In recent years, remarkable breakthroughs have been seen in both deep neural networks and quantum computing. It is an intriguing topic to study the intersection of these two cutting-edge research fields. Quantum neural network (QNN) is one of the most promising directions for finding advantageous applications of near-term quantum computers. However, the advantages of QNN and deep QNN models over classical models remain open questions, which require further study. This session will focus on recent advances in QNN model architectures, programing platforms for developing QNN algorithms, and potential applications of QNN in various learning tasks such as computer vision, finance and weather prediction.
The topics of interest include, but are not limited to:
- Search and Optimization of Parameterized Quantum Circuits
- When AI Meets Quantum Computing
- Expressivity of Quantum Neural Networks
- The Programing Platforms for Developing QNN Models
- Quantum Self-attention Neural Networks
Organizer: Prof. Yongjian Gu, Ocean University of China, China
Co-Organizer: Assoc. Prof. Zhimin Wang, Ocean University of China, China
Submission Guideline
Please submit your manuscript via Electronic Submission System (account is needed) at the link: http://confsys.iconf.org/submission/iccpr2023. (Please remark the session number when you make the submission.)
Important Dates
- Submission of Full Papers: Sept. 15, 2023
- Notification of Review Result of Papers from Special Sessions: Sept. 30, 2023
- Registration Deadline: Oct. 07, 2023
