Quantum Machine Learning bridges the gap between abstract
developments in quantum computing and the applied research on
machine learning. Paring down the complexity of the disciplines
involved, it focuses on providing a synthesis that explains the
most important machine learning algorithms in a quantum framework.
Theoretical advances in quantum computing are hard to follow for
computer scientists, and sometimes even for researchers involved in
the field. The lack of a step-by-step guide hampers the broader
understanding of this emergent interdisciplinary body of research.
Quantum Machine Learning sets the scene for a deeper understanding
of the subject for readers of different backgrounds. The author has
carefully constructed a clear comparison of classical learning
algorithms and their quantum counterparts, thus making differences
in computational complexity and learning performance apparent. This
book synthesizes of a broad array of research into a manageable and
concise presentation, with practical examples and applications.
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