Optimisation challenge for superconducting adiabatic neural network implementing XOR and OR boolean functions
This work addresses a domain-specific challenge in analog superconducting neural networks, presenting an incremental improvement for signal transmission optimization.
The authors tackled the problem of optimizing superconducting adiabatic neural networks for implementing XOR and OR boolean functions by developing a gradient descent method to adjust circuit parameters, achieving efficient signal transmission between layers as demonstrated in the system.
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.