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

2021/10/01 by C. B. Pronin, Pronin, Cesar Borisovich, А. В. Остроух +1
Computer Science · #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #Neural Networks and Applications #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2110.01443

openalex publication_date 2021/10/01 · openalex created_date 2021/10/11 · openalex updated_date 2026/07/28

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.

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