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

doi:10.1016/j.physleta.2022.128054

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

School of Mathematics and Physics

Research output: Contribution to journal › Article › peer-review

39 Citations (Scopus)

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 language	English
Article number	128054
Number of pages	13
Journal	Physics Letters A
Volume	434
Early online date	08 Mar 2022
DOIs	https://doi.org/10.1016/j.physleta.2022.128054
Publication status	Published - 16 May 2022

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10.1016/j.physleta.2022.128054Licence: Unspecified

Machine learning applications in quantum state engineering
Brown, J. P. (Author), Paternostro, M. (Supervisor) & Ferraro, A. (Supervisor), Dec 2023
Student 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 2024
Student thesis: Doctoral Thesis › Doctor of Philosophy

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Cite this

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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 .",

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AU - Falci, Giuseppe

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

Abstract

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Student theses

Machine learning applications in quantum state engineering

Reinforcement learning based methods for optimal control and design of quantum systems

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