Quantum Entanglement and Probability Matrices

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum entanglement, a fundamental phenomenon in quantum mechanics, presents a unique opportunity for enhancing computational power. This paper explores the application of quantum self-organization principles to probability matrices, aiming to revolutionize both quantum computing and machine learning. We propose a novel method for constructing probability matrices leveraging quantum entanglement and superposition, leveraging these principles to significantly boost matrix computational efficiency and expressiveness. This work delves into the theoretical groundwork, the potential advantages, and the challenges associated with this approach. The core mechanism centers around utilizing quantum entanglement to accelerate matrix calculations while simultaneously enhancing the representation of complex data. The paper concludes with a discussion of the research's implications and future directions.

DOI: 10.5281/zenodo.22635252

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