Simulating the Spread of Infection in Networks with Quantum Computers

TL;DR

This paper introduces a quantum computing approach to simulate infection spread in networks, linking quantum thermal models with classical epidemiological processes, demonstrated through SARS-CoV-2 Omicron variant simulations.

Contribution

It presents a novel quantum simulation framework for infection dynamics, connecting quantum spin models with classical Markovian epidemiological processes.

Findings

Quantum thermal models can simulate classical infection spread.

The method accurately reproduces SI model dynamics.

Application to SARS-CoV-2 Omicron demonstrates practical viability.

Abstract

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.