Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2110.11921 · International Symposium on Lattice Field Theory (LATTICE)

Hybrid stochastic method for the tensor renormalization group

Hiroshi Ohki, Masaaki Tomii, Erika Arai

Published May 16, 2022• 0 citations
arXivHTMLPDF

Abstract

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 the tensor using the truncated singular value decomposition (SVD), whose computational cost increases as the bond dimension increases, so that efficient cost reduction techniques are highly demanded. We use noise vectors for the low-rank approximation with the truncated SVD, by which the truncation error is replaced with a statistical error due to noise, and an error estimation could be improved. We test this method in the classical Ising model and observe a better accuracy than TRG. We also discuss a cross contamination issue in a multiple use of the same noise vectors, and to remove this systematic error we consider position-dependent noise vectors.

DOI
10.22323/1.396.0051

Related papers

All-mode renormalization for tensor network with stochastic noise

Erika Arai, Hiroshi Ohki, Shinji Takeda +1 more

In usual (nonstochastic) tensor network calculations, the truncated singular value decomposition is often used for approximating a tensor, and it causes systematic errors. By introducing stochastic noise in the approximation, however, one can avoid such systematic errors at the expense of statistical errors, which can be straightforwardly controlled. Therefore in principle, exact results can be obtained even at finite bond dimension up to the statistical errors. A previous study of the unbiased method implemented in tensor renormalization group algorithm, however, showed that the statistical errors for physical quantity are not negligible, and furthermore the computational cost is linearly proportional to a system volume. In this paper, we introduce a new way of stochastic noise such that the statistical error is suppressed, and moreover, in order to reduce the computational cost we propose common noise method whose cost is proportional to the logarithm of volume. We find that the method provides better accuracy for the free energy compared with the truncated singular value decomposition when applying to tensor renormalization group for Ising model on square lattice. Although the common noise method introduces systematic error originated from a correlation of noises, we show that the error can be described by a simple functional form in terms of the number of noises, thus the error can be straightforwardly controlled in an actual analysis. We also apply the method to the graph independent local truncation algorithm and show that the accuracy is further improved.

arXiv:2211.13107•Physical review. D/Physical review. D.•Jun 20, 2023•4 citations

Cost-efficient cutoff method for tensor renormalization group with randomized singular value decomposition

Haruka Yamada, Akira Imakura, Tetsuya Sakurai

Tensor renormalization group (TRG) is a coarse-graining algorithm for approximating the partition function using a tensor network in the field of elementary particle physics.Although the computational cost of TRG can be reduced using a randomized singular value decomposition, its computation time is still large.In this paper, we propose a cost-efficient cutoff method for calculating TRG by truncating small tensor elements.Numerical experiments showed that the proposed method is faster than the conventional one without degrading accuracy.

JSIAM Letters•Jan 1, 2018•0 citations

Randomized higher-order tensor renormalization group

Katsumasa Nakayama

The higher-order tensor renormalization group (HOTRG) is a fundamental method to calculate the physical quantities by using a tensor network representation. This method is based on the singular value decomposition (SVD) to take the contraction of all indices in the network with an approximation. For the SVD, randomized singular value decomposition (R-SVD) is a powerful method to reduce computational costs of SVD. However, HOTRG with the randomized method is not established. We propose a randomized HOTRG method in a dimension $d$ with the computational cost $O(D^{3d})$ depending on the truncated bond dimension $D$. We also introduce the minimally-decomposed TRG (MDTRG) as the R-HOTRG on the tensor of order $d+1$ with $O(D^{2d + 1})$ and a triad representation of the MDTRG (Triad-MDTRG) with $O(D^{d+3})$. The results from these formulations are consistent with the HOTRG result with the same truncated bond dimension $D$.

arXiv:2307.14191•Jul 26, 2023•2 citations

Application of the projective truncation and randomized singular value decomposition to a higher dimension

Katsumasa Nakayama

We study the tensor renormalization group (TRG) in the dimension larger than two as the Higher-order TRG (HOTRG) with the randomized SVD method. The randomized SVD and the detailed discussion on the low order tensor representation, we can calculate the HOTRG with the reduced computational cost. We also represent our method by using the cost function, and the details of the cost function for the isometry determine the precision, stability, and calculation time. In our study, we show calculation order improvement using randomized SVD. We also propose that the internal line respect for any TRG method improves the calculation without changing the order of the computational cost.

arXiv:2401.06389•International Symposium on Lattice Field Theory (LATTICE)•May 5, 2024•2 citations

Tensor renormalization group algorithms with a projective truncation method

Yoshifumi Nakamura, Hideaki Oba, Shinji Takeda

We apply the projective truncation technique to the tensor renormalization group (TRG) algorithm in order to reduce the computational cost from $O({\ensuremath{\chi}}^{6})$ to $O({\ensuremath{\chi}}^{5})$, where $\ensuremath{\chi}$ is the bond dimension, and propose three kinds of algorithms for demonstration. On the other hand, the technique causes a systematic error due to the incompleteness of a projector composed of isometries and in addition requires iteration steps to determine the isometries. Nevertheless, we find that the accuracy of the free energy for the Ising model on a square lattice is recovered to the level of TRG with a few iteration steps even at the critical temperature for $\ensuremath{\chi}=32$, 48, and 64.

arXiv:1809.08030•Physical review. B./Physical review. B•Apr 1, 2019•18 citations

All-mode approach for general tensor renormalization group method

Katsumasa Nakayama, Shinji Takeda

We apply the stochastic all-mode technique to general coarse-graining schemes of the tensor renormalization group (TRG) approach. The technique possesses a notable characteristic of being entirely free from systematic errors which can potentially arise in the coarse-graining step, while it has statistical errors caused by random noise. The key element in our generalization is employing a method called squeezer, and the all-mode technique can be used when it is constructed. Since many TRG methods can be described in terms of the squeezers, our approach can be applicable in all such cases. In this work, we demonstrate the new method with higher-order TRG in the two- and three-dimensional Ising model.

arXiv:2610.02394•Oct 1, 2026•0 citations
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 the tensor using the truncated singular value decomposition (SVD), whose computational cost increases as the bond dimension increases, so that efficient cost reduction techniques are highly demanded. We use noise vectors for the low-rank approximation with the truncated SVD, by which the truncation error is replaced with a statistical error due to noise, and an error estimation could be improved. We test this method in the classical Ising model and observe a better accuracy than TRG. We also discuss a cross contamination issue in a multiple use of the same noise vectors, and to remove this systematic error we consider position-dependent noise vectors.
In usual (nonstochastic) tensor network calculations, the truncated singular value decomposition is often used for approximating a tensor, and it causes systematic errors. By introducing stochastic noise in the approximation, however, one can avoid such systematic errors at the expense of statistical errors, which can be straightforwardly controlled. Therefore in principle, exact results can be obtained even at finite bond dimension up to the statistical errors. A previous study of the unbiased method implemented in tensor renormalization group algorithm, however, showed that the statistical errors for physical quantity are not negligible, and furthermore the computational cost is linearly proportional to a system volume. In this paper, we introduce a new way of stochastic noise such that the statistical error is suppressed, and moreover, in order to reduce the computational cost we propose common noise method whose cost is proportional to the logarithm of volume. We find that the method provides better accuracy for the free energy compared with the truncated singular value decomposition when applying to tensor renormalization group for Ising model on square lattice. Although the common noise method introduces systematic error originated from a correlation of noises, we show that the error can be described by a simple functional form in terms of the number of noises, thus the error can be straightforwardly controlled in an actual analysis. We also apply the method to the graph independent local truncation algorithm and show that the accuracy is further improved.
Tensor renormalization group (TRG) is a coarse-graining algorithm for approximating the partition function using a tensor network in the field of elementary particle physics.Although the computational cost of TRG can be reduced using a randomized singular value decomposition, its computation time is still large.In this paper, we propose a cost-efficient cutoff method for calculating TRG by truncating small tensor elements.Numerical experiments showed that the proposed method is faster than the conventional one without degrading accuracy.
The higher-order tensor renormalization group (HOTRG) is a fundamental method to calculate the physical quantities by using a tensor network representation. This method is based on the singular value decomposition (SVD) to take the contraction of all indices in the network with an approximation. For the SVD, randomized singular value decomposition (R-SVD) is a powerful method to reduce computational costs of SVD. However, HOTRG with the randomized method is not established. We propose a randomized HOTRG method in a dimension ddd with the computational cost O(D3d)O(D^{3d})O(D3d) depending on the truncated bond dimension DDD. We also introduce the minimally-decomposed TRG (MDTRG) as the R-HOTRG on the tensor of order d+1d+1d+1 with O(D2d+1)O(D^{2d + 1})O(D2d+1) and a triad representation of the MDTRG (Triad-MDTRG) with O(Dd+3)O(D^{d+3})O(Dd+3). The results from these formulations are consistent with the HOTRG result with the same truncated bond dimension DDD.
We study the tensor renormalization group (TRG) in the dimension larger than two as the Higher-order TRG (HOTRG) with the randomized SVD method. The randomized SVD and the detailed discussion on the low order tensor representation, we can calculate the HOTRG with the reduced computational cost. We also represent our method by using the cost function, and the details of the cost function for the isometry determine the precision, stability, and calculation time. In our study, we show calculation order improvement using randomized SVD. We also propose that the internal line respect for any TRG method improves the calculation without changing the order of the computational cost.
We apply the projective truncation technique to the tensor renormalization group (TRG) algorithm in order to reduce the computational cost from $O({\ensuremath{\chi}}^{6})$ to $O({\ensuremath{\chi}}^{5})$, where $\ensuremath{\chi}$ is the bond dimension, and propose three kinds of algorithms for demonstration. On the other hand, the technique causes a systematic error due to the incompleteness of a projector composed of isometries and in addition requires iteration steps to determine the isometries. Nevertheless, we find that the accuracy of the free energy for the Ising model on a square lattice is recovered to the level of TRG with a few iteration steps even at the critical temperature for $\ensuremath{\chi}=32$, 48, and 64.
We apply the stochastic all-mode technique to general coarse-graining schemes of the tensor renormalization group (TRG) approach. The technique possesses a notable characteristic of being entirely free from systematic errors which can potentially arise in the coarse-graining step, while it has statistical errors caused by random noise. The key element in our generalization is employing a method called squeezer, and the all-mode technique can be used when it is constructed. Since many TRG methods can be described in terms of the squeezers, our approach can be applicable in all such cases. In this work, we demonstrate the new method with higher-order TRG in the two- and three-dimensional Ising model.