A Hybrid Spiking Neural Network-Quantum Classifier Framework: A Case Study Using EEG Data

A Hybrid Spiking Neural Network-Quantum Classifier Framework: A Case Study Using EEG Data | 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 A Hybrid Spiking Neural Network-Quantum Classifier Framework: A Case Study Using EEG Data Ravi Kumar Jha, Nikola Kasabov, Saugat Bhattacharyya, Damien Coyle, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6173906/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Nov, 2025 Read the published version in EPJ Quantum Technology → Version 1 posted 9 You are reading this latest preprint version Abstract The study introduces a hybrid computational framework that combines neuro-inspired information processing using spiking neural networks (SNN) and quantum information processing using quantum kernels to develop quantum-enhanced machine learning models, demonstrated through the classification of EEG data as a case study. In the proposed SNN-quantum kernel classifier (SNN-QC), SNN with spike time information representation is employed to learn spatio-temporal interactions (EEG recorded from multiple channels over time). Frequency-based (rate-based) information as spike frequency state vectors are extracted from the SNN and classified using a quantum classifier. In the latter part, we use the quantum kernel approach utilizing feature maps for classification tasks. The proposed SNN-QC is demonstrated on a benchmark EEG dataset to classify three distinct wrist movement tasks in six binary classification setups as a proof of concept. We introduce a novel feature map with high-order nonlinearity, which has outperformed current state-of-the-art feature maps and various machine learning methods in most of the case studies. Furthermore, the role of hyperparameters for enhanced feature maps is also highlighted. The performance of SNN-QC is evaluated using statistical metrics and cross-validation techniques, demonstrating its 1 efficacy across multiple binary classifiers. An experimental validation is also performed on an IBM QPU. The results demonstrate that the SNN-QC significantly outperforms the models that use statistical features rather than features extracted from the SNN as SNN accounts for the temporal interaction between the spatio-temporal input variables. Finally, we conclude that the SNN-QC offers a potential pathway for developing more accurate neuromorphic-quantum enhanced systems that are both energy-efficient and biologically-inspired, well-suited for dealing with spatio-temporal data. Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.pdf Cite Share Download PDF Status: Published Journal Publication published 11 Nov, 2025 Read the published version in EPJ Quantum Technology → Version 1 posted Editorial decision: Revision requested 31 Jul, 2025 Reviews received at journal 31 Jul, 2025 Reviews received at journal 29 Mar, 2025 Reviewers agreed at journal 12 Mar, 2025 Reviewers agreed at journal 10 Mar, 2025 Reviewers invited by journal 10 Mar, 2025 Editor assigned by journal 10 Mar, 2025 Submission checks completed at journal 10 Mar, 2025 First submitted to journal 06 Mar, 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. 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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-6173906","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431746607,"identity":"488eaf93-ca48-41f9-ac5e-02dea65a1886","order_by":0,"name":"Ravi Kumar 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