Built on

L. Simon, R. Webster, and J. Rabin, “Revisiting precision and recall definition for generative model evaluation,” (2019), arXiv:1905.05441 [cs.LG]
Original
1905
Earlier work this paper cites.
M. Garey and D. Johnson, Computers and Intractability. A Guide to the Theory of NP-Completeness (W.H. Freeman and Co., NY, 1979)
1979
Earlier work this paper cites.
P. M. Nadkarni, L. Ohno-Machado, and W. W. Chapman, Journal of the American Medical Informatics Association 18
2011
Earlier work this paper cites.
G. Hinton, “A practical guide to training restricted boltzmann machines,” (Springer Berlin Heidelberg, 2012) pp. 599–619
2012
Earlier work this paper cites.
G. K. Dziugaite, D. M. Roy, and Z. Ghahramani, arXiv:1505.03906 (2015)
Original
2015
Earlier work this paper cites.
H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, R. Anil, Z. Haque, L. Hong, V. Jain, X. Liu, and H. Shah, arXiv:1606.07792 (2016)
Original
2016
Earlier work this paper cites.
I. G. Y. Bengio and A. Courville, MIT Press (2016)
2016
Earlier work this paper cites.
I. Goodfellow, arXiv:1701.00160 (2016)
Original
2016
Earlier work this paper cites.
N. Hansen, arXiv:1604.00772 (2016)
Original
2016
Earlier work this paper cites.
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” (2017), arXiv:1611.03530 [cs.LG]
Original
2017
Earlier work this paper cites.
H. Huang, P. S. Yu, and C. Wang, “An introduction to image synthesis with generative adversarial nets,” (2018), arXiv:1803.04469 [cs.CV]
Original
2018
Earlier work this paper cites.
A. Perdomo-Ortiz, M. Benedetti, J. Realpe-Gómez, and R. Biswas, Quantum Science and Technology 3
2018
Earlier work this paper cites.
M. Benedetti, D. Garcia-Pintos, Y. Nam, and A. Perdomo-Ortiz, npj Quantum Information 5
2018
Earlier work this paper cites.
J.-G. Liu and L. Wang, PRA 98
2018
Earlier work this paper cites.
Y. Du, M.-H. Hsieh, T. Liu, and D. Tao, Physical Review Research 2
2018
Earlier work this paper cites.
M. S. M. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly, in NeurIPS (2018)
2018
Earlier work this paper cites.
S. Zhao, H. Ren, A. Yuan, J. Song, N. Goodman, and S. Ermon, Advances in Neural Information Processing Systems 31
2018
Earlier work this paper cites.

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Then

A. M. Alaa, B. van Breugel, E. Saveliev, and M. van der Schaar, arXiv:2102.08921 (2021)
Original
2021
Later among the works it cites.
L. Ruthotto and E. Haber, GAMM-Mitteilungen (2021), 10.1002/gamm.202100008
2021
Later among the works it cites.
C. Leadbeater, L. Sharrock, B. Coyle, and M. Benedetti, Entropy 23
2021
Later among the works it cites.
Y. Suzuki, Y. Kawase, Y. Masumura, Y. Hiraga, M. Nakadai, J. Chen, K. M. Nakanishi, K. Mitarai, R. Imai, S. Tamiya, T. Yamamoto, T. Yan, T. Kawakubo, Y. O. Nakagawa, Y. Ibe, Y. Zhang, H. Yamashita, H. Yoshimura, A. Hayashi, and K. Fujii, Quantum 5
2021
Later among the works it cites.
J. Alcazar, M. G. Vakili, C. B. Kalayci, and A. Perdomo-Ortiz, arXiv:2101.06250 (2021)
Original
2021

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2022

Do Quantum Circuit Born Machines Generalize?

Gili, Kaitlin, Hibat-Allah, Mohamed, Mauri, Marta et al.

Understand

In recent proposals of quantum circuit models for generative tasks, the discussion about their performance has been limited to their ability to reproduce a known target distribution.

For example, expressive model families such as Quantum Circuit Born Machines (QCBMs) have been almost entirely evaluated on their capability to learn a given target distribution with high accuracy.
While this aspect may be ideal for some tasks, it limits the scope of a generative model's assessment to its ability to memorize data rather than generalize.
As a result, there has been little understanding of a model's generalization performance and the relation between such capability and the resource requirements, e.g., the circuit depth and the amount of training data.

Built on

L. Simon, R. Webster, and J. Rabin, “Revisiting precision and recall definition for generative model evaluation,” (2019), arXiv:1905.05441 [cs.LG]
Original
1905
Earlier work this paper cites.
M. Garey and D. Johnson, Computers and Intractability. A Guide to the Theory of NP-Completeness (W.H. Freeman and Co., NY, 1979)
1979
Earlier work this paper cites.
P. M. Nadkarni, L. Ohno-Machado, and W. W. Chapman, Journal of the American Medical Informatics Association 18
2011
Earlier work this paper cites.
G. Hinton, “A practical guide to training restricted boltzmann machines,” (Springer Berlin Heidelberg, 2012) pp. 599–619
2012
Earlier work this paper cites.
G. K. Dziugaite, D. M. Roy, and Z. Ghahramani, arXiv:1505.03906 (2015)
Original
2015
Earlier work this paper cites.
H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, R. Anil, Z. Haque, L. Hong, V. Jain, X. Liu, and H. Shah, arXiv:1606.07792 (2016)
Original
2016
Earlier work this paper cites.
I. G. Y. Bengio and A. Courville, MIT Press (2016)
2016
Earlier work this paper cites.
I. Goodfellow, arXiv:1701.00160 (2016)
Original
2016
Earlier work this paper cites.
N. Hansen, arXiv:1604.00772 (2016)
Original
2016
Earlier work this paper cites.
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” (2017), arXiv:1611.03530 [cs.LG]
Original
2017
Earlier work this paper cites.
H. Huang, P. S. Yu, and C. Wang, “An introduction to image synthesis with generative adversarial nets,” (2018), arXiv:1803.04469 [cs.CV]
Original
2018
Earlier work this paper cites.
A. Perdomo-Ortiz, M. Benedetti, J. Realpe-Gómez, and R. Biswas, Quantum Science and Technology 3
2018
Earlier work this paper cites.
M. Benedetti, D. Garcia-Pintos, Y. Nam, and A. Perdomo-Ortiz, npj Quantum Information 5
2018
Earlier work this paper cites.
J.-G. Liu and L. Wang, PRA 98
2018
Earlier work this paper cites.
Y. Du, M.-H. Hsieh, T. Liu, and D. Tao, Physical Review Research 2
2018
Earlier work this paper cites.
M. S. M. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly, in NeurIPS (2018)
2018
Earlier work this paper cites.
S. Zhao, H. Ren, A. Yuan, J. Song, N. Goodman, and S. Ermon, Advances in Neural Information Processing Systems 31
2018
Earlier work this paper cites.

Similar

Z.-Y. Han, J. Wang, H. Fan, L. Wang, and P. Zhang, PRX 8
2018
Cited alongside, same era.
J. Mcclean, S. Boixo, V. Smelyanskiy, R. Babbush, and H. Neven, Nature Communications 9
2018
Cited alongside, same era.
D. Zhu, N. M. Linke, M. Benedetti, K. A. Landsman, N. H. Nguyen, C. H. Alderete, A. Perdomo-Ortiz, N. Korda, A. Garfoot, C. Brecque, et al. , Science advances 5
2019
Cited alongside, same era.
K. E. Hamilton, E. F. Dumitrescu, and R. C. Pooser, Physical Review A 99
2019
Cited alongside, same era.
C. Zoufal, A. Lucchi, and S. Woerner, npj Quantum Information 5
2019
Cited alongside, same era.
I. Glasser, R. Sweke, N. Pancotti, J. Eisert, and I. Cirac, Advances in neural information processing systems 32
2019

Then

A. M. Alaa, B. van Breugel, E. Saveliev, and M. van der Schaar, arXiv:2102.08921 (2021)
Original
2021
Later among the works it cites.
L. Ruthotto and E. Haber, GAMM-Mitteilungen (2021), 10.1002/gamm.202100008
2021
Later among the works it cites.
C. Leadbeater, L. Sharrock, B. Coyle, and M. Benedetti, Entropy 23
2021
Later among the works it cites.
Y. Suzuki, Y. Kawase, Y. Masumura, Y. Hiraga, M. Nakadai, J. Chen, K. M. Nakanishi, K. Mitarai, R. Imai, S. Tamiya, T. Yamamoto, T. Yan, T. Kawakubo, Y. O. Nakagawa, Y. Ibe, Y. Zhang, H. Yamashita, H. Yoshimura, A. Hayashi, and K. Fujii, Quantum 5
2021
Later among the works it cites.
J. Alcazar, M. G. Vakili, C. B. Kalayci, and A. Perdomo-Ortiz, arXiv:2101.06250 (2021)
Original
2021

Beyond the bibliography

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

Open on alphaXiv

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Cited alongside, same era.

T. Kynkäänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila, arXiv:1904.06991 (2019)

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2019

Cited alongside, same era.

T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020) pp. 8110–8119

2020

Cited alongside, same era.

J. Alcazar, V. Leyton-Ortega, and A. Perdomo-Ortiz, Machine Learning: Science and Technology 1

2020

Cited alongside, same era.

B. Coyle, D. Mills, V. Danos, and E. Kashefi, npj Quantum Information 6

2020

Cited alongside, same era.

C. Meehan, K. Chaudhuri, and S. Dasgupta, arXiv:2004.05675 (2020)

Original

2020

Cited alongside, same era.