Development and Training of Quantum Neural Networks, Based on the Principles of Grover's Algorithm

TL;DR

This paper proposes a novel approach to training quantum neural networks using Grover's Search Algorithm, integrating quantum search principles with neural network structures for potential quantum computing applications.

Contribution

It introduces a new method of training quantum neural networks based on Grover's algorithm, combining quantum search with neural network architecture.

Findings

Demonstrates the concept with a simple perceptron model

Shows potential for quantum-enhanced neural network training

Lays groundwork for future quantum neural network development

Abstract

This paper highlights the possibility of creating quantum neural networks that are trained by Grover's Search Algorithm. The purpose of this work is to propose the concept of combining the training process of a neural network, which is performed on the principles of Grover's algorithm, with the functional structure of that neural network, interpreted as a quantum circuit. As a simple example of a neural network, to showcase the concept, a perceptron with one trainable parameter - the weight of a synapse connected to a hidden neuron.