Overview of Learning Quantum States - Part 3

Learn about quantum state learning and tomography protocols in this comprehensive lecture from IBM Quantum researcher Srinivasan Arunachalam at the PCMI Graduate Summer School. Explore key concepts in quantum learning theory including Boolean function learning, quantum state learning with multiple copies, and distribution learning from quantum circuits. Dive into specific topics like quantum state tomography, PAC learning protocols, shadow tomography, quantum hypothesis selection, and classical shadows. Designed for those interested in quantum computation, no prior knowledge of quantum learning is required. Part of a broader 3-week summer program featuring expert-led minicourses on quantum computing topics like quantum Fourier transforms, information theory, LDPC codes, Hamiltonian complexity and more. Includes accompanying lecture notes and practice exercises to develop hands-on understanding of the material.