Charged particle tracking with quantum annealing optimization

Abstract At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for physics analysis will need to be upgraded scale with density. Quantum annealing has shown promise in its ability solve combinatorial optimization problems amidst an ongoing effort establish evidence of a quantum speedup. As step towards exploiting such potential speedup, we investigate approach by adapting existing geometric Denby-Peterson (Hopfield) network method framework HL-LHC conditions. We develop additional embed problem onto and near-term hardware. Results using simulated D-Wave 2X system on TrackML open dataset presented, demonstrating successful application algorithm challenge. find can effectively reconstruct tracks, suggesting possible applications fast hardware-specific implementations at while leaving possibility speedup tracking.