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arXiv 2103.09818quant-phcs.DM
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通过Grover搜索实现更快的量子集中

Faster Quantum Concentration via Grover's Search

Cem M. Unsal, A. Yavuz Oruc

摘要:

我们提出了量子算法的路由集中分配满容量的脂肪和苗条的集中器,有界的脂肪和苗条的集中器,并定期脂肪和苗条的集中器。通常,在所有这些集中器上,浓度分配都需要$O(n)$时间,其中$n$是输入的数量。由Grover的量子搜索算法提供支持,我们的算法需要$O(\sqrt{nc}\ln{c})$时间,其中$c$是集中器的容量。因此,当$c\ln^2{c}=o(n)$时,我们的量子算法渐近地快于它们的经典算法。通常,$c = n^μ,$满足$c\ln^2{c}=o(n),$,这意味着对于任何$μ, 0 < μ< 1.$,时间复杂度为$O(n^{0.5(1+ μ)} \ln n),$

英文摘要:

We present quantum algorithms for routing concentration assignments on full capacity fat-and-slim concentrators, bounded fat-and-slim concentrators, and regular fat-and-slim concentrators. Classically, the concentration assignment takes $O(n)$ time on all these concentrators, where $n$ is the number of inputs. Powered by Grover's quantum search algorithm, our algorithms take $O(\sqrt{nc}\ln{c})$ time, where $c$ is the capacity of the concentrator. Thus, our quantum algorithms are asymptotically faster than their classical counterparts, when $c\ln^2{c}=o(n)$.In general, $c = n^μ,$ satisfies $c\ln^2{c}=o(n),$ implying a time complexity of $O(n^{0.5(1+ μ)} \ln n),$ for any $μ, 0 < μ< 1.$

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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