squlearn 0.4.0

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

sQUlearn 0.4.0

sQUlearn is a novel Python package introducing Quantum Machine Learning (QML) capabilities to traditional machine learning pipelines via a high-level / low-level design approach. The package features an array of algorithms including Quantum Support Vector Machines, Quantum Gaussian Processes, Quantum Kernel Ridge Regression and Quantum Neural Networks (QNN), all designed to seamlessly integrate with scikit-learn. The QNN engine facilitates efficient gradient computation and automated training with non-linear parametrized circuits. Users can further customize their QNN models, enhancing flexibility and potential outcomes. sQUlearn's kernel engines are designed to meet various needs, with fidelity kernels and projected quantum kernels, the latter leveraging the QNN engine for optimization. A encoding circuit tool allows for efficient layer-wise design based on strings, encouraging innovation beyond standard implementations. Lastly, backend integration with IBM quantum computers is handled with a custom execution engine that optimizes session management on quantum backends and simulators, ensuring optimal use of quantum resources and creating an accessible environment for QML experimentation.

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

Imprint

This project is maintained by the quantum computing group at the Fraunhofer Institute for Manufacturing Engineering and Automation IPA. It started as a collection of implementations of quantum machine learning methods.

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

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