• Corpus ID: 252595986

Interpretable Quantum Advantage in Neural Sequence Learning

@inproceedings{Anschuetz2022InterpretableQA,
  title={Interpretable Quantum Advantage in Neural Sequence Learning},
  author={Eric R. Anschuetz and Hong-ye Hu and Jin-Long Huang and Xun Gao},
  year={2022}
}
Quantum neural networks have been widely studied in recent years, given their potential practical utility and recent results regarding their ability to efficiently express certain classical data. However, analytic results to date rely on assumptions and arguments from complexity theory. Due to this, there is little intuition as to the source of the expressive power of quantum neural networks or for which classes of classical data any advantage can be reasonably expected to hold. Here, we study… 

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  • 2021