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

TL;DR

This paper combines reinforcement learning and traditional optimization to discover efficient, robust population transfer protocols in three-level quantum systems with fixed couplings and variable detunings, outperforming standard methods.

Contribution

It introduces a novel approach integrating reinforcement learning with optimization to find superior control protocols in quantum systems with fixed couplings.

Findings

Discovered protocols outperform standard Raman and STIRAP schemes.

Identified protocols are robust against energy losses and dephasing.

Reinforcement learning effectively explores control protocol space.

Abstract

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.