Quantum Annealing for Combinatorial Optimization of Neural Network Training

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper investigates the application of quantum annealing to the combinatorial optimization problem inherent in neural network training, specifically focusing on hyperparameter tuning. Traditional methods for hyperparameter optimization, such as grid search and random search, can be computationally expensive and often fail to efficiently explore the vast hyperparameter space. We propose a novel approach that maps the hyperparameter optimization task onto a quantum annealing landscape. The quantum annealer, leveraging quantum tunneling, is then utilized to navigate this landscape and identify optimal hyperparameter values. This approach aims to accelerate the training process and enhance the performance of neural networks. The core concept involves formulating the hyperparameter tuning problem as an Ising model, which can be directly implemented on quantum annealing hardware. We explore the theoretical underpinnings of this mapping and discuss the potential advantages and limitations of this method. The results, although not based on experimental validation due to the nature of the research, demonstrate a promising trajectory for integrating quantum computing with machine learning. Further research and development are required to fully realize the potential of this approach.

DOI: 10.5281/zenodo.22273235

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