Quantum Convolution Neural Network

Authors

  • Victor Sineglazov National Aviation University, Kyiv, Ukraine https://orcid.org/0000-0002-3297-9060
  • Petro Chynnyk National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

DOI:

https://doi.org/10.18372/1990-5548.76.17667

Keywords:

quantum computer, quantum method of support vectors, quantum convolutional neural network, quantum computing, classification, machine learning

Abstract

In this work, quantum convolutional neural networks are considered in the task of recognizing handwritten digits. A proprietary quantum scheme for the convolutional layer of a quantum convolutional neural network is proposed. A proprietary quantum scheme for the pooling layer of a quantum convolutional neural network is proposed. The results of learning quantum convolutional neural networks are analyzed. The built models were compared and the best one was selected based on the accuracy, recall, precision and f1-score metrics. A comparative analysis was made with the classic convolutional neural network based on accuracy, recall, precision and f1-score metrics. The object of the study is the task of recognizing numbers. The subject of research is convolutional neural network, quantum convolutional neural network. The result of this work can be applied in the further research of quantum computing in the tasks of artificial intelligence.

Author Biographies

Victor Sineglazov , National Aviation University, Kyiv, Ukraine

Doctor of Engineering Science

Professor

Head of the Department of Aviation Computer-Integrated Complexes

Faculty of Air Navigation Electronics and Telecommunications

Petro Chynnyk , National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Post-graduate student

Artificial Intelligence Department

Institute Applied System Analysis

References

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Published

2023-06-23

Issue

Vol. 2 No. 76 (2023)

Section

COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES

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