Quantum-ML

A professional library for Hybrid Quantum-Classical Machine Learning, developed by opendev-labs.

🚀 Overview

Quantum-ML bridges the gap between classical machine learning and quantum computing. It provides a robust framework for creating, training, and deploying hybrid models that leverage the power of quantum circuits alongside traditional deep learning architectures.

✨ Key Features

• Hybrid Architectures: Seamlessly integrate Quantum Variational Circuits with PyTorch/TensorFlow.
• Quantum Kernels: Efficient implementation of quantum kernel methods for SVMs and other kernel-based models.
• Noise Resilience: Built-in error mitigation strategies for NISQ (Noisy Intermediate-Scale Quantum) devices.
• Optimized Backends: High-performance simulation compatible with major quantum providers.

🛠️ Installation

pip install -r requirements.txt
# or via setup.py
pip install .

💻 Usage

from quantum_ml import QuantumModel
from quantum_ml.circuits import VariationalCircuit

# Initialize a hybrid model
model = QuantumModel(
    backend='simulator',
    n_qubits=4,
    circuit=VariationalCircuit(depth=3)
)

# Train with classical data
model.fit(X_train, y_train, epochs=10)

# Predict
predictions = model.predict(X_test)

🤝 Contributing

We welcome contributions! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

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

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