Quantum Information Matrix Optimization via Entanglement-Based Probability Distribution

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum information matrices are fundamental in quantum computing, serving as the basis for various quantum algorithms and protocols. However, efficiently managing and optimizing these matrices, particularly in complex scenarios like high-dimensional quantum states, remains a significant challenge. This paper presents a novel optimization algorithm, Quantum Entanglement-Based Probability Distribution (QEBPD), designed to address this challenge by leveraging the unique properties of quantum entanglement. The core mechanism involves introducing a probabilistic distribution function that utilizes entanglement to guide the optimization process, thereby reducing noise and improving the reliability of quantum information matrices. The algorithm's design focuses on enhancing the representation of the matrix's probability distribution, specifically through the manipulation of entanglement to achieve a more stable and efficient optimization. This research explores the potential of QEBPD to significantly improve the performance of quantum information matrix operations, offering a promising approach to scaling quantum computation.

DOI: 10.5281/zenodo.22625768

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