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Meta Variational Monte Carlo
November 20, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Tianchen Zhao, James Stokes, Oliver Knitter, Brian Chen, Shravan Veerapaneni
arXiv ID
2011.10614
Category
quant-ph: Quantum Computing
Cross-listed
cs.LG
Citations
3
Venue
arXiv.org
Last Checked
5 months ago
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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