Quantum Adaptive Optimization Algorithm

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel quantum adaptive optimization algorithm, leveraging quantum self-adaptivity to enhance algorithm efficiency and precision. Traditional optimization methods often rely on static parameter settings, which struggle to effectively handle complex system dynamics. This algorithm utilizes quantum entanglement and dynamic state changes to create a quantum self-adaptive optimization structure, providing a significant improvement over existing approaches. The core mechanism centers around constructing a quantum system that dynamically adjusts to the problem's characteristics, leading to faster convergence and improved solution quality. The paper details the algorithm's architecture, the underlying quantum principles, and preliminary experimental results demonstrating its effectiveness across a range of optimization problems.

DOI: 10.5281/zenodo.22592999

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