Adaptive Optimization of Quantum Algorithms

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum algorithms, renowned for their potential to solve complex problems beyond the capabilities of classical computers, often exhibit inherent sensitivity to initial conditions and parameter choices. This sensitivity can significantly impact algorithm performance, hindering their ability to achieve optimal solutions. This paper proposes a novel adaptive optimization method specifically designed for quantum algorithms, focusing on dynamically adjusting algorithm parameters based on runtime analysis. We introduce a framework that leverages quantum algorithm properties to automatically refine parameter settings, thereby maximizing efficiency and exploring a broader range of solution spaces. This approach aims to surpass the limitations of traditional optimization techniques by proactively addressing the unique challenges posed by quantum computation.

DOI: 10.5281/zenodo.22596167

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