Quantum Annealing Sampling with a Bias Field
Tobias Graß · Physical Review Applied · 2022
The presence of a bias field, encoding some information about a 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, with 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.