Dataset Complexity and Predictive Advantage in Quantum Machine Learning: Evidence from 208 Benchmarks

Name: Dataset Complexity and Predictive Advantage in Quantum Machine Learning: Evidence from 208 Benchmarks
Creator: Sungmin Kim
Published: 2026-09-15T09:17:30.741Z
Keywords: Quantum Machine Learning

Kim, Sungmin

doi:10.17632/z5vgcrn8sm.1

Dataset Complexity and Predictive Advantage in Quantum Machine Learning: Evidence from 208 Benchmarks

Published: 15 September 2026| Version 1 | DOI: 10.17632/z5vgcrn8sm.1

Contributor:

Sungmin Kim

Description

This repository analyzes the relationship between dataset complexity and the relative predictive performance of quantum machine learning (QML) using 208 dataset records extracted from the literature. It examines whether conditions associated with an observed advantage in the sample generalize into a model-selection rule for new datasets.

Dataset Complexity and Predictive Advantage in Quantum Machine Learning: Evidence from 208 Benchmarks

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