Traditional IM approaches consider each user/node independently as a potential target customer.

However, prior work has shown that the data can actually be recovered by the server using so-called gradient inversion attacks.

Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities.

We formalize coherence --a requirement that erroneous predictions, within a slice, should be wrong for the same reason --as a key property that any slice discovery method should satisfy.

Despite these advancements, QAOA's practical efficacy is challenged by the quantum coherence limits of modern quantum devices, as there is a ceiling on the allowable maximum circuit depth