Parameterized quantum circuits as machine learning models

TL;DR

This paper reviews the use of parameterized quantum circuits as machine learning models, highlighting their components, applications, and potential for real-world use in hybrid quantum-classical systems.

Contribution

It provides a comprehensive overview of parameterized quantum circuits in machine learning, emphasizing their components, applications, and recent experimental developments.

Findings

Growing experimental demonstrations on quantum hardware

Active software development in the field

Potential for broad real-world applications

Abstract

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.