Title: Probabilistic Symmetry Discovery via Wave Function Collapse

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper explores the application of probabilistic algorithms to identify and quantify symmetry within complex wave function ensembles, offering a novel approach to predicting the stability and evolution of systems exhibiting high degrees of disorder. The core mechanism centers around a Bayesian network model designed to capture the probability of symmetry formation, enabling the discovery of hidden patterns and predictive capabilities. We address the challenge of quantifying disorder and uncovering fundamental symmetry within systems often inaccessible through traditional statistical methods. The algorithm leverages the inherent probabilistic nature of quantum mechanics to provide a framework for understanding and manipulating the emergence of symmetry. This work presents a significant advancement in the field of symmetry detection, potentially unlocking new insights into complex systems and offering a pathway to more accurate modeling and prediction.

DOI: 10.5281/zenodo.22247111

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