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Quantum Optimization
Quantum Machine Learning
Meta Variational Monte Carlo
arXiv
Authors: Tianchen Zhao, James Stokes, Oliver Knitter, Brian Chen, Shravan Veerapaneni
Year
2020
Paper ID
19113
Status
Preprint
Abstract Read
~2 min
Abstract Words
58
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2020 reference point for readers tracking recent quantum research.
- 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.
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