Boosting Computational Power through Spatial Multiplexing in Quantum Reservoir Computing-Reference-Cited by-同舟云学术

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Boosting Computational Power through Spatial Multiplexing in Quantum Reservoir Computing

Published:2019-03-08 Issue:3 Volume:11 Page:
ISSN:2331-7019
Container-title:Physical Review Applied
language:en
Short-container-title:Phys. Rev. Applied

Author:

Nakajima Kohei,Fujii Keisuke,Negoro Makoto,Mitarai Kosuke,Kitagawa Masahiro

Funder

New Energy and Industrial Technology Development Organization

JST ERATO

JST CREST

JST PRESTO

JSPS KAKENHI

KAKENHI

Publisher

American Physical Society (APS)

Subject

General Physics and Astronomy

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevApplied.11.034021/fulltext

Reference30 articles.

1. A million spiking-neuron integrated circuit with a scalable communication network and interface

2. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication

3. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations

4. An experimental unification of reservoir computing methods

5. Reservoir computing approaches to recurrent neural network training

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1. Strain-mediated reservoir computing with temporal and spatial co-multiplexing in multiferroic heterostructures;Applied Physics Letters;2024-09-02

2. Quantum reservoir complexity by the Krylov evolution approach;Physical Review A;2024-08-30

3. Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective;Dynamics;2024-08-12

4. Quantum extreme learning of molecular potential energy surfaces and force fields;Machine Learning: Science and Technology;2024-07-18

5. Application of Quantum Extreme Learning Machines for QoS Prediction of Elevators’ Software in an Industrial Context;Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering;2024-07-10

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