Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation

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) 𝒪 (N s 3) with the number of orbitals Nₛ N s. The implementation is benchmarked on the two-dimensional t-V t − V model of spinless fermions on the square lattice.