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Despite the huge theoretical potential of neural quantum states, their use in describing generic, highly-correlated quantum many-body systems still often poses practical difficulties.
Colloquium: Area laws for the entanglement entropy
Eisert, J., Cramer, M. & Plenio, M. B · 2010
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Later among the works it cites.
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