机读格式显示(MARC)

• 000 02653cam a2200337 i 4500

• 001 0000827331

• 003 CAL

• 005 20250816203700.0

• 008 220803s2022 maua b 000 0 eng d

• 020 __ |a 9781638280583

• 020 __ |a 1638280584

• 035 __ |a (OCoLC)1338131598

• 040 __ |a YDX |b eng |c YDX |d UKMGB |d BDX |d OCLCQ |d OCLCO

• 082 04 |a 004.1 |q OCoLC |2 23/eng/20220826

• 090 __ |a TP385/X1

• 093 __ |a TP385 |2 5

• 099 __ |a CAL 022024018374

• 100 1_ |a Simeone, Osvaldo, |e author.

• 245 13 |a An introduction to quantum machine learning for engineers / |c Osvaldo Simeone.

• 260 __ |a Boston : |b Now Publishers, |c [2022]

• 300 __ |a 228 pages : |b illustrations (black and white) ; |c 24 cm.

• 336 __ |a text |2 rdacontent

• 337 __ |a unmediated |2 rdamedia

• 338 __ |a volume |2 rdacarrier

• 490 0_ |a Foundations and trends in signal processing, |x 1932-8346 ; |v volume 16, issue 1-2

• 490 0_ |a Foundations and Trends庐 in Signal Processing

• 500 __ |a "Now Publishers"

• 504 __ |a Includes bibliographical references (pages 227-228).

• 520 __ |a 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.

• 650 _0 |a Quantum computing.

• 650 _0 |a Signal processing.