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Error-correction records can reveal noise yet supply the information needed for control too slowly. In a surface code under Gaussian dephasing, encoded calibration learns a correlation that reverses the preferred control mainly through rare events. A product probe exposes it more often through the same checks, reducing ideal-record sample complexity from inverse-square to inverse-linear in phase variance. Counting both calibration and execution costs, we show that this gain can repay probe overhead and enable protected tasks within budgets that exclude encoded calibration.

生成讲解博客的中文版本

生成AI智能校验的中文版本