Skip to main content
Log in

Minor-embedding in adiabatic quantum computation: I. The parameter setting problem

  • Published:
Quantum Information Processing Aims and scope Submit manuscript

Abstract

We show that the NP-hard quadratic unconstrained binary optimization (QUBO) problem on a graph G can be solved using an adiabatic quantum computer that implements an Ising spin-1/2 Hamiltonian, by reduction through minor-embedding of G in the quantum hardware graph U. There are two components to this reduction: embedding and parameter setting. The embedding problem is to find a minor-embedding G emb of a graph G in U, which is a subgraph of U such that G can be obtained from G emb by contracting edges. The parameter setting problem is to determine the corresponding parameters, qubit biases and coupler strengths, of the embedded Ising Hamiltonian. In this paper, we focus on the parameter setting problem. As an example, we demonstrate the embedded Ising Hamiltonian for solving the maximum independent set (MIS) problem via adiabatic quantum computation (AQC) using an Ising spin-1/2 system. We close by discussing several related algorithmic problems that need to be investigated in order to facilitate the design of adiabatic algorithms and AQC architectures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Aharonov, D., van Dam, W., Kempe, J., Landau, Z., Lloyd, S., Regev,O.: Adiabatic quantum computation is equaivalent to standard quantum computation. In: Proc. 45th FOCS, pp. 42–51 (2004)

  2. Amin M.H.S., Love P.J., Truncik C.J.S.: Thermally assisted adiabatic quantum computation. Phys. Rev. Lett. 100, 060503 (2008)

    Article  ADS  Google Scholar 

  3. Amin, M.H.S., Truncik, C.J.S., Averin, D.V.: The role of single qubit decoherence time in adiabatic quantum computation. arXiv:quant-ph/0803.1196 (2008)

  4. Baker B.S.: Approximation algorithms for NP-complete problems on planar graphs. J. ACM 41(1), 153–180 (1994)

    Article  MATH  Google Scholar 

  5. Bansal, N., Bravyi, S., Terhal, B.M.: A classical approximation scheme for the ground-state energy of Ising spin Hamiltonians on planar graphs. arXiv:quant-ph/0705.1115 (2007)

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

    Article  ADS  MathSciNet  Google Scholar 

  7. Boros E., Hammer P.: Pseudo-Boolean optimization. Discrete Appl. Math. 123, 155–225 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  8. Boros, E., Hammer, P.L., Tavares, G.: Preprocessing of quadratic unconstrained binary optimization. Technical Report RRR 10-2006, RUTCOR Research Report (2006)

  9. Bravyi, S., DiVincenzo, D.P., Loss, D., Terhal, B.M.: Simulation of many-body Hamiltonians using perturbation theory with bounded-strength interactions. arXiv:quant-ph/0803.2686 (2008)

  10. Childs, A., Farhi, E., Preskill, J.: Robustness of adiabatic quantum computation. Phys. Rev. A 65, 012322 (10 pp.) (2001)

    Google Scholar 

  11. Diestel R.: Graph Theory. Springer-Verlag, Heidelberg (2005)

    MATH  Google Scholar 

  12. 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), 472–476 (2001)

    Article  ADS  MathSciNet  Google Scholar 

  13. Farhi, E., Goldstone, J., Gutmann, S., Sipser, M.: Quantum computation by adiabatic evolution. arXiv:quant-ph/0001106 (2000)

  14. Ioannou, L.M., Mosca, M.: Limitations of some simple adiabatic quantum algorithms. arXiv:quant-ph/ 0702241 (2007)

  15. Kaminsky W.M., Lloyd S.: Scalable architecture for adiabatic quantum computing of NP-hard problems. In: Leggett, A.J., Ruggiero, B., Silvestrini, P. (eds) Quantum Computing and Quantum Bits in Mesoscopic Systems, Kluwer, New York (2004)

    Google Scholar 

  16. Kaminsky, W.M., Lloyd, S., Orlando, T.P.: Scalable superconducting architecture for adiabatic quantum computation. arXiv:quant-ph/0403090 (2004)

  17. Kempe J., Kitaev A., Regev., O: The complexity of the local Hamiltonian problem. SIAM J. Comput. 35, 1070 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  18. Kleinberg, J.M., Rubinfeld, R.: Short paths in expander graphs. In: IEEE Symposium on Foundations of Computer Science, pp. 86–95 (1996)

  19. Lidar, D.A., Rezakhani, A.T., Hamma, A.: Adiabatic approximation with better than exponential accuracy for many-body systems and quantum computation. arXiv:quant-ph/0808.2697 (2008)

  20. Oliveira, R., Terhal, B.M.: The complexity of quantum spin systems on a two-dimensional square lattice. arXiv:quant-ph/0504050 (2005)

  21. Reichardt, B.W.: The quantum adiabatic optimization algorithm and local minima. In: STOC ’04: Proceedings of the Thirty-sixth Annual ACM Symposium on Theory of Computing, pp. 502–510. ACM, New York, NY, USA (2004)

  22. Robertson N., Seymour P.D.: Graph minors. xiii: the disjoint paths problem. J. Comb. Theory Ser. B 63(1), 65–110 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  23. Thomson, L.F.: Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes. Morgan Kaufmann, San Mateo, California (1992)

  24. van Dam, W., Mosca, M., Vazirani, U.: How powerful is adiabatic quantum computation? In: Proc. 42nd FOCS, pp. 279–287 (2001)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vicky Choi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Choi, V. Minor-embedding in adiabatic quantum computation: I. The parameter setting problem. Quantum Inf Process 7, 193–209 (2008). https://doi.org/10.1007/s11128-008-0082-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11128-008-0082-9

Keywords

Navigation