Research on the Freezing Phenomenon of Quantum Correlation by Machine Learning

Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics,especially in the field of quantum information.Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods,and all these quantification methods exhibit the peculiar freezing phenomenon.The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon,rather than only study the conditions which generate this phenomenon under a certain quantum system.In essence,this is a classification problem.Machine learning has become an effective method for researchers to study classification and feature generation.In this work,we prove that the machine learning can solve the problem of X form quantum states,which is a problem of physical significance.Subsequently,we apply the density-based spatial clustering of applications with noise(DBSCAN)algorithm and the decision tree to divide quantum states into two different groups.Our goal is to classify the quantum correlations of quantum states into two classes:one is the quantum correlation with freezing phenomenon for both Rènyi discord(α=2)and the geometric discord(Bures distance),the other is the quantum correlation of non-freezing phenomenon.The results demonstrate that the machine learning method has reasonable performance in quantum correlation research.

Intention Estimation of Adversarial Spatial Target Based on Fuzzy Inference

Estimating the intention of space objects plays an important role in air-craft design,aviation safety,military and otherfields,and is an important refer-ence basis for air situation analysis and command decision-making.This paper studies an intention estimation method based on fuzzy theory,combining prob-ability to calculate the intention between two objects.This method takes a space object as the origin of coordinates,observes the target’s distance,speed,relative heading angle,altitude difference,steering trend and etc.,then introduces the spe-cific calculation methods of these parameters.Through calculation,values are input into the fuzzy inference model,andfinally the action intention of the target is obtained through the fuzzy rule table and historical weighted probability.Ver-ified by simulation experiment,the target intention inferred by this method is roughly the same as the actual behavior of the target,which proves that the meth-od for identifying the target intention is effective.

Improved Prediction and Understanding of Glass-Forming Ability Based on Random Forest Algorithm

As an ideal material,bulk metallic glass(MG)has a wide range of applications because of its unique properties such as structural,functional and biomedical materials.However,it is difficult to predict the glass-forming ability(GFA)even given the criteria in theory and this problem greatly limits the application of bulk MG in industrial field.In this work,the proposed model uses the random forest classification method which is one of machine learning methods to solve the GFA prediction for binary metallic alloys.Compared with the previous SVM algorithm models of all features combinations,this new model is successfully constructed based on the random forest classification method with a new combination of features and it obtains better prediction results.Simultaneously,it further shows the degree of feature parameters influence on GFA.Finally,a normalized evaluation indicator of binary alloy for machine learning model performance is put forward for the first time.The result shows that the application of machine learning in MGs is valuable.