Efficient and Accurate Estimation of Lipschitz Constants for Hybrid Quantum-Classical Decision Models

In this paper, we propose a novel framework for efficiently and accurately estimating Lipschitz constants in hybrid quantum-classical decision models. Our approach integrates classical neural network with quantum variational circuits to address critical issues in learning theory such as fairness verification, robust training, and generalization.By a unified convex optimization formulation, we extend existing classical methods to capture the interplay between classical and quantum layers. This integrated strategy not only provide a tight bound on the Lipschitz constant but also improves computational efficiency with respect to the previous methods.

@article{hashemian2025_2503.07992,
  title={ Efficient and Accurate Estimation of Lipschitz Constants for Hybrid Quantum-Classical Decision Models },
  author={ Sajjad Hashemian and Mohammad Saeed Arvenaghi },
  journal={arXiv preprint arXiv:2503.07992},
  year={ 2025 }
}