Fetching the paper…
Reading the bibliography…
In the current Noisy Intermediate Scale Quantum (NISQ) era, the presence of noise deteriorates the performance of quantum computing algorithms.
E. N. Lorenz, Predictability: A problem partly solved, in Proc. Seminar on predictability , Vol. 1 (Reading, 1996)
1996
Earlier work this paper cites.
J. Preskill, Fault-tolerant quantum computation, in Introduction to quantum computation and information (World Scientific, 1998) pp. 213–269
1998
Earlier work this paper cites.
H. Jaeger, The” echo state” approach to analysing and training recurrent neural networks-with an erratum note’, Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148
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.
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.
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.
P. Lansky and S. Ditlevsen, A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models, Biological cybernetics 99
2008
Earlier work this paper cites.
F. GABBIANI and S. J. COX, Chapter 10 - reduced single neuron models, in Mathematics for Neuroscientists , edited by F. GABBIANI and S. J. COX (Academic Press, London, 2010) pp. 143–154
2010
Earlier work this paper cites.
S. K. Jha and R. Yadava, Denoising by singular value decomposition and its application to electronic nose data processing, IEEE Sensors Journal 11
2010
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.
R. W. Schafer, What is a savitzky-golay filter?[lecture notes], IEEE Signal processing magazine 28
2011
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. Shlens, A tutorial on principal component analysis, arXiv preprint arXiv:1404.1100 (2014)
2014
Earlier work this paper cites.
T. Schanze, Removing noise in biomedical signal recordings by singular value decomposition, current directions in biomedical engineering 3
2017
Earlier work this paper cites.
S. Aaronson, Shadow tomography of quantum states, in Proceedings of the 50th annual ACM SIGACT symposium on theory of computing (2018) pp. 325–338
2018
Cited alongside, same era.
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
Cited alongside, same era.
C. Teeter, R. Iyer, V. Menon, N. Gouwens, D. Feng, J. Berg, A. Szafer, N. Cain, H. Zeng, M. Hawrylycz, et al. , Generalized leaky integrate-and-fire models classify multiple neuron types, Nature communications 9
2018
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.
A. Racca and L. Magri, Data-driven prediction and control of extreme events in a chaotic flow, Physical Review Fluids 7
2022
Later among the works it cites.
M. Yu, Y. Liu, P. Yang, M. Gong, Q. Cao, S. Zhang, H. Liu, M. Heyl, T. Ozawa, N. Goldman, et al. , Quantum fisher information measurement and verification of the quantum cramér–rao bound in a solid-state qubit, npj Quantum Information 8
2022
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.
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.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. L. Carroll and L. M. Pecora, Network structure effects in reservoir computers, Chaos: An Interdisciplinary Journal of Nonlinear Science 29
2019
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
2019
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.
T. L. Carroll, Dimension of reservoir computers, Chaos: An Interdisciplinary Journal of Nonlinear Science 30
2020
Cited alongside, same era.
M. Schuld and F. Petruccione, Machine learning with quantum computers (Springer, 2021)
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, in Reservoir Computing: Theory, Physical Implementations, and Applications , edited by K. Nakajima and I. Fischer (Springer Singapore, Singapore, 2021) pp. 423–450
2021
Cited alongside, same era.
P. Mujal, R. Martínez-Peña, J. Nokkala, J. García-Beni, G. L. Giorgi, M. C. Soriano, and R. Zambrini, Opportunities in Quantum Reservoir Computing and Extreme Learning Machines (2021), publication Title: arXiv e-prints ADS Bibcode: 2021arXiv210211831M
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2023
Later among the works it cites.
L. Domingo, G. Carlo, and F. Borondo, Taking advantage of noise in quantum reservoir computing, Scientific Reports 13
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.
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.
2024
Closest in time.
D. A. Kreplin and M. Roth, Reduction of finite sampling noise in quantum neural networks, Quantum 8
2024
Closest in time.
IBM Quantum https://quantum.ibm.com/, (2024)
2024
Closest in time.