Q-OmniNet: Quantum-Inspired Hybrid TensorNetworks with Quantum Kernel Estimation for Polysemy-Aware Multimodal Affective Analysis of Code-Mixed Hindi–English Text

Q-OmniNet: Quantum-Inspired Hybrid TensorNetworks with Quantum Kernel Estimation for Polysemy-Aware Multimodal Affective Analysis of Code-Mixed Hindi–English Text | 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 Q-OmniNet: Quantum-Inspired Hybrid TensorNetworks with Quantum Kernel Estimation for Polysemy-Aware Multimodal Affective Analysis of Code-Mixed Hindi–English Text Rishu Kumar, Prof. Rajiv Misra, Prof. T. N. Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8371777/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The rapid growth of Hinglish communication onsocial media has introduced complex linguistic, cultural, andmultimodal patterns that conventional affective computing sys-tems struggle to handle. While deep learning and transformer-based models perform well on monolingual and grammaticallyconsistent text, they are limited in code-mixed Hinglish settings.These models rely on deterministic, single-meaning embeddingsthat inadequately represent polysemy, fail to capture context-dependent polarity shifts across Hindi–English transitions, andtreat multimodal signals—such as text, emojis, and visuals—asweakly coupled rather than jointly dependent. Consequently,emotional nuance, irony, and ambiguity common in real-worldHinglish interactions are often misinterpreted.To overcome these limitations, this work proposes Q-OmniNet,a quantum-inspired hybrid tensor network that models affectivemeaning as contextual superposition and captures non-separable(entangled) relationships across modalities. The framework inte-grates (i) a tensor-network-based contextual encoder to simulatemultimodal entanglement, (ii) complex-valued embeddings to en-code semantic magnitude and contextual phase, and (iii) quantumkernel estimation to model higher-order affective similarity viaquantum-state overlap. Together, these components provide aricher representation of Hinglish emotion dynamics than classicalor transformer-based approaches.Experimental results on real-world multimodal Hinglishdatasets show substantial improvements. Q-OmniNet achievesaccuracies of 0.89 for emotion classification, 0.93 for sentimentanalysis, and 0.97 for sarcasm detection, outperforming tradi-tional baselines by 15–25% and transformer models by 8–14%.The quantum-hybrid variant demonstrates superior robustnessunder noisy and ambiguous conditions, achieving an averageaccuracy of 0.9275 compared to 0.7617 for conventional models.These findings highlight the effectiveness of quantum-inspiredarchitectures for context-sensitive, multilingual, and multimodalaffective understanding. Quantum Computing QTN QKM Transformer LLMs Multimodal Affective Computing Full Text Additional Declarations No competing interests reported. Supplementary Files Q21.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 01 Apr, 2026 Editor assigned by journal 19 Feb, 2026 Submission checks completed at journal 16 Dec, 2025 First submitted to journal 15 Dec, 2025 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-8371777","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618460386,"identity":"24bb2fa8-75c8-4524-8198-ef27e8b893aa","order_by":0,"name":"Rishu Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie3RP0vEMBjH8V94IF2eXteUFu4tFA5ahGLfSqBwk/gKDiwIutT9Bl9MpVAX/6yCg0ih0w2FW1w8jdVFtL1VMN8hEJIPeSCAzfYXI0izpuQ5BaA5Zfl1wHvI0vHLCujDJUu5j2AgtRc9aIh1Wn/up0rOna5lUIAqb7d8dB/O5hcVXlYIkxES1pwsGHIhiiYO+ObRDDbTomzAB8XvRBHLgME5iVKSW34QjuAW4KgaI05niDo5I6atu7sbiHidJDDzIDKPMfw1VwOh6Vc49i+hSbGMVc+5Ice6Dhs1TrzrTm3wRtlT2/aaD7P56e3V82aVZmNkSOy+bRnmspq4/7Opf7fZbLb/2DvRwkLsepSalwAAAABJRU5ErkJggg==","orcid":"","institution":"Indian Institute of Technology Patna","correspondingAuthor":true,"prefix":"","firstName":"Rishu","middleName":"","lastName":"Kumar","suffix":""},{"id":618460387,"identity":"3bb3975b-1d6e-451c-8451-f215e6452386","order_by":1,"name":"Prof. Rajiv Misra","email":"","orcid":"","institution":"Indian Institute of Technology Patna","correspondingAuthor":false,"prefix":"","firstName":"Prof.","middleName":"Rajiv","lastName":"Misra","suffix":""},{"id":618460388,"identity":"aa9d6eb5-810b-41b4-bd50-bab5613e2740","order_by":2,"name":"Prof. T. 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The framework inte-grates (i) a tensor-network-based contextual encoder to simulatemultimodal entanglement, (ii) complex-valued embeddings to en-code semantic magnitude and contextual phase, and (iii) quantumkernel estimation to model higher-order affective similarity viaquantum-state overlap. Together, these components provide aricher representation of Hinglish emotion dynamics than classicalor transformer-based approaches.Experimental results on real-world multimodal Hinglishdatasets show substantial improvements. 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