论文标题
具有自回旋神经量子状态的动力学：应用至关重要的动力学
Dynamics with autoregressive neural quantum states: application to critical quench dynamics
论文作者
论文摘要
尽管结果非常有希望，但使用几个问题捕获具有神经网络的复杂量子系统的动力学已经困扰着，其中一个是随机噪声，这使动力学不稳定且高度依赖于某些正则化超标剂。我们提出了一种替代的一般方案，可以使人们以稳定的方式捕获量子系统的长期动力学，前提是对ANSATZ进行了归一化，可以通过所选ANSATZ的自回归属性来确保这一点。然后，我们通过研究二维量子iSing模型中的千里桑岛机理，将方案应用于时间依赖性的淬灭动力学。我们发现与小型系统的精确动态达成了极好的一致性，并且能够与其他变异方法恢复比例定律。
Despite very promising results, capturing the dynamics of complex quantum systems with neural-network ansätze has been plagued by several problems, one of which being stochastic noise that makes the dynamics unstable and highly dependent on some regularization hyperparameters. We present an alternative general scheme that enables one to capture long-time dynamics of quantum systems in a stable fashion, provided the neural-network ansatz is normalized, which can be ensured by the autoregressive property of the chosen ansatz. We then apply the scheme to time-dependent quench dynamics by investigating the Kibble-Zurek mechanism in the two-dimensional quantum Ising model. We find an excellent agreement with exact dynamics for small systems and are able to recover scaling laws in agreement with other variational methods.
