Parallel-Chain Monte Carlo Based on Generative Neural Networks

We design generative neural networks that generate Monte Carlo configurations with complete absence of autocorrelation and from which direct measurements of physical observables can be employed, irrespective of the system locating at the classical critical point, fermionic Mott insulator, Dirac semimetal and quantum critical point. We further propose a generic parallel-chain Monte Carlo scheme based on such neural networks, which provides independent samplings and accelerates the Monte Carlo simulations by reducing the thermalization process. We demonstrate the performance of our approach on the two-dimensional Ising and fermion Hubbard models.