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  • Open Access

Large time-step discretization of adiabatic quantum dynamics

Dong An1,2,*, Pedro C. S. Costa3,4, and Dominic W. Berry4

  • *Contact author: dongan@pku.edu.cn

Phys. Rev. Research 8, 043098 – Published 28 October, 2026

DOI: https://doi.org/10.1103/kh4h-m8z9

Abstract

Adiabatic quantum computing is a general framework for preparing eigenstates of Hamiltonians on quantum devices. However, its digital implementation requires an efficient Hamiltonian simulation subroutine, which may introduce extra computational overhead or complicated quantum control logic. In this work, we show that the time-step sizes in time discretization can be much larger than expected, and the overall complexity is greatly reduced. Remarkably, regardless of the general convergence order of the numerical method, we can choose a uniform time-step size independent of tolerated error and evolution time for sufficiently accurate simulation. Furthermore, with the boundary cancellation condition where the continuous diabatic errors are exponentially suppressed, we provide strong evidence for a superpolynomial convergence of first-order Trotter with uniform time-step size. We apply our analysis to the example of adiabatic unstructured search and show several preferable features of the Trotterized adiabatic approach: It can match the Grover lower bound, it does not require a priori knowledge of the number of marked states, and its performance can be asymptotically comparable with that of the quantum approximate optimization algorithm.

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References (54)

  1. T. Albash and D. A. Lidar, Adiabatic quantum computation, Rev. Mod. Phys. 90, 015002 (2018).
  2. M. Hochbruck and A. Ostermann, Exponential integrators, Acta Numer. 19, 209 (2010).
  3. B. F. Schiffer, J. Tura, and J. I. Cirac, Adiabatic spectroscopy and a variational quantum adiabatic algorithm, PRX Quantum 3, 020347 (2022).
  4. E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser, Quantum computation by adiabatic evolution, arXiv:quant-ph/0001106.
  5. W. van Dam, M. Mosca, and U. Vazirani, How powerful is adiabatic quantum computation? in Proceedings 42nd IEEE Symposium on Foundations of Computer Science (IEEE Computer Society, Los Alamitos, CA, USA, 2001), pp. 279–287.
  6. 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).
  7. D. An, D. Fang, and L. Lin, Time-dependent unbounded Hamiltonian simulation with vector norm scaling, Quantum 5, 459 (2021).
  8. M. Suzuki, General decomposition theory of ordered exponentials. Proc. Jpn. Acad. Ser. B 69, 161 (1993).
  9. 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).
  10. G. H. Low and N. Wiebe, Hamiltonian simulation in the interaction picture, arXiv:1805.00675.
  11. Y. Su, D. W. Berry, N. Wiebe, N. Rubin, and R. Babbush, Fault-tolerant quantum simulations of chemistry in first quantization, PRX Quantum, 2, 040332 (2021).
  12. A. Rajput, A. Roggero, and N. Wiebe, Hybridized methods for quantum simulation in the interaction picture, Quantum 6, 780 (2022).
  13. G. Nenciu, Linear adiabatic theory. Exponential estimates, Commun. Math. Phys. 152, 479 (1993).
  14. S. Morita, Faster annealing schedules for quantum annealing, J. Phys. Soc. Jpn. 76, 104001 (2007).
  15. D. A. Lidar, A. T. Rezakhani, and A. Hamma, Adiabatic approximation with exponential accuracy for many-body systems and quantum computation, J. Math. Phys. 50, 102106 (2009).
  16. N. Wiebe and N. S Babcock, Improved error-scaling for adiabatic quantum evolutions, New J. Phys. 14, 013024 (2012).
  17. A. Elgart and G. A. Hagedorn, A note on the switching adiabatic theorem, J. Math. Phys. 53, 102202 (2012).
  18. Y. Ge, A. Molnár, and J. I. Cirac, Rapid adiabatic preparation of injective projected entangled pair states and Gibbs states, Phys. Rev. Lett. 116, 080503 (2016).
  19. E. Farhi, J. Goldstone, and S. Gutmann, A quantum approximate optimization algorithm, arXiv:1411.4028.
  20. C. Yi, Success of digital adiabatic simulation with large Trotter step, Phys. Rev. A 104, 052603 (2021).
  21. L. K. Kovalsky, F. A. Calderon-Vargas, M. D. Grace, A. B. Magann, J. B. Larsen, A. D. Baczewski, and M. Sarovar, Self-healing of Trotter error in digital adiabatic state preparation, Phys. Rev. Lett. 131, 060602 (2023).
  22. N. Wiebe, D. Berry, P. Høyer, and B. C. Sanders, Higher order decompositions of ordered operator exponentials, J. Phys. A: Math. Theor. 43, 065203 (2010).
  23. H. Yoshida, Construction of higher order symplectic integrators, Phys. Lett. A 150, 262 (1990).
  24. M. E. S. Morales, P. C. S. Costa, G. Pantaleoni, D. K. Burgarth, Y. R. Sanders, and D. W. Berry, Selection and improvement of product formulae for best performance of quantum simulation, Quantum Inf. Comput. 25, 1 (2025).
  25. S. Blanes, F. Casas, and A. Murua, Splitting methods for differential equations, Acta Numer. 33, 1 (2024).
  26. S. Jansen, Mary-Beth Ruskai, and R. Seiler, Bounds for the adiabatic approximation with applications to quantum computation, J. Math. Phys. 48, 102111 (2007).
  27. T. Hogg, Adiabatic quantum computing for random satisfiability problems, Phys. Rev. A 67, 022314 (2003).
  28. A. Tanaka, Adiabatic theorem for discrete time evolution, J. Phys. Soc. Jpn. 80, 125002 (2011).
  29. V. Kremenetski, A. Apte, T. Hogg, S. Hadfield, and N. M. Tubman, Quantum alternating operator ansatz (QAOA) beyond low depth with gradually changing unitaries, arXiv:2305.04455.
  30. A. Dranov, J. Kellendonk, and R. Seiler, Discrete time adiabatic theorems for quantum mechanical systems, J. Math. Phys. 39, 1340 (1998).
  31. P. C. S. Costa, D. An, Y. R. Sanders, Y. Su, R. Babbush, and D. W. Berry, Optimal scaling quantum linear-systems solver via discrete adiabatic theorem, PRX Quantum 3, 040303 (2022).
  32. Y. Subaşı, R. D. Somma, and D. Orsucci, Quantum algorithms for systems of linear equations inspired by adiabatic quantum computing, Phys. Rev. Lett. 122, 060504 (2019).
  33. D. An and L. Lin, Quantum linear system solver based on time-optimal adiabatic quantum computing and quantum approximate optimization algorithm, ACM Trans. Quantum Comput. 3, 1 (2022).
  34. D. W. Berry, A. M. Childs, R. Cleve, R. Kothari, and R. D. Somma, Exponential improvement in precision for simulating sparse Hamiltonians, in Proceedings of the 46th Annual ACM Symposium on Theory of Computing, STOC '14 (ACM, New York, NY, USA, 2014), pp. 283–292.
  35. J. E. Avron and A. Elgart, Adiabatic theorem without a gap condition, Commun. Math. Phys. 203, 445 (1999).
  36. J. Roland and N. J. Cerf, Quantum search by local adiabatic evolution, Phys. Rev. A 65, 042308 (2002).
  37. A. M. Dalzell, T. J. Yoder, and I. L. Chuang, Fixed-point adiabatic quantum search, Phys. Rev. A 95, 012311 (2017).
  38. T. J. Yoder, G. H. Low, and I. L. Chuang, Fixed-point quantum search with an optimal number of queries, Phys. Rev. Lett. 113, 210501 (2014).
  39. L. K. Grover, A fast quantum mechanical algorithm for database search, in Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, STOC '96 (Association for Computing Machinery, New York, NY, USA, 1996), pp. 212–219.
  40. Z. Jiang, E. G. Rieffel, and Z. Wang, Near-optimal quantum circuit for Grover's unstructured search using a transverse field, Phys. Rev. A 95, 062317 (2017).
  41. 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, USA, 2019), pp. 193–204.
  42. G. H. Low and I. L. Chuang, Hamiltonian simulation by qubitization, Quantum 3, 163 (2019).
  43. E. Crosson, E. Farhi, C. Y.-Y. Lin, H.-H. Lin, and P. Shor, Different strategies for optimization using the quantum adiabatic algorithm, arXiv:1401.7320.
  44. E. Farhi, J. Goldstone, D. Gosset, S. Gutmann, H. B. Meyer, and P. Shor, Quantum adiabatic algorithms, small gaps, and different paths, Quantum Inf. Comput. 11, 181 (2011).
  45. L. Zeng, J. Zhang, and M. Sarovar, Schedule path optimization for adiabatic quantum computing and optimization, J. Phys. A: Math. Theor. 49, 165305 (2016).
  46. L. Hormozi, E. W. Brown, G. Carleo, and M. Troyer, Nonstoquastic Hamiltonians and quantum annealing of an Ising spin glass, Phys. Rev. B 95, 184416 (2017).
  47. Y. Seki and H. Nishimori, Quantum annealing with antiferromagnetic fluctuations, Phys. Rev. E 85, 051112 (2012).
  48. B. Seoane and H. Nishimori, Many-body transverse interactions in the quantum annealing of the p-spin ferromagnet, J. Phys. A: Math. Theor. 45, 435301 (2012).
  49. Y. Seki and H. Nishimori, Quantum annealing with antiferromagnetic transverse interactions for the Hopfield model, J. Phys. A: Math. Theor. 48, 335301 (2015).
  50. H. Nishimori and K. Takada, Exponential enhancement of the efficiency of quantum annealing by non-stoquastic Hamiltonians, Front. ICT 4, 2 (2017).
  51. D. An, Simulating many-body quantum dynamics on classical and quantum computers, UC Berkeley Electronic Theses and Dissertations, University of California, Berkeley, 2021.
  52. R. Bhatia and C. Davis, A bound for the spectral variation of a unitary operator, Linear Multilinear Algebra 15, 71 (1984).
  53. L. M. Milne-Thomson, The Calculus of Finite Differences (Macmillan and Co, London, 1933).
  54. L. Elsner and C. He, Perturbation and interlace theorems for the unitary eigenvalue problem, Linear Algebra Appl. 188-189, 207 (1993).

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