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

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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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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.

Original languageEnglish
Article number093035
Number of pages15
JournalNew Journal of Physics
Volume23
Issue number9
DOIs
Publication statusPublished - Sep 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • condensed matter physics
  • quantum control
  • reinforcement learning

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