Self-learning Monte Carlo with deep neural networks

Self-learning Monte Carlo (SLMC) method is a general algorithm to speedup MC simulations. Its efficiency has been demonstrated in various systems by introducing an effective model to propose global moves in the configuration space. In this paper, we show that deep neural networks can be naturally incorporated into SLMC, and without any prior knowledge, can learn the original model accurately and efficiently. Demonstrated in quantum impurity models, we reduce the complexity for a local update from $ \mathcal{O}(\beta^2) $ in Hirsch-Fye algorithm to $ \mathcal{O}(\beta \ln \beta) $, which is a significant speedup especially for systems at low temperatures.