Quantum-Enhanced Optimization of Neural Network Architecture

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum computing holds the potential to revolutionize various fields, and the field of neural networks is no exception. This research investigates the application of quantum optimization algorithms to enhance the design of neural network architectures, aiming to improve performance across specific tasks. We explore the feasibility of utilizing a novel quantum optimization technique to systematically explore network architectures and identify configurations that yield superior results. This work presents a theoretical framework and preliminary results demonstrating the potential for quantum acceleration in neural network architecture design. The core claim centers around the design of a quantum algorithm capable of efficiently navigating a complex space of possible network architectures, ultimately leading to optimized neural network configurations. The research will focus on identifying key architectural parameters and evaluating their impact on performance using benchmark datasets. This paper provides a foundational analysis of this approach and outlines potential avenues for future exploration.

DOI: 10.5281/zenodo.22482041

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