Papers
Topics
Authors
Recent
Search
2000 character limit reached

General, efficient, and robust Hamiltonian engineering

Published 25 Oct 2024 in quant-ph | (2410.19903v2)

Abstract: Implementing the time evolution under a desired target Hamiltonian is critical for various applications in quantum science. Due to the exponential increase of parameters in the system size and due to experimental imperfections this task can be challenging in quantum many-body settings. We introduce an efficient and robust scheme to engineer arbitrary local many-body Hamiltonians. This is achieved by applying simple single-qubit gates simultaneously to an always-on system Hamiltonian, which we assume to be native to a given platform. These sequences are constructed by efficiently solving a linear program (LP) which minimizes the total evolution time. In this way, we can engineer target Hamiltonians that are only limited by the locality of the Pauli terms in the system Hamiltonian. Based on average Hamiltonian theory and by using robust composite pulses, we make our schemes robust against errors including finite pulse time errors and various calibration errors. To demonstrate the performance of our scheme, we provide numerical simulations. In particular, we solve the Hamiltonian engineering problem for arbitrary two-body Hamiltonians on a 2D square lattice with $225$ qubits in only $60$ seconds. Moreover, we simulate the time evolution of Heisenberg Hamiltonians for smaller system sizes with a fidelity larger than $99.9\%$, which is orders of magnitude better than non-robust implementations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (21)
  1. R. Blatt and C. F. Roos, Quantum simulations with trapped ions, Nat. Phys. 8, 277 (2012).
  2. J. Preskill, Quantum Computing in the NISQ era and beyond, Quantum 2, 79 (2018), arXiv:1801.00862.
  3. R. Trivedi, A. Franco Rubio, and J. I. Cirac, Quantum advantage and stability to errors in analogue quantum simulators, Nature Communications 15, 6507 (2024), arXiv:2212.04924.
  4. D. Leung, Simulation and reversal of n-qubit Hamiltonians using Hadamard matrices, Journal of Modern Optics 49, 1199 (2002), arXiv:quant-ph/0107041.
  5. D. Hayes, S. T. Flammia, and M. J. Biercuk, Programmable quantum simulation by dynamic Hamiltonian engineering, New J. Phys. 16, 10.1088/1367-2630/16/8/083027 (2014), arXiv:1309.6736.
  6. J. K. Pachos and M. B. Plenio, Three-spin interactions in optical lattices and criticality in cluster Hamiltonians, Phys. Rev. Lett. 93, 056402 (2004), arXiv:quant-ph/0401106.
  7. H. P. Büchler, A. Micheli, and P. Zoller, Three-body interactions with cold polar molecules, Nature Physics 3, 726 (2007), arXiv:cond-mat/0703688.
  8. S. P. Boyd and L. Vandenberghe, Convex optimization (Cambridge University Press, Cambridge, UK ; New York, 2004).
  9. I. Bárány, A generalization of Carathéodory’s theorem, Discrete Mathematics 40, 141 (1982).
  10. D. A. Spielman and S.-H. Teng, Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time, Journal of the ACM 51, 385 (2004), arXiv:cs/0111050.
  11. J. Farkas, Theorie der einfachen Ungleichungen, Journal für die Reine und Angewandte Mathematik 124, 1 (1902).
  12. E. Stiemke, Über positive Lösungen homogener linearer Gleichungen, Mathematische Annalen 76, 340 (1915).
  13. S. Hayakawa, T. Lyons, and H. Oberhauser, Estimating the probability that a given vector is in the convex hull of a random sample, Probability Theory and Related Fields 185, 705 (2023), arXiv:2101.04250.
  14. J. G. Wendel, A problem in geometric probability., MATHEMATICA SCANDINAVICA 11, 109–112 (1962).
  15. U. Wagner and E. Welzl, A continuous analogue of the upper bound theorem, Discrete & Computational Geometry 26, 205 (2001).
  16. S. Diamond and S. Boyd, CVXPY: A Python-embedded modeling language for convex optimization, J. Mach. Learn. Res. 17, 1 (2016), arXiv:1603.00943.
  17. MOSEK ApS, MOSEK Optimizer API for Python 9.3.14 (2022).
  18. P. Baßler, Source code for “Efficient Hamiltonian engineering”, https://github.com/paba92/EffHamEng (2024).
  19. M. A. Ali Ahmed, G. A. Álvarez, and D. Suter, Robustness of dynamical decoupling sequences, Phys. Rev. A 87, 042309 (2013), arXiv:1211.5001.
  20. H. L. Gevorgyan and N. V. Vitanov, Ultrahigh-fidelity composite rotational quantum gates, Phys. Rev. A 104, 012609 (2021), arXiv:2012.14692.
  21. F. Casas, A. Murua, and M. Nadinic, Efficient computation of the Zassenhaus formula, Computer Physics Communications 183, 2386 (2012), arXiv:1204.0389.

Summary

No one has generated a summary of this paper yet.

Paper to Video (Beta)

No one has generated a video about this paper yet.

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.

Collections

Sign up for free to add this paper to one or more collections.

Tweets

Sign up for free to view the 2 tweets with 25 likes about this paper.