论文标题
使用机器学习对强无序的拓扑线进行分类
Classification of Strongly Disordered Topological Wires Using Machine Learning
论文作者
论文摘要
在本文中，我们将随机的森林机器学习模型在存在强障碍时对1D拓扑阶段进行分类。我们表明，使用纠缠频谱作为训练功能该模型具有很高的分类精度。训练有素的模型可以扩展到相空间中的其他区域，甚至可以扩展到其他未经训练的对称类别，并且仍然提供准确的结果。在对受过训练的模型进行详细分析后，我们发现其主导分类标准捕获了纠缠谱中的堕落性。
In this article we apply the random forest machine learning model to classify 1D topological phases when strong disorder is present. We show that using the entanglement spectrum as training features the model gives high classification accuracy. The trained model can be extended to other regions in phase space, and even to other symmetry classes on which it was not trained and still provides accurate results. After performing a detailed analysis of the trained model we find that its dominant classification criteria captures degeneracy in the entanglement spectrum.
