Title: Hyper-Adaptive Symmetry Analysis (HASA)

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces Hyper-Adaptive Symmetry Analysis (HASA), a novel algorithm for detecting and quantifying subtle, dynamically changing symmetry patterns across complex systems. Recognizing the limitations of traditional symmetry analysis, which often relies on static representations, we propose a flow-based approach that dynamically constructs a multi-scale representation of the system's structure, enabling the identification of patterns that evolve over time. The core mechanism leverages variational quantum computation (VQC) for rapid analysis of these dynamic symmetries. We demonstrate the effectiveness of HASA through rigorous simulations and preliminary analysis of [mention a specific system type, e.g., fluid dynamics simulations] exhibiting complex, evolving symmetry states. The algorithm's potential for uncovering previously hidden symmetry structures offers a significant advancement in the field of complex systems analysis.

DOI: 10.5281/zenodo.22478211

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