Proposers
Prof. Giovanni Acampora, Prof. Autilia Vitiello, Prof. Manuel Pegalajar Cuéllar and Dr. Amir Pourabdollah
Workshop Code
Please use the following code when submitting your paper to this Workshop: W8-QUANTUM
Scope and Aims
Quantum artificial intelligence is an emerging field of computer science that combines the advantages of two prominent research domains: artificial intelligence and quantum computing. Indeed, on the one hand, the potential boost in computational speed from quantum computing could enhance the eFiciency of artificial intelligence in mimicking human abilities, thus revealing an entirely new landscape for the creation and evolution of intelligent systems. On the other hand, present artificial intelligence methods for problem solving, reasoning, and learning could aid in crafting superior methodologies for quantum technology design, accelerating the shift from the noisy intermediate-scale quantum (NISQ) phase to the era of fault-tolerant quantum computing (FTQC), marking a noteworthy advancement towards achieving quantum utility. This workshop aims to bring together researchers and practitioners from the fields of artificial intelligence and quantum computing to explore this emerging paradigm and provide a forum for discussing theoretical foundations, hybrid architectures, and practical implementations that bridge the gap between classical and quantum intelligence.
In line with the mission of IEEE Computational Intelligence Society, IEEE Computer Society, IEEE System, Man and Cybernetics Society and IEEE Quantum Technical Community, this event aims to promote interdisciplinary collaboration, stimulate discussion on benchmarking and reproducibility, and support the development of shared frameworks and standards for quantum artificial intelligence research and applications.
Content and Objectives
The workshop will feature invited talks, contributed papers, and panel discussions addressing the following topics
- Quantum Machine Learning
- Quantum Data Preprocessing
- Quantum Kernels
- Quantum Variational Models
- Quantum Generative AI
- Quantum Natural Language Processing
- Quantum Game Theory
- Quantum Automated Reasoning
- Quantum Logic
- Quantum Fuzzy Reasoning
- Quantum Optimization
- Quantum Evolutionary Algorithms
- AI for Quantum Circuit Optimization
- AI for Quantum Calibration
- AI for Quantum Error Mitigation and Correction
- AI for Automated Quantum Algorithms Design
The objectives are to:
- Establish a shared understanding of the current challenges and frontiers of QAI.
- Identify promising research directions for hybrid AI–Quantum paradigms.
- Encourage collaboration between AI and Quantum Computing communities.
- Support open and standardized approaches to software frameworks and evaluation practices in Quantum AI.
The workshop’s intended outcome is to consolidate a sustainable research community that fosters long-
term collaboration and contributes to shaping the emerging Quantum Artificial Intelligence landscape
within IEEE Computational Intelligence initiatives.