当前位置: X-MOL 学术Eur. Phys. J. B › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Tensor network renormalization group study of spin-1 random Heisenberg chains
The European Physical Journal B ( IF 1.6 ) Pub Date : 2020-04-06 , DOI: 10.1140/epjb/e2020-100585-8
Zheng-Lin Tsai , Pochung Chen , Yu-Cheng Lin

Abstract

We use a tensor network strong-disorder renormalization group (tSDRG) method to study spin-1 random Heisenberg antiferromagnetic chains. The ground state of the clean spin-1 Heisenberg chain with uniform nearest-neighbor couplings is a gapped phase known as the Haldane phase. Here we consider disordered chains with random couplings, in which the Haldane gap closes in the strong disorder regime. As the randomness strength is increased further and exceeds a certain threshold, the random chain undergoes a phase transition to a critical random-singlet phase. The strong-disorder renormalization group method formulated in terms of a tree tensor network provides an efficient tool for exploring ground-state properties of disordered quantum many-body systems. Using this method we detect the quantum critical point between the gapless Haldane phase and the random-singlet phase via the disorder-averaged string order parameter. We determine the critical exponents related to the average string order parameter, the average end-to-end correlation function and the average bulk spin-spin correlation function, both at the critical point and in the random-singlet phase. Furthermore, we study energy-length scaling properties through the distribution of energy gaps for a finite chain. Our results are in closer agreement with the theoretical predictions than what was found in previous numerical studies. As a benchmark, a comparison between tSDRG results for the average spin correlations of the spin-1/2 random Heisenberg chain with those obtained by using unbiased zero-temperature QMC method is also provided.

Graphical abstract



中文翻译:

自旋1随机Heisenberg链的张量网络重正化群研究。

摘要

我们使用张量网络强序重整化组(tSDRG)方法来研究自旋1随机海森堡反铁磁链。具有均匀最近邻居的干净自旋为1的海森堡链的基态是称为Haldane相的带隙相。在这里,我们考虑具有随机耦合的无序链,其中在强无序状态下,Haldane间隙闭合。随着随机强度进一步增加并超过特定阈值,随机链经历到临界随机单相的相变。根据树张量网络制定的强无序重归一化组方法为探索无序量子多体系统的基态特性提供了有效的工具。使用这种方法,我们通过无序平均弦序参数检测了无间隙Haldane相和随机-单相之间的量子临界点。我们确定在临界点和随机单相中与平均字符串顺序参数,平均端到端相关函数和平均整体自旋旋转相关函数有关的临界指数。此外,我们通过有限链的能隙分布研究能级定标特性。与以前的数值研究相比,我们的结果与理论预测更接近。作为基准,还提供了自旋1/2随机海森堡链的平均自旋相关性的tSDRG结果与使用无偏零温度QMC方法获得的结果之间的比较。

图形概要

更新日期:2020-04-06
down
wechat
bug