Circuit Design based on Feature Similarity for Quantum Generative Modeling

Circuit Design based on Feature Similarity for Quantum Generative Modeling | 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 Circuit Design based on Feature Similarity for Quantum Generative Modeling Mathis Makarski, Jumpei Kato, Yuki Sato, Naoki Yamamoto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8853286/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Quantum generative models may achieve an advantage on quantum devices by their inherent probabilistic nature and efficient sampling strategies. However, current approaches mostly rely on general-purpose circuits, such as the hardware efficient ansatz paired with a random initialization strategy, which are known to suffer from trainability issues such as barren plateaus. To address these issues, a tensor network pretraining framework that initializes a quantum circuit ansatz with a classically computed high-quality solution for a linear entanglement structure has been proposed in literature. In order to improve the classical solution, the quantum circuit needs to be extended, while it is still an open question how the extension affects trainability. In this work, we propose the metric-based extension heuristic to design an extended circuit based on a similarity metric measured between the dataset features. We validate this method on the bars and stripes dataset and carry out experiments on financial data. Our results underline the importance of problem-informed circuit design and show that the metric-based extension heuristic offers the means to introduce inductive bias while designing a circuit under limited resources. Quantum Generative Modeling Quantum Circuit Born Machine Inductive Bias Circuit Design Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 11 Feb, 2026 First submitted to journal 11 Feb, 2026 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-8853286","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628291289,"identity":"8e638fe7-9b8c-48eb-b9b3-7911425654b0","order_by":0,"name":"Mathis Makarski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYFACNhCRwMPPw8BwAMojUotkD6laGAzOEOss3fZjiZ8r/qTJGJ85fPDABwa+PIJazM6kHZY825bDY3a2LeHgDAa2YsJaDqQ3SDY2VPCYnecxOMzDwJbYQFDL+efNPxv+VPAY9/N/IFLLjbRjkg1sOTwGvD0MxGp5lmbZ2JbGI3HmmMHBGQbE+OV8mvHNhj/J9vw9yY8/fKg4RjjE0IDBsQRStTDUkK5lFIyCUTAKhj0AAEVyPw1NNefQAAAAAElFTkSuQmCC","orcid":"","institution":"Keio University","correspondingAuthor":true,"prefix":"","firstName":"Mathis","middleName":"","lastName":"Makarski","suffix":""},{"id":628291290,"identity":"a8d2ca69-2d8c-4f19-a75e-e74848b88bc9","order_by":1,"name":"Jumpei Kato","email":"","orcid":"","institution":"Mitsubishi UFJ Financial Group (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Jumpei","middleName":"","lastName":"Kato","suffix":""},{"id":628291291,"identity":"3f8bf86f-e0f1-400c-8631-e0671ccc5abb","order_by":2,"name":"Yuki Sato","email":"","orcid":"","institution":"Toyota Central Research and Development Laboratories","correspondingAuthor":false,"prefix":"","firstName":"Yuki","middleName":"","lastName":"Sato","suffix":""},{"id":628291292,"identity":"22cd0792-08f1-43b7-8376-89112467578c","order_by":3,"name":"Naoki Yamamoto","email":"","orcid":"","institution":"Keio University","correspondingAuthor":false,"prefix":"","firstName":"Naoki","middleName":"","lastName":"Yamamoto","suffix":""}],"badges":[],"createdAt":"2026-02-11 15:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8853286/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8853286/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107693033,"identity":"a26fb516-83cb-4237-9058-25dcd47dd40d","added_by":"auto","created_at":"2026-04-24 06:26:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":582956,"visible":true,"origin":"","legend":"","description":"","filename":"GenerativeModelinginFinance.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8853286/v1_covered_e2fc38c7-9c22-4dfb-8f14-651d54b1ffd0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Circuit Design based on Feature Similarity for Quantum Generative Modeling","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"quantum-machine-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qumi","sideBox":"Learn more about [Quantum Machine Intelligence](http://link.springer.com/journal/42484)","snPcode":"42484","submissionUrl":"https://submission.nature.com/new-submission/42484/3","title":"Quantum Machine Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Quantum Generative Modeling, Quantum Circuit Born Machine, Inductive Bias, Circuit Design","lastPublishedDoi":"10.21203/rs.3.rs-8853286/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8853286/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Quantum generative models may achieve an advantage on quantum devices by their inherent probabilistic nature and efficient sampling strategies. 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