Title: Adaptive Quantum State Optimization Algorithm

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces an adaptive optimization algorithm based on quantum state principles, leveraging quantum state superposition to model and solve complex systems effectively. The core mechanism centers around utilizing quantum state manipulation to accelerate optimization processes. This approach addresses existing algorithmic limitations by exploiting quantum properties, promising improved efficiency and scalability. The proposed algorithm seeks to overcome computational bottlenecks inherent in traditional optimization methods through the application of quantum entanglement. This research investigates the potential of quantum state representations for enhanced optimization capabilities, offering a novel methodology for tackling challenging optimization problems.

DOI: 10.5281/zenodo.22632959

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