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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](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.107.042615).
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.
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