# Algorithm engineering for a quantum annealing platform

arXiv: https://arxiv.org/abs/1410.2628 · HTML: https://mlsanity.com/paper/1410.2628/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2014.

## Built on by (most cited first, 3 of 3)

- [Adiabatic Quantum Graph Matching with Permutation Matrix Constraints](https://mlsanity.com/paper/2107.04032/) (2021)
- [Skipper: Improving the Reach and Fidelity of Quantum Annealers by Skipping Long Chains](https://mlsanity.com/paper/2312.00264/) (2023)
- [Addressing the Minor-Embedding Problem in Quantum Annealing and Evaluating State-of-the-Art Algorithm Performance](https://mlsanity.com/paper/2504.13376/) (2025)

## Abstract (arXiv)

Recent advances bring within reach the viability of solving combinatorial problems using a quantum annealing algorithm implemented on a purpose-built platform that exploits quantum properties. However, the question of how to tune the algorithm for most effective use in this framework is not well understood. In this paper we describe some operational parameters that drive performance, discuss approaches for mitigating sources of error, and present experimental results from a D-Wave Two quantum annealing processor.