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Quantum simulation of wave optics in weakly inhomogeneous media using block encoding

Siavash Davani1,2,*, Martin Gärttner3,†, and Falk Eilenberger4,1,2,‡

  • *Contact author: siavash.davani@uni-jena.de
  • †Contact author: martin.gaerttner@uni-jena.de
  • ‡Contact author: falk.eilenberger@uni-jena.de

Phys. Rev. Research 8, 043117 – Published 2 November, 2026

DOI: https://doi.org/10.1103/snzq-fp39

Abstract

We propose a quantum algorithm that simulates the propagation of a light field through a weakly inhomogeneous medium. In the paraxial approximation, the wave equation in an inhomogeneous material takes the form of the Schrödinger equation with a time-dependent Hamiltonian. This reduction is used to simulate wave optical dynamics on a quantum computer. Beam propagator operators for a short propagation distance are constructed using an efficient and flexible block encoding that enables the simulation of various optical setups. The algorithm is showcased by simulating the propagation of a one-dimensional Gaussian beam through a lens of finite thickness, and the resulting spherical aberration is demonstrated.

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References (50)

  1. A. J. Daley, I. Bloch, C. Kokail, S. Flannigan, N. Pearson, M. Troyer, and P. Zoller, Practical quantum advantage in quantum simulation, Nature (London) 607, 667 (2022).
  2. E. Altman, K. R. Brown, G. Carleo, L. D. Carr, E. Demler, C. Chin, B. DeMarco, S. E. Economou, M. A. Eriksson, K.-M. C. Fu, et al., Quantum simulators: Architectures and opportunities, PRX Quantum 2, 017003 (2021).
  3. S. Pirandola, U. L. Andersen, L. Banchi, M. Berta, D. Bunandar, R. Colbeck, D. Englund, T. Gehring, C. Lupo, C. Ottaviani, J. L. Pereira, M. Razavi, J. S. Shaari, M. Tomamichel, V. C. Usenko, G. Vallone, P. Villoresi, and P. Wallden, Advances in quantum cryptography, Adv. Opt. Photonics 12, 1012 (2020).
  4. M. Cerezo, G. Verdon, H.-Y. Huang, L. Cincio, and P. J. Coles, Challenges and opportunities in quantum machine learning, Nat. Comput. Sci. 2, 567 (2022).
  5. A. M. Dalzell, S. McArdle, M. Berta, P. Bienias, C.-F. Chen, A. Gilyén, C. T. Hann, M. J. Kastoryano, E. T. Khabiboulline, A. Kubica, G. Salton, S. Wang, and F. G. S. L. Brandão, Quantum Algorithms: A Survey of Applications and End-to-End Complexities (Cambridge University Press, Cambridge, 2025).
  6. R. Santagati, A. Aspuru-Guzik, R. Babbush, M. Degroote, L. González, E. Kyoseva, N. Moll, M. Oppel, R. M. Parrish, N. C. Rubin, M. Streif, C. S. Tautermann, H. Weiss, N. Wiebe, and C. Utschig-Utschig, Drug design on quantum computers, Nat. Phys. 20, 549 (2024).
  7. D. Herman, C. Googin, X. Liu, Y. Sun, A. Galda, I. Safro, M. Pistoia, and Y. Alexeev, Quantum computing for finance, Nat. Rev. Phys. 5, 450 (2023).
  8. A. Di Meglio, K. Jansen, I. Tavernelli, C. Alexandrou, S. Arunachalam, C. W. Bauer, K. Borras, S. Carrazza, A. Crippa, V. Croft, et al., Quantum computing for high-energy physics: State of the art and challenges, PRX Quantum 5, 037001 (2024).
  9. G. Mazzola, Quantum computing for chemistry and physics applications from a Monte Carlo perspective, J. Chem. Phys. 160, 010901 (2024).
  10. R. P. Feynman, Simulating physics with computers, Int. J. Theor. Phys. 21, 467 (1982).
  11. S. Lloyd, Universal quantum simulators, Science 273, 1073 (1996).
  12. I. M. Georgescu, S. Ashhab, and F. Nori, Quantum simulation, Rev. Mod. Phys. 86, 153 (2014).
  13. A. W. Harrow, A. Hassidim, and S. Lloyd, Quantum algorithm for linear systems of equations, Phys. Rev. Lett. 103, 150502 (2009).
  14. D. W. Berry, High-order quantum algorithm for solving linear differential equations, J. Phys. A: Math. Theor. 47, 105301 (2014).
  15. P. Webster, M. Vasmer, T. R. Scruby, and S. D. Bartlett, Universal fault-tolerant quantum computing with stabilizer codes, Phys. Rev. Res. 4, 013092 (2022).
  16. R. Toshio, Y. Akahoshi, J. Fujisaki, H. Oshima, S. Sato, and K. Fujii, Practical quantum advantage on partially fault-tolerant quantum computer, Phys. Rev. X 15, 021057 (2025).
  17. F. Battistel, C. Chamberland, K. Johar, R. W. J. Overwater, F. Sebastiano, L. Skoric, Y. Ueno, and M. Usman, Real-time decoding for fault-tolerant quantum computing: Progress, challenges and outlook, Nano Futures 7, 032003 (2023).
  18. E. T. Campbell, B. M. Terhal, and C. Vuillot, Roads towards fault-tolerant universal quantum computation, Nature (London) 549, 172 (2017).
  19. A. M. Childs and N. Wiebe, Hamiltonian simulation using linear combinations of unitary operations, Quantum Inf. Comput. 12, 901 (2012).
  20. G. H. Low and I. L. Chuang, Hamiltonian simulation by qubitization, Quantum 3, 163 (2019).
  21. A. Gilyén, Y. Su, G. H. Low, and N. Wiebe, Quantum singular value transformation and beyond: Exponential improvements for quantum matrix arithmetics, in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, STOC 2019 (Association for Computing Machinery, New York, NY, 2019), pp. 193–204.
  22. J. M. Martyn, Z. M. Rossi, A. K. Tan, and I. L. Chuang, Grand unification of quantum algorithms, PRX Quantum 2, 040203 (2021).
  23. J. D. Schmidt, J. A. Tellez, and G. J. Gbur, Semi-analytic simulation of optical wave propagation through turbulence, Appl. Opt. 61, 9439 (2022).
  24. R. Heintzmann, L. Loetgering, and F. Wechsler, Scalable angular spectrum propagation, Optica 10, 1407 (2023).
  25. M. Wende, J. Drozella, A. Toulouse, and A. M. Herkommer, Fast vector wave optical simulation methods for application on 3D-printed microoptics, J. Opt. Microsyst. 4, 024501 (2024).
  26. Y. Liu, F. Guo, Z. Zhang, and R. Wu, Wave optical propagation in realistic lens systems through multi-slice decomposition with phase compensation, Appl. Opt. 63, F18 (2024).
  27. Y. Sung, B. Nelson, and R. Gupta, Realistic wave-optics simulation of X-ray dark-field imaging at a human scale, Sci. Rep. 15, 26748 (2025).
  28. J. Lu, D. Qu, J. Qu, R. Fong, G. H. Ahn, and J. Vuckovic, A systolic update scheme to overcome memory bandwidth limitations in GPU-accelerated FDTD simulations, arXiv:2502.20610.
  29. Z. Wu, Y. You, X. Zhou, and F. Zhang, Accelerated time-domain simulation of complex photonic structures with a data-aware Fourier neural operator, Opt. Commun. 604, 132838 (2026).
  30. R. Shams and P. Sadeghi, On optimization of finite-difference time-domain (FDTD) computation on heterogeneous and GPU clusters, J. Parallel Distrib. Comput. 71, 584 (2011).
  31. B. D. Clader, B. C. Jacobs, and C. R. Sprouse, Preconditioned quantum linear system algorithm, Phys. Rev. Lett. 110, 250504 (2013).
  32. C. Cholsuk, S. Davani, L. O. Conlon, T. Vogl, and F. Eilenberger, Efficient light propagation algorithm using quantum computers, Phys. Scr. 99, 045110 (2024).
  33. J. S. Kottmann, M. Krenn, T. H. Kyaw, S. Alperin-Lea, and A. Aspuru-Guzik, Quantum computer-aided design of quantum optics hardware, Quantum Sci. Technol. 6, 035010 (2021).
  34. J. M. Burzler, S. Hughes, and B. S. Wherrett, Split-step Fourier methods applied to model nonlinear refractive effects in optically thick media, Appl. Phys. B 62, 389 (1996).
  35. M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, 10th ed. (Cambridge University Press, Cambridge, New York, 2010).
  36. B. E. A. Saleh and M. C. Teich, Fundamentals of Photonics, 1st ed. (Wiley, New York, 1991).
  37. H. F. Trotter, On the product of semi-groups of operators, Proc. Am. Math. Soc. 10, 545 (1959).
  38. S.-J. Ran, Encoding of matrix product states into quantum circuits of one- and two-qubit gates, Phys. Rev. A 101, 032310 (2020).
  39. J. Iaconis, S. Johri, and E. Y. Zhu, Quantum state preparation of normal distributions using matrix product states, npj Quantum Inf. 10, 15 (2024).
  40. M. Rosenkranz, E. Brunner, G. Marin-Sanchez, N. Fitzpatrick, S. Dilkes, Y. Tang, Y. Kikuchi, and M. Benedetti, Quantum state preparation for multivariate functions, Quantum 9, 1703 (2025).
  41. M. Moosa, T. W. Watts, Y. Chen, A. Sarma, and P. L. McMahon, Linear-depth quantum circuits for loading Fourier approximations of arbitrary functions, Quantum Sci. Technol. 9, 015002 (2024).
  42. X. Yang, X. Nie, Y. Ji, T. Xin, D. Lu, and J. Li, Improved quantum computing with higher-order Trotter decomposition, Phys. Rev. A 106, 042401 (2022).
  43. M. Kieferová, A. Scherer, and D. W. Berry, Simulating the dynamics of time-dependent Hamiltonians with a truncated Dyson series, Phys. Rev. A 99, 042314 (2019).
  44. M. Schuld and F. Petruccione, Representing data on a quantum computer, in Machine Learning with Quantum Computers, edited by M. Schuld and F. Petruccione (Springer International Publishing, Cham, 2021), pp. 147–176.
  45. A. Gilyén, S. Arunachalam, and N. Wiebe, Optimizing quantum optimization algorithms via faster quantum gradient computation, in Proceedings of the 2019 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (Society for Industrial and Applied Mathematics, Philadelphia, PA, 2019), pp. 1425–1444.
  46. S. Davani, Blackwild/qiu: Code repository snapshot 2026-03-30, Zenodo, 2026, https://doi.org/10.5281/zenodo.19333736.
  47. S. Davani, M. Gärttner, and F. Eilenberger, Dataset of the publication: Quantum simulation of wave optics in weakly inhomogeneous media using block-encoding, Zenodo, 2026, http://doi.org/10.5281/zenodo.19333783.
  48. S. Blanes, F. Casas, J. A. Oteo, and J. Ros, The Magnus expansion and some of its applications, Phys. Rep. 470, 151 (2009).
  49. M. Hochbruck and C. Lubich, On Magnus integrators for time-dependent Schrödinger equations, SIAM J. Numer. Anal. 41, 945 (2003).
  50. A. M. Childs, Y. Su, M. C. Tran, N. Wiebe, and S. Zhu, Theory of Trotter error with commutator scaling, Phys. Rev. X 11, 011020 (2021).

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