Autonomous Floquet Engineering of Bosonic Codes via Reinforcement Learning
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908611913973760 |
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| author | Wu, Zheping Guo, Lingzhen Shi, Haobin Zhang, Wei-Wei |
| author_facet | Wu, Zheping Guo, Lingzhen Shi, Haobin Zhang, Wei-Wei |
| contents | Bosonic codes represent a promising route toward quantum error correction in continuous-variable systems, with direct relevance to experimental platforms such as circuit QED and optomechanics. However, their preparation and stabilization remain highly challenging, requiring ultra-precise control of nonlinear interactions to create entangled superpositions, suppress decoherence, and mitigate dynamic errors. Here, we introduce a reinforcement-learning-assisted Floquet engineering approach for the autonomous preparation of bosonic codes that is general, efficient, and noise-resilient. By leveraging machine learning to optimize Floquet driving parameters, our method achieves over two orders of magnitude reduction in evolution time-requiring only about one percent of that in conventional adiabatic schemes-while maintaining high-fidelity state generation even under strong dissipative and dephasing noise. This approach not only demonstrates the power of artificial intelligence in quantum control but also establishes a scalable and experimentally feasible route toward fault-tolerant bosonic quantum computation. Beyond the specific application to bosonic code preparation, our results suggest a general paradigm for integrating machine learning and Floquet engineering to overcome decoherence challenges in next-generation quantum technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22227 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Autonomous Floquet Engineering of Bosonic Codes via Reinforcement Learning Wu, Zheping Guo, Lingzhen Shi, Haobin Zhang, Wei-Wei Quantum Physics Bosonic codes represent a promising route toward quantum error correction in continuous-variable systems, with direct relevance to experimental platforms such as circuit QED and optomechanics. However, their preparation and stabilization remain highly challenging, requiring ultra-precise control of nonlinear interactions to create entangled superpositions, suppress decoherence, and mitigate dynamic errors. Here, we introduce a reinforcement-learning-assisted Floquet engineering approach for the autonomous preparation of bosonic codes that is general, efficient, and noise-resilient. By leveraging machine learning to optimize Floquet driving parameters, our method achieves over two orders of magnitude reduction in evolution time-requiring only about one percent of that in conventional adiabatic schemes-while maintaining high-fidelity state generation even under strong dissipative and dephasing noise. This approach not only demonstrates the power of artificial intelligence in quantum control but also establishes a scalable and experimentally feasible route toward fault-tolerant bosonic quantum computation. Beyond the specific application to bosonic code preparation, our results suggest a general paradigm for integrating machine learning and Floquet engineering to overcome decoherence challenges in next-generation quantum technologies. |
| title | Autonomous Floquet Engineering of Bosonic Codes via Reinforcement Learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2510.22227 |