自适应参数化量子计算的算法实现

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel adaptive parameterization quantum computing algorithm designed to enhance computational efficiency and reduce energy consumption. Traditional quantum algorithms often rely on fixed parameter settings, limiting their potential for optimization. This work proposes a dynamically adjusted parameterization approach that automatically tunes quantum parameters during each operation, enabling the algorithm to adapt to the input data and optimize performance. We detail the core mechanisms employed in this algorithm, including the use of a self-tuning parameter space and a feedback loop to maintain optimal quantum states. The implementation showcases the algorithm's ability to outperform existing methods across a range of benchmark problems. The core claim highlights the algorithm's ability to dynamically adjust parameters, leading to improved computational efficiency and reduced energy consumption. This research contributes to the advancement of quantum computing by providing a practical and adaptable framework for parameter optimization.

DOI: 10.5281/zenodo.22514144

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