Quantum Machine Learning: A Review and Case Studies

• Published in: Entropy (Basel, Switzerland) Vol. 25; no. 2; p. 287
• Main Authors: Zeguendry, Amine, Jarir, Zahi, Quafafou, Mohamed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.02.2023; MDPI
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Case studies; Classifiers; Computers; Datasets; Deep learning; Handwriting recognition; Hilbert space; Linear algebra; Machine learning; Methods; Neural networks; Optimization algorithms; Optimization techniques; QSVM; quantum algorithms; quantum classification; Quantum computers; Quantum computing; Quantum Machine Learning (QML); Quantum physics; Quantum theory; Regression analysis; Review; Variational Quantum Circuit (VQC); Morocco; Quanvolutional Neural Network (QNN); Quantum Machine Learning (QML); quantum computing; Variational Quantum Classifier (VQC); quantum classification; Variational Quantum Circuit (VQC); quantum algorithms; QSVM; quantum encoding
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e25020287

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.