Quantum Entanglement-Based Probability Matrix Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre phenomenon in quantum mechanics, offers a unique perspective on optimization problems. This paper explores the application of quantum entanglement to probability matrix optimization, a crucial task in machine learning. We propose a novel algorithm that leverages the inherent correlations within entangled states to guide the optimization process. The core mechanism focuses on encoding and manipulating entangled states to create a 'quantum state' that mimics the dynamics of a quantum system, ultimately leading to improved model accuracy and training efficiency. This work represents a significant step towards harnessing the power of quantum mechanics for machine learning, potentially revolutionizing the field.

DOI: 10.5281/zenodo.22291598

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