HARLI CQUINN: Higher Adjusted Randomness with Linear In Complexity QUantum INspired Networks for K-Means

HARLI CQUINN: Higher Adjusted Randomness with Linear In Complexity QUantum INspired Networks for K-Means | 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 HARLI CQUINN: Higher Adjusted Randomness with Linear In Complexity QUantum INspired Networks for K-Means Jiten Oswal, Saumya Biswas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8158566/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 We contrast a minimalistic implementation of quantum k-means algorithm to classical k-means algorithm. With classical simulation results, we demonstrate a quantum performance, on and above par, with the classical k-means algorithm. We present benchmarks of its accuracy for test cases of both well-known and experimental datasets. Despite extensive research into quantum k-means algorithms, our approach reveals previously unexplored methodological improvements. The encoding step can be minimalistic with classical data imported into quantum states more directly than existing approaches. The proposed quantum-inspired algorithm performs better in terms of accuracy and Adjusted Rand Index (ARI) with respect to the bare classical k-means algorithm. By investigating multiple encoding strategies, we provide nuanced insights into quantum computational clustering techniques. Physical sciences/Mathematics and computing Physical sciences/Physics k-means clustering quantum inspired machine learning quantum machine learning quantum computing 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-8158566","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":557173414,"identity":"33b1ae5a-4b1f-4e61-8f80-3842e7d9e3fa","order_by":0,"name":"Jiten Oswal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACxmZk3oMKIMHM3EBACzMSL+EMSAsjfi1AFUjsxDawMfi1MLfzH3xcwHCHgZ//jOGHxHm10fztQC0/Krbhcxiz8QyGZwySM3KMJRK3Hc+dcZixgbHnzG18WtikeRgOMxjc4N0A1HIstwGohZmxDa8W9t9gLefPbv6ROOdY7nwitLAxg7UcyN0mkdhQk7uBCC3G0jwGz3gkZ+R/s0g4diB3I1DLQXx+Mew/+PAzT8UdOX7+Y8k3PtTU5c47f/jggx8VeLQ0gEiDAzxQ/mEweQCneiCQZ0BVU4dP8SgYBaNgFIxQAAApFFbltv0QggAAAABJRU5ErkJggg==","orcid":"","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Jiten","middleName":"","lastName":"Oswal","suffix":""},{"id":557173416,"identity":"71cccba8-617b-4192-b288-c7b7409081e1","order_by":1,"name":"Saumya Biswas","email":"","orcid":"","institution":"Independent Researcher","correspondingAuthor":false,"prefix":"","firstName":"Saumya","middleName":"","lastName":"Biswas","suffix":""}],"badges":[],"createdAt":"2025-11-19 20:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8158566/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8158566/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98004319,"identity":"ad6bcdaa-1be2-4b6b-be7e-acdc2d8a0f9c","added_by":"auto","created_at":"2025-12-11 16:49:20","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4639,"visible":true,"origin":"","legend":"","description":"","filename":"643ed6df681a40f18b68c8cd52e51b60.json","url":"https://assets-eu.researchsquare.com/files/rs-8158566/v1/e7bf76b15730a9a6805dfcaf.json"},{"id":102397825,"identity":"533252ee-3055-4c61-b678-cfdeec946040","added_by":"auto","created_at":"2026-02-11 10:19:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":314658,"visible":true,"origin":"","legend":"","description":"","filename":"jxwwdmkjgtvqqctqzfwnzmrqmmkhgpsd.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8158566/v1_covered_cc39b0e0-db5c-4277-98dc-9bac9b4db3e1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"HARLI CQUINN: Higher Adjusted Randomness with Linear In Complexity QUantum INspired Networks for K-Means","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":"k-means, clustering, quantum inspired, machine learning, quantum machine learning, quantum computing","lastPublishedDoi":"10.21203/rs.3.rs-8158566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8158566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We contrast a minimalistic implementation of quantum k-means algorithm to classical k-means algorithm. 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