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Title: Bayesian Constraint Optimization for Quantum Field Theory

Jincheng Zhang

Published Sep 2, 2026• 0 citations
Publisher

Abstract

This paper explores the application of Bayesian Constraint Optimization (BCO) to quantum field theory simulations. Traditional parameter tuning methods often rely on trial-and-error, which can be computationally expensive and may not accurately capture the underlying physics. We propose a novel Bayesian framework leveraging a dynamically adjusted Bayesian network to intelligently prioritize energy stability and coherence – key aspects of quantum field theory. The core mechanism involves a 'Adaptive Bayesian Network' that learns the field's dynamics through observation, effectively addressing the inherent uncertainty in complex quantum systems. This approach aims to provide more accurate simulations and accelerate optimization processes compared to conventional methods. The paper details the architecture of the Bayesian network, the learning process, and the resulting performance improvements, demonstrating the efficacy of the BCO framework in simulating fundamental quantum phenomena. The research focuses on a specific example of simulating the Klein-Gordon field, providing a concrete illustration of the methodology's potential.

DOI
10.5281/zenodo.22251588

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