Neural network is a powerful learning paradigm for data feature learning in the era of big data. However, most neural network models are deterministic models that ignore the uncertainty of data. Fuzzy neural networks are proposed to address this problem. FDNN is a hierarchical deep neural network that derives information from both fuzzy and neural representations, the representations are then fused to form representation to be classified. FDNN perform well on uncertain data classification tasks. In this paper, we proposed a novel hierarchical fused quantum fuzzy neural network (HQFNN). Different from classical FDNN, HQFNN uses quantum neural networks to learn fuzzy membership functions in fuzzy neural network. We conducted simulated experiment on two types of datasets (Dirty-MNIST and 15-Scene), the results show that the proposed model can outperform several existing methods. In addition, we demonstrate the robustness of the proposed quantum circuit.

翻译：神经网络是大数据时代用于数据特征学习的强大学习范式。然而，多数神经网络模型为确定性模型，忽视了数据的不确定性。为解决该问题，模糊神经网络被提出。FDNN是一种分层次深度神经网络，它从模糊表示和神经表示中提取信息，随后将两种表示融合形成待分类的表示。FDNN在不确定性数据分类任务中表现优异。本文提出了一种新型分层次融合量子模糊神经网络(HQFNN)。与经典FDNN不同，HQFNN采用量子神经网络学习模糊神经网络中的模糊隶属函数。我们在两组数据集（Dirty-MNIST和15-Scene）上开展了仿真实验，结果表明所提模型在性能上优于若干现有方法。此外，我们还验证了所用量子电路的鲁棒性。