Corpus ID: 218889878

Trainability of Dissipative Perceptron-Based Quantum Neural Networks

@article{Sharma2020TrainabilityOD,
  title={Trainability of Dissipative Perceptron-Based Quantum Neural Networks},
  author={Kunal Sharma and M Cerezo and Lukasz Cincio and Patrick J. Coles},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.12458}
}
Several architectures have been proposed for quantum neural networks (QNNs), with the goal of efficiently performing machine learning tasks on quantum data. Rigorous scaling results are urgently needed for specific QNN constructions to understand which, if any, will be trainable at a large scale. Here, we analyze the gradient scaling (and hence the trainability) for a recently proposed architecture that we called dissipative QNNs (DQNNs), where the input qubits of each layer are discarded at… Expand
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