Quantum Walk-Based Search Algorithms for Massive Datasets

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of quantum walk-based search algorithms to address the scalability challenges inherent in classical search algorithms when dealing with massive datasets. The core premise is that quantum superposition and interference, inherent properties of quantum systems, offer a potential pathway to significantly enhance the efficiency of search operations. We propose and analyze quantum walk algorithms designed to explore search spaces more effectively than their classical counterparts. The focus is on theoretical development and algorithmic design, without experimental validation. We explore key concepts such as walker position, probability amplitudes, and interference effects within the framework of quantum walks. The algorithms are designed to be adaptable to both quantum hardware and quantum simulators. This work aims to demonstrate the theoretical feasibility and potential advantages of utilizing quantum walk algorithms for large-scale data search, contributing to the growing field of quantum search algorithms.

DOI: 10.5281/zenodo.22201782

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