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.
- Identical syndrome histories allow an AI adviser to give sign-flipped, harmful advice
- Calibration measurements supply missing sign information for certified recovery updates
- Evaluator rejects harmful updates without assuming adviser correctness
- Surface-code experiment with stochastic circuit faults corroborates results
- Products
- toric codesurface code
From the original article
1 min read at arxiv.orgCan an attacker turn influence over an artificial intelligence (AI) adviser into a harmful quantum error-correction update? We identify an ambiguity in passive syndrome records that obstructs recovery selection, then show how additional calibration measurements support certified recovery updates under uncertainty and drift.
By A. Barış Özgüler · arxiv.org
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