Quantum-Driven Constraint Optimization for Neural Networks

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper investigates the potential of quantum-enhanced constraint optimization to accelerate the training and improve generalization of neural networks. Traditional optimization methods often struggle with the complexity of large neural networks, requiring significant computational resources and time. We propose a novel quantum algorithm, employing quantum annealing or quantum-enhanced gradient descent, to dynamically adjust the weights of the neural network. The core claim is that this approach can significantly reduce training time and improve the model's ability to generalize to unseen data. The paper details the theoretical framework, algorithm implementation, and preliminary experimental results demonstrating the potential benefits of this quantum-driven approach.

DOI: 10.5281/zenodo.22290471

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