GrassmannTN: a Python package for Grassmann tensor network computations

Abstract

We present GrassmannTN, a Python package for the computation of the Grassmann tensor network. The package is built to assist in the numerical computation without the need to input the fermionic sign factor manually. It prioritizes coding readability by designing every tensor manipulating function around the tensor subscripts. The computation of the Grassmann tensor renormalization group and Grassmann isometries using GrassmannTN are given as the use case examples.

35 pages, 9 figures; v2: eq(A.36) corrected; v3: references added

References in corpus (9)

• Array Programming with NumPy
• Tensor renormalization group approach to 2D classical lattice models
• Grassmann Tensor Renormalization Group Approach to One-Flavor Lattice Schwinger Model
• Critical behavior of lattice Schwinger model with topological term at $\theta =\pi$ using Grassmann tensor renormalization group
• Tensor renormalization group study of the non-Abelian Higgs model in two dimensions
• Higher order tensor renormalization group for relativistic fermion systems
• Grassmann tensor network states and its renormalization for strongly correlated fermionic and bosonic states
• Tensor renormalization group study of (3+1)-dimensional $Z_2$ gauge-Higgs model at finite density
• A new technique to incorporate multiple fermion flavors in tensor renormalization group method for lattice gauge theories

Cited by in corpus (4)

• Reduced tensor network formulation for non-Abelian gauge theories in arbitrary dimensions
• The Cytnx Library for Tensor Networks
• Grassmann tensor networks
• Phase diagram of the single-flavor Gross--Neveu--Wilson model from the Grassmann corner transfer matrix renormalization group