Quantum-Inspired Optimization for Depth-Based AR Markerless Registration

Quantum-Inspired Optimization for Depth-Based AR Markerless Registration | 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-Inspired Optimization for Depth-Based AR Markerless Registration Michael R. Stevens, Anna Chen, Daniel K. Brooks, Laura M. Torres, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7987936/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Conventional optimization algorithms for AR markerless registration often converge to local minima under noisy depth data. We propose a quantum-inspired optimization approach that leverages a variational quantum annealing algorithm simulated on classical hardware to enhance global convergence in point cloud alignment. The method introduces probabilistic tunneling to escape poor local optima, integrated with adaptive ICP refinement. In 500 phantom trials, our method reduced registration error from 2.4 mm (ICP) and 1.7 mm (genetic algorithm) to 1.0 mm, while requiring 35% fewer iterations. Average runtime was 41 ms per frame, sustaining real-time AR visualization at 24 fps. This demonstrates the potential of quantum-inspired algorithms in advancing surgical AR registration accuracy and efficiency. Biomedical Engineering Artificial Intelligence and Machine Learning quantum-inspired optimization variational annealing global registration AR surgery depth alignment Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-7987936","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537402750,"identity":"8aa578b2-b3c5-453e-a48c-56443b44f5aa","order_by":0,"name":"Michael R. 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