Quantum State-Driven Dynamic Quantum Optimization Algorithm

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel quantum state-driven dynamic quantum optimization algorithm designed to address the limitations of traditional optimization methods. The algorithm leverages the inherent quantum properties of superposition and entanglement to dynamically adjust optimization parameters based on the evolving quantum state. We propose a new approach to optimization that surpasses existing methods by offering improved efficiency and accuracy through automated parameter tuning. The core mechanism involves a quantum state representation, iterative adjustment of parameters based on quantum state analysis, and the application of a modified quantum optimization framework. The potential impact of this algorithm on diverse optimization problems is significant, offering a pathway towards more robust and efficient solutions.

DOI: 10.5281/zenodo.22298874

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