Quantum-Driven Bayesian Optimization of Neural Network Architecture

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum-driven Bayesian optimization (QBO) presents a novel approach to neural network architecture search, employing quantum annealing or QAOA to efficiently explore the vast design space. This research investigates the potential of quantum algorithms to accelerate the discovery of optimal neural network architectures, addressing limitations of traditional methods like grid search. The core mechanism centers on utilizing quantum annealing to sample neural network designs, enabling a significantly broader exploration of potential architectures compared to classical methods. The study will analyze the impact of quantum optimization on the search process, focusing on speed and the ability to uncover unexpected architectural patterns. The paper will detail the implementation of a quantum algorithm, its performance compared to classical approaches, and the potential benefits of leveraging quantum mechanics for neural network design. This work aims to provide a fundamental understanding of the application of quantum optimization to neural network architecture discovery.

DOI: 10.5281/zenodo.22510034

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