quantum-learn

quantum-learn is an open-source Python library that simplifies Quantum Machine Learning (QML) using PennyLane.

Inspired by scikit-learn and fastai, it provides a high-level interface that abstracts both hybrid and pure quantum machine learning.

Features

• Simple setup that abstracts the process of training quantum models
• Supports both hybrid quantum and pure quantum machine learning:
  • Pure: Variational Quantum Circuits (VQC)
  • Hybrid: (Generalized) Classification, Clustering, Regression
• Works with PennyLane, scikit-learn, and standard ML tools
• Can be run on any simulated or real quantum hardware supported by Pennylane (includes the majority of industry standards)

Installation

quantum-learn requires Python 3.6+. Install it via pip:

pip install quantum-learn

Or install from source:

git clone https://github.com/OsamaMIT/quantum-learn.git
cd quantum-learn
pip install .

Documentation

For tutorials, examples, and details on the classes, check out the quantum-learn documentation.

Dependencies

The required dependencies can be installed by

pip install -r requirements.txt

Planned Features

• Implement quantum kernel methods
• Implement categorical feature maps

Contributing

Contributions are welcome! To contribute:

1. Fork the repository
2. Create a new branch (feature-branch)
3. Commit your changes and open a pull request

License

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