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

arXiv: https://arxiv.org/abs/1808.09058 · HTML: https://mlsanity.com/paper/1808.09058/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2018.

## Built on by (most cited first, 2 of 2)

- [Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition](https://mlsanity.com/paper/2301.08292/) (2023)
- [EP-PQM: Efficient Parametric Probabilistic Quantum Memory with Fewer Qubits and Gates](https://mlsanity.com/paper/2201.07265/) (2022)

## Abstract (arXiv)

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.