Securing quantum error correction against misleading advice from AI agents
The paper says passive syndrome histories can hide the sign of a quantum error, making an AI-suggested correction look safe when it is not.
Özgüler shows that calibration measurements can supply the missing information before a recovery update is accepted. A separate evaluator only approves an update when uncertainty and drift bounds still certify improvement over the current recovery. In simulations, those checks reject harmful advice while preserving beneficial updates under honest advice, but breaking the drift assumption can let harmful updates through. ArXiv · AI/CL/LG's note
Özgüler shows that calibration measurements can supply the missing information before a recovery update is accepted. A separate evaluator only approves an update when uncertainty and drift bounds still certify improvement over the current recovery. In simulations, those checks reject harmful advice while preserving beneficial updates under honest advice, but breaking the drift assumption can let harmful updates through. ArXiv · AI/CL/LG's note
score 4