论文标题
通过机器学习进行高效且通用的纠缠检测
Towards efficient and generic entanglement detection by machine learning
论文作者
论文摘要
检测纠缠是实用量子计算和通信的必不可少的步骤。与基于忠诚度的常规纠缠证人方法相比，我们提出了一种灵活的机器学习辅助纠缠检测方案，该方案对不同类型的噪声和样本有效。在此协议中，通过使用合成数据集训练经典的机器学习模型来获得通用纠缠状态的纠缠分类器。该数据集包含两种状态及其标签的经典特征（纠缠或可分离）。状态的经典特征是通过经典影子方法估算样本的估计值。在数值模拟中，我们的分类器可以检测具有连贯噪声的4 Qubit GHz状态的纠缠，W状态与大白噪声混合，精度很高。
Detection of entanglement is an indispensable step to practical quantum computation and communication. Compared with the conventional entanglement witness method based on fidelity, we propose a flexible, machine learning assisted entanglement detection protocol that is robust to different types of noises and sample efficient. In this protocol, an entanglement classifier for a generic entangled state is obtained by training a classical machine learning model with a synthetic dataset. The dataset contains classical features of two types of states and their labels (either entangled or separable). The classical features of a state, which are expectation values of a set of k-local Pauli observables, are estimated sample-efficiently by the classical shadow method. In the numerical simulation, our classifier can detect the entanglement of 4-qubit GHZ states with coherent noise and W states mixed with large white noise, with high accuracy.
