A gradient-based LSTM meta-learner outperforms L-BFGS-B, Bayesian optimization, evolutionary strategies, and Nelder-Mead at tuning QAOA and VQE parameters in simulated noisy settings.
the AND of a number of disjunc- tive two-variable OR clauses), MAX-SAT is the NP-hard problem of determining the maximum number of clauses which may be simultaneously satisfied
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
quant-ph 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Optimizing quantum heuristics with meta-learning
A gradient-based LSTM meta-learner outperforms L-BFGS-B, Bayesian optimization, evolutionary strategies, and Nelder-Mead at tuning QAOA and VQE parameters in simulated noisy settings.