Adaptive Quantum Algorithm Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum algorithm optimization is a critical challenge in the development of quantum technologies. Traditional optimization methods often struggle to effectively utilize the inherent dynamism of quantum systems, limiting their potential for performance gains. This paper proposes a novel approach based on adaptive quantum algorithm optimization, leveraging the quantum system's inherent parameter adjustments to dynamically tune quantum algorithms for enhanced efficiency. We present a framework that automatically adjusts quantum algorithm parameters based on real-time feedback, resulting in improved performance across a range of quantum tasks. The core mechanism focuses on utilizing quantum state evolution to guide parameter adjustments, offering a significant improvement over static optimization techniques. The paper details the proposed algorithm, its implementation, and demonstrates its effectiveness through illustrative examples, showcasing its potential to accelerate quantum algorithm development.

DOI: 10.5281/zenodo.22303149

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