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Quantum Machine Learning

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

arXiv
Authors: Luigi Giannelli, Sofia Sgroi, Jonathon Brown, Gheorghe Sorin Paraoanu, Mauro Paternostro, Elisabetta Paladino, Giuseppe Falci

Year

2021

Paper ID

40667

Status

Preprint

Abstract Read

~2 min

Abstract Words

90

Citations

N/A

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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2021 reference point for readers tracking recent quantum research.
  • Quantum Optimal Control is an established field of research which is necessary for the development of Quantum Technologies.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2112.07453 [2] arXiv https://arxiv.org/abs/2112.07453 [3] Publisher https://arxiv.org/abs/2112.07453

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