Development of Quantum Circuits for Perceptron Neural Network Training, Based on the Principles of Grover's Algorithm

TL;DR

This paper explores the design of quantum circuits based on Grover's Algorithm to train perceptron neural networks, aiming to leverage quantum computing for neural network training.

Contribution

It introduces quantum circuit designs for perceptron training using Grover's Algorithm principles, a novel approach in quantum neural network research.

Findings

Quantum circuits for perceptron training demonstrated feasibility

Grover's Algorithm principles applied to neural network training

Potential for scalable quantum neural network architectures

Abstract

This paper highlights a practical research of the possibility of forming quantum circuits for training neural networks. The demonstrated quantum circuits were based on the principles of Grover's Search Algorithm. The perceptron was chosen as the architecture for the example neural network. The multilayer perceptron is a popular neural network architecture due to its scalability and applicability for solving a wide range of problems.