Learning to Predict Arbitrary Quantum Processes

This course aims to teach learners how to efficiently learn and predict local properties in the output state of an arbitrary quantum circuit/process over a wide range of randomly sampled input quantum states. The course covers the potential for learning algorithms to predict the output of complex quantum dynamics faster than the time needed to run the process. The teaching method involves a talk based on research findings, with a panel discussion featuring experts in the field. The intended audience for this course includes individuals interested in quantum computing, machine learning, and quantum information theory.