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Titel:

Scaling of Neural‐Network Quantum States for Time Evolution

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Lin, Sheng-Hsuan; Pollmann, Frank
Abstract:
Simulating quantum many-body dynamics on classical computers is a challenging problem due to the exponential growth of the Hilbert space. Artificial neural networks have recently been introduced as a new tool to approximate quantum many-body states. The variational power of the restricted Boltzmann machine quantum states and different shallow and deep neural autoregressive quantum states to simulate the global quench dynamics of a non-integrable quantum Ising chain is benchmarked. It is found th...     »
Zeitschriftentitel:
physica status solidi (b) 2021-12
Jahr:
2022
Band / Volume:
259
Heft / Issue:
5
Volltext / DOI:
doi:10.1002/pssb.202100172
Verlag / Institution:
Wiley
E-ISSN:
0370-19721521-3951
Publikationsdatum:
07.01.2022
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