QUANTUM SEARCH IN THE NISQ ERA: A COMPREHENSIVE SURVEY OF GROVER’S ALGORITHM, NOISE RESILIENCE, AND APPLICATIONS IN INFORMATION RETRIEVAL

Abstract

This study provides a comprehensive review of Grover's Algorithm in quantum computing, emphasizing quantum information retrieval during the Noisy Intermediate-Scale Quantum (NISQ) era. The essential element in quantum information retrieval is Grover's Algorithm, which has been shown to be the most efficient one possible, offering a quadratic acceleration of O(sqrt(N)) compared to the classical O(N) unstructured database search. This survey offers an algorithm taxonomy by methodically examining 18 peer-reviewed works published from 1996 to 2026, systematically analyzing the Grover search, hybrid quantum-classical models, variants of amplitude estimation, distributed quantum search, adaptive learning oracle design and NISQ optimized circuit implementation. A structured comparative analysis is performed on the performance of classical and quantum approaches, with experimental results from IBM Quantum's 127-qubit superconducting processors. Systematic identification and discussion of critical research gaps such as the sub-O(√N) complexity barrier, noise resilience, scalability limits and quantum data-loading bottleneck. Future directions include fault-tolerant hardware, adaptive oracle learning, integration of quantum computers with AI, federated quantum search, and standardizing the benchmarking of quantum computers. The aim of this survey is to give an integrated structured reference for researchers interested in the field of quantum computing and information retrieval.