Esra Yeniaras
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QCIVET introduces a novel contract-based framework to ensure the integrity of hybrid quantum-classical pipelines by verifying both the structure (syntactic) and the behavior (semantic) of quantum stages.
QML-PipeGuard introduces a contract-based framework that monitors the behavioral fingerprint of quantum machine learning pipelines to detect both hardware drift and malicious channel substitution.
Papers
QML-PipeGuard: Drift-Aware Behavioral Fingerprinting for Quantum Machine Learning Pipeline Integrity
QML-PipeGuard introduces a contract-based framework that monitors the behavioral fingerprint of quantum machine learning pipelines to detect both hardware drift and malicious channel substitution.