An Introduction to Quantum Machine Learning for Engineers. (arXiv:2205.09510v1 [quant-ph])

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 …

arxiv engineers introduction learning machine machine learning quantum