# Optimizing quantum heuristics with meta-learning

@article{Wilson2019OptimizingQH, title={Optimizing quantum heuristics with meta-learning}, author={Max L. Wilson and Sam Stromswold and Filip Wudarski and Stuart Hadfield and Norm M. Tubman and Eleanor G. Rieffel}, journal={ArXiv}, year={2019}, volume={abs/1908.03185} }

Variational quantum algorithms, a class of quantum heuristics, are promising candidates for the demonstration of useful quantum computation. Finding the best way to amplify the performance of these methods on hardware is an important task. Here, we evaluate the optimization of quantum heuristics with an existing class of techniques called `meta-learners'. We compare the performance of a meta-learner to Bayesian optimization, evolutionary strategies, L-BFGS-B and Nelder-Mead approaches, for two… CONTINUE READING

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