An Introduction to Quantum Machine Learning for Engineers
It provides an introductory resource for engineers to understand and apply QML, but it is incremental as it synthesizes existing concepts without new results.
The monograph introduces quantum machine learning (QML) as a paradigm for programming gate-based quantum computers in the NISQ era, covering parametrized quantum circuits and their applications in optimization, generative modeling, and inference.
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.