Built on

Y. LeCun and Y. Bengio, The Handbook of Brain Theory and Neural Networks . MIT Press, 1995
1995
Earlier work this paper cites.
C. M. Bishop, “Training with noise is equivalent to tikhonov regularization,” Neural computation , vol. 7, no. 1, pp. 108–116, 1995
1995
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , 1998
1998
Earlier work this paper cites.
G. Brassard, P. Hoyer, M. Mosca, and A. Tapp, “Quantum amplitude amplification and estimation,” Contemporary Mathematics , vol. 305, pp. 53–74, 2002
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Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
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S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum algorithms for supervised and unsupervised machine learning,” arXiv , vol. 1307.0411, pp. 1–11, 7 2013. [Online]. Available: http://arxiv.org/abs/1307.0411
Original
2013
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M. Schuld, I. Sinayskiy, and F. Petruccione, “The quest for a quantum neural network,” Quantum Information Processing , vol. 13, no. 11, pp. 2567–2586, 2014
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S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum principal component analysis,” Nature Physics , vol. 10, no. 9, p. 631, 2014
2014
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N. Wiebe, A. Kapoor, and K. M. Svore, “Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning,” arXiv:1401.2142v2 , 2014. [Online]. Available: https://arxiv.org/pdf/1401.2142.pdf
Original
2014
Earlier work this paper cites.
N. Wiebe, A. Kapoor, and K. M. Svore, “Quantum deep learning,” arXiv preprint arXiv:1412.3489 , 2014
Original
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.

Similar

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014
Original
2014
Cited alongside, same era.
M. Bojarski, A. Choromanska, K. Choromanski, B. Firner, L. Jackel, U. Muller, and K. Zieba, “Visualbackprop: efficient visualization of cnns,” arXiv preprint arXiv:1611.05418 , 2016
Original
2016
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning . MIT press, 2016
2016
Cited alongside, same era.
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 , vol. 529, no. 7587, p. 484, 2016
2016
Cited alongside, same era.
I. Kerenidis and A. Prakash, “Quantum gradient descent for linear systems and least squares,” arXiv:1704.04992 , 2017

Then

P. Rebentrost, T. R. Bromley, C. Weedbrook, and S. Lloyd, “Quantum hopfield neural network,” Physical Review A , vol. 98, no. 4, p. 042308, 2018
2018
Later among the works it cites.
D. George and E. Huerta, “Deep learning for real-time gravitational wave detection and parameter estimation: Results with advanced ligo data,” Physics Letters B , vol. 778, pp. 64–70, 2018
2018
Later among the works it cites.
I. Cong, S. Choi, and M. D. Lukin, “Quantum convolutional neural networks,” arXiv preprint arXiv:1810.03787 , 2018
Original
2018
Later among the works it cites.
E. Farhi and H. Neven, “Classification with quantum neural networks on near term processors,” arXiv preprint arXiv:1802.06002 , 2018
Original
2018
Later among the works it cites.
N. Killoran, T. R. Bromley, J. M. Arrazola, M. Schuld, N. Quesada, and S. Lloyd, “Continuous-variable quantum neural networks,” arXiv preprint arXiv:1806.06871 , 2018

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2019

Quantum Algorithms for Deep Convolutional Neural Networks

Kerenidis, Iordanis, Landman, Jonas, Prakash, Anupam

Understand

Quantum computing is a new computational paradigm that promises applications in several fields, including machine learning.

In the last decade, deep learning, and in particular Convolutional neural networks (CNN), have become essential for applications in signal processing and image recognition.
Quantum deep learning, however remains a challenging problem, as it is difficult to implement non linearities with quantum unitaries.
In this paper we propose a quantum algorithm for applying and training deep convolutional neural networks with a potential speedup.

Built on

Y. LeCun and Y. Bengio, The Handbook of Brain Theory and Neural Networks . MIT Press, 1995
1995
Earlier work this paper cites.
C. M. Bishop, “Training with noise is equivalent to tikhonov regularization,” Neural computation , vol. 7, no. 1, pp. 108–116, 1995
1995
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , 1998
1998
Earlier work this paper cites.
G. Brassard, P. Hoyer, M. Mosca, and A. Tapp, “Quantum amplitude amplification and estimation,” Contemporary Mathematics , vol. 305, pp. 53–74, 2002
2002
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
2009
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 , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum algorithms for supervised and unsupervised machine learning,” arXiv , vol. 1307.0411, pp. 1–11, 7 2013. [Online]. Available: http://arxiv.org/abs/1307.0411
Original
2013
Earlier work this paper cites.
M. Schuld, I. Sinayskiy, and F. Petruccione, “The quest for a quantum neural network,” Quantum Information Processing , vol. 13, no. 11, pp. 2567–2586, 2014
2014
Earlier work this paper cites.
S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum principal component analysis,” Nature Physics , vol. 10, no. 9, p. 631, 2014
2014
Earlier work this paper cites.
N. Wiebe, A. Kapoor, and K. M. Svore, “Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning,” arXiv:1401.2142v2 , 2014. [Online]. Available: https://arxiv.org/pdf/1401.2142.pdf
Original
2014
Earlier work this paper cites.
N. Wiebe, A. Kapoor, and K. M. Svore, “Quantum deep learning,” arXiv preprint arXiv:1412.3489 , 2014
Original
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.

Similar

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014
Original
2014
Cited alongside, same era.
M. Bojarski, A. Choromanska, K. Choromanski, B. Firner, L. Jackel, U. Muller, and K. Zieba, “Visualbackprop: efficient visualization of cnns,” arXiv preprint arXiv:1611.05418 , 2016
Original
2016
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning . MIT press, 2016
2016
Cited alongside, same era.
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 , vol. 529, no. 7587, p. 484, 2016
2016
Cited alongside, same era.
I. Kerenidis and A. Prakash, “Quantum gradient descent for linear systems and least squares,” arXiv:1704.04992 , 2017

Then

P. Rebentrost, T. R. Bromley, C. Weedbrook, and S. Lloyd, “Quantum hopfield neural network,” Physical Review A , vol. 98, no. 4, p. 042308, 2018
2018
Later among the works it cites.
D. George and E. Huerta, “Deep learning for real-time gravitational wave detection and parameter estimation: Results with advanced ligo data,” Physics Letters B , vol. 778, pp. 64–70, 2018
2018
Later among the works it cites.
I. Cong, S. Choi, and M. D. Lukin, “Quantum convolutional neural networks,” arXiv preprint arXiv:1810.03787 , 2018
Original
2018
Later among the works it cites.
E. Farhi and H. Neven, “Classification with quantum neural networks on near term processors,” arXiv preprint arXiv:1802.06002 , 2018
Original
2018
Later among the works it cites.
N. Killoran, T. R. Bromley, J. M. Arrazola, M. Schuld, N. Quesada, and S. Lloyd, “Continuous-variable quantum neural networks,” arXiv preprint arXiv:1806.06871 , 2018

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Original

2017

Cited alongside, same era.

J. Wu, “Introduction to convolutional neural networks,” https://pdfs.semanticscholar.org/450c/a19932fcef1ca6d0442cbf52fec38fb9d1e5.pdf , 2017

2017

Cited alongside, same era.

I. Kerenidis and A. Prakash, “Quantum recommendation systems,” Proceedings of the 8th Innovations in Theoretical Computer Science Conference , 2017

2017

Cited alongside, same era.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017

2017

Cited alongside, same era.

W. Samek, T. Wiegand, and K.-R. Müller, “Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models,” arXiv preprint arXiv:1708.08296 , 2017

Original

2017

Cited alongside, same era.

H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,” arXiv preprint arXiv:1708.07747 , 2017

Original

2017

Cited alongside, same era.

I. Kerenidis, J. Landman, A. Luongo, and A. Prakash, “q-means: A quantum algorithm for unsupervised machine learning,” arXiv preprint arXiv:1812.03584 , 2018

Original