When cheap gradients fail: the measurement cost of attacking quantum classifiers
Adversarial perturbations threaten machine learning classifiers, including variational quantum classifiers. We show that finite quantum measurement statistics (shot noise) act as a built-in defense against gradient-based test-time attacks whose cost scales unfavorably for the attacker. Because every gradient component must be inferred from repeated circuit executions under any unbiased gradient-estimation rule, white-box extraction consumes a dimension-dependent measurement budget that measurement grouping cannot remove in expressive circuits. Under stated assumptions, single-step attacks need at least quadratically many shots in the input dimension $d$, growing as $d^{5/2}$ under norm-co...
| Authors | Bacui Li, Chandra Thapa, Tansu Alpcan et al. |
|---|---|
| Primary category | quant-ph |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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