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This research introduces a machine learning protocol to optimize continuous-time quantum error correction, effectively tailoring recovery strategies for complex noise environments ranging from Markovian to non-Markovian regimes. By simultaneously adjusting the code space and recovery map, the method aims to maximize logical state fidelity across arbitrary device-level noise correlations. This advancement represents a significant step toward making fault-tolerant quantum computing viable by adapting error correction to real-world hardware imperfections.
Optimizing continuous-time quantum error correction for Markovian and non-Markovian noise models
arXiv:2506.21707v3 Announce Type: replace Abstract: We present a protocol using machine learning (ML) to simultaneously optimize the quantum error-correcting code space and the corresponding recovery map in the framework of continuous-time quantum error correction. Given a Hilbert space and a noise process -- potentially correlated across both space and time -- the protocol identifies the optimal recovery strategy, measured by the average logical state fidelity. This approach enables the discovery of recovery schemes tailored to arbitrary device-level noise.
Abstract:We present a protocol using machine learning (ML) to simultaneously optimize the quantum error-correcting code space and the corresponding recovery map in the framework of continuous-time quantum error correction. Given a Hilbert space and a noise process -- potentially correlated across both space and time -- the protocol identifies the optimal recovery strategy, measured by the average logical state fidelity. This approach enables the discovery of recovery schemes tailored to arbitrary device-level noise.
arXiv:2506.21707 [quant-ph]
(or arXiv:2506.21707v3 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2506.21707 Focus to learn more arXiv-issued DOI via DataCite
Phys. Rev. A 114, 032438 (2026)
https://doi.org/10.1103/5zyg-lhjt Focus to learn more DOI(s) linking to related resources
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