  We investigate whether long-run persistent chain Monte Carlo simulation of
Langevin dynamics improves the quality of the representations achieved by
energy-based models (EBM). We consider a scheme wherein Monte Carlo simulation
of a diffusion process using a trained EBM is used to improve the adversarial
robustness and the calibration score of an independent classifier network. Our
results show that increasing the computational budget of Gibbs sampling in
persistent contrastive divergence improves the calibration and adversarial
robustness of the model, elucidating the practical merit of realizing new
quantum and classical hardware and software for efficient Gibbs sampling from
continuous energy potentials.
