Parameterized quantum circuits as machine learning models
This work addresses the challenge of utilizing quantum computers effectively for machine learning, though it is incremental as it reviews existing developments in the field.
The paper reviews parameterized quantum circuits as machine learning models, highlighting their expressive power and application to data-driven tasks like supervised and generative learning, with increasing experimental demonstrations on quantum hardware.
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.