Meta Variational Monte Carlo
Tianchen Zhao, JAMES M. STOKES, Oliver Knitter +2 more
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:2011.10614•Nov 20, 2020•1 citation