In this talk, I will first give a brief overview of three different aspects of the connection between machine learning and quantum physics. Then, the central part of this talk will focus on quantum neural networks, and especially, I will discuss how to analyze quantum neural networks from the quantum information scrambling perspective. Quantum information scrambling can be described by quantities such as the out-of-time-ordered correlator, the tripartite information, and the operator size. I will show that both the learning dynamics and the design of network architecture can be understood with the help of these quantities.
Link to talk: https://osu.zoom.us/rec/share/DLI5eI1yX2qNoPAs3mZoAa-gW0bIQV2zLf0WPpqBnjRRxxwzYDLWQFjNjA3NrgRN.t9Hab7iGhF_wqnkf
