Machine learning discovery of new phases in programmable quantum simulator snapshots

Abstract

Machine learning has recently emerged as a promising approach for studying complex phenomena characterized by rich datasets. In particular, data-centric approaches lend 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 analyze 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 characterize each phase by training fully interpretable CCNNs and extracting the relevant correlations for each phase. The characteristic spatial weightings and snippets of correlations specifically recognized in each phase capture quantum fluctuations in the striated phase and identify two previously undetected phases, the rhombic and boundary-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.

9 pages, 5 figures + 12 pages, 10 figures appendix

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Cited by in corpus (20)

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• Variational Monte Carlo with Large Patched Transformers
• Investigating Topological Order using Recurrent Neural Networks
• Data-Enhanced Variational Monte Carlo Simulations for Rydberg Atom Arrays
• Machine learning on quantum experimental data toward solving quantum many-body problems
• Anomalous suppression of large-scale density fluctuations in classical and quantum spin liquids
• Mapping out phase diagrams with generative classifiers
• Error-tolerant quantum convolutional neural networks for symmetry-protected topological phases
• Principal component analysis of absorbing state phase transitions
• Machine learning reveals features of spinon Fermi surface
• Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model
• Extracting Off-Diagonal Order from Diagonal Basis Measurements
• On the fluctuations of the number of atoms in the condensate
• Fluctuation based interpretable analysis scheme for quantum many-body snapshots
• Learning interactions between Rydberg atoms
• Attention to Quantum Complexity
• Artificial intelligence for representing and characterizing quantum systems
• Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders
• Neural network ensemble for computing cross sections for rotational transitions in H${}_2$O + H${}_2$O collisions
• Doped resonating-valence-bond states: Robustness of the spin-ice phases in three-dimensional Rydberg arrays