论文标题
使用暹罗神经网络的Rydberg Atom阵列相图的无监督学习
Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks
论文作者
论文摘要
我们引入了一种基于暹罗神经网络（SNN）的无监督的机器学习方法，以检测相界。该方法应用于ISing型系统和Rydberg Atom阵列的蒙特卡洛模拟。在这两种情况下，SNN都揭示了与先前研究一致的相边界。利用前馈神经网络的力量，无监督的学习以及学习多个阶段而不了解其存在的能力的结合，为探索物质的新阶段和未知阶段提供了有力的方法。
We introduce an unsupervised machine learning method based on Siamese Neural Networks (SNN) to detect phase boundaries. This method is applied to Monte-Carlo simulations of Ising-type systems and Rydberg atom arrays. In both cases the SNN reveals phase boundaries consistent with prior research. The combination of leveraging the power of feed-forward neural networks, unsupervised learning and the ability to learn about multiple phases without knowing about their existence provides a powerful method to explore new and unknown phases of matter.
