Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Optimal Embedding of Complete Graphs intoBroken Chimera Graphs

Elisabeth Lobe, Lukas Schürmann, Tobias Stollenwerk

Published Jun 24, 2019• 0 citations
OpenAlex

Related papers

Systematic and Deterministic Graph-Minor Embedding of Cartesian Products of Complete Graphs

Arman Zaribafiyan, D. J. J. Marchand, Seyed Saeed Changiz Rezaei

Bulletin of the American Physical Society•Mar 15, 2016•1 citation

Quantum walks on the chimera graph and its variants

Barry C. Sanders, Xiangxiang Sun, Shu Xu +3 more

Bulletin of the American Physical Society•Mar 13, 2017•0 citations

Fast clique minor generation in Chimera qubit connectivity graphs

Tomas Boothby, Andrew D. King, Aidan Roy

Quantum Information Processing•Oct 27, 2015•159 citations

Optimised Quantum Embedding: A Universal Minor-Embedding Framework for Large Complete Bipartite Graph

Salvatore Sinno, Thomas Groß, Nicholas Chancellor +2 more

IEEE International Conference on Quantum Artificial Intelligence (QAI)•Nov 2, 2025•0 citations

Embedding of complete graphs in broken Chimera graphs

Elisabeth Lobe, Lukas Schürmann, Tobias Stollenwerk

Abstract In order to solve real-world combinatorial optimization problems with a D-Wave quantum annealer, it is necessary to embed the problem at hand into the D-Wave hardware graph, namely Chimera or Pegasus. Most hard real-world problems exhibit a strong connectivity. For the worst-case scenario of a complete graph, there exists an efficient solution for the embedding into the ideal Chimera graph. However, since real machines almost always have broken qubits, it is necessary to find an embedding into the broken hardware graph. We present a new approach to the problem of embedding complete graphs into broken Chimera graphs. This problem can be formulated as an optimization problem, more precisely as a matching problem with additional linear constraints. Although being NP-hard in general, it is fixed-parameter tractable in the number of inaccessible vertices in the Chimera graph. We tested our exact approach on various instances of broken hardware graphs, both related to real hardware and randomly generated. For fixed runtime, we were able to embed larger complete graphs compared to previous, heuristic approaches. As an extension, we developed a fast heuristic algorithm which enables us to solve even larger instances. We compared the performance of our heuristic and exact approaches.

arXiv:2012.12720•Quantum Information Processing•Jul 1, 2021•19 citations

Graph Embedding Using Quantum Commute Times

David Emms, Richard C. Wilson, Edwin R. Hancock

Lecture notes in computer science•Aug 20, 2007•25 citations
Abstract In order to solve real-world combinatorial optimization problems with a D-Wave quantum annealer, it is necessary to embed the problem at hand into the D-Wave hardware graph, namely Chimera or Pegasus. Most hard real-world problems exhibit a strong connectivity. For the worst-case scenario of a complete graph, there exists an efficient solution for the embedding into the ideal Chimera graph. However, since real machines almost always have broken qubits, it is necessary to find an embedding into the broken hardware graph. We present a new approach to the problem of embedding complete graphs into broken Chimera graphs. This problem can be formulated as an optimization problem, more precisely as a matching problem with additional linear constraints. Although being NP-hard in general, it is fixed-parameter tractable in the number of inaccessible vertices in the Chimera graph. We tested our exact approach on various instances of broken hardware graphs, both related to real hardware and randomly generated. For fixed runtime, we were able to embed larger complete graphs compared to previous, heuristic approaches. As an extension, we developed a fast heuristic algorithm which enables us to solve even larger instances. We compared the performance of our heuristic and exact approaches.