Neural-Network Quantum States for Periodic Systems in Continuous Space
- URL: http://arxiv.org/abs/2112.11957v1
- Date: Wed, 22 Dec 2021 15:27:30 GMT
- Title: Neural-Network Quantum States for Periodic Systems in Continuous Space
- Authors: Gabriel Pescia, Jiequn Han, Alessandro Lovato, Jianfeng Lu, Giuseppe
Carleo
- Abstract summary: We introduce a family of neural quantum states for the simulation of strongly interacting systems in the presence of periodicity.
For one-dimensional systems we find very precise estimations of the ground-state energies and the radial distribution functions of the particles.
In two dimensions we obtain good estimations of the ground-state energies, comparable to results obtained from more conventional methods.
- Score: 66.03977113919439
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce a family of neural quantum states for the simulation of strongly
interacting systems in the presence of spatial periodicity. Our variational
state is parameterized in terms of a permutationally-invariant part described
by the Deep Sets neural-network architecture. The input coordinates to the Deep
Sets are periodically transformed such that they are suitable to directly
describe periodic bosonic systems. We show example applications to both one and
two-dimensional interacting quantum gases with Gaussian interactions, as well
as to $^4$He confined in a one-dimensional geometry. For the one-dimensional
systems we find very precise estimations of the ground-state energies and the
radial distribution functions of the particles. In two dimensions we obtain
good estimations of the ground-state energies, comparable to results obtained
from more conventional methods.
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