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arXiv 2001.10565cond-mat.dis-nncond-mat.mes-hall
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基于机器学习的强无序拓扑线分类

Classification of Strongly Disordered Topological Wires Using Machine Learning

Ye Zhuang, Luiz H. Santos, Taylor L. Hughes

摘要:

在这篇文章中,我们应用随机森林机器学习模型来分类强无序时的1D拓扑相位。我们表明,使用纠缠谱作为训练功能的模型给出了高的分类精度。经过训练的模型可以扩展到相空间中的其他区域,甚至扩展到其他没有经过训练的对称类,并且仍然提供准确的结果。在对训练模型进行详细分析后,我们发现其主要分类标准捕获了纠缠谱中的简并。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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