Sparse sampling and tensor network representation of two-particle Green's functions
Abstract
Many-body calculations at the two-particle level require a compact representation of two-particle Green's functions. In this paper, we introduce a sparse sampling scheme in the Matsubara frequency domain as well as a tensor network representation for two-particle Green's functions. The sparse sampling is based on the intermediate representation basis and allows an accurate extraction of the generalized susceptibility from a reduced set of Matsubara frequencies. The tensor network representation provides a system independent way to compress the information carried by two-particle Green's functions. We demonstrate efficiency of the present scheme for calculations of static and dynamic susceptibilities in single- and two-band Hubbard models in the framework of dynamical mean-field theory.
Keywords
Cite
@article{arxiv.1909.07519,
title = {Sparse sampling and tensor network representation of two-particle Green's functions},
author = {Hiroshi Shinaoka and Dominique Geffroy and Markus Wallerberger and Junya Otsuki and Kazuyoshi Yoshimi and Emanuel Gull and Jan Kuneš},
journal= {arXiv preprint arXiv:1909.07519},
year = {2020}
}
Comments
27 pages in single column format, 12 pages (added missing references)