A Theoretical Framework for Universal Latency Reduction in Hybrid Quantum-Classical Systems

A Theoretical Framework for Universal Latency Reduction in Hybrid Quantum-Classical Systems | 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 A Theoretical Framework for Universal Latency Reduction in Hybrid Quantum-Classical Systems Aneesh Nandan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6682595/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 This paper introduces a novel quantum-classical latency reduction formula, representing a theoretical breakthrough in optimizing performance across hybrid systems. Central to our work is \textit{Aneesh Nandan’s Quantum-Classical Latency Reduction Formula}, which models hybrid latency as an exponential function of baseline classical latency and simulated quantum enhancement. The baseline latency (( L_c )) was computed using classical performance metrics—CPU usage, GPU load, and frame rate—measured over a 60-second interval. These data were combined with a simulated range of quantum enhancement values and a noise model to account for experimental variability. The resulting hybrid latencies display a consistent exponential decay as quantum contribution increases, validating the formula’s predictive behavior in theory. To the best of our knowledge, this is the first systematic study to propose a dedicated quantum-classical latency reduction formula, establishing a theoretical framework applicable to domains such as virtual reality, augmented reality, gaming engines, autonomous systems, cloud infrastructure, and quantum-edge computing. All quantum computations were simulated on classical hardware; therefore, the observed latency reductions are theoretical. Future validation using physical quantum systems is necessary to confirm the real-world applicability and effectiveness of this model. Quantum-Classical Latency Reduction Formula Hybrid Quantum-Classical Optimization Latency Modeling Performance Optimization Virtual and Augmented Reality High-Performance Computing Exponential Decay Model Full Text Additional Declarations No competing interests reported. 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. 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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-6682595","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538678139,"identity":"0b379879-b193-44ce-a9a4-1bd09220dbc8","order_by":0,"name":"Aneesh 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