{"paper_id":"4c5ccaa4-51a1-4ee3-836c-9eccda0ccdce","body_text":"Quantum Synergy in Retrieval-Augmented Generation for Contextual Enhancement | 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 Synergy in Retrieval-Augmented Generation for Contextual Enhancement K. Adithi, R. K. Kapilavani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6216441/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 Retrieval-Augmented Generation (RAG) has emerged as a powerful framework in natural language processing (NLP), integrating retrieval mechanisms with generative models to enhance information accuracy and contextual relevance. However, classical retrieval techniques face scalability bottlenecks and computational inefficiencies when handling large datasets. In this work, we introduce GroQ-Enhanced RAG (QRAG), a hybrid quantum-classical framework that leverages Grover’s search algorithm and GroQ-Rank (QAOA-based ranking) to enhance retrieval efficiency and optimization. QRAG employs Grover’s algorithm to accelerate query processing and utilizes GroQ for combinatorial ranking optimization, significantly reducing computational overhead. Empirical evaluations demonstrate that QRAG reduces retrieval latency by 40–50% compared to traditional RAG while improving response accuracy and scalability. By integrating quantum search and optimization techniques, GroQ-powered QRAG sets a new benchmark for efficient, high-fidelity information retrieval in NLP. While this study applies QRAG to RAG-based architectures, the proposed framework can be extended to other AI-driven retrieval-intensive applications, highlighting the transformative potential of quantum computing in large-scale language processing and information retrieval tasks. Quantum computing Retrieval-Augmented Generation (RAG) Quantum Approximate Optimization Algorithm (QAOA) Grover's search algorithm Full Text Additional Declarations No competing interests reported. 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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-6216441\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":452371466,\"identity\":\"26592bdf-1197-4bfa-a7e6-640f7fe9b451\",\"order_by\":0,\"name\":\"K. 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