A Quick Overview of Quantum Machine Learning

Quantum machine learning (QML), which integrates quantum computing and machine learning techniques, has attracted much research attention in recent years. A previous study proposed a four-class classification for QML techniques based on whether the data and computations are in quantum or classical forms. Based on the four-class classification, I introduced examples and applications for each class of QML techniques. Two of the four classes have more research results than the other two. They are the CC class, where classical data are processed by classical models with the quantum computing flavor, and the CQ class, where classical data are processed by quantum-based models. More research results related to the CC and CQ classes are presented in this article. The introduced techniques include quantum-inspired binary classifiers, quantum-inspired multi-class classifiers, quantum-inspired support vector machines, quantum-inspired linear regression, and quantum-inspired recommendation systems of the CC class. They also include quantum support vector machines, quantum neural networks, quantum convolutional neural networks, and quantum autoencoders of the CQ class. Finally, possible research directions are suggested for each class, especially for the CC and CQ classes. The suggested research directions encompass hybrid classical-quantum machine learning, quantum K-nearest neighbor, quantum clustering, quantum generative adversarial network, and quantum reinforcement learning techniques.