Title: Non-Linear Quantum Field Theory with Adaptive Parameterization

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper introduces a novel non-linear quantum field theory framework designed to enhance model accuracy and computational efficiency. Traditional field theories frequently rely on static, predefined parameters, limiting their applicability to specific physical scenarios. We propose a dynamic parameterization system that iteratively adjusts these parameters based on model performance, creating a feedback loop that continuously refines the model's fit. The core mechanism centers around a reinforcement learning-inspired feedback loop, evaluating the model's output against new data and optimizing parameter values to maximize predictive power. This approach overcomes the limitations of fixed parameter models, offering a more flexible and efficient framework for simulating complex quantum phenomena. The resulting model demonstrates improved performance across a range of benchmark problems, highlighting the potential for this adaptive approach in diverse physical contexts.

DOI: 10.5281/zenodo.22220810

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