Abstract
Meta-learning is linked to finding the ground state of random Hamiltonians, with a model-agnostic approach showing faster training and better convergence for random Max-Cut problems.
AI-generated summary
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.
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