ADAPTIVE GROVER ITERATION STRATEGY FOR EFFICIENT QUANTUM SEARCH OPTIMIZATION

Abstract

Quantum computing has been found to have the potential for use as an efficient computing model for addressing complicated computational problems. Several algorithms exist within the quantum computing framework, but Grover's algorithm has been found to offer substantial benefits in searching processes using a quadratic speed-up. In this regard, the purpose of the paper is to conduct a critical analysis of Grover's algorithm as a quantum search algorithm. The paper also provides a critical examination of the principles of operation, parts, and applications of the algorithm. Furthermore, the paper discusses various research efforts regarding the areas of amplitude amplification, oracle construction, noise effects, and optimization methods. In addition, the limitations of Grover's algorithm are presented in the paper. The limitations are found to be limited to the constant number of iterations and overshooting issues. Also, gaps in the field of research due to the lack of adaptive iteration and dynamic system conditions are discussed. In summary, it can be observed that adaptive methods can be employed to enhance quantum search algorithms.