Abstract Pneumonia is a prevalent respiratory disease for which medical imaging plays an important role in diagnosis. With the growing integration of quantum computing and deep learning, Quantum Convolutional Neural Networks (QCNNs) have emerged as a promising framework for medical image analysis by exploiting quantum parallelism. However, their further development is constrained by the limitations of core quantum gates. To address key shortcomings in conventional QCNNs, particularly the error coupling caused by the asymmetric two-angle rotation in the Reconfigurable Beam Splitter (RBS) gate and the confinement of quantum states to real-valued space, this study proposes an optimized RBS gate with symmetric complex-phase modulation. Specifically, the proposed design replaces the asymmetric two-angle rotation with a symmetric single-angle rotation to alleviate rotation-mismatch-induced error coupling, and introduces an R z gate to enable complex-phase control. The optimized gate is then integrated into a baseline QCNN for pneumonia image diagnosis. Simulations on MNIST, Fashion-MNIST, and PneumoniaMNIST show that the proposed model achieves classification accuracies of 95.4%, 87.8%, and 82.1%, respectively, and outperforms the compared QCNN variants under the current setting. These findings suggest that gate-level optimization of the RBS gate may serve as an effective design strategy for enhancing QCNN-based medical image classification under controlled simulation settings.
Abstract Pneumonia is a prevalent respiratory disease for which medical imaging plays an important role in diagnosis. With the growing integration of quantum computing and deep learning, Quantum Convolutional Neural Networks (QCNNs) have emerged as a promising framework for medical image analysis by exploiting quantum parallelism. However, their further development is constrained by the limitations of core quantum gates. To address key shortcomings in conventional QCNNs, particularly the error coupling caused by the asymmetric two-angle rotation in the Reconfigurable Beam Splitter (RBS) gate and the confinement of quantum states to real-valued space, this study proposes an optimized RBS gate with symmetric complex-phase modulation. Specifically, the proposed design replaces the asymmetric two-angle rotation with a symmetric single-angle rotation to alleviate rotation-mismatch-induced error coupling, and introduces an R z gate to enable complex-phase control. The optimized gate is then integrated into a baseline QCNN for pneumonia image diagnosis. Simulations on MNIST, Fashion-MNIST, and PneumoniaMNIST show that the proposed model achieves classification accuracies of 95.4%, 87.8%, and 82.1%, respectively, and outperforms the compared QCNN variants under the current setting. These findings suggest that gate-level optimization of the RBS gate may serve as an effective design strategy for enhancing QCNN-based medical image classification under controlled simulation settings.
Pneumonia poses a significant global health challenge, demanding accurate and timely diagnosis. While deep learning, particularly Convolutional Neural Networks (CNNs), has shown promise in medical image analysis for pneumonia detection, CNNs often suffer from high computational costs, limitations in feature representation, and challenges in generalizing from smaller datasets. To address these limitations, we explore the application of Quanvolutional Neural Networks (QNNs), leveraging quantum computing for enhanced feature extraction. This paper introduces a novel hybrid quantum-classical model for pneumonia detection using the PneumoniaMNIST dataset. Our approach utilizes a quanvolutional layer with a parameterized quantum circuit (PQC) to process 2×2 image patches, employing rotational Y-gates for data encoding and entangling layers to generate non-classical feature representations. These quantum-extracted features are then fed into a classical neural network for classification. Experimental results demonstrate that the proposed QNN achieves a higher validation accuracy of 83.33% compared to a comparable classical CNN which achieves 73.33%. This enhanced convergence and sample efficiency highlight the potential of QNNs for medical image analysis, particularly in scenarios with limited labeled data. This research lays the foundation for integrating quantum computing into deep learning-driven medical diagnostic systems, offering a computationally efficient alternative to traditional approaches.
Medical imaging plays an important role in the early diagnosis of various diseases. In machine learning algorithms, texture, color, and shape features are primarily used. This may result in a system with inadequate model generalization capabilities. The development of an end-to-end model that can classify unprocessed medical images is now possible by recent advances in deep learning. The heavy demands of deep learning on memory and computer resources result in further advancement in the processing of complex data. As a result, quantum computing offers potential solutions that take advantage of quantum mechanics concepts like entanglement, superposition, and interference. This chapter introduces a new medical image classification model based on the quantum convolutional neural network (QCNN). Three benchmark datasets are used to evaluate the performance of QCNN; PneumoniaMNIST, VesselMNIST3D, and brain tumor MRI images. The experimental results showed that the proposed model based on the proposed QCNN is very promising.
Medical image classification poses significant challenges due to the complex and nonlinear patterns present in imaging data. Traditional deep learning models often struggle to capture the intricate relationships inherent in such data, especially under conditions of noise and training instability. To address these limitations, this study proposes a novel Hybrid Quantum Convolutional Neural Network (HQCNN) integrated with a Gradient Variance-Controlled Optimizer (GVCO). The aim is to enhance diagnostic accuracy by combining classical convolutional layers for shallow feature extraction with a parameterized quantum circuit (PQC) that enables richer and more discriminative feature representations. The GVCO dynamically adjusts learning rates and momentum coefficients based on real-time gradient variance, improving convergence speed and generalization performance. The proposed HQCNN-GVCO model was evaluated on two public brain imaging datasets: the Kaggle Brain MRI dataset and the REMBRANDT dataset. It achieved classification accuracies of 99.2% and 98.2%, respectively. Compared to baseline HQCNN and other state-of-the-art models, HQCNN-GVCO demonstrated superior and more consistent performance. These findings highlight the potential of integrating quantum computing techniques with adaptive optimization strategies to advance reliable and accurate medical image analysis.
Quantum Convolutional Neural Networks (QCNNs) have became a promising field for quantum advantage in feature extraction and classification of different datasets like Handwritten datasets, Fashion datasets, particularly useful for complex datasets like medical images. Medical image classification has emerged as a critical component in modern healthcare diagnostics, particularly for tumor detection and cancer diagnosis. Normally, Classical CNN is used for image classification but they may face certain challenges in achieving further improvements in accuracy, computational efficiency, processing time. Also, it becomes difficult to train for high-dimensional medical datasets and extracting complex feature representations. Therefore, In this proposal, Image classification through a Scalable and Resource efficient QCNN is proposed which integrates classical CNN with quantum computing. This review examines the current state of medical image classification for tumor detection, analyzes the transition from classical CNNs to quantum-based architectures, and explores the potential of QCNNs in revolutionizing medical diagnostics. This research includes QCNN architecture which will include selective feature encoding using encoding technique such as Z Feature map and quantum circuits for convolutional layers and pooling layers to handle multi-modal medical data (e.g., MRI and CT scans). We aim to achieve superior accuracy with reduced parameters over classical CNNs. This work advances quantum ML toward practical deployment in resource-limited healthcare settings.
Abstract Classical deep learning models often struggle to fully capture complex visual patterns and to generalize effectively when trained on limited or imbalanced medical imaging data. To address this limitation, this paper presents a novel Hybrid Quantum Deep Learning (Hybrid QDL) framework that leverages the strengths of both deep neural networks and quantum circuits for advanced medical image interpretation. The proposed model integrates a pre-trained EfficientNet-B4 as the feature extractor and a variational quantum circuit as the classification head, enabling joint learning of hierarchical and quantum-enhanced representations. The Hybrid QDL model is evaluated on three clinically relevant datasets: COVIDx (for 3-class COVID-19 diagnosis using chest X-rays), PneumoniaMNIST (for pneumonia detection), and OrganAMNIST (for multi-organ abdominal classification). The model achieves state-of-the-art results, with 98.48% accuracy and 98.33% F1-score on COVIDx, outperforming both classical and prior quantum baselines. On PneumoniaMNIST and OrganAMNIST, it reaches 98.28% and 95.27% accuracy, respectively, along with high macro F1-scores. Ablation studies confirm that increasing quantum circuit depth and entanglement enhances discriminative power, particularly in complex multi-class scenarios. These results demonstrate that the Hybrid QDL framework is a robust, scalable solution for automated medical diagnosis and holds significant promise for real-world clinical decision support and COVID-19 detection.
Recently, Quantum Computing has started being widely used for classical problem solving. We can use Quantum mechanics principles for problem solving. CNN (Convolutional Neural Network) had been used in classical computing and problem solving for many years, the implementation of this neural network model in Quantum devices and circuits is known as QCNN (Quantum Convolutional Neural Network). QCNNs are built using quantum circuits. QCNNs can read complicated pictures more quickly, they are allegedly better at extracting features and identifying patterns, these features can make it extremely good at image classification. By analyzing multiple features at the same time, QCNNs process data faster than classical CNNs and reduce the reliance on big datasets, therefore it addresses difficulties related to limited data. We can also see how robust QCNN is to noise, maintaining good accuracy despite many changes in image quality. Good speed and accuracy are demonstrated in experimental results on training datasets, indicating that QCNNs have the potential to transform medical diagnoses and enhance patient outcomes. We have made use of the chest x-ray image dataset which have three labels, Covid, Pneumonia, and Normal.