Year: 2023
Type: preprint
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.
Source: arXiv (Cornell University)
Institution: Rigetti Computing (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4386081865