Quantum speedup for track reconstruction in particle accelerators

TL;DR

This paper demonstrates the first rigorous evidence of quantum speedup in high-energy physics data processing, specifically in track reconstruction for particle accelerators, by analyzing fundamental routines and applying quantum search algorithms.

Contribution

It identifies key routines in local tracking methods and shows how quantum algorithms can reduce their computational complexity, providing a novel quantum advantage in particle physics data analysis.

Findings

Quantum algorithms can lower complexity of certain tracking routines.

First evidence of quantum speedup in high-energy physics data processing.

Potential for improved efficiency in future high-luminosity accelerators.

Abstract

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 analyse 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.