Quantum Image Classification on NISQ Devices

Shuroog Al-Ogbi; Abdulrahman Ashour; Muhamad Felemban

doi:10.1109/CICN56167.2022.10008259

Quantum Image Classification on NISQ Devices

Shuroog Al-Ogbi
, Abdulrahman Ashour
, Muhamad Felemban

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Quantum computing is an emerging computing field that is expected to make a huge impact on several scopes of science and technology. In this paper, we investigate the role of quantum computing in image classification, as an important branch of machine learning with widely used applications in healthcare, military, and IR4.0. In particular, we systemically compare the performance of two well-known classical image classification systems, i.e., Support Vector Machine (SVM) and Convolutions Neural Network (CNN), with equivalent quantum image classification algorithms, i.e., Quantum Support Vector Machine (Q-SVM) and Quantum Convolutional Neural Network (Q-CNN). Both quantum and classical algorithms are implemented on available Noisy-Intermediate Scale Quantum (NISQ) devices using MNIST dataset. Performance of models were compared regarding accuracy and training time. The results show that classical algorithms outperform the quantum algorithms for the given tasks. However, we observe that large-scale fault-tolerant quantum computing can effectively perform image classification tasks in the future.

Original language	English
Title of host publication	Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	83-89
Number of pages	7
ISBN (Electronic)	9781665487719
DOIs	https://doi.org/10.1109/CICN56167.2022.10008259
State	Published - 2022
Event	14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 - Al-Khobar, Saudi Arabia Duration: 4 Dec 2022 → 6 Dec 2022

Publication series

Name	Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

Conference

Conference	14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Country/Territory	Saudi Arabia
City	Al-Khobar
Period	4/12/22 → 6/12/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Machine Learning
Quantum Computing
Quantum Machine Learning

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/CICN56167.2022.10008259

Cite this

Al-Ogbi, S., Ashour, A., & Felemban, M. (2022). Quantum Image Classification on NISQ Devices. In Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 (pp. 83-89). (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICN56167.2022.10008259

Al-Ogbi, Shuroog ; Ashour, Abdulrahman ; Felemban, Muhamad. / Quantum Image Classification on NISQ Devices. Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 83-89 (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022).

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title = "Quantum Image Classification on NISQ Devices",

abstract = "Quantum computing is an emerging computing field that is expected to make a huge impact on several scopes of science and technology. In this paper, we investigate the role of quantum computing in image classification, as an important branch of machine learning with widely used applications in healthcare, military, and IR4.0. In particular, we systemically compare the performance of two well-known classical image classification systems, i.e., Support Vector Machine (SVM) and Convolutions Neural Network (CNN), with equivalent quantum image classification algorithms, i.e., Quantum Support Vector Machine (Q-SVM) and Quantum Convolutional Neural Network (Q-CNN). Both quantum and classical algorithms are implemented on available Noisy-Intermediate Scale Quantum (NISQ) devices using MNIST dataset. Performance of models were compared regarding accuracy and training time. The results show that classical algorithms outperform the quantum algorithms for the given tasks. However, we observe that large-scale fault-tolerant quantum computing can effectively perform image classification tasks in the future.",

keywords = "Machine Learning, Quantum Computing, Quantum Machine Learning",

author = "Shuroog Al-Ogbi and Abdulrahman Ashour and Muhamad Felemban",

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year = "2022",

doi = "10.1109/CICN56167.2022.10008259",

language = "English",

series = "Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022",

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Al-Ogbi, S, Ashour, A & Felemban, M 2022, Quantum Image Classification on NISQ Devices. in Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, Institute of Electrical and Electronics Engineers Inc., pp. 83-89, 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, Al-Khobar, Saudi Arabia, 4/12/22. https://doi.org/10.1109/CICN56167.2022.10008259

Quantum Image Classification on NISQ Devices. / Al-Ogbi, Shuroog; Ashour, Abdulrahman; Felemban, Muhamad.
Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 83-89 (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Al-Ogbi, Shuroog

AU - Ashour, Abdulrahman

AU - Felemban, Muhamad

PY - 2022

Y1 - 2022

N2 - Quantum computing is an emerging computing field that is expected to make a huge impact on several scopes of science and technology. In this paper, we investigate the role of quantum computing in image classification, as an important branch of machine learning with widely used applications in healthcare, military, and IR4.0. In particular, we systemically compare the performance of two well-known classical image classification systems, i.e., Support Vector Machine (SVM) and Convolutions Neural Network (CNN), with equivalent quantum image classification algorithms, i.e., Quantum Support Vector Machine (Q-SVM) and Quantum Convolutional Neural Network (Q-CNN). Both quantum and classical algorithms are implemented on available Noisy-Intermediate Scale Quantum (NISQ) devices using MNIST dataset. Performance of models were compared regarding accuracy and training time. The results show that classical algorithms outperform the quantum algorithms for the given tasks. However, we observe that large-scale fault-tolerant quantum computing can effectively perform image classification tasks in the future.

AB - Quantum computing is an emerging computing field that is expected to make a huge impact on several scopes of science and technology. In this paper, we investigate the role of quantum computing in image classification, as an important branch of machine learning with widely used applications in healthcare, military, and IR4.0. In particular, we systemically compare the performance of two well-known classical image classification systems, i.e., Support Vector Machine (SVM) and Convolutions Neural Network (CNN), with equivalent quantum image classification algorithms, i.e., Quantum Support Vector Machine (Q-SVM) and Quantum Convolutional Neural Network (Q-CNN). Both quantum and classical algorithms are implemented on available Noisy-Intermediate Scale Quantum (NISQ) devices using MNIST dataset. Performance of models were compared regarding accuracy and training time. The results show that classical algorithms outperform the quantum algorithms for the given tasks. However, we observe that large-scale fault-tolerant quantum computing can effectively perform image classification tasks in the future.

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KW - Quantum Computing

KW - Quantum Machine Learning

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DO - 10.1109/CICN56167.2022.10008259

M3 - Conference contribution

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T3 - Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

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BT - Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 4 December 2022 through 6 December 2022

ER -

Al-Ogbi S, Ashour A, Felemban M. Quantum Image Classification on NISQ Devices. In Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 83-89. (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022). doi: 10.1109/CICN56167.2022.10008259

Quantum Image Classification on NISQ Devices

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this