基于动态量子算法的优化计算

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper investigates the potential of dynamic quantum algorithms to revolutionize optimization calculations, particularly when dealing with complex and evolving data. Traditional quantum algorithms often rely on static parameters, limiting their applicability to relatively simple problems. We propose a novel approach utilizing a "Quantum State Evolution" mechanism, allowing the algorithm to dynamically adjust its parameters based on the input data, thereby achieving enhanced efficiency as the problem size increases. This work introduces a new framework for optimization computation that surpasses existing methods in its adaptability and potential for scalability. The core mechanism centers around iteratively refining the quantum state to minimize a predefined objective function. This paper will present a detailed theoretical analysis of the proposed algorithm, demonstrate its effectiveness through simulations, and discuss the implications of this approach for various optimization problems.

DOI: 10.5281/zenodo.22248911

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