Interpretable Artificial Intelligence (AI) Analysis of Strongly Correlated Electrons

TL;DR

This paper introduces AI workflows with attention-based architectures to analyze complex quantum correlations in the 2D Hubbard model, providing new insights into strongly correlated electron systems and aiding ultracold-atom simulations.

Contribution

The paper develops novel AI methods tailored for tensor-network simulation data of the 2D Hubbard model, enhancing interpretability and understanding of strongly correlated electrons.

Findings

AI captures complex quantum correlations in the 2D Hubbard model.

Provides interpretable insights into phenomena like Mott insulators and high-$T_c$ superconductivity.

Enables universal omnimetry for ultracold-atom simulations.

Abstract

Artificial Intelligence (AI) has become an exceptionally powerful tool for analyzing scientific data. In particular, attention-based architectures have demonstrated a remarkable capability to capture complex correlations and to furnish interpretable insights into latent, otherwise inconspicuous patterns. This progress motivates the application of AI techniques to the analysis of strongly correlated electrons, which remain notoriously challenging to study using conventional theoretical approaches. Here, we propose novel AI workflows for analyzing snapshot datasets from tensor-network simulations of the two-dimensional (2D) Hubbard model over a broad range of temperature and doping. The 2D Hubbard model is an archetypal strongly correlated system, hosting diverse intriguing phenomena including Mott insulators, anomalous metals, and high-$T_c$ superconductivity. Our AI techniques yield fresh perspectives on the intricate quantum correlations underpinning these phenomena and facilitate universal omnimetry for ultracold-atom simulations of the corresponding strongly correlated systems.