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Format Python code with psf/black push #1

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346 changes: 263 additions & 83 deletions quanestimation/AdaptiveScheme/Adapt.py
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46 changes: 22 additions & 24 deletions quanestimation/AdaptiveScheme/Adapt_MZI.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,8 +17,8 @@ class Adapt_MZI:
-- Initial state (density matrix).

"""
def __init__(self, x, p, rho0):

def __init__(self, x, p, rho0):
self.x = x
self.p = p
self.rho0 = rho0
Expand All @@ -33,18 +33,16 @@ def online(self, target="sharpness", output="phi"):
Parameters
----------
> **target:** `string`
-- Setting the target function for calculating the tunable phase. Options are:
"sharpness" (default) -- Sharpness.
"MI" -- Mutual information.
-- Setting the target function for calculating the tunable phase. Options are:
"sharpness" (default) -- Sharpness.
"MI" -- Mutual information.

> **output:** `string`
-- The output the class. Options are:
"phi" (default) -- The tunable phase.
"dphi" -- Phase difference.
-- The output the class. Options are:
"phi" (default) -- The tunable phase.
"dphi" -- Phase difference.
"""
phi = QJL.adaptMZI_online(
self.x, self.p, self.rho0, output, target
)
phi = QJL.adaptMZI_online(self.x, self.p, self.rho0, output, target)

def offline(
self,
Expand All @@ -65,13 +63,13 @@ def offline(
Parameters
----------
> **target:** `string`
-- Setting the target function for calculating the tunable phase. Options are:
"sharpness" (default) -- Sharpness.
"MI" -- Mutual information.
-- Setting the target function for calculating the tunable phase. Options are:
"sharpness" (default) -- Sharpness.
"MI" -- Mutual information.

> **method:** `string`
-- The method for the adaptive phase estimation. Options are:
"DE" (default) -- DE algorithm for the adaptive phase estimation.
-- The method for the adaptive phase estimation. Options are:
"DE" (default) -- DE algorithm for the adaptive phase estimation.
"PSO" -- PSO algorithm for the adaptive phase estimation.

If the `method=DE`, the parameters are:
Expand All @@ -83,7 +81,7 @@ def offline(

> **max_episode:** `int`
-- The number of episodes.

> **c:** `float`
-- Mutation constant.

Expand All @@ -95,28 +93,28 @@ def offline(

> **eps:** `float`
-- Machine epsilon.

If the `method=PSO`, the parameters are:

> **deltaphi0:** `list`
-- Initial guesses of phase difference.

> **max_episode:** `int or list`
-- If it is an integer, for example max_episode=1000, it means the
-- If it is an integer, for example max_episode=1000, it means the
program will continuously run 1000 episodes. However, if it is an
array, for example max_episode=[1000,100], the program will run
1000 episodes in total but replace states of all the particles
array, for example max_episode=[1000,100], the program will run
1000 episodes in total but replace states of all the particles
with global best every 100 episodes.

> **c0:** `float`
-- The damping factor that assists convergence, also known as inertia weight.

> **c1:** `float`
-- The exploitation weight that attracts the particle to its best previous
-- The exploitation weight that attracts the particle to its best previous
position, also known as cognitive learning factor.

> **c2:** `float`
-- The exploitation weight that attracts the particle to the best position
-- The exploitation weight that attracts the particle to the best position
in the neighborhood, also known as social learning factor.

> **eps:** `float`
Expand All @@ -127,7 +125,7 @@ def offline(
np.array([int(list(comb_tp[i])[j]) for j in range(self.N)])
for i in range(2**self.N)
]

if method == "DE":
QJL.DE_deltaphiOpt(
self.x,
Expand Down
70 changes: 31 additions & 39 deletions quanestimation/AsymptoticBound/AnalogCramerRao.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,31 +13,31 @@ def HCRB(rho, drho, W, eps=1e-8):
The HCRB is defined as:

$$
\min_{\{X_i\}} \left\{ \mathrm{Tr}(\mathrm{Re}Z) + \mathrm{Tr}(| \mathrm{Im} Z |) \right\},
\min_{\{X_i\}} \left\{ \mathrm{Tr}(\mathrm{Re}Z) + \mathrm{Tr}(| \mathrm{Im} Z |) \right\},
$$

where $Z_{ij} = \mathrm{Tr}(\rho X_i X_j)$ and $V$ is the covariance matrix.

Args:
rho (np.array):
rho (np.array):
Density matrix.
drho (list):
Derivatives of the density matrix with respect to unknown parameters.
drho (list):
Derivatives of the density matrix with respect to unknown parameters.
For example, `drho[0]` is the derivative with respect to the first parameter.
W (np.array):
W (np.array):
Weight matrix for the bound.
eps (float, optional):
eps (float, optional):
Machine epsilon for numerical stability.

Returns:
(float):
Returns:
(float):
The value of the Holevo Cramer-Rao bound.

Raises:
TypeError: If `drho` is not a list.

Notes:
In the single-parameter scenario, the HCRB is equivalent to the QFI.
In the single-parameter scenario, the HCRB is equivalent to the QFI.
For a rank-one weight matrix, the HCRB is equivalent to the inverse of the QFIM.
"""

Expand All @@ -50,7 +50,7 @@ def HCRB(rho, drho, W, eps=1e-8):
"Returning QFI value."
)
return QFIM(rho, drho, eps=eps)

if matrix_rank(W) == 1:
print(
"For rank-one weight matrix, HCRB is equivalent to QFIM. "
Expand All @@ -70,8 +70,7 @@ def HCRB(rho, drho, W, eps=1e-8):
vec_drho = []
for param_idx in range(num_params):
components = [
np.real(np.trace(drho[param_idx] @ basis_mat))
for basis_mat in basis
np.real(np.trace(drho[param_idx] @ basis_mat)) for basis_mat in basis
]
vec_drho.append(np.array(components))

Expand All @@ -91,12 +90,7 @@ def HCRB(rho, drho, W, eps=1e-8):
X = cp.Variable((num, num_params))

# Define constraints
constraints = [
cp.bmat([
[V, X.T @ R.conj().T],
[R @ X, np.identity(num)]
]) >> 0
]
constraints = [cp.bmat([[V, X.T @ R.conj().T], [R @ X, np.identity(num)]]) >> 0]

# Add linear constraints
for i in range(num_params):
Expand All @@ -113,65 +107,63 @@ def HCRB(rho, drho, W, eps=1e-8):

return problem.value


def NHB(rho, drho, W):
r"""
Calculation of the Nagaoka-Hayashi bound (NHB) via the semidefinite program (SDP).

The NHB is defined as:

$$
\min_{X} \left\{ \mathrm{Tr}[W \mathrm{Re}(Z)] + \|\sqrt{W} \mathrm{Im}(Z) \sqrt{W}\|_1 \right\},
\min_{X} \left\{ \mathrm{Tr}[W \mathrm{Re}(Z)] + \|\sqrt{W} \mathrm{Im}(Z) \sqrt{W}\|_1 \right\},
$$

where $Z_{ij} = \mathrm{Tr}(\rho X_i X_j)$ and $V$ is the covariance matrix.

Args:
rho (np.array):
rho (np.array):
Density matrix.
drho (list):
Derivatives of the density matrix with respect to unknown parameters.
drho (list):
Derivatives of the density matrix with respect to unknown parameters.
For example, `drho[0]` is the derivative with respect to the first parameter.
W (np.array):
W (np.array):
Weight matrix for the bound.

Returns:
(float):
Returns:
(float):
The value of the Nagaoka-Hayashi bound.

Raises:
TypeError:
TypeError:
If `drho` is not a list.
"""
if not isinstance(drho, list):
raise TypeError("drho must be a list of derivative matrices")

dim = len(rho)
num_params = len(drho)

# Initialize a temporary matrix for L components
L_temp = [[None for _ in range(num_params)] for _ in range(num_params)]

# Create Hermitian variables for the upper triangle and mirror to lower triangle
for i in range(num_params):
for j in range(i, num_params):
L_temp[i][j] = cp.Variable((dim, dim), hermitian=True)
if i != j:
L_temp[j][i] = L_temp[i][j]

# Construct the block matrix L
L_blocks = [cp.hstack(L_temp[i]) for i in range(num_params)]
L = cp.vstack(L_blocks)

# Create Hermitian variables for X
X = [cp.Variable((dim, dim), hermitian=True) for _ in range(num_params)]

# Construct the block matrix constraint
block_matrix = cp.bmat([
[L, cp.vstack(X)],
[cp.hstack(X), np.identity(dim)]
])
block_matrix = cp.bmat([[L, cp.vstack(X)], [cp.hstack(X), np.identity(dim)]])
constraints = [block_matrix >> 0]

# Add trace constraints
for i in range(num_params):
constraints.append(cp.trace(X[i] @ rho) == 0)
Expand All @@ -180,7 +172,7 @@ def NHB(rho, drho, W):
constraints.append(cp.trace(X[i] @ drho[j]) == 1)
else:
constraints.append(cp.trace(X[i] @ drho[j]) == 0)

# Define and solve the optimization problem
objective = cp.Minimize(cp.real(cp.trace(cp.kron(W, rho) @ L)))
prob = cp.Problem(objective, constraints)
Expand Down
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