Mini-scale Traffic Flow Optimization: An Iterative QUBOs Approach Converting from Hybrid Solver to Pure Quantum Processing Unit

Mini-scale Traffic Flow Optimization: An Iterative QUBOs Approach Converting from Hybrid Solver to Pure Quantum Processing Unit | 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 Mini-scale Traffic Flow Optimization: An Iterative QUBOs Approach Converting from Hybrid Solver to Pure Quantum Processing Unit Hadi Salloum, Sanzhar Zhanalin, Amer Al Badr, Yaroslav Kholodov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5332824/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Traffic congestion continues to pose a significant challenge in urban environments, necessitating innovative approaches to traffic management. This paper explores the application of Quantum Annealing (QA) for real-world traffic optimization, expanding on the pioneering work of Volkswagen and D-Wave. In 2017, a collaborative team demonstrated the potential of QA to optimize traffic flow by solving a complex Quadratic Unconstrained Binary Optimization (QUBO) problem involving 418 cars, which required 1,254 qubits. Later, this research culminated in a pilot project at the Web Summit conference in Lisbon, one of Europe’s largest technology events, showcasing quantum computing-based traffic optimization. Since the QPU alone could not directly handle the full problem size, the team employed a hybrid classical-quantum approach, leading to significant improvements in traffic distribution. This paper builds on that foundation by investigating potential speedups using a purely quantum approach, particularly by utilizing the QPU for smaller QUBO problems. The proposed method (MTF) enhances traffic management by decomposing the overall optimization problem into smaller, more manageable subproblems. This decomposition enables us to harness the advantages of the QPU while tackling more complex traffic scenarios that previous approaches struggled to manage. By breaking the problem into smaller parts, we mitigate the challenges associated with embedding large-scale problems into the QPU, which often presents computational difficulties. To evaluate our approach, we conducted experiments involving 100, 200, 300, 400, and 500 cars on a complex traffic map featuring multiple start and end points. We successfully embedded the problem into the D-Wave Advantage Quantum Processing Unit, utilizing the "Pegasus" topology, which resulted in a significant acceleration of the solution process. The experiment results show improved speed and effectiveness in real-world scenarios by leveraging the QPU for better traffic optimization. Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Physical sciences/Physics/Quantum physics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Feb, 2025 Reviews received at journal 06 Feb, 2025 Reviewers agreed at journal 18 Jan, 2025 Reviewers agreed at journal 16 Jan, 2025 Reviews received at journal 21 Dec, 2024 Reviewers agreed at journal 11 Dec, 2024 Reviewers agreed at journal 17 Nov, 2024 Reviewers invited by journal 12 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Editor invited by journal 11 Nov, 2024 Submission checks completed at journal 09 Nov, 2024 First submitted to journal 25 Oct, 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. 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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-5332824","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":382899035,"identity":"e2ed4e2c-c897-42a2-8059-d84a7f307153","order_by":0,"name":"Hadi Salloum","email":"data:image/png;base64,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","orcid":"","institution":"Innopolis University","correspondingAuthor":true,"prefix":"","firstName":"Hadi","middleName":"","lastName":"Salloum","suffix":""},{"id":382899036,"identity":"cad0e26a-34cb-4640-9b8a-8206923e78a8","order_by":1,"name":"Sanzhar Zhanalin","email":"","orcid":"","institution":"Innopolis University","correspondingAuthor":false,"prefix":"","firstName":"Sanzhar","middleName":"","lastName":"Zhanalin","suffix":""},{"id":382899037,"identity":"728137ab-1e29-4a30-89f9-7c65f0912238","order_by":2,"name":"Amer Al Badr","email":"","orcid":"","institution":"Innopolis University","correspondingAuthor":false,"prefix":"","firstName":"Amer","middleName":"Al","lastName":"Badr","suffix":""},{"id":382899038,"identity":"d7b698a4-b785-405b-aa65-ff830a16be7f","order_by":3,"name":"Yaroslav Kholodov","email":"","orcid":"","institution":"Innopolis University","correspondingAuthor":false,"prefix":"","firstName":"Yaroslav","middleName":"","lastName":"Kholodov","suffix":""}],"badges":[],"createdAt":"2024-10-25 13:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5332824/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5332824/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-04568-2","type":"published","date":"2025-07-02T15:58:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86181067,"identity":"d924e43e-906e-4d1c-89f6-11f55c7a0620","added_by":"auto","created_at":"2025-07-07 16:23:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1408509,"visible":true,"origin":"","legend":"","description":"","filename":"MiniscaleTrafficFlowOptimizationSR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5332824/v1_covered_d32c7963-1f6d-45b5-85dc-58e85f07838c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mini-scale Traffic Flow Optimization: An Iterative QUBOs Approach Converting from Hybrid Solver to Pure Quantum Processing Unit","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5332824/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5332824/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Traffic congestion continues to pose a significant challenge in urban environments, necessitating innovative approaches to traffic management. This paper explores the application of Quantum Annealing (QA) for real-world traffic optimization, expanding on the pioneering work of Volkswagen and D-Wave. In 2017, a collaborative team demonstrated the potential of QA to optimize traffic flow by solving a complex Quadratic Unconstrained Binary Optimization (QUBO) problem involving 418 cars, which required 1,254 qubits. Later, this research culminated in a pilot project at the Web Summit conference in Lisbon, one of Europe’s largest technology events, showcasing quantum computing-based traffic optimization. Since the QPU alone could not directly handle the full problem size, the team employed a hybrid classical-quantum approach, leading to significant improvements in traffic distribution. This paper builds on that foundation by investigating potential speedups using a purely quantum approach, particularly by utilizing the QPU for smaller QUBO problems. 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