{
  "id": 3010,
  "url": "https://arxiv.org/abs/2607.11095v1",
  "title": "When cheap gradients fail: the measurement cost of attacking quantum classifiers",
  "summary": "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 measureme",
  "authors": "Bacui Li, Chandra Thapa, Tansu Alpcan, Udaya Parampalli",
  "category": "research",
  "topics": "military-security",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-13T05:03:41.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-arxiv-cs-cr-ai-security",
  "source_name": "arXiv cs.CR (AI security)",
  "source_homepage": "https://arxiv.org/list/cs.CR/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/3010",
  "original_url": "https://arxiv.org/abs/2607.11095v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}