Training of quantum circuits on a hybrid quantum computer

@article{Zhu2019TrainingOQ,
  title={Training of quantum circuits on a hybrid quantum computer},
  author={D. Zhu and N. M. Linke and M. Benedetti and K. Landsman and N. Nguyen and C. H. Alderete and A. Perdomo-Ortiz and N. Korda and A. Garfoot and C. Brecque and L. Egan and O. Perdomo and C. Monroe},
  journal={Science Advances},
  year={2019},
  volume={5}
}
We train generative modeling circuits on a quantum hybrid computer showing an optimization strategy and a resource trade-off. Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to take advantage of the additional resources provided by near-term quantum computers. Here, we implement a data-driven quantum circuit training algorithm on the canonical Bars-and-Stripes dataset… Expand
Parameterized quantum circuits as machine learning models
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