Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and designan unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in the context of entanglement detection, we propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. It is shown via numerical examples, ranging from two-qubit to ten-qubit systems, that our network is able to achieve high detection accuracy which is above 97.5% on average. Moreover, it is capable of revealing rich structures of entanglement, such as partial entanglement among subsystems. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, and thus our work could provide a powerful tool to extract quantum features hidden in multipartite quantum data.

纠缠和相干等量子特性是各种量子信息处理任务中不可或缺的资源。然而，仍然缺乏一种有效且可扩展的方法来检测这些有用的特征，尤其是对于高维和多方量子系统。在这项工作中，我们利用没有所需量子特征的样本的凸性，并设计了一种无监督的机器学习方法来检测异常等特征的存在。特别是，在纠缠检测的背景下，我们提出了一个由伪孪生网络和生成对抗网络组成的复值神经网络，然后只用可分离的状态训练它来构建纠缠的非线性见证。它通过数值示例显示，范围从两个量子位到十个量子位系统，我们的网络能够达到平均超过 97.5% 的高检测准确率。此外，它能够揭示丰富的纠缠结构，例如子系统之间的部分纠缠。我们的结果很容易应用于检测其他量子资源，例如贝尔非局域性和可操纵性，因此我们的工作可以提供一个强大的工具来提取隐藏在多方量子数据中的量子特征。