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

Varování

Publikace nespadá pod Ústav výpočetní techniky, ale pod Přírodovědeckou fakultu. Oficiální stránka publikace je na webu muni.cz.

Autoři	SHINAOKA Hiroshi GEFFROY Dominique Alain WALLERBERGER Markus OTSUKI Junya YOSHIMI Kazuyoshi GULL Emanuel KUNES Jan
Rok publikování	2020
Druh	Článek v odborném periodiku
Časopis / Zdroj	SciPost Physics
Fakulta / Pracoviště MU	Přírodovědecká fakulta
Citace
www	https://scipost.org/10.21468/SciPostPhys.8.1.012
Doi	https://doi.org/10.21468/SciPostPhys.8.1.012
Klíčová slova	Green's function; Hubbard model; Tensor networks
Popis	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.
Související projekty:	Frustrované a nefrustrované spinové systémy s měkkými spiny v relativistických Mottových izolantech