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.
Quantum communication has been rapidly developed due to its unconditional security and successfully implemented through optical fibers and free-space air in experiment [1][2][3]. To build a complete quantum communication network involving satellites in space and submersibles in ocean, underwater quantum channel has been investigated in both theory and experiment [4][5][6]. However, the question of whether the polarization encoded qubit can survive through a long-distance and high-loss underwater channel, which is considered as the restricted area for satellite-borne radio waves, still remains. Here, we experimentally demonstrate the transmission of blue-green photonic polarization states through 55-meter-long water. We prepare six universal quantum states at single photon level and observe their faithful transmission in a large marine test platform. We obtain the complete information of the channel by quantum process tomography. The distance demonstrated in this work reaches a region allowing potential real applications, representing a step further towards air-to-sea quantum communication.
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