  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.
