Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation

Abstract

Direct sampling from a Slater determinant is combined with an autoregressive deep neural network as a Jastrow factor into a fully autoregressive Slater-Jastrow ansatz for variational quantum Monte Carlo, which allows for uncorrelated sampling. The elimination of the autocorrelation time leads to a stochastic algorithm with provable cubic scaling (with a potentially large prefactor), i.e. the number of operations for producing an uncorrelated sample and for calculating the local energy scales like O(Ns3) with the number of orbitals Ns. The implementation is benchmarked on the two-dimensional t - V model of spinless fermions on the square lattice.