Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network

Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network | 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 Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network Fan Fan, Yilei Shi, Tobias Guggemos, Xiao Xiang Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8765173/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Quantum machine learning (QML) has gained increasing attention as a potential solution to address the challenges of computation requirements in the future. Earth observation (EO) has entered the era of Big Data, and the computational demands for effectively analyzing large EO data with complex deep learning models have become a bottleneck. Motivated by this, we aim to leverage quantum computing for EO data classification and explore its advantages despite the current limitations of quantum devices. This paper presents a hybrid model that incorporates multitask learning to assist efficient data encoding and employs a location weight module with quantum convolution operations to extract valid features for classification. The validity of our proposed model was evaluated using multiple EO benchmarks. Additionally, we experimentally explored the generalizability of our model and investigated the factors contributing to its advantage, highlighting the potential of QML in EO data analysis. Quantum Machine Learning Quantum Circuit Image Classification Multitask Learning Generalizability Earth Observation Remote Sensing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 10 May, 2026 Reviews received at journal 10 May, 2026 Reviews received at journal 08 May, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 02 Feb, 2026 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. 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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-8765173","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620934063,"identity":"d6f18688-760e-4b1d-8d0c-1c1487a845f4","order_by":0,"name":"Fan Fan","email":"","orcid":"","institution":"Technical University of Munich (TUM)","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Fan","suffix":""},{"id":620934064,"identity":"369c80fc-93c9-4854-9afb-b39229d463ed","order_by":1,"name":"Yilei Shi","email":"","orcid":"","institution":"Technical University of Munich (TUM)","correspondingAuthor":false,"prefix":"","firstName":"Yilei","middleName":"","lastName":"Shi","suffix":""},{"id":620934065,"identity":"8cd6daf2-b16d-45cf-8977-274e2f37dcf0","order_by":2,"name":"Tobias Guggemos","email":"","orcid":"","institution":"German Aerospace Center","correspondingAuthor":false,"prefix":"","firstName":"Tobias","middleName":"","lastName":"Guggemos","suffix":""},{"id":620934066,"identity":"16a2b747-5662-4a50-a5c7-f2bb771071d4","order_by":3,"name":"Xiao Xiang Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACZhBhAGayMTBUMCC4RGo5Y0CEFiTAxsDYRoQWg+PMDx/zFNjlyTuwP3vMO+9P4toG5o0P8GmRbGYzNpxhkFxseIAh3Zh3m0HitgNsxXit4WdmMJP4YMCcuLGB4Zg0RAuPmQRe9zOzf5NIMKgHamFsk+adA9Zi/gO/LTwgWw4nzmdgZpPmbYDYgk8H0C88xUC/HE/cwMzGJjnnmLHxtsNsxXgdZnD++MbHPH+qE+e3tz+TeFMjJ7vtePPGD3itges9DGMxE6UeCOQbiFU5CkbBKBgFIw4AAN45QgsMchmfAAAAAElFTkSuQmCC","orcid":"","institution":"Technical University of Munich (TUM)","correspondingAuthor":true,"prefix":"","firstName":"Xiao","middleName":"Xiang","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2026-02-02 12:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8765173/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8765173/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107481149,"identity":"3a8416e8-8e7b-409c-a672-7a3f80dcf135","added_by":"auto","created_at":"2026-04-22 02:16:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1698899,"visible":true,"origin":"","legend":"","description":"","filename":"MultitaskLearningforEarthObservationDataClassificationwithHybridQuantumNetworkQMI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8765173/v1_covered_f7d325c2-0012-43c2-a327-f78eb15f3123.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"quantum-machine-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qumi","sideBox":"Learn more about [Quantum Machine Intelligence](http://link.springer.com/journal/42484)","snPcode":"42484","submissionUrl":"https://submission.nature.com/new-submission/42484/3","title":"Quantum Machine Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Quantum Machine Learning, Quantum Circuit, Image Classification, Multitask Learning, Generalizability, Earth Observation, Remote Sensing","lastPublishedDoi":"10.21203/rs.3.rs-8765173/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8765173/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eQuantum machine learning (QML) has gained increasing attention as a potential solution to address the challenges of computation requirements in the future. 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