Bulletin of the American Physical Society
2023 APS March Meeting
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session D62: Machine Learning for Quantum Matter I
3:00 PM–5:48 PM,
Monday, March 6, 2023
Room: Room 417
Sponsoring
Unit:
DCOMP
Chair: Ziyan Zhu, Stanford University
Abstract: D62.00010 : Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation
5:12 PM–5:24 PM
Presenter:
Stephan Humeniuk
(Rutgers University)
Authors:
Stephan Humeniuk
(Rutgers University)
Yuan Wan
(Institute of Physics)
Lei Wang
(Institute of Physics, Chinese Academy of Sciences)
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 (lattice sites) Ns , largely independent of the filling. In
cases where the autocorrelation time scales with the system size this drastically changes the scaling
of the computational effort for achieving a certain Monte Carlo error. Direct sampling also improves
the sampling efficiency compared to the Metropolis update which can suffer from bottlenecks due
to low acceptance rate. The implementation is benchmarked on the two-dimensional t − V model of
spinless fermions on the square lattice by comparison with exact diagonalization, yielding consistent
results for a variational ansatz which is limited by the sign structure of a single Slater determinant.
By co-optimizing the reference Slater determinant in the presence of the Jastrow factor the sign
structure of the initial Hartree-Fock Slater determinant can be changed considerably, which aids the
optimization.
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