Quantum Circuit Optimization

A comprehensive toolkit for quantum circuit optimization using large language models (LLMs). This project provides tools for generating quantum circuit datasets and optimizing them using the OpenRouter AI provider.

Features

• Dataset Generation: Create random quantum circuits using Cirq with configurable parameters
• Circuit Simulation: Simulate circuits with optional noise models
• AI-Powered Optimization: Use LLMs (OpenRouter) for circuit optimization via in-context learning
• Unified CLI: Simple command-line interface
• Extensible Architecture: Easy to add new optimization providers and circuit types

Project Structure

quantum-circuit-optimization/
├── src/
│   ├── data_generation/          # Quantum circuit dataset generation
│   │   ├── qc/                   # Core quantum computing modules
│   │   └── scripts/              # Data generation scripts
│   └── model_training/           # AI-powered circuit optimization
│       ├── cli/                  # Unified command-line interface
│       ├── config.py             # Configuration management
│       └── optimizer.py          # OpenRouter API integration
├── tests/                        # Test suites
├── requirements.txt              # Python dependencies
└── README.md                     # This file

Installation

Prerequisites

• Python 3.10+
• Virtual environment support (venv, uv, conda, etc.)

Quick Setup

1. Clone the repository:

   git clone <repository_url>
   cd quantum-circuit-optimization

2. Create and activate virtual environment:

   # Using uv (recommended)
   .venv/Scripts/python.exe -m uv venv .venv
   .venv/Scripts/python.exe -m ensurepip
   .venv/Scripts/python.exe -m pip install uv
   .venv/Scripts/python.exe -m uv pip install -r requirements.txt
   
   # Or using standard venv
   python -m venv .venv
   source .venv/bin/activate  # Linux/macOS
   # or
   .venv\Scripts\activate     # Windows
   pip install -r requirements.txt

Data Generation

Generate quantum circuit datasets for training and testing:

# Generate 100 circuits with default parameters
python src/data_generation/scripts/generate_dataset.py

# Generate custom dataset
python src/data_generation/scripts/generate_dataset.py \
    --n_circuits 500 \
    --min_gates 2 \
    --max_gates 10 \
    --n_qubits 3 \
    --output_file custom_dataset.jsonl

Configuration Options

• --n_circuits: Number of circuits to generate (default: 100)
• --min_gates/--max_gates: Gate count range per circuit
• --n_qubits: Number of qubits (default: 5)
• --noise_level: Simulation noise level (default: 0.01)

Circuit Optimization

Optimize quantum circuits using OpenRouter AI:

Quick Start

1. Set up API credentials:

   # For OpenRouter (default)
   export OPENROUTER_API_KEY="***"
   # Or create ~/.api-openrouter file with your key

2. Optimize a circuit:

   python -m src.model_training.cli.predict \
       --input_circuit "H 0 ; CNOT 0 1 ; H 0"

Advanced Usage

# Custom examples for in-context learning
python -m src.model_training.cli.predict \
    --input_circuit "X 0 ; X 0 ; Y 1" \
    --example "H 0 ; CNOT 0 1 ; H 0||CNOT 0 1" \
    --example "X 0 ; X 0 ; Y 1||Y 1" \
    --timeout 120 \
    --verbose

# Override model
python -m src.model_training.cli.predict \
    --model "deepseek/deepseek-chat-v3-0324" \
    --input_circuit "H 0 ; CNOT 0 1 ; H 0"

Configuration Files

Create configuration files for model selection:

• ~/.model-openrouter: Specify OpenRouter model (one line)
• ~/.api-openrouter: OpenRouter API key

Testing

Run the test suite to ensure everything works correctly:

# Install test dependencies
pip install -e .[test]

# Run all tests
pytest

# Run specific test suites
pytest src/data_generation/tests/
pytest src/model_training/tests/

Development

Adding New Providers

1. Add provider configuration to src/model_training/config.py
2. Implement optimization function following the pattern in the existing optimizer
3. Add provider to the CLI in src/model_training/cli/predict.py

Code Quality

• All code follows PEP 8 style guidelines
• Type hints are used throughout the codebase
• Comprehensive error handling and logging
• Extensive test coverage

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

Future Work

Data Generation

• Support for custom gate sets
• Advanced noise models
• Circuit complexity metrics
• Integration with other quantum frameworks

Model Training

• Support for additional AI providers (Claude, GPT-4, etc.)
• Automated evaluation metrics
• Reinforcement learning approaches
• Error mitigation strategies

Performance

• Batch processing capabilities
• Caching mechanisms
• Async API calls
• Resource optimization

License

This project is licensed under the MIT-0 License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Support

If you encounter any issues or have questions:

1. Check the Issues page
2. Create a new issue with detailed information
3. Provide sample inputs and expected outputs