This monograph provides a state-of-the-art treatment of learning
and robust control in quantum technology. It presents a systematic
investigation of control design and algorithm realisation for
several classes of quantum systems using control-theoretic tools
and machine-learning methods. The approaches rely heavily on
examples and the authors cover: sliding mode control of quantum
systems; control and classification of inhomogeneous quantum
ensembles using sampling-based learning control; robust and optimal
control design using machine-learning methods; robust
stability of quantum systems; and H∞ and
fault-tolerant control of quantum systems. Both theoretical
algorithm design and potential practical applications are
considered. Methods for enhancing robustness of performance are
developed in the context of quantum state preparation,
quantum gate construction, and ultrafast control of molecules.
Researchers and graduates studying systems and control theory,
quantum control, and quantum engineering, especially from
backgrounds in electrical engineering, applied mathematics and
quantum information will find Learning and Robust Control in
Quantum Technology to be a valuable reference for the investigation
of learning and robust control of quantum systems. The material
contained in this book will also interest chemists and physicists
working on chemical physics, quantum optics, and quantum
information technology.
General
| Imprint: |
Springer International Publishing AG
|
| Country of origin: |
Switzerland |
| Series: |
Communications and Control Engineering |
| Release date: |
March 2023 |
| First published: |
2023 |
| Authors: |
Daoyi Dong
• Ian R. Petersen
|
| Dimensions: |
235 x 155mm (L x W) |
| Format: |
Hardcover
|
| Pages: |
252 |
| Edition: |
1st ed. 2023 |
| ISBN-13: |
978-3-03-120244-5 |
| Categories: |
Books
Promotions
|
| LSN: |
3-03-120244-9 |
| Barcode: |
9783031202445 |
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