The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to large increases in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a Quadratic Unconstrained Binary Optimization (QUBO) using software and quantum annealing. At track densities comparable with current LHC conditions, our approach achieves physics performance competitive with state-of-the-art pattern recognition algorithms. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.
@article{arxiv.1902.08324,
title = {A pattern recognition algorithm for quantum annealers},
author = {Frederic Bapst and Wahid Bhimji and Paolo Calafiura and Heather Gray and Wim Lavrijsen and Lucy Linder},
journal= {arXiv preprint arXiv:1902.08324},
year = {2019}
}