Posner computing: a quantum neural network model

TL;DR

This paper introduces a quantum neural network model called Posner computing, implemented in Quipper, demonstrating a probabilistic quantum algorithm for classical function computation inspired by quantum cognition theories.

Contribution

It presents a novel quantum neural network model and a quantum algorithm implemented in Quipper, linking quantum information processing with neural network concepts.

Findings

Quantum neural network model defined and implemented in Quipper

Probabilistic quantum algorithm for classical function computation

Inspired by quantum cognition research

Abstract

We present a construction, rendered in Quipper, of a quantum algorithm which probabilistically computes a classical function from n bits to n bits. The construction is intended to be of interest primarily for the features of Quipper it highlights. However, intrigued by the utility of quantum information processing in the context of neural networks, we present the algorithm as a simplest example of a particular quantum neural network which we first define. As the definition is inspired by recent work of Fisher concerning possible quantum substrates to cognition, we precede it with a short description of that work.