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008			221216s2023 si | o |||| 0|eng d
020			_a9789811968976
024	7		_a10.1007/978-981-19-6897-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.889 _b2023 EB
100	1		_aPastorello, Davide _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689427
245	1	0	_aConcise Guide to Quantum Machine Learning _cby Davide Pastorello
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_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.
988			_aSpringer_Computer_2023
650		7	_2embne _9166087 _aOrdenadores cuánticos
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-6897-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eb _zSI