Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909032381415424 |
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| author | Cantone, Riccardo Mukherjee, Shreyasi Giannelli, Luigi Paladino, Elisabetta Falci, Giuseppe A. |
| author_facet | Cantone, Riccardo Mukherjee, Shreyasi Giannelli, Luigi Paladino, Elisabetta Falci, Giuseppe A. |
| contents | We apply a machine-learning-enhanced greybox framework to a quantum optimal control protocol for open quantum systems. Combining a whitebox physical model with a neural-network blackbox trained on synthetic data, the method captures non-Markovian noise effects and achieves gate fidelities above 90% under Random Telegraph and Ornstein-Uhlenbeck noise. Critical issues of the approach are discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24393 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise Cantone, Riccardo Mukherjee, Shreyasi Giannelli, Luigi Paladino, Elisabetta Falci, Giuseppe A. Quantum Physics We apply a machine-learning-enhanced greybox framework to a quantum optimal control protocol for open quantum systems. Combining a whitebox physical model with a neural-network blackbox trained on synthetic data, the method captures non-Markovian noise effects and achieves gate fidelities above 90% under Random Telegraph and Ornstein-Uhlenbeck noise. Critical issues of the approach are discussed. |
| title | Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2512.24393 |