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Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of dynamic matrix decomposition (DMD) techniques within the framework of quantum computing to optimize a complex problem. The core focus is on utilizing quantum algorithms to accelerate the matrix decomposition process, thereby enhancing the efficiency of the optimization algorithm. We present a novel quantum approach that leverages quantum superposition and entanglement to improve the search space and dimensionality reduction, ultimately leading to faster convergence and enhanced solution quality. The study demonstrates the potential of this methodology for tackling computationally intensive optimization problems. The core mechanism centers on exploiting quantum phenomena to enhance the efficiency of matrix decomposition, which is then translated into a significant speedup for the optimization process.

DOI: 10.5281/zenodo.22193363

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