Core Claim: Design a matrix multiplication algorithm based on quantum self-adaptation, utilizing quantum entanglement to accelerate the computational speed of matrix operations.

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper investigates a novel matrix multiplication algorithm leveraging quantum self-adaptation to enhance computational efficiency and stability. Traditional matrix multiplication relies on classical computation, presenting limitations in terms of speed and robustness. This work proposes a quantum self-adaptation mechanism that introduces quantum entanglement into the matrix multiplication process, fundamentally altering the way computations are performed. The algorithm's core design centers around exploiting the unique properties of quantum entanglement to accelerate the core steps of the matrix multiplication. We present a theoretical analysis and preliminary implementation demonstrating a significant speedup compared to traditional methods. The study focuses on the potential for quantum algorithms to surpass classical capabilities in computationally intensive tasks, specifically within the realm of matrix operations. The paper concludes with a discussion of future research directions and potential applications.

DOI: 10.5281/zenodo.22291010

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