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基于量子纠缠的动态参数优化

Jincheng Zhang

Published Sep 5, 2026• 0 citations
Publisher

Abstract

This paper introduces a novel dynamic parameter optimization algorithm based on quantum entanglement. Traditional optimization algorithms often struggle with computationally intensive tasks, particularly when dealing with complex parameter spaces. We propose a method that leverages the unique properties of quantum entanglement to dynamically adjust parameters, thereby significantly enhancing algorithmic efficiency. The core mechanism involves creating and manipulating entangled states to represent the parameter space, allowing for a more efficient exploration of the solution space. This approach offers potential advantages over existing optimization techniques, particularly in scenarios requiring high-dimensional parameter spaces and complex constraints. The theoretical analysis and initial experimental validation demonstrate the effectiveness of this quantum-inspired optimization strategy.

DOI
10.5281/zenodo.22314622

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