Bio-Inspired Algorithms for Quantum Computing Resource Allocation

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper explores a novel approach to quantum computing resource allocation by leveraging principles derived from biological neural networks. The core idea centers on mimicking the resource competition and cooperation mechanisms observed in biological systems to optimize the distribution and scheduling of quantum computational resources. We propose a resource management system built upon adaptive competition and cooperation algorithms, modeled using neural networks to dynamically allocate quantum resources. The system's architecture incorporates elements like synaptic plasticity, neuronal firing rates, and signaling pathways to create a robust and efficient allocation strategy. This research demonstrates the potential for bio-inspired algorithms to significantly improve the efficiency and performance of quantum computing, addressing key challenges in scaling quantum systems. The proposed framework offers a promising path towards more intelligent and adaptive resource management in the evolving field of quantum computation.

DOI: 10.5281/zenodo.22589037

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