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This course aims to teach learners how to efficiently learn quantum many-body systems. The learning outcomes include understanding classical probability distributions, quantum state operators, and dual optimization. The course covers topics such as strong convexity and complexity bounds. The teaching method involves theoretical explanations and proofs. This course is intended for individuals interested in quantum computing and machine learning.
