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SJTU Jin Xianmin Team Made Breakthrough in Cross-Domain Research of AI and Quantum IT

June 13, 2018      Author: Gao Jun

Recently, in international authoritative physics journal the Physical Review Letters, SJTU Jin Xianmin's team published "Experimental Machine Learning of Quantum States" as an important breakthrough in the field of artificial intelligence and quantum information technology. Cooperating with Professor Weng Wenkang's team from Southern University of Science and Technology, the research team applied machine learning technology to quantum information problem for the first time, and realized a quantum state classifier based on artificial neural network through experiment. AI might bring a new revolution to quantum information technology in the future.

This research was the first to experimentally demonstrate the application of machine learning algorithm to quantum information problem, marking a deep cross between machine learning and quantum information, and also moving an important step towards the development of various derivative technologies.


The research was supported by the major project of Shanghai Science and Technology Commission, the key project of National Natural Science Foundation of China, Organization Department of the Central Committee of the CPC "Young Overseas High-Level Talents Introduction Plan", National Key Research and Development Program, and Shanghai Education Commission.

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.


Translated by Zhang Qianqian    Reviewed by Wang Bingyu