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Solving NP-Complete Problems with Quantum Search

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4957))

Abstract

In his seminal paper, Grover points out the prospect of faster solutions for an NP-complete problem like SAT. If there are n variables, then an obvious classical deterministic algorithm checks out all 2n truth assignments in about 2n steps, while his quantum search algorithm can find a satisfying truth assignment in about 2n/2 steps.

For several NP-complete problems, many sophisticated classical algorithms have been designed. They are still exponential, but much faster than the brute force algorithms. The question arises whether their running time can still be decreased from T(n) to \(\tilde{O}(\sqrt{T(n)})\) by using a quantum computer. Isolated positive examples are known, and some speed-up has been obtained for wider classes. Here, we present a simple method to obtain the full T(n) to \(\tilde{O}(\sqrt{T(n)})\) speed-up for most of the many non-trivial exponential time algorithms for NP-hard problems. The method works whenever the widely used technique of recursive decomposition is employed.

This included all currently known algorithms for which such a speed-up has not yet been known.

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References

  1. Grover, L.K.: Quantum mechanics helps in searching for a needle in a haystack. Physical Review Letters 79(2), 325–328 (1997)

    Article  Google Scholar 

  2. Grover, L.K.: A framework for fast quantum mechanical algorithms. In: Proceedings of the 30th Annual ACM Symposium on Theory of Computing (STOC 1998), pp. 53–62. ACM Press, New York (1998)

    Chapter  Google Scholar 

  3. Angelsmark, O., Dahllöf, V., Jonsson, P.: Finite domain constraint satisfaction using quantum computation. In: Diks, K., Rytter, W. (eds.) MFCS 2002. LNCS, vol. 2420, pp. 93–103. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  4. Eppstein, D.: Improved algorithms for 3-Coloring, 3-Edge-Coloring, and constraint satisfaction. In: Proceedings of the Twelfth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2001), pp. 329–337. ACM Press, New York (2001)

    Google Scholar 

  5. Cerf, N., Grover, L., Williams, C.: Nested quantum search and structured problems. Phys. Rev. A 61(3) (2000) 14 032303.

    Google Scholar 

  6. Brassard, G., Høyer, P., Mosca, M., Tapp, A.: Quantum amplitude amplification and estimation. In: Lomonaco Jr., S.J., Brandt, H.E. (eds.) Quantum Computation and Information, AMS Contemporary Mathematics, vol. 305, pp. 53–74 (2002), http://arxiv.org/abs/quant-ph/0005055

  7. Ambainis, A.: Quantum search algorithms. ACM SIGACT News 35(2), 22–35 (2004)

    Article  Google Scholar 

  8. Dantsin, E., Kreinovich, V., Wolpert, A.: On quantum versions of record-breaking algorithms for sat. ACM SIGACT News 36(4), 103–108 (2005)

    Article  Google Scholar 

  9. Schöning, U.: A probabilistic algorithm for k-SAT and constraint satisfaction problems. In: 40th Annual Symposium on Foundations of Computer Science (FOCS 1999), Washington - Brussels - Tokyo, pp. 410–414. IEEE, Los Alamitos (1999)

    Google Scholar 

  10. Davis, M., Putnam, H.: A computing procedure for quantification theory. Journal of Association Computer Machinery 7, 201–215 (1960)

    MathSciNet  MATH  Google Scholar 

  11. Davis, M., Logemann, G., Loveland, D.: A machine program for theorem-proving. Communications of the ACM 5(7), 394–397 (1962)

    Article  MathSciNet  MATH  Google Scholar 

  12. Woeginger, G.: Exact algorithms for NP-hard problems: A survey. In: Jünger, M., Reinelt, G., Rinaldi, G. (eds.) Combinatorial Optimization - Eureka, You Shrink! LNCS, vol. 2570, pp. 185–207. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  13. Dantsin, E., Goerdt, A., Hirsch, E.A., Kannan, R., Kleinberg, J., Papadimitriou, C., Raghavan, P., Schöning, U.: A deterministic (2 − 2/(k + 1))n algorithm for k-SAT based on local search. Theoretical Computer Science 289(1), 69–83 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  14. Kullmann, O.: New methods for 3-SAT decision and worst-case analysis. Theoretical Computer Science 223, 1–72 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  15. Dahllöf, V., Jonsson, P., Wahlström, M.: Counting models for 2SAT and 3SAT formulae. Theoretical Computer Science 332(1-3), 265–291 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  16. Boyer, M., Brassard, G., Høyer, P., Tapp, A.: Tight bounds on quantum searching. Fortsch. Phys. 46, 493–506 (1998)

    Article  Google Scholar 

  17. Grover, L.K.: Quantum computers can search rapidly by using almost any transformation. Physical Review Letters 80, 4329–4332 (1998)

    Article  Google Scholar 

  18. Grover, L.K.: Rapid sampling through quantum computing. In: Proceedings of the 32nd Annual ACM Symposium on Theory of Computing (STOC 2000), pp. 618–626 (2000)

    Google Scholar 

  19. Chen, G., Sun, S.: Generalization of Grover’s algorithm to multiobject search in quantum computing, Part II: general unitary transformations. In: Brylinski, R., Chen, G. (eds.) Mathematics of Quantum Computation. Computational Mathematics, pp. 161–168. Chapman & Hall/CRC, Boca Raton, London, New York, Washington, D.C (2002)

    Google Scholar 

  20. Tarjan, R.E., Trojanowski, A.E.: Finding a maximum independent set. SIAM J. Comput. 6(3), 537–546 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  21. Beigel, R.: Finding maximum independent sets in sparse and general graphs. In: SODA 1999. Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, pp. 856–857 (1999)

    Google Scholar 

  22. Dantsin, E., Hirsch, E.A., Ivanov, S., Vsemirnov, M.: Algorithms for SAT and upper bounds on their complexity. Technical Report TR01-012, Electronic Colloquium on Computational Complexity (ECCC) (2001)

    Google Scholar 

  23. Fürer, M.: A faster algorithm for finding maximum independent sets in sparse graphs. In: Correa, J.R., Hevia, A., Kiwi, M. (eds.) LATIN 2006. LNCS, vol. 3887, pp. 491–501. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  24. Fürer, M., Kasiviswanathan, S.P.: Exact Max 2-SAT: Easier and faster. In: van Leeuwen, J., Italiano, G.F., van der Hoek, W., Meinel, C., Sack, H., Plášil, F. (eds.) SOFSEM 2007. LNCS, vol. 4362, pp. 272–283. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

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Eduardo Sany Laber Claudson Bornstein Loana Tito Nogueira Luerbio Faria

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Fürer, M. (2008). Solving NP-Complete Problems with Quantum Search. In: Laber, E.S., Bornstein, C., Nogueira, L.T., Faria, L. (eds) LATIN 2008: Theoretical Informatics. LATIN 2008. Lecture Notes in Computer Science, vol 4957. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78773-0_67

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  • DOI: https://doi.org/10.1007/978-3-540-78773-0_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78772-3

  • Online ISBN: 978-3-540-78773-0

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