Early Career Scientist ML Seminar TODAY at 10 AM

[Gaps]

Hi GAPS,

We are happy to invite you to the latest talk in our Early Career Scientist seminar series! Today's talk will take place at 10 AM in the MOB Theory Room for those of you onsite (light snacks will be provided) and on Zoom for those that aren't (information listed below). The seminar is structured as a 40 minute talk followed by 15 minutes for you to ask any and all questions you might have for the speaker.

These seminars are initially focusing on machine learning and its connections with the physics we do here at TRIUMF. Today we are happy to invite Dr. Stefanie Czischek from the University of Ottawa. Prior to joining the University of Ottawa as an Assistant Professor in the Department of Physics, Stefanie completed her doctoral degree at Heidelberg University with Thomas Gasenzer and further a post-doctoral fellowship at the University of Waterloo with Roger Melko. Her research interest focuses on advancing quantum science with artificial neural networks, with a specific focus on realizations of neural networks on biologically inspired neuromorphic devices or general physical systems. During her Ph.D., Dr. Czischek performed pioneering works leading toward a representation of strongly entangled quantum states on the BrainScaleS spiking neuromorphic hardware. In 2020, Dr. Czischek received the Springer Thesis Award, which recognizes outstanding Ph.D. research. She is now focused on growing her research team APRIQuOt with a focus on Artificial and Physically Realizable Intelligence for Quantum at uOttawa.

More information about the talk and Zoom coordinates can be found below. See you later today!

Your GAPS, on behalf of the Early Career Scientist Talks organizing committee

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Speaker: Dr. Stefanie Czischek (remote)

Title: The Need for Smart Tools: Leveraging Artificial Intelligence to Advance Quantum Science

Abstract: Recent years have witnessed remarkable advances in artificial intelligence (AI), reshaping a wide range of fields beyond traditional applications like text prediction, game playing, and traffic sign detection. Notably, AI is also driving significant breakthroughs in quantum science. While theoretical studies predict that quantum computation and simulation devices hold immense promise for overcoming limitations of conventional computers, experimental realizations remain constrained by current limitations in size and precision. Thus, as the excitement around quantum algorithms intensifies, so does the urgency to deepen our understanding of quantum systems. In this talk, I will present multiple examples demonstrating how AI can help overcome existing barriers to our understanding of quantum science, paving the way toward accessible quantum computation and simulation by modeling quantum systems, optimizing experimental control, and interpreting experimental measurement outcomes.

Zoom: https://ubc.zoom.us/j/61466731330?pwd=YZAntytn03BQfN2yJSzcf8zeJO9vvX.1
Meeting ID:614 6673 1330
Passcode: 231517

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