Beyond Optimization: Harnessing Quantum Annealer Dynamics for Machine Learning
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Quantum Physics arXiv:2601.09938 (quant-ph) [Submitted on 14 Jan 2026] Title:Beyond Optimization: Harnessing Quantum Annealer Dynamics for Machine Learning Authors:Akitada Sakurai, Aoi Hayashi, Tadayoshi Matumori, Daisuke Kaji, Tadashi Kadowaki, Kae Nemoto View a PDF of the paper titled Beyond Optimization: Harnessing Quantum Annealer Dynamics for Machine Learning, by Akitada Sakurai and 5 other authors View PDF HTML (experimental) Abstract:Quantum annealing is typically regarded as a tool for combinatorial optimization, but its coherent dynamics also offer potential for machine learning. We present a model that encodes classical data into an Ising Hamiltonian, evolves it on a quantum annealer, and uses the resulting probability distributions as feature maps for classification. Experiments on the quantum annealer machine with the Digits dataset, together with simulations on MNIST, demonstrate that short annealing times yield higher classification accuracy, while longer times reduce accuracy but lower sampling costs. We introduce the participation ratio as a measure of the effective model size and show its strong correlation with generalization. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2601.09938 [quant-ph] (or arXiv:2601.09938v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.09938 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Akitada Sakurai [view email] [v1] Wed, 14 Jan 2026 23:49:45 UTC (1,947 KB) Full-text links: Access Paper: View a PDF of the paper titled Beyond Optimization: Harnessing Quantum Annealer Dynamics for Machine Learning, by Akitada Sakurai and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-01 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?) Links to Code Toggle Papers with Code (What is Papers with Code?) 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?)