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Status: Unchecked ×Keyword: sign problem ×Clear all

3 claims from 1 paper

1. Physics and Astronomy › Quantum many-body systems

   Neural network wave functions and the sign problem

   Szabó and Castelnovo · Physical Review Research · 2020

   The authors propose a neural network with an explicit phase ansatz that handles sign structure and reaches state-of-the-art variational energies for conventional and frustrated antiferromagnets.

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   1. UncheckedFor frustrated antiferromagnets, the authors' neural network method finds low-energy states obeying the Marshall sign rule, unlike the expected ground state.“In the latter case, our approach uncovers low-energy states that exhibit the Marshall sign rule and are therefore inconsistent with the expected ground state.”
   2. UncheckedLow-energy states obeying the Marshall sign rule are the likely reason neural-network variational methods fail to reach true ground states of frustrated antiferromagnets.“Such states are the likely cause of the obstruction for NQS-based variational Monte Carlo to access the true ground states of these systems.”
   3. UncheckedA neural network with an explicit, interpretable phase ansatz is reported to reach state-of-the-art variational energies for conventional and frustrated antiferromagnets.“We tackle this problem by proposing a neural network architecture with a simple, explicit, and interpretable phase ansatz, which can robustly represent such states and achieve state-of-the-art variational energies for both conventional and frustrated antiferromagnets.”