Adding qubits is the industry's answer to almost everything, but it only works if you're truly below the error-correction threshold everywhere on the chip. Real processors aren't uniform — a few bad couplers or one noisy region pull the effective threshold down, by roughly 25% in our simulations. And what trips a logical qubit isn't the chip's average error rate; it's the weakest path through the code. Hot spots make that path far worse than the average, so a chip can clear the "below threshold" bar on its average while the path that actually decides the outcome is already over the line. Add qubits expecting the exponential payoff, and the logical error rate flattens instead of falling — qubits spent, no return.
The miss is in what gets measured. A logical failure isn't "a lot of errors" — it's a connected error chain spanning the code, and average error rates and raw defect counts can't see one forming. We model every interface as a finite budget, capacity against load, and watch the saturation map: where the correction burden is concentrating. That signal flags a forming failure earlier than raw defect count does — in time to steer correction toward the interfaces nearest the line.
It's vendor-agnostic control-plane software that runs on the syndrome and decoder data a code already produces, on superconducting, ion-trap, or neutral-atom hardware. It doesn't wait on our own chips to ship.
Provisional filed (App. 64/070,628). Results are simulation-validated, not yet silicon-proven.
The saturation/percolation picture of the fault-tolerance threshold, the predictive-observable results, and the information-theoretic early-warning divergence were demonstrated on toric-code simulations under standard decoding. Carry-over to full circuit-level noise is the next validation step.