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Scaling of Neural-Network Quantum States for Time Evolution
Sheng Hsuan Lin,
Frank Pollmann
Chair of Theoretical Solid-State Physics
Technical University of Munich
Research output
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Contribution to journal
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Article
›
peer-review
18
Scopus citations
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Keyphrases
Neural Network
100%
Quantum State
100%
Artificial Neural Network
25%
Network Architecture
25%
Growth Rate
25%
Variational
25%
Design Decisions
25%
Number of Parameters
25%
Hilbert Space
25%
Integrable
25%
Given Accuracy
25%
Filter Length
25%
Exponential Growth
25%
Deep Neural
25%
Quantum Many-body Dynamics
25%
Classical Computer
25%
Quench Dynamics
25%
Restricted Boltzmann Machine
25%
Shallow Network
25%
Quantum Ising Chain
25%
Accuracy Increase
25%
Challenging Problems
25%
Deep Network
25%
Shortcut Connection
25%
Quantum Many-body States
25%
Engineering
Quantum State
100%
Design Choice
25%
Boltzmann Equation
25%
Exponential Growth
25%
Artificial Neural Network
25%
Physics
Neural Network
100%
Hilbert Spaces
50%