Dynamic Optimization Based on Dynamic Quantum States

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm, Dynamic Optimization Based on Dynamic Quantum States, that leverages the inherent dynamics of quantum states to dynamically adjust optimization parameters. Traditional optimization methods often rely on fixed, pre-defined parameters, limiting their adaptability to complex and evolving problems. This algorithm utilizes quantum state fluctuations to automatically tune optimization parameters, resulting in improved efficiency and accuracy compared to conventional approaches. The core mechanism involves monitoring quantum state changes and instantaneously adapting optimization strategies based on these fluctuations. This adaptive nature offers a significant departure from static optimization techniques and holds potential for tackling a broader range of optimization challenges.

DOI: 10.5281/zenodo.22624828

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