Explore recent advancements in Hamiltonian learning algorithms through this 47-minute conference talk presented by Yu Tong from the California Institute of Technology at IPAM's Quantum Algorithms for Scientific Computation Workshop. Gain an overview of provably efficient algorithms for learning Hamiltonians from real-time dynamics, and delve into the challenges of reaching the Heisenberg limit, the fundamental precision limit imposed by quantum mechanics. Discover how quantum control, conservation laws, and thermalization play crucial roles in achieving this limit. Examine the fundamentally different techniques required to push the boundaries of Hamiltonian learning and consider open problems critical for practical implementation of these algorithms.