Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)

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Identifying AI Generated Scientific Abstracts using Quantum Machine Learning

Authors
Md Siam Ansary1, 2, *
1Ahsanullah University of Science and Technology, Dhaka, 1208, Bangladesh
2Institute of Information Technology, University of Dhaka, Dhaka, 1200, Bangladesh
*Corresponding author. Email: siamansary.cse@gmail.com
Corresponding Author
Md Siam Ansary
Available Online 8 June 2026.
DOI
10.2991/978-94-6239-664-7_70How to use a DOI?
Keywords
dataset; quantum machine learning; natural language processing; artificial intelligence
Abstract

Ever since artificial intelligence has gained popularity, it is being used and applied to many day-to-day things, which was quite unimaginable previously. Currently, AI tools are used to write many texts and documents. Even for preparing academic as well as business reports such platforms are being utilized. The ability of AI to produce texts and reports is tremendous, but it is also important to be able to identify which text is written by humans and which has been generated using AI platforms for the prevention of potential misuse and fraud. In this research, we have tried to address this issue for identification of AI Generated scientific abstracts because such abstracts are essentially fake and can lead to extremely dangerous and harmful scenarios. We have created a dataset for the task and fifty percent of it is AI generated while the rest are authentic and human written. On the dataset, we have employed state-of-the-art Quantum Machine Learning technique for classification and the approach achieved promising results. All research materials of this study will be made publicly available so that research community can utilize them.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
8 June 2026
ISBN
978-94-6239-664-7
ISSN
1951-6851
DOI
10.2991/978-94-6239-664-7_70How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Md Siam Ansary
PY  - 2026
DA  - 2026/06/08
TI  - Identifying AI Generated Scientific Abstracts using Quantum Machine Learning
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 1027
EP  - 1039
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_70
DO  - 10.2991/978-94-6239-664-7_70
ID  - Ansary2026
ER  -