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.

arXiv comment7 pages, 5 figures

References in corpus (26)

• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Machine learning phases of matter
• Learning phase transitions by confusion
• Discovering Phase Transitions with Unsupervised Learning
• Quantum Entanglement in Neural Network States
• Machine learning quantum phases of matter beyond the fermion sign problem
• Efficient Representation of Quantum Many-body States with Deep Neural Networks
• Machine Learning Topological States
• Quantum Loop Topography for Machine Learning
• Machine Learning Phases of Strongly Correlated Fermions
• Neural-Network Quantum States, String-Bond States, and Chiral Topological States
• Superconductivity and non-Fermi liquid behavior near a nematic quantum critical point
• Sign-problem-free quantum Monte Carlo of the onset of antiferromagnetism in metals
• Machine learning vortices at the Kosterlitz-Thouless transition
• Probing many-body localization with neural networks
• Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis
• Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems
• Machine Learning ${\mathbb{Z}}_2$ Quantum Spin Liquids with Quasi-particle Statistics
• What makes the Tc of monolayer FeSe on SrTiO3 so high: a sign-problem-free quantum Monte Carlo study
• Machine learning of quantum phase transitions
• Monte Carlo Studies of Quantum Critical Metals
• Competing Orders in a Nearly Antiferromagnetic Metal
• Deep Learning the Quantum Phase Transitions in Random Electron Systems: Applications to Three Dimensions
• Quantum critical properties of a metallic spin density wave transition
• Supervised learning magnetic skyrmion phases
• Quantum phase recognition via unsupervised machine learning

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