01562nam a22002417a 4500 20260714154943.0 260714s2024 |||||||| |||| 001 0 eng d 9783031442285 PK-LaUMT 006.31 CON-Q Conti, Claudio 13660 Quantum machine learning : thinking and exploration in neural network models for quantum science and quantum computing / Claudio Conti Cham : Springer, 2024 xxiii, 378 p. Quantum science and technology Includes bibliographical references and index. This 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. Eng Machine learning Neural networking 13661 Quantum computing 8569 BK 141425 141425 0 0 0 0 01 01 2026-06-13 0 006.31 CON-Q 153358 2026-07-14 15:43:26 2026-07-14 BK