High Energy Particle Physics

   

Quantum Machine Learning in High Energy Physics: the Future Prospects

Authors: 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.

Comments: 06 Pages. Quantum machine learning, High Eenergy Physics, Quantum Information

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Submission history

[v1] 2018-05-01 00:31:45

Unique-IP document downloads: 152 times

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