Q-SCOPE: Towards characterizing Quantum State geometry for Out-of-distribution Prediction and benchmark Evaluation

Q-SCOPE: Towards characterizing Quantum State geometry for Out-of-distribution Prediction and benchmark Evaluation | 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-SCOPE: Towards characterizing Quantum State geometry for Out-of-distribution Prediction and benchmark Evaluation Shahreen Sultana, Mutasim Fuad Sarker, Md Faiyaz Bin Younus, Md Adnan Arefeen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8378688/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 Hybrid classical-quantum neural network (HCQNN) models have recently emerged as a powerful approach for classification problems, due to their strong computational representation capabilities coming along with the integration of neural networks and quantum computing. However, these models are not free from the fundamental issue of poor separation between the in-distribution (ID) and Out-Of-Distribution (OOD) samples in the classification output space, which is observed in classical neural network classifiers too. Despite this, to the best of our knowledge, there is no current work that systematically studies the OOD detection problem in the context of the HCQNN models. Towards this end, we benchmark the existing approaches for OOD detection in the classical neural network literature domain on the HCQNN classifier models using the standard datasets and metrics and note their limitations. Thereby we propose a novel strategy suitable for OOD prediction in the HCQNN classifier models using the representational properties of the quantum features' space. Particularly, we find subspaces within the feature space based on their categorical label information, and make use of a metric called the fidelity score that is extensively used in the quantum computing literature for measuring the similarity between the quantum states. Finally, we substantiate our claims on the efficacy of the fidelity score for OOD detection by demonstrating empirically that we can separate the ID from OOD samples across many standard benchmarks using the class-wise boundary defined charaterized by the fidelity scores. Quantum Machine Learning Neural Networks Out-Of-Distribution Hilbert Space in Quantum States Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 09 Mar, 2026 Editor assigned by journal 19 Feb, 2026 Submission checks completed at journal 17 Dec, 2025 First submitted to journal 16 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-8378688","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":562052335,"identity":"c785bc0d-9062-4861-9e6a-14de1be8a399","order_by":0,"name":"Shahreen Sultana","email":"","orcid":"","institution":"North South University","correspondingAuthor":false,"prefix":"","firstName":"Shahreen","middleName":"","lastName":"Sultana","suffix":""},{"id":562052336,"identity":"c98084eb-2773-4777-acf1-db723adecb13","order_by":1,"name":"Mutasim Fuad Sarker","email":"","orcid":"","institution":"North South 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