Design and Analysis of Quantum Transfer Fractal Priority Replay with Dynamic Memory (QTFPR-DM) algorithm to Enhancing Robotic Decision-Making by Quantum computing

Design and Analysis of Quantum Transfer Fractal Priority Replay with Dynamic Memory (QTFPR-DM) algorithm to Enhancing Robotic Decision-Making by Quantum computing | 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 Design and Analysis of Quantum Transfer Fractal Priority Replay with Dynamic Memory (QTFPR-DM) algorithm to Enhancing Robotic Decision-Making by Quantum computing R. Palanivel, P. Muthulakshmi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4194374/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 In the field of robotics, efficient decision-making is essential for navigating complex environments and accomplishing diverse tasks. Quantum Reinforcement Learning (QRL) has emerged as a promising approach to address these challenges, leveraging quantum principles to enhance decision-making processes. However, existing QRL algorithms often face limitations in terms of sample efficiency and convergence speed. To overcome these limitations, we propose a novel algorithm, Quantum Transfer Fractal Priority Replay with Dynamic Memory (QTFPR-DM), designed specifically for robotic decision-making tasks. QTFPR-DM integrates quantum circuits for action selection within a QRL framework. It utilizes a replay buffer to store past experiences and dynamic memory to adaptively update based on experience access frequency. Additionally , a novel priority formula is introduced to prioritize experiences for learning, focusing on those with high learning potential. Experimental results demonstrate the effectiveness of QTFPR-DM in various robotic environments. The algorithm shows significant improvements in convergence speed and sample efficiency compared to conventional approaches. Furthermore, analysis of the exploration-exploitation trade-off reveals how dynamic memory management enhances exploration capabilities while maintaining exploitation efficiency in robotic contexts. QTFPR-DM represents a promising advancement in ro-botic decision-making, offering improved performance and efficiency. By lev-eraging quantum principles alongside dynamic memory and priority replay mechanisms, QTFPR-DM demonstrates its potential to revolutionize decision-making processes in robotics. The insights gained from this study pave the way for the development of more adaptive and efficient robotic systems, with broad implications for real-world applications. Quantum Reinforcement learning Transfer Learning Fractal Techniques Priority Replay Dynamic Memory Robotics 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. 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-4194374","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285935198,"identity":"198755c9-f08f-4802-9344-8d6ef705d6f5","order_by":0,"name":"R. 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The algorithm shows significant improvements in convergence speed and sample efficiency compared to conventional approaches. Furthermore, analysis of the exploration-exploitation trade-off reveals how dynamic memory management enhances exploration capabilities while maintaining exploitation efficiency in robotic contexts. QTFPR-DM represents a promising advancement in ro-botic decision-making, offering improved performance and efficiency. By lev-eraging quantum principles alongside dynamic memory and priority replay mechanisms, QTFPR-DM demonstrates its potential to revolutionize decision-making processes in robotics. 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