Lectures related to Shor Algorithm: Measurement Process Analysis

Explores the Simon Problem 4, analyzing quantum states and algorithm complexity.

Covers algorithmic complexity and travel time analysis, focusing on measuring the time taken by algorithms and evaluating their performance.

Covers classical and quantum computational complexity concepts and implications.

Explores algorithmic complexity, comparing growth rates using Theta notation and characterizing different complexity classes.

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Explores algorithm correctness, worst-case complexity analysis, and efficiency comparison based on input size.

Explores groups and numbers, emphasizing the hidden subgroup problem and its complexities in classic and quantum algorithms.

Explores algorithmic complexity, visualization of functions, and algorithm efficiency analysis using Python.

Introduces an improved algorithm for three-color parity games, focusing on progress measures, acceleration, and practical speed-up.

Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.

Covers Dijkstra's algorithm and its application to the all-pairs shortest path problem.

Analyzes the growth of functions to understand algorithm efficiency and uses Big-O notation for characterization.

Analyzes the growth of functions to understand algorithm complexity and efficiency.

Covers the Big-O notation to analyze algorithm efficiency and provides examples of polynomial and factorial function estimates.

Explains the Quantum Phase Estimation (QPE) algorithm and its complexity using two registers and SWAP gates.

Covers quantum computing basics, quantum algorithms, error correction, and quantum bit manipulation.

Covers the concept of algorithms, loop invariants, and examples of algorithmic problem-solving.

Explores constraints, efficiency, and complexity in linear algebra, emphasizing convexity and worst-case complexity in algorithm analysis.

Covers control operations, algorithmic complexity, function calls, and travel time analysis.

Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.