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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
11#
發(fā)表于 2025-3-23 13:28:22 | 只看該作者
Potential Quantum Advantages,irst have a closer look at different criteria for a quantum advantage, and then consider data mining on coherent or “quantum data”. We finally summarise some future perspectives for quantum machine learning research as a concluding outlook.
12#
發(fā)表于 2025-3-23 14:51:40 | 只看該作者
13#
發(fā)表于 2025-3-23 18:53:30 | 只看該作者
14#
發(fā)表于 2025-3-23 23:10:45 | 只看該作者
15#
發(fā)表于 2025-3-24 04:21:21 | 只看該作者
16#
發(fā)表于 2025-3-24 07:09:00 | 只看該作者
Machine Learning,ell as the basic concepts of training, regularisation and generalisation. A number of important models—such as linear models, neural networks, probabilistic graphical models and kernel methods—are introduced as a foundation and reference for later chapters.
17#
發(fā)表于 2025-3-24 11:34:02 | 只看該作者
18#
發(fā)表于 2025-3-24 15:45:39 | 只看該作者
Representing Data on a Quantum Computer,etimes an entire dataset, can be encoded into quantum states. We present quantum routines for this task, discuss their runtimes and review their interpretation as feature maps known in classical machine learning.
19#
發(fā)表于 2025-3-24 20:23:02 | 只看該作者
20#
發(fā)表于 2025-3-25 00:19:43 | 只看該作者
Potential Quantum Advantages,irst have a closer look at different criteria for a quantum advantage, and then consider data mining on coherent or “quantum data”. We finally summarise some future perspectives for quantum machine learning research as a concluding outlook.
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