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.

KEYWORDS:

• Machine learning
• quantum computing
• quantum entanglement
• quantum machine learning
• quantum neural networks

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was partially supported by the National Key Research and Development Program of China [grant numbers 2022YFA1602200 and 2021YFA1000600], the National Natural Science Foundation of China [grant number 62072170 and 62202156], the international partnership program of the Chinese Academy of Sciences [grant number 211134KYSB20200057], the Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology [grant 2020WLZC004], the Science and Technology Project of Hunan Provincial Department of Transportation [grant number 202101], the Key Technologies Research and Development Program of Hunan Province [grant number 2022GK2015], the Natural Science Foundation of Fujian Province [grant number 2023J011460].