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In chaotic dynamical systems, extreme events manifest in time series as unpredictable large-amplitude peaks.
1912
Earlier work this paper cites.
S. Boyd and L. Chua, Fading memory and the problem of approximating nonlinear operators with Volterra series, IEEE Transactions on Circuits and Systems 32
1985
Earlier work this paper cites.
P. Werbos, Backpropagation through time: what it does and how to do it, Proceedings of the IEEE 78
1990
Earlier work this paper cites.
P. Shor, Algorithms for quantum computation: discrete logarithms and factoring, in Proceedings 35th Annual Symposium on Foundations of Computer Science (1994) pp. 124–134
1994
Earlier work this paper cites.
E. Lorenz, Predictability: a problem partly solved , Ph.D. thesis, Shinfield Park, Reading (1995)
1995
Earlier work this paper cites.
H. Jaeger, Short term memory in echo state networks (GMD Forschungszentrum Informationstechnik, 2001)
2001
Earlier work this paper cites.
W. Maass, T. Natschläger, and H. Markram, Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations, Neural Computation 14
2002
Earlier work this paper cites.
G. Boffetta, M. Cencini, M. Falcioni, and A. Vulpiani, Predictability: a way to characterize complexity, Physics Reports 356
2002
Earlier work this paper cites.
J. Moehlis, H. Faisst, and B. Eckhardt, A low-dimensional model for turbulent shear flows, New Journal of Physics 6
2004
Earlier work this paper cites.
C. Goutte and E. Gaussier, A probabilistic interpretation of precision, recall and f-score, with implication for evaluation, in Advances in Information Retrieval , edited by D. E. Losada and J. M. Fernandez-Luna (Springer Berlin Heidelberg, Berlin, Heidelberg, 2005) pp. 345–359
2005
Earlier work this paper cites.
2006
Earlier work this paper cites.
H. Jaeger, Mantas, D. Popovici, and U. Siewert, Optimization and applications of echo state networks with leaky- integrator neurons, Neural Networks 20
2007
Earlier work this paper cites.
A. W. Harrow, A. Hassidim, and S. Lloyd, Quantum algorithm for linear systems of equations, Physical Review Letters 103
2009
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, Scikit-learn: Machine learning in python, J. Mach. Learn. Res. 12
2011
Earlier work this paper cites.
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information: 10th Anniversary Edition (Cambridge University Press, 2011)
2011
Earlier work this paper cites.
J. Boedecker, O. Obst, J. T. Lizier, N. M. Mayer, and M. Asada, Information processing in echo state networks at the edge of chaos, Theory in Biosciences 131
2012
Earlier work this paper cites.
M. lukosevicius, A Practical Guide to Applying Echo State Networks, in Neural Networks: Tricks of the trade, Second Edition , Lecture Notes in Computer Science, edited by G. Montavon, G. B. Orr, and K.-R. Muller (Springer, Berlin, Heidelberg, 2012) pp. 659–686
2012
Earlier work this paper cites.
J. Snoek, H. Larochelle, and R. P. Adams, Practical bayesian optimization of machine learning algorithms, in Advances in Neural Information Processing Systems , Vol. 25, edited by F. Pereira, C. Burges, L. Bottou, and K. Weinberger (Curran Associates, Inc., 2012)
2012
Earlier work this paper cites.
C. Cheng, A. Sa-Ngasoongsong, O. Beyca, T. Le, H. Yang, Z. Kong, and S. T. Bukkapatnam, Time series forecasting for nonlinear and non-stationary processes: a review and comparative study, Iie Transactions 47
2015
Earlier work this paper cites.
I. Farkaš, R. Bosák, and P. Gergeľ, Computational analysis of memory capacity in echo state networks, Neural Networks 83
2016
Earlier work this paper cites.
K. Fujii and K. Nakajima, Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning, Physical Review Applied 8
2017
Cited alongside, same era.
R. P. Feynman, Simulating physics with computers, in Feynman and computation (CRC Press, 2018) pp. 133–153
2018
Cited alongside, same era.
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, Quantum circuit learning, Physical Review A 98
2018
Cited alongside, same era.
J. Pathak, A. Wikner, R. Fussell, S. Chandra, B. R. Hunt, M. Girvan, and E. Ott, Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model, Chaos: An Interdisciplinary Journal of Nonlinear Science 28
2018
Cited alongside, same era.
P. A. Srinivasan, L. Guastoni, H. Azizpour, P. Schlatter, and R. Vinuesa, Predictions of turbulent shear flows using deep neural networks, Physical Review Fluids 4
A. Ghadami and B. I. Epureanu, Data-driven prediction in dynamical systems: recent developments, Philosophical Transactions of the Royal Society A 380
2022
Later among the works it cites.
D. Rolnick, P. L. Donti, L. H. Kaack, K. Kochanski, A. Lacoste, K. Sankaran, A. S. Ross, N. Milojevic-Dupont, N. Jaques, A. Waldman-Brown, et al. , Tackling climate change with machine learning, ACM Computing Surveys (CSUR) 55
2022
Later among the works it cites.
A. Racca and L. Magri, Statistical prediction of extreme events from small datasets, in Computational Science – ICCS 2022 , edited by D. Groen, C. de Mulatier, M. Paszynski, V. V. Krzhizhanovskaya, J. J. Dongarra, and P. M. A. Sloot (Springer International Publishing, Cham, 2022) pp. 707–713
2022
Later among the works it cites.
K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke, et al. , Noisy intermediate-scale quantum algorithms, Reviews of Modern Physics 94
2022
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2019
Cited alongside, same era.
N. A. K. Doan, W. Polifke, and L. Magri, Physics-informed echo state networks for chaotic systems forecasting, in Computational Science – ICCS 2019 , edited by J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, R. Lam, V. V. Krzhizhanovskaya, M. H. Lees, J. J. Dongarra, and P. M. Sloot (Springer International Publishing, Cham, 2019) pp. 192–198
2019
Cited alongside, same era.
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
Cited alongside, same era.
O. B. Sezer, M. U. Gudelek, and A. M. Ozbayoglu, Financial time series forecasting with deep learning : A systematic literature review: 2005–2019, Applied Soft Computing 90
2020
Cited alongside, same era.
P. R. Vlachas, J. Pathak, B. R. Hunt, T. P. Sapsis, M. Girvan, E. Ott, and P. Koumoutsakos, Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics, Neural Networks 126
2020
Cited alongside, same era.
F. Huhn and L. Magri, Learning ergodic averages in chaotic systems, in Computational Science – ICCS 2020 , edited by V. V. Krzhizhanovskaya, G. Závodszky, M. H. Lees, J. J. Dongarra, P. M. A. Sloot, S. Brissos, and J. Teixeira (Springer International Publishing, Cham, 2020) pp. 124–132
2020
Cited alongside, same era.
J. Herteux and C. Räth, Breaking symmetries of the reservoir equations in echo state networks, Chaos: An Interdisciplinary Journal of Nonlinear Science 30
2020
Cited alongside, same era.
H.-Y. Huang, R. Kueng, and J. Preskill, Predicting many properties of a quantum system from very few measurements, Nature Physics 16
2020
Cited alongside, same era.
Later among the works it cites.
2022
Later among the works it cites.
P. Pfeffer, F. Heyder, and J. Schumacher, Hybrid quantum-classical reservoir computing of thermal convection flow, Physical Review Research 4
2022
Later among the works it cites.
Y. Suzuki, Q. Gao, K. C. Pradel, K. Yasuoka, and N. Yamamoto, Natural quantum reservoir computing for temporal information processing, Scientific Reports 12
2022
Later among the works it cites.
2022
Later among the works it cites.
L. Storm, K. Gustavsson, and B. Mehlig, Constraints on parameter choices for successful time-series prediction with echo-state networks, Machine Learning: Science and Technology 3
2022
Later among the works it cites.
G. Margazoglou and L. Magri, Stability analysis of chaotic systems from data, Nonlinear Dynamics 111
2023
Later among the works it cites.
C. Bravo-Prieto, R. LaRose, M. Cerezo, Y. Subasi, L. Cincio, and P. J. Coles, Variational quantum linear solver, Quantum 7
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Dudas, B. Carles, E. Plouet, F. A. Mizrahi, J. Grollier, and D. Marković, Quantum reservoir computing implementation on coherently coupled quantum oscillators, npj Quantum Information 9
2023
Later among the works it cites.
N. Götting, F. Lohof, and C. Gies, Exploring quantumness in quantum reservoir computing, Phys. Rev. A 108
2023
Later among the works it cites.
P. Pfeffer, F. Heyder, and J. Schumacher, Reduced-order modeling of two-dimensional turbulent rayleigh-bénard flow by hybrid quantum-classical reservoir computing, Physical Review Research 5
2023
Later among the works it cites.
Qiskit contributors, Qiskit: An open-source framework for quantum computing (2023)
2023
Later among the works it cites.
P. Mujal, R. Martínez-Peña, G. L. Giorgi, M. C. Soriano, and R. Zambrini, Time-series quantum reservoir computing with weak and projective measurements, npj Quantum Information 9
2023
Later among the works it cites.
F. Hu, G. Angelatos, S. A. Khan, M. Vives, E. Türeci, L. Bello, G. E. Rowlands, G. J. Ribeill, and H. E. Türeci, Tackling sampling noise in physical systems for machine learning applications: Fundamental limits and eigentasks, Physical Review X 13
2023
Later among the works it cites.
S. Čindrak, B. Donvil, K. Lüdge, and L. Jaurigue, Enhancing the performance of quantum reservoir computing and solving the time-complexity problem by artificial memory restriction, Physical Review Research 6
2024
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