Securing quantum error correction against misleading advice from AI agents
Researchers design calibration-based certified checks that let quantum error-correction systems safely reject harmful recovery updates proposed by compromised AI advisers.
The paper shows that opposite coherent X rotations in an odd-distance square toric code yield identical passive syndrome histories, creating ambiguity an AI adviser could exploit to recommend harmful recovery updates. It introduces terminal logical measurements on calibration states plus an independent evaluator that accepts updates only when calibration uncertainty and drift bounds certify improvement. Simulated advice attacks showed calibration-confidence checks reject harmful proposals while retaining most beneficial updates, and the authors derive sufficient limits on calibration age.