Unsupervised Learning Eigenstate Phases of Matter

Abstract

Supervised Learning has been successfully used to produce phase diagrams and identify phase boundaries when local order parameters are unavailable. Here, we apply unsupervised learning to this task. By using readily available clustering algorithms, we are able to extract the distinct eigenstate phases of matter within the transverse-field Ising model in the presence of interactions and disorder. We compare our results to those found through supervised learning and observe remarkable agreement. However, as opposed to the supervised procedure, our method requires no strict assumptions concerning the number of phases present, no labeled training data, and no prior knowledge of the phase diagram. We conclude with a discussion of clustering and its limits.

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• Neural network setups for a precise detection of the many-body localization transition: finite-size scaling and limitations
• Unsupervised learning using topological data augmentation
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• Conservation Laws and Spin System Modeling through Principal Component Analysis
• Deep Learning of Phase Transitions for Quantum Spin Chains from Correlation Aspects
• Deep learning of topological phase transitions from entanglement aspects: An unsupervised way
• Manifold formation and crossings of ultracold lattice spinor atoms in the intermediate interaction regime