Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Evidence for quantum annealing with more than one hundred qubits

Subjects

Abstract

Quantum technology is maturing to the point where quantum devices, such as quantum communication systems, quantum random number generators and quantum simulators may be built with capabilities exceeding classical computers. A quantum annealer, in particular, solves optimization problems by evolving a known initial configuration at non-zero temperature towards the ground state of a Hamiltonian encoding a given problem. Here, we present results from tests on a 108 qubit D-Wave One device based on superconducting flux qubits. By studying correlations we find that the device performance is inconsistent with classical annealing or that it is governed by classical spin dynamics. In contrast, we find that the device correlates well with simulated quantum annealing. We find further evidence for quantum annealing in the form of small-gap avoided level crossings characterizing the hard problems. To assess the computational power of the device we compare it against optimized classical algorithms.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Success probability distributions.
Figure 2: Correlations.
Figure 3: Correlations of gauge-averaged data.
Figure 4: Evolution of the lowest spectral gap.
Figure 5: Correlation of success probability and the Hamming distance from excited states to the nearest ground state.
Figure 6: Scaling with problem size.

Similar content being viewed by others

References

  1. Muhly, J. in The beginning of the use of metals and alloys (ed Maddin, R.) The beginnings of metallurgy in the Old World. 2–20 (MIT Press, 1988).

    Google Scholar 

  2. Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. Optimization by simulated annealing. Science 220, 671–680 (1983).

    Article  ADS  MathSciNet  Google Scholar 

  3. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. & Teller, E. Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092 (1953).

    Article  ADS  Google Scholar 

  4. Ray, P., Chakrabarti, B. K. & Chakrabarti, A. Sherrington–Kirkpatrick model in a transverse field: Absence of replica symmetry breaking due to quantum fluctuations. Phys. Rev. B 39, 11828–11832 (1989).

    Article  ADS  Google Scholar 

  5. Finnila, A., Gomez, M., Sebenik, C., Stenson, C. & Doll, J. Quantum annealing: A new method for minimizing multidimensional functions. Chem. Phys. Lett. 219, 343–348 (1994).

    Article  ADS  Google Scholar 

  6. Kadowaki, T. & Nishimori, H. Quantum annealing in the transverse Ising model. Phys. Rev. E 58, 5355–5363 (1998).

    Article  ADS  Google Scholar 

  7. Martoňák, R., Santoro, G. E. & Tosatti, E. Quantum annealing by the path-integral Monte Carlo method: The two-dimensional random Ising model. Phys. Rev. B 66, 094203 (2002).

    Article  ADS  Google Scholar 

  8. Santoro, G. E., Martoňák, R., Tosatti, E. & Car, R. Theory of quantum annealing of an Ising spin glass. Science 295, 2427–2430 (2002).

    Article  ADS  Google Scholar 

  9. Battaglia, D. A., Santoro, G. E. & Tosatti, E. Optimization by quantum annealing: Lessons from hard satisfiability problems. Phys. Rev. E 71, 066707 (2005).

    Article  ADS  Google Scholar 

  10. Brooke, J., Bitko, D., Rosenbaum, F. T. & Aeppli, G. Quantum annealing of a disordered magnet. Science 284, 779–781 (1999).

    Article  ADS  Google Scholar 

  11. Johnson, M. W. et al. Quantum annealing with manufactured spins. Nature 473, 194–198 (2011).

    Article  ADS  Google Scholar 

  12. Boixo, S., Albash, T., Spedalieri, F. M., Chancellor, N. & Lidar, D. A. Experimental signature of programmable quantum annealing. Nature Commun. 4, 2067 (2013).

    Article  ADS  Google Scholar 

  13. Dickson, N. G. et al. Thermally assisted quantum annealing of a 16-qubit problem. Nature Commun. 4, 1903 (2013).

    Article  ADS  Google Scholar 

  14. Barahona, F. On the computational complexity of Ising spin glass models. J. Phys. A 15, 3241–3253 (1982).

    Article  ADS  MathSciNet  Google Scholar 

  15. Farhi, E. et al. A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science 292, 472–475 (2001).

    Article  ADS  MathSciNet  Google Scholar 

  16. Jörg, T., Krzakala, F., Semerjian, G. & Zamponi, F. First-order transitions and the performance of quantum algorithms in random optimization problems. Phys. Rev. Lett. 104, 207206 (2010).

    Article  ADS  Google Scholar 

  17. Hen, I. & Young, A. P. Exponential complexity of the quantum adiabatic algorithm for certain satisfiability problems. Phys. Rev. E 84, 061152 (2011).

    Article  ADS  Google Scholar 

  18. Farhi, E. et al. Performance of the quantum adiabatic algorithm on random instances of two optimization problems on regular hypergraphs. Phys. Rev. A 86, 052334 (2012).

    Article  ADS  Google Scholar 

  19. Bian, Z., Chudak, F., Macready, W. G., Clark, L. & Gaitan, F. Experimental determination of Ramsey numbers. Phys. Rev. Lett. 111, 130505 (2013).

    Article  ADS  Google Scholar 

  20. Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G. & Aspuru-Guzik, A. Finding low-energy conformations of lattice protein models by quantum annealing. Sci. Rep. 2, 571 (2012).

    Article  ADS  Google Scholar 

  21. Kashurnikov, V. A., Prokof’ev, N. V., Svistunov, B. V. & Troyer, M. Quantum spin chains in a magnetic field. Phys. Rev. B 59, 1162–1167 (1999).

    Article  ADS  Google Scholar 

  22. Young, A. P., Knysh, S. & Smelyanskiy, V. N. Size dependence of the minimum excitation gap in the quantum adiabatic algorithm. Phys. Rev. Lett. 101, 170503 (2008).

    Article  ADS  Google Scholar 

  23. Altshuler, B., Krovi, H. & Roland, J. Anderson localization makes adiabatic quantum optimization fail. Proc. Natl Acad. Sci. USA 107, 12446–12450 (2010).

    Article  ADS  Google Scholar 

  24. Dechter, R. Bucket elimination: A unifying framework for reasoning. Artif. Intell. 113, 41–85 (1999).

    Article  MathSciNet  Google Scholar 

  25. McGeoch, C. C. & Wang, C. Proc. 2013 ACM Conf. Comput. Frontiers (ACM, 2013).

    Google Scholar 

  26. Choi, V. Minor-embedding in adiabatic quantum computation: II. Minor-universal graph design. Quant. Inform. Process. 10, 343–353 (2011).

    Article  MathSciNet  Google Scholar 

  27. Harris, R. et al. Experimental demonstration of a robust and scalable flux qubit. Phys. Rev. B 81, 134510 (2010).

    Article  ADS  Google Scholar 

  28. Harris, R. et al. Experimental investigation of an eight-qubit unit cell in a superconducting optimization processor. Phys. Rev. B 82, 024511 (2010).

    Article  ADS  Google Scholar 

  29. Berkley, A. J. et al. A scalable readout system for a superconducting adiabatic quantum optimization system. Supercond. Sci. Tech. 23, 105014 (2010).

    Article  ADS  Google Scholar 

Download references

Acknowledgements

We acknowledge useful discussions with M. H. Amin, M. H. Freedman, H. G. Katzgraber, C. Marcus, B. Smith and K. Svore. We thank L. Wang for providing data of spin dynamics simulations, G. Wagenbreth for help in optimizing the belief propagation code and P. Messmer for help in optimizing the GPU codes. We are grateful to J. Smolin and G. Smith for suggesting that we test classical spin dynamics. Simulations were performed on the Brutus cluster at ETH Zurich and on computing resources of Microsoft Research with the help of J. Jernigan. This work was supported by the Swiss National Science Foundation through the NCCR QSIT, by ARO grant number W911NF-12-1-0523, by ARO MURI grant number W911NF-11-1-0268, by Lockheed Martin Corporation, by DARPA grant number FA8750-13-2-0035, and by NSF grant number CHE-1037992. MT acknowledges the hospitality of the Aspen Center for Physics, supported by NSF grant PHY-1066293. The initial planning of the tests by MT was funded by Microsoft Research.

Author information

Authors and Affiliations

Authors

Contributions

M.T., J.M.M. and D.A.L. designed the tests and wrote the manuscript, with input from all other authors. S.B. and Z.W. performed the tests on D-Wave One. S.V.I., T.F.R. and M.T. wrote the simulated classical and quantum annealing codes and T.F.R., S.V.I., M.T. and D.W. performed the simulations. S.B. and T.F.R. wrote the bucket sort code and the divide-and-conquer codes. T.F.R., S.V.I., M.T., S.B., Z.W. and D.A.L. evaluated the data. All authors contributed to the discussion and presentation of the results.

Corresponding author

Correspondence to Matthias Troyer.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Information (PDF 2232 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Boixo, S., Rønnow, T., Isakov, S. et al. Evidence for quantum annealing with more than one hundred qubits. Nature Phys 10, 218–224 (2014). https://doi.org/10.1038/nphys2900

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nphys2900

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing