Quantum-Driven Optimization of Parameter Space for Neural Network Architecture

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum-driven optimization of parameter space for neural network architecture seeks to enhance model performance through automated exploration of the network's structure. This paper presents a novel approach utilizing quantum annealing to efficiently search the parameter space of neural networks, identifying configurations with superior performance. The core mechanism leverages quantum annealing's ability to escape local optima and navigate complex landscapes, resulting in improved model accuracy and generalization. The research demonstrates the potential of this technique to significantly boost neural network capabilities.

DOI: 10.5281/zenodo.22219146

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