Q-SafeML applies quantum distance metrics, such as trace distance and fidelity, to compare correct and incorrect predictions of quantum classifiers, offering a way to flag unsafe or unreliable QML behavior.
In: Computer Safety, Reliability, and Security
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Q-SafeML: Safety Assessment of Quantum Machine Learning via Quantum Distance Metrics
Q-SafeML applies quantum distance metrics, such as trace distance and fidelity, to compare correct and incorrect predictions of quantum classifiers, offering a way to flag unsafe or unreliable QML behavior.