{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T06:20:27Z","timestamp":1775888427109,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an increasing number of machine learning researchers are investigating the possible advantages of quantum computing. The scientific literature on Quantum Machine Learning is now enormous, and a review of its current state that can be comprehended without a physics background is necessary. The objective of this study is to present a review of Quantum Machine Learning from the perspective of conventional techniques. Departing from giving a research path from fundamental quantum theory through Quantum Machine Learning algorithms from a computer scientist\u2019s perspective, we discuss a set of basic algorithms for Quantum Machine Learning, which are the fundamental components for Quantum Machine Learning algorithms. We implement the Quanvolutional Neural Networks (QNNs) on a quantum computer to recognize handwritten digits, and compare its performance to that of its classical counterpart, the Convolutional Neural Networks (CNNs). Additionally, we implement the QSVM on the breast cancer dataset and compare it to the classical SVM. Finally, we implement the Variational Quantum Classifier (VQC) and many classical classifiers on the Iris dataset to compare their accuracies.<\/jats:p>","DOI":"10.3390\/e25020287","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T05:09:20Z","timestamp":1675400960000},"page":"287","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":164,"title":["Quantum Machine Learning: A Review and Case Studies"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5864-5667","authenticated-orcid":false,"given":"Amine","family":"Zeguendry","sequence":"first","affiliation":[{"name":"Laboratoire d\u2019Ing\u00e9nierie des Syst\u00e8mes d\u2019Information, Faculty of Sciences, Cadi Ayyad University, Marrakech 40000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6425-1031","authenticated-orcid":false,"given":"Zahi","family":"Jarir","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Ing\u00e9nierie des Syst\u00e8mes d\u2019Information, Faculty of Sciences, Cadi Ayyad University, Marrakech 40000, Morocco"}]},{"given":"Mohamed","family":"Quafafou","sequence":"additional","affiliation":[{"name":"Laboratoire des Sciences de l\u2019Information et des Syst\u00e8mes, Unit\u00e9 Mixte de Recherche 7296, Aix-Marseille University, 13007 Marseille, France"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1103\/PhysRevLett.79.325","article-title":"Quantum Mechanics Helps in Searching for a Needle in a Haystack","volume":"79","author":"Grover","year":"1997","journal-title":"Phys. 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