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 language | English |
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Article number | 093035 |
Journal | New Journal of Physics |
Volume | 23 |
Issue number | 9 |
DOIs | |
Publication status | Published - 24 Sept 2021 |
Keywords
- Paper
- quantum control
- reinforcement learning
- condensed matter physics
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Dive into the research topics of 'Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems'. Together they form a unique fingerprint.Student theses
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Machine learning applications in quantum state engineering
Brown, J. P. (Author), Paternostro, M. (Supervisor) & Ferraro, A. (Supervisor), Dec 2023Student thesis: Doctoral Thesis › Doctor of Philosophy
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Reinforcement learning based methods for optimal control and design of quantum systems
Sgroi, S. (Author), Paternostro, M. (Supervisor) & Ferraro, A. (Supervisor), Jul 2024Student thesis: Doctoral Thesis › Doctor of Philosophy
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