Title: Non-Euclidean Probability Space Modeling

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores a novel probabilistic framework for modeling probability distributions across non-Euclidean spaces, leveraging the properties of quantum entanglement to enhance data representation and detection of correlations. The core claim posits that traditional probability models struggle to adequately capture the complexity of real-world data, particularly when dealing with high-dimensional spaces. We propose a new type of probability distribution based on quantum entanglement, which offers the potential to overcome these limitations by enabling the exploration of higher-dimensional spaces and the detection of subtle correlations that are currently hidden by classical methods. This work presents a theoretical framework and preliminary experimental investigations demonstrating the efficacy of this approach in a controlled setting.

DOI: 10.5281/zenodo.22201615

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