While hybrid quantum–classical architectures and knowledge distillation have each been explored independently in medical imaging and model compression, no prior work has unified these domains within a single framework. This study addresses this gap by investigating the integration of quantum–classical neural networks with knowledge distillation for medical image analysis, with a focus on circuit behavior, parameter efficiency, and diagnostic performance. The framework (QuantumMedKD) employs classical CNN architectures (ResNet-50, EfficientNet-B0, Xception) as teacher models, transferring learned representations to parameter-efficient quantum student networks via parameterized quantum circuits across qubit configurations (3-8), demonstrated through pneumonia detection from pediatric chest radiographs. Experimental validation reveals remarkable parameter efficiency, requiring only 24-36 trainable parameters compared to millions for classical counterparts, achieving compression ratios exceeding 1 0 5 while maintaining competitive diagnostic performance. Optimal configurations achieve 84.00% accuracy (EfficientNet-B0, 6-qubit) and 73.33% (Xception, 4-qubit), with knowledge distillation providing statistically significant improvements ( p -vallues < 0.001, Cohen’s d > 2.0). Medical evaluation confirms diagnostic capability with sensitivity 81.4%, specificity 73.9%, and AUC 0.89, establishing quantum–classical knowledge transfer viability for resource-constrained healthcare deployment. QuantumMedKD reveals essential principles and proof-of-concept for quantum-enhanced healthcare AI systems within and beyond the noisy intermediate-scale quantum (NISQ) era for high efficiency and deployability, paving the way for future advancements in the field.
While hybrid quantum–classical architectures and knowledge distillation have each been explored independently in medical imaging and model compression, no prior work has unified these domains within a single framework. This study addresses this gap by investigating the integration of quantum–classical neural networks with knowledge distillation for medical image analysis, with a focus on circuit behavior, parameter efficiency, and diagnostic performance. The framework (QuantumMedKD) employs classical CNN architectures (ResNet-50, EfficientNet-B0, Xception) as teacher models, transferring learned representations to parameter-efficient quantum student networks via parameterized quantum circuits across qubit configurations (3-8), demonstrated through pneumonia detection from pediatric chest radiographs. Experimental validation reveals remarkable parameter efficiency, requiring only 24-36 trainable parameters compared to millions for classical counterparts, achieving compression ratios exceeding 1 0 5 while maintaining competitive diagnostic performance. Optimal configurations achieve 84.00% accuracy (EfficientNet-B0, 6-qubit) and 73.33% (Xception, 4-qubit), with knowledge distillation providing statistically significant improvements ( p -vallues < 0.001, Cohen’s d > 2.0). Medical evaluation confirms diagnostic capability with sensitivity 81.4%, specificity 73.9%, and AUC 0.89, establishing quantum–classical knowledge transfer viability for resource-constrained healthcare deployment. QuantumMedKD reveals essential principles and proof-of-concept for quantum-enhanced healthcare AI systems within and beyond the noisy intermediate-scale quantum (NISQ) era for high efficiency and deployability, paving the way for future advancements in the field.
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.
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.
Quantum neural networks (QNNs), harnessing superposition and entanglement, have shown potential to surpass classical methods in complex learning tasks but remain limited by hardware constraints and noisy conditions. In this work, we present a novel framework for transferring knowledge from classical convolutional neural networks (CNNs) to QNNs via knowledge distillation, thereby reducing the need for resource intensive quantum training and error mitigation. We conduct extensive experiments using two parameterized quantum circuits (PQCs) with 4 and 8 qubits on MNIST, Fashion MNIST, and CIFAR10 datasets. The approach demonstrates consistent accuracy improvements attributed to distilled knowledge from larger classical networks. Through ablation studies, we systematically compare the effect of state of the art dimensionality reduction techniques fully connected layers, center cropping, principal component analysis, and pooling to compress high-dimensional image data prior to quantum encoding. Our findings reveal that fully connected layers retain the most salient features for QNN inference, thereby surpassing other down sampling approaches. Additionally, we examine state of the art data encoding methods (amplitude, angle, and qubit encoding) and identify amplitude encoding as the optimal strategy, yielding superior accuracy across all tested datasets and qubit configurations. Through computational analyses, we show that our distilled 4-qubit and 8-qubit QNNs achieve competitive performance while utilizing significantly fewer parameters than their classical counterparts. Our results establish a promising paradigm for bridging classical deep learning and emerging quantum computing, paving the way for more powerful, resource conscious models in quantum machine intelligence.
Purpose This study investigates the integration of quantum-enhanced machine learning (QML) into medical image analysis, focusing on classification and feature extraction tasks. It aims to evaluate whether hybrid quantum-classical models can overcome the limitations of traditional machine learning in handling high-dimensional, clinically relevant imaging data. Design/methodology/approach Hybrid architectures combining classical preprocessing with quantum components, specifically variational quantum circuits (VQCs) and quantum kernel methods, were developed and implemented in simulated quantum environments using Qiskit and PennyLane on Google Colab. Publicly available datasets (ChestX-ray14, Brain Tumor MRI and CT Hemorrhage) were preprocessed and encoded into quantum states. Model performance was evaluated against classical baselines (MLP and CNN) using accuracy, precision, recall, F1-score, convergence speed and trainable parameters. Findings Quantum-enhanced models achieved competitive or superior performance compared to classical models with significantly fewer trainable parameters and faster convergence. The Quantum Kernel SVM demonstrated the highest precision (89.1%) and F1-score (88.5%) using only 8 parameters, while VQC models converged up to 2× faster than classical baselines. These results highlight the potential of QML to improve efficiency, robustness and scalability in diagnostic imaging workflows, particularly in resource-constrained or data-limited settings. Research limitations/implications The study relies on simulated quantum environments, which do not capture hardware noise or qubit limitations. Future work will extend to real quantum devices, multimodal data integration and interpretability strategies. Practical implications Quantum kernel methods and lightweight VQC architectures enable faster, resource-efficient diagnostic modeling, making them suitable for clinical environments with limited computational capacity or time-sensitive decision-making needs. Social implications Improving the efficiency and accessibility of medical image analysis has the potential to support faster diagnosis and enhance healthcare delivery, particularly in underserved or resource-limited regions. Originality/value This study provides a practical evaluation of hybrid quantum-classical models applied to multiple real medical imaging datasets in a reproducible simulated environment. It offers an early-stage framework demonstrating how QML techniques can be integrated into existing machine learning pipelines for medical image analysis.
Abstract Pneumonia remains a leading cause of mortality globally, necessitating rapid and accurate diagnostic tools to assist radiologists. While classical Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), have demonstrated high efficacy in medical image analysis, they are often characterized by high computational complexity and massive parameter requirements. This research proposes a novel Hybrid Quantum-Classical Neural Network (HQCNN) to detect Pneumonia from chest X-ray images with enhanced computational efficiency. By integrating a classical ResNet-18 architecture for feature extraction with a Variational Quantum Circuit (VQC) based on PennyLane, we leverage the quantum mechanical principles of superposition and entanglement for pattern recognition. The proposed model was trained and validated on a standard chest X-ray dataset and benchmarked against a classical transfer learning equivalent. Experimental results indicate that the hybrid quantum model achieved a validation accuracy of 78% with significantly fewer trainable parameters (~ 24) compared to the classical baseline (62% accuracy, ~ 1026 parameters). Furthermore, the quantum model demonstrated superior sensitivity (100% Recall), successfully identifying all positive pneumonia cases in the validation batch. This study highlights the potential of Quantum Machine Learning (QML) as a resource-efficient, high-sensitivity alternative for future medical diagnostic systems.
Quantum-Enhanced Machine Learning (QEML) is a new computational paradigm that introduces the concept of quantum mechanics with the latest artificial intelligence algorithms to enhance the performance of the diagnostic process in the field of medical imaging. The early identification of diseases is crucial in mortality reduction and improving patient outcome, especially in capacities dealing with such diseases as cancer, neurological diseases, heart diseases, and infections of the lungs. Although conventional deep learning architectures, in particular, Convolutional Neural Networks (CNNs) have been highly successful in processing complex medical images such as MRI scans, CT images, X-rays, and histopathological data, they can be computationally expensive, need large data sets, and suffer in optimization of high dimensional feature spaces. The limitations that QEML tries to overcome are based on the quantum mechanical principles of superposition, entanglement and interference to facilitate better data representation and processing. The suggested structure is based on the classical deep learning architectures and variational quantum circuits together, to create a hybrid quantum-classical system, which improves features extraction and classification levels. Under this method, the classical image characteristics are coded into quantum states and processed in high-dimensional Hilbert spaces, which can enhance the separability of subtle disease patterns, which would have otherwise been challenging to obtain with conventional techniques. The objective of this hybrid system is to enhance the critical diagnostic measures like sensitivity and specificity without affecting the interpretability of such measures to be used in the clinical context. The proposed framework is also assessed in terms of its feasibility, its computational performance and scalability to the capabilities of present-day Noisy Intermediate-Scale Quantum (NISQ) devices. The model effectiveness is evaluated using objective evaluation metrics, which, in a way, guarantee the feasibility of the model application within a real-life healthcare context. On balance, quantum computing and medical image analysis integration is an exciting field of work of the future intelligent healthcare systems and has a potential of providing more efficient, accurate, and scalable diagnostic solutions.