Steerability detection of an arbitrary two-qubit state via machine learning
Steerability detection of an arbitrary two-qubit state via machine learning
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通过机器学习对任意两个量子位状态进行可操纵性检测
DOI：
10.1103/physreva.100.022314
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发表时间：
2019
影响因子：
2.9
通讯作者：
Chen Changbo
中科院分区：
文献类型：
--
作者：
Ren Changliang;Chen Changbo
量子操纵是量子信息处理的重要非经典资源。然而，即使有很多的导向准则存在，它仍然是非常困难的，有效地确定是否由Alice和Bob共享的任意两个量子比特状态是可操纵的或不，因为Alice的最佳测量方向是未知的。在这项工作中，我们提供了一个有效的量子转向检测方案的任意2量子比特状态的帮助下，机器学习，其中爱丽丝和鲍勃只需要在几个固定的测量方向进行测量。为了证明该方法的有效性，我们首先实现了一个高性能的全信息量子导向分类器。在此基础上，实现了一个具有部分信息的高性能量子导向分类器，其中Alice和Bob只需要在三个固定的测量方向上进行测量.我们的方法在速度和准确性方面优于一般情况下的现有方法，开辟了通过机器学习方法探索量子转向的途径。
Quantum steering is an important nonclassical resource for quantum information processing. However, even lots of steering criteria exist, it is still very difficult to efficiently determine whether an arbitrary two-qubit state shared by Alice and Bob is steerable or not, because the optimal measurement directions of Alice are unknown. In this work, we provide an efficient quantum steering detection scheme for arbitrary 2-qubit states with the help of machine learning, where Alice and Bob only need to measure in a few fixed measurement directions. In order to prove the validity of this method, we firstly realize a high performance quantum steering classifier with the whole information. Furthermore, a high performance quantum steering classifier with partial information is realized, where Alice and Bob only need to measure in three fixed measurement directions. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum steering via the machine learning approach.