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arXiv 2103.04804quant-phcs.LG
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利用无监督学习检测量子纠缠

Detecting quantum entanglement with unsupervised learning

Zhejiang University(浙江大学);The Hong Kong Polytechnic University(香港理工大学);The Hong Kong Polytechnic University Shenzhen Research Institute(香港理工大学深圳研究院);Tongji University(同济大学)

Yiwei Chen, Yu Pan, Guofeng Zhang, Shuming Cheng

摘要:

量子特性,如纠缠和相干,是各种量子信息处理任务中不可或缺的资源。然而,仍然缺乏一种有效的和可扩展的方法来检测这些有用的功能,特别是对于高维和多体量子系统。在这项工作中,我们利用了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这些特征的存在。特别地,在纠缠检测方面,我们提出了一种由伪连体网络和生成对抗网络组成的复值神经网络,然后用可分离态对其进行训练,构造纠缠的非线性见证.数值实验结果表明,从2量子比特到10量子比特系统,我们的网络都能达到平均97.5%以上的高检测精度,并且能够揭示出丰富的纠缠结构,如子系统间的部分纠缠.我们的结果很容易适用于其他量子资源,如贝尔非定域性和可操纵性的检测,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体量子数据的量子特征。

英文摘要:

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 design an 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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