Stanford EE

Optimization and error analysis in quantum tomography, and learning control of quantum autoencoders

Professor Daoyi Dong (School of Engineering, Australian National University)
Spilker 232

ABSTRACT: Quantum technology is a promising future technology where unique quantum characteristics are taken advantage of to develop faster computation, securer communication, and high-precision sensing than their classical (non-quantum) counterparts. Quantum tomography is a fundamental task for developing powerful quantum technology and quantum autoencoders are promising for quantum data compression. In this talk, we will first present several results on optimization, adaptivity, computational complexity and error analysis in quantum state tomography, quantum detector tomography, quantum Hamiltonian identification, and quantum process tomography. Then we will present a couple of results on the compression rate of quantum autoencoders and how to use mixed reference states and learning control to optimize the performance of quantum autoencoders.



Daoyi Dong is currently a Professor at the Australian National University, Australia. He is an IEEE Fellow, and a Future Fellow of the Australian Research Council. He received a B.E. degree in automatic control and a Ph.D. degree in engineering from the University of Science and Technology of China, in 2001 and 2006, respectively. His research interests include quantum control, machine learning, system identification and renewable energy. He was awarded an ACA Temasek Young Educator Award by the Asian Control Association and is a recipient of an International Collaboration Award and a Future Fellowship from the Australian Research Council and Humboldt Research Fellowship from Alexander von Humboldt Foundation in Germany. He is the founding chair of the Technical Committee on Quantum Computing, Systems and Control, IEEE Control Systems Society.