Effect of Gate Variations on MNIST Classification in QNN: Experimental Study and Analysis 


Vol. 49,  No. 2, pp. 254-263, Feb.  2024
10.7840/kics.2024.49.2.254


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  Abstract

Recently, QNNs have emerged as a promising method for big data processing because they can process data at high speeds while using fewer parameters than existing machine learning algorithms. In the structure of QNN, the performance is determined by which quantum gates are used in the PQC, which constitutes the quantum circuit. Therefore, we conduct a performance comparison experiment in this paper using various quantum gates in QNNs. Through four comparison experiments using the MNIST dataset, we confirm that performance differences occur depending on the use of gates in QNNs and discuss the reasons for the performance differences. Therefore, in this paper, we verify that the performance of QNNs varies depending on the quantum gate variation.

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  Cite this article

[IEEE Style]

S. B. Son, H. Baek, S. Park, "Effect of Gate Variations on MNIST Classification in QNN: Experimental Study and Analysis," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 2, pp. 254-263, 2024. DOI: 10.7840/kics.2024.49.2.254.

[ACM Style]

Seok Bin Son, Hankyul Baek, and Soohyun Park. 2024. Effect of Gate Variations on MNIST Classification in QNN: Experimental Study and Analysis. The Journal of Korean Institute of Communications and Information Sciences, 49, 2, (2024), 254-263. DOI: 10.7840/kics.2024.49.2.254.

[KICS Style]

Seok Bin Son, Hankyul Baek, Soohyun Park, "Effect of Gate Variations on MNIST Classification in QNN: Experimental Study and Analysis," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 2, pp. 254-263, 2. 2024. (https://doi.org/10.7840/kics.2024.49.2.254)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880