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Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs

Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs

Lukas Gonon
A. Jacquier
ArXivPDFHTML

Papers citing "Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs"

6 / 6 papers shown
Title
RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks
RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks
Guglielmo Gattiglio
Lyudmila Grigoryeva
M. Tamborrino
39
1
0
09 Nov 2024
Coherence influx is indispensable for quantum reservoir computing
Coherence influx is indispensable for quantum reservoir computing
Shumpei Kobayashi
Quoc Hoan Tran
Kohei Nakajima
18
2
0
19 Sep 2024
On fundamental aspects of quantum extreme learning machines
On fundamental aspects of quantum extreme learning machines
Weijie Xiong
Giorgio Facelli
Mehrad Sahebi
Owen Agnel
Thiparat Chotibut
Supanut Thanasilp
Zoë Holmes
31
23
0
23 Dec 2023
Unsupervised Random Quantum Networks for PDEs
Unsupervised Random Quantum Networks for PDEs
Josh Dees
Antoine Jacquier
Sylvain Laizet
24
2
0
21 Dec 2023
Exponential Quantum Communication Advantage in Distributed Inference and Learning
Exponential Quantum Communication Advantage in Distributed Inference and Learning
H. Michaeli
D. Gilboa
Daniel Soudry
Jarrod R. McClean
FedML
19
0
0
11 Oct 2023
Learning in High Dimension Always Amounts to Extrapolation
Learning in High Dimension Always Amounts to Extrapolation
Randall Balestriero
J. Pesenti
Yann LeCun
41
103
0
18 Oct 2021