Quantum-Inspired Resource Allocation in Distributed Systems

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper presents a novel approach to resource allocation within distributed systems, drawing inspiration from the principles of quantum annealing and quantum tunneling. Traditional resource allocation techniques often rely on classical optimization algorithms, which can struggle with the inherent complexity and scale of modern distributed environments. We propose a framework that models resource allocation as a quantum optimization problem, specifically utilizing simulated quantum annealing (SQA) to navigate the solution space. The core idea is to translate the resource allocation challenge into an equivalent quantum problem, where the probabilistic nature of quantum mechanics can accelerate the search for optimal solutions. We define a mathematical model incorporating key elements such as node heterogeneity, task demands, and resource constraints. The simulation of SQA provides a computationally efficient means to explore a wider range of potential solutions compared to deterministic classical algorithms. The effectiveness of this approach is demonstrated through theoretical analysis and simulation results, highlighting its potential to improve resource utilization and system performance in distributed systems. Furthermore, we explore the limitations of the SQA simulation and discuss potential extensions for more accurate quantum emulation.

DOI: 10.5281/zenodo.22221652

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