Sparse sampling and tensor network representation of two-particle green’s functions

Hiroshi Shinaoka, Dominique Geffroy, Markus Wallerberger, Junya Otsuki, Kazuyoshi Yoshimi, Emanuel Gull, Jan Kuneš

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Article number012
JournalSciPost Physics
Volume8
Issue number1
DOIs
Publication statusPublished - Jan 2020

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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