Optics and machine learning are natural symbionts. I will present three examples of how these fields can benefit each other based on our recent experimental work:
• optical neural networks and their all-optical training;
• robotic alignment of optical experiments;
• application of machine learning in linear-optical far-field superresolution imaging.
Alexander Lvovsky is an experimental physicist. He was born and raised in Moscow and did his undergraduate in Physics at the Moscow Institute of Physics and Technology. In 1993, he became a graduate student in Physics at Columbia University in New York City. His thesis research, conducted under the supervision of Dr. Sven R. Hartmann, was in the field of coherent optical transients in atomic gases. After completing his Ph. D. in 1998, he spent a year at the University of California, Berkeley as a postdoctoral fellow in the Department of Physics, and then five years at Universität Konstanz in Germany, first as an Alexander von Humboldt postdoctoral fellow, then as a research group leader in quantum-optical information technology. In 2004 he became Professor in the Department of Physics and Astronomy at the University of Calgary, and from autumn 2018, a professor at the University of Oxford. Alexander is a past Canada Research Chair, a lifetime member of the American Physical Society, a Fellow of the Optical Society and a winner of many awards – most notably the International Quantum Communications award, commendation letter from the Prime Minister of Canada and the Emmy Noether research award of the German Science Foundation. His work has been featured by CBC, NBC, Wired, New Scientist, MIT Technology Review, the Guardian, TASS and even Daily Mail.