论文标题
用于图像分类的新量子CNN模型
A New Quantum CNN Model for Image Classification
论文作者
论文摘要
量子密度矩阵代表整个量子系统的所有信息，使用密度矩阵的新型含义模型自然地对语言现象进行了模型，例如替诺语和语言歧义，以及其他在量子问题回答任务中的含义。自然，我们认为量子密度矩阵可以增强图像特征信息以及经典图像分类的特征之间的关系。具体而言，我们（i）将密度矩阵和CNN结合在一起以设计一种新的机制； （ii）将新机制应用于某些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具有在不同数据集上的概括和高效率。量子密度矩阵的应用在经典问题回答任务和经典图像分类任务中都显示出更有效的性能。
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.
