Applied quantum annealing for particle tracking: Optimisation for the HL-LHC

Advancement in particle physics tracking techniques is a seemingly inevitable requirement for the future of higher luminosity experiments at the Large Hadron Collider (LHC). With the advancements in quantum annealing, it is now possible to place a minimisation based track reconstruction algorithm on a quantum computer in the form of a quadratic unconstrained binary optimisation problem (QUBO). The quantum annealing approach requires sufficient resources to generate a QUBO. Unfortunately, this QUBO is too large for current annealing hardware and must be partitioned by slicing the dataset. This has a detrimental impact on scoring metrics such as efficiency and purity, but reduces the overall runtime of the algorithm by a factor of two from the non-sliced counterpart. The ATLAS experiment is one of the experiments at the LHC. ATLAS is able to provide a simulated dataset, which can then be used to determine the effectiveness of the QUBO in a fully realistic event similar to the incoming High Luminosity Large Hadron Collider. Depending on the hard cuts applied to pre-QUBO generation for dense events, the realistic dataset leads to either a considerable drop in performance metrics, or an exponential growth in size of the QUBO. For these reasons it is probable that quantum annealing techniques in track reconstruction will remain limited until the size of quantum annealing chips (and therefore the size of the QUBO) increases.