Fast clique minor generation in Chimera qubit connectivity graphs
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Abstract (arXiv)
The current generation of D-Wave quantum annealing processor is designed to minimize the energy of an Ising spin configuration whose pairwise interactions lie on the edges of a em Chimera graph mathcal CM,N,L. In order to solve an Ising spin problem with arbitrary pairwise interaction structure, the corresponding graph must be minor-embedded into a Chimera graph. We define a combinatorial class of em native clique minors in Chimera graphs with vertex images of uniform, near minimal size, and provide a polynomial-time algorithm that finds a maximum native clique minor in a given induced subgraph of a Chimera graph. These minors allow improvement over recent work and have immediate practical applications in the field of quantum annealing.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jul 2015.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 1; 2025: 3; 2026: 1.
10 papers.
- 2022A Hybrid Quantum-Classical Algorithm for Robust Fittingcited by 16
- 2021A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applicationscited by 7
- 2023Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustnesscited by 4
- 2024CHARME: A chain-based reinforcement learning approach for the minor embedding problemcited by 3
- 2022Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitationscited by 2
- 2025Minor Embedding for Quantum Annealing with Reinforcement Learningcited by 1
- 2023Skipper: Improving the Reach and Fidelity of Quantum Annealers by Skipping Long Chainscited by 0
- 2025Energy Scale Degradation in Sparse Quantum Solvers: A Barrier to Quantum Utilitycited by 0
- 2025Addressing the Minor-Embedding Problem in Quantum Annealing and Evaluating State-of-the-Art Algorithm Performancecited by 0
- 2026Infeasibility Aware Large Language Models for Combinatorial Optimizationcited by 0
- 2026Infeasibility Aware Large Language Models for Combinatorial Optimization1 Apr 2026
- 2025Minor Embedding for Quantum Annealing with Reinforcement Learning21 Jul 2025
- 2025Addressing the Minor-Embedding Problem in Quantum Annealing and Evaluating State-of-the-Art Algorithm Performance17 Apr 2025
- 2025Energy Scale Degradation in Sparse Quantum Solvers: A Barrier to Quantum Utility11 Mar 2025
- 2024CHARME: A chain-based reinforcement learning approach for the minor embedding problem11 Jun 2024
- 2023Skipper: Improving the Reach and Fidelity of Quantum Annealers by Skipping Long Chains1 Dec 2023
- 2023Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness30 Apr 2023
- 2022Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitations2 Dec 2022
- 2022A Hybrid Quantum-Classical Algorithm for Robust Fitting25 Jan 2022
- 2021A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applications5 Jun 2021