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020 _a9789811968976
_9978-981-19-6897-6
024 7 _a10.1007/978-981-19-6897-6
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
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082 0 4 _a006.3
_223
100 1 _aPastorello, Davide.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aConcise Guide to Quantum Machine Learning
_h[electronic resource] /
_cby Davide Pastorello.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aX, 138 p. 12 illus., 5 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
505 0 _aChapter 1: Introduction -- Chapter 2: Basics of Quantum Mechanics -- Chapter 3: Basics of Quantum Computing -- Chapter 4: Relevant Quantum Algorithms -- Chapter 5: QML Toolkit -- Chapter 6: Quantum Clustering -- Chapter 7: Quantum Classification -- Chapter 8: Quantum Pattern Recognition -- Chapter 9: Quantum Neural Networks -- Chapter 10: Concluding Remarks.
520 _aThis 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 suitable for physicists looking to enter the field of QML.
650 0 _aArtificial intelligence.
650 0 _aMachine learning.
650 0 _aQuantum computers.
650 1 4 _aArtificial Intelligence.
650 2 4 _aMachine Learning.
650 2 4 _aQuantum Computing.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811968969
776 0 8 _iPrinted edition:
_z9789811968983
776 0 8 _iPrinted edition:
_z9789811968990
830 0 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
856 4 0 _uhttps://doi.org/10.1007/978-981-19-6897-6
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c173435
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