Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Biomedical Signal Processing and Control

A quantum-inspired medical scalable convolutional neural network for Intelligent pneumonia diagnosis

Dongfen Li, Yuchen Sun, Yuhang Yuan, Zhikang Hu, Qiuyu Xiang, Yangyang Jiang, Yonghao Zhu, You Fu, Xiaoyu Hua

Published Aug 30, 2025• 4 citations
Publisher
DOI
10.1016/j.bspc.2025.108440

Related papers

A classical–quantum convolutional neural network for detecting pneumonia from chest radiographs

Viraj Kulkarni, Sanjesh Pawale, Amit T Kharat

arXiv:2202.10452•Neural Computing and Applications•Apr 12, 2023•28 citations

Hybrid Quantum-Classical Neural Networks for Medical Image Diagnosis

Mayank Genwa

International Conference on Cognitive Computing and Networking Systems (ICC-CNS)•Jun 1, 2026•0 citations

Quanvolutional Neural Networks for Pneumonia Detection: An Efficient Quantum-Assisted Feature Extraction Paradigm

Gazi Tanbhir, Md. Farhan Shahriyar, Abdullah Md Raihan Chy

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.

arXiv:2510.23660•International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM)•Jun 27, 2025•4 citations

QUANTUM NEURAL NETWORKS FOR DISEASE TREATMENT IDENTIFICATION

Sriramakrishnan Chandrasekaran, Vicente García‐Díaz, Vishal Dutt

European Journal of Molecular & Clinical Medicine•Dec 20, 2020•12 citations

Effective quantum convolutional neural network on symmetric complex-phase modulated RBS gate in pneumonia medical imaging diagnosis

Yan Liang, Wei Ding, Hong Xu +4 more

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.

Physica Scripta•May 2, 2026•1 citation

Quantum Convolutional Neural Networks for Medical Image Diagnosis

Asha S Manek, K Shakthi Ganesh, Mohammad Abuzar +3 more

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.

IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)•Sep 26, 2025•0 citations
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.
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.
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.