A Survey of Recent Advances in Quantum Generative Adversarial Networks
Quantum mechanics studies nature and its behavior at the scale of atoms and subatomic particles. By applying quantum mechanics, a lot of problems can be solved in a more convenient way thanks to its special quantum properties, such as superposition and entanglement. In the current noisy intermediate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/4/856 |
| _version_ | 1797621356347523072 |
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| author | Tuan A. Ngo Tuyen Nguyen Truong Cong Thang |
| author_facet | Tuan A. Ngo Tuyen Nguyen Truong Cong Thang |
| author_sort | Tuan A. Ngo |
| collection | DOAJ |
| description | Quantum mechanics studies nature and its behavior at the scale of atoms and subatomic particles. By applying quantum mechanics, a lot of problems can be solved in a more convenient way thanks to its special quantum properties, such as superposition and entanglement. In the current noisy intermediate-scale quantum era, quantum mechanics finds its use in various fields of life. Following this trend, researchers seek to augment machine learning in a quantum way. The generative adversarial network (GAN), an important machine learning invention that excellently solves generative tasks, has also been extended with quantum versions. Since the first publication of a quantum GAN (QuGAN) in 2018, many QuGAN proposals have been suggested. A QuGAN may have a fully quantum or a hybrid quantum–classical architecture, which may need additional data processing in the quantum–classical interface. Similarly to classical GANs, QuGANs are trained using a loss function in the form of max likelihood, Wasserstein distance, or total variation. The gradients of the loss function can be calculated by applying the parameter-shift method or a linear combination of unitaries in order to update the parameters of the networks. In this paper, we review recent advances in quantum GANs. We discuss the structures, optimization, and network evaluation strategies of QuGANs. Different variants of quantum GANs are presented in detail. |
| first_indexed | 2024-03-11T08:54:47Z |
| format | Article |
| id | doaj.art-3e830782ea3046d59f9a8eb41eebaa32 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T08:54:47Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-3e830782ea3046d59f9a8eb41eebaa322023-11-16T20:11:02ZengMDPI AGElectronics2079-92922023-02-0112485610.3390/electronics12040856A Survey of Recent Advances in Quantum Generative Adversarial NetworksTuan A. Ngo0Tuyen Nguyen1Truong Cong Thang2Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, JapanDepartment of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, JapanDepartment of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, JapanQuantum mechanics studies nature and its behavior at the scale of atoms and subatomic particles. By applying quantum mechanics, a lot of problems can be solved in a more convenient way thanks to its special quantum properties, such as superposition and entanglement. In the current noisy intermediate-scale quantum era, quantum mechanics finds its use in various fields of life. Following this trend, researchers seek to augment machine learning in a quantum way. The generative adversarial network (GAN), an important machine learning invention that excellently solves generative tasks, has also been extended with quantum versions. Since the first publication of a quantum GAN (QuGAN) in 2018, many QuGAN proposals have been suggested. A QuGAN may have a fully quantum or a hybrid quantum–classical architecture, which may need additional data processing in the quantum–classical interface. Similarly to classical GANs, QuGANs are trained using a loss function in the form of max likelihood, Wasserstein distance, or total variation. The gradients of the loss function can be calculated by applying the parameter-shift method or a linear combination of unitaries in order to update the parameters of the networks. In this paper, we review recent advances in quantum GANs. We discuss the structures, optimization, and network evaluation strategies of QuGANs. Different variants of quantum GANs are presented in detail.https://www.mdpi.com/2079-9292/12/4/856quantum machine learninggenerative adversarial networksquantum GANhybrid quantum–classical system |
| spellingShingle | Tuan A. Ngo Tuyen Nguyen Truong Cong Thang A Survey of Recent Advances in Quantum Generative Adversarial Networks Electronics quantum machine learning generative adversarial networks quantum GAN hybrid quantum–classical system |
| title | A Survey of Recent Advances in Quantum Generative Adversarial Networks |
| title_full | A Survey of Recent Advances in Quantum Generative Adversarial Networks |
| title_fullStr | A Survey of Recent Advances in Quantum Generative Adversarial Networks |
| title_full_unstemmed | A Survey of Recent Advances in Quantum Generative Adversarial Networks |
| title_short | A Survey of Recent Advances in Quantum Generative Adversarial Networks |
| title_sort | survey of recent advances in quantum generative adversarial networks |
| topic | quantum machine learning generative adversarial networks quantum GAN hybrid quantum–classical system |
| url | https://www.mdpi.com/2079-9292/12/4/856 |
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