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Time dynamics with matrix product states: Many-body localization transition of large systems revisited

机译:与矩阵产品状态的时间动态:重新审视大型系统的许多身体定位转换

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摘要

We compare the accuracy of two prime time evolution algorithms involving matrix product states-tDMRG (time-dependent density matrix renormalization group) and TDVP (time-dependent variational principle). The latter is supposed to be superior within a limited and fixed auxiliary space dimension. Surprisingly, we find that the performance of algorithms depends on the model considered. In particular, many-body localized systems as well as the crossover regions between localized and delocalized phases are better described by tDMRG, contrary to the delocalized regime where TDVP indeed outperforms tDMRG in terms of accuracy and reliability. As an example, we study many-body localization transition in a large size Heisenberg chain. We discuss drawbacks of previous estimates [Phys. Rev. B 98, 174202 (2018)] of the critical disorder strength for large systems.
机译:我们比较涉及矩阵产品状态-TDMRG(时间依赖密度矩阵重整组)和TDVP(时间依赖性变分原理)的精度的准确性。 后者应该在有限且固定的辅助空间尺寸内优越。 令人惊讶的是,我们发现算法的性能取决于所考虑的模型。 特别地,通过TDMRG更好地描述了许多身体局部系统以及局部和分层阶段之间的交叉区,与划分的划分状态相反,TDVP在准确性和可靠性方面表达TDMRG。 作为一个例子,我们研究了大尺寸的Heisenberg链中的许多身体定位过渡。 我们讨论以前估计的缺点[物理。 大型系统的临界障碍强度的Rev. B 98,174202(2018)。

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