Meta Variational Monte Carlo
Quantum Physics
2020-11-24 v1 Machine Learning
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.
Cite
@article{arxiv.2011.10614,
title = {Meta Variational Monte Carlo},
author = {Tianchen Zhao and James Stokes and Oliver Knitter and Brian Chen and Shravan Veerapaneni},
journal= {arXiv preprint arXiv:2011.10614},
year = {2020}
}
Comments
To appear at the Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020)