Abstract The study is dedicated to the development of quantum epidemiology which is the expected next stage in epidemiology transformation as new quantum technologies have emerged. At the present time, epidemiology is entering the digital era and undergoes a paradigm shift from data-driven to value-driven strategy. The epidemiology data are characterized by uncertainty, multidimensionality, and disconnection, which drive to prefer the quantum approach for data exposition, creation of value, and modeling. The Quantum Data Lake concept is proposed. The data about DNA viruses with symptoms and diseases are shown as example of epidemiology data complexity. The Quantum Data Lake concept consists of several layers and quantum tools, including PT-symmetry and non-Hermiticity as intuitive modeling tools. PT-symmetry breaking is able to detect the hidden shift in the information which is permanently updated in the Data Lake. The duality of PT-symmetry breaking can be compared with the estimation of the best and worst scenarios simultaneously. In contrast to the widely discussed advantages of quantum computing such as high-speed performance and very large parallel scale, the proposed approach emphasizes the fundamental uniqueness of quantum theory for modeling. The study highlights the necessity to investigate the native processes of viruses’ interaction with the human population by relying on quantum theory’s natural properties. Implementation of quantum logic and reliance on a quantum theory is the fundamental difference between the current digital epidemiology and future quantum epidemiology.
Abstract The study is dedicated to the development of quantum epidemiology which is the expected next stage in epidemiology transformation as new quantum technologies have emerged. At the present time, epidemiology is entering the digital era and undergoes a paradigm shift from data-driven to value-driven strategy. The epidemiology data are characterized by uncertainty, multidimensionality, and disconnection, which drive to prefer the quantum approach for data exposition, creation of value, and modeling. The Quantum Data Lake concept is proposed. The data about DNA viruses with symptoms and diseases are shown as example of epidemiology data complexity. The Quantum Data Lake concept consists of several layers and quantum tools, including PT-symmetry and non-Hermiticity as intuitive modeling tools. PT-symmetry breaking is able to detect the hidden shift in the information which is permanently updated in the Data Lake. The duality of PT-symmetry breaking can be compared with the estimation of the best and worst scenarios simultaneously. In contrast to the widely discussed advantages of quantum computing such as high-speed performance and very large parallel scale, the proposed approach emphasizes the fundamental uniqueness of quantum theory for modeling. The study highlights the necessity to investigate the native processes of viruses’ interaction with the human population by relying on quantum theory’s natural properties. Implementation of quantum logic and reliance on a quantum theory is the fundamental difference between the current digital epidemiology and future quantum epidemiology.
The continuous manifestations of emerging and reemerging infectious diseases across the world is championed by microorganisms particularly, viruses and bacteria. These manifestations consistently keep researchers at work in establishing approaches towards minimising the burden of infectious diseases both locally and internationally. Microorganisms, especially bacteria and viruses, possess very disparate features like the presence of sensors, in-built biological engines, immaculate communication means, processing architecture, among others. Researchers are introducing these biological entities into the quantum theory network where their dynamical physical properties and concepts can be used to develop response mechanisms during infection stages. This article aims to review and present an account of quantum approaches derived from quantum mechanics as an emerging fundamental enabler and a novel concept in the treatment of infectious diseases. It also looks at the possible application of these quantum approaches in revolutionising discovery, development, and production of vaccines at a quantum scale and speed. A descriptive review of the literature was conducted by searching these electronic databases: Web of Science, PubMed, and SciELO. Findings in this article show that through the advent of single-particle approaches and high-resolution techniques, viral dynamics can be scrutinised during and after their assembly towards studying their infection development pathway. Additionally, quantum information in biological entities has been found to provide hereditary information insights supporting genetic studies in relation to infectious diseases. The introduction of quantum-inspired genetic algorithms towards predicting the secondary structures of RNA depicts certain superiority over strategies currently in existence. Ongoing research is putting quantum-based studies in an ideal direction towards shaping the understanding and perhaps treatment of infectious diseases.
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.
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.
Quantum computing is an emerging paradigm that will soon transform the face of several industries, such as health care. This paper focuses on applying quantum computing in optimizing analytics on healthcare data, especially amidst big data challenges in the digital age. A health system generates tremendous amounts of complex datasets requiring advanced tools to draw meaningful insights from such data. Traditionally, most of these methods fail to meet the demands for real-time processing, predictiveness, and data security. Taking leaps with extraordinary computing power from quantum computing, health analytics can be taken towards leaps of revolutionary precision in medicine, resource allocation, and early detection of diseases. This paper explains how quantum algorithms synergistically find applications in health data. Areas of applicability, technological constraints, and issues related to ethics form other emphases. This, in turn, provides a comprehensive literature review, a method for quantum-enhanced analytics, and an analysis of the results of simulated quantum experiments, which points toward this integration as transformative. Furthermore, an architectural framework for quantum-driven healthcare systems, as depicted in the study, is postulated. The results are illustrated, including figures and corresponding tables for descriptive performance benefits and challenges. Implications to clinical workflows, patient outcomes, and the larger health ecosystem are demonstrated in the discussion. The conclusion presents the limitations and avenues for further research focusing on interdisciplinarity with quantum computing for the best healthcare.
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.