Title: Adaptive Parameter Adjustment for Dynamic Quantum Algorithms

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum algorithms, renowned for their potential to solve complex problems intractable for classical computers, rely heavily on precise parameter control. Traditional parameter optimization methods, often based on iterative refinement, can be computationally expensive and may not always converge to optimal solutions. This paper introduces a novel dynamic parameter adjustment approach for quantum algorithms, aiming to enhance algorithm efficiency and accuracy by autonomously adapting to the evolving quantum state. We propose a method that employs a feedback loop, dynamically adjusting parameters based on the observed quantum state, thereby mitigating the effects of data changes and improving the overall algorithm performance. The core mechanism lies in leveraging a continuous, self-regulating parameter update strategy driven by the quantum algorithm's trajectory. This approach offers a significant advancement over existing optimization techniques by providing a more adaptive and robust solution.

DOI: 10.5281/zenodo.22265967

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