We provide more sample-efficient versions of some basic routines in quantum data analysis, along with simpler proofs. Particularly, we give a quantum "Threshold Search" algorithm that requires only $O((\log^2 m)/\epsilon^2)$ samples of a $d$-dimensional state $\rho$. That is, given observables $0 \le A_1, A_2, ..., A_m \le 1$ such that $\mathrm{tr}(\rho A_i) \ge 1/2$ for at least one $i$, the algorithm finds $j$ with $\mathrm{tr}(\rho A_j) \ge 1/2-\epsilon$. As a consequence, we obtain a Shadow Tomography algorithm requiring only $\tilde{O}((\log^2 m)(\log d)/\epsilon^4)$ samples, which simultaneously achieves the best known dependence on each parameter $m$, $d$, $\epsilon$. This yields the same sample complexity for quantum Hypothesis Selection among $m$ states; we also give an alternative Hypothesis Selection method using $\tilde{O}((\log^3 m)/\epsilon^2)$ samples.

翻译：我们提供了量子数据分析中一些基本程序的更高效样本版本，并附有更简洁的证明。具体而言，我们提出了一种量子“阈值搜索”算法，该算法仅需$O((\log^2 m)/\epsilon^2)$个样本，即可处理一个$d$维量子态$\rho$。即，给定观测算子$0 \le A_1, A_2, ..., A_m \le 1$，且满足至少存在一个$i$使得$\mathrm{tr}(\rho A_i) \ge 1/2$，该算法能够找到$j$使得$\mathrm{tr}(\rho A_j) \ge 1/2-\epsilon$。作为推论，我们获得了一种阴影层析成像算法，仅需$\tilde{O}((\log^2 m)(\log d)/\epsilon^4)$个样本，该算法在参数$m$、$d$、$\epsilon$的依赖关系上同时达到了已知最优水平。这为在$m$个量子态中进行量子假设选择提供了相同的样本复杂度；我们还给出了一种替代的假设选择方法，仅需$\tilde{O}((\log^3 m)/\epsilon^2)$个样本。