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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2506.21707 |
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| _version_ | 1866914284344180736 |
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| author | Lanka, Anirudh Hegde, Shashank Brun, Todd A. |
| author_facet | Lanka, Anirudh Hegde, Shashank Brun, Todd A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21707 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing continuous-time quantum error correction for arbitrary noise Lanka, Anirudh Hegde, Shashank Brun, Todd A. Quantum Physics 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. |
| title | Optimizing continuous-time quantum error correction for arbitrary noise |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2506.21707 |