  Quantum density matrix represents all the information of the entire quantum
system, and novel models of meaning employing density matrices naturally model
linguistic phenomena such as hyponymy and linguistic ambiguity, among others in
quantum question answering tasks. Naturally, we argue that the quantum density
matrix can enhance the image feature information and the relationship between
the features for the classical image classification. Specifically, we (i)
combine density matrices and CNN to design a new mechanism; (ii) apply the new
mechanism to some representative classical image classification tasks. A series
of experiments show that the application of quantum density matrix in image
classification has the generalization and high efficiency on different
datasets. The application of quantum density matrix both in classical question
answering tasks and classical image classification tasks show more effective
performance.
