Absence of Barren Plateaus in Quantum Convolutional Neural Networks

Guillaume Verdon, Michael Broughton, Jarrod R McClean, Kevin J Sung, Ryan Babbush, Zhang Jiang, Hartmut Neven, and Masoud Mohseni, “Learning to learn with quantum neural networks via classical neural networks,” arXiv preprint arXiv:1907.05415 (2019b)

Original

1907

Earlier work this paper cites.

Guillaume Verdon, Jacob Marks, Sasha Nanda, Stefan Leichenauer, and Jack Hidary, “Quantum hamiltonian-based models and the variational quantum thermalizer algorithm,” arXiv preprint arXiv:1910.02071 (2019a)

Original

1910

Earlier work this paper cites.

David H Hubel and Torsten N Wiesel, “Receptive fields and functional architecture of monkey striate cortex,” The Journal of physiology 195

1968

Earlier work this paper cites.

Kunihiko Fukushima and Sei Miyake, “Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition,” in Competition and cooperation in neural nets (Springer, 1982) pp. 267–285

1982

Earlier work this paper cites.

David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams, “Learning representations by back-propagating errors,” Nature 323

1986

Earlier work this paper cites.

Yann LeCun, Bernhard E Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne E Hubbard, and Lawrence D Jackel, “Handwritten digit recognition with a back-propagation network,” in Advances in neural information processing systems (1990) pp. 396–404

1990

Earlier work this paper cites.

Simon Haykin, Neural networks: a comprehensive foundation (Prentice Hall PTR, 1994)

1994

Earlier work this paper cites.

Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE 86

1998

Earlier work this paper cites.

Sepp Hochreiter, Yoshua Bengio, Paolo Frasconi, Jürgen Schmidhuber, et al. , “Gradient flow in recurrent nets: the difficulty of learning long-term dependencies,” (2001)

2001

Earlier work this paper cites.

M. Cerezo, Kunal Sharma, Andrew Arrasmith, and Patrick J Coles, “Variational quantum state eigensolver,” arXiv preprint arXiv:2004.01372 (2020b)

Original

2004

Earlier work this paper cites.

Farrokh Vatan and Colin Williams, “Optimal quantum circuits for general two-qubit gates,” Physical Review A 69

2004

Earlier work this paper cites.

Kunal Sharma, M. Cerezo, Lukasz Cincio, and Patrick J Coles, “Trainability of dissipative perceptron-based quantum neural networks,” arXiv preprint arXiv:2005.12458 (2020c)

Original

2005

Earlier work this paper cites.

Benoît Collins and Piotr Śniady, “Integration with respect to the haar measure on unitary, orthogonal and symplectic group,” Communications in Mathematical Physics 264

2006

Earlier work this paper cites.

Kunal Sharma, M. Cerezo, Zoë Holmes, Lukasz Cincio, Andrew Sornborger, and Patrick J Coles, “Reformulation of the no-free-lunch theorem for entangled data sets,” arXiv preprint arXiv:2007.04900 (2020a)

Original

2007

Earlier work this paper cites.

Mustapha Raıssouli and Iqbal H Jebril, “Various proofs for the decrease monotonicity of the schatten’s power norm, various families of r n- norms and some open problems,” Int. J. Open Problems Compt. Math 3

2010

Earlier work this paper cites.

Xavier Glorot, Antoine Bordes, and Yoshua Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics (2011) pp. 315–323

2011

Earlier work this paper cites.

Roger A Horn and Charles R Johnson, Matrix analysis (Cambridge university press, 2012)

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,” Nature Communications 5

2014

Earlier work this paper cites.

E. Farhi, J. Goldstone, and S. Gutmann, “A quantum approximate optimization algorithm,” (2014), arXiv:1411.4028 [quant-ph]

Original

2014

Earlier work this paper cites.

Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione, “The quest for a quantum neural network,” Quantum Information Processing 13

2014

Earlier work this paper cites.

Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione, “An introduction to quantum machine learning,” Contemporary Physics 56

2015

Earlier work this paper cites.

Waseem Rawat and Zenghui Wang, “Deep convolutional neural networks for image classification: A comprehensive review,” Neural computation 29

2017

Cited alongside, same era.

Ahmed Ali Mohammed Al-Saffar, Hai Tao, and Mohammed Ahmed Talab, “Review of deep convolution neural network in image classification,” in 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) (IEEE, 2017) pp. 26–31

2017

Cited alongside, same era.

Marvin Minsky and Seymour A Papert, Perceptrons: An introduction to computational geometry (MIT press, 2017)

2017

Cited alongside, same era.

Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd, “Quantum machine learning,” Nature 549

2017

Cited alongside, same era.

J. Romero, J. P. Olson, and A. Aspuru-Guzik, “Quantum autoencoders for efficient compression of quantum data,” Quantum Science and Technology 2

Wen Guan, Gabriel Perdue, Arthur Pesah, Maria Schuld, Koji Terashi, Sofia Vallecorsa, and Jean-Roch Vlimant, “Quantum machine learning in high energy physics,” Machine Learning: Science and Technology (2020)

2020

Closest in time.

Cristina Cirstoiu, Zoe Holmes, Joseph Iosue, Lukasz Cincio, Patrick J Coles, and Andrew Sornborger, “Variational fast forwarding for quantum simulation beyond the coherence time,” npj Quantum Information 6

2020

Closest in time.

Kerstin Beer, Dmytro Bondarenko, Terry Farrelly, Tobias J Osborne, Robert Salzmann, Daniel Scheiermann, and Ramona Wolf, “Training deep quantum neural networks,” Nature Communications 11

2020

Closest in time.

Francesco Tacchino, Panagiotis Barkoutsos, Chiara Macchiavello, Ivano Tavernelli, Dario Gerace, and Daniele Bajoni, “Quantum implementation of an artificial feed-forward neural network,” Quantum Science and Technology 5

2020

Closest in time.