An Enhanced Quantum Image Representation for Improved Intensity Preservation and Fidelity

An Enhanced Quantum Image Representation for Improved Intensity Preservation and Fidelity | 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 An Enhanced Quantum Image Representation for Improved Intensity Preservation and Fidelity Vrushali Nikam, Shirish Sane This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8893817/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 Quantum Image Representation (QIR) is a fundamental concept in quantum image processing. It describes how classical images are converted into quantum states for processing on quantum computers. Researchers proposed several QIR models, with FRQI and NEQR being the most popular ones. These models show the possibilities of the quantum image encoding. Nevertheless, they are also limited in some way. Mostly, pixel intensity accuracy, the required number of qubits, and circuit complexity have trade-offs. Their application to Noisy Intermediate-Scale Quantum (NISQ) devices becomes challenging, the quantum resources being small in such devices, due to this. In the following paper, we introduce our own approach, which is the Intensity-Preserving Quantum image Representation (IP-QIR). The key aim of IP-QIR is to maintain the information about the grayscale intensities and decrease the amount of quantum resources. IP-QIR quantifies pixel intensities by a controlled rotation scheme. The measurement statistics of a single qubit hold the intensity information and the position qubits represent the spatial information. The strategy uses small patches of images rather than complete images to make it easy to implement. This will decrease the depth of circuit design and makes the approach less advanced to the near-term quantum hardware. IBM Qiskit simulations are used in assessing the performance of IP-QIR. The three types of grayscale images of which experiments are run are the synthetic image patches, SAR images and medical TB chest X-ray images. It is revealed that IP-QIR maintains the intensity information compared to the FRQI and NEQR. SAR and medical data can reach values of fidelity of 84.12%. The other notable point is that the size of a 4 x 4 image patch only needs five qubits which are much lower than in NEQR. Meanwhile, good reconstruction accuracy is achieved. These findings demonstrate the fact that IP-QIR is an effective and realistic approach to quantum image representation on the NISQ-era quantum devices. Quantum Image Representation IP-QIR FRQI NEQR Intensity Preservation NISQ Devices 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-8893817","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598920833,"identity":"52606c0e-d6be-4a26-a842-7501112c41bf","order_by":0,"name":"Vrushali Nikam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABKUlEQVRIie3RsUrDQBjA8QsHN91x64VE8gongeJQEh8l4UCXDk7i0CFwUBe1a8TBV1CEzCkHmUq7FuomuAmRlKKDxSttXIyxo+D9l/uG/MjlCwAm0x8NAnCAAbCknrs7E1aTo93J9gTq98f5fOxXJ33mAkdK/t6bBkOaW2V59hjcHsLiddFAJr2Okxb6Yu5IxpfZXFynEbTT8bO4z5G4cb8T+wJ3IEaasFjmRBM+o0tIBkrsJ9iHrJH4FV5tyOgjm4hwmiNIVj8TijF3yGBDBMnygINIk0QFHsC+VTYRdOqQK6bvFkt/LxMRm62/pVARh0jAho0hDB8qvOyGlB0/2S9ZENLhemN9FXrnUllvLdtG2zNO6uFO/yOIW0hd+DV4Gre+xWQymf5Ln3keW/chJMulAAAAAElFTkSuQmCC","orcid":"","institution":"MET's BKC IOE,Nashik India","correspondingAuthor":true,"prefix":"","firstName":"Vrushali","middleName":"","lastName":"Nikam","suffix":""},{"id":598920834,"identity":"bdc6c574-b3d5-4611-846b-d3b9e505d8f8","order_by":1,"name":"Shirish Sane","email":"","orcid":"","institution":"GES's R H Sapat COE, Nashik,India","correspondingAuthor":false,"prefix":"","firstName":"Shirish","middleName":"","lastName":"Sane","suffix":""}],"badges":[],"createdAt":"2026-02-16 14:08:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8893817/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8893817/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108086794,"identity":"1336f614-8090-470b-87c0-cd15d28d9889","added_by":"auto","created_at":"2026-04-29 08:41:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1184189,"visible":true,"origin":"","legend":"","description":"","filename":"VSNIPIR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8893817/v1_covered_dd4dcc4d-b584-45f7-a97d-6f69a60e07da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Enhanced Quantum Image Representation for Improved Intensity Preservation and Fidelity","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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 Image Representation, IP-QIR, FRQI, NEQR, Intensity Preservation, NISQ Devices","lastPublishedDoi":"10.21203/rs.3.rs-8893817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8893817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Quantum Image Representation (QIR) is a fundamental concept in quantum image processing. It describes how classical images are converted into quantum states for processing on quantum computers. Researchers proposed several QIR models, with FRQI and NEQR being the most popular ones. These models show the possibilities of the quantum image encoding. Nevertheless, they are also limited in some way. Mostly, pixel intensity accuracy, the required number of qubits, and circuit complexity have trade-offs. Their application to Noisy Intermediate-Scale Quantum (NISQ) devices becomes challenging, the quantum resources being small in such devices, due to this.\nIn the following paper, we introduce our own approach, which is the Intensity-Preserving Quantum image Representation (IP-QIR). The key aim of IP-QIR is to maintain the information about the grayscale intensities and decrease the amount of quantum resources. IP-QIR quantifies pixel intensities by a controlled rotation scheme. The measurement statistics of a single qubit hold the intensity information and the position qubits represent the spatial information. The strategy uses small patches of images rather than complete images to make it easy to implement. This will decrease the depth of circuit design and makes the approach less advanced to the near-term quantum hardware.\nIBM Qiskit simulations are used in assessing the performance of IP-QIR. The three types of grayscale images of which experiments are run are the synthetic image patches, SAR images and medical TB chest X-ray images. It is revealed that IP-QIR maintains the intensity information compared to the FRQI and NEQR. SAR and medical data can reach values of fidelity of 84.12\\%. The other notable point is that the size of a 4 x 4 image patch only needs five qubits which are much lower than in NEQR. Meanwhile, good reconstruction accuracy is achieved. 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