基于量子概率的算法优化设计

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper investigates the application of quantum probability to algorithm optimization, specifically targeting machine learning model optimization. Traditional optimization methods often struggle with complex, high-dimensional problems, while quantum mechanics offers the potential for enhanced performance through inherent probabilistic properties. We propose a novel algorithm, Quantum-Guided Optimization (QGO), that leverages quantum probability to guide the search process and improve model accuracy. The core mechanism of QGO centers around quantum state manipulation to represent and explore the solution space efficiently. This approach demonstrates improved performance compared to classical optimization techniques on a benchmark dataset of neural network training.

DOI: 10.5281/zenodo.22621631

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