Quantum semi-supervised generative adversarial network for enhanced data classification
Abstract In this paper, we propose the quantum semi-supervised generative adversarial network (qSGAN). The system is composed of a quantum generator and a classical discriminator/classifier (D/C). The goal is to train both the generator and the D/C, so that the latter may get a high classification a...
Enregistré dans:
Auteurs principaux: | Kouhei Nakaji, Naoki Yamamoto |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/28d3e769cea14ac5bf6d99df44acb399 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Textured Mesh Generation Using Multi-View and Multi-Source Supervision and Generative Adversarial Networks
par: Mingyun Wen, et autres
Publié: (2021) -
Quantum generative adversarial networks with multiple superconducting qubits
par: Kaixuan Huang, et autres
Publié: (2021) -
Generative adversarial network based on chaotic time series
par: Makoto Naruse, et autres
Publié: (2019) -
Connectivity-informed drainage network generation using deep convolution generative adversarial networks
par: Sung Eun Kim, et autres
Publié: (2021) -
A novel semi-supervised framework for UAV based crop/weed classification.
par: Shahbaz Khan, et autres
Publié: (2021)