000			a
999			_c29663 _d29663
008			191104b xxu||||| |||| 00| 0 eng d
020			_a9783319964232
082			_a530.1201514 _bSCH
100			_aSchuld, Maria
245			_aSupervised learning with quantum computers
260			_bSpringer _c2018 _aCham
300			_axiii, 287 p. _bill. _c24 cm.
365			_b119.99 _cEUR _d9863.18
490			_aQuantum science and technology.
520			_aQuantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
650			_aPattern recognition
650			_aQuantum physics
650			_aMathematical physics
650			_aArtificial intelligence
650			_aPhysics
650			_aComputers
650			_aComputer vision and pattern recognition
650			_aInformation technology
650			_aIntelligence and​ semantics
650			_aMathematical theory of computation
650			_aQuantum theory
650			_aMachine learning
650			_aData processing
650			_aOptical pattern recognition
650			_aQuantum computing
650			_aQuantum information technology, spintronics
650			_aNumerical and computational physics, simulation
942			_2ddc _cBK