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arXiv 1708.09401cond-mat.mes-hallcond-mat.dis-nncond-mat.str-elcs.AIcs.LG
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基于神经网络的机器学习拓扑不变量

Machine Learning Topological Invariants with Neural Networks

Pengfei Zhang, Huitao Shen, Hui Zhai

摘要:

在这封信中,我们监督训练神经网络,以区分不同的拓扑相位的拓扑带绝缘体的上下文中。在用具有手征对称性的一维绝缘体的哈密顿量进行训练后,神经网络可以以接近100%的准确率预测它们的拓扑缠绕数,即使对于训练数据中不包括的具有较大缠绕数的哈密顿量也是如此。这些结果表明,神经网络可以从局部输入中捕获量子相位的全局和非线性拓扑特征。通过打开神经网络,我们确认网络确实学习了缠绕数公式的离散版本。我们还对对称性的作用和正则化技术在将机器学习应用于物理系统时的相反效果做了一些评论。

英文摘要:

In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

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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