Work

Expressive power of parametrized quantum circuits

Year: 2020

Type: article

Abstract: The authors demonstrate that parametrized quantum circuits possess a better expressive power than classical neural networks, such as restricted and deep Boltzmann machines. Based on the advanced expressive power, the authors propose a Bayesian quantum circuit that enables parametrized quantum circuits to perform machine learning tasks

Source: Physical Review Research

DOI: 10.1103/physrevresearch.2.033125

Authors: Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao

Institutions: The University of Sydney, University of Technology Sydney

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625

Funder: Australian Research Council