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Benchmarking Quantum Annealers with Near-Optimal Minor-Embedded Instances (2405.01378v2)

Published 2 May 2024 in quant-ph, cs.ET, and cs.PF

Abstract: Benchmarking Quantum Process Units (QPU) at an application level usually requires considering the whole programming stack of the quantum computer. One critical task is the minor-embedding (resp. transpilation) step, which involves space-time overheads for annealing-based (resp. gate-based) quantum computers. This paper establishes a new protocol to generate graph instances with their associated near-optimal minor-embedding mappings to D-Wave Quantum Annealers (QA). This set of favorable mappings is used to generate a wide diversity of optimization problem instances. We use this method to benchmark QA on large instances of unconstrained and constrained optimization problems and compare the performance of the QPU with efficient classical solvers. The benchmark aims to evaluate and quantify the key characteristics of instances that could benefit from the use of a quantum computer. In this context, existing QA seem best suited for unconstrained problems on instances with densities less than $10\%$. For constrained problems, the penalty terms used to encode the hard constraints restrict the performance of QA and suggest that these QPU will be less efficient on these problems of comparable size.

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References (35)
  1. F. Arute, K. Arya, R. Babbush, et al., “Quantum supremacy using a programmable superconducting processor,” Nature, vol. 574, no. 7779, p. 505–510, 2019.
  2. H.-S. Zhong, H. Wang, Y.-H. Deng, et al., “Quantum computational advantage using photons,” Science, vol. 370, no. 6523, pp. 1460–1463, 2020.
  3. L. S. Madsen, F. Laudenbach, M. F. Askarani, et al., “Quantum computational advantage with a programmable photonic processor,” Nature, vol. 606, no. 7912, p. 75–81, 2022.
  4. A. D. King, A. Nocera, M. M. Rams, et al., “Computational supremacy in quantum simulation,” arXiv preprint arXiv:2403.00910, 2024.
  5. S. Martiel, T. Ayral, and C. Allouche, “Benchmarking quantum coprocessors in an application-centric, hardware-agnostic, and scalable way,” IEEE Transactions on Quantum Engineering, vol. 2, pp. 1–11, 2021.
  6. D. Mills, S. Sivarajah, T. L. Scholten, and R. Duncan, “Application-motivated, holistic benchmarking of a full quantum computing stack,” Quantum, vol. 5, p. 415, 2021.
  7. J. R. Finžgar, P. Ross, L. Hölscher, et al., “Quark: A framework for quantum computing application benchmarking,” in 2022 IEEE international conference on quantum computing and engineering (QCE), pp. 226–237, IEEE, 2022.
  8. T. Lubinski, S. Johri, P. Varosy, et al., “Application-oriented performance benchmarks for quantum computing,” IEEE Transactions on Quantum Engineering, 2023.
  9. F. Barbaresco, L. Rioux, C. Labreuche, et al., “Bacq–application-oriented benchmarks for quantum computing,” arXiv preprint arXiv:2403.12205, 2024.
  10. T. Tomesh, P. Gokhale, V. Omole, et al., “Supermarq: A scalable quantum benchmark suite,” in 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA), pp. 587–603, IEEE, 2022.
  11. Springer Nature Switzerland, 2023.
  12. N. P. Sawaya, D. Marti-Dafcik, Y. Ho, et al., “Hamlib: A library of hamiltonians for benchmarking quantum algorithms and hardware,” in 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), vol. 2, pp. 389–390, IEEE, 2023.
  13. N. Quetschlich, L. Burgholzer, and R. Wille, “Mqt bench: Benchmarking software and design automation tools for quantum computing,” Quantum, vol. 7, p. 1062, 2023.
  14. I. Dunning, S. Gupta, and J. Silberholz, “What works best when? a systematic evaluation of heuristics for max-cut and qubo,” INFORMS Journal on Computing, vol. 30, no. 3, p. 608–624, 2018.
  15. F. Furini, E. Traversi, P. Belotti, et al., “Qplib: a library of quadratic programming instances,” Mathematical Programming Computation, vol. 11, no. 2, p. 237–265, 2018.
  16. A. Abbas, A. Ambainis, B. Augustino, et al., “Quantum optimization: Potential, challenges, and the path forward,” arXiv preprint arXiv:2312.02279, 2023.
  17. “D-wave system. solver properties and parameters.” https://docs.dwavesys.com/docs/latest/doc_solver_ref.html [Accessed 20-04-2024].
  18. V. Choi, “Minor-embedding in adiabatic quantum computation: I. the parameter setting problem,” Quantum Information Processing, vol. 7, pp. 193–209, 2008.
  19. E. Pelofske, A. Bärtschi, and S. Eidenbenz, “Quantum annealing vs. qaoa: 127 qubit higher-order ising problems on nisq computers,” in International Conference on High Performance Computing, pp. 240–258, Springer, 2023.
  20. Y. Pang, C. Coffrin, A. Y. Lokhov, and M. Vuffray, “The potential of quantum annealing for rapid solution structure identification,” Constraints, vol. 26, no. 1, pp. 1–25, 2021.
  21. B. Tasseff, T. Albash, Z. Morrell, et al., “On the emerging potential of quantum annealing hardware for combinatorial optimization,” arXiv preprint arXiv:2210.04291, 2022.
  22. T. Lubinski, C. Coffrin, C. McGeoch, et al., “Optimization applications as quantum performance benchmarks,” arXiv preprint arXiv:2302.02278, 2023.
  23. T. Albash and D. A. Lidar, “Adiabatic quantum computation,” Reviews of Modern Physics, vol. 90, no. 1, p. 015002, 2018.
  24. N. Robertson and P. Seymour, “Graph minors .xiii. the disjoint paths problem,” Journal of Combinatorial Theory, Series B, vol. 63, no. 1, p. 65–110, 1995.
  25. J. Cai, W. G. Macready, and A. Roy, “A practical heuristic for finding graph minors,” arXiv preprint arXiv:1406.2741, 2014.
  26. T. Boothby, A. D. King, and A. Roy, “Fast clique minor generation in chimera qubit connectivity graphs,” Quantum Information Processing, vol. 15, pp. 495–508, 2016.
  27. G. Palubeckis, “Multistart tabu search strategies for the unconstrained binary quadratic optimization problem,” Annals of Operations Research, vol. 131, no. 1–4, p. 259–282, 2004.
  28. Gurobi Optimization, LLC, “Gurobi Optimizer Reference Manual,” 2023.
  29. V. Gilbert and S. Louise, “Quantum annealers chain strengths: A simple heuristic to set them all,” arXiv preprint arXiv:2404.05443, 2024.
  30. D. Willsch, M. Willsch, C. D. Gonzalez Calaza, et al., “Benchmarking advantage and d-wave 2000q quantum annealers with exact cover problems,” Quantum Information Processing, vol. 21, no. 4, p. 141, 2022.
  31. T. V. Le, M. V. Nguyen, T. N. Nguyen, et al., “Benchmarking chain strength: An optimal approach for quantum annealing,” in 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), IEEE, 2023.
  32. J. I. Adame and P. L. McMahon, “Inhomogeneous driving in quantum annealers can result in orders-of-magnitude improvements in performance,” Quantum Science and Technology, vol. 5, no. 3, p. 035011, 2020.
  33. M. Khezri, X. Dai, R. Yang, et al., “Customized quantum annealing schedules,” Physical Review Applied, vol. 17, no. 4, 2022.
  34. K. Chern, K. Boothby, J. Raymond, et al., “Tutorial: Calibration refinement in quantum annealing,” arXiv preprint arXiv:2304.10352, 2023.
  35. D. Rehfeldt, T. Koch, and Y. Shinano, “Faster exact solution of sparse maxcut and qubo problems,” Mathematical Programming Computation, vol. 15, no. 3, p. 445–470, 2023.
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