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Article: Tangent Space Approach for Thermal Tensor Network Simulations of the 2D Hubbard Model

TitleTangent Space Approach for Thermal Tensor Network Simulations of the 2D Hubbard Model
Authors
Issue Date2023
Citation
Physical Review Letters, 2023, v. 130, n. 22, article no. 226502 How to Cite?
AbstractAccurate simulations of the two-dimensional (2D) Hubbard model constitute one of the most challenging problems in condensed matter and quantum physics. Here we develop a tangent space tensor renormalization group (tanTRG) approach for the calculations of the 2D Hubbard model at finite temperature. An optimal evolution of the density operator is achieved in tanTRG with a mild O(D3) complexity, where the bond dimension D controls the accuracy. With the tanTRG approach we boost the low-temperature calculations of large-scale 2D Hubbard systems on up to a width-8 cylinder and 10×10 square lattice. For the half-filled Hubbard model, the obtained results are in excellent agreement with those of determinant quantum Monte Carlo (DQMC). Moreover, tanTRG can be used to explore the low-temperature, finite-doping regime inaccessible for DQMC. The calculated charge compressibility and Matsubara Green's function are found to reflect the strange metal and pseudogap behaviors, respectively. The superconductive pairing susceptibility is computed down to a low temperature of approximately 1/24 of the hopping energy, where we find d-wave pairing responses are most significant near the optimal doping. Equipped with the tangent-space technique, tanTRG constitutes a well-controlled, highly efficient and accurate tensor network method for strongly correlated 2D lattice models at finite temperature.
Persistent Identifierhttp://hdl.handle.net/10722/330321
ISSN
2023 Impact Factor: 8.1
2023 SCImago Journal Rankings: 3.040
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Qiaoyi-
dc.contributor.authorGao, Yuan-
dc.contributor.authorHe, Yuan Yao-
dc.contributor.authorQi, Yang-
dc.contributor.authorChen, Bin Bin-
dc.contributor.authorLi, Wei-
dc.date.accessioned2023-09-05T12:09:35Z-
dc.date.available2023-09-05T12:09:35Z-
dc.date.issued2023-
dc.identifier.citationPhysical Review Letters, 2023, v. 130, n. 22, article no. 226502-
dc.identifier.issn0031-9007-
dc.identifier.urihttp://hdl.handle.net/10722/330321-
dc.description.abstractAccurate simulations of the two-dimensional (2D) Hubbard model constitute one of the most challenging problems in condensed matter and quantum physics. Here we develop a tangent space tensor renormalization group (tanTRG) approach for the calculations of the 2D Hubbard model at finite temperature. An optimal evolution of the density operator is achieved in tanTRG with a mild O(D3) complexity, where the bond dimension D controls the accuracy. With the tanTRG approach we boost the low-temperature calculations of large-scale 2D Hubbard systems on up to a width-8 cylinder and 10×10 square lattice. For the half-filled Hubbard model, the obtained results are in excellent agreement with those of determinant quantum Monte Carlo (DQMC). Moreover, tanTRG can be used to explore the low-temperature, finite-doping regime inaccessible for DQMC. The calculated charge compressibility and Matsubara Green's function are found to reflect the strange metal and pseudogap behaviors, respectively. The superconductive pairing susceptibility is computed down to a low temperature of approximately 1/24 of the hopping energy, where we find d-wave pairing responses are most significant near the optimal doping. Equipped with the tangent-space technique, tanTRG constitutes a well-controlled, highly efficient and accurate tensor network method for strongly correlated 2D lattice models at finite temperature.-
dc.languageeng-
dc.relation.ispartofPhysical Review Letters-
dc.titleTangent Space Approach for Thermal Tensor Network Simulations of the 2D Hubbard Model-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1103/PhysRevLett.130.226502-
dc.identifier.pmid37327445-
dc.identifier.scopuseid_2-s2.0-85161954854-
dc.identifier.volume130-
dc.identifier.issue22-
dc.identifier.spagearticle no. 226502-
dc.identifier.epagearticle no. 226502-
dc.identifier.eissn1079-7114-
dc.identifier.isiWOS:001009437500001-

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