  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.
