中国物理B ›› 2026, Vol. 35 ›› Issue (6): 67101-067101.doi: 10.1088/1674-1056/ae56e3

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Grassmann corner transfer-matrix renormalization group approach to one-dimensional fermionic models

Jian-Gang Kong(孔建刚)1 and Zhi-Yuan Xie(谢志远)1,2,†   

  1. 1 School of Physics, Renmin University of China, Beijing 100872, China;
    2 Key Laboratory of Quantum State Construction and Manipulation of MoE, Renmin University of China, Beijing 100872, China
  • 收稿日期:2026-01-24 修回日期:2026-03-15 接受日期:2026-03-25 出版日期:2026-05-28 发布日期:2026-05-28
  • 通讯作者: Zhi-Yuan Xie E-mail:qingtaoxie@ruc.edu.cn
  • 基金资助:
    This work was supported by the National Natural Science Foundation of China (Grant No. 12274458) and the National Key R&D Program of China (Grant Nos. 2024YFA1408604 and 2023YFA1406500).

Grassmann corner transfer-matrix renormalization group approach to one-dimensional fermionic models

Jian-Gang Kong(孔建刚)1 and Zhi-Yuan Xie(谢志远)1,2,†   

  1. 1 School of Physics, Renmin University of China, Beijing 100872, China;
    2 Key Laboratory of Quantum State Construction and Manipulation of MoE, Renmin University of China, Beijing 100872, China
  • Received:2026-01-24 Revised:2026-03-15 Accepted:2026-03-25 Online:2026-05-28 Published:2026-05-28
  • Contact: Zhi-Yuan Xie E-mail:qingtaoxie@ruc.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (Grant No. 12274458) and the National Key R&D Program of China (Grant Nos. 2024YFA1408604 and 2023YFA1406500).

摘要: The strongly correlated fermions play a vital role in modern physics. For a given fermionic Hamiltonian system, the most widely used approach to exploring the underlying physics is to study the wave function that incorporates Fermi-Dirac statistics, which can be obtained variationally by energy minimization or by imaginary-time evolution. In this work, we develop an accurate tensor network method for one-dimensional interacting fermionic models based on the coherentstate path-integral representation of the fermionic partition function. Employing the coherent-state representation, the partition function is effectively represented as a (1+1)-dimensional anisotropic Grassmann-valued tensor network, and the Grassmann version of the corner transfer-matrix renormalization group algorithm is developed to contract the tensor network and evaluate physical quantities. We validate our method on the one-dimensional fermionic Hubbard model with a magnetic field, where the essential features of the phase diagram in the (μ,B) plane are quantitatively captured. Our work offers a promising approach to interacting fermionic models within the framework of tensor networks.

关键词: Grassmann tensor network, fermionic path integral, partition function

Abstract: The strongly correlated fermions play a vital role in modern physics. For a given fermionic Hamiltonian system, the most widely used approach to exploring the underlying physics is to study the wave function that incorporates Fermi-Dirac statistics, which can be obtained variationally by energy minimization or by imaginary-time evolution. In this work, we develop an accurate tensor network method for one-dimensional interacting fermionic models based on the coherentstate path-integral representation of the fermionic partition function. Employing the coherent-state representation, the partition function is effectively represented as a (1+1)-dimensional anisotropic Grassmann-valued tensor network, and the Grassmann version of the corner transfer-matrix renormalization group algorithm is developed to contract the tensor network and evaluate physical quantities. We validate our method on the one-dimensional fermionic Hubbard model with a magnetic field, where the essential features of the phase diagram in the (μ,B) plane are quantitatively captured. Our work offers a promising approach to interacting fermionic models within the framework of tensor networks.

Key words: Grassmann tensor network, fermionic path integral, partition function

中图分类号:  (Lattice fermion models (Hubbard model, etc.))

  • 71.10.Fd
05.10.Cc (Renormalization group methods)