Скачать или смотреть AIP & CQC2T Webinar: Machine Learining for Quantum Systems - Dr Aaron Tranter

Machine learning has been at the forefront of many recent technological improvements. From self-driving cars, to biomedical imaging and even personal assistants, machine learning techniques allow us to process data in a way unlike before. One of the key performers in this area is deep learning, using structures modelled after neural connections found in the brain. In parallel, we have seen the rise of quantum technology, offering advantages over classical technology such as superior sensing, faster computing, and provably secure communications. But building and controlling this technology is a challenging endeavour. We will cover the application of machine learning to quantum systems, examining how these powerful techniques can serve as a useful tool for researchers and engineers, uncovering potentially new and exciting phenomena. We will also detail how we have already been able to demonstrate a deep learning approach overturning the best human intuition for cooling atoms using lasers, automatically aligning lasers, and helping researchers examine atomic scale images.

Speaker: Dr Aaron Tranter, Postdoctoral researcher, Australian National University

Dr Aaron Tranter is a postdoctoral researcher in the Department of Quantum Science and Technology at the Australian National University. Aaron's current research at CQC2T covers cold atoms as a quantum memory platform, engineering atom-light interactions and machine learning applied to problems in quantum physics.


#CQC2T is one of the world's largest teams undertaking fundamental research to create a universal quantum computing ecosystem.

Learn more at https://cqc2t.org/