Design of Quantum Machine Learning Course for a Computer Science Program

Sathish Kumar; Temitope Adeniyi; Ahmad Alomari; Santanu Ganguly

doi:10.1109/QCE57702.2023.20326

Design of Quantum Machine Learning Course for a Computer Science Program

Sathish Kumar
, Temitope Adeniyi
, Ahmad Alomari
, Santanu Ganguly

Cleveland State University
Photonic Inc.

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

2 Scopus citations

Abstract

In this work, we present the design and plan of Quantum machine learning (QML) course in a computer science (CS) University program at senior undergraduate level / first year graduate level. Based on our survey, there is a lack of detailed design and assessment plan for the delivery of QML course. In this paper we have presented the QML course design with week by week details of QML concepts and hands on activities that are covered in the course. We also present how this QML course can be assessed from CS program learning outcomes perspective.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023
Editors	Brian La Cour, Lia Yeh, Marek Osinski
Place of Publication	usa
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	68-77
Number of pages	10
Volume	3
ISBN (Electronic)	9798350343236
DOIs	https://doi.org/10.1109/QCE57702.2023.20326
State	Published - Jan 1 2023
Event	4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023 - Bellevue, United States Duration: Sep 17 2023 → Sep 22 2023

Conference

Conference	4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023
Country/Territory	United States
City	Bellevue
Period	09/17/23 → 09/22/23

Keywords

Course Assessment
Course Design
QML Workflow
Quantum computing
Quantum machine learning

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10.1109/QCE57702.2023.20326

Cite this

Kumar, S., Adeniyi, T., Alomari, A., & Ganguly, S. (2023). Design of Quantum Machine Learning Course for a Computer Science Program. In B. La Cour, L. Yeh, & M. Osinski (Eds.), Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023 (Vol. 3, pp. 68-77). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/QCE57702.2023.20326

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abstract = "In this work, we present the design and plan of Quantum machine learning (QML) course in a computer science (CS) University program at senior undergraduate level / first year graduate level. Based on our survey, there is a lack of detailed design and assessment plan for the delivery of QML course. In this paper we have presented the QML course design with week by week details of QML concepts and hands on activities that are covered in the course. We also present how this QML course can be assessed from CS program learning outcomes perspective.",

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author = "Sathish Kumar and Temitope Adeniyi and Ahmad Alomari and Santanu Ganguly",

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booktitle = "Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023",

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Kumar, S, Adeniyi, T, Alomari, A & Ganguly, S 2023, Design of Quantum Machine Learning Course for a Computer Science Program. in B La Cour, L Yeh & M Osinski (eds), Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023. vol. 3, Institute of Electrical and Electronics Engineers Inc., usa, pp. 68-77, 4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023, Bellevue, United States, 09/17/23. https://doi.org/10.1109/QCE57702.2023.20326

Design of Quantum Machine Learning Course for a Computer Science Program. / Kumar, Sathish; Adeniyi, Temitope; Alomari, Ahmad et al.
Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023. ed. / Brian La Cour; Lia Yeh; Marek Osinski. Vol. 3 usa: Institute of Electrical and Electronics Engineers Inc., 2023. p. 68-77.

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

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AB - In this work, we present the design and plan of Quantum machine learning (QML) course in a computer science (CS) University program at senior undergraduate level / first year graduate level. Based on our survey, there is a lack of detailed design and assessment plan for the delivery of QML course. In this paper we have presented the QML course design with week by week details of QML concepts and hands on activities that are covered in the course. We also present how this QML course can be assessed from CS program learning outcomes perspective.

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Design of Quantum Machine Learning Course for a Computer Science Program

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