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Titlebook: Machine Learning with Quantum Computers; Maria Schuld,Francesco Petruccione Book 2021Latest edition The Editor(s) (if applicable) and The

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樓主: aspirant
21#
發(fā)表于 2025-3-25 05:08:44 | 只看該作者
https://doi.org/10.1007/978-3-030-83098-4Hidden Markov Models; Deep Belief Network; Grover Search; Hopfield Model; Artificial Neural Network; Qsam
22#
發(fā)表于 2025-3-25 11:16:21 | 只看該作者
23#
發(fā)表于 2025-3-25 14:46:38 | 只看該作者
24#
發(fā)表于 2025-3-25 17:33:37 | 只看該作者
Variational Circuits as Machine Learning Models,We explain how parametrised quantum circuits—quantum algorithms that are popular in near-term quantum computing—can be used as machine learning models, and review techniques to analyse and train such quantum models in a deep-learning fashion, including measures of expressivity and trainability, as well as parameter-shift rules.
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發(fā)表于 2025-3-25 23:59:24 | 只看該作者
26#
發(fā)表于 2025-3-26 01:39:46 | 只看該作者
978-3-030-83100-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
27#
發(fā)表于 2025-3-26 06:23:32 | 只看該作者
Machine Learning with Quantum Computers978-3-030-83098-4Series ISSN 2364-9054 Series E-ISSN 2364-9062
28#
發(fā)表于 2025-3-26 11:58:56 | 只看該作者
2364-9054 me in the literature.Focuses on the developments in near-ter.This book offers an introduction into quantum machine learning research,?covering approaches that range from "near-term"?to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us under
29#
發(fā)表于 2025-3-26 14:53:53 | 只看該作者
30#
發(fā)表于 2025-3-26 18:36:44 | 只看該作者
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