• Corpus ID: 240070921

Subtleties in the trainability of quantum machine learning models

@article{Thanasilp2021SubtletiesIT,
  title={Subtleties in the trainability of quantum machine learning models},
  author={Supanut Thanasilp and Samson Wang and Nhat A. Nghiem and Patrick J. Coles and Mar{\'i}a Cerezo},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.14753}
}
A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called Quantum Machine Learning (QML), aims to achieve a speedup over traditional machine learning for data analysis. However, its success usually hinges on efficiently training the parameters in quantum neural networks, and the field of QML is still lacking theoretical scaling results for their trainability. Some trainability results have been proven for a closely related… 

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