Improved coarse-graining methods on two dimensional tensor networks including fermions

Abstract

We show how to apply renormalization group algorithms incorporating entanglement filtering methods and a loop optimization to a tensor network which includes Grassmann variables which represent fermions in an underlying lattice field theory. As a numerical test a variety of quantities are calculated for two dimensional Wilson--Majorana fermions and for the two flavor Gross--Neveu model. The improved algorithms show much better accuracy for quantities such as the free energy and the determination of Fisher's zeros.

21 pages, 21 figures

References in corpus (6)

• Tensor renormalization group approach to 2D classical lattice models
• Grassmann Tensor Renormalization Group Approach to One-Flavor Lattice Schwinger Model
• Massive fermions without fermion bilinear condensates
• Tensor renormalization group study of the non-Abelian Higgs model in two dimensions
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Cited by in corpus (3)

• Matrix product decomposition for two- and three-flavor Wilson fermions: Benchmark results in the lattice Gross-Neveu model at finite density
• Forward-mode automatic differentiation for the tensor renormalization group and its relation to the impurity method
• Phase diagram of the single-flavor Gross--Neveu--Wilson model from the Grassmann corner transfer matrix renormalization group