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Books > Science & Mathematics > Physics > Quantum physics (quantum mechanics)
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Neural-Network Simulation of Strongly Correlated Quantum Systems (Paperback, 1st ed. 2020)
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Neural-Network Simulation of Strongly Correlated Quantum Systems (Paperback, 1st ed. 2020)
Series: Springer Theses
Expected to ship within 10 - 15 working days
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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.
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