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Machine Learning Phases of Strongly Correlated Fermions
Kelvin Ch'ng, Juan Carrasquilla, Roger G. Melko +1
Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to dist…
Machine learning quantum phases of matter beyond the fermion sign problem
Peter Broecker, Juan Carrasquilla, Roger G. Melko +1
State-of-the-art machine learning techniques promise to become a powerful tool in statistical mechanics via their capacity to distinguish different phases of matter in an automated…
Machine learning phases of matter
Juan Carrasquilla, Roger G. Melko
Neural networks can be used to identify phases and phase transitions in condensed matter systems via supervised machine learning. Readily programmable through modern software libra…