基于量子拓扑的算法优化

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper explores the potential of quantum topology as a novel algorithmic framework for optimization. Traditional optimization methods often struggle with complex, high-dimensional problems, while quantum topology offers a unique geometric approach to search and solution. We propose a new algorithm, termed "Quantum Topology Optimization," which leverages quantum superposition and entanglement to identify and refine optimal parameter configurations for various applications, including machine learning and chemical reaction path optimization. The core mechanism involves constructing a quantum state representation that captures the inherent topological structure of the problem domain, enabling efficient exploration of the solution space and the identification of global optima. The algorithm demonstrates promising results across several benchmark problems, highlighting the potential of quantum topology to surpass conventional optimization techniques.

DOI: 10.5281/zenodo.22639337

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