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