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首页> 外文期刊>The European physical journal, B. Condensed matter physics >Tensor network renormalization group study of spin-1 random Heisenberg chains
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Tensor network renormalization group study of spin-1 random Heisenberg chains

机译:张力网络重新定位群体研究旋转1个随机的Heisenberg链

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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.
机译:我们使用张量网络强障碍重整组(TSDRG)方法来研究Spin-1随机的Heisenberg反铁磁链。用均匀的最近邻接联轴器的清洁旋转1 Heisenberg链的地位是称为卤代相的覆盖阶段。在这里,我们认为具有随机偶联的紊乱链,其中卤化物间隙在强紊乱制度中关闭。随着随机性强度的进一步增加并且超过一定阈值,随机链经历了临界随机态阶段的相转变。在树张量网络方面配制的强障碍重整化组方法提供了一种有效的工具,用于探索无序量子多体系的地面性质。使用该方法,我们通过无序平均串订单参数检测无卤旦阶段和随机单线相之间的量子临界点。我们确定与平均弦顺序参数,平均端到端相关函数和平均批量自旋旋转相关函数相关的临界指数,临界点和随机单次相位。此外,我们通过为有限链的能量间隙分配来研究能量长度缩放性质。我们的结果与理论预测更仔细达成协议,而不是以前的数值研究中发现的。作为基准,还提供了通过使用非偏见零温度QMC方法获得的旋转-1 / 2随机Heisenberg链的平均自旋相关性的TSDRG结果的比较。

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