Machine Learning Phases of Strongly Correlated Fermions

By K. Ch'ng, Juan Felipe Carrasquilla, Roger G. Melko, Ehsan Khatami. Physical Review X, 2016

310 citations recorded by the source corpus.

Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated fermions on cubic lattices. We show that a three dimensional convolutional network trained on auxiliary field configurations produced by quantum Monte Carlo simulations of the Hubbard model can correctly predict the magnetic phase diagram of the model at the average density of one (half filling). We then use the network, trained at half filling, to explore the trend in the transition temperature as the system is doped away from half filling. This transfer learning approach predicts that the instability to the magnetic phase extends to at least 5% doping in this region. Our results pave the way for other machine learning applications in correlated quantum many-body systems.

Subfields: Atomic and Molecular Physics, and Optics, Condensed Matter Physics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/4ec306ab408839fa060cfe4f6ce969c43df65ed9

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In Atomic and Molecular Physics, and Optics

In Condensed Matter Physics

In Materials Chemistry

Citations in the corpus

Quantum Entanglement in Neural Network States (2017, background): “Exciting progress has been made in identifying quantum phases and transitions among them (either conventional symmetry-broken [33, 35, 37, 38] or topological phases [32]), modeling thermodynamic observables [36], constructing decoders for topological codes [39], accelerating Monte Carlo simulations…”
Transfer learning in hybrid classical-quantum neural networks (2019, methodology): “Up to now, the transfer learning approach has been largely unexplored in the quantum domain with the exception of a few interesting applications, for example, in modeling many-body quantum systems [12, 23, 52], in the connection of a classical autoencoder to a quantum Boltzmann machine [35] and in t”