High-Order Matrix Optimization Algorithm for Quantum Information Storage

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum information storage presents a significant challenge due to the need for precise control and efficient manipulation of quantum states. Traditional optimization algorithms often struggle to effectively address the complex constraints inherent in this field. This paper proposes a novel high-order matrix optimization algorithm designed to address these challenges, aiming to dramatically improve the efficiency of quantum information storage. The algorithm leverages the inherent properties of high-order matrices to efficiently navigate the space of possible quantum state configurations, offering a pathway towards enhanced storage density and reduced error rates. The core mechanism involves a sophisticated matrix manipulation strategy, coupled with predictive modeling based on calculated efficiency, offering a promising approach to optimizing quantum information storage. The proposed algorithm demonstrates significant improvements in computational efficiency compared to existing methods, and the resulting predictions offer insights into the potential for enhanced storage capabilities.

DOI: 10.5281/zenodo.22466669

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