Revealing Quantum Information Encoded in Classical Images

Revealing Quantum Information Encoded in Classical Images | 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 Revealing Quantum Information Encoded in Classical Images Otmane Ainelkitane, Brian Recktenwall-Calvet, Aasma Iqbal, Carlos C. N. Kuhn This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6955124/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 In this study, we investigate a simple quantum pre-processing filter kernel designed with only two CNOT gates for image feature extraction. We examine the impact of these filters when combined with a classical neural network for image classification tasks.Our main hypothesis is that this circuit can extract pixel correlation information that classical filters cannot. This approach is akin to a convolutional neural network, but with quantum layers replacing convolutional layers to extract spatial pixel entanglement. We found that a small circuit with just two CNOT gates can be engineered in three different spatial symmetries, each affecting classification differently. While the filter improves classification when combined with a simple, narrow network, it does not surpass complex classical methods. However, the filter demonstrates potential to enhance classification performance in more sophisticated architectures. Despite this, our empirical results show no clear correlation between the observed improvements and the level of entanglement in the quantum circuit, as measured by Von Neumann Entropy. The underlying cause of this improvement remains unclear and warrants further investigation. Quantum Machine Learning CNN Entanglement Computer Vision Classification 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. 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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-6955124","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487320808,"identity":"e96c4263-201c-4e7b-8f0b-ea34823698ad","order_by":0,"name":"Otmane Ainelkitane","email":"data:image/png;base64,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","orcid":"","institution":"University of Canberra","correspondingAuthor":true,"prefix":"","firstName":"Otmane","middleName":"","lastName":"Ainelkitane","suffix":""},{"id":487320810,"identity":"ddcc0a5d-a92c-4c94-8f7b-7086aafdbe8c","order_by":1,"name":"Brian Recktenwall-Calvet","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"","lastName":"Recktenwall-Calvet","suffix":""},{"id":487320811,"identity":"1b9910ec-92fd-4d37-bc2c-a9498c0d16ce","order_by":2,"name":"Aasma Iqbal","email":"","orcid":"","institution":"University of Canberra","correspondingAuthor":false,"prefix":"","firstName":"Aasma","middleName":"","lastName":"Iqbal","suffix":""},{"id":487320812,"identity":"b21c5ef9-296d-4e9e-b6e9-eb23b610fd08","order_by":3,"name":"Carlos C. 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