Covers the Quantum Approximate Optimization Algorithm, physically inspired unitary coupled cluster ansatz, hardware-efficient ansatz, and variational quantum eigensolver.
Covers coin rendering and the limitations of the greedy algorithm in finding optimal solutions.
Covers the Branch & Bound algorithm for efficient exploration of feasible solutions and discusses LP relaxation, portfolio optimization, Nonlinear Programming, and various optimization problems.
Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.
Explores choosing a step size in optimization on manifolds, including backtracking line-search and the Armijo method.
Discusses algorithms for balancing exploration and exploitation in decision-making processes.
