Hybrid quantum-classical systems make it possible to utilise existing quantum computers to their fullest extent. Within this framework, parameterised 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 modelling.

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