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This research addresses a critical bottleneck in quantum error correction by demonstrating how product probes can accelerate the learning of control correlations, reducing sample complexity requirements significantly. By showing that these gains offset probe overhead, the study paves the way for implementing protected tasks within practical operational budgets without relying on expensive encoded calibration routines. This advancement directly impacts the efficiency and scalability of fault-tolerant quantum computing architectures.
Preparation Changes the Cost of Calibration for Quantum Control
arXiv:2609.30910v1 Announce Type: new Abstract: 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.
Abstract: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.
arXiv:2609.30910 [quant-ph]
(or arXiv:2609.30910v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.30910 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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