Entanglement-based tensor-network strong-disorder renormalization group
Entanglement-based tensor-network strong-disorder renormalization group
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基于纠缠的张量网络强无序重整化群
DOI：
10.1103/physrevb.104.134405
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发表时间：
2021
影响因子：
3.7
通讯作者：
Okunishi Kouichi
中科院分区：
文献类型：
--
作者：
Seki Kouichi;Hikihara Toshiya;Okunishi Kouichi
针对淬灭随机性量子自旋系统，提出了一种基于纠缠的张量网络强无序重整化群（tSDRG）算法。与以前的基于重正化块哈密顿能谱的tSDRG算法不同，我们直接利用与要重正化的块相关的纠缠结构。我们检查的准确性的算法的随机反铁磁海森堡模型的一维，三角形和正方形晶格。然后我们发现，对于随机性较弱的正方形晶格模型，基于纠缠的tSDRG比之前的模型具有更好的准确性，而对于一维和三角形晶格模型，特别是在强随机性区域，它的效率较低。文中还讨论了该算法的理论背景和可能的改进。
We propose an entanglement-based algorithm of the tensor-network strong-disorder renormalization group (tSDRG) method for quantum spin systems with quenched randomness. In contrast to the previous tSDRG algorithm based on the energy spectrum of renormalized block Hamiltonians, we directly utilize the entanglement structure associated with the blocks to be renormalized. We examine accuracy of the algorithm for the random antiferromagnetic Heisenberg models on one-dimensional, triangular, and square lattices. We then find that the entanglement-based tSDRG achieves better accuracy than the previous one for the square-lattice model with weak randomness, while it is less efficient for the one-dimensional and triangular-lattice models particularly in the strong-randomness region. The theoretical background and possible improvements of the algorithm are also discussed.