Scope note (read first). This package studies whether a closed-form spinor wavepacket model (HSQ) reproduces discrete-time quantum-walk (DTQW) physics. It does not. Measured against a genuine 10-qubit Hadamard-coin DTQW (Qiskit Aer), at zero noise the HSQ model reaches only ~15.7% fidelity (steps=20; ~14.5% at steps=30), places >50% of its probability mass outside the DTQW light cone (where an ideal walk has exactly zero), and yields a peak-to-valley ratio of 1.00 (peak at the centre) versus ~5.1 for the true ballistic double-horn. This negative result is the finding. The HSQ core (hsq_qubit.py) is deliberately left unmodified: it is the object of study, not a bug to be patched. Reported "disagreement gap" values are larger = worse and are NOT fidelities.
This repository implements the official computing framework and evaluation suite for a container-native quantum simulation platform designed to benchmark the fault-tolerant transport dynamics of Hilbert-Space Spinor Quasiparticles (HSQ) under controlled phase-damping decoherence.
The platform employs a decoupled, cloud-native microservice topology to enforce strict spatial localization and cross-node hardware isolation, eliminating numerical memory contamination inherent in global state-vector computing.
install docker step1:
sudo systemctl start dockerinstall docker step2:
sudo systemctl enable dockercheck docker version:
sudo docker --versioninstall the mod to unzip this zip file.
sudo apt update && sudo apt install unzip -ypack hsq_qubit.py to make image.
sudo docker build --pull=false -t hsq_core:latest .step1:
sudo apt-get updatestep2:
sudo apt-get install -y nvidia-driver-535step3:
sudo rebootstep4:
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.liststep5:
sudo apt-get updatestep6:
sudo apt-get install -y nvidia-container-toolkitstep7:
sudo nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yamlstep8:
sudo systemctl restart dockerpreliminary work to install Python runtime environment. step1:
sudo apt install python3-pipstep2:
sudo apt update && sudo apt install python3-venv python3-full -ycreate your environment for python list:
python3 -m venv myenvEnter your environment instruction:
source myenv/bin/activateEnsure your Python runtime environment has the required auditing libraries installed:
pip install requests numpy scipy matplotlib pillow flask qiskit qiskit-aer psutil redisTo remore the container, enter the following command
sudo docker rm -f $(sudo docker ps -a -q)+-----------------------------------------------------------------+
| Windows Host (Main Orchestration & Control) |
| - Algorithmic Driver & Data Harvester: random_walk.py |
| - (no throughput profiler ships with this package) |
| - Dynamic DevOps Pure-Purge Orchestrator: deploy_orchestrator.py
+-----------------------------------------------------------------+
|
[ Loopback Restful HTTP API Gateways ]
(Port 5011 to N: Spawning Scalable Active Qubit Nodes)
|
v
+-----------------------------------------------------------------+
| WSL2 Linux Kernel / Docker Microservice Cluster |
| - Environment Isolation defined via /hsq_backend/Dockerfile |
| - Real-time Solver & Unitary Verification: hsq_qubit.py |
+-----------------------------------------------------------------+
|
[ NVIDIA Container Toolkit Pass-through ]
v
+-----------------------------------------------------------------+
| NVIDIA GPU CUDA Acceleration Layer |
| - CuPy-driven Parallelized Continuous Wavepacket Evolution |
| - Unitary Conserved Manifold via Spinor-Momentum Constraint |
+-----------------------------------------------------------------+
📂 Manifest Mapping & Component Index
deploy_orchestrator.py: Automated Ecosystem Purge & Cluster Orchestrator. Cleans defunct container clusters, wipes dangling registries, releases bound host OS communication ports, and dynamically scales up to N=100 isolated logical hardware nodes concurrently under native Linux/WSL2 environments.
random_walk.py: Top-level driver. Runs >= 20 seeds against the HSQ backend (NOTE: at noise=0 the closed-form model is deterministic, so those seeds return identical rows -- they are pseudo-replication, not samples), and simulates a genuine 10-qubit Hadamard-coin DTQW in Qiskit Aer to serve BOTH as the ideal reference (noise=0) and as the noisy A/B arms. The coin gate is applied ONCE before the evolution loop; the HSQ backend then advances t and re-evaluates its closed-form field. Scores the HSQ output against the DTQW reference and reports fidelity, total-variation, light-cone leakage and peak-to-valley ratio.
/hsq_backend/hsq_qubit.py: HSQ model core (the OBJECT OF STUDY -- intentionally unmodified). Evolves a two-component coin amplitude (a,b) and evaluates a CLOSED-FORM two-Gaussian wavepacket |a*exp(-(x+vg*t)^2/2s^2)*e^(i*phL) + b*exp(-(x-vg*t)^2/2s^2)*e^(i*phR)|^2 on a 512-point grid, with s(t)=sqrt(s0^2+alpha*t). It has no position register and no per-step conditional shift, so it is NOT a discrete-time quantum walk; quantifying that gap is the purpose of this study.
/hsq_backend/Dockerfile: Container Infrastructure Manifest. Builds the lightweight Ubuntu-based container environment pre-configured with the official NVIDIA CUDA 11.8.0 runtime to unleash GPU parallel matrix computations.
🚀 Quick Start & Reproducibility Guide PrerequisitesWindows 10 / 11 with WSL2 (Ubuntu Linux Kernel) enabled. Docker Desktop active and integrated with the WSL2 backend. NVIDIA Container Toolkit configured to allow GPU pass-through to Docker instances.
- Execute Cluster Orchestration and Deployment Run the main orchestration script from a Windows PowerShell or CMD terminal to automatically reset historic zombie dependencies and deploy a fresh logical network cluster(please running in wsl, if your computer system is window10/11):
python deploy_orchestrator.py(Designate your target qubit scale N through the interactive CLI prompt). 2. Launch Algorithmic Simulation Pipeline Once the backend container matrix is live and ports starting from Port 5011 are bound, open a separate shell and execute the master runtime control script to drive quantum gate injection, perform the 20-seed stress test, and compile the quantitative data:
python random_walk.py --seeds 20 --steps 20 --noise 0.00