  In the current noisy intermediate-scale quantum (NISQ) era, quantum machine
learning is emerging as a dominant paradigm to program gate-based quantum
computers. In quantum machine learning, the gates of a quantum circuit are
parametrized, and the parameters are tuned via classical optimization based on
data and on measurements of the outputs of the circuit. Parametrized quantum
circuits (PQCs) can efficiently address combinatorial optimization problems,
implement probabilistic generative models, and carry out inference
(classification and regression). This monograph provides a self-contained
introduction to quantum machine learning for an audience of engineers with a
background in probability and linear algebra. It first describes the necessary
background, concepts, and tools necessary to describe quantum operations and
measurements. Then, it covers parametrized quantum circuits, the variational
quantum eigensolver, as well as unsupervised and supervised quantum machine
learning formulations.
