QuantumMetaGPT 1.0.0

Autonomous quantum AI research agent

QuantumMetaGPT

Autonomous Quantum AI Research Agent

QuantumMetaGPT is a cutting-edge system that combines reinforcement learning, large language models, and quantum computing to automatically generate, optimize, and evaluate quantum algorithms.

🚀 Quick Start

Installation

# Install QuantumMetaGPT
pip install quantummetagpt

# Get your machine ID for licensing
python -c "import qmetagpt; print(f'Machine ID: {qmetagpt.get_machine_id()}')"

# Contact [e-mail] with your machine ID for license

Basic Usage

import qmetagpt
import numpy as np

# Initialize RL agent
agent = qmetagpt.PPOAgent(state_dim=8, action_dim=12)
agent.build_model()

# Generate quantum circuit
task_state = np.array([1, 0, 1, 0, 0, 1, 1, 0])
circuit = agent.generate_circuit(task_state)

# Evaluate performance
evaluator = qmetagpt.QuantumEvaluator()
results = evaluator.evaluate(circuit, shots=1024)

print(f"Fidelity: {results['fidelity']:.4f}")

📚 Documentation

Comprehensive Documentation Available

Our complete documentation is available at: https://yourusername.github.io/QuantumMetaGPT/

Documentation Sections

• Installation Guide - Complete setup instructions
• Quick Start - Get running in 10 minutes
• User Guide - Comprehensive usage documentation
• API Reference - Detailed API documentation
• Licensing Guide - License tiers and requirements
• Examples - Practical examples and tutorials
• Configuration - Advanced configuration options

🛠️ Building Documentation Locally

To build and serve the documentation locally:

# Install documentation dependencies
pip install mkdocs mkdocs-material

# Clone repository
git clone https://github.com/yourusername/QuantumMetaGPT.git
cd QuantumMetaGPT

# Serve documentation locally
mkdocs serve
# Visit http://127.0.0.1:8000

# Build static documentation
mkdocs build
# Output in site/ directory

🚀 Deploy Documentation to GitHub Pages

The repository includes automatic GitHub Pages deployment:

1. Enable GitHub Pages in repository settings
2. Set source to "GitHub Actions"
3. Push to main branch - documentation deploys automatically

The GitHub Actions workflow (.github/workflows/docs.yml) handles:

• Automatic builds on push to main
• Validation of documentation
• Deployment to GitHub Pages
• Link checking and quality assurance

🏗️ Architecture

graph TB
    A[Task Input] --> B[Task Synthesizer]
    B --> C[LLM Paper Parser]
    C --> D[Algorithm Generator]
    D --> E[RL Agents]
    E --> F[Quantum Circuits]
    F --> G[Evaluation Engine]
    G --> H[Optimizer Engine]
    H --> I[Report Generator]
    I --> J[Results Output]
    
    subgraph "License Protection"
    K[License Enforcer]
    K -.-> C
    K -.-> D
    K -.-> E
    K -.-> G
    K -.-> H
    K -.-> I
    end

Set environment variables

export IBMQ_TOKEN="your_ibmq_token" export OPENAI_API_KEY="your_openai_key"

Generate license

python -m qmetagpt.security_licensing.cli_license generate Usage python from QuantumMetaGPT import run_pipeline

Run full pipeline

run_pipeline(arxiv_id="quant-ph/2310.12345")

### Module Structure

```plaintext
quantummetagpt/
├── llm_paper_parser       # arXiv paper processing
├── task_synthesizer       # Quantum task formalization
├── quantum_algorithm_generator  # RL-based circuit generation
├── optimizer_engine       # Hybrid quantum-classical optimization
├── evaluation_engine      # Quantum hardware evaluation
├── report_generator       # Scientific report creation
├── security_licensing     # License management
└── utils                  # Logging and error handling

Contributing Contributions are welcome! Please see our contribution guidelines.

License Commercial license required. See LICENSE for details.

CONTRIBUTING.md

# Contribution Guidelines

We welcome contributions to QuantumMetaGPT! Please follow these guidelines:

## Development Setup
1. Fork the repository
2. Clone your fork: `git clone https://github.com/yourusername/QuantumMetaGPT.git`
3. Create a virtual environment: `python -m venv venv`
4. Activate environment: `source venv/bin/activate`
5. Install dependencies: `pip install -r requirements-dev.txt`

## Coding Standards
- Follow PEP 8 style guide
- Use type hints for all function signatures
- Document public methods with docstrings
- Write unit tests for new features
- Keep modules focused and cohesive

## Testing
Run the test suite with:
```bash
pytest --cov=qmetagpt --cov-report=html
Pull Requests
Create a new branch for your feature: git checkout -b feature-name

Implement your changes with tests

Ensure all tests pass: pytest

Update documentation if needed

Submit a pull request to the main repository

Reporting Issues
Please use GitHub issues to report bugs or request features. Include:

Detailed description of the issue

Steps to reproduce

Expected vs actual behavior

Environment details


### 16. `LICENSE`
```text
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/

TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

1. Definitions.

"License" shall mean the terms and conditions for use, reproduction, and distribution...

[Standard Apache 2.0 License Text]
17. .env.example
env
# IBM Quantum credentials
IBMQ_TOKEN="your_ibmq_token"

# OpenAI API
OPENAI_API_KEY="your_openai_key"

# LLM configuration
LLM_MODEL="gpt-4"
LLM_TEMPERATURE=0.7

# Quantum backend
QUANTUM_BACKEND="ibmq_manila"
USE_HARDWARE=false
SHOTS=1024

# RL training
RL_AGENT="PPO"
RL_TIMESTEPS=50000