Title: Quantum-Based Topological Structure Learning Algorithm Based on Probabilistic Perturbations

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel topological structure learning algorithm based on quantum simulations, employing probabilistic perturbations as the learning signal. The algorithm leverages the inherent properties of quantum simulations to efficiently learn and refine topological structures. The core mechanism involves generating quantum simulations of potential topological landscapes, using these simulations to iteratively refine the topology, and subsequently using the resulting topology to guide a non-linear optimization process. Traditional approaches often rely on handcrafted models, while this method offers a more adaptable and potentially higher-performing solution through quantum simulation. We demonstrate the effectiveness of this algorithm through a series of simulations and optimization tasks, showcasing its ability to learn complex topological structures and achieve significant improvements in optimization performance.

DOI: 10.5281/zenodo.22638064

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