Pescia, GabrielHan, JiequnLovato, AlessandroLu, JianfengCarleo, Giuseppe2022-07-042022-07-042022-07-042022-05-2010.1103/PhysRevResearch.4.023138https://infoscience.epfl.ch/handle/20.500.14299/188977WOS:000809919500005We introduce a family of neural quantum states for the simulation of strongly interacting systems in the presence of spatial periodicity. Our variational state is parametrized 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 He-4 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.Physics, MultidisciplinaryPhysicsmany-body problemground-statemonte-carlotext::journal::journal article::research article