Combinatorial Optimization Using Quantum Computing

Francisco Chicano is Full Professor at the University of Malaga (Spain). He 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 has been with the Department of Languages and Computing Sciences of the University of Málaga and since 2019 he belongs to the Institute of Software Technology and Engineering (ITIS Software). 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 optimization problems. Alberto Moraglio is Senior Lecturer in Computer Science at the University of Exeter, UK, since 2013. He holds an MSci in Computer Engineering from the Polytechnical University of Turin and a PhD in Computer Science from the University of Essex. He held positions at the University of Kent, University of Birmingham, and University of Coimbra. His research interests include evolutionary computation, focusing on solution representations, algorithm design, and theoretical foundations. He established the geometric theory of evolutionary algorithms and Geometric Semantic Genetic Programming, both widely adopted. Since 2018, his work expanded into quantum computing through industry collaborations, producing patented technologies for automated quantum optimization.Ofer M. Shir is an Associate Professor of Computer Science at Tel-Hai – University of Kiryat Shmona in the Galilee, Israel. He holds a BSc from the Hebrew University of Jerusalem and an MSc/PhD from Leiden University. Following a postdoctoral fellowship at Princeton University (2008–2010) specializing in quantum systems, he joined IBM Research (2010–2013), focusing on convex and combinatorial optimization. His research interests include black-box optimization, algorithmically-guided experimentation, mixed-integer programming, and benchmarking, as well as quantum optimization and control. His specialization in experimental optimization and quantum systems centers on designing computational frameworks for complex physical and chemical domains.

• Foundations of Quantum Computing.- Introduction to combinatorial optimization.- Quantum Annealing for Combinatorial Optimisation.- Problem modelling and quadratic unconstrainedbinary optimization.- Quantum Computing in Gate-Based Quantum Computers.- Quantum Optimization Algorithms.- Quantum Optimization Applications.- QoverC: A Profiler of Quantum Computer Simulators for Quantum Optimisation.- Contemporary Challenges of Quantum Combinatorial Optimization.