# Solving large Minimum Vertex Cover problems on a quantum annealer

arXiv: https://arxiv.org/abs/1904.00051 · HTML: https://mlsanity.com/paper/1904.00051/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [Quantum-Assisted Support Vector Regression](https://mlsanity.com/paper/2111.09304/) (2021)

## Abstract (arXiv)

We consider the minimum vertex cover problem having applications in e.g. biochemistry and network security. Quantum annealers can find the optimum solution of such NP-hard problems, given they can be embedded on the hardware. This is often infeasible due to limitations of the hardware connectivity structure. This paper presents a decomposition algorithm for the minimum vertex cover problem: The algorithm recursively divides an arbitrary problem until the generated subproblems can be embedded and solved on the annealer. To speed up the decomposition, we propose several pruning and reduction techniques. The performance of our algorithm is assessed in a simulation study.