Processes of viral diffusion are important in biological, technological, and social systems alike. Several mathematical models of infection have been developed to predict diffusion through networks, such as nonlinear dynamical systems (NLDSs). Such models generally offer accurate representations of real-world diffusion, particularly for networks with static topologies. However, simulations of viral diffusion are computationally expensive, rendering them infeasible for large-scale networks. Here, a new approach is shown that leverages quantum computing to make viral diffusion simulations feasible for large networks, independent of network topology. Simulations of an error-free quantum circuit accurately modeled viral diffusion, with multivariate Euclidean distances from predicted infection probabilities capped near 8% for a network with N = 5 nodes and t = 20 time-steps. This is sufficient accuracy to distinguish the relative susceptibility of nodes and to identify significant changes, such as periods of especially high susceptibility. The results illustrate the potential for quantum computational network simulation to provide accurate models of diffusion through large networks, an important real-world application of quantum computing. The ability to simulate viral diffusion is invaluable for researchers across disciplines who aim to understand, anticipate, prepare for, and intervene in ongoing diffusion processes.
Processes of viral diffusion are important in biological, technological, and social systems alike. Several mathematical models of infection have been developed to predict diffusion through networks, such as nonlinear dynamical systems (NLDSs). Such models generally offer accurate representations of real-world diffusion, particularly for networks with static topologies. However, simulations of viral diffusion are computationally expensive, rendering them infeasible for large-scale networks. Here, a new approach is shown that leverages quantum computing to make viral diffusion simulations feasible for large networks, independent of network topology. Simulations of an error-free quantum circuit accurately modeled viral diffusion, with multivariate Euclidean distances from predicted infection probabilities capped near 8% for a network with N = 5 nodes and t = 20 time-steps. This is sufficient accuracy to distinguish the relative susceptibility of nodes and to identify significant changes, such as periods of especially high susceptibility. The results illustrate the potential for quantum computational network simulation to provide accurate models of diffusion through large networks, an important real-world application of quantum computing. The ability to simulate viral diffusion is invaluable for researchers across disciplines who aim to understand, anticipate, prepare for, and intervene in ongoing diffusion processes.
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.
Biological neuronal networks represent a fundamental component of complex systems, including the brain. Simulating these networks accurately and efficiently is a significant challenge, largely due to the intricate, non-linear interactions between neurons. Traditional simulation methods, often relying on numerical integration techniques like the Hodgkin-Huxley model, are computationally intensive, particularly when scaling to large networks. This paper proposes a novel approach to neuronal network simulation inspired by quantum mechanics, specifically leveraging concepts of superposition and entanglement. We introduce a framework that utilizes quantum-inspired algorithms to represent and evolve neuronal states, aiming to dramatically reduce computational overhead. The core idea involves mapping neuronal dynamics onto a quantum system, where neurons are represented by qubits, and synaptic connections are modeled as quantum gates. The simulation process then utilizes quantum algorithms to evolve the system, effectively capturing the parallel and interconnected nature of neuronal networks. We present a mathematical formulation of the simulation process, focusing on the evolution equation for neuronal states. We explore key concepts like wave function collapse and measurement in the context of neuronal network simulation, offering a potential pathway for more realistic and efficient modeling of biological neural systems. The ultimate goal is to provide a computationally tractable method for simulating large-scale neuronal networks, leading to improved understanding of brain function and potentially facilitating the development of advanced neurotechnologies. The presented framework offers a distinct alternative to conventional methods, promising significant advantages in terms of speed and scalability.
This paper investigates the potential of quantum simulation to model self-organizing network topologies, leveraging the principles of quantum topology. We propose a novel approach to network analysis, employing a quantum-based simulation to understand the intricate relationships between nodes and explore potential applications within quantum information processing. The core of this work lies in the construction of a quantum network simulator, utilizing quantum information to model the dynamics of node interactions and uncover the underlying quantum network topology. The research focuses on analyzing the inherent properties of quantum systems and their influence on network structure. This investigation highlights the promise of this approach to provide a more fundamental understanding of network behavior and opens doors to novel applications in quantum computing and communication.
Recent advances in quantum communication have enabled long-distance secure information transfer through quantum channels, giving rise to quantum networks with unique physical and statistical properties. However, as in classical networks, the propagation of viruses in these systems could have severe consequences. Here, we investigate the critical problem of virus spreading in quantum networks. We develop quantitative tools, particularly a modified nonlinear dynamical system model, for performing epidemiological analyses on quantum networks. Our results show that quantum networks tend to be more resilient to viral infections, exhibiting higher epidemic thresholds than classical networks with identical graph topologies. This apparent robustness, however, arises primarily from the sparser connectivity inherent to the quantum networks. When the comparison is made at a fixed average connectivity, classical and quantum networks display comparable epidemic thresholds. These findings provide key insights into the security and reliability of future large-scale quantum communication systems. Our work bridges the fields of quantum information science, network theory, and epidemiology, paving the way for future studies of quantum epidemiological dynamics.
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.