Quantum Machine Learning in High Energy Physics

TL;DR

This paper reviews the emerging field of quantum machine learning applications in high energy physics, discussing initial ideas and future prospects enabled by quantum computing hardware.

Contribution

It provides a comprehensive review of early quantum machine learning approaches in high energy physics and explores potential future applications.

Findings

Initial quantum algorithms applied to high energy physics problems

Potential for quantum computing to enhance data analysis in physics

Future research directions in quantum machine learning for physics

Abstract

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.