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arXiv quant-ph/9807052quant-ph
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一种量子计算学习算法

A Quantum Computational Learning Algorithm

Dan Ventura, Tony Martinez

摘要:

一个有趣的经典结果,由于杰克逊允许多项式时间学习的函数类DNF使用成员查询。由于在大多数实际的学习情况下,访问一个成员资格的预言是不现实的,本文探讨了量子计算可能允许学习算法DNF,只依赖于示例查询的可能性。基于傅立叶的学习到量子域的自然扩展。该算法只需要一个示例oracle,并且它的运行时间为O(sqrt(2^n)),这是一个经典上不可能的结果。该算法在量子算法中是独特的,因为它不假设函数的先验知识,也不对包括所有可能状态的叠加进行操作。

英文摘要:

An interesting classical result due to Jackson allows polynomial-time learning of the function class DNF using membership queries. Since in most practical learning situations access to a membership oracle is unrealistic, this paper explores the possibility that quantum computation might allow a learning algorithm for DNF that relies only on example queries. A natural extension of Fourier-based learning into the quantum domain is presented. The algorithm requires only an example oracle, and it runs in O(sqrt(2^n)) time, a result that appears to be classically impossible. The algorithm is unique among quantum algorithms in that it does not assume a priori knowledge of a function and does not operate on a superposition that includes all possible states.

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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