Evaluating quantum generative models via imbalanced data classification benchmarks
Abstract
A limited set of tools exist for assessing whether the behavior of quantum machine learning models diverges from conventional models, outside of abstract or theoretical settings. We present a systematic application of explainable artificial intelligence techniques to analyze synthetic data generated from a hybrid quantum-classical neural network adapted from twenty different real-world data sets, including solar flares, cardiac arrhythmia, and speech data. Each of these data sets exhibits varying degrees of complexity and class imbalance. We benchmark the quantum-generated data relative to state-of-the-art methods for mitigating class imbalance for associated classification tasks. We leverage this approach to elucidate the qualities of a problem that make it more or less likely to be amenable to a hybrid quantum-classical generative model.
Cite
@article{arxiv.2308.10847,
title = {Evaluating quantum generative models via imbalanced data classification benchmarks},
author = {Graham R. Enos and Matthew J. Reagor and Eric Hulburd},
journal= {arXiv preprint arXiv:2308.10847},
year = {2023}
}