Evaluating quantum generative models via imbalanced data classification benchmarks


New paper, who dis?

I’m excited to announce that our paper on using classification performance on imbalanced datasets as a proxy for measuring generative model quality up on the arXiv! It combines a lot of interesting techniques, like post-hoc testing, the Bayesian boostrap, and explainable AI techniques, all to get an idea of how well a quantum generative model performs. Please check it out!