Adaptive Quantum Field Models: An Optimization Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces an adaptive quantum field model optimization algorithm designed to enhance the efficiency of quantum computation. Traditional quantum field models often rely on fixed models, limiting their ability to optimize quantum state distributions. This work proposes a novel approach utilizing self-adaptive parameter adjustment to dynamically optimize quantum field configurations. The core mechanism involves introducing a "self-adaptive quantum field" concept, allowing for continuous parameter refinement based on a dynamic feedback loop. The algorithm's goal is to improve quantum computation performance by systematically optimizing the quantum state distribution. The paper details the algorithm's architecture, the self-adaptive parameter adjustment mechanism, and the resulting performance gains compared to traditional methods.

DOI: 10.5281/zenodo.22197394

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