Probing transport in quantum many-fermion simulations via quantum loop topography

Abstract

Quantum many-fermion systems give rise to diverse states of matter that often reveal themselves in distinctive transport properties. While some of these states can be captured by microscopic models accessible to numerical exact quantum Monte Carlo simulations, it nevertheless remains challenging to numerically access their transport properties. Here we demonstrate that quantum loop topography (QLT) can be used to directly probe transport by machine learning current-current correlations in imaginary time. We showcase this approach by studying the emergence of superconducting fluctuations in the negative-U Hubbard model and a spin-fermion model for a metallic quantum critical point. For both sign-free models, we find that the QLT approach detects a change in transport in very good agreement with their established phase diagrams. These proof-of-principle calculations combined with the numerical efficiency of the QLT approach point a way to identify hitherto elusive transport phenomena such as non-Fermi liquids using machine learning algorithms.

7 pages, 5 figures

References in corpus (8)

• Learning phase transitions by confusion
• Sign-problem-free quantum Monte Carlo of the onset of antiferromagnetism in metals
• Machine learning vortices at the Kosterlitz-Thouless transition
• Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis
• Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems
• Deep Learning the Quantum Phase Transitions in Random Electron Systems: Applications to Three Dimensions
• Quantum critical properties of a metallic spin density wave transition
• Quantum phase recognition via unsupervised machine learning