Quantum Entangled Dynamic Matrix Transformation Algorithm

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel dynamic matrix transformation algorithm leveraging the principles of quantum entanglement. Traditional matrix transformations rely on fixed parameters, limiting their effectiveness and potential for optimization. We propose a system where the transformation process is dynamically adjusted based on the quantum state of the input matrix, achieving enhanced performance through self-adaptive parameter adjustments. This approach allows for a more robust and flexible transformation process, potentially offering improved efficiency and generalization capabilities compared to conventional methods. The theoretical framework underpinning this algorithm is based on quantum state evolution and entanglement. We explore the implementation of this algorithm using a simplified, illustrative model, demonstrating its potential for significant improvements in certain matrix transformation scenarios. The core mechanism centers around the manipulation of quantum entanglement to guide the transformation process.

DOI: 10.5281/zenodo.22634897

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