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Evaluation of Quantum and Hybrid Solvers for Combinatorial Optimization (2403.10455v1)

Published 15 Mar 2024 in quant-ph, cs.DC, and cs.PF

Abstract: Academic and industrial sectors have been engaged in a fierce competition to develop quantum technologies, fueled by the explosive advancements in quantum hardware. While universal quantum computers have been shown to support up to hundreds of qubits, the scale of quantum annealers has reached three orders of magnitude (i.e., thousands of qubits). Therefore, quantum algorithms are becoming increasingly popular in a variety of fields, with optimization being one of the most prominent. This work aims to explore the topic of quantum optimization by comprehensively evaluating the technologies provided by D-Wave Systems. To do so, a model for the energy optimization of data centers is proposed as a benchmark. D-Wave quantum and hybrid solvers are compared, in order to identify the most suitable one for the considered application. To highlight its advantageous performance capabilities and associated solving potential, the selected D-Wave hybrid solver is then contrasted with CPLEX, a highly efficient classical solver.

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References (30)
  1. A. Abbas, A. Ambainis, B. Augustino, A. Bärtschi, H. Buhrman, C. Coffrin, G. Cortiana, V. Dunjko, D. J. Egger, B. G. Elmegreen, N. Franco, F. Fratini, B. Fuller, J. Gacon, C. Gonciulea, S. Gribling, S. Gupta, S. Hadfield, R. Heese, G. Kircher, T. Kleinert, T. Koch, G. Korpas, S. Lenk, J. Marecek, V. Markov, G. Mazzola, S. Mensa, N. Mohseni, G. Nannicini, C. O’Meara, E. P. Tapia, S. Pokutta, M. Proissl, P. Rebentrost, E. Sahin, B. C. B. Symons, S. Tornow, V. Valls, S. Woerner, M. L. Wolf-Bauwens, J. Yard, S. Yarkoni, D. Zechiel, S. Zhuk, and C. Zoufal, “Quantum optimization: Potential, challenges, and the path forward,” 2023.
  2. D-Wave Systems Inc., “D-wave systems web page,” 2023. [Online]. Available: https://www.dwavesys.com/
  3. IBM Corporation, “Ilog cplex optimization studio,” 2024. [Online]. Available: https://www.ibm.com/docs/en/icos/22.1.1
  4. E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser, “Quantum computation by adiabatic evolution,” 2000.
  5. M. Born and V. Fock, “Beweis des adiabatensatzes,” Zeitschrift für Physik, vol. 51, no. 3-4, pp. 165–180, 1928.
  6. C. Mcgeoch and C. Wang, “Experimental evaluation of an adiabiatic quantum system for combinatorial optimization,” in Proceedings of the 2013 ACM Conference on Computing Frontiers.   Ischia, Italy: ACM, 05 2013.
  7. S. Boixo, T. F. Rønnow, S. V. Isakov, Z. Wang, D. Wecker, D. A. Lidar, J. M. Martinis, and M. Troyer, “Evidence for quantum annealing with more than one hundred qubits,” Nature Physics, vol. 10, no. 3, p. 218–224, Feb. 2014. [Online]. Available: http://dx.doi.org/10.1038/nphys2900
  8. K. Zheng, X. Wang, L. Li, and X. Wang, “Joint power optimization of data center network and servers with correlation analysis,” in IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.   Toronto, Canada: IEEE, 2014, pp. 2598–2606.
  9. B. Lu, S. S. Dayapule, F. Yao, J. Wu, G. Venkataramani, and S. Subramaniam, “Popcorns: Power optimization using a cooperative network-server approach for data centers,” in 2018 27th International Conference on Computer Communication and Networks (ICCCN).   Hangzhou, China: IEEE, 2018, pp. 1–9.
  10. A. Jayanetti and R. Buyya, “J-opt: A joint host and network optimization algorithm for energy-efficient workflow scheduling in cloud data centers,” in Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing, ser. UCC’19.   New York, NY, USA: Association for Computing Machinery, 2019, p. 199–208. [Online]. Available: https://doi.org/10.1145/3344341.3368822
  11. M. Amoretti, D. Ferrari, and A. Manzalini, “Classical and quantum solvers for joint network/servers power optimization,” arXiv:2205.01165, 2022.
  12. H. I. Christensen, A. Khan, S. Pokutta, and P. Tetali, “Multidimensional bin packing and other related problems: A survey,” 2016.
  13. L. Hyafil and R. L. Rivest, “Graph partitioning and constructing optimal decision trees are polynomial complete problems,” Iria Laboratory, Tech. Rep., 1973.
  14. M. Garey, “Some simplified NP-complete graph problems,” Theoretical Computer Science, vol. 1, pp. 237–267, 1976.
  15. H. Jin, T. Cheocherngngarn, D. Levy, A. Smith, D. Pan, J. Liu, and N. Pissinou, “Joint host-network optimization for energy-efficient data center networking,” in 2013 IEEE 27th International Symposium on Parallel and Distributed Processing.   Boston, MA, USA: IEEE, 2013, pp. 623–634.
  16. Y. Han, J. Li, J.-Y. Chung, J.-H. Yoo, and J. W.-K. Hong, “SAVE: Energy-aware virtual data center embedding and traffic engineering using SDN,” in Proceedings of the 2015 1st IEEE Conference on Network Softwarization (NetSoft).   London, UK: IEEE, 2015, pp. 1–9.
  17. C. Guo, K. Xu, G. Shen, and M. Zukerman, “Temperature-aware virtual data center embedding to avoid hot spots in data centers,” IEEE Transactions on green communications and networking, vol. 5, no. 1, pp. 497–511, 2020.
  18. Z. Wang, C. Guo, S. K. Bose, and G. Shen, “Frequency-adaptive vdc embedding to minimize energy consumption of data centers,” IEEE Transactions on Green Communications and Networking, vol. 6, no. 1, pp. 447–461, 2021.
  19. C. Pham, N. H. Tran, S. Ren, W. Saad, and C. S. Hong, “Traffic-aware and energy-efficient vnf placement for service chaining: Joint sampling and matching approach,” IEEE Transactions on Services Computing, vol. 13, no. 1, pp. 172–185, 2017.
  20. G. Sun, D. Liao, S. Bu, H. Yu, Z. Sun, and V. Chang, “The efficient framework and algorithm for provisioning evolving vdc in federated data centers,” Future Generation Computer Systems, vol. 73, pp. 79–89, 2017.
  21. F. Carpio, W. Bziuk, and A. Jukan, “Replication of virtual network functions: Optimizing link utilization and resource costs,” in 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).   Opatija, Croatia: IEEE, 2017, pp. 521–526.
  22. G. Colucci, S. van der Linde, and F. Phillipson, “Power network optimization: a quantum approach,” IEEE Access, vol. 11, pp. 98 926–98 938, 2023.
  23. D-Wave Systems Inc., “Hybrid solver for constrained quadratic models,” 2021. [Online]. Available: https://www.dwavesys.com/media/rldh2ghw/14-1055a-a_hybrid_solver_for_constrained_quadratic_models.pdf
  24. ——, “D-wave documentation web page,” 2023. [Online]. Available: https://docs.dwavesys.com/docs/latest/index.html
  25. ——, “Ocean sdk web page,” 2023. [Online]. Available: https://docs.ocean.dwavesys.com/en/stable/#
  26. D-Wave Systems Inc, “Quantum solvers,” 2024. [Online]. Available: https://docs.ocean.dwavesys.com/en/stable/overview/qpu.html
  27. ——, “Hybrid workflow solvers,” 2024. [Online]. Available: https://docs.ocean.dwavesys.com/en/stable/docs_hybrid/intro/overview.html#overview-hybrid
  28. ——, “Leap’s hybrid solvers,” 2024. [Online]. Available: https://docs.ocean.dwavesys.com/en/stable/overview/hybrid.html#leap-hybrid-solvers
  29. D-Wave Systems Inc., “Leap web page,” 2024. [Online]. Available: https://cloud.dwavesys.com/leap/
  30. IBM Corporation, “Ibm decision optimization cplex modeling for python,” 2024. [Online]. Available: https://ibmdecisionoptimization.github.io/docplex-doc/
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