Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Constant geometric speed schedule for adiabatic state preparation

Mancheon Han1,*, Hyowon Park2,3, and Sangkook Choi1,†

  • *Contact author: mchan@kias.re.kr
  • †Contact author: sangkookchoi@kias.re.kr

Phys. Rev. Research 8, 023233 – Published 1 June, 2026

DOI: https://doi.org/10.1103/ygs3-xgb1

Abstract

The efficiency of adiabatic quantum evolution is governed by the evolution time T, which typically scales as O(Δ−2) with the minimum energy gap Δ. However, the rigorous lower bound is O(LΔ−1), where L is the adiabatic path length. Although L is formally upper bounded by O(Δ−1), such a bound is often too loose in practice, and L can be bounded independently of Δ. This indicates the potential for a quadratic speedup through adiabatic schedule construction. Here, we introduce the constant geometric speed (CGS) schedule, which traverses the adiabatic path at a uniform rate. We show that this approach reduces the scaling of the evolution time by a factor of Δ−1, provided L remains bounded independently of Δ. We propose a segmented CGS protocol where path segment lengths are computed from eigenstate overlaps on the fly, reducing the prior spectral-knowledge requirement from the full gap function Δ(s) to just a global lower bound on the energy gap. Numerical tests on adiabatic unstructured search, N2, and a [2Fe-2S] cluster demonstrate the optimal Δ−1 scaling, confirming a quadratic speedup over the standard linear schedule.

View figure in article

Physics Subject Headings (PhySH)

Article Text

Supplemental Material

References (41)

  1. M. Born and V. Fock, Beweis des Adiabatensatzes, Z. Phys. 51, 165 (1928).
  2. T. Kato, On the adiabatic theorem of quantum mechanics, J. Phys. Soc. Jpn. 5, 435 (1950).
  3. S. Jansen, M.-B. Ruskai, and R. Seiler, Bounds for the adiabatic approximation with applications to quantum computation, J. Math. Phys. 48, 102111 (2007).
  4. E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser, Quantum computation by adiabatic evolution, arXiv:quant-ph/0001106.
  5. I. M. Georgescu, S. Ashhab, and F. Nori, Quantum simulation, Rev. Mod. Phys. 86, 153 (2014).
  6. T. Albash and D. A. Lidar, Adiabatic quantum computation, Rev. Mod. Phys. 90, 015002 (2018).
  7. A. Aspuru-Guzik, A. D. Dutoi, P. J. Love, and M. Head-Gordon, Simulated quantum computation of molecular energies, Science 309, 1704 (2005).
  8. L. Veis and J. Pittner, Adiabatic state preparation study of methylene, J. Chem. Phys. 140, 214111 (2014).
  9. C. Mc Keever and M. Lubasch, Towards adiabatic quantum computing using compressed quantum circuits, PRX Quantum 5, 020362 (2024).
  10. M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information (Cambridge University Press, Cambridge, UK, 2010).
  11. A. Y. Kitaev, Quantum measurements and the Abelian stabilizer problem, arXiv:quant-ph/9511026.
  12. D. S. Abrams and S. Lloyd, Quantum algorithm providing exponential speed increase for finding eigenvalues and eigenvectors, Phys. Rev. Lett. 83, 5162 (1999).
  13. J.-H. Chen, Lower bounds for adiabatic quantum algorithms by quantum speed limits, Phys. Rev. Res. 5, 033175 (2023).
  14. S. Boixo and R. D. Somma, Necessary condition for the quantum adiabatic approximation, Phys. Rev. A 81, 032308 (2010).
  15. J. Roland and N. J. Cerf, Quantum search by local adiabatic evolution, Phys. Rev. A 65, 042308 (2002).
  16. A. T. Rezakhani, W.-J. Kuo, A. Hamma, D. A. Lidar, and P. Zanardi, Quantum adiabatic brachistochrone, Phys. Rev. Lett. 103, 080502 (2009).
  17. A. T. Rezakhani, D. F. Abasto, D. A. Lidar, and P. Zanardi, Intrinsic geometry of quantum adiabatic evolution and quantum phase transitions, Phys. Rev. A 82, 012321 (2010).
  18. S. Isermann, On the optimal schedule of adiabatic quantum computing, Quantum Inf. Process. 20, 300 (2021).
  19. S. Matsuura, S. Buck, V. Senicourt, and A. Zaribafiyan, Variationally scheduled quantum simulation, Phys. Rev. A 103, 052435 (2021).
  20. Y. Shingu and T. Hatomura, Geometrical scheduling of adiabatic control without information of energy spectra, Phys. Rev. A 112, 022410 (2025).
  21. M. Jarret, B. Lackey, A. Liu, and K. Wan, Quantum adiabatic optimization without heuristics, arXiv:1810.04686.
  22. A. Braida, S. Chakraborty, A. Chaudhuri, J. Cunningham, R. Menavlikar, L. Novo, and J. Roland, Unstructured adiabatic quantum optimization: Optimality with limitations, Quantum 9, 1790 (2025).
  23. S. Boixo, E. Knill, and R. D. Somma, Eigenpath traversal by phase randomization, Quantum Inf. Comput. 9, 833 (2009).
  24. J.-F. Chen, Speeding up quantum adiabatic processes with a dynamical quantum geometric tensor, Phys. Rev. Res. 4, 023252 (2022).
  25. B. O’Neill, Elementary Differential Geometry, 2nd ed. (Academic Press, Burlington, MA, 2006).
  26. P. M. Alsing and C. Cafaro, From the classical Frenet-Serret apparatus to the curvature and torsion of quantum-mechanical evolutions. Part II. Nonstationary Hamiltonians, Int. J. Geom. Methods Mod. Phys. 21, 2450151 (2024).
  27. See Supplemental Material at http://link.aps.org/supplemental/10.1103/ygs3-xgb1 for additional proofs and numerical data.
  28. A. Elgart and G. A. Hagedorn, A note on the switching adiabatic theorem, J. Math. Phys. 53, 102202 (2012).
  29. A. D. Becke, Density-functional thermochemistry. III. The role of exact exchange, J. Chem. Phys. 98, 5648 (1993).
  30. C. Lee, W. Yang, and R. G. Parr, Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density, Phys. Rev. B 37, 785 (1988).
  31. L. K. Grover, Quantum mechanics helps in searching for a needle in a haystack, Phys. Rev. Lett. 79, 325 (1997).
  32. R. F. Stewart, Small Gaussian expansions of Slater-type orbitals, J. Chem. Phys. 52, 431 (1970).
  33. H. Beinert, R. H. Holm, and E. Münck, Iron-sulfur clusters: Nature's versatile electron-transfer agents, Science 277, 653 (1997).
  34. M. K. Johnson and A. D. Smith, Iron–Sulfur Proteins, in Encyclopedia of Inorganic Chemistry, edited by R. B. King, R. H. Crabtree, C. M. Lukehart, D. A. Atwood, and R. A. Scott (John Wiley & Sons, New Jersey, 2005).
  35. M. Reiher, N. Wiebe, K. M. Svore, D. Wecker, and M. Troyer, Elucidating reaction mechanisms on quantum computers, Proc. Natl. Acad. Sci. USA 114, 7555 (2017).
  36. S. Sharma, K. Sivalingam, F. Neese, and G. K.-L. Chan, Low-energy spectrum of iron-sulfur clusters directly from many-particle quantum mechanics, Nat. Chem. 6, 927 (2014).
  37. S. Lee, J. Lee, H. Zhai, Y. Tong, A. M. Dalzell, A. Kumar, P. Helms, J. Gray, Z.-H. Cui, W. Liu, et al., Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry, Nat. Commun. 14, 1952 (2023).
  38. https://github.com/mchan90/codes_for_published_paper/tree/main/CGS.
  39. M. Han, H. Park, and S. Choi, Quantum Zeno Monte Carlo for computing observables, npj Quantum Inf. 11, 46 (2025).
  40. F. N. Fritsch and J. Butland, A method for constructing local monotone piecewise cubic interpolants, SIAM J. Sci. Stat. Comput. 5, 300 (1984).
  41. P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, et al., SciPy 1.0: Fundamental algorithms for scientific computing in Python, Nat. Methods 17, 261 (2020).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation