Abstract With the rapid advancement of quantum machine learning, numerous hybrid quantum–classical convolutional neural network (HQCNN) architectures have been proposed. However, most existing HQCNNs adopt serial topologies-either classical convolution followed by quantum processing or vice versa-which suffer from low feature propagation efficiency, limited representational capacity, and poor robustness due to inherent information bottlenecks. To address these limitations, this paper proposes a novel parallel dual-branch HQCNN ( PDB-HQCNN ). The proposed architecture comprises two mutually independent branches: a classical feature branch for extracting local structural features and a quantum feature branch for capturing global high-dimensional correlations. Features from both branches are concatenated and fed into a fully connected layer to generate classification predictions based on quantum measurement outcomes. Extensive experiments on MNIST, Fashion-MNIST, and Medical MNIST datasets demonstrate that the proposed PDB-HQCNN achieves classification accuracy exceeding 96% across all benchmarks. Compared with existing serial hybrid architectures, pure classical convolutional neural networks, and pure quantum neural networks, the proposed parallel dual-branch design attains state-of-the-art classification accuracy on all test datasets, validating its effectiveness in leveraging complementary classical and quantum representational strengths.
Abstract With the rapid advancement of quantum machine learning, numerous hybrid quantum–classical convolutional neural network (HQCNN) architectures have been proposed. However, most existing HQCNNs adopt serial topologies-either classical convolution followed by quantum processing or vice versa-which suffer from low feature propagation efficiency, limited representational capacity, and poor robustness due to inherent information bottlenecks. To address these limitations, this paper proposes a novel parallel dual-branch HQCNN ( PDB-HQCNN ). The proposed architecture comprises two mutually independent branches: a classical feature branch for extracting local structural features and a quantum feature branch for capturing global high-dimensional correlations. Features from both branches are concatenated and fed into a fully connected layer to generate classification predictions based on quantum measurement outcomes. Extensive experiments on MNIST, Fashion-MNIST, and Medical MNIST datasets demonstrate that the proposed PDB-HQCNN achieves classification accuracy exceeding 96% across all benchmarks. Compared with existing serial hybrid architectures, pure classical convolutional neural networks, and pure quantum neural networks, the proposed parallel dual-branch design attains state-of-the-art classification accuracy on all test datasets, validating its effectiveness in leveraging complementary classical and quantum representational strengths.
Abstract Quantum Neural Networks (QNNs) present a promising research direction in image recognition, demonstrating significant potential for various applications. However, QNNs with a fixed circuit topology structure are prone to encountering the barren plateaus during training when processing exponential number of qubits, which limit their performance and scalability. In this paper, a novel Hybrid Quantum-Classical Inception-inspired Convolutional Neural Networks (HQCNN) is proposed. The HQCNN enhances feature learning capability and mitigates the barren plateau issue by integrating quantum convolutional kernels with diverse circuit topologies across multiple channels. The architecture employs an amplitude encoding layer to map multi-channel classical data into quantum states, followed by diverse quantum convolutional kernels that extract feature representations. Furthermore, Hadamard gates are used to intergrate multi-channel features and incorporate quantum pooling to reduce feature dimensionality, with a fully connected layer mapping the quantum-enhancing features into class labels for classification tasks. Finally, by analyzing the influence of the circuit structures and quantum gate combinations on the models' performance, the optimal HQCNN architecture is constructed. Experimental results demonstrate that HQCNN achieves 98.5% accuracy on the MNIST-4 classification task and 92.5% accuracy on the MNIST-10 classification task, which indicates that the proposed model outperforms state-of-the-art HQCNN models and classical CNN in image classification with about 10% parameters. This study provides an effective approach for alleviating the barren plateau issue and designing multi-channel quantum convolutional neural networks for processing big classic data with fewer quantum resources.
Abstract This paper presents a parallel quantum–classical hybrid architecture for image classification that integrates classical feature extraction with parallel quantum processing while maintaining competitive performance and improving parameter efficiency. Low-level and intermediate image features are first extracted by a shared convolutional neural network and then processed by multiple parallel quantum branches. Each branch employs a multilayer variational quantum circuit with a rotation–entanglement–rotation structure. To enhance representation diversity, two encoding-specific strategies are introduced. For amplitude encoding, deterministic spatial permutations generate distinct structural views of the same feature map across branches. For angle encoding, a layer-wise data re-uploading mechanism injects complementary feature subsets into different branches and circuit layers. Heterogeneous entanglement topologies with dynamic routing further diversify qubit interactions without increasing the number of trainable parameters. The measured expectation values from all branches are concatenated and fed into a compact linear classifier. Experiments on MNIST, CIFAR-10, and a thermal image dataset show that the proposed architecture achieves 98.35% accuracy on MNIST and competitive results on the other two datasets, while using substantially fewer parameters in the classification head than a fully classical baseline with the same feature extractor. These results demonstrate that parallel quantum processing combined with structured data embedding can serve as an effective and parameter-efficient decision stage for image classification.
Abstract Quantum Machine Learning (QML) has emerged as a promising paradigm that leverages the principles of quantum mechanics—such as superposition and entanglement—to overcome the limitations of classical machine learning in handling complex, high-dimensional data. However, practical challenges such as limited coherence times of qubits and the integration of quantum circuits with classical architectures have hindered the full realization of QML’s potential. To address these challenges, this paper proposes a novel hybrid architecture, termed the Classical-Quantum Hybrid Convolutional Neural Network (CQHCNN), which integrates quantum-inspired layers into conventional deep learning frameworks. These quantum layers utilize quantum information processing to extract complex features, while the classical components perform feature extraction and classification. This paper evaluates the performance of our model on several benchmark datasets, including MNIST, Fashion-MNIST, CIFAR-10 and COVID-19 Radiography, demonstrating its ability to outperform some other Quantum Convolutional Neural Networks (QCNNs) in terms of many metrics. Experimental results indicate that the hybrid model achieves significant improvements in classification accuracy and computational efficiency, highlighting the potential of quantum-inspired techniques to enhance classical machine learning models.
The growing complexity and scale of image processing tasks challenge classical convolutional neural networks (CNNs) with high computational costs. Hybrid quantum-classical convolutional neural networks (HQCNNs) show potential to improve performance by accelerating processing speed, enhancing classification accuracy, and reducing model parameters, though studies have primarily focused on the RGB color space. However, the effectiveness of HQCNNs in non-RGB color spaces, such as Lab, YCrCb, and HSV, remains largely unexplored. We propose an HQCNN to evaluate image classification across diverse color spaces. The HQCNN integrates parameterized quantum circuits (PQCs) with a classical CNN, leveraging quantum entanglement and trainable gates to enhance expressiveness across varied color representations. We assess performance on MNIST, CIFAR-10, EuroSAT, and SAT-4 datasets. Experimental results demonstrate that the HQCNN outperforms the classical CNN across all tested color spaces for the ten-class MNIST task, achieving a best accuracy of 94.3% in Lab compared to 92.8% in RGB for the CNN, with superior performance on other datasets in various color spaces. These findings highlight the potential of non-RGB color spaces and optimized PQC designs to improve classification performance. We provide new insights for advancing hybrid quantum-classical computer vision through optimized PQC architectures and diverse color space applications.
Medical image classification is a critical component of modern healthcare; however, accurate diagnosis remains challenging due to limited annotated datasets, class imbalance, and the high dimensionality of medical imaging data. To address these challenges, a hybrid quantum-classical neural network (HQCNN) is proposed, integrating classical deep learning with variational quantum learning for medical image classification. The proposed architecture combines a five-layer convolutional neural network (CNN) for hierarchical feature extraction with a lightweight 4-qubit variational quantum circuit (VQC) incorporating quantum state encoding, superposition and entanglement mechanisms, and a quantum attention-Fourier (QAF) module. This hybrid design aims to improve nonlinear feature representation and quantum parameter efficiency while maintaining a shallow quantum circuit suitable for noisy intermediate-scale quantum (NISQ)-era constraints. Experimental evaluation on six MedMNIST benchmark datasets demonstrated competitive performance across both binary and multi-class classification tasks. HQCNN achieved 98.88% accuracy on the binary subset of PathMNIST (classes 0 vs. 1), 97.61% accuracy on the multi-class OrganAMNIST dataset, and 86.29% accuracy on BreastMNIST. Comparative experiments and statistical analyses demonstrated consistent improvements over the controlled BHQNN baseline, while component-wise ablation studies showed that the QAF module, superposition and entanglement mechanisms, and expressive parameterized rotations contributed to classification performance in a complementary and dataset-dependent manner. Moreover, HQCNN reduced the number of trainable quantum parameters by approximately 55.6% compared with the baseline hybrid quantum neural network (BHQNN). Noise-aware simulations further showed that the model retained relatively stable predictive performance under moderate depolarizing noise, supporting further evaluation under near-term quantum computing conditions. Overall, the results demonstrate that HQCNN provides a parameter-efficient hybrid quantum-classical framework for medical image classification and offers a promising foundation for further investigation of quantum-enhanced medical image analysis.
Deep learning techniques have significantly enhanced medical image classification for disease diagnosis. However, traditional deep learning models face several challenges, such as dependence on large annotated datasets, complex architectural layers, and millions of trainable parameters, which can increase computational load and cause overfitting. Although quantum computing offers potential for parallel processing and improved feature representation, its practical use is limited by the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) hardware. To overcome these issues, Hybrid Quantum–Classical Convolutional Neural Networks (HQCNNs) have been developed, using quantum properties like superposition and entanglement to improve feature extraction and classification of medical images. This review offers a thorough analysis of 50 peer-reviewed studies published from 2021 to 2026 and introduces a taxonomy of HQCNN architectures. It classifies existing approaches into three categories: Quantum-to-Classical, Classical-to-Quantum, and Hybrid Quantum Transfer Learning. The studies reviewed encompass a diverse range of medical imaging applications, including binary and multi-class disease classification using X-ray, CT, MRI, ultrasound, dermoscopic, retinal, and mammographic images. Additionally, the review compares HQCNNs with their classical CNN counterparts, exploring how factors like quantum circuit design, dataset size, class imbalance, task complexity, evaluation protocols, and hardware limitations influence classification performance. It concludes with an analysis of the key limitations of current HQCNN models, discusses challenges in deploying NISQ devices practically, and proposes future research directions to bridge the gap between experimental HQCNN frameworks and reliable clinical diagnostic systems.