Sparse sampling and tensor network representation of two-particle Green's functions

0534736 - FZÚ 2021 RIV NL eng J - Journal Article
Shinaoka, H. - Geffroy, D. - Wallerberger, M. - Otsuki, J. - Yoshimi, K. - Gull, E. - Kuneš, Jan
Sparse sampling and tensor network representation of two-particle Green's functions.
SciPost Physics. Roč. 8, č. 1 (2020), s. 1-23, č. článku 012. ISSN 2542-4653. E-ISSN 2542-4653
EU Projects: European Commission(XE) 646807 - EXMAG
Grant - others:GA MŠk(CZ) LM2015042
Research Infrastructure: IT4Innovations - 90070; CESNET II - 90042
Institutional support: RVO:68378271
Keywords : green functions * impurity solver
OECD category: Condensed matter physics (including formerly solid state physics, supercond.)
Impact factor: 6.093, year: 2020
Method of publishing: Open access

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.
Permanent Link: http://hdl.handle.net/11104/0312915