Variational Quantum Classifier for Fraudulent Transaction Detection in Synthetic Banking Data: A Noise-Aware Benchmarking Study Using PKTRON

Variational Quantum Classifier for Fraudulent Transaction Detection in Synthetic Banking Data: A Noise-Aware Benchmarking Study Using PKTRON | 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 Variational Quantum Classifier for Fraudulent Transaction Detection in Synthetic Banking Data: A Noise-Aware Benchmarking Study Using PKTRON Dr. Zuhair Ahmed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9553749/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 Financial fraud represents one of the most costly and persistent threats to the global banking sector, with annual losses exceeding $485 billion USD. Machine learning-based fraud detection has become the industry standard, with classical algorithms such as Support Vector Machines (SVM) and Random Forests achieving near-perfect performance on well-structured datasets. As quantum computing hardware matures, the question arises: can variational quantum classifiers (VQCs) approach or exceed classical performance on banking fraud detection, and how does realistic hardware noise affect their viability? In this work, we address this question through a systematic, noise-aware benchmarking study executed on PKTRON v3.7.3, using the PK Falcon 27Q and PK NoisyLab 8Q virtual quantum processors. We generate a 200-sample synthetic banking transaction dataset with six clinically motivated fraud features — transaction amount, time of day, merchant category, transaction velocity, geographic distance, and account age — and evaluate a 4-qubit, 2-layer Variational Quantum Circuit against classical SVM and Random Forest baselines across four experimental phases. Our results reveal that the ideal VQC achieves 88.00% accuracy and 0.6940 Banking Quantum Readiness Score (BQRS), compared to 100.00% and 98.00% for classical SVM and Random Forest respectively. Under noise, amplitude damping at p = 0.10 achieves the highest noisy BQRS of 0.5189, while depolarizing noise is most destructive, collapsing recall and F1 to zero across all tested strengths. Phase damping produces a counter-intuitive pattern of high AUC with zero recall, indicating preserved ranking ability but collapsed decision boundary. We introduce the Banking Quantum Readiness Score (BQRS) — a recall-weighted composite metric (40% recall, 30% F1, 30% AUC-ROC) — as a standardized benchmark for evaluating quantum classifier readiness for banking deployment. Our findings establish the current performance gap between quantum and classical classifiers on fraud detection and define the noise thresholds that must be overcome for practical quantum advantage in financial cybersecurity. Scientific Communication variational quantum classifier fraud detection banking security quantum machine learning NISQ noise characterization PKTRON quantum benchmarking Banking Quantum Readiness Score Full Text Additional Declarations The authors declare no competing interests. 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-9553749","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631038217,"identity":"66f4c362-27b7-4281-8ecc-2ac304b46488","order_by":0,"name":"Dr. Zuhair Ahmed","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0008-6643-2857","institution":"Centre of Excellence for Technology Quantum and AI Canada","correspondingAuthor":true,"prefix":"Dr.","firstName":"Zuhair","middleName":"","lastName":"Ahmed","suffix":""}],"badges":[],"createdAt":"2026-04-28 11:53:45","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9553749/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9553749/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108083931,"identity":"d7991a3d-7e17-4f75-882d-5a63c89998b1","added_by":"auto","created_at":"2026-04-29 08:18:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":194927,"visible":true,"origin":"","legend":"","description":"","filename":"NoiseAwareVQCforFraudDetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9553749/v1_covered_1a683ed7-c6fa-4385-825b-2e4a04bfcd37.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eVariational Quantum Classifier for Fraudulent Transaction Detection in Synthetic Banking Data: A Noise-Aware Benchmarking Study Using PKTRON\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The Centre of Excellence for Technology Quantum and AI Canada","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, fraud detection, banking security, quantum machine learning, NISQ, noise characterization, PKTRON, quantum benchmarking, Banking Quantum Readiness Score","lastPublishedDoi":"10.21203/rs.3.rs-9553749/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9553749/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFinancial fraud represents one of the most costly and persistent threats to the global banking sector, with annual losses exceeding $485 billion USD. Machine learning-based fraud detection has become the industry standard, with classical algorithms such as Support Vector Machines (SVM) and Random Forests achieving near-perfect performance on well-structured datasets. As quantum computing hardware matures, the question arises: can variational quantum classifiers (VQCs) approach or exceed classical performance on banking fraud detection, and how does realistic hardware noise affect their viability? In this work, we address this question through a systematic, noise-aware benchmarking study executed on PKTRON v3.7.3, using the PK Falcon 27Q and PK NoisyLab 8Q virtual quantum processors. We generate a 200-sample synthetic banking transaction dataset with six clinically motivated fraud features — transaction amount, time of day, merchant category, transaction velocity, geographic distance, and account age — and evaluate a 4-qubit, 2-layer Variational Quantum Circuit against classical SVM and Random Forest baselines across four experimental phases. Our results reveal that the ideal VQC achieves 88.00% accuracy and 0.6940 Banking Quantum Readiness Score (BQRS), compared to 100.00% and 98.00% for classical SVM and Random Forest respectively. Under noise, amplitude damping at p = 0.10 achieves the highest noisy BQRS of 0.5189, while depolarizing noise is most destructive, collapsing recall and F1 to zero across all tested strengths. Phase damping produces a counter-intuitive pattern of high AUC with zero recall, indicating preserved ranking ability but collapsed decision boundary. We introduce the Banking Quantum Readiness Score (BQRS) — a recall-weighted composite metric (40% recall, 30% F1, 30% AUC-ROC) — as a standardized benchmark for evaluating quantum classifier readiness for banking deployment. 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