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
• H8 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.

Autorentext

Daoyi Dong is currently a Scientia Associate Professor at the University of New South Wales, Canberra, Australia, and an Australian Research Council Future Fellow. His research interests include quantum control and machine learning. He has published more than 100 journal papers and more than 40 conference papers. Associate Professor Dong was awarded an ACA Temasek Young Educator Award by The Asian Control Association, a Humboldt Fellowship by Alexander von Humboldt Foundation, and is a recipient of a Future Fellowship, an International Collaboration Award and an Australian Post-Doctoral Fellowship from the Australian Research Council. He served as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (2015-2021) and currently a Technical Editor of IEEE/ASME Transactions on Mechatronics, an Associate Editor of IEEE Transactions on Cybernetics, and a Guest Editor of Annual Reviews in Control. Ian R. Petersen is currently a professor at the Australian National University. He held an Australian Research Council Professorial Fellowship from 2005 to 2007, an Australian Research Council Federation Fellowship from 2007 to 2012, and an Australian Research Council Laureate Fellowship from 2012 to 2016. He has served as an Associate Editor for the IEEE Transactions on Automatic Control, Systems and Control Letters, Automatica, and SIAM Journal on Control and Optimization. Currently he is an Editor for Automatica in the area of optimization in systems and control. He is a fellow of the IFAC, the IEEE and the Australian Academy of Science. His main research interests are in robust control theory, quantum control theory and stochastic control theory. Ian Petersen was elected IFAC Council Member for the 2014-2017 and 2018-2021 Trienniums. He was also elected to be a member of the IEEE Control Systems Society Board of Governors for the periods 2011-2013 and 2015-2017. He was Vice-president for Technical Activity for the Asian Control Association and was General Chair of the 2012 Australia Control Conference. He was General Chair of the 2015 IEEE Multi-Conference on Systems and Control.