基于生物神经网络的量子算法优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper proposes a novel approach to quantum algorithm optimization by integrating the structural and functional principles of biological neural networks with quantum computation. The core idea is to leverage the inherent parallelism and adaptive learning capabilities of biological neural networks to refine the parameters of quantum algorithms, thereby enhancing both their efficiency and stability. We introduce a framework where a neural network acts as a dynamic optimizer, continuously adjusting the quantum algorithm's parameters based on feedback, mimicking the learning process observed in biological systems. The theoretical foundation relies on mapping quantum operations to neural network connections and utilizing activation functions to represent the probabilistic nature of quantum measurements. This synergistic combination aims to overcome some of the limitations associated with traditional quantum algorithm optimization techniques, particularly in scenarios with complex parameter landscapes. The proposed method offers a potentially more robust and adaptable solution for optimizing quantum algorithms across diverse applications. We explore the fundamental concepts and outline the potential benefits of this hybrid approach, emphasizing its ability to bridge the gap between classical and quantum computing.

DOI: 10.5281/zenodo.22268522

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