  Pattern recognition algorithms are commonly employed to simplify the
challenging and necessary step of track reconstruction in sub-atomic physics
experiments. Aiding in the discrimination of relevant interactions, pattern
recognition seeks to accelerate track reconstruction by isolating signals of
interest. In high collision rate experiments, such algorithms can be
particularly crucial for determining whether to retain or discard information
from a given interaction even before the data is transferred to tape. As data
rates, detector resolution, noise, and inefficiencies increase, pattern
recognition becomes more computationally challenging, motivating the
development of higher efficiency algorithms and techniques. Quantum associative
memory is an approach that seeks to exploits quantum mechanical phenomena to
gain advantage in learning capacity, or the number of patterns that can be
stored and accurately recalled. Here, we study quantum associative memory based
on quantum annealing and apply it to the particle track classification. We
focus on discrimination models based on Ising formulations of quantum
associative memory model (QAMM) recall and quantum content-addressable memory
(QCAM) recall. We characterize classification performance of these approaches
as a function detector resolution, pattern library size, and detector
inefficiencies, using the D-Wave 2000Q processor as a testbed. Discrimination
criteria is set using both solution-state energy and classification labels
embedded in solution states. We find that energy-based QAMM classification
performs well in regimes of small pattern density and low detector
inefficiency. In contrast, state-based QCAM achieves reasonably high accuracy
recall for large pattern density and the greatest recall accuracy robustness to
a variety of detector noise sources.
