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arXiv 2004.00026quant-ph
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小型量子计算机和大型经典数据集

Small quantum computers and large classical data sets

Aram W. Harrow

摘要:

我们引入混合经典量子算法的问题,涉及一个大型的经典数据集X和一个空间的模型Y,使量子计算机有叠加访问Y,但不是X。这些算法使用数据简化技术来构造X的加权子集,称为coreset,其对每个模型产生近似相同的损失。核心集可以由经典计算机单独构建,也可以通过一个交互式协议来构建,在该协议中,量子计算机的输出被用来帮助决定使用X的哪些元素。通过使用量子计算机来执行Grover搜索或拒绝采样,这产生了极大似然估计、贝叶斯推断和鞍点优化的量子加速。具体的应用包括k-means聚类,逻辑回归,零和游戏和提升。

英文摘要:

We introduce hybrid classical-quantum algorithms for problems involving a large classical data set X and a space of models Y such that a quantum computer has superposition access to Y but not X. These algorithms use data reduction techniques to construct a weighted subset of X called a coreset that yields approximately the same loss for each model. The coreset can be constructed by the classical computer alone, or via an interactive protocol in which the outputs of the quantum computer are used to help decide which elements of X to use. By using the quantum computer to perform Grover search or rejection sampling, this yields quantum speedups for maximum likelihood estimation, Bayesian inference and saddle-point optimization. Concrete applications include k-means clustering, logistical regression, zero-sum games and boosting.

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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