基于动态自适应的量子算法优化器 (DQS-O)

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents the design and implementation of a novel quantum algorithm optimization technique, termed Dynamic Adaptive Quantum Algorithm Optimizer (DQS-O). Traditional quantum algorithm optimization methods often rely on static optimization strategies, limiting their effectiveness in complex scenarios. DQS-O addresses this limitation by dynamically adapting the optimization process to the specific characteristics of the target quantum algorithm, leading to significantly improved results. This work explores the utilization of quantum phenomena – specifically, superposition and entanglement – to achieve enhanced optimization capabilities. We detail the core mechanism of DQS-O, outlining its iterative refinement and sensitivity to quantum state transformations, ultimately resulting in more efficient quantum computations. The paper also discusses the advantages and challenges associated with this approach, emphasizing its potential to revolutionize the design and execution of quantum algorithms.

DOI: 10.5281/zenodo.22486964

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