Charged particle tracking with quantum annealing-inspired optimization
This addresses scaling issues in particle physics data analysis for LHC researchers, but is incremental as it adapts existing methods to new hardware.
The researchers tackled track reconstruction challenges at the High Luminosity Large Hadron Collider by adapting a geometric Denby-Peterson network method to quantum annealing, demonstrating successful application on the TrackML dataset using simulated and quantum annealing with D-Wave 2X.
At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for analysis are expected to face challenges due to scaling with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework and to HL-LHC conditions. Furthermore, we develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML dataset are presented, demonstrating the successful application of a quantum annealing-inspired algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the LHC while leaving open the possibility of a quantum speedup for tracking.