Abstract
High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics.This article is part of the theme issue ‘Quantum technologies in particle physics’.
Highlights
The high-energy physics experiments at the Large Hadron Collider (LHC) located just outside Geneva, Switzerland have transformed the field of particle physics, most notably through the exciting discovery of the Higgs boson [1,2] by the ATLAS [3] and CMS [4] experiments
Graph neural networks are a popular technique from machine learning that are currently being explored for a wide range of high-energy physics applications including track pattern recognition [37]
The pattern recognition of charged particle trajectories is a challenging computational problem motivating the exploration of novel algorithms
Summary
The high-energy physics experiments at the Large Hadron Collider (LHC) located just outside Geneva, Switzerland have transformed the field of particle physics, most notably through the exciting discovery of the Higgs boson [1,2] by the ATLAS [3] and CMS [4] experiments. To illustrate the challenge that will be posed by the HL-LHC, figure 1 shows the dependence of the processing time per event as a function of pile up using data recorded by the ATLAS experiment using a pattern recognition sequence based on the Kalman filter. At future hadron colliders, such as the proposed hadron-hadron collider as part of the Future Circular Collider project [13], even more pile up is expected, with potentially up to 1000 additional interactions per event Due to this challenge, the development of new algorithms and new techniques for pattern recognition for high-energy physics is currently a very active field of development. This article reviews current studies of the potential of quantum computers for charged particle pattern recognition. The studies use the open dataset produced in the context of the tracking machine learning challenge (TrackML) [26,27]
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