TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.14.6.171
TI  - Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation
PY  - 2023/06/27
UR  - https://scipost.org/SciPostPhys.14.6.171
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 14
IS  - 6
SP  - 171
A1  - Humeniuk, Stephan
AU  - Wan, Yuan
AU  - Wang, Lei
AB  - 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 $\mathcal{O}(N_s^3)$ with the number of orbitals $N_s$. The implementation is benchmarked on the two-dimensional $t-V$ model of spinless fermions on the square lattice.
ER  -

@Article{10.21468/SciPostPhys.14.6.171,
	title={{Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation}},
	author={Stephan Humeniuk and Yuan Wan and Lei Wang},
	journal={SciPost Phys.},
	volume={14},
	pages={171},
	year={2023},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.14.6.171},
	url={https://scipost.org/10.21468/SciPostPhys.14.6.171},
}