In this talk, I'll first give an introduction to quantum machine learning and how one can trade quantum entanglement for data resources to get an experimental reduction in data resource requirements for quantum machine learning. I'll present a brief tutorial to give an idea of how quantum machine learning works and why this sort of tradeoff is possible. Then, I'll go on to show how quantum machine learning can be used to improve quantum simulation on near-term quantum computers showing some of our most recent results. Here, again, I'll focus on describing the structure of the problem to give the audience an appreciation of how the techniques work.