Extracting electronic many-body correlations from local measurements with artificial neural networks

Abstract

The characterization of many-body correlations provides a powerful tool for analyzing correlated quantum materials. However, experimental extraction of quantum entanglement in correlated electronic systems remains an open problem in practice. In particular, the correlation entropy quantifies the strength of quantum correlations in interacting electronic systems, yet it requires measuring all the single-particle correlators of a macroscopic sample. To circumvent this bottleneck, we introduce a strategy to obtain the correlation entropy of electronic systems solely from a set of local measurements. We demonstrate that by combining local particle-particle and density-density correlations with a neural-network algorithm, the correlation entropy can be predicted accurately. Specifically, we show that for a generalized interacting fermionic model, our algorithm yields an accurate prediction of the correlation entropy from a set of noisy local correlators. Our work demonstrates that the correlation entropy in interacting electron systems can be reconstructed from local measurements, providing a starting point to experimentally extract many-body correlations with local probes.

13 pages, 3 figures

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Cited by in corpus (8)

• Hamiltonian learning with real-space impurity tomography in topological moire superconductors
• Machine learning the Kondo entanglement cloud from local measurements
• Topological zero modes and correlation pumping in an engineered Kondo lattice
• Extracting Many-Body Quantum Resources within One-Body Reduced Density Matrix Functional Theory
• Orbital-Free Quasi-Density Functional Theory
• Transfer learning from Hermitian to non-Hermitian quantum many-body physics
• Hamiltonian-learning quantum magnets with non-local impurity tomography
• Transfer learning of many-body electronic correlation entropy from local measurements