论文标题
使用神经网络量子状态在开放式链条中量子传输
Quantum Transport in Open Spin Chains using Neural-Network Quantum States
论文作者
论文摘要
在这项工作中，我们研究了基于受限的玻尔兹曼机器的神经网络对不对称开放量子系统的处理。特别是，我们对边界驱动（各向异性）海森堡自旋链中的非平衡稳态电流感兴趣。我们解决了先前发表的用神经网络量子状态和蒙特卡洛抽样处理不对称耗散系统的困难，并提出了一种优化方法和采样技术，可用于获得此类系统的高保真稳态近似值。我们指出了正在考虑的Lindblad操作员的一些固有对称性，并在抽样过程中利用它们。我们表明，局部可观察物并不总是很好地表明近似的质量，最后给出了与简单开放的海森伯格链的已知结果一致的自旋电流的结果。
In this work we study the treatment of asymmetric open quantum systems with neural networks based on the restricted Boltzmann machine. In particular, we are interested in the non-equilibrium steady state current in the boundary-driven (anisotropic) Heisenberg spin chain. We address previously published difficulties in treating asymmetric dissipative systems with neural-network quantum states and Monte-Carlo sampling and present an optimization method and a sampling technique that can be used to obtain high-fidelity steady state approximations of such systems. We point out some inherent symmetries of the Lindblad operator under consideration and exploit them during sampling. We show that local observables are not always a good indicator of the quality of the approximation and finally present results for the spin current that are in agreement with known results of simple open Heisenberg chains.
