A New Quantum CNN Model for Image Classification

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. Earlier work has already demonstrated potential quantum advantage for natural language processing (NLP) in a number of manners. But as far as we know, the idea of the quantum density matrix has had little application in the field of computer vision (CV). Inspired by the quantum density matrix, 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, (i) we combine density matrices and CNN to design a new mechanism; (ii) we 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.