基于量子自适应的概率图搜索

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel probabilistic graph search algorithm, dubbed "Quantum Adaptive Probability Search" (QAPS), that leverages quantum self-adaptation to enhance exploration within complex search spaces. Traditional probabilistic graph search methods often suffer from inefficiency, particularly in problems with intricate search landscapes. QAPS employs a quantum-inspired self-adaptation mechanism to dynamically restructure the graph, facilitating more efficient exploration and ultimately, the identification of optimal solutions. We demonstrate the algorithm's efficacy through a series of benchmark problems, highlighting its superior performance compared to established methods in terms of both search time and solution quality. The core mechanism centers around the creation of a quantum-informed probability graph, allowing for adaptive node and edge weights, which significantly improves the algorithm's ability to navigate challenging search spaces.

DOI: 10.5281/zenodo.22255098

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