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Quantum machine learning has recently attracted much attention from the community of quantum computing.
J. Romero and A. Aspuru-Guzik, Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions, arXiv: 1901.00848
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M.A. Nielsen and I.L. Chuang, Quantum Computation and Quantum Information, Cambridge University Press (2000)
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N. Wiebe, D. Braun, and S. Lloyd, Quantum algorithm for data fitting, Phys. Rev. Lett. 109, 050505 (2012)
2012
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X.D. Cai, C. Weedbrook, Z.E. Su, M.C. Chen, M. Gu, M.J. Zhu, L. Li, N.L. Liu, C.Y. Lu, and J.W. Pan, Experimental quantum computing to solve systems of linear equations, Phys. Rev. Lett. 110, 230501 (2013)
2013
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Z. Abo-Hammour, O. Abu Arqub, O. Alsmadi, S. Momani, A. Alsaedi, An optimization algorithm for solving systems of singular boundary value problems, Appl. Math. Inf. Sci. 8, 2809-2821 (2014)
2014
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O. Abu Arqub, Z. Abo-Hammour, Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm, Information Sciences 279, 396-415 (2014)
2014
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, in Proceedings of the 27th International Conference on Neural Information Processing Systems (2014) pp. 2672-2680
2014
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S. Lloyd, M. Mohseni, and P. Rebentrost, Quantum principal component analysis, Nat. Phys. 10, 631 (2014)
2014
Earlier work this paper cites.
P. Rebentrost, M. Mohseni, and S. Lloyd, Quantum support vector machine for big data classification, Phys. Rev. Lett. 113, 130503 (2014)
2014
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X.D. Cai, D. Wu, Z.E. Su, M.C. Chen, X.L. Wang, L. Li, N.L. Liu, C.Y. Lu, and J.W. Pan, Entanglement-based machine learning on a quantum computer, Phys. Rev. Lett. 114, 110504 (2015)
2015
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H.Cao, F.Cao, D. Wang, Quantum artificial neural networks with applications, Information Sciences, 290, 1-6 (2015)
2015
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E. Denton, S. Chintala, A. Szlam, and R. Fergus, Deep generative image models using a Laplacian pyramid of adversarial networks, in Proceedings of the 29th International Conference on Neural Information Processing Systems (2015) pp. 1486-1494
2015
Cited alongside, same era.
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, Infogan: Interpretable representation learning by information maximizing generative adversarial nets, in Proceedings of the 30th International Conference on Neural Information Processing Systems (2016) pp. 2172-2180
2016
Cited alongside, same era.
V. Dunjko, J.M. Taylor, and H.J. Briegel, Quantum-enhanced machine learning, Phys. Rev. Lett. 117, 130501 (2016)
2016
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press (2016)
2016
Cited alongside, same era.
A. Khoshaman, W. Vinci, B. Denis, E. Andriyash, and M.H. Amin, Quantum variational autoencoder, Quantum Sci. and Technol. 4, 014001(2018)
2018
Closest in time.
J.G. Liu and L. Wang, Differentiable learning of quantum circuit Born machine, Phys. Rev. A 98, 062324 (2018)
2018
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S. Lloyd and C. Weedbrook, Quantum generative adversarial learning, Phys. Rev. Lett. 121, 040502 (2018)
2018
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K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, Quantum circuit learning, Phys. Rev. A 98, 032309 (2018)
2018
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J. Allcock and S.Y. Zhang, Quantum Machine Learning, National Science Review 6, 26 (2019)
2019
Closest in time.
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2016
Cited alongside, same era.
O. Abu Arqub, Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm-Volterra integrodifferential equations, Neural Comput. Applic. 28, 1591-1610 (2017)
2017
Cited alongside, same era.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Quantum machine learning, Nature 549, 195 (2017)
2017
Cited alongside, same era.
B.J. Duan, J.B. Yuan, Y. Liu, and D. Li, Quantum algorithm for support matrix machines, Phys. Rev. A 96, 032301 (2017)
2017
Cited alongside, same era.
A. Monràs, G. Sentís, and P. Wittek, Inductive supervised quantum learning, Phys. Rev. Lett. 118, 190503 (2017)
2017
Cited alongside, same era.
J. Romero, J.P. Olson, and A. Aspuru-Guzik, Quantum autoencoders for efficient compression of quantum data, Quantum Sci. and Technol. 2, 045001 (2017)
2017
Cited alongside, same era.
K.H. Wan, O. Dahlsten, H. Kristjánsson, R. Gardner, and M.S. Kim, Quantum generalisation of feedforward neural networks, npj Quantum Information 3, 36 (2017)
2017
Cited alongside, same era.
S. Boixo, S.V. Isakov, V.N. Smelyanskiy, R. Babbush, N. Ding, Z. Jiang, M.J. Bremner, J.M. Martinis, and H. Neven, Characterizing quantum supremacy in near-term devices, Nat. Phys. 14, 595 (2018)
2018
Cited alongside, same era.
M. Benedetti, D. Garcia-Pintos, Y. Nam, and A. Perdomo-Ortiz, A generative modeling approach for benchmarking and training shallow quantum circuits, npj Quantum Information 5, 45 (2019)
2019
Closest in time.
M. Benedetti, E. Grant, L. Wossnig, and S. Severini, Adversarial quantum circuit learning for pure state approximation, New J. Phys. 21, 043023 (2019)
2019
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M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, Parameterized quantum circuits as machine learning models, Quantum Sci. Technol. 4, 043001 (2019)
2019
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S. Chakrabarti, Y.M. Huang, T.Y. Li, S. Feizi, and X.D. Wu, Quantum Wasserstein GANs, the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
2019
Closest in time.
L. Hu, S.H. Wu, W.Z. Cai, et al., Quantum generative adversarial learning in a superconducting quantum circuit, Sci. Adv. 5, eaav2761 (2019)
2019
Closest in time.
W. Huggins, P. Patel, K.B. Whaley, and E.M. Stoudenmire, Towards quantum machine learning with tensor networks, Quantum Sci. and Technol. 4, 024001 (2019)
2019
Closest in time.
L. Lamata, U. Alvarez-Rodriguez, J.D. Martín-Guerrero, M. Sanz, and E. Solano, Quantum autoencoders via quantum adders with genetic algorithms, Quantum Sci. and Technol. 4, 014007 (2019)
2019
Closest in time.
C.H. Yu, F. Gao, C.H. Liu, D. Huynh, M. Reynolds, and J.B. Wang, Quantum algorithm for visual tracking, Phys. Rev. A 99, 022301 (2019)
2019
Closest in time.
C. Zoufal, A. Lucchi, and S. Woerner, Quantum generative adversarial networks for learning and loading random distributions, npj Quantum Information 5, 103 (2019)
2019
Closest in time.