A survey that categorizes known adversarial threats to quantum machine learning systems and reviews existing defenses, from logic locking to hardware-aware watermarking.
Evaluating efficacy of model stealing attacks and defenses on quantum neural networks,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
quant-ph 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses
A survey that categorizes known adversarial threats to quantum machine learning systems and reviews existing defenses, from logic locking to hardware-aware watermarking.