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The paradigm of reservoir computing exploits the nonlinear dynamics of a physical reservoir to perform complex time-series processing tasks such as speech recognition and forecasting.
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2013
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2013
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2015
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2015
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2019
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M. Rafayelyan, J. Dong, Y. Tan, F. Krzakala, and S. Gigan, Large-Scale Optical Reservoir Computing for Spatiotemporal Chaotic Systems Prediction, Physical Review X 10
2020
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2020
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J. Dong, M. Rafayelyan, F. Krzakala, and S. Gigan, Optical reservoir computing using multiple light scattering for chaotic systems prediction, IEEE Journal of Selected Topics in Quantum Electronics 26
2020
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K. Nakajima, Physical reservoir computing—an introductory perspective, Japanese Journal of Applied Physics 59
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N. D. Haynes, M. C. Soriano, D. P. Rosin, I. Fischer, and D. J. Gauthier, Reservoir computing with a single time-delay autonomous Boolean node, Physical Review E 91
2015
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O. Černotík, D. V. Vasilyev, and K. Hammerer, Adiabatic elimination of Gaussian subsystems from quantum dynamics under continuous measurement, Physical Review A 92
2015
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A. Metelmann and A. A. Clerk, Nonreciprocal photon transmission and amplification via reservoir engineering, Phys. Rev. X 5
2015
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K. Sliwa, M. Hatridge, A. Narla, S. Shankar, L. Frunzio, R. Schoelkopf, and M. Devoret, Reconfigurable Josephson Circulator/Directional Amplifier, Physical Review X 5
2015
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C. Macklin, K. O’Brien, D. Hover, M. E. Schwartz, V. Bolkhovsky, X. Zhang, W. D. Oliver, and I. Siddiqi, A near–quantum-limited Josephson traveling-wave parametric amplifier, Science 350
2015
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S. L. Brunton, J. L. Proctor, and J. N. Kutz, Discovering governing equations from data by sparse identification of nonlinear dynamical systems
2016
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A. Roy and M. Devoret, Introduction to parametric amplification of quantum signals with Josephson circuits
2016
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J. Pathak, Z. Lu, B. R. Hunt, M. Girvan, and E. Ott, Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data, Chaos: An Interdisciplinary Journal of Nonlinear Science 27
2017
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2020
Later among the works it cites.
2020
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Y.-S. Ra, A. Dufour, M. Walschaers, C. Jacquard, T. Michel, C. Fabre, and N. Treps, Non-Gaussian quantum states of a multimode light field
2020
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T.-C. Chien, O. Lanes, C. Liu, X. Cao, P. Lu, S. Motz, G. Liu, D. Pekker, and M. Hatridge, Multiparametric amplification and qubit measurement with a Kerr-free Josephson ring modulator, Physical Review A 101
2020
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D. J. Gauthier, E. Bollt, A. Griffith, and W. A. S. Barbosa, Next generation reservoir computing
2021
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D. J. Gauthier and I. Fischer, Predicting hidden structure in dynamical systems
2021
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2021
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D. Canaday, A. Pomerance, and D. J. Gauthier, Model-free control of dynamical systems with deep reservoir computing
2021
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W. A. S. Barbosa, A. Griffith, G. E. Rowlands, L. C. G. Govia, G. J. Ribeill, M.-H. Nguyen, T. A. Ohki, and D. J. Gauthier, Symmetry-aware reservoir computing, Physical Review E 104
2021
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J. Nokkala, R. Martínez-Peña, G. L. Giorgi, V. Parigi, M. C. Soriano, and R. Zambrini, Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
2021
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S. Apostel, N. D. Haynes, E. Schöll, O. D’Huys, and D. J. Gauthier, Reservoir Computing Using Autonomous Boolean Networks Realized on Field-Programmable Gate Arrays
2021
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2021
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2021
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2021
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K. Nakajima and I. Fischer, Reservoir Computing: Theory, Physical Implementations, and Applications (Springer Nature, 2021)
2021
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L. C. G. Govia, G. J. Ribeill, G. E. Rowlands, H. K. Krovi, and T. A. Ohki, Quantum reservoir computing with a single nonlinear oscillator, Physical Review Research 3
2021
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R. Martínez-Peña, G. L. Giorgi, J. Nokkala, M. C. Soriano, and R. Zambrini, Dynamical Phase Transitions in Quantum Reservoir Computing, Physical Review Letters 127
2021
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2021
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A. Blais, A. L. Grimsmo, S. Girvin, and A. Wallraff, Circuit quantum electrodynamics, Reviews of Modern Physics 93
2021
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Y. Wu, W.-S. Bao, S. Cao, F. Chen, M.-C. Chen, X. Chen, T.-H. Chung, H. Deng, Y. Du, D. Fan, M. Gong, C. Guo, C. Guo, S. Guo, L. Han, L. Hong, H.-L. Huang, Y.-H. Huo, L. Li, N. Li, S. Li, Y. Li, F. Liang, C. Lin, J. Lin, H. Qian, D. Qiao, H. Rong, H. Su, L. Sun, L. Wang, S. Wang, D. Wu, Y. Xu, K. Yan, W. Yang, Y. Yang, Y. Ye, J. Yin, C. Ying, J. Yu, C. Zha, C. Zhang, H. Zhang, K. Zhang, Y. Zhang, H. Zhao, Y. Zhao, L. Zhou, Q. Zhu, C.-Y. Lu, C.-Z. Peng, X. Zhu, and J.-W. Pan, Strong Quantum Computational Advantage Using a Superconducting Quantum Processor, Physical Review Letters 127
2021
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E. Bollt, On explaining the surprising success of reservoir computing forecaster of chaos? the universal machine learning dynamical system with contrast to var and dmd, Chaos: An Interdisciplinary Journal of Nonlinear Science 31
2021
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