| 书目名称 | Neural-Network Simulation of Strongly Correlated Quantum Systems | | 编辑 | Stefanie Czischek | | 视频video | | | 概述 | Nominated as an outstanding Ph.D. thesis by the Heidelberg University, Heidelberg, Germany.General introduction to quantum many-body physics and artificial neural networks.Deep discussions of simulati | | 丛书名称 | Springer Theses | | 图书封面 |  | | 描述 | .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. | | 出版日期 | Book 2020 | | 关键词 | Quantum Many-Body Systems; Quantum Machine Learning; Artificial Neural Networks; Neural-Network Quantum | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-52715-0 | | isbn_softcover | 978-3-030-52717-4 | | isbn_ebook | 978-3-030-52715-0Series ISSN 2190-5053 Series E-ISSN 2190-5061 | | issn_series | 2190-5053 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 17:39:32
