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arXiv 2109.00973quant-ph
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强化学习增强的三能级量子系统中相干布居转移协议

Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems

Jonathon Brown, Sofia Sgroi, Luigi Giannelli, Gheorghe Sorin Paraoanu, Elisabetta Paladino, Giuseppe Falci, Mauro Paternostro, Alessandro Ferraro

AI总结:

结合强化学习与传统优化技术,提出固定耦合率与时变失谐下的多能级布居转移最优协议,发现优于标准绝热方案且抗能量损耗和退相干的新协议。

摘要:

我们部署了基于强化学习的方法与更传统的优化技术的组合,以识别多能级系统中布居转移的最优协议。我们将策略限制在固定耦合率但时变失谐的情况下,这种情况将大大简化在相关实验平台(如半导体和超导平台)中实施布居转移的难度。我们的方法能够探索可能的控制协议空间,揭示出高效协议的存在,值得注意的是,这些协议不同于(且可能优于)标准的Raman、STIRAP或其他绝热方案。我们识别出的新协议对能量损耗和退相干均具有鲁棒性。

英文摘要:

We deploy a combination of reinforcement learning-based approaches and more traditional optimization techniques to identify optimal protocols for population transfer in a multi-level system. We constraint our strategy to the case of fixed coupling rates but time-varying detunings, a situation that would simplify considerably the implementation of population transfer in relevant experimental platforms, such as semiconducting and superconducting ones. Our approach is able to explore the space of possible control protocols to reveal the existence of efficient protocols that, remarkably, differ from (and can be superior to) standard Raman, STIRAP or other adiabatic schemes. The new protocols that we identify are robust against both energy losses and dephasing.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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