Effective epidemic control is crucial for mitigating the spread of infectious diseases, particularly when pharmaceutical interventions such as vaccines or treatments are limited. Nonpharmaceutical strategies, including mobility restrictions, are key in reducing transmission rates but require careful optimization to balance public health benefits and socioeconomic costs. Quantum computing is emerging as a powerful tool for solving complex optimization problems that are intractable for classical methods and can thus be leveraged to handle mobility restrictions. This article presents a new approach to optimizing epidemic control strategies using quantum computing techniques. We focus on non-pharmaceutical interventions, particularly mobility restriction, modeled as a discrete-time network epidemic process based on the susceptible-infected-susceptible and sus-ceptible-infected-removed frameworks. The control problem is formulated as a combinatorial optimization task, inherently NPhard due to the binary nature of intervention decisions. To tackle this computational complexity, we derive a Quadratic Unconstrained Binary Optimization representation of the control problem, enabling its efficient solution via quantum computing resources. Our methodology is validated through numerical simulations on realistic case studies, showcasing the potential of quantum algorithms for enhancing epidemic control strategies. These findings pave the way for leveraging quantum optimization in broader applications of networked dynamical systems, demonstrating its viability for complex decision-making processes in public health management.
Effective epidemic control is crucial for mitigating the spread of infectious diseases, particularly when pharmaceutical interventions such as vaccines or treatments are limited. Nonpharmaceutical strategies, including mobility restrictions, are key in reducing transmission rates but require careful optimization to balance public health benefits and socioeconomic costs. Quantum computing is emerging as a powerful tool for solving complex optimization problems that are intractable for classical methods and can thus be leveraged to handle mobility restrictions. This article presents a new approach to optimizing epidemic control strategies using quantum computing techniques. We focus on non-pharmaceutical interventions, particularly mobility restriction, modeled as a discrete-time network epidemic process based on the susceptible-infected-susceptible and sus-ceptible-infected-removed frameworks. The control problem is formulated as a combinatorial optimization task, inherently NPhard due to the binary nature of intervention decisions. To tackle this computational complexity, we derive a Quadratic Unconstrained Binary Optimization representation of the control problem, enabling its efficient solution via quantum computing resources. Our methodology is validated through numerical simulations on realistic case studies, showcasing the potential of quantum algorithms for enhancing epidemic control strategies. These findings pave the way for leveraging quantum optimization in broader applications of networked dynamical systems, demonstrating its viability for complex decision-making processes in public health management.
This review explores the integration of quantum machine learning (QML) algorithms into real-time epidemic surveillance systems and health policy simulation frameworks. As conventional machine learning models face scalability and interpretability challenges with high-dimensional epidemiological data, QML offers a transformative paradigm by leveraging quantum computing principles to enhance pattern recognition, data compression, and forecasting precision. The study examines emerging QML architectures and their application to real-time tracking of infectious diseases, anomaly detection in public health datasets, and dynamic modeling of transmission patterns. Additionally, it assesses the role of QML in simulating the impact of health interventions, policy shifts, and behavioral dynamics on epidemic trajectories. Emphasis is placed on multi-modal data integration from genomics, environmental monitoring, and mobility trends to refine predictive accuracy. Key implementation challenges—such as quantum decoherence, algorithmic instability, regulatory constraints, and limited access to quantum hardware—are critically analyzed. The paper concludes by proposing a roadmap for the adoption of hybrid quantum-classical frameworks and policy-informed computational modeling to strengthen global epidemic preparedness and responsive health governance.
This review explores the integration of quantum machine learning (QML) algorithms into real-time epidemic surveillance systems and health policy simulation frameworks. As conventional machine learning models face scalability and interpretability challenges with high-dimensional epidemiological data, QML offers a transformative paradigm by leveraging quantum computing principles to enhance pattern recognition, data compression, and forecasting precision. The study examines emerging QML architectures and their application to real-time tracking of infectious diseases, anomaly detection in public health datasets, and dynamic modeling of transmission patterns. Additionally, it assesses the role of QML in simulating the impact of health interventions, policy shifts, and behavioral dynamics on epidemic trajectories. Emphasis is placed on multi-modal data integration from genomics, environmental monitoring, and mobility trends to refine predictive accuracy. Key implementation challenges—such as quantum decoherence, algorithmic instability, regulatory constraints, and limited access to quantum hardware—are critically analyzed. The paper concludes by proposing a roadmap for the adoption of hybrid quantum-classical frameworks and policy-informed computational modeling to strengthen global epidemic preparedness and responsive health governance.
Quantum computing has emerged as an intellectual model of computing that is able to address complex and high-dimensional problems in ways that classical systems cannot. The predictive analysis of epidemic outbreak in the sphere of public health is an activity that requires working with big and heterogeneous data, electronic health records, genetic sequences, mobility patterns, environment, and social interaction network. Traditional machine learning and deep neural Net-based approaches have significantly improved the ability to predict epidemics, but they lack scalability, computing capabilities, and the capacity to model complex nonlinear interactions. This research proposes a dedicated quantum architecture of forecasted epidemic outbursts according to vast datasets of health data to foster an improved degree of forecasting, speed of computation, and early identification. The suggested system is a hybrid quantum-classical system, integrating quantum machine learning algorithms, including variational quantum circuits, quantum support vector machines, and quantum optimization algorithms. Data is processed using classical systems to extract features whereas quantum processors solve complex problems of probabilistic inference and optimization. Through quantum superposition and entanglement, the framework can study multiple transmission situations simultaneously, and this allows one to identify the pattern of outbreaks and even hotspots within a short period of time. The model is also driven by real-time healthcare systems and global surveillance network data streams to enhance the responsiveness of the model to health threats. Simulation analysis indicates that the enhanced quantum framework is more precise in predictions, and the convergence will be fast and consistent as compared to traditional models. The system can recognize the trend of an outbreak sooner than and can effectively manage a high-dimensional epidemiological data. Besides, the issue of such factors as data privacy, scalability, and disadvantages of current quantum hardware is discussed in the paper. Overall, the proposed solution suggests that quantum computing can serve as a future epidemic intelligence system, in which a reaction to a global health crisis can be proactive and information-oriented.
The convergence of quantum computing and epidemiological modeling represents a paradigm shift in pandemic preparedness and response strategies. This study explores the development and implementation of quantum-powered epidemiological models designed to enable proactive responses to pandemics and emerging health threats. Traditional computational approaches face significant limitations in processing the vast, multidimensional datasets required for accurate disease forecasting, while quantum computing offers exponential speedups in simulation and optimization tasks. Through a comprehensive analysis of quantum algorithms, machine learning integration, and real-world applications, this research demonstrates how quantum-enhanced models can predict disease transmission patterns with unprecedented accuracy and speed. The findings reveal that quantum computing can reduce computational time for complex epidemiological simulations from weeks to hours, enabling real-time decision-making during health crises. Our study also identifies key challenges including hardware limitations, algorithm development, and the need for interdisciplinary collaboration. The results suggest that quantum-powered epidemiological models hold transformative potential for global health security, offering healthcare systems and policymakers the tools necessary to anticipate, prepare for, and mitigate future pandemic threats before they escalate into global crises.
Optimization is a crucial challenge across various domains, including finance, resource allocation, and mobility. Quantum computing has the potential to redefine the way we handle complex problems by reducing computational complexity and enhancing solution quality. Optimization, particularly of objective functions, stands to benefit significantly from quantum solvers, which leverage principles of quantum mechanics like superposition, entanglement, and tunneling. The Ising and Quadratic Unconstrained Binary Optimization (QUBO) models are the most suitable formulations for these solvers, involving binary variables and constraints treated as penalties within the overall objective function. To harness quantum approaches for optimization, two primary strategies are employed: exploiting quantum annealers—special-purpose optimization devices—and designing algorithms based on quantum circuits. This review provides a comprehensive overview of quantum optimization methods, examining their advantages, challenges, and limitations. It demonstrates their application to real-world scenarios and outlines the steps to convert generic optimization problems into quantum-compliant models. Lastly, it discusses available tools and frameworks that facilitate the exploration of quantum solutions for optimization tasks.
In this work, we introduce a quantum-inspired epidemic model to study the dynamics of an infectious disease in a population divided into compartments. By treating the healthy population as a large reservoir, we construct a framework based on open quantum systems and a Hilbert space formalism to model the spread of the infection. This approach allows for a mathematical framework that captures both Markovian and semi-Markovian dynamics in the evolution equations. Through numerical experiments, we examine the effect of varying memory parameters on the epidemic evolution, focusing in particular on the conditions under which the model remains physically admissible.This article is part of the theme issue 'Quantum theory and topology in models of decision making (Part 1)'.