Quantum Entanglement-Based Adaptive Matrix Reconstruction

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper investigates the application of quantum entanglement to a novel matrix reconstruction algorithm. The core claim is the development of an adaptive matrix reconstruction method predicated on the inherent correlations of entangled states, allowing for a more intelligent and robust reconstruction process than traditional methods. We introduce a '纠缠度量' parameter to dynamically adjust the entanglement strength and shape, optimizing the reconstruction for specific input matrices. The algorithm leverages the instantaneous correlation between entangled particles to perform matrix transformations without requiring explicit computation. The paper details the theoretical underpinnings of the algorithm, describes the implementation of the core mechanism, and presents preliminary results demonstrating its effectiveness in reconstructing complex matrices. We explore the impact of the '纠缠度量' parameter on reconstruction quality and stability.

DOI: 10.5281/zenodo.22604581

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