danmohedano/scikit-qlearn

Proof-Of-Concept Quantum Enhanced Machine Learning Library

scikit-qlearn: Quantum Enhanced Machine Learning

Quantum-enhanced Machine Learning focuses on the improvement of classical machine learning algorithms with the help of quantum subroutines.

This package offers a variety of functions and classes related to quantum computing and quantum enhanced machine learning algorithms. From data encoding to clustering algorithms based on quantum subroutines. For further information on the features of the package, refer to the documentation.

The package makes use of the open-source Qiskit SDK for the execution of the quantum subroutines, which gives access to simulators and real quantum computers.

The package was orginally developed as a proof of concept as part of my Bachelor Thesis for my Computer Engineering degree at UAM.

Documentation

The documentation is available at danmohedano.github.io/scikit-qlearn/. It includes detailed information of the classes and methods offered by the package, tutorials to guide the user and the changes made on every version of the package.

Installation

Currently, the package is available for Python versions 3.7-3.10, regardless of platform. Stable versions are available for install via PyPI:

pip install scikit-qlearn

The latest version can also be manually installed by cloning the main branch of the repository:

git clone https://github.com/danmohedano/scikit-qlearn.git
pip install ./scikit-qlearn

Requirements

scikit-qlearn depends on the following packages:

• qiskit - Open-source SDK for working with quantum computers at the level of pulses, circuits, and algorithms
• numpy - The fundamental package for scientific computing with Python