Resource Allocation Based on Physical Models for Quantum Computing

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum computing holds immense potential for solving complex problems currently intractable for classical computers. However, realizing this potential necessitates efficient resource allocation within quantum computing systems. Traditional resource allocation approaches often lack a fundamental understanding of the physical constraints inherent in quantum computation, such as decoherence and the limitations of superposition. This paper explores a novel approach to quantum resource allocation that integrates physical models directly into the allocation strategy. We propose a framework where algorithms are designed to balance resource utilization with information fidelity, considering the underlying physics of quantum systems. The core claim is that utilizing physical models for resource allocation can optimize quantum computing performance and efficiency. This work introduces a mechanism based on quantum computing's physical properties – decoherence, superposition – to design resource allocation algorithms, aiming to achieve a better balance between resource utilization and information fidelity. We present a formalization of the resource allocation problem and outline a potential implementation strategy.

DOI: 10.5281/zenodo.22446489

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