"Quantum Evolution Kernel"

The Quantum Evolution Kernel is a Python library designed for the machine learning community to help users design quantum-driven similarity metrics for graphs and to use them inside kernel-based machine learning algorithms for graph data.

The core of the library is focused on the development of a classification algorithm for molecular-graph dataset as it is presented in the published paper Quantum feature maps for graph machine learning on a neutral atom quantum processor.

Users setting their first steps into quantum computing will learn how to implement the core algorithm in a few simple steps and run it using the Pasqal Neutral Atom QPU. More experienced users will find this library to provide the right environment to explore new ideas - both in terms of methodologies and data domain - while always interacting with a simple and intuitive QPU interface.

Getting started

We provide several tutorials:

• tutorial 1 - Using a Quantum Device to Extract Machine-Learning Features
• tutorial 2 - Machine-Learning with the Quantum EvolutionKernel

Getting in touch

• Pasqal Community Portal (forums, chat, tutorials, examples, code library).
• GitHub Repository (source code, issue tracker).
• Professional Support (if you need tech support, custom licenses, a variant of this library optimized for your workload, your own QPU, remote access to a QPU, ...)

Contribute

The GitHub repository is open for contributions!

Don't forget to read the Contributor License Agreement.