Quantum adversarial machine learning
Lu, S., Duan, L., Deng, D.
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 systemVulnerability (computing)QuantumComputer scienceArtificial intelligenceWork (physics)Machine learningTheoretical computer scienceComputer securityQuantum mechanicsPhysicsAdversarial Robustness in Machine LearningQuantum Computing Algorithms and ArchitectureQuantum Information and Cryptography