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A quantum computing concept for 1-D elastic wave simulation with exponential speedup (2312.14747v2)

Published 22 Dec 2023 in physics.geo-ph and quant-ph

Abstract: Quantum computing has attracted considerable attention in recent years because it promises speed-ups that conventional supercomputers cannot offer, at least for some applications. Though existing quantum computers are, in most cases, still too small to solve significant problems, their future impact on domain sciences is already being explored now. Within this context, we present a quantum computing concept for 1-D elastic wave propagation in heterogeneous media with two components: a theoretical formulation and an implementation on a real quantum computer. The method rests on a finite-difference approximation, followed by a sparsity-preserving transformation of the discrete elastic wave equation to a Schr\"{o}dinger equation, which can be simulated directly on a gate-based quantum computer. An implementation on an error-free quantum simulator verifies our approach and forms the basis of numerical experiments with small problems on the real quantum computer IBM Brisbane. The latter produce simulation results that qualitatively agree with the error-free version but are contaminated by quantum decoherence and noise effects. Complementing the discrete transformation to the Schr\"{o}dinger equation by a continuous version allows the replacement of finite differences by other spatial discretisation schemes, such as the spectral-element method. Anticipating the emergence of error-corrected quantum chips, an analogy between our method and analyses of coupled mass-spring systems suggests that our quantum computing approach may lead to wave field simulations that run exponentially faster than simulations on classical computers.

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References (63)
  1. Quantum algorithm implementations for beginners. ACM Transactions on Quantum Computing 3(4), 18:1–18:92.
  2. Al-Mohy, A. H. and N. J. Higham (2010). A new scaling and squaring algorithm for the matrix exponential. SIAM Journal on Matrix Analysis and Applications 31(3), 970–989.
  3. Qiskit: An open-source framework for quantum computing.
  4. Exponential quantum speedup in simulating coupled classical oscillators. arXiv preprint arXiv:2303.13012.
  5. Principles of quantum computation and information-volume I: Basic concepts. World scientific.
  6. Efficient quantum algorithms for simulating sparse hamiltonians. Communications in Mathematical Physics 270, 359–371.
  7. Simulating Hamiltonian dynamics with a truncated Taylor series. Phys. Rev. Lett. 114, 090502–1–090502–5.
  8. Global adjoint tomography: First-generation model. Geophys. J. Int. 207, 1739–1766.
  9. High-threshold and low-overhead fault-tolerant quantum memory. arXiv preprint arXiv:2308.07915.
  10. The boundary for quantum advantage in gaussian boson sampling. Science advances 8(4), eabl9236.
  11. Toward the first quantum simulation with quantum speedup. Proceedings of the National Academy of Sciences 115(38), 9456–9461.
  12. Ibm unveils breakthrough 127-qubit quantum processor. IBM Newsroom.
  13. Quantum algorithm for simulating the wave equation. Physical Review A 99(1), 012323.
  14. Practical quantum advantage in quantum simulation. Nature 607(7920), 667–676.
  15. Dhand, I. and B. C. Sanders (2014). Stability of the Trotter–Suzuki decomposition. Journal of Physics A: Mathematical and Theoretical 47(26), 265206.
  16. Dormand, J. R. and P. J. Prince (1980). A family of embedded runge-kutta formulae. Journal of computational and applied mathematics 6(1), 19–26.
  17. A game of quantum advantage: Linking verification and simulation. Quantum 6, 753.
  18. Gambetta, J. (2020). Ibm’s roadmap for scaling quantum technology. IBM Research Blog (September 2020).
  19. Seamless GPU acceleration for C++‐based physics with the Metal Shading Language on Apple’s M series unified chips. Seismological Research Letters 94(3), 1670–1675.
  20. Quantum simulation. Reviews of Modern Physics 86(1), 153.
  21. Gibney, E. (2019). Hello quantum world! google publishes landmark quantum supremacy claim. Nature 574(7779), 461–463.
  22. An efficient algorithm for sparse quantum state preparation. In Proceedings: 58th Design Automation Conference, pp.  433–438. IEEG.
  23. Grover, L. K. (1996). A fast quantum mechanical algorithm for database search. In Proceedings of the twenty-eighth annual ACM symposium on Theory of computing, pp.  212–219.
  24. Towards quantum advantage via topological data analysis. Quantum 6, 855.
  25. Quantum algorithm for linear systems of equations. Physical review letters 103(15), 150502.
  26. Finding exponential product formulas of higher orders, pp.  37–68. Springer.
  27. A framework for demonstrating practical quantum advantage: Racing quantum against classical generative models.
  28. Quantum advantage in learning from experiments. Science 376(6598), 1182–1186.
  29. Waveform inversion of marine reflection seismograms for P impedance and Poisson’s ratio. Geophys. J. Int. 124, 363–371.
  30. Anisotropic wave propagation through FD grids. Geophysics 60, 1203–1216.
  31. Quantum simulation of maxwell’s equations via schröodingersation. arXiv preprint arXiv:2308.08408.
  32. Quantum simulation of partial differential equations via schrodingerisation: Technical details. arXiv preprint arXiv:2212.14703.
  33. Kak, S. (1999). The initialization problem in quantum computing. Foundations of Physics 29, 267–279.
  34. Kane, C. L. and T. C. Lubensky (2014). Topological boundary modes in isostatic lattices. Nature Physics 10(1), 39–45.
  35. Evidence for the utility of quantum computing before fault tolerance. Nature 618(7965), 500–505.
  36. Knill, E. (2005). Quantum computing with realistically noisy devices. Nature 434(7029), 39–44.
  37. A rigorous and robust quantum speed-up in supervised machine learning. Nature Physics 17(9), 1013–1017.
  38. Closing the” quantum supremacy” gap: Achieving real-time simulation of a random quantum circuit using a new sunway supercomputer. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC ’21, New York, NY, USA, pp.  1–12. Association for Computing Machinery.
  39. Low, G. H. and I. L. Chuang (2019). Hamiltonian simulation by qubitization. Quantum 3, 163.
  40. Quantum computational advantage with a programmable photonic processor. Nature 606(7912), 75–81.
  41. Quantum advantage for computations with limited space. Nature Physics 17(8), 894–897.
  42. The finite-difference modelling of earthquake motions: Waves and ruptures. CUP.
  43. Montanaro, A. (2016). Quantum algorithms: An overview. npj Quantum Information 2(1), 1–8.
  44. Quantum computing in geophysics: Algorithms, computational costs, and future applications. In SEG International Exposition and Annual Meeting, pp.  SEG–2018. SEG.
  45. When quantum computers arrive on seismology’s doorstep. Canadian Journal of Exploration Geophysics 44, 1–20.
  46. Nielsen, M. A. and I. L. Chuang (2010). Quantum computation and quantum information. CUP.
  47. Preskill, J. (2018). Quantum computing in the nisq era and beyond. Quantum 2, 79.
  48. Press, F. (1968). Earth models obtained by Monte-Carlo inversion. J. Geophys. Res. 73, 5223–5234.
  49. Sanders, B. C. (2021). Quantum leap for quantum primacy. Physics 14, 147.
  50. Sevilla, J. and C. J. Riedel (2020). Forecasting timelines of quantum computing. arXiv preprint arXiv:2009.05045.
  51. Shampine, L. F. (1986). Some practical runge-kutta formulas. Mathematics of Computation 46(173), 135–150.
  52. Shor, P. W. (1994). Algorithms for quantum computation: Discrete logarithms and factoring. In Proceedings: 35th Annual Symposium on Foundations of Computer Science, pp.  124–134. IEEG.
  53. Shor, P. W. (1999). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM review 41(2), 303–332.
  54. Real-time quantum error correction beyond break-even. Nature 616(7955), 50–55.
  55. Efficient method for computing the maximum-likelihood quantum state from measurements with additive gaussian noise. Physical review letters 108(7), 070502.
  56. Practical quantum computing: Solving the wave equation using a quantum approach. ACM Transactions on Quantum Computing 2(1), 1–35.
  57. Süsstrunk, R. and S. D. Huber (2016). Classification of topological phonons in linear mechanical metamaterials. Proceedings of the National Academy of Sciences 113(33), E4767–E4775.
  58. Suzuki, M. (1976). Generalized trotter’s formula and systematic approximants of exponential operators and inner derivations with applications to many-body problems. Communications in Mathematical Physics 51(2), 183–190.
  59. Suzuki, M. (1991). General theory of fractal path integrals with applications to many-body theories and statistical physics. Journal of Mathematical Physics 32(2), 400–407.
  60. Trotter, H. F. (1959). On the product of semi-groups of operators. Proceedings of the American Mathematical Society 10(4), 545–551.
  61. Watrous, J. (2018). The theory of quantum information. CUP.
  62. Spectral analysis of product formulas for quantum simulation. npj Quantum Information 8(1), 1–6.
  63. On circuit complexity of quantum access models for encoding classical data. arXiv preprint arXiv.2311.11365.
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