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

Abstract

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.