000 | 03569nam a22005535i 4500 | ||
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001 | 978-981-19-6897-6 | ||
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_a9789811968976 _9978-981-19-6897-6 |
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_a10.1007/978-981-19-6897-6 _2doi |
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_aPastorello, Davide. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aX, 138 p. 12 illus., 5 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aMachine Learning: Foundations, Methodologies, and Applications, _x2730-9916 |
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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 |
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912 | _aZDB-2-SXCS | ||
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