# A quantum algorithm for approximating the influences of Boolean functions and its applications

A quantum algorithm for approximating the influences of Boolean functions and its applications We investigate the influences of variables on a Boolean function \$\$f\$\$ f based on the quantum Bernstein–Vazirani algorithm. A previous paper (Floess et al. in Math Struct Comput Sci 23:386, 2013) has proved that if an \$\$n\$\$ n -variable Boolean function \$\$f(x_1,\ldots ,x_n)\$\$ f ( x 1 , … , x n ) does not depend on an input variable \$\$x_i\$\$ x i , using the Bernstein–Vazirani circuit for \$\$f\$\$ f will always output \$\$y\$\$ y that has a 0 in the \$\$i\$\$ i th position. We generalize this result and show that, after running this algorithm once, the probability of getting a 1 in each position \$\$i\$\$ i is equal to the dependence degree of \$\$f\$\$ f on the variable \$\$x_i\$\$ x i , i.e., the influence of \$\$x_i\$\$ x i on \$\$f\$\$ f . Based on this, we give an approximation algorithm to evaluate the influence of any variable on a Boolean function. Next, as an application, we use it to study the Boolean functions with juntas and construct probabilistic quantum algorithms to learn certain Boolean functions. Compared with the deterministic algorithms given by Floess et al., our probabilistic algorithms are faster. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quantum Information Processing Springer Journals

# A quantum algorithm for approximating the influences of Boolean functions and its applications

Quantum Information Processing, Volume 14 (6) – Mar 5, 2015
11 pages

/lp/springer_journal/a-quantum-algorithm-for-approximating-the-influences-of-boolean-dUAan0HhwI
Publisher
Springer Journals
Subject
Physics; Quantum Information Technology, Spintronics; Quantum Computing; Data Structures, Cryptology and Information Theory; Quantum Physics; Mathematical Physics
ISSN
1570-0755
eISSN
1573-1332
D.O.I.
10.1007/s11128-015-0954-8
Publisher site
See Article on Publisher Site

### Abstract

We investigate the influences of variables on a Boolean function \$\$f\$\$ f based on the quantum Bernstein–Vazirani algorithm. A previous paper (Floess et al. in Math Struct Comput Sci 23:386, 2013) has proved that if an \$\$n\$\$ n -variable Boolean function \$\$f(x_1,\ldots ,x_n)\$\$ f ( x 1 , … , x n ) does not depend on an input variable \$\$x_i\$\$ x i , using the Bernstein–Vazirani circuit for \$\$f\$\$ f will always output \$\$y\$\$ y that has a 0 in the \$\$i\$\$ i th position. We generalize this result and show that, after running this algorithm once, the probability of getting a 1 in each position \$\$i\$\$ i is equal to the dependence degree of \$\$f\$\$ f on the variable \$\$x_i\$\$ x i , i.e., the influence of \$\$x_i\$\$ x i on \$\$f\$\$ f . Based on this, we give an approximation algorithm to evaluate the influence of any variable on a Boolean function. Next, as an application, we use it to study the Boolean functions with juntas and construct probabilistic quantum algorithms to learn certain Boolean functions. Compared with the deterministic algorithms given by Floess et al., our probabilistic algorithms are faster.

### Journal

Quantum Information ProcessingSpringer Journals

Published: Mar 5, 2015

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