Quantum Machine Learning (QML) is fast becoming a powerful instrument in medical diagnostics because it can process high-dimensional data and small samples efficiently. This paper is a detailed analysis of QML models used for disease prediction, with a relative comparison of Variational Quantum Classifiers (VQC), Quantum Convolutional Neural Networks (QCNN), and Quantum Support Vector Machines (QSVM) in clinical environments. We suggest a new multimodal quantumclassical hybrid approach that analyzes electronic health records (EHR), medical imaging, and biosignal data by means of dedicated QML pipelines with late fusion for ultimate decisionmaking. Recent IEEE literature is integrated in experimental performance glimpses to demonstrate the merits, difficulties, and viability of QML in real-world healthcare applications. The envisioned architecture provides the foundation for future applications of quantum-enhanced diagnostics with existing quantum simulators as well as emerging hardware.
Quantum Machine Learning (QML) is fast becoming a powerful instrument in medical diagnostics because it can process high-dimensional data and small samples efficiently. This paper is a detailed analysis of QML models used for disease prediction, with a relative comparison of Variational Quantum Classifiers (VQC), Quantum Convolutional Neural Networks (QCNN), and Quantum Support Vector Machines (QSVM) in clinical environments. We suggest a new multimodal quantumclassical hybrid approach that analyzes electronic health records (EHR), medical imaging, and biosignal data by means of dedicated QML pipelines with late fusion for ultimate decisionmaking. Recent IEEE literature is integrated in experimental performance glimpses to demonstrate the merits, difficulties, and viability of QML in real-world healthcare applications. The envisioned architecture provides the foundation for future applications of quantum-enhanced diagnostics with existing quantum simulators as well as emerging hardware.
The healthcare systems produce a tremendous volume of medical data which may be in the form of patient records, laboratory test results and diagnostic reports. It is quite necessary to analyze such data accurately and efficiently in order to detect diseases early and plan appropriate treatment . Conventional machine learning methods have a tendency to struggling with large-dimensional and complicated medical data. To address these shortcomings, this project suggests a hybrid Quantum Machine Learning (QML) scheme of analyzing medical data and diagnosing a disease. The suggested system combines the classical data preprocessing with the quantum-enhanced data learning algorithms including Variational Quantum Circuits (VQC) and Quantum Support Vector Machines (QSVM). Medical data is first processed by cleaning, normalizing and encoding to quantum states. Complex patterns and correlations that traditional algorithms can hardly be able to learn are then learned with the quantum model. The measures used to judge the performance are accuracy, precision, recall and F1-score. The results of the experimental process show that the model based on QML yields better diagnostic accuracy than the conventional machine learning. This is where there is great potential in real time medical decision support systems, with the future prediction of disease and the personalized solution of care. The suggested framework indicates the potential and benefits of switching to quantum computing methods in healthcare analytics.
Quantum machine learning (QML) is an emerging field combining quantum computing and artificial intelligence, with promising applications in medicine and healthcare. This survey reviews more than 60 studies published between 2018 and 2025, highlighting a sharp increase in research activity, especially in the last three years. We address seven core research questions related to publication trends, the use of real quantum hardware versus simulators, quantum architectures overview, dataset types, medical domains, algorithmic frameworks, and reported results. Our analysis shows that most QML research in healthcare is conducted on simulators due to limited hardware access, and it relies on small datasets. Quantum convolutional neural network (QCNN) architectures dominate image-based medical tasks such as tumor detection, pneumonia diagnosis, and ECG interpretation, while feature-based datasets are mainly analyzed with variational quantum classifiers and quantum support vector machines. Despite hardware constraints, QML models often match or surpass classical machine learning approaches in accuracy, frequently reaching 95–99%. However, these performance statements should be qualified to recognize experimental limitations and avoid excessive optimism and should not be interpreted as definitive proof of quantum superiority at this stage. Additionally, issues with reproducibility and reporting of hardware details persist, which is a significant research gap. This review emphasizes the need for standardized benchmarks, more real hardware testing, and architecture-aware algorithm design. With the potential for accelerated diagnostics and personalized healthcare, QML represents a strategic direction for future medical research.
Prompt diagnosis of disease is a strong driver for decreasing mortality, improving patient care and healthcare expenditures. Classical machine learning (ML), which has shown great potential in bolstering predictive modeling throughout oncology, cardiology, and neurology, runs the risk of excessive data dimensionality along with high computational complexity. Quantum computing is an exciting new paradigm for computation that takes advantage of superposition and entanglement which can be beneficial in learning patterns in high-dimensional spaces and for kernel-based learning. This review provides a systematic summary of the preliminary work on quantum machine learning (QML) with empirical applications to early disease diagnosis, while emphasizing its theoretical foundations, algorithmic developments and application in healthcare. We scope the conversation on quantum support vector machines, variational quantum classifiers, quantum kernels and hybrid architectures for a quantum classical. We also examine current hardware limitations in the NISQ era and assess how close these methods are to being ready for translation into public health applications. Although widespread clinical application is still far away, hybrid QML approaches are finding utility in low-data and high-complexity settings. We conclude with a discussion of research challenges, regulation and strategy towards realizing such quantum-enhanced precision diagnostics through U.S. public health innovation initiatives
Early diagnosis of chronic diseases is paramount to improving patient survival rates, optimizing treatment trajectories, and mitigating escalating healthcare expenditures. While the recent explosion of heterogeneous healthcare data presents robust opportunities for predictive modeling, it simultaneously introduces severe computational bottlenecks regarding data dimensionality and non-linear complexity. This paper proposes a novel, high-performance hybrid prediction framework that seamlessly fuses classical machine learning topologies with Variational Quantum Circuits (VQCs) within a Quantum Machine Learning (QML) paradigm. The proposed architecture targets the multi-classification and risk profiling of five high-prevalence chronic conditions: diabetes, cardiovascular disease, breast cancer, chronic liver disease, and chronic kidney disease This project introduces “Quantum Health,” a Quantum Machine Learning (QML)-based multi disease prediction system that integrates classical and quantum computing to enhance diagnostic performance. It employs five quantum-enhanced models: Quantum Random Forest for breast cancer, Quantum Neural Network for diabetes, Quantum k-Nearest Neighbors (QkNN) for heart disease, Quantum AdaBoost for liver disease, and Quantum Naive Bayes for kidney disease. The models are evaluated using both classical methods and PennyLane simulations, enabling direct comparison of performance. The system is deployed as a Flask-based web application featuring a user-friendly dashboard, disease-specific prediction modules, prediction history with CSV export, and an analytics dashboard comparing classical and quantum accuracies. Each module provides predictions, confidence scores, and feature importance insights. Unlike monolithic classical models, our approach incorporates a parameterized quantum layer that maps low-dimensional classical medical vectors into high-dimensional Hilbert spaces, capturing intricate, non-linear correlations that evade classical kernels. Experimental evaluations executed on simulated quantum backends demonstrate that the proposed Quantum Hybrid Model drastically outperforms conventional baselines, achieving an overall classification accuracy of 96.8% and an Area Under the Curve (AUC) of 98.4%. The findings establish that integrating quantum circuit state vectors with ensemble classical architectures provides a scalable, computationally efficient, and highly generalizable paradigm for next-generation clinical decision support systems.
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.
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.