In superconducting devices running variational workloads, gate and readout fidelities drift on hour timescales, while existing runtime schedulers treat backend quality as static. The temporal dimension of calibration remains unresolved. We formulate runtime calibration as a state-trajectory feedback-control problem under a fixed wall-clock budget, and investigate whether spending time on calibration now can improve the future optimization trajectory. Calibration quality proxy is represented as a drifting equivalent-age state, recovery action is modeled as costly state reset, and policies are evaluated by time-integrated optimization gap over the full execution window. Using a finite-horizon rollout controller, we compare feedback calibration against a strengthened family of open-loop baselines across three latency regimes: cloud-like (25 ms), local-millisecond (1 ms), and tight-loop (4 $\mathrmμ$s). The results show a clear ordering: cloud-like feedback is generally uncompetitive, while local-ms and tight-loop regimes open a positive-gain region that grows with workload quality-sensitivity and initial calibration age. Crucially, the gap between local-ms and tight-loop control is modest for single-target recovery. The advantage of tight-loop integration emerges under capacity pressure, when many calibration targets must be processed within the same control window.

翻译：在超导器件运行变分工作负载时，门操作和读出保真度在小时量级的时间尺度上发生漂移，而现有的运行时调度器将后端质量视为静态量。标定的时间维度仍未得到解决。我们将运行时标定形式化为固定挂钟预算下的状态轨迹反馈控制问题，并探究当前在标定上耗费时间能否改善未来的优化轨迹。标定质量代理被表示为漂移的等效老化状态，恢复动作被建模为代价高昂的状态重置，策略通过整个执行窗口上时间积分的优化差距进行评估。采用有限时域滚动控制器，我们在三种延迟场景（类云：25 ms、本地毫秒：1 ms、紧循环：4 $\mathrmμ$s）下将反馈标定与增强的开环基线族进行了比较。结果表明了明确的排序：类云反馈通常不具备竞争力，而本地毫秒和紧循环场景则开启了一个正增益区域，该区域随工作负载质量敏感性和初始标定老化程度而增长。关键在于，对于单目标恢复，本地毫秒与紧循环控制之间的差距较小。当容量压力出现时——即在同一控制窗口内必须处理多个标定目标——紧循环集成的优势才得以凸显。