The Australian Institute of Physics (AIP) in partnership with CQC²T is hosting a webinar on Machine Learning for Quantum Technology – on Friday 10th December, kicking off at 11am AEDT.
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.
In this webinar, CQC²T Research Fellow Dr Aaron Tranter from the Australian National University, 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. He 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.
Aaron’s current research in CQC²T covers cold atoms as a quantum memory platform, engineering atom-light interactions and machine learning applied to problems in quantum physics.
University: Australian National University
Authors Centre Participants: Mr. Aaron Tranter
Other Source: AIP