##基于多尺度自组织神经网络的量子算法优化

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper explores a novel approach to quantum algorithm optimization by leveraging the inherent capabilities of multi-scale self-organizing neural networks (MSSONNs). The core idea is to transform the optimization of quantum algorithms into a training problem for MSSONNs, allowing the network to autonomously discover optimal structures and parameters. We propose a framework where the quantum algorithm's objective function is mapped onto the MSSONN's loss function. Through iterative training, the MSSONN adapts and evolves, effectively automating the design and refinement of quantum algorithms. This work demonstrates the potential for significantly reducing the manual effort involved in quantum algorithm development, accelerating the discovery of efficient solutions, and ultimately unlocking the full potential of quantum computation. The key contribution lies in the synergistic combination of neural network self-organization and quantum algorithm optimization, leading to an adaptive and automated process. ---

DOI: 10.5281/zenodo.22137874

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