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arXiv · 2011.10614

Meta Variational Monte Carlo

Tianchen Zhao·James Stokes·Oliver Knitter·Brian Chen·Shravan Veerapaneni
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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.

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Tianchen Zhao, James Stokes, Oliver Knitter, Brian Chen, Shravan Veerapaneni. 2020-11-20. Meta Variational Monte Carlo. https://arxiv.org/abs/2011.10614

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