Comparative Analysis of Asymmetric Readout Errors on Variational Quantum Classifiers: Scaling from 2 to 4 Qubits

Comparative Analysis of Asymmetric Readout Errors on Variational Quantum Classifiers: Scaling from 2 to 4 Qubits | 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 Comparative Analysis of Asymmetric Readout Errors on Variational Quantum Classifiers: Scaling from 2 to 4 Qubits Feras Shita This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8801440/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 Variational Quantum Classifiers (VQCs) represent a promising approach forquantum-enhanced machine learning on Noisy Intermediate-Scale Quantum(NISQ) processors. This paper presents a comprehensive comparative studyinvestigating the impact of asymmetric readout errors on VQC performanceacross system scales. Through systematic numerical simulations of both 2-qubitand 4-qubit architectures trained on synthetic binary classification tasks, weanalyze how qubit role dependence and scalability interact with noise resilience.Our 2-qubit results demonstrate the classical optimizer’s remarkable capacityto compensate for measurement qubit noise, achieving 28.3% lower empiricalloss than theoretical predictions, while auxiliary qubit noise proves significantlymore detrimental. Extending to 4-qubit systems reveals persistent compensatorymechanisms, though with different quantitative patterns: 10% measurementqubit noise yields optimal accuracy (0.500 vs. 0.380 ideal), representing a 31.6%improvement. We observe consistent theoretical-empirical alignment across scales,with theoretical MSE values for 4-qubit systems ranging from 0.9423-0.9462for 5-20% error rates. The study demonstrates that while fundamental noiseresilience patterns persist during scaling, quantitative trade-offs and optimaloperating points shift, providing crucial insights for practical VQC deploymenton asymmetric NISQ hardware. Physical sciences/Mathematics and computing Physical sciences/Physics Variational Quantum Classifier Quantum Machine Learning Readout Error NISQ Error Mitigation Asymmetric Noise Scalability Analysis. 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. 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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-8801440","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":592045381,"identity":"b586bae4-d449-41f3-b02d-b37f219f915c","order_by":0,"name":"Feras Shita","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIie3RoQoCMQDG8U8GW5mnUYu+womgQdFXmQhesl4xeBYtglXf4h5hsnDluGw8y2XjFcF5YjJsNpH922A/vsEAl+sHYxEoBNB5HSngmQiXTyLQB8iLUCuiZ2aRPQEr8ms5CeLkIHELFWhLmAgf+kLMl3GqUDtmFmQKru8Isowvc5D61mqFFZqsA/9J7nYEA02UqEjNihCuySLpnVLln/dZwCnPDYTtinY5XnW9ZHPNy3DUaTDDSvUb72T10qaJfNaQXxOXy+X67x6s/DpPue4ZyAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Tripoli","correspondingAuthor":true,"prefix":"","firstName":"Feras","middleName":"","lastName":"Shita","suffix":""}],"badges":[],"createdAt":"2026-02-06 01:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8801440/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8801440/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107281894,"identity":"735bc5b8-32d1-4837-91a6-467cbb113ed5","added_by":"auto","created_at":"2026-04-20 02:25:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":276360,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8801440/v1_covered_965c9b72-9c53-466e-b757-1e9b23adfe95.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Analysis of Asymmetric Readout Errors on Variational Quantum Classifiers: Scaling from 2 to 4 Qubits","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Variational Quantum Classifier, Quantum Machine Learning, Readout Error, NISQ, Error Mitigation, Asymmetric Noise, Scalability Analysis.","lastPublishedDoi":"10.21203/rs.3.rs-8801440/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8801440/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Variational Quantum Classifiers (VQCs) represent a promising approach forquantum-enhanced machine learning on Noisy Intermediate-Scale Quantum(NISQ) processors. This paper presents a comprehensive comparative studyinvestigating the impact of asymmetric readout errors on VQC performanceacross system scales. Through systematic numerical simulations of both 2-qubitand 4-qubit architectures trained on synthetic binary classification tasks, weanalyze how qubit role dependence and scalability interact with noise resilience.Our 2-qubit results demonstrate the classical optimizer’s remarkable capacityto compensate for measurement qubit noise, achieving 28.3% lower empiricalloss than theoretical predictions, while auxiliary qubit noise proves significantlymore detrimental. Extending to 4-qubit systems reveals persistent compensatorymechanisms, though with different quantitative patterns: 10% measurementqubit noise yields optimal accuracy (0.500 vs. 0.380 ideal), representing a 31.6%improvement. We observe consistent theoretical-empirical alignment across scales,with theoretical MSE values for 4-qubit systems ranging from 0.9423-0.9462for 5-20% error rates. 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