State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.
Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
Phys Rev Lett. 2018 Jun 15;120(24):240501. doi: 10.1103/PhysRevLett.120.240501.
Quantum information technologies provide promising applications in communication and computation, while machine learning has become a powerful technique for extracting meaningful structures in "big data." A crossover between quantum information and machine learning represents a new interdisciplinary area stimulating progress in both fields. Traditionally, a quantum state is characterized by quantum-state tomography, which is a resource-consuming process when scaled up. Here we experimentally demonstrate a machine-learning approach to construct a quantum-state classifier for identifying the separability of quantum states. We show that it is possible to experimentally train an artificial neural network to efficiently learn and classify quantum states, without the need of obtaining the full information of the states. We also show how adding a hidden layer of neurons to the neural network can significantly boost the performance of the state classifier. These results shed new light on how classification of quantum states can be achieved with limited resources, and represent a step towards machine-learning-based applications in quantum information processing.
量子信息技术在通信和计算方面提供了有前景的应用，而机器学习已经成为从“大数据”中提取有意义结构的强大技术。量子信息和机器学习的交叉代表了一个新的交叉学科领域，刺激了这两个领域的进展。传统上，量子态的特征是量子态层析，当扩展时，这是一个资源消耗的过程。在这里，我们通过实验证明了一种机器学习方法来构建量子态分类器，以识别量子态的可分离性。我们表明，通过人工神经网络进行实验训练，可以有效地学习和分类量子态，而无需获取状态的全部信息。我们还展示了向神经网络添加神经元隐藏层如何可以显著提高状态分类器的性能。这些结果为如何在有限的资源下实现量子态分类提供了新的思路，并代表了迈向基于机器学习的量子信息处理应用的一步。
