Semantic Scholar Open Access 2019 1183 sitasi

Parameterized quantum circuits as machine learning models

Marcello Benedetti Erika Lloyd Stefan H. Sack Mattia Fiorentini

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science Physics Mathematics

Penulis (4)

Marcello Benedetti

Erika Lloyd

Stefan H. Sack

Mattia Fiorentini

Format Sitasi

APA MLA BibTeX

Benedetti, M., Lloyd, E., Sack, S.H., Fiorentini, M. (2019). Parameterized quantum circuits as machine learning models. https://doi.org/10.1088/2058-9565/ab4eb5

Akses Cepat

Lihat di Sumber doi.org/10.1088/2058-9565/ab4eb5

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 1183×
Sumber Database: Semantic Scholar
DOI: 10.1088/2058-9565/ab4eb5
Akses: Open Access ✓