Non-Linear Matrix Optimization with Quantum Entanglement

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the potential of quantum entanglement to accelerate the optimization of non-linear matrices. Traditional matrix factorization and linear algebra algorithms often struggle with computationally intensive problems, particularly when dealing with large matrices and complex dependencies. We propose a novel approach leveraging quantum entanglement to enhance optimization by introducing a parallel computation scheme. This mechanism aims to overcome inherent computational bottlenecks in these areas. The core of the work involves designing a quantum circuit that utilizes entanglement to efficiently explore the solution space, leading to faster convergence and improved performance. We detail the theoretical framework underpinning this approach, provide a preliminary implementation of a quantum optimization algorithm, and discuss the challenges and future directions for research in this area. This research addresses the limitations of existing methods and explores a fundamentally new paradigm for matrix optimization.

DOI: 10.5281/zenodo.22197117

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