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Usage of cutting-edge artificial intelligence will be the baseline at futurehigh energy colliders such as the High Luminosity Large Hadron Collider, tocope with the enormously increasing demand of the computing resources. Therapid development of quantum machine learning could bring in furtherparadigm-shifting improvement to this challenge. One of the two highestCPU-consuming components, the charged particle reconstruction, the so-calledtrack reconstruction, can be considered as a quadratic unconstrained binaryoptimization (QUBO) problem. The Quantum Approximate Optimization Algorithm(QAOA) is one of the most promising algorithms to solve such combinatorialproblems and to seek for a quantum advantage in the era of the NoisyIntermediate-Scale Quantum computers. It is found that the QAOA shows promisingperformance and demonstrated itself as one of the candidates for the trackreconstruction using quantum computers.

Charged particle reconstruction for future high energy colliders with Quantum Approximate Optimization Algorithm