Quantum speedup for track reconstruction in particle 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.

See our talk about the paper at https://www.youtube.com/watch?v=6fJe_5kMUCU&t=1s. Version 2 contains slight generalization to local track reconstruction algorithms. Version 3 includes an appendix comparing our approach with previous works

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• Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
• Machine Learning for Anomaly Detection in Particle Physics
• Quantum walk approach to simulating parton showers
• Quantum anomaly detection in the latent space of proton collision events at the LHC
• Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
• Dynamical quantum phase transitions of the Schwinger model: real-time dynamics on IBM Quantum
• Efficient Quantum Simulation of QCD Jets on the Light Front
• Quantum Simulation of Bound State Scattering
• Guided Quantum Compression for High Dimensional Data Classification
• A quantum algorithm for track reconstruction in the LHCb vertex detector
• Quantum-Annealing-Inspired Algorithms for Track Reconstruction at High-Energy Colliders
• Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction
• Classification and transformations of quantum circuit decompositions for permutation operations