Dynamic Matrix Quantum Algorithm Optimization

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel dynamic matrix quantum algorithm optimization framework designed for quantum computers. Traditional approaches often rely on static matrices, limiting the potential for efficient computation. We propose a mechanism that transforms linear algebra calculations into quantum operations, enabling dynamic adaptation and ultimately, improved computational efficiency. The core claim is that this framework, utilizing the unique properties of dynamic matrices, offers a significant advancement in the optimization of quantum algorithms. This research explores the potential of dynamically adjusting the matrix structure during the execution of quantum computations, leading to enhanced performance. The paper details the algorithm's architecture, its mathematical underpinnings, and its potential impact on various quantum algorithms.

DOI: 10.5281/zenodo.22575401

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