thinking and exploration in neural network models for quantum science and quantum computing Conti, Claudio creator text Cham Springer 2024 monographic eng

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xxiii, 378 p. 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. Claudio Conti Includes bibliographical references and index. Eng Machine learning Neural networking Quantum computing 006.31 CON-Q 9783031442285 260714 20260714154943.0