Quantum-Guided Optimization for Neural Network Architecture Design

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum-Guided Optimization (QGO) represents a novel approach to neural network architecture design, leveraging the principles of quantum mechanics to enhance exploration and feedback loops. This paper investigates the potential of QGO to improve the performance and efficiency of neural networks by intelligently guiding their structure through a quantum-inspired optimization process. We explore the fundamental concepts behind QGO, detailing the core mechanism of quantum-enhanced exploration and feedback, and presenting preliminary results demonstrating significant improvements across various benchmark datasets. The paper concludes with a discussion of the challenges and future directions for this promising field.

DOI: 10.5281/zenodo.22296898

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