References

  1. S. H. Adachi & M. P. Henderson (2015): Application of Quantum Annealing to Training of Deep Neural Networks. Available at https://arxiv.org/abs/1510.06356.
  2. E. Farhi, J. Goldstone, S. Gutmann, J. Lapan, A. Lundgren & D. Preda (2001): A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science 292, pp. 472–475, doi:10.1126/science.1057726.
  3. J. Brooke, D. Bitko, T. F. Rosenbaum & G. Aeppli (1999): Quantum annealing of a disordered magnet. Science 284, pp. 779–781, doi:10.1126/science.284.5415.779.
  4. M. H. Amin, E. Andriyash, J. Rolfe, B. Kulchytskyy & R. Melko (2016): Quantum Boltzmann Machine. Available at https://arxiv.org/abs/1601.02036.
  5. M. W. Johnson (2011): Quantum annealing with manufactured spins. Nature 473, pp. 194–198, doi:10.1038/nature10012.
  6. N. Chancellor, S. Szoke, W. Vinci, G. Aeppli & P. A. Warburton (2016): Maximum-Entropy Inference with a Programmable Annealer. Scientific Reports 6(22318), doi:10.1038/srep22318.
  7. N. G. Dickson (2013): Thermally assisted quantum annealing of a 16-qubit problem. Nature Communications 4(1903), doi:10.1038/ncomms2920.
  8. S. Boixo (2016): Computational Role of Multiqubit Tunneling in a Quantum Annealer. Nature Communications 7(10327), doi:10.1038/ncomms10327.
  9. W. Vinci, K. Markström, S. Boixo, A. Roy, F. M. Spedalieri, P. A. Warburton & S. Severini (2014): Hearing the Shape of the Ising Model with a Programmable Superconducting-Flux Annealer. Scientific Reports 4(5703), doi:10.1038/srep05703.
  10. Y. Otsubo, J. I. Inoue, K. Nagata & M. Okada (2012): Effect of quantum fluctuation in error-correcting codes. Physical Review E 86(051138), doi:10.1103/PhysRevE.86.051138.
  11. Y. Otsubo, J. I. Inoue, K. Nagata & M. Okada (2014): Code-division multiple-access multiuser demodulator by using quantum fluctuations. Physical Review E 90(012126), doi:10.1103/PhysRevE.90.012126.
  12. T. Albash, S. Boixo, D. A. Lidar & P. Zanardi (2014): Quantum Adiabatic Markovian Master Equations. New Journal of Physics 14(123016), doi:10.1088/1367-2630/14/12/123016.
  13. H. P. Breuer & F. Petruccione (2002): The Theory of Open Quantum Systems. Oxford University Press.
  14. N. Chancellor (to appear): Modernizing Quantum Annealing using Local Searches.
  15. N. Chancellor, J. Morley, V. Kendon & S. Bose (in preparation): Adiabatic and quantum walk search algorithms as quantum annealing extremes.
  16. N. Chancellor, P. A. Warburton & G. Aeppli (2016): Experimental Freezing of mid-Evolution Fluctuations with a Programmable Annealer. Available at http://arxiv.org/abs/arXiv:1605.07549.
  17. V. Choi (2010): Adiabatic Quantum Algorithms for the NP-Complete Maximum-Weight Independent Set, Exact Cover and 3SAT Problems. Available at http://arxiv.org/abs/1004.2226.
  18. G. E. Coxson, C. R. Hill & J. C. Russo (2014): Adiabatic quantum computing for finding low-peak-sidelobe codes. High Performance Extreme Computing conference, IEEE 7, pp. 3910–3916, doi:10.1039/b509983h.
  19. D. J. Earl & M. W. Deem (2005): Parallel Tempering: Theory, Applications, and New Perspectives. Physical Chemistry Chemical Physics 7, pp. 3910–3916, doi:10.1039/b509983h.
  20. A. B. Finilla, M. A. Gomez, C. Sebenik & J. D. Doll (1994): Quantum annealing: A new method for minimizing multidimensional functions. Chemical Physics Letters 219, pp. 343–348, doi:10.1016/0009-2614(94)00117-0.
  21. K. Hukushima & Y. Iba (2003): The Monte Carlo Method in the Physical Sciences: Celebrating the 50th Anniversary of the Metropolis Algorithm 690. AIP.
  22. S. P. Jordan, E. Farhi & P. W. Shor (2006): Error-correcting codes for adiabatic quantum computation. Physical Review A 74(052322), doi:10.1103/PhysRevA.74.052322.
  23. M. Marzec (2014): Portfolio Optimization: Applications in Quantum Computing. Social Science Research Network Technical Report, doi:10.2139/ssrn.2278729.
  24. J. Matcha (2010): Population Annealing with Weighted Averages: A Monte Carlo Method for Rough Free Energy Landscapes. Physical Review E 82(026704), doi:10.1103/PhysRevE.82.026704.
  25. R. H. Swendsen & J. S. Wang (1986): Replica Monte Carlo Simulation of Spin-Glasses. Physical Review Letters 57(2607), doi:10.1103/PhysRevLett.57.2607.
  26. W. Wang, J. Matcha & H. G. Katzgraber (2015): Population annealing: Theory and application in spin glasses. Physical Review E 92(063307), doi:10.1103/PhysRevE.92.063307.
  27. W. K. Wootters & W. H. Zurek (1982): A single quantum cannot be cloned. Nature 299, pp. 802–803, doi:10.1038/299802a0.
  28. Z. Zhu, A. H. Ochoa & H. G. Katzgraber (2015): Efficient Cluster Algorithm for Spin Glasses in Any Space Dimension. Physical Review Letters 115(077201), doi:10.1103/PhysRevLett.115.077201.

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