Tactile Network Resource Allocation enabled by Quantum Annealing based on ILP Modeling

TL;DR

This paper explores using quantum annealing to solve network resource allocation problems modeled as ILPs, demonstrating feasibility on small networks and estimating hardware needs for larger systems.

Contribution

It introduces a method to map network ILP models to QUBO problems suitable for quantum annealers and evaluates the approach on a three-node network.

Findings

Feasible solutions close to classical ILP solutions were obtained.

QUBO embedding was achieved for networks up to 6 nodes.

Hardware requirements for larger networks were estimated.

Abstract

Agile networks with fast adaptation and reconfiguration capabilities are required for on-demand provisioning of various network services. We propose a new methodical framework for short-time network optimization based on quantum computing (QC) and integer linear program (ILP) models, which has the potential of realizing a real-time network automation. We define methods to map a nearly real-world ILP model for resource provisioning to a quadratic unconstrained binary optimization (QUBO) problem, which is solvable on quantum annealer (QA). We concentrate on the three-node network to evaluate our approach and its obtainable quality of solution using the state-of-the-art quantum annealer D-Wave Advantage 5.2/5.3. By studying the annealing process, we find annealing configuration parameters that obtain feasible solutions close to the reference solution generated by the classical ILP-solver CPLEX. Further, we studied the scaling of the network problem and provide estimations on quantum annealer's hardware requirements to enable a proper QUBO problem embedding of larger networks. We achieved the QUBO embedding of networks with up to 6 nodes on the D-Wave Advantage. According to our estimates a real-sized network with 12 to 16 nodes require a QA hardware with at least 50000 qubits or more.