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

Exponential optimization of adiabatic quantum-state preparation

Davide Cugini1, Davide Nigro1, Mattia Bruno2, and Dario Gerace1

Phys. Rev. Research 7, L012074 – Published 19 March, 2025

DOI: https://doi.org/10.1103/PhysRevResearch.7.L012074

Abstract

The preparation of a given quantum state on a quantum computing register is a typically demanding operation, requiring a number of elementary gates that scales exponentially with the size of the problem. Using the adiabatic theorem for state preparation, whose error decreases exponentially as a function of the preparation time, we derive an explicit analytic expression for the dependence of the characteristic time on the Hamiltonian used in the adiabatic evolution. Exploiting this knowledge, we then design a preconditioning term that modifies the adiabatic preparation, thus reducing its characteristic time and hence giving an exponential advantage in state preparation. We prove the efficiency of our method with extensive numerical experiments on prototypical spin models, which gives a promising strategy to perform quantum simulations of manybody models via Trotter evolution on near-term quantum processors.

View figure in article

Physics Subject Headings (PhySH)

Article Text

Supplemental Material

References (44)

  1. J. Preskill, Quantum computing in the NISQ era and beyond, Quantum 2, 79 (2018).
  2. D. P. DiVincenzo, The physical implementation of quantum computation, Fortschr. Phys. 48, 771 (2000).
  3. S. A. Moses et al., A race-track trapped-ion quantum processor, Phys. Rev. X 13, 041052 (2023).
  4. Y. Kim et al., Evidence for the utility of quantum computing before fault tolerance, Nature (London) 618, 500 (2023).
  5. F. Tacchino, A. Chiesa, S. Carretta, and D. Gerace, Quantum computers as universal quantum simulators: State-of-the-art and perspectives, Adv. Quantum Technol. 3, 1900052 (2020).
  6. A. J. Daley et al., Practical quantum advantage in quantum simulation, Nature (London) 607, 667 (2022).
  7. D. S. Abrams and S. Lloyd, Simulation of many-body Fermi systems on a universal quantum computer, Phys. Rev. Lett. 79, 2586 (1997).
  8. M. Troyer and U.-J. Wiese, Computational complexity and fundamental limitations to fermionic quantum Monte Carlo simulations, Phys. Rev. Lett. 94, 170201 (2005).
  9. A. Kandala et al., Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets, Nature (London) 549, 242 (2017).
  10. A. Kandala, K. Temme, A. D. Córcoles, A. Mezzacapo, J. M. Chow, and J. M. Gambetta, Error mitigation extends the computational reach of a noisy quantum processor, Nature (London) 567, 491 (2019).
  11. M. Motta and J. E. Rice, Emerging quantum computing algorithms for quantum chemistry, WIREs Computat. Mol. Sci. 12, e1580 (2022).
  12. A. Chiesa, F. Tacchino, M. Grossi, P. Santini, I. Tavernelli, D. Gerace, and S. Carretta, Quantum hardware simulating four-dimensional inelastic neutron scattering, Nat. Phys. 15, 455 (2019).
  13. F. Arute et al., Observation of separated dynamics of charge and spin in the Fermi-Hubbard model, arXiv:2010.07965.
  14. K. Klymko et al., Real-time evolution for ultracompact Hamiltonian eigenstates on quantum hardware, PRX Quantum 3, 020323 (2022).
  15. E. A. Martinez et al., Real-time dynamics of lattice gauge theories with a few-qubit quantum computer, Nature (London) 534, 516 (2016).
  16. N. Klco, E. F. Dumitrescu, A. J. McCaskey, T. D. Morris, R. C. Pooser, M. Sanz, E. Solano, P. Lougovski, and M. J. Savage, Quantum-classical computation of Schwinger model dynamics using quantum computers, Phys. Rev. A 98, 032331 (2018).
  17. W. A. de Jong, K. Lee, J. Mulligan, M. Ploskon, F. Ringer, and X. Yao, Quantum simulation of nonequilibrium dynamics and thermalization in the Schwinger model, Phys. Rev. D 106, 054508 (2022).
  18. B. Chakraborty, M. Honda, T. Izubuchi, Y. Kikuchi, and A. Tomiya, Classically emulated digital quantum simulation of the Schwinger model with a topological term via adiabatic state preparation, Phys. Rev. D 105, 094503 (2022).
  19. N. H. Nguyen, M. C. Tran, Y. Zhu, A. M. Green, C. H. Alderete, Z. Davoudi, and N. M. Linke, Digital quantum simulation of the Schwinger model and symmetry protection with trapped ions, PRX Quantum 3, 020324 (2022).
  20. M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information (Cambridge University Press, Cambridge, 2000).
  21. A. Peruzzo et al., A variational eigenvalue solver on a photonic quantum processor, Nat. Commun. 5, 4213 (2014).
  22. J. R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik, The theory of variational hybrid quantum-classical algorithms, New J. Phys. 18, 023023 (2016).
  23. N. Moll et al., Quantum optimization using variational algorithms on near-term quantum devices, Quantum Sci. Technol. 3, 030503 (2018).
  24. A. Anand et al., A quantum computing view on unitary coupled cluster theory, Chem. Soc. Rev. 51, 1659 (2022).
  25. J. R. McClean et al., Barren plateaus in quantum neural network training landscapes, Nat. Commun. 9, 4812 (2018).
  26. M. Motta et al., Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution, Nat. Phys. 16, 205 (2020).
  27. M. Born and V. Fock, Beweis des adiabatensatzes, Z. Phys. 51, 165 (1928).
  28. T. Albash and D. A. Lidar, Adiabatic quantum computation, Rev. Mod. Phys. 90, 015002 (2018).
  29. A. Ambainis and O. Regev, An elementary proof of the quantum adiabatic theorem, arXiv:quant-ph/0411152.
  30. S. Jansen, M.-B. Ruskai, and R. Seiler, Bounds for the adiabatic approximation with applications to quantum computation, J. Math. Phys. 48, 102111 (2007).
  31. A. Elgart and G. A. Hagedorn, A note on the switching adiabatic theorem, J. Math. Phys. 53, 102202 (2012).
  32. G. Nenciu, Linear adiabatic theory exponential estimates, Commun. Math. Phys. 152, 479 (1993).
  33. C. Lanczos, An iteration method for the solution of the eigenvalue problem of linear differential and integral operators, J. Res. Natl. Bur. Stand. B 45, 255 (1950).
  34. P. Virtanen et al., SciPy 1.0: Fundamental algorithms for scientific computing in python, Nat. Meth. 17, 261 (2020).
  35. See Supplemental Material at http://link.aps.org/supplemental/10.1103/PhysRevResearch.7.L012074 for details concerning the calculations and additional data on which the main text is based. Specifically, the Supplemental Material file contains the following sections: Exponential behaviour of the adiabatic error; Quantum circuit design; Numerical results the Ising model in a transverse field; Periodic boundary conditions and site parity; Reduction of g̃ for large systems; Comparing exponential and power-like bounds.
  36. C. Mc Keever and M. Lubasch, Towards adiabatic quantum computing using compressed quantum circuits, PRX Quantum 5, 020362 (2024).
  37. E. Granet and H. Dreyer, Continuous Hamiltonian dynamics on noisy digital quantum computers without Trotter error, npj Quantum Inf. 10, 82 (2024).
  38. H. F. Trotter, On the product of semi-groups of operators, Proc. Am. Math. Soc. 10, 545 (1959).
  39. C. F. Gauss, Methodus nova integralium valores per approximationem inveniendi, in Werke, Cambridge Library Collection--Mathematics (Cambridge University Press, Cambridge, 2011), pp. 165–196.
  40. A. Quarteroni et al., Scientific Computing with MATLAB and Octave (Springer, Berlin, 2006), Vol. 3.
  41. 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).
  42. E. A. C. Pérez, J. Bonitati, D. Lee, S. Quaglioni, and K. A. Wendt, Quantum state preparation by adiabatic evolution with customized gates, Phys. Rev. A 105, 032403 (2022).
  43. Qiskit contributors, Qiskit An open-source framework for quantum computing, Zenodo (2023), doi:10.5281/zenodo.2562110.
  44. D. C. McKay, C. J. Wood, S. Sheldon, J. M. Chow, and J. M. Gambetta, Efficient Z gates for quantum computing, Phys. Rev. A 96, 022330 (2017).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation