Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.
Covers the density operator formalism, polynomial time, NP problems, BPP, QMA, and probabilistic algorithms.
Covers the Quantum Approximate Optimization Algorithm (QAOA) for solving combinatorial optimization problems using quantum computers and its application to Boolean Satisfiability (SAT) problems.
Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.
Delves into complexity theory, focusing on the P vs NP problem and the classification of computational problems based on efficiency.
