AI Seminar: Machine learning applications of quantum annealing in high energy physics
Due to their limitations, noisy intermediate-scale quantum (NISQ) devices often pose challenges in encoding real-world problems and in achieving sufficiently high fidelity computations. We present methodologies and results for overcoming these challenges on the D-Wave 2X quantum annealer for two problems in high energy physics: Higgs boson classification and charged particle tracking. Each problem is solved with a different construction, offering distinct perspectives on applications of quantum annealing. The quantum annealing for machine learning (QAML) algorithm ensembles weak classifiers to create a strong classifier from the excited states in the vicinity of the ground state, taking advantage of the noise that characterizes NISQ devices to help achieve comparable results to state-of-the-art classical machine learning methods in the Higgs signal-versus-background classification problem. Under a Hopfield network formulation, we also find successful results for charged particle tracking on simulated Large Hadron Collider data. Novel classical methods are proposed to overcome the limited size and connectivity of the D-Wave architecture, enabling the analysis of events with pileup at the scale of the Large Hadron Collider during its discovery of the Higgs boson. Furthermore, the time complexity of these classical pre-processing procedures is found to scale better with track density than current state-of-the-art tracking techniques, leaving open the possibility of a quantum speedup for tracking in the future.