Quantum annealing sampling with a bias field

TL;DR

This paper explores how applying a bias field in quantum annealing improves sampling performance, especially for larger problems, by benchmarking biased and unbiased methods on a D-Wave quantum computer.

Contribution

It demonstrates that bias fields can enhance quantum annealing sampling efficiency, particularly for larger problem sizes, and suggests avenues for optimizing bias configurations.

Findings

Biased annealing performs better on larger problem instances.

Bias reduces the importance of Hamming distance for larger problems.

Future work can optimize bias configurations via quantum or classical methods.

Abstract

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.