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.