This paper presents a comprehensive framework for integrating Quantum Machine situating the problem: rising healthcare costs and the increasing complexity of health-claims data demand more powerful predictive models than standard generalized linear models (GLMs) alone can reliably deliver. We review classical actuarial techniques and contemporary Learning (QML) into actuarial prediction pipelines for health insurance. We begin by machine learning (ML) practices in healthcare-cost and claims prediction and summarize the current state of quantum computing and QML with special attention to algorithms and methods applicable in the near-term (NISQ) era. We then introduce a hybrid quantum–classical actuarial pipeline (HQCAP) that maps actuarial targets (claim frequency, severity, high-cost utilizers, readmission risk) to QML model families (quantum kernel methods, variational quantum circuits, and quantum-enhanced optimization), together with detailed data-encoding strategies, loss functions and calibration techniques appropriate for insurance. We present an experimental plan (datasets, baselines, metrics) for evaluating HQCAP on representative healthcare cost and claims problems (using MEPS/MIMIC public datasets and synthetic insurance-claims data), and discuss implementation considerations (noise, qubit counts, simulators, software stacks), regulatory and auditability constraints, interpretability and fairness, and an economic analysis of when QML could be preferable to classical approaches. We close with limitations, open research directions, and practical recommendations for actuaries and insurers exploring QML today. Key contributions: (1) a detailed, implementable hybrid QML/actuarial workflow; (2) explicit mapping between actuarial targets and QML primitives; and (3) reproducible experimental protocols for academic and industry evaluation.
This paper presents a comprehensive framework for integrating Quantum Machine situating the problem: rising healthcare costs and the increasing complexity of health-claims data demand more powerful predictive models than standard generalized linear models (GLMs) alone can reliably deliver. We review classical actuarial techniques and contemporary Learning (QML) into actuarial prediction pipelines for health insurance. We begin by machine learning (ML) practices in healthcare-cost and claims prediction and summarize the current state of quantum computing and QML with special attention to algorithms and methods applicable in the near-term (NISQ) era. We then introduce a hybrid quantum–classical actuarial pipeline (HQCAP) that maps actuarial targets (claim frequency, severity, high-cost utilizers, readmission risk) to QML model families (quantum kernel methods, variational quantum circuits, and quantum-enhanced optimization), together with detailed data-encoding strategies, loss functions and calibration techniques appropriate for insurance. We present an experimental plan (datasets, baselines, metrics) for evaluating HQCAP on representative healthcare cost and claims problems (using MEPS/MIMIC public datasets and synthetic insurance-claims data), and discuss implementation considerations (noise, qubit counts, simulators, software stacks), regulatory and auditability constraints, interpretability and fairness, and an economic analysis of when QML could be preferable to classical approaches. We close with limitations, open research directions, and practical recommendations for actuaries and insurers exploring QML today. Key contributions: (1) a detailed, implementable hybrid QML/actuarial workflow; (2) explicit mapping between actuarial targets and QML primitives; and (3) reproducible experimental protocols for academic and industry evaluation.
Health insurance actuarial science has traditionally relied on classical statistical models to predict claims, assess risk, and set premiums. However, the growing complexity and high dimensionality of healthcare datasets challenge conventional techniques. Quantum Machine Learning (QML), integrating quantum computing and artificial intelligence, presents a promising paradigm for accelerating computations and capturing intricate correlations in insurance datasets. This paper develops a hybrid quantum-classical framework for actuarial predictions, encompassing claims forecasting, mortality and morbidity risk modeling, and dynamic premium optimization. Extensive simulation studies demonstrate QML’s ability to improve predictive accuracy, handle high-dimensional data, and enable risk-adjusted pricing strategies. The paper concludes with discussions on operational integration, ethical considerations, and future research directions.
Healthcare 5.0 combines artificial intelligence, the Internet of Medical Things, edge and cloud computing, and digital twins to support safer, more personal, and more efficient care. Interest in quantum machine learning is growing in this space, but the current evidence is fragmented and hard to compare across studies. Many reports focus on small tasks, use weak or inconsistent baselines, and do not separate algorithmic gains from costs caused by data encoding, repeated circuit execution, and hardware noise. This survey addresses these gaps by providing a structured synthesis of Healthcare 5.0 applications and by offering a practical comparison between classical machine learning and quantum machine learning using criteria that matter for clinical use. We review representative use cases in medical imaging, electronic health record based prediction, remote monitoring, decision support, and digital twin enabled systems. We then analyze QML integration patterns that are realistic today, with emphasis on hybrid pipelines where classical models extract features and quantum components act as narrow decision or optimization modules. Finally, we consolidate the barriers that currently limit adoption, including encoding overhead, limited qubit counts and circuit depth, training instability and reproducibility, privacy and governance constraints, and the need for careful benchmarking and reporting. We conclude with guidance on evaluation and study design to support fair comparison, clearer clinical relevance, and better alignment with Healthcare 5.0 engineering requirements.
The growing complexity, interdependencies, and rapid fluctuations inherent in modern financial markets create substantial challenges for accurate forecasting, portfolio optimization, and risk management. Conventional machine learning techniques, while powerful, often face limitations in capturing nonlinear relationships and processing high-dimensional datasets efficiently. Quantum machine learning (QML) has emerged as a promising paradigm that leverages quantum computing principles to enhance predictive modeling in finance. This study presents a comprehensive investigation into the application of QML methods—including variational quantum circuits, quantum kernel estimation, and quantum-enhanced support vector machines—for financial time-series prediction and asset price classification. We propose a hybrid quantum-classical framework that integrates quantum feature mapping with classical optimizers to improve model expressiveness and convergence. Empirical experiments are conducted using historical stock market data and synthetic datasets to benchmark QML approaches against established classical models such as long short-term memory networks and gradient boosting machines. The results demonstrate that QML techniques can achieve superior prediction accuracy and lower computational latency under certain data regimes, particularly when dealing with small-to-medium-sized datasets and high feature correlations. Additionally, the study examines scalability considerations, hardware constraints of near-term quantum devices, and the interpretability of quantum model outputs in financial decision-making contexts. The findings underscore the transformative potential of quantum machine learning as an innovative tool for advancing predictive analytics in finance and provide practical insights into how financial institutions can begin integrating QML capabilities into their workflows.
Quantum computing holds the potential to improve handling computational uncertainty, predictive modeling, and optimization challenges which is particularly promising for the insurance and risk management fields. This paper presents the current state of literature on quantum computing applications within the insurance industry, with a focus on actuarial science. The novel application opportunities of quantum technologies in probabilistic modeling, data analyses, and optimization in areas including insurance capital modeling, pricing, and reserving are examined. The paper categorizes the literature as follows: (1) technical applications, (2) security and healthcare implications, and (3) legal and societal perspectives. Regulatory and socioeconomic concerns are evaluated, stressing the necessity of infrastructure readiness against quantum threats, particularly in securing sensitive data. Governance frameworks and ethical guidelines tailored to the insurance sector are discussed. By integrating technical, regulatory, and societal aspects, this paper provides a comprehensive view of the opportunities and challenges quantum computing presents to the insurance industry. The need for coordinated efforts across actuarial science, legal scholarship, and strategic management is emphasized to ensure secure and ethical implementation.
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
Quantum Machine Learning (QML) has emerged as a promising interdisciplinary paradigm that integrates quantum computing and machine learning to address limitations of classical approaches in processing complex high-dimensional heterogeneous and imbalanced medical data. This study presents a systematic literature review of QML applications in healthcare based on 94 primary studies published between 2020 and April 2025. The review follows Kitchenham’s guidelines and the PRISMA framework to ensure rigor transparency and reproducibility. Seven research questions were formulated to examine publication trends sources datasets topics quantum model categories algorithms methods and evaluation metrics. Findings reveal a sharp increase in publication activity after 2023 indicating growing academic interest in healthcare-oriented QML. Medical imaging datasets were most frequently used followed by signal-based and tabular datasets with 86.5% of studies relying on public data. Hybrid classical-quantum approaches including CNN-QSVM Quantum Neural Networks and Variational Quantum Classifiers were dominant because they combine classical feature extraction with quantum-enhanced learning. However most studies were conducted in simulation environments rather than real hardware reflecting NISQ limitations including noise decoherence limited qubits and circuit depth constraints. Many results used small curated or imbalanced datasets raising concerns regarding generalizability reproducibility and clinical validity. Although numerous studies reported high accuracy standardized evaluation protocols external validation and uncertainty assessment remain limited. Overall QML in healthcare remains exploratory with substantial potential but significant barriers. This review highlights progress gaps and future directions emphasizing clinically representative datasets robust benchmarking interpretable noise-resilient models and real-hardware implementation to support trustworthy deployment in healthcare systems worldwide settings today.