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arXiv · 2506.21707

Optimizing continuous-time quantum error correction for Markovian and non-Markovian noise models

Anirudh Lanka·Shashank Hegde·Todd A. Brun
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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.

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Anirudh Lanka, Shashank Hegde, Todd A. Brun. 2026-09-18. Optimizing continuous-time quantum error correction for Markovian and non-Markovian noise models. https://doi.org/10.1103/5zyg-lhjt

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