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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.
Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.
Covers the Quantum Approximate Optimization Algorithm, physically inspired unitary coupled cluster ansatz, hardware-efficient ansatz, and variational quantum eigensolver.
Introduces the importance of studying algorithms, presents a clever algorithm for calculating an arithmetic series, and discusses efficiency and correctness in algorithms.
Covers the basics of numerical analysis, including adaptive feature computation, residual analysis, and the importance of well-posed problems.
