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Tremendous progress has been witnessed in artificial intelligence where neural network backed deep learning systems have been used, with applications in almost every domain.
S. Wold, K. Esbensen, and P. Geladi, “Principal component analysis,” Chemometrics and intelligent laboratory systems , vol. 2, no. 1-3, pp. 37–52, 1987
1987
Earlier work this paper cites.
P. Orponen et al. , “Computational complexity of neural networks: a survey,” Nordic Journal of Computing , 1994
1994
Earlier work this paper cites.
D. P. DiVincenzo, “Quantum gates and circuits,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences , vol. 454, no. 1969, pp. 261–276, 1998
1998
Earlier work this paper cites.
M. S. Nikulin, “Hellinger distance,” Encyclopedia of mathematics , vol. 78, 2001
2001
Earlier work this paper cites.
E. Aïmeur, G. Brassard, and S. Gambs, “Machine learning in a quantum world,” in Conference of the Canadian Society for Computational Studies of Intelligence . Springer, 2006, pp. 431–442
2006
Earlier work this paper cites.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of machine learning research , vol. 9, no. Nov, pp. 2579–2605, 2008
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
Y. LeCun, C. Cortes, and C. Burges, “Mnist handwritten digit database. 2010,” URL http://yann. lecun. com/exdb/mnist , vol. 7, p. 23, 2010
2010
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
N. Wiebe, A. Kapoor, and K. M. Svore, “Quantum deep learning,” arXiv preprint arXiv:1412.3489 , 2014
2014
Earlier work this paper cites.
S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum principal component analysis,” Nature Physics , vol. 10, no. 9, pp. 631–633, 2014
2014
Earlier work this paper cites.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” arXiv preprint arXiv:1701.07875 , 2017
2017
Earlier work this paper cites.
P. Kravtsov and P. Kuznetsov, “Creative adversarial networks,” https://github.com/mlberkeley/Creative-Adversarial-Networks , 2017
2017
Earlier work this paper cites.
J. Zhao, L. Xiong, P. Karlekar Jayashree, J. Li, F. Zhao, Z. Wang, P. Sugiri Pranata, P. Shengmei Shen, S. Yan, and J. Feng, “Dual-agent gans for photorealistic and identity preserving profile face synthesis,” in Advances in Neural Information Processing Systems 30 , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 66–76
2017
Earlier work this paper cites.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 105–114
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
H.-W. Dong, W.-Y. Hsiao, L.-C. Yang, and Y.-H. Yang, “Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Cited alongside, same era.
S. Lloyd and C. Weedbrook, “Quantum generative adversarial learning,” Physical review letters , vol. 121, no. 4, p. 040502, 2018
2018
Cited alongside, same era.
P.-L. Dallaire-Demers and N. Killoran, “Quantum generative adversarial networks,” Physical Review A , vol. 98, no. 1, p. 012324, 2018
2018
Cited alongside, same era.
J. Preskill, “Quantum computing in the nisq era and beyond,” Quantum , vol. 2, p. 79, 2018
2019
Later among the works it cites.
M. P. Kumar and P. Jayagopal, “Generative adversarial networks: a survey on applications and challenges,” International Journal of Multimedia Information Retrieval , pp. 1–24, 2020
2020
Closest in time.
Y. Liao, K. Schwarz, L. Mescheder, and A. Geiger, “Towards unsupervised learning of generative models for 3d controllable image synthesis,” in Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
Closest in time.
2020
Closest in time.
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2018
Cited alongside, same era.
S. Barratt and R. Sharma, “A note on the inception score,” arXiv preprint arXiv:1801.01973 , 2018
2018
Cited alongside, same era.
2019
Cited alongside, same era.
L. Hu, S.-H. Wu, W. Cai, Y. Ma, X. Mu, Y. Xu, H. Wang, Y. Song, D.-L. Deng, C.-L. Zou, and L. Sun, “Quantum generative adversarial learning in a superconducting quantum circuit,” Science Advances , vol. 5, no. 1, 2019. [Online]. Available: https://advances.sciencemag.org/content/5/1/eaav2761
2019
Cited alongside, same era.
F. Arute and et al, “Quantum supremacy using a programmable superconducting processor,” Nature , vol. 574, p. 505–510, 2019
2019
Cited alongside, same era.
L. Hu, S.-H. Wu, W. Cai, Y. Ma, X. Mu, Y. Xu, H. Wang, Y. Song, D.-L. Deng, C.-L. Zou et al. , “Quantum generative adversarial learning in a superconducting quantum circuit,” Science advances , vol. 5, no. 1, p. eaav2761, 2019
2019
Cited alongside, same era.
C. Zoufal, A. Lucchi, and S. Woerner, “Quantum generative adversarial networks for learning and loading random distributions,” npj Quantum Information , vol. 5, no. 1, pp. 1–9, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Lu, L.-M. Duan, and D.-L. Deng, “Quantum adversarial machine learning,” Phys. Rev. Research , vol. 2, p. 033212, Aug 2020. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevResearch.2.033212
2020
Closest in time.
K. Beer, D. Bondarenko, T. Farrelly, T. J. Osborne, R. Salzmann, D. Scheiermann, and R. Wolf, “Training deep quantum neural networks,” Nature communications , vol. 11, no. 1, pp. 1–6, 2020
2020
Closest in time.
2020
Closest in time.
M. Broughton, G. Verdon, T. McCourt, A. J. Martinez, J. H. Yoo, S. V. Isakov, P. Massey, M. Y. Niu, R. Halavati, E. Peters, M. Leib, A. Skolik, M. Streif, D. V. Dollen, J. R. McClean, S. Boixo, D. Bacon, A. K. Ho, H. Neven, and M. Mohseni, “Tensorflow quantum: A software framework for quantum machine learning,” 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
S. A. Stein, R. L’Abbate, W. Mu, Y. Liu, B. Baheri, Y. Mao, G. Qiang, A. Li, and B. Fang, “A hybrid system for learning classical data in quantum states,” in 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) . IEEE, 2021, pp. 1–7
2021
Closest in time.
C. Xue, Z.-Y. Chen, Y.-C. Wu, and G.-P. Guo, “Effects of quantum noise on quantum approximate optimization algorithm,” Chinese Physics Letters , vol. 38, no. 3, p. 030302, 2021
2021
Closest in time.
S. A. Stein, B. Baheri, D. Chen, Y. Mao, Q. Guan, A. Li, S. Xu, and C. Ding, “Quclassi: A hybrid deep neural network architecture based on quantum state fidelity,” Proceedings of Machine Learning and Systems , vol. 4, pp. 251–264, 2022
2022
Closest in time.
K. Roth, A. Lucchi, S. Nowozin, and T. Hofmann, “Stabilizing training of generative adversarial networks through regularization,” in Advances in neural information processing systems , 2017, pp. 2018–2028
2028
Closest in time.