Quantum-Augmented Hybrid Routing in Dynamic Networks: A Physics-Inspired Reinforcement Learning Approach

Quantum-Augmented Hybrid Routing in Dynamic Networks: A Physics-Inspired Reinforcement Learning Approach | 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 Quantum-Augmented Hybrid Routing in Dynamic Networks: A Physics-Inspired Reinforcement Learning Approach Haithem Abdelghany, Mohamed Ashour This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7124150/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 innovative dynamics of communication networks are being challenged unprecedentedly by rising data traffic, dynamically evolving topologies, and the increased complexity of distributed intelligent applications. Typically, routing algorithms are inadequate for adjusting in actual time to address network congestion, changes in computational needs and heterogeneity and real-time. In this paper, we propose a new hybrid quantum-augmented routing model that synergistically integrates quantum reinforcement learning with physics-motivated network modeling by reasoning in terms of a space‒time continuum. Here, the phenomenon of network congestion is simulated as a curvature on the fabric of space-time, and the computation facilities on nodes are envisioned as a gravitational source, which directs the flows of data. The most essential innovation is the development of an artificial intelligence awareness layer (AIAL), that functions with quantum-aid learning agents proactively weighing and choosing the routing paths in live environments. The quantum agents query the multiple parallel paths in SQL via quantum superposition and quantum parallelism to enhance convergence and global route optimization. The hybrid structure is built upon features of the local adaptability of quantum-enhanced Q-learning and global contextual knowledge of deep Q-networks (DQNs), which are constantly adjusted to changes in network metrics, such as the load, latency, and availability of nodes. Large simulations demonstrate that this structure has a reward/cost ratio of 138.16, scales well during states of extreme congestion and is structurally efficient in all cases, with higher resiliency and faster convergence of the framework than those of OSPF, the EIGRP, the IS-IS and the RIP. Although this is a positive finding, it is reached under certain simulation conditions and it is not directly applicable in real-life network with diverse topologies and traffic patterns. The findings corroborate that coupling quantum reinforcement learning with physics-inspired routing will make full use of the adaptive optimal routing discoverable in uncertain situations, changing network conditions in an autonomous manner. This study establishes a basis for a new generation of cognitively adaptive, self-optimizing networks to best meet the needs of 6G, edge computing and quantum-aware distributed infrastructures. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics Quantum reinforcement learning Deep Q-networks AI-aware routing Space-time modeling Edge computing 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-7124150","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":495417346,"identity":"889ccf91-fbc0-4a81-8ba0-73b484552e36","order_by":0,"name":"Haithem Abdelghany","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYBACAwYeBgmGAoYEIJvxAVyQsBYDsBZmA7ggsVrYJIjSYs5+9uCNDwa1efyze8yqbtTcsWdgb94mwVBRi1OLZU9esuUMg+PFEnfOmN3OOfYssYHnWJkEw5njuB12IMdMmsfgWGLDjRygFrbDCQwSOWYSjG3HcGs5/8ZM+g9Qy3ygluKcf4ftGeTfALX8w6MFqFKawaAmcQOQwZzbdpixQYIHqKWhBo+Wd8mWPQYHEjfeSCuWzu07nNjGk1ZskXDsAB6H5R688aOiLnHejeSNn3O+HbbnZz+88caHmjqcWqDgMBBzQKKDDUQkgEXwApCZ7A/QRUbBKBgFo2AUgAEAbZNbjSj7ADwAAAAASUVORK5CYII=","orcid":"","institution":"Mansoura University","correspondingAuthor":true,"prefix":"","firstName":"Haithem","middleName":"","lastName":"Abdelghany","suffix":""},{"id":495417347,"identity":"907aa306-80e3-476a-a99c-ea22732dcae8","order_by":1,"name":"Mohamed Ashour","email":"","orcid":"","institution":"Mansoura University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Ashour","suffix":""}],"badges":[],"createdAt":"2025-07-14 19:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7124150/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7124150/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90291879,"identity":"d0383121-c62c-4446-9821-db033ca364b8","added_by":"auto","created_at":"2025-09-01 07:32:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":857905,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7124150/v1_covered_b569cf1f-07dd-47ad-99cb-54cedc031cd3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantum-Augmented Hybrid Routing in Dynamic Networks: A Physics-Inspired Reinforcement Learning Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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 reinforcement learning, Deep Q-networks, AI-aware routing, Space-time modeling, Edge computing","lastPublishedDoi":"10.21203/rs.3.rs-7124150/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7124150/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe innovative dynamics of communication networks are being challenged unprecedentedly by rising data traffic, dynamically evolving topologies, and the increased complexity of distributed intelligent applications. 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The hybrid structure is built upon features of the local adaptability of quantum-enhanced Q-learning and global contextual knowledge of deep Q-networks (DQNs), which are constantly adjusted to changes in network metrics, such as the load, latency, and availability of nodes. Large simulations demonstrate that this structure has a reward/cost ratio of 138.16, scales well during states of extreme congestion and is structurally efficient in all cases, with higher resiliency and faster convergence of the framework than those of OSPF, the EIGRP, the IS-IS and the RIP. Although this is a positive finding, it is reached under certain simulation conditions and it is not directly applicable in real-life network with diverse topologies and traffic patterns. The findings corroborate that coupling quantum reinforcement learning with physics-inspired routing will make full use of the adaptive optimal routing discoverable in uncertain situations, changing network conditions in an autonomous manner. 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