High-Performance and Quantum Computing in Cancer Modeling: A Review and Hybrid HPC- Quantum Approach

High-Performance and Quantum Computing in Cancer Modeling: A Review and Hybrid HPC- Quantum 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 High-Performance and Quantum Computing in Cancer Modeling: A Review and Hybrid HPC- Quantum Approach Arpana Sinhal, Anay Sinhal, Amit Sinhal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7151271/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 High-performance computing (HPC) and quantum computing are increasingly being applied to accelerate the modeling of complex diseases such as cancer. This paper presents a detailed survey of the past five years of research on the independent contributions of HPC and quantum computing to cancer disease modeling, examines efforts to integrate these technologies, and proposes a novel hybrid approach. HPC has enabled large-scale, high-resolution cancer simulations (e.g., cm-scale tumor growth models and patient-specific “digital twin” ensembles) with significant speedups using GPU acceleration and distributed computing. Quantum computing, while still nascent, has shown promise in drug discovery, for example, generating novel KRAS inhibitor molecules, and in improving predictive modeling with quantum machine learning (achieving up to ~ 14% higher AUROC in mortality prediction for colorectal cancer). We compiled a literature survey table summarizing key studies, including their computational approaches (from MPI-based finite element simulations to variational quantum algorithms) and quantitative outcomes (speedups, accuracy gains, scalability limits). Four figures contrast classical HPC and quantum hardware architectures, performance scaling (GPU clusters vs. quantum processors), a hybrid HPC-quantum workflow, and the projected performance gains of our proposed integrated approach. From the synthesis of literature, we hypothesize that integrating real-time quantum solvers as accelerators for HPC cancer simulations, for example, using a quantum linear system solver as a preconditioner in an MPI-parallel tumor growth model, can reduce overall simulation time by ≥ 30% while maintaining sub-1% error margins. We support this hypothesis with back-of-the-envelope performance modeling and aggregated benchmark data. An experimental design is outlined to validate the hypothesis, involving coupling a quantum computing module with an existing HPC cancer simulator and measuring speedup, accuracy, and scalability on representative tumor modeling problems. This work aims to provide a comprehensive perspective on how HPC and quantum computing, separately and together, can push the frontiers of cancer modeling for improved understanding and treatment optimization. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing High-performance computing (HPC) quantum computing hybrid HPC-quantum integration cancer disease modeling tumor-growth simulation quantum linear system solver computational oncology digital twins multiscale simulation accelerators 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. 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-7151271","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":492131527,"identity":"69254ed1-2500-4c97-9587-4e38ee652154","order_by":0,"name":"Arpana Sinhal","email":"data:image/png;base64,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","orcid":"","institution":"Manipal University","correspondingAuthor":true,"prefix":"","firstName":"Arpana","middleName":"","lastName":"Sinhal","suffix":""},{"id":492131528,"identity":"decaa2a8-fe8f-49b7-b1f2-6beb9add7144","order_by":1,"name":"Anay Sinhal","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Anay","middleName":"","lastName":"Sinhal","suffix":""},{"id":492131529,"identity":"7324064a-d2d8-4a4a-8410-5e5a06c1bbe6","order_by":2,"name":"Amit Sinhal","email":"","orcid":"","institution":"JK Lakshmipat University","correspondingAuthor":false,"prefix":"","firstName":"Amit","middleName":"","lastName":"Sinhal","suffix":""}],"badges":[],"createdAt":"2025-07-17 17:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7151271/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7151271/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94985837,"identity":"3eeacd09-e0ed-413b-ba5d-70c49a0d2a5f","added_by":"auto","created_at":"2025-11-03 06:59:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":772796,"visible":true,"origin":"","legend":"","description":"","filename":"HighPerformanceandQuantumComputinginCancerModelingAReviewandHybridHPCQuantumApproach.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7151271/v1_covered_fdbe30fa-afde-41b8-92d5-b64af148f75c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"High-Performance and Quantum Computing in Cancer Modeling: A Review and Hybrid HPC- Quantum 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":"High-performance computing (HPC), quantum computing, hybrid HPC-quantum integration, cancer disease modeling, tumor-growth simulation, quantum linear system solver, computational oncology, digital twins, multiscale simulation, accelerators","lastPublishedDoi":"10.21203/rs.3.rs-7151271/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7151271/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-performance computing (HPC) and quantum computing are increasingly being applied to accelerate the modeling of complex diseases such as cancer. This paper presents a detailed survey of the past five years of research on the independent contributions of HPC and quantum computing to cancer disease modeling, examines efforts to integrate these technologies, and proposes a novel hybrid approach. HPC has enabled large-scale, high-resolution cancer simulations (e.g., cm-scale tumor growth models and patient-specific \u0026ldquo;digital twin\u0026rdquo; ensembles) with significant speedups using GPU acceleration and distributed computing. Quantum computing, while still nascent, has shown promise in drug discovery, for example, generating novel KRAS inhibitor molecules, and in improving predictive modeling with quantum machine learning (achieving up to ~\u0026thinsp;14% higher AUROC in mortality prediction for colorectal cancer). We compiled a literature survey table summarizing key studies, including their computational approaches (from MPI-based finite element simulations to variational quantum algorithms) and quantitative outcomes (speedups, accuracy gains, scalability limits). Four figures contrast classical HPC and quantum hardware architectures, performance scaling (GPU clusters vs. quantum processors), a hybrid HPC-quantum workflow, and the projected performance gains of our proposed integrated approach. From the synthesis of literature, we hypothesize that integrating real-time quantum solvers as accelerators for HPC cancer simulations, for example, using a quantum linear system solver as a preconditioner in an MPI-parallel tumor growth model, can reduce overall simulation time by \u0026ge;\u0026thinsp;30% while maintaining sub-1% error margins. We support this hypothesis with back-of-the-envelope performance modeling and aggregated benchmark data. An experimental design is outlined to validate the hypothesis, involving coupling a quantum computing module with an existing HPC cancer simulator and measuring speedup, accuracy, and scalability on representative tumor modeling problems. This work aims to provide a comprehensive perspective on how HPC and quantum computing, separately and together, can push the frontiers of cancer modeling for improved understanding and treatment optimization.\u003c/p\u003e","manuscriptTitle":"High-Performance and Quantum Computing in Cancer Modeling: A Review and Hybrid HPC- Quantum Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 07:39:18","doi":"10.21203/rs.3.rs-7151271/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"94e00a17-6498-4e4f-92b5-988ded5570ba","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52251429,"name":"Biological sciences/Cancer"},{"id":52251430,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":52251431,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-10-31T12:09:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 07:39:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7151271","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7151271","identity":"rs-7151271","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}