Machine learning discovery of new phases in programmable quantum simulator snapshots
Machine learning discovery of new phases in programmable quantum simulator snapshots
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DOI：
10.1103/physrevresearch.5.013026
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发表时间：
2023-01-19
影响因子：
4.2
通讯作者：
Kim, Eun-Ah
中科院分区：
文献类型：
--
作者：
Miles, Cole;Samajdar, Rhine;Kim, Eun-Ah
机器学习最近成为研究以丰富数据集为特征的复杂现象的一种有前途的方法。特别是，以数据为中心的方法可以自动发现人工检查可能错过的实验数据集中的结构。在这里，我们引入了一种可解释的无监督-监督混合机器学习方法，即混合相关卷积神经网络（hybrid- ccnn），并将其应用于使用基于Rydberg原子阵列的可编程量子模拟器生成的实验数据。具体来说，我们应用hybrid-CCNN来发现和识别具有可编程交互作用的方形晶格上的新量子相。初始的无监督降维和聚类阶段首先揭示了五个不同的量子相位区域。在第二个监督阶段，我们细化这些阶段边界，并通过训练多个CCNN分类器寻求对阶段的见解。在这项工作中，将学习的空间加权引入ccnn，可以在超出过滤器大小的尺度上发现空间结构。在每个相中识别的特征空间权重和相关性片段捕获了条纹相中的量子涨落，并确定了以前未检测到的边界有序相以及更奇特的有序相的基序。这些观察结果表明，可编程量子模拟器与机器学习的结合可以作为详细探索物质相关量子态的强大工具。
Machine learning has recently emerged as a promising approach for studying complex phenomena character-ized by rich datasets. In particular, data-centric approaches lead to the possibility of automatically discovering structures in experimental datasets that manual inspection may miss. Here, we introduce an interpretable unsupervised-supervised hybrid machine learning approach, the hybrid-correlation convolutional neural network (hybrid-CCNN), and apply it to experimental data generated using a programmable quantum simulator based on Rydberg atom arrays. Specifically, we apply hybrid-CCNN to discover and identify new quantum phases on square lattices with programmable interactions. The initial unsupervised dimensionality reduction and clustering stage first reveals five distinct quantum phase regions. In a second supervised stage, we refine these phase boundaries and seek insights into the phases by training multiple CCNN classifiers. A learned spatial weighting, introduced to the CCNNs in this work, enables discovery of spatial structure at scales beyond the filter size. The characteristic spatial weightings and snippets of correlations specifically recognized in each phase capture quantum fluctuations in the striated phase and identify a previously undetected boundary-ordered phase as well as motifs of more exotic ordered phases. These observations demonstrate that a combination of programmable quantum simulators with machine learning can be used as a powerful tool for detailed exploration of correlated quantum states of matter.