Meta Variational Monte Carlo

TL;DR

This paper introduces Meta Variational Monte Carlo, a novel approach that combines meta-learning with quantum ground state estimation, demonstrating accelerated training and better convergence on random Max-Cut problems.

Contribution

It proposes a model-agnostic meta-learning framework for quantum ground state problems, linking meta-learning with Hamiltonian ground state determination.

Findings

Accelerates training in quantum ground state estimation

Improves convergence in Max-Cut problem experiments

Establishes a connection between meta-learning and quantum physics

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.