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.
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.
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.
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.
Classical deep neural network models struggle to represent data uncertainty and capture dependencies between features simultaneously, especially under fuzzy or noisy conditions. Although a quantum-assisted hierarchical fuzzy neural network (QA-HFNN) was proposed to learn fuzzy membership for each feature, it cannot model dependencies between features due to its single-qubit encoding. To address this, this paper proposes a dual-qubit hierarchical fuzzy neural network (DQ-HFNN), encoding feature pairs onto a pair of entangled qubits, which extends the single-feature fuzzy model to a joint fuzzy representation. By introducing quantum entanglement, the dual-qubit circuit can encode non-classical correlations, enabling the model to directly learn relationship patterns between feature pairs. Experiments on benchmarks show that DQ-HFNN demonstrates higher classification accuracy than QA-HFNN, as well as classical deep learning baselines. Furthermore, ablation studies after controlling for circuit depth and parameter counts show that the performance gain mainly stems from the relational modeling capability enabled by entanglement rather than enhanced expressivity. The proposed DQ-HFNN model exhibits high parameter efficiency and fast inference speed. Experiments under noisy conditions suggest that it is robust against noise and has the potential to be implemented on noisy intermediate-scale quantum devices.
Image classification plays a vital role in large-scale data analysis, especially in object recognition tasks using advanced Deep Learning (DL) frameworks. However, the growing complexity and computational demands of modern DL models have introduced challenges related to scalability and efficiency. Quantum Computing (QC) has emerged as a promising alternative, capable of addressing these limitations by leveraging the principles of Quantum Machine Learning (QML). However, many existing QML models require a large number of qubits, which poses limitations within the current Noisy Intermediate-Scale Quantum (NISQ) era. This work introduces a novel Adaptive Quantum Convolutional Neural Network (AQCNN) designed for efficient and scalable image classification within the constraints of NISQ devices. Addressing the limitations of existing QML approaches, particularly the high qubit requirements, AQCNN incorporates a resource-efficient quantum convolutional layer that performs localized quantum filtering using parameterized quantum circuits. A classical preprocessing layer encodes input features to reduce qubit load, followed by quantum embedding and hybrid quantum-classical layers that optimize feature extraction and classification performance. The model leverages an adaptive quantum convolution strategy, minimizing quantum gate depth and circuit complexity while preserving critical spatial hierarchies in image data. Evaluated on benchmark datasets, AQCNN achieved 95.88% accuracy on MNIST and 95.68% on FMNIST, outperforming comparable QML architectures. Additionally, the model supports scalable execution through parallel quantum circuit arrays, enabling practical deployment on current quantum hardware. This architecture demonstrates a significant advance in quantum-assisted image classification, balancing performance with qubit and gate efficiency. The integration of adaptive quantum convolution and hybrid processing not only enhances classification accuracy but also provides a viable path forward for deploying QML solutions under realistic hardware constraints.
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.
Hybrid quantum classical neural networks integrate parameterized quantum circuits (PQCs) with established deep learning architectures, but their performance depends strongly on the choice of quantum circuit architecture, a choice that remains largely manual. Most existing approaches rely on hand-designed or fixed circuit ansätze, requiring circuit structure, gate composition, and qubit connectivity to be specified in advance with no guarantee that they suit the task. This limitation is especially acute in image classification, where quantum circuits must transform features extracted by classical networks while remaining compact enough for practical training, requirements that generic, task-agnostic ansätze are unlikely to satisfy simultaneously. We extend EXAQC, an evolutionary framework for automated quantum circuit discovery, to image classification. EXAQC evolves PQCs as intermediate processing modules while retaining classical feature-extraction and prediction layers. On MNIST, Fashion-MNIST, and CIFAR-10, EXAQC achieves 98.42%, 90.62%, and 85.47% accuracy, respectively, while using comparable gate counts to other quantum architecture-search methods. Against classical networks, evolved hybrid models maintain comparable accuracy with substantially fewer trainable parameters, reaching 85.68% on CIFAR-10 with over 25× fewer parameters than a 10-layer CNN. Encoding choice also matters: rotation-based encodings (RX, RY, U3) outperform amplitude encoding by 22-25 points on CIFAR-10. These results demonstrate that automated circuit discovery yields compact quantum modules that can replace larger classical components in vision architectures while retaining competitive accuracy.