Responsible Classic/Quantum AI Technologies for Industrial Applications

Dear Colleagues,

The rapid evolution of artificial intelligence has entered a new phase with the convergence of classical machine learning and emerging quantum computing techniques. While classical AI has already demonstrated transformative potential in diverse industrial domains such as manufacturing, energy systems, healthcare, logistics, and finance, quantum AI promises to address complex optimization and learning problems that are computationally intractable with conventional methods. However, alongside these opportunities arise pressing concerns of responsibility, transparency, and trustworthiness, particularly as AI technologies are deployed in safety-critical and high-stakes industrial environments. This multidisciplinary Topic seeks to provide a timely platform for advancing responsible AI by bridging classical and quantum paradigms, with an emphasis on practical industrial applications. Contributions will explore not only the design of hybrid classical–quantum models and algorithms but also frameworks that ensure fairness, accountability, and ethical use. Topics include but are not limited to responsible AI governance, privacy-preserving learning, robust optimization with quantum resources, interpretable machine learning models for industrial processes, and evaluation metrics for trustworthy deployments. By uniting theoretical advances with real-world case studies, this multidisciplinary Topic aims to shape a roadmap toward the responsible adoption of AI, both classical and quantum, for sustainable and ethical industrial innovation.

Dr. Youyang Qu
Dr. Khandakar Ahmed
Dr. Zhiyi Tian
Topic Editors