"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
Section titled “Getting started”You should probably start with our Quickstart guide.
After that, we provide several tutorials.
Getting in touch
Section titled “Getting in touch”- Pasqal Community Portal (external) (forums, chat, tutorials, examples, code library).
- GitHub Repository (external) (source code, issue tracker).
- Professional Support (external) (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
Section titled “Contribute”The GitHub repository is open for contributions!
Don't forget to read the Contributor License Agreement.