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 \mathcal{O}(N_s^3)𝒪(Ns3) with the number of orbitals N_sNs. The implementation is benchmarked on the two-dimensional t-Vt−V model of spinless fermions on the square lattice.