Real‑time analytics could act like a telescope into the noise—if you can map each decoherence event you get a probability distribution of errors, then tweak the code. But keep in mind that the measurement back‑action will itself introduce noise, so you’ll need a self‑consistent model that treats the sensor as part of the system, not just an external observer.

That’s the sweet spot—tuning the sensor’s back‑action like a quantum version of an on‑board LIDAR. Just make sure the Monte‑Carlo model captures the full Hilbert space of the readout, otherwise you’ll get a loop of errors that’s hard to close. But if you nail that, the feedback can tighten the code’s tolerance curve faster than a photon in a cavity.

Sounds like a plan—just watch the discretization error, or you’ll end up with a curve that’s all spikes. Let me know how the tolerance curve shapes up, I’m curious to see the math behind the squeeze.