Non-hemolytic Peptide Classification Using a Quantum Support Vector Machine

Non-hemolytic Peptide Classification Using a Quantum Support Vector Machine | 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 Non-hemolytic Peptide Classification Using a Quantum Support Vector Machine Shengxin Zhuang, John Tanner, Yusen Wu, Du Q. Huynh, Wei Liu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4514116/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Nov, 2024 Read the published version in Quantum Information Processing → Version 1 posted 9 You are reading this latest preprint version Abstract Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a classical nature and the search for practical, real-world applications of QML remains active. In this work, we apply the well-studied quantum support vector machine (QSVM), a powerful QML model, to a binary classification task which classifies peptides as either hemolytic or non-hemolytic. Using three peptide datasets,we apply and contrast the performance of the QSVM, numerous classical SVMs, and the best published results on the same peptide classification task, out of which the QSVM performs best. The contributions of this work include (i) the first application of the QSVM to this specific peptide classification task, (ii) an explicit demonstration of QSVMs outperform-ing the best published results attained with classical machine learning models on this classification task and (iii) empirical results showing that the QSVM is capable of outperforming many (and possibly all) classical SVMs on this classification 1 task. This foundational work paves the way to verifiable quantum advantages in the field of computational biology and facilitates safer therapeutic development. Quantum Computing Quantum Machine Learning Quantum Support Vector Machine Quantum Bioinformatics Biology Drug Design Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Nov, 2024 Read the published version in Quantum Information Processing → Version 1 posted Editorial decision: Revision requested 12 Aug, 2024 Reviews received at journal 11 Aug, 2024 Reviewers agreed at journal 30 Jul, 2024 Reviews received at journal 07 Jul, 2024 Reviewers agreed at journal 07 Jul, 2024 Reviewers invited by journal 27 Jun, 2024 Submission checks completed at journal 03 Jun, 2024 Editor assigned by journal 03 Jun, 2024 First submitted to journal 01 Jun, 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-4514116","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313178994,"identity":"808bd040-1b32-4da7-9110-6cdd9cb8ce2f","order_by":0,"name":"Shengxin Zhuang","email":"","orcid":"","institution":"The University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Shengxin","middleName":"","lastName":"Zhuang","suffix":""},{"id":313178995,"identity":"4caddc2e-e143-4c7d-9409-d383eb3a783f","order_by":1,"name":"John Tanner","email":"","orcid":"","institution":"The University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Tanner","suffix":""},{"id":313178996,"identity":"9823554a-30d0-4ae7-894c-aac59d3c52c0","order_by":2,"name":"Yusen Wu","email":"","orcid":"","institution":"The University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Yusen","middleName":"","lastName":"Wu","suffix":""},{"id":313178999,"identity":"7287e5f0-f3d0-4178-b9a4-fb4245bef58e","order_by":3,"name":"Du Q. 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