  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.
