Classifying snapshots of the doped Hubbard model with machine learning
Classifying snapshots of the doped Hubbard model with machine learning
复制标题
DOI：
10.1038/s41567-019-0565-x
复制
发表时间：
2019-09-01
期刊：
影响因子：
19.6
通讯作者：
Knap, Michael
中科院分区：
文献类型：
--
作者：
Bohrdt, Annabelle;Chiu, Christie S.;Knap, Michael
超冷原子的量子气体显微镜可以提供复杂多体系统的高分辨率真实空间快照。我们使用机器学习来分析和分类这些超冷原子的快照。具体地说，我们将二维Fermi-Hubbard模型的实验实现的数据与两种理论方法进行了比较：共振价键型掺杂量子自旋液态(1，2)和描述隐藏自旋序的几何弦理论(3，4)。这种技术考虑了所有可用的信息，而不会通过选择一个可观测对象来潜在地偏向一个特定的理论，因此可以选择更具预测性的理论。在中等掺杂值之前，我们的算法倾向于将实验快照归类为几何弦形状，而不是掺杂的自旋液体。我们的结果证明了机器学习在处理通过量子气体显微镜获得的丰富数据以获得新的物理见解方面的潜力。
Quantum gas microscopes for ultracold atoms can provide high-resolution real-space snapshots of complex many-body systems. We implement machine learning to analyse and classify such snapshots of ultracold atoms. Specifically, we compare the data from an experimental realization of the two-dimensional Fermi-Hubbard model to two theoretical approaches: a doped quantum spin liquid state of resonating valence bond type(1,2), and the geometric string theory(3,4), describing a state with hidden spin order. This technique considers all available information without a potential bias towards one particular theory by the choice of an observable and can therefore select the theory that is more predictive in general. Up to intermediate doping values, our algorithm tends to classify experimental snapshots as geometric-string-like, as compared to the doped spin liquid. Our results demonstrate the potential for machine learning in processing the wealth of data obtained through quantum gas microscopy for new physical insights.