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题名/责任者:

An introduction to quantum machine learning for engineers / Osvaldo Simeone.

出版发行项:

Boston : Now Publishers, [2022]

ISBN:

9781638280583

ISBN:

1638280584

载体形态项:

228 pages : illustrations (black and white) ; 24 cm.

丛编说明:

Foundations and trends in signal processing, 1932-8346 ; volume 16, issue 1-2

丛编说明:

Foundations and Trends庐 in Signal Processing

个人责任者:

Simeone, Osvaldo, author.

论题主题:

Quantum computing.

论题主题:

Signal processing.

中图法分类号:

TP385

一般附注:

"Now Publishers"

书目附注:

Includes bibliographical references (pages 227-228).

摘要附注:

This monograph is motivated by a number of recent developments that appear to define a possible new role for researchers with an engineering profile. First, there are now several software libraries - such as IBM's Qiskit, Google's Cirq, and Xanadu's PennyLane - that make programming quantum algorithms more accessible, while also providing cloud-based access to actual quantum computers. Second, a new framework is emerging for programming quantum algorithms to be run on current quantum hardware: quantum machine learning.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 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.

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