Impact of Adversarial Attacks on Quantum Transfer Learning Models

Investigate and characterize the impact of adversarial attacks on quantum transfer learning models by quantifying vulnerabilities and failure modes across threat models and datasets, and establishing comparative robustness relative to classical transfer learning baselines.

Background

The authors identify a gap in the literature regarding the paper of adversarial vulnerabilities specifically within quantum transfer learning, noting that while classical adversarial robustness is extensively examined, QTL’s susceptibility is underexplored.

They argue that this oversight has significant implications for reliability and security in critical domains, motivating dedicated analyses of QTL under adversarial perturbations to inform future defenses and deployments.