# Can quantum computing speed up Bayesian learning?

One of the biggest drawbacks of Bayesian learning against deep learning is runtime: applying Bayes' theorem requires knowledge on how the data is distributed, and this usually requires either expensive integrals or some sampling mechanism (with the corresponding drawbacks).

Since at the end of the day is all about distribution propagations, and this is (as far as I understand) the nature of quantum computing, is there a way to perform these efficiently? If yes, what limitations do apply?