Ascertain end-to-end quantum advantage in machine learning

Ascertain whether end-to-end quantum machine learning pipelines yield robust, practically useful quantum advantages over classical machine learning across broad and realistic families of tasks, beyond specially constructed instances.

Background

The paper surveys quantum machine learning’s promise and caveats: expensive data loading, prior dequantizations of proposed speedups, noisy and weakly structured real-world data, and training obstacles like barren plateaus.

While some rigorous advantages exist for contrived learning settings (e.g., specific generative or classification tasks under hardness assumptions), the authors stress that broad, practically meaningful end-to-end advantages remain to be demonstrated.