Evaluating quantum generative models via imbalanced data classification benchmarks
This work addresses the need for practical evaluation tools for quantum machine learning models, though it appears incremental as it benchmarks against existing methods without claiming major breakthroughs.
The authors tackled the problem of evaluating quantum generative models by applying explainable AI techniques to synthetic data from a hybrid quantum-classical neural network across 20 real-world imbalanced datasets, finding that this approach helps identify problem characteristics suitable for such models.
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.