Dy-Part: A Dynamic, Noise-Aware Scheduler for Optimizing Hybrid Quantum-Classical Algorithms

Dy-Part: A Dynamic, Noise-Aware Scheduler for Optimizing Hybrid Quantum-Classical Algorithms | 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 Dy-Part: A Dynamic, Noise-Aware Scheduler for Optimizing Hybrid Quantum-Classical Algorithms Mufakir Qamar Ansari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8041248/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Hybrid quantum-classical algorithms are a leading approach for applying noisy intermediate-scale quantum (NISQ) devices to practical problems. A key challenge in this paradigm is determining how to partition large computational problems into smaller sub-tasks that can be executed on size-limited quantum hardware. Current methods often rely on static, predetermined partitioning strategies that primarily consider the problem's structure, often failing to adapt to the quantum processor's dynamic noise characteristics. In this work, we introduce and analyze a dynamic scheduling framework, named Dy-Part, designed to address this challenge. Dy-Part automates the partitioning decision by using a heuristic cost model that balances two competing factors: the expected infidelity of the quantum computation, which increases with circuit size, and the classical post-processing overhead, which increases as the problem is broken into more pieces. To find the optimal partition size that minimizes this cost, our framework employs an efficient ternary search, whose output guides a fast, greedy graph partitioning heuristic. We demonstrate and validate this approach using the Variational Quantum Eigensolver (VQE) to solve the Max-Cut problem. Our results, averaged over 30 random graph instances, show that while a static strategy is effective in low-noise regimes, Dy-Part provides a more robust solution as noise increases. For instance, on 12-node graphs with a high gate error rate ( \((\epsilon_{\text{gate}} > 10^{-2})\) ), Dy-Part's dynamic strategy yields a mean approximation ratio more than double that of the static baseline. These results show that a dynamic, noise-aware scheduling approach can provide a robust method for configuring hybrid workflows, offering a practical pathway to maximizing the performance of near-term quantum computers. Hybrid Quantum-Classical Algorithms Workload Partitioning Noise-Aware Scheduling Variational Quantum Eigensolver (VQE) NISQ Quantum Computing Combinatorial Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Feb, 2026 Reviews received at journal 02 Dec, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers invited by journal 07 Nov, 2025 Editor assigned by journal 07 Nov, 2025 Submission checks completed at journal 06 Nov, 2025 First submitted to journal 05 Nov, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8041248","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":546428759,"identity":"0af82a68-d21c-4bff-b128-55b4f9af24dd","order_by":0,"name":"Mufakir Qamar 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Algorithms","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":"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":"Hybrid Quantum-Classical Algorithms, Workload Partitioning, Noise-Aware Scheduling, Variational Quantum Eigensolver (VQE), NISQ, Quantum Computing, Combinatorial Optimization","lastPublishedDoi":"10.21203/rs.3.rs-8041248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8041248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHybrid quantum-classical algorithms are a leading approach for applying noisy intermediate-scale quantum (NISQ) devices to practical problems. A key challenge in this paradigm is determining how to partition large computational problems into smaller sub-tasks that can be executed on size-limited quantum hardware. Current methods often rely on static, predetermined partitioning strategies that primarily consider the problem's structure, often failing to adapt to the quantum processor's dynamic noise characteristics. In this work, we introduce and analyze a dynamic scheduling framework, named Dy-Part, designed to address this challenge. Dy-Part automates the partitioning decision by using a heuristic cost model that balances two competing factors: the expected infidelity of the quantum computation, which increases with circuit size, and the classical post-processing overhead, which increases as the problem is broken into more pieces. To find the optimal partition size that minimizes this cost, our framework employs an efficient ternary search, whose output guides a fast, greedy graph partitioning heuristic. We demonstrate and validate this approach using the Variational Quantum Eigensolver (VQE) to solve the Max-Cut problem. Our results, averaged over 30 random graph instances, show that while a static strategy is effective in low-noise regimes, Dy-Part provides a more robust solution as noise increases. For instance, on 12-node graphs with a high gate error rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\epsilon_{\\text{gate}} \u0026gt; 10^{-2})\\)\u003c/span\u003e\u003c/span\u003e), Dy-Part's dynamic strategy yields a mean approximation ratio more than double that of the static baseline. These results show that a dynamic, noise-aware scheduling approach can provide a robust method for configuring hybrid workflows, offering a practical pathway to maximizing the performance of near-term quantum computers.\u003c/p\u003e","manuscriptTitle":"Dy-Part: A Dynamic, Noise-Aware Scheduler for Optimizing Hybrid Quantum-Classical Algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 07:29:21","doi":"10.21203/rs.3.rs-8041248/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-20T08:19:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T02:56:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304713314899012278974342639965007904508","date":"2025-11-08T13:39:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-07T15:57:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-07T15:56:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-06T08:07:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Quantum Machine Intelligence","date":"2025-11-05T18:51:45+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"f9de1c80-6c21-474e-a110-4ce663fdceef","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T15:23:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 07:29:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8041248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8041248","identity":"rs-8041248","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}