Session Chair(s)
Prof. Youyang Qu, Data61, CSIRO, Australia
Dr. Jun Bai, Mcgil University, Australia
Dr. Keshav Sood, Deakin University, Australia
Prof. Longxiang Gao, Qilu University of Technology, China
Aim and Scope:
Recent advances in artificial intelligence have increasingly emphasized responsibility, trustworthiness, and scalability in real-world deployment. As AI systems transition from
classical paradigms to quantum-enhanced models, ensuring ethical, secure, and
explainable intelligence becomes both more challenging and more critical. This special
session aims to bring together researchers and practitioners from academia, industry, and government to explore emerging methods, frameworks, and applications of
responsible Quantum/Classical AI.
The session will focus on cross-disciplinary innovations that combine ethical design
principles, trustworthy learning algorithms, and scalable computing architectures for
practical deployment across domains such as cybersecurity, energy systems, healthcare, finance, and intelligent manufacturing. It will serve as a forum for discussing
both theoretical foundations and real-world implementation challenges, including
governance, interpretability, and robustness of hybrid AI systems.
Topics of interest include (but are not limited to):
Trustworthy and explainable AI models in classical and quantum domains
Ethical governance frameworks for hybrid Quantum/Classical intelligence
Scalable quantum machine learning and federated AI architectures
Privacy-preserving and secure AI computation at scale
Responsible data management and unlearning in hybrid AI environments
Quantum-enhanced optimization for sustainable and ethical applications
Evaluation metrics and benchmarks for trustworthy hybrid AI
Real-world deployment case studies in energy, cybersecurity, and healthcare
Submission Process:
If you wish to participate in this special session--RQATSRS, please submit your manuscript through the ConfSync:https://confsync.cn/csae/submission and select the Section "Responsible Quantum/Classical AI for Trustworthy and Scalable Real-world Systems".We will assign your submission to Prof. Youyang Qu for a preliminary review. After passing the preliminary review, your manuscript will undergo a secondary review by experts. Notifications of acceptance will be issued concurrently with the main conference notifications. For any questions, please contact: info@confsync.cn