Optimisation challenge for superconducting adiabatic neural network implementing XOR and OR boolean functions

Autor:	Pashin, D. S., Bastrakova, M. V., Rybin, D. A., Soloviev, I. I., Schegolev, A. E., Klenov, N. V.
Rok vydání:	2024
Předmět:	Condensed Matter - Superconductivity Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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 the system implementing XOR and OR logical operations. Comment: 13 pages, 12 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.03521 Zobrazit plný text záznamu View this record from Arxiv