A Comparative Analysis of Grover's Algorithm with Classical Algorithms

Optimization algorithms are essential for solving complex problems in various domains. Grover’s quantum search algorithm, simulated annealing, and genetic algorithm are some of the optimization techniques. Grover’s quantum search algorithm is known for providing a quadratic speedup on searching unsorted databases through quantum principles, simulated annealing algorithm finds solution spaces based on probability, and genetic algorithm emulates how genes propagate using natural selection. This paper reviews these algorithms in detail, providing information on how they are grounded in theories, implemented, and their performance. Specifically, the implementation of Grover’s algorithm offers better results for conducting an unsorted database search than the classical methods because of the effective use of superposition and interference properties in quantum mechanics.