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

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 th...

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Bibliographic Details
Published in:Nanomaterials (Basel, Switzerland) Switzerland), 2024-05, Vol.14 (10), p.854
Main Authors: Pashin, Dmitrii S, Bastrakova, Marina V, Rybin, Dmitrii A, Soloviev, Igor I, Klenov, Nikolay V, Schegolev, Andrey E
Format: Article
Language:English
Subjects:
Adiabatic
Adiabatic flow
Algebra, Boolean
Analysis
Artificial intelligence
Artificial neural networks
Boolean functions
Cell activation
Energy efficiency
hardware implementation of neural networks
Methods
Neural networks
Neurons
nonlinear dynamics
optimisation procedure
Quantum computing
quantum parametron
Signal transmission
superconducting quantum interferometer
Superconductors
Citations: Items that this one cites
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Description
Summary: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.
ISSN:2079-4991
2079-4991
DOI:10.3390/nano14100854