Quantum-Driven Optimization of Neural Network Architecture

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-Driven Optimization of Neural Network Architecture explores the potential of quantum algorithms to revolutionize neural network design. This paper proposes a novel approach utilizing quantum annealing and variational quantum eigensolver (VQE) to dynamically adjust neural network architecture, moving beyond traditional heuristic methods. The core claim is the development of a quantum-guided optimization process that iteratively refines the network's structure – specifically, layer count and connection weights – to maximize performance on a given task. We detail the algorithm's design, the rationale behind its quantum-enhanced exploration, and the potential benefits compared to current approaches. This work represents a significant advancement in neural network architecture design, leveraging the unique capabilities of quantum computing.

DOI: 10.5281/zenodo.22270960

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