基于量子势场的自适应几何变换算法

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel algorithm for optimizing geometric transformations based on quantum fields. Traditional geometric transformation methods often struggle with complex shapes and require extensive parameter tuning. This work proposes a self-adaptive geometry transformation algorithm that leverages the inherent properties of quantum fields to dynamically adjust transformation parameters, resulting in improved computational efficiency and enhanced performance in various geometric optimization scenarios. We formulate the optimization problem as a quantum state evolution problem, utilizing a quantum kernel to represent the transformation process. The core mechanism involves a self-tuning kernel, which iteratively adjusts the transformation parameters based on feedback from the quantum field, achieving a more optimal and efficient transformation process. The algorithm demonstrates significant improvements in several benchmark geometric optimization tasks compared to existing state-space methods.

DOI: 10.5281/zenodo.22292890

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