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The Journal of Supercomputing

Quantum learning for medical image classification: a systematic review of algorithms, encoding strategies and hybrid architectures

N. Thirupathi Rao, V. V. Ramana CH

Published Oct 20, 2026• 0 citations
Publisher
DOI
10.1007/s11227-026-08932-5

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•Feb 12, 2026•1 citation
Quantum Learning (QL) has emerged as a promising approach to medical image classification, leveraging the principles of quantum mechanics to improve the performance and efficiency of machine learning algorithms. This systematic review provides a comprehensive critical assessment of the current status of QL techniques developed for medical image classification, with a specific focus on trends, methodologies, and future directions in this rapidly evolving field. A thorough literature search was conducted across five major databases, resulting in a total of 28 relevant studies published between 2018 and 2024. The studies were analyzed and classified based on the type of quantum algorithm, the medical image modality, and the performance metrics used. The analysis revealed a diverse range of QL techniques, including Quantum Support Vector Machines (QSVM), Quantum Convolutional Neural Networks (QCNN), and various hybrid quantum-classical approaches. These techniques have been applied to diverse medical image classification tasks, such as brain tumor classification, skin lesion classification, and COVID-19 detection, demonstrating promising results in terms of accuracy, sensitivity, and specificity. However, several challenges were identified, including the preprocessing and encoding of medical images for quantum processing, the limited scalability of current quantum hardware, and the need for interpretable and explainable QL models. This review underscores the immense potential of QL to revolutionize medical image classification, while also emphasizing the necessity of multidisciplinary collaborations and further research to overcome existing challenges and facilitate the integration of QL techniques into clinical practice. Reason for Expression of Concern:The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in ``Quantum Machine and Deep Learning for Medical Image Classification: A Systematic Review of Trends, Methodologies, and Future Directions (Manuscript 1252)''. The journal has initiated an additional editorial assessment of the article's methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal's policies and COPE guidance." See expression of concern available at: DOI: https://doi.org/10.52866/2788-7421.1387. Available at: http://ijcsm.researchcommons.org/ijcsm/vol7/iss1/41 We have found no evidence of misconduct or invalidity in the research. The gindings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research. See resolution of expression of concern available at: DOI: https://doi.org/10.52866/2788-7421.1408 Available at: http://ijcsm.researchcommons.org/ijcsm/vol7/iss2/8 The publication timeline is as follows: Original Article → Expression of Concern → Resolution.
This paper surveys the emerging field of hybrid quantum–classical (HQC) architectures for medical image analysis, with a particular focus on COVID-19 classification as a rep-resentative application. Despite extensive progress in quantum machine learning, there is still a lack of structured taxonomy-driven analysis that systematically characterizes how quantum components are integrated within hybrid imaging pipelines. This study pro-vides a structured review of representative HQC models, categorizing existing approaches based on the functional role of quantum modules, including front-end processing, feature extraction, and classification layers. A comparative analysis is conducted to examine how these architectural design choices relate to reported performance across different ex-perimental settings. The review shows that hybrid models can achieve strong binary classification performance under simulation conditions, particularly when quantum cir-cuits are used as feature extractors. However, performance in multi-class scenarios remains inconsistent and highly sensitive to encoding strategies, circuit design, and optimization settings. In addition, most existing studies rely on simulator-based evaluations, limited or single-source datasets, and non-standardized benchmarking protocols, which restrict reproducibility and generalization. Real-hardware validation remains scarce, and no consistent evidence of task-level quantum advantage over optimized classical baselines has been demonstrated. Overall, current HQC-based approaches should be considered exploratory rather than clinically validated solutions. Future progress requires stand-ardized benchmarking, multi-institutional validation, hardware-aware evaluation, and the development of interpretable hybrid architectures that respect NISQ-era constraints.
Abstract The convergence of quantum computing and medical image analysis has emerged as a promising frontier for advancing diagnostic intelligence beyond the limits of classical machine learning. Recent developments in quantum hardware and quantum machine learning (QML) algorithms have stimulated growing interest in their potential to enhance complex medical imaging tasks, including disease classification, image segmentation, and clinical decision support (Wei et al., 2023; Ullah and Garcia-Zapirain, 2024). This study presents a comprehensive review and synthesis of state-of-the-art QML methodologies applied to medical imaging, systematically examining both theoretical foundations and empirical implementations. We analyze quantum-based and hybrid quantum–classical models employed across modalities such as MRI, CT, X-ray, and histopathological imaging, with particular attention to classification accuracy, computational efficiency, and diagnostic robustness (Maheshwari et al., 2022; ElBedoui et al., 2025). Our findings reveal clear trends indicating that hybrid quantum–classical approaches currently dominate practical applications, consistently outperforming purely classical baselines in select low-dimensional and domain-specific diagnostic tasks. Performance gains are most evident in feature-rich yet data-constrained scenarios, where quantum kernels and variational circuits demonstrate enhanced representational capacity (Ajlouni et al., 2023; Landman et al., 2022). However, these advantages remain context-dependent and sensitive to noise, circuit depth, and encoding strategies. Overall, QML holds significant promise for improving medical image-based diagnosis, but its widespread clinical adoption is constrained by hardware scalability, limited qubit coherence, and unresolved data encoding challenges. Addressing these limitations will be critical for translating QML from experimental feasibility to routine medical practice (Marengo and Santamato, 2025; Rawas and AlSaeed, 2026).