Quantum Networks for Multi-Variable Probability Distribution Learning

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper explores the potential of quantum networks to overcome the computational limitations encountered in classical machine learning when dealing with high-dimensional multi-variable probability distributions. The core claim is that leveraging entangled resources within a quantum network can enable the efficient learning of these distributions, surpassing the bottlenecks inherent in classical computation. We introduce a novel learning algorithm designed to exploit the inherent correlations provided by quantum entanglement. This algorithm aims to achieve significantly improved learning speed and accuracy compared to traditional methods. The central mechanism relies on encoding probability distribution information within entangled quantum states, allowing for parallel processing and potentially revealing hidden correlations inaccessible to classical systems. This research contributes to the burgeoning field of quantum machine learning, offering a pathway towards more powerful and efficient data analysis techniques. We investigate the theoretical foundations of this approach, outlining the key mathematical formulations and highlighting the potential advantages offered by quantum networks. ---

DOI: 10.5281/zenodo.22153412

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