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Adiabatic quantum computers, such as the quantum annealers commercialized byD-Wave Systems Inc., are routinely used to tackle combinatorial optimizationproblems. In this article, we show how to exploit them to accelerateequilibrium Markov chain Monte Carlo simulations of computationally challengingspin-glass models at low but finite temperatures. This is achieved by traininggenerative neural networks on data produced by a D-Wave quantum annealer, andthen using them to generate smart proposals for the Metropolis-Hastingsalgorithm. In particular, we explore hybrid schemes by combining singlespin-flip and neural proposals, as well as D-Wave and classical Monte Carlotraining data. The hybrid algorithm outperforms the single spin-flipMetropolis-Hastings algorithm. It is competitive with parallel tempering interms of correlation times, with the significant benefit of a much shorterequilibration time.

Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning