TY - JOUR
T1 - A tutorial on optimal control and reinforcement learning methods for quantum technologies
AU - Giannelli, Luigi
AU - Sgroi, Pierpaolo
AU - Brown, Jonathon
AU - Paraoanu, Gheorghe Sorin
AU - Paternostro, Mauro
AU - Paladino, Elisabetta
AU - Falci, Giuseppe
PY - 2022/3/8
Y1 - 2022/3/8
N2 - Quantum Optimal Control is an established field of research which is necessary for the development of Quantum Technologies. In recent years, Machine Learning techniques have been proved useful to tackle a variety of quantum problems. In particular, Reinforcement Learning has been employed to address typical problems of control of quantum systems. In this tutorial we introduce the methods of Quantum Optimal Control and Reinforcement Learning by applying them to the problem of three-level population transfer. The jupyter notebooks to reproduce some of our results are open-sourced and available on github
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AB - Quantum Optimal Control is an established field of research which is necessary for the development of Quantum Technologies. In recent years, Machine Learning techniques have been proved useful to tackle a variety of quantum problems. In particular, Reinforcement Learning has been employed to address typical problems of control of quantum systems. In this tutorial we introduce the methods of Quantum Optimal Control and Reinforcement Learning by applying them to the problem of three-level population transfer. The jupyter notebooks to reproduce some of our results are open-sourced and available on github
1
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U2 - 10.1016/j.physleta.2022.128054
DO - 10.1016/j.physleta.2022.128054
M3 - Article
JO - Physics Letters A
JF - Physics Letters A
SN - 0375-9601
M1 - 128054
ER -