Speaker
Hala Siddig Mohamed Elhag
(CQTA (Centre f. Quantum Techno. a. Application))
Description
Quantum Convolutional Neural Networks (QCNNs) have emerged in recent years as a promising tool in the field of quantum machine learning. Numerous studies have demonstrated that QCNNs can achieve improved accuracy compared to their classical CNN counterparts in various tasks. However, it has recently been shown that QCNNs, when applied to classical data, are classically simulable, raising questions about their quantum advantage in such contexts. Nevertheless, QCNNs still remain a viable architecture for a wide range of applications, particularly in scenarios where their quantum properties can be effectively leveraged.
| Group | CQTA |
|---|---|
| Project Category | B1. Physics data analysis and performance (software-oriented) |
| DESY Site | Zeuthen |
Primary author
Hala Siddig Mohamed Elhag
(CQTA (Centre f. Quantum Techno. a. Application))
