AdvQuNN: A Methodology for Analyzing the Adversarial Robustness of Quanvolutional Neural Networks-Reference-Cited by-同舟云学术

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AdvQuNN: A Methodology for Analyzing the Adversarial Robustness of Quanvolutional Neural Networks

Published:2024-07-07 Issue: Volume: Page:175-181
ISSN:
Container-title:2024 IEEE International Conference on Quantum Software (QSW)
language:
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Author:

Maouaki Walid El¹,Marchisio Alberto²,Said Taoufik¹,Bennai Mohamed¹,Shafique Muhammad²

Affiliation:

1. Hassan II University of Casablanca,Quantum Physics and Magnetism Team, LPMC, Faculty of Sciences Ben M’Sik,Morocco

2. New York University Abu Dhabi (NYUAD),eBrain Lab, Division of Engineering,Abu Dhabi,UAE

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10646527/10646496/10646505.pdf?arnumber=10646505

Reference33 articles.

1. A survey on quantum machine learning: Current trends, challenges, opportunities, and the road ahead;Zaman;CoRR abs/2310.10315,2023

2. Programming Quantum Neural Networks on NISQ Systems: An Overview of Technologies and Methodologies

3. Quanvolutional neural networks: powering image recognition with quantum circuits

4. Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

5. Evaluating Accuracy and Adversarial Robustness of Quanvolutional Neural Networks

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. FedQNN: Federated Learning using Quantum Neural Networks;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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