论文标题
通过人工神经网络从局部测量中提取电子多体相关性
Extracting electronic many-body correlations from local measurements with artificial neural networks
论文作者
论文摘要
多体相关的表征为分析相关量子材料提供了有力的工具。但是，在实践中，相关电子系统中量子纠缠的实验提取仍然是一个开放的问题。特别是，相关熵量化了相互作用的电子系统中量子相关的强度，但它需要测量宏观样本的所有单粒子相关器。为了避免这种瓶颈，我们引入了一种策略，以从一组局部测量中获得电子系统的相关性熵。我们证明，通过将局部粒子粒子和密度密度相关性与神经网络算法相结合，可以准确预测相关熵。具体而言，我们表明，对于广义相互作用的费米模型，我们的算法从一组嘈杂的局部相关器中对相关熵进行了准确的预测。我们的工作表明，可以从局部测量结果重建相互作用的电子系统中的相关性熵，从而提供了一个起点，可以通过局部探针从实验中提取多体相关性。
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.
