Explores combinatorial optimization using simulated annealing to find ground states in frustrated systems and address challenges in satisfying all interactions simultaneously.
Covers the general logistics, course rationale, prerequisites, organization, credits, workload, grading, and course content, including swarm intelligence, foraging strategies, and collective phenomena.
Explores the Quantum Approximate Optimization Algorithm and its application in solving optimization problems efficiently using quantum adiabatic evolution.
Delves into the challenges and benefits of deep learning, highlighting the transition to convolutional neural networks and the impact of network width on the loss landscape.
