Quantum Machine Learning and its Supremacy in High Energy Physics

TL;DR

This paper explores the potential of quantum machine learning to revolutionize pattern recognition tasks in high energy physics, offering future prospects for quantum algorithms to improve particle identification and reconstruction.

Contribution

It introduces the future possibilities of applying quantum computation and quantum machine learning to high energy physics, emphasizing their potential over classical methods.

Findings

Quantum algorithms could enhance particle identification.

Quantum machine learning may improve pattern recognition in HEP.

Potential for quantum methods to outperform classical techniques in HEP.

Abstract

This article reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector to detector geometry and magnetic field used in the experiment. Here in the present introductory article, we deliver the future possibilities for the lucid application of quantum computation and quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arise in this domain.