Quantum State Matrix Decomposition Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum State Matrix Decomposition Optimization is a novel optimization algorithm designed to enhance the efficiency of matrix decomposition, particularly within the context of quantum computing and related applications. This paper introduces a dynamic matrix decomposition approach that leverages the inherent quantum state properties to optimize the computation process. We present a framework that dynamically adjusts parameters based on the quantum state, leading to significant improvements in computational speed and resource utilization. The core mechanism centers on incorporating quantum entanglement and superposition to guide the optimization process, enabling a more efficient and robust matrix decomposition. This work addresses limitations in traditional matrix decomposition methods by introducing a fundamentally different approach to parameter adjustment. The paper demonstrates the effectiveness of this algorithm through rigorous analysis and benchmark experiments, highlighting its potential to revolutionize matrix decomposition techniques.

DOI: 10.5281/zenodo.22193178

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