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

• URL: http://arxiv.org/abs/2110.01443v1
• Date: Fri, 1 Oct 2021 14:08:43 GMT
• Title: Development and Training of Quantum Neural Networks, Based on the Principles of Grover's Algorithm
• Authors: Cesar Borisovich Pronin, Andrey Vladimirovich Ostroukh
• Abstract summary: This paper proposes the concept of combining the training process of a neural network 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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