Variational approaches are among the most powerful techniques toapproximately solve quantum manybody problems. These encompass bothvariational states based on tensor or neural networks, and parameterizedquantum circuits in variational quantum eigensolvers. However,selfconsistent evaluation of the quality of variational wavefunctionsis a notoriously hard task. Using a recently developed Hamiltonianreconstruction method, we propose a multifaceted approach to evaluatingthe quality of neuralnetwork based wavefunctions. Specifically, weconsider convolutional neural network (CNN) and restricted Boltzmannmachine (RBM) states trained on a square latticespin
We advance the characterization of complexity in quantum manybody systems by examining
 Award ID(s):
 2014000
 NSFPAR ID:
 10477840
 Publisher / Repository:
 SciPost
 Date Published:
 Journal Name:
 SciPost Physics
 Volume:
 15
 Issue:
 4
 ISSN:
 25424653
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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