Temporal information processing on a 4,500-qubit quantum annealer

Quantum Physics arXiv:2609.19308 (quant-ph) [Submitted on 16 Sep 2026] Title:Temporal information processing on a 4,500-qubit quantum annealer Authors:Antonio Sannia, Roberto Menta, Pratik Sathe, Dario De Santis, Vittorio Giovannetti, Luis Pedro García-Pintos, Davide Venturelli, Gian Luca Giorgi, Roberta Zambrini, Francesco Caravelli View a PDF of the paper titled Temporal information processing on a 4,500-qubit quantum annealer, by Antonio Sannia and 9 other authors View PDF HTML (experimental) Abstract:Quantum machine learning could uncover statistical structure beyond the reach of classical models, but this requires quantum models large and expressive enough to be useful and cheap enough to read out. Most approaches optimize many quantum parameters and are thus limited by expensive training loops. Here we report a quantum machine-learning model implemented on a programmable superconducting quantum annealer that processes temporal data at large scale using up to 4,500 qubits-the largest quantum machine-learning experiment performed to date. Following the quantum reservoir computing paradigm, the untrained native many-body dynamics generated by reverse annealing is directly used to process temporal data. We prove that the interactions produced during annealing are indispensable-a non-interacting reservoir retains no memory of its input. We evaluate our model experimentally on standard memory benchmarks and demonstrate that it can successfully forecast chaotic time series. These results establish quantum annealers as a scalable platform for large-scale quantum machine learning. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2609.19308 [quant-ph] (or arXiv:2609.19308v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2609.19308 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Antonio Sannia [view email] [v1] Wed, 16 Sep 2026 18:17:03 UTC (1,267 KB) Full-text links: Access Paper: View a PDF of the paper titled Temporal information processing on a 4,500-qubit quantum annealer, by Antonio Sannia and 9 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-09 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?)