We show how to find all $k$ marked elements in a list of size $N$ using the optimal number $O(\sqrt{N k})$ of quantum queries and only a polylogarithmic overhead in the gate complexity, in the setting where one has a small quantum memory. Previous algorithms either incurred a factor $k$ overhead in the gate complexity, or had an extra factor $\log(k)$ in the query complexity. We then consider the problem of finding a multiplicative $\delta$-approximation of $s = \sum_{i=1}^N v_i$ where $v=(v_i) \in [0,1]^N$, given quantum query access to a binary description of $v$. We give an algorithm that does so, with probability at least $1-\rho$, using $O(\sqrt{N \log(1/\rho) / \delta})$ queries (under mild assumptions on $\rho$). This quadratically improves the dependence on $1/\delta$ and $\log(1/\rho)$ compared to a straightforward application of amplitude estimation. To obtain the improved $\log(1/\rho)$ dependence we use the first result.
翻译:我们展示了如何在大小列表中找到所有K$标记元素的方法, 使用量子查询的最优数字$O( sqrt{N k}) $N$, 使用量子查询中最优的数值$O( sqrt{N k}) 来查找量子查询中的所有K$标记元素, 并且只使用门复杂度小的设置, 在有量子内存的设置中, 使用门复杂度的设置中找到一个多倍数 $( sqrt{N), 或者在查询复杂度中找到一个额外的系数$( $== sumi=1 ⁇ N v_i) v_ i$ [0, 1\N$, 在量子查询中可以找到 $v$的二进位描述。 我们给出的算法, 使用$( sqr) r$( ng) 的可能性至少为1\ rho$( log) 。 然后我们考虑找到一个多倍的查询 $( 在 $( 在 $\ r$ rog_\\\ a restal a) distrat) imstest a advidustupdividustrat) difidifidificidust a.