04120nam 22006135 450 99641843210331620200827153241.03-030-52715-810.1007/978-3-030-52715-0(CKB)4100000011401215(MiAaPQ)EBC6320911(DE-He213)978-3-030-52715-0(PPN)250215411(EXLCZ)99410000001140121520200827d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierNeural-Network Simulation of Strongly Correlated Quantum Systems[electronic resource] /by Stefanie Czischek1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (212 pages) illustrationsSpringer Theses, Recognizing Outstanding Ph.D. Research,2190-50533-030-52714-X Introduction -- Quantum Mechanics and Spin Systems -- Artificial Neural Networks -- Discrete Truncated Wigner Approximation -- BM-Based Wave Function Parametrization -- Deep Neural Networks and Phase Reweighting -- Towards Neuromorphic Sampling of Quantum States -- Conclusion.Quantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both.Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5053Quantum physicsMachine learningNeural networks (Computer science) Condensed matterQuantum Physicshttps://scigraph.springernature.com/ontologies/product-market-codes/P19080Machine Learninghttps://scigraph.springernature.com/ontologies/product-market-codes/I21010Mathematical Models of Cognitive Processes and Neural Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/M13100Condensed Matter Physicshttps://scigraph.springernature.com/ontologies/product-market-codes/P25005Quantum physics.Machine learning.Neural networks (Computer science) .Condensed matter.Quantum Physics.Machine Learning.Mathematical Models of Cognitive Processes and Neural Networks.Condensed Matter Physics.530.12Czischek Stefanieauthttp://id.loc.gov/vocabulary/relators/aut843414MiAaPQMiAaPQMiAaPQBOOK996418432103316Neural-Network Simulation of Strongly Correlated Quantum Systems1882031UNISA