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Keyword: frustrated magnets ×Clear all

3 claims from 1 paper

1. Physics and Astronomy › Quantum many-body systems

   Generalization properties of neural network approximations to frustrated magnet ground states

   Westerhout, Astrakhantsev, Tikhonov, Katsnelson and Bagrov · Nature Communications · 2020

   The authors trained neural networks to approximate ground states of moderately sized frustrated magnet models and examined what limits their performance, finding generalisation, not expressibility, to be the main problem.

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   1. UncheckedIn this study, neural networks trained on a few samples of a magnet's ground state generalise much worse once frustration in the model is increased.“We notice that generalization quality, i.e. the ability to learn from a limited number of samples and correctly approximate the target state on the rest of the space, drops abruptly when frustration is increased.”
   2. Unchecked“We also show that learning the sign structure is considerably more difficult than learning amplitudes.”
   3. UncheckedThe authors conclude that, for neural quantum states to simulate realistic models, the main open problem is generalization rather than expressibility.“Finally, we conclude that the main issue to be addressed at this stage, in order to use the method of NQS for simulating realistic models, is that of generalization rather than expressibility.”