Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems
Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems
复制标题
DOI：
10.1088/1367-2630/ac2393
复制
发表时间：
2021-09-01
影响因子：
3.3
通讯作者：
Ferraro, Alessandro
中科院分区：
文献类型：
--
作者：
Brown, Jonathon;Sgroi, Pierpaolo;Ferraro, Alessandro
我们部署了基于强化学习的方法和更传统的优化技术的组合，以确定多级系统中人口转移的最佳协议。我们将我们的策略限制在固定耦合率但随时间变化的失谐的情况下，这种情况将大大简化相关实验平台（如半导体和超导平台）中人口转移的实现。我们的方法是能够探索空间的可能的控制协议，以揭示存在的有效的协议，显着地，不同于（并且可以是上级）标准拉曼，受激拉曼绝热通道或其他绝热方案。我们确定的新协议对能量损失和失相都是鲁棒的。
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 constrain 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, stimulated Raman adiabatic passage or other adiabatic schemes. The new protocols that we identify are robust against both energy losses and dephasing.