Quantum estimation, control and learning: Opportunities and challenges

Daoyi Dong; Ian R. Petersen

doi:10.1016/j.arcontrol.2022.04.011

Quantum estimation, control and learning: Opportunities and challenges

Daoyi Dong^*
, Ian R. Petersen

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

96 Citations (Scopus)

Abstract

The development of estimation and control theories for quantum systems is a fundamental task for practical quantum technology. This vision article presents a brief introduction to challenging problems and potential opportunities in the emerging areas of quantum estimation, control and learning. The topics cover quantum state estimation, quantum parameter identification, quantum filtering, quantum open-loop control, quantum feedback control, machine learning for estimation and control of quantum systems, and quantum machine learning.

Original language	English
Pages (from-to)	243-251
Number of pages	9
Journal	Annual Reviews in Control
Volume	54
DOIs	https://doi.org/10.1016/j.arcontrol.2022.04.011
Publication status	Published - Jan 2022

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10.1016/j.arcontrol.2022.04.011

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title = "Quantum estimation, control and learning: Opportunities and challenges",

abstract = "The development of estimation and control theories for quantum systems is a fundamental task for practical quantum technology. This vision article presents a brief introduction to challenging problems and potential opportunities in the emerging areas of quantum estimation, control and learning. The topics cover quantum state estimation, quantum parameter identification, quantum filtering, quantum open-loop control, quantum feedback control, machine learning for estimation and control of quantum systems, and quantum machine learning.",

keywords = "Quantum control, Quantum estimation, Quantum machine learning, Quantum sensing, Quantum system identification, Quantum systems",

author = "Daoyi Dong and Petersen, \{Ian R.\}",

note = "Publisher Copyright: {\textcopyright} 2022 The Author(s)",

year = "2022",

month = jan,

doi = "10.1016/j.arcontrol.2022.04.011",

language = "English",

volume = "54",

pages = "243--251",

journal = "Annual Reviews in Control",

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T2 - Opportunities and challenges

AU - Dong, Daoyi

AU - Petersen, Ian R.

PY - 2022/1

Y1 - 2022/1

N2 - The development of estimation and control theories for quantum systems is a fundamental task for practical quantum technology. This vision article presents a brief introduction to challenging problems and potential opportunities in the emerging areas of quantum estimation, control and learning. The topics cover quantum state estimation, quantum parameter identification, quantum filtering, quantum open-loop control, quantum feedback control, machine learning for estimation and control of quantum systems, and quantum machine learning.

AB - The development of estimation and control theories for quantum systems is a fundamental task for practical quantum technology. This vision article presents a brief introduction to challenging problems and potential opportunities in the emerging areas of quantum estimation, control and learning. The topics cover quantum state estimation, quantum parameter identification, quantum filtering, quantum open-loop control, quantum feedback control, machine learning for estimation and control of quantum systems, and quantum machine learning.

KW - Quantum control

KW - Quantum estimation

KW - Quantum machine learning

KW - Quantum sensing

KW - Quantum system identification

KW - Quantum systems

UR - https://www.scopus.com/pages/publications/85130884053

U2 - 10.1016/j.arcontrol.2022.04.011

DO - 10.1016/j.arcontrol.2022.04.011

M3 - Review article

SN - 1367-5788

VL - 54

SP - 243

EP - 251

JO - Annual Reviews in Control

JF - Annual Reviews in Control

ER -

Quantum estimation, control and learning: Opportunities and challenges

Abstract

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