Quantum machine learning in high energy physics

Abstract Machine learning has been used in high energy physics (HEP) for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated early 1980s as way to perform computations that would not be tractable classical computer. With advent of noisy intermediate-scale quantum devices, more algorithms are being developed aim exploiting capacity hardware machine applications. An interesting question is whether there ways apply HEP. This paper reviews first generation ideas use on problems HEP and provide an outlook future