Open Access

Nanomaterials, volume 14, issue 10, pages 854

Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions

Dmitrii Pashin ¹

Marina V. Bastrakova ^{1, 2}

Dmitrii A. Rybin ¹

Igor I. Soloviev ^{2, 3, 4}

Nikolay V. Klenov ^{4, 5}

Andrey E. Schegolev ^{3, 6}

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Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia |

Russian Quantum Centre, 143025 Moscow, Russia |

Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia |

⁴

National University of Science and Technology MISIS, 119049 Moscow, Russia |

⁵

Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia |

⁶

Science Department, Moscow Technical University of Communication and Informatics (MTUCI), 111024 Moscow, Russia |

Publication type: Journal Article

Publication date: 2024-05-14

MDPI

Journal: Nanomaterials

scimago Q1

SJR: 0.798

CiteScore: 8.5

Impact factor: 4.4

ISSN: 20794991

DOI: 10.3390/nano14100854

Copy DOI

PubMed ID: 38786810

Abstract

In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between the network layers. The proposed solution is demonstrated on the example of a system that implements XOR and OR logical operations.

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Publisher

MDPI

Journal

Nanomaterials

scimago Q1

SJR

0.798

CiteScore

8.5

Impact factor

4.4

ISSN

20794991 (Electronic)

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Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions

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