  We provide a robust defence to adversarial attacks on discriminative
algorithms. Neural networks are naturally vulnerable to small, tailored
perturbations in the input data that lead to wrong predictions. On the
contrary, generative models attempt to learn the distribution underlying a
dataset, making them inherently more robust to small perturbations. We use
Boltzmann machines for discrimination purposes as attack-resistant classifiers,
and compare them against standard state-of-the-art adversarial defences. We
find improvements ranging from 5% to 72% against attacks with Boltzmann
machines on the MNIST dataset. We furthermore complement the training with
quantum-enhanced sampling from the D-Wave 2000Q annealer, finding results
comparable with classical techniques and with marginal improvements in some
cases. These results underline the relevance of probabilistic methods in
constructing neural networks and highlight a novel scenario of practical
relevance where quantum computers, even with limited hardware capabilites,
could provide advantages over classical computers. This work is dedicated to
the memory of Peter Wittek.
