The rapid progress of technology over the past few decades has led to the emergence of two powerful computational paradigms known as quantum computing and machine learning. While machine learning tries to learn the solutions from data, quantum computing harnesses the quantum laws for more powerful computation compared to classical computers. In this talk, I will discuss three waves of quantum machine learning, each harnessing a particular aspect of quantum computers and targeting particular problems. The first domain scrutinizes the power of quantum computers to work with high-dimensional data and speed-up algebra, but raises the caveat of input/output due to the quantum measurement rules. The second domain circumvents this problem by using a hybrid architecture, performing optimization on a classical computer while evaluating parameterized states on a quantum circuit, chosen based on a particular problem. Finally, the third domain is inspired by brain-like computation and uses the natural interaction and unitary dynamic of a given quantum system as a source for learning.