Assessing Quantum Annealing to Solve the Minimum Vertex Multicut

Abstract

Cybersecurity in telecommunication networks often leads to hard combinatorial optimization problems that are challenging to solve with classical methods. This work investigates the practical feasibility of using quantum annealing to address the Restricted Vertex Minimum Multicut Problem. The problem is formulated as a Quadratic Unconstrained Binary Optimization model and implemented on D-Wave s quantum annealer. Rather than focusing on solution quality alone, we analyze key aspects of the quantum workflow including minor embedding techniques, chain length, topology constraints, chain strength selection, unembedding procedures, and postprocessing. Our results show that quantum annealing faces substantial hardware-level constraints limitations in embedding and scalability, especially for large instances, while hybrid quantum-classical solvers provide improved feasibility. This study offers a realistic assessment of the D-Wave system s current capabilities and identifies crucial parameters that govern the success of quantum optimization in cybersecurity-related network problems.