The detection of entangled states is essential in both fundamental and applied quantum physics. However, this task proves to be challenging, especially for general quantum states. One can execute full state tomography but this method is time demanding, especially in complex systems. Other approaches use entanglement witnesses: these methods tend to be less demanding but lack reliability. Here, we demonstrate that artificial neural networks (ANNs) provide a balance between the two approaches. In this paper, we make a comparison of ANN performance with witness-based methods for random general two-qubit quantum states without any prior information on the states. Furthermore, we apply our approach to a real experimental data set.

中文翻译：

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通过人工神经网络和人为设计的纠缠证人进行纠缠检测的准确性

纠缠态的检测在基本和应用量子物理学中都是必不可少的。但是，这一任务被证明是具有挑战性的，特别是对于一般的量子态。可以执行全状态层析成像，但是这种方法非常耗时，尤其是在复杂的系统中。其他方法使用纠缠证人：这些方法要求不高，但缺乏可靠性。在这里，我们证明了人工神经网络（ANN）提供了两种方法之间的平衡。在本文中，我们将ANN的性能与基于见证人的方法进行比较，得出了随机的一般两量子位量子态，而没有关于态的任何先验信息。此外，我们将我们的方法应用于真实的实验数据集。