Related papers: A Tutorial on Optimal Control and Reinforcement Learning methods for Quantum Technologies

A Tutorial on Optimal Control and Reinforcement Learning methods for Quantum Technologies

URL: http://arxiv.org/abs/2112.07453v4
Date: Fri, 18 Mar 2022 08:45:41 GMT
Title: A Tutorial on Optimal Control and Reinforcement Learning methods for Quantum Technologies
Authors: Luigi Giannelli, Pierpaolo Sgroi, Jonathon Brown, Gheorghe Sorin Paraoanu, Mauro Paternostro, Elisabetta Paladino, and Giuseppe Falci
Abstract summary: This tutorial introduces 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.
Score: 0.0
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
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 usefull 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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