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

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Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

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Abstrakti

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.

Alkuperäiskieli	Englanti
Artikkeli	128054
Sivut	1-13
Sivumäärä	13
Julkaisu	Physics Letters, Section A: General, Atomic and Solid State Physics
Vuosikerta	434
DOI - pysyväislinkit	https://doi.org/10.1016/j.physleta.2022.128054
Tila	Julkaistu - 16 toukok. 2022
OKM-julkaisutyyppi	A1 Julkaistu artikkeli, soviteltu

Pääsy asiakirjaan

10.1016/j.physleta.2022.128054

https://arxiv.org/abs/2112.07453

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Muut tiedostot ja linkit

Linkki julkaisuun Scopuksessa

Quantum-Enhanced Detection
Paraoanu, G., Björkman, I., McCord, J. & Sultanov, A.
01/01/2020 → 31/12/2022
Projekti: Academy of Finland: Other research funding
Kvanttiteknologian huippuyksikkö
Paraoanu, G., Dogra, S., Petrovnin, K., Lan, D. & McCord, J.
01/01/2018 → 31/12/2020
Projekti: Academy of Finland: Other research funding

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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",

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pages = "1--13",

journal = "Physics Letters A",

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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

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DO - 10.1016/j.physleta.2022.128054

M3 - Article

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VL - 434

SP - 1

EP - 13

JO - Physics Letters A

JF - Physics Letters A

SN - 0375-9601

M1 - 128054

ER -

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

Abstrakti

Pääsy asiakirjaan

OpenUrl-saatavuus

Muut tiedostot ja linkit

Sormenjälki

Projektit

Quantum-Enhanced Detection

Kvanttiteknologian huippuyksikkö

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