Inchworm quasi Monte Carlo for quantum impurities
2024 (English)In: Physical Review B, ISSN 2469-9950, E-ISSN 2469-9969, Vol. 110, no 12, article id L121120Article in journal (Refereed) Published
Abstract [en]
The inchworm expansion is a promising approach to solving strongly correlated quantum impurity models due to its reduction of the sign problem in real and imaginary time. However, inchworm Monte Carlo is computationally expensive, converging as 1/root N where N is the number of samples. We show that the imaginary-time integration is amenable to quasi Monte Carlo, with parametrically better 1/N convergence, by mapping the Sobol low-discrepancy sequence from the hypercube to the simplex with the so-called Root transform. This extends the applicability of the inchworm method to, e.g., multiorbital Anderson impurity models with off-diagonal hybridization, relevant for materials simulation, where continuous-time hybridization expansion Monte Carlo has a severe sign problem.
Place, publisher, year, edition, pages
American Physical Society , 2024. Vol. 110, no 12, article id L121120
National Category
Condensed Matter Physics
Identifiers
URN: urn:nbn:se:oru:diva-116749DOI: 10.1103/PhysRevB.110.L121120ISI: 001327409300003Scopus ID: 2-s2.0-85205137450OAI: oai:DiVA.org:oru-116749DiVA, id: diva2:1906121
Funder
EU, Horizon 2020
Note
H.U.R.S. and I.K. acknowledge funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 854843-FASTCORR). We acknowledge the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through Grant Agreement No. 2022-06725, for awarding this project access to the LUMI supercomputer, owned by the EuroHPC Joint Undertaking and hosted by CSC IT Center for Science (Finland) and the LUMI consortium, as well as computer resources hosted by the PDC Center for High Performance Computing and the National Supercomputer Centre (Projects No. SNIC 2022/1-18, No. SNIC 2022/6-113, No. SNIC 2022/13-9, No. SNIC 2022/21-15, No. NAISS 2023/1-44, and No. NAISS 2023/6-129).
2024-10-162024-10-162024-10-16Bibliographically approved