  We present dimension-free convergence and discretization error bounds for the
unadjusted Hamiltonian Monte Carlo algorithm applied to high-dimensional
probability distributions of mean-field type. These bounds require the
discretization step to be sufficiently small, but do not require strong
convexity of either the unary or pairwise potential terms present in the
mean-field model. To handle high dimensionality, our proof uses a particlewise
coupling that is contractive in a complementary particlewise metric.
