论文标题
学习误差统计数据以检测量子阶段
Learning of error statistics for the detection of quantum phases
论文作者
论文摘要
我们提出了基于神经网络的二元分类器，以检测大图量子阶段。通过考虑描述间隙阶段的合适参考状态之上的误差，我们表明对错误训练的神经网络可以捕获误差之间的相关性，并可用于检测间隙量子相位的相位边界。我们证明了该方法用于矩阵乘积状态计算的应用，以表现出局部对称性阶，受对称性保护的拓扑顺序和内在拓扑顺序的不同量子相。
We present a binary classifier based on neural networks to detect gapped quantum phases. By considering the errors on top of a suitable reference state describing the gapped phase, we show that a neural network trained on the errors can capture the correlation between the errors and can be used to detect the phase boundaries of the gapped quantum phase. We demonstrate the application of the method for matrix product state calculations for different quantum phases exhibiting local symmetry-breaking order, symmetry-protected topological order, and intrinsic topological order.
