Implementing An Artificial Quantum Perceptron

TL;DR

This paper develops and tests a quantum version of a perceptron, demonstrating exponential growth advantages and potential as a pattern classifier through simulation and circuit implementation.

Contribution

It introduces a quantum perceptron model, compares mechanisms, and provides insights into designing spike-dependent quantum perceptrons.

Findings

Quantum perceptron shows exponential growth advantage.

The model functions effectively as a pattern classifier.

Simulation results support quantum perceptron viability.

Abstract

A Perceptron is a fundamental building block of a neural network. The flexibility and scalability of perceptron make it ubiquitous in building intelligent systems. Studies have shown the efficacy of a single neuron in making intelligent decisions. Here, we examined and compared two perceptrons with distinct mechanisms, and developed a quantum version of one of those perceptrons. As a part of this modeling, we implemented the quantum circuit for an artificial perception, generated a dataset, and simulated the training. Through these experiments, we show that there is an exponential growth advantage and test different qubit versions. Our findings show that this quantum model of an individual perceptron can be used as a pattern classifier. For the second type of model, we provide an understanding to design and simulate a spike-dependent quantum perceptron. Our code is available at https://github.com/ashutosh1919/quantum-perceptron