Quantum algorithms for testing Boolean functions

Dominik F. Floess, E. Andersson, M. Hillery
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引用次数: 14

Abstract

We discuss quantum algorithms, based on the Bernstein-Vazirani algorithm, for finding which variables a Boolean function depends on. There are 2^n possible linear Boolean functions of n variables; given a linear Boolean function, the Bernstein-Vazirani quantum algorithm can deterministically identify which one of these Boolean functions we are given using just one single function query. The same quantum algorithm can also be used to learn which input variables other types of Boolean functions depend on, with a success probability that depends on the form of the Boolean function that is tested, but does not depend on the total number of input variables. We also outline a procedure to futher amplify the success probability, based on another quantum algorithm, the Grover search.
测试布尔函数的量子算法
我们讨论量子算法,基于Bernstein-Vazirani算法,用于查找布尔函数所依赖的变量。有2^n个可能的n个变量的线性布尔函数;给定一个线性布尔函数,Bernstein-Vazirani量子算法可以只用一个函数查询就确定地识别出给定的这些布尔函数中的哪一个。同样的量子算法也可以用来学习其他类型的布尔函数所依赖的输入变量,其成功概率取决于被测试的布尔函数的形式,但不取决于输入变量的总数。我们还概述了一个程序,以进一步扩大成功的概率,基于另一种量子算法,格罗弗搜索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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