Precursor Detection of Systematic Bias Drift in Cryogenic Quantum Control: A Fail-Closed Estimator Validated in Simulation and on Live Hardware Calibration

Cryogenic control electronics for quantum hardware accumulate systematic, non-zero-mean bias that integrates in a feedback loop until the actuator saturates; a threshold controller detects this only after saturation. We present a precursor estimator that detects the onset of systematic drift from the dynamics of the control signal, built as a finite observer (declared error budget, explicit competence boundary, fail-closed abstention) in the sense of a companion paper on the physical limits of finite observation. It uses path-functional invariants (a Fisher–Rao metric stability bound, a maximum-likelihood bias decoder, a cumulative-damage functional) rather than eigenvalue analysis, which we show is neither necessary nor sufficient for finite-time survival. In simulation, across 30 seeds × 7 sensor-environment conditions (including a 1/f dephasing spectrum), it detects 96.7% of drift-collapse scenarios before saturation at a 12 s median lead with 0% false positives over 210 healthy runs. On real data, the same governed outlier-and-drift detector, applied to live publicly accessible IBM quantum-hardware calibration time-series (a 156-qubit IBM Heron device), flags genuinely degraded qubits and catches a real relaxation-time decline (one qubit's T1 falling 88.0→19.1 µs, −78%, over 43 days) while staying silent on a qubit that is poor but stable and on the other 155 — detection of change, not of mere badness. The work is TRL 4: an advisory detection layer, not a deployed controller, and it does not emulate entanglement, non-local correlation, or contextuality. The contribution is the integrated, calibrated estimator and its operating discipline, demonstrated in simulation and against real hardware-calibration data.