Deep neural networks classifying transfer efficiency in complex networks

Year
2020
Type(s)
Author(s)
Alexey A. Melnikov, Leonid E. Fedichkin, Ray-Kuang Lee, Alexander Alodjants
Source
IEEE 2020 Opto-Electronics and Communications Conference, pp. 1-3 (2020)
Url(s)
https://doi.org/10.1109/OECC48412.2020.9273550
BibTeX
BibTeX

Transport of particles in networks is a broad interdisciplinary scientific field. Efficient transport of particles is of practical importance for communication tasks. To improve the efficiency of the transport, the quantum phenomenon of interference is of relevance. A particle that is transferred through a network interferes with itself, allowing for transfer properties that are different from non-quantum, classical, dynamics. However, it is not known in which networks quantum properties provide an advantage. It was shown in Melnikov et al. [New J. Phys., Vol. 21, 125002, 2019] and [Adv. Quantum Technol., Vol. 3, 1900115, 2020] that convolutional neural networks are well-suited for classifying which complex networks can have a fast quantum transport. Here we discuss these results and applicability of the approach.

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