A Quantum Approach for Optimal Transient Control in Network-Based Epidemic Models

TL;DR

This paper introduces a quantum computing-based method to optimize non-pharmaceutical epidemic control strategies, specifically mobility restrictions, modeled as a complex combinatorial problem on networked disease spread.

Contribution

It formulates the epidemic control problem as a QUBO, enabling quantum algorithms to efficiently find optimal mobility restriction strategies in network epidemic models.

Findings

Quantum algorithms can effectively solve the QUBO formulation of epidemic control.

Numerical simulations demonstrate improved optimization of mobility restrictions.

The approach shows promise for real-world epidemic management applications.

Abstract

Effective epidemic control is crucial for mitigating the spread of infectious diseases, particularly when pharmaceutical interventions such as vaccines or treatments are limited. Non-pharmaceutical 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 susceptible-infected-removed frameworks. The control problem is formulated as a combinatorial optimization task, inherently NP-hard 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.