squlearn 0.5.0

A library for quantum machine learning following the scikit-learnstandard.

sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library's dual-layer architecture serves both QML researchers and practitioners, enabling efficient prototyping, experimentation, and pipelining. sQUlearn provides a comprehensive toolset that includes both quantum kernel methods and quantum neural networks, along with features like customizable data encoding strategies, automated execution handling, and specialized kernel regularization techniques. By focusing on NISQ-compatibility and end-to-end automation, sQUlearn aims to bridge the gap between current quantum computing capabilities and practical machine learning applications.

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Prerequisites

The package requires at least Python 3.9.

Installation

Stable Release

To install the stable release version of sQUlearn, run the following command:

pip install squlearn

Alternatively, you can install sQUlearn directly from GitHub via

pip install git+ssh://[e-mail]:sQUlearn/squlearn.git

Examples

There are several more elaborate examples available in the folder ./examples which display the features of this package. Tutorials for beginners can be found at ./examples/tutorials.

To install the required packages, run

pip install .[examples]

Contribution

Thanks for considering to contribute to sQUlearn! Please read our contribution guidelines before you submit a pull request.

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License

Apache License 2.0

Contact

This project is maintained by the quantum computing group at the Fraunhofer Institute for Manufacturing Engineering and Automation IPA.

http://www.ipa.fraunhofer.de/quantum

For general questions regarding sQUlearn, feel free to contact [e-mail].

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