Adversarial Robustness Guarantees for Quantum Classifiers

arXiv:2405.10360v1 Announce Type: cross
Abstract: Despite their ever more widespread deployment throughout society, machine learning algorithms remain critically vulnerable to being spoofed by subtle adversarial tampering with their input data. The prospect of near-term quantum computers being capable of running {quantum machine learning} (QML) algorithms has therefore generated intense interest in their adversarial vulnerability. Here we show that quantum properties of QML algorithms can confer fundamental protections against such attacks, in certain scenarios guaranteeing robustness against classically-armed adversaries. We leverage …

abstract adversarial algorithms arxiv classifiers computers cond-mat.stat-mech cs.lg data deployment ever generated machine machine learning machine learning algorithms near nlin.cd qml quant-ph quantum quantum computers robustness running society type vulnerability vulnerable