论文标题
通过机器学习检测纠缠结构
Entanglement Structure Detection via Machine Learning
论文作者
论文摘要
检测N量状态的纠缠结构（例如完整性和深度）对于理解实验中状态制备的不完善性很重要。但是，识别这种结构通常需要指数级的局部测量值。在这封信中，我们提出了一种基于机器学习的方法，以同时预测纠缠完整和深度。该分类器的概括能力得到了令人信服的证明，因为它可以准确区分培训过程中永远不存在的纯普通GHz状态。特别是，学识渊博的分类器可以发现NONISE GHz状态的纠缠完整性和深度界限，为此，确切的界限仅部分知道。
Detecting the entanglement structure, such as intactness and depth, of an n-qubit state is important for understanding the imperfectness of the state preparation in experiments. However, identifying such structure usually requires an exponential number of local measurements. In this letter, we propose an efficient machine learning based approach for predicting the entanglement intactness and depth simultaneously. The generalization ability of this classifier has been convincingly proved, as it can precisely distinguish the whole range of pure generalized GHZ states which never exist in the training process. In particular, the learned classifier can discover the entanglement intactness and depth bounds for the noised GHZ state, for which the exact bounds are only partially known.
