CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, People's Republic of China.
CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, People's Republic of China.
Phys Rev Lett. 2019 Nov 8;123(19):190401. doi: 10.1103/PhysRevLett.123.190401.
Nonclassical correlations can be regarded as resources for quantum information processing. However, the classification problem of nonclassical correlations for quantum states remains a challenge, even for finite-size systems. Although there exists a set of criteria for determining individual nonclassical correlations, a unified framework that is capable of simultaneously classifying multiple correlations is still missing. In this Letter, we experimentally explored the possibility of applying machine-learning methods for simultaneously identifying nonclassical correlations. Specifically, by using partial information, we applied an artificial neural network, support vector machine, and decision tree for learning entanglement, quantum steering, and nonlocality. Overall, we found that, for a family of quantum states, all three approaches can achieve high accuracy for the classification problem. Moreover, the run time of the machine-learning methods to output the state label is experimentally found to be significantly less than that of state tomography.
非经典相关性可以被视为量子信息处理的资源。然而，即使对于有限大小的系统，量子态的非经典相关性的分类问题仍然是一个挑战。虽然已经存在一组用于确定单个非经典相关性的标准，但仍然缺少能够同时分类多个相关性的统一框架。在这封信中，我们实验探索了应用机器学习方法同时识别非经典相关性的可能性。具体来说，通过使用部分信息，我们应用了人工神经网络、支持向量机和决策树来学习纠缠、量子导引和非局域性。总的来说，我们发现，对于一类量子态，所有三种方法都可以实现分类问题的高精度。此外，实验发现，机器学习方法输出状态标签的运行时间明显小于状态层析成像的运行时间。
