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Meta Variational Monte Carlo
- Publication Year :
- 2020
-
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.<br />Comment: To appear at the Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020)
- Subjects :
- Quantum Physics
Computer Science - Machine Learning
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2011.10614
- Document Type :
- Working Paper