Quantum-Inspired Optimization for Neural Network Training

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum-inspired optimization offers a promising approach to accelerate and stabilize neural network training, addressing the limitations of classical optimization methods. This paper explores the implementation of a novel quantum-inspired technique, incorporating quantum annealing and enhanced gradient descent, to enhance the learning process and mitigate overfitting. We provide a detailed examination of the theoretical underpinnings, experimental results, and potential applications of this approach. The core claim centers around achieving significant improvements in training speed and model robustness compared to traditional optimization methods. We present a comprehensive analysis of the algorithm's effectiveness, highlighting its advantages and limitations.

DOI: 10.5281/zenodo.22156771

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