Quantum enhanced cross-validation for near-optimal neural networks architecture selection

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.

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• Quantum Machine Learning
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Cited by in corpus (2)

• Parametric Probabilistic Quantum Memory
• EP-PQM: Efficient Parametric Probabilistic Quantum Memory with Fewer Qubits and Gates