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F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. Brandao, D. A. Buell, et al., arXiv:1910.11333 (2019b), URL https://arxiv.org/abs/1910.11333
Original
1910
Earlier work this paper cites.
F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, A. Bengtsson, S. Boixo, M. Broughton, B. B. Buckley, et al., arXiv preprint arXiv:2010.07965 (2020a)
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2010
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S. Aaronson and A. Arkhipov, in Proceedings of the Forty-Third Annual ACM Symposium on Theory of Computing (Association for Computing Machinery, New York, NY, USA, 2011), STOC ’11, p. 333–342, ISBN 9781450306911, URL https://doi.org/10.1145/1993636.1993682
2011
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C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, arXiv preprint arXiv:1312.6199 (2013)
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2013
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J. C. Garcia-Escartin and P. Chamorro-Posada, Phys. Rev. A 87
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, in Advances in neural information processing systems (2014), pp. 2672–2680
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T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, and X. Chen, in Advances in Neural Information Processing Systems , edited by D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Curran Associates, Inc., 2016), vol. 29, pp. 2234–2242, URL https://proceedings.neurips.cc/paper/2016/file/8a3363abe792db2d8761d6403605aeb7-Paper.pdf
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S. Boixo, S. V. Isakov, V. N. Smelyanskiy, R. Babbush, N. Ding, Z. Jiang, J. M. Martinis, and H. Neven, arXiv preprint arXiv:1608.00263 (2016)
Original
2016
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J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, arXiv:1611.09347 (2016)
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2016
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S. Aaronson, The complexity of quantum states and transformations: From quantum money to black holes (2016), eprint 1607.05256
2016
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C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al., in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
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E. Putin, A. Asadulaev, Y. Ivanenkov, V. Aladinskiy, B. Sanchez-Lengeling, A. Aspuru-Guzik, and A. Zhavoronkov, Journal of Chemical Information and Modeling 58
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Cited alongside, same era.
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2018

Then

L. Mescheder, A. Geiger, and S. Nowozin, in Proceedings of the 35th International Conference on Machine Learning , edited by J. Dy and A. Krause (PMLR, Stockholmsmässan, Stockholm Sweden, 2018), vol. 80 of Proceedings of Machine Learning Research , pp. 3481–3490, URL http://proceedings.mlr.press/v80/mescheder18a.html
2018
Later among the works it cites.
C. Bădescu, R. O’Donnell, and J. Wright, in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (2019), pp. 503–514
2019
Later among the works it cites.
M. Y. Niu, A. M. Dai, L. Li, A. Odena, Z. Zhao, V. Smelyanskyi, H. Neven, and S. Boixo, arXiv preprint arXiv:2010.11983 (2020)
Original
2020
Later among the works it cites.
N. Klco and M. J. Savage, Phys. Rev. A 102
2020
Later among the works it cites.
S. Lloyd, M. Schuld, A. Ijaz, J. Izaac, and N. Killoran, arXiv preprint arXiv:2001.03622 (2020)

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2021

Entangling Quantum Generative Adversarial Networks

Niu, Murphy Yuezhen, Zlokapa, Alexander, Broughton, Michael et al.

Understand

Generative adversarial networks (GANs) are one of the most widely adopted semisupervised and unsupervised machine learning methods for high-definition image, video, and audio generation.

In this work, we propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) that overcomes some limitations of previously proposed quantum GANs.
Leveraging the entangling power of quantum circuits, EQ-GAN guarantees the convergence to a Nash equilibrium under minimax optimization of the discriminator and generator circuits by performing entangling operations between both the generator output and true quantum data.
We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.

Built on

F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. Brandao, D. A. Buell, et al., arXiv:1910.11333 (2019b), URL https://arxiv.org/abs/1910.11333
Original
1910
Earlier work this paper cites.
F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, A. Bengtsson, S. Boixo, M. Broughton, B. B. Buckley, et al., arXiv preprint arXiv:2010.07965 (2020a)
Original
2010
Earlier work this paper cites.
S. Aaronson and A. Arkhipov, in Proceedings of the Forty-Third Annual ACM Symposium on Theory of Computing (Association for Computing Machinery, New York, NY, USA, 2011), STOC ’11, p. 333–342, ISBN 9781450306911, URL https://doi.org/10.1145/1993636.1993682
2011
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, arXiv preprint arXiv:1312.6199 (2013)
Original
2013
Earlier work this paper cites.
J. C. Garcia-Escartin and P. Chamorro-Posada, Phys. Rev. A 87
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, in Advances in neural information processing systems (2014), pp. 2672–2680
2014
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, and X. Chen, in Advances in Neural Information Processing Systems , edited by D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Curran Associates, Inc., 2016), vol. 29, pp. 2234–2242, URL https://proceedings.neurips.cc/paper/2016/file/8a3363abe792db2d8761d6403605aeb7-Paper.pdf
2016
Earlier work this paper cites.
S. Boixo, S. V. Isakov, V. N. Smelyanskiy, R. Babbush, N. Ding, Z. Jiang, J. M. Martinis, and H. Neven, arXiv preprint arXiv:1608.00263 (2016)
Original
2016
Earlier work this paper cites.

Similar

J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, arXiv:1611.09347 (2016)
Original
2016
Cited alongside, same era.
S. Aaronson, The complexity of quantum states and transformations: From quantum money to black holes (2016), eprint 1607.05256
2016
Cited alongside, same era.
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al., in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Cited alongside, same era.
E. Putin, A. Asadulaev, Y. Ivanenkov, V. Aladinskiy, B. Sanchez-Lengeling, A. Aspuru-Guzik, and A. Zhavoronkov, Journal of Chemical Information and Modeling 58
2018
Cited alongside, same era.
P.-L. Dallaire-Demers and N. Killoran, Phys. Rev. A 98
2018

Then

L. Mescheder, A. Geiger, and S. Nowozin, in Proceedings of the 35th International Conference on Machine Learning , edited by J. Dy and A. Krause (PMLR, Stockholmsmässan, Stockholm Sweden, 2018), vol. 80 of Proceedings of Machine Learning Research , pp. 3481–3490, URL http://proceedings.mlr.press/v80/mescheder18a.html
2018
Later among the works it cites.
C. Bădescu, R. O’Donnell, and J. Wright, in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (2019), pp. 503–514
2019
Later among the works it cites.
M. Y. Niu, A. M. Dai, L. Li, A. Odena, Z. Zhao, V. Smelyanskyi, H. Neven, and S. Boixo, arXiv preprint arXiv:2010.11983 (2020)
Original
2020
Later among the works it cites.
N. Klco and M. J. Savage, Phys. Rev. A 102
2020
Later among the works it cites.
S. Lloyd, M. Schuld, A. Ijaz, J. Izaac, and N. Killoran, arXiv preprint arXiv:2001.03622 (2020)

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

S. Lloyd and C. Weedbrook, Phys. Rev. Lett. 121

2018

Cited alongside, same era.

E. Farhi and H. Neven, arXiv preprint arXiv:1802.06002 (2018)

Original

2018

Cited alongside, same era.

A. Brock, J. Donahue, and K. Simonyan, arXiv preprint arXiv:1809.11096 (2018)

Original

2018

Cited alongside, same era.

F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. S. L. Brandao, D. A. Buell, et al., Nature 574

Cited in the paper.

F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, S. Boixo, M. Broughton, B. B. Buckley, D. A. Buell, et al., Science 369

Cited in the paper.

Original

2020

Later among the works it cites.

V. Bergholm, J. Izaac, M. Schuld, C. Gogolin, M. S. Alam, S. Ahmed, J. M. Arrazola, C. Blank, A. Delgado, S. Jahangiri, et al., Pennylane: Automatic differentiation of hybrid quantum-classical computations (2020), eprint 1811.04968

2020

Later among the works it cites.

B. T. Kiani, G. D. Palma, M. Marvian, Z.-W. Liu, and S. Lloyd, Quantum earth mover’s distance: A new approach to learning quantum data (2021), eprint 2101.03037

2021

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