Machine learning has been used in high energy physics since a long time,
primarily at the analysis level with supervised classification. Quantum
computing was postulated in the early 1980s as way to perform computations that
would not be tractable with a classical computer. With the advent of noisy
intermediate-scale quantum computing devices, more quantum algorithms are being
developed with the aim at exploiting the capacity of the hardware for machine
learning applications. An interesting question is whether there are ways to
combine quantum machine learning with High Energy Physics. This paper reviews
the first generation of ideas that use quantum machine learning on problems in
high energy physics and provide an outlook on future applications.Comment: 25 pages, 9 figures, submitted to Machine Learning: Science and
Technology, Focus on Machine Learning for Fundamental Physics collectio