Quantum Annealing-Driven Deep Learning Model Architecture Design

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper proposes a novel approach to deep learning model architecture design by leveraging the principles of quantum annealing. The core idea is to map the complex search space of deep learning architectures to the energy landscape of a quantum annealing system. The quantum annealing algorithm is then employed to explore this landscape, iteratively minimizing the energy and ultimately identifying a globally optimal or near-optimal architecture. We introduce a framework integrating deep learning architecture search with quantum annealing, addressing the limitations of classical optimization methods prone to getting trapped in local minima. The method's effectiveness is demonstrated conceptually through a detailed architecture design process, highlighting the potential for accelerated discovery of superior models. The key contributions include a methodology for translating architectural parameters into a quantum annealing problem formulation and a strategy for interpreting the resulting quantum annealing solution as a viable deep learning architecture. Further research will focus on optimizing the annealing process and exploring different quantum annealing hardware platforms.

DOI: 10.5281/zenodo.22154774

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