QoS-Aware Reinforcement Learning Routing for Entanglement Networks

QoS-Aware Reinforcement Learning Routing for Entanglement Networks | 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 QoS-Aware Reinforcement Learning Routing for Entanglement Networks Diego Abreu, Antônio Abelém This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8455670/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Entanglement-based quantum networks require intelligent routing mechanisms that not only maximize fidelity and connectivity but also ensure service differentiation under varying traffic conditions. In this work, we propose a novel QoS-aware traffic modeling and scheduling framework integrated into a reinforcement learning-based quantum routing agent. Unlike prior approaches that assume deterministic or uniform request arrival patterns, our model introduces stochastic and bursty traffic scenarios using Erlang and ON/OFF distributions to emulate realistic quantum workloads. Each entanglement request is assigned a set of QoS attributes, including fidelity requirements, time-to-live constraints, and priority levels. We then extend the reward function of the agent to consider deadline compliance and resource efficiency in addition to fidelity. Through simulation, we compare the proposed method against state-of-the-art baselines, including DQRA and Proactive RL, under both best-effort and QoS-prioritized scheduling modes. Results demonstrate that our approach maintains higher success rates and lower latency for high-priority requests, while preserving entanglement fidelity and efficient resource allocation across varying network loads. This study highlights the importance of integrating QoS mechanisms into quantum routing to support scalable and differentiated quantum services Quantum Routing Quality of Service Quantum Entanglement Network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Feb, 2026 Reviews received at journal 26 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviews received at journal 31 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 25 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 05 Jan, 2026 Submission checks completed at journal 05 Jan, 2026 First submitted to journal 05 Jan, 2026 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-8455670","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578182351,"identity":"f9f621ad-4570-4f99-a7e4-957c0e48bcb1","order_by":0,"name":"Diego Abreu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYDCCwxAqAYgZHySA2YwPgMQBorQwGySA9TAb4NdyAKGFTYKBGC18x5kPv/jAYJdn3t5jVvHwh408g0Qy24MfDHfycWmRPMyWZjmDIblY5swZsxsJCWmGDRLJ7IY9DM8sG3BoMTjMY2bMw8CcOEMiB6TlcAKDRP4xCR6Gwwa4bDE4zP8NqKU+cYb8G7OChIT/CSCHSf7Bq4WH+THQTKAtPGYMCQkHwFqk8dkC9IsZ4wyD48USPGnFEglpyYZtPI/ZjWUMnuHUwnf+8OMPHyqq8yTYD2/8+MPGTp6fPZnt4ZuKOzi1MICjA1maDYzwaQDG3AcMQ/CqHwWjYBSMghEHAGV2UY9Q2UMGAAAAAElFTkSuQmCC","orcid":"","institution":"Federal University of Para","correspondingAuthor":true,"prefix":"","firstName":"Diego","middleName":"","lastName":"Abreu","suffix":""},{"id":578182352,"identity":"7e6a506f-60f9-4ef8-b669-cb6eed65e5e1","order_by":1,"name":"Antônio Abelém","email":"","orcid":"","institution":"Federal University of Para","correspondingAuthor":false,"prefix":"","firstName":"Antônio","middleName":"","lastName":"Abelém","suffix":""}],"badges":[],"createdAt":"2025-12-26 13:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8455670/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8455670/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100894884,"identity":"674beb01-4483-4b9f-8503-fe30c405758a","added_by":"auto","created_at":"2026-01-22 14:07:18","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4999,"visible":true,"origin":"","legend":"","description":"","filename":"d02ca47c439f4ad98617029fc15117e1.json","url":"https://assets-eu.researchsquare.com/files/rs-8455670/v1/5012f3c56f952ff537e0fc49.json"},{"id":100894932,"identity":"c65560e8-50ff-44ae-bd29-bf86d50554a5","added_by":"auto","created_at":"2026-01-22 14:07:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":379192,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerQOS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8455670/v1_covered_8f623cac-0ad4-450e-bee8-e8a43a752059.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"QoS-Aware Reinforcement Learning Routing for Entanglement Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-networks","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Networks](https://link.springer.com/journal/44354)","snPcode":"44354","submissionUrl":"https://submission.springernature.com/new-submission/44354/3","title":"Discover Networks","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Quantum Routing, Quality of Service, Quantum Entanglement Network","lastPublishedDoi":"10.21203/rs.3.rs-8455670/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8455670/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Entanglement-based quantum networks require intelligent routing mechanisms that not only maximize fidelity and connectivity but also ensure service differentiation under varying traffic conditions. 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