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
  • Letter
  • Open Access

Tight bound for the total time in digital-analog quantum computation

Mikel Garcia de Andoin*

Mikel Sanz

  • *Contact author: mikel.garciadeandoin@ehu.eus

Phys. Rev. Research 8, L022035 – Published 26 May, 2026

DOI: https://doi.org/10.1103/pkz9-vjkh

Abstract

Digital-analog quantum computing (DAQC) is a universal computational paradigm that combines the evolution under an entangling Hamiltonian with the application of single-qubit gates. Since any unitary operation can be decomposed into a sequence of evolutions generated by two-body Hamiltonians, DAQC is inherently well suited for realizing such operations. Suboptimal upper bounds for the total time required to perform these evolutions have been previously proposed. Here, we improve these limits by providing a tight bound for this crucial parameter, which shows a linear dependence with the number of couplings. This result enables a precise estimation of the time resources needed for quantum simulations and quantum algorithms implemented within the DAQC framework, facilitating a rigorous comparison with other approaches.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (32)

  1. I. Georgescu, S. Ashhab, and F. Nori, Quantum simulation, Rev. Mod. Phys. 86, 153 (2014).
  2. D. E. Deutsch, A. Barenco, and A. Ekert, Universality in quantum computation, Proc. R. Soc. London A Math. 449, 669 (1995).
  3. E. Manousakis, A quantum-dot array as model for copper-oxide superconductors: A dedicated quantum simulator for the many-fermion problem, J. Low Temp. Phys. 126, 1501 (2002).
  4. D. Porras and J. I. Cirac, Effective quantum spin systems with trapped ions, Phys. Rev. Lett. 92, 207901 (2004).
  5. J. L. Dodd, M. A. Nielsen, M. J. Bremner, and R. T. Thew, Universal quantum computation and simulation using any entangling Hamiltonian and local unitaries, Phys. Rev. A 65, 040301(R) (2002).
  6. A. Parra-Rodriguez, P. Lougovski, L. Lamata, E. Solano, and M. Sanz, Digital-analog quantum computation, Phys. Rev. A 101, 022305 (2020).
  7. A. Galicia, B. Ramón, E. Solano, and M. Sanz, Enhanced connectivity of quantum hardware with digital-analog control, Phys. Rev. Res. 2, 033103 (2020).
  8. P. García-Molina, A. Martin, M. Garcia de Andoin, and M. Sanz, Mitigating noise in digital and digital–analog quantum computation, Commun. Phys. 7, 321 (2024).
  9. V. P. Canelles, M. G. Algaba, H. Heimonen, M. Papič, M. Ponce, J. Rönkkö, M. J. Thapa, I. de Vega, and A. Auer, Benchmarking digital–analog quantum computation for the inhomogeneous two-body Ising model, Quantum Sci. Technol. 10, 035029 (2025).
  10. A. Martin, L. Lamata, E. Solano, and M. Sanz, Digital-analog quantum algorithm for the quantum Fourier transform, Phys. Rev. Res. 2, 013012 (2020).
  11. A. Martin, R. Ibarrondo, and M. Sanz, Digital-analog co-design of the Harrow-Hassidim-Lloyd algorithm, Phys. Rev. Appl. 19, 064056 (2023).
  12. P. Chandarana, K. Paul, K. R. Swain, X. Chen, and A. del Campo, Lyapunov controlled counterdiabatic quantum optimization, arXiv:2409.12525.
  13. A. Simen, C. Flores-Garrigos, N. N. Hegade, I. Montalban, Y. Vives-Gilabert, E. Michon, Q. Zhang, E. Solano, and J. D. Martín-Guerrero, Digital-analog quantum convolutional neural networks for image classification, Phys. Rev. Res. 6, L042060 (2024).
  14. L. Lamata, A. Parra-Rodriguez, M. Sanz, and E. Solano, Digital-analog quantum simulations with superconducting circuits, Adv. Phys.: X 3, 1457981 (2018).
  15. T. Gonzalez-Raya, R. Asensio-Perea, A. Martin, L. C. Céleri, M. Sanz, P. Lougovski, and E. F. Dumitrescu, Digital-analog quantum simulations using the cross-resonance effect, PRX Quantum 2, 020328 (2021).
  16. A. Llenas and L. Lamata, Digital-analog quantum genetic algorithm using Rydberg-atom arrays, Phys. Rev. A 110, 042603 (2024).
  17. S. Kumar, N. N. Hegade, M. Henrique de Oliveira, E. Solano, A. Gomez Cadavid, and F. Albarrán-Arriagada, Digital-analog counterdiabatic quantum optimization with trapped ions, Quantum Sci. Technol. 10, 015023 (2025).
  18. D. V. Babukhin, A. A. Zhukov, and W. V. Pogosov, Hybrid digital-analog simulation of many-body dynamics with superconducting qubits, Phys. Rev. A 101, 052337 (2020).
  19. M. Gong et al., Quantum neuronal sensing of quantum many-body states on a 61-qubit programmable superconducting processor, Sci. Bull. 68, 906 (2023).
  20. Y. Lu, W. Chen, S. Zhang, K. Zhang, J. Zhang, J.-N. Zhang, and K. Kim, Implementing arbitrary Ising models with a trapped-ion quantum processor, Phys. Rev. Lett. 134, 050602 (2025).
  21. M. Suzuki, Generalized Trotter's formula and systematic approximants of exponential operators and inner derivations with applications to many-body problems, Commun. Math. Phys. 51, 183 (1976).
  22. M. Garcia-de-Andoin, A. Saiz, P. Pérez-Fernández, L. Lamata, I. Oregi, and M. Sanz, Digital-analog quantum computation with arbitrary two-body Hamiltonians, Phys. Rev. Res. 6, 013280 (2024).
  23. P. Baßler, M. Heinrich, and M. Kliesch, General, efficient, and robust Hamiltonian engineering, PRX Quantum 6, 040346 (2025).
  24. M. Garcia de Andoin, A. Álvarez Ahedo, A. Franco Rubio, and M. Sanz, Impact and mitigation of Hamiltonian characterization errors in digital-analog quantum computation, arXiv:2505.03642.
  25. P. Baßler, M. Heinrich, and M. Kliesch, Time-optimal multi-qubit gates: Complexity, efficient heuristic and gate-time bounds, Quantum 8, 1279 (2024).
  26. L. Khachiyan, E. Boros, K. Borys, K. Elbassioni, and V. Gurvich, Generating all vertices of a polyhedron is hard, Discrete Comput. Geom. 39, 174 (2008).
  27. Q. Zhao, Y. Zhou, A. F. Shaw, T. Li, and A. M. Childs, Hamiltonian simulation with random inputs, Phys. Rev. Lett. 129, 270502 (2022).
  28. D. Layden, First-order Trotter error from a second-order perspective, Phys. Rev. Lett. 128, 210501 (2022).
  29. S. Zhuk, N. F. Robertson, and S. Bravyi, Trotter error bounds and dynamic multi-product formulas for Hamiltonian simulation, Phys. Rev. Res. 6, 033309 (2024).
  30. T. Feng, Y. Cao, and Q. Zhao, Trotterization, operator scrambling, and entanglement, arXiv:2506.23345.
  31. P. K. Faehrmann, M. Steudtner, R. Kueng, M. Kieferova, and J. Eisert, Randomizing multi-product formulas for Hamiltonian simulation, Quantum 6, 806 (2022).
  32. M. Bagherimehrab, L. M. Calderon, D. W. Berry, P. Schleich, M. G. Vakili, A. Aldossary, J. A. C. G. Angulo, C. Gorgulla, and A. Aspuru-Guzik, Faster algorithmic quantum and classical simulations by corrected product formulas, arXiv:2409.08265.

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation