Papers › Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation
Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation
Stephan Humeniuk, Yuan Wan, Lei Wang
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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.
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