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Recently, the tensor network description with bond weights on its edges has been proposed as a novel improvement for the tensor renormalization group algorithm. The bond weight is controlled by a single hyperparameter, whose optimal value…

High Energy Physics - Lattice · Physics 2022-11-09 Shinichiro Akiyama

We review the basic ideas of the Tensor Renormalization Group method and show how they can be applied for lattice field theory models involving relativistic fermions and Grassmann variables in arbitrary dimensions. We discuss recent…

High Energy Physics - Lattice · Physics 2024-01-17 Shinichiro Akiyama , Yannick Meurice , Ryo Sakai

We apply the Grassmann tensor renormalization group (GTRG) to the one-flavor lattice Gross-Neveu model in the presence of chemical potential. We compute the fermion number density and its susceptibility and confirm the validity of GTRG for…

High Energy Physics - Lattice · Physics 2015-04-01 Shinji Takeda , Yusuke Yoshimura

We apply the higher order tensor renormalization group to two and three dimensional relativistic fermion systems on the lattice. In order to perform a coarse-graining of tensor networks including Grassmann variables, we introduce Grassmann…

High Energy Physics - Lattice · Physics 2017-08-07 Ryo Sakai , Shinji Takeda , Yusuke Yoshimura

We propose an improved tensor renormalization group (TRG) algorithm, the bond-weighted TRG (BTRG). In BTRG, we generalize the conventional TRG by introducing bond weights on the edges of the tensor network. We show that BTRG outperforms the…

Statistical Mechanics · Physics 2022-03-03 Daiki Adachi , Tsuyoshi Okubo , Synge Todo

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…

High Energy Physics - Lattice · Physics 2023-01-12 Muhammad Asaduzzaman , Simon Catterall , Yannick Meurice , Ryo Sakai , Goksu Can Toga

We construct a Grassmann tensor network representing the partition function of (1+1)-dimensional two-color QCD with staggered fermions. The Grassmann path integral is rewritten as the trace of a Grassmann tensor network by introducing…

High Energy Physics - Lattice · Physics 2025-04-01 Kwok Ho Pai , Shinichiro Akiyama , Synge Todo

We propose a real-space renormalization group algorithm for accurately coarse-graining two-dimensional tensor networks. The central innovation of our method lies in utilizing variational boundary tensors as a globally optimized environment…

Statistical Mechanics · Physics 2026-03-03 Feng-Feng Song , Naoki Kawashima

We use the Grassmann tensor renormalization group method to investigate the $N_f=2$ Schwinger model with the staggered fermions in the presence of a $2\pi$ periodic $\theta$ term in a broad range of mass. The method allows us to deal with…

High Energy Physics - Lattice · Physics 2025-11-12 Hayato Kanno , Shinichiro Akiyama , Kotaro Murakami , Shinji Takeda

We derive a general formula of the tensor network representation for $d$-dimensional lattice fermions with ultra-local interactions, including Wilson fermions, staggered fermions, and domain-wall fermions. The Grassmann tensor is concretely…

High Energy Physics - Lattice · Physics 2021-10-26 Shinichiro Akiyama , Daisuke Kadoh

We formulate the path integral of two- and three-flavor Wilson fermion in two dimensions as a multilayer Grassmann tensor network by the matrix product decomposition. Thanks to this new description, the memory cost scaling is reduced from…

High Energy Physics - Lattice · Physics 2023-09-04 Shinichiro Akiyama

We propose a new technique to incorporate multiple fermion flavors in the tensor renormalization group method for lattice gauge theories, where fermions are treated by the Grassmann tensor network formalism. The basic idea is to separate…

High Energy Physics - Lattice · Physics 2023-09-06 Atis Yosprakob , Jun Nishimura , Kouichi Okunishi

We propose a second renormalization group (SRG) in the triad representation of tensor networks. The SRG method improves two parts of the triad tensor renormalization group, which are the decomposition of intermediate tensors and the…

Strongly Correlated Electrons · Physics 2022-05-11 Daisuke Kadoh , Hideaki Oba , Shinji Takeda

We study a tensor network formulation of the two dimensional lattice $\mathcal{N}=1$ Wess-Zumino model with Wilson derivatives for both fermions and bosons. The tensor renormalization group allows us to compute the partition function…

High Energy Physics - Lattice · Physics 2018-04-18 Ryo Sakai , Daisuke Kadoh , Yoshinobu Kuramashi , Yoshifumi Nakamura , Shinji Takeda , Yusuke Yoshimura

We develop calculational method for fermionic Green functions in the framework of Grassmann higher-order tensor renormalization group. The validity of the method is tested by applying it to three-dimensional free Wilson fermion system. We…

High Energy Physics - Lattice · Physics 2018-03-28 Yusuke Yoshimura , Yoshinobu Kuramashi , Yoshifumi Nakamura , Shinji Takeda , Ryo Sakai

We propose a hybrid stochastic method for the tensor renormalization group (TRG) approach. TRG is known as a powerful tool to study the many-body systems and quantum field theory on the lattice. It is based on a low-rank approximation of…

High Energy Physics - Lattice · Physics 2021-10-25 Hiroshi Ohki , Erika Arai , Masaaki Tomii

We analyze classical dimer models on the square and triangular lattice using a tensor network representation of the dimers. The correlation functions are numerically calculated using the recently developed "Tensor renormalization group"…

Strongly Correlated Electrons · Physics 2015-05-20 Krishanu Roychowdhury , Ching-Yu Huang

We investigate the $N_f=2$ Schwinger model with the massive staggered fermions in the presence of a $2\pi$ periodic $\theta$ term, using the Grassmann tensor renormalization group. Thanks to the Grassmann tensor network formulation, there…

High Energy Physics - Lattice · Physics 2025-01-27 Hayato Kanno , Shinichiro Akiyama , Kotaro Murakami , Shinji Takeda

Tensor renormalization group method (TRG) is a real space renormalization group approach. It has been successfully applied to both classical and quantum systems. In this paper, we study a disordered and frustrated system, the…

Disordered Systems and Neural Networks · Physics 2014-10-27 Chuang Wang , Shao-Meng Qin , Hai-Jun Zhou

We report recent progress on the application of the tensor renormalization group (TRG) to quantum field theories pursued by the Tsukuba group. We explain how to treat the scalar, fermion, and gauge theories with the TRG method presenting…

High Energy Physics - Lattice · Physics 2022-09-21 Shinichiro Akiyama , Yoshinobu Kuramashi , Yusuke Yoshimura
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