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Can Neural Quantum States Learn Volume-Law Ground States?

MPG-Autoren
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Hofmann,  D.
Theoretical Description of Pump-Probe Spectroscopies in Solids, Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society;
Center for Free-Electron Laser Science (CFEL);

/persons/resource/persons284220

Grunwald,  L.
Institut für Theorie der Statistischen Physik, RWTH Aachen University and JARA-Fundamentals of Future Information Technology;
Theory Group, Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society;
Center for Free-Electron Laser Science (CFEL);

/persons/resource/persons182604

Sentef,  M. A.
Theoretical Description of Pump-Probe Spectroscopies in Solids, Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society;
Center for Free-Electron Laser Science (CFEL);
H H Wills Physics Laboratory, University of Bristol;

/persons/resource/persons245033

Kennes,  D. M.
Institut für Theorie der Statistischen Physik, RWTH Aachen University and JARA-Fundamentals of Future Information Technology;
Theory Group, Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society;
Center for Free-Electron Laser Science (CFEL);

Externe Ressourcen

https://arxiv.org/abs/2212.02204
(Preprint)

https://doi.org/10.1103/PhysRevLett.131.036502
(Verlagsversion)

Volltexte (beschränkter Zugriff)
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Volltexte (frei zugänglich)

PhysRevLett.131.036502.pdf
(Verlagsversion), 2MB

Ergänzendes Material (frei zugänglich)

supplemental_material.pdf
(Ergänzendes Material), 5MB

Zitation

Passetti, G., Hofmann, D., Neitemeier, P., Grunwald, L., Sentef, M. A., & Kennes, D. M. (2023). Can Neural Quantum States Learn Volume-Law Ground States? Physical Review Letters, 131(3): 036502. doi:10.1103/PhysRevLett.131.036502.


Zitierlink: https://hdl.handle.net/21.11116/0000-000B-B5BE-3
Zusammenfassung
We study whether neural quantum states based on multilayer feed-forward networks can find ground states which exhibit volume-law entanglement entropy. As a testbed, we employ the paradigmatic Sachdev-Ye-Kitaev model. We find that both shallow and deep feed-forward networks require an exponential number of parameters in order to represent the ground state of this model. This demonstrates that sufficiently complicated quantum states, although being physical solutions to relevant models and not pathological cases, can still be difficult to learn to the point of intractability at larger system sizes. Hence, the variational neural network approach offers no benefits over exact diagonalization methods in this case. This highlights the importance of further investigations into the physical properties of quantum states amenable to an efficient neural representation.