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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2401.17190 |
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| _version_ | 1866929480045428736 |
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| author | Guatto, Manuel Susto, Gian Antonio Ticozzi, Francesco |
| author_facet | Guatto, Manuel Susto, Gian Antonio Ticozzi, Francesco |
| contents | Obtaining reliable state preparation protocols is a key step towards practical implementation of many quantum technologies, and one of the main tasks in quantum control. In this work, different reinforcement learning approaches are used to derive a feedback law for state preparation of a desired state in a target system. In particular, we focus on the robustness of the obtained strategies with respect to different types and amount of noise. Comparing the results indicates that the learned controls are more robust to unmodeled perturbations with respect to simple feedback strategy based on optimized population transfer, and that training on simulated nominal model retain the same advantages displayed by controllers trained on real data. The possibility of effective off-line training of robust controllers promises significant advantages towards practical implementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17190 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving robustness of quantum feedback control with reinforcement learning Guatto, Manuel Susto, Gian Antonio Ticozzi, Francesco Quantum Physics Obtaining reliable state preparation protocols is a key step towards practical implementation of many quantum technologies, and one of the main tasks in quantum control. In this work, different reinforcement learning approaches are used to derive a feedback law for state preparation of a desired state in a target system. In particular, we focus on the robustness of the obtained strategies with respect to different types and amount of noise. Comparing the results indicates that the learned controls are more robust to unmodeled perturbations with respect to simple feedback strategy based on optimized population transfer, and that training on simulated nominal model retain the same advantages displayed by controllers trained on real data. The possibility of effective off-line training of robust controllers promises significant advantages towards practical implementation. |
| title | Improving robustness of quantum feedback control with reinforcement learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2401.17190 |