Track clustering with a quantum annealer for primary vertex reconstruction at hadron colliders

Clustering of charged particle tracks along the beam axis is the first step in reconstructing the positions of hadronic interactions, also known as primary vertices, at hadron collider experiments. We use a 2048 qubit D-Wave quantum annealer to perform track clustering in a limited capacity on artificial events where the positions of primary vertices and tracks are drawn from distributions measured by the Compact Muon Solenoid experiment at the Large Hadron Collider. The clustering algorithm is tested on a variety of event topologies from 2 primary vertices and 10 tracks to 5 primary vertices and 15 tracks. We are limited by the convergence efficiency of the annealer, which is shown to decrease with increasing problem complexity measured by the number of qubits used to encode the problem. We identify three obstacles to reaching event complexities relevant for high luminosity hadron collider experiments, and suggest directions of research to overcome each.