An Introduction to Quantum Machine Learning for Engineers

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1-223
Number of pages223
JournalFoundations and Trends in Signal Processing
Volume16
Issue number1-2
DOIs
StatePublished - Jul 27 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing

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