A Hierarchical Fused Quantum Fuzzy Neural Network for Image
  Classification
Sheng-Yao Wu Run-Ze Li Yan-Qi Song Su-Juan Qin Qiao-Yan Wen Fei Gao
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.