  In this paper, we investigate the strong convergence analysis of parareal
algorithms for stochastic Maxwell equations with the damping term driven by
additive noise. The proposed parareal algorithms proceed as two-level temporal
parallelizable integrators with the stochastic exponential integrator as the
coarse propagator and both the exact solution integrator and the stochastic
exponential integrator as the fine propagator. It is proved that the
convergence order of the proposed algorithms linearly depends on the iteration
number. Numerical experiments are performed to illustrate the convergence order
of the algorithms for different choices of the iteration number, the damping
coefficient and the scale of noise.
