Experimental demonstration of adversarial examples in learning topological phases

Abstract

Machine learning has been applied to problems in condensed matter physics, but its performance in an experimental setting needs testing. Zhang et al. study the effects of adversarial perturbations on a neural-network-based topological phase classifier, applied to experimental data from an NV center in diamond.