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arXiv:2206.02388 · SciPost Physics Core

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

Faluke Aikebaier, Teemu Ojanen, José Luis Lado

Published Apr 17, 2023• 12 citations
arXivHTMLPDF

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 show 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 shows 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.

DOI
10.21468/scipostphyscore.6.2.030

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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 show 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 shows 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.
Characterizing quantum correlations in many-body systems is essential for understanding 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 enable the correlation entropy to be learned 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 variants of Hamiltonians never considered in the training set. We demonstrate this transfer learning methodology in a wide variety of interacting models, including local and nonlocal attractive and repulsive many-body interactions, long-range hopping, doping, magnetic field, and spin-orbit coupling. Furthermore, we show that this transfer learning methodology enables the detection of phases in quantum many-body systems beyond those present in the training set. Our results demonstrate that correlation entropy learning can be performed experimentally without requiring training in the experimentally realized Hamiltonian.
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 one to spatially map the entangled electronic modes in the vicinity of the impurity site from experimentally accessible data. We demonstrate that local correlators allow reconstruction of 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 nonlocal 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 machine-learning methods to map many-body entanglement from real-space measurements.
In this thesis, the properties of the interacting non-Hermitian Aubry–André–Harper (AAH) model are investigated using a many-body tight binding method. The non-Hermitian AAH model possesses a localization transition and exceptional points, both of which are inherited in the interacting model Correlation entropy is a powerful tool for characterizing fermionic many-body interactions, and for the AAH model, the exceptional points appear as peaks in the correlation entropy-- a result which is shown to be distinct from the behavior of the interacting Hermitian AAH model. However, measuring the correlation entropy is experimentally challenging as it requires knowledge of all particle--particle correlators, a task that becomes impractical in the thermodynamic limit. In addition, these correlators are complex-valued and cannot be directly measured. Nevertheless, machine learning can be used to extract the correlation entropy from a reduced set of inputs using the related and real-valued local density--density correlators. This study elucidates the relationship between exceptional points and correlation entropy, extends the applicability of correlation entropy to non-Hermitian systems, and provides a machine learning algorithm to predict the correlation entropy using only real-valued quantities.
Entanglement entropy is a fundamental measure of quantum correlations and a key resource underpinning advances in quantum information and many-body physics. We uncover a universal relationship between bipartite entanglement entropy and particle number after the barrier in a one-dimensional Fermi-Hubbard system with an external asymmetric potential. Decomposing the von Neumann entropy into number entropy S n and configurational entropy S c , we show that in the barrier-dominated tunneling regime both components are individually well-defined functions of the postbarrier particle density n A , even though S c encodes off-diagonal coherences that are not directly accessible from density measurements alone. Using Kolmogorov-Arnold networks—a novel machine learning architecture—we learn the relationship for entropy and its components across a broad range of interaction strengths and barrier heights with high predictive accuracy. Furthermore, we propose a simple analytical binary-entropy-like expression that quantitatively captures the observed correlation for fixed parameters. Our findings open avenues for characterizing quantum correlations in transport phenomena and provide a powerful framework for estimating the full von Neumann entropy—including its configurational component—from a single transport observable.
Measuring complex properties in quantum systems, such as measures of quantum entanglement and Bell nonlocality, is inherently challenging. Traditional methods, like quantum state tomography (QST), require a full reconstruction of the density matrix for a given system and demand resources that scale exponentially with system size. We propose an alternative strategy that reduces the required information by combining multicopy measurements with artificial neural networks (ANNs), resulting in a 67% reduction in measurement requirements compared to QST. We have successfully measured two-qubit quantum correlations of Bell states subjected to a depolarizing channel (resulting in Werner states) and an amplitude-damping channel (leading to Horodecki states) using the multicopy approach on real quantum hardware. Our experiments, conducted with transmon qubits on IBMQ quantum processors, quantified the violation of Bell's inequality and the negativity of two-qubit entangled states. We compared these results with those obtained from the standard QST approach and applied a maximum likelihood method to mitigate noise. We trained ANNs to estimate degrees of entanglement and nonlocality measures using optimized sets of projections identified through Shapley's (SHAP) analysis for the Werner and Horodecki states. The ANN output, based on this reduced set of projections, aligns well with expected values and enhances noise robustness. This approach simplifies and improves the error robustness of multicopy measurements, eliminating the need for complex nonlinear equation analysis. It represents a significant advancement in AI-assisted quantum measurements, making the practical implementation on current quantum hardware more feasible. The experimental results demonstrate improved noise robustness on the current noisy intermediate-scale quantum (NISQ) hardware, representing a practical advance in resource-efficient characterization of quantum correlations.
Global entanglement in quantum many-body systems is inherently nonlocal, raising the question of whether it can be inferred from local observations. We investigate this problem in monitored quantum circuits, where projective measurements generate classical records distributed across spacetime. Using graph neural networks (GNNs), we represent individual quantum trajectories as directed spacetime graphs and reconstruct the half-chain entanglement entropy from local measurement data alone. Because information propagates through the network via local message passing, the architecture directly controls the spacetime region over which correlations can be aggregated. By systematically varying this accessible scale -- through network depth and hierarchical spacetime coarse-graining -- we probe how much measurement information is required to reconstruct global entanglement. We find that prediction accuracy improves as the accessible spacetime region grows and that results from different architectures collapse when expressed in terms of an effective spacetime scale combining depth and coarse-graining. These results demonstrate that the information required to reconstruct global entanglement is organized in spacetime scales and show that graph-based learning architectures provide a controlled operational framework for probing how global quantum correlations emerge from local measurement data.