THE EFFECT OF SUPERPOSITION AND ENTANGLEMENT ON HYBRID QUANTUM MACHINE LEARNING FOR WEATHER FORECASTING

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dc.contributor.authorOur, Ber
dc.contributor.authorYılmaz, Hsan
dc.date.accessioned2024-07-12T21:40:17Z
dc.date.available2024-07-12T21:40:17Z
dc.date.issued2023en_US
dc.department[Belirlenecek]en_US
dc.description.abstractRecently, proposed algorithms for quantum computing and generated quantum computer technologies continue to evolve. On the other hand, machine learning has become an essential method for solving many problems such as computer vision, natural language processing, prediction and classification. Quantum machine learning is a new field devel-oped by combining the advantages of these two primary methods. As a hybrid approach to quantum and classical computing, variational quantum circuits are a form of machine learning that allows predicting an output value against input variables. In this study, the effects of superposition and entanglement on weather forecasting, were investigated using a variational quantum circuit model when the dataset size is small. The use of the entanglement layer between the variational layers has made significant improvements on the circuit performance. The use of the superposition layer before the data encoding layer resulted in the use of less variational layers.en_US
dc.description.sponsorshipBeir Ours doctoral thesis.en_US
dc.description.sponsorshipThis work was produced within the framework of Beir Ours doctoral thesis. We would like to thank anonymous referees for valuable suggestions.en_US
dc.identifier.endpage194en_US
dc.identifier.issn1533-7146
dc.identifier.issue3.Nisen_US
dc.identifier.scopus2-s2.0-85147327127en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage181en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12415/7227
dc.identifier.volume23en_US
dc.identifier.wosWOS:000936571400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherRinton Press, Incen_US
dc.relation.ispartofQuantum Information & Computationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY05165
dc.subjectQuantum Computingen_US
dc.subjectMachine Learningen_US
dc.subjectWeather Forcastingen_US
dc.subjectVariational Quantum Circuiten_US
dc.subjectHybrid Quantum-Classic Neural Networken_US
dc.titleTHE EFFECT OF SUPERPOSITION AND ENTANGLEMENT ON HYBRID QUANTUM MACHINE LEARNING FOR WEATHER FORECASTINGen_US
dc.typeArticle
dspace.entity.typePublication

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