From Bloch oscillations to many-body localization in clean interacting systems
From Bloch oscillations to many-body localization in clean interacting systems
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DOI：
10.1073/pnas.1819316116
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
Everard van Nieuwenburg;Y. Baum;G. Refael
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文献类型：
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作者：
Everard van Nieuwenburg;Y. Baum;G. Refael
意义多体局域相提供了一个普通量子相互作用系统的例子，它没有达到热平衡，因此违反了统计物理学的最基本原理。出于这个原因，它提供了实现强大的量子存储器的有希望的途径，因为它能够保留其初始配置的知识。这种遍历性的打破或可积性的出现，是由于系统中无序和相互作用的本质结合。然而，在我们的工作中，我们表明，即使没有无序，也可以得到一个显示多体局域化的所有主要特征的系统，这表明可能有更容易和更可重复的实现它的方法。在这项工作中，我们证明，导致单粒子局域化的非随机机制也可能导致多体局域化，即使在没有无序的情况下。特别是，我们考虑相互作用的自旋和费米子的存在下的线性潜力。在非相互作用的限制，这些模型显示出著名的Wannier-Stark本地化。我们分析了这种本地化的相互作用的存在下的命运。值得注意的是，我们发现，超过一个临界值的潜在梯度，这些模型表现出非遍历的行为所示的光谱和动力学性质。因此，这些模型构成了一类不能热化的通用非随机模型。因此，他们提出了实验探索和理解多体局域化现象的新方向。我们补充我们的工作表明，通过使用机器学习技术的系统的水平统计可以计算不产生和对角化的哈密顿量，这允许产生大的统计。
Significance The many-body localized phase provides an example of a generic quantum interacting system that does not reach thermal equilibrium and thereby violates the most fundamental principles of statistical physics. For that reason it provides promising pathways to implement robust quantum memory as it is able to retain knowledge of its initial configuration. This breaking of ergodicity, or the emergence of integrability, is due to the essential combination of disorder and interactions in the system. In our work we show, however, that even without disorder one can obtain a system that shows all of the main characteristics of many-body localization, suggesting that there may be easier and more reproducible ways of realizing it. In this work we demonstrate that nonrandom mechanisms that lead to single-particle localization may also lead to many-body localization, even in the absence of disorder. In particular, we consider interacting spins and fermions in the presence of a linear potential. In the noninteracting limit, these models show the well-known Wannier–Stark localization. We analyze the fate of this localization in the presence of interactions. Remarkably, we find that beyond a critical value of the potential gradient these models exhibit nonergodic behavior as indicated by their spectral and dynamical properties. These models, therefore, constitute a class of generic nonrandom models that fail to thermalize. As such, they suggest new directions for experimentally exploring and understanding the phenomena of many-body localization. We supplement our work by showing that by using machine-learning techniques the level statistics of a system may be calculated without generating and diagonalizing the Hamiltonian, which allows a generation of large statistics.