This open access book is meant as a textbook for Computer Science students who are looking for a gentle introduction to the world of quantum computing. More specifically, it is written for readers who have basic knowledge of Artificial Intelligence (AI) and Machine Learning (ML) and have a certain familiarity with search algorithms, optimization techniques, and neural networks. This is not because the authors are interested in Quantum AI or Quantum ML, but because they start from the basic premise that there exists a conceptual bridge between certain AI/ML models and certain quantum computing models. The purpose of this book is therefore 1) to revisit these AI/ML models and their applications, and 2) to build on this familiar foundation to segue into the study of quantum computing and its possible use cases. The presentation is technical but pragmatic and practice oriented. The authors cover theory to the necessary extent but largely proceed in an example-driven manner. Most of the examples are concerned with combinatorial optimization and consider problems that can be cast as quadratic unconstrained binary optimization problems.Numerous python/numpy/scipy codes support the mathematical discussion and demonstrate how to put theory into practice, accompanied by exercises for each chapter. Parts of the material were adopted from long running lectures on pattern recognition, on the foundations of quantum computing, and on quantum computing algorithms, which are taught by the authors in the Computer Science master’s program at the University of Bonn.

This open access book is meant as a textbook for Computer Science students who are looking for a gentle introduction to the world of quantum computing. More specifically, it is written for readers who have basic knowledge of Artificial Intelligence (AI) and Machine Learning (ML) and have a certain familiarity with search algorithms, optimization techniques, and neural networks. This is not because the authors are interested in Quantum AI or Quantum ML, but because they start from the basic premise that there exists a conceptual bridge between certain AI/ML models and certain quantum computing models. The purpose of this book is therefore 1) to revisit these AI/ML models and their applications, and 2) to build on this familiar foundation to segue into the study of quantum computing and its possible use cases. The presentation is technical but pragmatic and practice oriented. The authors cover theory to the necessary extent but largely proceed in an example-driven manner. Most of the examples are concerned with combinatorial optimization and consider problems that can be cast as quadratic unconstrained binary optimization problems.Numerous python/numpy/scipy codes support the mathematical discussion and demonstrate how to put theory into practice, accompanied by exercises for each chapter. Parts of the material were adopted from long running lectures on pattern recognition, on the foundations of quantum computing, and on quantum computing algorithms, which are taught by the authors in the Computer Science master’s program at the University of Bonn.