论文标题
通过神经网络量子状态的本地和顺序更新的基础状态搜索
Ground state search by local and sequential updates of neural network quantum states
论文作者
论文摘要
神经网络量子状态是分析复杂量子系统的有前途的工具。但是，很难有效地优化这种类型的ANSATZ参数。在这里，我们提出了一个局部优化程序，该过程与随机重新配置集成时，优于先前使用的全局优化方法。具体而言，我们分析了具有限制性玻尔兹曼机器的不可融合倾斜的ISING模型的基态能量和相关性。我们发现，根据局部更新的神经网络部分的大小，连续的局部更新可能会导致更快地收敛到具有能量和相关性的状态，这些状态更接近地面状态。为了显示该方法的一般性，我们将其应用于1D和2D不可综合的自旋系统。
Neural network quantum states are a promising tool to analyze complex quantum systems given their representative power. It can however be difficult to optimize efficiently and effectively the parameters of this type of ansatz. Here we propose a local optimization procedure which, when integrated with stochastic reconfiguration, outperforms previously used global optimization approaches. Specifically, we analyze both the ground state energy and the correlations for the non-integrable tilted Ising model with restricted Boltzmann machines. We find that sequential local updates can lead to faster convergence to states which have energy and correlations closer to those of the ground state, depending on the size of the portion of the neural network which is locally updated. To show the generality of the approach we apply it to both 1D and 2D non-integrable spin systems.
