Quantum Vision Clustering

Quantum Vision Clustering | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantum Vision Clustering Xuan-Bac Nguyen, Hugh Churchill, Khoa Luu, Samee U. Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5022498/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Unsupervised visual clustering has garnered significant attention recently, aiming to characterize distributions of unlabeled visual images through clustering based on a parameterized appearance approach. Alternatively, clustering algorithms can be viewed as assignment problems where an unlabelled sample needs to be assigned into a specific cluster. This problem can be formulated as Quadratic Unconstrained Binary Optimization that is characterized as NP-hard, yet precisely solvable for small instances on contemporary hardware. Adiabatic quantum computing (AQC) emerges as a promising solution to offer a good speed in solving the NP-hard optimization problems. However, existing clustering approaches face challenges in designing the problems to be solved by the quantum computer. In this study, we present the first clustering formulation tailored for resolution using Adiabatic quantum computing. An Ising model is introduced to represent the quantum mechanical system implemented on AQC. The proposed approach demonstrates high competitiveness compared to state-of-the-art optimization-based methods, even when utilizing off-the-shelf integer programming solvers. Lastly, this work showcases the solvability of the proposed clustering problem on current-generation real quantum computers for small examples and analyzes the properties of the obtained solutions. Quantum Computing Visual Clustering Quantum Annealing Neural Networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Jun, 2025 Editor assigned by journal 04 Jun, 2025 Reviews received at journal 25 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers invited by journal 12 May, 2025 Submission checks completed at journal 12 May, 2025 First submitted to journal 10 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5022498","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458979116,"identity":"c9142f19-ddc0-433e-94b3-399df37acf07","order_by":0,"name":"Xuan-Bac Nguyen","email":"","orcid":"","institution":"University of Arkansas at Fayetteville","correspondingAuthor":false,"prefix":"","firstName":"Xuan-Bac","middleName":"","lastName":"Nguyen","suffix":""},{"id":458979117,"identity":"7cfca820-e4a6-4cf3-bd32-2423688f707b","order_by":1,"name":"Hugh Churchill","email":"","orcid":"","institution":"University of Arkansas at Fayetteville","correspondingAuthor":false,"prefix":"","firstName":"Hugh","middleName":"","lastName":"Churchill","suffix":""},{"id":458979118,"identity":"2473ccaf-2c1a-439c-a829-1702c60dc44f","order_by":2,"name":"Khoa Luu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACCQkg8YCBQQ5IMUMxMVoSGBiMSdeS2EC0FsnZzQc/JLYdTt9w/OxjA4YKa5Be/EBa5liyBFBL7oYz6cYJDGfSCWuRk8gxgGg5kMZ8gLHtMFFajH+AHGZw/hlQyz8itEhL5JiBbEkwuJHGnMDYQIQWyRlpaRYJ59INZ954xmyQcCzdmKAWiRvJh298KLOW5zufxizxocZalqAWVJBAmvJRMApGwSgYBbgAAAUxPkIjjhJ4AAAAAElFTkSuQmCC","orcid":"","institution":"University of Arkansas at Fayetteville","correspondingAuthor":true,"prefix":"","firstName":"Khoa","middleName":"","lastName":"Luu","suffix":""},{"id":458979119,"identity":"dd4ea6bf-672e-4a90-b5f5-5d97c979c2de","order_by":3,"name":"Samee U. 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