自适应拓扑的量子计算几何优化

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel quantum computing geometric optimization algorithm based on adaptive topology. Traditional optimization methods often struggle with complex geometric structures, hindering the potential for enhanced quantum computation. Our approach leverages the inherent properties of quantum topology to dynamically adapt the optimization process, leading to improved efficiency and solution quality. The core mechanism involves mapping the geometric optimization problem to a quantum-mechanical representation, utilizing quantum algorithms to represent and manipulate the topology, ultimately optimizing the solution space. We present a preliminary implementation demonstrating the effectiveness of this method on a specific geometric optimization task, highlighting its potential for advancing quantum algorithm design and efficiency.

DOI: 10.5281/zenodo.22575479

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