Hybrid Quantum-Classical Recurrent Models for Sarcasm Detection: A Comparative Study

Hybrid Quantum-Classical Recurrent Models for Sarcasm Detection: A Comparative Study | 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 Hybrid Quantum-Classical Recurrent Models for Sarcasm Detection: A Comparative Study Islam DJEMMAL, Hacene BELHADEF This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9296095/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 Sarcasm detection is a challenging task in Natural Language Processing (NLP) due to its reliance on subtle and implicit linguistic cues that are often not explicitly expressed in text. Designing models capable of capturing such nuanced patterns remains an open problem. In this work, we investigate hybrid quantum–classical recurrent architectures for sarcasm detection by conducting a comparative study of classical and quantum-enhanced sequence models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and their quantum counterparts, Quantum LSTM (QLSTM) and Quantum GRU (QGRU).We employ a pre-trained BERT model to extract contextualized token embeddings, which are subsequently compressed through classical neural layers to ensure compatibility with Noisy Intermediate-Scale Quantum (NISQ) devices. In the proposed quantum-enhanced models, classical linear transformations within recurrent gates are replaced by Variational Quantum Circuits (VQCs), enabling the exploitation of quantum properties such as superposition and entanglement within a hybrid learning framework.Experimental results on a balanced sarcasm-labeled dataset indicate that both QLSTM and QGRU achieve performance levels comparable to their classical counterparts, despite utilizing significantly fewer trainable parameters. Notably, the quantum-enhanced models require approximately 10% of the parameters used by their classical equivalents while maintaining similar macro F1 scores. These findings suggest that hybrid quantum recurrent architectures can serve as compact and parameter-efficient alternatives to classical sequence models, highlighting their potential applicability in quantum natural language processing under near-term hardware constraints. Quantum computing Quantum machine learning Quantum natural language processing Sarcasm detection Hybrid Quantum-Classical Quantum recurrent neural network 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-9296095","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616918808,"identity":"d2733018-e2fe-4413-a865-25912d0f5d14","order_by":0,"name":"Islam DJEMMAL","email":"data:image/png;base64,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","orcid":"","institution":"Centre Universitaire de Mila","correspondingAuthor":true,"prefix":"","firstName":"Islam","middleName":"","lastName":"DJEMMAL","suffix":""},{"id":616918809,"identity":"6d26ec1f-ce5f-4936-9e03-6d4cd2517248","order_by":1,"name":"Hacene BELHADEF","email":"","orcid":"","institution":"Université Constantine 2","correspondingAuthor":false,"prefix":"","firstName":"Hacene","middleName":"","lastName":"BELHADEF","suffix":""}],"badges":[],"createdAt":"2026-04-01 21:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9296095/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9296095/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107705951,"identity":"176d570c-1437-483f-ba5c-86a52bd21e2a","added_by":"auto","created_at":"2026-04-24 09:15:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1457892,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9296095/v1_covered_225565cc-47ce-46ff-867a-85d24082433d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid Quantum-Classical Recurrent Models for Sarcasm Detection: A Comparative Study","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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 computing, Quantum machine learning, Quantum natural language processing, Sarcasm detection,Hybrid Quantum-Classical, Quantum recurrent neural network","lastPublishedDoi":"10.21203/rs.3.rs-9296095/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9296095/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSarcasm detection is a challenging task in Natural Language Processing (NLP) due to its reliance on subtle and implicit linguistic cues that are often not explicitly expressed in text. Designing models capable of capturing such nuanced patterns remains an open problem. In this work, we investigate hybrid quantum\u0026ndash;classical recurrent architectures for sarcasm detection by conducting a comparative study of classical and quantum-enhanced sequence models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and their quantum counterparts, Quantum LSTM (QLSTM) and Quantum GRU (QGRU).We employ a pre-trained BERT model to extract contextualized token embeddings, which are subsequently compressed through classical neural layers to ensure compatibility with Noisy Intermediate-Scale Quantum (NISQ) devices. 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