Transfer learning of many-body electronic correlation entropy from local measurements

TL;DR

This paper introduces a transfer learning approach to estimate many-body correlation entropy in quantum systems from limited measurements, enabling phase detection without prior training on specific Hamiltonians.

Contribution

It presents a novel transfer learning method that generalizes correlation entropy estimation to unseen Hamiltonians, reducing measurement requirements in quantum many-body systems.

Findings

Transfer learning accurately predicts correlation entropy in unseen Hamiltonian families.

Method detects quantum phases not included in training data.

Applicable to diverse models with local and non-local interactions.

Abstract

The characterization of quantum correlations in many-body systems is instrumental to understanding the nature of emergent phenomena in quantum materials. The correlation entropy serves as a key metric for assessing the complexity of a quantum many-body state in interacting electronic systems. However, its determination requires the measurement of all single-particle correlators across a macroscopic sample, which can be impractical. Machine learning methods have been shown to allow learning the correlation entropy from a reduced set of measurements, yet these methods assume that the targeted system is contained in the set of training Hamiltonians. Here we show that a transfer learning strategy enables correlation entropy learning from a reduced set of measurements in families of Hamiltonians never considered in the training set. We demonstrate this transfer learning methodology in a wide variety of interacting models including local and non-local attractive and repulsive many-body interactions, long-range hopping, doping, magnetic field, and spin-orbit coupling. Furthermore, we show how this transfer learning methodology allows detecting quantum many-body phases never observed during their training set without prior knowledge about them. Our results demonstrate that correlation entropy learning can be potentially performed experimentally without requiring training in the experimentally realized Hamiltonian.