qcraft 0.1.3

Qcraft: Quantum Circuit Design, Optimization, and Surface Code Mapping Platform

Qcraft: Quantum Circuit Design, Optimization, and Surface Code Mapping Platform

What is Qcraft?

Qcraft is an advanced, research-grade platform for quantum circuit design, optimization, and surface code mapping. It leverages reinforcement learning (RL), curriculum learning, and hardware-aware optimization to enable scalable, high-fidelity quantum circuit compilation and error correction. Qcraft is modular, extensible, and production-ready, supporting both classical and quantum error-corrected circuit workflows.

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Key Features

• Reinforcement Learning for Quantum Circuit Optimization: Device-aware, reward-driven optimization of quantum circuits, supporting gate fusion, commutation, SWAP insertion, and more.
• Surface Code Multi-Patch Mapping: RL-based mapping of multiple logical surface code patches to hardware, with advanced reward shaping and curriculum learning.
• Curriculum Learning: Progressive training with increasing difficulty, dynamic reward weighting, and robust convergence.
• Hardware Awareness: Supports IBM devices (IonQ in progress), native gate sets, and device-specific constraints.
• Modular and Configurable: YAML/JSON-driven configuration for all workflows, environments, and training parameters.
• Logging and Artifact Management: Automated tracking of training runs, metrics, and model artifacts for reproducibility.

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Installation

Requirements

• Python: 3.9–3.11 (3.11 recommended)
• CUDA: 12.4 (required for RL training with surface code agents)
• Tested on: Linux, NVIDIA RTX 3070, CUDA 12.4, IBM Q devices

Install from PyPI

pip install qcraft

Install from Source (Recommended for Research)

# Clone the repository
$ git clone <repo-url>
$ cd quantum-surface-code-generator-using-reinforcement-learning

# (Recommended) Create and activate a Python 3.11 virtual environment
$ python3.11 -m venv venv
$ source venv/bin/activate

# Install dependencies
$ pip install -r requirements.txt

# Build and install the package locally
$ pip install .

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Usage

Main GUI

qcraft

RL Training (Examples)

• Circuit Optimization RL Training:

  python -m circuit_designer.workflow_bridge --config configs/optimizer_config.yaml
  

• Surface Code Multi-Patch RL Training:

  python -m scode.rl_agent.train_multi_patch --config configs/multi_patch_rl_agent.yaml
  

Evaluation and Simulation

• Evaluation:

  python -m evaluation.evaluation_framework --config configs/your_eval_config.yaml
  

• Execution Simulation:

  python -m execution_simulation.execution_simulator --config configs/your_exec_config.yaml
  

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Reward Functions: Overview

Surface Code Multi-Patch Agent

• Highly configurable reward function: Encourages valid mappings, hardware connectivity, adjacency, resource utilization, error minimization, and logical correctness.
• Curriculum learning: Dynamic reward weights and phase multipliers across training stages.
• See configs/multi_patch_rl_agent.yaml for all tunable parameters.

Circuit Optimization Module

• Reward engine: Penalizes gate count, depth, and SWAPs; rewards native gate usage and penalizes invalid gates.
• Curriculum learning: Difficulty and reward weights progress as training advances.
• See configs/optimizer_config.yaml for all tunable parameters.

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Configuration and Customization

• All major workflows and RL environments are configured via YAML files in the configs/ directory.
• Surface Code Agent: configs/multi_patch_rl_agent.yaml
• Circuit Optimization Agent: configs/optimizer_config.yaml
• Device/Hardware: configs/ibm_devices.yaml, configs/ionq_devices.yaml
• Other: Logging, visualization, and more via their respective YAML files.

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Packaging and PyPI Publishing

To build and publish your own version:

# Clean previous builds
rm -rf dist/*

# Build the package
python3 setup.py sdist bdist_wheel

# Check the package
pip install twine

# Upload to PyPI
twine upload dist/*

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Support and Extensibility

• Qcraft is modular and extensible for new devices, reward functions, and optimization passes.
• Contributions and feedback are welcome for further research and development.

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Citation

If you use Qcraft in academic work, please cite the corresponding paper or this repository.

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For detailed technical documentation, architecture, and workflow explanations, please refer to the full README in the source repository.