基于自适应的量子态的优化算法

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm, termed "Adaptive Quantum State Optimization" (AQSO), leveraging the dynamic properties of quantum states to address complex system optimization challenges, particularly in areas such as protein structure prediction and molecular dynamics simulations. Traditional optimization methods often struggle with the inherent complexity and non-linearity of these systems, requiring extensive parameter tuning and potentially leading to suboptimal solutions. AQSO addresses this limitation by dynamically adjusting quantum states to guide the optimization process, offering a more robust and adaptable approach. We present a theoretical framework and initial implementation demonstrating the algorithm's effectiveness in a simplified, yet representative, example of protein structure prediction. The core mechanism centers around the concept of quantum state evolution, allowing the system to explore a wider range of solutions and escape local optima. The paper concludes with a discussion of the potential applications and future research directions for this innovative algorithm.

DOI: 10.5281/zenodo.22197093

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