Evaluating quantum generative models via imbalanced data classification
  benchmarks
Graham R. Enos Matthew J. Reagor Eric Hulburd
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.