Saved in:
Bibliographic Details
Main Authors: Gyurik, Casper, Wudarski, Filip, Philip, Evan, Sannia, Antonio, Sadeghi, Hossein, Kyriienko, Oleksandr, Venturelli, Davide, Gentile, Antonio A.
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2510.01797
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866912623017066496
author Gyurik, Casper
Wudarski, Filip
Philip, Evan
Sannia, Antonio
Sadeghi, Hossein
Kyriienko, Oleksandr
Venturelli, Davide
Gentile, Antonio A.
author_facet Gyurik, Casper
Wudarski, Filip
Philip, Evan
Sannia, Antonio
Sadeghi, Hossein
Kyriienko, Oleksandr
Venturelli, Davide
Gentile, Antonio A.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2510_01797
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle From quantum feature maps to quantum reservoir computing: perspectives and applications
Gyurik, Casper
Wudarski, Filip
Philip, Evan
Sannia, Antonio
Sadeghi, Hossein
Kyriienko, Oleksandr
Venturelli, Davide
Gentile, Antonio A.
Quantum Physics
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.
title From quantum feature maps to quantum reservoir computing: perspectives and applications
topic Quantum Physics
url https://arxiv.org/abs/2510.01797