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

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.

Automated summary

A combination of reinforcement learning and traditional optimization techniques is used to identify optimal protocols for population transfer in a multi-level system. The new protocols identified in the study are efficient and different from standard methods, showing robustness against energy losses and dephasing. This research has the potential to simplify the implementation of population transfer in experimental platforms like semiconducting and superconducting systems.