  With the advent of large-scale quantum annealing devices, several challenges
have emerged. For example, it has been shown that the performance of a device
can be significantly affected by several degrees of freedom when programming
the device; a common example being gauge selection. To date, no
experimentally-tested strategy exists to select the best programming
specifications. We developed a score function that can be calculated from a
number of readouts much smaller than the number of readouts required to find
the desired solution. We show how this performance estimator can be used to
guide, for example, the selection of the optimal gauges out of a pool of random
gauge candidates and how to select the values of parameters for which we have
no a priori knowledge of the optimal value. For the latter, we illustrate the
concept by applying the score function to set the strength of the parameter
intended to enforce the embedding of the logical graph into the hardware
architecture, a challenge frequently encountered in the implementation of
real-world problem instances. Since the harder the problem instances, the more
useful the strategies proposed in this work are, we expect the programming
strategies proposed to significantly reduce the time of future benchmark
studies and in help finding the solution of hard-to-solve real-world
applications implemented in the next generation of quantum annealing devices.
