Quantum estimation, control and learning: opportunities and challenges
Daoyi Dong, Ian R Petersen
TL;DR
This paper discusses the emerging challenges and opportunities in quantum estimation, control, and learning, highlighting key areas like quantum state estimation, control strategies, and machine learning applications for quantum systems.
Contribution
It provides a comprehensive overview of current problems and potential research directions in quantum estimation, control, and learning technologies.
Findings
Identifies key open problems in quantum state estimation and control.
Highlights potential for machine learning to enhance quantum system management.
Outlines future research opportunities in quantum information processing.
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.
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Taxonomy
TopicsQuantum Information and Cryptography · Spectroscopy Techniques in Biomedical and Chemical Research · Quantum Computing Algorithms and Architecture