Quantum adiabatic machine learning
10.1007/s11128-012-0506-4
Crossref journal-article
Springer Science and Business Media LLC
Quantum Information Processing (297)
Bibliography

Pudenz, K. L., & Lidar, D. A. (2012). Quantum adiabatic machine learning. Quantum Information Processing, 12(5), 2027–2070.

Authors 2
  1. Kristen L. Pudenz (first)
  2. Daniel A. Lidar (additional)
References 53 Referenced 102
  1. Vapnik, V.N.: Statistical Learning Theory. Wiley, London (1998) / Statistical Learning Theory by VN Vapnik (1998)
  2. Servedio, R.A., Gortler, S.J.: Equivalences and separations between quantum and classical learnability. SIAM J. Comput. 33, 1067 (2004) (10.1137/S0097539704412910) / SIAM J. Comput. by RA Servedio (2004)
  3. Aïmeur, E., Brassard, G., Gambs, S.: Machine learning in a quantum world. In: Lamontagne, L., Marchand, M. (eds.) Advances in Artificial Intelligence, vol. 4013 of Lecture Notes in Computer Science, p. 431. Springer, Berlin (2006) (10.1007/11766247_37)
  4. Meir, R., Rätsch, G.: An introduction to boosting and leveraging. In: Mendelson, S., Smola, A. (eds.) Advanced Lectures on Machine Learning, vol. 2600 of Lecture Notes in Computer Science, p. 118. Springer, Berlin (2003) (10.1007/3-540-36434-X_4)
  5. Freund, Y., Schapire, R., Abe, N.: A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14, 771 (1999) / J. Jpn. Soc. Artif. Intell. by Y Freund (1999)
  6. Neven, H., Denchev, V.S., Rose, G., Macready, W.G.: Training a binary classifier with the quantum adiabatic algorithm. eprint arXiv:0811.0416
  7. Neven, H., Denchev., V.S., Drew-Brook, M., Zhang, J., Macready, W.G., Rose, G.: NIPS 2009 demonstration: Binary classification using hardware implementation of quantum annealing (2009)
  8. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009) (10.1145/1541880.1541882) / ACM Comput. Surv. (CSUR) by V Chandola (2009)
  9. Dijkstra, E.W.: Notes on structured programming. In: Dahl, O.-J., Dijkstra, E.W., Hoare, C.A.R. (eds.) Structured Programming, p. 1. Academic Press, New York (1972) / Structured Programming by EW Dijkstra (1972)
  10. Tassey, G.: The economic impacts of inadequate infrastructure for software testing. National Institute of Standards and Technology, RTI Project 7007.011 (2002)
  11. Bryce, R., Kuhn, R., Lei, Y., Kacker, R.: Combinatorial testing. In: Ramachandran, M., de Carvalho, R.A. (eds.) Handbook of Software Engineering Research and Productivity Technologies, p. 196. IGI Global (2009) (10.4018/978-1-60566-731-7.ch014)
  12. Kuhn, D.R., Kacker, R.N., Lei, Y.: Practical combinatorial testing. NIST Special, Publication 800–142 (2010) (10.6028/NIST.SP.800-142)
  13. Grindal, M., Offutt, J., Andler, S.F.: Combination Testing Strategies: A survey. GMU Technical, Report ISE-TR-04-05 (2004)
  14. Cohen, D.M., Dalal, S.R., Parelius, J., Patton, G.C.: The combinatorial design approach to automatic test generation. Softw. IEEE 13, 83 (1996) (10.1109/52.536462) / Softw. IEEE by DM Cohen (1996)
  15. D’Silva, V., Kroening, D., Weissenbacher, G.: A survey of automated techniques for formal software verification. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 27, 1165 (2008) (10.1109/TCAD.2008.923410) / IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. by V D’Silva (2008)
  16. Weber, T., Amjad, H.: Efficiently checking propositional refutations in HOL theorem provers. J. Appl. Log. 7, 26 (2009) (10.1016/j.jal.2007.07.003) / J. Appl. Log. by T Weber (2009)
  17. Neven, H., Rose, G., Macready, W.G.: Image recognition with an adiabatic quantum computer I. mapping to quadratic unconstrained binary optimization. eprint arXiv:0804.4457
  18. Neven, H., Denchev, V.S., Rose, G., Macready, W.G.: Training a large scale classifier with the quantum adiabatic algorithm. eprint arXiv:0912.0779
  19. Bian, Z., Chudak, F., Macready, W.G., Rose, G.: The Ising model: teaching an old problem new tricks. D-Wave Systems (2010)
  20. Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5, 197 (1990) / Mach. Learn. by RE Schapire (1990)
  21. Farhi, E., Goldstone, J., Gutmann, S., Sipser, M.: Quantum computation by adiabatic evolution. eprint quant-ph/0001106
  22. Farhi, E., Goldstone, J., Gutmann, S., Lapan, J., Lundgren, A., Preda, D.: A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science 292(5516), 472 (2001) (10.1126/science.1057726) / Science by E Farhi (2001)
  23. Aharonov, D., van Dam, W., Kempe, J., Landau, Z., Lloyd, S., Regev, O.: Adiabatic quantum computation is equivalent to standard quantum computation. SIAM J. Comput. 37, 166 (2007) (10.1137/S0097539705447323) / SIAM J. Comput. by D Aharonov (2007)
  24. Mizel, A., Lidar, D.A., Mitchell, M.: Simple proof of equivalence between adiabatic quantum computation and the circuit model. Phys. Rev. Lett. 99, 070502 (2007) (10.1103/PhysRevLett.99.070502) / Phys. Rev. Lett. by A Mizel (2007)
  25. Jordan, S.P., Farhi, E., Shor, P.W.: Error-correcting codes for adiabatic quantum computation. Phys. Rev. A 74, 052322 (2006) (10.1103/PhysRevA.74.052322) / Phys. Rev. A by SP Jordan (2006)
  26. Lidar, Daniel A.: Towards fault tolerant adiabatic quantum computation. Phys. Rev. Lett. 100, 160506 (2008) (10.1103/PhysRevLett.100.160506) / Phys. Rev. Lett. by Daniel A Lidar (2008)
  27. Childs, Andrew M., Edward, Farhi, John, Preskill: Robustness of adiabatic quantum computation. Phys. Rev. A 65, 012322 (2001) (10.1103/PhysRevA.65.012322) / Phys. Rev. A by Andrew M Childs (2001)
  28. Sarandy, M.S., Lidar, D.A.: Adiabatic quantum computation in open systems. Phys. Rev. Lett. 95, 250503 (2005) (10.1103/PhysRevLett.95.250503) / Phys. Rev. Lett. by MS Sarandy (2005)
  29. Stehle, E., Lynch, K., Shevertalov, M., Rorres, C., Mancoridis, S.: On the use of computational geometry to detect software faults at runtime. ICAC10, June 711. Washington, DC, USA (2010) (10.1145/1809049.1809069)
  30. Le Traon, Y., Baudry, B., Jezequel, J.-M.: Design by contract to improve software vigilance. IEEE Trans. Softw. Eng. 32, 571 (2006) (10.1109/TSE.2006.79) / IEEE Trans. Softw. Eng. by Y Le Traon (2006)
  31. Mannor, S., Meir, R.: Geometric bounds for generalization in boosting. In: Helmbold, D., Williamson, B. (eds.) Computational Learning Theory, vol. 2111 of Lecture Notes in Computer Science, pp. 461–472. Springer, Berlin (2001) (10.1007/3-540-44581-1_30)
  32. Kotsiantis, S.B.: Supervised machine learning: A review of classification techniques. Informatica 31, 249 (2007) / Informatica by SB Kotsiantis (2007)
  33. Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205 (2004) / J. Mach. Learn. Res. by L Yu (2004)
  34. Zhang, S., Zhang, C., Yang, Q.: Data preparation for data mining. Appl. Artif. Intell. 17, 375 (2003) (10.1080/713827180) / Appl. Artif. Intell. by S Zhang (2003)
  35. Cheng, H., Yan, X., Han, J., Hsu, C.-W.: Discriminative frequent pattern analysis for effective classification. In: International Conference on Data Engineering, p. 716 (2007) (10.1109/ICDE.2007.367917)
  36. Breiman, L.: Arcing classifiers. Ann. Stat. 26, 801 (1998) (10.1214/aos/1024691079) / Ann. Stat. by L Breiman (1998)
  37. Blumer, A., Ehrenfeucht, A., Haussler, D., Warmuth, M.K.: Occam’s razor. Inf. Process. Lett. 24, 377 (1987) (10.1016/0020-0190(87)90114-1) / Inf. Process. Lett. by A Blumer (1987)
  38. Biamonte, J.D., Peter, Love: Realizable Hamiltonians for universal adiabatic quantum computers. Phys. Rev. A 78, 012352 (2008) (10.1103/PhysRevA.78.012352) / Phys. Rev. A by JD Biamonte (2008)
  39. Choi, V.: Minor-embedding in adiabatic quantum computation: I. The parameter setting problem. Quantum Inf. Process. 7, 193 (2008) (10.1007/s11128-008-0082-9) / Quantum Inf. Process. by V Choi (2008)
  40. Karimi, K., Dickson, N.G., Hamze, F., Amin, M.H.S., Drew-Brook, M., Chudak, F.A., Bunyk, P.I., Macready, W.G., Rose, G.: Investigating the performance of an adiabatic quantum optimization processor. Quantum Inf. Process. 11(1), 77 (2012) (10.1007/s11128-011-0235-0)
  41. Harris, R., Johnson, M.W., Lanting, T., Berkley, A.J., Johansson, J., Bunyk, P., Tolkacheva, E., Ladizinsky, E., Ladizinsky, N., Oh, T., Cioata, F., Perminov, I., Spear, P., Enderud, C., Rich, C., Uchaikin, S., Thom, M.C., Chapple, E.M., Wang, J., Wilson, B., Amin, M.H.S., Dickson, N., Karimi, K., Macready, W., Truncik, C.J.S., Rose, G.: Experimental investigation of an eight-qubit unit cell in a superconducting optimization processor. Phys. Rev. B 82, 024511 (2010) (10.1103/PhysRevB.82.024511) / Phys. Rev. B by R Harris (2010)
  42. Cheng, H., Yan, X., Han, J., Hsu, C.-W.: Discriminative frequent pattern analysis for effective classification. In: IEEE 23rd International Conference on Data Engineering, Istanbul, Turkey (2007) (10.1109/ICDE.2007.367917)
  43. Teufel, S.: Adiabatic Perturbation Theory in Quantum Dynamics. Springer, Berlin (2003) (10.1007/b13355) / Adiabatic Perturbation Theory in Quantum Dynamics by S Teufel (2003)
  44. Jansen, S., Ruskai, M.-B., Seiler, R.: Bounds for the adiabatic approximation with applications to quantum computation. J. Math. Phys. 48, 102111 (2007) (10.1063/1.2798382) / J. Math. Phys. by S Jansen (2007)
  45. Lidar, D.A., Rezakhani, A.T., Hamma, A.: Adiabatic approximation with exponential accuracy for many-body systems and quantum computation. J. Math. Phys. 50, 102106 (2009) (10.1063/1.3236685) / J. Math. Phys. by DA Lidar (2009)
  46. Roland, J., Cerf, N.J.: Quantum search by local adiabatic evolution. Phys. Rev. A 65, 042308 (2002) (10.1103/PhysRevA.65.042308) / Phys. Rev. A by J Roland (2002)
  47. Rezakhani, A.T., Pimachev, A.K., Lidar, D.A.: Accuracy versus run time in an adiabatic quantum search. Phys. Rev. A 82, 052305 (2010) (10.1103/PhysRevA.82.052305) / Phys. Rev. A by AT Rezakhani (2010)
  48. 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) (10.1103/PhysRevLett.101.170503) / Phys. Rev. Lett. by AP Young (2008)
  49. Slepian, D.: On the number of symmetry types of Boolean functions of N variables. Can. J. Math. 5, 185 (1953) (10.4153/CJM-1953-020-x) / Can. J. Math. by D Slepian (1953)
  50. Bryant, R.E.: Graph-based algorithms for Boolean function manipulation. IEEE Trans. Comput. C–35, 677 (1986) (10.1109/TC.1986.1676819) / IEEE Trans. Comput. by RE Bryant (1986)
  51. Jordan, Stephen P., Edward, Farhi: Perturbative gadgets at arbitrary orders. Phys. Rev. A 77, 062329 (2008) (10.1103/PhysRevA.77.062329) / Phys. Rev. A by Stephen P Jordan (2008)
  52. Rezakhani, A.T., Kuo, W.-J., Hamma, A., Lidar, D.A., Zanardi, P.: Quantum adiabatic brachistochrone. Phys. Rev. Lett. 103, 080502 (2009) (10.1103/PhysRevLett.103.080502) / Phys. Rev. Lett. by AT Rezakhani (2009)
  53. Rezakhani, A.T., Abasto, D.F., Lidar, D.A., Zanardi, P.: Intrinsic geometry of quantum adiabatic evolution and quantum phase transitions. Phys. Rev. A 82, 012321 (2010) (10.1103/PhysRevA.82.012321) / Phys. Rev. A by AT Rezakhani (2010)
Dates
Type When
Created 12 years, 9 months ago (Nov. 22, 2012, 1:06 p.m.)
Deposited 6 years, 2 months ago (July 5, 2019, 7:34 p.m.)
Indexed 1 month ago (Aug. 2, 2025, 1:26 a.m.)
Issued 12 years, 9 months ago (Nov. 21, 2012)
Published 12 years, 9 months ago (Nov. 21, 2012)
Published Online 12 years, 9 months ago (Nov. 21, 2012)
Published Print 12 years, 4 months ago (May 1, 2013)
Funders 0

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@article{Pudenz_2012, title={Quantum adiabatic machine learning}, volume={12}, ISSN={1573-1332}, url={http://dx.doi.org/10.1007/s11128-012-0506-4}, DOI={10.1007/s11128-012-0506-4}, number={5}, journal={Quantum Information Processing}, publisher={Springer Science and Business Media LLC}, author={Pudenz, Kristen L. and Lidar, Daniel A.}, year={2012}, month=nov, pages={2027–2070} }