Parameterized quantum circuits as machine learning models

Understand

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.