Software
Quantum Applications
Ready-to-use quantum algorithms designed to address specific computational challenges.
Pasqal’s Quantum Evolution Kernel (QEK) is an open-source Python library that leverages neutral-atom quantum computing to enhance graph-based machine learning tasks.
By enabling the design of quantum-driven similarity metrics for graphs, QEK integrates seamlessly with kernel-based machine learning algorithms, offering a novel approach to complex computational challenges in machine learning.
QEK
Quantum Evolution Kernel (QEK) is an open-source Python library designed for exploring quantum-enhanced graph machine learning.
It provides tools to compute similarities between graph-structured data using the time evolution of a quantum system on Pasqal’s Neutral Atom QPU
Dive deep into Quantum Evolution Kernel
To dive into the foundational research behind QEK, refer to the seminal paper Quantum evolution kernel: Machine learning on graphs with programmable arrays of qubits.
Contribute to QEK
QEK is open-source, and we welcome contributions! Whether you’re improving the code, sharing feedback, or discussing ideas, you can help shape the future of quantum-enhanced machine learning. Join the conversation on GitHub, Reddit, and our community Slack, and our developer forums!
Join the conversation on Slack
Explore how to contribute to advanced graph machine learning with neutral-atom quantum computing
QEK applied on Pasqal QPUs