Adaptive pruning-based optimization of parameterized quantum circuits

@article{Sim2020AdaptivePO,
  title={Adaptive pruning-based optimization of parameterized quantum circuits},
  author={Sukin Sim and Jonathan Romero and J{\'e}r{\^o}me F Gonthier and Alexander A Kunitsa},
  journal={Quantum Science \& Technology},
  year={2020},
  volume={6}
}
Variational hybrid quantum–classical algorithms are powerful tools to maximize the use of noisy intermediate-scale quantum devices. While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by the difficulty of optimizing in the vast parameter space. In this work, we propose a heuristic optimization strategy for such ansatze used in variational quantum algorithms, which we call ‘parameter-efficient circuit training (PECT)’. Instead of… 
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