Machine learning the Kondo entanglement cloud from local measurements

TL;DR

This paper presents a machine learning approach to map the quantum entanglement cloud around magnetic impurities in metals using local measurements, enabling real-space reconstruction of many-body correlations without long-range correlator measurements.

Contribution

The authors develop a transferable machine learning method that reconstructs the Kondo entanglement cloud from local correlators, bypassing the need for non-local measurements.

Findings

Successfully reconstructs the entanglement cloud in double Kondo systems.

Method is robust to noisy data and transferable across system sizes.

Enables real-space mapping of quantum correlations in Kondo systems.

Abstract

A quantum coherent screening cloud around a magnetic impurity in metallic systems is the hallmark of the antiferromagnetic Kondo effect. Despite the central role of the Kondo effect in quantum materials, the structure of quantum correlations of the screening cloud has defied direct observations. In this work, we introduce a machine-learning algorithm that allows to spatially map the entangled electronic modes in the vicinity of the impurity site from experimentally accessible data. We demonstrate that local correlators allow reconstructing the local many-body correlation entropy in real-space in a double Kondo system with overlapping entanglement clouds. Our machine learning methodology allows bypassing the typical requirement of measuring long-range non-local correlators with conventional methods. We show that our machine learning algorithm is transferable between different Kondo system sizes, and we show its robustness in the presence of noisy correlators. Our work establishes the potential of machine learning methods to map many-body entanglement from real-space measurements.