Dynamic Self-Organizing Particle Networks for Quantum Topology Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum topology optimization is a challenging field requiring sophisticated algorithms to explore complex multi-dimensional spaces. This paper introduces a novel dynamic self-organizing particle network approach to quantum topology optimization, aiming to overcome limitations of traditional methods. We present a new algorithm that leverages the inherent dynamics of particle networks to efficiently search for optimal solutions. This approach offers improved path finding and stability compared to existing methods, particularly in scenarios involving intricate topology. The core mechanism centers on the self-organizing process, which dynamically adapts to the problem's structure, enabling the discovery of more effective solutions. This innovation addresses a critical challenge in quantum topology optimization, offering a promising path towards increased computational efficiency and robustness.

DOI: 10.5281/zenodo.22266406

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