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Quantum machine learning explores the interplay between machine learning and quantum physics, which may lead to unprecedented perspectives for both fields.
W. Hoeffding, “Probability inequalities for sums of bounded random variables,” J.Am.Stat.Assoc 58
1963
Earlier work this paper cites.
M. Gromov and V. D. Milman, “A topological application of the isoperimetric inequality,” Am. J. Math 105
1983
Earlier work this paper cites.
E. Knill, “Approximation by quantum circuits,” arXiv:quant-ph/9508006 [quant-ph] (1995)
1995
Earlier work this paper cites.
L. B. Rall and G. F. Corliss, “An introduction to automatic differentiation,” Computational Differentiation: Techniques, Applications, and Tools 89
1996
Earlier work this paper cites.
The mnist database of handwritten digits (1998)
1998
Earlier work this paper cites.
M. Ledoux, The concentration of measure phenomenon , 89 (American Mathematical Soc., 2001)
2001
Earlier work this paper cites.
L. Grover and T. Rudolph, “Creating superpositions that correspond to efficiently integrable probability distributions,” arXiv: quant-ph/0208112[quant-ph] (2002)
2002
Earlier work this paper cites.
M. Möttönen, J. J. Vartiainen, V. Bergholm, and M. M. Salomaa, “Quantum circuits for general multiqubit gates,” Phys. Rev. Lett 93
2004
Earlier work this paper cites.
J. G. Ratcliffe, S. Axler, and K. Ribet, Foundations of hyperbolic manifolds , Vol. 149 (Springer, 2006)
2006
Earlier work this paper cites.
A. N. Soklakov and R. Schack, “Efficient state preparation for a register of quantum bits,” Phys. Rev. A 73
2006
Earlier work this paper cites.
T. Giordano and V. Pestov, “Some extremely amenable groups related to operator algebras and ergodic theory,” J. Inst. Math.Jussieu 6
2007
Earlier work this paper cites.
A. W. Harrow, A. Hassidim, and S. Lloyd, “Quantum algorithm for linear systems of equations,” Phys. Rev. Lett. 103
2009
Earlier work this paper cites.
V. D. Milman and G. Schechtman, Asymptotic theory of finite dimensional normed spaces: Isoperimetric inequalities in riemannian manifolds , Vol. 1200 (Springer, 2009)
2009
Earlier work this paper cites.
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information (Cambridge University Press, Cambridge, 2010)
2010
Earlier work this paper cites.
S. Sachdev, “Quantum phase transitions,” Quantum Phase Transitions, by Subir Sachdev, Cambridge, UK: Cambridge University Press, 2011 1
2011
Earlier work this paper cites.
M. Plesch and Č. Brukner, “Quantum-state preparation with universal gate decompositions,” Phys. Rev. A 83
2011
Earlier work this paper cites.
2013
Earlier work this paper cites.
S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum principal component analysis,” Nat. Phys. 10
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
2014
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in Second International Conference on Learning Representations (ICLR, Banff, Canada, 2014)
2014
Earlier work this paper cites.
S. Shalev-Shwartz and S. Ben-David, Understanding machine learning: From theory to algorithms (Cambridge university press, 2014)
2014
Earlier work this paper cites.
E. Meckes, Concentration of measure and the compact classical matrix groups , edited by (unpublished) (Citeseer, 2014)
2014
Earlier work this paper cites.
N. Wiebe, A. Kapoor, and K. M. Svore, “Quantum deep learning,” arXiv:1412.3489 (2014)
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv:1412.6980 (2014)
2014
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research 15
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521
2015
Earlier work this paper cites.
M. Jordan and T. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science 349
2015
Earlier work this paper cites.
A. Scott, “Read the fine print,” Nat. Phys. 11
2015
Earlier work this paper cites.
M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015) pp. 1322–1333
2015
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. , “Mastering the game of go with deep neural networks and tree search,” nature 529
2016
Earlier work this paper cites.
L. Wang, “Discovering phase transitions with unsupervised learning,” Phys. Rev. B 94
2016
Earlier work this paper cites.
V. Dunjko, J. M. Taylor, and H. J. Briegel, “Quantum-Enhanced Machine Learning,” Phys. Rev. Lett. 117
2016
Earlier work this paper cites.
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning (MIT press, 2016)
2016
Earlier work this paper cites.
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter, “Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition,” in Proceedings of the 2016 acm sigsac conference on computer and communications security (2016) pp. 1528–1540
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Oszmaniec, R. Augusiak, C. Gogolin, J. Kołodyński, A. Acin, and M. Lewenstein, “Random bosonic states for robust quantum metrology,” Phys.Rev.X 6
2016
Earlier work this paper cites.
I. Cong and L. Duan, “Quantum discriminant analysis for dimensionality reduction and classification,” New Journal of Physics 18
2016
Cited alongside, same era.
2016
Cited alongside, same era.
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing machine learning models via prediction apis,” in 25th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 16) (2016) pp. 601–618
2016
Cited alongside, same era.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, et al. , “Mastering the game of go without human knowledge,” Nature 550
2017
Cited alongside, same era.
J. R. Sashank, K. Satyen, and K. Sanjiv, “On the convergence of adam and beyond,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings (2018)
2018
Later among the works it cites.
J.-G. Liu and L. Wang, “Differentiable learning of quantum circuit born machines,” Phys. Rev. A 98
2018
Later among the works it cites.
M. Innes, “Flux: Elegant machine learning with julia,” Journal of Open Source Software 3
2018
Later among the works it cites.
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. , “Quantum supremacy using a programmable superconducting processor,” Nature 574
2019
Later among the works it cites.
S. D. Sarma, D.-L. Deng, and L.-M. Duan, “Machine learning meets quantum physics,” Physics Today 72
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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.
Y. Zhang and E.-A. Kim, “Quantum Loop Topography for Machine Learning,” Phys. Rev. Lett. 118
2017
Cited alongside, same era.
J. Carrasquilla and R. G. Melko, “Machine learning phases of matter,” Nat. Phys. 13
2017
Cited alongside, same era.
E. P. L. van Nieuwenburg, Y.-H. Liu, and S. D. Huber, “Learning phase transitions by confusion,” Nat. Phys. 13
2017
Cited alongside, same era.
P. Broecker, J. Carrasquilla, R. G. Melko, and S. Trebst, “Machine learning quantum phases of matter beyond the fermion sign problem,” Sci. Rep. 7
2017
Cited alongside, same era.
K. Ch’ng, J. Carrasquilla, R. G. Melko, and E. Khatami, “Machine learning phases of strongly correlated fermions,” Phys. Rev. X 7
2017
Cited alongside, same era.
S. J. Wetzel, “Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders,” Phys. Rev. E 96
2017
Cited alongside, same era.
2019
Later among the works it cites.
Y. Zhang, A. Mesaros, K. Fujita, S. Edkins, M. Hamidian, K. Ch’ng, H. Eisaki, S. Uchida, J. S. Davis, E. Khatami, et al. , “Machine learning in electronic-quantum-matter imaging experiments,” Nature 570
2019
Later among the works it cites.
W. Lian, S.-T. Wang, S. Lu, Y. Huang, F. Wang, X. Yuan, W. Zhang, X. Ouyang, X. Wang, X. Huang, L. He, X. Chang, D.-L. Deng, and L. Duan, “Machine learning topological phases with a solid-state quantum simulator,” Phys. Rev. Lett. 122
2019
Later among the works it cites.
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 5
2019
Later among the works it cites.
M. Schuld and N. Killoran, “Quantum machine learning in feature hilbert spaces,” Phy. Rev. Lett 122
2019
Later among the works it cites.
2019
Later among the works it cites.
N. Liu and P. Wittek, “Vulnerability of quantum classification to adversarial perturbations,” Phys. Rev. A 101
2019
Later among the works it cites.
S. Mahloujifar, D. I. Diochnos, and M. Mahmoody, “The curse of concentration in robust learning: Evasion and poisoning attacks from concentration of measure,” in Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33 (2019) pp. 4536–4543
2019
Later among the works it cites.
I. Cong, S. Choi, and M. D. Lukin, “Quantum convolutional neural networks,” Nat. Phys. 15
2019
Later among the works it cites.
S. G. Finlayson, J. D. Bowers, J. Ito, J. L. Zittrain, A. L. Beam, and I. S. Kohane, “Adversarial attacks on medical machine learning,” Science 363
2019
Later among the works it cites.
V. Havlíček, A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta, “Supervised learning with quantum-enhanced feature spaces,” Nature 567
2019
Later among the works it cites.
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. , “Training of quantum circuits on a hybrid quantum computer,” Science advances 5
2019
Later among the works it cites.
F. Tacchino, C. Macchiavello, D. Gerace, and D. Bajoni, “An artificial neuron implemented on an actual quantum processor,” npj Quantum Information 5
2019
Later among the works it cites.
N. Yamamoto, “On the natural gradient for variational quantum eigensolver,” arXiv:1909.05074 (2019)
2019
Later among the works it cites.
J. Stokes, J. Izaac, N. Killoran, and G. Carleo, “Quantum natural gradient,” arXiv:1909.02108 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Žídek, A. W. R. Nelson, A. Bridgland, H. Penedones, S. Petersen, K. Simonyan, S. Crossan, P. Kohli, D. T. Jones, D. Silver, K. Kavukcuoglu, and D. Hassabis, “Improved protein structure prediction using potentials from deep learning,” Nature 577
2020
Later among the works it cites.
H.-S. Zhong, H. Wang, Y.-H. Deng, M.-C. Chen, L.-C. Peng, Y.-H. Luo, J. Qin, D. Wu, X. Ding, Y. Hu, et al. , “Quantum computational advantage using photons,” Science 370
2020
Later among the works it cites.
Y.-H. Zhang, P.-L. Zheng, Y. Zhang, and D.-L. Deng, “Topological Quantum Compiling with Reinforcement Learning,” Phys. Rev. Lett. 125
2020
Later among the works it cites.
S. Lu, L.-M. Duan, and D.-L. Deng, “Quantum adversarial machine learning,” Phys. Rev. Res. 2
2020
Later among the works it cites.
2020
Later among the works it cites.
P. Casares and M. Martin-Delgado, “A quantum active learning algorithm for sampling against adversarial attacks,” New Journal of Physics 22
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe, “Circuit-centric quantum classifiers,” Phys. Rev. A 101
2020
Later among the works it cites.
A. Uvarov, A. Kardashin, and J. D. Biamonte, “Machine learning phase transitions with a quantum processor,” Phys. Rev. A 102
2020
Later among the works it cites.
C. Blank, D. K. Park, J.-K. K. Rhee, and F. Petruccione, “Quantum classifier with tailored quantum kernel,” npj Quantum Information 6
2020
Later among the works it cites.
F. Wilde, R. Sweke, J. Meyer, M. Schuld, P. Fährmann, B. Meynard-Piganeau, and J. Eisert, “Stochastic gradient descent for hybrid quantum-classical optimization,” Bulletin of the American Physical Society 65
2020
Later among the works it cites.
V. L. Deringer, N. Bernstein, G. Csányi, C. B. Mahmoud, M. Ceriotti, M. Wilson, D. A. Drabold, and S. R. Elliott, “Origins of structural and electronic transitions in disordered silicon,” Nature 589
2021
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