Self-Adaptive Quantum Field Optimization Algorithm

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

The current landscape of quantum field theory faces significant challenges in achieving precise control and optimization of complex quantum fields. Traditional optimization algorithms often struggle with high dimensionality and non-linearity, leading to suboptimal solutions. This paper introduces a novel self-adaptive quantum field optimization algorithm that leverages the unique properties of dynamic quantum fields to dynamically adjust parameters and achieve accurate control and optimization. We detail the core mechanism, including the use of a dynamic feedback loop, to automatically tune the quantum field based on input data, effectively addressing the limitations of static optimization techniques. The algorithm demonstrates significant improvements in optimization performance compared to existing methods across various benchmark problems.

DOI: 10.5281/zenodo.22153815

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