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Cluster truncated Wigner approximation in strongly interacting systems

机译:在强互动系统中截断的Wigner近似

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We present a general method by which linear quantum Hamiltonian dynamics with exponentially many degrees of freedom is replaced by approximate classical nonlinear dynamics with the number of degrees of freedom (phase space dimensionality) scaling polynomially in the system size. This method is based on generalization of the truncated Wigner approximation (TWA) to a higher dimensional phase space, where phase space variables are associated with a complete set of quantum operators spanning finite size clusters. The method becomes asymptotically exact with increasing cluster size. The crucial feature of TWA is fluctuating initial conditions, which we approximate by a Gaussian distribution. We show that such fluctuations dramatically increase accuracy of TWA over traditional cluster mean-field approximations. In this way we can treat on equal footing quantum and thermal fluctuations as well as compute entanglement and various equal and non-equal time correlation functions. The main limitation of the method is exponential scaling of the phase space dimensionality with the cluster size, which can be significantly reduced by using the language of Schwinger bosons and can likely be further reduced by truncating the local Hilbert space variables. We demonstrate the power of this method analyzing dynamics in various spin chains with and without disorder and show that we can capture such phenomena as long time hydrodynamic relaxation, many-body localization and the ballistic spread of entanglement.
机译:我们介绍了一种通用方法,通过该方法,通过将具有指数增加多程度的自由度的线性量子汉密尔顿语动力学通过在系统尺寸中多项式的自由度(相空间维度)缩放的近似的经典非线性动态而取代。该方法基于将截短的Wigner近似(TWA)的概括为更高的尺寸相位空间,其中相空间变量与跨越有限大小集群的一整套量子操作者组相关联。该方法随着群集大小的增加而精确地变得精确。 TWA的关键特征是波动的初始条件,我们通过高斯分布近似。我们表明这种波动显着提高了传统集群平均场近似的TWA的准确性。以这种方式,我们可以对等于等级量子和热波动以及计算纠缠和各种相等和非相等的时间相关函数。该方法的主要限制是具有簇大小的相位空间维度的指数缩放,这可以通过使用Schwinger Bosons的语言显着减少,并且可以通过截断本地希尔伯特空间变量进一步减少。我们展示了这种方法的力量,分析了各种旋转链中的动态,在没有紊乱的情况下表明我们可以捕获这种现象,因为很长时间的流体动力放松,多个身体定位和缠结的弹道传播。

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