  Classical and quantum annealing are two heuristic optimization methods that
search for an optimal solution by slowly decreasing thermal or quantum
fluctuations. Optimizing annealing schedules is important both for performance
and fair comparisons between classical annealing, quantum annealing, and other
algorithms. Here we present a heuristic approach for the optimization of
annealing schedules for quantum annealing and apply it to 3D Ising spin glass
problems. We find that if both classical and quantum annealing schedules are
similarly optimized, classical annealing outperforms quantum annealing for
these problems when considering the residual energy obtained in slow annealing.
However, when performing many repetitions of fast annealing, simulated quantum
annealing is seen to outperform classical annealing for our benchmark problems.
