Experimental demonstration of adversarial examples in learning topological phases.

ABSTRACT: Classification and identification of different phases and the transitions between them is a central task in condensed matter physics. Machine learning, which has achieved dramatic success in a wide range of applications, holds the promise to bring unprecedented perspectives for this challenging task. However, despite the exciting progress made along this direction, the reliability of machine-learning approaches in experimental settings demands further investigation. Here, with the nitrogen-vacancy center platform, we report a proof-of-principle experimental demonstration of adversarial examples in learning topological phases. We show that the experimental noises are more likely to act as adversarial perturbations when a larger percentage of the input data are dropped or unavailable for the

SUBMITTER: Zhang H 

PROVIDER: S-EPMC9411630 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature