Quantum Computing Adaptive Parameter Adjustment Algorithm

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum computing offers unprecedented computational power, but realizing this potential requires sophisticated algorithms for parameter optimization. Traditional optimization methods often struggle with the inherent complexities of quantum systems, particularly when dealing with large, multi-dimensional parameter spaces. This paper introduces a novel quantum computing algorithm, the Quantum Parameter Adjustment Algorithm (QPAA), designed to address these challenges by dynamically analyzing quantum process to automatically adjust parameters, thereby enhancing both computational efficiency and accuracy. This algorithm leverages machine learning techniques to achieve self-adaptive parameter correction, offering a fundamentally new approach to quantum optimization. We provide a detailed explanation of the algorithm's core mechanisms, including the dynamic analysis framework and the machine learning component, and discuss the potential benefits and limitations of this approach.

DOI: 10.5281/zenodo.22467746

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