Title: Quantum-Enhanced Topological Data Analysis (QETDA)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-Enhanced Topological Data Analysis (QETDA) presents a novel methodology for analyzing topological data, leveraging the power of quantum-inspired algorithms to uncover hidden patterns and relationships within complex datasets. The core mechanism centers around a quantum-enhanced sampling algorithm that employs superposition to explore the data space, complemented by a "quantum entanglement clustering" algorithm designed to identify correlated patterns. This approach aims to overcome the computational limitations of traditional data analysis techniques, offering the potential for deeper insights and more efficient discovery in topological data. The paper details the implementation of the quantum sampling algorithm, its associated quantum entanglement clustering, and a preliminary assessment of its performance on a representative set of topological datasets.

DOI: 10.5281/zenodo.22194817

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