Detection states of ions in a Paul trap  via conventional and quantum machine learning algorithms

Detection states of ions in a Paul trap via conventional and quantum machine learning algorithms | 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 Detection states of ions in a Paul trap via conventional and quantum machine learning algorithms Ilia Khomchenko, Andrei Fionov, Artem Alekseev, Daniil Volkov, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6635946/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 Trapped ions are among the leading platforms for quantum technologies, particularly in the field of quantum computing. Detecting states of trapped ions is essential for ensuring high-fidelity readouts of quantum states.In this work, we develop and benchmark a set of methods for ion quantum state detection using images obtained by a highly sensitive camera.By transforming the images from the camera and applying conventional and quantum machine learning methods, including convolution, support vector machine (classical and quantum), and quantum annealing, we demonstrate a possibility to detect the positions and quantum states of ytterbium ions in a Paul trap. Quantum state detection is performed with an electron shelving technique: depending on the quantum state of the ion its fluorescence under the influence of a 369.5 nm laser beam is either suppressed or not. We estimate fidelities for conventional and quantum detection techniques. In particular, conventional algorithms for detecting $^{171}$Yb$^{+}$, such as the support vector machine and photon statistics-based method,as well as our quantum annealing-based approach, have achieved perfect fidelity, which is beneficial compared to standard techniques. This result may pave the way for ultrahigh-fidelity detection of trapped ions via conventional and quantum machine learning techniques. Quantum Machine Learning Quantum Annealing Quantum Support Vector Machine Trapped-ion Quantum Computing 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-6635946","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475993333,"identity":"282b33b1-ad5e-4527-87fa-8aa162dcd2fa","order_by":0,"name":"Ilia Khomchenko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACNnYGBsbGBgYGCQbmA0C+hAxhLcxwLWwJIC08hK1BaOExAPEJa+Fj5jH8OHOHTbRk+5nPr27UWPAwsB8+ugG/w3iMJTeeScudzZO7zTrnGNBhPGlpNwhoMZB82HY4dx5D7jbjHDagFgkeM0JajH+CtfC/eWac8484LWaSG4FaZkvkMD/ObSNKC1uZ5cy2tNyZM56ZMef2SfCwEfKLfHvz5pu9bTa5M84nP/6c861Ojp/98DG8WhgYOAzgNkqASfzKQYD9AYzF/IGw6lEwCkbBKBiJAAB/8UTZhoZHwAAAAABJRU5ErkJggg==","orcid":"","institution":"P.N. 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Detecting states of trapped ions is essential for ensuring high-fidelity readouts of quantum states.In this work, we develop and benchmark a set of methods for ion quantum state detection using images obtained by a highly sensitive camera.By transforming the images from the camera and applying conventional and quantum machine learning methods, including convolution, support vector machine (classical and quantum), and quantum annealing, we demonstrate a possibility to detect the positions and quantum states of ytterbium ions in a Paul trap. Quantum state detection is performed with an electron shelving technique: depending on the quantum state of the ion its fluorescence under the influence of a 369.5 nm laser beam is either suppressed or not. We estimate fidelities for conventional and quantum detection techniques. 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