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Scientists use artificial intelligence to achieve simultaneous classification of multiple non-classical associations
[ Instrument Network Instrument Development ] Guo Guangcan, a member of the Chinese Academy of Sciences and a professor at the University of Science and Technology of China, made new progress in the field of artificial intelligence and quantum mechanics. Li Chuanfeng and Xu Jinshi of the laboratory cooperated with Weng Wenkang, a professor at Southern University of Science and Technology, and Ren Changliang, a researcher at the Chongqing Institute of Green and Intelligent Technology of the Chinese Academy of Sciences. They applied machine learning technology to the basic problems of quantum mechanics and realized machine-based learning for the first time. Simultaneous classification of multiple non-classical associations of algorithms. The results were published on the November 6th in the International Journal of Physics, Physical Review Letters.
In 1935, Einstein, Podolski, and Rosen published a famous article questioning the completeness of quantum mechanics, which was later called EPR佯谬. With the in-depth study of EPR佯谬 by many scientists such as Schrödinger and Bell, people gradually understand that Einstein’s “ghost-like super-distance effect†comes from the delocalization of the quantum world, and it can be further subdivided. It is a level of Quantum Entanglement, Quantum Steering, and Bell Nonlocality. On the other hand, with the rise of quantum information research, various quantum correlations have become a key resource in the field of quantum information, playing an important role in quantum computing, quantum communication and quantum precision measurement.
However, the characterization of non-classical associations in any given quantum state still presents significant challenges. First of all, many mathematical forms of judgment are extremely complicated for multi-body systems. Secondly, many methods are known to require the density matrix information of the entire quantum state, so that the complete quantum state chromatography is required experimentally, and the data acquisition time increases exponentially with the increase of the system particles. Finally, since each non-classical association has its own different criteria, it is not clear whether there is a unified framework that can achieve simultaneous differentiation of all these non-classical associations through the same set of measurements or observables.
Machine learning is an important branch of artificial intelligence, through a series of training data to get a function or model that can output prediction results. Li Chuanfeng, Xu Jinshi and others applied machine learning technology to the distinction of non-classical associations. The first experiment realized the simultaneous classification of multiple quantum correlations. Through ingenious experimental design, they prepared a cluster of two-bit quantum states with adjustable parameters in the optical system. By inputting only the partial information of the quantum state (two observable values), the machine learning model such as neural network, support vector machine and decision tree is used to learn the non-classical association properties of 455 quantum states, and the multiple non-success is successfully realized. Classic associative classifier. The experimental results show that the classifier based on machine learning algorithm can simultaneously identify different quantum correlation properties such as quantum entanglement, quantum guidance and Bell non-locality with high matching degree of more than 90%, and it is in terms of resource consumption and time complexity. Both are much smaller than the quantum state chromatography methods that traditional criteria rely on.
The integration of quantum information and artificial intelligence is one of the most popular research directions, and many important advances have been made. This work experimentally applies the machine learning algorithm to the simultaneous differentiation of multiple non-classical associations, and promotes the deep intersection of artificial intelligence and quantum information technology. In the future, machine learning as an effective analysis tool will help solve more quantum science problems.
The first author of the paper is Yang Mu (experiment), a doctoral student of the Key Laboratory of Quantum Information of the Chinese Academy of Sciences, and Ren Changliang (theory) of the Chongqing Research Institute. The work was supported by the Ministry of Science and Technology, the National Fund Committee, the Chinese Academy of Sciences, and Anhui Province.