Quantum Computing offers the theoretical promise of dramatically faster computation through direct utilization of the underlying quantum aspects of reality. This idea, first proposed in the early 1980s, exploded in interest in 1994 with Peter Shor's discovery of a polynomial time integer factoring algorithm. Today the first experimental platforms realizing small-scale quantum algorithms are becoming commonplace. Interestingly, machine learning may be the "killer app" for quantum computing. We will introduce quantum algorithms, with focus on a recent quantum computational model that will be familiar to researchers with a background in graphical models. We will show how a particular quantum algorithm -- quantum annealing -- running on current quantum hardware can be applied to certain optimization problems arising in machine learning. In turn, we will describe a number of challenges to further progress in quantum computing, and suggest that machine learning researchers may be well-positioned to drive the first real-world applications of quantum annealing.