Quantum speedup for track reconstruction in particle accelerators

To investigate the fundamental nature of matter and its interactions, particles are accelerated to very high energies and collided inside detectors, producing a multitude of other particles that are scattered in all directions. As charged particles traverse the detector, they leave signals of their passage. The problem of track reconstruction is to recover the original trajectories from these signals. This challenging data analysis task will become even more demanding as the luminosity of future accelerators increases, leading to collision events with a more complex structure. We identify four fundamental routines present in every local tracking method and analyze how they scale in the context of a standard tracking algorithm. We show that for some of these routines we can reach a lower computational complexity with quantum search algorithms. Although the found quantum speedups are mild, this constitutes, to the best of our knowledge, the first rigorous evidence of a quantum advantage for a high-energy physics data processing task.

Automated summary

To investigate the nature of matter and its interactions, scientists accelerate particles and collide them inside detectors, generating scattered particles in all directions. By analyzing the signals left by charged particles passing through the detector, the original trajectories can be recovered. As accelerator luminosity increases, leading to more complex collision events, the data analysis task becomes more challenging. Researchers have found that quantum search algorithms can reduce the computational complexity of some routines, providing the first rigorous evidence of a quantum advantage for high-energy physics data processing tasks.