Quantum Agent-Based Modeling of Disease Dynamics
By Ryan
Introduction
The proposed research aims to explore the potential advantage quantum computing may have in modeling the dynamics of disease spread using agent-based simulations. Agent-based models (ABMs) are a widely used approach in epidemiology, as they allow disease dynamics to emerge from local interactions between individuals rather than from aggregate equations. In their simplest form, these models implement Susceptible–Infected–Recovered (SIR) dynamics using classical probabilistic rules governing agent movement, contact, and transmission. While such classical ABMs are computationally effective and have been used extensively to study infectious disease spread, recent advances in quantum-inspired and quantum-based modeling frameworks suggest that quantum representations may offer advantages in certain multi-agent systems. However, it remains unclear whether these advantages extend to epidemiological models, particularly those that lack strategic decision-making or equilibrium-seeking behavior. This project proposes a controlled comparison between classical and quantum agent-based disease models to determine whether quantum agents provide measurable benefits in this context.
Intellectual Merit
The intellectual merit of this research lies in its systematic examination of the conditions under which quantum agent-based modeling provides advantages over classical approaches. Previous work has demonstrated that quantum or quantum-like models can more efficiently represent uncertainty, interference effects, and adaptive behavior in complex social systems. Related studies have also explored the application of quantum-mechanics–inspired frameworks to infectious disease modeling, often within compartmental or network-based differential equation systems. By contrast, classical agent-based disease models rely on fixed probabilistic transitions and do not naturally encode higher-order correlations or behavioral uncertainty. By implementing both classical and quantum formulations of a minimal SIR-style agent-based model and directly comparing their dynamics, this research will clarify whether quantum agents offer improvements in simulation efficiency, expressiveness, or emergent behavior. Importantly, this work also engages with recent findings on the fundamental limits of predictability in epidemic models, helping to establish where quantum approaches meaningfully contribute and where they do not.
Broader Impact
Agent-based disease models play an increasingly important role in public health research, policy evaluation, and risk assessment, where transparency, reliability, and computational efficiency are critical. By rigorously evaluating quantum agent-based methods in a foundational epidemiological setting, this project contributes to responsible and evidence-based integration of quantum computing into scientific modeling. Clarifying the specific modeling features required for quantum advantage to emerge can help guide future interdisciplinary research at the intersection of quantum science, epidemiology, and complex systems. More broadly, this work supports informed decision-making around the adoption of emerging computational technologies, ensuring that quantum methods are applied where they provide genuine value rather than novelty alone.