Q-PACT : Quantum-Parallel Agentic Coordination Toolkit

Q-PACT : Quantum-Parallel Agentic Coordination Toolkit | 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PDF Research Article Q-PACT : Quantum-Parallel Agentic Coordination Toolkit Sourodeep Kundu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8492548/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 The convergence of quantum optimization and autonomous agentic reasoning defines a new computational frontier in large-scale resource coordination. This work introduces Q-PACT, a Quantum-Parallel Agentic Coordination Toolkit that unifies quantum optimization, fairness-driven orchestration, and adaptive multi-agent intelligence into a reproducible hybrid framework. Q-PACT addresses a key limitation in distributed and cloud-based quantum infrastructures; inefficient, inequitable scheduling of scarce quantum resources among competing agents and tasks. The proposed architecture formulates multi-agent scheduling as a Quadratic Unconstrained Binary Optimization (QUBO) model, solved through a heterogeneous pipeline integrating quantum annealing (D-Wave), variational optimization (QAOA on IonQ/Azure), and classical meta-heuristics. A noise-aware encoder, solver-adapter layer, and agentic manager collaborate to maintain fairness, adaptability, and load balance, while the QML-AURA subsystem continuously refines solver hyperparameters through feedback-driven variational learning. Q-PACT’s hybrid orchestration layer enables dynamic re-optimization, fault-tolerant fallback execution, and secure artifact exchange across distributed backends, achieving resilience under the noise and queueing constraints of NISQ-era hardware. Empirical evaluations demonstrate significant improvements in makespan reduction, workload variance, and energy minimization compared with classical heuristics, establishing the viability of quantum-assisted agentic scheduling at scale. By bridging quantum optimization paradigms, multi-agent systems, and fairness-aware orchestration, Q-PACT provides a foundational blueprint for the next generation of autonomous, cloud-integrated, and quantum-enabled coordination systems. Theoretical Computer Science Artificial Intelligence and Machine Learning Computer Architecture and Engineering Quantum optimization Hybrid quantum–classical computing QUBO QAOA Multi-agent coordination Fairness-aware scheduling Distributed orchestration Quantum annealing QML-driven meta-learning 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. 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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-8492548","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":567942589,"identity":"d398d9b7-cc26-428f-b1b3-eb68f55b7d99","order_by":0,"name":"Sourodeep Kundu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYNACNiDmAWMbOX6QQEIB8VrSjCUbQFoMiNdyOHHDAZAIHi3y7T1mHz6U2ciZ85wxe/C27XDi5vOrEz88MGCQ5xc7gFWLwZkzxjNnnEsztuztMTec25ZuvO3G280SQIcZzpydgF2LRI4xMy/Q8A3necykedusZbfdOLsBpCXB4DZ2LfIzwFr+10O1MDNunnF28w98WhhugLUcSDA42wPS4qy4gb93G15bDM4cK2accS7ZcGfPsTLJOUBPSdzg3WaRYCCB0y/y7c2bGT6U2cmb8yRvk3gDDDr+/rObb/6osJHnl8bhMLh1cJYEWKUEfuWoWvgPEFY9CkbBKBgFIwoAADglXnhbrU+qAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Sourodeep","middleName":"","lastName":"Kundu","suffix":""}],"badges":[],"createdAt":"2025-12-31 23:59:04","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8492548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8492548/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99794585,"identity":"c7562ecf-f1fd-4b0b-8ab3-ba646be35006","added_by":"auto","created_at":"2026-01-08 13:35:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2443083,"visible":true,"origin":"","legend":"","description":"","filename":"QPACTQuantumParallelAgenticCoordinationToolkit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8492548/v1_covered_89473bec-6085-445a-9409-785a1dd506f0.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eQ-PACT : Quantum-Parallel Agentic Coordination Toolkit\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Quantum optimization, Hybrid quantum–classical computing, QUBO, QAOA, Multi-agent coordination, Fairness-aware scheduling, Distributed orchestration, Quantum annealing, QML-driven meta-learning","lastPublishedDoi":"10.21203/rs.3.rs-8492548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8492548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe convergence of quantum optimization and autonomous agentic reasoning defines a new computational frontier in large-scale resource coordination. 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