Noise-Resilient Optimization of Parameterized Quantum Circuits for Near-Term Quantum Devices

Noise-Resilient Optimization of Parameterized Quantum Circuits for Near-Term Quantum Devices | 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 Noise-Resilient Optimization of Parameterized Quantum Circuits for Near-Term Quantum Devices Deepika D, Vanishree K, Munishamaiah Krishna This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8510087/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 Noisy Intermediate-Scale Quantum (NISQ) devices are near-term quantum computing devices that inherently suffer from imperfect gate implementation, decoherence, and measurement noise. The main type of variational quantum algorithms(VQAs) is based on Parameterized Quantum Circuits (PQCs), which, however, face practical challenges due to optimization instability of optimization caused by noise and reduced performance. In this paper, we suggest a comprehensive noise-resilient optimization framework for PQCs, which explicitly incorporates noise-resilient circuit architecture design, incorporating noise-aware training strategies into the variational optimization loop. The framework uses circuit structures that are efficient in depth and are hardware compatible in addition to adaptive optimizer choice, stochastic optimization, and averaging as a part of repeated measurement to reduce the impacts of realistic noise. Large-scale experiments are performed with different amounts of qubits, depths of the circuit and noise strengths with realistic noise models. The findings indicate that the convergence rate is faster, the stability is increased, the variability of run-to-run variability is minimized, and other robustness is better than the noise-unaware optimization techniques. Moreover, systematic studies determine optimal operating regions that optimally trade-off expressibility and noise tolerance to offer useful design advice on trusted implementation of VQAs with near-term quantum hardware. Parameterized quantum circuits Noise-resilient optimization Variational quantum algorithms NISQ quantum computing Quantum circuit scalability 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-8510087","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580965994,"identity":"854ab001-678f-4e39-b873-1dbc5a1f1401","order_by":0,"name":"Deepika D","email":"","orcid":"","institution":"RV College of Engineering, India","correspondingAuthor":false,"prefix":"","firstName":"Deepika","middleName":"","lastName":"D","suffix":""},{"id":580965995,"identity":"78343438-0a42-4202-bced-3cf9af71c294","order_by":1,"name":"Vanishree K","email":"","orcid":"","institution":"RV College of Engineering, India","correspondingAuthor":false,"prefix":"","firstName":"Vanishree","middleName":"","lastName":"K","suffix":""},{"id":580965996,"identity":"cd421061-a30d-4302-8385-75ff2fced6b4","order_by":2,"name":"Munishamaiah Krishna","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACCQbGBmYQgx8qwNgAQkRpkWwgXgsDA1iLwQG4FgJAcvbh5s+FO+yijW+fMfzwgcFGdsMB5rYH+LRI8yW2Sc88k5y77VyOseQMhjTjDQcY2w3waZHjYWxj5m1jzt12hi2NmYfhcCJQS5sEAS3Nn3nb6nM39wC1/GH4T1iLNA9jgzRv2+HcDTzMx4DhcICwFskexjagluO5M84wH5bsMUg2nnmYgBaJM+yPgQ6rzu3vYWz88KPCTrbvePszvFrQACiomElQPwpGwSgYBaMAOwAAGz1Fz//Fj6wAAAAASUVORK5CYII=","orcid":"","institution":"RV College of Engineering, India","correspondingAuthor":true,"prefix":"","firstName":"Munishamaiah","middleName":"","lastName":"Krishna","suffix":""}],"badges":[],"createdAt":"2026-01-04 03:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8510087/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8510087/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105372924,"identity":"91af8f2f-e17f-472a-85c7-1237aa0013ea","added_by":"auto","created_at":"2026-03-25 09:43:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":954999,"visible":true,"origin":"","legend":"","description":"","filename":"paper3modified1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8510087/v1_covered_89f42d9c-41ad-4776-a3c9-62c92bde0582.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Noise-Resilient Optimization of Parameterized Quantum Circuits for Near-Term Quantum Devices","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":"Parameterized quantum circuits, Noise-resilient optimization, Variational quantum algorithms, NISQ quantum computing, Quantum circuit scalability","lastPublishedDoi":"10.21203/rs.3.rs-8510087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8510087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNoisy Intermediate-Scale Quantum (NISQ) devices are near-term quantum computing devices that inherently suffer from imperfect gate implementation, decoherence, and measurement noise. The main type of variational quantum algorithms(VQAs) is based on Parameterized Quantum Circuits (PQCs), which, however, face practical challenges due to optimization instability of optimization caused by noise and reduced performance. In this paper, we suggest a comprehensive noise-resilient optimization framework for PQCs, which explicitly incorporates noise-resilient circuit architecture design, incorporating noise-aware training strategies into the variational optimization loop. The framework uses circuit structures that are efficient in depth and are hardware compatible in addition to adaptive optimizer choice, stochastic optimization, and averaging as a part of repeated measurement to reduce the impacts of realistic noise. Large-scale experiments are performed with different amounts of qubits, depths of the circuit and noise strengths with realistic noise models. 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