Adaptive Matrix Quantumization Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents an innovative adaptive matrix quantumization algorithm designed to enhance the precision and stability of quantum computations. Traditional quantum algorithms often rely on static parameters, limiting their ability to adapt to complex matrix structures. This research introduces a dynamic quantumization strategy that automatically adjusts quantum parameter values based on the matrix's evolving characteristics. The algorithm's core mechanism involves optimizing matrix operations using dynamic parameter adjustments, thereby minimizing computational errors and improving overall accuracy. We demonstrate the effectiveness of this approach through simulations and experimental analysis, showcasing significant improvements in computation fidelity compared to static quantumization methods.

DOI: 10.5281/zenodo.22189025

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