Quantum Walk-Based Optimization of Neural Network Architecture

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper proposes a novel approach to neural network architecture search (NAS) leveraging the principles of quantum walks. Traditional NAS methods suffer from significant computational bottlenecks, limiting their scalability and practicality. We introduce a framework where quantum walks are employed to efficiently explore the vast architectural search space. The core idea is to map the architectural choices – such as the number of layers, the type of activation functions, and the connections between neurons – to a quantum state, allowing for parallel exploration guided by the inherent probabilistic nature of quantum walks. The algorithm iteratively evolves the quantum state, favoring architectures that maximize performance metrics (e.g., accuracy, inference time) while minimizing computational complexity. This approach offers the potential to dramatically accelerate the NAS process, enabling the discovery of high-performing neural network architectures with reduced computational cost. The theoretical framework is presented, along with a detailed explanation of the quantum walk implementation and its convergence properties. We demonstrate the feasibility and potential of this method through a series of simulations, showcasing its ability to outperform conventional NAS techniques in terms of both speed and architectural quality.

DOI: 10.5281/zenodo.22138534

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