Quantum-Inspired Swarm Intelligence for Dynamic Resource Allocation in Edge Computing Environments

Posted: May 20, 2024

• Dr. Elena Rodriguez
• Prof. Kenji Tanaka
• Dr. Maria Chen

Abstract

This paper introduces a novel hybrid optimization framework that combines quantum computing principles with swarm intelligence algorithms to address dynamic resource allocation in edge computing environments. Unlike traditional approaches that rely on classical optimization techniques, our method leverages quantum superposition and entanglement concepts to enhance particle swarm optimization, enabling more efficient exploration of complex solution spaces. We demonstrate that our Quantum-Inspired Swarm Optimization (QISO) algorithm achieves 37% faster convergence and 42% better resource utilization compared to conventional methods in dynamic edge computing scenarios. The framework incorporates quantum bit representation for solution encoding and quantum rotation gates for position updates, providing a fundamentally different approach to resource management in distributed systems.