Quantum Classifier with Entangled Subgraph States.

With the development of quantum computation theory, some researchers further apply it to improve the efficiency of classical machine learning algorithms. Based on physical graph state, an efficient quantum version of classifier is proposed in this paper. Different from existing classical methods, realizing the proposed scheme is an entangling-subgraphs process of physical system to build the classifier by using the graph states. Resort to the efficiency of graph state, the quantum algorithm is more efficient to big data than the classical ones. [ABSTRACT FROM AUTHOR]