A tutorial on optimal control and reinforcement learning methods for quantum technologies

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

Research output: Contribution to journalArticlepeer-review

Abstract

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 .
Original languageEnglish
Article number128054
JournalPhysics Letters A
Early online date08 Mar 2022
DOIs
Publication statusEarly online date - 08 Mar 2022

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