Entity Matching with Quantum Neural Networks

Entity Matching with Quantum Neural Networks | 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 Entity Matching with Quantum Neural Networks Lukas Bischof, Stefan Teodoropol, Rudolf M. Füchslin, Kurt Stockinger This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5366343/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Modern technology and scientific experiments increasingly generate larger and larger amounts of data. This data is sometimesredundant, incomplete or inaccurate and needs to be cleaned and merged with other data before becoming useful for scientificexploration. Hence, entity matching, i.e. the process of linking data about a given entity gathered from multiple data sets, is amajor problem in artificial intelligence with applications in science and industry. Typical methods for entity matching either usespecialized algorithms or supervised machine learning. While the problem is well studied on classical computers, it is unclearhow quantum approaches would tackle these challenges. In this paper, we evaluate quantum machine learning algorithmsfor entity matching on a handcrafted data set and compare them to similar classical algorithms. We do this by implementinga neural network with a classical embedding layer and extending it with quantum layers. Our experimental results suggestthat our hybrid quantum neural network can improve the performance of some classical approaches while requiring fewerparameters than its classical counterpart. Furthermore, we also show that a model trained on a quantum simulator is portableand thus transferable to a real quantum computer. Physical sciences/Physics/Quantum physics/Quantum information Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 Dec, 2024 Reviews received at journal 05 Dec, 2024 Reviews received at journal 27 Nov, 2024 Reviewers agreed at journal 25 Nov, 2024 Reviewers agreed at journal 25 Nov, 2024 Reviewers invited by journal 25 Nov, 2024 Editor assigned by journal 25 Nov, 2024 Editor invited by journal 19 Nov, 2024 Submission checks completed at journal 19 Nov, 2024 First submitted to journal 31 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-5366343","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379911539,"identity":"ce9d3102-2212-43da-ace3-247f62b43dc1","order_by":0,"name":"Lukas Bischof","email":"","orcid":"","institution":"Zurich University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lukas","middleName":"","lastName":"Bischof","suffix":""},{"id":379911540,"identity":"f2da6fec-2296-4414-965b-51a1539fed6c","order_by":1,"name":"Stefan Teodoropol","email":"","orcid":"","institution":"Zurich University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Teodoropol","suffix":""},{"id":379911541,"identity":"897addff-c341-4ebf-b39b-5f8d85d08842","order_by":2,"name":"Rudolf M. 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