From quantum feature maps to quantum reservoir computing: perspectives and applications
Quantum Physics
2025-10-03 v1
Abstract
We explore the interplay between two emerging paradigms: reservoir computing and quantum computing. We observe how quantum systems featuring beyond-classical correlations and vast computational spaces can serve as non-trivial, experimentally viable reservoirs for typical tasks in machine learning. With a focus on neutral atom quantum processing units, we describe and exemplify a novel quantum reservoir computing (QRC) workflow. We conclude exploratively discussing the main challenges ahead, whilst arguing how QRC can offer a natural candidate to push forward reservoir computing applications.
Cite
@article{arxiv.2510.01797,
title = {From quantum feature maps to quantum reservoir computing: perspectives and applications},
author = {Casper Gyurik and Filip Wudarski and Evan Philip and Antonio Sannia and Hossein Sadeghi and Oleksandr Kyriienko and Davide Venturelli and Antonio A. Gentile},
journal= {arXiv preprint arXiv:2510.01797},
year = {2025}
}