"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 1.
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
You should probably start with our Quickstart guide.
After that, we provide several tutorials.
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.