---
abstract: 'We propose a quantum generalisation of a classical neural network. The classical neurons are firstly rendered reversible by adding ancillary bits. Then they are generalised to being quantum reversible, i.e. unitary. (The classical networks we generalise are called feedforward, and have step-function activation functions.) The quantum network can be trained efficiently using gradient descent on a cost function to perform quantum generalisations of classical tasks. We demonstrate numerically that it can: (i) compress quantum states onto a minimal number of qubits, creating a quantum autoencoder, and (ii) discover quantum communication protocols such as teleportation. Our general recipe is theoretical and implementation-independent. The quantum neuron module can naturally be implemented photonically.'
author:
- Kwok Ho Wan
- Oscar Dahlsten
- Hlér Kristjánsson
- Robert Gardner
- 'M.S. Kim'
bibliography:
- 'bibliography\_bibtex.bib'
title: Quantum generalisation of feedforward neural networks
---

Introduction {#introduction .unnumbered}
============

Artificial neural networks mimic biological neural networks to perform information processing tasks. They are highly versatile, applying to vehicle control, trajectory prediction, game-playing, decision making, pattern recognition (such as facial recognition, spam filters), financial time series prediction, automated trading systems, mimicking unpredictable processes, and data mining [@Nielsen15; @Azoff94]. The networks can be trained to perform tasks 