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arXiv 2007.14451quant-phcs.LG
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On the Quantum versus Classical Learnability of Discrete Distributions

Dahlem Center for Complex Quantum Systems, Freie Universität Berlin(柏林自由大学达勒姆复杂量子系统中心);TU Berlin(柏林工业大学);FhG SIT(弗劳恩霍夫系统与创新研究所);Helmholtz Center Berlin(柏林亥姆霍兹中心);Department of Mathematics and Computer Science, Freie Universität Berlin(柏林自由大学数学与计算机科学系)

Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter, Jens Eisert

英文摘要:

Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct (PAC) framework. More specifically we consider the following task: Given samples from some unknown discrete probability distribution, output with high probability an efficient algorithm for generating new samples from a good approximation of the original distribution. Our primary result is the explicit construction of a class of discrete probability distributions which, under the decisional Diffie-Hellman assumption, is provably not efficiently PAC learnable by a classical generative modelling algorithm, but for which we construct an efficient quantum learner. This class of distributions therefore provides a concrete example of a generative modelling problem for which quantum learners exhibit a provable advantage over classical learning algorithms. In addition, we discuss techniques for proving classical generative modelling hardness results, as well as the relationship between the PAC learnability of Boolean functions and the PAC learnability of discrete probability distributions.

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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