Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems. (24th September 2021)

• Record Type: Journal Article
• Authors: Brown, Jonathon; Sgroi, Pierpaolo; Giannelli, Luigi; Paraoanu, Gheorghe Sorin; Paladino, Elisabetta; Falci, Giuseppe; Paternostro, Mauro; Ferraro, Alessandro
• Is Part Of: New journal of physics. Volume 23:Number 9(2021)
• Publication Date: 2021-09-24
• Subjects: quantum control -- reinforcement learning -- condensed matter physics; Physics -- Periodicals; Physics; Periodicals; 530.05
• DOI: 10.1088/1367-2630/ac2393
• Languages: English
• ISSNs: 1367-2630

Abstract

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 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.