Evaluating analytic gradients on quantum hardware

@article{Schuld2019EvaluatingAG,
  title={Evaluating analytic gradients on quantum hardware},
  author={Maria Schuld and Ville Bergholm and Christian Gogolin and Josh A. Izaac and Nathan Killoran},
  journal={Physical Review A},
  year={2019}
}
An important application for near-term quantum computing lies in optimization tasks, with applications ranging from quantum chemistry and drug discovery to machine learning. In many settings --- most prominently in so-called parametrized or variational algorithms --- the objective function is a result of hybrid quantum-classical processing. To optimize the objective, it is useful to have access to exact gradients of quantum circuits with respect to gate parameters. This paper shows how… 

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