On fundamental aspects of quantum extreme learning machines

P. W. Shor, Algorithms for quantum computation: discrete logarithms and factoring, in Proceedings 35th annual symposium on foundations of computer science (Ieee, 1994) pp. 124–134

1994

Earlier work this paper cites.

A. Odlyzko, Discrete logarithms: The past and the future, Towards a Quarter-Century of Public Key Cryptography: A Special Issue of DESIGNS, CODES AND CRYPTOGRAPHY An International Journal. Volume 19, No. 2/3 (2000) , 59 (2000)

2000

Earlier work this paper cites.

G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541) , Vol. 2 (Ieee, 2004) pp. 985–990

2004

Earlier work this paper cites.

G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, Extreme learning machine: theory and applications, Neurocomputing 70

2006

Earlier work this paper cites.

A. B. Tsybakov, Introduction to Nonparametric Estimation (Springer, 2009)

2009

Earlier work this paper cites.

J. Eisert, M. Cramer, and M. B. Plenio, Colloquium: Area laws for the entanglement entropy, Reviews of modern physics 82

2010

Earlier work this paper cites.

G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, Extreme learning machine for regression and multiclass classification, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42

2011

Earlier work this paper cites.

S. Ding, H. Zhao, Y. Zhang, X. Xu, and R. Nie, Extreme learning machine: algorithm, theory and applications, Artificial Intelligence Review 44

2015

Earlier work this paper cites.

G. Huang, G.-B. Huang, S. Song, and K. You, Trends in extreme learning machines: A review, Neural Networks 61

2015

Earlier work this paper cites.

P. De Chazal, J. Tapson, and A. Van Schaik, A comparison of extreme learning machines and back-propagation trained feed-forward networks processing the mnist database, in 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP) (IEEE, 2015) pp. 2165–2168

2015

Earlier work this paper cites.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT Press, 2016)

2016

Earlier work this paper cites.

K. Fujii and K. Nakajima, Harnessing disordered-ensemble quantum dynamics for machine learning, Physical Review Applied 8

2017

Earlier work this paper cites.

D. A. Roberts and B. Yoshida, Chaos and complexity by design, Journal of High Energy Physics 2017

2017

Earlier work this paper cites.

J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, Barren plateaus in quantum neural network training landscapes, Nature Communications 9

2018

Earlier work this paper cites.

K. Nakajima, K. Fujii, M. Negoro, K. Mitarai, and M. Kitagawa, Boosting computational power through spatial multiplexing in quantum reservoir computing, Phys. Rev. Appl. 11

2019

Earlier work this paper cites.

A. Kutvonen, K. Fujii, and T. Sagawa, Optimizing a quantum reservoir computer for time series prediction, Scientific Reports 10

2020

Earlier work this paper cites.

P. Mujal, R. Martí nez-Peña, J. Nokkala, J. García-Beni, G. L. Giorgi, M. C. Soriano, and R. Zambrini, Opportunities in quantum reservoir computing and extreme learning machines, Advanced Quantum Technologies 4

2021

Earlier work this paper 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, Nature Communications 12

2021

Earlier work this paper cites.

M. Schuld, R. Sweke, and J. J. Meyer, Effect of data encoding on the expressive power of variational quantum-machine-learning models, Physical Review A 103

2021

Earlier work this paper cites.

M. Cerezo and P. J. Coles, Higher order derivatives of quantum neural networks with barren plateaus, Quantum Science and Technology 6

2021

Earlier work this paper cites.

C. O. Marrero, M. Kieferová, and N. Wiebe, Entanglement-induced barren plateaus, PRX Quantum 2

2021

Earlier work this paper cites.

A. Uvarov and J. D. Biamonte, On barren plateaus and cost function locality in variational quantum algorithms, Journal of Physics A: Mathematical and Theoretical 54

2021

Cited alongside, same era.

A. Arrasmith, M. Cerezo, P. Czarnik, L. Cincio, and P. J. Coles, Effect of barren plateaus on gradient-free optimization, Quantum 5

2021

Cited alongside, same era.

R. Martínez-Peña, G. L. Giorgi, J. Nokkala, M. C. Soriano, and R. Zambrini, Dynamical phase transitions in quantum reservoir computing, Phys. Rev. Lett. 127

2021

Cited alongside, same era.

T. Goto, Q. H. Tran, and K. Nakajima, Universal approximation property of quantum machine learning models in quantum-enhanced feature spaces, Physical Review Letters 127

2021

Cited alongside, same era.

K. Fujii and K. Nakajima, Quantum reservoir computing: A reservoir approach toward quantum machine learning on near-term quantum devices, Reservoir Computing: Theory, Physical Implementations, and Applications , 423 (2021)

M. S. Rudolph, S. Lerch, S. Thanasilp, O. Kiss, S. Vallecorsa, M. Grossi, and Z. Holmes, Trainability barriers and opportunities in quantum generative modeling, arXiv preprint arXiv:2305.02881 (2023)

Original

2023

Closest in time.

M. Ragone, B. N. Bakalov, F. Sauvage, A. F. Kemper, C. O. Marrero, M. Larocca, and M. Cerezo, A unified theory of barren plateaus for deep parametrized quantum circuits, arXiv preprint arXiv:2309.09342 (2023)

Original

2023

Closest in time.

E. Fontana, D. Herman, S. Chakrabarti, N. Kumar, R. Yalovetzky, J. Heredge, S. Hari Sureshbabu, and M. Pistoia, The adjoint is all you need: Characterizing barren plateaus in quantum ansätze, arXiv preprint arXiv:2309.07902 (2023)

Original

2023

Closest in time.

D. García-Martín, M. Larocca, and M. Cerezo, Deep quantum neural networks form gaussian processes, arXiv preprint arXiv:2305.09957 (2023)

Original

2023

Closest in time.