An Efficient Prediction Based Dynamic Resource Allocation Framework in Quantum Cloud Using Knowledge Based Offline Reinforcement Learning

An Efficient Prediction Based Dynamic Resource Allocation Framework in Quantum Cloud Using Knowledge Based Offline Reinforcement Learning | 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 An Efficient Prediction Based Dynamic Resource Allocation Framework in Quantum Cloud Using Knowledge Based Offline Reinforcement Learning Valarmathi K, Mohnish Karthikeyan B, Navaneetha Krishnan S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5125318/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2025 Read the published version in Quantum Machine Intelligence → Version 1 posted 9 You are reading this latest preprint version Abstract Quantum Cloud Computing (QCC) is a practice of setting up the cloud platform for delivering computing assets over the internet via a pay-as-you-go model with the help of Quantum Computing (QC) paradigm. Real-time applications have scrupulous compliance regarding performance requirements due to the low-speed of traditional computers. Estimating cloud data center asset usage is a challenging task due to its dynamic nature. It employs a contemporary model to precisely estimate data center CPU utilization and applies an effective resource controller for optimized resource allocation using quantum computers. The proposed design ensures efficient resource estimation, scaling up or down based on predictions. An efficient dynamic resource controller is crucial to solving the scaling process with quantum computing support. Existing systems use a Reinforcement-Based resource controller with a Markov decision process that decides based on the current state of the environment, leading to long scaling and processing times. Our proposed model, the Prediction-Based Offline Reinforcement Learning (PB-ORL) Model, enhances this by considering historical information for prediction-based decisions. This approach achieves accurate and high-performance prediction, optimizing resource allocation proactively and dynamically. The model is analyzed using a real cloud data set with quantum cloud and machine learning approaches, which reduces latency and bandwidth traffic. Empirical results show that the proposed quantum computer-based machine learning approach outperforms previous methods, achieving 30–50% improved accuracy in CPU resource utilization and reducing time complexity by 33–42% in resource allocation. Quantum Computers Quantum Cloud Computing Cloud Computing Machine Learning Offline Reinforcement Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2025 Read the published version in Quantum Machine Intelligence → Version 1 posted Editorial decision: Revision requested 26 Nov, 2024 Reviews received at journal 24 Nov, 2024 Reviewers agreed at journal 04 Nov, 2024 Reviews received at journal 25 Oct, 2024 Reviewers agreed at journal 24 Oct, 2024 Reviewers invited by journal 24 Oct, 2024 Editor assigned by journal 24 Oct, 2024 Submission checks completed at journal 24 Sep, 2024 First submitted to journal 20 Sep, 2024 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-5125318","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":382906555,"identity":"895e0102-c364-4004-b100-2b1dc3f9ba24","order_by":0,"name":"Valarmathi K","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3RMUsDMRjG8ecItEsh65tvEXGwHexnMQQ6nXtFOC8cZBJc/RidgmNLhi4nzm5mcXIQXOxm7o5Cl8a6OeQPIRD48SYEyOX+ZazuN8nA8AkaDq+SpOgJdaR4BNEfSJzHJtiPSSS3G/N1U86rizHY+aWdVhj7N4SnBGlVI56dplkDpq9tvNhkIaHa40TUqhbGMZIezJdt95YSUDZBHkKzM+5uINOO8I804aRsnOJ7orGMhH6ZwinYmXFbsfJFc3a/JGHpXa5TZMS1fzXulssX7+lbVpxzHcIuQQ4aPgijuNYngVwul8sd7Qff9UZ7C0TNtwAAAABJRU5ErkJggg==","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Valarmathi","middleName":"","lastName":"K","suffix":""},{"id":382906560,"identity":"7f24e994-fae9-4b4b-9a34-4fde395bb1b0","order_by":1,"name":"Mohnish Karthikeyan B","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mohnish","middleName":"Karthikeyan","lastName":"B","suffix":""},{"id":382906570,"identity":"2e856bb6-0ffe-40c8-8103-bf523c32de19","order_by":2,"name":"Navaneetha Krishnan S","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Navaneetha","middleName":"Krishnan","lastName":"S","suffix":""}],"badges":[],"createdAt":"2024-09-20 18:07:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5125318/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5125318/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s42484-025-00257-5","type":"published","date":"2025-03-05T15:58:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78190951,"identity":"abeae103-0d2a-4189-9065-d03f918aedac","added_by":"auto","created_at":"2025-03-10 19:51:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":575519,"visible":true,"origin":"","legend":"","description":"","filename":"AnEfficientPredictionBasedDynamicResourceAllocationFrameworkinQuantumCloudUsingKnowledgeBasedOfflineReinforcementLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5125318/v1_covered_84e2b80d-e99b-4a4d-b485-b8d053dc5847.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Efficient Prediction Based Dynamic Resource Allocation Framework in Quantum Cloud Using Knowledge Based Offline Reinforcement Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"quantum-machine-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qumi","sideBox":"Learn more about [Quantum Machine Intelligence](http://link.springer.com/journal/42484)","snPcode":"42484","submissionUrl":"https://submission.nature.com/new-submission/42484/3","title":"Quantum Machine Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Quantum Computers, Quantum Cloud Computing, Cloud Computing, Machine Learning, Offline Reinforcement Learning","lastPublishedDoi":"10.21203/rs.3.rs-5125318/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5125318/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eQuantum Cloud Computing (QCC) is a practice of setting up the cloud platform for delivering computing assets over the internet via a pay-as-you-go model with the help of Quantum Computing (QC) paradigm. Real-time applications have scrupulous compliance regarding performance requirements due to the low-speed of traditional computers. Estimating cloud data center asset usage is a challenging task due to its dynamic nature. It employs a contemporary model to precisely estimate data center CPU utilization and applies an effective resource controller for optimized resource allocation using quantum computers. The proposed design ensures efficient resource estimation, scaling up or down based on predictions. An efficient dynamic resource controller is crucial to solving the scaling process with quantum computing support. Existing systems use a Reinforcement-Based resource controller with a Markov decision process that decides based on the current state of the environment, leading to long scaling and processing times. Our proposed model, the Prediction-Based Offline Reinforcement Learning (PB-ORL) Model, enhances this by considering historical information for prediction-based decisions. 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