Quantum computing is being investigated as a computational approach for medical image analysis, but its potential role in cardiovascular imaging remains unclear. This review examines gate-based, quantum annealing, and hybrid quantum-classical techniques for cardiovascular image enhancement, image reconstruction, image segmentation, image feature extraction, and image-based disease classification, and it treats quantum-inspired classical algorithms as a separate category. The literature was identified through a structured search of four bibliographic databases and one preprint server, using stated eligibility criteria and study-level data extraction. Applications are considered in echocardiography, computed tomography, cardiac magnetic resonance imaging, nuclear imaging, and coronary angiography, and direct cardiovascular evidence is distinguished from methods developed for other medical imaging tasks. One adjacent cardiac study based on chest radiography is reported separately because it falls outside these five modalities. For each study, the imaging data, qubit count, encoding strategy, execution on a simulator or physical hardware, classical baseline, and validation design are tabulated. The review also defines the forms of quantum advantage that a study could claim, quantifies the qubit and state-preparation costs of common image encodings for realistic cardiac image sizes, and compares quantum methods with classical machine learning and deep learning in terms of computational requirements, training, interpretability, and reported performance. The existing evidence is exploratory and relies mostly on small datasets, compressed image representations, and quantum simulators. Some results are encouraging, but no consistent advantage over well-tuned classical approaches has been established. The key obstacles are image encoding, hardware noise, circuit depth, trainability, end-to-end computational cost, and the lack of external validation. Hybrid designs offer a practical setting for testing selected quantum components within current imaging pipelines. Future progress requires reproducible cardiovascular benchmarks, evaluation on physical quantum devices, and staged clinical validation. Quantum computing is therefore an emerging field of study whose role in cardiovascular imaging has to be defined through rigorous technical and clinical comparison.
Quantum computing is being investigated as a computational approach for medical image analysis, but its potential role in cardiovascular imaging remains unclear. This review examines gate-based, quantum annealing, and hybrid quantum-classical techniques for cardiovascular image enhancement, image reconstruction, image segmentation, image feature extraction, and image-based disease classification, and it treats quantum-inspired classical algorithms as a separate category. The literature was identified through a structured search of four bibliographic databases and one preprint server, using stated eligibility criteria and study-level data extraction. Applications are considered in echocardiography, computed tomography, cardiac magnetic resonance imaging, nuclear imaging, and coronary angiography, and direct cardiovascular evidence is distinguished from methods developed for other medical imaging tasks. One adjacent cardiac study based on chest radiography is reported separately because it falls outside these five modalities. For each study, the imaging data, qubit count, encoding strategy, execution on a simulator or physical hardware, classical baseline, and validation design are tabulated. The review also defines the forms of quantum advantage that a study could claim, quantifies the qubit and state-preparation costs of common image encodings for realistic cardiac image sizes, and compares quantum methods with classical machine learning and deep learning in terms of computational requirements, training, interpretability, and reported performance. The existing evidence is exploratory and relies mostly on small datasets, compressed image representations, and quantum simulators. Some results are encouraging, but no consistent advantage over well-tuned classical approaches has been established. The key obstacles are image encoding, hardware noise, circuit depth, trainability, end-to-end computational cost, and the lack of external validation. Hybrid designs offer a practical setting for testing selected quantum components within current imaging pipelines. Future progress requires reproducible cardiovascular benchmarks, evaluation on physical quantum devices, and staged clinical validation. Quantum computing is therefore an emerging field of study whose role in cardiovascular imaging has to be defined through rigorous technical and clinical comparison.
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).
Deep learning has transformed radiology, but the computational cost of training and deploying modern models is outpacing what classical hardware can support. Quantum computing has been proposed as one response to this bottleneck: an n-qubit system can represent 2n states simultaneously, although practical speedups require efficient algorithms, data encoding, and measurement strategies rather than superposition alone. This review introduces radiologists to five core quantum concepts, qubits, superposition, entanglement, quantum gates, and quantum circuits, and to the superconducting, trapped-ion, and photonic hardware platforms that define the current noisy intermediate-scale quantum (NISQ) era. We then summarize the quantum machine learning architectures most often applied to medical imaging (quantum support vector machines, quantum neural networks, variational quantum circuits, and quantum convolutional neural networks) and quantum image representations such as the flexible representation of quantum images (FRQI) and the novel enhanced quantum representation (NEQR). Reported applications include brain tumor classification and segmentation on MRI, pneumonia detection on chest radiographs, skin and breast lesion classification, CT and PET reconstruction, and ultrasound denoising. Nearly all published systems are hybrid quantum-classical pipelines evaluated on small, downsampled benchmark datasets or simulators, and clinical deployment is not yet feasible. We conclude with current limitations and translational priorities for radiology.
Quantum Machine Learning (QML) is an emerging, powerful paradigm at the intersection of quantum computing and artificial intelligence, with the potential to enhance medical image analysis. We compared their performance with classical counterparts, while critically examining the gap between theoretical quantum advantages and the practical constraints of current Noisy Intermediate-Scale Quantum (NISQ) implementations. This review explored the current landscape of QML-based methodologies applied to key medical imaging modalities - Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-rays, and histopathology. We systematically analyzed how quantum-enhanced models were employed for critical tasks such as image classification, segmentation, and reconstruction, and we compared their performance with that of classical counterparts. The review delved into the architectural innovations that enabled these quantum approaches, including hybrid quantum-classical frameworks, variational quantum circuits, and quantum convolutional neural networks. Particular emphasis was given to the clinical relevance of QML applications, from early diagnostics to real-time surgery planning, and how quantum advantage might influence data efficiency, interpretability, along computational scalability. Finally, we highlighted current challenges in data encoding, noise resilience, and hardware limitations, and discussed promising future directions that could enable the practical deployment of QML in healthcare settings. This comprehensive review aimed to serve as a foundational reference for researchers and practitioners looking to harness quantum computing in medical imaging and precision medicine.
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, demanding innovations that transcend the limitations of conventional diagnostic modalities. Traditional imaging technologies—such as echocardiography, CT angiography, and MRI—though well-established, often struggle with trade-offs between speed, resolution, and predictive capabilities. Emerging advances in quantum science, particularly quantum computing1 and quantum sensing2, promise to redefine cardiac diagnostics by enabling unprecedented computational power, enhanced imaging fidelity, and predictive analytics. This paper explores the paradigm shift from traditional cardiac imaging to quantum-assisted diagnostics, emphasizing its transformative potential in precision cardiology. Quantum computing harnesses the principles of superposition3, entanglement4, and quantum tunneling5 to perform complex computations at speeds exponentially faster than classical systems. In cardiology, this capability translates into ultra-rapid analysis of massive multi-modal datasets—integrating genetic profiles, imaging scans, wearable sensor data, and electronic health records—to derive predictive risk scores and detect disease progression at its earliest molecular signatures. Quantum machine learning algorithms can identify subtle, non-linear patterns6 in cardiac pathophysiology that conventional statistical models fail to capture, thus enabling clinicians to anticipate adverse events such as arrhythmias, myocardial infarction, or heart failure with higher accuracy and lead time. Quantum sensing further enhances diagnostic precision through highly sensitive magnetometers and photon detectors, capable of mapping cardiac electrophysiology and microvascular changes at nanoscale resolution without invasive procedures. These innovations address current bottlenecks in traditional imaging—such as motion artifacts, limited temporal resolution, and radiation exposure—offering safer, faster, and more detailed diagnostic pathways. By coupling quantum sensors with real-time data processing, clinicians can monitor dynamic cardiac functions continuously, bridging the gap between snapshot diagnostics and continuous cardiovascular health tracking. This research paper presents a comparative analysis of traditional versus quantum-enabled cardiac diagnostics, highlighting the future potential of quantum technologies in cardiac detection and medical assistance. The study emphasizes advancements in imaging clarity, predictive accuracy, and clinical outcome improvement enabled by quantum approaches. A progressive implementation model is proposed, outlining how quantum-assisted systems can be integrated into existing hospital infrastructure through cloud-based quantum computing platforms, thereby reducing costs and minimizing operational disruptions. The paper explores regulatory, ethical, and training considerations essential for the medical community in adopting these emerging technologies. The paper shows that using quantum technology in heart disease diagnosis can make the process much faster and more accurate. This means doctors can plan treatments better and help reduce serious illness by detecting problems early. Quantum methods can also predict heart issues even before symptoms appear, making it possible to screen large groups of people at low cost and without painful tests. In the future, combining quantum-powered artificial intelligence (AI) with telemedicine could bring advanced heart care to people everywhere, especially in areas where hospitals and doctors are limited. In conclusion, combining quantum computing with heart care could bring a big change in how we treat heart diseases. Instead of waiting for problems to appear, doctors could use quantum tools to predict risks early and give more accurate treatments. This can help reduce heart disease worldwide. Even though there are challenges in making these technologies widely available, their potential is huge. This research shows why it is important to adopt such tools early, encourage new ideas, and work together so that patients can benefit from these advances in the near future.
Cardiac risk prediction is critical for the early detection and prevention of cardiovascular diseases, a leading global cause of mortality. In response to the growing volume and complexity of healthcare data, there has been increasing reliance on computational approaches to enhance clinical decision-making and improve early detection of cardiac risks. Although classical machine learning techniques have demonstrated strong performance in cardiovascular disease prediction, their efficiency and scalability are increasingly challenged by high-dimensional and large-scale medical datasets. Emerging advances in quantum computing have introduced quantum machine learning (QML) as a promising alternative, offering novel computational paradigms with the potential to outperform classical methods in terms of speed and problem-solving capability. This review analyzed twelve studies, evaluating data types, quantum architecture, performance metrics, and comparative efficacy against classical machine learning models. Our findings indicate that QNNs show promise for enhanced predictive accuracy and computational efficiency. However, significant challenges in scalability, noise resilience, and clinical integration persist. The translation of quantum advantage into clinical practice necessitates further validation on large-scale with diverse datasets.
Abstract Image segmentation remains one of the most extensively studied problems in computer vision, with applications ranging from medical image analysis and autonomous driving to remote sensing and industrial inspection. While deep learning approaches currently dominate the field, interest in quantum computing has motivated the development of quantum and quantum-inspired segmentation techniques that explore alternative strategies for image representation, optimization, and learning. This survey provides a comprehensive review of quantum and quantum-inspired image segmentation methods within the broader context of modern segmentation research. To establish this context, we first examine the evolution of image segmentation methodologies, covering classical image processing techniques, machine learning approaches, and contemporary deep learning architectures.We then organize the existing quantum image segmentation literature into four categories: quantum-inspired, tensor network-based, hybrid quantum-classical, and fully quantum methods. Across these categories, we analyze computational principles, image encoding and representation strategies, application domains, and datasets. We further compare quantum and quantum-inspired approaches with established segmentation paradigms in terms of supervision, scalability, and hardware requirements. Our analysis indicates that quantum-inspired methods currently represent the most developed direction, while hybrid approaches are emerging and fully quantum methods remain constrained by quantum hardware, data encoding costs, and scalability challenges. By connecting quantum image segmentation with the broader evolution of segmentation research, this survey provides a unified perspective on the field. It identifies current trends, open challenges, and promising future directions for researchers exploring the emerging role of quantum computing in image segmentation.