Benchmarking Quantum Optimization for the Maximum-Cut Problem on a Superconducting Quantum Computer
Abstract: Achieving high-quality solutions faster than classical solvers on computationally hard problems is a challenge for quantum optimization to deliver utility. Using a superconducting quantum computer, we experimentally investigate the performance of a hybrid quantum-classical algorithm inspired by semidefinite programming approaches for solving the maximum-cut problem on 3-regular graphs up to several thousand variables. We leverage the structure of the input problems to address sizes beyond what current quantum machines can naively handle. We attain an average approximation ratio of 99% over a random ensemble of thousands of problem instances. We benchmark the quantum solver against similarly high-performing classical heuristics, including the Gurobi optimizer, simulated annealing, and the Burer-Monteiro algorithm. A run-time analysis shows that the quantum solver on large-scale problems is competitive against Gurobi but short of others on a projected 100-qubit quantum computer. We explore multiple leads to close the gap and discuss prospects for a practical quantum speedup.
- E. Farhi, J. Goldstone, and S. Gutmann, arXiv:1411.4028 (2014a).
- T. Albash and D. A. Lidar, Rev. Mod. Phys. 90, 015002 (2018).
- M. Sipser, Introduction to the Theory of Computation, 3rd ed. (Course Technology, Boston, MA, 2013).
- J. Håstad, J. ACM 48, 798–859 (2001).
- M. X. Goemans and D. P. Williamson, J. ACM 42, 1115–1145 (1995).
- Gurobi Optimization, LLC, Gurobi Optimizer Reference Manual (2024).
- S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Science 220, 671 (1983).
- S. Burer, R. D. C. Monteiro, and Y. Zhang, SIAM J. Optim. 12, 503 (2002).
- S. Burer and R. D. C. Monteiro, Math. Program. 95, 329 (2003).
- S. H. Sack and D. J. Egger, Phys. Rev. Res. 6, 013223 (2024).
- A. Lucas, Front. Phys. 2, 5 (2014).
- E. Pelofske, A. Bärtschi, and S. Eidenbenz, Npj Quantum Inf. 10, 30 (2024).
- M. Dupont and B. Sundar, Phys. Rev. A 109, 012429 (2024).
- Devising an algorithm for the maximum cut problem on random 3333-regular graphs guaranteeing an approximation ratio of at least 99.7%percent99.799.7\%99.7 % [48] is NP-hard, with the current record sitting at α≃93.3%similar-to-or-equals𝛼percent93.3\alpha\simeq 93.3\%italic_α ≃ 93.3 % [49] from a modified version of the Goemans-Williamson algorithm based on semidefinite programming.
- See supplementary materials.
- J. Wurtz and P. Love, Phys. Rev. A 103, 042612 (2021).
- J. Wurtz and D. Lykov, arXiv:2107.00677 (2021).
- T. W. B. Kibble, J. Phys. A Math. 9, 1387 (1976).
- W. H. Zurek, Nature 317, 505 (1985).
- C.-W. Liu, A. Polkovnikov, and A. W. Sandvik, Phys. Rev. B 89, 054307 (2014).
- C.-W. Liu, A. Polkovnikov, and A. W. Sandvik, Phys. Rev. Lett. 114, 147203 (2015).
- G. Biroli, L. F. Cugliandolo, and A. Sicilia, Phys. Rev. E 81, 050101 (2010).
- E. Farhi, D. Gamarnik, and S. Gutmann, arXiv:2004.09002 (2020).
- S. J. Devitt, W. J. Munro, and K. Nemoto, Rep. Prog. Phys. 76, 076001 (2013).
- E. Halperin, D. Livnat, and U. Zwick, J. Algorithms 53, 169 (2004).
- W. K. Hastings, Biometrika 57, 97 (1970).
- V. Granville, M. Krivanek, and J.-P. Rasson, IEEE Trans. Pattern Anal. Mach. Intell. 16, 652 (1994).
- A. Nolte and R. Schrader, in Operations Research Proceedings 1996 (Springer Berlin Heidelberg, Berlin, Heidelberg, 1997) pp. 175–180.
- K. Hukushima and K. Nemoto, J. Phys. Soc. Jpn. 65, 1604 (1996).
- I. Dunning, S. Gupta, and J. Silberholz, INFORMS J. Comput. 30 (2018).
- M. Conforti, G. Cornuéjols, and G. Zambelli, Integer programming (Springer, 2014).
- D. P. Williamson and D. B. Shmoys, The Design of Approximation Algorithms (Cambridge university press, 2011).
- B. Mohar and S. Poljak, Czechoslov. Math. J. 40, 343 (1990).
- C. Delorme and S. Poljak, Math. Program. 62, 557 (1993a).
- C. Delorme and S. Poljak, Discrete Math. 111, 145 (1993b).
- S. Poljak and F. Rendl, Discret. Appl. Math. 62, 249 (1995).
- B. O’Donoghue, SIAM J. Optim. 31, 1999 (2021).
- E. Farhi, J. Goldstone, and S. Gutmann, arXiv:1412.6062 (2014b).
- E. Farhi and A. W. Harrow, arXiv:1602.07674 (2016).
- J. Wurtz and P. J. Love, Quantum 6, 635 (2022).
- D. Sherrington and S. Kirkpatrick, Phys. Rev. Lett. 35, 1792 (1975).
- A. STEGER and N. C. WORMALD, Comb. Probab. Comput. 8, 377–396 (1999).
- OEIS Foundation Inc., The On-Line Encyclopedia of Integer Sequences (2024), published electronically at http://oeis.org.
- N. C. Wormald, J. Comb. Theory, Ser. B 31, 168 (1981).
- B. D. McKay, N. C. Wormald, and B. Wysocka, Electron. J. Comb. 11, https://doi.org/10.37236/1819 (2004).
- H. L. Bodlaender and A. M. Koster, Inf. Comput. 208, 259 (2010).
- L. Babai, arXiv:1512.03547 (2015).
- H. A. Helfgott, J. Bajpai, and D. Dona, arXiv:1710.04574 (2017).
- A. MATSUO, S. YAMASHITA, and D. J. EGGER, IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E106.A, 1424 (2023).
- G. Audemard and L. Simon, Int. J. Artif. Intell. Tools 27, 1840001 (2018).
- N. Eén and N. Sörensson, in Theory and Applications of Satisfiability Testing, edited by E. Giunchiglia and A. Tacchella (Springer Berlin Heidelberg, Berlin, Heidelberg, 2004) pp. 502–518.
- A. Ignatiev, A. Morgado, and J. Marques-Silva, in SAT (2018) pp. 428–437.
- S. H. Sack and D. J. Egger, arXiv:2307.14427 (2023).
- J. J. Wallman and J. Emerson, Phys. Rev. A 94, 052325 (2016).
- Z. Cai and S. C. Benjamin, Sci. Rep. 9, 11281 (2019).
- M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information: 10th Anniversary Edition, 10th ed. (Cambridge University Press, 2010).
- S. K. Lam, A. Pitrou, and S. Seibert, in Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC (2015) pp. 1–6.
- L. T. Brady and S. Hadfield, arXiv:2309.13110 (2023).
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