Optical Routing with Binary Optimisation and Quantum Annealing

TL;DR

This work tackles the scalability of demand-driven optical routing in elastic DWDM/Flexgrid networks by formulating routing and spectrum-assignment problems as quadratic unconstrained binary optimization (QUBO) problems, solvable by quantum annealing. It presents two QUBO formulations—time-oriented and path-oriented—to compute resilient, latency-bounded multicast trees and extends them to routing and wavelength/spectrum assignment (RWA/RSA) in both unicast and multicast settings. Through experiments with a D-Wave Hybrid solver, the authors demonstrate feasible, if sometimes nonoptimal, solutions and discuss qubit-resource trade-offs and the influence of hyperparameters on solution quality. The results suggest that QUBO-based approaches can enable dynamic, resource-efficient optical networks with resilience against faults and cyber threats, while also outlining avenues for further benchmarking and solver-testing across problem classes and platforms.

Abstract

A challenge for scalability of demand-responsive, elastic optical Dense Wavelength Division Multiplexing (DWDM) and Flexgrid networks is the computational complexity of allocating many optical routes on large networks. We demonstrate that demand satisfaction problems in communication networks can be formulated as quadratic unconstrained binary optimisation (QUBO) problems, and solved using a hybrid quantum annealer. Efficient encodings are developed which solve both unicast and multicast multicommodity-flow problems, while also adhering to individual requirements for maximum latency and resilience for each route. We present several QUBO formulations and analyse the qubit scaling. We demonstrate solutions using a hybrid solver, D-Wave Quantum Advantage QPU. Progress in generating optimal solutions with efficient use of computational resources will be beneficial to telecoms operators, enabling them to run dynamic optical network infrastructures which use resources efficiently, are resilient to local faults and cyber-attacks, and can be elastically responsive to demands.

Figures (2)

• Figure 1: In this diagram, S are sources, T are sinks, Labels on edges indicate latency of the link (arb. units). Problem A (left) - Single multicast, the goal is to find two edge-disjoint multicast trees, with latency $\leq$ 20. Problem B (right) - Two simultaneous multicasts on non-planar graph, the goal is to find, for each, a pair of node-resilient multicast trees that connects each source to its respective sinks with latency $\leq$ 10.
• Figure 2: Solution quality for problems A and B of Figure \ref{['Toy problems solution']} using the Time and Path algorithms. Valid solutions are dark green.