TY - JOUR
T1 - An Introduction to Quantum Machine Learning for Engineers
AU - Simeone, Osvaldo
N1 - Funding Information:
Finally, I would like to gratefully acknowledge the support of the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 725731).
Funding Information:
I would like to thank Prof. Lajos Hanzo for the inspiration, as well as Prof. Bipin Rajendran, Dr. Hari Chittoor, Dr. Sharu Jose, and Dr. Ivana Nikoloska, who have been ideal companions during my ongoing journey of discovery of the field of quantum machine learning. My gratitude goes also to the other members of my research team at King's who have provided useful feedback, comments, and encouragement: Kfir Cohen, Dr. Sangwoo Park, Clement Ruah, and Matteo Zecchin. Finally, I would like to gratefully acknowledge the support of the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (Grant Agreement No. 725731).
Publisher Copyright:
© 2022 O. Simeone.
PY - 2022/7/27
Y1 - 2022/7/27
N2 - 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 parameterized, and the parameters are tuned via classical optimization based on data and on measurements of the outputs of the circuit. Parameterized 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 parameterized quantum circuits, the variational quantum eigensolver, as well as unsupervised and supervised quantum machine learning formulations.
AB - 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 parameterized, and the parameters are tuned via classical optimization based on data and on measurements of the outputs of the circuit. Parameterized 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 parameterized quantum circuits, the variational quantum eigensolver, as well as unsupervised and supervised quantum machine learning formulations.
UR - http://www.scopus.com/inward/record.url?scp=85135382350&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135382350&partnerID=8YFLogxK
U2 - 10.1561/2000000118
DO - 10.1561/2000000118
M3 - Article
AN - SCOPUS:85135382350
SN - 1932-8346
VL - 16
SP - 1
EP - 223
JO - Foundations and Trends in Signal Processing
JF - Foundations and Trends in Signal Processing
IS - 1-2
ER -