Meta Variational Monte Carlo

Tianchen Zhao, James Stokes, Oliver Knitter, Brian Chen, Shravan Veerapaneni

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.

Knowledge Graph

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