Classical shadows of local observables combined with unsupervised ML distinguish phases in the axial next-nearest-neighbor Ising model and Kitaev-Heisenberg ladder, with sample complexity scaling logarithmically in the number of features.
Quantum spin liquids: a review
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Distinguishing Ordered Phases using Machine Learning and Classical Shadows
Classical shadows of local observables combined with unsupervised ML distinguish phases in the axial next-nearest-neighbor Ising model and Kitaev-Heisenberg ladder, with sample complexity scaling logarithmically in the number of features.