Challenges and opportunities in quantum machine learning for high-energy physics

Marco Cerezo, Guillaume Verdon, Hsin-Yuan Huang +2 more

Leonard Wossnig, Simone Severini B

Alejandro Perdomo‐Ortiz

Marco Trenti, L. Sestini, A. Gianelle +4 more

Kapil K. Sharma

This article reveals the future prospects of quantum machine learning in high energy physics (HEP). Particle identication, 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 led used in the experiment. Here in the present introductory article, we deliver the future possibilities for the lucid application of quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arise in this domain.

Kapil K. Sharma

This paper 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 this paper, 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 arising in this domain.