Assessing Quantum Annealing to Solve the Minimum Vertex Multicut

TL;DR

This paper evaluates the practical use of quantum annealing on D-Wave hardware for solving the complex Restricted Vertex Minimum Multicut Problem in cybersecurity networks, highlighting hardware limitations and potential hybrid solutions.

Contribution

It provides a detailed analysis of quantum annealing's feasibility for a cybersecurity network problem, including implementation challenges and parameter insights.

Findings

Quantum annealing faces hardware embedding and scalability constraints.

Hybrid quantum-classical methods improve problem-solving feasibility.

Key parameters like chain length and strength significantly affect outcomes.

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.