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Title: Hamiltonian reconstruction as metric for variational studies
Variational approaches are among the most powerful techniques toapproximately solve quantum many-body problems. These encompass bothvariational states based on tensor or neural networks, and parameterizedquantum circuits in variational quantum eigensolvers. However,self-consistent evaluation of the quality of variational wavefunctionsis a notoriously hard task. Using a recently developed Hamiltonianreconstruction method, we propose a multi-faceted approach to evaluatingthe quality of neural-network based wavefunctions. Specifically, weconsider convolutional neural network (CNN) and restricted Boltzmannmachine (RBM) states trained on a square latticespin-1/2 1 / 2 J_1\!-\!J_2 J 1 J 2 Heisenberg model. We find that the reconstructed Hamiltonians aretypically less frustrated, and have easy-axis anisotropy near the highfrustration point. In addition, the reconstructed Hamiltonians suppressquantum fluctuations in the largeJ_2 J 2 limit. Our results highlight the critical importance of thewavefunction’s symmetry. Moreover, the multi-faceted insight from theHamiltonian reconstruction reveals that a variational wave function canfail to capture the true ground state through suppression of quantumfluctuations.  more » « less
Award ID(s):
1934714
PAR ID:
10486265
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
scipost.org
Date Published:
Journal Name:
SciPost Physics
Volume:
13
Issue:
3
ISSN:
2542-4653
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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