Simulating a perceptron on a quantum computer

TL;DR

This paper presents a quantum perceptron model based on quantum phase estimation, which imitates classical perceptron activation and offers resource-efficient implementation for quantum neural networks.

Contribution

It introduces a novel quantum perceptron scheme using quantum phase estimation, enabling efficient quantum neural network structures.

Findings

Resource complexity is linear in input size ($\\mathcal{O}(n)$).

The model effectively imitates classical perceptron activation functions.

Potential for scalable quantum neural network applications.

Abstract

Perceptrons are the basic computational unit of artificial neural networks, as they model the activation mechanism of an output neuron due to incoming signals from its neighbours. As linear classifiers, they play an important role in the foundations of machine learning. In the context of the emerging field of quantum machine learning, several attempts have been made to develop a corresponding unit using quantum information theory. Based on the quantum phase estimation algorithm, this paper introduces a quantum perceptron model imitating the step-activation function of a classical perceptron. This scheme requires resources in $\mathcal{O}(n)$ (where $n$ is the size of the input) and promises efficient applications for more complex structures such as trainable quantum neural networks.