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Titlebook: Concise Guide to Quantum Machine Learning; Davide Pastorello Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive li

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發(fā)表于 2025-3-21 18:25:48 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Concise Guide to Quantum Machine Learning
編輯Davide Pastorello
視頻videohttp://file.papertrans.cn/236/235112/235112.mp4
概述Offers a brief but effective introduction to quantum machine learning.Reviews those quantum algorithms most relevant to machine learning.Does not require a background in quantum computing or machine l
叢書名稱Machine Learning: Foundations, Methodologies, and Applications
圖書封面Titlebook: Concise Guide to Quantum Machine Learning;  Davide Pastorello Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive li
描述.This book offers a brief but effective introduction to quantum machine learning (QML). QML is not merely a translation of classical machine learning techniques into the language of quantum computing, but rather a new approach to data representation and processing. Accordingly, the content is not divided into a “classical part” that describes standard machine learning schemes and a “quantum part” that addresses their quantum counterparts. Instead, to immerse the reader in the quantum realm from the outset, the book starts from fundamental notions of quantum mechanics and quantum computing. Avoiding unnecessary details, it presents the concepts and mathematical tools that are essential for the required quantum formalism. In turn, it reviews those quantum algorithms most relevant to machine learning. Later chapters highlight the latest advances in this field and discuss the most promising directions for future research...To gain the most from this book, a basic grasp of statistics and linear algebra is sufficient; no previous experience with quantum computing or machine learning is needed. The book is aimed at researchers and students with no background in quantum physics and is also
出版日期Book 2023
關鍵詞Quantum Computing; Quantum Neural Networks; Quantum Annealing; Machine Leaning; Quantum Algorithms
版次1
doihttps://doi.org/10.1007/978-981-19-6897-6
isbn_softcover978-981-19-6899-0
isbn_ebook978-981-19-6897-6Series ISSN 2730-9908 Series E-ISSN 2730-9916
issn_series 2730-9908
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 20:53:05 | 只看該作者
板凳
發(fā)表于 2025-3-22 04:00:06 | 只看該作者
Quantum Pattern Recognition,In this chapter we introduce the quantum implementation of an associative memory based on a modification of the Grover algorithm. Then we review the application of the quantum Fourier transform to pattern recognistion and an adiabatic algorithm to retrieve binary patterns from a quantum memory.
地板
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Giuseppe Spolaore,Pierdaniele Giarettas. It is the most prominent application of quantum information theory and delivers algorithms to solve efficiently some problems which are hard for classical computers. This chapter is focused on the fundamentals of quantum computing like the abstract notion of a universal quantum computer and the c
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發(fā)表于 2025-3-22 20:22:19 | 只看該作者
Giuseppe Spolaore,Pierdaniele Giarettag schemes. In particular, the quantum Fourier transform is a quantum implementation of the discrete Fourier transform [Co94], Grover’s algorithm and amplitude amplification are quantum search algorithms in an unsorted database [Gr96, BH97], the phase estimation algorithm allows to estimate the eigen
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https://doi.org/10.1007/978-3-030-82700-7labelled (classified) data. In this section we introduce some quantum algorithms to make predictions on labels of previously unseen data instances: two examples of quantum distance based classifiers, a quantum versions of the k-nearest neighbors algorithm, and the quantum support vector machine. Mor
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