  Hybrid quantum-classical systems make it possible to utilize existing quantum
computers to their fullest extent. Within this framework, parameterized quantum
circuits can be regarded as machine learning models with remarkable expressive
power. This Review presents the components of these models and discusses their
application to a variety of data-driven tasks, such as supervised learning and
generative modeling. With an increasing number of experimental demonstrations
carried out on actual quantum hardware and with software being actively
developed, this rapidly growing field is poised to have a broad spectrum of
real-world applications.
