English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A limited set of tools exist for assessing whether the behavior of quantummachine learning models diverges from conventional models, outside of abstractor theoretical settings. We present a systematic application of explainableartificial intelligence techniques to analyze synthetic data generated from ahybrid quantum-classical neural network adapted from twenty differentreal-world data sets, including solar flares, cardiac arrhythmia, and speechdata. Each of these data sets exhibits varying degrees of complexity and classimbalance. We benchmark the quantum-generated data relative to state-of-the-artmethods for mitigating class imbalance for associated classification tasks. Weleverage this approach to elucidate the qualities of a problem that make itmore or less likely to be amenable to a hybrid quantum-classical generativemodel.

Evaluating quantum generative models via imbalanced data classification benchmarks