Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

@inproceedings{Mujal2021OpportunitiesIQ,
  title={Opportunities in Quantum Reservoir Computing and Extreme Learning Machines},
  author={Pere Mujal and Rodrigo Mart{\'i}nez-Pe{\~n}a and Johannes Nokkala and Jorge Garc{\'i}a‐Beni and Gian Luca Giorgi and Miguel C. Soriano and Roberta Zambrini},
  year={2021}
}
Quantum reservoir computing (QRC) and quantum extreme learning machines (QELM) are two emerging approaches that have demonstrated their potential both in classical and quantum machine learning tasks. They exploit the quantumness of physical systems combined with an easy training strategy, achieving an excellent performance. The increasing interest in these unconventional computing approaches is fueled by the availability of diverse quantum platforms suitable for implementation and the… Expand

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References

SHOWING 1-10 OF 90 REFERENCES
L
  • C. G. Govia, (Preprint) arXiv:2101.11729
  • 2021
H
  • E. Türeci, (Preprint) arXiv:2011.09652
  • 2020
K
  • Nakajima, (Preprint) arXiv:2011.04890
  • 2020
T
  • C. H. Liew, (Preprint) arXiv:2003.09569
  • 2020
{m
TLDR
The master programme in Applied Geology aims to provide comprehensive knowledge based on various branches of Geology, with special focus on Applied geology subjects in the areas of Geomorphology, Structural geology, Hydrogeology, Petroleum Geologists, Mining Geology), Remote Sensing and Environmental geology. Expand
R
  • Mart́ınez-Peña, G. L. Giorgi, V. Parigi, M. C. Soriano, R. Zambrini,
  • 2006
A
  • Banerjee, (Preprint) arXiv:2004.08240
  • 2020
K
  • Nakajima, (Preprint) arXiv:2006.08999
  • 2020
G
  • Van der Sande, Photonic Reservoir Computing: Optical Recurrent Neural Networks, Walter de Gruyter GmbH & Co KG
  • 2019
Creating and concentrating quantum resource states in noisy environments using a quantum neural network
TLDR
This work provides a versatile unified state preparation scheme based on a driven quantum network composed of randomly-coupled fermionic nodes that can be utilized to create almost perfect maximally entangled, NOON, W, cluster, and discorded states. Expand
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2
3
4
5
...