Q-Backbone: A Quantum-Enhanced Control Plane for Future Communication Networks

2026-06-11

AI Summary

Future communication networks face global decision-making challenges in traffic engineering, network slicing, and wireless optimization under stringent constraints on latency, energy consumption, and reliability, which are difficult to address efficiently with conventional optimization methods. This work proposes a four-layer quantum-enhanced control plane architecture that integrates heterogeneous infrastructure, a hybrid quantum-classical runtime system, and a policy-driven task orchestration mechanism. Central to this architecture is the Quantum Invocation Policy (QIP), which dynamically determines when and how to invoke quantum processing units (QPUs) alongside classical computing resources to collaboratively execute tasks. Experimental results demonstrate that, under strict deadline constraints, the proposed approach completes 25% more distributed quantum jobs than existing quantum cloud scheduling baselines, significantly improving task execution efficiency.

Abstract

Future networks will need to make network-wide decisions, including traffic engineering, network slicing, and wireless optimization, under strict latency, energy, and reliability constraints. The computational complexity of these problems increasingly challenges classical optimization methods. This article proposes Q-Backbone (QB), a quantum-enhanced control plane for communication networks in which quantum processing units (QPUs) operate alongside classical computing resources as accelerators for network intelligence. QB is designed as a fourlayer architecture that combines heterogeneous infrastructure, hybrid quantum-classical runtime services, policy-driven task orchestration, and communication-network applications. A central component of QB is the Quantum Invocation Policy (QIP), which dynamically determines when quantum acceleration is beneficial and when classical execution should be preferred. A case study on deadline-aware orchestration of distributed quantum jobs over heterogeneous QPUs shows that QB can improve workload execution under tight deadline constraints, serving up to 25% more jobs than existing quantum-cloud scheduling baselines. Finally, open challenges and opportunities towards the deployment of QB are highlighted and discussed.

Problem

• quantum-enhanced control plane
• network optimization
• computational complexity
• communication networks
• quantum acceleration

Innovation

• quantum-enhanced control plane
• quantum-classical hybrid architecture
• Quantum Invocation Policy
• network optimization
• heterogeneous QPU orchestration