论文标题
张量 - 网络强度重新归一化组，用于二维的随机量子自旋系统
Tensor-network strong-disorder renormalization groups for random quantum spin systems in two dimensions
论文作者
论文摘要
二维（2D）量子自旋系统中的新型随机性诱导的无序基态一直引起人们的兴趣。为了对此类随机量子自旋系统进行定量分析，最有希望的数值方法之一是张量 - 网络强度重新归一化组（TSDRG），基本上是针对一维（1D）系统建立的。在本文中，我们提出了将其算法推向2D随机旋转系统的可能改进，重点是张量的树网络结构的生成过程，并精确地检查了它们的性能，不仅在1D链上，而且在正方形和三角形局势上，不仅在1D链上，而且还研究了它们的性能。基于与最多36个站点系统的确切数值结果进行比较，我们证明了最佳TSDRG算法的准确性即使对于强随机状态中的2D系统，最佳的TSDRG算法也得到了显着改善。
Novel randomness-induced disordered ground states in two-dimensional (2D) quantum spin systems have been attracting much interest. For quantitative analysis of such random quantum spin systems, one of the most promising numerical approaches is the tensor-network strong-disorder renormalization group (tSDRG), which was basically established for one-dimensional (1D) systems. In this paper, we propose a possible improvement of its algorithm toward 2D random spin systems, focusing on a generating process of the tree network structure of tensors, and precisely examine their performances for the random antiferromagnetic Heisenberg model not only on the 1D chain but also on the square- and triangular-lattices. On the basis of comparison with the exact numerical results up to 36 site systems, we demonstrate that accuracy of the optimal tSDRG algorithm is significantly improved even for the 2D systems in the strong-randomness regime.
