Learning discovery manual#

A learned spatial weighting, introduced to the CCNNs in this work, enables discovery of spatial structure at scales beyond the filter size. In a second supervised stage, we refine these phase boundaries and seek insights into the phases by training multiple CCNN classifiers. The initial unsupervised dimensionality reduction and clustering stage first reveals five distinct quantum phase regions. Specifically, we apply hybrid-CCNN to discover and identify new quantum phases on square lattices with programmable interactions. 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.

Learning discovery manual#

In particular, data-centric approaches lead to the possibility of automatically discovering structures in experimental datasets that manual inspection may miss. Machine learning has recently emerged as a promising approach for studying complex phenomena characterized by rich datasets.