Quantum reservoir networks based on decoherence-free subspaces
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Quantum Physics arXiv:2605.27427 (quant-ph) [Submitted on 21 May 2026] Title:Quantum reservoir networks based on decoherence-free subspaces Authors:V.V. Akshay, M.V. Altaisky, N.E. Kaputkina View a PDF of the paper titled Quantum reservoir networks based on decoherence-free subspaces, by V.V. Akshay and 2 other authors View PDF Abstract: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. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.27427 [quant-ph] (or arXiv:2605.27427v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.27427 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mikhail Altaisky [view email] [v1] Thu, 21 May 2026 04:54:14 UTC (889 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum reservoir networks based on decoherence-free subspaces, by V.V. Akshay and 2 other authorsView PDFTeX Source view license Current browse context: quant-ph new | recent | 2026-05 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)