Meta Variational Monte Carlo
This work addresses the problem of efficiently finding ground states for random Hamiltonians, which is relevant for researchers working on optimization and quantum computing, by proposing a meta-learning approach.
This paper identifies meta-learning with finding the ground state of a random Hamiltonian. The authors propose a model-agnostic meta-learning approach, Meta Variational Monte Carlo, which experimentally accelerates training and improves convergence for random Max-Cut problems.
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.