  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.
