Quantum adversarial machine learning
Sirui Lu, Lu-Ming Duan, Dong-Ling Deng
Abstract
This work uncovers the vulnerability aspect for quantum machine learning, by showing that quantum classifiers are vulnerable to adversarial perturbations. The authors give generic recipes on how to generate adversarial perturbations and mitigate the vulnerability problem in various adversarial scenarios.
Topics & Concepts
Adversarial systemComputer scienceVulnerability (computing)Artificial intelligenceQuantumWork (physics)Machine learningTheoretical computer scienceKey (lock)Vulnerability assessmentAdversarial machine learningQuantum computerQuantum machine learningQuantum Computing Algorithms and ArchitectureQuantum Information and CryptographyAdversarial Robustness in Machine Learning