Quantum reservoir networks based on decoherence-free subspaces

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Akshay, V. V., Altaisky, M. V., Kaputkina, N. E.
Format: Preprint
Published: 2026
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866914605536641024
author Akshay, V. V.
Altaisky, M. V.
Kaputkina, N. E.
author_facet Akshay, V. V.
Altaisky, M. V.
Kaputkina, N. E.
contents We present numerical simulation of a six-qubit quantum reservoir network with an output implemented on a 5-dimensional decoherence-free subspace (DFS), working as a classifier between entangled and product states of the input quantum system, fed to the reservoir during a finite learning time. Since the dynamics of DFS is not affected by external fluctuations, no cooling is required, and the proposed model seems a promising candidate for future quantum artificial intelligence systems working at room temperatures and free of huge energy consumption.
format Preprint
id arxiv_https___arxiv_org_abs_2605_27427
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Quantum reservoir networks based on decoherence-free subspaces
Akshay, V. V.
Altaisky, M. V.
Kaputkina, N. E.
Quantum Physics
We present numerical simulation of a six-qubit quantum reservoir network with an output implemented on a 5-dimensional decoherence-free subspace (DFS), working as a classifier between entangled and product states of the input quantum system, fed to the reservoir during a finite learning time. Since the dynamics of DFS is not affected by external fluctuations, no cooling is required, and the proposed model seems a promising candidate for future quantum artificial intelligence systems working at room temperatures and free of huge energy consumption.
title Quantum reservoir networks based on decoherence-free subspaces
topic Quantum Physics
url https://arxiv.org/abs/2605.27427