Meta Variational Monte Carlo

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.

To appear at the Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020)

References in corpus (3)

• Theoretical Models of Learning to Learn
• Learning to learn with quantum neural networks via classical neural networks
• Classical Quantum Optimization with Neural Network Quantum States