June 8 - 12, 2025, Hangzhou, China
Quantum Computing (QC) is a computation paradigm that leverages the peculiar properties of quantum mechanics
such as superposition and entanglement. Several QC paradigms exist but the two principal ones would be the
gate-based and the adiabatic one. The first one describes the computation as quantum circuits that acts on the
qubits using some set of quantum gates, while the second paradigm is based on the adiabatic theorem in order to
solve discrete optimisation problems. Each of the paradigms has some advantages and shortfalls and a range of
applicability, where a plethora of perspectives rises.
QC has a wide domain of applications but one of the principal and most promising is optimisation-problem solving,
where QC algorithms are proven to provide, in some cases, very promising theoretical and practical speedup regarding
the classical computation techniques. On the other hand, classical Artificial Intelligence (AI) techniques
(e.g. Evolutionary Computation (EC), unsupervised/supervised machine learning, etc.) are also proven to be a promising
alternative for certain classes of tasks as well. Thus, it is widely believed that QC will not replace classical
computation but instead will collaboratively work together taking profit from one another strengths. Indeed, efforts
in the community are made to enhance classical AI methods using quantum routines, or on the reverse enhance quantum
processes using classical AI and ultimately, design efficient fully-quantum, hybrid quantum-classical or quantum-inspired AI techniques.
In this same line of thoughts, this special session explores how QC can take profit from classical AI especially EC, and vice versa,
where the topics include but are not limited to:
- The design of quantum, hybrid and quantum-inspired AI techniques.
- Optimisation-problem solving using quantum, hybrid and quantum-inspired AI.
- Machine learning through quantum, hybrid and quantum-inspired AI.
- AI for quantum simulation.
- AI for embedding in adiabatic quantum computers.
- AI for transpilation in gate-based quantum computers.
- AI for quantum variational algorithms.
- etc.
- Paper Submission: January 15th, 2025.
- Paper Acceptance: March 15th, 2025.
- Final Paper Submission: May 1st, 2025.
- For More Information About the:
- Template,
- Submission System,
- Number of pages, etc.
- Please Visit: CEC 2025 conference submission page
Zakaria A. DAHI: Zakaria A. Dahi is a researcher and lecturer and member of the NEO research group. He has an experience in scientific research and teaching at the university level. He has been actively working, participating and elaborating several national and international research projects on artificial intelligence and quantum computing. His research interests include the design and use of artificial intelligence and quantum computing for solving real-world problems in various domains such as smart cities, logistics, etc. He has published works in several well-established journals and conferences. He has also participated in the organisation of several national and international conferences.
Gabriel Luque: Dr. Luque finished his PhD studies in Computer Engineering at the University of Málaga with honors. He is currently an Associate Professor in the Department of Languages and Computer Science and also member of the NEO research group at the University of Málaga (Spain), where he has taught for the last 12 years at national and international levels. As a researcher, he is the author of more than 70 international publications of impact, including 16 publications in prestigious scientific journals, 13 book chapters, 3 conference proceedings and the recent book on Parallel Genetic Algorithms. His research interests include quantum computing and artificial intelligence such as the design of new metaheuristics, especially in the parallel domain, and their application to complex problems in bioinformatics, smart cities and combinatorial optimisation in general. Finally, he also has organised several conferences and special sessions on metaheuristics, parallel techniques and tools for the development of optimisation techniques.
Francisco Chicano: Dr. Chicano holds a PhD in Computer Science from the University of Málaga and a Degree in Physics from the National Distance Education University. Since 2008 he is with the Department of Languages and Computing Sciences of the University of Málaga and also a member of the NEO research group. His research interests include quantum computing, the application of search techniques to Software Engineering problems and the use of theoretical results to efficiently solve combinatorial optimisation problems. He is in the editorial board of Evolutionary Computation Journal, Engineering Applications of Artificial Intelligence, Journal of Systems and Software, ACM Transactions on Evolutionary Learning and Optimisation and Mathematical Problems in Engineering. He has also been programme chair and Editor-in-Chief in international events.
For any inquiries, please e-mail: zadahi@uma.es