当纠错使情况更糟：预算感知评估、外推失败与校准信任边界
When Error Mitigation Makes Things Worse: Budget-Aware Evaluation, Extrapolation Failure, and the Calibration Trust Boundary
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中文总结 AI 辅助
研究纠错在量子计算中可能放大误差，提出预算感知评估与校准信任边界，发现神经校正器在低预算下有效但硬件迁移失败。
中文摘要 AI 辅助
纠错（error mitigation）预期将噪声量子测量转化为更有用的估计。我们表明，它反而可能放大误差。在控制偏移失校准（control-offset miscalibration）下，评估的无约束零噪声外推（ZNE）估计器在38-63%的模拟实例上比不进行纠错更差，并产生极端尾部误差。一项小型IBM Heron研究发现，在两个深度下的18个实例中，80-88%的实例出现恶化，平均误差为原始误差的3.0-4.1倍。在模拟中，中位数变化很小，因此仅监测中位数会遗漏这些失败。随后，我们在每次评估的相同在线射击预算下比较方法，并单独报告离线训练成本。在该成本摊销后，一个预算条件的神经校正器占据了模拟精度-成本前沿的低预算端，并在集成不一致时回退到原始估计；其硬件迁移仍不成功。最后，我们将提供商报告的校准视为不绑定到执行时设备状态的输入。对此元数据的白盒投影梯度压力测试在训练使用的特征范围内将校正器的误差增加了8.1倍，并在半数恶化案例中规避了不一致监测器。综合来看，这些结果将预算感知评估、尾部风险和校准完整性联系起来，应用于近期量子学习流水线。
英文摘要
Error mitigation is expected to turn noisy quantum measurements into more useful estimates. We show that it can instead amplify error. Under control-offset miscalibration, the evaluated unconstrained zero-noise extrapolation (ZNE) estimators are worse than no mitigation on 38-63% of simulated instances and produce extreme tail errors. A small IBM Heron study finds worsening on 80-88% of 18 instances across two depths, with mean error 3.0-4.1 times the raw error. In simulation, the median changes little, so median-only monitoring misses the failures. We then compare methods at the same online shot budget per evaluation and report offline training cost separately. After that cost is amortized, a budget-conditioned neural corrector occupies the low-budget end of the simulated accuracy-cost frontier and falls back to the raw estimate when an ensemble disagrees; its hardware transfer remains unsuccessful. Finally, we treat provider-reported calibration as an input that is not bound to the execution-time device state. A white-box projected-gradient stress test on this metadata increases the corrector's error 8.1 times within the feature ranges used for training and evades the disagreement monitor on half of the worsened cases. Together, the results connect budget-aware evaluation, tail risk, and calibration integrity in near-term quantum learning pipelines.
发表机构
- Workday AI Research（Workday 人工智能研究）
机构由 AI 辅助整理，请以论文原文为准。
