Evidence for quantum annealing with more than one hundred qubits

Nature Physics

Volumn 10, Issue 3, 2014, Pages 218-224

  Boixo, Sergio ^a   Rønnow, Troels F ^b   Isakov, Sergei V ^b   Wang, Zhihui ^c   Wecker, David ^d   Lidar, Daniel A ^c   Martinis, John M ^e   Troyer, Matthias ^b  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^b ETH ZURICH   (Switzerland)

^c UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^d MICROSOFT RESEARCH   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANNEALING; GROUND STATE; NUMBER THEORY; QUANTUM COMMUNICATION; QUBITS; RANDOM NUMBER GENERATION; SPIN DYNAMICS;

COMPUTATIONAL POWER; DEVICE PERFORMANCE; INITIAL CONFIGURATION; OPTIMIZATION PROBLEMS; QUANTUM RANDOM NUMBER GENERATORS; QUANTUM SIMULATORS; QUANTUM TECHNOLOGIES; SUPERCONDUCTING FLUX;

QUANTUM THEORY;

EID: 84897631365     PISSN: 17452473     EISSN: 17452481     Source Type: Journal    
DOI: 10.1038/nphys2900     Document Type: Article

References (29)

1. Muhly, J. in The beginning of the use of metals and alloys (ed. Maddin, R.) 2-20 (MIT Press, 1988
2. Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. Optimization by simulated annealing. Science 220, 671-680 (1983
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
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
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
6. Kadowaki, T. & Nishimori, H. Quantum annealing in the transverse Ising model. Phys. Rev. E 58, 5355-5363 (1998
7. Marto‡k, 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
8. Santoro, G. E.,Marto‡k, R., Tosatti, E. & Car, R. Theory of quantum annealing of an Ising spin glass. Science 295, 2427-2430 (2002
9. Battaglia, D. A., Santoro, G. E. & Tosatti, E. Optimization by quantum annealing: Lessons from hard satisfiability problems. Phys. Rev. E 71, 066707 (2005
10. Brooke, J., Bitko, D. F. T., Rosenbaum, & Aeppli, G. Quantum annealing of a disordered magnet. Science 284, 779-781 (1999
11. Johnson, M.W. et al. Quantum annealing with manufactured spins. Nature 473, 194-198 (2011
12. Boixo, S., Albash, T., Spedalieri, F. M., Chancellor, N. & Lidar, D. A. Experimental signature of programmable quantum annealing. Nature Commun. 4, 2067 (2013
13. Dickson, N. G. et al. Thermally assisted quantum annealing of a 16-qubit problem. Nature Commun. 4, 1903 (2013
14. Barahona, F. On the computational complexity of Ising spin glass models. J. Phys. A 15, 3241-3253 (1982
15. Farhi, E. et al. A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science 292, 472-475 (2001
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
17. Hen, I. & Young, A. P. Exponential complexity of the quantum adiabatic algorithm for certain satisfiability problems. Phys. Rev. E 84, 061152 (2011
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
19. Bian, Z., Chudak, F., Macready, W. G., Clark, L. & Gaitan, F. Experimental determination of Ramsey numbers. Phys. Rev. Lett. 111, 130505 (2013
20. Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G. & Aspuru-Guzik, A. Finding lowenergy conformations of lattice protein models by quantum annealing. Sci. Rep. 2, 571 (2012
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
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
23. Altshuler, B., Krovi, H. & Roland, J. Anderson localization makes adiabatic quantum optimization fail. Proc. Natl Acad. Sci. USA 107, 12446-12450 (2010
24. Dechter, R. Bucket elimination: A unifying framework for reasoning. Artif. Intell. 113, 41-85 (1999
25. McGeoch, C. C. &Wang, C. Proc. 2013 ACM Conf. Comput. Frontiers (ACM, 2013
26. Choi, V. Minorembedding in adiabatic quantum computation: II. Minor-universal graph design. Quant. Inform. Process. 10, 343-353 (2011
27. Harris, R. et al. Experimental demonstration of a robust and scalable flux qubit. Phys. Rev. B 81, 134510 (2010
28. Harris, R. et al. Experimental investigation of an eight-qubit unit cell in a superconducting optimization processor. Phys. Rev. B 82, 024511 (2010
29. Berkley, A. J. et al. A scalable readout system for a superconducting adiabatic quantum optimization system. Supercond. Sci. Tech. 23, 105014 (2010