Shima Bab Hadiashar, Department of Combinatorics & Optimization, University of Waterloo
"Quantum PAC-Learning"
Abstract: Quantum learning theory lies in the intersection of (classical) Machine learning theory and quantum computing. In learning theory, the goal is to predict the outcome of an (unknown) function f for an arbitrary input when we have access to the value of the function on a small subset of inputs. In this talk, we'll discuss the basics of learning theory, more specifically the PAC learning model, and whether using quantum computers for learning is of any advantage.
P.S.: No background in quantum computing or machine learning is assumed.
Zoom link: https://us02web.zoom.us/j/3158960678?pwd=MmJUVDBGbW1rQXdUK0Iwa0VqK3I1Zz09
Note: The talk will start at 4:30pm, but the Zoom link will be active at 4:15pm for anyone who would like to chat prior to the talk. After the talk (at 5:30pm) there will be the opportunity to split into smaller rooms by topic of interest and chat.