  Usage of cutting-edge artificial intelligence will be the baseline at future
high energy colliders such as the High Luminosity Large Hadron Collider, to
cope with the enormously increasing demand of the computing resources. The
rapid development of quantum machine learning could bring in further
paradigm-shifting improvement to this challenge. One of the two highest
CPU-consuming components, the charged particle reconstruction, the so-called
track reconstruction, can be considered as a quadratic unconstrained binary
optimization (QUBO) problem. The Quantum Approximate Optimization Algorithm
(QAOA) is one of the most promising algorithms to solve such combinatorial
problems and to seek for a quantum advantage in the era of the Noisy
Intermediate-Scale Quantum computers. It is found that the QAOA shows promising
performance and demonstrated itself as one of the candidates for the track
reconstruction using quantum computers.
