自适应量子概率场场论的非线性建模

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper explores the development of a novel self-adaptive quantum probability field model. Traditional probabilistic models often rely on static parameter settings, limiting their ability to accurately represent complex systems. We propose a method leveraging quantum circuit simulation to dynamically adjust probability field parameters, enabling a more flexible and potentially more accurate system modeling process. The core mechanism involves iterative quantum circuit simulation based on input data and system state, allowing the model to continuously refine its representation of the underlying dynamics. This approach addresses a significant limitation of existing probabilistic frameworks, offering a pathway towards enhanced system understanding and predictive capability. We present a preliminary implementation demonstrating the efficacy of this self-adaptive approach in a simplified system, showcasing its potential for broader applicability.

DOI: 10.5281/zenodo.22224099

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