Abstract

This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures. The proposed algorithm is based on a probabilistic quantum memory and the possibility to train artificial neural networks in superposition. We obtain an exponential quantum speedup in the evaluation of neural networks. We also verify experimentally through a reduced experimental analysis that the proposed algorithm can be used to select near-optimal neural networks.

BibTeX

 @article{Santos2018,
  title={Quantum enhanced cross-validation for near-optimal neural networks architecture selection},
  author={Priscila G. M. dos Santos and Rodrigo S. Sousa and Ismael C. S. Araujo and Adenilton J. da Silva},
  journal={International Journal of Quantum Information},
  volume={16},
  number={6},
  pages={1840005},
  year={2018}
}