000			01562nam a22002417a 4500
005			20260714154943.0
008			260714s2024 |||||||| |||| 001 0 eng d
020			_a9783031442285
040			_cPK-LaUMT
082			_a006.31 _bCON-Q
100	1		_aConti, Claudio _913660
245	1	0	_aQuantum machine learning : _bthinking and exploration in neural network models for quantum science and quantum computing / _cClaudio Conti
260			_aCham : _bSpringer, _c2024
300			_axxiii, 378 p.
490			_aQuantum science and technology
500			_aIncludes bibliographical references and index.
520			_aThis book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits' performance.
546			_aEng
650			_aMachine learning
650			_aNeural networking _913661
650			_aQuantum computing _98569
942			_cBK
999			_c141425 _d141425