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.

翻译：量子密度矩阵表征了整个量子系统的全部信息，基于密度矩阵的新型意义模型能够自然地对语言现象进行建模，例如在量子问答任务中的下义关系及语言歧义性等。由此，我们认为量子密度矩阵能够增强经典图像分类中的图像特征信息及特征间关联性。具体而言，我们：(i) 将密度矩阵与CNN相结合，设计了一种新的机制；(ii) 将该新机制应用于若干具有代表性的经典图像分类任务。一系列实验表明，量子密度矩阵在图像分类中的应用在不同数据集上均展现出泛化性与高效性。量子密度矩阵在经典问答任务与经典图像分类任务中的应用均表现出更优越的性能。