Performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics models

Recent years have seen a rise in the application of machine learning techniques to aid the simulation of hard-to-sample systems that cannot be studied using traditional methods. Despite the introduction of many different architectures and procedures, a wide theoretical understanding is still lacking, with the risk of suboptimal implementations. As a first step to close this gap, we provide a complete analytic study of the widely used global annealing (also called sequential tempering) procedure applied to a shallow MADE architecture for the Curie-Weiss model. The contribution of this work is twofold: first, we give a description of the optimal weights and of the training under gradient descent optimization. Second, we compare what happens in global annealing with and without the addition of local Metropolis Monte Carlo steps. We are thus able to give theoretical insight into the best procedure to apply in this case. This work establishes a clear theoretical basis for the integration of machine learning techniques into Monte Carlo sampling and optimization.