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

^*Corresponding author for this work

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

12 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 language	English
Article number	128054
Pages (from-to)	1-13
Number of pages	13
Journal	Physics Letters, Section A: General, Atomic and Solid State Physics
Volume	434
DOIs	https://doi.org/10.1016/j.physleta.2022.128054
Publication status	Published - 16 May 2022
MoE publication type	A1 Journal article-refereed

Keywords

Machine learning
Optimal control
Quantum control
Quantum technologies
Reinforcement learning
STIRAP

Access to Document

10.1016/j.physleta.2022.128054

https://arxiv.org/abs/2112.07453

OpenUrl availability

Full text

Cite this

@article{23efba0245cb4860b1333c5a53de3f7e,

title = "A tutorial on optimal control and reinforcement learning methods for quantum technologies",

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.",

keywords = "Machine learning, Optimal control, Quantum control, Quantum technologies, Reinforcement learning, STIRAP",

author = "Luigi Giannelli and Pierpaolo Sgroi and Jonathon Brown and Paraoanu, {Gheorghe Sorin} and Mauro Paternostro and Elisabetta Paladino and Giuseppe Falci",

note = "| openaire: EC/H2020/766900/EU//TEQ Funding Information: The authors thank Alessandro Ferraro, Nicola Macr{\`i}, Federico Roy, Phila Rembold, Riccardo Sessa, Francesco M. D. Pellegrino, Christiane P. Koch, and Dominique Sugny for useful discussions. This work was supported by the Northern Ireland Department for Economy (DfE) , the EU H2020 framework through Collaborative Projects TEQ (Grant Agreement No. 766900 ), the DfE-SFI Investigator Programme (Grant No. 15/IA/2864 ), the Leverhulme Trust Research Project Grant UltraQute (Grant No. RGP-2018-266 ), COST Action CA15220 , the Royal Society Wolfson Research Fellowship scheme ( RSWF/R3/183013 ) and International Mobility Programme, the UK EPSRC (Grant No. EP/T028106/1 ), the Finnish Center of Excellence in Quantum Technology QTF (projects 312296 , 336810 ) of the Academy of Finland, and RADDESS programme (project 328193 ), Grant No. FQXi-IAF19-06 (“Exploring the fundamental limits set by thermodynamics in the quantum regime”) of the Foundational Questions Institute Fund (FQXi), the QuantERA grant SiUCs (Grant No. 731473 QuantERA), and by University of Catania , Piano per la Ricerca 2016–18 - linea di intervento “Chance”, Piano di Incentivi per la Ricerca di Ateneo 2020/2022, proposal Q-ICT. Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",

year = "2022",

month = may,

day = "16",

doi = "10.1016/j.physleta.2022.128054",

language = "English",

volume = "434",

pages = "1--13",

journal = "Physics Letters, Section A: General, Atomic and Solid State Physics",

issn = "0375-9601",

publisher = "Elsevier Science B.V.",

}

TY - JOUR

T1 - A tutorial on optimal control and reinforcement learning methods for quantum technologies

AU - Giannelli, Luigi

AU - Sgroi, Pierpaolo

AU - Brown, Jonathon

AU - Paraoanu, Gheorghe Sorin

AU - Paternostro, Mauro

AU - Paladino, Elisabetta

AU - Falci, Giuseppe

N1 - | openaire: EC/H2020/766900/EU//TEQ Funding Information: The authors thank Alessandro Ferraro, Nicola Macrì, Federico Roy, Phila Rembold, Riccardo Sessa, Francesco M. D. Pellegrino, Christiane P. Koch, and Dominique Sugny for useful discussions. This work was supported by the Northern Ireland Department for Economy (DfE) , the EU H2020 framework through Collaborative Projects TEQ (Grant Agreement No. 766900 ), the DfE-SFI Investigator Programme (Grant No. 15/IA/2864 ), the Leverhulme Trust Research Project Grant UltraQute (Grant No. RGP-2018-266 ), COST Action CA15220 , the Royal Society Wolfson Research Fellowship scheme ( RSWF/R3/183013 ) and International Mobility Programme, the UK EPSRC (Grant No. EP/T028106/1 ), the Finnish Center of Excellence in Quantum Technology QTF (projects 312296 , 336810 ) of the Academy of Finland, and RADDESS programme (project 328193 ), Grant No. FQXi-IAF19-06 (“Exploring the fundamental limits set by thermodynamics in the quantum regime”) of the Foundational Questions Institute Fund (FQXi), the QuantERA grant SiUCs (Grant No. 731473 QuantERA), and by University of Catania , Piano per la Ricerca 2016–18 - linea di intervento “Chance”, Piano di Incentivi per la Ricerca di Ateneo 2020/2022, proposal Q-ICT. Publisher Copyright: © 2022 Elsevier B.V.

PY - 2022/5/16

Y1 - 2022/5/16

N2 - 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.

AB - 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.

KW - Machine learning

KW - Optimal control

KW - Quantum control

KW - Quantum technologies

KW - Reinforcement learning

KW - STIRAP

UR - http://www.scopus.com/inward/record.url?scp=85126309792&partnerID=8YFLogxK

U2 - 10.1016/j.physleta.2022.128054

DO - 10.1016/j.physleta.2022.128054

M3 - Article

AN - SCOPUS:85126309792

SN - 0375-9601

VL - 434

SP - 1

EP - 13

JO - Physics Letters, Section A: General, Atomic and Solid State Physics

JF - Physics Letters, Section A: General, Atomic and Solid State Physics

M1 - 128054

ER -

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

Abstract

Keywords

Access to Document

OpenUrl availability

Other files and links

Fingerprint

-: Quantum-Enhanced Detection

Finnish Centre of Excellence in Quantum Technology

Cite this