This book is intended for students of computer science 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 are passingly familiar with search
algorithms, optimization techniques, and neural networks. This is not because we
are interested in quantum AI or quantum ML but because our basic premise is that
there exists a conceptual bridge between certain AI/ML models and certain quantum
computing models. Our purpose in 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.
Our presentation is technical but pragmatic and practice oriented. We cover theory
to the necessary extent but largely proceed in an example-driven manner. Most of
our examples are concerned with combinatorial optimization and consider problems that can be cast as quadratic unconstrained binary optimization problems.
Numerous Python/NumPy/SciPy codes will support our mathematical discussion
and demonstrate how to put theory into practice.