对撞机系统误差与量子估计不确定性下量子事件分类器的条件有效性
Conditional validity of quantum event classifiers under collider systematics and quantum estimation uncertainty
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中文总结 AI 辅助
针对对撞机系统误差与量子估计不确定性，开发了故障关闭型量子分类器审计框架，在希格斯对撞机基准测试中验证了有限次量子采样会影响分类器指标，且无量子优势。
中文摘要 AI 辅助
已部署的量子机器学习分类器的相关论断可能会在目标数据发生偏移或有限次量子采样评估使模型本身随机化时失效。我们开发了一种基于信息条件的、故障关闭的审计框架，该框架在声明的采样协议下，对每个论断返回支持、反驳或未解决的裁决，并具有随时有效的错误控制。在希格斯到τ对撞机基准测试中，稳定的分类器指标并不能保证有效的信号强度推断：在研究的有限模板统计下，当模板被独立估计且未明确建模模板统计不确定性时，即使在无偏移的控制样本中，固定模板轮廓也会失去覆盖率。在30个冻结的有限次量子核部署中，每个实现的Gram矩阵都经过重新拟合、校准和阈值选择；在主要原始流程中，这些扰动几乎未改变排名，但使阈值化的目标指标移动了约0.02，翻转了基于理想锚定的论断，且对角加载敏感性的分解方式不同。匹配的经典控制样本消除了明显的量子特定名义性能和传感效应。我们不主张量子优势。
英文摘要
Claims about a deployed quantum machine-learning classifier can fail when target data shift or when finite-shot quantum evaluation randomizes the model itself. We develop an information-conditional, fail-closed auditing framework that returns supported, refuted or unresolved verdicts with anytime-valid per-claim error control under a declared sampling protocol. On a Higgs-to-tau-tau collider benchmark, stable classifier metrics do not guarantee valid signal-strength inference: at the studied finite-template statistics, the fixed-template profile can lose coverage, even in shift-free controls, when its templates are estimated independently and template-statistical uncertainty is not modeled explicitly. Across 30 frozen finite-shot quantum-kernel deployments, every realized Gram matrix is propagated through refitting, calibration and threshold selection; in the primary raw pipeline these perturbations leave ranking nearly unchanged yet move thresholded target metrics by about 0.02, flipping ideal-anchored claims, and the diagonal-loading sensitivity decomposes differently. Matched classical controls remove apparent quantum-specific nominal-performance and sensing effects. We claim no quantum advantage.
发表机构
- Faculty of Engineering, University of Deusto（德乌斯托大学工程学院）
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