Dynamic Matrix Quantum Computing Framework

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel dynamic matrix quantum computing framework designed to enhance the efficiency of quantum algorithms. Traditional quantum computing frameworks often rely on static matrices, limiting the flexibility and adaptability needed for complex algorithms. This framework leverages the dynamic nature of matrices, allowing for continuous adjustment and optimization during computation, ultimately leading to improved performance. We present a theoretical analysis and preliminary implementation demonstrating the framework's potential for accelerating quantum computations. The core mechanism focuses on a dynamic matrix adjustment algorithm, which iteratively modifies the matrix to achieve optimal results. This approach addresses limitations of conventional methods and offers a promising pathway toward more efficient quantum processing.

DOI: 10.5281/zenodo.22315399

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