Quantum artificial intelligence for pattern recognition at high-energy colliders: Tales of Three "Quantum's"

Quantum Physics arXiv:2511.16713 (quant-ph) [Submitted on 20 Nov 2025] Title:Quantum artificial intelligence for pattern recognition at high-energy colliders: Tales of Three "Quantum's" Authors:Hideki Okawa View a PDF of the paper titled Quantum artificial intelligence for pattern recognition at high-energy colliders: Tales of Three "Quantum's", by Hideki Okawa View PDF HTML (experimental) Abstract:Quantum computing applications are an emerging field in high-energy physics. Its ambitious fusion with artificial intelligence is expected to deliver significant efficiency gains over existing methods and/or enable computation from a fundamentally different perspective. High-energy physics is a big data science that utilizes large-scale facilities, detectors, high-performance computing, and its worldwide networks. The experimental workflow consumes a significant amount of computing resources, and its annual cost will continue to grow exponentially at future colliders. In particular, pattern recognition is one of the most crucial and computationally intensive tasks. Three types of quantum computing technologies, i.e., quantum gates, quantum annealing, and quantum-inspired, are all actively investigated for high-energy physics applications, and each has its pros and cons. This article reviews the current status of quantum computing applications for pattern recognition at high-energy colliders. Comments: Subjects: Quantum Physics (quant-ph); High Energy Physics - Experiment (hep-ex); High Energy Physics - Phenomenology (hep-ph) Cite as: arXiv:2511.16713 [quant-ph] (or arXiv:2511.16713v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2511.16713 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hideki Okawa [view email] [v1] Thu, 20 Nov 2025 09:17:59 UTC (2,361 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum artificial intelligence for pattern recognition at high-energy colliders: Tales of Three "Quantum's", by Hideki OkawaView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-11 Change to browse by: hep-ex hep-ph 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?)