Meta Variational Monte Carlo
arXiv:2011.10614
Abstract
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.
arXiv commentTo appear at the Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020)
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