CITATION.cff

cff-version: 1.2.0
title: Gradient based Optimization of Chaogates
message: Please cite this software using these metadata.
type: software
authors:
  - given-names: Anil
    family-names: Radhakrishnan
    email: aradhak5@ncsu.edu
    affiliation: North Carolina State University
    orcid: 'https://orcid.org/0000-0002-8084-9527'
  - given-names: Sudeshna
    family-names: Sinha
    email: sudeshna@iisermohali.ac.in
    affiliation: >-
      Indian Institute of Science Education and Research
      Mohali
    orcid: 'https://orcid.org/0000-0002-1364-5276'
  - given-names: Krishna
    family-names: Murali
    email: kmurali@annauniv.edu
    affiliation: >-
      Department of Physics, Anna University,
      Chennai 600025, India
    orcid: 'https://orcid.org/0000-0001-8055-1117'
  - given-names: William
    name-particle: L
    family-names: Ditto
    email: wditto@ncsu.edu
    affiliation: North Carolina State Universty
    orcid: 'https://orcid.org/0000-0002-7416-8012'
repository-code: 'https://github.com/NonlinearArtificialIntelligenceLab/ChaoGateNN'
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.
keywords:
  - Machine Learning
  - Nonlinear
  - chaogate
  - optimization
license: MIT