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Quantum enhanced cross-validation for near-optimal neural networks architecture selection
Priscila G. M. dos Santos, Rodrigo S. Sousa, Ismael C. S. Araújo, A. J. D. Silva·August 27, 2018·DOI: 10.1142/S0219749918400051
PhysicsComputer Science
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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 (PQM) and the possibility to train artificial neural networks (ANN) 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.