Quantum autoencoders with enhanced data encoding

Bravo-Prieto, Carlos (2021) Quantum autoencoders with enhanced data encoding. Machine Learning: Science and Technology, 2 (3). 035028. ISSN 2632-2153

[thumbnail of Bravo-Prieto_2021_Mach._Learn.__Sci._Technol._2_035028.pdf] Text
Bravo-Prieto_2021_Mach._Learn.__Sci._Technol._2_035028.pdf - Published Version

Download (885kB)

Abstract

We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity. The key idea of the algorithm is to define a parameterized quantum circuit that depends upon adjustable parameters and a feature vector that characterizes such a model. We assess the validity of the method in simulations by compressing ground states of the Ising model and classical handwritten digits. The results show that EF-QAE improves the performance compared to the standard quantum autoencoder using the same amount of quantum resources, but at the expense of additional classical optimization. Therefore, EF-QAE makes the task of compressing quantum information better suited to be implemented in near-term quantum devices.

Item Type: Article
Subjects: STM Digital Press > Multidisciplinary
Depositing User: Unnamed user with email support@stmdigipress.com
Date Deposited: 04 Jul 2023 04:29
Last Modified: 07 Jun 2024 10:30
URI: http://publications.articalerewriter.com/id/eprint/1277

Actions (login required)

View Item
View Item