  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.

Quantum Physics