Noise Mitigation An Efficient Approach for Quantum Convolutional Neural Networks to Enhance Performance in NISQ

Noise Mitigation An Efficient Approach for Quantum Convolutional Neural Networks to Enhance Performance in NISQ | 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 Mitigation An Efficient Approach for Quantum Convolutional Neural Networks to Enhance Performance in NISQ Yuze Ding, Shibin Zhang, Xiaoyu Li, Yan Chang, Lili Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4594670/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 Quantum machine learning has achieved great success in many areas of image classification, however, in the Noisy Intermediate-Scale Quantum (NISQ) era, most of the quantum machine learning experimental results are obtained from simulations on classical computers, and most of the experimental environments do not take into account the detrimental effects of quantum noise on quantum classifiers. In fact, the prevalent quantum noise, such as Depolarization, Phase flip, etc., has the potential to significantly undermine the classification performance of quantum circuits. Addressing the challenge of mitigating the disturbing impact of quantum noise on quantum neural networks has become a prominent research focus in recent years. In this paper, we propose a Noise Mitigation method based on the image classification task, which is deployed on the constructed quantum convolutional neural network with noise, aiming to improve the classification accuracy by using Noise Mitigation to reduce the expressibility of quantum circuits. The designed Noise Mitigation method can adjust the parameters according to the complexity of the circuit to achieve better results. Numerical simulations conducted on the MNIST and Fashion-MNIST datasets substantiate the efficacy of the proposed methodology. Quantum machine learning Quantum noise Quantum convolutional neural network Noise mitigation 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-4594670","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321917080,"identity":"49612174-aba2-4616-b5a2-dfbbdd87b3c6","order_by":0,"name":"Yuze Ding","email":"","orcid":"","institution":"Chengdu University of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuze","middleName":"","lastName":"Ding","suffix":""},{"id":321917081,"identity":"5ef94c53-bba5-4959-a127-790d28e733a6","order_by":1,"name":"Shibin Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYFACxgYgcUAOymAmSksjUOkBYyiDKC1g0w8kNkAYRGjhn5Hc/vDHnzvp/UDGA4YK68QG9rMH8GqRuJHY2CDZ9ix3BojBcCY9sYEnLwGvFgMJoErDhsO5G0AMxrbDiQ0SPAaEtST8OZwOZjD+I1bLAbbDCRAtDURokTjzsHFmY9szwxlAxoyEY+nGbTw5+LXwt6c/+AgMMXkQ48OHGmvZfvYz+LWgggQgZiNB/SgYBaNgFIwCHAAAN75NlAEBY8EAAAAASUVORK5CYII=","orcid":"","institution":"Chengdu University of Information Technology","correspondingAuthor":true,"prefix":"","firstName":"Shibin","middleName":"","lastName":"Zhang","suffix":""},{"id":321917082,"identity":"f7b340ea-c0d2-4112-98e5-c43961f9a735","order_by":2,"name":"Xiaoyu Li","email":"","orcid":"","institution":"Kashi Research Institute of Electronic Information Industry Technology Kashi","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Li","suffix":""},{"id":321917083,"identity":"af182995-2c59-4be3-b230-624ed4387963","order_by":3,"name":"Yan Chang","email":"","orcid":"","institution":"Chengdu University of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Chang","suffix":""},{"id":321917084,"identity":"21332691-ad98-49e5-87df-080ff73d5959","order_by":4,"name":"Lili Yan","email":"","orcid":"","institution":"Chengdu University of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2024-06-17 14:12:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4594670/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4594670/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61398772,"identity":"21d28859-657f-4844-b755-1cd1edd042fe","added_by":"auto","created_at":"2024-07-30 09:29:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":976329,"visible":true,"origin":"","legend":"","description":"","filename":"Draft.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4594670/v1_covered_5be7ec25-31d0-4dd2-b6f0-77cfa54820ff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Noise Mitigation An Efficient Approach for Quantum Convolutional Neural Networks to Enhance Performance in NISQ","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":"Quantum machine learning; Quantum noise; Quantum convolutional neural network; Noise mitigation","lastPublishedDoi":"10.21203/rs.3.rs-4594670/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4594670/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Quantum machine learning has achieved great success in many areas of image classification, however, in the Noisy Intermediate-Scale Quantum (NISQ) era, most of the quantum machine learning experimental results are obtained from simulations on classical computers, and most of the experimental environments do not take into account the detrimental effects of quantum noise on quantum classifiers. In fact, the prevalent quantum noise, such as Depolarization, Phase flip, etc., has the potential to significantly undermine the classification performance of quantum circuits. Addressing the challenge of mitigating the disturbing impact of quantum noise on quantum neural networks has become a prominent research focus in recent years. In this paper, we propose a Noise Mitigation method based on the image classification task, which is deployed on the constructed quantum convolutional neural network with noise, aiming to improve the classification accuracy by using Noise Mitigation to reduce the expressibility of quantum circuits. The designed Noise Mitigation method can adjust the parameters according to the complexity of the circuit to achieve better results. 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