Meta-variational Monte Carlo
Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), 2020
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. [Paper]
Recommended citation: Tianchen Zhao, James Stokes, Oliver Knitter, Brian Chen, and Shravan Veerapaneni. Meta-variational Monte Carlo. In Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020)<\i>, 2020.