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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

27 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 github1.

Original languageEnglish
Article number128054
Pages (from-to)1-13
Number of pages13
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Volume434
DOIs
Publication statusPublished - 16 May 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Machine learning
  • Optimal control
  • Quantum control
  • Quantum technologies
  • Reinforcement learning
  • STIRAP

Fingerprint

Dive into the research topics of 'A tutorial on optimal control and reinforcement learning methods for quantum technologies'. Together they form a unique fingerprint.

Cite this