Boltzmann sampling with quantum annealers via fast Stein correction

Despite the attempts to apply a quantum annealer to Boltzmann sampling, it is still impossible to perform accurate sampling at arbitrary temperatures. Conventional distribution correction methods such as importance sampling and resampling cannot be applied, because the analytical expression of sampling distribution is unknown for a quantum annealer. Stein correction [Q. Liu and J. Lee, in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research (PMLR, 2017), Vol. 54, pp. 952–961] can correct the samples by weighting without the knowledge of the sampling distribution, but the naive implementation requires the solution of a large-scale quadratic program, hampering usage in practical problems. In this article, a fast and approximate method based on a random feature map and exponentiated gradient updates is developed to compute the sample weights and is used to correct the samples generated by D-Wave quantum annealers. In benchmarking problems, it is observed that the residual error of thermal average calculations is reduced significantly. If combined with our method, quantum annealers may emerge as a viable alternative to long-established Markov chain Monte Carlo methods.