Thank you for your interest in contributing to SuperQuantX! This guide will help you get started with contributing to our quantum AI research platform.
- Python 3.9 or higher
- Git
- Basic understanding of quantum computing concepts
- Familiarity with Python package development
-
Fork and Clone
git clone https://github.com/yourusername/superquantx.git cd superquantx -
Create Development Environment
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
-
Install in Development Mode
pip install -e ".[dev]" -
Run Tests
pytest tests/
git checkout -b feature/your-feature-name
# or
git checkout -b fix/your-bugfix-name- Follow our Code Standards
- Write tests for new functionality
- Update documentation as needed
- Ensure all tests pass
# Run tests
pytest tests/
# Check code formatting
black --check src/
# Run linting
flake8 src/
# Type checking
mypy src/- Push your branch to your fork
- Create a pull request against the main branch
- Fill out the PR template completely
- Wait for review and address feedback
- New Quantum Algorithms: Implement quantum ML algorithms
- Backend Integrations: Add support for new quantum frameworks
- Performance Improvements: Optimize existing code
- Bug Fixes: Fix issues in existing functionality
- Tutorials: Create educational content
- API Documentation: Improve docstrings and examples
- User Guides: Write comprehensive guides
- Blog Posts: Share research findings
- Unit Tests: Test individual components
- Integration Tests: Test component interactions
- Performance Tests: Benchmark quantum algorithms
- Example Scripts: Create working examples
SuperQuantX is designed for research. We welcome:
- Novel quantum machine learning algorithms
- Quantum-classical hybrid approaches
- Error mitigation techniques
- Noise modeling improvements
- Hardware validation on real quantum devices
- Benchmarking against classical methods
- Comparative studies across quantum frameworks
- Performance analysis
- Code follows project style guidelines
- Tests written and passing
- Documentation updated
- No breaking changes (or clearly documented)
- CHANGELOG.md updated
- Commit messages are clear and descriptive
## Description
Brief description of changes
## Type of Change
- [ ] Bug fix
- [ ] New feature
- [ ] Breaking change
- [ ] Documentation update
- [ ] Research contribution
## Testing
- [ ] All tests pass
- [ ] New tests added
- [ ] Manual testing performed
## Checklist
- [ ] Code follows style guidelines
- [ ] Self-review completed
- [ ] Documentation updated
- [ ] No merge conflicts- Check existing issues
- Verify on latest version
- Reproduce with minimal example
**Bug Description**
Clear description of the bug
**To Reproduce**
Steps to reproduce the behavior:
1. Code example
2. Expected behavior
3. Actual behavior
**Environment**
- SuperQuantX version:
- Python version:
- OS:
- Quantum backend:
**Additional Context**
Any other relevant information**Feature Description**
Clear description of the proposed feature
**Use Case**
Why is this feature needed?
**Proposed Implementation**
Technical details of how this could be implemented
**Alternatives Considered**
Other approaches that were considered
**Research Context**
How does this relate to current quantum computing research?src/superquantx/
├── algorithms/ # Quantum algorithms
├── backends/ # Backend integrations
├── circuits/ # Circuit construction
├── gates/ # Gate definitions
├── measurements/ # Measurement handling
├── noise/ # Noise modeling
└── utils/ # Utility functions
- Modularity: Each component should be independently testable
- Extensibility: Easy to add new backends and algorithms
- Performance: Optimize for research workloads
- Reproducibility: Ensure experiments are reproducible
- Unit Tests: Test individual functions/classes
- Integration Tests: Test component interactions
- Backend Tests: Test quantum backend integrations
- Algorithm Tests: Validate quantum algorithm correctness
def test_quantum_algorithm():
"""Test quantum algorithm with known expected result."""
# Setup
backend = get_backend('simulator')
algorithm = QuantumSVM(backend=backend)
# Test data with known classification
X_test = np.array([[0, 0], [1, 1]])
y_test = np.array([0, 1])
# Execute
result = algorithm.fit_predict(X_test, y_test)
# Verify
assert result.accuracy > 0.9
assert len(result.predictions) == len(y_test)Use Google-style docstrings:
def quantum_function(param1: int, param2: str) -> dict:
"""
Brief description of the function.
Longer description explaining the quantum algorithm,
its use cases, and any important implementation details.
Args:
param1: Description of first parameter
param2: Description of second parameter
Returns:
Dictionary containing results with keys:
- 'result': Main computation result
- 'metadata': Additional information
Raises:
ValueError: When parameters are invalid
QuantumError: When quantum computation fails
Example:
>>> result = quantum_function(4, "test")
>>> print(result['result'])
42
"""- Include complete, runnable examples
- Use realistic data and parameters
- Show both basic and advanced usage
- Include error handling examples
Contributors should be aware of our release process:
- Feature Freeze: No new features 1 week before release
- Release Candidate: Create RC for testing
- Documentation Review: Ensure docs are current
- Final Testing: Run full test suite
- Release: Tag and publish to PyPI
- Be respectful and inclusive
- Focus on constructive feedback
- Help newcomers learn
- Encourage diverse perspectives
- GitHub Issues: Bug reports and feature requests
- Pull Requests: Code review and discussion
- Email: research@super-agentic.ai for questions
Contributors are recognized through:
- Contributors List: Listed in README and documentation
- Release Notes: Contributions highlighted in releases
- Research Papers: Co-authorship opportunities for significant contributions
- Conference Talks: Speaking opportunities at quantum computing events
Thank you for contributing to SuperQuantX! Your efforts help advance quantum computing research and make quantum AI more accessible to researchers worldwide.