Gradient based optimization of Chaogates

Abstract

We present a method for configuring Chaogates to replicate standard Boolean logic gate behavior using gradient-based optimization. By defining a differentiable formulation of the Chaogate encoding, we optimize its tunable parameters to reconfigure the Chaogate for standard logic gate functions. This novel approach allows us to bring the well established tools of machine learning to optimizing Chaogates without the cost of high parameter count neural networks. We further extend this approach to the simultaneous optimization of multiple gates for tuning logic circuits. Experimental results demonstrate the viability of this technique across different nonlinear systems and configurations, offering a pathway to automate parameter discovery for nonlinear computational devices.