Quantum Spin Resonance Dynamics

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This research explores the application of Quantum Spin Resonance (QSPR) dynamics to model and predict complex system behavior, specifically focusing on the self-adaptive properties of quantum spin rotations. Traditional spin simulation methods often rely on static representations, limiting the ability to capture dynamic behavior and predict stability and controllability. This work proposes a novel dynamic modeling approach leveraging these self-adaptive characteristics to provide a more realistic and predictive framework for complex quantum systems. We aim to build a dynamic adjustment parameter-based dynamics model that effectively simulates the behavior of quantum spin rotations. The core claim is that this model will establish a robust and versatile tool for simulating complex systems, especially those exhibiting self-adjusting dynamics. The research will be conducted through a detailed mathematical analysis and simulation of a specific system, demonstrating the potential for improved prediction and control.

DOI: 10.5281/zenodo.22266399

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