Apr
01
Submission deadline: 01-April-2026
Publication date: 01-Oct-2026
Quantum information science is rapidly emerging as a transformative frontier, offering unprecedented capabilities in computation, communication, and sensing. While often perceived as a domain for physicists, its foundations—rooted in linear algebra, probability, optimization, and information theory—are deeply aligned with the core expertise of the signal processing community.
This Special Issue aims to bridge the theoretical advances in quantum information processing with the mathematical and algorithmic rigor of signal processing. We seek contributions that illuminate fundamental principles, inspire novel paradigms, and develop analytical tools that can inform or redefine traditional and emerging signal processing challenges.
This Special Issue seeks to bring together the latest advancements in Quantum Information Processing and its related topics. Topics of interest include, but are not limited to:
- Quantum machine learning and quantum-inspired algorithms
- AI-Enhanced Quantum System Design
- Quantum statistical inference
- Quantum algorithms and complexity
- Continuous-variable quantum information
- Quantum channel capacities and fundamental limits
- Quantum resource theories
- Quantum error correction
- Quantum cryptography
- Mathematical foundations of quantum information
Prospective authors should follow the instructions given on the IEEE JSTSP webpage and submit their manuscripts via the IEEE Author Portal submission system.
Important Dates (Tentative)
- Manuscript Submission Deadline: April 1, 2026
- First Decision: July 1, 2026
- Revised Manuscript: August 1, 2026
- Final Decision: September 1, 2026
- Final Manuscript Due: September 15, 2026
- Publication Date: October 1, 2026
Guest Editors
Masahito Hayashi (Lead GE), The Chinese University of Hong Kong, Shenzhen, hmasahito@cuhk.edu.cn
Mario Berta, RWTH Aachen University, berta@physik.rwth-aachen.de
Matthias Christandl, University of Copenhagen, christandl@math.ku.dk
Min-Hsiu Hsieh, Hon Hai (Foxconn), min-hsiu.hsieh@foxconn.com
François Le Gall, Nagoya University, Japan – legall@math.nagoya-u.ac.jp
Iman Marvian, Duke University, iman.marvian@duke.edu
Dacheng Tao, Nanyang Technological University, dacheng.tao@ntu.edu.sg
