Intensively Quantized Integrated Fuzzy Neural Network for High-Accuracy Image Classification

Modern vision networks achieve high accuracy but depend on millions of weights and offer limited insight into prediction confidence. Fuzzy reasoning produces graded rule strengths that quantify uncertainty, and variational quantum circuits add compact expressive power, yet quantum neural networks and fuzzy reasoning rarely appear together in a unified model. We present the Intensively Quantized Integrated Fuzzy Neural Network (IQI-FNN), a quantized architecture that replaces the membership and defuzzification blocks of a classical fuzzy neural network with a shallow parameterized quantum circuit while retaining only minimal classical layers. Its quantum-fuzzy module merges single-qubit membership encoding, angle reuploading, a lightweight rule layer and a clustered-CNOT defuzzifier, which keeps gate count linear in feature dimension. Across standard image classification benchmarks IQI-FNN surpasses relevant state-of-the-art models and attains peak accuracy with modest hyperparameter tuning. Complexity analysis shows gate depth and parameter size grow gently with input dimension, the network stays lightweight, and fidelity remains high under representative gate-level noise in simulation. IQI-FNN therefore offers a compact, interpretable and noise-resilient alternative to conventional vision backbones and advances quantum-native fuzzy learning.