  The presence of a bias field, encoding some information about the target
state, can enhance the performance of quantum optimization methods. Here we
investigate the effect of such a bias field on the outcome of quantum annealing
sampling, at the example of the exact cover problem. The sampling is carried
out on a D-Wave machine, and different bias configurations are benchmarked
against the unbiased sampling procedure. It is found that the biased annealing
algorithm works particularly well for larger problem sizes, where the Hamming
distance between bias and target configuration becomes less important. This
work motivates future research efforts for finding good bias configurations,
either on the quantum machine itself, or in a hybrid fashion via classical
algorithms.
