Dynamic Adaptive Quantum Algorithm Optimization Framework

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum algorithms, particularly those designed for large-scale computation, present significant challenges in terms of efficiency and accuracy. This paper introduces a dynamic adaptive quantum algorithm optimization framework that automatically adjusts quantum algorithm parameters to optimize both efficiency and accuracy. The framework leverages a learned performance metric to dynamically adjust parameters, offering a significant improvement over traditional methods. We present a comprehensive design of this framework, including a method for learning the performance metric and a detailed implementation of the optimization process. The results demonstrate the framework's effectiveness in optimizing various quantum algorithms, showcasing enhanced performance and improved accuracy compared to baseline approaches. This work contributes to the advancement of quantum algorithm design and optimization, paving the way for more efficient and reliable quantum computations.

DOI: 10.5281/zenodo.22190012

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