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Ab-Initio Study of Interacting Fermions at Finite Temperature with Neural Canonical Transformation

Year:    2022

Author:    Hao Xie, Linfeng Zhang, Lei Wang

Journal of Machine Learning, Vol. 1 (2022), Iss. 1 : pp. 38–59

Abstract

We present a variational density matrix approach to the thermal properties of interacting fermions in the continuum. The variational density matrix is parametrized by a permutation equivariant many-body unitary transformation together with a discrete probabilistic model. The unitary transformation is implemented as a quantum counterpart of neural canonical transformation, which incorporates correlation effects via a flow of fermion coordinates. As the first application, we study electrons in a two-dimensional quantum dot with an interaction-induced crossover from Fermi liquid to Wigner molecule. The present approach provides accurate results in the low-temperature regime, where conventional quantum Monte Carlo methods face severe difficulties due to the fermion sign problem. The approach is general and flexible for further extensions, thus holds the promise to deliver new physical results on strongly correlated fermions in the context of ultracold quantum gases, condensed matter, and warm dense matter physics.

Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jml.220113

Journal of Machine Learning, Vol. 1 (2022), Iss. 1 : pp. 38–59

Published online:    2022-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    22

Keywords:    Interacting fermions Thermodynamics Variational free energy Normalizing flows.

Author Details

Hao Xie Email

Linfeng Zhang Email

Lei Wang Email

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