Title: Self-Learning Monte Carlo Method: Continuous-Time Algorithm,
Abstract: The recently-introduced self-learning Monte Carlo method is a general-purpose
numerical method that speeds up Monte Carlo simulations by training an
effective model to propose uncorrelated configurations in the Markov chain. We
implement this method in the framework of continuous time Monte Carlo method
with auxiliary field in quantum impurity models. We introduce and train a
diagram generating function (DGF) to model the probability distribution of
auxiliary field configurations in continuous imaginary time, at all orders of
diagrammatic expansion. By using DGF to propose global moves in configuration
space, we show that the self-learning continuous-time Monte Carlo method can
significantly reduce the computational complexity of the simulation.