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

Continuous-time quantum control across an exponentially small bottleneck in a frustrated Ising ring model

Vincenzo Roberto Arezzo1,*, Kiran Thengil1, and Giuseppe E. Santoro1,2

  • *Contact author: varezzo@sissa.it

Phys. Rev. A 114, 042438 – Published 19 October, 2026

DOI: https://doi.org/10.1103/wwqn-c9cb

Abstract

Continuous-time quantum annealing (QA) is a strategy for preparing the ground state of nontrivial many-body systems. In its standard form, the dynamics is generated by a time-dependent interpolation between a simple driving Hamiltonian and the target problem Hamiltonian, usually implemented through a linear schedule. This approach faces the crucial bottleneck of small spectral gaps, which may require exponentially long annealing times to ensure adiabaticity. Here, we show how to implement quantum control over the annealing schedule in a frustrated Ising ring, one of the simplest models exhibiting an exponentially small bottleneck gap. By optimizing smooth continuous-time annealing schedules with a dressed chopped random basis (CRAB) approach, and using a digitized representation of the dynamics to efficiently evaluate gradients, we construct protocols that strongly outperform standard fixed schedules. The optimized dynamics bypasses the bottleneck through a strongly nonadiabatic mechanism, leading to efficient ground-state preparation despite the exponentially small minimum gap. In particular, the annealing time required to reach a fixed residual-energy threshold is found to grow linearly with system size rather than exponentially. We further examine a lowest-order variational counterdiabatic correction and find that, once schedule optimization is allowed, it does not lead to any improvement.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (54)

  1. A. B. Finnila, M. A. Gomez, C. Sebenik, C. Stenson, and J. D. Doll, Quantum annealing: A new method for minimizing multidimensional functions, Chem. Phys. Lett. 219, 343 (1994).
  2. T. Kadowaki and H. Nishimori, Quantum annealing in the transverse Ising model, Phys. Rev. E 58, 5355 (1998).
  3. G. E. Santoro, R. Martoňák, E. Tosatti, and R. Car, Theory of quantum annealing of an Ising spin glass, Science 295, 2427 (2002).
  4. G. E. Santoro and E. Tosatti, Optimization using quantum mechanics: Quantum annealing through adiabatic evolution, J. Phys. A: Math. Gen. 39, R393 (2006).
  5. T. Albash and D. A. Lidar, Adiabatic quantum computation, Rev. Mod. Phys. 90, 015002 (2018).
  6. J. Roland and N. J. Cerf, Quantum search by local adiabatic evolution, Phys. Rev. A 65, 042308 (2002).
  7. L. K. Grover, Quantum mechanics helps in searching for a needle in a haystack, Phys. Rev. Lett. 79, 325 (1997).
  8. A. Grabarits, F. Balducci, B. C. Sanders, and A. del Campo, Nonadiabatic quantum optimization for crossing quantum phase transitions, Phys. Rev. A 111, 012215 (2025).
  9. T. Caneva, R. Fazio, and G. E. Santoro, Adiabatic quantum dynamics of a random Ising chain across its quantum critical point, Phys. Rev. B 76, 144427 (2007).
  10. S. Knysh, Zero-temperature quantum annealing bottlenecks in the spin-glass phase, Nat. Commun. 7, 12370 (2016).
  11. D. Roberts, L. Cincio, A. Saxena, A. Petukhov, and S. Knysh, Noise amplification at spin-glass bottlenecks of quantum annealing: A solvable model, Phys. Rev. A 101, 042317 (2020).
  12. V. Bapst, L. Foini, F. Krzakala, G. Semerjian, and F. Zamponi, The quantum adiabatic algorithm applied to random optimization problems: The quantum spin glass perspective, Phys. Rep. 523, 127 (2013).
  13. S. Matsuura, S. Buck, V. Senicourt, and A. Zaribafiyan, Variationally scheduled quantum simulation, Phys. Rev. A 103, 052435 (2021).
  14. J. Côté, F. Sauvage, M. Larocca, M. Jonsson, L. Cincio, and T. Albash, Diabatic quantum annealing for the frustrated ring model, Quantum Sci. Technol. 8, 045033 (2023).
  15. G. Quiroz, Robust quantum control for adiabatic quantum computation, Phys. Rev. A 99, 062306 (2019).
  16. P. R. Hegde, G. Passarelli, A. Scocco, and P. Lucignano, Genetic optimization of quantum annealing, Phys. Rev. A 105, 012612 (2022).
  17. G. Passarelli, V. Cataudella, and P. Lucignano, Improving quantum annealing of the ferromagnetic p-spin model through pausing, Phys. Rev. B 100, 024302 (2019).
  18. T. Caneva, T. Calarco, and S. Montangero, Chopped random-basis quantum optimization, Phys. Rev. A 84, 022326 (2011).
  19. N. Rach, M. M. Müller, T. Calarco, and S. Montangero, Dressing the chopped-random-basis optimization: A bandwidth-limited access to the trap-free landscape, Phys. Rev. A 92, 062343 (2015).
  20. M. V. Berry, Transitionless quantum driving, J. Phys. A: Math. Theor. 42, 365303 (2009).
  21. M. Kolodrubetz, D. Sels, P. Mehta, and A. Polkovnikov, Geometry and non-adiabatic response in quantum and classical systems, Phys. Rep. 697, 1 (2017).
  22. D. Guéry-Odelin, A. Ruschhaupt, A. Kiely, E. Torrontegui, S. Martínez-Garaot, and J. G. Muga, Shortcuts to adiabaticity: Concepts, methods, and applications, Rev. Mod. Phys. 91, 045001 (2019).
  23. E. Torrontegui, S. Ibáñez, S. Martínez-Garaot, M. Modugno, A. del Campo, D. Guéry-Odelin, A. Ruschhaupt, X. Chen, and J. G. Muga, Shortcuts to adiabaticity, in Advances in Atomic, Molecular, and Optical Physics, edited by E. Arimondo, P. R. Berman, and C. C. Lin, Vol. 62 of Advances In Atomic, Molecular, and Optical Physics (Academic, New York, 2013), pp. 117–169.
  24. E. Farhi, J. Goldstone, and S. Gutmann, A quantum approximate optimization algorithm, arXiv:1411.4028.
  25. N. N. Hegade, X. Chen, and E. Solano, Digitized counterdiabatic quantum optimization, Phys. Rev. Res. 4, L042030 (2022).
  26. P. Chandarana, N. N. Hegade, K. Paul, F. Albarrán-Arriagada, E. Solano, A. del Campo, and X. Chen, Digitized-counterdiabatic quantum approximate optimization algorithm, Phys. Rev. Res. 4, 013141 (2022).
  27. I. Čepaitė, A. Polkovnikov, A. J. Daley, and C. W. Duncan, Counterdiabatic optimized local driving, PRX Quantum 4, 010312 (2023).
  28. C. Mc Keever and M. Lubasch, Towards adiabatic quantum computing using compressed quantum circuits, PRX Quantum 5, 020362 (2024).
  29. R. Wang, V. R. Arezzo, K. Thengil, G. Pecci, and G. E. Santoro, From exponential to quadratic: Optimal control for a frustrated Ising ring model, Quantum Sci. Technol. 10, 035052 (2025).
  30. A. Grabarits, F. Balducci, and A. del Campo, Fighting exponentially small gaps by counterdiabatic driving, PRX Quantum 7, 010322 (2026).
  31. P. Jordan and E. Wigner, Über das Paulische Äquivalenzverbot, Z. Phys. 47, 631 (1928).
  32. G. B. Mbeng, A. Russomanno, and G. E. Santoro, The quantum Ising chain for beginners, SciPost Phys. Lect. Notes 82 (2024).
  33. V. R. Arezzo, R. Wang, K. Thengil, G. Pecci, and G. E. Santoro, Digital controllability of transverse-field Ising chains, Phys. Rev. A 113, 012610 (2026).
  34. M. Werner, M. Jonsson, A. García-Sáez, A. Riera, and T. Albash, Leveraging Landau-Zener-Stückelberg interference for accelerating diabatic quantum annealing, arXiv:2606.09706.
  35. K. Thengil, V. R. Arezzo, and G. E. Santoro, Digital techniques for the frustrated Ising ring: The role of counter-diabatic terms, arXiv:2607.23074.
  36. P. Doria, T. Calarco, and S. Montangero, Optimal control technique for many-body quantum dynamics, Phys. Rev. Lett. 106, 190501 (2011).
  37. C. P. Koch, U. Boscain, T. Calarco, G. Dirr, S. Filipp, S. J. Glaser, R. Kosloff, S. Montangero, T. Schulte-Herbrüggen, D. Sugny, et al., Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe, EPJ Quantum Technol. 9, 19 (2022).
  38. S. Morita and H. Nishimori, Mathematical foundation of quantum annealing, J. Math. Phys. 49, 125210 (2008).
  39. R. Barends, A. Shabani, L. Lamata, J. Kelly, A. Mezzacapo, U. L. Heras, R. Babbush, A. G. Fowler, B. Campbell, Y. Chen, et al., Digitized adiabatic quantum computing with a superconducting circuit, Nature (London) 534, 222 (2016).
  40. G. B. Mbeng, L. Arceci, and G. E. Santoro, Optimal working point in digitized quantum annealing, Phys. Rev. B 100, 224201 (2019).
  41. G. B. Mbeng, R. Fazio, and G. E. Santoro, Quantum annealing: A journey through digitalization, control, and hybrid quantum variational schemes, arXiv:1906.08948.
  42. G. Pecci, R. Wang, P. Torta, G. B. Mbeng, and G. E. Santoro, Beyond quantum annealing: Optimal control solutions to MaxCut problems, Quantum Sci. Technol. 9, 045013 (2024).
  43. J. Nocedal and S. J. Wright, Numerical Optimization (Springer, New York, 1999).
  44. S. Machnes, E. Assémat, D. Tannor, and F. K. Wilhelm, Tunable, flexible, and efficient optimization of control pulses for practical qubits, Phys. Rev. Lett. 120, 150401 (2018).
  45. F. Sauvage and F. Mintert, Optimal control of families of quantum gates, Phys. Rev. Lett. 129, 050507 (2022).
  46. M. L. Goh, M. Larocca, L. Cincio, M. Cerezo, and F. Sauvage, Lie-algebraic classical simulations for quantum computing, Phys. Rev. Res. 7, 033266 (2025).
  47. This is a natural choice for positive quantities spanning several orders of magnitude. It should not be understood as assuming that the residual energies εP(r)(τ) are exactly log-normally distributed.
  48. L. T. Brady, C. L. Baldwin, A. Bapat, Y. Kharkov, and A. V. Gorshkov, Optimal protocols in quantum annealing and quantum approximate optimization algorithm problems, Phys. Rev. Lett. 126, 070505 (2021).
  49. V. R. Arezzo and G. E. Santoro (unpublished).
  50. W. Rossmann, Lie Groups: An Introduction through Linear Groups, Oxford Mathematics (Oxford University, New York, 2006).
  51. J. Wurtz and P. J. Love, Counterdiabaticity and the quantum approximate optimization algorithm, Quantum 6, 635 (2022).
  52. G. Lami, P. Torta, G. E. Santoro, and M. Collura, Quantum annealing for neural network optimization problems: A new approach via tensor network simulations, SciPost Phys. 14, 117 (2023).
  53. M. Collura, G. Lami, N. Ranabhat, and A. Santini, Tensor Network Techniques for Quantum Computation (OAPEN, The Hague, 2025).
  54. V. R. Arezzo, K. Thengil, and G. E. Santoro, Data and notebook for “Continuous-time quantum control across an exponentially small bottleneck in a frustrated Ising ring model”, Version 1.0, Zenodo, 2026, doi: 10.5281/zenodo.23268801.

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation