Quantum Machine Learning: A Review and Case Studies
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/25/2/287 |
| _version_ | 1797621145379274752 |
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| author | Amine Zeguendry Zahi Jarir Mohamed Quafafou |
| author_facet | Amine Zeguendry Zahi Jarir Mohamed Quafafou |
| author_sort | Amine Zeguendry |
| collection | DOAJ |
| description | 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’s 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. |
| first_indexed | 2024-03-11T08:51:32Z |
| format | Article |
| id | doaj.art-1bb75130ca694c7ab562afd627e3201a |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T08:51:32Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-1bb75130ca694c7ab562afd627e3201a2023-11-16T20:23:25ZengMDPI AGEntropy1099-43002023-02-0125228710.3390/e25020287Quantum Machine Learning: A Review and Case StudiesAmine Zeguendry0Zahi Jarir1Mohamed Quafafou2Laboratoire d’Ingénierie des Systèmes d’Information, Faculty of Sciences, Cadi Ayyad University, Marrakech 40000, MoroccoLaboratoire d’Ingénierie des Systèmes d’Information, Faculty of Sciences, Cadi Ayyad University, Marrakech 40000, MoroccoLaboratoire des Sciences de l’Information et des Systèmes, Unité Mixte de Recherche 7296, Aix-Marseille University, 13007 Marseille, FranceDespite 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’s 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.https://www.mdpi.com/1099-4300/25/2/287quantum computingquantum algorithmsQuantum Machine Learning (QML)quantum classificationVariational Quantum Circuit (VQC)QSVM |
| spellingShingle | Amine Zeguendry Zahi Jarir Mohamed Quafafou Quantum Machine Learning: A Review and Case Studies Entropy quantum computing quantum algorithms Quantum Machine Learning (QML) quantum classification Variational Quantum Circuit (VQC) QSVM |
| title | Quantum Machine Learning: A Review and Case Studies |
| title_full | Quantum Machine Learning: A Review and Case Studies |
| title_fullStr | Quantum Machine Learning: A Review and Case Studies |
| title_full_unstemmed | Quantum Machine Learning: A Review and Case Studies |
| title_short | Quantum Machine Learning: A Review and Case Studies |
| title_sort | quantum machine learning a review and case studies |
| topic | quantum computing quantum algorithms Quantum Machine Learning (QML) quantum classification Variational Quantum Circuit (VQC) QSVM |
| url | https://www.mdpi.com/1099-4300/25/2/287 |
| work_keys_str_mv | AT aminezeguendry quantummachinelearningareviewandcasestudies AT zahijarir quantummachinelearningareviewandcasestudies AT mohamedquafafou quantummachinelearningareviewandcasestudies |