Triad second renormalization group
Triad second renormalization group
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DOI：
10.1007/jhep04(2022)121
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发表时间：
2021-07
影响因子：
5.4
通讯作者：
D. Kadoh;H. Ōba;S. Takeda
中科院分区：
文献类型：
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作者：
D. Kadoh;H. Ōba;S. Takeda
我们提出了张量网络的三重表示中的第二重正化群（SRG）。SRG方法考虑了环境张量的影响，改进了三元组张量重整化群的两个部分：中间张量的分解和等距线的制备.每个基本张量包括环境张量被给定为秩为3的张量，所提出的算法的计算成本与采用随机SVD的（χ 5）成比例，其中χ是张量的键维数。我们在二维正方形格子上的经典Ising模型中测试了这种方法，并发现在固定的计算时间内得到了很好的数值结果。
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 preparation of isometries, taking the influence of environment tensors into account. Every fundamental tensor including environment tensor is given as a rank-3 tensor, and the computational cost of the proposed algorithm scales with(χ 5) employing the randomized SVD where χ is the bond dimension of tensors. We test this method in the classical Ising model on the two dimensional square lattice, and find that numerical results are obtained in good accuracy for a fixed computational time.