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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2204.07710 |
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| _version_ | 1866913180843769856 |
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| author | Sarma, Bijita Borah, Sangkha Kani, A Twamley, Jason |
| author_facet | Sarma, Bijita Borah, Sangkha Kani, A Twamley, Jason |
| contents | Achieving fast cooling of motional modes is a prerequisite for leveraging such bosonic quanta for high-speed quantum information processing. In this work, we address the aspect of reducing the time limit for cooling below that constrained by the conventional sideband cooling techniques; and propose a scheme to apply deep reinforcement learning (DRL) to achieve this. In particular, we have shown how the scheme can be used effectively to accelerate the dynamic motional cooling of a macroscopic magnonic sphere, and how it can be uniformly extended for more complex systems, for example, a tripartite opto-magno-mechanical system to obtain cooling of the motional mode below the time bound of coherent cooling. While conventional sideband cooling methods do not work beyond the well-known rotating wave approximation (RWA) regimes, our proposed DRL scheme can be applied uniformly to regimes operating within and beyond the RWA, and thus this offers a new and complete toolkit for rapid control and generation of macroscopic quantum states for application in quantum technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_07710 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Accelerated Magnonic Motional Cooling with Deep Reinforcement Learning Sarma, Bijita Borah, Sangkha Kani, A Twamley, Jason Quantum Physics Achieving fast cooling of motional modes is a prerequisite for leveraging such bosonic quanta for high-speed quantum information processing. In this work, we address the aspect of reducing the time limit for cooling below that constrained by the conventional sideband cooling techniques; and propose a scheme to apply deep reinforcement learning (DRL) to achieve this. In particular, we have shown how the scheme can be used effectively to accelerate the dynamic motional cooling of a macroscopic magnonic sphere, and how it can be uniformly extended for more complex systems, for example, a tripartite opto-magno-mechanical system to obtain cooling of the motional mode below the time bound of coherent cooling. While conventional sideband cooling methods do not work beyond the well-known rotating wave approximation (RWA) regimes, our proposed DRL scheme can be applied uniformly to regimes operating within and beyond the RWA, and thus this offers a new and complete toolkit for rapid control and generation of macroscopic quantum states for application in quantum technologies. |
| title | Accelerated Magnonic Motional Cooling with Deep Reinforcement Learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2204.07710 |