Constructing neural stationary states for open quantum many-body systems
Constructing neural stationary states for open quantum many-body systems
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DOI：
10.1103/physrevb.99.214306
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发表时间：
2019-02
影响因子：
3.7
通讯作者：
N. Yoshioka;Ryusuke Hamazaki
中科院分区：
文献类型：
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作者：
N. Yoshioka;Ryusuke Hamazaki
提出了一种新的基于神经网络量子态的变分方法来模拟开放量子多体系统的定态。使用的高表达能力的变分的anananomaly所描述的限制玻尔兹曼机，我们称之为神经定态anomaly，我们计算的量子动力学的定态服从Lindblad主方程。映射到找到一个零能量基态的一个适当的埃尔米特算子的稳态搜索问题，使我们能够应用传统的变分蒙特卡罗方法的优化。我们的方法被证明可以有效地模拟各种自旋系统，即，一维和二维的横向场伊辛模型以及一维的XYZ模型。
We propose a new variational scheme based on the neural-network quantum states to simulate the stationary states of open quantum many-body systems. Using the high expressive power of the variational ansatz described by the restricted Boltzmann machines, which we dub as the neural stationary state ansatz, we compute the stationary states of quantum dynamics obeying the Lindblad master equations. The mapping of the stationary-state search problem into finding a zero-energy ground state of an appropriate Hermitian operator allows us to apply the conventional variational Monte Carlo method for the optimization. Our method is shown to simulate various spin systems efficiently, i.e., the transverse-field Ising models in both one and two dimensions and the XYZ model in one dimension.