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

J 2020

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

SHINAOKA, Hiroshi, Dominique Alain GEFFROY, Markus WALLERBERGER, Junya OTSUKI, Kazuyoshi YOSHIMI et. al.

Základní údaje

Originální název

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

Autoři

SHINAOKA, Hiroshi, Dominique Alain GEFFROY (250 Francie, garant, domácí), Markus WALLERBERGER, Junya OTSUKI, Kazuyoshi YOSHIMI, Emanuel GULL a Jan KUNES

Vydání

SciPost Physics, Amsterdam, SciPost Foundation, 2020, 2542-4653

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10302 Condensed matter physics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 6.093

Kód RIV

RIV/00216224:14310/20:00114507

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.21468/SciPostPhys.8.1.012

UT WoS

000512377100012

Klíčová slova anglicky

Green's function; Hubbard model; Tensor networks

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 30. 4. 2021 18:20, Mgr. Marie Šípková, DiS.

Anotace

V originále

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.

Návaznosti

GA19-16937S, projekt VaV

Název: Frustrované a nefrustrované spinové systémy s měkkými spiny v relativistických Mottových izolantech

Investor: Grantová agentura ČR, Frustrované a nefrustrované spinové systémy s měkkými spiny v relativistických Mottových izolantech

90042, velká výzkumná infrastruktura

Název: CESNET II

90070, velká výzkumná infrastruktura

Název: IT4Innovations

Citovat

SHINAOKA, Hiroshi, Dominique Alain GEFFROY, Markus WALLERBERGER, Junya OTSUKI, Kazuyoshi YOSHIMI, Emanuel GULL a Jan KUNES. Sparse sampling and tensor network representation of two-particle Green's functions. SciPost Physics. Amsterdam: SciPost Foundation, 2020, roč. 8, č. 1, s. 1-23. ISSN 2542-4653. Dostupné z: https://dx.doi.org/10.21468/SciPostPhys.8.1.012.

@article{1699356,
   author = {Shinaoka, Hiroshi and Geffroy, Dominique Alain and Wallerberger, Markus and Otsuki, Junya and Yoshimi, Kazuyoshi and Gull, Emanuel and Kunes, Jan},
   article_location = {Amsterdam},
   article_number = {1},
   doi = {http://dx.doi.org/10.21468/SciPostPhys.8.1.012},
   keywords = {Green's function; Hubbard model; Tensor networks},
   language = {eng},
   issn = {2542-4653},
   journal = {SciPost Physics},
   title = {Sparse sampling and tensor network representation of two-particle Green's functions},
   url = {https://scipost.org/10.21468/SciPostPhys.8.1.012},
   volume = {8},
   year = {2020}
}

TY  - JOUR
ID  - 1699356
AU  - Shinaoka, Hiroshi - Geffroy, Dominique Alain - Wallerberger, Markus - Otsuki, Junya - Yoshimi, Kazuyoshi - Gull, Emanuel - Kunes, Jan
PY  - 2020
TI  - Sparse sampling and tensor network representation of two-particle Green's functions
JF  - SciPost Physics
VL  - 8
IS  - 1
SP  - 1-23
EP  - 1-23
PB  - SciPost Foundation
SN  - 25424653
KW  - Green's function
KW  - Hubbard model
KW  - Tensor networks
UR  - https://scipost.org/10.21468/SciPostPhys.8.1.012
L2  - https://scipost.org/10.21468/SciPostPhys.8.1.012
N2  - 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.
ER  -

SHINAOKA, Hiroshi, Dominique Alain GEFFROY, Markus WALLERBERGER, Junya OTSUKI, Kazuyoshi YOSHIMI, Emanuel GULL a Jan KUNES. Sparse sampling and tensor network representation of two-particle Green's functions. \textit{SciPost Physics}. Amsterdam: SciPost Foundation, 2020, roč.~8, č.~1, s.~1-23. ISSN~2542-4653. Dostupné z: https://dx.doi.org/10.21468/SciPostPhys.8.1.012.

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