Quantum Pattern Recognition for Tracking in High Energy Physics

The High-Luminosity LHC (HL-LHC) [1] is expected to deliver 5 to 7 times the nominal instantaneous luminosity of the LHC. While this upgrade has its many advantages, e.g. a larger proton-proton dataset permitting more precise measurements, numerous hardwareand software-based innovations will be necessary to withstand the immense number of per-event particle multiplicities as a result of many simultaneous proton-proton collisions. Given the finite computing resources available, processing the full dataset therefore poses a serious challenge for existing event reconstruction methods. However, the field of quantum information science and computation have been rapidly evolving in recent years, and show potential for unparalleled computing capabilities over classical machines for special classes of problems [2]. It is pivotal (and natural) that high energy physics (HEP) experimentalists and theorists get in on the ground floor to begin exploring the potential of quantum computation, specifically.