Machine Learning Phase Diagram in the Half-filled One-dimensional Extended Hubbard Model

Abstract

We demonstrate that supervised machine learning (ML) with entanglement spectrum can give useful information for constructing phase diagram in the half-filled one-dimensional extended Hubbard model. Combining ML with infinite-size density-matrix renormalization group, we confirm that bond-order-wave phase remains stable in the thermodynamic limit.

2 pages, 2 figures

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• Learning phase transitions by confusion
• Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems
• Phase diagram of the one-dimensional half-filled extended Hubbard model
• Entanglement in extended Hubbard models and quantum phase transitions
• Density-Matrix Renormalization Group Study of Extended Kitaev-Heisenberg Model