  An identification is found between meta-learning and the problem of
determining the ground state of a randomly generated Hamiltonian drawn from a
known ensemble. A model-agnostic meta-learning approach is proposed to solve
the associated learning problem and a preliminary experimental study of random
Max-Cut problems indicates that the resulting Meta Variational Monte Carlo
accelerates training and improves convergence.
