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This paper gives a precise characterization of when the ambient address space yields a genuine quantum advantage for linked-list search, and extends the results and gives the same tight asymptotic bounds for the natural double linked-list version as well.
Morohoshi, Nakayama, Manabe, and Mitarai proposed a physically motivated quantum machine learning problem in which the goal is to predict quantities of the form $\operatorname{Tr}[f(H)\rho]$ from classical descriptions of a Hamiltonian $H$ and a quantum state $\rho$, where $f$ is an unknown function. We call this probl...
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