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.
A perspective view and survey of meta-learning,
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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.