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 progresses 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.

本研究通过机器学习方法，实验性地构造了一个量子态分类器，能够识别量子态可分离性，此方法能有效地学习和分类量子态，并不需要获取完整的量子态信息。同时本研究表明，通过添加神经网络的隐藏层，可以显著提高状态分类器的性能。这些成果为解决如何在有限的资源下实现量子态分类提供了新的思路，并代表了机器学习在量子信息处理领域的应用进展。