Abstract Hybrid quantum-classical neural networks (QCNNs) integrate principles from quantum computing principle and classical neural networks, offering a novel computational approach for image classification tasks. However, current QCNNs with sequential structures encounter limitations in accuracy and robustness, especially when dealing with tasks involving numerous classes. In this study, we propose a novel solution - the hybrid Parallel Quantum Classical Neural Network (PQCNN) - for image classification tasks. This architecture seamlessly integrates the parallel processing capabilities of quantum computing with the hierarchical feature extraction abilities of classical neural networks, aiming to overcome the constraints of conventional sequential structures in multi-class classification tasks. Extensive experimentation demonstrates the superiority of PQCNN over traditional concatenative structures in binary classification datasets, displaying heightened accuracy and robustness against noise. Particularly noteworthy is PQCNN's significantly improved accuracy on datasets with 5 and 10 classes. These findings underscore the transformative potential of the PQCNN architecture as an advanced solution for enhancing the performance of quantum-classical-based classifiers, particularly in the domain of image classification.
Abstract Hybrid quantum-classical neural networks (QCNNs) integrate principles from quantum computing principle and classical neural networks, offering a novel computational approach for image classification tasks. However, current QCNNs with sequential structures encounter limitations in accuracy and robustness, especially when dealing with tasks involving numerous classes. In this study, we propose a novel solution - the hybrid Parallel Quantum Classical Neural Network (PQCNN) - for image classification tasks. This architecture seamlessly integrates the parallel processing capabilities of quantum computing with the hierarchical feature extraction abilities of classical neural networks, aiming to overcome the constraints of conventional sequential structures in multi-class classification tasks. Extensive experimentation demonstrates the superiority of PQCNN over traditional concatenative structures in binary classification datasets, displaying heightened accuracy and robustness against noise. Particularly noteworthy is PQCNN's significantly improved accuracy on datasets with 5 and 10 classes. These findings underscore the transformative potential of the PQCNN architecture as an advanced solution for enhancing the performance of quantum-classical-based classifiers, particularly in the domain of image classification.
Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are employed to extract image features, while Multilayer Perceptrons (MLPs) handle decision making. However, parameterized quantum circuits offer the potential to capture complex image features and define sophisticated decision boundaries. In this paper, we present a novel Hybrid Quantum–Classical Neural Network (H-QNN) for image classification, and demonstrate its effectiveness using the MNIST dataset. Our model combines quantum computing with classical supervised learning to enhance classification accuracy and computational efficiency. In this study, we detail the architecture of the H-QNN, emphasizing its capability in feature learning and image classification. Experimental results demonstrate that the proposed H-QNN model outperforms conventional deep learning methods in various training scenarios, showcasing its effectiveness in high-dimensional image classification tasks. Additionally, we explore the broader applicability of hybrid quantum–classical approaches in other domains. Our findings contribute to the growing body of work in quantum machine learning, and underscore the potential of quantum-enhanced models for image recognition and classification.
Image classification is an important application for deep learning. With the advent of quantum technology, quantum neural networks (QNNs) have become the focus of research. Traditional deep learning-based image classification involves using a convolutional neural network (CNN) to extract features from the image and a multi-layer perceptron (MLP) network to create the decision boundaries. However, quantum circuits with parameters can extract rich features from images and also create complex decision boundaries. This paper proposes a hybrid QNN (H-QNN) model designed for binary image classification that capitalizes on the strengths of quantum computing and classical neural networks. Our H-QNN model uses a compact, two-qubit quantum circuit integrated with a classical convolutional architecture, making it highly efficient for computation on noisy intermediate-scale quantum (NISQ) devices that are currently leading the way in practical quantum computing applications. Our H-QNN model significantly enhances classification accuracy, achieving a 90.1% accuracy rate on binary image datasets. In addition, we have extensively evaluated baseline CNN and our proposed H-QNN models for image retrieval tasks. The obtained quantitative results exhibit the generalization of our H-QNN for downstream image retrieval tasks. Furthermore, our model addresses the issue of overfitting for small datasets, making it a valuable tool for practical applications.
Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter several challenges, including limitations in accuracy improvement, computational efficiency, explain ability, and generalization. As data dimensionality or model complexity increases, CNNs often become more difficult to train efficiently, leading to slower processing times. To overcome these challenges, Quantum Convolutional Neural Networks (QCNNs) offer a promising quantum-based approach to enhance existing models, particularly by improving training efficiency and reducing processing times. This paper provides a comparative analysis of classical CNNs and a novel quantum circuit architecture tailored for image-based tasks. The analysis emphasizes the adaptability and potential of quantum circuits to enhance feature extraction, recognition accuracy, and processing efficiency.We applied various QCNN architectures to the Chinese-MNIST dataset and compared their performance with traditional CNN models. Although QCNNs achieved slightly lower accuracy, they demonstrated significantly greater stability. These findings high- light QCNNs’ potential to overcome the computational limitations of conventional deep learning, offering a promising pathway for quantum-enhanced solutions in computer vision. Future work will focus on refining quantum circuit designs, developing hybrid quantum-classical models, and extending QCNN applications to larger and more diverse datasets.
Abstract Quantum neural networks have emerged as a promising approach to solving complex problems across various domains, especially when integrated with classical methods. Several hybrid quantum-classical architectures have been developed to leverage the potential of quantum advantages for image classification tasks. The design of the quantum layer plays an important role in exploiting quantum properties such as superposition and entanglement. In this research, we propose hybrid quantum neural networks with multiple quantum layers, utilizing sequential circuits for enhanced feature representation through structured depth, and non-sequential circuits to reduce complexity and improve performance. Our experimental results demonstrate that stacking multiple layers in the quantum circuit enhances performance significantly. Furthermore, the results indicate that the optimal range of 6–10 qubits achieves the best trade-off between accuracy and computational efficiency. The results also show that amplitude embedding consistently outperformed angle embedding for image classification tasks. Notably, our proposed hybrid sequential model with amplitude embedding outperforms traditional convolutional neural networks on MNIST and Fashion-MNIST datasets, while requiring fewer parameters. These findings provide valuable insights for advancing quantum machine learning in real-world applications.
Classification of medical images plays a vital role in medical image analysis; however, it remains challenging due to the limited availability of labeled data, class imbalances, and the complexity of medical patterns. To overcome these challenges, we propose a novel Hybrid Quantum-Classical Neural Network (HQCNN) for both binary and multi-class classification. The architecture of HQCNN integrates a five-layer classical convolutional backbone with a 4-qubit variational quantum circuit that incorporates quantum state encoding, superpositional entanglement, and a Fourier-inspired quantum attention mechanism. We evaluate the model on six MedMNIST v2 benchmark datasets. The HQCNN consistently outperforms classical and quantum baselines, achieving up to 99.91% accuracy and 100.00% AUC on PathMNIST (binary) and 99.95% accuracy on OrganAMNIST (multi-class) with strong robustness on noisy datasets like BreastMNIST (87.18% accuracy). The model demonstrates superior generalization capability and computational efficiency, accomplished with significantly fewer trainable parameters, making it suitable for data-scarce scenarios. Our findings provide strong empirical evidence that hybrid quantum-classical models can advance medical imaging tasks.
The recent emergence of the hybrid quantum-classical neural network (HQCNN) architecture has garnered considerable attention due to the potential advantages associated with integrating quantum principles to enhance various facets of machine learning algorithms and computations. However, the current investigated serial structure of HQCNN, wherein information sequentially passes from one network to another, often imposes limitations on the trainability and expressivity of the network. In this study, we introduce a novel architecture termed Parallel Proportional Fusion of Quantum and Spiking Neural Networks (PPF-QSNN). The dataset information is simultaneously fed into both the spiking neural network and the variational quantum circuits, with the outputs amalgamated in proportion to their individual contributions. We systematically assess the impact of diverse PPF-QSNN parameters on network performance for image classification, aiming to identify the optimal configuration. Numerical results on the MNIST dataset unequivocally illustrate that our proposed PPF-QSNN outperforms both the existing spiking neural network and the serial quantum neural network across metrics such as accuracy, loss, and robustness. This study introduces a novel and effective amalgamation approach for HQCNN, thereby laying the groundwork for the advancement and application of quantum advantage in artificial intelligent computations.