# Weightless neural network parameters and architecture selection in a quantum computer

arXiv: https://arxiv.org/abs/1601.03277 · HTML: https://mlsanity.com/paper/1601.03277/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2016.

## Built on by (most cited first, 1 of 1)

- [Quantum activation functions for quantum neural networks](https://mlsanity.com/paper/2201.03700/) (2022)

## Abstract (arXiv)

Training artificial neural networks requires a tedious empirical evaluation to determine a suitable neural network architecture. To avoid this empirical process several techniques have been proposed to automatise the architecture selection process. In this paper, we propose a method to perform parameter and architecture selection for a quantum weightless neural network (qWNN). The architecture selection is performed through the learning procedure of a qWNN with a learning algorithm that uses the principle of quantum superposition and a non-linear quantum operator. The main advantage of the proposed method is that it performs a global search in the space of qWNN architecture and parameters rather than a local search.