论文标题
通过卷积神经网络在纠缠检测中找到有效的可观察操作员
Finding efficient observable operators in entanglement detection via convolutional neural network
论文作者
论文摘要
在量子信息中，有效检测纠缠非常重要。通常，它需要量子断层扫描才能获得状态密度矩阵。但是，它将消耗大量的测量资源，关键是如何减少消费。在本文中，我们发现了人工神经网络的卷积层与可观察到的量子在量子力学中的平均值之间的关系。然后，我们设计了一个分支卷积神经网络，该网络可用于检测2克量子系统中的纠缠。在这里，我们可以自动找到Werner State，Werner State，广义Werner State和General 2 Quibent State的纠缠以及可观察到的可观察的操作员，这些操作员可以自动找到适合检测的纠缠。除了与特殊作品相比，我们的方法可以实现更高的精度，而对具有特定形式的量子状态的测量更少。结果表明，卷积神经网络对于有效检测量子纠缠非常有用。
In quantum information, it is of high importance to efficiently detect entanglement. Generally, it needs quantum tomography to obtain state density matrix. However, it would consumes a lot of measurement resources, and the key is how to reduce the consumption. In this paper, we discovered the relationship between convolutional layer of artificial neural network and the average value of an observable operator in quantum mechanics. Then we devise a branching convolutional neural network which can be applied to detect entanglement in 2-qubit quantum system. Here, we detect the entanglement of Werner state, generalized Werner state and general 2-qubit states, and observable operators which are appropriate for detection can be automatically found. Beside, compared with privious works, our method can achieve higher accuracy with fewer measurements for quantum states with specific form. The results show that the convolutional neural network is very useful for efficiently detecting quantum entanglement.
