000			a
999			_c33075 _d33075
008			240320b xxu||||| |||| 00| 0 eng d
020			_a9783030830977
082			_a006.3 _bSCH
100			_aSchuld, Maria
245			_aMachine learning with quantum computers
250			_a2nd ed.
260			_bSpringer, _c2021 _aCham :
300			_axiv, 312 p. ; _bill., _c25 cm.
365			_b119.99 _c€ _d93.50
490			_aQuantum science and technology
504			_aIncludes bibliographical references and index.
520			_aThis book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.
650			_aMachine Learning
650			_aQuantum Computing
650			_aHidden Markov Models;
650			_aDeep Belief Network
650			_aGrover Search
650			_aHopfield Model
650			_aArtificial Neural Network
650			_aQsample Encoding
650			_aDeutsch-Josza Algorithm
650			_aKernel Methods
650			_aQuantum BLAS
700			_aPetruccione, F.
942			_2ddc _cBK