1887

Abstract

Summary

In this work we closely examine the overlap between (1) the computationally challenging problems in geophysical applications in the upstream business and (2) the capabilities of quantum annealing (QA) quantum processing units (QPUs). We analyze the strengths and limitations of the latter and we explain how and why QPUs need to be assisted with CPUs and GPUs to solve combinatorial optimization problems at industrial scale. We illustrate the discussion with three potential use cases: stack power maximization for residual statics estimation, “ray tracing” and compressive sensing.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.2023630003
2023-09-25
2026-02-07
Loading full text...

Full text loading...

References

  1. Ayanzadeh, R., Halem, M. and Finin, T. [2020] An ensemble approach for compressive sensing with quantum annealers. In: IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 3517–3520.
    [Google Scholar]
  2. van den Berg, E. and Friedlander, M.P. [2009] Probing the Pareto frontier for basis pursuit solutions.Siam journal on scientific computing, 31(2), 890–912.
    [Google Scholar]
  3. Borle, A. and Lomonaco, S.J. [2022] How viable is quantum annealing for solving linear algebra problems?arXiv preprint arXiv:2206.10576.
    [Google Scholar]
  4. Brooke, J., Bitko, D., Rosenbaum and Aeppli, G. [1999] Quantum annealing of a disordered magnet.Science, 284(5415), 779–781.
    [Google Scholar]
  5. Chancellor, N. [2019] Domain wall encoding of discrete variables for quantum annealing and QAOA.Quantum Science and Technology, 4(4), 045004.
    [Google Scholar]
  6. Dukalski, M., Oudejans, B. and Kontakis, A. [2023a] Practically feasible shortest path estimation with a quantum annealer (submitted). In: SEG/AAPG International Meeting for Applied Geoscience & Energy.
    [Google Scholar]
  7. Dukalski, M., Rovetta, D., van der Linde, S., Möller, M., Neumann, N. and Phillipson, F. [2023b] Quantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimation.Geophysics, 88(2), V75–V91.
    [Google Scholar]
  8. Krauss, T. and McCollum, J. [2020] Solving the network shortest path problem on a quantum annealer.IEEE Transactions on Quantum Engineering, 1, 1–12.
    [Google Scholar]
  9. Ledoux, R. and Dukalski, M. [2023] Advantages of domain-wall encoded formulation of refraction residual statics estimation on a quantum annealer (submitted). In: SEG/AAPG International Meeting for Applied Geoscience & Energy.
    [Google Scholar]
  10. McGeoch, C., Farre, P. and Bernoudy, W. [2020] D-Wave hybrid solver service+ advantage: Technology update.D-Wave: The Quantum Computing Company, Tech. Rep.
    [Google Scholar]
  11. Mücke, S., Heese, R., Müller, S., Wolter, M. and Piatkowski, N. [2023] Feature selection on quantum computers.Quantum Machine Intelligence, 5(1), 11.
    [Google Scholar]
  12. Nguyen, M.T., Liu, J.G., Wurtz, J., Lukin, M.D., Wang, S.T. and Pichler, H. [2023] Quantum optimization with arbitrary connectivity using Rydberg atom arrays.PRX Quantum, 4(1), 010316.
    [Google Scholar]
  13. Raymond, J., Stevanovic, R., Bernoudy, W., Boothby, K., McGeoch, C.C., Berkley, A.J., Farré, P., Pasvolsky, J. and King, A.D. [2022] Hybrid quantum annealing for larger-than-QPU lattice-structured problems.ACM Transactions on Quantum Computing.
    [Google Scholar]
  14. Romano, Y., Primack, H., Vaknin, T., Meirzada, I., Karpas, I., Furman, D., Tradonsky, C. and Shlomi, R.B. [2022] Quantum Sparse Coding.arXiv preprint arXiv:2209.03788.
    [Google Scholar]
  15. Rovetta, D., Dukalski, M. and Kontakis, A. [2023] Quantum annealer-assisted residual refraction statics estimation on the SEAM Arid model dataset. In: 84th EAGE Annual Conference & Exhibition, 2023. 1–5.
    [Google Scholar]
  16. da Silva Coelho, W., Henriet, L. and Henry, L.P. [2023] A quantum pricing-based column generation framework for hard combinatorial problems.arXiv preprint arXiv:2301.02637.
    [Google Scholar]
/content/papers/10.3997/2214-4609.2023630003
Loading
/content/papers/10.3997/2214-4609.2023630003
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error