Q GPT: A New Direction in Quantum AI Integrating Large Language Models with Quantum Optimization for Accelerated Industrial Problem Solving

Q GPT: A New Direction in Quantum AI Integrating Large Language Models with Quantum Optimization for Accelerated Industrial Problem Solving | 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 Article Q GPT: A New Direction in Quantum AI Integrating Large Language Models with Quantum Optimization for Accelerated Industrial Problem Solving Hadi Salloum, Osama Orabi, Yaroslav Kholodov, Suleiman Karim Eddin, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6336584/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 As industries increasingly encounter complex optimization challenges, the convergence of Artificial Intelligence (AI) and Quantum Computing offers a promising pathway to scalable, efficient solutions. This work proposes a novel framework — including the introduction of Q GPT, a purpose-built AI system designed to solve complex optimization problems using quantum resources — that integrates Large Language Models (LLMs) with Quantum Optimization techniques to bridge the gap between classical and quantum paradigms in industrial applications. LLMs, with their advanced reasoning and language capabilities, serve as intelligent interfaces that translate high‑level industrial problems into quantum‑compatible formulations such as Ising or QUBO models. These formulations are then processed using state‑of‑the‑art quantum optimization algorithms, including quantum annealing and variational approaches. By combining the semantic understanding of LLMs with the computational power of quantum systems, this hybrid approach significantly reduces the overhead for domain experts in accessing quantum technologies. We explore architectural designs, use cases in logistics and energy, and the potential of this synergy to accelerate decision‑making, lower cost barriers, and foster broader quantum adoption in real‑world industrial settings. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology 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. 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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-6336584","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":446496394,"identity":"6256ad41-0cbc-4456-b42e-99082e5804c9","order_by":0,"name":"Hadi 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