Math @ Duke
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Publications [#364048] of Jianfeng Lu
Papers Published
- Pescia, G; Han, J; Lovato, A; Lu, J; Carleo, G, Neural-network quantum states for periodic systems in continuous space,
Physical Review Research, vol. 4 no. 2
(June, 2022) [doi]
(last updated on 2024/04/19)
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 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 He4 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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