Design and analysis of quantum machine learning: a survey

Abstract

Machine learning has demonstrated tremendous potential in solving real-world problems. However, with the exponential growth of data amount and the increase of model complexity, the processing efficiency of machine learning declines rapidly. Meanwhile, the emergence of quantum computing has given rise to quantum machine learning, which relies on superposition and entanglement, exhibiting exponential optimisation compared to traditional machine learning. Therefore, in the paper, we survey the basic concepts, algorithms, applications and challenges of quantum machine learning. Concretely, we first review the basic concepts of quantum computing including qubit, quantum gates, quantum entanglement, etc. Secondly, we in-depth discuss 5 quantum machine learning algorithms of quantum support vector machine, quantum neural network, quantum k-nearest neighbour, quantum principal component analysis and quantum k-Means algorithm. Thirdly, we conduct discussions on the applications of quantum machine learning in image recognition, drug efficacy prediction and cybersecurity. Finally, we summarise the challenges of quantum machine learning consisting of algorithm design, hardware limitations, data encoding, quantum landscapes, noise and decoherence.