Quantum Approaches to the Minimum Edge Multiway Cut Problem

TL;DR

This paper compares three quantum computing paradigms—quantum annealing, photonic variational circuits, and gate-based QAOA—for solving the minimum edge multiway cut problem, revealing current scalability and feasibility differences.

Contribution

It benchmarks quantum approaches for the problem, providing insights into their relative performance and practical limitations in early-stage quantum optimization.

Findings

Quantum annealing shows the best scalability among tested methods.

Photonic and gate-based approaches are limited by hardware and simulation depth.

Results inform quantum workflow design for telecom network resilience.

Abstract

We investigate the minimum edge multiway cut problem, a fundamental task in evaluating the resilience of telecommunication networks. This study benchmarks the problem across three quantum computing paradigms: quantum annealing on a D-Wave quantum processing unit, photonic variational quantum circuits simulated on Quandela s Perceval platform, and IBM s gate-based Quantum Approximate Optimization Algorithm (QAOA). We assess the comparative feasibility of these approaches for early-stage quantum optimization, highlighting trade-offs in circuit constraints, encoding overhead, and scalability. Our findings suggest that quantum annealing currently offers the most scalable performance for this class of problems, while photonic and gate-based approaches remain limited by hardware and simulation depth. These results provide actionable insights for designing quantum workflows targeting combinatorial optimization in telecom security and resilience analysis.