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arXiv:2503.14223 · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences

Modelling epidemics with memory effects: an open quantum system approach

Fabio Bagarello, Francesco Gargano, Polina Khrennikova

Published Nov 27, 2025• 0 citations
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Abstract

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)'.

DOI
10.1098/rsta.2024.0619

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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)'.
We explore the nonequilibrium evolution and stationary states of an open many-body system that displays epidemic spreading dynamics in a classical and a quantum regime. Our study is motivated by recent experiments conducted in strongly interacting gases of highly excited Rydberg atoms where the facilitated excitation of Rydberg states competes with radiative decay. These systems approximately implement open quantum versions of models for population dynamics or disease spreading where species can be in a healthy, infected or immune state. We show that in a two-dimensional lattice, depending on the dominance of either classical or quantum effects, the system may display a different kind of nonequilibrium phase transition. We moreover discuss the observability of our findings in laser driven Rydberg gases with particular focus on the role of long-range interactions.
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.
Inspired by previous works on epidemic-like processes in open quantum systems, we derive an elementary quantum epidemic model that is simple enough to be studied via Quantum Jump Monte Carlo simulations at reasonably large system sizes. We show how some weak symmetries of the Lindblad equation allow us to map the dynamics onto a classical Kinetic Monte Carlo; this simplified, effective dynamics can be described via local stochastic jumps coupled with a local deterministic component. Simulations are then used to reconstruct a phase diagram which displays stationary features completely equivalent to those of completely classical epidemic processes, but richer dynamics with multiple, recurrent waves of infection.
We propose to use quantum computers to simulate infection spreading in networks. We first show the analogy between the infection distribution and spin-lattice configurations with Ising-type interactions. Then, since the spreading process can be modeled as a classical Markovian process, we show that the spreading process can be simulated using the evolution of a quantum thermal dynamic model with a parameterized Hamiltonian. In particular, we analytically and numerically analyze the evolution behavior of the Hamiltonian, and prove that the evolution simulates a classical Markovian process, which describes the well-known epidemiological stochastic susceptible and infectious (SI) model. A practical method to determine the parameters of the thermal dynamic Hamiltonian from epidemiological inputs is exhibited. As an example, we simulate the infection spreading process of the SARS-Cov-2 variant Omicron in a small-world network.
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In this Thesis various aspects of memory effects in the dynamics of open quantum systems are studied. We develop a general theoretical framework for open quantum systems beyond the Markov approximation which allows us to investigate different sources of memory effects and to develop methods for harnessing them in order to realise controllable open quantum systems. \n \nIn the first part of the Thesis a characterisation of non-Markovian dynamics in terms of information flow is developed and applied to study different sources of memory effects. Namely, we study nonlocal memory effects which arise due to initial correlations between two local environments and further the memory effects induced by initial correlations between the open system and the environment. \n \nThe last part focuses on describing two all-optical experiment in which through selective preparation of the initial environment states the information flow between the system and the environment can be controlled. In the first experiment the system is driven from the Markovian to the non- Markovian regime and the degree of non-Markovianity is determined. In the second experiment we observe the nonlocal nature of the memory effects and provide a novel method to experimentally quantify frequency correlations in photonic environments via polarisation measurements.