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 applying the quantum density matrix into classical Question Answering (QA) tasks can show more effective performance. Specifically, we (i) design a new mechanism based on Long Short-Term Memory (LSTM) to accommodate the case when the inputs are matrixes; (ii) apply the new mechanism to QA problems with Convolutional Neural Network (CNN) and gain the LSTM-based QA model with the quantum density matrix. Experiments of our new model on TREC-QA and WIKI-QA data sets show encouraging results. Similarly, we argue that the quantum density matrix can also enhance the image feature information and the relationship between the features for the classical image classification. Thus, 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，而采用密度矩阵的新型意义模型能够自然模拟量子问答任务中的下位关系、语言歧义等语言现象。自然地，我们主张将量子密度矩阵应用于经典问答任务能够展现更优性能。具体而言，我们：（i）设计了一种基于长短期记忆网络的新机制以处理矩阵输入场景；（ii）将新机制与卷积神经网络结合应用于问答问题，构建了基于量子密度矩阵的LSTM问答模型。在TREC-QA和WIKI-QA数据集上的实验取得了令人振奋的结果。同样地，我们主张量子密度矩阵还能增强经典图像分类中的图像特征信息及特征间关联性。因此，我们：（i）融合密度矩阵与卷积神经网络设计新机制；（ii）将新机制应用于若干典型经典图像分类任务。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具有泛化性和高效性。量子密度矩阵在经典问答任务与经典图像分类任务中的双重应用均展现出更优性能。