基于量子态的概率模型优化

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm based on quantum state probability models, designed to efficiently optimize complex systems. The algorithm leverages the inherent probabilistic nature of quantum states to enhance parameter optimization, potentially leading to significant improvements in performance compared to traditional approaches. We propose a new framework that utilizes quantum state representations to map the problem space, enabling a more nuanced and adaptive optimization process. The core mechanism revolves around quantifying and manipulating the probability distribution associated with potential parameter solutions, fostering a shift towards a probabilistic approach to parameter selection. The study demonstrates the effectiveness of this algorithm through theoretical analysis and preliminary experimental validation, showcasing its potential to address challenges in optimization of complex systems.

DOI: 10.5281/zenodo.22322230

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