Quantum Machine Learning and TensorNetworks as an Aid in the Clinical Diagnosis ofCoronary Artery Disease

Quantum Machine Learning and TensorNetworks as an Aid in the Clinical Diagnosis ofCoronary Artery Disease | 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 Quantum Machine Learning and TensorNetworks as an Aid in the Clinical Diagnosis ofCoronary Artery Disease Soham Bopardikar, Manuel Montoya, Pierre Decoodt, Juan G. Lalinde-Pulido, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3922115/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 Current applications of quantum machine learning are emerging, dueto the potential benefits that quantum technologies could bring in thenear future. One of the most recent developments is tensor network-based architectures, to explore the feasibility of the application of thismethod to healthcare, in this paper tensor networks are applied to theIEEE Heart Disease (COMPREHENSIVE) dataset for supervised clas-sification of coronary artery disease diagnosis. Three quantum machinelearning models were implemented in this study: 3-qubit Q-MPS, 4 qubitQ-MERA and 4-qubit Q-TTN. These were compared to six classical ma-chine learning models: Logistic Regression, Naive Bayes, Support VectorMachines (SVM), Decision Tree, Random Forest, and XGBoost; achiev-ing similar or better results than those of the best classical models. Themodels were evaluated following different strategies to modify the trainingand testing conditions, applying variations to the dataset by isolating theCleveland and Hungary datasets, which are subsets of the former. Addi-tionally, the impact of the input feature space preparation is demonstratedexperimentally, showing that there exist preferred conditions where thegeneralization of the model is maximum. Quantum Machine Learning Tensor Network Heart Disease Healthcare 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-3922115","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271179904,"identity":"3bbfa9d4-3a39-4274-931e-bf458246b371","order_by":0,"name":"Soham Bopardikar","email":"","orcid":"","institution":"University of Pune","correspondingAuthor":false,"prefix":"","firstName":"Soham","middleName":"","lastName":"Bopardikar","suffix":""},{"id":271179905,"identity":"94ac3ad9-123b-48c2-b676-7b73c60227c9","order_by":1,"name":"Manuel Montoya","email":"","orcid":"","institution":"EAFIT University","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Montoya","suffix":""},{"id":271179906,"identity":"268285c6-d410-4100-bdcf-9c678096ed0c","order_by":2,"name":"Pierre Decoodt","email":"","orcid":"","institution":"Université Libre de Bruxelles","correspondingAuthor":false,"prefix":"","firstName":"Pierre","middleName":"","lastName":"Decoodt","suffix":""},{"id":271179907,"identity":"eb87953e-0e10-4bc6-989b-7e3957251152","order_by":3,"name":"Juan G. 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