Efficient time-series prediction on NISQ devices via time-delayed quantum extreme learning machine
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Quantum Physics arXiv:2602.21544 (quant-ph) [Submitted on 25 Feb 2026] Title:Efficient time-series prediction on NISQ devices via time-delayed quantum extreme learning machine Authors:Mio Kawanabe (1), Saud Cindrak (2), Kathy Luedge (2), Jun-ichi Shirakashi (1), Tetsuo Shibuya (3), Hiroshi Imai (4) ((1) Tokyo University of Agriculture and Technology, Japan, (2) Technische Universitaet Ilmenau, Germany, (3) The University of Tokyo, Japan, (4) The University of Tokyo, Japan) View a PDF of the paper titled Efficient time-series prediction on NISQ devices via time-delayed quantum extreme learning machine, by Mio Kawanabe (1) and 12 other authors View PDF Abstract:We proposed a time-delayed quantum extreme learning machine (TD-QELM) for efficient time-series prediction on noisy intermediate-scale quantum (NISQ) devices. By encoding multiple past inputs simultaneously, TD-QELM achieves shallow circuit depth independent of sequence length, thereby, mitigating noise accumulation and reducing computational complexity. Experiments using the NARMA benchmark on both noiseless simulations and IBM's 127-qubit processor demonstrate that TD-QELM consistently outperforms conventional quantum reservoir computing in prediction accuracy and noise robustness. These results highlight TD-QELM as a practical and scalable framework for time-series learning on current NISQ hardware. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2602.21544 [quant-ph] (or arXiv:2602.21544v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.21544 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jun-Ichi Shirakashi [view email] [v1] Wed, 25 Feb 2026 04:03:41 UTC (1,038 KB) Full-text links: Access Paper: View a PDF of the paper titled Efficient time-series prediction on NISQ devices via time-delayed quantum extreme learning machine, by Mio Kawanabe (1) and 12 other authorsView PDFTeX Source view license Current browse context: quant-ph new | recent | 2026-02 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?)