Faster quantum simulation by randomization

Andrew M. Childs; Aaron Ostrander; Yuan Su

doi:10.22331/q-2019-09-02-182

Faster quantum simulation by randomization

Andrew M. Childs^1,2,3, Aaron Ostrander^2,3,4, and Yuan Su^1,2,3

¹Department of Computer Science, University of Maryland
²Institute for Advanced Computer Studies, University of Maryland
³Joint Center for Quantum Information and Computer Science, University of Maryland
⁴Department of Physics, University of Maryland

Published:	2019-09-02, volume 3, page 182
Eprint:	arXiv:1805.08385v2
Doi:	https://doi.org/10.22331/q-2019-09-02-182
Citation:	Quantum 3, 182 (2019).

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Abstract

Product formulas can be used to simulate Hamiltonian dynamics on a quantum computer by approximating the exponential of a sum of operators by a product of exponentials of the individual summands. This approach is both straightforward and surprisingly efficient. We show that by simply randomizing how the summands are ordered, one can prove stronger bounds on the quality of approximation for product formulas of any given order, and thereby give more efficient simulations. Indeed, we show that these bounds can be asymptotically better than previous bounds that exploit commutation between the summands, despite using much less information about the structure of the Hamiltonian. Numerical evidence suggests that the randomized approach has better empirical performance as well.

► BibTeX data

@article{Childs2019fasterquantum,
  doi = {10.22331/q-2019-09-02-182},
  url = {https://doi.org/10.22331/q-2019-09-02-182},
  title = {Faster quantum simulation by randomization},
  author = {Childs, Andrew M. and Ostrander, Aaron and Su, Yuan},
  journal = {{Quantum}},
  issn = {2521-327X},
  publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
  volume = {3},
  pages = {182},
  month = sep,
  year = {2019}
}

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