We create classical (non-quantum) dynamic data structures supporting queries for recommender systems and least-squares regression that are comparable to their quantum analogues. De-quantizing such algorithms has received a flurry of attention in recent years; we obtain sharper bounds for these problems. More significantly, we achieve these improvements by arguing that the previous quantum-inspired algorithms for these problems are doing leverage or ridge-leverage score sampling in disguise; these are powerful and standard techniques in randomized numerical linear algebra. With this recognition, we are able to employ the large body of work in numerical linear algebra to obtain algorithms for these problems that are simpler or faster (or both) than existing approaches.

翻译：我们创建了古典(非量子)动态数据结构,支持对推荐系统和最小方位回归的查询,这些查询与数量类比相仿。 量化的这种算法近年来引起了人们的注意;我们为这些问题获得了更清晰的界限。 更重要的是,我们之所以能够实现这些改进,是因为我们辩称,以前针对这些问题的量子驱动算法正在以伪装方式进行杠杆或脊脊裂分评分抽样;这些是随机数字直线代数的强大和标准技术。 有了这种认识,我们便能够利用数字线性代数的大量工作来获取比现有方法简单或更快( 或两者都) 的这些问题的算法。