



@article {2174,
	title = {Faster quantum simulation by randomization},
	journal = {Quantum },
	volume = {3},
	year = {2019},
	month = {08/28/2019},
	abstract = {<p><span style="font-family: \&quot;Lucida Grande\&quot;, helvetica, arial, verdana, sans-serif; font-size: 14.4px;">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 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 our randomized algorithm may be advantageous even for near-term quantum simulation.</span></p>},
	doi = {https://doi.org/10.22331/q-2019-09-02-182},
	url = {https://arxiv.org/abs/1805.08385},
	author = {Andrew M. Childs and Aaron Ostrander and Yuan Su}
}
