Hardware-Agnostic Quantum Kernel Feature Mapping for Anomaly Detection in Critical Infrastructure: A Cross-Testbed Validation on NISQ Processors

Hardware-Agnostic Quantum Kernel Feature Mapping for Anomaly Detection in Critical Infrastructure: A Cross-Testbed Validation on NISQ Processors | 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 Hardware-Agnostic Quantum Kernel Feature Mapping for Anomaly Detection in Critical Infrastructure: A Cross-Testbed Validation on NISQ Processors Shujaatali Badami This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8682004/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Mar, 2026 Read the published version in IEEE Access → Version 1 posted You are reading this latest preprint version Abstract Modern Industrial Control Systems (ICS) face sophisticated cyber-physical attacks that exploit nonlinear correlations between process variables, rendering traditional linear classifiers ineffective. This paper presents a hardware-agnostic Quantum Support Vector Machine (QSVM) framework employing an 8-qubit ZZFeatureMap kernel for anomaly detection in critical water treatment and thermal power infrastructure. Performance benchmarks were established via noise-free statevector simulation to determine theoretical quantum advantage. Physical realizability was separately validated via circuit execution on IBM's 156-qubit ibm_fez processor, with completed hardware jobs enabling quantitative analysis of the simulation-to-hardware performance gap. Through rigorous cross-testbed validation on the SWaT (Secure Water Treatment) and HAI (Hardware-in-the-Loop Augmented ICS) datasets, our simulated approach achieves an AUC-ROC of 0.9912 ± 0.004 on SWaT and 0.8309 ± 0.050 on HAI, demonstrating consistent quantum advantage of +10.8% AUC over classical RBF-kernel SVMs on the more challenging HAI testbed. Statistical robustness is established through 5-seed cross-validation with stratified sampling. Hardware execution on ibm_fez confirms physical realizability with circuit depth 76 and 28 CNOT gates, while revealing an expected fidelity degradation of approximately 17–20% compared to ideal simulation due to gate errors and decoherence. The quantum kernel's Z i Z j entanglement structure geometrically captures pairwise feature correlations, providing inductive bias well-suited for coupled sensor dynamics. Unlike deep learning approaches that require massive attack datasets unavailable in critical infrastructure domains, our kernel method generalizes effectively from limited training samples. All experimental artifacts are publicly available at https://github.com/Ali-Badami/Quantum-IDS. IBM Quantum job identifiers: d5l9htjh36vs73bgsi3g (SWaT), d5l9huk8d8hc73cfb0pg (HAI). Artificial Intelligence and Machine Learning Electrical Engineering Systems Engineering Systems and Networking Anomaly detection critical infrastructure security cyber-physical systems HAI industrial control systems IDS NISQ computing quantum kernels quantum machine learning QSVM SWaT Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 29 Mar, 2026 Read the published version in IEEE Access → 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. 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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-8682004","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":579547689,"identity":"4dbb7636-6bf5-48c8-8ce4-a4295bf261fb","order_by":0,"name":"Shujaatali Badami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3RrQ7CMBDA8SNNOtMxCyGwV7hllo9XGVmCmphEIFBVDdglvAQKXdIEzARyAkGCRYAhCL4GDkQHjoT+TdWvd8kBmEy/WAUAH2+ZECm/IQiU0OBzAk8CDD8T7kRgfBqcXWqxg9z3m+DUlistwXWKnligx4k9nSdpD6qjKNaTSoTIKJYeRNlcAaYs0C+WROidr9jhhG3U5XorJpBF6NscuzkBVRrKnFhSv1jWi/36yA85oTgXi5BVBdMPcZNw5u2OjdbYUdv9adBuOMza6A0AfblHPqL4QOT90+IpJpPJ9F/dATBxQo43IQ4+AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0003-5262-021X","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Shujaatali","middleName":"","lastName":"Badami","suffix":""}],"badges":[],"createdAt":"2026-01-23 19:01:35","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-8682004/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8682004/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1109/ACCESS.2026.3679234","type":"published","date":"2026-03-30T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":105827698,"identity":"9852b4d8-b80a-4d89-8c4f-b9bb911fd333","added_by":"auto","created_at":"2026-03-31 14:13:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3850090,"visible":true,"origin":"","legend":"","description":"","filename":"HardwareAgnosticQuantumKernelFeatureMappingforAnomalyDetectioninCriticalInfrastructureACrossTestbedValidationonNISQProcessors.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8682004/v1_covered_dc676d12-8534-4d88-bf7d-09e4a6eacd92.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eHardware-Agnostic Quantum Kernel Feature Mapping for Anomaly Detection in Critical Infrastructure: A Cross-Testbed Validation on NISQ Processors\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Anomaly detection, critical infrastructure security, cyber-physical systems, HAI, industrial control systems, IDS, NISQ computing, quantum kernels, quantum machine learning, QSVM, SWaT","lastPublishedDoi":"10.21203/rs.3.rs-8682004/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8682004/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eModern Industrial Control Systems (ICS) face sophisticated cyber-physical attacks that exploit nonlinear correlations between process variables, rendering traditional linear classifiers ineffective. This paper presents a hardware-agnostic Quantum Support Vector Machine (QSVM) framework employing an 8-qubit ZZFeatureMap kernel for anomaly detection in critical water treatment and thermal power infrastructure. Performance benchmarks were established via noise-free statevector simulation to determine theoretical quantum advantage. Physical realizability was separately validated via circuit execution on IBM's 156-qubit ibm_fez processor, with completed hardware jobs enabling quantitative analysis of the simulation-to-hardware performance gap. Through rigorous cross-testbed validation on the SWaT (Secure Water Treatment) and HAI (Hardware-in-the-Loop Augmented ICS) datasets, our simulated approach achieves an AUC-ROC of 0.9912 ± 0.004 on SWaT and 0.8309 ± 0.050 on HAI, demonstrating consistent quantum advantage of +10.8% AUC over classical RBF-kernel SVMs on the more challenging HAI testbed. Statistical robustness is established through 5-seed cross-validation with stratified sampling. Hardware execution on ibm_fez confirms physical realizability with circuit depth 76 and 28 CNOT gates, while revealing an expected fidelity degradation of approximately 17–20% compared to ideal simulation due to gate errors and decoherence. The quantum kernel's Z\u003csub\u003ei\u003c/sub\u003eZ\u003csub\u003ej\u0026nbsp;\u003c/sub\u003eentanglement structure geometrically captures pairwise feature correlations, providing inductive bias well-suited for coupled sensor dynamics. Unlike deep learning approaches that require massive attack datasets unavailable in critical infrastructure domains, our kernel method generalizes effectively from limited training samples. All experimental artifacts are publicly available at https://github.com/Ali-Badami/Quantum-IDS. 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