Superpolynomial quantum-classical separation for density modeling

Density modeling is a machine learning task with the goal of learning an underlying probability distribution from samples. Here the authors show that, when solving certain density modeling problems, algorithms that use fault-tolerant quantum computers have an advantage over classical ones.