Recorded 02 October 2023. Yu Tong of the California Institute of Technology presents "Recent progress in Hamiltonian learning" at IPAM's Quantum Algorithms for Scientific Computation Workshop. Abstract: In the last few years a number of works have proposed and improved provably efficient algorithms for learning the Hamiltonian from real-time dynamics. In this talk, I will first provide an overview of these developments, and then discuss how the Heisenberg limit, the fundamental precision limit imposed by quantum mechanics, can be reached for this task. I will demonstrate that reaching the Heisenberg limit requires techniques that are fundamentally different from previous ones, and the important roles played by quantum control, conservation laws, and thermalization. I will also discuss open problems that are crucial to the practical implementation of these algorithms. Learn more online at: https://www.ipam.ucla.edu/programs/workshops/workshop-i-quantum-algorithms-for-scientific-computation/?tab=schedule