  Quantum annealing offers a novel approach to finding the optimal solutions
for a variety of computational problems, where the quantum annealing controls
influence the observed performance and error mechanisms by tuning the
underlying quantum dynamics. However, the influence of the available controls
is often poorly understood, and methods for evaluating the effects of these
controls are necessary to tune quantum computational performance. Here we use
portfolio optimization as a case study by which to benchmark quantum annealing
controls and their relative effects on computational accuracy. We compare
empirical results from the D-Wave 2000Q quantum annealer to the computational
ground truth for a variety of portfolio optimization instances. We evaluate
both forward and reverse annealing methods and we identify control variations
that yield optimal performance in terms of probability of success and
probability of chain breaks.
