Quantum classifier based on open quantum systems with amplitude information loading

Quantum classifier based on open quantum systems with amplitude information loading | 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 Quantum classifier based on open quantum systems with amplitude information loading Eduardo Barreto Brito, Fernando M. De Paula Neto, Nadja K. Bernardes This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3944517/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Oct, 2024 Read the published version in Quantum Information Processing → Version 1 posted 12 You are reading this latest preprint version Abstract Although the studies on quantum algorithms have been progressing, it is still necessary to broaden the investigation of open quantum systems. In this study, we present the use of an open quantum system to implement a quantum classifier algorithm. Zhang et al. propose a one QuBit system interacting with the environment through a unitary operator that comes from the Hamiltonian. In our proposal, the input data is loaded into the amplitude of the environment instead of being in the unitary operator. This change positively impacts the performance of different databases tested and causes a difference in the system entanglement behavior. For evaluation, both the Zhang et. al model and the proposed model were tested in four real-world datasets and seven other toy problems. The results are evaluated according to accuracy and F1-Score. A deeper analysis of the Iris dataset is also done, checking the creation of entanglement and an extensive random search for better parameters on the proposed model. The results show that for most real-world datasets configurations, the proposed model, although having a simpler decision area, performed better than the one inspired by the Zhang et. al model, and that there is no pattern for the system entanglement in the Iris Dataset. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Oct, 2024 Read the published version in Quantum Information Processing → Version 1 posted Editorial decision: Revision requested 26 Jun, 2024 Reviews received at journal 24 Jun, 2024 Reviews received at journal 24 Jun, 2024 Reviewers agreed at journal 16 Jun, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviews received at journal 20 Mar, 2024 Reviewers agreed at journal 14 Mar, 2024 Reviewers agreed at journal 05 Mar, 2024 Reviewers invited by journal 27 Feb, 2024 Editor assigned by journal 13 Feb, 2024 Submission checks completed at journal 09 Feb, 2024 First submitted to journal 09 Feb, 2024 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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