Combinatorial Optimization Using Quantum Computing
Description
This book provides a comprehensive introduction to quantum optimization, bridging quantum computing and combinatorial optimization for readers without prior background in quantum mechanics. It covers both the theoretical foundations and practical implementation of optimization problems on current quantum hardware.
The book is organized in three parts: Part I (Foundations) introduces essential concepts in quantum computing and combinatorial optimization, establishing the mathematical and computational framework needed for subsequent chapters. Part II (Methodologies) covers the two main quantum computing paradigms: quantum annealing (as implemented on D-Wave systems) and gate-based quantum computing (including variational algorithms such as QAOA and VQE). Part III (Applications and Tools) presents practical case studies and implementation details, including executable code that readers can study, modify, and apply to their own problems. Each chapter emphasizes clear explanations and worked examples. Where appropriate, chapters include code implementations using standard quantum computing frameworks, enabling readers to experiment with algorithms on simulators and actual quantum hardware.
The book is intended for researchers, graduate students, and practitioners in Optimization, Operations Research, Computer Science, and related fields who seek to understand and apply quantum computing methods to combinatorial optimization problems.
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Persons
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.
Content
Foundations of Quantum Computing.- Introduction to combinatorial optimization.- Quantum Annealing for Combinatorial Optimisation.- Problem modelling and quadratic unconstrained
binary 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.