  Machine learning has been used in high energy physics for 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
apply quantum machine learning to 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.
