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

Posted: Oct 28, 2025

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

Abstract

Edge computing has emerged as a critical paradigm for supporting latency-sensitive applications and reducing bandwidth consumption in distributed systems. However, the dynamic nature of edge environments, characterized by fluctuating resource demands and heterogeneous computing nodes, presents significant challenges for efficient resource allocation. Traditional optimization approaches often struggle with the combinatorial complexity and real-time requirements of these systems. This paper introduces a fundamentally new approach that bridges quantum computing principles with swarm intelligence to address these challenges. Our Quantum-Inspired Swarm Optimization (QISO) framework represents a departure from conventional methods by incorporating quantum mechanical concepts into the optimization process. The novelty of our work lies in three key aspects: (1) the application of quantum superposition to maintain multiple potential solutions simultaneously, (2) the use of quantum entanglement to model complex relationships between resources, and (3) the integration of quantum rotation gates for dynamic solution space exploration.