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Advances in computational intelligence and robotics book series

Using Quantum Fuzzy Neural Networks for Novel Structured Categorization of Images

N. Nithiyanandam, G. K. Sandhia, K. Vijiyakumar

Published Apr 17, 2025• 0 citations
Publisher

Abstract

This research presents a new architecture for structured picture categorization problems called Hierarchical Fused Quantum Fuzzy Neural Network (HFQ-FNN). It combines the ideas of quantum computing with fuzzy logic and neural networks. With an accuracy of 84.04%, the suggested HFQ-FNN beats CNN and FDNN, two industry standards, on the Dirty-MNIST dataset, by a margin of 2.9% and 1.4%, respectively. Also, on the 15-Scene dataset, HFQ-FNN achieves an accuracy of 74.27% and shows a considerably higher F1 value, which is 3.0% better than DNN and 0.6% better than FDNN. The robustness analysis shows that HFQ-FNN is resilient against quantum noise, with a fidelity of 0.95 at a 0.1 noise probability. These findings show that HFQ-FNN is a powerful tool for improving picture classification accuracy through the use of quantum computing, which could lead to major developments in the field of complicated data classification.

DOI
10.4018/979-8-3693-6225-9.ch011

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This research presents a new architecture for structured picture categorization problems called Hierarchical Fused Quantum Fuzzy Neural Network (HFQ-FNN). It combines the ideas of quantum computing with fuzzy logic and neural networks. With an accuracy of 84.04%, the suggested HFQ-FNN beats CNN and FDNN, two industry standards, on the Dirty-MNIST dataset, by a margin of 2.9% and 1.4%, respectively. Also, on the 15-Scene dataset, HFQ-FNN achieves an accuracy of 74.27% and shows a considerably higher F1 value, which is 3.0% better than DNN and 0.6% better than FDNN. The robustness analysis shows that HFQ-FNN is resilient against quantum noise, with a fidelity of 0.95 at a 0.1 noise probability. These findings show that HFQ-FNN is a powerful tool for improving picture classification accuracy through the use of quantum computing, which could lead to major developments in the field of complicated data classification.
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.
Deep learning is a powerful technique for data-driven learning in the era of Big Data. However, most deep learning models are deterministic models that ignore the uncertainty of data. Fuzzy neural networks are proposed to tackle this type of problem. In this article, we proposed a novel quantum assisted hierarchical fuzzy neural network (QA-HFNN). Different from classical fuzzy neural networks, QA-HFNN uses quantum neural networks (QNNs) to learn fuzzy membership functions. The model is a multifeature fusion learning algorithm with a parallel structural design that integrates quantum and classical neural networks. The classical network is used to capture high-dimensional neural features, the QNNs are designed to capture fuzzy logic features of the data, then, the two features are fused to form the final features to be classified. The experiment is performed on a classical computer, and the quantum circuit is built through a simulated quantum environment. The results indicate that the accuracy of QA-HFNN can equal to or even surpass classical methods in image classification tasks. The quantum circuit utilizes only a single qubit which is easy to implement. In addition, the fidelity of quantum circuit in a quantum noise environment is assessed, demonstrating that QA-HFNN has strong robustness. The time and computational complexity of QNNs was analyzed, further proving the effectiveness of the model.
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.
Abstract With the significant enhancement of quantum computing capabilities and their expanding applications in various fields, quantum image processing combined with quantum computing has become a key area of research in quantum computing, holding important theoretical value. In the face of the challenges posed by the digital information era, image classification technology demands higher standards from machine learning algorithms in terms of accuracy, computational speed, generalization capability, and complexity. This paper studies a series of quantum data encoding methods and parametric quantum circuit architectures, and proposes the application of Hybrid Hierarchical Encoding (HHE) and Hybrid Quantum Convolutional Neural Network (HQCNN). The new encoding method HHE integrates the benefits of angle encoding and enhances the hybrid amplitude encoding design to achieve optimal classification performance without compromising the depth of quantum circuits.HQCNN demonstrates outstanding classification performance and remarkable control over model complexity by leveraging the data processing capabilities of classical convolution and the complex computational power of quantum convolution. Experimental results show that HHE and HQCNN achieve better classification accuracy on the MNIST and Fashion-MNIST datasets compared to traditional classical CNN models, particularly when the number of parameters is significantly reduced.
Deep learning vision systems excel at pattern recognition yet falter when inputs are noisy or the model must explain its own confidence. Fuzzy inference, with its graded memberships and rule transparency, offers a remedy, while parameterized quantum circuits can embed features in richly entangled Hilbert spaces with striking parameter efficiency. Bridging these ideas, this study introduces a innovative Highly Quantized Fuzzy Neural Network (HQFNN) that realises the entire fuzzy pipeline inside a shallow quantum circuit and couples the resulting quantum signal to a lightweight CNN feature extractor. Each image feature is first mapped to a single qubit membership state through repeated angle reuploading. Then a compact rule layer refines these amplitudes, and a clustered CNOT defuzzifier collapses them into one crisp value that is fused with classical features before classification. Evaluated on standard image benchmarks, HQFNN consistently surpasses classical, fuzzy enhanced and quantum only baselines while using several orders of magnitude fewer trainable weights, and its accuracy degrades only marginally under simulated depolarizing and amplitude damping noise, evidence of intrinsic robustness. Gate count analysis further shows that circuit depth grows sublinearly with input dimension, confirming the model's practicality for larger images. These results position the model as a compact, interpretable and noise tolerant alternative to conventional vision backbones and provide a template for future quantum native fuzzy learning frameworks.
Image classification is a fundamental task in computer vision that involves assigning labels to images based on their content. In recent years, deep learning techniques based on classical neural networks (CNN) have achieved modern performance on many image classification tasks. However, there is growing interest in using quantum neural networks (QNNs) for image classification, as quantum computing has the potential to improve the accuracy and speed of computation. We demonstrate the potential of our QNN model and QCNN model by applying it to the fashion MNIST dataset. The results indicate that the QNN model exhibits faster training speed and higher testing set accuracy compared to a QCNN with a similar architecture.