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Quantum estimation, control and learning

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Date

2022

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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.

Description

Keywords

Quantum control, Quantum estimation, Quantum machine learning, Quantum sensing, Quantum system identification, Quantum systems

Citation

URI

https://dspace-test.anu.edu.au/handle/1885/733748371

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DevOps Pure Testing

Source

Annual Reviews in Control

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Review article

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DOI

10.1016/j.arcontrol.2022.04.011

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