - const addonTutorialFiles = await syntheticAddonTutorialFiles(); const otherFiles = [ ...( await globby("public/{docs,learning}/{images,videos,open-source}/**/*") ).map((fp) => new File(fp, new Set())), ...SYNTHETIC_FILES.map((fp) => new File(fp, new Set(), true)), - ...addonTutorialFiles, ]; let allGood = true; diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index 7e938b45d4f..f0424db8192 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -609,29 +609,7 @@ function _addonStaleTutorialLinks(): FilesToIgnores { "docs/addons/qiskit-addon-aqc-tensor/index.mdx": [ "tutorials/index", ], - "docs/api/qiskit-addon-cutting/instructions-move.mdx": [ - "/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction", - ], - "docs/api/qiskit-addon-cutting/release-notes.mdx": [ - "/docs/addons/qiskit-addon-cutting/tutorials/index", - ], - "docs/api/qiskit-addon-sqd/release-notes.mdx": [ - "/docs/addons/qiskit-addon-sqd/tutorials/index", - ], - "docs/addons/qiskit-addon-mpf/explanations/mpf-stability.ipynb": [ - "/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started", - ], - "docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps.ipynb": [ - "/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started", - ], - "docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model.ipynb": [ - "/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started", - ], - "docs/addons/qiskit-addon-sqd/how-tos/integrate-dice-solver.ipynb": [ - "/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian", - ], "docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb": [ - "/docs/addons/qiskit-addon-obp/tutorials/01-getting-started-ipynb", "/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html", ], "docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb": [ From 1819731ab5f6b17dba34fd2b498f641e154c2968 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 26 May 2026 22:08:11 -0400 Subject: [PATCH 090/104] adds mthree --- docs/addons/qiskit-addon-mthree/_toc.json | 100 +++++ docs/addons/qiskit-addon-mthree/advanced.mdx | 203 +++++++++ docs/addons/qiskit-addon-mthree/balanced.mdx | 47 ++ docs/addons/qiskit-addon-mthree/basic.mdx | 97 +++++ docs/addons/qiskit-addon-mthree/cal-io.mdx | 66 +++ docs/addons/qiskit-addon-mthree/citing.mdx | 13 + .../qiskit-addon-mthree/collections.mdx | 71 ++++ docs/addons/qiskit-addon-mthree/error.mdx | 85 ++++ docs/addons/qiskit-addon-mthree/expvals.mdx | 125 ++++++ docs/addons/qiskit-addon-mthree/grouped.mdx | 104 +++++ docs/addons/qiskit-addon-mthree/index.mdx | 25 ++ .../qiskit-addon-mthree/installation.mdx | 60 +++ docs/addons/qiskit-addon-mthree/marginals.mdx | 130 ++++++ docs/addons/qiskit-addon-mthree/papers.mdx | 217 ++++++++++ docs/addons/qiskit-addon-mthree/probs.mdx | 64 +++ docs/addons/qiskit-addon-mthree/runtime.mdx | 25 ++ .../addons/qiskit-addon-mthree/sampling.ipynb | 402 ++++++++++++++++++ .../addons/qiskit-addon-mthree/transpiled.mdx | 62 +++ docs/addons/qiskit-addon-mthree/utils.mdx | 137 ++++++ docs/api/qiskit-addon-mthree/_package.json | 4 + docs/api/qiskit-addon-mthree/_toc.json | 98 +++++ docs/api/qiskit-addon-mthree/classes.mdx | 26 ++ docs/api/qiskit-addon-mthree/main.mdx | 18 + .../mthree-classes-prob-collection.mdx | 133 ++++++ .../mthree-classes-prob-distribution.mdx | 103 +++++ .../mthree-classes-quasi-collection.mdx | 148 +++++++ 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create mode 100644 public/docs/images/addons/qiskit-addon-mthree/sampling/extracted-outputs/40654bec-0.avif create mode 100644 public/docs/images/addons/qiskit-addon-mthree/transpiled_0_0.avif create mode 100644 public/docs/images/addons/qiskit-addon-mthree/transpiled_1_0.avif create mode 100644 public/docs/images/addons/qiskit-addon-mthree/truncation.avif create mode 100644 public/docs/images/addons/qiskit-addon-mthree/utils_0_0.avif create mode 100644 public/docs/images/addons/qiskit-addon-mthree/utils_1_0.avif diff --git a/docs/addons/qiskit-addon-mthree/_toc.json b/docs/addons/qiskit-addon-mthree/_toc.json new file mode 100644 index 00000000000..4ed0ce80bfe --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/_toc.json @@ -0,0 +1,100 @@ +{ + "parentUrl": "/docs/guides/addons", + "parentLabel": "Documentation", + "title": "Matrix-free Measurement Mitigation (M3) 3.0.0", + "collapsed": true, + "children": [ + { + "title": "", + "children": [ + { + "title": "Home", + "url": "/docs/addons/qiskit-addon-mthree" + }, + { + "title": "Advanced usage", + "url": "/docs/addons/qiskit-addon-mthree/advanced" + }, + { + "title": "Balanced calibrations", + "url": "/docs/addons/qiskit-addon-mthree/balanced" + }, + { + "title": "Basic usage", + "url": "/docs/addons/qiskit-addon-mthree/basic" + }, + { + "title": "Saving and loading calibration data", + "url": "/docs/addons/qiskit-addon-mthree/cal-io" + }, + { + "title": "Citing", + "url": "/docs/addons/qiskit-addon-mthree/citing" + }, + { + "title": "Using distribution collections", + "url": "/docs/addons/qiskit-addon-mthree/collections" + }, + { + "title": "Error analysis", + "url": "/docs/addons/qiskit-addon-mthree/error" + }, + { + "title": "Obtaining expectation values", + "url": "/docs/addons/qiskit-addon-mthree/expvals" + }, + { + "title": "Grouped operators", + "url": "/docs/addons/qiskit-addon-mthree/grouped" + }, + { + "title": "Installation", + "url": "/docs/addons/qiskit-addon-mthree/installation" + }, + { + "title": "Low-weight observables (Marginals)", + "url": "/docs/addons/qiskit-addon-mthree/marginals" + }, + { + "title": "Papers using M3", + "url": "/docs/addons/qiskit-addon-mthree/papers" + }, + { + "title": "Converting to probabilities", + "url": "/docs/addons/qiskit-addon-mthree/probs" + }, + { + "title": "Using Runtime execution modes", + "url": "/docs/addons/qiskit-addon-mthree/runtime" + }, + { + "title": "Mitigating sampling problems", + "url": "/docs/addons/qiskit-addon-mthree/sampling" + }, + { + "title": "Using M3 with transpiled circuits", + "url": "/docs/addons/qiskit-addon-mthree/transpiled" + }, + { + "title": "Utility functions", + "url": "/docs/addons/qiskit-addon-mthree/utils" + }, + { + "title": "GitHub", + "url": "https://github.com/Qiskit/qiskit-addon-mthree" + } + ], + "collapsible": false + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Matrix-free Measurement Mitigation (M3) API reference", + "url": "/docs/api/qiskit-addon-mthree" + } + ] + } + ] +} diff --git a/docs/addons/qiskit-addon-mthree/advanced.mdx b/docs/addons/qiskit-addon-mthree/advanced.mdx new file mode 100644 index 00000000000..c98b9c2229c --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/advanced.mdx @@ -0,0 +1,203 @@ +--- +title: "Advanced usage" +description: "Advanced usage for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="advanced" /> + +# Advanced usage + +## Options for M3Mitigation class + +<span id="iter-threshold" /> + +### `iter_threshold` + +The main [`mthree.M3Mitigation`](/docs/api/qiskit-addon-mthree/mthree-m-3-mitigation#mthree.M3Mitigation "mthree.M3Mitigation") class accepts the iter\_threshold keyword argument that determines when the automated method selector picks the iterative method over direct LU factorization (selection also depends on free memory). + +```python +from qiskit_ibm_runtime.fake_provider import FakeCasablancaV2 +import mthree + +backend = FakeCasablancaV2() +mit = mthree.M3Mitigation(backend, iter_threshold=4321) +``` + +## Options for calibration + +<span id="shots" /> + +### `shots` + +When calibrating the mitigator, it is possible to vary the number of shots per calibration circuit: + +```python +mit = mthree.M3Mitigation(backend) +mit.cals_from_system(range(5), shots=4321) +``` + +``` +[<qiskit_aer.jobs.aerjob.AerJob at 0x10cb079d0>] +``` + +<span id="method" /> + +### `method` + +There are three ways to do the calibration. The default `balanced` method a set of circuits with varying bitstring patterns such that the $|0\rangle$ and $|1\rangle$ states are each prepared an even number of times, as is their pair-wise correlations. For example, the balanced bit-strings over four qubits are: + +```python +gen = mthree.generators.HadamardGenerator(4) +list(gen) +``` + +``` +[array([0, 0, 0, 0], dtype=uint8), + array([1, 0, 1, 0], dtype=uint8), + array([0, 1, 1, 0], dtype=uint8), + array([1, 1, 0, 0], dtype=uint8), + array([0, 0, 0, 1], dtype=uint8), + array([1, 0, 1, 1], dtype=uint8), + array([0, 1, 1, 1], dtype=uint8), + array([1, 1, 0, 1], dtype=uint8)] +``` + +The `independent` method also sends $2N$ circuits but measures only a single qubit at a time. As such, this is a truly uncorrelated calibration process. + +Finally, a `marginal` calibration can also be done that sends only two circuits, $|0\rangle^{\otimes N}$ and $|1\rangle^{\otimes N}$, and marginalizes over the results to get the needed $2N$ error rates. These two states are a sub-set of the `balanced` calibrations, and are what Qiskit uses in its readout mitigation. This method is very sensitive to state preparation errors, and should not be used in practice. + +An example setting the method is + +```python +mit = mthree.M3Mitigation(backend) +mit.cals_from_system(range(5), method='independent') +``` + +``` +[<qiskit_aer.jobs.aerjob.AerJob at 0x113760850>] +``` + +<span id="rep-delay" /> + +### `rep_delay` + +<Admonition title="Caution" type="note"> + Do not set this unless you know what you are doing. +</Admonition> + +The `rep_delay` keyword argument sets the time between calibration circuits on IBM Quantum systems. This option exists to test and reduce the presence of state prep errors. The calibration circuits in M3 all contain conditional resets at the beginning so as to minimize the need for setting this option. + +```python +mit = mthree.M3Mitigation(backend) +mit.cals_from_system(range(5), rep_delay=400e-6) +``` + +``` +[<qiskit_aer.jobs.aerjob.AerJob at 0x1137355d0>] +``` + +<span id="initial-reset" /> + +## `initial_reset` + +<Admonition title="Caution" type="note"> + Do not set this unless you know what you are doing. +</Admonition> + +A boolean value that specifies whether reset instructions should be used at the beginning of the calibration circuits. Ideally this helps to suppress any residual state prep errors that occur from imperfect reset of the qubits. Can be used in concert with, or as an alternative to `rep_delay`. Note that, in order for this to work, the circuits that need to be mitigated must also have reset instructions at the beginning. Otherwise you are calibrating for no state-prep errors, but the actual circuits may still suffer from these errors. + +## Options when applying corrections + +Here we first calibrate a mitigator and generate raw counts: + +```python +from qiskit import * + +qc = QuantumCircuit(6) +qc.reset(range(6)) +qc.h(3) +qc.cx(3,1) +qc.cx(3,5) +qc.cx(1,0) +qc.cx(5,4) +qc.cx(1,2) +qc.measure_all() + +mit = mthree.M3Mitigation(backend) +mit.cals_from_system(range(6)) + +trans_qc = transpile(qc, backend) +raw_counts = backend.run(trans_qc, shots=8192).result().get_counts() +``` + +<span id="id1" /> + +### `method` + +There are two ways to solve the linear system of equations generated by M3. First, the `direct` method uses LU-factorization by constructing the reduced assignment matrix. Second the `iterative` method uses preconditioned iterative solvers to find the solution vector without explicit matrix construction. By default M3 uses an `auto` method that selects the appropriate solution method based on the number of unique bit-strings and the available free memory on the computer. To override this, one can simply set the option: + +```python +quasis = mit.apply_correction(raw_counts, range(6), method='iterative') +``` + +<span id="distance" /> + +### `distance` + +Optionally one may truncate the M3 assignment matrix to only those elements of the matrix that are transitions between elements less than or equal to a given Hamming distance away from each other. This does not change the dimensionality of the underlying matrix, but rather changes the sparsity pattern of the elements. This is done using the `distance` keyword argument. By default, M3 computes the corrections out to the full distance. In practice, including only up to `distance=3` elements yields accurate answers in most cases. + +```python +quasis = mit.apply_correction(raw_counts, range(6), distance=3) +``` + +<span id="details" /> + +### `details` + +Allows one to see additional information about the solution. This changes the return of the :method:`mthree.M3Mitigation.apply_correction` method to a tuple of two values: + +```python +quasis, details = mit.apply_correction(raw_counts, range(6), details=True) +print(details) +``` + +``` +{'method': 'direct', 'time': 0.0003400829737074673, 'dimension': 44, 'col_norms': array([0.9994606 , 0.9851231 , 0.98098224, 0.9847878 , 0.98490703, + 0.9848617 , 0.97361916, 0.99131244, 0.9914631 , 0.9924751 , + 0.969823 , 0.9843446 , 0.9990387 , 0.9703281 , 0.97626734, + 0.95813066, 0.9912847 , 0.98026615, 0.9515494 , 0.9799929 , + 0.98020184, 0.93811506, 0.978891 , 0.96814924, 0.9288414 , + 0.9763872 , 0.95401514, 0.96020985, 0.9307259 , 0.93472075, + 0.9966233 , 0.9681019 , 0.9053964 , 0.9027333 , 0.91178197, + 0.9701343 , 0.9986021 , 0.96056825, 0.9553432 , 0.9967951 , + 0.9599821 , 0.99687046, 0.9965531 , 0.9993191 ], dtype=float32)} +``` + +<span id="max-iter" /> + +### `max_iter` + +<Admonition title="Caution" type="note"> + Do not set this unless you know what you are doing. +</Admonition> + +Sets the maximum number of iterations performed by the iterative solver. + +```python +quasis = mit.apply_correction(raw_counts, range(6), method='iterative', max_iter=10) +``` + +<span id="tol" /> + +### `tol` + +<Admonition title="Caution" type="note"> + Do not set this unless you know what you are doing. +</Admonition> + +Sets the tolerance of the iterative solver. Might need adjustments to `max_iter` if value is set too low. + +```python +quasis = mit.apply_correction(raw_counts, range(6), method='iterative', tol=1e-5) +``` + diff --git a/docs/addons/qiskit-addon-mthree/balanced.mdx b/docs/addons/qiskit-addon-mthree/balanced.mdx new file mode 100644 index 00000000000..e20f6bf10f6 --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/balanced.mdx @@ -0,0 +1,47 @@ +--- +title: "Balanced calibrations" +description: "Balanced calibrations for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="balanced" /> + +# Balanced calibrations + +The default calibration method for M3 is what is called “balanced” calibration. Balanced calibrations sample all independent and pair-wise readout error rates evenly; hence the name “balanced”. In M3 v3 and higher, this routine has been updated to use a method from Bravyi et al, Phys. Rev. A 103, 042605 (2021). + +To see the bit-patterns used to generate the calibration circuits one can call the generator explicitly. E.g. to see the strings for 5-qubits one would do + +```python +import mthree + +gen = mthree.generators.HadamardGenerator(5) +list(gen) +``` + +``` +[array([0, 0, 0, 0, 0], dtype=uint8), + array([1, 0, 1, 0, 1], dtype=uint8), + array([0, 1, 1, 0, 0], dtype=uint8), + array([1, 1, 0, 0, 1], dtype=uint8), + array([0, 0, 0, 1, 1], dtype=uint8), + array([1, 0, 1, 1, 0], dtype=uint8), + array([0, 1, 1, 1, 1], dtype=uint8), + array([1, 1, 0, 1, 0], dtype=uint8)] +``` + +For every position in the bit-string you will see that 0 or 1 appear an equal number of times, and the same is true for all pair-wise combinations of 0 and 1. If there is a 0, then that circuit samples the $|0\rangle$ state for that qubit, similarly for the 1 element. So when we execute the balanced calibration circuits using shots number of samples, each error rate in the calibration data is actually being sampled more times than requested. Thus, when you pass the shots value to M3, in the balanced calibration mode internally it divides by the number of measured qubits so that the precision of each error rate matches the precision of the other methods. That is to say that the following: + +> ```python +> from qiskit_ibm_runtime.fake_provider import FakeAthensV2 +> +> backend = FakeAthensV2() +> mit = mthree.M3Mitigation(backend) +> mit.cals_from_system(method='balanced') +> ``` +> +> ``` +> [<qiskit_aer.jobs.aerjob.AerJob at 0x113063750>] +> ``` + +Will sample each independent qubit error rate 10000 times (or the max allowed by the target system if less) regardless of which method is used. All pair-wise correlations are measured half this number of times. This yields a calibration process whose overhead is independent of the number of qubits used; there is no additional cost to compute the calibration over a full device. Note that, when using a simulator or “fake” device, M3 defaults to independent calibration mode for efficiency. As such, to enable balanced calibration on a simulator one must explicitly set the method\` as done above. + diff --git a/docs/addons/qiskit-addon-mthree/basic.mdx b/docs/addons/qiskit-addon-mthree/basic.mdx new file mode 100644 index 00000000000..6f2a3676e29 --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/basic.mdx @@ -0,0 +1,97 @@ +--- +title: "Basic usage" +description: "Basic usage for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="basic" /> + +# Basic usage + +Using M3 involves three steps (steps one and two can usually be done in reverse order if desired). + +> 1. Select a system and calibrate over the desired set of qubits. +> 2. Run the circuit(s) of interest on the system. +> 3. Apply the readout correction and post-process. + +## Simple example + +Here we use a noisy simulator to perform the three steps above. First we import the needed modules, and construct a circuit of interest. + +```python +import numpy as np +from qiskit import * +from qiskit_ibm_runtime.fake_provider import FakeCasablancaV2 +import mthree + +qc = QuantumCircuit(6) +qc.reset(range(6)) +qc.h(3) +qc.cx(3,1) +qc.cx(3,5) +qc.cx(1,0) +qc.cx(5,4) +qc.cx(1,2) +qc.measure_all() +qc.draw('mpl') +``` + +![\_images/basic\_0\_1.png](/docs/images/addons/qiskit-addon-mthree/basic_0_1.avif) + +Next we calibrate an M3 mitigator instance over qubits 0 -> 6 (Step #1): + +```python +backend = FakeCasablancaV2() +mit = mthree.M3Mitigation(backend) +mit.cals_from_system(range(6)) +``` + +``` +[<qiskit_aer.jobs.aerjob.AerJob at 0x1140acd50>] +``` + +Transpile and execute our circuit (Step #2): + +```python +trans_qc = transpile(qc, backend) +raw_counts = backend.run(trans_qc).result().get_counts() +``` + +Finally, apply the correction and post-process (Step #3). Here our post-processing is simply computing the expectation value from the returned quasi-probabilities: + +```python +quasis = mit.apply_correction(raw_counts, range(6)) +print('Expectation value:',quasis.expval()) +``` + +``` +Expectation value: 0.9226210117340088 +``` + +## Specifying qubits to mitigate over + +The circuit above also fits on other systems without SWAP mapping provided that we select the correct layout. + +```python +from qiskit_ibm_runtime.fake_provider import FakeMontrealV2 + +backend = FakeMontrealV2() +mit2 = mthree.M3Mitigation(backend) +``` + +In our case, `qubits = [10, 12, 15, 13, 11, 14]` is an appropriate layout. Importantly, the zeroth entry of the list tells us what physical qubit is readout to generate bit 0 in the output bit-strings. We must pass this list to both the calibration and correction steps of M3. + +```python +qubits = [10, 12, 15, 13, 11, 14] +mit2.cals_from_system(qubits) + +trans_qc = transpile(qc, backend, initial_layout=qubits) +raw_counts2 = backend.run(trans_qc).result().get_counts() + +quasis2 = mit2.apply_correction(raw_counts2, qubits) +print('Expectation value:',quasis2.expval()) +``` + +``` +Expectation value: 0.9692449569702148 +``` + diff --git a/docs/addons/qiskit-addon-mthree/cal-io.mdx b/docs/addons/qiskit-addon-mthree/cal-io.mdx new file mode 100644 index 00000000000..109283941c7 --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/cal-io.mdx @@ -0,0 +1,66 @@ +--- +title: "Saving and loading calibration data" +description: "Saving and loading calibration data for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="calio" /> + +# Saving and loading calibration data + +It is possible to save calibration data and, optionally, re-load it at a later time. Let us generate some calibration data and save it. + +```python +from qiskit_ibm_runtime.fake_provider import FakeCasablancaV2 +import mthree + +backend = FakeCasablancaV2() + +mit = mthree.M3Mitigation(backend) +mit.cals_from_system([1, 3, 5], cals_file='my_cals.json') +mit.single_qubit_cals +``` + +``` +[None, + array([[0.993042 , 0.02490234], + [0.00695801, 0.97509766]], dtype=float32), + None, + array([[0.984375 , 0.01989746], + [0.015625 , 0.98010254]], dtype=float32), + None, + array([[0.993042 , 0.0279541 ], + [0.00695801, 0.9720459 ]], dtype=float32), + None] +``` + +or, + +```python +mit = mthree.M3Mitigation(backend) +mit.cals_from_system([1, 3, 5]) +mit.cals_to_file('my_cals.json') +``` + +We can then load this data at a later point in time using: + +```python +import mthree + +mit2 = mthree.M3Mitigation() +mit2.cals_from_file('my_cals.json') +mit2.single_qubit_cals +``` + +``` +[None, + array([[0.99328613, 0.02148438], + [0.00671387, 0.9785156 ]], dtype=float32), + None, + array([[0.9842529 , 0.01550293], + [0.01574707, 0.9844971 ]], dtype=float32), + None, + array([[0.9935303 , 0.02587891], + [0.00646973, 0.9741211 ]], dtype=float32), + None] +``` + diff --git a/docs/addons/qiskit-addon-mthree/citing.mdx b/docs/addons/qiskit-addon-mthree/citing.mdx new file mode 100644 index 00000000000..203e943ed7b --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/citing.mdx @@ -0,0 +1,13 @@ +--- +title: "Citing" +description: "Citing for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="id1" /> + +# Citing + +If you find M3 useful, we would be delighted if you cite it in your work: + +> “Scalable Mitigation of Measurement Errors on Quantum Computers”, Paul D. Nation, Hwajung Kang, Neereja Sundaresan, and Jay M. Gambetta, [PRX Quantum 2, 040326 (2021)](https://doi.org/10.1103/PRXQuantum.2.040326). + diff --git a/docs/addons/qiskit-addon-mthree/collections.mdx b/docs/addons/qiskit-addon-mthree/collections.mdx new file mode 100644 index 00000000000..47a8014d1df --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/collections.mdx @@ -0,0 +1,71 @@ +--- +title: "Using distribution collections" +description: "Using distribution collections for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="collections" /> + +# Using distribution collections + +When you mitigate over multiple circuits the return object is a [`mthree.classes.QuasiCollection`](/docs/api/qiskit-addon-mthree/mthree-classes-quasi-collection#mthree.classes.QuasiCollection "mthree.classes.QuasiCollection") + +```python +from qiskit import * +from qiskit_ibm_runtime.fake_provider import FakeCasablancaV2 +import mthree + +qc = QuantumCircuit(6) +qc.reset(range(6)) +qc.h(3) +qc.cx(3,1) +qc.cx(3,5) +qc.cx(1,0) +qc.cx(5,4) +qc.cx(1,2) +qc.measure_all() + +backend = FakeCasablancaV2() +mit = mthree.M3Mitigation(backend) +mit.cals_from_system(range(6)) + +trans_qc = transpile([qc]*10, backend) +raw_counts = backend.run(trans_qc, shots=4000).result().get_counts() + +quasis = mit.apply_correction(raw_counts, range(6), return_mitigation_overhead=True) +type(quasis) +``` + +``` +mthree.classes.QuasiCollection +``` + +`QuasiCollection` objects allow one to work with multiple distributions in the same manner as a single one. E.g. we can get the mitigation overhead of the whole collection + +```python +quasis.mitigation_overhead +``` + +``` +array([1.7319335, 1.7434431, 1.7570623, 1.7362996, 1.7422098, 1.7463065, + 1.7460448, 1.7474257, 1.7384585, 1.7354022], dtype=float32) +``` + +or compute expectation values and standard deviations over the full set: + +```python +quasis.expval_and_stddev('IZIZIZ') +``` + +``` +[(0.06401509046554565, 0.0208082523361218), + (0.006104528903961182, 0.02087727925403053), + (0.03547114133834839, 0.020958663872588774), + (0.06163862347602844, 0.020834464102203316), + (0.016530781984329224, 0.020869893662517725), + (0.03425535559654236, 0.02089441633079794), + (0.03737133741378784, 0.02089285001670664), + (0.01931297779083252, 0.020901110175608882), + (0.06001356244087219, 0.020847412652591435), + (0.04732754826545715, 0.02082907978781109)] +``` + diff --git a/docs/addons/qiskit-addon-mthree/error.mdx b/docs/addons/qiskit-addon-mthree/error.mdx new file mode 100644 index 00000000000..47a6fd3c498 --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/error.mdx @@ -0,0 +1,85 @@ +--- +title: "Error analysis" +description: "Error analysis for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="error" /> + +# Error analysis + +Mitigating readout errors does not come for free. Instead, there is an overhead that results in increased uncertainty in the computed results. M3 will optionally compute this overhead and return an upper-bound on the expected standard deviation (variance) of the computed expectation values. + +Let us first calibrate the mitigator and get raw results: + +```python +from qiskit import * +from qiskit_ibm_runtime.fake_provider import FakeCasablancaV2 +import mthree + +qc = QuantumCircuit(6) +qc.reset(range(6)) +qc.h(3) +qc.cx(3,1) +qc.cx(3,5) +qc.cx(1,0) +qc.cx(5,4) +qc.cx(1,2) +qc.measure_all() +qc.draw('mpl') +``` + +![\_images/error\_0\_0.png](/docs/images/addons/qiskit-addon-mthree/error_0_0.avif) + +```python +backend = FakeCasablancaV2() +mit = mthree.M3Mitigation(backend) +mit.cals_from_system(range(6)) +``` + +``` +[<qiskit_aer.jobs.aerjob.AerJob at 0x11002a750>] +``` + +```python +trans_qc = transpile(qc, backend) +raw_counts = backend.run(trans_qc, shots=8192).result().get_counts() +``` + +In order to compute the standard deviation of our mitigated expectation values we need to request the information be computed: + +```python +quasis = mit.apply_correction(raw_counts, range(6), return_mitigation_overhead=True) +``` + +The mitigation overhead is returned as an attribute of the returned quasi-probabilities + +```python +quasis.mitigation_overhead +``` + +``` +np.float64(1.7767365338952634) +``` + +that, together with the number of shots taken, determines the upper-bound on the standard deviation: + +```python +quasis.stddev() +``` + +``` +0.014727076557238428 +``` + +It is also possible to return both the expectation value and standard deviation in a single call: + +```python +quasis.expval_and_stddev() +``` + +``` +(0.8965940475463867, 0.014727076557238428) +``` + +Although this standard deviation is an upper-bound, it is usually a tight upper-bound that can be faithfully used further analysis. + diff --git a/docs/addons/qiskit-addon-mthree/expvals.mdx b/docs/addons/qiskit-addon-mthree/expvals.mdx new file mode 100644 index 00000000000..9a876d59ce4 --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/expvals.mdx @@ -0,0 +1,125 @@ +--- +title: "Obtaining expectation values" +description: "Obtaining expectation values for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="expvals" /> + +# Obtaining expectation values + +Given a quasi- or standard probability distribution, it is possible to compute the expectation value of diagonal operators directly from the distributions (or collections) of distributions. This can be done using string representation for standard diagonal operators such as `I`, `Z`, `0` or `1`, or via dictionaries for custom operators. + +Let us first generate some quasi-distributions by mitigating 2- and 3-qubit GHZ circuits on a noisy-simulator. + +```python +import numpy as np +from qiskit import * +from qiskit_ibm_runtime.fake_provider import FakeAthensV2 +import mthree + +backend = FakeAthensV2() + +ghz2 = QuantumCircuit(2) +ghz2.h(0) +ghz2.cx(0,1) +ghz2.measure_all() + +trans_ghz2 = transpile(ghz2, backend) + +ghz3 = QuantumCircuit(3) +ghz3.h(0) +ghz3.cx(0,1) +ghz3.cx(1,2) +ghz3.measure_all() + +trans_ghz3 = transpile(ghz3, backend) + +raw2 = backend.run(trans_ghz2, shots=4000).result().get_counts() +raw3 = backend.run(trans_ghz3, shots=4000).result().get_counts() + +mit = mthree.M3Mitigation(backend) +mit.cals_from_system() + +quasi2 = mit.apply_correction(raw2, [0,1], return_mitigation_overhead=True) +quasi3 = mit.apply_correction(raw3, [0,1,2], return_mitigation_overhead=True) +``` + +Now let us compute the expectation values of these distributions for the default case of `Z` operators on each qubit: + +```python +print('GHZ2:', quasi2.expval()) +print('GHZ3:', quasi3.expval()) +``` + +``` +GHZ2: 0.9362177848815918 +GHZ3: 0.029018640518188477 +``` + +The values are close to one and zero, respectively. We can use strings to repeat the above via: + +```python +print('GHZ2:', quasi2.expval('ZZ')) +print('GHZ3:', quasi3.expval('ZZZ')) +``` + +``` +GHZ2: 0.9362177848815918 +GHZ3: 0.029018640518188477 +``` + +Replacing a `Z` measurement with an `I` on one of the qubits has the affect of changing the sign for the $|1>^{\otimes N}$ component: + +```python +print('GHZ2:', quasi2.expval('IZ')) +print('GHZ3:', quasi3.expval('ZIZ')) +``` + +``` +GHZ2: 0.021010488271713257 +GHZ3: 0.9692444801330566 +``` + +We can also pass lists of strings: + +```python +quasi3.expval_and_stddev(['ZZZ','ZIZ']) +``` + +``` +(array([0.02901864, 0.9692445 ], dtype=float32), 0.018216806955407207) +``` + +Alternatively, users can specify their own custom diagonal operators using dictionaries. Here we form the projectors on the all ones and zeros states: + +```python +all_zeros_proj = {'000': 1} +all_ones_proj = {'111': 1} +quasi3.expval(all_zeros_proj) +``` + +``` +0.5033974051475525 +``` + +Like strings, one can pass an array of dicts: + +```python +quasi3.expval([all_zeros_proj, all_ones_proj]) +``` + +``` +array([0.5033974 , 0.48120362], dtype=float32) +``` + +We can verify that the projectors return the correct values: + +```python +p0s, p1s = quasi3.expval([all_zeros_proj, all_ones_proj]) +np.allclose([p0s, p1s], [quasi3['000'], quasi3['111']]) +``` + +``` +True +``` + diff --git a/docs/addons/qiskit-addon-mthree/grouped.mdx b/docs/addons/qiskit-addon-mthree/grouped.mdx new file mode 100644 index 00000000000..f7a4626522e --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/grouped.mdx @@ -0,0 +1,104 @@ +--- +title: "Grouped operators" +description: "Grouped operators for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="grouped" /> + +# Grouped operators + +Often times when evaluating expectation values it is possible to group operators together if the Pauli operators from which they are comprised commute with each other. when possible, this batching can greatly reduce the number of executions needed. To see how to do this consider the following simple example: + +```python +from qiskit import * +from qiskit_ibm_runtime.fake_provider import FakeAthensV2 +import mthree + +qc = QuantumCircuit(4) +qc.h(0) +qc.cx(0, range(1, 4)) +qc.measure_all() +qc.draw('mpl') +``` + +![\_images/grouped\_0\_0.png](/docs/images/addons/qiskit-addon-mthree/grouped_0_0.avif) + +Here we will generate and execute two circuits on the target system (fake system in this case), and, because we have transpiled, find the final measurement mapping: + +```python +backend = FakeAthensV2() +trans_circs = transpile([qc]*2, backend, optimization_level=3, approximation_degree=0) +mappings = mthree.utils.final_measurement_mapping(trans_circs) +``` + +Let us execute and get the resultant counts: + +```python +job = backend.run(trans_circs, shots=10000) +counts = job.result().get_counts() +``` + +We can now mitigate as usual: + +```python +mit = mthree.M3Mitigation(backend) +mit.cals_from_system(mappings, shots=10000) +quasis = mit.apply_correction(counts, mappings, return_mitigation_overhead=True) +``` + +Now that we have the final mitigated quasi-distributions we can compute expectation values. For batched operators, the expectation values are grouped together based on which quasi-distribution you want to evaluate them with: + +```python +quasis.expval([['IIII', 'ZZZZ', '0000', '1111'], ['IIII']]) +``` + +``` +[array([0.9999999 , 0.907704 , 0.4837907 , 0.45387825], dtype=float32), + array([0.9999999], dtype=float32)] +``` + +The return list has the expectation values grouped into NumPy arrays. A similar procedure works for expectation values and standard deviations: + +```python +quasis.expval_and_stddev([['IIII', 'ZZZZ', '0000', '1111'], ['IIII']]) +``` + +``` +[(array([0.9999999 , 0.907704 , 0.4837907 , 0.45387825], dtype=float32), + 0.01225381672502408), + (array([0.9999999], dtype=float32), 0.012211955337045909)] +``` + +If you have a single expectation value string then you do not need to wrap it in a list: + +```python +quasis.expval([['IIII', 'ZZZZ', '0000', '1111'], 'IIII']) +``` + +``` +[array([0.9999999 , 0.907704 , 0.4837907 , 0.45387825], dtype=float32), + 0.9999998807907104] +``` + +Of course probability-distributions work the same way as quasi-distributions: + +```python +probs = quasis.nearest_probability_distribution() +probs.expval([['IIII', 'ZZZZ', '0000', '1111'], ['IIII']]) +``` + +``` +[array([1. , 0.90862715, 0.483733 , 0.45382056], dtype=float32), + array([1.], dtype=float32)] +``` + +```python +probs.expval_and_stddev([['IIII', 'ZZZZ', '0000', '1111'], ['IIII']]) +``` + +``` +[(array([1. , 0.90862715, 0.483733 , 0.45382056], dtype=float32), + 0.01225381672502408), + (array([1.], dtype=float32), 0.012211955337045909)] +``` + diff --git a/docs/addons/qiskit-addon-mthree/index.mdx b/docs/addons/qiskit-addon-mthree/index.mdx new file mode 100644 index 00000000000..5406abca4d5 --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/index.mdx @@ -0,0 +1,25 @@ +--- +title: "mthree (3.0.0)" +description: "Documentation for the latest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="mthree-version" /> + +# mthree (3.0.0) + +mthree is a package for scalable quantum measurement error mitigation that need not explicitly form the assignment matrix, or its inverse, as is thus a **m**atrix-free **m**easurement **m**itigation (M3) routine. + +M3 works in a reduced subspace defined by the noisy input bitstrings that are to be corrected. Because the number of unique bitstrings can be much smaller than the dimensionality of the full multi-qubit Hilbert space, the resulting linear system of equations is nominally much easier to solve. + +![\_images/truncation.png](/docs/images/addons/qiskit-addon-mthree/truncation.avif) + +It is often the case that this linear equation is trivial to solve using LU decomposition, using only modest computing resources. However, if the number of unique bitstrings is large, and / or one has very tight memory constraints, then the problem can be solved in a matrix-free manner using a preconditioned iterative linear solution method, e.g. the Generalized minimal residual (GMRES) or biconjugate gradient stabilized (BiCGSTAB) methods. + +M3 is suitable for problems amenable to using quasi-probabilities such as those formulated in terms of expectation values, or sampling problems where, for example, one is interested in the bit-string with the highest probability. Quasi-probabilities can be projected onto the nearest probability distribution if true probabilities are desired, but this makes error analysis more difficult. M3 works for mid-circuit measurements as well, provided that one is interested in ensemble averages, as opposed to correcting single-shot measurements; it cannot mitigate single-shot measurements used for conditional-gate logic. + +<Admonition title="Tip" type="note"> + Not sure where to get started? + + Have a look at how others have utilized M3: [Papers using M3](papers#papers). +</Admonition> + diff --git a/docs/addons/qiskit-addon-mthree/installation.mdx b/docs/addons/qiskit-addon-mthree/installation.mdx new file mode 100644 index 00000000000..573681324a3 --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/installation.mdx @@ -0,0 +1,60 @@ +--- +title: "Installation" +description: "Installation for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +# Installation + +You can pip install M3 in serial mode using PyPi via: + +```bash +pip install mthree +``` + +Alternatively, one can install from source: + +```bash +python setup.py install +``` + +To enable OpenMP one must have an OpenMP 3.0+ enabled compiler and install with: + +```bash +python setup.py install --with-openmp +``` + +Optionally one can also set `-march=native` using: + +```bash +python setup.py install --with-native +``` + +The `openmp` and `native` flags can be used simultaneously using a comma. + +## OpenMP on OSX + +On OSX one must install LLVM using homebrew (You cannot use GCC): + +```bash +brew install llvm +``` + +after which the following (or the like) must be executed in the terminal: + +```bash +export PATH="/usr/local/opt/llvm/bin:$PATH" +``` + +and + +```bash +export LDFLAGS="-L/usr/local/opt/llvm/lib -Wl,-rpath,/usr/local/opt/llvm/lib" +export CPPFLAGS="-I/usr/local/opt/llvm/include" +``` + +Then installation with openmp can be accomplished using: + +```bash +CC=clang CXX=clang python setup.py install --with-openmp +``` + diff --git a/docs/addons/qiskit-addon-mthree/marginals.mdx b/docs/addons/qiskit-addon-mthree/marginals.mdx new file mode 100644 index 00000000000..ebf9c018fec --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/marginals.mdx @@ -0,0 +1,130 @@ +--- +title: "Low-weight observables (Marginals)" +description: "Low-weight observables (Marginals) for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="marginals" /> + +# Low-weight observables (Marginals) + +Often one is interested in computing expectation values of “low-weight” observables, where the operator is comprised of one or more identity operators, e.g. $\langle IIZZII\rangle$. Qubits with an identity do not need to be corrected because the eigenvalues associated with the $|0\rangle$ and $|1\rangle$ states are the same. Because M3 scales with the number of bit-strings, removing these unneeded elements can reduce the amount of computation required. Below is an example of how to use M3 to marginalize and correct over the smaller distribution. + +Consider the following circuit that we would like to evaluate: + +```python +from qiskit import * +from qiskit_ibm_runtime.fake_provider import FakeGuadalupeV2 +import mthree + +N = 6 +qc = QuantumCircuit(N) +qc.x(range(0, N)) +qc.h(range(0, N)) +for kk in range(N//2,0,-1): + qc.ch(kk, kk-1) +for kk in range(N//2, N-1): + qc.ch(kk, kk+1) +qc.measure_all() +qc.draw('mpl') +``` + +![\_images/marginals\_0\_0.png](/docs/images/addons/qiskit-addon-mthree/marginals_0_0.avif) + +Let us first map this onto the target system and compute the final measurement mapping: + +```python +backend = FakeGuadalupeV2() + +trans_qc = transpile(qc, backend, optimization_level=3, seed_transpiler=12345) +mapping = mthree.utils.final_measurement_mapping(trans_qc) +mapping +``` + +``` +{0: 14, 1: 13, 2: 12, 3: 10, 4: 7, 5: 6} +``` + +Now assume we are only interested in many low-weight operators such as $\langle ZIIZIZ\rangle$ or $\langle ZZIIII\rangle$. How would we compute the corrected expectation values? Because there are identity operators, we can marginalize the distributions to be only over those qubits that actually play a role in the expectation value. However, we must remember to keep track of which physical qubits go to which classical bits. We can do this with [`mthree.utils.marginal_distribution()`](/docs/api/qiskit-addon-mthree/mthree-utils-marginal-distribution#mthree.utils.marginal_distribution "mthree.utils.marginal_distribution"):. + +First let us first get a raw distribution: + +```python +counts = backend.run(trans_qc, shots=10000).result().get_counts() +``` + +We can get the marginal distributions in two ways. First we can directly pass a list of indices, e.g. for the operator $\langle ZIIZIZ\rangle$ the list would be \[0, 2, 5]: + +```python +mthree.utils.marginal_distribution(counts, [0, 2, 5]) +``` + +``` +{'101': 1599, + '011': 822, + '110': 309, + '111': 5986, + '100': 503, + '001': 553, + '010': 48, + '000': 180} +``` + +or we can use the operator itself: + +```python +mthree.utils.marginal_distribution(counts, 'ZIIZIZ') +``` + +``` +{'111': 5986, + '100': 503, + '010': 48, + '011': 822, + '110': 309, + '000': 180, + '001': 553, + '101': 1599} +``` + +Now we have a marginal distribution and in principle we have enough. However, having a reduced mapping of physical qubits that correspond to the bits in the marginal distribution would be handy to have. We can pass a mapping to marginal\_distribution and get such a reduced mapping: + +```python +marginal_counts, reduced_map = mthree.utils.marginal_distribution(counts, 'ZIIZIZ', mapping=mapping) +reduced_map +``` + +``` +{0: 14, 1: 12, 2: 6} +``` + +We can now mitigate in a smaller probability space: + +```python +mit = mthree.M3Mitigation(backend) +mit.cals_from_system(reduced_map, shots=25000) + +quasi = mit.apply_correction(marginal_counts, reduced_map) +quasi +``` + +``` +{'000': np.float32(0.014724134), + '001': np.float32(0.05168699), + '010': np.float32(0.0011133504), + '011': np.float32(0.073390774), + '100': np.float32(0.044632103), + '101': np.float32(0.14970978), + '110': np.float32(0.0052394327), + '111': np.float32(0.6595034)} +``` + +Because the only non-identity operators are typically Z operators it is easy to compute the eigenvalues because the operator will be all Z’s: + +```python +quasi.expval() +``` + +``` +-0.5138717293739319 +``` + diff --git a/docs/addons/qiskit-addon-mthree/papers.mdx b/docs/addons/qiskit-addon-mthree/papers.mdx new file mode 100644 index 00000000000..a6ef547c21f --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/papers.mdx @@ -0,0 +1,217 @@ +--- +title: "Papers using M3" +description: "Papers using M3 for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="papers" /> + +# Papers using M3 + +1. “Achieving computational gains with quantum error correction primitives: Generation of long-range entanglement enhanced by error detection”, Haoran Liao, Gavin S. Hartnett, Ashish Kakkar, Adrian Tan, Michael Hush, Pranav S. Mundada, Michael J. Biercuk, Yuval Baum, [arXiv:2411.14638](https://doi.org/10.48550/arXiv:2411.14638). +2. “Next Generation Power System Planning and Operation With Quantum Computation”, Priyanka Arkalgud Ganeshamurthy, Kumar Ghosh, Corey O’Meara, Giorgio Cortiana, Jan Schiefelbein-Lach, Antonello Monti, [EEE Access, vol. 12, 182673, 2024](https://doi.org/10.1109/ACCESS.2024.3509743). +3. “Mitigating exponential concentration in covariant quantum kernels for subspace and real-world data”, Gabriele Agliardi, Giorgio Cortiana, Anton Dekusar, Kumar Ghosh, Naeimeh Mohseni, Corey O’Meara, Víctor Valls, Kavitha Yogaraj, Sergiy Zhuk, [arXiv:2412.07915](https://doi.org/10.48550/arXiv:2412.07915). +4. “Sparse Non-Markovian Noise Modeling of Transmon-Based Multi-Qubit Operations”, Yasuo Oda, Kevin Schultz, Leigh Norris, Omar Shehab, Gregory Quiroz, [arXiv:2412.16092](https://doi.org/10.48550/arXiv:2412.16092). +5. “Experimental computations of atomic properties on a superconducting quantum processor”,Akhil Pratap Singh, Kenji Sugisaki, Srinivasa Prasannaa, Bijaya Kumar Sahoo, Bhanu Pratap Das, and Yasunobu Nakamura, [Phys. Rev. A 110, 062620 (2024)](https://doi.org/10.1103/PhysRevA.110.062620). +6. “Enhancing computational accuracy with parallel parameter optimization in variational quantum eigensolver”, Daisuke Tsukayama, Jun-ichi Shirakashi, Tetsuo Shibuya, Hiroshi Imai, [AIP Advances 15, 015226 (2025)](https://doi.org/10.1063/5.0236028). +7. “Quantum Error Correction Without Encoding via the Circulant Structure of Pauli Noise and the Fast Fourier Transform”, Alvin Gonzales, [arXiv:2501.01953](https://doi.org/10.48550/arXiv:2501.01953). +8. “Developing techniques for Simulation of SU(3) Quantum Field Theories on State-of-the-Art Quantum Devices”, Ivan Chernyshev, [arXiv:2502.02502](https://doi.org/10.48550/arXiv:2502.02502). +9. “Certified Random Number Generation using Quantum Computers”, Pingal Pratyush Nath, Aninda Sinha, Urbasi Sinha, [arXiv:2502.02973](https://doi.org/10.48550/arXiv:2502.02973). +10. “First-principle crosstalk dynamics and Hamiltonian learning via Rabi experiments”, Jan Balewski, Adam Winick, Yilun Xu, Neel Vora, Gang Huang, David Santiago, Joseph Emerson, Irfan Siddiqi, [arXiv:2502.05362](https://doi.org/10.48550/arXiv:2502.05362). +11. “Variational Quantum Algorithms for Many-Body Systems”, Mirko Consiglio, [arXiv:2502.11985](https://doi.org/10.48550/arXiv:2502.11985). +12. “Benchmarking MedMNIST dataset on real quantum hardware”, Gurinder Singh, Hongni Jin, Kenneth M. Merz Jr, [arXiv:2502.13056](https://doi.org/10.48550/arXiv:2502.13056). +13. “Efficient Quantum Chemistry Calculations on Noisy Quantum Hardware”, Nora Bauer, Kübra Yeter-Aydeniz, George Siopsis, [arXiv:2503.02778](https://doi.org/10.48550/arXiv:2503.02778). +14. “Non-equilibrium thermodynamics of precision through a quantum-centric computation”, Mario Motta, Antonio Mezzacapo, Giacomo Guarnieri, [arXiv:2503.03868](https://doi.org/10.48550/arXiv:2503.03868). +15. “Noise-Robust Estimation of Quantum Observables in Noisy Hardware”, Amin Hosseinkhani, Fedor Šimkovic, Alessio Calzona, Tianhan Liu, Adrian Auer, Inés de Vega, [arXiv:2503.06695](https://doi.org/10.48550/arXiv:2503.06695). +16. “A Superconducting Qubit-Resonator Quantum Processor with Effective All-to-All Connectivity”, Michael Renger, Jeroen Verjauw, Nicola Wurz, Amin Hosseinkhani, Caspar Ockeloen-Korppi, Wei Liu, Aniket Rath, Manish J. Thapa, Florian Vigneau, Elisabeth Wybo, Ville Bergholm, Chun Fai Chan, Bálint Csatári, Saga Dahl, Rakhim Davletkaliyev, Rakshyakar Giri, Daria Gusenkova, Hermanni Heimonen, Tuukka Hiltunen, Hao Hsu, Eric Hyyppä, Joni Ikonen, Tyler Jones, Shabeeb Khalid, Seung-Goo Kim, Miikka Koistinen, Anton Komlev, Janne Kotilahti, Vladimir Kukushkin, Julia Lamprich, Alessandro Landra, Lan-Hsuan Lee, Tianyi Li, Per Liebermann, Sourav Majumder, Janne Mäntylä, Fabian Marxer, Arianne Meijer - van de Griend, Vladimir Milchakov, Jakub Mrożek, Jayshankar Nath, Tuure Orell, Miha Papič, Matti Partanen, Alexander Plyushch, Stefan Pogorzalek, Jussi Ritvas, Pedro Figuero Romero, Ville Sampo, Marko Seppälä, Ville Selinmaa, Linus Sundström, Ivan Takmakov, Brian Tarasinski, Jani Tuorila, Olli Tyrkkö, Alpo Välimaa, Jaap Wesdorp, Ping Yang, Liuqi Yu, Johannes Heinsoo, Antti Vepsäläinen, William Kindel, Hsiang-Sheng Ku, Frank Deppe, [arXiv:2503.10903](https://doi.org/10.48550/arXiv:2503.10903). +17. “Refining Noise Mitigation in NISQ Hardware Through Qubit Error Probability”, Nahual Sobrino, Unai Aseginolaza, Joaquim Jornet-Somoza, Juan Borge, [arXiv:2503.10204](https://doi.org/10.48550/arXiv:2503.10204). +18. “Entanglement teleportation along a regenerating hamster-wheel graph state”, Haiyue Kang, John F. Kam, Gary J. Mooney, Lloyd C. L. Hollenberg, [arXiv:2411.13060](https://doi.org/10.48550/arXiv.2411.13060). +19. “Ptychographic estimation of pure multiqubit states in a quantum device”, Warley M. S. Alves, Leonardo Neves, [APL Quantum 1, 046115 (2024)](https://doi.org/10.1063/5.0236968). +20. “Reducing PEC Overhead by Pauli Error Propagation”, Timon Scheiber, Paul Haubenwallner, Matthias Heller, [arXiv:2412.01311](https://doi.org/10.48550/arXiv.2412.01311). +21. “QSRA: A QPU Scheduling and Resource Allocation Approach for Cloud-Based Quantum Computing”, Binhan Lu, Zhaoyun Chen, Yuchun Wu, [arXiv:2411.05283](https://doi.org/10.48550/arXiv.2411.05283). +22. “Towards quantum computing Feynman diagrams in hybrid qubit-oscillator devices”, S. Varona, S. Saner, O. Băzăvan, G. Araneda, G. Aarts, A. Bermudez, [arXiv:2411.05092](https://doi.org/10.48550/arXiv.2411.05092). +23. “Quantum-centric computation of molecular excited states with extended sample-based quantum diagonalization”, Stefano Barison, Javier Robledo Moreno, Mario Motta, [arXiv:2411.00468](https://doi.org/10.48550/arXiv.2411.00468). +24. “Experimental demonstration of the Bell-type inequalities for four qubit Dicke state using IBM Quantum Processing Unit”, Tomis, Harsh Mehta, Shreya Banerjee, Prasanta K. Panigrahi, V. Narayanan, [arXiv:2410.20241](https://doi.org/10.48550/arXiv.2410.20241). +25. “Contextual Subspace Variational Quantum Eigensolver Calculation of the Dissociation Curve of Molecular Nitrogen on a Superconducting Quantum Computer”, Tim Weaving, Alexis Ralli, Peter J. Love, Sauro Succi, Peter V. Coveney, [arXiv:2312.04392](https://doi.org/10.48550/arXiv.2312.04392). +26. “Implementing Quantum Secret Sharing on Current Hardware”, Jay Graves, Mike Nelson, Eric Chitambar, [arXiv:2410.11640](https://doi.org/10.48550/arXiv.2410.11640). +27. “Statistics of topological defects across a phase transition in a superconducting quantum processor”, Daniil Teplitskiy, Oriel Kiss, Michele Grossi, Antonio Mandarino, [arXiv:2410.06250](https://doi.org/10.48550/arXiv.2410.06250). +28. “LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder”, Alexis Vieloszynski, Soumaya Cherkaoui, Jean-Frederic Laprade, Oliver Nahman-Levesque, Abdallah Aaraba, Shengrui Wang, [arXiv:2409.14622](https://doi.org/10.48550/arXiv.2409.14622). +29. “Experimental Sample-Efficient Quantum State Tomography via Parallel Measurements”, Chang-Kang Hu, Chao Wei, Chilong Liu, Liangyu Che, Yuxuan Zhou, Guixu Xie, Haiyang Qin, Guantian Hu, Haolan Yuan, Ruiyang Zhou, Song Liu, Dian Tan, Tao Xin, and Dapeng Yu, [Phys. Rev. Lett. 133, 160801 (2024)](https://doi.org/10.1103/PhysRevLett.133.160801). +30. “Solving the Hele-Shaw flow using the Harrow-Hassidim-Lloyd algorithm on superconducting devices: A study of efficiency and challenges”, Muralikrishnan Gopalakrishnan Meena, Kalyana C. Gottiparthi, Justin G. Lietz, Antigoni Georgiadou, Eduardo Antonio Coello Perez, [Physics of Fluids 36, 101705 (2024)](https://doi.org/10.1063/5.0231929). +31. “High-fidelity heralded quantum state preparation and measurement”, A. S. Sotirova, J. D. Leppard, A. Vazquez-Brennan, S. M. Decoppet, F. Pokorny, M. Malinowski, C. J. Ballance, [arXiv:2409.05805](https://doi.org/10.48550/arXiv.2409.05805). +32. “Practical techniques for high precision measurements on near-term quantum hardware: a Case Study in Molecular Energy Estimation”, Keijo Korhonen, Hetta Vappula, Adam Glos, Marco Cattaneo, Zoltan Zimboras, Elsi-Mari Borrelli, Matteo A. C. Rossi, Guillermo Garcia-Perez, Daniel Cavalcanti, [arXiv:2409.02575](https://doi.org/10.48550/arXiv.2409.02575). +33. “Purity-Assisted Zero-Noise Extrapolation for Quantum Error Mitigation”, Tian-Ren Jin, Yun-Hao Shi, Zheng-An Wang, Tian-Ming Li, Kai Xu, Heng Fan, [Adv Quantum Technol. 2024, 2400150](https://doi.org/10.1002/qute.202400150). +34. “Technology and Performance Benchmarks of IQM’s 20-Qubit Quantum Computer”, Leonid Abdurakhimov, Janos Adam, Hasnain Ahmad, Olli Ahonen, Manuel Algaba, Guillermo Alonso, Ville Bergholm, Rohit Beriwal, Matthias Beuerle, Clinton Bockstiegel, Alessio Calzona, Chun Fai Chan, Daniele Cucurachi, Saga Dahl, Rakhim Davletkaliyev, Olexiy Fedorets, Alejandro Gomez Frieiro, Zheming Gao, Johan Guldmyr, Andrew Guthrie, Juha Hassel, Hermanni Heimonen, Johannes Heinsoo, Tuukka Hiltunen, Keiran Holland, Juho Hotari, Hao Hsu, Antti Huhtala, Eric Hyyppä, Aleksi Hämäläinen, Joni Ikonen, Sinan Inel, David Janzso, Teemu Jaakkola, Mate Jenei, Shan Jolin, Kristinn Juliusson, Jaakko Jussila, Shabeeb Khalid, Seung-Goo Kim, Miikka Koistinen, Roope Kokkoniemi, Anton Komlev, Caspar Ockeloen-Korppi, Otto Koskinen, Janne Kotilahti, Toivo Kuisma, Vladimir Kukushkin, Kari Kumpulainen, Ilari Kuronen, Joonas Kylmälä, Niclas Lamponen, Julia Lamprich, Alessandro Landra, Martin Leib, Tianyi Li, Per Liebermann, Aleksi Lintunen, Wei Liu, Jürgen Luus, Fabian Marxer, Arianne Meijer-van de Griend, Kunal Mitra, Jalil Khatibi Moqadam, Jakub Mrożek, Henrikki Mäkynen, Janne Mäntylä, Tiina Naaranoja, Francesco Nappi, Janne Niemi, Lucas Ortega, Mario Palma, Miha Papič, Matti Partanen, Jari Penttilä, Alexander Plyushch, Wei Qiu, Aniket Rath, Kari Repo, Tomi Riipinen, Jussi Ritvas, Pedro Figueroa Romero, Jarkko Ruoho, Jukka Räbinä, Sampo Saarinen, Indrajeet Sagar, Hayk Sargsyan, Matthew Sarsby, Niko Savola, Mykhailo Savytskyi, Ville Selinmaa, Pavel Smirnov, Marco Marín Suárez, Linus Sundström, Sandra Słupińska, Eelis Takala, Ivan Takmakov, Brian Tarasinski, Manish Thapa, Jukka Tiainen et al., [arXiv:2408.12433](https://doi.org/10.48550/arXiv.2408.12433). +35. “Performance-Oriented Layout Synthesis for Quantum Computing”, Chi-Chou Kao and Hung-Yi Lin, [Computer Systems Science and Engineering (2024)](https://doi.org/10.32604/csse.2024.055073). +36. “Accurately Simulating the Time Evolution of an Ising Model with Echo Verified Clifford Data Regression on a Superconducting Quantum Computer”, Tim Weaving, Alexis Ralli, Peter J. Love, Sauro Succi, Peter V. Coveney, [arXiv:2408.07439](https://doi.org/10.48550/arXiv.2408.07439). +37. “Characterization of entanglement on superconducting quantum computers of up to 414 qubits”, John F. Kam, Haiyue Kang, Charles D. Hill, Gary J. Mooney, and Lloyd C. L. Hollenberg, [Phys. Rev. Research 6, 033155](https://doi.org/10.1103/PhysRevResearch.6.033155). +38. “Measurement-Based Long-Range Entangling Gates in Constant Depth”, Elisa Bäumer, Stefan Woerner, [arXiv:2408.03064](https://doi.org/10.48550/arXiv.2408.03064). +39. “Quantum Algorithms for the Variational Optimization of Correlated Electronic States with Stochastic Reconfiguration and the Linear Method”, Mario Motta, Kevin J. Sung, James Shee, [J. Phys. Chem. A 2024, 128, 40, 8762](https://doi.org/10.1021/acs.jpca.4c02847). +40. “Quantum noise modeling through Reinforcement Learning”, Simone Bordoni, Andrea Papaluca, Piergiorgio Buttarini, Alejandro Sopena, Stefano Giagu, Stefano Carrazza, [arXiv:2408.01506](https://doi.org/10.48550/arXiv.2408.01506). +41. “Multiaxis quantum noise spectroscopy robust to errors in state preparation and measurement”, Muhammad Qasim Khan, Wenzheng Dong, Leigh M. Norris, and Lorenza Viola, [Phys. Rev. Applied 22, 024074 (2024)](https://doi.org/10.1103/PhysRevApplied.22.024074). +42. “Bayesian mitigation of measurement errors in multi-qubit experiments”, F. Cosco, F. Plastina, N. Lo Gullo, [arXiv:2408.00869](https://doi.org/10.48550/arXiv.2408.00869). +43. “On the use of calibration data in error-aware compilation techniques for NISQ devices”, Handy Kurniawan, Laura Rodríguez-Soriano, Daniele Cuomo, Carmen G. Almudever, Francisco García Herrero, [arXiv:2407.21462](https://doi.org/10.48550/arXiv.2407.21462). +44. “Quantum simulation of entanglement dynamics in a quantum processor”, Sebastián Saavedra-Pino, Cristian Inzulza, Pablo Román, Francisco Albarrán-Arriagada, and Juan Carlos Retamal, [Phys. Scr. 99 095105 (2024)](https://doi.org/10.1088/1402-4896/ad624a). +45. “A Noise Validation for Quantum Circuit Scheduling Through a Service-Oriented Architecture”, Javier Romero-Álvarez, Jaime Alvarado-Valiente, Jorge Casco-Seco, Enrique Moguel, Jose Garcia-Alonso, and Juan M. Murillo, [International Journal of Software Engineering and Knowledge Engineering 2024](https://doi.org/10.1142/S0218194024410018). +46. “Variational Gibbs state preparation on noisy intermediate-scale quantum devices”, Mirko Consiglio, Jacopo Settino, Andrea Giordano, Carlo Mastroianni, Francesco Plastina, Salvatore Lorenzo, Sabrina Maniscalco, John Goold, and Tony J. G. Apollaro, [Phys. Rev. A 110, 012445 (2024)](https://doi.org/10.1103/PhysRevA.110.012445). +47. “Microscopic parametrizations for gate set tomography under coloured noise”, P. Viñas, A. Bermudez, [arXiv:2407.11539](https://doi.org/10.48550/arXiv.2407.11539). +48. “Superconducting Quantum Simulation for Many-Body Physics beyond Equilibrium”, Yunyan Yao and Liang Xiang, [Entropy 2024, 26(7), 592](https://doi.org/10.3390/e26070592). +49. “Teleporting two-qubit entanglement across 19 qubits on a superconducting quantum computer”, Haiyue Kang, John F. Kam, Gary J. Mooney, Lloyd C. L. Hollenberg, [arXiv:2407.02858](https://doi.org/10.48550/arXiv.2407.02858). +50. “Realizing string-net condensation: Fibonacci anyon braiding for universal gates and sampling chromatic polynomials”, Zlatko K. Minev, Khadijeh Najafi, Swarnadeep Majumder, Juven Wang, Ady Stern, Eun-Ah Kim, Chao-Ming Jian, Guanyu Zhu, [arXiv:2406.12820](https://doi.org/10.48550/arXiv.2406.12820). +51. “Readout Error Mitigation for Mid-Circuit Measurements and Feedforward”, Jin Ming Koh, Dax Enshan Koh, Jayne Thompson, [arXiv:2406.07611](https://doi.org/10.48550/arXiv.2406.07611). +52. “Ground-state-energy calculation for the water molecule on a superconducting quantum processor”, Michael A. Jones, Harish J. Vallury, and Lloyd C.L. Hollenberg, [Phys. Rev. Applied 21, 064017 (2024)](https://doi.org/10.1103/PhysRevApplied.21.064017). +53. “mRNA secondary structure prediction using utility-scale quantum computers”, Dimitris Alevras, Mihir Metkar, Takahiro Yamamoto, Vaibhaw Kumar, Triet Friedhoff, Jae-Eun Park, Mitsuharu Takeori, Mariana LaDue, Wade Davis, Alexey Galda, [arXiv:2405.20328](https://doi.org/10.48550/arXiv.2405.20328). +54. “Full Band Structure Calculation of Semiconducting Materials on a Noisy Quantum Processor”, Shaobo Zhang, Akib Karim, Harry M. Quiney, Muhammad Usman, [arXiv:2405.09122](https://doi.org/10.48550/arXiv.2405.09122). +55. “Feed-Forward Probabilistic Error Cancellation with Noisy Recovery Gates”, Leo Kurosawa, Yoshiyuki Saito, Xinwei Lee, Xinjian Yan, Ningyi Xie, Dongsheng Cai, Jungpil Shin, Nobuyoshi Asai, [arXiv:2405.01833](https://doi.org/10.48550/arXiv.2405.01833). +56. “Sachdev-Ye-Kitaev model on a noisy quantum computer”, Muhammad Asaduzzaman, Raghav G. Jha, and Bharath Sambasivam, [Phys. Rev. D 109, 105002 (2024)](https://doi.org/10.1103/PhysRevD.109.105002). +57. “Simulation of a spin-boson model by iterative optimization of a parametrized quantum circuit”, Takanori Nishi, Kaoru Yamanouchi , [AVS Quantum Sci. 6, 023801 (2024)](https://doi.org/10.1116/5.0193981). +58. “Benchmarking digital quantum simulations above hundreds of qubits using quantum critical dynamics”, Alexander Miessen, Daniel J. Egger, Ivano Tavernelli, Guglielmo Mazzola, [arXiv:2404.08053](https://doi.org/10.48550/arXiv.2404.08053). +59. “Quantum Tunneling: From Theory to Error-Mitigated Quantum Simulation”, Sorana Catrina, Alexandra Băicoianu, [Adv. Quantum Technol. 2024, 2400163](https://doi.org/10.1002/qute.202400163). +60. “Analyzing the Temporal Behavior of Noisy Intermediate-Scale Quantum Nodes and Algorithm Fidelity”, Carlo Podda, Giuliana Siddi Moreau, Lorenzo Pisani, Lidia Leoni, Giacomo Cao, [Adv. Quantum Technol. 2024, 2300451](https://doi.org/10.1002/qute.202300451). +61. “Surrogate optimization of variational quantum circuits”, Erik J. Gustafson, Juha Tiihonen, Diana Chamaki, Farshud Sorourifar, J. Wayne Mullinax, Andy C. Y. Li, Filip B. Maciejewski, Nicolas PD Sawaya, Jaron T. Krogel, David E. Bernal Neira, Norm M. Tubman, [arXiv:2404.02951](https://doi.org/10.48550/arXiv.2404.02951). +62. “Quantum circuit scheduler for QPUs usage optimization”, Javier Romero-Alvarez, Jaime Alvarado-Valiente, Jorge Casco-Seco, Enrique Moguel, Jose Garcia-Alonso, Javier Berrocal, Juan M. Murillo, [arXiv:2404.01055](https://doi.org/10.48550/arXiv.2404.01055). +63. “Circuit-noise-resilient virtual distillation”, Xu, XY., Ding, C., Zhang, S. et al., [Commun Phys 7, 325 (2024)](https://doi.org/10.1038/s42005-024-01815-2). +64. “Lindblad-like quantum tomography for non-Markovian quantum dynamical maps”, Santiago Varona, Markus Müller, Alejandro Bermudez, [arXiv:2403.19799](https://doi.org/10.48550/arXiv.2403.19799). +65. “Quantum-Enhanced Simulation-Based Optimization for Newsvendor Problems”, Monit Sharma, Hoong Chuin Lau, Rudy Raymond, [arXiv:2403.17389](https://doi.org/10.48550/arXiv.2403.17389). +66. “Quantum State Preparation for Probability Distributions with Mirror Symmetry Using Matrix Product States”, Yuichi Sano, Ikko Hamamura, [arXiv:2403.16729](https://doi.org/10.48550/arXiv.2403.16729). +67. “Quantum Fourier Transform Using Dynamic Circuits”, Elisa Bäumer, Vinay Tripathi, Alireza Seif, Daniel Lidar, and Derek S. Wang, [Phys. Rev. Lett. 133, 150602 (2024)](https://doi.org/10.1103/PhysRevLett.133.150602). +68. “Simulation of a Diels-Alder Reaction on a Quantum Computer”, Ieva Liepuoniute, Mario Motta, Thaddeus Pellegrini, Julia E. Rice, Tanvi P. Gujarati, Sofia Gil, Gavin O. Jones, [arXiv:2403.08107](https://doi.org/10.48550/arXiv.2403.08107). +69. “Low-Rank Variational Quantum Algorithm for the Dynamics of Open Quantum Systems”, Sara Santos, Xinyu Song, Vincenzo Savona, [arXiv:2403.05908](https://doi.org/10.48550/arXiv.2403.05908). +70. “Quantum many-body simulations on digital quantum computers: State-of-the-art and future challenges”, Benedikt Fauseweh, [Nat Commun 15, 2123 (2024)](https://doi.org/10.1038/s41467-024-46402-9). +71. “Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance”, Shengbin Wang, Peng Wang, Guihui Li, Shubin Zhao, Dongyi Zhao, Jing Wang, Yuan Fang, Menghan Dou, Yongjian Gu, Yu-Chun Wu, Guo-Ping Guo, [arXiv:2403.04296](https://doi.org/10.48550/arXiv.2403.04296). +72. “An Energy Estimation Benchmark for Quantum Computing Hardware”, Andreas J. C. Woitzik, Lukas Hoffmann, Andreas Buchleitner, and Edoardo G. Carnio, [Open Systems & Information Dynamics 2024 31:01](https://doi.org/10.1142/S1230161224500069). +73. “Empirical learning of dynamical decoupling on quantum processors”, Christopher Tong, Helena Zhang, Bibek Pokharel, [arXiv:2403.02294](https://doi.org/10.48550/arXiv.2403.02294). +74. “Scalable measurement error mitigation via iterative bayesian unfolding”, Bibek Pokharel, Siddarth Srinivasan, Gregory Quiroz, and Byron Boots, [Phys. Rev. Research 6, 013187 (2024)](https://doi.org/10.1103/PhysRevResearch.6.013187). +75. “Maximum Likelihood Quantum Error Mitigation for Algorithms with a Single Correct Output”, Dror Baron, Hrushikesh Pramod Patil, Huiyang Zhou, [arXiv:2402.11830](https://doi.org/10.48550/arXiv.2402.11830). +76. “Scalable Quantum Algorithms for Noisy Quantum Computers”, Julien Gacon, [arXiv:2403.00940](https://doi.org/10.48550/arXiv.2403.00940). +77. “Quantum Algorithm Exploration using Application-Oriented Performance Benchmarks”, Thomas Lubinski, Joshua J. Goings, Karl Mayer, Sonika Johri, Nithin Reddy, Aman Mehta, Niranjan Bhatia, Sonny Rappaport, Daniel Mills, Charles H. Baldwin, Luning Zhao, Aaron Barbosa, Smarak Maity, Pranav S. Mundada, [arXiv:2402.08985](https://doi.org/10.48550/arXiv.2402.08985). +78. “On-premises superconducting quantum computer for education and research”, Jami Rönkkö, Olli Ahonen, Ville Bergholm, Alessio Calzona, Attila Geresdi, Hermanni Heimonen, Johannes Heinsoo, Vladimir Milchakov, Stefan Pogorzalek, Matthew Sarsby, Mykhailo Savytskyi, Stefan Seegerer, Fedor Šimkovic, P. V. Sriluckshmy, Panu T. Vesanen and Mikio Nakahara, [EPJ Quantum Technol., 11 1 (2024) 32](https://doi.org/10.1140/epjqt/s40507-024-00243-z). +79. “Robust projective measurements through measuring code-inspired observables”, Yingkai Ouyang, [arXiv:2402.04093](https://doi.org/10.48550/arXiv.2402.04093). +80. “Comparative study of quantum error correction strategies for the heavy-hexagonal lattice”, César Benito, Esperanza López, Borja Peropadre, Alejandro Bermudez, [arXiv:2402.02185](https://doi.org/10.48550/arXiv.2402.02185). +81. “Assessing the Benefits and Risks of Quantum Computers”, Travis L. Scholten, Carl J. Williams, Dustin Moody, Michele Mosca, William Hurley (“whurley”), William J. Zeng, Matthias Troyer, Jay M. Gambetta, [arXiv:2401.16317](https://doi.org/10.48550/arXiv.2401.16317). +82. “Quantifying the effect of gate errors on variational quantum eigensolvers for quantum chemistry”, Dalton, K., Long, C.K., Yordanov, Y.S. et al., [npj Quantum Inf 10, 18 (2024)](https://doi.org/10.1038/s41534-024-00808-x). +83. “Quantum error mitigation for Fourier moment computation”, Oriel Kiss, Michele Grossi, Alessandro Roggero, [arXiv:2401.13048](https://doi.org/10.48550/arXiv.2401.13048). +84. “Quantum simulations of hadron dynamics in the Schwinger model using 112 qubits”, Roland C. Farrell, Marc Illa, Anthony N. Ciavarella, and Martin J. Savage, [Phys. Rev. D 109, 114510 (2024)](https://doi.org/10.1103/PhysRevD.109.114510). +85. “Quantum simulation of the one-dimensional Fermi-Hubbard model as a Z2 lattice-gauge theory”, Uliana E. Khodaeva, Dmitry L. Kovrizhin, and Johannes Knolle, [Phys. Rev. Research 6, 013032 (2024)](https://doi.org/10.1103/PhysRevResearch.6.013032). +86. “Applying the noiseless extrapolation error mitigation protocol to calculate real-time quantum field theory scattering phase shifts”, Zachary Parks, Arnaud Carignan-Dugas, Erik Gustafson, Yannick Meurice, and Patrick Dreher, [Phys. Rev. D 109, 014505 (2024)](https://doi.org/10.1103/PhysRevD.109.014505). +87. “Nonlinear dynamics as a ground-state solution on quantum computers”, Albert J. Pool, Alejandro D. Somoza, Conor Mc Keever, Michael Lubasch, and Birger Horstmann, [Phys. Rev. Research 6, 033257 (2024)](https://doi.org/10.1103/PhysRevResearch.6.033257). +88. “High-Fidelity, Multiqubit Generalized Measurements with Dynamic Circuits”, Petr Ivashkov, Gideon Uchehara, Liang Jiang, Derek S. Wang, and Alireza Seif, [PRX Quantum 5, 030315 (2024)](https://doi.org/10.1103/PRXQuantum.5.030315). +89. “SantaQlaus: A resource-efficient method to leverage quantum shot-noise for optimization of variational quantum algorithms”, Kosuke Ito, Keisuke Fujii, [arXiv:2312.15791](https://doi.org/10.48550/arXiv.2312.15791). +90. “Quantum error mitigation and correction mediated by Yang-Baxter equation and artificial neural network”, Sahil Gulania, Yuri Alexeev, Stephen K. Gray, Bo Peng, Niranjan Govind, [arXiv:2401.17116](https://doi.org/10.48550/arXiv.2401.17116). +91. “Inverted-circuit zero-noise extrapolation for quantum gate error mitigation”, Kathrin F. Koenig, Finn Reinecke, Walter Hahn, Thomas Wellens, [arXiv:2403.01608](https://doi.org/10.48550/arXiv.2403.01608). +92. “Quantum State Compression Shadow”, Chen Ding, Xiao-Yue Xu, Shuo Zhang, Wan-Su Bao, He-Liang Huang, [arXiv:2312.13036](https://doi.org/10.48550/arXiv.2312.13036). +93. “Enhancing quantum utility: Simulating large-scale quantum spin chains on superconducting quantum computers”, Talal Ahmed Chowdhury, Kwangmin Yu, Mahmud Ashraf Shamim, M. L. Kabir, and Raza Sabbir Sufian, [Phys. Rev. Research 6, 033107 (2024)](https://doi.org/10.1103/PhysRevResearch.6.033107). +94. “Subspace methods for electronic structure simulations on quantum computers”, Mario Motta, William Kirby, Ieva Liepuoniute, Kevin J Sung, Jeffrey Cohn, Antonio Mezzacapo, Katherine Klymko, Nam Nguyen, Nobuyuki Yoshioka, and Julia E Rice, [Electron. Struct. 6 013001 (2024)](https://doi.org/10.1088/2516-1075/ad3592). +95. “Provable bounds for noise-free expectation values computed from noisy samples”, Samantha V. Barron, Daniel J. Egger, Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz, Matthis Lehmkuehler, Stefan Woerner, [arXiv:2312.00733](https://doi.org/10.48550/arXiv.2312.00733). +96. “Exploiting Maximally Mixed States for Spectral Estimation by Time Evolution”, Kaelyn J. Ferris, Zihang Wang, Itay Hen, Amir Kalev, Nicholas T. Bronn, Vojtech Vlcek, [arXiv:2312.00687](https://doi.org/10.48550/arXiv.2312.00687). +97. “Quantum simulations for strong-field QED”, Luis Hidalgo and Patrick Draper, [Phys. Rev. D 109, 076004 (2024)](https://doi.org/10.1103/PhysRevD.109.076004). +98. “Quantum Simulation of an Open System: A Dissipative 1+1D Ising Model”, Erik Gustafson, Michael Hite, Jay Hubisz, Bharath Sambasivam, Judah Unmuth-Yockey, [arXiv:2311.18728](https://doi.org/10.48550/arXiv.2311.18728). +99. “Improving the performance of digitized counterdiabatic quantum optimization via algorithm-oriented qubit mapping”, Yanjun Ji, Kathrin F. Koenig, and Ilia Polian, [Phys. Rev. A 110, 032421 (2024)](https://doi.org/10.1103/PhysRevA.110.032421). +100. “Quantum Diffusion Models”, Andrea Cacioppo, Lorenzo Colantonio, Simone Bordoni, Stefano Giagu, [arXiv:2311.15444](https://doi.org/10.48550/arXiv.2311.15444). +101. “An approach to solve the coarse-grained Protein folding problem in a Quantum Computer”, Jaya Vasavi P, Soham Bopardikar, Avinash D, Ashwini K, Kalyan Dasgupta, Sanjib Senapati, [arXiv:2311.14141](https://doi.org/10.48550/arXiv.2311.14141). +102. “Perspectives of running self-consistent DMFT calculations for strongly correlated electron systems on noisy quantum computing hardware”, Jannis Ehrlich, Daniel Urban, Christian Elsässer, [arXiv:2311.10402](https://doi.org/10.48550/arXiv.2311.10402). +103. “Observation of the non-Hermitian skin effect and Fermi skin on a digital quantum computer”, Ruizhe Shen, Tianqi Chen, Bo Yang, Ching Hua Lee, [arXiv:2311.10143](https://doi.org/10.48550/arXiv.2311.10143). +104. “Comparison of current quantum devices for quantum computing of Heisenberg spin chain dynamics”, Erik Lötstedt and Kaoru Yamanouchi, [Chemical Physics Letters 836, 140975 (2024)](https://doi.org/10.1016/j.cplett.2023.140975). +105. “ADAPT-QSCI: Adaptive Construction of Input State for Quantum-Selected Configuration Interaction”, Yuya O. Nakagawa, Masahiko Kamoshita, Wataru Mizukami, Shotaro Sudo, Yu-ya Ohnishi, [arXiv:2311.01105](https://doi.org/10.48550/arXiv.2311.01105). +106. “Efficient separate quantification of state preparation errors and measurement errors on quantum computers and their mitigation”, Hongye Yu, Tzu-Chieh Wei, [arXiv:2310.18881](https://doi.org/10.48550/arXiv.2310.18881). +107. “Quantum error mitigation”, Zhenyu Cai, Ryan Babbush, Simon C. Benjamin, Suguru Endo, William J. Huggins, Ying Li, Jarrod R. McClean, and Thomas E. O’Brien, [Rev. Mod. Phys. 95, 045005 (2023)](https://doi.org/10.1103/RevModPhys.95.045005). +108. “Quantum Simulation for High-Energy Physics”, Christian W. Bauer et al., [PRX Quantum 4, 027001 (2023)](https://doi.org/10.1103/PRXQuantum.4.027001). +109. “Scalable Circuits for Preparing Ground States on Digital Quantum Computers: The Schwinger Model Vacuum on 100 Qubits”, Roland C. Farrell, Marc Illa, Anthony N. Ciavarella, and Martin J. Savage, [PRX Quantum 5, 020315 (2024)](https://doi.org/10.1103/PRXQuantum.5.020315). +110. “Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation”, Huang, HL., Xu, XY., Guo, C. et al., [Sci. China Phys. Mech. Astron. 66, 250302 (2023)](https://doi.org/10.1007/s11433-022-2057-y). +111. “Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware”, Johannes Weidenfeller, Lucia C. Valor, Julien Gacon, Caroline Tornow, Luciano Bello, Stefan Woerner, Daniel J. Egger, [Quantum 6, 870 (2022)](https://doi.org/10.22331/q-2022-12-07-870). +112. “Deterministic Constant-Depth Preparation of the AKLT State on a Quantum Processor Using Fusion Measurements”, Kevin C. Smith, Eleanor Crane, Nathan Wiebe, and S.M. Girvin, [PRX Quantum 4, 020315 (2023)](https://doi.org/10.1103/PRXQuantum.4.020315). +113. “Biology and medicine in the landscape of quantum advantages”, Benjamin A. Cordier, Nicolas P. D. Sawaya, Gian Giacomo Guerreschi and Shannon K. McWeeney, [J. R. Soc. Interface.1920220541](https://doi.org/10.1098/rsif.2022.0541). +114. “Quantum computing of the 6Li nucleus via ordered unitary coupled clusters”, Oriel Kiss, Michele Grossi, Pavel Lougovski, Federico Sanchez, Sofia Vallecorsa, and Thomas Papenbrock, [Phys. Rev. C 106, 034325 (2022)](https://doi.org/10.1103/PhysRevC.106.034325). +115. “Demonstration of Algorithmic Quantum Speedup”, Bibek Pokharel and Daniel A. Lidar, [Phys. Rev. Lett. 130, 210602 (2023)](https://doi.org/10.1103/PhysRevLett.130.210602). +116. “Digitized Counterdiabatic Quantum Algorithm for Protein Folding”, Pranav Chandarana, Narendra N. Hegade, Iraitz Montalban, Enrique Solano, and Xi Chen, [Phys. Rev. Applied 20, 014024 (2023)](https://doi.org/10.1103/PhysRevApplied.20.014024). +117. “Universal Sampling Lower Bounds for Quantum Error Mitigation”, Ryuji Takagi, Hiroyasu Tajima, and Mile Gu, [Phys. Rev. Lett. 131, 210602 (2023)](https://doi.org/10.1103/PhysRevLett.131.210602). +118. “Measurement error mitigation in quantum computers through classical bit-flip correction”, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Paolo Stornati, and Xiaoyang Wang, [Phys. Rev. A 105, 062404 (2022)](https://doi.org/10.1103/PhysRevA.105.062404). +119. “Measuring nonstabilizerness via multifractal flatness”, Xhek Turkeshi, Marco Schirò, and Piotr Sierant, [Phys. Rev. A 108, 042408 (2023)](https://doi.org/10.1103/PhysRevA.108.042408). +120. “Experimental Benchmarking of an Automated Deterministic Error-Suppression Workflow for Quantum Algorithms”, Pranav S. Mundada, Aaron Barbosa, Smarak Maity, Yulun Wang, Thomas Merkh, T.M. Stace, Felicity Nielson, Andre R.R. Carvalho, Michael Hush, Michael J. Biercuk, and Yuval Baum, [Phys. Rev. Applied 20, 024034 (2023)](https://doi.org/10.1103/PhysRevApplied.20.024034). +121. “Uncovering Local Integrability in Quantum Many-Body Dynamics”, Oles Shtanko, Derek S. Wang, Haimeng Zhang, Nikhil Harle, Alireza Seif, Ramis Movassagh, Zlatko Minev, [arXiv:2307.07552](https://doi.org/10.48550/arXiv.2307.07552). +122. “Dissipative Dynamics of Graph-State Stabilizers with Superconducting Qubits”, Liran Shirizly, Grégoire Misguich, and Haggai Landa, [Phys. Rev. Lett. 132, 010601 (2024)](https://doi.org/10.1103/PhysRevLett.132.010601). +123. “Blueprint for a Molecular-Spin Quantum Processor”, A. Chiesa, S. Roca, S. Chicco, M.C. de Ory, A. Gómez-León, A. Gomez, D. Zueco, F. Luis, and S. Carretta, [Phys. Rev. Applied 19, 064060 (2023)](https://doi.org/10.1103/PhysRevApplied.19.064060). +124. “Primitive quantum gates for dihedral gauge theories”, M. Sohaib Alam, Stuart Hadfield, Henry Lamm, and Andy C. Y. Li (SQMS Collaboration), [Phys. Rev. D 105, 114501 (2022)](https://doi.org/10.1103/PhysRevD.105.114501). +125. “Pulse variational quantum eigensolver on cross-resonance-based hardware”, Daniel J. Egger, Chiara Capecci, Bibek Pokharel, Panagiotis Kl. Barkoutsos, Laurin E. Fischer, Leonardo Guidoni, and Ivano Tavernelli, [Phys. Rev. Research 5, 033159 (2023)](https://doi.org/10.1103/PhysRevResearch.5.033159). +126. “Simulating large-size quantum spin chains on cloud-based superconducting quantum computers”, Hongye Yu (余泓烨), Yusheng Zhao, and Tzu-Chieh Wei, [Phys. Rev. Research 5, 013183 (2023)](https://doi.org/10.1103/PhysRevResearch.5.013183). +127. “Steering-enhanced quantum metrology using superpositions of noisy phase shifts”, Kuan-Yi Lee, Jhen-Dong Lin, Adam Miranowicz, Franco Nori, Huan-Yu Ku, and Yueh-Nan Chen, [Phys. Rev. Research 5, 013103 (2023)](https://doi.org/10.1103/PhysRevResearch.5.013103). +128. “Effective calculation of the Green’s function in the time domain on near-term quantum processors”, Francesco Libbi, Jacopo Rizzo, Francesco Tacchino, Nicola Marzari, and Ivano Tavernelli, [Phys. Rev. Research 4, 043038 (2022)](https://doi.org/10.1103/PhysRevResearch.4.043038). +129. “N-Electron Valence Perturbation Theory with Reference Wave Functions from Quantum Computing: Application to the Relative Stability of Hydroxide Anion and Hydroxyl Radical”, Alessandro Tammaro, Davide E. Galli, Julia E. Rice, Mario Motta, [J. Phys. Chem. A 2023, 127, 3, 817–827](https://doi.org/10.1021/acs.jpca.2c07653). +130. “Efficient quantum readout-error mitigation for sparse measurement outcomes of near-term quantum devices”, Bo Yang, Rudy Raymond, and Shumpei Uno, [Phys. Rev. A 106, 012423 (2022)](https://doi.org/10.1103/PhysRevA.106.012423). +131. “Finite-size criticality in fully connected spin models on superconducting quantum hardware”, Michele Grossi, Oriel Kiss, Francesco De Luca, Carlo Zollo, Ian Gremese, and Antonio Mandarino, [Phys. Rev. E 107, 024113 (2023)](https://doi.org/10.1103/PhysRevE.107.024113). +132. “Hybrid Gate-Pulse Model for Variational Quantum Algorithms”, Zhiding Liang; Zhixin Song; Jinglei Cheng; Zichang He; Ji Liu; Hanrui Wang, [60th ACM/IEEE Design Automation Conference (DAC) (2023)](https://doi.org/10.1109/DAC56929.2023.10247923). +133. “Computing the Many-Body Green’s Function with Adaptive Variational Quantum Dynamics”, Niladri Gomes, David B. Williams-Young, Wibe A. de Jong, [J. Chem. Theory Comput. 2023, 19, 11, 3313](https://doi.org/10.1021/acs.jctc.3c00150). +134. “Characterizing Crosstalk of Superconducting Transmon Processors”, Andreas Ketterer and Thomas Wellens, [Phys. Rev. Applied 20, 034065 (2023)](https://doi.org/10.1103/PhysRevApplied.20.034065). +135. “Preparing valence-bond-solid states on noisy intermediate-scale quantum computers”, Bruno Murta, Pedro M. Q. Cruz, and J. Fernández-Rossier, [Phys. Rev. Research 5, 013190 (2023)](https://doi.org/10.1103/PhysRevResearch.5.013190). +136. “Configurable Readout Error Mitigation in Quantum Workflows “, Beisel M, Barzen J, Leymann F, Truger F, Weder B, Yussupov V, [Electronics. 2022; 11(19):2983](https://doi.org/10.3390/electronics11192983). +137. “Quantum Algorithm for Imaginary-Time Green’s Functions”, Diksha Dhawan, Dominika Zgid, Mario Motta, [J. Chem. Theory Comput. 2024, 20, 11, 4629](https://doi.org/10.1021/acs.jctc.4c00241). +138. “Best Practices for Quantum Error Mitigation with Digital Zero-Noise Extrapolation”, Ritajit Majumdar; Pedro Rivero; Friedrike Metz; Areeq Hasan; Derek S. Wang, [2023 IEEE International Conference on Quantum Computing and Engineering (QCE)](https://doi.org/10.1109/QCE57702.2023.00102). +139. “Conditional Born machine for Monte Carlo event generation”, Oriel Kiss, Michele Grossi, Enrique Kajomovitz, and Sofia Vallecorsa, [Phys. Rev. A 106, 022612 (2022)](https://doi.org/10.1103/PhysRevA.106.022612). +140. “Quantum approximate optimization via learning-based adaptive optimization”, Cheng, L., Chen, YQ., Zhang, SX. et al., [Commun Phys 7, 83 (2024)](https://doi.org/10.1038/s42005-024-01577-x). +141. “Folded Spectrum VQE: A Quantum Computing Method for the Calculation of Molecular Excited States”, Lila Cadi Tazi, Alex J. W. Thom, [J. Chem. Theory Comput. 2024, 20, 6, 2491](https://doi.org/10.1021/acs.jctc.3c01378). +142. “Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research”, Travis S. Humble, Andrea Delgado, Raphael Pooser, Christopher Seck, Ryan Bennink, Vicente Leyton-Ortega, C.-C. Joseph Wang, Eugene Dumitrescu, Titus Morris, Kathleen Hamilton, Dmitry Lyakh, Prasanna Date, Yan Wang, Nicholas A. Peters, Katherine J. Evans, Marcel Demarteau, Alex McCaskey, Thien Nguyen, Susan Clark, Melissa Reville, Alberto Di Meglio, Michele Grossi, Sofia Vallecorsa, Kerstin Borras, Karl Jansen, Dirk Krücker, [arXiv:2203.07091](https://doi.org/10.48550/arXiv.2203.07091). +143. “Adaptive POVM implementations and measurement error mitigation strategies for near-term quantum devices”, Adam Glos, Anton Nykänen, Elsi-Mari Borrelli, Sabrina Maniscalco, Matteo A. C. Rossi, Zoltán Zimborás, Guillermo García-Pérez, [arXiv:2208.07817](https://doi.org/10.48550/arXiv.2208.07817). +144. “Quantum Gaussian process regression for Bayesian optimization”, Frederic Rapp & Marco Roth, [Quantum Mach. Intell. 6, 5 (2024)](https://doi.org/10.1007/s42484-023-00138-9). +145. “Quantum Ising model on two-dimensional anti–de Sitter space”, Muhammad Asaduzzaman, Simon Catterall, Yannick Meurice, and Goksu Can Toga, [Phys. Rev. D 109, 054513 (2024)](https://doi.org/10.1103/PhysRevD.109.054513). +146. “Advances in Quantum Computation and Quantum Technologies: A Design Automation Perspective”, G. De Micheli, J. -H. R. Jiang, R. Rand, K. Smith and M. Soeken, [IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 12, no. 3, pp. 584 (2022)](https://doi.org/10.1109/JETCAS.2022.3205174). +147. “Performance Study of Variational Quantum Algorithms for Solving the Poisson Equation on a Quantum Computer”, Mazen Ali and Matthias Kabel, [Phys. Rev. Applied 20, 014054 (2023)](https://doi.org/10.1103/PhysRevApplied.20.014054). +148. “Leveraging quantum computing for dynamic analyses of logical networks in systems biology”, Weidner, Felix M. et al., [Patterns, Volume 4, Issue 3, 100705 (2023)](https://doi.org/10.1016/j.patter.2023.100705). +149. “Improved financial forecasting via quantum machine learning”, Thakkar, S., Kazdaghli, S., Mathur, N. et al., [Quantum Mach. Intell. 6, 27 (2024)](https://doi.org/10.1007/s42484-024-00157-0). +150. “Evaluating the resilience of variational quantum algorithms to leakage noise”, Chen Ding, Xiao-Yue Xu, Shuo Zhang, He-Liang Huang, and Wan-Su Bao, [Phys. Rev. A 106, 042421 (2022)](https://doi.org/10.1103/PhysRevA.106.042421). +151. “Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses”, Majumder, Swarnadeep and Yale, Christopher G. and Morris, Titus D. and Lobser, Daniel S. and Burch, Ashlyn D. and Chow, Matthew N. H. and Revelle, Melissa C. and Clark, Susan M. and Pooser, Raphael C., [Quantum 7, 1006 (2023)](https://doi.org/10.22331/q-2023-05-15-1006). +152. “Demonstrating quantum computation for quasiparticle band structures”, Takahiro Ohgoe, Hokuto Iwakiri, Masaya Kohda, Kazuhide Ichikawa, Yuya O. Nakagawa, Hubert Okadome Valencia, and Sho Koh, [Phys. Rev. Research 6, L022022 (2024)](https://doi.org/10.1103/PhysRevResearch.6.L022022). +153. “Ising meson spectroscopy on a noisy digital quantum simulator”, Lamb, C., Tang, Y., Davis, R. et al., [Nat Commun 15, 5901 (2024)](https://doi.org/10.1038/s41467-024-50206-2). +154. “Adaptive quantum error mitigation using pulse-based inverse evolutions”, Henao, I., Santos, J.P. & Uzdin, R., [npj Quantum Inf 9, 120 (2023)](https://doi.org/10.1038/s41534-023-00785-7). +155. “Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning”, N. Meyer, D. D. Scherer, A. Plinge, C. Mutschler and M. J. Hartmann, [2023 IEEE International Conference on Quantum Computing and Engineering (QCE)](https://doi.org/10.1109/QCE57702.2023.10181). +156. “Extending the variational quantum eigensolver to finite temperatures”, Johannes Selisko et al, [2024 Quantum Sci. Technol. 9 015026](https://doi.org/10.1088/2058-9565/ad1340). +157. “A Robust Large-Period Discrete Time Crystal and its Signature in a Digital Quantum Computer”, Tianqi Chen, Ruizhe Shen, Ching Hua Lee, Bo Yang, Raditya Weda Bomantara, [arXiv:2309.11560](https://doi.org/10.48550/arXiv.2309.11560). +158. “Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning”, Raoul Heese, Thore Gerlach, Sascha Mücke, Sabine Müller, Matthias Jakobs, Nico Piatkowski, [arXiv:2301.09138](https://doi.org/10.48550/arXiv.2301.09138). +159. “Shallow unitary decompositions of quantum Fredkin and Toffoli gates for connectivity-aware equivalent circuit averaging”, Pedro M. Q. Cruz, Bruno Murta, [APL Quantum 1, 016105 (2024)](https://doi.org/10.1063/5.0187026). +160. “A Bayesian Approach for Characterizing and Mitigating Gate and Measurement Errors”, Zheng, Muqing and Li, Ang and Terlaky, Tamas and Yang, Xiu, [ACM Transactions on Quantum Computing 4, 21 (2023)](https://doi.org/10.1145/3563397). +161. “Quantum simulations of molecular systems with intrinsic atomic orbitals”, Stefano Barison, Davide E. Galli, and Mario Motta, [Phys. Rev. A 106, 022404 (2023)](https://doi.org/10.1103/PhysRevA.106.022404). +162. “Hardware-Tailored Diagonalization Circuits”, Daniel Miller, Laurin E. Fischer, Kyano Levi, Eric J. Kuehnke, Igor O. Sokolov, Panagiotis Kl. Barkoutsos, Jens Eisert, Ivano Tavernelli, [arXiv:2203.03646](https://doi.org/10.48550/arXiv.2203.03646). +163. “Information-theoretic approach to readout-error mitigation for quantum computers”, Hai-Chau Nguyen, [Phys. Rev. A 108, 052419 (2023)](https://doi.org/10.1103/PhysRevA.108.052419). +164. “Defining Best Practices for Quantum Benchmarks”, Mirko Amico; Helena Zhang; Petar Jurcevic; Lev S. Bishop; Paul Nation; Andrew Wack, [2023 IEEE International Conference on Quantum Computing and Engineering (QCE)](https://doi.org/10.1109/QCE57702.2023.00084). +165. “Simulating Majorana zero modes on a noisy quantum processor”, Kevin J Sung et al , [2023 Quantum Sci. Technol. 8 025010](https://doi.org/10.1088/2058-9565/acb796). +166. “Noise-resistant quantum state compression readout”, Ding, C., Xu, XY., Niu, YF. et al , [Sci. China Phys. Mech. Astron. 66, 230311 (2023)](https://doi.org/10.1007/s11433-022-2005-x). +167. “Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning”, S. Park and O. Simeone, [EEE Transactions on Quantum Engineering, vol. 5, pp. 1-24, 2024](https://doi.org/10.1109/TQE.2023.3333224). +168. “Variational preparation of entangled states on quantum computers”, Vu Tuan Hai, Nguyen Tan Viet, Le Bin Ho, [arXiv:2306.174226](https://doi.org/10.48550/arXiv.2306.17422). +169. “PyQBench: A Python library for benchmarking gate-based quantum computers”, Konrad Jałowiecki, Paulina Lewandowska, Łukasz Pawela, [SoftwareX 24, 101558 (2023)](https://doi.org/10.1016/j.softx.2023.101558). +170. “Mapping Topology-Localization Phase Diagram with Quasiperiodic Disorder Using a Programmable Superconducting Simulator”, Xuegang Li, Huikai Xu, Junhua Wang, Ling-Zhi Tang, Dan-Wei Zhang, Chuhong Yang, Tang Su, Chenlu Wang, Zhenyu Mi, Weijie Sun, Xuehui Liang, Mo Chen, Chengyao Li, Yingshan Zhang, Kehuan Linghu, Jiaxiu Han, Weiyang Liu, Yulong Feng, Pei Liu, Guangming Xue, Jingning Zhang, Yirong Jin, Shi-Liang Zhu, Haifeng Yu, S. P. Zhao, Qi-Kun Xue, [arXiv:2301.12138](https://doi.org/10.48550/arXiv.2301.12138). +171. “Universal framework for simultaneous tomography of quantum states and SPAM noise”, Abhijith Jayakumar, Stefano Chessa, Carleton Coffrin, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra, [Quantum 8, 1426 (2024)](https://doi.org/10.22331/q-2024-07-30-1426). +172. “Stochastic Approximation of Variational Quantum Imaginary Time Evolution”, Julien Gacon; Christa Zoufal; Giuseppe Carleo; Stefan Woerner, [2023 IEEE International Conference on Quantum Computing and Engineering (QCE)](https://doi.org/10.1109/QCE57702.2023.10367741). +173. “Simulating Polaritonic Ground States on Noisy Quantum Devices”, Mohammad Hassan, Mohammad Hassan, Fabijan Pavošević, Derek S. Wang, Johannes Flick, [Phys. Chem. Lett. 2024, 15, 5, 1373](https://doi.org/10.1021/acs.jpclett.3c02875). +174. “High-fidelity realization of the AKLT state on a NISQ-era quantum processors”, Tianqi Chen, Ruizhe Shen, Ching Hua Lee, Bo Yang, [SciPost Phys. 15, 170 (2023)](https://doi.org/10.21468/SciPostPhys.15.4.170). +175. “Error mitigated quantum circuit cutting”, Ritajit Majumdar, Christopher J. Wood, [arXiv:2211.13431](https://doi.org/10.48550/arXiv.2211.13431). +176. “Quantum computation of π → π\* and n → π\* excited states of aromatic heterocycles”, Castellanos, M. A., Motta, M., & Rice, J. E., [Molecular Physics, 122(7–8) (2023)](https://doi.org/10.1080/00268976.2023.2282736). +177. “QuCT: A Framework for Analyzing Quantum Circuit by Extracting Contextual and Topological Features”, Tan, Siwei and Lang, Congliang and Xiang, Liang and Wang, Shudi and Jia, Xinghui and Tan, Ziqi and Li, Tingting and Yin, Jieming and Shang, Yongheng and Python, Andre and Lu, Liqiang and Yin, Jianwei, [Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture (2023)](https://doi.org/10.1145/3613424.36142746). +178. “Fair Sampling Error Analysis on NISQ Devices”, Golden, John and Bartschi, Andreas and O’Malley, Daniel and Eidenbenz, Stephan, [ACM Transactions on Quantum Computing 3, 8 (2022)](https://doi.org/10.1145/3510857). +179. “Quantum circuits for discrete graphical models”, Piatkowski, N., Zoufal, C., [Quantum Mach. Intell. 6, 37 (2024)](https://doi.org/10.1007/s42484-024-00175-y). +180. “Benchmarking noisy intermediate scale quantum error mitigation strategies for ground state preparation of the HCl molecule”, Tim Weaving, Alexis Ralli, William M. Kirby, Peter J. Love, Sauro Succi, and Peter V. Coveney, [Phys. Rev. Research 5, 043054 (2024)](https://doi.org/10.1103/PhysRevResearch.5.043054). +181. “Braiding fractional quantum Hall quasiholes on a superconducting quantum processor”, Ammar Kirmani, Derek S. Wang, Pouyan Ghaemi, and Armin Rahmani, [Phys. Rev. B 108, 064303 (2023)](https://doi.org/10.1103/PhysRevB.108.064303). +182. “Mitigating Coupling Map Constrained Correlated Measurement Errors on Quantum Devices”, Robertson, Alan and Song, Shuaiwen, [Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (2023)](https://doi.org/10.1145/3581784.3607039). +183. “Modular quantum circuits for secure communication”, Andrea Ceschini, Antonello Rosato, Massimo Panella, [IET Quantum Communication 4, 208 (2023)](https://doi.org/10.1049/qtc2.12065). +184. “Amplitude-based implementation of the unit step function on a quantum computer”, Jonas Koppe and Mark-Oliver Wolf, [Phys. Rev. A 107, 022606 (2023)](https://doi.org/10.1103/PhysRevA.107.022606). +185. “Perturbative readout-error mitigation for near-term quantum computers”, Evan Peters, Andy C. Y. Li, and Gabriel N. Perdue, [Phys. Rev. A 107, 062426 (2023)](https://doi.org/10.1103/PhysRevA.107.062426). +186. “Calculation of the moscovium ground-state energy by quantum algorithms”, V. A. Zaytsev, M. E. Groshev, I. A. Maltsev, A. V. Durova, V. M. Shabaev, [International Journal of Quantum Chemistry 124, e27232 (2023)](https://doi.org/10.1002/qua.272326). +187. “Scalable evaluation of incoherent infidelity in quantum devices”, Jader P. Santos, Ivan Henao, Raam Uzdin, [arXiv:2305.19359](https://doi.org/10.48550/arXiv.2305.19359). +188. “Application of the Variational Quantum Eigensolver to the Ultimate Pit Problem”, Yousef Hindy; Jessica Pointing; Meltem Tolunay; Sreeram Venkatarao; Mario Motta; Joseph A. Latone, [2023 IEEE International Conference on Quantum Computing and Engineering (QCE)](https://doi.org/10.1109/QCE57702.2023.00083). +189. “Dynamical mean-field theory for the Hubbard-Holstein model on a quantum device”, Steffen Backes, Yuta Murakami, Shiro Sakai, and Ryotaro Arita, [Phys. Rev. B 107, 165155 (2023)](https://doi.org/10.1103/PhysRevB.107.165155). +190. “Identifying Bottlenecks of NISQ-friendly HHL algorithms”, Marc Andreu Marfany, Alona Sakhnenko, Jeanette Miriam Lorenz, [arXiv:2406.06288](https://doi.org/10.48550/arXiv.2406.06288). +191. “Dual-GSE: Resource-efficient Generalized Quantum Subspace Expansion”, Bo Yang, Nobuyuki Yoshioka, Hiroyuki Harada, Shigeo Hakkaku, Yuuki Tokunaga, Hideaki Hakoshima, Kaoru Yamamoto, Suguru Endo, [arXiv:2309.14171](https://doi.org/10.48550/arXiv.2309.14171). +192. “Energy Risk Analysis with Dynamic Amplitude Estimation and Piecewise Approximate Quantum Compiling”, K. Ghosh et al, [IEEE Transactions on Quantum Engineering](https://doi.org/10.1109/TQE.2024.3425969). +193. “QuFEM: Fast and Accurate Quantum Readout Calibration Using the Finite Element Method”, Tan, Siwei and Lu, Liqiang and Zhang, Hanyu and Yu, Jia and Lang, Congliang and Shang, Yongheng and Zhao, Xinkui and Chen, Mingshuai and Liang, Yun and Yin, Jianwei, [ASPLOS ‘24: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems](https://doi.org/10.1145/3620665.3640380). +194. “Quantum Risk Analysis: Beyond (Conditional) Value-at-Risk”, Christian Laudagé, Ivica Turkalj, [arXiv:2211.04456](https://doi.org/10.48550/arXiv.2211.04456). +195. “Correlation thresholds for effective composite pulse quantum error mitigation”, Ido Kaplan, Haim Suchowski, Yaron Oz, [arXiv:2308.08691](https://doi.org/10.48550/arXiv.2308.08691). +196. “Robust design under uncertainty in quantum error mitigation”, Piotr Czarnik, Michael McKerns, Andrew T. Sornborger, Lukasz Cincio, [arXiv:2307.05302](https://doi.org/10.48550/arXiv.2307.05302). +197. “Qubit Assignment Using Time Reversal”, Evan Peters, Prasanth Shyamsundar, Andy C.Y. Li, and Gabriel Perdue, [PRX Quantum 3, 040333 (2022)](https://doi.org/10.1103/PRXQuantum.3.040333). +198. “Quantum Simulations for Carbon Capture on Metal-Organic Frameworks”, Gopal Ramesh Dahale, [2023 IEEE International Conference on Quantum Computing and Engineering (QCE),](https://doi.org/10.1109/QCE57702.2023.10189). +199. “Measuring qubit stability in a gate-based NISQ hardware processor”, Yeter-Aydeniz, K., Parks, Z., Thekkiniyedath, A.N. et al., [Quantum Inf Process 22, 96 (2023),](https://doi.org/10.1007/s11128-023-03826-4). +200. “Self-consistent quantum measurement tomography based on semidefinite programming”, Marco Cattaneo, Matteo A. C. Rossi, Keijo Korhonen, Elsi-Mari Borrelli, Guillermo García-Pérez, Zoltán Zimborás, and Daniel Cavalcanti, [Phys. Rev. Research 5, 033154 (2023),](https://doi.org/10.1103/PhysRevResearch.5.033154). +201. “Adaptive variational simulation for open quantum systems”, Huo Chen, Niladri Gomes, Siyuan Niu, Wibe Albert de Jong, [Quantum 8, 1252 (2024),](https://doi.org/10.22331/q-2024-02-13-1252). +202. “Conditions for a quadratic quantum speedup in nonlinear transforms with applications to energy contract pricing”, Gabriele Agliardi, Corey O’Meara, Kavitha Yogaraj, Kumar Ghosh, Piergiacomo Sabino, Marina Fernández-Campoamor, Giorgio Cortiana, Juan Bernabé-Moreno, Francesco Tacchino, Antonio Mezzacapo, Omar Shehab, [arXiv:2304.10385](https://doi.org/10.48550/arXiv.2304.10385). +203. “Error estimation in current noisy quantum computers”, Aseguinolaza, U., Sobrino, N., Sobrino, G. et al, [Quantum Inf Process 23, 181 (2024),](https://doi.org/10.1007/s11128-024-04384-z). +204. “Probing The Unitarity of Quantum Evolution Through Periodic Driving”, Alaina M. Green, Tanmoy Pandit, C. Huerta Alderete, Norbert M. Linke, Raam Uzdin, [arXiv:2212.10771](https://doi.org/10.48550/arXiv.2212.10771). +205. “Universal compilation for quantum state preparation and tomography”, Vu Tuan Hai, Le Bin Ho, [arXiv:2204.11635](https://doi.org/10.48550/arXiv.2204.11635). +206. “Folding-Free ZNE: A Comprehensive Quantum Zero-Noise Extrapolation Approach for Mitigating Depolarizing and Decoherence Noise”, Hrushikesh Pramod Patil; Peiyi Li; Ji Liu; Huiyang Zhou, [2023 IEEE International Conference on Quantum Computing and Engineering (QCE),](https://doi.org/10.1109/QCE57702.2023.00104). +207. “Testing the necessity of complex numbers in quantum mechanics with IBM quantum computers”, Jarrett L. Lancaster, Nicholas M. Palladino, [arXiv:2205.01262](https://doi.org/10.48550/arXiv.2205.01262). + diff --git a/docs/addons/qiskit-addon-mthree/probs.mdx b/docs/addons/qiskit-addon-mthree/probs.mdx new file mode 100644 index 00000000000..68e1a2681b8 --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/probs.mdx @@ -0,0 +1,64 @@ +--- +title: "Converting to probabilities" +description: "Converting to probabilities for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="probs" /> + +# Converting to probabilities + +M3 natively works with quasi-probability distributions; distributions that contain negative values but nonetheless sum to one. This is useful for mitigating expectation values, but there could be situations where a true probability distribution is useful / needed. To this end, it is possible to find the closest probability distribution to a quasi-probability distribution in terms of $L2$-norm using: [`mthree.classes.QuasiDistribution.nearest_probability_distribution()`](/docs/api/qiskit-addon-mthree/mthree-classes-quasi-distribution#mthree.classes.QuasiDistribution.nearest_probability_distribution "mthree.classes.QuasiDistribution.nearest_probability_distribution"). This conversion is done in linear time. + +```python +from qiskit import * +from qiskit_ibm_runtime.fake_provider import FakeCasablancaV2 +import mthree + +qc = QuantumCircuit(6) +qc.reset(range(6)) +qc.h(3) +qc.cx(3,1) +qc.cx(3,5) +qc.cx(1,0) +qc.cx(5,4) +qc.cx(1,2) +qc.measure_all() + +backend = FakeCasablancaV2() +mit = mthree.M3Mitigation(backend) +mit.cals_from_system(range(6)) + +trans_qc = transpile(qc, backend) +raw_counts = backend.run(trans_qc, shots=8192).result().get_counts() + +quasis = mit.apply_correction(raw_counts, range(6)) + +# Here is where the conversion happens +quasis.nearest_probability_distribution() +``` + +``` +{'010111': np.float32(1.8458784e-05), + '001101': np.float32(7.82907e-05), + '101011': np.float32(0.0001266693), + '101000': np.float32(0.00019029173), + '101101': np.float32(0.00033072094), + '000010': np.float32(0.00038816765), + '000110': np.float32(0.0006664434), + '110011': np.float32(0.0007018661), + '101111': np.float32(0.00095244986), + '001000': np.float32(0.0015221569), + '110111': np.float32(0.0019342253), + '110000': np.float32(0.0025582286), + '111001': np.float32(0.0028622507), + '010000': np.float32(0.003231837), + '001111': np.float32(0.0034692101), + '100000': np.float32(0.0035603144), + '111101': np.float32(0.0043579647), + '111000': np.float32(0.005170968), + '000111': np.float32(0.007296363), + '111011': np.float32(0.017431116), + '111111': np.float32(0.4505793), + '000000': np.float32(0.49257272)} +``` + diff --git a/docs/addons/qiskit-addon-mthree/runtime.mdx b/docs/addons/qiskit-addon-mthree/runtime.mdx new file mode 100644 index 00000000000..747c02eaf71 --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/runtime.mdx @@ -0,0 +1,25 @@ +--- +title: "Using Runtime execution modes" +description: "Using Runtime execution modes for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="runtime" /> + +# Using Runtime execution modes + +The Qiskit Runtime has two execution modes, Batch and Session, that allow for grouping multiple jobs. You can include M3 calibration jobs in these modes using the runtime\_mode argument in mthree.M3Mitigation.cals\_from\_system. For example: + +```python +from qiskit_ibm_runtime import Batch +from qiskit_ibm_runtime.fake_provider import FakeCasablancaV2 +import mthree + +backend = FakeCasablancaV2() +batch = Batch(backend=backend) + +mit = mthree.M3Mitigation(backend) +mit.cals_from_system(runtime_mode=batch); # This is where the Batch or Session goes +``` + +Note that if no runtime\_mode is set, and the passed system is an IBM backend, then jobs are submitted in Job mode, i.e. independently, by default. + diff --git a/docs/addons/qiskit-addon-mthree/sampling.ipynb b/docs/addons/qiskit-addon-mthree/sampling.ipynb new file mode 100644 index 00000000000..2ea56829c8c --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/sampling.ipynb @@ -0,0 +1,402 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "frontmatter", + "metadata": {}, + "source": [ + "---\n", + "title: \"Mitigating sampling problems\"\n", + "description: \"Mitigating sampling problems for the latest version of Matrix-free Measurement Mitigation (M3)\"\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "43f3f626", + "metadata": {}, + "source": [ + "# Mitigating sampling problems\n", + "\n", + "There are many cases where one is interested in computing the most probable bitstring in the output distribution from a circuit. Although, M3 returns quasi-probabilities, the most likely bit-string is something that is always positive, and we can mitigate sampling problems of this form. Below is a simple example for Bernstein–Vazirani (BV) circuits." + ] + }, + { + "cell_type": "markdown", + "id": "faf78437", + "metadata": {}, + "source": [ + "## Frontmatter" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0d41ee42", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:27.975572Z", + "iopub.status.busy": "2026-05-22T15:06:27.975341Z", + "iopub.status.idle": "2026-05-22T15:06:28.853671Z", + "shell.execute_reply": "2026-05-22T15:06:28.853381Z" + } + }, + "outputs": [], + "source": [ + "from qiskit import *\n", + "from qiskit_ibm_runtime.fake_provider import FakeKolkataV2\n", + "\n", + "import mthree\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('quantum-light')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ee597ad5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:28.855507Z", + "iopub.status.busy": "2026-05-22T15:06:28.855319Z", + "iopub.status.idle": "2026-05-22T15:06:28.876282Z", + "shell.execute_reply": "2026-05-22T15:06:28.876004Z" + } + }, + "outputs": [], + "source": [ + "backend = FakeKolkataV2()" + ] + }, + { + "cell_type": "markdown", + "id": "eafbbf6f", + "metadata": {}, + "source": [ + "## Setup experiment\n", + "\n", + "Here we generate BV circuits for all-ones bit-strings of various lengths, and transpile them against our target backend. We also create a (not-rigourous) function that finds the most likely bit-string from a distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8765752b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:28.877990Z", + "iopub.status.busy": "2026-05-22T15:06:28.877901Z", + "iopub.status.idle": "2026-05-22T15:06:28.881393Z", + "shell.execute_reply": "2026-05-22T15:06:28.881158Z" + } + }, + "outputs": [], + "source": [ + "def bv_ones_circs(N):\n", + " \"\"\"Create a Bernstein–Vazirani (BV) circuit of width N\n", + "\n", + " Here the bit-string length is N-1\n", + "\n", + " Parameters:\n", + " N (int): Number of qubits in circuit\n", + "\n", + " Returns:\n", + " QuantumCircuit: BV circuit\n", + " \"\"\"\n", + " qc = QuantumCircuit(N, N-1)\n", + " qc.x(N-1)\n", + " qc.h(range(N))\n", + " qc.cx(range(N-1), N-1)\n", + " qc.h(range(N-1))\n", + " qc.barrier()\n", + " qc.measure(range(N-1), range(N-1))\n", + " return qc\n", + "\n", + "\n", + "def most_likely_bitstring(dist):\n", + " \"\"\"Computes the most likely bit-string from a given distribution\n", + "\n", + " Parameters:\n", + " dist (dict): Input distribution\n", + "\n", + " Returns:\n", + " str: Most likely bit-string\n", + " int, float: Most likely value\n", + " \"\"\"\n", + " sorted_dist = dict(sorted(dist.items(), key=lambda item: item[1], reverse=True))\n", + " key = next(iter(sorted_dist.keys()))\n", + " val = next(iter(sorted_dist.values()))\n", + " return key, val" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6dae48a2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:28.882625Z", + "iopub.status.busy": "2026-05-22T15:06:28.882541Z", + "iopub.status.idle": "2026-05-22T15:06:28.885329Z", + "shell.execute_reply": "2026-05-22T15:06:28.885078Z" + } + }, + "outputs": [], + "source": [ + "bit_range = range(2, 13)\n", + "\n", + "circs = [bv_ones_circs(N+1) for N in bit_range]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3fa878bc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:28.886636Z", + "iopub.status.busy": "2026-05-22T15:06:28.886556Z", + "iopub.status.idle": "2026-05-22T15:06:28.947845Z", + "shell.execute_reply": "2026-05-22T15:06:28.947596Z" + } + }, + "outputs": [], + "source": [ + "trans_circs = transpile(circs, backend, optimization_level=3)" + ] + }, + { + "cell_type": "markdown", + "id": "b4c3ffb3", + "metadata": {}, + "source": [ + "## Perform experiment\n", + "\n", + "We generate the raw data for the circuits, and then perform our mitigation in the usual way." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "45a3d55d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:28.949373Z", + "iopub.status.busy": "2026-05-22T15:06:28.949293Z", + "iopub.status.idle": "2026-05-22T15:06:40.643782Z", + "shell.execute_reply": "2026-05-22T15:06:40.643415Z" + } + }, + "outputs": [], + "source": [ + "shots = 8192\n", + "counts = backend.run(trans_circs, shots=shots).result().get_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "07c46949", + "metadata": {}, + "source": [ + "Get the success probability, validating that our returned bit-string is what we expect." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "58a21b32", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:40.646018Z", + "iopub.status.busy": "2026-05-22T15:06:40.645917Z", + "iopub.status.idle": "2026-05-22T15:06:40.648505Z", + "shell.execute_reply": "2026-05-22T15:06:40.648069Z" + } + }, + "outputs": [], + "source": [ + "raw_success_probs = []\n", + "\n", + "for idx, num_bits in enumerate(bit_range):\n", + " max_bitstring, max_value = most_likely_bitstring(counts[idx])\n", + " if max_bitstring != '1'*num_bits:\n", + " raise Exception('Returned wrong bit-string')\n", + " raw_success_probs.append(max_value/shots)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fe41dbda", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:40.650173Z", + "iopub.status.busy": "2026-05-22T15:06:40.650072Z", + "iopub.status.idle": "2026-05-22T15:06:40.653956Z", + "shell.execute_reply": "2026-05-22T15:06:40.653621Z" + } + }, + "outputs": [], + "source": [ + "mit = mthree.M3Mitigation(backend)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e8a48d65", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:40.655448Z", + "iopub.status.busy": "2026-05-22T15:06:40.655379Z", + "iopub.status.idle": "2026-05-22T15:06:40.657794Z", + "shell.execute_reply": "2026-05-22T15:06:40.657465Z" + } + }, + "outputs": [], + "source": [ + "mappings = mthree.utils.final_measurement_mapping(trans_circs)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "34c20334", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:40.659684Z", + "iopub.status.busy": "2026-05-22T15:06:40.659596Z", + "iopub.status.idle": "2026-05-22T15:06:40.913805Z", + "shell.execute_reply": "2026-05-22T15:06:40.913488Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<qiskit_aer.jobs.aerjob.AerJob at 0x113251710>]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mit.cals_from_system(mappings)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3092a76a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:40.915482Z", + "iopub.status.busy": "2026-05-22T15:06:40.915385Z", + "iopub.status.idle": "2026-05-22T15:06:40.923491Z", + "shell.execute_reply": "2026-05-22T15:06:40.923141Z" + } + }, + "outputs": [], + "source": [ + "quasis = mit.apply_correction(counts, mappings)" + ] + }, + { + "cell_type": "markdown", + "id": "409f4771", + "metadata": {}, + "source": [ + "Get the mitigated success probability, validating that our returned bit-string is what we expect." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c547dd1e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:40.925619Z", + "iopub.status.busy": "2026-05-22T15:06:40.925515Z", + "iopub.status.idle": "2026-05-22T15:06:40.927855Z", + "shell.execute_reply": "2026-05-22T15:06:40.927581Z" + } + }, + "outputs": [], + "source": [ + "mit_success_probs = []\n", + "\n", + "for idx, num_bits in enumerate(bit_range):\n", + " max_bitstring, max_value = most_likely_bitstring(quasis[idx])\n", + " if max_bitstring != '1'*num_bits:\n", + " raise Exception('Returned wrong bit-string')\n", + " mit_success_probs.append(max_value)" + ] + }, + { + "cell_type": "markdown", + "id": "d099e9e0", + "metadata": {}, + "source": [ + "## Plot results\n", + "\n", + "Here we plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "40654bec", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-22T15:06:40.929516Z", + "iopub.status.busy": "2026-05-22T15:06:40.929424Z", + "iopub.status.idle": "2026-05-22T15:06:41.009652Z", + "shell.execute_reply": "2026-05-22T15:06:41.009199Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<Image src=\"/docs/images/addons/qiskit-addon-mthree/sampling/extracted-outputs/40654bec-0.avif\" alt=\"Output of the previous code cell\" />" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(bit_range, raw_success_probs, label='Raw')\n", + "ax.plot(bit_range, mit_success_probs, label='Mitigated')\n", + "ax.set_ylabel('Success probability')\n", + "ax.set_xlabel('Bit-string length')\n", + "ax.legend();" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-mthree/transpiled.mdx b/docs/addons/qiskit-addon-mthree/transpiled.mdx new file mode 100644 index 00000000000..2e7942384a5 --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/transpiled.mdx @@ -0,0 +1,62 @@ +--- +title: "Using M3 with transpiled circuits" +description: "Using M3 with transpiled circuits for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="transpiled" /> + +# Using M3 with transpiled circuits + +In the [Basic usage](basic#basic) section we looked at circuits with a qubit layout that was known ahead of time. However, when taking an arbitrary circuit and compiling it down to hardware, SWAP mapping permutes qubits such that the final mapping between virtual circuit qubits and physical qubits is not a-priori known. For example consider the Bernstein-Vazirani circuit + +```python +from qiskit import * +from qiskit_ibm_runtime.fake_provider import FakeCasablancaV2 +import mthree + +qc = QuantumCircuit(5, 4) +qc.reset(range(5)) +qc.x(4) +qc.h(range(5)) +qc.cx(range(4), 4) +qc.draw() +qc.h(range(4)) +qc.barrier() +qc.measure(range(4), range(4)) +qc.draw('mpl') +``` + +![\_images/transpiled\_0\_0.png](/docs/images/addons/qiskit-addon-mthree/transpiled_0_0.avif) + +The target Casablanca system does not have the needed connectivity to natively embed the circuit and we must SWAP map it: + +```python +backend = FakeCasablancaV2() +trans_qc = transpile(qc, backend, optimization_level=3, seed_transpiler=12345) +trans_qc.draw('mpl') +``` + +![\_images/transpiled\_1\_0.png](/docs/images/addons/qiskit-addon-mthree/transpiled_1_0.avif) + +We can see from the measurements at the end that what was circuit qubit 0 is now mapped to physical qubit 5, circuit qubit 1 is mapped to physical qubit 3, etc… This information is needed to correctly mitigate the final results, yet outside of visually inspecting the circuit there is no easy way to obtain this information in Qiskit. As such, M3 includes the [`mthree.utils.final_measurement_mapping()`](/docs/api/qiskit-addon-mthree/mthree-utils-final-measurement-mapping#mthree.utils.final_measurement_mapping "mthree.utils.final_measurement_mapping") routine to compute this for you: + +```python +mapping = mthree.utils.final_measurement_mapping(trans_qc) +print(mapping) +``` + +``` +{0: 1, 1: 3, 2: 4, 3: 6} +``` + +We see that the keys of the returned dictionary label the classical bits used, and the corresponding values show which qubit was measured into that bit. Using this mapping in M3 is easy: + +```python +mit = mthree.M3Mitigation(backend) +mit.cals_from_system(mapping) +``` + +``` +[<qiskit_aer.jobs.aerjob.AerJob at 0x113a7f390>] +``` + diff --git a/docs/addons/qiskit-addon-mthree/utils.mdx b/docs/addons/qiskit-addon-mthree/utils.mdx new file mode 100644 index 00000000000..a8a92d3ed55 --- /dev/null +++ b/docs/addons/qiskit-addon-mthree/utils.mdx @@ -0,0 +1,137 @@ +--- +title: "Utility functions" +description: "Utility functions for the lastest version of Matrix-free Measurement Mitigation (M3)" +--- + +<span id="utils" /> + +# Utility functions + +There are a couple of functionalities that are needed for easy mitigation, but are not part of the Qiskit SDK and thus are included in M3. Each is detailed below. + +## Final-measurement mapping + +We have already seen the [`mthree.utils.final_measurement_mapping()`](/docs/api/qiskit-addon-mthree/mthree-utils-final-measurement-mapping#mthree.utils.final_measurement_mapping "mthree.utils.final_measurement_mapping") routine in the section on detailing with [Transpiled circuits](transpiled#transpiled), where the addition of SWAP gates made it difficult to determine exactly which physical qubits are being used, and which classical bits they correspond to. Here we show another example of this usage. First we start with a 7-qubit GHZ state: + +```python +from qiskit import * +from qiskit_ibm_runtime.fake_provider import FakeCasablancaV2 +import mthree + +qc = QuantumCircuit(7) +qc.h(0) +qc.cx(0, range(1,7)) +qc.measure_all() +qc.draw('mpl') +``` + +![\_images/utils\_0\_0.png](/docs/images/addons/qiskit-addon-mthree/utils_0_0.avif) + +and then transpile it: + +```python +backend = FakeCasablancaV2() +trans_qc = transpile(qc, backend, optimization_level=3, seed_transpiler=54321) +trans_qc.draw('mpl') +``` + +![\_images/utils\_1\_0.png](/docs/images/addons/qiskit-addon-mthree/utils_1_0.avif) + +Once again we see that the physical qubits used and to which classical bits they map to is non-trivial to find. Yet this information is critical for successfully mitigating the results. This is where the [`mthree.utils.final_measurement_mapping()`](/docs/api/qiskit-addon-mthree/mthree-utils-final-measurement-mapping#mthree.utils.final_measurement_mapping "mthree.utils.final_measurement_mapping") plays a critical role: + +```python +mapping = mthree.utils.final_measurement_mapping(trans_qc) +mapping +``` + +``` +{0: 5, 1: 0, 2: 1, 3: 2, 4: 4, 5: 3, 6: 6} +``` + +The keys of this mapping index the classical bits, whereas the values tell you which qubits were measured to obtain the bit values. The mapping is ordered in terms of the classical bit indices. You can just pass the generated `mapping` into M3 functions. + +## Evaluating raw counts data + +When mitigating results one often wants to know how much better the results are with mitigation compared to without. However, Qiskit does not have great support for computing things like expectation values. So M3 includes the generic functions [`mthree.utils.expval()`](/docs/api/qiskit-addon-mthree/mthree-utils-expval#mthree.utils.expval "mthree.utils.expval"), [`mthree.utils.stddev()`](/docs/api/qiskit-addon-mthree/mthree-utils-stddev#mthree.utils.stddev "mthree.utils.stddev"), and [`mthree.utils.expval_and_stddev()`](/docs/api/qiskit-addon-mthree/mthree-utils-expval-and-stddev#mthree.utils.expval_and_stddev "mthree.utils.expval_and_stddev") that operate on the native `Counts` objects in Qiskit. + +For example let us compare raw data versus the mitigated results in a simple case. + +```python +from qiskit_ibm_runtime.fake_provider import FakeAthensV2 +backend = FakeAthensV2() +qc = QuantumCircuit(4) +qc.h(2) +qc.cx(2, 1) +qc.cx(2, 3) +qc.cx(1, 0) +qc.measure_all() + +raw_counts = backend.run(qc).result().get_counts() +mit = mthree.M3Mitigation(backend) +mit.cals_from_system() +mit_counts = mit.apply_correction(raw_counts, qubits=range(4), + return_mitigation_overhead=True) + +print('Raw counts expval', mthree.utils.expval(raw_counts)) +print('Mitigated expval', mit_counts.expval()) +``` + +``` +Raw counts expval 0.83203125 +Mitigated expval 0.9472091794013977 +``` + +We can also compare things like upper-bounds on the standard deviation: + +```python +print('Raw counts uncertainty', mthree.utils.stddev(raw_counts)) +print('Mitigated uncertainty', mit_counts.stddev()) +``` + +``` +Raw counts uncertainty 0.03125 +Mitigated uncertainty 0.038421723312172806 +``` + +where the uncertainty for the raw `Counts` data is just $1/\sqrt{\rm{shots}}$. + +These convenence functions work in the same manner as the methods for the distribution classes [`mthree.classes.QuasiDistribution`](/docs/api/qiskit-addon-mthree/mthree-classes-quasi-distribution#mthree.classes.QuasiDistribution "mthree.classes.QuasiDistribution") and [`mthree.classes.ProbDistribution`](/docs/api/qiskit-addon-mthree/mthree-classes-prob-distribution#mthree.classes.ProbDistribution "mthree.classes.ProbDistribution") and collections [`mthree.classes.QuasiCollection`](/docs/api/qiskit-addon-mthree/mthree-classes-quasi-collection#mthree.classes.QuasiCollection "mthree.classes.QuasiCollection") and [`mthree.classes.ProbCollection`](/docs/api/qiskit-addon-mthree/mthree-classes-prob-collection#mthree.classes.ProbCollection "mthree.classes.ProbCollection"). That is to say that, for example, I can pass operators to `expval` function: + +```python +print('These should be equal:', mthree.utils.expval(raw_counts, 'IIII'), + mit_counts.expval('IIII')) +``` + +``` +These should be equal: 1.0 1.0 +``` + +The routines also allow you to pass the native M3 distributions and collections. E.g. + +```python +print(mthree.utils.expval(mit_counts), mit_counts.expval()) +``` + +``` +0.9472092 0.9472091794013977 +``` + +Finally we note that you can pass multiple values at the same time. Here we run and mitigate 5 circuits: + +```python +raw_counts = backend.run([qc]*5).result().get_counts() +mit = mthree.M3Mitigation(backend) +mit.cals_from_system() +mit_counts = mit.apply_correction(raw_counts, qubits=range(4), + return_mitigation_overhead=True) + + +print('Raw counts expval', mthree.utils.expval(raw_counts)) +print('Mitigated expval', mit_counts.expval()) +``` + +``` +Raw counts expval [0.8300781 0.8066406 0.8691406 0.8574219 0.83984375] +Mitigated expval [0.93635666 0.91089356 0.9790215 0.97029066 0.94678086] +``` + diff --git a/docs/api/qiskit-addon-mthree/_package.json b/docs/api/qiskit-addon-mthree/_package.json new file mode 100644 index 00000000000..878038d00ce --- /dev/null +++ b/docs/api/qiskit-addon-mthree/_package.json @@ -0,0 +1,4 @@ +{ + "name": "qiskit-addon-mthree", + "version": "3.0.0" +} diff --git a/docs/api/qiskit-addon-mthree/_toc.json b/docs/api/qiskit-addon-mthree/_toc.json new file mode 100644 index 00000000000..37d224d6c16 --- /dev/null +++ b/docs/api/qiskit-addon-mthree/_toc.json @@ -0,0 +1,98 @@ +{ + "title": "Matrix-free Measurement Mitigation (M3)", + "children": [ + { + "title": "API index", + "url": "/docs/api/qiskit-addon-mthree" + }, + { + "title": "Release notes", + "useDivider": true, + "url": "/docs/api/qiskit-addon-mthree/release-notes" + }, + { + "title": "mthree", + "untranslatable": true, + "children": [ + { + "title": "Module overview", + "url": "/docs/api/qiskit-addon-mthree/main" + }, + { + "title": "M3Mitigation", + "url": "/docs/api/qiskit-addon-mthree/mthree-m-3-mitigation", + "untranslatable": true + } + ] + }, + { + "title": "mthree.classes", + "untranslatable": true, + "children": [ + { + "title": "Module overview", + "url": "/docs/api/qiskit-addon-mthree/classes" + }, + { + "title": "ProbCollection", + "url": "/docs/api/qiskit-addon-mthree/mthree-classes-prob-collection", + "untranslatable": true + }, + { + "title": "ProbDistribution", + "url": "/docs/api/qiskit-addon-mthree/mthree-classes-prob-distribution", + "untranslatable": true + }, + { + "title": "QuasiCollection", + "url": "/docs/api/qiskit-addon-mthree/mthree-classes-quasi-collection", + "untranslatable": true + }, + { + "title": "QuasiDistribution", + "url": "/docs/api/qiskit-addon-mthree/mthree-classes-quasi-distribution", + "untranslatable": true + } + ] + }, + { + "title": "mthree.utils", + "untranslatable": true, + "children": [ + { + "title": "Module overview", + "url": "/docs/api/qiskit-addon-mthree/utils" + }, + { + "title": "expval", + "url": "/docs/api/qiskit-addon-mthree/mthree-utils-expval", + "untranslatable": true + }, + { + "title": "expval_and_stddev", + "url": "/docs/api/qiskit-addon-mthree/mthree-utils-expval-and-stddev", + "untranslatable": true + }, + { + "title": "final_measurement_mapping", + "url": "/docs/api/qiskit-addon-mthree/mthree-utils-final-measurement-mapping", + "untranslatable": true + }, + { + "title": "marginal_distribution", + "url": "/docs/api/qiskit-addon-mthree/mthree-utils-marginal-distribution", + "untranslatable": true + }, + { + "title": "stddev", + "url": "/docs/api/qiskit-addon-mthree/mthree-utils-stddev", + "untranslatable": true + } + ] + } + ], + "collapsed": true, + "untranslatable": true, + "parentUrl": "/docs/addons/qiskit-addon-mthree", + "parentLabel": "Matrix-free Measurement Mitigation (M3)" +} diff --git a/docs/api/qiskit-addon-mthree/classes.mdx b/docs/api/qiskit-addon-mthree/classes.mdx new file mode 100644 index 00000000000..a08762e654b --- /dev/null +++ b/docs/api/qiskit-addon-mthree/classes.mdx @@ -0,0 +1,26 @@ +--- +title: classes (latest version) +description: API reference for mthree.classes in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: mthree.classes +--- + +<span id="module-mthree.classes" /> + +<span id="mthree-classes" /> + +# Distributions + +| | | +| ---------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | +| [`QuasiDistribution`](/docs/api/qiskit-addon-mthree/mthree-classes-quasi-distribution "mthree.classes.QuasiDistribution")(data\[, shots, ...]) | A dict-like class for representing quasi-probabilities. | +| [`ProbDistribution`](/docs/api/qiskit-addon-mthree/mthree-classes-prob-distribution "mthree.classes.ProbDistribution")(data\[, shots, ...]) | A generic dict-like class for probability distributions. | + +# Distribution collections + +| | | +| ------------------------------------------------------------------------------------------------------------------------- | ---------------------------- | +| [`QuasiCollection`](/docs/api/qiskit-addon-mthree/mthree-classes-quasi-collection "mthree.classes.QuasiCollection")(data) | QuasiCollection constructor. | +| [`ProbCollection`](/docs/api/qiskit-addon-mthree/mthree-classes-prob-collection "mthree.classes.ProbCollection")(data) | ProbCollection constructor. | + diff --git a/docs/api/qiskit-addon-mthree/main.mdx b/docs/api/qiskit-addon-mthree/main.mdx new file mode 100644 index 00000000000..b9f1f29a1cb --- /dev/null +++ b/docs/api/qiskit-addon-mthree/main.mdx @@ -0,0 +1,18 @@ +--- +title: mthree (latest version) +description: API reference for mthree in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: mthree +--- + +<span id="module-mthree" /> + +<span id="mthree-main" /> + +# Mitigation classes + +| | | +| ----------------------------------------------------------------------------------------------------------------------- | -------------------------- | +| [`M3Mitigation`](/docs/api/qiskit-addon-mthree/mthree-m-3-mitigation "mthree.M3Mitigation")(\[system, iter\_threshold]) | Main M3 calibration class. | + diff --git a/docs/api/qiskit-addon-mthree/mthree-classes-prob-collection.mdx b/docs/api/qiskit-addon-mthree/mthree-classes-prob-collection.mdx new file mode 100644 index 00000000000..53518b4cb77 --- /dev/null +++ b/docs/api/qiskit-addon-mthree/mthree-classes-prob-collection.mdx @@ -0,0 +1,133 @@ +--- +title: ProbCollection (latest version) +description: API reference for mthree.classes.ProbCollection in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: mthree.classes.ProbCollection +--- + +<span id="mthree-classes-probcollection" /> + +# mthree.classes.ProbCollection + +<Class id="mthree.classes.ProbCollection" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mthree/tree/stable/3.0/mthree/classes.py" signature="ProbCollection(data)" modifiers="class"> + ProbCollection constructor. + + **Parameters** + + **data** (*list or* [*ProbCollection*](#mthree.classes.ProbCollection "mthree.classes.ProbCollection")) – List of ProbDistribution instances. + + **Raises** + + **TypeError** – Must be list of ProbDistribution only. + + ## Methods + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------- | + | [`expval`](#mthree.classes.ProbCollection.expval "mthree.classes.ProbCollection.expval")(\[exp\_ops]) | Expectation value over entire collection. | + | [`expval_and_stddev`](#mthree.classes.ProbCollection.expval_and_stddev "mthree.classes.ProbCollection.expval_and_stddev")(\[exp\_ops]) | Expectation value and standard deviation over entire collection. | + | [`stddev`](#mthree.classes.ProbCollection.stddev "mthree.classes.ProbCollection.stddev")() | Standard deviation over entire collection. | + + ## Attributes + + | | | + | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------- | + | [`mitigation_overhead`](#mthree.classes.ProbCollection.mitigation_overhead "mthree.classes.ProbCollection.mitigation_overhead") | Mitigation overhead over entire collection. | + | [`shots`](#mthree.classes.ProbCollection.shots "mthree.classes.ProbCollection.shots") | Number of shots taken over collection. | + + ### expval + + <Function id="mthree.classes.ProbCollection.expval" signature="expval(exp_ops='')"> + Expectation value over entire collection. + + **Parameters** + + **exp\_ops** (*str or dict or list*) – Diagonal operators over which to compute expval. + + **Returns** + + Array of expectation values. + + **Return type** + + ndarray + + **Raises** + + **M3Error** – Length of passes operators does not match container length. + + **Notes** + + The dict operator format is a sparse diagonal format using bitstrings as the keys. + </Function> + + ### expval\_and\_stddev + + <Function id="mthree.classes.ProbCollection.expval_and_stddev" signature="expval_and_stddev(exp_ops='')"> + Expectation value and standard deviation over entire collection. + + **Parameters** + + **exp\_ops** (*str or dict or list*) – Diagonal operators over which to compute expval. + + **Returns** + + Tuples of expval and stddev pairs. + + **Return type** + + list + + **Raises** + + **M3Error** – Length of passes operators does not match container length. + + **Notes** + + The dict operator format is a sparse diagonal format using bitstrings as the keys. + </Function> + + ### mitigation\_overhead + + <Attribute id="mthree.classes.ProbCollection.mitigation_overhead"> + Mitigation overhead over entire collection. + + **Returns** + + Array of mitigation overhead values. + + **Return type** + + ndarray + </Attribute> + + ### shots + + <Attribute id="mthree.classes.ProbCollection.shots"> + Number of shots taken over collection. + + **Returns** + + Array of shots values. + + **Return type** + + ndarray + </Attribute> + + ### stddev + + <Function id="mthree.classes.ProbCollection.stddev" signature="stddev()"> + Standard deviation over entire collection. + + **Returns** + + Array of standard deviations. + + **Return type** + + ndarray + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-mthree/mthree-classes-prob-distribution.mdx b/docs/api/qiskit-addon-mthree/mthree-classes-prob-distribution.mdx new file mode 100644 index 00000000000..4bae199554a --- /dev/null +++ b/docs/api/qiskit-addon-mthree/mthree-classes-prob-distribution.mdx @@ -0,0 +1,103 @@ +--- +title: ProbDistribution (latest version) +description: API reference for mthree.classes.ProbDistribution in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: mthree.classes.ProbDistribution +--- + +<span id="mthree-classes-probdistribution" /> + +# mthree.classes.ProbDistribution + +<Class id="mthree.classes.ProbDistribution" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mthree/tree/stable/3.0/mthree/classes.py" signature="ProbDistribution(data, shots=None, mitigation_overhead=None)" modifiers="class"> + A generic dict-like class for probability distributions. + + **Parameters** + + * **data** (*dict or Counts or* [*ProbDistribution*](#mthree.classes.ProbDistribution "mthree.classes.ProbDistribution") *or*[*QuasiDistribution*](mthree-classes-quasi-distribution "mthree.classes.QuasiDistribution")) – Input data. + * **shots** (*int*) – Number shots taken to form distribution. + + **Raises** + + **M3Error** – Input not derived from discrete samples. + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------- | + | [`expval`](#mthree.classes.ProbDistribution.expval "mthree.classes.ProbDistribution.expval")(\[exp\_ops]) | Compute expectation value from distribution. | + | [`expval_and_stddev`](#mthree.classes.ProbDistribution.expval_and_stddev "mthree.classes.ProbDistribution.expval_and_stddev")(\[exp\_ops]) | Compute expectation value and standard deviation from distribution. | + | [`stddev`](#mthree.classes.ProbDistribution.stddev "mthree.classes.ProbDistribution.stddev")() | Compute standard deviation from distribution. | + + ### expval + + <Function id="mthree.classes.ProbDistribution.expval" signature="expval(exp_ops='')"> + Compute expectation value from distribution. + + **Parameters** + + **exp\_ops** (*str or dict or list*) – String representation of diagonal qubit operators used in computing the expectation value. + + **Returns** + + Expectation value. + + **Return type** + + float + + **Raises** + + **M3Error** – Invalid type passed to exp\_ops + + **Notes** + + The dict operator format is a sparse diagonal format using bitstrings as the keys. + </Function> + + ### expval\_and\_stddev + + <Function id="mthree.classes.ProbDistribution.expval_and_stddev" signature="expval_and_stddev(exp_ops='')"> + Compute expectation value and standard deviation from distribution. + + **Parameters** + + **exp\_ops** (*str or dict*) – String or dict representation of diagonal qubit operators used in computing the expectation value. + + **Returns** + + Expectation value. float: Standard deviation. + + **Return type** + + float + + **Notes** + + The dict operator format is a sparse diagonal format using bitstrings as the keys. + </Function> + + ### stddev + + <Function id="mthree.classes.ProbDistribution.stddev" signature="stddev()"> + Compute standard deviation from distribution. + + **Returns** + + Standard deviation. + + **Return type** + + float + + **Raises** + + **M3Error** – Distribution is missing info. + + **Notes** + + The dict operator format is a sparse diagonal format using bitstrings as the keys. + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-mthree/mthree-classes-quasi-collection.mdx b/docs/api/qiskit-addon-mthree/mthree-classes-quasi-collection.mdx new file mode 100644 index 00000000000..e57211b2556 --- /dev/null +++ b/docs/api/qiskit-addon-mthree/mthree-classes-quasi-collection.mdx @@ -0,0 +1,148 @@ +--- +title: QuasiCollection (latest version) +description: API reference for mthree.classes.QuasiCollection in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: mthree.classes.QuasiCollection +--- + +<span id="mthree-classes-quasicollection" /> + +# mthree.classes.QuasiCollection + +<Class id="mthree.classes.QuasiCollection" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mthree/tree/stable/3.0/mthree/classes.py" signature="QuasiCollection(data)" modifiers="class"> + QuasiCollection constructor. + + **Parameters** + + **data** (*list or* [*QuasiCollection*](#mthree.classes.QuasiCollection "mthree.classes.QuasiCollection")) – List of QuasiDistribution instances. + + **Raises** + + **TypeError** – Must be list of QuasiDistribution only. + + ## Methods + + | | | + | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------- | + | [`expval`](#mthree.classes.QuasiCollection.expval "mthree.classes.QuasiCollection.expval")(\[exp\_ops]) | Expectation value over entire collection. | + | [`expval_and_stddev`](#mthree.classes.QuasiCollection.expval_and_stddev "mthree.classes.QuasiCollection.expval_and_stddev")(\[exp\_ops]) | Expectation value and standard deviation over entire collection. | + | [`nearest_probability_distribution`](#mthree.classes.QuasiCollection.nearest_probability_distribution "mthree.classes.QuasiCollection.nearest_probability_distribution")() | Nearest probability distribution over collection | + | [`stddev`](#mthree.classes.QuasiCollection.stddev "mthree.classes.QuasiCollection.stddev")() | Standard deviation over entire collection. | + + ## Attributes + + | | | + | --------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------- | + | [`mitigation_overhead`](#mthree.classes.QuasiCollection.mitigation_overhead "mthree.classes.QuasiCollection.mitigation_overhead") | Mitigation overhead over entire collection. | + | [`shots`](#mthree.classes.QuasiCollection.shots "mthree.classes.QuasiCollection.shots") | Number of shots taken over collection. | + + ### expval + + <Function id="mthree.classes.QuasiCollection.expval" signature="expval(exp_ops='')"> + Expectation value over entire collection. + + **Parameters** + + **exp\_ops** (*str or dict or list*) – Diagonal operators over which to compute expval. + + **Returns** + + Array of expectation values. + + **Return type** + + ndarray + + **Raises** + + **M3Error** – Length of passes operators does not match container length. + + **Notes** + + The dict operator format is a sparse diagonal format using bitstrings as the keys. + </Function> + + ### expval\_and\_stddev + + <Function id="mthree.classes.QuasiCollection.expval_and_stddev" signature="expval_and_stddev(exp_ops='')"> + Expectation value and standard deviation over entire collection. + + **Parameters** + + **exp\_ops** (*str or dict or list*) – Diagonal operators over which to compute expval. + + **Returns** + + Tuples of expval and stddev pairs. + + **Return type** + + list + + **Raises** + + **M3Error** – Length of passes operators does not match container length. + + **Notes** + + The dict operator format is a sparse diagonal format using bitstrings as the keys. + </Function> + + ### mitigation\_overhead + + <Attribute id="mthree.classes.QuasiCollection.mitigation_overhead"> + Mitigation overhead over entire collection. + + **Returns** + + Array of mitigation overhead values. + + **Return type** + + ndarray + </Attribute> + + ### nearest\_probability\_distribution + + <Function id="mthree.classes.QuasiCollection.nearest_probability_distribution" signature="nearest_probability_distribution()"> + Nearest probability distribution over collection + + **Returns** + + Collection of ProbDistributions. + + **Return type** + + [ProbCollection](mthree-classes-prob-collection "mthree.classes.ProbCollection") + </Function> + + ### shots + + <Attribute id="mthree.classes.QuasiCollection.shots"> + Number of shots taken over collection. + + **Returns** + + Array of shots values. + + **Return type** + + ndarray + </Attribute> + + ### stddev + + <Function id="mthree.classes.QuasiCollection.stddev" signature="stddev()"> + Standard deviation over entire collection. + + **Returns** + + Array of standard deviations. + + **Return type** + + ndarray + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-mthree/mthree-classes-quasi-distribution.mdx b/docs/api/qiskit-addon-mthree/mthree-classes-quasi-distribution.mdx new file mode 100644 index 00000000000..05747fc7940 --- /dev/null +++ b/docs/api/qiskit-addon-mthree/mthree-classes-quasi-distribution.mdx @@ -0,0 +1,115 @@ +--- +title: QuasiDistribution (latest version) +description: API reference for mthree.classes.QuasiDistribution in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: mthree.classes.QuasiDistribution +--- + +<span id="mthree-classes-quasidistribution" /> + +# mthree.classes.QuasiDistribution + +<Class id="mthree.classes.QuasiDistribution" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mthree/tree/stable/3.0/mthree/classes.py" signature="QuasiDistribution(data, shots=None, mitigation_overhead=None)" modifiers="class"> + A dict-like class for representing quasi-probabilities. + + **Parameters** + + * **data** (*dict*) – Input data. + * **shots** (*int*) – Number shots taken to form quasi-distribution. + * **mitigation\_overhead** (*float*) – Overhead from performing mitigation. + + ## Methods + + | | | + | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------- | + | [`expval`](#mthree.classes.QuasiDistribution.expval "mthree.classes.QuasiDistribution.expval")(\[exp\_ops]) | Compute expectation value from distribution. | + | [`expval_and_stddev`](#mthree.classes.QuasiDistribution.expval_and_stddev "mthree.classes.QuasiDistribution.expval_and_stddev")(\[exp\_ops]) | Compute expectation value and standard deviation estimate from distribution. | + | [`nearest_probability_distribution`](#mthree.classes.QuasiDistribution.nearest_probability_distribution "mthree.classes.QuasiDistribution.nearest_probability_distribution")(\[...]) | Takes a quasiprobability distribution and maps it to the closest probability distribution as defined by the L2-norm. | + | [`stddev`](#mthree.classes.QuasiDistribution.stddev "mthree.classes.QuasiDistribution.stddev")() | Compute standard deviation estimate from distribution. | + + ### expval + + <Function id="mthree.classes.QuasiDistribution.expval" signature="expval(exp_ops='')"> + Compute expectation value from distribution. + + **Parameters** + + **exp\_ops** (*str or dict or list*) – String or dict representation of diagonal qubit operators used in computing the expectation value. + + **Returns** + + Expectation value. + + **Return type** + + float + + **Raises** + + **M3Error** – Invalid type passed to exp\_ops. + </Function> + + ### expval\_and\_stddev + + <Function id="mthree.classes.QuasiDistribution.expval_and_stddev" signature="expval_and_stddev(exp_ops='')"> + Compute expectation value and standard deviation estimate from distribution. + + **Parameters** + + **exp\_ops** (*str or dict*) – String or dict representation of diagonal qubit operators used in computing the expectation value. + + **Returns** + + Expectation value. float: Estimate of standard deviation upper-bound. + + **Return type** + + float + + **Notes** + + The dict operator format is a sparse diagonal format using bitstrings as the keys. + </Function> + + ### nearest\_probability\_distribution + + <Function id="mthree.classes.QuasiDistribution.nearest_probability_distribution" signature="nearest_probability_distribution(return_distance=False)"> + Takes a quasiprobability distribution and maps it to the closest probability distribution as defined by the L2-norm. + + **Parameters** + + **return\_distance** (*bool*) – Return the L2 distance between distributions. + + **Returns** + + Nearest probability distribution. float: Euclidean (L2) distance of distributions. + + **Return type** + + [ProbDistribution](mthree-classes-prob-distribution "mthree.classes.ProbDistribution") + + **Notes** + + Method from Smolin et al., Phys. Rev. Lett. 108, 070502 (2012). + </Function> + + ### stddev + + <Function id="mthree.classes.QuasiDistribution.stddev" signature="stddev()"> + Compute standard deviation estimate from distribution. + + **Returns** + + Estimate of standard deviation upper-bound. + + **Return type** + + float + + **Raises** + + **M3Error** – Missing shots or mitigation\_overhead information. + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-mthree/mthree-m-3-mitigation.mdx b/docs/api/qiskit-addon-mthree/mthree-m-3-mitigation.mdx new file mode 100644 index 00000000000..de8461034e5 --- /dev/null +++ b/docs/api/qiskit-addon-mthree/mthree-m-3-mitigation.mdx @@ -0,0 +1,276 @@ +--- +title: M3Mitigation (latest version) +description: API reference for mthree.M3Mitigation in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 1 +python_api_type: class +python_api_name: mthree.M3Mitigation +--- + +<span id="mthree-m3mitigation" /> + +# mthree.M3Mitigation + +<Class id="mthree.M3Mitigation" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mthree/tree/stable/3.0/mthree/mitigation.py" signature="M3Mitigation(system=None, iter_threshold=4096)" modifiers="class"> + Main M3 calibration class. + + **Parameters** + + * **system** (*Backend*) – Target backend. + * **iter\_threshold** (*int*) – Sets the bitstring count at which iterative mode is turned on (assuming reasonable error rates). + + ### system + + <Attribute id="mthree.M3Mitigation.system"> + The target system or execution manager. + + **Type** + + Backend + </Attribute> + + ### system\_info + + <Attribute id="mthree.M3Mitigation.system_info"> + Information needed about the system + + **Type** + + dict + </Attribute> + + ### cal\_method + + <Attribute id="mthree.M3Mitigation.cal_method"> + Calibration method used + + **Type** + + str + </Attribute> + + ### cal\_timestamp + + <Attribute id="mthree.M3Mitigation.cal_timestamp"> + Time at which cals were taken + + **Type** + + str + </Attribute> + + ### single\_qubit\_cals + + <Attribute id="mthree.M3Mitigation.single_qubit_cals"> + 1Q calibration matrices + + **Type** + + list + </Attribute> + + ## Methods + + | | | + | ---------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- | + | [`apply_correction`](#mthree.M3Mitigation.apply_correction "mthree.M3Mitigation.apply_correction")(counts, qubits\[, distance, ...]) | Applies correction to given counts. | + | [`cals_from_file`](#mthree.M3Mitigation.cals_from_file "mthree.M3Mitigation.cals_from_file")(cals\_file) | Generated the calibration data from a previous runs output | + | [`cals_from_matrices`](#mthree.M3Mitigation.cals_from_matrices "mthree.M3Mitigation.cals_from_matrices")(matrices) | Init calibration data from list of NumPy arrays. | + | [`cals_from_system`](#mthree.M3Mitigation.cals_from_system "mthree.M3Mitigation.cals_from_system")(\[qubits, shots, method, ...]) | Grab calibration data from system. | + | [`cals_to_file`](#mthree.M3Mitigation.cals_to_file "mthree.M3Mitigation.cals_to_file")(\[cals\_file]) | Save calibration data to JSON file. | + | [`cals_to_matrices`](#mthree.M3Mitigation.cals_to_matrices "mthree.M3Mitigation.cals_to_matrices")() | Return single qubit cals as list of NumPy arrays | + | [`readout_fidelity`](#mthree.M3Mitigation.readout_fidelity "mthree.M3Mitigation.readout_fidelity")(\[qubits]) | Compute readout fidelity for calibrated qubits. | + | [`reduced_cal_matrix`](#mthree.M3Mitigation.reduced_cal_matrix "mthree.M3Mitigation.reduced_cal_matrix")(counts, qubits\[, distance]) | Return the reduced calibration matrix used in the solution. | + | [`tensored_cals_from_file`](#mthree.M3Mitigation.tensored_cals_from_file "mthree.M3Mitigation.tensored_cals_from_file")(cals\_file) | Generated the tensored calibration data from a previous runs output | + | [`tensored_cals_from_system`](#mthree.M3Mitigation.tensored_cals_from_system "mthree.M3Mitigation.tensored_cals_from_system")(\[qubits, shots, ...]) | Grab calibration data from system. | + + ### apply\_correction + + <Function id="mthree.M3Mitigation.apply_correction" signature="apply_correction(counts, qubits, distance=None, method='auto', max_iter=25, tol=0.0001, return_mitigation_overhead=False, details=False)"> + Applies correction to given counts. + + **Parameters** + + * **counts** (*dict, list*) – Input counts dict or list of dicts. + * **qubits** (*dict, array\_like*) – Qubits on which measurements applied. + * **distance** (*int*) – Distance to correct for. Default=num\_bits + * **method** (*str*) – Solution method: ‘auto’, ‘direct’ or ‘iterative’. + * **max\_iter** (*int*) – Max. number of iterations, Default=25. + * **tol** (*float*) – Convergence tolerance of iterative method, Default=1e-4. + * **return\_mitigation\_overhead** (*bool*) – Returns the mitigation overhead, default=False. + * **details** (*bool*) – Return extra info, default=False. + + **Returns** + + **Dictionary of quasiprobabilities if** + + input is a single dict, else a collection of quasiprobabilities. + + **Return type** + + [QuasiDistribution](mthree-classes-quasi-distribution "mthree.classes.QuasiDistribution") or [QuasiCollection](mthree-classes-quasi-collection "mthree.classes.QuasiCollection") + + **Raises** + + **M3Error** – Bitstring length does not match number of qubits given. + </Function> + + ### cals\_from\_file + + <Function id="mthree.M3Mitigation.cals_from_file" signature="cals_from_file(cals_file)"> + Generated the calibration data from a previous runs output + + **Parameters** + + **cals\_file** (*str*) – A string path to the saved counts file from an earlier run. + + **Raises** + + **M3Error** – Calibration in progress. + </Function> + + ### cals\_from\_matrices + + <Function id="mthree.M3Mitigation.cals_from_matrices" signature="cals_from_matrices(matrices)"> + Init calibration data from list of NumPy arrays. + + Missing entries are set to None elements. + + **Parameters** + + **matrices** (*list\_like*) – List of cals as NumPy arrays + + **Raises** + + **M3Error** – If system set error if list length != num\_qubits on system + </Function> + + ### cals\_from\_system + + <Function id="mthree.M3Mitigation.cals_from_system" signature="cals_from_system(qubits=None, shots=None, method=None, initial_reset=False, rep_delay=None, cals_file=None, async_cal=True, runtime_mode=None)"> + Grab calibration data from system. + + **Parameters** + + * **qubits** (*array\_like*) – Qubits over which to correct calibration data. Default is all. + * **shots** (*int*) – Number of shots per circuit. min(1e4, max\_shots). + * **method** (*str*) – Type of calibration, ‘balanced’ (default for hardware), ‘independent’ (default for simulators), or ‘marginal’. + * **initial\_reset** (*bool*) – Use resets at beginning of calibration circuits, default=False. + * **rep\_delay** (*float*) – Delay between circuits on IBM Quantum backends. + * **cals\_file** (*str*) – Output path to write JSON calibration data to. + * **async\_cal** (*bool*) – Do calibration async in a separate thread, default is True. + * **runtime\_mode** (*Batch or Session*) – Mode to run jobs in if using IBM system, default=None + + **Returns** + + List of jobs submitted. + + **Return type** + + list + + **Raises** + + **M3Error** – Called while a calibration currently in progress. + </Function> + + ### cals\_to\_file + + <Function id="mthree.M3Mitigation.cals_to_file" signature="cals_to_file(cals_file=None)"> + Save calibration data to JSON file. + + **Parameters** + + **cals\_file** (*str*) – File in which to store calibrations. + + **Raises** + + * **M3Error** – Calibration filename missing. + * **M3Error** – Mitigator is not calibrated. + </Function> + + ### cals\_to\_matrices + + <Function id="mthree.M3Mitigation.cals_to_matrices" signature="cals_to_matrices()"> + Return single qubit cals as list of NumPy arrays + + **Returns** + + List of cals as NumPy arrays + + **Return type** + + list + </Function> + + ### readout\_fidelity + + <Function id="mthree.M3Mitigation.readout_fidelity" signature="readout_fidelity(qubits=None)"> + Compute readout fidelity for calibrated qubits. + + **Parameters** + + **qubits** (*array\_like*) – Qubits to compute over, default is all. + + **Returns** + + List of qubit fidelities. + + **Return type** + + list + + **Raises** + + * **M3Error** – Mitigator is not calibrated. + * **M3Error** – Qubit indices out of range. + </Function> + + ### reduced\_cal\_matrix + + <Function id="mthree.M3Mitigation.reduced_cal_matrix" signature="reduced_cal_matrix(counts, qubits, distance=None)"> + Return the reduced calibration matrix used in the solution. + + **Parameters** + + * **counts** (*dict*) – Input counts dict. + * **qubits** (*array\_like*) – Qubits on which measurements applied. + * **distance** (*int*) – Distance to correct for. Default=num\_bits + + **Returns** + + 2D array of reduced calibrations. dict: Counts in order they are displayed in matrix. + + **Return type** + + ndarray + + **Raises** + + **M3Error** – If bit-string length does not match passed number of qubits. + </Function> + + ### tensored\_cals\_from\_file + + <Function id="mthree.M3Mitigation.tensored_cals_from_file" signature="tensored_cals_from_file(cals_file)"> + Generated the tensored calibration data from a previous runs output + + **Parameters** + + **cals\_file** (*str*) – A string path to the saved counts file from an earlier run. + </Function> + + ### tensored\_cals\_from\_system + + <Function id="mthree.M3Mitigation.tensored_cals_from_system" signature="tensored_cals_from_system(qubits=None, shots=None, method='balanced', rep_delay=None, cals_file=None)"> + Grab calibration data from system. + + **Parameters** + + * **qubits** (*array\_like*) – Qubits over which to correct calibration data. Default is all. + * **shots** (*int*) – Number of shots per circuit. Default is min(1e4, max\_shots). + * **method** (*str*) – Type of calibration, ‘balanced’ (default), ‘independent’, or ‘marginal’. + * **rep\_delay** (*float*) – Delay between circuits on IBM Quantum backends. + * **cals\_file** (*str*) – Output path to write JSON calibration data to. + </Function> +</Class> + diff --git a/docs/api/qiskit-addon-mthree/mthree-utils-expval-and-stddev.mdx b/docs/api/qiskit-addon-mthree/mthree-utils-expval-and-stddev.mdx new file mode 100644 index 00000000000..85b477d837c --- /dev/null +++ b/docs/api/qiskit-addon-mthree/mthree-utils-expval-and-stddev.mdx @@ -0,0 +1,18 @@ +--- +title: expval_and_stddev (latest version) +description: API reference for mthree.utils.expval_and_stddev in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 1 +python_api_type: function +python_api_name: mthree.utils.expval_and_stddev +--- + +<span id="mthree-utils-expval-and-stddev" /> + +# mthree.utils.expval\_and\_stddev + +<Function id="mthree.utils.expval_and_stddev" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mthree/tree/stable/3.0/mthree/utils.py" signature="expval_and_stddev(items, exp_ops='')"> + Compute expectation values from distributions. + + <Admonition title="Added in version 0.16.0" type="info" /> +</Function> + diff --git a/docs/api/qiskit-addon-mthree/mthree-utils-expval.mdx b/docs/api/qiskit-addon-mthree/mthree-utils-expval.mdx new file mode 100644 index 00000000000..da840327fdb --- /dev/null +++ b/docs/api/qiskit-addon-mthree/mthree-utils-expval.mdx @@ -0,0 +1,18 @@ +--- +title: expval (latest version) +description: API reference for mthree.utils.expval in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 1 +python_api_type: function +python_api_name: mthree.utils.expval +--- + +<span id="mthree-utils-expval" /> + +# mthree.utils.expval + +<Function id="mthree.utils.expval" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mthree/tree/stable/3.0/mthree/utils.py" signature="expval(items, exp_ops='')"> + Compute expectation values from distributions. + + <Admonition title="Added in version 0.16.0" type="info" /> +</Function> + diff --git a/docs/api/qiskit-addon-mthree/mthree-utils-final-measurement-mapping.mdx b/docs/api/qiskit-addon-mthree/mthree-utils-final-measurement-mapping.mdx new file mode 100644 index 00000000000..827467909df --- /dev/null +++ b/docs/api/qiskit-addon-mthree/mthree-utils-final-measurement-mapping.mdx @@ -0,0 +1,30 @@ +--- +title: final_measurement_mapping (latest version) +description: API reference for mthree.utils.final_measurement_mapping in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 1 +python_api_type: function +python_api_name: mthree.utils.final_measurement_mapping +--- + +<span id="mthree-utils-final-measurement-mapping" /> + +# mthree.utils.final\_measurement\_mapping + +<Function id="mthree.utils.final_measurement_mapping" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mthree/tree/stable/3.0/mthree/utils.py" signature="final_measurement_mapping(circuit)"> + Return the final measurement mapping for the circuit. + + Dict keys label measured qubits, whereas the values indicate the classical bit onto which that qubits measurement result is stored. + + **Parameters** + + **circuit** (*QuantumCircuit or list*) – Input Qiskit QuantumCircuit or circuits. + + **Returns** + + Mapping of classical bits to qubits for final measurements. + + **Return type** + + dict or list +</Function> + diff --git a/docs/api/qiskit-addon-mthree/mthree-utils-marginal-distribution.mdx b/docs/api/qiskit-addon-mthree/mthree-utils-marginal-distribution.mdx new file mode 100644 index 00000000000..eb5f3728094 --- /dev/null +++ b/docs/api/qiskit-addon-mthree/mthree-utils-marginal-distribution.mdx @@ -0,0 +1,39 @@ +--- +title: marginal_distribution (latest version) +description: API reference for mthree.utils.marginal_distribution in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 1 +python_api_type: function +python_api_name: mthree.utils.marginal_distribution +--- + +<span id="mthree-utils-marginal-distribution" /> + +# mthree.utils.marginal\_distribution + +<Function id="mthree.utils.marginal_distribution" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mthree/tree/stable/3.0/mthree/utils.py" signature="marginal_distribution(dist, indices, mapping=None)"> + Grab the marginal counts from a given distribution. + + If an operator is passed for the indices then the position of the non-identity elements in the string will be used to set the indices to marginalize over. + + The mapping passed will be marginalized so that it can be directly used in applying the correction. The type of mapping at output is the same as that input. + + **Parameters** + + * **dist** (*dict*) – Input distribution + * **indices** (*array\_like or str*) – Indices (qubits) to keep or operator string + * **mapping** (*dict or array\_like*) – Optional, final measurement mapping. + + **Returns** + + Marginal distribution list or dict: The reduced mapping if an optional mapping (list or dict) is given + + **Return type** + + dict + + **Raises** + + * **M3Error** – Operator length does not equal bit-string length + * **M3Error** – One or more indices is out of bounds +</Function> + diff --git a/docs/api/qiskit-addon-mthree/mthree-utils-stddev.mdx b/docs/api/qiskit-addon-mthree/mthree-utils-stddev.mdx new file mode 100644 index 00000000000..34095ce0aa0 --- /dev/null +++ b/docs/api/qiskit-addon-mthree/mthree-utils-stddev.mdx @@ -0,0 +1,18 @@ +--- +title: stddev (latest version) +description: API reference for mthree.utils.stddev in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 1 +python_api_type: function +python_api_name: mthree.utils.stddev +--- + +<span id="mthree-utils-stddev" /> + +# mthree.utils.stddev + +<Function id="mthree.utils.stddev" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-addon-mthree/tree/stable/3.0/mthree/utils.py" signature="stddev(items)"> + Compute expectation values from distributions. + + <Admonition title="Added in version 0.16.0" type="info" /> +</Function> + diff --git a/docs/api/qiskit-addon-mthree/utils.mdx b/docs/api/qiskit-addon-mthree/utils.mdx new file mode 100644 index 00000000000..504a93b4582 --- /dev/null +++ b/docs/api/qiskit-addon-mthree/utils.mdx @@ -0,0 +1,22 @@ +--- +title: utils (latest version) +description: API reference for mthree.utils in the latest version of qiskit-addon-mthree +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: mthree.utils +--- + +<span id="module-mthree.utils" /> + +<span id="mthree-utils" /> + +# Utility functions + +| | | +| ----------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------- | +| [`final_measurement_mapping`](/docs/api/qiskit-addon-mthree/mthree-utils-final-measurement-mapping "mthree.utils.final_measurement_mapping")(circuit) | Return the final measurement mapping for the circuit. | +| [`expval`](/docs/api/qiskit-addon-mthree/mthree-utils-expval "mthree.utils.expval")(items\[, exp\_ops]) | Compute expectation values from distributions. | +| [`stddev`](/docs/api/qiskit-addon-mthree/mthree-utils-stddev "mthree.utils.stddev")(items) | Compute expectation values from distributions. | +| [`expval_and_stddev`](/docs/api/qiskit-addon-mthree/mthree-utils-expval-and-stddev "mthree.utils.expval_and_stddev")(items\[, exp\_ops]) | Compute expectation values from distributions. | +| [`marginal_distribution`](/docs/api/qiskit-addon-mthree/mthree-utils-marginal-distribution "mthree.utils.marginal_distribution")(dist, indices\[, mapping]) | Grab the marginal counts from a given distribution. | + diff --git a/public/docs/api/qiskit-addon-mthree/objects.inv b/public/docs/api/qiskit-addon-mthree/objects.inv new file mode 100644 index 0000000000000000000000000000000000000000..5e98a3b8af9f88037fe4b4792112c42215a5c8e1 GIT binary patch literal 1630 zcmV-k2BG;QAX9K?X>NERX>N99Zgg*Qc_4OWa&u{KZXhxWBOp+6Z)#;@bUGkSbZByA zWgs&yFfK3(BOq2~a&u{KZaN?{E-)@I3L_v?Xk{RBWo=<;Ze(S0Aa7<MbZBXFAZBT7 zWguyDAY*TBaB^jHb7f>8b#rNMXCQiPX<{x4c-qZdOK;;i48HHL2;g4b?xMF|XRZV6 z9`<Y~vC<mwYb?2#{QH4li7b&4si^H7n~)zrN)+|T*G9h$QXcxtOVgEOEf0Sm8d0^` z@>l+F$p30)lgA&=KPp|l3SD*WWPhrKQip#I%h!36Kj+QO0Cam4eP3USQ#TCqwBa;T zzjY-BlsPUi@=4Uiiqv0Tgw{iK9JNGY4SO6=SB+F!G(8>j=7Pei;yiRsajt4f$lr1Z zp*BJf)k!KA^3^GXt}aT;hK0Slc#w6+8jH=`kH{Sd(*;Hzq$s;l7w4*!b)_!^WXA<V zF2|EBi@7Ez62C~;juVWos`j;(#n16r>B(BrirIBT;IAb?!1UF^@?}xA=Z=H6IKYrv 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--- a/scripts/config/historical-pages-to-latest.json +++ b/scripts/config/historical-pages-to-latest.json @@ -1511,6 +1511,7 @@ "noise-management-post-selection": "/" } }, + "qiskit-addon-mthree": {}, "qiskit-addon-pna": {}, "qiskit-addon-slc": {}, "qiskit-addon-opt-mapper": {}, diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index bddae65df3f..ecb71b6e561 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -68,7 +68,7 @@ export class Pkg { "qiskit-addon-sqd-hpc", "qiskit-addon-cutting", "qiskit-addon-utils", - // "qiskit-addon-mthree", + "qiskit-addon-mthree", "qiskit-addon-pna", "qiskit-addon-slc", "qiskit-addon-opt-mapper", From 5f3b57208fb142b0d37abb5b5eba91c742eb3492 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 26 May 2026 22:09:07 -0400 Subject: [PATCH 091/104] updates guides for mthree --- docs/guides/addons.mdx | 2 +- docs/guides/error-mitigation-overview.mdx | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index e36821f60f7..f4bbfc27b79 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -121,7 +121,7 @@ These techniques are useful for managing noise on sampling results. <Card title="Matrix-free Measurement Mitigation (M3)" description="Mitigate measurement errors by processing in a reduced subspace defined by noisy bitstrings." - href="https://qiskit.github.io/qiskit-addon-mthree/" + href="/docs/addons/qiskit-addon-mthree/" analyticsName="Documentation page: M3" linkText="Browse documentation" /> diff --git a/docs/guides/error-mitigation-overview.mdx b/docs/guides/error-mitigation-overview.mdx index 729c455eb50..bc0ff2024a9 100644 --- a/docs/guides/error-mitigation-overview.mdx +++ b/docs/guides/error-mitigation-overview.mdx @@ -123,7 +123,7 @@ When executing quantum workloads, there are multiple ways to reduce the impact o <Card title="Matrix-free Measurement Mitigation" description="Matrix-free Measurement Mitigation (M3) is a package for scalable quantum measurement error mitigation that can be computed in parallel." - href="https://qiskit.github.io/qiskit-addon-mthree/" + href="/docs/addons/qiskit-addon-mthree/" analyticsName="Documentation page: M3" linkText="Browse documentation" /> From 0ace261e412c259cda1356f4fef881285af32720 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 26 May 2026 22:29:41 -0400 Subject: [PATCH 092/104] remove release notes from mthree --- docs/api/qiskit-addon-mthree/_toc.json | 5 ----- scripts/js/lib/api/Pkg.ts | 1 + 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/docs/api/qiskit-addon-mthree/_toc.json b/docs/api/qiskit-addon-mthree/_toc.json index 37d224d6c16..9a3a6da5746 100644 --- a/docs/api/qiskit-addon-mthree/_toc.json +++ b/docs/api/qiskit-addon-mthree/_toc.json @@ -5,11 +5,6 @@ "title": "API index", "url": "/docs/api/qiskit-addon-mthree" }, - { - "title": "Release notes", - "useDivider": true, - "url": "/docs/api/qiskit-addon-mthree/release-notes" - }, { "title": "mthree", "untranslatable": true, diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index ecb71b6e561..8bc3526bf3b 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -254,6 +254,7 @@ export class Pkg { githubSlug: "Qiskit/qiskit-addon-mthree", kebabCaseAndShortenUrls: true, language: "Python", + releaseNotesConfig: new ReleaseNotesConfig({ enabled: false }), }); } if (name === "qiskit-addon-pna") { From fb51da863547fabf850593d1daf2619a4477c9c2 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 26 May 2026 22:40:16 -0400 Subject: [PATCH 093/104] rename main to index --- scripts/js/lib/api/specialCaseResults.ts | 1 + 1 file changed, 1 insertion(+) diff --git a/scripts/js/lib/api/specialCaseResults.ts b/scripts/js/lib/api/specialCaseResults.ts index 41ad208bf25..96050f4cbe7 100644 --- a/scripts/js/lib/api/specialCaseResults.ts +++ b/scripts/js/lib/api/specialCaseResults.ts @@ -21,6 +21,7 @@ export function transformSpecialCaseUrl(url: string): string { .replace(/(?<=^|\/)explanation(?=\/|#|$)/g, "explanations") .replace(/(?<=^|\/)terra(?=#|$)/g, "index") .replace(/(?<=^|\/)ibm-runtime(?=#|$)/g, "index") + .replace(/(?<=^|\/)main(?=#|$)/g, "index") ); } From afaeadff0350a14f799e41412edc21d47f73f4c6 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Tue, 26 May 2026 22:40:48 -0400 Subject: [PATCH 094/104] rename main to index --- docs/api/qiskit-addon-mthree/_toc.json | 2 +- .../{main.mdx => index.mdx} | 7 ++----- .../docs/api/qiskit-addon-mthree/objects.inv | Bin 1630 -> 1631 bytes 3 files changed, 3 insertions(+), 6 deletions(-) rename docs/api/qiskit-addon-mthree/{main.mdx => index.mdx} (73%) diff --git a/docs/api/qiskit-addon-mthree/_toc.json b/docs/api/qiskit-addon-mthree/_toc.json index 9a3a6da5746..ed4eca1f671 100644 --- a/docs/api/qiskit-addon-mthree/_toc.json +++ b/docs/api/qiskit-addon-mthree/_toc.json @@ -11,7 +11,7 @@ "children": [ { "title": "Module overview", - "url": "/docs/api/qiskit-addon-mthree/main" + "url": "/docs/api/qiskit-addon-mthree" }, { "title": "M3Mitigation", diff --git a/docs/api/qiskit-addon-mthree/main.mdx b/docs/api/qiskit-addon-mthree/index.mdx similarity index 73% rename from docs/api/qiskit-addon-mthree/main.mdx rename to docs/api/qiskit-addon-mthree/index.mdx index b9f1f29a1cb..02e506236db 100644 --- a/docs/api/qiskit-addon-mthree/main.mdx +++ b/docs/api/qiskit-addon-mthree/index.mdx @@ -1,9 +1,6 @@ --- -title: mthree (latest version) -description: API reference for mthree in the latest version of qiskit-addon-mthree -in_page_toc_min_heading_level: 2 -python_api_type: module -python_api_name: mthree +title: Matrix-free Measurement Mitigation (M3) API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-addon-mthree. --- <span id="module-mthree" /> diff --git 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z84BG*jOjCqJG1n@7H?BWf}SdO5){BOfSExsp*UI6*n6E#;v9`z7HJ4g34e|sjvh1% z#9?sCK9@2oW%}S@MAc4#Im87|G<ZIlj2J(HbM|MX78Ia;@^pY&wLo3Ka<wolk3dF< z$>f-QT^e3#;3o|=sc}PX#?xD{z8vgqPzSy@_FgF&m7^s_oI%R&gli^E$)IVCX$*I2 zKGOly|Lq^!U@hc>MQS@&ZD^eKdc_BbmpMR?obUmlwwsfkg4i4g@;eszY-#=HlnWdu zqeEkTw-=@-8cS*dMzsIO1P{ply&Dfl%rNHX5A;9;#0eflDW|IdF5}?D+0W6acW=?u VodsnOy<wpJ6%B=V(chBM-A2Zi=B5Au From 8a7b6633a2971b1dcd5f44e380ab5bb6198dd45f Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 28 May 2026 12:26:34 -0400 Subject: [PATCH 095/104] simplify addon toc --- docs/addons/qiskit-addon-aqc-tensor/_toc.json | 14 +- docs/addons/qiskit-addon-cutting/_toc.json | 91 +++- docs/addons/qiskit-addon-mpf/_toc.json | 19 +- docs/addons/qiskit-addon-mthree/_toc.json | 140 ++++-- docs/addons/qiskit-addon-obp/_toc.json | 6 +- docs/addons/qiskit-addon-opt-mapper/_toc.json | 8 +- .../how-tos/01-optimization-problem.ipynb | 62 +-- ...converters-for-optimization-problems.ipynb | 46 +- .../how-tos/04-translate-docplex.ipynb | 18 +- docs/addons/qiskit-addon-pna/_toc.json | 8 +- docs/addons/qiskit-addon-slc/_toc.json | 21 +- docs/addons/qiskit-addon-sqd-hpc/_toc.json | 4 - docs/addons/qiskit-addon-sqd/_toc.json | 8 +- docs/addons/qiskit-addon-utils/_toc.json | 8 +- .../color-device-edges-to-improve-depth.ipynb | 2 +- scripts/js/lib/api/addonDocsPipeline.ts | 10 +- scripts/js/lib/api/generateAddonToc.test.ts | 427 ++++++++---------- scripts/js/lib/api/generateAddonToc.ts | 270 ++++------- 18 files changed, 578 insertions(+), 584 deletions(-) diff --git a/docs/addons/qiskit-addon-aqc-tensor/_toc.json b/docs/addons/qiskit-addon-aqc-tensor/_toc.json index 51c88c93780..fc2eb44e170 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/_toc.json +++ b/docs/addons/qiskit-addon-aqc-tensor/_toc.json @@ -8,20 +8,20 @@ "title": "", "children": [ { - "title": "Home", + "title": "Documentation Home", "url": "/docs/addons/qiskit-addon-aqc-tensor" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-aqc-tensor/install" }, { - "title": "Guides", + "title": "Explanatory Material", + "url": "/docs/addons/qiskit-addon-aqc-tensor" + }, + { + "title": "How-To Guides", "children": [ - { - "title": "Explanatory Material on AQC-Tensor", - "url": "/docs/addons/qiskit-addon-aqc-tensor/explanations/index" - }, { "title": "How to use quimb.tensor.TNOptimizer directly", "url": "/docs/addons/qiskit-addon-aqc-tensor/how-tos/01-quimb-tnoptimizer" diff --git a/docs/addons/qiskit-addon-cutting/_toc.json b/docs/addons/qiskit-addon-cutting/_toc.json index 4d2c86ede6c..15bc63ed477 100644 --- a/docs/addons/qiskit-addon-cutting/_toc.json +++ b/docs/addons/qiskit-addon-cutting/_toc.json @@ -8,20 +8,20 @@ "title": "", "children": [ { - "title": "Home", + "title": "Documentation Home", "url": "/docs/addons/qiskit-addon-cutting" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-cutting/install" }, { - "title": "Guides", + "title": "Explanatory Material", + "url": "/docs/addons/qiskit-addon-cutting" + }, + { + "title": "How-To Guides", "children": [ - { - "title": "Explanatory material", - "url": "/docs/addons/qiskit-addon-cutting/explanations/index" - }, { "title": "How to generate exact quasiprobability distributions from Sampler", "url": "/docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-quasi-dists-from-sampler" @@ -40,6 +40,83 @@ } ] }, + { + "title": "API References", + "children": [ + { + "title": "Circuit cutting (qiskit_addon_cutting)", + "url": "/docs/addons/qiskit-addon-cutting/apidocs/qiskit-addon-cutting" + }, + { + "title": "Instructions (qiskit_addon_cutting.instructions)", + "url": "/docs/addons/qiskit-addon-cutting/apidocs/instructions" + }, + { + "title": "CutWire", + "url": "/docs/addons/qiskit-addon-cutting/stubs/instructions-cut-wire" + }, + { + "title": "Move", + "url": "/docs/addons/qiskit-addon-cutting/stubs/instructions-move" + }, + { + "title": "Quasi-Probability Decomposition (QPD) (qiskit_addon_cutting.qpd)", + "url": "/docs/addons/qiskit-addon-cutting/apidocs/qpd" + }, + { + "title": "QPDBasis", + "url": "/docs/addons/qiskit-addon-cutting/stubs/qpd-qpd-basis" + }, + { + "title": "BaseQPDGate", + "url": "/docs/addons/qiskit-addon-cutting/stubs/qpd-base-qpd-gate" + }, + { + "title": "SingleQubitQPDGate", + "url": "/docs/addons/qiskit-addon-cutting/stubs/qpd-single-qubit-qpd-gate" + }, + { + "title": "TwoQubitQPDGate", + "url": "/docs/addons/qiskit-addon-cutting/stubs/qpd-two-qubit-qpd-gate" + }, + { + "title": "Bitwise utilities (qiskit_addon_cutting.utils.bitwise)", + "url": "/docs/addons/qiskit-addon-cutting/apidocs/utils-bitwise" + }, + { + "title": "Iteration utilities (qiskit_addon_cutting.utils.iteration)", + "url": "/docs/addons/qiskit-addon-cutting/apidocs/utils-iteration" + }, + { + "title": "Observable grouping (qiskit_addon_cutting.utils.observable_grouping)", + "url": "/docs/addons/qiskit-addon-cutting/apidocs/utils-observable-grouping" + }, + { + "title": "Observable terms (qiskit_addon_cutting.utils.observable_terms)", + "url": "/docs/addons/qiskit-addon-cutting/apidocs/utils-observable-terms" + }, + { + "title": "Simulation utilities (qiskit_addon_cutting.utils.simulation)", + "url": "/docs/addons/qiskit-addon-cutting/apidocs/utils-simulation" + }, + { + "title": "Transforms (qiskit_addon_cutting.utils.transforms)", + "url": "/docs/addons/qiskit-addon-cutting/apidocs/utils-transforms" + }, + { + "title": "Transpiler passes (qiskit_addon_cutting.utils.transpiler_passes)", + "url": "/docs/addons/qiskit-addon-cutting/apidocs/utils-transpiler-passes" + }, + { + "title": "RemoveFinalReset", + "url": "/docs/addons/qiskit-addon-cutting/stubs/utils-transpiler-passes-remove-final-reset" + }, + { + "title": "ConsolidateResets", + "url": "/docs/addons/qiskit-addon-cutting/stubs/utils-transpiler-passes-consolidate-resets" + } + ] + }, { "title": "GitHub", "url": "https://github.com/Qiskit/qiskit-addon-cutting" diff --git a/docs/addons/qiskit-addon-mpf/_toc.json b/docs/addons/qiskit-addon-mpf/_toc.json index 3e6dae0fc7c..0a416df9fc3 100644 --- a/docs/addons/qiskit-addon-mpf/_toc.json +++ b/docs/addons/qiskit-addon-mpf/_toc.json @@ -8,20 +8,16 @@ "title": "", "children": [ { - "title": "Home", + "title": "Documentation Home", "url": "/docs/addons/qiskit-addon-mpf" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-mpf/install" }, { - "title": "Guides", + "title": "How-To Guides", "children": [ - { - "title": "Understanding the stability of MPFs", - "url": "/docs/addons/qiskit-addon-mpf/explanations/mpf-stability" - }, { "title": "How to choose the Trotter steps for an MPF", "url": "/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps" @@ -32,6 +28,15 @@ } ] }, + { + "title": "Explanations", + "children": [ + { + "title": "Understanding the stability of MPFs", + "url": "/docs/addons/qiskit-addon-mpf/explanations/mpf-stability" + } + ] + }, { "title": "GitHub", "url": "https://github.com/Qiskit/qiskit-addon-mpf" diff --git a/docs/addons/qiskit-addon-mthree/_toc.json b/docs/addons/qiskit-addon-mthree/_toc.json index 4ed0ce80bfe..b24a0b45e21 100644 --- a/docs/addons/qiskit-addon-mthree/_toc.json +++ b/docs/addons/qiskit-addon-mthree/_toc.json @@ -8,80 +8,156 @@ "title": "", "children": [ { - "title": "Home", + "title": "mthree (3.0.0)", "url": "/docs/addons/qiskit-addon-mthree" }, { - "title": "Advanced usage", - "url": "/docs/addons/qiskit-addon-mthree/advanced" + "title": "Installation", + "url": "/docs/addons/qiskit-addon-mthree/installation" }, { - "title": "Balanced calibrations", - "url": "/docs/addons/qiskit-addon-mthree/balanced" + "title": "Citing", + "url": "/docs/addons/qiskit-addon-mthree/citing" + }, + { + "title": "Papers using M3", + "url": "/docs/addons/qiskit-addon-mthree/papers" }, { "title": "Basic usage", "url": "/docs/addons/qiskit-addon-mthree/basic" }, { - "title": "Saving and loading calibration data", - "url": "/docs/addons/qiskit-addon-mthree/cal-io" + "title": "Using Runtime modes", + "url": "/docs/addons/qiskit-addon-mthree/runtime" }, { - "title": "Citing", - "url": "/docs/addons/qiskit-addon-mthree/citing" + "title": "Transpiled circuits", + "url": "/docs/addons/qiskit-addon-mthree/transpiled" }, { - "title": "Using distribution collections", - "url": "/docs/addons/qiskit-addon-mthree/collections" + "title": "Expectation values", + "url": "/docs/addons/qiskit-addon-mthree/expvals" }, { - "title": "Error analysis", - "url": "/docs/addons/qiskit-addon-mthree/error" + "title": "Sampling problems", + "url": "/docs/addons/qiskit-addon-mthree/sampling" }, { - "title": "Obtaining expectation values", - "url": "/docs/addons/qiskit-addon-mthree/expvals" + "title": "Using collections", + "url": "/docs/addons/qiskit-addon-mthree/collections" }, { "title": "Grouped operators", "url": "/docs/addons/qiskit-addon-mthree/grouped" }, { - "title": "Installation", - "url": "/docs/addons/qiskit-addon-mthree/installation" + "title": "Error analysis", + "url": "/docs/addons/qiskit-addon-mthree/error" }, { - "title": "Low-weight observables (Marginals)", + "title": "Low-weight operators", "url": "/docs/addons/qiskit-addon-mthree/marginals" }, { - "title": "Papers using M3", - "url": "/docs/addons/qiskit-addon-mthree/papers" + "title": "Obtaining probabilities", + "url": "/docs/addons/qiskit-addon-mthree/probs" }, { - "title": "Converting to probabilities", - "url": "/docs/addons/qiskit-addon-mthree/probs" + "title": "Saving and loading calibrations", + "url": "/docs/addons/qiskit-addon-mthree/cal-io" }, { - "title": "Using Runtime execution modes", - "url": "/docs/addons/qiskit-addon-mthree/runtime" + "title": "Utility functions", + "url": "/docs/addons/qiskit-addon-mthree/utils" }, { - "title": "Mitigating sampling problems", - "url": "/docs/addons/qiskit-addon-mthree/sampling" + "title": "Balanced calibration", + "url": "/docs/addons/qiskit-addon-mthree/balanced" }, { - "title": "Using M3 with transpiled circuits", - "url": "/docs/addons/qiskit-addon-mthree/transpiled" + "title": "Advanced usage", + "url": "/docs/addons/qiskit-addon-mthree/advanced" }, { - "title": "Utility functions", - "url": "/docs/addons/qiskit-addon-mthree/utils" + "title": "M3 paper example", + "url": "/docs/addons/qiskit-addon-mthree/01-m-3-ex-1" + }, + { + "title": "Correcting probabilities", + "url": "/docs/addons/qiskit-addon-mthree/02-correcting-probs" + }, + { + "title": "Quantum Volume", + "url": "/docs/addons/qiskit-addon-mthree/03-qv-example" + }, + { + "title": "Dynamic Bernstein–Vazirani", + "url": "/docs/addons/qiskit-addon-mthree/04-dynamic-bv" + }, + { + "title": "Basic mitigated VQE", + "url": "/docs/addons/qiskit-addon-mthree/10-basic-vqe" }, { - "title": "GitHub", - "url": "https://github.com/Qiskit/qiskit-addon-mthree" + "title": "Mitigation class", + "children": [ + { + "title": "mthree.M3Mitigation", + "url": "/docs/addons/qiskit-addon-mthree/stubs/mthree-m-3-mitigation" + } + ] + }, + { + "title": "Distributions", + "children": [ + { + "title": "mthree.classes.QuasiDistribution", + "url": "/docs/addons/qiskit-addon-mthree/stubs/mthree-classes-quasi-distribution" + }, + { + "title": "mthree.classes.ProbDistribution", + "url": "/docs/addons/qiskit-addon-mthree/stubs/mthree-classes-prob-distribution" + } + ] + }, + { + "title": "Distribution collections", + "children": [ + { + "title": "mthree.classes.QuasiCollection", + "url": "/docs/addons/qiskit-addon-mthree/stubs/mthree-classes-quasi-collection" + }, + { + "title": "mthree.classes.ProbCollection", + "url": "/docs/addons/qiskit-addon-mthree/stubs/mthree-classes-prob-collection" + } + ] + }, + { + "title": "Utility functions", + "children": [ + { + "title": "mthree.utils.final_measurement_mapping", + "url": "/docs/addons/qiskit-addon-mthree/stubs/mthree-utils-final-measurement-mapping" + }, + { + "title": "mthree.utils.expval", + "url": "/docs/addons/qiskit-addon-mthree/stubs/mthree-utils-expval" + }, + { + "title": "mthree.utils.stddev", + "url": "/docs/addons/qiskit-addon-mthree/stubs/mthree-utils-stddev" + }, + { + "title": "mthree.utils.expval_and_stddev", + "url": "/docs/addons/qiskit-addon-mthree/stubs/mthree-utils-expval-and-stddev" + }, + { + "title": "mthree.utils.marginal_distribution", + "url": "/docs/addons/qiskit-addon-mthree/stubs/mthree-utils-marginal-distribution" + } + ] } ], "collapsible": false diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index 2904fc91318..551fdb61e67 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -8,15 +8,15 @@ "title": "", "children": [ { - "title": "Home", + "title": "Documentation Home", "url": "/docs/addons/qiskit-addon-obp" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-obp/install" }, { - "title": "Guides", + "title": "How-To Guides", "children": [ { "title": "Using different Lp-norms for Pauli term truncation", diff --git a/docs/addons/qiskit-addon-opt-mapper/_toc.json b/docs/addons/qiskit-addon-opt-mapper/_toc.json index cb7d36142ea..7879e772ac3 100644 --- a/docs/addons/qiskit-addon-opt-mapper/_toc.json +++ b/docs/addons/qiskit-addon-opt-mapper/_toc.json @@ -8,15 +8,15 @@ "title": "", "children": [ { - "title": "Home", + "title": "Documentation Home", "url": "/docs/addons/qiskit-addon-opt-mapper" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-opt-mapper/install" }, { - "title": "Guides", + "title": "How-To Guides", "children": [ { "title": "Migrate from qiskit-optimization to qiskit-addon-opt-mapper", @@ -42,7 +42,7 @@ }, { "title": "GitHub", - "url": "https://github.com/Qiskit/qiskit-addon-opt-mapper" + "url": "https://github.com/qiskit/qiskit-addon-opt-mapper" } ], "collapsible": false diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb index bcdb9da39a4..c0cab801745 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "0c65ec9e-01c8-4b6a-ba81-e74e44c02711", + "id": "e8fc2c6a-087b-4f14-97d9-cae76af6b82b", "metadata": {}, "source": [ "# Model quantum optimization problems with the `OptimizationProblem` class" @@ -21,7 +21,7 @@ }, { "cell_type": "markdown", - "id": "ff54c935-6139-4ac0-b233-35b9474fee71", + "id": "30c053dd-154d-4490-9d89-502802410f86", "metadata": {}, "source": [ "## Introduction" @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "c83a1b13-6ae5-4e23-ba20-d5283b2bad38", + "id": "6071d6b4-8642-4dae-8af2-a83784a391ef", "metadata": {}, "source": [ "This guide demonstrates how to use the **Optimization Mapper Qiskit addon** for quantum optimization modeling. The addon introduces the `OptimizationProblem` class, which provides a flexible way to handle variables, constraints, and objective functions.\n", @@ -54,7 +54,7 @@ }, { "cell_type": "markdown", - "id": "17c0ee8e-c8bb-4127-b31b-c42d22fa237d", + "id": "4f69a709-7d80-48c4-99de-32f04c7acc6c", "metadata": {}, "source": [ "## Construct an `OptimizationProblem`\n", @@ -66,7 +66,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "c48bd056-f1d8-44e5-b9b8-7f5ca6d697f0", + "id": "70e725ad-8ebc-4dbc-a65d-096fe60ca1a7", "metadata": {}, "outputs": [ { @@ -96,7 +96,7 @@ }, { "cell_type": "markdown", - "id": "96a266c6-4d80-4fb3-b215-1af831b2c3f3", + "id": "03cb8b33-bb97-4a5b-80e2-8212ff0a888d", "metadata": {}, "source": [ "The `OptimizationProblem` class supports four types of variables:\n", @@ -112,7 +112,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "ed67020e-7355-4614-b178-3c9ffc6b0d73", + "id": "e85233c1-fff8-4803-9f66-ba31dd057872", "metadata": {}, "outputs": [ { @@ -153,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "cfcc4bb5-457b-440c-a6d5-614431394732", + "id": "d52126c4-6927-4510-b06d-146e06ee1df8", "metadata": {}, "source": [ "You set the objective function by calling `OptimizationProblem.minimize` or `OptimizationProblem.maximize`.\n", @@ -177,7 +177,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "4b59317f-beca-4d9f-b6d2-7b87f1e4beb0", + "id": "19e13d69-56a8-4025-92d8-37700786a8f1", "metadata": {}, "outputs": [ { @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "56d70f2f-817e-4d18-a833-98e6f96ebc29", + "id": "b6123dee-a140-493b-b867-f653bbb2284a", "metadata": {}, "source": [ "You can also specify the objective function by using arrays:\n", @@ -233,7 +233,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "d823b230-7406-4a9f-a2e0-d172e156168b", + "id": "225872fc-0ee5-4016-ac26-1f916642f122", "metadata": {}, "outputs": [ { @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "94011cc6-f710-4c67-9c4c-a8819dda5f6a", + "id": "f5f9bbe7-9e73-4990-bbe8-c41e6463d6a7", "metadata": {}, "source": [ "You can access the constant, the linear term, the quadratic term, and the higher-order terms by looking at\n", @@ -307,7 +307,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "942008dd-1664-40de-852d-9cd8286ed57c", + "id": "c6263ac0-6e60-4c29-977d-f2333ed6fc39", "metadata": {}, "outputs": [ { @@ -412,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "d21d38b4-e77c-4e82-b9ce-7a142e18eeda", + "id": "39bdeddb-90ad-4020-8e73-2334bf75e860", "metadata": {}, "source": [ "## Add or remove linear, quadratic, and higher-order terms to and from the constraints" @@ -420,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "149562e2-bd48-4dcc-9954-236203478789", + "id": "8393d2cd-f962-4f73-a302-f3309ba6dc95", "metadata": {}, "source": [ "You can add linear constraints by setting name, linear expression, sense, and right-hand-side value (rhs).\n", @@ -430,7 +430,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "c5e41b95-1b1c-4bd4-91d7-e3d98c734ef8", + "id": "5aca058c-a8f5-4879-afb9-fe0aadc58ef8", "metadata": {}, "outputs": [ { @@ -473,7 +473,7 @@ }, { "cell_type": "markdown", - "id": "a0a67bda-dde4-474c-a67a-67e4ceb3db67", + "id": "b1fd62ac-0b53-459c-a864-5cc504ca73a6", "metadata": {}, "source": [ "You can add quadratic constraints by setting name, quadratic expression, sense, and right-hand-side value (rhs)." @@ -482,7 +482,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "bf3b36ac-1cbe-4842-a0d3-54d404b3e2f1", + "id": "265da26a-7f8d-40b9-ba77-23ce050df118", "metadata": {}, "outputs": [ { @@ -548,7 +548,7 @@ }, { "cell_type": "markdown", - "id": "9f9e61d8-a297-4fcd-91a6-6a957ded0ee4", + "id": "f52690df-af2a-4507-8304-ddf08b60cf15", "metadata": {}, "source": [ "You can also add higher-order constraints by setting name, higher-order expression, sense, and right-hand-side value (rhs)." @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "5a426243-abc6-4a7a-9494-c93214f69f96", + "id": "0136ca38-7b22-4a08-ad12-f7baa83d1a7b", "metadata": {}, "outputs": [ { @@ -631,7 +631,7 @@ }, { "cell_type": "markdown", - "id": "7d0a8a99-30ad-4f32-b3d8-37230d37cc22", + "id": "338437ed-f98e-4133-a7d6-2c355729fe90", "metadata": {}, "source": [ "You can access linear and quadratic terms of linear and quadratic constraints in the same way as the objective function." @@ -640,7 +640,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "a3f5b8de-527b-4a80-908e-d9202b09d38d", + "id": "187b6eff-e19f-435f-9d6d-3392efa6617e", "metadata": {}, "outputs": [ { @@ -677,7 +677,7 @@ }, { "cell_type": "markdown", - "id": "36b12d53-f294-41ef-94b9-4ae0c3c4b48d", + "id": "f71b6fec-f2b4-477f-b49f-ae34c727b61a", "metadata": {}, "source": [ "You can also remove linear/quadratic/higher-order constraints by using the `remove_linear_constraint`, `remove_quadratic_constraint`, and `remove_higher_order_constraint` methods, respectively." @@ -686,7 +686,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "ea41f100-e6f2-4328-ab8e-ad094e04bcea", + "id": "ba0e58c8-c84b-4348-ae76-6bccc0095774", "metadata": {}, "outputs": [ { @@ -736,7 +736,7 @@ }, { "cell_type": "markdown", - "id": "789b0e85-3417-4cef-b390-d6b39450cd95", + "id": "696be3ca-206c-452d-925b-9f8b778af8d8", "metadata": {}, "source": [ "## Substitute variables" @@ -744,7 +744,7 @@ }, { "cell_type": "markdown", - "id": "4e9eb647-d535-4670-9fd5-035b7a154cec", + "id": "e225840a-b8b2-464c-965c-2ced7fa60911", "metadata": {}, "source": [ "You can substitute some of the variables with constants or other variables.\n", @@ -757,7 +757,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "6b6b4ac8-c87f-411e-ba21-b4af83d45a5c", + "id": "fd4d822b-735a-477e-a39e-55b99e5c39bb", "metadata": {}, "outputs": [ { @@ -798,7 +798,7 @@ }, { "cell_type": "markdown", - "id": "ba2bc2cf-0504-4376-9e65-b680fe32d65f", + "id": "ab5c7ea1-8b1f-453d-a9b0-70c6118f9caf", "metadata": {}, "source": [ "If the resulting problem is infeasible due to lower bounds or upper bounds, the methods returns the status `Status.INFEASIBLE`.\n", @@ -808,7 +808,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "a2d48dbd-7d8f-4d60-8471-4579c6699856", + "id": "94ae5749-d328-41a7-b0b7-1d12d091ad9b", "metadata": {}, "outputs": [ { @@ -833,7 +833,7 @@ }, { "cell_type": "markdown", - "id": "8f4007a4-2568-42c0-a453-3a34af4a8a99", + "id": "369a14e3-c1b4-416b-8370-80a285a757fc", "metadata": {}, "source": [ "You cannot substitute variables multiple times.\n", @@ -843,7 +843,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "74eeda1d-2149-476a-b246-01b951db3879", + "id": "84f6cdad-96b7-4c5e-bce7-b7b2b053984d", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb index 19749f46a7a..8a173c1eff7 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "3166c3e5-fd8f-434c-80c4-631d8cdfcd78", + "id": "79a16c7b-61c0-4809-ada2-f8507f2c81b7", "metadata": {}, "source": [ "# Convert formulations of an `OptimizationProblem`\n", @@ -48,7 +48,7 @@ }, { "cell_type": "markdown", - "id": "712dd5a8-eeab-43df-8f73-74ce110907a2", + "id": "12f8183b-0797-46ed-a1c8-d4eeb7a821f4", "metadata": {}, "source": [ "## Code examples\n", @@ -65,7 +65,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "27cbce37-6bba-455f-bedb-27f28715dfd6", + "id": "12da4c6d-6737-49a4-9753-c59d984ff27f", "metadata": {}, "outputs": [ { @@ -128,7 +128,7 @@ }, { "cell_type": "markdown", - "id": "144b06d7-73b0-4692-a04a-7b1595d9fd97", + "id": "b412249f-2bdb-4b0f-8d33-656e5f0b9d46", "metadata": {}, "source": [ "## Binary to spin conversion\n", @@ -138,7 +138,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "ef35cfd9-1b19-4498-ba65-711ec5f70577", + "id": "97ff25f9-de9d-4d8d-8271-ee2211b4c21e", "metadata": {}, "outputs": [ { @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "143ddc4d-c12e-4089-8fda-f718648c4e6a", + "id": "b9982058-53a6-471f-b845-9c86341e5f63", "metadata": {}, "source": [ "## Spin to binary conversion\n", @@ -204,7 +204,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "2e57d6a7-b0bb-4407-a4c5-ed08df804f07", + "id": "490b511a-94ff-4c41-98b5-bf9523e65c0c", "metadata": {}, "outputs": [ { @@ -258,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "99286b6f-727b-42c3-90c6-8858a12830a9", + "id": "09fc621c-4438-46da-b098-e67bad02db9c", "metadata": {}, "source": [ "## Inequality to equality conversion\n", @@ -272,7 +272,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "178593fb-7cd5-4239-b1ba-6106f260f0f1", + "id": "1cda3f87-a499-450d-bdba-d1602801ec9d", "metadata": {}, "outputs": [ { @@ -336,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "e9d91a6e-b45d-42d4-b83f-945eaa9f753f", + "id": "c98b31ac-2416-481b-b3c7-01dd21d8125e", "metadata": {}, "source": [ "## Equality to penalty conversion\n", @@ -352,7 +352,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "126a6da5-c8c4-4e3e-b3d3-05117cb44e9a", + "id": "d33c7ead-3854-424e-b4be-d8f5300ea422", "metadata": {}, "outputs": [ { @@ -408,7 +408,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "1dee8a29-0be4-4a1b-9ab7-23630080b40b", + "id": "804c1a07-c77e-4dee-bf41-876eb2be8026", "metadata": {}, "outputs": [ { @@ -441,7 +441,7 @@ }, { "cell_type": "markdown", - "id": "2e04cd7e-e528-4e36-99fb-fc6202688ea3", + "id": "b43cf584-452b-409a-852a-9d5b252dae6b", "metadata": {}, "source": [ "## Linear inequality to penalty conversion\n", @@ -464,7 +464,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "e65424e6-dce2-4b9f-afeb-0ab3adef3d7c", + "id": "2ea182b1-dc5d-4990-975c-3fd51e44a36d", "metadata": {}, "outputs": [ { @@ -520,7 +520,7 @@ }, { "cell_type": "markdown", - "id": "f9e0a0d3-e1cd-43af-b42f-e93d58d76da1", + "id": "64f4457f-a0a5-423e-b90b-ef07d955d693", "metadata": {}, "source": [ "## Maximize to minimize conversion\n", @@ -535,7 +535,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "22b33dc8-88c3-4de1-87ba-2894e8454647", + "id": "384b5fd1-d66b-4f71-b51d-904f350b6024", "metadata": {}, "outputs": [ { @@ -590,7 +590,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "e1140dba-b00c-410b-be9c-33b1d36fb048", + "id": "cc0e2a50-7d79-47da-bf63-65acda0aa969", "metadata": {}, "outputs": [ { @@ -623,7 +623,7 @@ }, { "cell_type": "markdown", - "id": "40e0fb19-11f1-4211-884f-c12fd21b72bf", + "id": "e5e652b0-0e1e-4543-accd-50bdf617367b", "metadata": {}, "source": [ "## QUBO conversion\n", @@ -635,7 +635,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "7bf1d674-c04d-4851-bc97-2dec64663c7e", + "id": "0fc65baf-c9fa-4210-8470-2bcc555a5058", "metadata": {}, "outputs": [ { @@ -705,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "9626b79d-6fbb-4700-9838-8946849af534", + "id": "ec03c318-3906-411d-bcee-9b285c9d368d", "metadata": {}, "source": [ "## HUBO conversion\n", @@ -715,7 +715,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "6d972780-083d-4762-8e15-db09e2af3d6d", + "id": "88e8d05d-4070-4783-99a6-d5b2b62be0bd", "metadata": {}, "outputs": [ { @@ -779,7 +779,7 @@ }, { "cell_type": "markdown", - "id": "c9ae2df1-4436-4e43-bc55-61133277cb6c", + "id": "d5aa4990-8128-4b17-b8c5-23c3afdf900a", "metadata": {}, "source": [ "## Ising transformation\n", @@ -790,7 +790,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "cf16ef1b-3d17-4595-b115-e10b119c0fde", + "id": "cca9d12b-5d7c-443d-83db-038c4794ef6e", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb b/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb index 127ddcf418a..6238c885c66 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "59fb2de9-030f-4237-b363-d7046344b04a", + "id": "fcdb0387-f94e-4b20-b2e3-d7eb3dc7c65a", "metadata": {}, "source": [ "# Translate between `OptimizationProblem` and Docplex\n", @@ -29,7 +29,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "a97d6ced-ea16-496e-bdf9-2298a5a3618f", + "id": "6f92cf08-8531-46bb-9c51-9cf5e41bd3fa", "metadata": {}, "outputs": [ { @@ -80,7 +80,7 @@ }, { "cell_type": "markdown", - "id": "f50723ec-622b-4b56-824e-66c02812a475", + "id": "cb0126a6-5060-421c-9988-d9981b8532b3", "metadata": {}, "source": [ "When you display your problem in LP format using `export_as_lp_string`,\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "49f74476-882a-4c5f-8a60-76d880f71379", + "id": "f3572344-618e-4c02-9a3b-48e80431731d", "metadata": {}, "outputs": [ { @@ -135,7 +135,7 @@ }, { "cell_type": "markdown", - "id": "429d4ee5-fb4f-47f7-8d7b-6220333a7b54", + "id": "d800860d-cb74-45e0-806a-df38658cba2d", "metadata": {}, "source": [ "As stated before, you can also convert a `OptimizationProblem` to a Docplex model using the `to_docplex` function." @@ -144,7 +144,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "eec70c45-5194-44e7-b1c7-57c2c1d19e94", + "id": "8962d91d-b7d5-4a65-9e38-e3571af107f3", "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ }, { "cell_type": "markdown", - "id": "41401bed-0f84-4e3b-b152-8d404bcc5ab5", + "id": "3dc5e633-62c2-45fb-acbc-0f26c68367af", "metadata": {}, "source": [ "## Load LP files using Docplex" @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "1971686b-fec2-4054-8e8f-3a3d279e1991", + "id": "dfcf4fcc-ee34-427b-9a38-628e972007a3", "metadata": {}, "source": [ "Once you are familia with the Docplex translators, loading from LP files becomes very simple. You can use the `docplex.mp.ModelReader` class followed by a translation step:" @@ -203,7 +203,7 @@ { "cell_type": "code", "execution_count": null, - "id": "93254d1e-dcae-4601-a4e7-4a24f47aafa2", + "id": "5f1e4f21-e5b5-4a74-ba0d-1c52b4961307", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-pna/_toc.json b/docs/addons/qiskit-addon-pna/_toc.json index a5dd866fada..cc37aae08ec 100644 --- a/docs/addons/qiskit-addon-pna/_toc.json +++ b/docs/addons/qiskit-addon-pna/_toc.json @@ -8,15 +8,15 @@ "title": "", "children": [ { - "title": "Home", + "title": "Documentation Home", "url": "/docs/addons/qiskit-addon-pna" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-pna/install" }, { - "title": "Guides", + "title": "How-To Guides", "children": [ { "title": "How-to guides coming soon", @@ -26,7 +26,7 @@ }, { "title": "GitHub", - "url": "https://github.com/Qiskit/qiskit-addon-pna" + "url": "https://github.com/qiskit/qiskit-addon-pna" } ], "collapsible": false diff --git a/docs/addons/qiskit-addon-slc/_toc.json b/docs/addons/qiskit-addon-slc/_toc.json index 746d0699392..93cbe802437 100644 --- a/docs/addons/qiskit-addon-slc/_toc.json +++ b/docs/addons/qiskit-addon-slc/_toc.json @@ -8,25 +8,20 @@ "title": "", "children": [ { - "title": "Home", + "title": "Documentation Home", "url": "/docs/addons/qiskit-addon-slc" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-slc/install" }, { - "title": "Guides", - "children": [ - { - "title": "Explanations", - "url": "/docs/addons/qiskit-addon-slc/explanations/index" - }, - { - "title": "How-To Guides", - "url": "/docs/addons/qiskit-addon-slc/how-tos/index" - } - ] + "title": "How-To Guides", + "url": "/docs/addons/qiskit-addon-slc" + }, + { + "title": "Explanations", + "url": "/docs/addons/qiskit-addon-slc" }, { "title": "GitHub", diff --git a/docs/addons/qiskit-addon-sqd-hpc/_toc.json b/docs/addons/qiskit-addon-sqd-hpc/_toc.json index 7feb9e1e697..3fddd3daf7a 100644 --- a/docs/addons/qiskit-addon-sqd-hpc/_toc.json +++ b/docs/addons/qiskit-addon-sqd-hpc/_toc.json @@ -7,10 +7,6 @@ { "title": "", "children": [ - { - "title": "Home", - "url": "/docs/addons/qiskit-addon-sqd-hpc" - }, { "title": "Compilation flags", "url": "/docs/addons/qiskit-addon-sqd-hpc/compilation-flags" diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json index e14c84824eb..d1a20fdc603 100644 --- a/docs/addons/qiskit-addon-sqd/_toc.json +++ b/docs/addons/qiskit-addon-sqd/_toc.json @@ -8,15 +8,15 @@ "title": "", "children": [ { - "title": "Home", + "title": "Documentation Home", "url": "/docs/addons/qiskit-addon-sqd" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-sqd/install" }, { - "title": "Guides", + "title": "How-To Guides", "children": [ { "title": "Add fermionic transitions to the pool of configurations", @@ -50,7 +50,7 @@ }, { "title": "GitHub", - "url": "https://github.com/Qiskit/qiskit-addon-sqd" + "url": "https://github.com/qiskit/qiskit-addon-sqd" } ], "collapsible": false diff --git a/docs/addons/qiskit-addon-utils/_toc.json b/docs/addons/qiskit-addon-utils/_toc.json index 9781982eda2..bec41500745 100644 --- a/docs/addons/qiskit-addon-utils/_toc.json +++ b/docs/addons/qiskit-addon-utils/_toc.json @@ -8,15 +8,15 @@ "title": "", "children": [ { - "title": "Home", + "title": "Documentation Home", "url": "/docs/addons/qiskit-addon-utils" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/qiskit-addon-utils/install" }, { - "title": "Guides", + "title": "How-To Guides", "children": [ { "title": "Use edge coloring to reduce the depth of quantum circuits", @@ -30,7 +30,7 @@ }, { "title": "GitHub", - "url": "https://github.com/Qiskit/qiskit-addon-utils" + "url": "https://github.com/qiskit/qiskit-addon-utils" } ], "collapsible": false diff --git a/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb b/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb index 0a49c598f06..c86fb46d70e 100644 --- a/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb +++ b/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb @@ -18,7 +18,7 @@ "source": [ "# Use edge coloring to reduce the depth of quantum circuits\n", "\n", - "This guide will show how to use the [qiskit_addon_utils.coloring](/docs/api/qiskit-addon-utils/coloring) module to color edges of a coupling map and use the coloring to more efficiently place gates in a quantum circuit, leading to shallower circuits.\n", + "This guide will show how to use the [qiskit_addon_utils.coloring](/docs/api/qiskit-addon-utils/coloring#module-qiskit_addon_utils.coloring) module to color edges of a coupling map and use the coloring to more efficiently place gates in a quantum circuit, leading to shallower circuits.\n", "\n", "The point of edge-coloring a graph is to find a set of edge colors such that no two edges of the same color share a common node. For a quantum processor, this means that gates along like-colored edges (qubit connections) may be run simultaneously and that the circuit will execute faster. We can use this to define our circuit in a way that is more amenable to the hardware it should run on." ] diff --git a/scripts/js/lib/api/addonDocsPipeline.ts b/scripts/js/lib/api/addonDocsPipeline.ts index d9981a8eea7..3a3e0dd1be4 100644 --- a/scripts/js/lib/api/addonDocsPipeline.ts +++ b/scripts/js/lib/api/addonDocsPipeline.ts @@ -126,16 +126,8 @@ export async function runAddonDocsPipeline( notebookImages, ); - await writeTocFile(pkg, docsBaseFolder, outputPath); -} - -async function writeTocFile( - pkg: Pkg, - docsBaseFolder: string, - outputPath: string, -): Promise<void> { console.log("Generating addon toc"); - const toc = await generateAddonToc(pkg, docsBaseFolder); + const toc = await generateAddonToc(pkg, docsBaseFolder, artifactPath); await writeFile( `${outputPath}/_toc.json`, JSON.stringify(toc, null, 2) + "\n", diff --git a/scripts/js/lib/api/generateAddonToc.test.ts b/scripts/js/lib/api/generateAddonToc.test.ts index 9c2e9c594de..d87fa357052 100644 --- a/scripts/js/lib/api/generateAddonToc.test.ts +++ b/scripts/js/lib/api/generateAddonToc.test.ts @@ -37,63 +37,80 @@ async function makePkg(name = "my-addon", githubSlug?: string): Promise<Pkg> { }); } -/** Creates a temp directory, writes the given files into it, returns the path. */ -async function makeTmpAddonDir( - files: Record<string, string>, -): Promise<{ tmpDir: string; addonDir: string }> { - const tmpDir = await mkdtemp(path.join(os.tmpdir(), "addon-toc-test-")); - const addonDir = path.join(tmpDir, "addons", "my-addon"); - await mkdir(addonDir, { recursive: true }); - - for (const [relPath, content] of Object.entries(files)) { - const abs = path.join(addonDir, relPath); - await mkdir(path.dirname(abs), { recursive: true }); - await writeFile(abs, content, "utf-8"); - } - - return { tmpDir, addonDir }; -} +/** + * Builds a minimal Sphinx-style index.html with a sidebar tree. + * Each item in `navItems` can be: + * - a string href (top-level page link) + * - { href, title } (top-level page with custom title) + * - { href, title, children: [{ href, title }] } (section with sub-pages) + */ +type NavChild = { href: string; title: string }; +type NavItem = + | string + | { href: string; title?: string; children?: NavChild[] }; + +function makeIndexHtml(navItems: NavItem[]): string { + const items = navItems.map((item) => { + if (typeof item === "string") { + const title = item.replace(/\.html$/, "").replace(/[-_]/g, " "); + return `<li class="toctree-l1"><a class="reference internal" href="${item}">${title}</a></li>`; + } + const { href, title = href.replace(/\.html$/, ""), children } = item; + if (children && children.length > 0) { + const l2s = children + .map( + (c) => + `<li class="toctree-l2"><a class="reference internal" href="${c.href}">${c.title}</a></li>`, + ) + .join("\n "); + return `<li class="toctree-l1 has-children"><a class="reference internal" href="${href}">${title}</a> + <ul>${l2s}</ul> + </li>`; + } + return `<li class="toctree-l1"><a class="reference internal" href="${href}">${title}</a></li>`; + }); -function mdx(title: string, h1 = title): string { - return `---\ntitle: "${title}"\n---\n\n# ${h1}\n\nSome content.\n`; + return `<!DOCTYPE html> +<html> +<body> +<div class="sidebar-tree"> + <ul> + ${items.join("\n ")} + </ul> +</div> +</body> +</html>`; } -function notebook(h1: string): string { - return JSON.stringify({ - cells: [ - { - id: "frontmatter", - cell_type: "markdown", - source: [`---\ntitle: "Frontmatter title"\n---`], - metadata: {}, - outputs: [], - }, - { - id: "content", - cell_type: "markdown", - source: [`# ${h1}\n\nSome content.`], - metadata: {}, - outputs: [], - }, - ], - metadata: {}, - nbformat: 4, - nbformat_minor: 5, - }); +/** Creates a temp directory with an artifact (index.html) and optional docs files. */ +async function makeTestDirs( + navItems: NavItem[], +): Promise<{ artifactDir: string; docsDir: string }> { + const tmpDir = await mkdtemp(path.join(os.tmpdir(), "addon-toc-test-")); + const artifactDir = path.join(tmpDir, "artifact"); + const docsDir = path.join(tmpDir, "addons"); + await mkdir(artifactDir, { recursive: true }); + await mkdir(path.join(docsDir, "my-addon"), { recursive: true }); + await writeFile( + path.join(artifactDir, "index.html"), + makeIndexHtml(navItems), + "utf-8", + ); + return { artifactDir, docsDir }; } // --------------------------------------------------------------------------- // Tests // --------------------------------------------------------------------------- -test("minimal addon: only index.mdx and install.mdx", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("My Addon home page", "My Addon"), - "install.mdx": mdx("Installation Instructions"), - }); +test("minimal addon: only index and install in sidebar", async () => { + const { artifactDir, docsDir } = await makeTestDirs([ + { href: "index.html", title: "My Addon" }, + { href: "install.html", title: "Installation" }, + ]); const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const toc = await generateAddonToc(pkg, docsDir, artifactDir); expect(toc).toEqual({ parentUrl: "/docs/guides/addons", @@ -105,11 +122,8 @@ test("minimal addon: only index.mdx and install.mdx", async () => { title: "", collapsible: false, children: [ - { title: "Home", url: "/docs/addons/my-addon" }, - { - title: "Installation instructions", - url: "/docs/addons/my-addon/install", - }, + { title: "My Addon", url: "/docs/addons/my-addon" }, + { title: "Installation", url: "/docs/addons/my-addon/install" }, ], }, { @@ -126,247 +140,172 @@ test("minimal addon: only index.mdx and install.mdx", async () => { }); }); -test("install.mdx in a subdirectory uses its h1, not the hardcoded title", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - "how-tos/install.mdx": mdx("Custom install title"), - }); - - const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); - const main = toc.children[0]; - - const guides = main.children?.find((c) => c.title === "Guides"); - expect(guides?.children?.[0].title).toBe("Custom install title"); -}); - -test("github link is appended after top-level files when githubSlug is set", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home", "My Addon"), - }); - - const pkg = await makePkg("my-addon", "Qiskit/my-addon"); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); - const main = toc.children[0]; - - expect(main.children?.at(-1)).toEqual({ - title: "GitHub", - url: "https://github.com/Qiskit/my-addon", - }); -}); - -test("how-tos directory becomes Guides subsection in main section", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - "how-tos/index.mdx": mdx("Guides Index", "How-To Guides"), - "how-tos/alpha.ipynb": notebook("How to do alpha"), - "how-tos/beta.mdx": mdx("How to do beta"), - }); +test("sidebar order is preserved exactly", async () => { + const { artifactDir, docsDir } = await makeTestDirs([ + { href: "index.html", title: "Home" }, + { href: "install.html", title: "Install" }, + { href: "changelog.html", title: "Changelog" }, + { href: "faq.html", title: "FAQ" }, + ]); const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const toc = await generateAddonToc(pkg, docsDir, artifactDir); const main = toc.children[0]; - const guidesEntry = main.children?.find((c) => c.title === "Guides"); - expect(guidesEntry).toEqual({ - title: "Guides", - children: [ - { - title: "How to do alpha", - url: "/docs/addons/my-addon/how-tos/alpha", - }, - { - title: "How to do beta", - url: "/docs/addons/my-addon/how-tos/beta", - }, - ], - }); -}); - -test("api reference section is always last", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - }); - - const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); - const last = toc.children.at(-1); - - expect(last?.title).toBe("API reference"); - expect(last?.children?.[0].url).toBe("/docs/api/my-addon"); + expect(main.children?.map((c) => c.title)).toEqual([ + "Home", + "Install", + "Changelog", + "FAQ", + ]); }); -test("unknown subdirectory uses capitalized directory name as section title", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - "references/item.mdx": mdx("Some reference"), - }); +test("subdirectory section preserves l2 children order from sidebar", async () => { + const { artifactDir, docsDir } = await makeTestDirs([ + { href: "index.html", title: "Home" }, + { + href: "how_tos/index.html", + title: "Guides", + children: [ + { href: "how_tos/beta.html", title: "Beta guide" }, + { href: "how_tos/alpha.html", title: "Alpha guide" }, + { href: "how_tos/gamma.html", title: "Gamma guide" }, + ], + }, + ]); const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const toc = await generateAddonToc(pkg, docsDir, artifactDir); const main = toc.children[0]; - const refSection = main.children?.find((c) => c.title === "References"); - expect(refSection).toBeDefined(); - expect(refSection?.children?.[0].title).toBe("Some reference"); + const guides = main.children?.find((c) => c.title === "Guides"); + expect(guides?.children?.map((c) => c.title)).toEqual([ + "Beta guide", + "Alpha guide", + "Gamma guide", + ]); }); -test("subdir with only index.mdx renders as dropdown with index as child", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - "references/index.mdx": mdx("References Index", "Reference material"), - }); +test("subdirectory section has correct child URLs", async () => { + const { artifactDir, docsDir } = await makeTestDirs([ + { href: "index.html", title: "Home" }, + { + href: "how_tos/index.html", + title: "Guides", + children: [ + { href: "how_tos/my_guide.html", title: "My Guide" }, + ], + }, + ]); const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const toc = await generateAddonToc(pkg, docsDir, artifactDir); const main = toc.children[0]; - const references = main.children?.find((c) => c.title === "References"); - expect(references).toEqual({ - title: "References", - children: [ - { - title: "Reference material", - url: "/docs/addons/my-addon/references/index", - }, - ], - }); - // No url on the section itself — the index is the child, not a parent link. - expect(references?.url).toBeUndefined(); + const guides = main.children?.find((c) => c.title === "Guides"); + expect(guides?.children?.[0].url).toBe( + "/docs/addons/my-addon/how-tos/my-guide", + ); }); -test("explanations directory is merged into the Guides section", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - "how-tos/guide.mdx": mdx("A guide"), - "explanations/background.mdx": mdx("Background"), - }); +test("items with titles matching SKIP_ITEMS are omitted", async () => { + const { artifactDir, docsDir } = await makeTestDirs([ + { href: "index.html", title: "Home" }, + { + href: "tutorials/index.html", + title: "Tutorials", + children: [{ href: "tutorials/demo.html", title: "Demo" }], + }, + { href: "install.html", title: "Install" }, + ]); const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const toc = await generateAddonToc(pkg, docsDir, artifactDir); const main = toc.children[0]; - const guideSections = main.children?.filter((c) => c.title === "Guides"); - // Only one Guides section — both dirs merged into it. - expect(guideSections).toHaveLength(1); - const guides = guideSections![0]; - expect(guides.children?.map((c) => c.title)).toEqual(["Background", "A guide"]); + expect(main.children?.map((c) => c.title)).toEqual(["Home", "Install"]); }); -test("subdir without index.mdx has no url on the section entry", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - "how-tos/guide.mdx": mdx("A guide"), - }); +test("external link (class=external) is passed through unchanged", async () => { + const { artifactDir, docsDir } = await makeTestDirs([ + { href: "index.html", title: "Home" }, + ]); + // Append an external link directly to the index.html sidebar + const indexPath = path.join(artifactDir, "index.html"); + const html = await import("fs/promises").then((fs) => + fs.readFile(indexPath, "utf-8"), + ); + await import("fs/promises").then((fs) => + fs.writeFile( + indexPath, + html.replace( + "</ul>", + `<li class="toctree-l1"><a class="reference external" href="https://github.com/Qiskit/my-addon">GitHub</a></li></ul>`, + ), + ), + ); const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const toc = await generateAddonToc(pkg, docsDir, artifactDir); const main = toc.children[0]; - const guidesEntry = main.children?.find((c) => c.title === "Guides"); - expect(guidesEntry?.url).toBeUndefined(); - expect(guidesEntry?.children).toHaveLength(1); -}); - -test("empty subdirectory is omitted from the TOC", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - // create directory with no content files - }); - await mkdir(path.join(tmpDir, "addons", "my-addon", "empty-dir"), { - recursive: true, + expect(main.children?.at(-1)).toEqual({ + title: "GitHub", + url: "https://github.com/Qiskit/my-addon", }); - - const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); - const main = toc.children[0]; - - expect(main.children?.some((c) => c.title === "Empty-dir")).toBe(false); }); -test("notebook title is read from first non-frontmatter markdown cell h1", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - "how-tos/demo.ipynb": notebook("The real title from h1"), - }); +test("index.html href maps to package root URL", async () => { + const { artifactDir, docsDir } = await makeTestDirs([ + { href: "index.html", title: "Home" }, + ]); const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const toc = await generateAddonToc(pkg, docsDir, artifactDir); const main = toc.children[0]; - const guides = main.children?.find((c) => c.title === "Guides"); - expect(guides?.children?.[0].title).toBe("The real title from h1"); + expect(main.children?.[0].url).toBe("/docs/addons/my-addon"); }); -test("notebook with h2 as first heading falls back to h2", async () => { - const nb = JSON.stringify({ - cells: [ - { - id: "frontmatter", - cell_type: "markdown", - source: [`---\ntitle: "FM"\n---`], - metadata: {}, - outputs: [], - }, - { - id: "content", - cell_type: "markdown", - source: [`## Section heading\n\nContent.`], - metadata: {}, - outputs: [], - }, - ], - metadata: {}, - nbformat: 4, - nbformat_minor: 5, - }); - - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - "how-tos/demo.ipynb": nb, - }); +test("api reference section is always last", async () => { + const { artifactDir, docsDir } = await makeTestDirs([ + { href: "index.html", title: "Home" }, + ]); const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); - const main = toc.children[0]; + const toc = await generateAddonToc(pkg, docsDir, artifactDir); + const last = toc.children.at(-1); - const guides = main.children?.find((c) => c.title === "Guides"); - expect(guides?.children?.[0].title).toBe("Section heading"); + expect(last?.title).toBe("API reference"); + expect(last?.children?.[0].url).toBe("/docs/api/my-addon"); }); -test("mdx h2 fallback when no h1 present", async () => { - const content = `---\ntitle: "FM"\n---\n\n## Only an h2 here\n\nContent.\n`; - - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - "how-tos/guide.mdx": content, - }); +test("missing index.html throws", async () => { + const tmpDir = await mkdtemp(path.join(os.tmpdir(), "addon-toc-test-")); + const artifactDir = path.join(tmpDir, "artifact"); + const docsDir = path.join(tmpDir, "addons"); + await mkdir(artifactDir, { recursive: true }); + await mkdir(path.join(docsDir, "my-addon"), { recursive: true }); const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); - const main = toc.children[0]; - - const guides = main.children?.find((c) => c.title === "Guides"); - expect(guides?.children?.[0].title).toBe("Only an h2 here"); + await expect(generateAddonToc(pkg, docsDir, artifactDir)).rejects.toThrow(); }); -test("content files in subdir are sorted alphabetically by filename", async () => { - const { tmpDir } = await makeTmpAddonDir({ - "index.mdx": mdx("Home"), - "how-tos/index.mdx": mdx("Guides"), - "how-tos/03-third.mdx": mdx("Third guide"), - "how-tos/01-first.mdx": mdx("First guide"), - "how-tos/02-second.mdx": mdx("Second guide"), - }); +test("subdirectory section has no url property on the section entry", async () => { + const { artifactDir, docsDir } = await makeTestDirs([ + { href: "index.html", title: "Home" }, + { + href: "how_tos/index.html", + title: "Guides", + children: [{ href: "how_tos/guide.html", title: "A guide" }], + }, + ]); const pkg = await makePkg(); - const toc = await generateAddonToc(pkg, path.join(tmpDir, "addons")); + const toc = await generateAddonToc(pkg, docsDir, artifactDir); const main = toc.children[0]; - const guides = main.children?.find((c) => c.title === "Guides"); - expect(guides?.children?.map((c) => c.title)).toEqual([ - "First guide", - "Second guide", - "Third guide", - ]); + const guidesEntry = main.children?.find((c) => c.title === "Guides"); + expect(guidesEntry?.url).toBeUndefined(); + expect(guidesEntry?.children).toHaveLength(1); }); diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts index 33abc1004e1..700f2498ee9 100644 --- a/scripts/js/lib/api/generateAddonToc.ts +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -13,38 +13,22 @@ // Generates the _toc.json for an addon's content pages. // // Addon TOCs have a fixed three-part shape: -// 1. A flat "main" section — top-level files (index, install, …) plus -// subdirectory sections (how-tos, explanations), and a GitHub link. +// 1. A "main" section built from the Sphinx sidebar tree in index.html — +// preserving the author's toctree order and titles exactly. // 2. An optional "Tutorials" section listing slugs from docs/tutorials/. // 3. An "API reference" section linking to docs/api/{pkg}. // -// Subdirectories whose DIR_LABELS entry is the same string are merged into a -// single section (e.g. both "how-tos" and "explanations" map to "Guides"). -// Title text is read from each file's first h1/h2 heading. -// // Called by addonDocsPipeline.ts; for API doc TOCs see generateToc.ts. import { readFile } from "fs/promises"; -import { join } from "path/posix"; +import { join, parse } from "path/posix"; -import { globby } from "globby"; +import { load } from "cheerio"; import { Pkg } from "./Pkg.js"; import { TocEntry } from "./generateToc.js"; import { DOCS_BASE_PATH } from "./paths.js"; -import { capitalize } from "lodash-es"; - -// labels for known subdirectory names. -const DIR_LABELS: Record<string, string> = { - "how-tos": "Guides", - explanations: "Guides", -}; - -// Hardcoded titles for specific top-level slugs (overrides the file's h1). -const TOP_LEVEL_TITLES: Record<string, string> = { - index: "Home", - install: "Installation instructions", -}; +import { kebabCaseAndShortenPage } from "./normalizeResultUrls.js"; type AddonTocSection = TocEntry & { collapsible?: boolean }; @@ -59,78 +43,29 @@ type AddonToc = { export async function generateAddonToc( pkg: Pkg, docsBaseFolder: string, + artifactPath: string, ): Promise<AddonToc> { - const addonDocsPath = pkg.outputDir(docsBaseFolder); const addonUrlBase = `${DOCS_BASE_PATH}/addons/${pkg.name}`; - // Discover top-level content files (e.g. index.mdx, install.mdx). - const topLevelFiles = await globby(["*.mdx", "*.ipynb"], { - cwd: addonDocsPath, - }); - - // Discover top-level subdirectories that contain content. - const subdirNames = await globby(["*/"], { - cwd: addonDocsPath, - onlyDirectories: true, - }).then((dirs) => dirs.map((d) => d.replace(/\/$/, "")).sort()); + const mainChildren = await buildMainFromSidebar( + artifactPath, + pkg, + addonUrlBase, + ); - const mainChildren: TocEntry[] = []; const topLevelSections: AddonTocSection[] = []; - // Top-level files → entries in the main section, in filename order. - for (const file of topLevelFiles.sort()) { - const slug = file.replace(/\.(mdx|ipynb)$/, ""); - const isIndex = slug === "index"; - const url = isIndex ? addonUrlBase : `${addonUrlBase}/${slug}`; - const title = - TOP_LEVEL_TITLES[slug] ?? (await readTitle(join(addonDocsPath, file))); - // ensure index is first in the toc - isIndex - ? mainChildren.unshift({ title, url }) - : mainChildren.push({ title, url }); - } - - // Subdirectories → either nested subsections or top-level sections. - // Dirs that share the same label are merged into one section. - const mergedSections = new Map<string, TocEntry>(); - for (const dir of subdirNames) { - const entry = await buildSubdirEntry(dir, addonDocsPath, addonUrlBase); - if (!entry) continue; - - const existingSection = mergedSections.get(entry.title); - if (existingSection) { - existingSection.children = [ - ...(existingSection.children ?? []), - ...(entry.children ?? []), - ]; - } else { - mergedSections.set(entry.title, entry); - mainChildren.push(entry); - } - } - - if (pkg.githubSlug) { - mainChildren.push({ - title: "GitHub", - url: `https://github.com/${pkg.githubSlug}`, - }); - } - - const mainSection: AddonTocSection = { - title: "", - children: mainChildren, - collapsible: false, - }; - if (pkg.tutorials.length > 0) { - const tutorialsDocsPath = join(docsBaseFolder, "..", "tutorials"); - const tutorialChildren: TocEntry[] = await Promise.all( - pkg.tutorials.map(async (slug) => { - const filePath = join(tutorialsDocsPath, `${slug}.ipynb`); - const title = await readTitle(filePath); - return { title, url: `${DOCS_BASE_PATH}/tutorials/${slug}` }; - }), + const tutorialsToc = JSON.parse( + await readFile( + join(docsBaseFolder, "..", "tutorials", "_toc.json"), + "utf-8", + ), ); + const tutorialChildren: TocEntry[] = pkg.tutorials.map((slug) => ({ + title: findTutorialTitle(tutorialsToc, slug) ?? slug, + url: `${DOCS_BASE_PATH}/tutorials/${slug}`, + })); topLevelSections.push({ title: "Tutorials", collapsible: false, @@ -154,113 +89,92 @@ export async function generateAddonToc( parentLabel: "Documentation", title: `${pkg.title} ${pkg.version}`, collapsed: true, - children: [mainSection, ...topLevelSections], + children: [ + { title: "", children: mainChildren, collapsible: false }, + ...topLevelSections, + ], }; } +const SKIP_ITEMS = ["api reference", "release notes", "tutorials"]; + /** - * Builds a TocEntry for a subdirectory. - * - * The section title uses the known label for the directory name, or falls back - * to a capitalized version of the directory name. Content files (non-index) - * become children, sorted by filename, with titles read from their h1 headings. - * If an index.mdx exists, the section entry gets a url pointing to it. - * Returns undefined if the directory has no content. + * Builds the main TOC section from the sidebar tree in the Sphinx index.html. + * Each toctree-l1 item becomes either a flat entry (top-level page) or a + * section with children (subdirectory). Titles come directly from the sidebar + * link text — exactly what the docs author wrote in their toctree. */ -async function buildSubdirEntry( - dirName: string, - addonDocsPath: string, +async function buildMainFromSidebar( + artifactPath: string, + pkg: Pkg, addonUrlBase: string, -): Promise<TocEntry | undefined> { - const dirPath = join(addonDocsPath, dirName); - const files = await globby(["*.mdx", "*.ipynb"], { cwd: dirPath }).catch( - (): string[] => [], - ); - if (files.length === 0) return undefined; - - const hasIndex = files.includes("index.mdx"); - const contentFiles = files.filter((f) => f !== "index.mdx").sort(); - - const sectionTitle = DIR_LABELS[dirName] ?? capitalize(dirName); - - const children: TocEntry[] = await Promise.all( - contentFiles.map(async (file) => { - const slug = file.replace(/\.(mdx|ipynb)$/, ""); - const title = await readTitle(join(dirPath, file)); - return { title, url: `${addonUrlBase}/${dirName}/${slug}` }; - }), - ); +): Promise<TocEntry[]> { + const html = await readFile(join(artifactPath, "index.html"), "utf-8"); + const $ = load(html); + const entries: TocEntry[] = []; + + $(".sidebar-tree > ul > li").each((_, l1) => { + const $l1 = $(l1); + const a = $l1.children("a").first(); + const href = a.attr("href") ?? ""; + const title = a.text().trim(); + + if (SKIP_ITEMS.includes(title.toLowerCase())) return; + + // External link (e.g. GitHub) + if (a.hasClass("external")) { + entries.push({ title, url: href }); + return; + } - // When only an index page exists, include it as a child so the section - // always renders as a dropdown rather than a bare link. - if (hasIndex && children.length === 0) { - const title = await readTitle(join(dirPath, "index.mdx")); - children.push({ title, url: `${addonUrlBase}/${dirName}/index` }); - } + // href="#" means "this page" (index.html) in Sphinx when current-page is active + if (href === "#") { + entries.push({ title, url: addonUrlBase }); + return; + } - return { title: sectionTitle, children }; -} + // Subdirectory section: has-children class with l2 items + if ($l1.hasClass("has-children")) { + const children: TocEntry[] = []; + $l1.find("ul > li > a").each((_, a2) => { + const childHref = $(a2).attr("href") ?? ""; + const dir = childHref.split("/")[0].replace(/_/g, "-"); + const slug = hrefToSlug(childHref, pkg); + children.push({ + title: $(a2).text().trim(), + url: `${addonUrlBase}/${dir}/${slug}`, + }); + }); + if (children.length > 0) entries.push({ title, children }); + return; + } -/** - * Reads the first h1 (or h2 as fallback) from an .mdx or .ipynb file. - * Falls back to the filename stem if no heading is found. - */ -async function readTitle(filePath: string): Promise<string> { - const content = await readFile(filePath, "utf-8"); + // Top-level page + const slug = hrefToSlug(href, pkg); + entries.push({ + title, + url: slug === "index" ? addonUrlBase : `${addonUrlBase}/${slug}`, + }); + }); - if (filePath.endsWith(".ipynb")) { - return readNotebookTitle(content, filePath); - } - return readMdxTitle(content, filePath); + return entries; } -function readMdxTitle(content: string, filePath: string): string { - const withoutFrontmatter = content.replace(/^---\n[\s\S]*?\n---\n/, ""); - // Try h1 first, fall back to h2. - const h1 = withoutFrontmatter.match(/^#\s+(.+)$/m); - if (h1) return stripInlineCode(h1[1].trim()); - const h2 = withoutFrontmatter.match(/^##\s+(.+)$/m); - if (h2) return stripInlineCode(h2[1].trim()); - return fileBasename(filePath); +/** Converts a Sphinx HTML href to the kebab-case slug used in the output MDX. */ +function hrefToSlug(href: string, pkg: Pkg): string { + const name = parse(href).name; + return pkg.kebabCaseAndShortenUrls + ? kebabCaseAndShortenPage(name, pkg.name) + : name; } -function readNotebookTitle(content: string, filePath: string): string { - let notebook: { - cells: Array<{ id?: string; cell_type: string; source: string[] }>; - }; - try { - notebook = JSON.parse(content); - } catch { - return fileBasename(filePath); - } - - for (const cell of notebook.cells) { - if (cell.cell_type !== "markdown") continue; - // Skip the frontmatter cell. - if (cell.id === "frontmatter") continue; - - const src = Array.isArray(cell.source) - ? cell.source.join("") - : String(cell.source); +type TocNode = { title?: string; url?: string; children?: TocNode[] }; - const h1 = src.match(/^#\s+(.+)$/m); - if (h1) return stripInlineCode(h1[1].trim()); - const h2 = src.match(/^##\s+(.+)$/m); - if (h2) return stripInlineCode(h2[1].trim()); +/** Recursively searches a TOC tree for a node whose URL ends with the given slug. */ +function findTutorialTitle(node: TocNode, slug: string): string | undefined { + if (node.url?.endsWith(`/${slug}`) && node.title) return node.title; + for (const child of node.children ?? []) { + const found = findTutorialTitle(child, slug); + if (found) return found; } - - return fileBasename(filePath); -} - -function stripInlineCode(text: string): string { - return text.replace(/``([^`]+)``/g, "$1").replace(/`([^`]+)`/g, "$1"); -} - -function fileBasename(filePath: string): string { - return ( - filePath - .split("/") - .pop() - ?.replace(/\.(mdx|ipynb)$/, "") ?? filePath - ); } From c7c4201d9fac037876313a6d99a03810de92f980 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 28 May 2026 15:07:15 -0400 Subject: [PATCH 096/104] fix aqc toc --- docs/addons/qiskit-addon-aqc-tensor/_toc.json | 2 +- scripts/js/lib/api/generateAddonToc.test.ts | 20 +++++------ scripts/js/lib/api/generateAddonToc.ts | 33 ++++++++++--------- 3 files changed, 28 insertions(+), 27 deletions(-) diff --git a/docs/addons/qiskit-addon-aqc-tensor/_toc.json b/docs/addons/qiskit-addon-aqc-tensor/_toc.json index fc2eb44e170..5b983684047 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/_toc.json +++ b/docs/addons/qiskit-addon-aqc-tensor/_toc.json @@ -17,7 +17,7 @@ }, { "title": "Explanatory Material", - "url": "/docs/addons/qiskit-addon-aqc-tensor" + "url": "/docs/addons/qiskit-addon-aqc-tensor/explanation/index" }, { "title": "How-To Guides", diff --git a/scripts/js/lib/api/generateAddonToc.test.ts b/scripts/js/lib/api/generateAddonToc.test.ts index d87fa357052..7b2f74f14fb 100644 --- a/scripts/js/lib/api/generateAddonToc.test.ts +++ b/scripts/js/lib/api/generateAddonToc.test.ts @@ -105,7 +105,7 @@ async function makeTestDirs( test("minimal addon: only index and install in sidebar", async () => { const { artifactDir, docsDir } = await makeTestDirs([ - { href: "index.html", title: "My Addon" }, + { href: "#", title: "My Addon" }, { href: "install.html", title: "Installation" }, ]); @@ -142,7 +142,7 @@ test("minimal addon: only index and install in sidebar", async () => { test("sidebar order is preserved exactly", async () => { const { artifactDir, docsDir } = await makeTestDirs([ - { href: "index.html", title: "Home" }, + { href: "#", title: "Home" }, { href: "install.html", title: "Install" }, { href: "changelog.html", title: "Changelog" }, { href: "faq.html", title: "FAQ" }, @@ -162,7 +162,7 @@ test("sidebar order is preserved exactly", async () => { test("subdirectory section preserves l2 children order from sidebar", async () => { const { artifactDir, docsDir } = await makeTestDirs([ - { href: "index.html", title: "Home" }, + { href: "#", title: "Home" }, { href: "how_tos/index.html", title: "Guides", @@ -188,7 +188,7 @@ test("subdirectory section preserves l2 children order from sidebar", async () = test("subdirectory section has correct child URLs", async () => { const { artifactDir, docsDir } = await makeTestDirs([ - { href: "index.html", title: "Home" }, + { href: "#", title: "Home" }, { href: "how_tos/index.html", title: "Guides", @@ -210,7 +210,7 @@ test("subdirectory section has correct child URLs", async () => { test("items with titles matching SKIP_ITEMS are omitted", async () => { const { artifactDir, docsDir } = await makeTestDirs([ - { href: "index.html", title: "Home" }, + { href: "#", title: "Home" }, { href: "tutorials/index.html", title: "Tutorials", @@ -228,7 +228,7 @@ test("items with titles matching SKIP_ITEMS are omitted", async () => { test("external link (class=external) is passed through unchanged", async () => { const { artifactDir, docsDir } = await makeTestDirs([ - { href: "index.html", title: "Home" }, + { href: "#", title: "Home" }, ]); // Append an external link directly to the index.html sidebar const indexPath = path.join(artifactDir, "index.html"); @@ -255,9 +255,9 @@ test("external link (class=external) is passed through unchanged", async () => { }); }); -test("index.html href maps to package root URL", async () => { +test("href='#' maps to package root URL", async () => { const { artifactDir, docsDir } = await makeTestDirs([ - { href: "index.html", title: "Home" }, + { href: "#", title: "Home" }, ]); const pkg = await makePkg(); @@ -269,7 +269,7 @@ test("index.html href maps to package root URL", async () => { test("api reference section is always last", async () => { const { artifactDir, docsDir } = await makeTestDirs([ - { href: "index.html", title: "Home" }, + { href: "#", title: "Home" }, ]); const pkg = await makePkg(); @@ -293,7 +293,7 @@ test("missing index.html throws", async () => { test("subdirectory section has no url property on the section entry", async () => { const { artifactDir, docsDir } = await makeTestDirs([ - { href: "index.html", title: "Home" }, + { href: "#", title: "Home" }, { href: "how_tos/index.html", title: "Guides", diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts index 700f2498ee9..a23e5ed59b0 100644 --- a/scripts/js/lib/api/generateAddonToc.ts +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -21,7 +21,7 @@ // Called by addonDocsPipeline.ts; for API doc TOCs see generateToc.ts. import { readFile } from "fs/promises"; -import { join, parse } from "path/posix"; +import { join } from "path/posix"; import { load } from "cheerio"; @@ -138,34 +138,35 @@ async function buildMainFromSidebar( const children: TocEntry[] = []; $l1.find("ul > li > a").each((_, a2) => { const childHref = $(a2).attr("href") ?? ""; - const dir = childHref.split("/")[0].replace(/_/g, "-"); - const slug = hrefToSlug(childHref, pkg); children.push({ title: $(a2).text().trim(), - url: `${addonUrlBase}/${dir}/${slug}`, + url: `${addonUrlBase}/${hrefToRelPath(childHref, pkg)}`, }); }); if (children.length > 0) entries.push({ title, children }); return; } - // Top-level page - const slug = hrefToSlug(href, pkg); - entries.push({ - title, - url: slug === "index" ? addonUrlBase : `${addonUrlBase}/${slug}`, - }); + // Top-level page (may be a subdirectory index like "explanation/index.html") + entries.push({ title, url: `${addonUrlBase}/${hrefToRelPath(href, pkg)}` }); }); return entries; } -/** Converts a Sphinx HTML href to the kebab-case slug used in the output MDX. */ -function hrefToSlug(href: string, pkg: Pkg): string { - const name = parse(href).name; - return pkg.kebabCaseAndShortenUrls - ? kebabCaseAndShortenPage(name, pkg.name) - : name; +/** + * Converts a Sphinx HTML href to a repo-relative path, applying the + * kebab-case transform to every path segment (dir and filename alike). + */ +function hrefToRelPath(href: string, pkg: Pkg): string { + const segments = href.replace(/\.html$/, "").split("/"); + return segments + .map((seg) => + pkg.kebabCaseAndShortenUrls + ? kebabCaseAndShortenPage(seg, pkg.name) + : seg, + ) + .join("/"); } type TocNode = { title?: string; url?: string; children?: TocNode[] }; From 3ae95e9eee7e92f466012d9fd5a84a458fe1c0f6 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 28 May 2026 17:02:45 -0400 Subject: [PATCH 097/104] don't map how-tos or explainations --- docs/addons/pauli-prop/_toc.json | 10 +- .../{how-tos => how_tos}/do-something.ipynb | 0 .../pauli-prop/{how-tos => how_tos}/index.mdx | 0 docs/addons/pauli-prop/index.mdx | 4 +- docs/addons/qiskit-addon-aqc-tensor/_toc.json | 2 +- .../{explanations => explanation}/index.mdx | 0 docs/addons/qiskit-addon-aqc-tensor/index.mdx | 2 +- docs/addons/qiskit-addon-cutting/_toc.json | 4 +- .../{explanations => explanation}/index.mdx | 0 docs/addons/qiskit-addon-cutting/index.mdx | 26 +++--- docs/addons/qiskit-addon-mpf/_toc.json | 2 +- .../explanations/mpf-stability.ipynb | 4 +- .../choose-trotter-steps.ipynb | 8 +- .../{how-tos => how_tos}/index.mdx | 0 .../using-approximate-model.ipynb | 10 +- docs/addons/qiskit-addon-mpf/index.mdx | 2 +- docs/addons/qiskit-addon-mthree/_toc.json | 12 +-- .../bound-error-using-p-norm.ipynb | 12 +-- .../{how-tos => how_tos}/index.mdx | 0 .../simulating-circuits-with-obp.ipynb | 8 +- .../truncate-operator-terms.ipynb | 20 ++-- docs/addons/qiskit-addon-obp/index.mdx | 2 +- docs/addons/qiskit-addon-opt-mapper/_toc.json | 2 +- .../00-migrate-from-qiskit-optimization.ipynb | 0 .../01-optimization-problem.ipynb | 62 ++++++------ ...converters-for-optimization-problems.ipynb | 46 ++++----- .../03-validate-with-solvers.ipynb | 6 +- .../04-translate-docplex.ipynb | 18 ++-- .../{how-tos => how_tos}/index.mdx | 0 docs/addons/qiskit-addon-pna/_toc.json | 2 +- .../{how-tos => how_tos}/index.mdx | 0 .../{how-tos => how_tos}/placeholder.ipynb | 0 docs/addons/qiskit-addon-pna/index.mdx | 2 +- docs/addons/qiskit-addon-slc/_toc.json | 6 +- .../{how-tos => how_tos}/index.mdx | 0 docs/addons/qiskit-addon-slc/index.mdx | 2 +- docs/addons/qiskit-addon-sqd-hpc/_toc.json | 2 +- docs/addons/qiskit-addon-sqd/_toc.json | 2 +- ...ic-excitations-to-configuration-pool.ipynb | 0 .../benchmark-pauli-projection.ipynb | 4 +- .../choose-subspace-dimension.ipynb | 2 +- .../{how-tos => how_tos}/index.mdx | 0 .../integrate-dice-solver.ipynb | 4 +- ...uli-operators-onto-hilbert-subspaces.ipynb | 0 .../select-open-closed-shell.ipynb | 0 ...use-oo-to-optimize-hamiltonian-basis.ipynb | 0 docs/addons/qiskit-addon-sqd/index.mdx | 6 +- docs/addons/qiskit-addon-utils/_toc.json | 2 +- .../color-device-edges-to-improve-depth.ipynb | 8 +- .../create-circuit-slices.ipynb | 18 ++-- .../{how-tos => how_tos}/index.mdx | 0 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how_tos}/create-circuit-slices/extracted-outputs/f883fa2e-f943-4c49-af45-fb26cadb72b6-0.avif (100%) diff --git a/docs/addons/pauli-prop/_toc.json b/docs/addons/pauli-prop/_toc.json index 8a7370287a8..60d58538d3b 100644 --- a/docs/addons/pauli-prop/_toc.json +++ b/docs/addons/pauli-prop/_toc.json @@ -1,22 +1,22 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Pauli propagation 0.2.0", + "title": "Pauli propagation 0.2", "collapsed": true, "children": [ { "title": "", "children": [ { - "title": "Home", + "title": "Documentation Home", "url": "/docs/addons/pauli-prop" }, { - "title": "Installation instructions", + "title": "Installation Instructions", "url": "/docs/addons/pauli-prop/install" }, { - "title": "Guides", + "title": "How-To Guides", "children": [ { "title": "How to do something", @@ -26,7 +26,7 @@ }, { "title": "GitHub", - "url": "https://github.com/Qiskit/pauli-prop" + "url": "https://github.com/qiskit/pauli-prop" } ], "collapsible": false diff --git a/docs/addons/pauli-prop/how-tos/do-something.ipynb b/docs/addons/pauli-prop/how_tos/do-something.ipynb similarity index 100% rename from docs/addons/pauli-prop/how-tos/do-something.ipynb rename to docs/addons/pauli-prop/how_tos/do-something.ipynb diff --git a/docs/addons/pauli-prop/how-tos/index.mdx b/docs/addons/pauli-prop/how_tos/index.mdx similarity index 100% rename from docs/addons/pauli-prop/how-tos/index.mdx rename to docs/addons/pauli-prop/how_tos/index.mdx diff --git a/docs/addons/pauli-prop/index.mdx b/docs/addons/pauli-prop/index.mdx index ef3179523ad..bea5e23dba0 100644 --- a/docs/addons/pauli-prop/index.mdx +++ b/docs/addons/pauli-prop/index.mdx @@ -13,12 +13,12 @@ This package provides a Rust-accelerated Python interface for performing Pauli p * Lightcone shading \[7] and other novel error mitigation techniques * Operator backpropagation (OBP) \[6] -* Classical simulation of expectation values \[[tutorials](/docs/addons/pauli-prop/tutorials/index)] +* Classical simulation of expectation values \[[tutorials](https://qiskit.github.io/pauli-prop/tutorials/index.html)] ### Technical details * Rust-accelerated Python interface -* Support for noisy simulations \[[tutorial 3](/docs/addons/pauli-prop/tutorials/03-simulate-noisy-expectation-values)] +* Support for noisy simulations \[[tutorial 3](https://qiskit.github.io/pauli-prop/tutorials/03_simulate_noisy_expectation_values.html)] * Ability to truncate terms during evolution based on an absolute coefficient tolerance, a fixed number of terms in the evolving operator, or a combination of both. * Ability to perform Pauli propagation in both the Schrödinger and Heisenberg frameworks. * Novel technique for approximating the conjugation of a Pauli-sum operator by another Pauli operator. This heuristic implementation greedily generates contributions to the product expected to be most significant. diff --git a/docs/addons/qiskit-addon-aqc-tensor/_toc.json b/docs/addons/qiskit-addon-aqc-tensor/_toc.json index 5b983684047..d33c34a9e9a 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/_toc.json +++ b/docs/addons/qiskit-addon-aqc-tensor/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Approximate quantum compilation (AQC-Tensor) 0.3.0", + "title": "Approximate quantum compilation (AQC-Tensor) 0.3", "collapsed": true, "children": [ { diff --git a/docs/addons/qiskit-addon-aqc-tensor/explanations/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx similarity index 100% rename from docs/addons/qiskit-addon-aqc-tensor/explanations/index.mdx rename to docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx diff --git a/docs/addons/qiskit-addon-aqc-tensor/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/index.mdx index fac2cb938a2..5cb188b6837 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/index.mdx +++ b/docs/addons/qiskit-addon-aqc-tensor/index.mdx @@ -74,7 +74,7 @@ If you use this package in your research, please cite the appropriate references * [Documentation Home]() * [Installation Instructions](install) * [Tutorials](tutorials/index) -* [Explanatory Material](explanations/index) +* [Explanatory Material](explanation/index) * [API Reference](/docs/api/qiskit-addon-aqc-tensor/index) * [How-To Guides](how-tos/index) * [GitHub](https://github.com/Qiskit/qiskit-addon-aqc-tensor) diff --git a/docs/addons/qiskit-addon-cutting/_toc.json b/docs/addons/qiskit-addon-cutting/_toc.json index 15bc63ed477..4813b3cf117 100644 --- a/docs/addons/qiskit-addon-cutting/_toc.json +++ b/docs/addons/qiskit-addon-cutting/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Circuit cutting 0.10.0", + "title": "Circuit cutting 0.10", "collapsed": true, "children": [ { @@ -17,7 +17,7 @@ }, { "title": "Explanatory Material", - "url": "/docs/addons/qiskit-addon-cutting" + "url": "/docs/addons/qiskit-addon-cutting/explanation/index" }, { "title": "How-To Guides", diff --git a/docs/addons/qiskit-addon-cutting/explanations/index.mdx b/docs/addons/qiskit-addon-cutting/explanation/index.mdx similarity index 100% rename from docs/addons/qiskit-addon-cutting/explanations/index.mdx rename to docs/addons/qiskit-addon-cutting/explanation/index.mdx diff --git a/docs/addons/qiskit-addon-cutting/index.mdx b/docs/addons/qiskit-addon-cutting/index.mdx index c276cf9c9c7..76ddbd9a524 100644 --- a/docs/addons/qiskit-addon-cutting/index.mdx +++ b/docs/addons/qiskit-addon-cutting/index.mdx @@ -11,7 +11,7 @@ description: "Documentation for the latest version of Circuit cutting" This package implements circuit cutting. In this technique, a handful of gates and/or wires are cut, resulting in smaller circuits that are better suited for execution on hardware. The result of the original circuit can then be reconstructed; however, the trade-off is that the overall number of shots must be increased by a factor exponential in the number of cuts. -For a more detailed discussion on circuit cutting, check out our [technical guide](./circuit_cutting/explanations/index-rst#overview-of-circuit-cutting). +For a more detailed discussion on circuit cutting, check out our [technical guide](./circuit_cutting/explanation/index-rst#overview-of-circuit-cutting). We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [Release Notes](/docs/api/qiskit-addon-cutting/release-notes#release-notes). @@ -77,18 +77,18 @@ The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qi * [Wire Cutting Phrased as a Two-Qubit `Move` Instruction](tutorials/03-wire-cutting-via-move-instruction) * [Automatically find cuts](tutorials/04-automatic-cut-finding) -* [Explanatory Material](explanations/index) - - * [Overview of circuit cutting](explanations/index#overview-of-circuit-cutting) - * [Key terms](explanations/index#key-terms) - * [Circuit cutting as a quasiprobability decomposition (QPD)](explanations/index#circuit-cutting-as-a-quasiprobability-decomposition-qpd) - * [An example: cutting a `RZZGate`](explanations/index#an-example-cutting-a-rzzgate) - * [More general cut two-qubit gates via the KAK decomposition](explanations/index#more-general-cut-two-qubit-gates-via-the-kak-decomposition) - * [Wire cutting phrased as a two-qubit `Move` operation](explanations/index#wire-cutting-phrased-as-a-two-qubit-move-operation) - * [Sample weights in the Qiskit addon for circuit cutting](explanations/index#sample-weights-in-the-qiskit-addon-for-circuit-cutting) - * [Sampling overhead reference table](explanations/index#sampling-overhead-reference-table) - * [Current limitations](explanations/index#current-limitations) - * [References](explanations/index#references) +* [Explanatory Material](explanation/index) + + * [Overview of circuit cutting](explanation/index#overview-of-circuit-cutting) + * [Key terms](explanation/index#key-terms) + * [Circuit cutting as a quasiprobability decomposition (QPD)](explanation/index#circuit-cutting-as-a-quasiprobability-decomposition-qpd) + * [An example: cutting a `RZZGate`](explanation/index#an-example-cutting-a-rzzgate) + * [More general cut two-qubit gates via the KAK decomposition](explanation/index#more-general-cut-two-qubit-gates-via-the-kak-decomposition) + * [Wire cutting phrased as a two-qubit `Move` operation](explanation/index#wire-cutting-phrased-as-a-two-qubit-move-operation) + * [Sample weights in the Qiskit addon for circuit cutting](explanation/index#sample-weights-in-the-qiskit-addon-for-circuit-cutting) + * [Sampling overhead reference table](explanation/index#sampling-overhead-reference-table) + * [Current limitations](explanation/index#current-limitations) + * [References](explanation/index#references) * [How-To Guides](how-tos/index) diff --git a/docs/addons/qiskit-addon-mpf/_toc.json b/docs/addons/qiskit-addon-mpf/_toc.json index 0a416df9fc3..27a06c1f024 100644 --- a/docs/addons/qiskit-addon-mpf/_toc.json +++ b/docs/addons/qiskit-addon-mpf/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Multi-product formulas (MPF) 0.3.0", + "title": "Multi-product formulas (MPF) 0.3", "collapsed": true, "children": [ { diff --git a/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.ipynb b/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.ipynb index c6c073e4c8b..271ae4fde5f 100644 --- a/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.ipynb +++ b/docs/addons/qiskit-addon-mpf/explanations/mpf-stability.ipynb @@ -25,7 +25,7 @@ "source": [ "# Understanding the stability of MPFs\n", "\n", - "Over in the guide [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps) we introduced a heuristic that limits the **smallest** $k_j$ value ($k_{\\text{min}}$) based on the total evolution time, $t$, that we would like to reach.\n", + "Over in the guide [How to choose the Trotter steps for an MPF](https://qiskit.github.io/qiskit-addon-mpf/how_tos/choose_trotter_steps.html) we introduced a heuristic that limits the **smallest** $k_j$ value ($k_{\\text{min}}$) based on the total evolution time, $t$, that we would like to reach.\n", "In particular, we state that $t/k_{\\text{min}} \\lt 1$ must be satisfied (noting that $t/k_{\\text{min}} \\leq 1$ empirically seems to work fine, too).\n", "\n", "On this page, we will analyze when the behavior of MPFs is not guaranteed to work well, such as when this constraint gets violated." @@ -45,7 +45,7 @@ "source": [ "## Setting up a simple model problem\n", "\n", - "For this simple example, we will reuse the model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial; the Ising model on a line of 10 sites:\n", + "For this simple example, we will reuse the model problem from the [Getting started](https://qiskit.github.io/qiskit-addon-mpf/tutorials/01_getting_started.html) tutorial; the Ising model on a line of 10 sites:\n", "\n", "$$\n", "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", diff --git a/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps.ipynb b/docs/addons/qiskit-addon-mpf/how_tos/choose-trotter-steps.ipynb similarity index 93% rename from docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps.ipynb rename to docs/addons/qiskit-addon-mpf/how_tos/choose-trotter-steps.ipynb index 41c4c9478fd..0496a1f3912 100644 --- a/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps.ipynb +++ b/docs/addons/qiskit-addon-mpf/how_tos/choose-trotter-steps.ipynb @@ -26,7 +26,7 @@ "# How to choose the Trotter steps for an MPF\n", "\n", "This guide teaches you how to find a good set of Trotter steps for an MPF.\n", - "We assume that you have already read and understood the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial." + "We assume that you have already read and understood the [Getting started](https://qiskit.github.io/qiskit-addon-mpf/tutorials/01_getting_started.html) tutorial." ] }, { @@ -49,7 +49,7 @@ "\n", "1. In practice, our **largest** $k_j$ ($k_{\\text{max}}$) value is simply bound by the depth of quantum circuit we can reliably execute.\n", "\n", - "2. Since the Trotter error is only well behaved for $\\text{d}t = t/k \\lt 1$ (see [Understanding the stability of MPFs](/docs/addons/qiskit-addon-mpf/explanations/mpf-stability)), our **smallest** $k_j$ ($k_{\\text{min}}$) should satisfy: $t/k_{\\text{min}} \\lt 1$ (Note: empirically $\\text{d}t \\leq 1$ seems to work fine, too).\n", + "2. Since the Trotter error is only well behaved for $\\text{d}t = t/k \\lt 1$ (see [Understanding the stability of MPFs](https://qiskit.github.io/qiskit-addon-mpf/explanations/mpf_stability.html)), our **smallest** $k_j$ ($k_{\\text{min}}$) should satisfy: $t/k_{\\text{min}} \\lt 1$ (Note: empirically $\\text{d}t \\leq 1$ seems to work fine, too).\n", "\n", "3. None of our $x_j$ values should be close to $0$, as this would imply that we might as well exclude the corresponding $k_j$ from the expansion.\n", "\n", @@ -74,7 +74,7 @@ "source": [ "## Applying these heuristics\n", "\n", - "Reusing the example from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial, we want to simulate up to time $t=8.0$.\n", + "Reusing the example from the [Getting started](https://qiskit.github.io/qiskit-addon-mpf/tutorials/01_getting_started.html) tutorial, we want to simulate up to time $t=8.0$.\n", "Thus, $k_{\\text{min}}=8$.\n", "\n", "For the sake of this example, let us assume that our deepest circuit is bound by $k_{\\text{max}}=20$.\n", @@ -280,7 +280,7 @@ "tags": [] }, "source": [ - "Recalling the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial, from these possible values we picked `ks=(8, 12, 19)` since it strikes a good balance of minimizing the re-weighted and plain L1-norms." + "Recalling the [Getting started](https://qiskit.github.io/qiskit-addon-mpf/tutorials/01_getting_started.html) tutorial, from these possible values we picked `ks=(8, 12, 19)` since it strikes a good balance of minimizing the re-weighted and plain L1-norms." ] } ], diff --git a/docs/addons/qiskit-addon-mpf/how-tos/index.mdx b/docs/addons/qiskit-addon-mpf/how_tos/index.mdx similarity index 100% rename from docs/addons/qiskit-addon-mpf/how-tos/index.mdx rename to docs/addons/qiskit-addon-mpf/how_tos/index.mdx diff --git a/docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model.ipynb b/docs/addons/qiskit-addon-mpf/how_tos/using-approximate-model.ipynb similarity index 98% rename from docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model.ipynb rename to docs/addons/qiskit-addon-mpf/how_tos/using-approximate-model.ipynb index 58f2ad43da4..1d28d55a8c3 100644 --- a/docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model.ipynb +++ b/docs/addons/qiskit-addon-mpf/how_tos/using-approximate-model.ipynb @@ -25,7 +25,7 @@ "source": [ "# How to use the approximate model\n", "\n", - "You have already seen a first example of the [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#setup_sum_of_squares_problem) function in the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial.\n", + "You have already seen a first example of the [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#setup_sum_of_squares_problem) function in the [Getting started](https://qiskit.github.io/qiskit-addon-mpf/tutorials/01_getting_started.html) tutorial.\n", "In this guide, we are going to revisit that function in more detail." ] }, @@ -43,7 +43,7 @@ "source": [ "## Setting up a simple model problem\n", "\n", - "For this guide, we will reuse the simple model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial; the Ising model on a line of 10 sites:\n", + "For this guide, we will reuse the simple model problem from the [Getting started](https://qiskit.github.io/qiskit-addon-mpf/tutorials/01_getting_started.html) tutorial; the Ising model on a line of 10 sites:\n", "\n", "$$\n", "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", @@ -103,7 +103,7 @@ "source": [ "## Choosing $k_j$\n", "\n", - "In this guide, we are going to focus on the tweaks that we can apply to the [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) and its solver that are returned by the [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#setup_sum_of_squares_problem). Therefore, we are not concerned with choosing any specific $k_j$ values and can simply reuse the ones from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial." + "In this guide, we are going to focus on the tweaks that we can apply to the [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) and its solver that are returned by the [setup_sum_of_squares_problem](/docs/api/qiskit-addon-mpf/costs#setup_sum_of_squares_problem). Therefore, we are not concerned with choosing any specific $k_j$ values and can simply reuse the ones from the [Getting started](https://qiskit.github.io/qiskit-addon-mpf/tutorials/01_getting_started.html) tutorial." ] }, { @@ -130,7 +130,7 @@ "source": [ "## Computing $x$ analytically\n", "\n", - "First, we have to construct our LSE, $Ax=b$. Once again, we reuse the settings from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial." + "First, we have to construct our LSE, $Ax=b$. Once again, we reuse the settings from the [Getting started](https://qiskit.github.io/qiskit-addon-mpf/tutorials/01_getting_started.html) tutorial." ] }, { @@ -836,7 +836,7 @@ "source": [ "## Comparing our results\n", "\n", - "We will now briefly repeat the computations from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01-getting-started) tutorial in order to assess the quality of the various coefficients." + "We will now briefly repeat the computations from the [Getting started](https://qiskit.github.io/qiskit-addon-mpf/tutorials/01_getting_started.html) tutorial in order to assess the quality of the various coefficients." ] }, { diff --git a/docs/addons/qiskit-addon-mpf/index.mdx b/docs/addons/qiskit-addon-mpf/index.mdx index e08f467e2a8..444796ec38f 100644 --- a/docs/addons/qiskit-addon-mpf/index.mdx +++ b/docs/addons/qiskit-addon-mpf/index.mdx @@ -16,7 +16,7 @@ This package currently contains the following main entry points for users: ## Documentation -All documentation is available [here](/docs/addons/qiskit-addon-mpf/). +All documentation is available [here](https://qiskit.github.io/qiskit-addon-mpf/). ## Installation diff --git a/docs/addons/qiskit-addon-mthree/_toc.json b/docs/addons/qiskit-addon-mthree/_toc.json index b24a0b45e21..d3607938eda 100644 --- a/docs/addons/qiskit-addon-mthree/_toc.json +++ b/docs/addons/qiskit-addon-mthree/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Matrix-free Measurement Mitigation (M3) 3.0.0", + "title": "Matrix-free Measurement Mitigation (M3) 3.0", "collapsed": true, "children": [ { @@ -81,23 +81,23 @@ }, { "title": "M3 paper example", - "url": "/docs/addons/qiskit-addon-mthree/01-m-3-ex-1" + "url": "/docs/addons/qiskit-addon-mthree/tutorials/01-m-3-ex-1" }, { "title": "Correcting probabilities", - "url": "/docs/addons/qiskit-addon-mthree/02-correcting-probs" + "url": "/docs/addons/qiskit-addon-mthree/tutorials/02-correcting-probs" }, { "title": "Quantum Volume", - "url": "/docs/addons/qiskit-addon-mthree/03-qv-example" + "url": "/docs/addons/qiskit-addon-mthree/tutorials/03-qv-example" }, { "title": "Dynamic Bernstein–Vazirani", - "url": "/docs/addons/qiskit-addon-mthree/04-dynamic-bv" + "url": "/docs/addons/qiskit-addon-mthree/tutorials/04-dynamic-bv" }, { "title": "Basic mitigated VQE", - "url": "/docs/addons/qiskit-addon-mthree/10-basic-vqe" + "url": "/docs/addons/qiskit-addon-mthree/tutorials/10-basic-vqe" }, { "title": "Mitigation class", diff --git a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb b/docs/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm.ipynb similarity index 95% rename from docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb rename to docs/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm.ipynb index b71536ec338..5bc702cd2e7 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm.ipynb @@ -24,7 +24,7 @@ "id": "5958e635-64c1-4599-82ca-225b83312906", "metadata": {}, "source": [ - "**Note:** If you have not yet read the how-to guide on [truncating Pauli terms](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms) you should do so before reading this guide." + "**Note:** If you have not yet read the how-to guide on [truncating Pauli terms](https://qiskit.github.io/qiskit-addon-obp/how_tos/truncate_operator_terms.html) you should do so before reading this guide." ] }, { @@ -43,7 +43,7 @@ "source": [ "## Constructing an example circuit\n", "\n", - "For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms):" + "For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/truncate_operator_terms.html):" ] }, { @@ -55,7 +55,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm/extracted-outputs/d6f281d4-706e-41ad-b7b5-bc7ebe106996-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm/extracted-outputs/d6f281d4-706e-41ad-b7b5-bc7ebe106996-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 1, @@ -154,7 +154,7 @@ "source": [ "## Using the L1 norm\n", "\n", - "By default, and as you have already seen in the [other how-to guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms), `p_norm=1` which means that the error is estimated as:\n", + "By default, and as you have already seen in the [other how-to guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/truncate_operator_terms.html), `p_norm=1` which means that the error is estimated as:\n", "$$\n", "|\\langle\\psi|\\Delta|\\psi\\rangle| \\leq \\sum_{P\\in\\mathcal{T}} |c_P|\n", "$$\n", @@ -270,7 +270,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm/extracted-outputs/ff72e77f-e2c0-4cce-8575-c5aa587f78fb-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm/extracted-outputs/ff72e77f-e2c0-4cce-8575-c5aa587f78fb-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, @@ -407,7 +407,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm/extracted-outputs/fe7f454c-7d79-4c61-b1dd-6a98564fb5f1-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm/extracted-outputs/fe7f454c-7d79-4c61-b1dd-6a98564fb5f1-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, diff --git a/docs/addons/qiskit-addon-obp/how-tos/index.mdx b/docs/addons/qiskit-addon-obp/how_tos/index.mdx similarity index 100% rename from docs/addons/qiskit-addon-obp/how-tos/index.mdx rename to docs/addons/qiskit-addon-obp/how_tos/index.mdx diff --git a/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb b/docs/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp.ipynb similarity index 98% rename from docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb rename to docs/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp.ipynb index 10ba5693c2e..e3845339a56 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp.ipynb @@ -37,7 +37,7 @@ "source": [ "## Constructing an example circuit\n", "\n", - "For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms):" + "For the purposes of this guide, we will use the same example circuit as in the [Pauli term truncation guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/truncate_operator_terms.html):" ] }, { @@ -114,7 +114,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp/extracted-outputs/8758f393-8691-4116-94ca-1185c6188bb4-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp/extracted-outputs/8758f393-8691-4116-94ca-1185c6188bb4-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 3, @@ -255,7 +255,7 @@ "The most aggressive strategy in terms of operator simplification can be achieved by slicing your circuit into slices of individual gates.\n", "\n", "Additionally, you can leverage all of the truncation mechanism built into the `backpropagate` method.\n", - "We did not do so above, effectively resulting in an exact expectation value, but you can learn how to in the [Pauli term truncation guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms)." + "We did not do so above, effectively resulting in an exact expectation value, but you can learn how to in the [Pauli term truncation guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/truncate_operator_terms.html)." ] }, { @@ -461,7 +461,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp/extracted-outputs/dadc314d-452f-465c-a382-d54685ce3f2a-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp/extracted-outputs/dadc314d-452f-465c-a382-d54685ce3f2a-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 10, diff --git a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb b/docs/addons/qiskit-addon-obp/how_tos/truncate-operator-terms.ipynb similarity index 97% rename from docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb rename to docs/addons/qiskit-addon-obp/how_tos/truncate-operator-terms.ipynb index ad3b70a0b16..9e8ae2fe6bf 100644 --- a/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/truncate-operator-terms.ipynb @@ -35,7 +35,7 @@ "such that the sum of the truncated coefficients is maximal but lower than the budget.\n", "\n", "**Note**: By default, the L1 norm is used to evaluate and bound the truncation error; however, the `p_norm` setting enables the specification of the Lp-norm to be used.\n", - "For more information on how to use that setting, please refer to [its how-to guide](/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm)." + "For more information on how to use that setting, please refer to [its how-to guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/bound_error_using_p_norm.html)." ] }, { @@ -65,7 +65,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/truncate-operator-terms/extracted-outputs/297fc3b0-d3b4-432d-96cd-7e175b7e6a52-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/truncate-operator-terms/extracted-outputs/297fc3b0-d3b4-432d-96cd-7e175b7e6a52-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 1, @@ -139,7 +139,7 @@ "As a first step towards understanding how this works, we look at the simplest case of a fixed truncation budget as specified by the user.\n", "\n", "The most straight-forward way to specify the truncation budget is via the `max_error_per_slice` argument.\n", - "In fact, this is what is being done in [this tutorial](/docs/addons/qiskit-addon-obp/tutorials/01-getting-started-ipynb). Setting `max_error_per_slice` to a `float` results in each slice being allotted a budget equaling that value.\n", + "In fact, this is what is being done in [this tutorial](https://qiskit.github.io/qiskit-addon-obp/tutorials/01_getting_started.ipynb). Setting `max_error_per_slice` to a `float` results in each slice being allotted a budget equaling that value.\n", "In the example below, we set this value to `0.001` which guarantees us an implicit truncation error of at most `0.018`, if the entire 18 slices were to be backpropagated.\n", "\n", "<div class=\"alert alert-info\">\n", @@ -230,7 +230,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/truncate-operator-terms/extracted-outputs/68f118df-6718-49b2-ba80-c37ef461f71a-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/truncate-operator-terms/extracted-outputs/68f118df-6718-49b2-ba80-c37ef461f71a-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, @@ -336,7 +336,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/truncate-operator-terms/extracted-outputs/b7167f80-5d29-4c32-be97-cba733f18b1d-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/truncate-operator-terms/extracted-outputs/b7167f80-5d29-4c32-be97-cba733f18b1d-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, @@ -446,7 +446,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/truncate-operator-terms/extracted-outputs/4c36768e-03e7-4e1b-81b3-59b6e9eab43e-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/truncate-operator-terms/extracted-outputs/4c36768e-03e7-4e1b-81b3-59b6e9eab43e-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, @@ -544,7 +544,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/truncate-operator-terms/extracted-outputs/69c9069a-9038-4319-8dc6-37a1be973ebf-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/truncate-operator-terms/extracted-outputs/69c9069a-9038-4319-8dc6-37a1be973ebf-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, @@ -641,7 +641,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/truncate-operator-terms/extracted-outputs/51d36d7e-0bd5-4388-bbb8-6831bf1f02e6-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/truncate-operator-terms/extracted-outputs/51d36d7e-0bd5-4388-bbb8-6831bf1f02e6-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, @@ -766,7 +766,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/truncate-operator-terms/extracted-outputs/b0bf5050-2928-41a1-9c3d-c9d79e918f6f-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/truncate-operator-terms/extracted-outputs/b0bf5050-2928-41a1-9c3d-c9d79e918f6f-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, @@ -884,7 +884,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-obp/how-tos/truncate-operator-terms/extracted-outputs/82271b0d-ddaf-4ad2-b5c6-e9e4c2a4cff7-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-obp/how_tos/truncate-operator-terms/extracted-outputs/82271b0d-ddaf-4ad2-b5c6-e9e4c2a4cff7-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, diff --git a/docs/addons/qiskit-addon-obp/index.mdx b/docs/addons/qiskit-addon-obp/index.mdx index 32337cfebb6..37f45ed19f6 100644 --- a/docs/addons/qiskit-addon-obp/index.mdx +++ b/docs/addons/qiskit-addon-obp/index.mdx @@ -15,7 +15,7 @@ There are a number of ways in which operator backpropagation can be performed, t ## Documentation -All documentation is available [here](/docs/addons/qiskit-addon-obp/). +All documentation is available [here](https://qiskit.github.io/qiskit-addon-obp/). ## Installation diff --git a/docs/addons/qiskit-addon-opt-mapper/_toc.json b/docs/addons/qiskit-addon-opt-mapper/_toc.json index 7879e772ac3..8aff86322b0 100644 --- a/docs/addons/qiskit-addon-opt-mapper/_toc.json +++ b/docs/addons/qiskit-addon-opt-mapper/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Optimization mapper 0.1.0", + "title": "Optimization mapper 0.1", "collapsed": true, "children": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/00-migrate-from-qiskit-optimization.ipynb b/docs/addons/qiskit-addon-opt-mapper/how_tos/00-migrate-from-qiskit-optimization.ipynb similarity index 100% rename from docs/addons/qiskit-addon-opt-mapper/how-tos/00-migrate-from-qiskit-optimization.ipynb rename to docs/addons/qiskit-addon-opt-mapper/how_tos/00-migrate-from-qiskit-optimization.ipynb diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb b/docs/addons/qiskit-addon-opt-mapper/how_tos/01-optimization-problem.ipynb similarity index 94% rename from docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb rename to docs/addons/qiskit-addon-opt-mapper/how_tos/01-optimization-problem.ipynb index c0cab801745..c16f0369c07 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how_tos/01-optimization-problem.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "e8fc2c6a-087b-4f14-97d9-cae76af6b82b", + "id": "d27660f8-6458-4de4-82cd-cde5b96c73b2", "metadata": {}, "source": [ "# Model quantum optimization problems with the `OptimizationProblem` class" @@ -21,7 +21,7 @@ }, { "cell_type": "markdown", - "id": "30c053dd-154d-4490-9d89-502802410f86", + "id": "e5fd0aed-ac9d-4762-a878-db3a244e5174", "metadata": {}, "source": [ "## Introduction" @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "6071d6b4-8642-4dae-8af2-a83784a391ef", + "id": "70f28ef6-d4a8-4a74-a01c-eac004fae5eb", "metadata": {}, "source": [ "This guide demonstrates how to use the **Optimization Mapper Qiskit addon** for quantum optimization modeling. The addon introduces the `OptimizationProblem` class, which provides a flexible way to handle variables, constraints, and objective functions.\n", @@ -54,7 +54,7 @@ }, { "cell_type": "markdown", - "id": "4f69a709-7d80-48c4-99de-32f04c7acc6c", + "id": "e1f48581-9e8d-4c9a-964d-db6636834dbf", "metadata": {}, "source": [ "## Construct an `OptimizationProblem`\n", @@ -66,7 +66,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "70e725ad-8ebc-4dbc-a65d-096fe60ca1a7", + "id": "0ac04756-6055-4597-bfed-6c36062fa851", "metadata": {}, "outputs": [ { @@ -96,7 +96,7 @@ }, { "cell_type": "markdown", - "id": "03cb8b33-bb97-4a5b-80e2-8212ff0a888d", + "id": "de281542-4e23-409a-92e7-180c2145cbc1", "metadata": {}, "source": [ "The `OptimizationProblem` class supports four types of variables:\n", @@ -112,7 +112,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "e85233c1-fff8-4803-9f66-ba31dd057872", + "id": "b0980e7e-d086-4112-ba12-558c431915e7", "metadata": {}, "outputs": [ { @@ -153,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "d52126c4-6927-4510-b06d-146e06ee1df8", + "id": "5a275b42-5c2f-4c67-b744-74c53fe83da4", "metadata": {}, "source": [ "You set the objective function by calling `OptimizationProblem.minimize` or `OptimizationProblem.maximize`.\n", @@ -177,7 +177,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "19e13d69-56a8-4025-92d8-37700786a8f1", + "id": "8c55acf9-a528-4843-a60f-29dd6ccbd739", "metadata": {}, "outputs": [ { @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "b6123dee-a140-493b-b867-f653bbb2284a", + "id": "d9a38311-bca8-458f-af30-b1481126e7ec", "metadata": {}, "source": [ "You can also specify the objective function by using arrays:\n", @@ -233,7 +233,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "225872fc-0ee5-4016-ac26-1f916642f122", + "id": "17147648-faeb-4250-8038-2a1d619260f2", "metadata": {}, "outputs": [ { @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "f5f9bbe7-9e73-4990-bbe8-c41e6463d6a7", + "id": "f08dfa6c-6a58-489a-8cfb-644b0fca7053", "metadata": {}, "source": [ "You can access the constant, the linear term, the quadratic term, and the higher-order terms by looking at\n", @@ -307,7 +307,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "c6263ac0-6e60-4c29-977d-f2333ed6fc39", + "id": "7b53d03b-8dc5-4a79-9405-03d25554367d", "metadata": {}, "outputs": [ { @@ -412,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "39bdeddb-90ad-4020-8e73-2334bf75e860", + "id": "e150888d-4060-42af-b840-afd1a710a78e", "metadata": {}, "source": [ "## Add or remove linear, quadratic, and higher-order terms to and from the constraints" @@ -420,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "8393d2cd-f962-4f73-a302-f3309ba6dc95", + "id": "1ebe7521-ebfb-41f5-99cb-c6a670c12c7a", "metadata": {}, "source": [ "You can add linear constraints by setting name, linear expression, sense, and right-hand-side value (rhs).\n", @@ -430,7 +430,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "5aca058c-a8f5-4879-afb9-fe0aadc58ef8", + "id": "dffd939f-b0db-4cbd-b17f-89cb379dfdfc", "metadata": {}, "outputs": [ { @@ -473,7 +473,7 @@ }, { "cell_type": "markdown", - "id": "b1fd62ac-0b53-459c-a864-5cc504ca73a6", + "id": "ba4b2ac8-676b-47a8-8473-62e7e0c330cb", "metadata": {}, "source": [ "You can add quadratic constraints by setting name, quadratic expression, sense, and right-hand-side value (rhs)." @@ -482,7 +482,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "265da26a-7f8d-40b9-ba77-23ce050df118", + "id": "f03aa130-ea67-469a-8c30-f7a9e449d3a5", "metadata": {}, "outputs": [ { @@ -548,7 +548,7 @@ }, { "cell_type": "markdown", - "id": "f52690df-af2a-4507-8304-ddf08b60cf15", + "id": "e5c93ddf-3134-469f-b6a5-c7b9a11af52b", "metadata": {}, "source": [ "You can also add higher-order constraints by setting name, higher-order expression, sense, and right-hand-side value (rhs)." @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "0136ca38-7b22-4a08-ad12-f7baa83d1a7b", + "id": "e2636a9a-f4f5-4b63-885e-7618f56c6098", "metadata": {}, "outputs": [ { @@ -631,7 +631,7 @@ }, { "cell_type": "markdown", - "id": "338437ed-f98e-4133-a7d6-2c355729fe90", + "id": "92cb02b8-14c1-49ef-af1d-d3ee654c96d1", "metadata": {}, "source": [ "You can access linear and quadratic terms of linear and quadratic constraints in the same way as the objective function." @@ -640,7 +640,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "187b6eff-e19f-435f-9d6d-3392efa6617e", + "id": "a6cb7c71-c928-459d-b580-afe998ff640f", "metadata": {}, "outputs": [ { @@ -677,7 +677,7 @@ }, { "cell_type": "markdown", - "id": "f71b6fec-f2b4-477f-b49f-ae34c727b61a", + "id": "49815ffd-df5d-4a0d-838c-4f6d42c7f26a", "metadata": {}, "source": [ "You can also remove linear/quadratic/higher-order constraints by using the `remove_linear_constraint`, `remove_quadratic_constraint`, and `remove_higher_order_constraint` methods, respectively." @@ -686,7 +686,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "ba0e58c8-c84b-4348-ae76-6bccc0095774", + "id": "9fcb15ca-f5af-4a52-8ce6-9c96f5f34102", "metadata": {}, "outputs": [ { @@ -736,7 +736,7 @@ }, { "cell_type": "markdown", - "id": "696be3ca-206c-452d-925b-9f8b778af8d8", + "id": "0f797eb2-eb52-43be-a8e1-0f0d4028a197", "metadata": {}, "source": [ "## Substitute variables" @@ -744,7 +744,7 @@ }, { "cell_type": "markdown", - "id": "e225840a-b8b2-464c-965c-2ced7fa60911", + "id": "10247c26-5bdd-4522-b78b-04bf67452e6e", "metadata": {}, "source": [ "You can substitute some of the variables with constants or other variables.\n", @@ -757,7 +757,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "fd4d822b-735a-477e-a39e-55b99e5c39bb", + "id": "c557dcb9-a935-44fa-bd61-4c89a56305b3", "metadata": {}, "outputs": [ { @@ -798,7 +798,7 @@ }, { "cell_type": "markdown", - "id": "ab5c7ea1-8b1f-453d-a9b0-70c6118f9caf", + "id": "31e7fe1a-0754-4ceb-afc8-dbcd351e7241", "metadata": {}, "source": [ "If the resulting problem is infeasible due to lower bounds or upper bounds, the methods returns the status `Status.INFEASIBLE`.\n", @@ -808,7 +808,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "94ae5749-d328-41a7-b0b7-1d12d091ad9b", + "id": "f1ca8356-f037-4918-bf60-cc7bfd6d32c7", "metadata": {}, "outputs": [ { @@ -833,7 +833,7 @@ }, { "cell_type": "markdown", - "id": "369a14e3-c1b4-416b-8370-80a285a757fc", + "id": "05e0fe2a-27d7-45bb-8924-870d4f6675bf", "metadata": {}, "source": [ "You cannot substitute variables multiple times.\n", @@ -843,7 +843,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "84f6cdad-96b7-4c5e-bce7-b7b2b053984d", + "id": "ca3203fe-32c6-4f84-bc64-d2109bb80a3e", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb b/docs/addons/qiskit-addon-opt-mapper/how_tos/02-converters-for-optimization-problems.ipynb similarity index 96% rename from docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb rename to docs/addons/qiskit-addon-opt-mapper/how_tos/02-converters-for-optimization-problems.ipynb index 8a173c1eff7..4a677f7ff4b 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how_tos/02-converters-for-optimization-problems.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "79a16c7b-61c0-4809-ada2-f8507f2c81b7", + "id": "aac5ddd7-62a0-491a-98a3-21aa74a3f794", "metadata": {}, "source": [ "# Convert formulations of an `OptimizationProblem`\n", @@ -48,7 +48,7 @@ }, { "cell_type": "markdown", - "id": "12f8183b-0797-46ed-a1c8-d4eeb7a821f4", + "id": "e7172a49-71fd-45f5-881a-f2bf3447fdbd", "metadata": {}, "source": [ "## Code examples\n", @@ -65,7 +65,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "12da4c6d-6737-49a4-9753-c59d984ff27f", + "id": "8207c198-7097-4f82-aaf0-21dbde146fcf", "metadata": {}, "outputs": [ { @@ -128,7 +128,7 @@ }, { "cell_type": "markdown", - "id": "b412249f-2bdb-4b0f-8d33-656e5f0b9d46", + "id": "5409088d-6a20-4f36-a691-365fe67bef74", "metadata": {}, "source": [ "## Binary to spin conversion\n", @@ -138,7 +138,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "97ff25f9-de9d-4d8d-8271-ee2211b4c21e", + "id": "c4d05a89-6b93-4e0c-a614-629fc77eb71a", "metadata": {}, "outputs": [ { @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "b9982058-53a6-471f-b845-9c86341e5f63", + "id": "827b71c6-f7db-47b4-8247-118a3ccd454c", "metadata": {}, "source": [ "## Spin to binary conversion\n", @@ -204,7 +204,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "490b511a-94ff-4c41-98b5-bf9523e65c0c", + "id": "3f8e7be4-3145-46f5-87df-151b70e00cd3", "metadata": {}, "outputs": [ { @@ -258,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "09fc621c-4438-46da-b098-e67bad02db9c", + "id": "5521322d-e178-4d20-a920-d2b433aba076", "metadata": {}, "source": [ "## Inequality to equality conversion\n", @@ -272,7 +272,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "1cda3f87-a499-450d-bdba-d1602801ec9d", + "id": "64cbcd38-51f7-4900-8cc2-b4fb8515017d", "metadata": {}, "outputs": [ { @@ -336,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "c98b31ac-2416-481b-b3c7-01dd21d8125e", + "id": "8274fafe-0fe0-41ae-80e2-28f853141838", "metadata": {}, "source": [ "## Equality to penalty conversion\n", @@ -352,7 +352,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "d33c7ead-3854-424e-b4be-d8f5300ea422", + "id": "939a8a82-0d93-46a3-b155-d0142925aac0", "metadata": {}, "outputs": [ { @@ -408,7 +408,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "804c1a07-c77e-4dee-bf41-876eb2be8026", + "id": "fbd2f00b-a51b-4d87-94d2-5916c93009cf", "metadata": {}, "outputs": [ { @@ -441,7 +441,7 @@ }, { "cell_type": "markdown", - "id": "b43cf584-452b-409a-852a-9d5b252dae6b", + "id": "05bc7818-5864-427c-a560-4842e1f8778e", "metadata": {}, "source": [ "## Linear inequality to penalty conversion\n", @@ -464,7 +464,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "2ea182b1-dc5d-4990-975c-3fd51e44a36d", + "id": "75c465cb-a715-4d79-abc7-da6e395c14be", "metadata": {}, "outputs": [ { @@ -520,7 +520,7 @@ }, { "cell_type": "markdown", - "id": "64f4457f-a0a5-423e-b90b-ef07d955d693", + "id": "e02c5a8d-0bb7-4855-8a96-a739d8da44b3", "metadata": {}, "source": [ "## Maximize to minimize conversion\n", @@ -535,7 +535,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "384b5fd1-d66b-4f71-b51d-904f350b6024", + "id": "6ba78a4d-3e35-4d2f-a4e6-fe479501116a", "metadata": {}, "outputs": [ { @@ -590,7 +590,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "cc0e2a50-7d79-47da-bf63-65acda0aa969", + "id": "5e1c217b-6a21-4df5-8e5f-c3930cfcb5f5", "metadata": {}, "outputs": [ { @@ -623,7 +623,7 @@ }, { "cell_type": "markdown", - "id": "e5e652b0-0e1e-4543-accd-50bdf617367b", + "id": "6ea31b89-747d-4379-a9a2-f70a4240cfc8", "metadata": {}, "source": [ "## QUBO conversion\n", @@ -635,7 +635,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "0fc65baf-c9fa-4210-8470-2bcc555a5058", + "id": "ee2d5c81-73f4-4d21-a81e-239d86a4454f", "metadata": {}, "outputs": [ { @@ -705,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "ec03c318-3906-411d-bcee-9b285c9d368d", + "id": "6d2a2342-a08b-4827-ab21-05bd4e21d0fb", "metadata": {}, "source": [ "## HUBO conversion\n", @@ -715,7 +715,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "88e8d05d-4070-4783-99a6-d5b2b62be0bd", + "id": "384d7253-0ce3-4b24-96eb-4e1a1ab7cc1c", "metadata": {}, "outputs": [ { @@ -779,7 +779,7 @@ }, { "cell_type": "markdown", - "id": "d5aa4990-8128-4b17-b8c5-23c3afdf900a", + "id": "9df4c491-969d-46a0-a0d9-1d5d6b80a029", "metadata": {}, "source": [ "## Ising transformation\n", @@ -790,7 +790,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "cca9d12b-5d7c-443d-83db-038c4794ef6e", + "id": "657a3774-19cb-4d4f-a0ac-cec38bc0bcd4", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers.ipynb b/docs/addons/qiskit-addon-opt-mapper/how_tos/03-validate-with-solvers.ipynb similarity index 98% rename from docs/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers.ipynb rename to docs/addons/qiskit-addon-opt-mapper/how_tos/03-validate-with-solvers.ipynb index 2a93164fa7f..2ecc0ec9e52 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how_tos/03-validate-with-solvers.ipynb @@ -78,7 +78,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers/extracted-outputs/27b1b8d3-e2b7-4304-b8d8-d5e63af5deec-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-opt-mapper/how_tos/03-validate-with-solvers/extracted-outputs/27b1b8d3-e2b7-4304-b8d8-d5e63af5deec-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, @@ -331,7 +331,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers/extracted-outputs/f1281552-b611-4911-bdb9-df7d1df97ad4-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-opt-mapper/how_tos/03-validate-with-solvers/extracted-outputs/f1281552-b611-4911-bdb9-df7d1df97ad4-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, @@ -359,7 +359,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers/extracted-outputs/309f88e4-5d1f-47f8-b63a-50346829ffeb-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-opt-mapper/how_tos/03-validate-with-solvers/extracted-outputs/309f88e4-5d1f-47f8-b63a-50346829ffeb-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb b/docs/addons/qiskit-addon-opt-mapper/how_tos/04-translate-docplex.ipynb similarity index 93% rename from docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb rename to docs/addons/qiskit-addon-opt-mapper/how_tos/04-translate-docplex.ipynb index 6238c885c66..4926985f15c 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how_tos/04-translate-docplex.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "fcdb0387-f94e-4b20-b2e3-d7eb3dc7c65a", + "id": "71c5646f-8ef9-40b4-9a06-3ca97098e1d3", "metadata": {}, "source": [ "# Translate between `OptimizationProblem` and Docplex\n", @@ -29,7 +29,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "6f92cf08-8531-46bb-9c51-9cf5e41bd3fa", + "id": "0a50d206-2e05-453f-ac75-314aa86ab212", "metadata": {}, "outputs": [ { @@ -80,7 +80,7 @@ }, { "cell_type": "markdown", - "id": "cb0126a6-5060-421c-9988-d9981b8532b3", + "id": "04754d18-ee01-4c00-be23-125bac8e7bfe", "metadata": {}, "source": [ "When you display your problem in LP format using `export_as_lp_string`,\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "f3572344-618e-4c02-9a3b-48e80431731d", + "id": "2c5d050c-e68e-44a8-8b75-7cd4ae6a6e46", "metadata": {}, "outputs": [ { @@ -135,7 +135,7 @@ }, { "cell_type": "markdown", - "id": "d800860d-cb74-45e0-806a-df38658cba2d", + "id": "397a7b32-d5ab-4f74-9e89-66c6e028a1d7", "metadata": {}, "source": [ "As stated before, you can also convert a `OptimizationProblem` to a Docplex model using the `to_docplex` function." @@ -144,7 +144,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "8962d91d-b7d5-4a65-9e38-e3571af107f3", + "id": "db49073c-600f-43a4-b758-168a280698da", "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ }, { "cell_type": "markdown", - "id": "3dc5e633-62c2-45fb-acbc-0f26c68367af", + "id": "24731f83-11a5-448a-ac95-66f0a9945221", "metadata": {}, "source": [ "## Load LP files using Docplex" @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "dfcf4fcc-ee34-427b-9a38-628e972007a3", + "id": "52812c75-c821-47d8-8c09-448f92d0b3f8", "metadata": {}, "source": [ "Once you are familia with the Docplex translators, loading from LP files becomes very simple. You can use the `docplex.mp.ModelReader` class followed by a translation step:" @@ -203,7 +203,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5f1e4f21-e5b5-4a74-ba0d-1c52b4961307", + "id": "aab13e97-6245-41e2-91b1-841775d2ed42", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how-tos/index.mdx b/docs/addons/qiskit-addon-opt-mapper/how_tos/index.mdx similarity index 100% rename from docs/addons/qiskit-addon-opt-mapper/how-tos/index.mdx rename to docs/addons/qiskit-addon-opt-mapper/how_tos/index.mdx diff --git a/docs/addons/qiskit-addon-pna/_toc.json b/docs/addons/qiskit-addon-pna/_toc.json index cc37aae08ec..bb1b3e79e5d 100644 --- a/docs/addons/qiskit-addon-pna/_toc.json +++ b/docs/addons/qiskit-addon-pna/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Propagated noise absorption (PNA) 0.2.0", + "title": "Propagated noise absorption (PNA) 0.2", "collapsed": true, "children": [ { diff --git a/docs/addons/qiskit-addon-pna/how-tos/index.mdx b/docs/addons/qiskit-addon-pna/how_tos/index.mdx similarity index 100% rename from docs/addons/qiskit-addon-pna/how-tos/index.mdx rename to docs/addons/qiskit-addon-pna/how_tos/index.mdx diff --git a/docs/addons/qiskit-addon-pna/how-tos/placeholder.ipynb b/docs/addons/qiskit-addon-pna/how_tos/placeholder.ipynb similarity index 100% rename from docs/addons/qiskit-addon-pna/how-tos/placeholder.ipynb rename to docs/addons/qiskit-addon-pna/how_tos/placeholder.ipynb diff --git a/docs/addons/qiskit-addon-pna/index.mdx b/docs/addons/qiskit-addon-pna/index.mdx index 9ba06df73ad..37c21d89d6a 100644 --- a/docs/addons/qiskit-addon-pna/index.mdx +++ b/docs/addons/qiskit-addon-pna/index.mdx @@ -5,7 +5,7 @@ description: "Documentation for the latest version of Propagated noise absorptio # Qiskit add-on: Propagated noise absorption (PNA) -PNA is a technique for mitigating errors in observable expectation values by “absorbing” the inverses of the learned noise channels into the observable using [Pauli propagation](/docs/addons/pauli-prop/). Each Pauli noise generator in the noise model is classically propagated to the end of the circuit and applied to the observable, resulting in a new observable that when measured on a QPU, mitigates the learned gate noise. Check out the [tutorial](https://github.com/qiskit-community/qdc-challenges-2025/blob/main/day3_tutorials/Track_A/pna/propagated_noise_absorption.ipynb) to see how it works! +PNA is a technique for mitigating errors in observable expectation values by “absorbing” the inverses of the learned noise channels into the observable using [Pauli propagation](https://qiskit.github.io/pauli-prop/). Each Pauli noise generator in the noise model is classically propagated to the end of the circuit and applied to the observable, resulting in a new observable that when measured on a QPU, mitigates the learned gate noise. Check out the [tutorial](https://github.com/qiskit-community/qdc-challenges-2025/blob/main/day3_tutorials/Track_A/pna/propagated_noise_absorption.ipynb) to see how it works! ## Overview diff --git a/docs/addons/qiskit-addon-slc/_toc.json b/docs/addons/qiskit-addon-slc/_toc.json index 93cbe802437..306e67a89f0 100644 --- a/docs/addons/qiskit-addon-slc/_toc.json +++ b/docs/addons/qiskit-addon-slc/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Shaded lightcones 0.1.0", + "title": "Shaded lightcones 0.1", "collapsed": true, "children": [ { @@ -17,11 +17,11 @@ }, { "title": "How-To Guides", - "url": "/docs/addons/qiskit-addon-slc" + "url": "/docs/addons/qiskit-addon-slc/how-tos/index" }, { "title": "Explanations", - "url": "/docs/addons/qiskit-addon-slc" + "url": "/docs/addons/qiskit-addon-slc/explanations/index" }, { "title": "GitHub", diff --git a/docs/addons/qiskit-addon-slc/how-tos/index.mdx b/docs/addons/qiskit-addon-slc/how_tos/index.mdx similarity index 100% rename from docs/addons/qiskit-addon-slc/how-tos/index.mdx rename to docs/addons/qiskit-addon-slc/how_tos/index.mdx diff --git a/docs/addons/qiskit-addon-slc/index.mdx b/docs/addons/qiskit-addon-slc/index.mdx index a91d7832b28..6a2593ee744 100644 --- a/docs/addons/qiskit-addon-slc/index.mdx +++ b/docs/addons/qiskit-addon-slc/index.mdx @@ -11,7 +11,7 @@ This package contains the Qiskit addon for shaded lightcones (SLC). These can be ## Documentation -All documentation is available [here](/docs/addons/qiskit-addon-slc/). +All documentation is available [here](https://qiskit.github.io/qiskit-addon-slc/). ## Installation diff --git a/docs/addons/qiskit-addon-sqd-hpc/_toc.json b/docs/addons/qiskit-addon-sqd-hpc/_toc.json index 3fddd3daf7a..ade73e5c325 100644 --- a/docs/addons/qiskit-addon-sqd-hpc/_toc.json +++ b/docs/addons/qiskit-addon-sqd-hpc/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "SQD for HPC 0.0.0", + "title": "SQD for HPC 0.0", "collapsed": true, "children": [ { diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json index d1a20fdc603..3e9f11cd0d9 100644 --- a/docs/addons/qiskit-addon-sqd/_toc.json +++ b/docs/addons/qiskit-addon-sqd/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Sample-based quantum diagonalization (SQD) 0.12.1", + "title": "Sample-based quantum diagonalization (SQD) 0.12", "collapsed": true, "children": [ { diff --git a/docs/addons/qiskit-addon-sqd/how-tos/add-fermionic-excitations-to-configuration-pool.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/add-fermionic-excitations-to-configuration-pool.ipynb similarity index 100% rename from docs/addons/qiskit-addon-sqd/how-tos/add-fermionic-excitations-to-configuration-pool.ipynb rename to docs/addons/qiskit-addon-sqd/how_tos/add-fermionic-excitations-to-configuration-pool.ipynb diff --git a/docs/addons/qiskit-addon-sqd/how-tos/benchmark-pauli-projection.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/benchmark-pauli-projection.ipynb similarity index 99% rename from docs/addons/qiskit-addon-sqd/how-tos/benchmark-pauli-projection.ipynb rename to docs/addons/qiskit-addon-sqd/how_tos/benchmark-pauli-projection.ipynb index 1627b0bd653..127270183dd 100644 --- a/docs/addons/qiskit-addon-sqd/how-tos/benchmark-pauli-projection.ipynb +++ b/docs/addons/qiskit-addon-sqd/how_tos/benchmark-pauli-projection.ipynb @@ -310,7 +310,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/how-tos/benchmark-pauli-projection/extracted-outputs/9abb110f-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/how_tos/benchmark-pauli-projection/extracted-outputs/9abb110f-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, @@ -432,7 +432,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/how-tos/benchmark-pauli-projection/extracted-outputs/6b961c81-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/how_tos/benchmark-pauli-projection/extracted-outputs/6b961c81-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, diff --git a/docs/addons/qiskit-addon-sqd/how-tos/choose-subspace-dimension.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/choose-subspace-dimension.ipynb similarity index 99% rename from docs/addons/qiskit-addon-sqd/how-tos/choose-subspace-dimension.ipynb rename to docs/addons/qiskit-addon-sqd/how_tos/choose-subspace-dimension.ipynb index 92ead16fbb8..f81b5d159a3 100644 --- a/docs/addons/qiskit-addon-sqd/how-tos/choose-subspace-dimension.ipynb +++ b/docs/addons/qiskit-addon-sqd/how_tos/choose-subspace-dimension.ipynb @@ -174,7 +174,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-sqd/how-tos/choose-subspace-dimension/extracted-outputs/caffd888-e89c-4aa9-8bae-4d1bb723b35e-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-sqd/how_tos/choose-subspace-dimension/extracted-outputs/caffd888-e89c-4aa9-8bae-4d1bb723b35e-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "metadata": {}, diff --git a/docs/addons/qiskit-addon-sqd/how-tos/index.mdx b/docs/addons/qiskit-addon-sqd/how_tos/index.mdx similarity index 100% rename from docs/addons/qiskit-addon-sqd/how-tos/index.mdx rename to docs/addons/qiskit-addon-sqd/how_tos/index.mdx diff --git a/docs/addons/qiskit-addon-sqd/how-tos/integrate-dice-solver.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/integrate-dice-solver.ipynb similarity index 95% rename from docs/addons/qiskit-addon-sqd/how-tos/integrate-dice-solver.ipynb rename to docs/addons/qiskit-addon-sqd/how_tos/integrate-dice-solver.ipynb index a66f66f1c26..0d2211a0a6f 100644 --- a/docs/addons/qiskit-addon-sqd/how-tos/integrate-dice-solver.ipynb +++ b/docs/addons/qiskit-addon-sqd/how_tos/integrate-dice-solver.ipynb @@ -18,9 +18,9 @@ "source": [ "# Scale SQD chemistry workflows with Dice solver\n", "\n", - "For information on how to install and use ``qiskit-addon-dice-solver``, [visit the docs](/docs/addons/qiskit-addon-dice-solver/).\n", + "For information on how to install and use ``qiskit-addon-dice-solver``, [visit the docs](https://qiskit.github.io/qiskit-addon-dice-solver/).\n", "\n", - "For more details on the SQD code used in this example, check out [tutorial 1](/docs/addons/qiskit-addon-sqd/tutorials/01-chemistry-hamiltonian)." + "For more details on the SQD code used in this example, check out [tutorial 1](https://qiskit.github.io/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian.html)." ] }, { diff --git a/docs/addons/qiskit-addon-sqd/how-tos/project-pauli-operators-onto-hilbert-subspaces.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/project-pauli-operators-onto-hilbert-subspaces.ipynb similarity index 100% rename from docs/addons/qiskit-addon-sqd/how-tos/project-pauli-operators-onto-hilbert-subspaces.ipynb rename to docs/addons/qiskit-addon-sqd/how_tos/project-pauli-operators-onto-hilbert-subspaces.ipynb diff --git a/docs/addons/qiskit-addon-sqd/how-tos/select-open-closed-shell.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/select-open-closed-shell.ipynb similarity index 100% rename from docs/addons/qiskit-addon-sqd/how-tos/select-open-closed-shell.ipynb rename to docs/addons/qiskit-addon-sqd/how_tos/select-open-closed-shell.ipynb diff --git a/docs/addons/qiskit-addon-sqd/how-tos/use-oo-to-optimize-hamiltonian-basis.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/use-oo-to-optimize-hamiltonian-basis.ipynb similarity index 100% rename from docs/addons/qiskit-addon-sqd/how-tos/use-oo-to-optimize-hamiltonian-basis.ipynb rename to docs/addons/qiskit-addon-sqd/how_tos/use-oo-to-optimize-hamiltonian-basis.ipynb diff --git a/docs/addons/qiskit-addon-sqd/index.mdx b/docs/addons/qiskit-addon-sqd/index.mdx index 62d83fa6027..5cf2ce317d1 100644 --- a/docs/addons/qiskit-addon-sqd/index.mdx +++ b/docs/addons/qiskit-addon-sqd/index.mdx @@ -20,7 +20,7 @@ This package contains the functionality for the classical processing of user-pro ## Documentation -All documentation is available [here](/docs/addons/qiskit-addon-sqd/). +All documentation is available [here](https://qiskit.github.io/qiskit-addon-sqd/). ## Installation @@ -40,11 +40,11 @@ The [`qiskit_addon_sqd.fermion.solve_fermion()`](/docs/api/qiskit-addon-sqd/ferm ## Choosing subspace dimensions -The choice of the subspace dimension affects the accuracy and runtime of the eigenstate solver. The larger the subspace the more accurate the calculation, at the cost of increasing the runtime and memory requirements. The optimal subspace size of a given system is not known, thus a convergence study with the subspace dimension may be performed, as described in this [guide](how-tos/choose-subspace-dimension). +The choice of the subspace dimension affects the accuracy and runtime of the eigenstate solver. The larger the subspace the more accurate the calculation, at the cost of increasing the runtime and memory requirements. The optimal subspace size of a given system is not known, thus a convergence study with the subspace dimension may be performed, as described in this [guide](how_tos/choose-subspace-dimension). ## The subspace dimension is set indirectly -In this package, the user controls the number of bitstrings contained in each subspace with the samples\_per\_batch argument in [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](/docs/api/qiskit-addon-sqd/subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"). The value of this argument sets an upper bound to the subspace dimension in the case of quantum chemistry applications. See this [example](how-tos/select-open-closed-shell) for more details. +In this package, the user controls the number of bitstrings contained in each subspace with the samples\_per\_batch argument in [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](/docs/api/qiskit-addon-sqd/subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"). The value of this argument sets an upper bound to the subspace dimension in the case of quantum chemistry applications. See this [example](how_tos/select-open-closed-shell) for more details. ## Citing this project diff --git a/docs/addons/qiskit-addon-utils/_toc.json b/docs/addons/qiskit-addon-utils/_toc.json index bec41500745..4b669aa0000 100644 --- a/docs/addons/qiskit-addon-utils/_toc.json +++ b/docs/addons/qiskit-addon-utils/_toc.json @@ -1,7 +1,7 @@ { "parentUrl": "/docs/guides/addons", "parentLabel": "Documentation", - "title": "Qiskit addon utilities 0.3.1", + "title": "Qiskit addon utilities 0.3", "collapsed": true, "children": [ { diff --git a/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb b/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.ipynb similarity index 97% rename from docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb rename to docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.ipynb index c86fb46d70e..8eb4a1e1eaf 100644 --- a/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth.ipynb +++ b/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.ipynb @@ -67,7 +67,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth/extracted-outputs/9b986eac-93c6-453a-bfe1-2546681ab38b-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth/extracted-outputs/9b986eac-93c6-453a-bfe1-2546681ab38b-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 2, @@ -146,7 +146,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth/extracted-outputs/c3f482a1-6868-4cda-b8ed-8a8489794707-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth/extracted-outputs/c3f482a1-6868-4cda-b8ed-8a8489794707-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 4, @@ -189,7 +189,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth/extracted-outputs/2806ec31-05e8-4a4c-832b-888150506191-1.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth/extracted-outputs/2806ec31-05e8-4a4c-832b-888150506191-1.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 5, @@ -239,7 +239,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth/extracted-outputs/03a5f389-94c0-4496-9fe6-262dca62a0e3-1.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth/extracted-outputs/03a5f389-94c0-4496-9fe6-262dca62a0e3-1.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 6, diff --git a/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices.ipynb b/docs/addons/qiskit-addon-utils/how_tos/create-circuit-slices.ipynb similarity index 95% rename from docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices.ipynb rename to docs/addons/qiskit-addon-utils/how_tos/create-circuit-slices.ipynb index cab3fd65d39..03ebc9cbedf 100644 --- a/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices.ipynb +++ b/docs/addons/qiskit-addon-utils/how_tos/create-circuit-slices.ipynb @@ -48,7 +48,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/9da8d173-8ce9-47a9-a24f-ab32e45f8337-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/create-circuit-slices/extracted-outputs/9da8d173-8ce9-47a9-a24f-ab32e45f8337-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 1, @@ -90,7 +90,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/f883fa2e-f943-4c49-af45-fb26cadb72b6-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/create-circuit-slices/extracted-outputs/f883fa2e-f943-4c49-af45-fb26cadb72b6-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 2, @@ -123,7 +123,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/bc5ebecb-6c8b-4f51-8199-b236a0ba3328-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/create-circuit-slices/extracted-outputs/bc5ebecb-6c8b-4f51-8199-b236a0ba3328-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 3, @@ -154,7 +154,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/1a5d5aff-f988-4f14-ad25-9f0105202b77-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/create-circuit-slices/extracted-outputs/1a5d5aff-f988-4f14-ad25-9f0105202b77-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 4, @@ -187,7 +187,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/110b5844-972e-46d1-bfc5-20a30b69f814-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/create-circuit-slices/extracted-outputs/110b5844-972e-46d1-bfc5-20a30b69f814-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 5, @@ -236,7 +236,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/948aa715-22e8-41e1-943d-de7eb0aa379c-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/create-circuit-slices/extracted-outputs/948aa715-22e8-41e1-943d-de7eb0aa379c-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 6, @@ -276,7 +276,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/9f766d69-e10c-4aa8-a3bc-2057dab7a69a-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/create-circuit-slices/extracted-outputs/9f766d69-e10c-4aa8-a3bc-2057dab7a69a-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 7, @@ -300,7 +300,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/a00c8743-f3b9-460c-ba2c-ffc634e99e0e-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/create-circuit-slices/extracted-outputs/a00c8743-f3b9-460c-ba2c-ffc634e99e0e-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 8, @@ -321,7 +321,7 @@ { "data": { "text/plain": [ - "<Image src=\"/docs/images/addons/qiskit-addon-utils/how-tos/create-circuit-slices/extracted-outputs/791bd07b-739a-45ec-a3e9-2d068f8bd960-0.avif\" alt=\"Output of the previous code cell\" />" + "<Image src=\"/docs/images/addons/qiskit-addon-utils/how_tos/create-circuit-slices/extracted-outputs/791bd07b-739a-45ec-a3e9-2d068f8bd960-0.avif\" alt=\"Output of the previous code cell\" />" ] }, "execution_count": 9, diff --git a/docs/addons/qiskit-addon-utils/how-tos/index.mdx b/docs/addons/qiskit-addon-utils/how_tos/index.mdx similarity index 100% rename from docs/addons/qiskit-addon-utils/how-tos/index.mdx rename to docs/addons/qiskit-addon-utils/how_tos/index.mdx diff --git a/docs/addons/qiskit-addon-utils/index.mdx b/docs/addons/qiskit-addon-utils/index.mdx index feab65bf65d..6c011bb6337 100644 --- a/docs/addons/qiskit-addon-utils/index.mdx +++ b/docs/addons/qiskit-addon-utils/index.mdx @@ -11,7 +11,7 @@ This package contains functionality which is meant to supplement workflows invol ## Documentation -All documentation is available [here](/docs/addons/qiskit-addon-utils/). +All documentation is available [here](https://qiskit.github.io/qiskit-addon-utils/). ## Installation diff --git a/docs/api/qiskit-addon-aqc-tensor/ansatz-generation.mdx b/docs/api/qiskit-addon-aqc-tensor/ansatz-generation.mdx index 68287f2bae8..17cac80ed2f 100644 --- a/docs/api/qiskit-addon-aqc-tensor/ansatz-generation.mdx +++ b/docs/api/qiskit-addon-aqc-tensor/ansatz-generation.mdx @@ -29,7 +29,7 @@ Tools for generating ansatz circuits. <Function id="qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit" github="https://github.com/Qiskit/qiskit-addon-aqc-tensor/tree/stable/0.3/qiskit_addon_aqc_tensor/ansatz_generation/from_connectivity.py#L164-L422" signature="generate_ansatz_from_circuit(qc, /, *, qubits_initially_zero=False, parameter_name='theta')"> Generate an ansatz from the two-qubit connectivity structure of a circuit. - See the [explanatatory material](/docs/addons/qiskit-addon-aqc-tensor/explanations/index#ansatz-generation-motivation) for motivation. + See the [explanatatory material](https://qiskit.github.io/qiskit-addon-aqc-tensor/explanation/index.html#ansatz-generation-motivation) for motivation. **Parameters** diff --git a/docs/api/qiskit-addon-cutting/instructions-move.mdx b/docs/api/qiskit-addon-cutting/instructions-move.mdx index 1799fb6b14c..89d1db36ae0 100644 --- a/docs/api/qiskit-addon-cutting/instructions-move.mdx +++ b/docs/api/qiskit-addon-cutting/instructions-move.mdx @@ -50,7 +50,7 @@ python_api_name: qiskit_addon_cutting.instructions.Move ![Output from the previous code.](/docs/images/api/qiskit-addon-cutting/qiskit_addon_cutting-instructions-Move-1.svg) - A full demonstration of the [`Move`](#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction is available in [the introductory tutorial on wire cutting](/docs/addons/qiskit-addon-cutting/tutorials/03-wire-cutting-via-move-instruction). + A full demonstration of the [`Move`](#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction is available in [the introductory tutorial on wire cutting](https://qiskit.github.io/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction.html). Create a [`Move`](#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction. diff --git a/docs/api/qiskit-addon-cutting/release-notes.mdx b/docs/api/qiskit-addon-cutting/release-notes.mdx index 56680c4efa8..0572c561fdd 100644 --- a/docs/api/qiskit-addon-cutting/release-notes.mdx +++ b/docs/api/qiskit-addon-cutting/release-notes.mdx @@ -154,7 +154,7 @@ The 0.7 release introduces an automated cut finding code for the new circuit cut * The cutting tutorials have been rephrased with the goal of reconstructing the expectation value of a single [`SparsePauliOp`](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) with many terms, rather than multiple independent [`Pauli`](/docs/api/qiskit/qiskit.quantum_info.Pauli) observables. -* The [circuit cutting explanation](/docs/addons/qiskit-addon-cutting/explanations/index) document has been expanded significantly. +* The [circuit cutting explanation](https://qiskit.github.io/qiskit-addon-cutting/explanation/index.html) document has been expanded significantly. <span id="release-notes-0-6-0" /> @@ -274,7 +274,7 @@ The primary goal of this release is to modify the circuit cutting workflow to en ### Deprecation Notes -* The `execute_experiments()` function has been deprecated. Going forward, users should first call [`generate_cutting_experiments()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") to generate the subexperiment circuits and then submit these circuits directly to the desired Sampler(s). The [tutorials](/docs/addons/qiskit-addon-cutting/tutorials/index) have been updated with this new workflow. +* The `execute_experiments()` function has been deprecated. Going forward, users should first call [`generate_cutting_experiments()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") to generate the subexperiment circuits and then submit these circuits directly to the desired Sampler(s). The [tutorials](https://qiskit.github.io/qiskit-addon-cutting/tutorials/index.html) have been updated with this new workflow. <span id="release-notes-0-3-0" /> @@ -319,7 +319,7 @@ The 0.3.0 release introduces significant new features while maintaining backward * `generate_qpd_weights()` now returns a mixture of exact and sampled weights when appropriate. Specifically, it exactly evaluates all weights greater than or equal to `1 / num_samples` and samples from the remaining weights (ones which are below this threshold). Previously, this function would only return exact weights if *all* weights were greater than or equal to `1 / num_samples`; otherwise, all weights were sampled. The new behavior is expected to improve performance on non-uniform quasi-probability decompositions, e.g. for cut instantiations of [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), and [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate) away from $\theta=\pi/2$. -* The `circuit_knitting.cutting` module now supports wire cutting. There is a [new tutorial](/docs/addons/qiskit-addon-cutting/tutorials/index) that explains how to use it. +* The `circuit_knitting.cutting` module now supports wire cutting. There is a [new tutorial](https://qiskit.github.io/qiskit-addon-cutting/tutorials/index.html) that explains how to use it. <span id="release-notes-0-3-0-upgrade-notes" /> @@ -376,11 +376,11 @@ The 0.3.0 release introduces significant new features while maintaining backward ### Prelude -0.2.0 is centered around the addition of functions which allow for the easy implementation of a circuit cutting technique called gate cutting. For more details on circuit cutting, check out our [explanation guide](/docs/addons/qiskit-addon-cutting/explanations/index). +0.2.0 is centered around the addition of functions which allow for the easy implementation of a circuit cutting technique called gate cutting. For more details on circuit cutting, check out our [explanation guide](https://qiskit.github.io/qiskit-addon-cutting/explanation/index.html). The foundation of the `circuit_cutting` package is the `circuit_knitting_toolbox.circuit_cutting.qpd` sub-package. The `qpd` package allows for easy transformation of [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) gates and wires into elements which may be decomposed to a probabilistic set of basis gates. See `QPDBasis` and `BaseQPDGate` classes for more information. -Additionally, 0.2.0 includes a set of functions which allow for easy implementation of gate cutting workflows. These functions are built on top of the `circuit_knitting_toolbox.circuit_cutting.qpd` package. Like all circuit knitting techniques, gate cutting can be described as three consecutive stages: *decomposition* of a problem, *execution* of many subexperiments, and *reconstruction* of a simulated output of the original problem. These steps may be implemented with the `circuit_cutting` package using only a few primary functions, namely, the [`partition_problem()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#partition_problem "qiskit_addon_cutting.partition_problem"), `decompose_gates()`, `execute_experiments()`, and [`reconstruct_expectation_values()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") functions. Check out the [tutorials](/docs/addons/qiskit-addon-cutting/tutorials/index) for a look at a couple of example circuit cutting workflows. +Additionally, 0.2.0 includes a set of functions which allow for easy implementation of gate cutting workflows. These functions are built on top of the `circuit_knitting_toolbox.circuit_cutting.qpd` package. Like all circuit knitting techniques, gate cutting can be described as three consecutive stages: *decomposition* of a problem, *execution* of many subexperiments, and *reconstruction* of a simulated output of the original problem. These steps may be implemented with the `circuit_cutting` package using only a few primary functions, namely, the [`partition_problem()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#partition_problem "qiskit_addon_cutting.partition_problem"), `decompose_gates()`, `execute_experiments()`, and [`reconstruct_expectation_values()`](/docs/api/qiskit-addon-cutting/qiskit-addon-cutting#reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") functions. Check out the [tutorials](https://qiskit.github.io/qiskit-addon-cutting/tutorials/index.html) for a look at a couple of example circuit cutting workflows. <span id="release-notes-0-2-0-new-features" /> diff --git a/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx b/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx index 3a77af32e70..27de95afe80 100644 --- a/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx +++ b/docs/api/qiskit-addon-obp/qiskit-addon-obp.mdx @@ -35,7 +35,7 @@ Main operator backpropagation functionality. * **observables** ([*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)*]*) – The observable(s) onto which the circuit is backpropagated. * **slices** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)*]*) – A sequence of `QuantumCircuit` objects representing a single circuit which has been separated into partitions spanning all qubits. **These “slices” will be backpropagated in reverse order.** Each slice must span all qubits. One may use the tools provided in [`qiskit_addon_utils.slicing`](/docs/api/qiskit-addon-utils/slicing#module-qiskit_addon_utils.slicing#module-qiskit_addon_utils.slicing "(in Qiskit addon utilities)") to slice a single [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit). - * **truncation\_error\_budget** ([*TruncationErrorBudget*](utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") *| None*) – The error budget used for truncating Pauli terms. Refer to the [how-to guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms) for a detailed discussion on truncating terms from the output operator and bounding the incurred error. + * **truncation\_error\_budget** ([*TruncationErrorBudget*](utils-truncating#truncationerrorbudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") *| None*) – The error budget used for truncating Pauli terms. Refer to the [how-to guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/truncate_operator_terms.html) for a detailed discussion on truncating terms from the output operator and bounding the incurred error. * **operator\_budget** ([*OperatorBudget*](utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") *| None*) – Constraints on how large the operator may grow during backpropagation. If `None`, a default instance of [`OperatorBudget`](utils-simplify#operatorbudget "qiskit_addon_obp.utils.simplify.OperatorBudget") will be used, and no constraints will be placed on the output operator size. * **max\_seconds** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – The maximum number of seconds to run the backpropagation. If this timeout is triggered before the function returns, the metadata of that moment will be returned. Note, that this metadata may contain only partial information for the last slice being backpropagated. diff --git a/docs/api/qiskit-addon-obp/utils-truncating.mdx b/docs/api/qiskit-addon-obp/utils-truncating.mdx index f53dee4039a..2e6b97735e1 100644 --- a/docs/api/qiskit-addon-obp/utils-truncating.mdx +++ b/docs/api/qiskit-addon-obp/utils-truncating.mdx @@ -23,7 +23,7 @@ Functions for truncating Pauli operators within given error budgets. A class for storing the constants that determine the truncation error budget. - Refer to the [how-to guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms) for a detailed discussion on truncating operator terms during backpropagation and bounding the incurred error. + Refer to the [how-to guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/truncate_operator_terms.html) for a detailed discussion on truncating operator terms during backpropagation and bounding the incurred error. **Parameters** @@ -53,7 +53,7 @@ Functions for truncating Pauli operators within given error budgets. <Attribute id="qiskit_addon_obp.utils.truncating.TruncationErrorBudget.p_norm" attributeTypeHint="int" attributeValue="1"> Indicates which Lp-norm is used for calculating truncation errors. - Refer to the [how-to guide](/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm) for a detailed conversation on bounding truncation error using higher Lp-norms. + Refer to the [how-to guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/bound_error_using_p_norm.html) for a detailed conversation on bounding truncation error using higher Lp-norms. </Attribute> #### per\_slice\_budget @@ -74,7 +74,7 @@ Functions for truncating Pauli operators within given error budgets. <Function id="qiskit_addon_obp.utils.truncating.setup_budget" github="https://github.com/Qiskit/qiskit-addon-obp/tree/stable/0.3/qiskit_addon_obp/utils/truncating.py#L61-L141" signature="setup_budget(*, max_error_per_slice=None, max_error_total=None, num_slices=None, p_norm=1)"> Calculate the budget available to each slice for observable term truncation. - This method makes the construction of a [`TruncationErrorBudget`](#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") easier for an end-user. This error budget can be provided to the [`backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") method to enable the truncation of low-weight Pauli terms. Refer to the [how-to guide](/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms) for a detailed discussion on truncating terms from the output operator and bounding the incurred error. + This method makes the construction of a [`TruncationErrorBudget`](#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") easier for an end-user. This error budget can be provided to the [`backpropagate()`](qiskit-addon-obp#backpropagate "qiskit_addon_obp.backpropagate") method to enable the truncation of low-weight Pauli terms. Refer to the [how-to guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/truncate_operator_terms.html) for a detailed discussion on truncating terms from the output operator and bounding the incurred error. **The construction logic is as follows:** @@ -94,7 +94,7 @@ Functions for truncating Pauli operators within given error budgets. * **max\_error\_per\_slice** ([*float*](https://docs.python.org/3/library/functions.html#float) *|*[*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*] | None*) – Specifies the maximum error per backpropagated slice. See above for more details. * **max\_error\_total** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – Specifies the total maximum error for the entire backpropagation. See above for more details. * **num\_slices** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – The number of slices over which to distribute the budget. See above for more details. - * **p\_norm** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The Lp norm of the error. This affects the gradual distribution of `max_error_total` in the case of `num_slices` also being set (see above). Refer to the [how-to guide](/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm) for a detailed conversation on bounding truncation error using higher Lp-norms. + * **p\_norm** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The Lp norm of the error. This affects the gradual distribution of `max_error_total` in the case of `num_slices` also being set (see above). Refer to the [how-to guide](https://qiskit.github.io/qiskit-addon-obp/how_tos/bound_error_using_p_norm.html) for a detailed conversation on bounding truncation error using higher Lp-norms. **Returns** diff --git a/docs/api/qiskit-addon-pna/qiskit-addon-pna.mdx b/docs/api/qiskit-addon-pna/qiskit-addon-pna.mdx index 801e0c570ad..bab4a4ba8e5 100644 --- a/docs/api/qiskit-addon-pna/qiskit-addon-pna.mdx +++ b/docs/api/qiskit-addon-pna/qiskit-addon-pna.mdx @@ -25,7 +25,7 @@ Primary propagated noise absortption (PNA) functionality. This function uses the Python `multiprocessing` module for parallel execution. This function should be called from within an `if __name__ == "__main__"` guard to prevent unintended process spawning. </Admonition> - Starting from the beginning of the circuit, the noise affecting each entangling layer is inverted and each Pauli anti-noise generator is then propagated forward through the remainder of the circuit and applied to the observable. The propagation routines used to implement this method are available in the [pauli-prop](/docs/addons/pauli-prop/) package. + Starting from the beginning of the circuit, the noise affecting each entangling layer is inverted and each Pauli anti-noise generator is then propagated forward through the remainder of the circuit and applied to the observable. The propagation routines used to implement this method are available in the [pauli-prop](https://qiskit.github.io/pauli-prop/) package. As each anti-noise generator is propagated forward through the circuit under the action of $N$ Pauli rotation gates of an \$M\$-qubit circuit, the number of terms will grow as $O(2^N)$ towards a maximum of $4^M$ unique Pauli components. To control the computational cost, terms with small coefficients must be truncated, which will result in some error in the evolved anti-noise channel. diff --git a/docs/api/qiskit-addon-slc/bounds.mdx b/docs/api/qiskit-addon-slc/bounds.mdx index 8fbf0b14abc..03a8be29939 100644 --- a/docs/api/qiskit-addon-slc/bounds.mdx +++ b/docs/api/qiskit-addon-slc/bounds.mdx @@ -27,7 +27,7 @@ This module provides various functions for computing the error bounds that make That is, compute $\| \left[ E_F, A_F \right] \|_2$ for all error terms, $E_F$, where $A_F$ is the target `observable` to be measured on `circuit`. - The error terms, $E_I$, are dictated by `noise_model_paulis`. This dictionary maps noise model identifiers (`samplomatic.InjectNoise.ref`) to a list of Pauli error terms. The corresponding terms will be used whenever a [`BoxOp`](/docs/api/qiskit/qiskit.circuit.BoxOp) with a matching [`InjectNoise`](/docs/addons/samplomatic/api/auto/samplomatic-inject-noise#samplomatic.InjectNoise "(in samplomatic)") annotation is encountered during the iteration over `circuit`. + The error terms, $E_I$, are dictated by `noise_model_paulis`. This dictionary maps noise model identifiers (`samplomatic.InjectNoise.ref`) to a list of Pauli error terms. The corresponding terms will be used whenever a [`BoxOp`](/docs/api/qiskit/qiskit.circuit.BoxOp) with a matching [`InjectNoise`](https://qiskit.github.io/samplomatic/api/auto/samplomatic.InjectNoise.html#samplomatic.InjectNoise "(in samplomatic)") annotation is encountered during the iteration over `circuit`. **Parameters** @@ -90,7 +90,7 @@ This module provides various functions for computing the error bounds that make That is, compute $\| \left[ E_I, \rho_I \right] \|_1$ (using the Schatten 1-norm aka nuclear norm) for all error terms, $E_I$, where $\rho_I$ is assumed to be the all-zero state, $\ket{0 \ldots 0}$, on all active qubits in `circuit`. - The error terms, $E_I$, are dictated by `noise_model_paulis`. This dictionary maps noise model identifiers (`samplomatic.InjectNoise.ref`) to a list of Pauli error terms. The corresponding terms will be used whenever a [`BoxOp`](/docs/api/qiskit/qiskit.circuit.BoxOp) with a matching [`InjectNoise`](/docs/addons/samplomatic/api/auto/samplomatic-inject-noise#samplomatic.InjectNoise "(in samplomatic)") annotation is encountered during the iteration over `circuit`. + The error terms, $E_I$, are dictated by `noise_model_paulis`. This dictionary maps noise model identifiers (`samplomatic.InjectNoise.ref`) to a list of Pauli error terms. The corresponding terms will be used whenever a [`BoxOp`](/docs/api/qiskit/qiskit.circuit.BoxOp) with a matching [`InjectNoise`](https://qiskit.github.io/samplomatic/api/auto/samplomatic.InjectNoise.html#samplomatic.InjectNoise "(in samplomatic)") annotation is encountered during the iteration over `circuit`. <Admonition title="Caution" type="note"> Before computing the bounds, this function removes **all** `Measure` operations from `circuit`. This is required because the circuit is being inverted before being processed in reverse order, which allows the backward evolution to be treated like a forward evolution (in the inverted circuit). @@ -147,7 +147,7 @@ This module provides various functions for computing the error bounds that make ### compute\_local\_scales <Function id="qiskit_addon_slc.bounds.compute_local_scales" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/bounds/local_scales.py#L35-L163" signature="compute_local_scales(circuit, bounds, /, noise_rates, *, sampling_cost_budget=inf, bias_tolerance=0.0)"> - Computes the `local_scales` argument to a [`Samplex`](/docs/addons/samplomatic/api/auto/samplomatic-samplex-samplex#samplomatic.samplex.Samplex "(in samplomatic)"). + Computes the `local_scales` argument to a [`Samplex`](https://qiskit.github.io/samplomatic/api/auto/samplomatic.samplex.Samplex.html#samplomatic.samplex.Samplex "(in samplomatic)"). This `local_scales` argument is used to specify which individual error terms to mitigate. @@ -167,7 +167,7 @@ This module provides various functions for computing the error bounds that make **Returns** - * the `local_scales` dictionary to be provided as the direct input to the [`samplomatic.samplex.Samplex.inputs()`](/docs/addons/samplomatic/api/auto/samplomatic-samplex-samplex#samplomatic.samplex.Samplex.inputs "(in samplomatic)"). + * the `local_scales` dictionary to be provided as the direct input to the [`samplomatic.samplex.Samplex.inputs()`](https://qiskit.github.io/samplomatic/api/auto/samplomatic.samplex.Samplex.html#samplomatic.samplex.Samplex.inputs "(in samplomatic)"). * the sampling cost overhead ($\gamma^2$) required to perform the sampling of `local_scales`. * the remaining bias on expectation values computed with these bounds. diff --git a/docs/api/qiskit-addon-slc/utils.mdx b/docs/api/qiskit-addon-slc/utils.mdx index f1c83659c75..460f538e618 100644 --- a/docs/api/qiskit-addon-slc/utils.mdx +++ b/docs/api/qiskit-addon-slc/utils.mdx @@ -52,13 +52,13 @@ This module provides a number of utility functions. Some of these exist only tem **Parameters** - * **instructions** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*CircuitInstruction*](/docs/api/qiskit/qiskit.circuit.CircuitInstruction)*]*) – the output of [`find_unique_box_instructions()`](/docs/addons/samplomatic/api/auto/samplomatic-utils-find-unique-box-instructions#samplomatic.utils.find_unique_box_instructions "(in samplomatic)"). Any of the provided instructions are assumed to either consists of only measurement gates or correspond to a layer of 2-qubit gate instructions. For the former, the generated noise model will contain only single-qubit `X` errors, for the latter all 1- and 2-weight Pauli errors on the reduced coupling map will be included. + * **instructions** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*CircuitInstruction*](/docs/api/qiskit/qiskit.circuit.CircuitInstruction)*]*) – the output of [`find_unique_box_instructions()`](https://qiskit.github.io/samplomatic/api/auto/samplomatic.utils.find_unique_box_instructions.html#samplomatic.utils.find_unique_box_instructions "(in samplomatic)"). Any of the provided instructions are assumed to either consists of only measurement gates or correspond to a layer of 2-qubit gate instructions. For the former, the generated noise model will contain only single-qubit `X` errors, for the latter all 1- and 2-weight Pauli errors on the reduced coupling map will be included. * **coupling\_map** ([*CouplingMap*](/docs/api/qiskit/qiskit.transpiler.CouplingMap) *| None*) – the coupling map of the backend on which the instructions have been laid out. If this is `None`, a 1d line of qubits is assumed. * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) *| None*) – the transpiled circuit which has been laid out on the provided coupling map. This may only be `None` when the `coupling_map` is also `None`. **Returns** - A dictionary mapping the `ref` attributes of the [`InjectNoise`](/docs/addons/samplomatic/api/auto/samplomatic-inject-noise#samplomatic.InjectNoise "(in samplomatic)") annotation of each unique Box to the 1- and 2-weight Pauli terms whose errors are learned for this box. + A dictionary mapping the `ref` attributes of the [`InjectNoise`](https://qiskit.github.io/samplomatic/api/auto/samplomatic.InjectNoise.html#samplomatic.InjectNoise "(in samplomatic)") annotation of each unique Box to the 1- and 2-weight Pauli terms whose errors are learned for this box. **Return type** @@ -122,7 +122,7 @@ This module provides a number of utility functions. Some of these exist only tem **Yields** - Tuples of length four, consisting of the encountered circuit instruction, the [canonical qubit indices](/docs/addons/samplomatic/guides/samplex-io#qubit-ordering-convention) (i.e. the integer indices of the acted-upon qubits in the context of the input `circuit`), the [`InjectNoise`](/docs/addons/samplomatic/api/auto/samplomatic-inject-noise#samplomatic.InjectNoise "(in samplomatic)") attributes: `modifier_ref` and `ref`. The latter two items may be `None` indicating a circuit instruction that was **not** part of an unrolled [`BoxOp`](/docs/api/qiskit/qiskit.circuit.BoxOp). + Tuples of length four, consisting of the encountered circuit instruction, the [canonical qubit indices](https://qiskit.github.io/samplomatic/guides/samplex_io.html#qubit-ordering-convention) (i.e. the integer indices of the acted-upon qubits in the context of the input `circuit`), the [`InjectNoise`](https://qiskit.github.io/samplomatic/api/auto/samplomatic.InjectNoise.html#samplomatic.InjectNoise "(in samplomatic)") attributes: `modifier_ref` and `ref`. The latter two items may be `None` indicating a circuit instruction that was **not** part of an unrolled [`BoxOp`](/docs/api/qiskit/qiskit.circuit.BoxOp). **Return type** @@ -132,7 +132,7 @@ This module provides a number of utility functions. Some of these exist only tem ### map\_modifier\_ref\_to\_ref <Function id="qiskit_addon_slc.utils.map_modifier_ref_to_ref" github="https://github.com/Qiskit/qiskit-addon-slc/tree/stable/0.1/qiskit_addon_slc/utils/annotations.py#L26-L47" signature="map_modifier_ref_to_ref(circuit)"> - Iterate a circuit and map [`InjectNoise`](/docs/addons/samplomatic/api/auto/samplomatic-inject-noise#samplomatic.InjectNoise "(in samplomatic)") annotation references. + Iterate a circuit and map [`InjectNoise`](https://qiskit.github.io/samplomatic/api/auto/samplomatic.InjectNoise.html#samplomatic.InjectNoise "(in samplomatic)") annotation references. **Parameters** diff --git a/docs/api/qiskit-addon-sqd/release-notes.mdx b/docs/api/qiskit-addon-sqd/release-notes.mdx index 7461e5880b6..a1cb27a832f 100644 --- a/docs/api/qiskit-addon-sqd/release-notes.mdx +++ b/docs/api/qiskit-addon-sqd/release-notes.mdx @@ -74,7 +74,7 @@ in_page_toc_max_heading_level: 2 ### New Features -* Added a new function [`qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian()`](/docs/api/qiskit-addon-sqd/fermion#diagonalize_fermionic_hamiltonian "qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian") to serve as the main entrypoint to the SQD algorithm. See the updated [tutorials](/docs/addons/qiskit-addon-sqd/tutorials/index) for a demonstration of how to use this function. +* Added a new function [`qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian()`](/docs/api/qiskit-addon-sqd/fermion#diagonalize_fermionic_hamiltonian "qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian") to serve as the main entrypoint to the SQD algorithm. See the updated [tutorials](https://qiskit.github.io/qiskit-addon-sqd/tutorials/index.html) for a demonstration of how to use this function. * [`qiskit_addon_sqd.fermion.solve_fermion()`](/docs/api/qiskit-addon-sqd/fermion#solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") now accepts a `shift` argument used to shift states which have the wrong spin, $(H + shift * S^2)|ψ> = E|ψ>$. diff --git a/docs/api/qiskit-addon-utils/exp-vals.mdx b/docs/api/qiskit-addon-utils/exp-vals.mdx index 0934d7b983f..27e37a40784 100644 --- a/docs/api/qiskit-addon-utils/exp-vals.mdx +++ b/docs/api/qiskit-addon-utils/exp-vals.mdx @@ -84,7 +84,7 @@ Tools for calculating expectation values. In the transpiled (or ISA) ordering, the qubits are indexed based on the “physical” layout of qubits in the device. - For info on canonical qubit ordering conventions see the [Samplomatic docs](/docs/addons/samplomatic/guides/samplex-io#qubit-ordering-convention)). + For info on canonical qubit ordering conventions see the [Samplomatic docs](https://qiskit.github.io/samplomatic/guides/samplex_io.html#qubit-ordering-convention)). **Parameters** @@ -105,7 +105,7 @@ Tools for calculating expectation values. <Function id="qiskit_addon_utils.exp_vals.map_observable_virtual_to_canonical" github="https://github.com/Qiskit/qiskit-addon-utils/tree/stable/0.3/qiskit_addon_utils/exp_vals/observable_mappings.py#L72-L125" signature="map_observable_virtual_to_canonical(virt_observable, layout, canonical_qubits)"> Map an observable with virtual qubit ordering to canonical box-order. - For info on canonical qubit ordering conventions see the [Samplomatic docs](/docs/addons/samplomatic/guides/samplex-io#qubit-ordering-convention)). + For info on canonical qubit ordering conventions see the [Samplomatic docs](https://qiskit.github.io/samplomatic/guides/samplex_io.html#qubit-ordering-convention)). **Parameters** diff --git a/docs/api/qiskit-addon-utils/slicing.mdx b/docs/api/qiskit-addon-utils/slicing.mdx index abfe7e8fd3e..e21c5fda9bd 100644 --- a/docs/api/qiskit-addon-utils/slicing.mdx +++ b/docs/api/qiskit-addon-utils/slicing.mdx @@ -16,7 +16,7 @@ python_api_name: qiskit_addon_utils.slicing Utility methods for circuit slicing. -For more information, check out the [how-to guide](/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices) which discusses this submodule. +For more information, check out the [how-to guide](https://qiskit.github.io/qiskit-addon-utils/how_tos/create_circuit_slices.html) which discusses this submodule. ### combine\_slices diff --git 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public/docs/images/addons/qiskit-addon-utils/how_tos/create-circuit-slices/extracted-outputs/f883fa2e-f943-4c49-af45-fb26cadb72b6-0.avif diff --git a/scripts/js/commands/api/updateAddonDocs.ts b/scripts/js/commands/api/updateAddonDocs.ts index 2f07e403f87..00e467e48dc 100644 --- a/scripts/js/commands/api/updateAddonDocs.ts +++ b/scripts/js/commands/api/updateAddonDocs.ts @@ -10,6 +10,8 @@ // copyright notice, and modified files need to carry a notice indicating // that they have been altered from the originals. +import { readdir } from "fs/promises"; + import yargs from "yargs/yargs"; import { hideBin } from "yargs/helpers"; @@ -17,20 +19,60 @@ import { Pkg } from "../../lib/api/Pkg.js"; import { runAddonDocsPipeline } from "../../lib/api/addonDocsPipeline.js"; import { zxMain } from "../../lib/zx.js"; import { parseMinorVersion } from "../../lib/apiVersions.js"; -import { - addSharedOptions, - deleteOutputDirs, - prepareSphinxFolder, - SharedArguments, -} from "./updateDocsShared.js"; +import { deleteOutputDirs, prepareSphinxFolder } from "./updateDocsShared.js"; + +type Args = { + package?: string; + all: boolean; + skipDownload: boolean; + sphinxArtifactFolder?: string; +}; -const readArgs = (): SharedArguments => { - return addSharedOptions( - yargs(hideBin(process.argv)).version(false), - ).parseSync() as unknown as SharedArguments; +const readArgs = (): Args => { + return yargs(hideBin(process.argv)) + .version(false) + .option("package", { + alias: "p", + type: "string", + choices: Pkg.ADDON_NAMES, + description: "Which addon package to update", + }) + .option("all", { + type: "boolean", + default: false, + description: "Update all addon packages in parallel", + }) + .option("skip-download", { + alias: "s", + type: "boolean", + default: false, + description: "Skip downloading the Sphinx artifact", + }) + .option("sphinx-artifact-folder", { + type: "string", + description: "Use a local artifact folder instead of downloading", + }) + .check((argv) => { + if (!argv.all && !argv.package) { + throw new Error("Either --package or --all is required"); + } + return true; + }) + .parseSync() as unknown as Args; }; -async function generateVersion(pkg: Pkg, args: SharedArguments): Promise<void> { +async function resolveVersion(pkgName: string): Promise<string> { + const entries = await readdir(`.sphinx-artifacts/${pkgName}`); + const versions = entries.filter((e) => /^\d/.test(e)).sort(); + if (versions.length === 0) { + throw new Error( + `No artifact versions found for ${pkgName}. Run without --skip-download first.`, + ); + } + return versions.at(-1)!; +} + +async function generateVersion(pkg: Pkg, args: Args): Promise<void> { const sphinxArtifactFolder = await prepareSphinxFolder(pkg, args); await deleteOutputDirs(pkg, { markdownDir: pkg.outputDir("docs/addons"), @@ -47,20 +89,18 @@ async function generateVersion(pkg: Pkg, args: SharedArguments): Promise<void> { ); } -zxMain(async () => { - const args = readArgs(); - const minorVersion = parseMinorVersion(args.version); +async function generatePackage(pkgName: string, args: Args): Promise<void> { + const version = await resolveVersion(pkgName); + const minorVersion = parseMinorVersion(version); if (minorVersion === null) { - throw new Error( - `Invalid --version. Expected the format 0.44.0, but received ${args.version}`, - ); + throw new Error(`Could not parse version ${version} for ${pkgName}`); } - - const pkg = await Pkg.fromArgs( - args.package, - args.version, - minorVersion, - "latest", - ); + const pkg = await Pkg.fromArgs(pkgName, version, minorVersion, "latest"); await generateVersion(pkg, args); +} + +zxMain(async () => { + const args = readArgs(); + const packages = args.all ? Pkg.ADDON_NAMES : [args.package!]; + await Promise.all(packages.map((pkgName) => generatePackage(pkgName, args))); }); diff --git a/scripts/js/commands/api/updateApiDocs.ts b/scripts/js/commands/api/updateApiDocs.ts index 85704b29fca..499a0065231 100644 --- a/scripts/js/commands/api/updateApiDocs.ts +++ b/scripts/js/commands/api/updateApiDocs.ts @@ -18,21 +18,48 @@ import { zxMain } from "../../lib/zx.js"; import { parseMinorVersion } from "../../lib/apiVersions.js"; import { runApiDocsPipeline } from "../../lib/api/apiDocsPipeline.js"; import { generateHistoricalRedirects } from "./generateHistoricalRedirects.js"; -import { - addSharedOptions, - deleteOutputDirs, - prepareSphinxFolder, - SharedArguments, -} from "./updateDocsShared.js"; +import { deleteOutputDirs, prepareSphinxFolder } from "./updateDocsShared.js"; -export type Arguments = SharedArguments & { +export type Arguments = { [x: string]: unknown; + package: string; + version: string; + skipDownload: boolean; + sphinxArtifactFolder?: string; historical: boolean; dev: boolean; -} +}; const readArgs = (): Arguments => { - return addSharedOptions(yargs(hideBin(process.argv)).version(false)) + return yargs(hideBin(process.argv)) + .version(false) + .option("package", { + alias: "p", + type: "string", + choices: Pkg.VALID_NAMES, + demandOption: true, + description: "Which package to update", + }) + .option("version", { + alias: "v", + type: "string", + demandOption: true, + description: "The full version string, e.g. 0.44.0", + }) + .option("skip-download", { + type: "boolean", + default: false, + description: + "Rather than downloading the artifact from Box, reuse what is already downloaded.", + }) + .option("sphinx-artifact-folder", { + alias: "a", + type: "string", + implies: "skip-download", + normalize: true, + description: + "Skip downloading the artifact from Box and instead use the given directory.", + }) .option("historical", { type: "boolean", default: false, diff --git a/scripts/js/commands/api/updateDocsShared.ts b/scripts/js/commands/api/updateDocsShared.ts index 68967c41ee7..10163ee73d5 100644 --- a/scripts/js/commands/api/updateDocsShared.ts +++ b/scripts/js/commands/api/updateDocsShared.ts @@ -14,67 +14,11 @@ // take --package/--version, download (or reuse) a Sphinx artifact, and wipe // an output directory before running a pipeline. -import { readFile, writeFile } from "fs/promises"; - import { $ } from "zx"; -import { mkdirp } from "mkdirp"; -import type { Argv } from "yargs"; import { Pkg } from "../../lib/api/Pkg.js"; import { pathExists, rmFilesInFolder } from "../../lib/fs.js"; import { downloadSphinxArtifact } from "../../lib/api/sphinxArtifacts.js"; -import { isValidVersion } from "../../lib/apiVersions.js"; - -/** Fields shared by both `updateApiDocs` and `updateAddonDocs` CLIs. */ -export type SharedArguments = { - package: string; - version: string; - skipDownload: boolean; - sphinxArtifactFolder?: string; -} - -/** - * Register the yargs options shared by both commands. Call on the builder - * before adding command-specific options. - */ -export function addSharedOptions(y: Argv): Argv { - return y - .option("package", { - alias: "p", - type: "string", - choices: Pkg.VALID_NAMES, - demandOption: true, - description: "Which package to update", - }) - .option("version", { - alias: "v", - type: "string", - demandOption: true, - description: "The full version string of the --package, e.g. 0.44.0", - coerce: (version) => { - if (!isValidVersion(version)) { - throw new Error( - "The version must include a major, a minor, and a patch. E.g. `-v 0.46.3`", - ); - } - return version; - }, - }) - .option("skip-download", { - type: "boolean", - default: false, - description: - "Rather than downloading the artifact from Box, reuse what is already downloaded. This can save time, but it risks using an outdated version of the docs.", - }) - .option("sphinx-artifact-folder", { - alias: "a", - type: "string", - implies: "skip-download", - normalize: true, - description: - "Skip downloading the artifact from Box and instead use the given directory as the root of the Sphinx HTML output.", - }); -} /** * Resolve the Sphinx artifact folder: either a user-provided path, a reused @@ -82,7 +26,7 @@ export function addSharedOptions(y: Argv): Argv { */ export async function prepareSphinxFolder( pkg: Pkg, - args: SharedArguments, + args: { skipDownload: boolean; sphinxArtifactFolder?: string }, ): Promise<string> { if (args.sphinxArtifactFolder) { if (!(await pathExists(args.sphinxArtifactFolder))) { diff --git a/scripts/js/lib/api/objectsInv.ts b/scripts/js/lib/api/objectsInv.ts index 3e57a827c4c..284311dde7d 100644 --- a/scripts/js/lib/api/objectsInv.ts +++ b/scripts/js/lib/api/objectsInv.ts @@ -27,8 +27,8 @@ const ENTRIES_TO_EXCLUDE = [ /^genindex(\.html)?$/, /^py-modindex(\.html)?$/, /^search(\.html)?$/, - /^explanation(\.html)?(?=\/|#|$)/, - /^how_tos?(\.html)?(?=\/|#|$)/, + /^explanations?(\.html)?(?=\/|#|$)/, + /^how[-_]tos?(\.html)?(?=\/|#|$)/, /^tutorials(\.html)?(?=\/|#|$)/, /^migration_guides(\.html)?(?=\/|#|$)/, /^configuration(\.html)?(?=#|$)/, diff --git a/scripts/js/lib/api/specialCaseResults.ts b/scripts/js/lib/api/specialCaseResults.ts index 96050f4cbe7..195ce10584e 100644 --- a/scripts/js/lib/api/specialCaseResults.ts +++ b/scripts/js/lib/api/specialCaseResults.ts @@ -17,8 +17,6 @@ export function transformSpecialCaseUrl(url: string): string { url // We use `-` rather than `_` as our delimiter. .replace(/(?<=^|\/)release_notes(?=\/|#|$)/g, "release-notes") - .replace(/(?<=^|\/)how_tos(?=\/|#|$)/g, "how-tos") - .replace(/(?<=^|\/)explanation(?=\/|#|$)/g, "explanations") .replace(/(?<=^|\/)terra(?=#|$)/g, "index") .replace(/(?<=^|\/)ibm-runtime(?=#|$)/g, "index") .replace(/(?<=^|\/)main(?=#|$)/g, "index") diff --git a/scripts/js/lib/api/updateLinks.ts b/scripts/js/lib/api/updateLinks.ts index 49b542217d3..28e3d2b783b 100644 --- a/scripts/js/lib/api/updateLinks.ts +++ b/scripts/js/lib/api/updateLinks.ts @@ -22,11 +22,11 @@ import remarkGfm from "remark-gfm"; import remarkMdx from "remark-mdx"; import remarkStringify from "remark-stringify"; -import { kebabCase } from "lodash-es"; import { removePart, removePrefix, removeSuffix } from "../stringUtils.js"; import { HtmlToMdResultWithUrl } from "./HtmlToMdResult.js"; import { remarkStringifyOptions } from "./commonParserConfig.js"; import { ObjectsInv } from "./objectsInv.js"; +import { Pkg } from "./Pkg.js"; import { transformSpecialCaseUrl } from "./specialCaseResults.js"; import { kebabCaseAndShortenPage } from "./normalizeResultUrls.js"; import { DOCS_BASE_PATH } from "./paths.js"; @@ -178,6 +178,8 @@ export function normalizeUrl( } export function relativizeLink(link: Link): Link | undefined { + rewriteQiskitAddonLinks(link); + const priorPrefixToNewPrefix = new Map([ ["https://qiskit.org/documentation/apidoc/", "/api/qiskit"], ["https://qiskit.org/documentation/stubs/", "/api/qiskit"], @@ -185,33 +187,16 @@ export function relativizeLink(link: Link): Link | undefined { ["https://docs.quantum-computing.ibm.com/", ""], ["https://quantum.cloud.ibm.com/docs", "/docs"], ["https://quantum.cloud.ibm.com/learning", "/learning"], - ["https://qiskit.github.io/", "/docs/addons"], ]); const priorPrefix = Array.from(priorPrefixToNewPrefix.keys()).find((prefix) => link.url.startsWith(prefix), ); if (!priorPrefix) return; - // github.io stubs/apidocs URLs are looked up via objects.inv by the caller. - if ( - priorPrefix === "https://qiskit.github.io/" && - /\/(stubs|apidocs|apidoc)\//.test(link.url) - ) { - return; - } let [url, anchor] = link.url.split("#"); url = removePrefix(url, priorPrefix); url = removeSuffix(url, ".html"); - // For github.io addons links, kebab-case each path segment after the package name. - if (priorPrefix === "https://qiskit.github.io/") { - const [pkg, ...rest] = url.split("/"); - url = [ - pkg, - ...rest.map((s) => kebabCase(s).replace(/-v-([0-9]+)/g, `-v$1`)), - ].join("/"); - } - if (anchor && anchor !== url) { url = `${url}#${anchor}`; } @@ -222,6 +207,25 @@ export function relativizeLink(link: Link): Link | undefined { return { url: `/${relativeUrl}`, text: newText }; } +function rewriteQiskitAddonLinks(link: Link) { + if (!link.url.startsWith("https://qiskit.github.io/")) return; + + // github.io stubs/apidocs URLs are looked up via objects.inv by the caller + if (/\/(stubs|apidocs|apidoc)\//.test(link.url)) return; + + const rest = removePrefix(link.url, "https://qiskit.github.io/"); + const [addonName, ...pathParts] = rest.split("#")[0].split("/"); + if (!addonName || !Pkg.ADDON_NAMES.includes(addonName)) return; + + const anchor = rest.includes("#") ? rest.split("#")[1] : undefined; + const pagePath = pathParts.map((s) => removeSuffix(s, ".html")).join("/"); + const url = anchor + ? `/docs/addons/${addonName}/${pagePath}#${anchor}` + : `/docs/addons/${addonName}/${pagePath}`; + const newText = link.url === link.text ? url : undefined; + return { url, text: newText }; +} + export async function updateLinks( results: HtmlToMdResultWithUrl[], kwargs: { diff --git a/scripts/js/lib/links/FileBatch.ts b/scripts/js/lib/links/FileBatch.ts index 03415c286ee..925a94be0cd 100644 --- a/scripts/js/lib/links/FileBatch.ts +++ b/scripts/js/lib/links/FileBatch.ts @@ -118,7 +118,10 @@ export function addLinksToMap( links: Set<string>, linksToOriginFiles: Map<string, string[]>, ): void { - const ignoreUrlsRegex = new RegExp(ALWAYS_IGNORED_URL_REGEXES.join("|"), "i"); + const ignoreUrlsRegex = + ALWAYS_IGNORED_URL_REGEXES.length > 0 + ? new RegExp(ALWAYS_IGNORED_URL_REGEXES.join("|"), "i") + : null; if (IGNORED_FILES.has(filePath)) return; links.forEach((link) => { if ( @@ -126,7 +129,7 @@ export function addLinksToMap( ALWAYS_IGNORED_URL_PREFIXES.some((prefix) => link.startsWith(prefix)) || ALWAYS_IGNORED_URL_SUFFIXES.some((suffix) => link.endsWith(suffix)) || FILES_TO_IGNORES[filePath]?.includes(link) || - ignoreUrlsRegex.test(link) + ignoreUrlsRegex?.test(link) ) { return; } diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index f0424db8192..f604ba74a52 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -228,22 +228,7 @@ export const ALWAYS_IGNORED_URLS = new Set([ // Always ignored URL regexes - be careful using this // ----------------------------------------------------------------------------------- -function _addonsObjectsInvRegexes(): string[] { - // Addons have non-API docs in their Sphinx build that translate into invalid links - // we should ignore - return ["how-tos", "how_tos", "install", "index", "explanations"].flatMap( - (path) => [ - // Latest version - `\/api\/qiskit-addon-[^\/]+\/${path}(\/.*|#.*|$)`, - // Historical versions - `\/api\/qiskit-addon-[^\/]+\/[0-9]+\.[0-9]{1,2}\/${path}(\/.*|#.*|$)`, - ], - ); -} - -export const ALWAYS_IGNORED_URL_REGEXES: string[] = [ - ..._addonsObjectsInvRegexes(), -]; +export const ALWAYS_IGNORED_URL_REGEXES: string[] = []; // ----------------------------------------------------------------------------------- // Always ignored URL suffixes - be careful using this @@ -587,7 +572,7 @@ function _qiskitCRegexes(): FilesToIgnores { }; } -function _addonStaleTutorialLinks(): FilesToIgnores { +function _addonContentLinksToFix(): FilesToIgnores { // These links point to old addon-repo tutorial slugs that no longer exist. // The addon source docs need to be updated to use the new paths. return { @@ -605,21 +590,20 @@ function _addonStaleTutorialLinks(): FilesToIgnores { "tutorials/04-automatic-cut-finding", "./circuit_cutting/explanations/index-rst#overview-of-circuit-cutting", "how-tos/how-to-specify-cut-wires", + "./circuit_cutting/explanation/index-rst#overview-of-circuit-cutting", ], - "docs/addons/qiskit-addon-aqc-tensor/index.mdx": [ - "tutorials/index", - ], - "docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms.ipynb": [ + "docs/addons/qiskit-addon-aqc-tensor/index.mdx": ["tutorials/index"], + "docs/addons/qiskit-addon-obp/how_tos/truncate-operator-terms.ipynb": [ "/docs/api/qiskit/qiskit.circuit.QuantumCircuit#html", ], - "docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp.ipynb": [ + "docs/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp.ipynb": [ "/api/qiskit-ibm-runtime/noise-learner-noise-learner", "/api/qiskit-ibm-runtime/noise-learner-result", ], "docs/addons/qiskit-addon-cutting/install.mdx": [ "/docs/start/install#operating-system-support", ], - "docs/addons/qiskit-addon-cutting/explanations/index.mdx": [ + "docs/addons/qiskit-addon-cutting/explanation/index.mdx": [ "#equation-eq-qpd", "how-tos/how-to-specify-cut-wires", "/circuit_cutting/explanations/index-rst#overview-of-circuit-cutting", @@ -627,17 +611,25 @@ function _addonStaleTutorialLinks(): FilesToIgnores { "docs/addons/qiskit-addon-cutting/how-tos/how-to-specify-cut-wires.ipynb": [ "../tutorials/03_wire_cutting_via_move_instruction.ipynb", ], - "docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.ipynb": [ - "../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb", - ], + "docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.ipynb": + ["../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb"], "docs/addons/qiskit-addon-sqd-hpc/index.mdx": [ "/docs/en/guides/bit-ordering", ], + "docs/api/pauli-prop/propagation.mdx": [ + "/docs/en/api/qiskit/qiskit.quantum_info.Clifford", + "/docs/en/api/qiskit/qiskit.quantum_info.Pauli#evolve", + ], + "docs/addons/qiskit-addon-opt-mapper/how_tos/index.mdx": [ + "01-optimization-problem#add-or-remove-linear,-quadratic,-and-higher-order-terms-to-and-from-the-constraints", + ], }; } -const FILES_TO_IGNORES__SHOULD_FIX: FilesToIgnores = - mergeFilesToIgnores(_qiskitCRegexes(), _addonStaleTutorialLinks()); +const FILES_TO_IGNORES__SHOULD_FIX: FilesToIgnores = mergeFilesToIgnores( + _qiskitCRegexes(), + _addonContentLinksToFix(), +); export const FILES_TO_IGNORES: FilesToIgnores = mergeFilesToIgnores( FILES_TO_IGNORES__EXPECTED, From cc602396934c626a1f2c0eaa14a05fa81fb7ff24 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 28 May 2026 17:20:49 -0400 Subject: [PATCH 098/104] fix toc links --- docs/addons/pauli-prop/_toc.json | 2 +- docs/addons/qiskit-addon-mpf/_toc.json | 4 +- docs/addons/qiskit-addon-obp/_toc.json | 6 +- docs/addons/qiskit-addon-opt-mapper/_toc.json | 10 +-- .../how_tos/01-optimization-problem.ipynb | 62 +++++++++---------- ...converters-for-optimization-problems.ipynb | 46 +++++++------- .../how_tos/04-translate-docplex.ipynb | 18 +++--- docs/addons/qiskit-addon-pna/_toc.json | 2 +- docs/addons/qiskit-addon-slc/_toc.json | 2 +- docs/addons/qiskit-addon-sqd/_toc.json | 14 ++--- docs/addons/qiskit-addon-utils/_toc.json | 4 +- scripts/js/lib/api/generateAddonToc.test.ts | 2 +- scripts/js/lib/api/generateAddonToc.ts | 29 ++++----- 13 files changed, 99 insertions(+), 102 deletions(-) diff --git a/docs/addons/pauli-prop/_toc.json b/docs/addons/pauli-prop/_toc.json index 60d58538d3b..975e23d27a9 100644 --- a/docs/addons/pauli-prop/_toc.json +++ b/docs/addons/pauli-prop/_toc.json @@ -20,7 +20,7 @@ "children": [ { "title": "How to do something", - "url": "/docs/addons/pauli-prop/how-tos/do-something" + "url": "/docs/addons/pauli-prop/how_tos/do-something" } ] }, diff --git a/docs/addons/qiskit-addon-mpf/_toc.json b/docs/addons/qiskit-addon-mpf/_toc.json index 27a06c1f024..95724645ec9 100644 --- a/docs/addons/qiskit-addon-mpf/_toc.json +++ b/docs/addons/qiskit-addon-mpf/_toc.json @@ -20,11 +20,11 @@ "children": [ { "title": "How to choose the Trotter steps for an MPF", - "url": "/docs/addons/qiskit-addon-mpf/how-tos/choose-trotter-steps" + "url": "/docs/addons/qiskit-addon-mpf/how_tos/choose-trotter-steps" }, { "title": "How to use the approximate model", - "url": "/docs/addons/qiskit-addon-mpf/how-tos/using-approximate-model" + "url": "/docs/addons/qiskit-addon-mpf/how_tos/using-approximate-model" } ] }, diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json index 551fdb61e67..2f699ac7bff 100644 --- a/docs/addons/qiskit-addon-obp/_toc.json +++ b/docs/addons/qiskit-addon-obp/_toc.json @@ -20,15 +20,15 @@ "children": [ { "title": "Using different Lp-norms for Pauli term truncation", - "url": "/docs/addons/qiskit-addon-obp/how-tos/bound-error-using-p-norm" + "url": "/docs/addons/qiskit-addon-obp/how_tos/bound-error-using-p-norm" }, { "title": "Classically simulating circuits with OBP", - "url": "/docs/addons/qiskit-addon-obp/how-tos/simulating-circuits-with-obp" + "url": "/docs/addons/qiskit-addon-obp/how_tos/simulating-circuits-with-obp" }, { "title": "Truncating Pauli terms during backpropagation", - "url": "/docs/addons/qiskit-addon-obp/how-tos/truncate-operator-terms" + "url": "/docs/addons/qiskit-addon-obp/how_tos/truncate-operator-terms" } ] }, diff --git a/docs/addons/qiskit-addon-opt-mapper/_toc.json b/docs/addons/qiskit-addon-opt-mapper/_toc.json index 8aff86322b0..93b38ea0416 100644 --- a/docs/addons/qiskit-addon-opt-mapper/_toc.json +++ b/docs/addons/qiskit-addon-opt-mapper/_toc.json @@ -20,23 +20,23 @@ "children": [ { "title": "Migrate from qiskit-optimization to qiskit-addon-opt-mapper", - "url": "/docs/addons/qiskit-addon-opt-mapper/how-tos/00-migrate-from-qiskit-optimization" + "url": "/docs/addons/qiskit-addon-opt-mapper/how_tos/00-migrate-from-qiskit-optimization" }, { "title": "Model quantum optimization problems with the OptimizationProblem class", - "url": "/docs/addons/qiskit-addon-opt-mapper/how-tos/01-optimization-problem" + "url": "/docs/addons/qiskit-addon-opt-mapper/how_tos/01-optimization-problem" }, { "title": "Convert formulations of an OptimizationProblem", - "url": "/docs/addons/qiskit-addon-opt-mapper/how-tos/02-converters-for-optimization-problems" + "url": "/docs/addons/qiskit-addon-opt-mapper/how_tos/02-converters-for-optimization-problems" }, { "title": "Validate your modeling with classical solvers", - "url": "/docs/addons/qiskit-addon-opt-mapper/how-tos/03-validate-with-solvers" + "url": "/docs/addons/qiskit-addon-opt-mapper/how_tos/03-validate-with-solvers" }, { "title": "Translate between OptimizationProblem and Docplex", - "url": "/docs/addons/qiskit-addon-opt-mapper/how-tos/04-translate-docplex" + "url": "/docs/addons/qiskit-addon-opt-mapper/how_tos/04-translate-docplex" } ] }, diff --git a/docs/addons/qiskit-addon-opt-mapper/how_tos/01-optimization-problem.ipynb b/docs/addons/qiskit-addon-opt-mapper/how_tos/01-optimization-problem.ipynb index c16f0369c07..4e716074223 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how_tos/01-optimization-problem.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how_tos/01-optimization-problem.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "d27660f8-6458-4de4-82cd-cde5b96c73b2", + "id": "7f77b188-c62c-4d30-b6be-856171f7020d", "metadata": {}, "source": [ "# Model quantum optimization problems with the `OptimizationProblem` class" @@ -21,7 +21,7 @@ }, { "cell_type": "markdown", - "id": "e5fd0aed-ac9d-4762-a878-db3a244e5174", + "id": "7a43a06d-e750-44e8-a8c8-80e514963643", "metadata": {}, "source": [ "## Introduction" @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "70f28ef6-d4a8-4a74-a01c-eac004fae5eb", + "id": "7b9b0f1d-c556-4c38-8fd8-53e639b75c9d", "metadata": {}, "source": [ "This guide demonstrates how to use the **Optimization Mapper Qiskit addon** for quantum optimization modeling. The addon introduces the `OptimizationProblem` class, which provides a flexible way to handle variables, constraints, and objective functions.\n", @@ -54,7 +54,7 @@ }, { "cell_type": "markdown", - "id": "e1f48581-9e8d-4c9a-964d-db6636834dbf", + "id": "da1594d2-c974-4bdf-92f6-31adbe73483b", "metadata": {}, "source": [ "## Construct an `OptimizationProblem`\n", @@ -66,7 +66,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "0ac04756-6055-4597-bfed-6c36062fa851", + "id": "b4cda501-3177-4c83-868b-c14170e02d12", "metadata": {}, "outputs": [ { @@ -96,7 +96,7 @@ }, { "cell_type": "markdown", - "id": "de281542-4e23-409a-92e7-180c2145cbc1", + "id": "d31d72b7-ec26-40fb-aa9d-ba7f278952e3", "metadata": {}, "source": [ "The `OptimizationProblem` class supports four types of variables:\n", @@ -112,7 +112,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "b0980e7e-d086-4112-ba12-558c431915e7", + "id": "c560c628-9456-441e-91cf-b71ca99d0e59", "metadata": {}, "outputs": [ { @@ -153,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "5a275b42-5c2f-4c67-b744-74c53fe83da4", + "id": "fb2ba7ee-07a2-417c-a49a-ba66a8278794", "metadata": {}, "source": [ "You set the objective function by calling `OptimizationProblem.minimize` or `OptimizationProblem.maximize`.\n", @@ -177,7 +177,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "8c55acf9-a528-4843-a60f-29dd6ccbd739", + "id": "6be48ff6-01b8-4999-bc93-ae402d6d79cf", "metadata": {}, "outputs": [ { @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "d9a38311-bca8-458f-af30-b1481126e7ec", + "id": "8564f7a9-6698-4885-92fe-b3b7f4619120", "metadata": {}, "source": [ "You can also specify the objective function by using arrays:\n", @@ -233,7 +233,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "17147648-faeb-4250-8038-2a1d619260f2", + "id": "06980260-774f-4acc-94cb-9faacee1d5d3", "metadata": {}, "outputs": [ { @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "f08dfa6c-6a58-489a-8cfb-644b0fca7053", + "id": "c234b32c-f83f-4d84-9158-81d3f8ab3a91", "metadata": {}, "source": [ "You can access the constant, the linear term, the quadratic term, and the higher-order terms by looking at\n", @@ -307,7 +307,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "7b53d03b-8dc5-4a79-9405-03d25554367d", + "id": "665c6291-9ac8-42b8-8403-3720f5815b1f", "metadata": {}, "outputs": [ { @@ -412,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "e150888d-4060-42af-b840-afd1a710a78e", + "id": "914e66d9-0e18-47d1-b972-78fb3aff1248", "metadata": {}, "source": [ "## Add or remove linear, quadratic, and higher-order terms to and from the constraints" @@ -420,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "1ebe7521-ebfb-41f5-99cb-c6a670c12c7a", + "id": "284e0064-e95c-431f-8f3f-665a533263d9", "metadata": {}, "source": [ "You can add linear constraints by setting name, linear expression, sense, and right-hand-side value (rhs).\n", @@ -430,7 +430,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "dffd939f-b0db-4cbd-b17f-89cb379dfdfc", + "id": "d58921f7-fade-4ab9-88a5-c0cf38e72445", "metadata": {}, "outputs": [ { @@ -473,7 +473,7 @@ }, { "cell_type": "markdown", - "id": "ba4b2ac8-676b-47a8-8473-62e7e0c330cb", + "id": "527ccb63-35af-44e6-9585-1a6d72cddac0", "metadata": {}, "source": [ "You can add quadratic constraints by setting name, quadratic expression, sense, and right-hand-side value (rhs)." @@ -482,7 +482,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "f03aa130-ea67-469a-8c30-f7a9e449d3a5", + "id": "21da83ad-e9ba-4eae-b659-ed507796e30f", "metadata": {}, "outputs": [ { @@ -548,7 +548,7 @@ }, { "cell_type": "markdown", - "id": "e5c93ddf-3134-469f-b6a5-c7b9a11af52b", + "id": "c7f01573-e870-40d0-96fe-7172d165f647", "metadata": {}, "source": [ "You can also add higher-order constraints by setting name, higher-order expression, sense, and right-hand-side value (rhs)." @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "e2636a9a-f4f5-4b63-885e-7618f56c6098", + "id": "d4cab12c-b500-4981-8c9d-635627119511", "metadata": {}, "outputs": [ { @@ -631,7 +631,7 @@ }, { "cell_type": "markdown", - "id": "92cb02b8-14c1-49ef-af1d-d3ee654c96d1", + "id": "aa27116d-b1c1-45d5-8e30-709f75de29f1", "metadata": {}, "source": [ "You can access linear and quadratic terms of linear and quadratic constraints in the same way as the objective function." @@ -640,7 +640,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "a6cb7c71-c928-459d-b580-afe998ff640f", + "id": "d11d8546-fc57-4a39-bbbc-50da55b6215a", "metadata": {}, "outputs": [ { @@ -677,7 +677,7 @@ }, { "cell_type": "markdown", - "id": "49815ffd-df5d-4a0d-838c-4f6d42c7f26a", + "id": "ae9462fe-6cf4-499e-bce7-92beafc6225f", "metadata": {}, "source": [ "You can also remove linear/quadratic/higher-order constraints by using the `remove_linear_constraint`, `remove_quadratic_constraint`, and `remove_higher_order_constraint` methods, respectively." @@ -686,7 +686,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "9fcb15ca-f5af-4a52-8ce6-9c96f5f34102", + "id": "160de065-b673-47e7-a5b0-11a4416132d5", "metadata": {}, "outputs": [ { @@ -736,7 +736,7 @@ }, { "cell_type": "markdown", - "id": "0f797eb2-eb52-43be-a8e1-0f0d4028a197", + "id": "930e6a65-7b64-4420-a348-74e6dbe8de1b", "metadata": {}, "source": [ "## Substitute variables" @@ -744,7 +744,7 @@ }, { "cell_type": "markdown", - "id": "10247c26-5bdd-4522-b78b-04bf67452e6e", + "id": "1792b9cb-54da-450c-a1aa-875ff8db49d0", "metadata": {}, "source": [ "You can substitute some of the variables with constants or other variables.\n", @@ -757,7 +757,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "c557dcb9-a935-44fa-bd61-4c89a56305b3", + "id": "83f0d5f9-5ed0-41b4-8e4a-ab476e568e8d", "metadata": {}, "outputs": [ { @@ -798,7 +798,7 @@ }, { "cell_type": "markdown", - "id": "31e7fe1a-0754-4ceb-afc8-dbcd351e7241", + "id": "8bf8e1b0-fa8d-4d98-a3ca-5ec146e32363", "metadata": {}, "source": [ "If the resulting problem is infeasible due to lower bounds or upper bounds, the methods returns the status `Status.INFEASIBLE`.\n", @@ -808,7 +808,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "f1ca8356-f037-4918-bf60-cc7bfd6d32c7", + "id": "a2286ecf-5778-42f6-875c-6b0e28a5a7f6", "metadata": {}, "outputs": [ { @@ -833,7 +833,7 @@ }, { "cell_type": "markdown", - "id": "05e0fe2a-27d7-45bb-8924-870d4f6675bf", + "id": "dce64b51-3755-4e17-bfc4-22590d05c8ae", "metadata": {}, "source": [ "You cannot substitute variables multiple times.\n", @@ -843,7 +843,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "ca3203fe-32c6-4f84-bc64-d2109bb80a3e", + "id": "0462f970-ca86-43fd-a74c-12cbf81eff35", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how_tos/02-converters-for-optimization-problems.ipynb b/docs/addons/qiskit-addon-opt-mapper/how_tos/02-converters-for-optimization-problems.ipynb index 4a677f7ff4b..85c70dfa54b 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how_tos/02-converters-for-optimization-problems.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how_tos/02-converters-for-optimization-problems.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "aac5ddd7-62a0-491a-98a3-21aa74a3f794", + "id": "df5166ad-f5d3-49af-bcc1-84d556a5589e", "metadata": {}, "source": [ "# Convert formulations of an `OptimizationProblem`\n", @@ -48,7 +48,7 @@ }, { "cell_type": "markdown", - "id": "e7172a49-71fd-45f5-881a-f2bf3447fdbd", + "id": "06977c6b-c242-4bc3-b078-947ddffd68f7", "metadata": {}, "source": [ "## Code examples\n", @@ -65,7 +65,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "8207c198-7097-4f82-aaf0-21dbde146fcf", + "id": "9fde7959-9e98-45ef-b273-c720b2a9060f", "metadata": {}, "outputs": [ { @@ -128,7 +128,7 @@ }, { "cell_type": "markdown", - "id": "5409088d-6a20-4f36-a691-365fe67bef74", + "id": "4b187df5-fcff-4e6f-8f9d-cffd79ab3f6b", "metadata": {}, "source": [ "## Binary to spin conversion\n", @@ -138,7 +138,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "c4d05a89-6b93-4e0c-a614-629fc77eb71a", + "id": "e87b0cc5-e10d-4875-b805-acaa09077fff", "metadata": {}, "outputs": [ { @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "827b71c6-f7db-47b4-8247-118a3ccd454c", + "id": "a6ebd68b-96c0-40a4-80d3-b15147ab36a4", "metadata": {}, "source": [ "## Spin to binary conversion\n", @@ -204,7 +204,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "3f8e7be4-3145-46f5-87df-151b70e00cd3", + "id": "8c132373-c749-4754-a6c8-cd5cd1ad0ea2", "metadata": {}, "outputs": [ { @@ -258,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "5521322d-e178-4d20-a920-d2b433aba076", + "id": "02aa42df-1b43-4790-bbcc-64acda9d5043", "metadata": {}, "source": [ "## Inequality to equality conversion\n", @@ -272,7 +272,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "64cbcd38-51f7-4900-8cc2-b4fb8515017d", + "id": "7acaf434-ad0e-4a99-97cf-a649182759fd", "metadata": {}, "outputs": [ { @@ -336,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "8274fafe-0fe0-41ae-80e2-28f853141838", + "id": "21d099e4-3bae-4545-83d0-25b8854920b6", "metadata": {}, "source": [ "## Equality to penalty conversion\n", @@ -352,7 +352,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "939a8a82-0d93-46a3-b155-d0142925aac0", + "id": "de9222cb-fae4-4e09-ab96-e870c8296499", "metadata": {}, "outputs": [ { @@ -408,7 +408,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "fbd2f00b-a51b-4d87-94d2-5916c93009cf", + "id": "da5e8a5c-ac36-4dd9-836e-92a1b218587f", "metadata": {}, "outputs": [ { @@ -441,7 +441,7 @@ }, { "cell_type": "markdown", - "id": "05bc7818-5864-427c-a560-4842e1f8778e", + "id": "a0991704-f37d-40b8-a1e8-a57ab08d29c8", "metadata": {}, "source": [ "## Linear inequality to penalty conversion\n", @@ -464,7 +464,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "75c465cb-a715-4d79-abc7-da6e395c14be", + "id": "7af101b6-533c-45bc-a777-935338a48a0d", "metadata": {}, "outputs": [ { @@ -520,7 +520,7 @@ }, { "cell_type": "markdown", - "id": "e02c5a8d-0bb7-4855-8a96-a739d8da44b3", + "id": "82ef9d9f-05ad-40a1-b44c-5d93623d652a", "metadata": {}, "source": [ "## Maximize to minimize conversion\n", @@ -535,7 +535,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "6ba78a4d-3e35-4d2f-a4e6-fe479501116a", + "id": "250533ef-950b-49aa-bfca-30cd9f6f17af", "metadata": {}, "outputs": [ { @@ -590,7 +590,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "5e1c217b-6a21-4df5-8e5f-c3930cfcb5f5", + "id": "fc8fc55a-2c6c-49d9-9ba1-fd936bcee824", "metadata": {}, "outputs": [ { @@ -623,7 +623,7 @@ }, { "cell_type": "markdown", - "id": "6ea31b89-747d-4379-a9a2-f70a4240cfc8", + "id": "55e7fdd0-7be4-4632-b08d-70d02a109796", "metadata": {}, "source": [ "## QUBO conversion\n", @@ -635,7 +635,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "ee2d5c81-73f4-4d21-a81e-239d86a4454f", + "id": "6e7fbf4a-8435-4e45-996c-27fc05131a4b", "metadata": {}, "outputs": [ { @@ -705,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "6d2a2342-a08b-4827-ab21-05bd4e21d0fb", + "id": "029f17d0-3b65-46c2-9628-fd2138f0e6a5", "metadata": {}, "source": [ "## HUBO conversion\n", @@ -715,7 +715,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "384d7253-0ce3-4b24-96eb-4e1a1ab7cc1c", + "id": "874e045e-f5fd-4a0a-82a7-7fd57b722ca0", "metadata": {}, "outputs": [ { @@ -779,7 +779,7 @@ }, { "cell_type": "markdown", - "id": "9df4c491-969d-46a0-a0d9-1d5d6b80a029", + "id": "bf36fff5-8580-4c60-b81d-4d30eb6705e8", "metadata": {}, "source": [ "## Ising transformation\n", @@ -790,7 +790,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "657a3774-19cb-4d4f-a0ac-cec38bc0bcd4", + "id": "19fc13d9-09e0-49be-93e3-b4c4b34a334d", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how_tos/04-translate-docplex.ipynb b/docs/addons/qiskit-addon-opt-mapper/how_tos/04-translate-docplex.ipynb index 4926985f15c..1c440e6a1c0 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how_tos/04-translate-docplex.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how_tos/04-translate-docplex.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "71c5646f-8ef9-40b4-9a06-3ca97098e1d3", + "id": "69cc70f8-cae3-4a0f-994c-70e7a71aaa72", "metadata": {}, "source": [ "# Translate between `OptimizationProblem` and Docplex\n", @@ -29,7 +29,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "0a50d206-2e05-453f-ac75-314aa86ab212", + "id": "39e59a1c-31f3-4eeb-b362-af47d7fb0399", "metadata": {}, "outputs": [ { @@ -80,7 +80,7 @@ }, { "cell_type": "markdown", - "id": "04754d18-ee01-4c00-be23-125bac8e7bfe", + "id": "76f4ff25-c3f6-41c5-b0c6-155929f75e7f", "metadata": {}, "source": [ "When you display your problem in LP format using `export_as_lp_string`,\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "2c5d050c-e68e-44a8-8b75-7cd4ae6a6e46", + "id": "79f7a069-6f67-4979-9984-de1cb767309d", "metadata": {}, "outputs": [ { @@ -135,7 +135,7 @@ }, { "cell_type": "markdown", - "id": "397a7b32-d5ab-4f74-9e89-66c6e028a1d7", + "id": "d60de99d-8aec-4753-a34d-170c14acd11e", "metadata": {}, "source": [ "As stated before, you can also convert a `OptimizationProblem` to a Docplex model using the `to_docplex` function." @@ -144,7 +144,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "db49073c-600f-43a4-b758-168a280698da", + "id": "02d71ca6-04fe-4e66-8696-40b12b31e519", "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ }, { "cell_type": "markdown", - "id": "24731f83-11a5-448a-ac95-66f0a9945221", + "id": "6686f79f-c928-46db-9010-3af9e5f6fd5c", "metadata": {}, "source": [ "## Load LP files using Docplex" @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "52812c75-c821-47d8-8c09-448f92d0b3f8", + "id": "97efbf26-a425-46fe-a36e-491f6dc5a652", "metadata": {}, "source": [ "Once you are familia with the Docplex translators, loading from LP files becomes very simple. You can use the `docplex.mp.ModelReader` class followed by a translation step:" @@ -203,7 +203,7 @@ { "cell_type": "code", "execution_count": null, - "id": "aab13e97-6245-41e2-91b1-841775d2ed42", + "id": "6dcd7d89-e71e-4d76-b317-af100f4e48e5", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-pna/_toc.json b/docs/addons/qiskit-addon-pna/_toc.json index bb1b3e79e5d..9c1b857a59a 100644 --- a/docs/addons/qiskit-addon-pna/_toc.json +++ b/docs/addons/qiskit-addon-pna/_toc.json @@ -20,7 +20,7 @@ "children": [ { "title": "How-to guides coming soon", - "url": "/docs/addons/qiskit-addon-pna/how-tos/placeholder" + "url": "/docs/addons/qiskit-addon-pna/how_tos/placeholder" } ] }, diff --git a/docs/addons/qiskit-addon-slc/_toc.json b/docs/addons/qiskit-addon-slc/_toc.json index 306e67a89f0..3c070c99956 100644 --- a/docs/addons/qiskit-addon-slc/_toc.json +++ b/docs/addons/qiskit-addon-slc/_toc.json @@ -17,7 +17,7 @@ }, { "title": "How-To Guides", - "url": "/docs/addons/qiskit-addon-slc/how-tos/index" + "url": "/docs/addons/qiskit-addon-slc/how_tos/index" }, { "title": "Explanations", diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json index 3e9f11cd0d9..2c3e56a95db 100644 --- a/docs/addons/qiskit-addon-sqd/_toc.json +++ b/docs/addons/qiskit-addon-sqd/_toc.json @@ -20,31 +20,31 @@ "children": [ { "title": "Add fermionic transitions to the pool of configurations", - "url": "/docs/addons/qiskit-addon-sqd/how-tos/add-fermionic-excitations-to-configuration-pool" + "url": "/docs/addons/qiskit-addon-sqd/how_tos/add-fermionic-excitations-to-configuration-pool" }, { "title": "Benchmark Pauli operator projection", - "url": "/docs/addons/qiskit-addon-sqd/how-tos/benchmark-pauli-projection" + "url": "/docs/addons/qiskit-addon-sqd/how_tos/benchmark-pauli-projection" }, { "title": "Bounding the subspace dimension", - "url": "/docs/addons/qiskit-addon-sqd/how-tos/choose-subspace-dimension" + "url": "/docs/addons/qiskit-addon-sqd/how_tos/choose-subspace-dimension" }, { "title": "Scale SQD chemistry workflows with Dice solver", - "url": "/docs/addons/qiskit-addon-sqd/how-tos/integrate-dice-solver" + "url": "/docs/addons/qiskit-addon-sqd/how_tos/integrate-dice-solver" }, { "title": "Project Pauli operators onto Hilbert subspaces", - "url": "/docs/addons/qiskit-addon-sqd/how-tos/project-pauli-operators-onto-hilbert-subspaces" + "url": "/docs/addons/qiskit-addon-sqd/how_tos/project-pauli-operators-onto-hilbert-subspaces" }, { "title": "Understand open-shell vs closed-shell options and its effect in the subspace construction", - "url": "/docs/addons/qiskit-addon-sqd/how-tos/select-open-closed-shell" + "url": "/docs/addons/qiskit-addon-sqd/how_tos/select-open-closed-shell" }, { "title": "Optimize Hamiltonian basis with orbital optimization", - "url": "/docs/addons/qiskit-addon-sqd/how-tos/use-oo-to-optimize-hamiltonian-basis" + "url": "/docs/addons/qiskit-addon-sqd/how_tos/use-oo-to-optimize-hamiltonian-basis" } ] }, diff --git a/docs/addons/qiskit-addon-utils/_toc.json b/docs/addons/qiskit-addon-utils/_toc.json index 4b669aa0000..cb3ae27d2ad 100644 --- a/docs/addons/qiskit-addon-utils/_toc.json +++ b/docs/addons/qiskit-addon-utils/_toc.json @@ -20,11 +20,11 @@ "children": [ { "title": "Use edge coloring to reduce the depth of quantum circuits", - "url": "/docs/addons/qiskit-addon-utils/how-tos/color-device-edges-to-improve-depth" + "url": "/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth" }, { "title": "Slicing circuits using qiskit_addon_utils.slicing", - "url": "/docs/addons/qiskit-addon-utils/how-tos/create-circuit-slices" + "url": "/docs/addons/qiskit-addon-utils/how_tos/create-circuit-slices" } ] }, diff --git a/scripts/js/lib/api/generateAddonToc.test.ts b/scripts/js/lib/api/generateAddonToc.test.ts index 7b2f74f14fb..1a7b2a54450 100644 --- a/scripts/js/lib/api/generateAddonToc.test.ts +++ b/scripts/js/lib/api/generateAddonToc.test.ts @@ -204,7 +204,7 @@ test("subdirectory section has correct child URLs", async () => { const guides = main.children?.find((c) => c.title === "Guides"); expect(guides?.children?.[0].url).toBe( - "/docs/addons/my-addon/how-tos/my-guide", + "/docs/addons/my-addon/how_tos/my-guide", ); }); diff --git a/scripts/js/lib/api/generateAddonToc.ts b/scripts/js/lib/api/generateAddonToc.ts index a23e5ed59b0..7eec021b08d 100644 --- a/scripts/js/lib/api/generateAddonToc.ts +++ b/scripts/js/lib/api/generateAddonToc.ts @@ -21,7 +21,7 @@ // Called by addonDocsPipeline.ts; for API doc TOCs see generateToc.ts. import { readFile } from "fs/promises"; -import { join } from "path/posix"; +import { join, parse } from "path/posix"; import { load } from "cheerio"; @@ -138,35 +138,32 @@ async function buildMainFromSidebar( const children: TocEntry[] = []; $l1.find("ul > li > a").each((_, a2) => { const childHref = $(a2).attr("href") ?? ""; + const dir = childHref.split("/")[0]; + const slug = hrefToSlug(childHref, pkg); children.push({ title: $(a2).text().trim(), - url: `${addonUrlBase}/${hrefToRelPath(childHref, pkg)}`, + url: `${addonUrlBase}/${dir}/${slug}`, }); }); if (children.length > 0) entries.push({ title, children }); return; } + const slug = hrefToSlug(href, pkg); // Top-level page (may be a subdirectory index like "explanation/index.html") - entries.push({ title, url: `${addonUrlBase}/${hrefToRelPath(href, pkg)}` }); + const path = href.includes("/") ? `${href.split("/")[0]}/${slug}` : slug; + entries.push({ title, url: `${addonUrlBase}/${path}` }); }); return entries; } -/** - * Converts a Sphinx HTML href to a repo-relative path, applying the - * kebab-case transform to every path segment (dir and filename alike). - */ -function hrefToRelPath(href: string, pkg: Pkg): string { - const segments = href.replace(/\.html$/, "").split("/"); - return segments - .map((seg) => - pkg.kebabCaseAndShortenUrls - ? kebabCaseAndShortenPage(seg, pkg.name) - : seg, - ) - .join("/"); +/** Converts a Sphinx HTML href filename to the kebab-case slug used in the output MDX. */ +function hrefToSlug(href: string, pkg: Pkg): string { + const name = parse(href).name; + return pkg.kebabCaseAndShortenUrls + ? kebabCaseAndShortenPage(name, pkg.name) + : name; } type TocNode = { title?: string; url?: string; children?: TocNode[] }; From a6ba2ca8574f5d8faa52c4ea6af6c7d82a2ca0fc Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 28 May 2026 17:39:20 -0400 Subject: [PATCH 099/104] removes check:addon-tutorials and fix frontmatter spelling --- .github/workflows/main.yml | 2 - docs/addons/pauli-prop/how_tos/index.mdx | 2 +- docs/addons/pauli-prop/install.mdx | 2 +- .../explanation/index.mdx | 2 +- .../qiskit-addon-aqc-tensor/how-tos/index.mdx | 2 +- .../qiskit-addon-aqc-tensor/install.mdx | 2 +- .../explanation/index.mdx | 2 +- .../qiskit-addon-cutting/how-tos/index.mdx | 2 +- docs/addons/qiskit-addon-cutting/install.mdx | 2 +- .../qiskit-addon-mpf/explanations/index.mdx | 2 +- .../addons/qiskit-addon-mpf/how_tos/index.mdx | 2 +- docs/addons/qiskit-addon-mpf/install.mdx | 2 +- docs/addons/qiskit-addon-mthree/advanced.mdx | 2 +- docs/addons/qiskit-addon-mthree/balanced.mdx | 2 +- docs/addons/qiskit-addon-mthree/basic.mdx | 2 +- docs/addons/qiskit-addon-mthree/cal-io.mdx | 2 +- docs/addons/qiskit-addon-mthree/citing.mdx | 2 +- .../qiskit-addon-mthree/collections.mdx | 2 +- docs/addons/qiskit-addon-mthree/error.mdx | 2 +- docs/addons/qiskit-addon-mthree/expvals.mdx | 2 +- docs/addons/qiskit-addon-mthree/grouped.mdx | 2 +- .../qiskit-addon-mthree/installation.mdx | 2 +- docs/addons/qiskit-addon-mthree/marginals.mdx | 2 +- docs/addons/qiskit-addon-mthree/papers.mdx | 2 +- docs/addons/qiskit-addon-mthree/probs.mdx | 2 +- docs/addons/qiskit-addon-mthree/runtime.mdx | 2 +- .../addons/qiskit-addon-mthree/transpiled.mdx | 2 +- docs/addons/qiskit-addon-mthree/utils.mdx | 2 +- .../addons/qiskit-addon-obp/how_tos/index.mdx | 2 +- docs/addons/qiskit-addon-obp/install.mdx | 2 +- .../how_tos/01-optimization-problem.ipynb | 62 +++++++++---------- ...converters-for-optimization-problems.ipynb | 46 +++++++------- .../how_tos/04-translate-docplex.ipynb | 18 +++--- .../qiskit-addon-opt-mapper/how_tos/index.mdx | 2 +- .../qiskit-addon-opt-mapper/install.mdx | 2 +- .../addons/qiskit-addon-pna/how_tos/index.mdx | 2 +- docs/addons/qiskit-addon-pna/install.mdx | 2 +- .../qiskit-addon-slc/explanations/index.mdx | 2 +- .../addons/qiskit-addon-slc/how_tos/index.mdx | 2 +- docs/addons/qiskit-addon-slc/install.mdx | 2 +- .../compilation-flags.mdx | 2 +- .../addons/qiskit-addon-sqd/how_tos/index.mdx | 2 +- docs/addons/qiskit-addon-sqd/install.mdx | 2 +- .../color-device-edges-to-improve-depth.ipynb | 2 +- .../qiskit-addon-utils/how_tos/index.mdx | 2 +- docs/addons/qiskit-addon-utils/install.mdx | 2 +- package.json | 1 - scripts/js/commands/api/updateApiDocs.ts | 46 ++++++++------ scripts/js/commands/checkAddonTutorials.ts | 56 ----------------- scripts/js/lib/api/htmlToMd.ts | 2 +- scripts/js/lib/api/objectsInv.ts | 14 ----- scripts/js/lib/api/pipelineStages.ts | 10 ++- scripts/js/lib/api/specialCaseResults.ts | 2 +- scripts/js/lib/api/updateLinks.ts | 2 +- 54 files changed, 143 insertions(+), 202 deletions(-) delete mode 100644 scripts/js/commands/checkAddonTutorials.ts diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index cbc2467010e..96d214f5227 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -105,8 +105,6 @@ jobs: run: npm run check:orphan-pages -- --apis - name: Check Qiskit and Qiskit C API versions run: npm run check:qiskit-versions - - name: Check addon tutorial slugs - run: npm run check:addon-tutorials - name: Check tutorials index run: python scripts/ci/check-tutorials-index.py - name: Infrastructure tests diff --git a/docs/addons/pauli-prop/how_tos/index.mdx b/docs/addons/pauli-prop/how_tos/index.mdx index ac260a7bcbb..0e511186c39 100644 --- a/docs/addons/pauli-prop/how_tos/index.mdx +++ b/docs/addons/pauli-prop/how_tos/index.mdx @@ -1,6 +1,6 @@ --- title: "How-To Guides" -description: "How-To Guides for the lastest version of Pauli propagation" +description: "How-To Guides for the latest version of Pauli propagation" --- # How-To Guides diff --git a/docs/addons/pauli-prop/install.mdx b/docs/addons/pauli-prop/install.mdx index 549ac1a86a0..28b662f11f5 100644 --- a/docs/addons/pauli-prop/install.mdx +++ b/docs/addons/pauli-prop/install.mdx @@ -1,6 +1,6 @@ --- title: "Installation Instructions" -description: "Installation Instructions for the lastest version of Pauli propagation" +description: "Installation Instructions for the latest version of Pauli propagation" --- # Installation Instructions diff --git a/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx index f989758a541..2679f07975b 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx +++ b/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx @@ -1,6 +1,6 @@ --- title: "Explanatory Material on AQC-Tensor" -description: "Explanatory Material on AQC-Tensor for the lastest version of Approximate quantum compilation (AQC-Tensor)" +description: "Explanatory Material on AQC-Tensor for the latest version of Approximate quantum compilation (AQC-Tensor)" --- <span id="explanation" /> diff --git a/docs/addons/qiskit-addon-aqc-tensor/how-tos/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/how-tos/index.mdx index 49c607d2869..9efb2839c94 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/how-tos/index.mdx +++ b/docs/addons/qiskit-addon-aqc-tensor/how-tos/index.mdx @@ -1,6 +1,6 @@ --- title: "AQC-Tensor How-To Guides" -description: "AQC-Tensor How-To Guides for the lastest version of Approximate quantum compilation (AQC-Tensor)" +description: "AQC-Tensor How-To Guides for the latest version of Approximate quantum compilation (AQC-Tensor)" --- # AQC-Tensor How-To Guides diff --git a/docs/addons/qiskit-addon-aqc-tensor/install.mdx b/docs/addons/qiskit-addon-aqc-tensor/install.mdx index c9b7cc31ba3..6b9a666ac32 100644 --- a/docs/addons/qiskit-addon-aqc-tensor/install.mdx +++ b/docs/addons/qiskit-addon-aqc-tensor/install.mdx @@ -1,6 +1,6 @@ --- title: "Installation Instructions" -description: "Installation Instructions for the lastest version of Approximate quantum compilation (AQC-Tensor)" +description: "Installation Instructions for the latest version of Approximate quantum compilation (AQC-Tensor)" --- <span id="installation-guide" /> diff --git a/docs/addons/qiskit-addon-cutting/explanation/index.mdx b/docs/addons/qiskit-addon-cutting/explanation/index.mdx index 052a80a80c3..e7914f0c6a1 100644 --- a/docs/addons/qiskit-addon-cutting/explanation/index.mdx +++ b/docs/addons/qiskit-addon-cutting/explanation/index.mdx @@ -1,6 +1,6 @@ --- title: "Explanatory material" -description: "Explanatory material for the lastest version of Circuit cutting" +description: "Explanatory material for the latest version of Circuit cutting" --- <span id="circuit-cutting-explanation" /> diff --git a/docs/addons/qiskit-addon-cutting/how-tos/index.mdx b/docs/addons/qiskit-addon-cutting/how-tos/index.mdx index c02a48eefe5..2fe97d4f684 100644 --- a/docs/addons/qiskit-addon-cutting/how-tos/index.mdx +++ b/docs/addons/qiskit-addon-cutting/how-tos/index.mdx @@ -1,6 +1,6 @@ --- title: "How-To Guides" -description: "How-To Guides for the lastest version of Circuit cutting" +description: "How-To Guides for the latest version of Circuit cutting" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-cutting/install.mdx b/docs/addons/qiskit-addon-cutting/install.mdx index 9c29528a3a2..5cf283e5dc4 100644 --- a/docs/addons/qiskit-addon-cutting/install.mdx +++ b/docs/addons/qiskit-addon-cutting/install.mdx @@ -1,6 +1,6 @@ --- title: "Installation Instructions" -description: "Installation Instructions for the lastest version of Circuit cutting" +description: "Installation Instructions for the latest version of Circuit cutting" --- # Installation Instructions diff --git a/docs/addons/qiskit-addon-mpf/explanations/index.mdx b/docs/addons/qiskit-addon-mpf/explanations/index.mdx index 20d11fba7b0..59ad6ab7b20 100644 --- a/docs/addons/qiskit-addon-mpf/explanations/index.mdx +++ b/docs/addons/qiskit-addon-mpf/explanations/index.mdx @@ -1,6 +1,6 @@ --- title: "Explanations" -description: "Explanations for the lastest version of Multi-product formulas (MPF)" +description: "Explanations for the latest version of Multi-product formulas (MPF)" --- # Explanations diff --git a/docs/addons/qiskit-addon-mpf/how_tos/index.mdx b/docs/addons/qiskit-addon-mpf/how_tos/index.mdx index 8d1ec7121f0..4405aeedc78 100644 --- a/docs/addons/qiskit-addon-mpf/how_tos/index.mdx +++ b/docs/addons/qiskit-addon-mpf/how_tos/index.mdx @@ -1,6 +1,6 @@ --- title: "How-To Guides" -description: "How-To Guides for the lastest version of Multi-product formulas (MPF)" +description: "How-To Guides for the latest version of Multi-product formulas (MPF)" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-mpf/install.mdx b/docs/addons/qiskit-addon-mpf/install.mdx index 54859d0b768..fb080188c40 100644 --- a/docs/addons/qiskit-addon-mpf/install.mdx +++ b/docs/addons/qiskit-addon-mpf/install.mdx @@ -1,6 +1,6 @@ --- title: "Installation Instructions" -description: "Installation Instructions for the lastest version of Multi-product formulas (MPF)" +description: "Installation Instructions for the latest version of Multi-product formulas (MPF)" --- # Installation Instructions diff --git a/docs/addons/qiskit-addon-mthree/advanced.mdx b/docs/addons/qiskit-addon-mthree/advanced.mdx index c98b9c2229c..0004be67548 100644 --- a/docs/addons/qiskit-addon-mthree/advanced.mdx +++ b/docs/addons/qiskit-addon-mthree/advanced.mdx @@ -1,6 +1,6 @@ --- title: "Advanced usage" -description: "Advanced usage for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Advanced usage for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="advanced" /> diff --git a/docs/addons/qiskit-addon-mthree/balanced.mdx b/docs/addons/qiskit-addon-mthree/balanced.mdx index e20f6bf10f6..4c80ccd6680 100644 --- a/docs/addons/qiskit-addon-mthree/balanced.mdx +++ b/docs/addons/qiskit-addon-mthree/balanced.mdx @@ -1,6 +1,6 @@ --- title: "Balanced calibrations" -description: "Balanced calibrations for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Balanced calibrations for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="balanced" /> diff --git a/docs/addons/qiskit-addon-mthree/basic.mdx b/docs/addons/qiskit-addon-mthree/basic.mdx index 6f2a3676e29..be8f2e77bc5 100644 --- a/docs/addons/qiskit-addon-mthree/basic.mdx +++ b/docs/addons/qiskit-addon-mthree/basic.mdx @@ -1,6 +1,6 @@ --- title: "Basic usage" -description: "Basic usage for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Basic usage for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="basic" /> diff --git a/docs/addons/qiskit-addon-mthree/cal-io.mdx b/docs/addons/qiskit-addon-mthree/cal-io.mdx index 109283941c7..0063e9f9c6b 100644 --- a/docs/addons/qiskit-addon-mthree/cal-io.mdx +++ b/docs/addons/qiskit-addon-mthree/cal-io.mdx @@ -1,6 +1,6 @@ --- title: "Saving and loading calibration data" -description: "Saving and loading calibration data for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Saving and loading calibration data for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="calio" /> diff --git a/docs/addons/qiskit-addon-mthree/citing.mdx b/docs/addons/qiskit-addon-mthree/citing.mdx index 203e943ed7b..586ff4f2d98 100644 --- a/docs/addons/qiskit-addon-mthree/citing.mdx +++ b/docs/addons/qiskit-addon-mthree/citing.mdx @@ -1,6 +1,6 @@ --- title: "Citing" -description: "Citing for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Citing for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="id1" /> diff --git a/docs/addons/qiskit-addon-mthree/collections.mdx b/docs/addons/qiskit-addon-mthree/collections.mdx index 47a8014d1df..14532749f52 100644 --- a/docs/addons/qiskit-addon-mthree/collections.mdx +++ b/docs/addons/qiskit-addon-mthree/collections.mdx @@ -1,6 +1,6 @@ --- title: "Using distribution collections" -description: "Using distribution collections for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Using distribution collections for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="collections" /> diff --git a/docs/addons/qiskit-addon-mthree/error.mdx b/docs/addons/qiskit-addon-mthree/error.mdx index 47a6fd3c498..55c693b139a 100644 --- a/docs/addons/qiskit-addon-mthree/error.mdx +++ b/docs/addons/qiskit-addon-mthree/error.mdx @@ -1,6 +1,6 @@ --- title: "Error analysis" -description: "Error analysis for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Error analysis for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="error" /> diff --git a/docs/addons/qiskit-addon-mthree/expvals.mdx b/docs/addons/qiskit-addon-mthree/expvals.mdx index 9a876d59ce4..95eb5bd56ec 100644 --- a/docs/addons/qiskit-addon-mthree/expvals.mdx +++ b/docs/addons/qiskit-addon-mthree/expvals.mdx @@ -1,6 +1,6 @@ --- title: "Obtaining expectation values" -description: "Obtaining expectation values for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Obtaining expectation values for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="expvals" /> diff --git a/docs/addons/qiskit-addon-mthree/grouped.mdx b/docs/addons/qiskit-addon-mthree/grouped.mdx index f7a4626522e..144e6c63b08 100644 --- a/docs/addons/qiskit-addon-mthree/grouped.mdx +++ b/docs/addons/qiskit-addon-mthree/grouped.mdx @@ -1,6 +1,6 @@ --- title: "Grouped operators" -description: "Grouped operators for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Grouped operators for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="grouped" /> diff --git a/docs/addons/qiskit-addon-mthree/installation.mdx b/docs/addons/qiskit-addon-mthree/installation.mdx index 573681324a3..cf52db656da 100644 --- a/docs/addons/qiskit-addon-mthree/installation.mdx +++ b/docs/addons/qiskit-addon-mthree/installation.mdx @@ -1,6 +1,6 @@ --- title: "Installation" -description: "Installation for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Installation for the latest version of Matrix-free Measurement Mitigation (M3)" --- # Installation diff --git a/docs/addons/qiskit-addon-mthree/marginals.mdx b/docs/addons/qiskit-addon-mthree/marginals.mdx index ebf9c018fec..6555d2315e5 100644 --- a/docs/addons/qiskit-addon-mthree/marginals.mdx +++ b/docs/addons/qiskit-addon-mthree/marginals.mdx @@ -1,6 +1,6 @@ --- title: "Low-weight observables (Marginals)" -description: "Low-weight observables (Marginals) for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Low-weight observables (Marginals) for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="marginals" /> diff --git a/docs/addons/qiskit-addon-mthree/papers.mdx b/docs/addons/qiskit-addon-mthree/papers.mdx index a6ef547c21f..5eabed83c40 100644 --- a/docs/addons/qiskit-addon-mthree/papers.mdx +++ b/docs/addons/qiskit-addon-mthree/papers.mdx @@ -1,6 +1,6 @@ --- title: "Papers using M3" -description: "Papers using M3 for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Papers using M3 for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="papers" /> diff --git a/docs/addons/qiskit-addon-mthree/probs.mdx b/docs/addons/qiskit-addon-mthree/probs.mdx index 68e1a2681b8..4aaef4cbc76 100644 --- a/docs/addons/qiskit-addon-mthree/probs.mdx +++ b/docs/addons/qiskit-addon-mthree/probs.mdx @@ -1,6 +1,6 @@ --- title: "Converting to probabilities" -description: "Converting to probabilities for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Converting to probabilities for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="probs" /> diff --git a/docs/addons/qiskit-addon-mthree/runtime.mdx b/docs/addons/qiskit-addon-mthree/runtime.mdx index 747c02eaf71..f13ccc71824 100644 --- a/docs/addons/qiskit-addon-mthree/runtime.mdx +++ b/docs/addons/qiskit-addon-mthree/runtime.mdx @@ -1,6 +1,6 @@ --- title: "Using Runtime execution modes" -description: "Using Runtime execution modes for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Using Runtime execution modes for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="runtime" /> diff --git a/docs/addons/qiskit-addon-mthree/transpiled.mdx b/docs/addons/qiskit-addon-mthree/transpiled.mdx index 2e7942384a5..3231721dfac 100644 --- a/docs/addons/qiskit-addon-mthree/transpiled.mdx +++ b/docs/addons/qiskit-addon-mthree/transpiled.mdx @@ -1,6 +1,6 @@ --- title: "Using M3 with transpiled circuits" -description: "Using M3 with transpiled circuits for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Using M3 with transpiled circuits for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="transpiled" /> diff --git a/docs/addons/qiskit-addon-mthree/utils.mdx b/docs/addons/qiskit-addon-mthree/utils.mdx index a8a92d3ed55..98fbd4acc54 100644 --- a/docs/addons/qiskit-addon-mthree/utils.mdx +++ b/docs/addons/qiskit-addon-mthree/utils.mdx @@ -1,6 +1,6 @@ --- title: "Utility functions" -description: "Utility functions for the lastest version of Matrix-free Measurement Mitigation (M3)" +description: "Utility functions for the latest version of Matrix-free Measurement Mitigation (M3)" --- <span id="utils" /> diff --git a/docs/addons/qiskit-addon-obp/how_tos/index.mdx b/docs/addons/qiskit-addon-obp/how_tos/index.mdx index ab269f92f73..04fab7ff53f 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/index.mdx +++ b/docs/addons/qiskit-addon-obp/how_tos/index.mdx @@ -1,6 +1,6 @@ --- title: "How-To Guides" -description: "How-To Guides for the lastest version of Operator backpropagation (OBP)" +description: "How-To Guides for the latest version of Operator backpropagation (OBP)" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-obp/install.mdx b/docs/addons/qiskit-addon-obp/install.mdx index 5d736d51453..8a183e71a15 100644 --- a/docs/addons/qiskit-addon-obp/install.mdx +++ b/docs/addons/qiskit-addon-obp/install.mdx @@ -1,6 +1,6 @@ --- title: "Installation Instructions" -description: "Installation Instructions for the lastest version of Operator backpropagation (OBP)" +description: "Installation Instructions for the latest version of Operator backpropagation (OBP)" --- # Installation Instructions diff --git a/docs/addons/qiskit-addon-opt-mapper/how_tos/01-optimization-problem.ipynb b/docs/addons/qiskit-addon-opt-mapper/how_tos/01-optimization-problem.ipynb index 4e716074223..70e94e86497 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how_tos/01-optimization-problem.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how_tos/01-optimization-problem.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "7f77b188-c62c-4d30-b6be-856171f7020d", + "id": "bf41b9ac-f020-484c-ab60-aa01ecaf4f00", "metadata": {}, "source": [ "# Model quantum optimization problems with the `OptimizationProblem` class" @@ -21,7 +21,7 @@ }, { "cell_type": "markdown", - "id": "7a43a06d-e750-44e8-a8c8-80e514963643", + "id": "291527af-911f-40b9-92c2-9e6abc542ce5", "metadata": {}, "source": [ "## Introduction" @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "7b9b0f1d-c556-4c38-8fd8-53e639b75c9d", + "id": "103163ec-23a2-4c19-a2de-1ecfd533b4f7", "metadata": {}, "source": [ "This guide demonstrates how to use the **Optimization Mapper Qiskit addon** for quantum optimization modeling. The addon introduces the `OptimizationProblem` class, which provides a flexible way to handle variables, constraints, and objective functions.\n", @@ -54,7 +54,7 @@ }, { "cell_type": "markdown", - "id": "da1594d2-c974-4bdf-92f6-31adbe73483b", + "id": "e3875941-7ba0-47b1-acc5-d7e628011aa4", "metadata": {}, "source": [ "## Construct an `OptimizationProblem`\n", @@ -66,7 +66,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "b4cda501-3177-4c83-868b-c14170e02d12", + "id": "14128e7a-10c7-4fd5-be9d-e5143cbd7700", "metadata": {}, "outputs": [ { @@ -96,7 +96,7 @@ }, { "cell_type": "markdown", - "id": "d31d72b7-ec26-40fb-aa9d-ba7f278952e3", + "id": "55bab9cd-3ce8-4b49-804e-c948bd0c0cee", "metadata": {}, "source": [ "The `OptimizationProblem` class supports four types of variables:\n", @@ -112,7 +112,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "c560c628-9456-441e-91cf-b71ca99d0e59", + "id": "b0076fb5-6b0a-4e7f-bfb7-085ff42a7da5", "metadata": {}, "outputs": [ { @@ -153,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "fb2ba7ee-07a2-417c-a49a-ba66a8278794", + "id": "e06126c8-0e42-4a7d-bb25-bc2b04c71a20", "metadata": {}, "source": [ "You set the objective function by calling `OptimizationProblem.minimize` or `OptimizationProblem.maximize`.\n", @@ -177,7 +177,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "6be48ff6-01b8-4999-bc93-ae402d6d79cf", + "id": "ce7544d7-4d80-4903-8f4e-8448256036d7", "metadata": {}, "outputs": [ { @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "8564f7a9-6698-4885-92fe-b3b7f4619120", + "id": "d932e5cd-6fd0-41c3-9e09-08f5d981f0c3", "metadata": {}, "source": [ "You can also specify the objective function by using arrays:\n", @@ -233,7 +233,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "06980260-774f-4acc-94cb-9faacee1d5d3", + "id": "e45ac176-fa21-4657-a95f-22d1def1d1b7", "metadata": {}, "outputs": [ { @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "c234b32c-f83f-4d84-9158-81d3f8ab3a91", + "id": "f954dfcf-f7bc-42de-a206-82920c45997e", "metadata": {}, "source": [ "You can access the constant, the linear term, the quadratic term, and the higher-order terms by looking at\n", @@ -307,7 +307,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "665c6291-9ac8-42b8-8403-3720f5815b1f", + "id": "791beecf-1f2e-4432-8805-8c40fdec35f9", "metadata": {}, "outputs": [ { @@ -412,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "914e66d9-0e18-47d1-b972-78fb3aff1248", + "id": "9ffda951-ad26-4d92-92f9-6dc26f233302", "metadata": {}, "source": [ "## Add or remove linear, quadratic, and higher-order terms to and from the constraints" @@ -420,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "284e0064-e95c-431f-8f3f-665a533263d9", + "id": "4f793e66-c20f-40d0-b53e-b6b259a44291", "metadata": {}, "source": [ "You can add linear constraints by setting name, linear expression, sense, and right-hand-side value (rhs).\n", @@ -430,7 +430,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "d58921f7-fade-4ab9-88a5-c0cf38e72445", + "id": "7e76c822-b874-4dc2-b4f7-85ce23666cb3", "metadata": {}, "outputs": [ { @@ -473,7 +473,7 @@ }, { "cell_type": "markdown", - "id": "527ccb63-35af-44e6-9585-1a6d72cddac0", + "id": "59de667a-d5e4-4044-b518-e5933465fb6f", "metadata": {}, "source": [ "You can add quadratic constraints by setting name, quadratic expression, sense, and right-hand-side value (rhs)." @@ -482,7 +482,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "21da83ad-e9ba-4eae-b659-ed507796e30f", + "id": "935f8deb-ab94-466d-a2e2-e658cda1c744", "metadata": {}, "outputs": [ { @@ -548,7 +548,7 @@ }, { "cell_type": "markdown", - "id": "c7f01573-e870-40d0-96fe-7172d165f647", + "id": "a3886db2-3a14-4180-af3d-020136344ce8", "metadata": {}, "source": [ "You can also add higher-order constraints by setting name, higher-order expression, sense, and right-hand-side value (rhs)." @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "d4cab12c-b500-4981-8c9d-635627119511", + "id": "35b9310c-2545-4734-aada-584d8db6e47a", "metadata": {}, "outputs": [ { @@ -631,7 +631,7 @@ }, { "cell_type": "markdown", - "id": "aa27116d-b1c1-45d5-8e30-709f75de29f1", + "id": "5694b1ed-55f7-4799-9456-4a3072e5b1e2", "metadata": {}, "source": [ "You can access linear and quadratic terms of linear and quadratic constraints in the same way as the objective function." @@ -640,7 +640,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "d11d8546-fc57-4a39-bbbc-50da55b6215a", + "id": "7d4d05ec-8a8c-482d-ad53-e32409816fb5", "metadata": {}, "outputs": [ { @@ -677,7 +677,7 @@ }, { "cell_type": "markdown", - "id": "ae9462fe-6cf4-499e-bce7-92beafc6225f", + "id": "3dbb362f-4b96-4b0f-8454-2d9bd7eff171", "metadata": {}, "source": [ "You can also remove linear/quadratic/higher-order constraints by using the `remove_linear_constraint`, `remove_quadratic_constraint`, and `remove_higher_order_constraint` methods, respectively." @@ -686,7 +686,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "160de065-b673-47e7-a5b0-11a4416132d5", + "id": "fe2a694f-852e-4f15-a676-7235830c8174", "metadata": {}, "outputs": [ { @@ -736,7 +736,7 @@ }, { "cell_type": "markdown", - "id": "930e6a65-7b64-4420-a348-74e6dbe8de1b", + "id": "d87f7732-6d5e-4555-b688-5c575cc6134a", "metadata": {}, "source": [ "## Substitute variables" @@ -744,7 +744,7 @@ }, { "cell_type": "markdown", - "id": "1792b9cb-54da-450c-a1aa-875ff8db49d0", + "id": "19971881-d0d9-4a37-9f4f-dd5a56a99400", "metadata": {}, "source": [ "You can substitute some of the variables with constants or other variables.\n", @@ -757,7 +757,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "83f0d5f9-5ed0-41b4-8e4a-ab476e568e8d", + "id": "a08b2b2d-364c-4f74-9a54-c94001670bd6", "metadata": {}, "outputs": [ { @@ -798,7 +798,7 @@ }, { "cell_type": "markdown", - "id": "8bf8e1b0-fa8d-4d98-a3ca-5ec146e32363", + "id": "5521704d-fcc3-4a0c-820e-1d1c99cf9937", "metadata": {}, "source": [ "If the resulting problem is infeasible due to lower bounds or upper bounds, the methods returns the status `Status.INFEASIBLE`.\n", @@ -808,7 +808,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "a2286ecf-5778-42f6-875c-6b0e28a5a7f6", + "id": "acb951c1-3517-4bf8-9ebc-149468193171", "metadata": {}, "outputs": [ { @@ -833,7 +833,7 @@ }, { "cell_type": "markdown", - "id": "dce64b51-3755-4e17-bfc4-22590d05c8ae", + "id": "71955e4c-dfb9-4a41-b871-cdef2d7c2a44", "metadata": {}, "source": [ "You cannot substitute variables multiple times.\n", @@ -843,7 +843,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "0462f970-ca86-43fd-a74c-12cbf81eff35", + "id": "d765e1c5-763b-4a61-a28c-337c7ffe6911", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how_tos/02-converters-for-optimization-problems.ipynb b/docs/addons/qiskit-addon-opt-mapper/how_tos/02-converters-for-optimization-problems.ipynb index 85c70dfa54b..bcb11b47ed9 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how_tos/02-converters-for-optimization-problems.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how_tos/02-converters-for-optimization-problems.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "df5166ad-f5d3-49af-bcc1-84d556a5589e", + "id": "0c329473-0a09-4ae5-bb85-ef49a0faad7f", "metadata": {}, "source": [ "# Convert formulations of an `OptimizationProblem`\n", @@ -48,7 +48,7 @@ }, { "cell_type": "markdown", - "id": "06977c6b-c242-4bc3-b078-947ddffd68f7", + "id": "a846cc6f-284c-4f97-b6d8-c4db3f0a91da", "metadata": {}, "source": [ "## Code examples\n", @@ -65,7 +65,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "9fde7959-9e98-45ef-b273-c720b2a9060f", + "id": "9a4fb20f-6a23-4cca-b56d-ae6309d69b17", "metadata": {}, "outputs": [ { @@ -128,7 +128,7 @@ }, { "cell_type": "markdown", - "id": "4b187df5-fcff-4e6f-8f9d-cffd79ab3f6b", + "id": "80036c42-d82d-4ca3-a685-e34b41aa022f", "metadata": {}, "source": [ "## Binary to spin conversion\n", @@ -138,7 +138,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "e87b0cc5-e10d-4875-b805-acaa09077fff", + "id": "9e94dbd3-1cec-4d1f-b52a-139c046a8c25", "metadata": {}, "outputs": [ { @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "a6ebd68b-96c0-40a4-80d3-b15147ab36a4", + "id": "c31722ed-5882-4b0b-9fe2-16c0c9857675", "metadata": {}, "source": [ "## Spin to binary conversion\n", @@ -204,7 +204,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "8c132373-c749-4754-a6c8-cd5cd1ad0ea2", + "id": "c3dda8dd-98b0-41af-8691-9d052395466b", "metadata": {}, "outputs": [ { @@ -258,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "02aa42df-1b43-4790-bbcc-64acda9d5043", + "id": "d946ee88-2252-4bdd-969e-a9154e5c25b4", "metadata": {}, "source": [ "## Inequality to equality conversion\n", @@ -272,7 +272,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "7acaf434-ad0e-4a99-97cf-a649182759fd", + "id": "0e0be0fd-f257-4366-9d65-3a5899e66580", "metadata": {}, "outputs": [ { @@ -336,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "21d099e4-3bae-4545-83d0-25b8854920b6", + "id": "92643b58-d3dd-4a1a-a315-3a33649c6219", "metadata": {}, "source": [ "## Equality to penalty conversion\n", @@ -352,7 +352,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "de9222cb-fae4-4e09-ab96-e870c8296499", + "id": "8a39358d-81ea-4af6-8ccb-7b41e7e1abf6", "metadata": {}, "outputs": [ { @@ -408,7 +408,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "da5e8a5c-ac36-4dd9-836e-92a1b218587f", + "id": "b6b7bd90-5a8b-4ca7-afac-a808282bd5b3", "metadata": {}, "outputs": [ { @@ -441,7 +441,7 @@ }, { "cell_type": "markdown", - "id": "a0991704-f37d-40b8-a1e8-a57ab08d29c8", + "id": "25a76025-6101-4185-9002-645311c02100", "metadata": {}, "source": [ "## Linear inequality to penalty conversion\n", @@ -464,7 +464,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "7af101b6-533c-45bc-a777-935338a48a0d", + "id": "362fa702-59fe-4bdd-9da0-74ba5e44afb6", "metadata": {}, "outputs": [ { @@ -520,7 +520,7 @@ }, { "cell_type": "markdown", - "id": "82ef9d9f-05ad-40a1-b44c-5d93623d652a", + "id": "86411dd9-27d5-4f4f-89c6-a84aa7c3ae41", "metadata": {}, "source": [ "## Maximize to minimize conversion\n", @@ -535,7 +535,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "250533ef-950b-49aa-bfca-30cd9f6f17af", + "id": "349388e2-2a54-428d-b468-29f40f65fafc", "metadata": {}, "outputs": [ { @@ -590,7 +590,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "fc8fc55a-2c6c-49d9-9ba1-fd936bcee824", + "id": "bbc29dad-9f31-4a04-a6b6-88ac51da2865", "metadata": {}, "outputs": [ { @@ -623,7 +623,7 @@ }, { "cell_type": "markdown", - "id": "55e7fdd0-7be4-4632-b08d-70d02a109796", + "id": "591e8c14-2ad2-4567-864f-a04530f51dec", "metadata": {}, "source": [ "## QUBO conversion\n", @@ -635,7 +635,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "6e7fbf4a-8435-4e45-996c-27fc05131a4b", + "id": "c0cf153c-7e69-496d-b0fe-da2a47d777b0", "metadata": {}, "outputs": [ { @@ -705,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "029f17d0-3b65-46c2-9628-fd2138f0e6a5", + "id": "25d6a591-8270-40d5-abba-1a0ddb31b255", "metadata": {}, "source": [ "## HUBO conversion\n", @@ -715,7 +715,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "874e045e-f5fd-4a0a-82a7-7fd57b722ca0", + "id": "26837fff-19da-444f-a9b1-8007e89f4556", "metadata": {}, "outputs": [ { @@ -779,7 +779,7 @@ }, { "cell_type": "markdown", - "id": "bf36fff5-8580-4c60-b81d-4d30eb6705e8", + "id": "7615f2f0-c516-4618-bd9c-405c47896eeb", "metadata": {}, "source": [ "## Ising transformation\n", @@ -790,7 +790,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "19fc13d9-09e0-49be-93e3-b4c4b34a334d", + "id": "909720ca-cd91-41bf-86e8-401075011934", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how_tos/04-translate-docplex.ipynb b/docs/addons/qiskit-addon-opt-mapper/how_tos/04-translate-docplex.ipynb index 1c440e6a1c0..df97470b4c7 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how_tos/04-translate-docplex.ipynb +++ b/docs/addons/qiskit-addon-opt-mapper/how_tos/04-translate-docplex.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "69cc70f8-cae3-4a0f-994c-70e7a71aaa72", + "id": "54445be0-0780-4877-9633-503da91cf288", "metadata": {}, "source": [ "# Translate between `OptimizationProblem` and Docplex\n", @@ -29,7 +29,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "39e59a1c-31f3-4eeb-b362-af47d7fb0399", + "id": "4fd054a2-382e-405d-80a7-b6a9fa601a96", "metadata": {}, "outputs": [ { @@ -80,7 +80,7 @@ }, { "cell_type": "markdown", - "id": "76f4ff25-c3f6-41c5-b0c6-155929f75e7f", + "id": "7a7f0cfa-22ab-482a-8954-88f02b374b25", "metadata": {}, "source": [ "When you display your problem in LP format using `export_as_lp_string`,\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "79f7a069-6f67-4979-9984-de1cb767309d", + "id": "2580db2a-3b28-40c6-8103-a6a743eb712e", "metadata": {}, "outputs": [ { @@ -135,7 +135,7 @@ }, { "cell_type": "markdown", - "id": "d60de99d-8aec-4753-a34d-170c14acd11e", + "id": "fd3416e9-47cc-4956-a891-11ea307837a6", "metadata": {}, "source": [ "As stated before, you can also convert a `OptimizationProblem` to a Docplex model using the `to_docplex` function." @@ -144,7 +144,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "02d71ca6-04fe-4e66-8696-40b12b31e519", + "id": "c1cc6e5b-ec69-4fac-804b-5bef4aed44b6", "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ }, { "cell_type": "markdown", - "id": "6686f79f-c928-46db-9010-3af9e5f6fd5c", + "id": "63007e1d-cfac-4870-896c-4c7214921d50", "metadata": {}, "source": [ "## Load LP files using Docplex" @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "97efbf26-a425-46fe-a36e-491f6dc5a652", + "id": "c9a20a5b-807c-4f9c-9e25-ab9fc05d1cae", "metadata": {}, "source": [ "Once you are familia with the Docplex translators, loading from LP files becomes very simple. You can use the `docplex.mp.ModelReader` class followed by a translation step:" @@ -203,7 +203,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6dcd7d89-e71e-4d76-b317-af100f4e48e5", + "id": "875f9ab1-f624-4a15-bec4-ee303ebb4ea9", "metadata": {}, "outputs": [ { diff --git a/docs/addons/qiskit-addon-opt-mapper/how_tos/index.mdx b/docs/addons/qiskit-addon-opt-mapper/how_tos/index.mdx index 3b62ad63afb..fc31188fdbe 100644 --- a/docs/addons/qiskit-addon-opt-mapper/how_tos/index.mdx +++ b/docs/addons/qiskit-addon-opt-mapper/how_tos/index.mdx @@ -1,6 +1,6 @@ --- title: "How-To Guides" -description: "How-To Guides for the lastest version of Optimization mapper" +description: "How-To Guides for the latest version of Optimization mapper" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-opt-mapper/install.mdx b/docs/addons/qiskit-addon-opt-mapper/install.mdx index a5fa3a2ad5e..88b0642e0e7 100644 --- a/docs/addons/qiskit-addon-opt-mapper/install.mdx +++ b/docs/addons/qiskit-addon-opt-mapper/install.mdx @@ -1,6 +1,6 @@ --- title: "Installation instructions" -description: "Installation instructions for the lastest version of Optimization mapper" +description: "Installation instructions for the latest version of Optimization mapper" --- # Installation instructions diff --git a/docs/addons/qiskit-addon-pna/how_tos/index.mdx b/docs/addons/qiskit-addon-pna/how_tos/index.mdx index 96d440df2e7..f71ebabd22b 100644 --- a/docs/addons/qiskit-addon-pna/how_tos/index.mdx +++ b/docs/addons/qiskit-addon-pna/how_tos/index.mdx @@ -1,6 +1,6 @@ --- title: "How-To Guides" -description: "How-To Guides for the lastest version of Propagated noise absorption (PNA)" +description: "How-To Guides for the latest version of Propagated noise absorption (PNA)" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-pna/install.mdx b/docs/addons/qiskit-addon-pna/install.mdx index 1243618fb59..8a561fcdbbc 100644 --- a/docs/addons/qiskit-addon-pna/install.mdx +++ b/docs/addons/qiskit-addon-pna/install.mdx @@ -1,6 +1,6 @@ --- title: "Installation Instructions" -description: "Installation Instructions for the lastest version of Propagated noise absorption (PNA)" +description: "Installation Instructions for the latest version of Propagated noise absorption (PNA)" --- # Installation Instructions diff --git a/docs/addons/qiskit-addon-slc/explanations/index.mdx b/docs/addons/qiskit-addon-slc/explanations/index.mdx index 581c6bd4124..a2e3b40f203 100644 --- a/docs/addons/qiskit-addon-slc/explanations/index.mdx +++ b/docs/addons/qiskit-addon-slc/explanations/index.mdx @@ -1,6 +1,6 @@ --- title: "Explanations" -description: "Explanations for the lastest version of Shaded lightcones" +description: "Explanations for the latest version of Shaded lightcones" --- # Explanations diff --git a/docs/addons/qiskit-addon-slc/how_tos/index.mdx b/docs/addons/qiskit-addon-slc/how_tos/index.mdx index feac3b47752..a4c0966d7be 100644 --- a/docs/addons/qiskit-addon-slc/how_tos/index.mdx +++ b/docs/addons/qiskit-addon-slc/how_tos/index.mdx @@ -1,6 +1,6 @@ --- title: "How-To Guides" -description: "How-To Guides for the lastest version of Shaded lightcones" +description: "How-To Guides for the latest version of Shaded lightcones" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-slc/install.mdx b/docs/addons/qiskit-addon-slc/install.mdx index d07e58abbc0..73ffb6c2e94 100644 --- a/docs/addons/qiskit-addon-slc/install.mdx +++ b/docs/addons/qiskit-addon-slc/install.mdx @@ -1,6 +1,6 @@ --- title: "Installation Instructions" -description: "Installation Instructions for the lastest version of Shaded lightcones" +description: "Installation Instructions for the latest version of Shaded lightcones" --- # Installation Instructions diff --git a/docs/addons/qiskit-addon-sqd-hpc/compilation-flags.mdx b/docs/addons/qiskit-addon-sqd-hpc/compilation-flags.mdx index 8bec25dd356..7d55e31cd8a 100644 --- a/docs/addons/qiskit-addon-sqd-hpc/compilation-flags.mdx +++ b/docs/addons/qiskit-addon-sqd-hpc/compilation-flags.mdx @@ -1,6 +1,6 @@ --- title: "Compilation flags" -description: "Compilation flags for the lastest version of SQD for HPC" +description: "Compilation flags for the latest version of SQD for HPC" --- # Compilation flags diff --git a/docs/addons/qiskit-addon-sqd/how_tos/index.mdx b/docs/addons/qiskit-addon-sqd/how_tos/index.mdx index 3c1ac25b421..661b693f3e7 100644 --- a/docs/addons/qiskit-addon-sqd/how_tos/index.mdx +++ b/docs/addons/qiskit-addon-sqd/how_tos/index.mdx @@ -1,6 +1,6 @@ --- title: "How-To Guides" -description: "How-To Guides for the lastest version of Sample-based quantum diagonalization (SQD)" +description: "How-To Guides for the latest version of Sample-based quantum diagonalization (SQD)" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-sqd/install.mdx b/docs/addons/qiskit-addon-sqd/install.mdx index 13b6f3df1ae..fc531c3f920 100644 --- a/docs/addons/qiskit-addon-sqd/install.mdx +++ b/docs/addons/qiskit-addon-sqd/install.mdx @@ -1,6 +1,6 @@ --- title: "Installation Instructions" -description: "Installation Instructions for the lastest version of Sample-based quantum diagonalization (SQD)" +description: "Installation Instructions for the latest version of Sample-based quantum diagonalization (SQD)" --- # Installation Instructions diff --git a/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.ipynb b/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.ipynb index 8eb4a1e1eaf..bb3fbddcd6c 100644 --- a/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.ipynb +++ b/docs/addons/qiskit-addon-utils/how_tos/color-device-edges-to-improve-depth.ipynb @@ -18,7 +18,7 @@ "source": [ "# Use edge coloring to reduce the depth of quantum circuits\n", "\n", - "This guide will show how to use the [qiskit_addon_utils.coloring](/docs/api/qiskit-addon-utils/coloring#module-qiskit_addon_utils.coloring) module to color edges of a coupling map and use the coloring to more efficiently place gates in a quantum circuit, leading to shallower circuits.\n", + "This guide will show how to use the [qiskit_addon_utils.coloring](/docs/api/qiskit-addon-utils/coloring) module to color edges of a coupling map and use the coloring to more efficiently place gates in a quantum circuit, leading to shallower circuits.\n", "\n", "The point of edge-coloring a graph is to find a set of edge colors such that no two edges of the same color share a common node. For a quantum processor, this means that gates along like-colored edges (qubit connections) may be run simultaneously and that the circuit will execute faster. We can use this to define our circuit in a way that is more amenable to the hardware it should run on." ] diff --git a/docs/addons/qiskit-addon-utils/how_tos/index.mdx b/docs/addons/qiskit-addon-utils/how_tos/index.mdx index 1cbddef3172..43d1f96fd27 100644 --- a/docs/addons/qiskit-addon-utils/how_tos/index.mdx +++ b/docs/addons/qiskit-addon-utils/how_tos/index.mdx @@ -1,6 +1,6 @@ --- title: "How-To Guides" -description: "How-To Guides for the lastest version of Qiskit addon utilities" +description: "How-To Guides for the latest version of Qiskit addon utilities" --- # How-To Guides diff --git a/docs/addons/qiskit-addon-utils/install.mdx b/docs/addons/qiskit-addon-utils/install.mdx index a74f772aa21..33e239378d2 100644 --- a/docs/addons/qiskit-addon-utils/install.mdx +++ b/docs/addons/qiskit-addon-utils/install.mdx @@ -1,6 +1,6 @@ --- title: "Installation Instructions" -description: "Installation Instructions for the lastest version of Qiskit addon utilities" +description: "Installation Instructions for the latest version of Qiskit addon utilities" --- # Installation Instructions diff --git a/package.json b/package.json index 1ea0b566849..55c889bb2f3 100644 --- a/package.json +++ b/package.json @@ -30,7 +30,6 @@ "check:katex-render": "tsx scripts/js/commands/checkKatexRender.ts", "check:qiskit-deprecation": "tsx scripts/js/commands/api/checkQiskitDeprecation.ts", "check:qiskit-versions": "tsx scripts/js/commands/checkQiskitApiVersions.ts", - "check:addon-tutorials": "tsx scripts/js/commands/checkAddonTutorials.ts", "regen-apis": "tsx scripts/js/commands/api/regenerateApiDocs.ts", "gen-api": "tsx scripts/js/commands/api/updateApiDocs.ts", "gen-addon": "tsx scripts/js/commands/api/updateAddonDocs.ts", diff --git a/scripts/js/commands/api/updateApiDocs.ts b/scripts/js/commands/api/updateApiDocs.ts index 499a0065231..08d350e6f73 100644 --- a/scripts/js/commands/api/updateApiDocs.ts +++ b/scripts/js/commands/api/updateApiDocs.ts @@ -15,20 +15,20 @@ import { hideBin } from "yargs/helpers"; import { Pkg } from "../../lib/api/Pkg.js"; import { zxMain } from "../../lib/zx.js"; -import { parseMinorVersion } from "../../lib/apiVersions.js"; +import { isValidVersion, parseMinorVersion } from "../../lib/apiVersions.js"; import { runApiDocsPipeline } from "../../lib/api/apiDocsPipeline.js"; import { generateHistoricalRedirects } from "./generateHistoricalRedirects.js"; import { deleteOutputDirs, prepareSphinxFolder } from "./updateDocsShared.js"; -export type Arguments = { +export interface Arguments { [x: string]: unknown; package: string; version: string; - skipDownload: boolean; - sphinxArtifactFolder?: string; historical: boolean; dev: boolean; -}; + skipDownload: boolean; + sphinxArtifactFolder?: string; +} const readArgs = (): Arguments => { return yargs(hideBin(process.argv)) @@ -44,13 +44,31 @@ const readArgs = (): Arguments => { alias: "v", type: "string", demandOption: true, - description: "The full version string, e.g. 0.44.0", + description: "The full version string of the --package, e.g. 0.44.0", + coerce: (version) => { + if (!isValidVersion(version)) { + throw new Error( + "The version must include a major, a minor, and a patch. E.g. `-v 0.46.3`", + ); + } + return version; + }, + }) + .option("historical", { + type: "boolean", + default: false, + description: "Is this a prior release?", + }) + .option("dev", { + type: "boolean", + default: false, + description: "Is this a dev release?", }) .option("skip-download", { type: "boolean", default: false, description: - "Rather than downloading the artifact from Box, reuse what is already downloaded.", + "Rather than downloading the artifact from Box, reuse what is already downloaded. This can save time, but it risks using an outdated version of the docs.", }) .option("sphinx-artifact-folder", { alias: "a", @@ -58,19 +76,9 @@ const readArgs = (): Arguments => { implies: "skip-download", normalize: true, description: - "Skip downloading the artifact from Box and instead use the given directory.", - }) - .option("historical", { - type: "boolean", - default: false, - description: "Is this a prior release?", - }) - .option("dev", { - type: "boolean", - default: false, - description: "Is this a dev release?", + "Skip downloading the artifact from Box and instead use the given directory as the root of the Sphinx HTML output.", }) - .parseSync() as unknown as Arguments; + .parseSync(); }; export async function generateVersion( diff --git a/scripts/js/commands/checkAddonTutorials.ts b/scripts/js/commands/checkAddonTutorials.ts deleted file mode 100644 index 7e60950c6f1..00000000000 --- a/scripts/js/commands/checkAddonTutorials.ts +++ /dev/null @@ -1,56 +0,0 @@ -// This code is a Qiskit project. -// -// (C) Copyright IBM 2026. -// -// This code is licensed under the Apache License, Version 2.0. You may -// obtain a copy of this license in the LICENSE file in the root directory -// of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -// -// Any modifications or derivative works of this code must retain this -// copyright notice, and modified files need to carry a notice indicating -// that they have been altered from the originals. - -// Checks that every tutorial slug referenced in Pkg.tutorials exists as a -// .ipynb file under docs/tutorials/. Run via `npm run check:addon-tutorials`. - -import { readdir } from "fs/promises"; - -import { Pkg } from "../lib/api/Pkg.js"; - -async function main(): Promise<void> { - const tutorialFiles = await readdir("docs/tutorials"); - const available = new Set( - tutorialFiles - .filter((f) => f.endsWith(".ipynb")) - .map((f) => f.replace(/\.ipynb$/, "")), - ); - - const errors: string[] = []; - - for (const name of Pkg.ADDON_NAMES) { - // Use a throwaway version — only the name matters for tutorial config. - const pkg = await Pkg.fromArgs(name, "0.1.0", "0.1", "latest"); - for (const slug of pkg.tutorials) { - if (!available.has(slug)) { - errors.push( - ` ${name}: '${slug}' not found in docs/tutorials/ (no docs/tutorials/${slug}.ipynb)`, - ); - } - } - } - - if (errors.length > 0) { - console.error( - "❌ Addon tutorial slugs reference notebooks that don't exist:\n", - ); - errors.forEach((e) => console.error(e)); - console.error( - "\nUpdate the tutorials list in Pkg.fromArgs() to match files in docs/tutorials/.", - ); - process.exit(1); - } - - console.log("✅ All addon tutorial slugs are valid."); -} - -main().then(() => process.exit()); diff --git a/scripts/js/lib/api/htmlToMd.ts b/scripts/js/lib/api/htmlToMd.ts index 6b7183422ab..9d4d5a43bdb 100644 --- a/scripts/js/lib/api/htmlToMd.ts +++ b/scripts/js/lib/api/htmlToMd.ts @@ -262,7 +262,7 @@ function buildAdmonition( { type: "mdxJsxAttribute", name: "title", - value: titleNode ? toText(titleNode) : "", + value: toText(titleNode), }, { type: "mdxJsxAttribute", diff --git a/scripts/js/lib/api/objectsInv.ts b/scripts/js/lib/api/objectsInv.ts index 284311dde7d..7cf13788272 100644 --- a/scripts/js/lib/api/objectsInv.ts +++ b/scripts/js/lib/api/objectsInv.ts @@ -55,20 +55,6 @@ function shouldIncludeEntry( if (entry.name.startsWith("group__")) return false; if (entry.name.startsWith("struct_")) return false; - // std: entries (labels, doc references) are only meaningful within the API - // reference pages themselves. Entries pointing outside apidocs/ or stubs/ - // (e.g. index, install, how_tos/, tutorials/) don't belong in the published - // inventory since no other package cross-references them via intersphinx. - if ( - entry.domainAndRole.startsWith("std:") && - !entry.uri.startsWith("apidocs/") && - !entry.uri.startsWith("stubs/") && - !entry.name.startsWith("/apidocs/") && - !entry.name.startsWith("/stubs/") - ) { - return false; - } - // This happens during link checking. if (packageLanguage === "any") return true; diff --git a/scripts/js/lib/api/pipelineStages.ts b/scripts/js/lib/api/pipelineStages.ts index ef7b4adf275..b125b755589 100644 --- a/scripts/js/lib/api/pipelineStages.ts +++ b/scripts/js/lib/api/pipelineStages.ts @@ -94,7 +94,7 @@ function extractHtmlFrontmatter(html: string, pkg: Pkg, url: string): string { const isRootIndex = url.endsWith(`/${pkg.name}/index`); const description = isRootIndex ? `Documentation for the latest version of ${pkg.title}` - : `${h1} for the lastest version of ${pkg.title}`; + : `${h1} for the latest version of ${pkg.title}`; return [`title: "${h1}"`, `description: "${description}"`].join("\n"); } @@ -204,5 +204,11 @@ export async function copyImages( [...results.flatMap((result) => result.images), ...extraImages], (image) => image.fileName, ); - await saveImages(allImages, `${artifactPath}/_images`, destFolder, pkg, artifactPath); + await saveImages( + allImages, + `${artifactPath}/_images`, + destFolder, + pkg, + artifactPath, + ); } diff --git a/scripts/js/lib/api/specialCaseResults.ts b/scripts/js/lib/api/specialCaseResults.ts index 195ce10584e..f9abd3eb559 100644 --- a/scripts/js/lib/api/specialCaseResults.ts +++ b/scripts/js/lib/api/specialCaseResults.ts @@ -16,7 +16,7 @@ export function transformSpecialCaseUrl(url: string): string { return ( url // We use `-` rather than `_` as our delimiter. - .replace(/(?<=^|\/)release_notes(?=\/|#|$)/g, "release-notes") + .replace(/(?<=^|\/)release_notes(?=#|$)/g, "release-notes") .replace(/(?<=^|\/)terra(?=#|$)/g, "index") .replace(/(?<=^|\/)ibm-runtime(?=#|$)/g, "index") .replace(/(?<=^|\/)main(?=#|$)/g, "index") diff --git a/scripts/js/lib/api/updateLinks.ts b/scripts/js/lib/api/updateLinks.ts index 28e3d2b783b..cba4371c07a 100644 --- a/scripts/js/lib/api/updateLinks.ts +++ b/scripts/js/lib/api/updateLinks.ts @@ -272,7 +272,7 @@ export async function updateLinks( }); if (relativizedLink) { - node.url = transformSpecialCaseUrl(relativizedLink.url); + node.url = relativizedLink.url; if (textNode && relativizedLink.text) { textNode.value = relativizedLink.text; } From 237f3759da4e1f1094ba5f6d35561b7e4be7c366 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Thu, 28 May 2026 17:46:37 -0400 Subject: [PATCH 100/104] updates READEME.md --- README.md | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 2bd440d0f6e..1cbc5a3a244 100644 --- a/README.md +++ b/README.md @@ -143,7 +143,7 @@ API docs authors can preview their changes to one of the APIs by using the `-a` Addon docs authors can preview guide and how-to changes by pointing the addon pipeline at a local Sphinx build: -1. Run `npm run gen-addon-docs -- -p <pkg-name> -v <version> -a <path/to/docs/_build/html>`. +1. Run `npm run gen-addon -- -p <pkg-name> --sphinx-artifact-folder <path/to/docs/_build/html>`. 2. Execute `./start` and navigate to `http://localhost:3000/docs/addons/<pkg-name>`. ## Run quality checks @@ -337,12 +337,16 @@ Addon docs (guides, how-tos, explanations, and notebook-based tutorials) for pac 2. Rename the zip to its minor version, e.g. `0.5.zip`, and upload it to the Box folder. 3. Run: ```sh - npm run gen-addon-docs -- -p <pkg-name> -v <version> + npm run gen-addon -- -p <pkg-name> ``` For example: ```sh - npm run gen-addon-docs -- -p qiskit-addon-cutting -v 0.10.0 - npm run gen-addon-docs -- -p pauli-prop -v 0.1.0 + npm run gen-addon -- -p qiskit-addon-cutting + npm run gen-addon -- -p pauli-prop + ``` + To regenerate all addon packages at once: + ```sh + npm run gen-addon -- --all ``` 4. Open a pull request with the generated changes. From 113a6ac034c3bbabba6448591ead15f93ac84345 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 29 May 2026 11:26:58 -0400 Subject: [PATCH 101/104] add qiskit-paulice --- docs/addons/qiskit-paulice/_toc.json | 36 ++++ docs/addons/qiskit-paulice/index.mdx | 89 +++++++++ docs/addons/qiskit-paulice/install.mdx | 64 +++++++ docs/api/qiskit-paulice/_package.json | 4 + docs/api/qiskit-paulice/_toc.json | 38 ++++ docs/api/qiskit-paulice/checked-circuit.mdx | 177 ++++++++++++++++++ docs/api/qiskit-paulice/checks.mdx | 67 +++++++ docs/api/qiskit-paulice/index.mdx | 14 ++ docs/api/qiskit-paulice/layout.mdx | 39 ++++ docs/api/qiskit-paulice/noise-models.mdx | 147 +++++++++++++++ public/docs/api/qiskit-paulice/objects.inv | Bin 0 -> 1535 bytes .../images/addons/qiskit-paulice/paulice.avif | Bin 0 -> 5932 bytes scripts/config/api-html-artifacts.json | 5 +- .../config/historical-pages-to-latest.json | 1 + scripts/js/lib/api/Pkg.ts | 2 +- scripts/js/lib/api/objectsInv.ts | 12 ++ scripts/js/lib/links/ignores.ts | 4 + 17 files changed, 697 insertions(+), 2 deletions(-) create mode 100644 docs/addons/qiskit-paulice/_toc.json create mode 100644 docs/addons/qiskit-paulice/index.mdx create mode 100644 docs/addons/qiskit-paulice/install.mdx create mode 100644 docs/api/qiskit-paulice/_package.json create mode 100644 docs/api/qiskit-paulice/_toc.json create mode 100644 docs/api/qiskit-paulice/checked-circuit.mdx create mode 100644 docs/api/qiskit-paulice/checks.mdx create mode 100644 docs/api/qiskit-paulice/index.mdx create mode 100644 docs/api/qiskit-paulice/layout.mdx create mode 100644 docs/api/qiskit-paulice/noise-models.mdx create mode 100644 public/docs/api/qiskit-paulice/objects.inv create mode 100644 public/docs/images/addons/qiskit-paulice/paulice.avif diff --git a/docs/addons/qiskit-paulice/_toc.json b/docs/addons/qiskit-paulice/_toc.json new file mode 100644 index 00000000000..c37eb690712 --- /dev/null +++ b/docs/addons/qiskit-paulice/_toc.json @@ -0,0 +1,36 @@ +{ + "parentUrl": "/docs/guides/addons", + "parentLabel": "Documentation", + "title": "Qiskit Paulice 0.1", + "collapsed": true, + "children": [ + { + "title": "", + "children": [ + { + "title": "Documentation Home", + "url": "/docs/addons/qiskit-paulice" + }, + { + "title": "Installation Instructions", + "url": "/docs/addons/qiskit-paulice/install" + }, + { + "title": "GitHub", + "url": "https://github.com/qiskit/qiskit-paulice" + } + ], + "collapsible": false + }, + { + "title": "API reference", + "collapsible": false, + "children": [ + { + "title": "Qiskit Paulice API reference", + "url": "/docs/api/qiskit-paulice" + } + ] + } + ] +} diff --git a/docs/addons/qiskit-paulice/index.mdx b/docs/addons/qiskit-paulice/index.mdx new file mode 100644 index 00000000000..1b44c4d6c12 --- /dev/null +++ b/docs/addons/qiskit-paulice/index.mdx @@ -0,0 +1,89 @@ +--- +title: "Qiskit addon: Paulice" +description: "Documentation for the latest version of Qiskit Paulice" +--- + +# Qiskit addon: Paulice + +## Overview + +`qiskit-paulice` is a package for embedding hardware efficient Pauli checks into arbitrary Clifford circuits on arbitrary qubit connectivities using spacetime stabilizer codes. These checks can be used to detect logical errors during circuit execution. Postselecting only samples with no detected errors can improve the fidelity of states sampled with a quantum processor at the cost of some ancilla qubits and an increase in sampling overhead. This method is particularly suited to near-term hardware since it has a much milder overhead in qubits and gates compared to fault tolerant quantum computing, while having a better sampling overhead than error mitigation methods such as ZNE or PEC <span id="id1" />[\[1\]](#id3). + +Although spacetime Pauli checks may be used to implement standalone error detection routines, they are also relevant in the context of error mitigation and error correction. Error detection can complement error mitigation techniques such as probabilistic error cancellation (PEC) by capturing some of the noise affecting gates and measurements, reducing the impact of the noise channel being inverted and thus reducing the sampling overhead. They may also be viewed as an early step toward practical fault tolerance since implementing stabilizer codes to protect data qubits from logical errors is a core concept of traditional error correction. Since this method provides single shot access to the quantum state it can be used in both sampling-based and expectation value-based workflows. + +### Finding good sets of spacetime Pauli checks + +[![Quantum circuit cartoon](/docs/images/addons/qiskit-paulice/paulice.avif)](_images/paulice-png) + +A set of spacetime Pauli checks is “good” if each check is valid, low weight, and effective. + +A check is comprised of a number of controlled Pauli rotations, $P$, placed on some wires in the circuit, $w$: $\{(P_1,w_1), ..., (P_k,w_k)\}$. For a given check, the controls lie on a single ancilla qubit, and the rotations occur on wires of a single target qubit. For Clifford circuits, a check is valid if its backpropagated product is a stabilizer of the state prepared by the ideal circuit: $\prod_{i}B(P_i,w_i) \in S$, where $B(P,w)$ is the backpropagator of $P$ from $w$ to the beginning of the circuit, and $S$ is the set of all stabilizers of the circuit. + +A check is low weight if it requires few entangling gates to implement. The check picking algorithm will favor checks that are low weight and provide the most effective error detection. + +A check is effective if it captures much more error than it introduces. A Pauli check is comprised of a number of entangling gates and thus introduces some additional gate noise into the calculation. It is important to ensure each additional check brings some additional error detection capability. The effectiveness of a set of checks can be approximated by composing the Pauli errors that are uncovered by the checks into a postselected noise channel and calculating its impact. Minimizing the sampling overhead for implementing the uncovered inverse noise channel is a solid heuristic for selecting good checks, as it gives an indication of how much error the checks can’t detect. A slower but more realistic approach is to perform a Monte Carlo sampling from the noisy state and empirically compute the logical error rate of the postselected distribution. Both of these approaches are available as built-in cost functions in the `qiskit_paulice.add_pauli_checks` function. + +### Postselecting samples based on syndrome data + +In this package a check is implemented using entangling gates between one ancilla qubit and one target qubit. Each ancilla starts in $|0\rangle$, so $Z_\text{anc}$ stabilizes its input state. Forward propagating $Z_\text{anc}$ from the beginning of the ancilla through the entire checked circuit yields a Pauli operator on the output which may be higher weight and extend into the payload circuit. The qubit indices on which this output operator has non-identity terms are called the check’s support, and the check passes if the bits, $b$, in the check’s support have even parity: $\bigoplus_{i=1} b_i = 0$. A sample is kept if each check produces $0$ for its parity check. + +## Code example + +Check out the [tutorial](tutorials/index) for an overview of how to use this package to improve the fidelity of a stabilizer state prepared from noisy QPU samples. + +## Features + +* Automatic noise model creation from backend benchmark data +* Rust accelerated check finding +* 3 built-in algorithms for check finding +* Evaluate efficacy of checks based on sampling overhead of postselected inverse noise channel or logical error rate based on Monte Carlo sampling of noisy state +* Helper functionality for finding ancilla/target qubit pairs for a given backend + +## Known issues + +* Idling noise is not provided via `NoiseModel.get_backend` and is ignored during check picking +* While many stochastic steps in the algorithm are controllable with a random seed, some features have randomness not controllable with a seed. Specifically, the following kwargs values for `add_pauli_checks` will cause undeterministic check picking: `cost="LER"`, `method="genetic"`, and `method="windowed_genetic"`. For deterministic behavior use `add_pauli_checks(..., cost="gamma", method="windowed")`, which are the default values. + +## Future work + +* Support for handling non-Clifford systems +* More support for analyzing postselected noise channel +* Handling idling noise when picking checks +* Controllable randomness for logical error rate cost function and genetic search algorithms + +## Installation + +We encourage installing this package via `pip`, when possible: + +```bash +pip install qiskit-paulice +``` + +For more installation information refer to the [installation instructions](install) in the documentation. + +## Deprecation Policy + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the release notes. + +## Contributing + +The source code is available [on GitHub](https://github.com/Qiskit/qiskit-paulice). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-paulice/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). + +We use [GitHub issues](https://github.com/Qiskit/qiskit-paulice/issues/new/choose) for tracking requests and bugs. + +## License + +[Apache License 2.0](https://github.com/Qiskit/qiskit-paulice/blob/main/LICENSE.txt) + +<span id="id2" /> + +## References + +<span id="id3" /> + +\[[1](#id1)] + +Simon Martiel, Ali Javadi-Abhari, [Low-overhead error detection with spacetime codes](https://arxiv.org/abs/2504.15725), arXiv:2504.15725 \[quant-ph]. + diff --git a/docs/addons/qiskit-paulice/install.mdx b/docs/addons/qiskit-paulice/install.mdx new file mode 100644 index 00000000000..18e727515d2 --- /dev/null +++ b/docs/addons/qiskit-paulice/install.mdx @@ -0,0 +1,64 @@ +--- +title: "Installation Instructions" +description: "Installation Instructions for the latest version of Qiskit Paulice" +--- + +# Installation Instructions + +Let’s see how to install the package. The first thing to do is ensure your Python environment is set up correctly. To create a new environment: + +## Pre-Installation + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +There are two primary ways to install this package – from PyPI or source. The preferred method is to install from PyPI: + +## Install from PyPI + +```sh +pip install qiskit-paulice +``` + +## Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +If so, the first step is to clone the `qiskit-paulice` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-paulice.git +``` + +Next, install the Rust toolchain, upgrade pip, and enter the repository. Refer to the [Rust documentation](https://www.rust-lang.org/tools/install) for instructions on installing the toolchain. + +```sh +### <INSTALL RUST HERE> ### +pip install --upgrade pip +cd pauli-prop +``` + +The next step is to install `qiskit-paulice` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab' +``` + diff --git a/docs/api/qiskit-paulice/_package.json b/docs/api/qiskit-paulice/_package.json new file mode 100644 index 00000000000..c707de5d01d --- /dev/null +++ b/docs/api/qiskit-paulice/_package.json @@ -0,0 +1,4 @@ +{ + "name": "qiskit-paulice", + "version": "0.1.0" +} diff --git a/docs/api/qiskit-paulice/_toc.json b/docs/api/qiskit-paulice/_toc.json new file mode 100644 index 00000000000..de9dd5684f5 --- /dev/null +++ b/docs/api/qiskit-paulice/_toc.json @@ -0,0 +1,38 @@ +{ + "title": "Qiskit Paulice", + "children": [ + { + "title": "API index", + "url": "/docs/api/qiskit-paulice" + }, + { + "title": "Release notes", + "useDivider": true, + "url": "/docs/api/qiskit-paulice/release-notes" + }, + { + "title": "qiskit_paulice.checked_circuit", + "url": "/docs/api/qiskit-paulice/checked-circuit", + "untranslatable": true + }, + { + "title": "qiskit_paulice.checks", + "url": "/docs/api/qiskit-paulice/checks", + "untranslatable": true + }, + { + "title": "qiskit_paulice.layout", + "url": "/docs/api/qiskit-paulice/layout", + "untranslatable": true + }, + { + "title": "qiskit_paulice.noise_models", + "url": "/docs/api/qiskit-paulice/noise-models", + "untranslatable": true + } + ], + "collapsed": true, + "untranslatable": true, + "parentUrl": "/docs/addons/qiskit-paulice", + "parentLabel": "Qiskit Paulice" +} diff --git a/docs/api/qiskit-paulice/checked-circuit.mdx b/docs/api/qiskit-paulice/checked-circuit.mdx new file mode 100644 index 00000000000..3b7701cd0f7 --- /dev/null +++ b/docs/api/qiskit-paulice/checked-circuit.mdx @@ -0,0 +1,177 @@ +--- +title: checked_circuit (latest version) +description: API reference for qiskit_paulice.checked_circuit in the latest version of qiskit-paulice +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_paulice.checked_circuit +--- + +<span id="module-qiskit_paulice.checked_circuit" /> + +<span id="checked-circuit-qiskit-paulice-checked-circuit" /> + +# Checked circuit + +`qiskit_paulice.checked_circuit` + +A class for specifying a circuit containing coherent spacetime Pauli checks. + +### CheckedCircuit + +<Class id="qiskit_paulice.checked_circuit.CheckedCircuit" github="https://github.com/Qiskit/qiskit-paulice/tree/main/qiskit_paulice/checked_circuit.py#L45-L176" signature="CheckedCircuit(circuit, target_qubits=(), check_qubits=(), check_support=(), cost=None, cost_metric=None)" modifiers="class"> + Bases: [`object`](https://docs.python.org/3/library/functions.html#object) + + A quantum circuit and information about spacetime Pauli checks it contains. + + **Parameters** + + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) + * **target\_qubits** ([*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*, ...]*) + * **check\_qubits** ([*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*, ...]*) + * **check\_support** ([*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*, ...], ...]*) + * **cost** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) + * **cost\_metric** ([*str*](https://docs.python.org/3/library/stdtypes.html#str) *| None*) + + #### circuit + + <Attribute id="qiskit_paulice.checked_circuit.CheckedCircuit.circuit"> + A quantum circuit containing `0` or more spacetime Pauli checks. + + **Type** + + [qiskit.circuit.quantumcircuit.QuantumCircuit](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) + </Attribute> + + #### target\_qubits + + <Attribute id="qiskit_paulice.checked_circuit.CheckedCircuit.target_qubits"> + Qubit indices of `circuit` which were used to entangle the check qubits to the payload. `None` if `circuit` contains no checks. + + **Type** + + [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[int](https://docs.python.org/3/library/functions.html#int), …] + </Attribute> + + #### check\_qubits + + <Attribute id="qiskit_paulice.checked_circuit.CheckedCircuit.check_qubits"> + Qubit indices of the ancilla qubits in `circuit`. The `i``th check uses ``check_qubits[i]` to detect errors on `target_qubits[i]` and other qubits in `check_support[i]`. + + **Type** + + [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[int](https://docs.python.org/3/library/functions.html#int), …] + </Attribute> + + #### check\_support + + <Attribute id="qiskit_paulice.checked_circuit.CheckedCircuit.check_support"> + For each check, the qubit indices whose measurement outcomes XOR together to give that check’s syndrome bit. + + **Type** + + [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[tuple](https://docs.python.org/3/library/stdtypes.html#tuple)\[[int](https://docs.python.org/3/library/functions.html#int), …], …] + </Attribute> + + #### cost + + <Attribute id="qiskit_paulice.checked_circuit.CheckedCircuit.cost"> + The value of the cost function with respect to the checks in `circuit` + + **Type** + + [float](https://docs.python.org/3/library/functions.html#float) | None + </Attribute> + + #### cost\_metric + + <Attribute id="qiskit_paulice.checked_circuit.CheckedCircuit.cost_metric"> + The metric used to evaluate check quality (`gamma` or `LER`) + + **Type** + + [str](https://docs.python.org/3/library/stdtypes.html#str) | None + </Attribute> + + #### get\_postselection\_method + + <Function id="qiskit_paulice.checked_circuit.CheckedCircuit.get_postselection_method" github="https://github.com/Qiskit/qiskit-paulice/tree/main/qiskit_paulice/checked_circuit.py#L139-L176" signature="get_postselection_method()"> + Return a function that maps a single shot’s outcome to a syndrome vector. + + No errors were detected iff every entry of the returned vector is zero. The returned function accepts either bitstrings or bit arrays. + + **Return type** + + [*Callable*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable)\[\[[str](https://docs.python.org/3/library/stdtypes.html#str) | [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)], [*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)] + </Function> + + #### uncovered\_paulis + + <Attribute id="qiskit_paulice.checked_circuit.CheckedCircuit.uncovered_paulis" attributeTypeHint="tuple[UncoveredPauli, ...]"> + Locations where a single qubit Pauli error is undetectable by some checks. + + Each entry is an `UncoveredPauli(qubit, after_instruction, pauli)` triple, where `qubit` is the qubit of the single-qubit error, `after_instruction` is the `circuit.data` index of the instruction which immediately precedes the error, and `pauli` is the type of error (`"X"`, `"Y"`, or `"Z"`). + + Only locations on input wires and immediately after 2-qubit gates are enumerated; errors after single qubit gates are folded into the next 2-qubit-gate wire. + </Attribute> +</Class> + +### UncoveredPauli + +<Class id="qiskit_paulice.checked_circuit.UncoveredPauli" github="https://github.com/Qiskit/qiskit-paulice/tree/main/qiskit_paulice/checked_circuit.py#L30-L42" signature="UncoveredPauli(qubit, after_instruction, pauli)" modifiers="class"> + Bases: [`NamedTuple`](https://docs.python.org/3/library/typing.html#typing.NamedTuple) + + A spacetime location at which a single qubit Pauli error is undetectable by a set of checks. + + **Parameters** + + * **qubit** ([*int*](https://docs.python.org/3/library/functions.html#int)) + * **after\_instruction** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) + * **pauli** ([*Literal*](https://docs.python.org/3/library/typing.html#typing.Literal)*\['X', 'Y', 'Z']*) + + #### qubit + + <Attribute id="qiskit_paulice.checked_circuit.UncoveredPauli.qubit"> + Index of the qubit where the undetected error sits + + **Type** + + [int](https://docs.python.org/3/library/functions.html#int) + </Attribute> + + #### after\_instruction + + <Attribute id="qiskit_paulice.checked_circuit.UncoveredPauli.after_instruction"> + Index (into `circuit.data`) of the instruction the error occurs after; `None` means the error sits on the qubit’s input wire. + + **Type** + + [int](https://docs.python.org/3/library/functions.html#int) | None + </Attribute> + + #### pauli + + <Attribute id="qiskit_paulice.checked_circuit.UncoveredPauli.pauli"> + The undetected Pauli error (`"X"`, `"Y"`, or `"Z"`) + + **Type** + + Literal\[‘X’, ‘Y’, ‘Z’] + </Attribute> + + Create new instance of UncoveredPauli(qubit, after\_instruction, pauli) + + #### count + + <Function id="qiskit_paulice.checked_circuit.UncoveredPauli.count" signature="count(value, /)"> + Return number of occurrences of value. + </Function> + + #### index + + <Function id="qiskit_paulice.checked_circuit.UncoveredPauli.index" signature="index(value, start=0, stop=9223372036854775807, /)"> + Return first index of value. + + Raises ValueError if the value is not present. + </Function> +</Class> + diff --git a/docs/api/qiskit-paulice/checks.mdx b/docs/api/qiskit-paulice/checks.mdx new file mode 100644 index 00000000000..f375a8e75dc --- /dev/null +++ b/docs/api/qiskit-paulice/checks.mdx @@ -0,0 +1,67 @@ +--- +title: checks (latest version) +description: API reference for qiskit_paulice.checks in the latest version of qiskit-paulice +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_paulice.checks +--- + +<span id="module-qiskit_paulice.checks" /> + +<span id="checks-qiskit-paulice-checks" /> + +# Checks + +`qiskit_paulice.checks` + +Functionality for finding effective spacetime Pauli checks. + +### add\_pauli\_checks + +<Function id="qiskit_paulice.checks.add_pauli_checks" github="https://github.com/Qiskit/qiskit-paulice/tree/main/qiskit_paulice/checks.py#L31-L320" signature="add_pauli_checks(circuit, target_qubits, noise_model, cost='gamma', cost_nshots=10000, method='windowed', ancilla_qubits=None, check_creg_name='checks_c', check_qreg_name='checks_q', seed=None)"> + Add spacetime Pauli checks to a Clifford circuit. + + The check picking algorithm finds valid, low weight checks on each target qubit in the order they are specified in `target_qubits` and chooses the check which provides the most error detection coverage (i.e. minimizes the `cost` value). Once a check is committed on `target_qubits[0]`, it will be set for the remainder of the algorithm and a search for a good check on `target_qubits[1]` will begin. For this reason, the ordering of `target_qubits` can have some impact on the set of checks produced by the function. + + This function produces `qiskit_paulice.CheckedCircuit` instances containing numbers of checks ranging from `0` to one check per target qubit. It can be instructive to view the convergence of the cost function as more checks are added, as one may see convergence of the cost using fewer checks. + + For details on finding effective spacetime Pauli checks, see [Supplemental Sec. II-VI of Martiel, Javadi](https://arxiv.org/abs/2504.15725). + + **Parameters** + + * **circuit** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit)) – The Clifford circuit to dress with spacetime Pauli checks. The circuit must be terminated with a measurement on at least one qubit. The circuit may be defined on virtual or physical qubits. If the circuit has a layout, the user must provide `ancilla_qubits`. + + * **target\_qubits** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – Qubit indices of `circuit` which will be used to entangle the check qubits to the payload. When `circuit` has a layout (ISA mode), these are physical qubit indices, in the same index space as `ancilla_qubits`. + + * **noise\_model** ([*NoiseModel*](noise-models#noisemodel "qiskit_paulice.noise_models.NoiseModel")) – A noise model describing the effect of noise on the target device. This model will be used to estimate the effect of a given check during the check picking process. While one can generate a noise model from learned Pauli-Lindblad noise, a rougher approximation of the noise generated from backend benchmark data is often sufficient. Ancilla/target edges introduced by check insertion that aren’t in the supplied `GateWiseNoise` or `LayeredNoise` are auto-inferred (median rate per Pauli pair across the supplied data); supply them explicitly to override. + + * **cost** ([*Literal*](https://docs.python.org/3/library/typing.html#typing.Literal)*\['gamma', 'LER']*) – + + Metric to optimize. Can be `"gamma"` or `"LER"` (logical error rate). + + * `"gamma"`: The gamma value associated with the inverse logical noise channel (i.e. the noise channel consisting of errors within the measurement lightcone that are undetectable by the checks). + * `"LER"`: The empirical logical error rate after postselection. Performs Monte Carlo simulations to calculate the fraction of postselected shots affected by uncovered logical noise. + + * **cost\_nshots** ([*int*](https://docs.python.org/3/library/functions.html#int)) – Number of Monte Carlo shots used by Monte Carlo-based cost metrics (currently only `"LER"`). + + * **method** ([*Literal*](https://docs.python.org/3/library/typing.html#typing.Literal)*\['windowed', 'genetic', 'windowed\_genetic']*) – + + Check picking method (`"windowed"`, `"genetic"`, or `"windowed_genetic"`). Each method will add checks to `target_qubits` sequentially. Once a check is committed to a given target qubit, it will not be undone as more checks are added. Each method picks checks which provide maximum error detection capability (i.e. lowest `cost`). + + * `"windowed"`: Sample small subsets of the wires on each target qubit to find good checks + * `"genetic"`: Evolve a set of candidate checks from the full wire-space of each target qubit + * `"windowed_genetic"`: Run a genetic search within random windows of wires on each target qubit + + * **ancilla\_qubits** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*] | None*) – List of physical qubit indices (one per index in `target_qubits`) specifying where to place the check ancillas in the output circuit. Required when `circuit.layout` is not `None`. `ancilla_qubits[i]` will share entangling gates with `target_qubits[i]` when implementing the `i`\ th check. + + * **check\_creg\_name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – Name of the classical register for check measurements (default: “checks\_c”) + + * **check\_qreg\_name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – Name of the quantum register holding the check ancillas in the output circuits (default: “checks\_q”). Ignored in ISA mode. + + * **seed** ([*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – Random seed for controlling randomness during the search for good checks. While this seed controls some randomness in the algorithm, some non-determinism still exists when using `LER` cost function, or either variety of genetic check picking method. The combination of `cost="gamma"` and `method="windowed"` is fully deterministic if `seed` is not `None`. + + **Returns** + + A list of `qiskit_paulice.CheckedCircuit` instances – instances containing the bare circuit with no checks and one for each added check. The final element in the output contains the `qiskit_paulice.CheckedCircuit` with checks on every target qubit, assuming a valid set of checks could be found. +</Function> + diff --git a/docs/api/qiskit-paulice/index.mdx b/docs/api/qiskit-paulice/index.mdx new file mode 100644 index 00000000000..b38af9fb2a7 --- /dev/null +++ b/docs/api/qiskit-paulice/index.mdx @@ -0,0 +1,14 @@ +--- +title: Qiskit Paulice API documentation (latest version) +description: Index of all the modules in the latest version of qiskit-paulice. +--- + +<span id="qiskit-paulice-api-reference" /> + +# `qiskit-paulice` API reference + +* [Checks (`qiskit_paulice.checks`)](checks) +* [Checked circuit (`qiskit_paulice.checked_circuit`)](checked-circuit) +* [Noise models (`qiskit_paulice.noise_models`)](noise-models) +* [Layout (`qiskit_paulice.layout`)](layout) + diff --git a/docs/api/qiskit-paulice/layout.mdx b/docs/api/qiskit-paulice/layout.mdx new file mode 100644 index 00000000000..0cc38cfb1d4 --- /dev/null +++ b/docs/api/qiskit-paulice/layout.mdx @@ -0,0 +1,39 @@ +--- +title: layout (latest version) +description: API reference for qiskit_paulice.layout in the latest version of qiskit-paulice +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_paulice.layout +--- + +<span id="module-qiskit_paulice.layout" /> + +<span id="layout-qiskit-paulice-layout" /> + +# Layout + +`qiskit_paulice.layout` + +Functionality for finding layouts for checks dressed with spacetime Pauli checks. + +### get\_low\_overhead\_ancillas + +<Function id="qiskit_paulice.layout.get_low_overhead_ancillas" github="https://github.com/Qiskit/qiskit-paulice/tree/main/qiskit_paulice/layout.py#L22-L48" signature="get_low_overhead_ancillas(coupling_map, layout)"> + Find ancilla qubits adjacent to the layout qubits in the coupling graph. + + This function identifies physical qubits that are not in the layout but are connected to one or more layout qubits via the coupling map. + + **Parameters** + + * **coupling\_map** ([*CouplingMap*](/docs/api/qiskit/qiskit.transpiler.CouplingMap)) – A qubit connectivity graph + * **layout** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – Physical qubit indices for which to find adjacent ancillas + + **Returns** + + A dictionary mapping ancilla qubit indices to lists of layout qubit indices to which it is adjacent. + + **Return type** + + [dict](https://docs.python.org/3/library/stdtypes.html#dict)\[[int](https://docs.python.org/3/library/functions.html#int), [list](https://docs.python.org/3/library/stdtypes.html#list)\[[int](https://docs.python.org/3/library/functions.html#int)]] +</Function> + diff --git a/docs/api/qiskit-paulice/noise-models.mdx b/docs/api/qiskit-paulice/noise-models.mdx new file mode 100644 index 00000000000..cc2a0386d58 --- /dev/null +++ b/docs/api/qiskit-paulice/noise-models.mdx @@ -0,0 +1,147 @@ +--- +title: noise_models (latest version) +description: API reference for qiskit_paulice.noise_models in the latest version of qiskit-paulice +in_page_toc_min_heading_level: 2 +python_api_type: module +python_api_name: qiskit_paulice.noise_models +--- + +<span id="module-qiskit_paulice.noise_models" /> + +<span id="noise-models-qiskit-paulice-noise-models" /> + +# Noise models + +`qiskit_paulice.noise_models` + +Noise model specification for evaluating spacetime Pauli checks. + +### NoiseModel + +<Class id="qiskit_paulice.noise_models.NoiseModel" github="https://github.com/Qiskit/qiskit-paulice/tree/main/qiskit_paulice/noise_models.py#L59-L292" signature="NoiseModel(gate_noise=None, readout_noise=None, idling_noise=None)" modifiers="class"> + Bases: [`object`](https://docs.python.org/3/library/functions.html#object) + + Noise model used to find optimal circuit locations for spacetime Pauli checks. + + **Parameters** + + * **gate\_noise** (*GateNoise | None*) + * **readout\_noise** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) + * **idling\_noise** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) + + #### from\_backend + + <Function id="qiskit_paulice.noise_models.NoiseModel.from_backend" github="https://github.com/Qiskit/qiskit-paulice/tree/main/qiskit_paulice/noise_models.py#L78-L198" signature="from_backend(backend, layout, uniform_gate_noise=False, pauli_bases=None)" modifiers="classmethod"> + Instantiate a [`NoiseModel`](#qiskit_paulice.noise_models.NoiseModel "qiskit_paulice.noise_models.NoiseModel") from backend calibration data. + + Edge keys in the resulting `GateWiseNoise` dict use **virtual** qubit indices (positions in `layout`). If the input circuit has a layout that maps virtual to physical consistently with `layout`, the keys also serve as physical indices. + + `idling_noise` is always `None` from this method; if `backend` lacks readout calibration, `readout_noise` will also be `None`. + + **Parameters** + + * **backend** ([*BackendV2*](/docs/api/qiskit/qiskit.providers.BackendV2)) – A backend containing two-qubit gate error probabilities for each coupling map edge as well as readout error information for each qubit in the layout. + * **layout** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – Physical qubit indices on the backend to include in the noise model. The order defines the virtual qubit indexing (virtual qubit 0 maps to `layout[0]`, etc.). + * **uniform\_gate\_noise** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – If `True`, the `gate_noise` field in the output [`NoiseModel`](#qiskit_paulice.noise_models.NoiseModel "qiskit_paulice.noise_models.NoiseModel") will be a `UniformGateNoise` and the error probability is assumed to be distributed uniformly among the `15` non-identity Pauli bases to form a uniform depolarizing channel which will affect all entangling gates equally. If `False`, `gate_noise` will be a `GateWiseNoise` instance where each edge is associated with a custom noise channel based on backend calibration data. + * **pauli\_bases** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*str*](https://docs.python.org/3/library/stdtypes.html#str)*] | None*) – For `GateWiseNoise` models, `pauli_bases` are the bases over which the error probability reported from the backend will be distributed. Each basis is a 2-character Pauli string paired left-to-right with the edge tuple, so `"XZ"` on edge `(a, b)` places `X` on `a` and `Z` on `b`. The default behavior is to use the full 2Q Pauli basis excluding `"II"`. For `UniformNoise`, the full basis is assumed, and this argument is ignored. + + **Returns** + + A [`NoiseModel`](#qiskit_paulice.noise_models.NoiseModel "qiskit_paulice.noise_models.NoiseModel") instance containing gate and readout noise derived from backend calibration data. + + **Raises** + + [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – Backend is missing calibration data or layout contains invalid qubit indices. + + **Return type** + + [*NoiseModel*](#qiskit_paulice.noise_models.NoiseModel "qiskit_paulice.noise_models.NoiseModel") + </Function> + + #### from\_pauli\_lindblad\_maps + + <Function id="qiskit_paulice.noise_models.NoiseModel.from_pauli_lindblad_maps" github="https://github.com/Qiskit/qiskit-paulice/tree/main/qiskit_paulice/noise_models.py#L200-L292" signature="from_pauli_lindblad_maps(layer_noise, readout_noise=None)" modifiers="classmethod"> + Create a NoiseModel from Pauli-Lindblad maps. + + This method constructs a [`NoiseModel`](#qiskit_paulice.noise_models.NoiseModel "qiskit_paulice.noise_models.NoiseModel") from `PauliLindbladMap` instances that represent noise channels affecting gates and, optionally, measurements. Gate noise is specified with the `layer_noise` argument. Each `PauliLindbladMap` instance is assumed to act on a unique layer of entangling gates. Readout noise is specified by a single `PauliLindbladMap` containing single-qubit Pauli-X generators and their associated rates. + + **Parameters** + + * **layer\_noise** ([*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)*]*) – A sequence of `PauliLindbladMap` objects, where each map represents the noise channel for one unique entangling layer in the circuit. + * **readout\_noise** ([*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap) *| None*) – Optional `PauliLindbladMap` containing Pauli X generators on each qubit for readout errors. + + **Returns** + + A [`NoiseModel`](#qiskit_paulice.noise_models.NoiseModel "qiskit_paulice.noise_models.NoiseModel") instance reflecting the noise model(s) defined in the input `PauliLindbladMap``s. ``idling_noise` will always be `None` for this method. + + **Raises** + + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – Weight of error generator greater than `2`. + * [**ValueError**](https://docs.python.org/3/library/exceptions.html#ValueError) – Readout error generator is not Pauli-X. + + **Return type** + + [*NoiseModel*](#qiskit_paulice.noise_models.NoiseModel "qiskit_paulice.noise_models.NoiseModel") + </Function> + + #### gate\_noise + + <Attribute id="qiskit_paulice.noise_models.NoiseModel.gate_noise" attributeTypeHint="GateNoise | None" attributeValue="None"> + Errors that occur during 2-qubit gate operations. Can be: + + * `UniformNoise`: Same error probability for all gates. The error probability is equally distributed to each Pauli basis represented in the channel. + * `LayeredNoise`: Pauli-Lindblad noise channel per unique entangling layer + * `GateWiseNoise`: Pauli-Lindblad noise channel per unique entangling edge + </Attribute> + + #### idling\_noise + + <Attribute id="qiskit_paulice.noise_models.NoiseModel.idling_noise" attributeTypeHint="float | None" attributeValue="None"> + Qubit decay rate during idle time. Total error probability is given as `1 - exp(-t / idling_noise)`. + </Attribute> + + #### readout\_noise + + <Attribute id="qiskit_paulice.noise_models.NoiseModel.readout_noise" attributeTypeHint="float | None" attributeValue="None"> + Probability of bit-flip during measurement. + </Attribute> +</Class> + +### UniformGateNoise + +<Attribute id="qiskit_paulice.noise_models.UniformGateNoise" attributeValue="<class 'float'>"> + A depolarizing probability applied uniformly to all 2-qubit gates. + + This probability will be equally distributed among the `15` non-identity Paulis in the 2-qubit Pauli basis to form the depolarizing channel. This noise channel will be applied **after** each entangling gate. +</Attribute> + +### LayeredGateNoise + +<Attribute id="qiskit_paulice.noise_models.LayeredGateNoise"> + Layered noise model mapping unique entangling layers to Pauli error generators. + + <Admonition title="Note" type="note"> + The Pauli-Lindblad error associated with each entangling layer is assumed to be defined **before** the gates in the layer. + </Admonition> + + Keys are tuples of qubit index pairs (edges) defining a layer. Values are lists of `(pauli, rate)` tuples where `pauli` is a Pauli or Pauli string defined over all qubits and `rate` is the associated error rate. + + alias of [`dict`](https://docs.python.org/3/library/stdtypes.html#dict)\[[`tuple`](https://docs.python.org/3/library/stdtypes.html#tuple)\[[`tuple`](https://docs.python.org/3/library/stdtypes.html#tuple)\[[`int`](https://docs.python.org/3/library/functions.html#int), [`int`](https://docs.python.org/3/library/functions.html#int)], …], [`list`](https://docs.python.org/3/library/stdtypes.html#list)\[[`tuple`](https://docs.python.org/3/library/stdtypes.html#tuple)\[[`Pauli`](/docs/api/qiskit/qiskit.quantum_info.Pauli) | [`str`](https://docs.python.org/3/library/stdtypes.html#str), [`float`](https://docs.python.org/3/library/functions.html#float)]]] +</Attribute> + +### GateWiseNoise + +<Attribute id="qiskit_paulice.noise_models.GateWiseNoise"> + Gate-wise noise model mapping qubit pairs to a Pauli noise channel. + + Keys are tuples of qubit index pairs representing edges. Values are lists of (pauli, rate) tuples where `pauli` is a 2-character Pauli string and `rate` is the associated error rate. The string is paired left-to-right with the edge tuple — matching [`PauliLindbladMap`](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)’s sparse form `(pauli_str, indices)`: for edge `(a, b)`, `"XZ"` places `X` on `a` and `Z` on `b`. + + alias of [`dict`](https://docs.python.org/3/library/stdtypes.html#dict)\[[`tuple`](https://docs.python.org/3/library/stdtypes.html#tuple)\[[`int`](https://docs.python.org/3/library/functions.html#int), [`int`](https://docs.python.org/3/library/functions.html#int)], [`list`](https://docs.python.org/3/library/stdtypes.html#list)\[[`tuple`](https://docs.python.org/3/library/stdtypes.html#tuple)\[[`str`](https://docs.python.org/3/library/stdtypes.html#str), [`float`](https://docs.python.org/3/library/functions.html#float)]]] +</Attribute> + +### GateNoise + +<Attribute id="qiskit_paulice.noise_models.GateNoise" attributeValue="float | dict[tuple[tuple[int, int], ...], list[tuple[qiskit.quantum_info.operators.symplectic.pauli.Pauli | str, float]]] | 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z(~vYXpie|uH?2EyH2?TDpCFATnDa63Q2refez$fm!%es>=~y|xZ$Y`mwVEtH^I-53 z)&;PJoV$||=O1a7c5uAAa!*)vzqwWF35`Zg)SSOF_2M^|_;7?4J6nygl}Lf1$(gi} zNI}ML@ywK`WmlA$tx<QrGP0Lw5w#m4K2g%uq%xj;K%Y;FQ+$X1U5sU1KO@4g>Qrtg z&x7$~CQ^)W##t>Vzq+8ntvv3-3R@(Xn_0sF9JX-6{Iet>4dhfdhIc;WBA9%Ls$F5; z)~-J0@;yQstkNfk_ZF*J9I~`32j(OXuzwDpq^rMyfD^7bwWlP%iOWMO?a~kds2Ul@ r-$JmuI>3&yumH4hD1eS4R9y_9Ks~6sS}y1x1+zI9{14w&_<;Wcq^m8d literal 0 HcmV?d00001 diff --git a/scripts/config/api-html-artifacts.json b/scripts/config/api-html-artifacts.json index 696dc7a48a1..8f644bc7dc5 100644 --- a/scripts/config/api-html-artifacts.json +++ b/scripts/config/api-html-artifacts.json @@ -140,7 +140,10 @@ "qiskit-fermions": { "0.0": "https://ibm.box.com/shared/static/xt6apdhkq6f653w6ck0247etidll0osz.zip" }, + "qiskit-paulice": { + "0.1": "https://ibm.box.com/shared/static/zo45lb3417dnh3kjf71ph6ummu7lztqb.zip" + }, "pauli-prop": { - "0.2": "https://ibm.box.com/shared/static/lfl06p9hvlo4890o0lx2dheiebf82msv.zip" + "0.2": "https://ibm.box.com/shared/static/lfl06p9hvlo4890o0lx2dheiebf82msv.zip" } } diff --git a/scripts/config/historical-pages-to-latest.json b/scripts/config/historical-pages-to-latest.json index 0e0ee23629d..212d4b60d58 100644 --- a/scripts/config/historical-pages-to-latest.json +++ b/scripts/config/historical-pages-to-latest.json @@ -1515,5 +1515,6 @@ "qiskit-addon-pna": {}, "qiskit-addon-slc": {}, "qiskit-addon-opt-mapper": {}, + "qiskit-paulice": {}, "pauli-prop": {} } diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index 8bc3526bf3b..7fdfcae01f2 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -73,7 +73,7 @@ export class Pkg { "qiskit-addon-slc", "qiskit-addon-opt-mapper", // "qiskit-fermions", - // "qiskit-paulice", + "qiskit-paulice", "pauli-prop", ]; diff --git a/scripts/js/lib/api/objectsInv.ts b/scripts/js/lib/api/objectsInv.ts index 7cf13788272..7a842759f8d 100644 --- a/scripts/js/lib/api/objectsInv.ts +++ b/scripts/js/lib/api/objectsInv.ts @@ -55,6 +55,18 @@ function shouldIncludeEntry( if (entry.name.startsWith("group__")) return false; if (entry.name.startsWith("struct_")) return false; + // std: entries are RST labels that don't correspond to published pages + // unless they point into apidocs/ or stubs/. + if ( + entry.domainAndRole.startsWith("std:") && + !entry.uri.startsWith("apidocs/") && + !entry.uri.startsWith("stubs/") && + !entry.name.startsWith("/apidocs/") && + !entry.name.startsWith("/stubs/") + ) { + return false; + } + // This happens during link checking. if (packageLanguage === "any") return true; diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index f604ba74a52..d2f1d7709d8 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -623,6 +623,10 @@ function _addonContentLinksToFix(): FilesToIgnores { "docs/addons/qiskit-addon-opt-mapper/how_tos/index.mdx": [ "01-optimization-problem#add-or-remove-linear,-quadratic,-and-higher-order-terms-to-and-from-the-constraints", ], + "docs/addons/qiskit-paulice/index.mdx": [ + "tutorials/index", + "_images/paulice-png", + ], }; } From 3e13cc411c601e6cdaa16ebff930953958eabce7 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 29 May 2026 11:40:52 -0400 Subject: [PATCH 102/104] rem release notes --- docs/api/qiskit-paulice/_toc.json | 5 ----- docs/guides/addons.mdx | 2 +- public/docs/api/qiskit-paulice/objects.inv | Bin 1535 -> 1146 bytes scripts/js/lib/api/Pkg.ts | 1 + 4 files changed, 2 insertions(+), 6 deletions(-) diff --git a/docs/api/qiskit-paulice/_toc.json b/docs/api/qiskit-paulice/_toc.json index de9dd5684f5..e3e8d055557 100644 --- a/docs/api/qiskit-paulice/_toc.json +++ b/docs/api/qiskit-paulice/_toc.json @@ -5,11 +5,6 @@ "title": "API index", "url": "/docs/api/qiskit-paulice" }, - { - "title": "Release notes", - "useDivider": true, - "url": "/docs/api/qiskit-paulice/release-notes" - }, { "title": "qiskit_paulice.checked_circuit", "url": "/docs/api/qiskit-paulice/checked-circuit", diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index f4bbfc27b79..334222b0022 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -143,7 +143,7 @@ Use these capabilities to support and compose your workflows that leverage other <Card title="Pauli propagation" description="A framework to approximate operator evolution, which can be used to simulate expectation values and implement error mitigation techniques, such as propagated noise absorption (PNA) and shaded lightcones." - href="https://qiskit.github.io/pauli-prop/" + href="/docs/addons/pauli-prop/" analyticsName="Documentation page: pauli propagation" linkText="Browse documentation" /> diff --git a/public/docs/api/qiskit-paulice/objects.inv b/public/docs/api/qiskit-paulice/objects.inv index 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zGDdc+d+r~+K*}&>JGkQ<LrIi`d<$iZYNF_#*vl6f`|$3$3&Ni*7HBNcSira7h_%B8 zhVODmVW&mIPcGi03Y_X^^9=N}%YQ)e&#_4T`k&!K$xEr72h=nubUOM-xBkT`xFqk4 ze=<m=?Bc`X)&H&wz!qE*J&~D=&e@^r)&9Cs`h)sAC7-ddW8Gi<gB$2LmTU*5SppJn zkR%v#2IYZa!S@83OU4L!QT*BkgB}bF80dcQ!9as=!b?W8+K;z`<Clv*Zhv&EFoPJ| zU`&NK%BIjeQ__naW(L&%&9FVRG|Co%_547;0qev1CBZ9sC&DI2n0uhd{jYf!Ww-l{ zW4p;@jtD%nms&w!YK_@60ctG>^j4d90c}VyuQPA2^SmOL^x~W2qyrLUnsii>hkBPT o&m_qB9&kzL9=1ouD!wj<)f4BzrxxUKjHBD`7iZ7^9~026T&Migpa1{> diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index 7fdfcae01f2..cec90724be8 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -300,6 +300,7 @@ export class Pkg { githubSlug: "Qiskit/qiskit-paulice", kebabCaseAndShortenUrls: true, language: "Python", + releaseNotesConfig: new ReleaseNotesConfig({ enabled: false }), }); } if (name === "pauli-prop") { From 8ed77e93d6e3170e5f1c4e7b0441028ada98a5aa Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 29 May 2026 12:04:23 -0400 Subject: [PATCH 103/104] rem fermions --- scripts/js/commands/checkInternalLinks.ts | 6 ------ scripts/js/lib/api/Pkg.ts | 18 +++++++++--------- 2 files changed, 9 insertions(+), 15 deletions(-) diff --git a/scripts/js/commands/checkInternalLinks.ts b/scripts/js/commands/checkInternalLinks.ts index 9527c6ffa34..6e29f351e20 100644 --- a/scripts/js/commands/checkInternalLinks.ts +++ b/scripts/js/commands/checkInternalLinks.ts @@ -216,11 +216,6 @@ async function determineFileBatches(args: Arguments): Promise<FileBatch[]> { ADDON_GLOBS_TO_LOAD, { check: args.historicalApis }, ); - const fermions = await determineHistoricalFileBatches( - "qiskit-fermions", - ADDON_GLOBS_TO_LOAD, - { check: args.historicalApis }, - ); const paulice = await determineHistoricalFileBatches( "qiskit-paulice", ADDON_GLOBS_TO_LOAD, @@ -244,7 +239,6 @@ async function determineFileBatches(args: Arguments): Promise<FileBatch[]> { ...pna, ...slc, ...optMapper, - ...fermions, ...paulice, ...pauliProp, ...runtime, diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index cec90724be8..283ec2ce0ed 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -284,15 +284,15 @@ export class Pkg { language: "Python", }); } - if (name === "qiskit-fermions") { - return new Pkg({ - ...args, - title: "Fermionic mapper", - githubSlug: "Qiskit/qiskit-fermions", - kebabCaseAndShortenUrls: true, - language: "Python", - }); - } + // if (name === "qiskit-fermions") { + // return new Pkg({ + // ...args, + // title: "Fermionic mapper", + // githubSlug: "Qiskit/qiskit-fermions", + // kebabCaseAndShortenUrls: true, + // language: "Python", + // }); + // } if (name === "qiskit-paulice") { return new Pkg({ ...args, From 9f2212d46b6524ffca57aa36ef99bdb83ace1092 Mon Sep 17 00:00:00 2001 From: Eric Harvey <Eric.Harvey@ibm.com> Date: Fri, 29 May 2026 13:00:38 -0400 Subject: [PATCH 104/104] rem sqd-hpc --- docs/addons/qiskit-addon-sqd-hpc/_toc.json | 32 ----- .../compilation-flags.mdx | 38 ------ docs/addons/qiskit-addon-sqd-hpc/index.mdx | 39 ------ docs/api/qiskit-addon-sqd-hpc/_package.json | 4 - docs/api/qiskit-addon-sqd-hpc/_toc.json | 38 ------ .../configuration-recovery.mdx | 38 ------ docs/api/qiskit-addon-sqd-hpc/fermion.mdx | 32 ----- docs/api/qiskit-addon-sqd-hpc/index.mdx | 12 -- .../qiskit-addon-sqd-hpc/postselection.mdx | 76 ------------ .../qiskit-addon-sqd-hpc/release-notes.mdx | 24 ---- docs/api/qiskit-addon-sqd-hpc/subsampling.mdx | 116 ------------------ docs/guides/addons.mdx | 2 +- docs/guides/error-mitigation-overview.mdx | 2 +- .../config/historical-pages-to-latest.json | 1 - scripts/js/commands/checkInternalLinks.ts | 6 - scripts/js/lib/api/Pkg.ts | 20 +-- scripts/js/lib/links/ignores.ts | 3 - 17 files changed, 12 insertions(+), 471 deletions(-) delete mode 100644 docs/addons/qiskit-addon-sqd-hpc/_toc.json delete mode 100644 docs/addons/qiskit-addon-sqd-hpc/compilation-flags.mdx delete mode 100644 docs/addons/qiskit-addon-sqd-hpc/index.mdx delete mode 100644 docs/api/qiskit-addon-sqd-hpc/_package.json delete mode 100644 docs/api/qiskit-addon-sqd-hpc/_toc.json delete mode 100644 docs/api/qiskit-addon-sqd-hpc/configuration-recovery.mdx delete mode 100644 docs/api/qiskit-addon-sqd-hpc/fermion.mdx delete mode 100644 docs/api/qiskit-addon-sqd-hpc/index.mdx delete mode 100644 docs/api/qiskit-addon-sqd-hpc/postselection.mdx delete mode 100644 docs/api/qiskit-addon-sqd-hpc/release-notes.mdx delete mode 100644 docs/api/qiskit-addon-sqd-hpc/subsampling.mdx diff --git a/docs/addons/qiskit-addon-sqd-hpc/_toc.json b/docs/addons/qiskit-addon-sqd-hpc/_toc.json deleted file mode 100644 index ade73e5c325..00000000000 --- a/docs/addons/qiskit-addon-sqd-hpc/_toc.json +++ /dev/null @@ -1,32 +0,0 @@ -{ - "parentUrl": "/docs/guides/addons", - "parentLabel": "Documentation", - "title": "SQD for HPC 0.0", - "collapsed": true, - "children": [ - { - "title": "", - "children": [ - { - "title": "Compilation flags", - "url": "/docs/addons/qiskit-addon-sqd-hpc/compilation-flags" - }, - { - "title": "GitHub", - "url": "https://github.com/Qiskit/qiskit-addon-sqd-hpc" - } - ], - "collapsible": false - }, - { - "title": "API reference", - "collapsible": false, - "children": [ - { - "title": "SQD for HPC API reference", - "url": "/docs/api/qiskit-addon-sqd-hpc" - } - ] - } - ] -} diff --git a/docs/addons/qiskit-addon-sqd-hpc/compilation-flags.mdx b/docs/addons/qiskit-addon-sqd-hpc/compilation-flags.mdx deleted file mode 100644 index 7d55e31cd8a..00000000000 --- a/docs/addons/qiskit-addon-sqd-hpc/compilation-flags.mdx +++ /dev/null @@ -1,38 +0,0 @@ ---- -title: "Compilation flags" -description: "Compilation flags for the latest version of SQD for HPC" ---- - -# Compilation flags - -This guide documents various `#define`s and other supported flags that may be passed to the compiler when using this library. - -## How to disable exceptions - -To disable exceptions, the following flags must the passed to the compiler: `-fno-exceptions -DQKA_SQD_DISABLE_EXCEPTIONS=1` - -When `QKA_SQD_DISABLE_EXCEPTIONS` is set to a non-zero value during compilation, the library will refrain from throwing exceptions. Instead, if there is an error, it will send a message to `STDERR` and call `std::terminate`. The default behavior of `std::terminate` is to abort the program, but this behavior can be configured by calling `std::set_terminate`. For an MPI program, it is recommended to install a custom handler that calls `MPI_Abort` instead. - -## How to disable run-time type information (RTTI) - -This library does not require RTTI, so the following flag can optionally be passed to the compiler to disable it: `-fno-rtti`. - -## Example: passing flags to cmake - -Compiler flags are passed to `cmake` via the `-DCMAKE_CXX_FLAGS` option. For instance, the test suite of this repository can be compiled without exceptions with the following commands: - -```sh -mkdir -p build -cd build -cmake .. -DCMAKE_CXX_FLAGS="-fno-exceptions -DQKA_SQD_DISABLE_EXCEPTIONS=1 -DDOCTEST_CONFIG_NO_EXCEPTIONS_BUT_WITH_ALL_ASSERTS" -make -``` - -Note that compiling this repository’s test suite requires that `#define DOCTEST_CONFIG_NO_EXCEPTIONS_BUT_WITH_ALL_ASSERTS` be set, as exceptions must be disabled in the doctest framework as well as the SQD library. - -<span id="concepts-c-20-and-later" /> - -## Concepts (C++20 and later) - -This library is designed to use C++ concepts if they are available (i.e., if the code is compiled according to C++20 or a later version of the standard). If possible, users are encouraged to use such a compiler when developing, as this will likely lead to better error messages from the compiler (e.g., when a template argument has an unexpected type). - diff --git a/docs/addons/qiskit-addon-sqd-hpc/index.mdx b/docs/addons/qiskit-addon-sqd-hpc/index.mdx deleted file mode 100644 index 8882190e0d9..00000000000 --- a/docs/addons/qiskit-addon-sqd-hpc/index.mdx +++ /dev/null @@ -1,39 +0,0 @@ ---- -title: "Qiskit addon: SQD (HPC-ready)" -description: "Documentation for the latest version of SQD for HPC" ---- - -# Qiskit addon: SQD (HPC-ready) - -This repository contains an HPC-ready implementation of the [Qiskit addon for Sample-based Quantum Diagonalization (SQD)](https://github.com/Qiskit/qiskit-addon-sqd). - -Unlike the existing Python addon, this code is written in modern C++17 and is designed to enable HPC workflows and applications, particularly those that require a single binary to be compiled for use with MPI. This library builds upon existing interfaces in the C++ standard template library (STL); specifically, this code-base is: - -* Able to leverage any pseudo-random number generator compatible with the `<random>` header. -* Reliant on standard interfaces for storing bitstrings, namely `std::bitset` and `boost::dynamic_bitset`, which are themselves aligned with [Qiskit’s bit-ordering conventions](/docs/en/guides/bit-ordering) (c.f. [to\_ulong](https://en.cppreference.com/w/cpp/utility/bitset/to_ulong.html) and [to\_string](https://en.cppreference.com/w/cpp/utility/bitset/to_string.html)). -* Compatible with STL containers, including `std::vector`. - -The code is cross-platform, fully tested, fully documented, and contains an integrated benchmark suite. - -## Table of Contents - -* [Compilation flags](compilation-flags) - - * [How to disable exceptions](compilation-flags#how-to-disable-exceptions) - * [How to disable run-time type information (RTTI)](compilation-flags#how-to-disable-run-time-type-information-rtti) - * [Example: passing flags to `cmake`](compilation-flags#example-passing-flags-to-cmake) - * [Concepts (C++20 and later)](compilation-flags#concepts-c-20-and-later) - -* [API Reference](/docs/api/qiskit-addon-sqd-hpc/index) - - * [Postselection](/docs/api/qiskit-addon-sqd-hpc/postselection) - * [Subsampling](/docs/api/qiskit-addon-sqd-hpc/subsampling) - * [Configuration recovery](/docs/api/qiskit-addon-sqd-hpc/configuration-recovery) - * [Fermion](/docs/api/qiskit-addon-sqd-hpc/fermion) - -* [GitHub](https://github.com/Qiskit/qiskit-addon-sqd-hpc) - -* [Release Notes](/docs/api/qiskit-addon-sqd-hpc/release-notes) - - * [Upcoming release (`main`)](/docs/api/qiskit-addon-sqd-hpc/release-notes#upcoming-release-main) - diff --git a/docs/api/qiskit-addon-sqd-hpc/_package.json b/docs/api/qiskit-addon-sqd-hpc/_package.json deleted file mode 100644 index 7e88252443e..00000000000 --- a/docs/api/qiskit-addon-sqd-hpc/_package.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "name": "qiskit-addon-sqd-hpc", - "version": "0.0.0" -} diff --git a/docs/api/qiskit-addon-sqd-hpc/_toc.json b/docs/api/qiskit-addon-sqd-hpc/_toc.json deleted file mode 100644 index 431065c8d2b..00000000000 --- a/docs/api/qiskit-addon-sqd-hpc/_toc.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "title": "SQD for HPC", - "children": [ - { - "title": "API index", - "url": "/docs/api/qiskit-addon-sqd-hpc" - }, - { - "title": "Release notes", - "useDivider": true, - "url": "/docs/api/qiskit-addon-sqd-hpc/release-notes" - }, - { - "title": "Configuration recovery", - "url": "/docs/api/qiskit-addon-sqd-hpc/configuration-recovery", - "untranslatable": true - }, - { - "title": "Fermion", - "url": "/docs/api/qiskit-addon-sqd-hpc/fermion", - "untranslatable": true - }, - { - "title": "Postselection", - "url": "/docs/api/qiskit-addon-sqd-hpc/postselection", - "untranslatable": true - }, - { - "title": "Subsampling", - "url": "/docs/api/qiskit-addon-sqd-hpc/subsampling", - "untranslatable": true - } - ], - "collapsed": true, - "untranslatable": true, - "parentUrl": "/docs/addons/qiskit-addon-sqd-hpc", - "parentLabel": "SQD for HPC" -} diff --git a/docs/api/qiskit-addon-sqd-hpc/configuration-recovery.mdx b/docs/api/qiskit-addon-sqd-hpc/configuration-recovery.mdx deleted file mode 100644 index 1b2659b862f..00000000000 --- a/docs/api/qiskit-addon-sqd-hpc/configuration-recovery.mdx +++ /dev/null @@ -1,38 +0,0 @@ ---- -title: Configuration recovery (latest version) -description: API reference for Configuration recovery in the latest version of qiskit-addon-sqd-hpc -in_page_toc_min_heading_level: 2 -python_api_type: module -python_api_name: Configuration recovery ---- - -# Configuration recovery - -## Functions - -This library provides a single public function for performing configuration recovery. - -### BitstringVectorType - -<Function id="BitstringVectorType" signature="template<typename BitstringVectorType, typename WeightVectorType, std::uniform_random_bit_generator RNGType>std::pair<BitstringVectorType, WeightVectorType> Qiskit::addon::sqd::recover_configurations(const BitstringVectorType &bitstrings, const WeightVectorType &probabilities, const std::array<std::vector<double>, 2> &avg_occupancies, std::array<std::uint64_t, 2> num_elec, RNGType &rng)"> - Refine bitstrings based on average orbital occupancy and a target Hamming weight. - - **Parameters** - - * **bitstrings** – **\[in]** A container (e.g., `std::vector`) of bitstrings. - * **probabilities** – **\[in]** A 1D array specifying a probability distribution over the bitstrings. Must contain the same number of elements as `bitstrings`. - * **avg\_occupancies** – **\[in]** Size-2 `std::array` of `std::vector<double>`s holding the mean occupancy of the spin-up and spin-down orbitals, respectively. Each vector’s size must be half the size of a single bitstring. - * **num\_elec** – **\[in]** Size-2 `std::array` containing the number of spin-up and spin-down electrons in the system, respectively. - * **rng** – **\[inout]** Random number generator. - - **Template Parameters** - - * **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. - * **WeightVectorType** – Type of `weights`, compatible with `std::vector<double>`. - * **RNGType** – Type of random number generator. - - **Returns** - - A refined `std::vector` of unique bitstrings and a parallel, updated probability array. -</Function> - diff --git a/docs/api/qiskit-addon-sqd-hpc/fermion.mdx b/docs/api/qiskit-addon-sqd-hpc/fermion.mdx deleted file mode 100644 index a841c36486a..00000000000 --- a/docs/api/qiskit-addon-sqd-hpc/fermion.mdx +++ /dev/null @@ -1,32 +0,0 @@ ---- -title: Fermion (latest version) -description: API reference for Fermion in the latest version of qiskit-addon-sqd-hpc -in_page_toc_min_heading_level: 2 -python_api_type: module -python_api_name: Fermion ---- - -# Fermion - -Functions for the study of fermionic systems. - -## Functions - -### BitstringVectorType - -<Function id="BitstringVectorType" signature="template<class BitstringVectorType>auto Qiskit::addon::sqd::bitstrings_to_ci_strings_symmetrize_spin(const BitstringVectorType &bitstrings, std::optional<unsigned int> max_dimension = std::nullopt, std::optional<std::reference_wrapper<const std::vector<internal::HalfSize<typename BitstringVectorType::value_type>>>> include_configurations = std::nullopt) -> std::vector<internal::HalfSize<typename BitstringVectorType::value_type>>"> - Convert bitstrings into CI strings (representations of determinants). - - This function separates each bitstring in bitstring\_matrix in half, combining the right and left halves of all the bitstrings into a single set of unique configurations. - - **Parameters** - - * **bitstrings** – **\[in]** Population of bitstrings. - * **max\_dimension** – **\[in]** Maximum dimension of returned CI strings. If less than the number of CI strings, the list of CI strings will be truncated. - * **include\_configurations** – **\[in]** A list of CI strings that will be included in the output, regardless of whether they are contained in `bitstrings`. - - **Template Parameters** - - **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. -</Function> - diff --git a/docs/api/qiskit-addon-sqd-hpc/index.mdx b/docs/api/qiskit-addon-sqd-hpc/index.mdx deleted file mode 100644 index 1aef280d137..00000000000 --- a/docs/api/qiskit-addon-sqd-hpc/index.mdx +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: SQD for HPC API documentation (latest version) -description: Index of all the modules in the latest version of qiskit-addon-sqd-hpc. ---- - -# SQD API reference - -* [Postselection](postselection) -* [Subsampling](subsampling) -* [Configuration recovery](configuration-recovery) -* [Fermion](fermion) - diff --git a/docs/api/qiskit-addon-sqd-hpc/postselection.mdx b/docs/api/qiskit-addon-sqd-hpc/postselection.mdx deleted file mode 100644 index 15d30d5be7f..00000000000 --- a/docs/api/qiskit-addon-sqd-hpc/postselection.mdx +++ /dev/null @@ -1,76 +0,0 @@ ---- -title: Postselection (latest version) -description: API reference for Postselection in the latest version of qiskit-addon-sqd-hpc -in_page_toc_min_heading_level: 2 -python_api_type: module -python_api_name: Postselection ---- - -# Postselection - -## Functions - -### BitstringVectorType - -<Function id="BitstringVectorType" signature="template<typename BitstringVectorType, typename WeightVectorType, typename CallableType>std::pair<BitstringVectorType, WeightVectorType> Qiskit::addon::sqd::postselect_bitstrings(const BitstringVectorType &bitstrings, const WeightVectorType &weights, CallableType filter_function)"> - Post-select bitstrings based on a given criteria. - - <span id="postselection_8hpp_1autotoc_md0" /> - - #### Example - - ```c - std::vector<std::bitset<6>> bitstrings = {0b011010, 0b100011}; - std::vector<double> weights = {0.1, 0.7}; - auto [new_bitstrings, new_weights] = Qiskit::addon::sqd::postselect_bitstrings( - bitstrings, weights, Qiskit::addon::sqd::MatchesRightLeftHamming(1, 2) - ); - ``` - - **Parameters** - - * **bitstrings** – **\[in]** Bitstrings to consider. - * **weights** – **\[in]** Relative weight of each bitstring (need not be normalized to 1). - * **filter\_function** – **\[in]** Callable which returns a boolean indicating whether a is to be kept. - - **Template Parameters** - - * **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. - * **WeightVectorType** – Type of `weights`, compatible with `std::vector<double>`. - * **CallableType** – Type of `filter_function`, compatible with `bool (*f)(const BitstringType &)`. - - **Returns** - - Post-selected bitstrings and their corresponding weights, normalized to 1. -</Function> - -## Classes - -**template\<typename UnsignedType = uint32\_t><span id="classQiskit_1_1addon_1_1sqd_1_1MatchesRightLeftHamming" />class MatchesRightLeftHamming** - -Functor which returns `true` if a bitstring has a predetermined Hamming weight. - -### MatchesRightLeftHamming - -<Function id="MatchesRightLeftHamming" signature="inline explicit MatchesRightLeftHamming(UnsignedType right_target, UnsignedType left_target)"> - Constructor. -</Function> - -### BitstringType - -<Function id="BitstringType" signature="template<typename BitstringType>inline bool operator()(const BitstringType &bitstring) const"> - Function to be used as filter. - - **Parameters** - - **bitstring** – **\[in]** Bitstring to consider. - - **Template Parameters** - - **BitstringType** – Type of `bitstring`, compatible with `boost::dynamic_bitset<>` (for example). - - **Returns** - - `true` if the bitstring matches the Hamming weight that the functor was initialized with. -</Function> - diff --git a/docs/api/qiskit-addon-sqd-hpc/release-notes.mdx b/docs/api/qiskit-addon-sqd-hpc/release-notes.mdx deleted file mode 100644 index ed721b1f538..00000000000 --- a/docs/api/qiskit-addon-sqd-hpc/release-notes.mdx +++ /dev/null @@ -1,24 +0,0 @@ ---- -title: SQD for HPC release notes -description: Changes made to SQD for HPC -in_page_toc_max_heading_level: 2 ---- - -<span id="release-notes" /> - -<span id="id1" /> - -# SQD for HPC release notes - -<span id="upcoming-release-main" /> - -<span id="release-notes-upcoming-release-main" /> - -## Upcoming release (`main`) - -<span id="release-notes-upcoming-release-main-new-features" /> - -### New Features - -* The `bitstrings_to_ci_strings_symmetrize_spin` function has been added, which allows one to obtain a collection of CI strings (with alpha and beta determinants symmetrized) from a collection of bitstrings. - diff --git a/docs/api/qiskit-addon-sqd-hpc/subsampling.mdx b/docs/api/qiskit-addon-sqd-hpc/subsampling.mdx deleted file mode 100644 index 4a7f3a439a4..00000000000 --- a/docs/api/qiskit-addon-sqd-hpc/subsampling.mdx +++ /dev/null @@ -1,116 +0,0 @@ ---- -title: Subsampling (latest version) -description: API reference for Subsampling in the latest version of qiskit-addon-sqd-hpc -in_page_toc_min_heading_level: 2 -python_api_type: module -python_api_name: Subsampling ---- - -# Subsampling - -## Functions - -This library provides functions for subsampling either a single batch or multiple batches from a pool of bitstrings. - -### BitstringVectorType - -<Function id="BitstringVectorType" signature="template<typename BitstringVectorType, typename WeightVectorType, std::uniform_random_bit_generator RNGType>BitstringVectorType Qiskit::addon::sqd::subsample(const BitstringVectorType &bitstrings, const WeightVectorType &weights, unsigned int samples_per_batch, RNGType &rng)"> - Subsample a single batch of bitstrings. - - Note: You must de-duplicate the bitstrings before calling this, otherwise you may get duplicate bitstrings in the output. - - **Parameters** - - * **bitstrings** – **\[in]** Population of bitstrings. - * **weights** – **\[in]** Relative weight of each bitstring (need not be normalized to 1). Must be the same length as `bitstrings` and contain only non-negative values. - * **samples\_per\_batch** – **\[in]** Number of samples to return in `batch`. Cannot be greater than the number of bitstrings. - * **rng** – **\[inout]** Random number generator to use for sampling. - - **Template Parameters** - - * **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. - * **WeightVectorType** – Type of `weights`, compatible with `std::vector<double>`. - * **RNGType** – Type of random number generator. - - **Returns** - - The subsampled bitstrings. -</Function> - -### BatchVectorType - -<Function id="BatchVectorType" signature="template<typename BatchVectorType, typename BitstringVectorType, typename WeightVectorType, std::uniform_random_bit_generator RNGType>void Qiskit::addon::sqd::subsample(BatchVectorType &batch, const BitstringVectorType &bitstrings, const WeightVectorType &weights, unsigned int samples_per_batch, RNGType &rng)"> - Subsample a single batch of bitstrings (mutating version) - - This version can be useful if you want to avoid reallocation by re-using an existing batch vector. - - Note: You must de-duplicate the bitstrings before calling this, otherwise you may get duplicate bitstrings in the output. - - **Parameters** - - * **batch** – **\[out]** This will be cleared and overwritten with the subsampled bitstrings. - * **bitstrings** – **\[in]** Population of bitstrings. - * **weights** – **\[in]** Relative weight of each bitstring (need not be normalized to 1). Must be the same length as `bitstrings` and contain only non-negative values. - * **samples\_per\_batch** – **\[in]** Number of samples to return in `batch`. Cannot be greater than the number of bitstrings. - * **rng** – **\[inout]** Random number generator to use for sampling. - - **Template Parameters** - - * **BatchVectorType** – Type of `batch`, must be compatible with `std::vector<boost::dynamic_bitset<>>`. - * **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. - * **WeightVectorType** – Type of `weights`, compatible with `std::vector<double>`. - * **RNGType** – Type of random number generator. -</Function> - -### BitstringVectorType - -<Function id="BitstringVectorType" signature="template<typename BitstringVectorType, typename WeightVectorType, std::uniform_random_bit_generator RNGType>std::vector<BitstringVectorType> Qiskit::addon::sqd::subsample_multiple_batches(const BitstringVectorType &bitstrings, const WeightVectorType &weights, unsigned int samples_per_batch, unsigned int num_batches, RNGType &rng)"> - Subsample multiple batches of bitstrings. - - Note: You must de-duplicate the bitstrings before calling this, otherwise you may get duplicate bitstrings in the output. - - **Parameters** - - * **bitstrings** – **\[in]** Population of bitstrings. - * **weights** – **\[in]** Relative weight of each bitstring (need not be normalized to 1). Must be the same length as `bitstrings` and contain only non-negative values. - * **samples\_per\_batch** – **\[in]** Number of samples to return per batch. Cannot be greater than the number of bitstrings. - * **num\_batches** – **\[in]** Number of batches. - * **rng** – **\[inout]** Random number generator to use for sampling. - - **Template Parameters** - - * **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. - * **WeightVectorType** – Type of `weights`, compatible with `std::vector<double>`. - * **RNGType** – Type of random number generator. - - **Returns** - - The batches of subsampled bitstrings. -</Function> - -### BatchesVectorType - -<Function id="BatchesVectorType" signature="template<typename BatchesVectorType, typename BitstringVectorType, typename WeightVectorType, std::uniform_random_bit_generator RNGType>void Qiskit::addon::sqd::subsample_multiple_batches(BatchesVectorType &batches, const BitstringVectorType &bitstrings, const WeightVectorType &weights, unsigned int samples_per_batch, unsigned int num_batches, RNGType &rng)"> - Subsample multiple batches of bitstrings (mutating version) - - This version can be useful if you want to avoid reallocation by re-using existing batch vectors. - - Note: You must de-duplicate the bitstrings before calling this, otherwise you may get duplicate bitstrings in the output. - - **Parameters** - - * **batches** – **\[out]** This will be overwritten with the batches of subsampled bitstrings. - * **bitstrings** – **\[in]** Population of bitstrings. - * **weights** – **\[in]** Relative weight of each bitstring (need not be normalized to 1). Must be the same length as `bitstrings` and contain only non-negative values. - * **samples\_per\_batch** – **\[in]** Number of samples to return per batch. Cannot be greater than the number of bitstrings. - * **num\_batches** – **\[in]** Number of batches. - * **rng** – **\[inout]** Random number generator to use for sampling. - - **Template Parameters** - - * **BatchesVectorType** – Type of `batches`, must be compatible with `std::vector<std::vector<boost::dynamic_bitset<>>>`. - * **BitstringVectorType** – Type of `bitstrings`, compatible with `std::vector<boost::dynamic_bitset<>>`. - * **WeightVectorType** – Type of `weights`, compatible with `std::vector<double>`. - * **RNGType** – Type of random number generator. -</Function> - diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx index 334222b0022..b01b4073d55 100644 --- a/docs/guides/addons.mdx +++ b/docs/guides/addons.mdx @@ -105,7 +105,7 @@ These techniques are useful for managing noise on sampling results. <Card title="SQD for HPC" description="An HPC-ready implementation of the SQD addon, written in modern C++17 standards and designed to enable HPC workflows and applications." - href="/docs/addons/qiskit-addon-sqd-hpc/" + href="https://qiskit.github.io/qiskit-addon-sqd-hpc/" analyticsName="Documentation page: SQD for HPC" linkText="Browse documentation" /> diff --git a/docs/guides/error-mitigation-overview.mdx b/docs/guides/error-mitigation-overview.mdx index bc0ff2024a9..f5744514f13 100644 --- a/docs/guides/error-mitigation-overview.mdx +++ b/docs/guides/error-mitigation-overview.mdx @@ -115,7 +115,7 @@ When executing quantum workloads, there are multiple ways to reduce the impact o <Card title="SQD for HPC" description="An HPC-ready implementation of the SQD addon. It is written in modern C++17 standards and is designed to create a single compiled binary for use with MPI." - href="docs/addons/qiskit-addon-sqd-hpc/" + href="https://qiskit.github.io/qiskit-addon-sqd-hpc/" analyticsName="Documentation page: SQD for HPC" linkText="Browse documentation" /> diff --git a/scripts/config/historical-pages-to-latest.json b/scripts/config/historical-pages-to-latest.json index 212d4b60d58..c5564e917c6 100644 --- a/scripts/config/historical-pages-to-latest.json +++ b/scripts/config/historical-pages-to-latest.json @@ -1501,7 +1501,6 @@ "0.8": {}, "0.9": {} }, - "qiskit-addon-sqd-hpc": {}, "qiskit-addon-cutting": { "0.9": {} }, diff --git a/scripts/js/commands/checkInternalLinks.ts b/scripts/js/commands/checkInternalLinks.ts index 6e29f351e20..e844ba3d414 100644 --- a/scripts/js/commands/checkInternalLinks.ts +++ b/scripts/js/commands/checkInternalLinks.ts @@ -191,11 +191,6 @@ async function determineFileBatches(args: Arguments): Promise<FileBatch[]> { ADDON_GLOBS_TO_LOAD, { check: args.historicalApis }, ); - const sqdHpc = await determineHistoricalFileBatches( - "qiskit-addon-sqd-hpc", - ADDON_GLOBS_TO_LOAD, - { check: args.historicalApis }, - ); const mthree = await determineHistoricalFileBatches( "qiskit-addon-mthree", ADDON_GLOBS_TO_LOAD, @@ -232,7 +227,6 @@ async function determineFileBatches(args: Arguments): Promise<FileBatch[]> { result.push( ...transpiler, ...sqd, - ...sqdHpc, ...mpf, ...utils, ...mthree, diff --git a/scripts/js/lib/api/Pkg.ts b/scripts/js/lib/api/Pkg.ts index 283ec2ce0ed..943b43e51ee 100644 --- a/scripts/js/lib/api/Pkg.ts +++ b/scripts/js/lib/api/Pkg.ts @@ -65,7 +65,7 @@ export class Pkg { "qiskit-addon-obp", "qiskit-addon-mpf", "qiskit-addon-sqd", - "qiskit-addon-sqd-hpc", + // "qiskit-addon-sqd-hpc", "qiskit-addon-cutting", "qiskit-addon-utils", "qiskit-addon-mthree", @@ -238,15 +238,15 @@ export class Pkg { language: "Python", }); } - if (name === "qiskit-addon-sqd-hpc") { - return new Pkg({ - ...args, - title: "SQD for HPC", - githubSlug: "Qiskit/qiskit-addon-sqd-hpc", - kebabCaseAndShortenUrls: true, - language: "C", - }); - } + // if (name === "qiskit-addon-sqd-hpc") { + // return new Pkg({ + // ...args, + // title: "SQD for HPC", + // githubSlug: "Qiskit/qiskit-addon-sqd-hpc", + // kebabCaseAndShortenUrls: true, + // language: "C", + // }); + // } if (name === "qiskit-addon-mthree") { return new Pkg({ ...args, diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index d2f1d7709d8..c07abe6977b 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -613,9 +613,6 @@ function _addonContentLinksToFix(): FilesToIgnores { ], "docs/addons/qiskit-addon-cutting/how-tos/how-to-generate-exact-sampling-coefficients.ipynb": ["../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb"], - "docs/addons/qiskit-addon-sqd-hpc/index.mdx": [ - "/docs/en/guides/bit-ordering", - ], "docs/api/pauli-prop/propagation.mdx": [ "/docs/en/api/qiskit/qiskit.quantum_info.Clifford", "/docs/en/api/qiskit/qiskit.quantum_info.Pauli#evolve",

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public/docs/images/addons/qiskit-addon-mpf/tutorials/01_getting_started/extracted-outputs/e15dd313-668d-48b3-8133-3cd4df9a3d8e-0.avif diff --git a/docs/addons/qiskit-addon-mpf/_toc.json b/docs/addons/qiskit-addon-mpf/_toc.json new file mode 100644 index 00000000000..e84804e2a81 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/_toc.json @@ -0,0 +1,48 @@ +{ + "title": "Multi-product formulas (MPF)", + "children": [ + { + "title": "Documentation Home", + "url": "/docs/addons/qiskit-addon-mpf" + }, + { + "title": "Installation Instructions", + "url": "/docs/addons/qiskit-addon-mpf/install" + }, + { + "title": "Tutorials", + "children": [ + { + "title": "Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas", + "url": "/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started" + } + ], + "url": "/docs/addons/qiskit-addon-mpf/tutorials/index" + }, + { + "title": "How-To Guides", + "children": [ + { + "title": "How to choose the Trotter steps for an MPF", + "url": "/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps" + }, + { + "title": "How to use the approximate model", + "url": "/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model" + } + ], + "url": "/docs/addons/qiskit-addon-mpf/how_tos/index" + }, + { + "title": "Explanations", + "children": [ + { + "title": "Understanding the stability of MPFs", + "url": "/docs/addons/qiskit-addon-mpf/explanations/mpf_stability" + } + ], + "url": "/docs/addons/qiskit-addon-mpf/explanations/index" + } + ], + "collapsed": true +} diff --git a/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb b/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb new file mode 100644 index 00000000000..d847946a9cf --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dafa4d64-0da8-4e18-aea8-73f13cac3d29", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# How to choose the Trotter steps for an MPF\n", + "\n", + "This guide teaches you how to find a good set of Trotter steps for an MPF.\n", + "We assume that you have already read and understood the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial." + ] + }, + { + "cell_type": "markdown", + "id": "fb022d51-3187-4822-8536-7778df8620f1", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Understanding some heuristics\n", + "\n", + "In principle, any $k_j$ values can be chosen but some choices of Trotter steps will lead to a larger noise amplification on real devices than others.\n", + "Thus, it is important that one tries to find _\"good\"_ values of $k_j$.\n", + "In the following we will discuss some heuristics to help guide you in doing so.\n", + "\n", + "1. In practice, our **largest** $k_j$ ($k_{\\text{max}}$) value is simply bound by the depth of quantum circuit we can reliably execute.\n", + "\n", + "2. Since the Trotter error is only well behaved for $\\text{d}t = t/k \\lt 1$ (see [Understanding the stability of MPFs](/docs/addons/qiskit-addon-mpf/explanations/mpf_stability)), our **smallest** $k_j$ ($k_{\\text{min}}$) should satisfy: $t/k_{\\text{min}} \\lt 1$ (Note: empirically $\\text{d}t \\leq 1$ seems to work fine, too).\n", + "\n", + "3. None of our $x_j$ values should be close to $0$, as this would imply that we might as well exclude the corresponding $k_j$ from the expansion.\n", + "\n", + "4. Similarly, we do not want the $x_j$ for $k_{\\text{max}}$ to be the only dominant one, since that implies that we are essentially just recovering a single product formula with $k=k_{\\text{max}}$.\n", + "\n", + "5. Finally, we want the L1-norm of our coefficients, $x_j$, to be small, as this indicates a well-conditioned MPF (see [Carrera Vazquez et al., 2023]). Furthermore, small coefficients minimize the variance amplification of the MPF.\n", + "\n", + "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/" + ] + }, + { + "cell_type": "markdown", + "id": "f0f2096f-5fb6-4861-a05e-3ded9ac65a82", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Applying these heuristics\n", + "\n", + "Reusing the example from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial, we want to simulate up to time $t=8.0$.\n", + "Thus, $k_{\\text{min}}=8$.\n", + "\n", + "For the sake of this example, let us assume that our deepest circuit is bound by $k_{\\text{max}}=20$.\n", + "\n", + "In addition to the constraints above, we will follow the heuristic presented by [Zhuk et al., 2023], where they minimize a re-weighted L1-norm of the coefficients:\n", + "\n", + "$$\n", + "\\text{min}_{k_j} \\sum_j \\frac{|c_j|}{k_j^{2\\chi}} \\, ,\n", + "$$\n", + "\n", + "where $\\chi$ is the order of our individual product formulas.\n", + "\n", + "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" + ] + }, + { + "cell_type": "markdown", + "id": "1ff9ff06-47bf-451d-815f-11d331cafaa3", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Preparing our LSE\n", + "\n", + "In a first step, we prepare the LSE, $Ax=b$, leveraging a feature of the [setup_static_lse](/docs/addons/qiskit-addon-mpf/stubs/qiskit_addon_mpf.static.setup_static_lse) function that allows us to use a [cvxpy.Parameter](https://www.cvxpy.org/api_reference/cvxpy.expressions.html#parameter) object for our $k_j$ values." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "bda8c5d3-b48a-4c9e-bef3-fb9f26846ad6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "order = 2\n", + "symmetric = True\n", + "\n", + "min_k = 8\n", + "max_k = 20\n", + "\n", + "max_l1_norm = 5.0\n", + "min_coeff = 0.01" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "3b68ec15-b8ed-45a1-bd46-ced7b29b159e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import cvxpy as cp\n", + "from qiskit_addon_mpf.static import setup_static_lse\n", + "\n", + "ks = cp.Parameter(3, integer=True)\n", + "lse = setup_static_lse(ks, order=order, symmetric=symmetric)" + ] + }, + { + "cell_type": "markdown", + "id": "92356e74-97b6-4231-bd35-43d5cd274dca", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Next, we loop over all possible 3-tuples of $k_j$ values within our specified range.\n", + "For each one, we compute the analytical coefficients, $x_j$, and then apply our filter criteria.\n", + "We keep track of those $k_j$ tuples which satisfy our criteria." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "da4c0610-8d1c-4596-b082-8cfd0098bc7c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "possible_ks = {}\n", + "\n", + "for k1 in range(min_k, max_k):\n", + " for k2 in range(k1 + 1, max_k):\n", + " for k3 in range(k2 + 1, max_k):\n", + " ks.value = [k1, k2, k3]\n", + " coeffs = lse.solve()\n", + "\n", + " if np.any(np.isclose(coeffs, 0.0, atol=min_coeff)):\n", + " continue\n", + "\n", + " norm1 = np.linalg.norm(coeffs, ord=1)\n", + " if norm1 > max_l1_norm:\n", + " continue\n", + "\n", + " weighted = np.sum(\n", + " [\n", + " np.abs(c) / k ** (2 * order)\n", + " for c, k in zip(coeffs, ks.value)\n", + " ]\n", + " )\n", + " possible_ks[tuple(int(k) for k in ks.value)] = {\n", + " \"coeffs\": tuple(coeffs),\n", + " \"weighted\": float(weighted),\n", + " \"norm1\": float(norm1),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "9f37b658-0e73-409b-add2-4cbf83708730", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Finally, we sort our $k_j$ by the re-weighted L1-norm presented by [Zhuk et al., 2023]:\n", + "\n", + "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "16d05ed1-c1ed-445d-afce-543f55275e5e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(8, 14, 19) {'coeffs': (0.10447913478216586, -1.7638200183654775, 2.6593408835833117), 'weighted': 9.182736455463762e-05, 'norm1': 4.527640036730955}\n", + "(8, 13, 19) {'coeffs': (0.13134519801186473, -1.4167162698412699, 2.285371071829405), 'weighted': 9.920634920634922e-05, 'norm1': 3.8334325396825397}\n", + "(8, 12, 19) {'coeffs': (0.17239057239057254, -1.1944700460829496, 2.022079473692377), 'weighted': 0.00011520737327188946, 'norm1': 3.388940092165899}\n", + "(9, 13, 19) {'coeffs': (0.2662743506493499, -1.6904000946969673, 2.4241257440476174), 'weighted': 0.00011837121212121191, 'norm1': 4.380800189393934}\n", + "(8, 13, 18) {'coeffs': (0.15003663003663004, -1.7549001536098316, 2.6048635235732016), 'weighted': 0.00012288786482334872, 'norm1': 4.509800307219663}\n", + "(8, 12, 18) {'coeffs': (0.19692307692307653, -1.4399999999999984, 2.243076923076922), 'weighted': 0.0001388888888888887, 'norm1': 3.879999999999997}\n", + "(8, 11, 19) {'coeffs': (0.24195168054817162, -1.070248538011695, 1.8282968574635234), 'weighted': 0.00014619883040935662, 'norm1': 3.14049707602339}\n", + "(9, 12, 19) {'coeffs': (0.37193877551020393, -1.5167873601053319, 2.144848584595128), 'weighted': 0.00014629507717065315, 'norm1': 4.033574720210664}\n", + "(8, 12, 17) {'coeffs': (0.22755555555555573, -1.7875862068965531, 2.5600306513409974), 'weighted': 0.00017241379310344843, 'norm1': 4.575172413793107}\n", + "(8, 11, 18) {'coeffs': (0.27638326585694917, -1.265318468585254, 1.988935202728305), 'weighted': 0.00017284590787313073, 'norm1': 3.530636937170508}\n", + "(9, 12, 18) {'coeffs': (0.42857142857142816, -1.8285714285714276, 2.3999999999999995), 'weighted': 0.00017636684303350958, 'norm1': 4.657142857142855}\n", + "(9, 11, 19) {'coeffs': (0.5858035714285708, -1.5251041666666654, 1.9393005952380946), 'weighted': 0.00020833333333333313, 'norm1': 4.05020833333333}\n", + "(8, 11, 17) {'coeffs': (0.3193762183235871, -1.5289264828738525, 2.2095502645502654), 'weighted': 0.00020885547201336693, 'norm1': 4.0578529657477045}\n", + "(8, 10, 19) {'coeffs': (0.383090160867938, -1.0642826734780744, 1.6811925126101364), 'weighted': 0.00021285653469561486, 'norm1': 3.1285653469561487}\n", + "(9, 11, 18) {'coeffs': (0.6750000000000009, -1.8030788177339916, 2.1280788177339907), 'weighted': 0.0002463054187192121, 'norm1': 4.606157635467984}\n", + "(8, 10, 18) {'coeffs': (0.43760683760683694, -1.2400793650793638, 1.8024725274725268), 'weighted': 0.0002480158730158727, 'norm1': 3.4801587301587276}\n", + "(8, 11, 16) {'coeffs': (0.3742690058479534, -1.9026640675763484, 2.528395061728395), 'weighted': 0.0002599090318388565, 'norm1': 4.805328135152697}\n", + "(8, 10, 17) {'coeffs': (0.5056790123456789, -1.4697236919459142, 1.9640446796002353), 'weighted': 0.00029394473838918284, 'norm1': 3.9394473838918285}\n", + "(8, 10, 16) {'coeffs': (0.592592592592593, -1.7806267806267817, 2.1880341880341887), 'weighted': 0.0003561253561253563, 'norm1': 4.561253561253563}\n", + "(8, 9, 19) {'coeffs': (0.8112497524262232, -1.3783613445378153, 1.5671115921115921), 'weighted': 0.00042016806722689084, 'norm1': 3.756722689075631}\n", + "(8, 9, 18) {'coeffs': (0.9266968325791858, -1.5882352941176474, 1.6615384615384616), 'weighted': 0.00048414427499394834, 'norm1': 4.176470588235295}\n", + "(8, 9, 17) {'coeffs': (1.0708496732026151, -1.855486425339368, 1.7846367521367528), 'weighted': 0.0005656108597285072, 'norm1': 4.710972850678736}\n" + ] + } + ], + "source": [ + "for ks_val in sorted(\n", + " possible_ks, key=lambda ks_val: possible_ks[ks_val].get(\"weighted\")\n", + "):\n", + " print(ks_val, possible_ks[ks_val])" + ] + }, + { + "cell_type": "markdown", + "id": "f3b709d3-ed27-4e6f-a2ba-f3f17326099e", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Recalling the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial, from these possible values we picked `ks=(8, 12, 19)` since it strikes a good balance of minimizing the re-weighted and plain L1-norms." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb b/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb new file mode 100644 index 00000000000..2b4e6110260 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb @@ -0,0 +1,1035 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8d428726-b143-45b7-9c23-d7aef8f3690b", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# How to use the approximate model\n", + "\n", + "You have already seen a first example of the [setup_sum_of_squares_problem](/docs/addons/qiskit-addon-mpf/stubs/qiskit_addon_mpf.costs.setup_sum_of_squares_problem) function in the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial.\n", + "In this guide, we are going to revisit that function in more detail." + ] + }, + { + "cell_type": "markdown", + "id": "c9e6b25b-6f6f-49c6-84ac-9493c151a968", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Setting up a simple model problem\n", + "\n", + "For this guide, we will reuse the simple model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial; the Ising model on a line of 10 sites:\n", + "\n", + "$$\n", + "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", + "$$\n", + "\n", + "where $J$ is the coupling strength between two sites and $h$ is the external magnetic field." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3c8d6feb-d0fb-45ac-8bf6-213b29bcb8a4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'],\n", + " coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j,\n", + " 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j,\n", + " 0.4+0.j, 0.4+0.j, 0.4+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "coupling_map = CouplingMap.from_line(10, bidirectional=False)\n", + "\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " coupling_map,\n", + " coupling_constants=(0.0, 0.0, 1.0),\n", + " ext_magnetic_field=(0.4, 0.0, 0.0),\n", + ")\n", + "print(hamiltonian)" + ] + }, + { + "cell_type": "markdown", + "id": "46ab2bb9-fd98-4c59-8d9c-66993652ff13", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Choosing $k_j$\n", + "\n", + "In this guide, we are going to focus on the tweaks that we can apply to the [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) and its solver that are returned by the [setup_sum_of_squares_problem](/docs/addons/qiskit-addon-mpf/stubs/qiskit_addon_mpf.costs.setup_sum_of_squares_problem). Therefore, we are not concerned with choosing any specific $k_j$ values and can simply reuse the ones from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2ec34c2f-1f6f-4346-9ba6-f7f1c0e3620e", + "metadata": {}, + "outputs": [], + "source": [ + "trotter_steps = (8, 12, 19)" + ] + }, + { + "cell_type": "markdown", + "id": "99c7ad3c-7a5a-4dfe-81a5-9457d68fbd9c", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Computing $x$ analytically\n", + "\n", + "First, we have to construct our LSE, $Ax=b$. Once again, we reuse the settings from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "62d48e9c-8b00-43ac-9ee6-90f6418cfb05", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit_addon_mpf.static import setup_static_lse\n", + "\n", + "lse = setup_static_lse(trotter_steps, order=2, symmetric=True)" + ] + }, + { + "cell_type": "markdown", + "id": "9b5c4989-a75d-40e2-bb8a-9070188baef4", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Next, we compute the analytical solution of $x$ and its L1-norm as our reference values." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e279b0a3-9639-4e68-8138-90ff5ae6640d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.17239057 -1.19447005 2.02207947]\n" + ] + } + ], + "source": [ + "coeff_analytical = lse.solve()\n", + "print(coeff_analytical)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a668e3bc-0cd1-4995-bc81-9ac8ca053f02", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.388940092165899\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "print(np.linalg.norm(coeff_analytical, ord=1))" + ] + }, + { + "cell_type": "markdown", + "id": "abda0fe8-c188-49e5-b121-e7d9b64b5313", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Setting up the approximate model\n", + "\n", + "Now, we will construct the approximate model.\n", + "The idea of using approximate solutions $\\tilde{x}$ in order to ensure a smaller L1-norm was proposed by [Zhuk et al., 2023].\n", + "The model is already explained in detail in the API documentation of [setup_sum_of_squares_problem](/docs/addons/qiskit-addon-mpf/stubs/qiskit_addon_mpf.costs.setup_sum_of_squares_problem) so we refrain from repeating it here.\n", + "Suffice to say, that the model consists of two constraints which ensure that the coefficients, $\\tilde{x}$, sum to $1$ and it enforces their L1-norm to be smaller than the user-provided `max_l1_norm` value.\n", + "The optimization objective is to minimize the deviation of $A\\tilde{x}=b$.\n", + "\n", + "We can see all of this in the output below.\n", + "Here, we are setting `max_l1_norm=3` because we already know that the L1-norm of the exact solution is approximately $3.4$ and we want to find a $\\tilde{x}$ that has a smaller L1-norm.\n", + "\n", + "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "de60da28-ce4f-427d-b8c6-11a91cae3472", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "minimize quad_over_lin(Vstack([1. 1. 1.] @ x + -1.0, [0.015625 0.00694444 0.00277008] @ x + -0.0, [2.44140625e-04 4.82253086e-05 7.67336039e-06] @ x + -0.0), 1.0)\n", + "subject to Sum(x, None, False) == 1.0\n", + " norm1(x) <= 3.0\n" + ] + } + ], + "source": [ + "from qiskit_addon_mpf.costs import setup_sum_of_squares_problem\n", + "\n", + "model, coeff_var = setup_sum_of_squares_problem(lse, max_l1_norm=3)\n", + "print(model)" + ] + }, + { + "cell_type": "markdown", + "id": "fc5da15f-7771-446b-b0cf-c9320836102a", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Solving the model with default settings\n", + "\n", + "First, let us see what happens when we solve the `model` with only default settings." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "153755d2-4c04-410c-a251-b939f460643a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===============================================================================\n", + " CVXPY \n", + " v1.5.3 \n", + "===============================================================================\n", + "(CVXPY) Sep 05 09:28:06 AM: Your problem has 3 variables, 2 constraints, and 0 parameters.\n", + "(CVXPY) Sep 05 09:28:06 AM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 05 09:28:06 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 05 09:28:06 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 05 09:28:06 AM: Your problem is compiled with the CPP canonicalization backend.\n", + "-------------------------------------------------------------------------------\n", + " Compilation \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:06 AM: Compiling problem (target solver=OSQP).\n", + "(CVXPY) Sep 05 09:28:06 AM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> OSQP\n", + "(CVXPY) Sep 05 09:28:06 AM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 05 09:28:06 AM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 05 09:28:06 AM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 05 09:28:06 AM: Applying reduction OSQP\n", + "(CVXPY) Sep 05 09:28:06 AM: Finished problem compilation (took 1.053e-02 seconds).\n", + "-------------------------------------------------------------------------------\n", + " Numerical solver \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:06 AM: Invoking solver OSQP to obtain a solution.\n", + "-----------------------------------------------------------------\n", + " OSQP v0.6.3 - Operator Splitting QP Solver\n", + " (c) Bartolomeo Stellato, Goran Banjac\n", + " University of Oxford - Stanford University 2021\n", + "-----------------------------------------------------------------\n", + "problem: variables n = 9, constraints m = 11\n", + " nnz(P) + nnz(A) = 33\n", + "settings: linear system solver = qdldl,\n", + " eps_abs = 1.0e-05, eps_rel = 1.0e-05,\n", + " eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04,\n", + " rho = 1.00e-01 (adaptive),\n", + " sigma = 1.00e-06, alpha = 1.60, max_iter = 10000\n", + " check_termination: on (interval 25),\n", + " scaling: on, scaled_termination: off\n", + " warm start: on, polish: on, time_limit: off\n", + "\n", + "iter objective pri res dua res rho time\n", + " 1 0.0000e+00 1.00e+00 2.00e+02 1.00e-01 6.26e-05s\n", + " 200 4.0661e-09 7.52e-06 1.44e-08 1.22e-05 1.28e-04s\n", + "\n", + "status: solved\n", + "solution polish: unsuccessful\n", + "number of iterations: 200\n", + "optimal objective: 0.0000\n", + "run time: 1.53e-04s\n", + "optimal rho estimate: 2.09e-06\n", + "\n", + "-------------------------------------------------------------------------------\n", + " Summary \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal\n", + "(CVXPY) Sep 05 09:28:07 AM: Optimal value: 4.092e-09\n", + "(CVXPY) Sep 05 09:28:07 AM: Compilation took 1.053e-02 seconds\n", + "(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 1.028e-03 seconds\n" + ] + }, + { + "data": { + "text/plain": [ + "4.092215822096244e-09" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.solve(verbose=True)" + ] + }, + { + "cell_type": "markdown", + "id": "0aafe4c0-c2cc-4de2-824f-ea3594960ca3", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can see from the final value of the cost function that the sum of squares of $A\\tilde{x}-b$ is almost zero.\n", + "\n", + "In the output above, we also get a lot more information from [cvxpy.Problem.solve](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem.solve), some of which we will get back to later.\n", + "\n", + "For now, let us inspect the final values of $\\tilde{x}$ and its L1-norm:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "662b35c2-cd7a-42ff-96a5-792184723f7c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.39646076 0.55556835 0.84089365]\n", + "1.7929227539822414\n" + ] + } + ], + "source": [ + "coeff_approx_plain = coeff_var.value\n", + "print(coeff_approx_plain)\n", + "print(np.linalg.norm(coeff_approx_plain, ord=1))" + ] + }, + { + "cell_type": "markdown", + "id": "aed22d40-140e-4a06-9f6d-90be062fd0cb", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can clearly see that the L1-norm has decreased compared to the analytical solution that we have computed before.\n", + "\n", + "Nonetheless, let us see if we can tweak our solution a bit.\n", + "In the end, we will compare the results obtained for this set of coefficients with all the other ones." + ] + }, + { + "cell_type": "markdown", + "id": "aa3484a3-ce6e-460a-a144-2bc94ab0ace4", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Tweaking the approximate model\n", + "\n", + "Rather than tweaking the input parameters like the chosen `trotter_steps` and `max_l1_norm` values, we will focus on the solver settings in this guide." + ] + }, + { + "cell_type": "markdown", + "id": "a078b0b7-93d0-418f-aaf7-f8584d6889db", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Using a different solver\n", + "\n", + "Notice, that we used the default solver method: `OSQP`. We can change that solver to see whether this has any effect." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "318369c1-7c54-4035-87b9-1e729aa0c048", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===============================================================================\n", + " CVXPY \n", + " v1.5.3 \n", + "===============================================================================\n", + "(CVXPY) Sep 05 09:28:07 AM: Your problem has 3 variables, 2 constraints, and 0 parameters.\n", + "(CVXPY) Sep 05 09:28:07 AM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 05 09:28:07 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 05 09:28:07 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 05 09:28:07 AM: Your problem is compiled with the CPP canonicalization backend.\n", + "-------------------------------------------------------------------------------\n", + " Compilation \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Compiling problem (target solver=CLARABEL).\n", + "(CVXPY) Sep 05 09:28:07 AM: Reduction chain: Dcp2Cone -> CvxAttr2Constr -> ConeMatrixStuffing -> CLARABEL\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction Dcp2Cone\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction ConeMatrixStuffing\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction CLARABEL\n", + "(CVXPY) Sep 05 09:28:07 AM: Finished problem compilation (took 8.657e-03 seconds).\n", + "-------------------------------------------------------------------------------\n", + " Numerical solver \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Invoking solver CLARABEL to obtain a solution.\n", + "-------------------------------------------------------------\n", + " Clarabel.rs v0.8.1 - Clever Acronym \n", + "\n", + " (c) Paul Goulart \n", + " University of Oxford, 2022 \n", + "-------------------------------------------------------------\n", + "\n", + "problem:\n", + " variables = 9\n", + " constraints = 11\n", + " nnz(P) = 3\n", + " nnz(A) = 30\n", + " cones (total) = 2\n", + " : Zero = 1, numel = 4\n", + " : Nonnegative = 1, numel = 7\n", + "\n", + "settings:\n", + " linear algebra: direct / qdldl, precision: 64 bit\n", + " max iter = 200, time limit = Inf, max step = 0.990\n", + " tol_feas = 1.0e-8, tol_gap_abs = 1.0e-8, tol_gap_rel = 1.0e-8,\n", + " static reg : on, ϵ1 = 1.0e-8, ϵ2 = 4.9e-32\n", + " dynamic reg: on, ϵ = 1.0e-13, δ = 2.0e-7\n", + " iter refine: on, reltol = 1.0e-13, abstol = 1.0e-12,\n", + " max iter = 10, stop ratio = 5.0\n", + " equilibrate: on, min_scale = 1.0e-4, max_scale = 1.0e4\n", + " max iter = 10\n", + "\n", + "iter pcost dcost gap pres dres k/t μ step \n", + "---------------------------------------------------------------------------------------------\n", + " 0 +7.1341e-05 -2.3333e+00 2.33e+00 2.40e-01 7.10e-01 1.00e+00 1.88e+00 ------ \n", + " 1 +7.1330e-05 -1.5432e-01 1.54e-01 1.64e-02 1.12e-01 3.28e-02 1.80e-01 9.61e-01 \n", + " 2 +6.9440e-05 -1.7266e-03 1.80e-03 2.10e-04 1.89e-03 5.65e-04 2.43e-03 9.87e-01 \n", + " 3 +2.2053e-05 -5.0002e-05 7.21e-05 5.56e-06 4.83e-05 1.98e-05 7.80e-05 9.87e-01 \n", + " 4 +1.1119e-06 -2.5543e-05 2.67e-05 1.80e-07 1.41e-06 3.21e-06 1.04e-05 9.90e-01 \n", + " 5 +7.9250e-09 -2.0484e-06 2.06e-06 2.06e-09 1.59e-08 2.11e-07 6.43e-07 9.90e-01 \n", + " 6 +2.2543e-09 -2.8390e-08 3.06e-08 2.07e-11 1.59e-10 3.12e-09 9.42e-09 9.90e-01 \n", + " 7 +1.8480e-09 -8.5733e-10 2.71e-09 1.79e-12 2.36e-10 3.19e-10 9.40e-10 9.15e-01 \n", + "---------------------------------------------------------------------------------------------\n", + "Terminated with status = Solved\n", + "solve time = 113.289µs\n", + "-------------------------------------------------------------------------------\n", + " Summary \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal\n", + "(CVXPY) Sep 05 09:28:07 AM: Optimal value: 1.848e-09\n", + "(CVXPY) Sep 05 09:28:07 AM: Compilation took 8.657e-03 seconds\n", + "(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 8.504e-04 seconds\n" + ] + }, + { + "data": { + "text/plain": [ + "1.8479854472132682e-09" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.solve(verbose=True, solver=\"CLARABEL\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "207a1f1d-00e1-40ef-bc2a-f904a58fd1ba", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.21284352 -0.00792683 1.22077035]\n", + "1.4415406931636157\n" + ] + } + ], + "source": [ + "coeff_approx_clarabel = coeff_var.value\n", + "print(coeff_approx_clarabel)\n", + "print(np.linalg.norm(coeff_approx_clarabel, ord=1))" + ] + }, + { + "cell_type": "markdown", + "id": "05b672be-9f72-4a11-a9ee-0d4701a8fc86", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Indeed, the outcome has changed. But now we have one major contribution from the largest $k_j$ value which is also not ideal.\n", + "\n", + "An exhaustive search of the various available solvers is beyond the scope of this guide. Instead we encourage you to try this yourself." + ] + }, + { + "cell_type": "markdown", + "id": "77ba957c-f083-4975-b793-5a96d77a8a37", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Changing the convergence thresholds\n", + "\n", + "Rather than changing the solver altogether, we can also tweak the convergence parameters.\n", + "For the `OSQP` solver the relevant ones are called `eps_abs` and `eps_rel` and they are both set to `1e-5` by default." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "74528a7b-2bba-445d-b324-a61a261d07c3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===============================================================================\n", + " CVXPY \n", + " v1.5.3 \n", + "===============================================================================\n", + "(CVXPY) Sep 05 09:28:07 AM: Your problem has 3 variables, 2 constraints, and 0 parameters.\n", + "(CVXPY) Sep 05 09:28:07 AM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 05 09:28:07 AM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 05 09:28:07 AM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 05 09:28:07 AM: Your problem is compiled with the CPP canonicalization backend.\n", + "-------------------------------------------------------------------------------\n", + " Compilation \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Compiling problem (target solver=OSQP).\n", + "(CVXPY) Sep 05 09:28:07 AM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> OSQP\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 05 09:28:07 AM: Applying reduction OSQP\n", + "(CVXPY) Sep 05 09:28:07 AM: Finished problem compilation (took 8.835e-03 seconds).\n", + "-------------------------------------------------------------------------------\n", + " Numerical solver \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Invoking solver OSQP to obtain a solution.\n", + "-----------------------------------------------------------------\n", + " OSQP v0.6.3 - Operator Splitting QP Solver\n", + " (c) Bartolomeo Stellato, Goran Banjac\n", + " University of Oxford - Stanford University 2021\n", + "-----------------------------------------------------------------\n", + "problem: variables n = 9, constraints m = 11\n", + " nnz(P) + nnz(A) = 33\n", + "settings: linear system solver = qdldl,\n", + " eps_abs = 1.0e-08, eps_rel = 1.0e-08,\n", + " eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04,\n", + " rho = 1.00e-01 (adaptive),\n", + " sigma = 1.00e-06, alpha = 1.60, max_iter = 10000\n", + " check_termination: on (interval 25),\n", + " scaling: on, scaled_termination: off\n", + " warm start: on, polish: on, time_limit: off\n", + "\n", + "iter objective pri res dua res rho time\n", + " 1 0.0000e+00 1.00e+00 2.00e+02 1.00e-01 5.02e-05s\n", + " 200 4.0661e-09 7.52e-06 1.44e-08 1.22e-05 2.10e-04s\n", + " 400 3.8209e-09 4.95e-06 2.88e-09 2.09e-06 3.72e-04s\n", + " 600 3.3607e-09 6.09e-06 1.96e-09 2.09e-06 4.33e-04s\n", + " 800 2.8975e-09 6.24e-06 1.84e-09 2.09e-06 4.87e-04s\n", + "1000 2.4704e-09 6.08e-06 1.70e-09 2.09e-06 5.40e-04s\n", + "1200 2.0921e-09 5.78e-06 1.56e-09 2.09e-06 5.92e-04s\n", + "1400 1.7645e-09 5.40e-06 1.44e-09 2.09e-06 6.44e-04s\n", + "1600 1.4845e-09 5.01e-06 1.32e-09 2.09e-06 7.01e-04s\n", + "1800 1.2471e-09 4.63e-06 1.21e-09 2.09e-06 7.53e-04s\n", + "2000 1.0467e-09 4.26e-06 1.11e-09 2.09e-06 8.05e-04s\n", + "2200 8.7801e-10 3.91e-06 1.01e-09 2.09e-06 8.57e-04s\n", + "2400 7.3890e-10 3.25e-06 7.74e-10 2.09e-06 9.10e-04s\n", + "2600 6.4003e-10 2.47e-06 7.11e-10 2.09e-06 9.62e-04s\n", + "2800 5.6692e-10 2.03e-06 6.68e-10 2.09e-06 1.01e-03s\n", + "3000 5.0872e-10 1.76e-06 6.33e-10 2.09e-06 1.07e-03s\n", + "3200 4.5991e-10 1.58e-06 6.02e-10 2.09e-06 1.12e-03s\n", + "3400 4.1754e-10 1.46e-06 5.74e-10 2.09e-06 1.17e-03s\n", + "3600 3.7999e-10 1.37e-06 5.47e-10 2.09e-06 1.22e-03s\n", + "3800 3.4628e-10 1.29e-06 5.22e-10 2.09e-06 1.32e-03s\n", + "4000 3.1580e-10 1.22e-06 4.99e-10 2.09e-06 1.37e-03s\n", + "4200 2.8813e-10 1.16e-06 4.76e-10 2.09e-06 1.43e-03s\n", + "4400 2.6295e-10 1.11e-06 4.55e-10 2.09e-06 1.48e-03s\n", + "4600 2.4000e-10 1.06e-06 4.35e-10 2.09e-06 1.53e-03s\n", + "4800 2.1907e-10 1.01e-06 4.15e-10 2.09e-06 1.58e-03s\n", + "5000 1.9997e-10 9.67e-07 3.97e-10 2.09e-06 1.63e-03s\n", + "5200 1.8255e-10 9.23e-07 3.79e-10 2.09e-06 1.69e-03s\n", + "5400 1.6664e-10 8.82e-07 3.62e-10 2.09e-06 1.74e-03s\n", + "5600 1.5219e-10 1.01e-06 2.60e-10 2.09e-06 1.79e-03s\n", + "5800 1.4049e-10 6.59e-07 2.47e-10 2.09e-06 1.84e-03s\n", + "6000 1.3096e-10 5.75e-07 2.39e-10 2.09e-06 1.90e-03s\n", + "6200 1.2274e-10 5.24e-07 2.31e-10 2.09e-06 1.95e-03s\n", + "6400 1.1538e-10 4.90e-07 2.24e-10 2.09e-06 2.00e-03s\n", + "6600 1.0863e-10 4.67e-07 2.17e-10 2.09e-06 2.05e-03s\n", + "6800 1.0237e-10 4.48e-07 2.11e-10 2.09e-06 2.10e-03s\n", + "7000 9.6519e-11 4.32e-07 2.05e-10 2.09e-06 2.16e-03s\n", + "7200 9.1024e-11 4.19e-07 1.99e-10 2.09e-06 2.21e-03s\n", + "7400 8.5854e-11 4.06e-07 1.93e-10 2.09e-06 2.29e-03s\n", + "7600 8.0984e-11 3.94e-07 1.88e-10 2.09e-06 2.36e-03s\n", + "7800 7.6393e-11 3.82e-07 1.82e-10 2.09e-06 2.41e-03s\n", + "8000 7.2065e-11 3.71e-07 1.77e-10 2.09e-06 2.46e-03s\n", + "8200 6.7982e-11 3.60e-07 1.72e-10 2.09e-06 2.51e-03s\n", + "8400 6.4131e-11 3.50e-07 1.67e-10 2.09e-06 2.56e-03s\n", + "8600 6.0499e-11 3.40e-07 1.62e-10 2.09e-06 2.62e-03s\n", + "8800 5.7072e-11 3.30e-07 1.57e-10 2.09e-06 2.67e-03s\n", + "9000 5.3859e-11 8.77e-06 8.55e-12 2.09e-06 2.72e-03s\n", + "9200 5.1785e-11 5.78e-07 2.18e-13 2.09e-06 2.79e-03s\n", + "9400 5.0736e-11 7.72e-08 8.32e-14 2.09e-06 2.87e-03s\n", + "9550 5.0303e-11 4.69e-08 8.73e-14 2.09e-06 2.96e-03s\n", + "plsh 4.9640e-11 1.94e-14 1.50e-12 -------- 3.00e-03s\n", + "\n", + "status: solved\n", + "solution polish: successful\n", + "number of iterations: 9550\n", + "optimal objective: 0.0000\n", + "run time: 3.00e-03s\n", + "optimal rho estimate: 2.96e-06\n", + "\n", + "-------------------------------------------------------------------------------\n", + " Summary \n", + "-------------------------------------------------------------------------------\n", + "(CVXPY) Sep 05 09:28:07 AM: Problem status: optimal\n", + "(CVXPY) Sep 05 09:28:07 AM: Optimal value: 4.964e-11\n", + "(CVXPY) Sep 05 09:28:07 AM: Compilation took 8.835e-03 seconds\n", + "(CVXPY) Sep 05 09:28:07 AM: Solver (including time spent in interface) took 3.988e-03 seconds\n" + ] + }, + { + "data": { + "text/plain": [ + "4.9640211694779584e-11" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.solve(verbose=True, eps_abs=1e-8, eps_rel=1e-8)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a5c9fe95-1752-4717-bb74-30766be22f98", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.10925065 -1. 1.89074935]\n", + "3.0\n" + ] + } + ], + "source": [ + "coeff_approx_tight = coeff_var.value\n", + "print(coeff_approx_tight)\n", + "print(np.linalg.norm(coeff_approx_tight, ord=1))" + ] + }, + { + "cell_type": "markdown", + "id": "c841fd73-87fa-49d5-b1a8-f0de4b4c53ff", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "As you can see, we have now obtained a comparatively higher L1-norm (which is still constrained by our choice of `max_l1_norm=3`). While the coefficient of the smallest $k_j$ value is still fairly small in magnitude, at least the other two provide a reasonable amount of mixing." + ] + }, + { + "cell_type": "markdown", + "id": "7f852761-5ef5-4cc6-ab62-d7660f41e82a", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Comparing our results\n", + "\n", + "We will now briefly repeat the computations from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial in order to assess the quality of the various coefficients." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "71e34633-b044-4fcc-aa63-cebc155a848f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.synthesis import SuzukiTrotter\n", + "from qiskit_addon_utils.problem_generators import (\n", + " generate_time_evolution_circuit,\n", + ")\n", + "\n", + "time = 8.0\n", + "circuits = []\n", + "for k in trotter_steps:\n", + " circ = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(order=2, reps=k),\n", + " time=time,\n", + " )\n", + " circuits.append(circ)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "acfc17f7-9817-4576-9709-1f73aa26cf05", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'],\n", + " coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j,\n", + " 0.05+0.j, 0.05+0.j, 0.05+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "L = 10\n", + "observable = SparsePauliOp.from_sparse_list(\n", + " [(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L\n", + ")\n", + "print(observable)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0ea7cac7-0af8-4909-ad23-7b1acb16b607", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.primitives import StatevectorEstimator\n", + "\n", + "estimator = StatevectorEstimator()\n", + "job = estimator.run([(circ, observable) for circ in circuits])\n", + "result = job.result()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2c2b6c4c-6a27-4601-846a-3e6757ad5156", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array(0.23799162), array(0.35754312), array(0.38649906)]\n" + ] + } + ], + "source": [ + "evs = [res.data.evs for res in result]\n", + "print(evs)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "655f4663-d19e-4617-89c3-f0355563d915", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from scipy.linalg import expm\n", + "\n", + "exp_H = expm(-1j * time * hamiltonian.to_matrix())\n", + "\n", + "initial_state = np.zeros(exp_H.shape[0])\n", + "initial_state[0] = 1.0\n", + "\n", + "time_evolved_state = exp_H @ initial_state\n", + "\n", + "exact_obs = (\n", + " time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "35bb0451-091b-4ed2-8776-a1aa7ccb2760", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Analytical coeff solution: 0.39548478559794153\n", + "Plain approx. solution: 0.42928990901998165\n", + "CLARABEL approx. solution: 0.41833743748333746\n", + "Tight approx. solution: 0.3992304700751579\n", + "Exact solution: 0.40060242487900255\n" + ] + } + ], + "source": [ + "print(\"Analytical coeff solution:\", evs @ coeff_analytical)\n", + "print(\"Plain approx. solution:\", evs @ coeff_approx_plain)\n", + "print(\"CLARABEL approx. solution:\", evs @ coeff_approx_clarabel)\n", + "print(\"Tight approx. solution:\", evs @ coeff_approx_tight)\n", + "print(\"Exact solution:\", exact_obs.real)" + ] + }, + { + "cell_type": "markdown", + "id": "adaea843-98fb-48ed-ad1a-0f6ad57f9a7b", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can clearly see now, that the various approximate solutions can yield quite different results.\n", + "Luckily, testing out different expansion coefficients for a fixed choice of $k_j$ values does not require us to rerun our quantum experiments.\n", + "Therefore, we encourage you to play around and tweak your expansion coefficients when using the approximate solutions." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-mpf/index.mdx b/docs/addons/qiskit-addon-mpf/index.mdx new file mode 100644 index 00000000000..116e92a4d7f --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/index.mdx @@ -0,0 +1,63 @@ +--- +title: "Qiskit addon: multi-product formulas (MPF)" +--- + +# Qiskit addon: multi-product formulas (MPF) + +[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. + +This package contains the Qiskit addon for multi-product formulas (MPFs). These can be used to reduce the Trotter error of Hamiltonian dynamics. + +This package currently contains the following main entry points for users: + +* `qiskit_addon_mpf.static` for working with static MPFs \[[1-2](#references)]. +* `qiskit_addon_mpf.dynamic` for working with dynamic MPFs \[[2-3](#references)]. + +## Documentation + +All documentation is available [here](/docs/addons/qiskit-addon-mpf). + +## Installation + +We encourage installing this package via `pip`, when possible: + +```bash +pip install 'qiskit-addon-mpf' +``` + +For more installation information refer to the [installation instructions](install) in the documentation. + +### Optional dependencies + +The `qiskit-addon-mpf` package has a number of optional dependencies which enable certain features. The dynamic MPF feature (see \[[2-3](#references)]) is one such example. You can install the related optional dependencies like so: + +```bash +pip install 'qiskit-addon-mpf[dynamic]' +``` + + + The optional dependency [TeNPy](https://github.com/tenpy/tenpy) was previously offered under a GPLv3 license. As of the release of [v1.0.4](https://github.com/tenpy/tenpy/releases/tag/v1.0.4) on October 2nd, 2024, it has been offered under the Apache v2 license. The license of this package is only compatible with Apache-licensed versions of TeNPy. + + +## Deprecation Policy + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [release notes](/docs/addons/qiskit-addon-mpf/release-notes). + +## Contributing + +The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-mpf). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-mpf/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). + +We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-mpf/issues/new/choose) for tracking requests and bugs. + +## References + +1. 1. Carrera Vazquez, D. J. Egger, D. Ochsner, and S. Wörner, [Well-conditioned multi-product formulas for hardware-friendly Hamiltonian simulation](https://quantum-journal.org/papers/q-2023-07-25-1067/), Quantum 7, 1067 (2023). +2. 19. Zhuk, N. Robertson, and S. Bravyi, [Trotter error bounds and dynamic multi-product formulas for Hamiltonian simulation](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309), Phys. Rev. Research 6, 033309 (2024). +3. 14. Robertson, et al. [Tensor Network enhanced Dynamic Multiproduct Formulas](https://arxiv.org/abs/2407.17405v2), arXiv:2407.17405v2 \[quant-ph]. + +## License + +[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-mpf/blob/main/LICENSE.txt) + diff --git a/docs/addons/qiskit-addon-mpf/install.mdx b/docs/addons/qiskit-addon-mpf/install.mdx new file mode 100644 index 00000000000..89bac4edda7 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/install.mdx @@ -0,0 +1,76 @@ +--- +title: "Installation Instructions" +--- + +# Installation Instructions + +Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: + +* [Option 1: Install from PyPI](#option-1) +* [Option 2: Install from Source](#option-2) + +## Pre-Installation + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +Note: If you are using Windows, use the following commands in PowerShell: + +```pwsh +python3 -m venv c:\path\to\virtual\environment +c:\path\to\virtual\environment\Scripts\Activate.ps1 +``` + + + +## Option 1: Install from PyPI + +The most straightforward way to install the `qiskit-addon-mpf` package is via `PyPI`. + +```sh +pip install 'qiskit-addon-mpf' +``` + + + +## Option 2: Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +If so, the first step is to clone the `qiskit-addon-mpf` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-mpf.git +``` + +Next, upgrade `pip` and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-mpf +``` + +The next step is to install `qiskit-addon-mpf` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab +``` + diff --git a/docs/addons/qiskit-addon-mpf/release-notes.mdx b/docs/addons/qiskit-addon-mpf/release-notes.mdx new file mode 100644 index 00000000000..3fcac204f98 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/release-notes.mdx @@ -0,0 +1,71 @@ +--- +title: "Release Notes" +--- + + + + + +# + + + + + +## 0.3.0 + + + +### New Features + +* TeNPy can disable `Sz` conservation. Previously, the [`initialize_from_lattice()`](qiskit_addon_mpf.backends.tenpy_tebd.MPOState#qiskit_addon_mpf.backends.tenpy_tebd.MPOState.initialize_from_lattice "qiskit_addon_mpf.backends.tenpy_tebd.MPOState.initialize_from_lattice") method did not support this, but a new keyword argument `conserve` has been added which allows the disabling of `Sz` conservation. The default remains to be `True`. + +* Guards against unexpected behavior when `N_steps != 1` in [`qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver.evolve()`](qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver#qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver.evolve "qiskit_addon_mpf.backends.tenpy_layers.LayerwiseEvolver.evolve"). + + + +### Upgrade Notes + +* This release adds support for Python 3.13. No code changes were necessary, so older releases are expected to work on Python 3.13 too. + +* This package is now compatible with Qiskit SDK 2.0. + +* The `keep_only_odd` attribute of [`quimb_layers.LayerModel`](qiskit_addon_mpf.backends.quimb_layers.LayerModel#qiskit_addon_mpf.backends.quimb_layers.LayerModel "qiskit_addon_mpf.backends.quimb_layers.LayerModel") has been removed. The internal workings have been refactored to ensure that data reported by its `terms` attribute (which is inherited from the base class) is already taking the `keep_only_odd` argument of the [`quimb_layers.LayerModel.from_quantum_circuit()`](qiskit_addon_mpf.backends.quimb_layers.LayerModel#qiskit_addon_mpf.backends.quimb_layers.LayerModel.from_quantum_circuit "qiskit_addon_mpf.backends.quimb_layers.LayerModel.from_quantum_circuit") constructor method. + +* The `scaling_factor` keyword argument of the `from_quantum_circuit` constructor methods has been removed. It is not actually needed and was merely adding an additional (confusing) re-scaling. + + + +### Bug Fixes + +* Fixes the [`DynamicMPF.evolve()`](qiskit_addon_mpf.dynamic#qiskit_addon_mpf.dynamic.DynamicMPF.evolve "qiskit_addon_mpf.dynamic.DynamicMPF.evolve") method when providing `time` values with numerical inaccuracies (e.g. `time=1.00000001`). Previously this could cause the LHS and RHS time evolvers to advance too far. The new default accuracy is 8 decimal places, but it may be configured via the [`DynamicMPF.TIME_DECIMALS`](qiskit_addon_mpf.dynamic#qiskit_addon_mpf.dynamic.DynamicMPF.TIME_DECIMALS "qiskit_addon_mpf.dynamic.DynamicMPF.TIME_DECIMALS") value. + + + + + +## 0.2.0 + + + + + +### New Features + +* Adds the ability to compute dynamic (i.e. time-dependent) MPF coefficients. For more details, refer to [`qiskit_addon_mpf.dynamic`](qiskit_addon_mpf.dynamic#module-qiskit_addon_mpf.dynamic "qiskit_addon_mpf.dynamic"). + + + + + +### Upgrade Notes + +* The code for the static MPF coefficients has been moved. It functions the same as before, but you have to update your imports and function names as summarized in the table below: + + | Old | New | + | ------------------------------------------------------------- | ----------------------------------------------------------------- | + | `from qiskit_addon_mpf.static import setup_lse` | `from qiskit_addon_mpf.static import setup_static_lse` | + | `from qiskit_addon_mpf.static import LSE` | `from qiskit_addon_mpf.costs import LSE` | + | `from qiskit_addon_mpf.static import setup_exact_model` | `from qiskit_addon_mpf.costs import setup_exact_problem` | + | `from qiskit_addon_mpf.static import setup_approximate_model` | `from qiskit_addon_mpf.costs import setup_sum_of_squares_problem` | + diff --git a/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb b/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb new file mode 100644 index 00000000000..68a04819670 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb @@ -0,0 +1,960 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b1b4d0d8-13f5-4b7a-abe0-2a2f034b8d35", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Reducing the Trotter error of Hamiltonian dynamics with multi-product formulas\n", + "\n", + "In this notebook, you will learn how to use a **Multi-Product Formula (MPF)** to achieve a lower Trotter error on our observable compared to the one incurred by the deepest Trotter circuit that we will actually execute.\n", + "You will do so by working through the steps of a **Qiskit pattern**:\n", + "\n", + "- **Step 1: Map to quantum problem**\n", + " - Initialize our problem's Hamiltonian\n", + " - Use an MPF to generate the Trotterized time-evolution circuits\n", + "- **Step 2: Optimize the problem**\n", + " - Here we transpile our circuits for a [GenericBackendV2](/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2)\n", + "- **Step 3: Execute experiments**\n", + " - Use a [StatevectorEstimator](/api/qiskit/qiskit.primitives.StatevectorEstimator) for sake of simplicity in this notebook\n", + "- **Step 4: Reconstruct results**\n", + " - Compute the MPF expectation value" + ] + }, + { + "cell_type": "markdown", + "id": "fd1bbb0f-120f-4425-a52a-e393f86c16cc", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Step 1: Map to quantum problem\n", + "\n", + "### 1a: Setting up our Hamiltonian\n", + "\n", + "We use the Ising model on a line of 10 sites:\n", + "\n", + "$$\n", + "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", + "$$\n", + "\n", + "where $J$ is the coupling strength between two sites and $h$ is the external magnetic field." + ] + }, + { + "cell_type": "markdown", + "id": "55db9ce4-3cfb-439c-bb0d-a64030a52183", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The [qiskit_addon_utils](/docs/addons/qiskit-addon-utils) package provides some reusable functionalities for various purposes.\n", + "\n", + "Its [qiskit_addon_utils.problem_generators](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators) module provides functions to generate Heisenberg-like Hamiltonians on a given connectivity graph.\n", + "This graph can be either a [rustworkx.PyGraph](https://www.rustworkx.org/apiref/rustworkx.PyGraph.html) or a [CouplingMap](/api/qiskit/qiskit.transpiler.CouplingMap) making it easy to use in Qiskit-centric workflows.\n", + "\n", + "In the following, we create a simple line of 10 qubits using the `CouplingMap.from_line` method." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "98617c1f-00cc-4fdd-8dbc-7f0c7efd8344", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "\n", + "# Generate some coupling map to use for this example\n", + "coupling_map = CouplingMap.from_line(10, bidirectional=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e15dd313-668d-48b3-8133-3cd4df9a3d8e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from rustworkx.visualization import graphviz_draw\n", + "\n", + "graphviz_draw(coupling_map.graph, method=\"circo\")" + ] + }, + { + "cell_type": "markdown", + "id": "fa7dcbde-531c-4442-8c16-9127d0b2d11b", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Next, we generate the [SparsePauliOp](/api/qiskit/qiskit.quantum_info.SparsePauliOp) on the provided connectivity with the desired constants." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c03810d1-cb9f-429e-85d2-055486d77d2b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'],\n", + " coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j,\n", + " 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j,\n", + " 0.4+0.j, 0.4+0.j, 0.4+0.j])\n" + ] + } + ], + "source": [ + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "# Get a qubit operator describing the Ising field model\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " coupling_map,\n", + " coupling_constants=(0.0, 0.0, 1.0),\n", + " ext_magnetic_field=(0.4, 0.0, 0.0),\n", + ")\n", + "print(hamiltonian)" + ] + }, + { + "cell_type": "markdown", + "id": "bdb73298-d30a-407a-9e5d-de7c57264f6b", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The observable that we will be measuring is the total magnetization which we can simply construct as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "77232669-aa00-4f74-9f50-758f0c960693", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'],\n", + " coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j,\n", + " 0.05+0.j, 0.05+0.j, 0.05+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "L = coupling_map.size()\n", + "observable = SparsePauliOp.from_sparse_list(\n", + " [(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L\n", + ")\n", + "print(observable)" + ] + }, + { + "cell_type": "markdown", + "id": "83dc1e84-df2c-4e08-a8c9-3ecd6595f04e", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### 1b: Multi-Product Formulas\n", + "\n", + "MPFs reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions.\n", + "\n", + "To make this more concrete, we define an MPF as:\n", + "\n", + "$$\n", + "\\mu(t) = \\sum_{j} x_j \\rho^{k_j}_{j}\\left(\\frac{t}{k_j}\\right) + \\text{some remaining Trotter error} \\, ,\n", + "$$\n", + "\n", + "where $x_j$ are our weighting coefficients, $\\rho^{k_j}_j$ is the density matrix corresponding to the pure state obtained by evolving the initial state with the product formula, $S^{k_j}$, involving $k_j$ Trotter steps, and $j$ indexes the number of product formulas that make up the MPF.\n", + "\n", + "The key here is that the remaining Trotter error is smaller than the Trotter error that one would obtain by simply using the largest $k_j$ value!\n", + "\n", + "You can view the usefulness of MPF from two perspectives:\n", + "\n", + "1. For a fixed budget of Trotter steps that you are able to execute, you can obtain results with a Trotter error that is smaller in total.\n", + "2. For a number of Trotter steps which results in deep circuits, you can use MPF to find several shorter-depth circuits to run which result in a similar Trotter error." + ] + }, + { + "cell_type": "markdown", + "id": "58ffeb8e-978b-4211-b949-21bebe98bd1f", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### An introduction to static MPFs\n", + "\n", + "_Static_ MPFs are those where the $x_j$ values do **NOT** depend on the evolution time, $t$.\n", + "\n", + "Determining the static MPF coefficients for a given set of $k_j$ values amounts to solving a linear system of equations:\n", + "$Ax=b$, where $x$ are our coefficients of interest, $A$ is a matrix depending on $k_j$ and the type of PF we use ($S$), and $b$ is a vector of constraints.\n", + "For brevity, we are not going to go into more detail here and instead refer you to the documentation of [LSE](/docs/addons/qiskit-addon-mpf/apidocs/qiskit_addon_mpf.costs#qiskit_addon_mpf.costs.LSE).\n", + "\n", + "We can find a solution for $x$ analytically as $x = A^{-1}b$, see e.g. [Carrera Vazquez et al., 2023] or [Zhuk et al., 2023].\n", + "However, this exact solution can be _\"ill-conditioned\"_ resulting in very large L1-norms of our coefficients, $x$, which can lead to bad performance of the MPF.\n", + "Instead, one can also obtain an approximate solution which minimizes the L1-norm of $x$ in order to attempt and optimize the MPF behavior.\n", + "\n", + "In the following, you will learn how to do all of this.\n", + "\n", + "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/\n", + "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" + ] + }, + { + "cell_type": "markdown", + "id": "b293d4ae-cee1-4fd6-bb56-926dc282e868", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Choosing $k_j$\n", + "\n", + "The choice of $k_j$ is up to the end-user.\n", + "In principle, any values can be chosen but some $k_j$'s will lead to a larger noise amplification on real devices than other choices.\n", + "Thus, it is important that one tries to find _\"good\"_ values of $k_j$.\n", + "\n", + "Here, we will simply pick some fixed values for $k_j$.\n", + "The smallest value is motivated by the target evolution time of $t=8.0$ which normally tells us to satisfy $t/k_{\\text{min}} \\lt 1$ but empirically we know that setting it equal to $1$ usually works, too.\n", + "If you want to learn more about this and how to choose your other $k_j$ values, refer to the respective guide: [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "89682993-64b3-44ea-ae95-cc9eea30a632", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "time = 8.0\n", + "trotter_steps = (8, 12, 19)" + ] + }, + { + "cell_type": "markdown", + "id": "c1732f75-1299-4ade-bf7f-eab0c4758cba", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Setting up the LSE\n", + "\n", + "Now that we have chosen our $k_j$s, we must first construct the LSE, $Ax=b$ as explained above.\n", + "The matrix $A$ depends not only on $k_j$ but also our choice of product formula (PF) -- in particular its _order_.\n", + "Additionally, one may take into account whether the PF is symmetric or not (see [Carrera Vazquez et al., 2023]), by setting `symmetric=True`.\n", + "However, this is not required as shown by [Zhuk et al., 2023].\n", + "\n", + "Here, we are going to use a second-order Suzuki-Trotter formula yielding `order=2` and we will set `symmetric=True`.\n", + "\n", + "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/\n", + "[Zhuk et al., 2023]: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033309" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4898872e-8dc5-43f6-9615-31081851a80a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LSE(A=array([[1.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n", + " [1.56250000e-02, 6.94444444e-03, 2.77008310e-03],\n", + " [2.44140625e-04, 4.82253086e-05, 7.67336039e-06]]), b=array([1., 0., 0.]))\n" + ] + } + ], + "source": [ + "from qiskit_addon_mpf.static import setup_static_lse\n", + "\n", + "lse = setup_static_lse(trotter_steps, order=2, symmetric=True)\n", + "print(lse)" + ] + }, + { + "cell_type": "markdown", + "id": "cb2fcfff-7d3c-4128-b4be-504a634f9a02", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Solving for $x$ analytically\n", + "\n", + "As mentioned before, we can find $x$ analytically:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e0db1488-21f5-4f7b-a9f4-98e2e945afde", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.17239057 -1.19447005 2.02207947]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "coeffs_analytical = lse.solve()\n", + "print(coeffs_analytical)" + ] + }, + { + "cell_type": "markdown", + "id": "a4517a21-61e2-45fe-bea1-82e87e48d8f7", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Optimizing for $x$ using an exact model\n", + "\n", + "Alternatively to computing $x=A^{-1}b$, you can also use [setup_exact_problem](/docs/addons/qiskit-addon-mpf/stubs/qiskit_addon_mpf.costs.setup_exact_problem) to construct a [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which uses the LSE as constraints and whose optimal solution will yield $x$.\n", + "\n", + "In the next section, it will be clear why this interface exists." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3bce6d85-a9dd-49e4-abe0-f8575394122c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.17239057 -1.19447005 2.02207947]\n" + ] + } + ], + "source": [ + "from qiskit_addon_mpf.costs import setup_exact_problem\n", + "\n", + "model_exact, coeffs_exact = setup_exact_problem(lse)\n", + "model_exact.solve()\n", + "print(coeffs_exact.value)" + ] + }, + { + "cell_type": "markdown", + "id": "bb9971aa-a4a5-4e04-85fe-2541ce21ac1f", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "As an indicator whether an MPF constructed with these coefficients will yield good results, we can use the L1-norm (see also [Carrera Vazquez et al., 2023]).\n", + "\n", + "[Carrera Vazquez et al., 2023]: https://quantum-journal.org/papers/q-2023-07-25-1067/" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b8ffca3a-87e7-4344-b189-af95ef5bf2f2", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.3889400921655914\n" + ] + } + ], + "source": [ + "print(np.linalg.norm(coeffs_exact.value, ord=1))" + ] + }, + { + "cell_type": "markdown", + "id": "a6f84839-0879-4f48-bba0-89cc671986c5", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Optimizing for $x$ using an approximate model\n", + "\n", + "It may happen that the L1 norm for the chosen set of $k_j$ values is deemed too high.\n", + "If that is the case and you cannot choose a different set of $k_j$ values, you can use an approximate solution to the LSE instead of an exact one.\n", + "\n", + "To do so, simply use [setup_sum_of_squares_problem](/docs/addons/qiskit-addon-mpf/stubs/qiskit_addon_mpf.costs.setup_sum_of_squares_problem) to construct a different [cvxpy.Problem](https://www.cvxpy.org/api_reference/cvxpy.problems.html#cvxpy.Problem) instance which constrains the L1-norm to a chosen threshold while minimizing the difference of $Ax$ and $b$." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "30dac7af-5a92-48f4-bc1a-1e140546cab2", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.40454257 0.57553173 0.8290123 ]\n", + "1.8090865903790838\n" + ] + } + ], + "source": [ + "from qiskit_addon_mpf.costs import setup_sum_of_squares_problem\n", + "\n", + "model_approx, coeffs_approx = setup_sum_of_squares_problem(\n", + " lse, max_l1_norm=3.0\n", + ")\n", + "model_approx.solve()\n", + "print(coeffs_approx.value)\n", + "print(np.linalg.norm(coeffs_approx.value, ord=1))" + ] + }, + { + "cell_type": "markdown", + "id": "2dc731cc-f1c6-484b-afba-dba6c1c0d1db", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Note, that you have complete freedom over how to solve this optimization problem, meaning that you can change the optimization solver, its convergence thresholds, and so on.\n", + "Check out the respective guide on [How to use the approximate model](/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model)." + ] + }, + { + "cell_type": "markdown", + "id": "e328b896-a303-45f8-bfc6-4918205a18ec", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### 1c: Setting up the Trotter circuits\n", + "\n", + "At this point, we have found our expansion coefficients, $x$, and all that is left to do is to generate the Trotterized quantum circuits.\n", + "Once again, the [qiskit_addon_utils.problem_generators](/docs/addons/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators) module comes to the rescue to do just that:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fe25afaf-2f43-4c82-a97b-946b9a86df98", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.synthesis import SuzukiTrotter\n", + "from qiskit_addon_utils.problem_generators import (\n", + " generate_time_evolution_circuit,\n", + ")\n", + "\n", + "circuits = []\n", + "for k in trotter_steps:\n", + " circ = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(order=2, reps=k),\n", + " time=time,\n", + " )\n", + " circuits.append(circ)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "01b3c52b-53cc-4ccc-a2f0-efd774486070", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuits[0].draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "73e2099d-e7a7-4b6b-b6be-5f75fb1ccc8d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuits[1].draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "25fcf8ff-3f91-4312-9334-ea55cf3efb14", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuits[2].draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "6675e269-a681-4569-82c4-a95800e82be8", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Step 2: Optimize the problem\n", + "\n", + "Normally, this is the step in the pattern during which you optimize your circuits for execution on hardware.\n", + "Here, since we only use a noiseless simulator, we simply transpile our circuit for a [GenericBackendV2](/api/qiskit/qiskit.providers.fake_provider.GenericBackendV2)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6c049ff6-5fc0-4673-a9d9-a37abcf96711", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.providers.fake_provider import GenericBackendV2\n", + "from qiskit.transpiler import generate_preset_pass_manager\n", + "\n", + "backend = GenericBackendV2(num_qubits=10)\n", + "transpiler = generate_preset_pass_manager(\n", + " optimization_level=2, backend=backend\n", + ")\n", + "\n", + "transpiled_circuits = [transpiler.run(circ) for circ in circuits]" + ] + }, + { + "cell_type": "markdown", + "id": "a996e888-ff56-4ca4-849c-cc85b07a6b73", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Step 3: Execute quantum experiments\n", + "\n", + "As explained in the very beginning, we will skip the optimization step 2 because we are simply going to compute our target observable's expectation values using a noise-free simulator, namely the [StatevectorEstimator](/api/qiskit/qiskit.primitives.StatevectorEstimator)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "494cb360-91ae-4ba3-88ca-97e017ef1de5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.primitives import StatevectorEstimator\n", + "\n", + "estimator = StatevectorEstimator()\n", + "job = estimator.run([(circ, observable) for circ in transpiled_circuits])\n", + "result = job.result()" + ] + }, + { + "cell_type": "markdown", + "id": "04445a72-7a16-4dc5-a4af-0917ad9f7124", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Step 4: Reconstruct results\n", + "\n", + "First, we extract the individual expectation values obtained for each of the Trotter circuits:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7779817f-638d-4728-87d2-473d75e3aef4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array(0.23799162), array(0.35754312), array(0.38649906)]\n" + ] + } + ], + "source": [ + "evs = [res.data.evs for res in result]\n", + "print(evs)" + ] + }, + { + "cell_type": "markdown", + "id": "cea904fd-34fe-492b-9470-42580f9620c2", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Next, we simply recombine them with our MPF coefficients to yield the total expectation values of the MPF. Below, we do so for each of the different ways by which we have computed $x$." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e5f536d5-ffce-4b7a-80f3-44846d50ee19", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Analytical solution: 0.3954847855980006\n", + "Exact model solution: 0.39548478559800204\n", + "Approx. model solution: 0.42991214253489807\n" + ] + } + ], + "source": [ + "print(\"Analytical solution:\", evs @ coeffs_analytical)\n", + "print(\"Exact model solution:\", evs @ coeffs_exact.value)\n", + "print(\"Approx. model solution:\", evs @ coeffs_approx.value)" + ] + }, + { + "cell_type": "markdown", + "id": "29852936-2a0e-49d2-a22c-f3d1e8137e36", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Finally, for this small problem we can compute the exact reference value using [scipy.linalg.expm](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.expm.html) as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "eeff7277-7295-4b78-a449-f151c8bd3e66", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.40060242487899755\n" + ] + } + ], + "source": [ + "from scipy.linalg import expm\n", + "\n", + "exp_H = expm(-1j * time * hamiltonian.to_matrix())\n", + "\n", + "initial_state = np.zeros(exp_H.shape[0])\n", + "initial_state[0] = 1.0\n", + "\n", + "time_evolved_state = exp_H @ initial_state\n", + "\n", + "exact_obs = (\n", + " time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", + ")\n", + "print(exact_obs.real)" + ] + }, + { + "cell_type": "markdown", + "id": "4ed84f18-d1e4-4b6c-b362-8e8a8a18633c", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can clearly see that the MPF has reduced the Trotter error compared to the one obtained with the deepest individual PF with $k_j=19$.\n", + "However, we also see that the approximate model is not flawless since it actually resulted in an expectation value that is worse than the exact solution. This shows the importance of using tight convergence criteria on the approximate model as you will learn in the guide [How to use the approximate model](/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-obp/release-notes.mdx b/docs/addons/qiskit-addon-obp/release-notes.mdx new file mode 100644 index 00000000000..ebf4396e714 --- /dev/null +++ b/docs/addons/qiskit-addon-obp/release-notes.mdx @@ -0,0 +1,171 @@ +--- +title: "Release Notes" +--- + + + + + +# + + + + + +## 0.3.0 + + + +### New Features + +* [`PauliLindbladError`](/docs/api/qiskit-ibm-runtime/utils-noise-learner-result-pauli-lindblad-error "(in Qiskit Runtime IBM Client)") objects can now be embedded in slices of `QuantumCircuit` using the [`PauliLindbladErrorInstruction`](qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction#qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction "qiskit_addon_obp.utils.noise.PauliLindbladErrorInstruction") and will be handled properly by [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate"). + + + +### Bug Fixes + +* Fixed a bug in [`qiskit_addon_obp.backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") which often caused resets to be applied to the wrong qubit. + + + + + +## 0.2.0 + + + + + +### New Features + +* A new parameter `show_legend` has been added to each function in the [`qiskit_addon_obp.utils.visualization`](qiskit_addon_obp.utils.visualization#module-qiskit_addon_obp.utils.visualization "qiskit_addon_obp.utils.visualization") module that can show or hide the legend on a plot. The legend is shown by default. This can be useful when the legend becomes long and obstructs the plot. + +* [`qiskit_addon_obp.utils.simplify.OperatorBudget`](qiskit_addon_obp.utils.simplify#qiskit_addon_obp.utils.simplify.OperatorBudget "qiskit_addon_obp.utils.simplify.OperatorBudget") now holds `atol` and `rtol` fields which are absolute and relative tolerances used to determine whether coefficients are zero while simplifying an operator. + +* [`truncate_binary_search()`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search "qiskit_addon_obp.utils.truncating.truncate_binary_search") now accepts a `tol` kwarg. Once an optimal truncation threshold, up to this value, has been found, the search for an optimal threshold will stop. The default tolerance is `1e-8`; whereas, the tolerance used prior to this release was `1e-10`. + + [`TruncationErrorBudget`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") now holds a `tol` field. This field is used by [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") as the `tol` argument to [`truncate_binary_search()`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.truncate_binary_search "qiskit_addon_obp.utils.truncating.truncate_binary_search"). + + + +### Upgrade Notes + +* This release adds support for Python 3.13. No code changes were necessary, so older releases are expected to work on Python 3.13 too. + +* This package is now compatible with Qiskit SDK 2.0. + + + + + +### Bug Fixes + +* The [`num_duplicate_paulis`](qiskit_addon_obp.utils.simplify#qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_duplicate_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_duplicate_paulis") was previously unable to differentiate from Pauli terms that were trimmed due to their coefficient being close to zero. This is now tracked correctly with these trimmed terms only counting towards [`num_trimmed_paulis`](qiskit_addon_obp.utils.simplify#qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_trimmed_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_trimmed_paulis"). + +* The reported number of unique Paulis in [`num_unique_paulis`](qiskit_addon_obp.utils.simplify#qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_unique_paulis "qiskit_addon_obp.utils.simplify.SimplifyMetadata.num_unique_paulis") is now correct in cases where all gates in a slice commute with an observable. + +* Fixed a bug in [`qiskit_addon_obp.backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") which caused slices with depth > 1 to be incorrectly forward-propagated. Gates from a given slice will now be correctly propagated into the observable in reverse order (i.e., from the back of the slice). + + + + + +## 0.1.0 + + + + + +### New Features + +* Added a [`OperatorBudget`](qiskit_addon_obp.utils.simplify#qiskit_addon_obp.utils.simplify.OperatorBudget "qiskit_addon_obp.utils.simplify.OperatorBudget") class for specifying how large an operator may grow during back-propagation. + +* Adds the `max_seconds` keyword-argument to the [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") function, allowing the end-user to specify a maximum wall clock time for the algorithm. This can (for example) be useful for exploring different truncation error budget strategies while limiting the CPU time. + +* Introduced a new `dataclass`, [`TruncationErrorBudget`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget"), for holding information about the observable truncation strategy. + +* Introduced a new function, [`setup_budget()`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.setup_budget "qiskit_addon_obp.utils.truncating.setup_budget"), which generates a [`TruncationErrorBudget`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") class, given an observable truncation strategy (e.g. `max_error_total`, `max_error_per_slice`, `p_norm`). + + + + + +### Upgrade Notes + +* The [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") function no longer accepts `max_paulis` and `max_qwc_groups` kwargs for constraining the size of the operator during back-propagation. Users should instead use the new `operator_budget` kwarg, which takes an [`OperatorBudget`](qiskit_addon_obp.utils.simplify#qiskit_addon_obp.utils.simplify.OperatorBudget "qiskit_addon_obp.utils.simplify.OperatorBudget") instance. + + To migrate, change this code + + ```python + from qiskit_addon_obp import backpropagate + + bp_obs, remaining_slices, metadata = backpropagate( + obs, + slices, + max_paulis=100, + max_qwc_groups=10, + simplify=True + ) + ``` + + to this + + ```python + from qiskit_addon_obp import backpropagate + from qiskit_addon_obp.utils.simplify import OperatorBudget + + op_budget = OperatorBudget(max_paulis=100, max_qwc_groups=10, simplify=True) + bp_obs, remaining_slices, metadata = backpropagate(obs, slices, operator_budget=op_budget) + ``` + +* The `max_slices` kwarg has been removed from [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate"). Users should now only pass in slices which they intend to back-propagate. If a user wants to attempt to only back-propagate the last `20` slices of an `N`-slice circuit, they would simply pass in the last `20` slices to [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") and, recombine any slices remaining after back-propagation with the original `N-20` slices. + + For example + + ```python + from qiskit_addon_obp import backpropagate + from qiskit_addon_obp.utils.truncating import setup_budget + from qiskit_addon_utils.slicing import combine_slices + + num_slices = 20 + truncation_error_budget = setup_budget(max_error_total=0.02, num_slices=num_slices, p_norm=1) + bp_obs, remaining_slices, metadata = backpropagate( + obs, slices[-num_slices:], truncation_error_budget=truncation_error_budget + ) + reduced_circuit = combine_slices(slices[:-num_slices] + remaining_slices) + ``` + +* The `max_slices` kwarg in [`setup_budget()`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.setup_budget "qiskit_addon_obp.utils.truncating.setup_budget") has been renamed to `num_slices`. + +* The `max_slices` attribute in [`OBPMetadata`](qiskit_addon_obp.utils.metadata.OBPMetadata#qiskit_addon_obp.utils.metadata.OBPMetadata "qiskit_addon_obp.utils.metadata.OBPMetadata") has been renamed to `num_slices`. + +* The project’s root Python namespace has been changed from `obp` to `qiskit_addon_obp`. All package imports must be updated. + + For example: + + ```python + from obp import backpropagate + ``` + + should be changed to: + + ```python + from qiskit_addon_obp import backpropagate + ``` + +* Removed the `max_error_total`, `max_error_per_slice`, and `p_norm` kwargs from the [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") signature. Instead, users must specify their observable truncation strategy with the new `truncation_error_budget` kwarg which accepts a [`TruncationErrorBudget`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") instance. + +* Removed the `per_slice_budget`, `max_error_total`, and `p_norm` fields from the [`OBPMetadata`](qiskit_addon_obp.utils.metadata.OBPMetadata#qiskit_addon_obp.utils.metadata.OBPMetadata "qiskit_addon_obp.utils.metadata.OBPMetadata") class. These fields will now be accessed through the new `truncation_error_budget` field, which holds a [`TruncationErrorBudget`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.TruncationErrorBudget "qiskit_addon_obp.utils.truncating.TruncationErrorBudget") instance. + + + + + +### Bug Fixes + +* The [`setup_budget()`](qiskit_addon_obp.utils.truncating#qiskit_addon_obp.utils.truncating.setup_budget "qiskit_addon_obp.utils.truncating.setup_budget") erroneously distributed the `max_error_total` when `num_slices` was also set. This has been fixed now, such that the budget always gets distributed evenly, regardless of the value of `p_norm`. + +* When the `max_seconds` argument to the [`backpropagate()`](qiskit_addon_obp#qiskit_addon_obp.backpropagate "qiskit_addon_obp.backpropagate") method is used, but the timeout is not reached during the actual OBP execution, the signal will now be reset properly, thereby avoiding cancellations at a (seemingly) random later point in time (of course, it is not random but actually after the specified amount of time has passed, but the rest of the code being executed after OBP could be doing anything at this point). + +* The computation of the [`accumulated_error()`](qiskit_addon_obp.utils.metadata.OBPMetadata#qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error "qiskit_addon_obp.utils.metadata.OBPMetadata.accumulated_error") and [`left_over_error_budget()`](qiskit_addon_obp.utils.metadata.OBPMetadata#qiskit_addon_obp.utils.metadata.OBPMetadata.left_over_error_budget "qiskit_addon_obp.utils.metadata.OBPMetadata.left_over_error_budget") were fixed to respect the [Minkowski inequality](https://en.wikipedia.org/wiki/Minkowski_inequality). 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Record; export const ADDON_DOCS_CONFIG: AddonDocsConfig = { + "qiskit-addon-mpf": { + version: "0.3.0", + include: [ + "index.html", + "install.html", + "release-notes.html", + "how_tos/*.ipynb", + "tutorials/*.ipynb", + "explainations/index.html", + "explainations/*.ipynb", + ], + }, "qiskit-addon-obp": { version: "0.3.0", include: [ "index.html", "install.html", + "release-notes.html", "how_tos/*.ipynb", "tutorials/*.ipynb", ], From 881b9757b4543f613fa0c0d9ec8ef762b24a4a2c Mon Sep 17 00:00:00 2001 From: Eric Harvey Date: Thu, 30 Apr 2026 16:28:25 -0400 Subject: [PATCH 006/104] fix addon-mfp includes --- .../explanations/mpf_stability.ipynb | 322 ++++++++++++++++++ ...45cf113-58da-454e-97b6-679d5a5ae9fb-0.avif | Bin 0 -> 18853 bytes scripts/config/addon-docs.ts | 3 +- 3 files changed, 323 insertions(+), 2 deletions(-) create mode 100644 docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb create mode 100644 public/docs/images/addons/qiskit-addon-mpf/explanations/mpf_stability/extracted-outputs/845cf113-58da-454e-97b6-679d5a5ae9fb-0.avif diff --git a/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb b/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb new file mode 100644 index 00000000000..40b909c8367 --- /dev/null +++ b/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb @@ -0,0 +1,322 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4aaf8397-4140-4d16-b4b5-1d9e20adea98", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Understanding the stability of MPFs\n", + "\n", + "Over in the guide [How to choose the Trotter steps for an MPF](/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps) we introduced a heuristic that limits the **smallest** $k_j$ value ($k_{\\text{min}}$) based on the total evolution time, $t$, that we would like to reach.\n", + "In particular, we state that $t/k_{\\text{min}} \\lt 1$ must be satisfied (noting that $t/k_{\\text{min}} \\leq 1$ empirically seems to work fine, too).\n", + "\n", + "On this page, we will analyze when the behavior of MPFs is not guaranteed to work well, such as when this constraint gets violated." + ] + }, + { + "cell_type": "markdown", + "id": "986a58b5-611a-4400-9bd5-ba35d4a14188", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Setting up a simple model problem\n", + "\n", + "For this simple example, we will reuse the model problem from the [Getting started](/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started) tutorial; the Ising model on a line of 10 sites:\n", + "\n", + "$$\n", + "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{9} J_{i,(i+1)} Z_i Z_{(i+1)} + \\sum_{i=1}^{10} h_i X_i \\, ,\n", + "$$\n", + "\n", + "where $J$ is the coupling strength between two sites and $h$ is the external magnetic field." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d77e337b-0a69-49a5-b449-ffe4ad17d7ab", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIZZI', 'IIIIIZZIII', 'IIIZZIIIII', 'IZZIIIIIII', 'IIIIIIIIZZ', 'IIIIIIZZII', 'IIIIZZIIII', 'IIZZIIIIII', 'ZZIIIIIIII', 'IIIIIIIIIX', 'IIIIIIIIXI', 'IIIIIIIXII', 'IIIIIIXIII', 'IIIIIXIIII', 'IIIIXIIIII', 'IIIXIIIIII', 'IIXIIIIIII', 'IXIIIIIIII', 'XIIIIIIIII'],\n", + " coeffs=[1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j, 1. +0.j,\n", + " 1. +0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j, 0.4+0.j,\n", + " 0.4+0.j, 0.4+0.j, 0.4+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "coupling_map = CouplingMap.from_line(10, bidirectional=False)\n", + "\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " coupling_map,\n", + " coupling_constants=(0.0, 0.0, 1.0),\n", + " ext_magnetic_field=(0.4, 0.0, 0.0),\n", + ")\n", + "print(hamiltonian)" + ] + }, + { + "cell_type": "markdown", + "id": "0f25e279-e987-4da5-a7e3-ffea41542d51", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The observable that we will be measuring is the total magnetization which we can simply construct as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eca935e2-206e-471e-a673-62a67a00add0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIIIZ', 'IIIIIIIIZI', 'IIIIIIIZII', 'IIIIIIZIII', 'IIIIIZIIII', 'IIIIZIIIII', 'IIIZIIIIII', 'IIZIIIIIII', 'IZIIIIIIII', 'ZIIIIIIIII'],\n", + " coeffs=[0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j, 0.05+0.j,\n", + " 0.05+0.j, 0.05+0.j, 0.05+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "L = coupling_map.size()\n", + "observable = SparsePauliOp.from_sparse_list(\n", + " [(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L\n", + ")\n", + "print(observable)" + ] + }, + { + "cell_type": "markdown", + "id": "b652e4ab-26c1-4c4e-a34d-47a464c60f26", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Simulating various Trotter circuits\n", + "\n", + "Our analysis simply consists of comparing the behavior of product formulas of various order (here, second and fourth order) to the exact solution (which we can compute for a problem as simple as this one).\n", + "\n", + "In the code cell below, we do exactly that, for evolution times ranging from $2$ up to $10$ and Trotter steps ranging from $1$ to $25$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e7c43eec-3809-4e3a-998a-7f6a7a8cf2a6", + "metadata": { + "editable": true, + "scrolled": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit.primitives import StatevectorEstimator\n", + "from qiskit.synthesis import SuzukiTrotter\n", + "from qiskit_addon_utils.problem_generators import (\n", + " generate_time_evolution_circuit,\n", + ")\n", + "from scipy.linalg import expm\n", + "\n", + "estimator = StatevectorEstimator()\n", + "times = np.array(range(2, 11))\n", + "ks = np.array(range(1, 25))\n", + "\n", + "exact = []\n", + "order2 = []\n", + "order4 = []\n", + "\n", + "for time in times:\n", + " exp_H = expm(-1j * time * hamiltonian.to_matrix())\n", + " initial_state = np.zeros(exp_H.shape[0])\n", + " initial_state[0] = 1.0\n", + " time_evolved_state = exp_H @ initial_state\n", + " exact_evs = (\n", + " time_evolved_state.conj()\n", + " @ observable.to_matrix()\n", + " @ time_evolved_state\n", + " )\n", + " exact.append(float(exact_evs))\n", + "\n", + " for k in ks:\n", + " order2_circ = generate_time_evolution_circuit(\n", + " hamiltonian, synthesis=SuzukiTrotter(reps=k, order=2), time=time\n", + " )\n", + " order4_circ = generate_time_evolution_circuit(\n", + " hamiltonian, synthesis=SuzukiTrotter(reps=k, order=4), time=time\n", + " )\n", + "\n", + " job = estimator.run(\n", + " [(order2_circ, observable), (order4_circ, observable)]\n", + " )\n", + " result = job.result()\n", + " order2.append(result[0].data.evs)\n", + " order4.append(result[1].data.evs)" + ] + }, + { + "cell_type": "markdown", + "id": "0da8acd2-6192-450e-99b3-e0d2b82541ee", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Analyzing the results\n", + "\n", + "We can now analyze the computed results by plotting them." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "845cf113-58da-454e-97b6-679d5a5ae9fb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "order2_reshaped = np.asarray(order2).reshape((len(times), len(ks)))\n", + "order4_reshaped = np.asarray(order4).reshape((len(times), len(ks)))\n", + "\n", + "fig, axes = plt.subplots(3, 3, figsize=(20, 10))\n", + "\n", + "for i, t in enumerate(times):\n", + " ax = axes[i // 3, i % 3]\n", + "\n", + " ax.axvline(1.0, color=\"grey\", label=\"$t/k=1$\")\n", + " ax.axhline(exact[i], color=\"tab:green\", label=\"exact\")\n", + "\n", + " ax.plot(t / ks, order2_reshaped[i], label=r\"$\\chi=2$\")\n", + " ax.plot(t / ks, order4_reshaped[i], label=r\"$\\chi=4$\")\n", + "\n", + " ax.semilogx()\n", + " ax.set_xticks([10**p for p in range(-1, 1)])\n", + " ax.set_xlabel(\"$t/k$\")\n", + "\n", + " ax.set_ylim([-0.33, 0.55])\n", + " ax.set_ylabel(r\"$\\langle O \\rangle$\")\n", + "\n", + " ax.set_title(f\"time={t}\")\n", + "\n", + "fig.legend(\n", + " *ax.get_legend_handles_labels(),\n", + " ncols=4,\n", + " loc=\"center\",\n", + " bbox_to_anchor=(0.5, 0.0),\n", + ")\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2f40b1ee-0e98-4f24-9d25-080306ba98e1", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can now clearly see that in the regime $t/k \\gt 1$ the dynamics are no longer reproduced faithfully by the Trotterized circuits. Thus, if an MPF were to use a product formula with a $k_j$ from that regime, its extrapolated expectation value cannot faithfully produce good results." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/public/docs/images/addons/qiskit-addon-mpf/explanations/mpf_stability/extracted-outputs/845cf113-58da-454e-97b6-679d5a5ae9fb-0.avif b/public/docs/images/addons/qiskit-addon-mpf/explanations/mpf_stability/extracted-outputs/845cf113-58da-454e-97b6-679d5a5ae9fb-0.avif new file mode 100644 index 0000000000000000000000000000000000000000..289bf7e1f2521ed7bfdb1db87216b36ec4c9a285 GIT binary patch literal 18853 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a/docs/addons/qiskit-addon-aqc-tensor/_toc.json b/docs/addons/qiskit-addon-aqc-tensor/_toc.json new file mode 100644 index 00000000000..3e8cb8aef77 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/_toc.json @@ -0,0 +1,38 @@ +{ + "title": "Approximate quantum compilation (AQC-Tensor)", + "children": [ + { + "title": "Documentation Home", + "url": "/docs/addons/qiskit-addon-aqc-tensor" + }, + { + "title": "Installation Instructions", + "url": "/docs/addons/qiskit-addon-aqc-tensor/install" + }, + { + "title": "Tutorials", + "children": [ + { + "title": "Reducing circuit depth with the AQC-Tensor Qiskit addon", + "url": "/docs/addons/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc" + } + ], + "url": "/docs/addons/qiskit-addon-aqc-tensor/tutorials/index" + }, + { + "title": "Explanatory Material", + "url": "/docs/addons/qiskit-addon-aqc-tensor/explanation/index" + }, + { + "title": "How-To Guides", + "children": [ + { + "title": "How to use quimb.tensor.TNOptimizer directly", + "url": "/docs/addons/qiskit-addon-aqc-tensor/how-tos/01_quimb_tnoptimizer" + } + ], + "url": "/docs/addons/qiskit-addon-aqc-tensor/how-tos/index" + } + ], + "collapsed": true +} diff --git a/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx b/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx new file mode 100644 index 00000000000..41fbe752e76 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/explanation/index.mdx @@ -0,0 +1,85 @@ +--- +title: "Explanatory Material on AQC-Tensor" +--- + + + +# Explanatory Material on AQC-Tensor + +## Background + +This Qiskit addon allows one to compress the *initial portion* of a circuit using Approximate Quantum Compilation (AQC) with tensor networks, following the method introduced by Ref. [\[1\]](#id4). + +In the following, we will assume that the input circuit has already been truncated such that its entire depth can be simulated by a tensor network with a reasonable bond dimension. In order for AQC to be useful on quantum hardware, one must use the resulting circuit as a state preparation procedure and then perform subsequent quantum computation on that state in a way that is beyond the reach of a tensor network simulator alone. + +In essence, AQC-Tensor requires three things as input: + +1. A description of the **target state** in the form of a tensor network. This may be generated by simulating a circuit on a tensor network simulator (this is what Ref. [\[1\]](#id4) did), or it could be generated in some other way, e.g., by performing time-dependent variational principle (TDVP) time evolution directly on a matrix-product state. +2. A parametrized **ansatz circuit**, ideally with hardware-efficient connectivity, such that it will have a reasonable depth on the target hardware. +3. **Initial parameters** to plug into the ansatz circuit, such that the resulting state is already a *good* approximation to the target state. + +The technique is to then use an iterative optimization method to make the *good* state as close to the target state as possible. + +\[The third item is not required *in principle*, but it really helps to give the optimizer a sensible starting point; otherwise, it is much more likely to get stuck in a local (but not global) minimum.] + +## Ansatz generation (motivation) + +As mentioned in the previous section, three closely related things are needed as input to AQC-Tensor. Rather than code all three individually, this package provides a [`generate_ansatz_from_circuit()`](ansatz_generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") function that can generate (2) and (3) from a circuit. In the case of Trotter evolution (and likely other circuit classes of interest), one can use the same circuit-generation function to generate (1) as well as the input to [`generate_ansatz_from_circuit()`](ansatz_generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"), thus enabling the generation of all three inputs from a single function. The tutorial is a good demonstration of this re-use of a single circuit-generation function. + +Specifically, [`generate_ansatz_from_circuit()`](ansatz_generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit") will take an input circuit and generate the ansatz circuit (2) and initial parameters (3) that follow the two-qubit connectivity of the input circuit. However, the returned circuit will be parametrized, based on generalized one- and two-qubit rotations. The initial parameters returned by the function, when plugged into the ansatz, will result in a circuit that is exactly equivalent to the circuit passed as input, up to a global phase. + +Given the ansatz generation function, the user must provide only two things in order to use AQC-Tensor: + +1. The **target state**, i.e., a *great* description of the desired state; and, +2. A **circuit** with the desired connectivity of the ansatz that prepares a *good* approximation to the target state. + +The optimization procedure then takes the *good* ansatz parameters and brings them as close as possible to the *great* description (the target state). + +In the case of a Trotter circuit, the same function can actually provide both things. For instance, the `spinhalf_trotter_circuit` function could be called with `num_trotter_steps=50` to produce the target state, then again with `num_trotter_steps=5` to produce the ansatz structure to be passed to `generate_ansatz_from_circuit`. (Note that the `evolution_time` is fixed, such that the time step `dt = evolution_time / num_trotter_steps`. the time per single Trotter step as `dt = evolution_time / num_trotter_steps`, so these two function calls would leave the total `evolution_time` fixed, while changing the time per Trotter step, `dt`.) So, in order for a user to apply AQC-Tensor to a new physical model of Trotter circuit, they would only need to generalize a single function with the details of that model. + +The ansatz used by `generate_ansatz_from_circuit` uses 9 parameters per two-qubit block, which is the theoretical minimum (see, e.g., Fig. 2 of [https://arxiv.org/abs/quant-ph/0308033](https://arxiv.org/abs/quant-ph/0308033)). The automatic ansatz is based on the [KAK decomposition](https://qiskit-extensions.github.io/circuit-knitting-toolbox/circuit_cutting/explanation/index.html#more-general-cut-two-qubit-gates-via-the-kak-decomposition), which parametrizes any two-qubit gate in terms of three parameters, up to single-qubit rotations. The single-qubit rotation are then decomposed as ZXZ, each of which has three parameters. So, each two-qubit block of the original circuit will result in 3 parameters for the two-qubit rotation, plus 3 parameters for an outgoing single-qubit rotation on each of the two qubits, for a total of 9 parameters per block. The ansatz is completed by adding a layer of single-qubit rotations to each active qubit at the start of the circuit. + +## Tensor-network simulation + +The simplest tensor network is a matrix-product state (MPS). + +Currently, AQC-Tensor supports the following tensor-network simulators: + +* Qiskit Aer’s MPS simulator +* Quimb’s [eager](https://quimb.readthedocs.io/en/latest/tensor/tensor-circuit-mps.html) [`CircuitMPS`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.CircuitMPS) simulator +* Quimb’s [lazy](https://quimb.readthedocs.io/en/latest/tensor/tensor-circuit.html) [`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.Circuit) simulator (may work only on small circuits so far; we’re working to fix this soon with more clever contractions) + +The most important parameter of a tensor network is its maximum bond dimension, which limits how much entanglement it can represent (and thus to what depth a given circuit can be faithfully simulated). The bond dimension is often represented by the Greek letter $\chi$. + +Given a general circuit on $L$ qubits, a matrix-product state needs at most a bond dimension of $\chi_\mathrm{exact} = 2^{\lfloor L/2 \rfloor}$ in order to be able to simulate it to *any* depth. Of course, general circuits on 100+ qubits cannot be classically simulated, so it will be intractable to set the bond dimension this high for those circuits. + +For this reason, if you are attempting to experiment with AQC-Tensor on a toy problem with few qubits, it is important to ensure that $\chi < 2^{\lfloor L/2 \rfloor}$. Otherwise, any circuit can be simulated to any depth, and there is no point in performing AQC. + +### Objective function + +Currently, this addon provides one very simple objective, [`MaximizeStateFidelity`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity"), whose [`loss_function()`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") is equivalent to Eq. (7) in Ref. [\[1\]](#id4). When called with an array of parameters, the [`loss_function()`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") method returns the value and gradient of the objective function as a 2-tuple. + +### Gradient + +This package provides a few different methods for calculating the gradient. + +The Aer backend always uses the explicit gradient code in the `explicit_gradient` module. + +The Quimb backend will typically be used with an automatic differentiation backend; the user is to select a backend from among those supported by Quimb. Alternatively, one can instead pass `"explicit"` as the `autodiff_backend` when instantiating the [`QuimbSimulator`](qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator#qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator "qiskit_addon_aqc_tensor.simulation.quimb.QuimbSimulator") object; in this case, the `explicit_gradient` module will be used. It is only recommended to use explicit gradients with Quimb’s eager [`CircuitMPS`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.CircuitMPS) simulator, not the lazy [`Circuit`](https://quimb.readthedocs.io/en/latest/autoapi/quimb/tensor/index.html#quimb.tensor.Circuit) simulator. + +Regardless of which backend is chosen, the gradient code can understand linear parameter expressions (ParameterExpression objects). This support is essential, as linear expressions are returned by the ansatz generation code. + +### Optimization method + +Users are encouraged to use [`scipy.optimize`](https://docs.scipy.org/doc/scipy/reference/optimize.html#module-scipy.optimize "(in SciPy v1.15.2)") to perform the optimization. + +L-BFGS is the optimizer demonstrated in the tutorial notebook. It works well in practice because it uses the function value and its gradient to approximate the Hessian. It works well when given an initial point and seems to work particularly well in the case of Trotter circuits. However, it might early terminate if it starts in a barren plateau. In that case, performing a handful of steps using the ADAM optimizer first may help. + +## References + + + +\[1] ([1](#id1),[2](#id2),[3](#id3)) + +[arXiv:2301.08609v6](https://arxiv.org/abs/2301.08609v6). + diff --git a/docs/addons/qiskit-addon-aqc-tensor/how-tos/01_quimb_tnoptimizer.ipynb b/docs/addons/qiskit-addon-aqc-tensor/how-tos/01_quimb_tnoptimizer.ipynb new file mode 100644 index 00000000000..97afa60750e --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/how-tos/01_quimb_tnoptimizer.ipynb @@ -0,0 +1,729 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d0791829-7938-47b6-8f01-068e981f60c5", + "metadata": {}, + "source": [ + "# How to use `quimb.tensor.TNOptimizer` directly\n", + "\n", + "Quimb provides [a mechanism for optimizing tensor networks](https://quimb.readthedocs.io/en/latest/tensor-optimization.html) through its `TNOptimizer` interface. The Quimb backend provided by this addon uses this under the hood. This how-to guide demonstrates how to work with this object directly, in case some users want more direct access to it.\n", + "\n", + "### Set up a model Hamiltonian" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8fdf1603-b541-46e7-871d-bf6b0ea8afce", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:31.116183Z", + "iopub.status.busy": "2025-03-28T17:48:31.115492Z", + "iopub.status.idle": "2025-03-28T17:48:31.665894Z", + "shell.execute_reply": "2025-03-28T17:48:31.665054Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "# Generate some coupling map to use for this example\n", + "coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False)\n", + "\n", + "# Choose a 10-qubit circle on this coupling map\n", + "reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 4, 15, 3, 9])\n", + "\n", + "# Get a qubit operator describing the Ising field model\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " reduced_coupling_map,\n", + " coupling_constants=(0.0, 0.0, 1.0),\n", + " ext_magnetic_field=(0.4, 0.0, 0.0),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3f46356f-e975-4705-8788-87566e2c4905", + "metadata": {}, + "source": [ + "### Set up quimb simulator with default options" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fc38c412-f55d-4453-8c38-b84286f43ee8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:31.669986Z", + "iopub.status.busy": "2025-03-28T17:48:31.669579Z", + "iopub.status.idle": "2025-03-28T17:48:50.572758Z", + "shell.execute_reply": "2025-03-28T17:48:50.571987Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/runner/work/qiskit-addon-aqc-tensor/qiskit-addon-aqc-tensor/.tox/docs/lib/python3.9/site-packages/cotengra/hyperoptimizers/hyper.py:57: UserWarning: Couldn't find `optuna`, `cmaes`, or `nevergrad` so will use completely random sampling in place of hyper-optimization.\n", + " warnings.warn(\n", + "/home/runner/work/qiskit-addon-aqc-tensor/qiskit-addon-aqc-tensor/.tox/docs/lib/python3.9/site-packages/cotengra/hyperoptimizers/hyper.py:76: UserWarning: Couldn't find `optuna`, `cmaes`, or `nevergrad` so will use completely random sampling in place of hyper-optimization.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import quimb.tensor as qtn\n", + "\n", + "from qiskit_addon_aqc_tensor.simulation.quimb import (\n", + " QuimbSimulator,\n", + " qiskit_ansatz_to_quimb,\n", + " recover_parameters_from_quimb,\n", + ")\n", + "\n", + "simulator = QuimbSimulator(qtn.Circuit)" + ] + }, + { + "cell_type": "markdown", + "id": "b145bc16-7271-4647-8661-9f88023a790c", + "metadata": {}, + "source": [ + "### Generate target circuit" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "072bfaff-5a7f-4639-899a-a5a78e6af3ba", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:50.575445Z", + "iopub.status.busy": "2025-03-28T17:48:50.575150Z", + "iopub.status.idle": "2025-03-28T17:48:55.771949Z", + "shell.execute_reply": "2025-03-28T17:48:55.771099Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.synthesis import SuzukiTrotter\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", + "\n", + "from qiskit_addon_aqc_tensor.simulation import (\n", + " compute_overlap,\n", + " tensornetwork_from_circuit,\n", + ")\n", + "\n", + "evolution_time = 0.4\n", + "\n", + "target_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=8),\n", + " time=evolution_time,\n", + ")\n", + "\n", + "target_tns = tensornetwork_from_circuit(target_circuit, simulator)" + ] + }, + { + "cell_type": "markdown", + "id": "4b2735b7-0350-41e6-af80-6c9cda2083b7", + "metadata": {}, + "source": [ + "### Generate ansatz from a shallower circuit" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5d07640a-d7ee-4966-80e8-dd084ffb0da3", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:55.774797Z", + "iopub.status.busy": "2025-03-28T17:48:55.774309Z", + "iopub.status.idle": "2025-03-28T17:48:59.492081Z", + "shell.execute_reply": "2025-03-28T17:48:59.491177Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_aqc_tensor.ansatz_generation import (\n", + " AnsatzBlock,\n", + " generate_ansatz_from_circuit,\n", + ")\n", + "\n", + "initial_shallow_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=2),\n", + " time=evolution_time,\n", + ")\n", + "ansatz, initial_parameters = generate_ansatz_from_circuit(initial_shallow_circuit)\n", + "ansatz = ansatz.decompose(AnsatzBlock)\n", + "ansatz.draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "172012af-5499-4bdf-82b1-c5ba2d0350fe", + "metadata": {}, + "source": [ + "### Initialize objective function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8b039335-3e00-409d-ac75-983cfbe88c3a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:59.495880Z", + "iopub.status.busy": "2025-03-28T17:48:59.495415Z", + "iopub.status.idle": "2025-03-28T17:48:59.499389Z", + "shell.execute_reply": "2025-03-28T17:48:59.498609Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity\n", + "\n", + "objective = MaximizeStateFidelity(target_tns, None, None)" + ] + }, + { + "cell_type": "markdown", + "id": "4ba05493-be0b-4d91-8ca7-1065f8b0bde8", + "metadata": {}, + "source": [ + "### Convert Qiskit ansatz and initial parameters to a Quimb parametrized circuit" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2d60342e-393d-4604-86af-f20e0674c982", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:59.501808Z", + "iopub.status.busy": "2025-03-28T17:48:59.501297Z", + "iopub.status.idle": "2025-03-28T17:48:59.675635Z", + "shell.execute_reply": "2025-03-28T17:48:59.674867Z" + } + }, + "outputs": [], + "source": [ + "circ, conversion_context = qiskit_ansatz_to_quimb(ansatz, initial_parameters)" + ] + }, + { + "cell_type": "markdown", + "id": "a05591a0-5877-49e8-82aa-5fe06c11b718", + "metadata": {}, + "source": [ + "### Perform optimization of Quimb circuit using automatic differentiation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7f3eaa20-3332-4fd1-811c-48a7a3078242", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:59.678703Z", + "iopub.status.busy": "2025-03-28T17:48:59.678458Z", + "iopub.status.idle": "2025-03-28T17:49:36.521776Z", + "shell.execute_reply": "2025-03-28T17:49:36.521028Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 0%| | 0/20 [00:00 + +# Installation Instructions + +Let’s see how to install AQC-Tensor. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: + +* [Option 1: Pip Installation](#option-1) +* [Option 2: Install from Source](#option-2) + +## Pre-Installation + +Users who wish to install locally (using either [Option 1: Pip Installation](#option-1) or [Option 2: Install from Source](#option-2)) are encouraged to follow a brief set of common instructions to prepare a Python environment for installation of AQC-Tensor: + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +Note: If you are using Windows, use the following commands in PowerShell: + +```pwsh +python3 -m venv c:\path\to\virtual\environment +c:\path\to\virtual\environment\Scripts\Activate.ps1 +``` + + + +## Option 1: Pip Installation + +Upgrade pip and install the AQC-Tensor package. To meaningfully use the package, you must also install at least one tensor network backend. The below snippet installs the addon, along with quimb (for tensor network support) and jax (for automatic differentiation). + +```sh +pip install --upgrade pip +pip install qiskit-addon-aqc-tensor[quimb-jax] +``` + + + +## Option 2: Install from Source + +Users who wish to develop in the repository or run the tutorials locally may want to install from source. + +In either case, the first step is to clone the AQC-Tensor repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-aqc-tensor.git +``` + +Next, upgrade pip and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-aqc-tensor +``` + +The next step is to install AQC-Tensor to the virtual environment. If you plan on running the tutorials, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox jupyterlab -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started with AQC-Tensor by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab +``` + + + +## Platform Support + +We expect this package to work on [any Tier 1 platform supported by Qiskit](/docs/start/install#operating-system-support). + diff --git a/docs/addons/qiskit-addon-aqc-tensor/release-notes.mdx b/docs/addons/qiskit-addon-aqc-tensor/release-notes.mdx new file mode 100644 index 00000000000..9594395fbe6 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/release-notes.mdx @@ -0,0 +1,72 @@ +--- +title: "Release Notes" +--- + + + + + +# + + + + + +## 0.2.0 + + + +### Prelude + +This release renames the primary objective to [`MaximizeStateFidelity`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity") and adds support for Qiskit 2.0. + + + +### New Features + +* Adds the [`parametrize_circuit()`](ansatz_generation#qiskit_addon_aqc_tensor.ansatz_generation.parametrize_circuit "qiskit_addon_aqc_tensor.ansatz_generation.parametrize_circuit") function for generating a parametrized version of a given circuit. In contrast to [`generate_ansatz_from_circuit()`](ansatz_generation#qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit "qiskit_addon_aqc_tensor.ansatz_generation.generate_ansatz_from_circuit"), `parametrize_circuit` does not change the types of gates in the circuit. It simply replaces numerical parameters with free parameters. + +* Support for Python 3.13. + + + +### Upgrade Notes + +* The `OneMinusFidelity` objective function has been renamed and is now known as [`MaximizeStateFidelity`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity"). As a related change, one should now call the [`loss_function()`](objective#qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function "qiskit_addon_aqc_tensor.objective.MaximizeStateFidelity.loss_function") method to obtain the value and gradient of the loss function, instead of calling the instance directly (through its `__call__` method). + +* Qiskit SDK version 1.3 or higher is now required. Qiskit SDK version 2.0 is now supported. + + + +### Bug Fixes + +* This release fixes the import of `quimb_gate()` to work on versions of qiskit-quimb greater than 0.0.5. The aforementioned function is now imported from the toplevel `qiskit_quimb` instead of `qiskit_quimb.circuit`. + + + + + +## 0.1.1 + + + + + +### New Features + +* This release adds quimb-autograd as an optional dependency, so that one can now run `pip install 'qiskit-addon-aqc-tensor[quimb-autograd]'` to install the addon along with quimb and autograd. + + + + + +## 0.1.0 + + + + + +### Prelude + +Initial release. + diff --git a/docs/addons/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.ipynb b/docs/addons/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.ipynb new file mode 100644 index 00000000000..94830a07502 --- /dev/null +++ b/docs/addons/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.ipynb @@ -0,0 +1,843 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0f0b5824", + "metadata": {}, + "source": [ + "# Reducing circuit depth with the AQC-Tensor Qiskit addon\n", + "\n", + "In this notebook, we will work through the steps of a [Qiskit pattern](/guides/intro-to-patterns) while using **approximate quantum compilation with tensor networks (AQC-Tensor)** to achieve a lower circuit depth than would ordinarily be needed to perform Trotter evolution.\n", + "\n", + "These are the steps that we will take:\n", + "\n", + "- **Step 1: Map to quantum problem**\n", + " - Initialize our problem's Hamiltonian and observable(s)\n", + " - Generate a target tensor-network state for the initial portion of the circuit\n", + " - Generate a low-depth circuit which approximates the portion being compressed\n", + " - Generate a general ansatz from that circuit\n", + " - Optimize the parameters to bring the ansatz as close as possible to the target\n", + " - Add subsequent Trotter steps to the optimized ansatz\n", + "- **Step 2: Optimize for target hardware**\n", + " - Transpile the circuit for hardware\n", + "- **Step 3: Execute experiments**\n", + " - Use a fake backend for simplicity\n", + "- **Step 4: Reconstruct results**\n", + " - N/A; instead, we just output the measured observable" + ] + }, + { + "cell_type": "markdown", + "id": "79042f19-241d-48fb-aa87-134a93082c94", + "metadata": {}, + "source": [ + "## Step 1: Map to quantum circuit and operator\n", + "\n", + "### Set up a model Hamiltonian and observable\n", + "\n", + "In this notebook, we use the Ising model on a circle of 10 sites:\n", + "$$\n", + "\\hat{\\mathcal{H}}_{\\text{Ising}} = \\sum_{i=1}^{10} J_{i,(i+1)} Z_i Z_{(i+1)} + h_i X_i \\, ,\n", + "$$\n", + "where the periodic boundary conditions imply that for $i=10$ we obtain $i+1=11\\rightarrow1$, $J$ is the coupling strength between two sites and $h$ is the external magnetic field." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1f700ca7-ad7e-45ac-a5eb-50962ce67b48", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:51.132868Z", + "iopub.status.busy": "2025-03-28T17:48:51.132589Z", + "iopub.status.idle": "2025-03-28T17:48:51.531783Z", + "shell.execute_reply": "2025-03-28T17:48:51.531048Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "# Generate some coupling map to use for this example\n", + "coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False)\n", + "\n", + "# Choose a 10-qubit circle on this coupling map\n", + "reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 4, 15, 3, 9])\n", + "\n", + "# Get a qubit operator describing the Ising field model\n", + "hamiltonian = generate_xyz_hamiltonian(\n", + " reduced_coupling_map,\n", + " coupling_constants=(0.0, 0.0, 1.0),\n", + " ext_magnetic_field=(0.4, 0.0, 0.0),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d88fd2c0-a4cd-46be-a1aa-7f6dec219791", + "metadata": {}, + "source": [ + "The observable we will measure is the total magnetization." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9c864767-c218-4656-b20d-3e4e8c6f47af", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:51.534447Z", + "iopub.status.busy": "2025-03-28T17:48:51.534173Z", + "iopub.status.idle": "2025-03-28T17:48:51.538492Z", + "shell.execute_reply": "2025-03-28T17:48:51.537835Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "L = reduced_coupling_map.size()\n", + "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)" + ] + }, + { + "cell_type": "markdown", + "id": "70aa63a7-7560-4b29-9391-e1f42abdf1fd", + "metadata": {}, + "source": [ + "### Determine how much of the time evolution to simulate classically\n", + "\n", + "Our overall goal is to simulate time evolution of the above model Hamiltonian. We do so by Trotter evolution, which we split into two portions:\n", + "\n", + "1. An initial portion that is simulable with matrix product states (MPS). We will \"compile\" this portion using AQC as presented in https://arxiv.org/abs/2301.08609.\n", + "2. A subsequent portion of the circuit that will be executed on hardware." + ] + }, + { + "cell_type": "markdown", + "id": "442de278-82f9-4fcf-8b30-88804e7c170d", + "metadata": {}, + "source": [ + "Let's plan to use AQC-Tensor to compress our time evolution circuit up to time $t=4$, then evolve using ordinary Trotter steps up to $t=5$." + ] + }, + { + "cell_type": "markdown", + "id": "bf2243b5-56c7-48e8-acc6-7c8c720a8997", + "metadata": {}, + "source": [ + "### Generate circuits before and after split\n", + "\n", + "Now that we have chosen to split at $t=4$, we will generate two circuits:\n", + "\n", + "1. A \"target\" circuit for the AQC portion of the evolution, from $t_i=0$ to $t_f=4$. Because this is being simulated by a tensor-network simulator, the number of layers affects execution time only by a constant factor, so we might as well use a generous number of layers to minimize Trotter error." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c7593867-ed56-4f01-b2a9-1641ea4a5d10", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:51.541167Z", + "iopub.status.busy": "2025-03-28T17:48:51.540901Z", + "iopub.status.idle": "2025-03-28T17:48:51.554770Z", + "shell.execute_reply": "2025-03-28T17:48:51.554075Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.synthesis import SuzukiTrotter\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", + "\n", + "aqc_evolution_time = 4.0\n", + "aqc_target_num_trotter_steps = 45\n", + "\n", + "aqc_target_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=aqc_target_num_trotter_steps),\n", + " time=aqc_evolution_time,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bc3a6acc-8eb3-486e-9dd8-a58a82a7ccf9", + "metadata": {}, + "source": [ + "2. A subsequent evolution circuit, which evolves from $t_i=4$ to $t_f=5$. Because this is being run on quantum hardware, it is desirable to use as few Trotter layers as possible." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c97ac3e8-b7bd-4727-8495-a513bf143b1d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:51.557394Z", + "iopub.status.busy": "2025-03-28T17:48:51.557174Z", + "iopub.status.idle": "2025-03-28T17:48:51.563062Z", + "shell.execute_reply": "2025-03-28T17:48:51.562342Z" + } + }, + "outputs": [], + "source": [ + "subsequent_evolution_time = 1.0\n", + "subsequent_num_trotter_steps = 5\n", + "\n", + "subsequent_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=subsequent_num_trotter_steps),\n", + " time=subsequent_evolution_time,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3731468d-073b-460a-a87c-4920a35d44d9", + "metadata": {}, + "source": [ + "For the sake of later comparison, let's also generate a third circuit: one that evolves for `aqc_evolution_time` but which has the same evolution time per Trotter step as the subsequent circuit. This is the circuit we would have working with had we not used a generous number of Trotter steps for the target circuit. We will refer to this as the _comparison circuit_." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "557cc3a4-5909-4087-903d-2b603927071d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:51.565090Z", + "iopub.status.busy": "2025-03-28T17:48:51.564894Z", + "iopub.status.idle": "2025-03-28T17:48:51.571882Z", + "shell.execute_reply": "2025-03-28T17:48:51.571204Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aqc_comparison_num_trotter_steps = int(\n", + " subsequent_num_trotter_steps / subsequent_evolution_time * aqc_evolution_time\n", + ")\n", + "aqc_comparison_num_trotter_steps" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4c9bb0cd-0bd4-4236-9046-55a5222a034a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:51.573836Z", + "iopub.status.busy": "2025-03-28T17:48:51.573642Z", + "iopub.status.idle": "2025-03-28T17:48:51.580207Z", + "shell.execute_reply": "2025-03-28T17:48:51.579553Z" + } + }, + "outputs": [], + "source": [ + "comparison_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=aqc_comparison_num_trotter_steps),\n", + " time=aqc_evolution_time,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0c7d8499-afd9-4fdd-88a6-8dc33e144ee2", + "metadata": {}, + "source": [ + "### Generate an ansatz and initial parameters from a Trotter circuit with fewer steps\n", + "\n", + "First, we construct a \"good\" circuit that has the same evolution time as the target circuit, but with fewer Trotter steps (and thus fewer layers).\n", + "\n", + "Then we pass this \"good\" circuit to AQC-Tensor's `generate_ansatz_from_circuit` function. This function analyzes the two-qubit connectivity of the circuit and returns two things:\n", + "1. a general, parametrized ansatz circuit with the same two-qubit connectivity as the input circuit; and,\n", + "2. parameters that, when plugged into the ansatz, yield the input (good) circuit.\n", + "\n", + "Soon we will take these parameters and iteratively adjust them to bring the ansatz circuit as close as possible to the target MPS." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "17c2f81f-dcfa-436a-b592-af3efb46d75a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:51.582608Z", + "iopub.status.busy": "2025-03-28T17:48:51.582412Z", + "iopub.status.idle": "2025-03-28T17:48:53.734541Z", + "shell.execute_reply": "2025-03-28T17:48:53.733760Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_aqc_tensor import generate_ansatz_from_circuit\n", + "\n", + "aqc_ansatz_num_trotter_steps = 5\n", + "\n", + "aqc_good_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=aqc_ansatz_num_trotter_steps),\n", + " time=aqc_evolution_time,\n", + ")\n", + "\n", + "aqc_ansatz, aqc_initial_parameters = generate_ansatz_from_circuit(\n", + " aqc_good_circuit, qubits_initially_zero=True\n", + ")\n", + "aqc_ansatz.draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6864f636-91f6-4ba4-86d4-da7521474977", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:53.737555Z", + "iopub.status.busy": "2025-03-28T17:48:53.736896Z", + "iopub.status.idle": "2025-03-28T17:48:53.757790Z", + "shell.execute_reply": "2025-03-28T17:48:53.757151Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Comparison circuit: depth 120\n", + "Target circuit: depth 270\n", + "Ansatz circuit: depth 23, with 515 parameters\n" + ] + } + ], + "source": [ + "print(f\"Comparison circuit: depth {comparison_circuit.depth()}\")\n", + "print(f\"Target circuit: depth {aqc_target_circuit.depth()}\")\n", + "print(f\"Ansatz circuit: depth {aqc_ansatz.depth()}, with {len(aqc_initial_parameters)} parameters\")" + ] + }, + { + "cell_type": "markdown", + "id": "79845e2d-d563-4776-9e59-3a5c9eeb5687", + "metadata": {}, + "source": [ + "### Choose settings for tensor network simulation\n", + "\n", + "Here, we use the Qiskit Aer matrix-product state (MPS) simulator, which is currently the only supported tensor network backend." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "da640da1-cd8a-45b7-b73d-9c02c09c658b", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:53.759849Z", + "iopub.status.busy": "2025-03-28T17:48:53.759640Z", + "iopub.status.idle": "2025-03-28T17:48:53.794235Z", + "shell.execute_reply": "2025-03-28T17:48:53.793413Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_aer import AerSimulator\n", + "\n", + "simulator_settings = AerSimulator(\n", + " method=\"matrix_product_state\",\n", + " matrix_product_state_max_bond_dimension=100,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cf375cc7-f8c5-4534-b0ec-b2e9d6582840", + "metadata": {}, + "source": [ + "### Construct matrix-product state representation of the AQC target state\n", + "\n", + "Next, we build a matrix-product representation of the state to be approximated by AQC." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a8a6bf5e-31ec-48e2-b96e-ce3d7cc54ff7", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:53.797398Z", + "iopub.status.busy": "2025-03-28T17:48:53.796654Z", + "iopub.status.idle": "2025-03-28T17:48:54.069958Z", + "shell.execute_reply": "2025-03-28T17:48:54.069033Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_aqc_tensor.simulation import tensornetwork_from_circuit\n", + "\n", + "aqc_target_mps = tensornetwork_from_circuit(aqc_target_circuit, simulator_settings)" + ] + }, + { + "cell_type": "markdown", + "id": "88071374-04e4-47ee-8463-16ad0f5fa33b", + "metadata": {}, + "source": [ + "Note that because we chose a generous number of Trotter steps for the target state, it actually has less Trotter error than the comparison circuit. We can calculate the fidelity ($| \\langle \\psi_1 | \\psi_2 \\rangle |^2$) of the state prepared by the comparison circuit vs. the target state:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "65c3febb-8688-4806-aca6-99e9b860b377", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:54.073025Z", + "iopub.status.busy": "2025-03-28T17:48:54.072767Z", + "iopub.status.idle": "2025-03-28T17:48:54.163511Z", + "shell.execute_reply": "2025-03-28T17:48:54.162747Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9997111919739693" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_aqc_tensor.simulation import compute_overlap\n", + "\n", + "comparison_mps = tensornetwork_from_circuit(comparison_circuit, simulator_settings)\n", + "comparison_fidelity = abs(compute_overlap(comparison_mps, aqc_target_mps)) ** 2\n", + "comparison_fidelity" + ] + }, + { + "cell_type": "markdown", + "id": "5859794d-efbd-4e5b-a1d2-2b7add48b8e4", + "metadata": {}, + "source": [ + "### Optimize the parameters of the ansatz using MPS calculations\n", + "\n", + "Here, we minimize the simplest possible cost function, `MaximizeStateFidelity`, by using the L-BFGS optimizer from scipy.\n", + "\n", + "We choose a stopping point for the fidelity such that it will be above what the comparison circuit would have been, without using AQC. Once this is reached, the compressed circuit has less Trotter error _and_ less depth than the original circuit. Given more processing time, further optimization steps can be performed to bring the fidelity even higher." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "66dddd19-51d6-4bd4-a3df-2c9b457d4149", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:48:54.165816Z", + "iopub.status.busy": "2025-03-28T17:48:54.165599Z", + "iopub.status.idle": "2025-03-28T17:49:47.483360Z", + "shell.execute_reply": "2025-03-28T17:49:47.482608Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.95084365\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.98409893\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99142033\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99521405\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99566673\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99650054\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99683487\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99720426\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99761726\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99809073\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99838244\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99861841\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99874617\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99892696\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99908129\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99917737\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99925456\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99933134\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99947173\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99956469\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99964488\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99967419\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.99968821\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intermediate result: Fidelity 0.9997448\n", + "Done after 24 iterations.\n" + ] + } + ], + "source": [ + "from scipy.optimize import OptimizeResult, minimize\n", + "\n", + "from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity\n", + "\n", + "objective = MaximizeStateFidelity(aqc_target_mps, aqc_ansatz, simulator_settings)\n", + "\n", + "stopping_point = 1 - comparison_fidelity\n", + "\n", + "\n", + "def callback(intermediate_result: OptimizeResult):\n", + " print(f\"Intermediate result: Fidelity {1 - intermediate_result.fun:.8}\")\n", + " if intermediate_result.fun < stopping_point:\n", + " # Good enough for now\n", + " raise StopIteration\n", + "\n", + "\n", + "result = minimize(\n", + " objective.loss_function,\n", + " aqc_initial_parameters,\n", + " method=\"L-BFGS-B\",\n", + " jac=True,\n", + " options={\"maxiter\": 100},\n", + " callback=callback,\n", + ")\n", + "if result.status not in (\n", + " 0,\n", + " 1,\n", + " 99,\n", + "): # 0 => success; 1 => max iterations reached; 99 => early termination via StopIteration\n", + " raise RuntimeError(f\"Optimization failed: {result.message} (status={result.status})\")\n", + "\n", + "print(f\"Done after {result.nit} iterations.\")\n", + "aqc_final_parameters = result.x" + ] + }, + { + "cell_type": "markdown", + "id": "4f668909-529d-4f2c-bfe7-e7766cd33ce6", + "metadata": {}, + "source": [ + "### Construct the final circuit to pass to the transpiler" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "dbf60899-6f9d-4222-a499-e68bd5eabaf5", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:49:47.486007Z", + "iopub.status.busy": "2025-03-28T17:49:47.485504Z", + "iopub.status.idle": "2025-03-28T17:49:50.147362Z", + "shell.execute_reply": "2025-03-28T17:49:50.146575Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_circuit = aqc_ansatz.assign_parameters(aqc_final_parameters)\n", + "final_circuit.compose(subsequent_circuit, inplace=True)\n", + "final_circuit.draw(\"mpl\", fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "817839e7-17e7-4dcb-b620-a7711aeb2edf", + "metadata": {}, + "source": [ + "## Step 2: Transpile for execution on target hardware\n", + "\n", + "In Step 2 of a [Qiskit pattern](/guides/intro-to-patterns), we transpile this circuit and any desired observable(s) for execution on a target device. Here we are using a fake backend provided by `qiskit-ibm-runtime`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "783aea48-3d0a-4d7b-a5ed-e3f4888c47a8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:49:50.150161Z", + "iopub.status.busy": "2025-03-28T17:49:50.149739Z", + "iopub.status.idle": "2025-03-28T17:49:50.853074Z", + "shell.execute_reply": "2025-03-28T17:49:50.852356Z" + } + }, + "outputs": [], + "source": [ + "from qiskit import transpile\n", + "from qiskit_ibm_runtime.fake_provider import FakeMelbourneV2\n", + "\n", + "backend = FakeMelbourneV2()\n", + "\n", + "isa_circuit = transpile(final_circuit, backend)\n", + "isa_observable = observable.apply_layout(isa_circuit.layout)" + ] + }, + { + "cell_type": "markdown", + "id": "5ff0be42-b45a-4af3-8317-3c1f87758771", + "metadata": {}, + "source": [ + "The resulting ISA circuit can then be sent for execution on the backend (step 3 of a [Qiskit pattern](/guides/intro-to-patterns))." + ] + }, + { + "cell_type": "markdown", + "id": "6600071d-e0b6-45cd-ae4e-0ffe882b0955", + "metadata": {}, + "source": [ + "## Step 3: Execute on quantum hardware" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "bd8039e4-08c4-4a7f-98ba-f8c11c8da795", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:49:50.855921Z", + "iopub.status.busy": "2025-03-28T17:49:50.855424Z", + "iopub.status.idle": "2025-03-28T17:49:52.989199Z", + "shell.execute_reply": "2025-03-28T17:49:52.988574Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import EstimatorV2 as Estimator\n", + "\n", + "estimator = Estimator(backend)\n", + "job = estimator.run([(isa_circuit, isa_observable)])\n", + "pub_result = job.result()[0]" + ] + }, + { + "cell_type": "markdown", + "id": "f56257c7-55c0-426a-835f-6e08b306536a", + "metadata": {}, + "source": [ + "## Step 4: Reconstruct\n", + "\n", + "Reconstruction is not necessary in our case. We can just look at the result." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "84a212df-8e89-4576-a407-903da48a0c5a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T17:49:52.991715Z", + "iopub.status.busy": "2025-03-28T17:49:52.991328Z", + "iopub.status.idle": "2025-03-28T17:49:52.995743Z", + "shell.execute_reply": "2025-03-28T17:49:52.995238Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.054003906250000004)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pub_result.data.evs[()]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/_toc.json b/docs/addons/qiskit-addon-cutting/_toc.json new file mode 100644 index 00000000000..7721219d701 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/_toc.json @@ -0,0 +1,62 @@ +{ + "title": "Circuit cutting", + "children": [ + { + "title": "Documentation Home", + "url": "/docs/addons/qiskit-addon-cutting" + }, + { + "title": "Installation Instructions", + "url": "/docs/addons/qiskit-addon-cutting/install" + }, + { + "title": "Tutorials", + "children": [ + { + "title": "Gate Cutting to Reduce Circuit Width", + "url": "/docs/addons/qiskit-addon-cutting/tutorials/01_gate_cutting_to_reduce_circuit_width" + }, + { + "title": "Gate Cutting to Reduce Circuit Depth", + "url": "/docs/addons/qiskit-addon-cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth" + }, + { + "title": "Wire Cutting Phrased as a Two-Qubit Move Instruction", + "url": "/docs/addons/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction" + }, + { + "title": "Automatically find cuts", + "url": "/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding" + } + ], + "url": "/docs/addons/qiskit-addon-cutting/tutorials/index" + }, + { + "title": "Explanatory Material", + "url": "/docs/addons/qiskit-addon-cutting/explanation/index" + }, + { + "title": "How-To Guides", + "children": [ + { + "title": "How to generate exact quasiprobability distributions from Sampler", + "url": "/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler" + }, + { + "title": "How to generate exact sampling coefficients", + "url": "/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_sampling_coefficients" + }, + { + "title": "How to perform a cutting workflow with multiple observables", + "url": "/docs/addons/qiskit-addon-cutting/how-tos/how_to_provide_multiple_observables" + }, + { + "title": "How to place wire cuts using a single-qubit CutWire instruction", + "url": "/docs/addons/qiskit-addon-cutting/how-tos/how_to_specify_cut_wires" + } + ], + "url": "/docs/addons/qiskit-addon-cutting/how-tos/index" + } + ], + "collapsed": true +} diff --git a/docs/addons/qiskit-addon-cutting/explanation/index.mdx b/docs/addons/qiskit-addon-cutting/explanation/index.mdx new file mode 100644 index 00000000000..5a7511683ed --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/explanation/index.mdx @@ -0,0 +1,145 @@ +--- +title: "Explanatory material" +--- + + + +# Explanatory material + +## Overview of circuit cutting + +Circuit cutting is a technique to increase the size of circuits we can run on quantum hardware at the cost of an additional sampling overhead. A larger quantum circuit can be decomposed by cutting its gates and/or wires, resulting in smaller circuits which can be executed within the constraints of available quantum hardware. The results of these smaller circuits are combined to reconstruct the outcome of the original problem. Circuit cutting can also be used to engineer gates between distant qubits which would otherwise require a large swap overhead. + +## Key terms + +* subcircuits: The set of circuits resulting from cutting gates in a [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) and then separating the disconnected qubit subsets into smaller circuits. These circuits contain [`SingleQubitQPDGate`](qiskit_addon_cutting.qpd.SingleQubitQPDGate#qiskit_addon_cutting.qpd.SingleQubitQPDGate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s and will be used to instantiate each unique subexperiment. +* subexperiments: A term used to describe the unique circuit samples associated with a subcircuit. These circuits have had their [`BaseQPDGate`](qiskit_addon_cutting.qpd.BaseQPDGate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate")s decomposed into local Qiskit gates and measurements. Subexperiments are the circuits sent to the backend for execution. +* decompose: We try to honor the Qiskit notion of “decompose” in the documentation and API, which loosely means transforming a gate into a less-abstracted representation. *Occasionally*, we may use the term “decompose” to refer to the act of inserting [`BaseQPDGate`](qiskit_addon_cutting.qpd.BaseQPDGate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate") instances into quantum circuits as “decomposing” a gate or wire; however, we try to use terms like “partition” and “cut” when referring to this to avoid ambiguity with Qiskit language. + +## Circuit cutting as a quasiprobability decomposition (QPD) + +Quasiprobability decomposition is a technique which can be used to simulate quantum circuit executions that go beyond the actual capabilities of current quantum hardware while using that same hardware. It forms the basis of many error mitigation techniques, which allow simulating a noise-free quantum computer using a noisy one. Circuit cutting techniques, which allow simulating a quantum circuit using fewer qubits than would otherwise be necessary, can also be phrased in terms of a quasiprobability decomposition. No matter the goal, the cost of the quasiprobability decomposition is an exponential overhead in the number of circuit executions which must be performed. In certain cases, this tradeoff is worth it, because it can allow the estimation of quantities that would otherwise be impossible on today’s hardware. + +There are two types of cuts: gate cuts and wire cuts. Gate cuts, also known as “space-like” cuts, exist when the cut goes through a gate operating on two (or more) qubits. Wire cuts, also known as “time-like” cuts, are direct cuts through a qubit wire, essentially a single-qubit identity gate that has been cut into two pieces. In this package, a wire cut is represented by introducing a new qubit into the circuit and moving remaining operations after the cut identity gate to the new qubit; see [Wire cutting phrased as a two-qubit Move operation](#wire-cutting-as-move), below. + +There are [three settings](https://research.ibm.com/blog/circuit-knitting-with-classical-communication) to consider for circuit cutting. The first is where only local operations (LO) \[i.e., local *quantum* operations] are available. The other settings introduce classical communication between the circuit executions, which is known in the quantum information literature as LOCC, for [local operations and classical communication](https://en.wikipedia.org/wiki/LOCC). The LOCC can be either near-time, one-directional communication between the circuit executions (the second setting), or real-time, bi-directional communication (the third setting). + +As mentioned above, the cost of any simulation based on quasiprobability distribution is an exponential sampling overhead. The sampling overhead is the factor by which the overall number of shots must increase for the quasiprobability decomposition to result in the same amount of error, $\epsilon$, as one would get by executing the original circuit. The overhead of a cut gate depends on which gate is cut; see the final appendix of \[[1](https://arxiv.org/abs/2205.00016)] for details. For instance, a single cut CNOT gate incurs a sampling overhead of 9 \[[2](https://arxiv.org/abs/1909.07534),[6](https://arxiv.org/abs/2312.11638)]. A circuit with $n$ wire cuts incurs a sampling overhead of O($16^n$) when classical communication is not available (LO setting); this is reduced to O($4^n$) when classical communication is available (LOCC setting) \[[4](https://arxiv.org/abs/2302.03366)]. However, wire cutting with classical communication (LOCC) is not yet supported by this package (see issue [#264](https://github.com/Qiskit/qiskit-addon-cutting/issues/264)). + +The QPD can be given explicitly as follows: + +$$ +\mathcal{U} = \sum_i a_i \mathcal{F}_i , +$$ + +where $\mathcal{U}$ is the channel implementing the desired operation, and each $a_i$ is a real coefficient corresponding to a quantum channel, $\mathcal{F}_i$, that is realizable on hardware. + +Note that because this is a sum of channels, *not* a sum of unitaries, expectation values can be evaluated efficiently \[[2](https://arxiv.org/abs/1909.07534)]. + +Results equivalent to original unitary channel can be obtained by a post-processing method, given the coefficients $a_i$ and the outcome of each experiment corresponding to the different local ($\mathcal{F}_i$) channels. This post-processing boosts the magnitude of each measurement outcome by $\sum_i \left| a_i \right|$, resulting in a sampling overhead of that quantity squared \[[6](https://arxiv.org/abs/2312.11638)]: + +$$ +\mathrm{Sampling\ overhead} = \left( \sum_i \lvert a_i \rvert \right)^2 . +$$ + +For more detailed information on the quasiprobability decomposition technique, refer to the paper, Error mitigation for short-depth quantum circuits \[[5](https://arxiv.org/abs/1612.02058)]. + +The essential idea of gate cutting is to replace a two-qubit gate with a linear combination of quantum channels \[Eq. [(1)](#equation-eq-qpd)] that, when recombined, will allow reconstruction of the result for physically measurable quantities like expectation values. Note that “global phase” is not something that can be physically measured, so we can disregard it when specifying quasiprobability decompositions. + + + +## An example: cutting a [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) + +As a basic and explicit example, let us consider the decomposition of a cut [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate). + +As shown in \[[2](https://arxiv.org/abs/1909.07534)], a quantum circuit which implements an [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) can be simulated by performing six subexperiments where the [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) in the original circuit has been replaced with only local (single-qubit) operations \[the $\mathcal{F}_i$‘s in Eq. [(1)](#equation-eq-qpd)]. The result is then reconstructed by combining the subexperiment results with certain coefficients \[the $a_i$‘s in Eq. [(1)](#equation-eq-qpd)], which can be either positive or negative. Given the $\theta$ parameter of the [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), the six subexperiments are as follows: + +1. With coefficient $a_1 = \cos^2 (\theta/2)$, do nothing ($I \otimes I$, where $I$ is the identity operation on a single qubit). +2. With coefficient $a_2 = \sin^2 (\theta/2)$, perform a [`ZGate`](/docs/api/qiskit/qiskit.circuit.library.ZGate) on each qubit ($Z \otimes Z$). +3. With coefficient $a_3 = -\sin(\theta)/2$, perform a projective measurement in the Z basis on the first qubit and an [`SGate`](/docs/api/qiskit/qiskit.circuit.library.SGate) gate on the second qubit (denote this as $M_z \otimes S$). If the result of the measurement is 1, flip the sign of that outcome’s contribution during reconstruction. +4. With coefficient $a_4 = \sin(\theta)/2$, perform a projective measurement in the Z basis on the first qubit and an [`SdgGate`](/docs/api/qiskit/qiskit.circuit.library.SdgGate) gate on the second qubit (denote this as $M_z \otimes S^\dagger$). If the result of the measurement is 1, flip the sign of that outcome’s contribution during reconstruction. +5. Same as term 3 ($a_5 = a_3$), but swap the qubits ($S \otimes M_z$). +6. Same as term 4 ($a_6 = a_4$), but swap the qubits ($S^\dagger \otimes M_z$). + +Equation [(1)](#equation-eq-qpd) for [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) can thus be written as a sum of the six terms listed above. The following plot shows the magnitude of each coeffient (negative coefficients are in orange) as a function of $\theta$. The square root of the optimal sampling overhead, denoted by $\gamma$, is given by the sum of the absolute coefficients. + +![../\_images/index-1.svg](docs/images/addons/qiskit-addon-cutting/index-1.svg) + +Let’s consider some special points in this plot: + +* When $\theta = 0$, the gate has no effect, and the sampling overhead is 1. (Because the overhead is multiplicative, this is equivalent to there being no overhead.) +* When $\theta = \pi$, the gate is equivalent to $Z \otimes Z$ up to a global phase, and the sampling overhead is again 1. +* The maximum sampling overhead of $3^2 = 9$ is reached at a ZZ Rotation of $\theta=\pi/2$. We call this point the [`CXGate`](/docs/api/qiskit/qiskit.circuit.library.CXGate) family because this rotation is equivalent to a CXGate up to (single-qubit) local unitary operations. This point is also equivalent, up to local unitary operations, to [`CZGate`](/docs/api/qiskit/qiskit.circuit.library.CZGate), [`CYGate`](/docs/api/qiskit/qiskit.circuit.library.CYGate), [`CHGate`](/docs/api/qiskit/qiskit.circuit.library.CHGate), and [`ECRGate`](/docs/api/qiskit/qiskit.circuit.library.ECRGate). +* The ZZ rotation at $\theta=\pi/4$ has sampling overhead of $3+2\sqrt{2} \approx 5.828$. We call this the [`CSGate`](/docs/api/qiskit/qiskit.circuit.library.CSGate) family because this rotation is equivalent to a CSGate up to (single-qubit) local operations. This family also includes [`CSdgGate`](/docs/api/qiskit/qiskit.circuit.library.CSdgGate) and [`CSXGate`](/docs/api/qiskit/qiskit.circuit.library.CSXGate). +* Likewise, [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), and [`RZXGate`](/docs/api/qiskit/qiskit.circuit.library.RZXGate) are all locally equivalent to [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate). The controlled rotations [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate), and [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) at an angle of $2\theta$ are locally equivalent to [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate) at an angle of $\theta$. + +## More general cut two-qubit gates via the KAK decomposition + +We can formalize this notion of local unitary equivalence and expand it to all two-qubit gates using the KAK decomposition, given by + +$$ +U = (V_1 \otimes V_2) \exp \left[ i \left( \theta_x \, X \otimes X + \theta_y \, Y \otimes Y + \theta_z \, Z \otimes Z \right) \right] (V_3 \otimes V_4) , +$$ + +where $V_1$, $V_2$, $V_3$, and $V_4$ are local, single-qubit operations, and the two-qubit portion of the interaction is parametrized entirely by $\vec{\theta} = (\theta_x, \theta_y, \theta_z)$. By convention, we have chosen $\vec{\theta}$ to be in the “Weyl chamber” restricted by $\pi/4 \geq \theta_x \geq \theta_y \geq | \theta_z | \geq 0$ \[[6](https://arxiv.org/abs/2312.11638)]. For more information on the KAK decomposition, see Ref. \[[7](https://arxiv.org/abs/quant-ph/0209120)]. + +The code that generates subexperiments from the KAK decomposition currently follows Ref. \[[3](https://arxiv.org/abs/2006.11174)], which is now known to be non-optimal. A provably optimal method has been presented in Ref. \[[6](https://arxiv.org/abs/2312.11638)], but this newer method has not yet been implemented in this package (see issue [#531](https://github.com/Qiskit/qiskit-addon-cutting/issues/531)). + + + + + +## Wire cutting phrased as a two-qubit [`Move`](qiskit_addon_cutting.instructions.Move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") operation + +A wire cut is represented fundamentally by this package as a two-qubit [`Move`](qiskit_addon_cutting.instructions.Move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction, which is defined as a reset of the second qubit followed by a swap of both qubits. Equivalently, the operation is defined as transferring the state of the first qubit wire to the second qubit wire, while simultaneously discarding the state of the second qubit wire (the first qubit ends up in state $\lvert 0 \rangle$). + +We have chosen to represent wire cuts in this way primarily because it is consistent with the way one must treat wire cuts when acting on physical qubits: for instance, a wire cut might take the state of physical qubit $n$ and continue it as physical qubit $m$ after the cut. Our choice also has the benefit of allowing us to think of “instruction cutting” as a unified framework for considering both wire cuts and gate cuts in the same formalism, being that a wire cut is just a cut [`Move`](qiskit_addon_cutting.instructions.Move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") instruction. + +More information on this formalism is given in Sec. 3 of Ref. \[[4](https://arxiv.org/abs/2302.03366)] + +If you prefer to place cut wires abstractly on a single qubit wire, please see the [how-to guide on placing wire cuts using a single-qubit instruction](how-tos/how_to_specify_cut_wires), which explains how to use the [`cut_wires()`](qiskit_addon_cutting#qiskit_addon_cutting.cut_wires "qiskit_addon_cutting.cut_wires") function to convert a circuit with [`CutWire`](qiskit_addon_cutting.instructions.CutWire#qiskit_addon_cutting.instructions.CutWire "qiskit_addon_cutting.instructions.CutWire") instructions to a circuit with [`Move`](qiskit_addon_cutting.instructions.Move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move")s on additional qubits. + +## Sample weights in the Qiskit addon for circuit cutting + +In this package, the number of samples taken from the distribution is generally controlled by a `num_samples` argument, and each sample has an associated weight which is used during expectation value reconstruction. Each weight with absolute value above a threshold of 1 / `num_samples` will be evaluated exactly. The remaining low-probability elements – those in the tail of the distribution – will then be sampled, resulting in at most `num_samples` unique weights. Setting `num_samples` to infinity indicates that all weights should be generated rigorously, rather than by sampling from the distribution. + +Much of the circuit cutting literature describes a process where we sample from the distribution, take a single shot, then sample from the distribution again and repeat; however, this is not feasible in practice, so we instead perform all sampling upfront. For now, because of limitations in version 1 of the Qiskit primitives, we take a fixed number of shots for each considered subexperiment and send the subexperiments to the backend(s) in batches. During reconstruction, each subexperiment contributes to the final result with proportion equal to its weight. One must ensure the number of shots taken is sufficient for the heaviest weighted subexperiment. In the future, we plan to support passing an individual `shots` count with each subexperiment to Qiskit Runtime, so that each subexperiment will be run with a number of shots proportional to that subexperiment’s weight in the final result (see issue [#532](https://github.com/Qiskit/qiskit-addon-cutting/issues/532)). This per-experiment shots count is a new feature enabled by version 2 of the Qiskit primitives. + +## Sampling overhead reference table + +The below table provides the sampling overhead factor for a variety of two-qubit instructions, provided that only a single instruction is cut. + +| Instruction(s) | KAK decomposition angles | Sampling overhead factor | | | | | | | +| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------ | --------------------------- | --------- | -------------------- | -------------------- | ----------------- | -------------------- | -------------------------------------------------------------- | +| [`CSGate`](/docs/api/qiskit/qiskit.circuit.library.CSGate), [`CSdgGate`](/docs/api/qiskit/qiskit.circuit.library.CSdgGate), [`CSXGate`](/docs/api/qiskit/qiskit.circuit.library.CSXGate) | $(\pi/8, 0, 0)$ | $3+2\sqrt{2} \approx 5.828$ | | | | | | | +| [`CXGate`](/docs/api/qiskit/qiskit.circuit.library.CXGate), [`CYGate`](/docs/api/qiskit/qiskit.circuit.library.CYGate), [`CZGate`](/docs/api/qiskit/qiskit.circuit.library.CZGate), [`CHGate`](/docs/api/qiskit/qiskit.circuit.library.CHGate), [`ECRGate`](/docs/api/qiskit/qiskit.circuit.library.ECRGate) | $(\pi/4, 0, 0)$ | $3^2=9$ | | | | | | | +| [`iSwapGate`](/docs/api/qiskit/qiskit.circuit.library.iSwapGate), [`DCXGate`](/docs/api/qiskit/qiskit.circuit.library.DCXGate) | $(\pi/4, \pi/4, 0)$ | $7^2=49$ | | | | | | | +| [`SwapGate`](/docs/api/qiskit/qiskit.circuit.library.SwapGate) | $(\pi/4,\pi/4,\pi/4)$ | | | | | | | | +| [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), [`RZXGate`](/docs/api/qiskit/qiskit.circuit.library.RZXGate) | \$( | \theta/2 | , 0, 0)\$ | \$\left\[1 + 2 \left | \sin(\theta)\right | \right]^2\$ | | | +| [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate), [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) | \$( | \theta/4 | , 0, 0)\$ | \$\left\[1 + 2 \left | \sin(\theta/2)\right | \right]^2\$ | | | +| [`XXPlusYYGate`](/docs/api/qiskit/qiskit.circuit.library.XXPlusYYGate), [`XXMinusYYGate`](/docs/api/qiskit/qiskit.circuit.library.XXMinusYYGate) | \$( | \theta/4 | , | \theta/4 | ,0)\$ | \$\left\[1+4\left | \sin(\theta/2)\right | +2\sin^2(\theta/2)\right]^2$(independent of$\beta\$ parameter) | +| [`Move`](qiskit_addon_cutting.instructions.Move#qiskit_addon_cutting.instructions.Move "qiskit_addon_cutting.instructions.Move") (cut wire) without classical communication (i.e., in the LO setting) | not applicable | $4^2=16$ | | | | | | | + +## Current limitations + +* The workflow only allows taking the *expectation value* of observables with respect to a circuit. Limited support for reconstructing an output probability distribution may be added to a future version of this package (see issue [#259](https://github.com/Qiskit/qiskit-addon-cutting/issues/259)). +* Due to current code limitations, some of the generated subexperiments are redundant. This can result in more subexperiments than expected, particularly when using wire cutting. This is tracked by issue [#262](https://github.com/Qiskit/qiskit-addon-cutting/issues/262). + +## References + +This module is based on the theory described in the following papers: + +\[1] Christophe Piveteau, David Sutter, *Circuit knitting with classical communication*, [https://arxiv.org/abs/2205.00016](https://arxiv.org/abs/2205.00016) + +\[2] Kosuke Mitarai, Keisuke Fujii, *Constructing a virtual two-qubit gate by sampling single-qubit operations*, [https://arxiv.org/abs/1909.07534](https://arxiv.org/abs/1909.07534) + +\[3] Kosuke Mitarai, Keisuke Fujii, *Overhead for simulating a non-local channel with local channels by quasiprobability sampling*, [https://arxiv.org/abs/2006.11174](https://arxiv.org/abs/2006.11174) + +\[4] Lukas Brenner, Christophe Piveteau, David Sutter, *Optimal wire cutting with classical communication*, [https://arxiv.org/abs/2302.03366](https://arxiv.org/abs/2302.03366) + +\[5] K. Temme, S. Bravyi, and J. M. Gambetta, *Error mitigation for short-depth quantum circuits*, [https://arxiv.org/abs/1612.02058](https://arxiv.org/abs/1612.02058) + +\[6] Lukas Schmitt, Christophe Piveteau, David Sutter, *Cutting circuits with multiple two-qubit unitaries*, [https://arxiv.org/abs/2312.11638](https://arxiv.org/abs/2312.11638) + +\[7] Jun Zhang, Jiri Vala, K. Birgitta Whaley, Shankar Sastry, *A geometric theory of non-local two-qubit operations*, [https://arxiv.org/abs/quant-ph/0209120](https://arxiv.org/abs/quant-ph/0209120) + diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb new file mode 100644 index 00000000000..6a41797a153 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb @@ -0,0 +1,175 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f9e40036", + "metadata": {}, + "source": [ + "## How to generate exact quasiprobability distributions from Sampler\n", + "\n", + "This how-to guide is intended to show users how they can generate statevector-based quasiprobability distributions through the `qiskit.primitives.BaseSampler` interface." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "072055cb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:50.653585Z", + "iopub.status.busy": "2025-03-28T16:50:50.653274Z", + "iopub.status.idle": "2025-03-28T16:50:51.088193Z", + "shell.execute_reply": "2025-03-28T16:50:51.087485Z" + } + }, + "outputs": [], + "source": [ + "from qiskit import QuantumCircuit\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "from qiskit_addon_cutting import (\n", + " partition_problem,\n", + " generate_cutting_experiments,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "940334fd", + "metadata": {}, + "source": [ + "Prepare inputs to `generate_cutting_experiments`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dc4af922", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.091193Z", + "iopub.status.busy": "2025-03-28T16:50:51.090943Z", + "iopub.status.idle": "2025-03-28T16:50:51.116996Z", + "shell.execute_reply": "2025-03-28T16:50:51.116317Z" + } + }, + "outputs": [], + "source": [ + "circuit = QuantumCircuit(2)\n", + "circuit.h(0)\n", + "circuit.cx(0, 1)\n", + "observable = SparsePauliOp([\"ZZ\"])\n", + "partitioned_problem = partition_problem(\n", + " circuit=circuit, partition_labels=\"AB\", observables=observable.paulis\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "subobservables = partitioned_problem.subobservables" + ] + }, + { + "cell_type": "markdown", + "id": "d6361a9d", + "metadata": {}, + "source": [ + "Call `generate_cutting_experiments`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d095701f", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.119755Z", + "iopub.status.busy": "2025-03-28T16:50:51.119556Z", + "iopub.status.idle": "2025-03-28T16:50:51.128723Z", + "shell.execute_reply": "2025-03-28T16:50:51.127940Z" + } + }, + "outputs": [], + "source": [ + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits,\n", + " observables=subobservables,\n", + " num_samples=1000,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bc59b1be", + "metadata": {}, + "source": [ + "In order to calculate exact quasiprobability distributions for circuits with mid-circuit measurements, users will need to use the `ExactSampler` class from `qiskit_addon_cutting.utils.simulation`. The Qiskit Samplers do not support mid-circuit measurements in statevector mode." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7a74f709", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.131476Z", + "iopub.status.busy": "2025-03-28T16:50:51.130959Z", + "iopub.status.idle": "2025-03-28T16:50:51.135282Z", + "shell.execute_reply": "2025-03-28T16:50:51.134551Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting.utils.simulation import ExactSampler\n", + "\n", + "exact_sampler = ExactSampler()" + ] + }, + { + "cell_type": "markdown", + "id": "8d050fbe", + "metadata": {}, + "source": [ + "If `ExactSampler` is used, the quasiprobability distributions returned from `generate_cutting_experiments` will be exact and generated from the statevectors of the subexperiments." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7019d781", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.137659Z", + "iopub.status.busy": "2025-03-28T16:50:51.137468Z", + "iopub.status.idle": "2025-03-28T16:50:51.148731Z", + "shell.execute_reply": "2025-03-28T16:50:51.147960Z" + } + }, + "outputs": [], + "source": [ + "results = {\n", + " label: exact_sampler.run(subexperiment).result()\n", + " for label, subexperiment in subexperiments.items()\n", + "}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb new file mode 100644 index 00000000000..cb09664a410 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ccbf48d3", + "metadata": {}, + "source": [ + "## How to generate exact sampling coefficients\n", + "\n", + "This how-to guide is intended to show users how they can generate exact sampling coefficients to be used in reconstructing the simulated expectation value of the original circuit.\n", + "\n", + "First, we set up a simple cutting problem following the [first tutorial](../tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "dc54656b", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:52.873918Z", + "iopub.status.busy": "2025-03-28T16:50:52.873283Z", + "iopub.status.idle": "2025-03-28T16:50:53.390761Z", + "shell.execute_reply": "2025-03-28T16:50:53.390002Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit.circuit.library import efficient_su2\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "from qiskit_addon_cutting import (\n", + " partition_problem,\n", + " generate_cutting_experiments,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dd147239", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:53.393546Z", + "iopub.status.busy": "2025-03-28T16:50:53.393230Z", + "iopub.status.idle": "2025-03-28T16:50:54.346876Z", + "shell.execute_reply": "2025-03-28T16:50:54.346177Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuit = efficient_su2(4, entanglement=\"linear\", reps=2)\n", + "circuit.assign_parameters([0.8] * len(circuit.parameters), inplace=True)\n", + "observable = SparsePauliOp([\"ZZZZ\"])\n", + "circuit.draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "0bce456c", + "metadata": {}, + "source": [ + "Partition the circuit between qubits 1 and 2 by cutting 2 CNOT gates." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d4ccf5b8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:54.349628Z", + "iopub.status.busy": "2025-03-28T16:50:54.349194Z", + "iopub.status.idle": "2025-03-28T16:50:54.584073Z", + "shell.execute_reply": "2025-03-28T16:50:54.583316Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "partitioned_problem = partition_problem(\n", + " circuit=circuit, partition_labels=\"AABB\", observables=observable.paulis\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "bases = partitioned_problem.bases\n", + "subobservables = partitioned_problem.subobservables\n", + "subcircuits[\"A\"].draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "44956cbb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:54.586659Z", + "iopub.status.busy": "2025-03-28T16:50:54.586391Z", + "iopub.status.idle": "2025-03-28T16:50:54.811013Z", + "shell.execute_reply": "2025-03-28T16:50:54.810276Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[\"B\"].draw(\"mpl\", scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "fce55187", + "metadata": {}, + "source": [ + "### Demonstrate how to obtain all weights exactly\n", + "\n", + "If you wish to calculate all weights exactly, no matter how small, you can achieve this by passing infinity (`np.inf`) to `num_samples`:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8c56282f", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:54.813264Z", + "iopub.status.busy": "2025-03-28T16:50:54.813049Z", + "iopub.status.idle": "2025-03-28T16:50:54.867519Z", + "shell.execute_reply": "2025-03-28T16:50:54.866771Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(np.float64(0.24999999999999992), ),\n", + " (np.float64(0.24999999999999992), ),\n", + " (np.float64(0.24999999999999992), ),\n", + " (np.float64(-0.24999999999999992), ),\n", + " (np.float64(0.24999999999999992), ),\n", + " (np.float64(-0.24999999999999992), ),\n", + " (np.float64(0.24999999999999992), ),\n", + " (np.float64(0.24999999999999992), ),\n", + " (np.float64(0.24999999999999992), ),\n", + " (np.float64(-0.24999999999999992), ),\n", + " (np.float64(0.24999999999999992), ),\n", + " (np.float64(-0.24999999999999992), ),\n", + " (np.float64(0.24999999999999992), ),\n", + " (np.float64(0.24999999999999992), ),\n", + " 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}, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits,\n", + " observables=subobservables,\n", + " num_samples=np.inf,\n", + ")\n", + "coefficients" + ] + }, + { + "cell_type": "markdown", + "id": "b5fc6579", + "metadata": {}, + "source": [ + "### Demonstrate how to find the minimum `num_samples` needed to retrieve all exact weights for 2 CNOT cuts\n", + "\n", + "When `num_samples` is set to a finite number, each weight whose absolute value is above a threshold of 1 / `num_samples` will be evaluated exactly. The remaining weights -- those in the tail of the distribution -- will then be sampled from, resulting in at most `num_samples` unique weights.\n", + "\n", + "In the case of a CNOT gate -- or any gate equivalent to it up to single-qubit unitaries -- each of the six weights of the quasi-probability decomposition have the same magnitude, so each gets sampled with a probability of $1/6$:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "78539fcc", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:54.870158Z", + "iopub.status.busy": "2025-03-28T16:50:54.869670Z", + "iopub.status.idle": "2025-03-28T16:50:54.874200Z", + "shell.execute_reply": "2025-03-28T16:50:54.873577Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mapping probabilities for a CNOT decomposition: [0.16666667 0.16666667 0.16666667 0.16666667 0.16666667 0.16666667]\n" + ] + } + ], + "source": [ + "print(f\"Mapping probabilities for a CNOT decomposition: {bases[0].probabilities}\")" + ] + }, + { + "cell_type": "markdown", + "id": "5770cf75", + "metadata": {}, + "source": [ + "In this example, we have cut two CNOT gates. Given that the probability of any given mapping in a CNOT decomposition is $1/6$, the probability of any given mapping in the _joint_ distribution combining the two cut CNOT gates is $(1/6)^2$. Therefore, we need to take at least $6^2$ weights in order to retrieve all exact weights from `generate_cutting_experiments`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f07a6cc3", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:54.876590Z", + "iopub.status.busy": "2025-03-28T16:50:54.876398Z", + "iopub.status.idle": "2025-03-28T16:50:54.881758Z", + "shell.execute_reply": "2025-03-28T16:50:54.881069Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of samples needed to retrieve exact weights: 36.0\n" + ] + } + ], + "source": [ + "from qiskit_addon_cutting.qpd import QPDBasis\n", + "from qiskit.circuit.library.standard_gates import CXGate\n", + "\n", + "qpd_basis_cx = QPDBasis.from_instruction(CXGate())\n", + "\n", + "\n", + "def _min_nonzero(seq, /):\n", + " \"\"\"Return the minimum value in a sequence, ignoring values near zero.\"\"\"\n", + " return min(x for x in seq if not np.isclose(x, 0))\n", + "\n", + "\n", + "num_cx_cuts = 2\n", + "\n", + "print(\n", + " f\"Number of samples needed to retrieve exact weights: {1 / _min_nonzero(qpd_basis_cx.probabilities)**num_cx_cuts}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "deb6ad1e", + "metadata": {}, + "source": [ + "#### Observe the coefficient weights returned from `generate_cutting_experiments` are `WeightType.EXACT`\n", + "\n", + "Above, we determined 36 samples would trigger the coefficients to be returned as exact. Here we set `num_samples` to exactly 36 to test this." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "43d32869", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:54.884011Z", + "iopub.status.busy": "2025-03-28T16:50:54.883576Z", + "iopub.status.idle": "2025-03-28T16:50:54.942752Z", + "shell.execute_reply": "2025-03-28T16:50:54.942054Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(np.float64(0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(0.25), ),\n", + " (np.float64(-0.25), ),\n", + " (np.float64(0.25), )]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits,\n", + " observables=subobservables,\n", + " num_samples=36,\n", + ")\n", + "coefficients" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how_to_provide_multiple_observables.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how_to_provide_multiple_observables.ipynb new file mode 100644 index 00000000000..87f2c1e2c90 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/how_to_provide_multiple_observables.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a42230b5-41d9-465c-904a-7d431cc72595", + "metadata": {}, + "source": [ + "# How to perform a cutting workflow with multiple observables" + ] + }, + { + "cell_type": "markdown", + "id": "6902602f-1392-4321-9cbe-5eb0921909b0", + "metadata": {}, + "source": [ + "### Create a circuit to cut" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5defa9fb-e928-4fcb-9ae3-bed8e8f2527b", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:57.096887Z", + "iopub.status.busy": "2025-03-28T16:50:57.096668Z", + "iopub.status.idle": "2025-03-28T16:50:58.481013Z", + "shell.execute_reply": "2025-03-28T16:50:58.480360Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.circuit.library import EfficientSU2\n", + "\n", + "qc = EfficientSU2(4, entanglement=\"linear\", reps=2).decompose()\n", + "qc.assign_parameters([0.4] * len(qc.parameters), inplace=True)\n", + "\n", + "qc.draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "99344052-a7cc-4857-a77e-0db02003cb7d", + "metadata": {}, + "source": [ + "### Specify the observables of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "42c2d640-7cce-41c3-9683-920449215c9d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:58.484791Z", + "iopub.status.busy": "2025-03-28T16:50:58.483701Z", + "iopub.status.idle": "2025-03-28T16:50:58.489908Z", + "shell.execute_reply": "2025-03-28T16:50:58.489261Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.quantum_info import Pauli, SparsePauliOp\n", + "\n", + "observables = [\n", + " Pauli(\"-XZII\"),\n", + " SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"]),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "466343c4-35ce-496e-a887-5a126e0a6b40", + "metadata": {}, + "source": [ + "### Gather unique observable terms" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "defb33f4-820a-44aa-89ec-4f7eb13a49f5", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:58.492460Z", + "iopub.status.busy": "2025-03-28T16:50:58.491798Z", + "iopub.status.idle": "2025-03-28T16:50:58.561027Z", + "shell.execute_reply": "2025-03-28T16:50:58.560336Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting.utils.observable_terms import gather_unique_observable_terms\n", + "\n", + "unique_observable_terms = gather_unique_observable_terms(observables)" + ] + }, + { + "cell_type": "markdown", + "id": "81c1c9e1-e8a4-4a5c-80f3-35d00298459f", + "metadata": {}, + "source": [ + "### Perform cutting workflow" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9830f036-904e-4900-98b1-7991da2915d0", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:58.568971Z", + "iopub.status.busy": "2025-03-28T16:50:58.568447Z", + "iopub.status.idle": "2025-03-28T16:50:58.579358Z", + "shell.execute_reply": "2025-03-28T16:50:58.578737Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import partition_problem\n", + "\n", + "partitioned_problem = partition_problem(\n", + " circuit=qc, partition_labels=\"AABB\", observables=unique_observable_terms\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "subobservables = partitioned_problem.subobservables" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "77577d6f-4acc-468b-962c-81a09cc31fcd", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:58.581924Z", + "iopub.status.busy": "2025-03-28T16:50:58.581453Z", + "iopub.status.idle": "2025-03-28T16:50:58.854801Z", + "shell.execute_reply": "2025-03-28T16:50:58.850920Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit_addon_cutting import generate_cutting_experiments\n", + "\n", + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits, observables=subobservables, num_samples=np.inf\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "37253e37-51f9-4aa4-9f5e-d8a032d639ce", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:58.859697Z", + "iopub.status.busy": "2025-03-28T16:50:58.859332Z", + "iopub.status.idle": "2025-03-28T16:50:59.766313Z", + "shell.execute_reply": "2025-03-28T16:50:59.765302Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", + "\n", + "backend = FakeManilaV2()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f15e198e-7f0b-4507-a73f-e4aab2f286ed", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:59.773825Z", + "iopub.status.busy": "2025-03-28T16:50:59.773001Z", + "iopub.status.idle": "2025-03-28T16:51:07.997499Z", + "shell.execute_reply": "2025-03-28T16:51:07.996531Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", + "\n", + "# Transpile the subexperiments to ISA circuits\n", + "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", + "isa_subexperiments = {\n", + " label: pass_manager.run(partition_subexpts)\n", + " for label, partition_subexpts in subexperiments.items()\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5383d427-3285-43b1-8564-f451e1b4287e", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:08.002734Z", + "iopub.status.busy": "2025-03-28T16:51:08.001594Z", + "iopub.status.idle": "2025-03-28T16:51:08.452499Z", + "shell.execute_reply": "2025-03-28T16:51:08.444876Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import SamplerV2, Batch\n", + "\n", + "# Submit each partition's subexperiments as a single batch\n", + "with Batch(backend=backend) as batch:\n", + " sampler = SamplerV2(mode=batch)\n", + " jobs = {\n", + " label: sampler.run(subsystem_subexpts, shots=2**12)\n", + " for label, subsystem_subexpts in isa_subexperiments.items()\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "154f4cf0-c45d-41b2-8091-b23a47b30c22", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:08.472013Z", + "iopub.status.busy": "2025-03-28T16:51:08.471699Z", + "iopub.status.idle": "2025-03-28T16:51:34.412339Z", + "shell.execute_reply": "2025-03-28T16:51:34.411608Z" + } + }, + "outputs": [], + "source": [ + "# Retrieve results\n", + "results = {label: job.result() for label, job in jobs.items()}" + ] + }, + { + "cell_type": "markdown", + "id": "35c57f33-a128-4b0f-9a23-705cfecae993", + "metadata": {}, + "source": [ + "### Reconstruct the expectation value of each unique term" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9f5e64a7-a334-4278-9c61-e78bbbc36368", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:34.415031Z", + "iopub.status.busy": "2025-03-28T16:51:34.414827Z", + "iopub.status.idle": "2025-03-28T16:51:39.000491Z", + "shell.execute_reply": "2025-03-28T16:51:38.999738Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import reconstruct_expectation_values\n", + "\n", + "# Get expectation values for each observable term\n", + "reconstructed_term_expvals = reconstruct_expectation_values(\n", + " results,\n", + " coefficients,\n", + " subobservables, # or unique_observable_terms if the circuit did not separate\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "792853df-d4f9-43d1-9f7d-50e38768ceb8", + "metadata": {}, + "source": [ + "### Reconstruct the expectation value of the original operators" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b134b767-ce53-4438-866f-a25952cb985b", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:39.003144Z", + "iopub.status.busy": "2025-03-28T16:51:39.002785Z", + "iopub.status.idle": "2025-03-28T16:51:39.006758Z", + "shell.execute_reply": "2025-03-28T16:51:39.006291Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting.utils.observable_terms import (\n", + " reconstruct_observable_expvals_from_terms,\n", + ")\n", + "\n", + "reconstructed_expvals = reconstruct_observable_expvals_from_terms(\n", + " observables, dict(zip(unique_observable_terms, reconstructed_term_expvals))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "fd71a9ef-37cd-4687-9fe1-9b4ea3264f91", + "metadata": {}, + "source": [ + "### Compare the reconstructed expectation values with the exact expectation value from the original circuit and observables" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "986e5ce8-64a5-464a-9c43-45ccf1a65b2d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:39.008795Z", + "iopub.status.busy": "2025-03-28T16:51:39.008594Z", + "iopub.status.idle": "2025-03-28T16:51:39.023519Z", + "shell.execute_reply": "2025-03-28T16:51:39.022762Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed expectation values: [-0.20063001 0.48694563]\n", + "Exact expectation values: [-0.17994235 0.56254612]\n", + "Error in estimation: [-0.02068766 -0.07560049]\n", + "Relative error in estimation: [ 0.11496824 -0.13438986]\n" + ] + } + ], + "source": [ + "from qiskit_aer.primitives import EstimatorV2\n", + "\n", + "estimator = EstimatorV2()\n", + "exact_expvals = estimator.run([(qc, observables)]).result()[0].data.evs\n", + "print(\n", + " f\"Reconstructed expectation values: {np.real(np.round(reconstructed_expvals, 8))}\"\n", + ")\n", + "print(f\"Exact expectation values: {np.round(exact_expvals, 8)}\")\n", + "print(\n", + " f\"Error in estimation: {np.real(np.round(reconstructed_expvals-exact_expvals, 8))}\"\n", + ")\n", + "print(\n", + " f\"Relative error in estimation: {np.real(np.round((reconstructed_expvals-exact_expvals) / exact_expvals, 8))}\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/how-tos/how_to_specify_cut_wires.ipynb b/docs/addons/qiskit-addon-cutting/how-tos/how_to_specify_cut_wires.ipynb new file mode 100644 index 00000000000..f87093ebc2f --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/how-tos/how_to_specify_cut_wires.ipynb @@ -0,0 +1,506 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b04b8bc7", + "metadata": {}, + "source": [ + "# How to place wire cuts using a single-qubit `CutWire` instruction\n", + "\n", + "This how-to guide is intended to demonstrate how to place wire cuts using single-qubit `CutWire` instructions, which may at times be more convenient than specifying them as two-qubit `Move` instructions." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1aa871cb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:41.715866Z", + "iopub.status.busy": "2025-03-28T16:51:41.715618Z", + "iopub.status.idle": "2025-03-28T16:51:42.298891Z", + "shell.execute_reply": "2025-03-28T16:51:42.298034Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit import QuantumCircuit\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "from qiskit_aer.primitives import EstimatorV2, SamplerV2\n", + "\n", + "from qiskit_addon_cutting import (\n", + " partition_problem,\n", + " generate_cutting_experiments,\n", + " reconstruct_expectation_values,\n", + ")\n", + "\n", + "from qiskit_addon_cutting.instructions import CutWire\n", + "from qiskit_addon_cutting import cut_wires, expand_observables" + ] + }, + { + "cell_type": "markdown", + "id": "06ae4c39", + "metadata": {}, + "source": [ + "### Prepare a circuit for cutting\n", + "\n", + "As in the [tutorial for wire cutting](../tutorials/03_wire_cutting_via_move_instruction.ipynb), we have used a circuit inspired by Fig. 1(a) of [arXiv:2302.03366v1](https://arxiv.org/abs/2302.03366v1). The cut locations are marked manually here with `CutWire` instructions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0ae22516", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:42.302264Z", + "iopub.status.busy": "2025-03-28T16:51:42.301906Z", + "iopub.status.idle": "2025-03-28T16:51:43.264296Z", + "shell.execute_reply": "2025-03-28T16:51:43.263022Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc_0 = QuantumCircuit(7)\n", + "for i in range(7):\n", + " qc_0.rx(np.pi / 4, i)\n", + "qc_0.cx(0, 3)\n", + "qc_0.cx(1, 3)\n", + "qc_0.cx(2, 3)\n", + "qc_0.append(CutWire(), [3])\n", + "qc_0.cx(3, 4)\n", + "qc_0.cx(3, 5)\n", + "qc_0.cx(3, 6)\n", + "qc_0.append(CutWire(), [3])\n", + "qc_0.cx(0, 3)\n", + "qc_0.cx(1, 3)\n", + "qc_0.cx(2, 3)\n", + "qc_0.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "7ee07a81", + "metadata": {}, + "source": [ + "### Recover the uncut circuit\n", + "\n", + "`CutWire` instructions decompose to nothing (they are equivalent to the identity), so the uncut circuit can be recovered as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "631286a6", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.267141Z", + "iopub.status.busy": "2025-03-28T16:51:43.266751Z", + "iopub.status.idle": "2025-03-28T16:51:43.652437Z", + "shell.execute_reply": "2025-03-28T16:51:43.651574Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc_0.decompose(\"cut_wire\").draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "dcd8dea0", + "metadata": {}, + "source": [ + "### Specify an observable\n", + "\n", + "This observable has 7 qubits, just like the original circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "847a3205", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.655017Z", + "iopub.status.busy": "2025-03-28T16:51:43.654586Z", + "iopub.status.idle": "2025-03-28T16:51:43.658036Z", + "shell.execute_reply": "2025-03-28T16:51:43.657602Z" + } + }, + "outputs": [], + "source": [ + "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])" + ] + }, + { + "cell_type": "markdown", + "id": "59730746", + "metadata": {}, + "source": [ + "### Transform cuts to moves\n", + "\n", + "The next step is to call `cut_wires`, which transforms each `CutWire` into a `TwoQubitQPDGate` which wraps a `Move` instruction. An additional qubit is added to the circuit for each `CutWire` in the input circuit.\n", + "\n", + "Notice that, unlike in the [wire cutting tutorial](../tutorials/03_wire_cutting_via_move_instruction.ipynb), where `Move` operations were placed manually, this function does not result in the _re_-use of a qubit. Because any method for qubit re-use is based on heuristics, this function naively allocates an additional qubit for each cut. Users wishing to re-use qubits might wish to experiment with [qiskit-qubit-reuse](https://github.com/qiskit-community/qiskit-qubit-reuse)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e4ee1559", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.660083Z", + "iopub.status.busy": "2025-03-28T16:51:43.659877Z", + "iopub.status.idle": "2025-03-28T16:51:43.924630Z", + "shell.execute_reply": "2025-03-28T16:51:43.924082Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc_1 = cut_wires(qc_0)\n", + "qc_1.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "c22b9572", + "metadata": {}, + "source": [ + "### Update the observable terms to account for the extra qubit\n", + "\n", + "The transformed circuit contains additional qubits (one for each `CutWire` instruction), so the observables must be updated for the new circuit. This can be done using the `expand_observables` function. Since the observable coefficients are ignored until reconstruction of the final expectation value, the input and output types of the observables for ``expand_observables`` are ``PauliList``s.\n", + "\n", + "The resulting observables have 9 qubits, just like the transformed circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "95fbeda0", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.926980Z", + "iopub.status.busy": "2025-03-28T16:51:43.926767Z", + "iopub.status.idle": "2025-03-28T16:51:43.931899Z", + "shell.execute_reply": "2025-03-28T16:51:43.931437Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PauliList(['ZIIIIIIII', 'IIIZIIIII', 'IIIIIIIIZ'])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observable_expanded_paulis = expand_observables(observable.paulis, qc_0, qc_1)\n", + "observable_expanded_paulis" + ] + }, + { + "cell_type": "markdown", + "id": "6f64acd6", + "metadata": {}, + "source": [ + "### Separate the circuit and observables\n", + "\n", + "In this case, `partition_labels` need not be passed to `partition_problem`, as the labels can be determined automatically from the connectivity of the circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "99bef123", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.933905Z", + "iopub.status.busy": "2025-03-28T16:51:43.933725Z", + "iopub.status.idle": "2025-03-28T16:51:43.940544Z", + "shell.execute_reply": "2025-03-28T16:51:43.940042Z" + } + }, + "outputs": [], + "source": [ + "partitioned_problem = partition_problem(\n", + " circuit=qc_1, observables=observable_expanded_paulis\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "subobservables = partitioned_problem.subobservables" + ] + }, + { + "cell_type": "markdown", + "id": "ce2bf0cd", + "metadata": {}, + "source": [ + "From here forward, the cutting workflow is the same as usual, but the remaining steps are spelled out here explicitly so one can follow along with the results." + ] + }, + { + "cell_type": "markdown", + "id": "bae9ac63", + "metadata": {}, + "source": [ + "### Visualize the decomposed problem\n", + "\n", + "Notice that once the circuits have been cut, some of the instructions are able to commute past each other. For instance, in subcircuit \"A\", half of the second `Move` operation is actually the _first_ operation on the final qubit." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "abeee650", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.942618Z", + "iopub.status.busy": "2025-03-28T16:51:43.942418Z", + "iopub.status.idle": "2025-03-28T16:51:43.946860Z", + "shell.execute_reply": "2025-03-28T16:51:43.946404Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: PauliList(['IIIII', 'ZIIII', 'IIIIZ']),\n", + " 1: PauliList(['ZIII', 'IIII', 'IIII'])}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subobservables" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "aaef5b3d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:43.948780Z", + "iopub.status.busy": "2025-03-28T16:51:43.948581Z", + "iopub.status.idle": "2025-03-28T16:51:44.164480Z", + "shell.execute_reply": "2025-03-28T16:51:44.163535Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[0].draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "975a3ca9", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:44.167652Z", + "iopub.status.busy": "2025-03-28T16:51:44.167358Z", + "iopub.status.idle": "2025-03-28T16:51:44.386632Z", + "shell.execute_reply": "2025-03-28T16:51:44.385952Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[1].draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "2e02632b", + "metadata": {}, + "source": [ + "### Generate and run the cutting experiments; reconstruct and compare against uncut expectation values" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "459dcee8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:44.389065Z", + "iopub.status.busy": "2025-03-28T16:51:44.388801Z", + "iopub.status.idle": "2025-03-28T16:51:44.496881Z", + "shell.execute_reply": "2025-03-28T16:51:44.496185Z" + } + }, + "outputs": [], + "source": [ + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits,\n", + " observables=subobservables,\n", + " num_samples=np.inf,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "dc9fe287", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:44.501113Z", + "iopub.status.busy": "2025-03-28T16:51:44.500852Z", + "iopub.status.idle": "2025-03-28T16:51:45.949307Z", + "shell.execute_reply": "2025-03-28T16:51:45.948450Z" + } + }, + "outputs": [], + "source": [ + "sampler = SamplerV2()\n", + "results = {\n", + " label: sampler.run(subexperiment, shots=2**12).result()\n", + " for label, subexperiment in subexperiments.items()\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e317a998", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:45.952706Z", + "iopub.status.busy": "2025-03-28T16:51:45.951971Z", + "iopub.status.idle": "2025-03-28T16:51:49.139646Z", + "shell.execute_reply": "2025-03-28T16:51:49.138857Z" + } + }, + "outputs": [], + "source": [ + "reconstructed_expval_terms = reconstruct_expectation_values(\n", + " results,\n", + " coefficients,\n", + " subobservables,\n", + ")\n", + "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5ae568ca", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:49.143349Z", + "iopub.status.busy": "2025-03-28T16:51:49.143053Z", + "iopub.status.idle": "2025-03-28T16:51:49.158786Z", + "shell.execute_reply": "2025-03-28T16:51:49.158077Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed expectation value: 1.62048799\n", + "Exact expectation value: 1.59099026\n", + "Error in estimation: 0.02949773\n", + "Relative error in estimation: 0.01854048\n" + ] + } + ], + "source": [ + "estimator = EstimatorV2()\n", + "exact_expval = (\n", + " estimator.run([(qc_0.decompose(\"cut_wire\"), observable)]).result()[0].data.evs\n", + ")\n", + "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", + "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", + "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", + "print(\n", + " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/index.mdx b/docs/addons/qiskit-addon-cutting/index.mdx new file mode 100644 index 00000000000..2ca74551654 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/index.mdx @@ -0,0 +1,126 @@ +--- +title: "Qiskit addon: circuit cutting" +--- + +# Qiskit addon: circuit cutting + +[![GitHub repository star counter badge](https://img.shields.io/github/stars/Qiskit/qiskit-addon-cutting?style=social)](https://github.com/Qiskit/qiskit-addon-cutting) + +[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. + +This package implements circuit cutting. In this technique, a handful of gates and/or wires are cut, resulting in smaller circuits that are better suited for execution on hardware. The result of the original circuit can then be reconstructed; however, the trade-off is that the overall number of shots must be increased by a factor exponential in the number of cuts. + +For a more detailed discussion on circuit cutting, check out our [technical guide](./circuit_cutting/explanation/index.rst#overview-of-circuit-cutting). + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [Release Notes](release-notes#release-notes). + + + This package was known as the Circuit Knitting Toolbox prior to September 2024. + + +## Citing this project + +If you use this package in your research, please cite it according to `CITATON.bib` file included in this repository: + +```bibtex +@misc{qiskit-addon-cutting, + author = { + Agata M. Bra\'{n}czyk + and Almudena {Carrera Vazquez} + and Daniel J. Egger + and Bryce Fuller + and Julien Gacon + and James R. Garrison + and Jennifer R. Glick + and Caleb Johnson + and Saasha Joshi + and Edwin Pednault + and C. D. Pemmaraju + and Pedro Rivero + and Ibrahim Shehzad + and Stefan Woerner + }, + title = {{Qiskit addon: circuit cutting}}, + howpublished = {\url{https://github.com/Qiskit/qiskit-addon-cutting}}, + year = {2024}, + doi = {10.5281/zenodo.7987997} +} +``` + +If you are using the entanglement forging tool in Circuit Knitting Toolbox version 0.5 or earlier, please use [an older version of the citation file](https://github.com/Qiskit/qiskit-addon-cutting/blob/stable/0.5/CITATION.bib) which includes the authors of that tool. + +If you are using the CutQC tool in Circuit Knitting Toolbox version 0.7 or earlier, please use [an older version of the citation file](https://github.com/Qiskit/qiskit-addon-cutting/blob/stable/0.7/CITATION.bib) which includes the authors of that tool. + +## Developer guide + +The source code to this package is available [on GitHub](https://github.com/Qiskit/qiskit-addon-cutting). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-cutting/blob/main/CONTRIBUTING.md) in the root of this project’s repository. + +## Contents + +* [Documentation Home]() + +* [Installation Instructions](install) + + * [Pre-Installation](install#pre-installation) + * [Option 1: Install from PyPI](install#option-1-install-from-pypi) + * [Option 2: Install from Source](install#option-2-install-from-source) + * [Option 3: Use within Docker](install#option-3-use-within-docker) + * [Platform Support](install#platform-support) + +* [Tutorials](tutorials/index) + + * [Gate Cutting to Reduce Circuit Width](tutorials/01_gate_cutting_to_reduce_circuit_width) + * [Gate Cutting to Reduce Circuit Depth](tutorials/02_gate_cutting_to_reduce_circuit_depth) + * [Wire Cutting Phrased as a Two-Qubit `Move` Instruction](tutorials/03_wire_cutting_via_move_instruction) + * [Automatically find cuts](tutorials/04_automatic_cut_finding) + +* [Explanatory Material](explanation/index) + + * [Overview of circuit cutting](explanation/index#overview-of-circuit-cutting) + * [Key terms](explanation/index#key-terms) + * [Circuit cutting as a quasiprobability decomposition (QPD)](explanation/index#circuit-cutting-as-a-quasiprobability-decomposition-qpd) + * [An example: cutting a `RZZGate`](explanation/index#an-example-cutting-a-rzzgate) + * [More general cut two-qubit gates via the KAK decomposition](explanation/index#more-general-cut-two-qubit-gates-via-the-kak-decomposition) + * [Wire cutting phrased as a two-qubit `Move` operation](explanation/index#wire-cutting-phrased-as-a-two-qubit-move-operation) + * [Sample weights in the Qiskit addon for circuit cutting](explanation/index#sample-weights-in-the-qiskit-addon-for-circuit-cutting) + * [Sampling overhead reference table](explanation/index#sampling-overhead-reference-table) + * [Current limitations](explanation/index#current-limitations) + * [References](explanation/index#references) + +* [How-To Guides](how-tos/index) + + * [How to generate exact quasiprobability distributions from Sampler](how-tos/how_to_generate_exact_quasi_dists_from_sampler) + * [How to generate exact sampling coefficients](how-tos/how_to_generate_exact_sampling_coefficients) + * [How to perform a cutting workflow with multiple observables](how-tos/how_to_provide_multiple_observables) + * [How to place wire cuts using a single-qubit `CutWire` instruction](how-tos/how_to_specify_cut_wires) + +* [API References](index) + + * [Circuit cutting (`qiskit_addon_cutting`)](qiskit_addon_cutting) + * [Instructions (`qiskit_addon_cutting.instructions`)](qiskit_addon_cutting.instructions) + * [Quasi-Probability Decomposition (QPD) (`qiskit_addon_cutting.qpd`)](qiskit_addon_cutting.qpd) + * [Bitwise utilities (`qiskit_addon_cutting.utils.bitwise`)](qiskit_addon_cutting.utils.bitwise) + * [Iteration utilities (`qiskit_addon_cutting.utils.iteration`)](qiskit_addon_cutting.utils.iteration) + * [Observable grouping (`qiskit_addon_cutting.utils.observable_grouping`)](qiskit_addon_cutting.utils.observable_grouping) + * [Observable terms (`qiskit_addon_cutting.utils.observable_terms`)](qiskit_addon_cutting.utils.observable_terms) + * [Simulation utilities (`qiskit_addon_cutting.utils.simulation`)](qiskit_addon_cutting.utils.simulation) + * [Transforms (`qiskit_addon_cutting.utils.transforms`)](qiskit_addon_cutting.utils.transforms) + * [Transpiler passes (`qiskit_addon_cutting.utils.transpiler_passes`)](qiskit_addon_cutting.utils.transpiler_passes) + +* [GitHub](https://github.com/Qiskit/qiskit-addon-cutting) + +* [Release Notes](release-notes) + + * [0.10.0](release-notes#release-notes-0-10-0) + * [0.9.0](release-notes#release-notes-0-9-0) + * [0.7.1](release-notes#release-notes-0-7-1) + * [0.7.0](release-notes#release-notes-0-7-0) + * [0.6.0](release-notes#release-notes-0-6-0) + * [0.5.0](release-notes#release-notes-0-5-0) + * [0.4.0](release-notes#release-notes-0-4-0) + * [0.3.0](release-notes#release-notes-0-3-0) + * [0.2.0](release-notes#release-notes-0-2-0) + * [0.1.0](release-notes#release-notes-0-1-0) + diff --git a/docs/addons/qiskit-addon-cutting/install.mdx b/docs/addons/qiskit-addon-cutting/install.mdx new file mode 100644 index 00000000000..4053a3f7b52 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/install.mdx @@ -0,0 +1,127 @@ +--- +title: "Installation Instructions" +--- + +# Installation Instructions + +Let’s see how to install the Qiskit addon for circuit cutting. The first thing to do is choose how you’re going to run and install the packages. There are three primary ways to do this: + +* [Option 1: Install from PyPI](#option-1) +* [Option 2: Install from Source](#option-2) +* [Option 3: Use within Docker](#option-3) + +Users who wish to run within a containerized environment may skip the pre-installation and move straight to [Option 3: Use within Docker](#option-3). + +## Pre-Installation + +Users who wish to install locally (using either [Option 1: Install from PyPI](#option-1) or [Option 2: Install from Source](#option-2)) are encouraged to follow a brief set of common instructions to prepare a Python environment for installation: + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +Note: If you are using Windows, use the following commands in PowerShell: + +```pwsh +python3 -m venv c:\path\to\virtual\environment +c:\path\to\virtual\environment\Scripts\Activate.ps1 +``` + + + +## Option 1: Install from PyPI + +The most straightforward way to install the `qiskit-addon-cutting` package is via PyPI. + +```sh +pip install --upgrade pip +pip install qiskit-addon-cutting +``` + + + +## Option 2: Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +In either case, the first step is to clone the `qiskit-addon-cutting` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-cutting.git +``` + +Next, upgrade pip and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-cutting +``` + +The next step is to install to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started with the addon by running the notebooks in the docs. + +```python +cd docs/ +jupyter notebook +``` + + + +## Option 3: Use within Docker + +We have provided a [Dockerfile](https://github.com/Qiskit/qiskit-addon-cutting/blob/main/Dockerfile), which can be used to build a Docker image, as well as a [compose.yaml](https://github.com/Qiskit/qiskit-addon-cutting/blob/main/compose.yaml) file, which allows one to use the Docker image with just a few simple commands. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-cutting.git +cd qiskit-addon-cutting +docker compose build +docker compose up +``` + +Depending on your system configuration, you may need to type `sudo` before each `docker compose` command. + + + If you are instead using [podman](https://podman.io/) and [podman-compose](https://github.com/containers/podman-compose), the commands are: + + ```sh + podman machine start + podman-compose --podman-pull-args short-name-mode="permissive" build + podman-compose up + ``` + + +Once the container is running, you should see a message like this: + +```python +notebook_1 | To access the server, open this file in a browser: +notebook_1 | file:///home/jovyan/.local/share/jupyter/runtime/jpserver-7-open.html +notebook_1 | Or copy and paste one of these URLs: +notebook_1 | http://e4a04564eb39:8888/lab?token=00ed70b5342f79f0a970ee9821c271eeffaf760a7dcd36ec +notebook_1 | or http://127.0.0.1:8888/lab?token=00ed70b5342f79f0a970ee9821c271eeffaf760a7dcd36ec +``` + +Locate the *last* URL in your terminal (the one that includes `127.0.0.1`), and navigate to that URL in a web browser to access the Jupyter Notebook interface. + +The home directory includes a subdirectory named `persistent-volume`. All work you’d like to save should be placed in this directory, as it is the only one that will be saved across different container runs. + + + +## Platform Support + +We expect this package to work on [any platform supported by Qiskit](/docs/start/install#operating-system-support). If you are experiencing issues running the software on your device, you may consider [using this package within Docker](#option-3). + diff --git a/docs/addons/qiskit-addon-cutting/release-notes.mdx b/docs/addons/qiskit-addon-cutting/release-notes.mdx new file mode 100644 index 00000000000..01c3210737b --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/release-notes.mdx @@ -0,0 +1,462 @@ +--- +title: "Release Notes" +--- + + + + + +# + + + + + +## 0.10.0 + + + +### Prelude + +This release adds compatibility with Qiskit SDK 2.0. + + + +### New Features + +* This release adds utility functions and a how-to guide for performing circuit cutting with `SparsePauliOp` observables, rather than a `PauliList` of observables. + + + +### Upgrade Notes + +* Qiskit SDK 1.3.1 or higher is now required. Qiskit SDK 2.0 is now supported. + + + +### Other Notes + +* This addon is now marked as being incompatible with Qiskit 1.3.0, as the second cutting tutorial fails to execute due to a bug in Qiskit 1.3.0. See discussion in [issue #714](https://github.com/Qiskit/qiskit-addon-cutting/issues/714) for more information. + + + + + +## 0.9.0 + + + + + +### Prelude + +The most notable change in this release is that the package has been renamed to `qiskit-cutting-addon`. It also marks the removal of this package’s original implementation of wire cutting, CutQC. + + + + + +### New Features + +* A new `minimum_reached` field has been added to the metadata outputted by `circuit_knitting.cutting.find_cuts()` to check if the cut-finder found a cut scheme that minimized the sampling overhead. Note that the search algorithm employed by the cut-finder is *guaranteed* to find the optimal solution, that is, the solution with the minimum sampling overhead, provided it is allowed to run long enough. The user is free to time-restrict the search by passing in suitable values for `max_backjumps` and/or `max_gamma` to [`OptimizationParameters`](qiskit_addon_cutting#qiskit_addon_cutting.OptimizationParameters "qiskit_addon_cutting.OptimizationParameters"). If the search is terminated prematurely in this way, the metadata may indicate that the minimum was not reached, even though the returned solution was actually the optimal solution. This would mean that the search that was performed was not exhaustive enough to prove that the returned solution was optimal. + +* When specifying instances of [`OptimizationParameters`](qiskit_addon_cutting#qiskit_addon_cutting.OptimizationParameters "qiskit_addon_cutting.OptimizationParameters") that are inputted to `circuit_knitting.cutting.find_cuts()`, the user can now control whether the cut-finder looks only for gate cuts, only for wire cuts, or both, by setting the bools `gate_lo` and `wire_lo` appropriately. The default value of both of these is set to `True` and so the default search considers the possibility of both gate and wire cuts. + + + + + +### Upgrade Notes + +* This package now requires updated versions of some dependencies: `qiskit` 1.1 or later, and `qiskit-ibm-runtime` 0.24.0 or later. + +* The search engine inside the automated cut-finder has been primed to avoid extraneous searches and is therefore expected to run faster. + +* The `CutQC` subpackage has been removed, along with its two associated utility modules, `circuit_knitting.utils.metrics` and `circuit_knitting.utils.conversion`. Users are now encouraged to use the automatic cut-finding and gate/wire cutting from the `circuit_knitting.cutting` package. + +* The behavior of [`separate_circuit()`](qiskit_addon_cutting.utils.transforms#qiskit_addon_cutting.utils.transforms.separate_circuit "qiskit_addon_cutting.utils.transforms.separate_circuit") and [`partition_problem()`](qiskit_addon_cutting#qiskit_addon_cutting.partition_problem "qiskit_addon_cutting.partition_problem") have changed so that idle qubits are discarded by default. Previously, each idle qubit was placed in its own subcircuit if `partition_labels` was not provided. + + + + + +## 0.7.1 + + + + + +### Prelude + +The 0.7.1 release provides a workaround to ensure that the experiments generated by the circuit cutting workflow will execute on IBM Quantum’s hardware backends. + + + +### Bug Fixes + +* Added a workaround so that the classical registers in the generated circuits will always contain at least one bit. This is currently necessary for the experiments to be able to reach IBM Quantum’s hardware backends due to an [openqasm parser issue](https://github.com/openqasm/qe-qasm/issues/37). + + + + + +### Other Notes + +* The [`generate_cutting_experiments()`](qiskit_addon_cutting#qiskit_addon_cutting.generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") function has been optimized for faster execution. + + + + + +## 0.7.0 + + + + + +### Prelude + +The 0.7 release introduces an automated cut finding code for the new circuit cutting workflow. With this milestone, the older cutting workflow (CutQC) is now deprecated. Additionally, this is the first CKT release to support version 2 of the Qiskit Runtime primitives. User are encouraged to [migrate to v2 primitives](/docs/guides/v2-primitives) as soon as possible. + + + + + +### New Features + +* Added a cut-finder function, `circuit_knitting.cutting.find_cuts()`, for automatically identifying locations to place LO gate and wire cuts such that the circuit is separable and runnable, given the maximum number of qubits per subcircuit as a parameter. The cut-finder will search for cut schemes which minimize the sampling overhead. Note, however, that for larger circuits, the number of cuts needed to separate the circuit will naturally grow larger, leading to an exponentially increasing sampling overhead. For instances of wire cuts, the cut-finder assumes no qubit reuse. Therefore, for each wire cut, a new wire is added to the circuit. In addition, the cut-finder requires that every gate in an input circuit be at most a two qubit gate. The search algorithm used by the cut-finder to identify cut locations is Dijkstra’s best first search algorithm which is guaranteed to find solutions with the lowest sampling overhead, provided any user-specified value for the maximum number of allowed backjumps or for the maximum sampling overhead does not prematurely stop the search. If the user wishes to time-restrict the search when running the cut-finder on large circuits, they can specify a maximum sampling overhead and/or a maximum number of allowed backjumps, in which case the cut-finder will return a valid albeit suboptimal cut scheme. + +* Circuit cutting reconstruction can now interpret the [`PrimitiveResult`](/docs/api/qiskit/qiskit.primitives.PrimitiveResult) object, which is returned by version 2 of the sampler primitive ([`BaseSamplerV2`](/docs/api/qiskit/qiskit.primitives.BaseSamplerV2)). See the [migration guide](/docs/guides/qiskit-1.0-features#qiskitprimitives) for details on upgrading to version 2 of the Qiskit primitives. + + + + + +### Upgrade Notes + +* CKT now requires updated versions of some dependencies: `qiskit` 1.0 or later, `qiskit-aer` 0.14.0 or later, and `qiskit-ibm-runtime` 0.23.0 or later. + +* The code in the `circuit_knitting.cutting.qpd.qpd` submodule has been split into three separate files. If you were importing directly from this submodule, you will now need to import from `circuit_knitting.cutting.qpd` instead. + + + +### Deprecation Notes + +* The `circuit_knitting.cutting.cutqc` package is deprecated and will be removed no sooner than Circuit Knitting Toolbox 0.8.0. The wire cutting functionality in the `circuit_knitting.cutting` package is what will be maintained going forward. Additionally, there is a new automated gate and wire cut-finding functionality in the `circuit_knitting.cutting.automated_cut_finding` module. A [tutorial](https://github.com/Qiskit/qiskit-addon-cutting/blob/stable/0.7/docs/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb) has been added to demonstrate automated cut-finding. + + + + + +### Other Notes + +* The cutting tutorials have been rephrased with the goal of reconstructing the expectation value of a single [`SparsePauliOp`](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp) with many terms, rather than multiple independent [`Pauli`](/docs/api/qiskit/qiskit.quantum_info.Pauli) observables. + +* The [circuit cutting explanation](/docs/addons/qiskit-addon-cutting/explanation/index) document has been expanded significantly. + + + + + +## 0.6.0 + + + + + +### Upgrade Notes + +* The minimum supported version of `qiskit` is now 0.45.0, and the minimum supported version of `qiskit-ibm-runtime` is now 0.12.2. CKT also now explicitly requires a version of `qiskit` less than 1.0, as there is no guarantee that the current version of CKT will work with Qiskit 1.0. + +* Removed the `circuit_knitting.cutting.qpd.QPDBasis.from_gate` method, which has been deprecated since the 0.3 release. [`QPDBasis.from_instruction()`](qiskit_addon_cutting.qpd.QPDBasis#qiskit_addon_cutting.qpd.QPDBasis.from_instruction "qiskit_addon_cutting.qpd.QPDBasis.from_instruction") should be used instead. + +* Removed the `circuit_knitting_toolbox` import path. Users should now import from `circuit_knitting` instead. + +* Removed the `circuit_knitting.cutting.decompose_gates` function, which has been deprecated since the 0.3 release. [`cut_gates()`](qiskit_addon_cutting#qiskit_addon_cutting.cut_gates "qiskit_addon_cutting.cut_gates") should be used instead. + +* Removed the `circuit_knitting.cutting.cutting_evaluation` module, which has been deprecated since the 0.4 release. Users should first call `circuit_knitting.cutting.generate_cutting_experiments()` to generate the subexperiment circuits and then submit these circuits directly to the desired Sampler(s). + +* Removed the `circuit_knitting.cutting.qpd.generate_qpd_samples` function, which has been deprecated since the 0.3 release. [`generate_qpd_weights()`](qiskit_addon_cutting.qpd#qiskit_addon_cutting.qpd.generate_qpd_weights "qiskit_addon_cutting.qpd.generate_qpd_weights") should be used instead. + +* Removed the `circuit_knitting.cutting.qpd.qpdbasis_from_gate` function, which has been deprecated since the 0.3 release. [`qpdbasis_from_instruction()`](qiskit_addon_cutting.qpd#qiskit_addon_cutting.qpd.qpdbasis_from_instruction "qiskit_addon_cutting.qpd.qpdbasis_from_instruction") should be used instead. + +* The [`generate_cutting_experiments()`](qiskit_addon_cutting#qiskit_addon_cutting.generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") function now performs some optimizations on the generated circuits before returning them to the user. In particular, it performs the [`RemoveResetInZeroState`](/docs/api/qiskit/qiskit.transpiler.passes.RemoveResetInZeroState), [`RemoveFinalReset`](qiskit_addon_cutting.utils.transpiler_passes.RemoveFinalReset#qiskit_addon_cutting.utils.transpiler_passes.RemoveFinalReset "qiskit_addon_cutting.utils.transpiler_passes.RemoveFinalReset"), and [`ConsolidateResets`](qiskit_addon_cutting.utils.transpiler_passes.ConsolidateResets#qiskit_addon_cutting.utils.transpiler_passes.ConsolidateResets "qiskit_addon_cutting.utils.transpiler_passes.ConsolidateResets") passes, so that circuits with cut wires and no re-used qubits are transformed into subexperiments that contain no `Reset`s. This allows such circuits to work on a greater variety of hardware backends. + + + + + +### Bug Fixes + +* It is now possible to serialize [`SingleQubitQPDGate`](qiskit_addon_cutting.qpd.SingleQubitQPDGate#qiskit_addon_cutting.qpd.SingleQubitQPDGate "qiskit_addon_cutting.qpd.SingleQubitQPDGate")s using [`qpy`](/docs/api/qiskit/qpy#module-qiskit.qpy), but some other issues with serialization and deserialization still remain. See issue [#455](https://github.com/Qiskit/qiskit-addon-cutting/issues/445) for details. + + + + + +### Other Notes + +* Removed the entanglement forging tool, as the Qiskit application modules are no longer supported, and packages in `Qiskit-Extensions` may not have dependency on those modules. With this change, CKT no longer depends on `qiskit-algorithms` or Qiskit Nature. + + + + + +## 0.5.0 + + + + + +### Prelude + +The primary purpose of this release is to swap the order of the classical registers in the circuits generated the `circuit_knitting.cutting` module. With this change, `"observable_measurements"` now comes before `"qpd_measurements"`, and there is no longer a need to insert `num_qpd_bits` into the result metadata by hand. + + + + + +### Upgrade Notes + +* CKT now depends on the [qiskit-algorithms](https://github.com/qiskit-community/qiskit-algorithms) package. This new package replaces the `qiskit.algorithms` module, which was deprecated in Qiskit 0.44. + +* The order of the classical registers in the generated experiments has been swapped. The `"observable_measurements"` register now comes first, and the `"qpd_measurements"` register now comes second. As a result of this change, it is no longer necessary to manually insert `num_qpd_bits` into the `metadata` for each experiment’s result. + +* Removed the `circuit_knitting.cutting.wire_cutting` module. Users are now expected to import from the `circuit_knitting.cutting.cutqc` module instead. + + + + + +### Other Notes + +* `qiskit-nature` is now pinned to version 0.6.X, as the entanglement forging code has not yet been updated to work with Qiskit Nature 0.7.0. Compatibility with Qiskit Nature 0.7.0 is tracked by [issue #406](https://github.com/Qiskit/qiskit-addon-cutting/issues/406). + + + + + +## 0.4.0 + + + + + +### Prelude + +The primary goal of this release is to modify the circuit cutting workflow to enable direct use of the Sampler primitive. Previously, the Sampler was called in `execute_experiments()`, a function which is now deprecated in favor of [`generate_cutting_experiments()`](qiskit_addon_cutting#qiskit_addon_cutting.generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments"). + + + + + +### New Features + +* Added a module, `circuit_knitting.cutting.cutting_experiments`, which is intended to hold functions used for generating the quantum experiments needed for circuit cutting. This module will initially hold one function, [`generate_cutting_experiments()`](qiskit_addon_cutting#qiskit_addon_cutting.generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments"), which can be used to generate quantum experiments, given an input circuit containing [`BaseQPDGate`](qiskit_addon_cutting.qpd.BaseQPDGate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate") instances, some observables, and a number of times the joint quasi-probability distribution for the cuts should be sampled. + + + + + +### Upgrade Notes + +* The `circuit-knitting-toolbox` Python package now depends on `qiskit` rather than `qiskit-terra`. This should have no user-visible effects, but it is something to keep in mind if one sees dependency errors when upgrading CKT. + +* The `execute_experiments()` function now returns a [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instance for each circuit partition, rather than the 3D list of quasi-distributions returned previously. The quasi-distribution for each subexperiment can be accessed via the `quasi_dists` field of [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult). The number of QPD bits contained in each subexperiment will be included in the `num_qpd_bits` field of the `metadata` dictionary for each experiment result. The output of this function is still valid as input to [`reconstruct_expectation_values()`](qiskit_addon_cutting#qiskit_addon_cutting.reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values"). + +* [`reconstruct_expectation_values()`](qiskit_addon_cutting#qiskit_addon_cutting.reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") now takes, as its first argument, a [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instance or a dictionary mapping partition labels to [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instances. This new `results` argument replaces the old `quasi_dists` argument. The [`SamplerResult`](/docs/api/qiskit/qiskit.primitives.SamplerResult) instances are expected to contain the number of QPD bits used in each circuit input to the Sampler. This should be specified in the `num_qpd_bits` field of the experiment result metadata. + + + + + +### Deprecation Notes + +* The `execute_experiments()` function has been deprecated. Going forward, users should first call [`generate_cutting_experiments()`](qiskit_addon_cutting#qiskit_addon_cutting.generate_cutting_experiments "qiskit_addon_cutting.generate_cutting_experiments") to generate the subexperiment circuits and then submit these circuits directly to the desired Sampler(s). The [tutorials](/docs/addons/qiskit-addon-cutting/tutorials/index) have been updated with this new workflow. + + + + + +## 0.3.0 + + + + + +### Prelude + +The 0.3.0 release introduces significant new features while maintaining backwards compatibility with the 0.2.0 release. The most striking change in this release is the shortened module names: `circuit_knitting_toolbox` has been renamed to `circuit_knitting`, `entanglement_forging` has been renamed to `forging`, and `circuit_cutting` has been renamed to `cutting`. The new circuit cutting module contains significant enhancements, including support for wire cutting and for the cutting of arbitrary two-qubit gates. + + + + + +### New Features + +* The circuit cutting module now supports the cutting of arbitrary two-qubit gates. This is supported via a KAK decomposition, using Qiskit’s `TwoQubitWeylDecomposition`, following the method in [arXiv:2006.11174](https://arxiv.org/abs/2006.11174). + + Additionally, this release adds *explicit* support (i.e., without relying on a KAK decomposition) for the following gates: + + * [`CHGate`](/docs/api/qiskit/qiskit.circuit.library.CHGate) + * [`CYGate`](/docs/api/qiskit/qiskit.circuit.library.CYGate) + * [`CSGate`](/docs/api/qiskit/qiskit.circuit.library.CSGate) + * [`CSXGate`](/docs/api/qiskit/qiskit.circuit.library.CSXGate) + * [`CSdgGate`](/docs/api/qiskit/qiskit.circuit.library.CSdgGate) + * [`CPhaseGate`](/docs/api/qiskit/qiskit.circuit.library.CPhaseGate) + * [`ECRGate`](/docs/api/qiskit/qiskit.circuit.library.ECRGate) + * [`SwapGate`](/docs/api/qiskit/qiskit.circuit.library.SwapGate) + * [`iSwapGate`](/docs/api/qiskit/qiskit.circuit.library.iSwapGate) + * [`DCXGate`](/docs/api/qiskit/qiskit.circuit.library.DCXGate) + +* [`partition_problem()`](qiskit_addon_cutting#qiskit_addon_cutting.partition_problem "qiskit_addon_cutting.partition_problem") now works even if `partition_labels` is not explicitly provided. In this case, the labels are determined automatically from the connectivity of the input circuit. For the sake of determining connectivity, [`TwoQubitQPDGate`](qiskit_addon_cutting.qpd.TwoQubitQPDGate#qiskit_addon_cutting.qpd.TwoQubitQPDGate "qiskit_addon_cutting.qpd.TwoQubitQPDGate")s are ignored, as these instructions are already marked for cutting. To support this workflow, this release also introduces a new method, [`TwoQubitQPDGate.from_instruction()`](qiskit_addon_cutting.qpd.TwoQubitQPDGate#qiskit_addon_cutting.qpd.TwoQubitQPDGate.from_instruction "qiskit_addon_cutting.qpd.TwoQubitQPDGate.from_instruction"), which allows one to create a [`TwoQubitQPDGate`](qiskit_addon_cutting.qpd.TwoQubitQPDGate#qiskit_addon_cutting.qpd.TwoQubitQPDGate "qiskit_addon_cutting.qpd.TwoQubitQPDGate") that wraps a given instruction. + +* Dynamic Definition code has been added to the `cutqc` module. See pull request [#285](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/pull/285) for details. + +* Users may now bypass experiments associated with the Hartree-Fock bitstring and replace their results with a specified Hartree-Fock value using the `hf_energy` class field in `circuit_knitting.forging.EntanglementForgingGroundStateSolver`. Refer to the `explanatory material ` for more information. + +* `generate_qpd_weights()` now returns a mixture of exact and sampled weights when appropriate. Specifically, it exactly evaluates all weights greater than or equal to `1 / num_samples` and samples from the remaining weights (ones which are below this threshold). Previously, this function would only return exact weights if *all* weights were greater than or equal to `1 / num_samples`; otherwise, all weights were sampled. The new behavior is expected to improve performance on non-uniform quasi-probability decompositions, e.g. for cut instantiations of [`RXXGate`](/docs/api/qiskit/qiskit.circuit.library.RXXGate), [`RYYGate`](/docs/api/qiskit/qiskit.circuit.library.RYYGate), [`RZZGate`](/docs/api/qiskit/qiskit.circuit.library.RZZGate), [`CRXGate`](/docs/api/qiskit/qiskit.circuit.library.CRXGate), [`CRYGate`](/docs/api/qiskit/qiskit.circuit.library.CRYGate), and [`CRZGate`](/docs/api/qiskit/qiskit.circuit.library.CRZGate) away from $\theta=\pi/2$. + +* The `circuit_knitting.cutting` module now supports wire cutting. There is a [new tutorial](/docs/addons/qiskit-addon-cutting/tutorials/index) that explains how to use it. + + + + + +### Upgrade Notes + +* `execute_experiments()` no longer creates separate jobs for each subcircuit by default. Now, separate jobs are only created if separate [BaseSampler](/docs/api/qiskit/1.4/qiskit.primitives.BaseSampler) instances are provided for each circuit partition. + +* Numpy 1.23.0 or later is now required. The [`kron()`](https://numpy.org/doc/stable/reference/generated/numpy.kron.html#numpy.kron) method in earlier versions [has known performance issues](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/pull/70#issuecomment-1500514513), and this method is used heavily by the CutQC wire cutting module. + +* The dependency on the `qiskit.opflow` module has been removed from entanglement forging. With this change, Qiskit Nature 0.6.0 is now required. However, Qiskit Nature 0.6.0 is incompatible with Quantum Serverless, so users that wish to use entanglement forging with Quantum Serverless must remain on version 0.2 of the Circuit Knitting Toolbox until [issue #108](https://github.com/Qiskit/qiskit-addon-cutting/issues/108) is resolved. + +* [`BaseQPDGate`](qiskit_addon_cutting.qpd.BaseQPDGate#qiskit_addon_cutting.qpd.BaseQPDGate "qiskit_addon_cutting.qpd.BaseQPDGate") instances in subcircuits returned from [`partition_circuit_qubits()`](qiskit_addon_cutting#qiskit_addon_cutting.partition_circuit_qubits "qiskit_addon_cutting.partition_circuit_qubits") and [`partition_problem()`](qiskit_addon_cutting#qiskit_addon_cutting.partition_problem "qiskit_addon_cutting.partition_problem") will now have labels prefixed with “cut”, rather than “qpd”. + + + + + +### Deprecation Notes + +* `decompose_gates()` is deprecated and will be removed no sooner than v0.4.0. Users should migrate to the identical [`cut_gates()`](qiskit_addon_cutting#qiskit_addon_cutting.cut_gates "qiskit_addon_cutting.cut_gates") function. + +* `generate_qpd_samples()` has been renamed to `generate_qpd_weights()`. The original name will be removed no sooner than version 0.4 of the Circuit Knitting Toolbox. + +* `QPDBasis.from_gate()` has been renamed to [`QPDBasis.from_instruction()`](qiskit_addon_cutting.qpd.QPDBasis#qiskit_addon_cutting.qpd.QPDBasis.from_instruction "qiskit_addon_cutting.qpd.QPDBasis.from_instruction"). The original name is deprecated and will be removed no sooner than CKT v0.4.0. + +* The top-level name for imports has been renamed from `circuit_knitting_toolbox` to `circuit_knitting`. Furthermore, the following renames have occurred one level deeper: + + * `circuit_knitting_toolbox.entanglement_forging` has been moved to `circuit_knitting.forging`. + * `circuit_knitting_toolbox.circuit_cutting` has been moved to `circuit_knitting.cutting`. + + The old import locations are now deprecated and will be removed in a future release of the Circuit Knitting Toolbox. + + + + + +### Bug Fixes + +* Fixed a bug in `decompose_qpd_instructions()` which would cause index errors if `TwoQubitQPDGate` indices were not specified in ascending order. + +* Fixed a bug in `circuit_knitting.forging.EntanglementForgingGroundStateSolver` which was causing `AttributeError`s when instantiating the :class:`circuit_knitting.forging.EntanglementForgingResult` in certain conditions, such as when reducing the orbitals over which to solve. + + + + + +## 0.2.0 + + + + + +### Prelude + +0.2.0 is centered around the addition of functions which allow for the easy implementation of a circuit cutting technique called gate cutting. For more details on circuit cutting, check out our [explanation guide](/docs/addons/qiskit-addon-cutting/explanation/index). + +The foundation of the `circuit_cutting` package is the `circuit_knitting_toolbox.circuit_cutting.qpd` sub-package. The `qpd` package allows for easy transformation of [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) gates and wires into elements which may be decomposed to a probabilistic set of basis gates. See `QPDBasis` and `BaseQPDGate` classes for more information. + +Additionally, 0.2.0 includes a set of functions which allow for easy implementation of gate cutting workflows. These functions are built on top of the `circuit_knitting_toolbox.circuit_cutting.qpd` package. Like all circuit knitting techniques, gate cutting can be described as three consecutive stages: *decomposition* of a problem, *execution* of many subexperiments, and *reconstruction* of a simulated output of the original problem. These steps may be implemented with the `circuit_cutting` package using only a few primary functions, namely, the [`partition_problem()`](qiskit_addon_cutting#qiskit_addon_cutting.partition_problem "qiskit_addon_cutting.partition_problem"), `decompose_gates()`, `execute_experiments()`, and [`reconstruct_expectation_values()`](qiskit_addon_cutting#qiskit_addon_cutting.reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") functions. Check out the [tutorials](/docs/addons/qiskit-addon-cutting/tutorials/index) for a look at a couple of example circuit cutting workflows. + + + + + +### New Features + +* Addition of a `qpd` package which allows for easy transformation of [`QuantumCircuit`](/docs/api/qiskit/qiskit.circuit.QuantumCircuit) gates and wires into elements which may be decomposed to a probabilistic set of basis gates. See `QPDBasis` and `BaseQPDGate` classes for more information. + +* Addition of `cutting_decomposition`, `cutting_execution`, and `cutting_reconstruction` modules. These modules provide several functions which allow for easy implementation of gate cutting workflows, namely, the [`partition_problem()`](qiskit_addon_cutting#qiskit_addon_cutting.partition_problem "qiskit_addon_cutting.partition_problem"), `decompose_gates()`, `execute_experiments()`, and [`reconstruct_expectation_values()`](qiskit_addon_cutting#qiskit_addon_cutting.reconstruct_expectation_values "qiskit_addon_cutting.reconstruct_expectation_values") functions. + + + +### Known Issues + +* The `circuit_cutting` package only supports [`PauliList`](/docs/api/qiskit/qiskit.quantum_info.PauliList) observables for calculating expectation values. Support for calculating expectation values for more observable types, including [`SparsePauliOp`](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp), is expected no sooner than 0.3.0. + +* The `Sampler`s from Qiskit and Qiskit Aer do not support mid-circuit measurements in statevector mode. For more on generating exact quasi-distributions using the [BaseSampler](/docs/api/qiskit/1.4/qiskit.primitives.BaseSampler) interface, check out our [how-to guide](https://github.com/Qiskit/qiskit-addon-cutting/blob/stable/0.7/docs/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb). + +* The `circuit_cutting` package generally does not yet support input circuits with user-added classical bits, so by extension, it does not yet support dynamic circuits. + + + + + +### Upgrade Notes + +* Support for running with Python 3.7 has been removed. To run the Circuit Knitting Toolbox, you now need Python version 3.8 or higher. + + + + + +### Deprecation Notes + +* The `circuit_knitting_toolbox.circuit_cutting.wire_cutting` namespace is now deprecated. It has been renamed to `circuit_knitting_toolbox.circuit_cutting.cutqc`. Both gate and wire cutting for simulating expectation values will be implemented directly into the `circuit_cutting` package, and the `circuit_cutting.cutqc` package will remain available for users of the automatic cut finding and/or the full probability distribution reconstruction. + + + + + +## 0.1.0 + + + + + +### New Features + +* Support for Python 3.11. + +* Support for Qiskit Nature 0.5. + + + + + +### Upgrade Notes + +* The minimum supported version of each Qiskit dependency has been updated. This release depends on `qiskit-terra>=0.23.3`, `qiskit-aer>=0.12.0`, `qiskit-nature>=0.5.2`, and `qiskit-ibm-runtime>=0.9.2`. `qiskit-ibmq-provider` is now deprecated and is no longer required by the Circuit Knitting Toolbox. + +* Support for Qiskit Nature \< 0.5 has been removed upon this release. + + Users are now be required to use the `qiskit_nature.second_q.problems.ElectronicStructureProblem`, as input to the `EntanglementForgingGroundStateSolver`, rather than the deprecated `qiskit_nature.problems.second_quantization.ElectronicStructureProblem`. + + For more information on migrating to Qiskit Nature 0.5, check out the [Qiskit Nature migration guide](https://qiskit-community.github.io/qiskit-nature/migration/0.5_a_intro.html). + + For more information on adapting your entanglement forging workflows, check out the `tutorials ` and `how-to guides `. + +* The `~circuit_knitting_toolbox.utils.IntegralDriver` class has been removed. The new Qiskit Nature API allows for a more flexible build-up of the `qiskit_nature.second_q.problems.ElectronicStructureProblem`, and this driver is no longer needed. + +* DOcplex and cplex are now optional dependencies. They must be installed for automatic wire cut finding to work. + + + + + +### Deprecation Notes + +* Support for running with Python 3.7 has been deprecated. Future versions of the Circuit Knitting Toolbox will require Python 3.8 or higher. + diff --git a/docs/addons/qiskit-addon-cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb new file mode 100644 index 00000000000..efcf4e0ddbb --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb @@ -0,0 +1,528 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ad1f14b4", + "metadata": {}, + "source": [ + "# Gate Cutting to Reduce Circuit Width\n", + "\n", + "In this notebook, we will work through the steps of a [Qiskit pattern](/guides/intro-to-patterns) while using **circuit cutting** to reduce the number of qubits in a circuit. We will cut gates to enable us to reconstruct the expectation value of a four-qubit circuit using only two-qubit experiments.\n", + "\n", + "These are the steps that we will take:\n", + "\n", + "- **Step 1: Map problem to quantum circuits and operators**:\n", + " - Map the hamiltonian onto a quantum circuit.\n", + "- **Step 2: Optimize for target hardware** [_Uses the cutting addon_]:\n", + " - Cut the circuit and observable.\n", + " - Transpile the subexperiments for hardware.\n", + "- **Step 3: Execute on target hardware**:\n", + " - Run the subexperiments obtained in Step 2 using a `Sampler` primitive.\n", + "- **Step 4: Post-process results** [_Uses the cutting addon_]:\n", + " - Combine the results of Step 3 to reconstruct the expectation value of the observable in question." + ] + }, + { + "cell_type": "markdown", + "id": "510910a6", + "metadata": {}, + "source": [ + "## Step 1: Map\n", + "\n", + "### Create a circuit to cut" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "96f5b72a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:50.252738Z", + "iopub.status.busy": "2025-03-28T16:50:50.252448Z", + "iopub.status.idle": "2025-03-28T16:50:51.350658Z", + "shell.execute_reply": "2025-03-28T16:50:51.349916Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.circuit.library import efficient_su2\n", + "\n", + "qc = efficient_su2(4, entanglement=\"linear\", reps=2)\n", + "qc.assign_parameters([0.4] * len(qc.parameters), inplace=True)\n", + "\n", + "qc.draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "8638fdf1", + "metadata": {}, + "source": [ + "### Specify an observable" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f75e8dd1", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.353234Z", + "iopub.status.busy": "2025-03-28T16:50:51.352789Z", + "iopub.status.idle": "2025-03-28T16:50:51.356422Z", + "shell.execute_reply": "2025-03-28T16:50:51.355858Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])" + ] + }, + { + "cell_type": "markdown", + "id": "162a5629", + "metadata": {}, + "source": [ + "## Step 2: Optimize\n", + "\n", + "### Separate the circuit and observable according to a specified qubit partitioning\n", + "\n", + "Each label in `partition_labels` corresponds to the `circuit` qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut.\n", + "\n", + "**Note:** The ``observables`` kwarg to `partition_problem` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "30326299", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.358249Z", + "iopub.status.busy": "2025-03-28T16:50:51.358050Z", + "iopub.status.idle": "2025-03-28T16:50:51.401645Z", + "shell.execute_reply": "2025-03-28T16:50:51.400913Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import partition_problem\n", + "\n", + "partitioned_problem = partition_problem(\n", + " circuit=qc, partition_labels=\"AABB\", observables=observable.paulis\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "subobservables = partitioned_problem.subobservables\n", + "bases = partitioned_problem.bases" + ] + }, + { + "cell_type": "markdown", + "id": "9d2d42c3", + "metadata": {}, + "source": [ + "### Visualize the decomposed problem" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6b54be63", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.404435Z", + "iopub.status.busy": "2025-03-28T16:50:51.403916Z", + "iopub.status.idle": "2025-03-28T16:50:51.410005Z", + "shell.execute_reply": "2025-03-28T16:50:51.409344Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'A': PauliList(['II', 'ZI', 'ZZ', 'XI', 'ZZ', 'IX']),\n", + " 'B': PauliList(['ZZ', 'IZ', 'II', 'XI', 'ZI', 'IX'])}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subobservables" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b7e06fac", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.411952Z", + "iopub.status.busy": "2025-03-28T16:50:51.411753Z", + "iopub.status.idle": "2025-03-28T16:50:51.547364Z", + "shell.execute_reply": "2025-03-28T16:50:51.546536Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[\"A\"].draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "11e45e83", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.549471Z", + "iopub.status.busy": "2025-03-28T16:50:51.549264Z", + "iopub.status.idle": "2025-03-28T16:50:51.738543Z", + "shell.execute_reply": "2025-03-28T16:50:51.737835Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[\"B\"].draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "4f8017b6-2954-4b51-8a07-f4de49832509", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Calculate the sampling overhead for the chosen cuts\n", + "\n", + "Here we cut two CNOT gates, resulting in a sampling overhead of $9^2$.\n", + "\n", + "For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](../explanation/index.rst)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3d606ef8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.741344Z", + "iopub.status.busy": "2025-03-28T16:50:51.740724Z", + "iopub.status.idle": "2025-03-28T16:50:51.745416Z", + "shell.execute_reply": "2025-03-28T16:50:51.744851Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling overhead: 81.0\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ef3b061c-cd1d-4931-85f6-155e00b355be", + "metadata": {}, + "source": [ + "### Generate the subexperiments to run on the backend\n", + "\n", + "`generate_cutting_experiments` accepts `circuits`/`observables` args as dictionaries mapping qubit partition labels to the respective `subcircuit`/`subobservables`.\n", + "\n", + "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2029d18e-0e91-4160-b8c9-02cb9e1ba3cb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.747765Z", + "iopub.status.busy": "2025-03-28T16:50:51.747387Z", + "iopub.status.idle": "2025-03-28T16:50:51.854533Z", + "shell.execute_reply": "2025-03-28T16:50:51.853782Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import generate_cutting_experiments\n", + "\n", + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits, observables=subobservables, num_samples=np.inf\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "444b0038-b3d5-469c-85b2-4c1d9d8fce05", + "metadata": {}, + "source": [ + "### Choose a backend\n", + "\n", + "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "36c89aa0-70aa-4615-8198-77ec85e8aa93", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:51.857664Z", + "iopub.status.busy": "2025-03-28T16:50:51.857181Z", + "iopub.status.idle": "2025-03-28T16:50:52.542985Z", + "shell.execute_reply": "2025-03-28T16:50:52.542361Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", + "\n", + "backend = FakeManilaV2()" + ] + }, + { + "cell_type": "markdown", + "id": "85a1d76a-5c47-4210-a742-9b22f02f7200", + "metadata": {}, + "source": [ + "### Prepare the subexperiments for the backend\n", + "\n", + "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "184e0f36-1279-48a3-aab7-b7603bb71f66", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:52.545759Z", + "iopub.status.busy": "2025-03-28T16:50:52.545267Z", + "iopub.status.idle": "2025-03-28T16:50:56.855540Z", + "shell.execute_reply": "2025-03-28T16:50:56.854763Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.transpiler import generate_preset_pass_manager\n", + "\n", + "# Transpile the subexperiments to ISA circuits\n", + "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", + "isa_subexperiments = {\n", + " label: pass_manager.run(partition_subexpts)\n", + " for label, partition_subexpts in subexperiments.items()\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "d8870454-2173-4454-90b4-a034779510e0", + "metadata": {}, + "source": [ + "## Step 3: Execute\n", + "\n", + "### Run the subexperiments using the Qiskit Runtime Sampler primitive" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2dbb8148-0482-4a66-8316-335125f8a2b3", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:56.858196Z", + "iopub.status.busy": "2025-03-28T16:50:56.857749Z", + "iopub.status.idle": "2025-03-28T16:50:57.056138Z", + "shell.execute_reply": "2025-03-28T16:50:57.048260Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import SamplerV2, Batch\n", + "\n", + "# Submit each partition's subexperiments to the Qiskit Runtime Sampler\n", + "# primitive, in a single batch so that the jobs will run back-to-back.\n", + "with Batch(backend=backend) as batch:\n", + " sampler = SamplerV2(mode=batch)\n", + " jobs = {\n", + " label: sampler.run(subsystem_subexpts, shots=2**12)\n", + " for label, subsystem_subexpts in isa_subexperiments.items()\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "64cef60b-5130-467e-8e01-7460d78560ed", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:50:57.132131Z", + "iopub.status.busy": "2025-03-28T16:50:57.130570Z", + "iopub.status.idle": "2025-03-28T16:51:20.447808Z", + "shell.execute_reply": "2025-03-28T16:51:20.446901Z" + } + }, + "outputs": [], + "source": [ + "# Retrieve results\n", + "results = {label: job.result() for label, job in jobs.items()}" + ] + }, + { + "cell_type": "markdown", + "id": "f0032570", + "metadata": {}, + "source": [ + "## Step 4: Post-process\n", + "\n", + "### Reconstruct the expectation value\n", + "\n", + "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7d57339c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:20.451351Z", + "iopub.status.busy": "2025-03-28T16:51:20.450827Z", + "iopub.status.idle": "2025-03-28T16:51:25.701123Z", + "shell.execute_reply": "2025-03-28T16:51:25.700268Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import reconstruct_expectation_values\n", + "\n", + "# Get expectation values for each observable term\n", + "reconstructed_expval_terms = reconstruct_expectation_values(\n", + " results,\n", + " coefficients,\n", + " subobservables,\n", + ")\n", + "\n", + "# Reconstruct final expectation value\n", + "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" + ] + }, + { + "cell_type": "markdown", + "id": "53beaca3", + "metadata": {}, + "source": [ + "### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e3385ba5", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:25.708190Z", + "iopub.status.busy": "2025-03-28T16:51:25.707798Z", + "iopub.status.idle": "2025-03-28T16:51:25.747493Z", + "shell.execute_reply": "2025-03-28T16:51:25.744142Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed expectation value: 0.58167797\n", + "Exact expectation value: 0.56254612\n", + "Error in estimation: 0.01913185\n", + "Relative error in estimation: 0.03400939\n" + ] + } + ], + "source": [ + "from qiskit_aer.primitives import EstimatorV2\n", + "\n", + "estimator = EstimatorV2()\n", + "exact_expval = estimator.run([(qc, observable)]).result()[0].data.evs\n", + "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", + "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", + "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", + "print(\n", + " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb new file mode 100644 index 00000000000..ce41f29bfc3 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb @@ -0,0 +1,573 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7ed4867f", + "metadata": {}, + "source": [ + "# Gate Cutting to Reduce Circuit Depth\n", + "\n", + "In this tutorial, we will reduce a circuit's depth by cutting distant gates, avoiding the swap gates that would otherwise be introduced by routing.\n", + "\n", + "These are the steps that we will take in this [Qiskit pattern](/guides/intro-to-patterns):\n", + "\n", + "- **Step 1: Map problem to quantum circuits and operators**:\n", + " - Map the hamiltonian onto a quantum circuit.\n", + "- **Step 2: Optimize for target hardware** [_Uses the cutting addon_]:\n", + " - Cut the circuit and observable.\n", + " - Transpile the subexperiments for hardware.\n", + "- **Step 3: Execute on target hardware**:\n", + " - Run the subexperiments obtained in Step 2 using a `Sampler` primitive.\n", + "- **Step 4: Post-process results** [_Uses the cutting addon_]:\n", + " - Combine the results of Step 3 to reconstruct the expectation value of the observable in question." + ] + }, + { + "cell_type": "markdown", + "id": "07f7f75d", + "metadata": {}, + "source": [ + "## Step 1: Map\n", + "\n", + "### Create a circuit to run on the backend" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "54ed0f13", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:29.327835Z", + "iopub.status.busy": "2025-03-28T16:51:29.326393Z", + "iopub.status.idle": "2025-03-28T16:51:31.050966Z", + "shell.execute_reply": "2025-03-28T16:51:31.050169Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.circuit.library import efficient_su2\n", + "\n", + "circuit = efficient_su2(num_qubits=4, entanglement=\"circular\")\n", + "circuit.assign_parameters([0.4] * len(circuit.parameters), inplace=True)\n", + "circuit.draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "452cd19e", + "metadata": {}, + "source": [ + "### Specify an observable" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "105e858d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:31.054439Z", + "iopub.status.busy": "2025-03-28T16:51:31.054037Z", + "iopub.status.idle": "2025-03-28T16:51:31.061841Z", + "shell.execute_reply": "2025-03-28T16:51:31.061246Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])" + ] + }, + { + "cell_type": "markdown", + "id": "699105a3-903e-49d8-826b-cc8b9b85c9df", + "metadata": {}, + "source": [ + "## Step 2: Optimize\n", + "\n", + "### Specify a backend\n", + "\n", + "You can provide either a fake backend or a hardware backend from Qiskit Runtime." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "756f2130-6947-479a-9fe7-97443c87a904", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:31.066068Z", + "iopub.status.busy": "2025-03-28T16:51:31.065650Z", + "iopub.status.idle": "2025-03-28T16:51:32.084249Z", + "shell.execute_reply": "2025-03-28T16:51:32.083362Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", + "\n", + "backend = FakeManilaV2()" + ] + }, + { + "cell_type": "markdown", + "id": "080a2a8b", + "metadata": {}, + "source": [ + "### Transpile the circuit, visualize the swaps, and note the depth\n", + "\n", + "We choose a layout that requires two swaps to execute the gates between qubits 3 and 0 and another two swaps to return the qubits to their initial positions." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b394da7a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:32.088232Z", + "iopub.status.busy": "2025-03-28T16:51:32.087771Z", + "iopub.status.idle": "2025-03-28T16:51:32.168573Z", + "shell.execute_reply": "2025-03-28T16:51:32.163841Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transpiled circuit depth: 30\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3574/3423257129.py:8: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes.\n", + " print(f\"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\")\n" + ] + } + ], + "source": [ + "from qiskit.transpiler import generate_preset_pass_manager\n", + "\n", + "pass_manager = generate_preset_pass_manager(\n", + " optimization_level=1, backend=backend, initial_layout=[0, 1, 2, 3]\n", + ")\n", + "\n", + "transpiled_qc = pass_manager.run(circuit)\n", + "print(f\"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4fe4af43", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:32.176421Z", + "iopub.status.busy": "2025-03-28T16:51:32.174292Z", + "iopub.status.idle": "2025-03-28T16:51:33.477300Z", + "shell.execute_reply": "2025-03-28T16:51:33.474323Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transpiled_qc.draw(\"mpl\", scale=0.4, idle_wires=False, fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "6c106db7", + "metadata": {}, + "source": [ + "### Replace distant gates with `TwoQubitQPDGate`s by specifying their indices\n", + "\n", + "`cut_gates` will replace the gates in the specified indices with `TwoQubitQPDGate`s and also return a list of `QPDBasis` instances -- one for each gate decomposition." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "12e73c69", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:33.480297Z", + "iopub.status.busy": "2025-03-28T16:51:33.479989Z", + "iopub.status.idle": "2025-03-28T16:51:34.022077Z", + "shell.execute_reply": "2025-03-28T16:51:34.021152Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_cutting import cut_gates\n", + "\n", + "# Find the indices of the distant gates\n", + "cut_indices = [\n", + " i\n", + " for i, instruction in enumerate(circuit.data)\n", + " if {circuit.find_bit(q)[0] for q in instruction.qubits} == {0, 3}\n", + "]\n", + "\n", + "# Decompose distant CNOTs into TwoQubitQPDGate instances\n", + "qpd_circuit, bases = cut_gates(circuit, cut_indices)\n", + "\n", + "qpd_circuit.draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "069eb942-e947-4eff-a250-9a99c5ec47f0", + "metadata": {}, + "source": [ + "### Generate the subexperiments to run on the backend\n", + "\n", + "`generate_cutting_experiments` accepts a circuit containing `TwoQubitQPDGate` instances and observables as a `PauliList`.\n", + "\n", + "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst).\n", + "\n", + "**Note:** The ``observables`` kwarg to `generate_cutting_experiments` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "83b1efed-bafa-48c4-bbf0-cf7eb9027ac5", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:34.027775Z", + "iopub.status.busy": "2025-03-28T16:51:34.027177Z", + "iopub.status.idle": "2025-03-28T16:51:34.976541Z", + "shell.execute_reply": "2025-03-28T16:51:34.975799Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit_addon_cutting import generate_cutting_experiments\n", + "\n", + "# Generate the subexperiments and sampling coefficients\n", + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=qpd_circuit, observables=observable.paulis, num_samples=np.inf\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c0acae3a", + "metadata": {}, + "source": [ + "### Calculate the sampling overhead for the chosen cuts\n", + "\n", + "Here we cut three CNOT gates, resulting in a sampling overhead of $9^3$.\n", + "\n", + "For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](../explanation/index.rst)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c7b28e2c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:34.979488Z", + "iopub.status.busy": "2025-03-28T16:51:34.979281Z", + "iopub.status.idle": "2025-03-28T16:51:34.983347Z", + "shell.execute_reply": "2025-03-28T16:51:34.982624Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling overhead: 729.0\n" + ] + } + ], + "source": [ + "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")" + ] + }, + { + "cell_type": "markdown", + "id": "6929264d", + "metadata": {}, + "source": [ + "### Demonstrate that the QPD subexperiments will be shallower after cutting distant gates\n", + "\n", + "Here is an example of an arbitrarily chosen subexperiment generated from the QPD circuit. Its depth has been reduced by more than half. Many of these probabilistic subexperiments must be generated and evaluated in order to reconstruct an expectation value of the deeper circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "70e2f1b6", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:34.985332Z", + "iopub.status.busy": "2025-03-28T16:51:34.985103Z", + "iopub.status.idle": "2025-03-28T16:51:35.433820Z", + "shell.execute_reply": "2025-03-28T16:51:35.432939Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original circuit depth after transpile: 30\n", + "QPD subexperiment depth after transpile: 7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3574/4252024425.py:5: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes.\n", + " f\"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\"\n", + "/tmp/ipykernel_3574/4252024425.py:8: DeprecationWarning: Treating CircuitInstruction as an iterable is deprecated legacy behavior since Qiskit 1.2, and will be removed in Qiskit 2.0. Instead, use the `operation`, `qubits` and `clbits` named attributes.\n", + " f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}\"\n" + ] + }, + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Transpile the decomposed circuit to the same layout\n", + "transpiled_qpd_circuit = pass_manager.run(subexperiments[100])\n", + "\n", + "print(\n", + " f\"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\"\n", + ")\n", + "print(\n", + " f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}\"\n", + ")\n", + "transpiled_qpd_circuit.draw(\"mpl\", scale=0.8, idle_wires=False, fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "fd9a126c", + "metadata": {}, + "source": [ + "### Prepare subexperiments for the backend" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "736a7d14-71b0-47a0-847d-4972ab886918", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:35.436030Z", + "iopub.status.busy": "2025-03-28T16:51:35.435823Z", + "iopub.status.idle": "2025-03-28T16:51:47.520198Z", + "shell.execute_reply": "2025-03-28T16:51:47.519092Z" + } + }, + "outputs": [], + "source": [ + "# Transpile the subeperiments to the backend's instruction set architecture (ISA)\n", + "isa_subexperiments = pass_manager.run(subexperiments)" + ] + }, + { + "cell_type": "markdown", + "id": "241fc5e5-d367-4a31-aa1f-424acdbbd8dd", + "metadata": {}, + "source": [ + "## Step 3: Execute\n", + "\n", + "### Run the subexperiments using the Qiskit Runtime Sampler primitive" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e4dcc97b-8336-4eb9-9cc0-0d9b87a287e3", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:47.522983Z", + "iopub.status.busy": "2025-03-28T16:51:47.522756Z", + "iopub.status.idle": "2025-03-28T16:51:48.086035Z", + "shell.execute_reply": "2025-03-28T16:51:48.081351Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import SamplerV2\n", + "\n", + "# Set up the Qiskit Runtime Sampler primitive. For a fake backend, this will use a local simulator.\n", + "sampler = SamplerV2(backend)\n", + "\n", + "# Submit the subexperiments\n", + "job = sampler.run(isa_subexperiments)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a83922bc-0489-4ee5-a2b6-796103aae9bb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:51:48.099798Z", + "iopub.status.busy": "2025-03-28T16:51:48.099494Z", + "iopub.status.idle": "2025-03-28T16:52:28.830126Z", + "shell.execute_reply": "2025-03-28T16:52:28.829495Z" + } + }, + "outputs": [], + "source": [ + "# Retrieve the results\n", + "results = job.result()" + ] + }, + { + "cell_type": "markdown", + "id": "f04d9134-651b-446e-93f4-aa0281786200", + "metadata": {}, + "source": [ + "## Step 4: Post-process\n", + "\n", + "### Reconstruct the expectation value\n", + "\n", + "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ace12f7f", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:28.832409Z", + "iopub.status.busy": "2025-03-28T16:52:28.832187Z", + "iopub.status.idle": "2025-03-28T16:52:30.916461Z", + "shell.execute_reply": "2025-03-28T16:52:30.915714Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import reconstruct_expectation_values\n", + "\n", + "reconstructed_expval_terms = reconstruct_expectation_values(\n", + " results,\n", + " coefficients,\n", + " observable.paulis,\n", + ")\n", + "# Reconstruct final expectation value\n", + "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" + ] + }, + { + "cell_type": "markdown", + "id": "3fc2327f", + "metadata": {}, + "source": [ + "### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4928e703", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:30.918925Z", + "iopub.status.busy": "2025-03-28T16:52:30.918548Z", + "iopub.status.idle": "2025-03-28T16:52:30.932594Z", + "shell.execute_reply": "2025-03-28T16:52:30.932017Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed expectation value: 0.51977539\n", + "Exact expectation value: 0.50497603\n", + "Error in estimation: 0.01479936\n", + "Relative error in estimation: 0.02930705\n" + ] + } + ], + "source": [ + "from qiskit_aer.primitives import EstimatorV2\n", + "\n", + "estimator = EstimatorV2()\n", + "exact_expval = estimator.run([(circuit, observable)]).result()[0].data.evs\n", + "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", + "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", + "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", + "print(\n", + " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb new file mode 100644 index 00000000000..9d4d19b1c87 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb @@ -0,0 +1,639 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9916de95-2376-49f0-91ad-07f07939dfb5", + "metadata": {}, + "source": [ + "# Wire Cutting Phrased as a Two-Qubit `Move` Instruction\n", + "\n", + "In this tutorial, we will reconstruct expectation values of a seven-qubit circuit by splitting it into two four-qubit circuits using wire cutting.\n", + "\n", + "These are the steps that we will take in this [Qiskit pattern](/guides/intro-to-patterns):\n", + "\n", + "- **Step 1: Map problem to quantum circuits and operators**:\n", + " - Map the hamiltonian onto a quantum circuit.\n", + "- **Step 2: Optimize for target hardware** [_Uses the cutting addon_]:\n", + " - Cut the circuit and observable.\n", + " - Transpile the subexperiments for hardware.\n", + "- **Step 3: Execute on target hardware**:\n", + " - Run the subexperiments obtained in Step 2 using a `Sampler` primitive.\n", + "- **Step 4: Post-process results** [_Uses the cutting addon_]:\n", + " - Combine the results of Step 3 to reconstruct the expectation value of the observable in question." + ] + }, + { + "cell_type": "markdown", + "id": "ae63d837-a7f5-40a5-8186-f98076bb4cd9", + "metadata": {}, + "source": [ + "## Step 1: Map\n", + "\n", + "### Create a circuit to cut\n", + "\n", + "First, we begin with a circuit inspired by Fig. 1(a) of [arXiv:2302.03366v1](https://arxiv.org/abs/2302.03366v1)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3bcae0ed-4308-4686-b85c-8595c6e916bc", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:33.241330Z", + "iopub.status.busy": "2025-03-28T16:52:33.241116Z", + "iopub.status.idle": "2025-03-28T16:52:33.572919Z", + "shell.execute_reply": "2025-03-28T16:52:33.572241Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from qiskit import QuantumCircuit\n", + "\n", + "qc_0 = QuantumCircuit(7)\n", + "for i in range(7):\n", + " qc_0.rx(np.pi / 4, i)\n", + "qc_0.cx(0, 3)\n", + "qc_0.cx(1, 3)\n", + "qc_0.cx(2, 3)\n", + "qc_0.cx(3, 4)\n", + "qc_0.cx(3, 5)\n", + "qc_0.cx(3, 6)\n", + "qc_0.cx(0, 3)\n", + "qc_0.cx(1, 3)\n", + "qc_0.cx(2, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1dcaff2d-2d1b-4cc0-87d1-0f4f5de823ff", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:33.575145Z", + "iopub.status.busy": "2025-03-28T16:52:33.574711Z", + "iopub.status.idle": "2025-03-28T16:52:34.182253Z", + "shell.execute_reply": "2025-03-28T16:52:34.181568Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc_0.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "bd1617a1-e793-43a9-a24a-c81c18fd2a1e", + "metadata": {}, + "source": [ + "### Specify an observable" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b791aa42-c485-453b-a110-3c790194adaf", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.184389Z", + "iopub.status.busy": "2025-03-28T16:52:34.184096Z", + "iopub.status.idle": "2025-03-28T16:52:34.187638Z", + "shell.execute_reply": "2025-03-28T16:52:34.187108Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])" + ] + }, + { + "cell_type": "markdown", + "id": "9f56b094-0c6f-456f-9641-1424395fc6bd", + "metadata": {}, + "source": [ + "## Step 2: Optimize\n", + "\n", + "### Create a new circuit where `Move` instructions have been placed at the desired cut locations\n", + "\n", + "Given the above circuit, we would like to place two wire cuts on the middle qubit line, so that the circuit can separate into two circuits of four qubits each. One way to do this is to manually place two-qubit `Move` instructions that move the state from one qubit wire to another. A `Move` instruction is conceptually equivalent to a reset operation on the second qubit, followed by a SWAP gate. The effect of this instruction is to transfer the state of the first (source) qubit to the second (detination) qubit, while discarding the incoming state of the second qubit. For this to work as intended, it is important that the second (destination) qubit share no entanglement with the remainder of the system; otherwise, the reset operation will cause the state of the remainder of the system to be partially collapsed.\n", + "\n", + "Here, we build a new circuit with one additional qubit and the `Move` operations in place. In this example, we are able to reuse a qubit: the source qubit of the first `Move` becomes the destination qubit of the second `Move` operation.\n", + "\n", + "Note: As an alternative to working directly with `Move` instructions, one may choose to mark wire cuts using a single-qubit `CutWire` instruction. The `cut_wires` function exists to transform `CutWire`s to `Move` instructions on newly allocated qubits. However, in contrast to the manual method, this automatic method does not allow for the re-use of qubit wires. See the `CutWire` [how-to guide](../how-tos/how_to_specify_cut_wires.ipynb) for details." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "22f19d29-a182-4758-b5d0-6b66f9a946be", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.189717Z", + "iopub.status.busy": "2025-03-28T16:52:34.189367Z", + "iopub.status.idle": "2025-03-28T16:52:34.502126Z", + "shell.execute_reply": "2025-03-28T16:52:34.501425Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_cutting.instructions import Move\n", + "\n", + "qc_1 = QuantumCircuit(8)\n", + "for i in [*range(4), *range(5, 8)]:\n", + " qc_1.rx(np.pi / 4, i)\n", + "qc_1.cx(0, 3)\n", + "qc_1.cx(1, 3)\n", + "qc_1.cx(2, 3)\n", + "qc_1.append(Move(), [3, 4])\n", + "qc_1.cx(4, 5)\n", + "qc_1.cx(4, 6)\n", + "qc_1.cx(4, 7)\n", + "qc_1.append(Move(), [4, 3])\n", + "qc_1.cx(0, 3)\n", + "qc_1.cx(1, 3)\n", + "qc_1.cx(2, 3)\n", + "\n", + "qc_1.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "088431d9-de2f-4b5d-90d2-7e7a5947dcb5", + "metadata": {}, + "source": [ + "### Create observable to go with the new circuit\n", + "\n", + "This observable corresponds with `observable`, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an \"I\" at index 4). Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d33d5580-879f-466f-ac87-dcc6a19fbab6", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.504553Z", + "iopub.status.busy": "2025-03-28T16:52:34.504138Z", + "iopub.status.idle": "2025-03-28T16:52:34.507751Z", + "shell.execute_reply": "2025-03-28T16:52:34.506956Z" + } + }, + "outputs": [], + "source": [ + "observable_expanded = SparsePauliOp([\"ZIIIIIII\", \"IIIIZIII\", \"IIIIIIIZ\"])" + ] + }, + { + "cell_type": "markdown", + "id": "a00aeede-3d0f-4589-b1dc-3cd7df9c4085", + "metadata": {}, + "source": [ + "### Separate the circuit and observables\n", + "\n", + "As in the previous tutorials, qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fc3738c7-2bb2-4d67-ae2b-7a3090b31e6a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.509830Z", + "iopub.status.busy": "2025-03-28T16:52:34.509640Z", + "iopub.status.idle": "2025-03-28T16:52:34.522487Z", + "shell.execute_reply": "2025-03-28T16:52:34.521787Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import partition_problem\n", + "\n", + "partitioned_problem = partition_problem(\n", + " circuit=qc_1, partition_labels=\"AAAABBBB\", observables=observable_expanded.paulis\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "subobservables = partitioned_problem.subobservables\n", + "bases = partitioned_problem.bases" + ] + }, + { + "cell_type": "markdown", + "id": "4c8a76eb-31db-486c-b7c2-a5fe3cb732c2", + "metadata": {}, + "source": [ + "### Visualize the decomposed problem" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9c53a862-f762-471a-bda7-b27e88292ac9", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.524677Z", + "iopub.status.busy": "2025-03-28T16:52:34.524311Z", + "iopub.status.idle": "2025-03-28T16:52:34.528872Z", + "shell.execute_reply": "2025-03-28T16:52:34.528334Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'A': PauliList(['IIII', 'ZIII', 'IIIZ']),\n", + " 'B': PauliList(['ZIII', 'IIII', 'IIII'])}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subobservables" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "69df912e-6709-45bb-8eb3-c66a70edefdc", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.530891Z", + "iopub.status.busy": "2025-03-28T16:52:34.530503Z", + "iopub.status.idle": "2025-03-28T16:52:34.747276Z", + "shell.execute_reply": "2025-03-28T16:52:34.746598Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[\"A\"].draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d851adcb-e524-48c8-8adc-0d1606613c8d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.749630Z", + "iopub.status.busy": "2025-03-28T16:52:34.749131Z", + "iopub.status.idle": "2025-03-28T16:52:34.879618Z", + "shell.execute_reply": "2025-03-28T16:52:34.878945Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[\"B\"].draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "9c177fb7-a729-4e0d-bfac-256df3e14c54", + "metadata": {}, + "source": [ + "### Calculate the sampling overhead for the chosen cuts\n", + "\n", + "Here we cut two wires, resulting in a sampling overhead of $4^4$.\n", + "\n", + "For more on the sampling overhead incurred by circuit cutting, refer to the [explanatory material](../explanation/index.rst)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7af74c54-3e68-4d58-a2e6-02bc212d911d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.881911Z", + "iopub.status.busy": "2025-03-28T16:52:34.881529Z", + "iopub.status.idle": "2025-03-28T16:52:34.885387Z", + "shell.execute_reply": "2025-03-28T16:52:34.884739Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling overhead: 256.0\n" + ] + } + ], + "source": [ + "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a1eda2d0-8d83-473b-8ea8-fe9880108140", + "metadata": {}, + "source": [ + "### Generate the subexperiments to run on the backend\n", + "\n", + "`generate_cutting_experiments` accepts `circuits`/`observables` args as dictionaries mapping qubit partition labels to the respective `subcircuit`/`subobservables`.\n", + "\n", + "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d28a8b82-8405-47b2-8142-62d56266409a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.887533Z", + "iopub.status.busy": "2025-03-28T16:52:34.887063Z", + "iopub.status.idle": "2025-03-28T16:52:34.963806Z", + "shell.execute_reply": "2025-03-28T16:52:34.963065Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import generate_cutting_experiments\n", + "\n", + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits, observables=subobservables, num_samples=np.inf\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9f66e3eb-7cbc-45a9-8f40-9f92b61b6e65", + "metadata": {}, + "source": [ + "### Choose a backend\n", + "\n", + "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6944546f-e3e0-4863-9049-9feed1086e68", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:34.966311Z", + "iopub.status.busy": "2025-03-28T16:52:34.965845Z", + "iopub.status.idle": "2025-03-28T16:52:35.537487Z", + "shell.execute_reply": "2025-03-28T16:52:35.536746Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n", + "\n", + "backend = FakeManilaV2()" + ] + }, + { + "cell_type": "markdown", + "id": "fdb47142-690c-4b1a-ad0b-85e1260a2cea", + "metadata": {}, + "source": [ + "### Prepare the subexperiments for the backend\n", + "\n", + "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0229e6f2-a637-481d-ac76-84dfaadc264f", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:35.540179Z", + "iopub.status.busy": "2025-03-28T16:52:35.539685Z", + "iopub.status.idle": "2025-03-28T16:52:38.804548Z", + "shell.execute_reply": "2025-03-28T16:52:38.803721Z" + } + }, + "outputs": [], + "source": [ + "from qiskit.transpiler import generate_preset_pass_manager\n", + "\n", + "# Transpile the subexperiments to ISA circuits\n", + "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", + "isa_subexperiments = {\n", + " label: pass_manager.run(partition_subexpts)\n", + " for label, partition_subexpts in subexperiments.items()\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "9578c64a-8087-4696-bb2b-ca53d45442ba", + "metadata": {}, + "source": [ + "## Step 3: Execute\n", + "\n", + "### Run the subexperiments using the Qiskit Runtime Sampler primitive" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bdf3bd2f-e10a-4148-a7ad-f354492614cc", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:38.807146Z", + "iopub.status.busy": "2025-03-28T16:52:38.806915Z", + "iopub.status.idle": "2025-03-28T16:52:38.958106Z", + "shell.execute_reply": "2025-03-28T16:52:38.957428Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import SamplerV2, Batch\n", + "\n", + "# Submit each partition's subexperiments to the Qiskit Runtime Sampler\n", + "# primitive, in a single batch so that the jobs will run back-to-back.\n", + "with Batch(backend=backend) as batch:\n", + " sampler = SamplerV2(mode=batch)\n", + " jobs = {\n", + " label: sampler.run(subsystem_subexpts, shots=2**12)\n", + " for label, subsystem_subexpts in isa_subexperiments.items()\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "abb436de-9662-4c47-92b1-659ee0334adc", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:38.965357Z", + "iopub.status.busy": "2025-03-28T16:52:38.965102Z", + "iopub.status.idle": "2025-03-28T16:52:51.060055Z", + "shell.execute_reply": "2025-03-28T16:52:51.059450Z" + } + }, + "outputs": [], + "source": [ + "# Retrieve results\n", + "results = {label: job.result() for label, job in jobs.items()}" + ] + }, + { + "cell_type": "markdown", + "id": "3f6e4eed-eff7-4e16-ad8d-8070011a555b", + "metadata": {}, + "source": [ + "## Step 4: Post-process\n", + "\n", + "### Reconstruct the expectation value\n", + "\n", + "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a1904c54-27fa-45e0-a9dd-727b4542db71", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:51.062743Z", + "iopub.status.busy": "2025-03-28T16:52:51.062254Z", + "iopub.status.idle": "2025-03-28T16:52:53.372289Z", + "shell.execute_reply": "2025-03-28T16:52:53.371616Z" + } + }, + "outputs": [], + "source": [ + "from qiskit_addon_cutting import reconstruct_expectation_values\n", + "\n", + "reconstructed_expval_terms = reconstruct_expectation_values(\n", + " results,\n", + " coefficients,\n", + " subobservables,\n", + ")\n", + "reconstructed_expval = np.dot(reconstructed_expval_terms, observable.coeffs)" + ] + }, + { + "cell_type": "markdown", + "id": "cdc793f2-3e2b-417b-b863-7b9d57b86a8f", + "metadata": {}, + "source": [ + "### Compare the reconstructed expectation value with the exact expectation value from the original circuit and observable" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6e3d63e4-a510-4712-bc43-48df6e2f7ded", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:53.374812Z", + "iopub.status.busy": "2025-03-28T16:52:53.374611Z", + "iopub.status.idle": "2025-03-28T16:52:53.385882Z", + "shell.execute_reply": "2025-03-28T16:52:53.385355Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed expectation value: 1.40369761\n", + "Exact expectation value: 1.59099026\n", + "Error in estimation: -0.18729265\n", + "Relative error in estimation: -0.1177208\n" + ] + } + ], + "source": [ + "from qiskit_aer.primitives import EstimatorV2\n", + "\n", + "estimator = EstimatorV2()\n", + "exact_expval = estimator.run([(qc_0, observable)]).result()[0].data.evs\n", + "print(f\"Reconstructed expectation value: {np.real(np.round(reconstructed_expval, 8))}\")\n", + "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n", + "print(f\"Error in estimation: {np.real(np.round(reconstructed_expval-exact_expval, 8))}\")\n", + "print(\n", + " f\"Relative error in estimation: {np.real(np.round((reconstructed_expval-exact_expval) / exact_expval, 8))}\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb b/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb new file mode 100644 index 00000000000..985d1785977 --- /dev/null +++ b/docs/addons/qiskit-addon-cutting/tutorials/04_automatic_cut_finding.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "49524788-7b8e-4499-9108-83dfc4d3db79", + "metadata": {}, + "source": [ + "# Automatically find cuts" + ] + }, + { + "cell_type": "markdown", + "id": "c45659c6-0948-44af-926b-afcd2773c46a", + "metadata": {}, + "source": [ + "## Step 1: Map\n", + "\n", + "#### Create a circuit and observables" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3272590c-2155-4058-8a69-7805f0ae2b9d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:55.792854Z", + "iopub.status.busy": "2025-03-28T16:52:55.792659Z", + "iopub.status.idle": "2025-03-28T16:52:56.800107Z", + "shell.execute_reply": "2025-03-28T16:52:56.799450Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from qiskit.circuit.random import random_circuit\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "circuit = random_circuit(7, 6, max_operands=2, seed=1242)\n", + "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])\n", + "\n", + "\n", + "circuit.draw(\"mpl\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "a37a82a8-0fe7-4a74-b271-c5dd750106cc", + "metadata": {}, + "source": [ + "## Step 2: Optimize\n", + "\n", + "#### Find cut locations, given a maximum of 4 qubits per subcircuit. This circuit can be separated in two by making a single wire cut and cutting one `CRZGate`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "580dbe6d-9a91-4349-9bee-d5c22d742d45", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:56.802390Z", + "iopub.status.busy": "2025-03-28T16:52:56.801930Z", + "iopub.status.idle": "2025-03-28T16:52:59.488947Z", + "shell.execute_reply": "2025-03-28T16:52:59.488280Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found solution using 8 cuts with a sampling overhead of 59776343.19975524.\n", + "Lowest cost solution found: False.\n", + "Gate Cut at circuit instruction index 6\n", + "Gate Cut at circuit instruction index 8\n", + "Gate Cut at circuit instruction index 9\n", + "Wire Cut at circuit instruction index 10\n", + "Gate Cut at circuit instruction index 15\n", + "Gate Cut at circuit instruction index 18\n", + "Gate Cut at circuit instruction index 19\n", + "Gate Cut at circuit instruction index 21\n" + ] + }, + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_cutting.automated_cut_finding import (\n", + " find_cuts,\n", + " OptimizationParameters,\n", + " DeviceConstraints,\n", + ")\n", + "\n", + "# Specify settings for the cut-finding optimizer\n", + "optimization_settings = OptimizationParameters(seed=111)\n", + "\n", + "# Specify the size of the QPUs available\n", + "device_constraints = DeviceConstraints(qubits_per_subcircuit=4)\n", + "\n", + "cut_circuit, metadata = find_cuts(circuit, optimization_settings, device_constraints)\n", + "print(\n", + " f'Found solution using {len(metadata[\"cuts\"])} cuts with a sampling '\n", + " f'overhead of {metadata[\"sampling_overhead\"]}.\\n'\n", + " f'Lowest cost solution found: {metadata[\"minimum_reached\"]}.'\n", + ")\n", + "for cut in metadata[\"cuts\"]:\n", + " print(f\"{cut[0]} at circuit instruction index {cut[1]}\")\n", + "cut_circuit.draw(\"mpl\", scale=0.8, fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "11e995ae-6f8b-448a-a895-b9ce22e41a9e", + "metadata": {}, + "source": [ + "#### Add ancillas for wire cuts and expand the observables to account for ancilla qubits" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "41f67453-4a5e-4f93-965c-1fd63570b868", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:59.491081Z", + "iopub.status.busy": "2025-03-28T16:52:59.490686Z", + "iopub.status.idle": "2025-03-28T16:52:59.752854Z", + "shell.execute_reply": "2025-03-28T16:52:59.752167Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit_addon_cutting import cut_wires, expand_observables\n", + "\n", + "qc_w_ancilla = cut_wires(cut_circuit)\n", + "observables_expanded = expand_observables(observable.paulis, circuit, qc_w_ancilla)\n", + "qc_w_ancilla.draw(\"mpl\", scale=0.8, fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "7bb0a17b-2213-4ac3-a09f-db61ee1bc6d7", + "metadata": {}, + "source": [ + "#### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "80736dfb-a5f4-43b9-81d5-1731f32ac501", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:59.755174Z", + "iopub.status.busy": "2025-03-28T16:52:59.754781Z", + "iopub.status.idle": "2025-03-28T16:52:59.767941Z", + "shell.execute_reply": "2025-03-28T16:52:59.767319Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling overhead: 59776343.19975523\n" + ] + } + ], + "source": [ + "from qiskit_addon_cutting import partition_problem\n", + "\n", + "partitioned_problem = partition_problem(\n", + " circuit=qc_w_ancilla, observables=observables_expanded\n", + ")\n", + "subcircuits = partitioned_problem.subcircuits\n", + "subobservables = partitioned_problem.subobservables\n", + "print(\n", + " f\"Sampling overhead: {np.prod([basis.overhead for basis in partitioned_problem.bases])}\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "65155899-b8d7-4509-a86a-4f11a8bb86ff", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:59.770081Z", + "iopub.status.busy": "2025-03-28T16:52:59.769722Z", + "iopub.status.idle": "2025-03-28T16:52:59.774244Z", + "shell.execute_reply": "2025-03-28T16:52:59.773599Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: PauliList(['IIII', 'IIZI', 'IIIZ']),\n", + " 1: PauliList(['ZIII', 'IIII', 'IIII'])}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subobservables" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "aab2325a-6043-45c3-9240-963412e15784", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:59.776009Z", + "iopub.status.busy": "2025-03-28T16:52:59.775815Z", + "iopub.status.idle": "2025-03-28T16:52:59.931154Z", + "shell.execute_reply": "2025-03-28T16:52:59.930440Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[0].draw(\"mpl\", style=\"iqp\", scale=0.8)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bef68fc6-0626-45ff-b881-19314a943601", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:52:59.933278Z", + "iopub.status.busy": "2025-03-28T16:52:59.933055Z", + "iopub.status.idle": "2025-03-28T16:53:00.110630Z", + "shell.execute_reply": "2025-03-28T16:53:00.110004Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subcircuits[1].draw(\"mpl\", style=\"iqp\", scale=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "e45c0046-c8c6-4e27-b6f6-30d71c6c2645", + "metadata": {}, + "source": [ + "#### Generate the experiments to run on the backend." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ec23c94c-0969-4964-a30d-275c21dacbe0", + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-28T16:53:00.112773Z", + "iopub.status.busy": "2025-03-28T16:53:00.112543Z", + "iopub.status.idle": "2025-03-28T16:53:02.920653Z", + "shell.execute_reply": "2025-03-28T16:53:02.919977Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2000 total subexperiments to run on backend.\n" + ] + } + ], + "source": [ + "from qiskit_addon_cutting import generate_cutting_experiments\n", + "\n", + "subexperiments, coefficients = generate_cutting_experiments(\n", + " circuits=subcircuits, observables=subobservables, num_samples=1_000\n", + ")\n", + "print(\n", + " f\"{len(subexperiments[0]) + len(subexperiments[1])} total subexperiments to run on backend.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8c548ee8-7c46-4a22-b18a-84e69bdd23aa", + "metadata": {}, + "source": [ + "Steps 3 and 4 of a [Qiskit pattern](/guides/intro-to-patterns) can then be performed as in the preceding tutorials." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb b/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb index 40b909c8367..e12ac085532 100644 --- a/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb +++ b/docs/addons/qiskit-addon-mpf/explanations/mpf_stability.ipynb @@ -121,9 +121,7 @@ "from qiskit.quantum_info import SparsePauliOp\n", "\n", "L = coupling_map.size()\n", - "observable = SparsePauliOp.from_sparse_list(\n", - " [(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L\n", - ")\n", + "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)\n", "print(observable)" ] }, @@ -165,9 +163,7 @@ "import numpy as np\n", "from qiskit.primitives import StatevectorEstimator\n", "from qiskit.synthesis import SuzukiTrotter\n", - "from qiskit_addon_utils.problem_generators import (\n", - " generate_time_evolution_circuit,\n", - ")\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", "from scipy.linalg import expm\n", "\n", "estimator = StatevectorEstimator()\n", @@ -183,11 +179,7 @@ " initial_state = np.zeros(exp_H.shape[0])\n", " initial_state[0] = 1.0\n", " time_evolved_state = exp_H @ initial_state\n", - " exact_evs = (\n", - " time_evolved_state.conj()\n", - " @ observable.to_matrix()\n", - " @ time_evolved_state\n", - " )\n", + " exact_evs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", " exact.append(float(exact_evs))\n", "\n", " for k in ks:\n", @@ -198,9 +190,7 @@ " hamiltonian, synthesis=SuzukiTrotter(reps=k, order=4), time=time\n", " )\n", "\n", - " job = estimator.run(\n", - " [(order2_circ, observable), (order4_circ, observable)]\n", - " )\n", + " job = estimator.run([(order2_circ, observable), (order4_circ, observable)])\n", " result = job.result()\n", " order2.append(result[0].data.evs)\n", " order4.append(result[1].data.evs)" @@ -273,12 +263,7 @@ "\n", " ax.set_title(f\"time={t}\")\n", "\n", - "fig.legend(\n", - " *ax.get_legend_handles_labels(),\n", - " ncols=4,\n", - " loc=\"center\",\n", - " bbox_to_anchor=(0.5, 0.0),\n", - ")\n", + "fig.legend(*ax.get_legend_handles_labels(), ncols=4, loc=\"center\", bbox_to_anchor=(0.5, 0.0))\n", "fig.tight_layout()" ] }, diff --git a/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb b/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb index d847946a9cf..80266c720fb 100644 --- a/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb +++ b/docs/addons/qiskit-addon-mpf/how_tos/choose_trotter_steps.ipynb @@ -186,12 +186,7 @@ " if norm1 > max_l1_norm:\n", " continue\n", "\n", - " weighted = np.sum(\n", - " [\n", - " np.abs(c) / k ** (2 * order)\n", - " for c, k in zip(coeffs, ks.value)\n", - " ]\n", - " )\n", + " weighted = np.sum([np.abs(c) / k ** (2 * order) for c, k in zip(coeffs, ks.value)])\n", " possible_ks[tuple(int(k) for k in ks.value)] = {\n", " \"coeffs\": tuple(coeffs),\n", " \"weighted\": float(weighted),\n", @@ -258,9 +253,7 @@ } ], "source": [ - "for ks_val in sorted(\n", - " possible_ks, key=lambda ks_val: possible_ks[ks_val].get(\"weighted\")\n", - "):\n", + "for ks_val in sorted(possible_ks, key=lambda ks_val: possible_ks[ks_val].get(\"weighted\")):\n", " print(ks_val, possible_ks[ks_val])" ] }, diff --git a/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb b/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb index 2b4e6110260..556a1c10498 100644 --- a/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb +++ b/docs/addons/qiskit-addon-mpf/how_tos/using_approximate_model.ipynb @@ -842,9 +842,7 @@ "outputs": [], "source": [ "from qiskit.synthesis import SuzukiTrotter\n", - "from qiskit_addon_utils.problem_generators import (\n", - " generate_time_evolution_circuit,\n", - ")\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", "\n", "time = 8.0\n", "circuits = []\n", @@ -883,9 +881,7 @@ "from qiskit.quantum_info import SparsePauliOp\n", "\n", "L = 10\n", - "observable = SparsePauliOp.from_sparse_list(\n", - " [(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L\n", - ")\n", + "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)\n", "print(observable)" ] }, @@ -956,9 +952,7 @@ "\n", "time_evolved_state = exp_H @ initial_state\n", "\n", - "exact_obs = (\n", - " time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", - ")" + "exact_obs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state" ] }, { diff --git a/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb b/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb index 68a04819670..f99c34cd431 100644 --- a/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb +++ b/docs/addons/qiskit-addon-mpf/tutorials/01_getting_started.ipynb @@ -214,9 +214,7 @@ "from qiskit.quantum_info import SparsePauliOp\n", "\n", "L = coupling_map.size()\n", - "observable = SparsePauliOp.from_sparse_list(\n", - " [(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L\n", - ")\n", + "observable = SparsePauliOp.from_sparse_list([(\"Z\", [i], 1 / L / 2) for i in range(L)], num_qubits=L)\n", "print(observable)" ] }, @@ -553,9 +551,7 @@ "source": [ "from qiskit_addon_mpf.costs import setup_sum_of_squares_problem\n", "\n", - "model_approx, coeffs_approx = setup_sum_of_squares_problem(\n", - " lse, max_l1_norm=3.0\n", - ")\n", + "model_approx, coeffs_approx = setup_sum_of_squares_problem(lse, max_l1_norm=3.0)\n", "model_approx.solve()\n", "print(coeffs_approx.value)\n", "print(np.linalg.norm(coeffs_approx.value, ord=1))" @@ -609,9 +605,7 @@ "outputs": [], "source": [ "from qiskit.synthesis import SuzukiTrotter\n", - "from qiskit_addon_utils.problem_generators import (\n", - " generate_time_evolution_circuit,\n", - ")\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", "\n", "circuits = []\n", "for k in trotter_steps:\n", @@ -739,9 +733,7 @@ "from qiskit.transpiler import generate_preset_pass_manager\n", "\n", "backend = GenericBackendV2(num_qubits=10)\n", - "transpiler = generate_preset_pass_manager(\n", - " optimization_level=2, backend=backend\n", - ")\n", + "transpiler = generate_preset_pass_manager(optimization_level=2, backend=backend)\n", "\n", "transpiled_circuits = [transpiler.run(circ) for circ in circuits]" ] @@ -913,9 +905,7 @@ "\n", "time_evolved_state = exp_H @ initial_state\n", "\n", - "exact_obs = (\n", - " time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", - ")\n", + "exact_obs = time_evolved_state.conj() @ observable.to_matrix() @ time_evolved_state\n", "print(exact_obs.real)" ] }, diff --git a/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb b/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb index 1cd49633864..aeb5dc7702e 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/bound_error_using_p_norm.ipynb @@ -74,9 +74,7 @@ " pauli_order_strategy=PauliOrderStrategy.InteractionThenColor,\n", ")\n", "# we evolve for some time\n", - "circuit = generate_time_evolution_circuit(\n", - " hamiltonian, synthesis=LieTrotter(reps=3), time=2.0\n", - ")\n", + "circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0)\n", "# slice the circuit by gate type\n", "slices = slice_by_gate_types(circuit)\n", "\n", @@ -203,16 +201,10 @@ "\n", "max_slices = 6\n", "l1_bp_obs, l1_remaining_slices, l1_metadata = backpropagate(\n", - " obs,\n", - " slices[-max_slices:],\n", - " truncation_error_budget=l1_truncation_error_budget,\n", - ")\n", - "l1_reduced_circuit = combine_slices(\n", - " slices[:-max_slices] + l1_remaining_slices\n", - ")\n", - "print(\n", - " f\"Backpropagated {max_slices - len(l1_remaining_slices)} circuit slices.\"\n", + " obs, slices[-max_slices:], truncation_error_budget=l1_truncation_error_budget\n", ")\n", + "l1_reduced_circuit = combine_slices(slices[:-max_slices] + l1_remaining_slices)\n", + "print(f\"Backpropagated {max_slices - len(l1_remaining_slices)} circuit slices.\")\n", "print(\n", " f\"New observable contains {len(l1_bp_obs)} terms and {len(l1_bp_obs.group_commuting(qubit_wise=True))} commuting groups.\"\n", ")" @@ -347,16 +339,10 @@ "source": [ "max_slices = 6\n", "l2_bp_obs, l2_remaining_slices, l2_metadata = backpropagate(\n", - " obs,\n", - " slices[-max_slices:],\n", - " truncation_error_budget=l2_truncation_error_budget,\n", - ")\n", - "l2_reduced_circuit = combine_slices(\n", - " slices[:-max_slices] + l2_remaining_slices\n", - ")\n", - "print(\n", - " f\"Backpropagated {max_slices - len(l2_remaining_slices)} circuit slices.\"\n", + " obs, slices[-max_slices:], truncation_error_budget=l2_truncation_error_budget\n", ")\n", + "l2_reduced_circuit = combine_slices(slices[:-max_slices] + l2_remaining_slices)\n", + "print(f\"Backpropagated {max_slices - len(l2_remaining_slices)} circuit slices.\")\n", "print(\n", " f\"New observable contains {len(l2_bp_obs)} terms and {len(l2_bp_obs.group_commuting(qubit_wise=True))} commuting groups.\"\n", ")" diff --git a/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb b/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb index 99d95be2bc7..aecdb5154e7 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/simulating_circuits_with_obp.ipynb @@ -57,9 +57,7 @@ " pauli_order_strategy=PauliOrderStrategy.InteractionThenColor,\n", ")\n", "# we evolve for some time\n", - "circuit = generate_time_evolution_circuit(\n", - " hamiltonian, synthesis=LieTrotter(reps=3), time=2.0\n", - ")\n", + "circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0)\n", "# slice the circuit by gate type\n", "slices = slice_by_gate_types(circuit)" ] @@ -304,10 +302,7 @@ "source": [ "import numpy as np\n", "from qiskit.quantum_info import PauliList\n", - "from qiskit_ibm_runtime.utils.noise_learner_result import (\n", - " LayerError,\n", - " PauliLindbladError,\n", - ")\n", + "from qiskit_ibm_runtime.utils.noise_learner_result import LayerError, PauliLindbladError\n", "\n", "# fmt: off\n", "pauli_errors_even = ['IIIIIIIIIX', 'IIIIIIIIIY', 'IIIIIIIIIZ', 'IIIIIIIIXI', 'IIIIIIIIXX', 'IIIIIIIIXY', 'IIIIIIIIXZ', 'IIIIIIIIYI', 'IIIIIIIIYX', 'IIIIIIIIYY', 'IIIIIIIIYZ', 'IIIIIIIIZI', 'IIIIIIIIZX', 'IIIIIIIIZY', 'IIIIIIIIZZ', 'IIIIIIIXII', 'IIIIIIIXXI', 'IIIIIIIXYI', 'IIIIIIIXZI', 'IIIIIIIYII', 'IIIIIIIYXI', 'IIIIIIIYYI', 'IIIIIIIYZI', 'IIIIIIIZII', 'IIIIIIIZXI', 'IIIIIIIZYI', 'IIIIIIIZZI', 'IIIIIIXIII', 'IIIIIIXXII', 'IIIIIIXYII', 'IIIIIIXZII', 'IIIIIIYIII', 'IIIIIIYXII', 'IIIIIIYYII', 'IIIIIIYZII', 'IIIIIIZIII', 'IIIIIIZXII', 'IIIIIIZYII', 'IIIIIIZZII', 'IIIIIXIIII', 'IIIIIXXIII', 'IIIIIXYIII', 'IIIIIXZIII', 'IIIIIYIIII', 'IIIIIYXIII', 'IIIIIYYIII', 'IIIIIYZIII', 'IIIIIZIIII', 'IIIIIZXIII', 'IIIIIZYIII', 'IIIIIZZIII', 'IIIIXIIIII', 'IIIIXXIIII', 'IIIIXYIIII', 'IIIIXZIIII', 'IIIIYIIIII', 'IIIIYXIIII', 'IIIIYYIIII', 'IIIIYZIIII', 'IIIIZIIIII', 'IIIIZXIIII', 'IIIIZYIIII', 'IIIIZZIIII', 'IIIXIIIIII', 'IIIXXIIIII', 'IIIXYIIIII', 'IIIXZIIIII', 'IIIYIIIIII', 'IIIYXIIIII', 'IIIYYIIIII', 'IIIYZIIIII', 'IIIZIIIIII', 'IIIZXIIIII', 'IIIZYIIIII', 'IIIZZIIIII', 'IIXIIIIIII', 'IIXXIIIIII', 'IIXYIIIIII', 'IIXZIIIIII', 'IIYIIIIIII', 'IIYXIIIIII', 'IIYYIIIIII', 'IIYZIIIIII', 'IIZIIIIIII', 'IIZXIIIIII', 'IIZYIIIIII', 'IIZZIIIIII', 'IXIIIIIIII', 'IXXIIIIIII', 'IXYIIIIIII', 'IXZIIIIIII', 'IYIIIIIIII', 'IYXIIIIIII', 'IYYIIIIIII', 'IYZIIIIIII', 'IZIIIIIIII', 'IZXIIIIIII', 'IZYIIIIIII', 'IZZIIIIIII', 'XIIIIIIIII', 'XXIIIIIIII', 'XYIIIIIIII', 'XZIIIIIIII', 'YIIIIIIIII', 'YXIIIIIIII', 'YYIIIIIIII', 'YZIIIIIIII', 'ZIIIIIIIII', 'ZXIIIIIIII', 'ZYIIIIIIII', 'ZZIIIIIIII']\n", @@ -414,53 +409,25 @@ " if slice_ == layer_error_even_xx.circuit:\n", " noisy_slices.append(\n", " QuantumCircuit.from_instructions(\n", - " [\n", - " (\n", - " PauliLindbladErrorInstruction(\n", - " layer_error_even_xx.error\n", - " ),\n", - " slice_.qubits,\n", - " )\n", - " ]\n", + " [(PauliLindbladErrorInstruction(layer_error_even_xx.error), slice_.qubits)]\n", " )\n", " )\n", " elif slice_ == layer_error_odd_xx.circuit:\n", " noisy_slices.append(\n", " QuantumCircuit.from_instructions(\n", - " [\n", - " (\n", - " PauliLindbladErrorInstruction(\n", - " layer_error_odd_xx.error\n", - " ),\n", - " slice_.qubits,\n", - " )\n", - " ]\n", + " [(PauliLindbladErrorInstruction(layer_error_odd_xx.error), slice_.qubits)]\n", " )\n", " )\n", " elif slice_ == layer_error_even_yy.circuit:\n", " noisy_slices.append(\n", " QuantumCircuit.from_instructions(\n", - " [\n", - " (\n", - " PauliLindbladErrorInstruction(\n", - " layer_error_even_yy.error\n", - " ),\n", - " slice_.qubits,\n", - " )\n", - " ]\n", + " [(PauliLindbladErrorInstruction(layer_error_even_yy.error), slice_.qubits)]\n", " )\n", " )\n", " elif slice_ == layer_error_odd_yy.circuit:\n", " noisy_slices.append(\n", " QuantumCircuit.from_instructions(\n", - " [\n", - " (\n", - " PauliLindbladErrorInstruction(\n", - " layer_error_odd_yy.error\n", - " ),\n", - " slice_.qubits,\n", - " )\n", - " ]\n", + " [(PauliLindbladErrorInstruction(layer_error_odd_yy.error), slice_.qubits)]\n", " )\n", " )\n", " noisy_slices.append(slice_)" @@ -492,9 +459,7 @@ } ], "source": [ - "combine_slices(noisy_slices, include_barriers=True).draw(\n", - " \"mpl\", fold=100, scale=0.8\n", - ")" + "combine_slices(noisy_slices, include_barriers=True).draw(\"mpl\", fold=100, scale=0.8)" ] }, { @@ -535,9 +500,7 @@ } ], "source": [ - "vacuum_state_noisy_obs.coeffs[\n", - " ~vacuum_state_noisy_obs.paulis.x.any(axis=1)\n", - "].sum()" + "vacuum_state_noisy_obs.coeffs[~vacuum_state_noisy_obs.paulis.x.any(axis=1)].sum()" ] }, { diff --git a/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb b/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb index 12f0db7a886..862fab2f568 100644 --- a/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb +++ b/docs/addons/qiskit-addon-obp/how_tos/truncate_operator_terms.ipynb @@ -83,9 +83,7 @@ " pauli_order_strategy=PauliOrderStrategy.InteractionThenColor,\n", ")\n", "# we evolve for some time\n", - "circuit = generate_time_evolution_circuit(\n", - " hamiltonian, synthesis=LieTrotter(reps=3), time=2.0\n", - ")\n", + "circuit = generate_time_evolution_circuit(hamiltonian, synthesis=LieTrotter(reps=3), time=2.0)\n", "# slice the circuit by gate type\n", "slices = slice_by_gate_types(circuit)\n", "\n", @@ -180,10 +178,7 @@ "\n", "op_budget = OperatorBudget(max_qwc_groups=10)\n", "bp_obs, remaining_slices, metadata = backpropagate(\n", - " obs,\n", - " slices,\n", - " operator_budget=op_budget,\n", - " truncation_error_budget=truncation_error_budget,\n", + " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", ")\n", "reduced_circuit = combine_slices(remaining_slices)\n", "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", @@ -281,9 +276,7 @@ "# Zero out the first 3 slices' budgets\n", "max_error_per_slice = [0.0] * 3 + [0.001] * (len(slices) - 3)\n", "\n", - "truncation_error_budget = setup_budget(\n", - " max_error_per_slice=max_error_per_slice\n", - ")\n", + "truncation_error_budget = setup_budget(max_error_per_slice=max_error_per_slice)\n", "print(truncation_error_budget)" ] }, @@ -304,10 +297,7 @@ ], "source": [ "bp_obs, remaining_slices, metadata = backpropagate(\n", - " obs,\n", - " slices,\n", - " operator_budget=op_budget,\n", - " truncation_error_budget=truncation_error_budget,\n", + " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", ")\n", "reduced_circuit = combine_slices(remaining_slices)\n", "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", @@ -391,9 +381,7 @@ "# This will be cycled over 3 times to be applied to the 18 slices\n", "max_error_per_slice = [0.0] * 4 + [0.003] * 2\n", "\n", - "truncation_error_budget = setup_budget(\n", - " max_error_per_slice=max_error_per_slice\n", - ")\n", + "truncation_error_budget = setup_budget(max_error_per_slice=max_error_per_slice)\n", "print(truncation_error_budget)" ] }, @@ -415,10 +403,7 @@ "source": [ "op_budget = OperatorBudget(max_qwc_groups=20)\n", "bp_obs, remaining_slices, metadata = backpropagate(\n", - " obs,\n", - " slices,\n", - " operator_budget=op_budget,\n", - " truncation_error_budget=truncation_error_budget,\n", + " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", ")\n", "reduced_circuit = combine_slices(remaining_slices)\n", "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", @@ -617,10 +602,7 @@ "source": [ "op_budget = OperatorBudget(max_qwc_groups=10)\n", "bp_obs, remaining_slices, metadata = backpropagate(\n", - " obs,\n", - " slices,\n", - " operator_budget=op_budget,\n", - " truncation_error_budget=truncation_error_budget,\n", + " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", ")\n", "reduced_circuit = combine_slices(remaining_slices)\n", "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", @@ -692,9 +674,7 @@ "source": [ "num_slices = 12\n", "\n", - "truncation_error_budget = setup_budget(\n", - " max_error_total=0.018, num_slices=num_slices, p_norm=1\n", - ")\n", + "truncation_error_budget = setup_budget(max_error_total=0.018, num_slices=num_slices, p_norm=1)\n", "print(truncation_error_budget)" ] }, @@ -859,10 +839,7 @@ ], "source": [ "bp_obs, remaining_slices, metadata = backpropagate(\n", - " obs,\n", - " slices,\n", - " operator_budget=op_budget,\n", - " truncation_error_budget=truncation_error_budget,\n", + " obs, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", ")\n", "reduced_circuit = combine_slices(remaining_slices)\n", "print(f\"Backpropagated {len(slices) - len(remaining_slices)} circuit slices.\")\n", diff --git a/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb b/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb index 966aa483260..4a74f1eaae3 100644 --- a/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb +++ b/docs/addons/qiskit-addon-obp/tutorials/01_getting_started.ipynb @@ -62,9 +62,7 @@ "coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False)\n", "\n", "# Choose a 10-qubit linear chain on this coupling map\n", - "reduced_coupling_map = coupling_map.reduce(\n", - " [0, 13, 1, 14, 10, 16, 5, 12, 8, 18]\n", - ")" + "reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 5, 12, 8, 18])" ] }, { @@ -182,9 +180,7 @@ ], "source": [ "from qiskit.synthesis import LieTrotter\n", - "from qiskit_addon_utils.problem_generators import (\n", - " generate_time_evolution_circuit,\n", - ")\n", + "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n", "\n", "circuit = generate_time_evolution_circuit(\n", " hamiltonian,\n", @@ -300,9 +296,7 @@ "observable = SparsePauliOp(\"IIIIIIIIIZ\")\n", "\n", "# Backpropagate slices onto the observable\n", - "bp_obs, remaining_slices, metadata = backpropagate(\n", - " observable, slices, operator_budget=op_budget\n", - ")\n", + "bp_obs, remaining_slices, metadata = backpropagate(observable, slices, operator_budget=op_budget)\n", "# Recombine the slices remaining after backpropagation\n", "bp_circuit = combine_slices(remaining_slices, include_barriers=True)\n", "\n", @@ -382,16 +376,11 @@ "source": [ "# Run the same experiment but truncate observable terms with small coefficients\n", "bp_obs_trunc, remaining_slices_trunc, metadata = backpropagate(\n", - " observable,\n", - " slices,\n", - " operator_budget=op_budget,\n", - " truncation_error_budget=truncation_error_budget,\n", + " observable, slices, operator_budget=op_budget, truncation_error_budget=truncation_error_budget\n", ")\n", "\n", "# Recombine the slices remaining after backpropagation\n", - "bp_circuit_trunc = combine_slices(\n", - " remaining_slices_trunc, include_barriers=True\n", - ")\n", + "bp_circuit_trunc = combine_slices(remaining_slices_trunc, include_barriers=True)\n", "\n", "print(f\"Backpropagated {metadata.num_backpropagated_slices} slices.\")\n", "print(\n", @@ -482,27 +471,17 @@ "estimator = Estimator()\n", "\n", "# Run the experiments using Estimator primitive\n", - "result_exact = (\n", - " estimator.run([(circuit_isa, observable_isa)]).result()[0].data.evs.item()\n", - ")\n", - "result_bp = (\n", - " estimator.run([(bp_circuit_isa, bp_obs_isa)]).result()[0].data.evs.item()\n", - ")\n", + "result_exact = estimator.run([(circuit_isa, observable_isa)]).result()[0].data.evs.item()\n", + "result_bp = estimator.run([(bp_circuit_isa, bp_obs_isa)]).result()[0].data.evs.item()\n", "result_bp_trunc = (\n", - " estimator.run([(bp_circuit_trunc_isa, bp_obs_trunc_isa)])\n", - " .result()[0]\n", - " .data.evs.item()\n", + " estimator.run([(bp_circuit_trunc_isa, bp_obs_trunc_isa)]).result()[0].data.evs.item()\n", ")\n", "\n", "print(f\"Exact expectation value: {result_exact}\")\n", "print(f\"Backpropagated expectation value: {result_bp}\")\n", "print(f\"Backpropagated expectation value with truncation: {result_bp_trunc}\")\n", - "print(\n", - " f\" - Expected Error for truncated observable: {metadata.accumulated_error(0):.3e}\"\n", - ")\n", - "print(\n", - " f\" - Observed Error for truncated observable: {abs(result_exact - result_bp_trunc):.3e}\"\n", - ")" + "print(f\" - Expected Error for truncated observable: {metadata.accumulated_error(0):.3e}\")\n", + "print(f\" - Observed Error for truncated observable: {abs(result_exact - result_bp_trunc):.3e}\")" ] } ], diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json new file mode 100644 index 00000000000..c828c198a3b --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/_toc.json @@ -0,0 +1,62 @@ +{ + "title": "Sample-based quantum diagonalization (SQD)", + "children": [ + { + "title": "Documentation Home", + "url": "/docs/addons/qiskit-addon-sqd" + }, + { + "title": "Installation Instructions", + "url": "/docs/addons/qiskit-addon-sqd/install" + }, + { + "title": "Tutorials", + "children": [ + { + "title": "Improving energy estimation of a chemistry Hamiltonian with SQD", + "url": "/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian" + }, + { + "title": "Improving energy estimation of a Fermionic lattice model with SQD", + "url": "/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian" + } + ], + "url": "/docs/addons/qiskit-addon-sqd/tutorials/index" + }, + { + "title": "How-To Guides", + "children": [ + { + "title": "Add fermionic transitions to the pool of configurations", + "url": "/docs/addons/qiskit-addon-sqd/how_tos/add_fermionic_excitations_to_configuration_pool" + }, + { + "title": "Benchmark Pauli operator projection", + "url": "/docs/addons/qiskit-addon-sqd/how_tos/benchmark_pauli_projection" + }, + { + "title": "Bounding the subspace dimension", + "url": "/docs/addons/qiskit-addon-sqd/how_tos/choose_subspace_dimension" + }, + { + "title": "Scale SQD chemistry workflows with Dice solver", + "url": "/docs/addons/qiskit-addon-sqd/how_tos/integrate_dice_solver" + }, + { + "title": "Project Pauli operators onto Hilbert subspaces", + "url": "/docs/addons/qiskit-addon-sqd/how_tos/project_pauli_operators_onto_hilbert_subspaces" + }, + { + "title": "Understand open-shell vs closed-shell options and its effect in the subspace construction", + "url": "/docs/addons/qiskit-addon-sqd/how_tos/select_open_closed_shell" + }, + { + "title": "Optimize Hamiltonian basis with orbital optimization", + "url": "/docs/addons/qiskit-addon-sqd/how_tos/use_oo_to_optimize_hamiltonian_basis" + } + ], + "url": "/docs/addons/qiskit-addon-sqd/how_tos/index" + } + ], + "collapsed": true +} diff --git a/docs/addons/qiskit-addon-sqd/how_tos/add_fermionic_excitations_to_configuration_pool.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/add_fermionic_excitations_to_configuration_pool.ipynb new file mode 100644 index 00000000000..4c2623e64cd --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/add_fermionic_excitations_to_configuration_pool.ipynb @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", + "metadata": {}, + "source": [ + "# Add fermionic transitions to the pool of configurations\n", + "\n", + "Here we demonstrate the functionalities to augment the pool of electronic\n", + "configurations obtained by the action of transition operators on each electronic\n", + "configuration.\n", + "\n", + "We demonstrate how to add single-electron hops of the type:\n", + "$$\n", + "c^\\dagger_{p\\sigma} c_{q\\sigma} |\\textbf{x} \\rangle\n", + "$$\n", + "for $p, q = 1, ..., N_\\textrm{orb}$ and $\\sigma \\in \\{ \\uparrow, \\downarrow\\}$,\n", + "and for all $|\\textbf{x} \\rangle$ in the batch of electronic configurations." + ] + }, + { + "cell_type": "markdown", + "id": "0a7e9fcd", + "metadata": {}, + "source": [ + "Let's begin by generating a batch of random electronic configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e9506e0b-ed64-48bb-a97a-ef851b604af1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "n_qubits = 8\n", + "n_orb = n_qubits // 2\n", + "\n", + "rand_seed = 22\n", + "np.random.seed(rand_seed)\n", + "\n", + "\n", + "# Generate some random bitstrings for testing\n", + "def random_bitstrings(n_samples, n_qubits):\n", + " return np.round(np.random.rand(n_samples, n_qubits)).astype(\"int\").astype(\"bool\")\n", + "\n", + "\n", + "bitstring_matrix = random_bitstrings(100, n_qubits)" + ] + }, + { + "cell_type": "markdown", + "id": "4155599f", + "metadata": {}, + "source": [ + "The excitation operators are specified inside a numpy array whose length is\n", + "equal to the number of fermionic nodes (or qubits). Each element of the array\n", + "must be a string that can take values:\n", + "\n", + "- ``'I'``: Identity\n", + "- ``'+'``: Creation operator\n", + "- ``'-'``: Annihilation operator\n", + "\n", + "Let's generate all possible single-electron transitions (amongst same spin\n", + "species)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "389284f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[['I' 'I' 'I' 'I' 'I' 'I' 'I' 'I']\n", + " ['+' '-' 'I' 'I' 'I' 'I' 'I' 'I']\n", + " ['-' '+' 'I' 'I' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' '+' '-' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' '-' '+' 'I' 'I']\n", + " ['+' 'I' '-' 'I' 'I' 'I' 'I' 'I']\n", + " ['-' 'I' '+' 'I' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' '+' 'I' '-' 'I']\n", + " ['I' 'I' 'I' 'I' '-' 'I' '+' 'I']\n", + " ['+' 'I' 'I' '-' 'I' 'I' 'I' 'I']\n", + " ['-' 'I' 'I' '+' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' '+' 'I' 'I' '-']\n", + " ['I' 'I' 'I' 'I' '-' 'I' 'I' '+']\n", + " ['I' '+' '-' 'I' 'I' 'I' 'I' 'I']\n", + " ['I' '-' '+' 'I' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' 'I' '+' '-' 'I']\n", + " ['I' 'I' 'I' 'I' 'I' '-' '+' 'I']\n", + " ['I' '+' 'I' '-' 'I' 'I' 'I' 'I']\n", + " ['I' '-' 'I' '+' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' 'I' '+' 'I' '-']\n", + " ['I' 'I' 'I' 'I' 'I' '-' 'I' '+']\n", + " ['I' 'I' '+' '-' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' '-' '+' 'I' 'I' 'I' 'I']\n", + " ['I' 'I' 'I' 'I' 'I' 'I' '+' '-']\n", + " ['I' 'I' 'I' 'I' 'I' 'I' '-' '+']]\n" + ] + } + ], + "source": [ + "transitions_single = np.array(\n", + " [[\"I\" for i in range(2 * n_orb)] for j in range(4 * (n_orb**2 - n_orb) // 2 + 1)]\n", + ")\n", + "count = 1\n", + "for i in range(n_orb):\n", + " for j in range(i + 1, n_orb):\n", + " # spin up\n", + " transitions_single[count, i] = \"+\"\n", + " transitions_single[count, j] = \"-\"\n", + " count += 1\n", + " transitions_single[count, i] = \"-\"\n", + " transitions_single[count, j] = \"+\"\n", + " count += 1\n", + "\n", + " # spin down\n", + " transitions_single[count, i + n_orb] = \"+\"\n", + " transitions_single[count, j + n_orb] = \"-\"\n", + " count += 1\n", + " transitions_single[count, i + n_orb] = \"-\"\n", + " transitions_single[count, j + n_orb] = \"+\"\n", + " count += 1\n", + "\n", + "print(transitions_single)" + ] + }, + { + "cell_type": "markdown", + "id": "c15c5b3f", + "metadata": {}, + "source": [ + "Let's now apply the transition operators to the configurations in ``bitstring_matrix``" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6a44f358", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(723, 8)\n", + "[[False False False ... False False True]\n", + " [False True False ... True False False]\n", + " [ True True True ... True False True]\n", + " ...\n", + " [ True False True ... False False True]\n", + " [ True True True ... True False True]\n", + " [False False False ... False False True]]\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.fermion import enlarge_batch_from_transitions\n", + "\n", + "bitstring_matrix_aug = enlarge_batch_from_transitions(bitstring_matrix, transitions_single)\n", + "\n", + "print(bitstring_matrix_aug.shape)\n", + "print(bitstring_matrix_aug)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/how_tos/benchmark_pauli_projection.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/benchmark_pauli_projection.ipynb new file mode 100644 index 00000000000..64fba504741 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/benchmark_pauli_projection.ipynb @@ -0,0 +1,471 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "55900f6c-2fb6-4d2c-8745-29bad8b66d9f", + "metadata": {}, + "source": [ + "# Benchmark Pauli operator projection" + ] + }, + { + "cell_type": "markdown", + "id": "b5caa5b4", + "metadata": {}, + "source": [ + "### Pauli string action on a computational basis state\n", + "\n", + "The action of a Pauli string on a computational basis state is rather trivial, and just a single computational basis state itself. This is a direct consequence of the structure of Pauli matrices, which only have a single nonzero element on each of their rows. Consequently, their action on a qubit is:\n", + "\n", + "-------------------------------------------\n", + "\n", + "$$\n", + "\\sigma_x |0 \\rangle = |1 \\rangle\n", + "$$\n", + "\n", + "$$\n", + "\\sigma_x |1 \\rangle = |0 \\rangle\n", + "$$\n", + "\n", + "-------------------------------------------\n", + "\n", + "$$\n", + "\\sigma_y |0 \\rangle = i|1 \\rangle\n", + "$$\n", + "\n", + "$$\n", + "\\sigma_y |1 \\rangle = -i|0 \\rangle\n", + "$$\n", + "\n", + "-------------------------------------------\n", + "\n", + "$$\n", + "\\sigma_z |0 \\rangle = |0 \\rangle\n", + "$$\n", + "\n", + "$$\n", + "\\sigma_z |1 \\rangle = -|1 \\rangle\n", + "$$\n", + "\n", + "-------------------------------------------\n", + "\n", + "$$\n", + "I |0 \\rangle = |0 \\rangle\n", + "$$\n", + "\n", + "$$\n", + "I |1 \\rangle = |1 \\rangle\n", + "$$\n", + "\n", + "-------------------------------------------\n", + "\n", + "Each bit on the bitstring labeling the computational basis will be labeled by $x \\in \\{0, 1 \\}$. In order to keep the implementation at light as possible, we will\n", + "represent the bitstrings with `bool` variables: $0\\rightarrow \\textrm{False}$ and\n", + "$1\\rightarrow \\textrm{True}$.\n", + "\n", + "To represent the action of each Pauli operator in a computational basis state\n", + "we will assign three variables to it: `diag`, `sign`, `imag`.\n", + "\n", + "- `diag` labels whether the operator is diagonal:\n", + "\n", + " - $\\textrm{diag}(I) = \\textrm{True}$\n", + " - $\\textrm{diag}(\\sigma_x) = \\textrm{False}$\n", + " - $\\textrm{diag}(\\sigma_y) = \\textrm{False}$\n", + " - $\\textrm{diag}(\\sigma_z) = \\textrm{True}$\n", + "\n", + "- `sign` Identifies if there is a sign change in the matrix element connected\n", + "to either 0 or 1:\n", + "\n", + " - $\\textrm{sign}(I) = \\textrm{False}$\n", + " - $\\textrm{sign}(\\sigma_x) = \\textrm{False}$\n", + " - $\\textrm{sign}(\\sigma_y) = \\textrm{True}$\n", + " - $\\textrm{sign}(\\sigma_z) = \\textrm{True}$\n", + "\n", + "- `imag` Identifies if there is a complex component to the matrix element:\n", + "\n", + " - $\\textrm{imag}(I) = \\textrm{False}$\n", + " - $\\textrm{imag}(\\sigma_x) = \\textrm{False}$\n", + " - $\\textrm{imag}(\\sigma_y) = \\textrm{True}$\n", + " - $\\textrm{imag}(\\sigma_z) = \\textrm{False}$\n", + "\n", + "Let's label an arbitrary Pauli operator as $\\sigma \\in \\{ I, \\sigma_x, \\sigma_y\n", + "\\sigma_z\\}$. The action of the Pauli operator on a computational basis state\n", + "can then be represented by the logic operation:\n", + "$$\n", + "\\sigma |x \\rangle = |x == \\textrm{diag}(\\sigma) \\rangle (-1)^{x\\textrm{ and sign}(\\sigma)}\n", + "(i)^{\\textrm{imag}(\\sigma)}.\n", + "$$\n", + "The same is straightforwardly generalized to arbitrary number of qubits." + ] + }, + { + "cell_type": "markdown", + "id": "a8fd7e11", + "metadata": {}, + "source": [ + "Let's check that this works:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "dcb15308", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------\n", + "I\n", + "|False> --> |False> ME:(1+0j)\n", + "|True> --> |True> ME:(1+0j)\n", + "-------------------\n", + "SX\n", + "|False> --> |True> ME:(1+0j)\n", + "|True> --> |False> ME:(1+0j)\n", + "-------------------\n", + "SZ\n", + "|False> --> |False> ME:(1+0j)\n", + "|True> --> |True> ME:(-1+0j)\n", + "-------------------\n", + "SY\n", + "|False> --> |True> ME:1j\n", + "|True> --> |False> ME:(-0-1j)\n" + ] + } + ], + "source": [ + "def connected_element_and_amplitude_bool(\n", + " x: bool, diag: bool, sign: bool, imag: bool\n", + ") -> tuple[bool, complex]:\n", + " \"\"\"\n", + " Finds the connected element to computational basis state |x> under\n", + " the action of the Pauli operator represented by (diag, sign, imag).\n", + "\n", + " Args:\n", + " x: Value of the bit, either True or False.\n", + " diag: Whether the Pauli operator is diagonal (I, Z)\n", + " sigma: Whether the Pauli operator's rows differ in sign (Y, Z)\n", + " imag: Whether the Pauli operator is purely imaginary (Y)\n", + "\n", + " Returns:\n", + " A length-2 tuple:\n", + " - The connected element to x, either False or True\n", + " - The matrix element\n", + " \"\"\"\n", + " return x == diag, (-1) ** (x and sign) * (1j) ** (imag)\n", + "\n", + "\n", + "sigma_indices = [0, 1, 2, 3]\n", + "sigma_string = [\"I\", \"SX\", \"SZ\", \"SY\"]\n", + "sigma_diag = [True, False, True, False]\n", + "sigma_sign = [False, False, True, True]\n", + "sigma_imag = [False, False, False, True]\n", + "qubit_values = [False, True]\n", + "\n", + "for xi in sigma_indices:\n", + " print(\"-------------------\")\n", + " print(sigma_string[xi])\n", + " for x in qubit_values:\n", + " x_p, matrix_element = connected_element_and_amplitude_bool(\n", + " x, sigma_diag[xi], sigma_sign[xi], sigma_imag[xi]\n", + " )\n", + " print(\"|\" + str(x) + \"> --> |\" + str(x_p) + \"> ME:\" + str(matrix_element))" + ] + }, + { + "cell_type": "markdown", + "id": "f40a3463", + "metadata": {}, + "source": [ + "Let's generate some large number of bitstrings (50 M) for a 40-qubit system" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "74c16c91-cc5c-46ce-aff8-17d0e71ac50f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of unique bitstrings: 49998839\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from qiskit_addon_sqd.qubit import sort_and_remove_duplicates\n", + "\n", + "rand_seed = 22\n", + "np.random.seed(rand_seed)\n", + "\n", + "# Generate some random bitstrings for testing\n", + "\n", + "\n", + "def random_bitstrings(n_samples, n_qubits):\n", + " return np.round(np.random.rand(n_samples, n_qubits)).astype(\"int\").astype(\"bool\")\n", + "\n", + "\n", + "n_qubits = 40\n", + "bts_matrix = random_bitstrings(50_000_000, n_qubits)\n", + "\n", + "# We need to sort the bitstrings and just keep the unique ones\n", + "# NOTE: It is essential for the projection code to have the bitstrings sorted!\n", + "bts_matrix = sort_and_remove_duplicates(bts_matrix).astype(\"bool\")\n", + "\n", + "# Final subspace dimension after getting rid of duplicated bitstrings\n", + "d = bts_matrix.shape[0]\n", + "\n", + "print(\"Total number of unique bitstrings: \" + str(d))" + ] + }, + { + "cell_type": "markdown", + "id": "184bf287", + "metadata": {}, + "source": [ + "### Benchmark SQD Pauli projection functions\n", + "\n", + "The Pauli string under consideration is $\\sigma_z \\otimes ... \\otimes \\sigma_z$.\n", + "\n", + "Different subspace dimensions are considered by just slicing the matrix of bitstrings. We time the subspace projection for the different subspace sizes." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8fe182bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0 took 0.201246s\n", + "Iteration 1 took 0.348222s\n", + "Iteration 2 took 0.576333s\n", + "Iteration 3 took 0.78356s\n", + "Iteration 4 took 1.016162s\n", + "Iteration 5 took 1.305325s\n", + "Iteration 6 took 1.392751s\n", + "Iteration 7 took 1.632433s\n", + "Iteration 8 took 1.826521s\n", + "Iteration 9 took 2.02903s\n", + "Iteration 10 took 2.297458s\n", + "Iteration 11 took 2.588042s\n", + "Iteration 12 took 2.738746s\n", + "Iteration 13 took 2.906144s\n", + "Iteration 14 took 3.148833s\n", + "Iteration 15 took 3.323253s\n", + "Iteration 16 took 3.664171s\n", + "Iteration 17 took 3.680663s\n", + "Iteration 18 took 4.008313s\n", + "Iteration 19 took 4.173532s\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "from qiskit.quantum_info import Pauli\n", + "from qiskit_addon_sqd.qubit import matrix_elements_from_pauli\n", + "\n", + "pauli = Pauli(\"Z\" * n_qubits)\n", + "\n", + "# Different subspace sizes to test\n", + "d_list = np.linspace(d / 1000, d, 20).astype(\"int\")\n", + "\n", + "# To store the walltime\n", + "time_array = np.zeros(20)\n", + "\n", + "for i in range(20):\n", + " int_bts_matrix = bts_matrix[: d_list[i], :]\n", + " time_1 = time.time()\n", + " _ = matrix_elements_from_pauli(int_bts_matrix, pauli)\n", + " time_array[i] = time.time() - time_1\n", + " print(f\"Iteration {i} took {round(time_array[i], 6)}s\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9abb110f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Data for energies plot\n", + "x1 = d_list\n", + "y1 = time_array\n", + "\n", + "# Plot energies\n", + "plt.title(\"Runtime vs subspace dimension 40 qubits\")\n", + "plt.xlabel(\"Subspace dimension (millions)\")\n", + "plt.ylabel(\"Wall time [s]\")\n", + "plt.xticks([1e7, 2e7, 3e7, 4e7, 5e7], [str(i) for i in [10, 20, 30, 40, 50]])\n", + "plt.plot(x1, y1, marker=\".\", markersize=20)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8c486bc7", + "metadata": {}, + "source": [ + "Let's do the same for 60 qubits" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "359ed3f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of unique bitstrings: 50000000\n" + ] + } + ], + "source": [ + "n_qubits = 60\n", + "bts_matrix = random_bitstrings(50_000_000, n_qubits)\n", + "\n", + "# We need to sort the bitstrings and just keep the unique ones\n", + "bts_matrix = sort_and_remove_duplicates(bts_matrix).astype(\"bool\")\n", + "\n", + "# Final subspace dimension after getting rid of duplicated bitstrings\n", + "d = bts_matrix.shape[0]\n", + "\n", + "print(\"Total number of unique bitstrings: \" + str(d))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7bb0d8d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0 took 0.236567s\n", + "Iteration 1 took 0.424116s\n", + "Iteration 2 took 0.673399s\n", + "Iteration 3 took 0.905164s\n", + "Iteration 4 took 1.168936s\n", + "Iteration 5 took 1.454204s\n", + "Iteration 6 took 1.74778s\n", + "Iteration 7 took 1.920795s\n", + "Iteration 8 took 2.259994s\n", + "Iteration 9 took 2.550674s\n", + "Iteration 10 took 2.681287s\n", + "Iteration 11 took 3.04411s\n", + "Iteration 12 took 3.293262s\n", + "Iteration 13 took 3.471247s\n", + "Iteration 14 took 3.726639s\n", + "Iteration 15 took 4.072854s\n", + "Iteration 16 took 4.221037s\n", + "Iteration 17 took 4.498535s\n", + "Iteration 18 took 4.741108s\n", + "Iteration 19 took 5.159038s\n" + ] + } + ], + "source": [ + "pauli = Pauli(\"Z\" * n_qubits)\n", + "\n", + "# Different subspace sizes to test\n", + "d_list = np.linspace(d / 1000, d, 20).astype(\"int\")\n", + "\n", + "# It is better to do this once\n", + "row_array = np.arange(d)\n", + "\n", + "# To store the walltime\n", + "time_array = np.zeros(20)\n", + "\n", + "for i in range(20):\n", + " int_bts_matrix = bts_matrix[: d_list[i], :]\n", + " int_row_array = row_array[: d_list[i]]\n", + " time_1 = time.time()\n", + " _ = matrix_elements_from_pauli(int_bts_matrix, pauli)\n", + " time_array[i] = time.time() - time_1\n", + " print(f\"Iteration {i} took {round(time_array[i], 6)}s\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6b961c81", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data for energies plot\n", + "x1 = d_list\n", + "y1 = time_array\n", + "\n", + "fig, axs = plt.subplots(1, 1, figsize=(6, 6))\n", + "\n", + "# Plot energies\n", + "axs.plot(x1, y1, marker=\".\", markersize=20)\n", + "axs.set_title(\"Runtime vs subspace dimension 60 qubits\")\n", + "axs.set_xlabel(\"Subspace dimension (millions)\")\n", + "plt.xticks([1e7, 2e7, 3e7, 4e7, 5e7], [str(i) for i in [10, 20, 30, 40, 50]])\n", + "axs.set_ylabel(\"Wall time [s]\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/how_tos/choose_subspace_dimension.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/choose_subspace_dimension.ipynb new file mode 100644 index 00000000000..d832f333321 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/choose_subspace_dimension.ipynb @@ -0,0 +1,219 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", + "metadata": {}, + "source": [ + "# Bounding the subspace dimension\n", + "\n", + "In this tutorial, we will show the effect of the subspace dimension in the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068).\n", + "\n", + "***A priori***, we do not know what is the correct subspace dimension to obtain a target level of accuracy. However, we do know that increasing the subspace dimension increases the accuracy of the method. Therefore, we can study the accuracy of the predictions as a function of the subspace dimension." + ] + }, + { + "cell_type": "markdown", + "id": "a6755afb-ca1e-4473-974b-ba89acc8abce", + "metadata": {}, + "source": [ + "Specify the molecule and its properties." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "converged SCF energy = -108.835236570775\n", + "CASCI E = -109.046671778080 E(CI) = -32.8155692383188 S^2 = 0.0000000\n" + ] + } + ], + "source": [ + "import warnings\n", + "\n", + "import pyscf\n", + "import pyscf.cc\n", + "import pyscf.mcscf\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "# Specify molecule properties\n", + "open_shell = False\n", + "spin_sq = 0\n", + "\n", + "# Build N2 molecule\n", + "mol = pyscf.gto.Mole()\n", + "mol.build(\n", + " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", + " basis=\"6-31g\",\n", + " symmetry=\"Dooh\",\n", + ")\n", + "\n", + "# Define active space\n", + "n_frozen = 2\n", + "active_space = range(n_frozen, mol.nao_nr())\n", + "\n", + "# Get molecular integrals\n", + "scf = pyscf.scf.RHF(mol).run()\n", + "num_orbitals = len(active_space)\n", + "n_electrons = int(sum(scf.mo_occ[active_space]))\n", + "num_elec_a = (n_electrons + mol.spin) // 2\n", + "num_elec_b = (n_electrons - mol.spin) // 2\n", + "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", + "mo = cas.sort_mo(active_space, base=0)\n", + "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", + "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", + "\n", + "# Compute exact energy\n", + "exact_energy = cas.run().e_tot" + ] + }, + { + "cell_type": "markdown", + "id": "c58e988c-a109-44cd-a975-9df43250c318", + "metadata": {}, + "source": [ + "Generate some random bitstrings to proxy QPU samples." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e9506e0b-ed64-48bb-a97a-ef851b604af1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit_addon_sqd.counts import generate_bit_array_uniform\n", + "\n", + "# Create a seed to control randomness throughout this workflow\n", + "rng = np.random.default_rng(24)\n", + "\n", + "# Generate random samples\n", + "bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng)" + ] + }, + { + "cell_type": "markdown", + "id": "82b062a3-d4ca-41e2-9b00-4a394f83cbdd", + "metadata": {}, + "source": [ + "Call SQD with increasing batch sizes." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e0847f28", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_sqd.fermion import diagonalize_fermionic_hamiltonian\n", + "\n", + "list_samples_per_batch = [50, 200, 400, 600]\n", + "\n", + "# SQD options\n", + "max_iterations = 5\n", + "\n", + "# Eigenstate solver options\n", + "num_batches = 10\n", + "max_davidson_cycles = 200\n", + "\n", + "energies = []\n", + "subspace_dimensions = []\n", + "\n", + "for samples_per_batch in list_samples_per_batch:\n", + " result = diagonalize_fermionic_hamiltonian(\n", + " hcore,\n", + " eri,\n", + " bit_array,\n", + " samples_per_batch=samples_per_batch,\n", + " norb=num_orbitals,\n", + " nelec=(num_elec_a, num_elec_b),\n", + " num_batches=num_batches,\n", + " max_iterations=max_iterations,\n", + " symmetrize_spin=True,\n", + " seed=rng,\n", + " )\n", + " energies.append(result.energy)\n", + " subspace_dimensions.append(np.prod(result.sci_state.amplitudes.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "9d78906b-4759-4506-9c69-85d4e67766b3", + "metadata": {}, + "source": [ + "This plot shows that increasing the subspace dimension leads to more accurate results." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "caffd888-e89c-4aa9-8bae-4d1bb723b35e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Data for energies plot\n", + "x1 = subspace_dimensions\n", + "y1 = np.array(energies) + nuclear_repulsion_energy\n", + "\n", + "fig, axs = plt.subplots(1, 1, figsize=(12, 6))\n", + "\n", + "# Plot energies\n", + "axs.plot(x1, y1, marker=\".\", markersize=20, label=\"Estimated\")\n", + "axs.set_xticks(x1)\n", + "axs.set_xticklabels(x1)\n", + "axs.axhline(y=exact_energy, color=\"red\", linestyle=\"--\", label=\"Exact\")\n", + "axs.set_title(\"Approximated Ground State Energy vs subspace dimension\")\n", + "axs.set_xlabel(\"Subspace dimension\")\n", + "axs.set_ylabel(\"Energy (Ha)\")\n", + "axs.legend()\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/how_tos/integrate_dice_solver.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/integrate_dice_solver.ipynb new file mode 100644 index 00000000000..5e71994df7a --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/integrate_dice_solver.ipynb @@ -0,0 +1,135 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "65adf860-4443-494f-ad90-5d8ea444ac4e", + "metadata": {}, + "source": [ + "# Scale SQD chemistry workflows with Dice solver\n", + "\n", + "For information on how to install and use ``qiskit-addon-dice-solver``, [visit the docs](/docs/addons/qiskit-addon-dice-solver).\n", + "\n", + "For more details on the SQD code used in this example, check out [tutorial 1](/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "baf7d074-4443-4695-875e-65f7fb8cef25", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "converged SCF energy = -108.835236570774\n", + "CASCI E = -109.046671778080 E(CI) = -32.8155692383187 S^2 = 0.0000000\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pyscf\n", + "import pyscf.cc\n", + "import pyscf.mcscf\n", + "from qiskit_addon_dice_solver import solve_sci_batch\n", + "from qiskit_addon_sqd.counts import generate_bit_array_uniform\n", + "from qiskit_addon_sqd.fermion import diagonalize_fermionic_hamiltonian\n", + "\n", + "# Specify molecule properties\n", + "num_orbitals = 16\n", + "num_elec_a = num_elec_b = 5\n", + "spin_sq = 0\n", + "\n", + "# Build N2 molecule\n", + "mol = pyscf.gto.Mole()\n", + "mol.build(\n", + " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", + " basis=\"6-31g\",\n", + " symmetry=\"Dooh\",\n", + ")\n", + "\n", + "# Define active space\n", + "n_frozen = 2\n", + "active_space = range(n_frozen, mol.nao_nr())\n", + "\n", + "# Get molecular integrals\n", + "scf = pyscf.scf.RHF(mol).run()\n", + "num_orbitals = len(active_space)\n", + "n_electrons = int(sum(scf.mo_occ[active_space]))\n", + "num_elec_a = (n_electrons + mol.spin) // 2\n", + "num_elec_b = (n_electrons - mol.spin) // 2\n", + "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", + "mo = cas.sort_mo(active_space, base=0)\n", + "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", + "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", + "\n", + "# Compute exact energy\n", + "exact_energy = cas.run().e_tot\n", + "\n", + "# Create a seed to control randomness throughout this workflow\n", + "rng = np.random.default_rng(24)\n", + "\n", + "\n", + "# Generate random samples\n", + "bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng)\n", + "\n", + "# Run SQD\n", + "result = diagonalize_fermionic_hamiltonian(\n", + " hcore,\n", + " eri,\n", + " bit_array,\n", + " samples_per_batch=300,\n", + " norb=num_orbitals,\n", + " nelec=(num_elec_a, num_elec_b),\n", + " num_batches=5,\n", + " max_iterations=5,\n", + " sci_solver=solve_sci_batch,\n", + " symmetrize_spin=True,\n", + " seed=rng,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "99709a88-f9ec-4dbc-a561-e682f90aa4c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exact energy: -109.04667177808028\n", + "Estimated energy: -109.03402667558743\n" + ] + } + ], + "source": [ + "print(f\"Exact energy: {exact_energy}\")\n", + "print(f\"Estimated energy: {result.energy + nuclear_repulsion_energy}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb new file mode 100644 index 00000000000..a41624b2ad1 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb @@ -0,0 +1,293 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "21a1d83f-183f-4da9-bb8c-71400120fd31", + "metadata": {}, + "source": [ + "# Project Pauli operators onto Hilbert subspaces" + ] + }, + { + "cell_type": "markdown", + "id": "c961d48a", + "metadata": {}, + "source": [ + "We show different ways of projecting a weighted linear combination of pauli strings\n", + "into a subspace defined by a subset of size $d$ computational basis states. The\n", + "projected operator is stored as a $d \\times d$ ``scipy.sparse.coo_matrix``.\n", + "\n", + "As an example we consider the Hamiltonian of the 1D Heisenberg model with periodic\n", + "boundary conditions and $L = 22$ spins:\n", + "$$\n", + "H = \\sum_{\\langle i, j \\rangle}\\left( \\sigma^x_i\\sigma^x_j + \\sigma^y_i\\sigma^y_j + \\sigma^z_i\\sigma^z_j \\right).\n", + "$$\n", + "\n", + "This package provides two tools to perform this projection:\n", + "\n", + "- ``qiskit_addon_sqd.qubit.matrix_elements_from_pauli()``: is a lower-level function\n", + "that returns the non-zero matrix elements of a Pauli string and the corresponding indices\n", + "of the non-zero elements.\n", + "\n", + "- ``qiskit_addon_sqd.qubit.project_operator_to_subspace()``: is a higher-level function that\n", + "directly returns a ``scipy.sparse`` matrix.\n", + "\n", + "This notebook shows how to use these two tools to produce the same sparse operator." + ] + }, + { + "cell_type": "markdown", + "id": "6c87d5e5", + "metadata": {}, + "source": [ + "### Subspace definition\n", + "\n", + "For this example we just generate length-22 random bitstrings. For the projection\n", + "functions to work as expected, it is essential that the bitstrings that define\n", + "the subspace are unique and sorted in ascending order according to their unsigned\n", + "integer representation.\n", + "\n", + "This can be achieved with the ``qiskit_addon_sqd.qubit.sort_and_remove_duplicates()``\n", + "function." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7b94f840", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subspace dimension: 49718\n", + "Full Hilbert space dimension: 4194304\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from qiskit_addon_sqd.qubit import sort_and_remove_duplicates\n", + "\n", + "num_spins = 22\n", + "rand_seed = 22\n", + "np.random.seed(rand_seed)\n", + "\n", + "\n", + "def random_bitstrings(n_samples, n_qubits):\n", + " return np.round(np.random.rand(n_samples, n_qubits)).astype(\"int\").astype(\"bool\")\n", + "\n", + "\n", + "bitstring_matrix = random_bitstrings(50_000, num_spins)\n", + "\n", + "# NOTE: It is essential for the projection code to have the bitstrings sorted!\n", + "bitstring_matrix = sort_and_remove_duplicates(bitstring_matrix)\n", + "\n", + "print(\"Subspace dimension: \" + str(bitstring_matrix.shape[0]))\n", + "print(\"Full Hilbert space dimension: \" + str(2**num_spins))" + ] + }, + { + "cell_type": "markdown", + "id": "5707b93c", + "metadata": {}, + "source": [ + "### First method: nonzero matrix elements and indices." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "74c16c91-cc5c-46ce-aff8-17d0e71ac50f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIXX', 'IIIIIIIIIIIIIIIIIIIIYY', 'IIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIXXII', 'IIIIIIIIIIIIIIIIIIYYII', 'IIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIXXIIII', 'IIIIIIIIIIIIIIIIYYIIII', 'IIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIXXIIIIII', 'IIIIIIIIIIIIIIYYIIIIII', 'IIIIIIIIIIIIIIZZIIIIII', 'IIIIIIIIIIIIXXIIIIIIII', 'IIIIIIIIIIIIYYIIIIIIII', 'IIIIIIIIIIIIZZIIIIIIII', 'IIIIIIIIIIXXIIIIIIIIII', 'IIIIIIIIIIYYIIIIIIIIII', 'IIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIXXIIIIIIIIIIII', 'IIIIIIIIYYIIIIIIIIIIII', 'IIIIIIIIZZIIIIIIIIIIII', 'IIIIIIXXIIIIIIIIIIIIII', 'IIIIIIYYIIIIIIIIIIIIII', 'IIIIIIZZIIIIIIIIIIIIII', 'IIIIXXIIIIIIIIIIIIIIII', 'IIIIYYIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIII', 'IIXXIIIIIIIIIIIIIIIIII', 'IIYYIIIIIIIIIIIIIIIIII', 'IIZZIIIIIIIIIIIIIIIIII', 'XXIIIIIIIIIIIIIIIIIIII', 'YYIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIII', 'XIIIIIIIIIIIIIIIIIIIIX', 'YIIIIIIIIIIIIIIIIIIIIY', 'ZIIIIIIIIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIIIIIIIXXI', 'IIIIIIIIIIIIIIIIIIIYYI', 'IIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIXXIII', 'IIIIIIIIIIIIIIIIIYYIII', 'IIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIXXIIIII', 'IIIIIIIIIIIIIIIYYIIIII', 'IIIIIIIIIIIIIIIZZIIIII', 'IIIIIIIIIIIIIXXIIIIIII', 'IIIIIIIIIIIIIYYIIIIIII', 'IIIIIIIIIIIIIZZIIIIIII', 'IIIIIIIIIIIXXIIIIIIIII', 'IIIIIIIIIIIYYIIIIIIIII', 'IIIIIIIIIIIZZIIIIIIIII', 'IIIIIIIIIXXIIIIIIIIIII', 'IIIIIIIIIYYIIIIIIIIIII', 'IIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIXXIIIIIIIIIIIII', 'IIIIIIIYYIIIIIIIIIIIII', 'IIIIIIIZZIIIIIIIIIIIII', 'IIIIIXXIIIIIIIIIIIIIII', 'IIIIIYYIIIIIIIIIIIIIII', 'IIIIIZZIIIIIIIIIIIIIII', 'IIIXXIIIIIIIIIIIIIIIII', 'IIIYYIIIIIIIIIIIIIIIII', 'IIIZZIIIIIIIIIIIIIIIII', 'IXXIIIIIIIIIIIIIIIIIII', 'IYYIIIIIIIIIIIIIIIIIII', 'IZZIIIIIIIIIIIIIIIIIII'],\n", + " coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n", + " 1.+0.j, 1.+0.j, 1.+0.j])\n" + ] + } + ], + "source": [ + "from qiskit.transpiler import CouplingMap\n", + "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n", + "\n", + "coupling_map = CouplingMap.from_ring(num_spins)\n", + "hamiltonian = generate_xyz_hamiltonian(coupling_map)\n", + "print(hamiltonian)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f31f5e40", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_sqd.qubit import matrix_elements_from_pauli\n", + "from scipy.sparse import coo_matrix\n", + "\n", + "d = bitstring_matrix.shape[0]\n", + "\n", + "# Initialize the coo_matrix object\n", + "operator_from_matrix_elements = coo_matrix((d, d), dtype=\"complex128\")\n", + "\n", + "for pauli in hamiltonian.paulis:\n", + " matrix_elements, row_indices, col_indices = matrix_elements_from_pauli(bitstring_matrix, pauli)\n", + " operator_from_matrix_elements += coo_matrix(\n", + " (matrix_elements, (row_indices, col_indices)), (d, d)\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "4479af24", + "metadata": {}, + "source": [ + "### Higher-level implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e58763df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Projecting term 1 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIXX ...\n", + "Projecting term 2 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIYY ...\n", + "Projecting term 3 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIZZ ...\n", + "Projecting term 4 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIXXII ...\n", + "Projecting term 5 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIYYII ...\n", + "Projecting term 6 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIZZII ...\n", + "Projecting term 7 out of 66: (1+0j) * IIIIIIIIIIIIIIIIXXIIII ...\n", + "Projecting term 8 out of 66: (1+0j) * IIIIIIIIIIIIIIIIYYIIII ...\n", + "Projecting term 9 out of 66: (1+0j) * IIIIIIIIIIIIIIIIZZIIII ...\n", + "Projecting term 10 out of 66: (1+0j) * IIIIIIIIIIIIIIXXIIIIII ...\n", + "Projecting term 11 out of 66: (1+0j) * IIIIIIIIIIIIIIYYIIIIII ...\n", + "Projecting term 12 out of 66: (1+0j) * IIIIIIIIIIIIIIZZIIIIII ...\n", + "Projecting term 13 out of 66: (1+0j) * IIIIIIIIIIIIXXIIIIIIII ...\n", + "Projecting term 14 out of 66: (1+0j) * IIIIIIIIIIIIYYIIIIIIII ...\n", + "Projecting term 15 out of 66: (1+0j) * IIIIIIIIIIIIZZIIIIIIII ...\n", + "Projecting term 16 out of 66: (1+0j) * IIIIIIIIIIXXIIIIIIIIII ...\n", + "Projecting term 17 out of 66: (1+0j) * IIIIIIIIIIYYIIIIIIIIII ...\n", + "Projecting term 18 out of 66: (1+0j) * IIIIIIIIIIZZIIIIIIIIII ...\n", + "Projecting term 19 out of 66: (1+0j) * IIIIIIIIXXIIIIIIIIIIII ...\n", + "Projecting term 20 out of 66: (1+0j) * IIIIIIIIYYIIIIIIIIIIII ...\n", + "Projecting term 21 out of 66: (1+0j) * IIIIIIIIZZIIIIIIIIIIII ...\n", + "Projecting term 22 out of 66: (1+0j) * IIIIIIXXIIIIIIIIIIIIII ...\n", + "Projecting term 23 out of 66: (1+0j) * IIIIIIYYIIIIIIIIIIIIII ...\n", + "Projecting term 24 out of 66: (1+0j) * IIIIIIZZIIIIIIIIIIIIII ...\n", + "Projecting term 25 out of 66: (1+0j) * IIIIXXIIIIIIIIIIIIIIII ...\n", + "Projecting term 26 out of 66: (1+0j) * IIIIYYIIIIIIIIIIIIIIII ...\n", + "Projecting term 27 out of 66: (1+0j) * IIIIZZIIIIIIIIIIIIIIII ...\n", + "Projecting term 28 out of 66: (1+0j) * IIXXIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 29 out of 66: (1+0j) * IIYYIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 30 out of 66: (1+0j) * IIZZIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 31 out of 66: (1+0j) * XXIIIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 32 out of 66: (1+0j) * YYIIIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 33 out of 66: (1+0j) * ZZIIIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 34 out of 66: (1+0j) * XIIIIIIIIIIIIIIIIIIIIX ...\n", + "Projecting term 35 out of 66: (1+0j) * YIIIIIIIIIIIIIIIIIIIIY ...\n", + "Projecting term 36 out of 66: (1+0j) * ZIIIIIIIIIIIIIIIIIIIIZ ...\n", + "Projecting term 37 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIXXI ...\n", + "Projecting term 38 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIYYI ...\n", + "Projecting term 39 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIZZI ...\n", + "Projecting term 40 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIXXIII ...\n", + "Projecting term 41 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIYYIII ...\n", + "Projecting term 42 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIZZIII ...\n", + "Projecting term 43 out of 66: (1+0j) * IIIIIIIIIIIIIIIXXIIIII ...\n", + "Projecting term 44 out of 66: (1+0j) * IIIIIIIIIIIIIIIYYIIIII ...\n", + "Projecting term 45 out of 66: (1+0j) * IIIIIIIIIIIIIIIZZIIIII ...\n", + "Projecting term 46 out of 66: (1+0j) * IIIIIIIIIIIIIXXIIIIIII ...\n", + "Projecting term 47 out of 66: (1+0j) * IIIIIIIIIIIIIYYIIIIIII ...\n", + "Projecting term 48 out of 66: (1+0j) * IIIIIIIIIIIIIZZIIIIIII ...\n", + "Projecting term 49 out of 66: (1+0j) * IIIIIIIIIIIXXIIIIIIIII ...\n", + "Projecting term 50 out of 66: (1+0j) * IIIIIIIIIIIYYIIIIIIIII ...\n", + "Projecting term 51 out of 66: (1+0j) * IIIIIIIIIIIZZIIIIIIIII ...\n", + "Projecting term 52 out of 66: (1+0j) * IIIIIIIIIXXIIIIIIIIIII ...\n", + "Projecting term 53 out of 66: (1+0j) * IIIIIIIIIYYIIIIIIIIIII ...\n", + "Projecting term 54 out of 66: (1+0j) * IIIIIIIIIZZIIIIIIIIIII ...\n", + "Projecting term 55 out of 66: (1+0j) * IIIIIIIXXIIIIIIIIIIIII ...\n", + "Projecting term 56 out of 66: (1+0j) * IIIIIIIYYIIIIIIIIIIIII ...\n", + "Projecting term 57 out of 66: (1+0j) * IIIIIIIZZIIIIIIIIIIIII ...\n", + "Projecting term 58 out of 66: (1+0j) * IIIIIXXIIIIIIIIIIIIIII ...\n", + "Projecting term 59 out of 66: (1+0j) * IIIIIYYIIIIIIIIIIIIIII ...\n", + "Projecting term 60 out of 66: (1+0j) * IIIIIZZIIIIIIIIIIIIIII ...\n", + "Projecting term 61 out of 66: (1+0j) * IIIXXIIIIIIIIIIIIIIIII ...\n", + "Projecting term 62 out of 66: (1+0j) * IIIYYIIIIIIIIIIIIIIIII ...\n", + "Projecting term 63 out of 66: (1+0j) * IIIZZIIIIIIIIIIIIIIIII ...\n", + "Projecting term 64 out of 66: (1+0j) * IXXIIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 65 out of 66: (1+0j) * IYYIIIIIIIIIIIIIIIIIII ...\n", + "Projecting term 66 out of 66: (1+0j) * IZZIIIIIIIIIIIIIIIIIII ...\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.qubit import project_operator_to_subspace\n", + "\n", + "operator = project_operator_to_subspace(bitstring_matrix, hamiltonian, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "id": "1fce97a2", + "metadata": {}, + "source": [ + "Check that both implementations yield the same coo_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4bf56509", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0j\n" + ] + } + ], + "source": [ + "print((operator.power(2) - operator_from_matrix_elements.power(2)).sum())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/how_tos/select_open_closed_shell.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/select_open_closed_shell.ipynb new file mode 100644 index 00000000000..7ca123516fe --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/select_open_closed_shell.ipynb @@ -0,0 +1,531 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "72420c62-2716-4f64-ad5b-73394b481cc1", + "metadata": {}, + "source": [ + "# Understand open-shell vs closed-shell options and its effect in the subspace construction\n", + "\n", + "In this \"how-to\", we will show how to choose subpace dimensions in the `qiskit_addon_sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068).\n", + "\n", + "More importantly, this \"how-to\" also highlights some differences in the behaviour in the susbapce construction when run in `open_shell = False` or `open_shell = True` modes:\n", + "\n", + "- `open_shell = False` only works when the number of spin-up and spin-down electrons is the same.\n", + "\n", + "- `open_shell = True` must be used when the number of spin-up and spin-down electrons is different. It can also be used when the number of spin-up and spin-down electrons is the same. However, in this last case, there is a difference in the sizes of the subspaces generated between `open_shell = False` and `open_shell = True`, as discussed in this notebook.\n", + "\n", + "**NOTE:** Some of the electronic-configuration (bitstring) manipulations in this package have as a goal to preserve the total spin symmetry $S^2$. Standard Selected Counfiguration Interaction (SCI) solvers cannot impose $S^2$ conservation exactly. Consequently, they do so approximately via a Lagrange multiplier.\n", + "\n", + "The choice of electronic configurations entering the eigenstate solver can also have a strong effect in the conservation of spin. For example, in a (2-electron,2-orbital) system, one may sample the configuration $|1001\\rangle$ (having a single spin-up excitation over the RHF state $|0101\\rangle$) which is a linear combination of the open-shell singlet and triplet states, respectively $(|1001\\rangle ± |0110\\rangle) /\\sqrt{2}$. If the configuration |0110⟩ is not sampled, one can construct neither eigenfunction of total spin, leading to spin contamination or redundancy (i.e. the configuration |1001⟩ is involved in a CI calculation, but has coefficient 0 in the CI vector). Consider that a single sample $|1001\\rangle$ is generated in the quantum computer, this is how the `sqd` package handles this situation:\n", + "\n", + "- `open_shell = False`:\n", + "\n", + " 1. The $1001$ bitstring is split in half, representing spin-up and spin-down configurations: $10$ (up) and $01$ (down).\n", + "\n", + " 2. The list of unique spin-polarized configurations is constructed: $\\mathcal{U} = [01, 10]$.\n", + "\n", + " 3. We then consider all possible combinations of $\\mathcal{U}$ elements to form the basis: $\\left \\{ |0101\\rangle, |0110\\rangle , |1001\\rangle , |1010\\rangle \\right \\}$, which contains the singlet and triplet states.\n", + "\n", + "\n", + "- `open_shell = True`:\n", + "\n", + " 1. Contrary to the `open_shell = False` case, we do not combine the halves of the bitstring to form the basis." + ] + }, + { + "cell_type": "markdown", + "id": "a6755afb-ca1e-4473-974b-ba89acc8abce", + "metadata": {}, + "source": [ + "### Closed-Shell\n", + "\n", + "This example shows how the bitstrings are manipulated in a (2-electron, 4-orbital) system." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", + "metadata": {}, + "outputs": [], + "source": [ + "# Specify molecule properties\n", + "num_orbitals = 4\n", + "num_elec_a = num_elec_b = 1\n", + "open_shell = False" + ] + }, + { + "cell_type": "markdown", + "id": "c58e988c-a109-44cd-a975-9df43250c318", + "metadata": {}, + "source": [ + "Specify by hand a dictionary of measurement outcomes" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e9506e0b-ed64-48bb-a97a-ef851b604af1", + "metadata": {}, + "outputs": [], + "source": [ + "counts_dict = {\"00010010\": 1 / 2.0 - 0.01, \"01001000\": 1 / 2.0 - 0.01, \"00010001\": 0.02}" + ] + }, + { + "cell_type": "markdown", + "id": "851bc98e-9c08-4e78-9472-36301abc11d8", + "metadata": {}, + "source": [ + "Transform the counts dict into a bitstring matrix and probability array for post-processing" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7a102a7f-aae6-4583-ab82-ae40fcb5496a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[False False False True False False True False]\n", + " [False True False False True False False False]\n", + " [False False False True False False False True]]\n", + "[0.49 0.49 0.02]\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.counts import counts_to_arrays\n", + "\n", + "# Convert counts into bitstring and probability arrays\n", + "bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict)\n", + "print(bitstring_matrix_full)\n", + "print(probs_arr_full)" + ] + }, + { + "cell_type": "markdown", + "id": "eb704101-0fe8-4d12-b572-b1d844e35a90", + "metadata": {}, + "source": [ + "Subsample a single batch of size two:\n", + "\n", + "- ``n_batches = 1``: Number of batches of configurations used by the different calls to the eigenstate solver\n", + "- ``samples_per_batch = 2``: Number of unique configurations to include in each batch" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fe60aee2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[False False False True False False True False]\n", + " [False True False False True False False False]]\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.subsampling import postselect_and_subsample\n", + "\n", + "n_batches = 1\n", + "samples_per_batch = 2\n", + "\n", + "# seed for random number generator\n", + "rand_seed = 48\n", + "\n", + "# Generate the batches\n", + "batches = postselect_and_subsample(\n", + " bitstring_matrix_full,\n", + " probs_arr_full,\n", + " hamming_right=num_elec_a,\n", + " hamming_left=num_elec_b,\n", + " samples_per_batch=samples_per_batch,\n", + " num_batches=n_batches,\n", + " rand_seed=rand_seed,\n", + ")\n", + "\n", + "print(batches[0])" + ] + }, + { + "cell_type": "markdown", + "id": "93a6d05a", + "metadata": {}, + "source": [ + "Obtain decimal representation of the spin-up and spin-down bitstrings used by the eigenstate solver\n", + "\n", + "The fist element in the tuple corresponds to the decimal representation of the spin-up configurations, while the second element in the tuple corresponds to the decimal representation of the spin-down configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ef90e039", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([1, 2, 4, 8], dtype=int64), array([1, 2, 4, 8], dtype=int64))\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.fermion import bitstring_matrix_to_ci_strs\n", + "\n", + "ci_strs = bitstring_matrix_to_ci_strs(batches[0], open_shell=open_shell)\n", + "print(ci_strs)" + ] + }, + { + "cell_type": "markdown", + "id": "70d7883c", + "metadata": {}, + "source": [ + "Note that while the number of samples per batch is 2, and the sampled bitstrings are: $00010010$ and $01001000$, four electronic configurations are generated per spin-species. In this case, the set of unique spin-polarized configurations is given by:\n", + "$$\n", + "\\mathcal{U} = \\{ 0001, 0010, 0100, 1000 \\}\n", + "$$\n", + "whose base-10 decimal representation is\n", + "$$\n", + "\\mathcal{U}_{10} = \\{ 1, 2, 4, 8 \\}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "03b32ed5", + "metadata": {}, + "source": [ + "Basis of the subspace:\n", + "\n", + "The eigenstate solver takes all possible pairs of spin-up and spin-down bitstrings to construnct the basis $\\mathcal{B}$ of the subspace:\n", + "\n", + "- Element 1: $|00010001\\rangle$\n", + "\n", + "- Element 2: $|00010010\\rangle$\n", + "\n", + "- Element 3: $|00010100\\rangle$\n", + "\n", + "- Element 4: $|00011000\\rangle$\n", + "\n", + "- Element 5: $|00100001\\rangle$\n", + "\n", + "- Element 6: $|00100010\\rangle$\n", + "\n", + "- Element 7: $|00100100\\rangle$\n", + "\n", + "- Element 8: $|00101000\\rangle$\n", + "\n", + "- Element 9: $|01000001\\rangle$\n", + "\n", + "- Element 10: $|01000010\\rangle$\n", + "\n", + "- Element 11: $|01000100\\rangle$\n", + "\n", + "- Element 12: $|01001000\\rangle$\n", + "\n", + "- Element 13: $|10000001\\rangle$\n", + "\n", + "- Element 14: $|10000010\\rangle$\n", + "\n", + "- Element 15: $|10000100\\rangle$\n", + "\n", + "- Element 16: $|10001000\\rangle$" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "11c924ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Basis elements of the subspace:\n", + "|00010001>\n", + "|00010010>\n", + "|00010100>\n", + "|00011000>\n", + "|00100001>\n", + "|00100010>\n", + "|00100100>\n", + "|00101000>\n", + "|01000001>\n", + "|01000010>\n", + "|01000100>\n", + "|01001000>\n", + "|10000001>\n", + "|10000010>\n", + "|10000100>\n", + "|10001000>\n" + ] + } + ], + "source": [ + "ci_strs_up, ci_strs_dn = ci_strs\n", + "\n", + "print(\"Basis elements of the subspace:\")\n", + "\n", + "for ci_str_up in ci_strs_up:\n", + " for ci_str_dn in ci_strs_dn:\n", + " format_name = \"{0:0\" + str(num_orbitals) + \"b}\"\n", + " print(\"|\" + format_name.format(ci_str_up) + format_name.format(ci_str_dn) + \">\")" + ] + }, + { + "cell_type": "markdown", + "id": "aa43a4fc", + "metadata": {}, + "source": [ + "**The subspace dimension is upper-bounded by**: $2 \\cdot$ (`samples_per_batch`)$^2$" + ] + }, + { + "cell_type": "markdown", + "id": "9412e52b", + "metadata": {}, + "source": [ + "### Open-Shell\n", + "\n", + "This example shows how the bitstrings are manipulated in a (2-electron, 4-orbital) system." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9b515b8a", + "metadata": {}, + "outputs": [], + "source": [ + "# Specify molecule properties\n", + "num_orbitals = 4\n", + "num_elec_a = num_elec_b = 1\n", + "open_shell = True" + ] + }, + { + "cell_type": "markdown", + "id": "9ef78560", + "metadata": {}, + "source": [ + "Specify by hand a dictionary of measurement outcomes" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "06b2185a", + "metadata": {}, + "outputs": [], + "source": [ + "counts_dict = {\"00010010\": 1 / 2.0 - 0.01, \"01001000\": 1 / 2.0 - 0.01, \"00010001\": 0.02}" + ] + }, + { + "cell_type": "markdown", + "id": "08b32957", + "metadata": {}, + "source": [ + "Transform the counts dict into a bitstring matrix and probability array for post-processing" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "90561893", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[False False False True False False True False]\n", + " [False True False False True False False False]\n", + " [False False False True False False False True]]\n", + "[0.49 0.49 0.02]\n" + ] + } + ], + "source": [ + "# Convert counts into bitstring and probability arrays\n", + "bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict)\n", + "print(bitstring_matrix_full)\n", + "print(probs_arr_full)" + ] + }, + { + "cell_type": "markdown", + "id": "416bfb6c", + "metadata": {}, + "source": [ + "Subsample a single batch of size two:\n", + "\n", + "- ``n_batches = 1``: Number of batches of configurations used by the different calls to the eigenstate solver\n", + "- ``samples_per_batch = 2``: Number of unique configurations to include in each batch" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cf4fe11d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[False False False True False False True False]\n", + " [False True False False True False False False]]\n" + ] + } + ], + "source": [ + "n_batches = 1\n", + "samples_per_batch = 2\n", + "\n", + "# seed for random number generator\n", + "rand_seed = 48\n", + "\n", + "# Generate the batches\n", + "batches = postselect_and_subsample(\n", + " bitstring_matrix_full,\n", + " probs_arr_full,\n", + " hamming_right=num_elec_a,\n", + " hamming_left=num_elec_b,\n", + " samples_per_batch=samples_per_batch,\n", + " num_batches=n_batches,\n", + " rand_seed=rand_seed,\n", + ")\n", + "\n", + "print(batches[0])" + ] + }, + { + "cell_type": "markdown", + "id": "54d699ca", + "metadata": {}, + "source": [ + "Obtain decimal representation of the spin-up and spin-down bitstrings used by the eigenstate solver\n", + "\n", + "The fist element in the tuple corresponds to the decimal representation of the spin-up configurations, while the second element in the tuple corresponds to the decimal representation of the spin-down configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b40b049b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([2, 8], dtype=int64), array([1, 4], dtype=int64))\n" + ] + } + ], + "source": [ + "ci_strs = bitstring_matrix_to_ci_strs(batches[0], open_shell=open_shell)\n", + "print(ci_strs)" + ] + }, + { + "cell_type": "markdown", + "id": "28921e56", + "metadata": {}, + "source": [ + "If we specify that `open_shell = True`, now we do not include all unique half-bitstrings as spin-up and spin-down configurations, thus yielding a smaller basis as when specifying `open_shell = False`" + ] + }, + { + "cell_type": "markdown", + "id": "e1959b72", + "metadata": {}, + "source": [ + "Basis of the subspace:\n", + "\n", + "The eigenstate solver takes all possible pairs of spin-up and spin-down bitstrings to construnct the basis $\\mathcal{B}$ of the subspace:\n", + "\n", + "- Element 1: $|00010010\\rangle$\n", + "\n", + "- Element 2: $|00011000\\rangle$\n", + "\n", + "- Element 3: $|01000010\\rangle$\n", + "\n", + "- Element 4: $|01001000\\rangle$" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a550aba2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Basis elements of the subspace:\n", + "|00100001>\n", + "|00100100>\n", + "|10000001>\n", + "|10000100>\n" + ] + } + ], + "source": [ + "ci_strs_up, ci_strs_dn = ci_strs\n", + "\n", + "print(\"Basis elements of the subspace:\")\n", + "\n", + "for ci_str_up in ci_strs_up:\n", + " for ci_str_dn in ci_strs_dn:\n", + " format_name = \"{0:0\" + str(num_orbitals) + \"b}\"\n", + " print(\"|\" + format_name.format(ci_str_up) + format_name.format(ci_str_dn) + \">\")" + ] + }, + { + "cell_type": "markdown", + "id": "7317bc49", + "metadata": {}, + "source": [ + "**The subspace dimension is upper-bounded by**: (`samples_per_batch`)$^2$" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb b/docs/addons/qiskit-addon-sqd/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb new file mode 100644 index 00000000000..49b8965d9cf --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb @@ -0,0 +1,384 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", + "metadata": {}, + "source": [ + "# Optimize Hamiltonian basis with orbital optimization\n", + "\n", + "In this tutorial, we will show how to use the `sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068) and then further optimize the ground state approximation using orbital optimization\n", + "\n", + "Refer to [Sec. II A 4](https://arxiv.org/pdf/2405.05068) for a more detailed discussion on this technique." + ] + }, + { + "cell_type": "markdown", + "id": "a6755afb-ca1e-4473-974b-ba89acc8abce", + "metadata": {}, + "source": [ + "### Specify the molecule and generate samples\n", + "\n", + "In this example, we will approximate the ground state energy of an $N_2$ molecule and then improve the answer using orbital optimization. This guide studies $N_2$ at equilibrium, which is mean-field dominated. This means the MO basis is already a good choice for our integrals; therefore, we will rotate our integrals **out** of the MO basis in order to illustrate the effects of orbital optimization." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "import numpy as np\n", + "import pyscf\n", + "import pyscf.cc\n", + "import pyscf.mcscf\n", + "from qiskit_addon_sqd.fermion import rotate_integrals\n", + "\n", + "# Specify molecule properties\n", + "open_shell = False\n", + "spin_sq = 0\n", + "\n", + "# Build N2 molecule\n", + "mol = pyscf.gto.Mole()\n", + "mol.build(\n", + " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", + " basis=\"6-31g\",\n", + " symmetry=\"Dooh\",\n", + ")\n", + "\n", + "# Define active space\n", + "n_frozen = 2\n", + "active_space = range(n_frozen, mol.nao_nr())\n", + "\n", + "# Get molecular integrals\n", + "scf = pyscf.scf.RHF(mol).run()\n", + "num_orbitals = len(active_space)\n", + "n_electrons = int(sum(scf.mo_occ[active_space]))\n", + "num_elec_a = (n_electrons + mol.spin) // 2\n", + "num_elec_b = (n_electrons - mol.spin) // 2\n", + "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", + "mo = cas.sort_mo(active_space, base=0)\n", + "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", + "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", + "\n", + "# Compute exact energy\n", + "exact_energy = cas.run().e_tot" + ] + }, + { + "cell_type": "markdown", + "id": "b23a74fc-708c-4bd0-af4e-c9cd189b6346", + "metadata": {}, + "source": [ + "The MO basis is already a good basis for this problem, so we will rotate out of that basis in this guide in order to highlight the effect of orbital optimization." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9f46d1c6-3b49-45ad-b9ed-972bd58f7e3c", + "metadata": {}, + "outputs": [], + "source": [ + "# Rotate our integrals out of MO basis\n", + "rng = np.random.default_rng(24)\n", + "num_params = (num_orbitals**2 - num_orbitals) // 2 # antisymmetric, specified by upper triangle\n", + "k_rot = (rng.random(num_params) - 0.5) * 0.5\n", + "hcore_rot, eri_rot = rotate_integrals(hcore, eri, k_rot)" + ] + }, + { + "cell_type": "markdown", + "id": "c58e988c-a109-44cd-a975-9df43250c318", + "metadata": {}, + "source": [ + "Generate samples" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e9506e0b-ed64-48bb-a97a-ef851b604af1", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_sqd.counts import counts_to_arrays, generate_counts_uniform\n", + "\n", + "# Generate random samples\n", + "counts_dict = generate_counts_uniform(10_000, num_orbitals * 2, rand_seed=rng)\n", + "\n", + "# Convert counts into bitstring and probability arrays\n", + "bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "eb704101-0fe8-4d12-b572-b1d844e35a90", + "metadata": {}, + "source": [ + "### Iteratively refine the samples using SQD and approximate the ground state" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b72c048e-fe8e-4fc2-b28b-03138249074e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting configuration recovery iteration 0\n", + "Starting configuration recovery iteration 1\n", + "Starting configuration recovery iteration 2\n", + "Starting configuration recovery iteration 3\n", + "Starting configuration recovery iteration 4\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.configuration_recovery import recover_configurations\n", + "from qiskit_addon_sqd.fermion import solve_fermion\n", + "from qiskit_addon_sqd.subsampling import postselect_and_subsample\n", + "\n", + "# SQSD options\n", + "iterations = 5\n", + "\n", + "# Eigenstate solver options\n", + "n_batches = 3\n", + "samples_per_batch = 100\n", + "max_davidson_cycles = 200\n", + "\n", + "# Self-consistent configuration recovery loop\n", + "e_hist = np.zeros((iterations, n_batches)) # energy history\n", + "s_hist = np.zeros((iterations, n_batches)) # spin history\n", + "occupancy_hist = []\n", + "avg_occupancy = None\n", + "for i in range(iterations):\n", + " print(f\"Starting configuration recovery iteration {i}\")\n", + " # On the first iteration, we have no orbital occupancy information from the\n", + " # solver, so we just post-select from the full bitstring set based on hamming weight.\n", + " if avg_occupancy is None:\n", + " bs_mat_tmp = bitstring_matrix_full\n", + " probs_arr_tmp = probs_arr_full\n", + "\n", + " # In following iterations, we use both the occupancy info and the target hamming\n", + " # weight to refine bitstrings.\n", + " else:\n", + " bs_mat_tmp, probs_arr_tmp = recover_configurations(\n", + " bitstring_matrix_full,\n", + " probs_arr_full,\n", + " avg_occupancy,\n", + " num_elec_a,\n", + " num_elec_b,\n", + " rand_seed=rng,\n", + " )\n", + "\n", + " # Throw out samples with incorrect hamming weight and create batches of subsamples.\n", + " batches = postselect_and_subsample(\n", + " bs_mat_tmp,\n", + " probs_arr_tmp,\n", + " hamming_right=num_elec_a,\n", + " hamming_left=num_elec_b,\n", + " samples_per_batch=samples_per_batch,\n", + " num_batches=n_batches,\n", + " rand_seed=rng,\n", + " )\n", + "\n", + " # Run eigenstate solvers in a loop. This loop should be parallelized for larger problems.\n", + " int_e = np.zeros(n_batches)\n", + " int_s = np.zeros(n_batches)\n", + " int_occs = []\n", + " cs = []\n", + " for j in range(n_batches):\n", + " energy_sci, coeffs_sci, avg_occs, spin = solve_fermion(\n", + " batches[j],\n", + " hcore_rot,\n", + " eri_rot,\n", + " open_shell=open_shell,\n", + " spin_sq=spin_sq,\n", + " max_cycle=max_davidson_cycles,\n", + " )\n", + " energy_sci += nuclear_repulsion_energy\n", + " int_e[j] = energy_sci\n", + " int_s[j] = spin\n", + " int_occs.append(avg_occs)\n", + " cs.append(coeffs_sci)\n", + "\n", + " # Combine batch results\n", + " avg_occupancy = tuple(np.mean(int_occs, axis=0))\n", + "\n", + " # Track optimization history\n", + " e_hist[i, :] = int_e\n", + " s_hist[i, :] = int_s\n", + " occupancy_hist.append(avg_occupancy)" + ] + }, + { + "cell_type": "markdown", + "id": "917cf2d0", + "metadata": {}, + "source": [ + "### Refine the subspace\n", + "\n", + "To refine the subspace, we will take the CI strings of the batch with the lowest energy\n", + "from the last configuration recovery step. Other strategies may be used, like taking the union\n", + "of the CI strings of the batches in the last configuration recovery iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2a587030", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subspace dimension: 32761\n", + "Energy of that batch from SQD: -108.7531706601421\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.fermion import bitstring_matrix_to_ci_strs\n", + "\n", + "best_batch = batches[np.argmin(e_hist[-1])]\n", + "ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(best_batch, open_shell=open_shell)\n", + "print(f\"Subspace dimension: {len(ci_strs_up) * len(ci_strs_dn)}\")\n", + "print(f\"Energy of that batch from SQD: {e_hist[-1, np.argmin(e_hist[-1])]}\")\n", + "\n", + "# Union strategy\n", + "\n", + "# batches_union = np.concatenate((batches[0], batches[1]), axis = 0)\n", + "# for i in range(n_batches-2):\n", + "# batches_union = np.concatenate((batches_union, batches[ i+ 2]))\n", + "# ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(\n", + "# batches_union, open_shell=open_shell\n", + "# )\n", + "# print (f\"Subspace dimension: {len(ci_strs_up) * len(ci_strs_dn)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e8c6d5e4", + "metadata": {}, + "source": [ + "### Perform orbital optimization to improve the energy approximation\n", + "\n", + "We now describe how to optimize the orbitals to further improve the quality of the sqd calculation.\n", + "\n", + "The orbital rotations that are implemented in this package are those described by:\n", + "$$\n", + "U(\\kappa) = e^{\\sum_{pq, \\sigma} \\kappa_{pq} c^\\dagger_{p\\sigma} c_{q\\sigma}},\n", + "$$\n", + "where $\\kappa_{p, q} \\in \\mathbb{R}$ and $\\kappa_{p, q} = -\\kappa_{q, p}$. The orbitals are optimized to\n", + "minimize the variational energy:\n", + "$$\n", + "E(\\kappa) = \\langle \\psi | U^\\dagger(\\kappa) H U(\\kappa) |\\psi \\rangle,\n", + "$$\n", + "with respect to $\\kappa$ using gradient descent with momentum. Recall that\n", + "$|\\psi\\rangle$ is spanned in a subspace defined by determinants.\n", + "\n", + "Since the change of basis alters the Hamiltonian, we allow $|\\psi\\rangle$ to\n", + "respond to the change in the Hamiltonian. This is done by performing a number of alternating\n", + "self-consistent optimizations of $\\kappa$ and $|\\psi\\rangle$. We recall that the optimal\n", + "$|\\psi\\rangle$ is given by the lowest eigenvector of the Hamiltonian projected into the\n", + "subspace.\n", + "\n", + "The ``sqd.fermion.fermion`` module provides the tools to perform this alternating\n", + "optimization. In particular, the function ``sqd.fermion.optimize_orbitals()``.\n", + "\n", + "Some of the arguments that define the optimization are:\n", + "\n", + "- ``num_iters``: number of self-consistent iterations.\n", + "- ``num_steps_grad``: number of gradient step updates performed when optimizing\n", + "$\\kappa$ on each self-consistent iteration.\n", + "- ``learning_rate``: step-size in the gradient descent optimization of $\\kappa$." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b5e56baf", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_addon_sqd.fermion import optimize_orbitals\n", + "\n", + "k_flat = (rng.random(num_params) - 0.5) * 0.1\n", + "num_iters = 20\n", + "num_steps_grad = 10_000 # relatively cheap to execute\n", + "learning_rate = 0.05\n", + "\n", + "e_improved, k_flat, orbital_occupancies = optimize_orbitals(\n", + " best_batch,\n", + " hcore_rot,\n", + " eri_rot,\n", + " k_flat,\n", + " open_shell=open_shell,\n", + " spin_sq=spin_sq,\n", + " num_iters=num_iters,\n", + " num_steps_grad=num_steps_grad,\n", + " learning_rate=learning_rate,\n", + " max_cycle=max_davidson_cycles,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e06f5c28-83d0-4dc2-b2bd-2ec92676745d", + "metadata": {}, + "source": [ + "Here we see that by optimizing rotation parameters for our Hamiltonian, we can improve the result from SQD." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "78a80e64", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exact energy: -109.04667177808032\n", + "SQD energy: -108.7531706601421\n", + "Energy after OO: -108.80400806164377\n" + ] + } + ], + "source": [ + "print(f\"Exact energy: {exact_energy}\")\n", + "print(f\"SQD energy: {np.min(e_hist[-1])}\")\n", + "print(f\"Energy after OO: {e_improved + nuclear_repulsion_energy}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/index.mdx b/docs/addons/qiskit-addon-sqd/index.mdx new file mode 100644 index 00000000000..4d52b88810e --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/index.mdx @@ -0,0 +1,103 @@ +--- +title: "Qiskit addon: sample-based quantum diagonalization (SQD)" +--- + +# Qiskit addon: sample-based quantum diagonalization (SQD) + +[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. + +This package contains the Qiskit addon for sample-based quantum diagonalization (SQD) – a technique for finding eigenvalues and eigenvectors of quantum operators, such as a quantum system Hamiltonian, using quantum and distributed classical computing together \[1-5]. This technique can be run on current quantum computers and has been shown to scale to problem sizes beyond what was possible with variational methods and even beyond the reach of exact classical diagonalization methods \[1,2]. + +SQD-based workflows involve first preparing one or more quantum states on a quantum device and sampling from them. Then, classical distributed computing is used to process those noisy samples. This processing occurs iteratively in two steps: First, a configuration recovery step corrects noisy samples using information about the input problem and second, the Hamiltonian is projected and diagonalized in the subspace spanned by those samples. These steps are repeated self-consistently until convergence. The result is an approximated lowest eigenvalue (energy) and lowest energy eigenstate of a given Hamiltonian. SQD is robust to samples corrupted by quantum noise; in fact, as long as a useful signal can be retrieved out of the quantum computer, the outcome of SQD will be insensitive to noisy bitstrings. + +SQD can be used in various ways in practice. For example, we can use two categories of quantum circuits to sample from: + +> 1. A variational circuit ansatz with parameters chosen such that sampling the circuit produces electronic configurations on which the target wavefunction (i.e., the ground state) has significant support. This is appealing for chemistry applications where Hamiltonians can have millions of interaction terms \[1]. For an example of this approach applied to chemistry see the [tutorial for approximating the ground state energy of the N2 molecule](/docs/en/tutorials/sample-based-quantum-diagonalization). +> 2. A set of Krylov basis states are prepared over increasing time intervals. Assuming a good initial state and sparsity of the ground state, this approach is proven to converge efficiently. As one needs to prepare Trotterized time evolution circuits on a quantum device, this approach is best for applications to lattice models \[2]. For an example of this approach applied to Fermionic lattice Hamiltonians, see the [tutorial for approximating the ground state energy of a simplified single-impurity Anderson model](/docs/en/tutorials/sample-based-krylov-quantum-diagonalization). + +This package contains the functionality for the classical processing of user-provided samples. It can target Hamiltonians expressed as linear combinations of Pauli operators or second-quantized Fermionic operators. The projection and diagonalization steps are performed by a classical solver. We provide here two generic solvers, one for Fermionic systems and another for qubit systems. Other solvers that might be more efficient for specific systems can be interfaced by the users. + +## Documentation + +All documentation is available [here](/docs/addons/qiskit-addon-sqd). + +## Installation + +We encourage installing this package via `pip`, when possible: + +```bash +pip install 'qiskit-addon-sqd' +``` + +For more installation information refer to the [installation instructions](install) in the documentation. + +## System sizes and computational requirements + +The computational cost of SQD is dominated by the eigenstate solver calls. At each step of the self-consistent configuration recovery iteration, n\_batches of eigenstate solver calls are performed. The different calls are embarrassingly parallel. In this [tutorial](tutorials/01_chemistry_hamiltonian), those calls are inside a for loop. **It is highly recommended to perform these calls in parallel**. + +The [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") function is multithreaded and capable of handling systems with \~25 spacial orbitals and \~10 electrons with subspace dimensions of \~\$10^7\$, using \~10-30 cores. + +## Choosing subspace dimensions + +The choice of the subspace dimension affects the accuracy and runtime of the eigenstate solver. The larger the subspace the more accurate the calculation, at the cost of increasing the runtime and memory requirements. The optimal subspace size of a given system is not known, thus a convergence study with the subspace dimension may be performed, as described in this [guide](how_tos/choose_subspace_dimension). + +## The subspace dimension is set indirectly + +In this package, the user controls the number of bitstrings contained in each subspace with the samples\_per\_batch argument in [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"). The value of this argument sets an upper bound to the subspace dimension in the case of quantum chemistry applications. See this [example](how_tos/select_open_closed_shell) for more details. + +## Citing this project + +If you use this package in your research, please cite it according to `CITATON.bib` file included in this repository: + +```bibtex +@software{qiskit-addon-sqd, + author = { + Abdullah Ash Saki + and Stefano Barison + and Bryce Fuller + and James R. Garrison + and Jennifer R. Glick + and Caleb Johnson + and Antonio Mezzacapo + and Javier Robledo-Moreno + and Max Rossmannek + and Paul Schweigert + and Iskandar Sitdikov + and Kevin J. Sung + }, + title = {{Qiskit addon: sample-based quantum diagonalization}}, + howpublished = {\url{https://github.com/Qiskit/qiskit-addon-sqd}}, + year = {2024} +} +``` + +## Deprecation Policy + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the release notes. + +## Contributing + +The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-sqd). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-sqd/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). + +We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-sqd/issues/new/choose) for tracking requests and bugs. + +## License + +[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-sqd/blob/main/LICENSE.txt) + + + +## References + +\[1] Javier Robledo-Moreno, et al., \[Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer]\([https://arxiv.org/abs/2405.05068](https://arxiv.org/abs/2405.05068)), arXiv:2405.05068 \[quant-ph]. + +\[2] Jeffery Yu, et al., \[Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization]\([https://arxiv.org/abs/2501.09702](https://arxiv.org/abs/2501.09702)), arXiv:2501.09702 \[quant-ph]. + +\[3] Keita Kanno, et al., \[Quantum-Selected Configuration Interaction: classical diagonalization of Hamiltonians in subspaces selected by quantum computers]\([https://arxiv.org/abs/2302.11320](https://arxiv.org/abs/2302.11320)), arXiv:2302.11320 \[quant-ph]. + +\[4] Kenji Sugisaki, et al., \[Hamiltonian simulation-based quantum-selected configuration interaction for large-scale electronic structure calculations with a quantum computer]\([https://arxiv.org/abs/2412.07218](https://arxiv.org/abs/2412.07218)), arXiv:2412.07218 \[quant-ph]. + +\[5] Mathias Mikkelsen, Yuya O. Nakagawa, \[Quantum-selected configuration interaction with time-evolved state]\([https://arxiv.org/abs/2412.13839](https://arxiv.org/abs/2412.13839)), arXiv:2412.13839 \[quant-ph]. + diff --git a/docs/addons/qiskit-addon-sqd/install.mdx b/docs/addons/qiskit-addon-sqd/install.mdx new file mode 100644 index 00000000000..515607e6add --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/install.mdx @@ -0,0 +1,62 @@ +--- +title: "Installation Instructions" +--- + +# Installation Instructions + +Let’s see how to install the package. The first thing to do is ensure your Python environment is set up correctly. To create a new environment: + +## Pre-Installation + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +There are two primary ways to install this package – from PyPI or source. The preferred method is to install from PyPI: + +## Install from PyPI + +```sh +pip install 'qiskit-addon-sqd' +``` + +## Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +If so, the first step is to clone the `qiskit-addon-sqd` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-sqd.git +``` + +Next, upgrade pip and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-sqd +``` + +The next step is to install `qiskit-addon-sqd` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab +``` + diff --git a/docs/addons/qiskit-addon-sqd/release-notes.mdx b/docs/addons/qiskit-addon-sqd/release-notes.mdx new file mode 100644 index 00000000000..cfbf3168eed --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/release-notes.mdx @@ -0,0 +1,420 @@ +--- +title: "Release Notes" +--- + + + + + +# + + + + + +## 0.12.1 + + + +### Upgrade Notes + +* Python 3.10 is now the minimum required version. Support for Python 3.9 has been dropped. + + + + + +## 0.12.0 + + + +### New Features + +* Added the `max_dim` argument to the [`qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian "qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian") function. This argument is used to limit the dimension of the SCI subspace. + +* Introduced the [`qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left "qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left") function. + + + + + +### Upgrade Notes + +* [`qiskit_addon_sqd.fermion.SCIState`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.SCIState "qiskit_addon_sqd.fermion.SCIState") now takes two additional required arguments, `norb` and `nelec`. This is a breaking change. + + + +### Deprecation Notes + +* [`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight()`](qiskit_addon_sqd.configuration_recovery#qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight "qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight") is now deprecated. Instead, use [`qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left "qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left"). + +* [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample") is now deprecated. Instead, use [`qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left "qiskit_addon_sqd.subsampling.postselect_by_hamming_right_and_left") and [`qiskit_addon_sqd.subsampling.subsample()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.subsample "qiskit_addon_sqd.subsampling.subsample"). + + + +### Bug Fixes + +* Fixed a bug in [`qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs "qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs") in which the conversion of bitstrings to CI strings could be incorrect when the number of orbitals is greater than 53. + +* Fixed a bug in which saving and loading [`qiskit_addon_sqd.fermion.SCIState`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.SCIState "qiskit_addon_sqd.fermion.SCIState") objects resulted in an error. + +* Added a release note to version 0.11 for a breaking change involving [`qiskit_addon_sqd.fermion.SCIState`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.SCIState "qiskit_addon_sqd.fermion.SCIState"). + + + + + +## 0.11.0 + + + + + +### New Features + +* Added a new function [`qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian "qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian") to serve as the main entrypoint to the SQD algorithm. See the updated [tutorials](/docs/addons/qiskit-addon-sqd/tutorials/index) for a demonstration of how to use this function. + +* [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") now accepts a `shift` argument used to shift states which have the wrong spin, $(H + shift * S^2)|ψ> = E|ψ>$. + + + + + +### Upgrade Notes + +* [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") now accept trailing `kwargs`, which will be passed directly to [pyscf.fci.selected\_ci.kernel\_fixed\_space](https://pyscf.org/pyscf_api_docs/pyscf.fci.html#pyscf.fci.selected_ci.kernel_fixed_space) under the hood to calculate the target state. The `max_davidson` argument should now be passed as `max_cycle` in both functions. + + + + + +### Bug Fixes + +* Fixed a float to integer type conversion affecting the `qubit` module + + + + + +## 0.10.0 + + + + + +### Upgrade Notes + +* Removed `qiskit_addon_sqd.fermion.flip_orbital_occupancies()`. Users no longer need to flip the orbital occupancies output from [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals"); they will be output in the order expected by [`qiskit_addon_sqd.configuration_recovery.recover_configurations()`](qiskit_addon_sqd.configuration_recovery#qiskit_addon_sqd.configuration_recovery.recover_configurations "qiskit_addon_sqd.configuration_recovery.recover_configurations"): `tuple(array([occ_a_0, ..., occ_a_N]), array([occ_b_0, ..., occ_b_N]))`. + + + + + +### Deprecation Notes + +* The `avg_occupancies` argument to [`qiskit_addon_sqd.configuration_recovery.recover_configurations()`](qiskit_addon_sqd.configuration_recovery#qiskit_addon_sqd.configuration_recovery.recover_configurations "qiskit_addon_sqd.configuration_recovery.recover_configurations") should now be a length-2 tuple containing the spin-up and spin-down occupancies, respectively. + + Old format: `array([occ_b_N, ..., occ_b_0, occ_a_N, ..., occ_a_0])` + + New format: `tuple(array([occ_a_0, ..., occ_a_N]), array([occ_b_0, ..., occ_b_N]))` + + + + + +### Bug Fixes + +* Fixed a bug which would cause the energy output from [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") to be incorrect when the input `spin_sq` deviates from the spin^2 of the output wavefunction. + + + + + +## 0.9.0 + + + + + +### Upgrade Notes + +* [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") and [`qiskit_addon_sqd.fermion.rotate_integrals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.rotate_integrals "qiskit_addon_sqd.fermion.rotate_integrals") now require `k_flat` to specify the upper triangle (not including diagonal) of the rotation matrix, rather than the entire matrix. + + + + + +### Bug Fixes + +* Fixed an indexing error in [`qiskit_addon_sqd.configuration_recovery()`](qiskit_addon_sqd.configuration_recovery#module-qiskit_addon_sqd.configuration_recovery "qiskit_addon_sqd.configuration_recovery") which caused bits in the right half of the bitstring to be flipped with respect to the occupancies of the oritals associated with the left half of the bitstring. + +* pyscf is no longer considered a dependency on Windows, where it fails to install. However, Windows remains an unsupported platform. + + + + + +## 0.8.0 + + + + + +### New Features + +* Added support for Python 3.9. + +* All functions which take a `rand_seed` argument now also accept a `numpy.random.Generator` instance as the `rand_seed`. + + + + + +### Upgrade Notes + +* The ground state returned by [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") will now be an instance of [`qiskit_addon_sqd.fermion.SCIState`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.SCIState "qiskit_addon_sqd.fermion.SCIState"), rather than a PySCF `SCIVector` instance. + + + + + +## 0.7.0 + + + + + +### Bug Fixes + +* Fixed a bug in [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") which causes the determinants for spin-up and spin-down to be incorrectly flipped before solving. + +* Fixed a bug in open-shell workflows which would cause [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") to crash with a `malloc` error. + + + + + +## 0.6.0 + + + + + +### Upgrade Notes + +* Specifying `addresses` as a keyword argument to [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") is no longer supported. Users may still pass `addresses` as the first positional argument; however, this usage is deprecated. Users are encouraged to pass the bitstring matrix defining the subspace as the first positional arguments to these functions, as shown below. + + To upgrade, change this code + + ```python + # DEPRECATED CODE + from qiskit_addon_sqd.fermion import ( + bitstring_matrix_to_sorted_addresses, + solve_fermion, + optimize_orbitals, + ) + + bitstring_matrix = ... + addresses = bitstring_matrix_to_sorted_addresses(bitstring_matrix, open_shell=open_shell) + energy, coeffs, occs, spin = solve_fermion( + addresses=addresses, + hcore=hcore, + eri=eri, + ) + ... + e_oo, rotation, occs_oo = optimize_orbitals( + addresses=addresses, + hcore=hcore, + eri=eri, + ) + + ### SHOULD BECOME ### + + # NEW CODE + from qiskit_addon_sqd.fermion import solve_fermion, optimize_orbitals + + bitstring_matrix = ... + energy, coeffs, occs, spin = solve_fermion( + bitstring_matrix, + hcore=hcore, + eri=eri, + ) + ... + e_oo, rotation, occs_oo = optimize_orbitals( + bitstring_matrix, + hcore=hcore, + eri=eri, + ) + ``` + + + + + +### Deprecation Notes + +* The `qiskit_addon_sqd.fermion.bitstring_matrix_to_sorted_addresses()` function has been deprecated in favor of [`qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs "qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs"). These two functions behave the same with one key exception – `qiskit_addon_sqd.fermion.bitstring_matrix_to_sorted_addresses()` returns the configurations as `tuple(spin_dn, spin_up)`; whereas, [`qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs "qiskit_addon_sqd.fermion.bitstring_matrix_to_ci_strs") returns the configurations as `tuple(spin_up, spin_dn)`. + + To migrate + + ```python + from qiskit_addon_sqd.fermion import ( + bitstring_matrix_to_sorted_addresses, + bitstring_matrix_to_ci_strs, + ) + + # DEPRECATED CODE + bs_matrix = ... + addr_dn, addr_up = bitstring_matrix_to_sorted_addresses(bs_matrix, open_shell=True) + + ### SHOULD BECOME ### + + # NEW CODE + bs_matrix = ... + ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(bs_matrix, open_shell=True) + ``` + +* The `addresses` argument to [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.solve_fermion "qiskit_addon_sqd.fermion.solve_fermion") and [`qiskit_addon_sqd.fermion.optimize_orbitals()`](qiskit_addon_sqd.fermion#qiskit_addon_sqd.fermion.optimize_orbitals "qiskit_addon_sqd.fermion.optimize_orbitals") has been deprecated in favor of `bitstring_matrix`. Users are no longer required to convert their configurations to integers; instead, they should now pass in the bitstring matrix specifying the subspace onto which to project and diagonalize the Hamiltonian. The conversion to the integer representation of determinants will be done internally. + + + + + +### Bug Fixes + +* Fixed a bug in [`qiskit_addon_sqd.configuration_recovery.recover_configurations()`](qiskit_addon_sqd.configuration_recovery#qiskit_addon_sqd.configuration_recovery.recover_configurations "qiskit_addon_sqd.configuration_recovery.recover_configurations") which would sometimes cause a divide-by-zero error when calculating individual bit-flip probability. + +* Fixes a bug that caused configuration recovery to fail on bitstrings of length greater than 72. + + + + + +## 0.5.0 + + + + + +### Upgrade Notes + +* The [`qiskit_addon_sqd.counts.generate_counts_bipartite_hamming()`](qiskit_addon_sqd.counts#qiskit_addon_sqd.counts.generate_counts_bipartite_hamming "qiskit_addon_sqd.counts.generate_counts_bipartite_hamming"), [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"), and [`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight()`](qiskit_addon_sqd.configuration_recovery#qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight "qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight") now require the `hamming_right` and `hamming_left` arguments to be specified as keyword arguments. Additionally, the `samples_per_batch` and `n_batches` arguments to [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample") should now be passed as keyword arguments. + + To upgrade + + ```python + from qiskit_addon_sqd.configuration_recovery import post_select_by_hamming_weight + from qiskit_addon_sqd.subsampling import postselect_and_subsample + from qiskit_addon_sqd.counts import generate_counts_bipartite_hamming + + counts = generate_counts_bipartite_hamming(num_samples, num_bits, num_elec_a, num_elec_b) + + ... + + bs_mat = post_select_by_hamming_weight(bs_mat_full, num_elec_a, num_elec_b) + + ... + + batches = postselect_and_subsample( + bs_mat, + probs_arr, + num_elec_a, + num_elec_b, + samples_per_batch, + num_batches, + ) + ``` + + should be changed to + + ```python + from qiskit_addon_sqd.configuration_recovery import post_select_by_hamming_weight + from qiskit_addon_sqd.subsampling import postselect_and_subsample + from qiskit_addon_sqd.counts import generate_counts_bipartite_hamming + + counts = generate_counts_bipartite_hamming(num_samples, num_bits, hamming_right=num_elec_a, hamming_left=num_elec_b) + + ... + + bs_mat = post_select_by_hamming_weight(bs_mat_full, hamming_right=num_elec_a, hamming_left=num_elec_b) + + ... + + batches = postselect_and_subsample( + bs_mat, + probs_arr, + hamming_right=num_elec_a, + hamming_left=num_elec_b, + samples_per_batch=samples_per_batch, + num_batches=num_batches, + ) + ``` + + + + + +## 0.4.0 + + + +### Prelude + +This is a minor release which introduces a couple of small, but important, breaking changes to to the API. These changes allow for a more consistent pattern in specifying the number of alpha and beta electrons throughout both the chemistry and non-chemistry functions in the API. + + + + + +### Upgrade Notes + +* The [`qiskit_addon_sqd.counts.generate_counts_bipartite_hamming()`](qiskit_addon_sqd.counts#qiskit_addon_sqd.counts.generate_counts_bipartite_hamming "qiskit_addon_sqd.counts.generate_counts_bipartite_hamming"), [`qiskit_addon_sqd.subsampling.postselect_and_subsample()`](qiskit_addon_sqd.subsampling#qiskit_addon_sqd.subsampling.postselect_and_subsample "qiskit_addon_sqd.subsampling.postselect_and_subsample"), and [`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight()`](qiskit_addon_sqd.configuration_recovery#qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight "qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight") now take the `hamming_right` positional argument before the `hamming_left` argument to better match the rest of the workflow. + + To upgrade + + ```python + from qiskit_addon_sqd.configuration_recovery import post_select_by_hamming_weight + from qiskit_addon_sqd.subsampling import postselect_and_subsample + from qiskit_addon_sqd.counts import generate_counts_bipartite_hamming + + counts = generate_counts_bipartite_hamming(num_samples, num_bits, num_elec_b, num_elec_a) + + ... + + bs_mat = post_select_by_hamming_weight(bs_mat_full, num_elec_b, num_elec_a) + + ... + + batches = postselect_and_subsample( + bs_mat, + probs_arr, + num_elec_b, + num_elec_a, + samples_per_batch, + n_batches, + ) + ``` + + should be changed to + + ```python + from qiskit_addon_sqd.configuration_recovery import post_select_by_hamming_weight + from qiskit_addon_sqd.subsampling import postselect_and_subsample + from qiskit_addon_sqd.counts import generate_counts_bipartite_hamming + + counts = generate_counts_bipartite_hamming(num_samples, num_bits, num_elec_a, num_elec_b) + + bs_mat = post_select_by_hamming_weight(bs_mat_full, num_elec_a, num_elec_b) + + ... + + batches = postselect_and_subsample( + bs_mat, + probs_arr, + num_elec_a, + num_elec_b, + samples_per_batch, + n_batches, + ) + ``` + diff --git a/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian.ipynb new file mode 100644 index 00000000000..cefefd907a6 --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/tutorials/01_chemistry_hamiltonian.ipynb @@ -0,0 +1,531 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e", + "metadata": {}, + "source": [ + "# Improving energy estimation of a chemistry Hamiltonian with SQD\n", + "\n", + "In this tutorial we implement a [Qiskit pattern](/docs/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of a chemistry Hamiltonian: the $N_2$ molecule at equilibrium in the 6-31G basis set. We will follow a [sample-based quantum diagonalization approach](https://arxiv.org/abs/2405.05068) to process samples taken from a ``36``-qubit quantum circuit ansatz (in this case, an LUCJ circuit). In order to account for the effect of quantum noise, the configuration recovery technique is used.\n", + "\n", + "The pattern can be described in four steps:\n", + "\n", + "1. **Step 1: Map to quantum problem**\n", + " - Generate an ansatz for estimating the ground state\n", + "2. **Step 2: Optimize the problem**\n", + " - Transpile the ansatz for the backend\n", + "3. **Step 3: Execute experiments**\n", + " - Draw samples from the ansatz using the ``Sampler`` primitive\n", + "4. **Step 4: Post-process results**\n", + " - Self-consistent configuration recovery loop\n", + " - Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration.\n", + " - Probabilistically create batches of subsamples from recovered bitstrings.\n", + " - Project and diagonalize the molecular Hamiltonian over each sampled subspace.\n", + " - Save the minimum ground state energy found across all batches and update the avg orbital occupancy.\n", + "\n", + "\n", + "For this example, the interacting-electron Hamiltonian takes the generic form:\n", + "\n", + "$$\n", + "\\hat{H} = \\sum_{ \\substack{pr\\\\\\sigma} } h_{pr} \\, \\hat{a}^\\dagger_{p\\sigma} \\hat{a}_{r\\sigma}\n", + "+\n", + "\\sum_{ \\substack{prqs\\\\\\sigma\\tau} }\n", + "\\frac{(pr|qs)}{2} \\,\n", + "\\hat{a}^\\dagger_{p\\sigma}\n", + "\\hat{a}^\\dagger_{q\\tau}\n", + "\\hat{a}_{s\\tau}\n", + "\\hat{a}_{r\\sigma}\n", + "$$\n", + "\n", + "$\\hat{a}^\\dagger_{p\\sigma}$/$\\hat{a}_{p\\sigma}$ are the fermionic creation/annihalation operators associated to the $p$-th basis set element and the spin $\\sigma$. $h_{pr}$ and $(pr|qs)$ are the one- and two-body electronic integrals.\n", + "\n", + "The SQD workflow with self-consistent configuration recovery is depicted in the following diagram.\n", + "\n", + "![SQD diagram](../_static/images/sqd_diagram.png)\n", + "\n", + "SQD is known to work well when the target eigenstate is sparse: the wave function is supported in a set of basis states $\\mathcal{S} = \\{|x\\rangle \\}$ whose size does not increase exponentially with the size of the problem. In this scenario, the diagonalization of the Hamiltonian projected into the subspace defined by $\\mathcal{S}$:\n", + "$$\n", + "H_\\mathcal{S} = P_\\mathcal{S} H P_\\mathcal{S} \\textrm{ with } P_\\mathcal{S} = \\sum_{x \\in \\mathcal{S}} |x \\rangle \\langle x |;\n", + "$$\n", + "yields a good approximation to the target eigenstate. The role of the quantum device is to produce samples of the members of $\\mathcal{S}$ only. First, a quantum circuit prepares the state $|\\Psi\\rangle$ in the quantum device. The Jordan-Wigner encoding is used. Consequently, members of the computational basis represent Fock states (electronic configurations/determinants). The circuit is sampled in the computational basis, yielding the set of noisy configurations $\\tilde{\\mathcal{X}}$. The configurations are represented by bitstrings. The set $\\tilde{\\mathcal{X}}$ is then passed into the classical post-processing block, where the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068) is used. In the SQD framework, the role of the quantum device is to produce a probability distribution." + ] + }, + { + "cell_type": "markdown", + "id": "afeb054c", + "metadata": {}, + "source": [ + "### Step 1: Map problem to a quantum circuit\n", + "\n", + "In this tutorial, we will approximate the ground state energy of an $N_2$ molecule. First, we will specify the molecule and its properties. Next, we will create a [local unitary cluster Jastrow (LUCJ)](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) ansatz (quantum circuit) to generate samples from a quantum computer for ground state energy estimation.\n", + "\n", + "First, we will specify the molecule and its properties." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "converged SCF energy = -108.835236570775\n", + "CASCI E = -109.046671778080 E(CI) = -32.8155692383188 S^2 = 0.0000000\n" + ] + } + ], + "source": [ + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import pyscf\n", + "import pyscf.cc\n", + "import pyscf.mcscf\n", + "\n", + "# Specify molecule properties\n", + "open_shell = False\n", + "spin_sq = 0\n", + "\n", + "# Build N2 molecule\n", + "mol = pyscf.gto.Mole()\n", + "mol.build(\n", + " atom=[[\"N\", (0, 0, 0)], [\"N\", (1.0, 0, 0)]],\n", + " basis=\"6-31g\",\n", + " symmetry=\"Dooh\",\n", + ")\n", + "\n", + "# Define active space\n", + "n_frozen = 2\n", + "active_space = range(n_frozen, mol.nao_nr())\n", + "\n", + "# Get molecular integrals\n", + "scf = pyscf.scf.RHF(mol).run()\n", + "num_orbitals = len(active_space)\n", + "n_electrons = int(sum(scf.mo_occ[active_space]))\n", + "num_elec_a = (n_electrons + mol.spin) // 2\n", + "num_elec_b = (n_electrons - mol.spin) // 2\n", + "cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\n", + "mo = cas.sort_mo(active_space, base=0)\n", + "hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\n", + "eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\n", + "\n", + "# Compute exact energy\n", + "exact_energy = cas.run().e_tot" + ] + }, + { + "cell_type": "markdown", + "id": "96bfe018", + "metadata": {}, + "source": [ + "Next, we will create the ansatz. The ``LUCJ`` ansatz is a parameterized quantum circuit, and we will initialize it with `t2` and `t1` amplitudes obtained from a CCSD calculation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "66270387", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "E(CCSD) = -109.0398256929734 E_corr = -0.2045891221988311\n" + ] + } + ], + "source": [ + "# Get CCSD t2 amplitudes for initializing the ansatz\n", + "ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run()\n", + "t1 = ccsd.t1\n", + "t2 = ccsd.t2" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f4d882fa", + "metadata": {}, + "source": [ + "We will use the [ffsim](https://github.com/qiskit-community/ffsim/tree/main) package to create and initialize the ansatz with `t2` and `t1` amplitudes computed above. Since our molecule has a closed-shell Hartree-Fock state, we will use the spin-balanced variant of the UCJ ansatz, [UCJOpSpinBalanced](https://qiskit-community.github.io/ffsim/api/ffsim.html#ffsim.UCJOpSpinBalanced).\n", + "\n", + "As our target IBM hardware has a heavy-hex topology, we will adopt the [_zig-zag_ pattern for qubit interactions](https://pubs.rsc.org/en/content/articlehtml/2023/sc/d3sc02516k). In this pattern, orbitals (represented by qubits) with the same spin are connected with a line topology (red and blue circles) where each line take a zig-zag shape due the heavy-hex connectivity of the target hardware. Again, due to the heavy-hex topology, orbitals for different spins have connections between every 4th orbital (0, 4, 8, etc.) (purple circles).\n", + "\n", + "![lucj_ansatz](../_static/images/lucj_ansatz_zig_zag_pattern.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dd69a86c", + "metadata": {}, + "outputs": [], + "source": [ + "import ffsim\n", + "from qiskit import QuantumCircuit, QuantumRegister\n", + "\n", + "n_reps = 1\n", + "alpha_alpha_indices = [(p, p + 1) for p in range(num_orbitals - 1)]\n", + "alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\n", + "\n", + "ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\n", + " t2=t2,\n", + " t1=t1,\n", + " n_reps=n_reps,\n", + " interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\n", + ")\n", + "\n", + "nelec = (num_elec_a, num_elec_b)\n", + "\n", + "# create an empty quantum circuit\n", + "qubits = QuantumRegister(2 * num_orbitals, name=\"q\")\n", + "circuit = QuantumCircuit(qubits)\n", + "\n", + "# prepare Hartree-Fock state as the reference state and append it to the quantum circuit\n", + "circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\n", + "\n", + "# apply the UCJ operator to the reference state\n", + "circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\n", + "circuit.measure_all()" + ] + }, + { + "cell_type": "markdown", + "id": "db11bf6d", + "metadata": {}, + "source": [ + "### Step 2: Optimize the problem" + ] + }, + { + "cell_type": "markdown", + "id": "0760b3f3", + "metadata": {}, + "source": [ + "Next, we will optimize our circuit for a target hardware. We need to choose the hardware device to use before optimizing our circuit. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "53a039d8", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeSherbrooke\n", + "\n", + "backend = FakeSherbrooke()" + ] + }, + { + "cell_type": "markdown", + "id": "057ebbf6", + "metadata": {}, + "source": [ + "Next, we recommend the following steps to optimize the ansatz and make it hardware-compatible.\n", + "\n", + "- Select physical qubits (`initial_layout`) from the target hardware that adheres to the zig-zag pattern described above. Laying out qubits in this pattern leads to an efficient hardware-compatible circuit with fewer gates.\n", + "- Generate a staged pass manager using the [generate_preset_pass_manager](/docs/api/qiskit/transpiler_preset#generate_preset_pass_manager) function from Qiskit with your choice of `backend` and `initial_layout`.\n", + "- Set the `pre_init` stage of your staged pass manager to `ffsim.qiskit.PRE_INIT`. `ffsim.qiskit.PRE_INIT` includes Qiskit transpiler passes that decompose gates into orbital rotations and then merges the orbital rotations, resulting in fewer gates in the final circuit.\n", + "- Run the pass manager on your circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7d554aa5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gate counts (w/o pre-init passes): OrderedDict({'rz': 4420, 'sx': 3432, 'ecr': 1366, 'x': 239, 'measure': 32, 'barrier': 1})\n", + "Gate counts (w/ pre-init passes): OrderedDict({'rz': 2460, 'sx': 2156, 'ecr': 730, 'x': 71, 'measure': 32, 'barrier': 1})\n" + ] + } + ], + "source": [ + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", + "\n", + "spin_a_layout = [0, 14, 18, 19, 20, 33, 39, 40, 41, 53, 60, 61, 62, 72, 81, 82]\n", + "spin_b_layout = [2, 3, 4, 15, 22, 23, 24, 34, 43, 44, 45, 54, 64, 65, 66, 73]\n", + "initial_layout = spin_a_layout + spin_b_layout\n", + "\n", + "pass_manager = generate_preset_pass_manager(\n", + " optimization_level=3, backend=backend, initial_layout=initial_layout\n", + ")\n", + "\n", + "# without PRE_INIT passes\n", + "isa_circuit = pass_manager.run(circuit)\n", + "print(f\"Gate counts (w/o pre-init passes): {isa_circuit.count_ops()}\")\n", + "\n", + "# with PRE_INIT passes\n", + "# We will use the circuit generated by this pass manager for hardware execution\n", + "pass_manager.pre_init = ffsim.qiskit.PRE_INIT\n", + "isa_circuit = pass_manager.run(circuit)\n", + "print(f\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0cc1edef", + "metadata": {}, + "source": [ + "### Step 3: Execute experiments" + ] + }, + { + "cell_type": "markdown", + "id": "cbf7ef9f", + "metadata": {}, + "source": [ + "After optimizing the circuit for hardware execution, we are ready to run it on the target hardware and collect samples for ground state energy estimation. As we only have one circuit, we will use Qiskit Runtime's [Job execution mode](/docs/guides/execution-modes) and execute our circuit.\n", + "\n", + "***Note: We have commented out the code for running the circuit on a QPU and left it for the user's reference. Instead of running on real hardware in this guide, we will just generate random samples drawn from the uniform distribution.***" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3da09100", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qiskit_addon_sqd.counts import generate_bit_array_uniform\n", + "\n", + "# from qiskit_ibm_runtime import SamplerV2 as Sampler\n", + "\n", + "# sampler = Sampler(mode=backend)\n", + "# job = sampler.run([isa_circuit], shots=10_000)\n", + "# primitive_result = job.result()\n", + "# pub_result = primitive_result[0]\n", + "# bit_array = pub_result.data.meas\n", + "\n", + "rng = np.random.default_rng(24)\n", + "bit_array = generate_bit_array_uniform(10_000, num_orbitals * 2, rand_seed=rng)" + ] + }, + { + "cell_type": "markdown", + "id": "6df05b6e", + "metadata": {}, + "source": [ + "### Step 4: Post-process results" + ] + }, + { + "cell_type": "markdown", + "id": "7a102a7f-aae6-4583-ab82-ae40fcb5496a", + "metadata": {}, + "source": [ + "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function.\n", + "\n", + "The solver included in the SQD addon uses PySCF's implementation of selected CI, specifically [pyscf.fci.selected_ci.kernel_fixed_space](https://pyscf.org/pyscf_api_docs/pyscf.fci.html#pyscf.fci.selected_ci.kernel_fixed_space). The example below also shows how to pass keyword arguments to that function via the included solver. Here we pass the `max_cycle` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6e873c11", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1\n", + "\tSubsample 0\n", + "\t\tEnergy: -105.45358671756313\n", + "\t\tSubspace dimension: 5476\n", + "Iteration 2\n", + "\tSubsample 0\n", + "\t\tEnergy: -107.95172900082163\n", + "\t\tSubspace dimension: 249001\n", + "Iteration 3\n", + "\tSubsample 0\n", + "\t\tEnergy: -108.97460330369815\n", + "\t\tSubspace dimension: 339889\n", + "Iteration 4\n", + "\tSubsample 0\n", + "\t\tEnergy: -109.02739376648793\n", + "\t\tSubspace dimension: 440896\n", + "Iteration 5\n", + "\tSubsample 0\n", + "\t\tEnergy: -109.030972328451\n", + "\t\tSubspace dimension: 597529\n" + ] + } + ], + "source": [ + "from functools import partial\n", + "\n", + "from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian, solve_sci_batch\n", + "\n", + "# SQD options\n", + "energy_tol = 1e-3\n", + "occupancies_tol = 1e-3\n", + "max_iterations = 5\n", + "\n", + "# Eigenstate solver options\n", + "num_batches = 1\n", + "samples_per_batch = 300\n", + "symmetrize_spin = True\n", + "carryover_threshold = 1e-4\n", + "max_cycle = 200\n", + "\n", + "# Pass options to the built-in eigensolver. If you just want to use the defaults,\n", + "# you can omit this step, in which case you would not specify the sci_solver argument\n", + "# in the call to diagonalize_fermionic_hamiltonian below.\n", + "sci_solver = partial(solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle)\n", + "\n", + "# List to capture intermediate results\n", + "result_history = []\n", + "\n", + "\n", + "def callback(results: list[SCIResult]):\n", + " result_history.append(results)\n", + " iteration = len(result_history)\n", + " print(f\"Iteration {iteration}\")\n", + " for i, result in enumerate(results):\n", + " print(f\"\\tSubsample {i}\")\n", + " print(f\"\\t\\tEnergy: {result.energy + nuclear_repulsion_energy}\")\n", + " print(f\"\\t\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\")\n", + "\n", + "\n", + "result = diagonalize_fermionic_hamiltonian(\n", + " hcore,\n", + " eri,\n", + " bit_array,\n", + " samples_per_batch=samples_per_batch,\n", + " norb=num_orbitals,\n", + " nelec=nelec,\n", + " num_batches=num_batches,\n", + " energy_tol=energy_tol,\n", + " occupancies_tol=occupancies_tol,\n", + " max_iterations=max_iterations,\n", + " sci_solver=sci_solver,\n", + " symmetrize_spin=symmetrize_spin,\n", + " carryover_threshold=carryover_threshold,\n", + " callback=callback,\n", + " seed=rng,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9d78906b-4759-4506-9c69-85d4e67766b3", + "metadata": {}, + "source": [ + "Now, we plot the results.\n", + "\n", + "The first plot shows that after a few iterations we estimate the ground state energy within ``~16 mH`` (chemical accuracy is typically accepted to be ``1 kcal/mol`` $\\approx$ ``1.6 mH``). Remember, the quantum samples in this demo were pure noise. The signal here comes from *a priori* knowledge of the electronic structure and molecular Hamiltonian.\n", + "\n", + "The second plot shows the average occupancy of each spatial orbital after the final iteration. We can see that both the spin-up and spin-down electrons occupy the first five orbitals with high probability in our solutions." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "caffd888-e89c-4aa9-8bae-4d1bb723b35e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exact energy: -109.04667 Ha\n", + "SQD energy: -109.03097 Ha\n", + "Absolute error: 0.01570 Ha\n" + ] + }, + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Data for energies plot\n", + "x1 = range(len(result_history))\n", + "min_e = [\n", + " min(result, key=lambda res: res.energy).energy + nuclear_repulsion_energy\n", + " for result in result_history\n", + "]\n", + "e_diff = [abs(e - exact_energy) for e in min_e]\n", + "yt1 = [1.0, 1e-1, 1e-2, 1e-3, 1e-4]\n", + "\n", + "# Chemical accuracy (+/- 1 milli-Hartree)\n", + "chem_accuracy = 0.001\n", + "\n", + "# Data for avg spatial orbital occupancy\n", + "y2 = np.sum(result.orbital_occupancies, axis=0)\n", + "x2 = range(len(y2))\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + "# Plot energies\n", + "axs[0].plot(x1, e_diff, label=\"energy error\", marker=\"o\")\n", + "axs[0].set_xticks(x1)\n", + "axs[0].set_xticklabels(x1)\n", + "axs[0].set_yticks(yt1)\n", + "axs[0].set_yticklabels(yt1)\n", + "axs[0].set_yscale(\"log\")\n", + "axs[0].set_ylim(1e-4)\n", + "axs[0].axhline(y=chem_accuracy, color=\"#BF5700\", linestyle=\"--\", label=\"chemical accuracy\")\n", + "axs[0].set_title(\"Approximated Ground State Energy Error vs SQD Iterations\")\n", + "axs[0].set_xlabel(\"Iteration Index\", fontdict={\"fontsize\": 12})\n", + "axs[0].set_ylabel(\"Energy Error (Ha)\", fontdict={\"fontsize\": 12})\n", + "axs[0].legend()\n", + "\n", + "# Plot orbital occupancy\n", + "axs[1].bar(x2, y2, width=0.8)\n", + "axs[1].set_xticks(x2)\n", + "axs[1].set_xticklabels(x2)\n", + "axs[1].set_title(\"Avg Occupancy per Spatial Orbital\")\n", + "axs[1].set_xlabel(\"Orbital Index\", fontdict={\"fontsize\": 12})\n", + "axs[1].set_ylabel(\"Avg Occupancy\", fontdict={\"fontsize\": 12})\n", + "\n", + "print(f\"Exact energy: {exact_energy:.5f} Ha\")\n", + "print(f\"SQD energy: {min_e[-1]:.5f} Ha\")\n", + "print(f\"Absolute error: {e_diff[-1]:.5f} Ha\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb b/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb new file mode 100644 index 00000000000..e80a745695a --- /dev/null +++ b/docs/addons/qiskit-addon-sqd/tutorials/02_fermionic_lattice_hamiltonian.ipynb @@ -0,0 +1,514 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e95ccc49-13da-4dc5-8bc8-e8c313d5c868", + "metadata": {}, + "source": [ + "# Improving energy estimation of a Fermionic lattice model with SQD\n", + "\n", + "In this tutorial we implement a [Qiskit pattern](/docs/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of a Fermionic lattice Hamiltonian known as the single-impurity Anderson model. We will follow a sample-based quantum diagonalization approach to process samples taken from a set of ``16``-qubit Krylov basis states over increasing time intervals. These states are realized on the quantum device using Trotterization of the time evolution. In order to account for the effect of quantum noise, the configuration recovery technique is used. Assuming a good initial state and sparsity of the ground state, [this approach is proven to converge efficiently](https://arxiv.org/abs/2501.09702).\n", + "\n", + "The pattern can be described in four steps:\n", + "\n", + "1. **Step 1: Map to quantum problem**\n", + " - Generate a set of Krylov basis states (i.e., Trotterized time-evolution circuits) over increasing time intervals for estimating the ground state\n", + "2. **Step 2: Optimize the problem**\n", + " - Transpile the circuits for the backend\n", + "3. **Step 3: Execute experiments**\n", + " - Draw samples from the circuits using the ``Sampler`` primitive\n", + "4. **Step 4: Post-process results**\n", + " - Self-consistent configuration recovery loop\n", + " - Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration\n", + " - Probabilistically create batches of subsamples from recovered bitstrings\n", + " - Project and diagonalize the Fermionic lattice Hamiltonian over each sampled subspace\n", + " - Save the minimum ground state energy found across all batches and update the avg orbital occupancy" + ] + }, + { + "cell_type": "markdown", + "id": "c324e5a3-9f5f-4fa0-ab16-10c1b21227de", + "metadata": {}, + "source": [ + "### Step 1: Map problem to a quantum circuit" + ] + }, + { + "cell_type": "markdown", + "id": "0c4a7125-fac3-4876-84fe-4eaf52313b95", + "metadata": {}, + "source": [ + "First, we will create the one- and two-body Hamiltonians describing the one-dimensional single-impurity Anderson model (SIAM) with ``7`` bath sites (``8`` electrons in ``8`` orbitals). This model is used to describe magnetic impurities embedded in metals.\n", + "\n", + "Then we will create the ``16``-qubit Trotter circuits used to generate the quantum Krylov subspace." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "81c1127b-82fc-453b-b77b-7824a096dfe3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "n_bath = 7 # number of bath sites\n", + "\n", + "V = 1 # hybridization amplitude\n", + "t = 1 # bath hopping amplitude\n", + "U = 10 # Impurity onsite repulsion\n", + "eps = -U / 2 # Chemical potential for the impurity\n", + "\n", + "# Place the impurity on the first qubit\n", + "impurity_index = 0\n", + "\n", + "# One body matrix elements in the \"position\" basis\n", + "h1e = -t * np.diag(np.ones(n_bath), k=1) - t * np.diag(np.ones(n_bath), k=-1)\n", + "h1e[impurity_index, impurity_index + 1] = -V\n", + "h1e[impurity_index + 1, impurity_index] = -V\n", + "h1e[impurity_index, impurity_index] = eps\n", + "\n", + "# Two body matrix elements in the \"position\" basis\n", + "h2e = np.zeros((n_bath + 1, n_bath + 1, n_bath + 1, n_bath + 1))\n", + "h2e[impurity_index, impurity_index, impurity_index, impurity_index] = U" + ] + }, + { + "cell_type": "markdown", + "id": "546ddc26-b91a-46f4-8dc0-f3987626dd60", + "metadata": {}, + "source": [ + "Next, we will generate the quantum Krylov subspace with a set of Trotterized quantum circuits. Here we create helpers for generating the initial (reference) state as well as the time evolution for the one- and two-body parts of the Hamiltonian. For a more detailed description of this model and how the circuits are designed, please refer to [the paper](https://arxiv.org/abs/2501.09702)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0ca03aea-723c-41b6-aab7-6c021f558c9b", + "metadata": {}, + "outputs": [], + "source": [ + "import ffsim\n", + "import scipy\n", + "from qiskit import QuantumCircuit, QuantumRegister\n", + "from qiskit.circuit.library import CPhaseGate, XGate, XXPlusYYGate\n", + "\n", + "n_modes = n_bath + 1\n", + "nelec = (n_modes // 2, n_modes // 2)\n", + "\n", + "dt = 0.2\n", + "Utar = scipy.linalg.expm(-1j * dt * h1e)\n", + "\n", + "\n", + "# The reference state\n", + "def initial_state(q_circuit, norb, nocc):\n", + " \"\"\"Prepare an initial state.\"\"\"\n", + " for i in range(nocc):\n", + " q_circuit.append(XGate(), [i])\n", + " q_circuit.append(XGate(), [norb + i])\n", + " rot = XXPlusYYGate(np.pi / 2, -np.pi / 2)\n", + "\n", + " for i in range(3):\n", + " for j in range(nocc - i - 1, nocc + i, 2):\n", + " q_circuit.append(rot, [j, j + 1])\n", + " q_circuit.append(rot, [norb + j, norb + j + 1])\n", + " q_circuit.append(rot, [j + 1, j + 2])\n", + " q_circuit.append(rot, [norb + j + 1, norb + j + 2])\n", + "\n", + "\n", + "# The one-body time evolution\n", + "free_fermion_evolution = ffsim.qiskit.OrbitalRotationJW(n_modes, Utar)\n", + "\n", + "\n", + "# The two-body time evolution\n", + "def append_diagonal_evolution(dt, U, impurity_qubit, num_orb, q_circuit):\n", + " \"\"\"Append two-body time evolution to a quantum circuit.\"\"\"\n", + " if U != 0:\n", + " q_circuit.append(\n", + " CPhaseGate(-dt / 2 * U),\n", + " [impurity_qubit, impurity_qubit + num_orb],\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "8ee0bd78-cc2e-4a81-9e21-3eff832399a9", + "metadata": {}, + "source": [ + "Generate ``d`` time-evolved states that specify the quantum Krylov subspace. Here, we have chosen ``d=8``. The error from sampling Krylov basis states converges with increasing ``d``. Note that the particulars of this problem instance allow for an efficient compilation of the one-body evolution using `OrbitalRotationJW`; however, in general, one could use Qiskit's [PauliEvolutionGate](/docs/api/qiskit/qiskit.circuit.library.PauliEvolutionGate) to implement the Trotterized time evolution of the full Hamiltonian." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "95cc557d-daf4-4489-ae55-ce37ba7e6398", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate the initial state\n", + "qubits = QuantumRegister(2 * n_modes, name=\"q\")\n", + "init_state = QuantumCircuit(qubits)\n", + "initial_state(init_state, n_modes, n_modes // 2)\n", + "init_state.draw(\"mpl\", scale=0.4, fold=-1)\n", + "\n", + "d = 8 # Number of Krylov basis states\n", + "circuits = []\n", + "for i in range(d):\n", + " circ = init_state.copy()\n", + " circuits.append(circ)\n", + " for _ in range(i):\n", + " append_diagonal_evolution(dt, U, impurity_index, n_modes, circ)\n", + " circ.append(free_fermion_evolution, qubits)\n", + " append_diagonal_evolution(dt, U, impurity_index, n_modes, circ)\n", + " circ.measure_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a9b37df5-8a09-46d1-ac9c-56336ef95669", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuits[0].draw(\"mpl\", scale=0.4, fold=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "22b7a11c-bdc6-4767-885a-569fd981c69f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuits[-1].draw(\"mpl\", scale=0.4, fold=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "efc1ec7a-621d-4833-8087-252b82cfcbf1", + "metadata": {}, + "source": [ + "### Step 2: Optimize the problem" + ] + }, + { + "cell_type": "markdown", + "id": "b2a21d4b-ec8b-4803-bfe8-8686a9d12e06", + "metadata": {}, + "source": [ + "After we have created the Trotterized circuits, we will optimize them for a target hardware. We need to choose the hardware device to use before optimization. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device. To run on a real QPU, just replace the fake backend with a real backend. Check out the [Qiskit IBM Runtime docs](/docs/guides/get-started-with-primitives#get-started-with-sampler) for more info." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "44010b25-740d-454e-b7e9-ea95ffce211e", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider.backends import FakeSherbrooke\n", + "\n", + "backend = FakeSherbrooke()" + ] + }, + { + "cell_type": "markdown", + "id": "0d4f202a-a487-41c8-8567-a313e92bc03d", + "metadata": {}, + "source": [ + "Next, we will transpile the circuits to the target backend using Qiskit." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f12c69fb-d308-4092-ba87-689ff4f1192a", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit.transpiler import generate_preset_pass_manager\n", + "\n", + "# The circuit needs to be transpiled to the AerSimulator target\n", + "pass_manager = generate_preset_pass_manager(optimization_level=3, backend=backend)\n", + "isa_circuits = pass_manager.run(circuits)" + ] + }, + { + "cell_type": "markdown", + "id": "7bf409f6-5581-48a1-9bf9-ae10561f844c", + "metadata": {}, + "source": [ + "### Step 3: Execute experiments" + ] + }, + { + "cell_type": "markdown", + "id": "a5bb4ecb-a094-44da-a483-79b1b6c67572", + "metadata": {}, + "source": [ + "After optimizing the circuits for hardware execution, we are ready to run them on the target hardware and collect samples for ground state energy estimation. Here we use ``SamplerV2`` from ``qiskit-ibm-runtime`` to simulate noisy samples taken from the ``ibm_sherbrooke`` backend. We then combine the counts from each of the Krylov basis states into a single counts dictionary and plot the top 20 most commonly sampled bitstrings.\n", + "\n", + "***Note: [Qiskit Aer](/docs/addons/qiskit-aer/index) is required to simulate samples from transpiled circuits.***" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6060559e-3520-4f3d-bd12-afbae24f171b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.primitives import BitArray\n", + "from qiskit.visualization import plot_histogram\n", + "from qiskit_ibm_runtime import SamplerV2 as Sampler\n", + "\n", + "# Sample from the circuits\n", + "noisy_sampler = Sampler(backend, options={\"simulator\": {\"seed_simulator\": 24}})\n", + "job = noisy_sampler.run(isa_circuits, shots=500)\n", + "\n", + "# Combine the counts from the individual Trotter circuits\n", + "bit_array = BitArray.concatenate_shots([result.data.meas for result in job.result()])\n", + "\n", + "plot_histogram(bit_array.get_counts(), number_to_keep=20)" + ] + }, + { + "cell_type": "markdown", + "id": "b89a4190-eea3-4b09-8202-755655126e57", + "metadata": {}, + "source": [ + "### Step 4: Post-process the results" + ] + }, + { + "cell_type": "markdown", + "id": "ac0f8e6a-ce60-40ce-be3b-efce3c74bcba", + "metadata": {}, + "source": [ + "Now, we run the SQD algorithm using the `diagonalize_fermionic_hamiltonian` function. See the [API documentation](../apidocs/qiskit_addon_sqd.fermion.rst#qiskit_addon_sqd.fermion.diagonalize_fermionic_hamiltonian) for explanations of the arguments to this function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eee1179e-2947-42f0-97d5-49c91ce551ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1\n", + "\tSubsample 0\n", + "\t\tEnergy: -13.257128325607997\n", + "\t\tSubspace dimension: 3969\n", + "\tSubsample 1\n", + "\t\tEnergy: -13.257128325607997\n", + "\t\tSubspace dimension: 3969\n", + "\tSubsample 2\n", + "\t\tEnergy: -13.257128325607997\n", + "\t\tSubspace dimension: 3969\n", + "Iteration 2\n", + "\tSubsample 0\n", + "\t\tEnergy: -13.319666127542039\n", + "\t\tSubspace dimension: 4096\n", + "\tSubsample 1\n", + "\t\tEnergy: -13.420534292304595\n", + "\t\tSubspace dimension: 4624\n", + "\tSubsample 2\n", + "\t\tEnergy: -9.136171594591085\n", + "\t\tSubspace dimension: 4624\n", + "Iteration 3\n", + "\tSubsample 0\n", + "\t\tEnergy: -13.422491814612833\n", + "\t\tSubspace dimension: 4900\n", + "\tSubsample 1\n", + "\t\tEnergy: -13.422491814612833\n", + "\t\tSubspace dimension: 4900\n", + "\tSubsample 2\n", + "\t\tEnergy: -13.422491814612833\n", + "\t\tSubspace dimension: 4900\n", + "Iteration 4\n", + "\tSubsample 0\n", + "\t\tEnergy: -13.422491814612833\n", + "\t\tSubspace dimension: 4900\n", + "\tSubsample 1\n", + "\t\tEnergy: -13.422491814612833\n", + "\t\tSubspace dimension: 4900\n", + "\tSubsample 2\n", + "\t\tEnergy: -13.422491814612833\n", + "\t\tSubspace dimension: 4900\n" + ] + } + ], + "source": [ + "from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian\n", + "\n", + "# List to capture intermediate results\n", + "result_history = []\n", + "\n", + "\n", + "def callback(results: list[SCIResult]):\n", + " result_history.append(results)\n", + " iteration = len(result_history)\n", + " print(f\"Iteration {iteration}\")\n", + " for i, result in enumerate(results):\n", + " print(f\"\\tSubsample {i}\")\n", + " print(f\"\\t\\tEnergy: {result.energy}\")\n", + " print(f\"\\t\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\")\n", + "\n", + "\n", + "rng = np.random.default_rng(24)\n", + "result = diagonalize_fermionic_hamiltonian(\n", + " h1e,\n", + " h2e,\n", + " bit_array,\n", + " samples_per_batch=300,\n", + " norb=n_modes,\n", + " nelec=nelec,\n", + " num_batches=3,\n", + " max_iterations=10,\n", + " symmetrize_spin=True,\n", + " callback=callback,\n", + " seed=rng,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ba6c24e6-af10-4f2a-9ad2-ccf9c2785de8", + "metadata": {}, + "source": [ + "Now, we plot the results. The first plot shows that after a few iterations, we obtain the exact ground state energy.\n", + "\n", + "This example is small enough that we are able to explore the full Hilbert space, as seen in the print statements above. Remember, the full Hilbert space is dimension ``(num_orbitals choose nelec_a) * (num_orbitals choose nelec_b)``. So for this problem: `(8 choose 4)**2 = 4900`. The subspace dimensions increase due enhanced configuration recovery, and also the fact that the SQD algorithm carries important configurations from one iteration to the next. By the last iteration, we are diagonalizing in the entire Hilbert space.\n", + "\n", + "The second plot shows the average occupancy of each spatial orbital across all batches' solutions. We see that with high probability each orbital contains one electron." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "45b2ca6b-c570-4724-b811-e42e25ef7918", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exact energy: -13.42249\n", + "SQD energy: -13.42249\n", + "Absolute error: 0.00000\n" + ] + }, + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "exact_energy = -13.422491814605827\n", + "min_es = [min(result, key=lambda res: res.energy).energy for result in result_history]\n", + "min_id, min_e = min(enumerate(min_es), key=lambda x: x[1])\n", + "\n", + "# Data for energies plot\n", + "x1 = range(len(result_history))\n", + "yt1 = list(np.arange(-13.5, -13.1, 0.1))\n", + "ytl = [f\"{i:.1f}\" for i in yt1]\n", + "\n", + "# Data for avg spatial orbital occupancy\n", + "y2 = np.sum(result.orbital_occupancies, axis=0)\n", + "x2 = range(len(y2))\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + "# Plot energies\n", + "axs[0].plot(x1, min_es, label=\"SQD energy\", marker=\"o\")\n", + "axs[0].set_xticks(x1)\n", + "axs[0].set_xticklabels(x1)\n", + "axs[0].set_yticks(yt1)\n", + "axs[0].set_yticklabels(ytl)\n", + "axs[0].axhline(y=exact_energy, color=\"#BF5700\", linestyle=\"--\", label=\"Exact energy\")\n", + "axs[0].set_title(\"Approximated Ground State Energy vs SQD Iterations\")\n", + "axs[0].set_xlabel(\"Iteration Index\", fontdict={\"fontsize\": 12})\n", + "axs[0].set_ylabel(\"Energy\", fontdict={\"fontsize\": 12})\n", + "axs[0].legend()\n", + "\n", + "# Plot orbital occupancy\n", + "axs[1].bar(x2, y2, width=0.8)\n", + "axs[1].set_xticks(x2)\n", + "axs[1].set_xticklabels(x2)\n", + "axs[1].set_title(\"Avg Occupancy per Spatial Orbital\")\n", + "axs[1].set_xlabel(\"Orbital Index\", fontdict={\"fontsize\": 12})\n", + "axs[1].set_ylabel(\"Avg Occupancy\", fontdict={\"fontsize\": 12})\n", + "\n", + "print(f\"Exact energy: {exact_energy:.5f}\")\n", + "print(f\"SQD energy: {min_e:.5f}\")\n", + "print(f\"Absolute error: {abs(min_e - exact_energy):.5f}\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/addons/qiskit-addon-utils/_toc.json b/docs/addons/qiskit-addon-utils/_toc.json new file mode 100644 index 00000000000..ffa73e62c08 --- /dev/null +++ b/docs/addons/qiskit-addon-utils/_toc.json @@ -0,0 +1,28 @@ +{ + "title": "Qiskit addon utilities", + "children": [ + { + "title": "Documentation Home", + "url": "/docs/addons/qiskit-addon-utils" + }, + { + "title": "Installation Instructions", + "url": "/docs/addons/qiskit-addon-utils/install" + }, + { + "title": "How-To Guides", + "children": [ + { + "title": "Use edge coloring to reduce the depth of quantum circuits", + "url": "/docs/addons/qiskit-addon-utils/how_tos/color_device_edges_to_improve_depth" + }, + { + "title": "Slicing circuits using qiskit_addon_utils.slicing", + "url": "/docs/addons/qiskit-addon-utils/how_tos/create_circuit_slices" + } + ], + "url": "/docs/addons/qiskit-addon-utils/how_tos/index" + } + ], + "collapsed": true +} diff --git a/docs/addons/qiskit-addon-utils/index.mdx b/docs/addons/qiskit-addon-utils/index.mdx new file mode 100644 index 00000000000..474e54f027d --- /dev/null +++ b/docs/addons/qiskit-addon-utils/index.mdx @@ -0,0 +1,44 @@ +--- +title: "Qiskit addon utilities" +--- + +# Qiskit addon utilities + +[Qiskit addons](/docs/guides/addons) are a collection of modular tools for building utility-scale workloads powered by Qiskit. + +This package contains functionality which is meant to supplement workflows involving one or more Qiskit addons. For example, this package contains functions for creating Hamiltonians, generating Trotter time evolution circuits, and slicing and combining quantum circuits in time-wise partitions. + +## Documentation + +All documentation is available [here](/docs/addons/qiskit-addon-utils). + +## Installation + +We encourage installing this package via `pip`, when possible: + +```bash +pip install 'qiskit-addon-utils' +``` + +For more installation information refer to the [installation instructions](install) in the documentation. + +## Deprecation Policy + +We follow [semantic versioning](https://semver.org/) and are guided by the principles in [Qiskit’s deprecation policy](https://github.com/Qiskit/qiskit/blob/main/DEPRECATION.md). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the [release notes](/docs/addons/qiskit-addon-utils/release-notes). + +## Contributing + +The source code is available [on GitHub](https://github.com/Qiskit/qiskit-addon-utils). + +The developer guide is located at [CONTRIBUTING.md](https://github.com/Qiskit/qiskit-addon-utils/blob/main/CONTRIBUTING.md) in the root of this project’s repository. By participating, you are expected to uphold Qiskit’s [code of conduct](https://github.com/Qiskit/qiskit/blob/main/CODE_OF_CONDUCT.md). + +We use [GitHub issues](https://github.com/Qiskit/qiskit-addon-utils/issues/new/choose) for tracking requests and bugs. + +## Citation + +If you use this package in your research, please cite it according to the [CITATION.bib](https://github.com/Qiskit/qiskit-addon-utils/blob/main/CITATION.bib) file. + +## License + +[Apache License 2.0](https://github.com/Qiskit/qiskit-addon-utils/blob/main/LICENSE.txt) + diff --git a/docs/addons/qiskit-addon-utils/install.mdx b/docs/addons/qiskit-addon-utils/install.mdx new file mode 100644 index 00000000000..e7ff94c2ebe --- /dev/null +++ b/docs/addons/qiskit-addon-utils/install.mdx @@ -0,0 +1,76 @@ +--- +title: "Installation Instructions" +--- + +# Installation Instructions + +Let’s see how to install the package. The first thing to do is choose how you’re going to run and install the packages. There are two primary ways to do this: + +* [Option 1: Install from PyPI](#option-1) +* [Option 2: Install from Source](#option-2) + +## Pre-Installation + +First, create a minimal environment with only Python installed in it. We recommend using [Python virtual environments](https://docs.python.org/3.10/tutorial/venv.html). + +```sh +python3 -m venv /path/to/virtual/environment +``` + +Activate your new environment. + +```sh +source /path/to/virtual/environment/bin/activate +``` + +Note: If you are using Windows, use the following commands in PowerShell: + +```pwsh +python3 -m venv c:\path\to\virtual\environment +c:\path\to\virtual\environment\Scripts\Activate.ps1 +``` + + + +## Option 1: Install from PyPI + +The most straightforward way to install the `qiskit-addon-utils` package is via `PyPI`. + +```sh +pip install 'qiskit-addon-utils' +``` + + + +## Option 2: Install from Source + +Users who wish to develop in the repository or run the notebooks locally may want to install from source. + +If so, the first step is to clone the `qiskit-addon-utils` repository. + +```sh +git clone git@github.com:Qiskit/qiskit-addon-utils.git +``` + +Next, upgrade pip and enter the repository. + +```sh +pip install --upgrade pip +cd qiskit-addon-utils +``` + +The next step is to install `qiskit-addon-utils` to the virtual environment. If you plan on running the notebooks, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, you may want to install the `dev` dependencies. + +Adjust the options below to suit your needs. + +```sh +pip install tox notebook -e '.[notebook-dependencies,dev]' +``` + +If you installed the notebook dependencies, you can get started by running the notebooks in the docs. + +```python +cd docs/ +jupyter lab +``` + diff --git a/docs/addons/qiskit-addon-utils/release-notes.mdx b/docs/addons/qiskit-addon-utils/release-notes.mdx new file mode 100644 index 00000000000..31d21da94a9 --- /dev/null +++ b/docs/addons/qiskit-addon-utils/release-notes.mdx @@ -0,0 +1,93 @@ +--- +title: "Release Notes" +--- + + + + + +# + + + + + +## 0.3.1 + + + +### Bug Fixes + +* Fixed bugs in [`qiskit_addon_utils.exp_vals.executor_expectation_values()`](qiskit_addon_utils.exp_vals#qiskit_addon_utils.exp_vals.executor_expectation_values "qiskit_addon_utils.exp_vals.executor_expectation_values") causing indexing errors when bit\_array dimensions associated with twirling randomizations were not specified in avg\_axis. + + + + + +## 0.3.0 + + + +### New Features + +* Added a new sub-package, [`qiskit_addon_utils.exp_vals`](qiskit_addon_utils.exp_vals#module-qiskit_addon_utils.exp_vals "qiskit_addon_utils.exp_vals"), containing functions for calculating expectation values from the outputs of Qiskit Runtime’s new `Executor` primitive prototype. This includes: + + > * [`qiskit_addon_utils.exp_vals.executor_expectation_values()`](qiskit_addon_utils.exp_vals#qiskit_addon_utils.exp_vals.executor_expectation_values "qiskit_addon_utils.exp_vals.executor_expectation_values") for expectation value calculation + > * [`qiskit_addon_utils.exp_vals.get_measurement_bases()`](qiskit_addon_utils.exp_vals#qiskit_addon_utils.exp_vals.get_measurement_bases "qiskit_addon_utils.exp_vals.get_measurement_bases") for identifying a minimal set of measurement bases to cover all terms in the observable. + > * [`qiskit_addon_utils.exp_vals.map_observable_isa_to_canonical()`](qiskit_addon_utils.exp_vals#qiskit_addon_utils.exp_vals.map_observable_isa_to_canonical "qiskit_addon_utils.exp_vals.map_observable_isa_to_canonical"), [`qiskit_addon_utils.exp_vals.map_observable_virtual_to_canonical()`](qiskit_addon_utils.exp_vals#qiskit_addon_utils.exp_vals.map_observable_virtual_to_canonical "qiskit_addon_utils.exp_vals.map_observable_virtual_to_canonical"), and [`qiskit_addon_utils.exp_vals.map_observable_isa_to_virtual()`](qiskit_addon_utils.exp_vals#qiskit_addon_utils.exp_vals.map_observable_isa_to_virtual "qiskit_addon_utils.exp_vals.map_observable_isa_to_virtual") for transforming the qubit order of Pauli observables. + +* Added two new functions to the [`qiskit_addon_utils.noise_management`](qiskit_addon_utils.noise_management#module-qiskit_addon_utils.noise_management "qiskit_addon_utils.noise_management") sub-package: + + > * [`qiskit_addon_utils.noise_management.trex_factors()`](qiskit_addon_utils.noise_management#qiskit_addon_utils.noise_management.trex_factors "qiskit_addon_utils.noise_management.trex_factors") for calculating TREX readout error mitigation scaling factors + > * [`qiskit_addon_utils.noise_management.gamma_from_noisy_boxes()`](qiskit_addon_utils.noise_management#qiskit_addon_utils.noise_management.gamma_from_noisy_boxes "qiskit_addon_utils.noise_management.gamma_from_noisy_boxes") for calculating the gamma factor for a given circuit and noise model. + + + + + +## 0.2.0 + + + + + +### New Features + +* Add the transpiler passes required to pre-process the circuits, as well as the `PostSelector` and `PostSelectionSummary` objects required to post-process the results. + + + +### Upgrade Notes + +* This addition requires bumping the version of Qiskit SDK to at least 2.0. + +* This package is now compatible with Qiskit SDK 2.0. + + + + + +### Bug Fixes + +* Support circuits that include classical bits when slicing a circuit by gate types. See #115 \<[https://github.com/Qiskit/qiskit-addon-utils/issues/115](https://github.com/Qiskit/qiskit-addon-utils/issues/115)> for details. + + + +### Other Notes + +* This release adds support for Python 3.13. No code changes were necessary, so older releases are expected to work on Python 3.13 too. + + + + + +## 0.1.0 + + + + + +### Upgrade Notes + +* The keyword argument `include_slice_barriers` of [`combine_slices()`](qiskit_addon_utils.slicing#qiskit_addon_utils.slicing.combine_slices "qiskit_addon_utils.slicing.combine_slices") has been renamed to `include_barriers`. + diff --git a/docs/ruff.toml b/docs/ruff.toml index 50ec7749e1f..71708e929c0 100644 --- a/docs/ruff.toml +++ b/docs/ruff.toml @@ -1 +1,2 @@ line-length=78 +exclude = ["addons"] diff --git a/public/docs/images/addons/qiskit-addon-aqc-tensor/aqc-compression.avif b/public/docs/images/addons/qiskit-addon-aqc-tensor/aqc-compression.avif new file mode 100644 index 0000000000000000000000000000000000000000..729a6dfbf2e6ab183c20cb18a30a66bddd3de98c GIT binary patch literal 29893 zcmYJXQ|6p57!+#vm!PeBp@IU&$Aha;Carz$w0C2E0cKJX2e;Llf(#7__ z1n}R=VrgS<{2wMF000E=9|Zs~`F|Mz!0G9q_Agu_{!anm{YN=0E$#k$jQ>_q{}I#w 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