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Quantum neural networks (QNNs) require an efficient training algorithm to achieve practical quantum advantages.
H. Robbins and S. Monro, A Stochastic Approximation Method, Ann. Math. Stat. 22
1951
Earlier work this paper cites.
M. J. D. Powell, An efficient method for finding the minimum of a function of several variables without calculating derivatives, The Computer Journal 7
1964
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature 323
1986
Earlier work this paper cites.
J. C. Spall, Multivariate stochastic approximation using a simultaneous perturbation gradient approximation, IEEE Transactions on Automatic Control 37
1992
Earlier work this paper cites.
D. P. DiVincenzo, Two-bit gates are universal for quantum computation, Phys. Rev. A 51
1995
Earlier work this paper cites.
S. Lloyd, Almost any quantum logic gate is universal, Phys. Rev. Lett. 75
1995
Earlier work this paper cites.
A. Barenco, C. H. Bennett, R. Cleve, D. P. DiVincenzo, N. Margolus, P. Shor, T. Sleator, J. A. Smolin, and H. Weinfurter, Elementary gates for quantum computation, Phys. Rev. A 52
1995
Earlier work this paper cites.
D. Gottesman, Stabilizer Codes and Quantum Error Correction , Ph.D. thesis , California Institute of Technology (1997)
1997
Earlier work this paper cites.
F. Albertini and D. D’Alessandro, Notions of controllability for quantum mechanical systems, in IEEE Conference on Decision and Control , Vol. 2 (2001)
2001
Earlier work this paper cites.
A. Y. Kitaev, Fault-tolerant quantum computation by anyons, Ann. Phys. (N. Y.) 303
2003
Earlier work this paper cites.
C. Dankert, Efficient simulation of random quantum states and operators, arXiv:quant-ph/0512217 [quant-ph] (2005)
2005
Earlier work this paper cites.
G. E. Hinton, S. Osindero, and Y.-W. Teh, A Fast Learning Algorithm for Deep Belief Nets, Neural Computation 18
2006
Earlier work this paper cites.
D. Gross, K. Audenaert, and J. Eisert, Evenly distributed unitaries: On the structure of unitary designs, J. Math. Phys. 48
2007
Earlier work this paper cites.
D. D’Alessandro, Introduction to quantum control and dynamics (Taylor & Francis Ltd, 2007)
2007
Earlier work this paper cites.
C. Dankert, R. Cleve, J. Emerson, and E. Livine, Exact and approximate unitary 2-designs and their application to fidelity estimation, Phys. Rev. A 80
2009
Earlier work this paper cites.
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Infomation (Cambridge University Press, 2010)
2010
Earlier work this paper cites.
R. Zeier and T. Schulte-Herbrüggen, Symmetry principles in quantum systems theory, Journal of Mathematical Physics 52
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, in Advances in Neural Information Processing Systems , Vol. 25 (2012)
2012
Earlier work this paper cites.
D. Horsman, A. G. Fowler, S. Devitt, and R. Van Meter, Surface code quantum computing by lattice surgery, New J. Phys. 14
2012
Earlier work this paper cites.
A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’Brien, A variational eigenvalue solver on a photonic quantum processor, Nat. Commun. 5
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
N. Wiebe, A. Kapoor, and K. M. Svore, Quantum Deep Learning, arXiv:1412.3489 [quant-ph] (2014)
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv:1412.6980 [cs.LG] (2014)
2014
Earlier work this paper cites.
M. Schuld, I. Sinayskiy, and F. Petruccione, An introduction to quantum machine learning, Contemp. Phys. 56
2015
Earlier work this paper cites.
T. Cohen and M. Welling, Group Equivariant Convolutional Networks, in International Conference on Machine Learning (PMLR, 2016) pp. 2990–2999
2016
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, Attention is All you Need, in Advances in Neural Information Processing Systems , Vol. 30 (2017)
2017
Cited alongside, same era.
G. Carleo and M. Troyer, Solving the quantum many-body problem with artificial neural networks, Science 355
2017
Cited alongside, same era.
A. G. Baydin, B. A. Pearlmutter, A. A. Radul, and J. M. Siskind, Automatic differentiation in machine learning: a survey, J. Mach. Learn. Res. 18
2017
Cited alongside, same era.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Quantum machine learning, Nature 549
2017
N. P. Breuckmann and J. N. Eberhardt, Quantum low-density parity-check codes, PRX quantum 2
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, Qulacs: a fast and versatile quantum circuit simulator for research purpose, Quantum 5
2021
Later among the works it cites.
C. Ortiz Marrero, M. Kieferová, and N. Wiebe, Entanglement-induced barren plateaus, PRX quantum 2
2021
Later among the works it cites.
S. Wang, E. Fontana, M. Cerezo, K. Sharma, A. Sone, L. Cincio, and P. J. Coles, Noise-induced barren plateaus in variational quantum algorithms, Nat. Commun. 12
2021
Later among the works it cites.
I. Marvian, Restrictions on realizable unitary operations imposed by symmetry and locality, Nat. Phys. 18
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2018
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K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, Quantum circuit learning, Phys. Rev. A 98
2018
Cited alongside, same era.
V. Dunjko and H. J. Briegel, Machine learning & artificial intelligence in the quantum domain: a review of recent progress, Rep. Prog. Phys. 81
2018
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Cited alongside, same era.
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2018
Cited alongside, same era.
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2018
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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X. Gong, H. Li, N. Zou, R. Xu, W. Duan, and Y. Xu, General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian, Nat. Commun. 14
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