Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn about Quantum Hamiltonian Descent (QHD) in this 37-minute QuICS talk exploring innovative approaches to continuous optimization problems. Discover how QHD builds upon standard gradient descent while leveraging quantum interference of classical paths to tackle complex optimization challenges. Explore the algorithm's proven efficiency in solving degree-4 polynomial optimization problems with exponentially many local minima. Examine both circuit-based and analog implementations through Hamiltonian embedding techniques for sparse Hamiltonian simulation. Get introduced to QHDOPT, an open-source software package demonstrating QHD's practical advantages in large-scale nonlinear optimization across applied mathematics, computer science, and operations research.