  At the intersection of machine learning and quantum computing, Quantum
Machine Learning (QML) has the potential of accelerating data analysis,
especially for quantum data, with applications for quantum materials,
biochemistry, and high-energy physics. Nevertheless, challenges remain
regarding the trainability of QML models. Here we review current methods and
applications for QML. We highlight differences between quantum and classical
machine learning, with a focus on quantum neural networks and quantum deep
learning. Finally, we discuss opportunities for quantum advantage with QML.
