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Researchers Achieve Simultaneous Classification of Multiple Nonclassical Correlations

Nov 18, 2019

In a recent study published in Physical Review Letters, a team led by Prof. GUO Guangcan from University of Science and Technology of China of the Chinese Academy of Sciences (CAS), collaborating with the researchers from the South University of Science and Technology of China and Chongqing Institute of Green and Intelligent Technology of CAS, made progress in the research of basic problems of quantum mechanics using machine learning techniques.

In 1935, Einstein, Podolsky, and Rosen (EPR) questioned the completeness of quantum mechanics (QM), as the theory seems to allow “spooky action at a distance" (known as the EPR paradox). Efforts have been made to achieve a deeper understanding of EPR’s paradox in terms of nonclassical correlations. 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.
The classification problem of nonclassical correlations for quantum states remains a challenge. A set of criteria have formed to determine individual nonclassical correlations, but a unified framework that is capable of simultaneously classifying multiple correlations is still missing. The question is how one may characterize the nonclassical correlation for any given quantum state.

In this study, the researchers experimentally constructed a statistical unified witnesses for the simultaneous characterizing of different classes of multiple nonclassical correlations through machine learning (ML), a branch of artificial intelligence, aiming at producing a predictive function or a computer program based on a set of training data.

Specifically, they compared three different multilabel state classifiers using three different ML methods, including an artificial neural network (ANN), a support vector machine (SVM), and a decision tree (DT), where each classifier only takes partial information for each member in a family of quantum states. It was shown that all three methods can be experimentally trained to efficiently learn and classify quantum states without state tomography.

For the trained models, the researchers experimentally prepared a different set of 455 states for testing, and the multiple nonclassical correlation classifier is successfully implemented. The results showed that for a family of quantum states, all three approaches can achieve high accuracy (more than 90%) for learning entanglement, quantum steering, and nonlocality.

Moreover, they found that both the resource consumption and time complexity are far lower than those of the traditional criteria.

This study experimentally applies machine learning algorithms to multiple nonclassical correlations and simultaneously classification, promoting the deep intersection between artificial intelligence and quantum information technology.

The fusion of quantum information and artificial intelligence is one of the most popular research directions at present. In the future, machine learning as an effective analytical tool will help solve more quantum science problems.

Experimental Simultaneous Learning of Multiple Nonclassical Correlations

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